<title>Introduction</title> <p>A small fraction of the estimated 1.5 million fungal species on Earth can colonise
and infect human beings. Among them, the ascomycete <italic>Aspergillus
fumigatus</italic> is the leading cause of mould-related death, most of which
results from invasive lung disease in immune-deficient patients##REF##10194462##[1]##. The
ascomycetes' ecologically important saprophytism demands metabolic
diversity and species-specific inventories of secreted enzymes. These are attributes
which may ultimately contribute to the pathogenicity of certain species in plants
and humans and have long influenced interpretations of virulence##REF##10194462##[1]##,##REF##16019255##[2]##. Despite
the importance of appropriate transcriptional control in orchestrating these
processes, accurate data from within the host niche has eluded researchers,
principally due to difficulties associated with sample recovery.</p> <p>Most <italic>A. fumigatus</italic> infections are a direct consequence of the
enormous propensity of <italic>A. fumigatus</italic> spores for airborne dispersal
in large quantities, such that the human lung is constantly exposed to them. If
infection ensues, its nature and severity is governed by the status of the host,
which determines whether the spores are cleared effectively or whether they go on to
germinate in, colonise, or even to invade the surrounding lung tissue##REF##17525130##[3]##.
<italic>Ex vivo</italic> and epidemiological analyses place macrophages and
neutrophils on the frontline of cell-mediated defense##REF##7037853##[4]##,##REF##16517740##[5]## against <italic>A.
fumigatus</italic> infection and have implicated the fungal secondary metabolite
gliotoxin (GT), in an immunotoxigenic capacity, as contributing to virulence##REF##6203127##[6]##.
This hypothesis was recently substantiated within a physiological context with the
finding that the proapoptotic Bcl-2 family member, Bak, is required for GT-induced
apoptosis in murine embryonic fibroblasts. Moreover, Bak knockout mice are resistant
to <italic>A. fumigatus</italic> infection with a GT-producing clinical isolate##REF##16893972##[7]##. Deletion
of gliotoxin biosynthetic genes can differentially affect virulence, dependent upon
immunsuppressive regimen, in murine infection supporting an important role of the
host environment in determining pathogenic potential of <italic>A.
fumigatus</italic>. This dichotomy of virulence phenotype renders the clinical
significance of gliotoxin uncertain at the current time##REF##17030582##[8]##–##REF##17601876##[12]##.</p> <p>Fungi that sporulate or produce fruiting bodies demonstrate co-ordinate expression of
biologically active secondary metabolites and spore-related products during
development##REF##12208999##[13]##. Such regulation is mediated at the level of
transcription, from clusters of physically linked, co-ordinately regulated genes and
is profoundly affected by both the developmental program under execution, and
environmental factors such as pH and nutrient availability##REF##16322742##[14]##. Renewed interest in
the significance of secondary metabolites in establishing
<italic>Aspergillus</italic> infection has been triggered by the discovery of a
global transcriptional regulator of <italic>A. fumigatus</italic> secondary
metabolite biosynthesis, LaeA##REF##16151250##[15]##. <italic>lae</italic>A deletion does not affect
gross changes in growth or sporulation in media <italic>in vitro</italic>, but it
does result in reduced virulence in mice. This impairment in the ability to cause
infection can be correlated with a loss of detectable gliotoxin as well as
mis-regulation of gene expression within 13 gene clusters##REF##17432932##[16]##. Siderophore-mediated
iron uptake and storage, also partially under LaeA control##REF##17432932##[16]## is indispensable for
<italic>A. fumigatus</italic> virulence##REF##15504822##[17]##,##UREF##0##[18]##, and adequate
nutrient acquisition within the host niche is inextricably linked to pathogenesis of
invasive disease being necessary to support sufficient growth to promote
infection##REF##9669338##[19]##–##REF##15101984##[21]##.</p> <p>Transcriptional profiling can greatly illuminate the host pathogen interaction but
the potential of this approach remains limited due to difficulties associated with
obtaining good quality RNA in sufficient quantities from sites of infection. The
feasibility of performing microarray analyses on limited material has been tested by
a number of researchers employing linear amplification of mRNA, an approach which
has recently proven successful in murine candidiasis##REF##17367383##[22]##, though a truly global
transcriptional signature has yet to be reported for any fungal pathogen initiating
mammalian invasive disease. The Eberwine method of mRNA amplification involves
reverse transcription of mRNA with an oligo dT primer bearing a T7 RNA polymerase
promoter site, to direct <italic>in vitro</italic> transcription of antisense RNA
(aRNA) after double stranded cDNA synthesis and is favoured for linear mRNA
amplification from limited quantities of starting material. It provides the basis
for the methodology employed in our study, and in the majority of reported instances
where mRNA amplification has been applied to samples destined for microarray
analysis##REF##12659661##[23]##.</p> <p>To identify fungal attributes preferentially employed during adaptation to the host
niche, and thus contributing to the virulence of the saprophytic parasite <italic>A.
fumigatus</italic>, we compared the transcriptomes of developmentally matched
<italic>A. fumigatus</italic> isolates following laboratory culture or
initiation of infection in the neutropenic murine lung. We report the development of
a highly robust methodology for global profiling of <italic>A. fumigatus</italic>
gene expression in germlings rescued directly from the murine lung, a tool which
will empower the analysis of virulence in this pathogen. Our methodology employs
facile molecular manipulations which, combined with custom bioinformatic scripts
based on the latest annotations of the <italic>A. fumigatus</italic> Af293 genome,
mark a significant advance in understanding orchestration of fungal virulence. Our
analyses identify iron limitation, alkaline stress and nutrient deprivation as
relevant host-imposed stresses during early-stage <italic>A. fumigatus</italic>
infection and reveal a biased distribution of host-adaptation genes (relative to
laboratory culture) in subtelomeric regions of chromosomes. Finally we assess
lineage specificity of functions favoured during initiation of infection within
varyingly virulent members of the genus <italic>Aspergillus</italic>.</p>
<title>Materials and Methods</title> <p>Full details of the methods used are available through Array Express (<ext-link ext-link-type="uri" xlink:href="http://www.ebi.ac.uk/microarray-as/aer/#ae-main0">http://www.ebi.ac.uk/microarray-as/aer/#ae-main0</ext-link>) Accession number
E-TABM-327. The sequenced <italic>A. fumigatus</italic> isolate Af293 has been
previously described##REF##16372009##[68]## and was used for all analysis reported in this
study.</p> <title>
<italic>A. fumigatus</italic> strains and growth conditions</title> <p>
<italic>A. fumigatus</italic> Af293 <italic>in vitro</italic> isolates for murine
infection experiments were grown in shaken liquid culture at 37°C in YPD
medium. For oxidative stress Af293 conidia were inoculated into
<italic>Aspergillus</italic> complete medium (CM)##REF##5940632##[69]## and grown for 16
hours with shaking at 37°C. The resultant hyphal culture was transferred
to CM containing 17 mM H<sub>2</sub>O<sub>2</sub> and aliquots were harvested
for RNA isolation upon transfer (T<sub>0</sub>) and at 60 minutes after the
initiation of H<sub>2</sub>O<sub>2</sub> exposure. For acid stress Af293 conidia
were incubated in liquid CM medium with shaking for 6 hours at 37°C. At
that time the culture was split, one portion of the culture being adjusted to
pH3 using HCl (time T<sub>0</sub>). Aliquots of each were taken at time 60 mins
for RNA purification and microarray expression analysis. pH 3 was verified at
T<sub>0</sub> and at the end of the time course. For alkaline stress
<italic>A. fumigatus</italic> germlings were cultured for 16 hours in shaken
liquid AMM containing 100 mM glycolic acid pH5.0, 10 ml/L vitamin solution##REF##16893972##[7]##, 5 mM
ammonium tartrate and 1% w/v glucose. Germlings were filtered using
Miracloth and shifted to similar, prewarmed medium containing 100 mM Tris-HCl
pH8.0. After 1 hour incubation at 37°C, mycelia were washed with cold
AMM pH5.0 and RNA extraction was performed immediately. For anaerobic stress
hyphae were grown in CM (10<sup>7</sup> conidia in 20 ml in 100 mm Petri dishes
without agitation) for 16 hours at 37°C. Three plates were placed in
anaerobic chambers and anaerobic conditions were established using 2 palladium
catalysts (GasPak Plus, Becton Dickinson). A control plate was placed in a
chamber without palladium catalysts and all plates were returned to a
37°C incubator for the duration of the experiment. Plates placed in
anaerobic chambers were removed at 60 minutes, the mycelium rapidly harvested on
Miracloth, rinsed with ice-cold water and rapidly frozen in liquid nitrogen. The
RNA was extracted and used in competitive hybridization with the 16 hour control
RNA sample. For neutrophil exposure 10<sup>7</sup> germlings grown in 20 ml of
RPMI1640 with L-glutamine, 25 mM Hepes, and 5% (v/v) fetal bovine
serum for 7 hours at 37°C in 100 mm Petri dishes were exposed to
10<sup>7</sup> human neutrophils. At 60 minutes of exposure to neutrophils,
RNA was prepared. For iron limitation <italic>A. fumigatus</italic> isolate
ATCC46645 was grown for 15 hours at 37°C in –Fe
<italic>Aspergillus</italic> minimal medium (AMM, iron depleted conditions)
according to Pontecorvo##REF##13040135##[63]## containing 1% (wt/vol) glucose as
carbon source, 20 mM glutamine as nitrogen source. After ths time, 10
µM FeSO<sub>4</sub> was added to the medium and germlings harvested
after a further 60 minutes. Growth curve analyses were performed in triplicate
using liquid YPD, or AMM containing 1% glucose and either 5 mM
ammonium tartrate or 5 mM hyroxyproline as nitrogen source. Cultures were
inoculated to a final concentration of 5×10<sup>6</sup> spores/ml and
incubated under aerobic conditions at 37°C with shaking at 150 rpm. At
selected timepoints mycelia were harvested on Miracloth, encased in Whatmann
paper and dried at 37°C for 48 hours before weighing.</p> <title>Murine infections</title> <p>Groups of 24 outbred male mice (strain CD1, 18–22 g, Harlan Ortech)
were housed in individually vented cages and allowed free access to food and
water. Mice received cyclophosphamide (150 mg/kg, ENDOXANA, Asta Medica) by
intraperitoneal injection on days −3 and −1. A single dose
of hydrocortisone acetate (112.5 mg/kg, HYDROCORTISTAB, Sovereign Medical) was
administered subcutaneously on day −1. All mice received tetracycline
hydrochloride 1 mg/l and ciprofloxacin 64 mg/l in drinking water as prophylaxis
against bacterial infection. <italic>Aspergillus</italic> spores for
inoculations were grown on solid ACM, containing 5 mM ammonium
(+)-tartrate and 1% (w/v) Oxoid Agar Number 3 for 5 days
prior to infection. Conidia were freshly harvested using sterile saline (Baxter
Healthcare Ltd. England) and filtered through MIRACLOTH (Calbiochem). Conidial
suspensions were spun for 5 minutes at 3000 <italic>g</italic>, washed twice
with sterile saline, counted using a hemocytometer and re-suspended at a
concentration of 2.5×10<sup>10</sup> colony forming units (c.f.u)/ml.
Viable counts from administered inocula were determined following serial
dilution by plating on <italic>Aspergillus</italic> complete medium and growth
at 37 °C. Mice were anesthetized by halothane inhalation and infected by
intranasal instillation of 10<sup>8</sup> conidia in 40 µl of saline.
Groups of infected mice were culled and processed collectively during a 2 hour
window corresponding to a time point 12–14 hours post-infection.
Bronchoalveolar lavage was performed immediately after culling using three 0.5
ml aliquots of pre-warmed sterile saline. BALFs were snap frozen immediately
following harvest using liquid nitrogen.</p> <title>RNA extraction and amplification</title> <p>Prior to RNA extraction snap-frozen BALF samples were centrifuged, washed with 1
ml of ice-cold sterile water to lyse contaminating host cells and pooled.
Following a further cycle of snap freezing pelleted pooled product was
homogenized using a pestle and mortar and liquid nitrogen. RNA extraction was
performed immediately using the ‘filamentous fungi’ protocol
of the RNeasy mini kit (Qiagen). RNA concentration and integrity was measured by
Nanodrop. Reference RNA was prepared from snap frozen ground mycelium. RNA from
reference samples was prepared from snap-frozen homogenized germlings or young
mycelium.</p> <title>Estimation of fidelity of transcript abundance following mRNA amplification</title> <p>The corrected, multiple t-testing methodology of Nygaard##REF##12659661##[23]## formed the basis
of our investigation. However, for computational ease it was modified to use the
limma linear modelling framework for the R statistical environment.</p> <p>Spots were filtered as previously described and systematic spatial effects were
normalized within arrays using the print-tip lowess method (limma package for
R); no between-array normalization was performed in order to preserve the
amplification-protocol variation existent between arrays. Least squares
regression was used to fit the average log ratio, and error term of each
transcript in each of the amplification protocols. The resulting coefficient
vectors contained the mean relative abundance of each transcript (T0 v T60) in
one of the three amplification protocols: aRNA<sub>r1</sub>, aRNA<sub>r2</sub>
or totRNA. The amplification protocol effects and their associated
Benjamini-Hochberg corrected <italic>p</italic>-values (contrast<sub>1</sub>,
contrast<sub>2</sub> and contrast<sub>3</sub> in ##SUPPL##0##Figure S1##)
were extracted by fitting a contrast matrix to the original linear model. We
defined the null hypothesis H<sub>0</sub> as no difference in log ratios due to
the amplification protocol, and an alternative hypothesis H<sub>1</sub> as loss
of log ratio fidelity due to the amplification protocol. Spots were partitioned
into the three sets based on the Benjamini-Hochberg adjusted p-values associated
with each of the fitted contrasts. The rejected, undetermined and conservative
sets were populated by genes whose adjusted p-values were
0&lt;<italic>p</italic>&lt;0.01,
0.01&lt;<italic>p</italic>&lt;0.1 and
≫0.1&lt;<italic>p</italic>&lt;1 respectively. This approach
rejected 2325 (8.49%) of the spots in the aRNA<sub>1</sub> - totRNA
comparison, i.e. the genes whose log ratios are significantly altered as a
result of 1 round of mRNA amplification. The analogous rejected sets for the
aRNA<sub>2</sub> – totRNA and aRNA<sub>1</sub> –
aRNA<sub>2</sub> comparisons contained 2604 (9.52%) and 73
(0.27%) rejected spots respectively. Comparison of the rejection sets
showed that 80.69% of the spots in the aRNA<sub>1</sub>- totRNA
rejection set were also present in the aRNA<sub>2</sub>- totRNA rejection set.
This finding, in addition to the observed small number of genes in the
aRNA<sub>1</sub> – aRNA<sub>2</sub> rejection set, suggests that
most of the amplification specific noise is introduced in the first round.</p> <title>Microarray hybridisations</title> <p>All experiments used Af293 DNA amplicon microarrays##REF##16372009##[68]##. Labelling
reactions with RNA and hybridisations were conducted as described in the TIGR
standard operating procedures found at <ext-link ext-link-type="uri" xlink:href="http://atarrays.tigr.org">http://atarrays.tigr.org</ext-link>.
Independent verification of selected log<sub>2</sub> ratios was achieved using
quantitative RT-PCR (See ##SUPPL##2##Figure S3## and ##SUPPL##11##Table S9##).</p> <p>Hybridised slides were scanned using the Axon GenePix 4000B microarray scanner
and the TIFF images generated were analyzed using TIGR Spotfinder (<ext-link ext-link-type="uri" xlink:href="http://www.tigr.org/software/">http://www.tigr.org/software/</ext-link>) to identify poor quality spots.
mRNA underwent two rounds of linear amplification by T7 promoter-directed
transcription. Data processing and analysis was performed in the R statistical
framework, using the limma, multcomp and fdrtool packages (<ext-link ext-link-type="uri" xlink:href="http://www.bioconductor.org">http://www.bioconductor.org</ext-link>). The intensity data retained after
filtering (91.5% of the total spots) were normalized within
(print-tip lowess) and between (scale) arrays. Least squares regression (lmFit)
was then used to estimate a vector of regression coefficients (representing the
mean log2 (Cy5/Cy3) across the 5 biological replicates for the <italic>in
vivo</italic> effect), the associated residuals and unadjusted
<italic>p</italic>-values. Differentially regulated transcripts
(Benjamini-Hochberg corrected <italic>p</italic>-value&lt;0.05) were further
categorized by the magnitude and direction of the fitted log ratios.
Up-regulated transcripts were defined as having log2 (Cy5/Cy3) greater than an
arbitrary threshold of plus, or minus, two. Accuracy of microarray data was
independently verified by quantitative RT-PCR on selected transcripts (See ##SUPPL##2##Figure S3##).
Oligonucleotides used for this analysis are detailed in ##SUPPL##11##Table S9##).</p> <p>For exposure to <italic>in vitro</italic> stresses microarray experiments were
performed as previously described##REF##16372009##[68]##.</p> <title>Automated biological theme determinations</title> <p>To identify over-represented Gene Ontology terms, loci showing significantly
different expression were further analyzed by the Expression Analysis Systematic
Explorer (EASE) (PMID:14519205), which is implemented in MEV within the TIGR TM4
microarray data analysis suite (<ext-link ext-link-type="uri" xlink:href="http://TM4.org">http://TM4.org</ext-link>). Numbers of genes in
the indicated Gene Ontology categories were subjected to statistical analysis by
EASE##REF##14519205##[25]## to identify categories overrepresented compared
with the whole genome data set. Only categories with Fisher's exact
test <italic>p</italic>-values&lt;0.05 were included.</p> <title>Core and lineage-specific gene sets</title> <p>Orthologous proteins in the genomes were identified using a
reciprocal-best-BLAST-hit (RBH) approach with a cut-off of 1e-05. The
orthologous clusters, as well as synteny visualization and comparative analysis
tools can be also found in the <italic>Aspergillus</italic> Comparative Database
at <ext-link ext-link-type="uri" xlink:href="http://www.tigr.org/sybil/asp">http://www.tigr.org/sybil/asp</ext-link>.</p> <title>Gene cluster identifications</title> <p>Clusters of co-regulated neighbouring genes were defined using a simple, two
parameter algorithm. The first parameter, minimum_block_size (mbs), specifies
the minimum number of up- or down- regulated genes that a cluster must contain.
The second parameter, maximum_gap_size (mgs), specifies the maximum number of
adjacent non-up- or down- regulated genes permissable per cluster. Four analyses
were performed corresponding to mgs values of
n = 0, 1, 2, 3 and 4. ##FIG##2##Figure 3## shows data from
mgs = 4. Assigned cluster numbers are based on
chromosomal position. More details on the clustering algorithm are available at
<ext-link ext-link-type="uri" xlink:href="http://sybil.sourceforge.net/documentation.html#algorithms">http://sybil.sourceforge.net/documentation.html#algorithms</ext-link>.
Secondary metabolite biosynthesis genes were identified using SMURF, a web-based
software tool (<ext-link ext-link-type="uri" xlink:href="http://www.tigr.org/software/genefinding.shtml">http://www.tigr.org/software/genefinding.shtml</ext-link> - temporarily
hosted at <ext-link ext-link-type="uri" xlink:href="http://binf.gmu.edu/fseifudd/smurf/index1.html">http://binf.gmu.edu/fseifudd/smurf/index1.html</ext-link>). Density of the
SM genes was estimated as the exon length per kb.</p>
<title>Results</title> <title>
<italic>A. fumigatus</italic> RNA extraction and mRNA amplification</title> <p>Microarray analyses are constrained by the availability of sufficient RNA for
fluorophore labelling and hybridisation. One strategy to overcome the
requirement for large quantities of material is global amplification of the
sample. This approach has been employed for a number of reported microarray
expression analyses##REF##12659661##[23]##,##REF##16473852##[24]## with favourable
results. We chose to analyse an early time point of infection to facilitate
separation of fungal cells from those of the host with the advantage of studying
a stage at which germinating hyphae have sensed, and are adapting to, their
host. Early time points of <italic>A. fumigatus</italic> infection represent a
vulnerable phase of morphogenesis <italic>in vivo</italic> since epithelial
invasion and formation of mycelial mass have yet to occur. They also mark a
point at which diagnoses capable of distinguishing infection from carriage of
fungal spores would be most desirable, and antifungal therapy most effective.</p> <p>We firstly characterised the time course of hyphal development in the sequenced
clinical isolate Af293 by histopathological examination of infected neutropenic
murine lung tissues (##FIG##0##Figure
1##). Lung sections collected and formalin-fixed at 4, 6, 8 and 12 hours
post-infection contained numerous <italic>A. fumigatus</italic> spores in close
association with murine epithelium in the bronchioles and alveoli (##FIG##0##Figure 1A##). At 12–14
hours post-infection &gt;80% of <italic>A. fumigatus</italic>
conidia had undergone germination and primary hyphal production. We therefore
performed all BAL extractions for downstream analyses on concurrently infected
neutropenic mice within a two hour window of infectious growth corresponding to
12–14 hours post-infection. At this time point recovery of germlings
in BAL fluid was routinely achievable in the order of 10<sup>3</sup> germlings
per lavaged lung (##FIG##0##Figure
1B##).</p> <p>To isolate fungal RNA from the site of <italic>A. fumigatus</italic> infection we
inoculated pools of 24 neutropenic CD1 male mice with 10<sup>8</sup>
conidiospores and culled after 12–14 hours. Bronchoalveolar lavage was
performed immediately using pre-warmed sterile saline and samples (BALFs) were
snap frozen prior to RNA extraction and amplification. Within infection groups
BALFs were pooled prior to RNA extraction and mRNA amplification. Total RNA
yields from pooled BALFs ranged from 108–800 ng and yielded up to 258
µg aRNA after 2 rounds of linear amplification. Amplification factors
therefore ranged from
2.8×10<sup>3</sup>–3.9×10<sup>5</sup> based
upon 2% of the total RNA population being mRNA (##SUPPL##3##Table S1##).
<italic>In vitro</italic> reference RNA samples were similarly prepared from
developmentally matched <italic>A. fumigatus</italic> germlings (##FIG##0##Figure 1C##) which were
harvested, and snap-frozen, following 12 hour culture at 37°C in rich
medium and subjected to two rounds of mRNA amplification prior to
co-hybridisation (##SUPPL##3##Table S1##).</p> <title>Impact of mRNA amplification on preservation of transcript ratios</title> <p>A suitable global mRNA amplification protocol should provide sufficient material
for fluorophore cDNA labelling reactions whilst preserving the samples'
original relative transcript abundance. To estimate amplification-related error
(and distinguish such error from systematic error inherent to microarray
methodologies) a mock experiment was devised to quantify the proportion of
transcripts having significantly aberrant log<sub>2</sub> ratios as a result of
RNA amplification. This was achieved by indirectly comparing cDNA samples from
different amplification protocols in a statistical linear model and fitting
relevant contrast matrices (see <xref ref-type="sec" rid="s4">Materials and
Methods</xref> and ##SUPPL##0##Figure S1##). For the mock experiment total RNA
(totRNA) was isolated from two <italic>A. fumigatus</italic> cell populations T0
and T60, and subjected to either one (aRNA<sub>r1</sub>) or two
(aRNA<sub>r2</sub>) rounds of mRNA amplification prior to cDNA fluorophore
labelling and microarray hybridisation (##FIG##1##Figure 2##). To evaluate whether ratios are
preserved between amplification protocols, we adopted the approach of Nygaard
<italic>et. al.</italic>
##REF##12659661##[23]## who used corrected gene-wise t-tests to
identify differences in the mean log intensity between aRNA<sub>2</sub> and
total RNA populations. In preparing the data for multiple t-testing, we excluded
spots that were flagged by the the TIGR spotfinder software, or where the
intensity was lower than twice the background intensity in either the Cy5 or Cy3
channel. The proportion of excluded spots was used as an indication of the
hybridisation quality of the slide as a whole. Slides hybridised with aRNA had
fewer spots (13.49–18.22%) removed by the filtering process
than did slides hybridised with cDNA (44.56–48.95%), see
##SUPPL##4##Table
S2##. This apparent amplification-related improvement in hybridisation
quality has been previously reported##REF##16473852##[24]##. In our study,
this can be attributed to a greater signal to noise ratio, and more specifically
to increased foreground intensity on these slides (data not shown). We
identified 8.49% of retained spots showing evidence of amplification
protocol dependent differences in the log<sub>2</sub> ratios. Thus our estimated
measures of confidence from the above quality-control (QC) exercises fall within
a range conducive for deriving biologically useful information, based upon
reports of analytical studies published to date##REF##12659661##[23]##,##REF##16473852##[24]##
where Pearson correlation coefficients range from 0.75 to 0.99 (##FIG##1##Figure 2##) and rejected gene
sets approximate 10% of spots included in QC analysis.</p> <title>
<italic>A. fumigatus</italic> transcript profile during initiation of
mammalian infection</title> <p>A common reference design was adopted for the microarray experiment from which
data were analysed and processed as described in the <xref ref-type="sec" rid="s4">Materials and Methods</xref> section. We performed the infection
experiment 5 times in total generating pooled
(n = 24) BALFs from five independent Af293
infections (samples A to E, ##SUPPL##3##Table S1##). These samples were co-hybridised
with similarly amplified mRNA prepared from developmentally matched laboratory
cultured Af293 germlings (<xref ref-type="sec" rid="s4">Materials and
Methods</xref> and ##SUPPL##3##Table S1##). Fluorescent signals from 9075 out
of a possible 9516 represented ORFs were detectable from these hybridisation
analyses (##SUPPL##12##Dataset S1## and Array Express (<ext-link ext-link-type="uri" xlink:href="http://www.ebi.ac.uk/microarray-as/aer/#ae-main0">http://www.ebi.ac.uk/microarray-as/aer/#ae-main0</ext-link> Accession
number E-TABM-327). Of 2180 genes (22.6% of the whole genome) having
a fold-change in log intensity ratio of 2 or greater, 1281 were up-regulated and
897 were down-regulated. The entire expression dataset is graphically
represented in ##FIG##2##Figure 3##
which plots log<sub>2</sub> ratios against chromosomal locus. Initial
interpretations of the dataset were performed by the Expression Analysis
Systematic Explorer##REF##14519205##[25]## (EASE) to infer function by homology to
<italic>Saccharomyces cerevisiae</italic> and identify over-represented Gene
Ontology (GO) terms among differentially expressed genes. The results of these
analyses are partially listed in ##TAB##0##Table 1## (full listings in ##SUPPL##5##Table S3##). Distinct trends among favoured
cellular processes are evident from these analyses which reveal a marked
investment, by the host-adapting <italic>A. fumigatus</italic> cell, into
transport of metal ion, cation, carbohydrate and siderophore iron. Given the
importance of iron acquisition for microbial pathogenesis in general, and the
absolute requirement for siderophore biosynthesis during murine <italic>A.
fumigatus</italic> pathogenesis in this model of infection ##REF##15504822##[17]##,##UREF##0##[18]## we expected
transcripts from genes involved in iron mobilization and transport to be
differentially abundant in this analysis, relative to iron-rich laboratory
culture media. Accordingly we could identify a minimum of eleven siderophore
biosynthesis/transport genes as important during growth in the murine lung
(##TAB##1##Table 2##) including
two ferric-chelate reductases (Afu1g17270 and Afu6g13750). Thirteen amino acid
permease genes were more abundantly represented during host-adaptation than
growth in YPD (##TAB##1##Table 2##)
including 4 GABA (Afu8g01450, Afu7g0040, Afu5g14660 and Afu5g00710), and three
proline permeases (Afu2g11220, Afu8g02200 and Afu7g01090) as well as the general
amino acid permease, Gap1 (Afu7g04290). Nine genes annotated as maltose
permeases or transporters in the current Af293 annotation were also more
abundantly represented during initiation of murine infection (##TAB##1##Table 2##). Extracellular
proteases have been implicated as virulence factors in invasive aspergillosis,
as well as antigens causing inflammatory irregularity during allergic <italic>A.
fumigatus</italic> disease##REF##10194462##[1]##. Our analysis identified increased abundance
of transcripts from the elastinolytic metalloprotease (Mep) (Afu8g07080), an
aorsin-like serine protease (Afu6g6g10250) and three dipeptidylpeptidases
(Afu4g09320, Afu2g09030 and AfuAfu3g07850). Thus transcription of this subset of
<italic>A fumigatus</italic> proteases is significantly higher in the murine
lung relative to rich laboratory culture. Functional categories of ergosterol
biosynthesis, heme biosynthesis and aerobic respiration were significant among
genes underrepresented during infection, relative to laboratory culture (##TAB##0##Table 1##) as well as multiple
functional categories representing ribosome biogenesis and assembly, and protein
biosynthesis and processing. This may reflect the poor nutritional value of
murine lung relative to YPD and/or reduced growth (due to any number of
stresses) during host-adaptation compared to broth culture. This trend is
evidenced on multiple levels within our dataset, comprising repression of genes
directing ribosomal protein synthesis, rRNA synthesis, RNA polymerase I and II
activity, translation initiation and elongation, tRNA processing and synthesis,
intracellular trafficking, secretion and vesicular trafficking (##TAB##0##Table 1## and ##SUPPL##5##Table S3##).
While such metabolic dampening is often observed in microbial systems under
stress, the observed <italic>A. fumigatus</italic> regulatory signature mimics
that of rapamycin-mediated TOR kinase##REF##10617575##[26]## inhibition and
typifies fungal starvation. The <italic>S. cerevisiae</italic> TOR proteins
<italic>TOR1</italic> and <italic>TOR2</italic> are phosphatidylinositol
kinase homologues, first identified as the targets of the
immunophilin-immunosuppressant complex FKBP-rapamycin##REF##1715094##[27]##, combined deletion
of which causes yeast cells to arrest growth, undergo a reduction in protein
synthesis, accumulate the storage carbohydrate glycogen and acquire
thermotolerance. Comparison of our dataset to that obtained following
rapamycin-induced TOR inhibition in <italic>S. cerevisiae</italic>
##REF##10617575##[26]##
reveals extensive overlap in induced (n = 35)
and repressed (n = 90) homologous genes between
the two datasets (##SUPPL##6##Table S4##). Thus a clear TOR repression-like
starvation signature, relative to laboratory culture, is observable during
early-stage infection, which may derive from the relative nutritional status of
the tested conditions and/or slower growth within the context of our experiment.
The indicated cellular down-turn in metabolism observed is strongly countered by
up-regulation of genes encoding functions associated with amino acid and
carbohydrate catabolism (##TAB##0##Tables
1## and ##SUPPL##5##S3##).</p> <title>Lineage specificity and locational analyses of differentially expressed genes</title> <p>From an evolutionary perspective, relative proportions of genes being over- and
underrepresented in the analysis differed significantly within lineage-specific
gene cohorts (##FIG##3##Figure 4A## and
##TAB##2##Table 3##). Genes having
increased transcript abundance during infection are significantly enriched
(p&lt;0.0001 by chi-square analysis, see ##TAB##2##Table 3##) among differentially expressed genes
(n = 64) unique to the <italic>A.
fumigatus</italic> lineage. Thus 93.6% of <italic>A.
fumigatus</italic> genes having orthologues restricted to two very closely
related, but differentially virulent, species <italic>Neosartorya
fischeri</italic> (anamorph of <italic>Aspergillus fischerianus</italic>)
(AAKE00000000) and <italic>Aspergillus clavatus</italic> (AAKD00000000) are more
abundantly represented during the initiation of infection (##TAB##2##Table 3##). In contrast only
8% of genes having orthologues in all six
<italic>Aspergillus</italic> species sequenced to date (i.e. the
<italic>Aspergillus</italic> ‘core’ genome) are more
abundantly represented under these conditions
(n = 5095). This invariable
‘core’ genome encodes many functions associated with
information processing, central metabolism and cell growth, retention of which
is most likely to be essential for cellular survival##REF##18404212##[28]##. Narrowing the
phylogenetic sampling to include only <italic>A. fumigatus</italic> and its
relatives <italic>N. fischeri</italic> and <italic>A. clavatus</italic>
distinguishes several subtelomeric ‘genomic islands’ upon
which phenotypic variation between species, including differing pathogenicity,
might depend##REF##18404212##[28]##. Accordingly we found differentially
expressed genes to be unevenly distributed amongst <italic>A. fumigatus</italic>
chromosomes (##FIG##3##Figure 4B## and
##TAB##2##Table 3##). Induced
genes form a significantly increased proportion of differentially regulated
functions in intermediate (p&lt;0.001) and subtelomeric (p&lt;0.001)
regions of the chromosomes (##FIG##3##Figure
4B## and ##TAB##2##Table
3##). While only 16% of the predicted <italic>A.
fumigatus</italic> gene repertoire is housed within 300 kb of telomeres (classed
as the subtelomeric gene repertoire in our analyses), 29% of
transcripts having increased abundance, relative to laboratory culture, in the
murine lung are located to such subtelomeric areas, compared to just
11% of down-regulated transcripts. Moreover, 28% of the
entire subtelomeric gene repertoire is represented in the induced dataset
compared to only 8% of subtelomeric genes represented among
down-regulated functions (##TAB##2##Table
3##).</p> <title>Clustering of induced genes</title> <p>Regarding our <italic>A. fumigatus</italic> gene expression dataset as a function
of chromosomal locus (##FIG##2##Figure
3##) we identified that many induced <italic>A. fumigatus</italic> genes
are found in contiguous clusters. To investigate this further we generated a
custom script to automate cluster identification which identified numerous
genomic loci within which co-ordinate regulation of a minimum of 5 closely
neighbouring genes can be observed (##FIG##2##Figure 3## and ##SUPPL##8##Table S6##). Co-ordinate expression of
physically clustered genes is a prominent feature of the induced, but not
repressed, gene set and we observe a large proportion (40%) of
up-regulated physically clustered genes to reside within 300 kb of chromosome
ends (##FIG##2##Figure 3## and ##TAB##3##Table 4##). The clusters are
comprised of up to 34 co-ordinately expressed genes and include loci directing
biosynthesis of siderophores (cluster 33) and two known secondary metabolites,
pseurotin and gliotoxin (clusters 72 and 60, respectively). The gliotoxin
biosynthetic cluster is not subtelomerically located being 700 kb from the
telomere (as annotated by Perrin <italic>et. al.</italic>
##REF##17432932##[16]##). The pseurotin
biosynthetic cluster, however, (as annotated by Maiya <italic>et. al.</italic>
##REF##17722120##[29]##)
is contained within the fumitremorgen cluster (Afu8g00100–8g00720) at
100 kb from the telomere##REF##17432932##[16]##. Pseurotin##REF##17722120##[29]##–##REF##8931737##[31]##
is a neuritogenic, nematicidal quinone and gliotoxin an immunotoxin which
supports <italic>A. fumigatus</italic> virulence in some murine models of
invasive pulmonary aspergillosis##REF##17030582##[8]##–##REF##17601876##[12]##. We observed four
other postulated, but uncharacterised, secondary metabolite gene clusters
induced during early stage <italic>A. fumigatus</italic> infection, including a
large proportion of genes on the left arm of chromosome 8, predicted to encode a
fumitremorgen biosynthesis supercluster##UREF##1##[32]##. Thus, it would
seem that selective expression of a subset of secondary metabolite loci
facilitates initiation of mammalian infection. While gliotoxin biosynthesis is
dispensable for virulence in some murine models, our analysis demonstrates that
this host environment is nonetheless conducive to immunotoxin production, and
further insights on virulence mechanisms relevant to neutropenia and/or
corticosteroid therapy await comparative analyses of fungal gene expression in
each of these strikingly different host settings, an analysis which is currently
underway in our laboratory. A fully annotated table of clusters, indicating
between-species synteny within cluster loci, and all accession numbers, can be
viewed in ##SUPPL##13##Dataset S2##.</p> <title>Comparative analyses of murine adaptation- and <italic>in vitro</italic> gene
expression datasets</title> <p>The complexity of the transcriptional signature derived from host adaptation
analyses is likely to originate from convergence of multiple environmental cues,
coupled with metabolic and morphologic effects. Although functional
categorisation of differentially expressed functions (##TAB##0##Tables 1## and ##SUPPL##5##S3##) could
identify metabolic and physiological trends during initiation of infection,
environment-related signatures were less easy to discern. To ascertain
physiologically relevant features of the host environment we compared the
transcriptome of host-adapting germlings to those of <italic>in vitro A.
fumigatus</italic> cultures exposed to iron limitation, nutrient limitation,
alkaline stress, acid stress, neutrophils, oxidative stress or anaerobic stress.
The resulting transcriptomic responses varied in magnitude (##TAB##3##Table 4##) but assignment of
cut-off log<sub>2</sub> ratio values of +2 and −2 across all
of the analyses enabled us to distinguish several important aspects of the
<italic>A. fumigatus</italic> host-adaptive response (##FIG##4##Figure 5##). The transcriptional signatures of
paramount importance among those examined were alkaline adaptation, iron
deprivation and nutrient starvation, which are remarkably prominent in the
infection dataset. Using the aforementioned log<sub>2</sub> cut-offs, 24 of 43
iron-regulated genes were identified as differentially expressed during host
adaptation, of these 18 were more abundantly represented (##FIG##4##Figure 5## and ##SUPPL##14##Dataset
S3##), and 6 less abundantly represented. Relaxing the cut-off criterion to
encompass all differentially regulated genes in the host adaptation dataset
allowed complete capture of the iron regulon (##FIG##5##Figure 6##). Among these genes are the
siderophore biosynthetic genes <italic>sid</italic>A (Afu2g07680),
<italic>sid</italic>D (Afu3g03420) and <italic>sid</italic>C (Afu1g17200), the
latter two discerning biosynthesis of both intra- and extracellular siderophores
during infection as substantiated by previous findings##UREF##0##[18]##, and four
siderochrome/siderophore transporter proteins Afu7g06060, Afu7g04730, Afu3g03440
and afu3g03640. Preferential gene expression following a 60 minute shift from
acid to alkaline medium could also be strongly correlated with that observed
during infection (##FIG##4##Figure 5##
and ##SUPPL##14##Dataset
S3##). Alkaline adaptive capability, previously found to be essential for
<italic>A. nidulans</italic> virulence in neutropenic mice##REF##15686555##[33]##,
is likely to be important for growth of <italic>A. fumigatus</italic> spores at
physiological pH. Accordingly we identified 102 genes preferentially expressed
during both murine infection and <italic>in vitro</italic> alkaline adaptation
(##FIG##4##Figure 5## and ##SUPPL##14##Dataset
S3##). Among them are 36 genes having unknown function, two sodium ATPases
(Afu6g03690 and Afu4g09440), the plasma membrane zinc ion transporter
(Afu6g00470) and an alkaline phosphatase (Afu3g14030). Interestingly, we found
no concordance between iron starvation and alkaline adaptation (##FIG##4##Figure 5##). Since acidification
of the macrophage phagolysosome is an essential step in ROS-mediated <italic>A.
fumigatus</italic> killing we also assessed the transcriptome of Af293
germlings upon shift from pH7 to pH3, using a rich medium and hydrochloric acid.
No concordance between the resulting dataset and that of host adaptation was
evident, indeed (despite the magnitude of the murine infection dataset) most
functions upregulated in response to an <italic>in vitro</italic> acid shift
were less abundantly represented in our infection analyses
(n = 18, ##SUPPL##1##Figure S2##)
thus we can confidently conclude that acid stress, at least within the context
tested in this analysis (which can only approximate conditions encountered in
the host) is not relevant during host adaptation. This agrees with our
observation that alkaline adaptation is a physiological cue of primary
importance in this murine model of infection (##FIG##4##Figure 5##).</p> <p>An essential component of phagocyte defense against <italic>A. fumigatus</italic>
conidia and hyphae is the NADPH oxidase-mediated respiratory burst which
generates reactive oxygen species (ROS) required for fungal killing. To assess
the <italic>A. fumigatus</italic> transcriptional response to oxidative stress,
conidia were grown in rich medium at 37°C prior to shift into similar
medium containing 17 mM hydrogen peroxide. Comparison of the resulting dataset
to that obtained from host-adapting germlings revealed some concordance, this
time revealing a subset of genes having decreased transcript abundance both
<italic>in vitro</italic> and during murine infection (##SUPPL##1##Figure S2##).
A common theme among this group of genes is ergosterol and heme biosynthesis,
evidenced by common behaviour of both 14-alpha sterol demethylase
Cyp51A-encoding genes (Afu4g06890 and Afu7g03740) and a coproporphyrinogen III
oxidase homologue (Afu1g07480). Oxygen depletion was achieved by transfer of
<italic>A. fumigatus</italic> hyphae, following 16 hour growth in rich
medium, to anaerobic chambers containing two palladium catalysts. We were unable
to correlate gene expression under these anaerobic stress conditions with gene
expression during infection (##FIG##5##Figure
6##). Finally, we assessed germlings grown in 20 ml of RPMI1640 with
L-glutamine, 25 mM HEPES and 5% fetal bovine serum for 7 hours at
37°C following exposure to human neutrophils at a multiplicity of
infection of 1∶1 for 60 minutes, the reference sample for this
analysis being germlings incubated in the absence of neutrophils. Of 57
<italic>A. fumigatus</italic> genes upregulated in response to neutrophil
exposure <italic>in vitro</italic>, 18 (60%) were also more
abundantly represented during initiation of murine infection (##FIG##5##Figures 6## and ##SUPPL##1##S2##).
Interestingly these included the two major <italic>A. fumigatus</italic>
antioxidant enzymes, Mn superoxide dismutase (Afu1g14550) and the bifunctional
catalase-peroxidase Cat2 (Afu8g01670). Whether representation of these
transcipts among those differentially expressed in both murine and laboratory
culture is indeed neutrophil-specific remains to be determined.</p> <title>Nutrient limitation is a relevant physiological cue during <italic>A.
fumigatus</italic> initiation of murine infection</title> <p>To investigate the relevance of nutrient starvation during host adaptation we
made several growth and gene expression analyses. Hypothesising that YPD is
nutritionally more robust than murine lung tissue we compared radial growth of
<italic>A. fumigatus</italic>, in triplicate from an inoculum of 100 spores
grown at an agar/air interface on Petri dishes, on YPD and on a synthesized
murine lung tissue medium (MLT) composed of homogenised murine lung tissue
(80%) and water/agar (20%), overlaid upon a water/agar
baseplate. Growth of <italic>A. fumigatus</italic> was completely unsupported by
water/agar base with no evidence of conidial germination after 7 days. Growth on
YPD produced conidiating colonies averaging 66 mm
(n = 3) in diameter after 7 days at
37°C, whereas MLT as growth medium supported significantly less growth,
reaching a maximum colony diameter of 44 mm (##FIG##6##Figure 7A##). Any concern that reduced radial
growth observed on MLT medium originates from iron deprivation can be allayed by
reference to radial growth analysis of two independent wild type <italic>A.
fumigatus</italic> isolates, CEA10 and ATTC46645##REF##15504822##[17]##, where equivalent
growth is observed in the presence and absence of iron, and on blood agar
medium. Thus, from this solid growth analysis, we conclude that MLT supports
slower <italic>A. fumigatus</italic> colony growth than YPD under laboratory
conditions. Our MLT analysis could not support the volume of liquid culture
required to perform growth curve analyses, moreover, the viscosity of the medium
would have hindered dry weight measurements. To support our conclusions on
nutritive status of the host environment, within the context of the
experimentation performed during our infection analyses, we assessed
log<sub>2</sub> ratios obtained from competitive microarray hybridisation, using
doubly amplified <italic>A. fumigatus</italic> mRNA extracted from nitrogen
starved germlings, and the same YPD reference sample used for the initial host
adaptation analysis (samples F and G, respectively, ##SUPPL##3##Table S1##).
Nitrogen starvation was exerted in shaken liquid culture using minimal medium,
with hydroxyproline as nitrogen source, for a period of five hours.
Hydroxyproline is a rational candidate nitrogen source during initiation of
mammalian pulmonary infection, being a widely used surrogate marker of lung
injury whose concentration in bronchoalveolar lavage fluid permits quantitative
assessment of collagen breakdown##REF##2463695##[34]##,##UREF##2##[35]##.
Transcript levels of the proline permeases afu2g11220, Afu8g02200 and Afu7g01090
suggest induction of proline uptake during initiation of infection, and
laboratory culture on solid medium confirms that hydroxyproline can support
aconidial filamentous growth of <italic>A. fumigatus</italic> on minimal medium
in the presence of a repressing carbon source such as glucose (data not shown).
To confirm the inferiority of hydroxyproline as a nitrogen source (relative to
YPD) we performed dry weight growth curve analyses (##FIG##6##Figure 7B##) including a widely used
<italic>Aspergillus</italic> minimal medium (MM) for comparison.</p> <p>Nitrogen starvation rendered 1047 genes subject to differential expression,
relative to the doubly amplified YPD reference. Several notable features of the
resultant dataset (##SUPPL##15##Dataset S4##) support our conclusion that,
relative to YPD laboratory culture, initiation of murine infection occurs under
nutrient stress. As with our analyses of host adaptation, over-represented Gene
Ontology (GO) terms among differentially expressed genes identified ribosome
biogenesis and assembly, and protein biosynthesis and processing as the most
significantly down-regulated functional categories (##SUPPL##9##Table S7##).
We also identified significant over-representation of cell cycle-related
functions among genes preferentially expressed during growth in hydroxyproline,
including cell cycle regulation, mitosis, nuclear migration, chromosome
segregation and karyogamy (##SUPPL##9##Table S7##). This is a particularly satisfying
finding since, supported by our growth curve analyses (##FIG##6##Figure 7B##), a clear distinction between
<italic>A. fumigatus</italic> growth phase in the compared media is
discernable. This was not a feature of the murine-YPD comparison which,
importantly, lessens the probability that differences associated with cell cycle
stage or growth rate preside over environmental cues within the host adaptation
dataset. Direct comparisons of the murine and nitrogen starvation datasets
identified an overlap of 280 differentially expressed genes common to both, of
these 24 genes are also common to the TOR kinase nutrient limitation geneset,
and are indicated in ##SUPPL##6##Table S4##. 156 genes preferentially expressed
under nitrogen starvation conditions (relative to YPD laboratory culture) were
similarly favoured during host adaptation (##SUPPL##14##Dataset
S3##). Assessing locational bias among the hydroxyproline dataset,
29% of the geneset was found to reside subtelomerically (##TAB##3##Table 4##) with 180 out of 634
preferentially expressed genes housed within 300 kb of telomeres. This is
comparable to the level of subtelomeric gene expression identified during
initiation of murine infection (##TAB##3##Table 4##) and indicates that, relative to growth in a nutrient-rich
laboratory culture, adaptation to growth in a nutritionally challenging
environment prompts expression of a subtelomeric gene repertoire. A number of
physically linked coregulated genes came to prominence in the hydroxyproline
starvation dataset (##SUPPL##10##Table S8##). 16 clusters conforming to the
previously applied cluster algorithm were identified, 8 of which were
subtelomeric and 3 of which encompass genes in secondary metabolite loci, as
defined by bioinformatic analyses. Of the three latter loci one cluster
(Afu6g03390–6g03490) subject to regulation by the LaeA
methyltransferase##REF##17432932##[16]##, the product of which is currently unknown,
is also expressed during murine infection. Beyond this, correlation between
clustered gene regulation in murine and nitrogen starved growth was modest.</p> <title>The LaeA regulon is represented among genes preferentially expressed during
intitation of murine infection</title> <p>Given the predominance of clustered and subtelomeric gene loci among host
adaptation genes, we compared our dataset to that produced by Perrin <italic>et.
al.</italic>
##REF##17432932##[16]## during study of <italic>lae</italic>A gene
deletion. An <italic>A. fumigatus</italic> Δ<italic>lae</italic>A
(1g14660) mutant, which has decreased virulence in both neutropenic and
hydrocortisone treated mice##UREF##1##[32]##,##REF##17630330##[36]##, demonstrates
significantly lower expression of genes in 13 secondary metabolite biosynthetic
clusters including that of gliotoxin. LaeA was found to influence expression of
a subset of lineage- and species-specific genes therefore we tested the overlap
between functions down-regulated in the absence of LaeA and functions having
greater transcript abundance during murine infection, hoping to decipher a link
between genes under LaeA control, and those active during initiation of murine
infection. Out of 415 genes down-regulated in the absence of LaeA we identified
99 genes having increased abundance during initiation of murine infection (##FIG##7##Figure 8##). Functional
categorisation of shared genes revealed that 40%
(n = 40) were involved in secondary metabolite
biosynthesis, among these we could identify three complete secondary metabolite
clusters, those directing gliotoxin and pseurotin biosynthesis as well as the
genetic locus mentioned above (Afu6g03390–6g03490) whose biosynthetic
product is unknown. Perrin <italic>et. al.</italic> also found 54% of
the LaeA-regulated gene clusters showing differential expression under
laboratory conditions were located within 300 kb of the telomeres. We therefore
extended our analysis still further, determining the proportions of subtelomeric
and secondary metabolism cluster genes shared between the two datasets. This
identified 49 and 40 genes, having subtelomeric locations and secondary
metabolite biosynthetic functions respectively (##FIG##7##Figure 8##).</p> <p>Finally we analysed all of the <italic>in vitro</italic> and <italic>in
vivo</italic> datasets comparatively to assess the occurrence of subtelomeric
bias among differentially expressed genes, aiming to determine whether the
induction of genes at the telomeric extremities of chromosomes was a standard
feature of adaptation to environmental alterations rather than a host adaptation
phenomenon (##TAB##3##Table 4##). The
analysis identified murine adaptation, <italic>lae</italic>A deletion,
neutrophil exposure and nitrogen starvation as the conditions most enriching for
transcript abundance among subtelomeric genes, where 29%,
32%, 27% and 29% of the respective cohorts were
located. Interestingly, removal of secondary metabolism genes from the analysis
dramatically reduced the LaeA regulated subtelomeric gene cohort to
22% while only minimally impacting representation of murine
adaptation genes (##TAB##3##Table
4##).</p> <p>Taken together these analyses indicate that a significant component of the LaeA
regulon, comprised mainly of secondary metabolism genes, is represented among
transcripts more abundant during infection. Furthermore the subtelomeric bias
observed among differentially expressed murine adaptation genes extends beyond
secondary metabolite biosynthesis and does not appear to be a general feature of
adaptation to environmental change.</p>
<title>Discussion</title> <p>We present a methodology for <italic>A. fumigatus</italic> transcript profiling
during initiation of murine infection and a comparative analysis of global
transcriptional programming, in laboratory culture and the mammalian lung. We were
able to optimize a technical and statistical framework sufficiently robust to
reproducibly quantify relative transcript abundances using minute samplings of
<italic>A. fumigatus</italic> germlings. The statistical analysis, though
seemingly complex, utilizes standard statistical methods which are well documented
in biological analyses, particularly in microarrays##UREF##3##[37]##. We chose to
co-hybridize mock samples from the same amplification protocol (thereby determining
the ‘true’ biological effect in a benchmark sample, in this case
total RNA) and then, by comparison to cohybridisations using unamplified samples,
indirectly estimate the systematic effect of the amplification in our analysis. The
t-testing framework##REF##12659661##[23]## then allowed us to identify genes where the log
ratios are significantly different due to <italic>one</italic> factor i.e. the
amplification protocol. This enabled an estimation of the proportion of genes
showing amplification-dependent bias which we carried into the murine experiment,
where comparison of unamplified versus amplified material is impossible. Clearly, we
assume that this estimate can be reliably applied to similar experiments, in doing
so we also assume that the amplification process depends only on the protocol
adopted, and not the underlying gene expression dynamics. A further technical
consideration in planning experiments comparing murine and laboratory samples is
differential treatments. Given the nature of our analysis, differential treatment of
the germling samples was necessary to avoid osmotic shock in either instance. Water
lavage would impose an osmotic shift on germlings rescued from the lung and saline
treatment would osmotically shock laboratory cultured germlings. Thus the technical
limitations imposed by such comparative analysis must be accepted, however, in terms
of maximally preserving transcript abundances within the context of the experiment,
we believe our treatments of the samples to be appropriate.</p> <p>To identify factors governing adaptation to the host niche we compared laboratory and
murine lung samples aiming to find fungal attributes preferentially employed during
infection. Therefore our findings document genes having increased transcript
abundance in the <italic>A. fumigatus</italic> sample of murine origin, relative to
that derived from laboratory cultured fungus. We performed our comparative analysis
using doubly amplified mRNA from developmentally matched <italic>A.
fumigatus</italic> germlings following laboratory culture, or growth in neutropenic
murine lungs. In total we identified 2164 genes having altered transcript abundance.
Functional analysis of the dataset flagged certain putatively relevant physiological
cues which we then pursued by additional <italic>in vitro</italic> analyses.</p> <p>Careful interpretation of the dataset with respect to specific nutrient acquisition
mechanisms, and within the context of the comparison performed here, can lend
powerful insight into accessible nutrients in the mammalian niche. Nitrate
assimilation (which is strongly inducible by nitrate in the absence of preferred
nitrogen sources ammonium and glutamine)##REF##9508795##[38]## is not preferentially
employed in the murine host as evidenced by co-ordinate down-regulation of
<italic>crn</italic>A (Afu1g12850), <italic>nia</italic>D (Afu12830) and
<italic>nii</italic>A (Afu1g12840). This may be due to a) equivalent nutritional
status of YPD and murine lung or b) to the absence of nitrate as a nitrogen source
during initiation of infection, or both. We were able to confirm, by radial growth
analyses <italic>in vitro</italic> using YPD and a synthetic lung tissue medium MLT,
that YPD is nutritionally superior to MLT based upon the rate of radial growth
supported (##FIG##6##Figure 7A##). Coupled
with the observation that the nitrogen metabolite repression gene product AreA is
required for full virulence##REF##9669338##[19]## a likely explanation for slowed growth and
repression of nitrogen assimilation, is the utilisation of alternative non-preferred
nitrogen sources during establishment of disease, such as amino acids. Strong
support for this conclusion is provided by high level induction of the
<italic>are</italic>A-dependent nitrogen-scavenging enzyme L-amino acid oxidase LaoA
(Afu7g06810) which enables <italic>Aspergilli</italic> to catabolise a broad
spectrum of amino acids in nitrogen starvation conditions##REF##16000761##[39]##. Notably a by-product of
such catabolism is ammonium. However the likliehood that sufficient ammonium is
produced by these reactions to prevent starvation is diminished by the observed
general starvation response in our dataset. Catabolism of amino acids during
initiation of infection is also evidenced by induction of the methyl citrate
synthase enzyme (Afu6g03590) which acts to detoxify the intermediates of
propionyl-coA generating carbon sources##REF##16008561##[40]##, such as cysteine,
isoleucine and methionine. An essential role for methylcitrate synthase in murine
aspergillosis has recently been demonstrated##UREF##4##[41]## thereby
demonstrating the value of our approach in generating physiological information on
virulence mechanisms within the context of murine infection.</p> <p>Strong themes among genes having lowered transcript abundance during murine infection
are ribosome biogenesis and assembly, and protein bisoynthesis and folding (##TAB##0##Table 1##). Such signatures are
commonly observed among microbes under stress, and in this instance might indicate a
slowing of growth in the murine lung, relative to laboratory culture. We reasoned
that a mechanism possibly linking such transcriptional profiles to nutrient
starvation is TOR mediated ribosomal gene regulation, which tightly couples protein
synthesis and cellular growth to availability of nutrients and physiological status
to balance the opposing forces of protein synthesis and degradation. This is pivotal
for cellular fate determination in many organisms##REF##15788394##[42]##,##REF##17721444##[43]##, propelling cells
towards <italic>either</italic> proliferation (through the cell cycle) or vegetative
growth (increase in size). Comparison of our dataset to that generated following
rapamycin-mediated TOR kinase inhibition in <italic>S. cerevisiae</italic> revealed
a very marked overlap (n = 125) in differentially
regulated homologous genes (##SUPPL##6##Table S4##). This is in keeping with defined roles
for TOR kinase function in <italic>S. cerevisiae</italic>, which includes the
regulation of transcription in response to nutrients##REF##10617575##[26]##. A strong correlation
between developmental programming and microbial secondary metabolite biosynthesis
has been well-documented##REF##16322742##[14]## and while plasticity of nitrogen metabolism
demonstrably supports <italic>A. fumigatus</italic> virulence##REF##16110790##[20]## the role of the
single, and likely essential, <italic>A. fumigatus</italic> TOR kinase homologue,
TorA, (Afu2g10270) in this process remains untested. Intriguingly, however, a link
between TOR kinase function and secondary metabolite production, partially through
AreA, has recently been established in the rice pathogen <italic>Fusarium
fujikuroi</italic>
##REF##17031002##[44]## where, in addition to the target genes in common
with yeast and other eukaryotes, the AreA-regulated giberellin and bikaverin
biosyntheis genes are also under the control of TOR. This raises the possibility of
a relatively limited investment in secondary metabolism, within the context of our
analysis, at the tested timepoint of murine infection.</p> <p>Genes expressed during nitrogen starvation are also expressed during virulence in
<italic>Magneporthe grisea</italic>
##REF##16731015##[45]##, where two wide-range
regulators of nitrogen catabolism genes, <italic>NPR1</italic> and <italic>NPR2</italic>
##REF##12239399##[46]## are
required for virulence. Contrary to our analysis nitrate and nitrite reductase
activities were found to be relevant for <italic>M. grisea</italic> during rice
infection##REF##16731015##[45]##, however, a number of amino acid transporting
proteins predominated among induced functions during both <italic>in vitro</italic>
nitrogen starvation and infection. Common to that analysis, and ours, was increased
abundance of proline oxidase (Afu3g02300) and proline permease (Afu2g11220,
Afu7g10190, Afu8g02200) proteins, which, in the context of our experiment might have
special relevance given the high hydroxyproline of collagen tissue, and the notable
proline requirement of an attenuated <italic>A. fumigatus</italic> deletion mutant
lacking the Ras-related protein RhbA##REF##12704156##[47]##. To test the effect
of nitrogen limitation on gene expression, in the context of the host adaptation
study performed, we returned to the YPD reference sample employed for the initial
analysis (sample F, ##SUPPL##3##Table S1##), this time performing competitive hybridisations with
doubly-amplified RNA obtained from a nitrogen-starved laboratory culture (sample G,
##SUPPL##3##Table
S1##). Given the predominance of hydroxyproline among collagen amino acids
content, and its release into bronchoalveolar lavage fluid upon tissue injury##REF##2463695##[34]## we
adopted hydroxyproline as sole source of nitrogen for the starvation experiment.
Hydroxyproline supported markedly slowed growth of <italic>A. fumigatus</italic> in
liquid culture (##FIG##6##Figure 7B##)
relative to YPD. We identified a significant overlap between the two datasets
amounting to 280 genes. Functional anlaysis of the resulting datasets revealed that,
in keeping with the murine adaptation dataset, ribosome biogenesis and protein
biosynthesis were markedly down regulated, moreover, functions associated with
mitosis and cell cycle were more abundantly represented among the categories
favoured under nitrogen starvation, relative to rich laboratory growth. Given the
differences between the tested media in terms of ability to support <italic>A.
fumigatus</italic> growth (##FIG##6##Figure
7B##) it is pleasing to see that such a theme was not apparent from the
murine adaptation dataset where the degree of nutrient limitation is unlikely to be
as severe as that imposed in the nitrogen starvation conditions we used here.
Interestingly 29% of the genes preferentially expressed during nitrogen
starvation were subtelomerically located, and a degree of clustered gene expression
was observable. Thus induction of the subtelomeric gene repertoire becomes important
during nutrient deprivation, a trend which was not observable in response to any of
the other <italic>in vitro</italic> stresses, other than neutrophil exposure the
physiological relevance of which requires further investigation.</p> <p>The uptake and catabolism of other amino acids released by proteolytic digestion of
murine lung parenchyma might provide a source of nitrogen during <italic>A.
fumigatus</italic> infection, which is supported in our analyses by increased
transcript abundance of various secreted proteases (##TAB##1##Table 2##). Extracellular proteases are implicated
as virulence actors in invasive aspergillosis##REF##10194462##[1]##. An elastolytic protease,
produced when <italic>A. fumigatus</italic> is cultured on the insoluble matrix from
bovine lung is also produced during spore germination in infected lungs of
neutropenic mice, as judged by immunogold cytochemical localization##REF##8500876##[48]##,
and mutationally-derived mutants unable to produce this protease were deficient for
virulence in the murine model. An elastinolytic metalloprotease, characterized by
the same group was similarly visualized during murine lung infection##REF##8188335##[49]##.
Many species of human pathogenic fungi secrete proteases <italic>in vitro</italic>
or during infection. Full virulence associated with <italic>A. fumigatus</italic>
protease mutants ##REF##10194462##[1]##,##REF##7715453##[50]## is presumed to be
due to redundancy among the many enzymes produced by this organism which may degrade
the lung parenchyma to release utilisable carbon and nitrogen during infection. A
directed analysis of the role of such proteins in virulence might now be possible on
the strength of our data. Importantly, however, any conclusions reached on the basis
of this study reflect the comparative nature of the anlaysis, thus transcripts
equally abundant under both conditions tested will not have been be identified. Time
course analyses of <italic>A. fumigatus</italic> growth during murine infection will
reveal stage-specific gene expression in the absence of confounding sample
treatments. These analyses are underway in our laboratories.</p> <p>Fungal oxygen-sensing mechanisms have been linked to cell membrane sterol levels in
<italic>Schizosaccharomyces pombe</italic> and <italic>Cryptococcus
neoformans</italic> where homologues of the mammalian Sterol Regulatory Element
Binding Protein (SREBP) transcription factors, in complex with SREBP
cleavage-activating protein (SCAP) partners, undergo cellular translocation (from
the endoplasmic reticulum to the golgi) prior to proteolytic SREBP activation##REF##15797383##[51]##,##REF##17462012##[52]##. The
<italic>C. neoformans</italic> SREBP homologue, Sre1p, plays an important role
in low oxygen adaptation and infection##REF##17462012##[52]##. Biosynthesis of sterols
and unsaturated fatty acids is an aerobic process in <italic>Saccharomyces
cerevisiae</italic> and in <italic>C. neoformans</italic>, the hypoxia-mimicking
agent cobalt chloride and oxygen limitation target sterol biosynthetic gene
expression. This was intriguing to us given the broadly observed reduction in
transcript abundance among ergosterol biosynthetic genes, relative to laboratory
culture (##TAB##1##Table 2##). Our initial
hypothesis attributed this effect to oxygen limitation in the murine lung
environment since airway obstruction, intraalveolar exudates and inflammation, or
damage to alveolar capillaries (all observed in our murine modelling of pulmonary
aspergillosis) pose a significant barrier to proper oxygenation in human lungs##REF##18040654##[53]##. Oxygen
deprivation in mammals leads to a transcriptional induction of genes for adaptation
to hypoxia##REF##12943226##[54]## and efforts to characterise the transcription profile
of murine immune responses to <italic>A. fumigatus</italic> infection indicate that
hypoxia is relevant in the neutropenic murine lung at 24 hours post-infection
(Turnbull, personal communication). Co-hybridisation of murine lung cDNAs, derived
from infected immunocompetent and immunocompromised mice with a murine immunology
array set identified upregulation of murine ARNT (log<sub>2</sub>
ratio = 2.09) the obligate heterodimeric binding
partner for the hypoxia induced factor HIF-1α##REF##12943226##[54]##. However, on comparing
the <italic>A. fumigatus</italic> murine adaptation gene expression signature to
that observed following exposure to anaerobic stress <italic>in vitro</italic>
(##FIG##4##Figure 5A##) no support for
this hypothesis could be gleaned. Rather, repression of the two <italic>A.
fumigatus</italic> 14-alpha sterol demethylase genes (Afu4g06890 and Afu Afu03740),
representing a critical step in ergosterol biosynthesis, was observed following
hydrogen peroxide-mediated oxygen stress. Therefore, it would seem that this
component of the H<sub>2</sub>O<sub>2</sub>-mediated oxidative stress response is
relevant <italic>in vivo</italic>. This was found to be distinct from antioxidative
action of the <italic>A. fumigatus</italic> Mn superoxide dismutase (Afu1g14550) and
the bifunctional catalase-peroxidase Cat2 (Afu8g01670), both of which were more
abundant following murine lung (##TAB##1##Table
2##) or neutrophil exposure (##FIG##5##Figures 6## and ##SUPPL##1##S2##), suggesting multiple modes of oxidative
stress encountered during murine infection. Interestingly H<sub>2</sub>O<sub>2</sub>
gradients are detectable across the <italic>Saccharomyces cerevisiae</italic> plasma
membrane upon H<sub>2</sub>O<sub>2</sub> exposure, suggesting a mechanism other than
diffusion for H<sub>2</sub>O<sub>2</sub> entry into cells. This, coupled with the
observation that <italic>S. cerevisiae</italic> mutants <italic>erg3Δ and
erg6Δ</italic>, having increased ergosterol biosynthesis, show increased
permeability to H<sub>2</sub>O<sub>2</sub> might suggest down-regulation of
ergosterol biosynthesis as a protective response against oxidative stress##REF##14645222##[55]##.</p> <p>All of the previously characterised components of the <italic>A. fumigatus</italic>
siderophore biosynthetic pathway##REF##15504822##[17]##,##UREF##0##[18]## were more abundantly
represented at transcript level during murine infection. Comparison of the murine
dataset with that generated during <italic>in vitro</italic> iron limitation
confirmed the importance of this environmental deficit during murine adaptation
(##FIG##5##Figure 6##). With respect to
extracellular iron mobilization fungal siderophores bind ferric iron with a high
affinity, delivering the ferric chelate to specific receptors at the cell surface
for translocation into the cytoplasm. Existing evidence supports an essential role
for this uptake mechanism during infection##REF##15504822##[17]##,##UREF##0##[18]##. Reductive iron
assimilation (RIA) is dispensable for murine virulence##REF##15504822##[17]## but may assist in
iron acquisition during infection since RIA inhibition in the absence of
extracellular siderophore biosynthesis prevents growth <italic>in
vitro</italic>
##UREF##0##[18]##. This hypothesis is supported by our finding that
numerous RIA components are induced during initiation of infection (##TAB##1##Table 2##) in addition to
siderophore biosynthetic genes. We were also able to correlate differential gene
expression following <italic>in vitro</italic> alkaline shift to the host adaptation
transcriptome (##FIG##4##Figure 5##). This
was to be expected given the broad requirement for fungal pH adaptation during
mammalian pathogenesis##UREF##5##[56]## but is nonetheless pleasing to observe
particularly given that the <italic>in vitro</italic> acid stress transcriptome
showed an opposite trend (##SUPPL##1##Figure S2##) and since pleiotropic activity of the
virulence-directing family of PacC/Rim101 pH sensing transcription factors##UREF##5##[56]## does
not permit virulence defects to be soley, or thus far absolutely, correlated with pH
growth phenotypes.</p> <p>Fungal secondary metabolite biosynthesis is mediated at the level of transcription,
from clusters of physically linked, co-ordinately regulated genes and is profoundly
affected by environmental factors such as pH and nutrient availability##REF##16322742##[14]##. For
the most part, defined cues prompting gene expression from such clusters remain
unknown, as do the functions of the molecules produced by them in the natural
environment, but popular theory regards microbial secondary metabolites as chemical
determinants of selective advantage##REF##17686752##[57]##. Noting that clusters of physically linked,
co-ordinately regulated genes were a prominent feature of our dataset we applied a
custom script to systematically identify them. To limit the identification of false
negatives from our analysis (due, for example, to poor spot quality or absence on
array) we allowed for ‘gaps’ of up to a maximum of 4 non-up- or
down- regulated genes permissable per cluster. Gap size variation influenced the
architecture of clusters detectable in the dataset, further widening the extremities
of pre-existing clusters rather than creating new ones (a complete breakdown of the
cluster anlaysis is available as ##SUPPL##13##Dataset S2##). Thus a gap size of zero identified
14 upregulated gene clusters and a gap size of one identified 38. The maximum gap
size applied (gap size = 4) identified 65 groups of
physically linked genes having increased transcript abundance during murine
infection (##FIG##2##Figure 3##). A large
proportion (34%) of up-regulated, physically clustered, genes was found
to reside within 300 kb of chromosome ends (##FIG##2##Figure 3## and ##TAB##3##Table 4##). The clusters are comprised of up to 34
co-ordinately expressed genes and include loci directing biosynthesis of
siderophores (cluster 33) and two known secondary metabolites, pseurotin and
gliotoxin (clusters 70 and 59, respectively). The importance of fungal gene clusters
in virulence has recently been characterised in the fungal plant pathogen,
<italic>Ustilago maydis</italic> where co-induction of physically linked,
secreted protein-encoding genes is seen during infection##REF##17276906##[58]##. We were unable to
find evidence of clustered secreted protein genes from our analyses of <italic>A.
fumigatus</italic> (data not shown) however, from an evolutionary perspective,
clusters 8, 40, 53, 68 (down-regulated), 69 and 70 (##SUPPL##8##Table S6##)
deviate significantly from the least virulent sequenced species considered here,
<italic>N. fischeri</italic>, and therefore merit further analysis. <italic>N.
fischeri</italic> is extremely rarely identified as a human pathogen##REF##9730310##[59]##–##REF##1624579##[61]##, while prolonged
exposure to <italic>A. clavatus</italic> spores can cause extrinsic allergic
alveolitis known as malt worker's lung##REF##589785##[62]##. From a functional
perspective some relevant deductions regarding the clusters identifiedby our
analyses might be possible using present genome annotations. However, closer
scrutinization of clusters is required to address the likelihood that <italic>A.
fumigatus</italic> genes located in them encode functions required for
virulence. We anticipate that the inference of shared functionality among
neighbouring co-regulated genes in our dataset will empower the functional
annotation of the <italic>A. fumigatus</italic> genome; moreover, it significantly
raises the profile of such gene-regulatory paradigms within the context of fungal
pathogenicity.</p> <p>Our observations of biased localization among genes overrepresented during growth in
the murine lung (##FIG##2##Figures 3## and
##FIG##3##4##, ##TAB##2##Table 3##) prompted comparison of our dataset to
that obtained by Perrin <italic>et. al.</italic>
##REF##17432932##[16]## who identified genes
under control of the global seconday metabolism regulator, LaeA. Significant overlap
is observable, notably among secondary metabolism genes, most specifically between
genes in the gliotoxin and pseurotin biosynthetic gene clusters. Hypovirulence of a
Δ<italic>lae</italic>A deletion mutant cannot be explained solely on the
basis of a lack of gliotoxin biosynthesis, and no investigation of the role of other
secondary metabolite clusters, with respect to virulence, has been undertaken in
<italic>A. fumigatus</italic>, therefore at this time it is not possible to
reach any conclusions on the contribution made to virulence by the molecules whose
synthesis is directed by these loci. With regard to our comparison of functions
under LaeA control and those important during murine infection, differing
experimental conditions (Perrin study##REF##17432932##[16]## performed at
25°C in liquid shaking culture and glucose minimal medium##REF##13040135##[63]## for
60 hours) and methodologies pose a contentious issue. A truly illuminating analysis
of LaeA activity during murine pathogenesis might be forthcoming from time course
analyses in neutropenic mice, which we are currently attempting. DNA sequences in
subtelomeric regions undergo ectopic recombination at a much higher rate than
expected for homologous recombination##REF##17610516##[64]## allowing the expansion
and diversification of gene families located at chromosome ends. For some organisms
gene expression is governed by subtelomeric localisation, for example two <italic>A.
nidulans</italic> secondary metabolism gene clusters, directing penicillin and
sterigmatocystin biosynthesis, are activated following deletion of the
<italic>hda</italic>A histone deacetylase gene##REF##17616629##[65]##. In other organisms
subtelomeres provide an ideal setting for genes involved in antigenic variation,
such as in the parasites <italic>Plasmodium falciparum</italic> and
<italic>Trypanosoma brucei</italic>
##REF##12547344##[66]## and in cytoadhesion,
such as the EPA family of <italic>Candida glabrata</italic> adhesins##REF##12952896##[67]##. Thus
the importance of sub-telomeric chromosomal regions within the context of eukaryotic
pathogenesis is gaining significance in this post-genomic era of microbial studies.</p> <p>These analyses of transcript abundance, drawn from minute quantities of fungal
material convey a programme of <italic>A. fumigatus</italic> cellular regulation
directly from the site of pulmonary infection. From a fungal physiological
perspective the mammalian host restricts iron, and likely various nutrients, as well
as exerting multiple degrees of oxidative stress. Regarded as a function of the
genomic landscape the observed transcriptional changes reveal genome organisation
and subtelomeric diversity as effectors of the remarkable versatility of <italic>A.
fumigatus</italic> with respect to the niches it successfully inhabits, one of
which is the neutropenic human lung.</p>
<p>Conceived and designed the experiments: AM NDF YY SK OL DAJ KH HH MS GM WCN
EB. Performed the experiments: YY SK DC OL TC DAJ HH MS GM WCN EB. Analyzed
the data: AM NDF JC SK OL GG KH WCN EB. Contributed
reagents/materials/analysis tools: AM NDF JC WCN EB. Wrote the paper: AM NDF
JC GG KH WCN EB.</p> <p>
<italic>Aspergillus fumigatus</italic> is a common mould whose spores are a
component of the normal airborne flora. Immune dysfunction permits developmental
growth of inhaled spores in the human lung causing aspergillosis, a significant
threat to human health in the form of allergic, and life-threatening invasive
infections. The success of <italic>A. fumigatus</italic> as a pathogen is unique
among close phylogenetic relatives and is poorly characterised at the molecular
level. Recent genome sequencing of several <italic>Aspergillus</italic> species
provides an exceptional opportunity to analyse fungal virulence attributes
within a genomic and evolutionary context. To identify genes preferentially
expressed during adaptation to the mammalian host niche, we generated multiple
gene expression profiles from minute samplings of <italic>A. fumigatus</italic>
germlings during initiation of murine infection. They reveal a highly
co-ordinated <italic>A. fumigatus</italic> gene expression programme, governing
metabolic and physiological adaptation, which allows the organism to prosper
within the mammalian niche. As functions of phylogenetic conservation and
genetic locus, 28% and 30%, respectively, of the
<italic>A. fumigatus</italic> subtelomeric and lineage-specific gene
repertoires are induced relative to laboratory culture, and physically clustered
genes including loci directing pseurotin, gliotoxin and siderophore biosyntheses
are a prominent feature. Locationally biased <italic>A. fumigatus</italic> gene
expression is not prompted by <italic>in vitro</italic> iron limitation, acid,
alkaline, anaerobic or oxidative stress. However, subtelomeric gene expression
is favoured following <italic>ex vivo</italic> neutrophil exposure and in
comparative analyses of richly and poorly nourished laboratory cultured
germlings. We found remarkable concordance between the <italic>A.
fumigatus</italic> host-adaptation transcriptome and those resulting from
<italic>in vitro</italic> iron depletion, alkaline shift, nitrogen
starvation and loss of the methyltransferase LaeA. This first transcriptional
snapshot of a fungal genome during initiation of mammalian infection provides
the global perspective required to direct much-needed diagnostic and therapeutic
strategies and reveals genome organisation and subtelomeric diversity as
potential driving forces in the evolution of pathogenicity in the genus
<italic>Aspergillus</italic>.</p> <title>Author Summary</title> <p>Airborne spores of the fungus <italic>Aspergillus fumigatus</italic> are present
in significant quantities worldwide and are responsible for a range of illnesses
from allergy to deadly invasive lung infection. A number of fungal properties
are likely required for germination and growth of the fungus in the host, and
now that the genome sequence of <italic>A. fumigatus</italic> is available it is
possible to address which genes become important during initiation of infection.
Understanding this might lead to new therapeutics and diagnostic tools. We have
compared <italic>A. fumigatus</italic> gene activation during infection in a
murine model to that in a laboratory culture to identify fungal attributes
preferentially employed during disease. Our analysis entailed measurement of
activity from most of the &gt;9000 <italic>A. fumigatus</italic> genes,
identifying iron limitation, alkaline stress, and nitrogen starvation as
prominent stresses imposed by the host environment. We also found that genes
preferentially employed for infection occur in clusters and are more likely to
reside near the end of chromosomes, otherwise known as telomeres.</p>
<title>Supporting Information</title>
<p>We thank H. N. Arst Jnr., Sven Krappman and Nora Khaldi for critical discussion and
reading of the manuscript and Jimena Gonzalez Gonzalez, Tatiana Munera and C. Kim
Nguyen for technical support.</p>
<fig id="ppat-1000154-g001" position="float"><object-id pub-id-type="doi">10.1371/journal.ppat.1000154.g001</object-id><label>Figure 1</label><caption><title>Comparative time-course of <italic>A. fumigatus</italic> Af293
germination and hyphal development in the murine lung, and laboratory
culture.</title><p>(A) Time-course of Af293 germination and hyphal development in the
neutropenic murine lung. (B) Microscopic appearance of Af293 germlings
recovered from a typical single murine BALF, (harvested at
12–14 hours post-infection). (C) Microscopy of developmentally
matched laboratory cultured Af293 germlings, following liquid culture
for 12 hours in YPD at 37°C.</p></caption></fig> <fig id="ppat-1000154-g002" position="float"><object-id pub-id-type="doi">10.1371/journal.ppat.1000154.g002</object-id><label>Figure 2</label><caption><title>Correlation of log<sub>2</sub> ratios resulting from comparative
transcriptional analysis of the laboratory cultured <italic>A.
fumigatus</italic> cell populations T0 and T60 under varying mRNA
amplification protocols.</title><p>Correlation of technically duplicated log<sub>2</sub> ratios between
competitive hybridisations using single (aRNA<sub>1</sub>), double
(aRNA<sub>2</sub>) and unamplified (totRNA) RNA samples. (A and B)
Correlation between log<sub>2</sub> ratios obtained using cDNA derived
from amplified and total RNA (totRNA v aRNA<sub>r1</sub>
<italic>r</italic> = 0.74–0.80,
totRNA v aRNA<sub>r2</sub>
<italic>r</italic> = 0.74–0.80)
(C) Cross-protocol pairings revealed highest correlations between slides
using cDNA derived from amplified RNA (aRNA<sub>r1</sub> v aRNA<sub>r2</sub>
<italic>r</italic> = 0.88–0.91)
Surprisingly, technical replicates of slides using cDNA derived from
total RNA (totRNA
<italic>r</italic> = 0.80) were
comparable to cross-protocol pairings (data not shown).</p></caption></fig> <fig id="ppat-1000154-g003" position="float"><object-id pub-id-type="doi">10.1371/journal.ppat.1000154.g003</object-id><label>Figure 3</label><caption><title>A genome-wide transcriptional snapshot of <italic>A.
fumigatus</italic> Af293 during intiation of murine infection.</title><p>Red and green vertical lines correspond to individual up- and
down-regulated genes, respectively. Thin light gray vertical lines
indicate the positions of all other genes. (SM) and (asp_core) are
density graphs of secondary metabolite and
<italic>Aspergillus</italic>-core genes, respectively, expressed as a
percentage of the total bases contained per gene type, per
non-overlapping 2 kb of chromosomal sequence. Induced and repressed gene
clusters, are depicted by red and green rectangles, respectively, below
each chromosome. A complete listing of genes housed in these
co-regulated clusters can be found in ##SUPPL##8##Table
S6##. Light blue/gray vertical bars represent putative centromeres
and the pink vertical bar in chromosome 4 represents a region of
ribosomal DNA.</p></caption></fig> <fig id="ppat-1000154-g004" position="float"><object-id pub-id-type="doi">10.1371/journal.ppat.1000154.g004</object-id><label>Figure 4</label><caption><title>Distribution of lineage specific and telomere-proximal genes among
differentially expressed host adaptation dataset.</title><p>(A) Lineage specificity of <italic>A. fumigatus</italic> genes having
altered transcript abundances, relative to laboratory culture, in the
murine lung. The <italic>Aspergillus</italic>-core (Asp-core) set
contains <italic>A. fumigatus</italic> Af293 proteins that have
orthologues in <italic>A. clavatus</italic> (AAKD00000000), <italic>N.
fischeri</italic> (AAKE00000000), <italic>Aspergillus
terreus</italic> NIH2624 (AAJN01000000), <italic>Aspergillus
oryzae</italic> RIB40##REF##16372010##[70]##, <italic>A. nidulans</italic> FGSC
A4##REF##16372000##[71]## and <italic>Aspergillus
niger</italic> CBS 513.55##REF##17259976##[72]## The Affc-core set were defined as
<italic>A. <underline>f</underline>umigatus</italic> Af293 proteins
that have ortholouges in <italic>N. fischeri</italic> and <italic>A.
clavatus</italic>. The Affc-unique set is a sub-set of Affc-core
proteins that do not have ortholouges in <italic>A. terreus</italic>,
<italic>A. oryzae</italic>, <italic>A. nidulans</italic> or
<italic>A. niger</italic>. Asterisks indicate gene sets which are
listed in ##SUPPL##7##Table S5##. Underlined values
significantly deviate from the null hypothesis that an equal number of
induced and repressed genes will occur in each cohort, as estimated by
Chi-square analysis (##TAB##2##Table 3##). (B) Chromosomal distribution of <italic>A.
fumigatus</italic> genes having altered transcript abundances,
relative to laboratory culture, in the murine lung. Distances from
telomeres (kb) are noted above pie charts. Asterisked gene sets are
listed in Supplementary ##SUPPL##7##Table S5##. Underlined values
significantly deviate from the null hypothesis that an equal number of
induced and repressed genes will occur in each cohort, as estimated by
Chi-square analysis (##TAB##2##Table 3##).</p></caption></fig> <fig id="ppat-1000154-g005" position="float"><object-id pub-id-type="doi">10.1371/journal.ppat.1000154.g005</object-id><label>Figure 5</label><caption><title>Overlap between murine adaptation and <italic>in vitro</italic>
stress datasets.</title><p>Venn diagrammatic representation of overlap between murine adaptation
dataset and those of nitrogen starvation, iron starvation and alkaline
shift. Genes are listed in ##SUPPL##14##Dataset S3##.</p></caption></fig> <fig id="ppat-1000154-g006" position="float"><object-id pub-id-type="doi">10.1371/journal.ppat.1000154.g006</object-id><label>Figure 6</label><caption><title>Comparative analysis of <italic>A. fumigatus</italic> gene expression
datsets.</title><p>A pan-experimental comparison of <italic>A. fumigatus</italic> gene
expression aligning log<sub>2</sub> ratios obtained during host
adaptation (mice); exposure to neutrophils (neut), increased expression
in parental strain versus Δ<italic>lae</italic>A mutant, acid
shift (acid), iron starvation (iron), oxygen depletion (anaer) and
oxidative stress (H<sub>2</sub>O<sub>2</sub>) for various genes. The
colour bar indicates the range of log<sub>2</sub> expression ratios,
grey bars indicate genes from which signals were undetectable for
technical reasons. Experimental conditions are described in <xref ref-type="sec" rid="s4">Materials and Methods</xref>. LaeA dataset
is taken from Perrin <italic>et. al.</italic>
##REF##17432932##[16]##. Comparative analyses were implemented in
TM4 <ext-link ext-link-type="uri" xlink:href="http://www.jcvi.org/cms/research/software/">http://www.jcvi.org/cms/research/software/</ext-link>.</p></caption></fig> <fig id="ppat-1000154-g007" position="float"><object-id pub-id-type="doi">10.1371/journal.ppat.1000154.g007</object-id><label>Figure 7</label><caption><title>Characterisation of <italic>A. fumigatus</italic> growth, relative to
YPD.</title><p>(A) Comparative analysis of Af293 radial growth on YPD and synthetic
murine lung tissue medium (MLT). Triplicated, spot-inoculated plates
containing single 100 spore inocula were incubated at 37°C. (B)
Growth curve analysis of Af293, performed in triplicate using liquid
YPD, or AMM containing 1% glucose and either 5 mM ammonium
tartrate or 5 mM hyroxyproline as nitrogen source. Cultures were
inoculated to a final concentration of 5×10<sup>6</sup>
spores/ml and incubated under aerobic conditions at 37°C with
shaking at 150 rpm. At selected timepoints mycelia were harvested on
Miracloth, encased in Whatmann paper and dried at 37°C for 48
hours before weighing.</p></caption></fig> <fig id="ppat-1000154-g008" position="float"><object-id pub-id-type="doi">10.1371/journal.ppat.1000154.g008</object-id><label>Figure 8</label><caption><title>Expression of LaeA-regulated genes during initiation of murine
infection.</title><p>Venn diagram representation of overlap between genes repressed in
ΔLaeA##REF##17432932##[16]## and those having increased transcript
abundance during murine infection, according to proportions having
subtelomeric locations, and secondary metabolism functionality (on the
basis of annotation).</p></caption></fig>
<table-wrap id="ppat-1000154-t001" position="float"><object-id pub-id-type="doi">10.1371/journal.ppat.1000154.t001</object-id><label>Table 1</label><caption><title>Over-represented Gene Ontology terms among differentially expressed
genes.</title></caption><alternatives><table frame="hsides" rules="groups"><colgroup span="1"><col align="left" span="1"/><col align="center" span="1"/><col align="center" span="1"/><col align="center" span="1"/><col align="center" span="1"/><col align="center" span="1"/><col align="center" span="1"/></colgroup><thead><tr><td colspan="7" align="left" rowspan="1">Over represented biological processes
among genes induced <italic>in vivo</italic>
</td></tr><tr><td align="left" rowspan="1" colspan="1">GO ID</td><td align="left" rowspan="1" colspan="1">GO term</td><td align="left" rowspan="1" colspan="1">List hits</td><td align="left" rowspan="1" colspan="1">List size</td><td align="left" rowspan="1" colspan="1">Pop.hits</td><td align="left" rowspan="1" colspan="1">Pop. Size</td><td align="left" rowspan="1" colspan="1">Fisher's Exact</td></tr></thead><tbody><tr><td align="left" rowspan="1" colspan="1">GO:0006810</td><td align="left" rowspan="1" colspan="1">transport</td><td align="left" rowspan="1" colspan="1">133</td><td align="left" rowspan="1" colspan="1">448</td><td align="left" rowspan="1" colspan="1">761</td><td align="left" rowspan="1" colspan="1">4219</td><td align="left" rowspan="1" colspan="1">1.08E-10</td></tr><tr><td align="left" rowspan="1" colspan="1">GO:0008643</td><td align="left" rowspan="1" colspan="1">carbohydrate transport</td><td align="left" rowspan="1" colspan="1">19</td><td align="left" rowspan="1" colspan="1">448</td><td align="left" rowspan="1" colspan="1">40</td><td align="left" rowspan="1" colspan="1">4219</td><td align="left" rowspan="1" colspan="1">3.41E-09</td></tr><tr><td align="left" rowspan="1" colspan="1">GO:0009063</td><td align="left" rowspan="1" colspan="1">amino acid catabolism</td><td align="left" rowspan="1" colspan="1">14</td><td align="left" rowspan="1" colspan="1">448</td><td align="left" rowspan="1" colspan="1">35</td><td align="left" rowspan="1" colspan="1">4219</td><td align="left" rowspan="1" colspan="1">5.37E-06</td></tr><tr><td align="left" rowspan="1" colspan="1">GO:0044242</td><td align="left" rowspan="1" colspan="1">cellular lipid catabolism</td><td align="left" rowspan="1" colspan="1">10</td><td align="left" rowspan="1" colspan="1">448</td><td align="left" rowspan="1" colspan="1">19</td><td align="left" rowspan="1" colspan="1">4219</td><td align="left" rowspan="1" colspan="1">6.30E-06</td></tr><tr><td align="left" rowspan="1" colspan="1">GO:0044270</td><td align="left" rowspan="1" colspan="1">nitrogen compound catabolism</td><td align="left" rowspan="1" colspan="1">15</td><td align="left" rowspan="1" colspan="1">448</td><td align="left" rowspan="1" colspan="1">42</td><td align="left" rowspan="1" colspan="1">4219</td><td align="left" rowspan="1" colspan="1">1.28E-05</td></tr><tr><td align="left" rowspan="1" colspan="1">GO:0030001</td><td align="left" rowspan="1" colspan="1">metal ion transport</td><td align="left" rowspan="1" colspan="1">19</td><td align="left" rowspan="1" colspan="1">448</td><td align="left" rowspan="1" colspan="1">74</td><td align="left" rowspan="1" colspan="1">4219</td><td align="left" rowspan="1" colspan="1">1.76E-04</td></tr><tr><td align="left" rowspan="1" colspan="1">GO:0015892</td><td align="left" rowspan="1" colspan="1">siderophore-iron transport</td><td align="left" rowspan="1" colspan="1">5</td><td align="left" rowspan="1" colspan="1">448</td><td align="left" rowspan="1" colspan="1">8</td><td align="left" rowspan="1" colspan="1">4219</td><td align="left" rowspan="1" colspan="1">5.63E-04</td></tr><tr><td align="left" rowspan="1" colspan="1">GO:0006812</td><td align="left" rowspan="1" colspan="1">cation transport</td><td align="left" rowspan="1" colspan="1">21</td><td align="left" rowspan="1" colspan="1">448</td><td align="left" rowspan="1" colspan="1">94</td><td align="left" rowspan="1" colspan="1">4219</td><td align="left" rowspan="1" colspan="1">6.45E-04</td></tr><tr><td align="left" rowspan="1" colspan="1">GO:0019541</td><td align="left" rowspan="1" colspan="1">propionate metabolism</td><td align="left" rowspan="1" colspan="1">3</td><td align="left" rowspan="1" colspan="1">448</td><td align="left" rowspan="1" colspan="1">4</td><td align="left" rowspan="1" colspan="1">4219</td><td align="left" rowspan="1" colspan="1">4.38E-03</td></tr><tr><td align="left" rowspan="1" colspan="1">GO:0006830</td><td align="left" rowspan="1" colspan="1">high-affinity zinc ion transport</td><td align="left" rowspan="1" colspan="1">2</td><td align="left" rowspan="1" colspan="1">448</td><td align="left" rowspan="1" colspan="1">2</td><td align="left" rowspan="1" colspan="1">4219</td><td align="left" rowspan="1" colspan="1">1.13E-02</td></tr><tr><td align="left" rowspan="1" colspan="1">GO:0006631</td><td align="left" rowspan="1" colspan="1">fatty acid metabolism</td><td align="left" rowspan="1" colspan="1">13</td><td align="left" rowspan="1" colspan="1">448</td><td align="left" rowspan="1" colspan="1">62</td><td align="left" rowspan="1" colspan="1">4219</td><td align="left" rowspan="1" colspan="1">1.17E-02</td></tr></tbody></table><table frame="hsides" rules="groups"><colgroup span="1"><col align="left" span="1"/><col align="center" span="1"/><col align="center" span="1"/><col align="center" span="1"/><col align="center" span="1"/><col align="center" span="1"/><col align="center" span="1"/></colgroup><thead><tr><td colspan="7" align="left" rowspan="1">Over represented biological processes
among genes repressed <italic>in vivo</italic>
</td></tr><tr><td align="left" rowspan="1" colspan="1">GO ID</td><td align="left" rowspan="1" colspan="1">GO term</td><td align="left" rowspan="1" colspan="1">List hits</td><td align="left" rowspan="1" colspan="1">List size</td><td align="left" rowspan="1" colspan="1">Pop.hits</td><td align="left" rowspan="1" colspan="1">Pop. Size</td><td align="left" rowspan="1" colspan="1">Fisher's Exact</td></tr></thead><tbody><tr><td align="left" rowspan="1" colspan="1">GO:0019538</td><td align="left" rowspan="1" colspan="1">protein metabolism</td><td align="left" rowspan="1" colspan="1">174</td><td align="left" rowspan="1" colspan="1">561</td><td align="left" rowspan="1" colspan="1">781</td><td align="left" rowspan="1" colspan="1">4219</td><td align="left" rowspan="1" colspan="1">7.35E-15</td></tr><tr><td align="left" rowspan="1" colspan="1">GO:0042254</td><td align="left" rowspan="1" colspan="1">ribosome biogenesis and assembly</td><td align="left" rowspan="1" colspan="1">51</td><td align="left" rowspan="1" colspan="1">561</td><td align="left" rowspan="1" colspan="1">161</td><td align="left" rowspan="1" colspan="1">4219</td><td align="left" rowspan="1" colspan="1">5.56E-10</td></tr><tr><td align="left" rowspan="1" colspan="1">GO:0044237</td><td align="left" rowspan="1" colspan="1">cellular metabolism</td><td align="left" rowspan="1" colspan="1">366</td><td align="left" rowspan="1" colspan="1">561</td><td align="left" rowspan="1" colspan="1">2251</td><td align="left" rowspan="1" colspan="1">4219</td><td align="left" rowspan="1" colspan="1">6.51E-10</td></tr><tr><td align="left" rowspan="1" colspan="1">GO:0007046</td><td align="left" rowspan="1" colspan="1">ribosome biogenesis</td><td align="left" rowspan="1" colspan="1">46</td><td align="left" rowspan="1" colspan="1">561</td><td align="left" rowspan="1" colspan="1">140</td><td align="left" rowspan="1" colspan="1">4219</td><td align="left" rowspan="1" colspan="1">1.08E-09</td></tr><tr><td align="left" rowspan="1" colspan="1">GO:0016072</td><td align="left" rowspan="1" colspan="1">rRNA metabolism</td><td align="left" rowspan="1" colspan="1">41</td><td align="left" rowspan="1" colspan="1">561</td><td align="left" rowspan="1" colspan="1">118</td><td align="left" rowspan="1" colspan="1">4219</td><td align="left" rowspan="1" colspan="1">1.37E-09</td></tr><tr><td align="left" rowspan="1" colspan="1">GO:0006364</td><td align="left" rowspan="1" colspan="1">rRNA processing</td><td align="left" rowspan="1" colspan="1">40</td><td align="left" rowspan="1" colspan="1">561</td><td align="left" rowspan="1" colspan="1">115</td><td align="left" rowspan="1" colspan="1">4219</td><td align="left" rowspan="1" colspan="1">2.12E-09</td></tr><tr><td align="left" rowspan="1" colspan="1">GO:0044238</td><td align="left" rowspan="1" colspan="1">primary metabolism</td><td align="left" rowspan="1" colspan="1">334</td><td align="left" rowspan="1" colspan="1">561</td><td align="left" rowspan="1" colspan="1">2028</td><td align="left" rowspan="1" colspan="1">4219</td><td align="left" rowspan="1" colspan="1">3.30E-09</td></tr><tr><td align="left" rowspan="1" colspan="1">GO:0006457</td><td align="left" rowspan="1" colspan="1">protein folding</td><td align="left" rowspan="1" colspan="1">23</td><td align="left" rowspan="1" colspan="1">561</td><td align="left" rowspan="1" colspan="1">50</td><td align="left" rowspan="1" colspan="1">4219</td><td align="left" rowspan="1" colspan="1">1.48E-08</td></tr><tr><td align="left" rowspan="1" colspan="1">GO:0006412</td><td align="left" rowspan="1" colspan="1">protein biosynthesis</td><td align="left" rowspan="1" colspan="1">72</td><td align="left" rowspan="1" colspan="1">561</td><td align="left" rowspan="1" colspan="1">302</td><td align="left" rowspan="1" colspan="1">4219</td><td align="left" rowspan="1" colspan="1">1.63E-07</td></tr><tr><td align="left" rowspan="1" colspan="1">GO:0006783</td><td align="left" rowspan="1" colspan="1">heme biosynthesis</td><td align="left" rowspan="1" colspan="1">6</td><td align="left" rowspan="1" colspan="1">561</td><td align="left" rowspan="1" colspan="1">13</td><td align="left" rowspan="1" colspan="1">4219</td><td align="left" rowspan="1" colspan="1">4.03E-03</td></tr><tr><td align="left" rowspan="1" colspan="1">GO:0009060</td><td align="left" rowspan="1" colspan="1">aerobic respiration</td><td align="left" rowspan="1" colspan="1">13</td><td align="left" rowspan="1" colspan="1">561</td><td align="left" rowspan="1" colspan="1">48</td><td align="left" rowspan="1" colspan="1">4219</td><td align="left" rowspan="1" colspan="1">8.02E-03</td></tr><tr><td align="left" rowspan="1" colspan="1">GO:0006696</td><td align="left" rowspan="1" colspan="1">ergosterol biosynthesis</td><td align="left" rowspan="1" colspan="1">11</td><td align="left" rowspan="1" colspan="1">561</td><td align="left" rowspan="1" colspan="1">38</td><td align="left" rowspan="1" colspan="1">4219</td><td align="left" rowspan="1" colspan="1">8.45E-03</td></tr></tbody></table></alternatives></table-wrap> <table-wrap id="ppat-1000154-t002" position="float"><object-id pub-id-type="doi">10.1371/journal.ppat.1000154.t002</object-id><label>Table 2</label><caption><title>Functional classification of selected genes having altered transcript
abundance, relative to laboratory culture, in the murine lung.</title></caption><alternatives><table frame="hsides" rules="groups"><colgroup span="1"><col align="left" span="1"/><col align="center" span="1"/><col align="center" span="1"/><col align="center" span="1"/><col align="center" span="1"/><col align="center" span="1"/></colgroup><thead><tr><td colspan="2" align="left" rowspan="1">Ergosterol and Heme Biosynthesis</td><td align="left" rowspan="1" colspan="1"/><td colspan="2" align="left" rowspan="1">Iron acquisition</td><td align="left" rowspan="1" colspan="1"/></tr><tr><td align="left" rowspan="1" colspan="1">Log<sub>2</sub> Ratio</td><td align="left" rowspan="1" colspan="1">ORF Number</td><td align="left" rowspan="1" colspan="1">Gene annotation</td><td align="left" rowspan="1" colspan="1">Log<sub>2</sub> Ratio</td><td align="left" rowspan="1" colspan="1">ORF number</td><td align="left" rowspan="1" colspan="1">Gene annotation</td></tr></thead><tbody><tr><td align="left" rowspan="1" colspan="1">−2.072748506</td><td align="left" rowspan="1" colspan="1">Afu1g05720</td><td align="left" rowspan="1" colspan="1">c-14 sterol reductase</td><td align="left" rowspan="1" colspan="1">2.3932159</td><td align="left" rowspan="1" colspan="1">Afu7g06060</td><td align="left" rowspan="1" colspan="1">siderochrome-iron transporter (Sit1)</td></tr><tr><td align="left" rowspan="1" colspan="1">−2.135790025</td><td align="left" rowspan="1" colspan="1">Afu1g03950</td><td align="left" rowspan="1" colspan="1">cytochrome P450 sterol C-22 desaturase</td><td align="left" rowspan="1" colspan="1">2.5525794</td><td align="left" rowspan="1" colspan="1">Afu7g04730</td><td align="left" rowspan="1" colspan="1">siderochrome-iron transporter</td></tr><tr><td align="left" rowspan="1" colspan="1">−2.1829312</td><td align="left" rowspan="1" colspan="1">Afu8g07210</td><td align="left" rowspan="1" colspan="1">hydroxymethylglutaryl-CoA synthase</td><td align="left" rowspan="1" colspan="1">3.9717973</td><td align="left" rowspan="1" colspan="1">Afu6g13750</td><td align="left" rowspan="1" colspan="1">ferric-chelate reductase</td></tr><tr><td align="left" rowspan="1" colspan="1">−2.297904455</td><td align="left" rowspan="1" colspan="1">Afu5g02450</td><td align="left" rowspan="1" colspan="1">farnesyl-pyrophosphate synthetase</td><td align="left" rowspan="1" colspan="1">6.105128</td><td align="left" rowspan="1" colspan="1">Afu3g03440</td><td align="left" rowspan="1" colspan="1">MFS family siderophore transporter</td></tr><tr><td align="left" rowspan="1" colspan="1">−2.346162536</td><td align="left" rowspan="1" colspan="1">Afu7g03740</td><td align="left" rowspan="1" colspan="1">14-alpha sterol demethylase Cyp51B</td><td align="left" rowspan="1" colspan="1">3.4018665</td><td align="left" rowspan="1" colspan="1">Afu4g14640</td><td align="left" rowspan="1" colspan="1">low affinity iron transporter</td></tr><tr><td align="left" rowspan="1" colspan="1">−2.895486437</td><td align="left" rowspan="1" colspan="1">Afu1g07140</td><td align="left" rowspan="1" colspan="1">c-24(28) sterol reductase</td><td align="left" rowspan="1" colspan="1">2.6380832</td><td align="left" rowspan="1" colspan="1">Afu3g03650</td><td align="left" rowspan="1" colspan="1">sidG</td></tr><tr><td align="left" rowspan="1" colspan="1">−2.923648336</td><td align="left" rowspan="1" colspan="1">Afu1g03150</td><td align="left" rowspan="1" colspan="1">c-14 sterol reductase</td><td align="left" rowspan="1" colspan="1">6.4549049</td><td align="left" rowspan="1" colspan="1">Afu3g03640</td><td align="left" rowspan="1" colspan="1">siderochrome-iron transporter (MirB)</td></tr><tr><td align="left" rowspan="1" colspan="1">−2.970089164</td><td align="left" rowspan="1" colspan="1">Afu2g00320</td><td align="left" rowspan="1" colspan="1">sterol delta 5</td><td align="left" rowspan="1" colspan="1">6.1431709</td><td align="left" rowspan="1" colspan="1">Afu3g03420</td><td align="left" rowspan="1" colspan="1">sidD</td></tr><tr><td align="left" rowspan="1" colspan="1">−3.376200503</td><td align="left" rowspan="1" colspan="1">Afu6g05140</td><td align="left" rowspan="1" colspan="1">sterol delta 5</td><td align="left" rowspan="1" colspan="1">6.4520669</td><td align="left" rowspan="1" colspan="1">Afu3g03400</td><td align="left" rowspan="1" colspan="1">siderophore biosynthesis acetylase AceI (sidF)</td></tr><tr><td align="left" rowspan="1" colspan="1">−3.422246472</td><td align="left" rowspan="1" colspan="1">Afu5g14350</td><td align="left" rowspan="1" colspan="1">c-24(28) sterol reductase</td><td align="left" rowspan="1" colspan="1">3.4125222</td><td align="left" rowspan="1" colspan="1">Afu3g03390</td><td align="left" rowspan="1" colspan="1">siderophore biosynthesis lipase/esterase</td></tr><tr><td align="left" rowspan="1" colspan="1">−3.931510834</td><td align="left" rowspan="1" colspan="1">Afu4g06890</td><td align="left" rowspan="1" colspan="1">14-alpha sterol demethylase Cyp51A</td><td align="left" rowspan="1" colspan="1">4.8541282</td><td align="left" rowspan="1" colspan="1">Afu3g03350</td><td align="left" rowspan="1" colspan="1">nonribosomal peptide synthase (sidE)</td></tr><tr><td align="left" rowspan="1" colspan="1">−4.401905784</td><td align="left" rowspan="1" colspan="1">Afu4g09190</td><td align="left" rowspan="1" colspan="1">S-adenosyl-methionine-sterol-C- methyltransferase</td><td align="left" rowspan="1" colspan="1">4.0089025</td><td align="left" rowspan="1" colspan="1">Afu3g01360</td><td align="left" rowspan="1" colspan="1">siderochrome-iron transporter</td></tr><tr><td align="left" rowspan="1" colspan="1">−4.300123651</td><td align="left" rowspan="1" colspan="1">Afu1g07480</td><td align="left" rowspan="1" colspan="1">coproporphyrinogen III oxidase</td><td align="left" rowspan="1" colspan="1">5.2921431</td><td align="left" rowspan="1" colspan="1">Afu1g17270</td><td align="left" rowspan="1" colspan="1">ferric-chelate reductase (Fre2)</td></tr><tr><td align="left" rowspan="1" colspan="1">−2.435558859</td><td align="left" rowspan="1" colspan="1">Afu5g06270</td><td align="left" rowspan="1" colspan="1">5-aminolevulinic acid synthase</td><td align="left" rowspan="1" colspan="1">2.6644565</td><td align="left" rowspan="1" colspan="1">Afu1g17200</td><td align="left" rowspan="1" colspan="1">nonribosomal peptide synthase (sidC)</td></tr><tr><td align="left" rowspan="1" colspan="1">−2.128197751</td><td align="left" rowspan="1" colspan="1">Afu5g07750</td><td align="left" rowspan="1" colspan="1">ferrochelatase precursor</td><td align="left" rowspan="1" colspan="1">2.6144266</td><td align="left" rowspan="1" colspan="1">Afu8g01310</td><td align="left" rowspan="1" colspan="1">metalloreductase (FRE1)</td></tr><tr><td align="left" rowspan="1" colspan="1">−3.016960611</td><td align="left" rowspan="1" colspan="1">Afu6g07670</td><td align="left" rowspan="1" colspan="1">cytochrome c oxidase assembly protein cox15</td><td colspan="2" align="left" rowspan="1">
<bold>Carbohydrate transport</bold>
</td><td align="left" rowspan="1" colspan="1"/></tr><tr><td align="left" rowspan="1" colspan="1">
<bold>Nitrate assimilation</bold>
</td><td align="left" rowspan="1" colspan="1"/><td align="left" rowspan="1" colspan="1"/><td align="left" rowspan="1" colspan="1">5.7013458</td><td align="left" rowspan="1" colspan="1">Afu7g06390</td><td align="left" rowspan="1" colspan="1">maltose permease</td></tr><tr><td align="left" rowspan="1" colspan="1">−4.050567754</td><td align="left" rowspan="1" colspan="1">Afu1g12840</td><td align="left" rowspan="1" colspan="1">nitrite reductase</td><td align="left" rowspan="1" colspan="1">5.8795989</td><td align="left" rowspan="1" colspan="1">Afu7g05190</td><td align="left" rowspan="1" colspan="1">maltose permease</td></tr><tr><td align="left" rowspan="1" colspan="1">−0.297138231</td><td align="left" rowspan="1" colspan="1">Afu1g12850</td><td align="left" rowspan="1" colspan="1">nitrate transporter (nitrate permease)</td><td align="left" rowspan="1" colspan="1">4.6926397</td><td align="left" rowspan="1" colspan="1">Afu6g11920</td><td align="left" rowspan="1" colspan="1">maltose permease</td></tr><tr><td align="left" rowspan="1" colspan="1">−2.332483829</td><td align="left" rowspan="1" colspan="1">Afu1g12830</td><td align="left" rowspan="1" colspan="1">nitrate reductase NiaD</td><td align="left" rowspan="1" colspan="1">2.722422</td><td align="left" rowspan="1" colspan="1">Afu5g00500</td><td align="left" rowspan="1" colspan="1">maltose permease</td></tr><tr><td align="left" rowspan="1" colspan="1">0.471365637</td><td align="left" rowspan="1" colspan="1">Afu5g10420</td><td align="left" rowspan="1" colspan="1">nitrate reductase</td><td align="left" rowspan="1" colspan="1">4.7493339</td><td align="left" rowspan="1" colspan="1">Afu3g01700</td><td align="left" rowspan="1" colspan="1">maltose permease</td></tr><tr><td align="left" rowspan="1" colspan="1">7.059699432</td><td align="left" rowspan="1" colspan="1">Afu1g17470</td><td align="left" rowspan="1" colspan="1">high affinity nitrate transporter NrtB</td><td align="left" rowspan="1" colspan="1">2.917548</td><td align="left" rowspan="1" colspan="1">Afu2g10910</td><td align="left" rowspan="1" colspan="1">maltose permease</td></tr><tr><td align="left" rowspan="1" colspan="1">0.55716766</td><td align="left" rowspan="1" colspan="1">Afu6g13230</td><td align="left" rowspan="1" colspan="1">Nit protein 2</td><td align="left" rowspan="1" colspan="1">2.6084555</td><td align="left" rowspan="1" colspan="1">Afu1g03280</td><td align="left" rowspan="1" colspan="1">maltose permease</td></tr><tr><td align="left" rowspan="1" colspan="1">
<bold>Secreted Proteins</bold>
</td><td align="left" rowspan="1" colspan="1"/><td align="left" rowspan="1" colspan="1"/><td align="left" rowspan="1" colspan="1">3.2031907</td><td align="left" rowspan="1" colspan="1">Afu3g03380</td><td align="left" rowspan="1" colspan="1">maltose O-acetyltransferase</td></tr><tr><td align="left" rowspan="1" colspan="1">7.40411592</td><td align="left" rowspan="1" colspan="1">Afu5g14190</td><td align="left" rowspan="1" colspan="1">beta-glucanase</td><td align="left" rowspan="1" colspan="1">2.5614034</td><td align="left" rowspan="1" colspan="1">Afu8g07070</td><td align="left" rowspan="1" colspan="1">maltase</td></tr><tr><td align="left" rowspan="1" colspan="1">5.898216645</td><td align="left" rowspan="1" colspan="1">Afu1g17510</td><td align="left" rowspan="1" colspan="1">lipase/esterase</td><td align="left" rowspan="1" colspan="1">4.678081</td><td align="left" rowspan="1" colspan="1">Afu7g06380</td><td align="left" rowspan="1" colspan="1">maltase</td></tr><tr><td align="left" rowspan="1" colspan="1">5.644563577</td><td align="left" rowspan="1" colspan="1">Afu2g09380</td><td align="left" rowspan="1" colspan="1">cutinase</td><td align="left" rowspan="1" colspan="1">3.4802952</td><td align="left" rowspan="1" colspan="1">Afu4g00150</td><td align="left" rowspan="1" colspan="1">MFS maltose transporter</td></tr><tr><td align="left" rowspan="1" colspan="1">5.496020587</td><td align="left" rowspan="1" colspan="1">Afu8g07090</td><td align="left" rowspan="1" colspan="1">extracellular proline-serine rich protein</td><td align="left" rowspan="1" colspan="1">2.2425193</td><td align="left" rowspan="1" colspan="1">Afu8g07240</td><td align="left" rowspan="1" colspan="1">MFS maltose permease</td></tr><tr><td align="left" rowspan="1" colspan="1">5.069338693</td><td align="left" rowspan="1" colspan="1">Afu2g05150</td><td align="left" rowspan="1" colspan="1">cell wall galactomannoprotein Mp2</td><td align="left" rowspan="1" colspan="1">3.0811489</td><td align="left" rowspan="1" colspan="1">Afu6g01860</td><td align="left" rowspan="1" colspan="1">MFS lactose permease</td></tr><tr><td align="left" rowspan="1" colspan="1">5.009338142</td><td align="left" rowspan="1" colspan="1">Afu5g00540</td><td align="left" rowspan="1" colspan="1">extracellular signaling protein FacC</td><td align="left" rowspan="1" colspan="1">2.0826028</td><td align="left" rowspan="1" colspan="1">Afu1g17310</td><td align="left" rowspan="1" colspan="1">MFS lactose permease</td></tr><tr><td align="left" rowspan="1" colspan="1">4.974193046</td><td align="left" rowspan="1" colspan="1">Afu7g01180</td><td align="left" rowspan="1" colspan="1">extracellular lipase</td><td align="left" rowspan="1" colspan="1">4.3502528</td><td align="left" rowspan="1" colspan="1">Afu3g01670</td><td align="left" rowspan="1" colspan="1">MFS hexose transporter</td></tr><tr><td align="left" rowspan="1" colspan="1">4.926922531</td><td align="left" rowspan="1" colspan="1">Afu1g16250</td><td align="left" rowspan="1" colspan="1">alpha-glucosidase B</td><td align="left" rowspan="1" colspan="1">4.6946836</td><td align="left" rowspan="1" colspan="1">Afu2g08120</td><td align="left" rowspan="1" colspan="1">MFS monosaccharide transporter (Hxt8)</td></tr><tr><td align="left" rowspan="1" colspan="1">4.701754948</td><td align="left" rowspan="1" colspan="1">Afu3g14030</td><td align="left" rowspan="1" colspan="1">alkaline phosphatase</td><td align="left" rowspan="1" colspan="1">3.3131505</td><td align="left" rowspan="1" colspan="1">Afu5g14540</td><td align="left" rowspan="1" colspan="1">MFS monosaccharide transporter</td></tr><tr><td align="left" rowspan="1" colspan="1">4.683929957</td><td align="left" rowspan="1" colspan="1">Afu2g00490</td><td align="left" rowspan="1" colspan="1">glycosyl hydrolase</td><td align="left" rowspan="1" colspan="1">4.8648019</td><td align="left" rowspan="1" colspan="1">Afu4g00800</td><td align="left" rowspan="1" colspan="1">MFS monosaccharide transporter</td></tr><tr><td align="left" rowspan="1" colspan="1">4.678081027</td><td align="left" rowspan="1" colspan="1">Afu7g06380</td><td align="left" rowspan="1" colspan="1">maltase</td><td align="left" rowspan="1" colspan="1">5.0594343</td><td align="left" rowspan="1" colspan="1">Afu7g00780</td><td align="left" rowspan="1" colspan="1">MFS monocarboxylate transporter</td></tr><tr><td align="left" rowspan="1" colspan="1">4.659742223</td><td align="left" rowspan="1" colspan="1">Afu8g01050</td><td align="left" rowspan="1" colspan="1">lipase/esterase</td><td align="left" rowspan="1" colspan="1">2.7697017</td><td align="left" rowspan="1" colspan="1">Afu3g03320</td><td align="left" rowspan="1" colspan="1">MFS monocarboxylate transporter</td></tr><tr><td align="left" rowspan="1" colspan="1">4.62947329</td><td align="left" rowspan="1" colspan="1">Afu3g14910</td><td align="left" rowspan="1" colspan="1">extracellular signalling protein (factor C)</td><td align="left" rowspan="1" colspan="1">3.0355716</td><td align="left" rowspan="1" colspan="1">Afu3g03240</td><td align="left" rowspan="1" colspan="1">MFS monocarboxylate transporter</td></tr><tr><td align="left" rowspan="1" colspan="1">4.628176816</td><td align="left" rowspan="1" colspan="1">Afu8g01130</td><td align="left" rowspan="1" colspan="1">alpha-galactosidase C</td><td align="left" rowspan="1" colspan="1">2.8084929</td><td align="left" rowspan="1" colspan="1">Afu6g03060</td><td align="left" rowspan="1" colspan="1">monosaccharide transporter</td></tr><tr><td align="left" rowspan="1" colspan="1">4.576610248</td><td align="left" rowspan="1" colspan="1">Afu4g01070</td><td align="left" rowspan="1" colspan="1">acid phosphatase</td><td align="left" rowspan="1" colspan="1">3.4709519</td><td align="left" rowspan="1" colspan="1">Afu5g01160</td><td align="left" rowspan="1" colspan="1">monosaccharide transporter</td></tr><tr><td align="left" rowspan="1" colspan="1">4.53846138</td><td align="left" rowspan="1" colspan="1">Afu7g05610</td><td align="left" rowspan="1" colspan="1">glucanase</td><td align="left" rowspan="1" colspan="1">3.7652613</td><td align="left" rowspan="1" colspan="1">Afu4g13080</td><td align="left" rowspan="1" colspan="1">monosaccharide transporter</td></tr><tr><td align="left" rowspan="1" colspan="1">4.316438927</td><td align="left" rowspan="1" colspan="1">Afu4g00870</td><td align="left" rowspan="1" colspan="1">antigenic cell wall galactomannoprotein</td><td align="left" rowspan="1" colspan="1">2.8100298</td><td align="left" rowspan="1" colspan="1">Afu7g05830</td><td align="left" rowspan="1" colspan="1">MFS sugar transporter</td></tr><tr><td align="left" rowspan="1" colspan="1">4.069715654</td><td align="left" rowspan="1" colspan="1">Afu6g02980</td><td align="left" rowspan="1" colspan="1">extracellular exo-polygalacturonase</td><td align="left" rowspan="1" colspan="1">6.9692415</td><td align="left" rowspan="1" colspan="1">Afu6g14500</td><td align="left" rowspan="1" colspan="1">MFS sugar transporter</td></tr><tr><td align="left" rowspan="1" colspan="1">3.883566446</td><td align="left" rowspan="1" colspan="1">Afu8g04710</td><td align="left" rowspan="1" colspan="1">xylosidase</td><td align="left" rowspan="1" colspan="1">2.0479063</td><td align="left" rowspan="1" colspan="1">Afu5g06720</td><td align="left" rowspan="1" colspan="1">MFS sugar transporter</td></tr><tr><td align="left" rowspan="1" colspan="1">2.176380223</td><td align="left" rowspan="1" colspan="1">Afu3g07850</td><td align="left" rowspan="1" colspan="1">dipeptidyl aminopeptidase Ste13</td><td align="left" rowspan="1" colspan="1">4.4698085</td><td align="left" rowspan="1" colspan="1">Afu1g11050</td><td align="left" rowspan="1" colspan="1">MFS sugar transporter</td></tr><tr><td align="left" rowspan="1" colspan="1">3.497276152</td><td align="left" rowspan="1" colspan="1">Afu2g09030</td><td align="left" rowspan="1" colspan="1">secreted dipeptidyl peptidase</td><td align="left" rowspan="1" colspan="1">2.4191786</td><td align="left" rowspan="1" colspan="1">Afu3g12010</td><td align="left" rowspan="1" colspan="1">high-affinity hexose transporter</td></tr><tr><td align="left" rowspan="1" colspan="1">3.821050502</td><td align="left" rowspan="1" colspan="1">Afu6g11500</td><td align="left" rowspan="1" colspan="1">dipeptidase</td><td align="left" rowspan="1" colspan="1">2.8865489</td><td align="left" rowspan="1" colspan="1">Afu3g00430</td><td align="left" rowspan="1" colspan="1">high-affinity glucose transporter</td></tr><tr><td align="left" rowspan="1" colspan="1">
<bold>Antigens</bold>
</td><td align="left" rowspan="1" colspan="1"/><td align="left" rowspan="1" colspan="1"/><td align="left" rowspan="1" colspan="1">3.2509899</td><td align="left" rowspan="1" colspan="1">Afu8g04480</td><td align="left" rowspan="1" colspan="1">hexose transporter protein</td></tr><tr><td align="left" rowspan="1" colspan="1">5.642295426</td><td align="left" rowspan="1" colspan="1">Afu4g09320</td><td align="left" rowspan="1" colspan="1">antigenic dipeptidyl-peptidase Dpp4</td><td align="left" rowspan="1" colspan="1">3.6472387</td><td align="left" rowspan="1" colspan="1">Afu6g06730</td><td align="left" rowspan="1" colspan="1">l-fucose permease</td></tr><tr><td align="left" rowspan="1" colspan="1">4.316438927</td><td align="left" rowspan="1" colspan="1">Afu4g00870</td><td align="left" rowspan="1" colspan="1">antigenic cell wall galactomannoprotein</td><td colspan="2" align="left" rowspan="1">
<bold>Amino acid transport</bold>
</td><td align="left" rowspan="1" colspan="1"/></tr><tr><td align="left" rowspan="1" colspan="1">7.128239041</td><td align="left" rowspan="1" colspan="1">Afu4g09580</td><td align="left" rowspan="1" colspan="1">major allergen Asp F2</td><td align="left" rowspan="1" colspan="1">4.7164066</td><td align="left" rowspan="1" colspan="1">Afu7g04290</td><td align="left" rowspan="1" colspan="1">amino acid permease (Gap1)</td></tr><tr><td align="left" rowspan="1" colspan="1">6.439814589</td><td align="left" rowspan="1" colspan="1">Afu8g07080</td><td align="left" rowspan="1" colspan="1">elastinolytic metalloproteinase Mep</td><td align="left" rowspan="1" colspan="1">4.0522077</td><td align="left" rowspan="1" colspan="1">Afu2g08800</td><td align="left" rowspan="1" colspan="1">amino acid permease (Dip5)</td></tr><tr><td align="left" rowspan="1" colspan="1">3.218425558</td><td align="left" rowspan="1" colspan="1">Afu6g10250</td><td align="left" rowspan="1" colspan="1">alkaline serine protease AorO</td><td align="left" rowspan="1" colspan="1">3.7333962</td><td align="left" rowspan="1" colspan="1">Afu8g06090</td><td align="left" rowspan="1" colspan="1">amino acid permease</td></tr><tr><td colspan="3" align="left" rowspan="1">
<bold>Carbohydrate and/or protein glycosylation</bold>
</td><td align="left" rowspan="1" colspan="1">5.6472247</td><td align="left" rowspan="1" colspan="1">Afu5g09440</td><td align="left" rowspan="1" colspan="1">amino acid permease</td></tr><tr><td align="left" rowspan="1" colspan="1">2.540391388</td><td align="left" rowspan="1" colspan="1">Afu8g02020</td><td align="left" rowspan="1" colspan="1">glycosyl transferase</td><td align="left" rowspan="1" colspan="1">2.9622857</td><td align="left" rowspan="1" colspan="1">Afu2g10560</td><td align="left" rowspan="1" colspan="1">amino acid permease</td></tr><tr><td align="left" rowspan="1" colspan="1">6.098633033</td><td align="left" rowspan="1" colspan="1">Afu4g14070</td><td align="left" rowspan="1" colspan="1">glycosyl transferase</td><td align="left" rowspan="1" colspan="1">3.2688804</td><td align="left" rowspan="1" colspan="1">Afu1g09120</td><td align="left" rowspan="1" colspan="1">amino acid permease</td></tr><tr><td align="left" rowspan="1" colspan="1">2.229215652</td><td align="left" rowspan="1" colspan="1">Afu5g00670</td><td align="left" rowspan="1" colspan="1">glycosyl hydrolase family 35</td><td align="left" rowspan="1" colspan="1">2.9580145</td><td align="left" rowspan="1" colspan="1">Afu8g02200</td><td align="left" rowspan="1" colspan="1">proline permease</td></tr><tr><td align="left" rowspan="1" colspan="1">2.266930252</td><td align="left" rowspan="1" colspan="1">Afu6g11910</td><td align="left" rowspan="1" colspan="1">glycosyl hydrolase family 3</td><td align="left" rowspan="1" colspan="1">5.7069031</td><td align="left" rowspan="1" colspan="1">Afu7g01090</td><td align="left" rowspan="1" colspan="1">proline permease</td></tr><tr><td align="left" rowspan="1" colspan="1">3.177447564</td><td align="left" rowspan="1" colspan="1">Afu4g00390</td><td align="left" rowspan="1" colspan="1">glycosyl hydrolase</td><td align="left" rowspan="1" colspan="1">3.3818247</td><td align="left" rowspan="1" colspan="1">Afu2g11220</td><td align="left" rowspan="1" colspan="1">proline permease</td></tr><tr><td align="left" rowspan="1" colspan="1">2.065677868</td><td align="left" rowspan="1" colspan="1">Afu2g03270</td><td align="left" rowspan="1" colspan="1">glycosyl hydrolase</td><td align="left" rowspan="1" colspan="1">4.1850538</td><td align="left" rowspan="1" colspan="1">Afu8g01450</td><td align="left" rowspan="1" colspan="1">GABA permease</td></tr><tr><td align="left" rowspan="1" colspan="1">4.683929957</td><td align="left" rowspan="1" colspan="1">Afu2g00490</td><td align="left" rowspan="1" colspan="1">glycosyl hydrolase</td><td align="left" rowspan="1" colspan="1">2.3940135</td><td align="left" rowspan="1" colspan="1">Afu7g00440</td><td align="left" rowspan="1" colspan="1">GABA permease</td></tr><tr><td align="left" rowspan="1" colspan="1">2.14112188</td><td align="left" rowspan="1" colspan="1">Afu2g03120</td><td align="left" rowspan="1" colspan="1">cell wall glucanase (Utr2)</td><td align="left" rowspan="1" colspan="1">2.9136458</td><td align="left" rowspan="1" colspan="1">Afu5g14660</td><td align="left" rowspan="1" colspan="1">GABA permease</td></tr><tr><td align="left" rowspan="1" colspan="1">3.080401687</td><td align="left" rowspan="1" colspan="1">Afu8g05610</td><td align="left" rowspan="1" colspan="1">cell wall glucanase (Scw11)</td><td align="left" rowspan="1" colspan="1">6.28253</td><td align="left" rowspan="1" colspan="1">Afu5g00710</td><td align="left" rowspan="1" colspan="1">GABA permease</td></tr><tr><td align="left" rowspan="1" colspan="1">5.069338693</td><td align="left" rowspan="1" colspan="1">Afu2g05150</td><td align="left" rowspan="1" colspan="1">cell wall galactomannoprotein Mp2</td><td align="left" rowspan="1" colspan="1">6.7336819</td><td align="left" rowspan="1" colspan="1">Afu1g14700</td><td align="left" rowspan="1" colspan="1">allantoate transporter</td></tr><tr><td colspan="2" align="left" rowspan="1">
<bold>Carbohydrate catabolism</bold>
</td><td align="left" rowspan="1" colspan="1"/><td colspan="3" align="left" rowspan="1">
<bold>Metal ion transport/homeostasis</bold>
</td></tr><tr><td align="left" rowspan="1" colspan="1">2.832926255</td><td align="left" rowspan="1" colspan="1">Afu6g14490</td><td align="left" rowspan="1" colspan="1">beta-glucosidase</td><td align="left" rowspan="1" colspan="1">4.9722279</td><td align="left" rowspan="1" colspan="1">Afu5g09360</td><td align="left" rowspan="1" colspan="1">calcineurin A</td></tr><tr><td align="left" rowspan="1" colspan="1">2.109492985</td><td align="left" rowspan="1" colspan="1">Afu3g00230</td><td align="left" rowspan="1" colspan="1">beta-glucosidase</td><td align="left" rowspan="1" colspan="1">5.7140897</td><td align="left" rowspan="1" colspan="1">Afu7g01030</td><td align="left" rowspan="1" colspan="1">Calcium-transporting ATPase 1 (PMC1)</td></tr><tr><td align="left" rowspan="1" colspan="1">7.40411592</td><td align="left" rowspan="1" colspan="1">Afu5g14190</td><td align="left" rowspan="1" colspan="1">beta-glucanase</td><td align="left" rowspan="1" colspan="1">3.30078</td><td align="left" rowspan="1" colspan="1">Afu3g10690</td><td align="left" rowspan="1" colspan="1">calcium-translocating P-type ATPase(PMCA-type)</td></tr><tr><td align="left" rowspan="1" colspan="1">3.165725818</td><td align="left" rowspan="1" colspan="1">Afu5g14550</td><td align="left" rowspan="1" colspan="1">beta-galactosidase</td><td align="left" rowspan="1" colspan="1">2.2461463</td><td align="left" rowspan="1" colspan="1">Afu3g08540</td><td align="left" rowspan="1" colspan="1">Ca2+ binding modulator protein (Alg2)</td></tr><tr><td align="left" rowspan="1" colspan="1">2.183493763</td><td align="left" rowspan="1" colspan="1">Afu1g14170</td><td align="left" rowspan="1" colspan="1">beta-galactosidase</td><td align="left" rowspan="1" colspan="1">2.7693449</td><td align="left" rowspan="1" colspan="1">Afu6g00470</td><td align="left" rowspan="1" colspan="1">plasma membrane zinc ion transporter</td></tr><tr><td align="left" rowspan="1" colspan="1">7.164853239</td><td align="left" rowspan="1" colspan="1">Afu7g06140</td><td align="left" rowspan="1" colspan="1">beta-D-glucoside glucohydrolase</td><td align="left" rowspan="1" colspan="1">4.2562039</td><td align="left" rowspan="1" colspan="1">Afu5g03550</td><td align="left" rowspan="1" colspan="1">plasma membrane H(+)ATPase</td></tr><tr><td align="left" rowspan="1" colspan="1">3.586205167</td><td align="left" rowspan="1" colspan="1">Afu6g08700</td><td align="left" rowspan="1" colspan="1">beta glucosidase</td><td align="left" rowspan="1" colspan="1">3.2581249</td><td align="left" rowspan="1" colspan="1">Afu1g02480</td><td align="left" rowspan="1" colspan="1">plasma membrane ATPase</td></tr><tr><td align="left" rowspan="1" colspan="1">3.25484732</td><td align="left" rowspan="1" colspan="1">Afu1g16700</td><td align="left" rowspan="1" colspan="1">beta galactosidase</td><td align="left" rowspan="1" colspan="1">4.1291169</td><td align="left" rowspan="1" colspan="1">Afu1g01550</td><td align="left" rowspan="1" colspan="1">high affinity zinc ion transporter</td></tr><tr><td align="left" rowspan="1" colspan="1">4.926922531</td><td align="left" rowspan="1" colspan="1">Afu1g16250</td><td align="left" rowspan="1" colspan="1">alpha-glucosidase B</td><td align="left" rowspan="1" colspan="1">3.103044</td><td align="left" rowspan="1" colspan="1">Afu8g01890</td><td align="left" rowspan="1" colspan="1">Na+/H+ exchanger family
protein</td></tr><tr><td align="left" rowspan="1" colspan="1">3.825416885</td><td align="left" rowspan="1" colspan="1">Afu4g10150</td><td align="left" rowspan="1" colspan="1">alpha-glucosidase</td><td align="left" rowspan="1" colspan="1">3.3213401</td><td align="left" rowspan="1" colspan="1">Afu7g04570</td><td align="left" rowspan="1" colspan="1">Na/K ATPase alpha 1 subunit</td></tr><tr><td align="left" rowspan="1" colspan="1">4.628176816</td><td align="left" rowspan="1" colspan="1">Afu8g01130</td><td align="left" rowspan="1" colspan="1">alpha-galactosidase C</td><td colspan="2" align="left" rowspan="1">
<bold>Oxidative stress resistance</bold>
</td><td align="left" rowspan="1" colspan="1"/></tr><tr><td align="left" rowspan="1" colspan="1">6.513162955</td><td align="left" rowspan="1" colspan="1">Afu1g01200</td><td align="left" rowspan="1" colspan="1">alpha-galactosidase</td><td align="left" rowspan="1" colspan="1">4.2704386</td><td align="left" rowspan="1" colspan="1">Afu1g14550</td><td align="left" rowspan="1" colspan="1">Mn superoxide dismutase MnSOD</td></tr><tr><td align="left" rowspan="1" colspan="1">2.86708433</td><td align="left" rowspan="1" colspan="1">Afu8g07300</td><td align="left" rowspan="1" colspan="1">alpha/beta hydrolase</td><td align="left" rowspan="1" colspan="1">−0.7096154</td><td align="left" rowspan="1" colspan="1">Afu4g11580</td><td align="left" rowspan="1" colspan="1">Mn superoxide dismutase (SodB)</td></tr><tr><td align="left" rowspan="1" colspan="1">4.46285962</td><td align="left" rowspan="1" colspan="1">Afu8g00570</td><td align="left" rowspan="1" colspan="1">alpha/beta hydrolase</td><td align="left" rowspan="1" colspan="1">2.3390424</td><td align="left" rowspan="1" colspan="1">Afu8g01670</td><td align="left" rowspan="1" colspan="1">bifunctional catalase-peroxidase Cat2</td></tr><tr><td align="left" rowspan="1" colspan="1">2.441133251</td><td align="left" rowspan="1" colspan="1">Afu8g00530</td><td align="left" rowspan="1" colspan="1">alpha/beta hydrolase</td><td align="left" rowspan="1" colspan="1"/><td align="left" rowspan="1" colspan="1"/><td align="left" rowspan="1" colspan="1"/></tr><tr><td align="left" rowspan="1" colspan="1">2.041648909</td><td align="left" rowspan="1" colspan="1">Afu7g00830</td><td align="left" rowspan="1" colspan="1">alpha/beta hydrolase</td><td align="left" rowspan="1" colspan="1"/><td align="left" rowspan="1" colspan="1"/><td align="left" rowspan="1" colspan="1"/></tr><tr><td align="left" rowspan="1" colspan="1">5.206583195</td><td align="left" rowspan="1" colspan="1">Afu3g01280</td><td align="left" rowspan="1" colspan="1">alpha/beta hydrolase</td><td align="left" rowspan="1" colspan="1"/><td align="left" rowspan="1" colspan="1"/><td align="left" rowspan="1" colspan="1"/></tr></tbody></table></alternatives></table-wrap> <table-wrap id="ppat-1000154-t003" position="float"><object-id pub-id-type="doi">10.1371/journal.ppat.1000154.t003</object-id><label>Table 3</label><caption><title>Distribution of induced and repressed genes among lineage specificity
cohorts and chromosomal locations.</title></caption><alternatives><table frame="hsides" rules="groups"><colgroup span="1"><col align="left" span="1"/><col align="center" span="1"/><col align="center" span="1"/><col align="center" span="1"/><col align="center" span="1"/><col align="center" span="1"/><col align="center" span="1"/><col align="center" span="1"/><col align="center" span="1"/></colgroup><thead><tr><td align="left" rowspan="1" colspan="1"/><td align="left" rowspan="1" colspan="1">Number of genes</td><td align="left" rowspan="1" colspan="1">Differentially expressed</td><td align="left" rowspan="1" colspan="1">UP' (observed)</td><td align="left" rowspan="1" colspan="1">DOWN' (observed)</td><td align="left" rowspan="1" colspan="1">UP' (expected)</td><td align="left" rowspan="1" colspan="1">DOWN' (expected)</td><td align="left" rowspan="1" colspan="1">χ2</td><td align="left" rowspan="1" colspan="1">p</td></tr></thead><tbody><tr><td align="left" rowspan="1" colspan="1">
<italic>Aspergillus</italic> core</td><td align="left" rowspan="1" colspan="1">5095</td><td align="left" rowspan="1" colspan="1">965</td><td align="left" rowspan="1" colspan="1">414</td><td align="left" rowspan="1" colspan="1">551</td><td align="left" rowspan="1" colspan="1">482.5</td><td align="left" rowspan="1" colspan="1">482.5</td><td align="left" rowspan="1" colspan="1">19.45</td><td align="left" rowspan="1" colspan="1">&lt;0.0001</td></tr><tr><td align="left" rowspan="1" colspan="1">Affc-core</td><td align="left" rowspan="1" colspan="1">6907</td><td align="left" rowspan="1" colspan="1">1468</td><td align="left" rowspan="1" colspan="1">791</td><td align="left" rowspan="1" colspan="1">677</td><td align="left" rowspan="1" colspan="1">734</td><td align="left" rowspan="1" colspan="1">734</td><td align="left" rowspan="1" colspan="1">8.853</td><td align="left" rowspan="1" colspan="1">0.0029</td></tr><tr><td align="left" rowspan="1" colspan="1">Affc-specific</td><td align="left" rowspan="1" colspan="1">180</td><td align="left" rowspan="1" colspan="1">64*</td><td align="left" rowspan="1" colspan="1">54</td><td align="left" rowspan="1" colspan="1">10</td><td align="left" rowspan="1" colspan="1">32</td><td align="left" rowspan="1" colspan="1">32</td><td align="left" rowspan="1" colspan="1">30.25</td><td align="left" rowspan="1" colspan="1">&lt;0.0001</td></tr><tr><td align="left" rowspan="1" colspan="1">Afu-specific</td><td align="left" rowspan="1" colspan="1">428</td><td align="left" rowspan="1" colspan="1">135*</td><td align="left" rowspan="1" colspan="1">77</td><td align="left" rowspan="1" colspan="1">58</td><td align="left" rowspan="1" colspan="1">67.5</td><td align="left" rowspan="1" colspan="1">67.5</td><td align="left" rowspan="1" colspan="1">2.674</td><td align="left" rowspan="1" colspan="1">0.102</td></tr></tbody></table><table frame="hsides" rules="groups"><colgroup span="1"><col align="left" span="1"/><col align="center" span="1"/><col align="center" span="1"/><col align="center" span="1"/><col align="center" span="1"/><col align="center" span="1"/><col align="center" span="1"/><col align="center" span="1"/><col align="center" span="1"/></colgroup><thead><tr><td align="left" rowspan="1" colspan="1"/><td align="left" rowspan="1" colspan="1">Number of genes</td><td align="left" rowspan="1" colspan="1">Differentially expressed</td><td align="left" rowspan="1" colspan="1">UP' (observed)</td><td align="left" rowspan="1" colspan="1">DOWN' (observed)</td><td align="left" rowspan="1" colspan="1">UP' (expected)</td><td align="left" rowspan="1" colspan="1">DOWN' (expected)</td><td align="left" rowspan="1" colspan="1">χ2</td><td align="left" rowspan="1" colspan="1">p</td></tr></thead><tbody><tr><td align="left" rowspan="1" colspan="1">0–300 kb</td><td align="left" rowspan="1" colspan="1">1326</td><td align="left" rowspan="1" colspan="1">470</td><td align="left" rowspan="1" colspan="1">367</td><td align="left" rowspan="1" colspan="1">103</td><td align="left" rowspan="1" colspan="1">235</td><td align="left" rowspan="1" colspan="1">235</td><td align="left" rowspan="1" colspan="1">148.289</td><td align="left" rowspan="1" colspan="1">&lt;0.0001</td></tr><tr><td align="left" rowspan="1" colspan="1">301–600 kb</td><td align="left" rowspan="1" colspan="1">1504</td><td align="left" rowspan="1" colspan="1">365</td><td align="left" rowspan="1" colspan="1">229</td><td align="left" rowspan="1" colspan="1">136</td><td align="left" rowspan="1" colspan="1">182.5</td><td align="left" rowspan="1" colspan="1">182.5</td><td align="left" rowspan="1" colspan="1">23.696</td><td align="left" rowspan="1" colspan="1">&lt;0.0001</td></tr><tr><td align="left" rowspan="1" colspan="1">&gt;600</td><td align="left" rowspan="1" colspan="1">5779</td><td align="left" rowspan="1" colspan="1">1345</td><td align="left" rowspan="1" colspan="1">686</td><td align="left" rowspan="1" colspan="1">659</td><td align="left" rowspan="1" colspan="1">672.5</td><td align="left" rowspan="1" colspan="1">672.5</td><td align="left" rowspan="1" colspan="1">0.5</td><td align="left" rowspan="1" colspan="1">0.4795</td></tr></tbody></table></alternatives></table-wrap> <table-wrap id="ppat-1000154-t004" position="float"><object-id pub-id-type="doi">10.1371/journal.ppat.1000154.t004</object-id><label>Table 4</label><caption><title>Comparative analysis of genetic distribution among genes having
differential transcript abundances in microarray analyses using
<italic>A. fumigatus</italic> RNA following exposure to <italic>in
vitro</italic> or murine-adaptive stress, or <italic>lae</italic>A
gene deletion.</title></caption><alternatives><table frame="hsides" rules="groups"><colgroup span="1"><col align="left" span="1"/><col align="center" span="1"/><col align="center" span="1"/><col align="center" span="1"/><col align="center" span="1"/><col align="center" span="1"/><col align="center" span="1"/><col align="center" span="1"/><col align="center" span="1"/><col align="center" span="1"/></colgroup><thead><tr><td align="left" rowspan="1" colspan="1">Experimental condition</td><td align="left" rowspan="1" colspan="1">Total genes in dataset</td><td align="left" rowspan="1" colspan="1">Proximal</td><td align="left" rowspan="1" colspan="1">%</td><td align="left" rowspan="1" colspan="1">Intermediate</td><td align="left" rowspan="1" colspan="1">%</td><td align="left" rowspan="1" colspan="1">Distal</td><td align="left" rowspan="1" colspan="1">%</td><td align="left" rowspan="1" colspan="1">
<italic>A. fumigatus</italic>-specific</td><td align="left" rowspan="1" colspan="1">%</td></tr></thead><tbody><tr><td align="left" rowspan="1" colspan="1">Genome stats</td><td align="left" rowspan="1" colspan="1">9632</td><td align="left" rowspan="1" colspan="1">1509</td><td align="left" rowspan="1" colspan="1">16</td><td align="left" rowspan="1" colspan="1">1634</td><td align="left" rowspan="1" colspan="1">17</td><td align="left" rowspan="1" colspan="1">6494</td><td align="left" rowspan="1" colspan="1">67</td><td align="left" rowspan="1" colspan="1">2201</td><td align="left" rowspan="1" colspan="1">23</td></tr><tr><td align="left" rowspan="1" colspan="1">Clustered <italic>in vivo</italic> up</td><td align="left" rowspan="1" colspan="1">658</td><td align="left" rowspan="1" colspan="1">227</td><td align="left" rowspan="1" colspan="1">34</td><td align="left" rowspan="1" colspan="1">129</td><td align="left" rowspan="1" colspan="1">20</td><td align="left" rowspan="1" colspan="1">302</td><td align="left" rowspan="1" colspan="1">46</td><td align="left" rowspan="1" colspan="1">218</td><td align="left" rowspan="1" colspan="1">33</td></tr><tr><td align="left" rowspan="1" colspan="1">
<italic>ΔlaeA</italic> up</td><td align="left" rowspan="1" colspan="1">415</td><td align="left" rowspan="1" colspan="1">102</td><td align="left" rowspan="1" colspan="1">32</td><td align="left" rowspan="1" colspan="1">59</td><td align="left" rowspan="1" colspan="1">14</td><td align="left" rowspan="1" colspan="1">254</td><td align="left" rowspan="1" colspan="1">61</td><td align="left" rowspan="1" colspan="1">122</td><td align="left" rowspan="1" colspan="1">29</td></tr><tr><td align="left" rowspan="1" colspan="1">
<italic>in vivo</italic> up</td><td align="left" rowspan="1" colspan="1">1282</td><td align="left" rowspan="1" colspan="1">367</td><td align="left" rowspan="1" colspan="1">29</td><td align="left" rowspan="1" colspan="1">229</td><td align="left" rowspan="1" colspan="1">18</td><td align="left" rowspan="1" colspan="1">686</td><td align="left" rowspan="1" colspan="1">54</td><td align="left" rowspan="1" colspan="1">374</td><td align="left" rowspan="1" colspan="1">30</td></tr><tr><td align="left" rowspan="1" colspan="1">Nitrogen starvation up</td><td align="left" rowspan="1" colspan="1">626</td><td align="left" rowspan="1" colspan="1">180</td><td align="left" rowspan="1" colspan="1">29</td><td align="left" rowspan="1" colspan="1">115</td><td align="left" rowspan="1" colspan="1">18</td><td align="left" rowspan="1" colspan="1">331</td><td align="left" rowspan="1" colspan="1">53</td><td align="left" rowspan="1" colspan="1">33</td><td align="left" rowspan="1" colspan="1">33</td></tr><tr><td align="left" rowspan="1" colspan="1">
<italic>in vivo</italic> up (no sec mets)</td><td align="left" rowspan="1" colspan="1">1196</td><td align="left" rowspan="1" colspan="1">327</td><td align="left" rowspan="1" colspan="1">27</td><td align="left" rowspan="1" colspan="1">223</td><td align="left" rowspan="1" colspan="1">19</td><td align="left" rowspan="1" colspan="1">646</td><td align="left" rowspan="1" colspan="1">54</td><td align="left" rowspan="1" colspan="1">355</td><td align="left" rowspan="1" colspan="1">30</td></tr><tr><td align="left" rowspan="1" colspan="1">Neutrophils 60 minutes up</td><td align="left" rowspan="1" colspan="1">312</td><td align="left" rowspan="1" colspan="1">83</td><td align="left" rowspan="1" colspan="1">27</td><td align="left" rowspan="1" colspan="1">81</td><td align="left" rowspan="1" colspan="1">26</td><td align="left" rowspan="1" colspan="1">148</td><td align="left" rowspan="1" colspan="1">47</td><td align="left" rowspan="1" colspan="1">80</td><td align="left" rowspan="1" colspan="1">26</td></tr><tr><td align="left" rowspan="1" colspan="1">Alkaline adaptation up</td><td align="left" rowspan="1" colspan="1">211</td><td align="left" rowspan="1" colspan="1">53</td><td align="left" rowspan="1" colspan="1">25</td><td align="left" rowspan="1" colspan="1">39</td><td align="left" rowspan="1" colspan="1">18</td><td align="left" rowspan="1" colspan="1">113</td><td align="left" rowspan="1" colspan="1">54</td><td align="left" rowspan="1" colspan="1">6</td><td align="left" rowspan="1" colspan="1">6</td></tr><tr><td align="left" rowspan="1" colspan="1">
<italic>ΔlaeA</italic> up (no sec mets)</td><td align="left" rowspan="1" colspan="1">318</td><td align="left" rowspan="1" colspan="1">70</td><td align="left" rowspan="1" colspan="1">22</td><td align="left" rowspan="1" colspan="1">58</td><td align="left" rowspan="1" colspan="1">18</td><td align="left" rowspan="1" colspan="1">190</td><td align="left" rowspan="1" colspan="1">60</td><td align="left" rowspan="1" colspan="1">50</td><td align="left" rowspan="1" colspan="1">16</td></tr><tr><td align="left" rowspan="1" colspan="1">Acid stress up</td><td align="left" rowspan="1" colspan="1">83</td><td align="left" rowspan="1" colspan="1">18</td><td align="left" rowspan="1" colspan="1">22</td><td align="left" rowspan="1" colspan="1">21</td><td align="left" rowspan="1" colspan="1">25</td><td align="left" rowspan="1" colspan="1">44</td><td align="left" rowspan="1" colspan="1">53</td><td align="left" rowspan="1" colspan="1">43</td><td align="left" rowspan="1" colspan="1">52</td></tr><tr><td align="left" rowspan="1" colspan="1">Iron limitation up</td><td align="left" rowspan="1" colspan="1">28</td><td align="left" rowspan="1" colspan="1">4</td><td align="left" rowspan="1" colspan="1">14</td><td align="left" rowspan="1" colspan="1">4</td><td align="left" rowspan="1" colspan="1">15</td><td align="left" rowspan="1" colspan="1">20</td><td align="left" rowspan="1" colspan="1">71</td><td align="left" rowspan="1" colspan="1">8</td><td align="left" rowspan="1" colspan="1">30</td></tr><tr><td align="left" rowspan="1" colspan="1">
<italic>ΔlaeA</italic> down</td><td align="left" rowspan="1" colspan="1">528</td><td align="left" rowspan="1" colspan="1">93</td><td align="left" rowspan="1" colspan="1">18</td><td align="left" rowspan="1" colspan="1">99</td><td align="left" rowspan="1" colspan="1">19</td><td align="left" rowspan="1" colspan="1">336</td><td align="left" rowspan="1" colspan="1">64</td><td align="left" rowspan="1" colspan="1">108</td><td align="left" rowspan="1" colspan="1">20</td></tr><tr><td align="left" rowspan="1" colspan="1">Oxidative stress up</td><td align="left" rowspan="1" colspan="1">54</td><td align="left" rowspan="1" colspan="1">9</td><td align="left" rowspan="1" colspan="1">17</td><td align="left" rowspan="1" colspan="1">9</td><td align="left" rowspan="1" colspan="1">17</td><td align="left" rowspan="1" colspan="1">36</td><td align="left" rowspan="1" colspan="1">67</td><td align="left" rowspan="1" colspan="1">22</td><td align="left" rowspan="1" colspan="1">54</td></tr><tr><td align="left" rowspan="1" colspan="1">
<italic>in vivo</italic> down</td><td align="left" rowspan="1" colspan="1">898</td><td align="left" rowspan="1" colspan="1">103</td><td align="left" rowspan="1" colspan="1">11</td><td align="left" rowspan="1" colspan="1">136</td><td align="left" rowspan="1" colspan="1">15</td><td align="left" rowspan="1" colspan="1">659</td><td align="left" rowspan="1" colspan="1">73</td><td align="left" rowspan="1" colspan="1">126</td><td align="left" rowspan="1" colspan="1">14</td></tr></tbody></table></alternatives></table-wrap>
<supplementary-material content-type="local-data" id="ppat.1000154.s001"><label>Figure S1</label><caption><p>Schematic representation of hybridisations for assessing maintenance of
relative transcript abundance during <italic>A. fumigatus</italic> mRNA
amplification. Boxes represent the biological source material from which the
RNA was isolated. The open and shaded arrows depict dye-swapped
hybridisations for all pairs of samples. For all comparisons Af293-derived
cDNA sampled at media shift (t0) was cohybridised with Af-derived cDNA
sampled at 60 minutes post-shift (t60). Horizontally aligned boxes depict
cohybridisation of samples subjected to one (aRNA1), two (aRNA2) or zero
(totRNA) rounds of linear mRNA amplification. Amplification protocols were
compared indirectly by fitting a contrast matrix (contrast1, contrast2,
contrast3).</p><p>(0.03 MB JPG)</p></caption></supplementary-material> <supplementary-material content-type="local-data" id="ppat.1000154.s002"><label>Figure S2</label><caption><p>Comparative analysis of <italic>A. fumigatus</italic> gene expression
datsets. A pan-experimental comparison of <italic>A. fumigatus</italic> gene
expression aligning log2 ratios obtained during host adaptation (mice);
exposure to neutrophils (neut), increased expression in parental strain
versus Δ<italic>lae</italic>A mutant, acid shift (acid), iron
starvation (iron), oxygen depletion (anaer) and oxidative stress (H2O2) for
various genes. The colour bar indicates the range of log2 expression ratios,
grey bars indicate genes from which signals were undetectable for technical
reasons. Experimental conditions are described in <xref ref-type="sec" rid="s4">Materials and Methods</xref>. LaeA dataset is taken from Perrin
et. al. ##REF##17432932##[16]##. Comparative analyses were implemented in
TM4 <ext-link ext-link-type="uri" xlink:href="http://www.jcvi.org/cms/research/software/">http://www.jcvi.org/cms/research/software/</ext-link>
</p><p>(70.39 MB TIF)</p></caption></supplementary-material> <supplementary-material content-type="local-data" id="ppat.1000154.s003"><label>Figure S3</label><caption><p>Concordance between estimates of relative abundance from triplicate qPCR
measurements (yellow) and microarray experiments (grey) for selected
<italic>A. fumigatus</italic> ORFs. Oligo sequences are given in ##SUPPL##11##Table
S9##.</p><p>(1.00 MB TIF)</p></caption></supplementary-material> <supplementary-material content-type="local-data" id="ppat.1000154.s004"><label>Table S1</label><caption><p>RNA samples</p><p>(0.05 MB DOC)</p></caption></supplementary-material> <supplementary-material content-type="local-data" id="ppat.1000154.s005"><label>Table S2</label><caption><p>Filtered genes</p><p>(0.03 MB DOC)</p></caption></supplementary-material> <supplementary-material content-type="local-data" id="ppat.1000154.s006"><label>Table S3</label><caption><p>GO analysis</p><p>(0.85 MB DOC)</p></caption></supplementary-material> <supplementary-material content-type="local-data" id="ppat.1000154.s007"><label>Table S4</label><caption><p>TOR analysis</p><p>(0.37 MB DOC)</p></caption></supplementary-material> <supplementary-material content-type="local-data" id="ppat.1000154.s008"><label>Table S5</label><caption><p>Asterisked gene lists</p><p>(0.34 MB DOC)</p></caption></supplementary-material> <supplementary-material content-type="local-data" id="ppat.1000154.s009"><label>Table S6</label><caption><p>Cluster overview</p><p>(1.33 MB DOC)</p></caption></supplementary-material> <supplementary-material content-type="local-data" id="ppat.1000154.s010"><label>Table S7</label><caption><p>Nitrogen starvation GO</p><p>(0.40 MB XLS)</p></caption></supplementary-material> <supplementary-material content-type="local-data" id="ppat.1000154.s011"><label>Table S8</label><caption><p>Nitrogen starvation gene clusters</p><p>(0.03 MB XLS)</p></caption></supplementary-material> <supplementary-material content-type="local-data" id="ppat.1000154.s012"><label>Table S9</label><caption><p>Oligo sequences for RT-PCR</p><p>(0.04 MB DOC)</p></caption></supplementary-material> <supplementary-material content-type="local-data" id="ppat.1000154.s013"><label>Dataset S1</label><caption><p>(0.58 MB XLS)</p></caption></supplementary-material> <supplementary-material content-type="local-data" id="ppat.1000154.s014"><label>Dataset S2</label><caption><p>Gene clusters fully annotated</p><p>(0.19 MB XLS)</p></caption></supplementary-material> <supplementary-material content-type="local-data" id="ppat.1000154.s015"><label>Dataset S3</label><caption><p>Lists of genes common to several analyses</p><p>(0.04 MB XLS)</p></caption></supplementary-material> <supplementary-material content-type="local-data" id="ppat.1000154.s016"><label>Dataset S4</label><caption><p>(0.93 MB XLS)</p></caption></supplementary-material>
<table-wrap-foot><fn id="nt101"><label/><p>Table lists selected biological processes significantly
over-represented among differentially expressed genes, with respect
to their occurrence in the <italic>A. fumigatus</italic> Af293
genome. To identify over-represented Gene Ontology terms, loci
having significantly different expression were analyzed by the
Expression Analysis Systematic Explorer (EASE) (PMID:14519205),
which is implemented in MEV within the TIGR TM4 microarray data
analysis suite (<ext-link ext-link-type="uri" xlink:href="http://TM4.org">http://TM4.org</ext-link>). Numbers
of genes in the indicated Gene Ontology categories were subjected to
statistical analysis by EASE##REF##14519205##[25]## to
identify categories overrepresented compared with the whole genome
data set. Only categories with Fisher's exact test
probabilities below 5.00E-02 were included. Full results of the
analysis can be found in ##SUPPL##5##Table S3##.</p></fn></table-wrap-foot> <table-wrap-foot><fn id="nt102"><label/><p>Differentially regulated transcripts were defined as having
log<sub>2</sub>(Cy5 – Cy3) greater than the arbitrary
thresholds of plus and minus two.</p></fn></table-wrap-foot> <table-wrap-foot><fn id="nt103"><label/><p>Chi-square analysis (with one degree of freedom) was used to test the
distribution of induced and repressed genes with respect to lineage
specificity and sub-genomic locations, based upon the null
hypothesis that equal numbers of induced and repressed genes occur
in each cohort. Asterisked gene populations are listed in
Supplementary ##SUPPL##7##Table S5##.</p></fn></table-wrap-foot> <table-wrap-foot><fn id="nt104"><label/><p>Gene density in telomere-proximal (o-300 kb from telomeres),
intermediate (300–600 kb from telomeres) and
telomere-distal (&gt;600 kb from telomeres) regions of the
<italic>A. fumigatus</italic> genome are indicated (Genome
stats). (no sec mets) indicates omission of secondary metabolite
biosynthetic genes (identified as detailed in the <xref ref-type="sec" rid="s4">Materials and Methods</xref> section)
from the tested dataset.</p></fn></table-wrap-foot> <fn-group><fn fn-type="COI-statement"><p>The authors have declared that no competing interests exist.</p></fn><fn fn-type="financial-disclosure"><p>We acknowledge the support of the Medical Research Council, award numbers
G0501164 (E. B.) and G0501397 (for K. H. and A. M.) awarded to Imperial College,
National Institute for Allergy and Infectious Diseases (NIAID) NIH, award
numbers NIAID U01AI48830 and R21 AI052236 (W. C. N.), The Austrian Science
Foundation, award number FWP P-18606-B11 (H. H.) and The National Institutes of
Health, award numbers AI051144, AI052236 and N01-AI-30041 (G. M.) and Cancer
Support Center, grant number CA16672 (G. M.). The Wellcome Trust (K. H. and D.
A-J.) and the Biotechnology and Biological Sciences Research Council (K. H.). G.
G. is supported by FAPESP and CNPq, Brazil.</p></fn></fn-group>
<graphic xlink:href="ppat.1000154.g001"/> <graphic xlink:href="ppat.1000154.g002"/> <graphic xlink:href="ppat.1000154.g003"/> <graphic id="ppat-1000154-t001-1" xlink:href="ppat.1000154.t001"/> <graphic id="ppat-1000154-t002-2" xlink:href="ppat.1000154.t002"/> <graphic xlink:href="ppat.1000154.g004"/> <graphic id="ppat-1000154-t003-3" xlink:href="ppat.1000154.t003"/> <graphic id="ppat-1000154-t004-4" xlink:href="ppat.1000154.t004"/> <graphic xlink:href="ppat.1000154.g005"/> <graphic xlink:href="ppat.1000154.g006"/> <graphic xlink:href="ppat.1000154.g007"/> <graphic xlink:href="ppat.1000154.g008"/>
<media xlink:href="ppat.1000154.s001.jpg"><caption><p>Click here for additional data file.</p></caption></media> <media xlink:href="ppat.1000154.s002.tif"><caption><p>Click here for additional data file.</p></caption></media> <media xlink:href="ppat.1000154.s003.tif"><caption><p>Click here for additional data file.</p></caption></media> <media xlink:href="ppat.1000154.s004.doc"><caption><p>Click here for additional data file.</p></caption></media> <media xlink:href="ppat.1000154.s005.doc"><caption><p>Click here for additional data file.</p></caption></media> <media xlink:href="ppat.1000154.s006.doc"><caption><p>Click here for additional data file.</p></caption></media> <media xlink:href="ppat.1000154.s007.doc"><caption><p>Click here for additional data file.</p></caption></media> <media xlink:href="ppat.1000154.s008.doc"><caption><p>Click here for additional data file.</p></caption></media> <media xlink:href="ppat.1000154.s009.doc"><caption><p>Click here for additional data file.</p></caption></media> <media xlink:href="ppat.1000154.s010.xls"><caption><p>Click here for additional data file.</p></caption></media> <media xlink:href="ppat.1000154.s011.xls"><caption><p>Click here for additional data file.</p></caption></media> <media xlink:href="ppat.1000154.s012.doc"><caption><p>Click here for additional data file.</p></caption></media> <media xlink:href="ppat.1000154.s013.xls"><caption><p>Click here for additional data file.</p></caption></media> <media xlink:href="ppat.1000154.s014.xls"><caption><p>Click here for additional data file.</p></caption></media> <media xlink:href="ppat.1000154.s015.xls"><caption><p>Click here for additional data file.</p></caption></media> <media xlink:href="ppat.1000154.s016.xls"><caption><p>Click here for additional data file.</p></caption></media>
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PLoS Pathog. 2008 Sep 12; 4(9):e1000154