Computational methods for structural gene annotation have propelled gene discovery but face certain drawbacks with regards to prokaryotic genome annotation. Identification of transcriptional start sites, demarcating overlapping gene boundaries, and identifying regulatory elements such as small RNA are not accurate using these approaches. In this study, we re-visit the structural annotation of Mannheimia haemolytica PHL213, a bovine respiratory disease pathogen. M. haemolytica is one of the causative agents of bovine respiratory disease that results in about $3 billion annual losses to the cattle industry. We used RNA-Seq and analyzed the data using freely-available computational methods and resources. The aim was to identify previously unannotated regions of the genome using RNA-Seq based expression profile to complement the existing annotation of this pathogen.
Using the Illumina Genome Analyzer, we generated 9,055,826 reads (average length ~76 bp) and aligned them to the reference genome using Bowtie. The transcribed regions were analyzed using SAMTOOLS and custom Perl scripts in conjunction with BLAST searches and available gene annotation information. The single nucleotide resolution map enabled the identification of 14 novel protein coding regions as well as 44 potential novel sRNA. The basal transcription profile revealed that 2,506 of the 2,837 annotated regions were expressed in vitro, at 95.25% coverage, representing all broad functional gene categories in the genome. The expression profile also helped identify 518 potential operon structures involving 1,086 co-expressed pairs. We also identified 11 proteins with mutated/alternate start codons.
The application of RNA-Seq based transcriptome profiling to structural gene annotation helped correct existing annotation errors and identify potential novel protein coding regions and sRNA. We used computational tools to predict regulatory elements such as promoters and terminators associated with the novel expressed regions for further characterization of these novel functional elements. Our study complements the existing structural annotation of Mannheimia haemolytica PHL213 based on experimental evidence. Given the role of sRNA in virulence gene regulation and stress response, potential novel sRNA described in this study can form the framework for future studies to determine the role of sRNA, if any, in M. haemolytica pathogenesis.
A systems-level understanding of organisms is not feasible by studying the functions of individual genes or proteins using reductionist approaches. It requires describing all molecular-level components that constitute building blocks of the system, identifying interactions among these components and determining regulatory modules to model emergent behavior . As such, identifying all functional elements including genes, RNA, and proteins is a prerequisite to generating predictive models of biological response to biotic or abiotic perturbations. The genome sequence encodes all the necessary information required to decipher its functions. Therefore, genome sequencing, with concomitant structural annotation, i.e., identification of the functional elements within the genome, including genes, gene structures, open reading frames and regulatory motifs, is a critical step for conducting systems biology research. It is imperative that current and up-to-date knowledge of molecular level components exists for a genome sequence. Therefore, re-annotation is key to identifying these fundamental components of biological processes.
De novo assembly of a genome is followed by mapping of functional elements using computational methods. Computational methods for prokaryotic gene annotation such as Gene Locator and Interpolated Markov ModelER (GLIMMER)  and GeneMark.hmm  use hidden Markov models  based on a sequence similarity measure generated from previously annotated genomes. These algorithms do not accurately identify all genes in the genome and sometimes result in errors, especially in positioning of translational start codons  and in the identification of small protein coding genes. Another major problem with computational approaches is over-annotation, which arises from the failure to discriminate between random open reading frames and those that are translated. Computational prediction of small non-coding RNA (sRNA), which lack sequence conservation in closely related species, has limited accuracy since transcriptional signal prediction (promoter and rho-independent terminator prediction) is also not accurate. Therefore, sRNA that regulate many biological processes, including virulence in bacterial pathogens, cannot be identified by computational approaches alone.
Experimental identification of expressed regions in the genome can help overcome some of the drawbacks of computational methods and is a complementary approach to computational genome annotation methods. DNA microarrays, serial analysis of gene expression (SAGE) or high throughput transcriptome sequencing technologies such as RNA-Seq, can all be used to measure genome expression [6–9]. Of these methods, RNA-Seq, which generates a single nucleotide resolution map of the transcriptome, can help annotate mRNA, non-coding RNA and sRNA, transcriptional structure of genes, and post-transcriptional modifications induced by alternate splicing in eukaryotes [10–13].
In this study, we report re-annotation of M. haemolytica, a gram-negative bacterial pathogen and one of the causative agents of bovine respiratory disease (BRD) in cattle. BRD is responsible for over $3 billion in losses to the cattle industry every year . M. haemolytica is most commonly isolated in field cases of BRD  and is considered to be the primary pathogen for this disease. Due to its importance for disease etiology, the genome of a bovine strain of M. haemolytica was sequenced in 2006. However, to date, the 2.6 Mb M. haemolytica PHL213 genome sequenced with an 8.4× coverage, is still in its draft phase. Despite being sequenced 6 years ago, the M. haemolytica genome sequence has not seen any improvement in its quality. Therefore, we chose to conduct RNA-Seq based re-annotation of M.haemolytica. The single nucleotide resolution map generated helped identify novel protein coding regions, sRNA, correct annotation errors, and operon structures.
M. haemolytica PHL213 was cultured in brain heart infusion (BHI) to mid-log phase (OD620 = 0.8). Cells from a single culture were treated with RNAprotect reagent (Qiagen, Valencia, CA) and stored at -80°C for subsequent RNA isolation. Total RNA from this single culture was extracted using the RNeasy mini kit (Qiagen, Valencia, CA), following manufacturer's protocols. It is to be noted that this kit allows for the extraction of transcripts that are at least 200 nucleotides and larger. RNA preparations were treated with RNase-free DNAse (Invitrogen, Carlsbad, CA) and the integrity of the RNA was determined using Bioanalyzer 2100 (Agilent Technologies, Santa Clara, CA). RNA sample with RNA Integrity Number (RIN) of 8 was used for the RNA-Seq experiment. From total RNA, mRNA was enriched by removing the rRNAs using MICROBExpress™ kit (Ambion, Austin, TX). This enrichment step specifically removes large rRNAs; small RNAs (i.e., tRNA and 5S rRNA) are not removed. In the first step of the MICROBExpress™ kit procedure, total RNA was mixed with an optimized set of capture oligonucleotides that bind to the bacterial 16S and 23S rRNAs. Next, the rRNA hybrids were removed from the solution using derivatized magnetic microbeads. The mRNA remained in the supernatant and was recovered by ethanol precipitation and quantified by Bioanalyzer 2100. Our RNA preparation did not include entities < 200 nucleotides in length.
A cDNA library was constructed using the Illumina mRNA-Seq sample prep kit (Illumina, San Diego, CA) with 100 ng RNA enriched for mRNA isolated from a single in vitro culture, following manufacturer's instructions. mRNA was chemically fragmented and randomly primed for reverse transcription and second-strand synthesis. The resulting cDNA was end-repaired and 'A' overhangs were added. Illumina paired-end sequence adaptors were ligated to the cDNA fragments. Fragments of approximately 200 bp were isolated from a 2% agarose gel and amplified (18 cycles) according to the Illumina protocol. Bioanalyzer 2100 (Agilent) was used to quantify and confirm the fragment size of each library. 1 nM of mRNA-seq library sample prepared for sequencing on the Illumina GAII (San Diego, CA) was denatured and diluted to 6 pM for clustering (v2) according to the manufacturer's protocol. Single read sequencing of the clustered flow cell was performed using Illumina's SBS chemistry (v3) and SCS data analysis pipeline v2.4. Flow-cell image analysis and cluster intensity calculations were carried out by Illumina Real Time Analysis (RTA v1.4.15.0) software. Subsequent base-calling was performed using the Illumina GA Pipeline v1.5.1 software. The resulting Illumina reads were quality-filtered by removing reads containing Ns.
The sequencing experiment produced 9,055,826 reads. FASTQ reads generated by Illumina were converted to Sanger FASTQ format using Perl scripts from the Mapping and Assembly with Qualities (MAQ) software package . Reads (Sanger FASTQ format) were mapped to the 2.6 Mb M. haemolytica PHL213 [GenBank: AASA00000000] reference genome using Bowtie . The parameters in Bowtie that control the speed and sensitivity were adjusted as follows: reads with no more than 2 mismatches per read (n = 2) were aligned, and any reads mapped to more than one location across the genome (ambiguous reads) were discarded (m = 1). Post alignment, a human-readable sequence alignment/map (.SAM) format file was converted to a "pileup" format file using SAMTools . This pileup file contains the count of reads per base aligned to each location across the length of the genome. The SAM file was also converted into a binary alignment/map (.BAM) format. These BAM formatted files are necessary for visualization of read alignments in Artemis viewer. The Artemis browser enabled the visual/manual inspection of alignment results in the context of the existing genome annotation. The pileup file, in conjunction with the annotation information of M. haemolytica PHL213, was processed using in-house Perl  scripts. Data generated from the RNA-Seq experiment was submitted to the NCBI Sequence Read Archive [SRA049621.1] as reads in FASTQ format [SRR402063.1] and the .BAM alignment file [SRR402079.4] generated by aligning the reads to the reference genome.
Identifying expressed regions within the genome that have not been previously annotated will improve the existing structural annotation of the M. haemolytica PHL213. Prior to the analysis of expressed regions in the genome, we determined the signal to noise ratio cutoff for background expression using the pileup file. Coverage depth (reads per base) greater than the lower tenth percentile of all reads was considered to be expressed and in this dataset, this corresponded to 7 reads/base [8, 20]. Based on this read/base cutoff, expressed intergenic regions (EIRs) were identified by applying an additional length cutoff of 70 bp. Shorter regions (less than 70 bp) were discarded to reduce the number of false positives. Custom Perl scripts were written to parse the pileup file and the existing genome structural annotation to identify (i) expressed annotated regions, (ii) expressed regions previously not annotated and, (iii) regions that are annotated but are not expressed. All EIRs were further analyzed using BLASTX  searches to determine their protein coding potential. If an EIR was found to be a perfect match (~100% coverage) for a protein, it was classified as a putative novel protein coding region. All EIRs with partial BLASTX hits were evaluated for the presence of an alternate start site or mutation in the start or stop codon associated with the annotated region. If the BLASTX search revealed a frameshift mutation, the EIR and the gene associated with the frameshift mutation were classified as a frameshift. EIRs with poor BLASTX hits and without any association to genes containing annotation errors were excluded from further analysis. EIRs without BLASTX hits were considered to be potential small non-coding RNA.
The Prokaryotic Promoter Prediction (PPP) program  (from PePPER suite ) and Transterm HP  were used to predict promoters and rho-independent terminators, respectively, in the forward and reverse strands of the M. haemolytica PHL213 genome. The locations of promoters and terminators were organized into .GFF files. A Perl script was written to identify putative sRNA i.e. EIRs with promoters or terminators associated to their loci. EIRs with no computationally-predicted promoters or rho-independent terminators were searched against the Rfam database  to determine whether these sequences were annotated in Rfam. EIRs that could not be classified as sRNA by Rfam were excluded from further analysis.
Using the annotation information (gene loci) of M. haemolytica PHL213 and the pileup file, all annotated regions that were expressed above the background signal to noise ratio cutoff with at least 60% coverage were considered to be expressed, which accounts for uniform evaluation of varying gene lengths. Similar measures have been used in other transcriptome profiling studies [8, 26, 27]. Annotated regions below 60% coverage were considered as 'not expressed' under the current experimental conditions. After having identified expressed genes, operon structures within the genome were also defined. The first step towards identifying an operon was to identify co-expressed pairs of coding regions. Two regions were considered to be co-expressed when they were identified as expressed on the same strand (5' to 3' or 3' to 5') and the region between them was also expressed. After such co-expressed pairs were identified, they were extended to construct operons by including additional co-expressed pairs in the vicinity satisfying the same conditions for co-expression as described earlier. Operon structures identified by RNA-Seq were compared to the computationally-predicted operon structures described by the Database for prOkaryotic OpeRons (DOOR)  for cross validation.
The M. haemolytica PHL213 is a 2.6 Mb draft genome containing 2,837 annotated regions of which 2,695 are protein coding with a 40% G+C content . For structural annotation of M. haemolytica at the RNA level, the transcriptome of M. haemolytica PHL213 was sequenced using RNA-Seq. Sequencing-based analysis of the transcriptome overcomes the limitations of the hybridization-based microarray approach. Head-on comparison of RNA-Seq with microarrays has shown that RNA-Seq has negligible technical variability , making it possible to obtain a reliable estimate of gene expression without replicate analysis. Therefore, we applied RNA-Seq for re-annotation of M. haemolytica and conducted the analysis from a single in vitro experiment. Reads with an average length of 76 bp generated on the Illumina platform were mapped onto the reference genome using the Bowtie read alignment program. Bowtie is an ultrafast, memory efficient alignment program that uses the Burrows-Wheeler transform  with a novel quality backtracking algorithm that permits mismatches. Bowtie performs better than Short Oligonucleotide Analysis Package (SOAP)  and MAQ, and its sensitivity at aligning reads is as good as both SOAP and MAQ. Of the 9,055,826 reads generated by Illumina, 3,917,458 reads (43.26%) that mapped uniquely to the genome were used for downstream analysis. 2,989,603 reads (33.01%) failed to align due to mismatches. The remaining 2,148,765 reads (23.73%), which mapped to more than one location in the genome (ambiguous reads), were excluded from analysis. For annotation purposes, reads that map to unique locations alone are used [8, 33–37]. The cutoff value for true-positive expression of a coding region of 7 reads/base was calculated from the expression (number of reads per base) in the tenth percentile of all reads [8, 20], as we did earlier for RNA-Seq based re-annotation of another BRD pathogen Histophilus somni .
We used the existing annotation of open reading frames in M.haemolytica PHL213 i.e., locus of each gene in the genome and reads identified as expressed by RNA-Seq, to identify expressed intergenic regions (EIRs). We identified 630 EIRs, previously un-annotated as expressed, of a minimum length of 70 bp. Each EIR was further characterized by adding computationally-predicted promoter and rho-independent terminators. Prokaryotic Promoter Prediction (PPP) identified 11,847 promoter regions and Transterm HP identified 1,204 rho-independent terminator regions, in forward and reverse strands of the genome. Identified EIRs, in conjunction with existing gene annotation information and loci of regulatory signals, were subjected to the analysis workflow described in Figure 1.
Data analysis flow-chart. RNA-Seq reads are aligned to the genome using Bowtie to generate a single nucleotide resolution map. Mapped reads analyzed in the context of existing annotation using SAMTools and Perl scripts generate expressed intergenic regions (EIRs). Homologs for EIRs, if present, are identified by BLASTX and are classified accordingly. An EIR with no BLASTX matches is subjected to computational search for regulatory elements (promoter/rho-independent terminator) in its vicinity. An EIR with a regulatory signal is classified as potential sRNA. EIR without BLASTX matches and predicted transcriptional signals are searched against Rfam database.
Artemis is a genome browser and annotation tool that allows visualization of sequence features, next generation sequencing data, and the results of the analyses within the context of the genome sequence . The Artemis genome browser illustrates all the six reading frames of the genome sequence along with the translated amino acid sequences, start and stop codons, as well open readings frames across the length of genome (Figure 2). We visualized the alignment file generated by Bowtie, the gene annotation file, promoter and terminator loci, and EIRs in Artemis. Artemis generated a base coverage graph, giving a pictorial representation of the expression in various regions of the genome.
Overview of Artemis genome browser. Description of the tracks used for data analysis in Artemis genome browser. (a) reads aligned to the genome (b) expression portrayed as coverage graph (c) reading frames 5'-3', (d) forward strand of the genome, (e) reverse strand of the genome, (f) reading frames 3'-5', (g) amino acid sequences corresponding to the 6 reading frames, (h) expressed intergenic region, (i) computationally predicted regulatory signal (promoter/rho-independent terminator), (j) annotated gene (white), based on M. haemolytica Genbank accession # AASA00000000, (k) ORFs (blue) of a specified minimum length, predicted by Artemis between two consecutive stop codons, (l) stop codons in all six reading frames (black) and, (m) start codons in all six reading frames (pink).
The protein coding potential of EIRs was determined by conducting BLASTX searches with the translated nucleotide sequence of EIRs, against the protein database containing all bacterial species. BLASTX results showed that 14 EIRs had full length matches to target sequences, indicating their potential for coding proteins. The Artemis browser was used to identify the boundaries of these 14 potential novel protein coding regions (Figure 3). These novel protein coding regions had an average G+C content of approximately 46%. The length of these regions was between 37 to 200 amino acids. While the RNA-Seq experiment itself was not strand specific, strand specificity of novel protein coding regions was inferred from the proteins identified as ~100% matches to these EIRs in BLASTX. EIR MHP4 aligned to PG1 protein of Lactobacillus crispatus ST1 while MHP12 aligned to serine acetyltransferase of Haemophilus influenzae NT127. The rest of the EIRs (Table 1) aligned to proteins classified as hypothetical.
A novel protein coding region. Identification of novel protein coding regions using expressed intergenic region (EIR, yellow) and its corresponding open reading frame (ORF, blue). A BLASTX search of the ORF identified a full length match to a protein with 98% coverage. The coverage graph for the region of interest is shown (dotted line).
Potential protein loci, length (aa), inferred strand direction, description of the corresponding BLASTX protein match.
Artemis genome browser creates open reading frames (ORFs) of a desired minimum length. It identifies ORFs as regions between two consecutive stop codons with the specified minimum length. Thus ORFs corresponding to EIRs can be generated and visualized in this browser. RNA-Seq based expression in relation to the existing genome annotation, when visualized in Artemis, enabled the identification of the actual locus and length for some of the annotated proteins. We identified 4 genes with a mutated start codon. This anomaly could be the result of computational gene prediction programs identifying the next available "AUG" as the start codon (Figure 4). Our observation is substantiated by the consecutive expression of an identified EIR preceding the 5' region of these genes. 4 genes had a mutation that led to the replacement of start codon by a leucine (L).
Mutated start site. Visualization of a gene identified with a mutation in the start site in Artemis genome browser. RNA-Seq based coverage graph clearly shows expression upstream of the annotated start site of the gene |gi|153091350. When the ORF encompassing the gene |gi|153091350 and the expressed intergenic region (EIR, yellow) upstream of the gene, was used for conducting BLASTX searches, we identified a full length match suggesting that the actual start site is at 'leucine' (probably mutated), instead of 'valine' (based on existing annotation).
Since Artemis allows marking the start codons within an ORF, it is possible to identify alternate start sites, if any, for any ORF associated with an EIR. ORFs created from EIRs in Artemis revealed possible alternate starts sites for 7 genes. BLASTX searches of the EIR and its translated protein revealed that the actual start site varied with respect to previous annotation (Figure 5). Where there was a discrepancy between the existing annotation and the current transcriptome based identification of start site, the consensus of start site of similar proteins identified in BLASTX was used to determine the actual start site. The suggested revisions to existing annotation are in Table 2 (detailed results in Additional file 1). BLASTX searches of EIRs also revealed mutations that lead to disruption of a protein coding region, resulting in a frameshift (Figure 6). Two such EIRs which had BLASTX alignments revealed frameshifts which would otherwise be protein coding regions (Additional file 1).
Alternate start site. Artemis genome browser identified an alternate start site for the gene |gi|153092504. The coverage graph for this gene showed that the start codon based on expression is different from annotated start site. In the 5' upstream region of annotated start site, there are two methionines that could be potential start sites. BLASTX search identified a full length match, and confirmed one on these two methionines as the actual start site.
P reviously annotated gene locus, length and strand specificity. Observed locus based on RNA-Seq expression along with gene length (bp) and protein length (aa) and the description of the annotation error. In case of a mutated start, the amino acid start codon identified by RNA-Seq.
Frameshift mutation. An expressed intergenic region (EIR, yellow) and its corresponding ORFs (highlighted) visualized in Artemis genome browser indicate a possible frameshift mutation. BLASTX search of the EIR identified a full length match, confirming the frameshift mutation.
Small RNA are known to have regulatory roles in Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa, Vibrio cholera and many other bacterial pathogens . Genome-scale identification of sRNA using RNA-Seq is reported for E. coli  and Vibrio cholerae , among other pathogenic bacteria. The identification of the loci of sRNA in the genome is an important pre-requisite for understanding their role in modulating bacterial physiology and virulence . sRNA are synthesized by RNA polymerase (RNAP) in a manner analogous to the synthesis of any RNA in bacteria (mRNA, rRNA, tRNA); sRNA promoters could be regulated by transcription factors or use of alternative sigma factors . Therefore, the presence of promoters and terminators for potential sRNA [8, 44] identified by experimental approaches like RNA-Seq, increases the confidence in their identification. Although the RNA extraction protocol used in this study does not facilitate extraction of smaller transcripts and strand specificity is lost during cDNA synthesis, we identified potential sRNA. EIRs with no protein coding potential, as observed via BLASTX searches, were considered to be candidate sRNA. It is possible that EIRs with no BLASTX matches are non-conserved ORFs; since there are no in silico methods to validate this assumption, we chose to consider all EIRs with no BLASTX as candidates for small RNA analysis. Candidate sRNA loci were searched for the presence of a promoter or terminator. For 44 EIRs that had no BLASTX matches, a promoter or a rho-independent terminator was identified either on the forward or the reverse strand (Figure 7) of their locus. Promoters/terminators were present in the transcriptional regulatory regions, i.e., a promoter was present in the -1 to -35 region or a terminator was present in the +1 to +20 position at the end of the EIR. Therefore, we classified the 44 candidate sRNA as potential novel small RNA in the M. haemolytica PHL213 genome (Table 3). The average length of the identified novel sRNA was approximately 100 bp and ranged between 70 to 253 bp. The average G+C content of sRNA was 34.35%, which is relatively lower than the G+C content of the genome. All identified sRNA had a promoter associated with their locus and sRNA MHS17 also had an associated terminator. When sequences of the identified sRNA were searched in the Rfam  database to identify their function, no matches were found.
Identification of potential sRNA. (a) Criterion for identifying potential sRNA. If a promoter located upstream of the expressed intergenic region (EIR) or a rho-independent terminator located downstream of the EIR, either in the forward or the reverse strands of the genome is identified, then the EIR is classified as a potential sRNA. (b) & (c) a promoter was identified upstream of the EIR in both the cases and the EIRs were classified as sRNA.
Identified potential sRNA, their locus, length (bp), flanking genes, predicted promoter and its strand specificity, top BLASTN hit (if any).
EIRs with no BLASTX matches, predicted promoter, or a rho-independent terminator, were searched against the Rfam database to identify potential matches with any of the known conserved RNA families in the database. Five EIRs mapped to five different functional categories within Rfam, shown in Table 4. MHS45 was classified as bacterial signal recognition particle RNA, a conserved ribonulceoprotein that directs movement of proteins within the cell and aids their secretion. MHS46 was classified as MOCO RNA motif which is presumed to be a riboswitch that binds to molybdenum cofactor or related tungsten cofactor. MHS47 was classified as a thiamine pyrophosphate (TPP) riboswitch that binds directly to thiamine pyrophosphate to regulate gene expression. MHS48 was classified as an alpha operon ribosome binding site that binds to ribosomal protein S4 which acts as a translational repressor. MHS49 was annotated by Rfam as a gcvB RNA that encodes small non-coding RNA involved in the regulation of amino acid transport systems and amino acid biosynthetic genes. All predicted functions will need to be validated by further experimentation.
EIRs that do not have either a BLASTX hits or predicted promoter or rho-independent terminator, but with matches in Rfam database. sRNA locus, strand specificity and description based on Rfam.
The M. haemolytica PHL213 genome consists of 2,837 annotated genes, 2,695 of which code for proteins. Genes were considered to be expressed if 60% of the gene length had at least 7 reads aligned/nucleotide. Based on this criteria, 2,506 of all annotated regions in the genome (87.63%) were identified as expressed with 95.25% coverage i.e. approximately 95% of the sequence of the annotated region had at least 7 reads aligned/nucleotide. Expressed annotated genes and their coverage are documented in Additional file 2.
Functional analysis of the expressed annotated regions was based on the existing annotation of M. haemolytica genome available at NCBI. It is interesting to note that genes that are described as virulence factors are also expressed under normal culture conditions. For example, genes related to leukotoxin (MHA_0253, MHA_0254, MHA_0255 and MHA_0266), an important virulence factor, were all expressed. Also, the 12 capsule forming genes whose role in virulence includes adherence to host and resistance to serum-mediated killing and phagocytosis [45, 46] were all found to be expressed. In addition to these, we also found that 40 genes associated with lipopolysaccharide or lipoproteins and contribute to virulence by initiating an inflammatory cytokine response [45, 47] to be expressed. Genes responsible for forming the type IV pilus associated with M. haemolytica that is responsible for DNA uptake, adhesion, and motility  were expressed. Filamentous hemagglutinin genes of M. haemolytica (MHA_0866, MHA_0867), responsible for adhesion to host mucosa , were expressed. Adhesins play an important role in virulence, and all annotated genes related to this function, such as MHA_2262, MHA_0708, MHA_2492, MHA_2701, MHA_1367, MHA_0563 and MHA_2800, among others, were all identified as expressed in our experiment. Genes responsible for resistance towards antibiotics such as β-lactams, tetracycline, streptomycin, and sulfonamides  in M. haemolytica were also expressed. Annotated regions that were not expressed had coverage of only 30%. Of the 331 annotated regions that were not expressed 236 were annotated as "hypothetical proteins" and 26 were "hypothetical bacteriophage proteins."
Using expression patterns of coding regions, we identified paired gene expression and operon structures. RNA-Seq based operon structures were compared to the computationally predicted structures using DOOR . We identified 1,086 co-expressed pairs of genes that could be organized into 518 potential operons. DOOR predicted 1,295 co-expressed pairs forming 599 operons (Additional file 3). The overlap between RNA-Seq based and DOOR-based co-expressed pairs was 854. Our study identified relatively fewer co-expressed pairs as compared to DOOR. This could be due to the fact that 331 of the 2,837 annotated regions were not expressed in our dataset. Furthermore, this method cannot detect genes whose expression is suppressed by polar mutations. The single nucleotide resolution map enabled the identification of co-expressed pairs and definition of operon structures and regulatory patterns. Availability of operon structures will facilitate understanding the coordinated regulation of genes in M. haemolytica to moderate metabolic pathways under different environmental conditions.
Identification of all functional elements of the genome is fundamental to understanding the dynamics of biological processes that occur within any living organism. Gene models are available for sequenced genomes that are based on computational approaches. However, a number of recent studies highlight the need for genome re-annotation, prior to conducting holistic systems biology analyses. Experimental approaches, at times, shed light on regions of the genome where computational methods of structural annotation fail. Re-annotation studies of several species including disease causing pathogens have revealed numerous genes, regulatory regions and complex metabolic pathways that remained undetected based on the initial annotation [8, 50–55]. In this study, we applied a combinatorial approach i.e. RNA-Seq based transcriptome analysis in conjunction with computational resources, to structurally annotate a bacterial genome at the RNA level. For the first time, we report RNA-Seq based annotation of the genome of M. haemolytica PHL213, one of the primary pathogens of Bovine Respiratory Disease in cattle . Its genome was sequenced with 8.4× coverage, and is in draft phase since 2006.
We have recently re-annotated Histophilus somni 2336, another BRD pathogen belonging to Pasteurellaceae like M. haemolytica. RNA-Seq based transcriptome analysis identified 38 novel protein coding regions and 82 sRNA in H. somni . Compared to the draft genome for M. haemolytica, H. somni has a complete genome sequence. Yet, re-annotation of this genome identified a number of functional elements missed in the initial annotation. The relatively poor quality of the existing structural annotation of M. haemolytica can be enhanced by re-annotation, and this was the motivation behind the current study.
Re-annotation enabled us to fix errors in existing annotation. A mutation that might have occurred during replication could alter the structure of the gene in its vicinity. Computational methods, when predicting a gene, seek to identify an ORF and its putative start and stop codons to define gene boundaries. Mutations in the sequence between the start or stop codon of a gene might not actually affect gene prediction or may sometimes result in a frameshift. If the mutation is to occur in the start or stop codon itself, algorithms would seek to identify the next available start or stop codon. This would lead to alteration in gene locus and a subsequent gene annotation error. Such annotation errors cannot be detected without experimental validations. The single nucleotide resolution transcription map generated by RNA-Seq is one of the most efficient ways to detect such annotation errors. As described in our workflow (Figure 1), once EIRs overlapping a certain gene were identified, BLASTX searches of these regions helped in defining the actual boundaries and correct annotation errors, if any. Mutations leading to a frameshift can result in a gene being completely disrupted. Such frameshifts remain undetected by automated approaches, but can be identified by experimental approaches such as RNA-Seq used in this study. Genome-wide studies using experimental methods can help validate these predictions and improve the quality of annotation across genomes and eliminate errors from being transferred from one genome to another during annotation of novel assemblies.
Understanding coordinated regulation of gene expression in bacteria requires the description of operon structures in the genome. Prior to this study, operon structures were unavailable for M. haemolytica. Since computationally-predicted operon structures were unavailable, we first generated a set of computationally-predicted operons using DOOR (Additional file 3). RNA-Seq enabled us to identify expressed gene pairs that could be expanded into potential operons. Comparison of DOOR predicted operons with RNA-Seq based operons in M. haemolytica showed a major overlap and cross-validated the findings in both approaches. Thus re-annotation helped validate 599 operons predicted by DOOR. We also identified 233 co-expressed pairs that were not identified by DOOR. Since the strand specificity of expression is lost in RNA-Seq experiment described here, at best the operons identified in this study should be considered 'potential operons' that will require experimental validation in future studies. Furthermore, this experiment-based identification of co-expression will not be able to identify genes that are expressed in a polar fashion within the operon. Analysis of the functions of genes identified as expressed by RNA-Seq resulted in an interesting finding. Genes that are annotated as being virulence factors were identified as expressed under normal culture conditions. These results are consistent with our findings in H. somni. Our results indicate that the expectation of 'virulence factor' being expressed only during pathogenesis may not be accurate. It is possible that there is a basal pervasive level of expression of these factors and that it is the difference in the expression level that actually corresponds to virulence.
Computational methods for identification of sRNA are not accurate, and transcriptome profiling using deep sequencing methods can help identify novel sRNA. sRNA play a crucial role in adaptive response to stress by directly or indirectly regulating virulence genes , as shown in Staphylococcus aureus , Pseudomonas aeruginosa  and Vibrio cholerae [59, 60]. However, a comprehensive understanding of sRNA regulatory roles during adaptive responses and pathogenesis is only possible after their identification. Despite the drawbacks in sample preparation and lack of strand specificity, we identified 44 potential novel sRNA. The identified novel sRNA were searched for homology in the sRNA database (sRNAdb) against other bacterial sRNA identified through similar transcriptomics studies and/or computational approaches . Only 15 sRNA had partial alignments of 20-30 nucleotides and the remaining had very poor sequence conservation across the database (Additional file 4). We also compared the 44 sRNA identified in the M. haemolytica genome with 82 H. somni sRNA using 'BLAST 2 sequences' megablast . No similarity was found, indicating poor consensus among non-coding RNA. These results suggest that regulation of sRNA is probably as diverse and as complex as gene or protein regulation.
The inherent limitations of our experimental setup i.e. lack of enrichment specifically for sRNA, lack of strand specificity information and lack of biological replicates, isolation of RNA at different stages of in vitro growth, etc, did not allow comprehensive identification of sRNA. Due to the same limitations, the identified gene co-expression also needs further validation work in future. However, as the results indicate, application of RNA-Seq enhanced the existing annotation of M. haemolytica. RNA-Seq based annotation is not the 'final' and conclusive step in identifying functional elements in this important bacterial pathogen. In fact, this work is part of the continuum in a typical systems biology work flow.
The RNA-Seq based transcriptome map of M. haemolytica PHL213 validated annotated open reading frames and led to the discovery of potential novel protein coding regions. We identified operon structures and were able to fix exiting annotation errors by correcting gene boundaries. The availability of experimentally validated open reading frames, potential novel sRNA, potential protein coding regions, and operon structures form the basis for future investigations to determine the role of these elements during BRD pathogenesis. This study also demonstrates the utility of free and easy to bioinformatics tools for RNA-Seq data analysis workflow.
This project was partially supported by the Institute for Genomics, Biocomputing and Biotechnology, and the National Science Foundation (Mississippi EPSCoR-0903787), and Mississippi INBRE funded by grants from the National Center for Research Resources (5P20RR016476-11) and the National Institute of General Medical Sciences (8 P20 GM103476-11) from the National Institutes of Health.
One of the authors, James M Watt, is currently employed with Eagle Applied Science. Since the research work for this manuscript was performed when he was an employee at College of Veterinary Medicine, Mississippi State, Mississippi, it does not alter the authors' adherence to all the BMC Bioinformatics policies on sharing data and materials. The authors declare that they have no competing interests.
JSR developed the analysis workflow with RK and BN, wrote all scripts required for analysis, carried out data analysis, and wrote the initial draft of this manuscript. JMW prepared the RNA for conducting RNA-Seq. SCB, MLL, and BN conceived and designed this collaborative study, and helped with data analysis and interpretation. BN helped draft the final version of the manuscript. All authors read and approved the final manuscript.


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redpajama_set_name: RedPajamaGithub


Yes, Part 3. This technique was published some time ago and as it gets further "field testing", new conditions have come to light which require a revision of this technique. Assuming this is the last post on this topic, I need to figure out how to duplicate this logic in PowerBI.
I consider this to be version 15 or so of this query since my first version was written in mid-September of 2010. The changes here are as follows.
First, the DTD definitions have been expanded to include all possible HTML codes. This should prevent a report blowing up when someone decides to use an unexpected symbol somewhere.
Second, I keep seeing random <br> tags in the fields, which ironically is breaking the query. I've added a replace statement for that as well.
Lastly, I've expanded the field sizes to Max where necessary to accommodate truncation issues.
Sample code is below. I strongly recommend copying into NotePad first so that you don't accidentally get curly quotes. I've also left it as straight text to make it easier to copy.
Here's a quick article on how to connect Project Pro to Project Online. The question seems to come up on a regular basis so I'm documenting it here.
In Project 2013 and Project Online, Microsoft added the ability for Project Pro to autoconfigure the connection to Project Web App. However, to make this happen, you need to start in PWA.
Project Pro is now configured to open and edit projects from Project Web App.
Going forward, you should open your project from PWA as opposed to Project Pro. This is the future interaction direction. The Edit button provides an option to edit the project in Project Pro.


Itqan is the only Saudi company offering custody services for private equity investments. Clients benefit from having a comprehensive record of all their holdings and transactions, with scrip, share certificates, and dividend receipts lodged together in secure custody. This also makes the entire process more efficient and helps maximize returns. Itqan prepares detailed reports on the investments held and liaises personally with company managers.
An important consideration is assisting clients to ensure they comply with legal requirements for retaining and recording a history of transactions, particularly valuable in estate planning and management - giving executors an up-to-date statement of the assets held.
Our highly competitive fee structure is based on differing rates for each asset type and role, instead of a fixed rate or percentage of gross value.


The Department of Finance (DOF) is proposing to widen the value-added tax (VAT) exemption threshold for micro-, small- and medium enterprises (MSMEs) to lessen the impact of higher fuel levies on low cost businesses.
During the Senate inquiry on the DOF's tax reform plan, Finance Undersecretary Karl Kendrick T. Chua told lawmakers that they are proposing to exempt entrepreneurs with annual gross revenues of P3 million or less from paying the 12 percent VAT.
Under the proposal, Chua said the VAT threshold on business' goods and services would be increased from the current P1.9 million.
"The protection really comes from the increase in their VAT threshold to P3 million so that the micro and small enterprises with gross sales of at most P3 million will not be affected by the broadening of the VAT base," Chua said.
But still, the finance official said MSMEs will still pay their percentage tax to the Bureau of Internal Revenues (BIR).
"Our proposal to raise the VAT threshold will help cushion the country's vulnerable sectors, like MSMEs, from the impact of the proposed fuel excise tax adjustments and removal of certain VAT exemptions," Chua said.
Finance Undersecretary Antonette C. Tionko also said the proposal will simplify the tax compliance of small business owners with the "complicated" VAT system.
"This is aimed to actually help the SMEs, because right now it's at P1.9 million, over and above is covered by VAT, so it will be beneficial for them. It also will simplify things for them, they don't have to file VAT returns," Tionko told reporters.
Along with SMEs, the DOF is along proposing that minimum-wage earners and other members of the working class identified as those earning more than P5,000 but not over P12,000 a month would be protected from the effects of the fuel excise tax adjustments.
Chua said the government is planning to provide drivers and operators of public utility vehicles with cash cards similar to the Pantawid Pasada Program to ensure that their pass-through costs would only be around 50 centavos.
"Also, if we address the other problems of traffic, of improving their engines to make these more efficient; if we address the corruption on the streets, which we hear some authorities collect bribes from jeepney drivers, [then we probably do not need to see any increase]," Chua said.


This Topic is dedicated to those cool spots that only guides (well very few people) know about in the Canyon! We can share all those cool places that are only known by spending a ton of time below the rim. From the blue and green rooms in Havasupai to the Cave of the Domes off of Horseshoe Mesa. Please include in your Post Reply: hidden places, favorite spots, cool places off the beaten path, amazing view points, ruins, petroglyphs, favorite campsites and why. Also, if possible please add photos or maps to help other guides find these awesome places.
To start off this Topic, I thought I would let everyone know about something NEW in the Canyon. So recently there was a rock fall and a clean up that included some blasting. When it was all said and done some new animal tracks on the Bright Angel Trail were discovered. They are right on the trail at about 2.5 miles down. Please take a few minutes to check out the link below so that you can show your guests this cool rock! It would be hard to miss since it is massive and facing the trail!
I recently ran a trip on the Bright Angel trail and found the new tracks that have been exposed. They are between 2.5 and 2.75 miles down from the rim. So around 4 switchbacks up from 3 mile rest house. They are located on the outside of the trail and they are oriented so you have a better chance of seeing them as you hike up the trail.
Below is the video i took of the tracks. If you have trouble locating them, let me know and ill try and give you more specific instructions on their location.
I hope everyone knows about the tracks on the Hermit Trail. They are on the left hand side of the trail when you are heading down and they are right after the human made wall that almost looks like stairs if you turn to look back up the trail. The better tracks are straight in front of you when you hit the switch back. if you are looking towards the south there is Juniper tree off to your right.
There is an old mine to the West of the amphitheater in Indian Garden. Walk back to the area where the benches are and look for a trail to the North that leads to a wash. Crawl up the wash about 200 feet and the cave is up about fifteen feet above the wash to the North side of the wash. It is easy to miss so keep looking back over your right shoulder as you go up in the wash. Bring a light!
Verizon and AT&T cell phone reception at the helipad above the common bathrooms at Indian Garden... Take BA from campground to Day Area watching for a trail on the right just before a tree that leads off diagonally - it leads behind and above the large day use bathroom unit and past a fire hose box to the helipad. AT&T is spotty and Verizon works.
The spot by the Clear Creek sign and N Kaibab trail for Verizon.
5 minutes to the North and 8 minutes to the South of Cottonwood - if you can see the South Rim, you can get reception to text from AT&T OR Verizon.
Here is a cool Spot if you have time at Cottonwood.... Cross BA Creek to (Transept Canyon I think....) and there is fairly warm water coming from it. Walk up a ways and find some pools to soak in in cooler weather! It is just across the creek and slightly to the North of Cottonwood Campground!
If you can find the Unconformity on the BA trail 1 mile north of Indian Garden.... It is the black line to the right in a convenient shady resting area to the right of the trail while going down, there are ruins to see there.
Turn your back to the Unconformity and look across the creek for a straight line which is a trail. It is right in front of the huge chunk of Tapeats that fell from above 20 yards downslope. Cross the creek and follow the trail up to the opposite wall of the Tapeats where you will find a (class c?) ruins. Please don't touch and take only pictures!
On the way back about 100 yards from before you cross back over Indian Creek look up at the wall above where you were originally standing and sighting the trail and you will see another ruins about 100 feet above the BA trail.


Discussion in 'New User Introductions' started by planethoff, Apr 26, 2011.
I stumbled upon this site doing a google search trying to solve a computer glitch and found the issue discussed on this forum. I was instantly head over heels as I am a ex-pro chef and full-on foodie. I have over 20 years in the restaurant/catering/private chef industry at all levels of service. My formal training is with Italian cuisine but have branched out to many others. My darkest culinary secret is that French cuisine is my weakest, but I am versed enough to fake it well.
I am not currently employed in the culinary world because my computer skills have provided me an income and schedule that no restaurant can match. However, I still cook for real all the time. I am very excited to meet other chefs and foodies to learn and share and am excited to help out those who are just starting out in this wonderful world of flavor and passion.
Pastry is an alien world to me, but I do have a 7qt kitchen aide mixer and curiosity. I am semi-fluent in wine and amateur brewmeister. Good food, drink, music, and company makes the world go round.
We are a wide and diverse membership here - from around the globe (I'm Scots) and all levels of culinary abilty. I am sure you will find a lot of kindred spirits here.
Feel free to join in on any thread you find interesting, or start your own in the relevant forum. The wikis, blogs, reviews, articles and photographs are all well worth spending time to view, too.
Welcome glad to have you and hope you enjoy the community. There are a lot of us former chefs around so you are most welcome.
Hello Ishbel, Thanks for the welcoming words, I do think I will enjoy this site very much. I saw a post from you on another thread regarding your love of Indian food and its prevalence in the UK. I have been toying with the differences between Authentic Indian, UK Indian, and American Indian. I am a capsaicinaholic so I love the real deal, but people in my parts aren't so adventurous. It seems UK style is the perfect middle-ground between Authentic and American style, so I usually emulate that in my dishes.
btw - I am a large fan of Scots and their culture. One of my closest friend hails from the Mcdermott clan, and I have learned much about the heritage.
Nicko - thanks also for the welcome, I'm sure we will bump into each other quite often in the future.
Our 'Indian' cuisine in the UK is mostly Bangladeshi in origin. According to my Indian friends, they, the Bangladeshis are the most talented chefs.
I was taught to make curries and other 'Indian' dishes by fellow students many, many years ago. I think 'ours' are pretty authentic. Certainly more authentic than the 'Indian' dishes I've eaten in Australia, for instance!
I'm a Scottish historian, and have a deep interest in our culture and cuisine - if you ever have a question about a 'genuine' Scots recipe, I'm your woman!
Welcome to this forum. Have a nice day!
Hello Mit. Thank you. Have a great day as well. EdB - greetings sir. Just a little north of you in SLC.
You will love it here, better sense of community here then most cooking forums!
Hello Linny. Thanks, I think you are right.


text:
Microstachys revoluta är en törelväxtart som först beskrevs av Ernst Heinrich Georg Ule, och fick sitt nu gällande namn av Hans-Joachim Esser. Microstachys revoluta ingår i släktet Microstachys och familjen törelväxter. Inga underarter finns listade i Catalogue of Life.

Källor

Törelväxter
revoluta

redpajama_set_name: RedPajamaWikipedia


text:
View all Nature Research journals
Age-related remodelling of oesophageal epithelia by mutated cancer drivers
Akira Yokoyama1,2 na1,
Nobuyuki Kakiuchi1,3 na1,
Tetsuichi Yoshizato1 na1,
Yasuhito Nannya1,
Hiromichi Suzuki1,
Yasuhide Takeuchi1,4,
Yusuke Shiozawa1,
Yusuke Sato1,
Kosuke Aoki1,
Soo Ki Kim1,
Yoichi Fujii1,
Kenichi Yoshida1,
Keisuke Kataoka1,
Masahiro M. Nakagawa1,
Yoshikage Inoue1,5,
Tomonori Hirano1,3,
Yuichi Shiraishi6,
Kenichi Chiba6,
Hiroko Tanaka7,
Masashi Sanada8,
Yoshitaka Nishikawa2,
Yusuke Amanuma2,
Shinya Ohashi2,
Ikuo Aoyama2,
Takahiro Horimatsu2,
Shin'ichi Miyamoto3,
Shigeru Tsunoda5,
Yoshiharu Sakai5,
Maiko Narahara9,
J. B. Brown10,
Yoshitaka Sato11,
Genta Sawada12,
Koshi Mimori12,
Sachiko Minamiguchi4,
Hironori Haga4,
Hiroshi Seno3,
Satoru Miyano6,7,
Hideki Makishima1,
Manabu Muto2 na2 &
Seishi Ogawa1,13 na2
Nature volume 565, pages312–317(2019)Cite this article
Clonal expansion in aged normal tissues has been implicated in the development of cancer. However, the chronology and risk dependence of the expansion are poorly understood. Here we intensively sequence 682 micro-scale oesophageal samples and show, in physiologically normal oesophageal epithelia, the progressive age-related expansion of clones that carry mutations in driver genes (predominantly NOTCH1), which is substantially accelerated by alcohol consumption and by smoking. Driver-mutated clones emerge multifocally from early childhood and increase their number and size with ageing, and ultimately replace almost the entire oesophageal epithelium in the extremely elderly. Compared with mutations in oesophageal cancer, there is a marked overrepresentation of NOTCH1 and PPM1D mutations in physiologically normal oesophageal epithelia; these mutations can be acquired before late adolescence (as early as early infancy) and significantly increase in number with heavy smoking and drinking. The remodelling of the oesophageal epithelium by driver-mutated clones is an inevitable consequence of normal ageing, which—depending on lifestyle risks—may affect cancer development.
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Fig. 1: Somatic mutations in PNE, dysplasia and cancer samples.
Fig. 2: Whole-genome sequencing of single-cell-derived colonies.
Fig. 3: Driver mutations in PNE and cancer samples.
Fig. 4: Fine architecture of clones in high-density sampling.
Fig. 5: Expansion of driver-mutated clones.
Fig. 6: Clonal evolution in PNE.
All the WES, WGS and SNP array data have been deposited in the European Genome-Phenome Archive (http://www.ebi.ac.uk/ega/) under accession numbers EGAS00001003008, EGAS00001003281 and EGAS00001003331, respectively. Data for Figures and Extended Data Figures are available as Source Data. All other data are available from the corresponding author on reasonable request.
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We thank the Ministry of Education, Culture, Sports, Science and Technology for support (grant references: hp150232 and 15H05909), together with many other funding bodies and individuals (Supplementary Note 1).
Nature thanks F. Ciccarelli, B. Lehner and the other anonymous reviewer(s) for their contribution to the peer review of this work.
These authors contributed equally: Akira Yokoyama, Nobuyuki Kakiuchi, Tetsuichi Yoshizato
These authors jointly supervised this work: Manabu Muto, Seishi Ogawa
Department of Pathology and Tumor Biology, Kyoto University, Kyoto, Japan
Akira Yokoyama, Nobuyuki Kakiuchi, Tetsuichi Yoshizato, Yasuhito Nannya, Hiromichi Suzuki, Yasuhide Takeuchi, Yusuke Shiozawa, Yusuke Sato, Kosuke Aoki, Soo Ki Kim, Yoichi Fujii, Kenichi Yoshida, Keisuke Kataoka, Masahiro M. Nakagawa, Yoshikage Inoue, Tomonori Hirano, Hideki Makishima & Seishi Ogawa
Department of Clinical Oncology, Kyoto University Hospital, Kyoto, Japan
Akira Yokoyama, Yoshitaka Nishikawa, Yusuke Amanuma, Shinya Ohashi, Ikuo Aoyama, Takahiro Horimatsu & Manabu Muto
Department of Gastroenterology and Hepatology, Kyoto University, Kyoto, Japan
Nobuyuki Kakiuchi, Tomonori Hirano, Shin'ichi Miyamoto & Hiroshi Seno
Department of Diagnostic Pathology, Kyoto University, Kyoto, Japan
Yasuhide Takeuchi, Sachiko Minamiguchi & Hironori Haga
Department of Surgery, Graduate School of Medicine, Kyoto University, Kyoto, Japan
Yoshikage Inoue, Shigeru Tsunoda & Yoshiharu Sakai
Laboratory of DNA Information Analysis, Human Genome Center, Institute of Medical Science, The University of Tokyo, Tokyo, Japan
Yuichi Shiraishi, Kenichi Chiba & Satoru Miyano
Laboratory of Sequence Analysis, Human Genome Center, Institute of Medical Science, The University of Tokyo, Tokyo, Japan
Hiroko Tanaka & Satoru Miyano
Department of Advanced Diagnosis, Clinical Research Center, Nagoya Medical Center, Nagoya, Japan
Masashi Sanada
Department of Human Genetics, McGill University, Montreal, Quebec, Canada
Maiko Narahara
Laboratory of Molecular Biosciences, Life Science Informatics Research Unit, Kyoto University, Kyoto, Japan
J. B. Brown
Sato Clinic, Okayama, Japan
Yoshitaka Sato
Department of Surgery, Kyushu University Beppu Hospital, Beppu, Japan
Genta Sawada & Koshi Mimori
Department of Medicine, Center for Hematology and Regenerative Medicine, Karolinska Institute, Stockholm, Sweden
Seishi Ogawa
Akira Yokoyama
Nobuyuki Kakiuchi
Tetsuichi Yoshizato
Yasuhito Nannya
Hiromichi Suzuki
Yasuhide Takeuchi
Yusuke Shiozawa
Kosuke Aoki
Soo Ki Kim
Yoichi Fujii
Kenichi Yoshida
Keisuke Kataoka
Masahiro M. Nakagawa
Yoshikage Inoue
Tomonori Hirano
Yuichi Shiraishi
Kenichi Chiba
Hiroko Tanaka
Yoshitaka Nishikawa
Yusuke Amanuma
Shinya Ohashi
Ikuo Aoyama
Takahiro Horimatsu
Shin'ichi Miyamoto
Shigeru Tsunoda
Yoshiharu Sakai
Genta Sawada
Koshi Mimori
Sachiko Minamiguchi
Hironori Haga
Hiroshi Seno
Satoru Miyano
Hideki Makishima
Manabu Muto
A.Y., H. Suzuki, T.Y., N.K., H.M., M.M. and S. Ogawa designed the study. S. Ohashi, Y.A., I.A., T. Horimatsu, S. Miyamoto, S.T., Y. Sakai, Yoshitaka Sato, H. Seno, M.M. and Y. Nishikawa provided specimens. Y.T., S. Minamiguchi and H.H. performed histological analysis. A.Y., H. Suzuki, T.Y., N.K., Y. Shiozawa, Yusuke Sato, K.A., Y.T., S.K.K., Y.F., K.Y., K.K., Y.I., T. Hirano and M.M.N. performed sample preparation. G.S. and K.M. provided sequencing data. M.S., A.Y., H. Suzuki, T.Y., N.K., Y. Shiozawa and Y. Nannya performed mutation calling, validated the results, and analysed copy-number alterations, mutational signature and clonal dynamics. A.Y., H. Suzuki, T.Y., N.K., Y. Shiozawa, K.A., Y.T., Y. Nannya, Y. Shiraishi, K.C., H.T., M.N., J.B.B. and S. Miyano performed bioinformatics analysis. A.Y., H. Suzuki, T.Y., N.K., Y. Nannya, H.M. and S. Ogawa prepared the manuscript.
Correspondence to Seishi Ogawa.
The authors declare no competing interests.
Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
Extended Data Fig. 1 Study design.
a–c, Summary of the subjects, samples, methods of sample collection, lifestyle ESCC risks, and histology, as well as sequencing platforms used for analysis of solitary samples (a), single-cell-derived colonies (b) and densely collected samples (c). *In one case (UPN53), only cancer and dysplasia samples were collected. §, UPN36, UPN37 and UPN39; ¶, UPN49; ‡, UPN88; #, UPN56 and UPN61; Φ, UPN58; ††, UPN55, UPN59, UPN60 and UPN65. Samples from these individuals were analysed in indicated multiple experiments (see a, b and c). †Out of 53 PNE samples, 11 were analysed in both solitary and high-density sampling (see a and c). Source data
Extended Data Fig. 2 WES of PNE and cancer samples from a surgical specimen.
a, Positions of 25 PNE and 2 cancer samples from a surgically resected oesophagus of a 58-year-old, high-risk patient with ESCC (UPN32) are shown by green and red circles, respectively. Calibration is shown on the right. EGJ, oesophagogastric junction. b, MCFs are compared between samples from PNE (green) (n = 25) and cancer (red) (n = 2) samples. c, d, MCF is plotted for all shared (orange) and private (grey) mutations for low-density (c) and high-density (d) sampling. Driver mutations are indicated by arrowheads in indicated colours. The position of each sample is shown on the left. Calibration for the number of mutations depicted in the horizontal axis is separately provided for each sampling. e, For preparation of PNE samples, after submucosal layers were removed from endoscopic biopsy specimens, 0.2-mm2, 0.8-mm2 or 4.0-mm2 samples were collected using a punch biopsy of 0.5 mm or 1.0 mm in diameter, or 2 × 2-mm rectangular excision, respectively, followed by DNA extraction. f, Photographs of punch biopsy devices for 0.5-mm-diameter and 1.0-mm-diameter sampling. Photographs copyright Kai Industries. g, Histogram of DNA recovered from 0.2-mm2, 0.8-mm2 or 4.0-mm2 samples. Box plots represent the median, first quartile and third quartile, with whiskers extending to the furthest value within 1.5× interquartile range. Source data
Extended Data Fig. 3 Somatic mutations in solitary sampling.
a, Summary of somatic mutations found in 157 PNE, 12 dysplasia and 20 cancer samples from individuals with (pink) or without (green) lifestyle ESCC risks. MCFs are plotted for all mutations (upper panel). Driver mutations are shown by coloured triangles as indicated. Information about histology and sample size is shown on the top lanes. The age of the subject and the total number of mutations are shown in the bottom panels. Status of ESCC, CNAs and mutations are also shown (middle panels). b–j, MCFs of somatic mutations detected in WES of multiple samples from PNE, as well as dysplastic and cancer tissues, are depicted separately for shared (orange) and non-shared mutations (grey) in representative cases from 16 independent individuals, for whom multi-regional sampling was performed. Mutations in TP53, NOTCH1, PPM1D and other drivers are indicated by arrowheads in different colours. Positions of tumours and biopsy samples are shown on the left in each panel. Distance from the maxillary dental arch is indicated. Mutations are shared only between cancer samples except in two cases (i and j), in which small numbers of mutations (between 3 and 16) are shared between mutually positioned samples as indicated. Source data
Extended Data Fig. 4 Signatures of mutations in PNE, dysplasia and cancer samples.
a, Number (middle) and relative frequency (bottom) of mutations allocated to signatures A–D in the individual samples. Information about pathology, lifestyle ESCC risks, cohort and age of the subject is indicated (top). b, Number of mutations allocated to indicated mutational signatures is plotted against the age of the subject for PNE samples from low-risk (blue) and high-risk (red) individuals. Numbers of samples from low-risk and high-risk individuals are 34 and 19 (0.2-mm2 sampling), 19 and 12 (0.8-mm2 sampling), and 40 and 33 (4-mm2 sampling), respectively. Regression lines for samples from low-risk individuals are indicated with R2. P values for significance between samples from high-risk and low-risk individuals are also provided (one-sided Mann–Whitney U-test). c, Mean (± 95% confidence interval) of standardized residuals of the number of mutations allocated to each signature in samples from high-risk individuals against the linear regression model in samples from low-risk individuals is plotted for 0.2-mm2, 0.8-mm2 and 4-mm2 samples and for combined samples in a random-effects model. P value in the random-effects model is indicated, together with the weight from each sample size during the model fitting (two-sided Wald test) (Methods). d, Mean (±s.d.) number of mutations allocated to signature 16 in oesophagus (ESCC) (n = 90) (left), hepatic (LIHC) (n = 361) (middle) and stomach (STAD) (n = 239) (right) cancers from TCGA, are plotted according to the status of the ALDH2 risk allele. P values for significance between samples from risk-positive and risk-negative individuals are also provided (two-sided Mann–Whitney U-test). e, Frequency of mutations allocated to signature C in Japanese ESCC samples is plotted according to the status of alcohol drinking and smoking. According the history of heavy drinking and smoking, the subjects were divided into four groups; drinking- and smoking-negative (n = 18); drinking-negative and smoking-positive (n = 3); drinking-positive and smoking-negative (n = 40); and drinking- and smoking-positive (n = 96), as indicated in the panel. Box plots represent the median, first quartile and third quartile, with whiskers extending to the furthest value within 1.5× interquartile range; points show outliers. Source data
Extended Data Fig. 5 Mutations detected in single-cell-derived colonies in WGS.
VAF histograms of somatic mutations detected in 13 single-cell-derived colonies analysed with WGS are depicted for each sample. Mutation number and driver genes mutated in each sample are indicated. Source data
Extended Data Fig. 6 Driver mutations in PNE samples.
a, Distribution of mutations in 157 PNE and 519 ESCC samples is shown for driver genes significantly mutated in PNE or ESCC samples, including NOTCH2, NOTCH3, CREBBP, FAT1, CHEK2, PAX9, EP300 and PIK3CA. Mutations are depicted separately for PNE (top) and ESCC (bottom). b, Frequencies of driver genes in paired cancer and PNE samples from 68 patients with ESCC. Genes in which mutations were observed in 5% or more samples either in PNE or ESCC were evaluated. Significantly differentially mutated genes (q < 0.05) between PNE and ESCC samples are indicated by asterisks. c, d, Number of driver mutations (c) and their maximum MCFs (d) in samples from high-risk and low-risk individuals are plotted against the age of subject, according to sample size. Regression lines are provided with R2. P values for the significance of lifestyle ESCC risks are also indicated (one-sided Mann–Whitney U-test). e, h, Mean (± 95% confidence interval) of standardized difference of mutation number (e) and maximum MCF (h) between samples from high-risk and low-risk individuals are plotted for each sampling size, and combined samples, using a random-effects model. P value in the random-effects model is also indicated, together with the weight from each sample size (in per cent) for the model fitting (two-sided Wald test) (Methods). f, g, Mean (± 95% confidence interval) of standardized residuals of the number of indicated driver mutations (f) and their maximum MCFs (g) in samples from high-risk individuals against the linear regression model in samples from low-risk individuals (Methods); plotted for 0.2-mm2, 0.8-mm2 and 4-mm2 samples. Standardized residuals for combined samples using a random-effects model are also shown. P-value in the random-effects model is also indicated, together with the weight from each sample size (in per cent) for the model fitting (two-sided Wald test) (Methods). In the analyses in c–h, the numbers of samples from low-risk and high-risk individuals, respectively, are 34 and 19 (0.2-mm2 sampling), 19 and 12 (0.8-mm2 sampling), and 40 and 33 (4-mm2 sampling) (c, e, f); 26 and 18 (0.2-mm2 sampling), 13 and 12 (0.8-mm2 sampling), and 30 and 31 (4-mm2 sampling) (d, h); 5 and 5 (0.2-mm2 sampling), 4 and 7 (0.8-mm2 sampling), and 8 and 19 (4-mm2 sampling) (TP53 in g); 14 and 18 (0.2-mm2 sampling), 7 and 11 (0.8-mm2 sampling), 21 and 28 (4-mm2 sampling) (NOTCH1 in g); 2 and 4 (0.2-mm2 sampling), 2 and 9 (4-mm2 sampling) (PPM1D in g). Source data
Extended Data Fig. 7 CNAs in PNE, dysplasia and cancer samples.
a, Colour-gradient maps of CNAs and UPDs as detected by SNP-array karyotyping or sequencing-based assays are shown for PNE samples from high-risk and low-risk individuals, as well as dysplastic tissues (n = 12) and cancer samples (n = 51) (middle panels). Fractions of genomes showing copy-number gains (red), losses (blue) and UPD (green) are also plotted (top panels). Information about histology, age of the subject, sample size and risks of developing ESCC are shown in top panels. b, Box plots of fractions of genomic regions showing CNAs in PNE (n = 188), dysplasia (n = 12) and cancer (n = 45) samples; the median, first and third quartiles, as well as outliers, are indicated with whiskers extending to the furthest value within 1.5 of the interquartile range. P values for significant differences are from two-sided Mann–Whitney U-test. DP, dysplasia. c, Effects of lifestyle ESCC risks and age on CNAs. Mean (± 95% confidence interval) of standardized residuals of the total fraction of genomic regions showing CNAs in samples from high-risk individuals, compared with the linear regression model in samples from low-risk individuals (left panel) and Fisher's z-transformed correlation of age to the total fraction of genomic regions showing CNAs in samples from low-risk individuals (right panel) are plotted for 0.2-mm2, 0.8-mm2, 4-mm2 and 8-mm2 samples, which are combined using a random-effects model. P value in the random-effects model is also indicated, together with the weight from each sample size (in per cent) for the model fitting (two-sided Wald test) (Methods). Numbers of samples from low-risk and high-risk individuals, respectively, are 34 and 19 (0.2-mm2 sampling), 19 and 12 (0.8-mm2 sampling), 40 and 33 (4-mm2 sampling), and 3 and 28 (8-mm2 sampling). Numbers of samples from low-risk individuals who are <50 years old and ≥50 years old, respectively, are 20 and 14 (0.2-mm2 sampling), 13 and 6 (0.8-mm2 sampling), and 7 and 33 (4-mm2 sampling), respectively. d, f, LOH maps of chromosomes 9 (d) and 17 (f) in PNE (top) and cancer (bottom) samples. Deletions and UPDs are shown by blue and green lines, respectively. Positions and mutation status of CDKN2A, NOTCH1 and TP53 genes are indicated. e, g, Bar plots of frequencies of 9q UPD (e) and 17p LOH (g) in samples from high-risk and low-risk individuals. Individuals of <50 years old (low risk, n = 40; high risk, n = 11) and those of ≥50 years old (low-risk, n = 56; high risk, n = 81) were analysed. Number (n) of samples in each group is also indicated. P values are for significant differences between both risk groups (two-sided Fisher's exact test). Whiskers indicate the upper bounds of 95% confidence intervals from the binomial distribution. Source data
Extended Data Fig. 8 Spatial architecture of clones in representative PNE biopsies.
a, Mutation analysis of 341 samples obtained by high-density sampling from 16 biopsy specimens from 14 individuals with different age, lifestyle ESCC risks, and ESCC status (bottom panels), using WES (249 from 10 biopsies) or targeted-capture sequencing of major drivers (92 from 6 biopsies). Total numbers of mutations (top) are depicted for samples analysed by WES. Shared mutations are shown in orange. Mutation status of major drivers and common CNAs are summarized (middle panels). b–e, MCFs of detected mutations are depicted for each sample obtained by high-density collection from PNE biopsies performed on low-risk healthy individuals in their 20s (b–d) and an 81-year-old, low-risk man (e). Four representative examples from 10 biopsies analysed by high-density WES sampling are shown. Mutations shared by a distinct set of samples or isolated samples are integrated to identify unique clones, as shown in different colours. Driver mutations are indicated by downward-pointing arrowheads. Rightward-pointing arrows indicate the clones in the same colour in Fig. 4b, c and Extended Data Fig. 9a, b. Source data
Extended Data Fig. 9 Clones detected in high-density sampling.
a–i, The spatial distribution of clones as revealed by WES or targeted sequencing of driver genes is depicted for biopsies from younger-aged low-risk (a, b), middle-aged low-risk and high-risk (c, d), 70-year-old low-risk (e, f) and elderly high-risk (g–i) subjects, as described in Fig. 4. For convenience, samples that share discrete sets of mutations—as indicated by different colours—are summarized for four subjects (a–d); in these subjects, mutations are frequently shared between distant samples, which suggests mosaicism. Sequencing platforms are indicated in each panel. Source data
Extended Data Fig. 10 Phylogenetic analysis of representative clones.
Phylogenetic trees mapped to the location of samples are depicted for additional representative clones from a total of 29 clones, seen in 5 biopsies from 5 elderly subjects. Imputed clonal structures are depicted in violin plots at the bottom, in which the estimated cellular prevalence of imputed mutational clusters (vertical axis) in each sample are shown as the distribution of posterior probabilities (width of the violin plots) calculated from the PyClone model. Colours correspond to those in the associated phylogenetic trees. Source data
This file contains Supplementary Methods 1-2 and Supplementary Note 1, which contains the acknowledgements.
Reporting Summary
Supplementary Tables
This file contains Supplementary Tables 1-10.
Source data
Source Data Fig. 1
Source Data Extended Data Fig. 1
Source Data Extended Data Fig. 10
Yokoyama, A., Kakiuchi, N., Yoshizato, T. et al. Age-related remodelling of oesophageal epithelia by mutated cancer drivers. Nature 565, 312–317 (2019). https://doi.org/10.1038/s41586-018-0811-x
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Mutations differ in normal and cancer cells of the oesophagus
Francesca D. Ciccarelli
Nature ISSN 1476-4687 (online)
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redpajama_set_name: RedPajamaCommonCrawl