title: Using joint models to adjust for informative drop-out when modelling a longitudinal biomarker: an application to type 2 diabetes disease progression

abstract:
Linear mixed effects models are frequently used in biomedical statistics to model the trajectory of a repeatedly measured longitudinal variable, such as a biomarker, over time. However, population-level estimates may be biased by censoring bias resulting from exit criteria that depend on the variable in question. A joint longitudinal-survival model, in which the exit criteria and longitudinal variable are modelled simultaneously, may address this bias. Using blood glucose progression (change in HbA1c) in type 2 diabetes patients on metformin monotherapy as an example, we study the potential benefit of using joint models to model trajectory of a biomarker in observational data. 7,712 patients with type 2 diabetes initiating metformin monotherapy were identified in UK Biobanks general practice (GP) linked records. Genetic information was extracted from UK Biobank, and prescription records, baseline clinical features and biomarkers, and longitudinal HbA1c measures were extracted from GP records. Exit criteria for follow-up for a patient was defined as progression to an additional glucose-lowering drug (which is more likely in patient with higher HbA1c). Estimates of HbA1c trajectory over time were compared using linear mixed effect model approaches (which do not account for censoring bias) and joint models. In the primary analysis, a 0.19 mmol/mol per year higher (p = 0.01) HbA1c gradient was estimated using the joint model compared to the linear mixed effects model. This difference between models was attenuated (0.13 mmol/mol per year higher, p=0.43) when baseline clinical features and biomarkers were included as additional covariates.

Censoring bias should be carefully considered when modelling trajectories of repeatedly measured longitudinal variables in observational data. Joint longitudinal-survival models are a useful approach to identify and potentially correct for censoring bias when estimating population-level trajectories.

Author SummaryModelling biomarkers that change over time in real world data is a challenging statistical problem due to many potential sources of bias. For example, when studying a chronic disease using a biomarker or other measurement that represents disease severity, medication intended to affect that measurement has a profound effect on how it will change over time. One common way to control for this is to study a cohort on the same treatment strategy. That way, results are not influenced by treatment change. If a patient progresses to stronger medication, then future data is no longer used. However, this approach introduces its own bias. Patients whose condition progress particularly quickly are more likely to change treatment more rapidly (and therefore be removed from further analysis, or  censored), so the cohort is biased towards those whose condition progresses slower. In this paper we apply a technique called joint longitudinal-survival modelling which can adjust for this censoring bias and produce less biased estimates of progression rates. We use HbA1c (a widely used measure of glucose control) in type 2 diabetes as an example, however our methods are theoretically applicable to a range of problems across medicine in which a biomarker or feature is repeatedly measured in an individual.


title: Natian and Ryabhatta--graphical user interfaces to create, analyze and visualize single-cell transcriptomic datasets

abstract: Single-cell transcriptomic analyses permit a high-resolution investigation of biological processes at the individual cell level. Single-cell transcriptomics technologies such as Drop-seq, Smart-seq, MARS-seq, sci-RNA-seq, and CELL-seq produce large volumes of data in the form of sequence reads. In general, the alignment of the reads to genomes and the enumeration of reads mapping to a specific gene results in a gene-count matrix. These gene-count matrix data require robust quality control and statistical analytical pipelines before data mining and interpretation. Among these post-alignment pipelines, the  Seurat package in  R is the most popular analytical pipeline for the analysis of single-cell data. This package provides quality control, normalization, principal component analysis, dimensional reduction, clustering, and marker identification among other functions needed to process and mine the single-cell transcriptomic data. While the Seurat package is continuously updated and includes a variety of functionalities, the user is still required to be proficient in the  R programming language and its data structures to be able to execute the Seurat functions. Hence, there is a demand for a graphical user interface (GUI) that takes in relevant input information and processes the single-cell data using the Seurat pipeline. A GUI will also highly improve the access to single-cell data for life sciences researchers who are not trained in the command-line operation of the  R platform. To meet this demand, we present R Shiny apps  Natian and  Ryabhatta to assist in the generation and analysis of Seurat files from a variety of different sources. The apps and example data can be downloaded from https://singlecelltranscriptomics.org. Natian allows users to create Seurat files from the output of multiple pipelines, integrate existing Seurat files, add metadata information, perform dimensional reduction analysis or upload dimensional reduction data, resume partially processed Seurat files and find cluster markers. Ryabhatta allows users to visualize gene expression using a variety of plotting options, analyze cluster markers, rename clusters, select cells from a graph or based on expression levels of markers, perform differential expression, count the number of cells in each condition, and perform pseudotime analysis using Monocle. We found that the use of these apps substantially improved the analytical and processing time and remove needless troubleshooting due to incompatible commands, typographical errors in scripts, and cluttering of the R environment with variables. We hope the use of these apps improves the use of single-cell data for life sciences research while also providing a tool to learn the functionalities of Seurat and R functions available for single-cell data analysis.

category: bioinformatics


title: Casein kinase 1D encodes a novel drug target in Hedgehog-GLI driven cancers and tumor-initiating cells resistant to SMO inhibition

abstract:
(1) BackgroundAberrant activation of the Hedgehog (HH)/GLI pathway in stem-like tumor initiating cells (TIC) is a frequent oncogenic driver signal in various human malignancies. Remarkable efficacy of anti-HH therapeutics led to the approval of HH inhibitors targeting the key pathway effector Smoothened (SMO) in basal cell carcinoma and acute myeloid leukemia. However, frequent development of drug resistance and severe adverse effects of SMO inhibitors pose major challenges that require alternative treatment strategies targeting HH/GLI in TIC downstream of SMO. We therefore investigated members of the casein kinase 1 (CSNK1) family as novel drug targets in HH/GLI driven malignancies.

(2) MethodsWe genetically and pharmacologically inhibited CSNK1D in HH-dependent cancer cells displaying either sensitivity or resistance to SMO inhibitors. To address the role of CSNK1D in oncogenic HH signaling and tumor growth and initiation, we quantitatively analyzed HH target gene expression, performed genetic and chemical perturbations of CSNK1D activity and monitored oncogenic transformation of TIC in vitro and in vivo using 3D clonogenic tumor spheroid assays and xenograft models.

(3) ResultsWe show that CSNK1D plays a critical role in controlling oncogenic GLI activity downstream of SMO. We provide evidence that inhibition of CSNK1D interferes with oncogenic HH signaling in both SMO-inhibitor sensitive and resistant tumor settings. Furthermore, genetic and pharmacologic perturbation of CSNK1D decreases the clonogenic growth of GLI-dependent tumor-initiating cancer cells in vitro and in vivo.

(4) ConclusionsPharmacologic targeting of CSNK1D represents a novel therapeutic approach for the treatment of both SMO inhibitor sensitive and resistant tumors.

category: cancer biology


title: Respiratory and C4-photosynthetic NAD-malic enzyme coexist in bundle sheath cells mitochondria and evolved via association of differentially adapted subunits

abstract: In different lineages of Cleomaceae, NAD-malic enzyme (NAD-ME) was independently co-opted to participate in C4 photosynthesis. In the C4 Cleome species Gynandropsis gynandra and Cleome angustifolia, all NAD-ME genes (NAD-ME, NAD-ME{beta}1, and NAD-ME{beta}2) were affected by C4 evolution and are expressed at higher levels than their orthologs in the C3 Cleome species Tarenaya hassleriana. In the latter C3 species, the NAD-ME housekeeping function is performed by two heteromers, NAD-ME/{beta}1 and NAD-ME/{beta}2, with similar biochemical properties. In both C4 species analyzed, this role is restricted the NAD-ME/{beta}2 heteromer. In the C4 species, NAD-ME/{beta}1 is exclusively present in the leaves, where it accounts for most of the enzymatic activity. GgNAD-ME/{beta}1 exhibits high catalytic efficiency and is differentially activated by the C4 intermediate aspartate, confirming its role as the C4-decarboxylase. During C4 evolution, GgNAD-ME{beta}1and CaNAD-ME{beta}1 lost their catalytic activity; their contribution to enzymatic activity results from a stabilizing effect on the associated -subunit. We conclude that in bundle sheath cell mitochondria of C4 Cleome species, the functions of NAD-ME as C4 photosynthetic decarboxylase and as a tricarboxylic acid cycle-associated housekeeping enzyme coexist and are performed by isoforms that combine the same  subunit with differentially adapted {beta} subunits.

category: plant biology


title: Engineered Bacteria Computationally Solve Chemically Generated 2X2 Maze Problems

abstract: Maze generating and solving are challenging problems in mathematics and computing. Here we generated simple 2X2 maze problems applying four chemicals and created a set of engineered bacteria, which in a mixed population worked as a computational solver for any such problem. The input-output matrices of a mathematical maze were mapped through a truth table, where the 1 and 0 logic values of four chemical inputs determined the sixteen different 2X2 maze problems on a chemical space. Our engineered bacteria, which consisted of six different genetic logic circuits and distributed among six cell populations processed the chemical information and solved the problems by expressing or not expressing four different fluorescent proteins according to the input-output matrices. The three available  solutions were visualized by glowing bacteria and for the thirteen cases, where there was no solution no bacteria glowed. This work may have significance in cell based computing and synthetic biology.

category: synthetic biology


title: Emergent Statistical Laws in Single-Cell Transcriptomic Data

abstract:
Large scale data on single-cell gene expression have the potential to unravel the specific transcriptional programs of different cell types. The structure of these expression datasets suggests a similarity with several other complex systems that can be analogously described through the statistics of their basic building blocks. Transcriptomes of single cells are collections of messenger RNA abundances transcribed from a common set of genes just as books are different collections of words from a shared vocabulary, genomes of different species are specific compositions of genes belonging to evolutionary families, and ecological niches can be described by their species abundances. Following this analogy, we identify several emergent statistical laws in single-cell transcriptomic data closely similar to regularities found in linguistics, ecology or genomics. A simple mathematical framework can be used to analyze the relations between different laws and the possible mechanisms behind their ubiquity. Importantly, treatable statistical models can be useful tools in transcriptomics to disentangle the actual biological variability from general statistical effects present in most component systems and from the consequences of the sampling process inherent to the experimental technique.

Author summaryGene expression profiles represent how different cells use their genetic information. Similarly, books are specific collections of words chosen from a shared vocabulary, and many complex systems can be ultimately described by the statistics of their basic components. Leveraging on this analogy, we identified several emergent statistical laws in single-cell transcriptomic data that are universally found in complex component systems. A simple mathematical description sets these laws in a treatable quantitative framework and represents a useful tool for dissecting the different sources of gene expression variability.

category: systems biology


title: A Unified Model for Entrainment by Circadian Clocks: Dynamic Circadian Integrated Response Characteristic (dCiRC)

abstract: Circadian rhythm is a ubiquitous phenomenon, and it is observed in all biological kingdoms. In nature, their primary characteristic or phenotype is the phase of entrainment. There are two main hypotheses related to how circadian clocks entrain, parametric and non-parametric models. The parametric model focuses on the gradual changes of the clock parameters in response to the changing ambient condition, whereas the non-parametric model focuses on the instantaneous change of the phase of the clock in response to the zeitgeber. There are ample empirical data supporting both models. However, only recently has a unifying model been proposed, the circadian integrated response characteristic (CiRC). In the current study, we developed a system of ordinary differential equations, dynamic CiRC (dCiRC), that describes parameters of circadian rhythms and predicts the phase of entrainment in zeitgeber cycles. dCiRC mathematically extracts the underlying information of velocity changes of the internal clock that reflects the parametric model and the phase shift trajectory that reflects the non-parametric model from phase data under entraining conditions. As a proof of concept, we measured clock parameters of 26 Neurospora crassa ecotypes in both cycling and constant conditions using dCiRC. Our data showed that the morning light shortens the period of the clock while the afternoon light lengthens it. We also found that individual ecotypes have different strategies of integrating light effects to accomplish the optimal phase of entrainment, a model feature that is consistent with our knowledge of how circadian clocks are organized and encoded. The unified model dCiRC will provide new insights into how circadian clocks function under different zeitgeber conditions. We suggest that this type of model may be useful in the advent of chronotherapies.


title: Presynaptic Mitochondrial Volume and Density Scale with Presynaptic Power Demand

abstract:
Stable neural function requires an energy supply that can meet the intense episodic power demands of neuronal activity. The bioenergetic machinery of glycolysis and oxidative phosphorylation is highly responsive to such demands, but it must occupy a minimum volume if it is to accommodate these demands. We examined the trade-off between presynaptic power demands and the volume available to the bioenergetic machinery. We quantified the energy demands of six Drosophila motor nerve terminals through direct measurements of neurotransmitter release and Ca2+ entry, and via theoretical estimates of Na+ entry and power demands at rest. Electron microscopy revealed that terminals with the highest power demands contained the greatest volume of mitochondria, indicating that mitochondria are allocated according to presynaptic power demands. In addition, terminals with the greatest power demand-to-volume ratio ([~]66 nmol{middle dot}min-1{middle dot}L-1) harbor the largest mitochondria packed at the greatest density. If we assume sequential and complete oxidation of glucose by glycolysis and oxidative phosphorylation, then these mitochondria are required to produce ATP at a rate of 52 nmol{middle dot}min-1{middle dot}L-1 at rest, rising to 963 during activity. Glycolysis would contribute ATP at 0.24 nmol{middle dot}min-1{middle dot}L-1 of cytosol at rest, rising to 4.36. These data provide a quantitative framework for presynaptic bioenergetics in situ, and reveal that, beyond an immediate capacity to accelerate ATP output from glycolysis and oxidative phosphorylation, over longer time periods presynaptic terminals optimize mitochondrial volume and density to meet power demand.

Significance StatementThe remarkable energy demands of the brain are supported by the complete oxidation of its fuel but debate continues regarding a division of labor between glycolysis and oxidative phosphorylation across different cell types. Here we leverage the neuromuscular synapse, a model for studying neurophysiology, to elucidate fundamental aspects of neuronal energy metabolism that ultimately constrain rates of neural processing. We quantified energy production rates required to sustain activity at individual nerve terminals and compared these data with the volume capable of oxidative phosphorylation (mitochondria) and glycolysis (cytosol). We find strong support for oxidative phosphorylation playing a primary role in presynaptic terminals and provide the first in vivo estimates of energy production rates per unit volume of presynaptic mitochondria and cytosol.

category: neuroscience


title: Time-resolved multivariate pattern analysis of infant EEG data

abstract: Time-resolved multivariate pattern analysis (MVPA), a popular technique for analyzing magneto- and electro-encephalography (M/EEG) neuroimaging data, quantifies the extent and time-course by which neural representations support the discrimination of relevant stimuli dimensions. As EEG is widely used for infant neuroimaging, time-resolved MVPA of infant EEG data is a particularly promising tool for infant cognitive neuroscience. MVPA methods have recently been applied to common infant imaging methods such as EEG and fNIRS. In this tutorial, we provide and describe code to implement time-resolved, within-subject MVPA with infant EEG data. A pipeline for time-resolved MVPA based on linear SVM classification is described and implemented with accompanying code in both Matlab and Python. Results from a test dataset indicated that in both infants and adults this method reliably produced above chance classification accuracy. Extensions of the core pipeline are presented including both geometric- and accuracy-based representational similarity analysis, implemented in Python. Common choices of implementation are presented and discussed. As the amount of artifact-free EEG data contributed by each participant is lower in studies of infants than in studies of children and adults, we also explore and discuss the impact of varying participant-level inclusion thresholds on resulting MVPA findings in these datasets.


title: Tau interactome mapping reveals dynamic processes in synapses and mitochondria associated with neurodegenerative disease

abstract: Tau (MAPT) drives neuronal dysfunction in Alzheimers disease (AD) and other tauopathies. To dissect the underlying mechanisms, we combined an engineered ascorbic acid peroxidase (APEX) approach with quantitative affinity purification mass spectrometry (AP-MS) followed by proximity ligation assay (PLA) to characterize Tau interactomes modified by neuronal activity and mutations that cause frontotemporal dementia (FTD) in human induced pluripotent stem cell (iPSC)-derived neurons. We established activity-dependent interactions of Tau with presynaptic vesicle proteins during Tau secretion and mapped the exact APEX-tau-induced biotinylated tyrosines to the cytosolic domains of the interacting vesicular proteins. We showed that FTD mutations impair bioenergetics and markedly diminished Taus interaction with mitochondria proteins, which were downregulated in AD brains of multiple cohorts and correlated with disease severity. These multi-modal and dynamic Tau interactomes with unprecedented spatiotemporal resolution shed novel insights into Taus role in neuronal function and disease-related processes with potential therapeutic targets to block Tau-mediated pathogenesis.


title: The Cost of Untracked Diversity in Brain-Imaging Prediction

abstract: Brain-imaging research enjoys increasing adoption of supervised machine learning for singlesubject disease classification. Yet, the success of these algorithms likely depends on population diversity, including demographic differences and other factors that may be outside of primary scientific interest. Here, we capitalize on propensity scores as a composite confound index to quantify diversity due to major sources of population stratification. We delineate the impact of population heterogeneity on the predictive accuracy and pattern stability in two separate clinical cohorts: the Autism Brain Imaging Data Exchange (ABIDE, n=297) and the Healthy Brain Network (HBN, n=551). Across various analysis scenarios, our results uncover the extent to which cross-validated prediction performances are interlocked with diversity. The instability of extracted brain patterns attributable to diversity is located preferentially to the default mode network. Our collective findings highlight the limitations of prevailing deconfounding practices in mitigating the full consequences of population diversity.


title: Concurrent contextual and time-distant mnemonic information co-exist as feedback in human visual cortex.

abstract:
Efficient processing of visual environment necessitates the integration of incoming sensory evidence with concurrent contextual inputs and mnemonic content from our past experiences. To delineate how this integration takes place in the brain, we studied modulations of feedback neural patterns in non-stimulated areas of the early visual cortex in humans (i.e., V1 and V2). Using functional magnetic resonance imaging and multivariate pattern analysis, we show that both, concurrent contextual and time-distant mnemonic information, coexist in V1/V2 as feedback signals. The extent to which mnemonic information is reinstated in V1/V2 depends on whether the information is retrieved episodically or semantically. These results demonstrate that our stream of visual experience contains not just information from the visual surrounding, but also memory-based predictions internally generated in the brain.

One-Sentence SummaryFeedback activity in human early visual cortex contains concurrent contextual and time-distant mnemonic information.


title: Task-Dependent Warping of Semantic Representations During Search for Visual Action Categories

abstract: Object and action perception in cluttered dynamic natural scenes relies on efficient allocation of limited brain resources to prioritize the attended targets over distractors. It has been suggested that during visual search for objects, distributed semantic representation of hundreds of object categories is warped to expand the representation of targets. Yet, little is known about whether and where in the brain visual search for action categories modulates semantic representations. To address this fundamental question, we studied human brain activity recorded via functional magnetic resonance imaging while subjects viewed natural movies and searched for either communication or locomotion actions. We find that attention directed to action categories elicits tuning shifts that warp semantic representations broadly across neocortex, and that these shifts interact with intrinsic selectivity of cortical voxels for target actions. These results suggest that attention serves to facilitate task performance during social interactions by dynamically shifting semantic selectivity towards target actions, and that tuning shifts are a general feature of conceptual representations in the brain.


title: Bioluminescent Genetically Encoded Glutamate Indicator for Molecular Imaging of Neuronal Activity

abstract: Genetically encoded optical sensors and advancements in microscopy instrumentation and techniques have revolutionized the scientific toolbox available for probing complex biological processes such as release of specific neurotransmitters. Most genetically encoded optical sensors currently used are based on fluorescence and have been highly successful tools for single-cell imaging in superficial brain regions. However, there remains a need to develop new tools for reporting neuronal activity in vivo within deeper structures without the need for hardware such as lenses or fibers to be implanted within the brain. Our approach to this problem is to replace the fluorescent elements of the existing biosensors with bioluminescent elements. This eliminates the need of external light sources to illuminate the sensor and overcomes several drawbacks of fluorescence imaging such as limited light penetration depth, excitation scattering, and tissue heating that are all associated with the external light needed for fluorescence imaging. Here we report the development of the first genetically encoded neurotransmitter indicators based on bioluminescent light emission. These probes exhibit robust changes in light output in response to extracellular presentation of the excitatory neurotransmitter glutamate. We expect this new approach to neurotransmitter indicator design to enable the engineering of specific bioluminescent probes for multiple additional neurotransmitters in the future, ultimately allowing neuroscientists to monitor activity associated with a specific neurotransmitter as it relates to behavior in a variety of neuronal and psychiatric disorders, among many other applications.


title: Coupling of Ca2+-triggered unclamping and membrane fusion during neurotransmitter release

abstract: Neurotransmitter (NT) release is accomplished by a machinery that unclamps fusion in response to calcium and then fuses the synaptic vesicle and plasma membranes. These are often thought of as distinct tasks assigned to non-overlapping components. Vesicle release rates have a power law dependence on [Ca2+] with an exponent of 3-5, long taken to indicate that 3-5 Ca2+ ions bind the calcium sensor Synaptotagmin to trigger release. However, dependencies at low [Ca] are inconsistent with simple sequential binding to a single Ca2+ sensor followed by a final fusion step. Here we developed coarse-grained molecular dynamics simulations of the NT release machinery accounting for Synaptotagmin-mediated unclamping and SNARE-mediated fusion. Calcium-triggered unclamping and SNARE-mediated fusion emerged from simulations as contemporaneous, coupled processes. Increasing cytosolic [Ca2+], the instantaneous fusion rate increased as SNAREpins were progressively and reversibly released by dissociation of Synaptotagmin-SNAREpin complexes. Simulations reproduced the observed dependence of release rates on [Ca2+], but the power law was unrelated to the number of Ca2+ ions required. Action potential-evoked vesicle release probabilities depended on the number of transiently unclamped SNAREpins, explaining experimental dependencies of release probabilities on both unclamping and membrane-fusing machinery components. These results describe a highly cooperative NT release machinery with intrinsically inseparable unclamping and membrane-fusing functionalities.


title: Unsupervised Models of Mouse Visual Cortex

abstract: Task-optimized deep convolutional neural networks are the most quantitatively accurate models of the primate ventral visual stream. However, such networks are implausible as models of the mouse visual system because mouse visual cortex has both lower retinal resolution and a shallower hierarchy than the primate. Moreover, the category supervision deep networks typically receive is neither ethologically relevant to the mouse in semantic content, nor realistic in quantity. As a result, standard supervised deep neural networks have proven quantitatively ineffective at modeling mouse visual data. Here, we develop and evaluate models that remedy these structural and functional gaps. We first demonstrate that shallow hierarchical architectures applied to lower resolution images improve match to neural responses, both in electro-physiological and calcium imaging data. We then show that networks trained using contrastive embedding methods, a recent unsupervised learning objective that requires no semantic labeling, achieve neural prediction performance that substantially exceed that of the same architectures trained in a supervised manner, across a wide variety of architecture types. Combining these better structural and functional priors yields models that are the most quantitatively accurate match to mouse visual responses to natural scenes, significantly surpassing that of prior attempts using primate-specific models, and approaching the inter-animal consistency level of the data itself. We further find that these shallow unsupervised models transfer to a wide variety of non-categorical visual tasks better than categorization-trained models. Taken together, our results suggest that mouse visual cortex is a low-resolution, shallow network that makes best use of the mouses limited resources to create a light-weight, general-purpose visual system - in contrast to the deep, high-resolution, and more task-specific visual system of primates.


title: Shallow Unsupervised Models Best Predict Neural Responses in Mouse Visual Cortex


title: Non-serotype 2 isolates from healthy pigs are a potential zoonotic reservoir of Streptococcus suis genetic diversity and antimicrobial resistance

abstract:
Streptococcus suis is a leading cause of bacterial meningitis in SE Asia, with frequent zoonotic transfer to humans associated with close contact with pigs. A small number of invasive lineages are responsible for endemic infection in the swine industry causing considerable global economic losses. A lack of surveillance and a rising trend in clinical treatment failure has raised concerns of growing antimicrobial resistance (AMR) among invasive S. suis. The source-sink dynamics between healthy and disease isolates is poorly understood and, in this study, we sample and sequence a collection of isolates predominantly from healthy pigs in Chiang Mai province, Northern Thailand. Pangenome comparisons with a selection of invasive serotype 2 isolates identified increased genetic diversity and more frequent AMR carriage in isolates from healthy pigs. Multiple antimicrobial resistance genes were identified conferring resistance to aminoglycosides, lincosamides, tetracycline and macrolides. All isolates were non-susceptinle to three or more different antimicrobial classes, and 75% of non-serotype 2 isolates were non-susceptible to 6 or more classes (compared to 37.5% of serotype 2 isolates). Antimicrobial resistance genes were found on integrative and conjugative elements (ICE) previously observed in other species, suggesting mobile gene pool which can be accessed by invasive disease isolates.

Significance statementThe zoonotic pathogen Streptococcus suis causes respiratory disease in pigs and is among the most common causative agents of human clinical bacterial meningitis in SE Asia. We collected isolates from farmed healthy pigs in Northern Thailand, representing a source population from which invasive isolates have recently emerged - linked to the pork production industry. Pangenome characterisation of the isolates revealed a reservoir of genetic diversity and antimicrobial resistance suggesting that One Health approaches may be beneficial in tackling the increase in antimicrobial resistance.

category: microbiology


title: Bacterial factors drive the differential targeting of Guanylate Binding Proteins to Francisella and Shigella

abstract:
Guanylate-Binding Proteins (GBPs) are interferon-inducible GTPases that play a key role in cell autonomous responses against intracellular pathogens. Seven GBPs are present in humans. Despite sharing high sequence similarity, subtle differences among GBPs translate into functional divergences that are still largely not understood. A key step for the antimicrobial activity of GBPs towards cytosolic bacteria is the formation of supramolecular GBP complexes on the bacterial surface. Such complexes are formed when GBP1 binds lipopolysaccharide (LPS) from Shigella and Salmonella and further recruits GBP2, 3, and 4.

Here, we investigated GBPs recruitment on Francisella novicida, a professional cytosol-dwelling pathogen with an atypical tetra-acylated LPS. Co-infection experiments demonstrated that GBPs target preferentially S. flexneri compared to F. novicida. F. novicida was coated by GBP1 and GBP2 in human macrophages but escaped targeting by GBP3 and GBP4. GBP1 and GBP2 features that drive recruitment to F. novicida were investigated revealing that GBP1 GDPase activity is required to initiate GBP recruitment to F. novicida but facultative to target S. flexneri. Furthermore, analysis of chimeric GBP2/5 proteins identified a central domain in GBP2 necessary and sufficient to target F. novicida. Finally, a F. novicida {Delta}lpxF mutant with a penta-acylated lipid A was targeted by GBP3 suggesting that lipid A tetra-acylation contributes to escape from GBP3. Altogether our results indicate that GBPs have different affinity for different bacteria and that the repertoire of GBPs recruited onto cytosolic bacteria is dictated by GBP-intrinsic features and specific bacterial factors, including the structure of the lipid A.

IMPORTANCEFew bacteria have adapted to thrive in the hostile environment of the cell cytosol. As a professional cytosol-dwelling pathogen, S. flexneri secretes several effectors to block cytosolic immune effectors, including GBPs. This study illustrates a different approach of adapting to the host cytosol: the stealth strategy developed by F. novicida. F. novicida bears an atypical hypoacylated LPS, which does not elicit neither TLR4 nor caspase-11 activation. Here, this atypical LPS was shown to promote escape from GBP3 targeting. Furthermore, the lower affinity of GBPs for F. novicida allowed to decipher the different domains that govern GBP recruitment to the bacterial surface. This study illustrates the importance of investigating different bacterial models to broaden our understanding of the intricacies of host-pathogen interactions.


title: Soil protists can actively redistribute beneficial bacteria along Medicago truncatula roots

abstract:
The rhizosphere is the region of soil directly influenced by plant roots. The microbial community in the rhizosphere includes fungi, protists, and bacteria, all of which play a significant role in plant health. The beneficial bacterium Sinorhizobium meliloti infects growing root hairs on nitrogen starved leguminous plants. Infection leads to the formation of a root nodule, where S. meliloti converts atmospheric nitrogen to ammonia, the usable form of nitrogen for plants. However, S. meliloti, often found in biofilms, travels slowly; whereas infectible root hairs are found at the growing root tip, potentially causing many root hairs to remain uninfected by S. meliloti when it is delivered as a seed inoculant. Soil protists are an important component of the rhizosphere system who prey on soil bacteria and have been known to egest undigested phagosomes. We show that the soil protist, Colpoda sp., plays an important role in transporting S. meliloti down Medicago truncatula roots. By using pseudo-3D soil microcosms we directly observed the presence of fluorescently labelled S. meliloti along M. truncatula roots and track the displacement of bacteria over time. In the presence of Colpoda sp., S. meliloti was detected 44 mm, on average, farther down the roots, compared with the Bacteria Only Treatment. Facilitating bacterial transport may be an important mechanism whereby soil protists promote plant health. Protist facilitated transport as a sustainable agriculture biotechnology has the potential to boost efficacy of bacterial inoculants, avoid overuse of nitrogen fertilizers, and enhance performance of no-till farming practices.

ImportanceSoil protists are an important part of the microbial community in the rhizosphere. Plants grown with protists fare better than plants grown without protists. Mechanisms through which protists support plant health include nutrient cycling, alteration of the bacterial community through selective feeding and consumption of plant pathogens. Here we provide data in support of an additional mechanism: protists act as a transport vectors for bacteria. We show that protist-facilitated transport can deliver plant-beneficial bacteria to the growing tips of roots that may otherwise be sparsely inhabited with bacteria from a seed-associated inoculum. By co-inoculating Medicago truncatula roots with both Sinorhizobium meliloti, a nitrogen fixing legume symbiont, and the ciliated protist, Colpoda sp., we show bacteria are distributed an average of 44 mm farther down plant roots than when the plants are inoculated by bacteria alone. Co-inoculation with shelf-stable encysted soil protists may be employed as a sustainable agriculture biotechnology to better distribute beneficial bacteria and enhance the performance of inoculants.


title: Proteomic changes induced by harmine in human brain organoids reveal signaling pathways related to neuroprotection

abstract: Harmine is a {beta}-carboline found in Banisteriopsis caapi, a constituent of ayahuasca brew. Ayahuasca is consumed as a beverage in native Americans sacred rituals and in religious ceremonies in Brazil. Throughout the years, the beneficial effects of ayahuasca to improve mental health and life quality have been reported, which propelled the investigation of its therapeutic potential to target neurological disorders such as depression and anxiety. Indeed, antidepressant effects of ayahuasca have been described, raising the question of which cellular mechanisms might underlie those effects. Previous animal studies describe potential neuroprotective mechanisms of harmine, including anti-inflammatory and antioxidant activities, and neurotrophin signaling activation. However, the cellular and molecular mechanisms modulated by harmine in human models remain less investigated. Here we analyzed the short-term changes in the proteome of human brain organoids treated with harmine using shotgun mass spectrometry. Harmine upregulates proteins related to synaptic vesicle cycle, cytoskeleton-dependent intracellular transport, cell cycle, glucose transporter-4 translocation, and neurotrophin signaling pathway. In addition, protein expression levels of Akt and phosphorylated CREB were increased after 24 hour-treatment. Our results shed light on the potential mechanisms that may underlie harmine-induced neuroprotective effects.

category: cell biology


title: Structural organization of the C1b projection within the ciliary central apparatus

abstract:
9+2 motile cilia contain 9 doublet microtubules and a central apparatus (CA) composed of two singlet microtubules with associated projections. The CA plays crucial roles in regulating ciliary motility. Defects in CA assembly or function usually result in motility-impaired or paralyzed cilia, which in humans causes disease. Despite their importance, the protein composition and functions of most CA projections remain largely unknown. Here, we combined genetic approaches and quantitative proteomics with cryo-electron tomography and subtomogram averaging to compare the CA of wild-type Chlamydomonas with those of two CA mutants. Our results show that two conserved proteins, FAP42 and FAP246, are localized to the L-shaped C1b projection of the CA. We also identified another novel CA candidate protein, FAP413, which interacts with both FAP42 and FAP246. FAP42 is a large protein that forms the peripheral  beam of the C1b projection, and the FAP246-FAP413 subcomplex serves as the  bracket between the beam (FAP42) and the C1b  pillar that attaches the projection to the C1 microtubule. The FAP246-FAP413-FAP42 complex is essential for stable assembly of both the C1b and C1f projections, and loss of any of these proteins leads to ciliary motility defects. Our results provide insight into the subunit organization and 3D structure of the C1b projection, suggesting that the FAP246-FAP413-FAP42 subcomplex is part of a large interconnected CA-network that provides mechanical support and may play a role in mechano-signaling between the CA and radial spokes to regulate dynein activity and ciliary beating.

Summary StatementThe present work provides insight into the subunit organization and 3D structure of the C1b projection of CA and the mechanism by which it regulates dynein activity and ciliary beating.


title: PLEKHA5, PLEKHA6 and PLEKHA7 bind to PDZD11 to target the Menkes ATPase ATP7A to the cell periphery and regulate copper homeostasis

abstract: Copper homeostasis is crucial for cellular physiology and development, and its dysregulation leads to disease. The Menkes ATPase ATP7A plays a key role in copper efflux, by trafficking from the Golgi to the plasma membrane upon cell exposure to elevated copper, but the mechanisms that target ATP7A to the cell periphery are poorly understood. PDZD11 interacts with the C-terminus of ATP7A, which contains sequences involved in ATP7A trafficking, but the role of PDZD11 in ATP7A localization is unknown. Here we identify PLEKHA5 and PLEKHA6 as new interactors of PDZD11, which similarly to the junctional protein PLEKHA7 bind to PDZD11 N-terminus through their WW domains. Using CRISPR-KO kidney epithelial cells, we show by immunofluorescence that WW-PLEKHAs (PLEKHA5, PLEKHA6, PLEKHA7) recruit PDZD11 to distinct plasma membrane localizations, and that they are required for the efficient anterograde targeting of ATP7A to the cell periphery in elevated copper. Pulldown experiments show that WW-PLEKHAs promote PDZD11 interaction with the C-terminus of ATP7A. However, WW-PLEKHAs and PDZD11 are not necessary for ATP7A Golgi localization in basal copper, ATP7A copper-induced exit from the Golgi, and ATP7A retrograde trafficking to the Golgi. Finally, measuring bioavailable copper with the labile copper probe CF4 shows that WW-PLEKHAs and PDZD11 are required to maintain low intracellular copper levels when cells are exposed to elevated copper. These data indicate that WW-PLEKHAs-PDZD11 complexes regulate the localization and function of ATP7A to modulate cellular copper homeostasis.


title: Transcription factor-like 5 is a potential DNA/RNA-binding protein essential for maintaining male fertility in mice.Running title: Tcfl5 is haploinsufficient in vivo

abstract: Tissue-specific transcription factors often play key roles in the development of specific cell lineages. Transcription factor-like 5 (TCFL5) is a testis-specific protein that contains the basic helix-loop-helix domain, although the in vivo functions of TCFL5 remain unknown. Herein, we generated CRISPR/Cas9-mediated knockout mice to dissect the function of TCFL5 in mouse testes. Surprisingly, we found that it was difficult to generate homozygous mice with the Tcfl5 deletion since the heterozygous males (Tcfl5+/-) were infertile. We did, however, observe markedly abnormal phenotypes of spermatids and spermatozoa in the testes and epididymides of Tcfl5+/- mice. Mechanistically, we demonstrated that TCFL5 transcriptionally regulated a set of genes participating in male germ cell development, which we uncovered via RNA-sequencing and TCFL5 ChIP-sequencing. We also found that TCFL5 interacted with RNA-binding proteins (RBPs) that regulated RNA processing, and further identified the fragile X mental retardation gene 1, autosomal homolog (FXR1, a known RBP) as an interacting partner of TCFL5 that may coordinate the transition and localization of TCFL5 in the nucleus. Collectively, we herein report for the first time that Tcfl5 is haploinsufficient in vivo and hypothesize that TCFL5 may be a dual-function protein that mediates DNA and RNA to regulate spermatogenesis.


title: Evolution of Drosophila buzzatii wings: Modular genetic organization, sex-biased integrative selection and intralocus sexual conflict

abstract: The Drosophila wing is a structure shared by males and females with the main function of flight. However, in males, wings are also used to produce songs, or visual displays during courtship. Thus, observed changes in wing phenotype depend on the interaction between sex-specific selective pressures and the genetic and ontogenetic restrictions imposed by a common genetic architecture. Here, we investigate these issues by studying how the wing has evolved in twelve populations of Drosophila buzzatii raised in common-garden conditions and using an isofemale line design. The between-population divergence shows that sexual dimorphism is greater when sex evolves in different directions. Multivariate Qst-Fst analyses confirm that male wing shape is the target for multiple selective pressures, leading males wings to diverge more than females wings. While the wing blade and the wing base appear to be valid modules at the genetic (G matrix) and among-population (D matrix) levels, the reconstruction of between-population adaptive landscapes ({Omega} matrix) shows selection as an integrative force. Also, cross-sex covariances reduced the predicted response to selection in the direction of the extant sexual dimorphism, suggesting that selection had to be intensified in order to circumvent the limitations imposed by G. However, such intensity of selection was not able to break the modularity pattern of the wing. The results obtained here show that the evolution of D. buzzatii wing shape is the product of a complex interplay between ontogenetic constraints and conflicting sexual and natural selections.

category: evolutionary biology


title: Universality of evolutionary dynamics with arbitrary demography

abstract: The assumption of constant population size is central in population genetics. It led to a large body of results, that are robust to modeling choices and that have proven successful to understand evolutionary dynamics. In reality, allele frequencies and population size are both determined by the interaction between a population and the environment. Relaxing the constant-population assumption have two big drawbacks. It increases the technical difficulty of the analysis, and it requires specifying a mechanism for the saturation of the population size, possibly making the results contingent on model details. Here, we develop a framework that encompasses a great variety of systems with an arbitrary mechanism for population growth limitation. By using techniques based on scale separation for stochastic processes, we are able to calculate analytically properties of evolutionary trajectories, such as the fixation probability. Remarkably, these properties assume a universal form with respect to our framework, which depends on only three parameters related to the inter-generation timescale, the invasion fitness, and the carrying capacity of the strains. In other words, different systems, such as Lotka-Volterra or a chemostat model (contained in our framework), share the same evolutionary outcomes after a proper re-mapping of their parameters. An important and surprising consequence of our results is that the direction of selection can be inverted, with a population evolving to reach lower values of invasion fitness.


title: Universality of evolutionary trajectories under arbitrary competition dynamics


title: Unconventional conservation reveals structure-function relationships in the synaptonemal complex

abstract:
Functional requirements constrain protein evolution, commonly manifesting in conserved primary amino acid sequence. Here, we extend this idea to secondary structural features by tracking their conservation in essential meiotic proteins with highly diverged sequences. The synaptonemal complex (SC) aligns parental chromosome pairs and regulates exchanges between them. In electron micrographs of meiocytes from all eukaryotic clades, the SC appears as a ~100 nm-wide ladder-like structure with regular striations. Despite the conserved ultrastructure and functions, the proteins that make up the SC are highly divergent in sequence. Here we found that, within the Caenorhabditis genus, SC proteins are significantly more diverged than other proteins. However, SC proteins have highly conserved protein length and coiled-coil domain structure. The same unconventional conservation signature holds true for SC proteins in Drosophila and mammals, suggesting it could be a universal feature of SC proteins. We used this evolutionary signature to identify a novel SC protein in the nematode Pristionchus pacificus, Ppa-SYP-1, which has no significant homology to any protein outside of Pristionchus. Our work suggests that the length and relative arrangement of coiled-coils play a key role in the structure and function of the SC. Furthermore, our analysis implies that expanding sequence analysis beyond measures of per-site identity or similarity can enhance our understanding of protein evolution and function.

Short abstractFunctional requirements constrain protein evolution, commonly manifesting in a conserved amino acid sequence. Here, we extend this idea to secondary structural features by tracking their conservation in essential meiotic proteins with highly diverged sequences. The synaptonemal complex (SC) is a ~100 nm-wide ladder-like meiotic structure present in all eukaryotic clades, where it aligns parental chromosomes and regulates exchanges between them. Despite the conserved ultrastructure and functions of the SC, SC proteins are highly divergent within Caenorhabditis. However, SC proteins have highly conserved length and coiled-coil domain structure. We found the same unconventional conservation signature in Drosophila and mammals, and used it to identify a novel SC protein in Pristionchus pacificus, Ppa-SYP-1. Our work suggests that coiled-coils play wide-ranging roles in the structure and function of the SC, and more broadly, that expanding sequence analysis beyond measures of per-site similarity can enhance our understanding of protein evolution and function.


title: Interpreting TMEM67 missense variants of uncertain significance (VUS) in an animal model

abstract:
PurposeBetter methods are required to interpret the pathogenicity of disease-associated variants of uncertain significance (VUS), which cannot be actioned clinically. In this study, we explore the use of a tractable animal model (Caenorhabditis elegans) for in vivo interpretation of missense VUS alleles of TMEM67, a cilia gene associated with ciliopathies.

MethodsCRISPR/Cas9 gene editing was used to generate homozygous knock-in C. elegans worm strains carrying TMEM67 patient variants engineered into the orthologous gene (mks-3). Quantitative phenotypic assays of sensory cilia structure and function measured if the variants affect mks-3 gene function. Results from worms were validated by a genetic complementation assay in a human TMEM67 knock-out hTERT-RPE1 cell line that tests a TMEM67 signaling function.

ResultsAssays in C. elegans accurately distinguished between known benign (Asp359Glu, Thr360Ala) and known pathogenic (Glu361Ter, Gln376Pro) variants. Analysis of eight missense VUS generated evidence that three are benign (Cys173Arg, Thr176Ile, Gly979Arg) and five are pathogenic (Cys170Tyr, His782Arg, Gly786Glu, His790Arg, Ser961Tyr).

ConclusionEfficient genome editing and quantitative functional assays in C. elegans make it a tractable in vivo animal model that allows rapid, cost-effective interpretation of ciliopathy-associated missense VUS alleles.

category: genetics


title: Interpreting ciliopathy-associated missense variants of uncertain significance (VUS) in an animal model


title: Single Cell Transcriptomics Reveals Global Markers of Transcriptional Diversity Across Different Forms of CellularSenescence

abstract: Cellular Senescence is a state of irreversible cell cycle arrest, and the accumulation of senescent cells contributes to age- related organismal decline. The detrimental effects of cellular senescence are due to the senescence associated secretory phenotype (SASP), an array of signaling molecules and growth factors secreted by senescent cells that contribute to the sterile inflammation associated with aging tissues. Recent studies, both in vivo and in vitro, have highlighted the heterogeneous nature of the senescence phenotype. In particular, single cell transcriptomics has revealed that Oncogene Induced Senescence (OIS) is characterized by the presence of subpopulations of cells expressing different SASP profiles. We have generated a comprehensive dataset via single-cell transcriptional profiling of genetically homogenous clonal cell lines from different forms of senescence, including OIS, Replicative Senescence (RS), and DNA Damage Induced Senescence (DDIS). We identified subpopulations of cells that are common to all three major forms of senescence and show that the expression profiles of these subpopulations are driven by markers formerly identified in individual forms of senescence. These common signatures are characterized by chromatin modifiers, inflammation, extracellular matrix remodeling, and Ribosomal protein expression. The expression patterns of these subpopulations recapitulate primary and secondary senescence, a phenomenon where a preexisting (primary) senescent cell induces senescence in a neighboring (secondary) cell through cell-to-cell contact. Since it is still unclear what type of senescence occurs in-vivo with age, it is important to know that the formation of primary and secondary populations is common to multiple types of senescence since this mechanism could help explain how senescent cells accumulate in aged organisms. Finally, we show that these subpopulations show differential susceptibility to the senolytic agent Navitoclax, suggesting that senolytic agents targeting the apoptotic pathways may be clearing only a subset of senescent cells based on their inflammatory profiles in-vivo.

category: genomics


title: SPEAR: a proteomics approach for simultaneous protein expression and redox analysis

abstract: Oxidation and reduction of protein cysteinyl thiols serve as molecular switches, which is considered the most central mechanism for redox regulation of biological processes, altering protein structure, biochemical activity, subcellular localization, and binding affinity. Redox proteomics allows for the global identification of redox-modified cysteine (Cys) sites and quantification of their oxidation/reduction responses, serving as a hypothesis-generating platform to stimulate redox biology mechanistic research. Here, we developed Simultaneous Protein Expression and Redox (SPEAR) analysis, a new redox-proteomics approach based on differential labeling of oxidized and reduced cysteines with light and heavy isotopic forms of commercially available isotopically-labeled N-ethylmaleimide (NEM). The presented method does not require enrichment for labeled peptides, thus enabling simultaneous quantification of Cys oxidation state and protein abundance. Using SPEAR, we were able to quantify the in-vivo oxidation state of thousands of cysteines across the Arabidopsis proteome under steady-state and oxidative stress conditions. Functional assignment of the identified redox-sensitive proteins demonstrated the widespread effect of oxidative conditions on various cellular functions and highlighted the enrichment of chloroplast-targeted proteins. SPEAR provides a simple, straightforward, and cost-effective means of studying redox proteome dynamics. The presented data provide a global quantitative view of cysteine oxidation of well-known redox-regulated active sites and many novel redox-sensitive sites whose role in plant acclimation to stress conditions remains to be further explored.

category: biochemistry


title: MatrixQCvis: shiny-based interactive data quality exploration for omics data

abstract:
MotivationFirst-line data quality assessment and exploratory data analysis are integral parts of any data analysis workflow. In high-throughput quantitative omics experiments (e.g. transcriptomics, proteomics, metabolomics), after initial processing, the data are typically presented as a matrix of numbers (feature IDs x samples). Efficient and standardized data-quality metrics calculation and visualization are key to track the within-experiment quality of these rectangular data types and to guarantee for high-quality data sets and subsequent biological question-driven inference.

ResultsWe present MatrixQCvis, which provides interactive visualization of data quality metrics at the per-sample and per-feature level using Rs shiny framework. It provides efficient and standardized ways to analyze data quality of quantitative omics data types that come in a matrix-like format (features IDs x samples). MatrixQCvis builds upon the Bioconductor SummarizedExperiment S4 class and thus facilitates the integration into existing workflows.

AvailabilityMatrixQCVis is implemented in R. It is available via Bioconductor and released under the GPL v3.0 license.

Contactthomas.naake@embl.de

Supplementary informationSupplementary Information is available at bioRxiv online.


title: YAPP-CD: Yet another protein-peptide complex database

abstract: Protein-peptide interactions are of great interest to the research community not only because they serve as mediators in many protein-protein interactions but also because of the increasing demand for peptide-based pharmaceutical products. Protein-peptide docking is a major tool for studying protein-peptide interactions, and several docking methods are currently available. Among various protein-peptide docking algorithms, template-based approaches, which utilize known protein-peptide complexes or templates to predict a new one, have been shown to yield more reliable results than template-free methods in recent comparative research. To obtain reliable results with a template-based docking method, the template database must be comprehensive enough; that is, there must be similar templates of protein-peptide complexes to the protein and peptide being investigated. Thus, the template database must be updated to leverage recent advances in structural biology. However, the template database distributed with GalaxyPepDock, one of the most widely used peptide docking programs, is outdated, limiting the prediction quality of the method. Here, we present an up-to-date protein-peptide complex database called YAPP-CD, which can be directly plugged into the GalaxyPepDock binary package to improve GalaxyPepDocks prediction quality by drawing on recent discoveries in structural biology. Experimental results show that YAPP-CD significantly improves GalaxyPepDocks prediction quality, e.g., the average Ligand/Interface RMSD of a benchmark set is reduced from 7.60 [A]/3.62 [A] to 3.47 [A]/1.71 [A].


title: A hierarchy in clusters of cephalopod mRNA editing sites

abstract: RNA editing in the form of substituting adenine to inosine (A-to-I editing) is the most frequent type of RNA editing, observed in many metazoan species. A-to-I editing sites form clusters in most studied species, and editing at clustered sites depends on editing of the adjacent sites. Although functionally important in some specific cases, A-to-I editing in most considered species is rare, the exception being soft-bodied cephalopods (coleoids), where tens of thousands of potentially important A-to-I editing sites have been identified, making coleoids an ideal object for studying of general properties and evolution of A-to-I editing sites. Here, we apply several diverse techniques to demonstrate a strong tendency of coleoid RNA editing sites to cluster along the transcript. We identify three distinct types of editing site clusters, varying in size, and describe RNA structural features and mechanisms likely underlying formation of these clusters. In particular, these observations may resolve the paradox of sequence conservation at large distances around editing sites.


title: Accurate Protein Function Prediction via Graph Attention Networks with Predicted Structure Information

abstract:
Experimental protein function annotation does not scale with the fast-growing sequence databases. Only a tiny fraction (<0.1%) of protein sequences in UniProtKB has experimentally determined functional annotations. Computational methods may predict protein function in a high-throughput way, but its accuracy is not very satisfactory. Based upon recent breakthroughs in protein structure prediction and protein language models, we develop GAT-GO, a graph attention network (GAT) method that may substantially improve protein function prediction by leveraging predicted inter-residue contact graphs and protein sequence embedding.

Our experimental results show that GAT-GO greatly outperforms the latest sequence- and structure-based deep learning methods. On the PDB-mmseqs testset where the train and test proteins share <15% sequence identity, GAT-GO yields Fmax(maximum F-score) 0.508, 0.416, 0.501, and AUPRC(area under the precision-recall curve) 0.427, 0.253, 0.411 for the MFO, BPO, CCO ontology domains, respectively, much better than homology-based method BLAST (Fmax 0.117,0.121,0.207 and AUPRC 0.120, 0.120, 0.163). On the PDB-cdhit testset where the training and test proteins share higher sequence identity, GAT-GO obtains Fmax 0.637, 0.501, 0.542 for the MFO, BPO, CCO ontology domains, respectively, and AUPRC 0.662, 0.384, 0.481, significantly exceeding the just-published graph convolution method DeepFRI, which has Fmax 0.542, 0.425, 0.424 and AUPRC 0.313, 0.159, 0.193.


title: ACES: Analysis of Conservation with Expansive Species

abstract:
MotivationAn abundance of new reference genomes are becoming available through large-scale sequencing efforts. While the reference FASTA for each genome is available, there is currently no automated mechanism to query a specific sequence across all new reference genomes.

ResultsWe developed ACES (Analysis of Conservation with Expansive Species) as a computational workflow to query specific sequences of interest (e.g., enhancers, promoters, exons) against reference genomes with an available reference FASTA. This automated workflow generates BLAST hits against each of the reference genomes, a multiple sequence alignment file, a graphical fragment assembly file, and a phylogenetic tree file. These data files can then be used by the researcher in several ways to provide key insights into conservation of the query sequence.

AvailabilityACES is available at https://github.com/TNTurnerLab/ACES

Contacttychele@wustl.edu

Supplementary informationSupplementary Figure 1 is available on bioRxiv.


title: Incorporation of data from multiple hypervariable regions when analyzing bacterial 16S rRNA sequencing data

abstract: Short read 16S rRNA amplicon sequencing is a common technique used in microbiome research. However, inaccuracies in estimated bacterial community composition can occur due to amplification bias of the targeted hypervariable region. A potential solution is to sequence and assess multiple hypervariable regions in tandem, yet there is currently no consensus as to the appropriate method for analyzing this data. Additionally, there are many sequence analysis resources for data produced from the Illumina platform, but fewer open-source options available for data from the Ion Torrent platform. Herein, we present an analysis pipeline using an open-source analysis platform that integrates data from multiple hypervariable regions and is compatible with data produced from the Ion Torrent platform. We used the ThermoFisher Ion 16S Metagenomics Kit and a mock community of 20 bacterial strains to assess taxonomic classification of amplicons from 6 separate hypervariable regions (V2, V3, V4, V6-7, V8, V9) using our analysis pipeline. We report that different hypervariable regions have different specificities for taxonomic classification, which also had implications for global level analyses such as alpha and beta diversity. Finally, we utilize a generalized linear modeling approach to statistically integrate the results from multiple hypervariable regions and apply this methodology to data from a small clinical cohort. We conclude that scrutinizing sequencing results separately by hypervariable region provides a more granular view of the taxonomic classification achieved by each primer set as well as the concordance of results across hypervariable regions. However, the data across all hypervariable regions can be combined using generalized linear models to statistically evaluate overall differences in community structure and relatedness among sample groups.


title: TMEM120A contains a specific coenzyme A-binding site and might not mediate poking- or stretch-induced channel activities in cells

abstract: TMEM120A, a member of the Transmembrane protein 120 (TMEM120) family, has pivotal function in adipocyte differentiation and metabolism, and may also contribute to sensing mechanical pain by functioning as an ion channel named TACAN. Here we report that expression of TMEM120A is not sufficient in mediating poking- or stretch-induced currents in cells, and have solved cryo-EM structures of human TMEM120A (HsTMEM120A) in complex with an endogenous metabolic cofactor (coenzyme A, CoASH) and in the apo form. HsTMEM120A forms a symmetrical homodimer with each monomer containing an amino-terminal coiled-coil motif followed by a transmembrane domain with six membrane-spanning helices. Within the transmembrane domain, a CoASH molecule is hosted in a deep cavity and forms specific interactions with nearby amino acid residues. Mutation of a central tryptophan residue involved in binding CoASH dramatically reduced the binding affinity of HsTMEM120A with CoASH. HsTMEM120A exhibits distinct conformations at the states with or without CoASH bound. Our results suggest that TMEM120A may have alternative functional roles potentially involved in CoASH transport, sensing or metabolism.

category: biophysics


title: Conformational and interface variability of bivalent RNF4-SIM diSUMO3 interaction

abstract: SUMO targeted ubiqutin ligases (STUbLs) like RNF4 or Arkadia/RNF111 recognize SUMO chains through multiple SUMO interacting motifs (SIMs). Typically, these are contained in disordered regions of these enzymes and also the individual SUMO domains of SUMO chains move relatively freely. It is assumed that binding the SIM region significantly restricts the conformational freedom of SUMO chains. Here, we present the results of extensive molecular dynamics simulations on the complex formed by the SIM2-SIM3 region of RNF4 and diSUMO3. Though our simulations highlight the importance of typical SIM-SUMO interfaces also in the multivalent situation, we observe that frequently other regions of the peptide than the canonical SIMs establish this interface. This variability regarding the individual interfaces leads to a conformationally highly flexible complex. Even though this is in contrast to previous models of the RNF4 SUMO chain interaction, we demonstrate that our simulations are clearly consistent with previous experimental results.


title: Image quantification technique reveals novel lung cancer cytoskeletal phenotype with partial EMT signature

abstract: Epithelial-mesenchymal Transition (EMT) is a multi-step process that involves cytoskeletal rearrangement. Here, using novel image quantification tools, we have identified an intermediate EMT state with a specific cytoskeletal signature. We have been able to partition EMT into two steps: (1) initial formation of transverse arcs and dorsal stress fibers and (2) their subsequent conversion to ventral stress fibers with a concurrent alignment of fibers. Using the Orientational Order Parameter (OOP) as a figure of merit, we have been able to track EMT progression in live cells as well as characterize and quantify drug responses. Our technique has improved throughput and is non-destructive, making it a viable candidate for studying a broad range of biological processes. Further, owing to the increased stiffness (and hence invasiveness) of the intermediate phenotype compared to mesenchymal cells, our work can be instrumental in aiding the search for new treatment strategies that combat metastasis by specifically targeting the fiber alignment process.


title: CUB domain containing protein 1 (CDPC1) is a target for radioligand therapy in castration resistant prostate cancer

abstract:
PurposeRadioligand therapy (RLT) is relatively unexplored in metastatic castration resistant prostate cancer (mCRPC), with much of the focus having been on bone seeking radionuclides and PSMA-directed RLT. Herein, we evaluated if CUB domain containing protein 1 (CDCP1) can be exploited to treat mCRPC with RLT, particularly for subsets like small cell neuroendocrine prostate cancer (SCNC) that would not be expected to respond to current options.

Experimental DesignCDCP1 mRNA levels were evaluated in the RNA-seq data from 119 recent mCRPC biopsies. Protein expression was assessed in twelve SCNC and adenocarcinoma patient derived xenografts. Saturation binding assays were performed with 4A06, a recombinant human antibody that targets the CDCP1 ectodomain. The feasibility of imaging and treating mCRPC in vivo was tested with 89Zr-4A06 and 177Lu-4A06.

ResultsCDCP1 mRNA expression was observed in over 90% of mCRPC biopsies, including SCNC and in adenocarcinoma with low FOLH1 (PSMA) levels. A modest anticorrelation was observed between CDCP1 and PTEN. Overall survival was not significantly different based on CDCP1 mRNA levels, regardless of PTEN status. Full length and/or cleaved CDCP1 was expressed in ten of twelve PDX samples. Bmax values of ~22,000 and ~6,200 fmol/mg were calculated for two human prostate cancer cell lines. Five prostate cancer models were readily detected in vivo with 89Zr-4A06. 177Lu-4A06 significantly suppressed the growth of DU145 tumors compared to control.

ConclusionsThe antitumor data and the overexpression of CDCP1 reported herein provide the first evidence promoting CDCP1 directed RLT as a treatment strategy for mCRPC.

Statement of Translational RelevanceNew targets for RLT are needed to address the subset of mCRPC that cannot be treated with bone seeking radionuclides or PSMA directed RLT. We report herein the first data credentialing CDCP1 as a target for mCRPC, in both adenocarcinoma and neuroendocrine subtypes. Combined with low expression in normal human tissues, these data provide a compelling scientific rationale for testing CDCP1 directed RLT clinically in mCRPC patients alone or in combination with other systemic therapies.


title: Multi-omics network-based functional annotation of unknown Arabidopsis genes

abstract:
Unraveling gene functions is pivotal to understand the signaling cascades controlling plant development and stress responses. Given that experimental profiling is costly and labor intensive, the need for high-confidence computational annotations is evident. In contrast to detailed gene-specific functional information, transcriptomics data is widely available in both model and crop species. Here, we developed a novel automated function prediction (AFP) algorithm, leveraging complementary information present in multiple expression datasets through the analysis of study-specific gene co-expression networks. Benchmarking the prediction performance on recently characterized Arabidopsis thaliana genes, we showed that our method outperforms state-of-the-art expression-based approaches. Next, we predicted biological process annotations for known (n=15,790) and unknown (n=11,865) genes in A. thaliana and validated our predictions using experimental protein-DNA and protein-protein interaction data (covering >220 thousand interactions in total), obtaining a set of high-confidence functional annotations. 5,054 (42.6%) unknown genes were assigned at least one validated annotation, and 3,408 (53.0%) genes with only computational annotations gained at least one novel validated function. These omics-supported functional annotations shed light on a variety of developmental processes and molecular responses, such as flower and root development, defense responses to fungi and bacteria, and phytohormone signaling, and help alleviate the knowledge gap of biological process annotations in Arabidopsis. An in-depth analysis of two context-specific networks, modeling seed development and response to water deprivation, shows how previously uncharacterized genes function within the respective networks. Moreover, our AFP approach can be applied in future studies to facilitate gene discovery for crop improvement.

Significance statementFor the majority of plant genes, it is unknown in which processes they are involved. Using a multi-omics approach, leveraging transcriptome, protein-DNA and protein-protein interaction data, we functionally annotated 42.6% of unknown Arabidopsis thaliana genes, providing insight into a variety of developmental processes and molecular responses, as well as a resource of annotations which can be explored by the community to facilitate future research.


title: 'Superbugs: A Pop-up Science Shop': Increasing public awareness and knowledge of antimicrobial resistance by taking science to the city

abstract:
Superbugs: A Pop-up Science Shop was a public engagement event in the school summer holidays of 2019, organised by members of Cardiff Universitys School of Medicine. We transformed an empty retail unit in the centre of Wales largest shopping centre into an interactive and immersive microbiology experience. We aimed to facilitate opportunities for two-way dialogue to impart positive impact on the awareness of antibiotic resistance, whilst concurrently evaluating the efficacy of an engagement strategy focused on the utilisation of public spaces to attract public demographics diverse to those who would normally engage with STEM outreach.

Over the course of 14 days, we welcomed 6,566 visitors, with 67% attending as part of the natural footfall of the shopping centre. We created 1,625 young Antibiotic Resistance Champions, located in over 200 schools. Through a multi-lateral evaluation strategy, we were able to collect quantitative and qualitative feedback on the success of our delivery model, and the impact on our stakeholders. Herein, we will discuss the evolution of  Superbugs from concept, planning and design, to the logistics of delivering an engagement event of this scale. We will focus in particular on the learning outcomes of the project, and how this will shape the future of our  Superbugs project, and engagement events beyond.

Key MessagesO_LICreating a multi-disciplinary core team is essential to the success of large-scale engagement events as well as the support and development of large numbers of colleagues/volunteers
C_LIO_LIUtilising themes of exhibition and gameplay alongside strong fear-empowerment messages is an impactful way to confer positive influence and behaviour around antimicrobial resistance (AMR) and the use of antibiotics
C_LIO_LI Pop-up shop is an effective mode of delivery to capture diverse public demographics far beyond those who would traditionally engage with scientific outreach and science engagement.
C_LI

category: scientific communication and education


title: Spatial structure, phase, and the contrast of natural images

abstract: The sensitivity of the human visual system is thought to be shaped by environmental statistics. A major endeavour in vision science, therefore, is to uncover the image statistics that predict perceptual and cognitive function. When searching for targets in natural images, for example, it has recently been proposed that target detection is inversely related to the spatial similarity of the target to its local background. We tested this hypothesis by measuring observers sensitivity to targets that were blended with natural image backgrounds. Targets were designed to have a spatial structure that was either similar or dissimilar to the background. Contrary to masking from similarity, we found that observers were most sensitive to targets that were most similar to their backgrounds. We hypothesised that a coincidence of phase-alignment between target and background results in a local contrast signal that facilitates detection when target-background similarity is high. We confirmed this prediction in a second experiment. Indeed, we show that, by solely manipulating the phase of a target relative to its background, the target can be rendered easily visible or undetectable. Our study thus reveals that, in addition to its structural similarity, the phase of the target relative to the background must be considered when predicting detection sensitivity in natural images.


title: Cellular and molecular signatures of in vivo GABAergic neurotransmission in the human brain

abstract: Diverse GABAergic interneuron microcircuits orchestrate information processing in the brain. Understanding the cellular and molecular composition of these microcircuits, and whether these can be imaged by available non-invasive in vivo methods is crucial for the study of GABAergic neurotransmission in health and disease. Here, we use human gene expression data and state-of-the-art imaging transcriptomics to uncover co-expression patterns between GABAA receptor subunits and interneuron subtype-specific markers, and to decode the cellular and molecular signatures of gold-standard GABA PET radiotracers, [11C]Ro15-4513 and [11C]flumazenil. We find that the interneuron marker somatostatin is co-expressed with GABAA receptor-subunit genes GABRA5 and GABRA2, and their distribution maps onto [11C]Ro15-4513 binding in vivo. In contrast, the interneuron marker parvalbumin co-expressed with more predominant GABAA receptor subunits (GABRA1, GABRB2 and GABRG2), and their distribution tracks [11C]flumazenil binding in vivo. These results have important implications for the non-invasive study of GABAergic microcircuit dysfunction in psychiatric conditions.


title: Dynamic interplay between reward and voluntary attention determines stimulus processing in visual cortex

abstract: Reward enhances stimulus processing in the visual cortex, but the mechanisms through which this effect occurs remain unclear. Reward prospect can both increase the deployment of voluntary attention and increase the salience of previously neutral stimuli. In this study we orthogonally manipulated reward and voluntary attention while human participants performed a global motion detection task. We recorded steady-state visual evoked potentials (SSVEPs) to simultaneously measure the processing of attended and unattended stimuli linked to different reward probabilities, as they compete for attentional resources. The processing of the high rewarded feature was enhanced independently of voluntary attention, but this gain diminished once rewards were no longer available. Neither the voluntary attention nor the salience account alone can fully explain these results. Instead, we propose how these two accounts can be integrated to allow for the flexible balance between reward-driven increase in salience and voluntary attention.


title: A Dynamic Efficient Sensory Encoding Approach to Adaptive Tuning in Neural Models of Visual Motion Processing

abstract: This paper introduces a self-tuning mechanism for capturing rapid adaptation to changing visual stimuli by a population of neurons. Building upon the principles of efficient sensory encoding, we show how neural tuning curve parameters can be continually updated to optimally encode a time-varying distribution of recently detected stimulus values. We implemented this mechanism in a neural model that produces human-like estimates of self-motion direction (i.e., heading) based on optic flow. The parameters of speed-sensitive units were dynamically tuned in accordance with efficient sensory encoding such that the network remained sensitive as the distribution of optic flow speeds varied. In two simulation experiments, we found that model performance with dynamic tuning yielded more accurate, shorter latency heading estimates compared to the model with static tuning. We conclude that dynamic efficient sensory encoding offers a plausible approach for capturing adaptation to varying visual environments in biological visual systems and neural models alike.


title: Effects of tDCS on the attentional blink revisited: A statistical evaluation of a replication attempt

abstract: The attentional blink (AB) phenomenon reveals a bottleneck of human information processing: the second of two targets is often missed when they are presented in rapid succession among distractors. In our previous work, we showed that the size of the AB can be changed by applying transcranial direct current stimulation (tDCS) over the left dorsolateral prefrontal cortex (lDLPFC) (London & Slagter, Journal of Cognitive Neuroscience, 33, 756-68, 2021). Although AB size at the group level remained unchanged, the effects of anodal and cathodal tDCS were negatively correlated: if a given individuals AB size decreased from baseline during anodal tDCS, their AB size would increase during cathodal tDCS, and vice versa. Here, we attempted to replicate this finding. We found no group effects of tDCS, as in the original study, but we no longer found a significant negative correlation. We present a series of statistical measures of replication success, all of which confirm that both studies are not in agreement. First, the correlation here is significantly smaller than a conservative estimate of the original correlation. Second, the difference between the correlations is greater than expected due to sampling error, and our data are more consistent with a zero-effect than with the original estimate. Finally, the overall effect when combining both studies is small and not significant. Our findings thus indicate that the effects of lDPLFC-tDCS on the AB are less substantial than observed in our initial study. Although this should be quite a common scenario, null findings can be difficult to interpret and are still under-represented in the brain stimulation and cognitive neuroscience literatures. An important auxiliary goal of this paper is therefore to provide a tutorial for other researchers, to maximize the evidential value from null findings.


title: Single-cell analysis reveals the subthreshold TLR response to Mycobacterium tuberculosis infection

abstract: Mycobacterium tuberculosis (Mtb) infection remains one of society's greatest human health challenges. Recognition of pathogens such as Mtb by phagocytes via pattern recognition receptors (PRRs) is critical for immune defense. Phagocytes and other innate immune cells must successfully discriminate between pathogen infection or noisy signals derived from transient non-pathogenic exposures to pattern recognition receptor ligands. Previous studies have underscored the importance of toll-like receptor 2 (TLR2) in the recognition of Mtb, and other studies have demonstrated that toll-like receptor 4 (TLR4) exhibits sigmoidal dynamics in inflammatory protein production in response to soluble ligands. How these dynamics extend to TLR2 in response to Mtb or soluble TLR2 ligands is not known. Here, we address a challenging question essential to the pathogenesis of tuberculosis (TB) disease: What is the quality of the early innate response to Mtb infection at low multiplicity of infection and how does the response to soluble TLR2 ligand stimulation reflect the intact pathogen response? Using soluble TLR2 ligands, we demonstrate that inflammatory protein production in response to TLR2 stimulation similarly follows sigmoidal activation. We further show that inflammatory protein production following Mtb infection operates in this subthreshold regime and cannot be attributed to irreversible disruption of inflammatory signaling pathways in Mtb-infected cells. Using highly sensitive assays to interrogate signaling in Mtb-infected primary human macrophages, we further show that Mtb infection results in limited phosphorylation of p38, a key regulator of inflammatory pathways. We further demonstrate that this poor activation is not driven by irreversible inhibition of p38 signaling by Mtb. Lastly, we demonstrate that the quality of canonical inflammatory signal input is decoupled from the ability to control Mtb growth. Together, these data demonstrate that pathways downstream of TLR signaling are differentially activated following Mtb infection and that barriers to activation of these pathways may contribute to functional partitioning of the inflammatory state of Mtb infected cells. Importantly, these studies provide a roadmap to dissect heterogeneous innate responses to Mtb infection as well as provide a tractable experimental system to simulate the early innate immune response to paucibacillary infection in humans.

category: immunology


title: Insight into the ecology of vaginal bacteria through integrative analyses of metagenomic and metatranscriptomic data

abstract: Vaginal bacterial communities dominated by Lactobacillus species are associated with a reduced risk to various adverse health outcomes. However, somewhat unexpectedly many healthy women have microbiota that are not dominated by lactobacilli. To determine the factors that drive vaginal community composition we characterized the genetic composition and transcriptional activities of vaginal microbiota in healthy women. We demonstrated that the abundance of a species is not always indicative of its transcriptional activity and that impending changes in community composition can be predicted from metatranscriptomic data. Functional comparisons highlight differences in the metabolic activities of these communities, notably in their degradation of host-produced mucin but not glycogen. Degradation of mucin by communities not dominated by Lactobacillus may play a role in their association with adverse health outcomes. Finally, we show that the transcriptional activities of L. crispatus, L. iners, and G. vaginalis vary with the taxonomic composition of the communities in which they reside. Notably, L. iners and G. vaginalis both demonstrated lower expression of their cholesterol-dependent cytolysins when co-resident with Lactobacillus spp. and higher expression when co-resident with other facultative and obligate anaerobes. The pathogenic potential of these species may depend on the communities in which they reside and thus could be modulated by interventional strategies. Our results provide insight to the functional ecology of vaginal microbiota and reveal strategies for management of these ecosystems.


title: Zebrafish pigment cells develop directly from persistent highly multipotent progenitors.

abstract: Neural crest cells (NCCs) are highly multipotent stem cells. A long-standing controversy exists over the mechanism of NCC fate specification, specifically regarding the presence and potency of intermediate progenitors. The direct fate restriction (DFR) model, based on early in vivo clonal studies, hypothesised that intermediates are absent and that migrating cells maintain full multipotency1-6. However, most authors favour progressive fate restriction (PFR) models, with fully multipotent early NCCs (ENCCs) transitioning to partially-restricted intermediates before committing to individual fates7-12. Here, single cell transcriptional profiling of zebrafish pigment cell development leads to us proposing a Cyclical Fate Restriction mechanism of NCC development that reconciles the DFR and PFR models. Our clustering of single NCC Nanostring transcriptional profiles identifies only broadly multipotent intermediate states between ENCCs and differentiated melanocytes and iridophores. Leukocyte tyrosine kinase (Ltk) marks the multipotent progenitor and iridophores, consistent with biphasic ltk expression13-15. Ltk inhibitor and constitutive activation studies support expression at an early multipotent stage, whilst lineage-tracing of ltk-expressing cells reveals their multipotency extends beyond pigment cell-types to neural fates. We conclude that pigment cell development does not involve a conventional PFR mechanism, but instead occurs directly and more dynamically from a broadly multipotent intermediate state.

category: developmental biology


title: Arabidopsis ABIG1 Functions in Laminar Growth and Polarity Formation through Regulation by REVOLUTA and KANADI

abstract:
Leaf laminar growth and adaxial-abaxial boundary formation are fundamental outcomes of plant development. Boundary and laminar growth coordinate the further patterning and growth of the leaf, directing the differentiation of cell types within the top and bottom domains and promoting initiation of lateral organs along their adaxial/abaxial axis. Leaf adaxial-abaxial polarity specification and laminar out-growth are regulated by two transcription factors, REVOLUTA (REV) and KANADI (KAN). ABA INSENSITIVE TO GROWTH 1 (ABIG1) is a HOMEODOMAIN-LEUCINE ZIPPER (HD-ZIP) Class II transcription factor and is a direct target of the adaxial-abaxial regulators REV and KAN. To investigate the role of ABIG1 in the leaf development and establishment of polarity, we examined the phenotypes of both gain-of-function and loss-of-function mutants. Through genetic interaction analysis with REV and KAN mutants, we have determined that ABIG1 plays a role in leaf laminar-growth as well as in adaxial-abaxial polarity establishment. Genetic and physical interaction assays showed that ABIG1 interacts with the transcriptional corepressor TOPLESS (TPL). This study provides new evidence that another HD-ZIP II gene, ABIG1, facilitates growth through the corepressor TPL.

HighlightABIG1, a HD-ZIP Class II transcription factor, promotes laminar growth and adaxial-abaxial polarity through the regulation of REV and KAN.


title: Parallel shifts of visual sensitivity and body colouration in replicate populations of extremophile fish.

abstract: Visual sensitivity and body pigmentation are often shaped by both natural selection from the environment and sexual selection from mate choice. One way of quantifying the impact of the environment is by measuring how traits have changed after colonization of a novel habitat. To do this, we studied Poecilia mexicana populations that have repeatedly adapted to extreme sulphidic (H2S containing) environments. We measured visual sensitivity using opsin gene expression, as well as body pigmentation and water transmission for populations in four independent drainages. Both visual sensitivity and body pigmentation showed significant parallel shifts towards greater medium wavelength sensitivity and reflectance in sulphidic populations. The light spectrum was only subtly different between environments and overall, we found no significant correlations between the light environment and visual sensitivity or body pigmentation. Altogether we found that sulphidic habitats select for differences in visual sensitivity and pigmentation; our data suggest that this effect is unlikely to be driven purely by the waters spectral properties and may instead be from other correlated ecological changes.


title: High heritabilities for antibiotic usage show potential to breed for disease resistance in finishing pigs

abstract:
The use of antimicrobials in animal production is under public debate, mainly due to the risk of transfer of resistance to pathogenic bacteria in humans. Therefore, measures have been taken during the last decades to reduce antibiotic usage in animals, for instance by national monitoring programmes and by improving animal health management. Although some initiatives exist in molecular genetic selection, quantitative genetic selection of animals towards decreased antibiotic usage is an underexplored area to reduce antibiotic usage. However, this strategy could yield cumulative effects.

In this study we derived new phenotypes from on-farm parenteral antibiotic records from commercially grown crossbred finishing pigs used in the progeny test of Pietrain terminal sires to investigate the heritability of antibiotics usage. Parenteral antibiotic records, production parameters and pedigree records of 2238 fullsib pens from two experimental farms in Belgium between 2014 and 2020 were analysed. Heritability estimates were moderate (18-44%) for phenotypes derived from all antibiotic treatments, and low (1-15%) for phenotypes derived from treatments against respiratory diseases only. Moreover, genetic correlations between these new phenotypes and mortality were low to moderate (0.08-0.60) and no strong adverse genetic correlations with production traits were found.

The high heritabilities and favourable genetic correlations suggest these new phenotypes derived from on-farm antibiotics records to be promising for inclusion in future pig breeding programs to breed for a decrease in antibiotics usage.


title: The chemical synthesis of knob domain antibody fragments

abstract: Cysteine-rich knob domains found in the ultralong complementarity determining regions of a subset of bovine antibodies, are capable of functioning autonomously as 3-6 kDa peptides. While they can be expressed recombinantly in cellular systems, in this paper we show that knob domains are also readily amenable to chemical synthesis, with a co-crystal structure of a chemically synthesised knob domain in complex with antigen showing structural equivalence to the biological product. For drug discovery, following immunisation of cattle, knob domain peptides can be synthesised directly from antibody sequence data, combining the power and diversity of the bovine immune repertoire with the ability to rapidly incorporate non-biological modifications. We demonstrate that, through rational design with non-natural amino acids, paratope diversity can be massively expanded, in this case improving the efficacy of an allosteric peptide. As a potential route to further improve stability, we also performed head-to-tail cyclisation, exploiting the unusual proximity of the N- and C-termini to synthesise functional, fully cyclic antibody fragments. Lastly, we highlight the stability of knob domains in plasma and, through pharmacokinetic studies, use palmitoylation as a route to extend the plasma half-life of knob domains in vivo. This study presents an antibody-derived medicinal chemistry platform, with protocols for solid-phase synthesis of knob domains; together with characterisation of their molecular structures, in vitro pharmacology and pharmacokinetics.


title: Enhancing the interoperability of glycan data flow between ChEBI, PubChem, and GlyGen.

abstract:
Glycans play a vital role in health, disease, bioenergy, biomaterials, and biotherapeutics. As a result, there is keen interest to identify and increase glycan data in bioinformatics databases like ChEBI and PubChem, and connecting them to resources at the EMBL-EBI and NCBI to facilitate access to important annotations at a global level. GlyTouCan is a comprehensive archival database that contains glycans obtained primarily through batch upload from glycan repositories, glycoprotein databases, and individual laboratories. In many instances, the glycan structures deposited in GlyTouCan may not be fully defined or have supporting experimental evidence and citations. Databases like ChEBI and PubChem were designed to accommodate complete atomistic structures with well-defined chemical linkages. As a result, they cannot easily accommodate the structural ambiguity inherent in glycan databases. Consequently, there is a need to improve the organization of glycan data coherently to enhance connectivity across the major NCBI, EMBL-EBI, and glycoscience databases.

This paper outlines a workflow developed in collaboration between GlyGen, ChEBI, and PubChem to improve the visibility and connectivity of glycan data across these resources. GlyGen hosts a subset of glycans (~29,000) from the GlyTouCan database and has submitted valuable glycan annotations to the PubChem database and integrated over 10,500 (including ambiguously defined) glycans into the ChEBI database. The integrated glycans were prioritized based on links to PubChem and connectivity to glycoprotein data. The pipeline provides a blueprint for how glycan data can be harmonized between different resources. The current PubChem, ChEBI, and GlyTouCan mappings can be downloaded from GlyGen (https://data.glygen.org).


title: splatPop: simulating population scale single-cell RNA sequencing data

abstract: With improving technology and decreasing costs, single-cell RNA sequencing (scRNA-seq) at the population scale has become more viable, opening up the doors to study functional genomics at the single-cell level. This development has lead to a rush to adapt bulk methods and develop new single-cell-specific methods and tools for computational analysis of these studies. Many single-cell methods have been tested, developed, and benchmarked using simulated data. However, current scRNA-seq simulation frameworks do not allow for the simulation of population-scale scRNA-seq data. Here, we present splatPop, a new Splatter model, for flexible, reproducible, and well documented simulation of population-scale scRNA-seq data with known expression quantitative trait loci (eQTL) effects. The splatPop model also allows for the simulation of complex batch effects, cell group effects, and conditional effects between individuals from different cohorts.