Biology subjects

Murray, D.

Publications and source records attributed to Murray, D..

17 recordsLinked to original sources

bioRxiv2026

Genomics reveals population structure despite high connectivity of common sole, Solea solea, and European plaice, Pleuronectes platessa, in the Celtic Sea and western English Channel.

Despite being two of the most commercially important flatfish (order Pleuronectiformes) in Europe, little is known of the population structure of common sole Solea solea and European plaice Pleuronectes platessa. To address this gap, we generated full-genome resequencing data for 244 sole and 189 plaice in the Celtic Sea and western English Channel region to analyse both neutral and adaptive loci and quantify population processes, such as reproductive isolation or adaptive differentiation in each species. For sole, there was no evidence of reproductive isolation or population structure at neutral loci. There was, however, adaptive differentiation as adaptive loci indicated two subpopulations, with separation in the western English Channel. This is consistent with previous studies using RAD-seq and gene-linked SNPs. For plaice, there was no evidence of population structure at either neutral or adaptive loci in the Celtic Seas and Western English Channel region. However, when considering a larger geographical area and utilising previously published genomic data, three distinct populations of plaice were identified (Iceland; North Sea, Kattegat and Western Baltic; Celtic Sea and western English Channel), with clear reproductive isolation indicated by neutral loci and adaptive differentiation indicated by adaptive loci. Moreover, three large chromosomal inversions were identified, which differed in their frequency between regions. These large structural variants represent putative key regions for adaptive differentiation. This study shows the benefit from quantifying neutral and adaptive loci to better understand population structure and genetic diversity of commercially important fish.

evolutionary biology↗

bioRxiv2025

Integrating structural homology with deep learning to achieve highly accurate protein-protein interface prediction for the human interactome

A significant portion of disease-causing mutations occur at protein-protein interfaces however, the number of structurally resolved multi-protein complexes is extremely small. Here we present a computational pipeline, PIONEER2, that integrates 3D structural similarity with geometric deep learning to accurately predict protein binding partner-specific interfacial residues. We compare the performance of PIONEER2 to that of AlphaFold3 and found, using a test set of PDB structures, that their performance is quite similar. However, about 20% of AlphaFold3 predictions for protein-protein complexes in the PDB have AlphaFold3 ranking scores below 0.5, which indicates an uncertain model. For these structures, PIONEER2 outperforms AlphaFold3 at discriminating interfacial from non-interfacial residues. Further, about half of the AlphaFold3 ranking scores on high confidence protein-protein interactions (PPIs) not associated with a PDB structure are below 0.5 indicating that PIONEER2 offers superior interface prediction for a large number of PPIs for which structures are not available. We created a comprehensive 3D structurally informed interactome encompassing all 352,124 experimentally detected binary human PPIs in the current literature and made PIONEER2 interface predictions for each. We experimentally validated these predictions by generating 1,866 mutations and testing their disruptive impact on 5,010 mutation-interaction pairs. PIONEER2-predicted interfaces are found to be comparable to PDB structures in their ability to predict disruptive mutations while AlphaFold3 performance is reduced. Similarly, PIONEER2-predicted interfaces outperform AlphaFold3 in accounting for the depletion of non-deleterious common population variants and the enrichment of disease-related mutations on protein surfaces. Overall, our results suggest that PIONEER2-predicted interfaces provide a valuable tool for studying disease etiology, advancing personalized medicine and for fundamental research. We further implemented PIONEER2 as a user-friendly web server (https://pioneer2.yulab.org) platform for users to explore our 3D interactome models and conduct genome-wide functional genomics studies.

bioinformatics↗

bioRxiv2025

Combining structural modeling and deep learning to calculate the E. coli protein interactome and functional networks

We report on the integration of three methods that are computationally efficient enough to predict, on a proteome-wide scale, whether two proteins are likely to form a binary complex. The methods include PrePPI, which uses three-dimensional structure information as a basis for predictions, Topsy-Turvy which analyzes sequences using a protein language model, and ZEPPI which uses evolutionary information to evaluate protein-protein interfaces. We demonstrate how these methods can be integrated and validate the performance of the integrated method and its separate components at predicting E. coli protein-protein interactions through testing on the HINT high-quality literature-curated database of binary interactions. The integrated method identifies more high-confidence (FPR [≤] 0.001) interactions ([~]20K) than any of the component methods. The AF3Complex algorithm was used to predict the structures of 400 protein-protein interactions, and 78% of the integrated method predictions resulted in models deemed accurate by the AF3Complex evaluation score. Notably, essentially all AF3Complex models have at least partially overlapping interfaces with PrePPI models of the complexes. Finally, we clustered the high-confidence E. coli interactome and obtained 385 subnetworks which have high functional coherence defined by enrichment of Gene Ontology Biological Process terms, thus, illustrating that our methods which contain no explicit functional information provide biologically meaningful protein interactions. Biological insights derived from the subnetworks, including the annotation of proteins of unknown function, are discussed in detail. Overall, independent validations support the accuracy of the comprehensive E. coli interactome we have presented.

systems biology↗

bioRxiv2025

EEG Responses to Exercise Intensity in Parkinson's Disease

1.1.1. BackgroundExercise is increasingly recognized as a beneficial intervention for Parkinsons disease (PD), yet the optimal type and intensity of exercise remain unclear. This study investigated the relationship between exercise intensity and neural responses in PD patients, using electroencephalography (EEG) to explore potential neural markers that could be ultimately used to guide exercise intensity. 1.2. MethodEEG data were collected from 14 PD patients (5 females) and 8 healthy controls (HC) performing stationary pedaling exercises at 60 RPM with resistance adjusted to target heart rates of 30%, 40%, 50%, 60%, and 70% of maximum heart rate. Subjects pedaled for 3 minutes at each intensity level in a counterbalanced order. Canonical Time-series Characteristics (Catch-22) features and Multi-set Canonical Correlation Analysis (MCCA) were utilized to identify common profiles of EEG features at increasing exercise intensity across subjects. 1.3. ResultsWe identified a statistically significant MCCA component demonstrating a monotonic relationship with pedaling intensity. The dominant feature in this component was Periodicity Wang (PW), related to the autocorrelation of the EEG. Analysis revealed a consistent trend across features: six features increased with intensity, indicating heightened rhythmic engagement and sustained neural activation, while three features decreased, suggesting reduced variability and enhanced predictability in neural responses. Notably, PD patients exhibited more rigid, consistent response patterns compared to healthy controls (HC), who showed greater flexibility and variability in their neural adaptation across intensities. 1.4. ConclusionThis study highlights the feasibility of using EEG-derived features to track exercise intensity in PD patients, identifying specific neural markers correlating with varying intensity levels. PD subjects demonstrate less inter-subject variability in motor responses to increasing intensity. Our results suggest that EEG biomarkers can be used to assess differing brain involvement with the same exercise of increasing intensity, potentially useful for guiding targeted therapeutic strategies and maximizing the neurological benefits of exercise in PD.

neuroscience↗

bioRxiv2024

Electrostatic changes enabled the diversification of an exocyst subunit via protein complex escape

The evolution of cellular complexity hinges on the capacity of multimeric protein complexes to diversify without compromising their ancestral functions. A central question is how individual subunits within these complexes can evolve novel functions while maintaining the integrity of the original assembly. Here, we explore this question by tracing the evolutionary trajectory of the plant exocyst, an octameric complex essential for exocytosis in eukaryotes. Remarkably, the Exo70 subunit underwent dramatic expansion and functional divergence in plants. We demonstrate that electrostatic alterations in the N-terminal region of the Exo70 subunit precipitated its dissociation from the exocyst complex. This release mitigated paralog interference, thereby facilitating the subunits extensive functional co-option. Our findings reveal a nuanced mechanism by which a protein subunit, ancestrally constrained within a multimeric complex, can escape those constraints and evolve novel functions, shedding light on the molecular underpinnings of cellular innovation. One-Sentence SummaryEvolutionary diversification of an exocyst subunit is driven by electrostatic shifts that dissociates it from the ancestral complex.

evolutionary biology↗

bioRxiv2023

Paternal starvation affects metabolic gene expression during zebrafish offspring development and life-long fitness

Dietary restriction is a putative key to a healthier and longer life, but these benefits may come at a trade-off with reproductive fitness and may affect the following generation(s). The potential inter- and transgenerational effects of starvation are particularly poorly understood in vertebrates when they originate from the paternal line. We utilised the externally fertilising zebrafish amenable to a split-egg clutch design to explore the male-specific effects of starvation on fertility and fitness of offspring independently of maternal contribution. Eighteen days of fasting resulted in reduced fertility in exposed males. While average offspring survival was not affected, we detected higher larval growth in offspring from starved males and increased malformation rates at 24 hours post fertilisation in the F2 embryos produced by the offspring of the starved males. The transcriptome analysis of embryos from starved and fed fathers revealed robust and reproducible induction of muscle composition genes and a contrasting repressive effect on lipid metabolism and lysosome genes. A large proportion of these genes showed enrichment in the yolk syncytial layer suggesting gene regulatory responses associated with metabolism of nutrients through paternal impact on extra embryonic tissues which are loaded with maternally deposited factors. We compared the embryo transcriptome to adult transcriptome datasets and demonstrated comparable repressive effects on metabolism-associated genes. These similarities suggest a physiologically relevant, directed and potentially adaptive response transmitted by the father, independently from the offsprings nutritional state, which was defined by the mother.

developmental biology↗

bioRxiv2023

ZIPPI: proteome-scale sequence-based evaluation of protein-protein interaction models

Predicting protein-protein interactions (PPI) is a challenging problem of central importance in fundamental biology. With the increasing number of available PPI prediction methods and databases, an effective evaluation model would be extremely valuable. Here we introduce ZIPPI (Z-score for Information about Protein-Protein Interfaces), which evaluates structural models of a complex based on sequence co-evolution and conservation involving residues that are in contact in the interface. The interface Z-score (ZIPPI score) is calculated by comparing metrics for interface contacts to metrics obtained from randomly chosen surface residues. Since contacting residues are defined by the structural model, this obviates the need of accounting for indirect interactions with methods such as Direct Coupling Analysis. Although ZIPPI relies on species-paired multiple sequence alignments, its focus on contacting interfacial residues and the avoidance of direct coupling methods makes it computationally efficient. The performance of ZIPPI is evaluated through applications to experimentally determined complexes from the Protein Data Bank (PDB) and to decoys from the Critical Assessment of PRedicted Interactions (CAPRI) experiment. We demonstrate how ZIPPI can be implemented on a genome-wide scale by calculating scores for millions of structural models of protein-protein interactions in the E. coli interactome as predicted by PrePPI. Many PrePPI predictions filtered by ZIPPI score are novel. In all, this proteome-scale method shows promising feasibility for applications to the full human protein interactome, which is not yet accessible to deep learning methods.

systems biology↗

bioRxiv2023

PrePPI: A structure informed proteome-wide database of protein-protein interactions

We present an updated version of the Predicting Protein-Protein Interactions (PrePPI) webserver which predicts PPIs on a proteome-wide scale. PrePPI combines structural and non-structural clues within a Bayesian framework to compute a likelihood ratio (LR) for essentially every possible pair of proteins in a proteome; the current database is for the human interactome. The structural modeling (SM) clue is derived from templatebased modeling and its application on a proteome-wide scale is enabled by a unique scoring function used to evaluate a putative complex. The updated version of PrePPI leverages AlphaFold structures that are parsed into individual domains. As has been demonstrated in earlier applications, PrePPI performs extremely well as measured by receiver operating characteristic curves derived from testing on E. coli and human protein-protein interaction (PPI) databases. A PrePPI database of ~1.3 million human PPIs can be queried with a webserver application that comprises multiple functionalities for examining query proteins, template complexes, 3D models for predicted complexes, and related features (https://honiglab.c2b2.columbia.edu/PrePPI). PrePPI is a state-of- the-art resource that offers an unprecedented structure-informed view of the human interactome. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=68 SRC="FIGDIR/small/530276v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@b66c15org.highwire.dtl.DTLVardef@71d817org.highwire.dtl.DTLVardef@21d984org.highwire.dtl.DTLVardef@4f6f55_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2023

Network-based elucidation of colon cancer drug resistance by phosphoproteomic time-series analysis

Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. By leveraging progress in proteomic technologies and network-based methodologies, over the past decade, we developed VESPA--an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations--and used it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogation of tumor-specific enzyme/substrate interactions accurately inferred kinase and phosphatase activity, based on their inferred substrate phosphorylation state, effectively accounting for signal cross-talk and sparse phosphoproteome coverage. The analysis elucidated time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring that was experimentally confirmed by CRISPRko assays, suggesting broad applicability to cancer and other diseases.

systems biology↗

bioRxiv2022

Amoeba Predation of Cryptococcus: A Quantitative and Population Genomic Evaluation of the Accidental Pathogen Hypothesis

The "Amoeboid Predator-Fungal Animal Virulence Hypothesis" posits that interactions with environmental phagocytes shape the evolution of virulence traits in fungal pathogens. In this hypothesis, selection to avoid predation by amoeba inadvertently selects for traits that contribute to fungal escape from phagocytic immune cells. Here, we investigate this hypothesis in the human fungal pathogens Cryptococcus neoformans and Cryptococcus deneoformans. Applying quantitative trait locus (QTL) mapping and comparative genomics, we discovered a cross-species QTL region that is responsible for variation in resistance to amoeba predation. In C. neoformans, this same QTL was found to have pleiotropic effects on melanization, an established virulence factor. Through fine mapping and population genomic comparisons, we identified the gene encoding the transcription factor Bzp4 that underlies this pleiotropic QTL and we show that decreased expression of this gene reduces melanization and increases susceptibility to amoeba predation. Despite the joint effects of BZP4 on amoeba resistance and melanin production, we find no relationship between BZP4 genotype and escape from macrophages or virulence in murine models of disease. Our findings provide new perspectives on how microbial ecology shapes the genetic architecture of fungal virulence, and suggests the need for more nuanced models for the evolution of pathogenesis that account for the complexities of both microbe-microbe and microbe-host interactions. Author summaryA prominent hypothesis for the evolution of many environmental pathogens proposes that opportunistic pathogenesis is an "accidental" by-product of selection to survive encounters with microbial predators. Chief among the predators that have been suggested as relevant to the evolution of virulence are phagocytic amoebae. Amoebae share many characteristics with macrophages and other primary immune cells that microbial pathogens encounter during infection of animal hosts. This has led to the suggestion that amoebae may act as "training grounds" for both bacterial and fungal pathogens. In this study we test key tenets of the accidental pathogen hypothesis by examining two related questions: "Do alleles important for survival in the face of amoeba predation correspond to known virulence genes? And does genetic variation that increases resistance to amoeba predation increase virulence potential?" We carried out quantitative trait locus (QTL) mapping in two species of the human fungal pathogen Cryptococcus and identified an orthologous QTL, shared by the two species, where allelic variation is a key predictor of resistance to amoeba predation. In C. neoformans we show that this QTL corresponds to a deletion upstream of a transcription factor gene, BZP4. Variation at BZP4 also predicts melanin synthesis, another trait implicated in Cryptococcus virulence. Although BZP4 genotype is a strong predictor of resistance to amoeba predation, we find no correlation between genetic variation at this locus and the ability to proliferate in macrophages or to kill animal hosts. Our findings suggest that the evolutionary landscape of fungal virulence is complex, and highlights the importance of accounting for natural genetic variation when evaluating evolutionary hypotheses.

genomics↗

bioRxiv2022

PrePCI: A structure- and chemical similarity-informed database of predicted protein compound interactions

We describe the Predicting Protein Compound Interactions (PrePCI) database which comprises over 5 billion predicted interactions between nearly 7 million chemical compounds and 19,797 human proteins. PrePCI relies on a proteome-wide database of structural models based on both traditional modeling techniques and the AlphaFold Protein Structure Database. Sequence and structural similarity-based metrics are established between template proteins in the Protein Data Bank, T, that bind small molecules, C, and proteins in the models database, Q. When these metrics pass a sequence threshold value, it is assumed that C also binds to Q with a probability derived from machine learning. If the relationship is based on structure, this probability is based on a scoring function that measures the extent to which C is compatible with the binding site of Q as described in the LT-scanner algorithm. For every predicted complex derived in this way, chemical similarity based on the Tanimoto Coefficient identifies other small molecules that may bind to Q. A likelihood ratio for the binding of C to Q is obtained from naive Bayesian statistics. The PrePCI algorithm performs well under different validations. It can be queried by entering a UniProt ID for a protein and obtaining a list of compounds predicted to bind to it along with associated probabilities. Alternatively, entering an identifier for the compound outputs a list of proteins it is predicted to bind. Specific applications of the database are described and a strategy is introduced to use PrePCI as a first step in a docking screen.

systems biology↗

bioRxiv2022

Network-based multi-omics integration reveals metabolic risk profile within treated HIV-infection

Multiomics technologies improve the biological understanding of health status in people living with HIV on antiretroviral therapy (PLWHART). Still, a systematic and in-depth characterization of metabolic risk profile during successful long-term treatment is lacking. Here, we used multi-omics (plasma lipidomic and metabolomic, and fecal 16s microbiome) data-driven stratification and characterization to identify the metabolic at-risk profile within PLWHART. Through network analysis and similarity network fusion (SNF), we identified three groups of PLWHART (SNF-1 to 3). The PLWHART at SNF-2 (45%) was a severe at-risk metabolic profile with increased visceral adipose tissue, BMI, higher incidence of metabolic syndrome (MetS), and increased di- and triglycerides despite having higher CD4+ T-cell counts than the other two clusters. However, the healthy-like and severe at-risk group had a similar metabolic profile differing from HC, with dysregulation of amino acid metabolism. At the microbiome profile, the healthy-like group had a lower -diversity, a lower proportion of MSM, and was enriched in Bacteroides. In contrast, in at-risk groups, there was an increase in Prevotella, with a high proportion of men who have sex with men (MSM) confirming the influence of sexual orientation on the microbiome profile The multi-omics integrative analysis reveals a complex microbial interplay by microbiome-derived metabolites in PLWHART. PLWHART those are severely at-risk clusters may benefit from personalized medicine and lifestyle intervention to improve their metabolic profile. SignificanceThe network and factorization-based integrative analysis of plasma metabolomics, lipidomics, and microbiome profile identified three different diseases state -omics phenotypes within PLWHART driven by metabolomics, lipidomics, and microbiome that a single omics or clinical feature could not explain. The severe at-risk group has a dysregulated metabolic profile that potentiates metabolic diseases that could be barriers to healthy aging. The at-risk group may benefit from personalized medicine and lifestyle intervention to improve their metabolic profile.

bioinformatics↗

bioRxiv2020

Pleiotropy and Epistasis Within and Between Signaling Pathways Defines the Genetic Architecture of Fungal Virulence

Cryptococcal disease is estimated to affect nearly a quarter of a million people annually. Environmental isolates of Cryptococcus deneoformans, which make up 15 to 30% of clinical infections in temperate climates such as Europe, vary in their pathogenicity, ranging from benign to hyper-virulent. Key traits that contribute to virulence, such as the production of the pigment melanin, an extracellular polysaccharide capsule, and the ability to grow at human body temperature have been identified, yet little is known about the genetic basis of variation in such traits. Here we investigate the genetic basis of melanization, capsule size, thermal tolerance, oxidative stress resistance, and antifungal drug sensitivity using quantitative trait locus (QTL) mapping in progeny derived from a cross between two divergent C. deneoformans strains. Using a "function-valued" QTL analysis framework that exploits both time-series information and growth differences across multiple environments, we identified QTL for each of these virulence traits and drug susceptibility. For three QTL we identified the underlying genes and nucleotide differences that govern variation in virulence traits. One of these genes, RIC8, which encodes a regulator of cAMP-PKA signaling, contributes to variation in four virulence traits: melanization, capsule size, thermal tolerance, and resistance to oxidative stress. Two major effect QTL for amphotericin B resistance map to the genes SSK1 and SSK2, which encode key components of the HOG pathway, a fungal-specific signal transduction network that orchestrates cellular responses to osmotic and other stresses. We also discovered complex epistatic interactions within and between genes in the HOG and cAMP-PKA pathways that regulate antifungal drug resistance and resistance to oxidative stress. Our findings advance the understanding of virulence traits among diverse lineages of Cryptococcus, and highlight the role of genetic variation in key stress-responsive signaling pathways as a major contributor to phenotypic variation. Author summaryDifferent environmental isolates (strains) of the same microbial species can vary greatly in their ability to cause disease, ranging from avirulent to hypervirulent. What makes some strains deadly pathogens, while others are relatively benign? This study describes the characterization of key genetic differences that underlie variation in traits thought to promote virulence in Cryptococcus deneoformans, a wide-spread opportunistic fungal pathogen. Using a combination of quantitative genetic and molecular genetic approaches we dissected the genetic architecture of virulence-related cellular traits (melanin production and the production of a polysaccharide capsule), physiological responses to stress (tolerance of thermal, oxidative, and osmotic stress), and sensitivity to multiple antifungal drugs. Strikingly we find that variation in most of these traits is governed by a small number of genetic differences that modify the function of two major cell signaling networks, cyclic AMP-Protein Kinase A (cAMP-PKA) signaling and a fungal specific MAP-kinase cascade called the high osmolarity glycerol (HOG) pathway. Similar to recent studies in a number of other fungal species, our findings point to an outsize role for a small number of highly pleiotropic signaling pathways in potentiating phenotypic variation both within and between fungal species.

genetics↗

bioRxiv2020

Transiently structured head domains control intermediate filament assembly

Low complexity (LC) head domains 92 and 108 residues in length are, respectfully, required for assembly of neurofilament light (NFL) and desmin intermediate filaments (IFs). As studied in isolation, these IF head domains interconvert between states of conformational disorder and labile, {beta}-strand-enriched polymers. Solid state nuclear magnetic resonance (ss-NMR) spectroscopic studies of NFL and desmin head domain polymers reveal spectral patterns consistent with structural order. A combination of intein chemistry and segmental isotope labeling allowed preparation of fully assembled NFL and desmin IFs that could also be studied by ss-NMR. Assembled IFs revealed spectra overlapping with those observed for {beta}-strand-enriched polymers formed from the isolated NFL and desmin head domains. Phosphorylation and disease causing mutations reciprocally alter NFL and desmin head domain self-association, yet commonly impede IF assembly. These observations show how facultative structural assembly of LC domains via labile, {beta}-strand-enriched self-interactions may broadly influence cell morphology.

biochemistry↗

bioRxiv2018

Gender and international diversity improves equity in peer review

The fairness of scholarly peer review has been challenged by evidence of disparities in publication outcomes based on author demographic characteristics. To assess this, we conducted an exploratory analysis of peer review outcomes of 23,876 initial submissions and 7,192 full submissions that were submitted to the biosciences journal eLife between 2012 and 2017. Women and authors from nations outside of North America and Europe were underrepresented both as gatekeepers (editors and peer reviewers) and authors. We found evidence of a homophilic relationship between the demographics of the gatekeepers and authors and the outcome of peer review; that is, there were higher rates of acceptance in the case of gender and country homophily. The acceptance rate for manuscripts with male last authors was seven percent, or 3.5 percentage points, greater than for female last authors (95% CI = [0.5, 6.4]); this gender inequity was greatest, at nine percent or about 4.8 percentage points (95% CI = [0.3, 9.1]), when the team of reviewers was all male; this difference was smaller and not significantly different for mixed-gender reviewer teams. Homogeny between countries of the gatekeeper and the corresponding author was also associated with higher acceptance rates for many countries. To test for the persistence of these effects after controlling for potentially confounding variables, we conducted a logistic regression including document and author metadata. Disparities in acceptance rates associated with gender and country of affiliation and the homophilic associations remained. We conclude with a discussion of mechanisms that could contribute to this effect, directions for future research, and policy implications. Code and anonymized data have been made available at https://github.com/murrayds/elife-analysis\n\nAuthor summaryPeer review, the primary method by which scientific work is evaluated, is ideally a fair and equitable process in which scientific work is judged solely on its own merit. However, the integrity of peer review has been called into question based on evidence that outcomes often differ between male and female authors, and for authors in different countries. We investigated such disparities at the biosciences journal eLife by analyzing the demographics of authors and gatekeepers (editors and peer reviewers), and peer review outcomes of all submissions between 2012 and 2017. Outcomes were more favorable for male authors and those affiliated with institutions in North America and Europe; these groups were also over-represented among gatekeepers. There was evidence that peer review outcomes were influenced by homophily --a preference of gatekeepers for manuscripts from authors with shared characteristics. We discuss mechanisms that could contribute to this effect, directions for future research, and policy implications.

scientific communication and education↗

bioRxiv2018

Systematic Elucidation and Validation of OncoProtein-Centric Molecular Interaction Maps

The largely incomplete and tissue-independent nature of cancer pathways represents a key limitation to the ability to elucidate mechanistic determinants of cancer phenotypes and to predict adaptive response to targeted therapy. To address these challenges, we propose replacing canonical cancer pathways with a more accurate, comprehensive, and context-specific architecture - dubbed a Protein-Centric molecular interaction Map (PC-Map) - representing modulators, effectors, and cognate binding-partners of any oncoprotein of interest. To reconstruct these complex molecular architectures de novo, we introduce a novel OncoSig algorithm. Validation of a lung adenocarcinoma specific (LUAD) KRAS-centric PC-Map recapitulated known KRAS biology and, more critically, identified a novel repertoire of proteins eliciting synthetic lethality in KRASG12D LUAD organoid cultures. Showing the generalizable nature of the algorithm, we elucidated PC-Maps for ten recurrently mutated oncoproteins, including KRAS, in distinct tumor contexts. This revealed a highly context-specific nature of cancers regulatory and signaling architectures to an unprecedented degree of resolution.

systems biology↗

bioRxiv2016

THE IDENTIFICATION OF SOURCE AND VECTOR OF A PROLIFIC MARINE INVADER

The source and vector of an introduced species inform its ecological and evolutionary history and may guide management that seeks to prevent future introductions. Surprisingly, few studies have successfully used genetic tools to independently inform the specific source and pathway of biological invasions. The ecological history of many introduced species, including their origins and vectors, is often based on suppositions or educated guesses. Here, we used mitochondrial and microsatellite genotyping to trace the invasion of the Asian seaweed Gracilaria vermiculophylla (Rhodophyta) along the three coastlines of the Northern Hemisphere to which it has been introduced: the western coast of North America, eastern coast of the United States and the coasts of Europe and northwest Africa. Analyzing 37 native and 53 introduced sites, we identified the Pacific coastline of northeastern Japan as the ultimate source of the Northern Hemisphere invasion. Coincidentally, most exports of the oyster Crassostrea gigas historically originated from this region and both species often grow in close proximity. Based on genetic signatures, each of the three coastlines likely received thalli directly from Japan, as well as material from another introduced coastline (i.e., a secondary invasion). Our ability to document a source region, which was enabled by a robust sampling of locations and loci that previous studies lacked, reflected strong phylogeographic structure along native coastlines. We suggest Gracilaria vermiculophylla is an important representative example of many species likely exported out of Japan by the oyster trade and its genetic signatures that may be a hallmark of oyster introduction legacies.

evolutionary biology↗