Biology subjects

Martin, M. J.

Publications and source records attributed to Martin, M. J..

18 recordsLinked to original sources

bioRxiv2026

Early terminated transcripts and missing proteins reflect artifacts in bacterial proteomes

The high redundancy of many bacterial proteomes can be used to evaluate proteome quality and identify sequence errors. We have used MMseqs2 clustering with subsequent filtering to identify clusters that contain sequences from at least 50% of the clustered proteomes to build sets of core proteins that include proteins from 95% of the clustered bacteria. These clusters typically capture more than 80% of proteins in the bacteria. Because these clusters have highly uniform length (the median cluster has more than 99% of its proteins at the mode length), short (<75% of mode length) or long (>133%) proteins are likely artifacts. Most "outlier" proteins are found in fewer than 10% of clusters, and "high-outlier" clusters are over-represented in a small fraction of proteomes, which often have poor proteome BUSCO fragment scores. Short-outlier proteins are artifacts; at least 80% of short-outlier genomes contain mode-length copies of the protein, which were missed because of frame-shifts, termination codons, or initiation codon choice. MMseqs2 clustering with 50% participation provides robust sets of core bacterial proteins and can be used to identify lower-quality proteomes and proteins.

bioinformatics↗

bioRxiv2026

Advances in protein function prediction from the fifth CAFA challenge

The Critical Assessment of Functional Annotation (CAFA) is a long-standing community effort to independently assess computational methods for protein function prediction, to highlight well-performing methodologies, to identify bottlenecks in the field, and to provide a forum for the dissemination of results and exchange of ideas. In its fifth round (CAFA5) of triennial challenges, a partnership with Kaggle Inc. facilitated participation from a large community of data scientists and computational biologists through a competitive prospective challenge on the crowdsourcing platform. In this work, we present an in-depth analysis of the submitted predictions and report improvements in accuracy over all methods from the previous CAFA challenges. We further introduce a new evaluation setting for proteins with pre-existing (incomplete) annotations and identify the need for methods that better leverage existing annotations to predict those that will be discovered later. Finally, we characterize the prospective evaluation framework by examining performance on a strict set of unpublished annotations and across intermediate database releases. Our results indicate that recent developments in the field, such as the availability of protein language models and accurately predicted 3D structures, as well as the growth of experimental annotations through biocuration, have all contributed to performance improvements. 1

bioinformatics↗

bioRxiv2025

Cumulative cgMLST provides increased discrimination of nested phylogenetic groups

BackgroundCore genome multilocus sequence typing (cgMLST) is a powerful method for bacterial strain genotyping. However, the size of the core genome decreases as the phylogenetic breadth of the target group increases, reducing discriminatory power. To overcome this discrimination/applicability tradeoff, here we developed a cumulative cgMLST approach, where sets of core loci conserved within nested phylogenetic entities are added. We illustrate this approach using the Klebsiella pneumoniae species complex (KpSC), for which a widely used cgMLST scheme (KpSC-cgMLST) comprises only 629 genes. MethodsWe created non-redundant cgMLST schemes for the individual species K. pneumoniae sensu stricto (Kpn-cgMLST scheme), and its multidrug resistant sublineages (SLs) SL147 and SL307. To extract core genes, we used 37,874 genome assemblies originating from over 80 countries worldwide. A methodology was set to filter redundant loci before importing them into the genotyping tool BIGSdb, where they were combined into schemes together with preexisting loci conserved at higher phylogenetic levels. The performance of the cumulative cgMLST schemes was evaluated on previously published datasets and on novel data from an inter-hospital outbreak of SL307. ResultsThe Kpn-cgMLST, SL147 and SL307 schemes comprise 2752, 852, and 947 additional loci, respectively. The mean allele call rate of the novel loci was >99% in the validation datasets. Compared to the KpSC scheme used alone, pairwise allelic distances among isolates increased on average 5.6-fold using the Kpn scheme, and further by 20% and 30% using the SL147 and SL307 schemes, respectively. We demonstrate the added value of this increased discriminatory power for epidemiological analyses and show nearly equal discrimination when compared to whole-genome single nucleotide polymorphisms analysis. ConclusionsThe cumulative cgMLST strategy combines broad phylogenetic applicability and nearly complete genotyping resolution, expanding the utility of this harmonized approach for genomic epidemiology.

microbiology↗

bioRxiv2025

Pathogenicity of Purpureocillium lilacinum and Clonostachys rosea Against Fall Armyworm (Spodoptera frugiperda) Under Laboratory Conditions

BackgroundFall armyworm, Spodoptera frugiperda (J.E. Smith) threatens staple crops across Africa. Integrating entomopathogenic fungi into Integrated Pest Management (IPM) offers a sustainable alternative to sole reliance on insecticides. This study quantified the pathogenicity of Purpureocillium lilacinum and Clonostachys rosea against S. frugiperda under controlled conditions. MethodsSecond-fifth instar larvae and eggs were exposed to 1x107, 1x108, and 1x109 conidia mL-1 of each fungus; sterile water served as control. Mortality was recorded over 3-9 days after treatment (DAT); feeding reduction was measured gravimetrically. Larval mortality was analyzed with GLMs/GLMMs (binomial-probit); feeding reduction by ANOVA/Tukey; LD50 and LT50 were estimated from dose-response models. ResultsMortality increased with dose and time, and decreased with larval stage. Peak larval mortality reached [~]44% for P. lilacinum and [~]49% for C. rosea at 1x109 conidia mL-1 by 9 DAT. Egg mortality was concentration--dependent (up to 82% and 88% for P. lilacinum and C. rosea, respectively, at 1x109 conidia mL-1). Feeding reduction reached 60-74% in early instars at the highest dose. Early instars were consistently more susceptible than late instars. Model outputs indicated significant effects of concentration and time; interaction concentrationxtime was significant, whereas fungal speciesxconcentration was not. Our findings supported the initial hypothesis. Mortality and feeding reduction increased with higher fungal concentrations and longer exposure times, and younger instars were consistently more susceptible than older instars. While both P. lilacinum and C. rosea were pathogenic, differences between species were minor compared to the effects of dose and instar stage. These outcomes validated the formulated hypotheses, and reinforce the potential of these native fungi as candidates for IPM. ConclusionsNative P. lilacinum and C. rosea display dose--, stage--, and time--dependent pathogenicity and feeding suppression against S. frugiperda. These species are promising candidates for IPM; field validation and formulation optimization are the next steps.

ecology↗

bioRxiv2024

High-quality peptide evidence for annotating non-canonical open reading frames as human proteins

A major scientific drive is to characterize the protein-coding genome as it provides the primary basis for the study of human health. But the fundamental question remains: what has been missed in prior genomic analyses? Over the past decade, the translation of non-canonical open reading frames (ncORFs) has been observed across human cell types and disease states, with major implications for proteomics, genomics, and clinical science. However, the impact of ncORFs has been limited by the absence of a large-scale understanding of their contribution to the human proteome. Here, we report the collaborative efforts of stakeholders in proteomics, immunopeptidomics, Ribo-seq ORF discovery, and gene annotation, to produce a consensus landscape of protein-level evidence for ncORFs. We show that at least 25% of a set of 7,264 ncORFs give rise to translated gene products, yielding over 3,000 peptides in a pan-proteome analysis encompassing 3.8 billion mass spectra from 95,520 experiments. With these data, we developed an annotation framework for ncORFs and created public tools for researchers through GENCODE and PeptideAtlas. This work will provide a platform to advance ncORF-derived proteins in biomedical discovery and, beyond humans, diverse animals and plants where ncORFs are similarly observed.

molecular biology↗

bioRxiv2024

Imaging of MAP kinase dynamics reveals endocytic regulation of pulsatile signalling and network re-wiring in response to targeted therapy in EGFR-mutant non-small cell lung cancer

A better understanding of the signalling mechanisms underlying transitions from drug-sensitive to drug-tolerant states is required to overcome therapy failure. We combined single-cell biosensor imaging with functional perturbations to investigate the regulation of oncogenic signalling in EGFR-mutant lung adenocarcinoma. We find that despite the constant presence of the mutant oncogene, ERK signalling exhibits pulsatile dynamics, with pulse characteristics determined by the endocytic machinery. Analysis of drug-tolerant persisters (DTPs) revealed that, after an initial phase of complete pathway shut-down, signalling was rewired leading to renewed ERK pulses that drive cell cycle progression. FAK- and SRC-regulated adhesion complexes replace mutant EGFR as the driver of reactivated ERK pulses in DTPs, yet they remain controlled by the membrane trafficking machinery. We show that DTPs rely on additional survival pathways including YAP signalling, and that the phosphatase PTPRS represents a key node in therapy resistant cells, coordinating regulation of ERK, the cytoskeleton, and YAP.

cancer biology↗

bioRxiv2024

Improved selection of canonical proteins for reference proteomes

The "canonical" protein sets distributed by UniProt are widely used for similarity searching, and functional and structural annotation. For many investigators, canonical sequences are the only version of a protein examined. However, higher eukaryotes often encode multiple isoforms of a protein from a single gene. For unreviewed (UniProtKB/TrEMBL) protein sequences, the longest sequence in a Gene-Centric group is chosen as canonical. This choice can create inconsistencies, selecting >95% identical orthologs with dramatically different lengths, which is biologically unlikely. We describe the ortho2tree pipeline, which examines Reference Proteome canonical and isoform sequences from sets of orthologous proteins, builds multiple alignments, constructs gap-distance trees, and identifies low-cost clades of isoforms with similar lengths. After examining 140,000 proteins from eight mammals in UniProtKB release 2022 05, ortho2tree proposed 7,804 canonical changes for release 2023 01, while confirming 53,434 canonicals. Gap distributions for isoforms selected by ortho2tree are similar to those in bacterial and yeast alignments, organisms unaffected by isoform selection, suggesting ortho2tree canonicals more accurately reflect genuine biological variation. 82% of ortho2tree proposed-changes agreed with MANE; for confirmed canonicals, 92% agreed with MANE. Ortho2tree can improve canonical assignment among orthologous sequences that are more than 60% identical, a group that includes vertebrates and higher plants.

bioinformatics↗

bioRxiv2023

Evolution and spread of a persistent P. aeruginosa outbreak clone over decades in a single US hospital.

Whole-genome sequencing is revolutionizing bacterial outbreak investigation but its application to the clinic remains limited. In 2020, prospective and retrospective surveillance detected a Pseudomonas aeruginosa outbreak with 253 isolates collected from 82 patients in 26 wards of a hospital. Its origin was dated to the late 90s, just after the facility opened, and patient-to-patient and environment-to-patient cases of transmission were inferred. Over time, two epidemic subclones evolved in separate hosts and hospital areas, including newly opened wards, and hospital-wide sampling confirmed reservoirs persisted in the plumbing. Pathoadaptive mutations in genes associated with virulence, cell wall biogenesis, and antibiotic resistance were identified. While the latter correlated with the acquisition of phenotypic resistances to 1st (cephalosporin), 2nd (carbapenems) and 3rd (colistin) lines of treatment, maximum parsimony suggested that a truncation in a lipopolysaccharide component coincided with the emergence of a subclone prevalent in long-term infections. Since initial identification, extensive infection control efforts guided by routine, near real-time surveillance have proved successful at slowing transmission.

microbiology↗

bioRxiv2023

Differentiation of hypervirulent and classical Klebsiella pneumoniae with acquired drug resistance

Distinguishing hypervirulent (hvKp) from classical Klebsiella pneumoniae (cKp) strains is important for clinical care, surveillance, and research. Some combination of iucA, iroB, peg-344, rmpA, and rmpA2 are most commonly used, but it is unclear what combination of genotypic or phenotypic markers (e.g. siderophore concentration, mucoviscosity) most accurately predicts the hypervirulent phenotype. Further, acquisition of antimicrobial resistance may affect virulence and confound identification. Therefore, 49 K. pneumoniae strains that possessed some combination of iucA, iroB, peg-344, rmpA, and rmpA2 and had acquired resistance were assembled and categorized as hypervirulent hvKp (hvKp) (N=16) or cKp (N=33) via a murine infection model. Biomarker number, siderophore production, mucoviscosity, virulence plasmids Mash/Jaccard distances to the canonical pLVPK, and Kleborate virulence score were measured and evaluated to accurately differentiate these pathotypes. Both stepwise logistic regression and a CART model were used to determine which variable was most predictive of the strain cohorts. The biomarker count alone was the strongest predictor for both analyses. For logistic regression the area under the curve for biomarker count was 0.962 (P = 0.004). The CART model generated the classification rule that a biomarker count = 5 would classify the strain as hvKP, resulting in a sensitivity for predicting hvKP of 94% (15/16), a specificity of 94% (31/33), and an overall accuracy of 94% (46/49). Although a count of [≥] 4 was 100% (16/16) sensitive for predicting hvKP, the specificity and accuracy decreased to 76% (25/33) and 84% (41/49) respectively. These findings can be used to inform the identification of hvKp. ImportanceHypervirulent Klebsiella pneumoniae (hvKp) is a concerning pathogen that can cause life-threatening infections in otherwise healthy individuals. Importantly, although strains of hvKp have been acquiring antimicrobial resistance, the effect on virulence is unclear. Therefore, it is of critical importance to determine whether a given antimicrobial resistant K. pneumoniae isolate is hypervirulent. This report determined which combination of genotypic and phenotypic markers could most accurately identify hvKp strains with acquired resistance. Both logistic regression and a machine-learning prediction model demonstrated that biomarker count alone was the strongest predictor. The presence of all 5 of the biomarkers iucA, iroB, peg-344, rmpA, and rmpA2 was most accurate (94%); the presence of [≥] 4 of these biomarkers was most sensitive (100%). Accurately identifying hvKp is vital for surveillance and research, and the availability of biomarker data could alert the clinician that hvKp is a consideration, which in turn would assist in optimizing patient care.

microbiology↗

bioRxiv2023

Global diversity of enterococci and description of 18 novel species

Enterococci are commensal gut microbes of most land animals. They diversified over hundreds of millions of years adapting to evolving hosts and host diets. Of over 60 known enterococcal species, Enterococcus faecalis and E. faecium uniquely emerged in the antibiotic era among leading causes of multidrug resistant hospital-associated infection. The basis for the association of particular enterococcal species with a host is largely unknown. To begin deciphering enterococcal species traits that drive host association, and to assess the pool of Enterococcus-adapted genes from which known facile gene exchangers such as E. faecalis and E. faecium may draw, we collected 886 enterococcal strains from nearly 1,000 specimens representing widely diverse hosts, ecologies and geographies. This provided data on the global occurrence and host associations of known species, identifying 18 new species in the process expanding genus diversity by >25%. The novel species harbor diverse genes associated with toxins, detoxification, and resource acquisition. E. faecalis and E. faecium were isolated from a wide diversity of hosts highlighting their generalist properties, whereas most other species exhibited more restricted distributions indicative of specialized host associations. The expanded species diversity permitted the Enterococcus genus phylogeny to be viewed with unprecedented resolution, allowing features to be identified that distinguish its four deeply rooted clades as well as genes associated with range expansion, such as B-vitamin biosynthesis and flagellar motility. Collectively, this work provides an unprecedentedly broad and deep view of the genus Enterococcus, potential threats to human health, and new insights into its evolution. SIGNIFICANCEEnterococci, host-associated microbes that are now leading drug-resistant hospital pathogens, arose as animals colonized land over 400 million years ago. To globally assess the diversity of enterococci now associated with land animals, we collected 886 enterococcal specimens from a wide range of geographies and ecologies, ranging from urban environments to remote areas generally inaccessible to humans. Species determination and genome analysis revealed host associations from generalists to specialists, and identified 18 new species, increasing the genus by over 25%. This added diversity provided greater resolution of the genus clade structure, identifying new features associated with species radiations. Moreover, the high rate of new species discovery shows that tremendous genetic diversity in Enterococcus remains to be discovered.

microbiology↗

bioRxiv2022

A Panel of Diverse Klebsiella pneumoniae Clinical Isolates for Research and Development

Klebsiella pneumoniae are a leading cause of healthcare associated infections worldwide. In particular, strains expressing extended-spectrum {beta}-lactamases (ESBLs) and carbapenemases pose serious treatment challenges, leading the World Health Organization (WHO) to designate ESBL and carbapenem-resistant Enterobacteriaceae (CRE) as "critical" threats to human health. Research efforts to combat these pathogens can be supported by accessibility to diverse and clinically relevant isolates for testing novel therapeutics. Here, we describe a panel of 100 diverse K. pneumoniae isolates publicly available to assist the research community in this endeavor. Whole-genome sequencing (WGS) was performed on 3,878 K. pneumoniae clinical isolates housed at the Multidrug-Resistant Organism Repository and Surveillance Network. The isolates were cultured from 63 facilities in 19 countries between 2001 and 2020. Core-genome multilocus sequence typing and high-resolution single nucleotide polymorphism based phylogenetic analyses captured the genetic diversity of the collection and were used to select the final panel of 100 isolates. In addition to known multi-drug resistant (MDR) pandemic lineages, the final panel includes hypervirulent lineages and isolates with specific and diverse resistance genes and virulence biomarkers. A broad range of antibiotic susceptibilities ranging from pan-sensitive to extensively drug resistant isolates are described. The panel collection, all associated metadata and genome sequences, are available at no additional cost and will be an important for the research community and for the design and development of novel antimicrobial agents and diagnostics against this important pathogen. ImportanceKlebsiella pneumoniae is a major cause of healthcare-associated infections that are increasingly difficult to treat due to the emergence of multi-drug resistant strains. In particular, strains expressing extended-spectrum {beta}-lactamases and carbapenemases have attained global notoriety, with the World Health Organization listing these strains as a "critical-priority" for the development of new therapeutics. Access to a diverse collection of strains for testing is critical for this endeavor, but few resources currently exist. Similarly, pivotal research of the genetic determinants underlying the pathogenesis of hypervirulent lineages is hampered by the lack of standardized, comparator strains. Herein we describe a panel of 100 diverse K. pneumoniae constructed to maximize genetic and phenotypic diversity from a repository of over 3,800 clinical isolates collected over 19 years. The panel, and all associated metadata and genome sequences, is provided at no cost and will greatly assist efforts by academic, government, and industry research groups.

microbiology↗

bioRxiv2022

Antimicrobial resistance and virulence characteristics of Klebsiella pneumoniae isolates in Kenya

Klebsiella pneumoniae is a globally significant opportunistic pathogen causing healthcare-associated and community-acquired infections. This study examined the epidemiology and the distribution of resistance and virulence genes in clinical K. pneumoniae strains in Kenya. Eighty-nine K. pneumoniae isolates were collected over six years from five counties in Kenya and were analyzed using whole genome sequencing and bioinformatics. These isolates were obtained from community-acquired (62/89) and healthcare-associated infections (21/89), and the hospital environment (6/89). Genetic analysis revealed the presence of blaNDM-1 and blaOXA-181 carbapenemase genes and the armA and rmtF genes known to confer pan-aminoglycoside resistance. The most abundant extended-spectrum beta-lactamase genes identified were blaCTX-M-15 (36/89), blaTEM (35/89), and blaOXA (18/89). In addition, one isolate had a mobile colistin resistance gene (mcr-8). Fluoroquinolone resistance-conferring mutations in gyrA and parC genes were also observed. The most notable virulence factors were those associated with hyper-virulence (rmpA/A2 and magA), yersiniabactin (ybt), salmochelin (iro), and aerobactin (iuc and iutA). Thirty-eight distinct sequence types were identified, including known global lineages ST14, ST15, ST147, and ST307, and a regional clone ST17 implicated in regional outbreaks. In addition, this study genetically characterized two potential hypervirulent isolates and two community-acquired ST147 high-risk clones that contained carbapenemase genes, yersiniabactin, and other multidrug resistance genes. These results demonstrate that the resistome and virulome of Kenyan clinical and hospital environmental K. pneumoniae isolates are diverse. The reservoir of high risk-clones capable of spreading resistance and virulence factors have the potential to cause unmanageable infection outbreaks with high morbidity and mortality. AUTHOR SUMMARYKlebsiella pneumoniae is one of the human-disease-causing bacteria that easily acquires and spreads antibiotic resistance genes and is thus a serious threat to human health. We studied both the antibiotic resistance genes and the genes it uses to cause disease (virulence). Forty-two percent of our isolates were multidrug resistant (MDR). They carried several resistance and virulence genes bound in mobile circular DNA molecules called plasmids which easily migrate and spread the genes between bacteria. We identified 38 distinct K. pneumoniae strains (STs) distributed within the study sites. Fifteen isolates were classified under the groups of K. pneumoniae strains known to cause global infection outbreaks such as ST14, ST15 and ST147 collected from Nairobi and Kisumu, hotspot areas for spread of resistance. In particular, two ST147 isolates were resistant to carbapenems and one isolate to colistin, which are last line antibiotics. We also identified two isolates with the potential to cause high levels of disease. We concluded that the presence of highly resistant and virulent strains in the hospital and community demonstrates a need for the continuous monitoring and management of MDR K. pneumoniae infections to prevent disease outbreaks that are difficult to control and that lead to high death rate.

microbiology↗

bioRxiv2021

A community-driven roadmap to advance research on translated open reading frames detected by Ribo-seq

Ribosome profiling (Ribo-seq) has catalyzed a paradigm shift in our understanding of the translational vocabulary of the human genome, discovering thousands of translated open reading frames (ORFs) within long non-coding RNAs and presumed untranslated regions of protein-coding genes. However, reference gene annotation projects have been circumspect in their incorporation of these ORFs due to uncertainties about their experimental reproducibility and physiological roles. Yet, it is indisputable that certain Ribo-seq ORFs make stable proteins, others mediate gene regulation, and many have medical implications. Ultimately, the absence of standardized ORF annotation has created a circular problem: while Ribo-seq ORFs remain unannotated by reference biological databases, this lack of characterisation will thwart research efforts examining their roles. Here, we outline the initial stages of a community-led effort supported by GENCODE / Ensembl, HGNC and UniProt to produce a consolidated catalog of human Ribo-seq ORFs.

genomics↗

bioRxiv2021

Anatomy of an Extensively Drug Resistant Klebsiella pneumoniae Outbreak in Tuscany, Italy

A protracted outbreak of New Delhi metallo-beta-lactamase (NDM)-producing carbapenem-resistant Klebsiella pneumoniae, started in Tuscany, Italy, in November 2018 and continued in 2020 and through 2021. To understand the regional emergence and transmission dynamics over time, we collected and sequenced the genomes of 117 extensively drug-resistant, NDM-producing K. pneumoniae isolates cultured over a 20-month period from 76 patients at several health care facilities in South-East Tuscany. All isolates belonged to high-risk clone ST-147 and were typically non-susceptible to all first line antibiotics. Albeit sporadic, resistances to colistin, tigecycline and fosfomycin were also observed as a result of repeated, independent mutations. Genomic analysis revealed that ST-147 isolates circulating in Tuscany were monophyletic, highly genetically related (including a network of 42 patients from the same hospital and sharing nearly identical isolates) and shared a recent ancestor with clinical isolates from the Middle East. While the blaNDM-1 gene was carried by an IncFIB-type plasmid, our investigations revealed that the ST-147 lineage from Italy also acquired a hybrid IncH-type plasmid carrying the 16S methyltransferase armA gene as well as key virulence biomarkers often found in hypervirulent isolates. This plasmid shared extensive homologies with mosaic plasmids circulating globally including from ST-11 and ST-307 convergent lineages. Phenotypically, the carriage of this hybrid plasmid resulted in increased siderophore production but did not confer virulence to the level of an archetypical, hypervirulent K. pneumoniae in a subcutaneous model of infection with immunocompetent CD1 mice. Our findings highlight the importance of performing genomic surveillance to identify emerging threats. Significance StatementCarbapenem-resistant Klebsiella pneumoniae belong to the "critical priority" tier of bacterial pathogens as identified by the World Health Organization. Emerging "high-risk" lineages are responsible for difficult-to-treat, hospital-acquired infections and outbreaks around the globe. By integrating genomic and epidemiological data for isolates collected over 20 months, this study revealed both the high, regional prevalence and the rapid spread, within a single hospital, of K. pneumoniae ST-147 in Italy. Besides resistance to nearly all antibiotics, we showed that this lineage carried a hybrid plasmid harboring a set of biomarker genes previously linked to hypervirulence. Convergence of multidrug resistance and hypervirulence is a major concern and these findings highlight the need for robust, global surveillance to monitor the emergence of high-risk K. pneumoniae.

microbiology↗

bioRxiv2020

A GO catalogue of human DNA-binding transcription factors

DNA-binding transcription factors recognise genomic addresses, specific sequence motifs in gene regulatory regions, to control gene transcription. A complete and reliable catalogue of all DNA-binding transcription factors is key to investigating the delicate balance of gene regulation in response to environmental and developmental stimuli. The need for such a catalogue of proteins is demonstrated by the many lists of DNA-binding transcription factors that have been produced over the past decade. The COST Action Gene Regulation Ensemble Effort for the Knowledge Commons (GREEKC) Consortium brought together experts in the field of transcription with the aim of providing high quality and interoperable gene regulatory data. The Gene Ontology (GO) Consortium provides strict definitions for gene product function, including factors that regulate transcription. The collaboration between the GREEKC and GO Consortia has enabled the application of those definitions to produce a new curated catalogue of human DNA-binding transcription factors, that can be accessed at https://www.ebi.ac.uk/QuickGO/targetset/dbTF. In addition, this curation effort has led to the GO annotation of almost sixty thousand DNA-binding transcription factors in over a hundred species. Thus, this work will aid researchers investigating the regulation of transcription in both biomedical and basic science.

bioinformatics↗

bioRxiv2020

Ancient BED-domain-containing immune receptor from wild barley confers widely effective resistance to leaf rust

Leaf rust, caused by Puccinia hordei is a devastating fungal disease affecting barley (Hordeum vulgare subsp. vulgare) production globally. Race-specific resistance (R) genes have been deployed widely; however, their durability is often compromised due to the rapid emergence of virulent P. hordei races, prompting the search for new sources of broad-spectrum resistance. Here we report on the cloning of Rph15, a broadly effective resistance gene derived from the wild progenitor Hordeum vulgare subsp. spontaneum. We demonstrate using introgression mapping, mutation and complementation that Rph15 encodes a coiled-coil nucleotide-binding leucine-rich repeat (NLR) protein with an integrated Zinc-finger BED (ZF-BED) domain. The allelic variation at the Rph15 locus was assessed using barley exome capture data that traced its origin to the western region of the Fertile Crescent bordering Jordan and Israel. To unravel the genetic relationship of two other leaf rust resistance genes (Rph14 and Rph16) mapped at similar locus on chromosome 2H, we re-sequenced the Rph15 gene from the near-isogenic line for Rph15 (Bowman+Rph15) and the two donor accessions of Rph14 (PI 584760) and Rph16, (PI 405292, Hs 680). Both whole genome and Sanger sequencing confirmed that Hs 680 carried Rph15, while Rph14 in PI 584760 was an independent locus. A perfect diagnostic KASP marker was developed and validated to permit efficient introduction of Rph15 into cultivated barley.

plant biology↗

bioRxiv2019

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Here we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility (P. aureginosa only). We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. We conclude that, while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. We finally report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bioontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.

bioinformatics↗

bioRxiv2018

DEEPScreen: High Performance Drug-Target Interaction Prediction with Convolutional Neural Networks Using 2-D Structural Compound Representations

The identification of physical interactions between drug candidate chemical substances and target biomolecules is an important step in the process of drug discovery, where the standard procedure is the systematic screening of chemical compounds against pre-selected target proteins. However, experimental screening procedures are expensive and time consuming, therefore, it is not possible to carry out comprehensive tests. Within the last decade, computational approaches have been developed with the objective of aiding experimental studies by predicting novel drug-target interactions (DTI), via the construction and application of statistical models. In this study, we propose a large-scale DTI interaction prediction system, DEEPScreen, for early stage drug discovery, using convolutional deep neural networks. One of the main advantages of DEEPScreen is employing readily available simple 2-D images of compounds at the input level instead of engineered complex feature vectors that displayed limited performance in DTI prediction tasks previously. DEEPScreen learns complex features inherently from the 2-D molecular representations, thus producing highly accurate predictions. DEEPScreen system was trained for 704 target proteins (using ChEMBL curated bioactivity data) and finalized with rigorous hyper-parameter optimization tests. We compared the performance of DEEPScreen against shallow classifiers such as the random forest, logistic regression and support vector machines, to indicate the effectiveness of the proposed deep learning approach. Additionally, we compared DEEPScreen with other deep learning based state-of-the-art DTI predictors on widely used benchmark datasets and showed that DEEPScreen produces better or comparable results to the top performers. The method proposed here can be employed to computationally scan a large portion of the recorded drug candidate compound and protein spaces to aid the experimentalists working in the field of drug discovery and repurposing by providing a preselection of interesting novel DTIs.

bioinformatics↗