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Our ability to understand the genotype-to-phenotype relationship is hindered by the lack of detailed understanding of phenotypes at a single-cell level. To systematically assess cell-to-cell phenotypic variability, we combined automated yeast genetics, high-content screening and neural network-based image analysis of single cells, focussing on genes that influence the architecture of four subcellular compartments of the endocytic pathway as a model system. Our unbiased assessment of the morphology of these compartments—endocytic patch, actin patch, late endosome and vacuole—identified 17 distinct mutant phenotypes associated with ~1,600 genes (~30% of all yeast genes). Approximately half of these mutants exhibited multiple phenotypes, highlighting the extent of morphological pleiotropy. Quantitative analysis also revealed that incomplete penetrance was prevalent, with the majority of mutants exhibiting substantial variability in phenotype at the single-cell level. Our single-cell analysis enabled exploration of factors that contribute to incomplete penetrance and cellular heterogeneity, including replicative age, organelle inheritance and response to stress.

Genes have been historically classified as either essential or non-essential based on their requirement for viability. However, some genes are essential in some genetic backgrounds but non-essential in others, thus challenging the binary classification of gene essentiality. Such dispensable essential genes represent a valuable model for understanding the incomplete penetrance of loss-of-function mutations that is often observed in natural populations. Here, we compiled data from multiple studies on essential gene dispensability inSaccharomyces cerevisiaeto comprehensively characterize these genes. In analyses spanning different evolutionary time-scales, ranging fromS. cerevisiaestrains to human cell lines, dispensable essential genes exhibited distinct phylogenetic properties compared to other essential and non-essential genes. Integration of interactions with suppressor genes that can bypass the gene essentiality revealed the high functional modularity of the bypass suppression network. Furthermore, dispensable essential and bypass suppressor gene pairs reflected simultaneous changes in the mutational landscape ofS. cerevisiaestrains. Importantly, species in which dispensable essential genes were non-essential tended to carry bypass suppressor mutations in their genomes. Overall, our study offers a comprehensive view of dispensable essential genes and illustrates how their interactions with bypass suppressor genes reflect evolutionary outcomes.

Genetic suppression occurs when the deleterious effects of a primary “query” mutation, such as a disease-causing mutation, are rescued by a suppressor mutation elsewhere in the genome. To capture existing knowledge on suppression relationships between human genes, we examined 2,400 published papers for potential interactions identified through either genetic modification of cultured human cells or through association studies in patients. The resulting network encompassed 476 unique suppression interactions that frequently linked genes that function in the same biological process. Suppressors were strongly enriched for genes with a role in stress response or signaling, suggesting that deleterious mutations can often be buffered by modulating signaling cascades or immune responses. Suppressor mutations tended to be deleterious when they occurred in absence of the query mutation, in apparent contrast with their protective role in the presence of the query. We formulated and quantified mechanisms of genetic suppression that could explain 71% of interactions and provided mechanistic insight into disease pathology. Finally, we used these observations to accurately predict suppressor genes in the human genome. The emerging frequency of suppression interactions and range of underlying mechanisms suggest that compensatory mutations may exist for the majority of genetic diseases.

The toroidal nucleus is a novel chromosomal instability (CIN) biomarker which complements the micronucleus. Understanding the specific biological stresses leading to the formation of each CIN-associated phenotype requires the evaluation of large panels of biological images collected from different genetic backgrounds and environmental conditions. However, the quantification of toroidal nuclei is currently a manual process which is unviable on a large scale. Here, we present PIQUE (Processing of Images for the QUantification of toroidal nuclEi), a tool that automates the identification of toroidal nuclei, minimizing false positives while highly agreeing with the manual quantifications. Additionally, PIQUE identifies micronuclei for a convenient comparison of both CIN biomarkers. PIQUE is an open-source ImageJ plugin with a user-friendly interface that enables a wide scientific community to easily study the novel toroidal nucleus for the analysis of CIN levels in cellular models.AvailabilityThe plugin, the user manual, and the images used for the evaluation of PIQUE are available upon request from the authors.Contactcarles.pons@irbbarcelona.organdcaroline.mauvezin@ub.edu

The EMBL-EBI Complex Portal is a knowledgebase of macromolecular complexes providing persistent stable identifiers. Entries are linked to literature evidence and provide details of complex membership, function, structure and complex-specific Gene Ontology annotations. Data are freely available and downloadable in HUPO-PSI community standards and missing entries can be requested for curation. In collaboration with Saccharomyces Genome Database and UniProt, the yeast complexome, a compendium of all known heteromeric assemblies from the model organism Saccharomyces cerevisiae, was curated. This expansion of knowledge and scope has led to a 50% increase in curated complexes compared to the previously published dataset, CYC2008. The yeast complexome is used as a reference resource for the analysis of complexes from large-scale experiments. Our analysis showed that genes coding for proteins in complexes tend to have more genetic interactions, are co-expressed with more genes, are more multifunctional, localize more often in the nucleus, and are more often involved in nucleic acid-related metabolic processes and processes where large machineries are the predominant functional drivers. A comparison to genetic interactions showed that about 40% of expanded co-complex pairs also have genetic interactions, suggesting strong functional links between complex members.

Chromosomal instability (CIN) is a hallmark of cancer, which is characterized by the gain or loss of chromosomes as well as the rearrangement of the genetic material during cell division. Detection of mitotic errors such as misaligned chromosomes or chromosomal bridges (also known as lagging chromosomes) is challenging as it requires the analysis and manual discrimination of chromosomal aberrations in mitotic cells by molecular techniques. In interphase cells, more frequent in the cell population than mitotic cells, two distinct nuclear phenotypes are associated with CIN: the micronucleus and the toroidal nucleus. Several methods are available for the detection of micronuclei, but none for toroidal nuclei. Here, we provide a method to quantify the presence of both nuclear biomarkers for the evaluation of CIN status in non-mitotic cells particularly suited for genotoxicity screens.

Motivation Two key steps in the analysis of uncultured viruses recovered from metagenomes are the taxonomic classification of the viral sequences and the identification of putative host(s). Both steps rely mainly on the assignment of viral proteins to orthologs in cultivated viruses. Viral Protein Families (VPFs) can be used for the robust identification of new viral sequences in large metagenomics datasets. Despite the importance of VPF information for viral discovery, VPFs have not yet been explored for determining viral taxonomy and host targets. Results In this work, we classified the set of VPFs from the IMG/VR database and developed VPF-Class. VPF-Class is a tool that automates the taxonomic classification and host prediction of viral contigs based on the assignment of their proteins to a set of classified VPFs. Applying VPF-Class on 731K uncultivated virus contigs from the IMG/VR database, we were able to classify 363K contigs at the genus level and predict the host of over 461K contigs. In the RefSeq database, VPF-class reported an accuracy of nearly 100% to classify dsDNA, ssDNA and retroviruses, at the genus level, considering a membership ratio and a confidence score of 0.2. The accuracy in host prediction was 86.4%, also at the genus level, considering a membership ratio of 0.3 and a confidence score of 0.5. And, in the prophages dataset, the accuracy in host prediction was 86% considering a membership ratio of 0.6 and a confidence score of 0.8. Moreover, from the Global Ocean Virome dataset, over 817K viral contigs out of 1 million were classified. Availability and implementation The implementation of VPF-Class can be downloaded from https://github.com/biocom-uib/vpf-tools. Supplementary information Supplementary data are available at Bioinformatics online.

Environmental impacts on gene networks A phenotype can be affected by genes interacting with other genes, the environment, or both other genes and the environment (a differential interaction). To better understand how these interactions function in yeast, Costanzo et al. mapped gene-gene interactions using single- and double-mutant deletions and temperature-sensitive alleles under 14 environmental conditions. Many deleted or temperature-sensitive nonessential genes affected yeast fitness both positively and negatively under at least one of the environmental conditions tested. In these cases, up to 24% of yeast genes were affected. A minority of these differential interactions point to previously unknown genetic connections across functional networks, informing on how genetic architecture responds to environmental variation. Science , this issue p. eabf8424

The evolutionary relationships among organisms have traditionally been represented using rooted phylogenetic trees. However, due to reticulate processes such as hybridization or lateral gene transfer, evolution cannot always be adequately represented by a phylogenetic tree, and rooted phylogenetic networks that describe such complex processes have been introduced as a generalization of rooted phylogenetic trees. In fact, estimating rooted phylogenetic networks from genomic sequence data and analyzing their structural properties is one of the most important tasks in contemporary phylogenetics. Over the last two decades, several subclasses of rooted phylogenetic networks (characterized by certain structural constraints) have been introduced in the literature, either to model specific biological phenomena or to enable tractable mathematical and computational analyses. In the present manuscript, we provide a thorough review of these network classes, as well as provide a biological interpretation of the structural constraints underlying these networks where possible. In addition, we discuss how imposing structural constraints on the network topology can be used to address the scalability and identifiability challenges faced in the estimation of phylogenetic networks from empirical data., Comment: 54 pages, 13 figures

Invariants for complicated objects such as those arising in phylogenetics, whether they are invariants as matrices, polynomials, or other mathematical structures, are important tools for distinguishing and working with such objects. In this paper, we generalize a complete polynomial invariant on trees to a class of phylogenetic networks called separable networks, which will include orchard networks. Networks are becoming increasingly important for their ability to represent reticulation events, such as hybridization, in evolutionary history. We provide a function from the space of internally multi-labelled phylogenetic networks, a more generic graph structure than phylogenetic networks where the reticulations are also labelled, to a polynomial ring. We prove that the separability condition allows us to characterize, via the polynomial, the phylogenetic networks with the same number of leaves and same number of reticulations by considering their internally labelled versions. While the invariant for trees is a polynomial in Z [ x 1 , … , x n , y ] where n is the number of leaves, the invariant for internally multi-labelled phylogenetic networks is an element of Z [ x 1 , … , x n , λ 1 , … , λ r , y ], where r is the number of reticulations in the network. When the networks are considered without leaf labels the number of variables reduces to r + 2.

The consequence of a mutation can be influenced by the context in which it operates. For example, loss of gene function may be tolerated in one genetic background, but lead to lethality in another. The extent to which mutant phenotypes are malleable, the complexity of the architecture of modifiers, and the identities of causal genes and pathways remain largely unknown. Here, we measure the fitness effects of ~1,500 temperature sensitive alleles of yeast essential genes in the context of variation from ten different natural genetic backgrounds, and map the modifiers for 19 combinations. Altogether, fitness defects for 183 of the 530 tested genes (35%) could be suppressed by standing genetic variation in at least one wild strain. Suppression was generally driven by gain-of-function of a single, strong modifier gene. The validated causes included both variants in protein interaction partners or pathway members suppressing specific genes, as well as general modifiers altering the effect of many temperature sensitive alleles. The emerging frequency of suppression and range of possible suppression mechanisms suggest that a substantial fraction of monogenic diseases could be repressed by modulating other gene products.

The EMBL-EBI Complex Portal is a knowledgebase of macromolecular complexes providing persistent stable identifiers. Entries are linked to literature evidence and provide details of complex membership, function, structure and complex-specific Gene Ontology annotations. Data is freely available and downloadable in HUPO-PSI community standards and missing entries can be requested for curation. In collaboration with Saccharomyces Genome Database and UniProt, the yeast complexome, a compendium of all known heteromeric assemblies from the model organism Saccharomyces cerevisiae, was curated. This expansion of knowledge and scope has led to a 50% increase in curated complexes compared to the previously published dataset, CYC2008. The yeast complexome is used as a reference resource for the analysis of complexes from large-scale experiments. Our analysis showed that genes coding for proteins in complexes tend to have more genetic interactions, are co-expressed with more genes, are multifunctional, localize more often in the nucleus, and are more often involved in nucleic acid-related metabolic processes and processes where large machineries are the predominant functional drivers. A comparison to genetic interactions showed that about 40% of expanded co-complex pairs also have genetic interactions, suggesting strong functional links between complex members.

Objective The 13C-urea breath test (UBT) is the most widely used non-invasive diagnostic test for Helicobacter pylori. Debate continues to surround the possible interference of antacid intake on its result. This study aims to confirm the non-interference of almagate in the determination of H. pylori by UBT. Patients and methods Observational, multicentre study in adult patients treated with almagate in whom a UBT (TAUKIT®) was indicated. When the UBT result was negative, use of almagate was stopped for 30 days and the UBT was repeated. When the result was positive, no further determinations were made. The primary endpoint was the percentage of patients who, having had a negative result in the first breath test, were positive in the second after having stopped taking almagate (UBT false negatives, possibly attributable to almagate). Results Of the 167 evaluable patients, 59% were female, average age was 49 and 97% had gastrointestinal symptoms. The result of the first UBT was negative in 71% of cases. Of these, in the second UBT test after stopping the almagate, the negative result was confirmed in 97.5%. Out of the total number of cases evaluated, the rate of false negatives was 1.8%. Conclusions Taking almagate has minimal or no interference in the result of UBT for the diagnosis of H. pylori infection. It can therefore be used in the weeks prior to a UBT.

We present an online resource, based on small-molecule bioactivity signatures and natural language processing, to expand the portfolio of compounds with potential to treat COVID-19. By comparing the set of drugs reported to be potentially active against SARS-CoV-2 to a universe of 1M bioactive molecules, we identify compounds that display analogous chemical and functional features to the current COVID-19 candidates. Searches can be filtered by level of evidence and mechanism of action, and results can be restricted to drug molecules or include the much broader space of bioactive compounds. Moreover, we allow users to contribute COVID-19 drug candidates, which are automatically incorporated to the pipeline once per day. The computational platform, as well as the source code, is available at https://sbnb.irbbarcelona.org/covid19.

The massive molecular profiling of thousands of cancer patients has led to the identification of many tumor type specific driver genes. However, only a few (or none) of them are present in each individual tumor and, to enable precision oncology, we need to interpret the alterations found in a single patient. Cancer PanorOmics (http://panoromics.irbbarcelona.org) is a web-based resource to contextualize genomic variations detected in a personal cancer genome within the body of clinical and scientific evidence available for 26 tumor types, offering complementary cohort- and patient-centric views. Additionally, it explores the cellular environment of mutations by mapping them on the human interactome and providing quasi-atomic structural details, whenever available. This ‘PanorOmic’ molecular view of individual tumors, together with the appropriate genetic counselling and medical advice, should contribute to the identification of actionable alterations ultimately guiding the clinical decision-making process.

et al., Global insights into cellular organization and genome function require comprehensive understanding of the interactome networks that mediate genotype–phenotype relationships1,2. Here we present a human ‘all-by-all’ reference interactome map of human binary protein interactions, or ‘HuRI’. With approximately 53,000 protein–protein interactions, HuRI has approximately four times as many such interactions as there are high-quality curated interactions from small-scale studies. The integration of HuRI with genome3, transcriptome4 and proteome5 data enables cellular function to be studied within most physiological or pathological cellular contexts. We demonstrate the utility of HuRI in identifying the specific subcellular roles of protein–protein interactions. Inferred tissue-specific networks reveal general principles for the formation of cellular context-specific functions and elucidate potential molecular mechanisms that might underlie tissue-specific phenotypes of Mendelian diseases. HuRI is a systematic proteome-wide reference that links genomic variation to phenotypic outcomes., This work was primarily supported by the National Institutes of Health (NIH) National Human Genome Research Institute (NHGRI) grant U41HG001715 (M.V., F.P.R., D.E.H., M.A.C., G.D.B. and J.T.) with additional support from NIH grants P50HG004233 (M.V. and F.P.R.), U01HL098166 (M.V.), U01HG007690 (M.V.), R01GM109199 (M.A.C.), Canadian Institute for Health Research (CIHR) Foundation Grants (F.P.R. and J. Rak), the Canada Excellence Research Chairs Program (F.P.R.) and an American Heart Association grant 15CVGPS23430000 (M.V.). D.-K.K. was supported by a Banting Postdoctoral Fellowship through the Natural Sciences and Engineering Research Council (NSERC) of Canada and by the Basic Science Research Program through the National Research Foundation (NRF) of Korea funded by the Ministry of Education (2017R1A6A3A03004385). C. Pons was supported by a Ramon Cajal fellowship (RYC-2017-22959). G.M.S. was supported by NIH Training Grant T32CA009361. M.V. is a Chercheurv Qualifié Honoraire from the Fonds de la Recherche Scientifique (FRS-FNRS, Wallonia-Brussels Federation, Belgium).

Global insights into cellular organization and function require comprehensive understanding of interactome networks. Similar to how a reference genome sequence revolutionized human genetics, a reference map of the human interactome network is critical to fully understand genotype-phenotype relationships. Here we present the first human “all-by-all” binary reference interactome map, or “HuRI”. With ~53,000 high-quality protein-protein interactions (PPIs), HuRI is approximately four times larger than the information curated from small-scale studies available in the literature. Integrating HuRI with genome, transcriptome and proteome data enables the study of cellular function within essentially any physiological or pathological cellular context. We demonstrate the use of HuRI in identifying specific subcellular roles of PPIs and protein function modulation via splicing during brain development. Inferred tissue-specific networks reveal general principles for the formation of cellular context-specific functions and elucidate potential molecular mechanisms underlying tissue-specific phenotypes of Mendelian diseases. HuRI thus represents an unprecedented, systematic reference linking genomic variation to phenotypic outcomes.

The Integrated Microbial Genome/Virus (IMG/VR) system v.2.0 (https://img.jgi.doe.gov/vr/) is the largest publicly available data management and analysis platform dedicated to viral genomics. Since the last report published in the 2016, NAR Database Issue, the data has tripled in size and currently contains genomes of 8389 cultivated reference viruses, 12 498 previously published curated prophages derived from cultivated microbial isolates, and 735112 viral genomic fragments computationally predicted from assembled shotgun metagenomes. Nearly 60% of the viral genomes and genome fragments are clustered into 110384 viral Operational Taxonomic Units (vOTUs) with two or more members. To improve data quality and predictions of host specificity, IMG/VR v.2.0 now separates prokaryotic and eukaryotic viruses, utilizes known prophage sequences to improve taxonomic assignments, and provides viral genome quality scores based on the estimated genome completeness. New features also include enhanced BLAST search capabilities for external queries. Finally, geographic map visualization to locate user-selected viral genomes or genome fragments has been implemented and download options have been extended. All of these features make IMG/VR v.2.0 a key resource for the study of viruses., US Department of Energy Joint Genome Institute, a DOE Office of Science User Facility [DE-AC02-05CH11231] and used resources of the National Energy Research Scientific Computing Center, supported by the Office of Science of the US Department of Energy. J.C.P. and M.L. were partially supported by the Spanish Ministry of Economy and Competitiveness and European Regional Development Fund [DPI2015-67082-P] (MINECO/FEDER). Funding for open access charge: The corresponding author will pay for publication charges.

INTRODUCTION Genetic interactions occur when mutations in different genes combine to result in a phenotype that is different from expectation based on those of the individual mutations. Negative genetic interactions occur when a combination of mutations leads to a fitness defect that is more exacerbated than expected. For example, synthetic lethality occurs when two mutations, neither of which is lethal on its own, generate an inviable double mutant. Alternatively, positive genetic interactions occur when genetic perturbations combine to generate a double mutant with a greater fitness than expected. Global digenic interaction studies have been useful for understanding the functional wiring diagram of the cell and may also provide insight into the genotype-to-phenotype relationship, which is important for tracking the missing heritability of human health and disease. Here we describe a network of higher-order trigenic interactions and explore its implications. RATIONALE Variation in phenotypic outcomes in different individuals is caused by genetic determinants that act as modifiers. Modifier loci are prevalent in human populations, but knowledge regarding how variants interact to modulate phenotype in different individuals is lacking. Similarly, in yeast, traits including conditional essentiality—in which certain genes are essential in one genetic background but nonessential in another—often result from an interplay of multiple modifier loci. Because complex modifiers may underlie the genetic basis of physiological states found in natural populations, it is critical to understand the landscape of higher-order genetic interactions. RESULTS To survey trigenic interactions, we designed query strains that sampled key features of the global digenic interaction network: (i) digenic interaction strength, (ii) average number of digenic interactions, and (iii) digenic interaction profile similarity. In total, we tested ~400,000 double and ~200,000 triple mutants for fitness defects and identified ~9500 digenic and ~3200 trigenic negative interactions. Although trigenic interactions tend to be weaker than digenic interactions, they were both enriched for functional relationships. About one-third of trigenic interactions identified “novel” connections that were not observed in our digenic control network, whereas the remaining approximately two-thirds of trigenic interactions “modified” a digenic interaction, suggesting that the global digenic interaction network is important for understanding the trigenic interaction network. Despite their functional enrichment, trigenic interactions also bridged distant bioprocesses. We estimate that the global trigenic interaction network is ~100 times as large as the global digenic network, highlighting the potential for complex genetic interactions to affect the biology of inheritance. CONCLUSION The extensive network of trigenic interactions and their ability to generate functionally diverse phenotypes suggest that higher-order genetic interactions may play a key role in the genotype-to-phenotype relationship, genome size, and speciation.

Previous genome-level genetic interaction screens with the single essential actin gene of yeast identified 238 nonessential genes that upon deletion result in deleterious, digenic complex haploinsufficiences with an actin null allele. Deletion alleles of these 238 genes were tested for complex heterozygous interactions with 32 actin alanine scan alleles, which target clusters of residues on the surface of actin. A total of 891 deleterious digenic combinations were identified with 203 of the 238 genes. Two-dimensional hierarchical cluster analysis of the interactions identified nine distinct groups, and the alleles within clusters tended to affect localized regions on the surface of actin. The mutants in one cluster all affect electrostatic interactions between stacked subunits in the long pitch helix of the actin filament. A second cluster that contains the most highly interactive alleles may disrupt the tropomyosin/myosin system, as one of the mutants in that cluster cannot support Type V myosin-dependent movement of secretory vesicles in haploids and causes processivity defects in heterozygous diploids. These examples suggest the clusters represent mutations with shared protein−protein interaction defects. These results show that complex heterozygous interaction screens have benefit for detecting actin-related genes and suggest that having actin filaments of mixed composition, containing both mutant and wild-type subunits, presents unique challenges to the cell.

Phylogenetic networks generalize phylogenetic trees by allowing the modelization of events of reticulate evolution. Among the different kinds of phylogenetic networks that have been proposed in the literature, the subclass of binary tree-child networks is one of the most studied ones. However, very little is known about the combinatorial structure of these networks. In this paper we address the problem of generating all possible binary tree-child (BTC) networks with a given number of leaves in an efficient way via reduction/augmentation operations that extend and generalize analogous operations for phylogenetic trees, and are biologically relevant. Since our solution is recursive, this also provides us with a recurrence relation giving an upper bound on the number of such networks. We also show how the operations introduced in this paper can be employed to extend the evolutive history of a set of sequences, represented by a BTC network, to include a new sequence. An implementation in python of the algorithms described in this paper, along with some computational experiments, can be downloaded from https://github.com/bielcardona/TCGenerators.

Conformational fluctuations in proteins play key roles in their functions and interactions. In this work, validated conformational ensembles for ubiquitin have been used in docking trials. The ensembles were used in a systematic predictive study of known ubiquitin complexes by applying a cross-docking strategy against the bound structure of each partner. The global docking predictions obtained with the complete ubiquitin ensembles were significantly better than those obtained with the crystallographic structure of free ubiquitin. Importantly, in all cases we identified an individual ensemble member that performed equally well, or even better, than the bound structure of ubiquitin. These results unequivocally demonstrate that, for proteins that recognize binding partners by conformational selection, the availability of conformational ensembles can greatly improve the performance of automatic docking predictions. Our results highlight the need for docking methodologies to capitalize on validated ensemble representations of biomacromolecules.

Phylogenetic networks have gained attention from the scientific community due to the evidence of the existence of evolutionary events that cannot be represented using trees. A variant of phylogenetic networks, called LGT networks, models specifically lateral gene transfer events, which cannot be properly represented with generic phylogenetic networks. In this paper we treat the problem of the reconstruction of LGT networks from substructures induced by three leaves, which we call tri-LGT-nets. We first restrict ourselves to a class of LGT networks that are both mathematically treatable and biologically significant, called BAN-LGT networks. Then, we study the decomposition of such networks in subnetworks with three leaves and ask whether or not this decomposition determines the network. The answer to this question is negative, but if we further impose time-consistency (species involved in a later gene transfer must coexist) the answer is affirmative, up to some redundancy that can never be recovered but is fully characterized.

Transcription factors (TFs) play a central role in controlling spatiotemporal gene expression and the response to environmental cues. A comprehensive understanding of gene regulation requires integrating physical protein–DNA interactions (PDIs) with TF regulatory activity, expression patterns, and phenotypic data. Although great progress has been made in mapping PDIs using chromatin immunoprecipitation, these studies have only characterized ~10% of TFs in any metazoan species. The nematode C. elegans has been widely used to study gene regulation due to its compact genome with short regulatory sequences. Here, we delineated the largest gene‐centered metazoan PDI network to date by examining interactions between 90% of C. elegans TFs and 15% of gene promoters. We used this network as a backbone to predict TF binding sites for 77 TFs, two‐thirds of which are novel, as well as integrate gene expression, protein–protein interaction, and phenotypic data to predict regulatory and biological functions for multiple genes and TFs.

Structural prediction of protein-protein complexes given the structures of the two interacting compounds in their unbound state is a key problem in biophysics. In addition to the problem of sampling of near-native orientations, one of the modeling main difficulties is to discriminate true from false positives. Here, we present a hierarchical protocol for docking refinement able to discriminate near native poses from a group of docking candidates. The main idea is to combine an efficient sampling of the full system hydrogen bond network and side chains, together with an all-atom force field and a surface generalized born implicit solvent. We tested our method on a set of twenty two complexes containing a near-native solution within the top 100 docking poses, obtaining a near native solution as the top pose in 70% of the cases. We show that all atom force fields optimized H-bond networks do improve significantly state of the art scoring functions.

We describe here our results in the last CAPRI edition. We have participated in all targets, both as predictors and as scorers, using our pyDock docking methodology. The new challenges (homology-based modeling of the interacting subunits, domain–domain assembling, and protein-RNA interactions) have pushed our computer tools to the limits and have encouraged us to devise new docking approaches. Overall, the results have been quite successful, in line with previous editions, especially considering the high difficulty of some of the targets. Our docking approaches succeeded in five targets as predictors or as scorers (T29, T34, T35, T41, and T42). Moreover, with the inclusion of available information on the residues expected to be involved in the interaction, our protocol would have also succeeded in two additional cases (T32 and T40). In the remaining targets (except T37), results were equally poor for most of the groups. We submitted the best model (in ligand RMSD) among scorers for the unbound-bound target T29, the second best model among scorers for the protein-RNA target T34, and the only correct model among predictors for the domain assembly target T35. In summary, our excellent results for the new proposed challenges in this CAPRI edition showed the limitations and applicability of our approaches and encouraged us to continue developing methodologies for automated biomolecular docking. Proteins 2010. © 2010 Wiley-Liss, Inc.

Secondary bacterial peritonitis in cirrhotic patients is an uncommon entity that has been little reported. Our aim is to analyse the frequency, clinical characteristics, treatment and prognosis of patients with secondary peritonitis in comparison to those of patients with spontaneous bacterial peritonitis (SBP).Retrospective analysis of 24 cirrhotic patients with secondary peritonitis compared with 106 SBP episodes.Secondary peritonitis represented 4.5% of all peritonitis in cirrhotic patients. Patients with secondary peritonitis showed a significantly more severe local inflammatory response than patients with SBP. Considering diagnosis of secondary peritonitis, the sensitivity of Runyon's criteria was 66.6% and specificity 89.7%, Runyon's criteria and/or polymicrobial ascitic fluid culture were present in 95.6%, and abdominal computed tomography was diagnostic in 85% of patients in whom diagnosis was confirmed by surgery or autopsy. Mortality during hospitalization was higher in patients with secondary peritonitis than in those with SBP (16/24, 66.6% vs. 28/106, 26.4%) (p0.001). There was a trend to lower mortality in secondary peritonitis patients who underwent surgery (7/13, 53.8%) than in those who received medical treatment only (9/11, 81.8%) (p=0.21). Considering surgically treated patients, the time between diagnostic paracentesis and surgery was shorter in survivors than in non-survivors (3.2+/-2.4 vs. 7.2+/-6.1 days, p=0.31).Secondary peritonitis is an infrequent complication in cirrhotic patients but mortality is high. A low threshold of suspicion on the basis of Runyon's criteria and microbiological data, together with an aggressive approach that includes prompt abdominal computed tomography and early surgical evaluation, could improve prognosis in these patients.

We present a systematic study of the ability of low-resolution experimental data, when combined with physical/statistical scoring functions, to improve the quality of theoretical structural models of proteins and protein complexes. Particularly, we have analyzed in detail the "extra value" added to the theoretical models by: electrospray mass spectrometry (ESI-MS), small-angle X-ray scattering (SAXS), and hydrodynamic measurements. We found that any low-resolution structural data, even when (as in the case of mass spectrometry) obtained in conditions far from the physiological ones, help to improve the quality of theoretical models, but not all the coarse-grained experimental results are equally rich in information. The best results are always obtained when using SAXS data as experimental constraints, but either hydrodynamics or gas phase CCS data contribute to improving model prediction. The combination of suitable scoring functions and broadly available low-resolution structural data (technically easier to obtain) yields structural models that are notably close to the real structures.

The study of protein-protein interactions that are involved in essential life processes can largely benefit from the recent upraising of computational docking approaches. Predicting the structure of a protein-protein complex from their separate components is still a highly challenging task, but the field is rapidly improving. Recent advances in sampling algorithms and rigid-body scoring functions allow to produce, at least for some cases, high quality docking models that are perfectly suitable for biological and functional annotations, as it has been shown in the CAPRI blind tests. However, important challenges still remain in docking prediction. For example, in cases with significant mobility, such as multidomain proteins, fully unrestricted rigid-body docking approaches are clearly insufficient so they need to be combined with restraints derived from domain-domain linker residues, evolutionary information, or binding site predictions. Other challenging cases are weak or transient interactions, such as those between proteins involved in electron transfer, where the existence of alternative bound orientations and encounter complexes complicates the binding energy landscape. Docking methods also struggle when using in silico structural models for the interacting subunits. Bringing these challenges to a practical point of view, we have studied here the limitations of our docking and energy-based scoring approach, and have analyzed different parameters to overcome the limitations and improve the docking performance. For that, we have used the standard benchmark and some practical cases from CAPRI. Based on these results, we have devised a protocol to estimate the success of a given docking run.

Protein-protein interactions are responsible for the transfer of information inside the cell and represent one of the most interesting research fields in structural biology. Unfortunately, after decades of intense research, experimental approaches still have difficulties in providing 3D structures for the hundreds of thousands of interactions formed between the different proteins in a living organism. The use of theoretical approaches like docking aims to complement experimental efforts to represent the structure of the protein interactome. However, we cannot ignore that current methods have limitations due to problems of sampling of the protein-protein conformational space and the lack of accuracy of available force fields. Cases that are especially difficult for prediction are those in which complex formation implies a non-negligible change in the conformation of the interacting proteins, i.e., those cases where protein flexibility plays a key role in protein-protein docking. In this work, we present a new approach to treat flexibility in docking by global structural relaxation based on ultrafast discrete molecular dynamics. On a standard benchmark of protein complexes, the method provides a general improvement over the results obtained by rigid docking. The method is especially efficient in cases with large conformational changes upon binding, in which structure relaxation with discrete molecular dynamics leads to a predictive success rate double that obtained with state-of-the-art rigid-body docking.

A wealth of physical interaction data between transcription factors (TFs) and DNA has been generated, but these interactions often do not have apparent regulatory consequences. Thus, equating physical interaction data with gene regulatory networks (GRNs) is problematic. Here, we comprehensively assay TF activity, rather than binding, to construct a network of gene regulatory interactions in the C. elegans intestine. By manually observing the in vivo tissue-specific knockdown of 921 TFs on a panel of 19 fluorescent transcriptional reporters, we identified a GRN of 411 interactions between 19 promoters and 177 TFs. This GRN shows only modest overlap with physical interactions, indicating that many regulatory interactions are indirect. We applied nested effects modeling to uncover information flow between TFs in the intestine that converges on a small set of physical TF-promoter interactions. We found numerous cell nonautonomous regulatory interactions, illustrating tissue-to-tissue communication. Altogether, our study illuminates the complexity of gene regulation in the context of a living animal.

Background: Lateral, or Horizontal, Gene Transfers are a type of asymmetric evolutionary events where genetic material is transferred from one species to another. In this paper we consider LGT networks, a general model of phylogenetic networks with lateral gene transfers which consist, roughly, of a principal rooted tree with its leaves labelled on a set of taxa, and a set of extra secondary arcs between nodes in this tree representing lateral gene transfers. An LGT network gives rise in a natural way to a principal phylogenetic subtree and a set of secondary phylogenetic subtrees, which, roughly, represent, respectively, the main line of evolution of most genes and the secondary lines of evolution through lateral gene transfers. Results: We introduce a set of simple conditions on an LGT network that guarantee that its principal and secondary phylogenetic subtrees are pairwise different and that these subtrees determine, up to isomorphism, the LGT network. We then give an algorithm that, given a set of pairwise different phylogenetic trees T-0, T-1, ... ,T-k on the same set of taxa, outputs, when it exists, the LGT network that satisfies these conditions and such that its principal phylogenetic tree is T-0 and its secondary phylogenetic trees are T-1, ... T-k., We thank the anonymous reviewers for many comments and suggestions that have substantially improved the quality and readability of the paper. The research reported in this paper has been partially supported by the Programa Pont La Caixa per a groups de recerca de la UIB.

An entire family of methodologies for predicting protein interactions is based on the observed fact that families of interacting proteins tend to have similar phylogenetic trees due to co-evolution. One application of this concept is the prediction of the mapping between the members of two interacting protein families (which protein within one family interacts with which protein within the other). The idea is that the real mapping would be the one maximizing the similarity between the trees. Since the exhaustive exploration of all possible mappings is not feasible for large families, current approaches use heuristic techniques which do not ensure the best solution to be found. This is why it is important to check the results proposed by heuristic techniques and to manually explore other solutions. Here we present TSEMA, the server for efficient mapping assessment. This system calculates an initial mapping between two families of proteins based on a Monte Carlo approach and allows the user to interactively modify it based on performance figures and/or specific biological knowledge. All the explored mappings are graphically shown over a representation of the phylogenetic trees. The system is freely available at http://pdg.cnb.uam.es/TSEMA. Standalone versions of the software behind the interface are available upon request from the authors.

In addition to protein-protein docking, this CAPRI edition included new challenges, like protein-water and protein-sugar interactions, or the prediction of binding affinities and ΔΔG changes upon mutation. Regarding the standard protein-protein docking cases, our approach, mostly based on the pyDock scheme, submitted correct models as predictors and as scorers for 67% and 57% of the evaluated targets, respectively. In this edition, available information on known interface residues hardly made any difference for our predictions. In one of the targets, the inclusion of available experimental small-angle X-ray scattering (SAXS) data using our pyDockSAXS approach slightly improved the predictions. In addition to the standard protein-protein docking assessment, new challenges were proposed. One of the new problems was predicting the position of the interface water molecules, for which we submitted models with 20% and 43% of the water-mediated native contacts predicted as predictors and scorers, respectively. Another new problem was the prediction of protein-carbohydrate binding, where our submitted model was very close to being acceptable. A set of targets were related to the prediction of binding affinities, in which our pyDock scheme was able to discriminate between natural and designed complexes with area under the curve = 83%. It was also proposed to estimate the effect of point mutations on binding affinity. Our approach, based on machine learning methods, showed high rates of correctly classified mutations for all cases. The overall results were highly rewarding, and show that the field is ready to move forward and face new interesting challenges in interactomics. Proteins 2013; 81:2192-2200. © 2013 Wiley Periodicals, Inc.

INTRODUCTION Genetic interactions occur when mutations in two or more genes combine to generate an unexpected phenotype. An extreme negative or synthetic lethal genetic interaction occurs when two mutations, neither lethal individually, combine to cause cell death. Conversely, positive genetic interactions occur when two mutations produce a phenotype that is less severe than expected. Genetic interactions identify functional relationships between genes and can be harnessed for biological discovery and therapeutic target identification. They may also explain a considerable component of the undiscovered genetics associated with human diseases. Here, we describe construction and analysis of a comprehensive genetic interaction network for a eukaryotic cell. RATIONALE Genome sequencing projects are providing an unprecedented view of genetic variation. However, our ability to interpret genetic information to predict inherited phenotypes remains limited, in large part due to the extensive buffering of genomes, making most individual eukaryotic genes dispensable for life. To explore the extent to which genetic interactions reveal cellular function and contribute to complex phenotypes, and to discover the general principles of genetic networks, we used automated yeast genetics to construct a global genetic interaction network. RESULTS We tested most of the ~6000 genes in the yeast Saccharomyces cerevisiae for all possible pairwise genetic interactions, identifying nearly 1 million interactions, including ~550,000 negative and ~350,000 positive interactions, spanning ~90% of all yeast genes. Essential genes were network hubs, displaying five times as many interactions as nonessential genes. The set of genetic interactions or the genetic interaction profile for a gene provides a quantitative measure of function, and a global network based on genetic interaction profile similarity revealed a hierarchy of modules reflecting the functional architecture of a cell. Negative interactions connected functionally related genes, mapped core bioprocesses, and identified pleiotropic genes, whereas positive interactions often mapped general regulatory connections associated with defects in cell cycle progression or cellular proteostasis. Importantly, the global network illustrates how coherent sets of negative or positive genetic interactions connect protein complex and pathways to map a functional wiring diagram of the cell. CONCLUSION A global genetic interaction network highlights the functional organization of a cell and provides a resource for predicting gene and pathway function. This network emphasizes the prevalence of genetic interactions and their potential to compound phenotypes associated with single mutations. Negative genetic interactions tend to connect functionally related genes and thus may be predicted using alternative functional information. Although less functionally informative, positive interactions may provide insights into general mechanisms of genetic suppression or resiliency. We anticipate that the ordered topology of the global genetic network, in which genetic interactions connect coherently within and between protein complexes and pathways, may be exploited to decipher genotype-to-phenotype relationships.

Gene duplication results in two identical paralogs that diverge through mutation, leading to loss or gain of interactions with other biomolecules. Here, we comprehensively characterize such network rewiring for C. elegans transcription factors (TFs) within and across four newly delineated molecular networks. Remarkably, we find that even highly similar TFs often have different interaction degree and partners. In addition, we find that most TF families have a member that is highly connected in multiple networks. Further, different TF families have opposing correlations between network connectivity and phylogenetic age, suggesting that they are subject to different evolutionary pressures. Finally, TFs that have similar partners in one network generally do not in another, indicating a lack of pressure to retain cross-network similarity. Our multiparameter analyses provide an unprecedented glimpse into the evolutionary dynamics that shaped TF networks.

Summary: The application of docking to large-scale experiments or the explicit treatment of protein flexibility are part of the new challenges in structural bioinformatics that will require large computer resources and more efficient algorithms. Highly optimized fast Fourier transform (FFT) approaches are broadly used in docking programs but their optimal code implementation leaves hardware acceleration as the only option to significantly reduce the computational cost of these tools. In this work we present Cell-Dock, an FFT-based docking algorithm adapted to the Cell BE processor. We show that Cell-Dock runs faster than FTDock with maximum speedups of above 200×, while achieving results of similar quality. Availability and implementation: The source code is released under GNU General Public License version 2 and can be downloaded from http://mmb.pcb.ub.es/~cpons/Cell-Dock. Contact: djimenez@ac.upc.edu or juanf@bsc.es Supplementary Information: Supplementary data are available at Bioinformatics online.

A 74-year-old woman presented obstructive jaundice. Computed tomography revealed a pancreatic head tumor with dilatation of the common bile duct (CBD) and pulmonary metastases. Biliary drainage by ERCP was indicated. Papilla had tumoral signs of infiltration. Cannulation was not achieved after several attempts with a papillotome. We accessed the distal CBD after performing a pre-cut, but the guidewire could not pass deeply. After replacing the duodenoscope with a linear echoendoscope with the intention of performing biliary drainage guided by EUS, a 10 mm duodenal perforation (type I, Stapfer) was visualized in the posterior wall of the duodenal bulb (Fig. 1A), surely caused by the tip of the echoenEndoscopic closure of duodenal perforation with an over-the-scope clip during endoscopic ultrasound-guided cholangiopancreatography

This short communication describes the diagnosis, treatment, and clinical course of a domestic short-haired cat with diaphragmatic hernia in which the herniated structure in the thoracic cavity contained a mummified fetus. The cat was pregnant when rescued from the street and, days later, gave birth without abnormalities. Some months later, during an ovariohysterectomy, an abnormal localization of the uterus was observed, and at that time the case was referred to our center. A thoracic radiograph showed an abnormal thoracic mass cranial to the heart. The main suspicion was the presence of a thoracic hernia with the uterus herniated and containing a mummified fetus. A thoracotomy was performed to confirm the nature of the mass and reduce the diaphragmatic hernia. Although this clinical case is quite rare, a mummified fetus can be observed in thoracic hernias.

Structural prediction of protein-protein complexes given the structures of the two interacting compounds in their unbound state is a key problem in biophysics. In addition to the problem of sampling of near-native orientations, one of the modeling main difficulties is to discriminate true from false positives. Here, we present a hierarchical protocol for docking refinement able to discriminate near native poses from a group of docking candidates. The main idea is to combine an efficient sampling of the full system hydrogen bond network and side chains, together with an all-atom force field and a surface generalized born implicit solvent. We tested our method on a set of twenty two complexes containing a near-native solution within the top 100 docking poses, obtaining a near native solution as the top pose in 70% of the cases. We show that all atom force fields optimized H-bond networks do improve significantly state of the art scoring functions.

A detailed and complete structural knowledge of the interactome is one of the grand challenges in Biology, and a variety of computational docking approaches have been developed to complement experimental efforts and help in the characterization of protein-protein interactions. Among the different docking scoring methods, those based on physicochemical considerations can give the maximum accuracy at the atomic level, but they are usually computationally demanding and necessarily noisy when implemented in rigid-body approaches. Coarser-grained knowledge-based potentials are less sensitive to details of atomic arrangements, thus providing an efficient alternative for scoring of rigid-body docking poses. In this study, we have extracted new statistical potentials from intermolecular pairs of exposed residues in known complex structures, which were then used to score protein-protein docking poses. The new method, called SIPPER (scoring by intermolecular pairwise propensities of exposed residues), combines the value of residue desolvation based on solvent-exposed area with the propensity-based contribution of intermolecular residue pairs. This new scoring function found a near-native orientation within the top 10 predictions in nearly one-third of the cases of a standard docking benchmark and proved to be also useful as a filtering step, drastically reducing the number of docking candidates needed by energy-based methods like pyDock. © 2011 American Chemical Society., This work was supported by the Spanish Ministry of Science (grants BIO2006-15557, BIO2008-02882, BIO2009-10964, and BFU2009-11527), the Consejo Superior de Investigaciones Científicas (grant 200420E578), the Consolider E-science project, the COMBIOMED ISCIII project, and the Fundacion Marcelino Botin.

Deciphering the whole network of protein interactions for a given proteome (interactome) is the goal of many experimental and computational efforts in Systems Biology. Separately the prediction of the structure of protein complexes by docking methods is a well-established scientific area. To date, docking programs have not been used to predict interaction partners. We provide a proof of principle for such an approach. Using a set of protein complexes representing known interactors in their unbound form, we show that a standard docking program can distinguish the true interactors from a background of 922 non-redundant potential interactors. We additionally show that true interactions can be distinguished from non-likely interacting proteins within the same structural family. Our approach may be put in the context of the proposed funnel-energy model; the docking algorithm may not find the native complex, but it distinguishes binding partners because of the higher probability of favourable models compared with a collection of non-binders. The potential exists to develop this proof of principle into new approaches for predicting interaction partners and reconstructing biological networks. © 2011 EMBO and Macmillan Publishers Limited., MNW was supported by a British Council Research Exchange award.GF was awarded a Marie Curie Action European Reintegration Grant (MERG-CT-2007-200179). This work was funded by the BioSapiens Network of Excellence (grant number LSHG-CT-2003-503265) and in part by the BIO2006-15318 project from the Spanish Ministry for Education and Science.

X-ray crystallography and NMR can provide detailed structural information of protein-protein complexes, but technical problems make their application challenging in the high-throughput regime. Other methods such as small-angle X-ray scattering (SAXS) are more promising for large-scale application, but at the cost of lower resolution, which is a problem that can be solved by complementing SAXS data with theoretical simulations. Here, we propose a novel strategy that combines SAXS data and accurate protein-protein docking simulations. The approach has been benchmarked on a large pool of known structures with synthetic SAXS data, and on three experimental examples. The combined approach (pyDockSAXS) provided a significantly better success rate (43% for the top 10 predictions) than either of the two methods alone. Further analysis of the influence of different docking parameters made it possible to increase the success rates for specific cases, and to define guidelines for improving the data-driven protein-protein docking protocols.

Despite the importance of protein-RNA interactions in the cellular context, the number of available protein-RNA complex structures is still much lower than those of other biomolecules. As a consequence, few computational studies have been addressed towards protein-RNA complexes, and to our knowledge, no systematic benchmarking of protein-RNA docking has been reported. In this study we have extracted new pairwise residue-ribonucleotide interface propensities for protein-RNA, which can be used as statistical potentials for scoring of protein-RNA docking poses. We show here a new protein-RNA docking approach based on FTDock generation of rigid-body docking poses, which are later scored by these statistical residue-ribonucleotide potentials. The method has been successfully benchmarked in a set of 12 protein-RNA cases. The results show that FTDock is able to generate near-native solutions in more than half of the cases, and that it can rank near-native solutions significantly above random. In practically all these cases, our propensity-based scoring helps to improve the docking results, finding a near-native solution within rank 100 in 43% of them. In a remarkable case, the near-native solution was ranked 1 after the propensity-based scoring. Other previously described propensity potentials can also be used for scoring, with slightly worse performance. This new protein-RNA docking protocol permits a fast scoring of rigid-body docking poses in order to select a small number of docking orientations, which can be later evaluated with more sophisticated energy-based scoring functions.

The study of protein-protein interactions that are involved in essential life processes can largely benefit from the recent upraising of computational docking approaches. Predicting the structure of a protein-protein complex from their separate components is still a highly challenging task, but the field is rapidly improving. Recent advances in sampling algorithms and rigid-body scoring functions allow to produce, at least for some cases, high quality docking models that are perfectly suitable for biological and functional annotations, as it has been shown in the CAPRI blind tests. However, important challenges still remain in docking prediction. For example, in cases with significant mobility, such as multidomain proteins, fully unrestricted rigid-body docking approaches are clearly insufficient so they need to be combined with restraints derived from domain-domain linker residues, evolutionary information, or binding site predictions. Other challenging cases are weak or transient interactions, such as those between proteins involved in electron transfer, where the existence of alternative bound orientations and encounter complexes complicates the binding energy landscape. Docking methods also struggle when using in silico structural models for the interacting subunits. Bringing these challenges to a practical point of view, we have studied here the limitations of our docking and energy-based scoring approach, and have analyzed different parameters to overcome the limitations and improve the docking performance. For that, we have used the standard benchmark and some practical cases from CAPRI. Based on these results, we have devised a protocol to estimate the success of a given docking run.

The two previous CAPRI experiments showed the success of our rigid-body and refinement approach. For this third edition of CAPRI, we have used a new faster protocol called pyDock, which uses electrostatics and desolvation energy to score docking poses generated with FFT-based algorithms. In target T24 (unbound/model), our best prediction had the highest value of fraction of native contacts (40%) among all participants, although it was not considered as acceptable by the CAPRI criteria. In target T25 (unbound/bound), we submitted a model with medium quality. In target T26 (unbound/unbound), we did not submit any acceptable model (but we would have submitted acceptable predictions if we had included available mutational information about the binding site). For targets T27 (unbound/unbound) and T28 (homo-dimer using model), nobody (including us) submitted any acceptable model. Intriguingly, the crystal structure of target T27 shows an alternative interface that correlates with available biological data (we would have submitted acceptable predictions if we had included this). We also participated in all targets of the SCORERS experiment, with at least acceptable accuracy in all valid cases. We submitted two medium and four acceptable scoring models of T25. Using additional distance restraints (from mutational data), we had two medium and two acceptable scoring models of T26. For target T27, we submitted two acceptable scoring models of the alternative interface in the crystal structure. In summary, CAPRI showed the excellent capabilities of pyDock in identifying near-native docking poses.

pyDockWEB is a web server for the rigid-body docking prediction of protein-protein complex structures using a new version of the pyDock scoring algorithm. We use here a new custom parallel FTDock implementation, with adjusted grid size for optimal FFT calculations, and a new version of pyDock, which dramatically speeds up calculations while keeping the same predictive accuracy. Given the 3D coordinates of two interacting proteins, pyDockWEB returns the best docking orientations as scored mainly by electrostatics and desolvation energy.The server does not require registration by the user and is freely accessible for academics at http://life.bsc.es/servlet/pydock.Supplementary data are available at Bioinformatics online.