A reference map of the human binary protein interactome

Low-dose Bisphenol A exposure alters the functionality and cellular environment in a human cardiomyocyte model

Early embryonic development represents a sensitive time-window during which the foetus might be vulnerable to the exposure of environmental contaminants, potentially leading to heart diseases also later in life. Bisphenol A (BPA), a synthetic chemical widely used in plastics manufacturing, has been associated with heart developmental defects, even in low concentrations. This study aims to investigate the effects of environmentally relevant doses of BPA on developing cardiomyocytes using a human induced pluripotent stem cell (hiPSC)-derived model. Firstly, a 2D in vitro differentiation system to obtain cardiomyocytes from hiPSCs (hiPSC-CMs) have been established and characterised to provide a suitable model for the early stages of cardiac development. Then, the effects of a repeated BPA exposure, starting from the undifferentiated stage throughout the differentiation process, were evaluated. The chemical significantly decreased the beat rate of hiPSC-CMs, extending the contraction and relaxation time in a dose-dependent manner. Quantitative proteomics analysis revealed a high abundance of basement membrane (BM) components (e.g., COL4A1, COL4A2, LAMC1, NID2) and a significant increase in TNNC1 and SERBP1 proteins in hiPSC-CMs treated with BPA. Network analysis of proteomics data supported altered extracellular matrix remodelling and provided a disease-gene association with well-known pathological conditions of the heart. Furthermore, upon hypoxia-reoxygenation challenge, hiPSC-CMs treated with BPA showed higher rate of apoptotic events. Taken together, our results revealed that a long-term treatment, even with low doses of BPA, interferes with hiPSC-CMs functionality and alters the surrounding cellular environment, providing new insights about diseases that might arise upon the toxin exposure. Our study contributes to the current understanding of BPA effects on developing human foetal cardiomyocytes, in correlation with human clinical observations and animal studies, and it provides a suitable model for New Approach Methodologies (NAMs) for environmental chemical hazard and risk assessment.

Global analysis of suppressor mutations that rescue human genetic defects

BACKGROUND

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.

METHODS

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.

RESULTS

The resulting network encompassed 476 unique suppression interactions covering a wide spectrum of diseases and biological functions. The interactions frequently linked genes that operate 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 predict suppressor genes in the human genome.

CONCLUSIONS

The global suppression network allowed us to define principles of genetic suppression that were conserved across diseases, model systems, and species. The emerging frequency of suppression interactions among human genes and range of underlying mechanisms, together with the prevalence of suppression in model organisms, suggest that compensatory mutations may exist for most genetic diseases.

Super enhancer-driven core transcriptional regulatory circuitry crosstalk with cancer plasticity and patient mortality in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is a clinically aggressive subtype of breast cancer. Core transcriptional regulatory circuitry (CRC) consists of autoregulated transcription factors (TFs) and their enhancers, which dominate gene expression programs and control cell fate. However, there is limited knowledge of CRC in TNBC. Herein, we systemically characterized the activated super-enhancers (SEs) and interrogated 14 CRCs in breast cancer. We found that CRCs could be broadly involved in DNA conformation change, metabolism process, and signaling response affecting the gene expression reprogramming. Furthermore, these CRC TFs are capable of coordinating with partner TFs bridging the enhancer-promoter loops. Notably, the CRC TF and partner pairs show remarkable specificity for molecular subtypes of breast cancer, especially in TNBC. USF1, SOX4, and MYBL2 were identified as the TNBC-specific CRC TFs. We further demonstrated that USF1 was a TNBC immunophenotype-related TF. Our findings that the rewiring of enhancer-driven CRCs was related to cancer immune and mortality, will facilitate the development of epigenetic anti-cancer treatment strategies.

Ohnologs and SSD Paralogs Differ in Genomic and Expression Features Related to Dosage Constraints

Gene duplication is recognized as a critical process in genome evolution; however, many questions about this process remain unanswered. Although gene duplicability has been observed to differ by duplication mechanism and evolutionary rate, there is so far no broad characterization of its determinants. Many features correlate with this difference in duplicability; however, our ability to exploit these observations to advance our understanding of the role of duplication in evolution is hampered by limitations within existing work. In particular, the existence of methodological differences across studies impedes meaningful comparison. Here, we use consistent definitions of duplicability in the human lineage to explore these associations, allow resolution of the impact of confounding factors, and define the overall relevance of individual features. Using a classifier approach and controlling for the confounding effect of duplicate longevity, we find a subset of gene features important in differentiating genes duplicable by small-scale duplication from those duplicable by whole-genome duplication, revealing critical roles for gene dosage and expression costs in duplicability. We further delve into patterns of functional enrichment and find a lack of constraint on duplicate retention in any context for genes duplicable by small-scale duplication.

Large sample size and nonlinear sparse models outline epistatic effects in inflammatory bowel disease

BACKGROUND

Despite clear evidence of nonlinear interactions in the molecular architecture of polygenic diseases, linear models have so far appeared optimal in genotype-to-phenotype modeling. A key bottleneck for such modeling is that genetic data intrinsically suffers from underdetermination ([Formula: see text]). Millions of variants are present in each individual while the collection of large, homogeneous cohorts is hindered by phenotype incidence, sequencing cost, and batch effects.

RESULTS

We demonstrate that when we provide enough training data and control the complexity of nonlinear models, a neural network outperforms additive approaches in whole exome sequencing-based inflammatory bowel disease case-control prediction. To do so, we propose a biologically meaningful sparsified neural network architecture, providing empirical evidence for positive and negative epistatic effects present in the inflammatory bowel disease pathogenesis.

CONCLUSIONS

In this paper, we show that underdetermination is likely a major driver for the apparent optimality of additive modeling in clinical genetics today.

Deep multiple-instance learning accurately predicts gene haploinsufficiency and deletion pathogenicity

Copy number losses (deletions) are a major contributor to the etiology of severe genetic disorders. Although haploinsufficient genes play a critical role in deletion pathogenicity, current methods for deletion pathogenicity prediction fail to integrate multiple lines of evidence for haploinsufficiency at the gene level, limiting their power to pinpoint deleterious deletions associated with genetic disorders. Here we introduce DosaCNV, a deep multiple-instance learning framework that, for the first time, models deletion pathogenicity jointly with gene haploinsufficiency. By integrating over 30 gene-level features potentially predictive of haploinsufficiency, DosaCNV shows unmatched performance in prioritizing pathogenic deletions associated with a broad spectrum of genetic disorders. Furthermore, DosaCNV outperforms existing methods in predicting gene haploinsufficiency even though it is not trained on known haploinsufficient genes. Finally, DosaCNV leverages a state-of-the-art technique to quantify the contributions of individual gene-level features to haploinsufficiency, allowing for human-understandable explanations of model predictions. Altogether, DosaCNV is a powerful computational tool for both fundamental and translational research.

Identification of disease modules using higher-order network structure

Motivation

Higher-order interaction patterns among proteins have the potential to reveal mechanisms behind molecular processes and diseases. While clustering methods are used to identify functional groups within molecular interaction networks, these methods largely focus on edge density and do not explicitly take into consideration higher-order interactions. Disease genes in these networks have been shown to exhibit rich higher-order structure in their vicinity, and considering these higher-order interaction patterns in network clustering have the potential to reveal new disease-associated modules.

Results

We propose a higher-order community detection method which identifies community structure in networks with respect to specific higher-order connectivity patterns beyond edges. Higher-order community detection on four different protein-protein interaction networks identifies biologically significant modules and disease modules that conventional edge-based clustering methods fail to discover. Higher-order clusters also identify disease modules from genome-wide association study data, including new modules that were not discovered by top-performing approaches in a Disease Module DREAM Challenge. Our approach provides a more comprehensive view of community structure that enables us to predict new disease-gene associations.

Availability and implementation

https://github.com/Reed-CompBio/graphlet-clustering.

Extending protein interaction networks using proteoforms and small molecules

MOTIVATION

Biological network analysis for high-throughput biomedical data interpretation relies heavily on topological characteristics. Networks are commonly composed of nodes representing genes or proteins that are connected by edges when interacting. In this study, we use the rich information available in the Reactome pathway database to build biological networks accounting for small molecules and proteoforms modeled using protein isoforms and post-translational modifications to study the topological changes induced by this refinement of the network representation.

RESULTS

We find that improving the interactome modeling increases the number of nodes and interactions, but that isoform and post-translational modification annotation is still limited compared to what can be expected biologically. We also note that small molecule information can distort the topology of the network due to the high connectedness of these molecules, which does not necessarily represent the reality of biology. However, by restricting the connections of small molecules to the context of biochemical reactions, we find that these improve the overall connectedness of the network and reduce the prevalence of isolated components and nodes. Overall, changing the representation of the network alters the prevalence of articulation points and bridges globally but also within and across pathways. Hence, some molecules can gain or lose in biological importance depending on the level of detail of the representation of the biological system, which might in turn impact network-based studies of diseases or druggability.

AVAILABILITY AND IMPLEMENTATION

Networks are constructed based on data publicly available in the Reactome Pathway knowledgebase: reactome.org.

Predicting synergistic anticancer drug combination based on low-rank global attention mechanism and bilinear predictor

MOTIVATION

Drug combination therapy has exhibited remarkable therapeutic efficacy and has gradually become a promising clinical treatment strategy of complex diseases such as cancers. As the related databases keep expanding, computational methods based on deep learning model have become powerful tools to predict synergistic drug combinations. However, predicting effective synergistic drug combinations is still a challenge due to the high complexity of drug combinations, the lack of biological interpretability, and the large discrepancy in the response of drug combinations in vivo and in vitro biological systems.

RESULTS

Here, we propose DGSSynADR, a new deep learning method based on global structured features of drugs and targets for predicting synergistic anticancer drug combinations. DGSSynADR constructs a heterogeneous graph by integrating the drug-drug, drug-target, protein-protein interactions and multi-omics data, utilizes a low-rank global attention (LRGA) model to perform global weighted aggregation of graph nodes and learn the global structured features of drugs and targets, and then feeds the embedded features into a bilinear predictor to predict the synergy scores of drug combinations in different cancer cell lines. Specifically, LRGA network brings better model generalization ability, and effectively reduces the complexity of graph computation. The bilinear predictor facilitates the dimension transformation of the features and fuses the feature representation of the two drugs to improve the prediction performance. The loss function Smooth L1 effectively avoids gradient explosion, contributing to better model convergence. To validate the performance of DGSSynADR, we compare it with seven competitive methods. The comparison results demonstrate that DGSSynADR achieves better performance. Meanwhile, the prediction of DGSSynADR is validated by previous findings in case studies. Furthermore, detailed ablation studies indicate that the one-hot coding drug feature, LRGA model and bilinear predictor play a key role in improving the prediction performance.

AVAILABILITY AND IMPLEMENTATION

DGSSynADR is implemented in Python using the Pytorch machine-learning library, and it is freely available at https://github.com/DHUDBlab/DGSSynADR.

HPC-Atlas: Computationally Constructing A Comprehensive Atlas of Human Protein Complexes

A fundamental principle of biology is that proteins tend to form complexes to play important roles in the core functions of cells. For a complete understanding of human cellular functions, it is crucial to have a comprehensive atlas of human protein complexes. Unfortunately, we still lack such a comprehensive atlas of experimentally validated protein complexes, which prevents us from gaining a complete understanding of the compositions and functions of human protein complexes, as well as the underlying biological mechanisms. To fill this gap, we built Human Protein Complexes Atlas (HPC-Atlas), as far as we know, the most accurate and comprehensive atlas of human protein complexes available to date. We integrated two latest protein interaction networks, and developed a novel computational method to identify nearly 9000 protein complexes, including many previously uncharacterized complexes. Compared with the existing methods, our method achieved outstanding performance on both testing and independent datasets. Furthermore, with HPC-Atlas we identified 751 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-affected human protein complexes, and 456 multifunctional proteins that contain many potential moonlighting proteins. These results suggest that HPC-Atlas can serve as not only a computing framework to effectively identify biologically meaningful protein complexes by integrating multiple protein data sources, but also a valuable resource for exploring new biological findings. The HPC-Atlas webserver is freely available at http://www.yulpan.top/HPC-Atlas.

Methylation of KRAS by SETD7 promotes KRAS degradation in non-small cell lung cancer

Oncogenic KRAS mutations are a key driver for initiation and progression in non-small cell lung cancer (NSCLC). However, how post-translational modifications (PTMs) of KRAS, especially methylation, modify KRAS activity remain largely unclear. Here, we show that SET domain containing histone lysine methyltransferase 7 (SETD7) interacts with KRAS and methylates KRAS at lysines 182 and 184. SETD7-mediated methylation of KRAS leads to degradation of KRAS and attenuation of the RAS/MEK/ERK signaling cascade, endowing SETD7 with a potent tumor-suppressive role in NSCLC, both in vitro and in vivo. Mechanistically, RABGEF1, a ubiquitin E3 ligase of KRAS, is recruited and promotes KRAS degradation in a K182/K184 methylation-dependent manner. Notably, SETD7 is inversely correlated with KRAS at the protein level in clinical NSCLC tissues. Low SETD7 or RABGEF1 expression is associated with poor prognosis in lung adenocarcinoma patients. Altogether, our results define a tumor-suppressive function of SETD7 that operates via modulating KRAS methylation and degradation.

Recent Advances in Molecular and Cellular Functions of S100A10

S100A10 (p11, annexin II light chain, calpactin light chain) is a multifunctional protein with a wide range of physiological activity. S100A10 is unique among the S100 family members of proteins since it does not bind to Ca2+, despite its sequence and structural similarity. This review focuses on studies highlighting the structure, regulation, and binding partners of S100A10. The binding partners of S100A10 were collated and summarized.

TMKit: a Python interface for computational analysis of transmembrane proteins

Transmembrane proteins are receptors, enzymes, transporters and ion channels that are instrumental in regulating a variety of cellular activities, such as signal transduction and cell communication. Despite tremendous progress in computational capacities to support protein research, there is still a significant gap in the availability of specialized computational analysis toolkits for transmembrane protein research. Here, we introduce TMKit, an open-source Python programming interface that is modular, scalable and specifically designed for processing transmembrane protein data. TMKit is a one-stop computational analysis tool for transmembrane proteins, enabling users to perform database wrangling, engineer features at the mutational, domain and topological levels, and visualize protein-protein interaction interfaces. In addition, TMKit includes seqNetRR, a high-performance computing library that allows customized construction of a large number of residue connections. This library is particularly well suited for assigning correlation matrix-based features at a fast speed. TMKit should serve as a useful tool for researchers in assisting the study of transmembrane protein sequences and structures. TMKit is publicly available through https://github.com/2003100127/tmkit and https://tmkit-guide.herokuapp.com/doc/overview.

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

We introduce ZEPPI (Z-score Evaluation of Protein-Protein Interfaces), a framework to evaluate structural models of a complex based on sequence co-evolution and conservation involving residues in protein-protein interfaces. The ZEPPI score is calculated by comparing metrics for an interface to those obtained from randomly chosen residues. Since contacting residues are defined by the structural model, this obviates the need to account for indirect interactions. Further, although ZEPPI relies on species-paired multiple sequence alignments, its focus on interfacial residues allows it to leverage quite shallow alignments. ZEPPI performance is evaluated through applications to experimentally determined complexes and to decoys from the CASP-CAPRI experiment. ZEPPI can be implemented on a proteome-wide scale as evidenced by calculations on millions of structural models of dimeric complexes in the E. coli and human interactomes found in the PrePPI database. A number of examples that illustrate how these tools can yield novel functional hypotheses are provided.

An unsupervised deep learning framework for predicting human essential genes from population and functional genomic data

BACKGROUND

The ability to accurately predict essential genes intolerant to loss-of-function (LOF) mutations can dramatically improve the identification of disease-associated genes. Recently, there have been numerous computational methods developed to predict human essential genes from population genomic data. While the existing methods are highly predictive of essential genes of long length, they have limited power in pinpointing short essential genes due to the sparsity of polymorphisms in the human genome.

RESULTS

Motivated by the premise that population and functional genomic data may provide complementary evidence for gene essentiality, here we present an evolution-based deep learning model, DeepLOF, to predict essential genes in an unsupervised manner. Unlike previous population genetic methods, DeepLOF utilizes a novel deep learning framework to integrate both population and functional genomic data, allowing us to pinpoint short essential genes that can hardly be predicted from population genomic data alone. Compared with previous methods, DeepLOF shows unmatched performance in predicting ClinGen haploinsufficient genes, mouse essential genes, and essential genes in human cell lines. Notably, at a false positive rate of 5%, DeepLOF detects 50% more ClinGen haploinsufficient genes than previous methods. Furthermore, DeepLOF discovers 109 novel essential genes that are too short to be identified by previous methods.

CONCLUSION

The predictive power of DeepLOF shows that it is a compelling computational method to aid in the discovery of essential genes.

The FAM104 proteins VCF1/2 promote the nuclear localization of p97/VCP

The ATPase p97 (also known as VCP, Cdc48) has crucial functions in a variety of important cellular processes such as protein quality control, organellar homeostasis, and DNA damage repair, and its de-regulation is linked to neuromuscular diseases and cancer. p97 is tightly controlled by numerous regulatory cofactors, but the full range and function of the p97-cofactor network is unknown. Here, we identify the hitherto uncharacterized FAM104 proteins as a conserved family of p97 interactors. The two human family members VCP nuclear cofactor family member 1 and 2 (VCF1/2) bind p97 directly via a novel, alpha-helical motif and associate with p97-UFD1-NPL4 and p97-UBXN2B complexes in cells. VCF1/2 localize to the nucleus and promote the nuclear import of p97. Loss of VCF1/2 results in reduced nuclear p97 levels, slow growth, and hypersensitivity to chemical inhibition of p97 in the absence and presence of DNA damage, suggesting that FAM104 proteins are critical regulators of nuclear p97 functions.

Identification of essential genes associated with SARS-CoV-2 infection as potential drug target candidates with machine learning algorithms

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) requires the fast discovery of effective treatments to fight this worldwide concern. Several genes associated with the SARS-CoV-2, which are essential for its functionality, pathogenesis, and survival, have been identified. These genes, which play crucial roles in SARS-CoV-2 infection, are considered potential therapeutic targets. Developing drugs against these essential genes to inhibit their regular functions could be a good approach for COVID-19 treatment. Artificial intelligence and machine learning methods provide powerful infrastructures for interpreting and understanding the available data and can assist in finding fast explanations and cures. We propose a method to highlight the essential genes that play crucial roles in SARS-CoV-2 pathogenesis. For this purpose, we define eleven informative topological and biological features for the biological and PPI networks constructed on gene sets that correspond to COVID-19. Then, we use three different unsupervised learning algorithms with different approaches to rank the important genes with respect to our defined informative features. Finally, we present a set of 18 important genes related to COVID-19. Materials and implementations are available at: https://github.com/MahnazHabibi/Gene_analysis .

The use of immunoaffinity purification approaches coupled with LC-MS/MS offers a powerful strategy to identify protein complexes in filamentous fungi

Fungi are a diverse group of organisms that can be both beneficial and harmful to mankind. They have advantages such as producing food processing enzymes and antibiotics, but they can also be pathogens and produce mycotoxins that contaminate food. Over the past two decades, there have been significant advancements in methods for studying fungal molecular biology. These advancements have led to important discoveries in fungal development, physiology, pathogenicity, biotechnology, and natural product research. Protein complexes and protein-protein interactions (PPIs) play crucial roles in fungal biology. Various methods, including yeast two-hybrid (Y2H) and bimolecular fluorescence complementation (BiFC), are used to investigate PPIs. However, affinity-based PPI methods like co-immunoprecipitation (Co-IP) are highly preferred because they represent the natural conditions of PPIs. In recent years, the integration of liquid chromatography coupled with mass spectrometry (LC-MS/MS) has been used to analyse Co-IPs, leading to the discovery of important protein complexes in filamentous fungi. In this review, we discuss the tandem affinity purification (TAP) method and single affinity purification methods such as GFP, HA, FLAG, and MYC tag purifications. These techniques are used to identify PPIs and protein complexes in filamentous fungi. Additionally, we compare the efficiency, time requirements, and material usage of Sepharose™ and magnetic-based purification systems. Overall, the advancements in fungal molecular biology techniques have provided valuable insights into the complex interactions and functions of proteins in fungi. The methods discussed in this review offer powerful tools for studying fungal biology and will contribute to further discoveries in this field.

ClinPrior: an algorithm for diagnosis and novel gene discovery by network-based prioritization

BACKGROUND

Whole-exome sequencing (WES) and whole-genome sequencing (WGS) have become indispensable tools to solve rare Mendelian genetic conditions. Nevertheless, there is still an urgent need for sensitive, fast algorithms to maximise WES/WGS diagnostic yield in rare disease patients. Most tools devoted to this aim take advantage of patient phenotype information for prioritization of genomic data, although are often limited by incomplete gene-phenotype knowledge stored in biomedical databases and a lack of proper benchmarking on real-world patient cohorts.

METHODS

We developed ClinPrior, a novel method for the analysis of WES/WGS data that ranks candidate causal variants based on the patient's standardized phenotypic features (in Human Phenotype Ontology (HPO) terms). The algorithm propagates the data through an interactome network-based prioritization approach. This algorithm was thoroughly benchmarked using a synthetic patient cohort and was subsequently tested on a heterogeneous prospective, real-world series of 135 families affected by hereditary spastic paraplegia (HSP) and/or cerebellar ataxia (CA).

RESULTS

ClinPrior successfully identified causative variants achieving a final positive diagnostic yield of 70% in our real-world cohort. This includes 10 novel candidate genes not previously associated with disease, 7 of which were functionally validated within this project. We used the knowledge generated by ClinPrior to create a specific interactome for HSP/CA disorders thus enabling future diagnoses as well as the discovery of novel disease genes.

CONCLUSIONS

ClinPrior is an algorithm that uses standardized phenotype information and interactome data to improve clinical genomic diagnosis. It helps in identifying atypical cases and efficiently predicts novel disease-causing genes. This leads to increasing diagnostic yield, shortening of the diagnostic Odysseys and advancing our understanding of human illnesses.

Total network controllability analysis discovers explainable drugs for Covid-19 treatment

BACKGROUND

The active pursuit of network medicine for drug repurposing, particularly for combating Covid-19, has stimulated interest in the concept of structural controllability in cellular networks. We sought to extend this theory, focusing on the defense rather than control of the cell against viral infections. Accordingly, we extended structural controllability to total structural controllability and introduced the concept of control hubs. Perturbing any control hub may render the cell uncontrollable by exogenous stimuli like viral infections, so control hubs are ideal drug targets.

RESULTS

We developed an efficient algorithm to identify all control hubs, applying it to a largest homogeneous network of human protein interactions, including interactions between human and SARS-CoV-2 proteins. Our method recognized 65 druggable control hubs with enriched antiviral functions. Utilizing these hubs, we categorized potential drugs into four groups: antiviral and anti-inflammatory agents, drugs acting on the central nervous system, dietary supplements, and compounds enhancing immunity. An exemplification of our approach's effectiveness, Fostamatinib, a drug initially developed for chronic immune thrombocytopenia, is now in clinical trials for treating Covid-19. Preclinical trial data demonstrated that Fostamatinib could reduce mortality rates, ICU stay length, and disease severity in Covid-19 patients.

CONCLUSIONS

Our findings confirm the efficacy of our novel strategy that leverages control hubs as drug targets. This approach provides insights into the molecular mechanisms of potential therapeutics for Covid-19, making it a valuable tool for interpretable drug discovery. Our new approach is general and applicable to repurposing drugs for other diseases.

BQsupports: systematic assessment of the support and novelty of new biomedical associations

MOTIVATION

Living a Big Data era in Biomedicine, there is an unmet need to systematically assess experimental observations in the context of available information. This assessment would offer a means for a comprehensive and robust validation of biomedical data results and provide an initial estimate of the potential novelty of the findings.

RESULTS

Here we present BQsupports, a web-based tool built upon the Bioteque biomedical descriptors that systematically analyzes and quantifies the current support to a given set of observations. The tool relies on over 1000 distinct types of biomedical descriptors, covering over 11 different biological and chemical entities, including genes, cell lines, diseases, and small molecules. By exploring hundreds of descriptors, BQsupports provide support scores for each observation across a wide variety of biomedical contexts. These scores are then aggregated to summarize the biomedical support of the assessed dataset as a whole. Finally, the BQsupports also suggests predictive features of the given dataset, which can be exploited in downstream machine learning applications.

AVAILABILITY AND IMPLEMENTATION

The web application and underlying data are available online (https://bqsupports.irbbarcelona.org).

Recombination map tailored to Native Hawaiians improves robustness of genomic scans for positive selection

Recombination events establish the patterns of haplotypic structure in a population and estimates of recombination rates are used in several downstream population and statistical genetic analyses. Using suboptimal maps from distantly related populations may reduce the efficacy of genomic analyses, particularly for underrepresented populations such as the Native Hawaiians. To overcome this challenge, we constructed recombination maps using genome-wide array data from two study samples of Native Hawaiians: one reflecting the current admixed state of Native Hawaiians (NH map), and one based on individuals of enriched Polynesian ancestries (PNS map) with the potential to be used for less admixed Polynesian populations such as the Samoans. We found the recombination landscape to be less correlated with those from other continental populations (e.g. Spearman's rho = 0.79 between PNS and CEU (Utah residents with Northern and Western European ancestry) compared to 0.92 between YRI (Yoruba in Ibadan, Nigeria) and CEU at 50 kb resolution), likely driven by the unique demographic history of the Native Hawaiians. PNS also shared the fewest recombination hotspots with other populations (e.g. 8% of hotspots shared between PNS and CEU compared to 27% of hotspots shared between YRI and CEU). We found that downstream analyses in the Native Hawaiian population, such as local ancestry inference, imputation, and IBD segment and relatedness detections, would achieve similar efficacy when using the NH map compared to an omnibus map. However, for genome scans of adaptive loci using integrated haplotype scores, we found several loci with apparent genome-wide significant signals (|Z-score| > 4) in Native Hawaiians that would not have been significant when analyzed using NH-specific maps. Population-specific recombination maps may therefore improve the robustness of haplotype-based statistics and help us better characterize the evolutionary history that may underlie Native Hawaiian-specific health conditions that persist today.

AutoCore: A network-based definition of the core module of human autoimmunity and autoinflammation

Although research on rare autoimmune and autoinflammatory diseases has enabled definition of nonredundant regulators of homeostasis in human immunity, because of the single gene-single disease nature of many of these diseases, contributing factors were mostly unveiled in sequential and noncoordinated individual studies. We used a network-based approach for integrating a set of 186 inborn errors of immunity with predominant autoimmunity/autoinflammation into a comprehensive map of human immune dysregulation, which we termed "AutoCore." The AutoCore is located centrally within the interactome of all protein-protein interactions, connecting and pinpointing multidisease markers for a range of common, polygenic autoimmune/autoinflammatory diseases. The AutoCore can be subdivided into 19 endotypes that correspond to molecularly and phenotypically cohesive disease subgroups, providing a molecular mechanism-based disease classification and rationale toward systematic targeting for therapeutic purposes. Our study provides a proof of concept for using network-based methods to systematically investigate the molecular relationships between individual rare diseases and address a range of conceptual, diagnostic, and therapeutic challenges.

Differential gene expression analysis combined with molecular dynamics simulation study to elucidate the novel potential biomarker involved in pulmonary TB

Tuberculosis (TB) is a lethal multisystem disease that attacks the lungs' first line of defense. A substantial threat to public health and a primary cause of death is pulmonary TB. This study aimed to identify and investigate the probable differentially expressed genes (DEGs) primarily involved in Pulmonary TB. Accordingly, three independent gene expression data sets, numbered GSE139825, GSE139871, and GSE54992, were utilized for this purpose. The identified DEGs were used for bioinformatics-based analysis, including physical gene interaction, Gene Ontology (GO), network analysis and pathway studies using the Kyoto Encyclopedia of Genes and Genomes pathway (KEGG). The computational analysis predicted that TNFAIP6 is the significant DEG in the gene expression profiling of TB datasets. According to gene ontology analysis, TNFAIP6 is also essential in injury and inflammation. Further, TNFA1P6 is strongly linked to arsenic poisoning, evident from the results of NetworkAnalyst, a comprehensive and interactive platform for gene expression profiling via network visual analytics. As a result, the TNFAIP6 gene was ultimately chosen as a candidate DEG and subsequently employed for in silico structural characterization studies. The tertiary structure of TNFAIP6 was modelled using the ROBETTA server, followed by validation with SAVES and ProSA webserver. Additionally, structural dynamic studies, including molecular dynamics simulation (MDS) and essential dynamics analysis, including principal component (PC) based free energy landscape (FEL) analysis, was used for checking the stability of TNFAIP6 models. The dynamics result established the structural rigidity of modelled TNFAIP6 through RMSD, RMSF and RoG results. The FEL analysis revealed the restricted conformational flexibility of TNFAIP6 by displaying a single minimum energy basin in the contour plot. The comprehensive computational analysis established that TNFAIP6 could serve as a viable biomarker to assess the severity of pulmonary TB.

Long Non-Coding RNAs and Their "Discrete" Contribution to IBD and Johne's Disease-What Stands out in the Current Picture? A Comprehensive Review

Non-coding RNAs (ncRNA) have paved the way to new perspectives on the regulation of gene expression, not only in biology and medicine, but also in associated fields and technologies, ensuring advances in diagnostic means and therapeutic modalities. Critical in this multistep approach are the associations of long non-coding RNA (lncRNA) with diseases and their causal genes in their networks of interactions, gene enrichment and expression analysis, associated pathways, the monitoring of the involved genes and their functional roles during disease progression from one stage to another. Studies have shown that Johne's Disease (JD), caused by Mycobacterium avium subspecies partuberculosis (MAP), shares common lncRNAs, clinical findings, and other molecular entities with Crohn's Disease (CD). This has been a subject of vigorous investigation owing to the zoonotic nature of this condition, although results are still inconclusive. In this review, on one hand, the current knowledge of lncRNAs in cells is presented, focusing on the pathogenesis of gastrointestinal-related pathologies and MAP-related infections and, on the other hand, we attempt to dissect the associated genes and pathways involved. Furthermore, the recently characterized and novel lncRNAs share common pathologies with IBD and JD, including the expression, molecular networks, and dataset analysis results. These are also presented in an attempt to identify potential biomarkers pertinent to cattle and human disease phenotypes.

Pan-cancer proteogenomics connects oncogenic drivers to functional states

Cancer driver events refer to key genetic aberrations that drive oncogenesis; however, their exact molecular mechanisms remain insufficiently understood. Here, our multi-omics pan-cancer analysis uncovers insights into the impacts of cancer drivers by identifying their significant cis-effects and distal trans-effects quantified at the RNA, protein, and phosphoprotein levels. Salient observations include the association of point mutations and copy-number alterations with the rewiring of protein interaction networks, and notably, most cancer genes converge toward similar molecular states denoted by sequence-based kinase activity profiles. A correlation between predicted neoantigen burden and measured T cell infiltration suggests potential vulnerabilities for immunotherapies. Patterns of cancer hallmarks vary by polygenic protein abundance ranging from uniform to heterogeneous. Overall, our work demonstrates the value of comprehensive proteogenomics in understanding the functional states of oncogenic drivers and their links to cancer development, surpassing the limitations of studying individual cancer types.

Zinc Ions Modulate YY1 Activity: Relevance in Carcinogenesis

YY1 is widely recognized as an intrinsically disordered transcription factor that plays a role in development of many cancers. In most cases, its overexpression is correlated with tumor progression and unfavorable patient outcomes. Our latest research focusing on the role of zinc ions in modulating YY1's interaction with DNA demonstrated that zinc enhances the protein's multimeric state and affinity to its operator. In light of these findings, changes in protein concentration appear to be just one element relevant to modulating YY1-dependent processes. Thus, alterations in zinc ion concentration can directly and specifically impact the regulation of gene expression by YY1, in line with reports indicating a correlation between zinc ion levels and advancement of certain tumors. This review concentrates on other potential consequences of YY1 interaction with zinc ions that may act by altering charge distribution, conformational state distribution, or oligomerization to influence its interactions with molecular partners that can disrupt gene expression patterns.

HSPB2 facilitates neural regeneration through autophagy for sensorimotor recovery after traumatic brain injury

Autophagy is a promising target for promoting neural regeneration, which is essential for sensorimotor recovery following traumatic brain injury (TBI). Whether neuronal heat shock protein B2 (HSPB2), a small molecular heat shock protein, reduces injury and promotes recovery following TBI remains unclear. In this study, we demonstrated that HSPB2 was significantly increased in the neurons of a TBI mouse model, patients, and primary neuron cultures subjected to oxygen/glucose deprivation and reperfusion treatment. Upon creating a tamoxifen-induced neuron-specific HSPB2 overexpression transgenic mouse model, we found that elevated HSPB2 levels promoted long-term sensorimotor recovery and alleviated tissue loss after TBI. We also demonstrated that HSPB2 enhanced white matter structural and functional integrity, promoted central nervous system (CNS) plasticity, and accelerated long-term neural remodeling. Moreover, we found that autophagy occurred around injured brain tissues in patients, and the pro-regenerative effects of HSPB2 relied on its autophagy-promoting function. Mechanistically, HSPB2 may regulate autophagy possibly by forming the HSPB2/BCL2-associated athanogene 3/sequestosome-1 complex to facilitate the clearance of erroneously accumulated proteins in the axons. Treatment with the autophagy inhibitor chloroquine during the acute stage or delayed induction of HSPB2 remarkably impeded HSPB2's long-term reparative function, indicating the importance of acute-stage autophagy in long-term neuro-regeneration. Our findings highlight the beneficial role of HSPB2 in neuro-regeneration and functional recovery following acute CNS injury, thereby emphasizing the therapeutic potential of autophagy regulation for enhancing neuro-regeneration.

Massively parallel protein-protein interaction measurement by sequencing (MP3-seq) enables rapid screening of protein heterodimers

Protein-protein interactions (PPIs) regulate many cellular processes, and engineered PPIs have cell and gene therapy applications. Here we introduce massively parallel protein-protein interaction measurement by sequencing (MP3-seq), an easy-to-use and highly scalable yeast-two-hybrid approach for measuring PPIs. In MP3-seq, DNA barcodes are associated with specific protein pairs, and barcode enrichment can be read by sequencing to provide a direct measure of interaction strength. We show that MP3-seq is highly quantitative and scales to over 100,000 interactions. We apply MP3-seq to characterize interactions between families of rationally designed heterodimers and to investigate elements conferring specificity to coiled-coil interactions. Finally, we predict coiled heterodimer structures using AlphaFold-Multimer (AF-M) and train linear models on physics simulation energy terms to predict MP3-seq values. We find that AF-M and AF-M complex prediction-based models could be valuable for pre-screening interactions, but that measuring interactions experimentally remains necessary to rank their strengths quantitatively.

Proteomics and disease network associations evaluation of environmentally relevant Bisphenol A concentrations in a human 3D neural stem cell model

Bisphenol A (BPA) exposure is associated with a plethora of neurodevelopmental abnormalities and brain disorders. Previous studies have demonstrated BPA-induced perturbations to critical neural stem cell (NSC) characteristics, such as proliferation and differentiation, although the underlying molecular mechanisms remain under debate. The present study evaluated the effects of a repeated-dose exposure of environmentally relevant BPA concentrations during the in vitro 3D neural induction of human induced pluripotent stem cells (hiPSCs), emulating a chronic exposure scenario. Firstly, we demonstrated that our model is suitable for NSC differentiation during the early stages of embryonic brain development. Our morphological image analysis showed that BPA exposure at 0.01, 0.1 and 1 µM decreased the average spheroid size by day 21 (D21) of the neural induction, while no effect on cell viability was detected. No alteration to the rate of the neural induction was observed based on the expression of key neural lineage and neuroectodermal transcripts. Quantitative proteomics at D21 revealed several differentially abundant proteins across all BPA-treated groups with important functions in NSC proliferation and maintenance (e.g., FABP7, GPC4, GAP43, Wnt-8B, TPPP3). Additionally, a network analysis demonstrated alterations to the glycolytic pathway, potentially implicating BPA-induced changes to glycolytic signalling in NSC proliferation impairments, as well as the pathophysiology of brain disorders including intellectual disability, autism spectrum disorders, and amyotrophic lateral sclerosis (ALS). This study enhances the current understanding of BPA-related NSC aberrations based mostly on acute, often high dose exposures of rodent in vivo and in vitro models and human GWAS data in a novel human 3D cell-based model with real-life scenario relevant prolonged and low-level exposures, offering further mechanistic insights into the ramifications of BPA exposure on the developing human brain and consequently, later life neurological disorders.

Identifying Significantly Perturbed Subnetworks in Cancer Using Multiple Protein-Protein Interaction Networks

BACKGROUND

The identification of cancer driver genes and key molecular pathways has been the focus of large-scale cancer genome studies. Network-based methods detect significantly perturbed subnetworks as putative cancer pathways by incorporating genomics data with the topological information of PPI networks. However, commonly used PPI networks have distinct topological structures, making the results of the same method vary widely when applied to different networks. Furthermore, emerging context-specific PPI networks often have incomplete topological structures, which pose serious challenges for existing subnetwork detection algorithms.

METHODS

In this paper, we propose a novel method, referred to as MultiFDRnet, to address the above issues. The basic idea is to model a set of PPI networks as a multiplex network to preserve the topological structure of individual networks, while introducing dependencies among them, and, then, to detect significantly perturbed subnetworks on the modeled multiplex network using all the structural information simultaneously.

RESULTS

To illustrate the effectiveness of the proposed approach, an extensive benchmark analysis was conducted on both simulated and real cancer data. The experimental results showed that the proposed method is able to detect significantly perturbed subnetworks jointly supported by multiple PPI networks and to identify novel modular structures in context-specific PPI networks.

Integrating animal experiments, mass spectrometry and network-based approach to reveal the sleep-improving effects of Ziziphi Spinosae Semen and γ-aminobutyric acid mixture

BACKGROUND

Ziziphi Spinosae Semen (ZSS) is a plant widely used as medicine and food in Asian countries due to its numerous health benefits. γ-aminobutyric acid (GABA), a non-proteinaceous amino acid, is one of the major inhibitory neurotransmitters with a relaxant function. In this study, a system pharmacology approach was employed to assess the effects of a mixture composed of ZSS and GABA (ZSSG) on sleep improvement.

METHODS

Mice were divided into five groups (n = 10) and received either no treatment, sodium pentobarbital, or sodium barbital with diazepam or ZSSG. The effects of ZSSG on sleep quality were evaluated in mice, and differential metabolites associated with sleep were identified among the control, ZSS, GABA, and ZSSG groups. Additionally, network-based ingredient-insomnia proximity analysis was applied to explore the major ingredients.

RESULTS

ZSSG significantly improved sleep quality by decreasing sleep latency and prolonging sleep duration in sodium pentobarbital-induced sleeping mouse model (P < 0.05). ZSSG significantly enhanced the brain content of GABA in mice. Furthermore, ZSSG also significantly decreased sleep latency-induced by sodium barbital in mice (P < 0.05). Metabolic analysis revealed significant differences in 10 metabolites between ZSSG group and the groups administering ZSS or GABA. Lastly, using the network-based ingredient screening model, we discovered potential four active ingredients and three pairwise ingredient combinations with synergistic effect on insomnia from ZSSG among 85 ingredients identified by UPLC-Q/TOF-MS. Also, we have constructed an online computation platform.

CONCLUSION

Our data demonstrated that ZSSG improved the sleeping quality of mice and helped to balance metabolic disorders-associated with sleep disorders. Moreover, based on the network-based prediction method, the four potential active ingredients in ZSSG could serve as quality markers-associated with insomnia. The network-based framework may open up a new avenue for the discovery of active ingredients of herbal medicine for treating complex chronic diseases or symptoms, such as insomnia.

A cryptic microdeletion del(12)(p11.21p11.23) within an unbalanced translocation t(7;12)(q21.13;q23.1) implicates new candidate loci for intellectual disability and Kallmann syndrome

In a patient diagnosed with both Kallmann syndrome (KS) and intellectual disability (ID), who carried an apparently balanced translocation t(7;12)(q22;q24)dn, array comparative genomic hybridization (aCGH) disclosed a cryptic heterozygous 4.7 Mb deletion del(12)(p11.21p11.23), unrelated to the translocation breakpoint. This novel discovery prompted us to consider the possibility that the combination of KS and neurological disorder in this patient could be attributed to gene(s) within this specific deletion at 12p11.21-12p11.23, rather than disrupted or dysregulated genes at the translocation breakpoints. To further support this hypothesis, we expanded our study by screening five candidate genes at both breakpoints of the chromosomal translocation in a cohort of 48 KS patients. However, no mutations were found, thus reinforcing our supposition. In order to delve deeper into the characterization of the 12p11.21-12p11.23 region, we enlisted six additional patients with small copy number variations (CNVs) and analyzed eight individuals carrying small CNVs in this region from the DECIPHER database. Our investigation utilized a combination of complementary approaches. Firstly, we conducted a comprehensive phenotypic-genotypic comparison of reported CNV cases. Additionally, we reviewed knockout animal models that exhibit phenotypic similarities to human conditions. Moreover, we analyzed reported variants in candidate genes and explored their association with corresponding phenotypes. Lastly, we examined the interacting genes associated with these phenotypes to gain further insights. As a result, we identified a dozen candidate genes: TSPAN11 as a potential KS candidate gene, TM7SF3, STK38L, ARNTL2, ERGIC2, TMTC1, DENND5B, and ETFBKMT as candidate genes for the neurodevelopmental disorder, and INTS13, REP15, PPFIBP1, and FAR2 as candidate genes for KS with ID. Notably, the high-level expression pattern of these genes in relevant human tissues further supported their candidacy. Based on our findings, we propose that dosage alterations of these candidate genes may contribute to sexual and/or cognitive impairments observed in patients with KS and/or ID. However, the confirmation of their causal roles necessitates further identification of point mutations in these candidate genes through next-generation sequencing.

Monocarboxylate Transporter 13 (MCT13/SLC16A13) Functions as a Novel Plasma Membrane Oligopeptide Transporter

SLC16A13, which encodes the monocarboxylate transporter 13 (MCT13), is a susceptibility gene for type 2 diabetes and is expressed in the liver and duodenum. Some peptidase-resistant oligopeptides are absorbed in the gastrointestinal tract and affect glycemic control in the body. Their efficient absorption is mediated by oligopeptide transporter(s) at the apical and basolateral membranes of the intestinal epithelia; however, the molecules responsible for basolateral oligopeptide transport have not been identified. In this study, we examined whether MCT13 functions as a novel basolateral oligopeptide transporter. We evaluated the uptake of oligopeptides and peptidomimetics in MCT13-transfected cells. The uptake of cephradine, a probe for peptide transport system(s), significantly increased in MCT13-transfected cells, and this increase was sensitive to membrane potential. The cellular accumulation of bioactive peptides, such as anserine and carnosine, was decreased by MCT13, indicating MCT13-mediated efflux transport activity. In polarized Caco-2 cells, MCT13 was localized at the basolateral membrane. MCT13 induction enhanced cephradine transport in an apical-to-basal direction across Caco-2 cells. These results indicate that MCT13 functions as a novel efflux transporter of oligopeptides and peptidomimetics, driven by electrochemical gradients across the plasma membrane, and it may be involved in the transport of these compounds across the intestinal epithelia.

Predicting molecular mechanisms of hereditary diseases by using their tissue-selective manifestation

How do aberrations in widely expressed genes lead to tissue-selective hereditary diseases? Previous attempts to answer this question were limited to testing a few candidate mechanisms. To answer this question at a larger scale, we developed "Tissue Risk Assessment of Causality by Expression" (TRACE), a machine learning approach to predict genes that underlie tissue-selective diseases and selectivity-related features. TRACE utilized 4,744 biologically interpretable tissue-specific gene features that were inferred from heterogeneous omics datasets. Application of TRACE to 1,031 disease genes uncovered known and novel selectivity-related features, the most common of which was previously overlooked. Next, we created a catalog of tissue-associated risks for 18,927 protein-coding genes (https://netbio.bgu.ac.il/trace/). As proof-of-concept, we prioritized candidate disease genes identified in 48 rare-disease patients. TRACE ranked the verified disease gene among the patient's candidate genes significantly better than gene prioritization methods that rank by gene constraint or tissue expression. Thus, tissue selectivity combined with machine learning enhances genetic and clinical understanding of hereditary diseases.

GATA2 co-opts TGFβ1/SMAD4 oncogenic signaling and inherited variants at 6q22 to modulate prostate cancer progression

BACKGROUND

Aberrant somatic genomic alteration including copy number amplification is a hallmark of cancer genomes. We previously profiled genomic landscapes of prostate cancer (PCa), yet the underlying causal genes with prognostic potential has not been defined. It remains unclear how a somatic genomic event cooperates with inherited germline variants contribute to cancer predisposition and progression.

METHODS

We applied integrated genomic and clinical data, experimental models and bioinformatic analysis to identify GATA2 as a highly prevalent metastasis-associated genomic amplification in PCa. Biological roles of GATA2 in PCa metastasis was determined in vitro and in vivo. Global chromatin co-occupancy and co-regulation of GATA2 and SMAD4 was investigated by coimmunoprecipitation, ChIP-seq and RNA-seq assays. Tumor cellular assays, qRT-PCR, western blot, ChIP, luciferase assays and CRISPR-Cas9 editing methods were performed to mechanistically understand the cooperation of GATA2 with SMAD4 in promoting TGFβ1 and AR signaling and mediating inherited PCa risk and progression.

RESULTS

In this study, by integrated genomics and experimental analysis, we identified GATA2 as a prevalent metastasis-associated genomic amplification to transcriptionally augment its own expression in PCa. Functional experiments demonstrated that GATA2 physically interacted and cooperated with SMAD4 for genome-wide chromatin co-occupancy and co-regulation of PCa genes and metastasis pathways like TGFβ signaling. Mechanistically, GATA2 was cooperative with SMAD4 to enhance TGFβ and AR signaling pathways, and activated the expression of TGFβ1 via directly binding to a distal enhancer of TGFβ1. Strinkingly, GATA2 and SMAD4 globally mediated inherited PCa risk and formed a transcriptional complex with HOXB13 at the PCa risk-associated rs339331/6q22 enhancer, leading to increased expression of the PCa susceptibility gene RFX6.

CONCLUSIONS

Our study prioritizes causal genomic amplification genes with prognostic values in PCa and reveals the pivotal roles of GATA2 in transcriptionally activating the expression of its own and TGFβ1, thereby co-opting to TGFβ1/SMAD4 signaling and RFX6 at 6q22 to modulate PCa predisposition and progression.

Cross-protein transfer learning substantially improves disease variant prediction

BACKGROUND

Genetic variation in the human genome is a major determinant of individual disease risk, but the vast majority of missense variants have unknown etiological effects. Here, we present a robust learning framework for leveraging saturation mutagenesis experiments to construct accurate computational predictors of proteome-wide missense variant pathogenicity.

RESULTS

We train cross-protein transfer (CPT) models using deep mutational scanning (DMS) data from only five proteins and achieve state-of-the-art performance on clinical variant interpretation for unseen proteins across the human proteome. We also improve predictive accuracy on DMS data from held-out proteins. High sensitivity is crucial for clinical applications and our model CPT-1 particularly excels in this regime. For instance, at 95% sensitivity of detecting human disease variants annotated in ClinVar, CPT-1 improves specificity to 68%, from 27% for ESM-1v and 55% for EVE. Furthermore, for genes not used to train REVEL, a supervised method widely used by clinicians, we show that CPT-1 compares favorably with REVEL. Our framework combines predictive features derived from general protein sequence models, vertebrate sequence alignments, and AlphaFold structures, and it is adaptable to the future inclusion of other sources of information. We find that vertebrate alignments, albeit rather shallow with only 100 genomes, provide a strong signal for variant pathogenicity prediction that is complementary to recent deep learning-based models trained on massive amounts of protein sequence data. We release predictions for all possible missense variants in 90% of human genes.

CONCLUSIONS

Our results demonstrate the utility of mutational scanning data for learning properties of variants that transfer to unseen proteins.

XGDAG: explainable gene-disease associations via graph neural networks

MOTIVATION

Disease gene prioritization consists in identifying genes that are likely to be involved in the mechanisms of a given disease, providing a ranking of such genes. Recently, the research community has used computational methods to uncover unknown gene-disease associations; these methods range from combinatorial to machine learning-based approaches. In particular, during the last years, approaches based on deep learning have provided superior results compared to more traditional ones. Yet, the problem with these is their inherent black-box structure, which prevents interpretability.

RESULTS

We propose a new methodology for disease gene discovery, which leverages graph-structured data using graph neural networks (GNNs) along with an explainability phase for determining the ranking of candidate genes and understanding the model's output. Our approach is based on a positive-unlabeled learning strategy, which outperforms existing gene discovery methods by exploiting GNNs in a non-black-box fashion. Our methodology is effective even in scenarios where a large number of associated genes need to be retrieved, in which gene prioritization methods often tend to lose their reliability.

AVAILABILITY AND IMPLEMENTATION

The source code of XGDAG is available on GitHub at: https://github.com/GiDeCarlo/XGDAG. The data underlying this article are available at: https://www.disgenet.org/, https://thebiogrid.org/, https://doi.org/10.1371/journal.pcbi.1004120.s003, and https://doi.org/10.1371/journal.pcbi.1004120.s004.

Spinocerebellar ataxia 38: structure-function analysis shows ELOVL5 G230V is proteotoxic, conformationally altered and a mutational hotspot

Fatty acid elongase ELOVL5 is part of a protein family of multipass transmembrane proteins that reside in the endoplasmic reticulum where they regulate long-chain fatty acid elongation. A missense variant (c.689G>T p.Gly230Val) in ELOVL5 causes Spinocerebellar Ataxia subtype 38 (SCA38), a neurodegenerative disorder characterized by autosomal dominant inheritance, cerebellar Purkinje cell demise and adult-onset ataxia. Having previously showed aberrant accumulation of p.G230V in the Golgi complex, here we further investigated the pathogenic mechanisms triggered by p.G230V, integrating functional studies with bioinformatic analyses of protein sequence and structure. Biochemical analysis showed that p.G230V enzymatic activity was normal. In contrast, SCA38-derived fibroblasts showed reduced expression of ELOVL5, Golgi complex enlargement and increased proteasomal degradation with respect to controls. By heterologous overexpression, p.G230V was significantly more active than wild-type ELOVL5 in triggering the unfolded protein response and in decreasing viability in mouse cortical neurons. By homology modelling, we generated native and p.G230V protein structures whose superposition revealed a shift in Loop 6 in p.G230V that altered a highly conserved intramolecular disulphide bond. The conformation of this bond, connecting Loop 2 and Loop 6, appears to be elongase-specific. Alteration of this intramolecular interaction was also observed when comparing wild-type ELOVL4 and the p.W246G variant which causes SCA34. We demonstrate by sequence and structure analyses that ELOVL5 p.G230V and ELOVL4 p.W246G are position-equivalent missense variants. We conclude that SCA38 is a conformational disease and propose combined loss of function by mislocalization and gain of toxic function by ER/Golgi stress as early events in SCA38 pathogenesis.

N-terminal proteoforms may engage in different protein complexes

Alternative translation initiation and alternative splicing may give rise to N-terminal proteoforms, proteins that differ at their N-terminus compared with their canonical counterparts. Such proteoforms can have altered localizations, stabilities, and functions. Although proteoforms generated from splice variants can be engaged in different protein complexes, it remained to be studied to what extent this applies to N-terminal proteoforms. To address this, we mapped the interactomes of several pairs of N-terminal proteoforms and their canonical counterparts. First, we generated a catalogue of N-terminal proteoforms found in the HEK293T cellular cytosol from which 22 pairs were selected for interactome profiling. In addition, we provide evidence for the expression of several N-terminal proteoforms, identified in our catalogue, across different human tissues, as well as tissue-specific expression, highlighting their biological relevance. Protein-protein interaction profiling revealed that the overlap of the interactomes for both proteoforms is generally high, showing their functional relation. We also showed that N-terminal proteoforms can be engaged in new interactions and/or lose several interactions compared with their canonical counterparts, thus further expanding the functional diversity of proteomes.

Overlaid Transcriptional and Proteome Analyses Identify Mitotic Kinesins as Important Targets of Arylsulfonamide-Mediated RBM39 Degradation

Certain arylsulfonamides (ArSulf) induce an interaction between the E3 ligase substrate adaptor DCAF15 and the critical splicing factor RBM39, ultimately causing its degradation. However, degradation of a splicing factor introduces complex pleiotropic effects that are difficult to untangle, since, aside from direct protein degradation, downstream transcriptional effects also influence the proteome. By overlaying transcriptional data and proteome datasets, we distinguish transcriptional from direct degradation effects, pinpointing those proteins most impacted by splicing changes. Using our workflow, we identify and validate the upregulation of the arginine-and-serine rich protein (RSRP1) and the downregulation of the key kinesin motor proteins KIF20A and KIF20B due to altered splicing in the absence of RBM39. We further show that kinesin downregulation is connected to the multinucleation phenotype observed upon RBM39 depletion by ArSulfs. Our approach should be helpful in the assessment of potential cancer drug candidates which target splicing factors.

IMPLICATIONS

Our approach provides a workflow for identifying and studying the most strongly modulated proteins when splicing is altered. The work also uncovers a splicing-based approach toward pharmacologic targeting of mitotic kinesins.

ER chaperone GRP78/BiP translocates to the nucleus under stress and acts as a transcriptional regulator

Cancer cells are commonly subjected to endoplasmic reticulum (ER) stress. To gain survival advantage, cancer cells exploit the adaptive aspects of the unfolded protein response such as upregulation of the ER luminal chaperone GRP78. The finding that when overexpressed, GRP78 can escape to other cellular compartments to gain new functions regulating homeostasis and tumorigenesis represents a paradigm shift. Here, toward deciphering the mechanisms whereby GRP78 knockdown suppresses EGFR transcription, we find that nuclear GRP78 is prominent in cancer and stressed cells and uncover a nuclear localization signal critical for its translocation and nuclear activity. Furthermore, nuclear GRP78 can regulate expression of genes and pathways, notably those important for cell migration and invasion, by interacting with and inhibiting the activity of the transcriptional repressor ID2. Our study reveals a mechanism for cancer cells to respond to ER stress via transcriptional regulation mediated by nuclear GRP78 to adopt an invasive phenotype.

PCGAN: a generative approach for protein complex identification from protein interaction networks

MOTIVATION

Protein complexes are groups of polypeptide chains linked by non-covalent protein-protein interactions, which play important roles in biological systems and perform numerous functions, including DNA transcription, mRNA translation, and signal transduction. In the past decade, a number of computational methods have been developed to identify protein complexes from protein interaction networks by mining dense subnetworks or subgraphs.

RESULTS

In this article, different from the existing works, we propose a novel approach for this task based on generative adversarial networks, which is called PCGAN, meaning identifying Protein Complexes by GAN. With the help of some real complexes as training samples, our method can learn a model to generate new complexes from a protein interaction network. To effectively support model training and testing, we construct two more comprehensive and reliable protein interaction networks and a larger gold standard complex set by merging existing ones of the same organism (including human and yeast). Extensive comparison studies indicate that our method is superior to existing protein complex identification methods in terms of various performance metrics. Furthermore, functional enrichment analysis shows that the identified complexes are of high biological significance, which indicates that these generated protein complexes are very possibly real complexes.

AVAILABILITY AND IMPLEMENTATION

https://github.com/yul-pan/PCGAN.

Maximum likelihood estimation for reversible mechanistic network models

Mechanistic network models specify the mechanisms by which networks grow and change, allowing researchers to investigate complex systems using both simulation and analytical techniques. Unfortunately, it is difficult to write likelihoods for instances of graphs generated with mechanistic models, and thus it is near impossible to estimate the parameters using maximum likelihood estimation. In this paper, we propose treating the node sequence in a growing network model as an additional parameter, or as a missing random variable, and maximizing over the resulting likelihood. We develop this framework in the context of a simple mechanistic network model, used to study gene duplication and divergence, and test a variety of algorithms for maximizing the likelihood in simulated graphs. We also run the best-performing algorithm on one human protein-protein interaction network and four nonhuman protein-protein interaction networks. Although we focus on a specific mechanistic network model, the proposed framework is more generally applicable to reversible models.

Integrated interactome and transcriptome analysis reveals key host factors critical for SARS-CoV-2 infection

The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has seriously threatened global public health and caused huge economic losses. Omics studies of SARS-CoV-2 can help understand the interaction between the virus and host, thereby providing a new perspective in guiding the intervention and treatment of the SARS-CoV-2 infection. Since large amount of SARS-CoV-2 omics data have been accumulated in public databases, this study aimed to identify key host factors involved in SARS-CoV-2 infection through systematic integration of transcriptome and interactome data. By manually curating published studies, we obtained a comprehensive SARS-CoV-2-human protein-protein interactions (PPIs) network, comprising 3591 human proteins interacting with 31 SARS-CoV-2 viral proteins. Using the RobustRankAggregation method, we identified 123 multiple cell line common genes (CLCGs), of which 115 up-regulated CLCGs showed host enhanced innate immunity and chemotactic response signatures. Combined with network analysis, co-expression and functional enrichment analysis, we discovered four key host factors involved in SARS-CoV-2 infection: IFITM1, SERPINE1, DDX60, and TNFAIP2. Furthermore, SERPINE1 was found to facilitate SARS-CoV-2 replication, and can alleviate the endoplasmic reticulum (ER) stress induced by ORF8 protein through interaction with ORF8. Our findings highlight the importance of systematic integration analysis in understanding SARS-CoV-2-human interactions and provide valuable insights for future research on potential therapeutic targets against SARS-CoV-2 infection.

A network medicine approach to study comorbidities in heart failure with preserved ejection fraction

BACKGROUND

Comorbidities are expected to impact the pathophysiology of heart failure (HF) with preserved ejection fraction (HFpEF). However, comorbidity profiles are usually reduced to a few comorbid disorders. Systems medicine approaches can model phenome-wide comorbidity profiles to improve our understanding of HFpEF and infer associated genetic profiles.

METHODS

We retrospectively explored 569 comorbidities in 29,047 HF patients, including 8062 HFpEF and 6585 HF with reduced ejection fraction (HFrEF) patients from a German university hospital. We assessed differences in comorbidity profiles between HF subtypes via multiple correspondence analysis. Then, we used machine learning classifiers to identify distinctive comorbidity profiles of HFpEF and HFrEF patients. Moreover, we built a comorbidity network (HFnet) to identify the main disease clusters that summarized the phenome-wide comorbidity. Lastly, we predicted novel gene candidates for HFpEF by linking the HFnet to a multilayer gene network, integrating multiple databases. To corroborate HFpEF candidate genes, we collected transcriptomic data in a murine HFpEF model. We compared predicted genes with the murine disease signature as well as with the literature.

RESULTS

We found a high degree of variance between the comorbidity profiles of HFpEF and HFrEF, while each was more similar to HFmrEF. The comorbidities present in HFpEF patients were more diverse than those in HFrEF and included neoplastic, osteologic and rheumatoid disorders. Disease communities in the HFnet captured important comorbidity concepts of HF patients which could be assigned to HF subtypes, age groups, and sex. Based on the HFpEF comorbidity profile, we predicted and recovered gene candidates, including genes involved in fibrosis (COL3A1, LOX, SMAD9, PTHL), hypertrophy (GATA5, MYH7), oxidative stress (NOS1, GSST1, XDH), and endoplasmic reticulum stress (ATF6). Finally, predicted genes were significantly overrepresented in the murine transcriptomic disease signature providing additional plausibility for their relevance.

CONCLUSIONS

We applied systems medicine concepts to analyze comorbidity profiles in a HF patient cohort. We were able to identify disease clusters that helped to characterize HF patients. We derived a distinct comorbidity profile for HFpEF, which was leveraged to suggest novel candidate genes via network propagation. The identification of distinctive comorbidity profiles and candidate genes from routine clinical data provides insights that may be leveraged to improve diagnosis and identify treatment targets for HFpEF patients.

The Impact of Genomic Variation on Function (IGVF) Consortium

Our genomes influence nearly every aspect of human biology from molecular and cellular functions to phenotypes in health and disease. Human genetics studies have now associated hundreds of thousands of differences in our DNA sequence ("genomic variation") with disease risk and other phenotypes, many of which could reveal novel mechanisms of human biology and uncover the basis of genetic predispositions to diseases, thereby guiding the development of new diagnostics and therapeutics. Yet, understanding how genomic variation alters genome function to influence phenotype has proven challenging. To unlock these insights, we need a systematic and comprehensive catalog of genome function and the molecular and cellular effects of genomic variants. Toward this goal, the Impact of Genomic Variation on Function (IGVF) Consortium will combine approaches in single-cell mapping, genomic perturbations, and predictive modeling to investigate the relationships among genomic variation, genome function, and phenotypes. Through systematic comparisons and benchmarking of experimental and computational methods, we aim to create maps across hundreds of cell types and states describing how coding variants alter protein activity, how noncoding variants change the regulation of gene expression, and how both coding and noncoding variants may connect through gene regulatory and protein interaction networks. These experimental data, computational predictions, and accompanying standards and pipelines will be integrated into an open resource that will catalyze community efforts to explore genome function and the impact of genetic variation on human biology and disease across populations.

A Transcriptome-Wide Analysis of Psoriasis: Identifying the Potential Causal Genes and Drug Candidates

Psoriasis is a chronic inflammatory skin disease characterized by cutaneous eruptions and pruritus. Because the genetic backgrounds of psoriasis are only partially revealed, an integrative and rigorous study is necessary. We conducted a transcriptome-wide association study (TWAS) with the new Genotype-Tissue Expression version 8 reference panels, including some tissue and multi-tissue panels that were not used previously. We performed tissue-specific heritability analyses on genome-wide association study data to prioritize the tissue panels for TWAS analysis. TWAS and colocalization (COLOC) analyses were performed with eight tissues from the single-tissue panels and the multi-tissue panels of context-specific genetics (CONTENT) to increase tissue specificity and statistical power. From TWAS, we identified the significant associations of 101 genes in the single-tissue panels and 64 genes in the multi-tissue panels, of which 26 genes were replicated in the COLOC. Functional annotation and network analyses identified that the genes were associated with psoriasis and/or immune responses. We also suggested drug candidates that interact with jointly significant genes through a conditional and joint analysis. Together, our findings may contribute to revealing the underlying genetic mechanisms and provide new insights into treatments for psoriasis.

FUBP1 is a general splicing factor facilitating 3' splice site recognition and splicing of long introns

Splicing of pre-mRNAs critically contributes to gene regulation and proteome expansion in eukaryotes, but our understanding of the recognition and pairing of splice sites during spliceosome assembly lacks detail. Here, we identify the multidomain RNA-binding protein FUBP1 as a key splicing factor that binds to a hitherto unknown cis-regulatory motif. By collecting NMR, structural, and in vivo interaction data, we demonstrate that FUBP1 stabilizes U2AF2 and SF1, key components at the 3' splice site, through multivalent binding interfaces located within its disordered regions. Transcriptional profiling and kinetic modeling reveal that FUBP1 is required for efficient splicing of long introns, which is impaired in cancer patients harboring FUBP1 mutations. Notably, FUBP1 interacts with numerous U1 snRNP-associated proteins, suggesting a unique role for FUBP1 in splice site bridging for long introns. We propose a compelling model for 3' splice site recognition of long introns, which represent 80% of all human introns.