SVD-phy: improved prediction of protein functional associations through singular value decomposition of phylogenetic profiles

Predicting Protein Function in the AI and Big Data Era

It is an exciting time for researchers working to link proteins to their functions. Most techniques for extracting functional information from genomic sequences were developed several years ago, with major progress driven by the availability of big data. Now, groundbreaking advances in deep-learning and AI-based methods have enriched protein databases with three-dimensional information and offer the potential to predict biochemical properties and biomolecular interactions, providing key functional insights. This progress is expected to increase the proportion of functionally bright proteins in databases and deepen our understanding of life at the molecular level.

Expression and immunological role of FUNDC2 in pan-cancer

FUNDC2 is a novel mitochondrial protein and is highly involved in various cancers. However, expression pattern and possible role and mechanism of FUNDC2 in pan-cancer remain to be investigated. TIMER 2.0 was used to investigate the expression patterns and immune infiltration of FUNDC2. GEPIA was applied to study the relationship between level of FUNDC2 and prognosis of the patients with pan-cancer. STRING was employed to analyze the potential interacting proteins of FUNDC2. The phosphorylation sites were predicted by cBioPortal and PhosphoNet. Furthermore, variations of FUNDC2 in cancers were investigated by cBioPortal. Finally, AlphaFold was used to predict the structure of FUNDC2. The data show that there were significant differences in the expression levels of FUNDC2 between cancer tissues and controls. Specifically, the levels of FUNDC2 in 8 cancers were significantly lower than the respective controls. The survival time of the cancer patients with higher levels of FUNDC2 was longer than that of lower FUNDC2 in most different types of cancers. The pattern of FUNDC2 was significantly related to immune infiltration of B cells of cancer patients. STRING analysis revealed that FUNDC2 can interact with FUNDC1, et al. Fifteen phosphorylation sites were predicted by PhosphoNet and cBioPortal, of which the S167 also overlapped with the mutation sites of FUNDC2. These data collectively show that the mitochondrial protein FUNDC2 may serve as a possible prognostic biomarker across various cancers and the mechanism may include immune infiltration.

EvoWeaver: large-scale prediction of gene functional associations from coevolutionary signals

The known universe of uncharacterized proteins is expanding far faster than our ability to annotate their functions through laboratory study. Computational annotation approaches rely on similarity to previously studied proteins, thereby ignoring unstudied proteins. Coevolutionary approaches hold promise for injecting new information into our knowledge of the protein universe by linking proteins through 'guilt-by-association'. However, existing coevolutionary algorithms have insufficient accuracy and scalability to connect the entire universe of proteins. We present EvoWeaver, a method that weaves together 12 signals of coevolution to quantify the degree of shared evolution between genes. EvoWeaver accurately identifies proteins involved in protein complexes or separate steps of a biochemical pathway. We show the merits of EvoWeaver by partly reconstructing known biochemical pathways without any prior knowledge other than that available from genomic sequences. Applying EvoWeaver to 1545 gene groups from 8564 genomes reveals missing connections in popular databases and potentially undiscovered links between proteins.

Whole Transcriptome RNA-Seq Reveals Drivers of Pathological Dysfunction in a Transgenic Model of Alzheimer's Disease

Alzheimer's disease (AD) affects more than 55 million people worldwide, yet current theories cannot fully explain its aetiology. Accordingly, gene expression profiling has been used to provide a holistic view of the biology underpinning AD. Focusing primarily on protein-coding genes, such approaches have highlighted a critical involvement of microglia-related inflammatory processes. Simultaneous investigation of transcriptional regulators and noncoding RNA (ncRNA) can offer further insight into AD biology and inform the development of disease-modifying therapies. We previously described a method for whole transcriptome sampling to simultaneously investigate protein-coding genes and ncRNA. Here, we use this technique to explore transcriptional changes in a murine model of AD (15-month-old APP/PS1 mice). We confirmed the extensive involvement of microglia-associated genes and gene networks, consistent with literature. We also report a wealth of differentially-expressed non-coding RNA - including microRNA, long non-coding RNA, small nuclear and small nucleolar RNA, and pseudogenes - many of which have been overlooked previously. Transcription factor analysis determined that six transcription factors likely regulate gene expression changes in this model (Irf8, Junb, c-Fos, Lmo2, Runx1, and Nfe2l2). We then utilised validated miRNA-target interactions, finding 60 interactions between 15 miRNA and 42 mRNA (messenger RNA) with largely consistent directionality. Furthermore, we found that eight transcription factors (Clock, Lmo2, Runx1, Nfe2l2, Egr2, c-Fos, Junb, and Nr4a1) are likely responsible for the regulation of miRNA expression. Taken together, these data indicate a complex interplay of coding and non-coding RNA, driven by a small number of specific transcription factors, contributing to transcriptional changes in 15-month-old APP/PS1 mice.

A large-scale benchmark for network inference from single-cell perturbation data

Mapping biological mechanisms in cellular systems is a fundamental step in early-stage drug discovery that serves to generate hypotheses on what disease-relevant molecular targets may effectively be modulated by pharmacological interventions. With the advent of high-throughput methods for measuring single-cell gene expression under genetic perturbations, we now have effective means for generating evidence for causal gene-gene interactions at scale. However, evaluating the performance of network inference methods in real-world environments is challenging due to the lack of ground-truth knowledge. Moreover, traditional evaluations conducted on synthetic datasets do not reflect the performance in real-world systems. We thus introduce CausalBench, a benchmark suite revolutionizing network inference evaluation with real-world, large-scale single-cell perturbation data. CausalBench, distinct from existing benchmarks, offers biologically-motivated metrics and distribution-based interventional measures, providing a more realistic evaluation of network inference methods. An initial systematic evaluation of state-of-the-art causal inference methods using our CausalBench suite highlights how poor scalability of existing methods limits performance. Moreover, methods that use interventional information do not outperform those that only use observational data, contrary to what is observed on synthetic benchmarks. CausalBench subsequently enables the development of numerous promising methods through a community challenge, thus demonstrating its potential as a transformative tool in the field of computational biology, bridging the gap between theoretical innovation and practical application in drug discovery and disease understanding. Thus, CausalBench opens new avenues for method developers in causal network inference research, and provides to practitioners a principled and reliable way to track progress in network methods for real-world interventional data.

In Vitro Functional Validation of an Anti-FREM2 Nanobody for Glioblastoma Cell Targeting

Background/Objectives: Glioblastomas are the most common brain malignancies. Despite the implementation of multimodal therapy, patient life expectancy after diagnosis is barely 12 to 18 months. Glioblastomas are highly heterogeneous at the genetic and epigenetic level and comprise multiple different cell subpopulations. Therefore, small molecules such as nanobodies, able to target membrane proteins specific to glioblastoma cells or specific cell types within the tumor are being investigated as novel tools to treat glioblastomas. Methods: Here, we describe the identification of such a nanobody and its in silico and in vitro validation. NB3F18, as we named it, is directed against the membrane-associated protein FREM2, overexpressed in glioblastoma stem cells. Results: Three dimensional in silico modeling indicated that NB3F18 and FREM2 form a stable complex. Surface plasmon resonance confirmed their interaction with moderate affinity. As we demonstrated by flow cytometry, NB3F18 binds to glioblastoma stem cells to a greater extent than to differentiated glioblastoma cells and astrocytes. Immunocytochemistry revealed surface localization of NB3F18 on glioblastoma stem cells, whereas cytoplasmic localization of NB3F18 was observed in other cell lines. NB3F18 was detected by transmission electron microscopy on the plasma membrane and in various compartments of the endocytic pathway, from endocytic vesicles to multivesicular bodies (endosomes) and lysosomes. Interestingly, NB3F18 was cytotoxic to glioblastoma stem cells. Conclusions: Collectively, NB3F18 has been qualified as an interesting tool to target glioblastoma cells and as a potential vehicle to deliver biological or pharmaceutical agents to these cells.

The STRING database in 2025: protein networks with directionality of regulation

Proteins cooperate, regulate and bind each other to achieve their functions. Understanding the complex network of their interactions is essential for a systems-level description of cellular processes. The STRING database compiles, scores and integrates protein-protein association information drawn from experimental assays, computational predictions and prior knowledge. Its goal is to create comprehensive and objective global networks that encompass both physical and functional interactions. Additionally, STRING provides supplementary tools such as network clustering and pathway enrichment analysis. The latest version, STRING 12.5, introduces a new 'regulatory network', for which it gathers evidence on the type and directionality of interactions using curated pathway databases and a fine-tuned language model parsing the literature. This update enables users to visualize and access three distinct network types-functional, physical and regulatory-separately, each applicable to distinct research needs. In addition, the pathway enrichment detection functionality has been updated, with better false discovery rate corrections, redundancy filtering and improved visual displays. The resource now also offers improved annotations of clustered networks and provides users with downloadable network embeddings, which facilitate the use of STRING networks in machine learning and allow cross-species transfer of protein information. The STRING database is available online at https://string-db.org/.

Assembling bacterial puzzles: piecing together functions into microbial pathways

Functional metagenomics enables the study of unexplored bacterial diversity, gene families, and pathways essential to microbial communities. However, discovering biological insights with these data is impeded by the scarcity of quality annotations. Here, we use a co-occurrence-based analysis of predicted microbial protein functions to uncover pathways in genomic and metagenomic biological systems. Our approach, based on phylogenetic profiles, improves the identification of functional relationships, or participation in the same biochemical pathway, between enzymes over a comparable homology-based approach. We optimized the design of our profiles to identify potential pathways using minimal data, clustered functionally related enzyme pairs into multi-enzymatic pathways, and evaluated our predictions against reference pathways in the KEGG database. We then demonstrated a novel extension of this approach to predict inter-bacterial protein interactions amongst members of a marine microbiome. Most significantly, we show our method predicts emergent biochemical pathways between known and unknown functions. Thus, our work establishes a basis for identifying the potential functional capacities of the entire metagenome, capturing previously unknown and abstract functions into discrete putative pathways.

PFAS and their association with the increased risk of cardiovascular disease in postmenopausal women

Cardiovascular diseases (CVDs) are one of the major causes of death globally. In addition to traditional risk factors such as unhealthy lifestyles (smoking, obesity, sedentary) and genetics, common environmental exposures, including persistent environmental contaminants, may also influence CVD risk. Per- and polyfluoroalkyl substances (PFASs) are a class of highly fluorinated chemicals used in household consumer and industrial products known to persist in our environment for years, causing health concerns that are now linked to endocrine disruptions and related outcomes in women, including interference of the cardiovascular and reproductive systems. In postmenopausal women, higher levels of PFAS are observed than in premenopausal women due to the cessation of menstruation, which is crucial for PFAS excretion. Because of these findings, we explored the association between perfluorooctanoic acid (PFOA), perfluorooctane sulfonate (PFOS), and perfluorobutanesulfonic acid in postmenopausal women from our previously established CVD study. We used liquid chromatography with tandem mass spectrometry, supported by machine learning approaches, and the detection and quantification of serum metabolites and proteins. Here, we show that PFOS can be a good predictor of coronary artery disease, whereas PFOA can be an intermediate predictor of coronary microvascular disease. We also found that the PFAS levels in our study are significantly associated with inflammation-related proteins. Our findings may provide new insight into the potential mechanisms underlying the PFAS-induced risk of CVDs in this population. This study shows that exposure to PFOA and PFOS is associated with an increased risk of cardiovascular disease in postmenopausal women. PFOS and PFOA levels correlate with amino acids and proteins related to inflammation. These circulating biomarkers contribute to the etiology of CVD and potentially implicate a mechanistic relationship between PFAS exposure and increased risk of cardiovascular events in this population.

Landscape of protein-protein interactions during hepatitis C virus assembly and release

Assembly of infectious hepatitis C virus (HCV) particles requires multiple cellular proteins including for instance apolipoprotein E (ApoE). To describe these protein-protein interactions, we performed an affinity purification mass spectrometry screen of HCV-infected cells. We used functional viral constructs with epitope-tagged envelope protein 2 (E2), protein (p) 7, or nonstructural protein 4B (NS4B) as well as cells expressing a tagged variant of ApoE. We also evaluated assembly stage-dependent remodeling of protein complexes by using viral mutants carrying point mutations abrogating particle production at distinct steps of the HCV particle production cascade. Five ApoE binding proteins, 12 p7 binders, 7 primary E2 interactors, and 24 proteins interacting with NS4B were detected. Cell-derived PREB, STT3B, and SPCS2 as well as viral NS2 interacted with both p7 and E2. Only GTF3C3 interacted with E2 and NS4B, highlighting that HCV assembly and replication complexes exhibit largely distinct interactomes. An HCV core protein mutation, preventing core protein decoration of lipid droplets, profoundly altered the E2 interactome. In cells replicating this mutant, E2 interactions with HSPA5, STT3A/B, RAD23A/B, and ZNF860 were significantly enhanced, suggesting that E2 protein interactions partly depend on core protein functions. Bioinformatic and functional studies including STRING network analyses, RNA interference, and ectopic expression support a role of Rad23A and Rad23B in facilitating HCV infectious virus production. Both Rad23A and Rad23B are involved in the endoplasmic reticulum (ER)-associated protein degradation (ERAD). Collectively, our results provide a map of host proteins interacting with HCV assembly proteins, and they give evidence for the involvement of ER protein folding machineries and the ERAD pathway in the late stages of the HCV replication cycle.IMPORTANCEHepatitis C virus (HCV) establishes chronic infections in the majority of exposed individuals. This capacity likely depends on viral immune evasion strategies. One feature likely contributing to persistence is the formation of so-called lipo-viro particles. These peculiar virions consist of viral structural proteins and cellular lipids and lipoproteins, the latter of which aid in viral attachment and cell entry and likely antibody escape. To learn about how lipo-viro particles are coined, here, we provide a comprehensive overview of protein-protein interactions in virus-producing cells. We identify numerous novel and specific HCV E2, p7, and cellular apolipoprotein E-interacting proteins. Pathway analyses of these interactors show that proteins participating in processes such as endoplasmic reticulum (ER) protein folding, ER-associated protein degradation, and glycosylation are heavily engaged in virus production. Moreover, we find that the proteome of HCV replication sites is distinct from the assembly proteome, suggesting that transport process likely shuttles viral RNA to assembly sites.

FAVA: high-quality functional association networks inferred from scRNA-seq and proteomics data

MOTIVATION

Protein networks are commonly used for understanding how proteins interact. However, they are typically biased by data availability, favoring well-studied proteins with more interactions. To uncover functions of understudied proteins, we must use data that are not affected by this literature bias, such as single-cell RNA-seq and proteomics. Due to data sparseness and redundancy, functional association analysis becomes complex.

RESULTS

To address this, we have developed FAVA (Functional Associations using Variational Autoencoders), which compresses high-dimensional data into a low-dimensional space. FAVA infers networks from high-dimensional omics data with much higher accuracy than existing methods, across a diverse collection of real as well as simulated datasets. FAVA can process large datasets with over 0.5 million conditions and has predicted 4210 interactions between 1039 understudied proteins. Our findings showcase FAVA's capability to offer novel perspectives on protein interactions. FAVA functions within the scverse ecosystem, employing AnnData as its input source.

AVAILABILITY AND IMPLEMENTATION

Source code, documentation, and tutorials for FAVA are accessible on GitHub at https://github.com/mikelkou/fava. FAVA can also be installed and used via pip/PyPI as well as via the scverse ecosystem https://github.com/scverse/ecosystem-packages/tree/main/packages/favapy.

An Enzymatic Cofactor Regeneration System for the in-Vitro Reduction of Isolated C=C Bonds by Geranylgeranyl Reductases

Cofactor regeneration systems are of major importance for the applicability of oxidoreductases in biocatalysis. Previously, geranylgeranyl reductases have been investigated for the enzymatic reduction of isolated C=C bonds. However, an enzymatic cofactor-regeneration system for in vitro use is lacking. In this work, we report a ferredoxin from the archaea Archaeoglobus fulgidus that regenerates the flavin of the corresponding geranylgeranyl reductase. The proteins were heterologously produced, and the regeneration was coupled to a ferredoxin reductase from Escherichia coli and a glucose dehydrogenase from Bacillus subtilis, thereby enabling the reduction of isolated C=C bonds by purified enzymes. The system was applied in crude, cell-free extracts and gave conversions comparable to those of a previous method using sodium dithionite for cofactor regeneration. Hence, an enzymatic approach to the reduction of isolated C=C bonds can be coupled with common systems for the regeneration of nicotinamide cofactors, thereby opening new perspectives for the application of geranylgeranyl reductases in biocatalysis.

An In Vitro Evaluation and Network Pharmacology Analysis of Prospective Anti-Prostate Cancer Activity from Perilla frutescens

Perilla frutescens (L.) Britt. is extensively cultivated in East Asia as a dietary vegetable, and nutraceuticals are reportedly rich in bioactive compounds, especially with anticancer activities. This study explored the in vitro cytotoxic effects of P. frutescens parts' (stems, leaves, and seeds) extracts on prostate cancer cells (DU-145) and possible interactions of putative metabolites to related prostate cancer targets in silico. The ethanol extract of P. frutescens leaves was the most cytotoxic for the prostate cancer cells. From high-performance liquid chromatography analysis, rosmarinic acid was identified as the major metabolite in the leaf extracts. Network analysis revealed interactions from multiple affected targets and pathways of the metabolites. From gene ontology enrichment analysis, P. frutescens leaf metabolites could significantly affect 14 molecular functions and 12 biological processes in five cellular components. Four (4) KEGG pathways, including for prostate cancer, and six (6) Reactome pathways were shown to be significantly affected. The molecular simulation confirmed the interactions of relevant protein targets with key metabolites, including rosmarinic acid. This study could potentially lead to further exploration of P. frutescens leaves or their metabolites for prostate cancer treatment and prevention.

SHE9 deletion mutants display fitness defects during diauxic shift in Saccharomyces cerevisiae

Saccharomyces cerevisiae protein She9 is localized to the inner mitochondrial membrane and is required for normal mitochondrial morphology. While deletion mutants of SHE9 ( she9Δ ) are viable and display large ring-like mitochondrial structures, the molecular function of SHE9 is still unknown. We report a decreased growth of she9Δ cells during a diauxic shift, where mitochondria are primarily employing oxidative phosphorylation to generate ATP versus the alternative mechanism of glycolysis in high glucose conditions. Further bioinformatics analysis reveal putative functional protein associations, and proposes a model to aid in the understanding of the molecular function of She9.

MSIF-LNP: microbial and human health association prediction based on matrix factorization noise reduction for similarity fusion and bidirectional linear neighborhood label propagation

Studies have shown that microbes are closely related to human health. Clarifying the relationship between microbes and diseases that cause health problems can provide new solutions for the treatment, diagnosis, and prevention of diseases, and provide strong protection for human health. Currently, more and more similarity fusion methods are available to predict potential microbe-disease associations. However, existing methods have noise problems in the process of similarity fusion. To address this issue, we propose a method called MSIF-LNP that can efficiently and accurately identify potential connections between microbes and diseases, and thus clarify the relationship between microbes and human health. This method is based on matrix factorization denoising similarity fusion (MSIF) and bidirectional linear neighborhood propagation (LNP) techniques. First, we use non-linear iterative fusion to obtain a similarity network for microbes and diseases by fusing the initial microbe and disease similarities, and then reduce noise by using matrix factorization. Next, we use the initial microbe-disease association pairs as label information to perform linear neighborhood label propagation on the denoised similarity network of microbes and diseases. This enables us to obtain a score matrix for predicting microbe-disease relationships. We evaluate the predictive performance of MSIF-LNP and seven other advanced methods through 10-fold cross-validation, and the experimental results show that MSIF-LNP outperformed the other seven methods in terms of AUC. In addition, the analysis of Cystic fibrosis and Obesity cases further demonstrate the predictive ability of this method in practical applications.

PPNet: Identifying Functional Association Networks by Phylogenetic Profiling of Prokaryotic Genomes

Identification of microbial functional association networks allows interpretation of biological phenomena and a greater understanding of the molecular basis of pathogenicity and also underpins the formulation of control measures. Here, we describe PPNet, a tool that uses genome information and analysis of phylogenetic profiles with binary similarity and distance measures to derive large-scale bacterial gene association networks of a single species. As an exemplar, we have derived a functional association network in the pig pathogen Streptococcus suis using 81 binary similarity and dissimilarity measures which demonstrates excellent performance based on the area under the receiver operating characteristic (AUROC), the area under the precision-recall (AUPR), and a derived overall scoring method. Selected network associations were validated experimentally by using bacterial two-hybrid experiments. We conclude that PPNet, a publicly available (https://github.com/liyangjie/PPNet), can be used to construct microbial association networks from easily acquired genome-scale data. IMPORTANCE This study developed PPNet, the first tool that can be used to infer large-scale bacterial functional association networks of a single species. PPNet includes a method for assigning the uniqueness of a bacterial strain using the average nucleotide identity and the average nucleotide coverage. PPNet collected 81 binary similarity and distance measures for phylogenetic profiling and then evaluated and divided them into four groups. PPNet can effectively capture gene networks that are functionally related to phenotype from publicly prokaryotic genomes, as well as provide valuable results for downstream analysis and experiment testing.

ProtInteract: A deep learning framework for predicting protein-protein interactions

Proteins mainly perform their functions by interacting with other proteins. Protein-protein interactions underpin various biological activities such as metabolic cycles, signal transduction, and immune response. However, due to the sheer number of proteins, experimental methods for finding interacting and non-interacting protein pairs are time-consuming and costly. We therefore developed the ProtInteract framework to predict protein-protein interaction. ProtInteract comprises two components: first, a novel autoencoder architecture that encodes each protein's primary structure to a lower-dimensional vector while preserving its underlying sequence attributes. This leads to faster training of the second network, a deep convolutional neural network (CNN) that receives encoded proteins and predicts their interaction under three different scenarios. In each scenario, the deep CNN predicts the class of a given encoded protein pair. Each class indicates different ranges of confidence scores corresponding to the probability of whether a predicted interaction occurs or not. The proposed framework features significantly low computational complexity and relatively fast response. The contributions of this work are twofold. First, ProtInteract assimilates the protein's primary structure into a pseudo-time series. Therefore, we leverage the nature of the time series of proteins and their physicochemical properties to encode a protein's amino acid sequence into a lower-dimensional vector space. This approach enables extracting highly informative sequence attributes while reducing computational complexity. Second, the ProtInteract framework utilises this information to identify protein interactions with other proteins based on its amino acid configuration. Our results suggest that the proposed framework performs with high accuracy and efficiency in predicting protein-protein interactions.

NADPH oxidase 4 is dispensable for skin myofibroblast differentiation and wound healing

Differentiation of fibroblasts to myofibroblasts is governed by the transforming growth factor beta (TGF-β) through a mechanism involving redox signaling and generation of reactive oxygen species (ROS). Myofibroblasts synthesize proteins of the extracellular matrix (ECM) and display a contractile phenotype. Myofibroblasts are predominant contributors of wound healing and several pathological states, including fibrotic diseases and cancer. Inhibition of the ROS-generating enzyme NADPH oxidase 4 (NOX4) has been proposed to mitigate fibroblast to myofibroblast differentiation and to offer a therapeutic option for the treatment of fibrotic diseases. In this study, we addressed the role of NOX4 in physiological wound healing and in TGF-β-induced myofibroblast differentiation. We explored the phenotypic changes induced by TGF-β in primary skin fibroblasts isolated from Nox4-deficient mice by immunofluorescence, Western blotting and RNA sequencing. Mice deficient for Cyba, the gene coding for p22^phox, a key subunit of NOX4 were used for confirmatory experiments as well as human primary skin fibroblasts. In vivo, the wound healing was similar in wild-type and Nox4-deficient mice. In vitro, despite a strong upregulation following TGF-β treatment, Nox4 did not influence skin myofibroblast differentiation although a putative NOX4 inhibitor GKT137831 and a flavoprotein inhibitor diphenylene iodonium mitigated this mechanism. Transcriptomic analysis revealed upregulation of the mitochondrial protein Ucp2 and the stress-response protein Hddc3 in Nox4-deficient fibroblasts, which had however no impact on fibroblast bioenergetics. Altogether, we provide extensive evidence that NOX4 is dispensable for wound healing and skin fibroblast to myofibroblast differentiation, and suggest that another H₂O₂-generating flavoprotein drives this mechanism.

Combining the In Silico and In Vitro Assays to Identify Strobilanthes cusia Kuntze Bioactives against Penicillin-Resistant Streptococcus pneumoniae

Leaves of Strobilanthes cusia Kuntze (S. cusia) are a widely used alexipharmic Traditional Chinese Medicine (TCM) in southern China for the prevention of cold and respiratory tract infectious diseases. One of the most common bacterial pathogens in the respiratory tract is the gram-positive bacterium Streptococcus pneumoniae. The antibiotic resistance of colonized S. pneumoniae makes it a more serious threat to public health. In this study, the leaves of S. cusia were found to perform antibacterial effects on the penicillin-resistant S. pneumoniae (PRSP). Confocal assay and Transmission Electron Microscopy (TEM) monitored the diminished cell wall integrity and capsule thickness of the PRSP with treatment. The following comparative proteomics analysis revealed that the glycometabolism-related pathways were enriched for the differentially expressed proteins between the samples with treatment and the control. To further delve into the specific single effective compound, the bio-active contents of leaves of S. cusia were analyzed by UPLC-UV-ESI-Q-TOF/MS, and 23 compounds were isolated for anti-PRSP screening. Among them, Tryptanthrin demonstrated the most promising effect, and it possibly inhibited the N-glycan degradation proteins, as suggested by reverse docking analysis in silico and further experimental verification by the surface plasmon resonance assay (SPR). Our study provided a research foundation for applications of the leaves of S. cusia as a TCM, and supplied a bio-active compound Tryptanthrin as a candidate drug skeleton for infectious diseases caused by the PRSP.

Developing Photoaffinity Probes for Dopamine Receptor D2 to Determine Targets of Parkinson's Disease Drugs

Dopaminergic pathways control highly consequential aspects of physiology and behavior. One of the most therapeutically important and best-studied receptors in these pathways is dopamine receptor D₂ (DRD2). Unfortunately, DRD2 is challenging to study with traditional molecular biological techniques, and most drugs designed to target DRD2 are ligands for many other receptors. Here, we developed probes able to both covalently bind to DRD2 using photoaffinity labeling and provide a chemical handle for detection or affinity purification. These probes behaved like good DRD2 agonists in traditional biochemical assays and were able to perform in chemical-biological assays of cell and receptor labeling. Rat whole brain labeling and affinity enrichment using the probes permitted proteomic analysis of the probes' interacting proteins. Bioinformatic study of the hits revealed that the probes bound noncanonically targeted proteins in Parkinson's disease network as well as the retrograde endocannabinoid signaling, neuronal nitric oxide synthase, muscarinic acetylcholine receptor M1, GABA receptor, and dopamine receptor D₁ (DRD1) signaling networks. Follow-up analysis may yield insights into how this pathway relates specifically to Parkinson's disease symptoms or provide new targets for treatments. This work reinforces the notion that the combination of chemical biology and omics-based approaches provides a broad picture of a molecule's "interactome" and may also give insight into the pleiotropy of effects observed for a drug or perhaps indicate new applications.

Defining hierarchical protein interaction networks from spectral analysis of bacterial proteomes

Cellular behaviors emerge from layers of molecular interactions: proteins interact to form complexes, pathways, and phenotypes. We show that hierarchical networks of protein interactions can be defined from the statistical pattern of proteome variation measured across thousands of diverse bacteria and that these networks reflect the emergence of complex bacterial phenotypes. Our results are validated through gene-set enrichment analysis and comparison to existing experimentally derived databases. We demonstrate the biological utility of our approach by creating a model of motility in Pseudomonas aeruginosa and using it to identify a protein that affects pilus-mediated motility. Our method, SCALES (Spectral Correlation Analysis of Layered Evolutionary Signals), may be useful for interrogating genotype-phenotype relationships in bacteria.

A Novel Sparse Graph-Regularized Singular Value Decomposition Model and Its Application to Genomic Data Analysis

Learning the gene coexpression pattern is a central challenge for high-dimensional gene expression analysis. Recently, sparse singular value decomposition (SVD) has been used to achieve this goal. However, this model ignores the structural information between variables (e.g., a gene network). The typical graph-regularized penalty can be used to incorporate such prior graph information to achieve more accurate discovery and better interpretability. However, the existing approach fails to consider the opposite effect of variables with negative correlations. In this article, we propose a novel sparse graph-regularized SVD model with absolute operator (AGSVD) for high-dimensional gene expression pattern discovery. The key of AGSVD is to impose a novel graph-regularized penalty ( | u|^T L| u| ). However, such a penalty is a nonconvex and nonsmooth function, so it brings new challenges to model solving. We show that the nonconvex problem can be efficiently handled in a convex fashion by adopting an alternating optimization strategy. The simulation results on synthetic data show that our method is more effective than the existing SVD-based ones. In addition, the results on several real gene expression data sets show that the proposed methods can discover more biologically interpretable expression patterns by incorporating the prior gene network.

Web-Based Protein Interactions Calculator Identifies Likely Proteome Coevolution with Alzheimer’s Disease-Associated Proteins

Protein–protein functional interactions arise from either transitory or permanent biomolecular associations and often lead to the coevolution of the interacting residues. Although mutual information has traditionally been used to identify coevolving residues within the same protein, its application between coevolving proteins remains largely uncharacterized. Therefore, we developed the Protein Interactions Calculator (PIC) to efficiently identify coevolving residues between two protein sequences using mutual information. We verified the algorithm using 2102 known human protein interactions and 233 known bacterial protein interactions, with a respective 1975 and 252 non-interacting protein controls. The average PIC score for known human protein interactions was 4.5 times higher than non-interacting proteins (p = 1.03 × 10⁻¹⁰⁸) and 1.94 times higher in bacteria (p = 1.22 × 10⁻³⁵). We then used the PIC scores to determine the probability that two proteins interact. Using those probabilities, we paired 37 Alzheimer’s disease-associated proteins with 8608 other proteins and determined the likelihood that each pair interacts, which we report through a web interface. The PIC had significantly higher sensitivity and residue-specific resolution not available in other algorithms. Therefore, we propose that the PIC can be used to prioritize potential protein interactions, which can lead to a better understanding of biological processes and additional therapeutic targets belonging to protein interaction groups.

Putative Biomarkers in Tears for Diabetic Retinopathy Diagnosis

Purpose

Tear fluid biomarkers may offer a non-invasive strategy for detecting diabetic patients with increased risk of developing diabetic retinopathy (DR) or increased disease progression, thus helping both improving diagnostic accuracy and understanding the pathophysiology of the disease. Here, we assessed the tear fluid of nondiabetic individuals, diabetic patients with no DR, and diabetic patients with nonproliferative DR (NPDR) or with proliferative DR (PDR) to find putative biomarkers for the diagnosis and staging of DR.

Methods

Tear fluid samples were collected using Schirmer test strips from a cohort with 12 controls and 54 Type 2 Diabetes (T2D) patients, and then analyzed using mass spectrometry (MS)-based shotgun proteomics and bead-based multiplex assay. Tear fluid-derived small extracellular vesicles (EVs) were analyzed by transmission electron microscopy, Western Blotting, and nano tracking.

Results

Proteomics analysis revealed that among the 682 reliably quantified proteins in tear fluid, 42 and 26 were differentially expressed in NPDR and PDR, respectively, comparing to the control group. Data are available via ProteomeXchange with identifier PXD033101. By multicomparison analyses, we also found significant changes in 32 proteins. Gene ontology (GO) annotations showed that most of these proteins are associated with oxidative stress and small EVs. Indeed, we also found that tear fluid is particularly enriched in small EVs. T2D patients with NPDR have higher IL-2/-5/-18, TNF, MMP-2/-3/-9 concentrations than the controls. In the PDR group, IL-5/-18 and MMP-3/-9 concentrations were significantly higher, whereas IL-13 was lower, compared to the controls.

Conclusions

Overall, the results show alterations in tear fluid proteins profile in diabetic patients with retinopathy. Promising candidate biomarkers identified need to be validated in a large sample cohort.

Glycation modulates glutamatergic signaling and exacerbates Parkinson's disease-like phenotypes

Alpha-synuclein (aSyn) is a central player in the pathogenesis of synucleinopathies due to its accumulation in typical protein aggregates in the brain. However, it is still unclear how it contributes to neurodegeneration. Type-2 diabetes mellitus is a risk factor for Parkinson's disease (PD). Interestingly, a common molecular alteration among these disorders is the age-associated increase in protein glycation. We hypothesized that glycation-induced neuronal dysfunction is a contributing factor in synucleinopathies. Here, we dissected the impact of methylglyoxal (MGO, a glycating agent) in mice overexpressing aSyn in the brain. We found that MGO-glycation potentiates motor, cognitive, olfactory, and colonic dysfunction in aSyn transgenic (Thy1-aSyn) mice that received a single dose of MGO via intracerebroventricular injection. aSyn accumulates in the midbrain, striatum, and prefrontal cortex, and protein glycation is increased in the cerebellum and midbrain. SWATH mass spectrometry analysis, used to quantify changes in the brain proteome, revealed that MGO mainly increase glutamatergic-associated proteins in the midbrain (NMDA, AMPA, glutaminase, VGLUT and EAAT1), but not in the prefrontal cortex, where it mainly affects the electron transport chain. The glycated proteins in the midbrain of MGO-injected Thy1-aSyn mice strongly correlate with PD and dopaminergic pathways. Overall, we demonstrated that MGO-induced glycation accelerates PD-like sensorimotor and cognitive alterations and suggest that the increase of glutamatergic signaling may underly these events. Our study sheds new light into the enhanced vulnerability of the midbrain in PD-related synaptic dysfunction and suggests that glycation suppressors and anti-glutamatergic drugs may hold promise as disease-modifying therapies for synucleinopathies.

Copy number and expression of CEP89, a protein required for ciliogenesis, are increased and predict poor prognosis in patients with ovarian cancer

CEP89 (centrosomal protein 89) is required for ciliogenesis and mitochondrial metabolism, but its role in cancer has yet to be clarified. We report that CEP89 is overexpressed in ovarian cancer (OC) compared to normal ovaries. Likewise, its expression is higher in malignant ovarian tumors than in borderline ovarian tumors with low malignant potential. More than a quarter of patients with OC have copy number gains in the CEP89 gene, and patients with high expression have more than a year shorter overall survival compared to those with low expression. Moreover, we found that CEP89 can be considered as a prognostic marker for poor overall survival in patients with OC, after adjusting for tumor stage and residual tumor. Nine out of the top 10 protein interactors of CEP89 have the highest percentage of total copy number variation (CNV) events in OC among all other cancer types. Furthermore, CEP89 messenger RNA (mRNA) levels are higher in OC patients with disease recurrence compared to those with no recurrence. We also analyzed CEP89 levels in OC cell lines in terms of CNV, mRNA, and protein levels; and observed that the FUOV-1 cell line has the highest levels among cell lines that originated from primary sites. Our study suggests that CEP89 may be a valuable prognostic predictor for the overall survival of patients with OC, and it could also be a novel therapeutic target in this malignancy.

Creatine utilization as a sole nitrogen source in Pseudomonas putida KT2440 is transcriptionally regulated by CahR

Glutamine amidotransferase-1 domain-containing AraC-family transcriptional regulators (GATRs) are present in the genomes of many bacteria, including all Pseudomonas species. The involvement of several characterized GATRs in amine-containing compound metabolism has been determined, but the full scope of GATR ligands and regulatory networks are still unknown. Here, we characterize Pseudomonas putida's detection of the animal-derived amine compound creatine, a compound particularly enriched in muscle and ciliated cells by a creatine-specific GATR, PP_3665, here named CahR (Creatine amidohydrolase Regulator). cahR is necessary for transcription of the gene encoding creatinase (PP_3667/creA) in the presence of creatine and is critical for P. putida's ability to utilize creatine as a sole source of nitrogen. The CahR/creatine regulon is small, and an electrophoretic mobility shift assay demonstrates strong and specific CahR binding only at the creA promoter, supporting the conclusion that much of the regulon is dependent on downstream metabolites. Phylogenetic analysis of creA orthologues associated with cahR orthologues highlights a strain distribution and organization supporting probable horizontal gene transfer, particularly evident within the genus Acinetobacter. This study identifies and characterizes the GATR that transcriptionally controls P. putida's metabolism of creatine, broadening the scope of known GATR ligands and suggesting GATR diversification during evolution of metabolism for aliphatic nitrogen compounds.

Protein-DNA/RNA interactions: Machine intelligence tools and approaches in the era of artificial intelligence and big data

With the development of artificial intelligence technologies and the availability of large amounts of biological data, computational methods for proteomics have undergone a developmental process from traditional machine learning to deep learning. This review focuses on computational approaches and tools for the prediction of protein-DNA/RNA interactions using machine intelligence techniques. We provide an overview of the development progress of computational methods and summarize the advantages and shortcomings of these methods. We further compiled applications in tasks related to the protein-DNA/RNA interactions, and pointed out possible future application trends. Moreover, biological sequence-digitizing representation strategies used in different types of computational methods are also summarized and discussed. This article is protected by copyright. All rights reserved.

Network based analysis identifies TP53m-BRCA1/2wt-homologous recombination proficient (HRP) population with enhanced susceptibility to Vigil immunotherapy

Thus far immunotherapy has had limited impact on ovarian cancer. Vigil (a novel DNA-based multifunctional immune-therapeutic) has shown clinical benefit to prolong relapse-free survival (RFS) and overall survival (OS) in the BRCA wild type and HRP populations. We further analyzed molecular signals related to sensitivity of Vigil treatment. Tissue from patients enrolled in the randomized double-blind trial of Vigil vs. placebo as maintenance in frontline management of advanced resectable ovarian cancer underwent DNA polymorphism analysis. Data was generated from a 981 gene panel to determine the tumor mutation burden and classify variants using Ingenuity Variant Analysis software (Qiagen) or NIH ClinVar. Only variants classified as pathogenic or likely pathogenic were included. STRING application (version 1.5.1) was used to create a protein-protein interaction network. Topological distance and probability of co-mutation were used to calculated the C-score and cumulative C-score (cumC-score). Kaplan-Meier analysis was used to determine the relationship between gene pairs with a high cumC-score and clinical parameters. Improved relapse free survival in Vigil treated patients was found for the TP53m-BRCAwt-HRP group compared to placebo (21.1 months versus 5.6 months p = 0.0013). Analysis of tumor mutation burden did not reveal statistical benefit in patients receiving Vigil versus placebo. Results suggest a subset of ovarian cancer patients with enhanced susceptibility to Vigil immunotherapy. The hypothesis-generating data presented invites a validation study of Vigil in target identified populations, and supports clinical consideration of STRING-generated network application to biomarker characterization with other cancer patients targeted with Vigil.

Analysis of Microarray Data from Medulloblastoma Tissue Samples

As a laboratory tool, microarray is used to detect the expression of thousands of genes at the same time. Typically, microscope slides have DNA microarrays that are printed with thousands of tiny spots in specified positions. Each spot contains a known DNA sequence or gene. These slides are commonly referred to as gene chips or DNA chips. The DNA molecules printed to each slide serve as probes to detect gene expression, which is also known as the transcriptome or the set of messenger RNA (mRNA) transcripts expressed by a group of genes. The goal of this chapter is to discuss the steps involved computational analysis of data after the completion of a typical microarray experiment.

Increased levels of TAR DNA-binding protein 43 in the hippocampus of subjects with bipolar disorder: a postmortem study

Bipolar disorder shares symptoms and pathological pathways with other neurodegenerative diseases, including frontotemporal dementia (FTD). Since TAR DNA-binding protein 43 (TDP-43) is a neuropathological marker of frontotemporal dementia and it is involved in synaptic transmission, we explored the role of TDP-43 as a molecular feature of bipolar disorder (BD). Homogenates were acquired from frozen hippocampus of postmortem brains of bipolar disorder subjects. TDP-43 levels were quantified using an ELISA-sandwich method and compared between the postmortem brains of bipolar disorder subjects and age-matched control group. We found higher levels of TDP-43 protein in the hippocampus of BD (n = 15) subjects, when compared to controls (n = 15). We did not find associations of TDP-43 with age at death, postmortem interval, or age of disease onset. Our results suggest that protein TDP-43 may be potentially implicated in behavioral abnormalities seen in BD. Further investigation is needed to validate these findings and to examine the role of this protein during the disease course and mood states.

Co-evolution based machine-learning for predicting functional interactions between human genes

Over the next decade, more than a million eukaryotic species are expected to be fully sequenced. This has the potential to improve our understanding of genotype and phenotype crosstalk, gene function and interactions, and answer evolutionary questions. Here, we develop a machine-learning approach for utilizing phylogenetic profiles across 1154 eukaryotic species. This method integrates co-evolution across eukaryotic clades to predict functional interactions between human genes and the context for these interactions. We benchmark our approach showing a 14% performance increase (auROC) compared to previous methods. Using this approach, we predict functional annotations for less studied genes. We focus on DNA repair and verify that 9 of the top 50 predicted genes have been identified elsewhere, with others previously prioritized by high-throughput screens. Overall, our approach enables better annotation of function and functional interactions and facilitates the understanding of evolutionary processes underlying co-evolution. The manuscript is accompanied by a webserver available at: https://mlpp.cs.huji.ac.il.

With the rise in number of eukaryotic species being fully sequenced, large scale phylogenetic profiling can give insights on gene function, Here, the authors describe a machine-learning approach that integrates co-evolution across eukaryotic clades to predict gene function and functional interactions among human genes.

A Deep Learning Framework for Gene Ontology Annotations With Sequence- and Network-Based Information

Knowledge of protein functions plays an important role in biology and medicine. With the rapid development of high-throughput technologies, a huge number of proteins have been discovered. However, there are a great number of proteins without functional annotations. A protein usually has multiple functions and some functions or biological processes require interactions of a plurality of proteins. Additionally, Gene Ontology provides a useful classification for protein functions and contains more than 40,000 terms. We propose a deep learning framework called DeepGOA to predict protein functions with protein sequences and protein-protein interaction (PPI) networks. For protein sequences, we extract two types of information: sequence semantic information and subsequence-based features. We use the word2vec technique to numerically represent protein sequences, and utilize a Bi-directional Long and Short Time Memory (Bi-LSTM) and multi-scale convolutional neural network (multi-scale CNN) to obtain the global and local semantic features of protein sequences, respectively. Additionally, we use the InterPro tool to scan protein sequences for extracting subsequence-based information, such as domains and motifs. Then, the information is plugged into a neural network to generate high-quality features. For the PPI network, the Deepwalk algorithm is applied to generate its embedding information of PPI. Then the two types of features are concatenated together to predict protein functions. To evaluate the performance of DeepGOA, several different evaluation methods and metrics are utilized. The experimental results show that DeepGOA outperforms DeepGO and BLAST.

GFICLEE: ultrafast tree-based phylogenetic profile method inferring gene function at the genomic-wide level

Background

Phylogenetic profiling is widely used to predict novel members of large protein complexes and biological pathways. Although methods combined with phylogenetic trees have significantly improved prediction accuracy, computational efficiency is still an issue that limits its genome-wise application.

Results

Here we introduce a new tree-based phylogenetic profiling algorithm named GFICLEE, which infers common single and continuous loss (SCL) events in the evolutionary patterns. We validated our algorithm with human pathways from three databases and compared the computational efficiency with current tree-based with 10 different scales genome dataset. Our algorithm has a better predictive performance with high computational efficiency.

Conclusions

The GFICLEE is a new method to infers genome-wide gene function. The accuracy and computational efficiency of GFICLEE make it possible to explore gene functions at the genome-wide level on a personal computer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-08070-7.

Targeting the VCP-binding motif of ataxin-3 improves phenotypes in Drosophila models of Spinocerebellar Ataxia Type 3

Of the family of polyglutamine (polyQ) neurodegenerative diseases, Spinocerebellar Ataxia Type 3 (SCA3) is the most common. Like other polyQ diseases, SCA3 stems from abnormal expansions in the CAG triplet repeat of its disease gene resulting in elongated polyQ repeats within its protein, ataxin-3. Various ataxin-3 protein domains contribute to its toxicity, including the valosin-containing protein (VCP)-binding motif (VBM). We previously reported that VCP, a homo-hexameric protein, enhances pathogenic ataxin-3 aggregation and exacerbates its toxicity. These findings led us to explore the impact of targeting the SCA3 protein by utilizing a decoy protein comprising the N-terminus of VCP (N-VCP) that binds ataxin-3's VBM. The notion was that N-VCP would reduce binding of ataxin-3 to VCP, decreasing its aggregation and toxicity. We found that expression of N-VCP in Drosophila melanogaster models of SCA3 ameliorated various phenotypes, coincident with reduced ataxin-3 aggregation. This protective effect was specific to pathogenic ataxin-3 and depended on its VBM. Increasing the amount of N-VCP resulted in further phenotype improvement. Our work highlights the protective potential of targeting the VCP-ataxin-3 interaction in SCA3, a key finding in the search for therapeutic opportunities for this incurable disorder.

The Brachypodium distachyon cold-acclimated plasma membrane proteome is primed for stress resistance

In order to survive subzero temperatures, some plants undergo cold acclimation (CA) where low, nonfreezing temperatures, and/or shortened day lengths allow cold-hardening and survival during subsequent freeze events. Central to this response is the plasma membrane (PM), where low temperature is perceived and cellular homeostasis must be preserved by maintaining membrane integrity. Here, we present the first PM proteome of cold-acclimated Brachypodium distachyon, a model species for the study of monocot crops. A time-course experiment investigated CA-induced changes in the proteome following two-phase partitioning PM enrichment and label-free quantification by nano-liquid chromatography-mass spectrophotometry. Two days of CA were sufficient for membrane protection as well as an initial increase in sugar levels and coincided with a significant change in the abundance of 154 proteins. Prolonged CA resulted in further increases in soluble sugars and abundance changes in more than 680 proteins, suggesting both a necessary early response to low-temperature treatment, as well as a sustained CA response elicited over several days. A meta-analysis revealed that the identified PM proteins have known roles in low-temperature tolerance, metabolism, transport, and pathogen defense as well as drought, osmotic stress, and salt resistance suggesting crosstalk between stress responses, such that CA may prime plants for other abiotic and biotic stresses. The PM proteins identified here present keys to an understanding of cold tolerance in monocot crops and the hope of addressing economic losses associated with modern climate-mediated increases in frost events.

HOPMCLDA: predicting lncRNA-disease associations based on high-order proximity and matrix completion

In recent years, emerging evidence has shown that long noncoding RNAs (lncRNAs) have important roles in the biological processes of complex diseases. However, experiments to determine the associations between diseases and lncRNAs are time consuming and costly. Therefore, there is a need to develop effective computational methods for exploring potential lncRNA-disease associations. In this study, we present a computational prediction method based on high-order proximity and matrix completion to predict lncRNA-disease associations (HOPMCLDA). HOPMCLDA integrates explicit similarity and high-order proximity information on lncRNAs and diseases and constructs a heterogeneous disease-lncRNA network to utilize similarity information. Finally, nuclear norm regularization is carried out on the heterogeneous network for the recovery of a lncRNA-disease association matrix. By implementing leave-one-out cross validation (LOOCV) and five-fold cross validation (5-fold CV), we compare HOPMCLDA with five other methods. HOPMCLDA outperforms the other methods, with area under the receiver operating characteristic curve values of 0.8755 and 0.8353 ± 0.0045 using LOOCV and 5-fold CV, respectively. Furthermore, case studies of three human diseases (gastric cancer, osteosarcoma, and hepatocellular carcinoma) confirm the reliable predictive performance of HOPMCLDA.

Bioinformatics analysis of the role of CXC ligands in the microenvironment of head and neck tumor

Chemokines play a significant role in cancer. CXC-motif chemokine ligands (CXCLs) are associated with the tumorigenesis and progression of head and neck squamous cell carcinoma (HNSC); however, their specific functions in the tumor microenvironment remain unclear. Here, we analyzed the molecular networks and transcriptional data of HNSC patients from the Oncomine, GEPIA, String, cBioPortal, Metascape, TISCH, and TIMER databases. To verify immune functions of CXCLs, their expression was analyzed in different immune cell types. To our knowledge, this is the first report on the correlation between CXCL9-12 and 14 expression and advanced tumor stage. CXCL2, 3, 8, 10, 13, and 16 were remarkably related to tumor immunity. Kaplan-Meier and TIMER survival analyses revealed that high expression of CXCL1, 2, 4, and 6-8 is correlated with low survival in HNSC patients, whereas high expression of CXCL9, 10, 13, 14, and 17 predicts high survival. Only CXCL13 and 14 were associated with overall survival in human papilloma virus (HPV)-negative patients. Single-cell datasets confirmed that CXCLs are associated with HNSC-related immune cells. Thus, CXCL1-6, 8-10, 12-14, and 17 could be prognostic targets for HNSC, and CXCL13 and 14 could be novel biomarkers of HPV-negative HNSC.

CladeOScope: functional interactions through the prism of clade-wise co-evolution

Mapping co-evolved genes via phylogenetic profiling (PP) is a powerful approach to uncover functional interactions between genes and to associate them with pathways. Despite many successful endeavors, the understanding of co-evolutionary signals in eukaryotes remains partial. Our hypothesis is that 'Clades', branches of the tree of life (e.g. primates and mammals), encompass signals that cannot be detected by PP using all eukaryotes. As such, integrating information from different clades should reveal local co-evolution signals and improve function prediction. Accordingly, we analyzed 1028 genomes in 66 clades and demonstrated that the co-evolutionary signal was scattered across clades. We showed that functionally related genes are frequently co-evolved in only parts of the eukaryotic tree and that clades are complementary in detecting functional interactions within pathways. We examined the non-homologous end joining pathway and the UFM1 ubiquitin-like protein pathway and showed that both demonstrated distinguished co-evolution patterns in specific clades. Our research offers a different way to look at co-evolution across eukaryotes and points to the importance of modular co-evolution analysis. We developed the 'CladeOScope' PP method to integrate information from 16 clades across over 1000 eukaryotic genomes and is accessible via an easy to use web server at http://cladeoscope.cs.huji.ac.il.

Integrated analysis of RNA-binding proteins in thyroid cancer

Recently, the incidence of thyroid cancer (THCA) has been on the rise. RNA binding proteins (RBPs) and their abnormal expression are closely related to the emergence and pathogenesis of tumor diseases. In this study, we obtained gene expression data and corresponding clinical information from the TCGA database. A total of 162 aberrantly expressed RBPs were obtained, comprising 92 up-regulated and 70 down-regulated RBPs. Then, we performed a functional enrichment analysis and constructed a PPI network. Through univariate Cox regression analysis of key genes and found that NOLC1 (p = 0.036), RPS27L (p = 0.011), TDRD9 (p = 0.016), TDRD6 (p = 0.002), IFIT2 (p = 0.037), and IFIT3 (p = 0.02) were significantly related to the prognosis. Through the online website Kaplan-Meier plotter and multivariate Cox analysis, we identified 2 RBP-coding genes (RPS27L and IFIT3) to construct a predictive model in the entire TCGA dataset and then validate in two subsets. In-depth analysis revealed that the data gave by this model, the patient's high-risk score is very closely related to the overall survival rate difference (p = 0.038). Further, we investigated the correlation between the model and the clinic, and the results indicated that the high-risk was in the male group (p = 0.011) and the T3-4 group (p = 0.046) was associated with a poor prognosis. On the whole, the conclusions of our research this time can make it possible to find more insights into the research on the pathogenesis of THCA, this could be beneficial for individualized treatment and medical decision making.

A proteomics approach for the identification of cullin-9 (CUL9) related signaling pathways in induced pluripotent stem cell models

CUL9 is a non-canonical and poorly characterized member of the largest family of E3 ubiquitin ligases known as the Cullin RING ligases (CRLs). Most CRLs play a critical role in developmental processes, however, the role of CUL9 in neuronal development remains elusive. We determined that deletion or depletion of CUL9 protein causes aberrant formation of neural rosettes, an in vitro model of early neuralization. In this study, we applied mass spectrometric approaches in human pluripotent stem cells (hPSCs) and neural progenitor cells (hNPCs) to identify CUL9 related signaling pathways that may contribute to this phenotype. Through LC-MS/MS analysis of immunoprecipitated endogenous CUL9, we identified several subunits of the APC/C, a major cell cycle regulator, as potential CUL9 interacting proteins. Knockdown of the APC/C adapter protein FZR1 resulted in a significant increase in CUL9 protein levels, however, CUL9 does not appear to affect protein abundance of APC/C subunits and adapters or alter cell cycle progression. Quantitative proteomic analysis of CUL9 KO hPSCs and hNPCs identified protein networks related to metabolic, ubiquitin degradation, and transcriptional regulation pathways that are disrupted by CUL9 deletion in both hPSCs. No significant changes in oxygen consumption rates or ATP production were detected in either cell type. The results of our study build on current evidence that CUL9 may have unique functions in different cell types and that compensatory mechanisms may contribute to the difficulty of identifying CUL9 substrates.

Optimization of co-evolution analysis through phylogenetic profiling reveals pathway-specific signals

SUMMARY

The exponential growth in available genomic data is expected to reach full sequencing of a million genomes in the coming decade. Improving and developing methods to analyze these genomes and to reveal their utility is of major interest in a wide variety of fields, such as comparative and functional genomics, evolution and bioinformatics. Phylogenetic profiling is an established method for predicting functional interactions between proteins based on similarities in their evolutionary patterns across species. Proteins that function together (i.e. generate complexes, interact in the same pathways or improve adaptation to environmental niches) tend to show coordinated evolution across the tree of life. The normalized phylogenetic profiling (NPP) method takes into account minute changes in proteins across species to identify protein co-evolution. Despite the success of this method, it is still not clear what set of parameters is required for optimal use of co-evolution in predicting functional interactions. Moreover, it is not clear if pathway evolution or function should direct parameter choice. Here, we create a reliable and usable NPP construction pipeline. We explore the effect of parameter selection on functional interaction prediction using NPP from 1028 genomes, both separately and in various value combinations. We identify several parameter sets that optimize performance for pathways with certain biological annotation. This work reveals the importance of choosing the right parameters for optimized function prediction based on a biological context.

AVAILABILITY AND IMPLEMENTATION

Source code and documentation are available on GitHub: https://github.com/iditam/CompareNPPs.

CONTACT

yuvaltab@ekmd.huji.ac.il.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets

Cellular life depends on a complex web of functional associations between biomolecules. Among these associations, protein–protein interactions are particularly important due to their versatility, specificity and adaptability. The STRING database aims to integrate all known and predicted associations between proteins, including both physical interactions as well as functional associations. To achieve this, STRING collects and scores evidence from a number of sources: (i) automated text mining of the scientific literature, (ii) databases of interaction experiments and annotated complexes/pathways, (iii) computational interaction predictions from co-expression and from conserved genomic context and (iv) systematic transfers of interaction evidence from one organism to another. STRING aims for wide coverage; the upcoming version 11.5 of the resource will contain more than 14 000 organisms. In this update paper, we describe changes to the text-mining system, a new scoring-mode for physical interactions, as well as extensive user interface features for customizing, extending and sharing protein networks. In addition, we describe how to query STRING with genome-wide, experimental data, including the automated detection of enriched functionalities and potential biases in the user's query data. The STRING resource is available online, at https://string-db.org/.

Machine learning methods accurately predict host specificity of coronaviruses based on spike sequences alone

Coronaviruses infect many animals, including humans, due to interspecies transmission. Three of the known human coronaviruses: MERS, SARS-CoV-1, and SARS-CoV-2, the pathogen for the COVID-19 pandemic, cause severe disease. Improved methods to predict host specificity of coronaviruses will be valuable for identifying and controlling future outbreaks. The coronavirus S protein plays a key role in host specificity by attaching the virus to receptors on the cell membrane. We analyzed 1238 spike sequences for their host specificity. Spike sequences readily segregate in t-SNE embeddings into clusters of similar hosts and/or virus species. Machine learning with SVM, Logistic Regression, Decision Tree, Random Forest gave high average accuracies, F₁ scores, sensitivities and specificities of 0.95-0.99. Importantly, sites identified by Decision Tree correspond to protein regions with known biological importance. These results demonstrate that spike sequences alone can be used to predict host specificity.

Scalable phylogenetic profiling using MinHash uncovers likely eukaryotic sexual reproduction genes

Phylogenetic profiling is a computational method to predict genes involved in the same biological process by identifying protein families which tend to be jointly lost or retained across the tree of life. Phylogenetic profiling has customarily been more widely used with prokaryotes than eukaryotes, because the method is thought to require many diverse genomes. There are now many eukaryotic genomes available, but these are considerably larger, and typical phylogenetic profiling methods require at least quadratic time as a function of the number of genes. We introduce a fast, scalable phylogenetic profiling approach entitled HogProf, which leverages hierarchical orthologous groups for the construction of large profiles and locality-sensitive hashing for efficient retrieval of similar profiles. We show that the approach outperforms Enhanced Phylogenetic Tree, a phylogeny-based method, and use the tool to reconstruct networks and query for interactors of the kinetochore complex as well as conserved proteins involved in sexual reproduction: Hap2, Spo11 and Gex1. HogProf enables large-scale phylogenetic profiling across the three domains of life, and will be useful to predict biological pathways among the hundreds of thousands of eukaryotic species that will become available in the coming few years. HogProf is available at https://github.com/DessimozLab/HogProf.

Genes that are involved in the same biological process tend to co-evolve. This property is exploited by the technique of phylogenetic profiling, which identifies co-evolving (and therefore likely functionally related) genes through patterns of correlated gene retention and loss in evolution and across species. However, conventional methods to computing and clustering these correlated genes do not scale with increasing numbers of genomes. HogProf is a novel phylogenetic profiling tool built on probabilistic data structures. It allows the user to construct searchable databases containing the evolutionary history of hundreds of thousands of protein families. Such fast detection of coevolution takes advantage of the rapidly increasing amount of genomic data publicly available, and can uncover unknown biological networks and guide in-vivo research and experimentation. We have applied our tool to describe the biological networks underpinning sexual reproduction in eukaryotes.

LncRNA ZFAS1 serves as a prognostic biomarker to predict the survival of patients with ovarian cancer

Ovarian cancer (OC) is one of the most fatal types of gynecological malignancy. Certain long non-coding RNAs (lncRNA) have been reported to have crucial roles in cancer progression. Zinc finger nuclear transcription factor, X-box binding 1-type containing 1 antisense RNA 1 (ZFAS1) is a novel regulator lncRNA in various cancer types. The expression pattern of most lncRNAs, including ZFAS1, in OC remains to be determined. In the present study, it was demonstrated that ZFAS1 was overexpressed in OC vs. normal cell lines. However, ZFAS1 was downregulated in OC compared with normal samples in the GEPIA dataset. Furthermore, OC samples of higher stages (stage III/IV) had higher levels of ZFAS1 compared with those in early-stage OC (stage I/II) samples. Of note, higher ZFAS1 expression was associated with shorter overall survival time and disease-free survival time of OC patients. Protein-protein interaction networks of proteins co-expressed with ZFAS1 in OC were constructed. Furthermore, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis of genes co-expressed with ZFAS1 indicated that ZFAS1 is associated with translation, mRNA splicing, cell-cell adhesion, DNA repair, protein sumoylation, positive regulation of GTPase activity and DNA replication. The present study may provide novel clues to validate ZFAS1 as a biomarker in OC.

High Functioning Autism with Missense Mutations in Synaptotagmin-Like Protein 4 (SYTL4) and Transmembrane Protein 187 (TMEM187) Genes: SYTL4- Protein Modeling, Protein-Protein Interaction, Expression Profiling and MicroRNA Studies

We describe a 7-year-old male with high functioning autism spectrum disorder (ASD) and maternally-inherited rare missense variant of Synaptotagmin-like protein 4 (SYTL4) gene (Xq22.1; c.835C>T; p.Arg279Cys) and an unknown missense variant of Transmembrane protein 187 (TMEM187) gene (Xq28; c.708G>T; p. Gln236His). Multiple in-silico predictions described in our study indicate a potentially damaging status for both X-linked genes. Analysis of predicted atomic threading models of the mutant and the native SYTL4 proteins suggest a potential structural change induced by the R279C variant which eliminates the stabilizing Arg279-Asp60 salt bridge in the N-terminal half of the SYTL4, affecting the functionality of the protein's critical RAB-Binding Domain. In the European (Non-Finnish) population, the allele frequency for this variant is 0.00042. The SYTL4 gene is known to directly interact with several members of the RAB family of genes, such as, RAB27A, RAB27B, RAB8A, and RAB3A which are known autism spectrum disorder genes. The SYTL4 gene also directly interacts with three known autism genes: STX1A, SNAP25 and STXBP1. Through a literature-based analytical approach, we identified three of five (60%) autism-associated serum microRNAs (miRs) with high predictive power among the total of 298 mouse Sytl4 associated/predicted microRNA interactions. Five of 13 (38%) miRs were differentially expressed in serum from ASD individuals which were predicted to interact with the mouse equivalent Sytl4 gene. TMEM187 gene, like SYTL4, is a protein-coding gene that belongs to a group of genes which host microRNA genes in their introns or exons. The novel Q236H amino acid variant in the TMEM187 in our patient is near the terminal end region of the protein which is represented by multiple sequence alignments and hidden Markov models, preventing comparative structural analysis of the variant harboring region. Like SYTL4, the TMEM187 gene is expressed in the brain and interacts with four known ASD genes, namely, HCFC1; TMLHE; MECP2; and GPHN. TMM187 is in linkage with MECP2, which is a well-known determinant of brain structure and size and is a well-known autism gene. Other members of the TMEM gene family, TMEM132E and TMEM132D genes are associated with bipolar and panic disorders, respectively, while TMEM231 is a known syndromic autism gene. Together, TMEM187 and SYTL4 genes directly interact with recognized important ASD genes, and their mRNAs are found in extracellular vesicles in the nervous system and stimulate target cells to translate into active protein. Our evidence shows that both these genes should be considered as candidate genes for autism. Additional biological testing is warranted to further determine the pathogenicity of these gene variants in the causation of autism.