The BioGRID database: A comprehensive biomedical resource of curated protein, genetic, and chemical interactions

Heterogeneous network approaches to protein pathway prediction

Understanding protein-protein interactions (PPIs) and the pathways they comprise is essential for comprehending cellular functions and their links to specific phenotypes. Despite the prevalence of molecular data generated by high-throughput sequencing technologies, a significant gap remains in translating this data into functional information regarding the series of interactions that underlie phenotypic differences. In this review, we present an in-depth analysis of heterogeneous network methodologies for modeling protein pathways, highlighting the critical role of integrating multifaceted biological data. It outlines the process of constructing these networks, from data representation to machine learning-driven predictions and evaluations. The work underscores the potential of heterogeneous networks in capturing the complexity of proteomic interactions, thereby offering enhanced accuracy in pathway prediction. This approach not only deepens our understanding of cellular processes but also opens up new possibilities in disease treatment and drug discovery by leveraging the predictive power of comprehensive proteomic data analysis.

Knowledge-guided learning methods for integrative analysis of multi-omics data

Integrative analysis of multi-omics data has the potential to yield valuable and comprehensive insights into the molecular mechanisms underlying complex diseases such as cancer and Alzheimer's disease. However, a number of analytical challenges complicate multi-omics data integration. For instance, -omics data are usually high-dimensional, and sample sizes in multi-omics studies tend to be modest. Furthermore, when genes in an important pathway have relatively weak signal, it can be difficult to detect them individually. There is a growing body of literature on knowledge-guided learning methods that can address these challenges by incorporating biological knowledge such as functional genomics and functional proteomics into multi-omics data analysis. These methods have been shown to outperform their counterparts that do not utilize biological knowledge in tasks including prediction, feature selection, clustering, and dimension reduction. In this review, we survey recently developed methods and applications of knowledge-guided multi-omics data integration methods and discuss future research directions.

RIscoper 2.0: A deep learning tool to extract RNA biomedical relation sentences from literature

RNA plays an extensive role in a multi-dimensional regulatory system, and its biomedical relationships are scattered across numerous biological studies. However, text mining works dedicated to the extraction of RNA biomedical relations remain limited. In this study, we established a comprehensive and reliable corpus of RNA biomedical relations, recruiting over 30,000 sentences manually curated from more than 15,000 biomedical literature. We also updated RIscoper 2.0, a BERT-based deep learning tool to extract RNA biomedical relation sentences from literature. Benefiting from approximately 100,000 annotated named entities, we integrated the text classification and named entity recognition tasks in this tool. Additionally, RIscoper 2.0 outperformed the original tool in both tasks and can discover new RNA biomedical relations. Additionally, we provided a user-friendly online search tool that enables rapid scanning of RNA biomedical relationships using local and online resources. Both the online tools and data resources of RIscoper 2.0 are available at http://www.rnainter.org/riscoper.

CSE1L as a prognostic biomarker associated with pan cancer immune infiltration and drug sensitivity

BACKGROUND

Rising cancer-related mortality underscores the importance of biomarkers for treatment and prognosis, with Chromosome Segregation 1 Like (CSE1L) linked to various cancers yet its roles remain partially understood. This study investigates CSE1L's expression and oncogenic mechanisms in solid tumors.

RESEARCH DESIGN AND METHODS

We analyzed multi-omics data from 31 solid tumors, measured CSE1L in 41 head and neck carcinoma patients post-chemotherapy via qRT-PCR, and evaluated the impact of CSE1L knockdown on cell proliferation in A549 and HepG2 cells.

RESULTS

In this study, we observed significantly elevated levels of CSE1L RNA in 13 tumor tissues and protein levels in 8 tumor tissues compared to their corresponding adjacent normal tissues. Additionally, our investigation unveiled a correlation between heightened CSE1L expression in tumor tissues and worsened patient prognosis, poor response to immunotherapy, and diminished effectiveness of neoadjuvant chemotherapy. Through an analysis of CSE1L mechanisms, we discovered its potential involvement in promoting tumor cell proliferation, enhancing drug resistance, and influencing immune infiltration, thereby impacting patient prognosis and treatment outcomes. Finally, we delved into the potential mechanisms underlying upregulation of CSE1L in tumor tissues.

CONCLUSION

Our findings demonstrate that CSE1L promotes tumor development in various malignancies, highlighting its potential as both a therapeutic target and prognostic indicator.

BAPCP: A comprehensive and user-friendly web tool for identifying biomarkers from protein microarray technologies

BACKGROUND AND OBJECTIVE

Proteome microarrays are one of the popular high-throughput screening methods for large-scale investigation of protein interactions in cells. These interactions can be measured on protein chips when coupled with fluorescence-labeled probes, helping indicate potential biomarkers or discover drugs. Several computational tools were developed to help analyze the protein chip results. However, existing tools fail to provide a user-friendly interface for biologists and present only one or two data analysis methods suitable for limited experimental designs, restricting the use cases.

METHODS

In order to facilitate the biomarker examination using protein chips, we implemented a user-friendly and comprehensive web tool called BAPCP (Biomarker Analysis tool for Protein Chip Platforms) in this research to deal with diverse chip data distributions.

RESULTS

BAPCP is well integrated with standard chip result files and includes 7 data normalization methods and 7 custom-designed quality control/differential analysis filters for biomarker extraction among experiment groups. Moreover, it can handle cost-efficient chip designs that repeat several blocks/samples within one single slide. Using experiments of the human coronavirus (HCoV) protein microarray and the E. coli proteome chip that helps study the immune response of Kawasaki disease as examples, we demonstrated that BAPCP can accelerate the time-consuming week-long manual biomarker identification process to merely 3 min.

CONCLUSIONS

The developed BAPCP tool provides substantial analysis support for protein interaction studies and conforms to the necessity of expanding computer usage and exchanging information in bioscience and medicine. The web service of BAPCP is available at https://cosbi.ee.ncku.edu.tw/BAPCP/.

Multi-objective context-guided consensus of a massive array of techniques for the inference of Gene Regulatory Networks

BACKGROUND AND OBJECTIVE

Gene Regulatory Network (GRN) inference is a fundamental task in biology and medicine, as it enables a deeper understanding of the intricate mechanisms of gene expression present in organisms. This bioinformatics problem has been addressed in the literature through multiple computational approaches. Techniques developed for inferring from expression data have employed Bayesian networks, ordinary differential equations (ODEs), machine learning, information theory measures and neural networks, among others. The diversity of implementations and their respective customization have led to the emergence of many tools and multiple specialized domains derived from them, understood as subsets of networks with specific characteristics that are challenging to detect a priori. This specialization has introduced significant uncertainty when choosing the most appropriate technique for a particular dataset. This proposal, named MO-GENECI, builds upon the basic idea of the previous proposal GENECI and optimizes consensus among different inference techniques, through a carefully refined multi-objective evolutionary algorithm guided by various objective functions, linked to the biological context at hand.

METHODS

MO-GENECI has been tested on an extensive and diverse academic benchmark of 106 gene regulatory networks from multiple sources and sizes. The evaluation of MO-GENECI compared its performance to individual techniques using key metrics (AUROC and AUPR) for gene regulatory network inference. Friedman's statistical ranking provided an ordered classification, followed by non-parametric Holm tests to determine statistical significance.

RESULTS

MO-GENECI's Pareto front approximation facilitates easy selection of an appropriate solution based on generic input data characteristics. The best solution consistently emerged as the winner in all statistical tests, and in many cases, the median precision solution showed no statistically significant difference compared to the winner.

CONCLUSIONS

MO-GENECI has not only demonstrated achieving more accurate results than individual techniques, but has also overcome the uncertainty associated with the initial choice due to its flexibility and adaptability. It is shown intelligently to select the most suitable techniques for each case. The source code is hosted in a public repository at GitHub under MIT license: https://github.com/AdrianSeguraOrtiz/MO-GENECI. Moreover, to facilitate its installation and use, the software associated with this implementation has been encapsulated in a Python package available at PyPI: https://pypi.org/project/geneci/.

Yeast Proteins: Proteomics, Extraction, Modification, Functional Characterization, and Structure: A Review

Proteins are essential for human tissues and organs, and they require adequate intake for normal physiological functions. With a growing global population, protein demand rises annually. Traditional animal and plant protein sources rely heavily on land and water, making it difficult to meet the increasing demand. The high protein content of yeast and the complete range of amino acids in yeast proteins make it a high-quality source of supplemental protein. Screening of high-protein yeast strains using proteomics is essential to increase the value of yeast protein resources and to promote the yeast protein industry. However, current yeast extraction methods are mainly alkaline solubilization and acid precipitation; therefore, it is necessary to develop more efficient and environmentally friendly techniques. In addition, the functional properties of yeast proteins limit their application in the food industry. To improve these properties, methods must be selected to modify the secondary and tertiary structures of yeast proteins. This paper explores how proteomic analysis can be used to identify nutrient-rich yeast strains, compares the process of preparing yeast proteins, and investigates how modification methods affect the function and structure of yeast proteins. It provides a theoretical basis for solving the problem of inadequate protein intake in China and explores future prospects.

MAGICAL: A multi-class classifier to predict synthetic lethal and viable interactions using protein-protein interaction network

Synthetic lethality (SL) and synthetic viability (SV) are commonly studied genetic interactions in the targeted therapy approach in cancer. In SL, inhibiting either of the genes does not affect the cancer cell survival, but inhibiting both leads to a lethal phenotype. In SV, inhibiting the vulnerable gene makes the cancer cell sick; inhibiting the partner gene rescues and promotes cell viability. Many low and high-throughput experimental approaches have been employed to identify SLs and SVs, but they are time-consuming and expensive. The computational tools for SL prediction involve statistical and machine-learning approaches. Almost all machine learning tools are binary classifiers and involve only identifying SL pairs. Most importantly, there are limited properties known that best describe and discriminate SL from SV. We developed MAGICAL (Multi-class Approach for Genetic Interaction in Cancer via Algorithm Learning), a multi-class random forest based machine learning model for genetic interaction prediction. Network properties of protein derived from physical protein-protein interactions are used as features to classify SL and SV. The model results in an accuracy of ~80% for the training dataset (CGIdb, BioGRID, and SynLethDB) and performs well on DepMap and other experimentally derived reported datasets. Amongst all the network properties, the shortest path, average neighbor2, average betweenness, average triangle, and adhesion have significant discriminatory power. MAGICAL is the first multi-class model to identify discriminatory features of synthetic lethal and viable interactions. MAGICAL can predict SL and SV interactions with better accuracy and precision than any existing binary classifier.

The TRIM4 E3 ubiquitin ligase degrades TPL2 and is modulated by oncogenic KRAS

Loss-of-function mutations in the C terminus of TPL2 kinase promote oncogenesis by impeding its proteasomal degradation, leading to sustained protein expression. However, the degradation mechanism for TPL2 has remained elusive. Through proximity-dependent biotin identification (BioID), we uncovered tripartite motif-containing 4 (TRIM4) as the E3 ligase that binds and degrades TPL2 by polyubiquitination of lysines 415 and 439. The naturally occurring TPL2 mutants R442H and E188K exhibit impaired TRIM4 binding, enhancing their stability. We further discovered that TRIM4 itself is stabilized by another E3 ligase, TRIM21, which in turn is regulated by KRAS. Mutant KRAS recruits RNF185 to degrade TRIM21 and subsequently TRIM4, thereby stabilizing TPL2. In the presence of mutant KRAS, TPL2 phosphorylates and degrades GSK3β, resulting in β-catenin stabilization and activation of the Wnt pathway. These findings elucidate the physiological mechanisms regulating TPL2 and its exploitation by mutant KRAS, underscoring the need to develop TPL2 inhibitors for KRAS-mutant cancers.

A Novel Confocal Scanning Protein-Protein Interaction Assay (PPI-CONA) Reveals Exceptional Selectivity and Specificity of CC0651, a Small Molecule Binding Enhancer of the Weak Interaction between the E2 Ubiquitin-Conjugating Enzyme CDC34A and Ubiquitin

Protein-protein interactions (PPIs) are some of the most challenging target classes in drug discovery. Highly sensitive detection techniques are required for the identification of chemical modulators of PPIs. Here, we introduce PPI confocal nanoscanning (PPI-CONA), a miniaturized, microbead based high-resolution fluorescence imaging assay. We demonstrate the capabilities of PPI-CONA by detecting low affinity ternary complex formation between the human CDC34A ubiquitin-conjugating (E2) enzyme, ubiquitin, and CC0651, a small molecule enhancer of the CDC34A-ubiquitin interaction. We further exemplify PPI-CONA with an E2 enzyme binding study on CC0651 and a CDC34A binding specificity study of a series of CC0651 analogues. Our results indicate that CC0651 is highly selective toward CDC34A. We further demonstrate how PPI-CONA can be applied to screening very low affinity interactions. PPI-CONA holds potential for high-throughput screening for modulators of PPI targets and characterization of their affinity, specificity, and selectivity.

Rapid profiling of transcription factor-cofactor interaction networks reveals principles of epigenetic regulation

Transcription factor (TF)-cofactor (COF) interactions define dynamic, cell-specific networks that govern gene expression; however, these networks are understudied due to a lack of methods for high-throughput profiling of DNA-bound TF-COF complexes. Here, we describe the Cofactor Recruitment (CoRec) method for rapid profiling of cell-specific TF-COF complexes. We define a lysine acetyltransferase (KAT)-TF network in resting and stimulated T cells. We find promiscuous recruitment of KATs for many TFs and that 35% of KAT-TF interactions are condition specific. KAT-TF interactions identify NF-κB as a primary regulator of acutely induced histone 3 lysine 27 acetylation (H3K27ac). Finally, we find that heterotypic clustering of CBP/P300-recruiting TFs is a strong predictor of total promoter H3K27ac. Our data support clustering of TF sites that broadly recruit KATs as a mechanism for widespread co-occurring histone acetylation marks. CoRec can be readily applied to different cell systems and provides a powerful approach to define TF-COF networks impacting chromatin state and gene regulation.

USP36 regulates the proliferation, survival, and differentiation of hFOB1.19 osteoblast

BACKGROUND

Effective bone formation relies on osteoblast differentiation, a process subject to intricate post-translational regulation. Ubiquitin-specific proteases (USPs) repress protein degradation mediated by the ubiquitin-proteasome pathway. Several USPs have been documented to regulate osteoblast differentiation, but whether other USPs are involved in this process remains elusive.

METHODS

In this study, we conducted a comparative analysis of 48 USPs in differentiated and undifferentiated hFOB1.19 osteoblasts, identifying significantly upregulated USPs. Subsequently, we generated USP knockdown hFOB1.19 cells and evaluated their osteogenic differentiation using Alizarin red staining. We also assessed cell viability, cell cycle progression, and apoptosis through MTT, 7-aminoactinomycin D staining, and Annexin V/PI staining assays, respectively. Quantitative PCR and Western blotting were employed to measure the expression levels of osteogenic differentiation markers. Additionally, we investigated the interaction between the USP and its target protein using co-immunoprecipitation (co-IP). Furthermore, we depleted the USP in hFOB1.19 cells to examine its effect on the ubiquitination and stability of the target protein using immunoprecipitation (IP) and Western blotting. Finally, we overexpressed the target protein in USP-deficient hFOB1.19 cells and evaluated its impact on their osteogenic differentiation using Alizarin red staining.

RESULTS

USP36 is the most markedly upregulated USP in differentiated hFOB1.19 osteoblasts. Knockdown of USP36 leads to reduced viability, cell cycle arrest, heightened apoptosis, and impaired osteogenic differentiation in hFOB1.19 cells. USP36 interacts with WD repeat-containing protein 5 (WDR5), and the knockdown of USP36 causes an increased level of WDR5 ubiquitination and accelerated degradation of WDR5. Excessive WDR5 improved the impaired osteogenic differentiation of USP36-deficient hFOB1.19 cells.

CONCLUSIONS

These observations suggested that USP36 may function as a key regulator of osteoblast differentiation, and its regulatory mechanism may be related to the stabilization of WDR5.

Malivhu: A Comprehensive Bioinformatics Resource for Filtering SARS and MERS Virus Proteins by Their Classification, Family and Species, and Prediction of Their Interactions Against Human Proteins

COVID 19 pandemic is still ongoing, having taken more than 6 million human lives with it, and it seems that the world will have to learn how to live with the virus around. In consequence, there is a need to develop different treatments against it, not only with vaccines, but also new medicines. To do this, human-virus protein-protein interactions (PPIs) play a key part in drug-target discovery, but finding them experimentally can be either costly or sometimes unreliable. Therefore, computational methods arose as a powerful alternative to predict these interactions, reducing costs and helping researchers confirm only certain interactions instead of trying all possible combinations in the laboratory. Malivhu is a tool that predicts human-virus PPIs through a 4-phase process using machine learning models, where phase 1 filters ssRNA(+) class virus proteins, phase 2 filters Coronaviridae family proteins and phase 3 filters severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) species proteins, and phase 4 predicts human-SARS-CoV/SARS-CoV-2/MERS protein-protein interactions. The performance of the models was measured with Matthews correlation coefficient, F1-score, specificity, sensitivity, and accuracy scores, getting accuracies of 99.07%, 99.83%, and 100% for the first 3 phases, respectively, and 94.24% for human-SARS-CoV PPI, 94.50% for human-SARS-CoV-2 PPI, and 95.45% for human-MERS PPI on independent testing. All the prediction models developed for each of the 4 phases were implemented as web server which is freely available at https://kaabil.net/malivhu/.

Decoding the genomic landscape of chromatin-associated biomolecular condensates

Biomolecular condensates play a significant role in chromatin activities, primarily by concentrating and compartmentalizing proteins and/or nucleic acids. However, their genomic landscapes and compositions remain largely unexplored due to a lack of dedicated computational tools for systematic identification in vivo. To address this, we develop CondSigDetector, a computational framework designed to detect condensate-like chromatin-associated protein co-occupancy signatures (CondSigs), to predict genomic loci and component proteins of distinct chromatin-associated biomolecular condensates. Applying this framework to mouse embryonic stem cells (mESC) and human K562 cells enable us to depict the high-resolution genomic landscape of chromatin-associated biomolecular condensates, and uncover both known and potentially unknown biomolecular condensates. Multi-omics analysis and experimental validation further verify the condensation properties of CondSigs. Additionally, our investigation sheds light on the impact of chromatin-associated biomolecular condensates on chromatin activities. Collectively, CondSigDetector provides an approach to decode the genomic landscape of chromatin-associated condensates, facilitating a deeper understanding of their biological functions and underlying mechanisms in cells.

Neuronal UBE3A substrates hold therapeutic potential for Angelman syndrome

Emerging therapies for Angelman syndrome, a severe neurodevelopmental disorder, are focused on restoring UBE3A gene expression in the brain. Further therapeutic opportunities may arise from a better understanding of how UBE3A gene products-both long and short isoforms of the ubiquitin ligase E3A (UBE3A)-function in neurons. Great strides have been made recently toward identifying ubiquitin substrates of UBE3A in vitro and in heterologous expression systems. From this work, a particularly close relationship between UBE3A and subunits of the 19S regulatory particle of the proteasome has become evident. We propose that further research cognizant of isoform-specific UBE3A functional roles will be instrumental in elucidating key UBE3A/substrate relationships within distinct neuronal compartments, lending to the discovery of novel therapeutic targets and valuable clinical biomarkers for the treatment of Angelman syndrome.

Dachsous cadherin related 1 (DCHS1) is a novel biomarker for immune infiltration and epithelial-mesenchymal transition in endometrial cancer via pan-cancer analysis

BACKGROUND

Dachsous cadherin related 1 (DCHS1) is one of calcium-dependent adhesion membrane proteins and is mainly involved in the development of mammalian tissues. There is a lack of more detailed research on the biological function of DCHS1 in pan-cancer.

MATERIALS AND METHODS

We evaluated the expression, the prognostic value, the diagnostic value and genomic alterations of DCHS1 by using the databases, including TCGA, UALCAN, HPA, GEPIA2.0 and GSCA. We employed the databases of UCSC, TIMER2.0, TISIDB, GSCA to analyze the association between DCHS1 expression and the immune microenvironment, stemness, TMB, MSI and anticancer drug sensitivity. BioGRID, STRING and GEPIA2.0 were used to perform protein interaction and functional enrichment analysis. Real-time quantitative PCR, CCK8, Transwell assay and Western blot were performed to determine the function of DCHS1 in UCEC.

RESULTS

DCHS1 is differentially expressed in many cancers and its expression is significantly associated with tumor prognosis and diagnosis. DCHS1 expression was significantly correlated with the infiltration of cancer-associated fibroblasts (CAFs), Endothelial cell (ECs), and Hematopoietic stem cell in most cancers. In addition, DCHS1 was significantly associated with sensitivity to many antitumor drugs. Functional enrichment analysis revealed that DCHS1-related proteins were involved in Focal adhesion, Endometrial cancer and Wnt signaling pathway. GSEA results showed that DCHS1 was related to epithelial-mesenchymal transition (EMT) in many cancers. In vitro experiments in UCEC showed that DCHS1 regulated cell proliferation, migration and EMT.

CONCLUSIONS

Our findings indicated that DCHS1 might be a novel prognostic and diagnostic biomarker and immunotherapy target, and plays an important role in the proliferation, migration and EMT in UCEC.

A ubiquitin-specific, proximity-based labeling approach for the identification of ubiquitin ligase substrates

Over 600 E3 ligases in humans execute ubiquitination of specific target proteins in a spatiotemporal manner to elicit desired signaling effects. Here, we developed a ubiquitin-specific proximity-based labeling method to selectively biotinylate substrates of a given ubiquitin ligase. By fusing the biotin ligase BirA and an Avi-tag variant to the candidate E3 ligase and ubiquitin, respectively, we were able to specifically enrich bona fide substrates of a ligase using a one-step streptavidin pulldown under denaturing conditions. We applied our method, which we named Ub-POD, to the really interesting new gene (RING) E3 ligase RAD18 and identified proliferating cell nuclear antigen and several other critical players in the DNA damage repair pathway. Furthermore, we successfully applied Ub-POD to the RING ubiquitin ligase tumor necrosis factor receptor-associated factor 6 and a U-box-type E3 ubiquitin ligase carboxyl terminus of Hsc70-interacting protein. We anticipate that our method could be widely adapted to all classes of ubiquitin ligases to identify substrates.

Cytoplasmic binding partners of the Integrator endonuclease INTS11 and its paralog CPSF73 are required for their nuclear function

INTS11 and CPSF73 are metal-dependent endonucleases for Integrator and pre-mRNA 3'-end processing, respectively. Here, we show that the INTS11 binding partner BRAT1/CG7044, a factor important for neuronal fitness, stabilizes INTS11 in the cytoplasm and is required for Integrator function in the nucleus. Loss of BRAT1 in neural organoids leads to transcriptomic disruption and precocious expression of neurogenesis-driving transcription factors. The structures of the human INTS9-INTS11-BRAT1 and Drosophila dIntS11-CG7044 complexes reveal that the conserved C terminus of BRAT1/CG7044 is captured in the active site of INTS11, with a cysteine residue directly coordinating the metal ions. Inspired by these observations, we find that UBE3D is a binding partner for CPSF73, and UBE3D likely also uses a conserved cysteine residue to directly coordinate the active site metal ions. Our studies have revealed binding partners for INTS11 and CPSF73 that behave like cytoplasmic chaperones with a conserved impact on the nuclear functions of these enzymes.

RNA 5-methylcytosine marks mitochondrial double-stranded RNAs for degradation and cytosolic release

Mitochondria are essential regulators of innate immunity. They generate long mitochondrial double-stranded RNAs (mt-dsRNAs) and release them into the cytosol to trigger an immune response under pathological stress conditions. Yet the regulation of these self-immunogenic RNAs remains largely unknown. Here, we employ CRISPR screening on mitochondrial RNA (mtRNA)-binding proteins and identify NOP2/Sun RNA methyltransferase 4 (NSUN4) as a key regulator of mt-dsRNA expression in human cells. We find that NSUN4 induces 5-methylcytosine (m5C) modification on mtRNAs, especially on the termini of light-strand long noncoding RNAs. These m5C-modified RNAs are recognized by complement C1q-binding protein (C1QBP), which recruits polyribonucleotide nucleotidyltransferase to facilitate RNA turnover. Suppression of NSUN4 or C1QBP results in increased mt-dsRNA expression, while C1QBP deficiency also leads to increased cytosolic mt-dsRNAs and subsequent immune activation. Collectively, our study unveils the mechanism underlying the selective degradation of light-strand mtRNAs and establishes a molecular mark for mtRNA decay and cytosolic release.

Multi-omics pan-cancer analysis reveals the prognostic values and immunological functions of PPA2, with a spotlight on breast cancer

Background

Recently, the role of inorganic pyrophosphatase 2 (PPA2) has been remaining merely superficial in many tumors. Hence, the aim was to analyze the potential functions of PPA2 in pan-cancer, focusing on its role in breast cancer.

Methods

A systematic pan-cancer analysis conducted primarily utilizing various open databases such as TCGA and GTEx. We explored the clinical value of PPA2 as well as various biological functions, including expression levels and subcellular localization, multi-dimensional immune-correlation analysis, co-expression networks, and gene heterogeneity. In addition, we not only verified the function of PPA2 through cell experiments but also analyzed PPA2 at the single-cell level and its drug sensitivity.

Results

PPA2 is abnormally expressed in various tumors, and it is mainly distributed in mitochondria. Furthermore, the indicators (OS, DSS, DFI, and PFI) of analysis hint that PPA2 exhibits significant prognostic value. At the same time, the genomic heterogeneity (including TMB, MSI, MATH, and NEO) of PPA2 in pan-cancer was analyzed. Across multiple tumors, the results showed a close correlation between PPA2 expression levels and different immune signatures (such as immune cell infiltration). All of these indicate that PPA2 could potentially be applied in the guidance of immunotherapy. We also have demonstrated that PPA2 promoted the process of breast cancer. Finally, some potential therapeutic agents (such as Fulvestrant) targeting the abnormal expression of PPA2 are revealed.

Conclusion

In conclusion, the results demonstrated the great value of PPA2 in pan-cancer research, as well as its potential as a therapeutic target for breast tumors.

Central role of SUMOylation in the regulation of chromatin interactions and transcriptional outputs of the androgen receptor in prostate cancer cells

The androgen receptor (AR) is pivotal in prostate cancer (PCa) progression and represents a critical therapeutic target. AR-mediated gene regulation involves intricate interactions with nuclear proteins, with many mediating and undergoing post-translational modifications that present alternative therapeutic avenues. Through chromatin proteomics in PCa cells, we identified SUMO ligases together with nuclear receptor coregulators and pioneer transcription factors within the AR's protein network. Intriguingly, this network displayed a significant association with SUMO2/3. To elucidate the influence of SUMOylation on AR chromatin interactions and subsequent gene regulation, we inhibited SUMOylation using ML-792 (SUMOi). While androgens generally facilitated the co-occupancy of SUMO2/3 and AR on chromatin, SUMOi induced divergent effects dependent on the type of AR-binding site (ARB). SUMOi augmented AR's pioneer-like binding on inaccessible chromatin regions abundant in androgen response elements (AREs) and diminished its interaction with accessible chromatin regions sparse in AREs yet rich in pioneer transcription factor motifs. The SUMOi-impacted ARBs divergently influenced AR-regulated genes; those associated with AR-mediated activation played roles in negative regulation of cell proliferation, while those with AR-mediated repression were involved in pattern formation. In conclusion, our findings underscore the pervasive influence of SUMOylation in shaping AR's role in PCa cells, potentially unveiling new therapeutic strategies.

Proximity labeling reveals dynamic changes in the SQSTM1 protein network

Sequestosome1 (SQSTM1) is an autophagy receptor that mediates the degradation of intracellular cargo, including protein aggregates, through multiple protein interactions. These interactions form the SQSTM1 protein network, and these interactions are mediated by SQSTM1 functional interaction domains, which include LIR, PB1, UBA, and KIR. Technological advances in cell biology continue to expand our knowledge of the SQSTM1 protein network and the relationship between the actions of the SQSTM1 protein network in cellular physiology and disease states. Here we apply proximity profile labeling to investigate the SQSTM1 protein interaction network by fusing TurboID with the human protein SQSTM1 (TurboID::SQSTM1). This chimeric protein displayed well-established SQSTM1 features including production of SQSTM1 intracellular bodies, binding to known SQSTM1 interacting partners, and capture of novel SQSTM1 protein interactors. Strikingly, aggregated tau protein altered the protein interaction network of SQSTM1 to include many stress-associated proteins. We demonstrate the importance of the PB1 and/or UBA domains for binding network members, including the K18 domain of tau. Overall, our work reveals the dynamic landscape of the SQSTM1 protein network and offers a resource to study SQSTM1 function in cellular physiology and disease state.

Endogenous Protein-Protein Interaction Network of the NPC Cholesterol Transporter 1 in the Cerebral Cortex

NPC intracellular cholesterol transporter 1 (NPC1) is a multipass, transmembrane glycoprotein mostly recognized for its key role in facilitating cholesterol efflux. Mutations in the NPC1 gene result in Niemann-Pick disease, type C (NPC), a fatal, lysosomal storage disease. Due to the progressively expanding implications of NPC1-related disorders, we investigated endogenous NPC1 protein-protein interactions in the mouse cortex and human-derived iPSCs neuronal models of the disease through coimmunoprecipitation-coupled with LC-MS based proteomics. The current study investigated protein-protein interactions specific to the wild-type and the most prevalent NPC1 mutation (NPC1I1061T) while filtering out any protein interactor identified in the Npc1-/- mouse model. Additionally, the results were matched across the two species to map the parallel interactome of wild-type and mutant NPC1I1061T. Most of the identified wild-type NPC1 interactors were related to cytoskeleton organization, synaptic vesicle activity, and translation. We found many putative NPC1 interactors not previously reported, including two SCAR/WAVE complex proteins that regulate ARP 2/3 complex actin nucleation and multiple membrane proteins important for neuronal activity at synapse. Moreover, we identified proteins important in trafficking specific to wild-type and mutant NPC1I1061T. Together, the findings are essential for a comprehensive understanding of NPC1 biological functions in addition to its classical role in sterol efflux.

Integrating Large-Scale Protein Structure Prediction into Human Genetics Research

The last five years have seen impressive progress in deep learning models applied to protein research. Most notably, sequence-based structure predictions have seen transformative gains in the form of AlphaFold2 and related approaches. Millions of missense protein variants in the human population lack annotations, and these computational methods are a valuable means to prioritize variants for further analysis. Here, we review the recent progress in deep learning models applied to the prediction of protein structure and protein variants, with particular emphasis on their implications for human genetics and health. Improved prediction of protein structures facilitates annotations of the impact of variants on protein stability, protein-protein interaction interfaces, and small-molecule binding pockets. Moreover, it contributes to the study of host-pathogen interactions and the characterization of protein function. As genome sequencing in large cohorts becomes increasingly prevalent, we believe that better integration of state-of-the-art protein informatics technologies into human genetics research is of paramount importance.

The Role and Clinical Relevance of Glycolysis-Associated Genes on Immune Infiltration in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) poses a significant challenge with dismal survival rates, necessitating a deeper understanding of its molecular mechanisms and the development of improved therapies. Metabolic reprogramming, particularly heightened glycolysis, plays a crucial role in HCC progression. Glycolysis-associated genes (GAGs) emerge as key players in HCC pathogenesis, influencing the tumor microenvironment and immune responses. This study aims to investigate the intricate interplay between GAGs and the immune landscape within HCC, offering valuable insights into potential prognostic markers and therapeutic targets to enhance treatment strategies and patient outcomes. Through the exploration of GAGs, we have identified two distinct molecular glycolytic subtypes in HCC patients, each exhibiting significant differences in both the immune microenvironment and prognosis. A risk model comprising five key GAGs was formulated and subsequently evaluated for their predictive accuracy. Our findings underscore the diverse tumor microenvironment and immune responses associated with the varying glycolytic subtypes observed in HCC. The identified key GAGs hold promise as prognostic indicators for evaluating HCC risk levels, predicting patient outcomes, and guiding clinical treatment decisions, particularly in the context of anticipating responses to immunotherapy drugs.

NFAT activation by FKBP52 promotes cancer cell proliferation by suppressing p53

FK506-binding protein 52 (FKBP52) is a member of the FKBP family of proline isomerases. FKBP52 is up-regulated in various cancers and functions as a positive regulator of steroid hormone receptors. Depletion of FKBP52 is known to inhibit cell proliferation; however, the detailed mechanism remains poorly understood. In this study, we found that FKBP52 depletion decreased MDM2 transcription, leading to stabilization of p53, and suppressed cell proliferation. We identified NFATc1 and NFATc3 as transcription factors that regulate MDM2 We also found that FKBP52 associated with NFATc3 and facilitated its nuclear translocation. In addition, calcineurin, a well-known Ca2+ phosphatase essential for activation of NFAT, plays a role in MDM2 transcription. Supporting this notion, MDM2 expression was found to be regulated by intracellular Ca2+ Taken together, these findings reveal a new role of FKBP52 in promoting cell proliferation via the NFAT-MDM2-p53 axis, and indicate that inhibition of FKBP52 could be a new therapeutic tool to activate p53 and inhibit cell proliferation.

Identifying actionable synthetically lethal cancer gene pairs using mutual exclusivity

Mutually exclusive loss-of-function alterations in gene pairs are those that occur together less frequently than may be expected and may denote a synthetically lethal relationship (SSL) between the genes. SSLs can be exploited therapeutically to selectively kill cancer cells. Here, we analysed mutation, copy number variation, and methylation levels in samples from The Cancer Genome Atlas, using the hypergeometric and the Poisson binomial tests to identify mutually exclusive inactivated genes. We focused on gene pairs where one is an inactivated tumour suppressor and the other a gene whose protein product can be inhibited by known drugs. This provided an abundance of potential targeted therapeutics and repositioning opportunities for several cancers. These data are available on the MexDrugs website, https://bioinformaticslab.sussex.ac.uk/mexdrugs.

Computational Approaches to Drug Repurposing: Methods, Challenges, and Opportunities

Drug repurposing refers to the inference of therapeutic relationships between a clinical indication and existing compounds. As an emerging paradigm in drug development, drug repurposing enables more efficient treatment of rare diseases, stratified patient populations, and urgent threats to public health. However, prioritizing well-suited drug candidates from among a nearly infinite number of repurposing options continues to represent a significant challenge in drug development. Over the past decade, advances in genomic profiling, database curation, and machine learning techniques have enabled more accurate identification of drug repurposing candidates for subsequent clinical evaluation. This review outlines the major methodologic classes that these approaches comprise, which rely on (a) protein structure, (b) genomic signatures, (c) biological networks, and (d) real-world clinical data. We propose that realizing the full impact of drug repurposing methodologies requires a multidisciplinary understanding of each method's advantages and limitations with respect to clinical practice.

Tissue specific tumor-gene link prediction through sampling based GNN using a heterogeneous network

A tissue sample is a valuable resource for understanding a patient's symptoms and health status in relation to tumor growth. Recent research seeks to establish a connection between tissue-specific tumor samples and genetic markers (genes). This breakthrough has paved the way for personalized cancer therapies. With this motivation, the proposed model constructs a heterogeneous network based on tumor sample-gene relation data and gene-gene interaction data. This network also incorporates tissue-specific gene expression and primary site-based gene counts as features, enabling tissue-specific predictions. Graph neural networks (GNNs) have proven effective in modeling complex interactions and predicting links within this network. The proposed model has successfully predicted tumor-gene associations by leveraging sampling-based GNNs and link layer embedding. The model's performance metrics, such as AUC-ROC scores, reached approximately 94%, demonstrating the potential of this heterogeneous network in predicting tissue-specific tumor sample-gene links. This paper's findings highlight the importance of tissue-specific associations in cancer research.

TTF1 control of LncRNA synthesis delineates a tumor suppressor pathway directly regulating the ribosomal RNA genes

The tumor suppressor p14/19ARF regulates ribosomal RNA (rRNA) synthesis by controlling the nucleolar localization of Transcription Termination Factor 1 (TTF1). However, the role played by TTF1 in regulating the rRNA genes and in potentially controlling growth has remained unclear. We now show that TTF1 expression regulates cell growth by determining the cellular complement of ribosomes. Unexpectedly, it achieves this by acting as a "roadblock" to synthesis of the noncoding LncRNA and pRNA that we show are generated from the "Spacer Promoter" duplications present upstream of the 47S pre-rRNA promoter on the mouse and human ribosomal RNA genes. Unexpectedly, the endogenous generation of these noncoding RNAs does not induce CpG methylation or gene silencing. Rather, it acts in cis to suppress 47S preinitiation complex formation and hence de novo pre-rRNA synthesis by a mechanism reminiscent of promoter interference or occlusion. Taken together, our data delineate a pathway from p19ARF to cell growth suppression via the regulation of ribosome biogenesis by noncoding RNAs and validate a key cellular growth law in mammalian cells.

Bioinformatics Analysis of Actin Interactome: Characterization of the Nuclear and Cytoplasmic Actin-Binding Proteins

Actin is present in the cytoplasm and nucleus of every eukaryotic cell. In the cytoplasm, framework and motor functions of actin are associated with its ability to polymerize to form F-actin. In the nucleus, globular actin plays a significant functional role. For a globular protein, actin has a uniquely large number of proteins with which it interacts. Bioinformatics analysis of the actin interactome showed that only a part of actin-binding proteins are both cytoplasmic and nuclear. There are proteins that interact only with cytoplasmic, or only with nuclear actin. The first pool includes proteins associated with the formation, regulation, and functioning of the actin cytoskeleton predominate, while nuclear actin-binding proteins are involved in the majority of key nuclear processes, from regulation of transcription to DNA damage response. Bioinformatics analysis of the structure of actin-binding proteins showed that these are mainly intrinsically disordered proteins, many of which are part of membrane-less organelles. Interestingly, although the number of intrinsically disordered actin-binding proteins in the nucleus is greater than in the cytoplasm, the drivers for the formation of the membrane-less organelles in the cytoplasm are significantly (four times) greater than in the nucleus.

A homozygous nonsense variant in the alternatively spliced VLDLR exon 4 causes a neurodevelopmental disorder without features of VLDLR cerebellar hypoplasia

VLDLR cerebellar hypoplasia is characterized by intellectual disability, non-progressive cerebellar ataxia, and seizures. The characteristic MRI findings include hypoplasia of the inferior portion of the cerebellar vermis and hemispheres, simplified cortical gyration, and a small brain stem. Biallelic VLDLR pathogenic variants cause loss-of-function of the encoded very low-density lipoprotein receptor. VLDLR exons 4 and 16 are alternatively spliced, resulting in the expression of four transcript variants, including two exon 4-lacking mRNAs expressed in the human brain. Previously reported VLDLR pathogenic variants affect all four transcript variants. Here we report on two sisters with facial dysmorphism, microcephaly, intellectual disability, and normal brain imaging. Exome sequencing in one patient identified the homozygous VLDLR nonsense variant c.376C>T; p.(Gln126*) in exon 4; her similarly affected sister also carried the homozygous variant and parents were heterozygous carriers. VLDLR transcript analysis identified mRNAs with and without exon 4 in patient fibroblasts, while exon 4-containing VLDLR mRNAs were predominantly detected in control fibroblasts. We found significantly reduced VLDLR mRNA levels in patient compared to control cells, likely caused by nonsense-mediated mRNA decay of exon 4-containing VLDLR transcripts. Expression of neuronal VLDLR isoforms produced from exon 4-lacking transcripts may have protected both patients from developing the cerebellar hypoplasia phenotype.

Reconstruction of gene regulatory networks for Caenorhabditis elegans using tree-shaped gene expression data

Constructing gene regulatory networks is a widely adopted approach for investigating gene regulation, offering diverse applications in biology and medicine. A great deal of research focuses on using time series data or single-cell RNA-sequencing data to infer gene regulatory networks. However, such gene expression data lack either cellular or temporal information. Fortunately, the advent of time-lapse confocal laser microscopy enables biologists to obtain tree-shaped gene expression data of Caenorhabditis elegans, achieving both cellular and temporal resolution. Although such tree-shaped data provide abundant knowledge, they pose challenges like non-pairwise time series, laying the inaccuracy of downstream analysis. To address this issue, a comprehensive framework for data integration and a novel Bayesian approach based on Boolean network with time delay are proposed. The pre-screening process and Markov Chain Monte Carlo algorithm are applied to obtain the parameter estimates. Simulation studies show that our method outperforms existing Boolean network inference algorithms. Leveraging the proposed approach, gene regulatory networks for five subtrees are reconstructed based on the real tree-shaped datatsets of Caenorhabditis elegans, where some gene regulatory relationships confirmed in previous genetic studies are recovered. Also, heterogeneity of regulatory relationships in different cell lineage subtrees is detected. Furthermore, the exploration of potential gene regulatory relationships that bear importance in human diseases is undertaken. All source code is available at the GitHub repository https://github.com/edawu11/BBTD.git.

Linking coronary artery disease to neurodegenerative diseases through systems genetics

Coronary artery disease (CAD) is still a leading cause of death worldwide despite the extensive research and the considerable progresses made through the years. As other cardiovascular diseases, CAD is the result of the complex interaction between genetic variants and environmental factors. Currently identified genetic loci associated to CAD revealed the contribution of multiple molecular pathways to its pathogenesis, suggesting the need for a systemic approach to understand the role of genetic determinants. In this study we wanted to investigate how GWAS variants associated to CAD interact with each other and with nearby genes in the context of the coronary artery molecular interactome. GWAS variants associated to CAD were selected from GWAS Catalog, then, a tissue-specific interactome was constructed integrating protein-protein interactions (PPI) from multiple public repositories and computationally inferred co-expression relationships. To focus on the part of the network most relevant for CAD, we selected the interactions connecting the genes carrying a variant associated to the disease. A functional enrichment analysis conducted on the subnetwork revealed that genes carrying genetic variants associated to CAD closely interact with genes related to relevant biological processes, such as extracellular matrix organization, lipoprotein clearance, arterial morphology and inflammatory response. These results confirm that the identified subnetwork reflects the molecular pathways altered in CAD and intercepted by the selected variants. Interestingly, the most connected nodes of the network included amyloid beta precursor protein (APP) and huntingtin (HTT), both implicated in neurodegenerative disorders. In recent years the interest in investigating the common processes between cardiovascular diseases and neurodegenerative disorders is increasing, with growing evidence of a link between CAD and Alzheimer's disease. The results obtained in this work support the association between such apparently unrelated diseases and highlight the necessity of a systems biology approach to better elucidate shared pathological mechanisms.

PARK2 suppresses the proliferation of high-grade serous ovarian carcinoma via inducing the proteasomal degradation of ZNF703

High-grade serous ovarian cancer (HGSC) is an aggressive disease with poor prognosis. The oncoprotein ZNF703 is implicated in driving HGSC pathogenesis, but factors regulating its abundance remain unclear. In this study, we aim to investigate the potential connection between ZNF703 dysregulation and ubiquitin-mediated protein degradation in HGSC. Bioinformatics prediction was performed using BioGRID database. HGSC representative cell lines were utilized for in vitro and in vivo studies. Results showed that ZNF703 protein was stabilized upon proteasome inhibition, suggesting a regulation via ubiquitination. The ubiquitin E3 ligase PARK2 was found to interact with ZNF703 in a dose-dependent manner, promoting its polyubiquitination and subsequent proteasomal degradation. Re-expression of PARK2 in HGSC cells led to reduced ZNF703 levels together with decreased Cyclin D1/E1 abundance and G1 cell cycle arrest. ZNF703 overexpression alone increased S phase cells, Cyclin D1/E1 levels, and xenograft tumor growth, while co-expression with PARK2 mitigated these oncogenic effects. Collectively, our findings identify ZNF703 as a bona fide substrate of PARK2, reveal a tumor suppressive function for PARK2 in attenuating ZNF703-mediated G1/S transition and HGSC growth through instigating its degradation. This study elucidates a pivotal PARK2-ZNF703 axis with therapeutic implications for targeted intervention in HGSC.

Diversity of post-translational modifications and cell signaling revealed by single cell and single organelle mass spectrometry

The rapid evolution of mass spectrometry-based single-cell proteomics now enables the cataloging of several thousand proteins from single cells. We investigated whether we could discover cellular heterogeneity beyond proteome, encompassing post-translational modifications (PTM), protein-protein interaction, and variants. By optimizing the mass spectrometry data interpretation strategy to enable the detection of PTMs and variants, we have generated a high-definition dataset of single-cell and nuclear proteomic-states. The data demonstrate the heterogeneity of cell-states and signaling dependencies at the single-cell level and reveal epigenetic drug-induced changes in single nuclei. This approach enables the exploration of previously uncharted single-cell and organellar proteomes revealing molecular characteristics that are inaccessible through RNA profiling.

ASACO: Automatic and Serial Analysis of CO-expression to discover gene modifiers with potential use in drug repurposing

Massive gene expression analyses are widely used to find differentially expressed genes under specific conditions. The results of these experiments are often available in public databases that are undergoing a growth similar to that of molecular sequence databases in the past. This now allows novel secondary computational tools to emerge that use such information to gain new knowledge. If several genes have a similar expression profile across heterogeneous transcriptomics experiments, they could be functionally related. These associations are usually useful for the annotation of uncharacterized genes. In addition, the search for genes with opposite expression profiles is useful for finding negative regulators and proposing inhibitory compounds in drug repurposing projects. Here we present a new web application, Automatic and Serial Analysis of CO-expression (ASACO), which has the potential to discover positive and negative correlator genes to a given query gene, based on thousands of public transcriptomics experiments. In addition, examples of use are presented, comparing with previous contrasted knowledge. The results obtained propose ASACO as a useful tool to improve knowledge about genes associated with human diseases and noncoding genes. ASACO is available at http://www.bioinfocabd.upo.es/asaco/.

Binding Promiscuity of Therapeutic Factor VIII

The binding promiscuity of proteins defines their ability to indiscriminately bind multiple unrelated molecules. Binding promiscuity is implicated, at least in part, in the off-target reactivity, nonspecific biodistribution, immunogenicity, and/or short half-life of potentially efficacious protein drugs, thus affecting their clinical use. In this review, we discuss the current evidence for the binding promiscuity of factor VIII (FVIII), a protein used for the treatment of hemophilia A, which displays poor pharmacokinetics, and elevated immunogenicity. We summarize the different canonical and noncanonical interactions that FVIII may establish in the circulation and that could be responsible for its therapeutic liabilities. We also provide information suggesting that the FVIII light chain, and especially its C1 and C2 domains, could play an important role in the binding promiscuity. We believe that the knowledge accumulated over years of FVIII usage could be exploited for the development of strategies to predict protein binding promiscuity and therefore anticipate drug efficacy and toxicity. This would open a mutational space to reduce the binding promiscuity of emerging protein drugs while conserving their therapeutic potency.

Hazard assessment of hexagonal boron nitride and hexagonal boron nitride reinforced thermoplastic polyurethane composites using human skin and lung cells

Hexagonal boron nitride (hBN) is an emerging two-dimensional material attracting considerable attention in the industrial sector given its innovative physicochemical properties. Potential risks are associated mainly with occupational exposure where inhalation and skin contact are the most relevant exposure routes for workers. Here we aimed at characterizing the effects induced by composites of thermoplastic polyurethane (TPU) and hBN, using immortalized HaCaT skin keratinocytes and BEAS-2B bronchial epithelial cells. The composite was abraded using a Taber® rotary abraser and abraded TPU and TPU-hBN were also subjected to photo-Fenton-mediated degradation mimicking potential weathering across the product life cycle. Cells were exposed to the materials for 24 h (acute exposure) or twice per week for 4 weeks (chronic exposure) and evaluated with respect to material internalization, cytotoxicity, and proinflammatory cytokine secretion. Additionally, comprehensive mass spectrometry-based proteomics and metabolomics (secretomics) analyses were performed. Overall, despite evidence of cellular uptake of the material, no significant cellular and/or protein expression profiles alterations were observed after acute or chronic exposure of HaCaT or BEAS-2B cells, identifying only few pro-inflammatory proteins. Similar results were obtained for the degraded materials. These results support the determination of hazard profiles associated with cutaneous and pulmonary hBN-reinforced polymer composites exposure.

Polypharmacology prediction: the long road toward comprehensively anticipating small-molecule selectivity to de-risk drug discovery

INTRODUCTION

Small molecules often bind to multiple targets, a behavior termed polypharmacology. Anticipating polypharmacology is essential for drug discovery since unknown off-targets can modulate safety and efficacy - profoundly affecting drug discovery success. Unfortunately, experimental methods to assess selectivity present significant limitations and drugs still fail in the clinic due to unanticipated off-targets. Computational methods are a cost-effective, complementary approach to predict polypharmacology.

AREAS COVERED

This review aims to provide a comprehensive overview of the state of polypharmacology prediction and discuss its strengths and limitations, covering both classical cheminformatics methods and bioinformatic approaches. The authors review available data sources, paying close attention to their different coverage. The authors then discuss major algorithms grouped by the types of data that they exploit using selected examples.

EXPERT OPINION

Polypharmacology prediction has made impressive progress over the last decades and contributed to identify many off-targets. However, data incompleteness currently limits most approaches to comprehensively predict selectivity. Moreover, our limited agreement on model assessment challenges the identification of the best algorithms - which at present show modest performance in prospective real-world applications. Despite these limitations, the exponential increase of multidisciplinary Big Data and AI hold much potential to better polypharmacology prediction and de-risk drug discovery.

BMP4 regulates asymmetric Pkd2 distribution in mouse nodal immotile cilia and ciliary mechanosensing required for left-right determination

BACKGROUND

Mouse nodal immotile cilia mechanically sense the bending direction for left-right (L-R) determination and activate the left-side-specific signaling cascade, leading to increased Nodal activity. Asymmetric distribution of Pkd2, a crucial channel for L-R determination, on immotile cilia has been reported recently. However, the causal relationship between the asymmetric Pkd2 distribution and direction-dependent flow sensing is not well understood. Furthermore, the underlying molecular mechanism directing this asymmetric Pkd2 distribution remains unclear.

RESULTS

The effects of several recombinant proteins and inhibitors on the Pkd2 distribution were analyzed using super-resolution microscopy. Notably, bone morphogenetic protein 4 (BMP4) affected the Pkd2 distribution. Additionally, three-dimensional manipulation of nodal immotile cilia using optical tweezers revealed that excess BMP4 caused defects in the mechanosensing ability of the cilia.

CONCLUSIONS

Experimental data together with model calculations suggest that BMP4 regulates the asymmetric distribution of Pkd2 in nodal immotile cilia, thereby affecting the ability of these cilia to sense the bending direction for L-R determination. This study, for the first time, provides insight into the relationship between the asymmetric protein distribution in cilia and their function.

Connecting the dots: Computational network analysis for disease insight and drug repurposing

Network biology is a powerful framework for studying the structure, function, and dynamics of biological systems, offering insights into the balance between health and disease states. The field is seeing rapid progress in all of its aspects: data availability, network synthesis, network analytics, and impactful applications in medicine and drug development. We review the most recent and significant results in network biomedicine, with a focus on the latest data, analytics, software resources, and applications in medicine. We also discuss what in our view are the likely directions of impactful development over the next few years.

Computational tools to predict context-specific protein complexes

Interactions between thousands of proteins define cells' protein-protein interaction (PPI) network. Some of these interactions lead to the formation of protein complexes. It is challenging to identify a protein complex in a haystack of protein-protein interactions, and it is even more difficult to predict all protein complexes of the complexome. Simulations and machine learning approaches try to crack these problems by looking at the PPI network or predicted protein structures. Clustering of PPI networks led to the first protein complex predictions, while most recently, atomistic models of protein complexes and deep-learning-based structure prediction methods have also emerged. The simulation of PPI level interactions even enables the quantitative prediction of protein complexes. These methods, the required data sources, and their potential future developments are discussed in this review.

Exploring COVID-19 Pandemic Disparities with Transcriptomic Meta-analysis from the Perspective of Personalized Medicine

Infection with SARS-CoV2, which is responsible for COVID-19, can lead to differences in disease development, severity and mortality rates depending on gender, age or the presence of certain diseases. Considering that existing studies ignore these differences, this study aims to uncover potential differences attributable to gender, age and source of sampling as well as viral load using bioinformatics and multi-omics approaches. Differential gene expression analyses were used to analyse the phenotypic differences between SARS-CoV-2 patients and controls at the mRNA level. Pathway enrichment analyses were performed at the gene set level to identify the activated pathways corresponding to the differences in the samples. Drug repurposing analysis was performed at the protein level, focusing on host-mediated drug candidates to uncover potential therapeutic differences. Significant differences (i.e. the number of differentially expressed genes and their characteristics) were observed for COVID-19 at the mRNA level depending on the sample source, gender and age of the samples. The results of the pathway enrichment show that SARS-CoV-2 can be combated more effectively in the respiratory tract than in the blood samples. Taking into account the different sample sources and their characteristics, different drug candidates were identified. Evaluating disease prediction, prevention and/or treatment strategies from a personalised perspective is crucial. In this study, we not only evaluated the differences in COVID-19 from a personalised perspective, but also provided valuable data for further experimental and clinical efforts. Our findings could shed light on potential pandemics.

A-MYB substitutes for B-MYB in activating cell cycle genes and in stimulating proliferation

A-MYB (MYBL1) is a transcription factor with a role in meiosis in spermatocytes. The related B-MYB protein is a key oncogene and a master regulator activating late cell cycle genes. To activate genes, B-MYB forms a complex with MuvB and is recruited indirectly to cell cycle genes homology region (CHR) promoter sites of target genes. Activation through the B-MYB-MuvB (MMB) complex is essential for successful mitosis. Here, we discover that A-MYB has a function in transcriptional regulation of the mitotic cell cycle and can substitute for B-MYB. Knockdown experiments in cells not related to spermatogenesis show that B-MYB loss alone merely delays cell cycle progression. Only dual knockdown of B-MYB and A-MYB causes G2/M cell cycle arrest, endoreduplication, and apoptosis. A-MYB can substitute for B-MYB in binding to MuvB. The resulting A-MYB-MuvB complex activates genes through CHR sites. We find that A-MYB activates the same target genes as B-MYB. Many of the corresponding proteins are central regulators of the cell division cycle. In summary, we demonstrate that A-MYB is an activator of the mitotic cell cycle by activating late cell cycle genes.

WeSA: a web server for improving analysis of affinity proteomics data

Protein-protein interaction experiments still yield many false positive interactions. The socioaffinity metric can distinguish true protein-protein interactions from noise based on available data. Here, we present WeSA (Weighted SocioAffinity), which considers large datasets of interaction proteomics data (IntAct, BioGRID, the BioPlex) to score human protein interactions and, in a statistically robust way, flag those (even from a single experiment) that are likely to be false positives. ROC analysis (using CORUM-PDB positives and Negatome negatives) shows that WeSA improves over other measures of interaction confidence. WeSA shows consistently good results over all datasets (up to: AUC = 0.93 and at best threshold: TPR = 0.84, FPR = 0.11, Precision = 0.98). WeSA is freely available without login (wesa.russelllab.org). Users can submit their own data or look for organized information on human protein interactions using the web server. Users can either retrieve available information for a list of proteins of interest or calculate scores for new experiments. The server outputs either pre-computed or updated WeSA scores for the input enriched with information from databases. The summary is presented as a table and a network-based visualization allowing the user to remove those nodes/edges that the method considers spurious.

SLiMAn 2.0: meaningful navigation through peptide-protein interaction networks

Among the myriad of protein-protein interactions occurring in living organisms, a substantial amount involves small linear motifs (SLiMs) recognized by structured domains. However, predictions of SLiM-based networks are tedious, due to the abundance of such motifs and a high portion of false positive hits. For this reason, a webserver SLiMAn (Short Linear Motif Analysis) was developed to focus the search on the most relevant SLiMs. Using SLiMAn, one can navigate into a given (meta-)interactome and tune a variety of parameters associated to each type of SLiMs in attempt to identify functional ELM motifs and their recognition domains. The IntAct and BioGRID databases bring experimental information, while IUPred and AlphaFold provide boundaries of folded and disordered regions. Post-translational modifications listed in PhosphoSite+ are highlighted. Links to PubMed accelerate scrutiny into the literature, to support (or not) putative pairings. Dedicated visualization features are also incorporated, such as Cytoscape for macromolecular networks and BINANA for intermolecular contacts within structural models generated by SCWRL 3.0. The use of SLiMAn 2.0 is illustrated on a simple example. It is freely available at https://sliman2.cbs.cnrs.fr.

DEGRONOPEDIA: a web server for proteome-wide inspection of degrons

E3 ubiquitin ligases recognize substrates through their short linear motifs termed degrons. While degron-signaling has been a subject of extensive study, resources for its systematic screening are limited. To bridge this gap, we developed DEGRONOPEDIA, a web server that searches for degrons and maps them to nearby residues that can undergo ubiquitination and disordered regions, which may act as protein unfolding seeds. Along with an evolutionary assessment of degron conservation, the server also reports on post-translational modifications and mutations that may modulate degron availability. Acknowledging the prevalence of degrons at protein termini, DEGRONOPEDIA incorporates machine learning to assess N-/C-terminal stability, supplemented by simulations of proteolysis to identify degrons in newly formed termini. An experimental validation of a predicted C-terminal destabilizing motif, coupled with the confirmation of a post-proteolytic degron in another case, exemplifies its practical application. DEGRONOPEDIA can be freely accessed at degronopedia.com.

Diverse Gene Regulatory Mechanisms Alter Rattlesnake Venom Gene Expression at Fine Evolutionary Scales

Understanding and predicting the relationships between genotype and phenotype is often challenging, largely due to the complex nature of eukaryotic gene regulation. A step towards this goal is to map how phenotypic diversity evolves through genomic changes that modify gene regulatory interactions. Using the Prairie Rattlesnake (Crotalus viridis) and related species, we integrate mRNA-seq, proteomic, ATAC-seq and whole-genome resequencing data to understand how specific evolutionary modifications to gene regulatory network components produce differences in venom gene expression. Through comparisons within and between species, we find a remarkably high degree of gene expression and regulatory network variation across even a shallow level of evolutionary divergence. We use these data to test hypotheses about the roles of specific trans-factors and cis-regulatory elements, how these roles may vary across venom genes and gene families, and how variation in regulatory systems drive diversity in venom phenotypes. Our results illustrate that differences in chromatin and genotype at regulatory elements play major roles in modulating expression. However, we also find that enhancer deletions, differences in transcription factor expression, and variation in activity of the insulator protein CTCF also likely impact venom phenotypes. Our findings provide insight into the diversity and gene-specificity of gene regulatory features and highlight the value of comparative studies to link gene regulatory network variation to phenotypic variation.

Bidirectional linkage of DNA barcodes for the multiplexed mapping of higher-order protein interactions in cells

Capturing the full complexity of the diverse hierarchical interactions in the protein interactome is challenging. Here we report a DNA-barcoding method for the multiplexed mapping of pairwise and higher-order protein interactions and their dynamics within cells. The method leverages antibodies conjugated with barcoded DNA strands that can bidirectionally hybridize and covalently link to linearize closely spaced interactions within individual 3D protein complexes, encoding and decoding the protein constituents and the interactions among them. By mapping protein interactions in cancer cells and normal cells, we found that tumour cells exhibit a larger diversity and abundance of protein complexes with higher-order interactions. In biopsies of human breast-cancer tissue, the method accurately identified the cancer subtype and revealed that higher-order protein interactions are associated with cancer aggressiveness.