The GOA database: gene Ontology annotation updates for 2015

Mutual annotation-based prediction of protein domain functions with Domain2GO

Identifying unknown functional properties of proteins is essential for understanding their roles in both health and disease states. The domain composition of a protein can reveal critical information in this context, as domains are structural and functional units that dictate how the protein should act at the molecular level. The expensive and time-consuming nature of wet-lab experimental approaches prompted researchers to develop computational strategies for predicting the functions of proteins. In this study, we proposed a new method called Domain2GO that infers associations between protein domains and function-defining gene ontology (GO) terms, thus redefining the problem as domain function prediction. Domain2GO uses documented protein-level GO annotations together with proteins' domain annotations. Co-annotation patterns of domains and GO terms in the same proteins are examined using statistical resampling to obtain reliable associations. As a use-case study, we evaluated the biological relevance of examples selected from the Domain2GO-generated domain-GO term mappings via literature review. Then, we applied Domain2GO to predict unknown protein functions by propagating domain-associated GO terms to proteins annotated with these domains. For function prediction performance evaluation and comparison against other methods, we employed Critical Assessment of Function Annotation 3 (CAFA3) challenge datasets. The results demonstrated the high potential of Domain2GO, particularly for predicting molecular function and biological process terms, along with advantages such as producing interpretable results and having an exceptionally low computational cost. The approach presented here can be extended to other ontologies and biological entities to investigate unknown relationships in complex and large-scale biological data. The source code, datasets, results, and user instructions for Domain2GO are available at https://github.com/HUBioDataLab/Domain2GO. Additionally, we offer a user-friendly online tool at https://huggingface.co/spaces/HUBioDataLab/Domain2GO, which simplifies the prediction of functions of previously unannotated proteins solely using amino acid sequences.

PomBase: a Global Core Biodata Resource-growth, collaboration, and sustainability

PomBase (https://www.pombase.org), the model organism database (MOD) for fission yeast, was recently awarded Global Core Biodata Resource (GCBR) status by the Global Biodata Coalition (GBC; https://globalbiodata.org/) after a rigorous selection process. In this MOD review, we present PomBase's continuing growth and improvement over the last 2 years. We describe these improvements in the context of the qualitative GCBR indicators related to scientific quality, comprehensivity, accelerating science, user stories, and collaborations with other biodata resources. This review also showcases the depth of existing connections both within the biocuration ecosystem and between PomBase and its user community.

Mouse Genome Informatics: an integrated knowledgebase system for the laboratory mouse

Mouse Genome Informatics (MGI) is a federation of expertly curated information resources designed to support experimental and computational investigations into genetic and genomic aspects of human biology and disease using the laboratory mouse as a model system. The Mouse Genome Database (MGD) and the Gene Expression Database (GXD) are core MGI databases that share data and system architecture. MGI serves as the central community resource of integrated information about mouse genome features, variation, expression, gene function, phenotype, and human disease models acquired from peer-reviewed publications, author submissions, and major bioinformatics resources. To facilitate integration and standardization of data, biocuration scientists annotate using terms from controlled metadata vocabularies and biological ontologies (e.g. Mammalian Phenotype Ontology, Mouse Developmental Anatomy, Disease Ontology, Gene Ontology, etc.), and by applying international community standards for gene, allele, and mouse strain nomenclature. MGI serves basic scientists, translational researchers, and data scientists by providing access to FAIR-compliant data in both human-readable and compute-ready formats. The MGI resource is accessible at https://informatics.jax.org. Here, we present an overview of the core data types represented in MGI and highlight recent enhancements to the resource with a focus on new data and functionality for MGD and GXD.

Comparative Proteomics Elucidates the Potential Mechanism of Sperm Capacitation of Chinese Mitten Crabs (Eriocheir sinensis)

Sperm capacitation is broadly defined as a suite of biochemical and biophysical changes resulting from the acquisition of fertilization ability. To gain insights into the regulation mechanism of crustacean sperm capacitation, 4D label-free quantitative proteomics was first applied to analyze the changes of sperm in Eriocheir sinensis under three sequential physiological conditions: seminal vesicles (X2), hatched with the seminal receptacle content (X3), and incubated with egg water (X5). In total, 1536 proteins were identified, among which 880 proteins were quantified, with 82 and 224 proteins significantly altered after incubation with the seminal receptacle contents and egg water. Most differentially expressed proteins were attributed to biological processes by Gene Ontology annotation analysis. As the fundamental bioenergetic metabolism of sperm, the oxidative phosphorylation, glycolysis, and the pentose phosphate pathway presented significant changes under the treatment of seminal receptacle contents, indicating intensive regulation for sperm in the seminal receptacle. Additionally, the seminal receptacle contents also significantly increased the oxidation level of sperm, whereas the enhancement of abundance in superoxide dismutase, peroxiredoxin 1, and glutathione S-transferase after incubation with egg water significantly improved the resistance against oxidation. These results provided a new perspective for reproduction studies in crustaceans.

Hierarchical Negative Sampling Based Graph Contrastive Learning Approach for Drug-Disease Association Prediction

Predicting potential drug-disease associations (RDAs) plays a pivotal role in elucidating therapeutic strategies for diseases and facilitating drug repositioning, making it of paramount importance. However, existing methods are constrained and rely heavily on limited domain-specific knowledge, impeding their ability to effectively predict candidate associations between drugs and diseases. Moreover, the simplistic definition of unknown information pertaining to drug-disease relationships as negative samples presents inherent limitations. To overcome these challenges, we introduce a novel hierarchical negative sampling-based graph contrastive model, termed HSGCLRDA, which aims to forecast latent associations between drugs and diseases. In this study, HSGCLRDA integrates the association information as well as similarity between drugs, diseases and proteins. Meanwhile, the model constructs a drug-disease-protein heterogeneous network. Subsequently, employing a hierarchical structural sampling technique, we establish reliable negative drug-disease samples utilizing PageRank algorithms. Utilizing meta-path aggregation within the heterogeneous network, we derive low-dimensional representations for drugs and diseases, thereby constructing global and local feature graphs that capture their interactions comprehensively. To obtain representation information, we adopt a self-supervised graph contrastive approach that leverages graph convolutional networks (GCNs) and second-order GCNs to extract feature graph information. Furthermore, we integrate a contrastive cost function derived from the cross-entropy cost function, facilitating holistic model optimization. Experimental results obtained from benchmark datasets not only showcase the superior performance of HSGCLRDA compared to various baseline methods in predicting RDAs but also emphasize its practical utility in identifying novel potential diseases associated with existing drugs through meticulous case studies.

Wide transcriptional outlook to uncover Penicillium expansum genes underlying fungal incompatible infection

Pathogenesis of P. expansum involved different processes and one of them is the recognition between pathogen-host, which in the case of P. expansum is preferably pome fruit. In this work, the possible mechanisms connected to host recognition are addressed through the generation of a subtractive library carried out during the incompatible P. expansum-orange interaction in the initial stages of infection. The generated library was analyzed by massive sequencing and bioinformatic analysis. Of the identified genes, a total of 24 were selected for subsequent expression analysis by RT-qPCR in two incompatible interaction situations. The characterization of the overexpressed genes revealed the presence of CWDEs, ATPases, aldolases, detoxifying enzymes and virulent determinants that could act as effectors related to fungal virulence independently of the host. However, several identified genes, which could not be associated with the virulence of P. expansum under compatible conditions, were related to enzymes to obtain the nutrients necessary for the growth and development of the pathogen under stress conditions through basal metabolism that contributes to expand the range of adaptation of the pathogen to the environment and different hosts.

Ribosomal mutations enable a switch between high fitness and high stress resistance in Listeria monocytogenes

Multiple stress resistant variants of Listeria monocytogenes with mutations in rpsU encoding ribosomal protein RpsU have previously been isolated after a single exposure to acid stress. These variants, including L. monocytogenes LO28 variant V14 with a complete deletion of the rpsU gene, showed upregulation of the general stress sigma factor Sigma B-mediated stress resistance genes and had a lower maximum specific growth rate than the LO28 WT, signifying a trade-off between stress resistance and fitness. In the current work V14 has been subjected to an experimental evolution regime, selecting for higher fitness in two parallel evolving cultures. This resulted in two evolved variants with WT-like fitness: 14EV1 and 14EV2. Comparative analysis of growth performance, acid and heat stress resistance, in combination with proteomics and RNA-sequencing, indicated that in both lines reversion to WT-like fitness also resulted in WT-like stress sensitivity, due to lack of Sigma B-activated stress defense. Notably, genotyping of 14EV1 and 14EV2 provided evidence for unique point-mutations in the ribosomal rpsB gene causing amino acid substitutions at the same position in RpsB, resulting in RpsB22Arg-His and RpsB22Arg-Ser, respectively. Combined with data obtained with constructed RpsB22Arg-His and RpsB22Arg-Ser mutants in the V14 background, we provide evidence that loss of function of RpsU resulting in the multiple stress resistant and reduced fitness phenotype, can be reversed by single point mutations in rpsB leading to arginine substitutions in RpsB at position 22 into histidine or serine, resulting in a WT-like high fitness and low stress resistance phenotype. This demonstrates the impact of genetic changes in L. monocytogenes' ribosomes on fitness and stress resistance.

Best practices for the manual curation of intrinsically disordered proteins in DisProt

The DisProt database is a resource containing manually curated data on experimentally validated intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) from the literature. Developed in 2005, its primary goal was to collect structural and functional information into proteins that lack a fixed three-dimensional structure. Today, DisProt has evolved into a major repository that not only collects experimental data but also contributes to our understanding of the IDPs/IDRs roles in various biological processes, such as autophagy or the life cycle mechanisms in viruses or their involvement in diseases (such as cancer and neurodevelopmental disorders). DisProt offers detailed information on the structural states of IDPs/IDRs, including state transitions, interactions and their functions, all provided as curated annotations. One of the central activities of DisProt is the meticulous curation of experimental data from the literature. For this reason, to ensure that every expert and volunteer curator possesses the requisite knowledge for data evaluation, collection and integration, training courses and curation materials are available. However, biocuration guidelines concur on the importance of developing robust guidelines that not only provide critical information about data consistency but also ensure data acquisition.This guideline aims to provide both biocurators and external users with best practices for manually curating IDPs and IDRs in DisProt. It describes every step of the literature curation process and provides use cases of IDP curation within DisProt. Database URL: https://disprot.org/.

Explaining protein-protein interactions with knowledge graph-based semantic similarity

The application of artificial intelligence and machine learning methods for several biomedical applications, such as protein-protein interaction prediction, has gained significant traction in recent decades. However, explainability is a key aspect of using machine learning as a tool for scientific discovery. Explainable artificial intelligence approaches help clarify algorithmic mechanisms and identify potential bias in the data. Given the complexity of the biomedical domain, explanations should be grounded in domain knowledge which can be achieved by using ontologies and knowledge graphs. These knowledge graphs express knowledge about a domain by capturing different perspectives of the representation of real-world entities. However, the most popular way to explore knowledge graphs with machine learning is through using embeddings, which are not explainable. As an alternative, knowledge graph-based semantic similarity offers the advantage of being explainable. Additionally, similarity can be computed to capture different semantic aspects within the knowledge graph and increasing the explainability of predictive approaches. We propose a novel method to generate explainable vector representations, KGsim2vec, that uses aspect-oriented semantic similarity features to represent pairs of entities in a knowledge graph. Our approach employs a set of machine learning models, including decision trees, genetic programming, random forest and eXtreme gradient boosting, to predict relations between entities. The experiments reveal that considering multiple semantic aspects when representing the similarity between two entities improves explainability and predictive performance. KGsim2vec performs better than black-box methods based on knowledge graph embeddings or graph neural networks. Moreover, KGsim2vec produces global models that can capture biological phenomena and elucidate data biases.

Evidence for widespread cytoplasmic structuring into mesoscale condensates

Compartmentalization is an essential feature of eukaryotic life and is achieved both via membrane-bound organelles, such as mitochondria, and membrane-less biomolecular condensates, such as the nucleolus. Known biomolecular condensates typically exhibit liquid-like properties and are visualized by microscopy on the scale of ~1 µm (refs. 1,2). They have been studied mostly by microscopy, examining select individual proteins. So far, several dozen biomolecular condensates are known, serving a multitude of functions, for example, in the regulation of transcription3, RNA processing4 or signalling5,6, and their malfunction can cause diseases7,8. However, it remains unclear to what extent biomolecular condensates are utilized in cellular organization and at what length scale they typically form. Here we examine native cytoplasm from Xenopus egg extract on a global scale with quantitative proteomics, filtration, size exclusion and dilution experiments. These assays reveal that at least 18% of the proteome is organized into mesoscale biomolecular condensates at the scale of ~100 nm and appear to be stabilized by RNA or gelation. We confirmed mesoscale sizes via imaging below the diffraction limit by investigating protein permeation into porous substrates with defined pore sizes. Our results show that eukaryotic cytoplasm organizes extensively via biomolecular condensates, but at surprisingly short length scales.

Repopulated spinal cord microglia exhibit a unique transcriptome and contribute to pain resolution

Microglia are implicated as primarily detrimental in pain models; however, they exist across a continuum of states that contribute to homeostasis or pathology depending on timing and context. To clarify the specific contribution of microglia to pain progression, we take advantage of a temporally controlled transgenic approach to transiently deplete microglia. Unexpectedly, we observe complete resolution of pain coinciding with microglial repopulation rather than depletion. We find that repopulated mouse spinal cord microglia are morphologically distinct from control microglia and exhibit a unique transcriptome. Repopulated microglia from males and females express overlapping networks of genes related to phagocytosis and response to stress. We intersect the identified mouse genes with a single-nuclei microglial dataset from human spinal cord to identify human-relevant genes that may ultimately promote pain resolution after injury. This work presents a comprehensive approach to gene discovery in pain and provides datasets for the development of future microglial-targeted therapeutics.

RiceProteomeDB (RPDB): a user-friendly database for proteomics data storage, retrieval, and analysis

Rice, feeding a significant portion of the world, poses unique proteomic challenges critical to agricultural research and global food security. The complexity of the rice proteome, influenced by various genetic and environmental factors, demands specialized analytical approaches for effective study. The central challenges in rice proteomics lie in developing custom methods suited to the unique aspects of rice biology. These include data preprocessing, method selection, and result validation, all of which are essential for advancing rice research. Our aim is to decode these proteomic intricacies to facilitate breakthroughs in strain improvement, disease resistance, and yield optimization, all vital for combating global food insecurity. To achieve this, we have created the RiceProteomeDB (RPDB), a React + Django database, offering a streamlined and comprehensive platform for the analysis of rice proteomics data. RiceProteomeDB (RPDB) simplifies proteomics data management and analysis. It offers features for data organization, preprocessing, method selection, result validation, and data sharing. Researchers can access processed rice proteomics data, conduct analyses, and explore experimental conditions. The user-friendly web interface enhances navigation and interaction. RPDB fosters collaboration by enabling data sharing and proper acknowledgment of sources, contributing to proteomics research and knowledge dissemination. Availability and implementation: Web application: http://riceproteome.plantprofile.net/ . The web application's source code, user's manual, and sample data: https://github.com/dongu7610/Riceproteome .

Single-cell RNA sequencing analysis identifies one subpopulation of endothelial cells that proliferates and another that undergoes the endothelial-mesenchymal transition in regenerating pig hearts

Background: In our previous work, we demonstrated that when newborn pigs undergo apical resection (AR) on postnatal day 1 (P1), the animals' hearts were completely recover from a myocardial infarction (MI) that occurs on postnatal day 28 (P28); single-nucleus RNA sequencing (snRNAseq) data suggested that this recovery was achieved by regeneration of pig cardiomyocyte subpopulations in response to MI. However, coronary vasculature also has a key role in promoting cardiac repair. Method: Thus, in this report, we used autoencoder algorithms to analyze snRNAseq data from endothelial cells (ECs) in the hearts of the same animals. Main results: Our results identified five EC clusters, three composed of vascular ECs (VEC1-3) and two containing lymphatic ECs (LEC1-2). Cells from VEC1 expressed elevated levels of each of five cell-cyclespecific markers (Aurora Kinase B [AURKB], Marker of Proliferation Ki-67 [MKI67], Inner Centromere Protein [INCENP], Survivin [BIRC5], and Borealin [CDCA8]), as well as a number of transcription factors that promote EC proliferation, while (VEC3 was enriched for genes that regulate intercellular junctions, participate in transforming growth factor β (TGFβ), bone morphogenic protein (BMP) signaling, and promote the endothelial mesenchymal transition (EndMT). The remaining VEC2 did not appear to participate directly in the angiogenic response to MI, but trajectory analyses indicated that it may serve as a reservoir for the generation of VEC1 and VEC3 ECs in response to MI. Notably, only the VEC3 cluster was more populous in regenerating (i.e., ARP1MIP28) than non-regenerating (i.e., MIP28) hearts during the 1-week period after MI induction, which suggests that further investigation of the VEC3 cluster could identify new targets for improving myocardial recovery after MI. Histological analysis of KI67 and EndMT marker PDGFRA demonstrated that while the expression of proliferation of endothelial cells was not significantly different, expression of EndMT markers was significantly higher among endothelial cells of ARP1MIP28 hearts compared to MIP28 hearts, which were consistent with snRNAseq analysis of clusters VEC1 and VEC3. Furthermore, upregulated secrete genes by VEC3 may promote cardiomyocyte proliferation via the Pi3k-Akt and ERBB signaling pathways, which directly contribute to cardiac muscle regeneration. Conclusion: In regenerative heart, endothelial cells may express EndMT markers, and this process could contribute to regeneration via a endothelial-cardiomyocyte crosstalk that supports cardiomyocyte proliferation.

TFCheckpoint database update, a cross-referencing system for transcription factors from human, mouse and rat

Prior knowledge about DNA-binding transcription factors (dbTFs), transcription co-regulators (coTFs) and general transcriptional factors (GTFs) is crucial for the study and understanding of the regulation of transcription. This is reflected by the many publications and database resources describing knowledge about TFs. We previously launched the TFCheckpoint database, an integrated resource focused on human, mouse and rat dbTFs, providing users access to a comprehensive overview of these proteins. Here, we describe TFCheckpoint 2.0 (https://www.tfcheckpoint.org/index.php), comprising 13 collections of dbTFs, coTFs and GTFs. TFCheckpoint 2.0 provides an easy and versatile cross-referencing system for users to view and download collections that may otherwise be cumbersome to find, compare and retrieve.

Enhanced bovine genome annotation through integration of transcriptomics and epi-transcriptomics datasets facilitates genomic biology

BACKGROUND

The accurate identification of the functional elements in the bovine genome is a fundamental requirement for high-quality analysis of data informing both genome biology and genomic selection. Functional annotation of the bovine genome was performed to identify a more complete catalog of transcript isoforms across bovine tissues.

RESULTS

A total of 160,820 unique transcripts (50% protein coding) representing 34,882 unique genes (60% protein coding) were identified across tissues. Among them, 118,563 transcripts (73% of the total) were structurally validated by independent datasets (PacBio isoform sequencing data, Oxford Nanopore Technologies sequencing data, de novo assembled transcripts from RNA sequencing data) and comparison with Ensembl and NCBI gene sets. In addition, all transcripts were supported by extensive data from different technologies such as whole transcriptome termini site sequencing, RNA Annotation and Mapping of Promoters for the Analysis of Gene Expression, chromatin immunoprecipitation sequencing, and assay for transposase-accessible chromatin using sequencing. A large proportion of identified transcripts (69%) were unannotated, of which 86% were produced by annotated genes and 14% by unannotated genes. A median of two 5' untranslated regions were expressed per gene. Around 50% of protein-coding genes in each tissue were bifunctional and transcribed both coding and noncoding isoforms. Furthermore, we identified 3,744 genes that functioned as noncoding genes in fetal tissues but as protein-coding genes in adult tissues. Our new bovine genome annotation extended more than 11,000 annotated gene borders compared to Ensembl or NCBI annotations. The resulting bovine transcriptome was integrated with publicly available quantitative trait loci data to study tissue-tissue interconnection involved in different traits and construct the first bovine trait similarity network.

CONCLUSIONS

These validated results show significant improvement over current bovine genome annotations.

Transcriptional profiles of pulmonary artery endothelial cells in pulmonary hypertension

Pulmonary arterial hypertension (PAH) is characterized by endothelial cell (EC) dysfunction. There are no data from living patients to inform whether differential gene expression of pulmonary artery ECs (PAECs) can discern disease subtypes, progression and pathogenesis. We aimed to further validate our previously described method to propagate ECs from right heart catheter (RHC) balloon tips and to perform additional PAEC phenotyping. We performed bulk RNA sequencing of PAECs from RHC balloons. Using unsupervised dimensionality reduction and clustering we compared transcriptional signatures from PAH to controls and other forms of pulmonary hypertension. Select PAEC samples underwent single cell and population growth characterization and anoikis quantification. Fifty-four specimens were analyzed from 49 subjects. The transcriptome appeared stable over limited passages. Six genes involved in sex steroid signaling, metabolism, and oncogenesis were significantly upregulated in PAH subjects as compared to controls. Genes regulating BMP and Wnt signaling, oxidative stress and cellular metabolism were differentially expressed in PAH subjects. Changes in gene expression tracked with clinical events in PAH subjects with serial samples over time. Functional assays demonstrated enhanced replication competency and anoikis resistance. Our findings recapitulate fundamental biological processes of PAH and provide new evidence of a cancer-like phenotype in ECs from the central vasculature of PAH patients. This "cell biopsy" method may provide insight into patient and lung EC heterogeneity to advance precision medicine approaches in PAH.

Multi-view progression diagnosis of thyroid cancer by integrating platelet transcriptomes and blood routine tests

Thyroid cancer is the most common type of endocrine system cancer. The pre-cancer and early stages are usually benign or slowly growing, and do not need invasive treatments. This study investigated the challenging classification task of four classes of samples, i.e., normal controls (N), thyroid adenomas (TA), papillary thyroid cancers (PTC) and metastasized papillary thyroid cancers (MPTC). We proposed a multi-view progression diagnosis framework ThyroidBloodTest to integrate the two views of RNAseq platelet transcriptomes (View-T) and blood routine (View-B) features. Platelet transcriptome represented the molecular-level information, while the blood routine features were easy to obtain in the clinical practice. Eleven feature selection algorithms and seven classifiers were evaluated for both views. The experimental data suggested the importance of choosing appropriate data analysis algorithms and feature engineering techniques like principal component analysis (PCA). The best ThyroidBloodTest model achieved Acc = 0.8750 for the four-class classification of the N/TA/PTC/MPTC samples based on the integrated feature space of View-T and View-B. The cellular localization cytosol and three post-translational modification types acetylation/phosphorylation/ubiquitination were observed to be enriched in the proteins encoded by the View-T biomarkers. The numbers of different immune cells also contributed positively to the progression diagnosis of thyroid cancer. The proposed multi-view prediction model demonstrated the necessity of integrating both platelet transcriptomes and blood routine tests for the progression diagnosis of thyroid cancer.

Expanding the coverage of regulons from high-confidence prior knowledge for accurate estimation of transcription factor activities

Gene regulation plays a critical role in the cellular processes that underlie human health and disease. The regulatory relationship between transcription factors (TFs), key regulators of gene expression, and their target genes, the so called TF regulons, can be coupled with computational algorithms to estimate the activity of TFs. However, to interpret these findings accurately, regulons of high reliability and coverage are needed. In this study, we present and evaluate a collection of regulons created using the CollecTRI meta-resource containing signed TF-gene interactions for 1186 TFs. In this context, we introduce a workflow to integrate information from multiple resources and assign the sign of regulation to TF-gene interactions that could be applied to other comprehensive knowledge bases. We find that the signed CollecTRI-derived regulons outperform other public collections of regulatory interactions in accurately inferring changes in TF activities in perturbation experiments. Furthermore, we showcase the value of the regulons by examining TF activity profiles in three different cancer types and exploring TF activities at the level of single-cells. Overall, the CollecTRI-derived TF regulons enable the accurate and comprehensive estimation of TF activities and thereby help to interpret transcriptomics data.

Prediction of lncRNA functions using deep neural networks based on multiple networks

BACKGROUND

More and more studies show that lncRNA is widely involved in various physiological processes of the organism. However, the functions of the vast majority of them continue to be unknown. In addition, data related to lncRNAs in biological databases are constantly increasing. Therefore, it is quite urgent to develop a computing method to make the utmost of these data.

RESULTS

In this paper, we propose a new computational method based on global heterogeneous networks to predict the functions of lncRNAs, called DNGRGO. DNGRGO first calculates the similarities among proteins, miRNAs, and lncRNAs, and annotates the functions of lncRNAs according to its similar protein-coding genes, which have been labeled with gene ontology (GO). To evaluate the performance of DNGRGO, we manually annotated GO terms to lncRNAs and implemented our method on these data. Compared with the existing methods, the results of DNGRGO show superior predictive performance of maximum F-measure and coverage.

CONCLUSIONS

DNGRGO is able to annotate lncRNAs through capturing the low-dimensional features of the heterogeneous network. Moreover, the experimental results show that integrating miRNA data can help to improve the predictive performance of DNGRGO.

The transcriptional legacy of developmental stochasticity

Genetic and environmental variation are key contributors during organism development, but the influence of minor perturbations or noise is difficult to assess. This study focuses on the stochastic variation in allele-specific expression that persists through cell divisions in the nine-banded armadillo (Dasypus novemcinctus). We investigated the blood transcriptome of five wild monozygotic quadruplets over time to explore the influence of developmental stochasticity on gene expression. We identify an enduring signal of autosomal allelic variability that distinguishes individuals within a quadruplet despite their genetic similarity. This stochastic allelic variation, akin to X-inactivation but broader, provides insight into non-genetic influences on phenotype. The presence of stochastically canalized allelic signatures represents a novel axis for characterizing organismal variability, complementing traditional approaches based on genetic and environmental factors. We also developed a model to explain the inconsistent penetrance associated with these stochastically canalized allelic expressions. By elucidating mechanisms underlying the persistence of allele-specific expression, we enhance understanding of development's role in shaping organismal diversity.

Comparative proteomics unveils the bacteriostatic mechanisms of Ga(III) on the regulation of metabolic pathways in Pseudomonas aeruginosa

Gallium has a long history as a chemotherapeutic agent. The mechanisms of action of Ga(III)-based anti-infectives are different from conventional antibiotics, which primarily result from the chemical similarities of Ga(III) with Fe(III) and substitution of gallium into iron-dependent biological pathways. However, more aspects of the molecular mechanisms of Ga(III) against human pathogens, especially the effects on bacterial metabolic processes, remain to be understood. Herein, by using conventional quantitative proteomics, we identified the protein changes of Pseudomonas aeruginosa (P. aeruginosa) in response to Ga(NO3)3 treatment. We show that Ga(III) exhibits bacteriostatic mode of action against P. aeruginosa through affecting the expressions of a number of key enzymes in the main metabolic pathways, including glycolysis, TCA cycle, amino acid metabolism, and protein and nucleic acid biosynthesis. In addition, decreased expressions of proteins associated with pathogenesis and virulence of P. aeruginosa were also identified. Moreover, the correlations between protein expressions and metabolome changes in P. aeruginosa upon Ga(III) treatment were identified and discussed. Our findings thus expand the understanding on the antimicrobial mechanisms of Ga(III) that shed light on enhanced therapeutic strategies. BIOLOGICAL SIGNIFICANCE: Mounting evidence suggest that the efficacy and resistance of clinical antibiotics are closely related to the metabolic homeostasis in bacterial pathogens. Ga(III)-based compounds have been repurposed as antibacterial therapeutic candidates against antibiotics resistant pathogens, and represent a safe and promising treatment for clinical human infections, while more thorough understandings of how bacteria respond to Ga(III) treatment are needed. In the present study, we provide evidences at the proteome level that indicate Ga(III)-induced metabolic perturbations in P. aeruginosa. We identified and discussed the interference of Ga(III) on the expressions and activities of enzymes in the main metabolic pathways in P. aeruginosa. In view of our previous report that the antimicrobial efficacy of Ga(III) could be modulated according to Ga(III)-induced metabolome changes in P. aeruginosa, our current analyses may provide theoretical basis at the proteome level for the development of efficient gallium-based therapies by exploiting bacterial metabolic mechanisms.

Mucosal expression of PI3, ANXA1, and VDR discriminates Crohn's disease from ulcerative colitis

Differential diagnosis of inflammatory bowel disease (IBD) to Crohn's disease (CD) or ulcerative colitis (UC) is crucial for treatment decision making. With the aim of generating a clinically applicable molecular-based tool to classify IBD patients, we assessed whole transcriptome analysis on endoscopy samples. A total of 408 patient samples were included covering both internal and external samples cohorts. Whole transcriptome analysis was performed on an internal cohort of FFPE IBD samples (CD, n = 16 and UC, n = 17). The 100 most significantly differentially expressed genes (DEG) were tested in two external cohorts. Ten of the DEG were further processed by functional enrichment analysis from which seven were found to show consistent significant performance in discriminating CD from UC: PI3, ANXA1, VDR, MTCL1, SH3PXD2A-AS1, CLCF1, and CD180. Differential expression of PI3, ANXA1, and VDR was reproduced by RT-qPCR, which was performed on an independent sample cohort of 97 patient samples (CD, n = 44 and UC, n = 53). Gene expression levels of the three-gene profile, resulted in an area under the curve of 0.84 (P = 0.02) in discriminating CD from UC, and therefore appear as an attractive molecular-based diagnostic tool for clinicians to distinguish CD from UC.

Collagen-I influences the post-translational regulation, binding partners and role of Annexin A2 in breast cancer progression

Introduction

The extracellular matrix (ECM) has been heavily implicated in the development and progression of cancer. We have previously shown that Annexin A2 is integral in the migration and invasion of breast cancer cells and in the clinical progression of ER-negative breast cancer, processes which are highly influenced by the surrounding tumor microenvironment and ECM.

Methods

We investigated how modulations of the ECM may affect the role of Annexin A2 in MDA-MB-231 breast cancer cells using western blotting, immunofluorescent confocal microscopy and immuno-precipitation mass spectrometry techniques.

Results

We have shown that the presence of collagen-I, the main constituent of the ECM, increases the post-translational phosphorylation of Annexin A2 and subsequently causes the translocation of Annexin A2 to the extracellular surface. In the presence of collagen-I, we identified fibronectin as a novel interactor of Annexin A2, using mass spectrometry analysis. We then demonstrated that reducing Annexin A2 expression decreases the degradation of fibronectin by cancer cells and this effect on fibronectin turnover is increased according to collagen-I abundance.

Discussion

Our results suggest that Annexin A2's role in promoting cancer progression is mediated by collagen-I and Annexin A2 maybe a therapeutic target in the bi-directional cross-talk between cancer cells and ECM remodeling that supports metastatic cancer progression.

Proteomic mapping reveals dysregulated angiogenesis in the cerebral arteries of rats with early-onset hypertension

Hypertension is associated with the presence of vascular abnormalities, including remodeling and rarefaction. These processes play an important role in cerebrovascular disease development; however, the mechanistic changes leading to these diseases are not well characterized. Using data-independent acquisition-based mass spectrometry analysis, here we determined the protein changes in cerebral arteries in pre- and early-onset hypertension from the spontaneously hypertensive rat (SHR), a model that resembles essential hypertension in humans. Our analysis identified 125 proteins with expression levels that were significantly upregulated or downregulated in 12-week-old spontaneously hypertensive rats compared to normotensive Wistar Kyoto rats. Using an angiogenesis enrichment analysis, we further identified a critical imbalance in angiogenic proteins that promoted an anti-angiogenic profile in cerebral arteries at early onset of hypertension. In a comparison to previously published data, we demonstrate that this angiogenic imbalance is not present in mesenteric and renal arteries from age-matched SHRs. Finally, we identified two proteins (Fbln5 and Cdh13), whose expression levels were critically altered in cerebral arteries compared to the other arterial beds. The observation of an angiogenic imbalance in cerebral arteries from the SHR reveals critical protein changes in the cerebrovasculature at the early onset of hypertension and provides novel insights into the early pathology of cerebrovascular disease.

A TRANSCRIPTOMIC APPRECIATION OF CHILDHOOD MENINGOCOCCAL AND POLYMICROBIAL SEPSIS FROM A PRO-INFLAMMATORY AND TRAJECTORIAL PERSPECTIVE, A ROLE FOR VASCULAR ENDOTHELIAL GROWTH FACTOR A AND B MODULATION?

ABSTRACT

This study investigated the temporal dynamics of childhood sepsis by analyzing gene expression changes associated with proinflammatory processes. Five datasets, including four meningococcal sepsis shock (MSS) datasets (two temporal and two longitudinal) and one polymicrobial sepsis dataset, were selected to track temporal changes in gene expression. Hierarchical clustering revealed three temporal phases: early, intermediate, and late, providing a framework for understanding sepsis progression. Principal component analysis supported the identification of gene expression trajectories. Differential gene analysis highlighted consistent upregulation of vascular endothelial growth factor A (VEGF-A) and nuclear factor κB1 (NFKB1), genes involved in inflammation, across the sepsis datasets. NFKB1 gene expression also showed temporal changes in the MSS datasets. In the postmortem dataset comparing MSS cases to controls, VEGF-A was upregulated and VEGF-B downregulated. Renal tissue exhibited higher VEGF-A expression compared with other tissues. Similar VEGF-A upregulation and VEGF-B downregulation patterns were observed in the cross-sectional MSS datasets and the polymicrobial sepsis dataset. Hexagonal plots confirmed VEGF-R (VEGF receptor)-VEGF-R2 signaling pathway enrichment in the MSS cross-sectional studies. The polymicrobial sepsis dataset also showed enrichment of the VEGF pathway in septic shock day 3 and sepsis day 3 samples compared with controls. These findings provide unique insights into the dynamic nature of sepsis from a transcriptomic perspective and suggest potential implications for biomarker development. Future research should focus on larger-scale temporal transcriptomic studies with appropriate control groups and validate the identified gene combination as a potential biomarker panel for sepsis.

Isolation and genomic characterization of Klebsiella Lw3 with polychlorinated biphenyl degradability

Bioremediation of sediment organic pollution has been intensely investigated, but the degradation of complex organic compounds, pesticide residues, and polychlorinated biphenyls (PCBs) remains poorly studied. In this study, sediments were collected from Zhanjiang Mangrove Reserve and inoculated in an inorganic salt medium using only biphenyl (BP) and PCBs as the carbon sources to obtain a PCB-degrading strain. A gram-negative bacterium that metabolized PCBs was isolated and identified as Klebsiella Lw3 by 16S rDNA phylogenetic analysis. Genomic sequencing showed that this bacterium possessed genes related to BP/PCB degradation, and its GC content was 58.2%; we identified 3326 cellular pathways. Gas chromatography-mass spectrometry was employed to test the PCB degrading ability; the results showed that the strain had a good degradation effect on PCB3 at concentrations of 5, 10, 20, 40, and 60 mg/L and that the final degradation rate was higher than 97% after 96 h. Interestingly, this strain showed good biodegradability of PCBs despite having no classical PCB degradation pathway, providing a new direction for Klebsiella research with practical significance for in situ bioremediation of PCB contamination. Overall, this study provides valuable insights into the genetic structure of PCB-degrading strains as well as eco-friendly and low-cost PCB degradation and lays a foundation for the discovery of new degradation pathways.

Post-insemination sexual selection in males indirectly masculinizes the female transcriptome

Sex-specific regulation of gene expression is the most plausible way for generating sexually differentiated phenotypes from an essentially shared genome. However, since genetic material is shared, sex-specific selection in one sex can have an indirect response in the other sex. From a gene expression perspective, this tethered response can move one sex away from their wildtype expression state and impact potentially many gene regulatory networks. Here, using experimental evolution in the model nematode Caenorhabditis elegans , we explore the coupling of direct sexual selection on males with the transcriptomic response in females over microevolutionary timescales to uncover the extent to which post-insemination reproductive traits share a genetic basis between the sexes. We find that differential gene expression is driven by female ancestral or evolved generation alone and that male generation has no impact on changes in gene expression. Almost all differentially expressed genes were downregulated in evolved females. Moreover, 80% of these gene were located on the X chromosome and have wildtype female-biased expression profiles. Changes in gene expression profiles were likely driven through trans -acting pathways that are shared between the sexes. We found no evidence that the core dosage compensation machinery was impacted by experimental evolution. Together these data suggest masculinization of the female transcriptome driven by direct selection on male sperm competitive ability. Our results indicate that on short evolutionary timescales sexual selection can generate sexual conflict in expression space.

LAY SUMMARY

Sexual selection drives the evolution of some of the most dramatic phenotypic differences between the sexes. Such sexual dimorphism is so common across multicellular organisms that we often overlook how remarkable it is for shared genetic material to create numerous and complex sex differences. At an evolutionary level, sexual dimorphism furthers the opportunity for sex-specific selection to optimize the fitness of a given sex. As a consequence, sex-specific selection, such as sexual selection, can have an indirect evolutionary response in the other sex due to genetic associations created by the sexes sharing the same genome. This correlated evolutionary response can create sexual conflict by shifting a sex away from their fitness optimum. At the functional level, sexual dimorphism is generated is through sex-specific regulation of gene expression. Bridging the evolutionary response to sexual selection with the evolution of sex-specific gene regulation during post-mating interactions has proved challenging. We previously used experimental evolution to increase male fertility by directly selecting for increased sperm competitive ability. In this study, we examined the effect of this direct selection on males on gene expression patterns in females. Differential gene expression was determined by whether a female was ancestral or evolved generation, indicating that gene expression changes were an evolved response due to indirect selection on females. Significantly differentially expressed genes were downregulated in evolved females. These genes tended to be female-biased in wildtype individuals and located on the X chromosome. The downregulation of X-linked genes suggests expression levels in females equal to or lower than that in males. Together these results indicate a less female-like transcriptome after experimental evolution. This supports a sexual conflict scenario by which direct sexual selection on males indirectly masculinizes the female transcriptome over short evolutionary timescales.

N-terminal proteoforms may engage in different protein complexes

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

A framework for community curation of interspecies interactions literature

The quantity and complexity of data being generated and published in biology has increased substantially, but few methods exist for capturing knowledge about phenotypes derived from molecular interactions between diverse groups of species, in such a way that is amenable to data-driven biology and research. To improve access to this knowledge, we have constructed a framework for the curation of the scientific literature studying interspecies interactions, using data curated for the Pathogen-Host Interactions database (PHI-base) as a case study. The framework provides a curation tool, phenotype ontology, and controlled vocabularies to curate pathogen-host interaction data, at the level of the host, pathogen, strain, gene, and genotype. The concept of a multispecies genotype, the 'metagenotype,' is introduced to facilitate capturing changes in the disease-causing abilities of pathogens, and host resistance or susceptibility, observed by gene alterations. We report on this framework and describe PHI-Canto, a community curation tool for use by publication authors.

Large-scale predicting protein functions through heterogeneous feature fusion

As the volume of protein sequence and structure data grows rapidly, the functions of the overwhelming majority of proteins cannot be experimentally determined. Automated annotation of protein function at a large scale is becoming increasingly important. Existing computational prediction methods are typically based on expanding the relatively small number of experimentally determined functions to large collections of proteins with various clues, including sequence homology, protein-protein interaction, gene co-expression, etc. Although there has been some progress in protein function prediction in recent years, the development of accurate and reliable solutions still has a long way to go. Here we exploit AlphaFold predicted three-dimensional structural information, together with other non-structural clues, to develop a large-scale approach termed PredGO to annotate Gene Ontology (GO) functions for proteins. We use a pre-trained language model, geometric vector perceptrons and attention mechanisms to extract heterogeneous features of proteins and fuse these features for function prediction. The computational results demonstrate that the proposed method outperforms other state-of-the-art approaches for predicting GO functions of proteins in terms of both coverage and accuracy. The improvement of coverage is because the number of structures predicted by AlphaFold is greatly increased, and on the other hand, PredGO can extensively use non-structural information for functional prediction. Moreover, we show that over 205 000 ($\sim $100%) entries in UniProt for human are annotated by PredGO, over 186 000 ($\sim $90%) of which are based on predicted structure. The webserver and database are available at http://predgo.denglab.org/.

Status of genome function annotation in model organisms and crops

Since the entry into genome-enabled biology several decades ago, much progress has been made in determining, describing, and disseminating the functions of genes and their products. Yet, this information is still difficult to access for many scientists and for most genomes. To provide easy access and a graphical summary of the status of genome function annotation for model organisms and bioenergy and food crop species, we created a web application (https://genomeannotation.rheelab.org) to visualize, search, and download genome annotation data for 28 species. The summary graphics and data tables will be updated semi-annually, and snapshots will be archived to provide a historical record of the progress of genome function annotation efforts. Clear and simple visualization of up-to-date genome function annotation status, including the extent of what is unknown, will help address the grand challenge of elucidating the functions of all genes in organisms.

Robust Framework for Medical Time Series Classification and Application to Real Scenarios in Modern Bioengineering

In this study, a novel unsupervised classification framework for time series of medical nature is presented. This framework is based on the intersection of machine learning, Hilbert Spaces algebra, and signal theory. The methodology is illustrated through the resolution of three biomedical engineering problems: neuronal activity tracking, protein functional classification, and non-invasive diagnosis of atrial flutter (AFL). The results indicate that the proposed algorithms exhibit high proficiency in solving these tasks and demonstrate robustness in identifying damaged neuronal units while tracking healthy ones. Moreover, the application of the framework in protein functional classification provides a new perspective for the development of pharmaceutical products and personalised medicine. Additionally, the controlled environment of the framework in AFL simulation problem underscores the algorithm's ability to encode information efficiently. These results offer valuable insights into the potential of this framework and lay the groundwork for future studies.Clinical relevance- The framework proposed in this study has the potential to yield novel insights into the effects of newly implanted electrodes in the brain. Furthermore, the categorization of proteins by function could facilitate the development of personalised and efficient medicines, ultimately reducing both time and cost. The simulation of atrial flutter also demonstrates the framework's ability to encode information for arrhythmia diagnosis and treatment, which has the potential to lead to improved patient outcomes.

Gemini: memory-efficient integration of hundreds of gene networks with high-order pooling

MOTIVATION

The exponential growth of genomic sequencing data has created ever-expanding repositories of gene networks. Unsupervised network integration methods are critical to learn informative representations for each gene, which are later used as features for downstream applications. However, these network integration methods must be scalable to account for the increasing number of networks and robust to an uneven distribution of network types within hundreds of gene networks.

RESULTS

To address these needs, we present Gemini, a novel network integration method that uses memory-efficient high-order pooling to represent and weight each network according to its uniqueness. Gemini then mitigates the uneven network distribution through mixing up existing networks to create many new networks. We find that Gemini leads to more than a 10% improvement in F1 score, 15% improvement in micro-AUPRC, and 63% improvement in macro-AUPRC for human protein function prediction by integrating hundreds of networks from BioGRID, and that Gemini's performance significantly improves when more networks are added to the input network collection, while Mashup and BIONIC embeddings' performance deteriorates. Gemini thereby enables memory-efficient and informative network integration for large gene networks and can be used to massively integrate and analyze networks in other domains.

AVAILABILITY AND IMPLEMENTATION

Gemini can be accessed at: https://github.com/MinxZ/Gemini.

Combining protein sequences and structures with transformers and equivariant graph neural networks to predict protein function

MOTIVATION

Millions of protein sequences have been generated by numerous genome and transcriptome sequencing projects. However, experimentally determining the function of the proteins is still a time consuming, low-throughput, and expensive process, leading to a large protein sequence-function gap. Therefore, it is important to develop computational methods to accurately predict protein function to fill the gap. Even though many methods have been developed to use protein sequences as input to predict function, much fewer methods leverage protein structures in protein function prediction because there was lack of accurate protein structures for most proteins until recently.

RESULTS

We developed TransFun-a method using a transformer-based protein language model and 3D-equivariant graph neural networks to distill information from both protein sequences and structures to predict protein function. It extracts feature embeddings from protein sequences using a pre-trained protein language model (ESM) via transfer learning and combines them with 3D structures of proteins predicted by AlphaFold2 through equivariant graph neural networks. Benchmarked on the CAFA3 test dataset and a new test dataset, TransFun outperforms several state-of-the-art methods, indicating that the language model and 3D-equivariant graph neural networks are effective methods to leverage protein sequences and structures to improve protein function prediction. Combining TransFun predictions and sequence similarity-based predictions can further increase prediction accuracy.

AVAILABILITY AND IMPLEMENTATION

The source code of TransFun is available at https://github.com/jianlin-cheng/TransFun.

Novel gold(III)-dithiocarbamate complex targeting bacterial thioredoxin reductase: antimicrobial activity, synergy, toxicity, and mechanistic insights

Introduction

Antimicrobial resistance is a pressing global concern that has led to the search for new antibacterial agents with novel targets or non-traditional approaches. Recently, organogold compounds have emerged as a promising class of antibacterial agents. In this study, we present and characterize a (C^S)-cyclometallated Au(III) dithiocarbamate complex as a potential drug candidate.

Methods and results

The Au(III) complex was found to be stable in the presence of effective biological reductants, and showed potent antibacterial and antibiofilm activity against a wide range of multidrug-resistant strains, particularly gram-positive strains, and gram-negative strains when used in combination with a permeabilizing antibiotic. No resistant mutants were detected after exposing bacterial cultures to strong selective pressure, indicating that the complex may have a low propensity for resistance development. Mechanistic studies indicate that the Au(III) complex exerts its antibacterial activity through a multimodal mechanism of action. Ultrastructural membrane damage and rapid bacterial uptake suggest direct interactions with the bacterial membrane, while transcriptomic analysis identified altered pathways related to energy metabolism and membrane stability including enzymes of the TCA cycle and fatty acid biosynthesis. Enzymatic studies further revealed a strong reversible inhibition of the bacterial thioredoxin reductase. Importantly, the Au(III) complex demonstrated low cytotoxicity at therapeutic concentrations in mammalian cell lines, and showed no acute in vivo toxicity in mice at the doses tested, with no signs of organ toxicity.

Discussion

Overall, these findings highlight the potential of the Au(III)-dithiocarbamate scaffold as a basis for developing novel antimicrobial agents, given its potent antibacterial activity, synergy, redox stability, inability to produce resistant mutants, low toxicity to mammalian cells both in vitro and in vivo, and non-conventional mechanism of action.

Systematic identification of anticancer drug targets reveals a nucleus-to-mitochondria ROS-sensing pathway

Multiple anticancer drugs have been proposed to cause cell death, in part, by increasing the steady-state levels of cellular reactive oxygen species (ROS). However, for most of these drugs, exactly how the resultant ROS function and are sensed is poorly understood. It remains unclear which proteins the ROS modify and their roles in drug sensitivity/resistance. To answer these questions, we examined 11 anticancer drugs with an integrated proteogenomic approach identifying not only many unique targets but also shared ones-including ribosomal components, suggesting common mechanisms by which drugs regulate translation. We focus on CHK1 that we find is a nuclear H2O2 sensor that launches a cellular program to dampen ROS. CHK1 phosphorylates the mitochondrial DNA-binding protein SSBP1 to prevent its mitochondrial localization, which in turn decreases nuclear H2O2. Our results reveal a druggable nucleus-to-mitochondria ROS-sensing pathway-required to resolve nuclear H2O2 accumulation and mediate resistance to platinum-based agents in ovarian cancers.

Quantitative model for genome-wide cyclic AMP receptor protein binding site identification and characteristic analysis

Cyclic AMP receptor proteins (CRPs) are important transcription regulators in many species. The prediction of CRP-binding sites was mainly based on position-weighted matrixes (PWMs). Traditional prediction methods only considered known binding motifs, and their ability to discover inflexible binding patterns was limited. Thus, a novel CRP-binding site prediction model called CRPBSFinder was developed in this research, which combined the hidden Markov model, knowledge-based PWMs and structure-based binding affinity matrixes. We trained this model using validated CRP-binding data from Escherichia coli and evaluated it with computational and experimental methods. The result shows that the model not only can provide higher prediction performance than a classic method but also quantitatively indicates the binding affinity of transcription factor binding sites by prediction scores. The prediction result included not only the most knowns regulated genes but also 1089 novel CRP-regulated genes. The major regulatory roles of CRPs were divided into four classes: carbohydrate metabolism, organic acid metabolism, nitrogen compound metabolism and cellular transport. Several novel functions were also discovered, including heterocycle metabolic and response to stimulus. Based on the functional similarity of homologous CRPs, we applied the model to 35 other species. The prediction tool and the prediction results are online and are available at: https://awi.cuhk.edu.cn/∼CRPBSFinder.

Prioritized mass spectrometry increases the depth, sensitivity and data completeness of single-cell proteomics

Major aims of single-cell proteomics include increasing the consistency, sensitivity and depth of protein quantification, especially for proteins and modifications of biological interest. Here, to simultaneously advance all these aims, we developed prioritized Single-Cell ProtEomics (pSCoPE). pSCoPE consistently analyzes thousands of prioritized peptides across all single cells (thus increasing data completeness) while maximizing instrument time spent analyzing identifiable peptides, thus increasing proteome depth. These strategies increased the sensitivity, data completeness and proteome coverage over twofold. The gains enabled quantifying protein variation in untreated and lipopolysaccharide-treated primary macrophages. Within each condition, proteins covaried within functional sets, including phagosome maturation and proton transport, similarly across both treatment conditions. This covariation is coupled to phenotypic variability in endocytic activity. pSCoPE also enabled quantifying proteolytic products, suggesting a gradient of cathepsin activities within a treatment condition. pSCoPE is freely available and widely applicable, especially for analyzing proteins of interest without sacrificing proteome coverage. Support for pSCoPE is available at http://scp.slavovlab.net/pSCoPE .

NetGO 3.0: Protein Language Model Improves Large-scale Functional Annotations

As one of the state-of-the-art automated function prediction (AFP) methods, NetGO 2.0 integrates multi-source information to improve the performance. However, it mainly utilizes the proteins with experimentally supported functional annotations without leveraging valuable information from a vast number of unannotated proteins. Recently, protein language models have been proposed to learn informative representations [e.g., Evolutionary Scale Modeling (ESM)-1b embedding] from protein sequences based on self-supervision. Here, we represented each protein by ESM-1b and used logistic regression (LR) to train a new model, LR-ESM, for AFP. The experimental results showed that LR-ESM achieved comparable performance with the best-performing component of NetGO 2.0. Therefore, by incorporating LR-ESM into NetGO 2.0, we developed NetGO 3.0 to improve the performance of AFP extensively. NetGO 3.0 is freely accessible at https://dmiip.sjtu.edu.cn/ng3.0.

Identification of chemotherapy targets reveals a nucleus-to-mitochondria ROS sensing pathway

Multiple chemotherapies are proposed to cause cell death in part by increasing the steady-state levels of cellular reactive oxygen species (ROS). However, for most of these drugs exactly how the resultant ROS function and are sensed is poorly understood. In particular, it's unclear which proteins the ROS modify and their roles in chemotherapy sensitivity/resistance. To answer these questions, we examined 11 chemotherapies with an integrated proteogenomic approach identifying many unique targets for these drugs but also shared ones including ribosomal components, suggesting one mechanism by which chemotherapies regulate translation. We focus on CHK1 which we find is a nuclear H ₂ O ₂ sensor that promotes an anti-ROS cellular program. CHK1 acts by phosphorylating the mitochondrial-DNA binding protein SSBP1, preventing its mitochondrial localization, which in turn decreases nuclear H ₂ O ₂ . Our results reveal a druggable nucleus-to-mitochondria ROS sensing pathway required to resolve nuclear H ₂ O ₂ accumulation, which mediates resistance to platinum-based chemotherapies in ovarian cancers.

A Database of Lung Cancer-Related Genes for the Identification of Subtype-Specific Prognostic Biomarkers

The molecular subtype is critical for accurate treatment and follow-up in patients with lung cancer; however, information regarding subtype-associated genes is dispersed among thousands of published studies. Systematic curation and cross-validation of the scientific literature would provide a solid foundation for comparative genetic studies of the major molecular subtypes of lung cancer. Here, we constructed a literature-based lung cancer gene database (LCGene). In the current release, we collected and curated 2507 unique human genes, including 2267 protein-coding and 240 non-coding genes from comprehensive manual examination of 10,960 PubMed article abstracts. Extensive annotations were added to aid identification of differentially expressed genes, potential gene editing sites, and non-coding gene regulation. For instance, we prepared 607 curated genes with CRISPR knockout information in 43 lung cancer cell lines. Further comparison of these implicated genes among different subtypes identified several subtype-specific genes with high mutational frequencies. Common tumor suppressors and oncogenes shared by lung adenocarcinoma and lung squamous cell carcinoma, for example, exhibited different mutational frequencies and prognostic features, suggesting the presence of subtype-specific biomarkers. Our retrospective analysis revealed 43 small cell lung cancer-specific genes. Moreover, 52 tumor suppressors and oncogenes shared by lung adenocarcinoma and squamous cell carcinoma confirmed the different molecular mechanisms of these two cancer subtypes. The subtype-based genetic differences, when combined, may provide insight into subtype-specific biomarkers for genetic testing.

The Integrator complex regulates microRNA abundance through RISC loading

MicroRNA (miRNA) homeostasis is crucial for the posttranscriptional regulation of their target genes during development and in disease states. miRNAs are derived from primary transcripts and are processed from a hairpin precursor intermediary to a mature 22-nucleotide duplex RNA. Loading of the duplex into the Argonaute (AGO) protein family is pivotal to miRNA abundance and its posttranscriptional function. The Integrator complex plays a key role in protein coding and noncoding RNA maturation, RNA polymerase II pause-release, and premature transcriptional termination. Here, we report that loss of Integrator results in global destabilization of mature miRNAs. Enhanced ultraviolet cross-linking and immunoprecipitation of Integrator uncovered an association with duplex miRNAs before their loading onto AGOs. Tracing miRNA fate from biogenesis to stabilization by incorporating 4-thiouridine in nascent transcripts pinpointed a critical role for Integrator in miRNA assembly into AGOs.

Integrating the serum proteomic and fecal metaproteomic to analyze the impacts of overweight/obesity on IBD: a pilot investigation

BACKGROUND

Inflammatory bowel disease (IBD) encompasses a group of chronic relapsing disorders which include ulcerative colitis (UC) and Crohn's disease (CD). The incidences of IBD and overweight/obesity are increasing in parallel. Here, we investigated alterations in proteomic in serum and metaproteomic in feces of IBD patients with overweight/obesity and aimed to explore the effect of overweight/ obesity on IBD and the underlying mechanism.

METHODS

This prospective observational study (n = 64) comprised 26 health control subjects (HC, 13 with overweight/obesity) and 38 IBD patients (19 with overweight/obesity) at a tertiary hospital. Overweight/obesity was evaluated by body mass index (BMI) and defined as a BMI greater than 24 kg/m². The comprehensive serum proteomic and fecal metaproteomic analyses were conducted by ultra-performance liquid chromatography-Orbitrap Exploris 480 mass spectrometry.

RESULTS

UC and CD presented similar serum molecular profiles but distinct gut microbiota. UC and CD serum exhibited higher levels of cytoskeleton organization- associated and inflammatory response-related proteins than the HC serum. Compared the serum proteome of UC and CD without overweight/obesity, inflammatory response-associated proteins were dramatically decreased in UC and CD with overweight/obesity. Fecal metaproteome identified 66 species in the feces. Among them, Parasutterella excrementihominis was increased in CD compared with that in HC. UC group had a significant enrichment of Moniliophthora roreri, but had dramatically decreased abundances of Alistipes indistinctus, Clostridium methylpentosum, Bacteroides vulgatus, and Schizochytrium aggregatum. In addition, overweight/obesity could improve the microbial diversity of UC. Specifically, the UC patients with overweight/obesity had increased abundance of some probiotics in contrast to those without overweight/obesity, including Parabacteroides distasonis, Alistipes indistincus, and Ruminococcus bromii.

CONCLUSION

This study provided high-quality multi-omics data of IBD serum and fecal samples, which enabled deciphering the molecular bases of clinical phenotypes of IBD, revealing the impacts of microbiota on IBD, and emphasizing the important role of overweight/obesity in IBD.

Proteome-Wide Detection and Annotation of Receptor Tyrosine Kinases (RTKs): RTK-PRED and the TyReK Database

Receptor tyrosine kinases (RTKs) form a highly important group of protein receptors of the eukaryotic cell membrane. They control many vital cellular functions and are involved in the regulation of complex signaling networks. Mutations in RTKs have been associated with different types of cancers and other diseases. Although they are very important for proper cell function, they have been experimentally studied in a limited range of eukaryotic species. Currently, there is no available database for RTKs providing information about their function, expression, and interactions. Therefore, the identification of RTKs in multiple organisms, the documentation of their characteristics, and the collection of related information would be very useful. In this paper, we present a novel RTK detection pipeline (RTK-PRED) and the Receptor Tyrosine Kinases Database (TyReK-DB). RTK-PRED combines profile HMMs with transmembrane topology prediction to identify and classify potential RTKs. Proteins of all eukaryotic reference proteomes of the UniProt database were used as input in RTK-PRED leading to a filtered dataset of 20,478 RTKs. Based on the information collected for these RTKs from multiple databases, the relational TyReK database was created.

Combining protein sequences and structures with transformers and equivariant graph neural networks to predict protein function

Motivation

Millions of protein sequences have been generated by numerous genome and transcriptome sequencing projects. However, experimentally determining the function of the proteins is still a time consuming, low-throughput, and expensive process, leading to a large protein sequence-function gap. Therefore, it is important to develop computational methods to accurately predict protein function to fill the gap. Even though many methods have been developed to use protein sequences as input to predict function, much fewer methods leverage protein structures in protein function prediction because there was lack of accurate protein structures for most proteins until recently.

Results

We developed TransFun - a method using a transformer-based protein language model and 3D-equivariant graph neural networks to distill information from both protein sequences and structures to predict protein function. It extracts feature embeddings from protein sequences using a pre-trained protein language model (ESM) via transfer learning and combines them with 3D structures of proteins predicted by AlphaFold2 through equivariant graph neural networks. Benchmarked on the CAFA3 test dataset and a new test dataset, TransFun outperforms several state-of-the-art methods, indicating the language model and 3D-equivariant graph neural networks are effective methods to leverage protein sequences and structures to improve protein function prediction. Combining TransFun predictions and sequence similarity-based predictions can further increase prediction accuracy.

Availability

The source code of TransFun is available at https://github.com/jianlin-cheng/TransFun.

Contact

chengji@missouri.edu.

A Human Hereditary Cardiomyopathy Shares a Genetic Substrate With Bicuspid Aortic Valve

BACKGROUND

The complex genetics underlying human cardiac disease is evidenced by its heterogenous manifestation, multigenic basis, and sporadic occurrence. These features have hampered disease modeling and mechanistic understanding. Here, we show that 2 structural cardiac diseases, left ventricular noncompaction (LVNC) and bicuspid aortic valve, can be caused by a set of inherited heterozygous gene mutations affecting the NOTCH ligand regulator MIB1 (MINDBOMB1) and cosegregating genes.

METHODS

We used CRISPR-Cas9 gene editing to generate mice harboring a nonsense or a missense MIB1 mutation that are both found in LVNC families. We also generated mice separately carrying these MIB1 mutations plus 5 additional cosegregating variants in the ASXL3, APCDD1, TMX3, CEP192, and BCL7A genes identified in these LVNC families by whole exome sequencing. Histological, developmental, and functional analyses of these mouse models were carried out by echocardiography and cardiac magnetic resonance imaging, together with gene expression profiling by RNA sequencing of both selected engineered mouse models and human induced pluripotent stem cell-derived cardiomyocytes. Potential biochemical interactions were assayed in vitro by coimmunoprecipitation and Western blot.

RESULTS

Mice homozygous for the MIB1 nonsense mutation did not survive, and the mutation caused LVNC only in heteroallelic combination with a conditional allele inactivated in the myocardium. The heterozygous MIB1 missense allele leads to bicuspid aortic valve in a NOTCH-sensitized genetic background. These data suggest that development of LVNC is influenced by genetic modifiers present in affected families, whereas valve defects are highly sensitive to NOTCH haploinsufficiency. Whole exome sequencing of LVNC families revealed single-nucleotide gene variants of ASXL3, APCDD1, TMX3, CEP192, and BCL7A cosegregating with the MIB1 mutations and LVNC. In experiments with mice harboring the orthologous variants on the corresponding Mib1 backgrounds, triple heterozygous Mib1 Apcdd1 Asxl3 mice showed LVNC, whereas quadruple heterozygous Mib1 Cep192 Tmx3;Bcl7a mice developed bicuspid aortic valve and other valve-associated defects. Biochemical analysis suggested interactions between CEP192, BCL7A, and NOTCH. Gene expression profiling of mutant mouse hearts and human induced pluripotent stem cell-derived cardiomyocytes revealed increased cardiomyocyte proliferation and defective morphological and metabolic maturation.

CONCLUSIONS

These findings reveal a shared genetic substrate underlying LVNC and bicuspid aortic valve in which MIB1-NOTCH variants plays a crucial role in heterozygous combination with cosegregating genetic modifiers.

NucEnvDB: A Database of Nuclear Envelope Proteins and Their Interactions

The nuclear envelope (NE) is a double-membrane system surrounding the nucleus of eukaryotic cells. A large number of proteins are localized in the NE, performing a wide variety of functions, from the bidirectional exchange of molecules between the cytoplasm and the nucleus to chromatin tethering, genome organization, regulation of signaling cascades, and many others. Despite its importance, several aspects of the NE, including its protein-protein interactions, remain understudied. In this work, we present NucEnvDB, a publicly available database of NE proteins and their interactions. Each database entry contains useful annotation including a description of its position in the NE, its interactions with other proteins, and cross-references to major biological repositories. In addition, the database provides users with a number of visualization and analysis tools, including the ability to construct and visualize protein-protein interaction networks and perform functional enrichment analysis for clusters of NE proteins and their interaction partners. The capabilities of NucEnvDB and its analysis tools are showcased by two informative case studies, exploring protein-protein interactions in Hutchinson-Gilford progeria and during SARS-CoV-2 infection at the level of the nuclear envelope.

Increasing the throughput of sensitive proteomics by plexDIA

Current mass spectrometry methods enable high-throughput proteomics of large sample amounts, but proteomics of low sample amounts remains limited in depth and throughput. To increase the throughput of sensitive proteomics, we developed an experimental and computational framework, called plexDIA, for simultaneously multiplexing the analysis of peptides and samples. Multiplexed analysis with plexDIA increases throughput multiplicatively with the number of labels without reducing proteome coverage or quantitative accuracy. By using three-plex non-isobaric mass tags, plexDIA enables quantification of threefold more protein ratios among nanogram-level samples. Using 1-hour active gradients, plexDIA quantified ~8,000 proteins in each sample of labeled three-plex sets and increased data completeness, reducing missing data more than twofold across samples. Applied to single human cells, plexDIA quantified ~1,000 proteins per cell and achieved 98% data completeness within a plexDIA set while using ~5 minutes of active chromatography per cell. These results establish a general framework for increasing the throughput of sensitive and quantitative protein analysis.

Photoreception and transcriptomic response to light during early development of a teleost with a life cycle tightly controlled by seasonal changes in photoperiod

Light cues vary along the axis of periodicity, intensity and spectrum and perception of light is dependent on the photoreceptive capacity encoded within the genome and the opsins expressed. A global approach was taken to analyze the photoreceptive capacity and the effect of differing light conditions on a developing teleost prior to first feeding. The transcriptomes of embryos and alevins of Atlantic salmon (Salmo salar) exposed to different light conditions were analyzed, including a developmental series and a circadian profile. The results showed that genes mediating nonvisual photoreception are present prior to hatching when the retina is poorly differentiated. The clock genes were expressed early, but the circadian profile showed that only two clock genes were significantly cycling before first feeding. Few genes were differentially expressed between day and night within a light condition; however, many genes were significantly different between light conditions, indicating that light environment has an impact on the transcriptome during early development. Comparing the transcriptome data from constant conditions to periodicity of white light or different colors revealed overrepresentation of genes related to photoreception, eye development, muscle contraction, degradation of metabolites and cell cycle among others, and in constant light, several clock genes were upregulated. In constant white light and periodicity of green light, genes associated with DNA replication, chromatin remodeling, cell division and DNA repair were downregulated. The study implies a direct influence of light conditions on the transcriptome profile at early developmental stages, by a complex photoreceptive system where few clock genes are cycling.

Genetic architecture and heritability of early-life telomere length in a wild passerine

Early-life telomere length (TL) is associated with fitness in a range of organisms. Little is known about the genetic basis of variation in TL in wild animal populations, but to understand the evolutionary and ecological significance of TL it is important to quantify the relative importance of genetic and environmental variation in TL. In this study, we measured TL in 2746 house sparrow nestlings sampled across 20 years and used an animal model to show that there is a small heritable component of early-life TL (h²  = 0.04). Variation in TL among individuals was mainly driven by environmental (annual) variance, but also brood and parental effects. Parent-offspring regressions showed a large maternal inheritance component in TL ( h maternal 2  = 0.44), but no paternal inheritance. We did not find evidence for a negative genetic correlation underlying the observed negative phenotypic correlation between TL and structural body size. Thus, TL may evolve independently of body size and the negative phenotypic correlation is likely to be caused by nongenetic environmental effects. We further used genome-wide association analysis to identify genomic regions associated with TL variation. We identified several putative genes underlying TL variation; these have been inferred to be involved in oxidative stress, cellular growth, skeletal development, cell differentiation and tumorigenesis in other species. Together, our results show that TL has a low heritability and is a polygenic trait strongly affected by environmental conditions in a free-living bird.