Rapidly Growing Protein-Centric Technologies to Extensively Identify Protein-RNA Interactions: Application to the Analysis of Co-Transcriptional RNA Processing

MPI-VGAE: protein-metabolite enzymatic reaction link learning by variational graph autoencoders

Enzymatic reactions are crucial to explore the mechanistic function of metabolites and proteins in cellular processes and to understand the etiology of diseases. The increasing number of interconnected metabolic reactions allows the development of in silico deep learning-based methods to discover new enzymatic reaction links between metabolites and proteins to further expand the landscape of existing metabolite-protein interactome. Computational approaches to predict the enzymatic reaction link by metabolite-protein interaction (MPI) prediction are still very limited. In this study, we developed a Variational Graph Autoencoders (VGAE)-based framework to predict MPI in genome-scale heterogeneous enzymatic reaction networks across ten organisms. By incorporating molecular features of metabolites and proteins as well as neighboring information in the MPI networks, our MPI-VGAE predictor achieved the best predictive performance compared to other machine learning methods. Moreover, when applying the MPI-VGAE framework to reconstruct hundreds of metabolic pathways, functional enzymatic reaction networks and a metabolite-metabolite interaction network, our method showed the most robust performance among all scenarios. To the best of our knowledge, this is the first MPI predictor by VGAE for enzymatic reaction link prediction. Furthermore, we implemented the MPI-VGAE framework to reconstruct the disease-specific MPI network based on the disrupted metabolites and proteins in Alzheimer's disease and colorectal cancer, respectively. A substantial number of novel enzymatic reaction links were identified. We further validated and explored the interactions of these enzymatic reactions using molecular docking. These results highlight the potential of the MPI-VGAE framework for the discovery of novel disease-related enzymatic reactions and facilitate the study of the disrupted metabolisms in diseases.

A method for in situ visualization of Protein-Nascent RNA interactions in single cell using Proximity Ligation Assay (IPNR-PLA) in mammalian cells

Proximity ligation assay (PLA) is an immunofluorescence assay, which determines in situ interaction of two biomolecules present within 40 nm close proximity. Here, we describe a modification of PLA for visual detection of in situ protein interactions with nascent RNA in a single cell (IPNR-PLA). In IPNR-PLA, nascent RNA is labeled by incorporating 5-fluorouridine (FU), a uridine nucleotide analogue, followed by covalent cross-linking of the interacting partners in proximity to newly synthesized RNA. By using combination of anti-BrdU antibody, which specifically binds to FU, and primary antibody against a protein of interest, the IPNR reaction results in fluorescent puncta as a positive signal, only if the candidate proteins are in proximity to nascent RNA. We have validated this method by demonstrating known CDK9 and elongating RNA pol II interaction with nascent RNA. Finally, we used this method to test for the presence of DNA double strand breaks as well as Poly (ADP-ribose) polymerase 1 (PARP1), an RNA binding protein, in the vicinity of nascent RNA in cancer cells. The capability of performing parallel IF labeling and quantifiable multiparameter measurements within heterogeneous cell populations makes IPNR-PLA very attractive for use in biological studies. Overall, we have developed the IPNR-PLA method for analysis of protein association with nascent RNA with single-cell resolution, which is highly sensitive, quantitative, efficient, and requires little starting experimental material.

Genome-scale enzymatic reaction prediction by variational graph autoencoders

Background

Enzymatic reaction networks are crucial to explore the mechanistic function of metabolites and proteins in biological systems and understanding the etiology of diseases and potential target for drug discovery. The increasing number of metabolic reactions allows the development of deep learning-based methods to discover new enzymatic reactions, which will expand the landscape of existing enzymatic reaction networks to investigate the disrupted metabolisms in diseases.

Results

In this study, we propose the MPI-VGAE framework to predict metabolite-protein interactions (MPI) in a genome-scale heterogeneous enzymatic reaction network across ten organisms with thousands of enzymatic reactions. We improved the Variational Graph Autoencoders (VGAE) model to incorporate both molecular features of metabolites and proteins as well as neighboring features to achieve the best predictive performance of MPI. The MPI-VGAE framework showed robust performance in the reconstruction of hundreds of metabolic pathways and five functional enzymatic reaction networks. The MPI-VGAE framework was also applied to a homogenous metabolic reaction network and achieved as high performance as other state-of-art methods. Furthermore, the MPI-VGAE framework could be implemented to reconstruct the disease-specific MPI network based on hundreds of disrupted metabolites and proteins in Alzheimer's disease and colorectal cancer, respectively. A substantial number of new potential enzymatic reactions were predicted and validated by molecular docking. These results highlight the potential of the MPI-VGAE framework for the discovery of novel disease-related enzymatic reactions and drug targets in real-world applications.

Data availability and implementation

The MPI-VGAE framework and datasets are publicly accessible on GitHub https://github.com/mmetalab/mpi-vgae .

Author Biographies

Cheng Wang received his Ph.D. in Chemistry from The Ohio State Univesity, USA. He is currently a Assistant Professor in School of Public Health at Shandong University, China. His research interests include bioinformatics, machine learning-based approach with applications to biomedical networks. Chuang Yuan is a research assistant at Shandong University. He obtained the MS degree in Biology at the University of Science and Technology of China. His research interests include biochemistry & molecular biology, cell biology, biomedicine, bioinformatics, and computational biology. Yahui Wang is a PhD student in Department of Chemistry at Washington University in St. Louis. Her research interests include biochemistry, mass spectrometry-based metabolomics, and cancer metabolism. Ranran Chen is a master graduate student in School of Public Health at University of Shandong, China. Yuying Shi is a master graduate student in School of Public Health at University of Shandong, China. Gary J. Patti is the Michael and Tana Powell Professor at Washington University in St. Louis, where he holds appointments in the Department of Chemisrty and the Department of Medicine. He is also the Senior Director of the Center for Metabolomics and Isotope Tracing at Washington University. His research interests include metabolomics, bioinformatics, high-throughput mass spectrometry, environmental health, cancer, and aging. Leyi Wei received his Ph.D. in Computer Science from Xiamen University, China. He is currently a Professor in School of Software at Shandong University, China. His research interests include machine learning and its applications to bioinformatics. Qingzhen Hou received his Ph.D. in the Centre for Integrative Bioinformatics VU (IBIVU) from Vrije Universiteit Amsterdam, the Netherlands. Since 2020, He has serveved as the head of Bioinformatics Center in National Institute of Health Data Science of China and Assistant Professor in School of Public Health, Shandong University, China. His areas of research are bioinformatics and computational biophysics.

Key points

Genome-scale heterogeneous networks of metabolite-protein interaction (MPI) based on thousands of enzymatic reactions across ten organisms were constructed semi-automatically.An enzymatic reaction prediction method called Metabolite-Protein Interaction Variational Graph Autoencoders (MPI-VGAE) was developed and optimized to achieve higher performance compared with existing machine learning methods by using both molecular features of metabolites and proteins.MPI-VGAE is broadly useful for applications involving the reconstruction of metabolic pathways, functional enzymatic reaction networks, and homogenous networks (e.g., metabolic reaction networks).By implementing MPI-VGAE to Alzheimer's disease and colorectal cancer, we obtained several novel disease-related protein-metabolite reactions with biological meanings. Moreover, we further investigated the reasonable binding details of protein-metabolite interactions using molecular docking approaches which provided useful information for disease mechanism and drug design.

Towards an Ideal In Cell Hybridization-Based Strategy to Discover Protein Interactomes of Selected RNA Molecules

RNA-binding proteins are crucial to the function of coding and non-coding RNAs. The disruption of RNA–protein interactions is involved in many different pathological states. Several computational and experimental strategies have been developed to identify protein binders of selected RNA molecules. Amongst these, ‘in cell’ hybridization methods represent the gold standard in the field because they are designed to reveal the proteins bound to specific RNAs in a cellular context. Here, we compare the technical features of different ‘in cell’ hybridization approaches with a focus on their advantages, limitations, and current and potential future applications.

Regulated splicing of large exons is linked to phase-separation of vertebrate transcription factors

Although large exons cannot be readily recognized by the spliceosome, many are evolutionarily conserved and constitutively spliced for inclusion in the processed transcript. Furthermore, whether large exons may be enriched in a certain subset of proteins, or mediate specific functions, has remained unclear. Here, we identify a set of nearly 3,000 SRSF3-dependent large constitutive exons (S3-LCEs) in human and mouse cells. These exons are enriched for cytidine-rich sequence motifs, which bind and recruit the splicing factors hnRNP K and SRSF3. We find that hnRNP K suppresses S3-LCE splicing, an effect that is mitigated by SRSF3 to thus achieve constitutive splicing of S3-LCEs. S3-LCEs are enriched in genes for components of transcription machineries, including mediator and BAF complexes, and frequently contain intrinsically disordered regions (IDRs). In a subset of analyzed S3-LCE-containing transcription factors, SRSF3 depletion leads to deletion of the IDRs due to S3-LCE exon skipping, thereby disrupting phase-separated assemblies of these factors. Cytidine enrichment in large exons introduces proline/serine codon bias in intrinsically disordered regions and appears to have been evolutionarily acquired in vertebrates. We propose that layered splicing regulation by hnRNP K and SRSF3 ensures proper phase-separation of these S3-LCE-containing transcription factors in vertebrates.

Tex13a Optimizes Sperm Motility via Its Potential Roles in mRNA Turnover

mRNAs have been found to undergo substantial selective degradation during the late stages of spermiogenesis. However, the mechanisms regulating this biological process are unknown. In this report, we have identified Tex13a, a spermatid-specific gene that interacts with the CCR4–NOT complex and is implicated in the targeted degradation of mRNAs encoding particular structural components of sperm. Deletion of Tex13a led to a delayed decay of these mRNAs, lowered the levels of house-keeping genes, and ultimately lowered several key parameters associated with the control of sperm motility, such as the path velocity (VAP, average path velocity), track speed (VCL, velocity curvilinear), and rapid progression.

Complexome Profiling: Assembly and Remodeling of Protein Complexes

Many proteins have been found to operate in a complex with various biomolecules such as proteins, nucleic acids, carbohydrates, or lipids. Protein complexes can be transient, stable or dynamic and their association is controlled under variable cellular conditions. Complexome profiling is a recently developed mass spectrometry-based method that combines mild separation techniques, native gel electrophoresis, and density gradient centrifugation with quantitative mass spectrometry to generate inventories of protein assemblies within a cell or subcellular fraction. This review summarizes applications of complexome profiling with respect to assembly ranging from single subunits to large macromolecular complexes, as well as their stability, and remodeling in health and disease.