Minor intron splicing is critical for survival of lethal prostate cancer.

The evolutionarily conserved minor spliceosome (MiS) is required for protein expression of ∼714 minor intron-containing genes (MIGs) crucial for cell-cycle regulation, DNA repair, and MAP-kinase signaling. We explored the role of MIGs and MiS in cancer, taking prostate cancer (PCa) as an exemplar. Both androgen receptor signaling and elevated levels of U6atac, a MiS small nuclear RNA, regulate MiS activity, which is highest in advanced metastatic PCa. siU6atac-mediated MiS inhibition in PCa in vitro model systems resulted in aberrant minor intron splicing leading to cell-cycle G1 arrest. Small interfering RNA knocking down U6atac was ∼50% more efficient in lowering tumor burden in models of advanced therapy-resistant PCa compared with standard antiandrogen therapy. In lethal PCa, siU6atac disrupted the splicing of a crucial lineage dependency factor, the RE1-silencing factor (REST). Taken together, we have nominated MiS as a vulnerability for lethal PCa and potentially other cancers.

Actionable Cytopathogenic Host Responses of Human Alveolar Type 2 Cells to SARS-CoV-2.

Human transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), causative pathogen of the COVID-19 pandemic, exerts a massive health and socioeconomic crisis. The virus infects alveolar epithelial type 2 cells (AT2s), leading to lung injury and impaired gas exchange, but the mechanisms driving infection and pathology are unclear. We performed a quantitative phosphoproteomic survey of induced pluripotent stem cell-derived AT2s (iAT2s) infected with SARS-CoV-2 at air-liquid interface (ALI). Time course analysis revealed rapid remodeling of diverse host systems, including signaling, RNA processing, translation, metabolism, nuclear integrity, protein trafficking, and cytoskeletal-microtubule organization, leading to cell cycle arrest, genotoxic stress, and innate immunity. Comparison to analogous data from transformed cell lines revealed respiratory-specific processes hijacked by SARS-CoV-2, highlighting potential novel therapeutic avenues that were validated by a high hit rate in a targeted small molecule screen in our iAT2 ALI system.

Multi-modal features-based human-herpesvirus protein-protein interaction prediction by using LightGBM.

The identification of human-herpesvirus protein-protein interactions (PPIs) is an essential and important entry point to understand the mechanisms of viral infection, especially in malignant tumor patients with common herpesvirus infection. While natural language processing (NLP)-based embedding techniques have emerged as powerful approaches, the application of multi-modal embedding feature fusion to predict human-herpesvirus PPIs is still limited. Here, we established a multi-modal embedding feature fusion-based LightGBM method to predict human-herpesvirus PPIs. In particular, we applied document and graph embedding approaches to represent sequence, network and function modal features of human and herpesviral proteins. Training our LightGBM models through our compiled non-rigorous and rigorous benchmarking datasets, we obtained significantly better performance compared to individual-modal features. Furthermore, our model outperformed traditional feature encodings-based machine learning methods and state-of-the-art deep learning-based methods using various benchmarking datasets. In a transfer learning step, we show that our model that was trained on human-herpesvirus PPI dataset without cytomegalovirus data can reliably predict human-cytomegalovirus PPIs, indicating that our method can comprehensively capture multi-modal fusion features of protein interactions across various herpesvirus subtypes. The implementation of our method is available at https://github.com/XiaodiYangpku/MultimodalPPI/.

The online competition between pro- and anti-vaccination views.

Distrust in scientific expertise1-14 is dangerous. Opposition to vaccination with a future vaccine against SARS-CoV-2, the causal agent of COVID-19, for example, could amplify outbreaks2-4, as happened for measles in 20195,6. Homemade remedies7,8 and falsehoods are being shared widely on the Internet, as well as dismissals of expert advice9-11. There is a lack of understanding about how this distrust evolves at the system level13,14. Here we provide a map of the contention surrounding vaccines that has emerged from the global pool of around three billion Facebook users. Its core reveals a multi-sided landscape of unprecedented intricacy that involves nearly 100 million individuals partitioned into highly dynamic, interconnected clusters across cities, countries, continents and languages. Although smaller in overall size, anti-vaccination clusters manage to become highly entangled with undecided clusters in the main online network, whereas pro-vaccination clusters are more peripheral. Our theoretical framework reproduces the recent explosive growth in anti-vaccination views, and predicts that these views will dominate in a decade. Insights provided by this framework can inform new policies and approaches to interrupt this shift to negative views. Our results challenge the conventional thinking about undecided individuals in issues of contention surrounding health, shed light on other issues of contention such as climate change11, and highlight the key role of network cluster dynamics in multi-species ecologies15.

SGPPI: structure-aware prediction of protein-protein interactions in rigorous conditions with graph convolutional network.

While deep learning (DL)-based models have emerged as powerful approaches to predict protein-protein interactions (PPIs), the reliance on explicit similarity measures (e.g. sequence similarity and network neighborhood) to known interacting proteins makes these methods ineffective in dealing with novel proteins. The advent of AlphaFold2 presents a significant opportunity and also a challenge to predict PPIs in a straightforward way based on monomer structures while controlling bias from protein sequences. In this work, we established Structure and Graph-based Predictions of Protein Interactions (SGPPI), a structure-based DL framework for predicting PPIs, using the graph convolutional network. In particular, SGPPI focused on protein patches on the protein-protein binding interfaces and extracted the structural, geometric and evolutionary features from the residue contact map to predict PPIs. We demonstrated that our model outperforms traditional machine learning methods and state-of-the-art DL-based methods using non-representation-bias benchmark datasets. Moreover, our model trained on human dataset can be reliably transferred to predict yeast PPIs, indicating that SGPPI can capture converging structural features of protein interactions across various species. The implementation of SGPPI is available at https://github.com/emerson106/SGPPI.

Deep learning-assisted prediction of protein-protein interactions in Arabidopsis thaliana.

Currently, the experimentally identified interactome of Arabidopsis (Arabidopsis thaliana) is still far from complete, suggesting that computational prediction methods can complement experimental techniques. Motivated by the prosperity and success of deep learning algorithms and natural language processing techniques, we introduce an integrative deep learning framework, DeepAraPPI, allowing us to predict protein-protein interactions (PPIs) of Arabidopsis utilizing sequence, domain and Gene Ontology (GO) information. Our current DeepAraPPI comprises: (i) a word2vec encoding-based Siamese recurrent convolutional neural network (RCNN) model; (ii) a Domain2vec encoding-based multiple-layer perceptron (MLP) model; and (iii) a GO2vec encoding-based MLP model. Finally, DeepAraPPI combines the prediction results of the three individual predictors through a logistic regression model. Compiling high-quality positive and negative training and test samples by applying strict filtering strategies, DeepAraPPI shows superior performance compared with existing state-of-the-art Arabidopsis PPI prediction methods. DeepAraPPI also provides better cross-species predictive ability in rice (Oryza sativa) than traditional machine learning methods, although the overall performance in cross-species prediction remains to be improved. DeepAraPPI is freely accessible at http://zzdlab.com/deeparappi/. In the meantime, we have also made the source code and data sets of DeepAraPPI available at https://github.com/zjy1125/DeepAraPPI.

HVIDB: a comprehensive database for human-virus protein-protein interactions.

While leading to millions of people's deaths every year the treatment of viral infectious diseases remains a huge public health challenge.Therefore, an in-depth understanding of human-virus protein-protein interactions (PPIs) as the molecular interface between a virus and its host cell is of paramount importance to obtain new insights into the pathogenesis of viral infections and development of antiviral therapeutic treatments. However, current human-virus PPI database resources are incomplete, lack annotation and usually do not provide the opportunity to computationally predict human-virus PPIs. Here, we present the Human-Virus Interaction DataBase (HVIDB, http://zzdlab.com/hvidb/) that provides comprehensively annotated human-virus PPI data as well as seamlessly integrates online PPI prediction tools. Currently, HVIDB highlights 48 643 experimentally verified human-virus PPIs covering 35 virus families, 6633 virally targeted host complexes, 3572 host dependency/restriction factors as well as 911 experimentally verified/predicted 3D complex structures of human-virus PPIs. Furthermore, our database resource provides tissue-specific expression profiles of 6790 human genes that are targeted by viruses and 129 Gene Expression Omnibus series of differentially expressed genes post-viral infections. Based on these multifaceted and annotated data, our database allows the users to easily obtain reliable information about PPIs of various human viruses and conduct an in-depth analysis of their inherent biological significance. In particular, HVIDB also integrates well-performing machine learning models to predict interactions between the human host and viral proteins that are based on (i) sequence embedding techniques, (ii) interolog mapping and (iii) domain-domain interaction inference. We anticipate that HVIDB will serve as a one-stop knowledge base to further guide hypothesis-driven experimental efforts to investigate human-virus relationships.

Protein-protein interactions of human viruses.

Viruses infect their human hosts by a series of interactions between viral and host proteins, indicating that detailed knowledge of such virus-host interaction interfaces are critical for our understanding of viral infection mechanisms, disease etiology and the development of new drugs. In this review, we primarily survey human host-virus interaction data that are available from public databases following the standardized PSI-MS format. Notably, available host-virus protein interaction information is strongly biased toward a small number of virus families including herpesviridae, papillomaviridae, orthomyxoviridae and retroviridae. While we explore the reliability and relevance of these protein interactions we also survey the current knowledge about viruses functional and topological targets. Furthermore, we assess emerging frontiers of host-virus protein interaction research, focusing on protein interaction interfaces of hosts that are infected by different viruses and viruses that infect multiple hosts. Finally, we cover the current status of research that investigates the relationships of virus-targeted host proteins to other comorbidities as well as the influence of host-virus protein interactions on human metabolism.

Transfer learning via multi-scale convolutional neural layers for human-virus protein-protein interaction prediction.

MOTIVATION: To complement experimental efforts, machine learning-based computational methods are playing an increasingly important role to predict human-virus protein-protein interactions (PPIs). Furthermore, transfer learning can effectively apply prior knowledge obtained from a large source dataset/task to a small target dataset/task, improving prediction performance. RESULTS: To predict interactions between human and viral proteins, we combine evolutionary sequence profile features with a Siamese convolutional neural network (CNN) architecture and a multi-layer perceptron. Our architecture outperforms various feature encodings-based machine learning and state-of-the-art prediction methods. As our main contribution, we introduce two transfer learning methods (i.e. 'frozen' type and 'fine-tuning' type) that reliably predict interactions in a target human-virus domain based on training in a source human-virus domain, by retraining CNN layers. Finally, we utilize the 'frozen' type transfer learning approach to predict human-SARS-CoV-2 PPIs, indicating that our predictions are topologically and functionally similar to experimentally known interactions. AVAILABILITY AND IMPLEMENTATION: The source codes and datasets are available at https://github.com/XiaodiYangCAU/TransPPI/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Integration of peripheral transcriptomics, genomics, and interactomics following trauma identifies causal genes for symptoms of post-traumatic stress and major depression.

Posttraumatic stress disorder (PTSD) is a debilitating syndrome with substantial morbidity and mortality that occurs in the aftermath of trauma. Symptoms of major depressive disorder (MDD) are also a frequent consequence of trauma exposure. Identifying novel risk markers in the immediate aftermath of trauma is a critical step for the identification of novel biological targets to understand mechanisms of pathophysiology and prevention, as well as the determination of patients most at risk who may benefit from immediate intervention. Our study utilizes a novel approach to computationally integrate blood-based transcriptomics, genomics, and interactomics to understand the development of risk vs. resilience in the months following trauma exposure. In a two-site longitudinal, observational prospective study, we assessed over 10,000 individuals and enrolled >700 subjects in the immediate aftermath of trauma (average 5.3 h post-trauma (range 0.5-12 h)) in the Grady Memorial Hospital (Atlanta) and Jackson Memorial Hospital (Miami) emergency departments. RNA expression data and 6-month follow-up data were available for 366 individuals, while genotype, transcriptome, and phenotype data were available for 297 patients. To maximize our power and understanding of genes and pathways that predict risk vs. resilience, we utilized a set-cover approach to capture fluctuations of gene expression of PTSD or depression-converting patients and non-converting trauma-exposed controls to find representative sets of disease-relevant dysregulated genes. We annotated such genes with their corresponding expression quantitative trait loci and applied a variant of a current flow algorithm to identify genes that potentially were causal for the observed dysregulation of disease genes involved in the development of depression and PTSD symptoms after trauma exposure. We obtained a final list of 11 driver causal genes related to MDD symptoms, 13 genes for PTSD symptoms, and 22 genes in PTSD and/or MDD. We observed that these individual or combined disorders shared ESR1, RUNX1, PPARA, and WWOX as driver causal genes, while other genes appeared to be causal driver in the PTSD only or MDD only cases. A number of these identified causal pathways have been previously implicated in the biology or genetics of PTSD and MDD, as well as in preclinical models of amygdala function and fear regulation. Our work provides a promising set of initial pathways that may underlie causal mechanisms in the development of PTSD or MDD in the aftermath of trauma.

Proteome Data Improves Protein Function Prediction in the Interactome of Helicobacter pylori.

Helicobacter pylori is a common pathogen that is estimated to infect half of the human population, causing several diseases such as duodenal ulcer. Despite one of the first pathogens to be sequenced, its proteome remains poorly characterized as about one-third of its proteins have no functional annotation. Here, we integrate and analyze known protein interactions with proteomic and genomic data from different sources. We find that proteins with similar abundances tend to interact. Such an observation is accompanied by a trend of interactions to appear between proteins of similar functions, although some show marked cross-talk to others. Protein function prediction with protein interactions is significantly improved when interactions from other bacteria are included in our network, allowing us to obtain putative functions of more than 300 poorly or previously uncharacterized proteins. Proteins that are critical for the topological controllability of the underlying network are significantly enriched with genes that are up-regulated in the spiral compared with the coccoid form of H. pylori Determining their evolutionary conservation, we present evidence that 80 protein complexes are identical in composition with their counterparts in Escherichia coli, while 85 are partially conserved and 120 complexes are completely absent. Furthermore, we determine network clusters that coincide with related functions, gene essentiality, genetic context, cellular localization, and gene expression in different cellular states.

Promoter conservation in HDACs points to functional implications.

BACKGROUND: Histone deacetylases (HDACs) are the proteins responsible for removing the acetyl group from lysine residues of core histones in chromosomes, a crucial component of gene regulation. Eleven known HDACs exist in humans and most other vertebrates. While the basic function of HDACs has been well characterized and new discoveries are still being made, the transcriptional regulation of their corresponding genes is still poorly understood. RESULTS: Here, we conducted a computational analysis of the eleven HDAC promoter sequences in 25 vertebrate species to determine whether transcription factor binding sites (TFBSs) are conserved in HDAC evolution, and if so, whether they provide useful information about HDAC expression and function. Furthermore, we used tissue-specific information of transcription factors to investigate the potential expression patterns of HDACs in different human tissues based on their transcription factor binding sites. We found that the TFBS profiles of most of the HDACs were well conserved in closely related species for all HDAC promoters except HDAC7 and HDAC10. HDAC5 had particularly strong conservation across over half of the species studied, with nearly identical profiles in the primate species. Our comparisons of TFBSs with the tissue specific gene expression profiles of their corresponding TFs showed that most HDACs had the ability to be ubiquitously expressed. A few HDAC promoters exhibited the potential for preferential expression in certain tissues, most notably HDAC11 in gall bladder, while HDAC9 seemed to have less propensity for expression in the nervous system. CONCLUSIONS: In general, we found evolutionary conservation in HDAC promoters that seems to be more prominent for the ubiquitously expressed HDACs. In turn, when conservation did not follow usual phylogeny, human TFBS patterns indicated possible functional relevance. While we found that HDACs appear to uniformly expressed, we confirm that the functional differences in HDACs may be less a matter of location of activity than a question of which proteins and which acetyl groups they may be acting on.

microRNA regulation in an ancient obligate endosymbiosis.

Although many insects are associated with obligate bacterial endosymbionts, the mechanisms by which these host/endosymbiont associations are regulated remain mysterious. While microRNAs (miRNAs) have been recently identified as regulators of host/microbe interactions, including host/pathogen and host/facultative endosymbiont interactions, the role miRNAs may play in mediating host/obligate endosymbiont interactions is virtually unknown. Here, we identified conserved miRNAs that potentially mediate symbiotic interactions between aphids and their obligate endosymbiont, Buchnera aphidicola. Using small RNA sequence data from Myzus persicae and Acyrthosiphon pisum, we annotated 93 M. persicae and 89 A. pisum miRNAs, among which 69 were shared. We found 14 miRNAs that were either highly expressed in aphid bacteriome, the Buchnera-housing tissue, or differentially expressed in bacteriome vs. gut, a non-Buchnera-housing tissue. Strikingly, 10 of these 14 miRNAs have been implicated previously in other host/microbe interaction studies. Investigating the interaction networks of these miRNAs using a custom computational pipeline, we identified 103 miRNA::mRNA interactions shared between M. persicae and A. pisum. Functional annotation of the shared mRNA targets revealed only two over-represented cluster of orthologous group categories: amino acid transport and metabolism, and signal transduction mechanisms. Our work supports a role for miRNAs in mediating host/symbiont interactions between aphids and their obligate endosymbiont Buchnera. In addition, our results highlight the probable importance of signal transduction mechanisms to host/endosymbiont coevolution.

Cell-free Determination of Binary Complexes That Comprise Extended Protein-Protein Interaction Networks of Yersinia pestis.

Binary protein interactions form the basic building blocks of molecular networks and dynamic assemblies that control all cellular functions of bacteria. Although these protein interactions are a potential source of targets for the development of new antibiotics, few high-confidence data sets are available for the large proteomes of most pathogenic bacteria. We used a library of recombinant proteins from the plague bacterium Yersinia pestis to probe planar microarrays of immobilized proteins that represented ∼85% (3552 proteins) of the bacterial proteome, resulting in >77,000 experimentally determined binary interactions. Moderate (KD ∼µm) to high-affinity (KD ∼nm) interactions were characterized for >1600 binary complexes by surface plasmon resonance imaging of microarrayed proteins. Core binary interactions that were in common with other gram-negative bacteria were identified from the results of both microarray methods. Clustering of proteins within the interaction network by function revealed statistically enriched complexes and pathways involved in replication, biosynthesis, virulence, metabolism, and other diverse biological processes. The interaction pathways included many proteins with no previously known function. Further, a large assembly of proteins linked to transcription and translation were contained within highly interconnected subregions of the network. The two-tiered microarray approach used here is an innovative method for detecting binary interactions, and the resulting data will serve as a critical resource for the analysis of protein interaction networks that function within an important human pathogen.

Links between critical proteins drive the controllability of protein interaction networks.

Focusing on the interactomes of Homo sapiens, Saccharomyces cerevisiae, and Escherichia coli, we investigated interactions between controlling proteins. In particular, we determined critical, intermittent, and redundant proteins based on their tendency to participate in minimum dominating sets. Independently of the organisms considered, we found that interactions that involved critical nodes had the most prominent effects on the topology of their corresponding networks. Furthermore, we observed that phosphorylation and regulatory events were considerably enriched when the corresponding transcription factors and kinases were critical proteins, while such interactions were depleted when they were redundant proteins. Moreover, interactions involving critical proteins were enriched with essential genes, disease genes, and drug targets, suggesting that such characteristics may be key for the detection of novel drug targets as well as assess their efficacy.

Bacterial protein meta-interactomes predict cross-species interactions and protein function.

BACKGROUND: Protein-protein interactions (PPIs) can offer compelling evidence for protein function, especially when viewed in the context of proteome-wide interactomes. Bacteria have been popular subjects of interactome studies: more than six different bacterial species have been the subjects of comprehensive interactome studies while several more have had substantial segments of their proteomes screened for interactions. The protein interactomes of several bacterial species have been completed, including several from prominent human pathogens. The availability of interactome data has brought challenges, as these large data sets are difficult to compare across species, limiting their usefulness for broad studies of microbial genetics and evolution. RESULTS: In this study, we use more than 52,000 unique protein-protein interactions (PPIs) across 349 different bacterial species and strains to determine their conservation across data sets and taxonomic groups. When proteins are collapsed into orthologous groups (OGs) the resulting meta-interactome still includes more than 43,000 interactions, about 14,000 of which involve proteins of unknown function. While conserved interactions provide support for protein function in their respective species data, we found only 429 PPIs (~1% of the available data) conserved in two or more species, rendering any cross-species interactome comparison immediately useful. The meta-interactome serves as a model for predicting interactions, protein functions, and even full interactome sizes for species with limited to no experimentally observed PPI, including Bacillus subtilis and Salmonella enterica which are predicted to have up to 18,000 and 31,000 PPIs, respectively. CONCLUSIONS: In the course of this work, we have assembled cross-species interactome comparisons that will allow interactomics researchers to anticipate the structures of yet-unexplored microbial interactomes and to focus on well-conserved yet uncharacterized interactors for further study. Such conserved interactions should provide evidence for important but yet-uncharacterized aspects of bacterial physiology and may provide targets for anti-microbial therapies.

Controllability in protein interaction networks.

Recently, the focus of network research shifted to network controllability, prompting us to determine proteins that are important for the control of the underlying interaction webs. In particular, we determined minimum dominating sets of proteins (MDSets) in human and yeast protein interaction networks. Such groups of proteins were defined as optimized subsets where each non-MDSet protein can be reached by an interaction from an MDSet protein. Notably, we found that MDSet proteins were enriched with essential, cancer-related, and virus-targeted genes. Their central position allowed MDSet proteins to connect protein complexes and to have a higher impact on network resilience than hub proteins. As for their involvement in regulatory functions, MDSet proteins were enriched with transcription factors and protein kinases and were significantly involved in bottleneck interactions, regulatory links, phosphorylation events, and genetic interactions.

A second-generation protein-protein interaction network of Helicobacter pylori.

Helicobacter pylori infections cause gastric ulcers and play a major role in the development of gastric cancer. In 2001, the first protein interactome was published for this species, revealing over 1500 binary protein interactions resulting from 261 yeast two-hybrid screens. Here we roughly double the number of previously published interactions using an ORFeome-based, proteome-wide yeast two-hybrid screening strategy. We identified a total of 1515 protein-protein interactions, of which 1461 are new. The integration of all the interactions reported in H. pylori results in 3004 unique interactions that connect about 70% of its proteome. Excluding interactions of promiscuous proteins we derived from our new data a core network consisting of 908 interactions. We compared our data set to several other bacterial interactomes and experimentally benchmarked the conservation of interactions using 365 protein pairs (interologs) of E. coli of which one third turned out to be conserved in both species.

Essentiality and centrality in protein interaction networks revisited.

BACKGROUND: Minimum dominating sets (MDSet) of protein interaction networks allow the control of underlying protein interaction networks through their topological placement. While essential proteins are enriched in MDSets, we hypothesize that the statistical properties of biological functions of essential genes are enhanced when we focus on essential MDSet proteins (e-MDSet). RESULTS: Here, we determined minimum dominating sets of proteins (MDSet) in interaction networks of E. coli, S. cerevisiae and H. sapiens, defined as subsets of proteins whereby each remaining protein can be reached by a single interaction. We compared several topological and functional parameters of essential, MDSet, and essential MDSet (e-MDSet) proteins. In particular, we observed that their topological placement allowed e-MDSet proteins to provide a positive correlation between degree and lethality, connect more protein complexes, and have a stronger impact on network resilience than essential proteins alone. In comparison to essential proteins we further found that interactions between e-MDSet proteins appeared more frequently within complexes, while interactions of e-MDSet proteins between complexes were depleted. Finally, these e-MDSet proteins classified into functional groupings that play a central role in survival and adaptability. CONCLUSIONS: The determination of e-MDSet of an organism highlights a set of proteins that enhances the enrichment signals of biological functions of essential proteins. As a consequence, we surmise that e-MDSets may provide a new method of evaluating the core proteins of an organism.