CoMI: consensus mutual information for tissue-specific gene signatures.

BACKGROUND: The gene signatures have been considered as a promising early diagnosis and prognostic analysis to identify disease subtypes and to determine subsequent treatments. Tissue-specific gene signatures of a specific disease are an emergency requirement for precision medicine to improve the accuracy and reduce the side effects. Currently, many approaches have been proposed for identifying gene signatures for diagnosis and prognostic. However, they often lack of tissue-specific gene signatures. RESULTS: Here, we propose a new method, consensus mutual information (CoMI) for analyzing omics data and discovering gene signatures. CoMI can identify differentially expressed genes in multiple cancer omics data for reflecting both cancer-related and tissue-specific signatures, such as Cell growth and death in multiple cancers, Xenobiotics biodegradation and metabolism in LIHC, and Nervous system in GBM. Our method identified 50-gene signatures effectively distinguishing the GBM patients into high- and low-risk groups (log-rank p = 0.006) for diagnosis and prognosis. CONCLUSIONS: Our results demonstrate that CoMI can identify significant and consistent gene signatures with tissue-specific properties and can predict clinical outcomes for interested diseases. We believe that CoMI is useful for analyzing omics data and discovering gene signatures of diseases.

Whole-Blood 3-Gene Signature as a Decision Aid for Rifapentine-based Tuberculosis Preventive Therapy.

BACKGROUND: Systemic drug reaction (SDR) is a major safety concern with weekly rifapentine plus isoniazid for 12 doses (3HP) for latent tuberculosis infection (LTBI). Identifying SDR predictors and at-risk participants before treatment can improve cost-effectiveness of the LTBI program. METHODS: We prospectively recruited 187 cases receiving 3HP (44 SDRs and 143 non-SDRs). A pilot cohort (8 SDRs and 12 non-SDRs) was selected for generating whole-blood transcriptomic data. By incorporating the hierarchical system biology model and therapy-biomarker pathway approach, candidate genes were selected and evaluated using reverse-transcription quantitative polymerase chain reaction (RT-qPCR). Then, interpretable machine learning models presenting as SHapley Additive exPlanations (SHAP) values were applied for SDR risk prediction. Finally, an independent cohort was used to evaluate the performance of these predictive models. RESULTS: Based on the whole-blood transcriptomic profile of the pilot cohort and the RT-qPCR results of 2 SDR and 3 non-SDR samples in the training cohort, 6 genes were selected. According to SHAP values for model construction and validation, a 3-gene model for SDR risk prediction achieved a sensitivity and specificity of 0.972 and 0.947, respectively, under a universal cutoff value for the joint of the training (28 SDRs and 104 non-SDRs) and testing (8 SDRs and 27 non-SDRs) cohorts. It also worked well across different subgroups. CONCLUSIONS: The prediction model for 3HP-related SDRs serves as a guide for establishing a safe and personalized regimen to foster the implementation of an LTBI program. Additionally, it provides a potential translational value for future studies on drug-related hypersensitivity.

MoNetFamily: a web server to infer homologous modules and module-module interaction networks in vertebrates.

A module is a fundamental unit forming with highly connected proteins and performs a certain kind of biological functions. Modules and module-module interaction (MMI) network are essential for understanding cellular processes and functions. The MoNetFamily web server can identify the modules, homologous modules (called module family) and MMI networks across multiple species for the query protein(s). This server first finds module candidates of the query by using BLASTP to search the module template database (1785 experimental and 1252 structural templates). MoNetFamily then infers the homologous modules of the selected module candidate using protein-protein interaction (PPI) families. According to homologous modules and PPIs, we statistically calculated MMIs and MMI networks across multiple species. For each module candidate, MoNetFamily identifies its neighboring modules and their MMIs in module networks of Homo sapiens, Mus musculus and Danio rerio. Finally, MoNetFamily shows the conserved proteins, PPI profiles and functional annotations of the module family. Our results indicate that the server can be useful for MMI network (e.g. 1818 modules and 9678 MMIs in H. sapiens) visualizations and query annotations using module families and neighboring modules. We believe that the server is able to provide valuable insights to determine homologous modules and MMI networks across multiple species for studying module evolution and cellular processes. The MoNetFamily sever is available at http://monetfamily.life.nctu.edu.tw.

Inferring homologous protein-protein interactions through pair position specific scoring matrix.

BACKGROUND: The protein-protein interaction (PPI) is one of the most important features to understand biological processes. For a PPI, the physical domain-domain interaction (DDI) plays the key role for biology functions. In the post-genomic era, to rapidly identify homologous PPIs for analyzing the contact residue pairs of their interfaces within DDIs on a genomic scale is essential to determine PPI networks and the PPI interface evolution across multiple species. RESULTS: In this study, we proposed "pair Position Specific Scoring Matrix (pairPSSM)" to identify homologous PPIs. The pairPSSM can successfully distinguish the true protein complexes from unreasonable protein pairs with about 90% accuracy. For the test set including 1,122 representative heterodimers and 2,708,746 non-interacting protein pairs, the mean average precision and mean false positive rate of pairPSSM were 0.42 and 0.31, respectively. Moreover, we applied pairPSSM to identify ~450,000 homologous PPIs with their interacting domains and residues in seven common organisms (e.g. Homo sapiens, Mus musculus, Saccharomyces cerevisiae and Escherichia coli). CONCLUSIONS: Our pairPSSM is able to provide statistical significance of residue pairs using evolutionary profiles and a scoring system for inferring homologous PPIs. According to our best knowledge, the pairPSSM is the first method for searching homologous PPIs across multiple species using pair position specific scoring matrix and a 3D dimer as the template to map interacting domain pairs of these PPIs. We believe that pairPSSM is able to provide valuable insights for the PPI evolution and networks across multiple species.

Genome-wide structural modelling of TCR-pMHC interactions.

BACKGROUND: The adaptive immune response is antigen-specific and triggered by pathogen recognition through T cells. Although the interactions and mechanisms of TCR-peptide-MHC (TCR-pMHC) have been studied over three decades, the biological basis for these processes remains controversial. As an increasing number of high-throughput binding epitopes and available TCR-pMHC complex structures, a fast genome-wide structural modelling of TCR-pMHC interactions is an emergent task for understanding immune interactions and developing peptide vaccines. RESULTS: We first constructed the PPI matrices and iMatrix, using 621 non-redundant PPI interfaces and 398 non-redundant antigen-antibody interfaces, respectively, for modelling the MHC-peptide and TCR-peptide interfaces, respectively. The iMatrix consists of four knowledge-based scoring matrices to evaluate the hydrogen bonds and van der Waals forces between sidechains or backbones, respectively. The predicted energies of iMatrix are high correlated (Pearson's correlation coefficient is 0.6) to 70 experimental free energies on antigen-antibody interfaces. To further investigate iMatrix and PPI matrices, we inferred the 701,897 potential peptide antigens with significant statistic from 389 pathogen genomes and modelled the TCR-pMHC interactions using available TCR-pMHC complex structures. These identified peptide antigens keep hydrogen-bond energies and consensus interactions and our TCR-pMHC models can provide detailed interacting models and crucial binding regions. CONCLUSIONS: Experimental results demonstrate that our method can achieve high precision for predicting binding affinity and potential peptide antigens. We believe that iMatrix and our template-based method can be useful for the binding mechanisms of TCR-pMHC complexes and peptide vaccine designs.

PAComplex: a web server to infer peptide antigen families and binding models from TCR-pMHC complexes.

One of the most adaptive immune responses is triggered by specific T-cell receptors (TCR) binding to peptide-major histocompatibility complexes (pMHC). Despite the availability of many prediction servers to identify peptides binding to MHC, these servers are often lacking in peptide-TCR interactions and detailed atomic interacting models. PAComplex is the first web server investigating both pMHC and peptide-TCR interfaces to infer peptide antigens and homologous peptide antigens of a query. This server first identifies significantly similar TCR-pMHC templates (joint Z-value ≥ 4.0) of the query by using antibody-antigen and protein-protein interacting scoring matrices for peptide-TCR and pMHC interfaces, respectively. PAComplex then identifies the homologous peptide antigens of these hit templates from complete pathogen genome databases (≥10(8) peptide candidates from 864,628 protein sequences of 389 pathogens) and experimental peptide databases (80,057 peptides in 2287 species). Finally, the server outputs peptide antigens and homologous peptide antigens of the query and displays detailed interacting models (e.g. hydrogen bonds and steric interactions in two interfaces) of hitTCR-pMHC templates. Experimental results demonstrate that the proposed server can achieve high prediction accuracy and offer potential peptide antigens across pathogens. We believe that the server is able to provide valuable insights for the peptide vaccine and MHC restriction. The PAComplex sever is available at http://PAcomplex.life.nctu.edu.tw.

Steric recognition of T-cell receptor contact residues is required to map mutant epitopes by immunoinformatical programmes.

MHC class I-restricted CD8 T-lymphocyte epitopes comprise anchor motifs, T-cell receptor (TCR) contact residues and the peptide backbone. Serial variant epitopes with substitution of amino acids at either anchor motifs or TCR contact residues have been synthesized for specific interferon-γ responses to clarify the TCR recognition mechanism as well as to assess the epitope prediction capacity of immunoinformatical programmes. CD8 T lymphocytes recognise the steric configuration of functional groups at the TCR contact side chain with a parallel observation that peptide backbones of various epitopes adapt to the conserved conformation upon binding to the same MHC class I molecule. Variant epitopes with amino acid substitutions at the TCR contact site are not recognised by specific CD8 T lymphocytes without compromising their binding capacity to MHC class I molecules, which demonstrates two discrete antigen presentation events for the binding of peptides to MHC class I molecules and for TCR recognition. The predicted outcome of immunoinformatical programmes is not consistent with the results of epitope identification by laboratory experiments in the absence of information on the interaction with TCR contact residues. Immunoinformatical programmes based on the binding affinity to MHC class I molecules are not sufficient for the accurate prediction of CD8 T-lymphocyte epitopes. The predictive capacity is further improved to distinguish mutant epitopes from the non-mutated epitopes if the peptide-TCR interface is integrated into the computing simulation programme.

PCFamily: a web server for searching homologous protein complexes.

The proteins in a cell often assemble into complexes to carry out their functions and play an essential role of biological processes. The PCFamily server identifies template-based homologous protein complexes [called protein complex family (PCF)] and infers functional modules of the query proteins. This server first finds homologous structure complexes of the query using BLASTP to search the structural template database (11,263 complexes). PCFamily then searches the homologous complexes of the templates (query) from a complete genomic database (Integr8 with 6,352,363 protein sequences in 2274 species). According to these homologous complexes across multiple species, this sever infers binding models (e.g. hydrogen-bonds and conserved amino acids in the interfaces), functional modules, and the conserved interacting domains and Gene Ontology annotations of the PCF. Experimental results demonstrate that the PCFamily server can be useful for binding model visualizations and annotating the query proteins. We believe that the server is able to provide valuable insights for determining functional modules of biological networks across multiple species. The PCFamily sever is available at http://pcfamily.life.nctu.edu.tw.

PPISearch: a web server for searching homologous protein-protein interactions across multiple species.

As an increasing number of reliable protein-protein interactions (PPIs) become available and high-throughput experimental methods provide systematic identification of PPIs, there is a growing need for fast and accurate methods for discovering homologous PPIs of a newly determined PPI. PPISearch is a web server that rapidly identifies homologous PPIs (called PPI family) and infers transferability of interacting domains and functions of a query protein pair. This server first identifies two homologous families of the query, respectively, by using BLASTP to scan an annotated PPIs database (290 137 PPIs in 576 species), which is a collection of five public databases. We determined homologous PPIs from protein pairs of homologous families when these protein pairs were in the annotated database and have significant joint sequence similarity (E < or = 10(-40)) with the query. Using these homologous PPIs across multiple species, this sever infers the conserved domain-domain pairs (Pfam and InterPro domains) and function pairs (Gene Ontology annotations). Our results demonstrate that the transferability of conserved domain-domain pairs between homologous PPIs and query pairs is 88% using 103 762 PPI queries, and the transferability of conserved function pairs is 69% based on 106 997 PPI queries. The PPISearch server should be useful for searching homologous PPIs and PPI families across multiple species. The PPISearch server is available through the website at http://gemdock.life.nctu.edu.tw/ppisearch/.

3D-interologs: an evolution database of physical protein- protein interactions across multiple genomes.

BACKGROUND: Comprehensive exploration of protein-protein interactions is a challenging route to understand biological processes. For efficiently enlarging protein interactions annotated with residue-based binding models, we proposed a new concept "3D-domain interolog mapping" with a scoring system to explore all possible protein pairs between the two homolog families, derived from a known 3D-structure dimmer (template), across multiple species. Each family consists of homologous proteins which have interacting domains of the template for studying domain interface evolution of two interacting homolog families. RESULTS: The 3D-interologs database records the evolution of protein-protein interactions database across multiple species. Based on "3D-domain interolog mapping" and a new scoring function, we infer 173,294 protein-protein interactions by using 1,895 three-dimensional (3D) structure heterodimers to search the UniProt database (4,826,134 protein sequences). The 3D- interologs database comprises 15,124 species and 283,980 protein-protein interactions, including 173,294 interactions (61%) and 110,686 interactions (39%) summarized from the IntAct database. For a protein-protein interaction, the 3D-interologs database shows functional annotations (e.g. Gene Ontology), interacting domains and binding models (e.g. hydrogen-bond interactions and conserved residues). Additionally, this database provides couple-conserved residues and the interacting evolution by exploring the interologs across multiple species. Experimental results reveal that the proposed scoring function obtains good agreement for the binding affinity of 275 mutated residues from the ASEdb. The precision and recall of our method are 0.52 and 0.34, respectively, by using 563 non-redundant heterodimers to search on the Integr8 database (549 complete genomes). CONCLUSIONS: Experimental results demonstrate that the proposed method can infer reliable physical protein-protein interactions and be useful for studying the protein-protein interaction evolution across multiple species. In addition, the top-ranked strategy and template interface score are able to significantly improve the accuracies of identifying protein-protein interactions in a complete genome. The 3D-interologs database is available at http://3D- interologs.life.nctu.edu.tw.

Transcriptomic and Proteomic Profiling of Human Mesenchymal Stem Cell Derived from Umbilical Cord in the Study of Preterm Birth.

OBJECTIVE: Mesenchymal stem cells (MSCs) hold great therapeutic potential in morbidities associated with preterm birth. However, the molecular expressions of MSCs in preterm birth infants are not systematically evaluated. In this study, the dual-omics analyses of umbilical-cord (UC)-derived MSCs to identify the dysregulated cellular functions are presented. MATERIALS AND METHODS: The UC-MSCs are collected from ten full-term and eight preterm birth infants for microarray and iTRAQ-based proteome profiling. RESULTS: The integrative analysis of dual-omics data discovered 5615 commonly identified genes/proteins of which 29 genes/proteins show consistent up- or downregulation in preterm birth. The Gene Ontology analysis reveals that dysregulation of mitochondrial translation and cellular response to oxidative stress are mainly enriched in 290 differential expression proteins (DEPs) while the 412 differential expression genes (DEGs) are majorly involved in single-organism biosynthetic process, cellular response to stress, and mitotic cell cycle in preterm birth. Besides, a 13-protein module involving CUL2 and CUL3 is identified, which plays an important role in cullin-RING-based ubiquitin ligase complex, as potential mechanism for preterm birth. CONCLUSION: The dual-omics data not only provide new insights to the molecular mechanism but also identify panel of candidate markers associated with preterm birth.

3D-partner: a web server to infer interacting partners and binding models.

The 3D-partner is a web tool to predict interacting partners and binding models of a query protein sequence through structure complexes and a new scoring function. 3D-partner first utilizes IMPALA to identify homologous structures (templates) of a query from a heterodimer profile library. The interacting-partner sequence profiles of these templates are then used to search interacting candidates of the query from protein sequence databases (e.g. SwissProt) by PSI-BLAST. We developed a new scoring function, which includes the contact-residue interacting score (e.g. the steric, hydrogen bonds, and electrostatic interactions) and the template consensus score (e.g. couple-conserved residue and the template similarity scores), to evaluate how well the interfaces between the query and interacting candidates. Based on this scoring function, 3D-partner provides the statistic significance, the binding models (e.g. hydrogen bonds and conserved amino acids) and functional annotations of interacting partners. The correlation between experimental energies and predicted binding affinities of our scoring function is 0.91 on 275 mutated residues from the ASEdb. The average precision of the server is 0.72 on 563 queries and the execution time of this server for a query is approximately 15 s on average. These results suggest that the 3D-partner server can be useful in protein-protein interaction predictions and binding model visualizations. The server is available online at: http://3D-partner.life.nctu.edu.tw.

A homologous mapping method for three-dimensional reconstruction of protein networks reveals disease-associated mutations.

BACKGROUND: One of the crucial steps toward understanding the associations among molecular interactions, pathways, and diseases in a cell is to investigate detailed atomic protein-protein interactions (PPIs) in the structural interactome. Despite the availability of large-scale methods for analyzing PPI networks, these methods often focused on PPI networks using genome-scale data and/or known experimental PPIs. However, these methods are unable to provide structurally resolved interaction residues and their conservations in PPI networks. RESULTS: Here, we reconstructed a human three-dimensional (3D) structural PPI network (hDiSNet) with the detailed atomic binding models and disease-associated mutations by enhancing our PPI families and 3D-domain interologs from 60,618 structural complexes and complete genome database with 6,352,363 protein sequences across 2274 species. hDiSNet is a scale-free network (γ = 2.05), which consists of 5177 proteins and 19,239 PPIs with 5843 mutations. These 19,239 structurally resolved PPIs not only expanded the number of PPIs compared to present structural PPI network, but also achieved higher agreement with gene ontology similarities and higher co-expression correlation than the ones of 181,868 experimental PPIs recorded in public databases. Among 5843 mutations, 1653 and 790 mutations involved in interacting domains and contacting residues, respectively, are highly related to diseases. Our hDiSNet can provide detailed atomic interactions of human disease and their associated proteins with mutations. Our results show that the disease-related mutations are often located at the contacting residues forming the hydrogen bonds or conserved in the PPI family. In addition, hDiSNet provides the insights of the FGFR (EGFR)-MAPK pathway for interpreting the mechanisms of breast cancer and ErbB signaling pathway in brain cancer. CONCLUSIONS: Our results demonstrate that hDiSNet can explore structural-based interactions insights for understanding the mechanisms of disease-associated proteins and their mutations. We believe that our method is useful to reconstruct structurally resolved PPI networks for interpreting structural genomics and disease associations.

A genome-wide comparison of mesenchymal stem cells derived from human placenta and umbilical cord.

OBJECTIVE: The human umbilical cord and placenta have been considered as attractive alternative sources for noninvasive isolation of human mesenchymal stem cells (hMSCs). Different sources of MSC may have individual differentiation potential and phenotype. In this study, we compared the genome-wide expression data of umbilical cord and placenta derived hMSCs to identify specific differential expression genes (DEGs) and corresponding functions. MATERIALS AND METHODS: We collected human placental tissues and umbilical cord from healthy full-term placenta (n = 17). The genome-wide gene expression data of hMSCs were used to analyze and compare with that of fibroblasts. We identified the differential expression genes (DEGs) based on the Student's t-test and one-way ANOVA. RESULTS: According to the DEGs of umbilical cord and placenta, we used the Venn diagram to evaluate the consistence and specific genes. There are 390 umbilical cord specific DEGs which functions are related to movement of sub-cellular component. Then, the DEGs derived from placenta have two major clusters (i.e., placenta-specific (AM-CM-specific) and UC-like (UC-CD-specific)). 247 placenta-specific DEGs are down-regulated and involved in cell communication. 278 UC-like genes are up-regulated and are involved in the cell cycle, cell division, and DNA repair process. Finally, we also identified 239 umbilical cord-placenta consistence DEGs. According to the umbilical cord-placenta consistence DEGs, 175 genes are down-regulated and involved in cell death, cell growth, cell developmental processes. CONCLUSION: We identified the consistence and specific DEGs of human placenta and umbilical cord based on the genome-wide comparison. Our results indicated that hMSCs derived from umbilical cord and placenta have different gene expression patterns, and most of specific genes are involved in the cell cycle, cell division, cell death, and cell developmental processes.

Reconstructing genome-wide protein-protein interaction networks using multiple strategies with homologous mapping.

BACKGROUND: One of the crucial steps toward understanding the biological functions of a cellular system is to investigate protein-protein interaction (PPI) networks. As an increasing number of reliable PPIs become available, there is a growing need for discovering PPIs to reconstruct PPI networks of interesting organisms. Some interolog-based methods and homologous PPI families have been proposed for predicting PPIs from the known PPIs of source organisms. RESULTS: Here, we propose a multiple-strategy scoring method to identify reliable PPIs for reconstructing the mouse PPI network from two well-known organisms: human and fly. We firstly identified the PPI candidates of target organisms based on homologous PPIs, sharing significant sequence similarities (joint E-value ≤ 1 × 10(-40)), from source organisms using generalized interolog mapping. These PPI candidates were evaluated by our multiple-strategy scoring method, combining sequence similarities, normalized ranks, and conservation scores across multiple organisms. According to 106,825 PPI candidates in yeast derived from human and fly, our scoring method can achieve high prediction accuracy and outperform generalized interolog mapping. Experiment results show that our multiple-strategy score can avoid the influence of the protein family size and length to significantly improve PPI prediction accuracy and reflect the biological functions. In addition, the top-ranked and conserved PPIs are often orthologous/essential interactions and share the functional similarity. Based on these reliable predicted PPIs, we reconstructed a comprehensive mouse PPI network, which is a scale-free network and can reflect the biological functions and high connectivity of 292 KEGG modules, including 216 pathways and 76 structural complexes. CONCLUSIONS: Experimental results show that our scoring method can improve the predicting accuracy based on the normalized rank and evolutionary conservation from multiple organisms. Our predicted PPIs share similar biological processes and cellular components, and the reconstructed genome-wide PPI network can reflect network topology and modularity. We believe that our method is useful for inferring reliable PPIs and reconstructing a comprehensive PPI network of an interesting organism.

Module organization and variance in protein-protein interaction networks.

A module is a group of closely related proteins that act in concert to perform specific biological functions through protein-protein interactions (PPIs) that occur in time and space. However, the underlying module organization and variance remain unclear. In this study, we collected module templates to infer respective module families, including 58,041 homologous modules in 1,678 species, and PPI families using searches of complete genomic database. We then derived PPI evolution scores and interface evolution scores to describe the module elements, including core and ring components. Functions of core components were highly correlated with those of essential genes. In comparison with ring components, core proteins/PPIs were conserved across multiple species. Subsequently, protein/module variance of PPI networks confirmed that core components form dynamic network hubs and play key roles in various biological functions. Based on the analyses of gene essentiality, module variance, and gene co-expression, we summarize the observations of module organization and variance as follows: 1) a module consists of core and ring components; 2) core components perform major biological functions and collaborate with ring components to execute certain functions in some cases; 3) core components are more conserved and essential during organizational changes in different biological states or conditions.

Template-based scoring functions for visualising biological insights of H-2Kb-peptide-TCR complexes.

Major Histocompatibility Complex (MHC), peptide and T-Cell Receptor (TCR) play an essential role of adaptive immune responses. Many prediction servers are available for identification of peptides that bind to MHC class I molecules but often lack detailed interacting residues for analysing MHC-peptide-TCR interaction mechanisms. This study considers both the interface similarity and the interacting force for identifying binding models. Our model, considering both the MHC-peptide and the peptide-TCR interfaces, is able to provide visualisation and the biological insights of binding models. We believe that our model is useful for the development of peptide-based vaccines.