In silico prediction of physical protein interactions and characterization of interactome orphans

Mapping the Human Cell Surface Interactome: A Key to Decode Cell-to-Cell Communication

Proteins on the surfaces of cells serve as physical connection points to bridge one cell with another, enabling direct communication between cells and cohesive structure. As biomedical research makes the leap from characterizing individual cells toward understanding the multicellular organization of the human body, the binding interactions between molecules on the surfaces of cells are foundational both for computational models and for clinical efforts to exploit these influential receptor pathways. To achieve this grander vision, we must assemble the full interactome of ways surface proteins can link together. This review investigates how close we are to knowing the human cell surface protein interactome. We summarize the current state of databases and systematic technologies to assemble surface protein interactomes, while highlighting substantial gaps that remain. We aim for this to serve as a road map for eventually building a more robust picture of the human cell surface protein interactome.

S1P Signaling Genes as Prominent Drivers of BCR-ABL1-Independent Imatinib Resistance and Six Herbal Compounds as Potential Drugs for Chronic Myeloid Leukemia

Imatinib (IM), a breakthrough in chronic myeloid leukemia (CML) treatment, is accompanied by discontinuation challenges owing to drug intolerance. Although BCR-ABL1 mutation is a key cause of CML resistance, understanding mechanisms independent of BCR-ABL1 is also important. This study investigated the sphingosine-1-phosphate (S1P) signaling-associated genes (SphK1 and S1PRs) and their role in BCR-ABL1-independent resistant CML, an area currently lacking investigation. Through comprehensive transcriptomic analysis of IM-sensitive and IM-resistant CML groups, we identified the differentially expressed genes and found a notable upregulation of SphK1, S1PR2, and S1PR5 in IM-resistant CML. Functional annotation revealed their roles in critical cellular processes such as proliferation and GPCR activity. Their network analysis uncovered significant clusters, emphasizing the interconnectedness of the S1P signaling genes. Further, we identified interactors such as BIRC3, TRAF6, and SRC genes, with potential implications for IM resistance. Additionally, receiver operator characteristic curve analysis suggested these genes' potential as biomarkers for predicting IM resistance. Network pharmacology analysis identified six herbal compounds-ampelopsin, ellagic acid, colchicine, epigallocatechin-3-gallate, cucurbitacin B, and evodin-as potential drug candidates targeting the S1P signaling genes. In summary, this study contributes to efforts to better understand the molecular mechanisms underlying BCR-ABL1-independent CML resistance. Moreover, the S1P signaling genes are promising therapeutic targets and plausible new innovation avenues to combat IM resistance in cancer clinical care in the future.

Identification of a chromatin-bound ERRα interactome network in mouse liver

OBJECTIVES

Estrogen-related-receptor α (ERRα) plays a critical role in the transcriptional regulation of cellular bioenergetics and metabolism, and perturbations in its activity have been associated with metabolic diseases. While several coactivators and corepressors of ERRα have been identified to date, a knowledge gap remains in understanding the extent to which ERRα cooperates with coregulators in the control of gene expression. Herein, we mapped the primary chromatin-bound ERRα interactome in mouse liver.

METHODS

RIME (Rapid Immuno-precipitation Mass spectrometry of Endogenous proteins) analysis using mouse liver samples from two circadian time points was used to catalog ERRα-interacting proteins on chromatin. The genomic crosstalk between ERRα and its identified cofactors in the transcriptional control of precise gene programs was explored through cross-examination of genome-wide binding profiles from chromatin immunoprecipitation-sequencing (ChIP-seq) studies. The dynamic interplay between ERRα and its newly uncovered cofactor Host cell factor C1 (HCFC1) was further investigated by loss-of-function studies in hepatocytes.

RESULTS

Characterization of the hepatic ERRα chromatin interactome led to the identification of 48 transcriptional interactors of which 42 were previously unknown including HCFC1. Interrogation of available ChIP-seq binding profiles highlighted oxidative phosphorylation (OXPHOS) under the control of a complex regulatory network between ERRα and multiple cofactors. While ERRα and HCFC1 were found to bind to a large set of common genes, only a small fraction showed their colocalization, found predominately near the transcriptional start sites of genes particularly enriched for components of the mitochondrial respiratory chain. Knockdown studies demonstrated inverse regulatory actions of ERRα and HCFC1 on OXPHOS gene expression ultimately dictating the impact of their loss-of-function on mitochondrial respiration.

CONCLUSIONS

Our work unveils a repertoire of previously unknown transcriptional partners of ERRα comprised of chromatin modifiers and transcription factors thus advancing our knowledge of how ERRα regulates metabolic transcriptional programs.

PathDIP 5: improving coverage and making enrichment analysis more biologically meaningful

Pathway Data Integration Portal (PathDIP) is an integrated pathway database that was developed to increase functional gene annotation coverage and reduce bias in pathway enrichment analysis. PathDIP 5 provides multiple improvements to enable more interpretable analysis: users can perform enrichment analysis using all sources, separate sources or by combining specific pathway subsets; they can select the types of sources to use or the types of pathways for the analysis, reducing the number of resulting generic pathways or pathways not related to users' research question; users can use API. All pathways have been mapped to seven representative types. The results of pathway enrichment can be summarized through knowledge-based pathway consolidation. All curated pathways were mapped to 53 pathway ontology-based categories. In addition to genes, pathDIP 5 now includes metabolites. We updated existing databases, included two new sources, PathBank and MetabolicAtlas, and removed outdated databases. We enable users to analyse their results using Drugst.One, where a drug-gene network is created using only the user's genes in a specific pathway. Interpreting the results of any analysis is now improved by multiple charts on all the results pages. PathDIP 5 is freely available at https://ophid.utoronto.ca/pathDIP.

CrMP-Sol database: classification, bioinformatic analyses and comparison of cancer-related membrane proteins and their water-soluble variant designs

Membrane proteins are critical mediators for tumor progression and present enormous therapeutic potentials. Although gene profiling can identify their cancer-specific signatures, systematic correlations between protein functions and tumor-related mechanisms are still unclear. We present here the CrMP-Sol database ( https://bio-gateway.aigene.org.cn/g/CrMP ), which aims to breach the gap between the two. Machine learning was used to extract key functional descriptions for protein visualization in the 3D-space, where spatial distributions provide function-based predictive connections between proteins and cancer types. CrMP-Sol also presents QTY-enabled water-soluble designs to facilitate native membrane protein studies despite natural hydrophobicity. Five examples with varying transmembrane helices in different categories were used to demonstrate the feasibility. Native and redesigned proteins exhibited highly similar characteristics, predicted structures and binding pockets, and slightly different docking poses against known ligands, although task-specific designs are still required for proteins more susceptible to internal hydrogen bond formations. The database can accelerate therapeutic developments and biotechnological applications of cancer-related membrane proteins.

Multidimensional computational study to understand non-coding RNA interactions in breast cancer metastasis

Metastasis is a major breast cancer hallmark due to which tumor cells tend to relocate to regional or distant organs from their organ of origin. This study is aimed to decipher the interaction among 113 differentially expressed genes, interacting non-coding RNAs and drugs (614 miRNAs, 220 lncRNAs and 3241 interacting drugs) associated with metastasis in breast cancer. For an extensive understanding of genetic interactions in the diseased state, a backbone gene co-expression network was constructed. Further, the mRNA-miRNA-lncRNA-drug interaction network was constructed to identify the top hub RNAs, significant cliques and topological parameters associated with differentially expressed genes. Then, the mRNAs from the top two subnetworks constructed are considered for transcription factor (TF) analysis. 39 interacting miRNAs and 1641 corresponding TFs for the eight mRNAs from the subnetworks are also utilized to construct an mRNA-miRNA-TF interaction network. TF analysis revealed two TFs (EST1 and SP1) from the cliques to be significant. TCGA expression analysis of miRNAs and lncRNAs as well as subclass-based and promoter methylation-based expression, oncoprint and survival analysis of the mRNAs are also done. Finally, functional enrichment of mRNAs is also performed. Significant cliques identified in the study can be utilized for identification of newer therapeutic interventions for breast cancer. This work will also help to gain a deeper insight into the complicated molecular intricacies to reveal the potential biomarkers involved with breast cancer progression in future.

Zinc Ions Modulate YY1 Activity: Relevance in Carcinogenesis

YY1 is widely recognized as an intrinsically disordered transcription factor that plays a role in development of many cancers. In most cases, its overexpression is correlated with tumor progression and unfavorable patient outcomes. Our latest research focusing on the role of zinc ions in modulating YY1's interaction with DNA demonstrated that zinc enhances the protein's multimeric state and affinity to its operator. In light of these findings, changes in protein concentration appear to be just one element relevant to modulating YY1-dependent processes. Thus, alterations in zinc ion concentration can directly and specifically impact the regulation of gene expression by YY1, in line with reports indicating a correlation between zinc ion levels and advancement of certain tumors. This review concentrates on other potential consequences of YY1 interaction with zinc ions that may act by altering charge distribution, conformational state distribution, or oligomerization to influence its interactions with molecular partners that can disrupt gene expression patterns.

ProtInteract: A deep learning framework for predicting protein-protein interactions

Proteins mainly perform their functions by interacting with other proteins. Protein-protein interactions underpin various biological activities such as metabolic cycles, signal transduction, and immune response. However, due to the sheer number of proteins, experimental methods for finding interacting and non-interacting protein pairs are time-consuming and costly. We therefore developed the ProtInteract framework to predict protein-protein interaction. ProtInteract comprises two components: first, a novel autoencoder architecture that encodes each protein's primary structure to a lower-dimensional vector while preserving its underlying sequence attributes. This leads to faster training of the second network, a deep convolutional neural network (CNN) that receives encoded proteins and predicts their interaction under three different scenarios. In each scenario, the deep CNN predicts the class of a given encoded protein pair. Each class indicates different ranges of confidence scores corresponding to the probability of whether a predicted interaction occurs or not. The proposed framework features significantly low computational complexity and relatively fast response. The contributions of this work are twofold. First, ProtInteract assimilates the protein's primary structure into a pseudo-time series. Therefore, we leverage the nature of the time series of proteins and their physicochemical properties to encode a protein's amino acid sequence into a lower-dimensional vector space. This approach enables extracting highly informative sequence attributes while reducing computational complexity. Second, the ProtInteract framework utilises this information to identify protein interactions with other proteins based on its amino acid configuration. Our results suggest that the proposed framework performs with high accuracy and efficiency in predicting protein-protein interactions.

Preliminary exploration of the co-regulation of Alzheimer's disease pathogenic genes by microRNAs and transcription factors

Background

Alzheimer's disease (AD) is the most common form of age-related neurodegenerative disease. Unfortunately, due to the complexity of pathological types and clinical heterogeneity of AD, there is a lack of satisfactory treatment for AD. Previous studies have shown that microRNAs and transcription factors can modulate genes associated with AD, but the underlying pathophysiology remains unclear.

Methods

The datasets GSE1297 and GSE5281 were downloaded from the gene expression omnibus (GEO) database and analyzed to obtain the differentially expressed genes (DEGs) through the "R" language "limma" package. The GSE1297 dataset was analyzed by weighted correlation network analysis (WGCNA), and the key gene modules were selected. Next, gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis for the key gene modules were performed. Then, the protein-protein interaction (PPI) network was constructed and the hub genes were identified using the STRING database and Cytoscape software. Finally, for the GSE150693 dataset, the "R" package "survivation" was used to integrate the data of survival time, AD transformation status and 35 characteristics, and the key microRNAs (miRNAs) were selected by Cox method. We also performed regression analysis using least absolute shrinkage and selection operator (Lasso)-Cox to construct and validate prognostic features associated with the four key genes using different databases. We also tried to find drugs targeting key genes through DrugBank database.

Results

GO and KEGG enrichment analysis showed that DEGs were mainly enriched in pathways regulating chemical synaptic transmission, glutamatergic synapses and Huntington's disease. In addition, 10 hub genes were selected from the PPI network by using the algorithm Between Centrality. Then, four core genes (TBP, CDK7, GRM5, and GRIA1) were selected by correlation with clinical information, and the established model had very good prognosis in different databases. Finally, hsa-miR-425-5p and hsa-miR-186-5p were determined by COX regression, AD transformation status and aberrant miRNAs.

Conclusion

In conclusion, we tried to construct a network in which miRNAs and transcription factors jointly regulate pathogenic genes, and described the process that abnormal miRNAs and abnormal transcription factors TBP and CDK7 jointly regulate the transcription of AD central genes GRM5 and GRIA1. The insights gained from this study offer the potential AD biomarkers, which may be of assistance to the diagnose and therapy of AD.

A review of causal discovery methods for molecular network analysis

BACKGROUND

With the increasing availability and size of multi-omics datasets, investigating the casual relationships between molecular phenotypes has become an important aspect of exploring underlying biology andgenetics. There are an increasing number of methodlogies that have been developed and applied to moleular networks to investigate these causal interactions.

METHODS

We have introduced and reviewed the available methods for building large-scale causal molecular networks that have been developed and applied in the past decade.

RESULTS

In this review we have identified and summarized the existing methods for infering causality in large-scale causal molecular networks, and discussed important factors that will need to be considered in future research in this area.

CONCLUSION

Existing methods to infering causal molecular networks have their own strengths and limitations so there is no one best approach, and it is instead down to the discretion of the researcher. This review also to discusses some of the current limitations to biological interpretation of these networks, and important factors to consider for future studies on molecular networks.

Could Amyloid-β 1-42 or α-Synuclein Interact Directly with Mitochondrial DNA? A Hypothesis

The amyloid β (Aβ) and the α-synuclein (α-syn) are shown to be translocated into mitochondria. Even though their roles are widely investigated in pathological conditions, information on the presence and functions of Aβ and α-syn in mitochondria in endogenous levels is somewhat limited. We hypothesized that endogenous Aβ fragments or α-syn could interact with mitochondrial DNA (mtDNA) directly or influence RNAs or transcription factors in mitochondria and change the mtDNA transcription profile. In this review, we summarized clues of these possible interactions.

PeSA 2.0: A software tool for peptide specificity analysis implementing positive and negative motifs and motif-based peptide scoring

There are a vast number of molecular interactions that occur at the cellular level. Among these molecular interactions, interactions between multiple proteins are a widely studied area of research due to the importance of these interactions in cellular function and their potential in drug development. PeSA is a desktop application developed to facilitate the in vitro peptide study analysis to predict protein-protein interactions. PeSA can effortlessly generate visual outputs like motifs, bar charts, and visual matrices. Our implementation of PeSA version 2.0 includes additional tools, including the ability to further score peptide lists for consensus amongst interactions. The software is also able to design de novo peptides based on sequence motifs (sequence generator), which can be used to help design additional experiments for motif validation. Further, the efficacy of the sequence generator was validated using the lysine methyltransferase, SETD8, to identify new substrates of methylation based on motif-based predictions developed using PeSA2.0.

A physical wiring diagram for the human immune system

The human immune system is composed of a distributed network of cells circulating throughout the body, which must dynamically form physical associations and communicate using interactions between their cell-surface proteomes1. Despite their therapeutic potential2, our map of these surface interactions remains incomplete3,4. Here, using a high-throughput surface receptor screening method, we systematically mapped the direct protein interactions across a recombinant library that encompasses most of the surface proteins that are detectable on human leukocytes. We independently validated and determined the biophysical parameters of each novel interaction, resulting in a high-confidence and quantitative view of the receptor wiring that connects human immune cells. By integrating our interactome with expression data, we identified trends in the dynamics of immune interactions and constructed a reductionist mathematical model that predicts cellular connectivity from basic principles. We also developed an interactive multi-tissue single-cell atlas that infers immune interactions throughout the body, revealing potential functional contexts for new interactions and hubs in multicellular networks. Finally, we combined targeted protein stimulation of human leukocytes with multiplex high-content microscopy to link our receptor interactions to functional roles, in terms of both modulating immune responses and maintaining normal patterns of intercellular associations. Together, our work provides a systematic perspective on the intercellular wiring of the human immune system that extends from systems-level principles of immune cell connectivity down to mechanistic characterization of individual receptors, which could offer opportunities for therapeutic intervention.

Multi-view heterogeneous molecular network representation learning for protein-protein interaction prediction

BACKGROUND

Protein-protein interaction (PPI) plays an important role in regulating cells and signals. Despite the ongoing efforts of the bioassay group, continued incomplete data limits our ability to understand the molecular roots of human disease. Therefore, it is urgent to develop a computational method to predict PPIs from the perspective of molecular system.

METHODS

In this paper, a highly efficient computational model, MTV-PPI, is proposed for PPI prediction based on a heterogeneous molecular network by learning inter-view protein sequences and intra-view interactions between molecules simultaneously. On the one hand, the inter-view feature is extracted from the protein sequence by k-mer method. On the other hand, we use a popular embedding method LINE to encode the heterogeneous molecular network to obtain the intra-view feature. Thus, the protein representation used in MTV-PPI is constructed by the aggregation of its inter-view feature and intra-view feature. Finally, random forest is integrated to predict potential PPIs.

RESULTS

To prove the effectiveness of MTV-PPI, we conduct extensive experiments on a collected heterogeneous molecular network with the accuracy of 86.55%, sensitivity of 82.49%, precision of 89.79%, AUC of 0.9301 and AUPR of 0.9308. Further comparison experiments are performed with various protein representations and classifiers to indicate the effectiveness of MTV-PPI in predicting PPIs based on a complex network.

CONCLUSION

The achieved experimental results illustrate that MTV-PPI is a promising tool for PPI prediction, which may provide a new perspective for the future interactions prediction researches based on heterogeneous molecular network.

Protein-protein interaction networks as miners of biological discovery

Protein-protein interactions (PPIs) form the basis of a myriad of biological pathways and mechanism, such as the formation of protein-complexes or the components of signaling cascades. Here, we reviewed experimental methods for identifying PPI pairs, including yeast two-hybrid, mass spectrometry, co-localization, and co-immunoprecipitation. Furthermore, a range of computational methods leveraging biochemical properties, evolution history, protein structures and more have enabled identification of additional PPIs. Given the wealth of known PPIs, we reviewed important network methods to construct and analyze networks of PPIs. These methods aid biological discovery through identifying hub genes and dynamic changes in the network, and have been thoroughly applied in various fields of biological research. Lastly, we discussed the challenges and future direction of research utilizing the power of PPI networks. This article is protected by copyright. All rights reserved.

JAK-STAT core cancer pathway: An integrative cancer interactome analysis

Through a comprehensive review and in silico analysis of reported data on STAT-linked diseases, we analysed the communication pathways and interactome of the seven STATs in major cancer categories and proposed rational targeting approaches for therapeutic intervention to disrupt critical pathways and addictions to hyperactive JAK/STAT in neoplastic states. Although all STATs follow a similar molecular activation pathway, STAT1, STAT2, STAT4 and STAT6 exert specific biological profiles associated with a more restricted pattern of activation by cytokines. STAT3 and STAT5A as well as STAT5B have pleiotropic roles in the body and can act as critical oncogenes that promote many processes involved in cancer development. STAT1, STAT3 and STAT5 also possess tumour suppressive action in certain mutational and cancer type context. Here, we demonstrated member-specific STAT activity in major cancer types. Through systems biology approaches, we found surprising roles for EGFR family members, sex steroid hormone receptor ESR1 interplay with oncogenic STAT function and proposed new drug targeting approaches of oncogenic STAT pathway addiction.

Deciphering the Host-Pathogen Interactome of the Wheat-Common Bunt System: A Step towards Enhanced Resilience in Next Generation Wheat

Common bunt, caused by two fungal species, Tilletia caries and Tilletia laevis, is one of the most potentially destructive diseases of wheat. Despite the availability of synthetic chemicals against the disease, organic agriculture relies greatly on resistant cultivars. Using two computational approaches—interolog and domain-based methods—a total of approximately 58 M and 56 M probable PPIs were predicted in T. aestivum–T. caries and T. aestivum–T. laevis interactomes, respectively. We also identified 648 and 575 effectors in the interactions from T. caries and T. laevis, respectively. The major host hubs belonged to the serine/threonine protein kinase, hsp70, and mitogen-activated protein kinase families, which are actively involved in plant immune signaling during stress conditions. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the host proteins revealed significant GO terms (O-methyltransferase activity, regulation of response to stimulus, and plastid envelope) and pathways (NF-kappa B signaling and the MAPK signaling pathway) related to plant defense against pathogens. Subcellular localization suggested that most of the pathogen proteins target the host in the plastid. Furthermore, a comparison between unique T. caries and T. laevis proteins was carried out. We also identified novel host candidates that are resistant to disease. Additionally, the host proteins that serve as transcription factors were also predicted.

Protein Interaction Network Reconstruction with a Structural Gated Attention Deep Model by Incorporating Network Structure Information

Protein-protein interactions (PPIs) provide a physical basis of molecular communications for a wide range of biological processes in living cells. Establishing the PPI network has become a fundamental but essential task for a better understanding of biological events and disease pathogenesis. Although many machine learning algorithms have been employed to predict PPIs, with only protein sequence information as the training features, these models suffer from low robustness and prediction accuracy. In this study, a new deep-learning-based framework named the Structural Gated Attention Deep (SGAD) model was proposed to improve the performance of PPI network reconstruction (PINR). The improved predictive performances were achieved by augmenting multiple protein sequence descriptors, the topological features and information flow of the PPI network, which were further implemented with a gating mechanism to improve its robustness to noise. On 11 independent test data sets and one combined data set, SGAD yielded area under the curve values of approximately 0.83-0.93, outperforming other models. Furthermore, the SGAD ensemble can learn more characteristics information on protein pairs through a two-layer neural network, serving as a powerful tool in the exploration of PPI biological space.

Homodimeric and Heterodimeric Interactions among Vertebrate Basic Helix-Loop-Helix Transcription Factors

The basic helix–loop–helix transcription factor (bHLH TF) family is involved in tissue development, cell differentiation, and disease. These factors have transcriptionally positive, negative, and inactive functions by combining dimeric interactions among family members. The best known bHLH TFs are the E-protein homodimers and heterodimers with the tissue-specific TFs or ID proteins. These cooperative and dynamic interactions result in a complex transcriptional network that helps define the cell’s fate. Here, the reported dimeric interactions of 67 vertebrate bHLH TFs with other family members are summarized in tables, including specifications of the experimental techniques that defined the dimers. The compilation of these extensive data underscores homodimers of tissue-specific bHLH TFs as a central part of the bHLH regulatory network, with relevant positive and negative transcriptional regulatory roles. Furthermore, some sequence-specific TFs can also form transcriptionally inactive heterodimers with each other. The function, classification, and developmental role for all vertebrate bHLH TFs in four major classes are detailed.

IID 2021: towards context-specific protein interaction analyses by increased coverage, enhanced annotation and enrichment analysis

Improved bioassays have significantly increased the rate of identifying new protein-protein interactions (PPIs), and the number of detected human PPIs has greatly exceeded early estimates of human interactome size. These new PPIs provide a more complete view of disease mechanisms but precise understanding of how PPIs affect phenotype remains a challenge. It requires knowledge of PPI context (e.g. tissues, subcellular localizations), and functional roles, especially within pathways and protein complexes. The previous IID release focused on PPI context, providing networks with comprehensive tissue, disease, cellular localization, and druggability annotations. The current update adds developmental stages to the available contexts, and provides a way of assigning context to PPIs that could not be previously annotated due to insufficient data or incompatibility with available context categories (e.g. interactions between membrane and cytoplasmic proteins). This update also annotates PPIs with conservation across species, directionality in pathways, membership in large complexes, interaction stability (i.e. stable or transient), and mutation effects. Enrichment analysis is now available for all annotations, and includes multiple options; for example, context annotations can be analyzed with respect to PPIs or network proteins. In addition to tabular view or download, IID provides online network visualization. This update is available at http://ophid.utoronto.ca/iid.

Characterization and comparison of gene-centered human interactomes

The complex web of macromolecular interactions occurring within cells-the interactome-is the backbone of an increasing number of studies, but a clear consensus on the exact structure of this network is still lacking. Different genome-scale maps of human interactome have been obtained through several experimental techniques and functional analyses. Moreover, these maps can be enriched through literature-mining approaches, and different combinations of various 'source' databases have been used in the literature. It is therefore unclear to which extent the various interactomes yield similar results when used in the context of interactome-based approaches in network biology. We compared a comprehensive list of human interactomes on the basis of topology, protein complexes, molecular pathways, pathway cross-talk and disease gene prediction. In a general context of relevant heterogeneity, our study provides a series of qualitative and quantitative parameters that describe the state of the art of human interactomes and guidelines for selecting interactomes in future applications.

Psychologic Stress Drives Progression of Malignant Tumors via DRD2/HIF1α Signaling

Although it is established that the sustained psychologic stress conditions under which patients with tumors often reside accelerates malignant progression of tumors, the molecular mechanism behind this association is unclear. In this work, the effect of psychologic stress on tumor progression was verified using a stress-stimulated tumor-bearing mouse model (Str-tumor). Both D2 dopamine receptor (DRD2) and hypoxia-inducible factor-1α (HIF1α) were highly expressed in the nucleus of Str-tumors. Treatment with trifluoperazine (TFP), a DRD2 inhibitor, elicited better antitumor effects in Str-tumors than the control group. These results indicate that DRD2 may mediate stress-induced malignant tumor progression. DRD2 interacted with von Hippel-Lindau (VHL) in the nucleus, and competitive binding of DRD2 and HIF1α to VHL resulted in reduced ubiquitination-mediated degradation of HIF1α, enhancing the epithelial-mesenchymal transition of tumor cells. TFP acted as an interface inhibitor between DRD2 and VHL to promote the degradation of HIF1α. In conclusion, DRD2 may promote the progression of malignant tumors induced by psychologic stress via activation of the oxygen-independent HIF1α pathway, and TFP may serve as a therapeutic strategy for stress management in patients with cancer. SIGNIFICANCE: This work identifies DRD2 regulation of HIF1α as a mechanism underlying the progression of malignant tumors stimulated by psychologic stress and suggests that DRD2 inhibition can mitigate these stress conditions in patients.See related commentary by Bernabé, p. 5144.

Large-Scale Protein Interactions Prediction by Multiple Evidence Analysis Associated With an In-Silico Curation Strategy

Predicting the physical or functional associations through protein-protein interactions (PPIs) represents an integral approach for inferring novel protein functions and discovering new drug targets during repositioning analysis. Recent advances in high-throughput data generation and multi-omics techniques have enabled large-scale PPI predictions, thus promoting several computational methods based on different levels of biological evidence. However, integrating multiple results and strategies to optimize, extract interaction features automatically and scale up the entire PPI prediction process is still challenging. Most procedures do not offer an in-silico validation process to evaluate the predicted PPIs. In this context, this paper presents the PredPrIn scientific workflow that enables PPI prediction based on multiple lines of evidence, including the structure, sequence, and functional annotation categories, by combining boosting and stacking machine learning techniques. We also present a pipeline (PPIVPro) for the validation process based on cellular co-localization filtering and a focused search of PPI evidence on scientific publications. Thus, our combined approach provides means to extensive scale training or prediction of new PPIs and a strategy to evaluate the prediction quality. PredPrIn and PPIVPro are publicly available at https://github.com/YasCoMa/predprin and https://github.com/YasCoMa/ppi_validation_process.

An atlas of protein-protein interactions across mouse tissues

Cellular processes arise from the dynamic organization of proteins in networks of physical interactions. Mapping the interactome has therefore been a central objective of high-throughput biology. However, the dynamics of protein interactions across physiological contexts remain poorly understood. Here, we develop a quantitative proteomic approach combining protein correlation profiling with stable isotope labeling of mammals (PCP-SILAM) to map the interactomes of seven mouse tissues. The resulting maps provide a proteome-scale survey of interactome rewiring across mammalian tissues, revealing more than 125,000 unique interactions at a quality comparable to the highest-quality human screens. We identify systematic suppression of cross-talk between the evolutionarily ancient housekeeping interactome and younger, tissue-specific modules. Rewired proteins are tightly regulated by multiple cellular mechanisms and are implicated in disease. Our study opens up new avenues to uncover regulatory mechanisms that shape in vivo interactome responses to physiological and pathophysiological stimuli in mammalian systems.

Transcriptomic analysis of castration, chemo-resistant and metastatic prostate cancer elucidates complex genetic crosstalk leading to disease progression

Prostate adenocarcinoma, with its rising numbers and high fatality rate, is a daunting healthcare challenge to clinicians and researchers alike. The mainstay of our meta-analysis was to decipher differentially expressed genes (DEGs), their corresponding transcription factors (TFs), miRNAs (microRNA) and interacting pathways underlying the progression of prostate cancer (PCa). We have chosen multiple datasets from primary, castration-resistant, chemo-resistant and metastatic prostate cancer stages for investigation. From our tissue-specific and disease-specific co-expression networks, fifteen hub genes such as ACTB, ACTN1, CDH1, CDKN1A, DDX21, ELF3, FLNA, FLNC, IKZF1, ILK, KRT13, KRT18, KRT19, SVIL and TRIM29 were identified and validated by molecular complex detection analysis as well as survival analysis. In our attempt to highlight hub gene-associated mutations and drug interactions, FLNC was found to be most commonly mutated and CDKN1A gene was found to have highest druggability. Moreover, from DAVID and gene set enrichment analysis, the focal adhesion and oestrogen signalling pathways were found enriched which indicates the involvement of hub genes in tumour invasiveness and metastasis. Finally by Enrichr tool and miRNet, we identified transcriptional factors SNAI2, TP63, CEBPB and KLF11 and microRNAs, namely hsa-mir-1-3p, hsa-mir-145-5p, hsa-mir-124-3p and hsa-mir-218-5p significantly controlling the hub gene expressions. In a nutshell, our report will help to gain a deeper insight into complex molecular intricacies and thereby unveil the probable biomarkers and therapeutic targets involved with PCa progression.

Computational and experimental characterization of the novel ECM glycoprotein SNED1 and prediction of its interactome

The extracellular matrix (ECM) is a complex meshwork of proteins and an essential component of multicellular life. We have recently reported the characterization of a novel ECM protein, SNED1, and showed that it promotes breast cancer metastasis and regulates craniofacial development. However, the mechanisms by which it does so remain unknown. ECM proteins exert their functions by binding to cell surface receptors and interacting with other ECM proteins, actions that we can predict using knowledge of protein's sequence, structure, and post-translational modifications. Here, we combined in-silico and in-vitro approaches to characterize the physico-chemical properties of SNED1 and infer its putative functions. To do so, we established a mammalian cell system to produce and purify SNED1 and its N-terminal fragment, which contains a NIDO domain, and demonstrated experimentally SNED1's potential to be glycosylated, phosphorylated, and incorporated into an insoluble ECM. We also determined the secondary and tertiary structures of SNED1 and its N-terminal fragment and obtained a model for its NIDO domain. Using computational predictions, we identified 114 proteins as putative SNED1 interactors, including the ECM protein fibronectin. Pathway analysis of the predicted SNED1 interactome further revealed that it may contribute to signaling through cell surface receptors, such as integrins, and participate in the regulation of ECM organization and developmental processes. Last, using fluorescence microscopy, we showed that SNED1 forms microfibrils within the ECM and partially colocalizes with fibronectin. Altogether, we provide a wealth of information on an understudied yet important ECM protein with the potential to decipher its pathophysiological functions.

Current status and future perspectives of computational studies on human-virus protein-protein interactions

The protein-protein interactions (PPIs) between human and viruses mediate viral infection and host immunity processes. Therefore, the study of human-virus PPIs can help us understand the principles of human-virus relationships and can thus guide the development of highly effective drugs to break the transmission of viral infectious diseases. Recent years have witnessed the rapid accumulation of experimentally identified human-virus PPI data, which provides an unprecedented opportunity for bioinformatics studies revolving around human-virus PPIs. In this article, we provide a comprehensive overview of computational studies on human-virus PPIs, especially focusing on the method development for human-virus PPI predictions. We briefly introduce the experimental detection methods and existing database resources of human-virus PPIs, and then discuss the research progress in the development of computational prediction methods. In particular, we elaborate the machine learning-based prediction methods and highlight the need to embrace state-of-the-art deep-learning algorithms and new feature engineering techniques (e.g. the protein embedding technique derived from natural language processing). To further advance the understanding in this research topic, we also outline the practical applications of the human-virus interactome in fundamental biological discovery and new antiviral therapy development.

A novel method for data fusion over Entity-Relation graphs and its application to protein-protein interaction prediction

MOTIVATION

Modern Bioinformatics is facing increasingly complex problems to solve, and we are indeed rapidly approaching an era in which the ability to seamlessly integrate heterogeneous sources of information will be crucial for the scientific progress. Here we present a novel non-linear data fusion framework that generalizes the conventional Matrix Factorization paradigm allowing inference over arbitrary Entity-Relation graphs, and we applied it to the prediction of Protein-Protein Interactions (PPIs). Improving our knowledge of Protein Protein Interaction (PPI) networks at the proteome scale is indeed crucial to understand protein function, physiological and disease states and cell life in general.

RESULTS

We devised three data-fusion based models for the proteome-level prediction of PPIs, and we show that our method outperforms state of the art approaches on common benchmarks. Moreover, we investigate its predictions on newly published PPIs, showing that this new data has a clear shift in its underlying distributions and we thus train and test our models on this extended dataset.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

TWIST1 and chromatin regulatory proteins interact to guide neural crest cell differentiation

Protein interaction is critical molecular regulatory activity underlining cellular functions and precise cell fate choices. Using TWIST1 BioID-proximity-labeling and network propagation analyses, we discovered and characterized a TWIST-chromatin regulatory module (TWIST1-CRM) in the neural crest cells (NCC). Combinatorial perturbation of core members of TWIST1-CRM: TWIST1, CHD7, CHD8, and WHSC1 in cell models and mouse embryos revealed that loss of the function of the regulatory module resulted in abnormal differentiation of NCCs and compromised craniofacial tissue patterning. Following NCC delamination, low level of TWIST1-CRM activity is instrumental to stabilize the early NCC signatures and migratory potential by repressing the neural stem cell programs. High level of TWIST1 module activity at later phases commits the cells to the ectomesenchyme. Our study further revealed the functional interdependency of TWIST1 and potential neurocristopathy factors in NCC development.

Shaping the head and face during development relies on a complex ballet of molecular signals that orchestrates the movement and specialization of various groups of cells. In animals with a backbone for example, neural crest cells (NCCs for short) can march long distances from the developing spine to become some of the tissues that form the skull and cartilage but also the pigment cells and nervous system.

NCCs mature into specific cell types thanks to a complex array of factors which trigger a precise sequence of binary fate decisions at the right time and place. Amongst these factors, the protein TWIST1 can set up a cascade of genetic events that control how NCCs will ultimately form tissues in the head. To do so, the TWIST1 protein interacts with many other molecular actors, many of which are still unknown.

To find some of these partners, Fan et al. studied TWIST1 in the NCCs of mice and cells grown in the lab. The experiments showed that TWIST1 interacted with CHD7, CHD8 and WHSC1, three proteins that help to switch genes on and off, and which contribute to NCCs moving across the head during development. Further work by Fan et al. then revealed that together, these molecular actors are critical for NCCs to form cells that will form facial bones and cartilage, as opposed to becoming neurons. This result helps to show that there is a trade-off between NCCs forming the face or being part of the nervous system.

One in three babies born with a birth defect shows anomalies of the head and face: understanding the exact mechanisms by which NCCs contribute to these structures may help to better predict risks for parents, or to develop new approaches for treatment.

Graph embeddings on gene ontology annotations for protein-protein interaction prediction

Background

Protein–protein interaction (PPI) prediction is an important task towards the understanding of many bioinformatics functions and applications, such as predicting protein functions, gene-disease associations and disease-drug associations. However, many previous PPI prediction researches do not consider missing and spurious interactions inherent in PPI networks. To address these two issues, we define two corresponding tasks, namely missing PPI prediction and spurious PPI prediction, and propose a method that employs graph embeddings that learn vector representations from constructed Gene Ontology Annotation (GOA) graphs and then use embedded vectors to achieve the two tasks. Our method leverages on information from both term–term relations among GO terms and term-protein annotations between GO terms and proteins, and preserves properties of both local and global structural information of the GO annotation graph.

Results

We compare our method with those methods that are based on information content (IC) and one method that is based on word embeddings, with experiments on three PPI datasets from STRING database. Experimental results demonstrate that our method is more effective than those compared methods.

Conclusion

Our experimental results demonstrate the effectiveness of using graph embeddings to learn vector representations from undirected GOA graphs for our defined missing and spurious PPI tasks.

Identification of Candidate Genes Associated with Charcot-Marie-Tooth Disease by Network and Pathway Analysis

Charcot-Marie-Tooth Disease (CMT) is the most common clinical genetic disease of the peripheral nervous system. Although many studies have focused on elucidating the pathogenesis of CMT, few focuses on achieving a systematic analysis of biology to decode the underlying pathological molecular mechanisms and the mechanism of its disease remains to be elucidated. So our study may provide further useful insights into the molecular mechanisms of CMT based on a systematic bioinformatics analysis. In the current study, by reviewing the literatures deposited in PUBMED, we identified 100 genes genetically related to CMT. Then, the functional features of the CMT-related genes were examined by R software and KOBAS, and the selected biological process crosstalk was visualized with the software Cytoscape. Moreover, CMT specific molecular network analysis was conducted by the Molecular Complex Detection (MCODE) Algorithm. The biological function enrichment analysis suggested that myelin sheath, axon, peripheral nervous system, mitochondrial function, various metabolic processes, and autophagy played important roles in CMT development. Aminoacyl-tRNA biosynthesis, metabolic pathways, and vasopressin-regulated water reabsorption were significantly enriched in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway network, suggesting that these pathways may play key roles in CMT occurrence and development. According to the crosstalk, the biological processes could be roughly divided into a correlative module and two separate modules. MCODE clusters showed that in top 3 clusters, 13 of CMT-related genes were included in the network and 30 candidate genes were discovered which might be potentially related to CMT. The study may help to update the new understanding of the pathogenesis of CMT and expand the potential genes of CMT for further exploration.

MASS SPECTROMETRY IN VIROLOGICAL SCIENCES

Virology, as a branch of the life sciences, discovered mass spectrometry (MS) to be the pivotal tool around two decades ago. The technique unveiled the complex network of interactions between the living world of pro- and eukaryotes and viruses, which delivered "a piece of bad news wrapped in protein" as defined by Peter Medawar, Nobel Prize Laureate, in 1960. However, MS is constantly evolving, and novel approaches allow for a better understanding of interactions in this micro- and nanoworld. Currently, we can investigate the interplay between the virus and the cell by analyzing proteomes, interactomes, virus-cell interactions, and search for the compounds that build viral structures. In addition, by using MS, it is possible to look at the cell from the broader perspective and determine the role of viral infection on the scale of the organism, for example, monitoring the crosstalk between infected tissues and the immune system. In such a way, MS became one of the major tools for the modern virology, allowing us to see the infection in the context of the whole cell or the organism. © 2019 John Wiley & Sons Ltd. Mass Spec Rev.

Advancing from protein interactomes and gene co-expression networks towards multi-omics-based composite networks: approaches for predicting and extracting biological knowledge

Prediction of biological interaction networks from single-omics data has been extensively implemented to understand various aspects of biological systems. However, more recently, there is a growing interest in integrating multi-omics datasets for the prediction of interactomes that provide a global view of biological systems with higher descriptive capability, as compared to single omics. In this review, we have discussed various computational approaches implemented to infer and analyze two of the most important and well studied interactomes: protein-protein interaction networks and gene co-expression networks. We have explicitly focused on recent methods and pipelines implemented to infer and extract biologically important information from these interactomes, starting from utilizing single-omics data and then progressing towards multi-omics data. Accordingly, recent examples and case studies are also briefly discussed. Overall, this review will provide a proper understanding of the latest developments in protein and gene network modelling and will also help in extracting practical knowledge from them.

Split Intein-Mediated Protein Ligation for detecting protein-protein interactions and their inhibition

Here, to overcome many limitations accompanying current available methods to detect protein-protein interactions (PPIs), we develop a live cell method called Split Intein-Mediated Protein Ligation (SIMPL). In this approach, bait and prey proteins are respectively fused to an intein N-terminal fragment (IN) and C-terminal fragment (IC) derived from a re-engineered split intein GP41-1. The bait/prey binding reconstitutes the intein, which splices the bait and prey peptides into a single intact protein that can be detected by regular protein detection methods such as Western blot analysis and ELISA, serving as readouts of PPIs. The method is robust and can be applied not only in mammalian cell lines but in animal models such as C. elegans. SIMPL demonstrates high sensitivity and specificity, and enables exploration of PPIs in different cellular compartments and tracking of kinetic interactions. Additionally, we establish a SIMPL ELISA platform that enables high-throughput screening of PPIs and their inhibitors.

Protein-protein interactions are fundamental to the regulation of protein activity and cellular phyisology. Here the authors present Split Intein-Mediated Protein Ligation, which uses bait and prey proteins fused to intein fragments to generate single intact proteins upon interaction.

Interactome of SARS-CoV-2 / nCoV19 modulated host proteins with computationally predicted PPIs

World over, people are looking for solutions to tackle the pandemic coronavirus disease (COVID-19) caused by the virus SARS-CoV-2/nCoV-19. Notable contributions in biomedical field have been characterizing viral genomes, host transcriptomes and proteomes, repurposable drugs and vaccines. In one such study, 332 human proteins targeted by nCoV19 were identified. We expanded this set of host proteins by constructing their protein interactome, including in it not only the known protein-protein interactions (PPIs) but also novel, hitherto unknown PPIs predicted with our High-precision Protein-Protein Interaction Prediction (HiPPIP) model that was shown to be highly accurate. In fact, one of the earliest discoveries made possible by HiPPIP is related to activation of immunity upon viral infection. We found that several interactors of the host proteins are differentially expressed upon viral infection, are related to highly relevant pathways, and that the novel interaction of NUP98 with CHMP5 may activate an antiviral mechanism leading to disruption of viral budding. We are making the interactions available as downloadable files to facilitate future systems biology studies and also on a web-server at http://hagrid.dbmi.pitt.edu/corona that allows not only keyword search but also queries such as "PPIs where one protein is associated with 'virus' and the interactors with 'pulmonary'".

Physiological Tau Interactome in Brain and Its Link to Tauopathies

Alzheimer's disease (AD) and most of the other tauopathies are incurable neurodegenerative diseases with unpleasant symptoms and consequences. The common hallmark of all of these diseases is tau pathology, but its connection with disease progress has not been completely understood so far. Therefore, uncovering novel tau-interacting partners and pathology affected molecular pathways can reveal the causes of diseases as well as potential targets for the development of AD treatment. Despite the large number of known tau-interacting partners, a limited number of studies focused on in vivo tau interactions in disease or healthy conditions are available. Here, we applied an in vivo cross-linking approach, capable of capturing weak and transient protein-protein interactions, to a unique transgenic rat model of progressive tau pathology similar to human AD. We have identified 175 potential novel and known tau-interacting proteins by MALDI-TOF mass spectrometry. Several of the most promising candidates for possible drug development were selected for validation by coimmunoprecipitation and colocalization experiments in animal and cellular models. Three proteins, Baiap2, Gpr37l1, and Nptx1, were confirmed as novel tau-interacting partners, and on the basis of their known functions and implications in neurodegenerative or psychiatric disorders, we proposed their potential role in tau pathology.

Expression of long noncoding RNAs in cancer-associated fibroblasts linked to patient survival in ovarian cancer

Cancer-associated fibroblasts (CAFs) are the most abundant cell type in the tumor microenvironment and are responsible for producing the desmoplastic reaction that is a poor prognostic factor in ovarian cancer. Long non-coding RNAs (lncRNAs) have been shown to play important roles in cancer. However, very little is known about the role of lncRNAs in the tumor microenvironment. We aimed to identify lncRNAs expressed in ovarian CAFs that were associated with patient survival and used computational approaches to predict their function. Increased expression of 9 lncRNAs and decreased expression of 1 lncRNA in ovarian CAFs were found to be associated with poorer overall survival. A "guilt-by-association" approach was used to predict the function of these lncRNAs. In particular, MIR155HG was predicted to play a role in immune response. Further investigation revealed high MIR155HG expression to be associated with higher infiltrates of immune cell subsets. In conclusion, these data indicate expression on several lncRNAs in CAFs are associated with patient survival and are likely to play an important role in regulating CAF function.

Familial globular glial tauopathy linked to MAPT mutations: molecular neuropathology and seeding capacity of a prototypical mixed neuronal and glial tauopathy

Globular glial tauopathy (GGT) is a progressive neurodegenerative disease involving the grey matter and white matter (WM) and characterized by neuronal deposition of hyper-phosphorylated, abnormally conformed, truncated, oligomeric 4Rtau in neurons and in glial cells forming typical globular astrocyte and oligodendrocyte inclusions (GAIs and GOIs, respectively) and coiled bodies. Present studies centre on four genetic GGT cases from two unrelated families bearing the P301T mutation in MAPT and one case of sporadic GGT (sGGT) and one case of GGT linked to MAPT K317M mutation, for comparative purposes. Clinical and neuropathological manifestations and biochemical profiles of phospho-tau are subjected to individual variations in patients carrying the same mutation, even in carriers of the same family, independently of the age of onset, gender, and duration of the disease. Immunohistochemistry, western blotting, transcriptomic, proteomics and phosphoproteomics, and intra-cerebral inoculation of brain homogenates to wild-type (WT) mice were the methods employed. In GGT cases linked to MAPT P301T mutation, astrocyte markers GFAP, ALDH1L1, YKL40 mRNA and protein, GJA1 mRNA, and AQ4 protein are significantly increased; glutamate transporter GLT1 (EAAT2) and glucose transporter (SLC2A1) decreased; mitochondrial pyruvate carrier 1 (MPC1) increased, and mitochondrial uncoupling protein 5 (UCP5) almost absent in GAIs in frontal cortex (FC). Expression of oligodendrocyte markers OLIG1 and OLIG2mRNA, and myelin-related genes MBP, PLP1, CNP, MAG, MAL, MOG, and MOBP are significantly decreased in WM; CNPase, PLP1, and MBP antibodies reveal reduction and disruption of myelinated fibres; and SMI31 antibodies mark axonal damage in the WM. Altered expression of AQ4, GLUC-t, and GLT-1 is also observed in sGGT and in GGT linked to MAPT K317M mutation. These alterations point to primary astrogliopathy and oligodendrogliopathy in GGT. In addition, GGT linked to MAPT P301T mutation proteotypes unveil a proteostatic imbalance due to widespread (phospho)proteomic dearrangement in the FC and WM, triggering a disruption of neuron projection morphogenesis and synaptic transmission. Identification of hyper-phosphorylation of variegated proteins calls into question the concept of phospho-tau-only alteration in the pathogenesis of GGT. Finally, unilateral inoculation of sarkosyl-insoluble fractions of GGT homogenates from GGT linked to MAPT P301T, sGGT, and GGT linked to MAPT K317M mutation in the hippocampus, corpus callosum, or caudate/putamen in wild-type mice produces seeding, and time- and region-dependent spreading of phosphorylated, non-oligomeric, and non-truncated 4Rtau and 3Rtau, without GAIs and GOIs but only of coiled bodies. These experiments prove that host tau strains are important in the modulation of cellular vulnerability and phenotypes of phospho-tau aggregates.

PlaPPISite: a comprehensive resource for plant protein-protein interaction sites

BACKGROUND

Protein-protein interactions (PPIs) play very important roles in diverse biological processes. Experimentally validated or predicted PPI data have become increasingly available in diverse plant species. To further explore the biological functions of PPIs, understanding the interaction details of plant PPIs (e.g., the 3D structural contexts of interaction sites) is necessary. By integrating bioinformatics algorithms, interaction details can be annotated at different levels and then compiled into user-friendly databases. In our previous study, we developed AraPPISite, which aimed to provide interaction site information for PPIs in the model plant Arabidopsis thaliana. Considering that the application of AraPPISite is limited to one species, it is very natural that AraPPISite should be evolved into a new database that can provide interaction details of PPIs in multiple plants.

DESCRIPTION

PlaPPISite (http://zzdlab.com/plappisite/index.php) is a comprehensive, high-coverage and interaction details-oriented database for 13 plant interactomes. In addition to collecting 121 experimentally verified structures of protein complexes, the complex structures of experimental/predicted PPIs in the 13 plants were also constructed, and the corresponding interaction sites were annotated. For the PPIs whose 3D structures could not be modelled, the associated domain-domain interactions (DDIs) and domain-motif interactions (DMIs) were inferred. To facilitate the reliability assessment of predicted PPIs, the source species of interolog templates, GO annotations, subcellular localizations and gene expression similarities are also provided. JavaScript packages were employed to visualize structures of protein complexes, protein interaction sites and protein interaction networks. We also developed an online tool for homology modelling and protein interaction site annotation of protein complexes. All data contained in PlaPPISite are also freely available on the Download page.

CONCLUSION

PlaPPISite provides the plant research community with an easy-to-use and comprehensive data resource for the search and analysis of protein interaction details from the 13 important plant species.

Informed Use of Protein-Protein Interaction Data: A Focus on the Integrated Interactions Database (IID)

Protein-protein interaction data is fundamental in molecular biology, and numerous online databases provide access to this data. However, the huge quantity, complexity, and variety of PPI data can be overwhelming, and rather than helping to address research problems, the data may add to their complexity and reduce interpretability. This protocol focuses on solutions for some of the main challenges of using PPI data, including accessing data, ensuring relevance by integrating useful annotations, and improving interpretability. While the issues are generic, we highlight how to perform such operations using Integrated Interactions Database (IID; http://ophid.utoronto.ca/iid ).

Salivary Scavenger and Agglutinin (SALSA) Is Expressed in Mucosal Epithelial Cells and Decreased in Bronchial Epithelium of Asthmatic Horses

The Salivary Scavenger and Agglutinin (SALSA) protein is an innate immune protein with various alleged functions, including the regulation of inflammation and tissue remodeling. Transcriptomic studies of severe equine asthma (SEA) showed downregulation of the gene encoding SALSA in bronchial epithelium of asthmatic compared to non-asthmatic horses. This study aimed to characterize expression of SALSA in equine tissues by immunohistochemistry (IHC), corroborate potential differences in epithelial gene expression between asthmatic and non-asthmatic horses, and assess the structure of equine SALSA. An antibody against SALSA was validated through immunoprecipitation followed by mass spectrometry and Western blotting to recognize the equine protein. This antibody was applied to tissue microarrays (TMAs) containing 22 tissues each from four horses. A quantitative PCR assay was designed to compare gene expression for SALSA between six asthmatic and six non-asthmatic horses, before and after an asthmatic challenge, using cDNA from endoscopic bronchial biopsies as source material. The SALSA gene from bronchial cDNA samples of 10 horses, was amplified and sequenced, and translated to characterize the protein structure. Immunostaining for SALSA was detected in the mucosal surfaces of the trachea, bronchi, bronchioles, stomach, small intestine and bladder, in pancreatic and salivary gland ducts, and in uterine gland epithelium. Staining was strongest in the duodenum, and the intercalated ducts and Demilune cells of the salivary gland. SALSA was concentrated in the apical regions of the epithelial cell cytoplasm, suggestive of a secreted protein. Gene expression was significantly lower (p = 0.031) in asthmatic compared to non-asthmatic horses. Equine SALSA consisted of three to five scavenger receptor cysteine-rich (SRCR) domains, two CUB (C1r/C1s, uegf, bmp-1) domains and one Zona Pellucida domain. These domains mediate the binding of ligands involved in innate immunity. Varying numbers of SRCR domains were identified in different horses, indicating different isoforms. In summary, equine SALSA has a predilection for mucosal sites, has multiple isoforms, and has decreased expression in asthmatic horses, suggesting alterations in innate immunity in equine asthma.

An Ensemble Classifier to Predict Protein-Protein Interactions by Combining PSSM-based Evolutionary Information with Local Binary Pattern Model

Protein plays a critical role in the regulation of biological cell functions. Among them, whether proteins interact with each other has become a fundamental problem, because proteins usually perform their functions by interacting with other proteins. Although a large amount of protein-protein interactions (PPIs) data has been produced by high-throughput biotechnology, the disadvantage of biological experimental technique is time-consuming and costly. Thus, computational methods for predicting protein interactions have become a research hot spot. In this research, we propose an efficient computational method that combines Rotation Forest (RF) classifier with Local Binary Pattern (LBP) feature extraction method to predict PPIs from the perspective of Position-Specific Scoring Matrix (PSSM). The proposed method has achieved superior performance in predicting Yeast, Human, and H. pylori datasets with average accuracies of 92.12%, 96.21%, and 86.59%, respectively. In addition, we also evaluated the performance of the proposed method on the four independent datasets of C. elegans, H. pylori, H. sapiens, and M. musculus datasets. These obtained experimental results fully prove that our model has good feasibility and robustness in predicting PPIs.

Membrane protein-regulated networks across human cancers

Alterations in membrane proteins (MPs) and their regulated pathways have been established as cancer hallmarks and extensively targeted in clinical applications. However, the analysis of MP-interacting proteins and downstream pathways across human malignancies remains challenging. Here, we present a systematically integrated method to generate a resource of cancer membrane protein-regulated networks (CaMPNets), containing 63,746 high-confidence protein-protein interactions (PPIs) for 1962 MPs, using expression profiles from 5922 tumors with overall survival outcomes across 15 human cancers. Comprehensive analysis of CaMPNets links MP partner communities and regulated pathways to provide MP-based gene sets for identifying prognostic biomarkers and druggable targets. For example, we identify CHRNA9 with 12 PPIs (e.g., ERBB2) can be a therapeutic target and find its anti-metastasis agent, bupropion, for treatment in nicotine-induced breast cancer. This resource is a study to systematically integrate MP interactions, genomics, and clinical outcomes for helping illuminate cancer-wide atlas and prognostic landscapes in tumor homo/heterogeneity.

Automated feature engineering improves prediction of protein-protein interactions

Over the last decade, various machine learning (ML) and statistical approaches for protein-protein interaction (PPI) predictions have been developed to help annotating functional interactions among proteins, essential for our system-level understanding of life. Efficient ML approaches require informative and non-redundant features. In this paper, we introduce novel types of expert-crafted sequence, evolutionary and graph features and apply automatic feature engineering to further expand feature space to improve predictive modeling. The two-step automatic feature-engineering process encompasses the hybrid method for feature generation and unsupervised feature selection, followed by supervised feature selection through a genetic algorithm (GA). The optimization of both steps allows the feature-engineering procedure to operate on a large transformed feature space with no considerable computational cost and to efficiently provide newly engineered features. Based on GA and correlation filtering, we developed a stacking algorithm GA-STACK for automatic ensembling of different ML algorithms to improve prediction performance. We introduced a unified method, HP-GAS, for the prediction of human PPIs, which incorporates GA-STACK and rests on both expert-crafted and 40% of newly engineered features. The extensive cross validation and comparison with the state-of-the-art methods showed that HP-GAS represents currently the most efficient method for proteome-wide forecasting of protein interactions, with prediction efficacy of 0.93 AUC and 0.85 accuracy. We implemented the HP-GAS method as a free standalone application which is a time-efficient and easy-to-use tool. HP-GAS software with supplementary data can be downloaded from: http://www.vinca.rs/180/tools/HP-GAS.php .

The olfactory bulb proteotype differs across frontotemporal dementia spectrum

Mild olfactory dysfunction has been observed in frontotemporal dementias (FTD). However, the underlying molecular mechanisms associated to this deficit are poorly understood. We applied quantitative proteomics to analyze pathological effects on the olfactory bulb (OB) from progressive supranuclear palsy (PSP) and frontotemporal lobar degeneration (FTLD-TDP43) subjects respect to elderly non-FTD group. Our data revealed: i) a mitochondrial and calcium homeostasis impairment in PSP and ii) a disruption of protein synthesis and vesicle trafficking in FTLD-TDP43. Although differential OB proteomes clearly differ between both FTD phenotypes, functional analyses pointed out an imbalance in survival signaling in both pathologies. A common alteration of olfactory mitogen-activated protein kinases (MAPKs), calcium/calmodulin dependent protein kinase II (CAMKII), and protein kinase C (PKC) signaling pathways was observed in PSP and FTLD subjects. In contrast, a specific shut off in mitogen-activated protein kinase kinase 4 (SEK1/MKK4)/stress-activated protein kinase (SAPK) axis was exclusively observed in PSP, whereas a specific phosphoinositide-dependent protein kinase 1 (PDK1) inactivation was observed in FTLD-TDP43. In summary, our data contribute to a better understanding of the molecular mechanisms that are modulated in PSP and FTLD-TDP43 at olfactory level, highlighting cross-disease similarities and differences in the regulation of survival pathways across FTD spectrum. SIGNIFICANCE: This work reflects differential olfactory molecular disarrangements in PSP and FTLD-TDP43, two clinically similar FTD disorders, but with different neuropathological signature. Besides FTDs present mild olfactory dysfunction, our data provide basic information for understanding the implication of the OB in the pathophysiology of FTDs.