Protein-protein Interaction Data Research Articles

Proteostasis networks in aging: novel insights from text-mining approaches

Aging is a topic of paramount importance in an increasingly elderly society and has been the focus of extensive research. Protein homeostasis (proteostasis) decline is a hallmark in aging and several age-related diseases, but which specific proteins and mechanisms are involved in proteostasis (de)regulation during the aging process remain largely unknown. Here, we used different text-mining tools complemented with protein–protein interaction data to address this complex topic. Analysis of the integrated protein interaction networks identified novel proteins and pathways associated to proteostasis mechanisms and aging or age-related disorders, indicating that this approach is useful to identify previously unknown links and for retrieving information of potential novel biomarkers or therapeutic targets.

Computational modeling of signal transduction networks without kinetic parameters: Petri net approaches.

More and more computational techniques have been applied to model biological systems, especially signaling pathways in medical systems. Due to the large number of experimental data driven by high-throughput technologies, new computational concepts have been developed. Nevertheless, often the necessary kinetic data cannot be determined in sufficient number and quality because of experimental complexity or ethical reasons. At the same time, the number of qualitative data drastically increased, for example, gene expression data, protein-protein interaction data, and imaging data. Especially for large-scale models, the application of kinetic modeling techniques can fail. On the other hand, many large-scale models have been constructed applying qualitative and semiquantitative techniques, for example, logical models or Petri net models. These techniques make it possible to explore system's dynamics without knowing kinetic parameters. Here, we summarize the work of the last 10 years for modeling signal transduction pathways in medical applications applying Petri net formalism. We focus on analysis techniques based on system's invariants without any kinetic parameters and show predictions of all signaling pathways of the system. We start with an intuitive introduction into Petri nets and system's invariants. We illustrate the main concepts using the tumor necrosis factor receptor 1 (TNFR1)-induced nuclear factor κ-light-chain-enhancer of activated B cells (NF-κB) pathway as a case study. Summarizing recent models, we discuss the advantages and challenges of Petri net applications to medical signaling systems. In addition, we provide exemplarily interesting Petri net applications to model signaling in medical systems of the last years that use the well-known stochastic and kinetic concepts developed about 50 years ago.

Refining Protein Interaction Network for Identifying Essential Proteins

Aim: The study aimed to reconstruct the protein-protein interaction network for the identification of essential proteins. Background: In a living organism, essential proteins play an indispensable role in its survival and development. Hence, how to identify essential proteins from the protein interaction network (PIN) has become a hot topic in the field of bioinformatics. However, existing methods’ accuracies for identifying essential proteins are still limited due to the false positives of the protein-protein interaction data. Objective: The objective of the study was to propose an efficient algorithm for the reconstruction of a protein-protein interaction network. Methods: In this paper, a method for the refinement of PIN based on three kinds of biological data (subcellular localization data, protein complex data, and gene expression data) is proposed. Through evaluating each interaction within the original PIN, a refined clean PIN could be obtained. To verify the effectiveness of the refined PIN for the identification of essential proteins, we applied eight networkbased essential protein discovery methods (DC, BC, CC, LC, HC, SC, LAC, and NC) to it. Result: Based on the obtained experimental results, we demonstrated that the precision for identifying essential proteins could be greatly improved by refining the original PIN using our method. Conclusion: Our method could effectively enhance the protein-protein interaction network and improve the accuracy of identifying essential proteins. In the future, we plan to integrate more biological information to enhance our refinement method and apply it to more species and more PIN-based discovery tasks, like the identification of protein complexes or functional modules.

Normalized L3-based link prediction in protein–protein interaction networks

BackgroundProtein–protein interaction (PPI) data is an important type of data used in functional genomics. However, high-throughput experiments are often insufficient to complete the PPI interactome of different organisms. Computational techniques are thus used to infer missing data, with link prediction being one such approach that uses the structure of the network of PPIs known so far to identify non-edges whose addition to the network would make it more sound, according to some underlying assumptions. Recently, a new idea called the L3 principle introduced biological motivation into PPI link predictions, yielding predictors that are superior to general-purpose link predictors for complex networks. Interestingly, the L3 principle can be interpreted in another way, so that other signatures of PPI networks can also be characterized for PPI predictions. This alternative interpretation uncovers candidate PPIs that the current L3-based link predictors may not be able to fully capture, underutilizing the L3 principle.ResultsIn this article, we propose a formulation of link predictors that we call NormalizedL3 (L3N) which addresses certain missing elements within L3 predictors in the perspective of network modeling. Our computational validations show that the L3N predictors are able to find missing PPIs more accurately (in terms of true positives among the predicted PPIs) than the previously proposed methods on several datasets from the literature, including BioGRID, STRING, MINT, and HuRI, at the cost of using more computation time in some of the cases. In addition, we found that L3-based link predictors (including L3N) ranked a different pool of PPIs higher than the general-purpose link predictors did. This suggests that different types of PPIs can be predicted based on different topological assumptions, and that even better PPI link predictors may be obtained in the future by improved network modeling.

BioPlexR and BioPlexPy: integrated data products for the analysis of human protein interactions.

The BioPlex project has created two proteome scale, cell-line-specific protein-protein interaction (PPI) networks: the first in 293T cells, including 120k interactions among 15k proteins; and the second in HCT116 cells, including 70k interactions between 10k proteins. Here, we describe programmatic access to the BioPlex PPI networks and integration with related resources from within R and Python. Besides PPI networks for 293T and HCT116 cells, this includes access to CORUM protein complex data, PFAM protein domain data, PDB protein structures, and transcriptome and proteome data for the two cell lines. The implemented functionality serves as a basis for integrative downstream analysis of BioPlex PPI data with domain-specific R and Python packages, including efficient execution of maximum scoring sub-network analysis, protein domain-domain association analysis, mapping of PPIs onto 3D protein structures and analysis of BioPlex PPIs at the interface of transcriptomic and proteomic data. The BioPlex R package is available from Bioconductor (bioconductor.org/packages/BioPlex), and the BioPlex Python package is available from PyPI (pypi.org/project/bioplexpy). Applications and downstream analyses are available from GitHub (github.com/ccb-hms/BioPlexAnalysis).

Proteomics investigation of human sera for determination of postoperative indicators of pulmonary cystic echinococcosis

BackgroundCystic echinococcosis (CE)/hydatidosis is an important zoonotic parasitic disease caused by the larval stage of Echinococcus granulosus. The disease is a major health problem all over the world. Finding specific and sensitive biomarkers for follow-up of CE in patients after surgery is essential. Using proteomics methods, the present study aimed to evaluate post-surgical treatment by finding probable biomarker/s in the serum of human lungs CE.MethodsA total of 24 human sera were tested. These sera included eight confirmed lung/s CE patients sera before surgery (BS), eight sera 12 months post-surgery (12MPS) as well as eight control sera from healthy people. Proteomics methods including 2DE and LC–MS/MS were performed on the specimens followed by bioinformatics analysis. Differentially expressed proteins (DEP) were detected and, separately integrated with protein–protein interaction (PPI) data to construct the PPI network.ResultsA total of 171 protein spots were detected in three groups including BS, 12MPS, and control groups; of which a total of 106 DEP have been expressed based on fold changes > = 2 and p-value < 0.05. More analysis was performed and a total of 10 protein spots were selected for identification by mass spectrometry showing the following proteins: APOA1, BGN, SPP2, EAF1, ACOXL, MRPL55, MCTP2, SEPTIN1, B4GALNT1, and ZNF843. Based on centrality parameters of the PPI network (degree and betweenness) five Hub-bottlenecks proteins with significant centrality values were found including APOA1, BGN, SPP2, EAF1, and ACOXL.ConclusionThis study showed five proteins as hub-bottleneck proteins; of which APOA1 was more prominent. It can be concluded that a change in expression of this protein in patients’ sera could be used as an indicator tool for the achievement of lungs CE surgical therapy.

Search, Retrieve, Visualize, and Analyze Protein-Protein Interactions from Multiple Databases: A Guide for Experimental Biologists.

Functional annotation is lacking for over half of the proteins encoded in genomes and model or representative organisms are not an exception to this trend. One of the popular ways of assigning putative functions to uncharacterized proteins is based on the functions of well-characterized proteins that physically interact with them, i.e., guilt-by-association or functional context approach. In the last two decades, several powerful experimental and computational techniques have been used to determine protein-protein interactions (PPIs) at genome level and are made available through many public databases. The PPI data are often complex and heterogeneously represented across databases posing unique challenges in retrieving, integrating, and analyzing the data even for trained computational biologists, the end users-experimental biologists often struggle to work around the data for the protein of their interests. This chapter provides stepwise protocols to import interaction network of the protein of interest in Cytoscape using PSICQUIC, stringApp, and IntAct App. These are next-generation applications that import PPI from multiple databases/resources and provide seamless functions to study the protein of interest and its functional context directly in Cytoscape.

Protein network analysis links the NSL complex to Parkinson's disease via mitochondrial and nuclear biology.

Whilst the majority of Parkinson's Disease (PD) cases are sporadic, much of our understanding of the pathophysiological basis of the disease can be traced back to the study of rare, monogenic forms of PD. In the past decade, the availability of genome-wide association studies (GWAS) has facilitated a shift in focus, toward identifying common risk variants conferring increased risk of developing PD across the population. A recent mitophagy screening assay of GWAS candidates has functionally implicated the non-specific lethal (NSL) complex in the regulation of PINK1-mitophagy. Here, a bioinformatics approach has been taken to investigate the proteome of the NSL complex, to unpick its relevance to PD pathogenesis. The NSL interactome has been built, using 3 online tools: PINOT, HIPPIE and MIST, to mine curated, literature-derived protein-protein interaction (PPI) data. We built (i) the 'mitochondrial' NSL interactome exploring its relevance to PD genetics and (ii) the PD-oriented NSL interactome to uncover biological pathways underpinning the NSL/PD association. In this study, we find the mitochondrial NSL interactome to be significantly enriched for the protein products of PD-associated genes, including the Mendelian PD genes LRRK2 and VPS35. In addition, we find nuclear processes to be amongst those most significantly enriched within the PD-associated NSL interactome. These findings strengthen the role of the NSL complex in sporadic and familial PD, mediated by both its mitochondrial and nuclear functions.

Protein-Protein Interaction Network Exploration Using Cytoscape.

As the protein-protein interaction (PPI) data increase exponentially, the development and usage of computational methods to analyze these datasets have become a new research horizon in systems biology. The PPI network analysis and visualization can help identify functional modules of the network, pathway genes involved in common cellular functions, and functional annotations of novel genes. Currently, a variety of tools are available for network graph visualization and analysis. Cytoscape, an open-source software tool, is one of them. It provides an interactive visualization interface along with other core features to import, navigate, filter, cluster, search, and export networks. It comes with hundreds of in-built Apps in App Manager to resolve research questions related to network visualization and integration. This chapter aims to illustrate the Cytoscape application to visualize and analyze the PPI network using Arabidopsis interactome-1 main (AI-1MAIN) PPI network dataset from Plant Interactome Database.

Using PlaPPISite to Predict and Analyze Plant Protein-Protein Interaction Sites.

Proteome-wide characterization of protein-protein interactions (PPIs) is crucial to understand the functional roles of protein machinery within cells systematically. With the accumulation of PPI data in different plants, the interaction details of binary PPIs, such as the three-dimensional (3D) structural contexts of interaction sites/interfaces, are urgently demanded. To meet this requirement, we have developed a comprehensive and easy-to-use database called PlaPPISite ( http://zzdlab.com/plappisite/index.php ) to present interaction details for 13 plant interactomes. Here, we provide a clear guide on how to search and view protein interaction details through the PlaPPISite database. Firstly, the running environment of our database is introduced. Secondly, the input file format is briefly introduced. Moreover, we discussed which information related to interaction sites can be achieved through several examples. In addition, some notes about PlaPPISite are also provided. More importantly, we would like to emphasize the importance of interaction site information in plant systems biology through this user guide of PlaPPISite. In particular, the easily accessible 3D structures of PPIs in the coming post-AlphaFold2 era will definitely boost the application of plant interactome to decipher the molecular mechanisms of many fundamental biological issues.

Drought Stress-Related Gene Identification in Rice by Random Walk with Restart on Multiplex Biological Networks

Drought stress-related gene identification is vital in revealing the drought resistance mechanisms underlying rice and for cultivating rice-resistant varieties. Traditional methods, such as Genome-Wide Association Studies (GWAS), usually identify hundreds of candidate stress genes, and further validation by biological experiements is then time-consuming and laborious. However, computational and prioritization methods can effectively reduce the number of candidate stress genes. This study introduces a random walk with restart algorithm (RWR), a state-of-the-art guilt-by-association method, to operate on rice multiplex biological networks. It explores the physical and functional interactions between biological molecules at different levels and prioritizes a set of potential genes. Firstly, we integrated a Protein–Protein Interaction (PPI) network, constructed by multiple protein interaction data, with a gene coexpression network into a multiplex network. Then, we implemented the RWR on multiplex networks (RWR-M) with known drought stress genes as seed nodes to identify potential drought stress-related genes. Finally, we conducted association analysis between the potential genes and the known drought stress genes. Thirteen genes were identified as rice drought stress-related genes, five of which have been reported in the recent literature to be involved in drought stress resistance mechanisms.

Konnect2prot: a web application to explore the protein properties in a functional protein-protein interaction network.

The regulation of proteins governs the biological processes and functions and, therefore, the organisms' phenotype. So there is an unmet need for a systematic tool for identifying the proteins that play a crucial role in information processing in a protein-protein interaction (PPI) network. However, the current protein databases and web servers still lag behind to provide an end-to-end pipeline that can leverage the topological understanding of a context-specific PPI network to identify the influential spreaders. Addressing this, we developed a web application, 'konnect2prot' (k2p), which can generate context-specific directional PPI network from the input proteins and detect their biological and topological importance in the network. We pooled together a large amount of ontological knowledge, parsed it down into a functional network, and gained insight into the molecular underpinnings of the disease development by creating a one-stop junction for PPI data. k2p contains both local and global information about a protein, such as protein class, disease mutations, ligands and PDB structure, enriched processes and pathways, multi-disease interactome and hubs and bottlenecks in the directional network. It also identifies spreaders in the network and maps them to disease hallmarks to determine whether they can affect the disease state or not. konnect2prot is freely accessible using the link https://konnect2prot.thsti.in. The code repository is https://github.com/samrat-lab/k2p_bioinfo-2022.

Integrated analysis of transcriptomic and protein-protein interaction data reveals cadmium stress response in Geobacter sulfurreducens

Bacteria have evolved several mechanisms to resist Cd toxicity, which are crucial for Cd detoxication and have the potential to be used for bioremediation of Cd. Geobacter species are widely found in anaerobic environments and play important roles in natural biogeochemical cycles. However, the transcriptomic response of Geobacter sulfurreducens under Cd stress have not been fully elucidated. Through integrated analysis of transcriptomic and protein-protein interaction (PPI) data, we uncovered a global view of mRNA changes in Cd-induced cellular processes in this study. We identified 182 differentially expressed genes (|log2(fold change)| > 1, adjusted P < 0.05) in G. sulfurreducens exposed to 0.1 mM CdCl2 using RNA sequencing (RNA-seq). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed that CdCl2 significantly affected sulfur compound metabolic processes. In addition, through PPI network analysis, hub genes related to molecular chaperones were identified to play important role in Cd stress response. We also identified a Cd-responsive transcriptional regulator ArsR2 (coded by GSU2149) and verified the function of ArsR2-ParsR2 regulatory circuit in Escherichia coli. This study provides new insight into Cd stress response in G. sulfurreducens, and identified a potential sensor element for Cd detection.

New insights into extracellular and intracellular redox status in COVID-19 patients

BackgroundThe imbalance of redox homeostasis induces hyper-inflammation in viral infections. In this study, we explored the redox system signature in response to SARS-COV-2 infection and examined the status of these extracellular and intracellular signatures in COVID-19 patients. MethodThe multi-level network was constructed using multi-level data of oxidative stress-related biological processes, protein-protein interactions, transcription factors, and co-expression coefficients obtained from GSE164805, which included gene expression profiles of peripheral blood mononuclear cells (PBMCs) from COVID-19 patients and healthy controls. Top genes were designated based on the degree and closeness centralities. The expression of high-ranked genes was evaluated in PBMCs and nasopharyngeal (NP) samples of 30 COVID-19 patients and 30 healthy controls. The intracellular levels of GSH and ROS/O2• − and extracellular oxidative stress markers were assayed in PBMCs and plasma samples by flow cytometry and ELISA. ELISA results were applied to construct a classification model using logistic regression to differentiate COVID-19 patients from healthy controls. ResultsCAT, NFE2L2, SOD1, SOD2 and CYBB were 5 top genes in the network analysis. The expression of these genes and intracellular levels of ROS/O2• − were increased in PBMCs of COVID-19 patients while the GSH level decreased. The expression of high-ranked genes was lower in NP samples of COVID-19 patients compared to control group. The activity of extracellular enzymes CAT and SOD, and the total oxidant status (TOS) level were increased in plasma samples of COVID-19 patients. Also, the 2-marker panel of CAT and TOS and 3-marker panel showed the best performance. ConclusionSARS-COV-2 disrupts the redox equilibrium in immune cells and the upper respiratory tract, leading to exacerbated inflammation and increased replication and entrance of SARS-COV-2 into host cells. Furthermore, utilizing markers of oxidative stress as a complementary validation to discriminate COVID-19 from healthy controls, seems promising.

Analyzing the multi-target pharmacological mechanism of folium Artemisia argyi acting on breast cancer: a network pharmacology approach.

Folium Artemisia argyi (FAA) is a traditional Chinese herbal medicine that is widely used in the clinic. However, the underlying mechanisms of its anticancer effects have not been fully elucidated. In this study, we applied a network pharmacology approach to identify the potential mechanisms of FAA against breast cancer. To be specific, we screened the active ingredients and potential targets of the FAA through the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. Meanwhile, we employed the oral bioavailability (OB) and drug-likeness (DL) to search for potential bioactive compounds of FAA. Breast cancer-related target genes data were gathered from the GeneCards and Online Mendelian Inheritance in Man (OMIM) databases, and the protein-protein interaction (PPI) data were acquired from the Search Tool for the Retrieval of Interacting Genes (STRING) database. In addition, we constructed the network and performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway Enrichment Analysis. We obtained a total of nine active ingredients and 236 potential targets from FAA to construct a network, which showed that quercetin served as the major ingredient in FAA. AKT1 (RAC-alpha serine/threonine-protein kinase), MYC (Myc proto-oncogene protein), CASP3 (Caspase-3), EGFR (Epidermal growth factor receptor), JUN (Transcription factor AP-1), CCND1 (G1/S-specific cyclin-D1), VEGFA (Vascular endothelial growth factor A), ESR1 (Estrogen receptor), MAPK1 (Mitogen-activated protein kinase 1), and EGF (pro-epidermal growth factor) were identified as key targets of FAA in the treatment of breast cancer. The PPI cluster demonstrated that AKT1 was the seed in this cluster, indicating that AKT1 played a crucial role in connecting other nodes in the PPI network. This enrichment demonstrated that FAA was highly related to signal transduction, endocrine system, replication and repair, as well as cell growth and death. The enrichment results also verified that the underlying mechanisms of FAA against breast cancer might be attributed to the coordinated regulation of several cancer-related pathways, such as the MAPK and mammalian target of rapamycin (mTOR) signaling pathways, among others. This study identified the potential targets and pathways of FAA in the treatment of breast cancer using a network pharmacology approach, and systematically elucidated the mechanisms of FAA in the treatment of breast cancer.

Recent developments of sequence-based prediction of protein-protein interactions.

The identification of protein-protein interactions (PPIs) can lead to a better understanding of cellular functions and biological processes of proteins and contribute to the design of drugs to target disease-causing PPIs. In addition, targeting host-pathogen PPIs is useful for elucidating infection mechanisms. Although several experimental methods have been used to identify PPIs, these methods can yet to draw complete PPI networks. Hence, computational techniques are increasingly required for the prediction of potential PPIs, which have never been seen experimentally. Recent high-performance sequence-based methods have contributed to the construction of PPI networks and the elucidation of pathogenetic mechanisms in specific diseases. However, the usefulness of these methods depends on the quality and quantity of training data of PPIs. In this brief review, we introduce currently available PPI databases and recent sequence-based methods for predicting PPIs. Also, we discuss key issues in this field and present future perspectives of the sequence-based PPI predictions.

Genomic profiling and network-level understanding uncover the potential genes and the pathways in hepatocellular carcinoma.

Data integration with phenotypes such as gene expression, pathways or function, and protein-protein interactions data has proven to be a highly promising technique for improving human complex diseases, particularly cancer patient outcome prediction. Hepatocellular carcinoma is one of the most prevalent cancers, and the most common cause is chronic HBV and HCV infection, which is linked to the majority of cases, and HBV and HCV play a role in multistep carcinogenesis progression. We examined the list of known hepatocellular carcinoma biomarkers with the publicly available expression profile dataset of hepatocellular carcinoma infected with HCV from day 1 to day 10 in this study. The study covers an overexpression pattern for the selected biomarkers in clinical hepatocellular carcinoma patients, a combined investigation of these biomarkers with the gathered temporal dataset, temporal expression profiling changes, and temporal pathway enrichment following HCV infection. Following a temporal analysis, it was discovered that the early stages of HCV infection tend to be more harmful in terms of expression shifting patterns, and that there is no significant change after that, followed by a set of genes that are consistently altered. PI3K, cAMP, TGF, TNF, Rap1, NF-kB, Apoptosis, Longevity regulating pathway, signaling pathways regulating pluripotency of stem cells, Cytokine-cytokine receptor interaction, p53 signaling, Wnt signaling, Toll-like receptor signaling, and Hippo signaling pathways are just a few of the most commonly enriched pathways. The majority of these pathways are well-known for their roles in the immune system, infection and inflammation, and human illnesses like cancer. We also find that ADCY8, MYC, PTK2, CTNNB1, TP53, RB1, PRKCA, TCF7L2, PAK1, ITPR2, CYP3A4, UGT1A6, GCK, and FGFR2/3 appear to be among the prominent genes based on the networks of genes and pathways based on the copy number alterations, mutations, and structural variants study.

Protein function annotation based on heterogeneous biological networks

BackgroundAccurate annotation of protein function is the key to understanding life at the molecular level and has great implications for biomedicine and pharmaceuticals. The rapid developments of high-throughput technologies have generated huge amounts of protein–protein interaction (PPI) data, which prompts the emergence of computational methods to determine protein function. Plagued by errors and noises hidden in PPI data, these computational methods have undertaken to focus on the prediction of functions by integrating the topology of protein interaction networks and multi-source biological data. Despite effective improvement of these computational methods, it is still challenging to build a suitable network model for integrating multiplex biological data.ResultsIn this paper, we constructed a heterogeneous biological network by initially integrating original protein interaction networks, protein-domain association data and protein complexes. To prove the effectiveness of the heterogeneous biological network, we applied the propagation algorithm on this network, and proposed a novel iterative model, named Propagate on Heterogeneous Biological Networks (PHN) to score and rank functions in descending order from all functional partners, Finally, we picked out top L of these predicted functions as candidates to annotate the target protein. Our comprehensive experimental results demonstrated that PHN outperformed seven other competing approaches using cross-validation. Experimental results indicated that PHN performs significantly better than competing methods and improves the Area Under the Receiver-Operating Curve (AUROC) in Biological Process (BP), Molecular Function (MF) and Cellular Components (CC) by no less than 33%, 15% and 28%, respectively.ConclusionsWe demonstrated that integrating multi-source data into a heterogeneous biological network can preserve the complex relationship among multiplex biological data and improve the prediction accuracy of protein function by getting rid of the constraints of errors in PPI networks effectively. PHN, our proposed method, is effective for protein function prediction.

Exploring the pharmacological mechanism of Shengjing capsule on male infertility by a network pharmacology approach

BackgroundShengjing capsule (SJC) is a traditional Chinese medicine (TCM) and has gained widespread clinical application for the treatment of male infertility (MI). However, the pharmacological mechanism of SJC against MI remains vague to date.MethodThe active ingredients of SJC and their targets were identified from the database, and MI-related genes were retrieved from several databases. Protein–protein interaction (PPI) data were obtained to construct the PPI networks. The candidate targets of SJC against MI were identified through topological analysis of the PPI network. Functional enrichment analysis of candidate targets was performed, and the key target genes were identified from the gene-pathway network.ResultsWe identified 154 active ingredients and 314 human targets of SJC, as well as 564 MI-related genes. Eight pharmacological network diagrams illustrating the interactions among herbs, active ingredients, targets, and pathways, were constructed. The four dominating network maps included a compound-target network of SJC, a compound-anti-MI targets network, a candidate targets PPI network, a pathway-gene network, and a drug-key compounds-hub targets-pathways network. Systematic analysis indicated that the targets of SJC in the treatment of MI mainly involved RPS6, MAPK1, MAPK3, MDM2, and DDX5. Pathway enrichment analysis showed that SJC had the potential to impact multiple biological pathways, such as cancer-related pathways, viral/bacterial infection-related pathways, and signal transduction-related pathways.ConclusionOur results preliminarily revealed the pharmacological basis and molecular mechanism SJC in treating MI, but further experimental research is required to verify these findings.

Integrated multi-omics reveals novel microbe-host lipid metabolism and immune interactions in the donkey hindgut.

Evidence has shown that gut microbiota play a key role in host metabolism and health; however, little is known about the microbial community in the donkey hindgut as well as the interactions that occur between gut microbes and the host. This study aimed to explore the gut microbiome differences by analyzing the microbial community and differentially expressed genes (DEGs) related to lipid metabolism and the immune system along the donkey hindgut. The hindgut tissues (cecum, ventral colon, and dorsal colon) were separated, and the contents of each section were collected from six male donkeys for multi-omics analysis. There were significant differences in terms of dominant bacteria among the three sections, especially between the cecum and dorsal colon sites. For instance, species belonging to Prevotella and Treponema were most abundant in the cecum, while the Clostridiales_bacterium, Streptococcus_equinus, Ruminococcaceae_bacterium, etc., were more abundant in the dorsal colon. Apart from propionate, the concentrations of acetate, isobutyrate, valerate and isovalerate were all lower in the cecum than in the dorsal colon (p < 0.05). Furthermore, we identified some interesting DEGs related to lipid metabolism (e.g., ME1, MBOAT1, ACOX1, ACOX2 and LIPH) and the immune system (e.g., MUC3B, mucin-2-like, IL17RC, IL1R2, IL33, C1QA, and MMP9) between the cecum and dorsal colon and found that the PPAR pathway was mainly enriched in the cecum. Finally, we found a complex relationship between the gut microbiome and gene expression, especially with respect to the immune system, and combined with protein-protein interaction (PPI) data, suggesting that the PPAR pathway might be responsible, at least in part, for the role of the hindgut microbiota in the donkeys' gut homeostasis. Our data provide an in-depth understanding of the interaction between the microbiota and function in the healthy equine hindgut and may also provide guidance for improving animal performance metrics (such as product quality) and equine welfare.