Reconstruction and Application of Protein-Protein Interaction Network

Warhead-bearing natural compounds for multi-pathway irreversible inhibition to overcome drug resistance in colorectal cancer

Colorectal cancer (CRC) remains the second leading cause of cancer-related deaths globally, with approximately 930000 fatalities recorded in 2020. Resistance to conventional therapies continues to be a major obstacle in colorectal cancer treatment, highlighting the need for novel therapeutic strategies to enhance efficacy. This study aims to bridge this gap by exploring a multi-target inhibition approach using naturally derived electrophilic compounds, offering a potential solution to overcome drug resistance. Key CRC-covalent targets-EGFR, SRC, AKT1, HER2, and ERK2-were identified through network pharmacology and protein-protein interaction analysis. A panel of natural compounds, including ophiobolin A, deoxyelephantopin, eupalmerin acetate, curcumin, andrographolide, and syringolin A, was assessed for their inhibitory potential, benchmarking their activity against reference chemotherapeutics. Covalent docking and covalent molecular dynamics (CMD) were performed for 30 ligand-protein complexes to evaluate the binding affinities of the studied compounds. Against EGFR, curcumin displayed a competitive docking score of - 9.458 kcal/mol and ΔGbind of - 23.00 kcal/mol, closely matching the performance of afatinib (- 10.134 kcal/mol and - 24.28 kcal/mol, respectively). Syringolin A and andrographolide also exhibited strong binding affinities for EGFR. Against SRC, curcumin and andrographolide demonstrated excellent binding potential, achieving docking scores of - 8.360 and - 6.585 kcal/mol and ΔGbind values of - 38.91 and - 34.00 kcal/mol, respectively. In the case of AKT1, andrographolide displayed a competitive performance (- 8.044 kcal/mol, ΔGbind: - 32.00 kcal/mol), followed by curcumin and syringolin A. Andrographolide achieved the strongest binding affinity among the natural compounds against HER2 (- 7.006 kcal/mol, ΔGbind: - 21.01 kcal/mol) and ERK2 (- 7.640 kcal/mol, ΔGbind: - 33.00 kcal/mol), outperforming curcumin (- 7.468 kcal/mol, ΔGbind: - 31.23 kcal/mol) and deoxyelephantopin (- 6.517 kcal/mol, ΔGbind: - 29.01 kcal/mol). These results underscore the strong binding affinities of natural compounds to CRC targets and suggest that these compounds, either as standalone agents or in combination therapies, could complement existing chemotherapeutics by overcoming treatment resistance, thereby improving therapeutic outcomes in CRC patients.

An evolutionary dynamics analysis of the plant DEK gene family reveals the role of BnaA02g08940D in drought tolerance

DEK is a chromatin protein that interacts with DNA to influence chromatin formation, thereby affecting plant growth, development, and stress response. This study investigates the molecular evolution of the DEK family in plants, with a particular focus on the Brassica species. A total of 127 DEK genes were identified in 34 plants and classified into seven groups based on the phylogenetic analysis. The distribution of motifs and gene structure is similar within each group, indicating a high degree of conservation. The results of the collinearity analysis indicated that the DEK protein has undergone a certain degree of evolutionary conservation. The expansion of the DEK family is primarily attributable to whole-genome duplication (WGD) or segmental duplication events. The DEK protein has undergone purification during its evolutionary history, and several positively selected sites have been identified. Moreover, the examination of cis-acting elements and expression patterns revealed that the BnDEKs play a significant role in plant growth and stress response. The protein-protein interaction network identified several noteworthy proteins that interact with DEK. These analyses enhance our comprehension of the DEK gene family and establish the foundation for additional validation of its function. Further research demonstrated that the overexpression of one DEK family member, BnaA02g08940D, enhanced the transgenic Arabidopsis tolerance to drought and osmosis. This indicates that the DEK family may respond when plants are subjected to drought stress, thereby strengthening the plant's resilience.

The Role of TRPA1 as a Prognostic Marker in Colon Adenocarcinoma and Its Correlation with Mutations and Immunity

Background

This study aimed to investigate the prognostic value of TRP ion channel genes (TRPICGs) in colorectal adenocarcinoma (COAD) and explore its related mechanisms.

Methods

The COAD dataset was downloaded from the Cancer Genome Atlas (TCGA) database. The differential expression genes (DEGs) were screened between COAD and normal samples. The differentially expressed TRPICGs (DE-TRPICGs) were obtained via intersection of DEGs and 28 TRPICGs. The Kaplan-Meier (K-M) survival curve was used to screen DE-TRPICGs with survival differences as prognostic markers. Afterward, the correlation of prognostic marker with clinical, immune cell, copy number variation were explored. Finally, immunohistochemistry (IHC) was used to verify the expression of prognostic marker.

Results

Overall, 6003 DEGs were screened, and 6 DE-TRPICGs were obtained. Only TRPA1 was identified as prognostic biomarker. Survival and clinical correlation analyses implied that TRPA1 played an inhibitory role in colon adenocarcinoma pathogenesis and progression. Gene Set Enrichment Analysis (GSEA) indicated that TRPA1 was associated with cell cycle and immune-related pathways. Immune infiltration analysis showed that TRPA1 expression was significantly correlated with the infiltration of B cells, CD4+T cells, CD8+T cells, neutrophils and dendritic cells. Eventually, TRPA1 expression was down-regulated at the protein level in COAD samples, which presented consistent results with expression in the database.

Conclusion

TRPA1 was identified in COAD as a prognostic marker associated with TRP ion channels, which provided a powerful reference value and a new direction for the diagnosis and treatment of COAD.

Network-based multi-omics integrative analysis methods in drug discovery: a systematic review

The integration of multi-omics data from diverse high-throughput technologies has revolutionized drug discovery. While various network-based methods have been developed to integrate multi-omics data, systematic evaluation and comparison of these methods remain challenging. This review aims to analyze network-based approaches for multi-omics integration and evaluate their applications in drug discovery. We conducted a comprehensive review of literature (2015-2024) on network-based multi-omics integration methods in drug discovery, and categorized methods into four primary types: network propagation/diffusion, similarity-based approaches, graph neural networks, and network inference models. We also discussed the applications of the methods in three scenario of drug discovery, including drug target identification, drug response prediction, and drug repurposing, and finally evaluated the performance of the methods by highlighting their advantages and limitations in specific applications. While network-based multi-omics integration has shown promise in drug discovery, challenges remain in computational scalability, data integration, and biological interpretation. Future developments should focus on incorporating temporal and spatial dynamics, improving model interpretability, and establishing standardized evaluation frameworks.

Identification of potential molecular targets and repurposed drugs for tuberculosis using network-based screening approach, molecular docking, and simulation

The spread of drug-resistant strains of tuberculosis has hampered efforts to control the disease worldwide. The Mycobacterium tuberculosis cell wall envelope is dynamic, with complex features that protect it from the host immunological response. As a result, the bacterial cell wall components represent a potential target for drug discovery. Protein-protein interaction networks (PPIN) are critical for understanding disease conditions and identifying precise therapeutic targets. We used a rational theoretical approach by constructing a PPIN with the proteins involved in cell wall biosynthesis. The PPIN was constructed through the STRING database and embB was identified as a key protein by using four topological measures, betweenness, closeness, degree, and eigenvector, in the CytoNCA tool in Cytoscape. The 'Drug repurposing' approach was employed to find suitable inhibitors against embB. We used the Schrödinger suites for molecular docking, molecular dynamics simulation, and binding free energy calculations to validate the binding of protein with the ligand. FDA-approved drugs from the ZINC database and DrugBank were screened against embB (PDB ID: 7BVF) using high-throughput virtual screening, standard precision, and extra precision docking. The drugs were screened based on the XP docking score of the standard drug ethambutol. Accordingly, from the top five hits, azilsartan and dihydroergotamine were selected based on the binding free energy values and were further subjected to Molecular Dynamics Simulation studies for 100 ns. Our study confirms that Azilsartan and Dihydroergotamine form stable complexes with embB and can be used as potential lead molecules based on further in vitro and in vivo experimental validation.Communicated by Ramaswamy H. Sarma.

Deciphering sperm functions using biological networks

The global human population is exponentially increasing, which requires the production of quality food through efficient reproduction as well as sustainable production of livestock. Lack of knowledge and technology for assessing semen quality and predicting bull fertility is hindering advances in animal science and food animal production and causing millions of dollars of economic losses annually. The intent of this systemic review is to summarize methods from computational biology for analysis of gene, metabolite, and protein networks to identify potential markers that can be applied to improve livestock reproduction, with a focus on bull fertility. We provide examples of available gene, metabolic, and protein networks and computational biology methods to show how the interactions between genes, proteins, and metabolites together drive the complex process of spermatogenesis and regulate fertility in animals. We demonstrate the use of the National Center for Biotechnology Information (NCBI) and Ensembl for finding gene sequences, and then use them to create and understand gene, protein and metabolite networks for sperm associated factors to elucidate global cellular processes in sperm. This study highlights the value of mapping complex biological pathways among livestock and potential for conducting studies on promoting livestock improvement for global food security.

Genome-wide identification of hormone biosynthetic and metabolism genes in the 2OGD family of tobacco and JOX genes silencing enhances drought tolerance in plants

Phytohormones play crucial roles in regulation of plant growth and tolerance to abiotic stresses. The 2-oxoglutarate-dependent dioxygenase (2OGD) superfamily responds to hormone biosynthesis and metabolism in plants. However, the Nt2OGD family in tobacco has not been fully explored. In this study, we identify 126 members of the Nt2OGD family, and 60 of them are involved in hormone biosynthesis and metabolism process (Nt2OGD-Hs), including 1-aminocyclopropane-1-carboxylic acid oxidases (ACO), dioxygenases for auxin oxidation (DAO), gibberellin (GA) 20-oxidases and 3-oxidases (GA20ox and GA3ox), carbon-19 and carbon-20 GA 2-oxidases (C19-GA2ox and C20-GA2ox), lateral branching oxidoreductases (LBO), jasmonate-induced oxygenases (JOX), downy mildew resistant 6, and DMR6-like oxygenases (DMR6/DLO). Gene duplication analysis suggests the segmental duplication and whole genome duplication (WGD) might be a potential mechanism for the expansion of this family. Expression analysis reveals that most of Nt2OGD-Hs show tissue-specific expression patterns, and some of them respond to environmental conditions. Of Nt2OGD-Hs, the expression of NtJOX3 and NtJOX5, which are involved in JA metabolism, exhibits remarkable changes during drought treatments. Silencing of NtJOX3 or NtJOX5 increases tobacco tolerance to drought stress. Furthermore, knocking out OsJOX3 and OsJOX4, respectively in rice, result in high tolerance to drought. Taken together, our work comprehensively identifies the Nt2OGD family in tobacco and provides new insights into roles of the JA pathway in drought tolerance in plants.

Detecting responsible nodes in differential Bayesian networks

To study the roles that different nodes play in differentiating Bayesian networks under two states, such as control versus disease, we formulate two node-specific scores to facilitate such assessment. The first score is motivated by the prediction invariance property of a causal model. The second score results from modifying an existing score constructed for differential analysis of undirected networks. We develop strategies based on these scores to identify nodes responsible for topological differences between two Bayesian networks. Synthetic data and real-life data from designed experiments are used to demonstrate the efficacy of the proposed methods in detecting responsible nodes.

Reconstruction of Eriocheir sinensis Protein-Protein Interaction Network Based on DGO-SVM Method

Eriocheir sinensis is an economically important aquatic animal. Its regulatory mechanisms underlying many biological processes are still vague due to the lack of systematic analysis tools. The protein-protein interaction network (PIN) is an important tool for the systematic analysis of regulatory mechanisms. In this work, a novel machine learning method, DGO-SVM, was applied to predict the protein-protein interaction (PPI) in E. sinensis, and its PIN was reconstructed. With the domain, biological process, molecular functions and subcellular locations of proteins as the features, DGO-SVM showed excellent performance in Bombyx mori, humans and five aquatic crustaceans, with 92-96% accuracy. With DGO-SVM, the PIN of E. sinensis was reconstructed, containing 14,703 proteins and 7,243,597 interactions, in which 35,604 interactions were associated with 566 novel proteins mainly involved in the response to exogenous stimuli, cellular macromolecular metabolism and regulation. The DGO-SVM demonstrated that the biological process, molecular functions and subcellular locations of proteins are significant factors for the precise prediction of PPIs. We reconstructed the largest PIN for E. sinensis, which provides a systematic tool for the regulatory mechanism analysis. Furthermore, the novel-protein-related PPIs in the PIN may provide important clues for the mechanism analysis of the underlying specific physiological processes in E. sinensis.

Reconstruction of Metabolic-Protein Interaction Integrated Network of Eriocheir sinensis and Analysis of Ecdysone Synthesis

Integrated networks have become a new interest in genome-scale network research due to their ability to comprehensively reflect and analyze the molecular processes in cells. Currently, none of the integrated networks have been reported for higher organisms. Eriocheir sinensis is a typical aquatic animal that grows through ecdysis. Ecdysone has been identified to be a crucial regulator of ecdysis, but the influence factors and regulatory mechanisms of ecdysone synthesis in E. sinensis are still unclear. In this work, the genome-scale metabolic network and protein-protein interaction network of E. sinensis were integrated to reconstruct a metabolic-protein interaction integrated network (MPIN). The MPIN was used to analyze the influence factors of ecdysone synthesis through flux variation analysis. In total, 236 integrated reactions (IRs) were found to influence the ecdysone synthesis of which 16 IRs had a significant impact. These IRs constitute three ecdysone synthesis routes. It is found that there might be alternative pathways to obtain cholesterol for ecdysone synthesis in E. sinensis instead of absorbing it directly from the feeds. The MPIN reconstructed in this work is the first integrated network for higher organisms. The analysis based on the MPIN supplies important information for the mechanism analysis of ecdysone synthesis in E. sinensis.

Whole Blood Transcriptome Analysis in Patients with Trigeminal Neuralgia: a Prospective Clinical Study

Trigeminal neuralgia (TN) brings a huge burden to patients, without long-term effective treatment. This study aimed to explore the differentially expressed genes (DEGs) and related enrichment pathways in patients with TN. This was a study of transcriptome sequencing and bioinformatics analysis of human samples. Whole blood samples were collected from the TN patients and pain-free controls. RNA was extracted to conduct the RNA-sequencing and the subsequent bioinformatics analysis. DEGs between the two groups were derived. Kyoto encyclopedia of genes and genomes (KEGG) and Gene ontology (GO) was used to find the enrichment pathways of DEGs. Protein protein interaction (PPI) network was used to depict the interaction between DEGs and find the most important gene, hub gene. Compared with the control group, there were 117 up-regulated DEGs and 103 down-regulated DEGs in the whole blood of patients in the TN group. Pathway enrichment analysis showed that DEGs were mainly enriched in the neuroimmune and metabolic pathways. The PPI network demonstrated that colony stimulating factor 2 (CSF2) was the most important hub gene in the whole blood of TN patients. This study shows the expression of the transcriptome in the whole blood samples of TN patients. The neuroimmune responses and key hub gene CSF2 in the whole blood cells play a vital role in the occurrence of TN. Our research provides a theoretical basis for the diagnosis and treatments of TN. This study was registered at clinicaltrials.gov in June 2021 (No. NCT04923399).

Identification of Key Prognostic Alternative Splicing Events of Costimulatory Molecule-Related Genes in Colon Cancer

OBJECTIVE

This study aimed to explore the key alternative splicing events in costimulatory molecule-related genes in colon cancer and to determine their correlation with prognosis.

METHODS

Gene expression RNA-sequencing data, clinical data, and SpliceSeq data of colon cancer were obtained from The Cancer Genome Atlas. Differentially expressed alternative splicing events in genes were identified, Followed by correlation analysis of genes corresponding to differentially expressed alternative splicing events with costimulatory molecule-related genes. Survival analysis was conducted using differentially expressed alternative splicing events in these genes and a prognostic model was constructed. Functional enrichment, proteinprotein interaction network, and splicing factor analyses were performed.

RESULTS

In total, 6504 differentially expressed alternative splicing events in 3949 genes were identified between tumor and normal tissues. Correlation analysis revealed 3499 differentially expressed alternative splicing events in 2168 costimulatory molecule-related genes. Moreover, 328 differentially expressed alternative splicing events in 288 costimulatory molecule-related genes were associated with overall survival. The prognostic models constructed using these showed considerable power in predicting survival. The ubiquitin A-52 residue ribosomal protein fusion product 1 and ribosomal protein S9 were the hub nodes in the protein-protein interaction network. Furthermore, one splicing factor, splicing factor proline and glutamine-rich, was significantly associated with patient prognosis. Four splicing factor-alternative splicing pairs were obtained from four alternative splicing events in three genes: TBC1 domain family member 8 B, complement factor H, and mitochondrial fission 1.

CONCLUSION

The identified differentially expressed alternative splicing events of costimulatory molecule-related genes may be used to predict patient prognosis and immunotherapy responses in colon cancer.

Overcoming phenotypic switching: targeting protein-protein interactions in cancer

Alternative protein-protein interactions (PPIs) arising from mutations or post-translational modifications (PTMs), termed phenotypic switching (PS), are critical for the transmission of alternative pathogenic signals and are particularly significant in cancer. In recent years, PPIs have emerged as promising targets for rational drug design, primarily because their high specificity facilitates targeting of disease-related signaling pathways. However, obstacles exist at the molecular level that arise from the properties of the interaction interfaces and the propensity of small molecule drugs to interact with more than one cleft surface. The difficulty in identifying small molecules that act as activators or inhibitors to counteract the biological effects of mutations raises issues that have not been encountered before. For example, small molecules can bind tightly but may not act as drugs or bind to multiple sites (interaction promiscuity). Another reason is the absence of significant clefts on protein surfaces; if a pocket is present, it may be too small, or its geometry may prevent binding. PS, which arises from oncogenic (alternative) signaling, causes drug resistance and forms the basis for the systemic robustness of tumors. In this review, the properties of PPI interfaces relevant to the design and development of targeting drugs are examined. In addition, the interactions between three tyrosine kinase inhibitors (TKIs) employed as drugs are discussed. Finally, potential novel targets of one of these drugs were identified in silico.

Genome-wide identification and analyses of the AHL gene family in rice (Oryza sativa)

AHL (AT-HOOK MOTIF CONTAINING NUCLEAR LOCALIZED) family members play a critical role in stress resistance regulation by DNA-protein and protein-protein interactions in a number of plant biological processes. Using genomic data, an attempt was made to evaluate AHL genes in rice. Using a genome database, we performed in silico detection and characterization of AHL family genes in rice. The data of the gene were obtained from the Rice Genome Annotation Project (RGAP) database. The rice genome data were analyzed using bioinformatics software. The main objectives of the research are genome-wide recognition, expression, structural examination, phylogenetic analysis of AHL gene family, classification of AHL proteins into different classes based on motif and domain composition, analysis of promoter regions to identify stress and phytohormone-associated cis-elements, expression analysis of OsAHL genes in diverse tissues and stressful situations and understanding the roles of AHLs in controlling rice plant development. The genome-wide recognition, expression, and structural examination of the AHL gene family were undertaken in this research to evaluate the structural activities of AHLs in rice. From the Oryza sativa genome, 26 AHL genes have been identified. WoLF PSORT analysis predicted different subcellular localizations for these proteins, including nuclear, cytoplasmic, chloroplast, and endoplasmic reticulum. According to a phylogenetic study, rice AHLs resulted in two clades: Clade-A with no introns (excluding OsAHL15 and OsAHL21) and Clade-B with four introns. Depending on the AT-hook motif (s) (AHM) and PPC/DUF 296 domain composition, the AHL proteins are categorized into the following three classes: Type-I, Type-II, and Type-III, among Type-I AHLs constituting Clade-A, Type-II, and Type-III creating Clade-B. Type-I was the largest gene family, representing 57.69% of OsAHL genes. The exon-intron organization within clades of OsAHL genes was similar. Multiple sequence alignment identified 15 conserved motifs, including AT-hook motifs and the PPC domain, suggesting DNA-binding functionality. OsAHL genes were distributed across 12 chromosomes, with chromosome 2 and 8 harboring the highest number of genes. Gene duplication analysis revealed eight paralogous pairs, indicating evolutionary divergence between 13.32 and 35.59 million years ago. The emergence of OsAHL paralogous pairs was favored by purifying selection. Synteny analysis between rice and Arabidopsis demonstrated collinearity among AHL gene pairs, implying comparable structure and function in the two species. The role of stress- and phytohormone-associated cis-elements in the OsAHL genes was discovered by promoter analysis. OsAHL genes participated in various biological processes, with a prominent involvement in cellular and metabolic processes. They exhibited a significant enrichment in binding functions, including a substantial proportion of transcription regulators. OsAHL genes displayed diverse expression patterns in different tissues and under abiotic stress conditions. According to their expression patterns, the majority of OsAHLs of Clade-B were expressed mainly in the pistil indicating their roles in flower formation, while Clade-A OsAHLs had the minimal expression in pistil and highly expressed in embryos, indicating that the AHLs within each clade had the same expression patterns. Some OsAHL genes were also expressed in stressful situations, such as cold, salt, and drought. Protein interaction analysis revealed networks involving AHL proteins and other proteins, suggesting their participation in phytohormone responses, abiotic stress, and plant development. In this work, 26 OsAHL genes were found in the genome of rice. Rice OsAHLs were grouped into two phylogenetic groups. It is further divided into three types on the basis of the motif and domain composition. At various phases of development, the expression analysis of OsAHLs showed numerous variations in expression levels in diverse tissues and stress situations. Our findings shed light on the significant roles of AHLs in controlling rice plant development.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03666-0.

Decipher the molecular evolution and expression patterns of Cupin family genes in oilseed rape

Cupin proteins are involved in plant growth and development as well as in response to various stresses. Here, a total of 173 Cupin genes were identified in Brassica napus, and their molecular evolution and expression patterns were analyzed. These genes were classified into ten groups. Motif and exon-intron structure indicated a high degree of conservation within each group during evolution. BnaCupins were distributed on 19 chromosomes and their expansion is mainly contributed by whole-genome duplication (WGD) and segmental duplication events. BnaCupins have undergone severe purifying selection during a long evolutionary process. Meanwhile, some positive selection sites were identified. Expression patterns and cis-element analysis indicated that BnaCupins play significant roles in plant growth and stress responses. In addition, the expression levels of some BnCupins were significantly altered when treated with different conditions (cold, salt, drought, IAA, ABA, and 6-BA). Some BnaCupin interacting proteins, such as glycosyl hydrolase5 (GHs5), carbohydrate kinase (CHKs), ATP-dependent 6-phosphofructokinase (ATP-PFK), S-adenosylmethionine synthase (S-MAT), and aldolase class II (ALD II), were identified by the protein-protein interaction network. It will contribute to enriching our knowledge of the Cupin gene family in B. napus and provide a basis for further studies of their functions.

Bacteriocin-Nanoconjugates (Bac10307-AgNPs) Biosynthesized from Lactobacillus acidophilus-Derived Bacteriocins Exhibit Enhanced and Promising Biological Activities

The proteinaceous compounds produced by lactic acid bacteria are called bacteriocins and have a wide variety of bioactive properties. However, bacteriocin's commercial availability is limited due to short stability periods and low yields. Therefore, the objective of this study was to synthesize bacteriocin-derived silver nanoparticles (Bac10307-AgNPs) extracted from Lactobacillus acidophilus (L. acidophilus), which may have the potential to increase the bioactivity of bacteriocins and overcome the hurdles. It was found that extracted and purified Bac10307 had a broad range of stability for both temperature (20-100 °C) and pH (3-12). Further, based on Sodium dodecyl-sulfate polyacrylamide gel electrophoresis (SDS-PAGE) analysis, its molecular weight was estimated to be 4.2 kDa. The synthesized Bac10307-AgNPs showed a peak of surface plasmon resonance at 430 nm λmax. Fourier transform infrared (FTIR) confirmed the presence of biological moieties, and transmission electron microscopy (TEM) coupled with Energy dispersive X-Ray (EDX) confirmed that AgNPs were spherical and irregularly shaped, with a size range of 9-20 nm. As a result, the Bac10307-AgNPs displayed very strong antibacterial activity with MIC values as low as 8 μg/mL for Staphylococcus aureus (S. aureus) and Pseudomonas aeruginosa (P. aeruginosa), when compared to Bac10307 alone. In addition, Bac10307-AgNPs demonstrated promising in vitro antioxidant activity against 2,2-diphenyl-1-picrylhydrazyl (DPPH) (IC₅₀ = 116.04 μg/mL) and in vitro cytotoxicity against human liver cancer cells (HepG2) (IC₅₀ = 135.63 μg/mL), more than Bac10307 alone (IC₅₀ = 139.82 μg/mL against DPPH and 158.20 μg/mL against HepG2). Furthermore, a protein-protein molecular docking simulation study of bacteriocins with target proteins of different biological functions was also carried out in order to ascertain the interactions between bacteriocins and target proteins.

S100A9 plays a key role in Clostridium perfringens beta2 toxin-induced inflammatory damage in porcine IPEC-J2 intestinal epithelial cells

BACKGROUND

As an important regulator of autoimmune responses and inflammation, S100A9 may serve as a therapeutic target in inflammatory diseases. However, the role of S100A9 in Clostridium perfringens type C infectious diarrhea is poorly studied. The aim of our study was to screen downstream target genes regulated by S100A9 in Clostridium perfringens beta2 (CPB2) toxin-induced IPEC-J2 cell injury. We constructed IPEC-J2 cells with S100A9 knockdown and a CPB2-induced cell injury model, screened downstream genes regulated by S100A9 using RNA-Seq technique, and performed functional enrichment analysis. The function of S100A9 was verified using molecular biology techniques.

RESULTS

We identified 316 differentially expressed genes (DEGs), of which 221 were upregulated and 95 were downregulated. Functional enrichment analysis revealed that the DEGs were significantly enriched in cilium movement, negative regulation of cell differentiation, immune response, protein digestion and absorption, and complement and coagulation cascades. The key genes of immune response were TNF, CCL1, CCR7, CSF2, and CXCL9. When CPB2 toxin-induced IPEC-J2 cells overexpressed S100A9, Bax expression increased, Bcl-2 expression and mitochondrial membrane potential decreased, and SOD activity was inhibited.

CONCLUSION

In conclusion, S100A9 was involved in CPB2-induced inflammatory response in IPEC-J2 cells by regulating the expression of downstream target genes, namely, TNF, CCL1, CCR7, CSF2, and CXCL9; promoting apoptosis; and aggravating oxidative cell damage. This study laid the foundation for further study on the regulatory mechanism underlying piglet diarrhea.

An optimized herbal combination for the treatment of liver fibrosis: Hub genes, bioactive ingredients, and molecular mechanisms

ETHNOPHARMACOLOGICAL RELEVANCE

Liver fibrosis is a chronic liver disease that can lead to cirrhosis, liver failure, and hepatocellular carcinoma, and it is associated with long-term adverse outcomes and mortality. As a primary resource for complementary and alternative medicine, traditional Chinese medicine (TCM) has accumulated a large number of effective formulas for the treatment of liver fibrosis in clinical practice. However, studies on how to systematically optimize TCM formulas are still lacking.

AIM OF THE REVIEW

To provide a methodological reference for the systematic optimization of TCM formulae against liver fibrosis and explored the underlying molecular mechanisms; To provide an efficient method for searching for lead compounds from natural sources and developing from herbal medicines; To enable clinicians and patients to make more reasonable choices and promote the effective treatment toward those patients with liver fibrosis.

MATERIALS AND METHODS

TCM formulas related to treating liver fibrosis were collected from the Web of Science, PubMed, the China National Knowledge Infrastructure (CNKI), Wan Fang, and the Chinese Scientific Journals Database (VIP). Furthermore, the TCM compatibility patterns were mined using association analysis. The core TCM combinations were found by designing an optimized formulas algorithm. Finally, the hub target proteins, potential molecular mechanisms, and active compounds were explored through integrative pharmacology and docking-based inverse virtual screening (IVS) approaches.

RESULTS

We found that the herbs for reinforcing deficiency, activating blood, removing blood stasis, and clearing heat were the basis of TCM formulae patterns. Furthermore, the combination of Salviae Miltiorrhizae (Salvia miltiorrhiza Bunge; Chinese salvia/Danshen), Astragali Radix (Astragalus membranaceus (Fisch.) Bunge; Astragalus/Huangqi), and Radix Bupleuri (Bupleurum chinense DC.; Bupleurum/Chaihu) was identified as core groups. A total of six targets (TNF, STAT3, EGFR, IL2, ICAM1, PTGS2) play a pivotal role in TCM-mediated liver fibrosis inhibition. (-)-Cryptotanshinone, Tanshinaldehyde, Ononin, Thymol, Daidzein, and Formononetin were identified as active compounds in TCM. And mechanistically, TCM could affect the development of liver fibrosis by regulating inflammation, immunity, angiogenesis, antioxidants, and involvement in TNF, MicroRNAs, Jak-STAT, NF-kappa B, and C-type lectin receptors (CLRs) signaling pathways. Molecular docking results showed that key components had good potential to bind to the target genes.

CONCLUSION

In summary, this study provides a methodological reference for the systematic optimization of TCM formulae and exploration of underlying molecular mechanisms.

Molecular evolutionary analysis of the SHI/STY gene family in land plants: A focus on the Brassica species

The plant-specific SHORT INTERNODES/STYLISH (SHI/STY) proteins belong to a family of transcription factors that are involved in the formation and development of early lateral roots. However, the molecular evolution of this family is rarely reported. Here, a total of 195 SHI/STY genes were identified in 21 terrestrial plants, and the Brassica species is the focus of our research. Their physicochemical properties, chromosome location and duplication, motif distribution, exon-intron structures, genetic evolution, and expression patterns were systematically analyzed. These genes are divided into four clades (Clade 1/2/3/4) based on phylogenetic analysis. Motif distribution and gene structure are similar in each clade. SHI/STY proteins are localized in the nucleus by the prediction of subcellular localization. Collinearity analysis indicates that the SHI/STYs are relatively conserved in evolution. Whole-genome duplication is the main factor for their expansion. SHI/STYs have undergone intense purifying selection, but several positive selection sites are also identified. Most promoters of SHI/STY genes contain different types of cis-elements, such as light, stress, and hormone-responsive elements, suggesting that they may be involved in many biological processes. Protein-protein interaction predicted some important SHI/STY interacting proteins, such as LPAT4, MBOATs, PPR, and UBQ3. In addition, the RNA-seq and qRT-PCR analysis were studied in detail in rape. As a result, SHI/STYs are highly expressed in root and bud, and can be affected by Sclerotinia sclerotiorum, drought, cold, and heat stresses. Moreover, quantitative real-time PCR (qRT-PCR) analyses indicates that expression levels of BnSHI/STYs are significantly altered in different treatments (cold, salt, drought, IAA, auxin; ABA, abscisic acid; 6-BA, cytokinin). It provides a new understanding of the evolution and expansion of the SHI/STY family in land plants and lays a foundation for further research on their functions.

Serial-Omics and Molecular Function Study Provide Novel Insight into Cucumber Variety Improvement

Cucumbers are rich in vitamins and minerals. The cucumber has recently become one of China’s main vegetable crops. More specifically, the adjustment of the Chinese agricultural industry’s structure and rapid economic development have resulted in increases in the planting area allocated to Chinese cucumber varieties and in the number of Chinese cucumber varieties. After complete sequencing of the “Chinese long” genome, the transcriptome, proteome, and metabolome were obtained. Cucumber has a small genome and short growing cycle, and these traits are conducive to the application of molecular breeding techniques for improving fruit quality. Here, we review the developments and applications of molecular markers and genetic maps for cucumber breeding and introduce the functions of gene families from the perspective of genomics, including fruit development and quality, hormone response, resistance to abiotic stress, epitomizing the development of other omics, and relationships among functions.

Bioinformatic Analysis Combined With Experimental Validation Reveals Novel Hub Genes and Pathways Associated With Focal Segmental Glomerulosclerosis

Background: Focal segmental glomerulosclerosis (FSGS) is a type of nephrotic syndrome leading to end-stage renal disease, and this study aimed to explore the hub genes and pathways associated with FSGS to identify potential diagnostic and therapeutic targets.

Methods: We downloaded the microarray datasets GSE121233 and GSE129973 from the Gene Expression Omnibus (GEO) database. The datasets comprise 25 FSGS samples and 25 normal samples. The differential expression genes (DEGs) were identified using the R package “limma”. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the database for Annotation, Visualization and Integrated Discovery (DAVID) to identify the pathways and functional annotation of the DEGs. The protein–protein interaction (PPI) was constructed based on the Search Tool for the Retrieval of Interacting Genes (STRING) database and visualized using Cytoscape software. The hub genes of the DEGs were then evaluated using the cytoHubba plugin of Cytoscape. The expression of the hub genes was validated by quantitative real-time polymerase chain reaction (qRT-PCR) using the FSGS rat model, and receiver operating characteristic (ROC) curve analysis was performed to validate the accuracy of these hub genes.

Results: A total of 45 DEGs including 18 upregulated and 27 downregulated DEGs, were identified in the two GSE datasets (GSE121233 and GSE129973). Among them, five hub genes with a high degree of connectivity were selected. From the PPI network, of the top five hub genes, FN1 was upregulated, while ALB, EGF, TTR, and KNG1 were downregulated. The qRT-PCR analysis of FSGS rats confirmed that the expression of FN1 was upregulated and that of EGF and TTR was downregulated. The ROC analysis indicated that FN1, EGF, and TTR showed considerable diagnostic efficiency for FSGS.

Conclusion: Three novel FSGS-specific genes were identified through bioinformatic analysis combined with experimental validation, which may promote our understanding of the molecular underpinning of FSGS and provide potential therapeutic targets for the clinical management.

Construction and analysis of the protein-protein interaction network for the detoxification enzymes of the silkworm, Bombyx mori

Detoxification enzymes are necessary for insects to metabolize toxic substances and maintain physiological activities. Cytochromes P450 (CYPs), glutathione S-transferases (GSTs), and carboxylesterase (CarEs) are the main detoxification enzymes in insects. In addition, UDP-glucosyltransferase and ATP-binding cassette transporter also participate in the process of material metabolism. This study collected proteins related to detoxification in the silkworm, Bombyx mori (Lepidoptera: Bombycidae). And we performed Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis on these proteins to understand their biological function. We constructed the protein-protein interaction network for the silkworm's detoxification enzymes and analyzed the network's topological properties. We found that BGIBMGA014046-TA, BGIBMGA003221-TA, BGIBMGA011092-TA, BGIBMGA000074-TA, and LOC732976 are the essential proteins in the network. These proteins are primarily involved in the process of ribosome biogenesis and may be related to protein synthesis. We integrated GO, KEGG, and network analysis and found that ribosome-associated protein and GSTs played a vital role in the detoxification process.

Computationally Reconstructed Interactome of Bradyrhizobium diazoefficiens USDA110 Reveals Novel Functional Modules and Protein Hubs for Symbiotic Nitrogen Fixation

Symbiotic nitrogen fixation is an important part of the nitrogen biogeochemical cycles and the main nitrogen source of the biosphere. As a classical model system for symbiotic nitrogen fixation, rhizobium-legume systems have been studied elaborately for decades. Details about the molecular mechanisms of the communication and coordination between rhizobia and host plants is becoming clearer. For more systematic insights, there is an increasing demand for new studies integrating multiomics information. Here, we present a comprehensive computational framework integrating the reconstructed protein interactome of B. diazoefficiens USDA110 with its transcriptome and proteome data to study the complex protein-protein interaction (PPI) network involved in the symbiosis system. We reconstructed the interactome of B. diazoefficiens USDA110 by computational approaches. Based on the comparison of interactomes between B. diazoefficiens USDA110 and other rhizobia, we inferred that the slow growth of B. diazoefficiens USDA110 may be due to the requirement of more protein modifications, and we further identified 36 conserved functional PPI modules. Integrated with transcriptome and proteome data, interactomes representing free-living cell and symbiotic nitrogen-fixing (SNF) bacteroid were obtained. Based on the SNF interactome, a core-sub-PPI-network for symbiotic nitrogen fixation was determined and nine novel functional modules and eleven key protein hubs playing key roles in symbiosis were identified. The reconstructed interactome of B. diazoefficiens USDA110 may serve as a valuable reference for studying the mechanism underlying the SNF system of rhizobia and legumes.

Systems biology for crop improvement

In recent years, generation of large-scale data from genome, transcriptome, proteome, metabolome, epigenome, and others, has become routine in several plant species. Most of these datasets in different crop species, however, were studied independently and as a result, full insight could not be gained on the molecular basis of complex traits and biological networks. A systems biology approach involving integration of multiple omics data, modeling, and prediction of the cellular functions is required to understand the flow of biological information that underlies complex traits. In this context, systems biology with multiomics data integration is crucial and allows a holistic understanding of the dynamic system with the different levels of biological organization interacting with external environment for a phenotypic expression. Here, we present recent progress made in the area of various omics studies-integrative and systems biology approaches with a special focus on application to crop improvement. We have also discussed the challenges and opportunities in multiomics data integration, modeling, and understanding of the biology of complex traits underpinning yield and stress tolerance in major cereals and legumes.

Prediction of protein-binding residues: dichotomy of sequence-based methods developed using structured complexes versus disordered proteins

MOTIVATION

There are over 30 sequence-based predictors of the protein-binding residues (PBRs). They use either structure-annotated or disorder-annotated training datasets, potentially creating a dichotomy where the structure-/disorder-specific models may not be able to cross-over to accurately predict the other type. Moreover, the structure-trained predictors were shown to substantially cross-predict PBRs among residues that interact with non-protein partners (nucleic acids and small ligands). We address these issues by performing first-of-its-kind comparative study of a representative collection of disorder- and structure-trained predictors using a comprehensive benchmark set with the structure- and disorder-derived annotations of PBRs (to analyze the cross-over) and the protein-, nucleic acid- and small ligand-binding proteins (to study the cross-predictions).

RESULTS

Three predictors provide accurate results: SCRIBER, ANCHOR and disoRDPbind. Some of the structure-trained methods make accurate predictions on the structure-annotated proteins. Similarly, the disorder-trained predictors predict well on the disorder-annotated proteins. However, the considered predictors generally fail to cross-over, with the exception of SCRIBER. Our study also reveals that virtually all methods substantially cross-predict PBRs, except for SCRIBER for the structure-annotated proteins and disoRDPbind for the disorder-annotated proteins. We formulate a novel hybrid predictor, hybridPBRpred, that combines results produced by disoRDPbind and SCRIBER to accurately predict disorder- and structure-annotated PBRs. HybridPBRpred generates accurate results that cross-over structure- and disorder-annotated proteins and produces relatively low amount of cross-predictions, offering an accurate alternative to predict PBRs.

AVAILABILITY AND IMPLEMENTATION

HybridPBRpred webserver, benchmark dataset and supplementary information are available at http://biomine.cs.vcu.edu/servers/hybridPBRpred/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Comprehensive analysis of AHL gene family and their expression under drought stress and ABA treatment in Populus trichocarpa

The AT-hook motif nuclear-localized (AHL) family is a plant transcription factor family, which plays an important role in growth and development and stress responses. We identified and analyzed 37 AHL genes in poplar (Populus trichocarpa). Phylogenetic analysis classified the PtrAHL members into three subfamilies based on their conserved domain. All PtrAHL paralogous pairs evolved under purifying selection. The promoter analysis revealed the presence of stress-related and phytohormone-related cis-elements of the PtrAHL genes. Our analysis of the tissue-specific expression pattern of PtrAHL genes indicated their significance in tissue and organ development. Network-based prediction suggested that PtrAHL genes may interact with histone deacetylases (HDAC) and participate in the development of organs, such as roots. Drought negatively impacts plant growth and development. ABA is produced under osmotic stress condition, and it takes an important part in the stress response and tolerance of plants. Real-time quantitative PCR (qRT-PCR) showed that PtrAHL genes were induced by drought stress and ABA treatment. These insights into the expression of PtrAHL genes under stress provide a basis for PtrAHL gene functional analysis. Our study will help develop new breeding strategies to improve drought tolerance in poplar.

Evaluation of the susceptibility and fatality of lung cancer patients towards the COVID-19 infection: A systemic approach through analyzing the ACE2, CXCL10 and their co-expressed genes

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The expression of ACE2 and CXCL10 is upregulated in lung cancer.

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64 and 6 mutations were identified in ACE2 and CXCL10 protein sequences, respectively.

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ACE2 and CXCL10 are found as the hub proteins in the PPI network of COVID-19 development.

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803 co-expressed genes of ACE2 are found to be involved in binding activity.

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68 co-expressed genes of CXCL10 are identified involving in the immune response.

Coronavirus disease-2019 (COVID-19) is a recent world pandemic disease that is caused by a newly discovered strain of the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS- CoV-2). Patients with comorbidities are most vulnerable to this disease. Therefore, cancer patients are reported to be more susceptible to COVID-19 infection, particularly lung cancer patients. To evaluate the probable reasons behind the excessive susceptibility and fatality of lung cancer patients to COVID-19 infection, we targeted the two most crucial agents, Angiotensin-converting enzyme 2 (ACE2) and C-X-C motif 10 (CXCL10). ACE2 is a receptor protein that plays a vital role in the entry of SARS-CoV-2 into the host cell and CXCL10 is a cytokine mainly responsible for the lung cell damage involving in a cytokine storm. By using the UALCAN and GEPIA2 databases, we observed that ACE2 and CXCL10 are mostly overexpressed in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). We then identified the functional significance of ACE2 and CXCL10 in lung cancer development by determining the genetic alteration frequency in their amino acid sequences using the cBioPortal web portal. Lastly, we did the pathological assessment of targeted genes using the PANTHER database. Here, we found that ACE2 and CXCL10 along with their commonly co-expressed genes are involved respectively in the binding activity and immune responses in case of lung cancer and COVID-19 infection. Finally, based on this systemic analysis, we concluded that ACE2 and CXCL10 are two possible biomarkers responsible for the higher susceptibility and fatality of lung cancer patients towards the COVID-19.

Insights Into the Molecular Evolution of AT-Hook Motif Nuclear Localization Genes in Brassica napus

AT-hook motif nuclear localization (AHL) proteins belong to a family of transcription factors, and play important roles in plant growth and development and response to various stresses through protein-DNA and protein-protein interactions. To better understand the Brassica napus AHL gene family, AHL genes in B. napus and related species were analyzed. Using Arabidopsis as a reference, 122 AHL gene family members were first identified in B. napus. According to the phylogenetic tree and gene organization, the BnaAHLs were classified into two clades (Clade-A and Clade-B) and three types (Type-I, Type-II, and Type-III). Gene organization and motif distribution analysis suggested that the AHL gene family is relatively conserved during evolution. These BnaAHLs are unevenly distributed on 38 chromosomes and expanded by whole-genome duplication (WGD) or segmental duplication. And large-scale loss events have also occurred in evolution. All types of BnaAHLs are subject to purification or neutral selection, while some positive selection sites are also identified in Type-II and Type-III groups. At the same time, the purification effect of Type-I members are stronger than that of the others. In addition, RNA-seq data and cis-acting element analysis also suggested that the BnaAHLs play important roles in B. napus growth and development, as well as in response to some abiotic and biotic stresses. Protein-protein interaction analysis identified some important BnaAHL-binding proteins, which also play key roles in plant growth and development. This study is helpful to fully understand the origin and evolution of the AHL gene in B. napus, and lays the foundation for their functional studies.

Identifying Potential Biomarkers in Colorectal Cancer and Developing Non-invasive Diagnostic Models Using Bioinformatics Approaches

Background: Colorectal cancer (CRC) is one of the most frequent causes of gastrointestinal tumors. Due to the invasiveness of the current diagnostic methods, there is an urgent need to develop non-invasive diagnostic approaches for CRC. The exact mechanisms and the most important genes associated with the development of CRC are not fully demonstrated. Objectives: This study aimed to identify differentially expressed miRNAs (DEMs), key genes, and their regulators associated with the pathogenesis of CRC. The signaling pathways and biological processes (BPs) that were significantly affected in CRC were also indicated. Moreover, two non-invasive models were constructed for CRC diagnosis. Methods: The miRNA dataset GSE59856 was downloaded from the Gene Expression Omnibus (GEO) database and analyzed to identify DEMs in CRC patients compared with healthy controls (HCs). A protein-protein interaction (PPI) network was built and analyzed. Significant clusters in the PPI networks were identified, and the BPs and pathways associated with these clusters were studied. The hub genes in the PPI network, as well as their regulators were identified. Results: A total of 569 DEMs were demonstrated with the criteria of P value <0.001. A total of 110 essential genes and 30 modules were identified in the PPI network. Functional analysis revealed that 1005 BPs, 9 molecular functions (MFs), 14 cellular components (CCs), and 887 pathways were significantly affected in CRC. A total of 22 transcription factors (TFs) were demonstrated as the regulators of the hubs. Conclusion: Our results may provide new insight into the pathogenesis of CRC and advance the diagnostic and therapeutic methods of the disease. However, confirmation is required in the future.

Deciphering Molecular Virulence Mechanism of Mycobacterium tuberculosis Dop isopeptidase Based on Its Sequence-Structure-Function Linkage

The pupylation pathway marks proteins for prokaryotic ubiquitin-like protein (Pup)-proteasomal degradation and survival strategy of mycobacteria inside of the host macrophages. Deamidase of Pup (Dop) plays a central role in the pupylation pathway. It is still a matter of investigation to know the function of Dop in virulence of mycobacterial lineage. Hence, the present study was intended to describe the sequence-structure-function-virulence link of Dop for understanding the molecular virulence mechanism of Mycobacterium tuberculosis H37Rv (Mtb). Phylogenetic analysis of this study indicated that Dop has extensively diverged across the proteasome-harboring bacteria. The functional part of Dop was converged across the pathogenic mycobacterial lineage. The genome-wide analysis pointed out that the pupylation gene locus was identical to each other, but its genome neighborhood differed from species to species. Molecular modeling and dynamic studies proved that the predicted structure of Mtb Dop was energetically stable and low conformational freedom. Moreover, evolutionary constraints in Mtb Dop were intensively analyzed for inferring its sequence-structure-function relationships for the full virulence of Mtb. It indicated that evolutionary optimization was extensively required to stabilize its local structural environment at the side chains of mutable residues. The sequence-structure-function-virulence link of Dop might have retained in Mtb by reordering hydrophobic and hydrogen bonding patterns in the local structural environment. Thus, the results of our study provide a quest to understand the molecular virulence and pathogenesis mechanisms of Mtb during the infection process.

Neuroprotective Role of Quercetin on Rotenone-Induced Toxicity in SH-SY5Y Cell Line Through Modulation of Apoptotic and Autophagic Pathways

The detrimental impact on the food chain due to the overuse of rotenone is partly responsible for alpha-synuclein (α-syn) mediated neurotoxicity. It is hypothesized that rotenone overdose leads to cytosolic proteopathy resulting in modulation of apoptosis and autophagic pathways. The aim of our study is to explore the neuroprotective role of quercetin, a beneficial polyphenol against rotenone-induced neurotoxicity in dopaminergic human SH-SY5Y cell lines. In our study we demonstrated the correlation of rotenone-induced neurotoxicity through elevation of intracellular reactive oxygen species (ROS) and imbalance in the mitochondrial membrane potential (MMP). Moreover, the morphological distortion of cell, condensation of nuclei, externalization of the inner phosphatidylserine, cleavage of caspase 3, and Poly ADP Ribose Polymerase (PARP) confirmed apoptosis. However, all these lethal effects were ameliorated by treatment of quercetin to the cells. On the other hand rotenone has a strong effect on autophagy which is a regulated degrading and recycling cellular process to remove dysfunctional proteins. Indeed, rotenone-mediated autophagy resulted in the enhancement of autophagosome-bound microtubule-associated protein light chain-3 (LC3-II) expression. Furthermore, excess accumulation of acidic vesicles was detected in presence of rotenone. Lysosome associated membrane protein (LAMP-2A) is yet another crucial protein that recruits overexpressed or misfolded proteins into the lumen of lysosome to trigger autophagy. In all cases the impact of rotenone on the cells acquired significant protection through quercetin treatment. In the present work we therefore opine the prospects of quercetin as a therapeutic candidate against neurotoxicity.

Comparative analysis of differential proteome-wide protein-protein interaction network of Methanobrevibacter ruminantium M1

A proteome-wide protein-protein interaction (PPI) network of Methanobrevibacter ruminantium M1 (MRU), a predominant rumen methanogen, was constructed from its metabolic genes using a gene neighborhood algorithm and then compared with closely related rumen methanogens Using proteome-wide PPI approach, we constructed network encompassed 2194 edges and 637 nodes interacting with 634 genes. Network quality and robustness of functional modules were assessed with gene ontology terms. A structure-function-metabolism mapping for each protein has been carried out with efforts to extract experimental PPI concomitant information from the literature. The results of our study revealed that some topological properties of its network were robust for sharing homologous protein interactions across heterotrophic and hydrogenotrophic methanogens. MRU proteome has shown to establish many PPI sub-networks for associated metabolic subsystems required to survive in the rumen environment. MRU genome found to share interacting proteins from its PPI network involved in specific metabolic subsystems distinct to heterotrophic and hydrogenotrophic methanogens. Across these proteomes, the interacting proteins from differential PPI networks were shared in common for the biosynthesis of amino acids, nucleosides, and nucleotides and energy metabolism in which more fractions of protein pairs shared with Methanosarcina acetivorans. Our comparative study expedites our knowledge to understand a complex proteome network associated with typical metabolic subsystems of MRU and to improve its genome-scale reconstruction in the future.

Graph cluster approach in identifying novel proteins and significant pathways involved in polycystic ovary syndrome

RESEARCH QUESTION

Polycystic ovary syndrome (PCOS) is a complex endocrine disorder with diverse clinical implications, such as infertility, metabolic disorders, cardiovascular diseases and psychological problems among others. The heterogeneity of conditions found in PCOS contribute to its various phenotypes, leading to difficulties in identifying proteins involved in this abnormality. Several studies, however, have shown the feasibility in identifying molecular evidence underlying other diseases using graph cluster analysis. Therefore, is it possible to identify proteins and pathways related to PCOS using the same approach?

METHODS

Known PCOS-related proteins (PCOSrp) from PCOSBase and DisGeNET were integrated with protein-protein interactions (PPI) information from Human Integrated Protein-Protein Interaction reference to construct a PCOS PPI network. The network was clustered with DPClusO algorithm to generate clusters, which were evaluated using Fisher's exact test. Pathway enrichment analysis using gProfileR was conducted to identify significant pathways.

RESULTS

The statistical significance of the identified clusters has successfully predicted 138 novel PCOSrp with 61.5% reliability and, based on Cronbach's alpha, this prediction is acceptable. Androgen signalling pathway and leptin signalling pathway were among the significant PCOS-related pathways corroborating the information obtained from the clinical observation, where androgen signalling pathway is responsible in producing male hormones in women with PCOS, whereas leptin signalling pathway is involved in insulin sensitivity.

CONCLUSIONS

These results show that graph cluster analysis can provide additional insight into the pathobiology of PCOS, as the pathways identified as statistically significant correspond to earlier biological studies. Therefore, integrative analysis can reveal unknown mechanisms, which may enable the development of accurate diagnosis and effective treatment in PCOS.

Nanoparticle Mobility over a Surface as a Probe for Weak Transient Disordered Peptide-Peptide Interactions

Weak interactions form the core basis of a vast number of biological processes, in particular, those involving intrinsically disordered proteins. Here, we establish a new technique capable of probing these weak interactions between synthetic unfolded polypeptides using a convenient yet efficient, quantitative method based on single particle tracking of peptide-coated gold nanoparticles over peptide-coated surfaces. We demonstrate that our technique is sensitive enough to observe the influence of a single amino acid mutation on the transient peptide-peptide interactions. Furthermore, the effects of buffer salinity, which are expected to alter weak electrostatic interactions, are also readily detected and examined in detail. The method presented here has the potential to evaluate, in a high-throughput manner, weak interactions for a wide range of disordered proteins, polypeptides, and other biomolecules.

Reconstruction and Functional Annotation of P311 Protein-Protein Interaction Network Reveals Its New Functions

P311 is a highly conserved multifunctional protein. However, it does not belong to any established family of proteins, and its biological function has not been entirely determined. This study aims to reveal the unknown molecular and cellular function of P311. OCG (Overlapping Cluster Generator) is a clustering method used to partition a protein-protein network into overlapping clusters. Multifunctional proteins are at the intersection of relevant clusters. DAVID is an analytic tool used to extract biological meaning from a large protein list. Here we presented OD2 (OCG + DAVID + 2 human PPI datasets), a novel strategy to increase the likelihood to identify biological functions most pertinent to the multifunctional proteins. The principle of OD2 is that OCG prepares the protein lists from multifunctional protein relevant overlapping clusters, for a functional enrichment analysis by DAVID, and the similar functional enrichments, which occurs simultaneously when analyzing two human PPI datasets, are supposed to be the predicted functions. By applying OD2 to two reconstructed human PPI datasets, we supposed the function of the P311 in inflammatory responses, cell proliferation and coagulation, which were confirmed by the following biological experiments. Collectively, our study preliminarily found that P311 could play a role in inflammatory responses, cell proliferation and coagulation. Further studies are required to validate and elucidate the underlying mechanism.

Network analysis of membranous glomerulonephritis based on metabolomics data

Membranous glomerulonephritis (MGN) is one of the most frequent causes of nephrotic syndrome in adults. It is characterized by the thickening of the glomerular basement membrane in the renal tissue. The current diagnosis of MGN is based on renal biopsy and the detection of antibodies to the few podocyte antigens. Due to the limitations of the current diagnostic methods, including invasiveness and the lack of sensitivity of the current biomarkers, there is a requirement to identify more applicable biomarkers. The present study aimed to identify diagnostic metabolites that are involved in the development of the disease using topological features in the component‑reaction‑enzyme‑gene (CREG) network for MGN. Significant differential metabolites in MGN compared with healthy controls were identified using proton nuclear magnetic resonance and gas chromatography‑mass spectrometry techniques, and multivariate analysis. The CREG network for MGN was constructed, and metabolites with a high centrality and a striking fold‑change in patients, compared with healthy controls, were introduced as putative diagnostic biomarkers. In addition, a protein‑protein interaction (PPI) network, which was based on proteins associated with MGN, was built and analyzed using PPI analysis methods, including molecular complex detection and ClueGene Ontology. A total of 26 metabolites were identified as hub nodes in the CREG network, 13 of which had salient centrality and fold‑changes: Dopamine, carnosine, fumarate, nicotinamide D‑ribonucleotide, adenosine monophosphate, pyridoxal, deoxyguanosine triphosphate, L‑citrulline, nicotinamide, phenylalanine, deoxyuridine, tryptamine and succinate. A total of 13 subnetworks were identified using PPI analysis. In total, two of the clusters contained seed proteins (phenylalanine‑4‑hydroxlylase and cystathionine γ‑lyase) that were associated with MGN based on the CREG network. The following biological processes associated with MGN were identified using gene ontology analysis: 'Pyrimidine‑containing compound biosynthetic process', 'purine ribonucleoside metabolic process', 'nucleoside catabolic process', 'ribonucleoside metabolic process' and 'aromatic amino acid family metabolic process'. The results of the present study may be helpful in the diagnostic and therapeutic procedures of MGN. However, validation is required in the future.

Analyzing of Molecular Networks for Human Diseases and Drug Discovery

Molecular networks represent the interactions and relations of genes/proteins, and also encode molecular mechanisms of biological processes, development and diseases. Among the molecular networks, protein-protein Interaction Networks (PINs) have become effective platforms for uncovering the molecular mechanisms of diseases and drug discovery. PINs have been constructed for various organisms and utilized to solve many biological problems. In human, most proteins present their complex functions by interactions with other proteins, and the sum of these interactions represents the human protein interactome. Especially in the research on human disease and drugs, as an emerging tool, the PIN provides a platform to systematically explore the molecular complexities of specific diseases and the references for drug design. In this review, we summarized the commonly used approaches to aid disease research and drug discovery with PINs, including the network topological analysis, identification of novel pathways, drug targets and sub-network biomarkers for diseases. With the development of bioinformatic techniques and biological networks, PINs will play an increasingly important role in human disease research and drug discovery.

Biomarker panels for characterizing microbial community biofilm formation as composite molecular process

Microbial consortia execute collaborative molecular processes with contributions from individual species, on such basis enabling optimized molecular function. Such collaboration and synergies benefit metabolic flux specifically in extreme environmental conditions as seen in acid mine drainage, with biofilms as relevant microenvironment. However, knowledge about community species composition is not sufficient for deducing presence and efficiency of composite molecular function. For this task molecular resolution of the consortium interactome is to be retrieved, with molecular biomarkers particularly suited for characterizing composite molecular processes involved in biofilm formation and maintenance. A microbial species set identified in 18 copper environmental sites provides a data matrix for deriving a cross-species molecular process model of biofilm formation composed of 191 protein coding genes contributed from 25 microbial species. Computing degree and stress centrality of biofilm molecular process nodes allows selection of network hubs and central connectors, with the top ranking molecular features proposed as biomarker candidates for characterizing biofilm homeostasis. Functional classes represented in the biomarker panel include quorum sensing, chemotaxis, motility and extracellular polysaccharide biosynthesis, complemented by chaperones. Abundance of biomarker candidates identified in experimental data sets monitoring different biofilm conditions provides evidence for the selected biomarkers as sensitive and specific molecular process proxies for capturing biofilm microenvironments. Topological criteria of process networks covering an aggregate function of interest support the selection of biomarker candidates independent of specific community species composition. Such panels promise efficient screening of environmental samples for presence of microbial community composite molecular function.

MTGO: PPI Network Analysis Via Topological and Functional Module Identification

Protein-protein interaction (PPI) networks are viable tools to understand cell functions, disease machinery, and drug design/repositioning. Interpreting a PPI, however, it is a particularly challenging task because of network complexity. Several algorithms have been proposed for an automatic PPI interpretation, at first by solely considering the network topology, and later by integrating Gene Ontology (GO) terms as node similarity attributes. Here we present MTGO - Module detection via Topological information and GO knowledge, a novel functional module identification approach. MTGO let emerge the bimolecular machinery underpinning PPI networks by leveraging on both biological knowledge and topological properties. In particular, it directly exploits GO terms during the module assembling process, and labels each module with its best fit GO term, easing its functional interpretation. MTGO shows largely better results than other state of the art algorithms (including recent GO-based ones) when searching for small or sparse functional modules, while providing comparable or better results all other cases. MTGO correctly identifies molecular complexes and literature-consistent processes in an experimentally derived PPI network of Myocardial infarction. A software version of MTGO is available freely for non-commercial purposes at https://gitlab.com/d1vella/MTGO .

Protein Complexes and Virus-Like Particle Technology

Virus-like particle (VLP) technologies are based on virus-inspired artificial structures and the intrinsic ability of viral proteins to self-assemble at controlled conditions. Therefore, the basic knowledge about the mechanisms of viral particle formation is highly important for designing of industrial applications. As an alternative to genetic and chemical processes, different physical methods are frequently used for VLP construction, including well characterized protein complexes for introduction of foreign molecules in VLP structures.This chapter shortly discusses the mechanisms how the viruses form their perfectly ordered structures as well as the principles and most interesting application examples, how to exploit the structural and assembly/disassembly properties of viral structures for creation of new nanomaterials.

Hub Protein Controversy: Taking a Closer Look at Plant Stress Response Hubs

Plant stress responses involve numerous changes at the molecular and cellular level and are regulated by highly complex signaling pathways. Studying protein-protein interactions (PPIs) and the resulting networks is therefore becoming increasingly important in understanding these responses. Crucial in PPI networks are the so-called hubs or hub proteins, commonly defined as the most highly connected central proteins in scale-free PPI networks. However, despite their importance, a growing amount of confusion and controversy seems to exist regarding hub protein identification, characterization and classification. In order to highlight these inconsistencies and stimulate further clarification, this review critically analyses the current knowledge on hub proteins in the plant interactome field. We focus on current hub protein definitions, including the properties generally seen as hub-defining, and the challenges and approaches associated with hub protein identification. Furthermore, we give an overview of the most important large-scale plant PPI studies of the last decade that identified hub proteins, pointing out the lack of overlap between different studies. As such, it appears that although major advances are being made in the plant interactome field, defining hub proteins is still heavily dependent on the quality, origin and interpretation of the acquired PPI data. Nevertheless, many hub proteins seem to have a reported role in the plant stress response, including transcription factors, protein kinases and phosphatases, ubiquitin proteasome system related proteins, (co-)chaperones and redox signaling proteins. A significant number of identified plant stress hubs are however still functionally uncharacterized, making them interesting targets for future research. This review clearly shows the ongoing improvements in the plant interactome field but also calls attention to the need for a more comprehensive and precise identification of hub proteins, allowing a more efficient systems biology driven unraveling of complex processes, including those involved in stress responses.

DIGNiFI: Discovering causative genes for orphan diseases using protein-protein interaction networks

BACKGROUND

An orphan disease is any disease that affects a small percentage of the population. Orphan diseases are a great burden to patients and society, and most of them are genetic in origin. Unfortunately, our current understanding of the genes responsible for inherited orphan diseases is still quite limited. Developing effective computational algorithms to discover disease-causing genes would help unveil disease mechanisms and may enable better diagnosis and treatment.

RESULTS

We have developed a novel method, named as DIGNiFI (Disease causIng GeNe FInder), which uses Protein-Protein Interaction (PPI) network-based features to discover and rank candidate disease-causing genes. Specifically, our approach computes topologically similar genes by taking into account both local and global connected paths in PPI networks via Direct Neighbors and Local Random Walks, respectively. Furthermore, since genes with similar phenotypes tend to be functionally related, we have integrated PPI data with gene ontology (GO) annotations and protein complex data to further improve the performance of this approach. Results of 128 orphan diseases with 1184 known disease genes collected from the Orphanet show that our proposed methods outperform existing state-of-the-art methods for discovering candidate disease-causing genes. We also show that further performance improvement can be achieved when enriching the human-curated PPI network data with text-mined interactions from the biomedical literature. Finally, we demonstrate the utility of our approach by applying our method to identifying novel candidate genes for a set of four inherited retinal dystrophies. In this study, we found the top predictions for these retinal dystrophies consistent with literature reports and online databases of other retinal dystrophies.

CONCLUSIONS

Our method successfully prioritizes orphan-disease-causative genes. This method has great potential to benefit the field of orphan disease research, where resources are scarce and greatly needed.