Interactome Network Analysis Research Articles

Zoledronic acid-treated rats show altered jaw histology and gene expression in non-exposed MRONJ

BackgroundDiagnosis and management of medication-related osteonecrosis of the jaws (MRONJ) prior to clinical exposure induced by trauma may lead to improved patient management. Currently, few studies have examined early histologic and molecular MRONJ-related changes in the jaws. PurposeThis study aimed to identify histological and gene expression changes in the maxilla and mandible of Sprague-Dawley (SD) rats after zoledronic acid (ZA) treatment. Study Design, Setting, and SampleThis was an in-vivo animal study. The experiments were conducted in the laboratory at the Stomatology Hospital of Tianjin Medical University. A total of 12 SD rats were included. Predictor variableThe predictor variable was ZA exposure. Twelve SD rats were divided into two groups: experimental (n = 6) and control (n = 6), and they were intraperitoneally injected with ZA and saline, respectively. Main outcome variableThe outcome variables were histological and molecular changes. The maxilla, mandible, and ilium bone tissue samples were examined using Masson's trichrome and hematoxylin-eosin staining. Gene expression changes were identified using transcriptome sequencing, Kyoto encyclopedia of genes and genomes (KEGG), and gene interactome network analysis. The key changes were validated using the quantitative real-time polymerase chain reaction and immunohistochemistry. CovariatesNone. AnalysesThe t-test, Chi-square test, and Fisher's exact probability method were used for statistical analyses using the Statistical package for the social sciences software (version 26.0). ResultsAll animals remained healthy during the experiments. Histological staining revealed that the percentage of empty bone lacunae in the maxilla and mandible was significantly higher than that in the ilium (P < 0.01). In total, 552 genes were screened using transcriptome sequencing. The sonic hedgehog (Shh) signaling pathway was highly enriched. The key gene for the Shh interaction was distal-less homeobox 5 (Dlx-5). The Shh, Dlx-5, and bone morphogenetic protein 2 genes and protein expression levels in the maxilla and mandible were higher in the experimental group than in the control group (P < 0.05). Conclusions and RelevanceMRONJ-induced osteonecrosis and gene expression changes precede trauma-induced clinical changes in the SD rat model. These findings may provide additional support for timely and clinically early diagnosis and intervention.

Metabolic endophenotype associated with right ventricular glucose uptake in pulmonary hypertension.

Alterations in metabolism and bioenergetics are hypothesized in the mechanisms leading to pulmonary vascular remodeling and heart failure in pulmonary hypertension (PH). To test this, we performed metabolomic analyses on 30 PH individuals and 12 controls. Furthermore, using 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography, we dichotomized PH patients into metabolic phenotypes of high and low right ventricle (RV) glucose uptake and followed them longitudinally. In support of metabolic alterations in PH and its progression, the high RV glucose group had higher RV systolic pressure (p < 0.001), worse RV function as measured by RV fractional area change and peak global longitudinal strain (both p < 0.05) and may be associated with poorer outcomes (33% death or transplantation in the high glucose RV uptake group compared to 7% in the low RV glucose uptake group at five years follow-up, log-ranked p = 0.07). Pathway enrichment analysis identified key metabolic pathways including fructose catabolism, arginine-nitric oxide metabolism, tricarboxylic acid cycle, and ketones metabolism. Integrative human protein-protein interactome network analysis of metabolomic and transcriptomic data identified key pathobiological pathways: arginine biosynthesis, tricarboxylic acid cycle, purine metabolism, hypoxia-inducible factor 1, and apelin signaling. These findings identify a PH metabolomic endophenotype, and for the first time link this to disease severity and outcomes.

Identifying miRNAs in multiple sclerosis gray matter lesions that correlate with atrophy measures.

ObjectiveMultiple sclerosis (MS) is an inflammatory, demyelinating and neurodegenerative disease of the central nervous system (CNS). Though MS was initially considered to be a white matter demyelinating disease, myelin loss in cortical gray matter has been reported in all disease stages. We previously identified microRNAs (miRNAs) in white matter lesions (WMLs) that are detected in serum from MS patients. However, miRNA expression profiles in gray matter lesions (GMLs) from progressive MS brains are understudied.MethodsWe used a combination of global miRNAs and gene expression profiling of GMLs and independent validation using real‐time quantitative polymerase chain reaction (RT‐qPCR), immuno‐in situ hybridization, and immunohistochemistry.ResultsCompared to matched myelinated gray matter (GM) regions, we identified 82 miRNAs in GMLs, of which 10 were significantly upregulated and 17 were significantly downregulated. Among these 82 miRNAs, 13 were also detected in serum and importantly were associated with brain atrophy in MS patients. The predicted target mRNAs of these miRNAs belonged to pathways associated with axonal guidance, TGF‐β signaling, and FOXO signaling. Further, using state‐of‐the‐art human protein–protein interactome network analysis, we mapped the four key GM atrophy‐associated miRNAs (hsa‐miR‐149*, hsa‐miR‐20a, hsa‐miR‐29c, and hsa‐miR‐25) to their target mRNAs that were also changed in GMLs.InterpretationOur study identifies miRNAs altered in GMLs in progressive MS brains that correlate with atrophy measures. As these miRNAs were also detected in sera of MS patients, these could act as markers of GML demyelination in MS.

A network-based approach to uncover microRNA-mediated disease comorbidities and potential pathobiological implications

Disease–disease relationships (e.g., disease comorbidities) play crucial roles in pathobiological manifestations of diseases and personalized approaches to managing those conditions. In this study, we develop a network-based methodology, termed meta-path-based Disease Network (mpDisNet) capturing algorithm, to infer disease–disease relationships by assembling four biological networks: disease–miRNA, miRNA–gene, disease–gene, and the human protein–protein interactome. mpDisNet is a meta-path-based random walk to reconstruct the heterogeneous neighbors of a given node. mpDisNet uses a heterogeneous skip-gram model to solve the network representation of the nodes. We find that mpDisNet reveals high performance in inferring clinically reported disease–disease relationships, outperforming that of traditional gene/miRNA-overlap approaches. In addition, mpDisNet identifies network-based comorbidities for pulmonary diseases driven by underlying miRNA-mediated pathobiological pathways (i.e., hsa-let-7a- or hsa-let-7b-mediated airway epithelial apoptosis and pro-inflammatory cytokine pathways) as derived from the human interactome network analysis. The mpDisNet offers a powerful tool for network-based identification of disease–disease relationships with miRNA-mediated pathobiological pathways.

Protein interactome network analysis predicts novel breast cancer candidate genes with molecular signatures

Abstract Background Advancement in molecular biology research has allowed the measurement of multiomics data points from a single tumor biopsy sample in a reasonable time frame for making significant clinical decisions. There have been tremendous advances from the bioinformatics and systems biology perspective to analyse integrated multi‐scale analysis of the data will prove invaluable for a combined systems‐level understanding of the important biological network processes contributing to the initiation, progression and management of cancer. Cancer networks have greater challenge due to the aberrant functions of hundreds of genes that are translated to altered protein function within each cancer cell, to understand this complexity. Network-based methods have been used to analyse the complexity molecular interactions in the cell and can contribute to prediction of candidate genes responsible for cancer. Methods HI-II- 14 is the largest experimentally determined binary protein – protein interaction map is used for the study. These interactions were mapped to NCBI Entrez gene ID and excluded self-loop and redundant interactions. The known human breast cancer genes were obtained from the Sanger Cancer Gene Census which is a comprehensive catalogue of genes implicated in breast cancer and from Uniprot database. In total, we obtained 913 genes from both for analyses. The genes degree of connections and the centrality in the protein -protein networks were considered. The genome-wide mutation datasets of breast cancer were downloaded from TCGA as maf files. For each gene, we obtained the mutation frequency for each gene is calculated which is defined as the number of mutated samples divided by the total number of samples. The frequency of the top 100 ranked genes and the bottom 100 ranked genes were compared by Wilcox rank sum test. Results In this study, HI-II-14 human protein-protein interactome network is applied to prediction of novel breast cancer genes. Conclusions Our study suggests integrating interactome networks with multiomics datasets could provide novel insights into breast cancer-associated genes and underlying molecular mechanisms. Legal entity responsible for the study M. Peer Mohammed. Funding Has not received any funding. Disclosure The author has declared no conflicts of interest.

Reprogramming of cancer invasiveness and macrophage education via a nanostructured antagonist of the TGFβ receptor

An interactome network analysis reveals functional intervention of TGFβ signaling by nanodiamonds, which diminishes cancer metastasis, tumor-associated macrophage recruitment and M2 macrophage polarization.

Traditional herbal medicine-derived sulforaphene promotes mitophagic cell death in lymphoma cells through CRM1-mediated p62/SQSTM1 accumulation and AMPK activation

Sulforaphene (LFS-01) is the major chemical constituent of Raphanus sativus, a medicinal herb used for over a thousand years in traditional Chinese medicine. Here we identified that LFS-01 can selectively eradicate lymphoma cells while sparing normal lymphocytes by triggering concomitant mitophagy and apoptosis. We demonstrated that LFS-01 can retain Nrf2 in the nucleus by covalently modulating CRM1 and consequently upregulate p62/SQSTM1, an essential structural component of the autophagosomes during mitophagic process. We found that LFS-01 treatment also stimulated AMPK and thereby inhibited the mTOR pathway. On the contrary, we revealed that AMPK inhibition can severely impair the LFS-01-mediated mitophagy. Transcriptomic studies confirmed that 15 autophagy-associated genes such as p62/SQSTM1, VCP and BCL2 were differentially expressed after LFS-01 treatment. Furthermore, protein interactome network analysis revealed that the events of apoptosis and the assembly of autophagy vacuole were significant upon LFS-01 exposure. Lastly, we found that LFS-01 exhibited strong efficacy in xenograft mouse model yet with the lack of apparent toxicity to animals. We concluded that LFS-01 triggered mitophagic cell death via CRM1-mediated p62 overexpression and AMPK activation. Our findings provide new insights into the mechanism of action for LFS-01 and highlight its potential applications in treating major human diseases.

Insights into specificity, redundancy and new cellular functions of C/EBPa and C/EBPb transcription factors through interactome network analysis

BackgroundC/EBPa and C/EBPb are transcription factors with tissue specific expression regulating several important cellular processes. They work by recruiting protein complexes to a common DNA recognition motif and both are able to compensate each other's absence in many cell types, thus showing functional redundancy. They also play distinct roles in specific cellular pathways and their abnormal functioning gives raise to different human pathologies. MethodsTo investigate the molecular basis of C/EBPa and C/EBPb specificity and redundancy we characterized their in vivo protein-protein interaction networks by Tandem Affinity Purification (TAP) and Mass Spectrometry (MS). To unravel the functional features of C/EBPa and C/EBPb proteomes we studied the statistical enrichment of binding partners related to Gene Ontology (GO) terms and KEGG pathways. ResultsOur data confirmed that the C/EBPa and C/EBPb regulate biological processes like cell proliferation, apoptosis and transformation. We found that both C/EBPa and C/EBPb are involved in other cellular pathways such as RNA maturation, RNA splicing and DNA repair. Specific interactions of C/EBPa with MRE11, RUVBL1 and RUVBL2 components of DNA repair system were confirmed by co-immunoprecipitation assays. ConclusionsOur comparative analysis of the C/EBPa and C/EBPb proteomes provides an insight for understanding both their redundant and specific roles in cells indicating their involvement in new pathways. Such novel predicted functions are relevant to normal cellular processes and disease phenotypes controlled by these transcription factors. General significanceFunctional characterization of C/EBPa and C/EBPb proteomes suggests they can regulate novel pathways and indicate potential molecular targets for therapeutic intervention.

Interactome network analysis identifies multiple caspase-6 interactors involved in the pathogenesis of HD.

Caspase-6 (CASP6) has emerged as an important player in Huntington disease (HD), Alzheimer disease (AD) and cerebral ischemia, where it is activated early in the disease process. CASP6 also plays a key role in axonal degeneration, further underscoring the importance of this protease in neurodegenerative pathways. As a protein's function is modulated by its protein-protein interactions, we performed a high-throughput yeast-2-hybrid (Y2H) screen against ∼17,000 human proteins to gain further insight into the function of CASP6. We identified a high-confidence list of 87 potential CASP6 interactors. From this list, 61% are predicted to contain a CASP6 recognition site. Of nine candidate substrates assessed, six are cleaved by CASP6. Proteins that did not contain a predicted CASP6 recognition site were assessed using a LUMIER assay approach, and 51% were further validated as interactors by this method. Of note, 54% of the high-confidence interactors identified show alterations in human HD brain at the mRNA level, and there is a significant enrichment for previously validated huntingtin (HTT) interactors. One protein of interest, STK3, a pro-apoptotic kinase, was validated biochemically to be a CASP6 substrate. Furthermore, our results demonstrate that in striatal cells expressing mutant huntingtin (mHTT), an increase in full length and fragment levels of STK3 are observed. We further show that caspase-3 is not essential for the endogenous cleavage of STK3. Characterization of the interaction network provides important new information regarding key pathways of interactors of CASP6 and highlights potential novel therapeutic targets for HD, AD and cerebral ischemia.

Anticancer activity of galactoxyloglucan polysaccharide-conjugated doxorubicin nanoparticles: Mechanistic insights and interactome analysis

Toxicity associated with chemotherapeutic drugs such as doxorubicin (Dox), is one of the major obstacles that is currently affecting patients. PST-Dox (Galactoxyloglucan, PST001-conjugated Dox) nanoparticles were synthesized by encapsulating Dox with polysaccharide PST001, isolated from Tamarindus indica (Ti) by ionic gelation with tripolyphosphate (TPP). Herein, we demonstrate a detailed mechanistic and interactome network analysis that is specific to PST-Dox action in cancer cells and normal lymphocytes. Our results show that PST-Dox is superior to its parental counterparts, exhibiting a greater cytotoxicity by the induction of apoptosis against a wide variety of cancers by enhanced cellular uptake of Dox from the nanoparticle conjugates. Also, PST-Dox nanoparticles were non-toxic to normal lymphocytes with limited immunostimulatory effects up to certain doses. Elucidation of molecular mechanism by whole genome microarray in cancer cells and lymphocytes revealed that a large number of genes were dysregulated specifically in cancer cells. Specifically, a unique target gene EGR1, contextually determined translational activation of P53 in the cancerous and non-cancerous cells. Most of the key downregulated genes were tyrosine kinases, indicating the potential inhibitory action of PST-Dox on tyrosine kinase oncogenic pathways. Western blotting of proteins corresponding to the genes that were altered at the genomic level was very well correlated in the majority of them, except in a few that demonstrated post-transcriptional modifications. The important findings and highly disciplined approaches highlighted in the present study will speed up the therapeutic potential of this augmented nanoparticle formulation for more robust clinical studies and testing in several cancers.

Changes in the transcriptional profile in response to overexpression of the osteopontin-c splice isoform in ovarian (OvCar-3) and prostate (PC-3) cancer cell lines.

BackgroundEspecially in human tumor cells, the osteopontin (OPN) primary transcript is subject to alternative splicing, generating three isoforms termed OPNa, OPNb and OPNc. We previously demonstrated that the OPNc splice variant activates several aspects of the progression of ovarian and prostate cancers. The goal of the present study was to develop cell line models to determine the impact of OPNc overexpression on main cancer signaling pathways and thus obtain insights into the mechanisms of OPNc pro-tumorigenic roles.MethodsHuman ovarian and prostate cancer cell lines, OvCar-3 and PC-3 cells, respectively, were stably transfected to overexpress OPNc. Transcriptomic profiling was performed on these cells and compared to controls, to identify OPNc overexpression-dependent changes in gene expression levels and pathways by qRT-PCR analyses.ResultsAmong 84 genes tested by using a multiplex real-time PCR Cancer Pathway Array approach, 34 and 16, respectively, were differentially expressed between OvCar-3 and PC-3 OPNc-overexpressing cells in relation to control clones. Differentially expressed genes are included in all main hallmarks of cancer, and several interacting proteins have been identified using an interactome network analysis. Based on marked up-regulation of Vegfa transcript in response to OPNc overexpression, we partially validated the array data by demonstrating that conditioned medium (CM) secreted from OvCar-3 and PC-3 OPNc-overexpressing cells significantly induced endothelial cell adhesion, proliferation and migration, compared to CM secreted from control cells.ConclusionsOverall, the present study elucidated transcriptional changes of OvCar-3 and PC-3 cancer cell lines in response to OPNc overexpression, which provides an assessment for predicting the molecular mechanisms by which this splice variant promotes tumor progression features.

Identification of functional hubs and modules by converting interactome networks into hierarchical ordering of proteins

BackgroundProtein-protein interactions play a key role in biological processes of proteins within a cell. Recent high-throughput techniques have generated protein-protein interaction data in a genome-scale. A wide range of computational approaches have been applied to interactome network analysis for uncovering functional organizations and pathways. However, they have been challenged because ofcomplex connectivity. It has been investigated that protein interaction networks are typically characterized by intrinsic topological features: high modularity and hub-oriented structure. Elucidating the structural roles of modules and hubs is a critical step in complex interactome network analysis.ResultsWe propose a novel approach to convert the complex structure of an interactome network into hierarchical ordering of proteins. This algorithm measures functional similarity between proteins based on the path strength model, and reveals a hub-oriented tree structure hidden in the complex network. We score hub confidence and identify functional modules in the tree structure of proteins, retrieved by our algorithm. Our experimental results in the yeast protein interactome network demonstrate that the selected hubs are essential proteins for performing functions. In network topology, they have a role in bridging different functional modules. Furthermore, our approach has high accuracy in identifying functional modules hierarchically distributed.ConclusionsDecomposing, converting, and synthesizing complex interaction networks are fundamental tasks for modeling their structural behaviors. In this study, we systematically analyzed complex interactome network structures for retrievingfunctional information. Unlike previous hierarchical clustering methods, this approach dynamically explores the hierarchical structure of proteins in a global view. It is well-applicable to the interactome networks in high-level organisms because of its efficiency and scalability.

'Edgetic' perturbation of a C. elegans BCL2 ortholog

Genes and gene products do not function in isolation but within highly interconnected “interactome” networks, modeled as graphs of nodes and edges representing macromolecules and interactions between them, respectively. We propose to investigate genotype-phenotype associations by methodical use of alleles that lack single interactions, while retaining all others, in contrast to genetic approaches designed to eliminate gene products completely. We describe an integrated strategy based on the reverse yeast two-hybrid system to isolate and characterize such edge-specific, or “edgetic” alleles. We establish a proof-of-concept with CED-9, a C. elegans BCL2 ortholog involved in apoptosis. Using ced-9 edgetic alleles, we uncover a new potential functional link between apoptosis and a centrosomal protein, demonstrating both the interest and efficiency of our strategy. This approach is amenable to higher throughput and is particularly applicable to interactome network analysis in organisms for which transgenesis is straightforward.