Biological Interaction Networks Research Articles

Inhibition of the NRF2/KEAP1 Axis: A Promising Therapeutic Strategy to Alter Redox Balance of Cancer Cells.

Significance: The nuclear factor erythroid 2-related factor 2/Kelch-like ECH-associated protein 1 (NRF2/KEAP1) pathway is a crucial and highly conserved defensive system that is required to maintain or restore the intracellular homeostasis in response to oxidative, electrophilic, and other types of stress conditions. The tight control of NRF2 function is maintained by a complex network of biological interactions between positive and negative regulators that ultimately ensure context-specific activation, culminating in the NRF2-driven transcription of cytoprotective genes. Recent Advances: Recent studies indicate that deregulated NRF2 activation is a frequent event in malignant tumors, wherein it is associated with metabolic reprogramming, increased antioxidant capacity, chemoresistance, and poor clinical outcome. On the other hand, the growing interest in the modulation of the cancer cells' redox balance identified NRF2 as an ideal therapeutic target. Critical Issues: For this reason, many efforts have been made to identify potent and selective NRF2 inhibitors that might be used as single agents or adjuvants of anticancer drugs with redox disrupting properties. Despite the lack of specific NRF2 inhibitors still represents a major clinical hurdle, the researchers have exploited alternative strategies to disrupt NRF2 signaling at different levels of its biological activation. Future Directions: Given its dualistic role in tumor initiation and progression, the identification of the appropriate biological context of NRF2 activation and the specific clinicopathological features of patients cohorts wherein its inactivation is expected to have clinical benefits, will represent a major goal in the field of cancer research. In this review, we will briefly describe the structure and function of the NRF2/ KEAP1 system and some of the most promising NRF2 inhibitors, with a particular emphasis on natural compounds and drug repurposing. Antioxid. Redox Signal. 34, 1428-1483.

True scale-free networks hidden by finite size effects

We analyze about 200 naturally occurring networks with distinct dynamical origins to formally test whether the commonly assumed hypothesis of an underlying scale-free structure is generally viable. This has recently been questioned on the basis of statistical testing of the validity of power law distributions of network degrees. Specifically, we analyze by finite size scaling analysis the datasets of real networks to check whether the purported departures from power law behavior are due to the finiteness of sample size. We find that a large number of the networks follows a finite size scaling hypothesis without any self-tuning. This is the case of biological protein interaction networks, technological computer and hyperlink networks, and informational networks in general. Marked deviations appear in other cases, especially involving infrastructure and transportation but also in social networks. We conclude that underlying scale invariance properties of many naturally occurring networks are extant features often clouded by finite size effects due to the nature of the sample data.

Bioinformatics analysis of the microRNA-mRNA network in sebaceous gland carcinoma of the eyelid.

Sebaceous gland carcinoma (SGC) of the eyelid is an uncommon aggressive tumor with a relatively high rate of local recurrence and a poor prognosis following metastasis. However, the molecular mechanisms underlying the pathogenesis of SGC remain unclear. The purpose of the present study was to clarify microRNA (miRNA) expression profiles in SGC and to explore novel miRNA-mRNA networks of SGC. A small RNA-sequencing analysis was performed to identify miRNAs differentially expressed between SGC and sebaceous adenoma control samples. Bioinformatics analyses were conducted to reveal biological functions, canonical pathways and molecular interaction networks using integrated miRNA-mRNA datasets, including mRNA expression profiles of SGC from our previous study. The present results demonstrated that 16 upregulated miRNAs and 516 downregulated mRNAs were associated with loss of lipid metabolism function and enriched in cholesterol biosynthesis pathways. By contrast, 29 downregulated miRNAs and 194 upregulated mRNAs were mainly associated with the promotion of cell survival and proliferation in addition to enrichment of DNA damage-induced cell cycle-regulation pathways. Furthermore, network analyses revealed that the upregulated miRNAs, miR-130a-3p and miR-939-5p, and the downregulated miRNAs, miR-146a-5p, miR-149-3p, miR-193a-3p, miR-195-5p and miR-4671-3p, could be upstream regulators related to these functional changes of SGC. These results improved the understanding of molecular mechanisms of SGC and may help to improve the diagnosis of SGC.

Network Building with the Cytoscape BioGateway App Explained in Five Use Cases.

The BioGateway App is a plugin for the Cytoscape network editor, allowing users to interactively build biological networks by querying the Biogateway Resource Description Framework (RDF) triple store. BioGateway contains information from several curated resources including UniProtKB, IntAct, Gene Ontology Annotations, various datasets containing transcription-factor regulatory relations to specific target genes, and more. The BioGateway App facilitates the step-by-step creation of complex SPARQL queries through an intuitive Graphical User Interface, allowing users to build and explore biological interaction networks to assess, among other things, gene regulatory relationships, gene ontology annotations, and protein-protein interactions. As the BioGateway information content is most abundant for human proteins and genes, this article describes the utility of the tool through a series of use cases on these human data, starting from the most basic levels and then detailing applications that address some of the rich complexity of the integrated data. Network refinement and display can be further optimized via the selection and filtering possibilities that the Cytoscape framework provides. The use cases also provide examples to explore network information in other species, as they become supported by BioGateway. © 2020 The Authors. Basic Protocol 1: Introducing a node from the canvas Basic Protocol 2: Introducing a node from the query builder Basic Protocol 3: Exploring molecular relationships between diseases Basic Protocol 4: Find proteins with protein kinase activity involved in a disease and explore the context around them Basic Protocol 5: Exploring the potential downstream effects after targeted inhibition of proteins Support Protocol: Installation of the BioGateway plugin through the Cytoscape App Manager and from source.

Metabolomics Deciphers Potential Targets of Xuefu Zhuyu Decoction Against Traumatic Brain Injury in Rat.

Xuefu Zhuyu decoction (XFZYD) performs multiple functions for traumatic brain injury (TBI) treatment. However, its clinical application is limited by the incomplete exploration of targets and inadequate discussion of mechanisms. We aimed to investigate the metabolic alterations of XFZYD in acute and chronic stages of TBI. Sprague-Dawley rats were randomly divided into the sham, controlled cortical impact (CCI) and XFZYD group. Behavioral and histopathological tests were used to evaluate the neuroprotective effects. Coagulation assays were performed to assess safety. Moreover, we analyzed the metabolomic profiling of hippocampal samples with different time intervals after CCI by high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). Differential metabolites were screened by multivariate data analysis. To further uncover the association between candidate metabolites and biological interaction networks, we applied bioinformatics analysis using MetaboAnalyst 4.0, STITCH 5.0 and TCMSP. The potential mechanism was verified by ELISA and Western blot. XFZYD ameliorated neurological deficiencies post-CCI without impairing blood coagulation in the rat’s model. Seventeen and fourteen metabolites were filtered on d 3 and 21, respectively. Eleven of potential metabolites were common at these time points, involving two significant pathways (arginine and proline metabolism, phenylalanine, tyrosine and tryptophan biosynthesis). Gamma-aminobutyric acid (GABA) and the related pathways were specifically affected by XFZYD at the acute phase of TBI, while biosynthesis of amino acids was the major pathway influenced at the chronic phase. This study provides broad insights into the therapeutic effects of XFZYD in treating TBI through the whole phases.

CaNDis: a web server for investigation of causal relationships between diseases, drugs and drug targets.

MotivationCausal biological interaction networks represent cellular regulatory pathways. Their fusion with other biological data enables insights into disease mechanisms and novel opportunities for drug discovery.ResultsWe developed Causal Network of Diseases (CaNDis), a web server for the exploration of a human causal interaction network, which we expanded with data on diseases and FDA-approved drugs, on the basis of which we constructed a disease–disease network in which the links represent the similarity between diseases. We show how CaNDis can be used to identify candidate genes with known and novel roles in disease co-occurrence and drug–drug interactions.Availabilityand implementationCaNDis is freely available to academic users at http://candis.ijs.si and http://candis.insilab.org.Supplementary information Supplementary data are available at Bioinformatics online.

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

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

Using the TCGA Database to Predict and Analyze Tumor Microenvironment Genes Related to Poor Prognosis of Colon Cancer.

BackgroundColon cancer (COAD) is a highly malignant gastrointestinal cancer. The existence of the TCGA database allows us to more easily perform gene expression profiling and data mining on colon cancer patients worldwide, and to more easily discover the correlation between genes and survival prognosis of colon cancer. Related reports show that the degree of infiltration of tumor immune cells and stromal cells in tumor microenvironment cells has a significant impact on the prognosis of cancer patients.Material/MethodsThe immune and stromal components in colon cancer can be quantitatively analyzed using relevant scores obtained by use of the ESTIMATE calculation method. To better explain the effect of relevant genes of cells associated with immunity and stroma on the survival prognosis of colon cancer, we divided the data from 191 downloaded case into high and low groups according to their scores of immunity and stroma, and identified differentially expressed genes.ResultsThe results showed that immune and stromal scores were significantly associated with survival prognosis. After performing biological function enrichment analysis and protein interaction network on the target genes, the results showed that these genes are mainly involved in inflammatory response, immune response, and chemotaxis. We then performed relevant survival prognosis analysis of these genes.ConclusionsWe found a number of genes that possess the properties of tumor immune microenvironment and can predict poor prognosis of colon cancer.

Metabolite Genome-Wide Association Study (mGWAS) and Gene-Metabolite Interaction Network Analysis Reveal Potential Biomarkers for Feed Efficiency in Pigs.

Metabolites represent the ultimate response of biological systems, so metabolomics is considered the link between genotypes and phenotypes. Feed efficiency is one of the most important phenotypes in sustainable pig production and is the main breeding goal trait. We utilized metabolic and genomic datasets from a total of 108 pigs from our own previously published studies that involved 59 Duroc and 49 Landrace pigs with data on feed efficiency (residual feed intake (RFI)), genotype (PorcineSNP80 BeadChip) data, and metabolomic data (45 final metabolite datasets derived from LC-MS system). Utilizing these datasets, our main aim was to identify genetic variants (single-nucleotide polymorphisms (SNPs)) that affect 45 different metabolite concentrations in plasma collected at the start and end of the performance testing of pigs categorized as high or low in their feed efficiency (based on RFI values). Genome-wide significant genetic variants could be then used as potential genetic or biomarkers in breeding programs for feed efficiency. The other objective was to reveal the biochemical mechanisms underlying genetic variation for pigs’ feed efficiency. In order to achieve these objectives, we firstly conducted a metabolite genome-wide association study (mGWAS) based on mixed linear models and found 152 genome-wide significant SNPs (p-value < 1.06 × 10−6) in association with 17 metabolites that included 90 significant SNPs annotated to 52 genes. On chromosome one alone, 51 significant SNPs associated with isovalerylcarnitine and propionylcarnitine were found to be in strong linkage disequilibrium (LD). SNPs in strong LD annotated to FBXL4, and CCNC consisted of two haplotype blocks where three SNPs (ALGA0004000, ALGA0004041, and ALGA0004042) were in the intron regions of FBXL4 and CCNC. The interaction network revealed that CCNC and FBXL4 were linked by the hub gene N6AMT1 that was associated with isovalerylcarnitine and propionylcarnitine. Moreover, three metabolites (i.e., isovalerylcarnitine, propionylcarnitine, and pyruvic acid) were clustered in one group based on the low-high RFI pigs. This study performed a comprehensive metabolite-based genome-wide association study (GWAS) analysis for pigs with differences in feed efficiency and provided significant metabolites for which there is significant genetic variation as well as biological interaction networks. The identified metabolite genetic variants, genes, and networks in high versus low feed efficient pigs could be considered as potential genetic or biomarkers for feed efficiency.

Trypanosoma Cruzi Induces Phospho‐proteome Profile in Primary Colon Epithelial Cells During Early Infection

Trypanosoma cruzi, an obligate intracellular protozoan parasite, is the etiological agent of Chagas Disease. Due to globalization, the disease is now present in all economically advanced countries of the world. About 30% of T. cruzi infected individuals will develop cardiac, neurological, and/or gastrointestinal pathology, particularly megacolon. Megacolon, one of Chagas disease major pathologies, is accompanied by gastrointestinal motility disorders that have been attributed to alterations in the number of interstitial cells of Cajal and enteric nervous system defects. The objective of this study is to begin to define early molecular mechanisms that contribute to chagasic megacolon that can be exploited for development of molecular intervention strategies. In order to do this, we challenged primary colon epithelial cells (HCoEpiC) with invasive T. cruzi trypomastigotes at different time points to decipher changes in levels of phosphorylated proteins. Using a proteome profiler Human phospho‐kinase array, we show that T. cruzi induced a unique phospho‐proteome profile. We validated the phospho‐kinase array data via immunoblotting and immunofluorescence. Modulators of host immune response such JAK2, STAT1, and NF‐kB showed increased activation. Increased phosphorylated levels of kinases such, as p‐Src and p‐Fyn and the Heat Shock protein (p‐HSP27) chaperone illustrate dysregulation of intracellular signaling. We found that p‐JNK and p‐AKT exhibited increased phosphorylation and their downstream effectors p‐CREB and p‐c‐Jun showed increased nuclear colocalization. Early infection also induced increase thrompsopondin‐1 expression, a known fibrogenic protein. We generated a biological interaction network using Ingenuity Pathway Analysis (IPA) to further characterize protein‐protein interactions within the phospho‐proteome profile. In conclusion, we demonstrate a unique phospho‐proteome profile induced by T. cruzi during early infection of HCoEpiC and illustrate biological networks and signal transduction pathways that are activated during infection.Support or Funding InformationThis work was supported by NIH grants SC1AI127352, 5U54MD007586 (The RCMI Program in Health Disparities Research), and 5R25GM059994‐19 (RISE).

Genomic and Transcriptomic Landscape of Tumor Clonal Evolution in Cholangiocarcinoma.

Cholangiocarcinoma remained a severe threat to human health. Deciphering the genomic and/or transcriptomic profiles of tumor has been proved to be a promising strategy for exploring the mechanism of tumorigenesis and development, which could also provide valuable insights into Cholangiocarcinoma. However, little knowledge has been obtained regarding to how the alteration among different omics levels is connected. Here, using whole exome sequencing and transcriptome sequencing, we performed a thorough evaluation for the landscape of genome and transcriptome in cholangiocarcinoma and illustrate the alteration of tumor on different biological levels. Meanwhile, we also identified the clonal structure of each included tumor sample and discovered different clonal evolution patterns related to patients’ survival. Furthermore, we extracted subnetworks that were greatly influenced by tumor clonal/subclonal mutations or transcriptome change. The topology relationship between genes affected by genomic/transcriptomic changes in biological interaction networks revealed that alteration of genome and transcriptome was highly correlated, and somatic mutations located on important genes might affect the expression of numerous genes in close range.

A novel parallel Markov clustering method in biological interaction network analysis under multi-GPU computing environment

The various clustering methods are widely applied in analyzing biological interaction networks, such as the protein–protein interaction and the genetic interaction networks. With the rapid growth of these biological datasets in scale, much longer runtime is required to make cluster analyses on them. In this paper, we propose a novel parallel Markov clustering (MCL) method based on the ELLPACK-R sparse matrix format that can run on multiple graphic processing units (GPUs) equipped standalone computers. The method is implemented using the Compute Unified Device Architecture (CUDA) programming framework, and fine-grained warp-level optimization is introduced for improving the performance. The BioGRID, a large-scale and freely accessible database of protein and genetic interactions, is adopted as the dataset in the experiment. The method has been assessed on a desktop computer equipped with two NVIDIA GTX 1070 GPUs. The results show that the proposed multi-GPU method can conduct MCL clustering on the full-size BioGRID database with about 6.5 min, that is much faster than the CPU serial MCL implementation which needs almost an hour and a half execution time.

Fatty acid-binding protein 5 predicts poor prognosis in patients with uveal melanoma.

Fatty acid-binding protein 5 (FABP5), which participates in mediating the biological properties of tumor cells, has been recognized in several neoplasms. The present study aims to investigate FABP5 transcriptional expression profiles, reveal its underlying biological interaction networks and define its prognostic value in uveal melanoma (UVM). A total of 80 patients with UVM and their RNA-sequence data, available from The Cancer Genome Atlas (TCGA) database, was analyzed. A differential transcriptional expression profile was obtained from TCGA and the Oncomine databases. The survival benefits were analyzed using the Kaplan-Meier method and log-rank test. The correlation between FABP5 expression and immune infiltration level was analyzed using the Tumor Immune Estimation Resource database. Functional enrichment analyses using Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and signaling hallmarks were utilized to describe the biological process, molecular functions, cellular component and significantly involved pathways. The elevated transcriptional expression of FABP5 was significantly associated with shorter overall survival (OS) and worse progression-free survival (PFS) times in patients with UVM (P<0.001). Moreover, FABP5 expression was significantly and positively correlated with tumor purity and CD8+ T cells and was negatively correlated with the infiltrating levels of CD4+ T cells and neutrophils. Gene Set Enrichment Analysis was performed to obtain 100 significantly associated genes of FABP5 and FABP5 was found to be critical in several hallmark pathways, including allograft rejection, complement, interleukin-6/Janus kinase-STAT3 signaling, interferon γ response, inflammatory response and tumor necrosis factor α signaling via NFκB. The present study is the first to demonstrate that FABP5 expression was positively associated with progression-associated clinicopathological factors and poor prognosis in UVM, which suggests its likely function as an oncogene and prognostic marker in patients with UVM.

Endotactic Networks and Toric Differential Inclusions

An important dynamical property of biological interaction networks is persistence, which intuitively means that “no species goes extinct." It has been conjectured that dynamical system models of weakly reversible networks (i.e., networks for which each reaction is part of a cycle) are persistent. The property of persistence is also related to the well-known global attractor conjecture. An approach for the proof of the global attractor conjecture uses an embedding of weakly reversible dynamical systems into toric differential inclusions. We show that the larger class of endotactic dynamical systems can also be embedded into toric differential inclusions. Moreover, we show that, essentially, endotactic networks form the largest class of networks with this property.

Differential network inference via the fused D-trace loss with cross variables

Detecting the change of biological interaction networks is of great importance in biological and medical research. We proposed a simple loss function, named as CrossFDTL, to identify the network change or differential network by estimating the difference between two precision matrices under Gaussian assumption. The CrossFDTL is a natural fusion of the D-trace loss for the considered two networks by imposing the $\ell _{1}$ penalty to the differential matrix to ensure sparsity. The key point of our method is to utilize the cross variables, which correspond to the sum and difference of two precision matrices instead of using their original forms. Moreover, we developed an efficient minimization algorithm for the proposed loss function and further rigorously proved its convergence. Numerical results showed that our method outperforms the existing methods in both accuracy and convergence speed for the simulated and real data.

Integrate multi-omics data with biological interaction networks using Multi-view Factorization AutoEncoder (MAE)

BackgroundComprehensive molecular profiling of various cancers and other diseases has generated vast amounts of multi-omics data. Each type of -omics data corresponds to one feature space, such as gene expression, miRNA expression, DNA methylation, etc. Integrating multi-omics data can link different layers of molecular feature spaces and is crucial to elucidate molecular pathways underlying various diseases. Machine learning approaches to mining multi-omics data hold great promises in uncovering intricate relationships among molecular features. However, due to the “big p, small n” problem (i.e., small sample sizes with high-dimensional features), training a large-scale generalizable deep learning model with multi-omics data alone is very challenging.ResultsWe developed a method called Multi-view Factorization AutoEncoder (MAE) with network constraints that can seamlessly integrate multi-omics data and domain knowledge such as molecular interaction networks. Our method learns feature and patient embeddings simultaneously with deep representation learning. Both feature representations and patient representations are subject to certain constraints specified as regularization terms in the training objective. By incorporating domain knowledge into the training objective, we implicitly introduced a good inductive bias into the machine learning model, which helps improve model generalizability. We performed extensive experiments on the TCGA datasets and demonstrated the power of integrating multi-omics data and biological interaction networks using our proposed method for predicting target clinical variables.ConclusionsTo alleviate the overfitting problem in deep learning on multi-omics data with the “big p, small n” problem, it is helpful to incorporate biological domain knowledge into the model as inductive biases. It is very promising to design machine learning models that facilitate the seamless integration of large-scale multi-omics data and biomedical domain knowledge for uncovering intricate relationships among molecular features and clinical features.

Using mechanistic models for the clinical interpretation of complex genomic variation

The sustained generation of genomic data in the last decade has increased the knowledge on the causal mutations of a large number of diseases, especially for highly penetrant Mendelian diseases, typically caused by a unique or a few genes. However, the discovery of causal genes in complex diseases has been far less successful. Many complex diseases are actually a consequence of the failure of complex biological modules, composed by interrelated proteins, which can happen in many different ways, which conferring a multigenic nature to the condition that can hardly be attributed to one or a few genes. We present a mechanistic model, Hipathia, implemented in a web server that allows estimating the effect that mutations, or changes in the expression of genes, have over the whole system of human signaling and the corresponding functional consequences. We show several use cases where we demonstrate how different the ultimate impact of mutations with similar loss-of-function potential can be and how the potential pathological role of a damaged gene can be inferred within the context of a signaling network. The use of systems biology-based approaches, such as mechanistic models, allows estimating the potential impact of loss-of-function mutations occurring in proteins that are part of complex biological interaction networks, such as signaling pathways. This holistic approach provides an elegant alternative to gene-centric approaches that can open new avenues in the interpretation of the genomic variability in complex diseases.

MYSM1-AR complex-mediated repression of Akt/c-Raf/GSK-3β signaling impedes castration-resistant prostate cancer growth.

Epigenetic alterations that lead to dysregulated gene expression in the progression of castration-resistant prostate cancer (CRPC) remain elusive. Here, we investigated the role of histone deubiquitinase MYSM1 in the pathogenesis of prostate cancer (PCa). Tissues and public datasets of PCa were evaluated for MYSM1 levels. We explored the effects of MYSM1 on cell proliferation, senescence and viability both in vitro and in vivo. Integrative database analyses and co-immunoprecipitation assays were performed to elucidate genomic association of MYSM1 and MYSM1-involved biological interaction network in PCa. We observed that MYSM1 were downregulated in CRPC compared to localized prostate tumors. Knockdown of MYSM1 promoted cell proliferation and suppressed senescence of CRPC cells under condition of androgen ablation. MYSM1 downregulation enhanced the tumorigenic ability in nude mice. Integrative bioinformatic analyses of the significantly associated genes with MYSM1 revealed MYSM1-correlated pathways, providing substantial clues as to the role of MYSM1 in PCa. MYSM1 was able to bind to androgen receptor instead of increasing its expression and knockdown of MYSM1 resulted in activation of Akt/c-Raf/GSK-3β signaling. Together, our findings indicate that MYSM1 is pivotal in CRPC pathogenesis and may be established as a potential target for future treatment.

Expression And Biological Interaction Network Of RHOC For Hepatic Carcinoma With Metastasis In PBMC Samples.

ObjectivesHepatic carcinoma with metastasis remains incurable, and clinical diagnostic methods lacked adequate sensitivity and specificity. Therefore, seeking effectively diagnostic biomarkers is still essential for it. RHOC was reported to be linked to metastasis of hepatic carcinoma. However, almost all of the studies used tissues as detection samples, which was not ideal for clinical course minoring. Therefore, here, it was aimed to use PBMC samples that were not only easily accessible but also minimally invasive to determine the expression and biological interaction network of RHOC for hepatic carcinoma with metastasis.MethodsPBMC samples were isolated. Then, RNA-seq was performed to identify the DEGs between hepatic carcinoma with metastasis and hepatic carcinoma with solitary tumor. Subsequently, q-RT-PCR was used to verify the expression level of RHOC. Finally, bioinformatic analysis was used to present the biological interaction network of RHOC for hepatic carcinoma with metastasis in PBMC samples.ResultsThe results of both RNA-seq and q-RT-PCR showed that the expression level of RHOC was significantly higher in the PBMC samples of hepatic carcinoma with metastasis than in those of hepatic carcinoma with solitary tumor. By using variety of bioinformatic analysis platforms, in PBMCs, 18 co-expression genes with RHOC were identified and their interaction network showed that MYL9 and RHOC had the highest edge evidence, and were involved in some cell migration-related pathways.ConclusionOur results indicated that RHOC in PBMCs could be potentially minimally invasive indicators for the diagnosis and clinical course supervision of hepatic carcinoma with metastasis, and its biological interaction network determined based on bioinformatic methods would lay a foundation for further study of the role of RHOC in tumor invasion and metastasis.

NMR characterization of solvent accessibility and transient structure in intrinsically disordered proteins

In order to understand the conformational behavior of intrinsically disordered proteins (IDPs) and their biological interaction networks, the detection of residual structure and long-range interactions is required. However, the large number of degrees of conformational freedom of disordered proteins require the integration of extensive sets of experimental data, which are difficult to obtain. Here, we provide a straightforward approach for the detection of residual structure and long-range interactions in IDPs under near-native conditions using solvent paramagnetic relaxation enhancement (sPRE). Our data indicate that for the general case of an unfolded chain, with a local flexibility described by the overwhelming majority of available combinations, sPREs of non-exchangeable protons can be accurately predicted through an ensemble-based fragment approach. We show for the disordered protein α-synuclein and disordered regions of the proteins FOXO4 and p53 that deviation from random coil behavior can be interpreted in terms of intrinsic propensity to populate local structure in interaction sites of these proteins and to adopt transient long-range structure. The presented modification-free approach promises to be applicable to study conformational dynamics of IDPs and other dynamic biomolecules in an integrative approach.