Construction Of Biological Networks Research Articles

Light and temperature culture conditions impact the metabolome of the cyanobacterium of therapeutic interest Planktothricoides raciborskii PMC 877.14

Cyanobacteria are ancient photosynthetic microorganisms with a long evolutionary history that have adapted to inhabit diverse environments, such as thermal waters and muds. To do so, they are known to produce a wide range of bioactive molecules likely involved in adaptative traits such as high light and/or high temperature resistance mechanisms, which makes them particularly interesting for inclusion in thermal treatments and various therapeutic applications.In this study, the impact of higher temperature and light intensity culture conditions on the metabolome of a promising cyanobacterial strain isolated from the muds of the Balaruc-les-Bains thermal station, Planktothricoides raciborskii PMC 877.14, was investigated through liquid chromatography and mass spectrometry analyses. Statistical analyses of the biological data and molecular network construction allowed for the exploration of potential metabolite induction under different culture conditions, in order to drive the production of specific metabolites aimed at coping with cellular stress. A global shift on intracellular metabolic composition was observed over time with increased biomass production. Higher light intensity was found to stimulate both growth and production of antioxidant and/or photoprotective molecules (such as ergothioneine, mycosporine-like amino acids or carotenoids) while an increase in temperature influenced the higher production of phycobilins (such as phycoerythrin and phycocyanin).These results highlight the importance of culture/environmental conditions in driving intracellular metabolite differentiation and open up new perspectives in selecting of the optimal parameters for the growth and production of bioactive molecules for therapeutic applications in the cyanobacterium P. raciborskii PMC 877.14.

Baitouweng decoction suppresses growth of esophageal carcinoma cells through miR-495-3p/BUB1/STAT3 axis.

Esophageal carcinoma (EC) is one of the most prevalent cancers in human populations worldwide. Baitouweng decoction is one of the most important Chinese medicine formulas, with the potential to treat cancer. To investigate the role and mechanism of Baitouweng decoction on EC cells. Differentially expressed genes (DEGs) in EC tissues and normal tissues were screened by the cDNA microarray technique and by bioinformatics methods. The target genes of microRNAs were predicted based on the TargetScan database and verified by dual luciferase gene reporter assay. We used Baitouweng decoction to intervene EC cells, and detected the activity of EC9706 and KYSE150 cells by the MTT method. Cell cycle and apoptosis were measured by flow cytometry. The expression of BUB1 mRNA and miR-495-3p was measured by qRT-PCR. The protein levels of BUB1, STAT3, p-STAT3, CCNB1, CDK1, Bax, Caspase3, and Caspase9 were measured by Western blot analysis. The migration and invasion abilities of the cells were measured by wound-healing assay and Transwell invasion assay, respectively. DEGs identified are involved in biological processes, signaling pathways, and network construction, which are mainly related to mitosis. BUB1 was the key hub gene, and it is also a target gene of miR-495-3p. Baitouweng decoction could upregulate miR-495-3p and inhibit BUB1 expression. In vitro experiments showed that Baitouweng decoction significantly inhibited the migration and invasion of EC cells and induced apoptosis and G2/M phase arrest. After treatment with Baitouweng decoction, the expression of Bax, Caspase 3, and Caspase 9 in EC cells increased significantly, while the expression of BUB1, CCNB1, and CDK1 decreased significantly. Moreover, the STAT3 signaling pathway may play an important role in this process. Baitouweng decoction has a significant inhibitory effect on EC cell growth. BUB1 is a potential therapeutic target for EC. Further analysis showed that Baitouweng decoction may inhibit the growth of EC cells by upregulating miR-495-3p targeting the BUB1-mediated STAT3 signal pathway.

Comparative study by adding bootstrapping stage in construction of biological networks

Comparative study by adding bootstrapping stage in construction of biological networks

Systematic analysis of various RNA transcripts and construction of biological regulatory networks at the post-transcriptional level for chronic obstructive pulmonary disease

BackgroundAlthough chronic inflammation, oxidative stress, airway remodeling, and protease-antiprotease imbalance have been implicated in chronic obstructive pulmonary disease (COPD), the exact pathogenesis is still obscure. Gene transcription and post-transcriptional regulation have been taken into account as key regulators of COPD occurrence and development. Identifying the hub genes and constructing biological regulatory networks at the post-transcriptional level will help extend current knowledge on COPD pathogenesis and develop potential drugs.MethodsAll lung tissues from non-smokers (n = 6), smokers without COPD (smokers, n = 7), and smokers with COPD (COPD, n = 7) were collected to detect messenger RNA (mRNA), microRNA (miRNA), circular RNA (circRNA), and long non-coding RNA (lncRNA) expression and identify the hub genes. Biological regulatory networks were constructed at the post-transcriptional level, including the RNA-binding protein (RBP)-hub gene interaction network and the competitive endogenous RNA (ceRNA) network. In addition, we assessed the composition and abundance of immune cells in COPD lung tissue and predicted potential therapeutic drugs for COPD. Finally, the hub genes were confirmed at both the RNA and protein levels.ResultsAmong the 20 participants, a total of 121169 mRNA transcripts, 1871 miRNA transcripts, 4244 circRNA transcripts, and 122130 lncRNA transcripts were detected. There were differences in the expression of 1561 mRNAs, 48 miRNAs, 33 circRNAs, and 545 lncRNAs between smokers and non-smokers, as well as 1289 mRNAs, 69 miRNAs, 32 circRNAs, and 433 lncRNAs between smokers and COPD patients. 18 hub genes were identified in COPD. TGF-β signaling and Wnt/β-catenin signaling may be involved in the development of COPD. Furthermore, the circRNA/lncRNA-miRNA-mRNA ceRNA networks and the RBP-hub gene interaction network were also constructed. Analysis of the immune cell infiltration level revealed that M2 macrophages and activated NK cells were increased in COPD lung tissues. Finally, we identified that the ITK inhibitor and oxybutynin chloride may be effective in treating COPD.ConclusionsWe identified several novel hub genes involved in COPD pathogenesis. TGF-β signaling and Wnt/β-catenin signaling were the most dysregulated pathways in COPD patients. Our study constructed post-transcriptional biological regulatory networks and predicted small-molecule drugs for the treatment of COPD, which enhanced the existing understanding of COPD pathogenesis and suggested an innovative direction for the therapeutic intervention of the disease.

Research and development practice of traditional Chinese medicine based on network target theory and technology

Network targets theory and technology have transcended the limitations of the "single gene, single target" model, aiming to decipher the mechanisms of traditional Chinese medicine(TCM) based on biological network from the perspective of informatics and system. As the core of TCM network pharmacology, with the development of computer science and high-throughput experimental techniques, the network target theory and technology are beginning to exhibit a trend of organic integration with artificial intelligence technology and high-throughput multi-modal multi-omics experimental techniques. Taking the network target analysis of TCM like Yinqiao Qingre Tablets as a typical case, network target theory and technology have achieved the systematic construction, in-depth analysis, and high-throughput multi-modal multi-omics validation of multi-level biological networks spanning from traditional Chinese and Western phenotypes to tissues, cells, molecules, and traditional Chinese and Western medicines. This development helps to address critical issues in the analysis of mechanisms of TCM, including the discovery of key targets, identification of functional components, discovery of synergistic effects among compound ingredients, and elucidation of the regulatory mechanisms of formulae. It provides powerful theoretical and technological support for advancing clinical precision diagnosis and treatment, precise positioning of TCM, and precise research and development of TCM. Thus, a new paradigm of TCM research gradually emerges, combining big data and artificial intelligence(AI) with the integration of human experience and scientific evidence.

Single-cell biological network inference using a heterogeneous graph transformer

Single-cell multi-omics (scMulti-omics) allows the quantification of multiple modalities simultaneously to capture the intricacy of complex molecular mechanisms and cellular heterogeneity. Existing tools cannot effectively infer the active biological networks in diverse cell types and the response of these networks to external stimuli. Here we present DeepMAPS for biological network inference from scMulti-omics. It models scMulti-omics in a heterogeneous graph and learns relations among cells and genes within both local and global contexts in a robust manner using a multi-head graph transformer. Benchmarking results indicate DeepMAPS performs better than existing tools in cell clustering and biological network construction. It also showcases competitive capability in deriving cell-type-specific biological networks in lung tumor leukocyte CITE-seq data and matched diffuse small lymphocytic lymphoma scRNA-seq and scATAC-seq data. In addition, we deploy a DeepMAPS webserver equipped with multiple functionalities and visualizations to improve the usability and reproducibility of scMulti-omics data analysis.

Identification of Survival-Associated Hub Genes in Pancreatic Adenocarcinoma Based on WGCNA.

Pancreatic adenocarcinoma is one of the leading causes of cancer-related death worldwide. Since little clinical symptoms were shown in the early period of pancreatic adenocarcinoma, most patients were found to carry metastases when diagnosis. The lack of effective diagnosis biomarkers and therapeutic targets makes pancreatic adenocarcinoma difficult to screen and cure. The fundamental problem is we know very little about the regulatory mechanisms during carcinogenesis. Here, we employed weighted gene co-expression network analysis (WGCNA) to build gene interaction network using expression profile of pancreatic adenocarcinoma from The Cancer Genome Atlas (TCGA). STRING was used for the construction and visualization of biological networks. A total of 22 modules were detected in the network, among which yellow and pink modules showed the most significant associations with pancreatic adenocarcinoma. Dozens of new genes including PKMYT1, WDHD1, ASF1B, and RAD18 were identified. Further survival analysis yielded their valuable effects on the diagnosis and treatment of pancreatic adenocarcinoma. Our study pioneered network-based algorithm in the application of tumor etiology and discovered several promising regulators for pancreatic adenocarcinoma detection and therapy.

Vine copula graphical models in the construction of biological networks

The copula Gaussian graphical model (CGGM) is one of the major mathematical models for high dimensional biological networks which provides a graphical representation, especially, for sparse networks. Basically, this model uses a regression of the Gaussian graphical model (GGM) whose precision matrix describes the conditional dependence between the variables to estimate the coefficients of the linear regression model. The Bayesian inference for the model parameters is used to overcome the dimensional limitation of GGM under sparse networks and small sample sizes. But from the application in bench-mark data sets, it is seen that although CGGM is successful in certain systems, it may not fit well for non-normal multivariate observations. In this study, we propose the vine copulas to relax the strict normality assumption of CGGM and to describe networks from a variety of copulas’ alternates besides the Gaussian copula. Accordingly, we evaluate the best fitted bivariate copula distribution for every pairwise gene and compute the estimated adjacency matrix which denotes the presence of an edge between the corresponding genes. We assess the performance of our proposed approach in three network data via distinct accuracy measures by comparing the outputs with the results of the CGGM.

Analysis Pipeline of PIWI‐Interacting RNA (piRNA) in an Inflammatory Parasitic Infectious Disease

Small noncoding RNAs (sncRNA) have been implicated as major contributors to the pathogenesis of various diseases. PIWI-Interacting RNA (piRNA) are a type of sncRNA. with several regulatory roles across the genome. They are primarily expressed from piRNA clusters within the genome, where they go on to regulate and maintain genomic stability. They are capable of silencing transposable elements and inducing epigenetic changes. Some studies also suggest that piRNAs can regulate gene transcription. This gene regulatory mechanism is thought to be similar to that of miRNAs. These sncRNA play important roles in regulating gene expression by forming complexes with Argonaute proteins to recognize specific target sequences. The specific goal of this project was to develop a method of computationally analyzing piRNA in the context of their diverse functions and relate them to biological responses occurring in disease. To accomplish this, we created a pipeline to analyze dysregulation of piRNA expression and predict how these changes contribute to Trypanosoma cruzi-induced cardiac pathology. We used primary human cardiac myocytes (PHCM) challenged by T. cruzi infection and computationally assessed the impact of piRNA during inflammatory response. Small RNA from PHCM challenged by T. cruzi infection was extracted and sequenced. The piRNA annotation program Piano was used to predict known piRNAs via a support vector machine algorithm with transposon interaction informatics. The NOISeq method was used to determine differentially expressed piRNAs. Prediction of binding interactions between coding transcripts and piRNAs was done by adapting the miRanda and RNA22 algorithms using strict pairing thresholds. Predicted target transcripts were then mapped to enriched KEGG pathways using WEBGESTALT. Mapped genes were then queried for biological interaction network construction with the GeneMANIA algorithm. In combination, these analyses were used to produce biological interaction networks involving piRNAs and predicted target genes related to inflammation, specifically pro-inflammatory AP-1 transcription factor genes. We demonstrate a method of ascertaining the dynamics of changes in piRNA expression using this disease model, from which more directed biological inquiries can subsequently be made.

Amphibian habitat network planning based on the graph theory: A case study of Pelophylax nigromaculata.

Both biodiversity conservation and the construction of biological habitat networks are key components of territorial spatial planning in China. Improving the landscape functional connectivity of biological habitat networks plays a vital role in biodiversity conservation. Although biological habitat network planning is a hot topic in literature, there is still lack of operable technological and methodological support in practical planning. According to the graph theory, the following three different aspects of biodiversity conservation and ecological network construction should be addressed in territorial spatial planning. First, the importance of biological habitat patches should be evaluated to determine the priority of patch protection. Second, the best locations for adding new elements should be identified to increase landscape functional connectivity of biological habitat network. Third, the impacts of construction projects should be judged and the potential impact of new construction projects according to the reduction of landscape function connectivity should be evaluated. We applied such framework to the network planning of amphibian (Pelophylax nigromaculata) habitat in Xiong'an New Area. The results showed that graph theory approach effectively met the requirements of those three aspects. The identification of the five optimal locations of new habitats of P. nigromaculata improved the overall landscape functional connectivity of habitat network by 19%. Four optimal locations of cross passage were identified to reduce the impacts of G45 expressway by assessing its impacts on functional connectivity of the amphibian habitat network.

A Meta-Analysis of the Effects of High-LET Ionizing Radiations in Human Gene Expression.

The use of high linear energy transfer (LET) ionizing radiation (IR) is progressively being incorporated in radiation therapy due to its precise dose localization and high relative biological effectiveness. At the same time, these benefits of particle radiation become a high risk for astronauts in the case of inevitable cosmic radiation exposure. Nonetheless, DNA Damage Response (DDR) activated via complex DNA damage in healthy tissue, occurring from such types of radiation, may be instrumental in the induction of various chronic and late effects. An approach to elucidating the possible underlying mechanisms is studying alterations in gene expression. To this end, we identified differentially expressed genes (DEGs) in high Z and high energy (HZE) particle-, γ-ray- and X-ray-exposed healthy human tissues, utilizing microarray data available in public repositories. Differential gene expression analysis (DGEA) was conducted using the R programming language. Consequently, four separate meta-analyses were conducted, after DEG lists were grouped depending on radiation type, radiation dose and time of collection post-irradiation. To highlight the biological background of each meta-analysis group, functional enrichment analysis and biological network construction were conducted. For HZE particle exposure at 8–24 h post-irradiation, the most interesting finding is the variety of DNA repair mechanisms that were downregulated, a fact that is probably correlated with complex DNA damage formation. Simultaneously, after X-ray exposure during the same hours after irradiation, DNA repair mechanisms continue to take place. Finally, in a further comparison of low- and high-LET radiation effects, the most prominent result is that autophagy mechanisms seem to persist and that adaptive immune induction seems to be present. Such bioinformatics approaches may aid in obtaining an overview of the cellular response to high-LET particles. Understanding these response mechanisms can consequently aid in the development of countermeasures for future space missions and ameliorate heavy ion treatments.

Multiscale Laplacian graph kernel combined with lexico-syntactic patterns for biomedical event extraction from literature

Bio-event extraction is an extensive research area in the field of biomedical text mining, this focuses on elaborating relationships between biomolecules and can provide various aspects of their nature. Bio-event extraction plays a vital role in biomedical literature mining applications such as biological network construction, pathway curation, and drug repurposing. Extracting biological events automatically is a difficult task because of the uncertainty and assortment of natural language processing such as negations and speculations, which provides further room for the development of feasible methodologies. This paper presents a hybrid approach that integrates an ensemble-learning framework by combining a Multiscale Laplacian Graph kernel and a feature-based linear kernel, using a pattern-matching engine to identify biomedical events with arguments. This graph-based kernel not only captures the topological relationships between the individual event nodes but also identifies the associations among the subgraphs for complex events. In addition, the lexico-syntactic patterns were used to automatically discover the semantic role of each word in the sentence. For performance evaluation, we used the gold standard corpora, namely BioNLP-ST (2009, 2011, and 2013) and GENIA-MK. Experimental results show that our approach achieved better performance than other state-of-the-art systems.

LAITOR4HPC: A text mining pipeline based on HPC for building interaction networks

BackgroundThe amount of published full-text articles has increased dramatically. Text mining tools configure an essential approach to building biological networks, updating databases and providing annotation for new pathways. PESCADOR is an online web server based on LAITOR and NLProt text mining tools, which retrieves protein-protein co-occurrences in a tabular-based format, adding a network schema. Here we present an HPC-oriented version of PESCADOR’s native text mining tool, renamed to LAITOR4HPC, aiming to access an unlimited abstract amount in a short time to enrich available networks, build new ones and possibly highlight whether fields of research have been exhaustively studied.ResultsBy taking advantage of parallel computing HPC infrastructure, the full collection of MEDLINE abstracts available until June 2017 was analyzed in a shorter period (6 days) when compared to the original online implementation (with an estimated 2 years to run the same data). Additionally, three case studies were presented to illustrate LAITOR4HPC usage possibilities. The first case study targeted soybean and was used to retrieve an overview of published co-occurrences in a single organism, retrieving 15,788 proteins in 7894 co-occurrences. In the second case study, a target gene family was searched in many organisms, by analyzing 15 species under biotic stress. Most co-occurrences regarded Arabidopsis thaliana and Zea mays. The third case study concerned the construction and enrichment of an available pathway. Choosing A. thaliana for further analysis, the defensin pathway was enriched, showing additional signaling and regulation molecules, and how they respond to each other in the modulation of this complex plant defense response.ConclusionsLAITOR4HPC can be used for an efficient text mining based construction of biological networks derived from big data sources, such as MEDLINE abstracts. Time consumption and data input limitations will depend on the available resources at the HPC facility. LAITOR4HPC enables enough flexibility for different approaches and data amounts targeted to an organism, a subject, or a specific pathway. Additionally, it can deliver comprehensive results where interactions are classified into four types, according to their reliability.

Study on components efficacy of Salviae Miltiorrhizae Radix et Rhizoma based on systematic traditional Chinese medicine

This paper aimed to establish efficacy systems of tanshinones and salvianolic acids, two representative substances in Salviae Miltiorrhizae Radix et Rhizoma by using literature mining and biological network construction, based on systematic traditional Chinese medicine theory. The systematic study on the efficacy of traditional Chinese medicine was carried out from the basic unit, the structure and relationship between the basic units, the boundary of the research object and the function of the system, so as to explain the overall efficacy of the two kinds of components at the molecular level. Firstly, we collected the elements of the efficacy systems of these two kinds of components by literature mining, and defined their boundaries based on biological processes. After that, the structure of the efficacy systems was clarified according to the relationship in the KEGG database. Finally, the function of the efficacy systems was analyzed from the level of pharmacology, pharmacodynamics, and efficacy, revealing the scientific connotation of traditional Chinese medicine efficacy system. The results showed that there were 201 targets(elements), 12 target sets(boundary), and 12 pathway networks(structure) in salvianolic acids' efficacy system. Meanwhile, there were 189 targets(elements), 11 target sets(boundary), and 11 pathway networks(structure) in tanshinones' efficacy system. The results suggested that the functions of salvia-nolic acids' and tanshinones' efficacy systems were different in pharmacology and pharmacodynamics from aspects of elements, boundary, relationship and structure, but they were same in functional level as both of them could promote blood circulation, remove blood stasis, clear away heart-fire, relieve restlessness, and soothe the nerves. Based on systematic traditional Chinese medicine, we constructed the efficacy system of two representative components in Salviae Miltiorrhizae Radix et Rhizoma in this paper, elucidated the overall efficacy and builded the bridge between reductionism and holism in traditional Chinese medicine.

A Gene Expression Profile of Metabolic Dysfunction in the Aorta of Male Sprague‐Dawley Rats Following a Short‐Term High‐Fat Diet

Diet‐induced metabolic dysfunction precedes multiple disease states, including diabetes, heart disease, and atherosclerosis. The critical role of the vasculature in disease progression is established, yet the details of gene expression remain an enigma. The objective of the current project was to demonstrate that a quantitative assessment of gene expression within the vascular wall (aorta) of healthy rats compared to those exhibiting metabolic dysfunction symptoms could reveal potential mediators of vascular dysfunction. RNA was extracted from the aorta of four rats from a larger experiment; two animals that were maintained on a high‐fat diet (HFD) exhibited symptoms of metabolic dysfunction (including hypertension, weight gain, glucose intolerance, and insulin insensitivity) and two healthy animals that were fed a standard chow diet (CHOW) served as age‐matched controls. Poly‐A libraries were constructed and subjected to Illumina sequencing protocols. The post‐sequencing workflow was conducted with the software package Lumenogix® and sequencing reads were mapped to the Rat Genome Assembly version Rnor_5.0 using the Genomic Mapping and Alignment Program (GMAP). The program Empirical Analysis of Digital Gene Expression data in R (EdgeR) was used to evaluate statistical significance. Genes with a P adjusted value of ≤ 0.05 were passed to Cytoscape App ClueGO/CluePedia for enrichment analysis using Gene Ontology (GO) Biological Process terms, network construction and visualization. Preliminary analyses indicated 93 genes were significantly differentially expressed in the aorta of HFD rats. Enrichment analysis and the resulting network contained 54 genes relevant to physiological processes including fat and protein metabolism, oxygen transport, hormone regulation, vascular regulation, thermoregulation, and circadian rhythm. The majority of differentially regulated genes were downregulated (82.5%) including Nicotinamide Phosphoribosyltransferase (visfatin), which plays a role in the modulation of the circadian clock function. In contrast, Leptin and 3‐hydroxy‐3‐methylglutaryl‐CoA synthase 2 (Hmgcs2) were notably upregulated. Leptin is involved in several major energy balance signaling pathways and Hmgcs2 is a mitochondrial enzyme that catalyzes the first reaction of ketogenesis. Together, these data describe the changes in gene expression within the aortic wall of HFD rats with early metabolic dysfunction and highlight potential pathways and signaling intermediates that impact vascular disease.

Comprehensive understanding of Saccharomyces cerevisiae phenotypes with whole-cell model WM_S288C.

Biological network construction for Saccharomyces cerevisiae is a widely used approach for simulating phenotypes and designing cell factories. However, due to a complicated regulatory mechanism governing the translation of genotype to phenotype, precise prediction of phenotypes remains challenging. Here, we present WM_S288C, a computational whole-cell model that includes 15 cellular states and 26 cellular processes and which enables integrated analyses of physiological functions of Saccharomyces cerevisiae. Using WM_S288C to predict phenotypes of S. cerevisiae, the functions of 1140 essential genes were characterized and linked to phenotypes at five levels. During the cell cycle, the dynamic allocation of intracellular molecules could be tracked in real-time to simulate cell activities. Additionally, one-third of non-essential genes were identified to affect cell growth via regulating nucleotide concentrations. These results demonstrated the value of WM_S288C as a tool for understanding and investigating the phenotypes of S. cerevisiae.

Convergence of a Belief Propagation Algorithm for Biological Networks

Constructing network models of biological systems is important for effective understanding and control of the biological systems. For the construction of biological networks, a stochastic approach for link weights has been recently developed by using experimental data and belief propagation on a factor graph. The link weights were variable nodes of the factor graph and determined from their marginal probability mass functions which were approximated by using an iterative scheme. However, there is no convergence analysis of the iterative scheme. In this paper, at first, we present a detailed explanation of the complicated multistep process step by step with a network of small size and artificial experimental data, and then we show a sufficient condition for the convergence of the iterative scheme. Numerical examples are given to illustrate the whole process and to verify our result.

Family specific genetic predisposition to breast cancer: results from Tunisian whole exome sequenced breast cancer cases

BackgroundA family history of breast cancer has long been thought to indicate the presence of inherited genetic events that predispose to this disease. In North Africa, many specific epidemio-genetic characteristics have been observed in breast cancer families when compared to Western populations. Despite these specificities, the majority of breast cancer genetics studies performed in North Africa remain restricted to the investigation of the BRCA1 and BRCA2 genes. Thus, comprehensive data at a whole exome or whole genome level from local patients are lacking.MethodsA whole exome sequencing (WES) of seven breast cancer Tunisian families have been performed using a family-based approach. We focused our analysis on BC-TN-F001 family that included two affected members that have been sequenced using WES. Relevant variants identified in BC-TN-F001 have been confirmed using Sanger sequencing. Then, we conducted an integrative analysis by combining our results with those from other WES studies in order to figure out the genetic transmission model of the newly identified genes. Biological network construction and protein–protein interactions analyses have been performed to decipher the molecular mechanisms likely accounting for the role of these genes in breast cancer risk.ResultsSequencing, filtering strategies, and validation analysis have been achieved. For BC-TN-F001, no deleterious mutations have been identified on known breast cancer genes. However, 373 heterozygous, exonic and rare variants have been identified on other candidate genes. After applying several filters, 12 relevant high-risk variants have been selected. Our results showed that these variants seem to be inherited in a family specific model. This hypothesis has been confirmed following a thorough analysis of the reported WES studies. Enriched biological process and protein–protein interaction networks resulted in the identification of four novel breast cancer candidate genes namely MMS19, DNAH3, POLK and KATB6.ConclusionsIn this first WES application on Tunisian breast cancer patients, we highlighted the impact of next generation sequencing technologies in the identification of novel breast cancer candidate genes which may bring new insights into the biological mechanisms of breast carcinogenesis. Our findings showed that the breast cancer predisposition in non-BRCA families may be ethnic and/or family specific.

Loop-based conic multivariate adaptive regression splines is a novel method for advanced construction of complex biological networks

The Gaussian Graphical Model (GGM) and its Bayesian alternative, called, the Gaussian copula graphical model (GCGM) are two widely used approaches to construct the undirected networks of biological systems. They define the interactions between species by using the conditional dependencies of the multivariate normality assumption. However, when the system’s dimension is high, the performance of the model becomes computationally demanding, and, particularly, the accuracy of GGM decreases when the observations are far from normality. Here, we suggest a Conic Multivariate Adaptive Regression Splines (CMARS) as an alternative to GGM and GCGM to ameliorate both problems. CMARS is a modified version of the Multivariate Adaptive Regression Spline, a well-known modeling approaches used in Operational Research (OR) to represent biological, environmental, and economic data. The main benefit of this model is its compatibility with high-dimensional and correlated measurements of serious nonlinearity, which allows for a wide field of application. We adapted CMARS to describe biological systems and called it “LCMARS” due to its loop-based description. We then applied LCMARS to simulated and real datasets, and LCMARS produced more accurate results compared to GGM and GCGM. Hereby, the ability to use LCMARS in the description of biological networks has the potential to open up new avenues in the application of OR to computational biology and bioinformatics, and can thus help us better understanding complex diseases like cancer and hepatitis.

Network pharmacology analysis of the anti-cancer pharmacological mechanisms of Ganoderma lucidum extract with experimental support using Hepa1-6-bearing C57 BL/6 mice

Ganoderma lucidum (GL) is an oriental medical fungus, which was used to prevent and treat many diseases. Previously, the effective compounds of Ganoderma lucidum extract (GLE) were extracted from two kinds of GL, [Ganoderma lucidum (Leyss. Ex Fr.) Karst.] and [Ganoderma sinense Zhao, Xu et Zhang], which have been used for adjuvant anti-cancer clinical therapy for more than 20 years. However, its concrete active compounds and its regulation mechanisms on tumor are unclear. In this study, we aimed to identify the main active compounds from GLE and to investigate its anti-cancer mechanisms via drug-target biological network construction and prediction. The main active compounds of GLE were identified by HPLC, EI-MS and NMR, and the compounds related targets were predicted using docking program. To investigate the functions of GL holistically, the active compounds of GL and related targets were predicted based on four public databases. Subsequently, the Identified-Compound-Target network and Predicted-Compound-Target network were constructed respectively, and they were overlapped to detect the hub potential targets in both networks. Furthermore, the qRT-PCR and western-blot assays were used to validate the expression levels of target genes in GLE treated Hepa1-6-bearing C57 BL/6 mice. In our work, 12 active compounds of GLE were identified, including Ganoderic acid A, Ganoderenic acid A, Ganoderic acid B, Ganoderic acid H, Ganoderic acid C2, Ganoderenic acid D, Ganoderic acid D, Ganoderenic acid G, Ganoderic acid Y, Kaemferol, Genistein and Ergosterol. Using the docking program, 20 targets were mapped to 12 compounds of GLE. Furthermore, 122 effective active compounds of GL and 116 targets were holistically predicted using public databases. Compare with the Identified-Compound-Target network and Predicted-Compound-Target network, 6 hub targets were screened, including AR, CHRM2, ESR1, NR3C1, NR3C2 and PGR, which was considered as potential markers and might play important roles in the process of GLE treatment. GLE effectively inhibited tumor growth in Hepa1-6-bearing C57 BL/6 mice. Finally, consistent with the results of qRT-PCR data, the results of western-blot assay demonstrated the expression levels of PGR and ESR1 were up-regulated, as well as the expression levels of NR3C2 and AR were down-regulated, while the change of NR3C1 and CHRM2 had no statistical significance. The results indicated that these 4 hub target genes, including NR3C2, AR, ESR1 and PGR, might act as potential markers to evaluate the curative effect of GLE treatment in tumor. And, the combined data provide preliminary study of the pharmacological mechanisms of GLE, which may be a promising potential therapeutic and chemopreventative candidate for anti-cancer.