Reconstruction Of Co-expression Networks Research Articles

Genetic architecture of inter-specific and -generic grass hybrids by network analysis on multi-omics data

BackgroundUnderstanding the mechanisms underlining forage production and its biomass nutritive quality at the omics level is crucial for boosting the output of high-quality dry matter per unit of land. Despite the advent of multiple omics integration for the study of biological systems in major crops, investigations on forage species are still scarce.ResultsOur results identified substantial changes in gene co-expression and metabolite-metabolite network topologies as a result of genetic perturbation by hybridizing L. perenne with another species within the genus (L. multiflorum) relative to across genera (F. pratensis). However, conserved hub genes and hub metabolomic features were detected between pedigree classes, some of which were highly heritable and displayed one or more significant edges with agronomic traits in a weighted omics-phenotype network. In spite of tagging relevant biological molecules as, for example, the light-induced rice 1 (LIR1), hub features were not necessarily better explanatory variables for omics-assisted prediction than features stochastically sampled and all available regressors.ConclusionsThe utilization of computational techniques for the reconstruction of co-expression networks facilitates the identification of key omic features that serve as central nodes and demonstrate correlation with the manifestation of observed traits. Our results also indicate a robust association between early multi-omic traits measured in a greenhouse setting and phenotypic traits evaluated under field conditions.

Comparative de novo transcriptome analysis of flower and root of Oliveria decumbens Vent. to identify putative genes in terpenes biosynthesis pathway.

The Oliveria decumbens Vent. is a wild, rare, annual medicinal plant and endemic plant of Iran that has metabolites (mostly terpenes) which make it a precious plant in Persian Traditional Medicine and also a potential chemotherapeutic agent. The lack of genetic resources has slowed the discovery of genes involved in the terpenes biosynthesis pathway. It is a wild relative of Daucus carota. In this research, we performed the transcriptomic differences between two samples, flower and root of Oliveria decumbens, and also analyze the expression value of the genes involved in terpenoid biosynthesis by RNA-seq and its essential oil’s phytochemicals analyzed by GC/MS. In total, 136,031,188 reads from two samples of flower and root have been produced. The result shows that the MEP pathway is mostly active in the flower and the MVA in the root. Three genes of GPP, FPPS, and GGPP that are the precursors in the synthesis of mono, di, and triterpenes are upregulated in root and 23 key genes were identified that are involved in the biosynthesis of terpenes. Three genes had the highest upregulation in the root including, and on the other hand, another three genes had the expression only in the flower. Meanwhile, 191 and 185 upregulated genes in the flower and root of the plant, respectively, were selected for the gene ontology analysis and reconstruction of co-expression networks. The current research is the first of its kind on Oliveria decumbens transcriptome and discussed 67 genes that have been deposited into the NCBI database. Collectively, the information obtained in this study unveils the new insights into characterizing the genetic blueprint of Oliveria decumbens Vent. which paved the way for medical/plant biotechnology and the pharmaceutical industry in the future.

Unraveling the transcriptional complexity of compactness in sistan grape cluster

Yaghooti grape of Sistan is the earliest ripening grape in Iran, harvested every May annually. It is adapted to dry conditions in Sistan region and its water requirement is less than the other grape cultivars. The transcriptional complexity of this grape was studied in three stages of cluster development. Totally, 24121 genes were expressed in different cluster development steps (step 1: cluster formation, step 2: berry formation, step 3: final size of cluster) of which 3040 genes in the first stage, 2381 genes in the second stage and 2400 genes in the third stage showed a significant increase in expression. GO analysis showed that when the clusters are ripening, the activity of the nucleus, cytoplasmic, cytosol, membrane and chloroplast genes in the cluster architecture cells decreases. In contrast, the activity of the endoplasmic reticulum, vacuole and extracellular region genes enhances. When Yaghooti grape is growing and developing, some of metabolic pathways were activated in the response to biotic and abiotic stresses. Gene co-expression network reconstruction showed that AGAMOUS is a key gene in compactness of Sistan grape cluster, because it influences on expression of GA gene which leads to increase cluster length and berries size.

Constructing a comprehensive gene co-expression based interactome in Bos taurus.

Integrating genomic information into cattle breeding is an important approach to exploring genotype-phenotype relationships for complex traits related to diary and meat production. To assist with genomic-based selection, a reference map of interactome is needed to fully understand and identify the functional relevant genes. To this end, we constructed a co-expression analysis of 92 tissues and this represents the systematic exploration of gene-gene relationship in Bos taurus. By using robust WGCNA (Weighted Gene Correlation Network Analysis), we described the gene co-expression network of 5,000 protein-coding genes with majority variations in expression across 92 tissues. Further module identifications found 55 highly organized functional clusters representing diverse cellular activities. To demonstrate the re-use of our interaction for functional genomics analysis, we extracted a sub-network associated with DNA binding genes in Bos taurus. The subnetwork was enriched within regulation of transcription from RNA polymerase II promoter representing central cellular functions. In addition, we identified 28 novel linker genes associated with more than 100 DNA binding genes. Our WGCNA-based co-expression network reconstruction will be a valuable resource for exploring the molecular mechanisms of incompletely characterized proteins and for elucidating larger-scale patterns of functional modulization in the Bos taurus genome.

Gene co-expression network reconstruction: a review on computational methods for inferring functional information from plant-based expression data

Reconstruction of gene co-expression networks is a powerful tool for better understanding of gene function, biological processes, and complex disease mechanisms. In essence, co-expression network analysis has been widely used for understanding which genes are highly co-expressed through special biological processes or differentially expressed in various conditions. Development of high-throughput experiments has provided a large amount of genomic and transcriptomic data for model and non-model organisms. The availability of genome-wide expression data has led to the development of in silico procedures for reconstruction of gene co-expression networks. Gene co-expression networks predict unknown genes’ functions; moreover, it has been successfully applied to understand important biological processes of living organisms such as plants. In this survey, we have reviewed the algorithms, databases, and tools of gene co-expression network reconstruction, which can lead to new landscapes for further research activities. Furthermore, we explain an application of some algorithms, databases, and tools that can significantly boost our current understanding of co-expression networks in Arabidopsis thaliana as a model plant using publicly available data. The presented example shows that using co-expression networks is an efficient way to detect genes, which may involve in various critical biological processes such as defense response.

Transcriptional Networks in Single Perivascular Cells Sorted from Human Adipose Tissue Reveal a Hierarchy of Mesenchymal Stem Cells.

Adipose tissue is a rich source of multipotent mesenchymal stem-like cells, located in the perivascular niche. Based on their surface markers, these have been assigned to two main categories: CD31- /CD45- /CD34+ /CD146- cells (adventitial stromal/stem cells [ASCs]) and CD31- /CD45- /CD34- /CD146+ cells (pericytes [PCs]). These populations display heterogeneity of unknown significance. We hypothesized that aldehyde dehydrogenase (ALDH) activity, a functional marker of primitivity, could help to better define ASC and PC subclasses. To this end, the stromal vascular fraction from a human lipoaspirate was simultaneously stained with fluorescent antibodies to CD31, CD45, CD34, and CD146 antigens and the ALDH substrate Aldefluor, then sorted by fluorescence-activated cell sorting. Individual ASCs (n = 67) and PCs (n = 73) selected from the extremities of the ALDH-staining spectrum were transcriptionally profiled by Fluidigm single-cell quantitative polymerase chain reaction for a predefined set (n = 429) of marker genes. To these single-cell data, we applied differential expression and principal component and clustering analysis, as well as an original gene coexpression network reconstruction algorithm. Despite the stochasticity at the single-cell level, covariation of gene expression analysis yielded multiple network connectivity parameters suggesting that these perivascular progenitor cell subclasses possess the following order of maturity: (a) ALDHbr ASC (most primitive); (b) ALDHdim ASC; (c) ALDHbr PC; (d) ALDHdim PC (least primitive). This order was independently supported by specific combinations of class-specific expressed genes and further confirmed by the analysis of associated signaling pathways. In conclusion, single-cell transcriptional analysis of four populations isolated from fat by surface markers and enzyme activity suggests a developmental hierarchy among perivascular mesenchymal stem cells supported by markers and coexpression networks. Stem Cells 2017;35:1273-1289.

Identification of candidate anti-cancer molecular mechanisms of Compound Kushen Injection using functional genomics.

Compound Kushen Injection (CKI) has been clinically used in China for over 15 years to treat various types of solid tumours. However, because such Traditional Chinese Medicine (TCM) preparations are complex mixtures of plant secondary metabolites, it is essential to explore their underlying molecular mechanisms in a systematic fashion. We have used the MCF-7 human breast cancer cell line as an initial in vitro model to identify CKI induced changes in gene expression. Cells were treated with CKI for 24 and 48 hours at two concentrations (1 and 2 mg/mL total alkaloids), and the effect of CKI on cell proliferation and apoptosis were measured using XTT and Annexin V/Propidium Iodide staining assays respectively. Transcriptome data of cells treated with CKI or 5-Fluorouracil (5-FU) for 24 and 48 hours were subsequently acquired using high-throughput Illumina RNA-seq technology. In this report we show that CKI inhibited MCF-7 cell proliferation and induced apoptosis in a dose-dependent fashion. We integrated and applied a series of transcriptome analysis methods, including gene differential expression analysis, pathway over-representation analysis, de novo identification of long non-coding RNAs (lncRNA) as well as co-expression network reconstruction, to identify candidate anti-cancer molecular mechanisms of CKI. Multiple pathways were perturbed and the cell cycle was identified as the potential primary target pathway of CKI in MCF-7 cells. CKI may also induce apoptosis in MCF-7 cells via a p53 independent mechanism. In addition, we identified novel lncRNAs and showed that many of them might be expressed as a response to CKI treatment.

Lynx: a knowledge base and an analytical workbench for integrative medicine.

Lynx (http://lynx.ci.uchicago.edu) is a web-based database and a knowledge extraction engine. It supports annotation and analysis of high-throughput experimental data and generation of weighted hypotheses regarding genes and molecular mechanisms contributing to human phenotypes or conditions of interest. Since the last release, the Lynx knowledge base (LynxKB) has been periodically updated with the latest versions of the existing databases and supplemented with additional information from public databases. These additions have enriched the data annotations provided by Lynx and improved the performance of Lynx analytical tools. Moreover, the Lynx analytical workbench has been supplemented with new tools for reconstruction of co-expression networks and feature-and-network-based prioritization of genetic factors and molecular mechanisms. These developments facilitate the extraction of meaningful knowledge from experimental data and LynxKB. The Service Oriented Architecture provides public access to LynxKB and its analytical tools via user-friendly web services and interfaces.

The application of gene co-expression network reconstruction based on CNVs and gene expression microarray data in breast cancer

Copy number variations (CNVs) are one type of the human genetic variations and are pervasive in the human genome. It has been confirmed that they can play a causal role in complex diseases. Previous studies of CNVs focused more on identifying the disease-specific CNV regions or candidate genes on these CNV regions, but less on the synergistic actions between genes on CNV regions and other genes. Our research combined the CNVs with related gene co-expression to reconstruct gene co-expression network by using single nucleotide polymorphism microarray datasets and gene microarray datasets of breast cancer, and then extracted the modules which connected densely inside and analyzed the functions of modules. Interestingly, all of these modules' functions were related to breast cancer according to our enrichment analysis, and most of the genes in these modules have been reported to be involved in breast cancer. Our findings suggested that integrating CNVs and gene co-expressed relations was an available way to analyze the roles of CNV genes and their synergistic genes in breast cancer, and provided a novel insight into the pathological mechanism of breast cancer.