Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

TSCCA: A tensor sparse CCA method for detecting microRNA-gene patterns from multiple cancers.

Existing studies have demonstrated that dysregulation of microRNAs (miRNAs or miRs) is involved in the initiation and progression of cancer. Many efforts have been devoted to identify microRNAs as potential biomarkers for cancer diagnosis, prognosis and therapeutic targets. With the rapid development of miRNA sequencing technology, a vast amount of miRNA expression data for multiple cancers has been collected. These invaluable data repositories provide new paradigms to explore the relationship between miRNAs and cancer. Thus, there is an urgent need to explore the complex cancer-related miRNA-gene patterns by integrating multi-omics data in a pan-cancer paradigm. In this study, we present a tensor sparse canonical correlation analysis (TSCCA) method for identifying cancer-related miRNA-gene modules across multiple cancers. TSCCA is able to overcome the drawbacks of existing solutions and capture both the cancer-shared and specific miRNA-gene co-expressed modules with better biological interpretations. We comprehensively evaluate the performance of TSCCA using a set of simulated data and matched miRNA/gene expression data across 33 cancer types from the TCGA database. We uncover several dysfunctional miRNA-gene modules with important biological functions and statistical significance. These modules can advance our understanding of miRNA regulatory mechanisms of cancer and provide insights into miRNA-based treatments for cancer

TBC2target: A Resource of Predicted Target Genes of Tea Bioactive Compounds

Tea is one of the most popular non-alcoholic beverages consumed worldwide. Numerous bioactive constituents of tea were confirmed to possess healthy benefits via the mechanisms of regulating gene expressions or protein activities. However, a complete interacting profile between tea bioactive compounds (TBCs) and their target genes is lacking, which put an obstacle in the study of healthy function of tea. To fill this gap, we developed a database of target genes of TBCs (TBC2target, http://camellia.ahau.edu.cn/TBC2target) based on a pharmacophore mapping approach. In TBC2target, 6,226 interactions between 240 TBCs and 673 target genes were documented. TBC2target contains detailed information about each interacting entry, such as TBC, CAS number, PubChem CID, source of compound (e.g., green, black), compound type, target gene(s) of TBC, gene symbol, gene ID, ENSEMBL ID, PDB ID, TBC bioactivity and the reference. Using the TBC-target associations, we constructed a bipartite network and provided users the global network and local sub-network visualization and topological analyses. The entire database is free for online browsing, searching and downloading. In addition, TBC2target provides a BLAST search function to facilitate use of the database. The particular strengths of TBC2target are the inclusion of the comprehensive TBC-target interactions, and the capacity to visualize and analyze the interacting networks, which may help uncovering the beneficial effects of tea on human health as a central resource in tea health community

Gene Discovery of Characteristic Metabolic Pathways in the Tea Plant (Camellia sinensis) Using ‘Omics’-Based Network Approaches: A Future Perspective

Characteristic secondary metabolites, including flavonoids, theanine and caffeine, in the tea plant (Camellia sinensis) are the primary sources of the rich flavors, fresh taste, and health benefits of tea. The decoding of genes involved in these characteristic components is still significantly lagging, which lays an obstacle for applied genetic improvement and metabolic engineering. With the popularity of high-throughout transcriptomics and metabolomics, ‘omics’-based network approaches, such as gene co-expression network and gene-to-metabolite network, have emerged as powerful tools for gene discovery of plant-specialized (secondary) metabolism. Thus, it is pivotal to summarize and introduce such system-based strategies in facilitating gene identification of characteristic metabolic pathways in the tea plant (or other plants). In this review, we describe recent advances in transcriptomics and metabolomics for transcript and metabolite profiling, and highlight ‘omics’-based network strategies using successful examples in model and non-model plants. Further, we summarize recent progress in ‘omics’ analysis for gene identification of characteristic metabolites in the tea plant. Limitations of the current strategies are discussed by comparison with ‘omics’-based network approaches. Finally, we demonstrate the potential of introducing such network strategies in the tea plant, with a prospects ending for a promising network discovery of characteristic metabolite genes in the tea plant

Comprehensive analysis of putative dihydroflavonol 4-reductase gene family in tea plant.

One identified dihydroflavonol 4-reductases (DFR) encoding gene (named as CsDFRa herein) and five putative DFRs (named as CsDFRb1, CsDFRb2, CsDFRb3, CsDFRc and CsDFRd) in tea (Camellia sinensis) have been widely discussed in recent papers concerning multi-omics data. However, except for CsDFRa, their function and biochemical characteristics are not clear. This study aims to compare all putative CsDFRs and preliminarily evaluate their function. We investigated the sequences of genes (coding and promoter regions) and predicted structures of proteins encoded, and determined the activities of heterologously expressed CsDFRs under various conditions. The results showed that the sequences of five putative CsDFRs were quite different from CsDFRa, and had lower expression levels as well. The five putative CsDFRs could not catalyze three dihydroflavonol substrates. The functional CsDFRa had the strongest affinity with dihydroquercetin, and performed best at pH around 7 and 35°C but was not stable at lower pHs or higher temperatures. Single amino acid mutation at position 141 modified the preference of CsDFRa for dihydroquercetin and dihydromyricetin, and also weakened its stability. These data suggest that only CsDFRa works in the pathway for generating anthocyanidins and catechins. This study provides new insights into the function of CsDFRs and may assist to develop new strategies to manipulate the composition of tea flavonoids in the future

Metabolome and Transcriptome Analysis Reveals Putative Genes Involved in Anthocyanin Accumulation and Coloration in White and Pink Tea (Camellia sinensis) Flower

A variant of tea tree (Camellia sinensis (L.)) with purple buds and leaves and pink flowers can be used as a unique ornamental plant. However, the mechanism of flower coloration remains unclear. To elucidate the molecular mechanism of coloration, as well as anthocyanin accumulation in white and pink tea flowers, metabolite profiling and transcriptome sequencing was analyzed in various tea flower developmental stages. Results of metabolomics analysis revealed that three specific anthocyanin substances could be identified, i.e., cyanidin O-syringic acid, petunidin 3-O-glucoside, and pelargonidin 3-O-β-d-glucoside, which only accumulated in pink tea flowers, and were not able to be detected in white flowers. RNA-seq and weighted gene co-expression network analysis revealed eight highly expressed structural genes involved in anthocyanin biosynthetic pathway, and particularly, different expression patterns of flavonol synthase and dihydroflavonol-4-reductase genes were observed. We deduced that the disequilibrium of expression levels in flavonol synthases and dihydroflavonol-4-reductases resulted in different levels of anthocyanin accumulation and coloration in white and pink tea flowers. Results of qRT-PCR performed for 9 key genes suggested that the expression profiles of differentially expressed genes were generally consistent with the results of high-throughput sequencing. These findings provide insight into anthocyanin accumulation and coloration mechanisms during tea flower development, which will contribute to the breeding of pink-flowered and anthocyanin-rich tea cultivars