Wang, J. J. | bioRxiv Science

bioRxiv2018

Unigene-based RNA-seq provides insights on drought stress responses in Marsdenia tenacissima

Marsdenia tenacissima is a well-known anti-cancer medicinal plant used in traditional Chinese medicine. Drought severely affects production and no information on its transcriptional responses to drought stress is available. In this study, cDNA libraries on control (CK), drought stress (T1), and re-watering (T2) treatments were constructed and HiSeq 2000 sequencing was performed using the Illumina platform. There were 43,129,228, 47,116,844, and 42,815,454 clean reads with Q20 values of 98.06, 98.04, and 97.88, respectively. A total of 8672, 6043, and 6537 differentially expressed genes (DEGs) were identified when CK vs. T1, CK vs. T2, and T1 vs. T2, respectively, were analyzed. In addition, 1039, 1016, and 980 transcription factors (TFs) were identified in CK, T1, and T2, respectively. Among them, 363, 267, and 299 TFs were identified as DEGs in CK vs. T1, CK vs. T2, and T1 vs. T2, respectively. These differentially expressed TFs mainly belonged to the bHLH, bZIP, C2H2, ERF, MYB, MYB-related, and NAC families. A comparative analysis of CK vs. T1 and T1 vs. T2 found that 1174 genes were up-regulated and 2344 were down-regulated under drought stress and this pattern was the opposite to that found after re-watering. Among the 1174 genes up-regulated by drought stress, 64 were homologous to known functional genes that directly protect plants against drought stress. Furthermore, 44 protein kinases and 38 TFs with opposite expression patterns under drought stress and re-watering were identified, which are possibly candidate regulators for drought stress resistance in M. tenacissima. Our study is the first to characterize the M. tenacissima transcriptome in response to drought stress, and will serve as a useful resource for future studies on the functions of candidate protein kinases and TFs involved in M. tenacissima drought stress resistance.

molecular biology↗

bioRxiv2017

A personalized, multi-omics approach identifies genes involved in cardiac hypertrophy and heart failure

Identifying genes underlying complex diseases remains a major challenge. Biomarkers are typically identified by comparing average levels of gene expression in populations of healthy and diseased individuals. However, genetic diversities may undermine the effort to uncover genes with significant but individual contribution to the spectrum of disease phenotypes within a population. Here we leverage the Hybrid Mouse Diversity Panel (HMDP), a model system of 100+ genetically diverse strains of mice exhibiting different complex disease traits, to develop a personalized differential gene expression analysis that is able to identify disease-associated genes missed by traditional population-wide methods. The population-level and personalized approaches are compared for isoproterenol(ISO)-induced cardiac hypertrophy and heart failure using pre- and post-ISO gene expression and phenotypic data. The personalized approach identifies 36 Fold-Change (FC) genes predictive of the severity of cardiac hypertrophy, and enriched in genes previously associated with cardiac diseases in human. Strikingly, these genes are either up- or down-regulated at the individual strain level, and are therefore missed when averaging at the population level. Using insights from the gene regulatory network and protein-protein interactome, we identify Hes1 as a strong candidate FC gene. We validate its role by showing that even a mild knockdown of 20-40% of Hes1 can induce a dramatic reduction of hypertrophy by 80-90% in rat neonatal cardiac cells. These findings emphasize the importance of a personalized approach to identify causal genes underlying complex diseases as well as to develop personalized therapies.\n\nSignificanceA traditional approach to investigate the genetic basis of complex diseases is to look for genes with a global change in expression between diseased and healthy individuals. Here, we investigate individual changes of gene expression by inducing heart failure in 100 strains of genetically distinct mice. We find that genes associated to the severity of the disease are either up- or down-regulated across individuals and are therefore missed by a traditional population level approach. However, they are enriched in human cardiac disease genes and form a coregulated module strongly interacting with a cardiac hypertrophic signaling network in the human interactome. Our analysis demonstrates that individualized approaches are crucial to reveal all genes involved in the development of complex diseases.

systems biology↗