BowStrap v1.0: Assigning Statistical Significance to Expressed Genes Using Short-Read Transcriptome Data
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Protein kinases catalyze the transfer of a phosphate moiety from a phosphate donor to the substrate molecule, thus playing critical roles in cell signaling and metabolism. Although plant genomes contain more than 1000 genes that encode kinases, knowledge is limited about the function of each of these kinases. A major obstacle that hinders progress towards kinase characterization is functional redundancy. To address this challenge, we previously developed the rice kinase database (RKD) that integrated omics-scale data within a phylogenetics context.
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Abstract Background Mercury (Hg) is highly toxic and has the potential to cause severe health problems for humans and foraging animals when transported into edible plant parts. Soil rhizobia that form symbiosis with legumes may possess mechanisms to prevent heavy metal translocation from roots to shoots in plants by exporting metals from nodules or compartmentalizing metal ions inside nodules. Horizontal gene transfer has potential to confer immediate de novo adaptations to stress. We used comparative genomics of high quality de novo assemblies to identify structural differences in the genomes of nitrogen-fixing rhizobia that were isolated from a mercury (Hg) mine site that show high variation in their tolerance to Hg. Results Our analyses identified multiple structurally conserved merA homologs in the genomes of Sinorhizobium medicae and Rhizobium leguminosarum but only the strains that possessed a Mer operon exhibited 10-fold increased tolerance to Hg. RNAseq analysis revealed nearly all genes in the Mer operon were significantly up-regulated in response to Hg stress in free-living conditions and in nodules. In both free-living and nodule environments, we found the Hg-tolerant strains with a Mer operon exhibited the fewest number of differentially expressed genes (DEGs) in the genome, indicating a rapid and efficient detoxification of Hg from the cells that reduced general stress responses to the Hg-treatment. Expression changes in S. medicae while in bacteroids showed that both rhizobia strain and host-plant tolerance affected the number of DEGs. Aside from Mer operon genes, nif genes which are involved in nitrogenase activity in S. medicae showed significant up-regulation in the most Hg-tolerant strain while inside the most Hg-accumulating host-plant. Transfer of a plasmid containing the Mer operon from the most tolerant strain to low-tolerant strains resulted in an immediate increase in Hg tolerance, indicating that the Mer operon is able to confer hyper tolerance to Hg. Conclusions Mer operons have not been previously reported in nitrogen-fixing rhizobia. This study demonstrates a pivotal role of the Mer operon in effective mercury detoxification and hypertolerance in nitrogen-fixing rhizobia. This finding has major implications not only for soil bioremediation, but also host plants growing in mercury contaminated soils.
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Background: Drought is a major environmental constraint to plant growth, development and productivity. Compared with most willows that are generally susceptible to drought, the desert willow Salix psammophila has extraordinary adaptation to drought stress. However, its molecular basis of drought tolerance is still largely unknown.Results: During polyethylene glycol 6000 (PEG 6000)-simulated drought stress, we found that the osmotic adjustment substances were accumulated and the antioxidant enzyme activities were enhanced in S. psammophila roots. A total of 8172 differentially expressed genes were identified in roots of S. psammophila through RNA-Sequencing. Based on K-means clustering, their expression patterns were classified into nine clusters, which were enriched in several stress-related processes including transcriptional regulation, response to various stresses, cell death, etc. Moreover, 672 transcription factors from 45 gene families were differentially expressed under drought stress. Furthermore, a weighted gene co-expression network was constructed, and eight genes were identified as hub genes. We demonstrated the function of two hub genes, magnesium-dependent phosphatase 1 (SpMDP1) and SpWRKY33, through overexpression in Arabidopsis thaliana. Overexpression of the two hub genes enhanced the drought tolerance in transgenic plants, suggesting that the identification of candidate drought tolerance genes in this study was highly efficient and credible.Conclusions: Our study analyzed the physiological and molecular responses to drought stress in S. psammophila, and these results contribute to dissect the mechanism of drought tolerance of S. psammophila and facilitate identification of critical genes involved in drought tolerance for willow breeding.
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Background Candida parapsilosis is a common cause of invasive candidiasis, especially in newborn infants, and infections have been increasing over the past two decades. C. parapsilosis has been primarily studied in pure culture, leaving gaps in understanding of its function in a microbiome context. Results. Here, we compare five unique C. parapsilosis genomes assembled from premature infant fecal samples, three of which are newly reconstructed, and analyze their genome structure, population diversity, and in situ activity relative to reference strains in pure culture. All five genomes contain hotspots of single nucleotide variants, some of which are shared by strains from multiple hospitals. A subset of environmental and hospital-derived genomes share variants within these hotspots suggesting derivation of that region from a common ancestor. Four of the newly reconstructed C. parapsilosis genomes have 4 to 16 copies of the gene RTA3, which encodes a lipid translocase and is implicated in antifungal resistance, potentially indicating adaptation to hospital antifungal use. Time course metatranscriptomics and metaproteomics on fecal samples from a premature infant with a C. parapsilosis blood infection revealed highly variable in situ expression patterns that are distinct from those of similar strains in pure cultures. For example, biofilm formation genes were relatively less expressed in situ, whereas genes linked to oxygen utilization were more highly expressed, indicative of growth in a relatively aerobic environment. In gut microbiome samples, C. parapsilosis co-existed with Enterococcus faecalis that shifted in relative abundance over time, accompanied by changes in bacterial and fungal gene expression and proteome composition. Conclusions The results reveal potentially medically relevant differences in Candida function in gut vs. laboratory environments, and constrain evolutionary processes that could contribute to hospital strain persistence and transfer into premature infant microbiomes.
To gain insight into the transcription programs activated during the formation of Drosophila larval structures, we carried out single cell RNA sequencing during two periods of Drosophila embryogenesis: stages 10-12, when most organs are first specified and initiate morphological and physiological specialization; and stages 13-16, when organs achieve their final mature architectures and begin to function. Our data confirm previous findings with regards to functional specialization of some organs – the salivary gland and trachea – and clarify the embryonic functions of another – the plasmatocytes. We also identify two early developmental trajectories in germ cells and uncover a potential role for proteolysis during germline stem cell specialization. We identify the likely cell type of origin for key components of the Drosophila matrisome and several commonly used Drosophila embryonic cell culture lines. Finally, we compare our findings with other recent related studies and with other modalities for identifying tissue-specific gene expression patterns. These data provide a useful community resource for identifying many new players in tissue-specific morphogenesis and functional specialization of developing organs.
High-throughput multi-omics studies and corresponding network analyses of multi-omic data have rapidly expanded their impact over the last 10 years. As biological features of different types (e.g. transcripts, proteins, metabolites) interact within cellular systems, the greatest amount of knowledge can be gained from networks that incorporate multiple types of -omic data. However, biological and technical sources of variation diminish the ability to detect cross-type associations, yielding networks dominated by communities comprised of nodes of the same type. We describe here network building methods that can maximize edges between nodes of different data types leading to integrated networks. We systematically rank several network inference methods and demonstrate that, in many cases, using a random forest method, GENIE3, produces the most integrated networks. This increase in integration does not come at the cost of accuracy as GENIE3 produces networks of approximately the same quality as the other network inference methods tested here. Using GENIE3, we also infer networks representing antibody-mediated Dengue virus cell invasion and receptor-mediated Dengue virus invasion. A number of functional pathways showed centrality differences between the two networks including genes responding to both GM-CSF and IL-4, which had a higher centrality value in an antibody-mediated vs. receptor-mediated Dengue network. Because a biological system involves the interplay of many different types of molecules, incorporating multiple data types into networks will improve their use as models of biological systems. The methods explored here are some of the first to specifically highlight and address the challenges associated with how such multi-omic networks can be assembled and how the greatest number of interactions can be inferred from different data types. The resulting networks can lead to the discovery of new host response patterns and interactions during viral infection, generate new hypotheses of pathogenic mechanisms and confirm disease associated mechanisms of disease.