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The ability of Staphylococcus aureus to infect many different tissue sites is enabled, in part, by its transcriptional regulatory network (TRN) that coordinates its gene expression to respond to different environments. We elucidated the organization and activity of this TRN by applying independent component analysis to a compendium of 108 RNA-sequencing expression profiles from two S. aureus clinical strains (TCH1516 and LAC). ICA decomposed the S. aureus transcriptome into 29 independently modulated sets of genes (i-modulons) that revealed: 1) High confidence associations between 21 i-modulons and known regulators; 2) an association between an i-modulon and σS, whose regulatory role was previously undefined; 3) the regulatory organization of 65 virulence factors in the form of three i-modulons associated with AgrR, SaeR, and Vim-3; 4) the roles of three key transcription factors (CodY, Fur, and CcpA) in coordinating the metabolic and regulatory networks; and 5) a low-dimensional representation, involving the function of few transcription factors of changes in gene expression between two laboratory media (RPMI, cation adjust Mueller Hinton broth) and two physiological media (blood and serum). This representation of the TRN covers 842 genes representing 76% of the variance in gene expression that provides a quantitative reconstruction of transcriptional modules in S. aureus, and a platform enabling its full elucidation.

Summary Fruit ripening is a critical phase in the production and marketing of fruits. Previous studies have indicated that fruit ripening is a highly coordinated process, mainly regulated at the transcriptional level, in which transcription factors play essential roles. Thus, identifying key transcription factors regulating fruit ripening as well as their associated regulatory networks promises to contribute to a better understanding of fruit ripening. In this study, temporal gene expression analyses were performed to investigate banana fruit ripening with the aim to discern the global architecture of gene regulatory networks underlying fruit ripening. Eight time points were profiled covering dynamic changes of phenotypes, the associated physiology and levels of known ripening marker genes. Combining results from a weighted gene co‐expression network analysis (WGCNA) as well as cis‐motif analysis and supported by EMSA, Y1H, tobacco‐, banana‐transactivation experimental results, the regulatory network of banana fruit ripening was constructed, from which 25 transcription factors were identified as prime candidates to regulate the ripening process by modulating different ripening‐related pathways. Our study presents the first global view of the gene regulatory network involved in banana fruit ripening, which may provide the basis for a targeted manipulation of fruit ripening to attain higher banana and loss‐reduced banana commercialization.

Numerous methods have been developed for inferring gene regulatory networks from expression data, however, both their absolute and comparative performance remain poorly understood. In this paper, we introduce a framework for critical performance assessment of methods for gene network inference. We present an in silico benchmark suite that we provided as a blinded, community-wide challenge within the context of the DREAM (Dialogue on Reverse Engineering Assessment and Methods) project. We assess the performance of 29 gene-network-inference methods, which have been applied independently by participating teams. Performance profiling reveals that current inference methods are affected, to various degrees, by different types of systematic prediction errors. In particular, all but the best-performing method failed to accurately infer multiple regulatory inputs (combinatorial regulation) of genes. The results of this community-wide experiment show that reliable network inference from gene expression data remains an unsolved problem, and they indicate potential ways of network reconstruction improvements.

Because cereal kernel texture is a determinant of maize end-use properties, it is desirable to elucidate the genetic control of kernel formation and thereby to optimize maize kernel texture for breeding. Basically, maize kernel texture is determined by the ratio of vitreous endosperm in the peripheral region to the floury endosperm in the center of the kernel. In contrast to the puroindoline proteins (Pins) as the major determinants of grain texture specific to wheat, maize kernel texture is a quantitative trait that is controlled by many minor-effect genes. Nonetheless, substantial progresses have been made in unravelling gene regulatory networks underlying maize kernel formation that is related to its texture. Here, we review the current knowledge on maize endosperm development, focusing on vitreous and floury endosperm formation, and summarize the potential transcription regulatory mechanisms for starch and zein biosynthesis. The integration of the information will potentially provide valuable candidate genes for breeding maize varieties with improved kernel texture and quality.

Predicting bacterial transcriptional regulatory networks (TRNs) through computational methods is a core challenge in systems biology, and there is still a long way to go. Here we propose a powerful, general, and stable computational framework called PGBTR (Powerful and General Bacterial Transcriptional Regulatory networks inference method), which employs Convolutional Neural Networks (CNN) to predict bacterial transcriptional regulatory relationships from gene expression data and genomic information. PGBTR consists of two main components: the input generation step PDGD (Probability Distribution and Graph Distance) and the deep learning model CNNBTR (Convolutional Neural Networks for Bacterial Transcriptional Regulation inference). On the real Escherichia coli and Bacillus subtilis datasets, PGBTR outperforms other advanced supervised and unsupervised learning methods in terms of AUROC (Area Under the Receiver Operating Characteristic Curve), AUPR (Area Under Precision-Recall Curve), and F1-score. Moreover, PGBTR exhibits greater stability in identifying real transcriptional regulatory interactions compared to existing methods. PGBTR provides a new software tool for bacterial TRNs inference, and its core ideas can be further extended to other molecular network inference tasks and other biological problems using gene expression data.

In Escherichia coli crosstalk between DNA supercoiling, nucleoid-associated proteins and major RNA polymerase σ initiation factors regulates growth phase-dependent gene transcription. We show that the highly conserved spatial ordering of relevant genes along the chromosomal replichores largely corresponds both to their temporal expression patterns during growth and to an inferred gradient of DNA superhelical density from the origin to the terminus. Genes implicated in similar functions are related mainly in trans across the chromosomal replichores, whereas DNA-binding transcriptional regulators interact predominantly with targets in cis along the replichores. We also demonstrate that macrodomains (the individual structural partitions of the chromosome) are regulated differently. We infer that spatial and temporal variation of DNA superhelicity during the growth cycle coordinates oxygen and nutrient availability with global chromosome structure, thus providing a mechanistic insight into how the organization of a complete bacterial chromosome encodes a spatiotemporal program integrating DNA replication and global gene expression.

Drought stress is one of the major abiotic stress factors that affect plant growth and crop productivity. Tartary buckwheat is a nutritionally balanced and flavonoid-rich pseudocereal crop and also has strong adaptability to different adverse environments including drought. However, little is known about its drought tolerance mechanism. In this study, we performed comparative physiological and transcriptomic analyses of two contrasting drought-resistant Tartary buckwheat genotypes under nature drought treatment in the reproductive stage. Under drought stress, the drought-tolerant genotype XZSN had significantly higher contents of relative water, proline, and soluble sugar, as well as lower relative electrolyte leakage in the leaves than the drought-susceptible LK3. A total of 5,058 (2,165 upregulated and 2,893 downregulated) and 5,182 (2,358 upregulated and 2,824 downregulated) potential drought-responsive genes were identified in XZSN and LK3 by transcriptome sequencing analysis, respectively. Among the potential drought-responsive genes of XZSN, 1,206 and 1,274 genes were identified to be potential positive and negative contributors for XZSN having higher drought resistance ability than LK3. Furthermore, 851 out of 1,206 positive drought-resistant genes were further identified to be the core drought-resistant genes of XZSN based on WGCNA analysis, and most of them were induced earlier and quicker by drought stress than those in LK3. Functional annotation of the 851 core drought-resistant genes found that a large number of stress-responsive genes were involved in TFs, abscisic acid (ABA) biosynthesis, signal transduction and response, non-ABA signal molecule biosynthesis, water holding, oxygen species scavenging, osmotic adjustment, cell damage prevention, and so on. Transcriptional regulatory network analyses identified the potential regulators of these drought-resistant functional genes and found that the HD-ZIP and MYB TFs might be the key downstream TFs of drought resistance in Tartary buckwheat. Taken together, these results indicated that the XZSN genotype was more drought-tolerant than the LK3 genotype as evidenced by triggering the rapid and dramatic transcriptional reprogramming of drought-resistant genes to reduce water loss, prevent cell damage, and so on. This research expands our current understanding of the drought tolerance mechanisms of Tartary buckwheat and provides important information for its further drought resistance research and variety breeding.

Summary Rice (Oryza sativa L.) is one of the most important food crops. Starch is the main substance of rice endosperm and largely determines the grain quality and yield. Starch biosynthesis in endosperm is very complex, requiring a series of enzymes which are also regulated by many transcription factors (TFs). But until now, the large‐scale regulatory network for rice endosperm starch biosynthesis has not been established. Here, we constructed a rice endosperm starch biosynthesis regulatory network comprised of 277 TFs and 15 starch synthesis enzyme‐encoding genes (SSEGs) using DNA affinity chromatography/pull‐down combined with liquid chromatography‐mass spectrometry (DNA pull‐down and LC–MS). In this regulatory network, each SSEG is directly regulated by 7–46 TFs. Based on this network, we found a new pathway ‘ABA‐OsABI5‐OsERF44‐SSEGs’ that regulates rice endosperm starch biosynthesis. We also knocked out five TFs targeting the key amylose synthesis enzyme gene OsGBSSI in japonica rice ‘Nipponbare’ background and found that all mutants had moderately decreased amylose content (AC) in endosperm and improved eating and cooking quality (ECQ). Notably, the knockout of OsSPL7 and OsB3 improves the ECQ without compromising the rice appearance quality, which was further validated in the indica rice ‘Zhongjiazao17’ background. In summary, this gene regulatory network for rice endosperm starch biosynthesis established here will provide important theoretical and practical guidance for the genetic improvement of rice quality.

Background Transcription bridges genetic information and phenotypes. Here, we evaluated how changes in transcriptional regulation enable maize ( Zea mays ), a crop originally domesticated in the tropics, to adapt to temperate environments. Result We generated 572 unique RNA-seq datasets from the roots of 340 maize genotypes. Genes involved in core processes such as cell division, chromosome organization and cytoskeleton organization showed lower heritability of gene expression, while genes involved in anti-oxidation activity exhibited higher expression heritability. An expression genome-wide association study (eGWAS) identified 19,602 expression quantitative trait loci (eQTLs) associated with the expression of 11,444 genes. A GWAS for alternative splicing identified 49,897 splicing QTLs (sQTLs) for 7614 genes. Genes harboring both cis -eQTLs and cis -sQTLs in linkage disequilibrium were disproportionately likely to encode transcription factors or were annotated as responding to one or more stresses. Independent component analysis of gene expression data identified loci regulating co-expression modules involved in oxidation reduction, response to water deprivation, plastid biogenesis, protein biogenesis, and plant-pathogen interaction. Several genes involved in cell proliferation, flower development, DNA replication, and gene silencing showed lower gene expression variation explained by genetic factors between temperate and tropical maize lines. A GWAS of 27 previously published phenotypes identified several candidate genes overlapping with genomic intervals showing signatures of selection during adaptation to temperate environments. Conclusion Our results illustrate how maize transcriptional regulatory networks enable changes in transcriptional regulation to adapt to temperate regions.

Abstract The aim of this investigation was to discover the promoters that drive expression of the sig genes encoding sigma factors of RNA polymerase in Rhodococcus erythropolis CCM2595 and classify these promoters according to the sigma factors which control their activity. To analyze the regulation of major sigma factors, which control large regulons that also contain genes expressed under exponential growth and non-stressed conditions, we used the R. erythropolis CCM2595 culture, which grew rapidly in minimal medium. The transcriptional start sites (TSSs) of the genes sigA, sigB, sigD, sigE, sigG, sigH, sigJ, and sigK were detected by primary 5′-end-specific RNA sequencing. The promoters localized upstream of the detected TSSs were defined by their −35 and −10 elements, which were identical or closely similar to these sequences in the related species Corynebacterium glutamicum and Mycobacterium tuberculosis. Regulation of the promoter activities by different sigma factors was demonstrated by two independent techniques (in vivo and in vitro). All analyzed sig genes encoding the sigma factors with extracytoplasmic function (ECF) were found to be also driven from additional housekeeping promoters. Based on the classification of the sig gene promoters, a model of the basic sigma transcriptional regulatory network in R. erythropolis was designed. The subject of this study is regulatory network formed by sigma subunits of RNA polymerase that is the major enzyme which starts gene expression in the bacterium Rhodococcus erythropolis.