An integrated approach to reconstructing genome-scale transcriptional regulatory networks

A Systems Biology Approach To Disentangle the Direct and Indirect Effects of Global Transcription Factors on Gene Expression in Escherichia coli

Delineating the pleiotropic effects of global transcriptional factors (TFs) is critical for understanding the system-wide regulatory response in a particular environment. Currently, with the availability of genome-wide TF binding and gene expression data for Escherichia coli, several gene targets can be assigned to the global TFs, albeit inconsistently. Here, using a systematic integrated approach with emphasis on metabolism, we characterized and quantified the direct effects as well as the growth rate-mediated indirect effects of global TFs using deletion mutants of FNR, ArcA, and IHF regulators (focal TFs) under glucose fermentative conditions. This categorization enabled us to disentangle the dense connections seen within the transcriptional regulatory network (TRN) and determine the exact nature of focal TF-driven epistatic interactions with other global and pathway-specific local regulators (iTFs). We extended our analysis to combinatorial deletions of these focal TFs to determine their cross talk effects as well as conserved patterns of regulatory interactions. Moreover, we predicted with high confidence several novel metabolite-iTF interactions using inferred iTF activity changes arising from the allosteric effects of the intracellular metabolites perturbed as a result of the absence of focal TFs. Further, using compendium level computational analyses, we revealed not only the coexpressed genes regulated by these focal TFs but also the coordination of the direct and indirect target expression in the context of the economy of intracellular metabolites. Overall, this study leverages the fundamentals of TF-driven regulation, which could serve as a better template for deciphering mechanisms underlying complex phenotypes. IMPORTANCE Understanding the pleiotropic effects of global TFs on gene expression and their relevance underlying a specific response in a particular environment has been challenging. Here, we distinguish the TF-driven direct effects and growth rate-mediated indirect effects on gene expression using single- and double-deletion mutants of FNR, ArcA, and IHF regulators under anaerobic glucose fermentation. Such dissection assists us in unraveling the precise nature of interactions existing between the focal TF(s) and several other TFs, including those altered by allosteric effects of intracellular metabolites. We were able to recapitulate the previously known metabolite-TF interactions and predict novel interactions with high confidence. Furthermore, we determined that the direct and indirect gene expression have a strong connection with each other when analyzed using the coexpressed- or coregulated-gene approach. Deciphering such regulatory patterns explicitly from the metabolism point of view would be valuable in understanding other unpredicted complex regulation existing in nature.

A novel strategy to improving Rhodobacter azotoformans denitrification efficiency: Insight into the role of a two-component system NtrX/Y in denitrification regulation

Transcription factors (TFs) can manage the coordinated expression of genes clusters or multiple genes. TF was used to improve bacterial denitrification ability in this study. During denitrification, the ntrY of R. azotoformans, which encodes the sensor of NtrX/Y system, was significantly upregulated in transcription. Denitrification of the mutant △ntrY was significantly inhibited, and it was recovered after replenishing this gene to the mutant, which indicates the NtrX/Y system plays an important role in regulating bacterial denitrification. According to additional research, the NtrX/Y system regulates bacterial denitrification by directly promoting the expression of the nitrite reductase. ntrY overexpression appears to accelerate bacterial denitrification, and the introduction of a strong promoter tac in conjunction with iron supply optimization increases the rate by 72% further. This study realizes bacterial denitrification enhancement from the perspective of global transcription regulation, which provides a novel strategy for improving microbial ability to degrade pollutants.

Hierarchy of transcription factor network in Escherichia coli K-12: H-NS-mediated silencing and Anti-silencing by global regulators

Transcriptional regulation for genome expression determines growth and adaptation of single-cell bacteria that are directly exposed to environment. The transcriptional apparatus in Escherichia coli K-12 is composed of RNA polymerase core enzyme and two groups of its regulatory proteins, seven species of promoter-recognition subunit sigma and about 300 species of transcription factors. The identification of regulatory targets for all these regulatory proteins is critical toward understanding the genome regulation as a whole. For this purpose, we performed a systematic search in vitro of the whole set of binding sites for each factor by gSELEX system. This review summarizes the accumulated knowledge of regulatory targets for more than 150 TFs from E. coli K-12. Overall TFs could be classified into four families: nucleoid-associated bifunctional TFs; global regulators; local regulators; and single-target regulators, in which the regulatory functions remain uncharacterized for the nucleoid-associated TFs. Here we overview the regulatory targets of two nucleoid-associated TFs, H-NS and its paralog StpA, both together playing the silencing role of a set of non-essential genes. Participation of LeuO and other global regulators have been indicated for the anti-silencing. Finally, we propose the hierarchy of TF network as a key framework of the bacterial genome regulation.

Manipulation of the Regulatory Genes ppsR and prrA in Rhodobacter sphaeroides Enhances Lycopene Production

Rhodobacter sphaeroides is a non-sulfur purple bacterium with great metabolic versatility, capable of producing a variety of valuable compounds that include carotenoids and CoQ₁₀. In order to enhance lycopene production, we deleted the photosynthetic gene cluster repressor ppsR from a lycopene-producing Rb. sphaeroides strain (RL1) constructed in a previous study to break the control of carotenoid synthesis by the oxygen level. Also, lycopene production was further increased by overexpression of the activator prrA. The superior lycopene producer DppsR/OprrA thus obtained had a high growth rate and a lycopene production of 150.15 mg/L with a yield of 21.45 mg/g dry cell weight (DCW) under high oxygen conditions; these values were ≥6.85-fold higher than those of RL1 (19.13 mg/L; 3.32 mg/g DCW). Our findings indicate that elimination of oxygen repression led to more efficient lycopene production by DppsR/OprrA and that its increased productivity under high oxygen conditions makes it a potentially useful strain for industrial-scale lycopene production.

Improving microbial bioremediation efficiency of intensive aquacultural wastewater based on bacterial pollutant metabolism kinetics analysis

How to effectively bioremediate aquacultural wastewater using microbes is an urgent issue for the application of aquaculture beneficial microorganisms. Purple non-sulfur bacteria (PNSB) are beneficial in preventing related pollution in aquaculture applications. An autochthonous PNSB Rhodobacter sphaeroides was employed in this study to explore an effective bioremediation strategy of aquacultural wastewater. The test bacterium showed high performance in the removal of ammonium (97.50% ± 0.78% of 42 mg L^-1 NH₄⁺-N) and phosphate (93.24% ± 0.71% of 50 mg L^-1 PO₄^3--P) in the synthetic wastewater, which are the two crucial indicators of the aquacultural wastewater bioremediation. The study also unveiled that the imbalanced ratio of nutrients in water was the principal reason for limiting the efficient bioremediation of shrimp-culture wastewater. Therefore, an effective microbial bioremediation strategy was proposed by comprehensively considering bacterial pollutant metabolism kinetics constants such as specific consumption yields of chemical oxygen demand (COD)/phosphorous and nitrogen/phosphorous. Finally, COD, total nitrogen (TN), total phosphorus (TP), and ammonium (NH₄⁺-N) in the wastewater were examined, and the results showed that they all decreased to the acceptable values. In conclusion, this study suggested a novel method for improved bioremediation efficiency of aquacultural wastewater, and the findings revealed that this strategy is promising due to its characteristics to be used in various aquaculture wastewater types.

Antisense RNA asPcrL regulates expression of photosynthesis genes in Rhodobacter sphaeroides by promoting RNase III-dependent turn-over of puf mRNA

Anoxygenic photosynthesis is an important pathway for Rhodobacter sphaeroides to produce ATP under oxygen-limiting conditions. The expression of its photosynthesis genes is tightly regulated at transcriptional and post-transcriptional levels in response to light and oxygen signals, to avoid photooxidative stress by the simultaneous presence of pigments, light and oxygen. The puf operon encodes pigment-binding proteins of the light-harvesting complex I (genes pufB and pufA), of the reaction centre (genes pufL and pufM), a scaffold protein (gene pufX) and includes the gene for sRNA PcrX. Segmental differences in the stability of the pufBALMX-pcrX mRNA contribute to the stoichiometry of LHI to RC complexes. With asPcrL we identified the third sRNA and the first antisense RNA that is involved in balancing photosynthesis gene expression in R. sphaeroides. asPcrL influences the stability of the pufBALMX-pcrX mRNA but not of the pufBA mRNA and consequently the stoichiometry of photosynthetic complexes. By base pairing to the pufL region asPcrL promotes RNase III-dependent degradation of the pufBALMX-prcX mRNA. Since asPcrL is activated by the same protein regulators as the puf operon including PcrX it is part of an incoherent feed-forward loop that fine-tunes photosynthesis gene expression.[Figure: see text].

Interplay between formation of photosynthetic complexes and expression of genes for iron-sulfur cluster assembly in Rhodobacter sphaeroides?

Formation of photosynthetic complexes leads to a higher demand for Fe-S clusters. We hypothesized that in the facultative phototrophic alpha-proteobacterium Rhodobacter sphaeroides expression of the isc-suf operon for Fe-S cluster formation may be increased under conditions that promote formation of photosynthetic complexes and that, vice versa, lack of the IscR regulator may also affect photosynthesis gene expression. To test this hypothesis, we monitored the activities of the isc-suf sense and anti-sense promoters under different growth conditions and in mutants which are impaired in formation of photosynthetic complexes. We also tested expression of photosynthesis genes in a mutant lacking the IscR regulator. Our results are not in agreement with a co-regulation of the Isc-Suf system and the photosynthetic apparatus at level of transcription. We provide evidence that, coordination of the systems occurs at post-transcriptional levels. Increased levels of isc-suf mRNAs under conditions promoting formation of photosynthetic complexes are due to higher RNA stability.

Multiple Sense and Antisense Promoters Contribute to the Regulated Expression of the isc-suf Operon for Iron-Sulfur Cluster Assembly in Rhodobacter

A multitude of biological functions relies on iron-sulfur clusters. The formation of photosynthetic complexes goes along with an additional demand for iron-sulfur clusters for bacteriochlorophyll synthesis and photosynthetic electron transport. However, photooxidative stress leads to the destruction of iron-sulfur clusters, and the released iron promotes the formation of further reactive oxygen species. A balanced regulation of iron-sulfur cluster synthesis is required to guarantee the supply of this cofactor, on the one hand, but also to limit stress, on the other hand. The phototrophic alpha-proteobacterium Rhodobacter sphaeroides harbors a large operon for iron-sulfur cluster assembly comprising the iscRS and suf genes. IscR (iron-sulfur cluster regulator) is an iron-dependent regulator of isc-suf genes and other genes with a role in iron metabolism. We applied reporter gene fusions to identify promoters of the isc-suf operon and studied their activity alone or in combination under different conditions. Gel-retardation assays showed the binding of regulatory proteins to individual promoters. Our results demonstrated that several promoters in a sense and antisense direction influenced isc-suf expression and the binding of the IscR, Irr, and OxyR regulatory proteins to individual promoters. These findings demonstrated a complex regulatory network of several promoters and regulatory proteins that helped to adjust iron-sulfur cluster assembly to changing conditions in Rhodobacter sphaeroides.

Common principles and best practices for engineering microbiomes

Despite broad scientific interest in harnessing the power of Earth's microbiomes, knowledge gaps hinder their efficient use for addressing urgent societal and environmental challenges. We argue that structuring research and technology developments around a design-build-test-learn (DBTL) cycle will advance microbiome engineering and spur new discoveries of the basic scientific principles governing microbiome function. In this Review, we present key elements of an iterative DBTL cycle for microbiome engineering, focusing on generalizable approaches, including top-down and bottom-up design processes, synthetic and self-assembled construction methods, and emerging tools to analyse microbiome function. These approaches can be used to harness microbiomes for broad applications related to medicine, agriculture, energy and the environment. We also discuss key challenges and opportunities of each approach and synthesize them into best practice guidelines for engineering microbiomes. We anticipate that adoption of a DBTL framework will rapidly advance microbiome-based biotechnologies aimed at improving human and animal health, agriculture and enabling the bioeconomy.

Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities

New technologies have enabled the investigation of biology and human health at an unprecedented scale and in multiple dimensions. These dimensions include myriad properties describing genome, epigenome, transcriptome, microbiome, phenotype, and lifestyle. No single data type, however, can capture the complexity of all the factors relevant to understanding a phenomenon such as a disease. Integrative methods that combine data from multiple technologies have thus emerged as critical statistical and computational approaches. The key challenge in developing such approaches is the identification of effective models to provide a comprehensive and relevant systems view. An ideal method can answer a biological or medical question, identifying important features and predicting outcomes, by harnessing heterogeneous data across several dimensions of biological variation. In this Review, we describe the principles of data integration and discuss current methods and available implementations. We provide examples of successful data integration in biology and medicine. Finally, we discuss current challenges in biomedical integrative methods and our perspective on the future development of the field.

Modulation of extracytoplasmic function (ECF) sigma factor promoter selectivity by spacer region sequence

The ability of bacteria to adapt to stress depends on the conditional expression of specific sets of genes. Bacillus subtilis encodes seven extracytoplasmic function (ECF) sigma (σ) factors that regulate functions important for survival under conditions eliciting cell envelope stress. Of these, four have been studied in detail: σ^M, σ^W, σ^X and σ^V. These four σ factors recognize overlapping sets of promoters, although the sequences that determine this overlapping recognition are incompletely understood. A major role in promoter selectivity has been ascribed to the core −10 and −35 promoter elements. Here, we demonstrate that a homopolymeric T-tract motif, proximal to the −35 element, functions in combination with the core promoter sequences to determine selectivity for ECF sigma factors. This motif is most critical for promoter activation by σ^V, and contributes variably to activation by σ^M, σ^X and σ^W. We propose that this motif, which is a feature of the deduced promoter consensus for a subset of ECF σ factors from many species, imparts intrinsic DNA curvature to influence promoter activity. The differential effect of this region among ECF σ factors thereby provides a mechanism to modulate the nature and extent of regulon overlap.

Unsupervised Gene Network Inference with Decision Trees and Random Forests

In this chapter, we introduce the reader to a popular family of machine learning algorithms, called decision trees. We then review several approaches based on decision trees that have been developed for the inference of gene regulatory networks (GRNs). Decision trees have indeed several nice properties that make them well-suited for tackling this problem: they are able to detect multivariate interacting effects between variables, are non-parametric, have good scalability, and have very few parameters. In particular, we describe in detail the GENIE3 algorithm, a state-of-the-art method for GRN inference.

Transcriptomic and evolutionary analyses of white pear (Pyrus bretschneideri) β-amylase genes reveals their importance for cold and drought stress responses

β-amylase (BAM) genes play essential roles in plant abiotic stress responses. Although the genome of Chinese white pear (Pyrus bretschneideri) has recently been made available, knowledge regarding the BAM family in pear, including gene function, evolutionary history and patterns of gene expression remains limited. In this study, we identified 17 PbBAMs in the pear genome. Of these, 12 PbBAM members were mapped onto 9 chromosomes and 5 PbBAM genes were located on scaffold contigs. Based on gene structure, protein motif analysis, and the topology of the phylogenetic tree of the PbBAM family, we classified member genes into 4 groups. All PbBAM genes were found to contain typical glycosyl hydrolysis 14 domain motifs. Interfamilial comparisons revealed that the phylogenetic relationships of BAM genes in other Rosaceae species were similar those found in pear. We also found that whole-genome duplication (WGD)/segmental duplication events played critical roles in the expansion of the BAM family. Next, we used transcriptomic data to study gene expression during the response of drought and low temperate responses, and found that genes in Group B were related to drought and cold stress. We identified four PbBAM genes associated with abiotic stress in Pear. Finally, by analyzing co-expression networks and co-regulatory genes, we found that PbBAM1a and PbBAM1b were associated with the pear abiotic stress response.

Genome-scale biological models for industrial microbial systems

The primary aims and challenges associated with microbial fermentation include achieving faster cell growth, higher productivity, and more robust production processes. Genome-scale biological models, predicting the formation of an interaction among genetic materials, enzymes, and metabolites, constitute a systematic and comprehensive platform to analyze and optimize the microbial growth and production of biological products. Genome-scale biological models can help optimize microbial growth-associated traits by simulating biomass formation, predicting growth rates, and identifying the requirements for cell growth. With regard to microbial product biosynthesis, genome-scale biological models can be used to design product biosynthetic pathways, accelerate production efficiency, and reduce metabolic side effects, leading to improved production performance. The present review discusses the development of microbial genome-scale biological models since their emergence and emphasizes their pertinent application in improving industrial microbial fermentation of biological products.

PredCRP: predicting and analysing the regulatory roles of CRP from its binding sites in Escherichia coli

Cyclic AMP receptor protein (CRP), a global regulator in Escherichia coli, regulates more than 180 genes via two roles: activation and repression. Few methods are available for predicting the regulatory roles from the binding sites of transcription factors. This work proposes an accurate method PredCRP to derive an optimised model (named PredCRP-model) and a set of four interpretable rules (named PredCRP-ruleset) for predicting and analysing the regulatory roles of CRP from sequences of CRP-binding sites. A dataset consisting of 169 CRP-binding sites with regulatory roles strongly supported by evidence was compiled. The PredCRP-model, using 12 informative features of CRP-binding sites, and cooperating with a support vector machine achieved a training and test accuracy of 0.98 and 0.93, respectively. PredCRP-ruleset has two activation rules and two repression rules derived using the 12 features and the decision tree method C4.5. This work further screened and identified 23 previously unobserved regulatory interactions in Escherichia coli. Using quantitative PCR for validation, PredCRP-model and PredCRP-ruleset achieved a test accuracy of 0.96 (=22/23) and 0.91 (=21/23), respectively. The proposed method is suitable for designing predictors for regulatory roles of all global regulators in Escherichia coli. PredCRP can be accessed at https://github.com/NctuICLab/PredCRP.

Use of transcriptomic data for extending a model of the AppA/PpsR system in Rhodobacter sphaeroides

Background

Photosynthetic (PS) gene expression in Rhodobacter sphaeroides is regulated in response to changes in light and redox conditions mainly by PrrB/A, FnrL and AppA/PpsR systems. The PrrB/A and FnrL systems activate the expression of them under anaerobic conditions while the AppA/PpsR system represses them under aerobic conditions. Recently, two mathematical models have been developed for the AppA/PpsR system and demonstrated how the interaction between AppA and PpsR could lead to a phenotype in which PS genes are repressed under semi-aerobic conditions. These models have also predicted that the transition from aerobic to anaerobic growth mode could occur via a bistable regime. However, they lack experimentally quantifiable inputs and outputs. Here, we extend one of them to include such quantities and combine all relevant micro-array data publically available for a PS gene of this bacterium and use that to parameterise the model. In addition, we hypothesise that the AppA/PpsR system alone might account for the observed trend of PS gene expression under semi-aerobic conditions.

Results

Our extended model of the AppA/PpsR system includes the biological input of atmospheric oxygen concentration and an output of photosynthetic gene expression. Following our hypothesis that the AppA/PpsR system alone is sufficient to describe the overall trend of PS gene expression we parameterise the model and suggest that the rate of AppA reduction in vivo should be faster than its oxidation. Also, we show that despite both the reduced and oxidised forms of PpsR binding to the PS gene promoters in vitro, binding of the oxidised form as a repressor alone is sufficient to reproduce the observed PS gene expression pattern. Finally, the combination of model parameters which fit the biological data well are broadly consistent with those which were previously determined to be required for the system to show (i) the repression of PS genes under semi-aerobic conditions, and (ii) bistability.

Conclusion

We found that despite at least three pathways being involved in the regulation of photosynthetic genes, the AppA/PpsR system alone is capable of accounting for the observed trends in photosynthetic gene expression seen at different oxygen levels.

Electronic supplementary material

The online version of this article (10.1186/s12918-017-0489-y) contains supplementary material, which is available to authorized users.

Links between Transcription, Environmental Adaptation and Gene Variability in Escherichia coli: Correlations between Gene Expression and Gene Variability Reflect Growth Efficiencies

Gene expression is known to be the principle factor explaining how fast genes evolve. Highly transcribed genes evolve slowly because any negative impact caused by a particular mutation is magnified by protein abundance. However, gene expression is a phenotype that depends both on the environment and on the strains or species. We studied this phenotypic plasticity by analyzing the transcriptome profiles of four Escherichia coli strains grown in three different culture media, and explored how expression variability was linked to gene allelic diversity. Genes whose expression changed according to the media and not to the strains were less polymorphic than other genes. Genes for which transcription depended predominantly on the strain were more polymorphic than other genes and were involved in sensing and responding to environmental changes, with an overrepresentation of two-component system genes. Surprisingly, we found that the correlation between transcription and gene diversity was highly variable among growth conditions and could be used to quantify growth efficiency of a strain in a medium. Genetic variability was found to increase with gene expression in poor growth conditions. As such conditions are also characterized by down-regulation of all DNA repair systems, including transcription-coupled repair, we suggest that gene expression under stressful conditions may be mutagenic and thus leads to a variability in mutation rate among genes in the genome which contributes to the pattern of protein evolution.

An experimentally supported model of the Bacillus subtilis global transcriptional regulatory network

Organisms from all domains of life use gene regulation networks to control cell growth, identity, function, and responses to environmental challenges. Although accurate global regulatory models would provide critical evolutionary and functional insights, they remain incomplete, even for the best studied organisms. Efforts to build comprehensive networks are confounded by challenges including network scale, degree of connectivity, complexity of organism–environment interactions, and difficulty of estimating the activity of regulatory factors. Taking advantage of the large number of known regulatory interactions in Bacillus subtilis and two transcriptomics datasets (including one with 38 separate experiments collected specifically for this study), we use a new combination of network component analysis and model selection to simultaneously estimate transcription factor activities and learn a substantially expanded transcriptional regulatory network for this bacterium. In total, we predict 2,258 novel regulatory interactions and recall 74% of the previously known interactions. We obtained experimental support for 391 (out of 635 evaluated) novel regulatory edges (62% accuracy), thus significantly increasing our understanding of various cell processes, such as spore formation.

CceR and AkgR regulate central carbon and energy metabolism in alphaproteobacteria

Many pathways of carbon and energy metabolism are conserved across the phylogeny, but the networks that regulate their expression or activity often vary considerably among organisms. In this work, we show that two previously uncharacterized transcription factors (TFs) are direct regulators of genes encoding enzymes of central carbon and energy metabolism in the alphaproteobacterium Rhodobacter sphaeroides. The LacI family member CceR (RSP_1663) directly represses genes encoding enzymes in the Entner-Doudoroff pathway, while activating those encoding the F1F0 ATPase and enzymes of the tricarboxylic acid (TCA) cycle and gluconeogenesis, providing a direct transcriptional network connection between carbon and energy metabolism. We identified bases that are important for CceR DNA binding and showed that DNA binding by this TF is inhibited by 6-phosphogluconate. We also showed that the GntR family TF AkgR (RSP_0981) directly activates genes encoding several TCA cycle enzymes, and we identified conditions where its activity is increased. The properties of single and double ΔCceR and ΔAkgR mutants illustrate that these 2 TFs cooperatively regulate carbon and energy metabolism. Comparative genomic analysis indicates that CceR and AkgR orthologs are found in other alphaproteobacteria, where they are predicted to have a conserved function in regulating central carbon metabolism. Our characterization of CceR and AkgR has provided important new insight into the networks that control central carbon and energy metabolism in alphaproteobacteria that can be exploited to modify or engineer new traits in these widespread and versatile bacteria.

IMPORTANCE

To extract and conserve energy from nutrients, cells coordinate a set of metabolic pathways into integrated networks. Many pathways that conserve energy or interconvert metabolites are conserved across cells, but the networks regulating these processes are often highly variable. In this study, we characterize two previously unknown transcriptional regulators of carbon and energy metabolism that are conserved in alphaproteobacteria, a group of abundant, environmentally and biotechnologically important organisms. We identify the genes they regulate, the DNA sequences they recognize, the metabolite that controls the activity of one of the regulators, and conditions where they are required for growth. We provide important new insight into conserved cellular networks that can also be used to improve a variety of hosts for converting feedstock into valuable products.

Transcriptional Regulatory Network Analysis of MYB Transcription Factor Family Genes in Rice

MYB transcription factor (TF) is one of the largest TF families and regulates defense responses to various stresses, hormone signaling as well as many metabolic and developmental processes in plants. Understanding these regulatory hierarchies of gene expression networks in response to developmental and environmental cues is a major challenge due to the complex interactions between the genetic elements. Correlation analyses are useful to unravel co-regulated gene pairs governing biological process as well as identification of new candidate hub genes in response to these complex processes. High throughput expression profiling data are highly useful for construction of co-expression networks. In the present study, we utilized transcriptome data for comprehensive regulatory network studies of MYB TFs by "top-down" and "guide-gene" approaches. More than 50% of OsMYBs were strongly correlated under 50 experimental conditions with 51 hub genes via "top-down" approach. Further, clusters were identified using Markov Clustering (MCL). To maximize the clustering performance, parameter evaluation of the MCL inflation score (I) was performed in terms of enriched GO categories by measuring F-score. Comparison of co-expressed cluster and clads analyzed from phylogenetic analysis signifies their evolutionarily conserved co-regulatory role. We utilized compendium of known interaction and biological role with Gene Ontology enrichment analysis to hypothesize function of coexpressed OsMYBs. In the other part, the transcriptional regulatory network analysis by "guide-gene" approach revealed 40 putative targets of 26 OsMYB TF hubs with high correlation value utilizing 815 microarray data. The putative targets with MYB-binding cis-elements enrichment in their promoter region, functional co-occurrence as well as nuclear localization supports our finding. Specially, enrichment of MYB binding regions involved in drought-inducibility implying their regulatory role in drought response in rice. Thus, the co-regulatory network analysis facilitated the identification of complex OsMYB regulatory networks, and candidate target regulon genes of selected guide MYB genes. The results contribute to the candidate gene screening, and experimentally testable hypotheses for potential regulatory MYB TFs, and their targets under stress conditions.