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Plants rely on complex regulatory mechanisms to ensure proper growth and development. As plants are sessile organisms, these mechanisms must be flexible enough to adapt to changes in the environment. GROWTH-REGULATING FACTORS (GRFs) are plant-specific transcription factors that act as a central hub controlling plant growth and development, which offer promising biotechnological applications to enhance plant performance. Here, we analyze the complex molecular mechanisms that regulate GRFs activity, and how their natural and synthetic variants can impact on plant growth and development. We describe the biological roles of the GRFs and examine how they regulate gene expression and contribute to the control of organ growth and plant responses to a changing environment. This review focuses on the premise that unlocking the full biotechnological potential of GRFs requires a thorough understanding of the various regulatory layers governing GRF activity, the functional divergence among GRF family members, and the gene networks that they regulate.

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Breast cancer encompasses a diverse array of subtypes, each exhibiting distinct clinical characteristics and treatment responses. Unraveling the underlying regulatory mechanisms that govern gene expression patterns in these subtypes is essential for advancing our understanding of breast cancer biology. Gene co-expression networks (GCNs) help us identify groups of genes that work in coordination. Previous research has revealed a marked reduction in the interaction of genes located on different chromosomes within GCNs for breast cancer, as well as for lung, kidney, and hematopoietic cancers. However, the reasons behind why genes on the same chromosome often co-express remain unclear. In this study, we investigate the role of transcription factors in shaping gene co-expression networks within the four main breast cancer subtypes: Luminal A, Luminal B, HER2+, and Basal, along with normal breast tissue. We identify communities within each GCN and calculate the transcription factors that may regulate these communities, comparing the results across different phenotypes. Our findings indicate that, in general, regulatory behavior is to a large extent similar among breast cancer molecular subtypes and even in healthy networks. This suggests that transcription factor motif usage does not fully determine long-range co-expression patterns. Specific transcription factor motifs, such as CCGGAAG, appear frequently across all phenotypes, even involving multiple highly connected transcription factors. Additionally, certain transcription factors exhibit unique actions in specific subtypes but with limited influence. Our research demonstrates that the loss of inter-chromosomal co-expression is not solely attributable to transcription factor regulation. Although the exact mechanism responsible for this phenomenon remains elusive, this work contributes to a better understanding of gene expression regulatory programs in breast cancer.

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Periodontitis is a chronic inflammatory disease characterized by the progressive and irreversible destruction of the periodontium. Its aetiopathogenesis lies in the constant challenge of the dysbiotic biofilm, which triggers a deregulated immune response responsible for the disease phenotype. Although the molecular mechanisms underlying periodontitis have been extensively studied, the regulatory mechanisms at the transcriptional level remain unclear. To generate transcriptomic data, we performed RNA shotgun sequencing of the oral mucosa of periodontitis-affected mice. Since genes are not expressed in isolation during pathological processes, we disclose here the complete repertoire of differentially expressed genes (DEG) and co-expressed modules to build Gene Regulatory Networks (GRNs) and identify the Master Transcriptional Regulators of periodontitis. The transcriptional changes revealed 366 protein-coding genes and 42 non-coding genes differentially expressed and enriched in the immune response. Furthermore, we found 13 co-expression modules with different representation degrees and gene expression levels. Our GRN comprises genes from 12 gene clusters, 166 nodes, of which 33 encode Transcription Factors, and 201 connections. Finally, using these strategies, 26 master regulators of periodontitis were identified. In conclusion, combining the transcriptomic analyses with the regulatory network construction represents a powerful and efficient strategy for identifying potential periodontitis-therapeutic targets.

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Huntington's disease (HD) is a disorder caused by an abnormal expansion of trinucleotide CAG repeats within the huntingtin (Htt) gene. Under normal conditions, the CREB Binding Protein interacts with CREB elements and acetylates Lysine 27 of Histone 3 to direct the expression of several genes. However, mutant Htt causes depletion of CBP, which in turn induces altered histone acetylation patterns and transcriptional deregulation. Here, we have studied a differential expression analysis and H3K27ac variation in 4- and 6-week-old R6/2 mice as a model of juvenile HD. The analysis of differential gene expression and acetylation levels were integrated into Gene Regulatory Networks revealing key regulators involved in the altered transcription cascade. Our results show changes in acetylation and gene expression levels that are related to impaired neuronal development, and key regulators clearly defined in 6-week-old mice are proposed to drive the downstream regulatory cascade in HD. Here, we describe the first approach to determine the relationship among epigenetic changes in the early stages of HD. We determined the existence of changes in pre-symptomatic stages of HD as a starting point for early onset indicators of the progression of this disease.

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Alzheimer's Disease (AD) is an irreversible neurodegenerative disease clinically characterized by the presence of ß-amyloid plaques and tau deposits in various regions of the brain. However, the underlying factors that contribute to the development of AD remain unclear. Recently, the fusiform gyrus has been identified as a critical brain region associated with mild cognitive impairment, which may increase the risk of AD development. In our study, we performed gene co-expression and differential co-expression network analyses, as well as gene-expression-based prediction, using RNA-seq transcriptome data from post-mortem fusiform gyrus tissue samples collected from both cognitively healthy individuals and those with AD. We accessed differential co-expression networks in large cohorts such as ROSMAP, MSBB, and Mayo, and conducted over-representation analyses of gene pathways and gene ontology. Our results comprise four exclusive gene hubs in co-expression modules of Alzheimer's Disease, including FNDC3A, MED23, NRIP1, and PKN2. Further, we identified three genes with differential co-expressed links, namely FAM153B, CYP2C8, and CKMT1B. The differential co-expressed network showed moderate predictive performance for AD, with an area under the curve ranging from 0.71 to 0.76 (+/- 0.07). The over-representation analysis identified enrichment for Toll-Like Receptors Cascades and signaling pathways, such as G protein events, PIP2 hydrolysis and EPH-Epherin mechanism, in the fusiform gyrus. In conclusion, our findings shed new light on the molecular pathophysiology of AD by identifying new genes and biological pathways involved, emphasizing the crucial role of gene regulatory networks in the fusiform gyrus.

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The functions of living organisms are affected by different kinds of perturbation, both internal and external, which in many cases have functional effects and phenotypic impact. The effects of these perturbations become particularly relevant for multicellular organisms with complex body patterns and cell type heterogeneity, where transcriptional programs controlled by gene regulatory networks determine, for example, the cell fate during embryonic development. Therefore, an essential aspect of development in these organisms is the ability to maintain the functionality of their genetic developmental programs even in the presence of genetic variation, changing environmental conditions and biochemical noise, a property commonly termed robustness. We discuss the implication of different molecular mechanisms of robustness involved in neurodevelopment, which is characterized by the interplay of many developmental programs at a molecular, cellular and systemic level. We specifically focus on processes affecting the function of gene regulatory networks, encompassing transcriptional regulatory elements and post-transcriptional processes such as miRNA-based regulation, but also higher order regulatory organization, such as gene network topology. We also present cases where impairment of robustness mechanisms can be associated with neurodevelopmental disorders, as well as reasons why understanding these mechanisms should represent an important part of the study of gene regulatory networks driving neural development.

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BACKGROUND: Plant domestication is a remarkable example of rapid phenotypic transformation of polygenic traits, such as organ size. Evidence from a handful of study cases suggests this transformation is due to gene regulatory changes that result in non-additive phenotypes. Employing data from published genetic crosses, we estimated the role of non-additive gene action in the modulation of transcriptional landscapes in three domesticated plants: maize, sunflower, and chili pepper. Using A. thaliana, we assessed the correlation between gene regulatory network (GRN) connectivity properties, transcript abundance variation, and gene action. Finally, we investigated the propagation of non-additive gene action in GRNs. RESULTS: We compared crosses between domesticated plants and their wild relatives to a set of control crosses that included a pair of subspecies evolving under natural selection and a set of inbred lines evolving under domestication. We found abundance differences on a higher portion of transcripts in crosses between domesticated-wild plants relative to the control crosses. These transcripts showed non-additive gene action more often in crosses of domesticated-wild plants than in our control crosses. This pattern was strong for genes associated with cell cycle and cell fate determination, which control organ size. We found weak but significant negative correlations between the number of targets of trans-acting genes (Out-degree) and both the magnitude of transcript abundance difference a well as the absolute degree of dominance. Likewise, we found that the number of regulators that control a gene's expression (In-degree) is weakly but negatively correlated with the magnitude of transcript abundance differences. We observed that dominant-recessive gene action is highly propagable through GRNs. Finally, we found that transgressive gene action is driven by trans-acting regulators showing additive gene action. CONCLUSIONS: Our study highlights the role of non-additive gene action on modulating domestication-related traits, such as organ size via regulatory divergence. We propose that GRNs are shaped by regulatory changes at genes with modest connectivity, which reduces the effects of antagonistic pleiotropy. Finally, we provide empirical evidence of the propagation of non-additive gene action in GRNs, which suggests a transcriptional epistatic model for the control of polygenic traits, such as organ size.

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Oxidative stress is a major cause of morbidity and mortality in human health and disease. In this review, we focus on the Forkhead Box (Fox) subclass O3 (FoxO3), an extensively studied transcription factor that plays a pleiotropic role in a wide range of physiological and pathological processes by regulating multiple gene regulatory networks involved in the modulation of numerous aspects of cellular metabolism, including fuel metabolism, cell death, and stress resistance. This review will also focus on regulatory mechanisms of FoxO3 expression and activity, such as crucial post-translational modifications and non-coding RNAs. Moreover, this work discusses and evidences some pathways to how this transcription factor and reactive oxygen species regulate each other, which may lead to the pathogenesis of various types of diseases. Therefore, in addition to being a promising therapeutic target, the FoxO3-regulated signaling pathways can also be used as reliable diagnostic and prognostic biomarkers and indicators for drug responsiveness.

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Transcriptional factors are well studied in bacteria for their global interactions and the effects they produce at the phenotypic level. Particularly, Bacillus subtilis has been widely employed as a model Gram-positive microorganism used to characterize these network interactions. Bacillus species are currently used as efficient commercial microbial platforms to produce diverse metabolites such as extracellular enzymes, antibiotics, surfactants, industrial chemicals, heterologous proteins, among others. However, the pleiotropic effects caused by the genetic modification of specific genes that codify for global regulators (transcription factors) have not been implicated commonly from a bioprocess point of view. Recently, these strategies have attracted the attention in Bacillus species because they can have an application to increase production efficiency of certain commercial interest metabolites. In this review, we update the recent advances that involve this trend in the use of genetic engineering (mutations, deletion, or overexpression) performed to global regulators such as Spo0A, CcpA, CodY and AbrB, which can provide an advantage for the development or improvement of bioprocesses that involve Bacillus species as production platforms. Genetic networks, regulation pathways and their relationship to the development of growth stages are also discussed to correlate the interactions that occur between these regulators, which are important to consider for application in the improvement of commercial-interest metabolites. Reported yields from these products currently produced mostly under laboratory conditions and, in a lesser extent at bioreactor level, are also discussed to give valuable perspectives about their potential use and developmental level directed to process optimization at large-scale.

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Neurodevelopmental disorders differ considerably between males and females, and fetal brain development is one of the most critical periods to determine risk for these disorders. Transcriptomic studies comparing male and female fetal brain have demonstrated that the highest difference in gene expression occurs in sex chromosomes, but several autossomal genes also demonstrate a slight difference that has not been yet explored. In order to investigate biological pathways underlying fetal brain sex differences, we applied medicine network principles using integrative methods such as co-expression networks (CEMiTool) and regulatory networks (netZoo). The pattern of gene expression from genes in the same pathway tend to reflect biologically relevant phenomena. In this study, network analysis of fetal brain expression reveals regulatory differences between males and females. Integrating two different bioinformatics tools, our results suggest that biological processes such as cell cycle, cell differentiation, energy metabolism and extracellular matrix organization are consistently sex-biased. MSET analysis demonstrates that these differences are relevant to neurodevelopmental disorders, including autism.

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Gene regulation is a key process for all microorganisms, as it allows them to adapt to different environmental stimuli. However, despite the relevance of gene expression control, for only a handful of organisms is there related information about genome regulation. In this work, we inferred the gene regulatory networks (GRNs) of bacterial and archaeal genomes by comparisons with six organisms with well-known regulatory interactions. The references we used are: Escherichia coli K-12 MG1655, Bacillus subtilis 168, Mycobacterium tuberculosis, Pseudomonas aeruginosa PAO1, Salmonella enterica subsp. enterica serovar typhimurium LT2, and Staphylococcus aureus N315. To this end, the inferences were achieved in two steps. First, the six model organisms were contrasted in an all-vs-all comparison of known interactions based on Transcription Factor (TF)-Target Gene (TG) orthology relationships and Transcription Unit (TU) assignments. In the second step, we used a guilt-by-association approach to infer the GRNs for 12,230 bacterial and 649 archaeal genomes based on TF-TG orthology relationships of the six bacterial models determined in the first step. Finally, we discuss examples to show the most relevant results obtained from these inferences. A web server with all the predicted GRNs is available at https://regulatorynetworks.unam.mx/ or http://132.247.46.6/.

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In this work, we inferred the gene regulatory network (GRN) of the fungus Fusarium oxysporum by using the regulatory networks of Aspergillus nidulans FGSC A4, Neurospora crassa OR74A, Saccharomyces cerevisiae S288c, and Fusarium graminearum PH-1 as templates for sequence comparisons. Topological properties to infer the role of transcription factors (TFs) and to identify functional modules were calculated in the GRN. From these analyzes, five TFs were identified as hubs, including FOXG_04688 and FOXG_05432, which regulate 2,404 and 1,864 target genes, respectively. In addition, 16 communities were identified in the GRN, where the largest contains 1,923 genes and the smallest contains 227 genes. Finally, the genes associated with virulence were extracted from the GRN and exhaustively analyzed, and we identified a giant module with ten TFs and 273 target genes, where the most highly connected node corresponds to the transcription factor FOXG_05265, homologous to the putative bZip transcription factor CPTF1 of Claviceps purpurea, which is involved in ergotism disease that affects cereal crops and grasses. The results described in this work can be used for the study of gene regulation in this organism and open the possibility to explore putative genes associated with virulence against their host.

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Understanding how roots modulate development under varied irrigation or rainfall is crucial for development of climate-resilient crops. We established a toolbox of tagged rice lines to profile translating mRNAs and chromatin accessibility within specific cell populations. We used these to study roots in a range of environments: plates in the lab, controlled greenhouse stress and recovery conditions, and outdoors in a paddy. Integration of chromatin and mRNA data resolves regulatory networks of the following: cycle genes in proliferating cells that attenuate DNA synthesis under submergence; genes involved in auxin signaling, the circadian clock, and small RNA regulation in ground tissue; and suberin biosynthesis, iron transporters, and nitrogen assimilation in endodermal/exodermal cells modulated with water availability. By applying a systems approach, we identify known and candidate driver transcription factors of water-deficit responses and xylem development plasticity. Collectively, this resource will facilitate genetic improvements in root systems for optimal climate resilience.

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Neurodevelopmental disorders differ considerably between males and females, and fetal brain development is one of the most critical periods to determine risk for these disorders. Transcriptomic studies comparing male and female fetal brain have demonstrated that the highest difference in gene expression occurs in sex chromosomes, but several autossomal genes also demonstrate a slight difference that has not been yet explored. In order to investigate biological pathways underlying fetal brain sex differences, we applied medicine network principles using integrative methods such as co-expression networks (CEMiTool) and regulatory networks (netZoo). The pattern of gene expression from genes in the same pathway tend to reflect biologically relevant phenomena. In this study, network analysis of fetal brain expression reveals regulatory differences between males and females. Integrating two different bioinformatics tools, our results suggest that biological processes such as cell cycle, cell differentiation, energy metabolism and extracellular matrix organization are consistently sex-biased. MSET analysis demonstrates that these differences are relevant to neurodevelopmental disorders, including autism.

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Noncoding RNAs (ncRNAs) play prominent roles in the regulation of gene expression via their interactions with other biological molecules such as proteins and nucleic acids. Although much of our knowledge about how these ncRNAs operate in different biological processes has been obtained from experimental findings, computational biology can also clearly substantially boost this knowledge by suggesting possible novel interactions of these ncRNAs with other molecules. Computational predictions are thus used as an alternative source of new insights through a process of mutual enrichment because the information obtained through experiments continuously feeds through into computational methods. The results of these predictions in turn shed light on possible interactions that are subsequently validated experimentally. This review describes the latest advances in databases, bioinformatic tools, and new in silico strategies that allow the establishment or prediction of biological interactions of ncRNAs, particularly miRNAs and lncRNAs. The ncRNA species described in this work have a special emphasis on those found in humans, but information on ncRNA of other species is also included.

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Sepsis remains a leading cause of death in ICUs all over the world, with pediatric sepsis accounting for a high percentage of mortality in pediatric ICUs. Its complexity makes it difficult to establish a consensus on genetic biomarkers and therapeutic targets. A promising strategy is to investigate the regulatory mechanisms involved in sepsis progression, but there are few studies regarding gene regulation in sepsis. This work aimed to reconstruct the sepsis regulatory network and identify transcription factors (TFs) driving transcriptional states, which we refer to here as master regulators. We used public gene expression datasets to infer the co-expression network associated with sepsis in a retrospective study. We identified a set of 15 TFs as potential master regulators of pediatric sepsis, which were divided into two main clusters. The first cluster corresponded to TFs with decreased activity in pediatric sepsis, and GATA3 and RORA, as well as other TFs previously implicated in the context of inflammatory response. The second cluster corresponded to TFs with increased activity in pediatric sepsis and was composed of TRIM25, RFX2, and MEF2A, genes not previously described as acting in a coordinated way in pediatric sepsis. Altogether, these results show how a subset of master regulators TF can drive pathological transcriptional states, with implications for sepsis biology and treatment.

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Leishmania parasites are the causal agent of leishmaniasis, an endemic disease in more than 90 countries worldwide. Over the years, traditional approaches focused on the parasite when developing treatments against leishmaniasis. Despite numerous attempts, there is not yet a universal treatment, and those available have allowed for the appearance of resistance. Here, we propose and follow a host-directed approach that aims to overcome the current lack of treatment. Our approach identifies potential therapeutic targets in the host cell and proposes known drug interactions aiming to improve the immune response and to block the host machinery necessary for the survival of the parasite. We started analyzing transcription factor regulatory networks of macrophages infected with Leishmania major. Next, based on the regulatory dynamics of the infection and available gene expression profiles, we selected potential therapeutic target proteins. The function of these proteins was then analyzed following a multilayered network scheme in which we combined information on metabolic pathways with known drugs that have a direct connection with the activity carried out by these proteins. Using our approach, we were able to identify five host protein-coding gene products that are potential therapeutic targets for treating leishmaniasis. Moreover, from the 11 drugs known to interact with the function performed by these proteins, 3 have already been tested against this parasite, verifying in this way our novel methodology. More importantly, the remaining eight drugs previously employed to treat other diseases, remain as promising yet-untested antileishmanial therapies. IMPORTANCE This work opens a new path to fight parasites by targeting host molecular functions by repurposing available and approved drugs. We created a novel approach to identify key proteins involved in any biological process by combining gene regulatory networks and expression profiles. Once proteins have been selected, our approach employs a multilayered network methodology that relates proteins to functions to drugs that alter these functions. By applying our novel approach to macrophages during the Leishmania infection process, we both validated our work and found eight drugs already approved for use in humans that to the best of our knowledge were never employed to treat leishmaniasis, rendering our work as a new tool in the box available to the scientific community fighting parasites.

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A new phenotypic variant may appear first in organisms through plasticity, that is, as a response to an environmental signal or other nongenetic perturbation. If such trait is beneficial, selection may increase the frequency of alleles that enable and facilitate its development. Thus, genes may take control of such traits, decreasing dependence on nongenetic disturbances, in a process called genetic assimilation. Despite an increasing amount of empirical studies supporting genetic assimilation, its significance is still controversial. Whether genetic assimilation is widespread depends, to a great extent, on how easily mutation and recombination reduce the trait's dependence on nongenetic perturbations. Previous research suggests that this is the case for mutations. Here we use simulations of gene regulatory network dynamics to address this issue with respect to recombination. We find that recombinant offspring of parents that produce a new phenotype through plasticity are more likely to produce the same phenotype without requiring any perturbation. They are also prone to preserve the ability to produce that phenotype after genetic and nongenetic perturbations. Our work also suggests that ancestral plasticity can play an important role for setting the course that evolution takes. In sum, our results indicate that the manner in which phenotypic variation maps unto genetic variation facilitates evolution through genetic assimilation in gene regulatory networks. Thus, we contend that the importance of this evolutionary mechanism should not be easily neglected.

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The cell expresses various genes in specific contexts with respect to internal and external perturbations to invoke appropriate responses. Transcription factors (TFs) orchestrate and define the expression level of genes by binding to their regulatory regions. Dysregulated expression of TFs often leads to aberrant expression changes of their target genes and is responsible for several diseases including cancers. In the last two decades, several studies experimentally identified target genes of several TFs. However, these studies are limited to a small fraction of the total TFs encoded by an organism, and only for those amenable to experimental settings. Experimental limitations lead to many computational techniques having been proposed to predict target genes of TFs. Linear modeling of gene expression is one of the most promising computational approaches, readily applicable to the thousands of expression datasets available in the public domain across diverse phenotypes. Linear models assume that the expression of a gene is the sum of expression of TFs regulating it. In this chapter, I introduce mathematical programming for the linear modeling of gene expression, which has certain advantages over the conventional statistical modeling approaches. It is fast, scalable to genome level and most importantly, allows mixed integer programming to tune the model outcome with prior knowledge on gene regulation.

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