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Engineering a cell's regulatory networks to dynamically control gene expression has been considered a new frontier in biological engineering. In cyanobacteria, the lack of well-characterized, modular gene regulatory elements makes regulatory network engineering challenging. Here, we suggest potential tools to modify various gene expression steps in cyanobacterial regulatory networks., Competing Interests: Declaration of interests No interests are declared., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The genetic architecture of Parkinson's disease (PD) is complex and multiple brain cell subtypes are involved in the neuropathological progression of the disease. Here we aimed to advance our understanding of PD genetic complexity at a cell subtype precision level. Using parallel single-nucleus (sn)RNA-seq and snATAC-seq analyses we simultaneously profiled the transcriptomic and chromatin accessibility landscapes in temporal cortex tissues from 12 PD compared to 12 control subjects at a granular single cell resolution. An integrative bioinformatic pipeline was developed and applied for the analyses of these snMulti-omics datasets. The results identified a subpopulation of cortical glutamatergic excitatory neurons with remarkably altered gene expression in PD, including differentially-expressed genes within PD risk loci identified in genome-wide association studies (GWAS). This was the only neuronal subtype showing significant and robust overexpression of SNCA. Further characterization of this neuronal-subpopulation showed upregulation of specific pathways related to axon guidance, neurite outgrowth and post-synaptic structure, and downregulated pathways involved in presynaptic organization and calcium response. Additionally, we characterized the roles of three molecular mechanisms in governing PD-associated cell subtype-specific dysregulation of gene expression: (1) changes in cis-regulatory element accessibility to transcriptional machinery; (2) changes in the abundance of master transcriptional regulators, including YY1, SP3, and KLF16; (3) candidate regulatory variants in high linkage disequilibrium with PD-GWAS genomic variants impacting transcription factor binding affinities. To our knowledge, this study is the first and the most comprehensive interrogation of the multi-omics landscape of PD at a cell-subtype resolution. Our findings provide new insights into a precise glutamatergic neuronal cell subtype, causal genes, and non-coding regulatory variants underlying the neuropathological progression of PD, paving the way for the development of cell- and gene-targeted therapeutics to halt disease progression as well as genetic biomarkers for early preclinical diagnosis., (© 2024. The Author(s).)

Currently, the biological understanding of Crohn's disease (CD) remains limited. PANoptosis is a revolutionary form of cell death reported to participate in numerous diseases, including CD. In our study, we aimed to uncover the roles of PANoptosis in CD. Differentially expressed PANoptosis-related genes (DE-PRGs) were identified by overlapping PANoptosis-related genes and differentially expressed genes between CD and normal samples in a combined microarray dataset. Three machine learning algorithms were adopted to detect hub DE-PRGs. To stratify the heterogeneity within CD patients, nonnegative matrix factorization clustering was conducted. In terms of immune landscape analysis, the "ssGSEA" method was applied. qRT-PCR was performed to examine the expression levels of the hub DE-PRGs in CD patients and colitis model mice. Ten hub DE-PRGs with satisfactory diagnostic performance were identified and validated: CD44, CIDEC, NDRG1, NUMA1, PEA15, RAG1, S100A8, S100A9, TIMP1 and XBP1. These genes displayed significant associations with certain immune cell types and CD-related genes. We also constructed gene‒microRNA, gene‒transcription factor and drug‒gene interaction networks. CD samples were classified into two PANoptosis patterns according to the expression levels of the hub DE-PRGs. Our results suggest that PANoptosis plays a nonnegligible role in CD by modulating the immune system and interacting with CD-related genes., (© 2024. The Author(s).)

Microbes typically live in complex habitats where they need to rapidly adapt to continuously changing growth conditions. To do so, they produce an astonishing array of natural products with diverse structures and functions. Actinobacteria stand out for their prolific production of bioactive molecules, including antibiotics, anticancer agents, antifungals, and immunosuppressants. Attention has been directed especially towards the identification of the compounds they produce and the mining of the large diversity of biosynthetic gene clusters (BGCs) in their genomes. However, the current return on investment in random screening for bioactive compounds is low, while it is hard to predict which of the millions of BGCs should be prioritized. Moreover, many of the BGCs for yet undiscovered natural products are silent or cryptic under laboratory growth conditions. To identify ways to prioritize and activate these BGCs, knowledge regarding the way their expression is controlled is crucial. Intricate regulatory networks control global gene expression in Actinobacteria, governed by a staggering number of up to 1000 transcription factors per strain. This review highlights recent advances in experimental and computational methods for characterizing and predicting transcription factor binding sites and their applications to guide natural product discovery. We propose that regulation-guided genome mining approaches will open new avenues toward eliciting the expression of BGCs, as well as prioritizing subsets of BGCs for expression using synthetic biology approaches., One-Sentence Summary: This review provides insights into advances in experimental and computational methods aimed at predicting transcription factor binding sites and their applications to guide natural product discovery., (© The Author(s) 2024. Published by Oxford University Press on behalf of Society of Industrial Microbiology and Biotechnology.)

Modeling cell signal transduction pathways via Boolean networks (BNs) has become an established method for analyzing intracellular communications over the last few decades. What's more, BNs provide a course-grained approach, not only to understanding molecular communications, but also for targeting pathway components that alter the long-term outcomes of the system. This has come to be known as phenotype control theory. In this review we study the interplay of various approaches for controlling gene regulatory networks such as: algebraic methods, control kernel, feedback vertex set, and stable motifs. The study will also include comparative discussion between the methods, using an established cancer model of T-Cell Large Granular Lymphocyte Leukemia. Further, we explore possible options for making the control search more efficient using reduction and modularity. Finally, we will include challenges presented such as the complexity and the availability of software for implementing each of these control techniques., (© 2023. The Author(s), under exclusive licence to Society for Mathematical Biology.)

The invasive trophoblast cell lineages in rat and human share crucial responsibilities in establishing the uterine-placental interface of the hemochorial placenta. These observations have led to the rat becoming an especially useful animal model for studying hemochorial placentation. However, our understanding of similarities or differences between regulatory mechanisms governing rat and human invasive trophoblast cell populations is limited. In this study, we generated single-nucleus ATAC-seq data from gestation day 15.5 and 19.5 rat uterine-placental interface tissues, and integrated the data with single-cell RNA-seq data generated at the same stages. We determined the chromatin accessibility profiles of invasive trophoblast, natural killer, macrophage, endothelial and smooth muscle cells, and compared invasive trophoblast chromatin accessibility with extravillous trophoblast cell accessibility. In comparing chromatin accessibility profiles between species, we found similarities in patterns of gene regulation and groups of motifs enriched in accessible regions. Finally, we identified a conserved gene regulatory network in invasive trophoblast cells. Our data, findings and analysis will facilitate future studies investigating regulatory mechanisms essential for the invasive trophoblast cell lineage., Competing Interests: Competing interests The authors declare no competing or financial interests., (© 2023. Published by The Company of Biologists Ltd.)

A transcriptional regulatory network (TRN) is a collection of transcription regulators with their associated downstream genes, which is highly condition-specific. Understanding how cell states can be programmed through small molecules/drugs or conditions by modulating the whole gene expression system granted us the potential to amend abnormal cells and cure diseases. Condition Orientated Regulatory Networks (CORN, https://qinlab.sysu.edu.cn/home) is a library of condition (small molecule/drug treatments and gene knockdowns)-based transcriptional regulatory sub-networks (TRSNs) that come with an online TRSN matching tool. It allows users to browse condition-associated TRSNs or match those TRSNs by inputting transcriptomic changes of interest. CORN utilizes transcriptomic changes data after specific conditional treatment in cells, and in vivo transcription factor (TF) binding data in cells, by combining TF binding information and calculations of significant expression alterations of TFs and genes after the conditional treatments, TRNs under the effect of different conditions were constructed. In short, CORN associated 1805 different types of specific conditions (small molecule/drug treatments and gene knockdowns) to 9553 TRSNs in 25 human cell lines, involving 204TFs. By linking and curating specific conditions to responsive TRNs, the scientific community can now perceive how TRNs are altered and controlled by conditions alone in an organized manner for the first time. This study demonstrated with examples that CORN can aid the understanding of molecular pathology, pharmacology and drug repositioning, and screened drugs with high potential for cancer and coronavirus disease 2019 (COVID-19) treatments., (© The Author(s) 2022. Published by Oxford University Press.)

One of the main theoretical questions in the field of discrete regulatory networks is the question what aspects of the dynamics - the structure of the state transition graph - are already imposed by structural descriptions of the network such as the interaction graph. For Boolean networks, prior work has concentrated on different versions of the Thomas conjectures that link feedback cycles in the network structure to attractor properties. Other approaches check algorithmically whether certain properties hold true for all models sharing specific structural constraints, e.g. by using model checking techniques. In this work we investigate the behavior of the pool of Boolean networks in agreement with a given interaction graph using a different approach. Grouping together states that are updated consistently across the pool we derive an equivalence relation and analyze a corresponding quotient graph on the state space. By construction this graph yields information about the dynamics of all functions in the pool. Our main result is that this graph can be computed efficiently without enumerating and analyzing all individual functions. This opens up new possibilities for applications, where such model pools arise when modeling under uncertainty., (Copyright © 2022. Published by Elsevier B.V.)

Background: Transposable elements (TE) make up a large portion of many plant genomes and are playing innovative roles in genome evolution. Several TEs can contribute to gene regulation by influencing expression of nearby genes as stress-responsive regulatory motifs. To delineate TE-mediated plant stress regulatory networks, we took a 2-step computational approach consisting of identifying TEs in the proximity of stress-responsive genes, followed by searching for cis-regulatory motifs in these TE sequences and linking them to known regulatory factors. Through a systematic meta-analysis of RNA-seq expression profiles and genome annotations, we investigated the relation between the presence of TE superfamilies upstream, downstream or within introns of nearby genes and the differential expression of these genes in various stress conditions in the TE-poor Arabidopsis thaliana and the TE-rich Solanum lycopersicum., Results: We found that stress conditions frequently expressed genes having members of various TE superfamilies in their genomic proximity, such as SINE upon proteotoxic stress and Copia and Gypsy upon heat stress in A. thaliana, and EPRV and hAT upon infection, and Harbinger, LINE and Retrotransposon upon light stress in S. lycopersicum. These stress-specific gene-proximal TEs were mostly located within introns and more detected near upregulated than downregulated genes. Similar stress conditions were often related to the same TE superfamily. Additionally, we detected both novel and known motifs in the sequences of those TEs pointing to regulatory cooption of these TEs upon stress. Next, we constructed the regulatory network of TFs that act through binding these TEs to their target genes upon stress and discovered TE-mediated regulons targeted by TFs such as BRB/BPC, HD, HSF, GATA, NAC, DREB/CBF and MYB factors in Arabidopsis and AP2/ERF/B3, NAC, NF-Y, MYB, CXC and HD factors in tomato., Conclusions: Overall, we map TE-mediated plant stress regulatory networks using numerous stress expression profile studies for two contrasting plant species to study the regulatory role TEs play in the response to stress. As TE-mediated gene regulation allows plants to adapt more rapidly to new environmental conditions, this study contributes to the future development of climate-resilient plants., (© 2021. The Author(s).)

We analyse paths through the regulatory networks that control gene-expression patterns in Yeast, in five different physiological states: cell cycle, DNA damage, stress response, diauxic shift, and sporulation. The network in each state is specified as a directed graph, containing different sets of edges connecting pairs selected from a combined set of 1475 nodes. Each network contains some nodes that have no parents, and others that have no children. We call these, respectively, 'source' and 'sink' nodes. For each network we enumerate paths between source and sink nodes. In a previous paper (Lesk and Konagurthu, 2020), we defined, extracted and compared the neighbourhoods of each transcription factor in different physiological states, and how the system reconfigures itself. Here we compare the usage of nodes and edges by different networks, and how they are assembled into paths. The picture that emerges is that the networks are not disjoint but show substantial sharing of nodes and edges; however, they assemble these materials into different sets of paths. Four of the networks, other than the cell-cycle network, contain paths between only a small fraction (<13%) of possible source-sink pairs. Although the cell-cycle network is not an outlier in terms of total number of nodes and edges, and number of sink nodes, it is very much an outlier in having a greater proportion of source-to-sink paths than the other networks., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

A number of bacteria are known to differentiate into cells with distinct phenotypic traits during processes such as biofilm formation or the development of reproductive structures. These cell types, by virtue of their specialized functions, embody a division of labor. However, how bacteria build spatial patterns of differentiated cells is not well understood. Here, we examine the factors that drive spatial patterns in divisions of labor in colonies of Streptomyces coelicolor , a multicellular bacterium capable of synthesizing an array of antibiotics and forming complex reproductive structures (e.g., aerial hyphae and spores). Using fluorescent reporters, we demonstrate that the pathways for antibiotic biosynthesis and aerial hypha formation are activated in distinct waves of gene expression that radiate outwards in S. coelicolor colonies. We also show that the spatiotemporal separation of these cell types depends on a key activator in the developmental pathway, AdpA. Importantly, when we manipulated local gradients by growing competing microbes nearby, or through physical disruption, expression in these pathways could be decoupled and/or disordered, respectively. Finally, the normal spatial organization of these cell types was partially restored with the addition of a siderophore, a public good made by these organisms, to the growth medium. Together, these results indicate that spatial divisions of labor in S. coelicolor colonies are determined by a combination of physiological gradients and regulatory network architecture, key factors that also drive patterns of cellular differentiation in multicellular eukaryotic organisms. IMPORTANCE Streptomyces coelicolor is a multicellular bacterium that differentiates into specialized cell types and produces a diverse array of natural products. While much is known about the genetic networks that regulate development and antibiotic biosynthesis in S. coelicolor , what drives the spatial organization of these activities within a colony remains to be explored. By using time-lapse microscopy to monitor gene expression in developmental and antibiotic biosynthesis pathways, we found that expression in these pathways occurs in spatiotemporally separated waves. Normally, expression of the antibiotic biosynthesis pathway preceded expression in the developmental pathway; however, this order was compromised in a mutant lacking a key developmental regulator. Furthermore, when we disrupted the local gradients during S. coelicolor growth, we observed disordered patterns of gene expression within colonies. Together, these results indicate that spatial divisions of labor in S. coelicolor colonies are determined by a combination of regulatory network architecture and physiological gradients., (Copyright © 2021 Zacharia et al.)

Circular RNAs (circRNA) have gained recent interest due to their functional versatility due to their interactions with other RNA species and proteins, all of which underline complex regulatory networks involved in pathogenic mechanisms. As a result, recent insights in circRNA biology are investigating their biomarker and therapeutic potential. One such circRNA is CircFOXO3, which consists of the circularized second exon of the FOXO3 mRNA, a member of the forkhead box transcription factor family involved in the regulation of developmental programs. Recent research focused on the role of circFOXO3 in the context of cancer has highlighted several implications in key tumorigenesis mechanisms, thus consolidating its relevance among other identified circRNAs. In this paper, we will focus on the currently identified case-specific implications of circFOXO3 in cancer, with a focus on the circFOXO3-miRNA-mRNA regulatory networks, its interactions with different proteins, and their cumulated biological effects upon tumor development. Therefore, we aim to provide an integrated perspective of the mechanistic implications of circFOXO3 in different cancers while also highlighting its biomarker or therapeutic potential based on the current evidence., (Copyright © 2020 Elsevier B.V. All rights reserved.)

To provide systematic insight into the composition and expression of transfer RNA (tRNA) derivatives transcriptome in colorectal cancer (CRC). tRNA derivatives expression profiles in three pairs of CRC and adjacent normal colon tissues were performed by tRNA-derived small RNA fragments (tRFs) and tRNA halves (tiRNA) sequencing, and microarray data of transcriptomes from CRC and paired controls were retrieved from Gene Expression Omnibus database. The differentially expressed tRFs and tiRNAs and differentially expressed genes between CRC and paired normal samples were screened. The functional regulations between tRF and tiRNA and gene were identified. A total of 60 upregulated and 48 downregulated tRNA derivatives and 7373 upregulated and 12,138 downregulated messenger RNA (mRNA) were identified. The tRF and tiRNA-gene regulatory modules were constructed by analyzing computational tRF and tiRNA-target predictions and inverse expression relationships between tRF and tiRNAs and mRNA. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway annotation showed that the function of targets of tiRNA-Tyr-GTA was mainly enriched in negative regulation of epithelial cell apoptotic process and peroxisome proliferator activated-receptors (PPAR) signaling pathway. Cellular response to monoamine stimulus and inflammatory bowel disease was enriched in function of tiRNA-Val-CAC. Two functions, including negative regulation of c-Jun N-terminal kinase (JNK) cascade and choline metabolism in cancer, were enriched in tRF-Gln-CTG. The function of mesenchymal to epithelial transition was enriched in tRF-Leu-TAG. For the first time to our knowledge, our study provided a landscape of tRNA derivatives expression profiles in CRC. Further tRF and tiRNA-gene regulatory modules construction explored the potential functions related to these tRNA derivatives in the pathogenesis of CRC.

Liver diseases are important causes of morbidity and mortality worldwide. The aim of this study was to identify differentially expressed microRNAs (miRNAs), target genes, and key pathways as innovative diagnostic biomarkers in liver patients with different pathology and functional state. We determined, using RT-qPCR, the expression of 472 miRNAs in 125 explanted livers from subjects with six different liver pathologies and from control livers. ANOVA was employed to obtain differentially expressed miRNAs (DEMs), and miRDB (MicroRNA target prediction database) was used to predict target genes. A miRNA-gene differential regulatory (MGDR) network was constructed for each condition. Key miRNAs were detected using topological analysis. Enrichment analysis for DEMs was performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). We identified important DEMs common and specific to the different patient groups and disease progression stages. hsa-miR-1275 was universally downregulated regardless the disease etiology and stage, while hsa-let-7a*, hsa-miR-195, hsa-miR-374, and hsa-miR-378 were deregulated. The most significantly enriched pathways of target genes controlled by these miRNAs comprise p53 tumor suppressor protein (TP53)-regulated metabolic genes, and those involved in regulation of methyl-CpG-binding protein 2 (MECP2) expression, phosphatase and tensin homolog (PTEN) messenger RNA (mRNA) translation and copper homeostasis. Our findings show a novel panel of deregulated miRNAs in the liver tissue from patients with different liver pathologies. These miRNAs hold potential as biomarkers for diagnosis and staging of liver diseases.

The logic of genetic discovery has changed little over time, but the focus of biology is shifting from simple genotype-phenotype relationships to complex metabolic, physiological, developmental, and behavioral traits. In light of this, the traditional reductionist view of individual genes as privileged difference-making causes of phenotypes is re-examined. The scope and nature of genetic effects in complex regulatory systems, in which dynamics are driven by regulatory feedback and hierarchical interactions across levels of organization are considered. This review argues that it is appropriate to treat genes as specific actual difference-makers for the molecular regulation of gene expression. However, they are often neither stable, proportional, nor specific as causes of the overall dynamic behavior of regulatory networks. Dynamical models, properly formulated and validated, provide the tools to probe cause-and-effect relationships in complex biological systems, allowing to go beyond the limitations of genetic reductionism to gain an integrative understanding of the causal processes underlying complex phenotypes., (© 2020 The Authors. BioEssays published by Wiley Periodicals, Inc.)

Regulatory networks control the spatiotemporal gene expression patterns that give rise to and define the individual cell types of multicellular organisms. In eumetazoa, distal regulatory elements called enhancers play a key role in determining the structure of such networks, particularly the wiring diagram of "who regulates whom." Mutations that affect enhancer activity can therefore rewire regulatory networks, potentially causing adaptive changes in gene expression. Here, we use whole-tissue and single-cell transcriptomic and chromatin accessibility data from mouse to show that enhancers play an additional role in the evolution of regulatory networks: They facilitate network growth by creating transcriptionally active regions of open chromatin that are conducive to de novo gene evolution. Specifically, our comparative transcriptomic analysis with three other mammalian species shows that young, mouse-specific intergenic open reading frames are preferentially located near enhancers, whereas older open reading frames are not. Mouse-specific intergenic open reading frames that are proximal to enhancers are more highly and stably transcribed than those that are not proximal to enhancers or promoters, and they are transcribed in a limited diversity of cellular contexts. Furthermore, we report several instances of mouse-specific intergenic open reading frames proximal to promoters showing evidence of being repurposed enhancers. We also show that open reading frames gradually acquire interactions with enhancers over macroevolutionary timescales, helping integrate genes-those that have arisen de novo or by other means-into existing regulatory networks. Taken together, our results highlight a dual role of enhancers in expanding and rewiring gene regulatory networks., (© The Author(s) 2019. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.)

Circular RNAs (circRNAs) are a novel class of non-coding RNAs that are characterized by a covalently closed circular structure. They have been widely found in Populus euphratica Oliv. heteromorphic leaves (P. hl). To study the role of circRNAs related to transcription factors (TFs) in the morphogenesis of P. hl, the expression profiles of circRNAs in linear, lanceolate, ovate, and broad-ovate leaves of P. euphratica were elucidated by strand-specific sequencing. We identified and characterized 22 circRNAs related to TFs in P. hl at the four developmental stages. Using the competing endogenous RNAs hypothesis as a guide, we constructed circRNA-miRNA-TF mRNA regulatory networks, which indicated that circRNAs antagonized microRNAs (miRNAs), thereby influencing the expression of the miRNA target genes and playing a significant role in transcriptional regulation. Gene ontology annotation of the target TF genes predicted that these circRNAs were associated mainly with the regulation of leaf development, leaf morphogenesis, signal transduction, and response to abiotic stress. These findings implied that the circRNAs affected the size and number of cells in P. hl by regulating the expression of TF mRNAs. Our results provide a basis for further studies of leaf development in poplar trees., (© 2019 The Author(s).)

Background: Alginate is an important cell wall component and mannitol is a soluble storage carbon substance in the brown seaweed Saccharina japonica. Their contents vary with kelp developmental periods and harvesting time. Alginate and mannitol regulatory networks and molecular mechanisms are largely unknown., Results: With WGCNA and trend analysis of 20,940 known genes and 4264 new genes produced from transcriptome sequencing of 30 kelp samples from different stages and tissues, we deduced that ribosomal proteins, light harvesting complex proteins and "imm upregulated 3" gene family are closely associated with the meristematic growth and kelp maturity. Moreover, 134 and 6 genes directly involved in the alginate and mannitol metabolism were identified, respectively. Mannose-6-phosphate isomerase (MPI2), phosphomannomutase (PMM1), GDP-mannose 6-dehydrogenase (GMD3) and mannuronate C5-epimerase (MC5E70 and MC5E122) are closely related with the high content of alginate in the distal blade. Mannitol accumulation in the basal blade might be ascribed to high expression of mannitol-1-phosphate dehydrogenase (M1PDH1) and mannitol-1-phosphatase (M1Pase) (in biosynthesis direction) and low expression of mannitol-2-dehydrogenase (M2DH) and Fructokinase (FK) (in degradation direction). Oxidative phosphorylation and photosynthesis provide ATP and NADH for mannitol metabolism whereas glycosylated cycle and tricarboxylic acid (TCA) cycle produce GTP for alginate biosynthesis. RNA/protein synthesis and transportation might affect alginate complex polymerization and secretion processes. Cryptochrome (CRY-DASH), xanthophyll cycle, photosynthesis and carbon fixation influence the production of intermediate metabolite of fructose-6-phosphate, contributing to high content of mannitol in the basal blade., Conclusions: The network of co-responsive DNA synthesis, repair and proteolysis are presumed to be involved in alginate polymerization and secretion, while upstream light-responsive reactions are important for mannitol accumulation in meristem of kelp. Our transcriptome analysis provides new insights into the transcriptional regulatory networks underlying the biosynthesis of alginate and mannitol during S. japonica developments.

Transcriptional regulation, determined by the chromatin structure and regulatory elements interacting at promoter regions, is a key step in plant responses to environmental cues. Nitrate (NO 3 - ) is a nutrient signal that regulates the expression of hundreds of genes in Arabidopsis thaliana. Here, we integrate mRNA sequencing, genome-wide RNA polymerase II (RNPII), chromatin immunoprecipitation sequencing, and DNase sequencing datasets to establish the relationship between RNPII occupancy and chromatin accessibility in response to NO 3 - treatments in Arabidopsis roots. Genomic footprinting allowed us to identify in vivo regulatory elements controlling gene expression in response to NO 3 - treatments. NO 3 - -modulated transcription factor (TF) footprints are important for a rapid increase in RNPII occupancy and transcript accumulation over time. We mapped key TF regulatory interactions and functionally validated the role of NAP, an NAC-domain containing TF, as a new regulatory factor in NO 3 - transport. Taken together, our study provides a comprehensive view of transcriptional networks in response to a nutrient signal in Arabidopsis roots., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

Data generation using high throughput technologies has led to the accumulation of diverse types of molecular data. These data have different types (discrete, real, string, etc.) and occur in various formats and sizes. Datasets including gene expression, miRNA expression, protein-DNA binding data (ChIP-Seq/ChIP-ChIP), mutation data (copy number variation, single nucleotide polymorphisms), annotations, interactions, and association data are some of the commonly used biological datasets to study various cellular mechanisms of living organisms. Each of them provides a unique, complementary and partly independent view of the genome and hence embed essential information about the regulatory mechanisms of genes and their products. Therefore, integrating these data and inferring regulatory interactions from them offer a system level of biological insight in predicting gene functions and their phenotypic outcomes. To study genome functionality through regulatory networks, different methods have been proposed for collective mining of information from an integrated dataset. We survey here integration methods that reconstruct regulatory networks using state-of-the-art techniques to handle multi-omics (i.e., genomic, transcriptomic, proteomic) and other biological datasets., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Leaf premature senescence largely determines maize ( Zea mays L.) grain yield and quality. A natural recessive premature-senescence mutant was selected from the breeding population, and near-isogenic lines were constructed using Jing24 as the recurrent parent. In the near-isogenic lines, the dominant homozygous material was wild-type (WT), and the recessive material of early leaf senescence was the premature-senescence-type ZmELS5 . To identify major genes and regulatory mechanisms involved in leaf senescence, a transcriptome analysis of the ZmELS5 and WT near-isogenic lines (NILs) was performed. A total of 8,796 differentially expressed transcripts were identified between ZmELS5 and WT, including 3,811 up-regulated and 4,985 down-regulated transcripts. By combining gene ontology, Kyoto Encyclopedia of Genes and Genomes, gene set, and transcription factor enrichment analyses, key differentially expressed genes were screened. The senescence regulatory network was predicted based on these key differentially expressed genes, which indicated that the senescence process is mainly regulated by bHLH, WRKY, and AP2/EREBP family transcription factors, leading to the accumulations of jasmonic acid and ethylene. This causes stress responses and reductions in the chlorophyll a/b-binding protein activity level. Then, decreased ATP synthase activity leads to increased photosystem II photodamage, ultimately leading to leaf senescence., Competing Interests: Authors declare no conflict of interest.

Disease-disease relationships (e.g., disease comorbidities) play crucial roles in pathobiological manifestations of diseases and personalized approaches to managing those conditions. In this study, we develop a network-based methodology, termed meta-path-based Disease Network (mpDisNet) capturing algorithm, to infer disease-disease relationships by assembling four biological networks: disease-miRNA, miRNA-gene, disease-gene, and the human protein-protein interactome. mpDisNet is a meta-path-based random walk to reconstruct the heterogeneous neighbors of a given node. mpDisNet uses a heterogeneous skip-gram model to solve the network representation of the nodes. We find that mpDisNet reveals high performance in inferring clinically reported disease-disease relationships, outperforming that of traditional gene/miRNA-overlap approaches. In addition, mpDisNet identifies network-based comorbidities for pulmonary diseases driven by underlying miRNA-mediated pathobiological pathways (i.e., hsa-let-7a- or hsa-let-7b-mediated airway epithelial apoptosis and pro-inflammatory cytokine pathways) as derived from the human interactome network analysis. The mpDisNet offers a powerful tool for network-based identification of disease-disease relationships with miRNA-mediated pathobiological pathways., Competing Interests: Competing interestsS.Y.C. has served as a consultant for Zogenix, Vivus, Aerpio, and United Therapeutics; S.Y.C. is a director, officer, and shareholder in Numa Therapeutics; S.Y.C. holds research grants from Actelion and Pfizer. S.Y.C. has filed patent applications regarding the targeting of metabolism in pulmonary hypertension., (© The Author(s) 2019.)

While gene expression profiling is commonly used to gain an overview of cellular processes, the identification of upstream processes that drive expression changes remains a challenge. To address this issue, we introduce CARNIVAL, a causal network contextualization tool which derives network architectures from gene expression footprints. CARNIVAL (CAusal Reasoning pipeline for Network identification using Integer VALue programming) integrates different sources of prior knowledge including signed and directed protein-protein interactions, transcription factor targets, and pathway signatures. The use of prior knowledge in CARNIVAL enables capturing a broad set of upstream cellular processes and regulators, leading to a higher accuracy when benchmarked against related tools. Implementation as an integer linear programming (ILP) problem guarantees efficient computation. As a case study, we applied CARNIVAL to contextualize signaling networks from gene expression data in IgA nephropathy (IgAN), a condition that can lead to chronic kidney disease. CARNIVAL identified specific signaling pathways and associated mediators dysregulated in IgAN including Wnt and TGF-β, which we subsequently validated experimentally. These results demonstrated how CARNIVAL generates hypotheses on potential upstream alterations that propagate through signaling networks, providing insights into diseases., Competing Interests: Competing interestsThe authors declare no competing interests, (© The Author(s) 2019.)

Background: Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia in western countries. The factors governing the progression of AF to a permanent chronic condition are still not well characterized. Among epigenetic factors, non-coding RNAs (ncRNAs) such as miRNAs and lncRNAs have been recently described as important players involved in the AF progression. We hypothesize about the existence of additional regulatory layers in AF involving an intricate cross-talk between different ncRNA species, namely miRNAs and circRNAs for the establishment of a chronic AF condition., Methods and Results: We have performed an unbiased study analyzing the expression profile for miRNAs and circRNAs in left-atrial biopsies from patients with paroxysmal and permanent AF by RNA-seq. The transition from paroxysmal to permanent AF is characterized by a pattern of down-regulated miRNAs, concomitant to the appearance of specific circRNA species. The analysis of the sponging activities of the circRNAs exclusively expressed in permanent AF samples, allowed us to determine that they could be responsible for the downregulation of specific miRNAs in establishment of a permanent AF condition., Conclusion: Sponging activity of circRNAs sequestering specific miRNAs is an important factor to be considered for the determination of the molecular mechanisms involved in AF progression., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Despite remarkable progress in medium-term overall survival benefit in the adjuvant, neoadjuvant and metastatic settings, with multiple recent targeted drug approvals, acquired resistance, late relapse, and cancer-related death rates remain challenging. Integrated technological systems have been developed to overcome these unmet needs. The characterization of structural and functional noncoding genome elements through next-generation sequencing (NGS) systems, Hi-C and CRISPR/Cas9, as well as computational models, allows for whole genome and transcriptome analysis. Rapid progress in large-scale single-biopsy genome analysis has identified several novel breast cancer driver genes and oncotargets. The exploration of spatiotemporal tumor evolution has returned exciting while inconclusive data on dynamic intratumor heterogeneity (ITH) through multiregional NGS and single-cell DNA/RNA sequencing and circulating genomic subclones (cGSs) by serial circulating cell-free DNA NGS to predict and overcome intrinsic and acquired therapeutic resistance. This review discusses reliable breast cancer genome analysis data and focuses on two major crucial perspectives. The validation of ITH, cGSs, and intrapatient genetic/genomic heterogeneity as predictive biomarkers, as well as the valid discovery of novel oncotargets within patient-centric genomic trials, encouraging early drug development, could optimize primary and secondary therapeutic decision-making. A longer-term goal is to identify the individualized landscape of both coding and noncoding key mutations. This progress will enable the understanding of molecular mechanisms perturbating regulatory networks, shaping the pharmaceutical controllability of deregulated transcriptional biocircuits., (© 2018 Wiley Periodicals, Inc.)

Pbx genes encode transcription factors that belong to the TALE (three-amino-acid loop extension) superclass of homeodomain proteins. We have witnessed a surge in information about the roles of this gene family as leading actors in the transcriptional control of development. PBX proteins represent a clear example of how transcription factors can regulate developmental processes by combinatorial properties, acting within multimeric complexes to implement activation or repression of transcription depending on their interaction partners. Here, we revisit long-emphasized functions of PBX transcription factors as cofactors for HOX proteins, major architects of the body plan. We further discuss new knowledge on roles of PBX proteins in different developmental contexts as upstream regulators of Hox genes-as factors that interact with non-HOX proteins and can work independently of HOX-as well as potential pioneer factors. Committed to building a perfect body, PBX proteins govern regulatory networks that direct essential morphogenetic processes and organogenesis in vertebrate development. Perturbations of PBX-dependent networks can cause human congenital disease and cancer., (© 2019 Selleri et al.; Published by Cold Spring Harbor Laboratory Press.)

Down syndrome, a genetic disorder of known attribution reveals several types of brain abnormalities resulting in mental retardation, inadequacy in speech and memory. In this study, we have presented a consolidative network approach to comprehend the intricacy of the associated genes of Down syndrome. In this analysis, the differentially expressed genes (DEG's) were identified and the central networks were constructed as upregulated and downregulated. Subsequently, GNB5, CDC42, SPTAN1, GNG2, GNAZ, PRKACB, SST, CD44, FGF2, PHLPP1, APP, and FYN were identified as the candidate hub genes by using topological parameters. Later, Fpclass a PPI tool identified WASP gene, a co-expression interacting partner with highest network topology. Moreover, an enhanced enrichment pathway namely Opioid signaling was obtained using ClueGo, depicting the roles of the hub genes in signaling and neuronal mechanisms. The transcriptional regulatory factors and the common miRNA connected to them were identified by using MatInspector and miRTarbase. Later, a regulatory network constructed showed that PLAG, T2FB, CREB, NEUR, and GATA were the most commonly connected transcriptional factors and hsa-miR-122-5p was the most prominent miRNA. In a nutshell, these hub genes and the enriched pathway could help understand at a molecular level and eventually used as therapeutic targets for Down syndrome., (Copyright © 2018 Elsevier B.V. All rights reserved.)

The visualization of regulatory networks is becoming increasingly important in order to understand molecular mechanisms and diseases. MicroRNAs (miRNAs) are small noncoding RNAs (ncRNAs) responsible of the post-transcriptional regulation of messenger RNAs (mRNAs) and other ncRNAs. MiRNAs are involved in numerous biological processes including development, cell proliferation, and apoptosis. They are also key molecules in major complex diseases such as cancer and cardiovascular diseases. A single miRNA can regulate many targets, making the analysis and visualization of these complex networks challenging. Here, we present standard and advanced visualization approaches to represent networks with a special focus on miRNA-target interactions.

Transcriptional regulatory networks specify the regulatory proteins of target genes that control the context-specific expression levels of genes. With our ability to profile the different types of molecular components of cells under different conditions, we are now uniquely positioned to infer regulatory networks in diverse biological contexts such as different cell types, tissues, and time points. In this chapter, we cover two main classes of computational methods to integrate different types of information to infer genome-scale transcriptional regulatory networks. The first class of methods focuses on integrative methods for specifically inferring connections between transcription factors and target genes by combining gene expression data with regulatory edge-specific knowledge. The second class of methods integrates upstream signaling networks with transcriptional regulatory networks by combining gene expression data with protein-protein interaction networks and proteomic datasets. We conclude with a section on practical applications of a network inference algorithm to infer a genome-scale regulatory network.

Background: How regulatory networks incorporate additional components and how novel genes are functionally integrated into well-established developmental processes are two important and intertwined questions whose answers have major implications for understanding the evolution of development. We recently discovered a set of lineage-restricted genes with strong and specific expression patterns along the dorsal-ventral (DV) axis of the embryo of the wasp Nasonia that may serve as a powerful system for addressing these questions. We sought to both understand the evolutionary history of these genes and to determine their functions in the Nasonia DV patterning system., Results: We have found that the novel DV genes are part of a large family of rapidly duplicating and diverging ankyrin domain-encoding genes that originated most likely by horizontal transfer from a prokaryote in a common ancestor of the wasp superfamily Chalcidoidea. We tested the function of those ankyrin-encoding genes expressed along the DV axis and found that they participate in early embryonic DV patterning. We also developed a new wasp model system (Melittobia) and found that some functional integration of ankyrin genes have been preserved for over 90 million years., Conclusions: Our results indicate that regulatory networks can incorporate novel genes that then become necessary for stable and repeatable outputs. Even a modest role in developmental networks may be enough to allow novel or duplicate genes to be maintained in the genome and become fully integrated network components.

Although the return on investment over the last 20 years for mass screening of individual plant genes to improve crop performance has been low, the investment in these activities was essential to establish the infrastructure and tools of modern plant genomics. Complex traits such as crop yield are likely multigenic, and the exhaustive screening of random gene combinations to achieve yield gains is not realistic. Clearly, smart approaches must be developed. In silico analyses of plant metabolism and gene networks can move a trait discovery program beyond trial-and-error approaches and towards rational design strategies. Metabolic models employing flux-balance analysis are useful to determine the contribution of individual genes to a trait, or to compare, optimize, or even design metabolic pathways. Regulatory association networks provide a transcriptome-based view of the plant and can lead to the identification of transcription factors that control expression of multiple genes affecting a trait. In this review, the use of these models from the perspective of an Ag innovation company's trait discovery and development program will be discussed. Important decisions that can have significant impacts on the cost and timeline to develop a commercial trait will also be presented., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Direct reprogramming of non-fibroblastic cells to the neuronal cell types including induced neurons (iNs) and induced neural stem cells (iNSCs) has provided an alternative approach for the direct reprogramming of fibroblasts to those cells. However, to increase the efficiency of the reprogramming process the underlying mechanisms should be clarified. In the current study, we analyzed the gene expression profiles of five different cellular conversions to understand the most significant molecular mechanisms and transcription factors (TFs) underlying each conversion. For each conversion, we found the list of differentially expressed genes (DEGs) and the list of differentially expressed TFs (DE-TFs) which regulate expression of DEGs. Moreover, we constructed gene regulatory networks based on the TF-binding sites' data and found the most central regulators and the most active part of the networks. Furthermore, protein complexes were identified from constructed protein-protein interaction networks for DE-TFs. Finally, we proposed a list of main regulators for each conversion; for example, in the direct conversion of epithelial-like cells (ECs) to iNSCs, combination of centrality with active modules or protein complex analyses highlighted the role of POU3F2, BACH1, AR, PBX1, SOX2 and NANOG genes in this conversion. To the best of our knowledge, this study is the first one that analyzed the direct conversion of non-fibroblastic cells toward iNs and iNSCs and we believe that the expression manipulation of identified genes may increase efficiency of these processes., (Copyright © 2018. Published by Elsevier Ltd.)

A microarray analysis generally contains expression data of thousands of genes, but most of them are irrelevant to the disease of interest, making analyzing the genes concerning specific diseases complicated. Therefore, filtering out a few essential genes as well as their regulatory networks is critical, and a disease can be easily diagnosed just depending on the expression profiles of a few critical genes. In this study, a target gene screening (TGS) system, which is a microarray-based information system that integrates F-statistics, pattern recognition matching, a two-layer K-means classifier, a Parameter Detection Genetic Algorithm (PDGA), a genetic-based gene selector (GBG selector) and the association rule, was developed to screen out a small subset of genes that can discriminate malignant stages of cancers. During the first stage, F-statistic, pattern recognition matching, and a two-layer K-means classifier were applied in the system to filter out the 20 critical genes most relevant to ovarian cancer from 9600 genes, and the PDGA was used to decide the fittest values of the parameters for these critical genes. Among the 20 critical genes, 15 are associated with cancer progression. In the second stage, we further employed a GBG selector and the association rule to screen out seven target gene sets, each with only four to six genes, and each of which can precisely identify the malignancy stage of ovarian cancer based on their expression profiles. We further deduced the gene regulatory networks of the 20 critical genes by applying the Pearson correlation coefficient to evaluate the correlationship between the expression of each gene at the same stages and at different stages. Correlationships between gene pairs were calculated, and then, three regulatory networks were deduced. Their correlationships were further confirmed by the Ingenuity pathway analysis. The prognostic significances of the genes identified via regulatory networks were examined using online tools, and most represented biomarker candidates. In summary, our proposed system provides a new strategy to identify critical genes or biomarkers, as well as their regulatory networks, from microarray data., (Copyright © 2018. Published by Elsevier Inc.)

Triple-negative breast cancer (TNBC) has gained considerable attention as it oversteps about 15% of the deaths caused by breast cancer in women and is well known for its aggressive impact, high proliferation rate, intensity of tumor, and metastasis. In this study, we have given a new insight into TNBC, by introducing an integrative network methodology to understand the complexity of the candidate genes in TNBC. In the course of this analysis, the central network was constructed by refining the candidate genes present in the various databases. Eventually, the hub genes SRC, EGFR, JUN, CTNNB1, and MYC were derived using distinct topological parameters such as degree, betweenness centrality, closeness centrality, and clustering coefficient, which implicated a central role in TNBC. Furthermore, the identified hub genes were validated by utilizing molecular complex detection cluster analysis. A regulatory network was constructed to locate the transcriptional factors regulating these hub genes via MatInspector. Interestingly, we characterized that ZF02, MZF1, and PLAG are the common transcription factors which activate the hub genes. Moreover, a functional enrichment pathway was obtained from ClueGo, which depicted that all the hub genes have a significant role in toxicity-associated pathways as well as physiochemical featured pathways. In a nutshell, these prioritized genes could be the way out to uncover the molecular and therapeutic targets, leading to the development of personalized medicine for TNBC.

Over the past decade, genetic studies have recognized hundreds of polymorphic DNA loci called response QTLs (reQTLs) as potential contributors to interindividual variation in transcriptional responses to stimulations. Such reQTLs commonly affect the transduction of signals along the regulatory network that controls gene transcription. Identifying the pathways through which reQTLs perturb the underlying network has been a major challenge. Here, we present GEVIN ("Genome-wide Embedding of Variation In Networks"), a methodology that simultaneously identifies a reQTL and the particular pathway in which the reQTL affects downstream signal transduction along the network. Using synthetic data, we show that this algorithm outperforms existing pathway identification and reQTL identification methods. We applied GEVIN to the analysis of murine and human dendritic cells in response to pathogenic components. These analyses revealed significant reQTLs together with their perturbed Toll-like receptor signaling pathways. GEVIN thus offers a powerful framework that renders a comprehensive picture of disease-related DNA loci and their molecular functions within regulatory networks., (Copyright © 2017 by the Genetics Society of America.)

Although all human tissues carry out common processes, tissues are distinguished by gene expression patterns, implying that distinct regulatory programs control tissue specificity. In this study, we investigate gene expression and regulation across 38 tissues profiled in the Genotype-Tissue Expression project. We find that network edges (transcription factor to target gene connections) have higher tissue specificity than network nodes (genes) and that regulating nodes (transcription factors) are less likely to be expressed in a tissue-specific manner as compared to their targets (genes). Gene set enrichment analysis of network targeting also indicates that the regulation of tissue-specific function is largely independent of transcription factor expression. In addition, tissue-specific genes are not highly targeted in their corresponding tissue network. However, they do assume bottleneck positions due to variability in transcription factor targeting and the influence of non-canonical regulatory interactions. These results suggest that tissue specificity is driven by context-dependent regulatory paths, providing transcriptional control of tissue-specific processes., (Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: The Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNE) represents one of the most effective tools to reconstruct gene regulatory networks from large-scale molecular profile datasets. However, previous implementations require intensive computing resources and, in some cases, restrict the number of samples that can be used. These issues can be addressed elegantly in a GPU computing framework, where repeated mathematical computation can be done efficiently, but requires extensive redesign to apply parallel computing techniques to the original serial algorithm, involving detailed optimization efforts based on a deep understanding of both hardware and software architecture., Result: Here, we present an accelerated parallel implementation of ARACNE (GPU-ARACNE). By taking advantage of multi-level parallelism and the Compute Unified Device Architecture (CUDA) parallel kernel-call library, GPU-ARACNE successfully parallelizes a serial algorithm and simplifies the user experience from multi-step operations to one step. Using public datasets on comparable hardware configurations, we showed that GPU-ARACNE is faster than previous implementations and is able to reconstruct equally valid gene regulatory networks., Conclusion: Given that previous versions of ARACNE are extremely resource demanding, either in computational time or in hardware investment, GPU-ARACNE is remarkably valuable for researchers who need to build complex regulatory networks from large expression datasets, but with limited budget on computational resources. In addition, our GPU-centered optimization of adaptive partitioning for Mutual Information (MI) estimation provides lessons that are applicable to other domains.

Background: Cell lines are an indispensable tool in biomedical research and often used as surrogates for tissues. Although there are recognized important cellular and transcriptomic differences between cell lines and tissues, a systematic overview of the differences between the regulatory processes of a cell line and those of its tissue of origin has not been conducted. The RNA-Seq data generated by the GTEx project is the first available data resource in which it is possible to perform a large-scale transcriptional and regulatory network analysis comparing cell lines with their tissues of origin., Results: We compared 127 paired Epstein-Barr virus transformed lymphoblastoid cell lines (LCLs) and whole blood samples, and 244 paired primary fibroblast cell lines and skin samples. While gene expression analysis confirms that these cell lines carry the expression signatures of their primary tissues, albeit at reduced levels, network analysis indicates that expression changes are the cumulative result of many previously unreported alterations in transcription factor (TF) regulation. More specifically, cell cycle genes are over-expressed in cell lines compared to primary tissues, and this alteration in expression is a result of less repressive TF targeting. We confirmed these regulatory changes for four TFs, including SMAD5, using independent ChIP-seq data from ENCODE., Conclusions: Our results provide novel insights into the regulatory mechanisms controlling the expression differences between cell lines and tissues. The strong changes in TF regulation that we observe suggest that network changes, in addition to transcriptional levels, should be considered when using cell lines as models for tissues.

Measuring gene expression at the single cell and single molecule level has recently made possible the quantitative measurement of stochasticity of gene expression. This enables identification of the probable sources and roles of noise. Gene expression noise can result in bacterial population heterogeneity, offering specific advantages for fitness and survival in various environments. This trait is therefore selected during the evolution of the species, and is consequently regulated by a specific genetic network architecture. Examples exist in stress-response mechanisms, as well as in infection and pathogenicity strategies, pointing to advantages for multicellularity of bacterial populations., (Copyright © 2017 Institut Pasteur. Published by Elsevier Masson SAS. All rights reserved.)

Homeobox a1 ( Hoxa1 ) is one of the most rapidly induced genes in ES cell differentiation and it is the earliest expressed Hox gene in the mouse embryo. In this study, we used genomic approaches to identify Hoxa1-bound regions during early stages of ES cell differentiation into the neuro-ectoderm. Within 2 h of retinoic acid treatment, Hoxa1 is rapidly recruited to target sites that are associated with genes involved in regulation of pluripotency, and these genes display early changes in expression. The pattern of occupancy of Hoxa1 is dynamic and changes over time. At 12 h of differentiation, many sites bound at 2 h are lost and a new cohort of bound regions appears. At both time points the genome-wide mapping reveals that there is significant co-occupancy of Nanog (Nanog homeobox) and Hoxa1 on many common target sites, and these are linked to genes in the pluripotential regulatory network. In addition to shared target genes, Hoxa1 binds to regulatory regions of Nanog , and conversely Nanog binds to a 3' enhancer of Hoxa1 This finding provides evidence for direct cross-regulatory feedback between Hoxa1 and Nanog through a mechanism of mutual repression. Hoxa1 also binds to regulatory regions of Sox2 (sex-determining region Y box 2), Esrrb (estrogen-related receptor beta), and Myc , which underscores its key input into core components of the pluripotential regulatory network. We propose a model whereby direct inputs of Nanog and Hoxa1 on shared targets and mutual repression between Hoxa1 and the core pluripotency network provides a molecular mechanism that modulates the fine balance between the alternate states of pluripotency and differentiation., Competing Interests: The authors declare no conflict of interest.

Changes in transcriptional regulatory networks can significantly contribute to species evolution and adaptation. However, identification of genome-scale regulatory networks is an open challenge, especially in non-model organisms. Here, we introduce multi-species regulatory network learning (MRTLE), a computational approach that uses phylogenetic structure, sequence-specific motifs, and transcriptomic data, to infer the regulatory networks in different species. Using simulated data from known networks and transcriptomic data from six divergent yeasts, we demonstrate that MRTLE predicts networks with greater accuracy than existing methods because it incorporates phylogenetic information. We used MRTLE to infer the structure of the transcriptional networks that control the osmotic stress responses of divergent, non-model yeast species and then validated our predictions experimentally. Interrogating these networks reveals that gene duplication promotes network divergence across evolution. Taken together, our approach facilitates study of regulatory network evolutionary dynamics across multiple poorly studied species., (Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.)

The model checking method has been long since established as an important tool for modelling and reverse engineering of biological systems. However, due to a high complexity of both the method and the biological systems, this approach often requires a vast amount of computational resources. In this article we show that by reducing the expressivity of the method we can gain performance while still being able to use all biologically relevant data. We utilize this approach to conduct a study of mutations in the EGFR signalling, motivated by a paper from Klinger et al. (2013). Here we aim at constructing approximated models of multiple cell-lines from sizeable sets of experimental data. Due to cancerous mutations in each cell line, there is a high degree of parameter uncertainty and the study would not be practically tractable without the performance optimizations described here., (Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.)

Candidiasis caused by primarily Candida albicans poses serious threat due to dry pipeline and ineffective antifungal strategy against resistance. In this study we propose to target genes involved in efflux mediated Multi drug resistance. The main objective of this study was to understand the regulatory interactions responsible for activating a major MFS transporter gene of Candida albicans. Another aim was to identify the docking effect of certain antifungal compounds upon the transcription factor effectively controlling FLU1. The in silico study carried out here aims at control of gene expression at initial levels. This approach helps to understand regulatory control of FLU1 based on which a predictive map was generated. This data focused on factors with major control that could be suitable target for antifungal agents. The docking results confirm the agreeable effect on the target transcription factor. Broadly this sort of study would account for understanding and targeting any significant gene which in turn would help in adjusting therapeutics accordingly. Further in silico ADMET analysis reported positive values that are indicative of a good antifungal compound with respect to pharmacokinetics. These tests are essential in assessment of good drug candidates because they not only help in refining better drug candidates but weeding out the unsuitable ones too., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Background: Comparative genomics can leverage the vast amount of available genomic sequences to reconstruct and analyze transcriptional regulatory networks in Bacteria, but the efficacy of this approach hinges on the ability to transfer regulatory network information from reference species to the genomes under analysis. Several methods have been proposed to transfer regulatory information between bacterial species, but the paucity and distributed nature of experimental information on bacterial transcriptional networks have prevented their systematic evaluation., Results: We report the compilation of a large catalog of transcription factor-binding sites across Bacteria and its use to systematically benchmark proposed transfer methods across pairs of bacterial species. We evaluate motif- and accuracy-based metrics to assess the results of regulatory network transfer and we identify the precision-recall area-under-the-curve as the best metric for this purpose due to the large class-imbalanced nature of the problem. Methods assuming conservation of the transcription factor-binding motif (motif-based) are shown to substantially outperform those assuming conservation of regulon composition (network-based), even though their efficiency can decrease sharply with increasing phylogenetic distance. Variations of the basic motif-based transfer method do not yield significant improvements in transfer accuracy. Our results indicate that detection of a large enough number of regulated orthologs is critical for network-based transfer methods, but that relaxing orthology requirements does not improve results. Using the transcriptional regulators LexA and Fur as case examples, we also show how DNA-binding domain sequence similarity can yield confounding results as an indicator of transfer efficiency for motif-based methods., Conclusions: Counter to standard practice, our evaluation of metrics to assess the efficiency of methods for regulatory network information transfer reveals that the area under precision-recall (PR) curves is a more precise and informative metric than that of receiver-operating-characteristic (ROC) curves, confirming similar findings in other class-imbalanced settings. Our systematic assessment of transfer methods reveals that simple approaches to both motif- and network-based transfer of regulatory information provide equal or better results than more elaborate methods. We also show that there are not effective predictors of transfer efficacy, substantiating the long-standing practice of manual curation in comparative genomics analyses.

Background: It is now well-demonstrated that histone demethylases play an important role in developmental controls, cell-fate decisions, and a variety of diseases such as cancer. Lysine-specific demethylase 5D (KDM5D) is a male-specific histone demethylase that specifically demethylates di- and tri-methyl H3K4 at the start site of active gene. In this light, the aim of this study was to investigate isoform/transcript-specific expression profiles of KDM5D in three prostate cancer cell lines, Du-145, LNCaP, and PC3., Methods: Real-time PCR analysis was performed to determine the expression levels of different KDM5D transcripts in the prostate cell lines. A gene regulatory network was established to analyze the gene expression profile., Results: Significantly different expression levels of both isoforms were found among the three cell lines. Interestingly, isoform I was expressed in three cell lines while isoform III did only in DU-145. The expression levels of both isoforms were higher in DU-145 when compared to other cell lines (P<0.0001). The observed expression profile was determined by using regulatory network analyses., Conclusion: The present study, for the first time, not only showed the expression profiles of KDM5D isoforms in prostate cancer cell lines but also evaluated the effects of the gene regulatory network on the expression profile of this gene.

Comparative functional genomics aims to measure and compare genome-wide functional data such as transcriptomes, proteomes, and epigenomes across multiple species to study the conservation and divergence patterns of such quantitative measurements. However, computational methods to systematically compare these quantitative genomic profiles across multiple species are in their infancy. We developed Arboretum, a novel algorithm to identify modules of co-expressed genes and trace their evolutionary history across multiple species from a complex phylogeny. To interpret the results from Arboretum we developed several measures to examine the extent of conservation and divergence in modules and their relationship to species lifestyle, cis-regulatory elements, and gene duplication. We applied Arboretum to study the evolution of modular transcriptional regulatory programs controlling transcriptional response to different environmental stresses in the yeast Ascomycota phylogeny. We found that modules of similar patterns of expression captured the transcriptional responses to different stresses across species; however, the genes exhibiting these patterns were not the same. Divergence in module membership was associated with changes in lifestyle and specific clades and that gene duplication was a major factor contributing to the divergence of module membership.

Blood-based microRNA (miRNA) signatures as biomarkers have been reported for various pathologies, including cancer, neurological disorders, cardiovascular diseases, and also infections. The regulatory mechanism behind respective miRNA patterns is only partially understood. Moreover, "preserved" miRNAs, i.e., miRNAs that are not dysregulated in any disease, and their biological impact have been explored to a very limited extent. We set out to systematically determine their role in regulatory networks by defining groups of highly-dysregulated miRNAs that contribute to a disease signature as opposed to preserved housekeeping miRNAs. We further determined preferential targets and pathways of both dysregulated and preserved miRNAs by computing multi-layer networks, which were compared between housekeeping and dysregulated miRNAs. Of 848 miRNAs examined across 1049 blood samples, 8 potential housekeepers showed very limited expression variations, while 20 miRNAs showed highly-dysregulated expression throughout the investigated blood samples. Our approach provides important insights into miRNAs and their role in regulatory networks. The methodology can be applied to systematically investigate the differences in target genes and pathways of arbitrary miRNA sets., (Copyright © 2015 The Authors. Production and hosting by Elsevier Ltd.. All rights reserved.)

The intricate regulatory network for floral organogenesis in plants that includes AP2/ERF, SPL and AGL transcription factors, miR172 and miR156 along with other components is well documented, though its complexity and size keep increasing. The miR172/AP2 node was recently proposed as essential regulator in the legume-rhizobia nitrogen-fixing symbiosis. Research from our group contributed to demonstrate the control of common bean (Phaseolus vulgaris) nodulation by miR172c/AP2-1, however no other components of such regulatory network have been reported. Here we propose AGLs as new protagonists in the regulation of common bean nodulation and discuss the relevance of future deeper analysis of the complex AP2 regulatory network for nodule organogenesis in legumes.

Environmental changes can not only trigger a regulatory response, but also impose evolutionary pressures that can modify the underlying regulatory network. Here, we review recent approaches that are beginning to disentangle this complex interplay between regulatory and evolutionary responses. Systematic genetic reconstructions have shown how evolutionary constraints arise from epistatic interactions between mutations in fixed environments. This approach is now being extended to more complex environments and systems. The first results suggest that epistasis is affected dramatically by environmental changes and, hence, can profoundly affect the course of evolution. Thus, external environments not only define the selection of favored phenotypes, but also affect the internal constraints that can limit the evolution of these phenotypes. These findings also raise new questions relating to the conditions for evolutionary transitions and the evolutionary potential of regulatory networks., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Current limitation in cancer genomic studies is a lack of the integration of various omics data generated through next generation sequencing technologies, as well as a lack of the sounding and comprehensive epigenomic and genomic information about a particular cancer cell type. In this review, we will discuss main aspects of current genomics research with its application in cancer topics. We will first overview the next-generation sequencing technologies, then outline the major computational approaches, particularly focusing on ChIP-based omics data, and list several remaining open questions facing computational biologists, further present regulatory network analysis inferred from the ChIP-based omics data; finally implicate the clinical outcomes from the network and pathway analysis., (Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.)