Function, dynamics and evolution of network motif modules in integrated gene regulatory networks of worm and plant

Gene regulatory networks (GRNs) consist of different molecular interactions that closely work together to establish proper gene expression in time and space. Especially in higher eukaryotes, many questions remain on how these interactions collectively coordinate gene regulation. We study high quality GRNs consisting of undirected protein-protein, genetic and homologous interactions, and directed protein-DNA, regulatory and miRNA-mRNA interactions in the worm Caenorhabditis elegans and the plant Ara-bidopsis thaliana. Our data-integration framework integrates interactions in composite network motifs, clusters these in biologically relevant, higher-order topological network motif modules, overlays these with gene expression profiles and discovers novel connections between modules and regulators. Similar modules exist in the integrated GRNs of worm and plant. We show how experimental or computational methodologies underlying a certain data type impact network topology. Through phylogenetic decomposition, we found that proteins of worm and plant tend to functionally interact with proteins of a similar age, while at the regulatory level TFs favor same age, but also older target genes. Despite some influence of the duplication mode difference, we also observe at the motif and module level for both species a preference for age homogeneity for undirected and age heterogeneity for directed interactions. This leads to a model where novel genes are added together to the GRNs in a specific biological functional context, regulated by one or more TFs that also target older genes in the GRNs. Overall, we detected topological, functional and evolutionary properties of GRNs that are potentially universal in all species

Nearby transposable elements impact plant stress gene regulatory networks : a meta-analysis in A. thaliana and S. lycopersicum

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. CONCLUSION: 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 plantshttp://www.biomedcentral.com/bmcgenomicsBiochemistryGeneticsMicrobiology and Plant Patholog

Bioavailability of angiotensin I converting enzyme inhibitory peptides

Hypertension or high blood pressure is a significant health problem worldwide. Bioactive peptides that inhibit angiotensin I converting enzyme (ACE) in the cardiovascular system can contribute to the prevention and treatment of hypertension. These ACE inhibitory peptides are derived from many food proteins, especially milk proteins. An ACE inhibitory activity in vitro does not always imply an antihypertensive effect in vivo. Even if it does, it is very difficult to establish a direct relationship between in vitro and in vivo activity. This is mainly due to the bioavailability of the ACE inhibitory peptides after oral administration and the fact that peptides may influence blood pressure by mechanisms other than ACE inhibition. To exert an antihypertensive effect after oral ingestion, ACE inhibitory peptides have to reach the cardiovascular system in an active form. Therefore, they need to remain active during digestion by human proteases and be transported through the intestinal wall into the blood. The bioavailability of some ACE inhibitory peptides has been studied. It is also known that (hydroxy)proline-containing peptides are generally resistant to degradation by digestive enzymes. Peptides can be absorbed intact through the intestine by paracellular and transcellular routes, but the potency of the bioactivity after absorption is inversely correlated to chain length. In addition, some strategies are proposed to increase the bioavailability of ACE inhibitory peptides. Further research into the bioavailability of ACE inhibitory peptides will lead to the development of more effective ACE inhibitory peptides and foods. [PUBLICATION ABSTRACT

RRM2 enhances MYCN-driven neuroblastoma formation and acts as a synergistic target with CHK1 inhibition

High-risk neuroblastoma, a pediatric tumor originating from the sympathetic nervous system, has a low mutation load but highly recurrent somatic DNA copy number variants. Previously, segmental gains and/or amplifications allowed identification of drivers for neuroblastoma development. Using this approach, combined with gene dosage impact on expression and survival, we identified ribonucleotide reductase subunit M2 (RRM2) as a candidate dependency factor further supported by growth inhibition upon in vitro knockdown and accelerated tumor formation in a neuroblastoma zebrafish model coexpressing human RRM2 with MYCN. Forced RRM2 induction alleviates excessive replicative stress induced by CHK1 inhibition, while high RRM2 expression in human neuroblastomas correlates with high CHK1 activity. MYCN-driven zebrafish tumors with RRM2 co-overexpression exhibit differentially expressed DNA repair genes in keeping with enhanced ATR-CHK1 signaling activity. In vitro, RRM2 inhibition enhances intrinsic replication stress checkpoint addiction. Last, combinatorial RRM2-CHK1 inhibition acts synergistic in high-risk neuroblastoma cell lines and patient-derived xenograft models, illustrating the therapeutic potential

Molecular systems biology approaches to investigate mechanisms of gut-brain communication in neurological diseases

Background: Whilst the incidence of neurological diseases is increasing worldwide, treatment remains mostly limited to symptom management. The gut-brain axis, which encompasses the communication routes between microbiota, gut and brain, has emerged as a crucial area of investigation for identifying new preventive and therapeutic targets in neurological disease. Methods: Due to the inter--organ, systemic nature of the gut-brain axis, together with the multitude of biomolecules and microbial species involved, molecular systems biology approaches are required to accurately investigate the mechanisms of gut-brain communication. High--throughput omics profiling, together with computational methodologies such as dimensionality reduction or clustering, machine learning, network inference and genome--scale metabolic models, allows novel biomarkers to be discovered and elucidates mechanistic insights. Results: In this review, the general concepts of experimental and computational methodologies for gut-brain axis research are introduced and their applications are discussed, mainly in human cohorts. Important aspects are further highlighted concerning rational study design, sampling procedures and data modalities relevant for gut-brain communication, strengths and limitations of methodological approaches and some future perspectives. Conclusion: Multi--omics analyses, together with advanced data mining, are essential to functionally characterize the gut-brain axis and put forward novel preventive or therapeutic strategies in neurological disease

A single-cell multimodal view on gene regulatory network inference from transcriptomics and chromatin accessibility data

Eukaryotic gene regulation is a combinatorial, dynamic, and quantitative process that plays a vital role in development and disease and can be modeled at a systems level in gene regulatory networks (GRNs). The wealth of multi-omics data measured on the same samples and even on the same cells has lifted the field of GRN inference to the next stage. Combinations of (single-cell) transcriptomics and chromatin accessibility allow the prediction of fine-grained regulatory programs that go beyond mere correlation of transcription factor and target gene expression, with enhancer GRNs (eGRNs) modeling molecular interactions between transcription factors, regulatory elements, and target genes. In this review, we highlight the key components for successful (e)GRN inference from (sc)RNA-seq and (sc)ATAC-seq data exemplified by state-of-the-art methods as well as open challenges and future developments. Moreover, we address preprocessing strategies, metacell generation and computational omics pairing, transcription factor binding site detection, and linear and three-dimensional approaches to identify chromatin interactions as well as dynamic and causal eGRN inference. We believe that the integration of transcriptomics together with epigenomics data at a single-cell level is the new standard for mechanistic network inference, and that it can be further advanced with integrating additional omics layers and spatiotemporal data, as well as with shifting the focus towards more quantitative and causal modeling strategies

SUBATOMIC : a SUbgraph BAsed mulTi-OMIcs clustering framework to analyze integrated multi-edge networks

Background: Representing the complex interplay between different types of biomolecules across different omics layers in multi-omics networks bears great potential to gain a deep mechanistic understanding of gene regulation and disease. However, multi-omics networks easily grow into giant hairball structures that hamper biological interpretation. Module detection methods can decompose these networks into smaller interpretable modules. However, these methods are not adapted to deal with multi-omics data nor consider topological features. When deriving very large modules or ignoring the broader network context, interpretability remains limited. To address these issues, we developed a SUbgraph BAsed mulTi-OMIcs Clustering framework (SUBATOMIC), which infers small and interpretable modules with a specific topology while keeping track of connections to other modules and regulators. Results: SUBATOMIC groups specific molecular interactions in composite network subgraphs of two and three nodes and clusters them into topological modules. These are functionally annotated, visualized and overlaid with expression profiles to go from static to dynamic modules. To preserve the larger network context, SUBATOMIC investigates statistically the connections in between modules as well as between modules and regulators such as miRNAs and transcription factors. We applied SUBATOMIC to analyze a composite Homo sapiens network containing transcription factor-target gene, miRNA-target gene, protein-protein, homologous and co-functional interactions from different databases. We derived and annotated 5586 modules with diverse topological, functional and regulatory properties. We created novel functional hypotheses for unannotated genes. Furthermore, we integrated modules with condition specific expression data to study the influence of hypoxia in three cancer cell lines. We developed two prioritization strategies to identify the most relevant modules in specific biological contexts: one considering GO term enrichments and one calculating an activity score reflecting the degree of differential expression. Both strategies yielded modules specifically reacting to low oxygen levels. Conclusions: We developed the SUBATOMIC framework that generates interpretable modules from integrated multi-omics networks and applied it to hypoxia in cancer. SUBATOMIC can infer and contextualize modules, explore condition or disease specific modules, identify regulators and functionally related modules, and derive novel gene functions for uncharacterized genes. The software is available at https://github.com/ CBIGR/SUBATOMIC

Optimisation and validation of an angiotensin-converting enzyme inhibition assay for the screening of bioactive peptides

Angiotensin-converting enzyme (ACE) plays a major role in the regulation of blood pressure. A diagnostic assay to measure angiotensin-converting enzyme (ACE) activity was transformed into an enzyme inhibition assay and optimised, which led to a more sensitive and less expensive assay. By this spectrophotometric method, ACE inhibition is measured using the substrate furanacryloyl-Phe-Gly-Gly and as ACE source rabbit lung acetone extract. The optimised as well as the original ACE inhibition assay were used to verify the ACE inhibitory activity of captopril. The ACE inhibition assay was further validated by enalapril, its active derivative enalaprilat and the ACE-inhibitory peptide Ala-Leu-Pro-Met-His-Ile-Arg, corresponding to a tryptic fragment of bovine β-lactoglobulin. Sigmoid curves could be fit adequately to the data points representing ACE inhibition in function of inhibitor concentration. IC50 values for these compounds corresponded well with literature data. Furthermore, pea and whey protein hydrolysates obtained by digestion with trypsin showed ACE inhibitory activity in the ACE inhibition assay. Hence, this optimised assay is suitable to screen for ACE inhibitory peptides derived from food proteins with a possible antihypertensive effect in vivo

Nearby transposable elements impact plant stress gene regulatory networks : a meta-analysis in A. thaliana and S. lycopersicum

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