Crosstalk between transcription factors and microRNAs in human protein interaction network

<p>Abstract</p> <p>Background</p> <p>Gene regulatory networks control the global gene expression and the dynamics of protein output in living cells. In multicellular organisms, transcription factors and microRNAs are the major families of gene regulators. Recent studies have suggested that these two kinds of regulators share similar regulatory logics and participate in cooperative activities in the gene regulatory network; however, their combinational regulatory effects and preferences on the protein interaction network remain unclear.</p> <p>Methods</p> <p>In this study, we constructed a global human gene regulatory network comprising both transcriptional and post-transcriptional regulatory relationships, and integrated the protein interactome into this network. We then screened the integrated network for four types of regulatory motifs: single-regulation, co-regulation, crosstalk, and independent, and investigated their topological properties in the protein interaction network.</p> <p>Results</p> <p>Among the four types of network motifs, the crosstalk was found to have the most enriched protein-protein interactions in their downstream regulatory targets. The topological properties of these motifs also revealed that they target crucial proteins in the protein interaction network and may serve important roles of biological functions.</p> <p>Conclusions</p> <p>Altogether, these results reveal the combinatorial regulatory patterns of transcription factors and microRNAs on the protein interactome, and provide further evidence to suggest the connection between gene regulatory network and protein interaction network.</p

Distinct Tumor Microenvironment at Tumor Edge as a Result of Astrocyte Activation Is Associated With Therapeutic Resistance for Brain Tumor

Tumor vasculatures and hypoxia are critical tumor micro-environmental factors associated with tumor response to the therapy and heterogeneous in both time- and location-dependent manner. Using a murine orthotopic anaplastic astrocytoma model, ALTS1C1, this study showed that brain tumor edge had a very unique microenvironment, having higher microvascular density (MVD) and better vessel function than the tumor core, but on the other hand was also positive for hypoxia markers, such as pimonidazole (PIMO), hypoxia inducible factor-1α (HIF-1α), and carbonic anhydrase IV (CAIX). The hypoxia at tumor edge was transient, named as peripheral hypoxia, and caused by different mechanisms from the chronic hypoxia in tumor core. The correlation of CAIX staining with astrocyte activation marker, glial fibrillary acid protein (GFAP), at the tumor edge indicated the involvement of astrocyte activation on the development of peripheral hypoxia. Peripheral hypoxia was a specific trait of orthotopic brain tumors at tumor edge, regardless of tumor origin. The hypoxic cells were resistant to the therapy, regardless of their location. Surviving cells, particularly those at the hypoxic region of tumor edge, are likely the cause of tumor recurrence after the therapy. New therapeutic platform that targets cells in tumor edge is likely to achieve better treatment outcomes

Revealing the Anti-Tumor Effect of Artificial miRNA p-27-5p on Human Breast Carcinoma Cell Line T-47D

microRNAs (miRNAs) cause mRNA degradation or translation suppression of their target genes. Previous studies have found direct involvement of miRNAs in cancer initiation and progression. Artificial miRNAs, designed to target single or multiple genes of interest, provide a new therapeutic strategy for cancer. This study investigates the anti-tumor effect of a novel artificial miRNA, miR P-27-5p, on breast cancer. In this study, we reveal that miR P-27-5p downregulates the differential gene expressions associated with the protein modification process and regulation of cell cycle in T-47D cells. Introduction of this novel artificial miRNA, miR P-27-5p, into breast cell lines inhibits cell proliferation and induces the first “gap” phase (G1) cell cycle arrest in cancer cell lines but does not affect normal breast cells. We further show that miR P-27-5p targets the 3′-untranslated mRNA region (3′-UTR) of cyclin-dependent kinase 4 (CDK4) and reduces both the mRNA and protein level of CDK4, which in turn, interferes with phosphorylation of the retinoblastoma protein (RB1). Overall, our data suggest that the effects of miR p-27-5p on cell proliferation and G1 cell cycle arrest are through the downregulation of CDK4 and the suppression of RB1 phosphorylation. This study opens avenues for future therapies targeting breast cancer

Serotonin receptor HTR6-mediated mTORC1 signaling regulates dietary restriction-induced memory enhancement

Dietary restriction (DR; sometimes called calorie restriction) has profound beneficial effects on physiological, psychological, and behavioral outcomes in animals and in humans. We have explored the molecular mechanism of DR-induced memory enhancement and demonstrate that dietary tryptophan-a precursor amino acid for serotonin biosynthesis in the brain-and serotonin receptor 5-hydroxytryptamine receptor 6 (HTR6) are crucial in mediating this process. We show that HTR6 inactivation diminishes DR-induced neurological alterations, including reduced dendritic complexity, increased spine density, and enhanced long-term potentiation (LTP) in hippocampal neurons. Moreover, we find that HTR6-mediated mechanistic target of rapamycin complex 1 (mTORC1) signaling is involved in DR-induced memory improvement. Our results suggest that the HTR6-mediated mTORC1 pathway may function as a nutrient sensor in hippocampal neurons to couple memory performance to dietary intake

Revealing the Functions of the Transketolase Enzyme Isoforms in Rhodopseudomonas palustris Using a Systems Biology Approach

BACKGROUND: Rhodopseudomonas palustris (R. palustris) is a purple non-sulfur anoxygenic phototrophic bacterium that belongs to the class of proteobacteria. It is capable of absorbing atmospheric carbon dioxide and converting it to biomass via the process of photosynthesis and the Calvin-Benson-Bassham (CBB) cycle. Transketolase is a key enzyme involved in the CBB cycle. Here, we reveal the functions of transketolase isoforms I and II in R. palustris using a systems biology approach. METHODOLOGY/PRINCIPAL FINDINGS: By measuring growth ability, we found that transketolase could enhance the autotrophic growth and biomass production of R. palustris. Microarray and real-time quantitative PCR revealed that transketolase isoforms I and II were involved in different carbon metabolic pathways. In addition, immunogold staining demonstrated that the two transketolase isoforms had different spatial localizations: transketolase I was primarily associated with the intracytoplasmic membrane (ICM) but transketolase II was mostly distributed in the cytoplasm. Comparative proteomic analysis and network construction of transketolase over-expression and negative control (NC) strains revealed that protein folding, transcriptional regulation, amino acid transport and CBB cycle-associated carbon metabolism were enriched in the transketolase I over-expressed strain. In contrast, ATP synthesis, carbohydrate transport, glycolysis-associated carbon metabolism and CBB cycle-associated carbon metabolism were enriched in the transketolase II over-expressed strain. Furthermore, ATP synthesis assays showed a significant increase in ATP synthesis in the transketolase II over-expressed strain. A PEPCK activity assay showed that PEPCK activity was higher in transketolase over-expressed strains than in the negative control strain. CONCLUSIONS/SIGNIFICANCE: Taken together, our results indicate that the two isoforms of transketolase in R. palustris could affect photoautotrophic growth through both common and divergent metabolic mechanisms

Genetic Drivers of Heterogeneity in Type 2 Diabetes Pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P \u3c 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P &lt; 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.</p

The trans-ancestral genomic architecture of glycemic traits

Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P < 5 x 10(-8)), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution. A trans-ancestry meta-analysis of GWAS of glycemic traits in up to 281,416 individuals identifies 99 novel loci, of which one quarter was found due to the multi-ancestry approach, which also improves fine-mapping of credible variant sets.Peer reviewe