Kim K.,University of California at Davis |
Warden C.H.,University of California at Davis |
Griffey S.M.,University of California at Davis |
Vilches-Moure J.G.,University of California at Davis |
And 5 more authors.
Physiological Genomics | Year: 2010
We have previously shown that 90% of outbred obese Zucker Lepr fa/fa rats die prematurely of renal disease. Thus, renal disease in obese Zucker Leprfa/fa rats may be caused by the LEPR mutation on chromosome 5, by the obesity, or it may be influenced by Zucker susceptibility alleles of genes on other chromosomes. We have searched for susceptibility genes on other chromosomes using urinary albumin excretion (UAE) as an early indicator of altered renal function in a backcross of (Brown Norway x inbred Zucker) F1 x inbred Zucker, which we name the BZZ cross. We killed 237 BZZ backcross animals at 15 wk of age. All included animals were homozygous for the fatty mutation of LEPR and were obese. Urinary creatinine measurements were used to calculate the albumin-to-creatinine ratio (ACR). We identified direct effect quantitative trait loci (QTLs) for UAE and ACR on chromosome 1 (LOD scores = 3.6 and 2.86, respectively) in males, and chromosome 4 (LOD score = 2.9) in females. Significant QTLs were identified for left kidney weight for females on chromosomes 3 and 12. We also demonstrated that kidneys from 15 wk old obese inbred Zucker rats already show evidence of kidney pathology: tubular dilation, proteinaceous fluid accumulation, evidence for inflammation, and mild mesangial and tubular membrane basement membrane thickening. Both lean Zucker rats and the Brown Norway rats showed no evidence for these changes. Thus, by removing the influence of the Leprfa/fa mutation from analysis we have identified UAE QTLs unlinked to LEPR. Copyright © 2010 the American Physiological Society.
Xu H.,Capital Medical University |
Chen X.,Capital Medical University |
Huang J.,Capital Medical University |
Deng W.,Functional Genomics Group |
And 5 more authors.
Biochemical and Biophysical Research Communications | Year: 2013
Matrix metalloproteinases (MMPs) are over-expressed in nearly all cancers. To study novel regulatory factors of MMP expression in head and neck cancer (HNC), we screened a total of 636 candidate genes encoding putative human transmembrane proteins using MMP promoter reporter in a dual luciferase assay system. Three genes GPR65, AXL and TNFRSF10B dramatically activated the induction of MMP3 expression. The induction of MMP expression by GPR65 was further confirmed in A549 and/or FaDu cells. GPR65 mediated MMP induction under acidic conditions. The AP-1 binding site in MMP3 promoter was crucial for MMP3 induction. Moreover, the A549 cells infected by recombinant adenovirus of GPR65 showed accelerated cell invasion. In conclusion, we validate that GPR65 is vital regulatory genes upstream of MMP3, and define a novel mechanism of MMP3 regulation by proton-sensing G-protein-coupled receptors. © 2013 Elsevier Inc.
Rowlands D.S.,Massey University |
Thomson J.S.,Massey University |
Timmons B.W.,McMaster University |
Raymond F.,Functional Genomics Group |
And 9 more authors.
Physiological Genomics | Year: 2011
Postexercise protein feeding regulates the skeletal muscle adaptive response to endurance exercise, but the transcriptome guiding these adaptations in welltrained human skeletal muscle is uncharacterized. In a crossover design, eight cyclists ingested beverages containing protein, carbohydrate and fat (PTN: 0.4, 1.2, 0.2 g/kg, respectively) or isocaloric carbohydrate and fat (CON: 1.6, 0.2 g/kg) at 0 and 1 h following 100 min of cycling. Biopsies of the vastus lateralis were collected at 3 and 48 h following to determine the early and late transcriptome and regulatory signaling responses via microarray and immunoblot. The top gene ontology enriched by PTN were: muscle contraction, extracellular matrix - signaling and structure, and nucleoside, nucleotide, and nucleic acid metabolism (3 and 48 h); developmental processes, immunity, and defense (3 h); glycolysis, lipid and fatty acid metabolism (48 h). The transcriptome was also enriched within axonal guidance, actin cytoskeletal, Ca 2+, cAMP, MAPK, and PPAR canonical pathways linking protein nutrition to exercise-stimulated signaling regulating extracellular matrix, slow-myofibril, and metabolic gene expression. At 3 h, PTN attenuated AMPKα1 Thr172 phosphorylation but increased mTORC1 Ser2448, rps6 Ser240/244, and 4E-BP1-γ phosphorylation, suggesting increased translation initiation, while at 48 h AMPKα1 Thr172 phosphorylation and PPARG and PPARGC1 A expression increased, supporting the late metabolic transcriptome, relative to CON. To conclude, protein feeding following endurance exercise affects signaling associated with cell energy status and translation initiation and the transcriptome involved in skeletal muscle development, slow-myofibril remodeling, immunity and defense, and energy metabolism. Further research should determine the time course and posttranscriptional regulation of this transcriptome and the phenotype responding to chronic postexercise protein feeding. © 2011 the American Physiological Society.
Teng M.,Dana-Farber Cancer Institute |
Teng M.,Harvard University |
Teng M.,Harbin Institute of Technology |
Love M.I.,Dana-Farber Cancer Institute |
And 14 more authors.
Genome Biology | Year: 2016
Obtaining RNA-seq measurements involves a complex data analytical process with a large number of competing algorithms as options. There is much debate about which of these methods provides the best approach. Unfortunately, it is currently difficult to evaluate their performance due in part to a lack of sensitive assessment metrics. We present a series of statistical summaries and plots to evaluate the performance in terms of specificity and sensitivity, available as a R/Bioconductor package (http://bioconductor.org/packages/rnaseqcomp). Using two independent datasets, we assessed seven competing pipelines. Performance was generally poor, with two methods clearly underperforming and RSEM slightly outperforming the rest. © 2016 Teng et al.
PubMed | Dana-Farber Cancer Institute, Stanford University, Institute for System Genomics, Functional Genomics Group and 2 more.
Type: | Journal: Genome biology | Year: 2016
Obtaining RNA-seq measurements involves a complex data analytical process with a large number of competing algorithms as options. There is much debate about which of these methods provides the best approach. Unfortunately, it is currently difficult to evaluate their performance due in part to a lack of sensitive assessment metrics. We present a series of statistical summaries and plots to evaluate the performance in terms of specificity and sensitivity, available as a R/Bioconductor package ( http://bioconductor.org/packages/rnaseqcomp ). Using two independent datasets, we assessed seven competing pipelines. Performance was generally poor, with two methods clearly underperforming and RSEM slightly outperforming the rest.
Pervouchine D.D.,Center for Genomic Regulation |
Pervouchine D.D.,Moscow State University |
Djebali S.,Center for Genomic Regulation |
Breschi A.,Center for Genomic Regulation |
And 21 more authors.
Nature Communications | Year: 2015
Mice have been a long-standing model for human biology and disease. Here we characterize, by RNA sequencing, the transcriptional profiles of a large and heterogeneous collection of mouse tissues, augmenting the mouse transcriptome with thousands of novel transcript candidates. Comparison with transcriptome profiles in human cell lines reveals substantial conservation of transcriptional programmes, and uncovers a distinct class of genes with levels of expression that have been constrained early in vertebrate evolution. This core set of genes captures a substantial fraction of the transcriptional output of mammalian cells, and participates in basic functional and structural housekeeping processes common to all cell types. Perturbation of these constrained genes is associated with significant phenotypes including embryonic lethality and cancer. Evolutionary constraint in gene expression levels is not reflected in the conservation of the genomic sequences, but is associated with conserved epigenetic marking, as well as with characteristic post-transcriptional regulatory programme, in which sub-cellular localization and alternative splicing play comparatively large roles. © 2015 Macmillan Publishers Limited.
Bassett A.R.,University of Oxford |
Akhtar A.,Chromatin |
Barlow D.P.,Austrian Academy of Sciences |
Bird A.P.,University of Edinburgh |
And 9 more authors.
eLife | Year: 2014
Although a small number of the vast array of animal long non-coding RNAs (lncRNAs) have known effects on cellular processes examined in vitro, the extent of their contributions to normal cell processes throughout development, differentiation and disease for the most part remains less clear. Phenotypes arising from deletion of an entire genomic locus cannot be unequivocally attributed either to the loss of the lncRNA per se or to the associated loss of other overlapping DNA regulatory elements. The distinction between cis- or trans-effects is also often problematic. We discuss the advantages and challenges associated with the current techniques for studying the in vivo function of lncRNAs in the light of different models of lncRNA molecular mechanism, and reflect on the design of experiments to mutate lncRNA loci. These considerations should assist in the further investigation of these transcriptional products of the genome. © Bassett et al.
Carvalho B.S.,University of Campinas |
Rustici G.,Functional Genomics Group
Briefings in Bioinformatics | Year: 2013
High-throughput technologies are widely used in the field of functional genomics and used in an increasing number of applications. For many 'wet lab' scientists, the analysis of the large amount of data generated by such technologies is a major bottleneck that can only be overcome through very specialized training in advanced data analysis methodologies and the use of dedicated bioinformatics software tools. In this article, we wish to discuss the challenges related to delivering training in the analysis of high-throughput sequencing data and how we addressed these challenges in the hands-on training courses that we have developed at the European Bioinformatics Institute. © The Author 2013.