335 research outputs found

    Bio-informatics analysis of a gene co-expression module in adipose tissue containing the diet-responsive gene Nnat

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    Background: Obesity causes insulin resistance in target tissues - skeletal muscle, adipose tissue, liver and the brain. Insulin resistance predisposes to type-2 diabetes (T2D) and cardiovascular disease (CVD). Adipose tissue inflammation is an essential characteristic of obesity and insulin resistance. Neuronatin (Nnat) expression has been found to be altered in a number of conditions related to inflammatory or metabolic disturbance, but its physiological roles and regulatory mechanisms in adipose tissue, brain, pancreatic islets and other tissues are not understood. Results: We identified transcription factor binding sites (TFBS) conserved in the Nnat promoter, and transcription factors (TF) abundantly expressed in adipose tissue. These include transcription factors concerned with the control of: adipogenesis (Ppar gamma, Klf15, Irf1, Creb1, Egr2, Gata3); lipogenesis (Mlxipl, Srebp1c); inflammation (Jun, Stat3); insulin signalling and diabetes susceptibility (Foxo1, Tcf7l2). We also identified NeuroD1 the only documented TF that controls Nnat expression. We identified KEGG pathways significantly associated with Nnat expression, including positive correlations with inflammation and negative correlations with metabolic pathways (most prominently oxidative phosphorylation, glycolysis and gluconeogenesis, pyruvate metabolism) and protein turnover. 27 genes, including; Gstt1 and Sod3, concerned with oxidative stress; Sncg and Cxcl9 concerned with inflammation; Ebf1, Lgals12 and Fzd4 involved in adipogenesis; whose expression co-varies with Nnat were identified, and conserved transcription factor binding sites identified on their promoters. Functional networks relating to each of these genes were identified. Conclusions: Our analysis shows that Nnat is an acute diet-responsive gene in white adipose tissue and hypothalamus; it may play an important role in metabolism, adipogenesis, and resolution of oxidative stress and inflammation in response to dietary exces

    BRCA2 polymorphic stop codon K3326X and the risk of breast, prostate, and ovarian cancers

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    Background: The K3326X variant in BRCA2 (BRCA2*c.9976A>T; p.Lys3326*; rs11571833) has been found to be associated with small increased risks of breast cancer. However, it is not clear to what extent linkage disequilibrium with fully pathogenic mutations might account for this association. There is scant information about the effect of K3326X in other hormone-related cancers. Methods: Using weighted logistic regression, we analyzed data from the large iCOGS study including 76 637 cancer case patients and 83 796 control patients to estimate odds ratios (ORw) and 95% confidence intervals (CIs) for K3326X variant carriers in relation to breast, ovarian, and prostate cancer risks, with weights defined as probability of not having a pathogenic BRCA2 variant. Using Cox proportional hazards modeling, we also examined the associations of K3326X with breast and ovarian cancer risks among 7183 BRCA1 variant carriers. All statistical tests were two-sided. Results: The K3326X variant was associated with breast (ORw = 1.28, 95% CI = 1.17 to 1.40, P = 5.9x10- 6) and invasive ovarian cancer (ORw = 1.26, 95% CI = 1.10 to 1.43, P = 3.8x10-3). These associations were stronger for serous ovarian cancer and for estrogen receptor–negative breast cancer (ORw = 1.46, 95% CI = 1.2 to 1.70, P = 3.4x10-5 and ORw = 1.50, 95% CI = 1.28 to 1.76, P = 4.1x10-5, respectively). For BRCA1 mutation carriers, there was a statistically significant inverse association of the K3326X variant with risk of ovarian cancer (HR = 0.43, 95% CI = 0.22 to 0.84, P = .013) but no association with breast cancer. No association with prostate cancer was observed. Conclusions: Our study provides evidence that the K3326X variant is associated with risk of developing breast and ovarian cancers independent of other pathogenic variants in BRCA2. Further studies are needed to determine the biological mechanism of action responsible for these associations

    Best practice data standards for discrete chemical oceanographic observations

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    © The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Jiang, L.-Q., Pierrot, D., Wanninkhof, R., Feely, R. A., Tilbrook, B., Alin, S., Barbero, L., Byrne, R. H., Carter, B. R., Dickson, A. G., Gattuso, J.-P., Greeley, D., Hoppema, M., Humphreys, M. P., Karstensen, J., Lange, N., Lauvset, S. K., Lewis, E. R., Olsen, A., Pérez, F. F., Sabine, C., Sharp, J. D., Tanhua, T., Trull, T. W., Velo, A., Allegra, A. J., Barker, P., Burger, E., Cai, W-J., Chen, C-T. A., Cross, J., Garcia, H., Hernandez-Ayon J. M., Hu, X., Kozyr, A., Langdon, C., Lee., K, Salisbury, J., Wang, Z. A., & Xue, L. Best practice data standards for discrete chemical oceanographic observations. Frontiers in Marine Science, 8, (2022): 705638, https://doi.org/10.3389/fmars.2021.705638.Effective data management plays a key role in oceanographic research as cruise-based data, collected from different laboratories and expeditions, are commonly compiled to investigate regional to global oceanographic processes. Here we describe new and updated best practice data standards for discrete chemical oceanographic observations, specifically those dealing with column header abbreviations, quality control flags, missing value indicators, and standardized calculation of certain properties. These data standards have been developed with the goals of improving the current practices of the scientific community and promoting their international usage. These guidelines are intended to standardize data files for data sharing and submission into permanent archives. They will facilitate future quality control and synthesis efforts and lead to better data interpretation. In turn, this will promote research in ocean biogeochemistry, such as studies of carbon cycling and ocean acidification, on regional to global scales. These best practice standards are not mandatory. Agencies, institutes, universities, or research vessels can continue using different data standards if it is important for them to maintain historical consistency. However, it is hoped that they will be adopted as widely as possible to facilitate consistency and to achieve the goals stated above.Funding for L-QJ and AK was from NOAA Ocean Acidification Program (OAP, Project ID: 21047) and NOAA National Centers for Environmental Information (NCEI) through NOAA grant NA19NES4320002 [Cooperative Institute for Satellite Earth System Studies (CISESS)] at the University of Maryland/ESSIC. BT was in part supported by the Australia’s Integrated Marine Observing System (IMOS), enabled through the National Collaborative Research Infrastructure Strategy (NCRIS). AD was supported in part by the United States National Science Foundation. AV and FP were supported by BOCATS2 Project (PID2019-104279GB-C21/AEI/10.13039/501100011033) funded by the Spanish Research Agency and contributing to WATER:iOS CSIC interdisciplinary thematic platform. MH was partly funded by the European Union’s Horizon 2020 Research and Innovation Program under grant agreement N°821001 (SO-CHIC)

    2q36.3 is associated with prognosis for oestrogen receptor-negative breast cancer patients treated with chemotherapy

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    Large population-based registry studies have shown that breast cancer prognosis is inherited. Here we analyse single-nucleotide polymorphisms (SNPs) of genes implicated in human immunology and inflammation as candidates for prognostic markers of breast cancer survival involving 1,804 oestrogen receptor (ER)-negative patients treated with chemotherapy (279 events) from 14 European studies in a prior large-scale genotyping experiment, which is part of the Collaborative Oncological Gene-environment Study (COGS) initiative. We carry out replication using Asian COGS samples (n=522, 53 events) and the Prospective Study of Outcomes in Sporadic versus Hereditary breast cancer (POSH) study (n=315, 108 events). Rs4458204-A near CCL20 (2q36.3) is found to be associated with breast cancer-specific death at a genome-wide significant level (n=2,641, 440 events, combined allelic hazard ratio (HR)=1.81 (1.49-2.19); P for trend=1.90 × 10 â ̂'9). Such survival-associated variants can represent ideal targets for tailored therapeutics, and may also enhance our current prognostic prediction capabilities

    2q36.3 is associated with prognosis for oestrogen receptor-negative breast cancer patients treated with chemotherapy.

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    Large population-based registry studies have shown that breast cancer prognosis is inherited. Here we analyse single-nucleotide polymorphisms (SNPs) of genes implicated in human immunology and inflammation as candidates for prognostic markers of breast cancer survival involving 1,804 oestrogen receptor (ER)-negative patients treated with chemotherapy (279 events) from 14 European studies in a prior large-scale genotyping experiment, which is part of the Collaborative Oncological Gene-environment Study (COGS) initiative. We carry out replication using Asian COGS samples (n=522, 53 events) and the Prospective Study of Outcomes in Sporadic versus Hereditary breast cancer (POSH) study (n=315, 108 events). Rs4458204_A near CCL20 (2q36.3) is found to be associated with breast cancer-specific death at a genome-wide significant level (n=2,641, 440 events, combined allelic hazard ratio (HR)=1.81 (1.49-2.19); P for trend=1.90 × 10(-9)). Such survival-associated variants can represent ideal targets for tailored therapeutics, and may also enhance our current prognostic prediction capabilities

    Copy number variants as modifiers of breast cancer risk for BRCA1/BRCA2 pathogenic variant carriers

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    The risk of germline copy number variants (CNVs) in BRCA1 and BRCA2 pathogenic variant carriers in breast cancer is assessed, with CNVs overlapping SULT1A1 decreasing breast cancer risk in BRCA1 carriers.The contribution of germline copy number variants (CNVs) to risk of developing cancer in individuals with pathogenic BRCA1 or BRCA2 variants remains relatively unknown. We conducted the largest genome-wide analysis of CNVs in 15,342 BRCA1 and 10,740 BRCA2 pathogenic variant carriers. We used these results to prioritise a candidate breast cancer risk-modifier gene for laboratory analysis and biological validation. Notably, the HR for deletions in BRCA1 suggested an elevated breast cancer risk estimate (hazard ratio (HR) = 1.21), 95% confidence interval (95% CI = 1.09-1.35) compared with non-CNV pathogenic variants. In contrast, deletions overlapping SULT1A1 suggested a decreased breast cancer risk (HR = 0.73, 95% CI 0.59-0.91) in BRCA1 pathogenic variant carriers. Functional analyses of SULT1A1 showed that reduced mRNA expression in pathogenic BRCA1 variant cells was associated with reduced cellular proliferation and reduced DNA damage after treatment with DNA damaging agents. These data provide evidence that deleterious variants in BRCA1 plus SULT1A1 deletions contribute to variable breast cancer risk in BRCA1 carriers.Peer reviewe

    A case-only study to identify genetic modifiers of breast cancer risk for BRCA1/BRCA2 mutation carriers.

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    Breast cancer (BC) risk for BRCA1 and BRCA2 mutation carriers varies by genetic and familial factors. About 50 common variants have been shown to modify BC risk for mutation carriers. All but three, were identified in general population studies. Other mutation carrier-specific susceptibility variants may exist but studies of mutation carriers have so far been underpowered. We conduct a novel case-only genome-wide association study comparing genotype frequencies between 60,212 general population BC cases and 13,007 cases with BRCA1 or BRCA2 mutations. We identify robust novel associations for 2 variants with BC for BRCA1 and 3 for BRCA2 mutation carriers, P < 10-8, at 5 loci, which are not associated with risk in the general population. They include rs60882887 at 11p11.2 where MADD, SP11 and EIF1, genes previously implicated in BC biology, are predicted as potential targets. These findings will contribute towards customising BC polygenic risk scores for BRCA1 and BRCA2 mutation carriers
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