90 research outputs found
Cyclin D2 is sufficient to drive β cell self-renewal and regeneration
Diabetes results from an inadequate mass of functional β cells, due to either β cell loss caused by autoimmune destruction (type I diabetes) or β cell failure in response to insulin resistance (type II diabetes). Elucidating the mechanisms that regulate β cell mass may be key to developing new techniques that foster β cell regeneration as a cellular therapy to treat diabetes. While previous studies concluded that cyclin D2 is required for postnatal β cell self-renewal in mice, it is not clear if cyclin D2 is sufficient to drive β cell self-renewal. Using transgenic mice that overexpress cyclin D2 specifically in β cells, we show that cyclin D2 overexpression increases β cell self-renewal post-weaning and results in increased β cell mass. β cells that overexpress cyclin D2 are responsive to glucose stimulation, suggesting they are functionally mature. β cells that overexpress cyclin D2 demonstrate an enhanced regenerative capacity after injury induced by streptozotocin toxicity. To understand if cyclin D2 overexpression is sufficient to drive β cell self-renewal, we generated a novel mouse model where cyclin D2 is only expressed in β cells of cyclin D2−/− mice. Transgenic overexpression of cyclin D2 in cyclin D2−/− β cells was sufficient to restore β cell mass, maintain normoglycaemia, and improve regenerative capacity when compared with cyclin D2−/− littermates. Taken together, our results indicate that cyclin D2 is sufficient to regulate β cell self-renewal and that manipulation of its expression could be used to enhance β cell regeneration
The co-existence of NAFLD and CHB is associated with suboptimal viral and biochemical response to CHB antiviral therapy: a systematic review and meta-analysis
Background and aimsChronic hepatitis B (CHB) and non-alcoholic fatty liver disease (NAFLD) are leading causes of liver-related morbidity and mortality. The interaction between these two disease processes is poorly defined and the impact of NAFLD on HBV-related cirrhosis and HCC remains unclear. The aim of this study was to evaluate the impact of NAFLD on response to antiviral CHB therapy to inform the debate on changing CHB treatment thresholds for these comorbid patients.MethodsStudies with a minimum of 50 adult CHB patients on nucleoside analogue therapy with or without concurrent NAFLD were identified from PubMed/Medline and EMBASE to February 21, 2023. Data extraction from each study included HBeAg and treatment status, diagnostic method of NAFLD, frequency of monitoring intervals, patient age, gender, grade of hepatic steatosis, BMI and metabolic comorbidities. The outcomes of interest, complete virological response (CVR), biochemical response (BR) and HBeAg loss/seroconversion, were recorded at each available monitoring interval. Comparing CHB-NAFLD and CHB-only groups, pooled odds ratios (OR) and 95% confidence intervals (CI) were calculated using random- or fixed-effects models depending on heterogeneity.ResultsFrom a search of 470 citations, we identified 32 potentially relevant papers. Overall, 11 studies, comprising 2580 unique patients, met the inclusion criteria of the meta-analysis. CHB-NAFLD patients exhibited significantly lower rates of CVR compared to CHB-only patients. This was demonstrated by an OR of 0.59 (0.38-0.93, p=0.001, I2 = 72%) at 12 months, which tapered off to an OR of 0.67 (0.48-0.95, p=0.02) at 60 months. CHB-NAFLD patients also exhibited significantly lower rates of BR compared to CHB-only patients, as demonstrated by ORs of 0.39 (0.24-0.62, p<0.0001, I2 = 53%) at 12 months and 0.33 (0.17-0.63, p=0.0008) at 24 months.ConclusionPatients with concurrent CHB and NAFLD experience delayed CVR to antiviral therapy and more persistent biochemical abnormalities in comparison to patients with CHB only. This supports the argument for earlier antiviral therapy in order to avert CHB complications in these multi-morbid patients, as the global disease burden of NAFLD continues to increase
A systematic review and meta-analysis of gestational diabetes mellitus and mental health among BAME populations
BACKGROUND: Gestational diabetes mellitus (GDM) is a common complication of pregnancy and is associated with an increased risk of mental health (MH) disorders including antenatal and postnatal depression (PND), anxiety and post-traumatic-stress-disorder (PTSD). We hypothesized GDM and MH disorders will disproportionately affect individuals from Black, Asian and Minority Ethnic backgrounds.METHODS: A systematic methodology was developed, and a protocol was published in PROSPERO (CRD42020210863) and a systematic review of publications between 1st January 1990 and 30th January 2021 was conducted. Multiple electronic databases were explored using keywords and MeSH terms. The finalised dataset was analysed using statistical methods such as random-effect models, subgroup analysis and sensitivity analysis. These were used to determine odds ratio (OR) and 95% confidence intervals (CI) to establish prevalence using variables of PND, anxiety, PTSD and stress to name a few.FINDINGS: Sixty studies were finalised from the 20,040 data pool. Forty-six studies were included systematically with 14 used to meta-analyze GDM and MH outcomes. A second meta-analysis was conducted using 7 studies to determine GDM risk among Black, Asian and Minority Ethnic women with pre-existing MH disorders. The results indicate an increased risk with pooled adjusted OR for both reflected at 1.23, 95% CI of 1.00-1.50 and 1.29, 95% CI of 1.11-1.50 respectively.INTERPRETATION: The available studies suggest a MH sequalae with GDM as well as a sequalae of GDM with MH among Black, Asian and Minority Ethnic populations. Our findings warrant further future exploration to better manage these patients.FUNDING: Not applicable.</p
Genome wide association mapping of grain arsenic, copper, molybdenum and zinc in rice (Oryza sativa L.) grown at four international field sites
Peer reviewedPublisher PD
Mapping genomic loci implicates genes and synaptic biology in schizophrenia
Schizophrenia has a heritability of 60-80%1, much of which is attributable to common risk alleles. Here, in a two-stage genome-wide association study of up to 76,755 individuals with schizophrenia and 243,649 control individuals, we report common variant associations at 287 distinct genomic loci. Associations were concentrated in genes that are expressed in excitatory and inhibitory neurons of the central nervous system, but not in other tissues or cell types. Using fine-mapping and functional genomic data, we identify 120 genes (106 protein-coding) that are likely to underpin associations at some of these loci, including 16 genes with credible causal non-synonymous or untranslated region variation. We also implicate fundamental processes related to neuronal function, including synaptic organization, differentiation and transmission. Fine-mapped candidates were enriched for genes associated with rare disruptive coding variants in people with schizophrenia, including the glutamate receptor subunit GRIN2A and transcription factor SP4, and were also enriched for genes implicated by such variants in neurodevelopmental disorders. We identify biological processes relevant to schizophrenia pathophysiology; show convergence of common and rare variant associations in schizophrenia and neurodevelopmental disorders; and provide a resource of prioritized genes and variants to advance mechanistic studies.</p
Fine-Scale Mapping of the 4q24 Locus Identifies Two Independent Loci Associated with Breast Cancer Risk
Background: A recent association study identified a common variant (rs9790517) at 4q24 to be associated with breast cancer risk. Independent association signals and potential functional variants in this locus have not been explored.
Methods: We conducted a fine-mapping analysis in 55,540 breast cancer cases and 51,168 controls from the Breast Cancer Association Consortium.
Results: Conditional analyses identified two independent association signals among women of European ancestry, represented by rs9790517 [conditional P = 2.51 × 10−4; OR, 1.04; 95% confidence interval (CI), 1.02–1.07] and rs77928427 (P = 1.86 × 10−4; OR, 1.04; 95% CI, 1.02–1.07). Functional annotation using data from the Encyclopedia of DNA Elements (ENCODE) project revealed two putative functional variants, rs62331150 and rs73838678 in linkage disequilibrium (LD) with rs9790517 (r2 ≥ 0.90) residing in the active promoter or enhancer, respectively, of the nearest gene, TET2. Both variants are located in DNase I hypersensitivity and transcription factor–binding sites. Using data from both The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), we showed that rs62331150 was associated with level of expression of TET2 in breast normal and tumor tissue.
Conclusion: Our study identified two independent association signals at 4q24 in relation to breast cancer risk and suggested that observed association in this locus may be mediated through the regulation of TET2.
Impact: Fine-mapping study with large sample size warranted for identification of independent loci for breast cancer risk
A framework for human microbiome research
A variety of microbial communities and their genes (the microbiome) exist throughout the human body, with fundamental roles in human health and disease. The National Institutes of Health (NIH)-funded Human Microbiome Project Consortium has established a population-scale framework to develop metagenomic protocols, resulting in a broad range of quality-controlled resources and data including standardized methods for creating, processing and interpreting distinct types of high-throughput metagenomic data available to the scientific community. Here we present resources from a population of 242 healthy adults sampled at 15 or 18 body sites up to three times, which have generated 5,177 microbial taxonomic profiles from 16S ribosomal RNA genes and over 3.5 terabases of metagenomic sequence so far. In parallel, approximately 800 reference strains isolated from the human body have been sequenced. Collectively, these data represent the largest resource describing the abundance and variety of the human microbiome, while providing a framework for current and future studies
Structure, function and diversity of the healthy human microbiome
Author Posting. © The Authors, 2012. This article is posted here by permission of Nature Publishing Group. The definitive version was published in Nature 486 (2012): 207-214, doi:10.1038/nature11234.Studies of the human microbiome have revealed that even healthy individuals differ remarkably in the microbes that occupy habitats such as the gut, skin and vagina. Much of this diversity remains unexplained, although diet, environment, host genetics and early microbial exposure have all been implicated. Accordingly, to characterize the ecology of human-associated microbial communities, the Human Microbiome Project has analysed the largest cohort and set of distinct, clinically relevant body habitats so far. We found the diversity and abundance of each habitat’s signature microbes to vary widely even among healthy subjects, with strong niche specialization both within and among individuals. The project encountered an estimated 81–99% of the genera, enzyme families and community configurations occupied by the healthy Western microbiome. Metagenomic carriage of metabolic pathways was stable among individuals despite variation in community structure, and ethnic/racial background proved to be one of the strongest associations of both pathways and microbes with clinical metadata. These results thus delineate the range of structural and functional configurations normal in the microbial communities of a healthy population, enabling future characterization of the epidemiology, ecology and translational applications of the human microbiome.This research was supported in
part by National Institutes of Health grants U54HG004969 to B.W.B.; U54HG003273
to R.A.G.; U54HG004973 to R.A.G., S.K.H. and J.F.P.; U54HG003067 to E.S.Lander;
U54AI084844 to K.E.N.; N01AI30071 to R.L.Strausberg; U54HG004968 to G.M.W.;
U01HG004866 to O.R.W.; U54HG003079 to R.K.W.; R01HG005969 to C.H.;
R01HG004872 to R.K.; R01HG004885 to M.P.; R01HG005975 to P.D.S.;
R01HG004908 to Y.Y.; R01HG004900 to M.K.Cho and P. Sankar; R01HG005171 to
D.E.H.; R01HG004853 to A.L.M.; R01HG004856 to R.R.; R01HG004877 to R.R.S. and
R.F.; R01HG005172 to P. Spicer.; R01HG004857 to M.P.; R01HG004906 to T.M.S.;
R21HG005811 to E.A.V.; M.J.B. was supported by UH2AR057506; G.A.B. was
supported by UH2AI083263 and UH3AI083263 (G.A.B., C. N. Cornelissen, L. K. Eaves
and J. F. Strauss); S.M.H. was supported by UH3DK083993 (V. B. Young, E. B. Chang,
F. Meyer, T. M. S., M. L. Sogin, J. M. Tiedje); K.P.R. was supported by UH2DK083990 (J.
V.); J.A.S. and H.H.K. were supported by UH2AR057504 and UH3AR057504 (J.A.S.);
DP2OD001500 to K.M.A.; N01HG62088 to the Coriell Institute for Medical Research;
U01DE016937 to F.E.D.; S.K.H. was supported by RC1DE0202098 and
R01DE021574 (S.K.H. and H. Li); J.I. was supported by R21CA139193 (J.I. and
D. S. Michaud); K.P.L. was supported by P30DE020751 (D. J. Smith); Army Research
Office grant W911NF-11-1-0473 to C.H.; National Science Foundation grants NSF
DBI-1053486 to C.H. and NSF IIS-0812111 to M.P.; The Office of Science of the US
Department of Energy under Contract No. DE-AC02-05CH11231 for P.S. C.; LANL
Laboratory-Directed Research and Development grant 20100034DR and the US
Defense Threat Reduction Agency grants B104153I and B084531I to P.S.C.; Research
Foundation - Flanders (FWO) grant to K.F. and J.Raes; R.K. is an HHMI Early Career
Scientist; Gordon&BettyMoore Foundation funding and institutional funding fromthe
J. David Gladstone Institutes to K.S.P.; A.M.S. was supported by fellowships provided by
the Rackham Graduate School and the NIH Molecular Mechanisms in Microbial
Pathogenesis Training Grant T32AI007528; a Crohn’s and Colitis Foundation of
Canada Grant in Aid of Research to E.A.V.; 2010 IBM Faculty Award to K.C.W.; analysis
of the HMPdata was performed using National Energy Research Scientific Computing
resources, the BluBioU Computational Resource at Rice University
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