Differential effects of dietary supplements on metabolomic profile of smokers versus non-smokers.

BackgroundCigarette smoking is well-known to associate with accelerated skin aging as well as cardiovascular disease and lung cancer, in large part due to oxidative stress. Because metabolites are downstream of genetic variation, as well as transcriptional changes and post-translational modifications of proteins, they are the most proximal reporters of disease states or reversal of disease states.MethodsIn this study, we explore the potential effects of commonly available oral supplements (containing antioxidants, vitamins and omega-3 fatty acids) on the metabolomes of smokers (n = 11) compared to non-smokers (n = 17). At baseline and after 12 weeks of supplementation, metabolomic analysis was performed on serum by liquid and gas chromatography with mass spectroscopy (LC-MS and GC-MS). Furthermore, clinical parameters of skin aging, including cutometry as assessed by three dermatologist raters blinded to subjects' age and smoking status, were measured.ResultsLong-chain fatty acids, including palmitate and oleate, decreased in smokers by 0.76-fold (P = 0.0045) and 0.72-fold (P = 0.0112), respectively. These changes were not observed in non-smokers. Furthermore, age and smoking status showed increased glow (P = 0.004) and a decrease in fine wrinkling (P = 0.038). Cutometry showed an increase in skin elasticity in smokers (P = 0.049) but not in non-smokers. Complexion analysis software (VISIA) revealed decreases in the number of ultraviolet spots (P = 0.031), and cutometry showed increased elasticity (P = 0.05) in smokers but not non-smokers.ConclusionsAdditional future work may shed light on the specific mechanisms by which long-chain fatty acids can lead to increased glow, improved elasticity measures and decreased fine wrinkling in smokers' skin. Our study provides a novel, medicine-focused application of available metabolomic technology to identify changes in sera of human subjects with oxidative stress, and suggests that oral supplementation (in particular, commonly available antioxidants, vitamins and omega-3 fatty acids) affects these individuals in a way that is unique (compared to non-smokers) on a broad level

Genome sequence of the organohalide-respiring Dehalogenimonas alkenigignens type strain (IP3-3(T))

Dehalogenimonas alkenigignens IP3-3(T) is a strictly anaerobic, mesophilic, Gram negative staining bacterium that grows by organohalide respiration, coupling the oxidation of H-2 to the reductive dehalogenation of polychlorinated alkanes. Growth has not been observed with any non-polyhalogenated alkane electron acceptors. Here we describe the features of strain IP3-3(T) together with genome sequence information and its annotation. The 1,849,792 bp high-quality-draft genome contains 1936 predicted protein coding genes, 47 tRNA genes, a single large subunit rRNA (23S-5S) locus, and a single, orphan, small unit rRNA (16S) locus. The genome contains 29 predicted reductive dehalogenase genes, a large majority of which lack cognate genes encoding membrane anchoring proteins.

Impact of the Coronavirus Disease 2019 pandemic on neoadjuvant chemotherapy use in patients diagnosed with epithelial type ovarian cancer

IntroductionThe Coronavirus Disease 2019 (COVID-19) pandemic posed critical challenges in providing care to ovarian cancer (OC) patients, including delays in OC diagnosis and treatment initiation. To accommodate for delays in OC surgery, the Society of Gynecologic Oncology (SGO) recommended preferential use of neoadjuvant chemotherapy during the pandemic. The purpose of this study was to assess the association of the COVID-19 pandemic with neoadjuvant chemotherapy use in patients diagnosed with OC.MethodsThis retrospective cohort study included patients diagnosed with stage II-IV ovarian cancer of epithelial subtype between 01/01/2017-06/30/2021 at Kaiser Permanente Southern California (KPSC), a large integrated healthcare system in the United States. Ovarian cancer patients diagnosed between 2017-2020 were identified from KPSC’s Surveillance, Epidemiology, and End Results (SEER)-affiliated cancer registry. Patients diagnosed in 2021 were identified from the electronic medical records (EMR) using ICD-10 diagnosis codes, followed by medical chart review to validate diagnosis and extract information on histology and stage at diagnosis. March 4, 2020 was used as the cut-off to define pre-pandemic and pandemic periods. Patients diagnosed with COVID-19 between OC diagnosis and treatment completion were excluded. Data on neoadjuvant chemotherapy use were extracted from the cancer registry and EMR, supplemented by chart review. Modified Poisson regression was used to evaluate the association of the pandemic with neoadjuvant chemotherapy use.ResultsOf 566 OC patients, 160 (28.3%) were diagnosed in the pandemic period. Patients diagnosed in the pandemic period were slightly younger (mean age 62.7 vs 64.9 years, p=0.07) and had a higher burden of Charlson comorbidities (p=0.05) than patients diagnosed in pre-pandemic period. No differences in time to treatment initiation were observed by pandemic periods. Neoadjuvant chemotherapy use was documented in 58.7% patients during the pandemic period compared to 47.3% in pre-pandemic period (p=0.01). After adjusting for covariates, patients diagnosed in the pandemic period were 29% more likely to receive neoadjuvant chemotherapy than patients diagnosed in pre-pandemic period [RR(95%CI): 1.29(1.12-1.49)].DiscussionsOvarian cancer patients diagnosed in the COVID-19 pandemic were more likely to receive neoadjuvant chemotherapy than patients diagnosed before the pandemic. Future research on patient outcomes and trends in the post-pandemic period are warranted

Genome-wide association study identifies 30 loci associated with bipolar disorder.

Bipolar disorder is a highly heritable psychiatric disorder. We performed a genome-wide association study (GWAS) including 20,352 cases and 31,358 controls of European descent, with follow-up analysis of 822 variants with P < 1 × 10-4 in an additional 9,412 cases and 137,760 controls. Eight of the 19 variants that were genome-wide significant (P < 5 × 10-8) in the discovery GWAS were not genome-wide significant in the combined analysis, consistent with small effect sizes and limited power but also with genetic heterogeneity. In the combined analysis, 30 loci were genome-wide significant, including 20 newly identified loci. The significant loci contain genes encoding ion channels, neurotransmitter transporters and synaptic components. Pathway analysis revealed nine significantly enriched gene sets, including regulation of insulin secretion and endocannabinoid signaling. Bipolar I disorder is strongly genetically correlated with schizophrenia, driven by psychosis, whereas bipolar II disorder is more strongly correlated with major depressive disorder. These findings address key clinical questions and provide potential biological mechanisms for bipolar disorder

Description of the data from the Collaborative Study on the Genetics of Alcoholism (COGA) and single-nucleotide polymorphism genotyping for Genetic Analysis Workshop 14

The data provided to the Genetic Analysis Workshop 14 (GAW 14) was the result of a collaboration among several different groups, catalyzed by Elizabeth Pugh from The Center for Inherited Disease Research (CIDR) and the organizers of GAW 14, Jean MacCluer and Laura Almasy. The DNA, phenotypic characterization, and microsatellite genomic survey were provided by the Collaborative Study on the Genetics of Alcoholism (COGA), a nine-site national collaboration funded by the National Institute of Alcohol and Alcoholism (NIAAA) and the National Institute of Drug Abuse (NIDA) with the overarching goal of identifying and characterizing genes that affect the susceptibility to develop alcohol dependence and related phenotypes. CIDR, Affymetrix, and Illumina provided single-nucleotide polymorphism genotyping of a large subset of the COGA subjects. This article briefly describes the dataset that was provided

Genome-wide and Ordered-Subset linkage analyses provide support for autism loci on 17q and 19p with evidence of phenotypic and interlocus genetic correlates

BACKGROUND: Autism is a neurobehavioral spectrum of phenotypes characterized by deficits in the development of language and social relationships and patterns of repetitive, rigid and compulsive behaviors. Twin and family studies point to a significant genetic etiology, and several groups have performed genomic linkage screens to identify susceptibility loci. METHODS: We performed a genome-wide linkage screen in 158 combined Tufts, Vanderbilt and AGRE (Autism Genetics Research Exchange) multiplex autism families using parametric and nonparametric methods with a categorical autism diagnosis to identify loci of main effect. Hypothesizing interdependence of genetic risk factors prompted us to perform exploratory studies applying the Ordered-Subset Analysis (OSA) approach using LOD scores as the trait covariate for ranking families. We employed OSA to test for interlocus correlations between loci with LOD scores ≥1.5, and empirically determined significance of linkage in optimal OSA subsets using permutation testing. Exploring phenotypic correlates as the basis for linkage increases involved comparison of mean scores for quantitative trait-based subsets of autism between optimal subsets and the remaining families. RESULTS: A genome-wide screen for autism loci identified the best evidence for linkage to 17q11.2 and 19p13, with maximum multipoint heterogeneity LOD scores of 2.9 and 2.6, respectively. Suggestive linkage (LOD scores ≥1.5) at other loci included 3p, 6q, 7q, 12p, and 16p. OSA revealed positive correlations of linkage between the 19p locus and 17q, between 19p and 6q, and between 7q and 5p. While potential phenotypic correlates for these findings were not identified for the chromosome 7/5 combination, differences indicating more rapid achievement of "developmental milestones" was apparent in the chromosome 19 OSA-defined subsets for 17q and 6q. OSA was used to test the hypothesis that 19p linkage involved more rapid achievement of these milestones and it revealed significantly increased LOD* scores at 19p13. CONCLUSIONS: Our results further support 19p13 as harboring an autism susceptibility locus, confirm other linkage findings at 17q11.2, and demonstrate the need to analyze more discreet trait-based subsets of complex phenotypes to improve ability to detect genetic effects

Novel App knock-in mouse model shows key features of amyloid pathology and reveals profound metabolic dysregulation of microglia.

BACKGROUND: Genetic mutations underlying familial Alzheimer\u27s disease (AD) were identified decades ago, but the field is still in search of transformative therapies for patients. While mouse models based on overexpression of mutated transgenes have yielded key insights in mechanisms of disease, those models are subject to artifacts, including random genetic integration of the transgene, ectopic expression and non-physiological protein levels. The genetic engineering of novel mouse models using knock-in approaches addresses some of those limitations. With mounting evidence of the role played by microglia in AD, high-dimensional approaches to phenotype microglia in those models are critical to refine our understanding of the immune response in the brain. METHODS: We engineered a novel App knock-in mouse model (App RESULTS: Leveraging multi-omics approaches, we discovered profound alteration of diverse lipids and metabolites as well as an exacerbated disease-associated transcriptomic response in microglia with high intracellular Aβ content. The App DISCUSSION: Our findings demonstrate that fibrillar Aβ in microglia is associated with lipid dyshomeostasis consistent with lysosomal dysfunction and foam cell phenotypes as well as profound immuno-metabolic perturbations, opening new avenues to further investigate metabolic pathways at play in microglia responding to AD-relevant pathogenesis. The in-depth characterization of pathological hallmarks of AD in this novel and open-access mouse model should serve as a resource for the scientific community to investigate disease-relevant biology

Low Rates of Breakthrough COVID-19 Infection After SARS-CoV-2 Vaccination in Patients With Inflammatory Bowel Disease

We demonstrate low rates of breakthrough coronavirus disease 2019 (COVID-19) infection and mild course of illness following severe acute respiratory syndrome coronavirus 2 vaccination in a large cohort of inflammatory bowel disease patients. Residence in southern United States and lower median anti-receptor binding antibody level were associated with development of COVID-19

Multi -ancestry genome-wide association analyses identify novel genetic mechanisms in rheumatoid arthritis.

Rheumatoid arthritis (RA) is a highly heritable complex disease with unknown etiology. Multi-ancestry genetic research of RA promises to improve power to detect genetic signals, fine-mapping resolution and performances of polygenic risk scores (PRS). Here, we present a large-scale genome-wide association study (GWAS) of RA, which includes 276,020 samples from five ancestral groups. We conducted a multi-ancestry meta-analysis and identified 124 loci (P < 5 × 10−8), of which 34 are novel. Candidate genes at the novel loci suggest essential roles of the immune system (for example, TNIP2 and TNFRSF11A) and joint tissues (for example, WISP1) in RA etiology. Multi-ancestry fine-mapping identified putatively causal variants with biological insights (for example, LEF1). Moreover, PRS based on multi-ancestry GWAS outperformed PRS based on single-ancestry GWAS and had comparable performance between populations of European and East Asian ancestries. Our study provides several insights into the etiology of RA and improves the genetic predictability of RA.We thank the Director of Health Malaysia for supporting the work described in the South Asian (SAS) population: the Malaysian Epidemiological Investigation of Rheumatoid Arthritis (MyEIRA) study. The MyEIRA study was funded by grants from Ministry of Health Malaysia (NMRR-08-820-1975) and the Swedish National Research Council (DNR-348-2009-6468). The GENRA study and the CARDERA genetics cohort genotyping were funded by Versus Arthritis (grant reference 19739 to I.C.S.). The Nurses’ Health Study (NHS cohort) is funded by the National Institutes of Health (NIH) (R01 AR049880, UM1 CA186107, R01 CA49449, U01 CA176726 and R01 CA67262). The Swedish EIRA study was supported by the Swedish Research Council (to L.K., L.P. and L.A.). S.S. was in part supported by the Mochida Memorial Foundation for Medical and Pharmaceutical Research, Kanae Foundation for the Promotion of Medical Science, Astellas Foundation for Research on Metabolic Disorders, JCR Grant for Promoting Basic Rheumatology, and Manabe Scholarship Grant for Allergic and Rheumatic Diseases. I.C.S. is funded by the National Institute for Health and Care Research (NIHR) Advanced Research Fellowship (grant reference NIHR300826). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. K.A.S. is supported by the Sherman Family Chair in Genomic Medicine and by a Canadian Institutes for Health Research Foundation Grant (FDN 148457) and grants from the Ontario Research Fund (RE-09-090) and Canadian Foundation for Innovation (33374). S.-C.B. is supported by the Basic Science Research Program through the NRF funded by the Ministry of Education (NRF-2021R1A6A1A03038899). R.P.K. and J.C.E. are funded by NIH (UL1 TR003096). C.M.L. is partly funded by the NIHR Maudsley Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. T. Arayssi was partially supported by the National Priorities Research Program (grant 4-344-3-105 from the Qatar National Research Fund, a member of Qatar Foundation). M. Kerick and J.M. are funded by Rheumatology Cooperative Research Thematic Network program RD16/0012/0013 from the Instituto de Salud Carlos III (Spanish Ministry of Science and Innovation). Y.O. is funded by JSPS KAKENHI (19H01021 and 20K21834), AMED (JP21km0405211, JP21ek0109413, JP21ek0410075, JP21gm4010006 and JP21km0405217), JST Moonshot R&D (JPMJMS2021 and JPMJMS2024), Takeda Science Foundation, and the Bioinformatics Initiative of Osaka University Graduate School of Medicine. Y. Kochi is funded by grants from Nanken-Kyoten, TMDU and Medical Research Center Initiative for High Depth Omics. S.R. is supported by UH2AR067677, U01HG009379, R01AR063759 and U01HG012009