A comprehensive 1000 Genomes-based genome-wide association meta-analysis of coronary artery disease

Existing knowledge of genetic variants affecting risk of coronary artery disease (CAD) is largely based on genome-wide association studies (GWAS) analysis of common SNPs. Leveraging phased haplotypes from the 1000 Genomes Project, we report a GWAS meta-analysis of 185 thousand CAD cases and controls, interrogating 6.7 million common (MAF>0.05) as well as 2.7 million low frequency (0.005<MAF<0.05) variants. In addition to confirmation of most known CAD loci, we identified 10 novel loci, eight additive and two recessive, that contain candidate genes that newly implicate biological processes in vessel walls. We observed intra-locus allelic heterogeneity but little evidence of low frequency variants with larger effects and no evidence of synthetic association. Our analysis provides a comprehensive survey of the fine genetic architecture of CAD showing that genetic susceptibility to this common disease is largely determined by common SNPs of small effect siz

Common Variants at 10 Genomic Loci Influence Hemoglobin A(1C) Levels via Glycemic and Nonglycemic Pathways

OBJECTIVE-Glycated hemoglobin (HbA(1c)), used to monitor and diagnose diabetes, is influenced by average glycemia over a 2- to 3-month period. Genetic factors affecting expression, turnover, and abnormal glycation of hemoglobin could also be associated with increased levels of HbA(1c). We aimed to identify such genetic factors and investigate the extent to which they influence diabetes classification based on HbA(1c) levels.RESEARCH DESIGN AND METHODS-We studied associations with HbA(1c) in up to 46,368 nondiabetic adults of European descent from 23 genome-wide association studies (GWAS) and 8 cohorts with de novo genotyped single nucleotide polymorphisms (SNPs). We combined studies using inverse-variance meta-analysis and tested mediation by glycemia using conditional analyses. We estimated the global effect of HbA(1c) loci using a multilocus risk score, and used net reclassification to estimate genetic effects on diabetes screening.RESULTS-Ten loci reached genome-wide significant association with HbA(1c), including six new loci near FN3K (lead SNP/P value, rs1046896/P = 1.6 x 10(-26)), HFE (rs1800562/P = 2.6 x 10(-20)), TMPRSS6 (rs855791/P = 2.7 x 10(-14)), ANK1 (rs4737009/P = 6.1 x 10(-12)), SPTA1 (rs2779116/P = 2.8 x 10(-9)) and ATP11A/TUBGCP3 (rs7998202/P = 5.2 x 10(-9)), and four known HbA(1c) loci: HK1 (rs16926246/P = 3.1 x 10(-54)), MTNR1B (rs1387153/P = 4.0 X 10(-11)), GCK (rs1799884/P = 1.5 x 10(-20)) and G6PC2/ABCB11 (rs552976/P = 8.2 x 10(-18)). We show that associations with HbA(1c) are partly a function of hyperglycemia associated with 3 of the 10 loci (GCK, G6PC2 and MTNR1B). The seven nonglycemic loci accounted for a 0.19 (%HbA(1c)) difference between the extreme 10% tails of the risk score, and would reclassify similar to 2% of a general white population screened for diabetes with HbA(1c).CONCLUSIONS-GWAS identified 10 genetic loci reproducibly associated with HbA(1c). Six are novel and seven map to loci where rarer variants cause hereditary anemias and iron storage disorders. Common variants at these loci likely influence HbA(1c) levels via erythrocyte biology, and confer a small but detectable reclassification of diabetes diagnosis by HbA(1c) Diabetes 59: 3229-3239, 201

New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk.

Levels of circulating glucose are tightly regulated. To identify new loci influencing glycemic traits, we performed meta-analyses of 21 genome-wide association studies informative for fasting glucose, fasting insulin and indices of beta-cell function (HOMA-B) and insulin resistance (HOMA-IR) in up to 46,186 nondiabetic participants. Follow-up of 25 loci in up to 76,558 additional subjects identified 16 loci associated with fasting glucose and HOMA-B and two loci associated with fasting insulin and HOMA-IR. These include nine loci newly associated with fasting glucose (in or near ADCY5, MADD, ADRA2A, CRY2, FADS1, GLIS3, SLC2A2, PROX1 and C2CD4B) and one influencing fasting insulin and HOMA-IR (near IGF1). We also demonstrated association of ADCY5, PROX1, GCK, GCKR and DGKB-TMEM195 with type 2 diabetes. Within these loci, likely biological candidate genes influence signal transduction, cell proliferation, development, glucose-sensing and circadian regulation. Our results demonstrate that genetic studies of glycemic traits can identify type 2 diabetes risk loci, as well as loci containing gene variants that are associated with a modest elevation in glucose levels but are not associated with overt diabetes

Metabolic Engineering of Cofactor F420 Production in Mycobacterium smegmatis

Cofactor F420 is a unique electron carrier in a number of microorganisms including Archaea and Mycobacteria. It has been shown that F420 has a direct and important role in archaeal energy metabolism whereas the role of F420 in mycobacterial metabolism has only begun to be uncovered in the last few years. It has been suggested that cofactor F420 has a role in the pathogenesis of M. tuberculosis, the causative agent of tuberculosis. In the absence of a commercial source for F420, M. smegmatis has previously been used to provide this cofactor for studies of the F420-dependent proteins from mycobacterial species. Three proteins have been shown to be involved in the F420 biosynthesis in Mycobacteria and three other proteins have been demonstrated to be involved in F420 metabolism. Here we report the over-expression of all of these proteins in M. smegmatis and testing of their importance for F420 production. The results indicate that co–expression of the F420 biosynthetic proteins can give rise to a much higher F420 production level. This was achieved by designing and preparing a new T7 promoter–based co-expression shuttle vector. A combination of co–expression of the F420 biosynthetic proteins and fine-tuning of the culture media has enabled us to achieve F420 production levels of up to 10 times higher compared with the wild type M. smegmatis strain. The high levels of the F420 produced in this study provide a suitable source of this cofactor for studies of F420-dependent proteins from other microorganisms and for possible biotechnological applications

Sequence Imputation of HPV16 Genomes for Genetic Association Studies

Human Papillomavirus type 16 (HPV16) causes over half of all cervical cancer and some HPV16 variants are more oncogenic than others. The genetic basis for the extraordinary oncogenic properties of HPV16 compared to other HPVs is unknown. In addition, we neither know which nucleotides vary across and within HPV types and lineages, nor which of the single nucleotide polymorphisms (SNPs) determine oncogenicity.A reference set of 62 HPV16 complete genome sequences was established and used to examine patterns of evolutionary relatedness amongst variants using a pairwise identity heatmap and HPV16 phylogeny. A BLAST-based algorithm was developed to impute complete genome data from partial sequence information using the reference database. To interrogate the oncogenic risk of determined and imputed HPV16 SNPs, odds-ratios for each SNP were calculated in a case-control viral genome-wide association study (VWAS) using biopsy confirmed high-grade cervix neoplasia and self-limited HPV16 infections from Guanacaste, Costa Rica.HPV16 variants display evolutionarily stable lineages that contain conserved diagnostic SNPs. The imputation algorithm indicated that an average of 97.5±1.03% of SNPs could be accurately imputed. The VWAS revealed specific HPV16 viral SNPs associated with variant lineages and elevated odds ratios; however, individual causal SNPs could not be distinguished with certainty due to the nature of HPV evolution.Conserved and lineage-specific SNPs can be imputed with a high degree of accuracy from limited viral polymorphic data due to the lack of recombination and the stochastic mechanism of variation accumulation in the HPV genome. However, to determine the role of novel variants or non-lineage-specific SNPs by VWAS will require direct sequence analysis. The investigation of patterns of genetic variation and the identification of diagnostic SNPs for lineages of HPV16 variants provides a valuable resource for future studies of HPV16 pathogenicity

Quantitative proteomic analysis of the influence of lignin on biofuel production by Clostridium acetobutylicum ATCC 824

Background: Clostridium acetobutylicum has been a focus of research because of its ability to produce high-value compounds that can be used as biofuels. Lignocellulose is a promising feedstock, but the lignin–cellulose–hemicellulose biomass complex requires chemical pre-treatment to yield fermentable saccharides, including cellulose-derived cellobiose, prior to bioproduction of acetone–butanol–ethanol (ABE) and hydrogen. Fermentation capability is limited by lignin and thus process optimization requires knowledge of lignin inhibition. The effects of lignin on cellular metabolism were evaluated for C. acetobutylicum grown on medium containing either cellobiose only or cellobiose plus lignin. Microscopy, gas chromatography and 8-plex iTRAQ-based quantitative proteomic technologies were applied to interrogate the effect of lignin on cellular morphology, fermentation and the proteome. Results: Our results demonstrate that C. acetobutylicum has reduced performance for solvent production when lignin is present in the medium. Medium supplemented with 1 g L−1 of lignin led to delay and decreased solvents production (ethanol; 0.47 g L−1 for cellobiose and 0.27 g L−1 for cellobiose plus lignin and butanol; 0.13 g L−1 for cellobiose and 0.04 g L−1 for cellobiose plus lignin) at 20 and 48 h, respectively, resulting in the accumulation of acetic acid and butyric acid. Of 583 identified proteins (FDR < 1 %), 328 proteins were quantified with at least two unique peptides. Up- or down-regulation of protein expression was determined by comparison of exponential and stationary phases of cellobiose in the presence and absence of lignin. Of relevance, glycolysis and fermentative pathways were mostly down-regulated, during exponential and stationary growth phases in presence of lignin. Moreover, proteins involved in DNA repair, transcription/translation and GTP/ATP-dependent activities were also significantly affected and these changes were associated with altered cell morphology. Conclusions: This is the first comprehensive analysis of the cellular responses of C. acetobutylicum to lignin at metabolic and physiological levels. These data will enable targeted metabolic engineering strategies to optimize biofuel production from biomass by overcoming limitations imposed by the presence of lignin

Genome Sequence of a Mesophilic Hydrogenotrophic Methanogen Methanocella paludicola, the First Cultivated Representative of the Order Methanocellales

We report complete genome sequence of a mesophilic hydrogenotrophic methanogen Methanocella paludicola, the first cultured representative of the order Methanocellales once recognized as an uncultured key archaeal group for methane emission in rice fields. The genome sequence of M. paludicola consists of a single circular chromosome of 2,957,635 bp containing 3004 protein-coding sequences (CDS). Genes for most of the functions known in the methanogenic archaea were identified, e.g. a full complement of hydrogenases and methanogenesis enzymes. The mixotrophic growth of M. paludicola was clarified by the genomic characterization and re-examined by the subsequent growth experiments. Comparative genome analysis with the previously reported genome sequence of RC-IMRE50, which was metagenomically reconstructed, demonstrated that about 70% of M. paludicola CDSs were genetically related with RC-IMRE50 CDSs. These CDSs included the genes involved in hydrogenotrophic methane production, incomplete TCA cycle, assimilatory sulfate reduction and so on. However, the genetic components for the carbon and nitrogen fixation and antioxidant system were different between the two Methanocellales genomes. The difference is likely associated with the physiological variability between M. paludicola and RC-IMRE50, further suggesting the genomic and physiological diversity of the Methanocellales methanogens. Comparative genome analysis among the previously determined methanogen genomes points to the genome-wide relatedness of the Methanocellales methanogens to the orders Methanosarcinales and Methanomicrobiales methanogens in terms of the genetic repertoire. Meanwhile, the unique evolutionary history of the Methanocellales methanogens is also traced in an aspect by the comparative genome analysis among the methanogens

A second isoform of 3-ketoacyl-CoA thiolase found in Caenorhabditis elegans, which is similar to sterol carrier protein x but lacks the sequence of sterol carrier protein 2

We cloned a full-length cDNA of the nematode Caenorhabditis elegans that encodes a 44-kDa protein (P-44, 412 residues) similar to sterol carrier protein x (SCPx). Mammalian SCPx is a bipartite protein: its 404-residue N-terminal and 143-residue C-terminal domains are similar to 3-ketoacyl-CoA thiolase and identical to the precursor of sterol carrier protein 2 (SCP2; also termed non-specific lipid-transfer protein), respectively. P-44 has 56(null)equence identity to the thiolase domain of SCx but lacks the SCP2 sequence. Northern blot analysis revealed only a single mRNA species of 1.4 kb, which agrees well with the length of the cDNA (1371 bp), making it improbable that alternative splicing produces an SCPx-like fusion protein. The sequence similarities of P-44 to conventional thiolases are lesser than that to SCPx. Purified recombinant P-44 cleaved long-chain 3-ketoacyl-CoAs (C8-16) in a thiolytic manner by the ping-pong bi-bi reaction mechanism. The inhibition of P-44 by acetyl-CoA was competitive with CoA and non-competitive with 3-ketooctanoyl-CoA. This pattern of inhibition is shared with SCPx but not with conventional 3-ketoacyl-CoA thiolase, which is inhibited uncompetitively with respect to 3-ketoacyl CoA. From these results, we concluded that nematode P-44 and mammalian SCPx constitute a second isoform of thiolase, which we propose to term type-II 3-ketoacyl-CoA thiolase

Current understanding of the human microbiome

Author Posting. © The Author(s), 2018. This is the author's version of the work. It is posted here by permission of Nature Publishing Group for personal use, not for redistribution. The definitive version was published in Nature Medicine 24 (2018): 392–400, doi:10.1038/nm.4517.Our understanding of the link between the human microbiome and disease, including obesity, inflammatory bowel disease, arthritis and autism, is rapidly expanding. Improvements in the throughput and accuracy of DNA sequencing of the genomes of microbial communities associated with human samples, complemented by analysis of transcriptomes, proteomes, metabolomes and immunomes, and mechanistic experiments in model systems, have vastly improved our ability to understand the structure and function of the microbiome in both diseased and healthy states. However, many challenges remain. In this Review, we focus on studies in humans to describe these challenges, and propose strategies that leverage existing knowledge to move rapidly from correlation to causation, and ultimately to translation.Many of the studies described here in our laboratories were supported by the NIH, NSF, DOE, and the Alfred P. Sloan Foundation.2018-10-1