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BACKGROUND: Shotgun metagenomic sequencing has improved our understanding of the human gut microbiota. Various DNA extraction methods have been compared to find protocols that robustly and most accurately reflect the original microbial community structures. However, these recommendations can be further refined by considering the time and cost demands in dealing with samples from very large human cohorts. Additionally, fungal DNA extraction performance has so far been little investigated. RESULTS: We compared 6 DNA extraction protocols, MagPure Fast Stool DNA KF Kit B, Macherey Nagel™ NucleoSpin™®Soil kit, Zymo Research Quick-DNA™ Fecal/Soil Microbe kit, MOBIO DNeasy PowerSoil kit, the manual non-commercial protocol MetaHIT, and the recently published protocol Q using 1 microbial mock community (MMC) (containing 8 bacterial and 2 fungal strains) and fecal samples. All samples were manually extracted and subjected to shotgun metagenomics sequencing. Extracting DNA revealed high reproducibility within all 6 protocols, but microbial extraction efficiencies varied. The MMC results demonstrated that bead size was a determining factor for fungal and bacterial DNA yields. In human fecal samples, the MagPure bacterial extraction performed as well as the standardized protocol Q but was faster and more cost-effective. Extraction using the PowerSoil protocol resulted in a significantly higher ratio of gram-negative to gram-positive bacteria than other protocols, which might contribute to reported gut microbial differences between healthy adults. CONCLUSIONS: We emphasize the importance of bead size selection for bacterial and fungal DNA extraction. More importantly, the performance of the novel protocol MP matched that of the recommended standardized protocol Q but consumed less time, was more cost-effective, and is recommended for further large-scale human gut metagenomic studies.

Background PPP1R3B has been suggested as a candidate gene for monogenic forms of diabetes as well as type 2 diabetes (T2D) due to its association with glycaemic trait and its biological role in glycogen synthesis. Objectives To study if rare missense variants in PPP1R3B increase the risk of maturity onset diabetes of the young (MODY), T2D or affect measures of glucose metabolism. Method Targeted resequencing of PPP1R3B was performed in 8,710 samples; MODY patients with unknown etiology (n = 54), newly diagnosed patients with T2D (n = 2,930) and population-based control individuals (n = 5,726, of whom n = 4,569 had normal glucose tolerance). All population-based sampled individuals were examined using an oral glucose tolerance test. Results Among n = 396 carriers, we identified twenty-three PPP1R3B missense mutations, none of which segregated with MODY. The burden of likely deleterious PPP1R3B variants was significantly increased with a total of 17 carriers among patients with T2D (0.58% (95% CI: 0.36–0.93)) compared to 18 carriers among non-diabetic individuals (0.31% (95% CI: 0.20–0.49)), resulting in an increased risk of T2D (OR (95% CI) = 2.57 (1.14–5.79), p = 0.02 (age and sex adjusted)). Furthermore, carriers with diabetes had less abdominal fat and a higher serum concentration of LDL-cholesterol compared to patients with T2D without rare missense PPP1R3B variants. In addition, non-diabetic carriers had a higher birth weight compared to non-carriers. Conclusion Rare missense PPP1R3B variants may predispose to T2D.

Type 2 diabetes (T2D) is a common form of diabetes and it is one of the main threats to human health in an industrial society [1]. The prevalence and incidence of T2D have been increasing rapidly worldwide. The etiology and pathogenesis of T2D include both genetic and environmental factors. In an attempt to investigate the underlying genetic pathogenesis of T2D, a total of 243 genetic loci, most of which are common variants, have been reported in the past decade [2]. However, as with most polygenic diseases, the discovered genes only account for a minority of the inherited risk (heritability) of T2D (< 20%). The gap of the heritability explained by common genetic variants and the heritability identified in familial studies have been termed as ‘missing heritability’ [3]. We are still in the process of fully dissecting the complexity of T2D. Until recently, the majority of genetic studies has been performed using array-based genotyping and genome wide association studies (GWAS), focusing on the effect of common variants. However, with the development of next generation sequencing (NGS), scientists can directly test associations between rare variants and complex diseases. The NGS heralds a new era of investigating the impact of genetic variation on T2D. This thesis provides an overview of the development of genetic aspects of diabetes, with emphasis on T2D. The etiology and pathogenesis of T2D are introduced. The current status of identification and association of genetic variants is discussed and related to the results obtained in my PhD project. Also, the methodological improvements in investigations of genetic variants made in the last decade are discussed, especially focusing on the development of NGS. The overall aim of the thesis is to explore the underlying genetic mechanisms of T2D and related metabolic traits, with focus on risk contributions of rare variants in two well-established T2D candidate genes - GLIS3 and PPP1R3B. Common variants in both GLIS3 and

Background Based on the association of common GLIS3 variants with various forms of diabetes and the biological role of GLIS3 in beta-cells, we sequenced GLIS3 in non-diabetic and diabetic Danes to investigate the effect of rare missense variants on glucose metabolism. Methods We sequenced 53 patients with maturity-onset diabetes of the young (MODY), 5,726 non-diabetic participants, 2,930 patients with newly diagnosed type 2 diabetes and 206 patients with glutamic acid decarboxylase antibody (GADA) -positive diabetes. Results In total we identified 86 rare (minor allele frequency < 0.1%) missense variants. None was considered causal for the presence of MODY. Among patients with type 2 diabetes, we observed a higher prevalence of rare GLIS3 missense variants (2.5%) compared to non-diabetic individuals (1.8%) (odds ratio of 1.37 (interquartile range:1.01–1.88, p = 0.04)). A significantly increased HbA1c was found among patients with type 2 diabetes and with GADA-positive diabetes carrying rare GLIS3 variants compared to non-carriers of rare GLIS3 variants with diabetes (p = 0.02 and p = 0.004, respectively). One variant (p.I28V) was found to have a minor allele frequency of only 0.03% among patients with type 2 diabetes compared to 0.2% among non-diabetic individuals suggesting a protective function (odds ratio of 0.20 (interquartile range: 0.005–1.4, p = 0.1)), an effect which was supported by publically available data. This variant was also associated with a lower level of fasting plasma glucose among non-diabetic individuals (p = 0.046). Conclusion Rare missense variants in GLIS3 associates nominally with increased level of HbA1c and increased risk of developing type 2 diabetes. In contrast, the rare p.I28V variant associate with reduced level of fasting plasma glucose and may be protective against type 2 diabetes.

Based on eddy covariance flux data from July 15, 2014, to December 31, 2015, the water and CO2 fluxes were compared over a semiarid alpine steppe (Bange, Tibetan Plateau) and a humid alpine meadow (Lijiang, Yunnan) on the Tibetan Plateau and its surrounding region. During the wet season, the evaporative fraction (EF) was strongly and linearly correlated with the soil water content (SWC) at Bange because of its sparse green grass cover. In contrast, the correlation between the EF at Lijiang and the SWC and the normalized difference vegetation index (NDVI) was very low because the atmosphere was close to saturation and the EF was relatively constant. In the dry season, the EF at both sites decreased with the SWC. The net ecosystem exchange (NEE) at Bange was largely depressed at noon, while this phenomenon did not occur at Lijiang. The saturated NEE at Bange was 24% of that at Lijiang. The temperature sensitivity coefficient of ecosystem respiration at Bange (1.7) was also much lower than that at Lijiang (3.4). The annual total NEE in 2015 was 21.8 and -230.0 g C m(-2) yr(-1) at Bange and Lijiang, respectively, and the NEE was tightly controlled by the NDVI at the two sites. The distinct differences in the water and CO2 fluxes at Bange and Lijiang are attributed to the large SWC difference and its effect on vegetation growth.