Ashfaq Ali

Gene × Physical Activity Interactions in Obesity: Combined Analysis of 111,421 Individuals of European Ancestry

Numerous obesity loci have been identified using genome-wide association studies. A UK study indicated that physical activity may attenuate the cumulative effect of 12 of these loci, but replication studies are lacking. Therefore, we tested whether the aggregate effect of these loci is diminished in adults of European ancestry reporting high levels of physical activity. Twelve obesity-susceptibility loci were genotyped or imputed in 111,421 participants. A genetic risk score (GRS) was calculated by summing the BMI-associated alleles of each genetic variant. Physical activity was assessed using self-administered questionnaires. Multiplicative interactions between the GRS and physical activity on BMI were tested in linear and logistic regression models in each cohort, with adjustment for age, age2, sex, study center (for multicenter studies), and the marginal terms for physical activity and the GRS. These results were combined using meta-analysis weighted by cohort sample size. The meta-analysis yielded a statistically significant GRS × physical activity interaction effect estimate (Pinteraction = 0.015). However, a statistically significant interaction effect was only apparent in North American cohorts (n = 39,810, Pinteraction = 0.014 vs. n = 71,611, Pinteraction = 0.275 for Europeans). In secondary analyses, both the FTO rs1121980 (Pinteraction = 0.003) and the SEC16B rs10913469 (Pinteraction = 0.025) variants showed evidence of SNP × physical activity interactions. This meta-analysis of 111,421 individuals provides further support for an interaction between physical activity and a GRS in obesity disposition, although these findings hinge on the inclusion of cohorts from North America, indicating that these results are either population-specific or non-causal.

An Adaptive Alignment Algorithm for Quality-controlled Label-free LC-MS

Label-free quantification using precursor-based intensities is a versatile workflow for large-scale proteomics studies. The method however requires extensive computational analysis and is therefore in need of robust quality control during the data mining stage. We present a new label-free data analysis workflow integrated into a multiuser software platform. A novel adaptive alignment algorithm has been developed to minimize the possible systematic bias introduced into the analysis. Parameters are estimated on the fly from the data at hand, producing a user-friendly analysis suite. Quality metrics are output in every step of the analysis as well as actively incorporated into the parameter estimation. We furthermore show the improvement of this system by comprehensive comparison to classical label-free analysis methodology as well as current state-of-the-art software.

Paranoid potato: phytophthora-resistant genotype shows constitutively activated defense.

Phytophthora is the most devastating pathogen of dicot plants. There is a need for resistance sources with different modes of action to counteract the fast evolution of this pathogen. In order to better understand mechanisms of defense against P. infestans, we analyzed several clones of potato. Two of the genotypes tested, Sarpo Mira and SW93-1015, exhibited strong resistance against P. infestans in field trials, whole plant assays and detached leaf assays. The resistant genotypes developed different sizes of hypersensitive response (HR)-related lesions. HR lesions in SW93-1015 were restricted to very small areas, whereas those in Sarpo Mira were similar to those in Solanum demissum, the main source of classical resistance genes. SW93-1015 can be characterized as a cpr (constitutive expressor of PR genes) genotype without spontaneous microscopic or macroscopic HR lesions. This is indicated by constitutive hydrogen peroxide (H2O2) production and PR1 (pathogenesis-related protein 1) secretion. SW93-1015 is one of the first plants identified as having classical protein-based induced defense expressed constitutively without any obvious metabolic costs or spontaneous cell death lesions.

Quantitative Label-Free Phosphoproteomics of Six Different Life Stages of the Late Blight Pathogen Phytophthora infestans Reveals Abundant Phosphorylation of Members of the CRN Effector Family

The oomycete Phytophthora infestans is the causal agent of late blight in potato and tomato. Since the underlying processes that govern pathogenicity and development in P. infestans are largely unknown, we have performed a large-scale phosphoproteomics study of six different P. infestans life stages. We have obtained quantitative data for 2922 phosphopeptides and compared their abundance. Life-stage-specific phosphopeptides include ATP-binding cassette transporters and a kinase that only occurs in appressoria. In an extended data set, we identified 2179 phosphorylation sites and deduced 22 phosphomotifs. Several of the phosphomotifs matched consensus sequences of kinases that occur in P. infestans but not Arabidopsis. In addition, we detected tyrosine phosphopeptides that are potential targets of kinases resembling mammalian tyrosine kinases. Among the phosphorylated proteins are members of the RXLR and Crinkler effector families. The latter are phosphorylated in several life stages and at multiple positions, in sites that are conserved between different members of the Crinkler family. This indicates that proteins in the Crinkler family have functions beyond their putative role as (necrosis-inducing) effectors. This phosphoproteomics data will be instrumental for studies on oomycetes and host-oomycete interactions. The data sets have been deposited to ProteomeXchange (identifier PXD000433).

Two thymidine kinases and one multisubstrate deoxyribonucleoside kinase salvage DNA precursors in Arabidopsis thaliana

Deoxyribonucleotides are the building blocks of DNA and can be synthesized via de novo and salvage pathways. Deoxyribonucleoside kinases (EC 2.7.1.145) salvage deoxyribonucleosides by transfer of a phosphate group to the 5′ of a deoxyribonucleoside. This salvage pathway is well characterized in mammals, but in contrast, little is known about how plants salvage deoxyribonucleosides. We show that during salvage, deoxyribonucleosides can be phosphorylated by extracts of Arabidopsis thaliana into corresponding monophosphate compounds with an unexpected preference for purines over pyrimidines. Deoxyribonucleoside kinase activities were present in all tissues during all growth stages. In the A. thaliana genome, we identified two types of genes that could encode enzymes which are involved in the salvage of deoxyribonucleosides. Thymidine kinase activity was encoded by two thymidine kinase 1 (EC 2.7.1.21)‐like genes (AtTK1a and AtTK1b). Deoxyadenosine, deoxyguanosine and deoxycytidine kinase activities were encoded by a single AtdNK gene. T‐DNA insertion lines of AtTK1a and AtTK1b mutant genes had normal growth, although AtTK1a AtTK1b double mutants died at an early stage, which indicates that AtTK1a and AtTK1b catalyze redundant reactions. The results obtained in the present study suggest a crucial role for the salvage of thymidine during early plant development.

Statistical power considerations in genotype-based recall randomized controlled trials

Randomized controlled trials (RCT) are often underpowered for validating gene-treatment interactions. Using published data from the Diabetes Prevention Program (DPP), we examined power in conventional and genotype-based recall (GBR) trials. We calculated sample size and statistical power for gene-metformin interactions (vs. placebo) using incidence rates, gene-drug interaction effect estimates and allele frequencies reported in the DPP for the rs8065082 SLC47A1 variant, a metformin transported encoding locus. We then calculated statistical power for interactions between genetic risk scores (GRS), metformin treatment and intensive lifestyle intervention (ILI) given a range of sampling frames, clinical trial sample sizes, interaction effect estimates, and allele frequencies; outcomes were type 2 diabetes incidence (time-to-event) and change in small LDL particles (continuous outcome). Thereafter, we compared two recruitment frameworks: GBR (participants recruited from the extremes of a GRS distribution) and conventional sampling (participants recruited without explicit emphasis on genetic characteristics). We further examined the influence of outcome measurement error on statistical power. Under most simulated scenarios, GBR trials have substantially higher power to observe gene-drug and gene-lifestyle interactions than same-sized conventional RCTs. GBR trials are becoming popular for validation of gene-treatment interactions; our analyses illustrate the strengths and weaknesses of this design.

Saturated Fat Is More Metabolically Harmful for the Human Liver Than Unsaturated Fat or Simple Sugars

OBJECTIVE Nonalcoholic fatty liver disease (i.e., increased intrahepatic triglyceride [IHTG] content), predisposes to type 2 diabetes and cardiovascular disease. Adipose tissue lipolysis and hepatic de novo lipogenesis (DNL) are the main pathways contributing to IHTG. We hypothesized that dietary macronutrient composition influences the pathways, mediators, and magnitude of weight gain-induced changes in IHTG. RESEARCH DESIGN AND METHODS We overfed 38 overweight subjects (age 48 ± 2 years, BMI 31 ± 1 kg/m2, liver fat 4.7 ± 0.9%) 1,000 extra kcal/day of saturated (SAT) or unsaturated (UNSAT) fat or simple sugars (CARB) for 3 weeks. We measured IHTG (1H-MRS), pathways contributing to IHTG (lipolysis ([2H5]glycerol) and DNL (2H2O) basally and during euglycemic hyperinsulinemia), insulin resistance, endotoxemia, plasma ceramides, and adipose tissue gene expression at 0 and 3 weeks. RESULTS Overfeeding SAT increased IHTG more (+55%) than UNSAT (+15%, P < 0.05). CARB increased IHTG (+33%) by stimulating DNL (+98%). SAT significantly increased while UNSAT decreased lipolysis. SAT induced insulin resistance and endotoxemia and significantly increased multiple plasma ceramides. The diets had distinct effects on adipose tissue gene expression. CONCLUSIONS Macronutrient composition of excess energy influences pathways of IHTG: CARB increases DNL, while SAT increases and UNSAT decreases lipolysis. SAT induced the greatest increase in IHTG, insulin resistance, and harmful ceramides. Decreased intakes of SAT could be beneficial in reducing IHTG and the associated risk of diabetes.

Novel genetic loci associated with long-term deterioration in blood lipid concentrations and coronary artery disease in European adults

Background Cross-sectional genome-wide association studies have identified hundreds of loci associated with blood lipids and related cardiovascular traits, but few genetic association studies have focused on long-term changes in blood lipids. Methods Participants from the GLACIER Study (Nmax = 3492) were genotyped with the MetaboChip array, from which 29 387 SNPs (single nucleotide polymorphisms; replication, fine-mapping regions and wildcard SNPs for lipid traits) were extracted for association tests with 10-year change in total cholesterol (ΔTC) and triglycerides (ΔTG). Four additional prospective cohort studies (MDC, PIVUS, ULSAM, MRC Ely; Nmax = 8263 participants) were used for replication. We conducted an in silico look-up for association with coronary artery disease (CAD) in the Coronary ARtery DIsease Genome-wide Replication and Meta-analysis (CARDIoGRAMplusC4D) Consortium (N ∼ 190 000) and functional annotation for the top ranking variants. Results In total, 956 variants were associated (P < 0.01) with either ΔTC or ΔTG in GLACIER. In GLACIER, chr19:50121999 at APOE was associated with ΔTG and multiple SNPs in the APOA1/A4/C3/A5 region at genome-wide significance (P < 5 × 10-8), whereas variants in four loci, DOCK7, BRE, SYNE1 and KCNIP1, reached study-wide significance (P < 1.7 × 10-6). The rs7412 variant at APOE was associated with ΔTC in GLACIER (P < 1.7 × 10-6). In pooled analyses of all cohorts, 139 SNPs at six and five loci were associated with ΔTC and for ΔTG, respectively (P < 10-3). Of these, a variant at CAPN3 (P = 1.2 × 10-4), multiple variants at HPR (Pmin = 1.5 × 10-6) and a variant at SIX5 (P = 1.9 × 10-4) showed evidence for association with CAD. Conclusions We identified seven novel genomic regions associated with long-term changes in blood lipids, of which three also raise CAD risk.

Do Genetic Factors Modify the Relationship between Obesity and Hypertriglyceridemia? Findings from the GLACIER and the MDC Studies

Background—Obesity is a major risk factor for dyslipidemia, but this relationship is highly variable. Recently published data from 2 Danish cohorts suggest that genetic factors may underlie some of this variability. Methods and Results—We tested whether established triglyceride-associated loci modify the relationship of body mass index (BMI) and triglyceride concentrations in 2 Swedish cohorts (the Gene–Lifestyle Interactions and Complex Traits Involved in Elevated Disease Risk [GLACIER Study; N=4312] and the Malmö Diet and Cancer Study [N=5352]). The genetic loci were amalgamated into a weighted genetic risk score (WGRSTG) by summing the triglyceride-elevating alleles (weighted by their established marginal effects) for all loci. Both BMI and the WGRSTG were strongly associated with triglyceride concentrations in GLACIER, with each additional BMI unit (kg/m2) associated with 2.8% (P=8.4×10–84) higher triglyceride concentration and each additional WGRSTG unit with 2% (P=7.6×10–48) higher triglyceride concentration. Each unit of the WGRSTG was associated with 1.5% higher triglyceride concentrations in normal weight and 2.4% higher concentrations in overweight/obese participants (Pinteraction=0.056). Meta-analyses of results from the Swedish cohorts yielded a statistically significant WGRSTG×BMI interaction effect (Pinteraction=6.0×10–4), which was strengthened by including data from the Danish cohorts (Pinteraction=6.5×10–7). In the meta-analysis of the Swedish cohorts, nominal evidence of a 3-way interaction (WGRSTG×BMI×sex) was observed (Pinteraction=0.03), where the WGRSTG×BMI interaction was only statistically significant in females. Using protein–protein interaction network analyses, we identified molecular interactions and pathways elucidating the metabolic relationships between BMI and triglyceride-associated loci. Conclusions—Our findings provide evidence that body fatness accentuates the effects of genetic susceptibility variants in hypertriglyceridemia, effects that are most evident in females.

Do genetic factors modify the relationship between obesity and hypertriglyceridemia

Background—Obesity is a major risk factor for dyslipidemia, but this relationship is highly variable. Recently published data from 2 Danish cohorts suggest that genetic factors may underlie some of this variability. Methods and Results—We tested whether established triglyceride-associated loci modify the relationship of body mass index (BMI) and triglyceride concentrations in 2 Swedish cohorts (the Gene–Lifestyle Interactions and Complex Traits Involved in Elevated Disease Risk [GLACIER Study; N=4312] and the Malmö Diet and Cancer Study [N=5352]). The genetic loci were amalgamated into a weighted genetic risk score (WGRSTG) by summing the triglyceride-elevating alleles (weighted by their established marginal effects) for all loci. Both BMI and the WGRSTG were strongly associated with triglyceride concentrations in GLACIER, with each additional BMI unit (kg/m2) associated with 2.8% (P=8.4×10–84) higher triglyceride concentration and each additional WGRSTG unit with 2% (P=7.6×10–48) higher triglyceride concentration. Each unit of the WGRSTG was associated with 1.5% higher triglyceride concentrations in normal weight and 2.4% higher concentrations in overweight/obese participants (Pinteraction=0.056). Meta-analyses of results from the Swedish cohorts yielded a statistically significant WGRSTG×BMI interaction effect (Pinteraction=6.0×10–4), which was strengthened by including data from the Danish cohorts (Pinteraction=6.5×10–7). In the meta-analysis of the Swedish cohorts, nominal evidence of a 3-way interaction (WGRSTG×BMI×sex) was observed (Pinteraction=0.03), where the WGRSTG×BMI interaction was only statistically significant in females. Using protein–protein interaction network analyses, we identified molecular interactions and pathways elucidating the metabolic relationships between BMI and triglyceride-associated loci. Conclusions—Our findings provide evidence that body fatness accentuates the effects of genetic susceptibility variants in hypertriglyceridemia, effects that are most evident in females.