Silke Szymczak

Genetics and Beyond – The Transcriptome of Human Monocytes and Disease Susceptibility

Background Variability of gene expression in human may link gene sequence variability and phenotypes; however, non-genetic variations, alone or in combination with genetics, may also influence expression traits and have a critical role in physiological and disease processes. Methodology/Principal Findings To get better insight into the overall variability of gene expression, we assessed the transcriptome of circulating monocytes, a key cell involved in immunity-related diseases and atherosclerosis, in 1,490 unrelated individuals and investigated its association with >675,000 SNPs and 10 common cardiovascular risk factors. Out of 12,808 expressed genes, 2,745 expression quantitative trait loci were detected (P<5.78×10−12), most of them (90%) being cis-modulated. Extensive analyses showed that associations identified by genome-wide association studies of lipids, body mass index or blood pressure were rarely compatible with a mediation by monocyte expression level at the locus. At a study-wide level (P<3.9×10−7), 1,662 expression traits (13.0%) were significantly associated with at least one risk factor. Genome-wide interaction analyses suggested that genetic variability and risk factors mostly acted additively on gene expression. Because of the structure of correlation among expression traits, the variability of risk factors could be characterized by a limited set of independent gene expressions which may have biological and clinical relevance. For example expression traits associated with cigarette smoking were more strongly associated with carotid atherosclerosis than smoking itself. Conclusions/Significance This study demonstrates that the monocyte transcriptome is a potent integrator of genetic and non-genetic influences of relevance for disease pathophysiology and risk assessment.

A trans-acting locus regulates an anti-viral expression network and type 1 diabetes risk

Combined analyses of gene networks and DNA sequence variation can provide new insights into the aetiology of common diseases that may not be apparent from genome-wide association studies alone. Recent advances in rat genomics are facilitating systems-genetics approaches. Here we report the use of integrated genome-wide approaches across seven rat tissues to identify gene networks and the loci underlying their regulation. We defined an interferon regulatory factor 7 (IRF7)-driven inflammatory network (IDIN) enriched for viral response genes, which represents a molecular biomarker for macrophages and which was regulated in multiple tissues by a locus on rat chromosome 15q25. We show that Epstein–Barr virus induced gene 2 (Ebi2, also known as Gpr183), which lies at this locus and controls B lymphocyte migration, is expressed in macrophages and regulates the IDIN. The human orthologous locus on chromosome 13q32 controlled the human equivalent of the IDIN, which was conserved in monocytes. IDIN genes were more likely to associate with susceptibility to type 1 diabetes (T1D)—a macrophage-associated autoimmune disease—than randomly selected immune response genes (P = 8.85 × 10−6). The human locus controlling the IDIN was associated with the risk of T1D at single nucleotide polymorphism rs9585056 (P = 7.0 × 10−10; odds ratio, 1.15), which was one of five single nucleotide polymorphisms in this region associated with EBI2 (GPR183) expression. These data implicate IRF7 network genes and their regulatory locus in the pathogenesis of T1D.

A Genome-wide Association Study Identifies LIPA as a Susceptibility Gene for Coronary Artery Disease

Background— eQTL analyses are important to improve the understanding of genetic association results. We performed a genome-wide association and global gene expression study to identify functionally relevant variants affecting the risk of coronary artery disease (CAD). Methods and Results— In a genome-wide association analysis of 2078 CAD cases and 2953 control subjects, we identified 950 single-nucleotide polymorphisms (SNPs) that were associated with CAD at P<10−3. Subsequent in silico and wet-laboratory replication stages and a final meta-analysis of 21 428 CAD cases and 38 361 control subjects revealed a novel association signal at chromosome 10q23.31 within the LIPA (lysosomal acid lipase A) gene (P=3.7×10−8; odds ratio, 1.1; 95% confidence interval, 1.07 to 1.14). The association of this locus with global gene expression was assessed by genome-wide expression analyses in the monocyte transcriptome of 1494 individuals. The results showed a strong association of this locus with expression of the LIPA transcript (P=1.3×10−96). An assessment of LIPA SNPs and transcript with cardiovascular phenotypes revealed an association of LIPA transcript levels with impaired endothelial function (P=4.4×10−3). Conclusions— The use of data on genetic variants and the addition of data on global monocytic gene expression led to the identification of the novel functional CAD susceptibility locus LIPA, located on chromosome 10q23.31. The respective eSNPs associated with CAD strongly affect LIPA gene expression level, which was related to endothelial dysfunction, a precursor of CAD.

Genome-wide association analysis identifies variation in vitamin D receptor and other host factors influencing the gut microbiota

Human gut microbiota is an important determinant for health and disease, and recent studies emphasize the numerous factors shaping its diversity. Here we performed a genome-wide association study (GWAS) of the gut microbiota using two cohorts from northern Germany totaling 1,812 individuals. Comprehensively controlling for diet and non-genetic parameters, we identify genome-wide significant associations for overall microbial variation and individual taxa at multiple genetic loci, including the VDR gene (encoding vitamin D receptor). We observe significant shifts in the microbiota of Vdr−/− mice relative to control mice and correlations between the microbiota and serum measurements of selected bile and fatty acids in humans, including known ligands and downstream metabolites of VDR. Genome-wide significant (P < 5 × 10−8) associations at multiple additional loci identify other important points of host–microbe intersection, notably several disease susceptibility genes and sterol metabolism pathway components. Non-genetic and genetic factors each account for approximately 10% of the variation in gut microbiota, whereby individual effects are relatively small.

Integrating genome-wide genetic variations and monocyte expression data reveals trans-regulated gene modules in humans.

One major expectation from the transcriptome in humans is to characterize the biological basis of associations identified by genome-wide association studies. So far, few cis expression quantitative trait loci (eQTLs) have been reliably related to disease susceptibility. Trans-regulating mechanisms may play a more prominent role in disease susceptibility. We analyzed 12,808 genes detected in at least 5% of circulating monocyte samples from a population-based sample of 1,490 European unrelated subjects. We applied a method of extraction of expression patterns—independent component analysis—to identify sets of co-regulated genes. These patterns were then related to 675,350 SNPs to identify major trans-acting regulators. We detected three genomic regions significantly associated with co-regulated gene modules. Association of these loci with multiple expression traits was replicated in Cardiogenics, an independent study in which expression profiles of monocytes were available in 758 subjects. The locus 12q13 (lead SNP rs11171739), previously identified as a type 1 diabetes locus, was associated with a pattern including two cis eQTLs, RPS26 and SUOX, and 5 trans eQTLs, one of which (MADCAM1) is a potential candidate for mediating T1D susceptibility. The locus 12q24 (lead SNP rs653178), which has demonstrated extensive disease pleiotropy, including type 1 diabetes, hypertension, and celiac disease, was associated to a pattern strongly correlating to blood pressure level. The strongest trans eQTL in this pattern was CRIP1, a known marker of cellular proliferation in cancer. The locus 12q15 (lead SNP rs11177644) was associated with a pattern driven by two cis eQTLs, LYZ and YEATS4, and including 34 trans eQTLs, several of them tumor-related genes. This study shows that a method exploiting the structure of co-expressions among genes can help identify genomic regions involved in trans regulation of sets of genes and can provide clues for understanding the mechanisms linking genome-wide association loci to disease.

Construction of a Large Signature-Tagged Mini-Tn5 Transposon Library and Its Application to Mutagenesis of Sinorhizobium meliloti†

ABSTRACT Sinorhizobium meliloti genome sequence determination has provided the basis for different approaches of functional genomics for this symbiotic nitrogen-fixing alpha-proteobacterium. One of these approaches is gene disruption with subsequent analysis of mutant phenotypes. This method is efficient for single genes; however, it is laborious and time-consuming if it is used on a large scale. Here, we used a signature-tagged transposon mutagenesis method that allowed analysis of the survival and competitiveness of many mutants in a single experiment. A novel set of signature tags characterized by similar melting temperatures and G+C contents of the tag sequences was developed. The efficiencies of amplification of all tags were expected to be similar. Thus, no preselection of the tags was necessary to create a library of 412 signature-tagged transposons. To achieve high specificity of tag detection, each transposon was bar coded by two signature tags. In order to generate defined, nonredundant sets of signature-tagged S. meliloti mutants for subsequent experiments, 12,000 mutants were constructed, and insertion sites for more than 5,000 mutants were determined. One set consisting of 378 mutants was used in a validation experiment to identify mutants showing altered growth patterns.

Machine learning in genome-wide association studies.

Recently, genome‐wide association studies have substantially expanded our knowledge about genetic variants that influence the susceptibility to complex diseases. Although standard statistical tests for each single‐nucleotide polymorphism (SNP) separately are able to capture main genetic effects, different approaches are necessary to identify SNPs that influence disease risk jointly or in complex interactions. Experimental and simulated genome‐wide SNP data provided by the Genetic Analysis Workshop 16 afforded an opportunity to analyze the applicability and benefit of several machine learning methods. Penalized regression, ensemble methods, and network analyses resulted in several new findings while known and simulated genetic risk variants were also identified. In conclusion, machine learning approaches are promising complements to standard single‐and multi‐SNP analysis methods for understanding the overall genetic architecture of complex human diseases. However, because they are not optimized for genome‐wide SNP data, improved implementations and new variable selection procedures are required. Genet. Epidemiol. 33 (Suppl. 1):S51–S57, 2009.

Association of single nucleotide polymorphisms in ATM, GSTP1, SOD2, TGFB1, XPD and XRCC1 with clinical and cellular radiosensitivity

PURPOSE To examine the association of polymorphisms in ATM (codon 158), GSTP1 (codon 105), SOD2 (codon 16), TGFB1 (position -509), XPD (codon 751), and XRCC1 (codon 399) with fibrosis and also individual radiosensitivity. METHODS AND MATERIALS Retrospective analysis with 69 breast cancer patients treated with breast-conserving radiotherapy; total dose delivered was restricted to vary between 54 and 55Gy. Fibrosis was evaluated according to LENT/SOMA score. DNA was extracted from blood samples; cellular radiosensitivity was measured using the G0 assay and polymorphisms by PCR-RFLP and MALDI-TOF, respectively. RESULTS Twenty-five percent of all patients developed fibrosis of grade 2 or 3. This proportion tends to be higher in patients being polymorphic in TGFB1 or XRCC1 when compared to patients with wildtype genotype, whereas for ATM, GSTP1, SOD2 and XPD the polymorphic genotype appears to be associated with a lower risk of fibrosis. However, none of these associations are significant. In contrast, when a risk score is calculated based on all risk alleles, there was significant association with an increased risk of fibrosis (per risk allele odds ratio (ORs)=2.09, 95% confidence interval (CI): 1.32-3.55, p=0.0005). All six polymorphisms were found to have no significant effect on cellular radiosensitivity. CONCLUSIONS It is most likely that risk for radiation-induced fibrosis can be assessed by a combination of risk alleles. This finding needs to be replicated in further studies.

Individual Radiosensitivity Measured With Lymphocytes May Predict the Risk of Acute Reaction After Radiotherapy

PURPOSE We tested whether the chromosomal radiosensitivity of in vitro irradiated lymphocytes could be used to predict the risk of acute reactions after radiotherapy. METHODS AND MATERIALS Two prospective studies were performed: study A with 51 patients included different tumor sites and study B included 87 breast cancer patients. Acute reaction was assessed using the Radiation Therapy Oncology Group score. In both studies, patients were treated with curative radiotherapy, and the mean tumor dose applied was 55 Gy (40-65) +/- boost with 11 Gy (6-31) in study A and 50.4 Gy +/- boost with 10 Gy in study B. Individual radiosensitivity was determined with lymphocytes irradiated in vitro with X-ray doses of either 3 or 6 Gy and scoring the number of chromosomal deletions. RESULTS Acute reactions displayed a typical spectrum with 57% in study A and 53% in study B showing an acute reaction of Grade 2-3. Individual radiosensitivity in both studies was characterized by a substantial variation and the fraction of patients with Grade 2-3 reaction was found to increase with increasing individual radiosensitivity measured at 6 Gy (study A, p = 0.238; study B, p = 0.023). For study B, this fraction increased with breast volume, and the impact of individual radiosensitivity on acute reaction was especially pronounced (p = 0.00025) for lower breast volume. No such clear association with acute reaction was observed when individual radiosensitivity was assessed at 3 Gy. CONCLUSION Individual radiosensitivity determined at 6 Gy seems to be a good predictor for risk of acute effects after curative radiotherapy.

Identification of Genes Relevant to Symbiosis and Competitiveness in Sinorhizobium meliloti Using Signature-Tagged Mutants

Sinorhizobium meliloti enters an endosymbiosis with alfalfa plants through the formation of nitrogen-fixing nodules. In order to identify S. meliloti genes required for symbiosis and competitiveness, a method of signature-tagged mutagenesis was used. Two sets, each consisting of 378 signature-tagged mutants with a known transposon insertion site, were used in an experiment in planta. As a result, 67 mutants showing attenuated symbiotic phenotypes were identified, including most of the exo, fix, and nif mutants in the sets. For 38 mutants in genes previously not described to be involved in competitiveness or symbiosis in S. meliloti, attenuated competitiveness phenotypes were tested individually. A large part of these phenotypes was confirmed. Moreover, additional symbiotic defects were observed for mutants in several novel genes such as infection deficiency phenotypes (ilvI and ilvD2 mutants) or delayed nodulation (pyrE, metA, thiC, thiO, and thiD mutants).