Differential RelA- and RelB-dependent gene transcription in LTβR-stimulated mouse embryonic fibroblasts

<p>Abstract</p> <p>Background</p> <p>Lymphotoxin signaling via the lymphotoxin-β receptor (LTβR) has been implicated in biological processes ranging from development of secondary lymphoid organs, maintenance of spleen architecture, host defense against pathogens, autoimmunity, and lipid homeostasis. The major transcription factor that is activated by LTβR crosslinking is NF-κB. Two signaling pathways have been described, the classical inhibitor of NF-κB α (IκBα)-regulated and the alternative p100-regulated pathway that result in the activation of p50-RelA and p52-RelB NF-κB heterodimers, respectively.</p> <p>Results</p> <p>Using microarray analysis, we investigated the transcriptional response downstream of the LTβR in mouse embryonic fibroblasts (MEFs) and its regulation by the RelA and RelB subunits of NF-κB. We describe novel LTβR-responsive genes that were regulated by RelA and/or RelB. The majority of LTβR-regulated genes required the presence of both RelA and RelB, revealing significant crosstalk between the two NF-κB activation pathways. Gene Ontology (GO) analysis confirmed that LTβR-NF-κB target genes are predominantly involved in the regulation of immune responses. However, other biological processes, such as apoptosis/cell death, cell cycle, angiogenesis, and taxis were also regulated by LTβR signaling. Moreover, LTβR activation inhibited expression of a key adipogenic transcription factor, peroxisome proliferator activated receptor-γ (<it>pparg</it>), suggesting that LTβR signaling may interfere with adipogenic differentiation.</p> <p>Conclusion</p> <p>Microarray analysis of LTβR-stimulated fibroblasts provided comprehensive insight into the transcriptional response of LTβR signaling and its regulation by the NF-κB family members RelA and RelB.</p

Integrated Assessment and Prediction of Transcription Factor Binding

Systematic chromatin immunoprecipitation (chIP-chip) experiments have become a central technique for mapping transcriptional interactions in model organisms and humans. However, measurement of chromatin binding does not necessarily imply regulation, and binding may be difficult to detect if it is condition or cofactor dependent. To address these challenges, we present an approach for reliably assigning transcription factors (TFs) to target genes that integrates many lines of direct and indirect evidence into a single probabilistic model. Using this approach, we analyze publicly available chIP-chip binding profiles measured for yeast TFs in standard conditions, showing that our model interprets these data with significantly higher accuracy than previous methods. Pooling the high-confidence interactions reveals a large network containing 363 significant sets of factors (TF modules) that cooperate to regulate common target genes. In addition, the method predicts 980 novel binding interactions with high confidence that are likely to occur in so-far untested conditions. Indeed, using new chIP-chip experiments we show that predicted interactions for the factors Rpn4p and Pdr1p are observed only after treatment of cells with methyl-methanesulfonate, a DNA-damaging agent. We outline the first approach for consistently integrating all available evidences for TF–target interactions and we comprehensively identify the resulting TF module hierarchy. Prioritizing experimental conditions for each factor will be especially important as increasing numbers of chIP-chip assays are performed in complex organisms such as humans, for which “standard conditions” are ill defined

Common variants at 10 Genomic loci influence hemoglobin A1C 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 × 10(−26)), HFE (rs1800562/P = 2.6 × 10(−20)), TMPRSS6 (rs855791/P = 2.7 × 10(−14)), ANK1 (rs4737009/P = 6.1 × 10(−12)), SPTA1 (rs2779116/P = 2.8 × 10(−9)) and ATP11A/TUBGCP3 (rs7998202/P = 5.2 × 10(−9)), and four known HbA(1c) loci: HK1 (rs16926246/P = 3.1 × 10(−54)), MTNR1B (rs1387153/P = 4.0 × 10(−11)), GCK (rs1799884/P = 1.5 × 10(−20)) and G6PC2/ABCB11 (rs552976/P = 8.2 × 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 ∼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)

Lymphotoxin β receptor signaling promotes tertiary lymphoid organogenesis in the aorta adventitia of aged ApoE−/− mice

Atherosclerosis involves a macrophage-rich inflammation in the aortic intima. It is increasingly recognized that this intimal inflammation is paralleled over time by a distinct inflammatory reaction in adjacent adventitia. Though cross talk between the coordinated inflammatory foci in the intima and the adventitia seems implicit, the mechanism(s) underlying their communication is unclear. Here, using detailed imaging analysis, microarray analyses, laser-capture microdissection, adoptive lymphocyte transfers, and functional blocking studies, we undertook to identify this mechanism. We show that in aged apoE−/− mice, medial smooth muscle cells (SMCs) beneath intimal plaques in abdominal aortae become activated through lymphotoxin β receptor (LTβR) to express the lymphorganogenic chemokines CXCL13 and CCL21. These signals in turn trigger the development of elaborate bona fide adventitial aortic tertiary lymphoid organs (ATLOs) containing functional conduit meshworks, germinal centers within B cell follicles, clusters of plasma cells, high endothelial venules (HEVs) in T cell areas, and a high proportion of T regulatory cells. Treatment of apoE−/− mice with LTβR-Ig to interrupt LTβR signaling in SMCs strongly reduced HEV abundance, CXCL13, and CCL21 expression, and disrupted the structure and maintenance of ATLOs. Thus, the LTβR pathway has a major role in shaping the immunological characteristics and overall integrity of the arterial wall

Pleiotropy among common genetic loci identified for cardiometabolic disorders and C-reactive protein.

Pleiotropic genetic variants have independent effects on different phenotypes. C-reactive protein (CRP) is associated with several cardiometabolic phenotypes. Shared genetic backgrounds may partially underlie these associations. We conducted a genome-wide analysis to identify the shared genetic background of inflammation and cardiometabolic phenotypes using published genome-wide association studies (GWAS). We also evaluated whether the pleiotropic effects of such loci were biological or mediated in nature. First, we examined whether 283 common variants identified for 10 cardiometabolic phenotypes in GWAS are associated with CRP level. Second, we tested whether 18 variants identified for serum CRP are associated with 10 cardiometabolic phenotypes. We used a Bonferroni corrected p-value of 1.1×10-04 (0.05/463) as a threshold of significance. We evaluated the independent pleiotropic effect on both phenotypes using individual level data from the Women Genome Health Study. Evaluating the genetic overlap between inflammation and cardiometabolic phenotypes, we found 13 pleiotropic regions. Additional analyses showed that 6 regions (APOC1, HNF1A, IL6R, PPP1R3B, HNF4A and IL1F10) appeared to have a pleiotropic effect on CRP independent of the effects on the cardiometabolic phenotypes. These included loci where individuals carrying the risk allele for CRP encounter higher lipid levels and risk of type 2 diabetes. In addition, 5 regions (GCKR, PABPC4, BCL7B, FTO and TMEM18) had an effect on CRP largely mediated through the cardiometabolic phenotypes. In conclusion, our results show genetic pleiotropy among inflammation and cardiometabolic phenotypes. In addition to reverse causation, our data suggests that pleiotropic genetic variants partially underlie the association between CRP and cardiometabolic phenotypes

Identification of Novel Genetic Loci Associated with Thyroid Peroxidase Antibodies and Clinical Thyroid Disease

Peer reviewe

Mean platelet volume is more important than age for defining reference intervals of platelet counts

<div><p>Background</p><p>Platelet count is known to be associated with sex, age and mean platelet volume (MPV). Sex and age were proposed for adjustment of platelet count reference intervals, but MPV is currently not used for further adjustment. We investigated the association of MPV, age and sex with platelet counts and established individualized reference ranges respecting MPV.</p><p>Methods</p><p>The association of platelet count with age, sex and MPV was assessed in healthy participants (n = 3,033 individuals; 1,542 women) in the cross-sectional population-based cohort Study of Health in Pomerania. Reference intervals respecting age, sex, and MPV were estimated using quantile regressions for the 2.5^th and 97.5^th percentile.</p><p>Results</p><p>Women had higher platelet counts than men (239 vs. 207 x10⁹/L, p<0.001). Platelet counts correlated with age (p<0.001) and MPV (p<0.001). Quantile regression of lower and upper platelet count limits correlated less with age in female (p = 0.047 for 2.5^th percentile; p = 0.906 for 97.5^th percentile) and male subjects (p = 0.029 for 2.5^th percentile; p = 0.195 for 97.5^th percentile) compared to MPV (p<0.001 for upper and lower limit for both sexes). After adjustment for MPV, age did no longer correlate with the 2.5^th (p = 0.165) or 97.5^th percentile (p = 0.999) of platelet count. In contrast, after adjustment for age, MPV levels still significantly correlated with 2.5^th, 50^th and 97.5^th percentile (p<0.001).</p><p>Conclusion</p><p>MPV and sex have a stronger association with platelet count than age. MPV should be considered to adjust platelet count reference intervals and needs to be respected as confounder for platelet counts in epidemiological studies and clinical practice.</p></div

Laser-Based 3D Body Scanning Reveals a Higher Prevalence of Abdominal Obesity than Tape Measurements: Results from a Population-Based Sample

Background: The global obesity epidemic is a major public health concern, and accurate diagnosis is essential for identifying at-risk individuals. Three-dimensional (3D) body scanning technology offers several advantages over the standard practice of tape measurements for diagnosing obesity. This study was conducted to validate body scan data from a German population-based cohort and explore clinical implications of this technology in the context of metabolic syndrome. Methods: We performed a cross-sectional analysis of 354 participants from the Study of Health in Pomerania that completed a 3D body scanning examination. The agreement of anthropometric data obtained from 3D body scanning with manual tape measurements was analyzed using correlation analysis and Bland–Altman plots. Classification agreement regarding abdominal obesity based on IDF guidelines was assessed using Cohen’s kappa. The association of body scan measures with metabolic syndrome components was explored using correlation analysis. Results: Three-dimensional body scanning showed excellent validity with slightly larger values that presumably reflect the true circumferences more accurately. Metabolic syndrome was highly prevalent in the sample (31%) and showed strong associations with central obesity. Using body scan vs. tape measurements of waist circumference for classification resulted in a 16% relative increase in the prevalence of abdominal obesity (61.3% vs. 52.8%). Conclusions: These results suggest that the prevalence of obesity may be underestimated using the standard method of tape measurements, highlighting the need for more accurate approaches