A novel CCCH-zinc finger protein family regulates proinflammatory activation of macrophages

Activated macrophages play an important role in many inflammatory diseases. However, the molecular mechanisms controlling macrophage activation are not completely understood. Here we report that a novel CCCH-zinc finger protein family, MCPIP1, 2, 3, and 4, encoded by four genes, Zc3h12a, Zc3h12b, Zc3h12c, and Zc3h12d, respectively, regulates macrophage activation. Northern blot analysis revealed that the expression of MCPIP1 and MCPIP3 was highly induced in macrophages in response to treatment with lipopolysaccharide (LPS). Although not affecting cell surface marker expression and phagocytotic function, overexpression of MCPIP1 significantly blunted LPS-induced inflammatory cytokine and NO(2)radical anion. production as well as their gene expression. Conversely, short interfering RNA-mediated reduction in MCPIP1 augmented LPS-induced inflammatory gene expression. Further studies demonstrated that MCPIP1 did not directly affect the mRNA stability of tumor necrosis factor alpha and monocyte chemoattractant protein 1 (MCP-1) but strongly inhibited LPS-induced tumor necrosis factor alpha and inducible nitric-oxide synthase promoter activation. Moreover, we found that forced expression of MCPIP1 significantly inhibited LPS-induced nuclear factor-kappa B activation. These results identify MCP-induced proteins, a novel CCCH-zinc finger protein family, as negative regulators in macrophage activation and may implicate them in host immunity and inflammatory diseases

Genome-Wide Survey and Expression Profiling of CCCH-Zinc Finger Family Reveals a Functional Module in Macrophage Activation

Previously, we have identified a novel CCCH zinc finger protein family as negative regulators of macrophage activation. To gain an overall insight into the entire CCCH zinc finger gene family and to evaluate their potential role in macrophage activation, here we performed a genome-wide survey of CCCH zinc finger genes in mouse and human. Totally 58 CCCH zinc finger genes in mouse and 55 in human were identified and most of them have not been reported previously. Phylogenetic analysis revealed that the mouse CCCH family was divided into 6 groups. Meanwhile, we employed quantitative real-time PCR to profile their tissue expression patterns in adult mice. Clustering analysis showed that most of CCCH genes were broadly expressed in all of tissues examined with various levels. Interestingly, several CCCH genes Mbnl3, Zfp36l2, Zfp36, Zc3h12a, Zc3h12d, Zc3h7a and Leng9 were enriched in macrophage-related organs such as thymus, spleen, lung, intestine and adipose. Consistently, a comprehensive assessment of changes in expression of the 58 members of the mouse CCCH family during macrophage activation also revealed that these CCCH zinc finger genes were associated with the activation of bone marrow-derived macrophages by lipopolysaccharide. Taken together, this study not only identified a functional module of CCCH zinc finger genes in the regulation of macrophage activation but also provided the framework for future studies to dissect the function of this emerging gene family

An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge

There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data were donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease-causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance. RESULTS: A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization. CONCLUSIONS: The CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups

Rare and low-frequency coding variants alter human adult height

Height is a highly heritable, classic polygenic trait with ~700 common associated variants identified so far through genome - wide association studies . Here , we report 83 height - associated coding variants with lower minor allele frequenc ies ( range of 0.1 - 4.8% ) and effects of up to 2 16 cm /allele ( e.g. in IHH , STC2 , AR and CRISPLD2 ) , >10 times the average effect of common variants . In functional follow - up studies, rare height - increasing alleles of STC2 (+1 - 2 cm/allele) compromise d proteolytic inhibition of PAPP - A and increased cleavage of IGFBP - 4 in vitro , resulting in higher bioavailability of insulin - like growth factors . The se 83 height - associated variants overlap genes mutated in monogenic growth disorders and highlight new biological candidates ( e.g. ADAMTS3, IL11RA, NOX4 ) and pathways ( e.g . proteoglycan/ glycosaminoglycan synthesis ) involved in growth . Our results demonstrate that sufficiently large sample sizes can uncover rare and low - frequency variants of moderate to large effect associated with polygenic human phenotypes , and that these variants implicate relevant genes and pathways

Mechanistic phenotypes: an aggregative phenotyping strategy to identify disease mechanisms using GWAS data.

A single mutation can alter cellular and global homeostatic mechanisms and give rise to multiple clinical diseases. We hypothesized that these disease mechanisms could be identified using low minor allele frequency (MAF<0.1) non-synonymous SNPs (nsSNPs) associated with mechanistic phenotypes, comprised of collections of related diagnoses. We studied two mechanistic phenotypes: (1) thrombosis, evaluated in a population of 1,655 African Americans; and (2) four groupings of cancer diagnoses, evaluated in 3,009 white European Americans. We tested associations between nsSNPs represented on GWAS platforms and mechanistic phenotypes ascertained from electronic medical records (EMRs), and sought enrichment in functional ontologies across the top-ranked associations. We used a two-step analytic approach whereby nsSNPs were first sorted by the strength of their association with a phenotype. We tested associations using two reverse genetic models and standard additive and recessive models. In the second step, we employed a hypothesis-free ontological enrichment analysis using the sorted nsSNPs to identify functional mechanisms underlying the diagnoses comprising the mechanistic phenotypes. The thrombosis phenotype was solely associated with ontologies related to blood coagulation (Fishers p = 0.0001, FDR p = 0.03), driven by the F5, P2RY12 and F2RL2 genes. For the cancer phenotypes, the reverse genetics models were enriched in DNA repair functions (p = 2×10-5, FDR p = 0.03) (POLG/FANCI, SLX4/FANCP, XRCC1, BRCA1, FANCA, CHD1L) while the additive model showed enrichment related to chromatid segregation (p = 4×10-6, FDR p = 0.005) (KIF25, PINX1). We were able to replicate nsSNP associations for POLG/FANCI, BRCA1, FANCA and CHD1L in independent data sets. Mechanism-oriented phenotyping using collections of EMR-derived diagnoses can elucidate fundamental disease mechanisms

Identifying gene-gene interactions that are highly associated with four quantitative lipid traits across multiple cohorts

Genetic loci explain only 25-30 % of the heritability observed in plasma lipid traits. Epistasis, or gene-gene interactions may contribute to a portion of this missing heritability. Using the genetic data from five NHLBI cohorts of 24,837 individuals, we combined the use of the quantitative multifactor dimensionality reduction (QMDR) algorithm with two SNP-filtering methods to exhaustively search for SNP-SNP interactions that are associated with HDL cholesterol (HDL-C), LDL cholesterol (LDL-C), total cholesterol (TC) and triglycerides (TG). SNPs were filtered either on the strength of their independent effects (main effect filter) or the prior knowledge supporting a given interaction (Biofilter). After the main effect filter, QMDR identified 20 SNP-SNP models associated with HDL-C, 6 associated with LDL-C, 3 associated with TC, and 10 associated with TG (permutation P value <0.05). With the use of Biofilter, we identified 2 SNP-SNP models associated with HDL-C, 3 associated with LDL-C, 1 associated with TC and 8 associated with TG (permutation P value <0.05). In an independent dataset of 7502 individuals from the eMERGE network, we replicated 14 of the interactions identified after main effect filtering: 11 for HDL-C, 1 for LDL-C and 2 for TG. We also replicated 23 of the interactions found to be associated with TG after applying Biofilter. Prior knowledge supports the possible role of these interactions in the genetic etiology of lipid traits. This study also presents a computationally efficient pipeline for analyzing data from large genotyping arrays and detecting SNP-SNP interactions that are not primarily driven by strong main effects

Mechanistic Phenotypes: An Aggregative Phenotyping Strategy to Identify Disease Mechanisms Using GWAS Data

<div><p>A single mutation can alter cellular and global homeostatic mechanisms and give rise to multiple clinical diseases. We hypothesized that these disease mechanisms could be identified using low minor allele frequency (MAF<0.1) non-synonymous SNPs (nsSNPs) associated with “mechanistic phenotypes”, comprised of collections of related diagnoses. We studied two mechanistic phenotypes: (1) thrombosis, evaluated in a population of 1,655 African Americans; and (2) four groupings of cancer diagnoses, evaluated in 3,009 white European Americans. We tested associations between nsSNPs represented on GWAS platforms and mechanistic phenotypes ascertained from electronic medical records (EMRs), and sought enrichment in functional ontologies across the top-ranked associations. We used a two-step analytic approach whereby nsSNPs were first sorted by the strength of their association with a phenotype. We tested associations using two reverse genetic models and standard additive and recessive models. In the second step, we employed a hypothesis-free ontological enrichment analysis using the sorted nsSNPs to identify functional mechanisms underlying the diagnoses comprising the mechanistic phenotypes. The thrombosis phenotype was solely associated with ontologies related to blood coagulation (Fisher's p = 0.0001, FDR p = 0.03), driven by the <i>F5, P2RY12</i> and <i>F2RL2</i> genes. For the cancer phenotypes, the reverse genetics models were enriched in DNA repair functions (p = 2×10−5, FDR p = 0.03) (<i>POLG/FANCI, SLX4/FANCP, XRCC1, BRCA1, FANCA, CHD1L</i>) while the additive model showed enrichment related to chromatid segregation (p = 4×10−6, FDR p = 0.005) (<i>KIF25, PINX1</i>). We were able to replicate nsSNP associations for <i>POLG/FANCI, BRCA1, FANCA</i> and <i>CHD1L</i> in independent data sets. Mechanism-oriented phenotyping using collections of EMR-derived diagnoses can elucidate fundamental disease mechanisms.</p></div

Enriched ontologies for genes associated with the cancer phenotypes in whites.

<p> All ontologies with an FDR p<0.05 are shown.</p><p> (1) The association p-value cut-off that gave the strongest enrichment.</p><p> (2) Number of genes with p-values below the p-value cut-off.</p><p> (3) Number of genes with the ontology.</p

Association of DNA repair genes in independent data sets.

<p> (1) This model compared minor allele homozygotes to matched common allele homozygotes.</p><p> = 3,092 subjects). (2) Genotype data for this nsSNP was only available for one eMERGE site (n</p