Rare variant associations with plasma protein levels in the UK Biobank.

Integrating human genomics and proteomics can help elucidate disease mechanisms, identify clinical biomarkers and discover drug targets1-4. Because previous proteogenomic studies have focused on common variation via genome-wide association studies, the contribution of rare variants to the plasma proteome remains largely unknown. Here we identify associations between rare protein-coding variants and 2,923 plasma protein abundances measured in 49,736 UK Biobank individuals. Our variant-level exome-wide association study identified 5,433 rare genotype-protein associations, of which 81% were undetected in a previous genome-wide association study of the same cohort5. We then looked at aggregate signals using gene-level collapsing analysis, which revealed 1,962 gene-protein associations. Of the 691 gene-level signals from protein-truncating variants, 99.4% were associated with decreased protein levels. STAB1 and STAB2, encoding scavenger receptors involved in plasma protein clearance, emerged as pleiotropic loci, with 77 and 41 protein associations, respectively. We demonstrate the utility of our publicly accessible resource through several applications. These include detailing an allelic series in NLRC4, identifying potential biomarkers for a fatty liver disease-associated variant in HSD17B13 and bolstering phenome-wide association studies by integrating protein quantitative trait loci with protein-truncating variants in collapsing analyses. Finally, we uncover distinct proteomic consequences of clonal haematopoiesis (CH), including an association between TET2-CH and increased FLT3 levels. Our results highlight a considerable role for rare variation in plasma protein abundance and the value of proteogenomics in therapeutic discovery.

Rare variant contribution to human disease in 281,104 UK Biobank exomes.

Genome-wide association studies have uncovered thousands of common variants associated with human disease, but the contribution of rare variants to common disease remains relatively unexplored. The UK Biobank contains detailed phenotypic data linked to medical records for approximately 500,000 participants, offering an unprecedented opportunity to evaluate the effect of rare variation on a broad collection of traits1,2. Here we study the relationships between rare protein-coding variants and 17,361 binary and 1,419 quantitative phenotypes using exome sequencing data from 269,171 UK Biobank participants of European ancestry. Gene-based collapsing analyses revealed 1,703 statistically significant gene-phenotype associations for binary traits, with a median odds ratio of 12.4. Furthermore, 83% of these associations were undetectable via single-variant association tests, emphasizing the power of gene-based collapsing analysis in the setting of high allelic heterogeneity. Gene-phenotype associations were also significantly enriched for loss-of-function-mediated traits and approved drug targets. Finally, we performed ancestry-specific and pan-ancestry collapsing analyses using exome sequencing data from 11,933 UK Biobank participants of African, East Asian or South Asian ancestry. Our results highlight a significant contribution of rare variants to common disease. Summary statistics are publicly available through an interactive portal ( http://azphewas.com/ ).

Effects of protein-coding variants on blood metabolite measurements and clinical biomarkers in the UK Biobank.

Genome-wide association studies (GWASs) have established the contribution of common and low-frequency variants to metabolic blood measurements in the UK Biobank (UKB). To complement existing GWAS findings, we assessed the contribution of rare protein-coding variants in relation to 355 metabolic blood measurements-including 325 predominantly lipid-related nuclear magnetic resonance (NMR)-derived blood metabolite measurements (Nightingale Health Plc) and 30 clinical blood biomarkers-using 412,393 exome sequences from four genetically diverse ancestries in the UKB. Gene-level collapsing analyses were conducted to evaluate a diverse range of rare-variant architectures for the metabolic blood measurements. Altogether, we identified significant associations (p < 1 × 10-8) for 205 distinct genes that involved 1,968 significant relationships for the Nightingale blood metabolite measurements and 331 for the clinical blood biomarkers. These include associations for rare non-synonymous variants in PLIN1 and CREB3L3 with lipid metabolite measurements and SYT7 with creatinine, among others, which may not only provide insights into novel biology but also deepen our understanding of established disease mechanisms. Of the study-wide significant clinical biomarker associations, 40% were not previously detected on analyzing coding variants in a GWAS in the same cohort, reinforcing the importance of studying rare variation to fully understand the genetic architecture of metabolic blood measurements.

A minimal role for synonymous variation in human disease.

Synonymous mutations change the DNA sequence of a gene without affecting the amino acid sequence of the encoded protein. Although some synonymous mutations can affect RNA splicing, translational efficiency, and mRNA stability, studies in human genetics, mutagenesis screens, and other experiments and evolutionary analyses have repeatedly shown that most synonymous variants are neutral or only weakly deleterious, with some notable exceptions. Based on a recent study in yeast, there have been claims that synonymous mutations could be as important as nonsynonymous mutations in causing disease, assuming the yeast findings hold up and translate to humans. Here, we argue that there is insufficient evidence to overturn the large, coherent body of knowledge establishing the predominant neutrality of synonymous variants in the human genome.

The effects of pathogenic and likely pathogenic variants for inherited hemostasis disorders in 140 214 UK Biobank participants.

Rare genetic diseases affect millions, and identifying causal DNA variants is essential for patient care. Therefore, it is imperative to estimate the effect of each independent variant and improve their pathogenicity classification. Our study of 140 214 unrelated UK Biobank (UKB) participants found that each of them carries a median of 7 variants previously reported as pathogenic or likely pathogenic. We focused on 967 diagnostic-grade gene (DGG) variants for rare bleeding, thrombotic, and platelet disorders (BTPDs) observed in 12 367 UKB participants. By association analysis, for a subset of these variants, we estimated effect sizes for platelet count and volume, and odds ratios for bleeding and thrombosis. Variants causal of some autosomal recessive platelet disorders revealed phenotypic consequences in carriers. Loss-of-function variants in MPL, which cause chronic amegakaryocytic thrombocytopenia if biallelic, were unexpectedly associated with increased platelet counts in carriers. We also demonstrated that common variants identified by genome-wide association studies (GWAS) for platelet count or thrombosis risk may influence the penetrance of rare variants in BTPD DGGs on their associated hemostasis disorders. Network-propagation analysis applied to an interactome of 18 410 nodes and 571 917 edges showed that GWAS variants with large effect sizes are enriched in DGGs and their first-order interactors. Finally, we illustrate the modifying effect of polygenic scores for platelet count and thrombosis risk on disease severity in participants carrying rare variants in TUBB1 or PROC and PROS1, respectively. Our findings demonstrate the power of association analyses using large population datasets in improving pathogenicity classifications of rare variants.

Pan-ancestry exome-wide association analyses of COVID-19 outcomes in 586,157 individuals.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19), a respiratory illness that can result in hospitalization or death. We used exome sequence data to investigate associations between rare genetic variants and seven COVID-19 outcomes in 586,157 individuals, including 20,952 with COVID-19. After accounting for multiple testing, we did not identify any clear associations with rare variants either exome wide or when specifically focusing on (1) 13 interferon pathway genes in which rare deleterious variants have been reported in individuals with severe COVID-19, (2) 281 genes located in susceptibility loci identified by the COVID-19 Host Genetics Initiative, or (3) 32 additional genes of immunologic relevance and/or therapeutic potential. Our analyses indicate there are no significant associations with rare protein-coding variants with detectable effect sizes at our current sample sizes. Analyses will be updated as additional data become available, and results are publicly available through the Regeneron Genetics Center COVID-19 Results Browser.

Gene-SCOUT: identifying genes with similar continuous trait fingerprints from phenome-wide association analyses.

Large-scale phenome-wide association studies performed using densely-phenotyped cohorts such as the UK Biobank (UKB), reveal many statistically robust gene-phenotype relationships for both clinical and continuous traits. Here, we present Gene-SCOUT, a tool used to identify genes with similar continuous trait fingerprints to a gene of interest. A fingerprint reflects the continuous traits identified to be statistically associated with a gene of interest based on multiple underlying rare variant genetic architectures. Similarities between genes are evaluated by the cosine similarity measure, to capture concordant effect directionality, elucidating clusters of genes in a high dimensional space. The underlying gene-biomarker population-scale association statistics were obtained from a gene-level rare variant collapsing analysis performed on over 1500 continuous traits using 394 692 UKB participant exomes, with additional metabolomic trait associations provided through Nightingale Health's recent study of 121 394 of these participants. We demonstrate that gene similarity estimates from Gene-SCOUT provide stronger enrichments for clinical traits compared to existing methods. Furthermore, we provide a fully interactive web-resource (http://genescout.public.cgr.astrazeneca.com) to explore the pre-calculated exome-wide similarities. This resource enables a user to examine the biological relevance of the most similar genes for Gene Ontology (GO) enrichment and UKB clinical trait enrichment statistics, as well as a detailed breakdown of the traits underpinning a given fingerprint.

Uncovering variable neoplasms between ATM protein-truncating and common missense variants using 394 694 UK Biobank exomes.

As an essential regulator of DNA damage, ataxia-telangiectasia mutated (ATM) gene has been widely studied in oncology. However, the independent effects of ATM missense variants and protein-truncating variants (PTVs) on neoplasms have not been heavily studied. Whole-exome sequencing data and the clinical health records of 394,694 UK Biobank European participants were used in this analysis. We mined genetic associations from gene-level and variant-level phenome-wide association studies, and conducted a variant-level conditional association study to test whether the effects of ATM missense variants on neoplasms were independent of ATM PTV carrier status. The gene-level PTV collapsing analysis was consistent with established ATM PTV literature showing that the aggregated impact of 286 ATM PTVs significantly (p < 2 × 10-9 ) associated with 31 malignant neoplasm phenotypes. Of 773 distinct protein-coding variants in ATM, three individual missense variants significantly (p < 2 × 10-9 ) associated with nine phenotypes. Remarkably, although the nine phenotypes were tumor-related, none overlapped the established ATM PTV-linked malignancies. A subsequent conditional analysis identified that the missense signals were acting independently of the known clinically relevant ATM PTVs.

Whole-exome sequencing in the evaluation of fetal structural anomalies: a prospective cohort study.

BACKGROUND: Identification of chromosomal aneuploidies and copy number variants that are associated with fetal structural anomalies has substantial value. Although whole-exome sequencing (WES) has been applied to case series of a few selected prenatal cases, its value in routine clinical settings has not been prospectively assessed in a large unselected cohort of fetuses with structural anomalies. We therefore aimed to determine the incremental diagnostic yield (ie, the added value) of WES following uninformative results of standard investigations with karyotype testing and chromosomal microarray in an unselected cohort of sequential pregnancies showing fetal structural anomalies. METHODS: In this prospective cohort study, the parents of fetuses who were found to have a structural anomaly in a prenatal ultrasound were screened for possible participation in the study. These participants were predominantly identified in or were referred to the Columbia University Carmen and John Thain Center for Prenatal Pediatrics (New York, NY, USA). Fetuses with confirmed aneuploidy or a causal pathogenic copy number variant were excluded from WES analyses. By use of WES of the fetuses and parents (parent-fetus trios), we identified genetic variants that indicated an underlying cause (diagnostic genetic variants) and genetic variants that met the criteria of bioinformatic signatures that had previously been described to be significantly enriched among diagnostic genetic variants. FINDINGS: Between April 24, 2015, and April 19, 2017, 517 sequentially identified pregnant women found to have fetuses with a structural anomaly were screened for their eligibility for inclusion in our study. 71 (14%) couples declined testing, 87 (17%) trios were missing at least one DNA sample (from either parent or the fetus), 69 (13%) trios had a clinically relevant abnormal karyotype or chromosomal microarray finding, 51 (10%) couples did not consent to WES or withdrew consent, and five (1%) samples were not of good enough quality for analysis. DNA samples from 234 (45%) eligible trios were therefore used for analysis of the primary outcome. By use of trio sequence data, we identified diagnostic genetic variants in 24 (10%) families. Mutations with bioinformatic signatures that were indicative of pathogenicity but with insufficient evidence to be considered diagnostic were also evaluated; 46 (20%) of the 234 fetuses assessed were found to have such signatures. INTERPRETATION: Our analysis of WES data in a prospective cohort of unselected fetuses with structural anomalies shows the value added by WES following the use of routine genetic tests. Our findings suggest that, in cases of fetal anomalies in which assessment with karyotype testing and chromosomal microarray fail to determine the underlying cause of a structural anomaly, WES can add clinically relevant information that could assist current management of a pregnancy. The unique challenges of WES-based prenatal diagnostics require analysis by a multidisciplinary team of perinatal practitioners and laboratory specialists. FUNDING: Institute for Genomic Medicine (Columbia University Irving Medical Center).

Optimizing genomic medicine in epilepsy through a gene-customized approach to missense variant interpretation.

Gene panel and exome sequencing have revealed a high rate of molecular diagnoses among diseases where the genetic architecture has proven suitable for sequencing approaches, with a large number of distinct and highly penetrant causal variants identified among a growing list of disease genes. The challenge is, given the DNA sequence of a new patient, to distinguish disease-causing from benign variants. Large samples of human standing variation data highlight regional variation in the tolerance to missense variation within the protein-coding sequence of genes. This information is not well captured by existing bioinformatic tools, but is effective in improving variant interpretation. To address this limitation in existing tools, we introduce the missense tolerance ratio (MTR), which summarizes available human standing variation data within genes to encapsulate population level genetic variation. We find that patient-ascertained pathogenic variants preferentially cluster in low MTR regions (P < 0.005) of well-informed genes. By evaluating 20 publicly available predictive tools across genes linked to epilepsy, we also highlight the importance of understanding the empirical null distribution of existing prediction tools, as these vary across genes. Subsequently integrating the MTR with the empirically selected bioinformatic tools in a gene-specific approach demonstrates a clear improvement in the ability to predict pathogenic missense variants from background missense variation in disease genes. Among an independent test sample of case and control missense variants, case variants (0.83 median score) consistently achieve higher pathogenicity prediction probabilities than control variants (0.02 median score; Mann-Whitney U test, P < 1 × 10-16). We focus on the application to epilepsy genes; however, the framework is applicable to disease genes beyond epilepsy.

Inhibition of microRNA 128 promotes excitability of cultured cortical neuronal networks.

Cultured neuronal networks monitored with microelectrode arrays (MEAs) have been used widely to evaluate pharmaceutical compounds for potential neurotoxic effects. A newer application of MEAs has been in the development of in vitro models of neurological disease. Here, we directly evaluated the utility of MEAs to recapitulate in vivo phenotypes of mature microRNA-128 (miR-128) deficiency, which causes fatal seizures in mice. We show that inhibition of miR-128 results in significantly increased neuronal activity in cultured neuronal networks derived from primary mouse cortical neurons. These results support the utility of MEAs in developing in vitro models of neuroexcitability disorders, such as epilepsy, and further suggest that MEAs provide an effective tool for the rapid identification of microRNAs that promote seizures when dysregulated.

meaRtools: An R package for the analysis of neuronal networks recorded on microelectrode arrays.

Here we present an open-source R package 'meaRtools' that provides a platform for analyzing neuronal networks recorded on Microelectrode Arrays (MEAs). Cultured neuronal networks monitored with MEAs are now being widely used to characterize in vitro models of neurological disorders and to evaluate pharmaceutical compounds. meaRtools provides core algorithms for MEA spike train analysis, feature extraction, statistical analysis and plotting of multiple MEA recordings with multiple genotypes and treatments. meaRtools functionality covers novel solutions for spike train analysis, including algorithms to assess electrode cross-correlation using the spike train tiling coefficient (STTC), mutual information, synchronized bursts and entropy within cultured wells. Also integrated is a solution to account for bursts variability originating from mixed-cell neuronal cultures. The package provides a statistical platform built specifically for MEA data that can combine multiple MEA recordings and compare extracted features between different genetic models or treatments. We demonstrate the utilization of meaRtools to successfully identify epilepsy-like phenotypes in neuronal networks from Celf4 knockout mice. The package is freely available under the GPL license (GPL> = 3) and is updated frequently on the CRAN web-server repository. The package, along with full documentation can be downloaded from: https://cran.r-project.org/web/packages/meaRtools/.

An Exome Sequencing Study to Assess the Role of Rare Genetic Variation in Pulmonary Fibrosis.

RATIONALE: Idiopathic pulmonary fibrosis (IPF) is an increasingly recognized, often fatal lung disease of unknown etiology. OBJECTIVES: The aim of this study was to use whole-exome sequencing to improve understanding of the genetic architecture of pulmonary fibrosis. METHODS: We performed a case-control exome-wide collapsing analysis including 262 unrelated individuals with pulmonary fibrosis clinically classified as IPF according to American Thoracic Society/European Respiratory Society/Japanese Respiratory Society/Latin American Thoracic Association guidelines (81.3%), usual interstitial pneumonia secondary to autoimmune conditions (11.5%), or fibrosing nonspecific interstitial pneumonia (7.2%). The majority (87%) of case subjects reported no family history of pulmonary fibrosis. MEASUREMENTS AND MAIN RESULTS: We searched 18,668 protein-coding genes for an excess of rare deleterious genetic variation using whole-exome sequence data from 262 case subjects with pulmonary fibrosis and 4,141 control subjects drawn from among a set of individuals of European ancestry. Comparing genetic variation across 18,668 protein-coding genes, we found a study-wide significant (P < 4.5 × 10-7) case enrichment of qualifying variants in TERT, RTEL1, and PARN. A model qualifying ultrarare, deleterious, nonsynonymous variants implicated TERT and RTEL1, and a model specifically qualifying loss-of-function variants implicated RTEL1 and PARN. A subanalysis of 186 case subjects with sporadic IPF confirmed TERT, RTEL1, and PARN as study-wide significant contributors to sporadic IPF. Collectively, 11.3% of case subjects with sporadic IPF carried a qualifying variant in one of these three genes compared with the 0.3% carrier rate observed among control subjects (odds ratio, 47.7; 95% confidence interval, 21.5-111.6; P = 5.5 × 10-22). CONCLUSIONS: We identified TERT, RTEL1, and PARN-three telomere-related genes previously implicated in familial pulmonary fibrosis-as significant contributors to sporadic IPF. These results support the idea that telomere dysfunction is involved in IPF pathogenesis.

The Intolerance of Regulatory Sequence to Genetic Variation Predicts Gene Dosage Sensitivity.

Noncoding sequence contains pathogenic mutations. Yet, compared with mutations in protein-coding sequence, pathogenic regulatory mutations are notoriously difficult to recognize. Most fundamentally, we are not yet adept at recognizing the sequence stretches in the human genome that are most important in regulating the expression of genes. For this reason, it is difficult to apply to the regulatory regions the same kinds of analytical paradigms that are being successfully applied to identify mutations among protein-coding regions that influence risk. To determine whether dosage sensitive genes have distinct patterns among their noncoding sequence, we present two primary approaches that focus solely on a gene's proximal noncoding regulatory sequence. The first approach is a regulatory sequence analogue of the recently introduced residual variation intolerance score (RVIS), termed noncoding RVIS, or ncRVIS. The ncRVIS compares observed and predicted levels of standing variation in the regulatory sequence of human genes. The second approach, termed ncGERP, reflects the phylogenetic conservation of a gene's regulatory sequence using GERP++. We assess how well these two approaches correlate with four gene lists that use different ways to identify genes known or likely to cause disease through changes in expression: 1) genes that are known to cause disease through haploinsufficiency, 2) genes curated as dosage sensitive in ClinGen's Genome Dosage Map, 3) genes judged likely to be under purifying selection for mutations that change expression levels because they are statistically depleted of loss-of-function variants in the general population, and 4) genes judged unlikely to cause disease based on the presence of copy number variants in the general population. We find that both noncoding scores are highly predictive of dosage sensitivity using any of these criteria. In a similar way to ncGERP, we assess two ensemble-based predictors of regional noncoding importance, ncCADD and ncGWAVA, and find both scores are significantly predictive of human dosage sensitive genes and appear to carry information beyond conservation, as assessed by ncGERP. These results highlight that the intolerance of noncoding sequence stretches in the human genome can provide a critical complementary tool to other genome annotation approaches to help identify the parts of the human genome increasingly likely to harbor mutations that influence risk of disease.

Maternal iron intake during pregnancy and birth outcomes: a cross-sectional study in Northwest China.

Previous studies have yielded conflicting results on the associations of maternal Fe intake with birth outcomes. This study aimed to investigate the associations between maternal Fe intake (total Fe from diet and supplements, dietary total Fe, haeme Fe, non-haeme Fe and Fe supplements use) and adverse birth outcomes in Shaanxi Province of Northwest China. In all, 7375 women were recruited using a stratified multistage random sampling method at 0-12 months (median 3; 10th-90th percentile 0-7) after delivery. Diets were collected by a validated FFQ and maternal characteristics were obtained via a standard questionnaire. The highest tertile of haeme Fe intake compared with the lowest tertile was negatively associated with low birth weight (LBW) (OR 0·68; 95 % CI 0·49, 0·94), small for gestational age (SGA) (OR 0·76; 95 % CI 0·62, 0·94) and birth defects (OR 0·55; 95 % CI 0·32, 0·89). Maternal haeme Fe intake was associated with a lower risk of intra-uterine growth retardation (IUGR) (medium tertile v. lowest tertile: OR 0·78; 95 % CI 0·61, 0·95; highest tertile v. lowest tertile: OR 0·76; 95 % CI 0·59, 0·93; P trend=0·045). The OR of LBW associated with Fe supplements use were as follows: during pregnancy: 0·72 (95 % CI 0·50, 0·95); in the second trimester: 0·67 (95 % CI 0·42, 0·98); in the third trimester: 0·47 (95 % CI 0·24, 0·93). We observed no associations of total Fe, dietary total Fe or non-haeme Fe intake with birth outcomes. The results suggest that maternal haeme Fe intake is associated with a reduced risk of LBW, SGA, IUGR and birth defects, and Fe supplements use during pregnancy reduces LBW risk.

A genomic strategy to elucidate modules of oncogenic pathway signaling networks.

Recent studies have emphasized the importance of pathway-specific interpretations for understanding the functional relevance of gene alterations in human cancers. Although signaling activities are often conceptualized as linear events, in reality, they reflect the activity of complex functional networks assembled from modules that each respond to input signals. To acquire a deeper understanding of this network structure, we developed an approach to deconstruct pathways into modules represented by gene expression signatures. Our studies confirm that they represent units of underlying biological activity linked to known biochemical pathway structures. Importantly, we show that these signaling modules provide tools to dissect the complexity of oncogenic states that define disease outcomes as well as response to pathway-specific therapeutics. We propose that this model of pathway structure constitutes a framework to study the processes by which information propogates through cellular networks and to elucidate the relationships of fundamental modules to cellular and clinical phenotypes.

Dietary intakes and dietary patterns among pregnant women in Northwest China.

OBJECTIVE: To describe nutrient intakes, characterize dietary patterns and analyse their associations with sociodemographic characteristics among pregnant women in Shaanxi, China. DESIGN: Population-based cross-sectional survey. SETTING: Twenty counties and ten districts in Shaanxi Province of Northwest China, 2013. SUBJECTS: Women (n 7462) were recruited using a stratified multistage random sampling method to report diets during pregnancy, at 0-12 months (median 3 months; 10th-90th percentile, 0-7 months) after delivery. RESULTS: Pregnant women had higher intakes of fat, niacin and vitamin E than the nutrient reference values, while most micronutrients such as vitamin A, folate, Ca and Zn were reportedly low. Women in the highest education, occupation and household income groups had higher nutrient intakes than those in the lowest groups. Nutrient intake differences also existed by geographic area, residence and maternal age at delivery. Three dietary patterns were identified: balanced pattern, vegetarian pattern and snacks pattern. Participants with high balanced pattern scores tended to be better educated, wealthier, 25-29 years old at delivery, working outside and living in urban areas and central Shaanxi. Women with high scores on the vegetarian pattern and snacks pattern tended to be in low balanced pattern score groups, and had lower nutrient intakes than those in the high balanced pattern score groups. CONCLUSIONS: The study suggested that pregnant women in Shaanxi, China had low intakes of most nutrients such as vitamin A, folate and Ca. Dietary patterns and most nutrient intakes varied by sociodemographic characteristics. Targeted programmes are needed to improve dietary intakes and dietary patterns among sociodemographically disadvantaged groups.

Genic intolerance to functional variation and the interpretation of personal genomes.

A central challenge in interpreting personal genomes is determining which mutations most likely influence disease. Although progress has been made in scoring the functional impact of individual mutations, the characteristics of the genes in which those mutations are found remain largely unexplored. For example, genes known to carry few common functional variants in healthy individuals may be judged more likely to cause certain kinds of disease than genes known to carry many such variants. Until now, however, it has not been possible to develop a quantitative assessment of how well genes tolerate functional genetic variation on a genome-wide scale. Here we describe an effort that uses sequence data from 6503 whole exome sequences made available by the NHLBI Exome Sequencing Project (ESP). Specifically, we develop an intolerance scoring system that assesses whether genes have relatively more or less functional genetic variation than expected based on the apparently neutral variation found in the gene. To illustrate the utility of this intolerance score, we show that genes responsible for Mendelian diseases are significantly more intolerant to functional genetic variation than genes that do not cause any known disease, but with striking variation in intolerance among genes causing different classes of genetic disease. We conclude by showing that use of an intolerance ranking system can aid in interpreting personal genomes and identifying pathogenic mutations.

TLR4 plays a crucial role in MSC-induced inhibition of NK cell function.

Mesenchymal stem cells (MSC) are a kind of stromal cell within the tumor microenvironment. In our research, MSC derived from acute myeloid leukemia patients' bone marrow (AML-MSC) and lung cancer tissues (LC-MSC) as well as normal bone marrow-derived MSC (BM-MSC) cultured in conditioned medium of HeLa cells were found to have higher expressions of Toll-like receptor (TLR4) mRNA compared with BM-MSC. The sorted TLR4-positive MSC (TLR4+ MSC) differed in cytokine (interleukin-6, interleukin-8, and monocyte chemoattractant protein-1) secretion from those of unsorted MSC. MSC was reported to inhibit natural killer (NK) cell proliferation and function. In this research, we confirmed that TLR4+ MSC aggravate this suppression. Furthermore, when TLR4 in the sorted cells were stimulated by LPS or following blocked by antibody, the suppression on NK cell proliferation and cytotoxicity were more intensive or recovered respectively. Compared to unsorted MSC, NKG2D receptor expression on NK cells were also inhibited by TLR4+ MSC. These findings suggest that activation of TLR4 pathway is important for TLR4+ MSC and MSC to obstruct anti-tumor immunity by inhibiting NK cell function, which may provide a potential stroma-targeted tumor therapy.

[Clinical features, prevention and treatment of respiratory distress syndrome in neonates of different gestational ages in tertiary hospitals in Northwest China].

OBJECTIVE: To investigate the clinical features, prevention and treatment of respiratory distress syndrome (RDS) in neonates of different gestational ages (GA) in the tertiary hospitals in Northwest China. METHODS: A total of 440 neonates diagnosed with RDS between January and December, 2011 in 12 tertiary hospitals in Northwest China were enrolled and classified into three groups: early preterm (GA<34 weeks; n=247), late preterm (GA 34-36(+6) weeks; n=131) and full-term (GA≥37 weeks; n=62). The clinical data, including perinatal factors, prevention and treatment, complications and prognosis, were comparatively analyzed among the three groups. RESULTS: The rate of multiple births in the early preterm group was higher than the other two groups. The two preterm groups showed a higher incidence of premature rupture of membranes than the full-term group. The full-term group had a higher rate of cesarean section without contractions than the two preterm groups. The early preterm group had the highest application rate of antenatal steroids. Compared with the other two groups, the early preterm group had a higher application rate of pulmonary surfactants (PS) and an earlier time of first application of PS. The full-term group had a lower resuscitation rate than the two preterm groups. The early preterm group showed a higher incidence of patent ductus arteriosus and intracranial hemorrhage than the other two groups. The cure rate of RDS (78.2%) was the highest in the full-term group, followed by the late preterm group (58.6%) and the early preterm group (42.9%). CONCLUSIONS: RDS infants of different GA in Northwest China have significant differences in perinatal factors, antenatal prevention, PS treatment, complications and prognosis.