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Background Age-related cataract is a highly prevalent eye disorder that results in the clouding of the crystalline lens and is one of the leading causes of visual impairment and blindness. The disease is influenced by multiple factors including genetics, prolonged exposure to ultraviolet radiation (UVR), and a history of diabetes. However, the extent to which each of these factors contributes to the development of cataracts remains unclear. Further research into the genetic basis of cataracts could provide valuable insights into the disease's etiology and lead to a better understanding of biological mechanisms that are associated with its development. Methods This study presents the largest genome-wide association study of cataracts to date, using data from 127,985 cases and 837,371 controls. We performed gene enrichment analysis to identify genes and biological pathways associated with cataracts. We integrated our results with gene expression reference datasets to identify genetic variants modifying risk for cataracts through changes in the expression of specific genes. We further explored drug-gene interactions to better understand the potential impact of pharmacological interventions on cataract development. Finally, we explored whether a causal relationship underlies the known comorbidity between type 1 diabetes and cataracts using a mendelian randomization framework, and the association between UV exposure and cataract risk in adults using a polygenic risk scoring approach. Findings Our study identified 85 independent genome-wide significant loci, 37 of which are novel. Gene-based association tests identified 126 genes associated with cataracts, hinting at a potential relationship between negative regulation of lipid biosynthesis and the development of cataracts. Four of the genes identified GNL3, JAG1, METTL21A, and CREB1 are involved in drug-gene interactions. Moreover, Mendelian Randomisation analysis identified a putative causal relationship between genetic predisposition to type 1 diabetes and an increased risk of cataracts. Lastly, we found evidence indicating that early-life exposure to UVR may have an impact on the later development of cataracts. Interpretation Our findings advance our understanding of the genetic basis of cataract and provide new insights into its etiology. We identified multiple genes and biological pathways associated with the condition, including associations with four genes from which drug repurposing could be proposed. Our results suggest a causal association between type 1 diabetes and cataracts. Also, we highlighted a surrogate measure of UV light exposure as a marker of cataract risk in adults and drug-genes interactions that has the potential of informing novel therapies.

Study ObjectivesDespite its association with severe health conditions, the etiology of sleep apnea (SA) remains understudied. This study sought to identify genetic variants robustly associated with SA risk.MethodsWe performed a genome-wide association study (GWAS) meta-analysis of SA across five cohorts (NTotal = 523 366), followed by a multi-trait analysis of GWAS (multi-trait analysis of genome-wide association summary statistics [MTAG]) to boost power, leveraging the high genetic correlation between SA and snoring. We then adjusted our results for the genetic effects of body mass index (BMI) using multi-trait-based conditional and joint analysis (mtCOJO) and sought replication of lead hits in a large cohort of participants from 23andMe, Inc (NTotal = 1 477 352; Ncases = 175 522). We also explored genetic correlations with other complex traits and performed a phenome-wide screen for causally associated phenotypes using the latent causal variable method.ResultsOur SA meta-analysis identified five independent variants with evidence of association beyond genome-wide significance. After adjustment for BMI, only one genome-wide significant variant was identified. MTAG analyses uncovered 49 significant independent loci associated with SA risk. Twenty-nine variants were replicated in the 23andMe GWAS adjusting for BMI. We observed genetic correlations with several complex traits, including multisite chronic pain, diabetes, eye disorders, high blood pressure, osteoarthritis, chronic obstructive pulmonary disease, and BMI-associated conditions.ConclusionOur study uncovered multiple genetic loci associated with SA risk, thus increasing our understanding of the etiology of this condition and its relationship with other complex traits.

Background Advances in the expression quantitative trait loci (eQTL) studies have provided valuable insights into the mechanism of diseases and traits-associated genetic variants. However, it remains challenging to evaluate and control the quality of multi-source heterogeneous eQTL raw data for researchers with limited computational background. There is an urgent need to develop a powerful and user-friendly tool to automatically process the raw datasets in various formats and perform the eQTL mapping afterward. Results In this work, we present a pipeline for eQTL analysis, termed eQTLQC, featured with automated data preprocessing for both genotype data and gene expression data. Our pipeline provides a set of quality control and normalization approaches, and utilizes automated techniques to reduce manual intervention. We demonstrate the utility and robustness of this pipeline by performing eQTL case studies using multiple independent real-world datasets with RNA-seq data and whole genome sequencing (WGS) based genotype data. Conclusions eQTLQC provides a reliable computational workflow for eQTL analysis. It provides standard quality control and normalization as well as eQTL mapping procedures for eQTL raw data in multiple formats. The source code, demo data, and instructions are freely available at https://github.com/stormlovetao/eQTLQC.

Background Whole‐genome sequencing data are available from several large studies across a variety of diseases and traits. However, massive storage and computation resources are required to use these data, and to achieve sufficient power for discoveries, harmonization of multiple cohorts is critical. Objectives The Accelerating Medicines Partnership Parkinson's Disease program has developed a research platform for Parkinson's disease (PD) that integrates the storage and analysis of whole‐genome sequencing data, RNA expression data, and clinical data, harmonized across multiple cohort studies. Methods The version 1 release contains whole‐genome sequencing data derived from 3941 participants from 4 cohorts. Samples underwent joint genotyping by the TOPMed Freeze 9 Variant Calling Pipeline. We performed descriptive analyses of these whole‐genome sequencing data using the Accelerating Medicines Partnership Parkinson's Disease platform. Results The clinical diagnosis of participants in version 1 release includes 2005 idiopathic PD patients, 963 healthy controls, 64 prodromal subjects, 62 clinically diagnosed PD subjects without evidence of dopamine deficit, and 705 participants of genetically enriched cohorts carrying PD risk‐associated GBA variants or LRRK2 variants, of whom 304 were affected. We did not observe significant enrichment of pathogenic variants in the idiopathic PD group, but the polygenic risk score was higher in PD both in nongenetically enriched cohorts and genetically enriched cohorts. The population analysis showed a correlation between genetically enriched cohorts and Ashkenazi Jewish ancestry. Conclusions We describe the genetic component of the Accelerating Medicines Partnership Parkinson's Disease platform, a solution to democratize data access and analysis for the PD research community. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. This article is a U.S. Government work and is in the public domain in the USA.

Exosomes have shown great potential in disease diagnostics and therapeutics. However, current isolation approaches are burdensome and suffer from low speed, yield and purity, limiting basic research and clinical applications. Here, we describe an efficient exosome detection method via the ultrafast-isolation system (EXODUS) that allows automated label-free purification of exosomes from varied biofluids. We obtained the ultra-efficient purification of exosomes by negative pressure oscillation and double coupled harmonic oscillator–enabled membrane vibration. Our two coupled oscillators generate dual-frequency transverse waves on the membranes, enabling EXODUS to outperform other isolation techniques in speed, purity and yield. We demonstrated EXODUS by purifying exosomes from urine samples of 113 patients and validated the practical relevance in exosomal RNA profiling with the high-resolution capability and high-throughput analysis. EXODUS is a high-speed isolation method for the enrichment of exosome from biological fluids with high purity and yield.

The genetic origins of nanoscale extracellular vesicles in our body fluids remains unclear. Here, we perform a tracking analysis of urinary exosomes via RNA sequencing, revealing that urine exosomes mostly express tissue-specific genes for the bladder and have close cell-genetic relationships to the endothelial cell, basal cell, monocyte, and dendritic cell. Tracking the differentially expressed genes of cancers and corresponding enrichment analysis show urine exosomes are intensively involved in immune activities, indicating that they may be harnessed as reliable biomarkers of noninvasive liquid biopsy in cancer genomic diagnostics and precision medicine.

Signaling pathway analysis has become a routine task after differentially expressed gene (DEG) studies across disease conditions, drug treatments, or developmental stages. A signaling pathway can be represented by a graph that consists of Genes and interactions (the genetic regulation) between them. However, existing signaling pathway analysis methods ignore the strength variations of interactions in signaling pathways under different conditions. Here, we developed a novel method named SPACI (Signaling Pathway Analysis Combined with the strength variations of Interactions between genes in signaling pathways under different conditions) to improve signaling pathway analysis after DEG studies. To further evaluate the performance of SPACI, we compared SPACI with nine other methods by using a benchmark of 28 gene expression datasets in two standard measures: sensitivity and prioritization. The False positive rate (FPR) of SPACI was also compared with five methods. The results show that SPACI is the second-ranked method in terms of prioritization and the third-ranked method in terms of sensitivity. SPACI is the top method when compared in terms of the sum value of the two ranks. Also, the FPR of SPACI is modest compared with the classic methods. Furthermore, the strength variation of the interaction is demonstrated as coherent with the biological problem. The interactions with high strength variations under different conditions can help improve the discovery of the underlying biological information. The R package of SPACI can be accessed at https://github.com/ZhenshenBao/SPACI .

Acne vulgaris is a highly heritable skin disorder that primarily impacts facial skin. Severely inflamed lesions may leave permanent scars that have been associated with long-term psychosocial consequences. Here, we perform a GWAS meta-analysis comprising 20,165 individuals with acne from nine independent European ancestry cohorts. We identify 29 novel genome-wide significant loci and replicate 14 of the 17 previously identified risk loci, bringing the total number of reported acne risk loci to 46. Using fine-mapping and eQTL colocalisation approaches, we identify putative causal genes at several acne susceptibility loci that have previously been implicated in Mendelian hair and skin disorders, including pustular psoriasis. We identify shared genetic aetiology between acne, hormone levels, hormone-sensitive cancers and psychiatric traits. Finally, we show that a polygenic risk score calculated from our results explains up to 5.6% of the variance in acne liability in an independent cohort.

A key driver of patients' well-being and clinical trials for Parkinson's disease (PD) is the course that the disease takes over time (progression and prognosis). To assess how genetic variation influences the progression of PD over time to dementia, a major determinant for quality of life, we performed a longitudinal genome-wide survival study of 11.2 million variants in 3,821 patients with PD over 31,053 visits. We discover RIMS2 as a progression locus and confirm this in a replicate population (hazard ratio (HR) = 4.77, P = 2.78 x 10(-11)), identify suggestive evidence for TMEM108 (HR = 2.86, P = 2.09 x 10(-8)) and WWOX (HR = 2.12, P = 2.37 x 10(-8)) as progression loci, and confirm associations for GBA (HR = 1.93, P = 0.0002) and APOE (HR = 1.48, P = 0.001). Polygenic progression scores exhibit a substantial aggregate association with dementia risk, while polygenic susceptibility scores are not predictive. This study identifies a novel synaptic locus and polygenic score for cognitive disease progression in PD and proposes diverging genetic architectures of progression and susceptibility.A genome-wide survival study identifies variants at RIMS2 associated with progression of Parkinson's disease to dementia and highlights divergence in the genetic architecture of disease onset and progression.

Background Age at onset (AAO) is an essential feature of Parkinson's disease (PD) and can help predict disease progression and mortality. Identification of genetic variants influencing AAO of PD could lead to a better understanding of the disease's biological mechanism and provide clinical guidance. However, genetic determinants for AAO of PD remain mostly unknown, especially in the Asian population. Objectives To identify genetic determinants for AAO of PD in the Asian population. Methods We performed a genome-wide association meta-analysis on AAO of PD in 5166 Chinese patients with PD (Ndiscovery = 3628, Nreplication = 1538). We then conducted a further cross-ethnic meta-analysis using our results and summary statistics for the AAO of PD from the European population. Results The total heritability of AAO of PD was around 0.10 ~ 0.14, similar to that (~0.11) estimated in populations of European ancestry. One novel significant intergenic locus rs9783733 (NDN; PWRN4) was identified (P = 3.14E-09, beta = 2.30, SE = 0.39). Remarkably, this variant could delay AAO of PD by ~2.43 years, with a more considerable effect on males (~3.18 years) than females (~1.45 years). The variant was suggestively significant in the cross-ethnic meta-analysis and suggested a positive selection in the East Asian population. Additionally, cross-ethnic meta-analysis identified a significant locus rs356203 in SNCA (P = 2.35E-11, beta = -0.71, SE = 0.01). Conclusions These findings improve the current understanding of the genetic etiology of AAO of PD in different ethnic groups, and provide a new target for further research on PD pathogenesis and potential therapeutic options. © 2021 International Parkinson and Movement Disorder Society.

In the last decade, massive omics datasets have been generated for human brain research. It is evolving so fast that a timely update is urgently needed. In this review, we summarize the main multi-omics data resources for the human brains of both healthy controls and neuropsychiatric disorders, including schizophrenia, autism, bipolar disorder, Alzheimer’s disease, Parkinson’s disease, progressive supranuclear palsy, etc. We also review the recent development of single-cell omics in brain research, such as single-nucleus RNA-seq, single-cell ATAC-seq and spatial transcriptomics. We further investigate the integrative multi-omics analysis methods for both tissue and single-cell data. Finally, we discuss the limitations and future directions of the multi-omics study of human brain disorders.

SummaryGenome-wide association studies (GWAS) have revealed thousands of genetic loci for common diseases. One of the main challenges in the post-GWAS era is to understand the causality of the genetic variants. Expression quantitative trait locus (eQTL) analysis has been proven to be an effective way to address this question by examining the relationship between gene expression and genetic variation in a sufficiently powered cohort. However, it is often tricky to determine the sample size at which a variant with a specific allele frequency will be detected to associate with gene expression with sufficient power. This is particularly demanding with single-cell RNAseq studies. Therefore, a user-friendly tool to perform power analysis for eQTL at both bulk tissue and single-cell level will be critical. Here, we presented an R package called powerEQTL with flexible functions to calculate power, minimal sample size, or detectable minor allele frequency in both bulk tissue and single-cell eQTL analysis. A user-friendly, program-free web application is also provided, allowing customers to calculate and visualize the parameters interactively.Availability and implementationThe powerEQTL R package source code and online tutorial are freely available at CRAN: https://cran.r-project.org/web/packages/powerEQTL/. The R shiny application is publicly hosted at https://bwhbioinfo.shinyapps.io/powerEQTL/.ContactXianjun Dong (xdong@rics.bwh.harvard.edu), Weiliang Qiu (weiliang.qiu@sanofi.com)Supplementary informationSupplementary data are available at Bioinformatics online.

BackgroundWhole-genome sequencing (WGS) data is available from several large studies across a variety of diseases and traits. However, massive storage and computation resources are required to use these data, and, to achieve the sufficient power for discoveries, harmonization of multiple cohorts is critical.ObjectivesThe Accelerating Medicines Partnership Parkinson’s Disease (AMP PD) program has developed a research platform for Parkinson’s disease (PD) which integrates the storage and analysis of WGS data, RNA expression data, and clinical data, harmonized across multiple cohort studies.MethodsThe version 1 release contains WGS data derived from 3,941 participants from 4 cohorts. Samples underwent joint genotyping by the TOPMed Freeze 9 Variant Calling Pipeline. We performed descriptive analyses of these WGS data using the AMP PD platform.ResultsThe clinical diagnosis of participants in version 1 release includes 2,005 idiopathic PD patients, 963 healthy controls, 64 prodromal subjects, 62 clinically diagnosed PD subjects without evidence of dopamine deficit (SWEDD) and 705 participants of genetically enriched cohorts carrying PD risk associated GBA variants or LRRK2 variants in which 304 were affected. We did not observe a significant enrichment of pathogenic variants in the idiopathic PD group, but the polygenic risk score (PRS) was higher in PD both in non-genetically enriched cohorts and genetically enriched cohorts. The population analysis showed a correlation between genetically enriched cohorts and Ashkenazi Jewish ancestry.ConclusionsWe describe the genetic component of the AMP PD platform, a solution to democratise data access and analysis for the PD research community.(d) Financial Disclosure/CoI

Previous observational studies have identified correlations between Parkinson’s disease (PD) risk and lifestyle factors. However, whether or not those associations are causal remains unclear. To infer causality between PD risk and smoking or alcohol intake, we conducted a two-sample Mendelian randomization study using genome-wide association study summary statistics from the GWAS & Sequencing Consortium of Alcohol and Nicotine use study (1.2 million participants) and the latest meta-analysis from the International Parkinson’s Disease Genomics Consortium (37,688 PD cases and 18,618 proxy-cases). We performed sensitivity analyses, including testing for pleiotropy with MR-Egger and MR-PRESSO, and multivariable MR modeling to account for the genetic effects of competing substance use traits on PD risk. Our results revealed causal associations of alcohol intake (OR 0.79; 95% CI 0.65–0.96; p = 0.021) and smoking continuation (which compares current vs. former smokers) (OR 0.64; 95% CI 0.46–0.89; p = 0.008) with lower PD risk. Multivariable MR analyses showed that the causal association between drinks per week and PD is unlikely due to confounding by smoking behavior. Finally, frailty analyses suggested that the causal effects of both alcohol intake and smoking continuation on PD risk estimated from MR analysis are not explained by the presence of survival bias alone. Our findings support the role of smoking as a protective factor against PD, but only when comparing current vs. former smokers. Similarly, increased alcohol intake had a protective effect over PD risk, with the alcohol dehydrogenase 1B (ADH1B) locus as a potential candidate for further investigation of the mechanisms underlying this association.

BackgroundSleep apnoea is characterised by periods of halted breathing during sleep. Despite its association with severe health conditions, the aetiology of sleep apnoea remains understudied, and previous genetic analyses have not identified many robustly associated genetic risk variants.MethodsWe performed a genome-wide association study (GWAS) meta-analysis of sleep apnoea across five cohorts (NTotal=523,366), followed by a multi-trait analysis of GWAS (MTAG) to boost power, leveraging the high genetic correlation between sleep apnoea and snoring. We then adjusted our results for the genetic effects of body mass index (BMI) using multi-trait-based conditional & joint analysis (mtCOJO) and sought replication of lead hits in a large cohort of participants from 23andMe, Inc (NTotal=1,477,352; Ncases=175,522). We also explored genetic correlations with other complex traits and performed a phenome-wide screen for causally associated phenotypes using the latent causal variable method.ResultsOur MTAG analysis uncovered 49 significant independent loci associated with sleep apnoea risk. Twenty-nine variants were replicated in the 23andMe cohort. We observed genetic correlations with several complex traits, including multisite chronic pain, diabetes, eye disorders, high blood pressure, osteoarthritis, chronic obstructive pulmonary disease, and BMI-associated conditions.ConclusionsOur study uncovered multiple genetic loci associated with sleep apnoea risk, thus increasing our understanding of the aetiology of this condition and its relationship with other complex traits.

Skeletal muscles can undergo atrophy and/or programmed cell death (PCD) during development or in response to a wide range of insults, including immobility, cachexia, and spinal cord injury. However, the protracted nature of atrophy and the presence of multiple cell types within the tissue complicate molecular analyses. One model that does not suffer from these limitations is the intersegmental muscle (ISM) of the tobacco hawkmoth Manduca sexta. Three days before the adult eclosion (emergence) at the end of metamorphosis, the ISMs initiate a nonpathological program of atrophy that results in a 40% loss of mass. The ISMs then generate the eclosion behavior and initiate a nonapoptotic PCD during the next 30 h. We have performed a comprehensive transcriptomics analysis of all mRNAs and microRNAs throughout ISM development to better understand the molecular mechanisms that mediate atrophy and death. Atrophy involves enhanced protein catabolism and reduced expression of the genes involved in respiration, adhesion, and the contractile apparatus. In contrast, PCD involves the induction of numerous proteases, DNA methylases, membrane transporters, ribosomes, and anaerobic metabolism. These changes in gene expression are largely repressed when insects are injected with the insect steroid hormone 20-hydroxyecdysone, which delays death. The expression of the death-associated proteins may be greatly enhanced by reductions in specific microRNAs that function to repress translation. This study not only provides fundamental new insights into basic developmental processes, it may also represent a powerful resource for identifying potential diagnostic markers and molecular targets for therapeutic intervention.

Gene set analysis using signaling pathway has become a popular downstream analysis following differential expression analysis. From a biological point of view, only some portions of a pathway are expected to be altered; however, a few approaches using the different importance of genes in signaling pathways, which encompass the constitutive functional nonequivalent roles of genes in real pathways, have been proposed and none of them tries to associate the importance of genes with the related disease. In this paper, we developed an extended method of signaling pathway impact analysis (SPIA), called gwSPIA, by incorporating three signaling pathway-based gene weight merits that reflect the importance of genes from different aspects and attempt to associate the importance of genes with the related diseases. By applying the gwSPIA to the gene expression data sets in comparison with other seven methods in three measures, sensitivity, prioritization, and specificity, we show that the gwSPIA ranks in the second place in both sensitivity and prioritization. Furthermore, the specificity of the gwSPIA is better than SPIA, which is lower than 25%. The results also suggest that the gene weight used in the gwSPIA can reflect the association between the genes and the related diseases. The R package of the gwSPIA can be accessed from https://github.com/sterding/gwSPIA.

BackgroundObservational studies have identified correlations between environmental and lifestyle factors and Parkinson’s disease (PD). However, the causal direction of many of these relationships remains unclear.ObjectiveTo infer causal relationships between smoking and alcohol intake and PD.MethodsWe use a two-sample Mendelian randomization (MR) experimental design to infer causal relationships between smoking (initiation, age of initiation, heaviness, and cessation) and alcohol (drinks per week) consumption as exposure variables and PD as the health outcome. We also conduct sensitivity analyses, including testing for pleiotropic effects MR-Egger and MR-PRESSO, and multivariable MR to jointly model the effects of drinking and smoking behavior on PD risk.ResultsBoth alcohol intake (OR = 0.69; 95% CI 0.56-0.86; p=0.001). and smoking cessation (comparing current vs. former smokers) (IVW OR = 0.39; 95% CI 0.22 to 0.69; p=0.001) were causally associated with a reduced risk of PD. In addition, our multivariable MR results provide additional assurance that the causal association between drinks per week and PD is unlikely due to confounding by smoking behavior.ConclusionOur findings support the role of smoking as a protective factor against PD, but only when comparing current vs. former smokers. Increased alcohol intake also had a protective effect over PD risk, and the alcohol dehydrogenase 1B (ADH1B) locus is a candidate for further investigating the mechanisms underlying this association.

Enhancers function as DNA logic gates and may control specialized functions of billions of neurons. Here we show a tailored program of noncoding genome elements active in situ in physiologically distinct dopamine neurons of the human brain. We found 71,022 transcribed noncoding elements, many of which were consistent with active enhancers and with regulatory mechanisms in zebrafish and mouse brains. Genetic variants associated with schizophrenia, addiction, and Parkinson’s dis- ease were enriched in these elements. Expression quantitative trait locus analysis revealed that Parkinson’s disease-associated variants on chromosome 17q21 cis-regulate the expression of an enhancer RNA in dopamine neurons. This study shows that enhancers in dopamine neurons link genetic variation to neuropsychiatric traits.

Expression quantitative trait loci (eQTL) analysis is of critical importance to understand the mechanism underlying trait associated variants. Evaluating and controlling the data quality of transcripts and genotypes, which are basis of eQTL analysis, remains challenging for researchers with limited computational backgrounds. There is a strong need for a user-friendly and comprehensive tool to pre-process those data sets automatically. Here we propose such a solution, eQTLQC, an automated quality control pipeline for preprocessing both RNA-seq and genotype data. The eQTLQC pipeline provides multiple informative quality control measurements and data normalization approaches. And it provides a easy-to-use configuration file for users to flexibly set up the parameters and control the pipeline. We demonstrate its utility by performing RNA-seq and genotype preprocessing on real data sets. eQTLQC is open source and freely available at https://github.com/ruanjunpeng/eQTLQC.

In the version of this article initially published, the legends for Supplementary Figs. 4–8 and 10–14 contained errors. The Supplementary Figure legends have been corrected in the HTML and PDF versions of the article.

There are no cures for neurodegenerative diseases and this is partially due to the difficulty of monitoring pathogenic molecules in patients during life. The Parkinson's disease gene α-synuclein (SNCA) is selectively expressed in blood cells and neurons. Here we show that SNCA transcripts in circulating blood cells are paradoxically reduced in early stage, untreated and dopamine transporter neuroimaging-supported Parkinson's disease in three independent regional, national, and international populations representing 500 cases and 363 controls and on three analogue and digital platforms with P < 0.0001 in meta-analysis. Individuals with SNCA transcripts in the lowest quartile of counts had an odds ratio for Parkinson's disease of 2.45 compared to individuals in the highest quartile. Disease-relevant transcript isoforms were low even near disease onset. Importantly, low SNCA transcript abundance predicted cognitive decline in patients with Parkinson's disease during up to 5 years of longitudinal follow-up. This study reveals a consistent association of reduced SNCA transcripts in accessible peripheral blood and early-stage Parkinson's disease in 863 participants and suggests a clinical role as potential predictor of cognitive decline. Moreover, the three independent biobank cohorts provide a generally useful platform for rapidly validating any biological marker of this common disease.

Animal germ cells produce PIWI-interacting RNAs (piRNAs), small silencing RNAs that suppress transposons and enable gamete maturation. Mammalian transposon-silencing piRNAs accumulate early in spermatogenesis, whereas pachytene piRNAs are produced later during postnatal spermatogenesis and account for >95% of all piRNAs in the adult mouse testis. Mutants defective for pachytene piRNA pathway proteins fail to produce mature sperm, but neither the piRNA precursor transcripts nor the trigger for pachytene piRNA production is known. Here, we show that the transcription factor A-MYB initiates pachytene piRNA production. A-MYB drives transcription of both pachytene piRNA precursor RNAs and the mRNAs for core piRNA biogenesis factors including MIWI, the protein through which pachytene piRNAs function. A-MYB regulation of piRNA pathway proteins and piRNA genes creates a coherent feedforward loop that ensures the robust accumulation of pachytene piRNAs. This regulatory circuit, which can be detected in rooster testes, likely predates the divergence of birds and mammals.

In the nuclei of eukaryotic cells, DNA wraps around the octamer of histone proteins to form the nucleosome, in a structure like ‘beads on a string’, which makes up the basic unit of chromatin. Chromatin further folds into higherlevel structures, loosely or tightly, which helps to determine the accessibility of the DNA. For instance, actively transcribed regions tend to be in looser chromatin structures so that transcription factors and RNA polymerases can access the genes. Chromatin structure can be altered by various post-translational modifications of the N-terminal tail residues of histone proteins. For example, acetylation of a lysine residue can neutralize its positive charge and weaken the binding between the histone and the negatively charged DNA, which exposes the DNA to regulatory proteins. Methylation is another common type of histone modification; for example, the lysine at the fourth position of the H3 histone can be mono-, dior tri-methylated (denoted as H3K4me1, H3K4me2 and H3K4me3, respectively). By examining histone modification patterns at highly conserved noncoding regions in mouse embryonic stem cells, Bernstein et al. found ‘bivalent domains’ of histone modifications (i.e., harboring both the repressive mark H3K27me3 and the active mark H3K4me3) near genes with poised transcription [1]. When embryonic stem cells differentiate into more specialized cells (e.g., neural precursor cells), a subset of the bivalent domains are resolved (i.e., H3K27me3 becomes weaker, while H3K4me3 becomes stronger, and these loci coincide with genes that are actively transcribed in neural precursor cells). Thus, combinations of histone marks are indicative of transcriptional states. Barski et al. mapped 20 histone methylations of lysine and arginine residues in human CD4 T cells using chromatin immunoprecipitation followed by sequencing (ChIP-seq) [2]. They found that monomethylated H3K27, H3K9, H4K20, H3K79 and H2BK5 were linked to gene activation, while trimethylated H3K27, H3K9 and H3K79 were linked to gene repression. In a later study, the group profiled 39 additional histone modifications in human CD4 T cells [3]. They identified more than 3000 genes that were highly expressed in these cells and the promoters of these genes showed high levels of 17 histone modifications (called a histone modification module). Other studies also investigated the correlation between individual histone marks and gene expression, although not in a quantitative way [4,5].

Elucidating the risk of Parkinson's disease High expression of the α-synuclein gene ( SNCA ) is a risk factor for Parkinson's disease (PD), but certain drugs may mitigate this risk. Mittal et al. ran a small-molecule screen to identify compounds that regulate levels of SNCA expression and found that several β2-adrenoreceptor (β2AR) agonists reduced them (see the Perspective by Snyder). These compounds modulated epigenetic marks at the SNCA gene, effectively suppressing SNCA transcription. The authors looked at the pharmaceutical history of more than 4 million Norwegians over an 11-year period and found a reduced risk of PD among those that were taking one of the β2AR agonists for other medical problems. Science , this issue p. 891 ; see also p. 869

Chromatin immunoprecipitation coupled with high-throughput sequencing (ChIP-seq) has become the dominant technique for mapping transcription factor (TF) binding regions genome-wide. We performed an integrative analysis centered around 457 ChIP-seq data sets on 119 human TFs generated by the ENCODE Consortium. We identified highly enriched sequence motifs in most data sets, revealing new motifs and validating known ones. The motif sites (TF binding sites) are highly conserved evolutionarily and show distinct footprints upon DNase I digestion. We frequently detected secondary motifs in addition to the canonical motifs of the TFs, indicating tethered binding and cobinding between multiple TFs. We observed significant position and orientation preferences between many cobinding TFs. Genes specifically expressed in a cell line are often associated with a greater occurrence of nearby TF binding in that cell line. We observed cell-line–specific secondary motifs that mediate the binding of the histone deacetylase HDAC2 and the enhancer-binding protein EP300. TF binding sites are located in GC-rich, nucleosome-depleted, and DNase I sensitive regions, flanked by well-positioned nucleosomes, and many of these features show cell type specificity. The GC-richness may be beneficial for regulating TF binding because, when unoccupied by a TF, these regions are occupied by nucleosomes in vivo. We present the results of our analysis in a TF-centric web repository Factorbook (http://factorbook.org) and will continually update this repository as more ENCODE data are generated.

To investigate epigenetic contributions to Huntington's disease (HD) pathogenesis, we carried out genome-wide mapping of the transcriptional mark, trimethyl-histone H3-lysine 4 (H3K4me3) in neuronal nuclei extracted from prefrontal cortex of HD cases and controls using chromatin immunoprecipitation followed by deep-sequencing. Neuron-specific mapping of the genome-wide distribution of H3K4me3 revealed 136 differentially enriched loci associated with genes implicated in neuronal development and neurodegeneration, including GPR3, TMEM106B, PDIA6 and the Notch signaling genes hairy and enhancer of split 4 (HES4) and JAGGED2, supporting the view that the neuronal epigenome is affected in HD. Importantly, loss of H3K4me3 at CpG-rich sequences on the HES4 promoter was associated with excessive DNA methylation, reduced binding of nuclear proteins to the methylated region and altered expression of HES4 and HES4 targeted genes MASH1 and P21 involved in striatal development. Moreover, hypermethylation of HES4 promoter sequences was strikingly correlated with measures of striatal degeneration and age-of-onset in a cohort of 25 HD brains (r = 0.56, P = 0.006). Lastly, shRNA knockdown of HES4 in human neuroblastoma cells altered MASH1 and P21 mRNA expression and markedly increased mutated HTT-induced aggregates and cell death. These findings, taken together, suggest that epigenetic dysregulation of HES4 could play a critical role in modifying HD disease pathogenesis and severity.