Pooled ChIP-Seq Links Variation in Transcription Factor Binding to Complex Disease Risk.

Cis-regulatory elements such as transcription factor (TF) binding sites can be identified genome-wide, but it remains far more challenging to pinpoint genetic variants affecting TF binding. Here, we introduce a pooling-based approach to mapping quantitative trait loci (QTLs) for molecular-level traits. Applying this to five TFs and a histone modification, we mapped thousands of cis-acting QTLs, with over 25-fold lower cost compared to standard QTL mapping. We found that single genetic variants frequently affect binding of multiple TFs, and CTCF can recruit all five TFs to its binding sites. These QTLs often affect local chromatin and transcription but can also influence long-range chromosomal contacts, demonstrating a role for natural genetic variation in chromosomal architecture. Thousands of these QTLs have been implicated in genome-wide association studies, providing candidate molecular mechanisms for many disease risk loci and suggesting that TF binding variation may underlie a large fraction of human phenotypic variation.

Bayesian-based noninvasive prenatal diagnosis of single-gene disorders.

In the last decade, noninvasive prenatal diagnosis (NIPD) has emerged as an effective procedure for early detection of inherited diseases during pregnancy. This technique is based on using cell-free DNA (cfDNA) and fetal cfDNA (cffDNA) in maternal blood, and hence, has minimal risk for the mother and fetus compared with invasive techniques. NIPD is currently used for identifying chromosomal abnormalities (in some instances) and for single-gene disorders (SGDs) of paternal origin. However, for SGDs of maternal origin, sensitivity poses a challenge that limits the testing to one genetic disorder at a time. Here, we present a Bayesian method for the NIPD of monogenic diseases that is independent of the mode of inheritance and parental origin. Furthermore, we show that accounting for differences in the length distribution of fetal- and maternal-derived cfDNA fragments results in increased accuracy. Our model is the first to predict inherited insertions-deletions (indels). The method described can serve as a general framework for the NIPD of SGDs; this will facilitate easy integration of further improvements. One such improvement that is presented in the current study is a machine learning model that corrects errors based on patterns found in previously processed data. Overall, we show that next-generation sequencing (NGS) can be used for the NIPD of a wide range of monogenic diseases, simultaneously. We believe that our study will lead to the achievement of a comprehensive NIPD for monogenic diseases.

Inferring Relevant Cell Types for Complex Traits by Using Single-Cell Gene Expression.

Previous studies have prioritized trait-relevant cell types by looking for an enrichment of genome-wide association study (GWAS) signal within functional regions. However, these studies are limited in cell resolution by the lack of functional annotations from difficult-to-characterize or rare cell populations. Measurement of single-cell gene expression has become a popular method for characterizing novel cell types, and yet limited work has linked single-cell RNA sequencing (RNA-seq) to phenotypes of interest. To address this deficiency, we present RolyPoly, a regression-based polygenic model that can prioritize trait-relevant cell types and genes from GWAS summary statistics and gene expression data. RolyPoly is designed to use expression data from either bulk tissue or single-cell RNA-seq. In this study, we demonstrated RolyPoly's accuracy through simulation and validated previously known tissue-trait associations. We discovered a significant association between microglia and late-onset Alzheimer disease and an association between schizophrenia and oligodendrocytes and replicating fetal cortical cells. Additionally, RolyPoly computes a trait-relevance score for each gene to reflect the importance of expression specific to a cell type. We found that differentially expressed genes in the prefrontal cortex of individuals with Alzheimer disease were significantly enriched with genes ranked highly by RolyPoly gene scores. Overall, our method represents a powerful framework for understanding the effect of common variants on cell types contributing to complex traits.

Effective genetic-risk prediction using mixed models.

For predicting genetic risk, we propose a statistical approach that is specifically adapted to dealing with the challenges imposed by disease phenotypes and case-control sampling. Our approach (termed Genetic Risk Scores Inference [GeRSI]), combines the power of fixed-effects models (which estimate and aggregate the effects of single SNPs) and random-effects models (which rely primarily on whole-genome similarities between individuals) within the framework of the widely used liability-threshold model. We demonstrate in extensive simulation that GeRSI produces predictions that are consistently superior to current state-of-the-art approaches. When applying GeRSI to seven phenotypes from the Wellcome Trust Case Control Consortium (WTCCC) study, we confirm that the use of random effects is most beneficial for diseases that are known to be highly polygenic: hypertension (HT) and bipolar disorder (BD). For HT, there are no significant associations in the WTCCC data. The fixed-effects model yields an area under the ROC curve (AUC) of 54%, whereas GeRSI improves it to 59%. For BD, using GeRSI improves the AUC from 55% to 62%. For individuals ranked at the top 10% of BD risk predictions, using GeRSI substantially increases the BD relative risk from 1.4 to 2.5.

Measuring missing heritability: inferring the contribution of common variants.

Genome-wide association studies (GWASs), also called common variant association studies (CVASs), have uncovered thousands of genetic variants associated with hundreds of diseases. However, the variants that reach statistical significance typically explain only a small fraction of the heritability. One explanation for the "missing heritability" is that there are many additional disease-associated common variants whose effects are too small to detect with current sample sizes. It therefore is useful to have methods to quantify the heritability due to common variation, without having to identify all causal variants. Recent studies applied restricted maximum likelihood (REML) estimation to case-control studies for diseases. Here, we show that REML considerably underestimates the fraction of heritability due to common variation in this setting. The degree of underestimation increases with the rarity of disease, the heritability of the disease, and the size of the sample. Instead, we develop a general framework for heritability estimation, called phenotype correlation-genotype correlation (PCGC) regression, which generalizes the well-known Haseman-Elston regression method. We show that PCGC regression yields unbiased estimates. Applying PCGC regression to six diseases, we estimate the proportion of the phenotypic variance due to common variants to range from 25% to 56% and the proportion of heritability due to common variants from 41% to 68% (mean 60%). These results suggest that common variants may explain at least half the heritability for many diseases. PCGC regression also is readily applicable to other settings, including analyzing extreme-phenotype studies and adjusting for covariates such as sex, age, and population structure.

Deep sequencing analysis of viral infection and evolution allows rapid and detailed characterization of viral mutant spectrum.

MOTIVATION: The study of RNA virus populations is a challenging task. Each population of RNA virus is composed of a collection of different, yet related genomes often referred to as mutant spectra or quasispecies. Virologists using deep sequencing technologies face major obstacles when studying virus population dynamics, both experimentally and in natural settings due to the relatively high error rates of these technologies and the lack of high performance pipelines. In order to overcome these hurdles we developed a computational pipeline, termed ViVan (Viral Variance Analysis). ViVan is a complete pipeline facilitating the identification, characterization and comparison of sequence variance in deep sequenced virus populations. RESULTS: Applying ViVan on deep sequenced data obtained from samples that were previously characterized by more classical approaches, we uncovered novel and potentially crucial aspects of virus populations. With our experimental work, we illustrate how ViVan can be used for studies ranging from the more practical, detection of resistant mutations and effects of antiviral treatments, to the more theoretical temporal characterization of the population in evolutionary studies. AVAILABILITY AND IMPLEMENTATION: Freely available on the web at http://www.vivanbioinfo.org CONTACT: : nshomron@post.tau.ac.il SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

lobSTR: A short tandem repeat profiler for personal genomes.

Short tandem repeats (STRs) have a wide range of applications, including medical genetics, forensics, and genetic genealogy. High-throughput sequencing (HTS) has the potential to profile hundreds of thousands of STR loci. However, mainstream bioinformatics pipelines are inadequate for the task. These pipelines treat STR mapping as gapped alignment, which results in cumbersome processing times and a biased sampling of STR alleles. Here, we present lobSTR, a novel method for profiling STRs in personal genomes. lobSTR harnesses concepts from signal processing and statistical learning to avoid gapped alignment and to address the specific noise patterns in STR calling. The speed and reliability of lobSTR exceed the performance of current mainstream algorithms for STR profiling. We validated lobSTR's accuracy by measuring its consistency in calling STRs from whole-genome sequencing of two biological replicates from the same individual, by tracing Mendelian inheritance patterns in STR alleles in whole-genome sequencing of a HapMap trio, and by comparing lobSTR results to traditional molecular techniques. Encouraged by the speed and accuracy of lobSTR, we used the algorithm to conduct a comprehensive survey of STR variations in a deeply sequenced personal genome. We traced the mutation dynamics of close to 100,000 STR loci and observed more than 50,000 STR variations in a single genome. lobSTR's implementation is an end-to-end solution. The package accepts raw sequencing reads and provides the user with the genotyping results. It is written in C/C++, includes multi-threading capabilities, and is compatible with the BAM format.

MARCKS protein mediates hydrogen peroxide regulation of endothelial permeability.

Impairment of endothelial barrier function is implicated in many vascular and inflammatory disorders. One prevalent mechanism of endothelial dysfunction is an increase in reactive oxygen species under oxidative stress. Previous reports have demonstrated that hydrogen peroxide (H(2)O(2)), a highly stable reactive oxygen species that modulates physiological signaling pathways, also enhances endothelial permeability, but the mechanism of this effect is unknown. Here, we identify the actin-binding protein myristoylated alanine-rich C-kinase substrate (MARCKS) as a key mediator of the H(2)O(2)-induced permeability change in bovine aortic endothelial cells. MARCKS knockdown and H(2)O(2) treatment alter the architecture of the actin cytoskeleton in endothelial cells, and H(2)O(2) induces the phosphorylation and translocation of MARCKS from the cell membrane to the cytosol. Using pharmacological inhibitors and small interference RNA constructs directed against specific proteins, we uncover a signaling cascade from Rac1 to Abl1, phospholipase Cγ1, and PKCδ that is triggered by H(2)O(2) and leads to MARCKS phosphorylation. Our findings establish a distinct role for MARCKS in the regulation of H(2)O(2)-induced permeability change in endothelial cells, and suggest potential new therapeutic targets for the treatment of disorders involving oxidative stress and altered endothelial permeability.

CD151 restricts the α6 integrin diffusion mode.

Laminin-binding integrins (α3ß1, α6ß1, α6ß4, α7ß1) are almost always expressed together with tetraspanin CD151. In every coexpressing cell analyzed to date, CD151 makes a fundamental contribution to integrin-dependent motility, invasion, morphology, adhesion and/or signaling. However, there has been minimal mechanistic insight into how CD151 affects integrin functions. In MDA-MB-231 mammary cells, tetraspanin CD151 knockdown impairs α6 integrin clustering and functions without decreasing α6 integrin expression or activation. Furthermore, CD151 knockdown minimally affects the magnitude of α6 integrin diffusion, as measured using single particle tracking. Instead, CD151 knockdown has a novel and unexpected dysregulating effect on the mode of α6 integrin diffusion. In control cells α6 integrin shows mostly random-confined diffusion (RCD) and some directed motion (DMO). In sharp contrast, in CD151-knockdown cells α6 integrin shows mostly DMO. In control cells α6 diffusion mode is sensitive to actin disruption, talin knockdown and phorbol ester stimulation. By contrast, CD151 knockdown cell α6 integrin is sensitive to actin disruption but desensitized to talin knockdown or phorbol ester stimulation, indicating dysregulation. Both phorbol ester and EGF stimulate cell spreading and promote α6 RCD in control cells. By contrast, CD151-ablated cells retain EGF effects but lose phorbol-ester-stimulated spreading and α6 RCD. For α6 integrins, physical association with CD151 promotes α6 RCD, in support of α6-mediated cable formation and adhesion. By comparison, for integrins not associated with CD151 (e.g. αv integrins), CD151 affects neither diffusion mode nor αv function. Hence, CD151 support of α6 RCD is specific and functionally relevant, and probably underlies diverse CD151 functions in skin, kidney and cancer cells.

Macrophages require Skap2 and Sirpα for integrin-stimulated cytoskeletal rearrangement.

Macrophages migrate to sites of insult during normal inflammatory responses. Integrins guide such migration, but the transmission of signals from integrins into the requisite cytoskeletal changes is poorly understood. We have discovered that the hematopoietic adaptor protein Skap2 is necessary for macrophage migration, chemotaxis, global actin reorganization and local actin reorganization upon integrin engagement. Binding of phosphatidylinositol [3,4,5]-triphosphate to the Skap2 pleckstrin-homology (PH) domain, which relieves its conformational auto-inhibition, is critical for this integrin-driven cytoskeletal response. Skap2 enables integrin-induced tyrosyl phosphorylation of Src-family kinases (SFKs), Adap, and Sirpα, establishing their roles as signaling partners in this process. Furthermore, macrophages lacking functional Sirpα unexpectedly have impaired local integrin-induced responses identical to those of Skap2(-/-) macrophages, and Skap2 requires Sirpα for its recruitment to engaged integrins and for coordinating downstream actin rearrangement. By revealing the positive-regulatory role of Sirpα in a Skap2-mediated mechanism connecting integrin engagement with cytoskeletal rearrangement, these data demonstrate that Sirpα is not exclusively immunoinhibitory, and illuminate previously unexplained observations implicating Skap2 and Sirpα in mouse models of inflammatory disease.

Inactivation of retinoblastoma protein does not overcome the requirement for human cytomegalovirus UL97 in lamina disruption and nuclear egress.

Human cytomegalovirus (HCMV) encodes one conventional protein kinase, UL97. During infection, UL97 phosphorylates the retinoblastoma tumor suppressor protein (pRb) on sites ordinarily phosphorylated by cyclin-dependent kinases (CDK), inactivating the ability of pRb to repress host genes required for cell cycle progression to S phase. UL97 is important for viral DNA synthesis in quiescent cells, but this function can be replaced by human papillomavirus type 16 E7, which targets pRb for degradation. However, viruses in which E7 replaces UL97 are still defective for virus production. UL97 is also required for efficient nuclear egress of viral nucleocapsids, which is associated with disruption of the nuclear lamina during infection, and phosphorylation of lamin A/C on serine 22, which antagonizes lamin polymerization. We investigated whether inactivation of pRb might overcome the requirement of UL97 for these roles, as pRb inactivation induces CDK1, and CDK1 phosphorylates lamin A/C on serine 22. We found that lamin A/C serine 22 phosphorylation during HCMV infection correlated with expression of UL97 and was considerably delayed in UL97-null mutants, even when E7 was expressed. E7 failed to restore gaps in the nuclear lamina seen in wild-type but not UL97-null virus infections. In electron microscopy analyses, a UL97-null virus expressing E7 was as impaired as a UL97-null mutant in cytoplasmic accumulation of viral nucleocapsids. Our results demonstrate that pRb inactivation is insufficient to restore efficient viral nuclear egress of HCMV in the absence of UL97 and instead argue further for a direct role of UL97 in this stage of the infectious cycle.

Using state machines to model the Ion Torrent sequencing process and to improve read error rates.

MOTIVATION: The importance of fast and affordable DNA sequencing methods for current day life sciences, medicine and biotechnology is hard to overstate. A major player is Ion Torrent, a pyrosequencing-like technology which produces flowgrams--sequences of incorporation values--which are converted into nucleotide sequences by a base-calling algorithm. Because of its exploitation of ubiquitous semiconductor technology and innovation in chemistry, Ion Torrent has been gaining popularity since its debut in 2011. Despite the advantages, however, Ion Torrent read accuracy remains a significant concern. RESULTS: We present FlowgramFixer, a new algorithm for converting flowgrams into reads. Our key observation is that the incorporation signals of neighboring flows, even after normalization and phase correction, carry considerable mutual information and are important in making the correct base-call. We therefore propose that base-calling of flowgrams should be done on a read-wide level, rather than one flow at a time. We show that this can be done in linear-time by combining a state machine with a Viterbi algorithm to find the nucleotide sequence that maximizes the likelihood of the observed flowgram. FlowgramFixer is applicable to any flowgram-based sequencing platform. We demonstrate FlowgramFixer's superior performance on Ion Torrent Escherichia coli data, with a 4.8% improvement in the number of high-quality mapped reads and a 7.1% improvement in the number of uniquely mappable reads. AVAILABILITY: Binaries and source code of FlowgramFixer are freely available at: http://www.cs.tau.ac.il/~davidgo5/flowgramfixer.html.

Feed-forward microprocessing and splicing activities at a microRNA-containing intron.

The majority of mammalian microRNA (miRNA) genes reside within introns of protein-encoding and non-coding genes, yet the mechanisms coordinating primary transcript processing into both mature miRNA and spliced mRNA are poorly understood. Analysis of melanoma invasion suppressor miR-211 expressed from intron 6 of melastatin revealed that microprocessing of miR-211 promotes splicing of the exon 6-exon 7 junction of melastatin by a mechanism requiring the RNase III activity of Drosha. Additionally, mutations in the 5' splice site (5'SS), but not in the 3'SS, branch point, or polypyrimidine tract of intron 6 reduced miR-211 biogenesis and Drosha recruitment to intron 6, indicating that 5'SS recognition by the spliceosome promotes microprocessing of miR-211. Globally, knockdown of U1 splicing factors reduced intronic miRNA expression. Our data demonstrate novel mutually-cooperative microprocessing and splicing activities at an intronic miRNA locus and suggest that the initiation of spliceosome assembly may promote microprocessing of intronic miRNAs.

Involvement of IGF-1R regulation by miR-515-5p modifies breast cancer risk among BRCA1 carriers.

Several lines of evidence indicate that sequence alterations within microRNA (miRNA)-binding sites can modify the binding to its target gene resulting in altered expression patterns. We hypothesized that a single nucleotide polymorphism (SNP) located in the miR-515-5p binding site of igf-1r gene may alter IGF-1R regulation, with consequent effects on breast cancer risk in BRCA1 mutation carriers. Computational prediction revealed that the rs28674628 SNP in the igf-1r 3' UTR is located within a predicted binding site for miR-515-5p. The effect of this SNP on breast cancer risk was evaluated by genotyping 115 Jewish Ashkenazi carriers of the 185delAG mutation in the BRCA1 gene using the Sequenom platform followed by Kaplan-Meier analysis. Additional data set of 378 Jewish BRCA1 carriers was analyzed to validate our results. MiRNA transfection, Western blot analysis, luciferase reporter assay, real time PCR, and immunohistochemistry were performed to assess direct regulation of igf-1r by miR-515-5p. We show direct regulation of IGF-1R by miR-515-5p. We identified that disrupting miR-515-5p and igf-1r 3' UTR binding by SNP may cause elevated IGF-1R protein levels. Interestingly, miR-515-5p is downregulated in tumor tissue compared to its non-neoplastic surrounding tissue while IGF-1R levels are elevated. This igf-1r SNP was found to be significantly associated with age at diagnosis of breast cancer in Jewish Ashkenazi BRCA1 mutation carriers. These findings support the hypothesis that a SNP located in igf-1r gene may alter miRNA regulation of IGF-1R, with a putative effect on BRCA1 penetrance and breast cancer risk.

Weighted pooling--practical and cost-effective techniques for pooled high-throughput sequencing.

MOTIVATION: Despite the rapid decline in sequencing costs, sequencing large cohorts of individuals is still prohibitively expensive. Recently, several sophisticated pooling designs were suggested that can identify carriers of rare alleles in large cohorts with a significantly smaller number of pools, thus dramatically reducing the cost of such large-scale sequencing projects. These approaches use combinatorial pooling designs where each individual is either present or absent from a pool. One can then infer the number of carriers in a pool, and by combining information across pools, reconstruct the identity of the carriers. RESULTS: We show that one can gain further efficiency and cost reduction by using 'weighted' designs, in which different individuals donate different amounts of DNA to the pools. Intuitively, in this situation, the number of mutant reads in a pool does not only indicate the number of carriers, but also their identity. We describe and study a powerful example of such weighted designs, using non-overlapping pools. We demonstrate that this approach is not only easier to implement and analyze but is also competitive in terms of accuracy with combinatorial designs when identifying rare variants, and is superior when sequencing common variants. We then discuss how weighting can be incorporated into existing combinatorial designs to increase their accuracy and demonstrate the resulting improvement using simulations. Finally, we argue that weighted designs have enough power to facilitate detection of common alleles, so they can be used as a cornerstone of whole-exome sequencing projects.

Membrane protein dynamics and functional implications in mammalian cells.

The organization of the plasma membrane is both highly complex and highly dynamic. One manifestation of this dynamic complexity is the lateral mobility of proteins within the plane of the membrane, which is often an important determinant of intermolecular protein-binding interactions, downstream signal transduction, and local membrane mechanics. The mode of membrane protein mobility can range from random Brownian motion to immobility and from confined or restricted motion to actively directed motion. Several methods can be used to distinguish among the various modes of protein mobility, including fluorescence recovery after photobleaching, single-particle tracking, fluorescence correlation spectroscopy, and variations of these techniques. Here, we present both a brief overview of these methods and examples of their use to elucidate the dynamics of membrane proteins in mammalian cells-first in erythrocytes, then in erythroblasts and other cells in the hematopoietic lineage, and finally in non-hematopoietic cells. This multisystem analysis shows that the cytoskeleton frequently governs modes of membrane protein motion by stably anchoring the proteins through direct-binding interactions, by restricting protein diffusion through steric interactions, or by facilitating directed protein motion. Together, these studies have begun to delineate mechanisms by which membrane protein dynamics influence signaling sequelae and membrane mechanical properties, which, in turn, govern cell function.

Accurate estimation of heritability in genome wide studies using random effects models.

MOTIVATION: Random effects models have recently been introduced as an approach for analyzing genome wide association studies (GWASs), which allows estimation of overall heritability of traits without explicitly identifying the genetic loci responsible. Using this approach, Yang et al. (2010) have demonstrated that the heritability of height is much higher than the ~10% associated with identified genetic factors. However, Yang et al. (2010) relied on a heuristic for performing estimation in this model. RESULTS: We adopt the model framework of Yang et al. (2010) and develop a method for maximum-likelihood (ML) estimation in this framework. Our method is based on Monte-Carlo expectation-maximization (MCEM; Wei et al., 1990), an expectation-maximization algorithm wherein a Markov chain Monte Carlo approach is used in the E-step. We demonstrate that this method leads to more stable and accurate heritability estimation compared to the approach of Yang et al. (2010), and it also allows us to find ML estimates of the portion of markers which are causal, indicating whether the heritability stems from a small number of powerful genetic factors or a large number of less powerful ones. CONTACT: saharon@post.tau.ac.il.

Ligation of complement receptor 1 increases erythrocyte membrane deformability.

Microbes as well as immune complexes and other continuously generated inflammatory particles are efficiently removed from the human circulation by red blood cells (RBCs) through a process called immune-adherence clearance. During this process, RBCs use complement receptor 1 (CR1, CD35) to bind circulating complement-opsonized particles and transfer them to resident macrophages in the liver and spleen for removal. We here show that ligation of RBC CR1 by antibody and complement-opsonized particles induces a transient Ca(++) influx that is proportional to the RBC CR1 levels and is inhibited by T1E3 pAb, a specific inhibitor of TRPC1 channels. The CR1-elicited RBC Ca(++) influx is accompanied by an increase in RBC membrane deformability that positively correlates with the number of preexisting CR1 molecules on RBC membranes. Biochemically, ligation of RBC CR1 causes a significant increase in phosphorylation levels of ß-spectrin that is inhibited by preincubation of RBCs with DMAT, a specific casein kinase II inhibitor. We hypothesize that the CR1-dependent increase in membrane deformability could be relevant for facilitating the transfer of CR1-bound particles from the RBCs to the hepatic and splenic phagocytes.

Analysis of molecular diffusion by first-passage time variance identifies the size of confinement zones.

The diffusion of receptors within the two-dimensional environment of the plasma membrane is a complex process. Although certain components diffuse according to a random walk model (Brownian diffusion), an overwhelming body of work has found that membrane diffusion is nonideal (anomalous diffusion). One of the most powerful methods for studying membrane diffusion is single particle tracking (SPT), which records the trajectory of a label attached to a membrane component of interest. One of the outstanding problems in SPT is the analysis of data to identify the presence of heterogeneity. We have adapted a first-passage time (FPT) algorithm, originally developed for the interpretation of animal movement, for the analysis of SPT data. We discuss the general application of the FPT analysis to molecular diffusion, and use simulations to test the method against data containing known regions of confinement. We conclude that FPT can be used to identify the presence and size of confinement within trajectories of the receptor LFA-1, and these results are consistent with previous reports on the size of LFA-1 clusters. The analysis of trajectory data for cell surface receptors by FPT provides a robust method to determine the presence and size of confined regions of diffusion.