Biology subjects

Lee, D.

Publications and source records attributed to Lee, D..

At least 289 records · Page 16Linked to original sources

bioRxiv2019

Deep sampling of Hawaiian Caenorhabditis elegans reveals high genetic diversity and admixture with global populations

Recent efforts to understand the natural niche of the keystone model organism Caenorhabditis elegans have suggested that this species is cosmopolitan and associated with rotting vegetation and fruits. However, most of the strains isolated from nature have low genetic diversity likely because recent chromosome-scale selective sweeps contain alleles that increase fitness in human-associated habitats. Strains from the Hawaii Islands are highly divergent from non-Hawaiian strains. This result suggests that Hawaiian strains might contain ancestral genetic diversity that was purged from most non-Hawaiian strains by the selective sweeps. To characterize the genetic diversity and niche of Hawaiian C. elegans, we sampled across the Hawaiian Islands and isolated 100 new C. elegans strains. We found that C. elegans strains are not associated with any one substrate but are found in cooler climates at high elevations. These Hawaiian strains are highly diverged compared to the rest of the global population. Admixture analysis identified 11 global populations, four of which are from Hawaii. Surprisingly, one of the Hawaiian populations shares recent ancestry with non-Hawaiian populations, including portions of globally swept haplotypes. This discovery provides the first evidence of gene flow between Hawaiian and non-Hawaiian populations. Most importantly, the high levels of diversity observed in Hawaiian strains might represent the complex patterns of ancestral genetic diversity in the C. elegans species before human influence.

ecology↗

bioRxiv2019

An integrative ENCODE resource for cancer genomics

ENCODE comprises thousands of functional genomics datasets, and the encyclopedia covers hundreds of cell types, providing a universal annotation for genome interpretation. However, for particular applications, it may be advantageous to use a customized annotation. Here, we develop such a custom annotation by leveraging advanced assays, such as eCLIP, Hi-C, and whole-genome STARR-seq on a number of data-rich ENCODE cell types. A key aspect of this annotation is comprehensive and experimentally derived networks of both transcription factors and RNA-binding proteins (TFs and RBPs). Cancer, a disease of system-wide dysregulation, is an ideal application for such a network-based annotation. Specifically, for cancer-associated cell types, we put regulators into hierarchies and measure their network change (rewiring) during oncogenesis. We also extensively survey TF-RBP crosstalk, highlighting how SUB1, a previously uncharacterized RBP, drives aberrant tumor expression and amplifies the effect of MYC, a well-known oncogenic TF. Furthermore, we show how our annotation allows us to place oncogenic transformations in the context of a broad cell space; here, many normal-to-tumor transitions move towards a stem-like state, while oncogene knockdowns show an opposing trend. Finally, we organize the resource into a coherent workflow to prioritize key elements and variants, in addition to regulators. We showcase the application of this prioritization to somatic burdening, cancer differential expression and GWAS. Targeted validations of the prioritized regulators, elements and variants using siRNA knockdowns, CRISPR-based editing, and luciferase assays demonstrate the value of the ENCODE resource.

genomics↗

bioRxiv2019

STARRPeaker: Uniform processing and accurate identification of whole human STARR-seq active regions

BackgroundHigh-throughput reporter assays, such as self-transcribing active regulatory region sequencing (STARR-seq), allow for unbiased and quantitative assessment of enhancers at a genome-wide scale. Recent advances in STARR-seq technology have employed progressively more complex genomic libraries and increased sequencing depths, to assay larger sized regions, up to the entire human genome. These advances necessitate a reliable processing pipeline and peak-calling algorithm. ResultsMost STARR-seq studies have relied on chromatin immunoprecipitation sequencing (ChIP-seq) processing pipelines. However, there are key differences in STARR-seq versus ChIP-seq. First, STARR-seq uses transcribed RNA to measure the activity of an enhancer, making an accurate determination of the basal transcription rate important. Second, STARR-seq coverage is highly non-uniform, overdispersed, and often confounded by sequencing biases, such as GC content and mappability. Lastly, here, we observed a clear correlation between RNA thermodynamic stability and STARR-seq readout, suggesting that STARR-seq may be sensitive to RNA secondary structure and stability. Considering these findings, we developed a negative-binomial regression framework for uniformly processing STARR-seq data, called STARRPeaker. In support of this, we generated whole-genome STARR-seq data from the HepG2 and K562 human cell lines and applied STARRPeaker to call enhancers. ConclusionsWe show STARRPeaker can unbiasedly detect active enhancers from both captured and whole-genome STARR-seq data. Specifically, we report [~]33,000 and [~]20,000 candidate enhancers from HepG2 and K562, respectively. Moreover, we show that STARRPeaker outperforms other peak callers in terms of identifying known enhancers with fewer false positives. Overall, we demonstrate an optimized processing framework for STARR-seq experiments can identify putative enhancers while addressing potential confounders.

bioinformatics↗

bioRxiv2019

A molecular logic of sensory coding revealed by optical tagging of physiologically-defined neuronal types

Neural circuit analysis relies on having molecular markers for specific cell types. However, for a cell type identified only by its circuit function, the process of identifying markers remains laborious. Here, we report physiological optical tagging sequencing (PhOTseq), a technique for tagging and expression-profiling cells based on their functional properties. We demonstrate that PhOTseq is capable of selecting rare cell types and enriching them by nearly one hundred-fold. We applied PhOTseq to the challenge of mapping receptor-ligand pairings among vomeronasal pheromone-sensing neurons in mice. Together with in vivo ectopic expression of vomeronasal chemoreceptors, PhOTseq identified the complete combinatorial receptor code for a specific set of ligands, and revealed that the primary sequence of a chemoreceptor was an unexpectedly strong predictor of functional similarity.

neuroscience↗

bioRxiv2019

PTMProphet: Fast and Accurate Mass Modification Localization for the Trans-Proteomic Pipeline

Spectral matching sequence database search engines commonly used on mass spectrometry-based proteomics experiments excel at identifying peptide sequence ions, and in addition, possible sequence ions carrying post-translational modifications (PTMs), but most do not provide confidence metrics for the exact localization of those PTMs when several possible sites are available. Localization is absolutely required for downstream molecular cell biology analysis of PTM function in vitro and in vivo. Therefore, we developed PTMProphet, a free and open-source software tool integrated into the Trans-Proteomic Pipeline, which reanalyzes identified spectra from any search engine for which pepXML output is available to provide localization confidence to enable appropriate further characterization of biologic events. Localization of any type of mass modification (e.g., phosphorylation) is supported. PTMProphet applies Bayesian mixture models to compute probabilities for each site/peptide spectrum match where a PTM has been identified. These probabilities can be combined to compute a global false localization rate at any threshold to guide downstream analysis. We describe the PTMProphet tool, its underlying algorithms and demonstrate its performance on ground-truth synthetic peptide reference datasets, one previously published small dataset, one new larger dataset, and also on a previously published phospho-enriched dataset where the correct sites of modification are unknown. Data have been deposited to ProteomeXchange with identifier PXD013210.\n\nO_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

bioinformatics↗

bioRxiv2019

Mapping gene regulatory networks of primary CD4+ T cells using single-cell genomics and genome engineering

Gene regulatory programs controlling the activation and polarization of CD4+ T cells are incompletely mapped and the interindividual variability in these programs remain unknown. We sequenced the transcriptomes of ~160k CD4+ T cells from 9 donors following pooled CRISPR perturbation targeting 140 regulators. We identified 134 regulators that affect T cell functionalization, including IRF2 as a positive regulator of Th2 polarization. Leveraging correlation patterns between cells, we mapped 194 pairs of interacting regulators, including known (e.g. BATF and JUN) and novel interactions (e.g. ETS1 and STAT6). Finally, we identified 80 natural genetic variants with effects on gene expression, 48 of which are modified by a perturbation. In CD4+ T cells, CRISPR perturbations can influence in vitro polarization and modify the effects of trans and cis regulatory elements on gene expression.

genomics↗

bioRxiv2019

IQSEC3 Mediates Npas4-dependent GABAergic Synaptic Transmission and Depressive-like Behavior

Organization of mammalian inhibitory synapses is thought to be crucial for normal brain functions, but the underlying molecular mechanisms have been still incompletely understood. IQSEC3 (IQ motif and Sec7 domain 3) is a guanine nucleotide exchange factor for ADP-ribosylation factor (ARF-GEF) that directly interacts with gephyrin. Here, we show that GABAergic synapse-specific transcription factor, Npas4 (neuronal PAS domain protein 4) directly binds to the promoter of Iqsec3 and regulates its transcription. Strikingly, an enriched environment (EE) induced Npas4 upregulation and concurrently increased IQSEC3 protein levels specifically in mouse CA1 stratum oriens layer somatostatin (SST)-expressing GABAergic interneurons, which are compromised in Npas4-knockout (KO) mice. Moreover, expression of wild-type (WT) IQSEC3, but not a dominant-negative (DN) ARF-GEF-inactive mutant, rescued the decreased GABAergic synaptic transmission in Npas4-deficient SST interneurons. Concurrently, expression of IQSEC3 WT normalized the altered GABAergic synaptic transmission in dendrites, but not soma, of Npas4-deficient CA1 pyramidal neurons. Furthermore, expression of IQSEC3 WT, but not IQSEC3 DN, in SST-expressing interneurons in CA1 SST Npas4-KO mice rescued the altered anxiety-like behavior. Collectively, our results suggest that IQSEC3 is a key GABAergic synapse component that is directed by Npas4 activity- and ARF activity-dependent gene programs in SST-expressing interneurons to orchestrate the functional excitation-to-inhibition balance.

neuroscience↗

bioRxiv2019

Consensus Machine Learning for Gene Target Selection in Pediatric AML Risk

Acute myeloid leukemia (AML) is a cancer of hematopoietic systems that poses high population burden, especially among pediatric populations. AML presents with high molecular heterogeneity, complicating patient risk stratification and treatment planning. While molecular and cytogenetic subtypes of AML are well described, significance of subtype-specific gene expression patterns is poorly understood and effective modeling of these patterns with individual algorithms is challenging. Using a novel consensus machine learning approach, we analyzed public RNA-seq and clinical data from pediatric AML patients (N = 137 patients) enrolled in the TARGET project.\n\nWe used a binary risk classifier (Low vs. Not-Low Risk) to study risk-specific expression patterns in pediatric AML. We applied the following workflow to identify important gene targets from RNA-seq data: (1) Reduce data dimensionality by identification of differentially expressed genes for AML risk (N = 1984 loci); (2) Optimize algorithm hyperparameters for each of 4 algorithm types (lasso, XGBoost, random forest, and SVM); (3) Study ablation test results for penalized methods (lasso and XGBoost); (4) Bootstrap Boruta permutations with a novel consensus importance metric.\n\nWe observed recurrently selected features across hyperparameter optimizations, ablation tests, and Boruta permutation bootstrap iterations, including HOXA9 and putative cofactors including MEIS1. Consensus feature selection from Boruta bootstraps identified a larger gene set than single penalized algorithm runs (lasso or XGBoost), while also including correlated and predictive genes from ablation tests.\n\nWe present a consensus machine learning approach to identify gene targets of likely importance for pediatric AML risk. The approach identified a moderately sized set of recurrent important genes from across 4 algorithm types, including genes identified across ablation tests with penalized algorithms (HOXA9 and MEIS1). Our approach mitigates exclusion biases of penalized algorithms (lasso and XGBoost) while obviating arbitrary importance cutoffs for other types (SVM and random forest). This approach is readily generalizable for research of other heterogeneous diseases, single-assay experiments, and high-dimensional data. Resources and code to recreate our findings are available online.

bioinformatics↗

bioRxiv2019

Robust Microfabrication of Highly Parallelized Three-Dimensional Microfluidics on Silicon

We present a new, robust three dimensional microfabrication method for highly parallel microfluidics, to improve the throughput of on-chip material synthesis by allowing parallel and simultaneous operation of many replicate devices on a single chip. Recently, parallelized microfluidic chips fabricated in Silicon and glass have been developed to increase the throughput of microfluidic materials synthesis to an industrially relevant scale. These parallelized microfluidic chips require large arrays (> 10,000) of Through Silicon Vias (TSVs) to deliver fluid from delivery channels to the parallelized devices. Ideally, these TSVs should have a small footprint to allow a high density of features to be packed into a single chip, have channels on both sides of the wafer, and at the same time minimize debris generation and wafer warping to enable permanent bonding of the device to glass. Because of these requirements and challenges, previous approaches cannot be easily applied to produce three dimensional microfluidic chips with a large array of TSVs. To address these issues, in this paper we report a fabrication strategy for the robust fabrication of three-dimensional Silicon microfluidic chips consisting of a dense array of TSVs, designed specifically for highly parallelized microfluidics. In particular, we have developed a two-layer TSV design that allows small diameter vias (d < 20 {micro}m) without sacrificing the mechanical stability of the chip and a patterned SiO2 etch-stop layer to replace the use of carrier wafers in Deep Reactive Ion Etching (DRIE). Our microfabrication strategy allows >50,000 (d = 15 {micro}m) TSVs to be fabricated on a single 4\" wafer, using only conventional semiconductor fabrication equipment, with 100% yield (M = 16 chips) compared to 30% using previous approaches. We demonstrated the utility of these fabrication strategies by developing a chip that incorporates 20,160 flow focusing droplet generators onto a single 4\" Silicon wafer, representing a 100% increase in the total number of droplet generators than previously reported. To demonstrate the utility of this chip for generating pharmaceutical microparticle formulations, we generated 5-9 {micro}m polycaprolactone particles with a CV <5% at a rate as high as 60 g/hr (> 1 trillion particles / hour).

bioengineering↗

bioRxiv2019

A methodology for morphological feature extraction and unsupervised cell classification

Cell morphology is an important indicator of cell state, function, stage of development, and fate in both normal and pathological conditions. Cell shape is among key indicators used by pathologists to identify abnormalities or malignancies. With rapid advancements in the speed and amount of biological data acquisition, including images and movies of cells, computer-assisted identification and analysis of images becomes essential. Here, we report on techniques for recognition of cells in microscopic images and automated cell shape classification. We illustrate how our unsupervised machine-learning-based approach can be used to classify distinct cell shapes from a large number of microscopic images.\n\nTechnical AbstractWe develop a methodology to segment cells from microscopy images and compute quantitative descriptors that characterize their morphology. Using unsupervised techniques for dimensionality reduction and density-based clustering, we perform label-free cell shape classification. Cells are identified with minimal user input using mathematical morphology and region-growing segmentation methods. Physical quantities describing cell shape and size (including area, perimeter, Feret diameters, etc.) are computed along with other features including shape factors and Hus image moments.\n\nCorrelated features are combined to obtain a low-dimensional (2-D or 3-D) embedding of data points corresponding to individual segmented cell shapes. Finally, a hierarchical density-based clustering algorithm (HDBSCAN) is used to classify cells. We compare cell classification results obtained from different combinations of features to identify a feature set that delivers optimum classification performance for our test data consisting of phase-contrast microscopy images of a pancreatic-cancer cell line, MIA PaCa-2.

bioinformatics↗

bioRxiv2019

Brain endothelial cells are exquisite sensors of age-related circulatory cues

Brain endothelial cells (BECs) are key elements of the blood-brain barrier (BBB), protecting the brain from pathogens and restricting access to circulatory factors. Recent studies have demonstrated that the circulatory environment can modulate brain aging, yet, the underlying processes remain largely unknown. Given the BBBs intermediary position, we hypothesized that BECs sense, adapt to, and relay signals between the aging blood and brain. We sequenced single endothelial cells from the hippocampus--a brain region key to learning, memory, and neurogenesis-- of healthy young and aged mice as well as post-exposure to inflammatory and age-related circulatory factors. We discovered that aged capillary BECs, compared with arterial and venous cells, exhibit the greatest transcriptional changes, upregulating innate immunity, antigen presentation, TGF-{beta} signaling and oxidative stress response pathways. Remarkably, short-term infusions of aged plasma into young mice recapitulated key aspects of this aging transcriptome, while infusions of young plasma into aged mice reversed select aging signatures, essentially rejuvenating the BBB endothelium transcriptome. We identify candidate pathways mediating blood-borne brain rejuvenation by comparing age-upregulated genes with those modulated by plasma exposure. Together, these findings suggest that the transcriptional age of BECs is exquisitely sensitive to age-related circulatory cues and pinpoint the BBB itself as a promising therapeutic target to treat brain disease.\n\nHighlightsO_LISingle-cell RNA sequencing of brain endothelial cells (BECs) reveals transcriptional segmentation into distinct arterial, capillary, and venous identities with age and experimental interventions\nC_LIO_LIChanges with age are heterogenous across vessel segments, with aged capillaries enriched in signatures of innate immunity, TGF-{beta} and VEGF signaling, hypoxia and oxidative stress\nC_LIO_LIBECs sense and respond transcriptionally to diverse circulatory cues: inflammatory, proaging, or rejuvenating\nC_LIO_LIAged plasma exposure recapitulates--and young plasma reverses--key transcriptomic signatures of normal BEC aging\nC_LIO_LIBEC response to aged and young plasma reveals cell non-autonomous mechanisms of blood-brain-barrier aging\nC_LI

neuroscience↗

bioRxiv2019

Selection and gene flow shape niche-associated copy-number variation of pheromone receptor genes

From quorum sensing in bacteria to pheromone signaling in social insects, chemical communication mediates interactions among individuals in a local population. In Caenorhabditis elegans, ascaroside pheromones can dictate local population density, in which high levels of pheromones inhibit the reproductive maturation of individuals. Little is known about how natural genetic diversity affects the pheromone responses of individuals from diverse habitats. Here, we show that a niche-associated copy-number variation (CNV) of pheromone receptor genes contributes to natural differences in pheromone responses. We found putative loss-of-function deletions that reduce copy number of duplicated pheromone receptor genes (srg-36 and srg-37), which were shown previously to be selected in population-dense laboratory cultures. A common natural deletion in the less functional copy (srg-37) arose from a single ancestral population that spread throughout the world and underlies reduced pheromone sensitivity across the global C. elegans population. This deletion is enriched in wild strains that were isolated from a rotting fruit niche, where proliferating populations are often found. Taken together, these results demonstrate that selection and gene flow together shape the copy number of pheromone receptor genes in natural C. elegans populations to facilitate local adaptation to diverse niches.

evolutionary biology↗

bioRxiv2019

A beneficial genomic rearrangement creates multiple versions of calcipressin in C. elegans

Gene duplication is a major source of genetic novelty and evolutionary adaptation, providing a molecular substrate that can generate biological complexity and diversity (Ohno 1967, Taylor and Raes 2004). Despite an abundance of genomic evidence from extant organisms suggesting the importance of gene duplication, consensus about how they arise and functionally diversify is lacking (Innan and Kondrashov 2010). In the process of studying the adaptation of laboratory strains of C. elegans to new food sources, we identified a recombinant inbred line (RIL) with higher relative fitness and hyperactive exploration behavior compared to either parental strain. Using bulk-segregant analysis and short-read resequencing, we identified a de novo beneficial, complex rearrangement of the rcan-1 gene, which we resolved into five new unique tandem inversion/duplications using Oxford Nanopore long-read sequencing. rcan-1 encodes an ortholog to human RCAN1/DSCR1, which has been implicated as a causal gene for Down syndrome (Fuentes, Genesca et al. 2000). The genomic rearrangement in rcan-1 causes two complete and two truncated versions of the rcan-1 coding region, with a variety of modified promoter and 3 regions, which ultimately reduce whole-body gene expression. This rearrangement does not phenocopy a loss-of-function allele, which indicates that the rearrangement was necessary for the observed fitness gains. Our results demonstrate that adaptation can occur through unexpectedly complex genetic changes that can simultaneously duplicate and diversify a gene, providing the molecular substrate for future evolutionary change.

evolutionary biology↗

bioRxiv2019

Mediator subunit MDT-15/MED15 and Nuclear Receptor HIZR-1/HNF4 cooperate to regulate toxic metal stress responses in Caenorhabditis elegans

Zinc is essential for cellular functions as it is a catalytic and structural component of many proteins. In contrast, cadmium is not required in biological systems and is toxic. Zinc and cadmium levels are closely monitored and regulated as their excess causes cell stress. To maintain homeostasis, organisms induce metal detoxification gene programs through stress responsive transcriptional regulatory complexes. In Caenorhabditis elegans, the MDT-15 subunit of the evolutionarily conserved Mediator transcriptional coregulator is required to induce genes upon exposure to excess zinc and cadmium. However, the regulatory partners of MDT-15 in this response, its role in cellular and physiological stress adaptation, and the putative role mammalian for MED15 in the metal stress responses remain unknown. Here, we show that MDT-15 interacts physically and functionally with the Nuclear Hormone Receptor HIZR-1 to promote molecular, cellular, and organismal adaptation to excess metals. Using gain- and loss-of-function mutants and qPCR and reporter analysis, we find that mdt-15 and hizr-1 cooperate to induce zinc and cadmium responsive genes. Moreover, the two proteins interact physically in yeast-two-hybrid assays and this interaction is enhanced by the addition of zinc or cadmium, the former a known ligand of HIZR-1. Functionally, mdt-15 and hizr-1 mutants show defective storage of excess zinc in the gut, and at the organismal level, mdt-15 mutants are hypersensitive to zinc- and cadmium-induced reductions in egg-laying. Lastly, mammalian MDT-15 orthologs bind genomic regulatory regions of metallothionein and zinc transporter genes in a metal-stimulated fashion, and human MED15 is required to induce a metallothionein gene in lung adenocarcinoma cells exposed to cadmium. Collectively, our data show that mdt-15 and hizr-1 cooperate to regulate metal detoxification and zinc storage and that this mechanism appears to be at least partially conserved in mammals.

genetics↗

bioRxiv2019

A nematode-specific gene underlies bleomycin-response variation in Caenorhabditis elegans

Bleomycin is a powerful chemotherapeutic drug used to treat a variety of cancers. However, individual patients vary in their responses to bleomycin. The identification of genetic differences that underlie this response variation could improve treatment outcomes by tailoring bleomycin dosages to each patient. We used the model organism Caenorhabditis elegans to identify genetic determinants of bleomycin-response differences by performing linkage mapping on recombinants derived from a cross between the laboratory strain (N2) and a wild strain (CB4856). This approach identified a small genomic region on chromosome V that underlies bleomycin-response variation. Using near-isogenic lines and strains with CRISPR-Cas9 mediated deletions and allele replacements, we discovered that a novel nematode-specific gene (scb-1) is required for bleomycin resistance. Although the mechanism by which this gene causes variation in bleomycin responses is unknown, we suggest that a rare variant present in the CB4856 strain might cause differences in the potential stress-response function of scb-1 between the N2 and CB4856 strains, thereby leading to differences in bleomycin resistance.

genetics↗

bioRxiv2019

A cellular taxonomy of the bone marrow stroma in homeostasis and leukemia demonstrates cancer-crosstalk with stroma to impair normal tissue function

Stroma is a poorly defined non-parenchymal component of virtually every organ with key roles in organ development, homeostasis and repair. Studies of the bone marrow stroma have defined individual populations in the stem cell niche regulating hematopoietic regeneration and capable of initiating leukemia. Here, we use single-cell RNA-seq to define a cellular taxonomy of the mouse bone marrow stroma and its perturbation by malignancy. We identified seventeen stromal subsets expressing distinct hematopoietic regulatory genes, spanning new fibroblastic, and osteoblastic subpopulations. Emerging acute myeloid leukemia resulted in impaired osteogenic differentiation and reduced production of hematopoietic regulatory molecules necessary for normal hematopoiesis. Thus, cancer can affect tissue stroma in which they reside to disadvantage normal parenchymal cells. Our taxonomy of the regulatory stromal compartment provides experimental support for a model where malignant clone is not a destroyer of normal tissue but an architect of it, remodeling tissue stroma to enable emergent cancer.

cell biology↗

bioRxiv2019

Perinatal Programming of Mucosal Stromal Cell Identity by the Lymphotoxin Pathway Regulates Mucosal Immune Responses in the Adult

Redundant mechanisms support IgA responses to intestinal antigens. These include multiple priming sites (mesenteric lymph nodes (MLN), Peyers patches and isolated lymphoid follicles) and various cytokines that promote class switch to IgA, even in the absence of T cells. In spite of these back-up mechanisms, vaccination against enteric pathogens such as Rotavirus has limited success in some populations. Genetic and environmental signals experienced during early life are known to influence mucosal immunity, yet the mechanisms for how these exposures operate remain unclear. Here we used Rotavirus infection to follow antigen-specific IgA responses through time and in different gut compartments. Using genetic and pharmacological approaches, we tested the role of a pathway known to support IgA responses (Lymphotoxin - LT) at different developmental stages. We found that LT-beta receptor (LT{beta}R) signalling in utero programs intestinal IgA responses in adulthood by affecting antibody class switch recombination to IgA and subsequent generation of IgA antibody-secreting cells within an intact MLN. In addition, in utero LT{beta}R signalling dictates the phenotype and function of MLN stromal cells in order to support IgA responses in the adult. Collectively, our studies uncover new mechanistic insights into how in utero LT{beta}R signalling impacts mucosal immune responses during adulthood. One Sentence SummaryEarly life LT{beta}R signalling is critical for programming the mesenteric lymph node stromal cell environment, impacting both antibody isotype switching to IgA and the differentiation of IgA+ antibody secreting cells. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=190 HEIGHT=200 SRC="FIGDIR/small/557652v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@4780dborg.highwire.dtl.DTLVardef@5cbe85org.highwire.dtl.DTLVardef@e0465eorg.highwire.dtl.DTLVardef@2bcae6_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2018

DISTMIX2: robust imputation of summary statistics for cosmopolitan cohorts using a large and diverse reference panel

Genotype imputation across populations of mixed ancestry is critical for optimal discovery in large-scale genome-wide association studies (GWAS). Methods for direct imputation of GWAS summary statistics were previously shown to be practically as accurate as summary statistics produced after raw genotype imputation, while incurring orders of magnitude lower computational burden. Given that direct imputation needs a precise estimation of linkage-disequilibrium (LD) and that most of the methods using a small reference panel e.g., ~2,500 subject coming from the 1000 Genome Project, there is a great need for much larger and more diverse reference panels. To accurately estimate the LD needed for an exhaustive analysis of any cosmopolitan cohort, we developed DISTMIX2. DISTMIX2: i) uses a much larger and more diverse reference panel and ii) estimates weights of ethnic mixture based solely on Z-scores (when AFs are not available). We applied DISTMIX2 to GWAS summary statistics from the Psychiatric Genetic Consortium (PGC). DISTMIX2 uncovered signals in numerous new regions, with most of these findings coming from the rarer variants. Rarer variants provide much sharper location for the signals compared with common variants, as the LD for rare variants extends over a lower distance than for common ones. For example, while the original PGC post-traumatic stress disorder (PTSD) study found only 3 marginal signals for common variants, we now uncover a very strong signal for a rare variant in PKN2, a gene associated with neuronal and hippocampal development. Thus, DISTMIX2 provides a robust and fast (re)imputation approach for most Psychiatric GWAS studies.

bioinformatics↗