Biology subjects

Chatterjee, N.

Publications and source records attributed to Chatterjee, N..

35 records · Page 2Linked to original sources

bioRxiv2020

Polygenic Risk Scores for Kidney Function to the Circulating Proteome, and Incident Kidney Diseases: the Atherosclerosis Risk in Communities Study

Genome-wide association studies (GWAS) have revealed numerous loci for kidney function (estimated glomerular filtration rate, eGFR). The relationship of polygenic predictors of eGFR, risk of incident adverse kidney outcomes, and the plasma proteome is not known. We developed a genome-wide polygenic risk score (PRS) using a weighted average of 1.2 million SNPs for eGFR using the LDpred algorithm, summary statistics generated by a European-ancestry (EA) meta-analysis of the CKDGen Consortium (N=558,423) and UK Biobank GWAS for eGFR (90% of the cohort; N=289,432), followed by best parameter selection using data from the remaining 10% of the UK Biobank (N=32,159). We then tested the association of the PRS among 8,886 EA participants in the Atherosclerosis Risk in Communities (ARIC) study (mean age: 54{+/-}6 years, 53% female) with incident chronic kidney disease (CKD), end stage kidney disease (ESKD), kidney failure (KF), and acute kidney injury (AKI). We also examined 4,877 plasma proteins measured at two time points (visit 3 (1993-95) and visit 5 (2011-13)) in relation to the PRS and compared associations between the proteome and eGFR itself. All models were adjusted for age, sex, center, and the first 10 principal components of ancestry. The developed PRS had an R2 for eGFR of 0.07 in ARIC. Over 30 years of follow up, the number of incident CKD, ESKD, KF, and AKI were 2,959, 137, 470, and 1,723, respectively. The PRS showed significant associations with all outcomes: hazard ratios (95% CI) per 1 SD lower PRS were 1.33 (1.28, 1.39), 1.20 (1.00, 1.42), 1.17 (1.06, 1.28), and 1.07 (1.02, 1.12) for incident CKD, ESKD, KF, and AKI respectively. The PRS was significantly associated (Bonferroni threshold P<1.02 x 10-5) with 108 proteins at both time points. The strongest associations were with cystatin-C (a marker of kidney function used in clinical practice), collagen alpha-1 (XV) chain, and desmocollin-2. All significant correlations with the PRS were negative, except those of testican-2 and angiostatin. Correlations of proteins with eGFR were much stronger than those with the PRS. Overall, we demonstrated that the PRS for eGFR is now sufficiently strong to capture risk for a spectrum of incident kidney diseases as well as broadly influence the plasma proteome.

genetics↗

bioRxiv2020

Homologous recombination defects in Shwachman-Diamond syndrome and Diamond-Blackfan anemia

Shwachman-Diamond syndrome (SDS) and Diamond-Blackfan anemia (DBA) are ribosomopathies characterized by impaired hematopoiesis and cancer predisposition. The mechanisms underlying cancer predisposition in these disorders are not well understood. We found that LCLs derived from patients with SDS or DBA had a prolonged DNA damage response and hypersensitivity to ionizing radiation, suggesting impaired DNA double strand break (DSB) repair. Consistent with this, depletion of SBDS and RPS19, the most common etiologic factors in SDS and DBA, respectively, resulted in reduced homologous recombination (HR) in HCT116 and U2OS cells. Surprisingly, depletion of EFL1, which functions with SBDS in ribosome biogenesis, did not impair HR and depletion of eIF6, which restores ribosome joining in SBDS-depleted cells, did not rescue the HR defect associated with SBDS depletion. Instead, we found SBDS and RPS19 recruitment to sites of DSBs suggesting that SBDS and RPS19 have more proximate roles in regulating HR, independent of their ribosomal functions. We propose that reduced HR shifts DSB repair toward error-prone NHEJ and this may contribute to oncogenesis in SDS and DBA. Additionally, we found SBDS and RPS19 depleted cells were hypersensitive to PARP inhibition, potentially uncovering a therapeutic target for SDS- and DBA-associated malignancies.

molecular biology↗

bioRxiv2020

A miR-494 dependent feedback loop regulates ER stress

Defects in stress responses are important contributors in many chronic conditions including cancer, cardiovascular disease, diabetes, and obesity-driven pathologies like non-alcoholic steatohepatitis (NASH). Specifically, endoplasmic reticulum (ER) stress is linked with these pathologies and control of ER stress can ameliorate tissue damage. MicroRNAs have a critical role in regulating diverse stress responses including ER stress. Here we show that miR-494 plays a functional role during ER stress. ER stress inducers (tunicamycin & thapsigargin) robustly increase the expression of miR-494 in vitro in an ATF6 dependent manner. Surprisingly, miR-494 pretreatment dampens the induction and magnitude of ER stress in response to tunicamycin in endothelial cells. Conversely, inhibition of miR-494 increases ER stress de novo and amplifies the effects of ER stress inducers. Using Mass Spectrometry (TMT-MS) we identified 23 proteins that are downregulated by both tunicamycin and miR-494. Among these, we found 6 transcripts which harbor a putative miR-494 binding site. We validated the anti-apoptotic gene BIRC5 (survivin) as one of the targets of miR-494 during ER stress. Finally, induction of ER stress in vivo increases miR-494 expression in the liver. Pretreatment of mice with a miR-494 plasmid via hydrodynamic injection decreased ER stress in response to tunicamycin in part by decreasing inflammatory chemokines and cytokines. In summary, our data indicates that ER stress driven miR-494 may act in a feedback inhibitory loop to dampen downstream ER stress signaling. We propose that RNA-based approaches targeting miR-494 or its targets may be attractive candidates for inhibiting ER stress dependent pathologies in human disease.

cell biology↗

bioRxiv2020

A Powerful Method for Pleiotropic Analysis under Composite Null Hypothesis Identifies Novel Shared Loci Between Type 2 Diabetes and Prostate Cancer

SO_SCPLOWUMMARYC_SCPLOWThere is increasing evidence that pleiotropy, the association of multiple traits with the same genetic variants/loci, is a very common phenomenon. Cross-phenotype association tests are often used to jointly analyze multiple traits from a GWAS. The underlying methods, however, are often designed to test the global null hypothesis that there is no association of a genetic variant with any of the traits, the rejection of which does not implicate pleiotropy. In this article, we propose a new statistical approach, PLACO, for specifically detecting pleiotropic loci between two traits by considering an underlying composite null hypothesis that a variant is associated with none or only one of the traits. We propose testing the null hypothesis based on the product of the Z-statistics of the SNPs across two studies and derive a null distribution of the test statistic in the form of a mixture distribution that allows for fractions of SNPs to be associated with none or only one of the traits. We borrow approaches from the statistical literature on mediation analysis that allow asymptotic approximation of the null distribution avoiding estimation of nuisance parameters related to mixture proportions and variance components. Simulation studies demonstrate that the proposed method can maintain type I error and can achieve major power gain over alternative simpler methods that are typically used for testing pleiotropy. PLACO allows correlation in summary statistics between studies that may arise due to sharing of controls between disease traits. Application of PLACO to publicly available summary data from two large case-control GWAS of Type 2 Diabetes and of Prostate Cancer implicated a number of novel shared genetic regions near ZBTB38 (3q23), RGS17 (6q25.3), HAUS6 (9p22.1), UBAP2 (9p13.3), RAPSN (11p11.2), AKAP6 (14q12), KNL1 (15q15) and ZNF236 (18q23).

genetics↗

bioRxiv2019

Kidney Function and Blood Pressure: A Bi-directional Mendelian Randomisation Study

ObjectiveTo evaluate the bi-directional causal relation between kidney function and blood pressure. DesignMendelian randomisation study. SettingWe performed two-sample Mendelian randomisation analyses. Genetic instruments of kidney function traits were selected from summary statistics of genome-wide association studies (GWAS) of glomerular filtration rate estimated from serum creatinine (eGFRcr) and blood urea nitrogen (BUN) and were required to be associated with both eGFRcr and BUN to ensure that the instruments were more likely to represent the underlying kidney function. Genetic instruments of blood pressure were selected from summary statistics of GWAS of systolic and diastolic blood pressure. We investigated Mendelian randomisation hypothesis using several alternative approaches, including methods that are most robust to the presence of horizontal pleiotropy. ParticipantsThe summary statistics of eGFRcr included 567,460 participants from 54 cohorts, and the summary statistics of BUN included 243,031 participants from 48 cohorts from the Chronic Kidney Disease Genetics (CKDGen) Consortium. The summary statistics of systolic and diastolic blood pressure included 757,601 participants from the UK Biobank and 78 cohorts from the International Consortium for Blood Pressure (ICBP). ResultsSignificant evidence supported the causal effects of higher kidney function on lower blood pressure with multiple methods. Based on the mode-based Mendelian randomisation analysis approach, known for its robustness to the presence of pleiotropic effect, the effect estimate for 1 SD higher in eGFRcr was -0.17 SD unit (95 % CI: -0.09 to -0.24) in systolic blood pressure (SBP) and -0.15 SD unit (95% CI: -0.07 to -0.22) in diastolic blood pressure (DBP). In contrast, the causal effects of blood pressure on kidney function were not statistically significant. ConclusionsMendelian randomisation analyses support causal effects of higher kidney function on lower blood pressure. These results suggest preventing kidney function decline can reduce the public health burden of hypertension.

genetics↗

bioRxiv2019

A Comprehensive Evaluation of Methods for Mendelian Randomization Using Realistic Simulations of Genome-wide Association Studies

BackgroundMendelian randomization (MR) has provided major opportunities for understanding the causal relationship among complex traits. Previous studies have often evaluated MR methods based on simulations that do not adequately reflect the data-generating mechanism in GWAS and there are often discrepancies in performance of MR methods in simulations and real datasets. MethodsWe use a simulation framework that generates data on full GWAS for two traits under realistic model for effect-size distribution coherent with heritability, co-heritability and polygenicity typically observed for complex traits. We further use recent data generated from GWAS of 38 biomarkers in the UK Biobank to investigate their causal effects on risk of type-2 diabetes using externally available GWAS summary-statistics. ResultsSimulation studies show that weighted mode and MRMix are the only two methods which maintain correct type-I error rate in a diverse set of scenarios. Between the two methods, MRMix tends to be more powerful for larger GWAS while the opposite being true for smaller sample sizes. Among the other methods, random-effect IVW, MR-Robust and MR-RAPS tend to perform best in maintaining low mean squared error when the InSIDE assumption is satisfied, but can produce large bias when InSIDE is violated. In real data analysis, some biomarkers showed major heterogeneity in estimates of their causal effects on risk of type-2 diabetes across the different methods, with patterns similar to those observed in simulation studies. ConclusionsRelative performance of different MR methods depends heavily on sample sizes of underlying GWAS, proportion of valid instruments and validity of the InSIDE assumption. Key MessagesO_LIMany previous simulations studies to evaluate Mendelian randomization methods do not adequately reflect the data-generating mechanism of genome-wide association studies (GWAS). C_LIO_LIWe use a simulation framework that generates data on full GWASs under realistic model informed by recent studies on effect-size distribution. We also used very recent GWAS data available on a large number of biomarkers to evaluate their causal effect on type-2 diabetes using alternative methods. C_LIO_LIAmong the 10 methods that were compared, relative performance of different methods depends heavily on sample sizes of underlying GWAS, proportion of valid instruments and validity of the InSIDE assumption. C_LIO_LIWeighted mode and MRMix are the only two methods that maintain correct type I error rate in a diverse set of scenarios. C_LI

genetics↗

bioRxiv2019

Case-only analysis of gene-environment interactions using polygenic risk scores

Investigations of gene (G)-environment (E) interactions have led to limited findings to date, possibly due to weak effects of individual genetic variants. Polygenic risk scores (PRS), which capture the genetic susceptibility associated with a set of variants, can be a powerful tool for detecting global patterns of interaction. Motivated by the case-only method for evaluating interactions with a single variant, we propose a case-only method for the analysis of interactions with a PRS in case-control studies. Assuming the PRS and E are independent, we show how a linear regression of the PRS on E in a sample of cases can be used to efficiently estimate the interaction parameter. Furthermore, if an estimate of the mean of the PRS in the underlying population is available, the proposed method can estimate the PRS main effect. Extensions allow for PRS-E dependence due to associations between variants in the PRS and E. Simulation studies indicate the proposed method offers appreciable gains in efficiency over logistic regression and can recover much of the efficiency of a cohort study. As an illustration, we apply the proposed method to investigate interactions between a PRS and epidemiologic factors on breast cancer risk in the UK Biobank study.

genetics↗

bioRxiv2018

Comparative validation of breast cancer risk prediction models and projections for future risk stratification

BackgroundWell-validated risk models are critical for risk stratified breast cancer prevention. We used the Individualized Coherent Absolute Risk Estimation (iCARE) tool for comparative model validation of five-year risk of invasive breast cancer in a prospective cohort, and to make projections for population risk stratification.\n\nMethodsPerformance of two recently developed models, iCARE-BPC3 and iCARE-Lit, were compared with two established models (BCRAT, IBIS) based on classical risk factors in a UK-based cohort of 64,874 women (863 cases) aged 35-74 years. Risk projections in US White non-Hispanic women aged 50-70 years were made to assess potential improvements in risk stratification by adding mammographic breast density (MD) and polygenic risk score (PRS).\n\nResultsThe best calibrated models were iCARE-Lit (expected to observed number of cases (E/O)=0.98 (95% confidence interval [CI]=0.87 to 1.11)) for women younger than 50 years; and iCARE-BPC3 (E/O=1.00 (0.93 to 1.09)) for women 50 years or older. Risk projections using iCARE-BPC3 indicated classical risk factors can identify ~500,000 women at moderate to high risk (>3% five-year risk). Additional information on MD and a PRS based on 172 variants is expected to increase this to ~3.6 million, and among them, ~155,000 invasive breast cancer cases are expected within five years.\n\nConclusionsiCARE models based on classical risk factors perform similarly or better than BCRAT or IBIS. Addition of MD and PRS can lead to substantial improvements in risk stratification. Independent prospective validation of integrated models is needed prior to clinical evaluation risk stratified breast cancer screening and prevention.

epidemiology↗

bioRxiv2018

A mixed-model approach for powerful testing of genetic associations with cancer risk incorporating tumor characteristics

AO_SCPLOWBSTRACTC_SCPLOWCancers are routinely classified into subtypes according to various features, including histopathological characteristics and molecular markers. Previous genome-wide association studies have reported heterogeneous associations between loci and cancer subtypes. However, it is not evident what is the optimal modeling strategy for handling correlated tumor features, missing data, and increased degrees-of-freedom in the underlying tests of associations. We propose to test for genetic associations using a mixed-effect two-stage polytomous model score test (MTOP). In the first stage, a standard polytomous model is used to specify all possible sub-types defined by the cross-classification of the tumor characteristics. In the second stage, the subtype-specific case-control odds ratios are specified using a more parsimonious model based on the case-control odds ratio for a baseline subtype, and the case-case parameters associated with tumor markers. Further, to reduce the degrees-of-freedom, we specify case-case parameters for additional exploratory markers using a random-effect model. We use the Expectation-Maximization (EM) algorithm to account for missing data on tumor markers. Through simulations across a range of realistic scenarios and data from the Polish Breast Cancer Study (PBCS), we show MTOP outperforms alternative methods for identifying heterogeneous associations between risk loci and tumor subtypes. The proposed methods have been implemented in a user-friendly and high-speed R statistical package called TOP (https://github.com/andrewhaoyu/TOP).

genetics↗

bioRxiv2018

Synthetic essentiality of metabolic regulator PDHK1 in PTEN-deficient cells and cancers

PTEN is a tumor suppressor that is often inactivated in cancer and possesses both lipid and protein phosphatase activities. We report the metabolic regulator PDHK1 (pyruvate dehydrogenase kinase1) is a synthetic-essential gene in PTEN-deficient cancer and normal cells. The predominant mechanism of PDHK1 regulation and dependency is the PTEN protein phosphatase dephosphorylates NF{kappa};B activating protein (NKAP) and limits NF{kappa}B activation to suppress expression of PDHK1, a NF{kappa}B target gene. Loss of the PTEN protein phosphatase upregulates PDHK1 to drive aerobic glycolysis and induce PDHK1 cellular dependence. PTEN-deficient human tumors harbor increased PDHK1, which is a biomarker of decreased patient survival, establishing clinical relevance. This study uncovers a PTEN-regulated signaling pathway and reveals PDHK1 as a potential target in PTEN-deficient cancers.\n\nSIGNIFICANCEThe tumor suppressor PTEN is widely inactivated in cancers and tumor syndromes. PTEN antagonizes PI3K/AKT signaling via its lipid phosphatase activity. The modest success of PI3K/AKT inhibition in PTEN-deficient cancer patients provides rationale for identifying other vulnerabilities in PTEN-deficient cancers to improve clinical outcomes. We show that PTEN-deficient cells are uniquely sensitive to PDHK1 inhibition. PTEN and PDHK1 co-suppression reduced colony formation and induced cell death in vitro and tumor regression in vivo. PDHK1 levels were high in PTEN-deficient patient tumors and associated with inferior patient survival, establishing clinical relevance. Our study identifies a PTEN-regulated signaling pathway linking the PTEN protein phosphatase to the metabolic regulator PDHK1 and provides a mechanistic basis for PDHK1 targeting in PTEN-deficient cancers.

cancer biology↗

bioRxiv2018

Mendelian Randomization Analysis Using Mixture Models (MRMix) for Genetic Effect-Size-Distribution Leads to Robust Estimation of Causal Effects

We propose a novel method for robust estimation of causal effects in two-sample Mendelian randomization analysis using potentially large number of genetic instruments. We consider a \"working model\" for bi-variate effect-size distribution across pairs of traits in the form of normal-mixtures which assumes existence of a fraction of the genetic markers that are valid instruments, i.e. they have only direct effect on one trait, while other markers can have potentially correlated, direct and indirect effects, or have no effects at all. We show that model motivates a simple method for estimating causal effect ({theta}) through a procedure for maximizing the probability concentration of the residuals, [Formula], at the \"null\" component of a two-component normal-mixture model. Simulation studies showed that MRMix provides nearly unbiased or/and substantially more robust estimates of causal effects compared to alternative methods under various scenarios. Further, the studies showed that MRMix is sensitive to direction and can achieve much higher efficiency (up to 3-4 fold) relative to other comparably robust estimators. We applied the proposed methods for conducting MR analysis using largest publicly available datasets across a number of risk-factors and health outcomes. Notable findings included identification of causal effects of genetically determined BMI and ageat-menarche, which have relationship among themselves, on the risk of breast cancer; detrimental effect of HDL on the risk of breast cancer; no causal effect of HDL and triglycerides on the risk of coronary artery disease; a strong detrimental effect of BMI, but no causal effect of years of education, on the risk of major depressive disorder.

genetics↗

bioRxiv2017

Heritability Informed Power Optimization (HIPO) Leads to Enhanced Detection of Genetic Associations Across Multiple Traits

Genome-wide association studies have shown that pleiotropy is a common phenomenon that can potentially be exploited for enhanced detection of susceptibility loci. We propose heritability informed power optimization (HIPO) for conducting powerful pleiotropic analysis using summary-level association statistics. We find optimal linear combinations of association coefficients across traits that are expected to maximize non-centrality parameter for the underlying test statistics, taking into account estimates of heritability, sample size variations and overlaps across the traits. Simulation studies show that the proposed method has correct type I error, robust to population stratification and leads to desired genome-wide enrichment of association signals. Application of the proposed method to publicly available data for three groups of genetically related traits, lipids (N=188,577), psychiatric diseases (Ncase=33,332, Ncontrol=27,888) and social science traits (N ranging between 161,460 to 298,420 across individual traits) increased the number of genome-wide significant loci by 12%, 200% and 50%, respectively, compared to those found by analysis of individual traits. Evidence of replication is present for many of these loci in subsequent larger studies for individual traits. HIPO can potentially be extended to high-dimensional phenotypes as a way of dimension reduction to maximize power for subsequent genetic association testing.

genetics↗

bioRxiv2017

Estimation of complex effect-size distributions using summary-level statistics from genome-wide association studies across 32 complex traits and implications for the future

Summary-level statistics from genome-wide association studies are now widely used to estimate heritability and co-heritability of traits using the popular linkage-disequilibrium-score (LD-score) regression method. We develop a likelihood-based approach for analyzing summary-level statistics and external LD information to estimate common variants effect-size distributions, characterized by proportion of underlying susceptibility SNPs and a flexible normal-mixture model for their effects. Analysis of summary-level results across 32 GWAS reveals that while all traits are highly polygenic, there is wide diversity in the degrees of polygenicity. The effect-size distributions for susceptibility SNPs could be adequately modeled by a single normal distribution for traits related to mental health and ability and by a mixture of two normal distributions for all other traits. Among quantitative traits, we predict the sample sizes needed to identify SNPs which explain 80% of GWAS heritability to be between 300K-500K for some of the early growth traits, between 1-2 million for some anthropometric and cholesterol traits and multiple millions for body mass index and some others. The corresponding predictions for disease traits are between 200K-400K for inflammatory bowel diseases, close to one million for a variety of adult onset chronic diseases and between 1-2 million for psychiatric diseases.

genetics↗

bioRxiv2017

MicroRNA perturbation of the MRN complex buffers DNA damage response from VEGF signaling

MicroRNAs contribute to biological robustness by buffering cellular processes from external perturbations. Here we report an unexpected link between DNA damage response and angiogenic signaling that is buffered by two distinct microRNAs. We demonstrate that genotoxic stress-induced miR-494 and miR-99b inhibit the DNA repair machinery by targeting the MRE11a-RAD50-NBN (MRN) complex. Functionally, gain and loss of function experiments show that miR-494 and miR-99b affect telomerase activity, activate p21 and Rb pathways and diminish angiogenic sprouting in vitro and in vivo. Genetic and pharmacological disruption of VEGFR-2 signaling and the MRN complex reveal a surprising co-dependency of these pathways in regulating endothelial senescence and proliferation. Vascular-targeted delivery of miR-494 decreases both growth factor-induced and tumor angiogenesis in mouse models. Mechanistically, disruption of the MRN complex induced CD44, a known driver of senescence and regulator of VEGF signaling in addition to suppressing IL-13 a stimulator of VEGF signaling. Our work identifies a putative miR-facilitated mechanism by which endothelial cells can be insulated against VEGF signaling to facilitate the onset of senescence and highlight the potential of targeting DNA repair to disrupt pathological angiogenesis.

cancer biology↗

bioRxiv2017

dMARCH8, a Drosophila ubiquitin ligase, regulates polymodifications of tubulin in the spermiogenic pathway

Ciliary stability and function are regulated by the covalent addition of polyglutamate and polyglycine chains to axonemal tubulin subunits. The Drosophila gene CG13442 encodes a predicted ubiquitin E3 ligase involved in the regulation of tubulin glycylation and glutamylation. Homologous to mammalian MARCH8, CG13442/dMARCH8 is required for male fertility. Sperm in dMARCH8 mutant testes appear to undergo a normal individualization process but fail to be transferred to the seminal vesicle. This phenotype is very similar to that of mutants in the Ntl gene, shown here to be a glycine transporter using a [3H]glycine uptake assay. Mutations in dMARCH8 are associated with a reduction of both polyglutamylation and polyglycylation of sperm tubulin. Polyglutamylation of tubulin is significantly increased in the Ntl- background, and recovers to wild-type levels in the Ntl--dMARCH8- double mutant background, indicating that glycine and glutamate compete for some common site(s) on tubulin molecules in this system. By analogy to the regulation of the mammalian glycine transporter GlyT2 through ubiquitin-mediated trafficking between the plasma membrane and endosome, dMARCH8 may target Ntl and glutamate transporters, or other upstream regulators of these proteins.

cell biology↗

bioRxiv2017

Simplified Power Calculations for Aggregate-level Association Tests Provide Insights to Challenges for Rare Variant Association Studies

Genome-wide association studies are now shifting focus from analysis of common to uncommon and rare variants with an anticipation to explain additional heritability of complex traits. As power for association testing for individual rare variants may often be low, various aggregate level association tests have been proposed to detect genetic loci that may contain clusters of susceptibility variants. Typically, power calculations for such tests require specification of large number of parameters, including effect sizes and allele frequencies of individual variants, making them difficult to use in practice. In this report, we approximate power to varying degree of accuracy using a smaller number of key parameters, including the total genetic variance explained by multiple variants within a locus. We perform extensive simulation studies to assess the accuracy of the proposed approximations in realistic settings. Using the simplified power calculation methods, we then develop an analytic framework to obtain bounds on genetic architecture of an underlying trait given results from a genome-wide study and observe important implications for the completely lack of or limited number of findings in many currently reported studies. Finally, we provide insights into the required quality of annotation/functional information for identification of likely causal variants to make meaningful improvement in power of subsequent association tests. A shiny application, Power Analysis for GEnetic AssociatioN Tests (PAGEANT), in R implementing the methods is made publicly available.

bioinformatics↗

bioRxiv2016

Retrotransposon Activation Contributes to Neurodegeneration in a DrosophilaTDP-43 Model of ALS.

Amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD) are two incurable neurodegenerative disorders that exist on a symptomological spectrum and share both genetic underpinnings and pathophysiological hallmarks. Functional abnormality of TAR DNA-binding protein 43 (TDP-43), an aggregation-prone RNA and DNA binding protein, is observed in the vast majority of both familial and sporadic ALS cases and in [~]40% of FTLD cases, but the cascade of events leading to cell death are not understood. We have expressed human TDP-43 (hTDP-43) in Drosophila neurons and glia, a model that recapitulates many of the characteristics of TDP-43-linked human disease including protein aggregation pathology, locomotor impairment, and premature death. We report that such expression of hTDP-43 impairs small interfering RNA (siRNA) silencing, which is the major post-transcriptional mechanism of retrotransposable element (RTE) control in somatic tissue. This is accompanied by de-repression of a panel of both LINE and LTR families of RTEs, with somewhat different elements being active in response to hTDP-43 expression in glia versus neurons. hTDP-43 expression in glia causes an early and severe loss of control of a specific RTE, the endogenous retrovirus (ERV) gypsy. We demonstrate that gypsy causes the degenerative phenotypes in these flies because we are able to rescue the toxicity of glial hTDP-43 either by genetically blocking expression of this RTE or by pharmacologically inhibiting RTE reverse transcriptase activity. Moreover, we provide evidence that activation of DNA damage-mediated programmed cell death underlies both neuronal and glial hTDP-43 toxicity, consistent with RTE-mediated effects in both cell types. Our findings suggest a novel mechanism in which RTE activity contributes to neurodegeneration in TDP-43-mediated diseases such as ALS and FTLD.\n\nAUTHOR SUMMARYFunctional abnormality of TAR DNA-binding protein 43 (TDP-43), an aggregation-prone RNA and DNA binding protein, is observed in the vast majority of both familial and sporadic ALS cases and in [~]40% of FTLD cases, and mutations in TDP-43 are causal in a subset of familial ALS cases. Although cytoplasmic inclusions of this mostly nuclear protein are a hallmark of the disease, the cascade of events leading to cell death are not understood. We demonstrate that expression of human TDP-43 (hTDP-43) in Drosophila neurons or glial cells, which results in toxic cytoplasmic accumulation of TDP-43, causes broad expression of retrotransposons. In the case of glial hTDP-43 expression, the endogenous retrovirus (ERV) gypsy causally contributes to degeneration because inhibiting gypsy genetically or pharmacologically is sufficient to rescue the phenotypic effects. Moreover, we demonstrate that activation of DNA damage-mediated programmed cell death underlies hTDP-43 and gypsy mediated toxicity. Finally, we find that hTDP-43 pathology impairs small interfering RNA silencing, which is an essential system that normally protects the genome from RTEs. These findings suggest a novel mechanism in which a storm of retrotransposon activation drives neurodegeneration in TDP-43 mediated diseases such as ALS and FTLD.

neuroscience↗