Biology subjects

Rao, A.

Publications and source records attributed to Rao, A..

7 recordsLinked to original sources

bioRxiv2018

TET enzymes augment AID expression via 5hmC modifications at the Aicda superenhancer

TET enzymes are dioxygenases that promote DNA demethylation by oxidizing the methyl group of 5-methylcytosine (5mC) to 5-hydroxymethylcytosine (5hmC). Here we report a close correspondence between 5hmC-marked regions, chromatin accessibility and enhancer activity in B cells, and a strong enrichment for consensus binding motifs for basic region-leucine zipper (bZIP) transcription factors at TET-responsive genomic regions. Functionally, Tet2 and Tet3 regulate class switch recombination (CSR) in murine B cells by enhancing expression of Aicda, encoding the cytidine deaminase AID essential for CSR. TET enzymes deposit 5hmC, demethylate and maintain chromatin accessibility at two TET-responsive elements, TetE1 and TetE2, located within a superenhancer in the Aicda locus. Transcriptional profiling identified BATF as the bZIP transcription factor involved in TET-dependent Aicda expression. 5hmC is not deposited at TetE1 in activated Batf-deficient B cells, indicating that BATF recruits TET proteins to the Aicda enhancer. Our data emphasize the importance of TET enzymes for bolstering AID expression, and highlight 5hmC as an epigenetic mark that captures enhancer dynamics during cell activation.

immunology↗

bioRxiv2018

Pooled functional genomic screens for intracellular calcium effectors

Cytoplasmic calcium transients relay cellular signals on timescales from milliseconds to hours, and the dynamic nature of calcium signals has slowed functional genomic screening for cellular calcium effectors. Here, we present a new strategy to identify calcium handling genes via a pooled knockdown employing the calcium-sensitive photo-switchable fluorescent protein, CaMPARI. This assay, cal-Seq, enabled identification of regulators for both cellular desensitization and histamine induced calcium signaling including GPR99, a leukotriene binding receptor, with implications in asthma treatment.

molecular biology↗

bioRxiv2018

Genetic analyses in UK Biobank identifies 78 novel loci associated with urinary biomarkers providing new insights into the biology of kidney function and chronic disease

BackgroundUrine biomarkers, such as creatinine, microalbumin, potassium and sodium are strongly associated with several common diseases including chronic kidney disease, cardiovascular disease and diabetes mellitus. Knowledge about the genetic determinants of the levels of these biomarker may shed light on pathophysiological mechanisms underlying the development of these diseases.\n\nMethodsWe performed genome-wide association studies of urinary levels of creatinine, microalbumin, potassium, and sodium in up to 326,441 unrelated individuals of European ancestry from the UK Biobank, a large population-based cohort study of over 500,000 individuals recruited across the United Kingdom in 2006-2010. Further, we explored genetic correlations, tissue-specific gene expression and possible causal genes related to these biomarkers.\n\nResultsWe identified 23 genome-wide significant independent loci associated with creatinine, 20 for microalbumin, 12 for potassium, and 38 for sodium. We confirmed several established associations including between the CUBN locus and microalbumin (rs141640975, p=3.11e-68). Variants associated with the levels of urinary creatinine, potassium, and sodium mapped to loci previously associated with obesity (GIPR, rs1800437, p=9.81e-10), caffeine metabolism (CYP1A1, rs2472297, p=1.61e-8) and triglycerides (GCKR, rs1260326, p=4.37e-16), respectively. We detected high pairwise genetic correlation between the levels of four urinary biomarkers, and significant genetic correlation between their levels and several anthropometric, cardiovascular, glycemic, lipid and kidney traits. We highlight GATM as causally implicated in the genetic control of urine creatinine, and GIPR, a potential diabetes drug target, as a plausible causal gene involved in regulation of urine creatinine and sodium.\n\nConclusionWe report 78 novel genome-wide significant associations with urinary levels of creatinine, microalbumin, potassium and sodium in the UK Biobank, confirming several previously established associations and providing new insights into the genetic basis of these traits and their connection to chronic diseases.\n\nAuthor SummaryUrine biomarkers, such as creatinine, microalbumin, potassium and sodium are strongly associated with several common diseases including chronic kidney disease, cardiovascular disease and diabetes mellitus. Knowledge about the genetic determinants of the levels of these biomarker may shed light on pathophysiological mechanisms underlying the development of these diseases. Here, we performed genome-wide association studies of urinary levels of creatinine, microalbumin, potassium and sodium in up to 326,441 unrelated individuals of European ancestry from the UK Biobank. Further, we explored genetic correlations, tissue-specific gene expression and possible causal genes related to these biomarkers. We identified 78 novel genome-wide significant associations with urinary biomarkers, confirming several previously established associations and providing new insights into the genetic basis of these traits and their connection to chronic diseases. Further, we highlight GATM as causally implicated in the genetic control of urine creatinine, and GIPR, a potential diabetes drug target, as a plausible causal gene involved in regulation of urine creatinine and sodium. The knowledge arising from our work may improve the predictive utility of the respective biomarker and point to new therapeutic strategies to prevent common diseases.

genetics↗

bioRxiv2018

Computational Modeling of the Dynamic Biomarker Cascade in Alzheimer’s Disease

BackgroundAlzheimers disease (AD) is a major public health concern and there is an urgent need to better understand its complex biology and develop effective therapies. AD progression can be tracked in patients though validated imaging and spinal fluid biomarkers of pathology and neuronal loss. We still, however, lack a coherent quantitative model that explains how these biomarkers interact and evolve over time. Such a model could potentially help identify the major drivers of disease in individual patients and simulate response to therapy prior to entry in clinical trials. A current theory of AD biomarker progression, known as the dynamic biomarker cascade model, hypothesizes AD biomarkers evolve in a sequential, but temporally overlapping manner. A computational model incorporating assumptions about the underlying biology of this theory and its variations would be useful to test and refine its accuracy with longitudinal biomarker data from clinical trials.\n\nMethodsWe implemented a causal model to simulate time-dependent biomarker data under the descriptive assumptions of the dynamic biomarker cascade theory. We modeled pathologic biomarkers (beta-amyloid and tau), neuronal loss biomarkers and cognitive impairment as non-linear first order ordinary differential equations (ODEs) to include amyloid-dependent and non-dependent neurodegenerative cascades. We tested the feasibility of the model by adjusting its parameters to simulate three specific natural history scenarios in early-onset autosomal dominant AD and late-onset AD, and determine whether computed biomarker trajectories agreed with current assumptions of AD biomarker progression. We also simulated the effects of anti-amyloid therapy in late-onset AD.\n\nResultsThe computational model of early-onset AD demonstrated the initial appearance of amyloid, followed by biomarkers of tau and neurodegeneration, followed by onset of cognitive decline based on cognitive reserve, as predicted by prior literature. Similarly, the late-onset AD computational models demonstrated the first appearance of amyloid or non-amyloid-related tauopathy, depending on the magnitude of comorbid pathology, and also closely matched the biomarker cascades predicted by prior literature. Forward simulation of anti-amyloid therapy in symptomatic late-onset AD failed to demonstrate any slowing in progression of cognitive decline, consistent with prior failed clinical trials in symptomatic patients.\n\nConclusionWe have developed and computationally implemented a mathematical causal model of the dynamic biomarker cascade theory in AD. We demonstrate the feasibility of this model by simulating biomarker evolution and cognitive decline in early and late-onset natural history scenarios, as well as in a treatment scenario targeted at core AD pathology. Models resulting from this causal approach can be further developed and refined using patient data from longitudinal biomarker studies, and may in the future play a key role in personalizing approaches to treatment.

neuroscience↗

bioRxiv2018

Genetic regulatory mechanisms of smooth muscle cells map to coronary artery disease risk loci

Coronary artery disease (CAD) is the leading cause of death globally. Genome-wide association studies (GWAS) have identified more than 95 independent loci that influence CAD risk, most of which reside in non-coding regions of the genome. To interpret these loci, we generated transcriptome and whole-genome datasets using human coronary artery smooth muscle cells (HCASMC) from 52 unrelated donors, as well as epigenomic datasets using ATAC-seq on a subset of 8 donors. Through systematic comparison with publicly available datasets from GTEx and ENCODE projects, we identified transcriptomic, epigenetic, and genetic regulatory mechanisms specific to HCASMC. We assessed the relevance of HCASMC to CAD risk using transcriptomic and epigenomic level analyses. By jointly modeling eQTL and GWAS datasets, we identified five genes (SIPA1, TCF21, SMAD3, FES, and PDGFRA) that modulate CAD risk through HCASMC, all of which have relevant functional roles in vascular remodeling. Comparison with GTEx data suggests that SIPA1 and PDGFRA influence CAD risk predominantly through HCASMC, while other annotated genes may have multiple cell and tissue targets. Together, these results provide new tissue-specific and mechanistic insights into the regulation of a critical vascular cell type associated with CAD in human populations.

genomics↗

bioRxiv2017

Bayesian estimation of MSM population size in Côte d’Ivoire

Cote dIvoire has one of the largest HIV epidemics in West Africa with around half million people living with HIV. Key populations like gay men and other men who have sex with men (MSM) are often disproportionately burdened with HIV due to specific acquisition and transmission risks. Quantifying the MSM population sizes at subnational level is critical to improving the HIV prevention interventions. While survey-based direct estimates of MSM numbers are available at a few urban centers in C{circumflex}ote dIvoire, no data on MSM population size exists at other areas without any community infrastructure to facilitate sufficient access to the MSM community. We use this limited data in a Bayesian regression setup to produce first empirically calculated estimates of the numbers of MSM in all areas of C{circumflex}ote dIvoire prioritized in the HIV response. Our hierarchical model imputes missing covariates using geospatial information and allows for proper uncertainty quantification leading to meaningful confidence bounds for the predicted MSM population size estimates. The intended impact of this process is to increase uptake and use of high quality, comprehensive epidemiologic and interventional data in program planning. These estimates will help design future surveys and support the planning of the scale and content of HIV prevention and treatment programs for MSM in C{circumflex}ote dIvoire.

epidemiology↗

bioRxiv2017

Associations between memory traces emerge in a generic neural circuit model through STDP

Memory traces and associations between them are fundamental for cognitive brain function. Neuron recordings suggest that distributed assemblies of neurons in the brain serve as memory traces for spatial information, real-world items, and concepts. How-ever, there is conflicting evidence regarding neural codes for associated memory traces. Some studies suggest the emergence of overlaps between assemblies during an association, while others suggest that the assemblies themselves remain largely unchanged and new assemblies emerge as neural codes for associated memory items. Here we study the emergence of neural codes for associated memory items in a generic computational model of recurrent networks of spiking neurons with a data-constrained rule for spike-timing-dependent plasticity (STDP). The model depends critically on two parameters, which control the excitability of neurons and the scale of initial synaptic weights. By modifying these two parameters, the model can reproduce both experimental data from the human brain on the fast formation of associations through emergent overlaps between assemblies, and rodent data where new neurons are recruited to encode the associated memories. Hence our findings suggest that the brain can use both of these two neural codes for associations, and dynamically switch between them during consolidation.

neuroscience↗