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Manikandan Narayanan

Bio: Manikandan Narayanan is an academic researcher from National Institutes of Health. The author has contributed to research in topics: Biology & Gene regulatory network. The author has an hindex of 14, co-authored 23 publications receiving 2054 citations. Previous affiliations of Manikandan Narayanan include Indian Institute of Technology Madras & Bosch.

Papers
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Journal ArticleDOI
10 Apr 2014-Cell
TL;DR: Independent of age and pre-existing antibody titers, accurate models could be constructed using pre-perturbation cell populations alone, which were validated using independent baseline time points.

352 citations

Journal ArticleDOI
TL;DR: Using expression profiles from postmortem prefrontal cortex samples of 624 dementia patients and non‐demented controls, this work identified a 242‐gene subnetwork enriched for independent AD/HD signatures, which revealed a surprising dichotomy of gained/lost correlations among two inter‐connected processes, chromatin organization and neural differentiation.
Abstract: Using expression profiles from postmortem prefrontal cortex samples of 624 dementia patients and non-demented controls, we investigated global disruptions in the co-regulation of genes in two neurodegenerative diseases, late-onset Alzheimer’s disease (AD) and Huntington’s disease (HD). We identified networks of differentially co-expressed (DC) gene pairs that either gained or lost correlation in disease cases relative to the control group, with the former dominant for both AD and HD and both patterns replicating in independent human cohorts of AD and aging. When aligning networks of DC patterns and physical interactions, we identified a 242-gene subnetwork enriched for independent AD/HD signatures. This subnetwork revealed a surprising dichotomy of gained/lost correlations among two inter-connected processes, chromatin organization and neural differentiation, and included DNA methyltransferases, DNMT1 and DNMT3A, of which we predicted the former but not latter as a key regulator. To validate the inter-connection of these two processes and our key regulator prediction, we generated two brain-specific knockout (KO) mice and show that Dnmt1 KO signature significantly overlaps with the subnetwork (P = 3.1 × 10 � 12 ), while Dnmt3a KO signature does not (P = 0.017).

145 citations

Journal ArticleDOI
TL;DR: The results suggest that CD8+ lymphocytes undergo chromatin remodeling in a progressive fashion, and have major implications for the understanding of peripheral T-cell ontogeny and the formation of immunological memory.
Abstract: To better elucidate epigenetic mechanisms that correlate with the dynamic gene expression program observed upon T-cell differentiation, we investigated the genomic landscape of histone modifications in naive and memory CD8+ T cells. Using a ChIP-Seq approach coupled with global gene expression profiling, we generated genome-wide histone H3 lysine 4 (H3K4me3) and H3 lysine 27 (H3K27me3) trimethylation maps in naive, T memory stem cells, central memory cells, and effector memory cells in order to gain insight into how histone architecture is remodeled during T cell differentiation. We show that H3K4me3 histone modifications are associated with activation of genes, while H3K27me3 is negatively correlated with gene expression at canonical loci and enhancers associated with T-cell metabolism, effector function, and memory. Our results also reveal histone modifications and gene expression signatures that distinguish the recently identified T memory stem cells from other CD8+ T-cell subsets. Taken together, our results suggest that CD8+ lymphocytes undergo chromatin remodeling in a progressive fashion. These findings have major implications for our understanding of peripheral T-cell ontogeny and the formation of immunological memory.

94 citations

Posted Content
TL;DR: A method that compares the protein interaction networks of two species to detect functionally similar (conserved) protein modules between them, based on an algorithm developed to identify matching subgraphs between two graphs that performs competitively with and sometimes better than two previous network comparison methods.
Abstract: We present a method that compares the protein interaction networks of two species to detect functionally similar (conserved) protein modules between them. The method is based on an algorithm we developed to identify matching subgraphs between two graphs. Unlike previous network comparison methods, our algorithm has provable guarantees on correctness and efficiency. Our algorithm framework also admits quite general connectivity and local matching criteria that define when two subgraphs match and constitute a conserved module. We apply our method to pairwise comparisons of the yeast protein network with the human, fruit fly and nematode worm protein networks, using a lenient criterion based on connectedness and matching edges, coupled with a betweenness clustering heuristic. We evaluate the detected conserved modules against reference yeast protein complexes using sensitivity and specificity measures. In these evaluations, our method performs competitively with and sometimes better than two previous network comparison methods. Further under some conditions (proper homolog and species selection), our method performs better than a popular single-species clustering method. Beyond these evaluations, we discuss the biology of a couple of conserved modules detected by our method. We demonstrate the utility of network comparison for transferring annotations from yeast proteins to human ones, and validate the predicted annotations.

69 citations


Cited by
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Journal ArticleDOI
Kristin G. Ardlie, David S. DeLuca, Ayellet V. Segrè, Timothy J. Sullivan, Taylor Young, Ellen Gelfand, Casandra A. Trowbridge, Julian Maller, Taru Tukiainen, Monkol Lek, Lucas D. Ward, Pouya Kheradpour, Benjamin Iriarte, Yan Meng, Cameron D. Palmer, Tõnu Esko, Wendy Winckler, Joel N. Hirschhorn, Manolis Kellis, Daniel G. MacArthur, Gad Getz, Andrey A. Shabalin, Gen Li, Yi-Hui Zhou, Andrew B. Nobel, Ivan Rusyn, Fred A. Wright, Tuuli Lappalainen, Pedro G. Ferreira, Halit Ongen, Manuel A. Rivas, Alexis Battle, Sara Mostafavi, Jean Monlong, Michael Sammeth, Marta Melé, Ferran Reverter, Jakob M. Goldmann, Daphne Koller, Roderic Guigó, Mark I. McCarthy, Emmanouil T. Dermitzakis, Eric R. Gamazon, Hae Kyung Im, Anuar Konkashbaev, Dan L. Nicolae, Nancy J. Cox, Timothée Flutre, Xiaoquan Wen, Matthew Stephens, Jonathan K. Pritchard, Zhidong Tu, Bin Zhang, Tao Huang, Quan Long, Luan Lin, Jialiang Yang, Jun Zhu, Jun Liu, Amanda Brown, Bernadette Mestichelli, Denee Tidwell, Edmund Lo, Mike Salvatore, Saboor Shad, Jeffrey A. Thomas, John T. Lonsdale, Michael T. Moser, Bryan Gillard, Ellen Karasik, Kimberly Ramsey, Christopher Choi, Barbara A. Foster, John Syron, Johnell Fleming, Harold Magazine, Rick Hasz, Gary Walters, Jason Bridge, Mark Miklos, Susan L. Sullivan, Laura Barker, Heather M. Traino, Maghboeba Mosavel, Laura A. Siminoff, Dana R. Valley, Daniel C. Rohrer, Scott D. Jewell, Philip A. Branton, Leslie H. Sobin, Mary Barcus, Liqun Qi, Jeffrey McLean, Pushpa Hariharan, Ki Sung Um, Shenpei Wu, David Tabor, Charles Shive, Anna M. Smith, Stephen A. Buia, Anita H. Undale, Karna Robinson, Nancy Roche, Kimberly M. Valentino, Angela Britton, Robin Burges, Debra Bradbury, Kenneth W. Hambright, John Seleski, Greg E. Korzeniewski, Kenyon Erickson, Yvonne Marcus, Jorge Tejada, Mehran Taherian, Chunrong Lu, Margaret J. Basile, Deborah C. Mash, Simona Volpi, Jeffery P. Struewing, Gary F. Temple, Joy T. Boyer, Deborah Colantuoni, Roger Little, Susan E. Koester, Latarsha J. Carithers, Helen M. Moore, Ping Guan, Carolyn C. Compton, Sherilyn Sawyer, Joanne P. Demchok, Jimmie B. Vaught, Chana A. Rabiner, Nicole C. Lockhart 
08 May 2015-Science
TL;DR: The landscape of gene expression across tissues is described, thousands of tissue-specific and shared regulatory expression quantitative trait loci (eQTL) variants are cataloged, complex network relationships are described, and signals from genome-wide association studies explained by eQTLs are identified.
Abstract: Understanding the functional consequences of genetic variation, and how it affects complex human disease and quantitative traits, remains a critical challenge for biomedicine. We present an analysi...

4,418 citations

Journal ArticleDOI
TL;DR: Genome-wide analysis suggests that several genes that increase the risk for sporadic Alzheimer's disease encode factors that regulate glial clearance of misfolded proteins and the inflammatory reaction.
Abstract: Increasing evidence suggests that Alzheimer's disease pathogenesis is not restricted to the neuronal compartment, but includes strong interactions with immunological mechanisms in the brain. Misfolded and aggregated proteins bind to pattern recognition receptors on microglia and astroglia, and trigger an innate immune response characterised by release of inflammatory mediators, which contribute to disease progression and severity. Genome-wide analysis suggests that several genes that increase the risk for sporadic Alzheimer's disease encode factors that regulate glial clearance of misfolded proteins and the inflammatory reaction. External factors, including systemic inflammation and obesity, are likely to interfere with immunological processes of the brain and further promote disease progression. Modulation of risk factors and targeting of these immune mechanisms could lead to future therapeutic or preventive strategies for Alzheimer's disease.

3,947 citations

Journal ArticleDOI
TL;DR: In a recent study, this article showed that low cerebrospinal fluid (CSF) Aβ42 and amyloid-PET positivity precede other AD manifestations by many years.
Abstract: Despite continuing debate about the amyloid β‐protein (or Aβ hypothesis, new lines of evidence from laboratories and clinics worldwide support the concept that an imbalance between production and clearance of Aβ42 and related Aβ peptides is a very early, often initiating factor in Alzheimer9s disease (AD). Confirmation that presenilin is the catalytic site of γ‐secretase has provided a linchpin: all dominant mutations causing early‐onset AD occur either in the substrate (amyloid precursor protein, APP) or the protease (presenilin) of the reaction that generates Aβ. Duplication of the wild‐type APP gene in Down9s syndrome leads to Aβ deposits in the teens, followed by microgliosis, astrocytosis, and neurofibrillary tangles typical of AD. Apolipoprotein E4, which predisposes to AD in > 40% of cases, has been found to impair Aβ clearance from the brain. Soluble oligomers of Aβ42 isolated from AD patients9 brains can decrease synapse number, inhibit long‐term potentiation, and enhance long‐term synaptic depression in rodent hippocampus, and injecting them into healthy rats impairs memory. The human oligomers also induce hyperphosphorylation of tau at AD‐relevant epitopes and cause neuritic dystrophy in cultured neurons. Crossing human APP with human tau transgenic mice enhances tau‐positive neurotoxicity. In humans, new studies show that low cerebrospinal fluid (CSF) Aβ42 and amyloid‐PET positivity precede other AD manifestations by many years. Most importantly, recent trials of three different Aβ antibodies (solanezumab, crenezumab, and aducanumab) have suggested a slowing of cognitive decline in post hoc analyses of mild AD subjects. Although many factors contribute to AD pathogenesis, Aβ dyshomeostasis has emerged as the most extensively validated and compelling therapeutic target.

3,824 citations

Journal ArticleDOI
21 Apr 2016-Cell
TL;DR: It is concluded that transcript levels by themselves are not sufficient to predict protein levels in many scenarios and to thus explain genotype-phenotype relationships and that high-quality data quantifying different levels of gene expression are indispensable for the complete understanding of biological processes.

1,996 citations

Journal ArticleDOI
TL;DR: Mash extends the MinHash dimensionality-reduction technique to include a pairwise mutation distance and P value significance test, enabling the efficient clustering and search of massive sequence collections.
Abstract: Mash extends the MinHash dimensionality-reduction technique to include a pairwise mutation distance and P value significance test, enabling the efficient clustering and search of massive sequence collections. Mash reduces large sequences and sequence sets to small, representative sketches, from which global mutation distances can be rapidly estimated. We demonstrate several use cases, including the clustering of all 54,118 NCBI RefSeq genomes in 33 CPU h; real-time database search using assembled or unassembled Illumina, Pacific Biosciences, and Oxford Nanopore data; and the scalable clustering of hundreds of metagenomic samples by composition. Mash is freely released under a BSD license ( https://github.com/marbl/mash ).

1,886 citations