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Saletin JM, Koopman-Verhoeff ME, Han G, Barker DH, Carskadon MA, Anders TF, Sheinkopf SJ. Sleep Problems and Autism Impairments in a Large Community Sample of Children and Adolescents. Child Psychiatry Hum Dev 2022:10.1007/s10578-022-01470-0. [PMID: 36515855 DOI: 10.1007/s10578-022-01470-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/12/2022] [Indexed: 12/15/2022]
Abstract
Sleep problems are common in individuals with autism spectrum disorder (ASD). How sleep problems reflect specific ASD phenotypes is unclear. We studied whether sleep problems indexed functional impairment in a heterogeneous community sample of individuals with ASD. We analyzed 977 probands (233 females; age = 11.27 ± 4.13 years) from the Rhode Island Consortium for Autism Research and Treatment dataset, a unique public-private-academic collaboration involving all major points of service for families in Rhode Island. We found that individuals with a confirmed diagnosis of ASD were more likely to have sleep problems. However, across the whole sample and above and beyond a formal diagnosis, sleep problems were dimensionally associated with worse social impairment and poorer adaptive functioning. By using a large dataset reflective of the diversity of presentations in the community, this study underscores the importance of considering sleep problems in clinical practice to improve adaptive functioning in individuals with ASD.
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Affiliation(s)
- Jared M Saletin
- Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA.
- Emma Pendleton Bradley Hospital, East Providence, RI, USA.
- Sleep Research Laboratory, Emma Pendleton Bradley Hospital, Providence, RI, USA.
| | - M Elisabeth Koopman-Verhoeff
- Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA
- Emma Pendleton Bradley Hospital, East Providence, RI, USA
- Sleep Research Laboratory, Emma Pendleton Bradley Hospital, Providence, RI, USA
| | - Gloria Han
- Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA
- Department of Anesthesiology, Vanderbilt Medical Center, Nashville, TN, USA
| | - David H Barker
- Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA
- The Bradley Hasbro Children's Research Center, Providence, RI, USA
| | - Mary A Carskadon
- Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA
- Emma Pendleton Bradley Hospital, East Providence, RI, USA
- Sleep Research Laboratory, Emma Pendleton Bradley Hospital, Providence, RI, USA
| | - Thomas F Anders
- Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA
- Emma Pendleton Bradley Hospital, East Providence, RI, USA
| | - Stephen J Sheinkopf
- Brown Center for the Study of Children at Risk, Women & Infants Hospital, Providence, RI, USA
- Thompson Center for Autism and Neurodevelopment, University of Missouri, Columbia, MO, USA
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2
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Bengani H, Grozeva D, Moyon L, Bhatia S, Louros SR, Hope J, Jackson A, Prendergast JG, Owen LJ, Naville M, Rainger J, Grimes G, Halachev M, Murphy LC, Spasic-Boskovic O, van Heyningen V, Kind P, Abbott CM, Osterweil E, Raymond FL, Roest Crollius H, FitzPatrick DR. Identification and functional modelling of plausibly causative cis-regulatory variants in a highly-selected cohort with X-linked intellectual disability. PLoS One 2021; 16:e0256181. [PMID: 34388204 PMCID: PMC8362966 DOI: 10.1371/journal.pone.0256181] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 08/01/2021] [Indexed: 11/18/2022] Open
Abstract
Identifying causative variants in cis-regulatory elements (CRE) in neurodevelopmental disorders has proven challenging. We have used in vivo functional analyses to categorize rigorously filtered CRE variants in a clinical cohort that is plausibly enriched for causative CRE mutations: 48 unrelated males with a family history consistent with X-linked intellectual disability (XLID) in whom no detectable cause could be identified in the coding regions of the X chromosome (chrX). Targeted sequencing of all chrX CRE identified six rare variants in five affected individuals that altered conserved bases in CRE targeting known XLID genes and segregated appropriately in families. Two of these variants, FMR1CRE and TENM1CRE, showed consistent site- and stage-specific differences of enhancer function in the developing zebrafish brain using dual-color fluorescent reporter assay. Mouse models were created for both variants. In male mice Fmr1CRE induced alterations in neurodevelopmental Fmr1 expression, olfactory behavior and neurophysiological indicators of FMRP function. The absence of another likely causative variant on whole genome sequencing further supported FMR1CRE as the likely basis of the XLID in this family. Tenm1CRE mice showed no phenotypic anomalies. Following the release of gnomAD 2.1, reanalysis showed that TENM1CRE exceeded the maximum plausible population frequency of a XLID causative allele. Assigning causative status to any ultra-rare CRE variant remains problematic and requires disease-relevant in vivo functional data from multiple sources. The sequential and bespoke nature of such analyses renders them time-consuming and challenging to scale for routine clinical use.
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Affiliation(s)
- Hemant Bengani
- MRC Human Genetics Unit, IGMM, University of Edinburgh (UoE), Edinburgh, United Kingdom
| | - Detelina Grozeva
- Cambridge Institute for Medical Research, University of Cambridge, Cambridge, United Kingdom
- Institute of Psychological Medicine & Clinical Neurosciences, Cardiff University, Cardiff, United Kingdom
| | - Lambert Moyon
- Ecole Normale Supérieure, Institut de Biologie de l’ENS, IBENS, Paris, France
| | - Shipra Bhatia
- MRC Human Genetics Unit, IGMM, University of Edinburgh (UoE), Edinburgh, United Kingdom
| | - Susana R. Louros
- Centre for Discovery Brain Sciences, Patrick Wild Centre, University of Edinburgh, Edinburgh, United Kingdom
- Simons Initiative for the Developing Brain, University of Edinburgh, Edinburgh, United Kingdom
| | - Jilly Hope
- Institute of Genomic and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom
| | - Adam Jackson
- Centre for Discovery Brain Sciences, Patrick Wild Centre, University of Edinburgh, Edinburgh, United Kingdom
| | | | - Liusaidh J. Owen
- MRC Human Genetics Unit, IGMM, University of Edinburgh (UoE), Edinburgh, United Kingdom
| | - Magali Naville
- Ecole Normale Supérieure, Institut de Biologie de l’ENS, IBENS, Paris, France
| | - Jacqueline Rainger
- MRC Human Genetics Unit, IGMM, University of Edinburgh (UoE), Edinburgh, United Kingdom
| | - Graeme Grimes
- Institute of Genomic and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom
| | - Mihail Halachev
- Institute of Genomic and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom
| | - Laura C. Murphy
- Institute of Genomic and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom
| | - Olivera Spasic-Boskovic
- East Midlands and East of England NHS Genomic Laboratory Hub, Molecular Genetics, Adden brooke’s Hospital, Cambridge University Hospitals NHS Foundation Trust Cambridge, Cambridge, United Kingdom
| | | | - Peter Kind
- Centre for Discovery Brain Sciences, Patrick Wild Centre, University of Edinburgh, Edinburgh, United Kingdom
- Simons Initiative for the Developing Brain, University of Edinburgh, Edinburgh, United Kingdom
| | - Catherine M. Abbott
- Simons Initiative for the Developing Brain, University of Edinburgh, Edinburgh, United Kingdom
- Institute of Genomic and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom
| | - Emily Osterweil
- Centre for Discovery Brain Sciences, Patrick Wild Centre, University of Edinburgh, Edinburgh, United Kingdom
- Simons Initiative for the Developing Brain, University of Edinburgh, Edinburgh, United Kingdom
| | - F. Lucy Raymond
- Cambridge Institute for Medical Research, University of Cambridge, Cambridge, United Kingdom
| | | | - David R. FitzPatrick
- MRC Human Genetics Unit, IGMM, University of Edinburgh (UoE), Edinburgh, United Kingdom
- Simons Initiative for the Developing Brain, University of Edinburgh, Edinburgh, United Kingdom
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3
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Morival JLP, Widyastuti HP, Nguyen CHH, Zaragoza MV, Downing TL. DNA methylation analysis reveals epimutation hotspots in patients with dilated cardiomyopathy-associated laminopathies. Clin Epigenetics 2021; 13:139. [PMID: 34246298 PMCID: PMC8272901 DOI: 10.1186/s13148-021-01127-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2021] [Accepted: 07/03/2021] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Mutations in LMNA, encoding lamin A/C, lead to a variety of diseases known as laminopathies including dilated cardiomyopathy (DCM) and skeletal abnormalities. Though previous studies have investigated the dysregulation of gene expression in cells from patients with DCM, the role of epigenetic (gene regulatory) mechanisms, such as DNA methylation, has not been thoroughly investigated. Furthermore, the impact of family-specific LMNA mutations on DNA methylation is unknown. Here, we performed reduced representation bisulfite sequencing on ten pairs of fibroblasts and their induced pluripotent stem cell (iPSC) derivatives from two families with DCM due to distinct LMNA mutations, one of which also induces brachydactyly. RESULTS Family-specific differentially methylated regions (DMRs) were identified by comparing the DNA methylation landscape of patient and control samples. Fibroblast DMRs were found to enrich for distal regulatory features and transcriptionally repressed chromatin and to associate with genes related to phenotypes found in tissues affected by laminopathies. These DMRs, in combination with transcriptome-wide expression data and lamina-associated domain (LAD) organization, revealed the presence of inter-family epimutation hotspots near differentially expressed genes, most of which were located outside LADs redistributed in LMNA-related DCM. Comparison of DMRs found in fibroblasts and iPSCs identified regions where epimutations were persistent across both cell types. Finally, a network of aberrantly methylated disease-associated genes revealed a potential molecular link between pathways involved in bone and heart development. CONCLUSIONS Our results identified both shared and mutation-specific laminopathy epimutation landscapes that were consistent with lamin A/C mutation-mediated epigenetic aberrancies that arose in somatic and early developmental cell stages.
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Affiliation(s)
- Julien L. P. Morival
- Department of Biomedical Engineering and The Edwards Lifesciences Center for Advanced Cardiovascular Technology, University of California Irvine, 2408 Engineering III, Irvine, CA 92697 USA
- NSF-Simons Center for Multiscale Cell Fate Research, University of California Irvine, Irvine, CA USA
- Center for Complex Biological Systems, University of California Irvine, Irvine, CA USA
| | - Halida P. Widyastuti
- UCI Cardiogenomics Program, Department of Pediatrics, Division of Genetics and Genomics and Department of Biological Chemistry, University of California Irvine, 2042 Hewitt Hall, Irvine, CA 92697 USA
| | - Cecilia H. H. Nguyen
- UCI Cardiogenomics Program, Department of Pediatrics, Division of Genetics and Genomics and Department of Biological Chemistry, University of California Irvine, 2042 Hewitt Hall, Irvine, CA 92697 USA
| | - Michael V. Zaragoza
- UCI Cardiogenomics Program, Department of Pediatrics, Division of Genetics and Genomics and Department of Biological Chemistry, University of California Irvine, 2042 Hewitt Hall, Irvine, CA 92697 USA
| | - Timothy L. Downing
- Department of Biomedical Engineering and The Edwards Lifesciences Center for Advanced Cardiovascular Technology, University of California Irvine, 2408 Engineering III, Irvine, CA 92697 USA
- NSF-Simons Center for Multiscale Cell Fate Research, University of California Irvine, Irvine, CA USA
- Center for Complex Biological Systems, University of California Irvine, Irvine, CA USA
- Department of Microbiology and Molecular Genetics, University of California Irvine, Irvine, CA USA
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4
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Baity-Jesi M, Biroli G, Reichman DR. Revisiting the concept of activation in supercooled liquids. Eur Phys J E Soft Matter 2021; 44:77. [PMID: 34125327 PMCID: PMC8203548 DOI: 10.1140/epje/s10189-021-00077-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 05/07/2021] [Indexed: 06/12/2023]
Abstract
In this work, we revisit the description of dynamics based on the concepts of metabasins and activation in mildly supercooled liquids via the analysis of the dynamics of a paradigmatic glass former between its onset temperature [Formula: see text] and mode-coupling temperature [Formula: see text]. First, we provide measures that demonstrate that the onset of glassiness is indeed connected to the landscape, and that metabasin waiting time distributions are so broad that the system can remain stuck in a metabasin for times that exceed [Formula: see text] by orders of magnitude. We then reanalyze the transitions between metabasins, providing several indications that the standard picture of activated dynamics in terms of traps does not hold in this regime. Instead, we propose that here activation is principally driven by entropic instead of energetic barriers. In particular, we illustrate that activation is not controlled by the hopping of high energetic barriers and should more properly be interpreted as the entropic selection of nearly barrierless but rare pathways connecting metabasins on the landscape.
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Affiliation(s)
| | - Giulio Biroli
- Departement de Physique Statistique, École Normale Supérieure, 75005, Paris, France
| | - David R Reichman
- Department of Chemistry, Columbia University, New York, NY, 10027, USA
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5
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Camacho-Aguilar E, Warmflash A, Rand DA. Quantifying cell transitions in C. elegans with data-fitted landscape models. PLoS Comput Biol 2021; 17:e1009034. [PMID: 34061834 PMCID: PMC8195438 DOI: 10.1371/journal.pcbi.1009034] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2021] [Revised: 06/11/2021] [Accepted: 05/03/2021] [Indexed: 12/19/2022] Open
Abstract
Increasing interest has emerged in new mathematical approaches that simplify the study of complex differentiation processes by formalizing Waddington's landscape metaphor. However, a rational method to build these landscape models remains an open problem. Here we study vulval development in C. elegans by developing a framework based on Catastrophe Theory (CT) and approximate Bayesian computation (ABC) to build data-fitted landscape models. We first identify the candidate qualitative landscapes, and then use CT to build the simplest model consistent with the data, which we quantitatively fit using ABC. The resulting model suggests that the underlying mechanism is a quantifiable two-step decision controlled by EGF and Notch-Delta signals, where a non-vulval/vulval decision is followed by a bistable transition to the two vulval states. This new model fits a broad set of data and makes several novel predictions.
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Affiliation(s)
- Elena Camacho-Aguilar
- Mathematics Institute, University of Warwick, Coventry, United Kingdom
- Department of Biosciences, Rice University, Houston, Texas, United States of America
| | - Aryeh Warmflash
- Department of Biosciences, Rice University, Houston, Texas, United States of America
- Department of Bioengineering, Rice University, Houston, Texas, United States of America
| | - David A. Rand
- Mathematics Institute, University of Warwick, Coventry, United Kingdom
- Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, University of Warwick, Coventry, United Kingdom
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6
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Wei Z, Lin BJ, Chen TW, Daie K, Svoboda K, Druckmann S. A comparison of neuronal population dynamics measured with calcium imaging and electrophysiology. PLoS Comput Biol 2020; 16:e1008198. [PMID: 32931495 PMCID: PMC7518847 DOI: 10.1371/journal.pcbi.1008198] [Citation(s) in RCA: 68] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Revised: 09/25/2020] [Accepted: 07/27/2020] [Indexed: 12/13/2022] Open
Abstract
Calcium imaging with fluorescent protein sensors is widely used to record activity in neuronal populations. The transform between neural activity and calcium-related fluorescence involves nonlinearities and low-pass filtering, but the effects of the transformation on analyses of neural populations are not well understood. We compared neuronal spikes and fluorescence in matched neural populations in behaving mice. We report multiple discrepancies between analyses performed on the two types of data, including changes in single-neuron selectivity and population decoding. These were only partially resolved by spike inference algorithms applied to fluorescence. To model the relation between spiking and fluorescence we simultaneously recorded spikes and fluorescence from individual neurons. Using these recordings we developed a model transforming spike trains to synthetic-imaging data. The model recapitulated the differences in analyses. Our analysis highlights challenges in relating electrophysiology and imaging data, and suggests forward modeling as an effective way to understand differences between these data.
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Affiliation(s)
- Ziqiang Wei
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, the United States of America
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland, the United States of America
| | - Bei-Jung Lin
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, the United States of America
- Institute of Neuroscience, National Yang-Ming University, Taipei, Taiwan
| | - Tsai-Wen Chen
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, the United States of America
- Institute of Neuroscience, National Yang-Ming University, Taipei, Taiwan
| | - Kayvon Daie
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, the United States of America
| | - Karel Svoboda
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, the United States of America
| | - Shaul Druckmann
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, the United States of America
- Department of Neurobiology, Stanford University, Stanford, California, the United States of America
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7
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Ly C, Shew WL, Barreiro AK. Efficient calculation of heterogeneous non-equilibrium statistics in coupled firing-rate models. J Math Neurosci 2019; 9:2. [PMID: 31073652 PMCID: PMC6509307 DOI: 10.1186/s13408-019-0070-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2019] [Accepted: 04/28/2019] [Indexed: 06/09/2023]
Abstract
Understanding nervous system function requires careful study of transient (non-equilibrium) neural response to rapidly changing, noisy input from the outside world. Such neural response results from dynamic interactions among multiple, heterogeneous brain regions. Realistic modeling of these large networks requires enormous computational resources, especially when high-dimensional parameter spaces are considered. By assuming quasi-steady-state activity, one can neglect the complex temporal dynamics; however, in many cases the quasi-steady-state assumption fails. Here, we develop a new reduction method for a general heterogeneous firing-rate model receiving background correlated noisy inputs that accurately handles highly non-equilibrium statistics and interactions of heterogeneous cells. Our method involves solving an efficient set of nonlinear ODEs, rather than time-consuming Monte Carlo simulations or high-dimensional PDEs, and it captures the entire set of first and second order statistics while allowing significant heterogeneity in all model parameters.
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Affiliation(s)
- Cheng Ly
- Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, Richmond, USA
| | - Woodrow L. Shew
- Department of Physics, University of Arkansas, Fayetteville, USA
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8
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Datta V, Hannenhalli S, Siddharthan R. ChIPulate: A comprehensive ChIP-seq simulation pipeline. PLoS Comput Biol 2019; 15:e1006921. [PMID: 30897079 PMCID: PMC6445533 DOI: 10.1371/journal.pcbi.1006921] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2018] [Revised: 04/02/2019] [Accepted: 03/04/2019] [Indexed: 12/17/2022] Open
Abstract
ChIP-seq (Chromatin Immunoprecipitation followed by sequencing) is a high-throughput technique to identify genomic regions that are bound in vivo by a particular protein, e.g., a transcription factor (TF). Biological factors, such as chromatin state, indirect and cooperative binding, as well as experimental factors, such as antibody quality, cross-linking, and PCR biases, are known to affect the outcome of ChIP-seq experiments. However, the relative impact of these factors on inferences made from ChIP-seq data is not entirely clear. Here, via a detailed ChIP-seq simulation pipeline, ChIPulate, we assess the impact of various biological and experimental sources of variation on several outcomes of a ChIP-seq experiment, viz., the recoverability of the TF binding motif, accuracy of TF-DNA binding detection, the sensitivity of inferred TF-DNA binding strength, and number of replicates needed to confidently infer binding strength. We find that the TF motif can be recovered despite poor and non-uniform extraction and PCR amplification efficiencies. The recovery of the motif is, however, affected to a larger extent by the fraction of sites that are either cooperatively or indirectly bound. Importantly, our simulations reveal that the number of ChIP-seq replicates needed to accurately measure in vivo occupancy at high-affinity sites is larger than the recommended community standards. Our results establish statistical limits on the accuracy of inferences of protein-DNA binding from ChIP-seq and suggest that increasing the mean extraction efficiency, rather than amplification efficiency, would better improve sensitivity. The source code and instructions for running ChIPulate can be found at https://github.com/vishakad/chipulate. DNA-binding proteins perform many key roles in biology, such as transcriptional regulation of gene expression and chromatin modification. ChIP-seq (Chromatin immunoprecipitation followed by high-throughput sequencing) is a widely used experimental technique to identify DNA-binding sites of specific proteins of interest, within cells, genome-wide. DNA fragments from genomic regions that are bound by a protein of interest, often a transcription factor (TF), are selectively extracted using specific antibodies, amplified using PCR, and sequenced. The sequences are mapped to the reference genome. Regions where many sequences map, called “peaks”, are used to infer the location of TF-bound loci (peaks), in vivo occupancy at those loci, and the sequence pattern (motif) to which the TF shows a binding affinity. But measurements of TF occupancy and motif inference are vulnerable to several biological and experimental sources of variation that are poorly understood and difficult to assess directly. Here, we simulate key steps of the ChIP-seq protocol with the aim of estimating the relative effects of various sources of variations on motif inference and binding affinity estimations. Besides providing specific insights and recommendations, we provide a general framework to simulate sequence reads in a ChIP-seq experiment, which should considerably aid in the development of software aimed at analyzing ChIP-seq data.
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Affiliation(s)
- Vishaka Datta
- Simons Centre for the Study of Living Machines, National Centre for Biological Sciences, TIFR, Bengaluru, Karnataka, India
- * E-mail:
| | - Sridhar Hannenhalli
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, United States of America
| | - Rahul Siddharthan
- The Institute of Mathematical Sciences/HBNI, Taramani, Chennai, India
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9
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Smith H, Carter AS, Blaser E, Kaldy Z. Successful attentional set-shifting in 2-year-olds with and without Autism Spectrum Disorder. PLoS One 2019; 14:e0213903. [PMID: 30870516 PMCID: PMC6417667 DOI: 10.1371/journal.pone.0213903] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2018] [Accepted: 03/04/2019] [Indexed: 11/19/2022] Open
Abstract
The development of executive function is necessary for flexible and voluntary control of behavior. Deficits in executive function are purported to be a primary cause of behavioral inflexibility—a core clinical symptom—in Autism Spectrum Disorder (ASD). Attentional set-shifting has traditionally been measured with the Dimensional Change Card Sort, however, this task requires following verbal instructions. Here, we used a novel visual search task that does not require verbal instructions in conjunction with eye-tracking to test attentional set-shifting in 2-year-old toddlers diagnosed with ASD (N = 29) and chronological age-matched typically developing controls (N = 30). On each trial, a relevant and an irrelevant target were embedded in a set of feature-conjunction distractors, and toddlers were tasked with searching for the relevant target. Critically, after a set of trials the targets switched roles (i.e., the previously relevant target became irrelevant, and the previously relevant target became irrelevant). We measured visual search performance prior to and following a target switch. We found that both groups of toddlers could readily switch targets, and found strikingly similar performance between typically developing toddlers and toddlers with ASD. Our results challenge the centrality of deficits in attentional set-shifting to early behavioral inflexibility in ASD.
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Affiliation(s)
- Hayley Smith
- Department of Psychology, University of Massachusetts Boston, Boston, Massachusetts, United States of America
| | - Alice S. Carter
- Department of Psychology, University of Massachusetts Boston, Boston, Massachusetts, United States of America
| | - Erik Blaser
- Department of Psychology, University of Massachusetts Boston, Boston, Massachusetts, United States of America
| | - Zsuzsa Kaldy
- Department of Psychology, University of Massachusetts Boston, Boston, Massachusetts, United States of America
- * E-mail:
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10
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Meinecke L, Sharma PP, Du H, Zhang L, Nie Q, Schilling TF. Modeling craniofacial development reveals spatiotemporal constraints on robust patterning of the mandibular arch. PLoS Comput Biol 2018; 14:e1006569. [PMID: 30481168 PMCID: PMC6258504 DOI: 10.1371/journal.pcbi.1006569] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2018] [Accepted: 10/16/2018] [Indexed: 12/11/2022] Open
Abstract
How does pattern formation occur accurately when confronted with tissue growth and stochastic fluctuations (noise) in gene expression? Dorso-ventral (D-V) patterning of the mandibular arch specifies upper versus lower jaw skeletal elements through a combination of Bone morphogenetic protein (Bmp), Endothelin-1 (Edn1), and Notch signaling, and this system is highly robust. We combine NanoString experiments of early D-V gene expression with live imaging of arch development in zebrafish to construct a computational model of the D-V mandibular patterning network. The model recapitulates published genetic perturbations in arch development. Patterning is most sensitive to changes in Bmp signaling, and the temporal order of gene expression modulates the response of the patterning network to noise. Thus, our integrated systems biology approach reveals non-intuitive features of the complex signaling system crucial for craniofacial development, including novel insights into roles of gene expression timing and stochasticity in signaling and gene regulation.
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Affiliation(s)
- Lina Meinecke
- Department of Mathematics, University of California, Irvine, CA, United States of America
- Center for Complex Biological Systems, University of California, Irvine, CA, United States of America
| | - Praveer P. Sharma
- Center for Complex Biological Systems, University of California, Irvine, CA, United States of America
- Department of Developmental and Cell Biology, University of California, Irvine, CA, United States of America
| | - Huijing Du
- Department of Mathematics, University of Nebraska, Lincoln, NE, United States of America
| | - Lei Zhang
- Beijing International Center for Mathematical Research, Peking University, Beijing, China
- Center for Quantitative Biology, Peking University, Beijing, China
| | - Qing Nie
- Department of Mathematics, University of California, Irvine, CA, United States of America
- Center for Complex Biological Systems, University of California, Irvine, CA, United States of America
- Department of Developmental and Cell Biology, University of California, Irvine, CA, United States of America
| | - Thomas F. Schilling
- Center for Complex Biological Systems, University of California, Irvine, CA, United States of America
- Department of Developmental and Cell Biology, University of California, Irvine, CA, United States of America
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11
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Abstract
Emergent patterns in complex systems are related with many intriguing phenomena in modern science. One question that has sparked vigorous debates is if difficulties in the modelization of emergent behaviours are a consequence of ontological or epistemological limitations. To elucidate this question, we propose a novel approximation through constructive logic. Under this framework, experimental measurements will be considered conceptual building blocks from which we aim to achieve a description of the microstates ensemble mapping the macroscopic emergent observation. This procedure allow us to have full control of any information loss, thus making the analysis of different systems fairly comparable. In particular, we aim to look for compact descriptions of the constraints underlying a dynamical system, as a necessary a priori step to develop explanatory (mechanistic) models. We apply our proposal to a synthetic system to show that the number and scope of the system’s constraints hinder our ability to build compact descriptions, being those systems under global constraints a limiting case in which such a description is unreachable. This result clearly links the epistemological limits of the framework selected with an ontological feature of the system, leading us to propose a definition of emergence strength which we make compatible with the scientific method through the active intervention of the observer on the system, following the spirit of Granger causality. We think that our approximation clarifies previous discrepancies found in the literature, reconciles distinct attempts to classify emergent processes, and paves the way to understand other challenging concepts such as downward causation.
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Affiliation(s)
- Alberto Pascual-García
- Centro de Biología Molecular “Severo Ochoa” CSIC-Universidad Autónoma de Madrid, Madrid, Spain
- Department of Life Sciences Imperial College London, Silwood Park, Ascot, United Kingdom
- * E-mail: ,
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12
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Lakshminarasimhan KJ, Pouget A, DeAngelis GC, Angelaki DE, Pitkow X. Inferring decoding strategies for multiple correlated neural populations. PLoS Comput Biol 2018; 14:e1006371. [PMID: 30248091 PMCID: PMC6188888 DOI: 10.1371/journal.pcbi.1006371] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2017] [Revised: 10/15/2018] [Accepted: 07/17/2018] [Indexed: 12/29/2022] Open
Abstract
Studies of neuron-behaviour correlation and causal manipulation have long been used separately to understand the neural basis of perception. Yet these approaches sometimes lead to drastically conflicting conclusions about the functional role of brain areas. Theories that focus only on choice-related neuronal activity cannot reconcile those findings without additional experiments involving large-scale recordings to measure interneuronal correlations. By expanding current theories of neural coding and incorporating results from inactivation experiments, we demonstrate here that it is possible to infer decoding weights of different brain areas at a coarse scale without precise knowledge of the correlation structure. We apply this technique to neural data collected from two different cortical areas in macaque monkeys trained to perform a heading discrimination task. We identify two opposing decoding schemes, each consistent with data depending on the nature of correlated noise. Our theory makes specific testable predictions to distinguish these scenarios experimentally without requiring measurement of the underlying noise correlations. The neocortex is structurally organized into distinct brain areas. The role of specific brain areas in sensory perception is typically studied using two kinds of laboratory experiments: those that measure correlations between neural activity and reported percepts, and those that inactivate a brain region and measure the resulting changes in percepts. The two types of experiments have generally been interpreted in isolation, in part because no theory has been able combine their outcomes. Here, we describe a mathematical framework that synthesizes both kinds of results, giving us a new way to assess how different brain areas contribute to perception. When we apply our framework to experiments on behaving monkeys, we discover two models that can explain the perplexing finding that one brain area can predict an animal’s reported percepts, even though the percepts are not affected when that brain area is inactivated. The two models ascribe dramatically different efficiencies to brain computation. We show that these two models could be distinguished by a proposed experiment that measures correlations while inactivating different brain areas.
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Affiliation(s)
| | - Alexandre Pouget
- Department of Basic Neuroscience, University of Geneva, Geneva, Switzerland
- Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY, United States of America
| | - Gregory C. DeAngelis
- Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY, United States of America
| | - Dora E. Angelaki
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, United States of America
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, United States of America
- Department of Mechanical and Aerospace Engineering, New York University, New York, United States of America
- Center for Neural Science, New York University, New York, United States of America
| | - Xaq Pitkow
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, United States of America
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, United States of America
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, United States of America
- * E-mail:
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13
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Dingens AS, Acharya P, Haddox HK, Rawi R, Xu K, Chuang GY, Wei H, Zhang B, Mascola JR, Carragher B, Potter CS, Overbaugh J, Kwong PD, Bloom JD. Complete functional mapping of infection- and vaccine-elicited antibodies against the fusion peptide of HIV. PLoS Pathog 2018; 14:e1007159. [PMID: 29975771 PMCID: PMC6049957 DOI: 10.1371/journal.ppat.1007159] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2018] [Revised: 07/17/2018] [Accepted: 06/15/2018] [Indexed: 11/19/2022] Open
Abstract
Eliciting broadly neutralizing antibodies (bnAbs) targeting envelope (Env) is a major goal of HIV vaccine development, but cross-clade breadth from immunization has only sporadically been observed. Recently, Xu et al (2018) elicited cross-reactive neutralizing antibody responses in a variety of animal models using immunogens based on the epitope of bnAb VRC34.01. The VRC34.01 antibody, which was elicited by natural human infection, targets the N terminus of the Env fusion peptide, a critical component of the virus entry machinery. Here we precisely characterize the functional epitopes of VRC34.01 and two vaccine-elicited murine antibodies by mapping all single amino-acid mutations to the BG505 Env that affect viral neutralization. While escape from VRC34.01 occurred via mutations in both fusion peptide and distal interacting sites of the Env trimer, escape from the vaccine-elicited antibodies was mediated predominantly by mutations in the fusion peptide. Cryo-electron microscopy of four vaccine-elicited antibodies in complex with Env trimer revealed focused recognition of the fusion peptide and provided a structural basis for development of neutralization breadth. Together, these functional and structural data suggest that the breadth of vaccine-elicited antibodies targeting the fusion peptide can be enhanced by specific interactions with additional portions of Env. Thus, our complete maps of viral escape both delineate pathways of resistance to these fusion peptide-directed antibodies and provide a strategy to improve the breadth or potency of future vaccine-induced antibodies against Env’s fusion peptide. A major goal of HIV-1 vaccine design is to elicit antibodies that neutralize diverse strains of HIV-1. Recently, some of us elicited such antibodies in animal models using immunogens based on the epitope of a broad antibody (VRC34.01) isolated from an infected individual. Further improving these vaccine-elicited antibody responses will require a detailed understanding of how the resulting antibodies target HIV’s envelope protein (Env). Here, we used mutational antigenic profiling to precisely map the epitopes of two vaccine-elicited antibodies and the template VRC34.01 antibody. We did this by quantifying the effect of all possible amino acid mutations to Env on antibody neutralization. Although all antibodies target a similar region of Env, we found clear differences in the functional interaction of Env with these vaccine- and infection-elicited antibodies. We combined these functional data with structural analyses to identify antibody–Env interactions that may contribute to the relatively greater breadth of the infection-elicited antibody and could improve the breadth of vaccine-elicited antibodies. These data thereby help to refine vaccination schemes to achieve broader responses.
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Affiliation(s)
- Adam S. Dingens
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
- Molecular and Cellular Biology PhD program, University of Washington, Seattle, Washington, United States of America
- Division of Human Biology and Epidemiology Program, Seattle, Washington, United States of America
| | - Priyamvada Acharya
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, United States of America
- National Resource for Automated Molecular Microscopy, Simons Electron Microscopy Center, New York Structural Biology Center, New York, New York, United States of America
| | - Hugh K. Haddox
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
- Molecular and Cellular Biology PhD program, University of Washington, Seattle, Washington, United States of America
| | - Reda Rawi
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Kai Xu
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Gwo-Yu Chuang
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Hui Wei
- National Resource for Automated Molecular Microscopy, Simons Electron Microscopy Center, New York Structural Biology Center, New York, New York, United States of America
| | - Baoshan Zhang
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, United States of America
| | - John R. Mascola
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Bridget Carragher
- National Resource for Automated Molecular Microscopy, Simons Electron Microscopy Center, New York Structural Biology Center, New York, New York, United States of America
| | - Clinton S. Potter
- National Resource for Automated Molecular Microscopy, Simons Electron Microscopy Center, New York Structural Biology Center, New York, New York, United States of America
| | - Julie Overbaugh
- Division of Human Biology and Epidemiology Program, Seattle, Washington, United States of America
| | - Peter D. Kwong
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, United States of America
- * E-mail: (PDK); (JDB)
| | - Jesse D. Bloom
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
- * E-mail: (PDK); (JDB)
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14
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Cho M, Vijay Mishra K, Xu W. Computable performance guarantees for compressed sensing matrices. EURASIP J Adv Signal Process 2018; 2018:16. [PMID: 29503664 PMCID: PMC5829123 DOI: 10.1186/s13634-018-0535-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2016] [Accepted: 02/07/2018] [Indexed: 06/08/2023]
Abstract
The null space condition for ℓ1 minimization in compressed sensing is a necessary and sufficient condition on the sensing matrices under which a sparse signal can be uniquely recovered from the observation data via ℓ1 minimization. However, verifying the null space condition is known to be computationally challenging. Most of the existing methods can provide only upper and lower bounds on the proportion parameter that characterizes the null space condition. In this paper, we propose new polynomial-time algorithms to establish upper bounds of the proportion parameter. We leverage on these techniques to find upper bounds and further develop a new procedure-tree search algorithm-that is able to precisely and quickly verify the null space condition. Numerical experiments show that the execution speed and accuracy of the results obtained from our methods far exceed those of the previous methods which rely on linear programming (LP) relaxation and semidefinite programming (SDP).
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Affiliation(s)
- Myung Cho
- Department of ECE, University of Iowa, Iowa City, 52242 IA USA
| | | | - Weiyu Xu
- Department of ECE, University of Iowa, Iowa City, 52242 IA USA
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15
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Menon R, Ramanan V, Korolev KS. Interactions between species introduce spurious associations in microbiome studies. PLoS Comput Biol 2018; 14:e1005939. [PMID: 29338008 PMCID: PMC5786326 DOI: 10.1371/journal.pcbi.1005939] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2017] [Revised: 01/26/2018] [Accepted: 12/21/2017] [Indexed: 12/30/2022] Open
Abstract
Microbiota contribute to many dimensions of host phenotype, including disease. To link specific microbes to specific phenotypes, microbiome-wide association studies compare microbial abundances between two groups of samples. Abundance differences, however, reflect not only direct associations with the phenotype, but also indirect effects due to microbial interactions. We found that microbial interactions could easily generate a large number of spurious associations that provide no mechanistic insight. Using techniques from statistical physics, we developed a method to remove indirect associations and applied it to the largest dataset on pediatric inflammatory bowel disease. Our method corrected the inflation of p-values in standard association tests and showed that only a small subset of associations is directly linked to the disease. Direct associations had a much higher accuracy in separating cases from controls and pointed to immunomodulation, butyrate production, and the brain-gut axis as important factors in the inflammatory bowel disease.
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Affiliation(s)
- Rajita Menon
- Department of Physics, Boston University, Boston, Massachusetts, United States of America
| | - Vivek Ramanan
- BRITE Bioinformatics REU Program, Boston University, Boston, Massachusetts, United States of America
- Department of Biology and Computer Science, Swarthmore College, Swarthmore, Pennsylvania, United States of America
| | - Kirill S. Korolev
- Department of Physics, Boston University, Boston, Massachusetts, United States of America
- Graduate Program in Bioinformatics, Boston University, Boston, Massachusetts, United States of America
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16
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Bukhari SA, Saul MC, Seward CH, Zhang H, Bensky M, James N, Zhao SD, Chandrasekaran S, Stubbs L, Bell AM. Temporal dynamics of neurogenomic plasticity in response to social interactions in male threespined sticklebacks. PLoS Genet 2017; 13:e1006840. [PMID: 28704398 PMCID: PMC5509087 DOI: 10.1371/journal.pgen.1006840] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2016] [Accepted: 05/27/2017] [Indexed: 11/18/2022] Open
Abstract
Animals exhibit dramatic immediate behavioral plasticity in response to social interactions, and brief social interactions can shape the future social landscape. However, the molecular mechanisms contributing to behavioral plasticity are unclear. Here, we show that the genome dynamically responds to social interactions with multiple waves of transcription associated with distinct molecular functions in the brain of male threespined sticklebacks, a species famous for its behavioral repertoire and evolution. Some biological functions (e.g., hormone activity) peaked soon after a brief territorial challenge and then declined, while others (e.g., immune response) peaked hours afterwards. We identify transcription factors that are predicted to coordinate waves of transcription associated with different components of behavioral plasticity. Next, using H3K27Ac as a marker of chromatin accessibility, we show that a brief territorial intrusion was sufficient to cause rapid and dramatic changes in the epigenome. Finally, we integrate the time course brain gene expression data with a transcriptional regulatory network, and link gene expression to changes in chromatin accessibility. This study reveals rapid and dramatic epigenomic plasticity in response to a brief, highly consequential social interaction. Social interactions provoke changes in the brain and behavior but their underlying molecular mechanisms remain obscure. Male sticklebacks are small fish whose fitness depends on their ability to defend a territory. Here, by measuring the time course of gene expression in response to a territorial challenge in two brain regions, we show that a single brief territorial intrusion provoked waves of gene expression that persisted for hours afterwards, with waves of transcription associated with distinct biological processes. Moreover, a single territorial challenge caused dramatic changes to the epigenome. Changes in chromatin accessibility corresponded to changes in gene expression, and to the activity of transcription factors operating within gene regulatory networks. This study reveals rapid and dramatic epigenomic plasticity in response to a brief, highly consequential social interaction. These results suggest that meaningful social interactions (even brief ones) can provoke waves of transcription and changes to the epigenome which lead to changes in neural functioning, and those changes are a mechanism by which animals update their assessment of their social world.
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Affiliation(s)
- Syed Abbas Bukhari
- Carl R. Woese Institute for Genomic Biology, University of Illinois, Urbana Champaign, Urbana, IL, United States of America
- Illinois Informatics Institute, University of Illinois, Urbana Champaign, Urbana, IL, United States of America
| | - Michael C. Saul
- Carl R. Woese Institute for Genomic Biology, University of Illinois, Urbana Champaign, Urbana, IL, United States of America
| | - Christopher H. Seward
- Carl R. Woese Institute for Genomic Biology, University of Illinois, Urbana Champaign, Urbana, IL, United States of America
| | - Huimin Zhang
- Carl R. Woese Institute for Genomic Biology, University of Illinois, Urbana Champaign, Urbana, IL, United States of America
| | - Miles Bensky
- Program in Ecology, Evolution and Conservation Biology, University of Illinois, Urbana Champaign, Urbana, IL, United States of America
| | - Noelle James
- Neuroscience Program, University of Illinois, Urbana Champaign, Urbana, IL, United States of America
| | - Sihai Dave Zhao
- Carl R. Woese Institute for Genomic Biology, University of Illinois, Urbana Champaign, Urbana, IL, United States of America
- Department of Statistics, University of Illinois, Urbana Champaign, Urbana, IL United States of America
| | - Sriram Chandrasekaran
- Harvard Society of Fellows, Harvard University, Cambridge, MA, United States of America
- Faculty of Arts and Sciences, Harvard University, Cambridge, MA, United States of America
- Broad Institute of MIT and Harvard, Cambridge, MA, United States of America
| | - Lisa Stubbs
- Carl R. Woese Institute for Genomic Biology, University of Illinois, Urbana Champaign, Urbana, IL, United States of America
- Department of Cell and Developmental Biology, University of Illinois, Urbana Champaign, Urbana, IL, United States of America
| | - Alison M. Bell
- Carl R. Woese Institute for Genomic Biology, University of Illinois, Urbana Champaign, Urbana, IL, United States of America
- Program in Ecology, Evolution and Conservation Biology, University of Illinois, Urbana Champaign, Urbana, IL, United States of America
- Neuroscience Program, University of Illinois, Urbana Champaign, Urbana, IL, United States of America
- * E-mail:
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17
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Abstract
Recent experimental advances are producing an avalanche of data on both neural connectivity and neural activity. To take full advantage of these two emerging datasets we need a framework that links them, revealing how collective neural activity arises from the structure of neural connectivity and intrinsic neural dynamics. This problem of structure-driven activity has drawn major interest in computational neuroscience. Existing methods for relating activity and architecture in spiking networks rely on linearizing activity around a central operating point and thus fail to capture the nonlinear responses of individual neurons that are the hallmark of neural information processing. Here, we overcome this limitation and present a new relationship between connectivity and activity in networks of nonlinear spiking neurons by developing a diagrammatic fluctuation expansion based on statistical field theory. We explicitly show how recurrent network structure produces pairwise and higher-order correlated activity, and how nonlinearities impact the networks’ spiking activity. Our findings open new avenues to investigating how single-neuron nonlinearities—including those of different cell types—combine with connectivity to shape population activity and function. Neuronal networks, like many biological systems, exhibit variable activity. This activity is shaped by both the underlying biology of the component neurons and the structure of their interactions. How can we combine knowledge of these two things—that is, models of individual neurons and of their interactions—to predict the statistics of single- and multi-neuron activity? Current approaches rely on linearizing neural activity around a stationary state. In the face of neural nonlinearities, however, these linear methods can fail to predict spiking statistics and even fail to correctly predict whether activity is stable or pathological. Here, we show how to calculate any spike train cumulant in a broad class of models, while systematically accounting for nonlinear effects. We then study a fundamental effect of nonlinear input-rate transfer–coupling between different orders of spiking statistic–and how this depends on single-neuron and network properties.
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Affiliation(s)
- Gabriel Koch Ocker
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Krešimir Josić
- Department of Mathematics and Department of Biology and Biochemistry, University of Houston, Houston, Texas, United States of America
- Department of BioSciences, Rice University, Houston, Texas, United States of America
| | - Eric Shea-Brown
- Allen Institute for Brain Science, Seattle, Washington, United States of America
- Department of Applied Mathematics, University of Washington, Seattle, Washington, United States of America
- Department of Physiology and Biophysics, and UW Institute of Neuroengineering, University of Washington, Seattle, Washington, United States of America
| | - Michael A. Buice
- Allen Institute for Brain Science, Seattle, Washington, United States of America
- Department of Applied Mathematics, University of Washington, Seattle, Washington, United States of America
- * E-mail:
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18
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Zylberberg J, Pouget A, Latham PE, Shea-Brown E. Robust information propagation through noisy neural circuits. PLoS Comput Biol 2017; 13:e1005497. [PMID: 28419098 PMCID: PMC5413111 DOI: 10.1371/journal.pcbi.1005497] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2016] [Revised: 05/02/2017] [Accepted: 04/03/2017] [Indexed: 12/31/2022] Open
Abstract
Sensory neurons give highly variable responses to stimulation, which can limit the amount of stimulus information available to downstream circuits. Much work has investigated the factors that affect the amount of information encoded in these population responses, leading to insights about the role of covariability among neurons, tuning curve shape, etc. However, the informativeness of neural responses is not the only relevant feature of population codes; of potentially equal importance is how robustly that information propagates to downstream structures. For instance, to quantify the retina’s performance, one must consider not only the informativeness of the optic nerve responses, but also the amount of information that survives the spike-generating nonlinearity and noise corruption in the next stage of processing, the lateral geniculate nucleus. Our study identifies the set of covariance structures for the upstream cells that optimize the ability of information to propagate through noisy, nonlinear circuits. Within this optimal family are covariances with “differential correlations”, which are known to reduce the information encoded in neural population activities. Thus, covariance structures that maximize information in neural population codes, and those that maximize the ability of this information to propagate, can be very different. Moreover, redundancy is neither necessary nor sufficient to make population codes robust against corruption by noise: redundant codes can be very fragile, and synergistic codes can—in some cases—optimize robustness against noise. Information about the outside world, which originates in sensory neurons, propagates through multiple stages of processing before reaching the neural structures that control behavior. While much work in neuroscience has investigated the factors that affect the amount of information contained in peripheral sensory areas, very little work has asked how much of that information makes it through subsequent processing stages. That’s the focus of this paper, and it’s an important issue because information that fails to propagate cannot be used to affect decision-making. We find a tradeoff between information content and information transmission: neural codes which contain a large amount of information can transmit that information poorly to subsequent processing stages. Thus, the problem of robust information propagation—which has largely been overlooked in previous research—may be critical for determining how our sensory organs communicate with our brains. We identify the conditions under which information propagates well—or poorly—through multiple stages of neural processing.
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Affiliation(s)
- Joel Zylberberg
- Department of Physiology and Biophysics, Center for Neuroscience, and Computational Bioscience Program, University of Colorado School of Medicine, Aurora, Colorado, United States of America
- Department of Applied Mathematics, University of Colorado, Boulder, Colorado, United States of America
- Department of Applied Mathematics, University of Washington, Seattle, Washington, United States of America
- Learning in Machines and Brains Program, Canadian Institute For Advanced Research, Toronto, Ontario, Canada
- * E-mail:
| | - Alexandre Pouget
- Department of Basic Neuroscience, University of Geneva, Geneva, Switzerland
- Gatsby Computational Neuroscience Unit, University College London, London, United Kingdom
| | - Peter E. Latham
- Gatsby Computational Neuroscience Unit, University College London, London, United Kingdom
| | - Eric Shea-Brown
- Department of Applied Mathematics, University of Washington, Seattle, Washington, United States of America
- Department of Physiology and Biophysics, Program in Neuroscience, University of Washington Institute for Neuroengineering, and Center for Sensorimotor Neural Engineering, University of Washington, Seattle, Washington, United States of America
- Allen Institute for Brain Science, Seattle, Washington, United States of America
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19
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Gupta AR, Westphal A, Yang DYJ, Sullivan CAW, Eilbott J, Zaidi S, Voos A, Vander Wyk BC, Ventola P, Waqar Z, Fernandez TV, Ercan-Sencicek AG, Walker MF, Choi M, Schneider A, Hedderly T, Baird G, Friedman H, Cordeaux C, Ristow A, Shic F, Volkmar FR, Pelphrey KA. Neurogenetic analysis of childhood disintegrative disorder. Mol Autism 2017; 8:19. [PMID: 28392909 PMCID: PMC5379515 DOI: 10.1186/s13229-017-0133-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2016] [Accepted: 03/15/2017] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND Childhood disintegrative disorder (CDD) is a rare form of autism spectrum disorder (ASD) of unknown etiology. It is characterized by late-onset regression leading to significant intellectual disability (ID) and severe autism. Although there are phenotypic differences between CDD and other forms of ASD, it is unclear if there are neurobiological differences. METHODS We pursued a multidisciplinary study of CDD (n = 17) and three comparison groups: low-functioning ASD (n = 12), high-functioning ASD (n = 50), and typically developing (n = 26) individuals. We performed whole-exome sequencing (WES), copy number variant (CNV), and gene expression analyses of CDD and, on subsets of each cohort, non-sedated functional magnetic resonance imaging (fMRI) while viewing socioemotional (faces) and non-socioemotional (houses) stimuli and eye tracking while viewing emotional faces. RESULTS We observed potential differences between CDD and other forms of ASD. WES and CNV analyses identified one or more rare de novo, homozygous, and/or hemizygous (mother-to-son transmission on chrX) variants for most probands that were not shared by unaffected sibling controls. There were no clearly deleterious variants or highly recurrent candidate genes. Candidate genes that were found to be most conserved at variant position and most intolerant of variation, such as TRRAP, ZNF236, and KIAA2018, play a role or may be involved in transcription. Using the human BrainSpan transcriptome dataset, CDD candidate genes were found to be more highly expressed in non-neocortical regions than neocortical regions. This expression profile was similar to that of an independent cohort of ASD probands with regression. The non-neocortical regions overlapped with those identified by fMRI as abnormally hyperactive in response to viewing faces, such as the thalamus, cerebellum, caudate, and hippocampus. Eye-tracking analysis showed that, among individuals with ASD, subjects with CDD focused on eyes the most when shown pictures of faces. CONCLUSIONS Given that cohort sizes were limited by the rarity of CDD, and the challenges of conducting non-sedated fMRI and eye tracking in subjects with ASD and significant ID, this is an exploratory study designed to investigate the neurobiological features of CDD. In addition to reporting the first multimodal analysis of CDD, a combination of fMRI and eye-tracking analyses are being presented for the first time for low-functioning individuals with ASD. Our results suggest differences between CDD and other forms of ASD on the neurobiological as well as clinical level.
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Affiliation(s)
- Abha R. Gupta
- Department of Pediatrics, Yale School of Medicine, New Haven, Connecticut USA
- Child Study Center, Yale School of Medicine, New Haven, Connecticut USA
| | - Alexander Westphal
- Child Study Center, Yale School of Medicine, New Haven, Connecticut USA
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut USA
| | - Daniel Y. J. Yang
- Child Study Center, Yale School of Medicine, New Haven, Connecticut USA
| | | | - Jeffrey Eilbott
- Child Study Center, Yale School of Medicine, New Haven, Connecticut USA
| | - Samir Zaidi
- Department of Genetics, Yale School of Medicine, New Haven, Connecticut USA
| | - Avery Voos
- Child Study Center, Yale School of Medicine, New Haven, Connecticut USA
| | | | - Pam Ventola
- Child Study Center, Yale School of Medicine, New Haven, Connecticut USA
| | - Zainulabedin Waqar
- Department of Pediatrics, Yale School of Medicine, New Haven, Connecticut USA
| | - Thomas V. Fernandez
- Child Study Center, Yale School of Medicine, New Haven, Connecticut USA
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut USA
| | | | - Michael F. Walker
- Child Study Center, Yale School of Medicine, New Haven, Connecticut USA
| | - Murim Choi
- Department of Genetics, Yale School of Medicine, New Haven, Connecticut USA
| | - Allison Schneider
- Child Study Center, Yale School of Medicine, New Haven, Connecticut USA
| | - Tammy Hedderly
- Evelina London Children’s Hospital, Guy’s and St. Thomas’ Trust, Kings Health Partners AHSC, London, UK
| | - Gillian Baird
- Evelina London Children’s Hospital, Guy’s and St. Thomas’ Trust, Kings Health Partners AHSC, London, UK
| | - Hannah Friedman
- Child Study Center, Yale School of Medicine, New Haven, Connecticut USA
| | - Cara Cordeaux
- Child Study Center, Yale School of Medicine, New Haven, Connecticut USA
| | - Alexandra Ristow
- Child Study Center, Yale School of Medicine, New Haven, Connecticut USA
| | - Frederick Shic
- Child Study Center, Yale School of Medicine, New Haven, Connecticut USA
| | - Fred R. Volkmar
- Child Study Center, Yale School of Medicine, New Haven, Connecticut USA
| | - Kevin A. Pelphrey
- Child Study Center, Yale School of Medicine, New Haven, Connecticut USA
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20
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Abstract
BACKGROUND The etiology of autism, a complex, heritable, neurodevelopmental disorder, remains largely unexplained. Given the unexplained risk and recent evidence supporting a role for epigenetic mechanisms in the development of autism, we explored the role of CpG and CpH (H = A, C, or T) methylation within the autism-affected cortical brain tissue. METHODS Reduced representation bisulfite sequencing (RRBS) was completed, and analysis was carried out in 63 post-mortem cortical brain samples (Brodmann area 19) from 29 autism-affected and 34 control individuals. Analyses to identify single sites that were differentially methylated and to identify any global methylation alterations at either CpG or CpH sites throughout the genome were carried out. RESULTS We report that while no individual site or region of methylation was significantly associated with autism after multi-test correction, methylated CpH dinucleotides were markedly enriched in autism-affected brains (~2-fold enrichment at p < 0.05 cutoff, p = 0.002). CONCLUSIONS These results further implicate epigenetic alterations in pathobiological mechanisms that underlie autism.
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Affiliation(s)
- Shannon E. Ellis
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205 USA
| | - Simone Gupta
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205 USA
| | - Anna Moes
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205 USA
| | - Andrew B. West
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL 35294 USA
| | - Dan E. Arking
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205 USA
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21
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Abstract
BACKGROUND Multiplexing multiple samples during Illumina sequencing is a common practice and is rapidly growing in importance as the throughput of the platform increases. Misassignments during de-multiplexing, where sequences are associated with the wrong sample, are an overlooked error mode on the Illumina sequencing platform. This results in a low rate of cross-talk among multiplexed samples and can cause detrimental effects in studies requiring the detection of rare variants or when multiplexing a large number of samples. RESULTS We observed rates of cross-talk averaging 0.24 % when multiplexing 14 different samples with unique i5 and i7 index sequences. This cross-talk rate corresponded to 254,632 misassigned reads on a single lane of the Illumina HiSeq 2500. Notably, all types of misassignment occur at similar rates: incorrect i5, incorrect i7, and incorrect sequence reads. We demonstrate that misassignments can be nearly eliminated by quality filtering of index reads while preserving about 90 % of the original sequences. CONCLUSIONS Cross-talk among multiplexed samples is a significant error mode on the Illumina platform, especially if samples are only separated by a single unique index. Quality filtering of index sequences offers an effective solution to minimizing cross-talk among samples. Furthermore, we propose a straightforward method for verifying the extent of cross-talk between samples and optimizing quality score thresholds that does not require additional control samples and can even be performed post hoc on previous runs.
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Affiliation(s)
- Erik Scott Wright
- Department of Bacteriology, UW-Madison, Madison, USA
- Wisconsin Institute for Discovery, UW-Madison, 330 N. Orchard St, Madison, 53715 WI USA
| | - Kalin Horen Vetsigian
- Department of Bacteriology, UW-Madison, Madison, USA
- Wisconsin Institute for Discovery, UW-Madison, 330 N. Orchard St, Madison, 53715 WI USA
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22
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Fishman L, Simmons D. Unconventional height functions in simultaneous Diophantine approximation. Mon Hefte Math 2016; 182:577-618. [PMID: 32269388 PMCID: PMC7114982 DOI: 10.1007/s00605-016-0983-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/23/2015] [Accepted: 10/03/2016] [Indexed: 06/11/2023]
Abstract
Simultaneous Diophantine approximation is concerned with the approximation of a point x ∈ R d by points r ∈ Q d , with a view towards jointly minimizing the quantities ‖ x - r ‖ and H ( r ) . Here H ( r ) is the so-called "standard height" of the rational point r . In this paper the authors ask: What changes if we replace the standard height function by a different one? As it turns out, this change leads to dramatic differences from the classical theory and requires the development of new methods. We discuss three examples of nonstandard height functions, computing their exponents of irrationality as well as giving more precise results. A list of open questions is also given.
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Affiliation(s)
- Lior Fishman
- Department of Mathematics, University of North Texas, 1155 Union Circle #311430, Denton, TX 76203-5017 USA
| | - David Simmons
- Department of Mathematics, University of York, Heslington, York YO10 5DD UK
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23
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Fok PW. Multi-Layer Mechanical Model of Glagov Remodeling in Coronary Arteries: Differences between In-Vivo and Ex-Vivo Measurements. PLoS One 2016; 11:e0159304. [PMID: 27427954 PMCID: PMC4948909 DOI: 10.1371/journal.pone.0159304] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2016] [Accepted: 06/30/2016] [Indexed: 11/18/2022] Open
Abstract
When blood vessels undergo remodeling because of the buildup of atherosclerotic plaque, it is thought that they first undergo compensatory or outward remodeling, followed by inward remodeling: the lumen area stays roughly constant or increases slightly and then decreases rapidly. The second phase of remodeling is supposed to start after the plaque burden exceeds about 40%. These changes in the vessel were first observed by S. Glagov who examined cross-sections of coronary arteries at different stages of the disease. In this paper, we use a mathematical model based on growth and elasticity theory to verify the main aspects of Glagov’s result. However, both our model and curve-fitting to the data suggest that the critical stenosis is around 20% rather than 40%. Our model and data from the PROSPECT trial also show that Glagov remodeling is qualitatively different depending on whether measurements are taken ex-vivo or in-vivo. Our results suggest that the first outward phase of “Glagov remodeling” is largely absent for in-vivo measurements: that is, the lumen area always decreases as plaque builds up. We advocate that care must be taken when infering how in-vivo vessels remodel from ex-vivo data.
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Affiliation(s)
- Pak-Wing Fok
- Department of Mathematical Sciences, University of Delaware, Newark, DE 19716, United States of America
- * E-mail:
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24
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Abstract
New experimental results on bacterial growth inspire a novel top-down approach to study cell metabolism, combining mass balance and proteomic constraints to extend and complement Flux Balance Analysis. We introduce here Constrained Allocation Flux Balance Analysis, CAFBA, in which the biosynthetic costs associated to growth are accounted for in an effective way through a single additional genome-wide constraint. Its roots lie in the experimentally observed pattern of proteome allocation for metabolic functions, allowing to bridge regulation and metabolism in a transparent way under the principle of growth-rate maximization. We provide a simple method to solve CAFBA efficiently and propose an “ensemble averaging” procedure to account for unknown protein costs. Applying this approach to modeling E. coli metabolism, we find that, as the growth rate increases, CAFBA solutions cross over from respiratory, growth-yield maximizing states (preferred at slow growth) to fermentative states with carbon overflow (preferred at fast growth). In addition, CAFBA allows for quantitatively accurate predictions on the rate of acetate excretion and growth yield based on only 3 parameters determined by empirical growth laws. The intracellular protein levels of exponentially growing bacteria are known to vary strongly with growth conditions, as described by quantitative “growth laws”. This work introduces a computational genome-scale framework (Constrained Allocation Flux Balance Analysis, CAFBA) which incorporates growth laws into canonical Flux Balance Analysis. Upon introducing 3 parameters based on established growth laws for E. coli, CAFBA accurately reproduces empirical results on the growth-rate dependent rate of carbon overflow and growth yield, and generates testable predictions about cellular energetic strategies and protein expression levels. CAFBA therefore provides a simple, quantitative approach to balancing the trade-off between growth and its associated biosynthetic costs at genome-scale, without the burden of tuning many inaccessible parameters.
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Affiliation(s)
- Matteo Mori
- Dipartimento di Fisica, Sapienza Università di Roma, Rome, Italy
- Departamento de Bioquímica y Biología Molecular I, Universidad Complutense de Madrid, Madrid, Spain
- Department of Physics, University of California at San Diego, La Jolla, California, United States of America
| | - Terence Hwa
- Department of Physics, University of California at San Diego, La Jolla, California, United States of America
- Institute for Theoretical Studies, ETH Zurich, Switzerland
| | - Olivier C. Martin
- GQE - Le Moulon, INRA, Univ. Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Andrea De Martino
- Dipartimento di Fisica, Sapienza Università di Roma, Rome, Italy
- Soft and Living Matter Lab, Istituto di Nanotecnologia (CNR-NANOTEC), Consiglio Nazionale delle Ricerche, Rome, Italy
- Center for Life Nano Science@Sapienza, Istituto Italiano di Tecnologia, Rome, Italy
- Human Genetics Foundation, Turin, Italy
- * E-mail:
| | - Enzo Marinari
- Dipartimento di Fisica, Sapienza Università di Roma, Rome, Italy
- Soft and Living Matter Lab, Istituto di Nanotecnologia (CNR-NANOTEC), Consiglio Nazionale delle Ricerche, Rome, Italy
- INFN, Sezione di Roma 1, Rome, Italy
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25
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Abstract
Neuroprosthetic brain-computer interfaces function via an algorithm which decodes neural activity of the user into movements of an end effector, such as a cursor or robotic arm. In practice, the decoder is often learned by updating its parameters while the user performs a task. When the user's intention is not directly observable, recent methods have demonstrated value in training the decoder against a surrogate for the user's intended movement. Here we show that training a decoder in this way is a novel variant of an imitation learning problem, where an oracle or expert is employed for supervised training in lieu of direct observations, which are not available. Specifically, we describe how a generic imitation learning meta-algorithm, dataset aggregation (DAgger), can be adapted to train a generic brain-computer interface. By deriving existing learning algorithms for brain-computer interfaces in this framework, we provide a novel analysis of regret (an important metric of learning efficacy) for brain-computer interfaces. This analysis allows us to characterize the space of algorithmic variants and bounds on their regret rates. Existing approaches for decoder learning have been performed in the cursor control setting, but the available design principles for these decoders are such that it has been impossible to scale them to naturalistic settings. Leveraging our findings, we then offer an algorithm that combines imitation learning with optimal control, which should allow for training of arbitrary effectors for which optimal control can generate goal-oriented control. We demonstrate this novel and general BCI algorithm with simulated neuroprosthetic control of a 26 degree-of-freedom model of an arm, a sophisticated and realistic end effector.
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Affiliation(s)
- Josh Merel
- Neurobiology and Behavior program, Columbia University, New York, New York, United States of America
- Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
| | - David Carlson
- Department of Statistics, Columbia University, New York, New York, United States of America
- Grossman Center for the Statistics of Mind, Columbia University, New York, New York, United States of America
| | - Liam Paninski
- Neurobiology and Behavior program, Columbia University, New York, New York, United States of America
- Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
- Department of Statistics, Columbia University, New York, New York, United States of America
- Grossman Center for the Statistics of Mind, Columbia University, New York, New York, United States of America
| | - John P. Cunningham
- Neurobiology and Behavior program, Columbia University, New York, New York, United States of America
- Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
- Department of Statistics, Columbia University, New York, New York, United States of America
- Grossman Center for the Statistics of Mind, Columbia University, New York, New York, United States of America
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