1
|
Ji C, Tang Y, Zhang Y, Huang X, Li C, Yang Y, Wu Q, Xia X, Cai Q, Qi XR, Zheng JC. Glutaminase 1 deficiency confined in forebrain neurons causes autism spectrum disorder-like behaviors. Cell Rep 2023; 42:112712. [PMID: 37384529 DOI: 10.1016/j.celrep.2023.112712] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 04/21/2023] [Accepted: 06/13/2023] [Indexed: 07/01/2023] Open
Abstract
An abnormal glutamate signaling pathway has been proposed in the mechanisms of autism spectrum disorder (ASD). However, less is known about the involvement of alterations of glutaminase 1 (GLS1) in the pathophysiology of ASD. We show that the transcript level of GLS1 is significantly decreased in the postmortem frontal cortex and peripheral blood of ASD subjects. Mice lacking Gls1 in CamKIIα-positive neurons display a series of ASD-like behaviors, synaptic excitatory and inhibitory (E/I) imbalance, higher spine density, and glutamate receptor expression in the prefrontal cortex, as well as a compromised expression pattern of genes involved in synapse pruning and less engulfed synaptic puncta in microglia. A low dose of lipopolysaccharide treatment restores microglial synapse pruning, corrects synaptic neurotransmission, and rescues behavioral deficits in these mice. In summary, these findings provide mechanistic insights into Gls1 loss in ASD symptoms and identify Gls1 as a target for the treatment of ASD.
Collapse
Affiliation(s)
- Chenhui Ji
- Center for Translational Neurodegeneration and Regenerative Therapy, Tongji Hospital Affiliated to Tongji University School of Medicine, Shanghai 200065, China
| | - Yalin Tang
- Center for Translational Neurodegeneration and Regenerative Therapy, Tongji Hospital Affiliated to Tongji University School of Medicine, Shanghai 200065, China
| | - Yanyan Zhang
- Center for Translational Neurodegeneration and Regenerative Therapy, Tongji Hospital Affiliated to Tongji University School of Medicine, Shanghai 200065, China
| | - Xiaoyan Huang
- Center for Translational Neurodegeneration and Regenerative Therapy, Tongji Hospital Affiliated to Tongji University School of Medicine, Shanghai 200065, China
| | - Congcong Li
- Center for Translational Neurodegeneration and Regenerative Therapy, Tongji Hospital Affiliated to Tongji University School of Medicine, Shanghai 200065, China
| | - Yuhong Yang
- Center for Translational Neurodegeneration and Regenerative Therapy, Tongji Hospital Affiliated to Tongji University School of Medicine, Shanghai 200065, China
| | - Qihui Wu
- Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital Affiliated to Tongji University School of Medicine, Shanghai 200081, China
| | - Xiaohuan Xia
- Center for Translational Neurodegeneration and Regenerative Therapy, Tongji Hospital Affiliated to Tongji University School of Medicine, Shanghai 200065, China; Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital Affiliated to Tongji University School of Medicine, Shanghai 200081, China; Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Clinical Research Center for Anesthesiology and Perioperative Medicine, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai 200434, China; Shanghai Frontiers Science Center of Nanocatalytic Medicine, Tongji University, Shanghai 200331, China
| | - Qingyuan Cai
- Franklin and Marshall College, 415 Harrisburg Avenue, Lancaster, PA 17603, USA
| | - Xin-Rui Qi
- Center for Translational Neurodegeneration and Regenerative Therapy, Tongji Hospital Affiliated to Tongji University School of Medicine, Shanghai 200065, China.
| | - Jialin C Zheng
- Center for Translational Neurodegeneration and Regenerative Therapy, Tongji Hospital Affiliated to Tongji University School of Medicine, Shanghai 200065, China; Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital Affiliated to Tongji University School of Medicine, Shanghai 200081, China; Collaborative Innovation Center for Brain Science, Tongji University, Shanghai 200092, China; Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Clinical Research Center for Anesthesiology and Perioperative Medicine, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai 200434, China; Shanghai Frontiers Science Center of Nanocatalytic Medicine, Tongji University, Shanghai 200331, China.
| |
Collapse
|
2
|
Hess JL, Quinn TP, Zhang C, Hearn GC, Chen S, Kong SW, Cairns M, Tsuang MT, Faraone SV, Glatt SJ. BrainGENIE: The Brain Gene Expression and Network Imputation Engine. Transl Psychiatry 2023; 13:98. [PMID: 36949060 PMCID: PMC10033657 DOI: 10.1038/s41398-023-02390-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 02/23/2023] [Accepted: 02/28/2023] [Indexed: 03/24/2023] Open
Abstract
In vivo experimental analysis of human brain tissue poses substantial challenges and ethical concerns. To address this problem, we developed a computational method called the Brain Gene Expression and Network-Imputation Engine (BrainGENIE) that leverages peripheral-blood transcriptomes to predict brain tissue-specific gene-expression levels. Paired blood-brain transcriptomic data collected by the Genotype-Tissue Expression (GTEx) Project was used to train BrainGENIE models to predict gene-expression levels in ten distinct brain regions using whole-blood gene-expression profiles. The performance of BrainGENIE was compared to PrediXcan, a popular method for imputing gene expression levels from genotypes. BrainGENIE significantly predicted brain tissue-specific expression levels for 2947-11,816 genes (false-discovery rate-adjusted p < 0.05), including many transcripts that cannot be predicted significantly by a transcriptome-imputation method such as PrediXcan. BrainGENIE recapitulated measured diagnosis-related gene-expression changes in the brain for autism, bipolar disorder, and schizophrenia better than direct correlations from blood and predictions from PrediXcan. We developed a convenient software toolset for deploying BrainGENIE, and provide recommendations for how best to implement models. BrainGENIE complements and, in some ways, outperforms existing transcriptome-imputation tools, providing biologically meaningful predictions and opening new research avenues.
Collapse
Affiliation(s)
- Jonathan L Hess
- Department of Psychiatry & Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, USA
| | - Thomas P Quinn
- Applied Artificial Intelligence Institute (A2I2), Deakin University, Geelong, Australia
| | - Chunling Zhang
- Department of Neuroscience & Physiology, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, USA
| | - Gentry C Hearn
- Department of Neuroscience & Physiology, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, USA
| | - Samuel Chen
- Department of Psychiatry & Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, USA
| | - Sek Won Kong
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Murray Cairns
- School of Biomedical Sciences & Pharmacy, Faculty of Health, The University of Newcastle, New South Wales, Callaghan, New South Wales, Australia
- Hunter Medical Research Institute, Newcastle, Australia
- Centre for Brain & Mental Health Research, The University of Newcastle, Callaghan, Australia
| | - Ming T Tsuang
- Center for Behavioral Genomics, Department of Psychiatry, Institute for Genomic Medicine, University of California, San Diego, La Jolla, CA, USA
- Harvard Institute of Psychiatric Epidemiology and Genetics, Boston, MA, USA
| | - Stephen V Faraone
- Department of Psychiatry & Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, USA
- Department of Neuroscience & Physiology, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, USA
| | - Stephen J Glatt
- Department of Psychiatry & Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, USA.
- Department of Neuroscience & Physiology, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, USA.
| |
Collapse
|
3
|
Zhang C, Yan L, Qiao J. Effect of advanced parental age on pregnancy outcome and offspring health. J Assist Reprod Genet 2022; 39:1969-1986. [PMID: 35925538 PMCID: PMC9474958 DOI: 10.1007/s10815-022-02533-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Accepted: 11/24/2021] [Indexed: 10/16/2022] Open
Abstract
PURPOSE Fertility at advanced age has become increasingly common, but the aging of parents may adversely affect the maturation of gametes and the development of embryos, and therefore the effects of aging are likely to be transmitted to the next generation. This article reviewed the studies in this field in recent years. METHODS We searched the relevant literature in recent years with the keywords of "advanced maternal/paternal age" combined with "adverse pregnancy outcome" or "birth defect" in the PubMed database and classified the effects of parental advanced age on pregnancy outcomes and birth defects. Related studies on the effect of advanced age on birth defects were classified as chromosomal abnormalities, neurological and psychiatric disorders, and other systemic diseases. The effect of assisted reproduction technology (ART) on fertility in advanced age was also discussed. RESULTS Differences in the definition of the range of advanced age and other confounding factors among studies were excluded, most studies believed that advanced parental age would affect pregnancy outcomes and birth defects in offspring. CONCLUSION To some extent, advanced parental age caused adverse pregnancy outcomes and birth defects. The occurrence of these results was related to the molecular genetic changes caused by aging, such as gene mutations, epigenetic variations, etc. Any etiology of adverse pregnancy outcomes and birth defects related to aging might be more than one. The detrimental effect of advanced age can be corrected to some extent by ART.
Collapse
Affiliation(s)
- Cong Zhang
- Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, No. 49, North garden road, Haidian district, Beijing, 100191, People's Republic of China
- National Clinical Research Center for Obstetrics and Gynecology (Peking University Third Hospital), Beijing, 100191, China
- Key Laboratory of Assisted Reproduction (Peking University), Ministry of Education, Beijing, 100191, China
- Beijing Key Laboratory of Reproductive Endocrinology and Assisted Reproductive Technology, Beijing, 100191, China
- Research Units of Comprehensive Diagnosis and Treatment of Oocyte Maturation Arrest (Chinese Academy of Medical Sciences), Beijing, 100191, China
- Savid Medical College (University of Chinese Academy of Sciences), Beijing, 100049, China
| | - Liying Yan
- Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, No. 49, North garden road, Haidian district, Beijing, 100191, People's Republic of China
- National Clinical Research Center for Obstetrics and Gynecology (Peking University Third Hospital), Beijing, 100191, China
- Key Laboratory of Assisted Reproduction (Peking University), Ministry of Education, Beijing, 100191, China
- Beijing Key Laboratory of Reproductive Endocrinology and Assisted Reproductive Technology, Beijing, 100191, China
- Research Units of Comprehensive Diagnosis and Treatment of Oocyte Maturation Arrest (Chinese Academy of Medical Sciences), Beijing, 100191, China
| | - Jie Qiao
- Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, No. 49, North garden road, Haidian district, Beijing, 100191, People's Republic of China.
- National Clinical Research Center for Obstetrics and Gynecology (Peking University Third Hospital), Beijing, 100191, China.
- Key Laboratory of Assisted Reproduction (Peking University), Ministry of Education, Beijing, 100191, China.
- Beijing Key Laboratory of Reproductive Endocrinology and Assisted Reproductive Technology, Beijing, 100191, China.
- Research Units of Comprehensive Diagnosis and Treatment of Oocyte Maturation Arrest (Chinese Academy of Medical Sciences), Beijing, 100191, China.
| |
Collapse
|
4
|
Characterization of a mGluR5 Knockout Rat Model with Hallmarks of Fragile X Syndrome. Life (Basel) 2022; 12:life12091308. [PMID: 36143345 PMCID: PMC9504063 DOI: 10.3390/life12091308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/17/2022] [Accepted: 08/18/2022] [Indexed: 11/16/2022] Open
Abstract
The number of reported cases of neurodevelopmental disorders has increased significantly in the last few decades, but the etiology of these diseases remains poorly understood. There is evidence of a fundamental link between genetic abnormalities and symptoms of autism spectrum disorders (ASDs), and the most common monogenetic inheritable form of ASDs is Fragile X Syndrome (FXS). Previous studies indicate that FXS is linked to glutamate signaling regulation by the G-protein-coupled metabotropic glutamate receptor 5 (mGluR5), which has been shown to have a regulatory role in neuroinflammation. We characterized the effect of knocking out mGluR5 in an organism known to have complex cognitive functions—the rat. The heterozygous phenotype is the most clinically relevant; therefore, we performed analysis in heterozygous pups. We showed developmental abnormalities in heterozygous mGluR5 knockout rats, as well as a significant increase in chemokine (C-X-C motif) ligand 1 (CXCL) expression, a hallmark indicator of early onset inflammation. We quantified an increase in microglial density in the knockout pups and quantified morphological phenotypes representative of greater reactivity in the male vs. female and postnatal day 28 heterozygous pups compared to postnatal day 14 heterozygous pups. In response to injury, reactive microglia release matrix metalloproteases, contribute to extracellular matrix (ECM) breakdown, and are responsible for eradicating cellular and molecular debris. In our study, the changes in microglial density and reactivity correlated with abnormalities in the mRNA expression levels of ECM proteins and with the density of perineuronal nets. We saw atypical neuropsychiatric behavior in open field and elevated plus tests in heterozygous pups compared to wild-type litter and age-matched controls. These results demonstrate the pathological potential of the mGluR5 knockout in rats and further support the presence of neuroinflammatory roots in ASDs.
Collapse
|
5
|
Forouzandeh A, Rutar A, Kalmady SV, Greiner R. Analyzing biomarker discovery: Estimating the reproducibility of biomarker sets. PLoS One 2022; 17:e0252697. [PMID: 35901020 PMCID: PMC9333302 DOI: 10.1371/journal.pone.0252697] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 06/29/2022] [Indexed: 11/19/2022] Open
Abstract
Many researchers try to understand a biological condition by identifying biomarkers. This is typically done using univariate hypothesis testing over a labeled dataset, declaring a feature to be a biomarker if there is a significant statistical difference between its values for the subjects with different outcomes. However, such sets of proposed biomarkers are often not reproducible – subsequent studies often fail to identify the same sets. Indeed, there is often only a very small overlap between the biomarkers proposed in pairs of related studies that explore the same phenotypes over the same distribution of subjects. This paper first defines the Reproducibility Score for a labeled dataset as a measure (taking values between 0 and 1) of the reproducibility of the results produced by a specified fixed biomarker discovery process for a given distribution of subjects. We then provide ways to reliably estimate this score by defining algorithms that produce an over-bound and an under-bound for this score for a given dataset and biomarker discovery process, for the case of univariate hypothesis testing on dichotomous groups. We confirm that these approximations are meaningful by providing empirical results on a large number of datasets and show that these predictions match known reproducibility results. To encourage others to apply this technique to analyze their biomarker sets, we have also created a publicly available website, https://biomarker.shinyapps.io/BiomarkerReprod/, that produces these Reproducibility Score approximations for any given dataset (with continuous or discrete features and binary class labels).
Collapse
Affiliation(s)
- Amir Forouzandeh
- Department of Computing Science, University of Alberta, Edmonton, Canada
- * E-mail:
| | - Alex Rutar
- Department of Pure Math, University of Waterloo, Waterloo, ON, Canada
| | - Sunil V. Kalmady
- Department of Computing Science, University of Alberta, Edmonton, Canada
- Canadian VIGOUR Centre, University of Alberta, Edmonton, Canada
| | - Russell Greiner
- Department of Computing Science, University of Alberta, Edmonton, Canada
- Alberta Machine Intelligence Institute, Edmonton, Canada
| |
Collapse
|
6
|
Zoo: Selecting Transcriptomic and Methylomic Biomarkers by Ensembling Animal-Inspired Swarm Intelligence Feature Selection Algorithms. Genes (Basel) 2021; 12:genes12111814. [PMID: 34828418 PMCID: PMC8621246 DOI: 10.3390/genes12111814] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 11/12/2021] [Accepted: 11/15/2021] [Indexed: 02/03/2023] Open
Abstract
Biological omics data such as transcriptomes and methylomes have the inherent “large p small n” paradigm, i.e., the number of features is much larger than that of the samples. A feature selection (FS) algorithm selects a subset of the transcriptomic or methylomic biomarkers in order to build a better prediction model. The hidden patterns in the FS solution space make it challenging to achieve a feature subset with satisfying prediction performances. Swarm intelligence (SI) algorithms mimic the target searching behaviors of various animals and have demonstrated promising capabilities in selecting features with good machine learning performances. Our study revealed that different SI-based feature selection algorithms contributed complementary searching capabilities in the FS solution space, and their collaboration generated a better feature subset than the individual SI feature selection algorithms. Nine SI-based feature selection algorithms were integrated to vote for the selected features, which were further refined by the dynamic recursive feature elimination framework. In most cases, the proposed Zoo algorithm outperformed the existing feature selection algorithms on transcriptomics and methylomics datasets.
Collapse
|
7
|
Use of relevancy and complementary information for discriminatory gene selection from high-dimensional gene expression data. PLoS One 2021; 16:e0230164. [PMID: 34613963 PMCID: PMC8494339 DOI: 10.1371/journal.pone.0230164] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2020] [Accepted: 09/21/2021] [Indexed: 12/22/2022] Open
Abstract
With the advent of high-throughput technologies, life sciences are generating a huge amount of varied biomolecular data. Global gene expression profiles provide a snapshot of all the genes that are transcribed in a cell or in a tissue under a particular condition. The high-dimensionality of such gene expression data (i.e., very large number of features/genes analyzed with relatively much less number of samples) makes it difficult to identify the key genes (biomarkers) that are truly attributing to a particular phenotype or condition, (such as cancer), de novo. For identifying the key genes from gene expression data, among the existing literature, mutual information (MI) is one of the most successful criteria. However, the correction of MI for finite sample is not taken into account in this regard. It is also important to incorporate dynamic discretization of genes for more relevant gene selection, although this is not considered in the available methods. Besides, it is usually suggested in current studies to remove redundant genes which is particularly inappropriate for biological data, as a group of genes may connect to each other for downstreaming proteins. Thus, despite being redundant, it is needed to add the genes which provide additional useful information for the disease. Addressing these issues, we proposed Mutual information based Gene Selection method (MGS) for selecting informative genes. Moreover, to rank these selected genes, we extended MGS and propose two ranking methods on the selected genes, such as MGSf—based on frequency and MGSrf—based on Random Forest. The proposed method not only obtained better classification rates on gene expression datasets derived from different gene expression studies compared to recently reported methods but also detected the key genes relevant to pathways with a causal relationship to the disease, which indicate that it will also able to find the responsible genes for an unknown disease data.
Collapse
|
8
|
Garbulowski M, Diamanti K, Smolińska K, Baltzer N, Stoll P, Bornelöv S, Øhrn A, Feuk L, Komorowski J. R.ROSETTA: an interpretable machine learning framework. BMC Bioinformatics 2021; 22:110. [PMID: 33676405 PMCID: PMC7937228 DOI: 10.1186/s12859-021-04049-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Accepted: 02/24/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Machine learning involves strategies and algorithms that may assist bioinformatics analyses in terms of data mining and knowledge discovery. In several applications, viz. in Life Sciences, it is often more important to understand how a prediction was obtained rather than knowing what prediction was made. To this end so-called interpretable machine learning has been recently advocated. In this study, we implemented an interpretable machine learning package based on the rough set theory. An important aim of our work was provision of statistical properties of the models and their components. RESULTS We present the R.ROSETTA package, which is an R wrapper of ROSETTA framework. The original ROSETTA functions have been improved and adapted to the R programming environment. The package allows for building and analyzing non-linear interpretable machine learning models. R.ROSETTA gathers combinatorial statistics via rule-based modelling for accessible and transparent results, well-suited for adoption within the greater scientific community. The package also provides statistics and visualization tools that facilitate minimization of analysis bias and noise. The R.ROSETTA package is freely available at https://github.com/komorowskilab/R.ROSETTA . To illustrate the usage of the package, we applied it to a transcriptome dataset from an autism case-control study. Our tool provided hypotheses for potential co-predictive mechanisms among features that discerned phenotype classes. These co-predictors represented neurodevelopmental and autism-related genes. CONCLUSIONS R.ROSETTA provides new insights for interpretable machine learning analyses and knowledge-based systems. We demonstrated that our package facilitated detection of dependencies for autism-related genes. Although the sample application of R.ROSETTA illustrates transcriptome data analysis, the package can be used to analyze any data organized in decision tables.
Collapse
Affiliation(s)
- Mateusz Garbulowski
- Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden
| | - Klev Diamanti
- Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Karolina Smolińska
- Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden
| | - Nicholas Baltzer
- Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden
- Department of Research, Cancer Registry of Norway, Oslo, Norway
| | - Patricia Stoll
- Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden
- Department of Biosystems Science and Engineering, ETH Zurich, Zurich, Switzerland
| | - Susanne Bornelöv
- Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | | | - Lars Feuk
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Jan Komorowski
- Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden.
- Swedish Collegium for Advanced Study, Uppsala, Sweden.
- Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland.
- Washington National Primate Research Center, Seattle, WA, USA.
| |
Collapse
|
9
|
Garbulowski M, Smolinska K, Diamanti K, Pan G, Maqbool K, Feuk L, Komorowski J. Interpretable Machine Learning Reveals Dissimilarities Between Subtypes of Autism Spectrum Disorder. Front Genet 2021; 12:618277. [PMID: 33719335 PMCID: PMC7946989 DOI: 10.3389/fgene.2021.618277] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 01/12/2021] [Indexed: 01/16/2023] Open
Abstract
Autism spectrum disorder (ASD) is a heterogeneous neuropsychiatric disorder with a complex genetic background. Analysis of altered molecular processes in ASD patients requires linear and nonlinear methods that provide interpretable solutions. Interpretable machine learning provides legible models that allow explaining biological mechanisms and support analysis of clinical subgroups. In this work, we investigated several case-control studies of gene expression measurements of ASD individuals. We constructed a rule-based learning model from three independent datasets that we further visualized as a nonlinear gene-gene co-predictive network. To find dissimilarities between ASD subtypes, we scrutinized a topological structure of the network and estimated a centrality distance. Our analysis revealed that autism is the most severe subtype of ASD, while pervasive developmental disorder-not otherwise specified and Asperger syndrome are closely related and milder ASD subtypes. Furthermore, we analyzed the most important ASD-related features that were described in terms of gene co-predictors. Among others, we found a strong co-predictive mechanism between EMC4 and TMEM30A, which may suggest a co-regulation between these genes. The present study demonstrates the potential of applying interpretable machine learning in bioinformatics analyses. Although the proposed methodology was designed for transcriptomics data, it can be applied to other omics disciplines.
Collapse
Affiliation(s)
- Mateusz Garbulowski
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden
| | - Karolina Smolinska
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden
| | - Klev Diamanti
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Gang Pan
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Khurram Maqbool
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Lars Feuk
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Jan Komorowski
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden.,Swedish Collegium for Advanced Study, Uppsala, Sweden.,Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland.,Washington National Primate Research Center, Seattle, WA, United States
| |
Collapse
|
10
|
Han Y, Huang L, Zhou F. A dynamic recursive feature elimination framework (dRFE) to further refine a set of OMIC biomarkers. Bioinformatics 2021; 37:2183-2189. [PMID: 33515240 DOI: 10.1093/bioinformatics/btab055] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 12/23/2020] [Accepted: 01/25/2021] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION A feature selection algorithm may select the subset of features with the best associations with the class labels. The recursive feature elimination (RFE) is a heuristic feature screening framework and has been widely used to select the biological OMIC biomarkers. This study proposed a dynamic recursive feature elimination (dRFE) framework with more flexible feature elimination operations. The proposed dRFE was comprehensively compared with 11 existing feature selection algorithms and five classifiers on the eight difficult transcriptome datasets from a previous study, the ten newly collected transcriptome datasets and the five methylome datasets. RESULTS The experimental data suggested that the regular RFE framework did not perform well, and dRFE outperformed the existing feature selection algorithms in most cases. The dRFE-detected features achieved Acc=1.0000 for the two methylome datasets GSE53045 and GSE66695. The best prediction accuracies of the dRFE-detected features were 0.9259, 0.9424, and 0.8601 for the other three methylome datasets GSE74845, GSE103186, and GSE80970, respectively. Four transcriptome datasets received Acc=1.0000 using the dRFE-detected features, and the prediction accuracies for the other six newly collected transcriptome datasets were between 0.6301 and 0.9917. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Yuanyuan Han
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, China, 130012
| | - Lan Huang
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, China, 130012
| | - Fengfeng Zhou
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, China, 130012
| |
Collapse
|
11
|
Hameed SS, Hassan R, Hassan WH, Muhammadsharif FF, Latiff LA. HDG-select: A novel GUI based application for gene selection and classification in high dimensional datasets. PLoS One 2021; 16:e0246039. [PMID: 33507983 PMCID: PMC7842997 DOI: 10.1371/journal.pone.0246039] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2020] [Accepted: 01/12/2021] [Indexed: 11/24/2022] Open
Abstract
The selection and classification of genes is essential for the identification of related genes to a specific disease. Developing a user-friendly application with combined statistical rigor and machine learning functionality to help the biomedical researchers and end users is of great importance. In this work, a novel stand-alone application, which is based on graphical user interface (GUI), is developed to perform the full functionality of gene selection and classification in high dimensional datasets. The so-called HDG-select application is validated on eleven high dimensional datasets of the format CSV and GEO soft. The proposed tool uses the efficient algorithm of combined filter-GBPSO-SVM and it was made freely available to users. It was found that the proposed HDG-select outperformed other tools reported in literature and presented a competitive performance, accessibility, and functionality.
Collapse
Affiliation(s)
- Shilan S. Hameed
- Computer Systems and Networks (CSN), Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia
- Directorate of Information Technology, Koya University, Koya, Kurdistan Region-F.R., Iraq
| | - Rohayanti Hassan
- School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru, Johor, Malaysia
| | - Wan Haslina Hassan
- Computer Systems and Networks (CSN), Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia
| | - Fahmi F. Muhammadsharif
- Department of Physics, Faculty of Science and Health, Koya University, Koya, Kurdistan Region-F.R., Iraq
| | - Liza Abdul Latiff
- U-BAN Research Group, Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia
| |
Collapse
|
12
|
Couture V, Delisle S, Mercier A, Pennings G. The other face of advanced paternal age: a scoping review of its terminological, social, public health, psychological, ethical and regulatory aspects. Hum Reprod Update 2020; 27:305-323. [PMID: 33201989 DOI: 10.1093/humupd/dmaa046] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 08/25/2020] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND There is a global tendency for parents to conceive children later in life. The maternal dimension of the postponement transition has been thoroughly studied, but interest in the paternal side is more recent. For the moment, most literature reviews on the topic have focused on the consequences of advanced paternal age (APA) on fertility, pregnancy and the health of the child. OBJECTIVE AND RATIONALE The present review seeks to move the focus away from the biological and medical dimensions of APA and synthesise the knowledge of the other face of APA. SEARCH METHODS We used the scoping review methodology. Searches of interdisciplinary articles databases were performed with keywords pertaining to APA and its dimensions outside of biology and medicine. We included scientific articles, original research, essays, commentaries and editorials in the sample. The final sample of 177 documents was analysed with qualitative thematic analysis. OUTCOMES We identified six themes highlighting the interdisciplinary nature of APA research. The 'terminological aspects' highlight the lack of consensus on the definition of APA and the strategies developed to offer alternatives. The 'social aspects' focus on the postponement transition towards reproducing later in life and its cultural dimensions. The 'public health aspects' refer to attempts to analyse APA as a problem with wider health and economic implications. The 'psychological aspects' focus on the consequences of APA and older fatherhood on psychological characteristics of the child. The 'ethical aspects' reflect on issues of APA emerging at the intersection of parental autonomy, children's welfare and social responsibility. The 'regulatory aspects' group different suggestions to collectively approach the implications of APA. Our results show that the field of APA is still in the making and that evidence is lacking to fully address the issues of APA. The review suggests promising avenues of research such as introducing the voice of fathers of advanced age into the research agenda. WIDER IMPLICATIONS The results of this review will be useful for developing policies and preconception health interventions that consider and include prospective fathers of advanced age.
Collapse
Affiliation(s)
- Vincent Couture
- Faculty of Nursing, Université Laval, Québec G1V 0A6, Canada.,Research Center of the CHU de Québec-Université Laval, Québec G1L 3L5, Canada
| | - Stéphane Delisle
- Research Center of the CHU de Québec-Université Laval, Québec G1L 3L5, Canada
| | - Alexis Mercier
- Faculty of Nursing, Université Laval, Québec G1V 0A6, Canada
| | - Guido Pennings
- Department of Philosophy and Moral Sciences, Bioethics Institute Ghent, Ghent University, Gent 9000, Belgium
| |
Collapse
|
13
|
Transcriptome signatures from discordant sibling pairs reveal changes in peripheral blood immune cell composition in Autism Spectrum Disorder. Transl Psychiatry 2020; 10:106. [PMID: 32291385 PMCID: PMC7156413 DOI: 10.1038/s41398-020-0778-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 02/14/2020] [Accepted: 02/26/2020] [Indexed: 12/22/2022] Open
Abstract
Notwithstanding several research efforts in the past years, robust and replicable molecular signatures for autism spectrum disorders from peripheral blood remain elusive. The available literature on blood transcriptome in ASD suggests that through accurate experimental design it is possible to extract important information on the disease pathophysiology at the peripheral level. Here we exploit the availability of a resource for molecular biomarkers in ASD, the Italian Autism Network (ITAN) collection, for the investigation of transcriptomic signatures in ASD based on a discordant sibling pair design. Whole blood samples from 75 discordant sibling pairs selected from the ITAN network where submitted to RNASeq analysis and data analyzed by complementary approaches. Overall, differences in gene expression between affected and unaffected siblings were small. In order to assess the contribution of differences in the relative proportion of blood cells between discordant siblings, we have applied two different cell deconvolution algorithms, showing that the observed molecular signatures mainly reflect changes in peripheral blood immune cell composition, in particular NK cells. The results obtained by the cell deconvolution approach are supported by the analysis performed by WGCNA. Our report describes the largest differential gene expression profiling in peripheral blood of ASD subjects and controls conducted by RNASeq. The observed signatures are consistent with the hypothesis of immune alterations in autism and an increased risk of developing autism in subjects exposed to prenatal infections or stress. Our study also points to a potential role of NMUR1, HMGB3, and PTPRN2 in ASD.
Collapse
|
14
|
Network Structure Analysis Identifying Key Genes of Autism and Its Mechanism. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2020; 2020:3753080. [PMID: 32273901 PMCID: PMC7125446 DOI: 10.1155/2020/3753080] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Revised: 11/04/2019] [Accepted: 02/22/2020] [Indexed: 01/16/2023]
Abstract
Identifying the key genes of autism is of great significance for understanding its pathogenesis and improving the clinical level of medicine. In this paper, we use the structural parameters (average degree) of gene correlation networks to identify genes related to autism and study its pathogenesis. Based on the gene expression profiles of 82 autistic patients (the experimental group, E) and 64 healthy persons (the control group, C) in NCBI database, spearman correlation networks are established, and their average degrees under different thresholds are analyzed. It is found that average degrees of C and E are basically separable at the full thresholds. This indicates that there is a clear difference between the network structures of C and E, and it also suggests that this difference is related to the mechanism of disease. By annotating and enrichment analysis of the first 20 genes (MD-Gs) with significant difference in the average degree, we find that they are significantly related to gland development, cardiovascular development, and embryogenesis of nervous system, which support the results in Alter et al.'s original research. In addition, FIGF and CSF3 may play an important role in the mechanism of autism.
Collapse
|
15
|
Davoudi A, Mahmoodian H. Stable gene selection by self-representation method in fuzzy sample classification. Med Biol Eng Comput 2020; 58:1213-1223. [PMID: 32212053 DOI: 10.1007/s11517-020-02160-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Accepted: 03/12/2020] [Indexed: 11/27/2022]
Abstract
In recent years, microarray technology and gene expression profiles have been widely used to detect, predict, or classify the samples of various diseases. The presence of large genes in these profiles and the small number of samples are known challenges in this field and are widely considered in previous papers. In previous studies, other topics such as the noise of microarray data or the dependence of selected genes on samples have been less considered. Therefore, we have tried to address these two issues by using a fuzzy classifier and stability index of selected genes, respectively. The proposed method is based on the regression function between the genes and class labels which is determined by the self-representing method. This regression function is determined individually for each class of the database. To minimize the effect of noise in microarray data, a fuzzy classifier is applied in the proposed model. Four databases of gene expression profiles are examined in this article, and the results indicate that the proposed model has a relative advantage over the previous methods. Graphical abstract.
Collapse
Affiliation(s)
- Armaghan Davoudi
- Electrical Engineering Faculty, Najafabad Branch, Islamic Azad University, Najafabad, Iran
| | - Hamid Mahmoodian
- Electrical Engineering Faculty, Najafabad Branch, Islamic Azad University, Najafabad, Iran.
- Digital Processing and Machine Vision Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran.
| |
Collapse
|
16
|
Tomoiaga D, Aguiar-Pulido V, Shrestha S, Feinstein P, Levy SE, Mason CE, Rosenfeld JA. Single-cell sperm transcriptomes and variants from fathers of children with and without autism spectrum disorder. NPJ Genom Med 2020; 5:14. [PMID: 32133155 PMCID: PMC7035312 DOI: 10.1038/s41525-020-0117-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Accepted: 01/02/2020] [Indexed: 11/17/2022] Open
Abstract
The human sperm is one of the smallest cells in the body, but also one of the most important, as it serves as the entire paternal genetic contribution to a child. Investigating RNA and mutations in sperm is especially relevant for diseases such as autism spectrum disorders (ASD), which have been correlated with advanced paternal age. Historically, studies have focused on the assessment of bulk sperm, wherein millions of individual sperm are present and only high-frequency variants can be detected. Using 10× Chromium single-cell sequencing technology, we assessed the transcriptome from >65,000 single spermatozoa across six sperm donors (scSperm-RNA-seq), including two who fathered multiple children with ASD and four fathers of neurotypical children. Using RNA-seq methods for differential expression and variant analysis, we found clusters of sperm mutations in each donor that are indicative of the sperm being produced by different stem cell pools. Finally, we have shown that genetic variations can be found in single sperm.
Collapse
Affiliation(s)
- Delia Tomoiaga
- 1Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY USA
| | - Vanessa Aguiar-Pulido
- 2The Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY USA
| | | | - Paul Feinstein
- 4Hunter College, City University of New York, New York, NY USA
| | - Shawn E Levy
- 3Hudson Alpha Institute for Biotechnology, Huntsville, AL USA
| | - Christopher E Mason
- 1Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY USA.,2The Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY USA.,5The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY USA.,6The WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, New York, NY USA
| | - Jeffrey A Rosenfeld
- 7Rutgers Cancer Institute of New Jersey, New Brunswick, NJ USA.,8Department of Pathology, Robert Wood Johnson Medical School, New Brunswick, NJ USA
| |
Collapse
|
17
|
Pietras CM, Power L, Slonim DK. aTEMPO: Pathway-Specific Temporal Anomalies for Precision Therapeutics. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2020; 25:683-694. [PMID: 31797638 PMCID: PMC7664835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Dynamic processes are inherently important in disease, and identifying disease-related disruptions of normal dynamic processes can provide information about individual patients. We have previously characterized individuals' disease states via pathway-based anomalies in expression data, and we have identified disease-correlated disruption of predictable dynamic patterns by modeling a virtual time series in static data. Here we combine the two approaches, using an anomaly detection model and virtual time series to identify anomalous temporal processes in specific disease states. We demonstrate that this approach can informatively characterize individual patients, suggesting personalized therapeutic approaches.
Collapse
|
18
|
New Horizons for Molecular Genetics Diagnostic and Research in Autism Spectrum Disorder. ADVANCES IN NEUROBIOLOGY 2020; 24:43-81. [PMID: 32006356 DOI: 10.1007/978-3-030-30402-7_2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Autism spectrum disorder (ASD) is a highly heritable, heterogeneous, and complex pervasive neurodevelopmental disorder (PND) characterized by distinctive abnormalities of human cognitive functions, social interaction, and speech development.Nowadays, several genetic changes including chromosome abnormalities, genetic variations, transcriptional epigenetics, and noncoding RNA have been identified in ASD. However, the association between these genetic modifications and ASDs has not been confirmed yet.The aim of this review is to summarize the key findings in ASD from genetic viewpoint that have been identified from the last few decades of genetic and molecular research.
Collapse
|
19
|
Saffari A, Arno M, Nasser E, Ronald A, Wong CCY, Schalkwyk LC, Mill J, Dudbridge F, Meaburn EL. RNA sequencing of identical twins discordant for autism reveals blood-based signatures implicating immune and transcriptional dysregulation. Mol Autism 2019; 10:38. [PMID: 31719968 PMCID: PMC6839145 DOI: 10.1186/s13229-019-0285-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Accepted: 09/01/2019] [Indexed: 11/13/2022] Open
Abstract
Background A gap exists in our mechanistic understanding of how genetic and environmental risk factors converge at the molecular level to result in the emergence of autism symptoms. We compared blood-based gene expression signatures in identical twins concordant and discordant for autism spectrum condition (ASC) to differentiate genetic and environmentally driven transcription differences, and establish convergent evidence for biological mechanisms involved in ASC. Methods Genome-wide gene expression data were generated using RNA-seq on whole blood samples taken from 16 pairs of monozygotic (MZ) twins and seven twin pair members (39 individuals in total), who had been assessed for ASC and autism traits at age 12. Differential expression (DE) analyses were performed between (a) affected and unaffected subjects (N = 36) and (b) within discordant ASC MZ twin pairs (total N = 11) to identify environmental-driven DE. Gene set enrichment and pathway testing was performed on DE gene lists. Finally, an integrative analysis using DNA methylation data aimed to identify genes with consistent evidence for altered regulation in cis. Results In the discordant twin analysis, three genes showed evidence for DE at FDR < 10%: IGHG4, EVI2A and SNORD15B. In the case-control analysis, four DE genes were identified at FDR < 10% including IGHG4, PRR13P5, DEPDC1B, and ZNF501. We find enrichment for DE of genes curated in the SFARI human gene database. Pathways showing evidence of enrichment included those related to immune cell signalling and immune response, transcriptional control and cell cycle/proliferation. Integrative methylomic and transcriptomic analysis identified a number of genes showing suggestive evidence for cis dysregulation. Limitations Identical twins stably discordant for ASC are rare, and as such the sample size was limited and constrained to the use of peripheral blood tissue for transcriptomic and methylomic profiling. Given these primary limitations, we focused on transcript-level analysis. Conclusions Using a cohort of ASC discordant and concordant MZ twins, we add to the growing body of transcriptomic-based evidence for an immune-based component in the molecular aetiology of ASC. Whilst the sample size was limited, the study demonstrates the utility of the discordant MZ twin design combined with multi-omics integration for maximising the potential to identify disease-associated molecular signals.
Collapse
Affiliation(s)
- Ayden Saffari
- 1Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
- 2Centre for Brain and Cognitive Development, Department of Psychological Sciences, Birkbeck, University of London, London, UK
| | - Matt Arno
- 3Edinburgh Genomics, University of Edinburgh, Edinburgh, Scotland UK
- 4King's Genomics Centre, King's College London, London, UK
| | - Eric Nasser
- 4King's Genomics Centre, King's College London, London, UK
| | - Angelica Ronald
- 2Centre for Brain and Cognitive Development, Department of Psychological Sciences, Birkbeck, University of London, London, UK
| | - Chloe C Y Wong
- 5Social Genetic and Developmental Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | | | - Jonathan Mill
- 7University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Frank Dudbridge
- 1Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
- 8Department of Health Sciences, University of Leicester, Leicester, UK
| | - Emma L Meaburn
- 2Centre for Brain and Cognitive Development, Department of Psychological Sciences, Birkbeck, University of London, London, UK
| |
Collapse
|
20
|
Mordaunt CE, Park BY, Bakulski KM, Feinberg JI, Croen LA, Ladd-Acosta C, Newschaffer CJ, Volk HE, Ozonoff S, Hertz-Picciotto I, LaSalle JM, Schmidt RJ, Fallin MD. A meta-analysis of two high-risk prospective cohort studies reveals autism-specific transcriptional changes to chromatin, autoimmune, and environmental response genes in umbilical cord blood. Mol Autism 2019; 10:36. [PMID: 31673306 PMCID: PMC6814108 DOI: 10.1186/s13229-019-0287-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Accepted: 09/08/2019] [Indexed: 12/17/2022] Open
Abstract
Background Autism spectrum disorder (ASD) is a neurodevelopmental disorder that affects more than 1% of children in the USA. ASD risk is thought to arise from both genetic and environmental factors, with the perinatal period as a critical window. Understanding early transcriptional changes in ASD would assist in clarifying disease pathogenesis and identifying biomarkers. However, little is known about umbilical cord blood gene expression profiles in babies later diagnosed with ASD compared to non-typically developing and non-ASD (Non-TD) or typically developing (TD) children. Methods Genome-wide transcript levels were measured by Affymetrix Human Gene 2.0 array in RNA from cord blood samples from both the Markers of Autism Risk in Babies-Learning Early Signs (MARBLES) and the Early Autism Risk Longitudinal Investigation (EARLI) high-risk pregnancy cohorts that enroll younger siblings of a child previously diagnosed with ASD. Younger siblings were diagnosed based on assessments at 36 months, and 59 ASD, 92 Non-TD, and 120 TD subjects were included. Using both differential expression analysis and weighted gene correlation network analysis, gene expression between ASD and TD, and between Non-TD and TD, was compared within each study and via meta-analysis. Results While cord blood gene expression differences comparing either ASD or Non-TD to TD did not reach genome-wide significance, 172 genes were nominally differentially expressed between ASD and TD cord blood (log2(fold change) > 0.1, p < 0.01). These genes were significantly enriched for functions in xenobiotic metabolism, chromatin regulation, and systemic lupus erythematosus (FDR q < 0.05). In contrast, 66 genes were nominally differentially expressed between Non-TD and TD, including 8 genes that were also differentially expressed in ASD. Gene coexpression modules were significantly correlated with demographic factors and cell type proportions. Limitations ASD-associated gene expression differences identified in this study are subtle, as cord blood is not the main affected tissue, it is composed of many cell types, and ASD is a heterogeneous disorder. Conclusions This is the first study to identify gene expression differences in cord blood specific to ASD through a meta-analysis across two prospective pregnancy cohorts. The enriched gene pathways support involvement of environmental, immune, and epigenetic mechanisms in ASD etiology.
Collapse
Affiliation(s)
- Charles E Mordaunt
- 1Department of Medical Microbiology and Immunology, Genome Center, and MIND Institute, University of California, Davis, CA USA
| | - Bo Y Park
- 2Department of Public Health, California State University, Fullerton, CA USA
| | - Kelly M Bakulski
- 3Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI USA
| | - Jason I Feinberg
- 4Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD USA
| | - Lisa A Croen
- 5Division of Research and Autism Research Program, Kaiser Permanente Northern California, Oakland, CA USA
| | | | - Craig J Newschaffer
- 6Department of Biobehavioral Health, College of Health and Human Development, Pennsylvania State University, University Park, PA USA
| | - Heather E Volk
- 4Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD USA
| | - Sally Ozonoff
- 7Psychiatry and Behavioral Sciences and MIND Institute, University of California, Davis, CA USA
| | - Irva Hertz-Picciotto
- 8Department of Public Health Sciences and MIND Institute, University of California, Davis, CA USA
| | - Janine M LaSalle
- 1Department of Medical Microbiology and Immunology, Genome Center, and MIND Institute, University of California, Davis, CA USA
| | - Rebecca J Schmidt
- 8Department of Public Health Sciences and MIND Institute, University of California, Davis, CA USA
| | - M Daniele Fallin
- 4Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD USA
| |
Collapse
|
21
|
Quinn TP, Lee SC, Venkatesh S, Nguyen T. Improving the classification of neuropsychiatric conditions using gene ontology terms as features. Am J Med Genet B Neuropsychiatr Genet 2019; 180:508-518. [PMID: 31025483 DOI: 10.1002/ajmg.b.32727] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2018] [Revised: 02/14/2019] [Accepted: 03/08/2019] [Indexed: 11/11/2022]
Abstract
Although neuropsychiatric disorders have an established genetic background, their molecular foundations remain elusive. This has prompted many investigators to search for explanatory biomarkers that can predict clinical outcomes. One approach uses machine learning to classify patients based on blood mRNA expression. However, these endeavors typically fail to achieve the high level of performance, stability, and generalizability required for clinical translation. Moreover, these classifiers can lack interpretability because not all genes have relevance to researchers. For this study, we hypothesized that annotation-based classifiers can improve classification performance, stability, generalizability, and interpretability. To this end, we evaluated the models of four classification algorithms on six neuropsychiatric data sets using four annotation databases. Our results suggest that the Gene Ontology Biological Process database can transform gene expression into an annotation-based feature space that is accurate and stable. We also show how annotation features can improve the interpretability of classifiers: as annotations are used to assign biological importance to genes, the biological importance of annotation-based features are the features themselves. In evaluating the annotation features, we find that top ranked annotations tend contain top ranked genes, suggesting that the most predictive annotations are a superset of the most predictive genes. Based on this, and the fact that annotations are used routinely to assign biological importance to genetic data, we recommend transforming gene-level expression into annotation-level expression prior to the classification of neuropsychiatric conditions.
Collapse
Affiliation(s)
- Thomas P Quinn
- Centre for Pattern Recognition and Data Analytics (PRaDA), Deakin University, Geelong, Victoria, Australia.,Centre for Molecular and Medical Research, Deakin University, Geelong, Victoria, Australia.,Bioinformatics Core Research Group, Deakin University, Geelong, Victoria, Australia
| | - Samuel C Lee
- Centre for Pattern Recognition and Data Analytics (PRaDA), Deakin University, Geelong, Victoria, Australia
| | - Svetha Venkatesh
- Centre for Pattern Recognition and Data Analytics (PRaDA), Deakin University, Geelong, Victoria, Australia
| | - Thin Nguyen
- Centre for Pattern Recognition and Data Analytics (PRaDA), Deakin University, Geelong, Victoria, Australia
| |
Collapse
|
22
|
Manzouri L, Yousefian S, Keshtkari A, Hashemi N. Advanced Parental Age and Risk of Positive Autism Spectrum Disorders Screening. Int J Prev Med 2019; 10:135. [PMID: 31516676 PMCID: PMC6710919 DOI: 10.4103/ijpvm.ijpvm_25_19] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Accepted: 04/02/2019] [Indexed: 12/27/2022] Open
Abstract
Background: Autism Spectrum Disorder (ASD) is a life -long neurodevelopmental disorder and significantly influences the quality of life in children. The screening of ASD in children aged between 16-30 months to early detection and early intervention for better prognosis. Methods: This cross-sectional study was conducted in the southwest of Iran (Yasuj) with dominant Lore ethnicity in 2017. A total of 1504 mother- child pairs with children aged between 16-30 months were selected through simple random sampling from the integrated national health system as the framework. ASD screening was implemented using the Modified checklist for autism in toddlers- revised, with follow-up interview (M-CHAT-R/F). Demographic data such as sex of children, and parental age at their time of pregnancy were collected for all children. Results: Risk of ASD was low, moderate and high in 1447 (96.2%), 54 (3.6%) and 3 (0.2%) in screening, respectively. The estimated rate of ASD prevalence was 80 per 10000 (12 out of 1504) or 1 in 125. Mother's age ≥35 (P value = 0.002, OR = 11.65, CI95%: 2.49-54.35) and father's age ≥40 (P value = 0.0001, OR = 19.64, CI95%: 4.89-78.82) were predicting factors of ASD in toddlers aged 16-30 months. Conclusions: Given that, increasing the age of marriage in Iran and recent trend towards delayed childbearing; children born to older parents are at a higher risk for having ASD. So, increasing the public awareness is necessary.
Collapse
Affiliation(s)
- Leila Manzouri
- Social Determinants of Health Research Center, Yasuj University of Medical Sciences, Yasuj, Iran
| | - Sepideh Yousefian
- Student Research Committee, Yasuj University of Medical Sciences, Yasuj, Iran
| | - Ali Keshtkari
- Department of Pediatrics, School of Medicine, Yasuj University of Medical Sciences, Yasuj, Iran
| | - Nazir Hashemi
- Department of Psychiatry, School of Medicine, Yasuj University of Medical Sciences, Yasuj, Iran
| |
Collapse
|
23
|
Lee SC, Quinn TP, Lai J, Kong SW, Hertz-Picciotto I, Glatt SJ, Crowley TM, Venkatesh S, Nguyen T. Solving for X: Evidence for sex-specific autism biomarkers across multiple transcriptomic studies. Am J Med Genet B Neuropsychiatr Genet 2019; 180:377-389. [PMID: 30520558 PMCID: PMC6551334 DOI: 10.1002/ajmg.b.32701] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2018] [Revised: 09/20/2018] [Accepted: 10/29/2018] [Indexed: 12/20/2022]
Abstract
Autism spectrum disorder (ASD) is a markedly heterogeneous condition with a varied phenotypic presentation. Its high concordance among siblings, as well as its clear association with specific genetic disorders, both point to a strong genetic etiology. However, the molecular basis of ASD is still poorly understood, although recent studies point to the existence of sex-specific ASD pathophysiologies and biomarkers. Despite this, little is known about how exactly sex influences the gene expression signatures of ASD probands. In an effort to identify sex-dependent biomarkers and characterize their function, we present an analysis of a single paired-end postmortem brain RNA-Seq data set and a meta-analysis of six blood-based microarray data sets. Here, we identify several genes with sex-dependent dysregulation, and many more with sex-independent dysregulation. Moreover, through pathway analysis, we find that these sex-independent biomarkers have substantially different biological roles than the sex-dependent biomarkers, and that some of these pathways are ubiquitously dysregulated in both postmortem brain and blood. We conclude by synthesizing the discovered biomarker profiles with the extant literature, by highlighting the advantage of studying sex-specific dysregulation directly, and by making a call for new transcriptomic data that comprise large female cohorts.
Collapse
Affiliation(s)
- Samuel C. Lee
- Centre for Pattern Recognition and Data Analytics (PRaDA), Deakin University, Geelong, 3220, Australia
| | - Thomas P. Quinn
- Centre for Pattern Recognition and Data Analytics (PRaDA), Deakin University, Geelong, 3220, Australia
- Centre for Molecular and Medical Research, Deakin University, Geelong, 3220, Australia
- Bioinformatics Core Research Group, Deakin University, Geelong, 3220, Australia
| | - Jerry Lai
- Deakin eResearch, Deakin University, Geelong, 3220, Australia | Intersect Australia, Sydney, 2000, Australia
| | - Sek Won Kong
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA, USA | Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Irva Hertz-Picciotto
- Department of Public Health Sciences and UC Davis MIND Institute, School of Medicine, Davis, California
| | - Stephen J. Glatt
- Psychiatric Genetic Epidemiology and Neurobiology Laboratory (PsychGENe Lab) | SUNY Upstate Medical University, Syracuse, NY, USA
| | - Tamsyn M. Crowley
- Centre for Molecular and Medical Research, Deakin University, Geelong, 3220, Australia
- Bioinformatics Core Research Group, Deakin University, Geelong, 3220, Australia
- Poultry Hub Australia, University of New England, Armidale, New South Wales, 2351, Australia
| | - Svetha Venkatesh
- Centre for Pattern Recognition and Data Analytics (PRaDA), Deakin University, Geelong, 3220, Australia
| | - Thin Nguyen
- Centre for Pattern Recognition and Data Analytics (PRaDA), Deakin University, Geelong, 3220, Australia
| |
Collapse
|
24
|
An integrated transcriptomic analysis of autism spectrum disorder. Sci Rep 2019; 9:11818. [PMID: 31413321 PMCID: PMC6694127 DOI: 10.1038/s41598-019-48160-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2018] [Accepted: 07/26/2019] [Indexed: 02/06/2023] Open
Abstract
Autism spectrum disorder (ASD) is not a single disease but a set of disorders. To find clues of ASD pathogenesis in transcriptomic data, we performed an integrated transcriptomic analysis of ASD. After screening based on several standards in Gene Expression Omnibus (GEO) database, we obtained 11 series of transcriptomic data of different human tissues of ASD patients and healthy controls. Multidimensional scaling analysis revealed that datasets from the same tissue had bigger similarity than from different tissues. Functional enrichment analysis demonstrated that differential expressed genes were significantly enriched in inflammation/immune response, mitochondrion-related function and oxidative phosphorylation. Interestingly, genes enriched in inflammation/immune response were up-regulated in the brain tissues and down-regulated in the blood. In addition, drug prediction provided several compounds which might reverse gene expression profiles of ASD patients. And we also replicated the methods and criteria of transcriptomic analysis with datasets of ASD animal models and healthy controls, the results from animal models consolidated the results of transcriptomic analysis of ASD human tissues. In general, the results of our study may provide researchers a new sight of understanding the etiology of ASD and clinicians the possibilities of developing medical therapies.
Collapse
|
25
|
Pichitpunpong C, Thongkorn S, Kanlayaprasit S, Yuwattana W, Plaingam W, Sangsuthum S, Aizat WM, Baharum SN, Tencomnao T, Hu VW, Sarachana T. Phenotypic subgrouping and multi-omics analyses reveal reduced diazepam-binding inhibitor (DBI) protein levels in autism spectrum disorder with severe language impairment. PLoS One 2019; 14:e0214198. [PMID: 30921354 PMCID: PMC6438570 DOI: 10.1371/journal.pone.0214198] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Accepted: 03/08/2019] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND The mechanisms underlying autism spectrum disorder (ASD) remain unclear, and clinical biomarkers are not yet available for ASD. Differences in dysregulated proteins in ASD have shown little reproducibility, which is partly due to ASD heterogeneity. Recent studies have demonstrated that subgrouping ASD cases based on clinical phenotypes is useful for identifying candidate genes that are dysregulated in ASD subgroups. However, this strategy has not been employed in proteome profiling analyses to identify ASD biomarker proteins for specific subgroups. METHODS We therefore conducted a cluster analysis of the Autism Diagnostic Interview-Revised (ADI-R) scores from 85 individuals with ASD to predict subgroups and subsequently identified dysregulated genes by reanalyzing the transcriptome profiles of individuals with ASD and unaffected individuals. Proteome profiling of lymphoblastoid cell lines from these individuals was performed via 2D-gel electrophoresis, and then mass spectrometry. Disrupted proteins were identified and compared to the dysregulated transcripts and reported dysregulated proteins from previous proteome studies. Biological functions were predicted using the Ingenuity Pathway Analysis (IPA) program. Selected proteins were also analyzed by Western blotting. RESULTS The cluster analysis of ADI-R data revealed four ASD subgroups, including ASD with severe language impairment, and transcriptome profiling identified dysregulated genes in each subgroup. Screening via proteome analysis revealed 82 altered proteins in the ASD subgroup with severe language impairment. Eighteen of these proteins were further identified by nano-LC-MS/MS. Among these proteins, fourteen were predicted by IPA to be associated with neurological functions and inflammation. Among these proteins, diazepam-binding inhibitor (DBI) protein was confirmed by Western blot analysis to be expressed at significantly decreased levels in the ASD subgroup with severe language impairment, and the DBI expression levels were correlated with the scores of several ADI-R items. CONCLUSIONS By subgrouping individuals with ASD based on clinical phenotypes, and then performing an integrated transcriptome-proteome analysis, we identified DBI as a novel candidate protein for ASD with severe language impairment. The mechanisms of this protein and its potential use as an ASD biomarker warrant further study.
Collapse
Affiliation(s)
- Chatravee Pichitpunpong
- M.Sc. Program in Clinical Biochemistry and Molecular Medicine, Department of Clinical Chemistry, Faculty of Allied Health Sciences, Chulalongkorn University, Bangkok, Thailand
| | - Surangrat Thongkorn
- PhD Program in Clinical Biochemistry and Molecular Medicine, Department of Clinical Chemistry, Faculty of Allied Health Sciences, Chulalongkorn University, Bangkok, Thailand
| | - Songphon Kanlayaprasit
- PhD Program in Clinical Biochemistry and Molecular Medicine, Department of Clinical Chemistry, Faculty of Allied Health Sciences, Chulalongkorn University, Bangkok, Thailand
| | - Wasana Yuwattana
- B.Sc. Program in Medical Technology, Faculty of Allied Health Sciences, Chulalongkorn University, Bangkok, Thailand
| | - Waluga Plaingam
- College of Oriental Medicine, Rangsit University, Pathum Thani, Thailand
| | - Siriporn Sangsuthum
- Department of Clinical Chemistry, Faculty of Allied Health Sciences, Chulalongkorn University, Bangkok, Thailand
| | - Wan Mohd Aizat
- Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia
| | - Syarul Nataqain Baharum
- Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia
| | - Tewin Tencomnao
- Age-related Inflammation and Degeneration Research Unit, Department of Clinical Chemistry, Faculty of Allied Health Sciences, Chulalongkorn University, Bangkok, Thailand
| | - Valerie Wailin Hu
- Department of Biochemistry and Molecular Medicine, George Washington University School of Medicine and Health Sciences, Washington, DC, United States of America
| | - Tewarit Sarachana
- Age-related Inflammation and Degeneration Research Unit, Department of Clinical Chemistry, Faculty of Allied Health Sciences, Chulalongkorn University, Bangkok, Thailand
| |
Collapse
|
26
|
Sunwoo JS, Jeon D, Lee ST, Moon J, Yu JS, Park DK, Bae JY, Lee DY, Kim S, Jung KH, Park KI, Jung KY, Kim M, Lee SK, Chu K. Maternal immune activation alters brain microRNA expression in mouse offspring. Ann Clin Transl Neurol 2018; 5:1264-1276. [PMID: 30349861 PMCID: PMC6186947 DOI: 10.1002/acn3.652] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2018] [Accepted: 08/27/2018] [Indexed: 01/15/2023] Open
Abstract
Objective Maternal immune activation (MIA) is associated with an increased risk of autism spectrum disorder (ASD) in offspring. Herein, we investigate the altered expression of microRNAs (miRNA), and that of their target genes, in the brains of MIA mouse offspring. Methods To generate MIA model mice, pregnant mice were injected with polyriboinosinic:polyribocytidylic acid on embryonic day 12.5. We performed miRNA microarray and mRNA sequencing in order to determine the differential expression of miRNA and mRNA between MIA mice and controls, at 3 weeks of age. We further identified predicted target genes of dysregulated miRNAs, and miRNA‐target interactions, based on the inverse correlation of their expression levels. Results Mice prenatally subjected to MIA exhibited behavioral abnormalities typical of ASD, such as a lack of preference for social novelty and reduced prepulse inhibition. We found 29 differentially expressed miRNAs (8 upregulated and 21 downregulated) and 758 differentially expressed mRNAs (542 upregulated and 216 downregulated) in MIA offspring compared to controls. Based on expression levels of the predicted target genes, 18 downregulated miRNAs (340 target genes) and three upregulated miRNAs (60 target genes) were found to be significantly enriched among the differentially expressed genes. miRNA and target gene interactions were most significant between mmu‐miR‐466i‐3p and Hfm1 (ATP‐dependent DNA helicase homolog), and between mmu‐miR‐877‐3p and Aqp6 (aquaporin 6). Interpretation Our results provide novel information regarding miRNA expression changes and their putative targets in the early postnatal period of brain development. Further studies will be needed to evaluate potential pathogenic roles of the dysregulated miRNAs.
Collapse
Affiliation(s)
- Jun-Sang Sunwoo
- Department of Neurology Soonchunhyang University College of Medicine Seoul South Korea
| | | | - Soon-Tae Lee
- Laboratory for Neurotherapeutics Department of Neurology Comprehensive Epilepsy Center Biomedical Research Institute Seoul National University Hospital Seoul South Korea.,Program in Neuroscience Seoul National University College of Medicine Seoul South Korea
| | - Jangsup Moon
- Laboratory for Neurotherapeutics Department of Neurology Comprehensive Epilepsy Center Biomedical Research Institute Seoul National University Hospital Seoul South Korea.,Program in Neuroscience Seoul National University College of Medicine Seoul South Korea.,Department of Neurosurgery Seoul National University Hospital Seoul South Korea
| | - Jung-Suk Yu
- Laboratory for Neurotherapeutics Department of Neurology Comprehensive Epilepsy Center Biomedical Research Institute Seoul National University Hospital Seoul South Korea
| | - Dong-Kyu Park
- Laboratory for Neurotherapeutics Department of Neurology Comprehensive Epilepsy Center Biomedical Research Institute Seoul National University Hospital Seoul South Korea
| | - Ji-Yeon Bae
- Laboratory for Neurotherapeutics Department of Neurology Comprehensive Epilepsy Center Biomedical Research Institute Seoul National University Hospital Seoul South Korea
| | - Doo Young Lee
- Laboratory for Neurotherapeutics Department of Neurology Comprehensive Epilepsy Center Biomedical Research Institute Seoul National University Hospital Seoul South Korea
| | - Sangwoo Kim
- Laboratory for Neurotherapeutics Department of Neurology Comprehensive Epilepsy Center Biomedical Research Institute Seoul National University Hospital Seoul South Korea
| | - Keun-Hwa Jung
- Laboratory for Neurotherapeutics Department of Neurology Comprehensive Epilepsy Center Biomedical Research Institute Seoul National University Hospital Seoul South Korea.,Program in Neuroscience Seoul National University College of Medicine Seoul South Korea
| | - Kyung-Il Park
- Department of Neurology Seoul National University Hospital Healthcare System Gangnam Center Seoul South Korea
| | - Ki-Young Jung
- Laboratory for Neurotherapeutics Department of Neurology Comprehensive Epilepsy Center Biomedical Research Institute Seoul National University Hospital Seoul South Korea.,Program in Neuroscience Seoul National University College of Medicine Seoul South Korea
| | - Manho Kim
- Laboratory for Neurotherapeutics Department of Neurology Comprehensive Epilepsy Center Biomedical Research Institute Seoul National University Hospital Seoul South Korea.,Program in Neuroscience Seoul National University College of Medicine Seoul South Korea.,Protein Metabolism Medical Research Center Seoul National University College of Medicine Seoul South Korea
| | - Sang Kun Lee
- Laboratory for Neurotherapeutics Department of Neurology Comprehensive Epilepsy Center Biomedical Research Institute Seoul National University Hospital Seoul South Korea.,Program in Neuroscience Seoul National University College of Medicine Seoul South Korea
| | - Kon Chu
- Laboratory for Neurotherapeutics Department of Neurology Comprehensive Epilepsy Center Biomedical Research Institute Seoul National University Hospital Seoul South Korea.,Program in Neuroscience Seoul National University College of Medicine Seoul South Korea
| |
Collapse
|
27
|
Tangsuwansri C, Saeliw T, Thongkorn S, Chonchaiya W, Suphapeetiporn K, Mutirangura A, Tencomnao T, Hu VW, Sarachana T. Investigation of epigenetic regulatory networks associated with autism spectrum disorder (ASD) by integrated global LINE-1 methylation and gene expression profiling analyses. PLoS One 2018; 13:e0201071. [PMID: 30036398 PMCID: PMC6056057 DOI: 10.1371/journal.pone.0201071] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2018] [Accepted: 07/06/2018] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND The exact cause and mechanisms underlying the pathobiology of autism spectrum disorder (ASD) remain unclear. Dysregulation of long interspersed element-1 (LINE-1) has been reported in the brains of ASD-like mutant mice and ASD brain tissues. However, the role and methylation of LINE-1 in individuals with ASD remain unclear. In this study, we aimed to investigate whether LINE-1 insertion is associated with differentially expressed genes (DEGs) and to assess LINE-1 methylation in ASD. METHODS To identify DEGs associated with LINE-1 in ASD, we reanalyzed previously published transcriptome profiles and overlapped them with the list of LINE-1-containing genes from the TranspoGene database. An Ingenuity Pathway Analysis (IPA) of DEGs associated with LINE-1 insertion was conducted. DNA methylation of LINE-1 was assessed via combined bisulfite restriction analysis (COBRA) of lymphoblastoid cell lines from ASD individuals and unaffected individuals, and the methylation levels were correlated with the expression levels of LINE-1 and two LINE-1-inserted DEGs, C1orf27 and ARMC8. RESULTS We found that LINE-1 insertion was significantly associated with DEGs in ASD. The IPA showed that LINE-1-inserted DEGs were associated with ASD-related mechanisms, including sex hormone receptor signaling and axon guidance signaling. Moreover, we observed that the LINE-1 methylation level was significantly reduced in lymphoblastoid cell lines from ASD individuals with severe language impairment and was inversely correlated with the transcript level. The methylation level of LINE-1 was also correlated with the expression of the LINE-1-inserted DEG C1orf27 but not ARMC8. CONCLUSIONS In ASD individuals with severe language impairment, LINE-1 methylation was reduced and correlated with the expression levels of LINE-1 and the LINE-1-inserted DEG C1orf27. Our findings highlight the association of LINE-1 with DEGs in ASD blood samples and warrant further investigation. The molecular mechanisms of LINE-1 and the effects of its methylation in ASD pathobiology deserve further study.
Collapse
Affiliation(s)
- Chayanin Tangsuwansri
- M.Sc. Program in Clinical Biochemistry and Molecular Medicine, Department of Clinical Chemistry, Faculty of Allied Health Sciences, Chulalongkorn University, Bangkok, Thailand
| | - Thanit Saeliw
- M.Sc. Program in Clinical Biochemistry and Molecular Medicine, Department of Clinical Chemistry, Faculty of Allied Health Sciences, Chulalongkorn University, Bangkok, Thailand
| | - Surangrat Thongkorn
- M.Sc. Program in Clinical Biochemistry and Molecular Medicine, Department of Clinical Chemistry, Faculty of Allied Health Sciences, Chulalongkorn University, Bangkok, Thailand
| | - Weerasak Chonchaiya
- Division of Growth and Development and Maximizing Thai Children’s Developmental Potential Research Unit, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, the Thai Red Cross Society, Bangkok, Thailand
| | - Kanya Suphapeetiporn
- Center of Excellence for Medical Genetics, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Excellence Center for Medical Genetics, King Chulalongkorn Memorial Hospital, the Thai Red Cross Society, Bangkok, Thailand
| | - Apiwat Mutirangura
- Center of Excellence in Molecular Genetics of Cancer and Human Diseases, Department of Anatomy, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Tewin Tencomnao
- Age-related Inflammation and Degeneration Research Unit, Department of Clinical Chemistry, Faculty of Allied Health Sciences, Chulalongkorn University, Bangkok, Thailand
| | - Valerie Wailin Hu
- Department of Biochemistry and Molecular Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, United States of America
| | - Tewarit Sarachana
- Age-related Inflammation and Degeneration Research Unit, Department of Clinical Chemistry, Faculty of Allied Health Sciences, Chulalongkorn University, Bangkok, Thailand
| |
Collapse
|
28
|
Algamal ZY, Alhamzawi R, Mohammad Ali HT. Gene selection for microarray gene expression classification using Bayesian Lasso quantile regression. Comput Biol Med 2018; 97:145-152. [DOI: 10.1016/j.compbiomed.2018.04.018] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2018] [Revised: 04/22/2018] [Accepted: 04/22/2018] [Indexed: 01/01/2023]
|
29
|
Golightly NP, Bell A, Bischoff AI, Hollingsworth PD, Piccolo SR. Curated compendium of human transcriptional biomarker data. Sci Data 2018; 5:180066. [PMID: 29664470 PMCID: PMC5903354 DOI: 10.1038/sdata.2018.66] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2017] [Accepted: 02/22/2018] [Indexed: 12/25/2022] Open
Abstract
One important use of genome-wide transcriptional profiles is to identify relationships between transcription levels and patient outcomes. These translational insights can guide the development of biomarkers for clinical application. Data from thousands of translational-biomarker studies have been deposited in public repositories, enabling reuse. However, data-reuse efforts require considerable time and expertise because transcriptional data are generated using heterogeneous profiling technologies, preprocessed using diverse normalization procedures, and annotated in non-standard ways. To address this problem, we curated 45 publicly available, translational-biomarker datasets from a variety of human diseases. To increase the data's utility, we reprocessed the raw expression data using a uniform computational pipeline, addressed quality-control problems, mapped the clinical annotations to a controlled vocabulary, and prepared consistently structured, analysis-ready data files. These data, along with scripts we used to prepare the data, are available in a public repository. We believe these data will be particularly useful to researchers seeking to perform benchmarking studies—for example, to compare and optimize machine-learning algorithms' ability to predict biomedical outcomes.
Collapse
Affiliation(s)
| | - Avery Bell
- Department of Biology, Brigham Young University, Provo, Utah 84602, USA
| | - Anna I Bischoff
- Department of Biology, Brigham Young University, Provo, Utah 84602, USA
| | - Parker D Hollingsworth
- Department of Biology, Brigham Young University, Provo, Utah 84602, USA.,Northeast Ohio Medical University, Rootstown, Ohio 44272, USA
| | - Stephen R Piccolo
- Department of Biology, Brigham Young University, Provo, Utah 84602, USA.,Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah 84602, USA
| |
Collapse
|
30
|
Saeliw T, Tangsuwansri C, Thongkorn S, Chonchaiya W, Suphapeetiporn K, Mutirangura A, Tencomnao T, Hu VW, Sarachana T. Integrated genome-wide Alu methylation and transcriptome profiling analyses reveal novel epigenetic regulatory networks associated with autism spectrum disorder. Mol Autism 2018; 9:27. [PMID: 29686828 PMCID: PMC5902935 DOI: 10.1186/s13229-018-0213-9] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2017] [Accepted: 04/03/2018] [Indexed: 12/20/2022] Open
Abstract
Background Alu elements are a group of repetitive elements that can influence gene expression through CpG residues and transcription factor binding. Altered gene expression and methylation profiles have been reported in various tissues and cell lines from individuals with autism spectrum disorder (ASD). However, the role of Alu elements in ASD remains unclear. We thus investigated whether Alu elements are associated with altered gene expression profiles in ASD. Methods We obtained five blood-based gene expression profiles from the Gene Expression Omnibus database and human Alu-inserted gene lists from the TranspoGene database. Differentially expressed genes (DEGs) in ASD were identified from each study and overlapped with the human Alu-inserted genes. The biological functions and networks of Alu-inserted DEGs were then predicted by Ingenuity Pathway Analysis (IPA). A combined bisulfite restriction analysis of lymphoblastoid cell lines (LCLs) derived from 36 ASD and 20 sex- and age-matched unaffected individuals was performed to assess the global DNA methylation levels within Alu elements, and the Alu expression levels were determined by quantitative RT-PCR. Results In ASD blood or blood-derived cells, 320 Alu-inserted genes were reproducibly differentially expressed. Biological function and pathway analysis showed that these genes were significantly associated with neurodevelopmental disorders and neurological functions involved in ASD etiology. Interestingly, estrogen receptor and androgen signaling pathways implicated in the sex bias of ASD, as well as IL-6 signaling and neuroinflammation signaling pathways, were also highlighted. Alu methylation was not significantly different between the ASD and sex- and age-matched control groups. However, significantly altered Alu methylation patterns were observed in ASD cases sub-grouped based on Autism Diagnostic Interview-Revised scores compared with matched controls. Quantitative RT-PCR analysis of Alu expression also showed significant differences between ASD subgroups. Interestingly, Alu expression was correlated with methylation status in one phenotypic ASD subgroup. Conclusion Alu methylation and expression were altered in LCLs from ASD subgroups. Our findings highlight the association of Alu elements with gene dysregulation in ASD blood samples and warrant further investigation. Moreover, the classification of ASD individuals into subgroups based on phenotypes may be beneficial and could provide insights into the still unknown etiology and the underlying mechanisms of ASD. Electronic supplementary material The online version of this article (10.1186/s13229-018-0213-9) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Thanit Saeliw
- 1M.Sc. Program in Clinical Biochemistry and Molecular Medicine, Department of Clinical Chemistry, Faculty of Allied Health Sciences, Chulalongkorn University, Bangkok, Thailand
| | - Chayanin Tangsuwansri
- 1M.Sc. Program in Clinical Biochemistry and Molecular Medicine, Department of Clinical Chemistry, Faculty of Allied Health Sciences, Chulalongkorn University, Bangkok, Thailand
| | - Surangrat Thongkorn
- 1M.Sc. Program in Clinical Biochemistry and Molecular Medicine, Department of Clinical Chemistry, Faculty of Allied Health Sciences, Chulalongkorn University, Bangkok, Thailand
| | - Weerasak Chonchaiya
- 2Maximizing Thai Children's Developmental Potential Research Unit, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, the Thai Red Cross Society, Bangkok, Thailand
| | - Kanya Suphapeetiporn
- 3Center of Excellence for Medical Genetics, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.,Excellence Center for Medical Genetics, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Apiwat Mutirangura
- 5Center of Excellence in Molecular Genetics of Cancer and Human Diseases, Department of Anatomy, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Tewin Tencomnao
- 6Age-related Inflammation and Degeneration Research Unit, Department of Clinical Chemistry, Faculty of Allied Health Sciences, Chulalongkorn University, 154 Soi Chula 12, Rama 1 Road, Wangmai, Pathumwan, Bangkok, 10330 Thailand
| | - Valerie W Hu
- 7Department of Biochemistry and Molecular Medicine, The George Washington University School of Medicine and Health Sciences, The George Washington University, Washington, DC USA
| | - Tewarit Sarachana
- 6Age-related Inflammation and Degeneration Research Unit, Department of Clinical Chemistry, Faculty of Allied Health Sciences, Chulalongkorn University, 154 Soi Chula 12, Rama 1 Road, Wangmai, Pathumwan, Bangkok, 10330 Thailand
| |
Collapse
|
31
|
Hameed SS, Hassan R, Muhammad FF. Selection and classification of gene expression in autism disorder: Use of a combination of statistical filters and a GBPSO-SVM algorithm. PLoS One 2017; 12:e0187371. [PMID: 29095904 PMCID: PMC5667738 DOI: 10.1371/journal.pone.0187371] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2017] [Accepted: 10/18/2017] [Indexed: 11/30/2022] Open
Abstract
In this work, gene expression in autism spectrum disorder (ASD) is analyzed with the goal of selecting the most attributed genes and performing classification. The objective was achieved by utilizing a combination of various statistical filters and a wrapper-based geometric binary particle swarm optimization-support vector machine (GBPSO-SVM) algorithm. The utilization of different filters was accentuated by incorporating a mean and median ratio criterion to remove very similar genes. The results showed that the most discriminative genes that were identified in the first and last selection steps included the presence of a repetitive gene (CAPS2), which was assigned as the gene most highly related to ASD risk. The merged gene subset that was selected by the GBPSO-SVM algorithm was able to enhance the classification accuracy.
Collapse
Affiliation(s)
- Shilan S. Hameed
- Department of Computer Science, Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, Malaysia
- Department of Software and Informatics Engineering, College of Engineering, Salahaddin University, Erbil, Kurdistan Region, Iraq
| | - Rohayanti Hassan
- Department of Software Engineering, Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, Malaysia
| | - Fahmi F. Muhammad
- Department of Physics, Faculty of Science & Health, Koya University, Koya, Kurdistan Region, Iraq
| |
Collapse
|
32
|
|
33
|
Kazemi M, Fayyazi-Bordbar MR, Mahdavi-Shahri N. Comparative Dermatoglyphic Study between Autistic Patients and Normal People in Iran. IRANIAN JOURNAL OF MEDICAL SCIENCES 2017; 42:392-396. [PMID: 28761206 PMCID: PMC5523047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
Autism is a neurodevelopmental disorder originating from early childhood; nevertheless, its diagnosis is in older ages. In addition to heredity, environmental factors are also of great significance in the etiology of the disease. Dermatoglyphic patterns, albeit varied, remain stable for a lifetime and yield a large number of patterns upon examination. Studies have shown a significant association between dermatoglyphics and some diseases, especially genetic ones. We compared fingerprints between patients with autism and normal individuals in a Fars population living in Khorasan-Razavi Province, Iran, in 2015. The right and left hand fingerprints of 104 autistic individuals (case group; age range=5-15 y) were collected using a fingerprint scanner. The same process was performed for 102 healthy individuals, in the age range of 6 to 25 years. All dermatoglyphic patterns and ridge counts were determined. The data were analyzed using the Mann-Whitney nonparametric test and binomial distribution. There was a significant difference in the distribution of the dermatoglyphic patterns on the right and left thumbs and the index fingers between the case and control groups (P<0.05). The patients had a significantly higher count of loops on their right and left thumbs and their index fingers. A significant decrease in ridge counts for the right and left thumbs and the index fingers was observed in the patients compared to the controls. The results suggested that the patterns were associated with the risk of autism. The patterns may be drawn upon as biometric parameters in the screening of children with autism.
Collapse
Affiliation(s)
- Mansoureh Kazemi
- Department of Biology, Mashhad Branch, Islamic Azad University, Mashhad, Iran
| | | | - Nasser Mahdavi-Shahri
- Department of Biology, Kavian Institute of Education, Mashhad, Iran,Correspondence: Nasser Mahdavi-Shahri, PhD; Kavian Institute of Mashhad, Kosar13, Kosar Blvd, Vakil Abad Blvd, Mashhad, Iran Tel/Fax: +98 51 38841809
| |
Collapse
|
34
|
Genetic variants in the transcription regulatory region of MEGF10 are associated with autism in Chinese Han population. Sci Rep 2017; 7:2292. [PMID: 28536440 PMCID: PMC5442155 DOI: 10.1038/s41598-017-02348-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2017] [Accepted: 04/10/2017] [Indexed: 12/27/2022] Open
Abstract
Multiple epidermal growth factor-like-domains 10 (MEGF10), a critical member of the apoptotic engulfment pathway, mediates axon pruning and synapse elimination during brain development. Previous studies indicated that synaptic pruning deficit was associated with autism-related phenotypes. However, the relationship between MEGF10 and autism remains poorly understood. Disease-associated variants are significantly enriched in the transcription regulatory regions. These include the transcription start site (TSS) and its cis-regulatory elements. To investigate the role of MEGF10 variants with putative transcription regulatory function in the etiology of autism, we performed a family-based association study in 410 Chinese Han trios. Our results indicate that three single nucleotide polymorphisms (SNPs), rs4836316, rs2194079 and rs4836317 near the TSS are significantly associated with autism following Bonferroni correction (p = 0.0011, p = 0.0088, and p = 0.0023, respectively). Haplotype T-A-G (rs4836316-rs2194079-rs4836317) was preferentially transmitted from parents to affected offspring (p permutation = 0.0055). Consistently, functional exploration further verified that the risk allele and haplotype might influence its binding with transcription factors, resulting in decreased transcriptional activity of MEGF10. Our findings indicated that the risk alleles and haplotype near the MEGF10 TSS might modulate transcriptional activity and increase the susceptibility to autism.
Collapse
|
35
|
Diaz-Beltran L, Esteban FJ, Varma M, Ortuzk A, David M, Wall DP. Cross-disorder comparative analysis of comorbid conditions reveals novel autism candidate genes. BMC Genomics 2017; 18:315. [PMID: 28427329 PMCID: PMC5399393 DOI: 10.1186/s12864-017-3667-9] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2016] [Accepted: 03/28/2017] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Numerous studies have highlighted the elevated degree of comorbidity associated with autism spectrum disorder (ASD). These comorbid conditions may add further impairments to individuals with autism and are substantially more prevalent compared to neurotypical populations. These high rates of comorbidity are not surprising taking into account the overlap of symptoms that ASD shares with other pathologies. From a research perspective, this suggests common molecular mechanisms involved in these conditions. Therefore, identifying crucial genes in the overlap between ASD and these comorbid disorders may help unravel the common biological processes involved and, ultimately, shed some light in the understanding of autism etiology. RESULTS In this work, we used a two-fold systems biology approach specially focused on biological processes and gene networks to conduct a comparative analysis of autism with 31 frequently comorbid disorders in order to define a multi-disorder subcomponent of ASD and predict new genes of potential relevance to ASD etiology. We validated our predictions by determining the significance of our candidate genes in high throughput transcriptome expression profiling studies. Using prior knowledge of disease-related biological processes and the interaction networks of the disorders related to autism, we identified a set of 19 genes not previously linked to ASD that were significantly differentially regulated in individuals with autism. In addition, these genes were of potential etiologic relevance to autism, given their enriched roles in neurological processes crucial for optimal brain development and function, learning and memory, cognition and social behavior. CONCLUSIONS Taken together, our approach represents a novel perspective of autism from the point of view of related comorbid disorders and proposes a model by which prior knowledge of interaction networks may enlighten and focus the genome-wide search for autism candidate genes to better define the genetic heterogeneity of ASD.
Collapse
Affiliation(s)
- Leticia Diaz-Beltran
- Division of Systems Medicine, Department of Pediatrics, School of Medicine, Stanford University, 1265 Welch Road, Stanford, CA, 94305-5488, USA
- Division of Systems Medicine, Department of Psychiatry, Stanford University, Stanford, CA, USA
- Systems Biology Unit, Department of Experimental Biology, University of Jaén, Jaén, Spain
| | - Francisco J Esteban
- Systems Biology Unit, Department of Experimental Biology, University of Jaén, Jaén, Spain
| | - Maya Varma
- Division of Systems Medicine, Department of Pediatrics, School of Medicine, Stanford University, 1265 Welch Road, Stanford, CA, 94305-5488, USA
- Division of Systems Medicine, Department of Psychiatry, Stanford University, Stanford, CA, USA
| | - Alp Ortuzk
- Division of Systems Medicine, Department of Pediatrics, School of Medicine, Stanford University, 1265 Welch Road, Stanford, CA, 94305-5488, USA
- Division of Systems Medicine, Department of Psychiatry, Stanford University, Stanford, CA, USA
| | - Maude David
- Division of Systems Medicine, Department of Pediatrics, School of Medicine, Stanford University, 1265 Welch Road, Stanford, CA, 94305-5488, USA
- Division of Systems Medicine, Department of Psychiatry, Stanford University, Stanford, CA, USA
| | - Dennis P Wall
- Division of Systems Medicine, Department of Pediatrics, School of Medicine, Stanford University, 1265 Welch Road, Stanford, CA, 94305-5488, USA.
- Division of Systems Medicine, Department of Psychiatry, Stanford University, Stanford, CA, USA.
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
| |
Collapse
|
36
|
Tylee DS, Hess JL, Quinn TP, Barve R, Huang H, Zhang-James Y, Chang J, Stamova BS, Sharp FR, Hertz-Picciotto I, Faraone SV, Kong SW, Glatt SJ. Blood transcriptomic comparison of individuals with and without autism spectrum disorder: A combined-samples mega-analysis. Am J Med Genet B Neuropsychiatr Genet 2017; 174:181-201. [PMID: 27862943 PMCID: PMC5499528 DOI: 10.1002/ajmg.b.32511] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2016] [Accepted: 10/21/2016] [Indexed: 12/25/2022]
Abstract
Blood-based microarray studies comparing individuals affected with autism spectrum disorder (ASD) and typically developing individuals help characterize differences in circulating immune cell functions and offer potential biomarker signal. We sought to combine the subject-level data from previously published studies by mega-analysis to increase the statistical power. We identified studies that compared ex vivo blood or lymphocytes from ASD-affected individuals and unrelated comparison subjects using Affymetrix or Illumina array platforms. Raw microarray data and clinical meta-data were obtained from seven studies, totaling 626 affected and 447 comparison subjects. Microarray data were processed using uniform methods. Covariate-controlled mixed-effect linear models were used to identify gene transcripts and co-expression network modules that were significantly associated with diagnostic status. Permutation-based gene-set analysis was used to identify functionally related sets of genes that were over- and under-expressed among ASD samples. Our results were consistent with diminished interferon-, EGF-, PDGF-, PI3K-AKT-mTOR-, and RAS-MAPK-signaling cascades, and increased ribosomal translation and NK-cell related activity in ASD. We explored evidence for sex-differences in the ASD-related transcriptomic signature. We also demonstrated that machine-learning classifiers using blood transcriptome data perform with moderate accuracy when data are combined across studies. Comparing our results with those from blood-based studies of protein biomarkers (e.g., cytokines and trophic factors), we propose that ASD may feature decoupling between certain circulating signaling proteins (higher in ASD samples) and the transcriptional cascades which they typically elicit within circulating immune cells (lower in ASD samples). These findings provide insight into ASD-related transcriptional differences in circulating immune cells. © 2016 Wiley Periodicals, Inc.
Collapse
Affiliation(s)
- Daniel S. Tylee
- Psychiatric Genetic Epidemiology & Neurobiology Laboratory (PsychGENe Lab); Departments of Psychiatry and Behavioral Sciences & Neuroscience and Physiology; SUNY Upstate Medical University; Syracuse, NY, U.S.A
| | - Jonathan L. Hess
- Psychiatric Genetic Epidemiology & Neurobiology Laboratory (PsychGENe Lab); Departments of Psychiatry and Behavioral Sciences & Neuroscience and Physiology; SUNY Upstate Medical University; Syracuse, NY, U.S.A
| | - Thomas P. Quinn
- Psychiatric Genetic Epidemiology & Neurobiology Laboratory (PsychGENe Lab); Departments of Psychiatry and Behavioral Sciences & Neuroscience and Physiology; SUNY Upstate Medical University; Syracuse, NY, U.S.A
| | - Rahul Barve
- Psychiatric Genetic Epidemiology & Neurobiology Laboratory (PsychGENe Lab); Departments of Psychiatry and Behavioral Sciences & Neuroscience and Physiology; SUNY Upstate Medical University; Syracuse, NY, U.S.A
| | - Hailiang Huang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA,Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Yanli Zhang-James
- Psychiatric Genetic Epidemiology & Neurobiology Laboratory (PsychGENe Lab); Departments of Psychiatry and Behavioral Sciences & Neuroscience and Physiology; SUNY Upstate Medical University; Syracuse, NY, U.S.A
| | - Jeffrey Chang
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Medical Center, Brooklyn, NY, U.S.A
| | - Boryana S. Stamova
- Department of Neurology, UC Davis School of Medicine, Sacramento, CA, USA
| | - Frank R. Sharp
- Department of Neurology, UC Davis School of Medicine, Sacramento, CA, USA
| | - Irva Hertz-Picciotto
- Department of Public Health Sciences and UC Davis MIND Institute, School of Medicine, Davis, CA
| | - Stephen V. Faraone
- Psychiatric Genetic Epidemiology & Neurobiology Laboratory (PsychGENe Lab); Departments of Psychiatry and Behavioral Sciences & Neuroscience and Physiology; SUNY Upstate Medical University; Syracuse, NY, U.S.A,K.G. Jebsen Centre for Research on Neuropsychiatric Disorders, University of Bergen, Bergen, Norway
| | - Sek Won Kong
- Computational Health Informatics Program, Boston Children’s Hospital; Department of Pediatrics, Harvard Medical School, Boston, MA, U.S.A
| | - Stephen J. Glatt
- Psychiatric Genetic Epidemiology & Neurobiology Laboratory (PsychGENe Lab); Departments of Psychiatry and Behavioral Sciences & Neuroscience and Physiology; SUNY Upstate Medical University; Syracuse, NY, U.S.A,To whom correspondence should be addressed: SUNY Upstate Medical University, 750 East Adams Street, Syracuse, NY 13210, Phone: (315) 464-7742,
| |
Collapse
|
37
|
Karimi P, Kamali E, Mousavi SM, Karahmadi M. Environmental factors influencing the risk of autism. JOURNAL OF RESEARCH IN MEDICAL SCIENCES : THE OFFICIAL JOURNAL OF ISFAHAN UNIVERSITY OF MEDICAL SCIENCES 2017; 22:27. [PMID: 28413424 PMCID: PMC5377970 DOI: 10.4103/1735-1995.200272] [Citation(s) in RCA: 112] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2016] [Revised: 11/06/2016] [Accepted: 11/30/2016] [Indexed: 12/16/2022]
Abstract
Autism is a developmental disability with age of onset in childhood (under 3 years old), which is characterized by definite impairments in social interactions, abnormalities in speech, and stereotyped pattern of behaviors. Due to the progress of autism in recent decades, a wide range of studies have been done to identify the etiological factors of autism. It has been found that genetic and environmental factors are both involved in autism pathogenesis. Hence, in this review article, a set of environmental factors involved in the occurrence of autism has been collected, and finally, some practical recommendations for reduction of the risk of this devastating disease in children are represented.
Collapse
Affiliation(s)
- Padideh Karimi
- Division of Genetics, Department of Biology, Faculty of Science, Tarbiat Modares University, Tehran, Iran
| | - Elahe Kamali
- Division of Genetics, Department of Biology, Faculty of Science, Isfahan University, Isfahan, Iran
| | - Seyyed Mohammad Mousavi
- Cellular and Molecular Research Center, Shahrekord University of Medical Sciences, Shahrekord, Iran
- Genetic and Identification Lab, Legal Medicine Center, Isfahan, Iran
| | - Mojgan Karahmadi
- Department of Psychiatry, School of Medicine, Isfahan University of Medical Sciences, Noor Hospital, Isfahan, Iran
| |
Collapse
|
38
|
Ansel A, Rosenzweig JP, Zisman PD, Melamed M, Gesundheit B. Variation in Gene Expression in Autism Spectrum Disorders: An Extensive Review of Transcriptomic Studies. Front Neurosci 2017; 10:601. [PMID: 28105001 PMCID: PMC5214812 DOI: 10.3389/fnins.2016.00601] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2016] [Accepted: 12/15/2016] [Indexed: 01/01/2023] Open
Abstract
Autism spectrum disorders (ASDs) are a group of complex neurodevelopmental conditions that present in early childhood and have a current estimated prevalence of about 1 in 68 US children, 1 in 42 boys. ASDs are heterogeneous, and arise from epigenetic, genetic and environmental origins, yet, the exact etiology of ASDs still remains unknown. Individuals with ASDs are characterized by having deficits in social interaction, impaired communication and a range of stereotyped and repetitive behaviors. Currently, a diagnosis of ASD is based solely on behavioral assessments and phenotype. Hundreds of diverse ASD susceptibility genes have been identified, yet none of the mutations found account for more than a small subset of autism cases. Therefore, a genetic diagnosis is not yet possible for the majority of the ASD population. The susceptibility genes that have been identified are involved in a wide and varied range of biological functions. Since the genetics of ASDs is so diverse, information on genome function as provided by transcriptomic data is essential to further our understanding. Gene expression studies have been extremely useful in comparing groups of individuals with ASD and control samples in order to measure which genes (or group of genes) are dysregulated in the ASD group. Transcriptomic studies are essential as a key link between measuring protein levels and analyzing genetic information. This review of recent autism gene expression studies highlights genes that are expressed in the brain, immune system, and processes such as cell metabolism and embryology. Various biological processes have been shown to be implicated with ASD individuals as well as differences in gene expression levels between different types of biological tissues. Some studies use gene expression to attempt to separate autism into different subtypes. An updated list of genes shown to be significantly dysregulated in individuals with autism from all recent ASD expression studies will help further research isolate any patterns useful for diagnosis or understanding the mechanisms involved. The functional relevance of transcriptomic studies as a method of classifying and diagnosing ASD cannot be underestimated despite the possible limitations of transcriptomic studies.
Collapse
|
39
|
Shen L, Lin Y, Sun Z, Yuan X, Chen L, Shen B. Knowledge-Guided Bioinformatics Model for Identifying Autism Spectrum Disorder Diagnostic MicroRNA Biomarkers. Sci Rep 2016; 6:39663. [PMID: 28000768 PMCID: PMC5175196 DOI: 10.1038/srep39663] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2016] [Accepted: 11/24/2016] [Indexed: 01/02/2023] Open
Abstract
Autism spectrum disorder (ASD) is a severe neurodevelopmental disease with a high incidence and effective biomarkers are urgently needed for its diagnosis. A few previous studies have reported the detection of miRNA biomarkers for autism diagnosis, especially those based on bioinformatics approaches. In this study, we developed a knowledge-guided bioinformatics model for identifying autism miRNA biomarkers. We downloaded gene expression microarray data from the GEO Database and extracted genes with expression levels that differed in ASD and the controls. We then constructed an autism-specific miRNA-mRNA network and inferred candidate autism biomarker miRNAs based on their regulatory modes and functions. We defined a novel parameter called the autism gene percentage as autism-specific knowledge to further facilitate the identification of autism-specific biomarker miRNAs. Finally, 11 miRNAs were screened as putative autism biomarkers, where eight miRNAs (72.7%) were significantly dysregulated in ASD samples according to previous reports. Functional enrichment results indicated that the targets of the identified miRNAs were enriched in autism-associated pathways, such as Wnt signaling (in KEGG and IPA), cell cycle (in KEGG), and glioblastoma multiforme signaling (in IPA), thereby supporting the predictive power of our model.
Collapse
Affiliation(s)
- Li Shen
- Center for Systems Biology, Soochow University, Suzhou, 215006, China.,Institute of Biological Sciences and Biotechnology, Donghua University, Shanghai, 201620, China
| | - Yuxin Lin
- Center for Systems Biology, Soochow University, Suzhou, 215006, China
| | - Zhandong Sun
- Center for Systems Biology, Soochow University, Suzhou, 215006, China
| | - Xuye Yuan
- Center for Systems Biology, Soochow University, Suzhou, 215006, China
| | - Luonan Chen
- Key laboratory of Systems Biology, Shanghai Institute of Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Bairong Shen
- Center for Systems Biology, Soochow University, Suzhou, 215006, China
| |
Collapse
|
40
|
Abstract
We consider the problem of predicting an outcome variable using p covariates that are measured on n independent observations, in a setting in which additive, flexible, and interpretable fits are desired. We propose the fused lasso additive model (FLAM), in which each additive function is estimated to be piecewise constant with a small number of adaptively-chosen knots. FLAM is the solution to a convex optimization problem, for which a simple algorithm with guaranteed convergence to a global optimum is provided. FLAM is shown to be consistent in high dimensions, and an unbiased estimator of its degrees of freedom is proposed. We evaluate the performance of FLAM in a simulation study and on two data sets. Supplemental materials are available online, and the R package flam is available on CRAN.
Collapse
Affiliation(s)
- Ashley Petersen
- Department of Biostatistics, University of Washington, Seattle WA 98195
| | - Daniela Witten
- Department of Biostatistics, University of Washington, Seattle WA 98195
| | - Noah Simon
- Department of Biostatistics, University of Washington, Seattle WA 98195
| |
Collapse
|
41
|
A common molecular signature in ASD gene expression: following Root 66 to autism. Transl Psychiatry 2016; 6:e705. [PMID: 26731442 PMCID: PMC5068868 DOI: 10.1038/tp.2015.112] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2015] [Revised: 06/04/2015] [Accepted: 06/14/2015] [Indexed: 12/27/2022] Open
Abstract
Several gene expression experiments on autism spectrum disorders have been conducted using both blood and brain tissue. Individually, these studies have advanced our understanding of the molecular systems involved in the molecular pathology of autism and have formed the bases of ongoing work to build autism biomarkers. In this study, we conducted an integrated systems biology analysis of 9 independent gene expression experiments covering 657 autism, 9 mental retardation and developmental delay and 566 control samples to determine if a common signature exists and to test whether regulatory patterns in the brain relevant to autism can also be detected in blood. We constructed a matrix of differentially expressed genes from these experiments and used a Jaccard coefficient to create a gene-based phylogeny, validated by bootstrap. As expected, experiments and tissue types clustered together with high statistical confidence. However, we discovered a statistically significant subgrouping of 3 blood and 2 brain data sets from 3 different experiments rooted by a highly correlated regulatory pattern of 66 genes. This Root 66 appeared to be non-random and of potential etiologic relevance to autism, given their enriched roles in neurological processes key for normal brain growth and function, learning and memory, neurodegeneration, social behavior and cognition. Our results suggest that there is a detectable autism signature in the blood that may be a molecular echo of autism-related dysregulation in the brain.
Collapse
|
42
|
Synaptic P-Rex1 signaling regulates hippocampal long-term depression and autism-like social behavior. Proc Natl Acad Sci U S A 2015; 112:E6964-72. [PMID: 26621702 DOI: 10.1073/pnas.1512913112] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Autism spectrum disorders (ASDs) are a group of highly inheritable mental disorders associated with synaptic dysfunction, but the underlying cellular and molecular mechanisms remain to be clarified. Here we report that autism in Chinese Han population is associated with genetic variations and copy number deletion of P-Rex1 (phosphatidylinositol-3,4,5-trisphosphate-dependent Rac exchange factor 1). Genetic deletion or knockdown of P-Rex1 in the CA1 region of the hippocampus in mice resulted in autism-like social behavior that was specifically linked to the defect of long-term depression (LTD) in the CA1 region through alteration of AMPA receptor endocytosis mediated by the postsynaptic PP1α (protein phosphase 1α)-P-Rex1-Rac1 (Ras-related C3 botulinum toxin substrate 1) signaling pathway. Rescue of the LTD in the CA1 region markedly alleviated autism-like social behavior. Together, our findings suggest a vital role of P-Rex1 signaling in CA1 LTD that is critical for social behavior and cognitive function and offer new insight into the etiology of ASDs.
Collapse
|
43
|
Bakos J, Bacova Z, Grant SG, Castejon AM, Ostatnikova D. Are Molecules Involved in Neuritogenesis and Axon Guidance Related to Autism Pathogenesis? Neuromolecular Med 2015; 17:297-304. [PMID: 25989848 DOI: 10.1007/s12017-015-8357-7] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2014] [Accepted: 05/08/2015] [Indexed: 12/27/2022]
Abstract
Autism spectrum disorder is a heterogeneous disease, and numerous alterations of gene expression come into play to attempt to explain potential molecular and pathophysiological causes. Abnormalities of brain development and connectivity associated with alterations in cytoskeletal rearrangement, neuritogenesis and elongation of axons and dendrites might represent or contribute to the structural basis of autism pathology. Slit/Robo signaling regulates cytoskeletal remodeling related to axonal and dendritic branching. Components of its signaling pathway (ABL and Cdc42) are suspected to be molecular bases of alterations of normal development. The present review describes the most important mechanisms underlying neuritogenesis, axon pathfinding and the role of GTPases in neurite outgrowth, with special emphasis on alterations associated with autism spectrum disorders. On the basis of analysis of publicly available microarray data, potential biomarkers of autism are discussed.
Collapse
Affiliation(s)
- Jan Bakos
- Institute of Experimental Endocrinology, Slovak Academy of Sciences, Vlarska 3, Bratislava, Slovakia,
| | | | | | | | | |
Collapse
|
44
|
Latkowski T, Osowski S. Computerized system for recognition of autism on the basis of gene expression microarray data. Comput Biol Med 2014; 56:82-8. [PMID: 25464350 DOI: 10.1016/j.compbiomed.2014.11.004] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2014] [Revised: 10/24/2014] [Accepted: 11/02/2014] [Indexed: 12/28/2022]
Abstract
The aim of this paper is to provide a means to recognize a case of autism using gene expression microarrays. The crucial task is to discover the most important genes which are strictly associated with autism. The paper presents an application of different methods of gene selection, to select the most representative input attributes for an ensemble of classifiers. The set of classifiers is responsible for distinguishing autism data from the reference class. Simultaneous application of a few gene selection methods enables analysis of the ill-conditioned gene expression matrix from different points of view. The results of selection combined with a genetic algorithm and SVM classifier have shown increased accuracy of autism recognition. Early recognition of autism is extremely important for treatment of children and increases the probability of their recovery and return to normal social communication. The results of this research can find practical application in early recognition of autism on the basis of gene expression microarray analysis.
Collapse
Affiliation(s)
- Tomasz Latkowski
- Military University of Technology, Institute of Electronic Systems, Warsaw, Kaliskiego 2, Poland.
| | - Stanislaw Osowski
- Military University of Technology, Institute of Electronic Systems, Warsaw, Kaliskiego 2, Poland; Warsaw University of Technology, Institute of the Theory of Electrical Engineering, Measurement and Information Systems, Warsaw, Koszykowa 75, Poland.
| |
Collapse
|
45
|
Markunas CA, Lock E, Soldano K, Cope H, Ding CKC, Enterline DS, Grant G, Fuchs H, Ashley-Koch AE, Gregory SG. Identification of Chiari Type I Malformation subtypes using whole genome expression profiles and cranial base morphometrics. BMC Med Genomics 2014; 7:39. [PMID: 24962150 PMCID: PMC4082616 DOI: 10.1186/1755-8794-7-39] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2014] [Accepted: 06/18/2014] [Indexed: 12/02/2022] Open
Abstract
Background Chiari Type I Malformation (CMI) is characterized by herniation of the cerebellar tonsils through the foramen magnum at the base of the skull, resulting in significant neurologic morbidity. As CMI patients display a high degree of clinical variability and multiple mechanisms have been proposed for tonsillar herniation, it is hypothesized that this heterogeneous disorder is due to multiple genetic and environmental factors. The purpose of the present study was to gain a better understanding of what factors contribute to this heterogeneity by using an unsupervised statistical approach to define disease subtypes within a case-only pediatric population. Methods A collection of forty-four pediatric CMI patients were ascertained to identify disease subtypes using whole genome expression profiles generated from patient blood and dura mater tissue samples, and radiological data consisting of posterior fossa (PF) morphometrics. Sparse k-means clustering and an extension to accommodate multiple data sources were used to cluster patients into more homogeneous groups using biological and radiological data both individually and collectively. Results All clustering analyses resulted in the significant identification of patient classes, with the pure biological classes derived from patient blood and dura mater samples demonstrating the strongest evidence. Those patient classes were further characterized by identifying enriched biological pathways, as well as correlated cranial base morphological and clinical traits. Conclusions Our results implicate several strong biological candidates warranting further investigation from the dura expression analysis and also identified a blood gene expression profile corresponding to a global down-regulation in protein synthesis.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | | | | | - Simon G Gregory
- Duke Center for Human Genetics, Duke University Medical Center, Durham, NC, USA.
| |
Collapse
|
46
|
Popov NT, Madjirova NP, Minkov IN, Vachev TI. Micro RNA HSA-486-3P Gene Expression Profiling in the Whole Blood of Patients with Autism. BIOTECHNOL BIOTEC EQ 2014. [DOI: 10.5504/bbeq.2012.0093] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
|
47
|
Smith RG, Fernandes C, Kember R, Schalkwyk LC, Buxbaum J, Reichenberg A, Mill J. Transcriptomic changes in the frontal cortex associated with paternal age. Mol Autism 2014; 5:24. [PMID: 24655730 PMCID: PMC3998024 DOI: 10.1186/2040-2392-5-24] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2013] [Accepted: 03/10/2014] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Advanced paternal age is robustly associated with several human neuropsychiatric disorders, particularly autism. The precise mechanism(s) mediating the paternal age effect are not known, but they are thought to involve the accumulation of de novo (epi)genomic alterations. In this study we investigate differences in the frontal cortex transcriptome in a mouse model of advanced paternal age. FINDINGS Transcriptomic profiling was undertaken for medial prefrontal cortex tissue dissected from the male offspring of young fathers (2 month old, 4 sires, n = 16 offspring) and old fathers (10 month old, 6 sires, n = 16 offspring) in a mouse model of advancing paternal age. We found a number of differentially expressed genes in the offspring of older fathers, many previously implicated in the aetiology of autism. Pathway analysis highlighted significant enrichment for changes in functional networks involved in inflammation and inflammatory disease, which are also implicated in autism. CONCLUSIONS We observed widespread alterations to the transcriptome associated with advanced paternal age with an enrichment of genes associated with inflammation, an interesting observation given previous evidence linking the immune system to several neuropsychiatric disorders including autism.
Collapse
Affiliation(s)
| | | | | | | | | | | | - Jonathan Mill
- Institute of Psychiatry, King's College London, De Crespigny Park, Denmark Hill, London SE5 8AF, UK.
| |
Collapse
|
48
|
Kim P, Choi CS, Park JH, Joo SH, Kim SY, Ko HM, Kim KC, Jeon SJ, Park SH, Han SH, Ryu JH, Cheong JH, Han JY, Ko KN, Shin CY. Chronic exposure to ethanol of male mice before mating produces attention deficit hyperactivity disorder-like phenotype along with epigenetic dysregulation of dopamine transporter expression in mouse offspring. J Neurosci Res 2014; 92:658-70. [PMID: 24510599 DOI: 10.1002/jnr.23275] [Citation(s) in RCA: 72] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2012] [Revised: 04/09/2013] [Accepted: 06/14/2013] [Indexed: 11/08/2022]
Abstract
Preconception exposure to EtOH through the paternal route may affect neurobehavioral and developmental features of offspring. This study investigates the effects of paternal exposure to EtOH before conception on the hyperactivity, inattention, and impulsivity behavior of male offspring in mice. Sire mice were treated with EtOH in a concentration range approximating human binge drinking (0-4 g/kg/day EtOH) for 7 weeks and mated with untreated females mice to produce offspring. EtOH exposure to sire mice induced attention deficit hyperactivity disorder (ADHD)-like hyperactive, inattentive, and impulsive behaviors in offspring. As a mechanistic link, both protein and mRNA expression of dopamine transporter (DAT), a key determinant of ADHD-like phenotypes in experimental animals and humans, were significantly decreased by paternal EtOH exposure in cerebral cortex and striatum of offspring mice along with increased methylation of a CpG region of the DAT gene promoter. The increase in methylation of DAT gene promoter was also observed in the sperm of sire mice, suggesting germline changes in the epigenetic methylation signature of DAT gene by EtOH exposure. In addition, the expression of two key regulators of methylation-dependent epigenetic regulation of functional gene expression, namely, MeCP2 and DNMT1, was markedly decreased in offspring cortex and striatum sired by EtOH-exposed mice. These results suggest that preconceptional exposure to EtOH through the paternal route induces behavioral changes in offspring, possibly via epigenetic changes in gene expression, which is essential for the regulation of ADHD-like behaviors.
Collapse
Affiliation(s)
- Pitna Kim
- Department of Neuroscience, School of Medicine and Neuroscience Research Center, Institute SMART-IABS, Konkuk University, Seoul, Korea
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
49
|
Fertilität bei Männern über 40 Jahren. GYNAKOLOGISCHE ENDOKRINOLOGIE 2014. [DOI: 10.1007/s10304-013-0581-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
|
50
|
Glatt SJ. How should we interpret and value the pursuit of blood-based biomarkers for autism spectrum disorders? J Am Acad Child Adolesc Psychiatry 2013; 52:1248-50. [PMID: 24290455 DOI: 10.1016/j.jaac.2013.08.022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2013] [Revised: 08/01/2013] [Accepted: 09/12/2013] [Indexed: 10/26/2022]
|