1
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Lu XY, Li MQ, Li YT, Yao JY, Zhang LX, Zeng ZH, Yu-Liu, Chen ZR, Li CQ, Zhou XF, Li F. Oral edaravone ameliorates behavioral deficits and pathologies in a valproic acid-induced rat model of autism spectrum disorder. Neuropharmacology 2024; 258:110089. [PMID: 39033904 DOI: 10.1016/j.neuropharm.2024.110089] [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: 05/07/2024] [Revised: 07/15/2024] [Accepted: 07/18/2024] [Indexed: 07/23/2024]
Abstract
Autism spectrum disorder (ASD) is neurodevelopmental disorder with a high incidence rate, characterized by social deficits and repetitive behaviors. There is currently no effective management available to treat the core symptoms of ASD; however, oxidative stress has been implicated in its pathogenesis. Edaravone (EDA), a free-radical scavenger, is used to treat amyotrophic lateral sclerosis (ALS) and acute ischemic stroke (AIS). Here, we hypothesized that an oral formula of EDA may have therapeutic efficacy in the treatment of core ASD symptoms. A rat model of autism was established by prenatal exposure to valproic acid (VPA), and the offsprings were orally treated with EDA at low (3 mg/kg), medium (10 mg/kg), and high (30 mg/kg) doses once daily for 28 days starting from postnatal day 25 (PND25). Oral EDA administration alleviated the core symptoms in VPA rats in a dose-dependent manner, including repetitive stereotypical behaviors and impaired social interaction. Furthermore, oral administration of EDA significantly reduced oxidative stress in a dose-dependent manner, as evidenced by a reduction in oxidative stress markers and an increase in antioxidants in the blood and brain. In addition, oral EDA significantly attenuated downstream pathologies, including synaptic and mitochondrial damage in the brain. Proteomic analysis further revealed that EDA corrected the imbalance in brain oxidative reduction and mitochondrial proteins induced by prenatal VPA administration. Overall, these findings demonstrate that oral EDA has therapeutic potential for ASD by targeting the oxidative stress pathway of disease pathogenesis and paves the way towards clinical studies.
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Affiliation(s)
- Xiao-Yu Lu
- Department of Anatomy and Neurobiology, School of Basic Medical Science, Central South University, Changsha 410013, Hunan Province, China
| | - Meng-Qing Li
- Department of Anatomy and Neurobiology, School of Basic Medical Science, Central South University, Changsha 410013, Hunan Province, China
| | | | - Jia-Yu Yao
- Department of Anatomy and Neurobiology, School of Basic Medical Science, Central South University, Changsha 410013, Hunan Province, China
| | - Lin-Xuan Zhang
- Department of Anatomy and Neurobiology, School of Basic Medical Science, Central South University, Changsha 410013, Hunan Province, China
| | - Ze-Hao Zeng
- Department of Anatomy and Neurobiology, School of Basic Medical Science, Central South University, Changsha 410013, Hunan Province, China
| | - Yu-Liu
- Department of Anatomy and Neurobiology, School of Basic Medical Science, Central South University, Changsha 410013, Hunan Province, China
| | - Zhao-Rong Chen
- Department of Anatomy and Neurobiology, School of Basic Medical Science, Central South University, Changsha 410013, Hunan Province, China
| | - Chang-Qi Li
- Department of Anatomy and Neurobiology, School of Basic Medical Science, Central South University, Changsha 410013, Hunan Province, China
| | - Xin-Fu Zhou
- Suzhou Auzone Biotechnology, Suzhou, China; Health and Biomedical Innovation, Clinical and Health Sciences, University of South Australia, Adelaide, SA 5000, Australia.
| | - Fang Li
- Department of Anatomy and Neurobiology, School of Basic Medical Science, Central South University, Changsha 410013, Hunan Province, China.
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2
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Kernfeld E, Keener R, Cahan P, Battle A. Transcriptome data are insufficient to control false discoveries in regulatory network inference. Cell Syst 2024; 15:709-724.e13. [PMID: 39173585 DOI: 10.1016/j.cels.2024.07.006] [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: 05/24/2023] [Revised: 05/31/2024] [Accepted: 07/22/2024] [Indexed: 08/24/2024]
Abstract
Inference of causal transcriptional regulatory networks (TRNs) from transcriptomic data suffers notoriously from false positives. Approaches to control the false discovery rate (FDR), for example, via permutation, bootstrapping, or multivariate Gaussian distributions, suffer from several complications: difficulty in distinguishing direct from indirect regulation, nonlinear effects, and causal structure inference requiring "causal sufficiency," meaning experiments that are free of any unmeasured, confounding variables. Here, we use a recently developed statistical framework, model-X knockoffs, to control the FDR while accounting for indirect effects, nonlinear dose-response, and user-provided covariates. We adjust the procedure to estimate the FDR correctly even when measured against incomplete gold standards. However, benchmarking against chromatin immunoprecipitation (ChIP) and other gold standards reveals higher observed than reported FDR. This indicates that unmeasured confounding is a major driver of FDR in TRN inference. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Eric Kernfeld
- Department of Biomedical Engineering, Johns Hopkins University, 3400 N. Charles Street, Wyman Park Building, Suite 400 West, Baltimore, MD 21218, USA
| | - Rebecca Keener
- Department of Biomedical Engineering, Johns Hopkins University, 3400 N. Charles Street, Wyman Park Building, Suite 400 West, Baltimore, MD 21218, USA
| | - Patrick Cahan
- Department of Biomedical Engineering, Johns Hopkins University, 3400 N. Charles Street, Wyman Park Building, Suite 400 West, Baltimore, MD 21218, USA; Institute for Cell Engineering, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Molecular Biology and Genetics, Johns Hopkins University, Baltimore, MD, USA.
| | - Alexis Battle
- Department of Biomedical Engineering, Johns Hopkins University, 3400 N. Charles Street, Wyman Park Building, Suite 400 West, Baltimore, MD 21218, USA; Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA; Department of Genetic Medicine, Johns Hopkins Medicine, Baltimore, MD, USA; Malone Center for Engineering and Healthcare, Johns Hopkins University, Baltimore, MD, USA; Data Science and AI Institute, Johns Hopkins University, Baltimore, MD, USA.
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3
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Yuan H, Mancuso CA, Johnson K, Braasch I, Krishnan A. Computational strategies for cross-species knowledge transfer and translational biomedicine. ARXIV 2024:arXiv:2408.08503v1. [PMID: 39184546 PMCID: PMC11343225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 08/27/2024]
Abstract
Research organisms provide invaluable insights into human biology and diseases, serving as essential tools for functional experiments, disease modeling, and drug testing. However, evolutionary divergence between humans and research organisms hinders effective knowledge transfer across species. Here, we review state-of-the-art methods for computationally transferring knowledge across species, primarily focusing on methods that utilize transcriptome data and/or molecular networks. We introduce the term "agnology" to describe the functional equivalence of molecular components regardless of evolutionary origin, as this concept is becoming pervasive in integrative data-driven models where the role of evolutionary origin can become unclear. Our review addresses four key areas of information and knowledge transfer across species: (1) transferring disease and gene annotation knowledge, (2) identifying agnologous molecular components, (3) inferring equivalent perturbed genes or gene sets, and (4) identifying agnologous cell types. We conclude with an outlook on future directions and several key challenges that remain in cross-species knowledge transfer.
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Affiliation(s)
- Hao Yuan
- Genetics and Genome Science Program; Ecology, Evolution, and Behavior Program, Michigan State University
| | - Christopher A. Mancuso
- Department of Biostatistics & Informatics, University of Colorado Anschutz Medical Campus
| | - Kayla Johnson
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus
| | - Ingo Braasch
- Department of Integrative Biology; Genetics and Genome Science Program; Ecology, Evolution, and Behavior Program, Michigan State University
| | - Arjun Krishnan
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus
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4
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Steele JW, Krishnan V, Finnell RH. Mechanisms of neurodevelopmental toxicity of topiramate. Crit Rev Toxicol 2024; 54:465-475. [PMID: 38995641 PMCID: PMC11296906 DOI: 10.1080/10408444.2024.2368552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 05/03/2024] [Accepted: 06/11/2024] [Indexed: 07/13/2024]
Abstract
Prescriptions for antiseizure medications (ASMs) have been rapidly growing over the last several decades due, in part, to an expanding list of clinical indications for which they are now prescribed. This trend has raised concern for potential adverse neurodevelopmental outcomes in ASM-exposed pregnancies. Recent large scale population studies have suggested that the use of topiramate (TOPAMAX, Janssen-Cilag), when prescribed for seizure control, migraines, and/or weight management, is associated with an increased risk for autism spectrum disorder (ASD), intellectual disability, and attention-deficit/hyperactivity disorder (ADHD) in exposed offspring. Here, we critically review epidemiologic evidence demonstrating the neurobehavioral teratogenicity of topiramate and speculate on the neuromolecular mechanisms by which prenatal exposure may perturb neurocognitive development. Specifically, we explore the potential role of topiramate's pharmacological interactions with ligand- and voltage-gated ion channels, especially GABAergic signaling, its effects on DNA methylation and histone acetylation, whether topiramate induces oxidative stress, and its association with fetal growth restriction as possible mechanisms contributing to neurodevelopmental toxicity. Resolving this biology will be necessary to reduce the risk of adverse pregnancy outcomes caused by topiramate or other ASMs.
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Affiliation(s)
- John W. Steele
- Center for Precision Environmental Health, Baylor College of Medicine, Houston, TX, USA
- Department of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Vaishnav Krishnan
- Departments of Neurology, Neuroscience and Psychiatry, and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA
| | - Richard H. Finnell
- Center for Precision Environmental Health, Baylor College of Medicine, Houston, TX, USA
- Department of Medicine, Baylor College of Medicine, Houston, TX, USA
- Departments of Molecular and Cellular Biology and Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
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5
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Kim IB, Kim MH, Jung S, Kim WK, Lee J, Ju YS, Webster MJ, Kim S, Kim JH, Kim HJ, Kim J, Kim S, Lee JH. Low-level brain somatic mutations in exonic regions are collectively implicated in autism with germline mutations in autism risk genes. Exp Mol Med 2024; 56:1750-1762. [PMID: 39085355 PMCID: PMC11372092 DOI: 10.1038/s12276-024-01284-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Revised: 04/15/2024] [Accepted: 05/12/2024] [Indexed: 08/02/2024] Open
Abstract
Low-level somatic mutations in the human brain are implicated in various neurological disorders. The contribution of low-level brain somatic mutations to autism spectrum disorder (ASD), however, remains poorly understood. Here, we performed high-depth exome sequencing with an average read depth of 559.3x in 181 cortical, cerebellar, and peripheral tissue samples to identify brain somatic single nucleotide variants (SNVs) in 24 ASD subjects and 31 controls. We detected ~2.4 brain somatic SNVs per exome per single brain region, with a variant allele frequency (VAF) as low as 0.3%. The mutational profiles, including the number, signature, and type, were not significantly different between the ASD patients and controls. Intriguingly, when considering genes with low-level brain somatic SNVs and ASD risk genes with damaging germline SNVs together, the merged set of genes carrying either somatic or germline SNVs in ASD patients was significantly involved in ASD-associated pathophysiology, including dendrite spine morphogenesis (p = 0.025), mental retardation (p = 0.012), and intrauterine growth retardation (p = 0.012). Additionally, the merged gene set showed ASD-associated spatiotemporal expression in the early and mid-fetal cortex, striatum, and thalamus (all p < 0.05). Patients with damaging mutations in the merged gene set had a greater ASD risk than did controls (odds ratio = 3.92, p = 0.025, 95% confidence interval = 1.12-14.79). The findings of this study suggest that brain somatic SNVs and germline SNVs may collectively contribute to ASD-associated pathophysiology.
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Affiliation(s)
- Il Bin Kim
- Department of Psychiatry, CHA Gangnam Medical Center, CHA University School of Medicine, Seoul, 06135, Republic of Korea
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea
| | - Myeong-Heui Kim
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea
| | - Saehoon Jung
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea
| | - Woo Kyeong Kim
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea
| | - Junehawk Lee
- Center for Supercomputing Applications, Division of National Supercomputing, Korea Institute of Science and Technology Information, Daejeon, 34141, Republic of Korea
| | - Young Seok Ju
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea
| | - Maree J Webster
- Stanley Medical Research Institute, Laboratory of Brain Research, 9800 Medical Center Drive, Suite C-050, Rockville, MD, 20850, USA
| | - Sanghyeon Kim
- Stanley Medical Research Institute, Laboratory of Brain Research, 9800 Medical Center Drive, Suite C-050, Rockville, MD, 20850, USA
| | - Ja Hye Kim
- Department of Pediatrics, Asan Medical Center Children's Hospital, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea
| | - Hyun Jung Kim
- Department of Anatomy, Korea University College of Medicine, Seoul, 02841, Republic of Korea
| | - Junho Kim
- Department of Biological Sciences, Sungkyunkwan University, Suwon, 16419, Republic of Korea.
| | - Sangwoo Kim
- Department of Biomedical Systems Informatics and Brain Korea 21 PLUS for Medical Science, Yonsei University College of Medicine, Seoul, 03722, Republic of Korea.
| | - Jeong Ho Lee
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea.
- SoVarGen, SoVarGen, Inc., Daejeon, 34141, Republic of Korea.
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6
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Wang L, Wen X, Morrison J. Imperfect gold standard gene sets yield inaccurate evaluation of causal gene identification methods. Commun Biol 2024; 7:873. [PMID: 39020054 PMCID: PMC11255313 DOI: 10.1038/s42003-024-06482-1] [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: 11/17/2023] [Accepted: 06/21/2024] [Indexed: 07/19/2024] Open
Abstract
Causal gene discovery methods are often evaluated using reference sets of causal genes, which are treated as gold standards (GS) for the purposes of evaluation. However, evaluation methods typically treat genes not in the GS positive set as known negatives rather than unknowns. This leads to inaccurate estimates of sensitivity, specificity, and AUC. Labeling biases in GS gene sets can also lead to inaccurate ordering of alternative causal gene discovery methods. We argue that the evaluation of causal gene discovery methods should rely on statistical techniques like those used for variant discovery rather than on comparison with GS gene sets.
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Affiliation(s)
- Lijia Wang
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Xiaoquan Wen
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
| | - Jean Morrison
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
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7
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Nussinov R, Yavuz BR, Demirel HC, Arici MK, Jang H, Tuncbag N. Review: Cancer and neurodevelopmental disorders: multi-scale reasoning and computational guide. Front Cell Dev Biol 2024; 12:1376639. [PMID: 39015651 PMCID: PMC11249571 DOI: 10.3389/fcell.2024.1376639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 06/10/2024] [Indexed: 07/18/2024] Open
Abstract
The connection and causality between cancer and neurodevelopmental disorders have been puzzling. How can the same cellular pathways, proteins, and mutations lead to pathologies with vastly different clinical presentations? And why do individuals with neurodevelopmental disorders, such as autism and schizophrenia, face higher chances of cancer emerging throughout their lifetime? Our broad review emphasizes the multi-scale aspect of this type of reasoning. As these examples demonstrate, rather than focusing on a specific organ system or disease, we aim at the new understanding that can be gained. Within this framework, our review calls attention to computational strategies which can be powerful in discovering connections, causalities, predicting clinical outcomes, and are vital for drug discovery. Thus, rather than centering on the clinical features, we draw on the rapidly increasing data on the molecular level, including mutations, isoforms, three-dimensional structures, and expression levels of the respective disease-associated genes. Their integrated analysis, together with chromatin states, can delineate how, despite being connected, neurodevelopmental disorders and cancer differ, and how the same mutations can lead to different clinical symptoms. Here, we seek to uncover the emerging connection between cancer, including pediatric tumors, and neurodevelopmental disorders, and the tantalizing questions that this connection raises.
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Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Cancer Innovation Laboratory, National Cancer Institute, Frederick, MD, United States
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv-Yafo, Israel
| | - Bengi Ruken Yavuz
- Cancer Innovation Laboratory, National Cancer Institute, Frederick, MD, United States
| | | | - M. Kaan Arici
- Graduate School of Informatics, Middle East Technical University, Ankara, Türkiye
| | - Hyunbum Jang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Cancer Innovation Laboratory, National Cancer Institute, Frederick, MD, United States
| | - Nurcan Tuncbag
- Department of Chemical and Biological Engineering, Koc University, Istanbul, Türkiye
- School of Medicine, Koc University, Istanbul, Türkiye
- Koc University Research Center for Translational Medicine (KUTTAM), Istanbul, Türkiye
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8
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Pravikova PD, Arssan MA, Zalivina EA, Kondaurova EM, Kulikova EA, Belokopytova II, Naumenko VS. Dopamine receptors and key elements of the neurotrophins (BDNF, CDNF) expression patterns during critical periods of ontogenesis in the brain structures of mice with autism-like behavior (BTBR) or its absence (С57BL/6 J). Vavilovskii Zhurnal Genet Selektsii 2024; 28:407-415. [PMID: 39027124 PMCID: PMC11253014 DOI: 10.18699/vjgb-24-46] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 03/11/2024] [Accepted: 03/12/2024] [Indexed: 07/20/2024] Open
Abstract
Analysis of the mechanisms underlying autism spectrum disorder (ASD) is an urgent task due to the ever-increasing prevalence of this condition. The study of critical periods of neuroontogenesis is of interest, since the manifestation of ASD is often associated with prenatal disorders of the brain development. One of the currently promising hypotheses postulates a connection between the pathogenesis of ASD and the dysfunction of neurotransmitters and neurotrophins. In this study, we investigated the expression of key dopamine receptors (Drd1, Drd2), brain-derived neurotrophic factor (Bdnf), its receptors (Ntrkb2, Ngfr) and the transcription factor Creb1 that mediates BDNF action, as well as cerebral dopamine neurotrophic factor (Cdnf) during the critical periods of embryogenesis (e14 and e18) and postnatal development (p14, p28, p60) in the hippocampus and frontal cortex of BTBR mice with autism-like behavior compared to the neurotypical C57BL/6 J strain. In BTBR embryos, on the 14th day of prenatal development, an increase in the expression of the Ngfr gene encoding the p75NTR receptor, which may lead to the activation of apoptosis, was found in the hippocampus and frontal cortex. A decrease in the expression of Cdnf, Bdnf and its receptor Ntrkb2, as well as dopamine receptors (Drd1, Drd2) was detected in BTBR mice in the postnatal period of ontogenesis mainly in the frontal cortex, while in the hippocampus of mature mice (p60), only a decrease in the Drd2 mRNA level was revealed. The obtained results suggest that the decrease in the expression levels of CDNF, BDNF-TrkB and dopamine receptors in the frontal cortex in the postnatal period can lead to significant changes in both the morphology of neurons and dopamine neurotransmission in cortical brain structures. At the same time, the increase in p75NTR receptor gene expression observed on the 14th day of embryogenesis, crucial for hippocampus and frontal cortex development, may have direct relevance to the manifestation of early autism.
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Affiliation(s)
- P D Pravikova
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - M A Arssan
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - E A Zalivina
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - E M Kondaurova
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - E A Kulikova
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - I I Belokopytova
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - V S Naumenko
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
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9
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Xu S, Wang J, Mao K, Jiao D, Li Z, Zhao H, Sun Y, Feng J, Lai Y, Peng R, Fu Y, Gan R, Chen S, Zhao HY, Wei HJ, Cheng Y. Generation and transcriptomic characterization of MIR137 knockout miniature pig model for neurodevelopmental disorders. Cell Biosci 2024; 14:86. [PMID: 38937838 PMCID: PMC11212353 DOI: 10.1186/s13578-024-01268-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2024] [Accepted: 06/19/2024] [Indexed: 06/29/2024] Open
Abstract
BACKGROUND Neurodevelopmental disorders (NDD), such as autism spectrum disorders (ASD) and intellectual disorders (ID), are highly debilitating childhood psychiatric conditions. Genetic factors are recognized as playing a major role in NDD, with a multitude of genes and genomic regions implicated. While the functional validation of NDD-associated genes has predominantly been carried out using mouse models, the significant differences in brain structure and gene function between mice and humans have limited the effectiveness of mouse models in exploring the underlying mechanisms of NDD. Therefore, it is important to establish alternative animal models that are more evolutionarily aligned with humans. RESULTS In this study, we employed CRISPR/Cas9 and somatic cell nuclear transplantation technologies to successfully generate a knockout miniature pig model of the MIR137 gene, which encodes the neuropsychiatric disorder-associated microRNA miR-137. The homozygous knockout of MIR137 (MIR137-/-) effectively suppressed the expression of mature miR-137 and led to the birth of stillborn or short-lived piglets. Transcriptomic analysis revealed significant changes in genes associated with neurodevelopment and synaptic signaling in the brains of MIR137-/- miniature pig, mirroring findings from human ASD transcriptomic data. In comparison to miR-137-deficient mouse and human induced pluripotent stem cell (hiPSC)-derived neuron models, the miniature pig model exhibited more consistent changes in critical neuronal genes relevant to humans following the loss of miR-137. Furthermore, a comparative analysis identified differentially expressed genes associated with ASD and ID risk genes in both miniature pig and hiPSC-derived neurons. Notably, human-specific miR-137 targets, such as CAMK2A, known to be linked to cognitive impairments and NDD, exhibited dysregulation in MIR137-/- miniature pigs. These findings suggest that the loss of miR-137 in miniature pigs affects genes crucial for neurodevelopment, potentially contributing to the development of NDD. CONCLUSIONS Our study highlights the impact of miR-137 loss on critical genes involved in neurodevelopment and related disorders in MIR137-/- miniature pigs. It establishes the miniature pig model as a valuable tool for investigating neurodevelopmental disorders, providing valuable insights for potential applications in human research.
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Affiliation(s)
- Shengyun Xu
- Institute of Biomedical Research, Yunnan University, Kunming, 650500, China
| | - Jiaoxiang Wang
- Key Laboratory for Porcine Gene Editing and Xenotransplantation in Yunnan Province, Yunnan Agricultural University, Kunming, 650201, China
| | - Kexin Mao
- Institute of Biomedical Research, Yunnan University, Kunming, 650500, China
- Southwest United Graduate School, Kunming, 650092, China
| | - Deling Jiao
- Key Laboratory for Porcine Gene Editing and Xenotransplantation in Yunnan Province, Yunnan Agricultural University, Kunming, 650201, China
| | - Zhu Li
- Institute of Biomedical Research, Yunnan University, Kunming, 650500, China
| | - Heng Zhao
- Key Laboratory for Porcine Gene Editing and Xenotransplantation in Yunnan Province, Yunnan Agricultural University, Kunming, 650201, China
| | - Yifei Sun
- Institute of Biomedical Research, Yunnan University, Kunming, 650500, China
| | - Jin Feng
- Institute of Biomedical Research, Yunnan University, Kunming, 650500, China
- Southwest United Graduate School, Kunming, 650092, China
| | - Yuanhao Lai
- Institute of Biomedical Research, Yunnan University, Kunming, 650500, China
| | - Ruiqi Peng
- Institute of Biomedical Research, Yunnan University, Kunming, 650500, China
| | - Yu Fu
- Institute of Biomedical Research, Yunnan University, Kunming, 650500, China
| | - Ruoyi Gan
- Institute of Biomedical Research, Yunnan University, Kunming, 650500, China
- Southwest United Graduate School, Kunming, 650092, China
| | - Shuhan Chen
- Key Laboratory for Porcine Gene Editing and Xenotransplantation in Yunnan Province, Yunnan Agricultural University, Kunming, 650201, China
| | - Hong-Ye Zhao
- Key Laboratory for Porcine Gene Editing and Xenotransplantation in Yunnan Province, Yunnan Agricultural University, Kunming, 650201, China.
| | - Hong-Jiang Wei
- Key Laboratory for Porcine Gene Editing and Xenotransplantation in Yunnan Province, Yunnan Agricultural University, Kunming, 650201, China.
| | - Ying Cheng
- Institute of Biomedical Research, Yunnan University, Kunming, 650500, China.
- Southwest United Graduate School, Kunming, 650092, China.
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10
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Wang Y, Thistlethwaite W, Tadych A, Ruf-Zamojski F, Bernard DJ, Cappuccio A, Zaslavsky E, Chen X, Sealfon SC, Troyanskaya OG. Automated single-cell omics end-to-end framework with data-driven batch inference. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.01.564815. [PMID: 37961197 PMCID: PMC10635042 DOI: 10.1101/2023.11.01.564815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
To facilitate single-cell multi-omics analysis and improve reproducibility, we present SPEEDI (Single-cell Pipeline for End to End Data Integration), a fully automated end-to-end framework for batch inference, data integration, and cell type labeling. SPEEDI introduces data-driven batch inference and transforms the often heterogeneous data matrices obtained from different samples into a uniformly annotated and integrated dataset. Without requiring user input, it automatically selects parameters and executes pre-processing, sample integration, and cell type mapping. It can also perform downstream analyses of differential signals between treatment conditions and gene functional modules. SPEEDI's data-driven batch inference method works with widely used integration and cell-typing tools. By developing data-driven batch inference, providing full end-to-end automation, and eliminating parameter selection, SPEEDI improves reproducibility and lowers the barrier to obtaining biological insight from these valuable single-cell datasets. The SPEEDI interactive web application can be accessed at https://speedi.princeton.edu/.
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Affiliation(s)
- Yuan Wang
- Department of Computer Science, Princeton University, Princeton, NJ, USA
- Lewis-Sigler Institute of Integrative Genomics, Princeton University, Princeton, NJ, USA
- These authors contributed equally
| | - William Thistlethwaite
- Lewis-Sigler Institute of Integrative Genomics, Princeton University, Princeton, NJ, USA
- These authors contributed equally
| | - Alicja Tadych
- Lewis-Sigler Institute of Integrative Genomics, Princeton University, Princeton, NJ, USA
| | | | - Daniel J Bernard
- Department of Pharmacology and Therapeutics, McGill University, Montreal, QC, H3G 1Y6, Canada
| | - Antonio Cappuccio
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Elena Zaslavsky
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Xi Chen
- Lewis-Sigler Institute of Integrative Genomics, Princeton University, Princeton, NJ, USA
- Center for Computational Biology, Flatiron Institute, New York, NY, USA
| | - Stuart C. Sealfon
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Olga G. Troyanskaya
- Department of Computer Science, Princeton University, Princeton, NJ, USA
- Lewis-Sigler Institute of Integrative Genomics, Princeton University, Princeton, NJ, USA
- Center for Computational Biology, Flatiron Institute, New York, NY, USA
- Lead contact
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11
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Neff RC, Stangis KA, Beniwal U, Hergenreder T, Ye B, Murphy GG. Cognitive behavioral phenotyping of DSCAM heterozygosity as a model for autism spectrum disorder. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.03.597158. [PMID: 38895491 PMCID: PMC11185729 DOI: 10.1101/2024.06.03.597158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
It is estimated that 1 in 36 children are affected by autism spectrum disorder (ASD) in the United States, which is nearly a twofold increase from a decade ago. Recent genetic studies have identified de novo loss-of-function (dnLoF) mutations in the Down Syndrome Cell Adhesion Molecule (DSCAM) as a strong risk factor for ASD. Previous research has shown that DSCAM ablation confers social interaction deficits and perseverative behaviors in mouse models. However, it remains unknown to what extent DSCAM underexpression captures the full range of behaviors, specifically cognitive phenotypes, presented in ASD. Here, we conducted a comprehensive cognitive behavioral phenotyping which revealed that loss of one copy of DSCAM , as in the DSCAM 2J +/- mice, displayed hyperactivity, increased anxiety, and motor coordination impairments. Additionally, hippocampal-dependent learning and memory was affected, including working memory, long-term memory, and contextual fear learning. Interestingly, implicit learning processes remained intact. Therefore, DSCAM LoF produces autistic-like behaviors that are similar to human cases of ASD. These findings further support a role for DSCAM dnLoF mutations in ASD and suggest DSCAM 2J +/- as a suitable model for ASD research. Summary Statement Autism spectrum disorder represents a growing patient population. Loss of one copy of the DSCAM gene provides a promising mouse model that reproduces autistic-like behaviors for research and therapeutic testing.
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Nóbrega IDS, Teles e Silva AL, Yokota-Moreno BY, Sertié AL. The Importance of Large-Scale Genomic Studies to Unravel Genetic Risk Factors for Autism. Int J Mol Sci 2024; 25:5816. [PMID: 38892002 PMCID: PMC11172008 DOI: 10.3390/ijms25115816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Revised: 05/17/2024] [Accepted: 05/21/2024] [Indexed: 06/21/2024] Open
Abstract
Autism spectrum disorder (ASD) is a common and highly heritable neurodevelopmental disorder. During the last 15 years, advances in genomic technologies and the availability of increasingly large patient cohorts have greatly expanded our knowledge of the genetic architecture of ASD and its neurobiological mechanisms. Over two hundred risk regions and genes carrying rare de novo and transmitted high-impact variants have been identified. Additionally, common variants with small individual effect size are also important, and a number of loci are now being uncovered. At the same time, these new insights have highlighted ongoing challenges. In this perspective article, we summarize developments in ASD genetic research and address the enormous impact of large-scale genomic initiatives on ASD gene discovery.
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Affiliation(s)
| | | | | | - Andréa Laurato Sertié
- Faculdade Israelita de Ciências da Saúde Albert Einstein, Hospital Israelita Albert Einstein, Rua Comendador Elias Jafet, 755. Morumbi, São Paulo 05653-000, Brazil; (I.d.S.N.); (A.L.T.e.S.); (B.Y.Y.-M.)
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13
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Courchesne E, Taluja V, Nazari S, Aamodt CM, Pierce K, Duan K, Stophaeros S, Lopez L, Barnes CC, Troxel J, Campbell K, Wang T, Hoekzema K, Eichler EE, Nani JV, Pontes W, Sanchez SS, Lombardo MV, de Souza JS, Hayashi MAF, Muotri AR. Embryonic origin of two ASD subtypes of social symptom severity: the larger the brain cortical organoid size, the more severe the social symptoms. Mol Autism 2024; 15:22. [PMID: 38790065 PMCID: PMC11127428 DOI: 10.1186/s13229-024-00602-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 05/08/2024] [Indexed: 05/26/2024] Open
Abstract
BACKGROUND Social affective and communication symptoms are central to autism spectrum disorder (ASD), yet their severity differs across toddlers: Some toddlers with ASD display improving abilities across early ages and develop good social and language skills, while others with "profound" autism have persistently low social, language and cognitive skills and require lifelong care. The biological origins of these opposite ASD social severity subtypes and developmental trajectories are not known. METHODS Because ASD involves early brain overgrowth and excess neurons, we measured size and growth in 4910 embryonic-stage brain cortical organoids (BCOs) from a total of 10 toddlers with ASD and 6 controls (averaging 196 individual BCOs measured/subject). In a 2021 batch, we measured BCOs from 10 ASD and 5 controls. In a 2022 batch, we tested replicability of BCO size and growth effects by generating and measuring an independent batch of BCOs from 6 ASD and 4 control subjects. BCO size was analyzed within the context of our large, one-of-a-kind social symptom, social attention, social brain and social and language psychometric normative datasets ranging from N = 266 to N = 1902 toddlers. BCO growth rates were examined by measuring size changes between 1- and 2-months of organoid development. Neurogenesis markers at 2-months were examined at the cellular level. At the molecular level, we measured activity and expression of Ndel1; Ndel1 is a prime target for cell cycle-activated kinases; known to regulate cell cycle, proliferation, neurogenesis, and growth; and known to be involved in neuropsychiatric conditions. RESULTS At the BCO level, analyses showed BCO size was significantly enlarged by 39% and 41% in ASD in the 2021 and 2022 batches. The larger the embryonic BCO size, the more severe the ASD social symptoms. Correlations between BCO size and social symptoms were r = 0.719 in the 2021 batch and r = 0. 873 in the replication 2022 batch. ASD BCOs grew at an accelerated rate nearly 3 times faster than controls. At the cell level, the two largest ASD BCOs had accelerated neurogenesis. At the molecular level, Ndel1 activity was highly correlated with the growth rate and size of BCOs. Two BCO subtypes were found in ASD toddlers: Those in one subtype had very enlarged BCO size with accelerated rate of growth and neurogenesis; a profound autism clinical phenotype displaying severe social symptoms, reduced social attention, reduced cognitive, very low language and social IQ; and substantially altered growth in specific cortical social, language and sensory regions. Those in a second subtype had milder BCO enlargement and milder social, attention, cognitive, language and cortical differences. LIMITATIONS Larger samples of ASD toddler-derived BCO and clinical phenotypes may reveal additional ASD embryonic subtypes. CONCLUSIONS By embryogenesis, the biological bases of two subtypes of ASD social and brain development-profound autism and mild autism-are already present and measurable and involve dysregulated cell proliferation and accelerated neurogenesis and growth. The larger the embryonic BCO size in ASD, the more severe the toddler's social symptoms and the more reduced the social attention, language ability, and IQ, and the more atypical the growth of social and language brain regions.
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Affiliation(s)
- Eric Courchesne
- Autism Center of Excellence, Department of Neurosciences, University of California, San Diego, 8110 La Jolla Shores Dr., La Jolla, CA, 92037, USA.
| | - Vani Taluja
- Autism Center of Excellence, Department of Neurosciences, University of California, San Diego, 8110 La Jolla Shores Dr., La Jolla, CA, 92037, USA
| | - Sanaz Nazari
- Autism Center of Excellence, Department of Neurosciences, University of California, San Diego, 8110 La Jolla Shores Dr., La Jolla, CA, 92037, USA
| | - Caitlin M Aamodt
- Department of Pediatrics and Department of Molecular and Cellular Medicine, University of California, San Diego, Gilman Drive, La Jolla, CA, 92093, USA
| | - Karen Pierce
- Autism Center of Excellence, Department of Neurosciences, University of California, San Diego, 8110 La Jolla Shores Dr., La Jolla, CA, 92037, USA
| | - Kuaikuai Duan
- Autism Center of Excellence, Department of Neurosciences, University of California, San Diego, 8110 La Jolla Shores Dr., La Jolla, CA, 92037, USA
| | - Sunny Stophaeros
- Autism Center of Excellence, Department of Neurosciences, University of California, San Diego, 8110 La Jolla Shores Dr., La Jolla, CA, 92037, USA
| | - Linda Lopez
- Autism Center of Excellence, Department of Neurosciences, University of California, San Diego, 8110 La Jolla Shores Dr., La Jolla, CA, 92037, USA
| | - Cynthia Carter Barnes
- Autism Center of Excellence, Department of Neurosciences, University of California, San Diego, 8110 La Jolla Shores Dr., La Jolla, CA, 92037, USA
| | - Jaden Troxel
- Autism Center of Excellence, Department of Neurosciences, University of California, San Diego, 8110 La Jolla Shores Dr., La Jolla, CA, 92037, USA
| | - Kathleen Campbell
- Autism Center of Excellence, Department of Neurosciences, University of California, San Diego, 8110 La Jolla Shores Dr., La Jolla, CA, 92037, USA
| | - Tianyun Wang
- Department of Medical Genetics, Center for Medical Genetics, Peking University Health Science Center, Beijing, 100191, China
- Neuroscience Research Institute, Peking University, Key Laboratory for Neuroscience, Ministry of Education of China and National Health Commission of China, Beijing, 100191, China
| | - Kendra Hoekzema
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, 98195, USA
| | - Evan E Eichler
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, 98195, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, 98195, USA
| | - Joao V Nani
- Department of Pediatrics and Department of Molecular and Cellular Medicine, University of California, San Diego, Gilman Drive, La Jolla, CA, 92093, USA
- Department of Pharmacology, Escola Paulista de Medicina (EPM), Universidade Federal de São Paulo (UNIFESP), São Paulo, SP, Brazil
| | - Wirla Pontes
- Department of Pediatrics and Department of Molecular and Cellular Medicine, University of California, San Diego, Gilman Drive, La Jolla, CA, 92093, USA
| | - Sandra Sanchez Sanchez
- Department of Pediatrics and Department of Molecular and Cellular Medicine, University of California, San Diego, Gilman Drive, La Jolla, CA, 92093, USA
| | - Michael V Lombardo
- Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Janaina S de Souza
- Department of Pediatrics and Department of Molecular and Cellular Medicine, University of California, San Diego, Gilman Drive, La Jolla, CA, 92093, USA
| | - Mirian A F Hayashi
- Department of Pharmacology, Escola Paulista de Medicina (EPM), Universidade Federal de São Paulo (UNIFESP), São Paulo, SP, Brazil
| | - Alysson R Muotri
- Department of Pediatrics and Department of Molecular and Cellular Medicine, University of California, San Diego, Gilman Drive, La Jolla, CA, 92093, USA.
- Rady Children's Hospital, Center for Academic Research and Training in Anthropogeny (CARTA), Archealization Center (ArchC), Kavli Institute for Brain and Mind, La Jolla, CA, USA.
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14
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Cai W, Song W, Yu S, Zhao M, Lin GN. Human lineage mutations regulate RNA-protein binding of conserved genes NTRK2 and ITPR1 involved in human evolution. Gen Psychiatr 2024; 37:e101425. [PMID: 38770356 PMCID: PMC11103204 DOI: 10.1136/gpsych-2023-101425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 04/14/2024] [Indexed: 05/22/2024] Open
Abstract
Background The role of human lineage mutations (HLMs) in human evolution through post-transcriptional modification is unclear. Aims To investigate the contribution of HLMs to human evolution through post-transcriptional modification. Methods We applied a deep learning model Seqweaver to predict how HLMs impact RNA-binding protein affinity. Results We found that only 0.27% of HLMs had significant impacts on RNA-binding proteins at the threshold of the top 1% of human common variations. These HLMs enriched in a set of conserved genes highly expressed in adult excitatory neurons and prenatal Purkinje neurons, and were involved in synapse organisation and the GTPase pathway. These genes also carried excess damaging coding mutations that caused neurodevelopmental disorders, ataxia and schizophrenia. Among these genes, NTRK2 and ITPR1 had the most aggregated evidence of functional importance, suggesting their essential roles in cognition and bipedalism. Conclusions Our findings suggest that a small subset of human-specific mutations have contributed to human speciation through impacts on post-transcriptional modification of critical brain-related genes.
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Affiliation(s)
- Wenxiang Cai
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China
| | - Weichen Song
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China
| | - Shunying Yu
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China
| | - Min Zhao
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China
| | - Guan Ning Lin
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China
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15
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Zhang S, Shu H, Zhou J, Rubin-Sigler J, Yang X, Liu Y, Cooper-Knock J, Monte E, Zhu C, Tu S, Li H, Tong M, Ecker JR, Ichida JK, Shen Y, Zeng J, Tsao PS, Snyder MP. Deconvolution of polygenic risk score in single cells unravels cellular and molecular heterogeneity of complex human diseases. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.14.594252. [PMID: 38798507 PMCID: PMC11118500 DOI: 10.1101/2024.05.14.594252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Polygenic risk scores (PRSs) are commonly used for predicting an individual's genetic risk of complex diseases. Yet, their implication for disease pathogenesis remains largely limited. Here, we introduce scPRS, a geometric deep learning model that constructs single-cell-resolved PRS leveraging reference single-cell chromatin accessibility profiling data to enhance biological discovery as well as disease prediction. Real-world applications across multiple complex diseases, including type 2 diabetes (T2D), hypertrophic cardiomyopathy (HCM), and Alzheimer's disease (AD), showcase the superior prediction power of scPRS compared to traditional PRS methods. Importantly, scPRS not only predicts disease risk but also uncovers disease-relevant cells, such as hormone-high alpha and beta cells for T2D, cardiomyocytes and pericytes for HCM, and astrocytes, microglia and oligodendrocyte progenitor cells for AD. Facilitated by a layered multi-omic analysis, scPRS further identifies cell-type-specific genetic underpinnings, linking disease-associated genetic variants to gene regulation within corresponding cell types. We substantiate the disease relevance of scPRS-prioritized HCM genes and demonstrate that the suppression of these genes in HCM cardiomyocytes is rescued by Mavacamten treatment. Additionally, we establish a novel microglia-specific regulatory relationship between the AD risk variant rs7922621 and its target genes ANXA11 and TSPAN14. We further illustrate the detrimental effects of suppressing these two genes on microglia phagocytosis. Our work provides a multi-tasking, interpretable framework for precise disease prediction and systematic investigation of the genetic, cellular, and molecular basis of complex diseases, laying the methodological foundation for single-cell genetics.
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Affiliation(s)
- Sai Zhang
- Department of Epidemiology, University of Florida, Gainesville, FL, USA
- Departments of Biostatistics & Biomedical Engineering, Genetics Institute, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
- These authors contributed equally: Sai Zhang, Hantao Shu, and Jingtian Zhou
| | - Hantao Shu
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
- These authors contributed equally: Sai Zhang, Hantao Shu, and Jingtian Zhou
| | - Jingtian Zhou
- Arc Institute, Palo Alto, CA, USA
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
- These authors contributed equally: Sai Zhang, Hantao Shu, and Jingtian Zhou
| | - Jasper Rubin-Sigler
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, University of Southern California, Los Angeles, CA, USA
| | - Xiaoyu Yang
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
| | - Yuxi Liu
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
| | - Johnathan Cooper-Knock
- Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, UK
| | - Emma Monte
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Chenchen Zhu
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Sharon Tu
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, University of Southern California, Los Angeles, CA, USA
| | - Han Li
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
| | - Mingming Tong
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Joseph R. Ecker
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Justin K. Ichida
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, University of Southern California, Los Angeles, CA, USA
| | - Yin Shen
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
- Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Jianyang Zeng
- School of Engineering, Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China
| | - Philip S. Tsao
- VA Palo Alto Healthcare System, Palo Alto, CA, USA
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Michael P. Snyder
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
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16
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Zhang S, Moll T, Rubin-Sigler J, Tu S, Li S, Yuan E, Liu M, Butt A, Harvey C, Gornall S, Alhalthli E, Shaw A, Souza CDS, Ferraiuolo L, Hornstein E, Shelkovnikova T, van Dijk CH, Timpanaro IS, Kenna KP, Zeng J, Tsao PS, Shaw PJ, Ichida JK, Cooper-Knock J, Snyder MP. Deep learning modeling of rare noncoding genetic variants in human motor neurons defines CCDC146 as a therapeutic target for ALS. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.30.24305115. [PMID: 38633814 PMCID: PMC11023684 DOI: 10.1101/2024.03.30.24305115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/19/2024]
Abstract
Amyotrophic lateral sclerosis (ALS) is a fatal and incurable neurodegenerative disease caused by the selective and progressive death of motor neurons (MNs). Understanding the genetic and molecular factors influencing ALS survival is crucial for disease management and therapeutics. In this study, we introduce a deep learning-powered genetic analysis framework to link rare noncoding genetic variants to ALS survival. Using data from human induced pluripotent stem cell (iPSC)-derived MNs, this method prioritizes functional noncoding variants using deep learning, links cis-regulatory elements (CREs) to target genes using epigenomics data, and integrates these data through gene-level burden tests to identify survival-modifying variants, CREs, and genes. We apply this approach to analyze 6,715 ALS genomes, and pinpoint four novel rare noncoding variants associated with survival, including chr7:76,009,472:C>T linked to CCDC146. CRISPR-Cas9 editing of this variant increases CCDC146 expression in iPSC-derived MNs and exacerbates ALS-specific phenotypes, including TDP-43 mislocalization. Suppressing CCDC146 with an antisense oligonucleotide (ASO), showing no toxicity, completely rescues ALS-associated survival defects in neurons derived from sporadic ALS patients and from carriers of the ALS-associated G4C2-repeat expansion within C9ORF72. ASO targeting of CCDC146 may be a broadly effective therapeutic approach for ALS. Our framework provides a generic and powerful approach for studying noncoding genetics of complex human diseases.
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Affiliation(s)
- Sai Zhang
- Department of Epidemiology, University of Florida, Gainesville, FL, USA
- J. Crayton Pruitt Family Department of Biomedical Engineering, Genetics Institute, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
- These authors contributed equally: Sai Zhang, Tobias Moll, and Jasper Rubin-Sigler
| | - Tobias Moll
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
- These authors contributed equally: Sai Zhang, Tobias Moll, and Jasper Rubin-Sigler
| | - Jasper Rubin-Sigler
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, University of Southern California, Los Angeles, CA, USA
- These authors contributed equally: Sai Zhang, Tobias Moll, and Jasper Rubin-Sigler
| | - Sharon Tu
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, University of Southern California, Los Angeles, CA, USA
| | - Shuya Li
- School of Engineering, Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China
| | - Enming Yuan
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
| | - Menghui Liu
- Department of Epidemiology, University of Florida, Gainesville, FL, USA
| | - Afreen Butt
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Calum Harvey
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Sarah Gornall
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Elham Alhalthli
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Allan Shaw
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Cleide Dos Santos Souza
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Laura Ferraiuolo
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Eran Hornstein
- Department of Molecular Genetics and Department of Molecular Neuroscience, Weizmann Institute of Science, Rehovot, Israel
| | - Tatyana Shelkovnikova
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Charlotte H. van Dijk
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Ilia S. Timpanaro
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Kevin P. Kenna
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Jianyang Zeng
- School of Engineering, Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China
| | - Philip S. Tsao
- VA Palo Alto Healthcare System, Palo Alto, CA, USA
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Pamela J. Shaw
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Justin K. Ichida
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, University of Southern California, Los Angeles, CA, USA
| | - Johnathan Cooper-Knock
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Michael P. Snyder
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
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17
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Kumari R, Ponte ME, Franczak E, Prom JC, O'Neil MF, Sardiu ME, Lutkewitte AJ, Christenson LK, Shankar K, Morris EM, Thyfault JP. VCD-induced menopause mouse model reveals reprogramming of hepatic metabolism. Mol Metab 2024; 82:101908. [PMID: 38432400 PMCID: PMC10944007 DOI: 10.1016/j.molmet.2024.101908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 02/23/2024] [Accepted: 02/26/2024] [Indexed: 03/05/2024] Open
Abstract
OBJECTIVE Menopause adversely impacts systemic energy metabolism and increases the risk of metabolic disease(s) including hepatic steatosis, but the mechanisms are largely unknown. Dosing female mice with vinyl cyclohexene dioxide (VCD) selectively causes follicular atresia in ovaries, leading to a murine menopause-like phenotype. METHODS In this study, we treated female C57BL6/J mice with VCD (160 mg/kg i.p. for 20 consecutive days followed by verification of the lack of estrous cycling) to investigate changes in body composition, energy expenditure (EE), hepatic mitochondrial function, and hepatic steatosis across different dietary conditions. RESULTS VCD treatment induced ovarian follicular loss and increased follicle-stimulating hormone (FSH) levels in female mice, mimicking a menopause-like phenotype. VCD treatment did not affect body composition, or EE in mice on a low-fat diet (LFD) or in response to a short-term (1-week) high-fat, high sucrose diet (HFHS). However, the transition to a HFHS lowered cage activity in VCD mice. A chronic HFHS diet (16 weeks) significantly increased weight gain, fat mass, and hepatic steatosis in VCD-treated mice compared to HFHS-fed controls. In the liver, VCD mice showed suppressed hepatic mitochondrial respiration on LFD, while chronic HFHS resulted in compensatory increases in hepatic mitochondrial respiration. Also, liver RNA sequencing revealed that VCD promoted global upregulation of hepatic lipid/cholesterol synthesis pathways. CONCLUSION Our findings suggest that the VCD-induced menopause model compromises hepatic mitochondrial function and lipid/cholesterol homeostasis that sets the stage for HFHS diet-induced steatosis while also increasing susceptibility to obesity.
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Affiliation(s)
- Roshan Kumari
- Department of Cell Biology and Physiology, University of Kansas Medical Center, Kansas City, KS, USA; Research Service, Kansas City Veterans Affairs Medical Center, Kansas City, MO, USA; KU Diabetes Institute and Kansas Center for Metabolism and Obesity, University of Kansas Medical Center, Kansas City, KS, USA; Center for Children's Healthy Lifestyles and Nutrition, Kansas City, MO, USA
| | - Michael E Ponte
- Department of Cell Biology and Physiology, University of Kansas Medical Center, Kansas City, KS, USA; KU Diabetes Institute and Kansas Center for Metabolism and Obesity, University of Kansas Medical Center, Kansas City, KS, USA
| | - Edziu Franczak
- Department of Cell Biology and Physiology, University of Kansas Medical Center, Kansas City, KS, USA; Research Service, Kansas City Veterans Affairs Medical Center, Kansas City, MO, USA; KU Diabetes Institute and Kansas Center for Metabolism and Obesity, University of Kansas Medical Center, Kansas City, KS, USA
| | - John C Prom
- Department of Cell Biology and Physiology, University of Kansas Medical Center, Kansas City, KS, USA
| | - Maura F O'Neil
- Department of Pathology, University of Kansas Medical Center, Kansas City, KS, USA
| | - Mihaela E Sardiu
- Department of Biostatistics and Data Science, University of Kansas Medical Center, Kansas City, KS, USA; KU Diabetes Institute and Kansas Center for Metabolism and Obesity, University of Kansas Medical Center, Kansas City, KS, USA
| | - Andrew J Lutkewitte
- KU Diabetes Institute and Kansas Center for Metabolism and Obesity, University of Kansas Medical Center, Kansas City, KS, USA; Department of Internal Medicine, Division of Endocrinology, Diabetes, and Clinical Pharmacology, University of Kansas Medical Center, Kansas City, KS, USA
| | - Lane K Christenson
- Department of Cell Biology and Physiology, University of Kansas Medical Center, Kansas City, KS, USA
| | - Kartik Shankar
- Department of Pediatrics, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - E Matthew Morris
- Department of Cell Biology and Physiology, University of Kansas Medical Center, Kansas City, KS, USA; KU Diabetes Institute and Kansas Center for Metabolism and Obesity, University of Kansas Medical Center, Kansas City, KS, USA; Center for Children's Healthy Lifestyles and Nutrition, Kansas City, MO, USA.
| | - John P Thyfault
- Department of Cell Biology and Physiology, University of Kansas Medical Center, Kansas City, KS, USA; Research Service, Kansas City Veterans Affairs Medical Center, Kansas City, MO, USA; KU Diabetes Institute and Kansas Center for Metabolism and Obesity, University of Kansas Medical Center, Kansas City, KS, USA; Department of Internal Medicine, Division of Endocrinology, Diabetes, and Clinical Pharmacology, University of Kansas Medical Center, Kansas City, KS, USA; Center for Children's Healthy Lifestyles and Nutrition, Kansas City, MO, USA.
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18
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Bandara D, Riccardi K. Graph Node Classification to Predict Autism Risk in Genes. Genes (Basel) 2024; 15:447. [PMID: 38674382 PMCID: PMC11049455 DOI: 10.3390/genes15040447] [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: 02/20/2024] [Revised: 03/28/2024] [Accepted: 03/28/2024] [Indexed: 04/28/2024] Open
Abstract
This study explores the genetic risk associations with autism spectrum disorder (ASD) using graph neural networks (GNNs), leveraging the Sfari dataset and protein interaction network (PIN) data. We built a gene network with genes as nodes, chromosome band location as node features, and gene interactions as edges. Graph models were employed to classify the autism risk associated with newly introduced genes (test set). Three classification tasks were undertaken to test the ability of our models: binary risk association, multi-class risk association, and syndromic gene association. We tested graph convolutional networks, Graph Sage, graph transformer, and Multi-Layer Perceptron (Baseline) architectures on this problem. The Graph Sage model consistently outperformed the other models, showcasing its utility in classifying ASD-related genes. Our ablation studies show that the chromosome band location and protein interactions contain useful information for this problem. The models achieved 85.80% accuracy on the binary risk classification, 81.68% accuracy on the multi-class risk classification, and 90.22% on the syndromic classification.
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Affiliation(s)
- Danushka Bandara
- Department of Computer Science and Engineering, Fairfield University, Fairfield, CT 06824, USA;
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19
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Lara MK, Brabec JL, Hernan AE, Scott RC, Tyler AL, Mahoney JM. Network-based analysis predicts interacting genetic modifiers from a meta-mapping study of spike-wave discharge in mice. GENES, BRAIN, AND BEHAVIOR 2024; 23:e12879. [PMID: 38444174 PMCID: PMC10915378 DOI: 10.1111/gbb.12879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 11/29/2023] [Accepted: 12/19/2023] [Indexed: 03/07/2024]
Abstract
Absence seizures are characterized by brief lapses in awareness accompanied by a hallmark spike-and-wave discharge (SWD) electroencephalographic pattern and are common to genetic generalized epilepsies (GGEs). While numerous genes have been associated with increased risk, including some Mendelian forms with a single causal allele, most cases of GGE are idiopathic and there are many unknown genetic modifiers of GGE influencing risk and severity. In a previous meta-mapping study, crosses between transgenic C57BL/6 and C3HeB/FeJ strains, each carrying one of three SWD-causing mutations (Gabrg2tm1Spet(R43Q) , Scn8a8j or Gria4spkw1 ), demonstrated an antagonistic epistatic interaction between loci on mouse chromosomes 2 and 7 influencing SWD. These results implicate universal modifiers in the B6 background that mitigate SWD severity through a common pathway, independent of the causal mutation. In this study, we prioritized candidate modifiers in these interacting loci. Our approach integrated human genome-wide association results with gene interaction networks and mouse brain gene expression to prioritize candidate genes and pathways driving variation in SWD outcomes. We considered candidate genes that are functionally associated with human GGE risk genes and genes with evidence for coding or non-coding allele effects between the B6 and C3H backgrounds. Our analyses output a summary ranking of gene pairs, one gene from each locus, as candidates for explaining the epistatic interaction. Our top-ranking gene pairs implicate microtubule function, cytoskeletal stability and cell cycle regulation as novel hypotheses about the source of SWD variation across strain backgrounds, which could clarify underlying mechanisms driving differences in GGE severity in humans.
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Affiliation(s)
- Montana Kay Lara
- Department of Neurological SciencesUniversity of VermontBurlingtonVermontUSA
| | - Jeffrey L. Brabec
- Department of Neurological SciencesUniversity of VermontBurlingtonVermontUSA
| | - Amanda E. Hernan
- Department of Neurological SciencesUniversity of VermontBurlingtonVermontUSA
- Division of NeuroscienceNemours Children's HealthWilmingtonDelawareUSA
- Department of Psychological and Brain SciencesUniversity of DelawareNewarkDelawareUSA
| | - Rod C. Scott
- Division of NeuroscienceNemours Children's HealthWilmingtonDelawareUSA
- Department of Psychological and Brain SciencesUniversity of DelawareNewarkDelawareUSA
| | | | - J. Matthew Mahoney
- Department of Neurological SciencesUniversity of VermontBurlingtonVermontUSA
- The Jackson LaboratoryBar HarborMaineUSA
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20
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Zadok N, Ast G, Sharan R. A network-based method for associating genes with autism spectrum disorder. FRONTIERS IN BIOINFORMATICS 2024; 4:1295600. [PMID: 38525240 PMCID: PMC10960359 DOI: 10.3389/fbinf.2024.1295600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Accepted: 02/26/2024] [Indexed: 03/26/2024] Open
Abstract
Autism spectrum disorder (ASD) is a highly heritable complex disease that affects 1% of the population, yet its underlying molecular mechanisms are largely unknown. Here we study the problem of predicting causal genes for ASD by combining genome-scale data with a network propagation approach. We construct a predictor that integrates multiple omic data sets that assess genomic, transcriptomic, proteomic, and phosphoproteomic associations with ASD. In cross validation our predictor yields mean area under the ROC curve of 0.87 and area under the precision-recall curve of 0.89. We further show that it outperforms previous gene-level predictors of autism association. Finally, we show that we can use the model to predict genes associated with Schizophrenia which is known to share genetic components with ASD.
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Affiliation(s)
- Neta Zadok
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
| | - Gil Ast
- Department of Human Molecular Genetics and Biochemistry, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Roded Sharan
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
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21
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Reed TJ, Tyl MD, Tadych A, Troyanskaya OG, Cristea IM. Tapioca: a platform for predicting de novo protein-protein interactions in dynamic contexts. Nat Methods 2024; 21:488-500. [PMID: 38361019 PMCID: PMC11249048 DOI: 10.1038/s41592-024-02179-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 01/12/2024] [Indexed: 02/17/2024]
Abstract
Protein-protein interactions (PPIs) drive cellular processes and responses to environmental cues, reflecting the cellular state. Here we develop Tapioca, an ensemble machine learning framework for studying global PPIs in dynamic contexts. Tapioca predicts de novo interactions by integrating mass spectrometry interactome data from thermal/ion denaturation or cofractionation workflows with protein properties and tissue-specific functional networks. Focusing on the thermal proximity coaggregation method, we improved the experimental workflow. Finely tuned thermal denaturation afforded increased throughput, while cell lysis optimization enhanced protein detection from different subcellular compartments. The Tapioca workflow was next leveraged to investigate viral infection dynamics. Temporal PPIs were characterized during the reactivation from latency of the oncogenic Kaposi's sarcoma-associated herpesvirus. Together with functional assays, NUCKS was identified as a proviral hub protein, and a broader role was uncovered by integrating PPI networks from alpha- and betaherpesvirus infections. Altogether, Tapioca provides a web-accessible platform for predicting PPIs in dynamic contexts.
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Affiliation(s)
- Tavis J Reed
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Carl Icahn Laboratory, Princeton, NJ, USA
- Department of Computer Science, Princeton University, Princeton, NJ, USA
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Matthew D Tyl
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Alicja Tadych
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Carl Icahn Laboratory, Princeton, NJ, USA
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Olga G Troyanskaya
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Carl Icahn Laboratory, Princeton, NJ, USA.
- Department of Computer Science, Princeton University, Princeton, NJ, USA.
- Flatiron Institute, Simons Foundation, New York City, NY, USA.
| | - Ileana M Cristea
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA.
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22
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Greenberg ZF, Ali S, Schmittgen TD, Han S, Hughes SJ, Graim KS, He M. Peptide-based capture-and-release purification of extracellular vesicles and statistical algorithm enabled quality assessment. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.06.578050. [PMID: 38370748 PMCID: PMC10871196 DOI: 10.1101/2024.02.06.578050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Circulating extracellular vesicles (EVs) have gained significant attention for discovering tumor biomarkers. However, isolating EVs with well-defined homogeneous populations from complex biological samples is challenging. Different isolation methods have been found to derive different EV populations carrying different molecular contents, which confounds current investigations and hinders subsequent clinical translation. Therefore, standardizing and building a rigorous assessment of isolated EV quality associated with downstream molecular analysis is essential. To address this need, we introduce a statistical algorithm (ExoQuality Index, EQI) by integrating multiple EV characterizations (size, particle concentration, zeta potential, total protein, and RNA), enabling direct EV quality assessment and comparisons between different isolation methods. We also introduced a novel capture-release isolation approach using a pH-responsive peptide conjugated with NanoPom magnetic beads (ExCy) for simple, fast, and homogeneous EV isolation from various biological fluids. Bioinformatic analysis of next-generation sequencing (NGS) data of EV total RNAs from pancreatic cancer patient plasma samples using our novel EV isolation approach and quality index strategy illuminates how this approach improves the identification of tumor associated molecular markers. Results showed higher human mRNA coverage compared to existing isolation approaches in terms of both pancreatic cancer pathways and EV cellular component pathways using gProfiler pathway analysis. This study provides a valuable resource for researchers, establishing a workflow to prepare and analyze EV samples carefully and contributing to the advancement of reliable and rigorous EV quality assessment and clinical translation.
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Affiliation(s)
- Zachary F. Greenberg
- Department of Pharmaceutics, College of Pharmacy, University of Florida, Gainesville, Florida 32610, United States
| | - Samantha Ali
- Department of Pharmaceutics, College of Pharmacy, University of Florida, Gainesville, Florida 32610, United States
| | - Thomas D. Schmittgen
- Department of Pharmaceutics, College of Pharmacy, University of Florida, Gainesville, Florida 32610, United States
| | - Song Han
- Department of Surgery, College of Medicine, University of Florida, Gainesville, Florida 32610, United States
| | - Steven J. Hughes
- Department of Surgery, College of Medicine, University of Florida, Gainesville, Florida 32610, United States
| | - Kiley S. Graim
- Department of Computer & Information Science & Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, Florida, 32610, USA
| | - Mei He
- Department of Pharmaceutics, College of Pharmacy, University of Florida, Gainesville, Florida 32610, United States
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23
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Goh CJ, Kwon HJ, Kim Y, Jung S, Park J, Lee IK, Park BR, Kim MJ, Kim MJ, Lee MS. Improving CNV Detection Performance in Microarray Data Using a Machine Learning-Based Approach. Diagnostics (Basel) 2023; 14:84. [PMID: 38201393 PMCID: PMC10871075 DOI: 10.3390/diagnostics14010084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 12/26/2023] [Accepted: 12/28/2023] [Indexed: 01/12/2024] Open
Abstract
Copy number variation (CNV) is a primary source of structural variation in the human genome, leading to several disorders. Therefore, analyzing neonatal CNVs is crucial for managing CNV-related chromosomal disabilities. However, genomic waves can hinder accurate CNV analysis. To mitigate the influences of the waves, we adopted a machine learning approach and developed a new method that uses a modified log R ratio instead of the commonly used log R ratio. Validation results using samples with known CNVs demonstrated the superior performance of our method. We analyzed a total of 16,046 Korean newborn samples using the new method and identified CNVs related to 39 genetic disorders were identified in 342 cases. The most frequently detected CNV-related disorder was Joubert syndrome 4. The accuracy of our method was further confirmed by analyzing a subset of the detected results using NGS and comparing them with our results. The utilization of a genome-wide single nucleotide polymorphism array with wave offset was shown to be a powerful method for identifying CNVs in neonatal cases. The accurate screening and the ability to identify various disease susceptibilities offered by our new method could facilitate the identification of CNV-associated chromosomal disease etiologies.
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Affiliation(s)
- Chul Jun Goh
- Eone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of Korea; (C.J.G.); (H.-J.K.); (Y.K.); (S.J.); (J.P.); (I.K.L.); (B.-R.P.); (M.-J.K.)
| | - Hyuk-Jung Kwon
- Eone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of Korea; (C.J.G.); (H.-J.K.); (Y.K.); (S.J.); (J.P.); (I.K.L.); (B.-R.P.); (M.-J.K.)
- Department of Computer Science and Engineering, Incheon National University (INU), Incheon 22012, Republic of Korea
| | - Yoonhee Kim
- Eone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of Korea; (C.J.G.); (H.-J.K.); (Y.K.); (S.J.); (J.P.); (I.K.L.); (B.-R.P.); (M.-J.K.)
| | - Seunghee Jung
- Eone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of Korea; (C.J.G.); (H.-J.K.); (Y.K.); (S.J.); (J.P.); (I.K.L.); (B.-R.P.); (M.-J.K.)
| | - Jiwoo Park
- Eone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of Korea; (C.J.G.); (H.-J.K.); (Y.K.); (S.J.); (J.P.); (I.K.L.); (B.-R.P.); (M.-J.K.)
| | - Isaac Kise Lee
- Eone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of Korea; (C.J.G.); (H.-J.K.); (Y.K.); (S.J.); (J.P.); (I.K.L.); (B.-R.P.); (M.-J.K.)
- Department of Computer Science and Engineering, Incheon National University (INU), Incheon 22012, Republic of Korea
- NGENI Foundation, San Diego, CA 92127, USA
| | - Bo-Ram Park
- Eone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of Korea; (C.J.G.); (H.-J.K.); (Y.K.); (S.J.); (J.P.); (I.K.L.); (B.-R.P.); (M.-J.K.)
| | - Myeong-Ji Kim
- Eone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of Korea; (C.J.G.); (H.-J.K.); (Y.K.); (S.J.); (J.P.); (I.K.L.); (B.-R.P.); (M.-J.K.)
| | - Min-Jeong Kim
- Diagnomics, Inc., 5795 Kearny Villa Rd., San Diego, CA 92123, USA;
| | - Min-Seob Lee
- Eone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of Korea; (C.J.G.); (H.-J.K.); (Y.K.); (S.J.); (J.P.); (I.K.L.); (B.-R.P.); (M.-J.K.)
- Diagnomics, Inc., 5795 Kearny Villa Rd., San Diego, CA 92123, USA;
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24
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Kumari R, Ponte ME, Franczak E, Prom JC, O'Neil MF, Sardiu ME, Lutkewitte AJ, Shankar K, Morris EM, Thyfault JP. VCD-induced menopause mouse model reveals reprogramming of hepatic metabolism. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.14.571644. [PMID: 38168213 PMCID: PMC10760158 DOI: 10.1101/2023.12.14.571644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Menopause adversely impacts systemic energy metabolism and increases the risk of metabolic disease(s) including hepatic steatosis, but the mechanisms are largely unknown. Dosing female mice with vinyl cyclohexene dioxide (VCD) selectively causes follicular atresia in ovaries, leading to a murine menopause-like phenotype. In this study, we treated female C57BL6/J mice with VCD (160mg/kg i.p. for 20 consecutive days followed by verification of the lack of estrous cycling) to investigate changes in body composition, energy expenditure (EE), hepatic mitochondrial function, and hepatic steatosis across different dietary conditions. VCD treatment induced ovarian follicular loss and increased follicle-stimulating hormone (FSH) levels in female mice, mimicking a menopause-like phenotype. VCD treatment did not affect body composition, or EE in mice on a low-fat diet or in response to a short-term (1-week) high-fat, high sucrose diet (HFHS). However, the transition to a HFHS lowered cage activity in VCD mice. A chronic HFHS diet (16 weeks) significantly increased weight gain, fat mass, and hepatic steatosis in VCD-treated mice compared to HFHS-fed controls. In the liver, VCD mice showed suppressed hepatic mitochondrial respiration on LFD, while chronic HFHS diet resulted in compensatory increases in hepatic mitochondrial respiration. Also, liver RNA sequencing revealed that VCD promoted global upregulation of hepatic lipid/cholesterol synthesis pathways. Our findings suggest that the VCD- induced menopause model compromises hepatic mitochondrial function and lipid/cholesterol homeostasis that sets the stage for HFHS diet-induced steatosis while also increasing susceptibility to obesity.
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25
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Karunakaran KB, Amemori KI. Spatiotemporal expression patterns of anxiety disorder-associated genes. Transl Psychiatry 2023; 13:385. [PMID: 38092764 PMCID: PMC10719387 DOI: 10.1038/s41398-023-02693-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 11/25/2023] [Accepted: 11/28/2023] [Indexed: 12/17/2023] Open
Abstract
Anxiety disorders (ADs) are the most common form of mental disorder that affects millions of individuals worldwide. Although physiological studies have revealed the neural circuits related to AD symptoms, how AD-associated genes are spatiotemporally expressed in the human brain still remains unclear. In this study, we integrated genome-wide association studies of four human AD subtypes-generalized anxiety disorder, social anxiety disorder, panic disorder, and obsessive-compulsive disorder-with spatial gene expression patterns. Our investigation uncovered a novel division among AD-associated genes, marked by significant and distinct expression enrichments in the cerebral nuclei, limbic, and midbrain regions. Each gene cluster was associated with specific anxiety-related behaviors, signaling pathways, region-specific gene networks, and cell types. Notably, we observed a significant negative correlation in the temporal expression patterns of these gene clusters during various developmental stages. Moreover, the specific brain regions enriched in each gene group aligned with neural circuits previously associated with negative decision-making and anxious temperament. These results suggest that the two distinct gene clusters may underlie separate neural systems involved in anxiety. As a result, our findings bridge the gap between genes and neural circuitry, shedding light on the mechanisms underlying AD-associated behaviors.
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Affiliation(s)
- Kalyani B Karunakaran
- Institute for the Advanced Study of Human Biology, Kyoto University, Yoshida Konoe-cho, Sakyo-ku, Kyoto, 606-8501, Japan
| | - Ken-Ichi Amemori
- Institute for the Advanced Study of Human Biology, Kyoto University, Yoshida Konoe-cho, Sakyo-ku, Kyoto, 606-8501, Japan.
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26
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Jia Q, Wang X, Zhou R, Ma B, Fei F, Han H. Systematic bibliometric and visualized analysis of research hotspots and trends in artificial intelligence in autism spectrum disorder. Front Neuroinform 2023; 17:1310400. [PMID: 38125308 PMCID: PMC10731312 DOI: 10.3389/fninf.2023.1310400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 11/20/2023] [Indexed: 12/23/2023] Open
Abstract
Background Artificial intelligence (AI) has been the subject of studies in autism spectrum disorder (ASD) and may affect its identification, diagnosis, intervention, and other medical practices in the future. Although previous studies have used bibliometric techniques to analyze and investigate AI, there has been little research on the adoption of AI in ASD. This study aimed to explore the broad applications and research frontiers of AI used in ASD. Methods Citation data were retrieved from the Web of Science Core Collection (WoSCC) database to assess the extent to which AI is used in ASD. CiteSpace.5.8. R3 and VOSviewer, two online tools for literature metrology analysis, were used to analyze the data. Results A total of 776 publications from 291 countries and regions were analyzed; of these, 256 publications were from the United States and 173 publications were from China, and England had the largest centrality of 0.33; Stanford University had the highest H-index of 17; and the largest cluster label of co-cited references was machine learning. In addition, keywords with a high number of occurrences in this field were autism spectrum disorder (295), children (255), classification (156) and diagnosis (77). The burst keywords from 2021 to 2023 were infants and feature selection, and from 2022 to 2023, the burst keyword was corpus callosum. Conclusion This research provides a systematic analysis of the literature concerning AI used in ASD, presenting an overall demonstration in this field. In this area, the United States and China have the largest number of publications, England has the greatest influence, and Stanford University is the most influential. In addition, the research on AI used in ASD mostly focuses on classification and diagnosis, and "infants, feature selection, and corpus callosum are at the forefront, providing directions for future research. However, the use of AI technologies to identify ASD will require further research.
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Affiliation(s)
- Qianfang Jia
- Hebei University of Chinese Medicine, Shijiazhuang, China
| | - Xiaofang Wang
- Hebei University of Chinese Medicine, Shijiazhuang, China
| | - Rongyi Zhou
- Children’s Brain Disease Diagnosis, Treatment and Rehabilitation Center of the First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
- School of Pediatric Medicine, Henan University of Chinese Medicine, Zhengzhou, China
| | - Bingxiang Ma
- Children’s Brain Disease Diagnosis, Treatment and Rehabilitation Center of the First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
- School of Pediatric Medicine, Henan University of Chinese Medicine, Zhengzhou, China
| | - Fangqin Fei
- Department of Nursing, the First People’s Hospital of Huzhou, Huzhou University, Huzhou, China
| | - Hui Han
- Department of Nursing, the First People’s Hospital of Huzhou, Huzhou University, Huzhou, China
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27
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Orlichenko A, Daly G, Zhou Z, Liu A, Shen H, Deng HW, Wang YP. ImageNomer: Description of a functional connectivity and omics analysis tool and case study identifying a race confound. NEUROIMAGE. REPORTS 2023; 3:100191. [PMID: 38125823 PMCID: PMC10732473 DOI: 10.1016/j.ynirp.2023.100191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Most packages for the analysis of fMRI-based functional connectivity (FC) and genomic data are used with a programming language interface, lacking an easy-to-navigate GUI frontend. This exacerbates two problems found in these types of data: demographic confounds and quality control in the face of high dimensionality of features. The reason is that it is too slow and cumbersome to use a programming interface to create all the necessary visualizations required to identify all correlations, confounding effects, or quality control problems in a dataset. FC in particular usually contains tens of thousands of features per subject, and can only be summarized and efficiently explored using visualizations. To remedy this situation, we have developed ImageNomer, a data visualization and analysis tool that allows inspection of both subject-level and cohort-level demographic, genomic, and imaging features. The software is Python-based, runs in a self-contained Docker image, and contains a browser-based GUI frontend. We demonstrate the usefulness of ImageNomer by identifying an unexpected race confound when predicting achievement scores in the Philadelphia Neurodevelopmental Cohort (PNC) dataset, which contains multitask fMRI and single nucleotide polymorphism (SNP) data of healthy adolescents. In the past, many studies have attempted to use FC to identify achievement-related features in fMRI. Using ImageNomer to visualize trends in achievement scores between races, we find a clear potential for confounding effects if race can be predicted using FC. Using correlation analysis in the ImageNomer software, we show that FCs correlated with Wide Range Achievement Test (WRAT) score are in fact more highly correlated with race. Investigating further, we find that whereas both FC and SNP (genomic) features can account for 10-15% of WRAT score variation, this predictive ability disappears when controlling for race. We also use ImageNomer to investigate race-FC correlation in the Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP) dataset. In this work, we demonstrate the advantage of our ImageNomer GUI tool in data exploration and confound detection. Additionally, this work identifies race as a strong confound in FC data and casts doubt on the possibility of finding unbiased achievement-related features in fMRI and SNP data of healthy adolescents.
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Affiliation(s)
- Anton Orlichenko
- Department of Biomedical Engineering, Tulane University, New Orleans, LA, USA
| | - Grant Daly
- College of Medicine, University of South Alabama, Mobile, AL, USA
| | - Ziyu Zhou
- Department of Biomedical Engineering, Tulane University, New Orleans, LA, USA
| | - Anqi Liu
- School of Medicine, Tulane University, New Orleans, LA, USA
| | - Hui Shen
- School of Medicine, Tulane University, New Orleans, LA, USA
| | - Hong-Wen Deng
- School of Medicine, Tulane University, New Orleans, LA, USA
| | - Yu-Ping Wang
- Department of Biomedical Engineering, Tulane University, New Orleans, LA, USA
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28
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Han YM, Chan MM, Shea CK, Mo FY, Yiu KW, Chung RC, Cheung MC, Chan AS. Effects of prefrontal transcranial direct current stimulation on social functioning in autism spectrum disorder: A randomized clinical trial. AUTISM : THE INTERNATIONAL JOURNAL OF RESEARCH AND PRACTICE 2023; 27:2465-2482. [PMID: 37151094 DOI: 10.1177/13623613231169547] [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] [Indexed: 05/09/2023]
Abstract
LAY ABSTRACT Currently available pharmacological and behavioral interventions for adolescents and young adults with autism spectrum disorder (ASD) yield only modest effect in alleviating their core behavioral and cognitive symptoms, and some of these treatment options are associated with undesirable side effects. Hence, developing effective treatment protocols is urgently needed. Given emerging evidence shows that the abnormal connections of the frontal brain regions contribute to the manifestations of ASD behavioral and cognitive impairments, noninvasive treatment modalities that are capable in modulating brain connections, such as transcranial direct current stimulation (tDCS), have been postulated to be potentially promising for alleviating core symptoms in ASD. However, whether tDCS can reduce behavioral symptoms and enhance cognitive performance in ASD remains unclear. This randomized controlled trial involving 105 adolescents and young adults with ASD showed that multiple sessions of a tDCS protocol, which was paired up with computerized cognitive training, was effective in improving social functioning in adolescents and young adults with ASD. No prolonged and serious side effects were observed. With more future studies conducted in different clinical settings that recruit participants from a wider age range, this tDCS protocol may be potentially beneficial to a broad spectrum of individuals with autism.
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Affiliation(s)
| | - Melody My Chan
- The Hong Kong Polytechnic University, Hong Kong
- The University of Queensland, Australia
| | - Caroline Ks Shea
- Hospital Authority, Hong Kong
- The Chinese University of Hong Kong, Hong Kong
| | - Flora Ym Mo
- Hospital Authority, Hong Kong
- The Chinese University of Hong Kong, Hong Kong
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Dy ABC, Tanchanco LBS, Sy JCY, Levantino MD, Hagerman RJ. Screening for Fragile X Syndrome Among Filipino Children with Autism Spectrum Disorder. J Autism Dev Disord 2023; 53:4465-4473. [PMID: 35972625 DOI: 10.1007/s10803-022-05707-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/29/2022] [Indexed: 10/15/2022]
Abstract
Individuals with autism spectrum disorder present with difficulties in social communication, restricted interests or behaviors and other co-morbidities. About 2 to 10% of cases of autism have a genetic cause, and Fragile X Syndrome (FXS) is reported in 0 to 6.5% of individuals with autism. However, the FXS and premutation prevalence among Filipino children has never been reported. The aim of the study was to establish the presence of FXS or premutation carriers among Filipino children with autism and to describe the phenotypic characteristic of cases identified. Blood was collected from 235 children aged 2-6 years old and diagnosed with autism. Samples were analyzed using PCR methods to amplify CGG repeats in the FMRI gene. The diagnosis of autism was confirmed through the Autism Diagnostic Observation Schedule-2. Additional characteristics were documented from a physical examination, Griffiths Scales of Child Development assessment and a parent-answered questionnaire using the Vineland Adaptive Behavior Scale. Fragile X testing through PCR methods in 235 children with diagnosed autism showed 220 (93.6%) were negative, no full mutations, 1 (0.436%) premutation carrier and 14 (5.95%) cases contained intermediate alleles. The FXS testing was limited to confirmed cases of autism, which is considered a high-risk group and does not provide prevalence for the general Filipino population. Subjects were self-referred or referred by clinicians, which may not represent the Filipino autism population with a bias towards those with means for clinical consultations and ability to travel to the place of testing. Samples were not measured for mosaicism, DNA methylation or AGG interspersion patterns. These may have effects on the CGG repeat expansion and overall presentation of FXS. Findings from a single premutation carrier cannot characterize features distinctly present in Filipinos with the mutation. Nevertheless, these results support the data that the prevalence of FXS in Asian populations may be lower than non-Asian populations. This can contribute to a better understanding of FXS and genetic causes of autism in the Philippines and other Asian populations.
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Affiliation(s)
- Angel Belle C Dy
- Ateneo de Manila University School of Medicine and Public Health, Don Eugenio Lopez Sr. Medical Complex, Pasig City, NCR, Philippines.
| | - Lourdes Bernadette S Tanchanco
- Ateneo de Manila University School of Medicine and Public Health, Don Eugenio Lopez Sr. Medical Complex, Pasig City, NCR, Philippines
| | - Jenica Clarisse Y Sy
- Ateneo de Manila University School of Medicine and Public Health, Don Eugenio Lopez Sr. Medical Complex, Pasig City, NCR, Philippines
| | - Myla Dominicina Levantino
- Ateneo de Manila University School of Medicine and Public Health, Don Eugenio Lopez Sr. Medical Complex, Pasig City, NCR, Philippines
| | - Randi J Hagerman
- University of California Davis, MIND Institute, Sacramento, CA, USA
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Ismail E, Gad W, Hashem M. A hybrid Stacking-SMOTE model for optimizing the prediction of autistic genes. BMC Bioinformatics 2023; 24:379. [PMID: 37803253 PMCID: PMC10559615 DOI: 10.1186/s12859-023-05501-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Accepted: 09/27/2023] [Indexed: 10/08/2023] Open
Abstract
PURPOSE Autism spectrum disorder(ASD) is a disease associated with the neurodevelopment of the brain. The autism spectrum can be observed in early childhood, where the symptoms of the disease usually appear in children within the first year of their life. Currently, ASD can only be diagnosed based on the apparent symptoms due to the lack of information on genes related to the disease. Therefore, in this paper, we need to predict the largest number of disease-causing genes for a better diagnosis. METHODS A hybrid stacking ensemble model with Synthetic Minority Oversampling TEchnique (Stack-SMOTE) is proposed to predict the genes associated with ASD. The proposed model uses the gene ontology database to measure the similarities between the genes using a hybrid gene similarity function(HGS). HGS is effective in measuring the similarity as it combines the features of information gain-based methods and graph-based methods. The proposed model solves the imbalanced ASD dataset problem using the Synthetic Minority Oversampling Technique (SMOTE), which generates synthetic data rather than duplicates the data to reduce the overfitting. Sequentially, a gradient boosting-based random forest classifier (GBBRF) is introduced as a new combination technique to enhance the prediction of ASD genes. Moreover, the GBBRF classifier combined with random forest(RF), k-nearest neighbor, support vector machine(SVM), and logistic regression(LR) to form the proposed Stacking-SMOTE model to optimize the prediction of ASD genes. RESULTS The proposed Stacking-SMOTE model is evaluated using the Simons Foundation Autism Research Initiative (SFARI) gene database and a set of candidates ASD genes.The results of the proposed model-based SMOTE outperform other reported undersampling and oversampling techniques. Sequentially, the results of GBBRF achieve higher accuracy than using the basic classifiers. Moreover, the experimental results show that the proposed Stacking-SMOTE model outperforms the existing ASD prediction models with approximately 95.5% accuracy. CONCLUSION The proposed Stacking-SMOTE model demonstrates that SMOTE is effective in handling the autism imbalanced data. Sequentially, the integration between the gradient boosting and random forest classifier (GBBRF) support to build a robust stacking ensemble model(Stacking-SMOTE).
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Affiliation(s)
- Eman Ismail
- Information Systems Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt
| | - Walaa Gad
- Information Systems Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt
| | - Mohamed Hashem
- Information Systems Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt
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Matrone C, Ferretti G. Semaphorin 3A influences neuronal processes that are altered in patients with autism spectrum disorder: Potential diagnostic and therapeutic implications. Neurosci Biobehav Rev 2023; 153:105338. [PMID: 37524141 DOI: 10.1016/j.neubiorev.2023.105338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 07/27/2023] [Accepted: 07/28/2023] [Indexed: 08/02/2023]
Abstract
Autism spectrum disorder (ASD) is a pervasive disorder that most frequently manifests in early childhood and lasts for their entire lifespan. Several behavioural traits characterise the phenotype of patients with ASD, including difficulties in reciprocal social communication as well as compulsive/repetitive stereotyped verbal and non-verbal behaviours. Although multiple hypotheses have been proposed to explain the aetiology of ASD and many resources have been used to improve our understanding of ASD, several aspects remain largely unexplored. Class 3 semaphorins (SEMA3) are secreted proteins involved in the organisation of structural and functional connectivity in the brain that regulate synaptic and dendritic development. Alterations in brain connectivity and aberrant neuronal development have been described in some patients with ASD. Mutations and polymorphisms in SEMA3A and alterations in its receptors and signalling have been associated with some neurological disorders such as schizophrenia and epilepsy, which are comorbidities in ASD, but also with ASD itself. In addition, SEMA3A is a key regulator of the immune response and neuroinflammatory processes, which have been found to be dysregulated in mothers of children who develop ASD and in affected patients. In this review, we highlight neurodevelopmental-related processes in which SEMA3A is involved, which are altered in ASD, and provide a viewpoint emphasising the development of strategies targeting changes in the SEMA3A signal to identify patterns of anomalies distinctive of ASD or to predict the prognosis of affected patients.
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Affiliation(s)
- Carmela Matrone
- Division of Pharmacology, Department of Neuroscience, School of Medicine, University of Naples "Federico II", Via Pansini 5, 80131 Naples, Italy.
| | - Gabriella Ferretti
- Division of Pharmacology, Department of Neuroscience, School of Medicine, University of Naples "Federico II", Via Pansini 5, 80131 Naples, Italy
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Sen T, Ju W, Nair V, Ladd P, Menon R, Otto EA, Pyle L, Vigers T, Nelson RG, Arnott C, Neal B, Hansen MK, Kretzler M, Bjornstad P, Heerspink HJL. Sodium glucose co-transporter 2 inhibition increases epidermal growth factor expression and improves outcomes in patients with type 2 diabetes. Kidney Int 2023; 104:828-839. [PMID: 37543256 DOI: 10.1016/j.kint.2023.07.007] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 06/30/2023] [Accepted: 07/21/2023] [Indexed: 08/07/2023]
Abstract
Underlying molecular mechanisms of the kidney protective effects of sodium glucose co-transporter 2 (SGLT2) inhibitors are not fully elucidated. Therefore, we studied the association between urinary epidermal growth factor (uEGF), a mitogenic factor involved in kidney repair, and kidney outcomes in patients with type 2 diabetes (T2D). The underlying molecular mechanisms of the SGLT2 inhibitor canagliflozin on EGF using single-cell RNA sequencing from kidney tissue were examined. Urinary EGF-to-creatinine ratio (uEGF/Cr) was measured in 3521 CANagliflozin cardioVascular Assessment Study (CANVAS) participants at baseline and week 52. Associations of uEGF/Cr with kidney outcome were assessed using multivariable-adjusted Cox regression models. Single-cell RNA sequencing was performed using protocol kidney biopsy tissue from ten young patients with T2D on SGLT2i, six patients with T2D on standard care only, and six healthy controls (HCs). In CANVAS, each doubling in baseline uEGF/Cr was associated with a 12% (95% confidence interval 1-22) decreased risk of kidney outcome. uEGF/Cr decreased after 52 weeks with placebo and remained stable with canagliflozin (between-group difference +7.3% (2.0-12.8). In young persons with T2D, EGF mRNA was primarily expressed in the thick ascending loop of Henle. Expression in biopsies from T2D without SGLT2i was significantly lower compared to HCs, whereas treatment with SGLT2i increased EGF levels closer to the healthy state. In young persons with T2D without SGLT2i, endothelin-1 emerged as a key regulator of the EGF co-expression network. SGLT2i treatment was associated with a shift towards normal EGF expression. Thus, decreased uEGF represents increased risk of kidney disease progression in patients with T2D. Canagliflozin increased kidney tissue expression of EGF and was associated with a downstream signaling cascade linked to tubular repair and reversal of tubular injury.
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Affiliation(s)
- Taha Sen
- Department of Clinical Pharmacy and Pharmacology, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands
| | - Wenjun Ju
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
| | - Viji Nair
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Patricia Ladd
- Department of Radiology, University of Colorado School of Medicine, Aurora, Colorado, USA
| | - Rajasree Menon
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
| | - Edgar A Otto
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Laura Pyle
- Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, Colorado, USA; Section of Endocrinology, Department of Pediatrics, University of Colorado School of Medicine, Aurora, Colorado, USA
| | - Tim Vigers
- Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, Colorado, USA; Section of Endocrinology, Department of Pediatrics, University of Colorado School of Medicine, Aurora, Colorado, USA
| | - Robert G Nelson
- Chronic Kidney Disease Section, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, Arizona, USA
| | - Clare Arnott
- The George Institute for Global Health, University New South Wales (UNSW), Sydney, New South Wales, Australia; Department of Cardiology, Royal Prince Alfred Hospital, Sydney, New South Wales, Australia
| | - Bruce Neal
- The George Institute for Global Health, University New South Wales (UNSW), Sydney, New South Wales, Australia
| | - Michael K Hansen
- Janssen Research & Development, LLC, Spring House, Pennsylvania, USA
| | - Matthias Kretzler
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA.
| | - Petter Bjornstad
- Section of Endocrinology, Department of Pediatrics, University of Colorado School of Medicine, Aurora, Colorado, USA; Division of Renal Diseases and Hypertension, Department of Medicine, University of Colorado School of Medicine, Aurora, Colorado, USA.
| | - Hiddo J L Heerspink
- Department of Clinical Pharmacy and Pharmacology, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands; The George Institute for Global Health, University New South Wales (UNSW), Sydney, New South Wales, Australia.
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Ruiz-Sobremazas D, Rodulfo-Cárdenas R, Ruiz-Coca M, Morales-Navas M, Teresa Colomina M, López-Granero C, Sánchez-Santed F, Perez-Fernandez C. Uncovering the link between air pollution and neurodevelopmental alterations during pregnancy and early life exposure: A systematic review. Neurosci Biobehav Rev 2023; 152:105314. [PMID: 37442496 DOI: 10.1016/j.neubiorev.2023.105314] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Revised: 06/09/2023] [Accepted: 07/08/2023] [Indexed: 07/15/2023]
Abstract
Air pollution plays, nowadays, a huge role in human's health and in the personal economy. Moreover, there has been a rise in the prevalence of neurodevelopmental disorders like the Autism Spectrum Disorder (ASD) in recent years. Current scientific studies have established a link between prenatal or perinatal exposure to environmental pollutants and ASD. This systematic review summarizes the current literature available about the relationship between exposure to air pollutants (particulate matter [PM], Second Organic Aerosols [SOA], Diesel Exhaust [DE], and Traffic Related Air Pollution [TRAP]) and neurodevelopmental disorders in preclinical models using rats and mice. The articles were selected and filtered using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, and bias-evaluated using the SYstematic Review Centre for Laboratory animal Experimentation (SYRCLE) tool. Overall, our findings suggest that air pollutants are associated with negative developmental outcomes characterized by ASD-like behaviors, abnormal biochemical patterns, and impaired achievement of developmental milestones in rodents. However, there is not sufficient information in certain domains to establish a clear relationship. Short phrases for indexing terms: Air pollution affects neurodevelopment; PM exposure modifies glutamate system; Prenatal exposure combined with postnatal affect more to behavioral / cognitive domain; Air pollution modifies social behavior in rodents; Cognitive deficits can be detected after gestational exposure to air pollution.
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Affiliation(s)
- Diego Ruiz-Sobremazas
- Department of Psychology, Health Research Center (CEINSA), Almeria University, 04120 Almeria, Spain
| | - Rocío Rodulfo-Cárdenas
- Universitat Rovira i Virgili, Research Group in Neurobehavior and Health (NEUROLAB), Tarragona, Spain; Universitat Rovira i Virgili, Department of Psychology and Research Center for Behavior Assessment (CRAMC), Tarragona, Spain; Universitat Rovira i Virgili, Laboratory of Toxicology and Environmental Health, School of Medicine, Reus, Spain
| | - Mario Ruiz-Coca
- Department of Psychology, Health Research Center (CEINSA), Almeria University, 04120 Almeria, Spain
| | - Miguel Morales-Navas
- Department of Psychology, Health Research Center (CEINSA), Almeria University, 04120 Almeria, Spain
| | - Maria Teresa Colomina
- Universitat Rovira i Virgili, Research Group in Neurobehavior and Health (NEUROLAB), Tarragona, Spain; Universitat Rovira i Virgili, Department of Psychology and Research Center for Behavior Assessment (CRAMC), Tarragona, Spain; Universitat Rovira i Virgili, Laboratory of Toxicology and Environmental Health, School of Medicine, Reus, Spain
| | | | - Fernando Sánchez-Santed
- Department of Psychology, Health Research Center (CEINSA), Almeria University, 04120 Almeria, Spain
| | - Cristian Perez-Fernandez
- Department of Psychology, Health Research Center (CEINSA), Almeria University, 04120 Almeria, Spain.
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Chen J, Engelhard M, Henao R, Berchuck S, Eichner B, Perrin EM, Sapiro G, Dawson G. Enhancing early autism prediction based on electronic records using clinical narratives. J Biomed Inform 2023; 144:104390. [PMID: 37182592 PMCID: PMC10526711 DOI: 10.1016/j.jbi.2023.104390] [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] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 04/14/2023] [Accepted: 05/09/2023] [Indexed: 05/16/2023]
Abstract
Recent work has shown that predictive models can be applied to structured electronic health record (EHR) data to stratify autism likelihood from an early age (<1 year). Integrating clinical narratives (or notes) with structured data has been shown to improve prediction performance in other clinical applications, but the added predictive value of this information in early autism prediction has not yet been explored. In this study, we aimed to enhance the performance of early autism prediction by using both structured EHR data and clinical narratives. We built models based on structured data and clinical narratives separately, and then an ensemble model that integrated both sources of data. We assessed the predictive value of these models from Duke University Health System over a 14-year span to evaluate ensemble models predicting later autism diagnosis (by age 4 years) from data collected from ages 30 to 360 days. Our sample included 11,750 children above by age 3 years (385 meeting autism diagnostic criteria). The ensemble model for autism prediction showed superior performance and at age 30 days achieved 46.8% sensitivity (95% confidence interval, CI: 22.0%, 52.9%), 28.0% positive predictive value (PPV) at high (90%) specificity (CI: 2.0%, 33.1%), and AUC4 (with at least 4-year follow-up for controls) reaching 0.769 (CI: 0.715, 0.811). Prediction by 360 days achieved 44.5% sensitivity (CI: 23.6%, 62.9%), and 13.7% PPV at high (90%) specificity (CI: 9.6%, 18.9%), and AUC4 reaching 0.797 (CI: 0.746, 0.840). Results show that incorporating clinical narratives in early autism prediction achieved promising accuracy by age 30 days, outperforming models based on structured data only. Furthermore, findings suggest that additional features learned from clinician narratives might be hypothesis generating for understanding early development in autism.
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Affiliation(s)
- Junya Chen
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, 27705, United States.
| | - Matthew Engelhard
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, 27705, United States
| | - Ricardo Henao
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, 27705, United States
| | - Samuel Berchuck
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, 27705, United States
| | - Brian Eichner
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, 27705, United States
| | - Eliana M Perrin
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, 27705, United States
| | - Guillermo Sapiro
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, 27705, United States
| | - Geraldine Dawson
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, 27705, United States
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Bauleo A, Montesanto A, Pace V, Guarasci F, Apa R, Brando R, De Stefano L, Sestito S, Concolino D, Falcone E. 7p22.2 Microduplication: A Pathogenic CNV? Genes (Basel) 2023; 14:1292. [PMID: 37372471 DOI: 10.3390/genes14061292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 06/15/2023] [Accepted: 06/17/2023] [Indexed: 06/29/2023] Open
Abstract
Partial duplication of the short arm of chromosome 7 is a rare chromosome rearrangement. The phenotype spectrum associated with this rearrangement is extremely variable even if in the last decade the use of high-resolution microarray technology for the investigation of patients carrying this rearrangement allowed for the identification of the 7p22.1 sub-band causative of this phenotype and to recognize the corresponding 7p22.1 microduplication syndrome. We report two unrelated patients that carry a microduplication involving the 7.22.2 sub-band. Unlike 7p22.1 microduplication carriers, both patients only show a neurodevelopmental disorder without malformations. We better characterized the clinical pictures of these two patients providing insight into the clinical phenotype associated with the microduplication of the 7p22.2 sub-band and support for a possible role of this sub-band in the 7p22 microduplication syndrome.
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Affiliation(s)
- Alessia Bauleo
- BIOGENET, Medical and Forensic Genetics Laboratory, 87100 Cosenza, Italy
| | - Alberto Montesanto
- Department of Biology, Ecology and Earth Sciences, University of Calabria, 87036 Rende, Italy
| | - Vincenza Pace
- BIOGENET, Medical and Forensic Genetics Laboratory, 87100 Cosenza, Italy
| | - Francesco Guarasci
- BIOGENET, Medical and Forensic Genetics Laboratory, 87100 Cosenza, Italy
| | - Rosalbina Apa
- BIOGENET, Medical and Forensic Genetics Laboratory, 87100 Cosenza, Italy
| | - Rossella Brando
- BIOGENET, Medical and Forensic Genetics Laboratory, 87100 Cosenza, Italy
| | - Laura De Stefano
- BIOGENET, Medical and Forensic Genetics Laboratory, 87100 Cosenza, Italy
| | - Simona Sestito
- Pediatric Unit, Department of Science of Health, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy
| | - Daniela Concolino
- Pediatric Unit, Department of Science of Health, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy
| | - Elena Falcone
- BIOGENET, Medical and Forensic Genetics Laboratory, 87100 Cosenza, Italy
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Fuller KNZ, Allen J, Kumari R, Akakpo JY, Ruebel M, Shankar K, Thyfault JP. Pre- and Post-Sexual Maturity Liver-specific ERα Knockout Does Not Impact Hepatic Mitochondrial Function. J Endocr Soc 2023; 7:bvad053. [PMID: 37197409 PMCID: PMC10184454 DOI: 10.1210/jendso/bvad053] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Indexed: 05/19/2023] Open
Abstract
Compared with males, premenopausal women and female rodents are protected against hepatic steatosis and present with higher functioning mitochondria (greater hepatic mitochondrial respiration and reduced H2O2 emission). Despite evidence that estrogen action mediates female protection against steatosis, mechanisms remain unknown. Here we validated a mouse model with inducible reduction of liver estrogen receptor alpha (ERα) (LERKO) via adeno-associated virus (AAV) Cre. We phenotyped the liver health and mitochondrial function of LERKO mice (n = 10-12 per group) on a short-term high-fat diet (HFD), and then tested whether timing of LERKO induction at 2 timepoints (sexually immature: 4 weeks old [n = 11 per group] vs sexually mature: 8-10 weeks old [n = 8 per group]) would impact HFD-induced outcomes. We opted for an inducible LERKO model due to known estrogen-mediated developmental programming, and we reported both receptor and tissue specificity with our model. Control mice were ERαfl/fl receiving AAV with green fluorescent protein (GFP) only. Results show that there were no differences in body weight/composition or hepatic steatosis in LERKO mice with either short-term (4-week) or chronic (8-week) high-fat feeding. Similarly, LERKO genotype nor timing of LERKO induction (pre vs post sexual maturity) did not alter hepatic mitochondrial O2 and H2O2 flux, coupling, or OXPHOS protein. Transcriptomic analysis showed that hepatic gene expression in LERKO was significantly influenced by developmental stage. Together, these studies suggest that hepatic ERα is not required in female protection against HFD-induced hepatic steatosis nor does it mediate sexual dimorphism in liver mitochondria function.
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Affiliation(s)
- Kelly N Z Fuller
- Department of Cell Biology and Physiology, University of Kansas Medical Center, Kansas City, KS 66160, USA
- Research Service, Kansas City Veterans Affairs Medical Center, Kansas City, KS 64128, USA
| | - Julie Allen
- Department of Cell Biology and Physiology, University of Kansas Medical Center, Kansas City, KS 66160, USA
- Research Service, Kansas City Veterans Affairs Medical Center, Kansas City, KS 64128, USA
| | - Roshan Kumari
- Department of Cell Biology and Physiology, University of Kansas Medical Center, Kansas City, KS 66160, USA
- Research Service, Kansas City Veterans Affairs Medical Center, Kansas City, KS 64128, USA
| | - Jephte Y Akakpo
- Department of Pharmacology and Toxicology, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Meghan Ruebel
- USDA-ARS, Southeast Area, Arkansas Children’s Nutrition Center, Little Rock, AR 72202, USA
| | - Kartik Shankar
- USDA-ARS, Southeast Area, Arkansas Children’s Nutrition Center, Little Rock, AR 72202, USA
| | - John P Thyfault
- Department of Cell Biology and Physiology, University of Kansas Medical Center, Kansas City, KS 66160, USA
- Research Service, Kansas City Veterans Affairs Medical Center, Kansas City, KS 64128, USA
- KU Diabetes Institute and Kansas Center for Metabolism and Obesity, University of Kansas Medical Center, Kansas City, KS 66160, USA
- Department of Internal Medicine, Division of Endocrinology and Metabolism, University of Kansas Medical Center, Kansas City, KS 66160, USA
- Center for Children’s Healthy Lifestyles and Nutrition, Kansas City, MO 64108, USA
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Asif M, Martiniano HFMC, Lamurias A, Kausar S, Couto FM. DGH-GO: dissecting the genetic heterogeneity of complex diseases using gene ontology. BMC Bioinformatics 2023; 24:171. [PMID: 37101154 PMCID: PMC10134522 DOI: 10.1186/s12859-023-05290-4] [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: 01/03/2023] [Accepted: 04/14/2023] [Indexed: 04/28/2023] Open
Abstract
BACKGROUND Complex diseases such as neurodevelopmental disorders (NDDs) exhibit multiple etiologies. The multi-etiological nature of complex-diseases emerges from distinct but functionally similar group of genes. Different diseases sharing genes of such groups show related clinical outcomes that further restrict our understanding of disease mechanisms, thus, limiting the applications of personalized medicine approaches to complex genetic disorders. RESULTS Here, we present an interactive and user-friendly application, called DGH-GO. DGH-GO allows biologists to dissect the genetic heterogeneity of complex diseases by stratifying the putative disease-causing genes into clusters that may contribute to distinct disease outcome development. It can also be used to study the shared etiology of complex-diseases. DGH-GO creates a semantic similarity matrix for the input genes by using Gene Ontology (GO). The resultant matrix can be visualized in 2D plots using different dimension reduction methods (T-SNE, Principal component analysis, umap and Principal coordinate analysis). In the next step, clusters of functionally similar genes are identified from genes functional similarities assessed through GO. This is achieved by employing four different clustering methods (K-means, Hierarchical, Fuzzy and PAM). The user may change the clustering parameters and explore their effect on stratification immediately. DGH-GO was applied to genes disrupted by rare genetic variants in Autism Spectrum Disorder (ASD) patients. The analysis confirmed the multi-etiological nature of ASD by identifying four clusters of genes that were enriched for distinct biological mechanisms and clinical outcome. In the second case study, the analysis of genes shared by different NDDs showed that genes causing multiple disorders tend to aggregate in similar clusters, indicating a possible shared etiology. CONCLUSION DGH-GO is a user-friendly application that allows biologists to study the multi-etiological nature of complex diseases by dissecting their genetic heterogeneity. In summary, functional similarities, dimension reduction and clustering methods, coupled with interactive visualization and control over analysis allows biologists to explore and analyze their datasets without requiring expert knowledge on these methods. The source code of proposed application is available at https://github.com/Muh-Asif/DGH-GO.
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Affiliation(s)
- Muhammad Asif
- Biomedical Data Science Lab, Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Faisalabad, 38000, Pakistan.
- LASIGE, Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal.
| | - Hugo F M C Martiniano
- Instituto Nacional de Saúde Doutor Ricardo Jorge, Avenida Padre Cruz, 1649-016, Lisbon, Portugal
- BioISI - Instituto de Biosistemas e Ciências Integrativas, Faculdade de Ciências, Universidade de Lisboa, 1749-016, Lisboa, Portugal
| | - Andre Lamurias
- Department of Computer Science, Aalborg University, Ålborg, Denmark
- NOVA LINCS, NOVA School of Science and Technology, Lisboa, Portugal
| | - Samina Kausar
- DeepOmicsVision, Avenue de Luminy, 13009, Marseille, France
| | - Francisco M Couto
- LASIGE, Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal.
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Wu S, Fan J, Tang F, Chen L, Zhang X, Xiao D, Li X. The role of RIM in neurotransmitter release: promotion of synaptic vesicle docking, priming, and fusion. Front Neurosci 2023; 17:1123561. [PMID: 37179554 PMCID: PMC10169678 DOI: 10.3389/fnins.2023.1123561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 04/06/2023] [Indexed: 05/15/2023] Open
Abstract
There are many special sites at the end of a synapse called active zones (AZs). Synaptic vesicles (SVs) fuse with presynaptic membranes at these sites, and this fusion is an important step in neurotransmitter release. The cytomatrix in the active zone (CAZ) is made up of proteins such as the regulating synaptic membrane exocytosis protein (RIM), RIM-binding proteins (RIM-BPs), ELKS/CAST, Bassoon/Piccolo, Liprin-α, and Munc13-1. RIM is a scaffold protein that interacts with CAZ proteins and presynaptic functional components to affect the docking, priming, and fusion of SVs. RIM is believed to play an important role in regulating the release of neurotransmitters (NTs). In addition, abnormal expression of RIM has been detected in many diseases, such as retinal diseases, Asperger's syndrome (AS), and degenerative scoliosis. Therefore, we believe that studying the molecular structure of RIM and its role in neurotransmitter release will help to clarify the molecular mechanism of neurotransmitter release and identify targets for the diagnosis and treatment of the aforementioned diseases.
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Affiliation(s)
- Shanshan Wu
- Emergency Department, West China Second University Hospital, Sichuan University, Chengdu, China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China
| | - Jiali Fan
- Emergency Department, West China Second University Hospital, Sichuan University, Chengdu, China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China
| | - Fajuan Tang
- Emergency Department, West China Second University Hospital, Sichuan University, Chengdu, China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China
| | - Lin Chen
- Emergency Department, West China Second University Hospital, Sichuan University, Chengdu, China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China
| | - Xiaoyan Zhang
- Emergency Department, West China Second University Hospital, Sichuan University, Chengdu, China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China
| | - Dongqiong Xiao
- Emergency Department, West China Second University Hospital, Sichuan University, Chengdu, China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China
| | - Xihong Li
- Emergency Department, West China Second University Hospital, Sichuan University, Chengdu, China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China
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Steele MM, Jaiswal A, Delclaux I, Dryg ID, Murugan D, Femel J, Son S, du Bois H, Hill C, Leachman SA, Chang YH, Coussens LM, Anandasabapathy N, Lund AW. T cell egress via lymphatic vessels is tuned by antigen encounter and limits tumor control. Nat Immunol 2023; 24:664-675. [PMID: 36849745 PMCID: PMC10998279 DOI: 10.1038/s41590-023-01443-y] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 01/25/2023] [Indexed: 03/01/2023]
Abstract
Antigen-specific CD8+ T cell accumulation in tumors is a prerequisite for effective immunotherapy, and yet the mechanisms of lymphocyte transit are not well defined. Here we show that tumor-associated lymphatic vessels control T cell exit from tumors via the chemokine CXCL12, and intratumoral antigen encounter tunes CXCR4 expression by effector CD8+ T cells. Only high-affinity antigen downregulates CXCR4 and upregulates the CXCL12 decoy receptor, ACKR3, thereby reducing CXCL12 sensitivity and promoting T cell retention. A diverse repertoire of functional tumor-specific CD8+ T cells, therefore, exit the tumor, which limits the pool of CD8+ T cells available to exert tumor control. CXCR4 inhibition or loss of lymphatic-specific CXCL12 boosts T cell retention and enhances tumor control. These data indicate that strategies to limit T cell egress might be an approach to boost the quantity and quality of intratumoral T cells and thereby response to immunotherapy.
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Affiliation(s)
- Maria M Steele
- Ronald O. Perelman Department of Dermatology, NYU Grossman School of Medicine, New York University Langone Health, New York, NY, USA
- Department of Cell, Developmental and Cancer Biology and Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA
| | - Abhinav Jaiswal
- Department of Dermatology, Microbiology and Immunology, Meyer Cancer Center, Englander Institute of Precision Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Ines Delclaux
- Ronald O. Perelman Department of Dermatology, NYU Grossman School of Medicine, New York University Langone Health, New York, NY, USA
| | - Ian D Dryg
- Ronald O. Perelman Department of Dermatology, NYU Grossman School of Medicine, New York University Langone Health, New York, NY, USA
| | - Dhaarini Murugan
- Department of Cell, Developmental and Cancer Biology and Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA
| | - Julia Femel
- Department of Cell, Developmental and Cancer Biology and Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA
| | - Sunny Son
- Ronald O. Perelman Department of Dermatology, NYU Grossman School of Medicine, New York University Langone Health, New York, NY, USA
- Applied Bioinformatics Laboratories, NYU Langone Health, New York, NY, USA
| | - Haley du Bois
- Ronald O. Perelman Department of Dermatology, NYU Grossman School of Medicine, New York University Langone Health, New York, NY, USA
| | - Cameron Hill
- Ronald O. Perelman Department of Dermatology, NYU Grossman School of Medicine, New York University Langone Health, New York, NY, USA
| | - Sancy A Leachman
- Department of Dermatology, Oregon Health and Science University, Portland, OR, USA
| | - Young H Chang
- Department of Biomedical Engineering and Computational Biology Program, Oregon Health and Science University, Portland, OR, USA
- OHSU Center for Spatial Systems Biomedicine, Oregon Health and Science University, Portland, OR, USA
| | - Lisa M Coussens
- Department of Cell, Developmental and Cancer Biology and Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA
| | - Niroshana Anandasabapathy
- Department of Dermatology, Microbiology and Immunology, Meyer Cancer Center, Englander Institute of Precision Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Amanda W Lund
- Ronald O. Perelman Department of Dermatology, NYU Grossman School of Medicine, New York University Langone Health, New York, NY, USA.
- Department of Cell, Developmental and Cancer Biology and Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA.
- Department of Pathology, NYU Grossman School of Medicine, New York University Langone Health, New York, NY, USA.
- Laura and Isaac Perlmutter Cancer Center, NYU Grossman School of Medicine, New York University Langone Health, New York, NY, USA.
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Bao J, Chang C, Zhang Q, Saykin AJ, Shen L, Long Q. Integrative analysis of multi-omics and imaging data with incorporation of biological information via structural Bayesian factor analysis. Brief Bioinform 2023; 24:bbad073. [PMID: 36882008 PMCID: PMC10387302 DOI: 10.1093/bib/bbad073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 01/14/2023] [Accepted: 02/10/2023] [Indexed: 03/09/2023] Open
Abstract
MOTIVATION With the rapid development of modern technologies, massive data are available for the systematic study of Alzheimer's disease (AD). Though many existing AD studies mainly focus on single-modality omics data, multi-omics datasets can provide a more comprehensive understanding of AD. To bridge this gap, we proposed a novel structural Bayesian factor analysis framework (SBFA) to extract the information shared by multi-omics data through the aggregation of genotyping data, gene expression data, neuroimaging phenotypes and prior biological network knowledge. Our approach can extract common information shared by different modalities and encourage biologically related features to be selected, guiding future AD research in a biologically meaningful way. METHOD Our SBFA model decomposes the mean parameters of the data into a sparse factor loading matrix and a factor matrix, where the factor matrix represents the common information extracted from multi-omics and imaging data. Our framework is designed to incorporate prior biological network information. Our simulation study demonstrated that our proposed SBFA framework could achieve the best performance compared with the other state-of-the-art factor-analysis-based integrative analysis methods. RESULTS We apply our proposed SBFA model together with several state-of-the-art factor analysis models to extract the latent common information from genotyping, gene expression and brain imaging data simultaneously from the ADNI biobank database. The latent information is then used to predict the functional activities questionnaire score, an important measurement for diagnosis of AD quantifying subjects' abilities in daily life. Our SBFA model shows the best prediction performance compared with the other factor analysis models. AVAILABILITY Code are publicly available at https://github.com/JingxuanBao/SBFA. CONTACT qlong@upenn.edu.
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Affiliation(s)
- Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, 19104, PA, USA
| | - Changgee Chang
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, 19104, PA, USA
| | - Qiyiwen Zhang
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, 19104, PA, USA
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, 46202, IN, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, 19104, PA, USA
| | - Qi Long
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, 19104, PA, USA
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Liao C, Moyses-Oliveira M, De Esch CE, Bhavsar R, Nuttle X, Li A, Yu A, Burt ND, Erdin S, Fu JM, Wang M, Morley T, Han L, Dion PA, Rouleau GA, Zhang B, Brennand KJ, Talkowski ME, Ruderfer DM. Convergent coexpression of autism-associated genes suggests some novel risk genes may not be detectable in large-scale genetic studies. CELL GENOMICS 2023; 3:100277. [PMID: 37082147 PMCID: PMC10112287 DOI: 10.1016/j.xgen.2023.100277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 12/01/2022] [Accepted: 02/10/2023] [Indexed: 03/21/2023]
Abstract
Autism spectrum disorder (ASD) is a heritable neurodevelopmental disorder characterized by deficits in social interactions and communication. Protein-altering variants in many genes have been shown to contribute to ASD; however, understanding the convergence across many genes remains a challenge. We demonstrate that coexpression patterns from 993 human postmortem brains are significantly correlated with the transcriptional consequences of CRISPR perturbations in human neurons. Across 71 ASD risk genes, there was significant tissue-specific convergence implicating synaptic pathways. Tissue-specific convergence was further demonstrated across schizophrenia and atrial fibrillation risk genes. The degree of ASD convergence was significantly correlated with ASD association from rare variation and differential expression in ASD brains. Positively convergent genes showed intolerance to functional mutations and had shorter coding lengths than known risk genes even after removing association with ASD. These results indicate that convergent coexpression can identify potentially novel genes that are unlikely to be discovered by sequencing studies.
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Carmona-Mora P, Knepp B, Jickling GC, Zhan X, Hakoupian M, Hull H, Alomar N, Amini H, Sharp FR, Stamova B, Ander BP. Monocyte, neutrophil, and whole blood transcriptome dynamics following ischemic stroke. BMC Med 2023; 21:65. [PMID: 36803375 PMCID: PMC9942321 DOI: 10.1186/s12916-023-02766-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 12/21/2022] [Indexed: 02/22/2023] Open
Abstract
BACKGROUND After ischemic stroke (IS), peripheral leukocytes infiltrate the damaged region and modulate the response to injury. Peripheral blood cells display distinctive gene expression signatures post-IS and these transcriptional programs reflect changes in immune responses to IS. Dissecting the temporal dynamics of gene expression after IS improves our understanding of immune and clotting responses at the molecular and cellular level that are involved in acute brain injury and may assist with time-targeted, cell-specific therapy. METHODS The transcriptomic profiles from peripheral monocytes, neutrophils, and whole blood from 38 ischemic stroke patients and 18 controls were analyzed with RNA-seq as a function of time and etiology after stroke. Differential expression analyses were performed at 0-24 h, 24-48 h, and >48 h following stroke. RESULTS Unique patterns of temporal gene expression and pathways were distinguished for monocytes, neutrophils, and whole blood with enrichment of interleukin signaling pathways for different time points and stroke etiologies. Compared to control subjects, gene expression was generally upregulated in neutrophils and generally downregulated in monocytes over all times for cardioembolic, large vessel, and small vessel strokes. Self-organizing maps identified gene clusters with similar trajectories of gene expression over time for different stroke causes and sample types. Weighted Gene Co-expression Network Analyses identified modules of co-expressed genes that significantly varied with time after stroke and included hub genes of immunoglobulin genes in whole blood. CONCLUSIONS Altogether, the identified genes and pathways are critical for understanding how the immune and clotting systems change over time after stroke. This study identifies potential time- and cell-specific biomarkers and treatment targets.
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Affiliation(s)
- Paulina Carmona-Mora
- Department of Neurology and M.I.N.D, Institute, M.I.N.D. Institute Bioscience Labs, School of Medicine, University of California at Davis, 2805 50th St, Room 2434, Sacramento, CA, 95817, USA.
| | - Bodie Knepp
- Department of Neurology and M.I.N.D, Institute, M.I.N.D. Institute Bioscience Labs, School of Medicine, University of California at Davis, 2805 50th St, Room 2434, Sacramento, CA, 95817, USA
| | - Glen C Jickling
- Division of Neurology, Department of Medicine, Faculty of Medicine and Dentistry, University of Alberta, 87 Avenue & 114 Street, Edmonton, AB, T6G 2J7, Canada
| | - Xinhua Zhan
- Department of Neurology and M.I.N.D, Institute, M.I.N.D. Institute Bioscience Labs, School of Medicine, University of California at Davis, 2805 50th St, Room 2434, Sacramento, CA, 95817, USA
| | - Marisa Hakoupian
- Department of Neurology and M.I.N.D, Institute, M.I.N.D. Institute Bioscience Labs, School of Medicine, University of California at Davis, 2805 50th St, Room 2434, Sacramento, CA, 95817, USA
| | - Heather Hull
- Department of Neurology and M.I.N.D, Institute, M.I.N.D. Institute Bioscience Labs, School of Medicine, University of California at Davis, 2805 50th St, Room 2434, Sacramento, CA, 95817, USA
| | - Noor Alomar
- Department of Neurology and M.I.N.D, Institute, M.I.N.D. Institute Bioscience Labs, School of Medicine, University of California at Davis, 2805 50th St, Room 2434, Sacramento, CA, 95817, USA
| | - Hajar Amini
- Department of Neurology and M.I.N.D, Institute, M.I.N.D. Institute Bioscience Labs, School of Medicine, University of California at Davis, 2805 50th St, Room 2434, Sacramento, CA, 95817, USA
| | - Frank R Sharp
- Department of Neurology and M.I.N.D, Institute, M.I.N.D. Institute Bioscience Labs, School of Medicine, University of California at Davis, 2805 50th St, Room 2434, Sacramento, CA, 95817, USA
| | - Boryana Stamova
- Department of Neurology and M.I.N.D, Institute, M.I.N.D. Institute Bioscience Labs, School of Medicine, University of California at Davis, 2805 50th St, Room 2434, Sacramento, CA, 95817, USA
| | - Bradley P Ander
- Department of Neurology and M.I.N.D, Institute, M.I.N.D. Institute Bioscience Labs, School of Medicine, University of California at Davis, 2805 50th St, Room 2434, Sacramento, CA, 95817, USA
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Mancuso CA, Liu R, Krishnan A. PyGenePlexus: a Python package for gene discovery using network-based machine learning. Bioinformatics 2023; 39:7017525. [PMID: 36721325 PMCID: PMC9900208 DOI: 10.1093/bioinformatics/btad064] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 11/29/2022] [Accepted: 01/30/2023] [Indexed: 02/02/2023] Open
Abstract
SUMMARY PyGenePlexus is a Python package that enables a user to gain insight into any gene set of interest through a molecular interaction network informed supervised machine learning model. PyGenePlexus provides predictions of how associated every gene in the network is to the input gene set, offers interpretability by comparing the model trained on the input gene set to models trained on thousands of known gene sets, and returns the network connectivity of the top predicted genes. AVAILABILITY AND IMPLEMENTATION https://pypi.org/project/geneplexus/ and https://github.com/krishnanlab/PyGenePlexus. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Christopher A Mancuso
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI 48824, USA.,Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado-Denver Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Renming Liu
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Arjun Krishnan
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI 48824, USA.,Department of Biomedical Informatics, University of Colorado-Denver Anschutz Medical Campus, Aurora, CO 80045, USA
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Bao B, Zahiri J, Gazestani VH, Lopez L, Xiao Y, Kim R, Wen TH, Chiang AWT, Nalabolu S, Pierce K, Robasky K, Wang T, Hoekzema K, Eichler EE, Lewis NE, Courchesne E. A predictive ensemble classifier for the gene expression diagnosis of ASD at ages 1 to 4 years. Mol Psychiatry 2023; 28:822-833. [PMID: 36266569 PMCID: PMC9908553 DOI: 10.1038/s41380-022-01826-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 09/13/2022] [Accepted: 09/27/2022] [Indexed: 11/09/2022]
Abstract
Autism Spectrum Disorder (ASD) diagnosis remains behavior-based and the median age of diagnosis is ~52 months, nearly 5 years after its first-trimester origin. Accurate and clinically-translatable early-age diagnostics do not exist due to ASD genetic and clinical heterogeneity. Here we collected clinical, diagnostic, and leukocyte RNA data from 240 ASD and typically developing (TD) toddlers (175 toddlers for training and 65 for test). To identify gene expression ASD diagnostic classifiers, we developed 42,840 models composed of 3570 gene expression feature selection sets and 12 classification methods. We found that 742 models had AUC-ROC ≥ 0.8 on both Training and Test sets. Weighted Bayesian model averaging of these 742 models yielded an ensemble classifier model with accurate performance in Training and Test gene expression datasets with ASD diagnostic classification AUC-ROC scores of 85-89% and AUC-PR scores of 84-92%. ASD toddlers with ensemble scores above and below the overall ASD ensemble mean of 0.723 (on a scale of 0 to 1) had similar diagnostic and psychometric scores, but those below this ASD ensemble mean had more prenatal risk events than TD toddlers. Ensemble model feature genes were involved in cell cycle, inflammation/immune response, transcriptional gene regulation, cytokine response, and PI3K-AKT, RAS and Wnt signaling pathways. We additionally collected targeted DNA sequencing smMIPs data on a subset of ASD risk genes from 217 of the 240 ASD and TD toddlers. This DNA sequencing found about the same percentage of SFARI Level 1 and 2 ASD risk gene mutations in TD (12 of 105) as in ASD (13 of 112) toddlers, and classification based only on the presence of mutation in these risk genes performed at a chance level of 49%. By contrast, the leukocyte ensemble gene expression classifier correctly diagnostically classified 88% of TD and ASD toddlers with ASD risk gene mutations. Our ensemble ASD gene expression classifier is diagnostically predictive and replicable across different toddler ages, races, and ethnicities; out-performs a risk gene mutation classifier; and has potential for clinical translation.
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Affiliation(s)
- Bokan Bao
- Autism Center of Excellence, Department of Neuroscience, University of California San Diego, La Jolla, CA, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
| | - Javad Zahiri
- Autism Center of Excellence, Department of Neuroscience, University of California San Diego, La Jolla, CA, USA
| | - Vahid H Gazestani
- Autism Center of Excellence, Department of Neuroscience, University of California San Diego, La Jolla, CA, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Linda Lopez
- Autism Center of Excellence, Department of Neuroscience, University of California San Diego, La Jolla, CA, USA
| | - Yaqiong Xiao
- Autism Center of Excellence, Department of Neuroscience, University of California San Diego, La Jolla, CA, USA
- Center for Language and Brain, Shenzhen Institute of Neuroscience, Shenzhen, China
| | - Raphael Kim
- Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA
- Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Teresa H Wen
- Autism Center of Excellence, Department of Neuroscience, University of California San Diego, La Jolla, CA, USA
| | - Austin W T Chiang
- Autism Center of Excellence, Department of Neuroscience, University of California San Diego, La Jolla, CA, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Srinivasa Nalabolu
- Autism Center of Excellence, Department of Neuroscience, University of California San Diego, La Jolla, CA, USA
| | - Karen Pierce
- Autism Center of Excellence, Department of Neuroscience, University of California San Diego, La Jolla, CA, USA
| | - Kimberly Robasky
- Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27514, US
- School of Information and Library Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Carolina Health and Informatics Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Tianyun Wang
- Department of Medical Genetics, Center for Medical Genetics, Peking University Health Science Center, 100191, Beijing, China
- Neuroscience Research Institute, Peking University; Key Laboratory for Neuroscience, Ministry of Education of China & National Health Commission of China, 100191, Beijing, China
| | - Kendra Hoekzema
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, 98195, USA
| | - Evan E Eichler
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, 98195, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, 98195, USA
| | - Nathan E Lewis
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA.
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA.
| | - Eric Courchesne
- Autism Center of Excellence, Department of Neuroscience, University of California San Diego, La Jolla, CA, USA.
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Liu R, Hirn M, Krishnan A. Accurately modeling biased random walks on weighted networks using node2vec. Bioinformatics 2023; 39:6998205. [PMID: 36688699 PMCID: PMC9891245 DOI: 10.1093/bioinformatics/btad047] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 01/16/2023] [Accepted: 01/20/2023] [Indexed: 01/24/2023] Open
Abstract
MOTIVATION Accurately representing biological networks in a low-dimensional space, also known as network embedding, is a critical step in network-based machine learning and is carried out widely using node2vec, an unsupervised method based on biased random walks. However, while many networks, including functional gene interaction networks, are dense, weighted graphs, node2vec is fundamentally limited in its ability to use edge weights during the biased random walk generation process, thus under-using all the information in the network. RESULTS Here, we present node2vec+, a natural extension of node2vec that accounts for edge weights when calculating walk biases and reduces to node2vec in the cases of unweighted graphs or unbiased walks. Using two synthetic datasets, we empirically show that node2vec+ is more robust to additive noise than node2vec in weighted graphs. Then, using genome-scale functional gene networks to solve a wide range of gene function and disease prediction tasks, we demonstrate the superior performance of node2vec+ over node2vec in the case of weighted graphs. Notably, due to the limited amount of training data in the gene classification tasks, graph neural networks such as GCN and GraphSAGE are outperformed by both node2vec and node2vec+. AVAILABILITY AND IMPLEMENTATION The data and code are available on GitHub at https://github.com/krishnanlab/node2vecplus_benchmarks. All additional data underlying this article are available on Zenodo at https://doi.org/10.5281/zenodo.7007164. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Renming Liu
- Department of Computational Mathematics, Science & Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Matthew Hirn
- Department of Computational Mathematics, Science & Engineering, Michigan State University, East Lansing, MI 48824, USA
- Department of Mathematics, Michigan State University, East Lansing, MI 48824, USA
- Center for Quantum Computing, Science & Engineering, Michigan State University, East Lansing, MI 48824, USA
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Liu H, Guan L, Deng M, Bolund L, Kristiansen K, Zhang J, Luo Y, Zhang Z. Integrative genetic and single cell RNA sequencing analysis provides new clues to the amyotrophic lateral sclerosis neurodegeneration. Front Neurosci 2023; 17:1116087. [PMID: 36875658 PMCID: PMC9983639 DOI: 10.3389/fnins.2023.1116087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 02/02/2023] [Indexed: 02/19/2023] Open
Abstract
Introduction The gradual loss of motor neurons (MNs) in the brain and spinal cord is a hallmark of amyotrophic lateral sclerosis (ALS), but the mechanisms underlying neurodegeneration in ALS are still not fully understood. Methods Based on 75 ALS-pathogenicity/susceptibility genes and large-scale single-cell transcriptomes of human/mouse brain/spinal cord/muscle tissues, we performed an expression enrichment analysis to identify cells involved in ALS pathogenesis. Subsequently, we created a strictness measure to estimate the dosage requirement of ALS-related genes in linked cell types. Results Remarkably, expression enrichment analysis showed that α- and γ-MNs, respectively, are associated with ALS-susceptibility genes and ALS-pathogenicity genes, revealing differences in biological processes between sporadic and familial ALS. In MNs, ALS-susceptibility genes exhibited high strictness, as well as the ALS-pathogenicity genes with known loss of function mechanism, indicating the main characteristic of ALS-susceptibility genes is dosage-sensitive and the loss of function mechanism of these genes may involve in sporadic ALS. In contrast, ALS-pathogenicity genes with gain of function mechanism exhibited low strictness. The significant difference of strictness between loss of function genes and gain of function genes provided a priori understanding for the pathogenesis of novel genes without an animal model. Besides MNs, we observed no statistical evidence for an association between muscle cells and ALS-related genes. This result may provide insight into the etiology that ALS is not within the domain of neuromuscular diseases. Moreover, we showed several cell types linked to other neurological diseases [i.e., spinocerebellar ataxia (SA), hereditary motor neuropathies (HMN)] and neuromuscular diseases [i.e. hereditary spastic paraplegia (SPG), spinal muscular atrophy (SMA)], including an association between Purkinje cells in brain and SA, an association between α-MNs in spinal cord and SA, an association between smooth muscle cells and SA, an association between oligodendrocyte and HMN, a suggestive association between γ-MNs and HMN, a suggestive association between mature skeletal muscle and HMN, an association between oligodendrocyte in brain and SPG, and no statistical evidence for an association between cell type and SMA. Discussion These cellular similarities and differences deepened our understanding of the heterogeneous cellular basis of ALS, SA, HMN, SPG, and SMA.
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Affiliation(s)
- Hankui Liu
- Hebei Industrial Technology Research Institute of Genomics in Maternal and Child Health, BGI-Shijiazhuang Medical Laboratory, Shijiazhuang, China.,BGI-Shenzhen, Shenzhen, China
| | - Liping Guan
- Hebei Industrial Technology Research Institute of Genomics in Maternal and Child Health, BGI-Shijiazhuang Medical Laboratory, Shijiazhuang, China.,Laboratory of Genomics and Molecular Biomedicine, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Min Deng
- Institute of Medical Innovation and Research, Peking University Third Hospital, Beijing, China
| | - Lars Bolund
- Lars Bolund Institute of Regenerative Medicine, BGI-Qingdao, Qingdao, China.,Department of Biomedicine, Aarhus University, Aarhus, Denmark
| | - Karsten Kristiansen
- Laboratory of Genomics and Molecular Biomedicine, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Jianguo Zhang
- Hebei Industrial Technology Research Institute of Genomics in Maternal and Child Health, BGI-Shijiazhuang Medical Laboratory, Shijiazhuang, China.,BGI-Shenzhen, Shenzhen, China
| | - Yonglun Luo
- BGI-Shenzhen, Shenzhen, China.,Lars Bolund Institute of Regenerative Medicine, BGI-Qingdao, Qingdao, China.,Department of Biomedicine, Aarhus University, Aarhus, Denmark
| | - Zhanchi Zhang
- Department of Human Anatomy, Hebei Medical University, Shijiazhuang, Hebei, China.,Hebei Key Laboratory of Neurodegenerative Disease Mechanism, Hebei Medical University, Shijiazhuang, China
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47
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Ismail E, Gad W, Hashem M. HEC-ASD: a hybrid ensemble-based classification model for predicting autism spectrum disorder disease genes. BMC Bioinformatics 2022; 23:554. [PMID: 36544099 PMCID: PMC9768984 DOI: 10.1186/s12859-022-05099-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 12/06/2022] [Indexed: 12/24/2022] Open
Abstract
PURPOSE Autism spectrum disorder (ASD) is the most prevalent disease today. The causes of its infection may be attributed to genetic causes by 80% and environmental causes by 20%. In spite of this, the majority of the current research is concerned with environmental causes, and the least proportion with the genetic causes of the disease. Autism is a complex disease, which makes it difficult to identify the genes that cause the disease. METHODS Hybrid ensemble-based classification (HEC-ASD) model for predicting ASD genes using gradient boosting machines is proposed. The proposed model utilizes gene ontology (GO) to construct a gene functional similarity matrix using hybrid gene similarity (HGS) method. HGS measures the semantic similarity between genes effectively. It combines the graph-based method, such as Wang method with the number of directed children's nodes of gene term from GO. Moreover, an ensemble gradient boosting classifier is adapted to enhance the prediction of genes forming a robust classification model. RESULTS The proposed model is evaluated using the Simons Foundation Autism Research Initiative (SFARI) gene database. The experimental results are promising as they improve the classification performance for predicting ASD genes. The results are compared with other approaches that used gene regulatory network (GRN), protein to protein interaction network (PPI), or GO. The HEC-ASD model reaches the highest prediction accuracy of 0.88% using ensemble learning classifiers. CONCLUSION The proposed model demonstrates that ensemble learning technique using gradient boosting is effective in predicting autism spectrum disorder genes. Moreover, the HEC-ASD model utilized GO rather than using PPI network and GRN.
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Affiliation(s)
- Eman Ismail
- grid.7269.a0000 0004 0621 1570Information Systems Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt
| | - Walaa Gad
- grid.7269.a0000 0004 0621 1570Information Systems Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt
| | - Mohamed Hashem
- grid.7269.a0000 0004 0621 1570Information Systems Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt
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48
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Lee K, Mills Z, Cheung P, Cheyne JE, Montgomery JM. The Role of Zinc and NMDA Receptors in Autism Spectrum Disorders. Pharmaceuticals (Basel) 2022; 16:ph16010001. [PMID: 36678498 PMCID: PMC9866730 DOI: 10.3390/ph16010001] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 12/12/2022] [Accepted: 12/15/2022] [Indexed: 12/24/2022] Open
Abstract
NMDA-type glutamate receptors are critical for synaptic plasticity in the central nervous system. Their unique properties and age-dependent arrangement of subunit types underpin their role as a coincidence detector of pre- and postsynaptic activity during brain development and maturation. NMDAR function is highly modulated by zinc, which is co-released with glutamate and concentrates in postsynaptic spines. Both NMDARs and zinc have been strongly linked to autism spectrum disorders (ASDs), suggesting that NMDARs are an important player in the beneficial effects observed with zinc in both animal models and children with ASDs. Significant evidence is emerging that these beneficial effects occur via zinc-dependent regulation of SHANK proteins, which form the backbone of the postsynaptic density. For example, dietary zinc supplementation enhances SHANK2 or SHANK3 synaptic recruitment and rescues NMDAR deficits and hypofunction in Shank3ex13-16-/- and Tbr1+/- ASD mice. Across multiple studies, synaptic changes occur in parallel with a reversal of ASD-associated behaviours, highlighting the zinc-dependent regulation of NMDARs and glutamatergic synapses as therapeutic targets for severe forms of ASDs, either pre- or postnatally. The data from rodent models set a strong foundation for future translational studies in human cells and people affected by ASDs.
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Wood KA, Goriely A. The impact of paternal age on new mutations and disease in the next generation. Fertil Steril 2022; 118:1001-1012. [PMID: 36351856 PMCID: PMC10909733 DOI: 10.1016/j.fertnstert.2022.10.017] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 10/09/2022] [Accepted: 10/11/2022] [Indexed: 11/09/2022]
Abstract
Advanced paternal age is associated with an increased risk of fathering children with genetic disorders and other adverse reproductive consequences. However, the mechanisms underlying this phenomenon remain largely unexplored. In this review, we focus on the impact of paternal age on de novo mutations that are an important contributor to genetic disease and can be studied both indirectly through large-scale sequencing studies and directly in the tissue in which they predominantly arise-the aging testis. We discuss the recent data that have helped establish the origins and frequency of de novo mutations, and highlight experimental evidence about the close link between new mutations, parental age, and genetic disease. We then focus on a small group of rare genetic conditions, the so-called "paternal age effect" disorders that show a strong association between paternal age and disease prevalence, and discuss the underlying mechanism ("selfish selection") and implications of this process in more detail. More broadly, understanding the causes and consequences of paternal age on genetic risk has important implications both for individual couples and for public health advice given that the average age of fatherhood is steadily increasing in many developed nations.
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Affiliation(s)
- Katherine A Wood
- Radcliffe Department of Medicine, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, United Kingdom; National Institute for Health and Care Research (NIHR) Oxford Biomedical Research Centre, Oxford, United Kingdom
| | - Anne Goriely
- Radcliffe Department of Medicine, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, United Kingdom; National Institute for Health and Care Research (NIHR) Oxford Biomedical Research Centre, Oxford, United Kingdom.
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50
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Barrio-Hernandez I, Beltrao P. Network analysis of genome-wide association studies for drug target prioritisation. Curr Opin Chem Biol 2022; 71:102206. [PMID: 36087372 DOI: 10.1016/j.cbpa.2022.102206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 07/29/2022] [Accepted: 08/05/2022] [Indexed: 01/27/2023]
Abstract
Over the past decades, genome-wide association studies (GWAS) have led to a dramatic expansion of genetic variants implicated with human traits and diseases. These advances are expected to result in new drug targets but the identification of causal genes and the cell biology underlying human diseases from GWAS remains challenging. Here, we review protein interaction network-based methods to analyse GWAS data. These approaches can rank candidate drug targets at GWAS-associated loci or among interactors of disease genes without direct genetic support. These methods identify the cell biology affected in common across diseases, offering opportunities for drug repurposing, as well as be combined with expression data to identify focal tissues and cell types. Going forward, we expect that these methods will further improve from advances in the characterisation of context specific interaction networks and the joint analysis of rare and common genetic signals.
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Affiliation(s)
- Inigo Barrio-Hernandez
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, CB10 1SD, UK; Open Targets, Wellcome Genome Campus, Cambridge, CB10 1SA, UK.
| | - Pedro Beltrao
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, CB10 1SD, UK; Open Targets, Wellcome Genome Campus, Cambridge, CB10 1SA, UK; Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, 8093, Switzerland.
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