1
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Ma J, Qi R, Wang J, Berto S, Wang GZ. Human-unique brain cell clusters are associated with learning disorders and human episodic memory activity. Mol Psychiatry 2024:10.1038/s41380-024-02722-2. [PMID: 39227435 DOI: 10.1038/s41380-024-02722-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 08/16/2024] [Accepted: 08/22/2024] [Indexed: 09/05/2024]
Abstract
The advanced evolution of the human cerebral cortex forms the basis for our high-level cognitive functions. Through a comparative analysis of single-nucleus transcriptome data from the human neocortex and that of chimpanzees, macaques, and marmosets, we discovered 20 subgroups of cell types unique to the human brain, which include 11 types of excitatory neurons. Many of these human-unique cell clusters exhibit significant overexpression of genes regulated by human-specific enhancers. Notably, these specific cell clusters also express genes associated with disease risk, particularly those related to brain dysfunctions like learning disorders. Furthermore, genes linked to cortical thickness and human episodic memory encoding activities show heightened expression within these cell subgroups. These findings underscore the critical role of human brain-unique cell clusters in the evolution of human brain functions.
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Affiliation(s)
- Junjie Ma
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Ruicheng Qi
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Jing Wang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Stefano Berto
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, 29425, USA
| | - Guang-Zhong Wang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
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2
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Chen R, Nie P, Wang J, Wang GZ. Deciphering brain cellular and behavioral mechanisms: Insights from single-cell and spatial RNA sequencing. WILEY INTERDISCIPLINARY REVIEWS. RNA 2024; 15:e1865. [PMID: 38972934 DOI: 10.1002/wrna.1865] [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: 01/31/2024] [Revised: 05/05/2024] [Accepted: 05/14/2024] [Indexed: 07/09/2024]
Abstract
The brain is a complex computing system composed of a multitude of interacting neurons. The computational outputs of this system determine the behavior and perception of every individual. Each brain cell expresses thousands of genes that dictate the cell's function and physiological properties. Therefore, deciphering the molecular expression of each cell is of great significance for understanding its characteristics and role in brain function. Additionally, the positional information of each cell can provide crucial insights into their involvement in local brain circuits. In this review, we briefly overview the principles of single-cell RNA sequencing and spatial transcriptomics, the potential issues and challenges in their data processing, and their applications in brain research. We further outline several promising directions in neuroscience that could be integrated with single-cell RNA sequencing, including neurodevelopment, the identification of novel brain microstructures, cognition and behavior, neuronal cell positioning, molecules and cells related to advanced brain functions, sleep-wake cycles/circadian rhythms, and computational modeling of brain function. We believe that the deep integration of these directions with single-cell and spatial RNA sequencing can contribute significantly to understanding the roles of individual cells or cell types in these specific functions, thereby making important contributions to addressing critical questions in those fields. This article is categorized under: RNA Evolution and Genomics > Computational Analyses of RNA RNA in Disease and Development > RNA in Development RNA in Disease and Development > RNA in Disease.
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Affiliation(s)
- Renrui Chen
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Pengxing Nie
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Jing Wang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Guang-Zhong Wang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
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3
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Negrón-Oyarzo I, Dib T, Chacana-Véliz L, López-Quilodrán N, Urrutia-Piñones J. Large-scale coupling of prefrontal activity patterns as a mechanism for cognitive control in health and disease: evidence from rodent models. Front Neural Circuits 2024; 18:1286111. [PMID: 38638163 PMCID: PMC11024307 DOI: 10.3389/fncir.2024.1286111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 03/11/2024] [Indexed: 04/20/2024] Open
Abstract
Cognitive control of behavior is crucial for well-being, as allows subject to adapt to changing environments in a goal-directed way. Changes in cognitive control of behavior is observed during cognitive decline in elderly and in pathological mental conditions. Therefore, the recovery of cognitive control may provide a reliable preventive and therapeutic strategy. However, its neural basis is not completely understood. Cognitive control is supported by the prefrontal cortex, structure that integrates relevant information for the appropriate organization of behavior. At neurophysiological level, it is suggested that cognitive control is supported by local and large-scale synchronization of oscillatory activity patterns and neural spiking activity between the prefrontal cortex and distributed neural networks. In this review, we focus mainly on rodent models approaching the neuronal origin of these prefrontal patterns, and the cognitive and behavioral relevance of its coordination with distributed brain systems. We also examine the relationship between cognitive control and neural activity patterns in the prefrontal cortex, and its role in normal cognitive decline and pathological mental conditions. Finally, based on these body of evidence, we propose a common mechanism that may underlie the impaired cognitive control of behavior.
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Affiliation(s)
- Ignacio Negrón-Oyarzo
- Instituto de Fisiología, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
| | - Tatiana Dib
- Instituto de Fisiología, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
| | - Lorena Chacana-Véliz
- Instituto de Fisiología, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
- Programa de Doctorado en Ciencias Mención en Neurociencia, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
| | - Nélida López-Quilodrán
- Instituto de Fisiología, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
- Programa de Doctorado en Ciencias Mención en Neurociencia, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
| | - Jocelyn Urrutia-Piñones
- Instituto de Fisiología, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
- Programa de Doctorado en Ciencias Mención en Neurociencia, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
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4
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Chatterjee S, Park BJ, Vanrobaeys Y, Heiney SA, Rhone AE, Nourski KV, Langmack L, Mukherjee U, Kovach CK, Kocsis Z, Kikuchi Y, Petkov CI, Hefti MM, Bahl E, Michaelson JJ, Kawasaki H, Oya H, Howard MA, Nickl-Jockschat T, Lin LC, Abel T. Activity-induced gene expression in the human brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.21.558812. [PMID: 37790527 PMCID: PMC10542502 DOI: 10.1101/2023.09.21.558812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Activity-induced gene expression underlies synaptic plasticity and brain function. Here, using molecular sequencing techniques, we define activity-dependent transcriptomic and epigenomic changes at the tissue and single-cell level in the human brain following direct electrical stimulation of the anterior temporal lobe in patients undergoing neurosurgery. Genes related to transcriptional regulation and microglia-specific cytokine activity displayed the greatest induction pattern, revealing a precise molecular signature of neuronal activation in the human brain.
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Affiliation(s)
- Snehajyoti Chatterjee
- Department of Neuroscience and Pharmacology, Iowa Neuroscience Institute Carver College of Medicine, University of Iowa, Iowa City, IA, USA
- Iowa Neuroscience Institute, University of Iowa, Iowa City, IA, USA
| | - Brian J. Park
- Department of Neurosurgery, University of Iowa Hospitals and Clinics, Iowa City, IA, USA
| | - Yann Vanrobaeys
- Department of Neuroscience and Pharmacology, Iowa Neuroscience Institute Carver College of Medicine, University of Iowa, Iowa City, IA, USA
| | - Shane A. Heiney
- Department of Neuroscience and Pharmacology, Iowa Neuroscience Institute Carver College of Medicine, University of Iowa, Iowa City, IA, USA
- Iowa Neuroscience Institute, University of Iowa, Iowa City, IA, USA
- Neural Circuits and Behavior Core, Iowa Neuroscience Institute Carver College of Medicine, University of Iowa, Iowa City, IA, USA
| | - Ariane E. Rhone
- Department of Neurosurgery, University of Iowa Hospitals and Clinics, Iowa City, IA, USA
| | - Kirill V. Nourski
- Department of Neurosurgery, University of Iowa Hospitals and Clinics, Iowa City, IA, USA
| | - Lucy Langmack
- Department of Neuroscience and Pharmacology, Iowa Neuroscience Institute Carver College of Medicine, University of Iowa, Iowa City, IA, USA
- Iowa Neuroscience Institute, University of Iowa, Iowa City, IA, USA
| | - Utsav Mukherjee
- Department of Neuroscience and Pharmacology, Iowa Neuroscience Institute Carver College of Medicine, University of Iowa, Iowa City, IA, USA
- Iowa Neuroscience Institute, University of Iowa, Iowa City, IA, USA
| | - Christopher K. Kovach
- Department of Neurosurgery, University of Iowa Hospitals and Clinics, Iowa City, IA, USA
| | - Zsuzsanna Kocsis
- Department of Neurosurgery, University of Iowa Hospitals and Clinics, Iowa City, IA, USA
- Biosciences Institute, Newcastle University Medical School, Newcastle upon Tyne, UK
| | - Yukiko Kikuchi
- Biosciences Institute, Newcastle University Medical School, Newcastle upon Tyne, UK
| | - Christopher I. Petkov
- Department of Neurosurgery, University of Iowa Hospitals and Clinics, Iowa City, IA, USA
- Biosciences Institute, Newcastle University Medical School, Newcastle upon Tyne, UK
| | - Marco M. Hefti
- Department of Pathology, University of Iowa Hospitals and Clinics, Iowa City, IA, USA
| | - Ethan Bahl
- Department of Psychiatry, University of Iowa Hospitals and Clinics, Iowa City, IA, USA
| | - Jacob J Michaelson
- Department of Psychiatry, University of Iowa Hospitals and Clinics, Iowa City, IA, USA
| | - Hiroto Kawasaki
- Department of Neurosurgery, University of Iowa Hospitals and Clinics, Iowa City, IA, USA
| | - Hiroyuki Oya
- Department of Neurosurgery, University of Iowa Hospitals and Clinics, Iowa City, IA, USA
| | - Matthew A. Howard
- Department of Neurosurgery, University of Iowa Hospitals and Clinics, Iowa City, IA, USA
| | - Thomas Nickl-Jockschat
- Department of Neuroscience and Pharmacology, Iowa Neuroscience Institute Carver College of Medicine, University of Iowa, Iowa City, IA, USA
- Iowa Neuroscience Institute, University of Iowa, Iowa City, IA, USA
- Department of Psychiatry, University of Iowa Hospitals and Clinics, Iowa City, IA, USA
| | - Li-Chun Lin
- Department of Neuroscience and Pharmacology, Iowa Neuroscience Institute Carver College of Medicine, University of Iowa, Iowa City, IA, USA
- Iowa Neuroscience Institute, University of Iowa, Iowa City, IA, USA
- Iowa NeuroBank Core, Iowa Neuroscience Institute Carver College of Medicine, University of Iowa, Iowa City, IA, USA
| | - Ted Abel
- Department of Neuroscience and Pharmacology, Iowa Neuroscience Institute Carver College of Medicine, University of Iowa, Iowa City, IA, USA
- Iowa Neuroscience Institute, University of Iowa, Iowa City, IA, USA
- Department of Psychiatry, University of Iowa Hospitals and Clinics, Iowa City, IA, USA
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5
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Wang W, Bo T, Zhang G, Li J, Ma J, Ma L, Hu G, Tong H, Lv Q, Araujo DJ, Luo D, Chen Y, Wang M, Wang Z, Wang GZ. Noncoding transcripts are linked to brain resting-state activity in non-human primates. Cell Rep 2023; 42:112652. [PMID: 37335775 DOI: 10.1016/j.celrep.2023.112652] [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: 09/21/2022] [Revised: 04/05/2023] [Accepted: 05/30/2023] [Indexed: 06/21/2023] Open
Abstract
Brain-derived transcriptomes are known to correlate with resting-state brain activity in humans. Whether this association holds in nonhuman primates remains uncertain. Here, we search for such molecular correlates by integrating 757 transcriptomes derived from 100 macaque cortical regions with resting-state activity in separate conspecifics. We observe that 150 noncoding genes explain variations in resting-state activity at a comparable level with protein-coding genes. In-depth analysis of these noncoding genes reveals that they are connected to the function of nonneuronal cells such as oligodendrocytes. Co-expression network analysis finds that the modules of noncoding genes are linked to both autism and schizophrenia risk genes. Moreover, genes associated with resting-state noncoding genes are highly enriched in human resting-state functional genes and memory-effect genes, and their links with resting-state functional magnetic resonance imaging (fMRI) signals are altered in the brains of patients with autism. Our results highlight the potential for noncoding RNAs to explain resting-state activity in the nonhuman primate brain.
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Affiliation(s)
- Wei Wang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Tingting Bo
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Clinical Neuroscience Center, Ruijin Hospital Luwan Branch, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ge Zhang
- Department of Medical Imaging, Henan Provincial People's Hospital & the People's Hospital of Zhengzhou University, No. 7 Weiwu Road, Zhengzhou, Henan, China
| | - Jie Li
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Junjie Ma
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Liangxiao Ma
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Ganlu Hu
- Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, Shanghai, China
| | - Huige Tong
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Qian Lv
- School of Psychological and Cognitive Sciences, Beijing Key Laboratory of Behavior and Mental Health, IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Daniel J Araujo
- Center for Cancer Immunotherapy, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - Dong Luo
- School of Biomedical Engineering, Hainan University, Haikou, Hainan, China
| | - Yuejun Chen
- Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai 201210, China
| | - Meiyun Wang
- Department of Medical Imaging, Henan Provincial People's Hospital & the People's Hospital of Zhengzhou University, No. 7 Weiwu Road, Zhengzhou, Henan, China
| | - Zheng Wang
- School of Psychological and Cognitive Sciences, Beijing Key Laboratory of Behavior and Mental Health, IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China; School of Biomedical Engineering, Hainan University, Haikou, Hainan, China.
| | - Guang-Zhong Wang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
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6
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Moore H, Lega BC, Konopka G. Riding brain "waves" to identify human memory genes. Curr Opin Cell Biol 2022; 78:102118. [PMID: 35947942 DOI: 10.1016/j.ceb.2022.102118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 06/24/2022] [Accepted: 06/28/2022] [Indexed: 01/31/2023]
Abstract
While there is extensive research on memory-related oscillations and brain gene expression, the relationship between oscillations and gene expression has rarely been studied. Recently, progress has been made to identify specific genes associated with oscillations that are correlated with episodic memory. Neocortical regions, in particular the temporal pole, have been examined in this line of research due to their accessibility during neurosurgical procedures. By harnessing this accessibility, a unique and powerful study design has allowed gene expression and intracranial oscillatory data to be sourced from the same human patients. These studies have identified a plethora of understudied gene targets that should be further characterized with respect to human brain function. Future work should extend to other brain regions to increase our understanding of the genetic signatures of oscillations and, ultimately, human cognition.
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Affiliation(s)
- Haley Moore
- Department of Neuroscience, UT Southwestern Medical Center, USA; Peter O'Donnell Jr. Brain Institute, UT Southwestern Medical Center, USA; Department of Neurosurgery, UT Southwestern Medical Center, USA
| | - Bradley C Lega
- Peter O'Donnell Jr. Brain Institute, UT Southwestern Medical Center, USA; Department of Neurosurgery, UT Southwestern Medical Center, USA.
| | - Genevieve Konopka
- Department of Neuroscience, UT Southwestern Medical Center, USA; Peter O'Donnell Jr. Brain Institute, UT Southwestern Medical Center, USA.
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7
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Arpi MNT, Simpson TI. SFARI genes and where to find them; modelling Autism Spectrum Disorder specific gene expression dysregulation with RNA-seq data. Sci Rep 2022; 12:10158. [PMID: 35710789 PMCID: PMC9203566 DOI: 10.1038/s41598-022-14077-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Accepted: 06/01/2022] [Indexed: 11/09/2022] Open
Abstract
Autism Spectrum Disorders (ASD) have a strong, yet heterogeneous, genetic component. Among the various methods that are being developed to help reveal the underlying molecular aetiology of the disease one approach that is gaining popularity is the combination of gene expression and clinical genetic data, often using the SFARI-gene database, which comprises lists of curated genes considered to have causative roles in ASD when mutated in patients. We build a gene co-expression network to study the relationship between ASD-specific transcriptomic data and SFARI genes and then analyse it at different levels of granularity. No significant evidence is found of association between SFARI genes and differential gene expression patterns when comparing ASD samples to a control group, nor statistical enrichment of SFARI genes in gene co-expression network modules that have a strong correlation with ASD diagnosis. However, classification models that incorporate topological information from the whole ASD-specific gene co-expression network can predict novel SFARI candidate genes that share features of existing SFARI genes and have support for roles in ASD in the literature. A statistically significant association is also found between the absolute level of gene expression and SFARI's genes and Scores, which can confound the analysis if uncorrected. We propose a novel approach to correct for this that is general enough to be applied to other problems affected by continuous sources of bias. It was found that only co-expression network analyses that integrate information from the whole network are able to reveal signatures linked to ASD diagnosis and novel candidate genes for the study of ASD, which individual gene or module analyses fail to do. It was also found that the influence of SFARI genes permeates not only other ASD scoring systems, but also lists of genes believed to be involved in other neurodevelopmental disorders.
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Affiliation(s)
| | - T Ian Simpson
- School of Informatics, University of Edinburgh, 10 Crichton Street, Edinburgh, EH8 9AB, UK. .,Simons Initiative for the Developing Brain (SIDB), Centre for Brain Discovery Sciences, University of Edinburgh, Edinburgh, UK.
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8
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Gupta C, Chandrashekar P, Jin T, He C, Khullar S, Chang Q, Wang D. Bringing machine learning to research on intellectual and developmental disabilities: taking inspiration from neurological diseases. J Neurodev Disord 2022; 14:28. [PMID: 35501679 PMCID: PMC9059371 DOI: 10.1186/s11689-022-09438-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 04/07/2022] [Indexed: 12/31/2022] Open
Abstract
Intellectual and Developmental Disabilities (IDDs), such as Down syndrome, Fragile X syndrome, Rett syndrome, and autism spectrum disorder, usually manifest at birth or early childhood. IDDs are characterized by significant impairment in intellectual and adaptive functioning, and both genetic and environmental factors underpin IDD biology. Molecular and genetic stratification of IDDs remain challenging mainly due to overlapping factors and comorbidity. Advances in high throughput sequencing, imaging, and tools to record behavioral data at scale have greatly enhanced our understanding of the molecular, cellular, structural, and environmental basis of some IDDs. Fueled by the "big data" revolution, artificial intelligence (AI) and machine learning (ML) technologies have brought a whole new paradigm shift in computational biology. Evidently, the ML-driven approach to clinical diagnoses has the potential to augment classical methods that use symptoms and external observations, hoping to push the personalized treatment plan forward. Therefore, integrative analyses and applications of ML technology have a direct bearing on discoveries in IDDs. The application of ML to IDDs can potentially improve screening and early diagnosis, advance our understanding of the complexity of comorbidity, and accelerate the identification of biomarkers for clinical research and drug development. For more than five decades, the IDDRC network has supported a nexus of investigators at centers across the USA, all striving to understand the interplay between various factors underlying IDDs. In this review, we introduced fast-increasing multi-modal data types, highlighted example studies that employed ML technologies to illuminate factors and biological mechanisms underlying IDDs, as well as recent advances in ML technologies and their applications to IDDs and other neurological diseases. We discussed various molecular, clinical, and environmental data collection modes, including genetic, imaging, phenotypical, and behavioral data types, along with multiple repositories that store and share such data. Furthermore, we outlined some fundamental concepts of machine learning algorithms and presented our opinion on specific gaps that will need to be filled to accomplish, for example, reliable implementation of ML-based diagnosis technology in IDD clinics. We anticipate that this review will guide researchers to formulate AI and ML-based approaches to investigate IDDs and related conditions.
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Affiliation(s)
- Chirag Gupta
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Pramod Chandrashekar
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Ting Jin
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Chenfeng He
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Saniya Khullar
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Qiang Chang
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Department of Medical Genetics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Department of Neurology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Daifeng Wang
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA.
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA.
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, 53706, USA.
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9
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Wang Z, Ji Y, Fu Y, Liu F, Du X, Liu H, Zhu W, Xue K, Qin W, Zhang Q. Gene expression associated with human brain activations in facial expression recognition. Brain Imaging Behav 2022; 16:1657-1670. [PMID: 35212890 DOI: 10.1007/s11682-022-00633-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/10/2022] [Indexed: 11/30/2022]
Abstract
Previous studies identified some genetic loci of emotion, but few focused on human emotion-related gene expression. In this study, the facial expression recognition (FER) task-based high-resolution fMRI data of 203 subjects in the Human Connectome Project (HCP) and expression data of the six healthy human postmortem brain tissues in the Allen Human Brain Atlas (AHBA) were used to conduct a transcriptome-neuroimaging spatial association analysis. Finally, 371 genes were identified to be significantly associated with FER-related brain activations. Enrichment analyses revealed that FER-related genes were mainly expressed in the brain, especially neurons, and might be related to cell junction organization, synaptic functions, and nervous system development regulation, indicating that FER was a complex polygenetic biological process involving multiple pathways. Moreover, these genes exhibited higher enrichment for psychiatric diseases with heavy emotion impairments. This study provided new insight into understanding the FER-related biological mechanisms and might be helpful to explore treatment methods for emotion-related psychiatric disorders.
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Affiliation(s)
- Zirui Wang
- Department of Medical imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, Tianjin, 300052, China
| | - Yuan Ji
- Department of Medical imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, Tianjin, 300052, China
| | - Yumeng Fu
- Department of Medical imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, Tianjin, 300052, China
| | - Feng Liu
- Department of Medical imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, Tianjin, 300052, China
| | - Xin Du
- Department of Medical imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, Tianjin, 300052, China
| | - Huaigui Liu
- Department of Medical imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, Tianjin, 300052, China
| | - Wenshuang Zhu
- Department of Medical imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, Tianjin, 300052, China
| | - Kaizhong Xue
- Department of Medical imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, Tianjin, 300052, China
| | - Wen Qin
- Department of Medical imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, Tianjin, 300052, China
| | - Quan Zhang
- Department of Medical imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, Tianjin, 300052, China.
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10
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Liu W, Peeters N, Fernández G, Kohn N. Common neural and transcriptional correlates of inhibitory control underlie emotion regulation and memory control. Soc Cogn Affect Neurosci 2021; 15:523-536. [PMID: 32507888 PMCID: PMC7328031 DOI: 10.1093/scan/nsaa073] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 05/05/2020] [Accepted: 05/22/2020] [Indexed: 12/30/2022] Open
Abstract
Inhibitory control is crucial for regulating emotions and may also enable memory control. However, evidence for their shared neurobiological correlates is limited. Here, we report meta-analyses of neuroimaging studies on emotion regulation, or memory control and link neural commonalities to transcriptional commonalities using the Allen Human Brain Atlas (AHBA). Based on 95 functional magnetic resonance imaging studies, we reveal a role of the right inferior parietal lobule embedded in a frontal–parietal–insular network during emotion regulation and memory control, which is similarly recruited during response inhibition. These co-activation patterns also overlap with the networks associated with ‘inhibition’, ‘cognitive control’ and ‘working memory’ when consulting the Neurosynth. Using the AHBA, we demonstrate that emotion regulation- and memory control-related brain activity patterns are associated with transcriptional profiles of a specific set of ‘inhibition-related’ genes. Gene ontology enrichment analysis of these ‘inhibition-related’ genes reveal associations with the neuronal transmission and risk for major psychiatric disorders as well as seizures and alcoholic dependence. In summary, this study identified a neural network and a set of genes associated with inhibitory control across emotion regulation and memory control. These findings facilitate our understanding of the neurobiological correlates of inhibitory control and may contribute to the development of brain stimulation and pharmacological interventions.
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Affiliation(s)
- Wei Liu
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, 6525 EN Nijmegen, The Netherlands
| | - Nancy Peeters
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, 6525 EN Nijmegen, The Netherlands
| | - Guillén Fernández
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, 6525 EN Nijmegen, The Netherlands
| | - Nils Kohn
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, 6525 EN Nijmegen, The Netherlands
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11
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Berto S, Fontenot MR, Seger S, Ayhan F, Caglayan E, Kulkarni A, Douglas C, Tamminga CA, Lega BC, Konopka G. Gene-expression correlates of the oscillatory signatures supporting human episodic memory encoding. Nat Neurosci 2021; 24:554-564. [PMID: 33686299 PMCID: PMC8016736 DOI: 10.1038/s41593-021-00803-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Accepted: 01/19/2021] [Indexed: 12/13/2022]
Abstract
In humans, brain oscillations support critical features of memory formation. However, understanding the molecular mechanisms underlying this activity remains a major challenge. Here, we measured memory-sensitive oscillations using intracranial electroencephalography recordings from the temporal cortex of patients performing an episodic memory task. When these patients subsequently underwent resection, we employed transcriptomics on the temporal cortex to link gene expression with brain oscillations and identified genes correlated with oscillatory signatures of memory formation across six frequency bands. A co-expression analysis isolated oscillatory signature-specific modules associated with neuropsychiatric disorders and ion channel activity, with highly correlated genes exhibiting strong connectivity within these modules. Using single-nucleus transcriptomics, we further revealed that these modules are enriched for specific classes of both excitatory and inhibitory neurons, and immunohistochemistry confirmed expression of highly correlated genes. This unprecedented dataset of patient-specific brain oscillations coupled to genomics unlocks new insights into the genetic mechanisms that support memory encoding.
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Affiliation(s)
- Stefano Berto
- Department of Neuroscience, UT Southwestern Medical Center, Dallas, TX, USA
| | - Miles R Fontenot
- Department of Neuroscience, UT Southwestern Medical Center, Dallas, TX, USA
| | - Sarah Seger
- Department of Neurosurgery, UT Southwestern Medical Center, Dallas, TX, USA
| | - Fatma Ayhan
- Department of Neuroscience, UT Southwestern Medical Center, Dallas, TX, USA
| | - Emre Caglayan
- Department of Neuroscience, UT Southwestern Medical Center, Dallas, TX, USA
| | | | - Connor Douglas
- Department of Neuroscience, UT Southwestern Medical Center, Dallas, TX, USA
| | - Carol A Tamminga
- Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX, USA
| | - Bradley C Lega
- Department of Neurosurgery, UT Southwestern Medical Center, Dallas, TX, USA.
| | - Genevieve Konopka
- Department of Neuroscience, UT Southwestern Medical Center, Dallas, TX, USA.
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12
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Xie X, Li L, Wu H, Hou F, Chen Y, Xue Q, Zhou Y, Zhang J, Gong J, Song R. Comprehensive Integrative Analyses Identify TIGD5 rs75547282 as a Risk Variant for Autism Spectrum Disorder. Autism Res 2021; 14:631-644. [PMID: 33393181 DOI: 10.1002/aur.2466] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 12/16/2020] [Accepted: 12/21/2020] [Indexed: 12/12/2022]
Abstract
Although recent genome-wide association studies have identified risk loci that strongly associates with autism spectrum disorder (ASD), how to pinpoint the causal genes remains a challenge. We aimed to pinpoint the potential causal genes and explore the possible susceptibility and mechanism. A convergent functional genomics (CFG) method was used to prioritize the candidate genes by combining lines of evidence, including Sherlock analysis, spatio-temporal expression patterns, expression analysis, protein-protein interactions, co-expression and association with brain structure. A higher score in the CFG approach suggested that more evidence supported this gene as an ASD risk gene. We screened genes with higher CFG scores for candidate functional single nucleotide polymorphisms (SNPs). A genotyping experiment (602 ASD children and 604 healthy sex-matched children) and the dual-luciferase reporter gene assay were followed to validate the effects of SNPs. We identified three genes (MAPT, ZNF285, and TIGD5) as candidate causal genes using the CFG approach. The genotyping experiment showed that TIGD5 rs75547282 was associated with an increased risk of ASD under the dominant model (OR = 1.37, 95% CI = 1.09-1.72, P = 0.006) though the statistical power was limited (5.2%). The T allele of rs75547282 activated the expression of TIGD5 compared with the C allele in the dual-luciferase reporter assay. Our study indicates that such comprehensive integrative analyses may be an effective way to explore promising ASD susceptibility variants and needs to be further investigated in future research. Genotyping experiments should, however, be based on a larger population sample to increase statistical power. LAY SUMMARY: We set out to pinpoint the potential causal genes of ASD and explore the possible susceptibility and mechanism by combining lines of evidence from different analyses. Our results show that TIGD5 rs75547282 is associated with the risk of ASD in the Han Chinese population. In addition, a similar framework to seek promising ASD risk variants could be further investigated in future research Autism Res 2021, 14: 631-644. © 2021 International Society for Autism Research and Wiley Periodicals LLC.
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Affiliation(s)
- Xinyan Xie
- Department of Maternal and Child Health and MOE (Ministry of Education) Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Li Li
- Maternity and Children Health Care Hospital of Luohu District, Shenzhen, China
| | - Hao Wu
- Department of Maternal and Child Health and MOE (Ministry of Education) Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Fang Hou
- Maternity and Children Health Care Hospital of Luohu District, Shenzhen, China
| | - Yanlin Chen
- Maternity and Children Health Care Hospital of Luohu District, Shenzhen, China
| | - Qi Xue
- Department of Maternal and Child Health and MOE (Ministry of Education) Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yu Zhou
- Department of Maternal and Child Health and MOE (Ministry of Education) Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jiajia Zhang
- Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, 29208, USA
| | - Jianhua Gong
- Maternity and Children Health Care Hospital of Luohu District, Shenzhen, China
| | - Ranran Song
- Department of Maternal and Child Health and MOE (Ministry of Education) Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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13
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Zhao X, Chen J, Xiao P, Feng J, Nie Q, Zhao XM. Identifying age-specific gene signatures of the human cerebral cortex with joint analysis of transcriptomes and functional connectomes. Brief Bioinform 2020; 22:6048938. [PMID: 33367491 DOI: 10.1093/bib/bbaa388] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 11/10/2020] [Accepted: 11/27/2020] [Indexed: 11/13/2022] Open
Abstract
The human cerebral cortex undergoes profound structural and functional dynamic variations across the lifespan, whereas the underlying molecular mechanisms remain unclear. Here, with a novel method transcriptome-connectome correlation analysis (TCA), which integrates the brain functional magnetic resonance images and region-specific transcriptomes, we identify age-specific cortex (ASC) gene signatures for adolescence, early adulthood and late adulthood. The ASC gene signatures are significantly correlated with the cortical thickness (P-value <2.00e-3) and myelination (P-value <1.00e-3), two key brain structural features that vary in accordance with brain development. In addition to the molecular underpinning of age-related brain functions, the ASC gene signatures allow delineation of the molecular mechanisms of neuropsychiatric disorders, such as the regulation between ARNT2 and its target gene ETF1 involved in Schizophrenia. We further validate the ASC gene signatures with published gene sets associated with the adult cortex, and confirm the robustness of TCA on other brain image datasets. Availability: All scripts are written in R. Scripts for the TCA method and related statistics result can be freely accessed at https://github.com/Soulnature/TCA. Additional data related to this paper may be requested from the authors.
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Affiliation(s)
- Xingzhong Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, China
| | - Jingqi Chen
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, China
| | - Peipei Xiao
- Department of Electronic and Information Engineering, Tongji University, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, China
| | - Qing Nie
- Department of Biomedical Engineering, University of California, Irvine, USA
| | - Xing-Ming Zhao
- ISTBI, RIICS, Fudan University, and MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and Frontiers Center for Brain Science, China
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14
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Cervera J, Levin M, Mafe S. Bioelectrical Coupling of Single-Cell States in Multicellular Systems. J Phys Chem Lett 2020; 11:3234-3241. [PMID: 32243754 DOI: 10.1021/acs.jpclett.0c00641] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
The spatiotemporal distributions of signaling ions and molecules that modulate biochemical pathways in nonexcitable cells are influenced by multicellular electric potentials. These potentials act as distributed controllers encoding instructive spatial patterns in development and regeneration. We review experimental facts and discuss recent bioelectrical models that provide new physical insights and complement biochemical approaches. Single-cell states are modulated at the multicellular level because of the coupling between neighboring cells, thus allowing memories and multicellular patterns. The model is based on (i) two generic voltage-gated ion channels that promote the polarized and depolarized cell states, (ii) a feedback mechanism for the transcriptional and bioelectrical regulations, and (iii) voltage-gated intercellular conductances that allow a dynamic intercellular connectivity. The simulations provide steady-state and oscillatory multicellular states that help explain aspects of development and guide experimental procedures attempting to establish instructive bioelectrical patterns based on electric potentials and currents to regulate cell behavior and morphogenesis.
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Affiliation(s)
- Javier Cervera
- Dept. Termodinàmica, Facultat de Física, Universitat de València, E-46100 Burjassot, Spain
| | - Michael Levin
- Dept. of Biology and Allen Discovery Center at Tufts University, Medford, Massachusetts 02155-4243, United States
| | - Salvador Mafe
- Dept. Termodinàmica, Facultat de Física, Universitat de València, E-46100 Burjassot, Spain
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15
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Wang W, Wang GZ. Twin-peak temporal regulation during human neocortical development. Cell Discov 2019; 5:61. [PMID: 31871735 PMCID: PMC6915741 DOI: 10.1038/s41421-019-0129-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Accepted: 09/11/2019] [Indexed: 01/05/2023] Open
Abstract
Understanding the temporal and spatial expression patterns of the human cerebral cortex is essential for expanding knowledge of its functionality. Previous analysis focused on the differentially expressed genes (DEGs) among cortical subregions revealed an hourglass model for interareal differences. However, the overall pattern of transcriptional differences during the development of every region remains to be fully explored. Here, analysing more than 800 neocortex samples from lifespan transcriptional profiles revealed that excitatory neurons are more regulated than inhibitory neurons in the foetal brain. Developmental DEGs tend to be resting state or memory encoding-related and are also involved in autism and Alzheimer’s disease. In addition, twin peaks of DEGs occur during the development of each neocortex region, with a first peak appearing in the perinatal period and an unexpected second peak appearing around childhood. Genes in these peaks have similar functions, but the second peak is more inhibitory neuron related. All these results emphasize the significance of this unique temporal regulatory pattern for human neocortical development.
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Affiliation(s)
- Wei Wang
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031 China
| | - Guang-Zhong Wang
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031 China
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16
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Distinct Genetic Signatures of Cortical and Subcortical Regions Associated with Human Memory. eNeuro 2019; 6:ENEURO.0283-19.2019. [PMID: 31818829 PMCID: PMC6917897 DOI: 10.1523/eneuro.0283-19.2019] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Revised: 11/12/2019] [Accepted: 11/19/2019] [Indexed: 11/21/2022] Open
Abstract
Despite the discovery of gene variants linked to memory performance, understanding the genetic basis of adult human memory remains a challenge. Here, we devised an unsupervised framework that relies on spatial correlations between human transcriptome data and functional neuroimaging maps to uncover the genetic signatures of memory in functionally-defined cortical and subcortical memory regions. Despite the discovery of gene variants linked to memory performance, understanding the genetic basis of adult human memory remains a challenge. Here, we devised an unsupervised framework that relies on spatial correlations between human transcriptome data and functional neuroimaging maps to uncover the genetic signatures of memory in functionally-defined cortical and subcortical memory regions. Results were validated with animal literature and showed that our framework is highly effective in identifying memory-related processes and genes compared to a control cognitive function. Genes preferentially expressed in cortical memory regions are linked to memory-related processes such as immune and epigenetic regulation. Genes expressed in subcortical memory regions are associated with neurogenesis and glial cell differentiation. Genes expressed in both cortical and subcortical memory areas are involved in the regulation of transcription, synaptic plasticity, and glutamate receptor signaling. Furthermore, distinct memory-associated genes such as PRKCD and CDK5 are linked to cortical and subcortical regions, respectively. Thus, cortical and subcortical memory regions exhibit distinct genetic signatures that potentially reflect functional differences in health and disease, and nominates gene candidates for future experimental investigations.
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17
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Malykh SB, Malykh AS, Karunas AS, Enikeeva RF, Davydova YD, Khusnutdinova EK. Molecular Genetic Studies of Cognitive Ability. RUSS J GENET+ 2019. [DOI: 10.1134/s1022795419070111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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18
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Cervera J, Manzanares JA, Mafe S, Levin M. Synchronization of Bioelectric Oscillations in Networks of Nonexcitable Cells: From Single-Cell to Multicellular States. J Phys Chem B 2019; 123:3924-3934. [PMID: 31003574 DOI: 10.1021/acs.jpcb.9b01717] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Biological networks use collective oscillations for information processing tasks. In particular, oscillatory membrane potentials have been observed in nonexcitable cells and bacterial communities where specific ion channel proteins contribute to the bioelectric coordination of large populations. We aim at describing theoretically the oscillatory spatiotemporal patterns that emerge at the multicellular level from the single-cell bioelectric dynamics. To this end, we focus on two key questions: (i) What single-cell properties are relevant to multicellular behavior? (ii) What properties defined at the multicellular level can allow an external control of the bioelectric dynamics? In particular, we explore the interplay between transcriptional and translational dynamics and membrane potential dynamics in a model multicellular ensemble, describe the spatiotemporal patterns that arise when the average electric potential allows groups of cells to act as a coordinated multicellular patch, and characterize the resulting synchronization phenomena. The simulations concern bioelectric networks and collective communication across different scales based on oscillatory and synchronization phenomena, thus shedding light on the physiological dynamics of a wide range of endogenous contexts across embryogenesis and regeneration.
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Affiliation(s)
- Javier Cervera
- Departament de Termodinàmica, Facultat de Física , Universitat de València , E-46100 Burjassot , Spain
| | - José Antonio Manzanares
- Departament de Termodinàmica, Facultat de Física , Universitat de València , E-46100 Burjassot , Spain
| | - Salvador Mafe
- Departament de Termodinàmica, Facultat de Física , Universitat de València , E-46100 Burjassot , Spain
| | - Michael Levin
- Allen Discovery Center at Tufts University, Department of Biology , Tufts University Medford , Massachusetts 02155-4243 , United States
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