LIVIVO - Search results -

Search results

Result 1 - 10 of total 18

Search options

Article ; Online: Predicting brain-regional gene regulatory networks from multi-omics for Alzheimer's disease phenotypes and Covid-19 severity.

Khullar, Saniya / Wang, Daifeng

2023 Volume 32, Issue 11, Page(s) 1797–1813

Abstract: Neuroinflammation and immune dysregulation play a key role in Alzheimer's disease (AD) and are also associated with severe Covid-19 and neurological symptoms. Also, genome-wide association studies found many risk single nucleotide polymorphisms (SNPs) ... ...

Abstract	Neuroinflammation and immune dysregulation play a key role in Alzheimer's disease (AD) and are also associated with severe Covid-19 and neurological symptoms. Also, genome-wide association studies found many risk single nucleotide polymorphisms (SNPs) for AD and Covid-19. However, our understanding of underlying gene regulatory mechanisms from risk SNPs to AD, Covid-19 and phenotypes is still limited. To this end, we performed an integrative multi-omics analysis to predict gene regulatory networks for major brain regions from population data in AD. Our networks linked transcription factors (TFs) to TF binding sites (TFBSs) on regulatory elements to target genes. Comparative network analyses revealed cross-region-conserved and region-specific regulatory networks, in which many immunological genes are present. Furthermore, we identified a list of AD-Covid genes using our networks involving known and Covid-19 genes. Our machine learning analysis prioritized 36 AD-Covid candidate genes for predicting Covid severity. Our independent validation analyses found that these genes outperform known genes for classifying Covid-19 severity and AD. Finally, we mapped genome-wide association study SNPs of AD and severe Covid that interrupt TFBSs on our regulatory networks, revealing potential mechanistic insights of those disease risk variants. Our analyses and results are open-source available, providing an AD-Covid functional genomic resource at the brain region level.
MeSH term(s)	Humans ; Alzheimer Disease/genetics ; Alzheimer Disease/metabolism ; Gene Regulatory Networks ; Genome-Wide Association Study ; Multiomics ; COVID-19/genetics ; Brain/metabolism ; Phenotype
Language	English
Publishing date	2023-01-14
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	1108742-0
ISSN	1460-2083 ; 0964-6906
ISSN (online)	1460-2083
ISSN	0964-6906
DOI	10.1093/hmg/ddad009
Database	MEDical Literature Analysis and Retrieval System OnLINE

In stock of ZB MED Cologne/Königswinter

Zs.A 3469: Show issues

Location:
Je nach Verfügbarkeit (siehe Angabe bei Bestand)
bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
Jg. 1995 - 2021: Lesesall (2.OG)
ab Jg. 2022: Lesesaal (EG)

Order via subito

This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

Details ▾
- See ZB MED holdings
- Order with fees

Article: Predicting gene regulatory networks from multi-omics to link genetic risk variants and neuroimmunology to Alzheimer's disease phenotypes.

Khullar, Saniya / Wang, Daifeng

bioRxiv : the preprint server for biology

2021

Abstract: Background: Genome-wide association studies have found many genetic risk variants associated with Alzheimer's disease (AD). However, how these risk variants affect deeper phenotypes such as disease progression and immune response remains elusive. Also, ... ...

Abstract	Background: Genome-wide association studies have found many genetic risk variants associated with Alzheimer's disease (AD). However, how these risk variants affect deeper phenotypes such as disease progression and immune response remains elusive. Also, our understanding of cellular and molecular mechanisms from disease risk variants to various phenotypes is still limited. To address these problems, we performed an integrative multi-omics analysis of genotype, transcriptomics, and epigenomics for revealing gene regulatory mechanisms from disease variants to AD phenotypes. Method: First, given the population gene expression data of a cohort, we construct and cluster its gene co-expression network to identify gene co-expression modules for various AD phenotypes. Next, we predict transcription factors (TFs) regulating co-expressed genes and AD risk SNPs that interrupt TF binding sites on regulatory elements. Finally, we construct a gene regulatory network (GRN) linking SNPs, interrupted TFs, and regulatory elements to target genes and gene modules for each phenotype in the cohort. This network thus provides systematic insights into gene regulatory mechanisms from risk variants to AD phenotypes. Results: Our analysis predicted GRNs in three major AD-relevant regions: Hippocampus, Dorsolateral Prefrontal Cortex (DLPFC), Lateral Temporal Lobe (LTL). Comparative analyses revealed cross-region-conserved and region-specific GRNs, in which many immunological genes are present. For instance, SNPs rs13404184 and rs61068452 disrupt SPI1 binding and regulation of AD gene INPP5D in the Hippocampus and LTL. However, SNP rs117863556 interrupts bindings of REST to regulate GAB2 in DLPFC only. Driven by emerging neuroinflammation in AD, we used Covid-19 as a proxy to identify possible regulatory mechanisms for neuroimmunology in AD. To this end, we looked at the GRN subnetworks relating to genes from shared AD-Covid pathways. From those subnetworks, our machine learning analysis prioritized the AD-Covid genes for predicting Covid-19 severity. Decision Curve Analysis also validated our AD-Covid genes outperform known Covid-19 genes for classifying severe Covid-19 patients. This suggests AD-Covid genes along with linked SNPs can be potential novel biomarkers for neuroimmunology in AD. Finally, our results are open-source available as a comprehensive functional genomic map for AD, providing a deeper mechanistic understanding of the interplay among multi-omics, brain regions, gene functions like neuroimmunology, and phenotypes.
Language	English
Publishing date	2021-09-20
Publishing country	United States
Document type	Preprint
DOI	10.1101/2021.06.21.449165
Database	MEDical Literature Analysis and Retrieval System OnLINE

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Details ▾

Article: NetREm: Network Regression Embeddings reveal cell-type transcription factor coordination for gene regulation.

Khullar, Saniya / Huang, Xiang / Ramesh, Raghu / Svaren, John / Wang, Daifeng

bioRxiv : the preprint server for biology

2023

Abstract: Transcription factor (TF) coordination plays a key role in gene regulation such as protein-protein interactions (PPIs) and DNA co-bindings. Single-cell technologies facilitate gene expression measurement for individual cells and cell-type identification, ...

Abstract	Transcription factor (TF) coordination plays a key role in gene regulation such as protein-protein interactions (PPIs) and DNA co-bindings. Single-cell technologies facilitate gene expression measurement for individual cells and cell-type identification, yet the connection between TF coordination and gene regulation of various cell types remains unclear. To address this, we have developed a novel computational approach, Network Regression Embedding (NetREm), to reveal cell-type TF-TF coordination activities for gene regulation. NetREm leverages network-constrained regularization using prior interaction knowledge (e.g., protein, chromatin, TF binding) to analyze single-cell gene expression data. We test NetREm by simulation data and apply it to analyze various cell types in both central and peripheral nerve systems (PNS) such as neuronal, glial and Schwann cells as well as in Alzheimer's disease (AD). Our findings uncover cell-type coordinating TFs and identify new TF-target gene candidate links. We also validate our top predictions using Cut&Run and knockout loss-of-function expression data in rat and mouse models and compare our results with additional functional genomic data including expression quantitative trait loci (eQTL) and Genome-Wide Association Studies (GWAS) to link genetic variants to TF coordination. NetREm is open-source available at https://github.com/SaniyaKhullar/NetREm .
Language	English
Publishing date	2023-10-30
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.10.25.563769
Database	MEDical Literature Analysis and Retrieval System OnLINE

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Details ▾

Article ; Online: Bringing machine learning to research on intellectual and developmental disabilities: taking inspiration from neurological diseases.

Gupta, Chirag / Chandrashekar, Pramod / Jin, Ting / He, Chenfeng / Khullar, Saniya / Chang, Qiang / Wang, Daifeng

Journal of neurodevelopmental disorders

2022 Volume 14, Issue 1, Page(s) 28

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 ...

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.
MeSH term(s)	Artificial Intelligence ; Autism Spectrum Disorder/diagnosis ; Child ; Child, Preschool ; Developmental Disabilities/diagnosis ; Humans ; Infant, Newborn ; Intellectual Disability/diagnosis ; Machine Learning
Language	English
Publishing date	2022-05-02
Publishing country	England
Document type	Journal Article ; Review ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2487174-6
ISSN	1866-1955 ; 1866-1955
ISSN (online)	1866-1955
ISSN	1866-1955
DOI	10.1186/s11689-022-09438-w
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Article ; Online: Single-cell network biology characterizes cell type gene regulation for drug repurposing and phenotype prediction in Alzheimer's disease.

Gupta, Chirag / Xu, Jielin / Jin, Ting / Khullar, Saniya / Liu, Xiaoyu / Alatkar, Sayali / Cheng, Feixiong / Wang, Daifeng

PLoS computational biology

2022 Volume 18, Issue 7, Page(s) e1010287

Abstract: Dysregulation of gene expression in Alzheimer's disease (AD) remains elusive, especially at the cell type level. Gene regulatory network, a key molecular mechanism linking transcription factors (TFs) and regulatory elements to govern gene expression, can ...

Abstract	Dysregulation of gene expression in Alzheimer's disease (AD) remains elusive, especially at the cell type level. Gene regulatory network, a key molecular mechanism linking transcription factors (TFs) and regulatory elements to govern gene expression, can change across cell types in the human brain and thus serve as a model for studying gene dysregulation in AD. However, AD-induced regulatory changes across brain cell types remains uncharted. To address this, we integrated single-cell multi-omics datasets to predict the gene regulatory networks of four major cell types, excitatory and inhibitory neurons, microglia and oligodendrocytes, in control and AD brains. Importantly, we analyzed and compared the structural and topological features of networks across cell types and examined changes in AD. Our analysis shows that hub TFs are largely common across cell types and AD-related changes are relatively more prominent in some cell types (e.g., microglia). The regulatory logics of enriched network motifs (e.g., feed-forward loops) further uncover cell type-specific TF-TF cooperativities in gene regulation. The cell type networks are also highly modular and several network modules with cell-type-specific expression changes in AD pathology are enriched with AD-risk genes. The further disease-module-drug association analysis suggests cell-type candidate drugs and their potential target genes. Finally, our network-based machine learning analysis systematically prioritized cell type risk genes likely involved in AD. Our strategy is validated using an independent dataset which showed that top ranked genes can predict clinical phenotypes (e.g., cognitive impairment) of AD with reasonable accuracy. Overall, this single-cell network biology analysis provides a comprehensive map linking genes, regulatory networks, cell types and drug targets and reveals cell-type gene dysregulation in AD.
MeSH term(s)	Alzheimer Disease/metabolism ; Biology ; Drug Repositioning ; Gene Expression Profiling ; Gene Regulatory Networks/genetics ; Humans ; Phenotype
Language	English
Publishing date	2022-07-18
Publishing country	United States
Document type	Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1010287
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Article ; Online: Illuminating links between cis-regulators and trans-acting variants in the human prefrontal cortex.

Liu, Shuang / Won, Hyejung / Clarke, Declan / Matoba, Nana / Khullar, Saniya / Mu, Yudi / Wang, Daifeng / Gerstein, Mark

Genome medicine

2022 Volume 14, Issue 1, Page(s) 133

Abstract: Background: Neuropsychiatric disorders afflict a large portion of the global population and constitute a significant source of disability worldwide. Although Genome-wide Association Studies (GWAS) have identified many disorder-associated variants, the ... ...

Abstract	Background: Neuropsychiatric disorders afflict a large portion of the global population and constitute a significant source of disability worldwide. Although Genome-wide Association Studies (GWAS) have identified many disorder-associated variants, the underlying regulatory mechanisms linking them to disorders remain elusive, especially those involving distant genomic elements. Expression quantitative trait loci (eQTLs) constitute a powerful means of providing this missing link. However, most eQTL studies in human brains have focused exclusively on cis-eQTLs, which link variants to nearby genes (i.e., those within 1 Mb of a variant). A complete understanding of disease etiology requires a clearer understanding of trans-regulatory mechanisms, which, in turn, entails a detailed analysis of the relationships between variants and expression changes in distant genes. Methods: By leveraging large datasets from the PsychENCODE consortium, we conducted a genome-wide survey of trans-eQTLs in the human dorsolateral prefrontal cortex. We also performed colocalization and mediation analyses to identify mediators in trans-regulation and use trans-eQTLs to link GWAS loci to schizophrenia risk genes. Results: We identified ~80,000 candidate trans-eQTLs (at FDR<0.25) that influence the expression of ~10K target genes (i.e., "trans-eGenes"). We found that many variants associated with these candidate trans-eQTLs overlap with known cis-eQTLs. Moreover, for >60% of these variants (by colocalization), the cis-eQTL's target gene acts as a mediator for the trans-eQTL SNP's effect on the trans-eGene, highlighting examples of cis-mediation as essential for trans-regulation. Furthermore, many of these colocalized variants fall into a discernable pattern wherein cis-eQTL's target is a transcription factor or RNA-binding protein, which, in turn, targets the gene associated with the candidate trans-eQTL. Finally, we show that trans-regulatory mechanisms provide valuable insights into psychiatric disorders: beyond what had been possible using only cis-eQTLs, we link an additional 23 GWAS loci and 90 risk genes (using colocalization between candidate trans-eQTLs and schizophrenia GWAS loci). Conclusions: We demonstrate that the transcriptional architecture of the human brain is orchestrated by both cis- and trans-regulatory variants and found that trans-eQTLs provide insights into brain-disease biology.
MeSH term(s)	Humans ; Genome-Wide Association Study ; Quantitative Trait Loci ; Polymorphism, Single Nucleotide ; Gene Expression Regulation ; Prefrontal Cortex
Language	English
Publishing date	2022-11-24
Publishing country	England
Document type	Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, N.I.H., Extramural
ZDB-ID	2484394-5
ISSN	1756-994X ; 1756-994X
ISSN (online)	1756-994X
ISSN	1756-994X
DOI	10.1186/s13073-022-01133-8
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Article ; Online: BOMA, a machine-learning framework for comparative gene expression analysis across brains and organoids.

He, Chenfeng / Kalafut, Noah Cohen / Sandoval, Soraya O / Risgaard, Ryan / Sirois, Carissa L / Yang, Chen / Khullar, Saniya / Suzuki, Marin / Huang, Xiang / Chang, Qiang / Zhao, Xinyu / Sousa, Andre M M / Wang, Daifeng

Cell reports methods

2023 Volume 3, Issue 2, Page(s) 100409

Abstract: Our machine-learning framework, brain and organoid manifold alignment (BOMA), first performs a global alignment of developmental gene expression data between brains and organoids. It then applies manifold learning to locally refine the alignment, ... ...

Abstract	Our machine-learning framework, brain and organoid manifold alignment (BOMA), first performs a global alignment of developmental gene expression data between brains and organoids. It then applies manifold learning to locally refine the alignment, revealing conserved and specific developmental trajectories across brains and organoids. Using BOMA, we found that human cortical organoids better align with certain brain cortical regions than with other non-cortical regions, implying organoid-preserved developmental gene expression programs specific to brain regions. Additionally, our alignment of non-human primate and human brains reveals highly conserved gene expression around birth. Also, we integrated and analyzed developmental single-cell RNA sequencing (scRNA-seq) data of human brains and organoids, showing conserved and specific cell trajectories and clusters. Further identification of expressed genes of such clusters and enrichment analyses reveal brain- or organoid-specific developmental functions and pathways. Finally, we experimentally validated important specific expressed genes through the use of immunofluorescence. BOMA is open-source available as a web tool for community use.
MeSH term(s)	Animals ; Brain ; Gene Expression Profiling ; Organoids/metabolism
Language	English
Publishing date	2023-02-15
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ISSN	2667-2375
ISSN (online)	2667-2375
DOI	10.1016/j.crmeth.2023.100409
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: DeepGAMI: deep biologically guided auxiliary learning for multimodal integration and imputation to improve genotype-phenotype prediction.

Chandrashekar, Pramod Bharadwaj / Alatkar, Sayali / Wang, Jiebiao / Hoffman, Gabriel E / He, Chenfeng / Jin, Ting / Khullar, Saniya / Bendl, Jaroslav / Fullard, John F / Roussos, Panos / Wang, Daifeng

Genome medicine

2023 Volume 15, Issue 1, Page(s) 88

Abstract: Background: Genotypes are strongly associated with disease phenotypes, particularly in brain disorders. However, the molecular and cellular mechanisms behind this association remain elusive. With emerging multimodal data for these mechanisms, machine ... ...

Abstract	Background: Genotypes are strongly associated with disease phenotypes, particularly in brain disorders. However, the molecular and cellular mechanisms behind this association remain elusive. With emerging multimodal data for these mechanisms, machine learning methods can be applied for phenotype prediction at different scales, but due to the black-box nature of machine learning, integrating these modalities and interpreting biological mechanisms can be challenging. Additionally, the partial availability of these multimodal data presents a challenge in developing these predictive models. Method: To address these challenges, we developed DeepGAMI, an interpretable neural network model to improve genotype-phenotype prediction from multimodal data. DeepGAMI leverages functional genomic information, such as eQTLs and gene regulation, to guide neural network connections. Additionally, it includes an auxiliary learning layer for cross-modal imputation allowing the imputation of latent features of missing modalities and thus predicting phenotypes from a single modality. Finally, DeepGAMI uses integrated gradient to prioritize multimodal features for various phenotypes. Results: We applied DeepGAMI to several multimodal datasets including genotype and bulk and cell-type gene expression data in brain diseases, and gene expression and electrophysiology data of mouse neuronal cells. Using cross-validation and independent validation, DeepGAMI outperformed existing methods for classifying disease types, and cellular and clinical phenotypes, even using single modalities (e.g., AUC score of 0.79 for Schizophrenia and 0.73 for cognitive impairment in Alzheimer's disease). Conclusion: We demonstrated that DeepGAMI improves phenotype prediction and prioritizes phenotypic features and networks in multiple multimodal datasets in complex brains and brain diseases. Also, it prioritized disease-associated variants, genes, and regulatory networks linked to different phenotypes, providing novel insights into the interpretation of gene regulatory mechanisms. DeepGAMI is open-source and available for general use.
MeSH term(s)	Animals ; Mice ; Machine Learning ; Neural Networks, Computer ; Genotype ; Phenotype ; Alzheimer Disease/genetics
Language	English
Publishing date	2023-10-31
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2484394-5
ISSN	1756-994X ; 1756-994X
ISSN (online)	1756-994X
ISSN	1756-994X
DOI	10.1186/s13073-023-01248-6
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Article ; Online: Single-cell network biology characterizes cell type gene regulation for drug repurposing and phenotype prediction in Alzheimer's disease.

Chirag Gupta / Jielin Xu / Ting Jin / Saniya Khullar / Xiaoyu Liu / Sayali Alatkar / Feixiong Cheng / Daifeng Wang

PLoS Computational Biology, Vol 18, Iss 7, p e

2022 Volume 1010287

Abstract	Dysregulation of gene expression in Alzheimer's disease (AD) remains elusive, especially at the cell type level. Gene regulatory network, a key molecular mechanism linking transcription factors (TFs) and regulatory elements to govern gene expression, can change across cell types in the human brain and thus serve as a model for studying gene dysregulation in AD. However, AD-induced regulatory changes across brain cell types remains uncharted. To address this, we integrated single-cell multi-omics datasets to predict the gene regulatory networks of four major cell types, excitatory and inhibitory neurons, microglia and oligodendrocytes, in control and AD brains. Importantly, we analyzed and compared the structural and topological features of networks across cell types and examined changes in AD. Our analysis shows that hub TFs are largely common across cell types and AD-related changes are relatively more prominent in some cell types (e.g., microglia). The regulatory logics of enriched network motifs (e.g., feed-forward loops) further uncover cell type-specific TF-TF cooperativities in gene regulation. The cell type networks are also highly modular and several network modules with cell-type-specific expression changes in AD pathology are enriched with AD-risk genes. The further disease-module-drug association analysis suggests cell-type candidate drugs and their potential target genes. Finally, our network-based machine learning analysis systematically prioritized cell type risk genes likely involved in AD. Our strategy is validated using an independent dataset which showed that top ranked genes can predict clinical phenotypes (e.g., cognitive impairment) of AD with reasonable accuracy. Overall, this single-cell network biology analysis provides a comprehensive map linking genes, regulatory networks, cell types and drug targets and reveals cell-type gene dysregulation in AD.
Keywords	Biology (General) ; QH301-705.5
Subject code	570
Language	English
Publishing date	2022-07-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article: Age-maintained human neurons demonstrate a developmental loss of intrinsic neurite growth ability.

Lear, Bo P / Thompson, Elizabeth A N / Rodriguez, Kendra / Arndt, Zachary P / Khullar, Saniya / Klosa, Payton C / Lu, Ryan J / Morrow, Christopher S / Risgaard, Ryan / Peterson, Ella R / Teefy, Brian B / Bhattacharyya, Anita / Sousa, Andre M M / Wang, Daifeng / Benayoun, Bérénice A / Moore, Darcie L

bioRxiv : the preprint server for biology

2023

Abstract: Injury to adult mammalian central nervous system (CNS) axons results in limited regeneration. Rodent studies have revealed a developmental switch in CNS axon regenerative ability, yet whether this is conserved in humans is unknown. Using human ... ...

Abstract	Injury to adult mammalian central nervous system (CNS) axons results in limited regeneration. Rodent studies have revealed a developmental switch in CNS axon regenerative ability, yet whether this is conserved in humans is unknown. Using human fibroblasts from 8 gestational-weeks to 72 years-old, we performed direct reprogramming to transdifferentiate fibroblasts into induced neurons (Fib-iNs), avoiding pluripotency which restores cells to an embryonic state. We found that early gestational Fib-iNs grew longer neurites than all other ages, mirroring the developmental switch in regenerative ability in rodents. RNA-sequencing and screening revealed ARID1A as a developmentally-regulated modifier of neurite growth in human neurons. These data suggest that age-specific epigenetic changes may drive the intrinsic loss of neurite growth ability in human CNS neurons during development.
Language	English
Publishing date	2023-05-24
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.05.23.541995
Database	MEDical Literature Analysis and Retrieval System OnLINE

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Details ▾

To top

More links

Kategorien

In stock of ZB MED Cologne/Königswinter

Order via subito

More links

Kategorien

Inter-library loan at ZB MED

More links

Kategorien

Inter-library loan at ZB MED

More links

Kategorien

Order via subito

More links

Kategorien

Order via subito

More links

Kategorien

Order via subito

More links

Kategorien

Order via subito

Inter-library loan at ZB MED

More links

Kategorien

Order via subito

Full text online

More links

Kategorien

Inter-library loan at ZB MED

More links

Kategorien

Inter-library loan at ZB MED