scholarly journals Convergent and distributed effects of the 3q29 deletion on the human neural transcriptome

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Esra Sefik ◽  
Ryan H. Purcell ◽  
Katrina Aberizk ◽  
Hallie Averbach ◽  
Emily Black ◽  
...  
Keyword(s):  
Posttranslational Modifications ◽  
Molecular Mechanisms ◽  
Single Gene ◽  
Normal Function ◽  
Cellular Functions ◽  
Protein Coding ◽  
Risk Genes ◽  
Disease Associations ◽  
Guilt By Association ◽  
The Impact

AbstractThe 3q29 deletion (3q29Del) confers high risk for schizophrenia and other neurodevelopmental and psychiatric disorders. However, no single gene in this interval is definitively associated with disease, prompting the hypothesis that neuropsychiatric sequelae emerge upon loss of multiple functionally-connected genes. 3q29 genes are unevenly annotated and the impact of 3q29Del on the human neural transcriptome is unknown. To systematically formulate unbiased hypotheses about molecular mechanisms linking 3q29Del to neuropsychiatric illness, we conducted a systems-level network analysis of the non-pathological adult human cortical transcriptome and generated evidence-based predictions that relate 3q29 genes to novel functions and disease associations. The 21 protein-coding genes located in the interval segregated into seven clusters of highly co-expressed genes, demonstrating both convergent and distributed effects of 3q29Del across the interrogated transcriptomic landscape. Pathway analysis of these clusters indicated involvement in nervous-system functions, including synaptic signaling and organization, as well as core cellular functions, including transcriptional regulation, posttranslational modifications, chromatin remodeling, and mitochondrial metabolism. Top network-neighbors of 3q29 genes showed significant overlap with known schizophrenia, autism, and intellectual disability-risk genes, suggesting that 3q29Del biology is relevant to idiopathic disease. Leveraging “guilt by association”, we propose nine 3q29 genes, including one hub gene, as prioritized drivers of neuropsychiatric risk. These results provide testable hypotheses for experimental analysis on causal drivers and mechanisms of the largest known genetic risk factor for schizophrenia and highlight the study of normal function in non-pathological postmortem tissue to further our understanding of psychiatric genetics, especially for rare syndromes like 3q29Del, where access to neural tissue from carriers is unavailable or limited.

scholarly journals Convergent and distributed effects of the schizophrenia-associated 3q29 deletion on the human neural transcriptome

2020 ◽  
Author(s):  
Esra Sefik ◽  
Ryan H. Purcell ◽  
Elaine F. Walker ◽  
Gary J. Bassell ◽  
Jennifer G. Mulle ◽  
...  
Keyword(s):  
Molecular Mechanisms ◽  
Single Gene ◽  
Normal Function ◽  
Cellular Functions ◽  
Protein Coding ◽  
Post Translational Modifications ◽  
Increased Risk ◽  
Disease Associations ◽  
Guilt By Association ◽  
The Impact

AbstractThe 3q29 deletion (3q29Del) confers >40-fold increased risk for schizophrenia. However, no single gene in this interval is definitively associated with disease, prompting the hypothesis that neuropsychiatric sequelae emerge upon loss of multiple functionally-connected genes. 3q29 genes are unevenly annotated and the impact of 3q29Del on the human neural transcriptome is unknown. To systematically formulate unbiased hypotheses about molecular mechanisms linking 3q29Del to neuropsychiatric illness, we conducted a systems-level network analysis of the non-pathological adult human cortical transcriptome and generated evidence-based predictions that relate 3q29 genes to novel functions and disease associations. The 21 protein-coding genes located in the interval segregated into seven clusters of highly co-expressed genes, demonstrating both convergent and distributed effects of 3q29Del across the interrogated transcriptomic landscape. Pathway analysis of these clusters indicated involvement in nervous-system functions, including synaptic signaling and organization, as well as core cellular functions, including transcriptional regulation, post-translational modifications, chromatin remodeling and mitochondrial metabolism. Top network-neighbors of 3q29 genes showed significant overlap with known schizophrenia, autism and intellectual disability-risk genes, suggesting that 3q29Del biology is relevant to idiopathic disease. Leveraging “guilt by association”, we propose nine 3q29 genes, including one hub gene, as prioritized drivers of neuropsychiatric risk. These results provide testable hypotheses for experimental analysis on causal drivers and mechanisms of the largest known genetic risk factor for schizophrenia and highlight the study of normal function in non-pathological post-mortem tissue to further our understanding of psychiatric genetics, especially for rare syndromes like 3q29Del, where access to neural tissue from carriers is unavailable or limited.

scholarly journals DeepPheno: Predicting single gene loss-of-function phenotypes using an ontology-aware hierarchical classifier

2019 ◽  
Author(s):  
Maxat Kulmanov ◽  
Robert Hoehndorf
Keyword(s):  
Research Group ◽  
Molecular Mechanisms ◽  
State Of The Art ◽  
Single Gene ◽  
Hierarchical Classification ◽  
Model Organisms ◽  
Loss Of Function ◽  
Protein Coding ◽  
Disease Associations ◽  
Molecular Aberrations

AbstractMotivationPredicting the phenotypes resulting from molecular perturbations is one of the key challenges in genetics. Both forward and reverse genetic screen are employed to identify the molecular mechanisms underlying phenotypes and disease, and these resulted in a large number of genotype–phenotype association being available for humans and model organisms. Combined with recent advances in machine learning, it may now be possible to predict human phenotypes resulting from particular molecular aberrations.ResultsWe developed DeepPheno, a neural network based hierarchical multi-class multi-label classification method for predicting the phenotypes resulting from complete loss-of-function in single genes. DeepPheno uses the functional annotations with gene products to predict the phenotypes resulting from a loss-of-function; additionally, we employ a two-step procedure in which we predict these functions first and then predict phenotypes. Prediction of phenotypes is ontology-based and we propose a novel ontology-based classifier suitable for very large hierarchical classification tasks. These methods allow us to predict phenotypes associated with any known protein-coding gene. We evaluate our approach using evaluation metrics established by the CAFA challenge and compare with top performing CAFA2 methods as well as several state of the art phenotype prediction approaches, demonstrating the improvement of DeepPheno over state of the art methods. Furthermore, we show that predictions generated by DeepPheno are applicable to predicting gene–disease associations based on comparing phenotypes, and that a large number of new predictions made by DeepPheno interact with a gene that is already associated with the predicted phenotype.Availabilityhttps://github.com/bio-ontology-research-group/[email protected]

scholarly journals DeepPheno: Predicting single gene loss-of-function phenotypes using an ontology-aware hierarchical classifier

2020 ◽  
Vol 16 (11) ◽  
pp. e1008453
Author(s):  
Maxat Kulmanov ◽  
Robert Hoehndorf
Keyword(s):  
Molecular Mechanisms ◽  
Single Gene ◽  
Hierarchical Classification ◽  
Model Organisms ◽  
Loss Of Function ◽  
Protein Coding ◽  
Functional Annotations ◽  
Step Procedure ◽  
Disease Associations ◽  
Molecular Aberrations

Predicting the phenotypes resulting from molecular perturbations is one of the key challenges in genetics. Both forward and reverse genetic screen are employed to identify the molecular mechanisms underlying phenotypes and disease, and these resulted in a large number of genotype–phenotype association being available for humans and model organisms. Combined with recent advances in machine learning, it may now be possible to predict human phenotypes resulting from particular molecular aberrations. We developed DeepPheno, a neural network based hierarchical multi-class multi-label classification method for predicting the phenotypes resulting from loss-of-function in single genes. DeepPheno uses the functional annotations with gene products to predict the phenotypes resulting from a loss-of-function; additionally, we employ a two-step procedure in which we predict these functions first and then predict phenotypes. Prediction of phenotypes is ontology-based and we propose a novel ontology-based classifier suitable for very large hierarchical classification tasks. These methods allow us to predict phenotypes associated with any known protein-coding gene. We evaluate our approach using evaluation metrics established by the CAFA challenge and compare with top performing CAFA2 methods as well as several state of the art phenotype prediction approaches, demonstrating the improvement of DeepPheno over established methods. Furthermore, we show that predictions generated by DeepPheno are applicable to predicting gene–disease associations based on comparing phenotypes, and that a large number of new predictions made by DeepPheno have recently been added as phenotype databases.

scholarly journals Long noncoding RNA: a dazzling dancer in tumor immune microenvironment

2020 ◽  
Vol 39 (1) ◽  
Author(s):  
Yalu Zhang ◽  
Qiaofei Liu ◽  
Quan Liao
Keyword(s):  
Noncoding Rna ◽  
Molecular Mechanisms ◽  
Immune Escape ◽  
Noncoding Rnas ◽  
Pathological Process ◽  
Cellular Functions ◽  
Immune Microenvironment ◽  
Protein Coding ◽  
Functional Roles ◽  
Tumor Immune Microenvironment

Abstract Long noncoding RNAs (lncRNAs) are a class of endogenous, non-protein coding RNAs that are highly linked to various cellular functions and pathological process. Emerging evidence indicates that lncRNAs participate in crosstalk between tumor and stroma, and reprogramming of tumor immune microenvironment (TIME). TIME possesses distinct populations of myeloid cells and lymphocytes to influence the immune escape of cancer, the response to immunotherapy, and the survival of patients. However, hitherto, a comprehensive review aiming at relationship between lncRNAs and TIME is missing. In this review, we focus on the functional roles and molecular mechanisms of lncRNAs within the TIME. Furthermore, we discussed the potential immunotherapeutic strategies based on lncRNAs and their limitations.

scholarly journals Construction the first gene co-expression-based interactome in cattle

2017 ◽  
Author(s):  
Yan Chen ◽  
Yining Liu ◽  
Min Du ◽  
Wengang Zhang ◽  
Xue Gao ◽  
...  
Keyword(s):  
Dna Binding ◽  
Rna Polymerase Ii ◽  
Complex Traits ◽  
Molecular Mechanisms ◽  
Meat Production ◽  
Regulation Of Transcription ◽  
Cattle Genome ◽  
Cellular Functions ◽  
Protein Coding ◽  
Important Approach

Integrating genomic information into cattle breeding is an important approach to exploring the molecular mechanism for complex traits related to diary and meat production. To assist with genomic-based selection, a reference map of interactome is needed to fully understand genotype-phenotype relationships. To this end we constructed a co-expression analysis of 92 tissues and this represents the first systematic exploration of gene-gene relationship in cattle. By using robust WGCNA (Weighted Gene Correlation Network Analysis), we described the gene co-expression network of 13,405 protein-coding genes from the cattle genome. Using the 5,000 genes with majority variations in expression across 92 tissues, we compiled a network with 72,306 co-associations and that provides functional insights into thousands of poorly characterized proteins. Further module identifications found 55 highly organized functional clusters representing diverse cellular activities. To demonstrate the re-use of our interaction for functional genomics analysis, we extracted a sub-network associated with DNA binding genes in cattle. The subnetwork was enriched within regulation of transcription from RNA polymerase II promoter representing central cellular functions. In addition, we identified 28 novel linker genes associated with more than 100 DNA binding genes. Our WGCNA-based co-expression network reconstruction will be a valuable resource for exploring the molecular mechanisms of incompletely characterized proteins and for elucidating larger-scale patterns of functional modulization in the cattle genome.

scholarly journals The role of isoforms in the evolution of cryptic coloration in Peromyscus mice

2016 ◽  
Author(s):  
Ricardo Mallarino ◽  
Tess A. Linden ◽  
Catherine R. Linnen ◽  
Hopi E. Hoekstra
Keyword(s):  
Evolutionary Biology ◽  
Molecular Mechanisms ◽  
Single Gene ◽  
Deer Mice ◽  
Alternative Transcript ◽  
Protein Coding ◽  
Transcript Processing ◽  
Cryptic Coloration

AbstractA central goal of evolutionary biology is to understand the molecular mechanisms underlying phenotypic adaptation. While the contribution of protein-coding and cis-regulatory mutations to adaptive traits have been well documented, additional sources of variation—such as the production of alternative RNA transcripts from a single gene, or isoforms—have been understudied. Here, we focus on the pigmentation gene Agouti, known to express multiple alternative transcripts, to investigate the role of isoform usage in the evolution of cryptic color phenotypes in deer mice (genus Peromyscus). We first characterize the Agouti isoforms expressed in the Peromyscus skin and find two novel isoforms not previously identified in Mus. Next, we show that a locally adapted light-colored population of P. maniculatus living on the Nebraska Sand Hills shows an up-regulation of a single Agouti isoform, termed 1C, compared to their ancestral dark-colored conspecifics. Using in vitro assays, we show that this preference for isoform 1C may be driven by isoform-specific differences in translation. In addition, using an admixed population of wild-caught mice, we find that variation in overall Agouti expression maps to a region near exon 1C, which also has patterns of nucleotide variation consistent with strong positive selection. Finally, we show that the independent evolution of cryptic light pigmentation in a different species, P. polionotus, has been driven by a preference for the same Agouti isoform. Together, these findings present an example of the role of alternative transcript processing in adaptation and demonstrate molecular convergence at the level of isoform regulation.

scholarly journals DNA Processing in The Context of Non-Coding Transcription

2020 ◽  
Author(s):  
Uthra Gowthaman ◽  
Desiré García-Pichardo ◽  
Yu Jin ◽  
Isabel Schwarz ◽  
Sebastian Marquardt
Keyword(s):  
Rna Polymerase Ii ◽  
Dna Sequences ◽  
Molecular Mechanisms ◽  
Environmental Changes ◽  
Biological Significance ◽  
Protein Coding ◽  
Non Coding Rna ◽  
Functional Dna ◽  
The Impact ◽  
Dna Processing

RNA polymerase II (RNAPII) frequently transcribes non-protein coding DNA sequences in eukaryotic genomes into long non-coding RNA (lncRNA). Here, we focus on the impact of the act of lncRNA transcription on nearby functional DNA units. Distinct molecular mechanisms linked to the position of lncRNA relative to the coding gene illustrate how non-coding transcription controls gene expression. We review the biological significance of the act of lncRNA transcription on DNA processing, highlighting common themes, such as mediating cellular responses to environmental changes. This review presents the background in chromatin signaling to appreciate examples in different organisms where we can interpret functions of non-coding DNA through the act of RNAPII transcription.

scholarly journals Construction the first gene co-expression-based interactome in cattle

2017 ◽  
Author(s):  
Yan Chen ◽  
Yining Liu ◽  
Min Du ◽  
Wengang Zhang ◽  
Xue Gao ◽  
...  
Keyword(s):  
Dna Binding ◽  
Rna Polymerase Ii ◽  
Complex Traits ◽  
Molecular Mechanisms ◽  
Meat Production ◽  
Regulation Of Transcription ◽  
Cattle Genome ◽  
Cellular Functions ◽  
Protein Coding ◽  
Important Approach

Integrating genomic information into cattle breeding is an important approach to exploring the molecular mechanism for complex traits related to diary and meat production. To assist with genomic-based selection, a reference map of interactome is needed to fully understand genotype-phenotype relationships. To this end we constructed a co-expression analysis of 92 tissues and this represents the first systematic exploration of gene-gene relationship in cattle. By using robust WGCNA (Weighted Gene Correlation Network Analysis), we described the gene co-expression network of 13,405 protein-coding genes from the cattle genome. Using the 5,000 genes with majority variations in expression across 92 tissues, we compiled a network with 72,306 co-associations and that provides functional insights into thousands of poorly characterized proteins. Further module identifications found 55 highly organized functional clusters representing diverse cellular activities. To demonstrate the re-use of our interaction for functional genomics analysis, we extracted a sub-network associated with DNA binding genes in cattle. The subnetwork was enriched within regulation of transcription from RNA polymerase II promoter representing central cellular functions. In addition, we identified 28 novel linker genes associated with more than 100 DNA binding genes. Our WGCNA-based co-expression network reconstruction will be a valuable resource for exploring the molecular mechanisms of incompletely characterized proteins and for elucidating larger-scale patterns of functional modulization in the cattle genome.

Using Functional Linkage Gene Networks to Study Human Diseases

2011 ◽  
pp. 275-293
Author(s):  
Bolan Linghu ◽  
Guohui Liu ◽  
Yu Xia
Keyword(s):  
Gene Networks ◽  
Molecular Mechanisms ◽  
Common Disease ◽  
Disease Genes ◽  
New Disease ◽  
Cellular Functions ◽  
Proteomic Data ◽  
Functional Linkage ◽  
Disease Associations ◽  
Shed Light

A major challenge in the post-genomic era is to understand the specific cellular functions of individual genes and how dysfunctions of these genes lead to different diseases. As an emerging area of systems biology, gene networks have been used to shed light on gene function and human disease. In this chapter, first the existence of functional association for genes working in a common biological process or implicated in a common disease is demonstrated. Next, approaches to construct the functional linkage gene network (FLN) based on genomic and proteomic data integration are reviewed. Finally, two FLN-based applications related to diseases are reviewed: prediction of new disease genes and therapeutic targets, and identification of disease-disease associations at the molecular level. Both of these applications bring new insights into the molecular mechanisms of diseases, and provide new opportunities for drug discovery.

scholarly journals Targeting Aberrant RAS/RAF/MEK/ERK Signaling for Cancer Therapy

Cells ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 198 ◽  
Author(s):  
Ufuk Degirmenci ◽  
Mei Wang ◽  
Jiancheng Hu
Keyword(s):  
Cancer Therapy ◽  
Molecular Mechanisms ◽  
Mapk Pathway ◽  
Mapk Signaling ◽  
Erk Signaling ◽  
Signaling Cascade ◽  
Cellular Functions ◽  
Cell Functions ◽  
Core Elements ◽  
The Impact

The RAS/RAF/MEK/ERK (MAPK) signaling cascade is essential for cell inter- and intra-cellular communication, which regulates fundamental cell functions such as growth, survival, and differentiation. The MAPK pathway also integrates signals from complex intracellular networks in performing cellular functions. Despite the initial discovery of the core elements of the MAPK pathways nearly four decades ago, additional findings continue to make a thorough understanding of the molecular mechanisms involved in the regulation of this pathway challenging. Considerable effort has been focused on the regulation of RAF, especially after the discovery of drug resistance and paradoxical activation upon inhibitor binding to the kinase. RAF activity is regulated by phosphorylation and conformation-dependent regulation, including auto-inhibition and dimerization. In this review, we summarize the recent major findings in the study of the RAS/RAF/MEK/ERK signaling cascade, particularly with respect to the impact on clinical cancer therapy.

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