A novel subnetwork representation learning method for uncovering disease-disease relationships

Peng, Jiajie; Guan, Jiaojiao; Hui, Weiwei; Shang, Xuequn

doi:10.1016/j.ymeth.2020.09.002

Cited by 18 publications

(10 citation statements)

References 33 publications

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“…The primary function of CD4 + T cells after HIV infection is related to DNA repair in response to DNA damage stimuli, along with positive regulation of cellular processes and other pathways, whereas CD8 + T-cell functions after infection are mainly related to cell mitosis, signal transduction, and transmission ( Xu et al, 2014 ). In addition, network-based methods have been widely used in biological data analysis ( Peng et al, 2021a ; Peng et al, 2021b ). Therefore, we use WGCNA analysis to find that CD4 + T cells from individuals in the early HIV infection stage were enriched in genes involved in metabolic and infection-related pathways, whereas CD8 + T cells were enriched in genes involved in cell-related changes, including the cell cycle and DNA replication.…”

Section: Discussionmentioning

confidence: 99%

Landscape of T Cells Transcriptional and Metabolic Modules During HIV Infection Based on Weighted Gene Co-expression Network Analysis

Pan

Liu

et al. 2021

Front. Genet.

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Human immunodeficiency virus (HIV) causes acquired immunodeficiency syndrome (AIDS). HIV infection affects the functions and metabolism of T cells, which may determine the fate of patients; however, the specific pathways activated in different T-cell subtypes (CD4+ and CD8+ T cells) at different stages of infection remain unclear. We obtained transcriptome data of five individuals each with early HIV infection, chronic progressive HIV infection, and no HIV infection. Weighted gene co-expression network analysis was used to evaluate changes in gene expression to determine the antiviral response. An advanced metabolic algorithm was then applied to compare the alterations in metabolic pathways in the two T-cell subtypes at different infection stages. We identified 23 and 20 co-expressed gene modules in CD4+ T and CD8+ T cells, respectively. CD4+ T cells from individuals in the early HIV infection stage were enriched in genes involved in metabolic and infection-related pathways, whereas CD8+ T cells were enriched in genes involved in cell cycle and DNA replication. Three key modules were identified in the network common to the two cell types: NLRP1 modules, RIPK1 modules, and RIPK2 modules. The specific role of NLRP1 in the regulation of HIV infection in the human body remains to be determined. Metabolic functional analysis of the two cells showed that the significantly altered metabolic pathways after HIV infection were valine, leucine, and isoleucine degradation; beta-alanine metabolism; and PPAR signaling pathways. In summary, we found the core gene expression modules and different pathways activated in CD4+ and CD8+ T cells, along with changes in their metabolic pathways during HIV infection progression. These findings can provide an overall resource for establishing biomarkers to facilitate early diagnosis and potential guidance for new targeted therapeutic strategies.

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Section: Discussionmentioning

confidence: 99%

Landscape of T Cells Transcriptional and Metabolic Modules During HIV Infection Based on Weighted Gene Co-expression Network Analysis

Pan

Liu

et al. 2021

Front. Genet.

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“…At present, machine learning methods are increasingly used in bioinformatics, and many achievements have been made ( Peng et al, 2021a , b ). We have conducted a lot of clustering experiments on the existing imputation method.…”

Section: Introductionmentioning

confidence: 99%

AdImpute: An Imputation Method for Single-Cell RNA-Seq Data Based on Semi-Supervised Autoencoders

Xue

et al. 2021

Front. Genet.

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Motivation: The emergence of single-cell RNA sequencing (scRNA-seq) technology has paved the way for measuring RNA levels at single-cell resolution to study precise biological functions. However, the presence of a large number of missing values in its data will affect downstream analysis. This paper presents AdImpute: an imputation method based on semi-supervised autoencoders. The method uses another imputation method (DrImpute is used as an example) to fill the results as imputation weights of the autoencoder, and applies the cost function with imputation weights to learn the latent information in the data to achieve more accurate imputation.Results: As shown in clustering experiments with the simulated data sets and the real data sets, AdImpute is more accurate than other four publicly available scRNA-seq imputation methods, and minimally modifies the biologically silent genes. Overall, AdImpute is an accurate and robust imputation method.

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“…The generation and development of psychiatric disorders are influenced by genetic and environmental factors ( Sklar et al, 2011 ; Nagel et al, 2018 ; Ruderfer et al, 2018 ; Peng et al, 2020 ; Peng et al, 2021a ; Peng et al, 2021b ). For genetic factors, based on genome-wide association analysis (GWAS), Purcell et al implicate the major histocompatibility complex, constructed a polygenic risk score (PRS) of schizophrenia (SCZ) and verified that the PRS also predicted bipolar disorder (BD) ( Purcell et al, 2009 ).…”

Section: Introductionmentioning

confidence: 99%

The Causal Effects of Insomnia on Bipolar Disorder, Depression, and Schizophrenia: A Two-Sample Mendelian Randomization Study

et al. 2021

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Psychiatric disorder, including bipolar disorder (BD), major depression (MDD), and schizophrenia (SCZ), affects millions of persons around the world. Understanding the disease causal mechanism underlying the three diseases and identifying the modifiable risk factors for them hold the key for the development of effective preventative and treatment strategies. We used a two-sample Mendelian randomization method to assess the causal effect of insomnia on the risk of BD, MDD, and SCZ in a European population. We collected one dataset of insomnia, three of BD, one of MDD, and three of SCZ and performed a meta-analysis for each trait, further verifying the analysis through extensive complementarity and sensitivity analysis. Among the three psychiatric disorders, we found that only insomnia is causally associated with MDD and that higher insomnia increases the risk of MDD. Specifically, the odds ratio of MDD increase of insomnia is estimated to be 1.408 [95% confidence interval (CI): 1.210–1.640, p = 1.03E-05] in the European population. The identified causal relationship between insomnia and MDD is robust with respect to the choice of statistical methods and is validated through extensive sensitivity analyses that guard against various model assumption violations. Our results provide new evidence to support the causal effect of insomnia on MDD and pave ways for reducing the psychiatric disorder burden.

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A novel subnetwork representation learning method for uncovering disease-disease relationships

Cited by 18 publications

References 33 publications

Landscape of T Cells Transcriptional and Metabolic Modules During HIV Infection Based on Weighted Gene Co-expression Network Analysis

Landscape of T Cells Transcriptional and Metabolic Modules During HIV Infection Based on Weighted Gene Co-expression Network Analysis

AdImpute: An Imputation Method for Single-Cell RNA-Seq Data Based on Semi-Supervised Autoencoders

The Causal Effects of Insomnia on Bipolar Disorder, Depression, and Schizophrenia: A Two-Sample Mendelian Randomization Study

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