Construction of A Dataset for All Expressed Transcripts for Alzheimer’s Disease Research DOI Creative Commons
Zhenyu Huang,

Bocheng Shi,

Xuechen Mu

et al.

Brain Sciences, Journal Year: 2024, Volume and Issue: 14(12), P. 1180 - 1180

Published: Nov. 25, 2024

Accurate identification and functional annotation of splicing isoforms non-coding RNAs (lncRNAs), alongside full-length protein-encoding transcripts, are critical for understanding gene (mis)regulation metabolic reprogramming in Alzheimer’s disease (AD). This study aims to provide a comprehensive accurate transcriptome resource improve existing AD transcript databases. Background/Objectives: Gene mis-regulation play key role AD, yet databases lack lncRNAs. generate refined dataset, expanding the onset progression. Methods: Publicly available RNA-seq data from pre-AD tissues were utilized. Advanced bioinformatics tools applied assemble annotate including lncRNAs, with an emphasis on correcting errors enhancing accuracy. Results: A significantly improved dataset was generated, which includes detailed annotations expands scope provides new insights into molecular mechanisms underlying AD. The findings demonstrate that captures more relevant details about progression compared publicly data. Conclusions: newly developed associated analysis offer valuable contribution research, providing deeper disease’s mechanisms. work supports future research regulation serves as foundation exploring novel therapeutic targets.

Language: Английский

Elucidating the Functional Roles of Long Non-coding RNAs in Alzheimer's Disease DOI Open Access
Zhenyu Huang,

Qiufen Chen,

Xuechen Mu

et al.

Published: July 10, 2024

Alzheimer's disease (AD) is a multifaceted neurodegenerative disorder characterized by cognitive decline and neuronal loss, representing most challenging health issue. We present computational analysis of transcriptomic data AD tissues vs. healthy controls, focused on elucidation functional roles played long non-coding RNAs (lncRNAs) throughout the progression. first assembled our own lncRNA transcripts from raw RNA-Seq generated 527 samples dorsolateral prefrontal cortex, resulting in identification 31,574 novel genes. Based co-expression analyses between mRNAs lncRNAs, network constructed. Maximal subnetworks with dense connections are identified as clusters. Pathway enrichment conducted over lncRNAs each cluster, which serve basis for inference involved key steps an development model that we have previously build based protein-encoding Detailed information presented about activities related to stress response, reprogrammed metabolism, cell-polarity, development. Our also revealed discerning power distinguishing stage controls. This study represents its kind.

Language: Английский

Citations

4
Identification and Functional Analysis of Novel Long Intergenic RNA in Chicken Macrophages Infected with Avian Pathogenic Escherichia coli DOI Creative Commons

Yuyi Ma,

Xingqi Cao,

Sumayya

et al.

Microorganisms, Journal Year: 2024, Volume and Issue: 12(8), P. 1594 - 1594

Published: Aug. 6, 2024

Avian pathogenic E. coli (APEC), a widespread bacterium, results in serious economic losses to the poultry industry annually, and it poses threat human health due contaminated retail meat eggs. Recently, has been demonstrated that long non-coding RNAs played important roles regulating gene expression animal immune response. This study aimed systematically explore function of novel intergenic transcript, lincRNA-73240, upon APEC infection. A bioinformatics analysis indicated lincRNA-73240 had no coding ability relative stable secondary structure with multiple hairpin rings. Moreover, RT-qPCR showed was highly expressed lungs, heart, liver, spleen, cecum tonsils, thymus, ileum, bursa Fabricius, harderian gland, muscles comparison cerebrum. Additionally, overexpression can promote levels inflammation, apoptosis, autophagy, oxidative stress-related genes, as well production reactive oxygen species (ROS), malondialdehyde (MDA), nitric oxide (NO) infection, which lead cellular injury apoptosis. These findings collectively establish foundation for biological chicken provide theoretical basis further research on molecular mechanisms

Language: Английский

Citations

2
Elucidating the Functional Roles of Long Non-Coding RNAs in Alzheimer’s Disease DOI Open Access
Zhenyu Huang,

Qiufen Chen,

Xuechen Mu

et al.

International Journal of Molecular Sciences, Journal Year: 2024, Volume and Issue: 25(17), P. 9211 - 9211

Published: Aug. 25, 2024

Alzheimer's disease (AD) is a multifaceted neurodegenerative disorder characterized by cognitive decline and neuronal loss, representing most challenging health issue. We present computational analysis of transcriptomic data AD tissues vs. healthy controls, focused on the elucidation functional roles played long non-coding RNAs (lncRNAs) throughout progression. first assembled our own lncRNA transcripts from raw RNA-Seq generated 527 samples dorsolateral prefrontal cortex, resulting in identification 31,574 novel genes. Based co-expression analyses between mRNAs lncRNAs, network was constructed. Maximal subnetworks with dense connections were identified as clusters. Pathway enrichment conducted over lncRNAs each cluster, which served basis for inference involved key steps an development model that we have previously built based protein-encoding Detailed information presented about activities related to stress response, reprogrammed metabolism, cell polarity, development. Our also revealed discerning power distinguish stage controls. This study represents its kind.

Language: Английский

Citations

2
Construction of A Dataset for All Expressed Transcripts for Alzheimer’s Disease Research DOI Creative Commons
Zhenyu Huang,

Bocheng Shi,

Xuechen Mu

et al.

Brain Sciences, Journal Year: 2024, Volume and Issue: 14(12), P. 1180 - 1180

Published: Nov. 25, 2024

Accurate identification and functional annotation of splicing isoforms non-coding RNAs (lncRNAs), alongside full-length protein-encoding transcripts, are critical for understanding gene (mis)regulation metabolic reprogramming in Alzheimer’s disease (AD). This study aims to provide a comprehensive accurate transcriptome resource improve existing AD transcript databases. Background/Objectives: Gene mis-regulation play key role AD, yet databases lack lncRNAs. generate refined dataset, expanding the onset progression. Methods: Publicly available RNA-seq data from pre-AD tissues were utilized. Advanced bioinformatics tools applied assemble annotate including lncRNAs, with an emphasis on correcting errors enhancing accuracy. Results: A significantly improved dataset was generated, which includes detailed annotations expands scope provides new insights into molecular mechanisms underlying AD. The findings demonstrate that captures more relevant details about progression compared publicly data. Conclusions: newly developed associated analysis offer valuable contribution research, providing deeper disease’s mechanisms. work supports future research regulation serves as foundation exploring novel therapeutic targets.

Language: Английский

Citations

1