scholarly journals Identifying molecular targets for reverse aging using integrated network analysis of transcriptomic and epigenomic changes during aging

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hwang-Yeol Lee ◽  
Yeonsu Jeon ◽  
Yeon Kyung Kim ◽  
Jae Young Jang ◽  
Yun Sung Cho ◽  
...  
Keyword(s):  
Network Model ◽  
Expression Profiles ◽  
Flow Analysis ◽  
Gene Expression Profiles ◽  
Molecular Targets ◽  
Regulatory Mechanisms ◽  
Integrated Network ◽  
Signal Flow ◽  
Biomarkers Of Aging ◽  
Candidate Target

AbstractAging is associated with widespread physiological changes, including skeletal muscle weakening, neuron system degeneration, hair loss, and skin wrinkling. Previous studies have identified numerous molecular biomarkers involved in these changes, but their regulatory mechanisms and functional repercussions remain elusive. In this study, we conducted next-generation sequencing of DNA methylation and RNA sequencing of blood samples from 51 healthy adults between 20 and 74 years of age and identified aging-related epigenetic and transcriptomic biomarkers. We also identified candidate molecular targets that can reversely regulate the transcriptomic biomarkers of aging by reconstructing a gene regulatory network model and performing signal flow analysis. For validation, we screened public experimental data including gene expression profiles in response to thousands of chemical perturbagens. Despite insufficient data on the binding targets of perturbagens and their modes of action, curcumin, which reversely regulated the biomarkers in the experimental dataset, was found to bind and inhibit JUN, which was identified as a candidate target via signal flow analysis. Collectively, our results demonstrate the utility of a network model for integrative analysis of omics data, which can help elucidate inter-omics regulatory mechanisms and develop therapeutic strategies against aging.

scholarly journals Bioinformatics analysis of differentially expressed genes and pathways in the development of cervical cancer

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Baojie Wu ◽  
Shuyi Xi
Keyword(s):  
Gene Expression ◽  
Cervical Cancer ◽  
Differentially Expressed Genes ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Molecular Targets ◽  
Enrichment Analysis ◽  
Ppi Network ◽  
Hub Genes ◽  
Kegg Pathways

Abstract Background This study aimed to explore and identify key genes and signaling pathways that contribute to the progression of cervical cancer to improve prognosis. Methods Three gene expression profiles (GSE63514, GSE64217 and GSE138080) were screened and downloaded from the Gene Expression Omnibus database (GEO). Differentially expressed genes (DEGs) were screened using the GEO2R and Venn diagram tools. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Gene set enrichment analysis (GSEA) was performed to analyze the three gene expression profiles. Moreover, a protein–protein interaction (PPI) network of the DEGs was constructed, and functional enrichment analysis was performed. On this basis, hub genes from critical PPI subnetworks were explored with Cytoscape software. The expression of these genes in tumors was verified, and survival analysis of potential prognostic genes from critical subnetworks was conducted. Functional annotation, multiple gene comparison and dimensionality reduction in candidate genes indicated the clinical significance of potential targets. Results A total of 476 DEGs were screened: 253 upregulated genes and 223 downregulated genes. DEGs were enriched in 22 biological processes, 16 cellular components and 9 molecular functions in precancerous lesions and cervical cancer. DEGs were mainly enriched in 10 KEGG pathways. Through intersection analysis and data mining, 3 key KEGG pathways and related core genes were revealed by GSEA. Moreover, a PPI network of 476 DEGs was constructed, hub genes from 12 critical subnetworks were explored, and a total of 14 potential molecular targets were obtained. Conclusions These findings promote the understanding of the molecular mechanism of and clinically related molecular targets for cervical cancer.

Validating Molecular Targets of Thalidomide in CLL: Net Effect of Increased Apoptosis through the Intrinsic Pathway and down Regulation of NF-kB Signaling- Validation Using Gene Expression Profile from the Phase I/II Clinical Trial of Thalidomide and Fludarabine.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 5043-5043
Author(s):  
Asher Alban Chanan-Khan ◽  
Swaminathan Padmanabhan ◽  
Leighton Stein ◽  
Jennifer Panzarella ◽  
Kena C. Miller ◽  
...  
Keyword(s):  
Gene Expression ◽  
B Cells ◽  
Expression Analysis ◽  
Clinical Success ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Molecular Targets ◽  
Post Treatment ◽  
Clinical Activity ◽  
Base Line

Abstract Thalidomide is novel agent with demonstrated antitumor activity in various tumor types. The exact mechanism of the antineoplastic effects of thalidomide remains unknown despite its clinical success. We recently reported on the clinical activity of T in combination with F in patients with treatment naive CLL. In this clinical study pts were treated with T alone for 7 days prior to initiating F. Anti-leukemic effects of T were noted as early as day 7. To investigate the molecular targets of T in malignant CLL cells we analyzed changes in gene expression profiles at base line and at day 7-post treatment with T. Here we report on oligonucleotide microarray expression analysis of these patients, the clinical data is presented separately. Materials and methods: Peripheral blood samples from 5 patients for gene expression profiling were collected on day 0(prior to thalidomide) and on day 7 after completing thalidomide but prior to fludarabine infusion. DNA was extracted from apoptotic cells. Purified B cells extracted by Ficoll-hypaque were homogenized in Trizol and total RNA was extracted from samples prepared for GeneChip analysis as described in the Affymetrix GeneChip Expression Analysis Manual and biotinylated using Bioarray. After QC the samples were hybridized to U95A chips, representing over 12,000 annotated genes from the Unigene database. QPCR analysis was performed using ABI7900 sequence detector system. The data obtained were analyzed using Rapid Multi-Array Analysis (RMA) with all gene ontology (GO) functional annotations and chromosomal locations determined using NetAffx and Locus Link. Pathway Analysis: Two approaches were used-one using Pathway Assist Software, genes altered by Thalidomide were compared to those implicated in established pathways and second by constructing Biological Association Networks (BANS) which identifies associations to over 140,000 biological facts extracted from PubMed. Results: In each of the 5 paired samples 51 genes consistently displayed increased expression- mainly genes whose primary function were related to the immune response and apoptosis while 53 genes- mainly the signal transduction were lower. In particular the apoptotic response include mainly the intrinsic pathways and the activation of the granule mediated pathways. In the surviving B cells many of the genes were directly or indirectly related to the upregulation of nuclear factor kappa B (NF-kB) suggesting possible mechanism of resistance to thalidomide. QPCR validation using five different genes CFLAR, HBD, ILIB, TNF-a and PTPNS1 were done in additional 12 paired CLL samples. The PTPNS1 gene involved in the NF-kB signaling pathway was elevated in 8 of the post treatment samples. Conclusions: Any treatment of CLL, a malignancy with failure of apoptotic cellular machinery must be able to overcome pro-survival mechanisms and induce apoptosis. Our results show that Thalidomide induces a net effect of increasing apoptosis-related responses in malignant B cells through several distinct mechanisms and decreasing those involving the NF-kB pathway. We will present the complete gene expression data at the Annual meeting.

Activation of intracellular signaling pathways as a new type of biomarkers for selection of target anticancer drugs.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e23142-e23142 ◽  
Author(s):  
Anton Buzdin ◽  
Maxim Sorokin ◽  
Alexander Glusker ◽  
Andrew Garazha ◽  
Elena Poddubskaya ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cancer Patients ◽  
Intracellular Signaling ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Molecular Targets ◽  
Signalling Pathways ◽  
Pathway Activation ◽  
Significant Difference ◽  
Selection Of

e23142 Background: Anticancer target drugs (ATDs) specifically bind and inhibit molecular targets that play important roles in tumorigenesis. More than 150 different ATDs have been approved for clinical use worldwide, and the clinicians are faced with the problem of choosing the best therapeutic solution for each patient. The problem of efficient ATD selection remains largely unsolved and personalized approaches are needed to select the best ATD candidates for individual patients. Methods: We propose a new approach termed OncoFinder. It is based on digesting gene expression profiles for the analysis of activation of intracellular signalling pathways as a marker for the selection of target therapies. The original bioinformatic algorithms were integrated with the databases featuring molecular drug targets, compositions of signalling pathways, including the functional role of each gene product, for more than 1700 pathways (Buzdin, Front.Genet 2014; Ozerov, Nature Communications 2016). Results: We showed that pathway activation strengths are more stable and reliable biomarkers of cancer than the expressions of individual genes. OncoFinder allows to detect changes at the level of pathway activation and to predict the effectiveness of drugs based on the knowledge of their molecular targets. We applied it to find new biomarkers of clinical response to the ATD cetuximab; for modelling the combined chemotherapy of acute myeloid leukemia and combined anti-VEGF/BRAF therapy of melanoma. For two unrelated datasets obtained for colon cancer patients before treatment with the ATD bevacizumab, we were able to distinguish between those who responded to treatment and not (p < 0.01). We next assayed biopsies for kidney cancer patients with known responses to the ATD sorafenib. The responders and non-responders showed a significant difference (p = 0.02). Finally, the OncoFinder platform was prospectively used for decision making support to patients with advanced metastatic solid tumors (n = 23). The efficiency of the ATD treatment was 61% (complete + partial response, RECIST). Conclusions: OncoFinder method may be effective for predicting response to ATD based on high throughput gene expression profiles.

A new LSTM-based gene expression prediction model: L-GEPM

2019 ◽  
Vol 17 (04) ◽  
pp. 1950022
Author(s):  
Huiqing Wang ◽  
Chun Li ◽  
Jianhui Zhang ◽  
Jingjing Wang ◽  
Yue Ma ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prediction Model ◽  
Target Genes ◽  
Prediction Models ◽  
Short Term Memory ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Integrated Network ◽  
Genome Wide ◽  
Nonlinear Features

Molecular biology combined with in silico machine learning and deep learning has facilitated the broad application of gene expression profiles for gene function prediction, optimal crop breeding, disease-related gene discovery, and drug screening. Although the acquisition cost of genome-wide expression profiles has been steadily declining, the requirement generates a compendium of expression profiles using thousands of samples remains high. The Library of Integrated Network-Based Cellular Signatures (LINCS) program used approximately 1000 landmark genes to predict the expression of the remaining target genes by linear regression; however, this approach ignored the nonlinear features influencing gene expression relationships, limiting the accuracy of the experimental results. We herein propose a gene expression prediction model, L-GEPM, based on long short-term memory (LSTM) neural networks, which captures the nonlinear features affecting gene expression and uses learned features to predict the target genes. By comparing and analyzing experimental errors and fitting the effects of different prediction models, the LSTM neural network-based model, L-GEPM, can achieve low error and a superior fitting effect.

scholarly journals De Novo Transcriptome Analysis Reveals Abundant Gonad-specific Genes in the Ovary and Testis of Henosepilachna vigintioctopunctata

2019 ◽  
Vol 20 (17) ◽  
pp. 4084 ◽  
Author(s):  
Wei Guo ◽  
Jing Lü ◽  
Mujuan Guo ◽  
Shimin Chen ◽  
Baoli Qiu ◽  
...  
Keyword(s):  
Target Genes ◽  
De Novo ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Oviposition Period ◽  
Hatching Rate ◽  
Treatment Groups ◽  
Candidate Target ◽  
Major Pest ◽  
Henosepilachna Vigintioctopunctata

Henosepilachna vigintioctopunctata (Coleoptera: Coccinellidae) is a major pest affecting Solanaceae plants in Asian countries. In this study, we sequenced the ovary and testis transcriptomes of H. vigintioctopunctata to identify gonad-related genes. Comparison of the unigene sequences in ovary and testis libraries identified 1,421 and 5,315 ovary- and testis-specific genes, respectively. Among the ovary-specific genes, we selected the RC2-like and PSHS-like genes to investigate the effects of gene silencing on the mortality, percentage infertility, pre-oviposition period, fecundity, daily number of eggs laid, and hatching rate in female adults. Although the percentage mortality and infertility of females did not differ significantly among dsRNA treatments, fecundity was significantly reduced in the dsRC2-like and dsPSHS-like treatment groups. Moreover, the pre-oviposition period was markedly prolonged in response to dsPSHS-like treatment. This is the first reported RNA sequencing of H. vigintioctopunctata. The transcriptome sequences and gene expression profiles of the ovary and testis libraries will provide useful information for the identification of gonad-related genes in H. vigintioctopunctata and facilitate further research on the reproductive biology of this species. Moreover, the gonad-specific genes identified may represent candidate target genes for inhibiting the population growth of H. vigintioctopunctata.

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