scholarly journals Gene Expression-Based Drug Repurposing to Target Ageing

2018 ◽  
Author(s):  
Handan Melike Dönertaş ◽  
Matías Fuentealba Valenzuela ◽  
Linda Partridge ◽  
Janet M. Thornton
Keyword(s):  
Gene Expression ◽  
Human Brain ◽  
Defense Mechanisms ◽  
De Novo ◽  
Expression Profiles ◽  
Model Organism ◽  
Drug Repurposing ◽  
Complex Process ◽  
Brain Ageing ◽  
Using Data

SummaryAgeing is the largest risk factor for a variety of non-communicable diseases. Model organism studies have shown that genetic and chemical perturbations can extend both life- and health-span. Ageing is a complex process, with parallel and interacting mechanisms contributing to its aetiology, posing a challenge for the discovery of new pharmacological candidates to ameliorate its effects. In this study, instead of a target-centric approach, we adopt a systems level drug repurposing methodology to discover drugs that could combat ageing in human brain. Using multiple gene expression datasets from brain tissue, taken from patients of different ages, we first identified the expression changes that characterise ageing. Then, we compared these changes in gene expression with drug perturbed expression profiles in the Connectivity Map. We thus identified 24 drugs with significantly associated changes. Some of these drugs may function as anti-ageing drugs by reversing the detrimental changes that occur during ageing, others by mimicking the cellular defense mechanisms. The drugs that we identified included significant number of already identified pro-longevity drugs, indicating that the method can discover de novo drugs that meliorate ageing. The approach has the advantages that, by using data from human brain ageing data it focuses on processes relevant in human ageing and that it is unbiased, making it possible to discover new targets for ageing studies.

scholarly journals Chemical-induced Gene Expression Ranking and its Application to Pancreatic Cancer Drug Repurposing

2021 ◽  
Author(s):  
Thai-Hoang Pham ◽  
Yue Qiu ◽  
Jiahui Liu ◽  
Steven Zimmer ◽  
Eric O'Neill ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Drug Discovery ◽  
Large Scale ◽  
Missing Values ◽  
De Novo ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Drug Repurposing ◽  
Cancer Drug

Chemical-induced gene expression profiles provide critical information on the mode of action, off-target effect, and cellar heterogeneity of chemical actions in a biological system, thus offer new opportunities for drug discovery, system pharmacology, and precision medicine. Despite their successful applications in drug repurposing, large-scale analysis that leverages these profiles is limited by sparseness and low throughput of the data. Several methods have been proposed to predict missing values in gene expression data. However, most of them focused on imputation and classification settings which have limited applications to real-world scenarios of drug discovery. Therefore, a new deep learning framework named chemical-induced gene expression ranking (CIGER) is proposed to target a more realistic but more challenging setting in which the model predicts the rankings of genes in the whole gene expression profiles induced by de novo chemicals. The experimental results show that CIGER significantly outperforms existing methods in both ranking and classification metrics for this prediction task. Furthermore, a new drug screening pipeline based on CIGER is proposed to select approved or investigational drugs for the potential treatments of pancreatic cancer. Our predictions have been validated by experiments, thereby showing the effectiveness of CIGER for phenotypic compound screening of precision drug discovery in practice.

scholarly journals MNBDR: A Module Network Based Method for Drug Repositioning

Genes ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 25
Author(s):  
He-Gang Chen ◽  
Xiong-Hui Zhou
Keyword(s):  
Gene Expression ◽  
Protein Interactions ◽  
Drug Response ◽  
Drug Repositioning ◽  
Expression Profiles ◽  
Drug Repurposing ◽  
Recent Decade ◽  
Data Set ◽  
Module Network ◽  
The Cross

Drug repurposing/repositioning, which aims to find novel indications for existing drugs, contributes to reducing the time and cost for drug development. For the recent decade, gene expression profiles of drug stimulating samples have been successfully used in drug repurposing. However, most of the existing methods neglect the gene modules and the interactions among the modules, although the cross-talks among pathways are common in drug response. It is essential to develop a method that utilizes the cross-talks information to predict the reliable candidate associations. In this study, we developed MNBDR (Module Network Based Drug Repositioning), a novel method that based on module network to screen drugs. It integrated protein–protein interactions and gene expression profile of human, to predict drug candidates for diseases. Specifically, the MNBDR mined dense modules through protein–protein interaction (PPI) network and constructed a module network to reveal cross-talks among modules. Then, together with the module network, based on existing gene expression data set of drug stimulation samples and disease samples, we used random walk algorithms to capture essential modules in disease development and proposed a new indicator to screen potential drugs for a given disease. Results showed MNBDR could provide better performance than popular methods. Moreover, functional analysis of the essential modules in the network indicated our method could reveal biological mechanism in drug response.

Comprehensive Analysis of Differential Expression Profiles of Long Non-coding RNAs with Associated Co-expression and Competing Endogenous RNA Networks in the Hippocampus of Patients with Alzheimer's Disease

2021 ◽  
Vol 18 ◽  
Author(s):  
Jian-Jun Zhang ◽  
Ze-Xuan-Zhu ◽  
Guang-Min-Xu ◽  
Peng Su ◽  
Qian Lei ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Expression Profiles ◽  
Gene Expression Omnibus ◽  
Competing Endogenous Rna ◽  
Pathogenic Genes ◽  
Non Coding Rna ◽  
Using Data ◽  
Trans Regulation

Background: Alzheimer's disease (AD) is still one of the major threats to human health. Although a satisfactory treatment for AD has not yet been discovered, it is necessary to continue to search for novel approaches to deal with this insidious and debilitating disease. Although numerous studies have shown that long non-coding RNA (lncRNA) occupy a significant role in a variety of diseases, their roles in AD remain unclear. Objectives: Using data analysis to explore the role of lncRNA in the course of AD, to further our understanding of AD, and to look forward to finding a new breakthrough for the treatment of AD. Methods: We downloaded and screened expression data of the hippocampal regions of patients with AD from the Gene Expression Omnibus database. We generated lncRNA-miRNA-mRNA networks based on the competing endogenous RNA (ceRNA) hypothesis, and according to gene expression level, we constructed a coding-noncoding co-expression (CNC) network and then executed cis- and trans-regulation analyses. Results: Through comprehensive and systematic analyses, we found that lncRNAs MALAT1, OIP5-AS1, LINC00657, and lnc-NUMB-1 regulated the expression of the key AD pathogenic genes APP, PSEN1, BACE1; and that these lncRNAs may promote the distribution of β-amyloid (Aβ protein) in the brain through exosomes. In addition, lncRNAs were found to adjust viral transcriptional expression, thereby further supporting viral pathogenesis for AD. Conclusions: The lncRNAs MALAT1, OIP5-AS1, LINC00657, and lnc-NUMB-1 that are present in the hippocampus of AD patients exert an important influence on the development of this disease.

scholarly journals Integrated analysis of the aging brain transcriptome and proteome in tauopathy

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Carl Grant Mangleburg ◽  
Timothy Wu ◽  
Hari K. Yalamanchili ◽  
Caiwei Guo ◽  
Yi-Chen Hsieh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Human Brain ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Differentially Expressed ◽  
Integrated Analysis ◽  
Mutant Form ◽  
Brain Gene Expression ◽  
Brain Transcriptome ◽  
Age Dependent

Abstract Background Tau neurofibrillary tangle pathology characterizes Alzheimer’s disease and other neurodegenerative tauopathies. Brain gene expression profiles can reveal mechanisms; however, few studies have systematically examined both the transcriptome and proteome or differentiated Tau- versus age-dependent changes. Methods Paired, longitudinal RNA-sequencing and mass-spectrometry were performed in a Drosophila model of tauopathy, based on pan-neuronal expression of human wildtype Tau (TauWT) or a mutant form causing frontotemporal dementia (TauR406W). Tau-induced, differentially expressed transcripts and proteins were examined cross-sectionally or using linear regression and adjusting for age. Hierarchical clustering was performed to highlight network perturbations, and we examined overlaps with human brain gene expression profiles in tauopathy. Results TauWT induced 1514 and 213 differentially expressed transcripts and proteins, respectively. TauR406W had a substantially greater impact, causing changes in 5494 transcripts and 697 proteins. There was a ~ 70% overlap between age- and Tau-induced changes and our analyses reveal pervasive bi-directional interactions. Strikingly, 42% of Tau-induced transcripts were discordant in the proteome, showing opposite direction of change. Tau-responsive gene expression networks strongly implicate innate immune activation. Cross-species analyses pinpoint human brain gene perturbations specifically triggered by Tau pathology and/or aging, and further differentiate between disease amplifying and protective changes. Conclusions Our results comprise a powerful, cross-species functional genomics resource for tauopathy, revealing Tau-mediated disruption of gene expression, including dynamic, age-dependent interactions between the brain transcriptome and proteome.

Gene Expression Profiles in Acute Myeloid Leukemia (AML): From Diagnosis to Prognosis.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 2996-2996
Author(s):  
Sanggyu Lee ◽  
Jianjun Chen ◽  
Goulin Zhou ◽  
Run Shi ◽  
Masha Kocherginsky ◽  
...  
Keyword(s):  
Gene Expression ◽  
De Novo ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Major Change ◽  
Molecular Signature ◽  
Human Leukemia ◽  
Prognostic Information ◽  
Myeloid Progenitor Cells ◽  
Novel Transcripts

Abstract Chromosome translocations are among the most common genetic abnormalities in human leukemia. The abnormally expressed genes from each translocation can be used to identify specific markers for clinical diagnosis of each translocation. Microarrays have identified genes differentially expressed in different translocations but the results between laboratories are not always compatible. We used SAGE to quantitate gene expression in bone marrow(BM) samples from 22 patients with four types of AML, [de novo AML M2 with t(8;21), AML M3 or M3V with t(15;17), AML M4Eo with inv(16), AML M5 with t(9;11) or secondary t(9;11)].We made SAGE libraries from CD15+ leukemic myeloid progenitor cells, collecting over 106 SAGE tags, of which 209,486 were unique tags; 136,010 were known genes and ESTs, and 73,476 were novel transcripts. SAGE tags for further analysis were selected based on a 5-fold difference between patient’s samples and normal CD15+ BM; they were also statistically significantly different at the 5% level. Using these strict criteria, we identified 2,381 unique tags, of which 2,053 were known genes and ESTs, and 328 were novel transcripts that were either specific for each translocation or were common(55) SAGE tags for all 4 translocations. The major change in all translocations was a decrease in expression in leukemia cells compared with normal cells; the decrease was least in the t(8;21) cells. Changes in expression of these known genes, which fall into different gene ontology functional categories, varied by translocation. Those associated with macromolecular biosynthesis, transport and transcription were most altered in the t(8;21); those related to defense response and apoptosis were altered in the t(15;17); cell proliferation genes were most affected by the t(9;11). From this analysis, we identified the functional molecular signature of each translocation. We designed a custom microarray to validate our SAGE data analysis. Our initial microarray contained 349 probes including 212 known genes, 61 ESTs, 28 novel sequences based on our data and 48 genes reported by others. We have now included 65 additional probes that appeared to be correlated with survival. Using 63 samples with the four translocations [16 inv(16), 4 t(9;11), 20 t(15;17), 4 t(8;21) and 19 other translocations], we are validating which genes provide a robust, reproducible “fingerprint” for each translocation, for all translocations, and which ones provide reliable information related to prognosis and survival. Our results will provide new insights into genes that collaborate with each translocation to lead to a fully leukemic phenotype as well as which genes appear to provide valid prognostic information.

scholarly journals Gammaherpesvirus Lytic Gene Expression as Characterized by DNA Array

2002 ◽  
Vol 76 (12) ◽  
pp. 6244-6256 ◽  
Author(s):  
Joo Wook Ahn ◽  
Kenneth L. Powell ◽  
Paul Kellam ◽  
Dagmar G. Alber
Keyword(s):  
Gene Expression ◽  
De Novo ◽  
Expression Profiles ◽  
Cdna Array ◽  
Gene Expression Profiles ◽  
Hierarchical Cluster ◽  
Murine Gammaherpesvirus ◽  
Transcription Profiles ◽  
A Genome

ABSTRACT Gammaherpesviruses are associated with a number of diseases including lymphomas and other malignancies. Murine gammaherpesvirus 68 (MHV-68) constitutes the most amenable animal model for this family of pathogens. However experimental characterization of gammaherpesvirus gene expression, at either the protein or RNA level, lags behind that of other, better-studied alpha- and beta-herpesviruses. We have developed a cDNA array to globally characterize MHV-68 gene expression profiles, thus providing an experimental supplement to a genome that is chiefly annotated by homology. Viral genes started to be transcribed as early as 3 h postinfection (p.i.), and this was followed by a rapid escalation of gene expression that could be seen at 5 h p.i. Individual genes showed their own transcription profiles, and most genes were still being expressed at 18 h p.i. Open reading frames (ORFs) M3 (chemokine-binding protein), 52, and M9 (capsid protein) were particularly noticeable due to their very high levels of expression. Hierarchical cluster analysis of transcription profiles revealed four main groups of genes and allowed functional predictions to be made by comparing expression profiles of uncharacterized genes to those of genes of known function. Each gene was also categorized according to kinetic class by blocking de novo protein synthesis and viral DNA replication in vitro. One gene, ORF 73, was found to be expressed with α-kinetics, 30 genes were found to be expressed with β-kinetics, and 42 genes were found to be expressed with γ-kinetics. This fundamental characterization furthers the development of this model and provides an experimental basis for continued investigation of gammaherpesvirus pathology.

scholarly journals Infectomic Analysis of Gene Expression Profiles of Human Brain Microvascular Endothelial Cells Infected withCryptococcus neoformans

2008 ◽  
Vol 2008 ◽  
pp. 1-7 ◽  
Author(s):  
Ambrose Jong ◽  
Chun-Hua Wu ◽  
Wensheng Zhou ◽  
Han-Min Chen ◽  
Sheng-He Huang
Keyword(s):  
Gene Expression ◽  
Endothelial Cells ◽  
Human Brain ◽  
Time Course ◽  
Expression Profiles ◽  
Host Cells ◽  
Microvascular Endothelial Cells ◽  
Brain Microvascular Endothelial Cells ◽  
Microvascular Endothelial ◽  
Time Point

In order to dissect the pathogenesis ofCryptococcus neoformansmeningoencephalitis, a genomic survey of the changes in gene expression of human brain microvascular endothelial cells infected byC.neoformanswas carried out in a time-course study. Principal component analysis (PCA) revealed sigificant fluctuations in the expression levels of different groups of genes during the pathogen-host interaction. Self-organizing map (SOM) analysis revealed that most genes were up- or downregulated 2 folds or more at least at one time point during the pathogen-host engagement. The microarray data were validated by Western blot analysis of a group of genes, includingβ-actin, Bcl-x, CD47, Bax, Bad, and Bcl-2. Hierarchical cluster profile showed that 61 out of 66 listed interferon genes were changed at least at one time point. Similarly, the active responses in expression of MHC genes were detected at all stages of the interaction. Taken together, our infectomic approaches suggest that the host cells significantly change the gene profiles and also actively participate in immunoregulations of the central nervous system (CNS) duringC.neoformansinfection.

scholarly journals Transcriptional signatures of somatic neoblasts and germline cells in Macrostomum lignano

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Magda Grudniewska ◽  
Stijn Mouton ◽  
Daniil Simanov ◽  
Frank Beltman ◽  
Margriet Grelling ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cells ◽  
De Novo ◽  
Transcriptome Assembly ◽  
Model Organism ◽  
Proliferating Cells ◽  
Macrostomum Lignano ◽  
Germline Cells

The regeneration-capable flatworm Macrostomum lignano is a powerful model organism to study the biology of stem cells in vivo. As a flatworm amenable to transgenesis, it complements the historically used planarian flatworm models, such as Schmidtea mediterranea. However, information on the transcriptome and markers of stem cells in M. lignano is limited. We generated a de novo transcriptome assembly and performed the first comprehensive characterization of gene expression in the proliferating cells of M. lignano, represented by somatic stem cells, called neoblasts, and germline cells. Knockdown of a selected set of neoblast genes, including Mlig-ddx39, Mlig-rrm1, Mlig-rpa3, Mlig-cdk1, and Mlig-h2a, confirmed their crucial role for the functionality of somatic neoblasts during homeostasis and regeneration. The generated M. lignano transcriptome assembly and gene expression signatures of somatic neoblasts and germline cells will be a valuable resource for future molecular studies in M. lignano.

scholarly journals De novo characterization of the Lycium ruthenicum transcriptome and analysis of its digital gene expression profiles during fruit development and ripening

2017 ◽  
Vol 69 (1) ◽  
pp. 181-190 ◽  
Author(s):  
Yong Peng ◽  
Huiqin Ma ◽  
Shangwu Chen
Keyword(s):  
Gene Expression ◽  
Developmental Stages ◽  
De Novo ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Digital Gene Expression ◽  
Orthologous Group ◽  
Functional Studies ◽  
Lycium Ruthenicum

Lycium ruthenicum Murr., which belongs to the family Solanaceae, is a resource plant for Chinese traditional medicine and nutraceutical foods. In this study, RNA sequencing was applied to obtain raw reads of L. ruthenicum fruit at different stages of ripening, and a de novo assembly of its sequence was performed. Approximately 52.45 million 100-bp paired-end raw reads were generated from the samples by deep RNA-seq analysis. These short reads were assembled to obtain 164814 contigs, and the contigs were assembled into 84968 non-redundant unigenes using the Trinity method. Assembled sequences were annotated with gene descriptions, gene ontology, clusters of orthologous group and KEGG (Kyoto Encyclopedia of Genes and Genomes)pathway terms. Digital gene expression analysis was applied to compare gene-expression patterns at different fruit developmental stages. These results contribute to existing sequence resources for Lycium spp. during the fruit-ripening stages, which is valuable for further functional studies of genes involved in L. ruthenicum fruit nutraceutical quality.

scholarly journals DNA Microarrays of the Complex Human Cytomegalovirus Genome: Profiling Kinetic Class with Drug Sensitivity of Viral Gene Expression

1999 ◽  
Vol 73 (7) ◽  
pp. 5757-5766 ◽  
Author(s):  
James Chambers ◽  
Ana Angulo ◽  
Dhammika Amaratunga ◽  
Hongqing Guo ◽  
Ying Jiang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Human Cytomegalovirus ◽  
Dna Sequences ◽  
De Novo ◽  
Expression Profiles ◽  
Viral Gene Expression ◽  
Viral Gene ◽  
Viral Dna ◽  
Viral Protein Synthesis ◽  
Entire Genome

ABSTRACT We describe, for the first time, the generation of a viral DNA chip for simultaneous expression measurements of nearly all known open reading frames (ORFs) in the largest member of the herpesvirus family, human cytomegalovirus (HCMV). In this study, an HCMV chip was fabricated and used to characterize the temporal class of viral gene expression. The viral chip is composed of microarrays of viral DNA prepared by robotic deposition of oligonucleotides on glass for ORFs in the HCMV genome. Viral gene expression was monitored by hybridization to the oligonucleotide microarrays with fluorescently labelled cDNAs prepared from mock-infected or infected human foreskin fibroblast cells. By using cycloheximide and ganciclovir to block de novo viral protein synthesis and viral DNA replication, respectively, the kinetic classes of array elements were classified. The expression profiles of known ORFs and many previously uncharacterized ORFs provided a temporal map of immediate-early (α), early (β), early-late (γ1), and late (γ2) genes in the entire genome of HCMV. Sequence compositional analysis of the 5′ noncoding DNA sequences of the temporal classes, performed by using algorithms that automatically search for defined and recurring motifs in unaligned sequences, indicated the presence of potential regulatory motifs for β, γ1, and γ2 genes. In summary, these fabricated microarrays of viral DNA allow rapid and parallel analysis of gene expression at the whole viral genome level. The viral chip approach coupled with global biochemical and genetic strategies should greatly speed the functional analysis of established as well as newly discovered large viral genomes.

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