In silico analysis of gene expression profiles in the olfactory mucosae of aging senescence-accelerated mice

2004 ◽  
Vol 77 (3) ◽  
pp. 430-452 ◽  
Author(s):  
Thomas V. Getchell ◽  
Xuejun Peng ◽  
C. Paul Green ◽  
Arnold J. Stromberg ◽  
Kuey-Chu Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
In Silico ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
In Silico Analysis ◽  
Senescence Accelerated Mice ◽  
Silico Analysis

scholarly journals In silico analysis suggests disruption of interactions between HAMP from hepatocytes and SLC40A1 from macrophages in hepatocellular carcinoma

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Liang Hu ◽  
Chao Wu
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
In Silico ◽  
Large Scale ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
In Silico Analysis ◽  
Hepatocyte Proliferation ◽  
Upregulated Genes ◽  
Silico Analysis

Abstract Background Identification of factors associated with proliferation in the hepatocellular carcinoma (HCC) microenvironment aids in understanding the mechanisms of disease progression and provides druggable targets. Gene expression profiles of individual cells in HCC and para-carcinoma tissues can be effectively obtained using the single-cell RNA sequencing (scRNA-Seq) technique. Here, we aimed to identify proliferative hepatocytes from HCC and para-carcinoma tissues, detect differentially expressed genes between the two types of proliferative hepatocytes, and investigate their potential roles in aberrant proliferation. Results Two respective gene signatures for proliferative cells and hepatocytes were established and used to identify proliferative hepatocytes from HCC and para-carcinoma tissues based on scRNA-Seq data. Gene expression profiles between the two types of proliferative hepatocytes were compared. Overall, 40 genes were upregulated in proliferative hepatocytes from para-carcinoma tissue, whereas no upregulated genes were detected in those from HCC tissue. Twelve of the genes, including HAMP, were specifically expressed in the liver tissue. Based on previous reports, we found that HAMP modulates cell proliferation through interaction with its receptor SLC40A1. Comprehensive analysis of cells in HCC and para-carcinoma tissues revealed that: (1) HAMP is specifically expressed in hepatocytes and significantly downregulated in malignant hepatocytes; (2) a subset of macrophages expressing SLC40A1 and genes reacting to various infections is present in para-carcinoma but not in HCC tissue. We independently validated the findings with scRNA-Seq and large-scale tissue bulk RNA-Seq/microarray analyses. Conclusion HAMP was significantly downregulated in malignant hepatocytes. In addition, a subset of macrophages expressing SLC40A1 and genes reacting to various infections was absent in HCC tissue. These findings support the involvement of HAMP-SLC40A1 signaling in aberrant hepatocyte proliferation in the HCC microenvironment. The collective data from our in silico analysis provide novel insights into the mechanisms underlying HCC progression and require further validation with wet laboratory experiments.

scholarly journals Connecting gene expression data from connectivity map and in silico target predictions for small molecule mechanism-of-action analysis

2015 ◽  
Vol 11 (1) ◽  
pp. 86-96 ◽  
Author(s):  
Aakash Chavan Ravindranath ◽  
Nolen Perualila-Tan ◽  
Adetayo Kasim ◽  
Georgios Drakakis ◽  
Sonia Liggi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ligand Binding ◽  
Gene Expression Data ◽  
Mechanism Of Action ◽  
In Silico ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Expression Data ◽  
Connectivity Map ◽  
Action Analysis

Integrating gene expression profiles with certain proteins can improve our understanding of the fundamental mechanisms in protein–ligand binding.

B553 In Silico Analysis of Global Gene Expression in Myeloma CCL: In Search of Stem Cell Genes

2009 ◽  
Vol 9 ◽  
pp. S148
Author(s):  
HE Johnsen ◽  
T Urup ◽  
AD Hoejfeldt ◽  
KB Fogd ◽  
KS Bergkvist ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cell ◽  
In Silico ◽  
In Silico Analysis ◽  
Global Gene Expression ◽  
Silico Analysis

scholarly journals In Silico Discovery of Candidate Drugs against Covid-19

Viruses ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 404 ◽  
Author(s):  
Claudia Cava ◽  
Gloria Bertoli ◽  
Isabella Castiglioni
Keyword(s):  
Gene Expression ◽  
In Silico ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Basic Mechanism ◽  
Cell Receptor ◽  
The Cancer Genome Atlas ◽  
Pathway Enrichment Analysis

Previous studies reported that Angiotensin converting enzyme 2 (ACE2) is the main cell receptor of SARS-CoV and SARS-CoV-2. It plays a key role in the access of the virus into the cell to produce the final infection. In the present study we investigated in silico the basic mechanism of ACE2 in the lung and provided evidences for new potentially effective drugs for Covid-19. Specifically, we used the gene expression profiles from public datasets including The Cancer Genome Atlas, Gene Expression Omnibus and Genotype-Tissue Expression, Gene Ontology and pathway enrichment analysis to investigate the main functions of ACE2-correlated genes. We constructed a protein-protein interaction network containing the genes co-expressed with ACE2. Finally, we focused on the genes in the network that are already associated with known drugs and evaluated their role for a potential treatment of Covid-19. Our results demonstrate that the genes correlated with ACE2 are mainly enriched in the sterol biosynthetic process, Aryldialkylphosphatase activity, adenosylhomocysteinase activity, trialkylsulfonium hydrolase activity, acetate-CoA and CoA ligase activity. We identified a network of 193 genes, 222 interactions and 36 potential drugs that could have a crucial role. Among possible interesting drugs for Covid-19 treatment, we found Nimesulide, Fluticasone Propionate, Thiabendazole, Photofrin, Didanosine and Flutamide.

Double hit viral parasitism, polymicrobial CNS residency and perturbed proteostasis in Alzheimer’s disease: A data driven, in silico analysis of gene expression data

2020 ◽  
Vol 127 ◽  
pp. 124-135
Author(s):  
George D. Vavougios ◽  
Christiane Nday ◽  
Sygliti-Henrietta Pelidou ◽  
Sotirios G. Zarogiannis ◽  
Konstantinos I. Gourgoulianis ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Gene Expression Data ◽  
In Silico ◽  
In Silico Analysis ◽  
Data Driven ◽  
Expression Data ◽  
Double Hit ◽  
Silico Analysis

scholarly journals In Silico analysis of Gastric carcinoma Serial Analysis of Gene Expression libraries reveals different profiles associated with ethnicity

2008 ◽  
Vol 7 (1) ◽  
pp. 22 ◽  
Author(s):  
Francisco J Ossandon ◽  
Cynthia Villarroel ◽  
Francisco Aguayo ◽  
Eudocia Santibanez ◽  
Naohide Oue ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Carcinoma ◽  
In Silico ◽  
In Silico Analysis ◽  
Expression Libraries ◽  
Silico Analysis

In Silico Prediction of Novel Drug Combinations to Combat Bortezomib-Resistant Multiple Myeloma

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 1344-1344
Author(s):  
Holly A. F. Stessman ◽  
Tian Xia ◽  
Aatif Mansoor ◽  
Raamesh Deshpande ◽  
Linda B. Baughn ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Cell Lines ◽  
In Silico ◽  
Research Funding ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Mouse Cell ◽  
Resistant Mouse ◽  
Novel Therapeutic

Abstract Abstract 1344 Bortezomib/VELCADE® (Bz) is a proteasome inhibitor that has been used successfully in the treatment of multiple myeloma (MM) patients. However, acquired resistance to Bz is an emerging problem. Thus, there is a need for novel therapeutic combinations that enhance Bz sensitivity or re-sensitize Bz resistant MM cells to Bz. The Connectivity Map (CMAP; Broad Institute) database contains treatment-induced transcriptional signatures from 1,309 bioactive compounds in 4 human cancer cell lines. An input signature can be used to query the database for correlated drug signatures, a technique that has been used previously to identify drugs that combat chemoresistance in cancer (Wei, et al. Cancer Cell (2006) 10:331). In this study we used in silico bioinformatic screening of gene expression profiles from isogenic pairs of Bz sensitive and resistant mouse cell lines derived from the iMycCα/Bcl-xL mouse model of plasma cell malignancy to identify compounds that combat Bz resistance. We established Bz-induced kinetic gene expression profiles (GEPs) in 3 pairs of Bz sensitive and resistant mouse cell lines over the course of 24 hours. GEPs were collected in the absence of large-scale cell death. The 16 and 24 hour time points were averaged and compared between each Bz sensitive and resistant pair. Genes in the sensitive cell line with a fold change greater than 2, relative to the resistant line, were given the binary distinction of “up” or “down” depending on the direction of change. Genes that met these criteria were assembled into signatures, and then used as inputs for CMAP queries to identify compounds that induce similar transcriptional responses. In all pairs, treatment of the Bz sensitive line correlated with GEPs of drugs that target the proteasome, NF-κB, HSP90 and microtubules, as indicated by positive connectivity scores. However eight compounds, all classified as Topoisomerase (Topo) I and/or II inhibitors, were negatively correlated to our input signature. A negative connectivity score could have two interpretations: (1) this could indicate simply that Topos are upregulated by Bz treatment in Bz sensitive lines, which has been previously reported (Congdan, et al. Biochem. Pharmacol. (2008) 74: 883); or (2) this score could be interpreted as Topos are inhibited in Bz resistant cells upon Bz treatment. This led us to ask whether Topo inhibitors could target Bz resistant MM cells and re-sensitize them to Bz. Indeed, we found that multiple Topo inhibitors were significantly more active against Bz resistant cells as single agents and restored sensitivity to Bz when combined with Bz as a cocktail regimen. This work demonstrates the potential of this in silico bioinformatic approach for identifying novel therapeutic combinations that overcome Bz resistance in MM. Furthermore, it identifies Topo inhibitors – drugs that are already approved for clinical use – as agents that may have utility in combating Bz resistance in refractory MM patients. Disclosures: Stessman: Millennium: The Takeda Oncology Company: Research Funding. Van Ness:Millennium: The Takeda Oncology Company: Research Funding.

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