scholarly journals Assessing Chemical-Induced Liver Injury In Vivo From In Vitro Gene Expression Data in the Rat: The Case of Thioacetamide Toxicity

2019 ◽  
Vol 10 ◽  
Author(s):  
Patric Schyman ◽  
Richard L. Printz ◽  
Shanea K. Estes ◽  
Tracy P. O’Brien ◽  
Masakazu Shiota ◽  
...  
Keyword(s):  
Gene Expression ◽  
Liver Injury ◽  
Gene Expression Data ◽  
Expression Data

scholarly journals Discovery and Validation of Novel Methylation Markers in Helicobacter pylori-Associated Gastric Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Huan Wang ◽  
Nian-Shuang Li ◽  
Cong He ◽  
Chuan Xie ◽  
Yin Zhu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Dna Methylation ◽  
Helicobacter Pylori ◽  
Gene Expression Data ◽  
Functional Enrichment ◽  
Expression Data ◽  
H Pylori

Previous studies have shown that abnormal methylation is an early key event in the pathogenesis of most human cancers, contributing to the development of tumors. However, little attention has been given to the potential of DNA methylation patterns as markers for Helicobacter pylori- (H. pylori-) associated gastric cancer (GC). In this study, an integrated analysis of DNA methylation and gene expression was conducted to identify some potential key epigenetic markers in H. pylori-associated GC. DNA methylation data of 28 H. pylori-positive and 168 H. pylori-negative GC samples were compared and analyzed. We also analyzed the gene expression data of 18 H. pylori-positive and 145 H. pylori-negative GC cases. Finally, the results were verified by in vitro and in vivo experiments. A total of 5609 differentially methylated regions associated with 2454 differentially methylated genes were identified. A total of 228 differentially expressed genes were identified from the gene expression data of H. pylori-positive and H. pylori-negative GC cases. The screened genes were analyzed for functional enrichment. Subsequently, we obtained 28 genes regulated by methylation through a Venn diagram, and we identified five genes (GSTO2, HUS1, INTS1, TMEM184A, and TMEM190) downregulated by hypermethylation. HUS1, GSTO2, and TMEM190 were expressed at lower levels in GC than in adjacent samples ( P < 0.05 ). Moreover, H. pylori infection decreased HUS1, GSTO2, and TMEM190 expression in vitro and in vivo. Our study identified HUS1, GSTO2, and TMEM190 as novel methylation markers for H. pylori-associated GC.

scholarly journals Clinical Drug Response Prediction by Using a Lq Penalized Network-Constrained Logistic Regression Method

2018 ◽  
Vol 51 (5) ◽  
pp. 2073-2084 ◽  
Author(s):  
Hai-Hui Huang ◽  
Jing-Guo Dai ◽  
Yong Liang
Keyword(s):  
Gene Expression ◽  
Logistic Regression ◽  
Personalized Medicine ◽  
Gene Expression Data ◽  
Drug Response ◽  
Prediction Models ◽  
Response Prediction ◽  
Expression Data

Background/Aims: One of the most important impacts of personalized medicine is the connection between patients’ genotypes and their drug responses. Despite a series of studies exploring this relationship, the predictive ability of such analyses still needs to be strengthened. Methods: Here we present the Lq penalized network-constrained logistic regression (Lq-NLR) method to meet this need, in which the predictors are integrated into the gene expression data and biological network knowledge and are combined with a more aggressive penalty function. Response prediction models for two cancer targeting drugs (erlotinib and sorafenib) were developed from gene expression data and IC50 values from a large panel of cancer cell lines by utilizing the proposed approach. Then the drug responders were tested with the baseline tumor gene expression data, yielding an in vivo drug sensitivity prediction. Results: These results demonstrated the high effectiveness of this approach. One of the best results achieved by our method was a correlation of 0.841 between the cell line in vitro drug response and patient’s in vivo drug response. We then applied these two drug prediction models to develop a personalized medicine approach in which the subsequent treatment depends on each patient’s gene-expression profile. Conclusion: The proposed method is much better than the existing approach and can capture a more accurate reflection of the relationship between genotypes and phenotypes.

In vitro gene expression data supporting a DNA non-reactive genotoxic mechanism for ochratoxin A

2007 ◽  
Vol 220 (2) ◽  
pp. 216-224 ◽  
Author(s):  
Leire Arbillaga ◽  
Amaia Azqueta ◽  
Joost H.M. van Delft ◽  
Adela López de Cerain
Keyword(s):  
Gene Expression ◽  
Ochratoxin A ◽  
Gene Expression Data ◽  
Expression Data

Leukemic Transformation of Primary Human CD34+ Progenitor Cells by MLL-AF9 Recapitulates the Phenotype and Gene Expression Profile of MLL Rearranged AML.

Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 2210-2210
Author(s):  
James C. Mulloy ◽  
Junping Wei ◽  
Catherine Rettig ◽  
Mark Wunderlich ◽  
Sara Alvarez ◽  
...  
Keyword(s):  
Gene Expression ◽  
Progenitor Cells ◽  
Gene Expression Data ◽  
Cultured Cells ◽  
Gene Expression Signature ◽  
Leukemic Cells ◽  
Support Vector ◽  
Expression Data ◽  
Hematopoietic Progenitor

Abstract The t(9;11)(p22;q23) results in the creation of an MLL-AF9 fusion gene and is observed in 27% of 11q23 leukemias. t(9;11) is commonly associated with an M5 monocytic leukemia and patients harboring this translocation are included in the intermediate or poor cytogenetic risk groups, with an average 42% overall survival at five years. Using primary human hematopoietic progenitor cells, we have developed a system that closely mimics the MLL-AF9 associated disease observed in human acute myeloid leukemia (AML). Injection of MLL-AF9 expressing cells into sublethally-irradiated NOD/SCID mice produced an aggressive FAB-M5-like leukemia that infiltrated the spleen and liver and induced death with an average latency of 8 weeks. Serial transplantation of leukemic cells recapitulated the disease with a similar latency. MLL-AF9 expression conferred the ability to grow indefinitely in culture, promoted an accumulation of cells resembling monoblasts and was associated with an increase in telomerase activity. Mono- or oligoclonal outgrowth arose in vitro as well as in vivo. Numeric karyotypic changes or normal karyotypes, in the absence of structural rearrangements, additions or deletions were detected in cultured cells as well as samples from leukemic mice. Principal Component Analysis of genome-wide gene expression data from MLL-AF9 expressing cultured cells revealed a gene expression signature that closely mirrors that observed in MLL-rearranged leukemic cells from patient samples. Furthermore, training of a (linear) Support Vector Machine with gene expression data from patient samples resulted in the correct classification of all MLL-AF9-expressing cell cultures as MLL-rearranged leukemia. We conclude that expression of MLL-AF9 in a normal human hematopoietic progenitor is sufficient to recapitulate many key aspects of the clinical disease. This model will yield valuable insights into the molecular pathogenesis of MLL fusion driven leukemia and will serve as a powerful in vivo model for testing much needed novel therapeutic targets.

Selection of Reference Genes for Transcription Studies on Adventitious Root Induction in Olive (Olea Europaea L.) Microshoots Considering Co-expression and Average Transcriptional Stability

2020 ◽  
Author(s):  
Carlos Noceda ◽  
Augusto Peixe ◽  
Birgit Arnholdt-Schmitt
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Reference Genes ◽  
Ad Hoc ◽  
Root Induction ◽  
Expression Data ◽  
Internal Reference ◽  
Experimental Conditions ◽  
Selection Of

Abstract BackgroungSelection of reference genes (RGs) for normalization of PCR-gene expression data includes two crucial steps: determination of the among-sample transcriptionally more stable genes and subsequent choosing of the most suitable genes as internal controls. Both steps can be carried-out through generally accepted strategies each having different strengths and weaknesses. The present study proposes to reinforce normalization of gene expression data by integrating and adding analytical revision at critical steps of those accepted procedures. Especially crucial is to counterbalance a higher representative number of RGs with a correspondent increase in their average transcriptional instability or a generalised co-expression trend among the samples. This methodological study used in vitro olive adventitious rooting as an experimental system, since the underlying morphogenetic process -wich is common to diverse species- is still not completely understood.ResultsFirstly, RG candidates were ranked according to transcriptional stability following a simple statistical method that reduces biasing effects of concomitant, systematic biological variations associated to experimental conditions, such as the variations caused by gene co-regulation. Those types of systematic co-variation are unconsidered by several popular ad hoc informatics programmes. To select the adequate genes among those already ranked, an algorithm of one of the ad hoc informatics programmes (GeNorm) was adapted to allow partial automatization of RG selection for any strategy of transcriptional-gene stability ordering. In order to delve into the resulting possible RG sets suitability for inter-assay comparisons and technical-error compensation, separate statistics were formulated. The achieved results were compared with those obtained by standard stability ranking methods. Finally, a double evaluation was performed to accurately contrast two choice RG sets. The whole strategy was applied to a panel considering several independent factors, but the suitability of the obtained putative RG sets was tested for cases restricted to fewer variables. H2B, OUB and ACT are valid for normalization in transcriptional studies on olive microshoot rooting when comparing treatments, time points and assays.ConclusionsThe set of genes identified as internal reference is now available for wider expression studies on any target gene in similar biological systems. The overall methodology aims to constitute a guide for general application.

scholarly journals Gene expression data from acetaminophen-induced toxicity in human hepatic in vitro systems and clinical liver samples

Data in Brief ◽  
2016 ◽  
Vol 7 ◽  
pp. 1052-1057 ◽  
Author(s):  
Robim M. Rodrigues ◽  
Olivier Govaere ◽  
Tania Roskams ◽  
Tamara Vanhaecke ◽  
Vera Rogiers ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Expression Data ◽  
In Vitro Systems

COXEN prediction of antineoplastic drug sensitivity in bladder cancer patients.

2016 ◽  
Vol 34 (2_suppl) ◽  
pp. 365-365
Author(s):  
Shalin Kothari ◽  
Daniel Gustafson ◽  
Keith Killian ◽  
James Costello ◽  
Daniel C. Edelman ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bladder Cancer ◽  
Gene Expression Data ◽  
Drug Sensitivity ◽  
Model Building ◽  
Response To Therapy ◽  
Antineoplastic Drug ◽  
In Vitro Assays ◽  
Expression Data

365 Background: COXEN (Co-eXpression ExtrapolatioN) uses molecular profiles as a “rosetta stone” for translating drug sensitivities of one set of cancers into predictions for another completely independent set of cell lines or human tumors. The ability of COXEN to predict drug effectiveness in pts using tumor samples from in vitro assays is unique. Methods: We tested the predictive value of COXEN for standard chemotherapies in a cohort of bladder cancer pts. Total RNA was extracted from formalin fixed paraffin embedded (FFPE) tissue and converted to cDNA, amplified with Ovation FFPE WTA, and hybridized to a GeneChip Human Genome U133 Plus 2.0 array. Using gene expression data from 278 independent bladder tumors, COXEN scores were generated using bioinformatics models originally built using the NCI-60 cell line panel and a model building algorithm (MiPP). Gene expression data was processed to score 76 FDA approved antineoplastic drugs. Results: A total of 24 samples were tested (15 tumors with 1 sample and 9 tumors with 2 biological replicas (2 samples from the same tumor)) from 15 pts who received chemotherapy (median age 64 (41-74); 73% male; with muscle invasive bladder cancer (MIBC) (12/15, 80%) or metastatic bladder cancer (mBC) (3/15, 20%)). Response to therapy was confirmed by pathologic response in MIBC pts and radiologic response in mBC pts. Chemotherapies evaluated included: methotrexate/vinblastine/doxorubicin/cisplatin; gemcitabine/cisplatin; gemcitabine/carboplatin; and cisplatin/etoposide. COXEN accurately predicted antineoplastic drug sensitivity in 11/15 (73%) pts (75% MIBC and 67% mBC), of which 7/11 pts had 2 biological samples. However, only 3/7 (43%) biological replicas confirmed COXEN prediction. COXEN accurately predicted drug sensitivity in 9/10 (90%) pts with response and 2/5 (40%) pts with resistance to therapy. Conclusions: COXEN did well in predicting antineoplastic drug response for the majority of bladder cancer pts in this cohort. However, predictions from 2 samples within the same tumor were not always consistent, likely due to the expected tumor heterogeneity found in bladder cancer tumors. A prospective clinical trial in patients with mBC using COXEN to select next best therapy is in development.

scholarly journals Computationally predicted gene regulatory networks in molluscan biomineralization identify extracellular matrix production and ion transportation pathways

2019 ◽  
Author(s):  
Victoria A Sleight ◽  
Philipp Antczak ◽  
Francesco Falciani ◽  
Melody S Clark
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Time Course ◽  
Shell Growth ◽  
Extracellular Proteins ◽  
Supplementary Information ◽  
Gene Interactions ◽  
Expression Data ◽  
Gene Regulatory

Abstract Motivation The molecular processes regulating molluscan shell production remain relatively uncharacterized, despite the clear evolutionary and societal importance of biomineralization. Results Here we built the first computationally predicted gene regulatory network (GRN) for molluscan biomineralization using Antarctic clam (Laternula elliptica) mantle gene expression data produced over an age-categorized shell damage-repair time-course. We used previously published in vivo in situ hybridization expression data to ground truth gene interactions predicted by the GRN and show that candidate biomineralization genes from different shell layers, and hence microstructures, were connected in unique modules. We characterized two biomineralization modules of the GRN and hypothesize that one module is responsible for translating the extracellular proteins required for growing, repairing or remodelling the nacreous shell layer, whereas the second module orchestrates the transport of both ions and proteins to the shell secretion site, which are required during normal shell growth, and repair. Our findings demonstrate that unbiased computational methods are particularly valuable for studying fundamental biological processes and gene interactions in non-model species where rich sources of gene expression data exist, but annotation rates are poor and the ability to carry out true functional tests are still lacking. Availability and implementation The raw RNA-Seq data is freely available for download from NCBI SRA (Accession: PRJNA398984), the assembled and annotated transcriptome can be viewed and downloaded from molluscDB (ensembl.molluscdb.org) and in addition, the assembled transcripts, reconstructed GRN, modules and detailed annotations are all available as Supplementary Files. Supplementary information Supplementary data are available at Bioinformatics online.

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