Translation of disease associated gene signatures across tissues

Kasim, Adetayo; Shkedy, Ziv; Lin, Dan; Sanden, Suzy Van; Abrahantes, José Cortiñas; Göhlmann, Hinrich W. H.; Bijnens, Luc; Yekutieli, Daniel; Camilleri, Michael; Aerssens, Jeroen; Talloen, Willem

doi:10.1504/ijdmb.2015.067321

Cited by 5 publications

(4 citation statements)

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“…Knowing how much concordance can be expected between studies carried out using different parameters will act as a measure of ‘representativeness’ of the recorded gene expression to true human PD, helping to establish whether animal models of disease are representative of the human condition at the transcriptomic level, and whether gene expression in more easily accessible surrogate tissues could be useful in PD research or diagnostics [24]. As the largest meta-analysis of PD to date, this study will analyse the effects of these four factors - species, tissue, platform, and sample size - to understand the reasons for the observed inconsistency between microarray studies of PD, aiming to eventually establish the relevance of these parameters to the representation of the human disease at the level of measured gene expression.…”

Section: Introductionmentioning

confidence: 99%

Concordance analysis of microarray studies identifies representative gene expression changes in Parkinson’s disease: a comparison of 33 human and animal studies

Oerton

2017

BMC Neurol

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BackgroundAs the popularity of transcriptomic analysis has grown, the reported lack of concordance between different studies of the same condition has become a growing concern, raising questions as to the representativeness of different study types, such as non-human disease models or studies of surrogate tissues, to gene expression in the human condition.MethodsIn a comparison of 33 microarray studies of Parkinson’s disease, correlation and clustering analyses were used to determine the factors influencing concordance between studies, including agreement between different tissue types, different microarray platforms, and between neurotoxic and genetic disease models and human Parkinson’s disease.ResultsConcordance over all studies is low, with correlation of only 0.05 between differential gene expression signatures on average, but increases within human patients and studies of the same tissue type, rising to 0.38 for studies of human substantia nigra. Agreement of animal models, however, is dependent on model type. Studies of brain tissue from Parkinson’s disease patients (specifically the substantia nigra) form a distinct group, showing patterns of differential gene expression noticeably different from that in non-brain tissues and animal models of Parkinson’s disease; while comparison with other brain diseases (Alzheimer’s disease and brain cancer) suggests that the mixed study types display a general signal of neurodegenerative disease. A meta-analysis of these 33 microarray studies demonstrates the greater ability of studies in humans and highly-affected tissues to identify genes previously known to be associated with Parkinson’s disease.ConclusionsThe observed clustering and concordance results suggest the existence of a ‘characteristic’ signal of Parkinson’s disease found in significantly affected human tissues in humans. These results help to account for the consistency (or lack thereof) so far observed in microarray studies of Parkinson’s disease, and act as a guide to the selection of transcriptomic studies most representative of the underlying gene expression changes in the human disease.Electronic supplementary materialThe online version of this article (doi:10.1186/s12883-017-0838-x) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

Concordance analysis of microarray studies identifies representative gene expression changes in Parkinson’s disease: a comparison of 33 human and animal studies

Oerton

2017

BMC Neurol

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“…A number of studies have assessed changes in colonic gene expression in IBS, mostly in the rectosigmoid. 21 Small bowel signalling is likely more important in understanding meal-induced symptoms in IBS. Most studies in the small intestine have assessed limited gene expression panels showing changes in mast cells, Toll-like receptors and tight junction signalling.…”

Section: Introductionmentioning

confidence: 99%

“…A number of studies have assessed changes in colonic gene expression in IBS, mostly in the rectosigmoid 21 . Small bowel signalling is likely more important in understanding meal‐induced symptoms in IBS.…”

Section: Introductionmentioning

confidence: 99%

Intestinal chemosensitivity in irritable bowel syndrome associates with small intestinal TRPV channel expression

Grover

Berumen

Peters

et al. 2021

Aliment Pharmacol Ther

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Background: Irritable bowel syndrome (IBS) patients often experience mealassociated symptoms. However, the underlying mechanisms are unclear. Aim:To determine small intestinal mechanisms of lipid-induced symptoms and rectal hypersensitivity in IBS Methods: We recruited 26 IBS patients (12 IBS-C, 14 IBS-D) and 15 healthy volunteers (HV). In vivo permeability was assessed using saccharide excretion assay.Rectal sensitivity was assessed using a barostat before and after small bowel lipid infusion; symptoms were assessed throughout. Next, an extended upper endoscopy with probe-based confocal laser endomicroscopy (pCLE) was performed with changes induced by lipids. Duodenal and jejunal mucosal biopsies were obtained for transcriptomics.Results: Following lipid infusion, a higher proportion of HV than IBS patients reported no pain, no nausea, no fullness and no urgency (P < 0.05 for all). In a model adjusted for sex and anxiety, IBS-C and IBS-D patients had lower thresholds for first rectal sensation (P = 0.0007) and pain (P = 0.004) than HV. In vivo small intestinal permeability and mean pCLE scores were similar between IBS patients and HV. Postlipid, pCLE scores were higher than pre-lipid but were not different between groups. Baseline duodenal transient receptor potential vanilloid (TRPV) 1 and 3 expression was increased in IBS-D, and TRPV3 in IBS-C. Duodenal TRPV1 expression correlated with abdominal pain (r = 0.51, FDR = 0.01), and inversely with first rectal sensation (r = −0.48, FDR = 0.01) and pain (r = −0.41, FDR = 0.02) thresholds. Conclusion:Lipid infusion elicits a greater symptom response in IBS patients than HV, which is associated with small intestinal expression of TRPV channels. TRPVmediated small intestinal chemosensitivity may mediate post-meal symptoms in IBS.

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“…Today, the analysis of gene expression data is one of the major topics in health informatics (Ng and Pei, 2007). This technology has proven capable of improving accuracy in tumour classification, drug target identification, prediction of the response to the therapy and early lesion detection efficiency (Elek et al, 1999;Kasim et al, 2015;Selaru et al, 2002). Classification of DNA microarray data allows the discovery of hidden patterns in expression profiles and opened the possibility for accurate classification.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network classification of microarray data using new hybrid gene selection method

Aziz

Verma

Jha

et al. 2017

IJDMB

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This paper proposed a new combination of feature selection/ extraction approach for Artificial Neural Networks (ANNs) classification of high-dimensional microarray data, which uses an Independent Component Analysis (ICA) as an extraction technique and Artificial Bee Colony (ABC) as an optimisation technique. The study evaluates the performance of the proposed ICA + ABC algorithm by conducting extensive experiments on fivebinary and one multi-class gene expression microarray data set and compared the proposed algorithm with ICA and ABC. The proposed method shows superior performance as it achieves the highest classification accuracy along with the lowest average number of selected genes. Furthermore, the present work compares the proposed ICA + ABC algorithm with popular filter techniques and with other similar bio-inspired algorithms with ICA. The experimental results show that the proposed algorithm gives more accurate classification rate for ANN classifier. Therefore, ICA + ABC are a promising approach for solving gene selection and cancer classification problems using microarray data.

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Translation of disease associated gene signatures across tissues

Cited by 5 publications

References 24 publications

Concordance analysis of microarray studies identifies representative gene expression changes in Parkinson’s disease: a comparison of 33 human and animal studies

Concordance analysis of microarray studies identifies representative gene expression changes in Parkinson’s disease: a comparison of 33 human and animal studies

Intestinal chemosensitivity in irritable bowel syndrome associates with small intestinal TRPV channel expression

Artificial neural network classification of microarray data using new hybrid gene selection method

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