Accessory proteins for heterotrimeric G-proteins in the kidney

Park, Frank

doi:10.3389/fphys.2015.00219

Cited by 13 publications

(10 citation statements)

References 143 publications

(212 reference statements)

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“…Second, genes upregulated in acute dehydration cluster around the GO term related to negative regulation of the MAPK cascade, which is an important regulator of transcrip-tion and translation (74), a phenomenon that is known to be a part of the response to hyperosmotic stress (12). Genes involved in this pathway, upregulated in the cactus mouse kidney, include ERRFI1 (FDR ϭ 2.1e-09, ERBB receptor feedback inhibitor 1), XBP1 (FDR ϭ 0.002, X box-binding protein 1), and RGS2 (FDR ϭ 4.6e-07, regulator of G protein signaling 2), the latter of which has been shown to decrease the rate at which water is reabsorbed in the collecting tubules and duct via negative regulation of V2 vasopressin receptor signaling (72,102).…”

Section: Global Analysis Of Differential Gene Expressionmentioning

confidence: 99%

Severe acute dehydration in a desert rodent elicits a transcriptional response that effectively prevents kidney injury

MacManes

2017

American Journal of Physiology-Renal Physiology

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Animals living in desert environments are forced to survive despite severe heat, intense solar radiation, and both acute and chronic dehydration. These animals have evolved phenotypes that effectively address these environmental stressors. To begin to understand the ways in which the desert-adapted rodent survives, reproductively mature adults were subjected to 72 h of water deprivation, during which they lost, on average, 23% of their body weight. The animals reacted via a series of changes in the kidney, which included modulating expression of genes responsible for reducing the rate of transcription and maintaining water and salt balance. Extracellular matrix turnover appeared to be decreased, and apoptosis was limited. In contrast to the canonical human response, serum creatinine and other biomarkers of kidney injury were not elevated, suggesting that changes in gene expression related to acute dehydration may effectively prohibit widespread kidney damage in the cactus mouse.

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Section: Global Analysis Of Differential Gene Expressionmentioning

confidence: 99%

Severe acute dehydration in a desert rodent elicits a transcriptional response that effectively prevents kidney injury

MacManes

2017

American Journal of Physiology-Renal Physiology

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“…The 'G-protein signaling' and 'Integrin-mediated cell adhesion' pathways were consistently selected in most of the runs (54% and 28%, respectively) with a median R 2 of ~ 0.57. Both pathways play known roles in kidney function: G-protein signaling is responsible for mediating response to various types of physical damage to the cell in a broad range of renal diseases [14,15]. Integrin-mediated cell adhesion plays a crucial structural role in the portions of the kidneys responsible for collecting waste from the circulatory system [16].…”

Section: Tg-gatesmentioning

confidence: 99%

Embedding covariate adjustments in tree-based automated machine learning for biomedical big data analyses

Manduchi

Romano

et al. 2020

BMC Bioinformatics

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Background A typical task in bioinformatics consists of identifying which features are associated with a target outcome of interest and building a predictive model. Automated machine learning (AutoML) systems such as the Tree-based Pipeline Optimization Tool (TPOT) constitute an appealing approach to this end. However, in biomedical data, there are often baseline characteristics of the subjects in a study or batch effects that need to be adjusted for in order to better isolate the effects of the features of interest on the target. Thus, the ability to perform covariate adjustments becomes particularly important for applications of AutoML to biomedical big data analysis. Results We developed an approach to adjust for covariates affecting features and/or target in TPOT. Our approach is based on regressing out the covariates in a manner that avoids ‘leakage’ during the cross-validation training procedure. We describe applications of this approach to toxicogenomics and schizophrenia gene expression data sets. The TPOT extensions discussed in this work are available at https://github.com/EpistasisLab/tpot/tree/v0.11.1-resAdj. Conclusions In this work, we address an important need in the context of AutoML, which is particularly crucial for applications to bioinformatics and medical informatics, namely covariate adjustments. To this end we present a substantial extension of TPOT, a genetic programming based AutoML approach. We show the utility of this extension by applications to large toxicogenomics and differential gene expression data. The method is generally applicable in many other scenarios from the biomedical field.

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“…Both pathways play known roles in kidney function: G-protein signaling is responsible for mediating response to various types of physical damage to the cell in a broad range of renal diseases [17,18]. Integrin-mediated cell adhesion plays a crucial structural role in the portions of the kidneys responsible for collecting waste from the circulatory system [19].…”

Section: Tg-gatesmentioning

confidence: 99%

Embedding covariate adjustments in tree-based automated machine learning for biomedical big data analyses

Manduchi

Romano

et al. 2020

Preprint

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Background: A typical task in bioinformatics consists of identifying which features are associated with a target outcome of interest and building a predictive model. Automated machine learning (AutoML) systems such as the Tree-based Pipeline Optimization Tool (TPOT) constitute an appealing approach to this end. However, in biomedical data, there are often baseline characteristics of the subjects in a study or batch effects that need to be adjusted for in order to better isolate the effects of the features of interest on the target. Thus, the ability to perform covariate adjustments becomes particularly important for applications of AutoML to biomedical big data analysis. Results: We present an approach to adjust for covariates affecting features and/or target in TPOT. Our approach is based on regressing out the covariates in a manner that avoids leakage during the cross-validation training procedure. We then describe applications of this approach to toxicogenomics and schizophrenia gene expression data sets. The TPOT extensions discussed in this work are available at https://github.com/EpistasisLab/tpot/tree/v0.11.1-resAdj. Conclusions: In this work, we address an important need in the context of AutoML, which is particularly crucial for applications to bioinformatics and medical informatics, namely covariate adjustments. To this end we present a substantial extension of TPOT, a genetic programming based AutoML approach. We show the utility of this extension by applications to large toxicogenomics and differential gene expression data. The method is generally applicable in many other scenarios from the biomedical field.

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Accessory proteins for heterotrimeric G-proteins in the kidney

Cited by 13 publications

References 143 publications

Severe acute dehydration in a desert rodent elicits a transcriptional response that effectively prevents kidney injury

Severe acute dehydration in a desert rodent elicits a transcriptional response that effectively prevents kidney injury

Embedding covariate adjustments in tree-based automated machine learning for biomedical big data analyses

Embedding covariate adjustments in tree-based automated machine learning for biomedical big data analyses

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