The consequences of increased 4E-BP1 in polycystic kidney disease

Holditch, Sara J.; Brown, Carolyn N.; Atwood, Daniel J.; Pokhrel, Deepak; Brown, Sara; Lombardi, Andrew M.; Nguyen, Khoa N.; Hill, Ryan C.; Lanaspa, Miguel A.; Hopp, Katharina; Weiser‐Evans, Mary C.M.; Edelstein, Charles L.

doi:10.1093/hmg/ddz244

Cited by 14 publications

(8 citation statements)

References 55 publications

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“…The top-ranked enriched pathways for low eGFR are related to translation. Since low eGFR is an indicator of kidney disease, this is in line with studies reporting increased translational activity to several kidney diseases [ 17 , 18 ]. For diabetes and metabolic syndrome, which is a risk factor for diabetes, 16 out of the top 20 significantly enriched pathways were found for both outcomes.…”

Section: Resultssupporting

confidence: 83%

Metabolomic predictors of phenotypic traits can replace and complement measured clinical variables in population-scale expression profiling studies

Niehues

Bizzarri²,

Reinders³

et al. 2022

BMC Genomics

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Population-scale expression profiling studies can provide valuable insights into biological and disease-underlying mechanisms. The availability of phenotypic traits is essential for studying clinical effects. Therefore, missing, incomplete, or inaccurate phenotypic information can make analyses challenging and prevent RNA-seq or other omics data to be reused. A possible solution are predictors that infer clinical or behavioral phenotypic traits from molecular data. While such predictors have been developed based on different omics data types and are being applied in various studies, metabolomics-based surrogates are less commonly used than predictors based on DNA methylation profiles.In this study, we inferred 17 traits, including diabetes status and exposure to lipid medication, using previously trained metabolomic predictors. We evaluated whether these metabolomic surrogates can be used as an alternative to reported information for studying the respective phenotypes using expression profiling data of four population cohorts. For the majority of the 17 traits, the metabolomic surrogates performed similarly to the reported phenotypes in terms of effect sizes, number of significant associations, replication rates, and significantly enriched pathways.The application of metabolomics-derived surrogate outcomes opens new possibilities for reuse of multi-omics data sets. In studies where availability of clinical metadata is limited, missing or incomplete information can be complemented by these surrogates, thereby increasing the size of available data sets. Additionally, the availability of such surrogates could be used to correct for potential biological confounding. In the future, it would be interesting to further investigate the use of molecular predictors across different omics types and cohorts.

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Section: Resultssupporting

confidence: 83%

Metabolomic predictors of phenotypic traits can replace and complement measured clinical variables in population-scale expression profiling studies

Niehues

Bizzarri²,

Reinders³

et al. 2022

BMC Genomics

View full text Add to dashboard Cite

show abstract

“…The top-ranked enriched pathways for low eGFR are related to translation. Since low eGFR is an indicator of kidney disease, this is in line with studies reporting increased translational activity to several kidney diseases [30,31]. For diabetes and metabolic syndrome, which is a risk factor for diabetes, 16 out of the top 20 significantly enriched pathways were found for both outcomes.…”

Section: Gene Set Enrichment Analysissupporting

confidence: 82%

Metabolic predictors of phenotypic traits can replace and complement measured clinical variables in transcriptome-wide association studies

Niehues

Bizzarri

Reinders

et al. 2022

Preprint

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Transcriptome-wide association studies (TWAS) can provide valuable insights into biological and disease-underlying mechanisms. For studying clinical effects, availability of (confounding) phenotypic traits is essential. The (re)use of RNA-seq or other omics data can be limited by missing, incomplete, or inaccurate phenotypic information. A possible solution are molecular predictors inferring clinical or behavioral phenotypic traits. Such predictors have been developed based on different omics data types and are being applied in various studies.In this study, we applied 17 metabolic predictors to infer various traits, including diabetes status or exposure to lipid medication. We evaluated whether these metabolic surrogates can be used as an alternative to reported information for studying the respective phenotypes using TWAS. Our results revealed that in most cases, the use of metabolic surrogates yields similar results compared to using reported information, making them suitable substitutes for such studies.The application of metabolomics-derived surrogate outcomes opens new possibilities for reuse of multi-omics data sets, especially in situations where availability of clinical metadata is limited. Missing or incomplete information can be complemented by these surrogates, thereby increasing the size of available data sets. Studies would likely also benefit from the use of such surrogates to correct for potential biological confounding. This should be further investigated.Author summaryTranscriptome-wide association studies (TWAS) can be used to associate gene expression levels with phenotypic traits. These associations can provide insights into biological mechanisms including those that underlie diseases. Such studies require molecular profiling data from a large number of individuals as well as information on the phenotypic trait of interest. Biobanks that collect samples and corresponding molecular data, also collect information on phenotypic traits or clinical information. However, this information can be heterogeneous and/or incomplete, or a certain piece of information could be missing entirely. In this study, we apply metabolic predictors to infer various traits, including diabetes status or exposure to lipid medication. We evaluate whether these metabolic surrogates can be used as an alternative to reported information for studying the respective phenotypes using TWAS. Our results reveal that in many cases, the use of metabolic surrogates yields similar results compared to using reported information, making them suitable substitutes for such studies. The possibility of using these surrogate outcomes can thus increase the size of data sets for studies where phenotypic information is incomplete or missing.

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“…The mTORC1 pathway is activated in cyst-lined epithelial cells in both ADPKD patients and 3 different mouse models. Pkd1RC/RC [31,32] and Pk-d2WS25/− mice have increased mTORC1 (4E-BP1) and mTORC2 (p-Akt Ser473) signaling in the kidney [33].…”

Section: Mammalian Target Of Rapamycin Signaling and Autophagymentioning

confidence: 99%

The Pathophysiology of Left Ventricular Hypertrophy, beyond Hypertension, in Autosomal Dominant Polycystic Kidney Disease

Oto

Edelstein²

2022

Nephron

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Heart disease is one of the leading causes of death in autosomal dominant polycystic kidney disease (ADPKD) patients. Left ventricular hypertrophy (LVH) is an early and severe complication in ADPKD patients. Two decades ago, the prevalence of LVH on echocardiography in hypertensive ADPKD patients was shown to be as high as 46%. Recent studies using cardiac magnetic resonance imaging have shown that the prevalence of LVH in ADPKD patients may be lower. The true prevalence of LVH in ADPKD patients is controversial. There is evidence that factors other than hypertension contribute to LVH in ADPKD patients. Studies have shown that young normotensive ADPKD adults and children have a higher left ventricular mass index compared to controls and that the prevalence of LVH is high in patients with ADPKD whose blood pressure is well controlled. Polycystin-1 (PC-1) and polycystin-2 (PC-2) control intracellular signaling pathways that can influence cardiac function. Perturbations of PC-1 or PC-2 in the heart can lead to profound changes in cardiac structure and function independently of kidney function or blood pressure. PC-1 can influence mammalian target of rapamycin and mitophagy and PC-2 can influence autophagy, processes that play a role in LVH. Polymorphisms in the angiotensin-converting enzyme gene may play a role in LVH in ADPKD. This review will detail the pathophysiology of LVH, beyond hypertension, in ADPKD.

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The consequences of increased 4E-BP1 in polycystic kidney disease

Cited by 14 publications

References 55 publications

Metabolomic predictors of phenotypic traits can replace and complement measured clinical variables in population-scale expression profiling studies

Metabolomic predictors of phenotypic traits can replace and complement measured clinical variables in population-scale expression profiling studies

Metabolic predictors of phenotypic traits can replace and complement measured clinical variables in transcriptome-wide association studies

The Pathophysiology of Left Ventricular Hypertrophy, beyond Hypertension, in Autosomal Dominant Polycystic Kidney Disease

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