Quantitating the epigenetic transformation contributing to cholesterol homeostasis using Gaussian process

Wang, Chao; Scott, Samantha M.; Subramanian, Kanagaraj; Loguercio, Salvatore; Zhao, Pei; Hutt, Darren M.; Farhat, Nicole Y.; Porter, Forbes D.; Balch, William E.

doi:10.1038/s41467-019-12969-x

Cited by 21 publications

(39 citation statements)

References 78 publications

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“…Besides, a machine learning approach has modeled the Niemann‐Pick C1 (NPC1) protein folding as well as the response of different NPC1 genetic variants to SAHA treatment (Wang et al, 2019). SAHA is an FDA‐approved epidrug acting as histone deacetyltransferase inhibitor (HDACi) leading to increased levels of Lys acetylation in specific NPC1 chain domains (Wang et al, 2019). Overall, this platform provided mechanistic findings in PPI contributing to NPC1 disease and predicted residues that are likely responsive to HDACi which are largely tested in clinical trials for major CV diseases (Schiano et al, 2020a).…”

Section: Artificial Intelligence In Personalized Treatment Of Dyslipidemiasmentioning

confidence: 99%

“…However, larger cohort studies are needed to confirm these findings in clinical practice. Besides, a machine learning approach has modeled the Niemann-Pick C1 (NPC1) protein folding as well as the response of different NPC1 genetic variants to SAHA treatment (Wang et al, 2019). SAHA is an FDA-approved epidrug acting as histone deacetyltransferase inhibitor (HDACi) leading to increased levels of Lys acetylation in specific NPC1 chain domains (Wang et al, 2019).…”

Section: Artificial Intelligence In Personalized Treatment Of Dyslipidemiasmentioning

confidence: 99%

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Network Medicine Approach in Prevention and Personalized Treatment of Dyslipidemias

Benincasa

Candia

Costa

et al. 2020

Lipids

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Dyslipidemias can affect molecular networks underlying the metabolic homeostasis and vascular function leading to atherogenesis at early stages of development. Since disease‐related proteins often interact with each other in functional modules, many advanced network‐oriented algorithms were applied to patient‐derived big data to identify the complex gene–environment interactions underlying the early pathophysiology of dyslipidemias and atherosclerosis. Both the proprotein convertase subtilisin/kexin type 7 (PCSK7) and collagen type 1 alpha 1 chain (COL1A1) genes arose from the application of TFfit and WGCNA algorithms, respectively, as potential useful therapeutic targets in prevention of dyslipidemias. Moreover, the Seed Connector algorithm (SCA) algorithm suggested a putative role of the neuropilin‐1 (NRP1) protein as drug target, whereas a regression network analysis reported that niacin may provide benefits in mixed dyslipidemias. Dyslipidemias are highly heterogeneous at the clinical level; thus, it would be helpful to overcome traditional evidence‐based paradigm toward a personalized risk assessment and therapy. Network Medicine uses omics data, artificial intelligence (AI), imaging tools, and clinical information to design personalized therapy of dyslipidemias and atherosclerosis. Recently, a novel non‐invasive AI‐derived biomarker, named Fat Attenuation Index (FAI™) has been established to early detect clinical signs of atherosclerosis. Moreover, an integrated AI‐radiomics approach can detect fibrosis and microvascular remodeling improving the customized risk assessment. Here, we offer a network‐based roadmap ranging from novel molecular pathways to digital therapeutics which can improve personalized therapy of dyslipidemias.

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Section: Artificial Intelligence In Personalized Treatment Of Dyslipidemiasmentioning

confidence: 99%

Section: Artificial Intelligence In Personalized Treatment Of Dyslipidemiasmentioning

confidence: 99%

Network Medicine Approach in Prevention and Personalized Treatment of Dyslipidemias

Benincasa

Candia

Costa

et al. 2020

Lipids

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“…A cloned cDNA construct of NPC1, named NPC1 WT‐V that has been used as wild‐type NPC1 in publications by us and others, also contains four variants in comparison to the Genbank reference sequence . These variants are 387 T>C (Y129Y), 1415 T>C (L472P), 1925 T>C (M642T) and 2587 T>C (S863P).…”

Section: Introductionmentioning

confidence: 99%

High‐content imaging and structure‐based predictions reveal functional differences between Niemann‐Pick C1 variants

Vanharanta

Peränen

Pfisterer

et al. 2020

Traffic

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www.traf c.dkCover legend: Structure of full-length Niemann-Pick C1 (NPC1) protein embedded in a bilayer composed of a binary mixture of POPC and cholesterol. The protein is shown in ribbon representation. Each lysosomal luminal domain is indicated with a different color and the transmembrane region is shown in white. The amino acids that differ in WT-V variant of NPC1 are highlighted with red space-filling representation. The POPC and cholesterol molecules are colored by element (oxygen in red and carbon in blue) and hydrogen atoms are not shown. See Vanharanta et al., Traffic 2020; 21(5):386-397. Aim and ScopeTraffic encourages and facilitates the publication of papers covering the cell biology of intracellular transport in health and disease. Areas of interest include protein, nucleic acid and lipid traffic, molecular motors, intracellular pathogens, intracellular proteolysis, nuclear import and export, cytokinesis and the cell cycle, protein translocation, antigen processing and presentation, organelle biogenesis, metabolism, cell polarity and organization, and organelle movement.All aspects of the structural, molecular biology, biochemistry, genetics, morphology, intracellular signaling and relationship to hereditary or infectious diseases will be covered. Manuscripts must provide a clear conceptual or mechanistic advance. The editors will reject papers that require major changes, including addition of significant experimental data or other significant revision.

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“…The striking difference in dynamical behavior can be described for WT NPC1 as a lateral movement of cholesterol toward the lipid bilayer, whereas in P691S, the cholesterol molecule travels upward toward the NTD, translocating nearly 10 Å over the 100 ns simulation time, via the putative transport tunnel previously predicted [18,20]. Importantly, the cholesterol motion in P691S follows a cholesterol flow path recently established using spatial covariance analyses [8]. We analyzed the structural features of the IBS to understand why the dynamical behavior of cholesterol differs when proline at position 691 is mutated to serine.…”

Section: Resultsmentioning

confidence: 79%

“…How specific mutations in NPC1 are related to disrupted cholesterol trafficking and ultimately to disease phenotype is not understood. Applying machine learning techniques, Wang et al recently analyzed the structural determinants to uncover connections between epigenetic factors and disease phenotype [8]. Indeed, some mutants, e.g., homozygous I1061T, give rise to a severe disease phenotype with abolished cholesterol trafficking, while others are related to a mild disease phenotype [9,10].…”

Section: Introductionmentioning

confidence: 99%

Cholesterol Transport in Wild-Type NPC1 and P691S: Molecular Dynamics Simulations Reveal Changes in Dynamical Behavior

Elghobashi-Meinhardt

2020

IJMS

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The Niemann-Pick C1 (NPC1) protein is the main protein involved in NPC disease, a fatal lysosomal lipid storage disease. NPC1, containing 1278 amino acids, is comprised of three lumenal domains (N-terminal, middle lumenal, C-terminal) and a transmembrane (TM) domain that contains a five helix bundle referred to as the sterol-sensing domain (SSD). The exact purpose of the SSD is not known, but it is believed that the SSD may bind cholesterol, either as a part of the lipid trafficking pathway or as part of a signaling mechanism. A recent cryo-EM structure has revealed an itraconazole binding site (IBS) in the SSD of human NPC1. Using this structural data, we constructed a model of cholesterol-bound wild-type (WT) and mutant P691S and performed molecular dynamics (MD) simulations of each cholesterol-bound protein. For WT NPC1, cholesterol migrates laterally, in the direction of the lipid bilayer. In the case of P691S, cholesterol is observed for the first time to migrate away from the SSD toward the N-terminal domain via a putative tunnel that connects the IBS with the lumenal domains. Structural features of the IBS are analyzed to identify the causes for different dynamical behavior between cholesterol-bound WT and cholesterol-bound P691S. The side chain of Ser691 in the P691S mutant introduces a hydrogen bond network that is not present in the WT protein. This change is likely responsible for the altered dynamical behavior observed in the P691S mutant and helps explain the disrupted cholesterol trafficking behavior observed in experiments.

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Quantitating the epigenetic transformation contributing to cholesterol homeostasis using Gaussian process

Cited by 21 publications

References 78 publications

Network Medicine Approach in Prevention and Personalized Treatment of Dyslipidemias

Network Medicine Approach in Prevention and Personalized Treatment of Dyslipidemias

High‐content imaging and structure‐based predictions reveal functional differences between Niemann‐Pick C1 variants

Cholesterol Transport in Wild-Type NPC1 and P691S: Molecular Dynamics Simulations Reveal Changes in Dynamical Behavior

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