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Biology subjects

Hu, F. B.

Publications and source records attributed to Hu, F. B..

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Renal tubule Cpt1a overexpression mitigates kidney fibrosis by restoring mitochondrial homeostasis

Chronic kidney disease (CKD) remains a major epidemiological, clinical and biomedical challenge. During CKD, renal tubular epithelial cells (TECs) suffer a persistent inflammatory and profibrotic response. Fatty acid oxidation (FAO), the main source of energy for TECs, is reduced in kidney fibrosis and contributes to its pathogenesis. To determine if FAO gain-of-function (FAO-GOF) could protect from fibrosis, we generated a conditional transgenic mouse model with overexpression of the fatty acid shuttling enzyme carnitine palmitoyl-transferase 1 A (CPT1A) in TECs. Cpt1a knock-in (CPT1A KI) mice subjected to three different models of renal fibrosis (unilateral ureteral obstruction, folic acid nephropathy-FAN and adenine induced nephrotoxicity) exhibited decreased expression of fibrotic markers, a blunted pro-inflammatory response and reduced epithelial cell damage and macrophage influx. Protection from fibrosis was also observed when Cpt1a overexpression was induced after FAN. FAO-GOF restituted oxidative metabolism and mitochondrial number and enhanced bioenergetics increasing palmitate oxidation and ATP levels, changes also recapitulated in TECs exposed to profibrotic stimuli. Studies in patients evidenced decreased CPT1 levels and increased accumulation of short and middle chain acyl-carnitines, reflecting impaired FAO in human CKD. We propose that strategies based on FAO-GOF may constitute powerful alternatives to combat fibrosis inherent to CKD. Graphical abstractOverexpression of CPT1A impedes tubular epithelial renal cell dedifferentiation and a pro-fibrotic phenotype by restoring mitochondrial metabolic bioenergetics. O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

biochemistry

genuMet: distinguish genuine untargeted metabolic features without quality control samples

MotivationLarge-scale untargeted metabolomics experiments lead to detection of thousands of novel metabolic features as well as false positive artifacts. With the incorporation of pooled QC samples and corresponding bioinformatics algorithms, those measurement artifacts can be well quality controlled. However, it is impracticable for all the studies to apply such experimental design.\n\nResultsWe introduce a post-alignment quality control method called genuMet, which is solely based on injection order of biological samples to identify potential false metabolic features. In terms of the missing pattern of metabolic signals, genuMet can reach over 95% true negative rate and 85% true positive rate with suitable parameters, compared with the algorithm utilizing pooled QC samples. genu-Met makes it possible for studies without pooled QC samples to reduce false metabolic signals and perform robust statistical analysis.\n\nAvailability and implementationgenuMet is implemented in a R package and available on https://github.com/liucaomics/genuMet under GPL-v2 license.\n\nContactLiming Liang: lliang@hsph.harvard.edu\n\nSupplementary informationSupplementary data are available at ....

bioinformatics