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Publications in Cell Metabolism by NOMIS researchers

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NOMIS Researcher(s)

Published in

January 4, 2022

Inexorable increases in insulin resistance, lipolysis, and hepatic glucose production (HGP) are hallmarks of type 2 diabetes. Previously, we showed that peripheral delivery of exogenous fibroblast growth factor 1 (FGF1) has robust anti-diabetic effects mediated by the adipose FGF receptor (FGFR) 1. However, its mechanism of action is not known. Here, we report that FGF1 acutely lowers HGP by suppressing adipose lipolysis. On a molecular level, FGF1 inhibits the cAMP-protein kinase A axis by activating phosphodiesterase 4D (PDE4D), which separates it mechanistically from the inhibitory actions of insulin via PDE3B. We identify Ser44 as an FGF1-induced regulatory phosphorylation site in PDE4D that is modulated by the feed-fast cycle. These findings establish the FGF1/PDE4 pathway as an alternate regulator of the adipose-HGP axis and identify FGF1 as an unrecognized regulator of fatty acid homeostasis.

Research field(s)
Health Sciences, Clinical Medicine, Endocrinology & Metabolism

NOMIS Researcher(s)

Published in

March 2, 2021
We appreciate the interest and comments on our article reporting a novel gut microbiome signature for predicting liver cirrhosis (). Dr. Chen raises concerns about the impact of proton pump inhibitor (PPI) treatment on the gut microbial profiles of cirrhosis patients in our main cohort (). He cites evidence that 4 species in our 19-species signature for cirrhosis (Veillonella parvulaVeillonella atypicaStreptococcus parasanguinis, and Streptococcus salivarius) are known to be impacted by PPI treatment. According to his analysis, these species alone were sufficient to detect cirrhosis in the Qin and Iebba cohorts. The question at hand is whether PPI usage in our training cohort may have skewed and thus compromised our gut microbiome signature for cirrhosis.
To address whether our 19-species signature remains valid, independent of PPI treatment status (Table S1), we first reviewed all 81 subjects in our training cohort (54 non-NAFLD controls and 27 NAFLD-cirrhosis patients) and identified 13 individuals who were using PPIs (4 non-NAFLD controls and 9 NAFLD-cirrhosis patients). After excluding those 13 subjects on PPIs, we retrained our 19-species Random Forest (RF) model on the revised cohort comprising only the 68 remaining non-PPI subjects (50 non-NAFLD controls and 18 NAFLD-cirrhosis patients) for cirrhosis prediction. For detection of cirrhosis, the model achieved an accuracy of AUC (area under the curve) 0.891, which is comparable to our original AUC of 0.91 (Figure S1A). In our original study, we included age during machine training as a default. Therefore, we also examined the 19 species+age in the non-PPI training set and achieved an AUC of 0.896, which again is comparable to our original AUC of 0.91 (Figure S1B). Furthermore, we tested the models comprising 19 species or 19 species+age with the independent dataset from the Qin et al. study (merged discovery and validation set, 114 controls and 123 cirrhosis) (). Notably, we still obtained AUCs of 0.851 and 0.832 for validation and testing scores, respectively (Figures S1C and S1D). Thus, even after removing subjects on PPIs from the training cohort, the revised model with 19 species still detected cirrhosis with high accuracy. This suggests the impact of PPI usage on the signature was minimal.
We observed no significant difference in the diagnostic accuracy of our gut microbiome signature for cirrhosis, regardless of whether it was evaluating mixed or only non-PPI cohorts. Although we agree that PPI drugs may have an effect on gut microbiota as a whole, our 19-species signature is robust and retains its diagnostic potential for distinguishing liver cirrhosis, independent of PPI treatment. Future studies encompassing more clinical samples and longitudinal follow-up will allow us to understand the specific effects of different classes of perturbants and provide more accuracy and robustness to power the machine-learning-based prediction model ().

Research field(s)
Clinical Medicine

NOMIS Researcher(s)

Published in

November 3, 2020

Oh et al. identify diagnostic signatures for fibrosis from stool metagenomic and metabolomic profiling that, when combined with serum AST levels, distinguishes cirrhosis in mixed fibrosis cohort. Moreover, this combination signature was validated in racially and geographically independent cohorts.

Research field(s)
Health Sciences, Clinical Medicine, Endocrinology & Metabolism

NOMIS Researcher(s)

Published in

August 6, 2019

Most neurons are not replaced during an animal’s lifetime. This nondividing state is characterized by extreme longevity and age-dependent decline of key regulatory proteins. To study the lifespans of cells and proteins in adult tissues, we combined isotope labeling of mice with a hybrid imaging method (MIMS-EM). Using 15N mapping, we show that liver and pancreas are composed of cells with vastly different ages, many as old as the animal. Strikingly, we also found that a subset of fibroblasts and endothelial cells, both known for their replicative potential, are characterized by the absence of cell division during adulthood. In addition, we show that the primary cilia of beta cells and neurons contains different structural regions with vastly different lifespans. Based on these results, we propose that age mosaicism across multiple scales is a fundamental principle of adult tissue, cell, and protein complex organization. Arrojo e Drigo et al. measure the age of cells and proteins using high-resolution isotope imaging and show that adult mouse organs are mosaics of cells of different ages. The liver, which has high turnover, contains cells as old as the animal, while cilia have differentially aged structural protein components.

Research field(s)
Health Sciences, Clinical Medicine, Endocrinology & Metabolism