Biology subjects

Hu, F. B.

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

5 recordsLinked to original sources

bioRxiv2024

Optimization-based dietary recommendations for healthy eating

Various diet scores have been developed to assess compliance with dietary guidelines. Yet, enhancing those diet scores is very challenging. Here, we tackle this issue by formalizing an optimization problem and solving it with simulated annealing. Our optimization-based dietary recommendation (ODR) approach, evaluated using Diet-Microbiome Association study data, provides efficient and reasonable recommendations for different diet scores. ODR has the potential to enhance nutritional counseling and promote dietary adherence for healthy eating.

bioinformatics↗

bioRxiv2023

Microbiome-based correction of nutrient profiles derived from self-reported dietary assessments

Since dietary intake is challenging to directly measure in large-scale cohort studies, we often rely on self-reported instruments (e.g., food frequency questionnaires, 24-hour recalls, and diet records) developed in nutritional epidemiology. Those self-reported instruments are prone to measurement errors, which can lead to inaccuracies in the calculation of nutrient profiles. Currently, few computational methods exist to address this problem. In the present study, we introduce a deep-learning approach --- Microbiome-based nutrient profile corrector (METRIC), which leverages gut microbial compositions to correct random errors in self-reported dietary assessments using 24-hour recalls or diet records. We demonstrate the excellent performance of METRIC in minimizing the simulated random errors, particularly for nutrients metabolized by gut bacteria in both synthetic and three real-world datasets. Further research is warranted to examine the utility of METRIC to correct actual measurement errors in self-reported dietary assessment instruments.

microbiology↗

bioRxiv2023

Predicting metabolic response to dietary intervention using deep learning

Due to highly personalized biological and lifestyle characteristics, different individuals may have different metabolite responses to specific foods and nutrients. In particular, the gut microbiota, a collection of trillions of microorganisms living in the gastrointestinal tract, is highly personalized and plays a key role in the metabolite responses to foods and nutrients. Accurately predicting metabolite responses to dietary interventions based on individuals gut microbial compositions holds great promise for precision nutrition. Existing prediction methods are typically limited to traditional machine learning models. Deep learning methods dedicated to such tasks are still lacking. Here we develop a method McMLP (Metabolite response predictor using coupled Multilayer Perceptrons) to fill in this gap. We provide clear evidence that McMLP outperforms existing methods on both synthetic data generated by the microbial consumer-resource model and real data obtained from six dietary intervention studies. Furthermore, we perform sensitivity analysis of McMLP to infer the tripartite food-microbe-metabolite interactions, which are then validated using the ground-truth (or literature evidence) for synthetic (or real) data, respectively. The presented tool has the potential to inform the design of microbiota-based personalized dietary strategies to achieve precision nutrition.

microbiology↗

bioRxiv2020

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↗

bioRxiv2019

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↗