Fecal let-7b and miR-21 directly modulate the intestinal microbiota, driving chronic inflammation.

Inflammatory bowel diseases (IBD) etiology is multifactorial. Luminal microRNAs (miRNAs) have been suspected to play a role in the promotion of chronic inflammation, but the extent to which fecal miRNAs are interacting with the intestinal ecosystem in a way that contribute to diseases, including IBD, remains unknown. Here, fecal let-7b and miR-21 were found elevated, associated with inflammation, and correlating with multiple bacteria in IBD patients and IL-10-/- mice, model of spontaneous colitis. Using an in vitro microbiota modeling system, we revealed that these two miRNAs can directly modify the composition and function of complex human microbiota, increasing their proinflammatory potential. In vivo investigations revealed that luminal increase of let-7b drastically alters the intestinal microbiota and enhances macrophages' associated proinflammatory cytokines (TNF, IL-6, and IL-1ß). Such proinflammatory effects are resilient and dependent on the bacterial presence. Moreover, we identified that besides impairing the intestinal barrier function, miR-21 increases myeloperoxidase and antimicrobial peptides secretion, causing intestinal dysbiosis. More importantly, in vivo inhibition of let-7b and miR-21 with anti-miRNAs significantly improved the intestinal mucosal barrier function and promoted a healthier host-microbiota interaction in the intestinal lining, which altogether conferred protection against colitis. In summary, we provide evidence of the functional significance of fecal miRNAs in host-microbiota communication, highlighting their therapeutic potential in intestinal inflammation and dysbiosis-related conditions, such as IBD.

Ubiquitination and De-Ubiquitination in the Synthesis of Cow Milk Fat: Reality and Prospects.

Ubiquitination modifications permit the degradation of labelled target proteins with the assistance of proteasomes and lysosomes, which is the main protein degradation pathway in eukaryotic cells. Polyubiquitination modifications of proteins can also affect their functions. De-ubiquitinating enzymes reverse the process of ubiquitination via cleavage of the ubiquitin molecule, which is known as a de-ubiquitination. It was demonstrated that ubiquitination and de-ubiquitination play key regulatory roles in fatty acid transport, de novo synthesis, and desaturation in dairy mammary epithelial cells. In addition, natural plant extracts, such as stigmasterol, promote milk fat synthesis in epithelial cells via the ubiquitination pathway. This paper reviews the current research on ubiquitination and de-ubiquitination in dairy milk fat production, with a view to providing a reference for subsequent research on milk fat and exploring new directions for the improvement of milk quality.

Unraveling the molecular mechanisms of Ace2-mediated post-COVID-19 cognitive dysfunction through systems genetics approach.

The dysregulation of Angiotensin-converting enzyme 2 (ACE2) in central nervous system is believed associates with COVID-19 induced cognitive dysfunction. However, the detailed mechanism remains largely unknown. In this study, we performed a comprehensive system genetics analysis on hippocampal ACE2 based on BXD mice panel. Expression quantitative trait loci (eQTLs) mapping showed that Ace2 was strongly trans-regulated, and the elevation of Ace2 expression level was significantly correlated with impaired cognitive functions. Further Gene co-expression analysis showed that Ace2 may be correlated with the membrane proteins in Calcium signaling pathway. Further, qRT-PCR confirmed that SARS-CoV-2 spike S1 protein upregulated ACE2 expression together with eight membrane proteins in Calcium Signaling pathway. Moreover, such elevation can be attenuated by recombinant ACE2. Collectively, our findings revealed a potential mechanism of Ace2 in cognitive dysfunction, which could be beneficial for COVID-19-induced cognitive dysfunction prevention and potential treatment.

Temporal trends in prevalence of liver cancer and etiology-specific liver cancer from 1990 to 2019.

BACKGROUND: Liver cancer (LC) remains a major cause of cancer death worldwide. Grasping prevalence trends is key to informing strategies for control and prevention. We analyzed the global, regional and national trends in LC prevalence and its major causes from 1990 to 2019. METHODS: We obtained LC age-standardized prevalence rate (ASPR) estimates from the Global Burden of Disease study 2019 and assessed trends using Joinpoint regression. LC cases were categorized into those due to hepatitis B virus (HBV), hepatitis C virus (HCV), alcohol use, nonalcoholic steatohepatitis (NASH) and other causes. RESULTS: While the ASPR of LC has shown a global decrease, there are specific regions where an increase in ASPR has been observed, with the highest rates in America. HBV remained the leading cause of LC (41.45 %) but significant increases occurred for HCV, alcohol use and NASH. Prevalence correlated with socioeconomic development. High-income countries had higher LC rates from HCV and alcohol but lower HBV-related LC. In high-income nations, LC prevalence climbs; the converse holds in middle- and low-income countries. CONCLUSIONS: Despite a global ASPR decrease, LC due to HCV, NASH, and alcohol is rising. Prevention strategies must prioritize HBV vaccination, HCV treatment, and alcohol regulation. IMPACT: The study informs targeted LC control policies and emphasizes the importance of continued monitoring and regional cooperation to combat LC.

Learning a neural network-based soft sensor with double-errors parallel optimization towards effluent variable prediction in wastewater treatment plants.

With the development of machine learning and artificial intelligence (ML/AI) models, data-driven soft sensors, especially the neural network-based, have widespread utilization for the prediction of key water quality indicators in wastewater treatment plants (WWTPs). However, recent research indicates that the prediction performance and computational efficiency are greatly compromised due to the time-varying, nonlinear and high-dimensional nature of the wastewater treatment process. This paper proposes a neural network-based soft sensor with double-errors parallel optimization to achieve more accurate prediction for effluent variables timely. Firstly, relying on the Activity Based Classification (ABC) principle, an ensemble variable selection method that combines Pearson correlation coefficient (PCC) and mutual information (MI) is introduced to select the optimal process variables as auxiliary variables, thereby reducing the data dimensionality and simplifying the model complexity. Subsequently, a double-errors parallel optimization methodology with minimizing both point prediction error and distribution error simultaneously is proposed, aiming to enhancing the training efficiency and the fitting quality of neural networks. Finally, the effectiveness is quantitatively assessed in two datasets collected from the Benchmark Simulation Model no. 1 (BMS1) and an actual oxidation ditch WWTP. The experimental results illustrate that the proposed soft sensor achieves precise effluent variable prediction, with RMSE, MAE and R2 values being 0.0606, 0.0486, 0.99930, and 0.06939, 0.05381, 0.98040, respectively. Consequently, this soft sensor can expedite the convergence speed in the neural network training process and enhance the prediction performance, thereby contributing to the effective optimization management of WWTPs.

The value of myocardial contraction fraction and long-axis strain to predict late gadolinium enhancement in multiple myeloma patients with secondary cardiac amyloidosis.

The aim of this study is to assess the effectiveness of conventional and two additional functional markers derived from standard cardiac magnetic resonance (CMR) images in detecting the occurrence of late gadolinium enhancement (LGE) in patients with secondary cardiac amyloidosis (CA) related to multiple myeloma (MM). This study retrospectively included 32 patients with preserved ejection fraction (EF) who had MM-CA diagnosed consecutively. Conventional left ventricular (LV) function markers and two additional functional markers, namely myocardial contraction fraction (MCF) and LV long-axis strain (LAS), were obtained using commercial cardiac post-processing software. Logistic regression analyses and receiver operating characteristic (ROC) analysis were performed to evaluate the predictive performances. (1) There were no notable distinctions in clinical features between the LGE+ and LGE- groups, with the exception of a reduced systolic blood pressure in the former (105.60 ± 18.85 mmHg vs. 124.50 ± 20.95 mmHg, P = 0.022). (2) Patients with MM-CA presented with intractable heart failure with preserved ejection fraction (HFpEF). The LVEF in the LGE+ group exhibited a greater reduction (54.27%, IQR 51.59-58.39%) in comparison to the LGE- group (P < 0.05). And MM-CA patients with LGE+ had significantly higher LVMI (90.15 ± 23.69 g/m2), lower MCF (47.39%, IQR 34.28-54.90%), and the LV LAS were more severely damaged (- 9.94 ± 3.42%) than patients with LGE- (all P values < 0.05). (3) The study found that MCF exhibited a significant independent association with LGE, as indicated by an odds ratio of 0.89 (P < 0.05). The cut-off value for MCF was determined to be 64.25% with a 95% confidence interval ranging from 0.758 to 0.983. The sensitivity and specificity of this association were calculated to be 95% and 83%, respectively. MCF is a simple reproducible predict marker of LGE in MM-CA patients. It is a potentially CMR-based method that promise to reduce scan times and costs, and boost the accessibility of CMR.

Process Manufacturing Intelligence Empowered by Industrial Metaverse: A Survey.

The intelligent goal of process manufacturing is to achieve high efficiency and greening of the entire production. Whereas the information system it used is functionally independent, resulting to knowledge gaps between each level. Decision-making still requires lots of knowledge workers making manually. The industrial metaverse is a necessary means to bridge the knowledge gaps by sharing and collaborative decision-making. Considering the safety and stability requirements of the process manufacturing, this article conducts a thorough survey on the process manufacturing intelligence empowered by industrial metaverse. First, it analyzes the current status and challenges of process manufacturing intelligence, and then summarizes the latest developments about key enabling technologies of industrial metaverse, such as interconnection technologies, artificial intelligence, cloud-edge computing, digital twin (DT), immersive interaction, and blockchain technology. On this basis, taking into account the characteristics of process manufacturing, a construction approach and architecture for the process industrial metaverse is proposed: a virtual-real fused industrial metaverse construction method that combines DTs with physical avatar, which can effectively ensure the safety of metaverse's application in industrial scenarios. Finally, we conducted preliminary exploration and research, to prove the feasibility of proposed method.

Sparse Wasserstein stationary subspace analysis for fault detection and diagnosis of nonstationary industrial processes.

Fault detection and diagnosis of nonstationary processes are crucial for ensuring the safety of industrial production systems. However, the nonstationarity of process data poses multifaceted challenges to them. First, conventional stationary fault detection methods encounter difficulties in discerning evolving trends within nonstationary data. Secondly, the majority of current nonstationary fault detection methods directly extract features from all variables, rendering them susceptible to redundant interference. Moreover, nonstationary trends possess the capacity to conceal and modify the correlations among variables. Coupled with the smearing effect of faults, it is challenging to achieve accurate fault diagnosis. To address these challenges, this paper proposes sparse Wasserstein stationary subspace analysis (SWSSA). Specifically, a ℓ2,p-norm constraint is introduced to endow the stationary subspace model with excellent sparse representation capability. Furthermore, recognizing that fault variables within the sparse stationary subspace influence only a limited subset of stationary sources, this paper proposes a novel contribution analysis method based on local dynamic preserving projection (LDPP), termed LDPPBC, which can effectively mitigate the smearing effect on nonstationary fault diagnosis. LDPPBC establishes a LDPP matrix by extracting the latent positional information of fault variables within the stationary subspace. This allows LDPPBC to selectively analyze the contributions of variables within the latent fault subspace to achieve precise fault diagnosis while avoiding the interference of variable contributions from the fault-free subspace. Finally, the superiority of the proposed method is thoroughly validated through a numerical simulation, a continuous stirred tank reactor, and a real industrial roaster.

Studies on the Increasing Saltiness and Antioxidant Effects of Peanut Protein Maillard Reaction Products.

The salt taste-enhancing and antioxidant effect of the Maillard reaction on peanut protein hydrolysates (PPH) was explored. The multi-spectroscopic and sensory analysis results showed that the Maillard reaction products (MRPs) of hexose (glucose and galactose) had slower reaction rates than those of pentose (xylose and arabinose), but stronger umami and increasing saltiness effects. The Maillard reaction can improve the flavor of PPH, and the galactose-Maillard reaction product (Ga-MRP) has the best umami and salinity-enhancing effects. The measured molecular weight of Ga-MRP were all below 3000 Da, among which the molecular weights between 500-3000 Da accounted for 46.7%. The products produced during the Maillard reaction process resulted in a decrease in brightness and an increase in red value of Ga-MRP. The amino acid analysis results revealed that compared with PPH, the content of salty and umami amino acids in Ga-MRPs decreased, but their proportion in total free amino acids increased, and the content of bitter amino acids decreased. In addition, the Maillard reaction enhances the reducing ability, DPPH radical scavenging ability, and Fe2+ chelating ability of PPH. Therefore, the Maillard reaction product of peanut protein can be expected to be used as a substitute for salt seasoning, with excellent antioxidant properties.

A novel role for KIFC1-MYH9 interaction in triple-negative breast cancer aggressiveness and racial disparity.

African American (AA) women are twice as likely to develop triple-negative breast cancer (TNBC) as women of European descent. Additionally, AA women with TNBC present a much more aggressive disease course than their European American (EA) counterparts. Thus, there is an unmet clinical need to identify race-specific biomarkers and improve survival outcomes in AA patients with TNBC. The minus-end directed microtubule motor protein kinesin family member C1 (KIFC1) promotes centrosome clustering and chromosomal instability and is often overexpressed in TNBC. Previous findings suggest that KIFC1 plays a role in cell proliferation and migration in TNBC cells from AAs and that the levels of nuclear KIFC1 (nKIFC1) are particularly high in AA patients with TNBC. The nuclear localization of KIFC1 in interphase may underlie its previously unrecognized race-specific association. In this study, we found that in TNBC cells derived from AAs, nKIFC1 interacted with the tumor suppressor myosin heavy chain 9 (MYH9) over EA cells. Treatment of AA TNBC cells with commercial inhibitors of KIFC1 and MYH9 disrupted the interaction between KIFC1 and MYH9. To characterize the racial differences in the KIFC1-MYH9-MYC axis in TNBC, we established homozygous KIFC1 knockout (KO) TNBC cell lines. KIFC1 KO significantly inhibited proliferation, migration, and invasion in AA TNBC cells but not in EA TNBC cells. RNA sequencing analysis showed significant downregulation of genes involved in cell migration, invasion, and metastasis upon KIFC1 KO in TNBC cell lines from AAs compared to those from EAs. These data indicate that mechanistically, the role of nKIFC1 in driving TNBC progression and metastasis is stronger in AA patients than in EA patients, and that KIFC1 may be a critical therapeutic target for AA patients with TNBC.

Systems Genetics Analyses Reveals Key Genes Related to Behavioral Traits in the Striatum of CFW Mice.

The striatum plays a central role in directing many complex behaviors ranging from motor control to action choice and reward learning. In our study, we used 55 male CFW mice with rapid decay linkage disequilibrium to systematically mine the striatum-related behavioral functional genes by analyzing their striatal transcriptomes and 79 measured behavioral phenotypic data. By constructing a gene coexpression network, we clustered the genes into 13 modules, with most of them being positively correlated with motor traits. Based on functional annotations as well as Fisher's exact and hypergeometric distribution tests, brown and magenta modules were identified as core modules. They were significantly enriched for striatal-related functional genes. Subsequent Mendelian randomization analysis verified the causal relationship between the core modules and dyskinesia. Through the intramodular gene connectivity analysis, Adcy5 and Kcnma1 were identified as brown and magenta module hub genes, respectively. Knock outs of both Adcy5 and Kcnma1 lead to motor dysfunction in mice, and KCNMA1 acts as a risk gene for schizophrenia and smoking addiction in humans. We also evaluated the cellular composition of each module and identified oligodendrocytes in the striatum to have a positive role in motor regulation.

Classification and identification of tea diseases based on improved YOLOv7 model of MobileNeXt.

To address the issues of low accuracy and slow response speed in tea disease classification and identification, an improved YOLOv7 lightweight model was proposed in this study. The lightweight MobileNeXt was used as the backbone network to reduce computational load and enhance efficiency. Additionally, a dual-layer routing attention mechanism was introduced to enhance the model's ability to capture crucial details and textures in disease images, thereby improving accuracy. The SIoU loss function was employed to mitigate missed and erroneous judgments, resulting in improved recognition amidst complex image backgrounds.The revised model achieved precision, recall, and average precision of 93.5%, 89.9%, and 92.1%, respectively, representing increases of 4.5%, 1.9%, and 2.6% over the original model. Furthermore, the model's volum was reduced by 24.69M, the total param was reduced by 12.88M, while detection speed was increased by 24.41 frames per second. This enhanced model efficiently and accurately identifies tea disease types, offering the benefits of lower parameter count and faster detection, thereby establishing a robust foundation for tea disease monitoring and prevention efforts.

Fluorescent Labeling and Imaging of IL-22 mRNA-Loaded Lipid Nanoparticles.

Lipid nanoparticle (LNP)-based drug delivery systems (DDSs) are widely recognized for their ability to enhance efficient and precise delivery of therapeutic agents, including nucleic acids like DNA and mRNA. Despite this acknowledgment, there is a notable knowledge gap regarding the systemic biodistribution and organ accumulation of these nanoparticles. The ability to track LNPs in vivo is crucial for understanding their fate within biological systems. Fluorescent labeling of LNPs facilitates real-time tracking, quantification, and visualization of their behavior within biological systems, providing valuable insights into biodistribution, cellular uptake, and the optimization of drug delivery strategies. Our prior research established reversely engineered LNPs as an exceptional mRNA delivery platform for treating ulcerative colitis. This study presents a detailed protocol for labeling interleukin-22 (IL-22) mRNA-loaded LNPs, their oral administration to mice, and visualization of DiR-labeled LNPs biodistribution in the gastrointestinal tract using IVIS spectrum. This fluorescence-based approach will assist researchers in gaining a dynamic understanding of nanoparticle fate in other models of interest. Key features • This protocol is developed to assess the delivery of IL-22 mRNA to ulcerative colitis sites using lipid nanoparticles. • This protocol uses fluorescent DiR dye for imaging of IL-22 mRNA-loaded lipid nanoparticles in the gastrointestinal tract of mice. • This protocol employs the IVIS spectrum for imaging.

Adaptive Neural Consensus Observer Networks Design for a Class of Semilinear Parabolic PDE Systems.

This article concerns the investigation on the consensus problem for the joint state-uncertainty estimation of a class of parabolic partial differential equation (PDE) systems with parametric and nonparametric uncertainties. We propose a two-layer network consisting of informed and uninformed boundary observers where novel adaptation laws are developed for the identification of uncertainties. Particularly, all observer agents in the network transmit their information with each other across the entire network. The proposed adaptation laws include a penalty term of the mismatch between the parameter estimates generated by the other observer agents. Moreover, for the nonparametric uncertainties, radial basis function (RBF) neural networks are employed for the universal approximation of unknown nonlinear functions. Given the persistently exciting condition, it is shown that the proposed network of adaptive observers can achieve exponential joint state-uncertainty estimation in the presence of parametric uncertainties and ultimate bounded estimation in the presence of nonparametric uncertainties based on the Lyapunov stability theory. The effects of the proposed consensus method are demonstrated through a typical reaction-diffusion system example, which implies convincing numerical findings.

Global, regional, and national burden of stroke from 1990 to 2019: A temporal trend analysis based on the Global Burden of Disease Study 2019.

BACKGROUND: Stroke is the second leading cause of death and the leading cause of disability worldwide. However, how the prevalence of stroke varies across the world is uncertain. AIMS: The aim of this study was to analyze temporal trends of prevalence for stroke, including ischemic stroke (IS), intracerebral hemorrhage (ICH), and subarachnoid hemorrhage (SAH) at the global, regional, and national levels. METHODS: The age-standardized prevalence rates (ASPR) of stroke, IS, ICH, and SAH, along with their corresponding 95% uncertainty intervals (UI), were derived from data in the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019. This provides estimates for the burden of 369 diseases and injuries globally in 2019, as well as their temporal trends over the past 30 years. Joinpoint regression analysis was used to analyze the 1990-2019 temporal trends by calculating the annual percentage change (APC) and average annual percentage change (AAPC), as well as their 95% confidence interval (CI). RESULTS: In 2019, the global ASPR of stroke was 1240.263 per 100,000 population (95% UI: 1139.711 to 1352.987), with ASPRs generally lower in Europe compared to other regions. Over the period from 1990 to 2019, a significant global decrease in ASPR was observed for stroke (AAPC -0.200, 95% CI: -0.215 to -0.183), IS (AAPC -0.059%, 95% CI: -0.077 to -0.043), SAH (AAPC -0.476, 95% CI: -0.483 to -0.469), and ICH (AAPC -0.626, 95% CI: -0.642 to -0.611). The trends of ASPR of stroke, IS, SAH, and ICH varied significantly across 204 countries and territories. CONCLUSION: Our findings highlight significant global disparities in stroke prevalence, emphasizing the need for ongoing monitoring and intensified efforts in developing regions to reduce the global burden of stroke.

Analysis of DNA Barcodes Using DNA Subway.

DNA Subway makes bioinformatic analysis of DNA barcodes classroom friendly, eliminating the need for software installations or command line tools. Subway bundles research-grade bioinformatics software into workflows with an easy-to-use interface. This chapter covers DNA Subway's DNA barcoding analysis workflow (Blue Line) starting with one or more Sanger sequence reads. During analysis, users can view trace files and sequence quality, pair and align forward and reverse reads, create and trim consensus sequences, perform BLAST searches, select reference data, align multiple sequences, and compute phylogenetic trees. High-quality sequences with the required metadata can also be submitted as barcode sequences to NCBI GenBank.

Hierarchical Self-Attention Network for Industrial Data Series Modeling With Different Sampling Rates Between the Input and Output Sequences.

For industrial processes, it is significant to carry out the dynamic modeling of data series for quality prediction. However, there are often different sampling rates between the input and output sequences. For the most traditional data series models, they have to carefully select the labeled sample sequence to build the dynamic prediction model, while the massive unlabeled input sequences between labeled samples are directly discarded. Moreover, the interactions of the variables and samples are usually not fully considered for quality prediction at each labeled step. To handle these problems, a hierarchical self-attention network (HSAN) is designed for adaptive dynamic modeling. In HSAN, a dynamic data augmentation is first designed for each labeled step to include the unlabeled input sequences. Then, a self-attention layer of variable level is proposed to learn the variable interactions and short-interval temporal dependencies. After that, a self-attention layer of sample level is further developed to model the long-interval temporal dependencies. Finally, a long short-term memory network (LSTM) network is constructed to model the new sequence that contains abundant interactions for quality prediction. The experiment on an industrial hydrocracking process shows the effectiveness of HSAN.

Quality Prediction Modeling for Industrial Processes Using Multiscale Attention-Based Convolutional Neural Network.

Soft sensors have been increasingly applied for quality prediction in complex industrial processes, which often have different scales of topology and highly coupled spatiotemporal features. However, the existing soft sensing models usually face difficulties in extracting the multiscale local spatiotemporal features in multicoupled complex process data and harnessing them to their full potential to improve the prediction performance. Therefore, a multiscale attention-based CNN (MSACNN) is proposed in this article to alleviate such problems. In MSACNN, convolutional kernels of different sizes are first designed in parallel in the convolutional layers, which can generate feature maps containing local spatiotemporal features at different scales. Meanwhile, a channel-wise attention mechanism is designed on the feature maps in parallel to get their attention weights, representing the significance of the local spatiotemporal feature at different scales. The superiority of the proposed MSACNN over the other state-of-the-art methods is validated through the performance evaluation in two real industrial processes.

Unleashing the Potential of Oral Deliverable Nanomedicine in the Treatment of Inflammatory Bowel Disease.

Inflammatory bowel disease (IBD), marked by chronic gastrointestinal tract inflammation, poses a significant global medical challenge. Current treatments for IBD, including corticosteroids, immunomodulators, and biologics, often require frequent systemic administration through parenteral delivery, leading to nonspecific drug distribution, suboptimal therapeutic outcomes, and adverse effects. There is a pressing need for a targeted drug delivery system to enhance drug efficacy and minimize its systemic impact. Nanotechnology emerges as a transformative solution, enabling precise oral drug delivery to inflamed intestinal tissues, reducing off-target effects, and enhancing therapeutic efficiency. The advantages include heightened bioavailability, sustained drug release, and improved cellular uptake. Additionally, the nano-based approach allows for the integration of theranostic elements, enabling simultaneous diagnosis and treatment. Recent preclinical advances in oral IBD treatments, particularly with nanoformulations such as functionalized polymeric and lipid nanoparticles, demonstrate remarkable cell-targeting ability and biosafety, promising to overcome the limitations of conventional therapies. These developments signify a paradigm shift toward personalized and effective oral IBD management. This review explores the potential of oral nanomedicine to enhance IBD treatment significantly, focusing specifically on cell-targeting oral drug delivery system for potential use in IBD management. We also examine emerging technologies such as theranostic nanoparticles and artificial intelligence, identifying avenues for the practical translation of nanomedicines into clinical applications.

TEAD1, MYO7A and NDUFC2 are novel functional genes associated with glucose metabolism in BXD recombinant inbred population.

AIM: The liver is an important metabolic organ that governs glucolipid metabolism, and its dysfunction may cause non-alcoholic fatty liver disease, type 2 diabetes mellitus, dyslipidaemia, etc. We aimed to systematic investigate the key factors related to hepatic glucose metabolism, which may be beneficial for understanding the underlying pathogenic mechanisms for obesity and diabetes mellitus. MATERIALS AND METHODS: Oral glucose tolerance test (OGTT) phenotypes and liver transcriptomes of BXD mice under chow and high-fat diet conditions were collected from GeneNetwork. QTL mapping was conducted to pinpoint genomic regions associated with glucose homeostasis. Candidate genes were further nominated using a multi-criteria approach and validated to confirm their functional relevance in vitro. RESULTS: Our results demonstrated that plasma glucose levels in OGTT were significantly affected by both diet and genetic background, with six genetic regulating loci were mapped on chromosomes 1, 4, and 7. Moreover, TEAD1, MYO7A and NDUFC2 were identified as the candidate genes. Functionally, siRNA-mediated TEAD1, MYO7A and NDUFC2 knockdown significantly decreased the glucose uptake and inhibited the transcription of genes related to insulin and glucose metabolism pathways. CONCLUSIONS: Our study contributes novel insights to the understanding of hepatic glucose metabolism, demonstrating the impact of TEAD1, MYO7A and NDUFC2 on mitochondrial function in the liver and their regulatory role in maintaining in glucose homeostasis.