Cell-type-specific mapping of enhancers and target genes from single-cell multimodal data.

Mapping enhancers and target genes in disease-related cell types has provided critical insights into the functional mechanisms of genetic variants identified by genome-wide association studies (GWAS). However, most existing analyses rely on bulk data or cultured cell lines, which may fail to identify cell-type-specific enhancers and target genes. Recently, single-cell multimodal data measuring both gene expression and chromatin accessibility within the same cells have enabled the inference of enhancer-gene pairs in a cell-type-specific and context-specific manner. However, this task is challenged by the data's high sparsity, sequencing depth variation, and the computational burden of analyzing a large number of enhancer-gene pairs. To address these challenges, we propose scMultiMap, a statistical method that infers enhancer-gene association from sparse multimodal counts using a joint latent-variable model. It adjusts for technical confounding, permits fast moment-based estimation and provides analytically derived p -values. In systematic analyses of blood and brain data, scMultiMap shows appropriate type I error control, high statistical power with greater reproducibility across independent datasets and stronger consistency with orthogonal data modalities. Meanwhile, its computational cost is less than 1% of existing methods. When applied to single-cell multimodal data from postmortem brain samples from Alzheimer's disease (AD) patients and controls, scMultiMap gave the highest heritability enrichment in microglia and revealed new insights into the regulatory mechanisms of AD GWAS variants in microglia.

Estimating cell-type-specific gene co-expression networks from bulk gene expression data with an application to Alzheimer's disease.

Inferring and characterizing gene co-expression networks has led to important insights on the molecular mechanisms of complex diseases. Most co-expression analyses to date have been performed on gene expression data collected from bulk tissues with different cell type compositions across samples. As a result, the co-expression estimates only offer an aggregated view of the underlying gene regulations and can be confounded by heterogeneity in cell type compositions, failing to reveal gene coordination that may be distinct across different cell types. In this paper, we introduce a flexible framework for estimating cell-type-specific gene co-expression networks from bulk sample data, without making specific assumptions on the distributions of gene expression profiles in different cell types. We develop a novel sparse least squares estimator, referred to as CSNet, that is efficient to implement and has good theoretical properties. Using CSNet, we analyzed the bulk gene expression data from a cohort study on Alzheimer's disease and identified previously unknown cell-type-specific co-expressions among Alzheimer's disease risk genes, suggesting cell-type-specific disease mechanisms.

Effects of different seat inclination angles on lumbar dynamic response and injury during lunar-earth reentry.

The inclination angle of the spacecraft seat is related to the astronaut's reentry angle, which in turn affects the safety of the astronauts. This study quantitatively analyzed the effects of different seat inclination angles on astronauts' lumbar spine injuries using the finite element method during the Lunar-Earth reentry. Firstly, a finite element model of the astronaut's lumbar spine was constructed based on reverse engineering technology, and the effectiveness of the model was verified through mesh sensitivity, vertebral range of motion, and spinal impact experiments. Then, simulation calculations were carried out for different seat inclination angles (0°, 10°, 20°, and 30°) under the typical reentry return loads of Chang'e 5T1 (CE-5T1) and Apollo 10, and the prediction and evaluation of lumbar spine injuries were conducted in conjunction with the biological tissue injury criteria. The results indicated that the stress on the vertebrae and annulus fibrosus increased under both reentry loads with the rise of the seat inclination angle, but the increasing rates decreased. When the acceleration peak of CE-5T1 approached 9G, the risk of tissue injury was higher under the seat angle exceeded 20°. According to the Multi-Axis Dynamic Response Criteria for spinal injury, neither of the two load conditions would directly cause injury to the astronauts' lumbar spine when the seat inclination angle was below 30°. The study findings provide a numerical basis for designing and improving the spacecraft's inclination angle in crewed lunar missions, ensuring the safety of astronauts.

USP7 promotes cardiometabolic disorders and mitochondrial homeostasis dysfunction in diabetic mice via stabilizing PGC1ß.

Diabetic cardiomyopathy (DCM) is a major complication of diabetes and is characterized by left ventricular dysfunction. Currently, there is a lack of effective treatments for DCM. Ubiquitin-specific protease 7 (USP7) plays a key role in various diseases. However, whether USP7 is involved in DCM has not been established. In this study, we demonstrated that USP7 was upregulated in diabetic mouse hearts and NMCMs co-treated with HG+PA or H9c2 cells treated with PA. Abnormalities in diabetic heart morphology and function were reversed by USP7 silencing through conditional gene knockout or chemical inhibition. Proteomic analysis coupled with biochemical validation confirmed that PCG1ß was one of the direct protein substrates of USP7 and aggravated myocardial damage through coactivation of the PPARα signaling pathway. USP7 silencing restored the expression of fatty acid metabolism-related proteins and restored mitochondrial homeostasis by inhibiting mitochondrial fission and promoting fusion events. Similar effects were also observed in vitro. Our data demonstrated that USP7 promoted cardiometabolic metabolism disorders and mitochondrial homeostasis dysfunction via stabilizing PCG1ß and suggested that silencing USP7 may be a therapeutic strategy for DCM.

Giant Parotid Abscess With Significant Skin Necrosis: A Case Report.

Poor blood glucose control is a common predisposing factor for parotid abscesses; however, extensive skin necrosis secondary to parotid abscesses has rarely been reported. In this article, we present the case of a 70-year-old man with poor glycemic control admitted to our hospital with swelling, congestion, and pain in the right parotid region that had gradually increased over 15 days prior to presentation. Based on the clinical, imaging, and laboratory findings, the patient was diagnosed with a giant parotid abscess with extensive skin necrosis caused by Klebsiella pneumoniae. The abscess responded poorly to long-term treatment with intravenous broad-spectrum antibiotics, and the patient underwent daily Bacillus exchange with blood glucose level management and electrolyte monitoring via routine blood tests. At the 3 month follow-up, complete resolution of the right parotid gland abscess and skin rupture was observed.

High-Dimensional Gaussian Graphical Regression Models with Covariates.

Though Gaussian graphical models have been widely used in many scientific fields, relatively limited progress has been made to link graph structures to external covariates. We propose a Gaussian graphical regression model, which regresses both the mean and the precision matrix of a Gaussian graphical model on covariates. In the context of co-expression quantitative trait locus (QTL) studies, our method can determine how genetic variants and clinical conditions modulate the subject-level network structures, and recover both the population-level and subject-level gene networks. Our framework encourages sparsity of covariate effects on both the mean and the precision matrix. In particular for the precision matrix, we stipulate simultaneous sparsity, i.e., group sparsity and element-wise sparsity, on effective covariates and their effects on network edges, respectively. We establish variable selection consistency first under the case with known mean parameters and then a more challenging case with unknown means depending on external covariates, and establish in both cases the ℓ2 convergence rates and the selection consistency of the estimated precision parameters. The utility and efficacy of our proposed method is demonstrated through simulation studies and an application to a co-expression QTL study with brain cancer patients.

PKM2 is a potential prognostic biomarker and related to immune infiltration in lung cancer.

Pyruvate kinase M2 (PKM2), a subtype of pyruvate kinase, plays a crucial role as a key enzyme in the final step of glycolysis. It is involved in regulating the tumor microenvironment and accelerating tumor progression. However, the relationship between PKM2 expression and the prognosis and immune infiltration remains unclear in lung cancer. In this study, we analyzed PKM2 expression in pan-cancer, and investigated its association with prognosis and immune cell infiltration of lung cancer by using multiple online databases, including Gent2, Tumor Immune Estimation Resource (TIMER), Gene Expression Profiling Interactive Analysis (GEPIA), PrognoScan, Kaplan-Meier plotter, and The Human Protein Atlas (HPA). The results showed that PKM2 expression is elevated in tumor tissues compared with the adjacent normal tissues of most cancers, including lung cancer. Prognostic analysis indicated that high expression of PKM2 was associated with poorer prognosis in overall lung cancer patients, especially in lung adenocarcinoma (LUAD). Notably, PKM2 exhibited a strong correlation with B cells and CD4+ T cells in LUAD; and with B cells, CD8+ T cells, CD4+ cells, and macrophages in lung squamous cell carcinoma (LUSC). Furthermore, PKM2 expression displayed a significant negative correlation with the expression of immune cell markers in both LUAD and LUSC. These findings suggested that PKM2 could serve as a promising prognostic biomarker for lung cancer and provided insights into its essential role in modulating the immune cell infiltration.

Liver macrophages and sinusoidal endothelial cells execute vaccine-elicited capture of invasive bacteria.

Vaccination has substantially reduced the morbidity and mortality of bacterial diseases, but mechanisms of vaccine-elicited pathogen clearance remain largely undefined. We report that vaccine-elicited immunity against invasive bacteria mainly operates in the liver. In contrast to the current paradigm that migrating phagocytes execute vaccine-elicited immunity against blood-borne pathogens, we found that invasive bacteria are captured and killed in the liver of vaccinated host via various immune mechanisms that depend on the protective potency of the vaccine. Vaccines with relatively lower degrees of protection only activated liver-resident macrophage Kupffer cells (KCs) by inducing pathogen-binding immunoglobulin M (IgM) or low amounts of IgG. IgG-coated pathogens were directly captured by KCs via multiple IgG receptors FcγRs, whereas IgM-opsonized bacteria were indirectly bound to KCs via complement receptors of immunoglobulin superfamily (CRIg) and complement receptor 3 (CR3) after complement C3 activation at the bacterial surface. Conversely, the more potent vaccines engaged both KCs and liver sinusoidal endothelial cells by inducing higher titers of functional IgG antibodies. Endothelial cells (ECs) captured densely IgG-opsonized pathogens by the low-affinity IgG receptor FcγRIIB in a "zipper-like" manner and achieved bacterial killing predominantly in the extracellular milieu via an undefined mechanism. KC- and endothelial cell-based capture of antibody-opsonized bacteria also occurred in FcγR-humanized mice. These vaccine protection mechanisms in the liver not only provide a comprehensive explanation for vaccine-/antibody-boosted immunity against invasive bacteria but also may serve as in vivo functional readouts of vaccine efficacy.

Cell-type-specific co-expression inference from single cell RNA-sequencing data.

The advancement of single cell RNA-sequencing (scRNA-seq) technology has enabled the direct inference of co-expressions in specific cell types, facilitating our understanding of cell-type-specific biological functions. For this task, the high sequencing depth variations and measurement errors in scRNA-seq data present two significant challenges, and they have not been adequately addressed by existing methods. We propose a statistical approach, CS-CORE, for estimating and testing cell-type-specific co-expressions, that explicitly models sequencing depth variations and measurement errors in scRNA-seq data. Systematic evaluations show that most existing methods suffered from inflated false positives as well as biased co-expression estimates and clustering analysis, whereas CS-CORE gave accurate estimates in these experiments. When applied to scRNA-seq data from postmortem brain samples from Alzheimer's disease patients/controls and blood samples from COVID-19 patients/controls, CS-CORE identified cell-type-specific co-expressions and differential co-expressions that were more reproducible and/or more enriched for relevant biological pathways than those inferred from existing methods.

Hyperhomocysteinemia in patients with riboflavin-responsive multiple acyl-CoA dehydrogenase deficiency.

INTRODUCTION/AIMS: Riboflavin-responsive multiple acyl-CoA dehydrogenase deficiency (RR-MADD) is an autosomal recessive disease chiefly caused by variants of ETFDH affecting fatty acid metabolism. In our cohort, hyperhomocysteinemia (HHcy) was common. In this study we aimed to identify the association between RR-MADD and HHcy. METHODS: We performed a retrospective review of 13 patients with RR-MADD. Thirty-three healthy controls were recruited, and logistic regression was used to investigate the association between RR-MADD and HHcy. Muscle tissues from six patients and six controls without myopathies were collected to measure the levels of flavin adenine dinucleotide (FAD), an active form of riboflavin. Whole-exome sequencing was performed to identify the disease-associated variants. RESULTS: The RR-MADD patients had a higher prevalence of HHcy (9 of 12) than controls (6 of 33, P < .001). In the multivariate analysis, RR-MADD was positively related to HHcy (P = .014). Muscular FAD levels were decreased in RR-MADD patients (P = .006). Thirteen variants (8 reported and 5 novel) were identified in ETFDH. Of these, c.250G > A was the most common pathogenic variant with an allelic frequency of 4 of 20. DISCUSSION: HHcy was associated with RR-MADD and may aid in the diagnosis of the disease. Our findings expand the mutational spectrum of RR-MADD.

Partially Observed Dynamic Tensor Response Regression.

In modern data science, dynamic tensor data prevail in numerous applications. An important task is to characterize the relationship between dynamic tensor datasets and external covariates. However, the tensor data are often only partially observed, rendering many existing methods inapplicable. In this article, we develop a regression model with a partially observed dynamic tensor as the response and external covariates as the predictor. We introduce the low-rankness, sparsity, and fusion structures on the regression coefficient tensor, and consider a loss function projected over the observed entries. We develop an efficient nonconvex alternating updating algorithm, and derive the finite-sample error bound of the actual estimator from each step of our optimization algorithm. Unobserved entries in the tensor response have imposed serious challenges. As a result, our proposal differs considerably in terms of estimation algorithm, regularity conditions, as well as theoretical properties, compared to the existing tensor completion or tensor response regression solutions. We illustrate the efficacy of our proposed method using simulations and two real applications, including a neuroimaging dementia study and a digital advertising study.

Detection of spatiotemporal changes in ecological quality in the Chinese mainland: Trends and attributes.

Global climate change and revegetation programs have significantly changed the ecological quality (EQ) in the Chinese mainland after 1999. Monitoring and assessing the changes in the regional EQ and analyzing their drivers are crucial for ensuring ecological restoration and rehabilitation. However, it is challenging to carry out a long-term and large-scale quantitative assessment of the EQ of a region based on traditional field investigations and experiment methods alone; notably, in previous studies, the effects of carbon and water cycles and human activities on the variations in EQ have not been studied comprehensively. Therefore, in addition to remote sensing data and principal component analysis, we used the remote sensing-based ecological index (RSEI), to assess the EQ changes in the Chinese mainland during 2000-2021. Additionally, we also analyzed the impacts of carbon and water cycles and anthropological activities on the changes in the RSEI. The main conclusions of this study were: since the beginning of the 21st century, we observed a fluctuating upward trend in the EQ changes in the Chinese mainland and eight climatic regions. From 2000 to 2021, in terms of the EQ, North China (NN) portrayed the highest increase rate (2.02 × 10-3 year-1, P < 0.05). There was a breaking point in 2011, the EQ in the region experienced a change, from a downward trend to an upward one. Northwest China, Northeast China, and NN portrayed an overall significant increasing trend in the RSEI, whereas the southwest part of the Southwest Yungui Plateau (YG) and a part of the plain region of the Changjiang (Yangtze) River (CJ) river region portrayed a significant decreasing trend in the EQ. Overall, the carbon and water cycles and human activities played a pivotal role in determining the spatial patterns and trends of the EQ in the Chinese mainland. In particular, the self-calibrating Palmer Drought Severity Index, actual evapotranspiration (AET), gross primary productivity (GPP), and soil water content (Soil_w) were identified as the key drivers of the RSEI. In the central and western Qinghai-Tibetan Plateau (QZ) and the northwest region of NW, the changes in RSEI were dominated by AET; however, in central NN, southeastern QZ, northern YG, and central NE, the changes were driven by GPP, and in the southeast region of NW, south region of NE, northern region of NN, middle YG region, and a part of the middle CJ region, the changes were driven by Soil_w. The population-density-related change in the RSEI was positive in the northern regions (NN and NW) but negative in the southern regions (SE), whereas the RSEI change related to ecosystem services was positive in the NE, NW, QZ, and YG regions. These results are beneficial for the adaptive management and protection of the environment and the realization of green and sustainable developmental strategies in the Chinese mainland.

eQTL studies: from bulk tissues to single cells.

An expression quantitative trait locus (eQTL) is a chromosomal region where genetic variants are associated with the expression levels of specific genes that can be both nearby or distant. The identifications of eQTLs for different tissues, cell types, and contexts have led to a better understanding of the dynamic regulations of gene expressions and implications of functional genes and variants for complex traits and diseases. Although most eQTL studies have been performed on data collected from bulk tissues, recent studies have demonstrated the importance of cell-type-specific and context-dependent gene regulations in biological processes and disease mechanisms. In this review, we discuss statistical methods that have been developed to enable the detection of cell-type-specific and context-dependent eQTLs from bulk tissues, purified cell types, and single cells. We also discuss the limitations of the current methods and future research opportunities.

Generalized Connectivity Matrix Response Regression with Applications in Brain Connectivity Studies.

Multiple-subject network data are fast emerging in recent years, where a separate connectivity matrix is measured over a common set of nodes for each individual subject, along with subject covariates information. In this article, we propose a new generalized matrix response regression model, where the observed network is treated as a matrix-valued response and the subject covariates as predictors. The new model characterizes the population-level connectivity pattern through a low-rank intercept matrix, and the effect of subject covariates through a sparse slope tensor. We develop an efficient alternating gradient descent algorithm for parameter estimation, and establish the non-asymptotic error bound for the actual estimator from the algorithm, which quantifies the interplay between the computational and statistical errors. We further show the strong consistency for graph community recovery, as well as the edge selection consistency. We demonstrate the efficacy of our method through simulations and two brain connectivity studies.

eQTL Studies: from Bulk Tissues to Single Cells.

An expression quantitative trait locus (eQTL) is a chromosomal region where genetic variants are associated with the expression levels of certain genes that can be both nearby or distant. The identifications of eQTLs for different tissues, cell types, and contexts have led to better understanding of the dynamic regulations of gene expressions and implications of functional genes and variants for complex traits and diseases. Although most eQTL studies to date have been performed on data collected from bulk tissues, recent studies have demonstrated the importance of cell-type-specific and context-dependent gene regulations in biological processes and disease mechanisms. In this review, we discuss statistical methods that have been developed to enable the detections of cell-type-specific and context-dependent eQTLs from bulk tissues, purified cell types, and single cells. We also discuss the limitations of the current methods and future research opportunities.

Effects of bamboo leaf extract on energy metabolism, antioxidant capacity, and biogenesis of small intestine mitochondria in broilers.

The present study was carried out to investigate the effects of bamboo leaf extract (BLE) on energy metabolism, antioxidant capacity, and biogenesis of broilers' small intestine mitochondria. A total of 384 one-day-old male Arbor Acres broiler chicks were randomly divided into four groups with six replicates each for 42 d. The control group was fed a basal diet, whereas the BLE1, BLE2, and BLE3 groups consumed basal diets with 1.0, 2.0, and 4.0 g/kg of BLE, respectively. Some markers of mitochondrial energy metabolism including isocitrate dehydrogenase, α-ketoglutarate dehydrogenase, and malate dehydrogenase and some markers of redox system including total superoxide dismutase, malondialdehyde, and glutathione were measured by commercial colorimetric kits. Mitochondrial and cellular antioxidant genes, mitochondrial biogenesis-related genes, and mitochondrial DNA copy number were measured by quantitative real-time-polymerase chain reaction (qRT-PCR). Data were analyzed using the SPSS 19.0, and differences were considered as significant at P < 0.05. BLE supplementation linearly increased jejunal mitochondrial isocitrate dehydrogenase (P < 0.05) and total superoxide dismutase (P < 0.05) activity. The ileal manganese superoxide dismutase mRNA expression was linearly affected by increased dietary BLE supplementation (P < 0.05). Increasing BLE supplementation linearly increased jejunal sirtuin 1 (P < 0.05) and nuclear respiratory factor 1 (P < 0.05) mRNA expression. Linear (P < 0.05) and quadratic (P < 0.05) responses of the ileal nuclear respiratory factor 2 mRNA expression occurred with increased dietary BLE levels. In conclusion, BLE supplementation was beneficial to the energy metabolism, antioxidant capacity, and biogenesis of small intestine mitochondria in broilers. The dose of 4.0 g/kg BLE demonstrated the best effects.

The intensive breeding model of broilers exposes broilers directly to oxidative stress, which is associated with mitochondrial dysfunction. Some researches have shown that bamboo leaf extract (BLE) exhibited antioxidant capacity both in vitro and vivo. However, few researches have been conducted to explore the effects of BLE supplementation on small intestine mitochondrial functions in broilers. The study aimed to evaluate whether BLE can improve energy metabolism, antioxidant capacity, and biogenesis of broilers' small intestine mitochondria. All broilers were randomly divided into four groups. The control (CTR) group was fed a basal diet, and the three experimental groups of BLE1, BLE2, and BLE3 were fed the basal diet supplemented with 1.0, 2.0, and 4.0 g of BLE per kg of feed between 1 d and 42 d of age, respectively. Based on our results, we obtained interesting evidence that BLE supplementation enhanced metabolic efficiency of small intestine mitochondria in broilers.

Cell-type-specific co-expression inference from single cell RNA-sequencing data.

The inference of gene co-expressions from microarray and RNA-sequencing data has led to rich insights on biological processes and disease mechanisms. However, the bulk samples analyzed in most studies are a mixture of different cell types. As a result, the inferred co-expressions are confounded by varying cell type compositions across samples and only offer an aggregated view of gene regulations that may be distinct across different cell types. The advancement of single cell RNA-sequencing (scRNA-seq) technology has enabled the direct inference of co-expressions in specific cell types, facilitating our understanding of cell-type-specific biological functions. However, the high sequencing depth variations and measurement errors in scRNA-seq data present significant challenges in inferring cell-type-specific gene co-expressions, and these issues have not been adequately addressed in the existing methods. We propose a statistical approach, CS-CORE, for estimating and testing cell-type-specific co-expressions, built on a general expression-measurement model that explicitly accounts for sequencing depth variations and measurement errors in the observed single cell data. Systematic evaluations show that most existing methods suffer from inflated false positives and biased co-expression estimates and clustering analysis, whereas CS-CORE has appropriate false positive control, unbiased co-expression estimates, good statistical power and satisfactory performance in downstream co-expression analysis. When applied to analyze scRNA-seq data from postmortem brain samples from Alzheimer’s disease patients and controls and blood samples from COVID-19 patients and controls, CS-CORE identified cell-type-specific co-expressions and differential co-expressions that were more reproducible and/or more enriched for relevant biological pathways than those inferred from other methods.

Construction and validation of a metabolic-related genes prognostic model for oral squamous cell carcinoma based on bioinformatics.

BACKGROUND: Oral squamous cell carcinoma (OSCC) accounts for a frequently-occurring head and neck cancer, which is characterized by high rates of morbidity and mortality. Metabolism-related genes (MRGs) show close association with OSCC development, metastasis and progression, so we constructed an MRGs-based OSCC prognosis model for evaluating OSCC prognostic outcome. METHODS: This work obtained gene expression profile as well as the relevant clinical information from the The Cancer Genome Atlas (TCGA) database, determined the MRGs related to OSCC by difference analysis, screened the prognosis-related MRGs by performing univariate Cox analysis, and used such identified MRGs for constructing the OSCC prognosis prediction model through Lasso-Cox regression. Besides, we validated the model with the GSE41613 dataset based on Gene Expression Omnibus (GEO) database. RESULTS: The present work screened 317 differentially expressed MRGs from the database, identified 12 OSCC prognostic MRGs through univariate Cox regression, and then established a clinical prognostic model composed of 11 MRGs by Lasso-Cox analysis. Based on the optimal risk score threshold, cases were classified as low- or high-risk group. As suggested by Kaplan-Meier (KM) analysis, survival rate was obviously different between the two groups in the TCGA training set (P < 0.001). According to subsequent univariate and multivariate Cox regression, risk score served as the factor to predict prognosis relative to additional clinical features (P < 0.001). Besides, area under ROC curve (AUC) values for patient survival at 1, 3 and 5 years were determined as 0.63, 0.70, and 0.76, separately, indicating that the prognostic model has good predictive accuracy. Then, we validated this clinical prognostic model using GSE41613. To enhance our model prediction accuracy, age, gender, risk score together with TNM stage were incorporated in a nomogram. As indicated by results of ROC curve and calibration curve analyses, the as-constructed nomogram had enhanced prediction accuracy compared with clinicopathological features alone, besides, combining clinicopathological characteristics with risk score contributed to predicting patient prognosis and guiding clinical decision-making. CONCLUSION: In this study, 11 MRGs prognostic models based on TCGA database showed superior predictive performance and had a certain clinical application prospect in guiding individualized.

Bisdemethoxycurcumin Alleviates Dextran Sodium Sulfate-Induced Colitis via Inhibiting NLRP3 Inflammasome Activation and Modulating the Gut Microbiota in Mice.

Our previous study showed that bisdemethoxycurcumin (BUR) exerts anti-inflammatory properties in lipopolysaccharide-induced intestinal injury, and studies have revealed that NOD-like receptor superfamily, pyrin domain containing 3 (NLRP3) inflammasome activation plays a vital role in the pathogenesis of colitis. However, it is not clear whether BUR could attenuate colitis-mediated intestinal inflammation via NLRP3 inflammasome inactivation and modulate the gut microbiota dysbiosis. The results demonstrated that BUR attenuated DSS-induced body weight decrease, histopathological changes, and epithelial apoptosis. BUR significantly improved the intestinal barrier defects and abrogated DSS-induced inflammatory response. Consistently, BUR reduced the expression of NLRP3 family members, confirming its inhibitory effects on NLRP3 inflammasome activation and pyroptosis. BUR regulated microbiota dysbiosis and altered the gut microbial community. BUR supplementation enriched the relative abundance of beneficial bacteria (such as Lactobacillus and Bifidobacterium), which showed significant negative correlations with the pro-inflammatory biomarkers. Collectively, these findings illustrated that BUR could ameliorate DSS-induced colitis by improving intestinal barrier function, reducing apoptosis, inhibiting NLRP3 inflammasome activation, and regulating the gut microbiota.

Supplementation of Dimethylglycine Sodium Salt in Sow Milk Reverses Skeletal Muscle Redox Status Imbalance and Mitochondrial Dysfunction of Intrauterine Growth Restriction Newborns.

The current study sought to understand the mechanism underlying skeletal muscle dysfunction brought on by intrauterine growth restriction (IUGR) and to explore the treatment benefits of applying dimethylglycine sodium salt (DMG-Na) in sow milk to newborns during the suckling period. Each of the 10 sows delivered one newborn with a normal birth weight (NBW) and one with an IUGR. Additionally, two NBW and two IUGR newborns were collected per litter of another 10 sows. The 20 NBW newborns were divided between the N (sow milk) and ND (sow milk + 0.1% DMG-Na) groups, while 20 IUGR newborns were divided between the I (sow milk) and ID (sow milk + 0.1% DMG-Na) groups. The skeletal muscle histomorphology, redox status, and levels of gene and protein expression were worse (p < 0.05) in the I group than in the N group. In addition, supplementation with DMG-Na (ND and ID groups) improved (p < 0.05) those parameters compared to the unsupplemented groups (N and I groups). Inhibited nuclear factor erythroid 2-related factor 2 (Nrf2)/sirtuin 1 (SIRT1)/peroxisome proliferator-activated receptorγcoactivator-1α (PGC-1α) activity resulted in decreased redox status, skeletal muscle structural damage, skeletal muscle mitochondrial function impairment, and decreased performance in IUGR newborns. Supplementation of DMG-Na in sow milk activated the Nrf2/SIRT1/PGC-1α in IUGR newborns, thereby improving their skeletal muscle performance.