Genomic structural variations link multiple genes to bone mineral density in a multi-ethnic cohort study: Louisiana osteoporosis study.

Osteoporosis, characterized by low bone mineral density (BMD), is a highly heritable metabolic bone disorder. While single nucleotide variations (SNVs) have been extensively studied, they explain only a fraction of BMD heritability. While genomic structural variations (SVs) are large-scale genomic alterations that contribute to genetic diversity in shaping phenotypic variations, the role of SVs in osteoporosis susceptibility remains poorly understood. This study aims to identify and prioritize genes that harbor BMD-related SVs. We performed whole genome sequencing on 4982 subjects from the Louisiana Osteoporosis Study. To obtain high-confidence SVs, the detection of SVs was performed using an ensemble approach. The SVs were tested for association with BMD variation at the hip (HIP), femoral neck (FNK), and lumbar spine (SPN), respectively. Additionally, we conducted co-occurrence analysis using multi-omics approaches to prioritize the identified genes based on their functional importance. Stratification was employed to explore the sex- and ethnicity-specific effects. We identified significant SV-BMD associations: 125 for FNK-BMD, 99 for SPN-BMD, and 83 for HIP-BMD. We observed SVs that were commonly associated with both FNK and HIP BMDs in our combined and stratified analyses. These SVs explain 13.3% to 19.1% of BMD variation. Novel bone-related genes emerged, including LINC02370, ZNF family genes, and ZDHHC family genes. Additionally, FMN2, carrying BMD-related deletions, showed associations with FNK or HIP BMDs, with sex-specific effects. The co-occurrence analysis prioritized an RNA gene LINC00494 and ZNF family genes positively associated with BMDs at different skeletal sites. Two potential causal genes, IBSP and SPP1, for osteoporosis were also identified. Our study uncovers new insights into genetic factors influencing BMD through SV analysis. We highlight BMD-related SVs, revealing a mix of shared and specific genetic influences across skeletal sites and gender or ethnicity. These findings suggest potential roles in osteoporosis pathophysiology, opening avenues for further research and therapeutic targets.

Variability in performance of genetic-enhanced DXA-BMD prediction models across diverse ethnic and geographic populations: A risk prediction study.

BACKGROUND: Osteoporosis is a major global health issue, weakening bones and increasing fracture risk. Dual-energy X-ray absorptiometry (DXA) is the standard for measuring bone mineral density (BMD) and diagnosing osteoporosis, but its costliness and complexity impede widespread screening adoption. Predictive modeling using genetic and clinical data offers a cost-effective alternative for assessing osteoporosis and fracture risk. This study aims to develop BMD prediction models using data from the UK Biobank (UKBB) and test their performance across different ethnic and geographical populations. METHODS AND FINDINGS: We developed BMD prediction models for the femoral neck (FNK) and lumbar spine (SPN) using both genetic variants and clinical factors (such as sex, age, height, and weight), within 17,964 British white individuals from UKBB. Models based on regression with least absolute shrinkage and selection operator (LASSO), selected based on the coefficient of determination (R2) from a model selection subset of 5,973 individuals from British white population. These models were tested on 5 UKBB test sets and 12 independent cohorts of diverse ancestries, totaling over 15,000 individuals. Furthermore, we assessed the correlation of predicted BMDs with fragility fractures risk in 10 years in a case-control set of 287,183 European white participants without DXA-BMDs in the UKBB. With single-nucleotide polymorphism (SNP) inclusion thresholds at 5×10-6 and 5×10-7, the prediction models for FNK-BMD and SPN-BMD achieved the highest R2 of 27.70% with a 95% confidence interval (CI) of [27.56%, 27.84%] and 48.28% (95% CI [48.23%, 48.34%]), respectively. Adding genetic factors improved predictions slightly, explaining an additional 2.3% variation for FNK-BMD and 3% for SPN-BMD over clinical factors alone. Survival analysis revealed that the predicted FNK-BMD and SPN-BMD were significantly associated with fragility fracture risk in the European white population (P < 0.001). The hazard ratios (HRs) of the predicted FNK-BMD and SPN-BMD were 0.83 (95% CI [0.79, 0.88], corresponding to a 1.44% difference in 10-year absolute risk) and 0.72 (95% CI [0.68, 0.76], corresponding to a 1.64% difference in 10-year absolute risk), respectively, indicating that for every increase of one standard deviation in BMD, the fracture risk will decrease by 17% and 28%, respectively. However, the model's performance declined in other ethnic groups and independent cohorts. The limitations of this study include differences in clinical factors distribution and the use of only SNPs as genetic factors. CONCLUSIONS: In this study, we observed that combining genetic and clinical factors improves BMD prediction compared to clinical factors alone. Adjusting inclusion thresholds for genetic variants (e.g., 5×10-6 or 5×10-7) rather than solely considering genome-wide association study (GWAS)-significant variants can enhance the model's explanatory power. The study highlights the need for training models on diverse populations to improve predictive performance across various ethnic and geographical groups.

Multi-scale variational autoencoder for imputation of missing values in untargeted metabolomics using whole-genome sequencing data.

BACKGROUND: Missing data is a common challenge in mass spectrometry-based metabolomics, which can lead to biased and incomplete analyses. The integration of whole-genome sequencing (WGS) data with metabolomics data has emerged as a promising approach to enhance the accuracy of data imputation in metabolomics studies. METHOD: In this study, we propose a novel method that leverages the information from WGS data and reference metabolites to impute unknown metabolites. Our approach utilizes a multi-scale variational autoencoder to jointly model the burden score, polygenetic risk score (PGS), and linkage disequilibrium (LD) pruned single nucleotide polymorphisms (SNPs) for feature extraction and missing metabolomics data imputation. By learning the latent representations of both omics data, our method can effectively impute missing metabolomics values based on genomic information. RESULTS: We evaluate the performance of our method on empirical metabolomics datasets with missing values and demonstrate its superiority compared to conventional imputation techniques. Using 35 template metabolites derived burden scores, PGS and LD-pruned SNPs, the proposed methods achieved R2-scores > 0.01 for 71.55 % of metabolites. CONCLUSION: The integration of WGS data in metabolomics imputation not only improves data completeness but also enhances downstream analyses, paving the way for more comprehensive and accurate investigations of metabolic pathways and disease associations. Our findings offer valuable insights into the potential benefits of utilizing WGS data for metabolomics data imputation and underscore the importance of leveraging multi-modal data integration in precision medicine research.

CD11b maintains West Nile virus replication through modulation of immune response in human neuroblastoma cells.

BACKGROUND: West Nile virus (WNV) is a rapidly spreading mosquito-borne virus accounted for neuroinvasive diseases. An insight into WNV-host factors interaction is necessary for development of therapeutic approaches against WNV infection. CD11b has key biological functions and been identified as a therapeutic target for several human diseases. The purpose of this study was to determine whether CD11b was implicated in WNV infection. METHODS: SH-SY5Y cells with and without MEK1/2 inhibitor U0126 or AKT inhibitor MK-2206 treatment were infected with WNV. CD11b mRNA levels were assessed by real-time PCR. WNV replication and expression of stress (ATF6 and CHOP), pro-inflammatory (TNF-α), and antiviral (IFN-α, IFN-ß, and IFN-γ) factors were evaluated in WNV-infected SH-SY5Y cells with CD11b siRNA transfection. Cell viability was determined by MTS assay. RESULTS: CD11b mRNA expression was remarkably up-regulated by WNV in a time-dependent manner. U0126 but not MK-2206 treatment reduced the CD11b induction by WNV. CD11b knockdown significantly decreased WNV replication and protected the infected cells. CD11b knockdown markedly increased TNF-α, IFN-α, IFN-ß, and IFN-γ mRNA expression induced by WNV. ATF6 mRNA expression was reduced upon CD11b knockdown following WNV infection. CONCLUSION: These results demonstrate that CD11b is involved in maintaining WNV replication and modulating inflammatory as well as antiviral immune response, highlighting the potential of CD11b as a target for therapeutics for WNV infection.

Mechanisms and clinical landscape of N6-methyladenosine (m6A) RNA modification in gastrointestinal tract cancers.

Epigenetics encompasses reversible and heritable chemical modifications of non-nuclear DNA sequences, including DNA and RNA methylation, histone modifications, non-coding RNA modifications, and chromatin rearrangements. In addition to well-studied DNA and histone methylation, RNA methylation has emerged as a hot topic in biological sciences over the past decade. N6-methyladenosine (m6A) is the most common and abundant modification in eukaryotic mRNA, affecting all RNA stages, including transcription, translation, and degradation. Advances in high-throughput sequencing technologies made it feasible to identify the chemical basis and biological functions of m6A RNA. Dysregulation of m6A levels and associated modifying proteins can both inhibit and promote cancer, highlighting the importance of the tumor microenvironment in diverse biological processes. Gastrointestinal tract cancers, including gastric, colorectal, and pancreatic cancers, are among the most common and deadly malignancies in humans. Growing evidence suggests a close association between m6A levels and the progression of gastrointestinal tumors. Global m6A modification levels are substantially modified in gastrointestinal tumor tissues and cell lines compared to healthy tissues and cells, possibly influencing various biological behaviors such as tumor cell proliferation, invasion, metastasis, and drug resistance. Exploring the diagnostic and therapeutic potential of m6A-related proteins is critical from a clinical standpoint. Developing more specific and effective m6A modulators offers new options for treating these tumors and deeper insights into gastrointestinal tract cancers.

omicsMIC: a comprehensive benchmarking platform for robust comparison of imputation methods in mass spectrometry-based omics data.

Mass spectrometry is a powerful and widely used tool for generating proteomics, lipidomics and metabolomics profiles, which is pivotal for elucidating biological processes and identifying biomarkers. However, missing values in mass spectrometry-based omics data may pose a critical challenge for the comprehensive identification of biomarkers and elucidation of the biological processes underlying human complex disorders. To alleviate this issue, various imputation methods for mass spectrometry-based omics data have been developed. However, a comprehensive comparison of these imputation methods is still lacking, and researchers are frequently confronted with a multitude of options without a clear rationale for method selection. To address this pressing need, we developed omicsMIC (mass spectrometry-based omics with Missing values Imputation methods Comparison platform), an interactive platform that provides researchers with a versatile framework to evaluate the performance of 28 diverse imputation methods. omicsMIC offers a nuanced perspective, acknowledging the inherent heterogeneity in biological data and the unique attributes of each dataset. Our platform empowers researchers to make data-driven decisions in imputation method selection based on real-time visualizations of the outcomes associated with different imputation strategies. The comprehensive benchmarking and versatility of omicsMIC make it a valuable tool for the scientific community engaged in mass spectrometry-based omics research. omicsMIC is freely available at https://github.com/WQLin8/omicsMIC.

Engagement of AKT and ERK signaling pathways facilitates infection of human neuronal cells with West Nile virus.

West Nile virus (WNV) is an important neurotropic virus that accounts for the emergence of human arboviral encephalitis and meningitis. The interaction of WNV with signaling pathways plays a key role in controlling WNV infection. We have investigated the roles of the AKT and ERK pathways in supporting WNV propagation and modulating the inflammatory response following WNV infection. WNV established a productive infection in neuronal cell lines originated from human and mouse. Expression of IL-11 and TNF-α was markedly up-regulated in the infected human neuronal cells, indicating elicitation of inflammation response upon WNV infection. WNV incubation rapidly activated signaling cascades of AKT (AKT-S6-4E-BP1) and ERK (MEK-ERK-p90RSK) pathways. Treatment with AKT inhibitor MK-2206 or MEK inhibitor U0126 abrogated WNV-induced AKT or ERK activation. Strong activation of AKT and ERK signaling pathways could be detectable at 24 h after WNV infection, while such activation was abolished at 48 h post infection. U0126 treatment or knockdown of ERK expression significantly increased WNV RNA levels and viral titers and efficiently decreased IL-11 production induced by WNV, suggesting the involvement of ERK pathway in WNV propagation and IL-11 induction. MK-2206 treatment enhanced WNV RNA replication accompanied with a moderate decrease in IL-11 production. These results demonstrate that engagement of AKT and ERK signaling pathways facilitates viral infection and may be implicated in WNV pathogenesis.

Magnolin alleviated DSS-induced colitis by inhibiting ALOX5-mediated ferroptosis.

Inflammatory bowel disease (IBD) is a chronic and incurable disorder associated with higher cancer risk and currently faces unsatisfactory treatment outcomes. Ferroptotic cells secrete damage-associated molecular patterns (DAMPs) that recruit and activate immune cells, particularly macrophages. Magnolin has excellent antioxidant and anti-inflammatory properties, but its effect on IBD has not yet been clearly understood. This study aimed to investigate the therapeutic effects and mechanism of magnolin in IBD. For this purpose, in vivo and in vitro colitis models were established using dextran sulfate sodium (DSS), followed by optimization of magnolin concentration 2.5 µg/mL in vitro and 5 mg/kg in vivo. Bioinformatics analysis identified potential magnolin target sites and evaluated ferroptosis-associated gene expressions. Body weight, food intake, disease activity index (DAI), pathological changes, and inflammation levels were assessed. The effect of magnolin on ferroptosis and macrophages was evaluated using quantitative real time-polymerase chain reaction (qRT-PCR), immunofluorescent staining, flow cytometry, enzyme-linked immunosorbent assay (ELISA), and western blotting. Results indicated that magnolin at a lower dose (5 mg/kg) alleviated DSS-induced colitis symptoms and reduced inflammation in mice. The bioinformatics analysis showed arachidonate 5-lipoxygenase (ALOX5) as a potential magnolin target. Furthermore, magnolin inhibited the expression of ALOX5 with no effect on GPX4. Moreover, magnolin regulated macrophage differentiation into the M2 phenotype and suppressed pro-inflammatory factors, that is, interleukin-6 and tumor necrosis factor-α (IL-6 and TNFα). These results suggested that magnolin possesses significant therapeutic potential in treating IBD by suppressing ALOX5-mediated ferroptosis, inhibiting M1 while promoting M2 macrophages, which is envisaged to provide novel strategies for treating IBD.

Multi-View Variational Autoencoder for Missing Value Imputation in Untargeted Metabolomics.

Background: Missing data is a common challenge in mass spectrometry-based metabolomics, which can lead to biased and incomplete analyses. The integration of whole-genome sequencing (WGS) data with metabolomics data has emerged as a promising approach to enhance the accuracy of data imputation in metabolomics studies. Method: In this study, we propose a novel method that leverages the information from WGS data and reference metabolites to impute unknown metabolites. Our approach utilizes a multi-view variational autoencoder to jointly model the burden score, polygenetic risk score (PGS), and linkage disequilibrium (LD) pruned single nucleotide polymorphisms (SNPs) for feature extraction and missing metabolomics data imputation. By learning the latent representations of both omics data, our method can effectively impute missing metabolomics values based on genomic information. Results: We evaluate the performance of our method on empirical metabolomics datasets with missing values and demonstrate its superiority compared to conventional imputation techniques. Using 35 template metabolites derived burden scores, PGS and LD-pruned SNPs, the proposed methods achieved R2-scores > 0.01 for 71.55% of metabolites. Conclusion: The integration of WGS data in metabolomics imputation not only improves data completeness but also enhances downstream analyses, paving the way for more comprehensive and accurate investigations of metabolic pathways and disease associations. Our findings offer valuable insights into the potential benefits of utilizing WGS data for metabolomics data imputation and underscore the importance of leveraging multi-modal data integration in precision medicine research.

Multi-view information fusion using multi-view variational autoencoder to predict proximal femoral fracture load.

Background: Hip fracture occurs when an applied force exceeds the force that the proximal femur can support (the fracture load or "strength") and can have devastating consequences with poor functional outcomes. Proximal femoral strengths for specific loading conditions can be computed by subject-specific finite element analysis (FEA) using quantitative computerized tomography (QCT) images. However, the radiation and availability of QCT limit its clinical usability. Alternative low-dose and widely available measurements, such as dual energy X-ray absorptiometry (DXA) and genetic factors, would be preferable for bone strength assessment. The aim of this paper is to design a deep learning-based model to predict proximal femoral strength using multi-view information fusion. Results: We developed new models using multi-view variational autoencoder (MVAE) for feature representation learning and a product of expert (PoE) model for multi-view information fusion. We applied the proposed models to an in-house Louisiana Osteoporosis Study (LOS) cohort with 931 male subjects, including 345 African Americans and 586 Caucasians. We performed genome-wide association studies (GWAS) to select 256 genetic variants with the lowest p-values for each proximal femoral strength and integrated whole genome sequence (WGS) features and DXA-derived imaging features to predict proximal femoral strength. The best prediction model for fall fracture load was acquired by integrating WGS features and DXA-derived imaging features. The designed models achieved the mean absolute percentage error of 18.04%, 6.84% and 7.95% for predicting proximal femoral fracture loads using linear models of fall loading, nonlinear models of fall loading, and nonlinear models of stance loading, respectively. Conclusion: The proposed models are capable of predicting proximal femoral strength using WGS features and DXA-derived imaging features. Though this tool is not a substitute for predicting FEA using QCT images, it would make improved assessment of hip fracture risk more widely available while avoiding the increased radiation exposure from QCT.

Safety and immunogenicity of primary vaccination with a SARS-CoV-2 mRNA vaccine (SYS6006) in Chinese participants aged 18 years or more: Two randomized, observer-blinded, placebo-controlled and dose-escalation phase 1 clinical trials.

Vaccination plays a key role in preventing morbidity and mortality caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We aimed to evaluate the safety and immunogenicity of a SARS-CoV-2 messenger ribonucleic acid (mRNA) vaccine SYS6006. In the two randomized, observer-blinded, placebo-controlled phase 1 trials, 40 adult participants aged 18-59 years and 40 elderly participants aged 60 years or more were randomized to receive two doses of SYS6006 or placebo (saline). Adverse events (AEs) were collected through 30 days post the second vaccination. Immunogenicity was assessed by live-virus neutralizing antibody (Nab), spike protein (S1) binding antibody (S1-IgG), and cellular immunity. The result showed that 7/15, 9/15 and 4/10 adult participants, and 9/15, 8/15 and 4/10 elderly participants reported at least one AE in the 20-µg, 30-µg and placebo groups, respectively. Most AEs were grade 1. Injection-site pain was the most common AE. Two adults and one elder reported fever. No vaccination-related serious AE was reported. SYS6006 elicited wild-type Nab response with a peak geometric mean titer of 232.1 and 130.6 (adults), and 48.7 and 66.7 (elders), in the 20-µg and 30-µg groups, respectively. SYS6006 induced moderate-to-robust Nab response against Delta, and slight Nab response against Omicron BA.2 and BA.5. Robust IgG response against wild type and BA.2 was observed. Cellular immune response was induced. In conclusion, two-dose primary vaccination with SYS6006 demonstrated good safety and immunogenicity during a follow-up period of 51 days in immunologically naive population aged 18 years or more. (Trial registry: Chictr.org.cn ChiCTR2200059103 and ChiCTR2200059104).

Phase I study of a non-S2P SARS-CoV-2 mRNA vaccine LVRNA009 in Chinese adults.

BACKGROUND: COVID-19 caused by SARS-CoV-2 is a great threat to public health. We present the safety and immunogenicity data from a phase I trial in China of an mRNA vaccine (LVRNA009). METHODS: In the single-centre, double-blind, placebo-controlled and dose-escalation study, 72 healthy unvaccinated adults aged 18-59 years were randomized (3:1) to receive LVRNA009 with one of three vaccine dosage (25, 50 and 100 µg) or placebo, to evaluate for the safety, tolerability and immunogenicity of LVRNA009. RESULTS: All these participants received two injections 28 days apart. No adverse events higher than grade 2 were reported during the study. A total of 30 participants (42 %) reported solicited adverse reactions during the first 14 days after vaccinations. Of the events reported, fever (n = 11, 15 %) was the most common systemic adverse reaction, and pain at the injection site (n = 17, 24 %) was the most frequent solicited local adverse reaction. Anti-S-protein IgG and neutralising antibodies were observed to have been induced 14 days after the first dose, significantly increased 7 days after the second dose, and remained at a high level 28 days after the second dose. Specific T-cell responses peaked 7 days and persisted 28 days after second vaccination. CONCLUSION: LVRNA009 has demonstrated promising results in safety and tolerability at all three dose levels among Chinese adults. LVRNA009 at three dose levels could rapidly induce strong humoral and cellular immune responses, including binding and neutralising antibody production and IFN- γ secretion, which showed good immunogenicity. CLINICAL TRIAL REGISTRATION NUMBER: Clinicaltrials.gov NCT05364047; Chictr.org.cn ChiCTR2100049349.

Metabolomic profiles associated with physical activity in White and African American adult men.

BACKGROUND: Physical activity (PA) is associated with various health benefits, especially in improving chronic health conditions. However, the metabolic changes in host metabolism in response to PA remain unclear, especially in racially/ethnically diverse populations. OBJECTIVE: This study is to assess the metabolic profiles associated with the frequency of PA in White and African American (AA) men. METHODS: Using the untargeted metabolomics data collected from 698 White and AA participants (mean age: 38.0±8.0, age range: 20-50) from the Louisiana Osteoporosis Study (LOS), we conducted linear regression models to examine metabolites that are associated with PA levels (assessed by self-reported regular exercise frequency levels: 0, 1-2, and ≥3 times per week) in White and AA men, respectively, as well as in the pooled sample. Covariates considered for statistical adjustments included race (only for the pooled sample), age, BMI, waist circumstance, smoking status, and alcohol drinking. RESULTS: Of the 1133 untargeted compounds, we identified 7 metabolites associated with PA levels in the pooled sample after covariate adjustment with a false discovery rate of 0.15. Specifically, compared to participants who did not exercise, those who exercised at a frequency ≥3 times/week showed higher abundances in uracil, orotate, 1-(1-enyl-palmitoyl)-2-oleoyl-GPE (P-16:0/18:1) (GPE), threonate, and glycerate, but lower abundances in salicyluric glucuronide and adenine in the pooled sample. However, in Whites, salicyluric glucuronide and orotate were not significant. Adenine, GPE, and threonate were not significant in AAs. In addition, the seven metabolites were not significantly different between participants who exercised ≥3 times/week and 1-2 times/week, nor significantly different between participants with 1-2 times/week and 0/week in the pooled sample and respective White and AA groups. CONCLUSIONS: Metabolite responses to PA are dose sensitive and may differ between White and AA populations. The identified metabolites may help advance our knowledge of guiding precision PA interventions. Studies with rigorous study designs are warranted to elucidate the relationship between PA and metabolites.

Curcumin alleviates AFB1-induced nephrotoxicity in ducks: regulating mitochondrial oxidative stress, ferritinophagy, and ferroptosis.

Aflatoxin B1 (AFB1), an extremely toxic mycotoxin that extensively contaminates feed and food worldwide, poses a major hazard to poultry and human health. Curcumin, a polyphenol derived from turmeric, has attracted great attention due to its wonderful antioxidant properties. Nevertheless, effects of curcumin on the kidneys of ducks exposed to AFB1 remain unclear. Additionally, the underlying mechanism between AFB1 and ferroptosis (based on excessive lipid peroxidation) has not been sufficiently elucidated. This study aimed to investigate the protective effects and potential mechanisms of curcumin against AFB1-induced nephrotoxicity in ducklings. The results indicated that curcumin alleviated AFB1-induced growth retardation and renal distorted structure in ducklings. Concurrently, curcumin inhibited AFB1-induced mitochondrial-mediated oxidative stress by reducing the expression levels of oxidative damage markers malondialdehyde (MDA) and 8-hydroxy-2 deoxyguanosine (8-OHdG) and improved the expression of mitochondria-related antioxidant enzymes and the Nrf2 pathway. Notably, curcumin attenuated iron accumulation in the kidney, inhibited ferritinophagy via the NCOA4 pathway, and balanced iron homeostasis, thereby alleviating AFB1-induced ferroptosis in the kidney. Collectively, our results suggest that curcumin alleviates AFB1-induced nephrotoxicity in ducks by inhibiting mitochondrial-mediated oxidative stress, ferritinophagy, and ferroptosis and provide new evidence for the mechanism of AFB1-induced nephrotoxicity in ducklings treated with curcumin.

Alcohol dehydrogenase 1 is a tubular mitophagy-dependent apoptosis inhibitor against septic acute kidney injury.

Alcohol dehydrogenase 1 (ADH1) is an alcohol-oxidizing enzyme with poorlydefined biology. Here we report that ADH1 is highly expressed in kidneys of mice with lethal endotoxemia and is transcriptionally upregulated in tubular cells by lipopolysaccharide (LPS) stimuli through TLR4/NF-κB cascade. The Adh1 knockout (Adh1KO) mice with lethal endotoxemia displayed increased susceptibility to acute kidney injury (AKI) but not systemic inflammatory response. Adh1KO mice develop more severe tubular cell apoptosis in comparison to Adh1 wild-type (Adh1WT) mice during course of lethal endotoxemia. ADH1 deficiency facilitates the LPS-induced tubular cell apoptosis in a caspase-dependent manner. Mechanistically, ADH1 deficiency dampens tubular mitophagy that relies on PINK1-Parkin pathway characterized by the reduced membrane potential, reactive oxygen species (ROS) and release of fragmented mtDNA to cytosol. Kidney-specific overexpression of PINK1 and Parkin by adeno-associated viral vector 9 (AAV9) delivery ameliorates AKI exacerbation in Adh1KO mice with lethal endotoxemia. Our study supports the notion that ADH1 is critical for blockade of tubular apoptosis mediated by mitophagy, allowing the rapid identification and targeting of alcohol-metabolic route applicable to septic AKI.

omicsMIC: a Comprehensive Benchmarking Platform for Robust Comparison of Imputation Methods in Mass Spectrometry-based Omics Data.

Mass spectrometry is a powerful and widely used tool for generating proteomics, lipidomics, and metabolomics profiles, which is pivotal for elucidating biological processes and identifying biomarkers. However, missing values in spectrometry-based omics data may pose a critical challenge for the comprehensive identification of biomarkers and elucidation of the biological processes underlying human complex disorders. To alleviate this issue, various imputation methods for mass spectrometry-based omics data have been developed. However, a comprehensive and systematic comparison of these imputation methods is still lacking, and researchers are frequently confronted with a multitude of options without a clear rationale for method selection. To address this pressing need, we developed omicsMIC (mass spectrometry-based omics with Missing values Imputation methods Comparison platform), an interactive platform that provides researchers with a versatile framework to simulate and evaluate the performance of 28 diverse imputation methods. omicsMIC offers a nuanced perspective, acknowledging the inherent heterogeneity in biological data and the unique attributes of each dataset. Our platform empowers researchers to make data-driven decisions in imputation method selection based on real-time visualizations of the outcomes associated with different imputation strategies. The comprehensive benchmarking and versatility of omicsMIC make it a valuable tool for the scientific community engaged in mass spectrometry-based omics research. OmicsMIC is freely available at https://github.com/WQLin8/omicsMIC.

Administration of A. muciniphila ameliorates pulmonary arterial hypertension by targeting miR-208a-3p/NOVA1 axis.

Pulmonary arterial hypertension (PH) is a chronic disease induced by a progressive increase in pulmonary vascular resistance and failure of the right heart function. A number of studies show that the development of PH is closely related to the gut microbiota, and lung-gut axis might be a potential therapeutic target in the PH treatment. A. muciniphila has been reported to play a critical role in treating cardiovascular disorders. In this study we evaluated the therapeutic effects of A. muciniphila against hypoxia-induced PH and the underlying mechanisms. Mice were pretreated with A. muciniphila suspension (2 × 108 CFU in 200 µL sterile anaerobic PBS, i.g.) every day for 3 weeks, and then exposed to hypoxia (9% O2) for another 4 weeks to induce PH. We showed that A. muciniphila pretreatment significantly facilitated the restoration of the hemodynamics and structure of the cardiopulmonary system, reversed the pathological progression of hypoxia-induced PH. Moreover, A. muciniphila pretreatment significantly modulated the gut microbiota in hypoxia-induced PH mice. miRNA sequencing analysis reveals that miR-208a-3p, a commensal gut bacteria-regulated miRNA, was markedly downregulated in lung tissues exposed to hypoxia, which was restored by A. muciniphila pretreatment. We showed that transfection with miR-208a-3p mimic reversed hypoxia-induced abnormal proliferation of human pulmonary artery smooth muscle cells (hPASMCs) via regulating the cell cycle, whereas knockdown of miR-208a-3p abolished the beneficial effects of A. muciniphila pretreatment in hypoxia-induced PH mice. We demonstrated that miR-208a-3p bound to the 3'-untranslated region of NOVA1 mRNA; the expression of NOVA1 was upregulated in lung tissues exposed to hypoxia, which was reversed by A. muciniphila pretreatment. Furthermore, silencing of NOVA1 reversed hypoxia-induced abnormal proliferation of hPASMCs through cell cycle modulation. Our results demonstrate that A. muciniphila could modulate PH through the miR-208a-3p/NOVA1 axis, providing a new theoretical basis for PH treatment.

New insights into endophytic fungi diversity and their potential correlation with polyphyllin levels of Paris polyphylla var. yunnanensis.

Endophytes confer fitness advantages to host plants. However, the ecological communities of endophytic fungi in the different tissues (rhizomes, stems, and leaves) of Paris polyphylla and the relationship of their endophytic fungi with polyphyllin levels remain unclear. In this study, the community diversity and differences of endophytic fungi in the rhizomes, stems, and leaves of P. polyphylla var. yunnanensis were investigated, and a comprehensively diverse community of endophytic fungi was represented, including 50 genera, 44 families, 30 orders, 12 classes, and 5 phyla. Distributions of endophytic fungi differed greatly across the three tissues, with six genera common to all tissues, and 11, 5, and 4 genera specific to the rhizomes, stems, and leaves, respectively. Seven genera showed a significantly positive correlation to polyphyllin contents, indicating their potential roles in polyphyllin accumulation. This study provides valuable information for further research of the ecological and biological functions of endophytic fungi of P. polyphylla.