HIV, asymptomatic STI, and the rectal mucosal immune environment among young men who have sex with men.

Young men who have sex with men (YMSM) are disproportionately affected by HIV and bacterial sexually transmitted infections (STI) including gonorrhea, chlamydia, and syphilis; yet research into the immunologic effects of these infections is typically pursued in siloes. Here, we employed a syndemic approach to understand potential interactions of these infections on the rectal mucosal immune environment among YMSM. We enrolled YMSM aged 18-29 years with and without HIV and/or asymptomatic bacterial STI and collected blood, rectal secretions, and rectal tissue biopsies. YMSM with HIV were on suppressive antiretroviral therapy (ART) with preserved blood CD4 cell counts. We defined 7 innate and 19 adaptive immune cell subsets by flow cytometry, the rectal mucosal transcriptome by RNAseq, and the rectal mucosal microbiome by 16S rRNA sequencing and examined the effects of HIV and STI and their interactions. We measured tissue HIV RNA viral loads among YMSM with HIV and HIV replication in rectal explant challenge experiments among YMSM without HIV. HIV, but not asymptomatic STI, was associated with profound alterations in the cellular composition of the rectal mucosa. We did not detect a difference in the microbiome composition associated with HIV, but asymptomatic bacterial STI was associated with a higher probability of presence of potentially pathogenic taxa. When examining the rectal mucosal transcriptome, there was evidence of statistical interaction; asymptomatic bacterial STI was associated with upregulation of numerous inflammatory genes and enrichment for immune response pathways among YMSM with HIV, but not YMSM without HIV. Asymptomatic bacterial STI was not associated with differences in tissue HIV RNA viral loads or in HIV replication in explant challenge experiments. Our results suggest that asymptomatic bacterial STI may contribute to inflammation particularly among YMSM with HIV, and that future research should examine potential harms and interventions to reduce the health impact of these syndemic infections.

Dynamic within-host cefiderocol heteroresistance caused by blaSHV-12 amplification in pandrug-resistant and hypervirulent Klebsiella pneumoniae sequence type 11.

AIMS: Although cefiderocol (FDC) is not prescribed in China, FDC-resistant pandrug-resistant hypervirulent Klebsiella pneumoniae (PDR-hvKp) is emerging. In this study, we performed FDC susceptibility testing of clinical Kp isolates to explore the prevalence of FDC-resistant isolates and the mechanism of FDC-resistance. METHODS: We retrospectively selected 151 carbapenem-resistant Kp isolates to assess FDC susceptibility. Seven isolates harboring blaSHV-12 from two patients were enrolled for whole-genome sequencing. The antimicrobial resistance, virulence, blaSHV-12 expression, and fitness costs in different media were examined. The amplification of blaSHV-12 was further investigated by qPCR and long-read sequencing. RESULTS: The 151 isolates showed a low MIC50/MIC90 (1/4 mg/L) of FDC. The seven isolates were ST11 PDR-hvKp, and two represented FDC-resistance (MIC=32 mg/L). The IncR/IncFII plasmids of two FDC-resistant isolates harbored 6 and 15 copies of blaSHV-12, whereas four FDC-susceptible isolates carried one copy and one harbored three copies. These blaSHV-12 genes concatenated together and were located within the same 7.3 kb fragment flanked by IS26, which contributed to the increased expression and FDC resistance without fitness costs. The amplification of blaSHV-12 and FDC resistance could be induced by FDC in vitro and reversed during continuous passage. CONCLUSIONS: The amplification of blaSHV-12 and the consequent dynamic within-host heteroresistance are important concerns for the rational application of antibiotics. Long-read sequencing might be a superior way to detect resistance gene amplification rapidly and accurately.

LOCOM: A logistic regression model for testing differential abundance in compositional microbiome data with false discovery rate control.

Compositional analysis is based on the premise that a relatively small proportion of taxa are differentially abundant, while the ratios of the relative abundances of the remaining taxa remain unchanged. Most existing methods use log-transformed data, but log-transformation of data with pervasive zero counts is problematic, and these methods cannot always control the false discovery rate (FDR). Further, high-throughput microbiome data such as 16S amplicon or metagenomic sequencing are subject to experimental biases that are introduced in every step of the experimental workflow. McLaren et al. [eLife 8, e46923 (2019)] have recently proposed a model for how these biases affect relative abundance data. Motivated by this model, we show that the odds ratios in a logistic regression comparing counts in two taxa are invariant to experimental biases. With this motivation, we propose logistic compositional analysis (LOCOM), a robust logistic regression approach to compositional analysis, that does not require pseudocounts. Inference is based on permutation to account for overdispersion and small sample sizes. Traits can be either binary or continuous, and adjustment for confounders is supported. Our simulations indicate that LOCOM always preserved FDR and had much improved sensitivity over existing methods. In contrast, analysis of composition of microbiomes (ANCOM) and ANCOM with bias correction (ANCOM-BC)/ANOVA-Like Differential Expression tool (ALDEx2) had inflated FDR when the effect sizes were small and large, respectively. Only LOCOM was robust to experimental biases in every situation. The flexibility of our method for a variety of microbiome studies is illustrated by the analysis of data from two microbiome studies. Our R package LOCOM is publicly available.

eQTL mapping using allele-specific count data is computationally feasible, powerful, and provides individual-specific estimates of genetic effects.

Using information from allele-specific gene expression (ASE) can improve the power to map gene expression quantitative trait loci (eQTLs). However, such practice has been limited, partly due to computational challenges and lack of clarification on the size of power gain or new findings besides improved power. We have developed geoP, a computationally efficient method to estimate permutation p-values, which makes it computationally feasible to perform eQTL mapping with ASE counts for large cohorts. We have applied geoP to map eQTLs in 28 human tissues using the data from the Genotype-Tissue Expression (GTEx) project. We demonstrate that using ASE data not only substantially improve the power to detect eQTLs, but also allow us to quantify individual-specific genetic effects, which can be used to study the variation of eQTL effect sizes with respect to other covariates. We also compared two popular methods for eQTL mapping with ASE: TReCASE and RASQUAL. TReCASE is ten times or more faster than RASQUAL and it provides more robust type I error control.

Compositional analysis of microbiome data using the linear decomposition model (LDM).

SUMMARY: There are compelling reasons to test compositional hypotheses about microbiome data. We present here linear decomposition model-centered log ratio (LDM-clr), an extension of our LDM approach to allow fitting linear models to centered-log-ratio-transformed taxa count data. As LDM-clr is implemented within the existing LDM program, this extension enjoys all the features supported by LDM, including a compositional analysis of differential abundance at both the taxon and community levels, while allowing for a wide range of covariates and study designs for either association or mediation analysis. AVAILABILITY AND IMPLEMENTATION: LDM-clr has been added to the R package LDM, which is available on GitHub at https://github.com/yijuanhu/LDM.

ST218 Klebsiella pneumoniae became a high-risk clone for multidrug resistance and hypervirulence.

BACKGROUND: The occurrence of multidrug-resistant and hypervirulent Klebsiella pneumoniae (MDR-hvKp) worldwide poses a great challenge for public health. Few studies have focused on ST218 MDR-hvKp. METHODS: Retrospective genomic surveillance was conducted at the Peking University Third Hospital from 2017 and clinical information was obtained. To understand genomic and microbiological characteristics, antimicrobial susceptibility testing, plasmid conjugation and stability, biofilm formation, serum killing, growth curves and whole-genome sequencing were performed. We also assessed the clinical and microbiological characteristics of ST218 compared with ST23. RESULTS: A total of eleven ST218 Kp isolates were included. The most common infection type was lower respiratory tract infection (72.7%, 8/11) in our hospital, whereas ST23 hvKp (72.7%, 8/11) was closely associated with bloodstream infection. Notably, nosocomial infections caused by ST218 (54.5%, 6/11) was slightly higher than ST23 (36.4%, 4/11). All of the ST218 and ST23 strains presented with the virulence genes combination of iucA + iroB + peg344 + rmpA + rmpA2. Interestingly, the virulence score of ST218 was lower than ST23, whereas one ST218 strain (pPEKP3107) exhibited resistance to carbapenems, cephalosporins, ß-lactamase/inhibitors and quinolones and harbored an ~ 59-kb IncN type MDR plasmid carrying resistance genes including blaNDM-1, dfrA14 and qnrS1. Importantly, blaNDM-1 and qnrS1 were flanked with IS26 located within the plasmid that could successfully transfer into E. coli J53. Additionally, PEKP2044 harbored an ~ 41-kb resistance plasmid located within tetA indicating resistance to doxycycline. CONCLUSION: The emergence of blaNDM-1 revealed that there is great potential for ST218 Kp to become a high-risk clone for MDR-hvKp, indicating the urgent need for enhanced genomic surveillance.

MERIT: Controlling Monte-Carlo error rate in large-scale Monte-Carlo hypothesis testing.

The use of Monte-Carlo (MC) p $$ p $$ -values when testing the significance of a large number of hypotheses is now commonplace. In large-scale hypothesis testing, we will typically encounter at least some p $$ p $$ -values near the threshold of significance, which require a larger number of MC replicates than p $$ p $$ -values that are far from the threshold. As a result, some incorrect conclusions can be reached due to MC error alone; for hypotheses near the threshold, even a very large number (eg, 1 0 6 $$ 1{0}^6 $$ ) of MC replicates may not be enough to guarantee conclusions reached using MC p $$ p $$ -values. Gandy and Hahn (GH)6-8 have developed the only method that directly addresses this problem. They defined a Monte-Carlo error rate (MCER) to be the probability that any decisions on accepting or rejecting a hypothesis based on MC p $$ p $$ -values are different from decisions based on ideal p $$ p $$ -values; their method then makes decisions by controlling the MCER. Unfortunately, the GH method is frequently very conservative, often making no rejections at all and leaving a large number of hypotheses "undecided". In this article, we propose MERIT, a method for large-scale MC hypothesis testing that also controls the MCER but is more statistically efficient than the GH method. Through extensive simulation studies, we demonstrate that MERIT controls the MCER while making more decisions that agree with the ideal p $$ p $$ -values than GH does. We also illustrate our method by an analysis of gene expression data from a prostate cancer study.

Downregulation of miR-337-3p in hypoxia/reoxygenation neuroblastoma cells increases KCTD11 expression.

Neurodegeneration is linked to the progressive loss of neural function and is associated with several diseases. Hypoxia is a hallmark in many of these diseases, and several therapies have been developed to treat this disease, including gene expression therapies that should be tightly controlled to avoid side effects. Cells experiencing hypoxia undergo a series of physiological responses that are induced by the activation of various transcription factors. Modulation of microRNA (miRNA) expression to alter transcriptional regulation has been demonstrated to be beneficial in treating multiple diseases, and in this study, we therefore explored potential miRNA candidates that could influence hypoxia-induced nerve cell death. Our data suggest that in mouse neuroblasts Neuro-2a cells with hypoxia/reoxygenation (H/R), miR-337-3p is downregulated to increase the expression of Potassium channel tetramerization domain containing 11 (KCTD11) and subsequently promote apoptosis. Here, we demonstrate for the first time that KCTD11 plays a role in the cellular response to hypoxia, and we also provide a possible regulatory mechanism by identifying the axis of miR-337-3p/KCTD11 as a promising candidate modulator of nerve cell survival after H/R exposure.

Young osteocyte-derived extracellular vesicles facilitate osteogenesis by transferring tropomyosin-1.

BACKGROUND: Bone marrow mesenchymal stem cells (BMSCs) can undergo inadequate osteogenesis or excessive adipogenesis as they age due to changes in the bone microenvironment, ultimately resulting in decreased bone density and elevated risk of fractures in senile osteoporosis. This study aims to investigate the effects of osteocyte senescence on the bone microenvironment and its influence on BMSCs during aging. RESULTS: Primary osteocytes were isolated from 2-month-old and 16-month-old mice to obtain young osteocyte-derived extracellular vesicles (YO-EVs) and senescent osteocyte-derived EVs (SO-EVs), respectively. YO-EVs were found to significantly increase alkaline phosphatase activity, mineralization deposition, and the expression of osteogenesis-related genes in BMSCs, while SO-EVs promoted BMSC adipogenesis. Neither YO-EVs nor SO-EVs exerted an effect on the osteoclastogenesis of primary macrophages/monocytes. Our constructed transgenic mice, designed to trace osteocyte-derived EV distribution, revealed abundant osteocyte-derived EVs embedded in the bone matrix. Moreover, mature osteoclasts were found to release osteocyte-derived EVs from bone slices, playing a pivotal role in regulating the functions of the surrounding culture medium. Following intravenous injection into young and elderly mouse models, YO-EVs demonstrated a significant enhancement of bone mass and biomechanical strength compared to SO-EVs. Immunostaining of bone sections revealed that YO-EV treatment augmented the number of osteoblasts on the bone surface, while SO-EV treatment promoted adipocyte formation in the bone marrow. Proteomics analysis of YO-EVs and SO-EVs showed that tropomyosin-1 (TPM1) was enriched in YO-EVs, which increased the matrix stiffness of BMSCs, consequently promoting osteogenesis. Specifically, the siRNA-mediated depletion of Tpm1 eliminated pro-osteogenic activity of YO-EVs both in vitro and in vivo. CONCLUSIONS: Our findings suggested that YO-EVs played a crucial role in maintaining the balance between bone resorption and formation, and their pro-osteogenic activity declining with aging. Therefore, YO-EVs and the delivered TPM1 hold potential as therapeutic targets for senile osteoporosis.

Acupuncture and Doxylamine-Pyridoxine for Nausea and Vomiting in Pregnancy : A Randomized, Controlled, 2 × 2 Factorial Trial.

BACKGROUND: An effective and safe treatment for nausea and vomiting of pregnancy (NVP) is lacking. OBJECTIVE: To assess the efficacy and safety of acupuncture, doxylamine-pyridoxine, and a combination of both in women with moderate to severe NVP. DESIGN: Multicenter, randomized, double-blind, placebo-controlled, 2 × 2 factorial trial. (ClinicalTrials.gov: NCT04401384). SETTING: 13 tertiary hospitals in mainland China from 21 June 2020 to 2 February 2022. PARTICIPANTS: 352 women in early pregnancy with moderate to severe NVP. INTERVENTION: Participants received daily active or sham acupuncture for 30 minutes and doxylamine-pyridoxine or placebo for 14 days. MEASUREMENTS: The primary outcome was the reduction in Pregnancy-Unique Quantification of Emesis (PUQE) score at the end of the intervention at day 15 relative to baseline. Secondary outcomes included quality of life, adverse events, and maternal and perinatal complications. RESULTS: No significant interaction was detected between the interventions (P = 0.69). Participants receiving acupuncture (mean difference [MD], -0.7 [95% CI, -1.3 to -0.1]), doxylamine-pyridoxine (MD, -1.0 [CI, -1.6 to -0.4]), and the combination of both (MD, -1.6 [CI, -2.2 to -0.9]) had a larger reduction in PUQE score over the treatment course than their respective control groups (sham acupuncture, placebo, and sham acupuncture plus placebo). Compared with placebo, a higher risk for births with children who were small for gestational age was observed with doxylamine-pyridoxine (odds ratio, 3.8 [CI, 1.0 to 14.1]). LIMITATION: The placebo effects of the interventions and natural regression of the disease were not evaluated. CONCLUSION: Both acupuncture and doxylamine-pyridoxine alone are efficacious for moderate and severe NVP. However, the clinical importance of this effect is uncertain because of its modest magnitude. The combination of acupuncture and doxylamine-pyridoxine may yield a potentially larger benefit than each treatment alone. PRIMARY FUNDING SOURCE: The National Key R&D Program of China and the Project of Heilongjiang Province "TouYan" Innovation Team.

Interaction between genetics and smoking in determining risk of coronary artery diseases.

Coronary artery disease (CAD) is a preeminent cause of death, and smoking is a strong risk factor for CAD. Genetic factors contribute to the development of CAD, but the interplay between genetic predisposition and smoking history in CAD remains unclear. Using data from the UK Biobank, we constructed several genetic risk scores (GRSs) based on known CAD loci and assessed their interactions with smoking for the development of incident CAD in 307,147 participants of European ancestry who were free of CAD. We fitted Cox proportional hazard models and assessed gene-smoking interaction on both multiplicative and additive scales. Overall, we found no multiplicative interactions, but observed a synergistic additive interaction of GRS with both smoking status and pack-years of smoking, finding that the absolute CAD risk due to smoking was higher for those with high genetic risk. Trait-based sub-GRSs suggested smoking status and smoking intensity measured by pack-years might confer gene-smoking interaction effects with different intermediate risk factors for CAD. Our study results suggest that genetics could modify the effects of smoking on CAD and highlight the value of addressing gene-lifestyle interactions on both additive and multiplicative scales.

Correction: Zhang et al. Parkin, as a Regulator, Participates in Arsenic Trioxide-Triggered Mitophagy in HeLa Cells. Curr. Issues Mol. Biol. 2022, 44, 2759-2771.

In the published publication [...].

A new approach to testing mediation of the microbiome at both the community and individual taxon levels.

MOTIVATION: Understanding whether and which microbes played a mediating role between an exposure and a disease outcome are essential for researchers to develop clinical interventions to treat the disease by modulating the microbes. Existing methods for mediation analysis of the microbiome are often limited to a global test of community-level mediation or selection of mediating microbes without control of the false discovery rate (FDR). Further, while the null hypothesis of no mediation at each microbe is a composite null that consists of three types of null, most existing methods treat the microbes as if they were all under the same type of null, leading to excessive false positive results. RESULTS: We propose a new approach based on inverse regression that regresses the microbiome data at each taxon on the exposure and the exposure-adjusted outcome. Then, the P-values for testing the coefficients are used to test mediation at both the community and individual taxon levels. This approach fits nicely into our Linear Decomposition Model (LDM) framework, so our new method LDM-med, implemented in the LDM framework, enjoys all the features of the LDM, e.g. allowing an arbitrary number of taxa to be tested simultaneously, supporting continuous, discrete, or multivariate exposures and outcomes (including survival outcomes), and so on. Using extensive simulations, we showed that LDM-med always preserved the FDR of testing individual taxa and had adequate sensitivity; LDM-med always controlled the type I error of the global test and had compelling power over existing methods. The flexibility of LDM-med for a variety of mediation analyses is illustrated by an application to a murine microbiome dataset, which identified several plausible mediating taxa. AVAILABILITY AND IMPLEMENTATION: Our new method has been added to our R package LDM, which is available on GitHub at https://github.com/yijuanhu/LDM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A rarefaction-without-resampling extension of PERMANOVA for testing presence-absence associations in the microbiome.

MOTIVATION: PERMANOVA is currently the most commonly used method for testing community-level hypotheses about microbiome associations with covariates of interest. PERMANOVA can test for associations that result from changes in which taxa are present or absent by using the Jaccard or unweighted UniFrac distance. However, such presence-absence analyses face a unique challenge: confounding by library size (total sample read count), which occurs when library size is associated with covariates in the analysis. It is known that rarefaction (subsampling to a common library size) controls this bias but at the potential costs of information loss and the introduction of a stochastic component into the analysis. RESULTS: Here, we develop a non-stochastic approach to PERMANOVA presence-absence analyses that aggregates information over all potential rarefaction replicates without actual resampling, when the Jaccard or unweighted UniFrac distance is used. We compare this new approach to three possible ways of aggregating PERMANOVA over multiple rarefactions obtained from resampling: averaging the distance matrix, averaging the (element-wise) squared distance matrix and averaging the F-statistic. Our simulations indicate that our non-stochastic approach is robust to confounding by library size and outperforms each of the stochastic resampling approaches. We also show that, when overdispersion is low, averaging the (element-wise) squared distance outperforms averaging the unsquared distance, currently implemented in the R package vegan. We illustrate our methods using an analysis of data on inflammatory bowel disease in which samples from case participants have systematically smaller library sizes than samples from control participants. AVAILABILITY AND IMPLEMENTATION: We have implemented all the approaches described above, including the function for calculating the analytical average of the squared or unsquared distance matrix, in our R package LDM, which is available on GitHub at https://github.com/yijuanhu/LDM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Integrative analysis of relative abundance data and presence-absence data of the microbiome using the LDM.

SUMMARY: We previously developed the LDM for testing hypotheses about the microbiome that performs the test at both the community level and the individual taxon level. The LDM can be applied to relative abundance data and presence-absence data separately, which work well when associated taxa are abundant and rare, respectively. Here, we propose LDM-omni3 that combines LDM analyses at the relative abundance and presence-absence data scales, thereby offering optimal power across scenarios with different association mechanisms. The new LDM-omni3 test is available for the wide range of data types and analyses that are supported by the LDM. AVAILABILITY AND IMPLEMENTATION: The LDM-omni3 test has been added to the R package LDM, which is available on GitHub at https://github.com/yijuanhu/LDM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Testing microbiome associations with survival times at both the community and individual taxon levels.

BACKGROUND: Finding microbiome associations with possibly censored survival times is an important problem, especially as specific taxa could serve as biomarkers for disease prognosis or as targets for therapeutic interventions. The two existing methods for survival outcomes, MiRKAT-S and OMiSA, are restricted to testing associations at the community level and do not provide results at the individual taxon level. An ad hoc approach testing each taxon with a survival outcome using the Cox proportional hazard model may not perform well in the microbiome setting with sparse count data and small sample sizes. METHODS: We have previously developed the linear decomposition model (LDM) for testing continuous or discrete outcomes that unifies community-level and taxon-level tests into one framework. Here we extend the LDM to test survival outcomes. We propose to use the Martingale residuals or the deviance residuals obtained from the Cox model as continuous covariates in the LDM. We further construct tests that combine the results of analyzing each set of residuals separately. Finally, we extend PERMANOVA, the most commonly used distance-based method for testing community-level hypotheses, to handle survival outcomes in a similar manner. RESULTS: Using simulated data, we showed that the LDM-based tests preserved the false discovery rate for testing individual taxa and had good sensitivity. The LDM-based community-level tests and PERMANOVA-based tests had comparable or better power than MiRKAT-S and OMiSA. An analysis of data on the association of the gut microbiome and the time to acute graft-versus-host disease revealed several dozen associated taxa that would not have been achievable by any community-level test, as well as improved community-level tests by the LDM and PERMANOVA over those obtained using MiRKAT-S and OMiSA. CONCLUSIONS: Unlike existing methods, our new methods are capable of discovering individual taxa that are associated with survival times, which could be of important use in clinical settings.

Ångstrom-scale silver particles ameliorate collagen-induced and K/BxN-transfer arthritis in mice via the suppression of inflammation and osteoclastogenesis.

OBJECTIVE: Nanoparticles (NPs) hold a great promise in combating rheumatoid arthritis, but are often compromised by their toxicities because the currently used NPs are usually synthesized by chemical methods. Our group has previously fabricated Ångstrom-scale silver particles (AgÅPs) and demonstrated the anti-tumor and anti-sepsis efficacy of fructose-coated AgÅPs (F-AgÅPs). This study aimed to uncover the efficacy and mechanisms of F-AgÅPs for arthritis therapy. METHODS: We evaluated the efficacy of F-AgÅPs in collagen-induced arthritis (CIA) mice. We also compared the capacities of F-AgÅPs, the commercial AgNPs, and the clinical drug methotrexate (MTX) in protecting against K/BxN serum-transfer arthritis (STA) mice. Moreover, we evaluated the effects of F-AgÅPs and AgNPs on inflammation, osteoclast formation, synoviocytes migration, and matrix metalloproteinases (MMPs) production in vitro and in vivo. Meanwhile, the toxicities of F-AgÅPs and AgNPs in vitro and in vivo were also tested. RESULTS: F-AgÅPs significantly prevented bone erosion, synovitis, and cartilage damage, attenuated rheumatic pain, and improved the impaired motor function in mouse models of CIA or STA, the anti-rheumatic effects of which were comparable or stronger than AgNPs and MTX. Further studies revealed that F-AgÅPs exhibited similar or greater inhibitory abilities than AgNPs to suppress inflammation, osteoclast formation, synoviocytes migration, and MMPs production. No obvious toxicities were observed in vitro and in vivo after F-AgÅPs treatment. CONCLUSIONS: F-AgÅPs can effectively alleviate arthritis without notable toxicities and their anti-arthritic effects are associated with the inhibition of inflammation, osteoclastogenesis, synoviocytes migration, and MMPs production. Our study suggests the prospect of F-AgÅPs as an efficient and low-toxicity agent for arthritis therapy.

Oral microbiome of electronic cigarette users: A cross-sectional exploration.

OBJECTIVE: Electronic cigarettes have increased in popularity globally. Vaping may be associated with oral symptoms and pathologies including dental and periodontal damage, both of which have an underlying microbial etiology. The primary aim of this pilot study, therefore, was to compare the oral microbiome of vapers and non-vapers. SUBJECTS AND METHODS: This secondary data analysis had a cross-sectional comparative descriptive design and included data for 36 adults. Bacterial 16S rRNA genes were extracted and amplified from soft tissue oral swab specimens and taxonomically classified using the Human Oral Microbiome Database. RESULTS: Data for 18 vapers and 18 non-vapers were included in this study. Almost 56% of the vapers also smoked conventional cigarettes. Beta diversity differences were identified between vapers and non-vapers. Vapers had a significantly higher relative abundance of an unclassified species of Veillonella compared with non-vapers. Dual users had higher alpha diversity compared with exclusive vapers. Beta diversity was also associated with dual use. Multiple OTUs were identified to be associated with dual use of e-cigarettes and conventional cigarettes. CONCLUSIONS: Vapers exhibit an altered oral microbiome. Dual use of electronic cigarettes and conventional cigarettes is associated with the presence of several known pathogenic microbes.

Probiotics-Fermented Grifola frondosa Total Active Components: Better Antioxidation and Microflora Regulation for Alleviating Alcoholic Liver Damage in Mice.

Alcoholic liver damage is caused by long-term drinking, and it further develops into alcoholic liver diseases. In this study, we prepared a probiotic fermentation product of Grifola frondosa total active components (PFGF) by fermentation with Lactobacillus acidophilus, Lactobacillus rhamnosus, and Pediococcus acidilactici. After fermentation, the total sugar and protein content in the PFGF significantly decreased, while the lactic acid level and antioxidant activity of the PFGF increased. Afterward, we investigated the alleviating effect of PFGF on alcoholic liver injury in alcohol-fed mice. The results showed that the PFGF intervention reduced the necrosis of the liver cells, attenuated the inflammation of the liver and intestines, restored the liver function, increased the antioxidant factors of the liver, and maintained the cecum tissue barrier. Additionally, the results of the 16S rRNA sequencing analysis indicated that the PFGF intervention increased the relative abundance of beneficial bacteria, such as Lactobacillus, Ruminococcaceae, Parabacteroids, Parasutterella, and Alistipes, to attenuate intestinal inflammation. These results demonstrate that PFGF can potentially alleviate alcoholic liver damage by restoring the intestinal barrier and regulating the intestinal microflora.

Transcriptome analysis revealed detoxification gene expression changes in Tetranychus cinnabarinus challenged with ethyl oleate.

Natural acaricides are potential biorational mite control alternatives to conventional chemical acaricides. However, little is known about the molecular mechanism of defense response to natural acaricides in mites. We previously reported significant acaricidal properties of ethyl oleate (EO) against Tetranychus cinnabarinus (here referred to as a sibling species of two-spotted spider mite, Tetranychus urticae), a highly polyphagous pest devastating crops in fields and greenhouses worldwide. In this study, we explored the molecular responses of T. cinnabarinus exposed to EO using RNA-Seq and differentially expressed gene (DEG) analysis. A total of 131, 185, and 154 DEGs were identified in T. cinnabarinus after 1, 6, and 24 h of EO treatment. In addition, 36 putative detoxification-related DEGs, including 10 cytochrome P450s (P450s), three glutathione S-transferases (GSTs), nine UDP-glycosyltransferases (UGTs), eight esterases (ESTs), and six ATP-binding cassette transporters (ABC transporters), were identified. Interestingly, the upregulation of these detoxification-related genes might be the main defense response of T. cinnabarinus exposed to EO. A quantitative real-time PCR analysis indicated that the expression profiles of 19 random DEGs were consistent with the RNA-Seq results. These findings serve as valuable information for a better understanding of the acaricide-mite interaction and molecular mechanisms involved in the defense response of T. cinnabarinus against EO.