Gender-affirming hormone therapy preserves skeletal maturation in young mice via the gut microbiome

Gender-affirming hormone therapy (GAHT) is often prescribed to transgender (TG) adolescents to alleviate gender dysphoria, but the effect of GAHT on the growing skeleton is unclear. We found GAHT to improve trabecular bone structure via increased bone formation in young male mice and not to affect trabecular structure in female mice. GAHT modified gut microbiome composition in both male and female mice. However, fecal microbiota transfers (FMTs) revealed that GAHT-shaped gut microbiome was a communicable regulator of bone structure and turnover in male, but not in female mice. Mediation analysis identified 2 species of Bacteroides as significant contributors to the skeletal effects of GAHT in male mice, with Bacteroides supplementation phenocopying the effects of GAHT on bone. Bacteroides have the capacity to expand Treg populations in the gut. Accordingly, GAHT expanded intestinal Tregs and stimulated their migration to the bone marrow (BM) in male but not in female mice. Attesting to the functional relevance of Tregs, pharmacological blockade of Treg expansion prevented GAHT-induced bone anabolism. In summary, in male mice GAHT stimulated bone formation and improved trabecular structure by promoting Treg expansion via a microbiome-mediated effect, while in female mice, GAHT neither improved nor impaired trabecular structure.

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 somep $$ p $$ -values near the threshold of significance, which require a larger number of MC replicates thanp $$ p $$ -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 MCp $$ p $$ -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 MCp $$ p $$ -values are different from decisions based on idealp $$ p $$ -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 idealp $$ p $$ -values than GH does. We also illustrate our method by an analysis of gene expression data from a prostate cancer study.

Unique microbial diversity, community composition, and networks among Pacific Islander endocervical and vaginal microbiomes with and without Chlamydia trachomatis infection in Fiji

IMPORTANCE

Chlamydia trachomatis (Ct) is the most common sexually transmitted bacterium globally. Endocervical and vaginal microbiome interactions are rarely examined within the context of Ct or among vulnerable populations. We evaluated 258 vaginal and 92 paired endocervical samples from Fijian women using metagenomic shotgun sequencing. Over 37% of the microbiomes could not be classified into sub-community state types (subCSTs). We, therefore, developed subCSTs IV-D0, IV-D1, IV-D2, and IV-E-dominated primarily by Gardnerella vaginalis-to improve classification. Among paired microbiomes, the endocervix had a significantly higher alpha diversity and, independently, higher diversity for high-risk human papilloma virus (HPV) genotypes compared to low-risk and no HPV. Ct-infected endocervical networks had smaller clusters without interactions with potentially beneficial Lactobacillus spp. Overall, these data suggest that G. vaginalis may generate polymicrobial biofilms that predispose to and/or promote Ct and possibly HPV persistence and pathogenicity. Our findings expand on the existing repertoire of endocervical and vaginal microbiomes and fill in knowledge gaps regarding Pacific Islanders.

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.

Comparison of genomic diversity between single and pooled Staphylococcus aureus colonies isolated from human colonization cultures

The most common approach to sampling the bacterial populations within an infected or colonized host is to sequence genomes from a single colony obtained from a culture plate. However, it is recognized that this method does not capture the genetic diversity in the population. Sequencing a mixture of several colonies (pool-seq) is a better approach to detect population heterogeneity, but it is more complex to analyse due to different types of heterogeneity, such as within-clone polymorphisms, multi-strain mixtures, multi-species mixtures and contamination. Here, we compared 8 single-colony isolates (singles) and pool-seq on a set of 2286 Staphylococcus aureus culture samples to identify features that can distinguish pure samples, samples undergoing intraclonal variation and mixed strain samples. The samples were obtained by swabbing 3 body sites on 85 human participants quarterly for a year, who initially presented with a methicillin-resistant S. aureus skin and soft-tissue infection (SSTI). We compared parameters such as sequence quality, contamination, allele frequency, nucleotide diversity and pangenome diversity in each pool to those for the corresponding singles. Comparing singles from the same culture plate, we found that 18% of sample collections contained mixtures of multiple multilocus sequence types (MLSTs or STs). We showed that pool-seq data alone could predict the presence of multi-ST populations with 95% accuracy. We also showed that pool-seq could be used to estimate the number of intra-clonal polymorphic sites in the population. Additionally, we found that the pool may contain clinically relevant genes such as antimicrobial resistance markers that may be missed when only examining singles. These results highlight the potential advantage of analysing genome sequences of total populations obtained from clinical cultures rather than single colonies.

Integrative analysis of microbial 16S gene and shotgun metagenomic sequencing data improves statistical efficiency

Background

The most widely used technologies for profiling microbial communities are 16S marker-gene sequencing and shotgun metagenomic sequencing. Interestingly, many microbiome studies have performed both sequencing experiments on the same cohort of samples. The two sequencing datasets often reveal consistent patterns of microbial signatures, highlighting the potential for an integrative analysis to improve power of testing these signatures. However, differential experimental biases, partially overlapping samples, and differential library sizes pose tremendous challenges when combining the two datasets. Currently, researchers either discard one dataset entirely or use different datasets for different objectives.

Methods

In this article, we introduce the first method of this kind, named Com-2seq, that combines the two sequencing datasets for testing differential abundance at the genus and community levels while overcoming these difficulties. The new method is based on our LOCOM model (Hu et al., 2022), which employs logistic regression for testing taxon differential abundance while remaining robust to experimental bias. To benchmark the performance of Com-2seq, we introduce two ad hoc approaches: applying LOCOM to pooled taxa count data and combining LOCOM p-values from analyzing each dataset separately.

Results

Our simulation studies indicate that Com-2seq substantially improves statistical efficiency over analysis of either dataset alone and works better than the two ad hoc approaches. An application of Com-2seq to two real microbiome studies uncovered scientifically plausible findings that would have been missed by analyzing individual datasets.

Conclusions

Com-2seq performs integrative analysis of 16S and metagenomic sequencing data, which improves statistical efficiency and has the potential to accelerate the search of microbial communities and taxa that are involved in human health and diseases.

Impact of Experimental Bias on Compositional Analysis of Microbiome Data

Microbiome data are subject to experimental bias that is caused by DNA extraction and PCR amplification, among other sources, but this important feature is often ignored when developing statistical methods for analyzing microbiome data. McLaren, Willis, and Callahan (2019) proposed a model for how such biases affect the observed taxonomic profiles; this model assumes the main effects of bias without taxon-taxon interactions. Our newly developed method for testing the differential abundance of taxa, LOCOM, is the first method to account for experimental bias and is robust to the main effect biases. However, there is also evidence for taxon-taxon interactions. In this report, we formulated a model for interaction biases and used simulations based on this model to evaluate the impact of interaction biases on the performance of LOCOM as well as other available compositional analysis methods. Our simulation results indicate that LOCOM remained robust to a reasonable range of interaction biases. The other methods tend to have an inflated FDR even when there were only main effect biases. LOCOM maintained the highest sensitivity even when the other methods could not control the FDR. We thus conclude that LOCOM outperforms the other methods for compositional analysis of microbiome data considered here.

[Evaluation of the efficacy and safety of Nocardia rubra cell wall skeleton immunotherapy for cervical high-risk HPV persistent infection]

Objective: To evaluate the efficacy and safety of Nocardia rubra cell wall skeleton (Nr-CWS) in the treatment of persistent cervical high-risk human papillomavirus (HR-HPV) infection. Methods: A randomized, double blind, multi-center trial was conducted. A total of 688 patients with clinically and pathologically confirmed HR-HPV infection of the cervix diagnosed in 13 hispital nationwide were recruited and divided into: (1) patients with simple HR-HPV infection lasting for 12 months or more; (2) patients with cervical intraepithelial neoplasia (CIN) Ⅰ and HR-HPV infection lasting for 12 months or more; (3) patients with the same HR-HPV subtype with no CINⅡ and more lesions after treatment with CINⅡ or CIN Ⅲ (CINⅡ/CIN Ⅲ). All participants were randomly divided into the test group and the control group at a ratio of 2∶1. The test group was locally treated with Nr-CWS freeze-dried powder and the control group was treated with freeze-dried powder without Nr-CWS. The efficacy and negative conversion rate of various subtypes of HR-HPV were evaluated at 1, 4, 8, and 12 months after treatment. The safety indicators of initial diagnosis and treatment were observed. Results: (1) This study included 555 patients with HR-HPV infection in the cervix (included 368 in the test group and 187 in the control group), with an age of (44.1±10.0) years. The baseline characteristics of the two groups of subjects, including age, proportion of Han people, weight, composition of HR-HPV subtypes, and proportion of each subgroup, were compared with no statistically significant differences (all P>0.05). (2) After 12 months of treatment, the effective rates of the test group and the control group were 91.0% (335/368) and 44.9% (84/187), respectively. The difference between the two groups was statistically significant (χ²=142.520, P<0.001). After 12 months of treatment, the negative conversion rates of HPV 16, 18, 52, and 58 infection in the test group were 79.2% (84/106), 73.3% (22/30), 83.1% (54/65), and 77.4% (48/62), respectively. The control group were 21.6% (11/51), 1/9, 35.1% (13/37), and 20.0% (8/40), respectively. The differences between the two groups were statistically significant (all P<0.001). (3) There were no statistically significant differences in vital signs (body weight, body temperature, respiration, pulse rate, systolic blood pressure, diastolic blood pressure, etc.) and laboratory routine indicators (blood cell analysis, urine routine examination) between the test group and the control group before treatment and at 1, 4, 8, and 12 months after treatment (all P>0.05); there was no statistically significant difference in the incidence of adverse reactions related to the investigational drug between the two groups of subjects [8.7% (32/368) vs 8.0% (15/187), respectively; χ²=0.073, P=0.787]. Conclusion: External use of Nr-CWS has good efficacy and safety in the treatment of high-risk HPV persistent infection in the cervix.

Integrative analysis of microbial 16S gene and shotgun metagenomic sequencing data improves statistical efficiency

The most widely used technologies for profiling microbial communities are 16S marker-gene sequencing and shotgun metagenomic sequencing. Interestingly, many microbiome studies have performed both sequencing experiments on the same cohort of samples. The two sequencing datasets often reveal consistent patterns of microbial signatures, highlighting the potential for an integrative analysis to improve power of testing these signatures. However, differential experimental biases, partially overlapping samples, and differential library sizes pose tremendous challenges when combining the two datasets. Currently, researchers either discard one dataset entirely or use different datasets for different objectives. In this article, we introduce the first method of this kind, named Com-2seq, that combines the two sequencing datasets for the objective of testing differential abundance at the genus and community levels while overcoming these difficulties. We demonstrate that Com-2seq substantially improves statistical efficiency over analysis of either dataset alone and works better than two ad hoc approaches.

Comparison of genomic diversity between single and pooled Staphylococcus aureus colonies isolated from human colonisation cultures

The most common approach to sampling the bacterial populations within an infected or colonised host is to sequence genomes from a single colony obtained from a culture plate. However, it is recognized that this method does not capture the genetic diversity in the population. An alternative is to sequence a mixture containing multiple colonies ("pool-seq"), but this has the disadvantage that it is a non-homogeneous sample, making it difficult to perform specific experiments. We compared differences in measures of genetic diversity between eight single-colony isolates (singles) and pool-seq on a set of 2286 S. aureus culture samples. The samples were obtained by swabbing three body sites on 85 human participants quarterly for a year, who initially presented with a methicillin-resistant S. aureus skin and soft-tissue infection (SSTI). We compared parameters such as sequence quality, contamination, allele frequency, nucleotide diversity and pangenome diversity in each pool to the corresponding singles. Comparing singles from the same culture plate, we found that 18% of sample collections contained mixtures of multiple Multilocus sequence types (MLSTs or STs). We showed that pool-seq data alone could predict the presence of multi-ST populations with 95% accuracy. We also showed that pool-seq could be used to estimate the number of polymorphic sites in the population. Additionally, we found that the pool may contain clinically relevant genes such as antimicrobial resistance markers that may be missed when only examining singles. These results highlight the potential advantage of analysing genome sequences of total populations obtained from clinical cultures rather than single colonies.

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 LDM-clr, an extension of our linear decomposition model (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, it 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 .

Contact

yijuan.hu@emory.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

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.

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.

[Antimicrobial resistance and plasmid-mediated colistin resistance mechanism of diarrheagenic Escherichia coli recovered from foods in parts of China in 2020]

Objective: To investigate the antimicrobial resistance of food-borne diarrheagenic Escherichia coli (DEC) and the prevalence of mcr genes that mediates mobile colistin resistance in parts of China, 2020. Methods: For 91 DEC isolates recovered from food sources collected from Fujian province, Hebei province, Inner Mongolia Autonomous Region and Shanghai city in 2020, Vitek2 Compact biochemical identification and antimicrobial susceptibility testing platform was used for the detection of antimicrobial susceptibility testing (AST) against to 18 kinds of antimicrobial compounds belonging to 9 categories, and multi-polymerase chain reaction (mPCR) was used to detect the mcr-1-mcr-9 genes, then a further AST, whole genome sequencing (WGS) and bioinformatics analysis were platformed for these DEC isolates which were PCR positive for mcr genes. Results: Seventy in 91 isolates showed different antimicrobial resistance levels to the drugs tested with a resistance rate of 76.92%. The isolates showed the highest antimicrobial resistance rates to ampicillin (69.23%, 63/91) and trimethoprim-sulfamethoxazole (59.34%, 54/91), respectively. The multiple drug-resistant rate was 47.25% (43/91). Two mcr-1 gene and ESBL (extended-spectrum beta-lactamase) positive EAEC (enteroaggregative Escherichia coli) strains were detected. One of them was identified as serotype of O11:H6, which showed a resistance profile to 25 tested drugs referring to 10 classes, and 38 drug resistance genes were predicted by genome analysis. The other one was O16:H48 serotype, which was resistant to 21 tested drugs belonging to 7 classes and carried a new variant of mcr-1 gene (mcr-1.35). Conclusion: An overall high-level antimicrobial resistance was found among foodborne DEC isolates recovered from parts of China in 2020, and so was the MDR (multi-drug resistance) condition. MDR strains carrying multiple resistance genes such as mcr-1 gene were detected, and a new variant of mcr-1 gene was also found. It is necessary to continue with a dynamic monitoring on DEC contamination and an ongoing research into antimicrobial resistance mechanisms.

Impact of experimental bias on compositional analysis of microbiome data

Microbiome data are subject to experimental bias that is caused by DNA extraction, PCR amplification among other sources, but this important feature is often ignored when developing statistical methods for analyzing microbiome data. McLaren, Willis and Callahan (2019) proposed a model for how such bias affects the observed taxonomic profiles, which assumes main effects of bias without taxon-taxon interactions. Our newly developed method, LOCOM (logistic regression for compositional analysis) for testing differential abundance of taxa, is the first method that accounted for experimental bias and is robust to the main effect biases. However, there is also evidence for taxon-taxon interactions. In this report, we formulated a model for interaction biases and used simulations based on this model to evaluate the impact of interaction biases on the performance of LOCOM as well as other available compositional analysis methods. Our simulation results indicated that LOCOM remained robust to a reasonable range of interaction biases. The other methods tended to have inflated FDR even when there were only main effect biases. LOCOM maintained the highest sensitivity even when the other methods cannot control the FDR. We thus conclude that LOCOM outperforms the other methods for compositional analysis of microbiome data considered here.

Use of high-resolution metabolomics to assess the biological perturbations associated with maternal exposure to Bisphenol A and Bisphenol F among pregnant African American women

BACKGROUND

Human and animal exposure to bisphenol A (BPA) has been associated with adverse developmental and reproductive effects. The molecular mechanisms by which BPA exposure exerts its effects are not well-understood, even less known about its analogues bisphenol F (BPF). To address these knowledge gaps, we conducted an untargeted metabolome-wide association study (MWAS) to identify metabolic perturbations associated with BPA/BPF exposures in a pregnant African American cohort.

METHODS

From a subset of study participants enrolled in the Atlanta African American Maternal-Child cohort, we collected both urine samples, for targeted exposure assessment of BPA (N = 230) and BPF (N = 48), and serum samples, for high-resolution metabolomics (HRM) profiling (N = 230), during early pregnancy (8-14 weeks' gestation). Using an established untargeted HRM workflow consisting of MWAS modeling, pathway enrichment analysis, and chemical annotation and confirmation, we investigated the potential metabolic pathways and features associated with BPA/BPF exposures.

RESULTS

The geometric mean creatinine-adjusted concentrations of urinary BPA and BPF were 0.85 ± 2.58 and 0.70 ± 4.71 µg/g creatinine, respectively. After false positive discovery rate correction at 20 % level, 264 and 733 unique metabolic features were significantly associated with urinary BPA and BPF concentrations, representing 10 and 12 metabolic pathways, respectively. Three metabolic pathways, including steroid hormones biosynthesis, lysine and lipoate metabolism, were significantly associated with both BPA and BPF exposure. Using chemical standards, we have confirmed the chemical identity of 16 metabolites significantly associated with BPA or BPF exposure.

CONCLUSIONS

Our findings support that exposure to BPA and BPF in pregnant women is associated with the perturbation of aromatic amino acid metabolism, xenobiotics metabolism, steroid biosynthesis, and other amino acid metabolism closely linked to stress responses, inflammation, neural development, reproduction, and weight regulation.

The rectal mucosal immune environment and HIV susceptibility among young men who have sex with men

Young men who have sex with men (YMSM) represent a particularly high-risk group for HIV acquisition in the US, despite similarly reported rates of sexual activity as older, adult MSM (AMSM). Increased rates of HIV infection among YMSM compared to AMSM could be partially attributable to differences within the rectal mucosal (RM) immune environment associated with earlier sexual debut and less lifetime exposure to receptive anal intercourse. Using an ex vivo explant HIV challenge model, we found that rectal tissues from YMSM supported higher levels of p24 at peak viral replication timepoints compared to AMSM. Among YMSM, the RM was characterized by increased CD4+ T cell proliferation, as well as lower frequencies of tissue resident CD8+ T cells and pro-inflammatory cytokine producing CD4+ and CD8+ T cells. In addition, the microbiome composition of YMSM was enriched for anaerobic taxa that have previously been associated with HIV acquisition risk, including Prevotella, Peptostreptococcus, and Peptoniphilus. These distinct immunologic and microbiome characteristics were found to be associated with higher HIV replication following ex vivo challenge of rectal explants, suggesting the RM microenvironment of YMSM may be uniquely conducive to HIV infection.

[Clinicopathological features and diagnosis of pericytic tumors of the kidney]

Objective: To investigate the clinicopathologic features,diagnosis and prognosis of pericytic tumor of the kidney. Methods: Three cases of pericytic tumor of the kidney (two cases were diagnosed as glomangiomyomas and one case as pericytic tumor,unclassified) were collected from the affiliated Hospital of Qingdao University between January 2014 to May 2021; the clinical and morphologic features, immunohistochemical and molecular characteristics were analyzed and the relevant literature was reviewed. Results: The three patients included one male and two females, with ages ranging from 21 to 70 years. In two patients the tumors were detected incidentally at physical examination and one patient presented with low back discomfort. Imaging showed a rounded nodular soft tissue density shadow in renal parenchyma, and enhancement scan showed uneven delayed enhancement. Grossly, two tumors were located in the renal hilum and one in the renal parenchyma; all were nodular. The tumors were measured in size from 1.6 cm to 5.1 cm (mean 4.1 cm) and showed gray or gray-red cut surface. Histologic examination showed the tumor cells were arranged in solid sheets or small nodules, closely related to vascular wall. Tumor cells were mostly epithelial-like with abundant cytoplasm, light eosinophilia, obscure boundary and round nuclei with visible nucleoli. Vague bundles and fascicular arrangements of smooth muscle component were noted in some areas, with transition of both components. There was no necrosis. By immunohistochemistry, the tumor cells strongly and diffusely expressed vimentin, SMA and collagen Ⅳ, two cases expressed CD34, all three cases expressed PDGFRB to varying extent, and the Ki-67 index was 2%-3%. PCR tests showed absent K-RAS, BRAF V600E gene mutation in all three cases. PDGFRB mutations in exons 3 and 18, respectively were found in two of the three cases by high-throughput sequencing, and no NOTCH 1/2/3 gene fusions were found in any of them. Follow-up information (range: 6-92 months) showed no evidence of local recurrence or distant metastasis in all three patients. Conclusions: Pericytic tumor of the kidney is a rare mesenchymal tumor originating in the kidney with differentiation to smooth muscle, most commonly glomus tumor. The mild pleomorphism, close relationship with vascular wall and spindled smooth muscle components suggest the diagnosis of the tumor. Expression of both epithelial and muscle-associated markers aids the diagnosis. PDGFRB gene mutations may have an important role in the development of this tumor. Most patients have a good prognosis, and a few cases have malignant biological behavior.

A robust and transformation-free joint model with matching and regularization for metagenomic trajectory and disease onset

BACKGROUND

To identify operational taxonomy units (OTUs) signaling disease onset in an observational study, a powerful strategy was selecting participants by matched sets and profiling temporal metagenomes, followed by trajectory analysis. Existing trajectory analyses modeled individual OTU or microbial community without adjusting for the within-community correlation and matched-set-specific latent factors.

RESULTS

We proposed a joint model with matching and regularization (JMR) to detect OTU-specific trajectory predictive of host disease status. The between- and within-matched-sets heterogeneity in OTU relative abundance and disease risk were modeled by nested random effects. The inherent negative correlation in microbiota composition was adjusted by incorporating and regularizing the top-correlated taxa as longitudinal covariate, pre-selected by Bray-Curtis distance and elastic net regression. We designed a simulation pipeline to generate true biomarkers for disease onset and the pseudo biomarkers caused by compositionality. We demonstrated that JMR effectively controlled the false discovery and pseudo biomarkers in a simulation study generating temporal high-dimensional metagenomic counts with random intercept or slope. Application of the competing methods in the simulated data and the TEDDY cohort showed that JMR outperformed the other methods and identified important taxa in infants' fecal samples with dynamics preceding host disease status.

CONCLUSION

Our method JMR is a robust framework that models taxon-specific trajectory and host disease status for matched participants without transformation of relative abundance, improving the power of detecting disease-associated microbial features in certain scenarios. JMR is available in R package mtradeR at https://github.com/qianli10000/mtradeR.

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.

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.

Abstract 5940: Impact of obesity on sexual functioning in women with gynecologic cancer pre-and post-radiotherapy

Background: As the prevalence of obesity in women increases in the US, a better understanding of the effect of obesity on women with gynecologic cancer (GynCa) is imperative. However, whether obesity impacts sexual functioning (SF) in GynCa patients has been understudied. Limited studies have used body mass index (BMI) as the only obesity indicator, which may result in a misunderstanding of the effect of obesity on SF in GynCa patients with different body compositions. This study aimed to explore the role of obesity via two different measures in determining SF in GynCa patients pre-and post-radiotherapy (RT).

Methods: In this secondary analysis, obesity was measured pre-RT by both BMI and body fat percentage (BFP). BFP was calculated by using the Clinica Universidad de Navarra-Body Adiposity Estimator. SF, physical and emotional well-being were assessed pre-RT by the Female Sexual Function Index and the Functional Assessment of Cancer Therapy-General. At 6 months post-RT, SF was reassessed. Generalized linear models (GLM) and generalized linear mixed models (GLMM) with a log-link were implemented to identify the effect of obesity (using BMI and BFP) on SF pre-and post-RT. Regression-based mediation analysis was used to identify mediating effects of physical and emotional well-being between obesity and SF.

Results: Among 54 (24 cervical and 30 endometrial) patients, 48% were Black, 52% were married or in a domestic partnership, and 59% were treated with surgery and/or chemotherapy pre-RT (i.e. prior treatment). Less patients were classified as being obese when obesity was measured by BMI (48%) compared to BFP (61%). Patients with endometrial cancer and prior treatment history were more likely to be obese and reported better SF. Unadjusted GLM showed that patients with obesity had better SF than those without obesity pre-RT regardless of obesity measures (BMI p = .018; BFP p = .016). However, we found no association between obesity (both BMI-and BFP-based obesity) and SF pre-RT after adjusting for cancer type and prior treatment history. GLMM showed that BFP-based obesity had a negative effect on the improvement in SF from pre-RT to post-RT (p = .013) after adjusting for cancer type and prior treatment history. Patients without BFP-based obesity showed a significant improvement post-RT (p = .019), but those with obesity (per BFP) showed no changes in SF post-RT (p = .949) compared to pre-RT. BMI-based obesity did not affect the changes in SF post-RT. We found no mediation effects of physical and emotional well-being between obesity and SF.

Conclusions: Obesity may play a negative role in SF improvement in GynCa patients post-RT. More rigorous research and clinical attention to obesity measurement are necessary to determine the association between obesity and SF in GynCa patients and thus lead to the discovery of new interventions for this long-neglected topic of SF in GynCa patients, particularly for those who are obese.

Citation Format: Haerim Lee, Deborah W. Bruner, Yi-Juan Hu, Jinbing Bai, Tony Y. Eng, Joseph W. Shelton, Pretesh R. Patel, Namita Khanna, Isabelle Scott, Katherine A. Yeager. Impact of obesity on sexual functioning in women with gynecologic cancer pre-and post-radiotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5940.

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.

[Phenotypic and genomic characterization for Salmonella isolates recovered from meat products in Beijing wholesale markets, 2014-2015]

Objective: To analyze the phenotypic and genomic characteristics of Salmonella isolates recovered from meat products in Beijing wholesale markets. Methods: A total of 336 Salmonella strains from meat products collected from wholesale markets in Beijing were tested for antimicrobial resistance to 25 antimicrobial compounds by micro-broth dilution method; whole genome data were sequenced, followed by the serotype and ST type prediction by Seqsero2 and SISTR software, and the drug resistance genes and virulence factors were also predicted with CARD and VFDB databases of Abricate software; Salmonella serotyping assay kit and serum agglutination method were used for serotype confirmation of some isolates with different genome prediction results. Results: The resistance rates to Nalidixic acid and Ampicillin were 62.5% (210/336) and 55.1% (185/336), respectively, and all isolates were susceptible to Tigecyclin, Cefoxitin and Carbapenem antimicrobial compounds; 207 isolates (61.6%, 207/336) were multi-drug resistant, some could even be resistant to ten categories of drugs at the same time, and the most common antimicrobial resistance spectrum was NAL-AMP-SAM. A total of 24 serotypes were detected with predominant serotypes of Enteritidis (34.5%, 116/336), Derby (17.3%, 58/336) and Indiana (10.4%, 35/336). A total of 27 ST types were detected, the dominant type was ST11; ST types were in good consistency with serotypes; The detection rates of resistant genes referred to aminoglycosides, fluoroquinolones, β-lactams, sulfonamides and tetracyclines are more than 48%, and the first two reached 100%. The prediction of drug resistance genes was consistent with the results of antimicrobial resistance phenotype. A total of 122 virulence genes were predicted, 74 of which existing among all isolates. Conclusion: Salmonella in meat from the wholesale markets of Beijing has a high proportion of multiple drug resistance, a complex drug resistance spectrum, a variety of serotypes and ST types, and a high carrying rate of drug resistance gene and virulence gene; drug resistance phenotype and genotype are relatively consistent.

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.

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.

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.

Exploring the Anal Microbiome in HIV Positive and High-Risk HIV Negative Women

This exploratory study sought to characterize the anal microbiome and explore associations among the anal microbiome, risk factors for anal cancer, and clinical factors. A pilot sample of 50 HIV infected and high-risk HIV negative women were recruited from the former Women's Interagency HIV Study. Microbiome characterization by 16S rRNA gene sequencing and datasets were analyzed using QIIME 2™. Composition of the anal microbiome and its associations with anal cancer risk factors and clinical factors were analyzed using linear decomposition model and permutational multivariate analysis of variance. Composition of the anal microbiome among HIV positive and high-risk negative women was dominated by Bacteroides, Prevotella, and Campylobacter. The overall taxonomic composition and microbial diversity of the anal microbiome did not significantly differ by HIV status. However, the abundance of Ruminococcus 1 belonging to the Rumincoccaceae family was associated with HIV status (q = .05). No anal cancer risk factors were associated with the anal microbiome composition. Clinical factors marginally associated with the anal microbiome composition included body mass index (BMI; p = .05) and hepatitis C virus (HCV; p = .05). Although HIV and risk factors for anal cancer were not associated with the composition of the anal microbiome in this pilot sample, other clinical factors such as BMI and HCV, may be worth further investigation in a larger study. Future research can build on these findings to explore the role of the microbiome and HIV comorbidities in women.

Monocarboxylate transporter 1-mediated lactate accumulation promotes nucleus pulposus degeneration under hypoxia in a 3D multilayered nucleus pulposus degeneration model

During intervertebral disc degeneration (IVDD), due to endplate calcification, diminished oxygen and nutrient concentrations and accumulated lactate are present in the microenvironment of the nucleus pulposus (NP). The disadvantages of 3D layered culture include uneven oxygen and nutrient gradients. In the present study, to mimic the in vivo microenvironment of the NP, a 5-layered 3D culture was constructed using clinical haemostatic gelatine sponges and developed as a NP degeneration (NPD) model. Subsequently, cell distribution as well as expression of NP chondrogenic markers (type II collagen and aggrecan), glycosaminoglycan (GAG) and degeneration markers [e.g. matrix metalloproteinase (MMP) 3] were measured from the top to the bottom layer. However, in a single NP-cell-loaded disc model, the chondrogenic potency in the middle or bottom layer was higher than that in the top layer. To further study the mechanism underlying the degeneration of NP cells in this NPD model, the contribution of secreted metabolites was examined. Lactate identified in the supernatant modulated GAG accumulation and MMP3 expression. Inhibition of lactate influx by the monocarboxylate transporter (MCT)-1 inhibitor, AZD3965, reversed the effect of lactate on GAG accumulation and MMP3 expression and further improved NP cell degeneration in the NPD model. Thanks to the homogenous expression of lactate in the model, it was possible to further identified that the combination of lactate and hypoxia enhanced MMP3 expression. Taken together, multilayered cell-loaded sponges, with oxygen and nutrient gradients as well as lactate accumulation, can represent a 3D multilayered NPD model for exploring potential agents for IVDD.

A Bottom-up Approach to Testing Hypotheses That Have a Branching Tree Dependence Structure, with Error Rate Control

Modern statistical analyses often involve testing large numbers of hypotheses. In many situations, these hypotheses may have an underlying tree structure that both helps determine the order that tests should be conducted but also imposes a dependency between tests that must be accounted for. Our motivating example comes from testing the association between a trait of interest and groups of microbes that have been organized into operational taxonomic units (OTUs) or amplicon sequence variants (ASVs). Given p-values from association tests for each individual OTU or ASV, we would like to know if we can declare a certain species, genus, or higher taxonomic group to be associated with the trait. For this problem, a bottom-up testing algorithm that starts at the lowest level of the tree (OTUs or ASVs) and proceeds upward through successively higher taxonomic groupings (species, genus, family etc.) is required. We develop such a bottom-up testing algorithm that controls a novel error rate that we call the false selection rate. By simulation, we also show that our approach is better at finding driver taxa, the highest level taxa below which there are dense association signals. We illustrate our approach using data from a study of the microbiome among patients with ulcerative colitis and healthy controls.

Condomless receptive anal intercourse is associated with markers of mucosal inflammation in a cohort of men who have sex with men in Atlanta, Georgia

Introduction

We previously showed that the rectal mucosal immune environment among men who have sex with men (MSM) engaging in condomless receptive anal intercourse (CRAI) is immunologically distinct from that of men who do not engage in anal intercourse (AI). Here, we further examined these differences with quantitative immunohistochemistry to better understand the geographic distribution of immune markers of interest.

Methods

We enrolled a cohort of MSM engaging in CRAI (n = 41) and men who do not engage in AI (n = 21) between October 2013 and April 2015. Participants were healthy, HIV‐negative men aged 18–45 from the metro Atlanta area. We performed rectal mucosal sampling via rigid sigmoidoscopy during two study visits separated by a median of nine weeks and timed with sexual activity for MSM engaging in CRAI. We used standardized, automated immunohistochemistry and quantitative image analysis to investigate the rectal mucosal distribution of neutrophils (MPO), IL‐17‐producing cells (IL‐17) and T_regs (FOXP3) in the lamina propria, and cellular proliferation (Ki67) and adherens junction protein (E‐cadherin) in the epithelium. We examined associations between biomarker expression and the rectal mucosal microbiota composition by 16s rRNA sequencing.

Results

Relative to the colonic crypt base, IL‐17, FOXP3, and MPO expression increased towards the rectal lumen, while Ki67 decreased and E‐cadherin was more uniformly distributed. Throughout the rectal mucosa distribution examined, MSM engaging in CRAI had higher mean lamina propria MPO expression (p = 0.04) and epithelial Ki67 (p = 0.04) compared to controls. There were no significant differences in IL‐17, FOXP3 or E‐cadherin expression. We found no significant associations of the five biomarkers with the global rectal microbiota composition or the individual taxa examined.

Conclusions

Understanding the mucosal distribution of inflammatory mediators can enhance our knowledge of the earliest events in HIV transmission. Neutrophil enrichment and crypt epithelial cell proliferation likely represent sub‐clinical inflammation in response to CRAI in the rectal mucosa of MSM, which could increase the risk for HIV acquisition. However, the contributory role of the microbiota in mucosal inflammation among MSM remains unclear. HIV prevention may be enhanced by interventions that reduce inflammation or capitalize on the presence of specific inflammatory mechanisms during HIV exposure.

The impact of particulate matter air pollution (PM2.5) on atherosclerosis in modernizing China: a report from the CATHAY study

BACKGROUND

Air pollution has been associated with an increase in cardiovascular diseases incidence. To evaluate whether air pollution can accelerate atherogenic processes, we assessed the effects of air pollution on important surrogate markers of atherosclerosis [brachial flow-mediated dilation (FMD) and carotid intima-media thickness (IMT)].

METHODS

A total of 1656 Han Chinese (mean age 46.0 + 11.2 years; male 47%) in Hong Kong, Macau, Pun Yu, Yu County and the 3-Gorges Territories (Yangtze River) were studied between 1996 and 2007 [Chinese Atherosclerosis in the Aged and Young Project (the CATHAY Study)]. Cardiovascular risk profiles were evaluated. Particulate matter with an aerodynamic diameter <2.5 µm (PM2.5) parameters were computed from satellite sensors. Brachial FMD and carotid IMT were measured by ultrasound.

RESULTS

Health parameters [age, gender, body mass index, waist : hip ratio (WHR) and glucose)] were similar in lowest and highest PM2.5 exposure tertiles, systolic and diastolic blood pressures and triglycerides were higher (P < 0.001) and low-density cholesterol (LDL-C) was lower in the top PM2.5 tertile (P < 0.001). Brachial FMD [7.84 ± 1.77, 95% confidence interval (CI) 7.59-8.10%, vs 8.50 ± 2.52, 95% CI 8.23-8.77%, P < 0.0001) was significantly lower and carotid IMT (0.68 ± 0.13 mm, 95% CI 0.67-0.69 mm vs 0.63 mm ± 0.15 mm 95% CI 0.62-0.64 mm; P < 0.0001) was significantly thicker in the top PM2.5 tertile compared with the lowest tertile. On multiple regression, FMD was inversely related to PM2.5 (beta = 0.134, P = 0.015) independent of gender, age and blood pressure (model R2 = 0.156, F-value = 7.6, P < 0.0001). Carotid IMT was significantly correlated with PM2.5 exposure (beta = 0.381, P < 0.0001) independent of age, location, gender, WHR, blood pressure and LDL-C (model R2 = 0.408, F-value = 51.4, P-value <0.0001).

CONCLUSIONS

Air pollution is strongly associated with markers of early atherosclerosis, suggesting a potential target for preventive intervention.

Constraining PERMANOVA and LDM to within-set comparisons by projection improves the efficiency of analyses of matched sets of microbiome data

BACKGROUND

Matched-set data arise frequently in microbiome studies. For example, we may collect pre- and post-treatment samples from a set of individuals, or use important confounding variables to match data from case participants to one or more control participants. Thus, there is a need for statistical methods for data comprised of matched sets, to test hypotheses against traits of interest (e.g., clinical outcomes or environmental factors) at the community level and/or the operational taxonomic unit (OTU) level. Optimally, these methods should accommodate complex data such as those with unequal sample sizes across sets, confounders varying within sets, and continuous traits of interest.

METHODS

PERMANOVA is a commonly used distance-based method for testing hypotheses at the community level. We have also developed the linear decomposition model (LDM) that unifies the community-level and OTU-level tests into one framework. Here we present a new strategy that can be used with both PERMANOVA and the LDM for analyzing matched-set data. We propose to include an indicator variable for each set as covariates, so as to constrain comparisons between samples within a set, and also permute traits within each set, which can account for exchangeable sample correlations. The flexible nature of PERMANOVA and the LDM allows discrete or continuous traits or interactions to be tested, within-set confounders to be adjusted, and unbalanced data to be fully exploited.

RESULTS

Our simulations indicate that our proposed strategy outperformed alternative strategies, including the commonly used one that utilizes restricted permutation only, in a wide range of scenarios. Using simulation, we also explored optimal designs for matched-set studies. The flexibility of PERMANOVA and the LDM for a variety of matched-set microbiome data is illustrated by the analysis of data from two real studies.

CONCLUSIONS

Including set indicator variables and permuting within sets when analyzing matched-set data with PERMANOVA or the LDM is a strategy that performs well and is capable of handling the complex data structures that frequently occur in microbiome studies. Video Abstract.

Shiftwork, functional bowel symptoms, and the microbiome

BACKGROUND

There are about 15 million Americans working full-time on evening, night, or rotating shifts. Between 48% and 81.9% of those working rotating or night shifts report abdominal pain, constipation, diarrhea and other symptoms of functional bowel disorders. The basis for this high prevalence of functional bowel disorders, including irritable bowel syndrome (IBS), among shift workers is unknown. Animal studies, however, suggest that circadian disruption, similar to that in shift workers, may contribute to the development of GI complaints among shift workers by altering the composition and normal diurnal rhythmicity of the resident intestinal microbes. Therefore, the present study was designed to determine if there were differences in (1) composition and diversity of the microbiome of night shift workers compared to day shift workers; and (2) the composition and diversity of the microbiome among shift workers experiencing functional bowel symptoms compared to shift workers who did not experience functional bowel symptoms.

METHODS

Fifty-one full time staff nurses who worked either 12-hour day or night shifts completed demographic information, and the Rome III IBS module. They also collected two samples of gut microbiota before the beginning and at the end of their last work shift on day 14, using validated field-tested methods consistent with the Human Microbiome Project. After DNA extraction, 16S rRNA sequencing and assignment to the genus level was completed, samples were then compared to determine if there were (1) differences in the diversity and profile of the microbiome by shift type; (2) if there were differences in the microbiome by time of day for collection; and (3) whether there were differences in the diversity and profile of the microbiome of nurses with IBS and those without IBS.

RESULTS

There were no differences in alpha or beta diversity of gut microbiota when specimens from day and night shift nurses were compared. There were however marginal differences in beta diversity when specimens collected at the beginning and end of the shifts were compared, with seven OTUs being differentially abundant when collected from day shift workers in the evening. There were also three OTUs to be differentially abundant in participants reporting IBS symptoms.

Vaginal Microbiome Composition in Early Pregnancy and Risk of Spontaneous Preterm and Early Term Birth Among African American Women

Objective

To evaluate the association between the early pregnancy vaginal microbiome and spontaneous preterm birth (sPTB) and early term birth (sETB) among African American women.

Methods

Vaginal samples collected in early pregnancy (8-14 weeks' gestation) from 436 women enrolled in the Emory University African American Vaginal, Oral, and Gut Microbiome in Pregnancy Study underwent 16S rRNA gene sequencing of the V3-V4 region, taxonomic classification, and community state type (CST) assignment. We compared vaginal CST and abundance of taxa for women whose pregnancy ended in sPTB (N = 44) or sETB (N= 84) to those who delivered full term (N = 231).

Results

Nearly half of the women had a vaginal microbiome classified as CST IV (Diverse CST), while one-third had CST III (L. iners dominated) and just 16% had CST I, II, or V (non-iners Lactobacillus dominated). Compared to vaginal CST I, II, or V (non-iners Lactobacillus dominated), both CST III (L. iners dominated) and CST IV (Diverse) were associated with sPTB with an adjusted odds ratio (95% confidence interval) of 4.1 (1.1, infinity) and 7.7 (2.2, infinity), respectively, in multivariate logistic regression. In contrast, no vaginal CST was associated with sETB. The linear decomposition model (LDM) based on amplicon sequence variant (ASV) relative abundance found a significant overall effect of the vaginal microbiome on sPTB (p=0.034) but not sETB (p=0.320), whereas the LDM based on presence/absence of ASV found no overall effect on sPTB (p=0.328) but a significant effect on sETB (p=0.030). In testing for ASV-specific effects, the LDM found that no ASV was significantly associated with sPTB considering either relative abundance or presence/absence data after controlling for multiple comparisons (FDR 10%), although in marginal analysis the relative abundance of Gardnerella vaginalis (p=0.011), non-iners Lactobacillus (p=0.016), and Mobiluncus curtisii (p=0.035) and the presence of Atopobium vaginae (p=0.049), BVAB2 (p=0.024), Dialister microaerophilis (p=0.011), and Prevotella amnii (p=0.044) were associated with sPTB. The LDM identified the higher abundance of 7 ASVs and the presence of 13 ASVs, all commonly residents of the gut, as associated with sETB at FDR < 10%.

Conclusions

In this cohort of African American women, an early pregnancy vaginal CST III or IV was associated with an increased risk of sPTB but not sETB. The relative abundance and presence of distinct taxa within the early pregnancy vaginal microbiome was associated with either sPTB or sETB.

[Analysis on serotype and antimicrobial resistance of invasive non-typhoidal Salmonella]

Objective: To investigate the serotypes and antimicrobial resistance of seven invasive non-typhoidal Salmonella (iNTS) isolates. Methods: For 7 iNTS strains collected, serotype identification, antimicrobial susceptibility testing and whole genome sequencing were performed. We identified, annotated and analyzed the serotypes, MLST types, and antimicrobial resistance genes. Results: Among the 7 tested iNTS isolates, we found one Salmonella Typhimurium strain and two Salmonella Ⅰ 4, [5], 12: i:- strains whose MLST types were ST34, two Salmonella Enteritidis strains, one Salmonella Corvallis strain and one strain of unknown serotype with the antigenic formulae of Ⅰ 4, [5], 12: d:- (ST279 type). Six of seven strains were monophasic and the deletion or pseudogenization of Salmonella Flagellum gene might contribute to the enhancement of Salmonella invasiveness. None was found to be resistant to tigarcycline, aztreonam, amikacin, cephalosporins and carbapenem and one Salmonella Typhimurium strain was found to be co-resistant to eight classes of antimicrobials at the same time. Resistance genes were generally in accord with relative resistant phenotypes. Conclusion: The iNTS strains could show high level multi-drug resistance, indicating that close attention should be paid to the resistance of iNTS though the overall resistance might be relatively not high.

Sexual Differences in Genetic Predisposition of Coronary Artery Disease

BACKGROUND

The genomic structure that contributes to the risk of coronary artery disease (CAD) can be evaluated as a risk score of multiple variants. However, sex differences have not been fully examined in applications of genetic risk score (GRS) of CAD.

METHODS

Using data from the UK Biobank, we constructed a CAD-GRS based on all known loci, 3 mediating trait-based (blood pressure, lipids, and body mass index) subscores, and a genome-wide polygenic risk score based on 1.1 million variants. The differences in genetic associations with prevalent and incident CAD between men and women were investigated among 317 509 unrelated individuals of the European ancestry. We also assessed interactions with sex for 161 individual loci included in the comprehensive GRS.

RESULTS

For both prevalent and incident CAD, the associations of comprehensive and genome-wide GRSs were stronger among men than women. Using a score of 161 loci, we observed a 2.4× higher risk for incident CAD comparing men with high genetic risk to men with low genetic risk but an 80% greater risk comparing women with high genetic risk to women with low genetic risk (interaction P=0.002). Of the 3 subscores, the blood pressure-associated subscore exhibited sex differences (interaction P=0.0004 per SD increase in subscore). Analysis of individual variants identified a novel gene-sex interaction at locus 21q22.11.

CONCLUSIONS

Sexual differences in genetic predisposition should be considered in future studies of CAD, and GRSs should not be assumed to perform equally well in men and women.

A rarefaction-based extension of the LDM for testing presence-absence associations in the microbiome

MOTIVATION

Many methods for testing association between the microbiome and covariates of interest (e.g., clinical outcomes, environmental factors) assume that these associations are driven by changes in the relative abundance of taxa. However, these associations may also result from changes in which taxa are present and which are absent. Analyses of such presence-absence associations 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 cost of information loss as well as the introduction of a stochastic component into the analysis. Currently, there is a need for robust and efficient methods for testing presence-absence associations in the presence of such confounding, both at the community level and at the individual-taxon level, that avoid the drawbacks of rarefaction.

RESULTS

We have previously developed the linear decomposition model (LDM) that unifies the community-level and taxon-level tests into one framework. Here we present an extension of the LDM for testing presence-absence associations. The extended LDM is a non-stochastic approach that repeatedly applies the LDM to all rarefied taxa count tables, averages the residual sum-of-squares (RSS) terms over the rarefaction replicates, and then forms an F-statistic based on these average RSS terms. We show that this approach compares favorably to averaging the F-statistic from R rarefaction replicates, which can only be calculated stochastically. The flexible nature of the LDM allows discrete or continuous traits or interactions to be tested while allowing confounding covariates to be adjusted for. Our simulations indicate that our proposed method is robust to any systematic differences in library size and has better power than alternative approaches. We illustrate our method using an analysis of data on inflammatory bowel disease (IBD) in which cases have systematically smaller library sizes than controls.

AVAILABILITY

The R package LDM is available on GitHub at https://github.com/yijuanhu/LDM in formats appropriate for Macintosh or Windows.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Testing hypotheses about the microbiome using the linear decomposition model (LDM)

MOTIVATION

Methods for analyzing microbiome data generally fall into one of two groups: tests of the global hypothesis of any microbiome effect, which do not provide any information on the contribution of individual operational taxonomic units (OTUs); and tests for individual OTUs, which do not typically provide a global test of microbiome effect. Without a unified approach, the findings of a global test may be hard to resolve with the findings at the individual OTU level. Further, many tests of individual OTU effects do not preserve the false discovery rate (FDR).

RESULTS

We introduce the linear decomposition model (LDM), that provides a single analysis path that includes global tests of any effect of the microbiome, tests of the effects of individual OTUs while accounting for multiple testing by controlling the FDR, and a connection to distance-based ordination. The LDM accommodates both continuous and discrete variables (e.g. clinical outcomes, environmental factors) as well as interaction terms to be tested either singly or in combination, allows for adjustment of confounding covariates, and uses permutation-based P-values that can control for sample correlation. The LDM can also be applied to transformed data, and an 'omnibus' test can easily combine results from analyses conducted on different transformation scales. We also provide a new implementation of PERMANOVA based on our approach. For global testing, our simulations indicate the LDM provided correct type I error and can have comparable power to existing distance-based methods. For testing individual OTUs, our simulations indicate the LDM controlled the FDR well. In contrast, DESeq2 often had inflated FDR; MetagenomeSeq generally had the lowest sensitivity. The flexibility of the LDM for a variety of microbiome studies is illustrated by the analysis of data from two microbiome studies. We also show that our implementation of PERMANOVA can outperform existing implementations.

AVAILABILITY AND IMPLEMENTATION

The R package LDM is available on GitHub at https://github.com/yijuanhu/LDM in formats appropriate for Macintosh or Windows.

CONTACT

yijuan.hu@emory.edu.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Comparison of vaginal microbiota in gynecologic cancer patients pre- and post-radiation therapy and healthy women

BACKGROUND

While the importance of commensal microbes in vaginal health is well appreciated, little is known about the effects of gynecological cancer (GynCa) and radiation therapy (RT) on the vaginal microbiome (VM) of postmenopausal women.

METHODS

We studied women with GynCa, pre- (N = 65) and post-RT (N = 25) and a group of healthy controls (N = 67) by sequencing the V4 region of the 16S rRNA gene from vaginal swabs and compared the diversity and composition of VMs between the three groups accounting for potential confounding factors in multivariate analysis of variance.

RESULTS

Comparisons of cancer vs healthy groups revealed that Lactobacillus and Bifidobacterium have significantly higher relative abundance in the healthy group, while the cancer group was enriched in 16 phylogroups associated with bacterial vaginosis (BV) and inflammation, including Sneathia, Prevotella, Peptoniphilus, Fusobacterium, Anaerococcus, Dialister, Moryella, and Peptostreptococcus. In our sample, RT affected the α-diversity and correlated with higher abundance of typically rare VM species, including several members of the Lacnospiraceae family, a taxon previously linked to vaginal dysbiosis. In addition to cancer and treatment modalities, age and vaginal pH were identified as significant parameters that structure the VM.

CONCLUSIONS

This is among the first reports identifying VM changes among postmenopausal women with cancer. RT alone seems to affect several phylogroups (12 bacterial genera), while gynecological cancer and its treatment modalities are associated with even greater significant shifts in the vaginal microbiota including the enrichment of opportunistic bacterial pathogens, which warrants further attention.

Detection of Hole Pockets in the Candidate Type-II Weyl Semimetal MoTe_{2} from Shubnikov-de Haas Quantum Oscillations

The bulk electronic structure of T_{d}-MoTe_{2} features large hole Fermi pockets at the Brillouin zone center (Γ) and two electron Fermi surfaces along the Γ-X direction. However, the large hole pockets, whose existence has important implications for the Weyl physics of T_{d}-MoTe_{2}, has never been conclusively detected in quantum oscillations. This raises doubt about the realizability of Majorana states in T_{d}-MoTe_{2}, because these exotic states rely on the existence of Weyl points, which originated from the same band structure predicted by density functional theory (DFT). Here, we report an unambiguous detection of these elusive hole pockets via Shubnikov-de Haas (SdH) quantum oscillations. At ambient pressure, the quantum oscillation frequencies for these pockets are 988 and 1513 T, when the magnetic field is applied along the c axis. The quasiparticle effective masses m^{*} associated with these frequencies are 1.50 and 2.77 m_{e}, respectively, indicating the importance of Coulomb interactions in this system. We further measure the SdH oscillations under pressure. At 13 kbar, we detected a peak at 1798 T with m^{*}=2.86m_{e}. Relative to the oscillation data at a lower pressure, the amplitude of this peak experienced an enhancement, which can be attributed to the reduced curvature of the hole pockets under pressure. Combining our experimental data with DFT+U calculations, where U is the Hubbard parameter, our results shed light on why these important hole pockets have not been detected until now.

[Differentiation of prostatitis and clinically significant prostate cancer in peripheral zone using the prostate imaging-reporting and data system]

Objective: To determine whether clinically significant prostate cancer (PCa) and prostatitis in the peripheral zone can be distinguished using prostate imaging reporting and data system version 1 (PI-RADS V1) and version 2(PI-RADS V2). Methods: Between September 2010 and August 2016, mpMRI data of 77 patients with PCa and 29 prostatitis obtained at 3.0 T were collected in Zhangjiagang Hospital Affiliated to Soochow University. Every lesion was scored according to PI-RADS (V1 and V2), as well as a sum score and a PI-RADS V2 score. The non-parametric Kruskal-Wallis test was used to assess differences between PCa and prostatitis regarding PS3, PS4 and PI-RADS V2 score. The diagnostic performance of PI-RADS V1 and V2 for detection of prostatitis in peripheral zone was compared by analyzing ROC curve. Results: The PI-RADS V1 score for PS3, PS4 and the PI-RADS V2-score were all significantly higher for PCa (PS3:12.1±2.1; PS4:16.2±2.9; V2:4.6±0.8) than for prostatitis (PS3:8.0±0.7; PS4:10.6±1.0; V2:3.0±0.5) (all P<0.01). Of these parameters, PS4 achieved the highest predictive value for the presence of prostatitis with an AUC of 0.937, sensitivity and specificity were 87.0%, 97.0% with a threshold of 12.5. Conclusion: Prostatitis can be differentiated from clinically significant PCa in peripheral zone on mpMRI using PI-RADS system, PS4 achieved better results compared to PS3 and V2.

Hypofractionated stereotactic radiotherapy combined with chemotherapy or not in the management of recurrent malignant gliomas: A systematic review and meta-analysis

Hypofractionated stereotactic radiotherapy (HFSRT) is a common salvage treatment for recurrent malignant glioma (MG). However, it remains controversial whether the combination of HFSRT and chemotherapy could improve survival for patients with recurrent MG compared to HFSRT alone. The present systematic review and meta-analysis aims to investigate this question, and tries to determine to what extent the addition of chemotherapy to HFSRT affects survival. A systematic review was performed to analyse the survival for patients treated with HFSRT combined with chemotherapy or not. Hazard ratios (HRs) with 95% confidence intervals (CIs) for overall survival (OS) were pooled with random effects; and standard mean difference (MD) with 95% CIs for OS were pooled using the same strategy. A total of 7 studies including 388 patients with recurrent MG were eligible for our study. The OS survival of patients receiving combination therapy ranged from 8.7 to 23 months, and the median OS of patients underwent HFSRT ranged from 3.9 to 12 months. The meta-analyses resulted in the pooled HR of 0.44 (95% CI 0.30-0.65, p < 0.0001) (Cochran Q statistic 4.70, P = 0.320, I² = 14.8%) and pooled standard MD of 0.80 months (95% CI 0.41-1.18, p < 0.001) (Cochran Q statistic 10.16, p = 0.71, I² = 50.8%). The present study suggests that HFSRT + chemotherapy confers a slight survival improvement for patients with recurrent MG as compared with sole HFSRT management. To draw a more solid conclusion, greater investigation is warranted.

Marsupialisation for the treatment of unicystic ameloblastoma of the mandible: a long-term follow up of 116 cases

Unicystic ameloblastoma is a unique histopathological type of ameloblastoma, and treatment is controversial. Marsupialisation is effective in reducing the size of cystic lesions and their complications. We have retrospectively analysed the clinical, histopathological, and prognostic data of affected patients who were treated by marsupialisation between 2003 and 2013 in three Chinese hospitals. Our aim was to evaluate the effects and prognosis, and the factors associated with outcome. A total of 116 patients with mandibular unicystic ameloblastomas were included, and 74, 26, and 16 patients were histopathologically classified as being luminal, intraluminal, and mural subtypes, respectively. Most responded well to marsupialisation, with an overall recurrence rate of 12%. Resorption of the root (p<0.001), perforation of the cortical bone (p=0.005), and histopathological subtype (p=0.013) were the main factors that predicted the outcome. Perforation of the cortical bone was the only reliable predictor of recurrence (p<0.001). Disease-free survival function curves indicated that patients with the mural subtype were at a higher risk of recurrence than patients with the other two subtypes (p=0.003). Poor outcomes of marsupialisation were treated surgically and, to date, no subsequent recurrences have been reported. Marsupialisation is effective for these patients, with a recurrence rate similar to that of radical treatment. The outcomes can be predicted using characteristics of the lesion such as resorption of the root, perforation of the cortical bone, and histopathological subtypes. However, additional studies are required to corroborate these findings.

[The reliability of using impenem, meropenem, cefoperazone-sulbactam and piperacillin-tazobactam to treat nosocomial Gram-negative bacterial infections with Monte Carlo simulation]

Objective: To evaluate the reliability of using imipenem, meropenem, cefoperazone-sulbactam, piperacillin-tazobactam in the treatment of hospital-acquired Gram-negative bacterial infections with Monte Carlo simulation(MCS). Methods: The MIC of the four agents collected from hospital-acquired infections were detected in accordance with broth dilution method of Clinical and Laboratory Standard Institute (CLSI). MCS were conducted with MICs and the pharmacokinetics parameters of the four agents based on conventional dose regimens.The cumulative fraction of response (CFR) of time over MIC target attainment in different dosing regimen were generated. Results: A total of 2 541 strains, including 2 093 strains of Enterobacteriaceae and 448 strains of glucose non-fermentative bacilli were collected.The MIC(90) of imipenem and meropenem against Enterobacteriaceae were less than 1 mg/L in general, whereas MIC(90) of two agents with β-lactamase inhibitors was around 64 mg/L.As to glucose non-fermenting bacteria, MICs of all the four agents were very high, especially to Acinetobacter baumannii, which indicated MIC(50) more than 32 mg/L.MCS revealed that carbapenems had significantly higher CFR than those with β-lactamase inhibitors.Imipenem and meropenem (1 g, q8 h) obtained CFRs of 74.69% and 81.42%, respectively.The CFR of cefoperazone-sulbactam (2 g, q8 h) and piperacillin-tazobactam (4 g, q6 h) (both excluding β-lactamase inhibitors) were just 49.59% and 27.66% respectively, which increased after excluding A. baumannii in piperacillin-tazobactam. Conclusions: The conventional dose regimens of imipenem and meropenem are reliable for the empiric therapy of Gram-negative hospital-acquired bacterial infections.Piperacillin-tazobactam is suggested to use with higher doses or prolonged infusion time to satisfy the time of drug concentration exceeded the MIC(T>MIC)requirement.More clinical studies of cefoperazone-sulbactam should be conducted to optimize its regimen and guarantee its efficacy.

[Advances on antimicrobial resistance research of Salmonella enterica Serovar Indiana]

Salmonella enterica Serovar Indiana is a common serotype of Salmonella isolated from food especially from poultry meat. Recently it demonstrated a raising tendency of infection cases and isolate numbers with high antimicrobial resistant rate against many common antimicrobials, including quinolones and cephalosporin which were regarded as the first line drug for the treatment of Salmonella infections, and this kind of Salmonella serotype was always carrying complex resistance mechanisms and also a variety of mobile elements, all of these features made the very clinical infections caused by Salmonella hard to treat and brought great difficulties and risks. Here, we review the prevalence of Samonella Indiana on national and international view, and we also anticipate the research progress on antimicrobial drug classes, multi drug resistance, co-resistance and resistance mechanism. We discuss the resistant genotypes, phenotypes, mechanism and transmission of Salmonella Indiana strains isolated from different origins. By introducing the resistance of Salmonella Indiana, we want to attract people's attention to this bacteria and its hazard, and offer some idea to evaluate and treat infections in clinical.