Adaptive Regularized Tri-Factor Non-Negative Matrix Factorization for Cell Type Deconvolution.

Motivation: Accurate deconvolution of cell types from bulk gene expression is crucial for understanding cellular compositions and uncovering cell-type specific differential expression and physiological states of diseased tissues. Existing deconvolution methods have limitations, such as requiring complete cellular gene expression signatures or neglecting partial biological information. Moreover, these methods often overlook varying cell-type mRNA amounts, leading to biased proportion estimates. Additionally, they do not effectively utilize valuable reference information from external studies, such as means and ranges of population cell-type proportions. Results: To address these challenges, we introduce an Adaptive Regularized Tri-factor non-negative matrix factorization approach for deconvolution (ARTdeConv). We rigorously establish the numerical convergence of our algorithm. Through benchmark simulations, we demonstrate the superior performance of ARTdeConv compared to state-of-the-art reference-free methods. In a real-world application, our method accurately estimates cell proportions, as evidenced by the nearly perfect Pearson's correlation between ARTdeConv estimates and flow cytometry measurements in a dataset from a trivalent influenza vaccine study. Moreover, our analysis of ARTdeConv estimates in COVID-19 patients reveals patterns consistent with important immunological phenomena observed in other studies. Availability and implementation: The proposed method, ARTdeConv, is implemented as an R package and can be accessed on GitHub for researchers and practitioners at https://github.com/gr8lawrence/ARTDeConv .

Comprehensive evaluation of methods for differential expression analysis of metatranscriptomics data.

Understanding the function of the human microbiome is important but the development of statistical methods specifically for the microbial gene expression (i.e. metatranscriptomics) is in its infancy. Many currently employed differential expression analysis methods have been designed for different data types and have not been evaluated in metatranscriptomics settings. To address this gap, we undertook a comprehensive evaluation and benchmarking of 10 differential analysis methods for metatranscriptomics data. We used a combination of real and simulated data to evaluate performance (i.e. type I error, false discovery rate and sensitivity) of the following methods: log-normal (LN), logistic-beta (LB), MAST, DESeq2, metagenomeSeq, ANCOM-BC, LEfSe, ALDEx2, Kruskal-Wallis and two-part Kruskal-Wallis. The simulation was informed by supragingival biofilm microbiome data from 300 preschool-age children enrolled in a study of childhood dental disease (early childhood caries, ECC), whereas validations were sought in two additional datasets from the ECC study and an inflammatory bowel disease study. The LB test showed the highest sensitivity in both small and large samples and reasonably controlled type I error. Contrarily, MAST was hampered by inflated type I error. Upon application of the LN and LB tests in the ECC study, we found that genes C8PHV7 and C8PEV7, harbored by the lactate-producing Campylobacter gracilis, had the strongest association with childhood dental disease. This comprehensive model evaluation offers practical guidance for selection of appropriate methods for rigorous analyses of differential expression in metatranscriptomics. Selection of an optimal method increases the possibility of detecting true signals while minimizing the chance of claiming false ones.

Unique T cell signatures are associated with reduced Chlamydia trachomatis reinfection in a highly exposed cohort.

Chlamydia trachomatis (CT) is the most common bacterial sexually transmitted infection (STI) in the United States, despite effective antibiotics. Information regarding natural immunity to CT will inform vaccine design. The objectives of this study were to determine immune cell populations and functional features associated with reduced risk of CT reinfection or endometrial CT infection. PBMCs were collected from a cohort of CT-exposed women who were tested for CT and other STIs at the cervix and endometrium (to determine ascension) and were repeatedly tested over the course of a year (to determine reinfection). Mass cytometry identified major immune populations and T cell subsets. Women with CT had increased CD4+ effector memory T cells (TEM) compared to uninfected women. Specifically, Th2, Th17, and Th17 DN CD4+ TEM were increased. Th17 and Th17 DN CD4+ central memory T cells (TCM) were increased in women who did not experience follow-up CT infection, suggesting that these cells may be important for protection. These data indicate that peripheral T cells display distinct features that correlate with natural immunity to CT and suggest that the highly plastic Th17 lineage plays a role in protection against reinfection.

A Machine Learning-Based Analytic Pipeline Applied to Clinical and Serum IgG Immunoproteome Data To Predict Chlamydia trachomatis Genital Tract Ascension and Incident Infection in Women.

We developed a reusable and open-source machine learning (ML) pipeline that can provide an analytical framework for rigorous biomarker discovery. We implemented the ML pipeline to determine the predictive potential of clinical and immunoproteome antibody data for outcomes associated with Chlamydia trachomatis (Ct) infection collected from 222 cis-gender females with high Ct exposure. We compared the predictive performance of 4 ML algorithms (naive Bayes, random forest, extreme gradient boosting with linear booster [xgbLinear], and k-nearest neighbors [KNN]), screened from 215 ML methods, in combination with two different feature selection strategies, Boruta and recursive feature elimination. Recursive feature elimination performed better than Boruta in this study. In prediction of Ct ascending infection, naive Bayes yielded a slightly higher median value of are under the receiver operating characteristic curve (AUROC) 0.57 (95% confidence interval [CI], 0.54 to 0.59) than other methods and provided biological interpretability. For prediction of incident infection among women uninfected at enrollment, KNN performed slightly better than other algorithms, with a median AUROC of 0.61 (95% CI, 0.49 to 0.70). In contrast, xgbLinear and random forest had higher predictive performances, with median AUROC of 0.63 (95% CI, 0.58 to 0.67) and 0.62 (95% CI, 0.58 to 0.64), respectively, for women infected at enrollment. Our findings suggest that clinical factors and serum anti-Ct protein IgGs are inadequate biomarkers for ascension or incident Ct infection. Nevertheless, our analysis highlights the utility of a pipeline that searches for biomarkers and evaluates prediction performance and interpretability. IMPORTANCE Biomarker discovery to aid early diagnosis and treatment using machine learning (ML) approaches is a rapidly developing area in host-microbe studies. However, lack of reproducibility and interpretability of ML-driven biomarker analysis hinders selection of robust biomarkers that can be applied in clinical practice. We thus developed a rigorous ML analytical framework and provide recommendations for enhancing reproducibility of biomarkers. We emphasize the importance of robustness in selection of ML methods, evaluation of performance, and interpretability of biomarkers. Our ML pipeline is reusable and open-source and can be used not only to identify host-pathogen interaction biomarkers but also in microbiome studies and ecological and environmental microbiology research.

Synthesis, Antifungal Activity, and Molecular Simulation Study of L-Carvone-Derived 1,3,4-Oxadiazole-Thioether Compounds.

To discover potent antifungal molecules with new and distinctive structures, 20 novel L-carvone-derived 1,3,4-oxadiazole-thioether compounds 5 a-5 t were synthesized through multi-step reaction of L-carvone, and their structures were confirmed by FT-IR, 1 H-NMR, 13 C-NMR, and HR-MS. The antifungal activities of compounds 5 a-5 t were preliminarily tested by in vitro method, and the results indicated that all of the title compounds displayed certain antifungal activities against the eight tested plant fungi, especially for P. piricola. Among them, compound 5 i (R=p-F) with the most significant antifungal activity deserved further study for discovering and developing novel natural product-based antifungal agents. Moreover, two molecular simulation technologies were employed for the investigation of their structure-activity relationships (SARs). Firstly, a reasonable and effective 3D-QSAR model was established by the comparative molecular field (CoMFA) method, and the relationship of the substituents linked with the benzene rings and the inhibitory activities of the title compounds against P. piricola was elucidated. Then, the binding mode of compound 5 i (R=p-F) and its potential biological target (CYP51) was simulated by molecular docking, and it was found that compound 5 i could readily bind with CYP51 in the active site, and the ligand-receptor interactions involved three hydrogen bonds and several hydrophobic effects.

A bivariate zero-inflated negative binomial model and its applications to biomedical settings.

The zero-inflated negative binomial distribution has been widely used for count data analyses in various biomedical settings due to its capacity of modeling excess zeros and overdispersion. When there are correlated count variables, a bivariate model is essential for understanding their full distributional features. Examples include measuring correlation of two genes in sparse single-cell RNA sequencing data and modeling dental caries count indices on two different tooth surface types. For these purposes, we develop a richly parametrized bivariate zero-inflated negative binomial model that has a simple latent variable framework and eight free parameters with intuitive interpretations. In the scRNA-seq data example, the correlation is estimated after adjusting for the effects of dropout events represented by excess zeros. In the dental caries data, we analyze how the treatment with Xylitol lozenges affects the marginal mean and other patterns of response manifested in the two dental caries traits. An R package "bzinb" is available on Comprehensive R Archive Network.

Selenomonas sputigena acts as a pathobiont mediating spatial structure and biofilm virulence in early childhood caries.

Streptococcus mutans has been implicated as the primary pathogen in childhood caries (tooth decay). While the role of polymicrobial communities is appreciated, it remains unclear whether other microorganisms are active contributors or interact with pathogens. Here, we integrate multi-omics of supragingival biofilm (dental plaque) from 416 preschool-age children (208 males and 208 females) in a discovery-validation pipeline to identify disease-relevant inter-species interactions. Sixteen taxa associate with childhood caries in metagenomics-metatranscriptomics analyses. Using multiscale/computational imaging and virulence assays, we examine biofilm formation dynamics, spatial arrangement, and metabolic activity of Selenomonas sputigena, Prevotella salivae and Leptotrichia wadei, either individually or with S. mutans. We show that S. sputigena, a flagellated anaerobe with previously unknown role in supragingival biofilm, becomes trapped in streptococcal exoglucans, loses motility but actively proliferates to build a honeycomb-like multicellular-superstructure encapsulating S. mutans, enhancing acidogenesis. Rodent model experiments reveal an unrecognized ability of S. sputigena to colonize supragingival tooth surfaces. While incapable of causing caries on its own, when co-infected with S. mutans, S. sputigena causes extensive tooth enamel lesions and exacerbates disease severity in vivo. In summary, we discover a pathobiont cooperating with a known pathogen to build a unique spatial structure and heighten biofilm virulence in a prevalent human disease.

BZINB Model-Based Pathway Analysis and Module Identification Facilitates Integration of Microbiome and Metabolome Data.

Integration of multi-omics data is a challenging but necessary step to advance our understanding of the biology underlying human health and disease processes. To date, investigations seeking to integrate multi-omics (e.g., microbiome and metabolome) employ simple correlation-based network analyses; however, these methods are not always well-suited for microbiome analyses because they do not accommodate the excess zeros typically present in these data. In this paper, we introduce a bivariate zero-inflated negative binomial (BZINB) model-based network and module analysis method that addresses this limitation and improves microbiome-metabolome correlation-based model fitting by accommodating excess zeros. We use real and simulated data based on a multi-omics study of childhood oral health (ZOE 2.0; investigating early childhood dental caries, ECC) and find that the accuracy of the BZINB model-based correlation method is superior compared to Spearman's rank and Pearson correlations in terms of approximating the underlying relationships between microbial taxa and metabolites. The new method, BZINB-iMMPath, facilitates the construction of metabolite-species and species-species correlation networks using BZINB and identifies modules of (i.e., correlated) species by combining BZINB and similarity-based clustering. Perturbations in correlation networks and modules can be efficiently tested between groups (i.e., healthy and diseased study participants). Upon application of the new method in the ZOE 2.0 study microbiome-metabolome data, we identify that several biologically-relevant correlations of ECC-associated microbial taxa with carbohydrate metabolites differ between healthy and dental caries-affected participants. In sum, we find that the BZINB model is a useful alternative to Spearman or Pearson correlations for estimating the underlying correlation of zero-inflated bivariate count data and thus is suitable for integrative analyses of multi-omics data such as those encountered in microbiome and metabolome studies.

BZINB model-based pathway analysis and module identification facilitates integration of microbiome and metabolome data.

Integration of multi-omics data is a challenging but necessary step to advance our understanding of the biology underlying human health and disease processes. To date, investigations seeking to integrate multi-omics (e.g., microbiome and metabolome) employ simple correlation-based network analyses; however, these methods are not always well-suited for microbiome analyses because they do not accommodate the excess zeros typically present in these data. In this paper, we introduce a bivariate zero-inflated negative binomial (BZINB) model-based network and module analysis method that addresses this limitation and improves microbiome-metabolome correlation-based model fitting by accommodating excess zeros. We use real and simulated data based on a multi-omics study of childhood oral health (ZOE 2.0; investigating early childhood dental disease, ECC) and find that the accuracy of the BZINB model-based correlation method is superior compared to Spearman’s rank and Pearson correlations in terms of approximating the underlying relationships between microbial taxa and metabolites. The new method, BZINB-iMMPath facilitates the construction of metabolite-species and species-species correlation networks using BZINB and identifies modules of (i.e., correlated) species by combining BZINB and similarity-based clustering. Perturbations in correlation networks and modules can be efficiently tested between groups (i.e., healthy and diseased study participants). Upon application of the new method in the ZOE 2.0 study microbiome-metabolome data, we identify that several biologically-relevant correlations of ECC-associated microbial taxa with carbohydrate metabolites differ between healthy and dental caries-affected participants. In sum, we find that the BZINB model is a useful alternative to Spearman or Pearson correlations for estimating the underlying correlation of zero-inflated bivariate count data and thus is suitable for integrative analyses of multi-omics data such as those encountered in microbiome and metabolome studies.

Reduced Endometrial Ascension and Enhanced Reinfection Associated With Immunoglobulin G Antibodies to Specific Chlamydia trachomatis Proteins in Women at Risk for Chlamydia.

BACKGROUND: Previous research revealed antibodies targeting Chlamydia trachomatis elementary bodies was not associated with reduced endometrial or incident infection in C. trachomatis-exposed women. However, data on the role of C. trachomatis protein-specific antibodies in protection are limited. METHODS: A whole-proteome C. trachomatis array screening serum pools from C. trachomatis-exposed women identified 121 immunoprevalent proteins. Individual serum samples were probed using a focused array. Immunoglobulin (Ig) G antibody frequencies and endometrial or incident infection relationships were examined using Wilcoxon rank sum test. The impact of the breadth and magnitude of protein-specific IgGs on ascension and incident infection were examined using multivariable stepwise logistic regression. Complementary RNA sequencing quantified C. trachomatis gene transcripts in cervical swab samples from infected women. RESULTS: IgG to pGP3 and CT_005 were associated with reduced endometrial infection; anti-CT_443, anti-CT_486, and anti-CT_123 were associated with increased incident infection. Increased breadth of protein recognition did not however predict protection from endometrial or incident infection. Messenger RNAs for immunoprevalent C. trachomatis proteins were highly abundant in the cervix. CONCLUSIONS: Protein-specific C. trachomatis antibodies are not sufficient to protect against ascending or incident infection. However, cervical C. trachomatis gene transcript abundance positively correlates with C. trachomatis protein immunogenicity. These abundant and broadly recognized antigens are viable vaccine candidates.

The association of apical periodontitis and type 2 diabetes mellitus: A large hospital network cross-sectional case-controlled study.

BACKGROUND: The relationship of apical periodontitis (AP) and type 2 diabetes mellitus (T2DM) is poorly studied in large populations. The aims of this study were to determine if there is an independent association between AP and T2DM in a large hospital network after controlling for confounding variables, as well as to determine if glycated hemoglobin levels were independently associated with AP. METHODS: An initial search of the Carolina Data Warehouse for Health yielded 5,995,011 patients, of whom 7,749 were diagnosed with AP in 2015 through 2018. Patients' demographics, T2DM status, HbA1c, periodontal disease, oral cellulitis, hypertension, atherosclerosis, kidney disease, smoking, body mass index, the use of metformin or statins, and hospital inpatient status were collected from their most recent visit. A control group of 7,749 patients without AP were sampled and matched according to the age, race, and sex of each patient with AP. Multiple logistic regression was used to determine the association between T2DM and AP, as well as between HbA1c and AP after controlling for the effects of the aforementioned confounding variables, using a matched cohort design. RESULTS: T2DM was independently associated with significantly greater prevalence of AP (odds ratio [OR], 2.05; 95% confidence interval [CI], 1.73 to 2.43). The use of metformin (OR, 0.82; 95% CI, 0.69 to 0.98) or statins (OR, 0.70; 95% CI, 0.62 to 0.78) was independently associated with significantly lower prevalence of AP. HbA1c greater than 8.0 (OR, 2.46; 95% CI, 1.83 to 3.35) was significantly associated with greater prevalence of AP. CONCLUSIONS: T2DM and poorly controlled glycemia were significantly associated with AP. Metformin and statin use were associated with lower prevalence of AP. PRACTICAL IMPLICATIONS: This study provides evidence linking T2DM and the level of glycemia to the increased prevalence of AP. Statins and metformin use may be protective in this relationship.

Mucosal Vaccination with UV-Inactivated Chlamydia suis in Pre-Exposed Outbred Pigs Decreases Pathogen Load and Induces CD4 T-Cell Maturation into IFN-γ+ Effector Memory Cells.

Chlamydia trachomatis (Ct) infections are the most frequent bacterial sexually transmitted disease, and they can lead to ectopic pregnancy and infertility. Despite these detrimental long-term sequelae, a vaccine is not available. Success in preclinical animal studies is essential for vaccines to move to human clinical trials. Pigs are the natural host to Chlamydia suis (Cs)-a chlamydia species closely related to Ct, and are susceptible to Ct, making them a valuable animal model for Ct vaccine development. Before making it onto market, Ct vaccine candidates must show efficacy in a high-risk human population. The high prevalence of human Ct infection combined with the fact that natural infection does not result in sterilizing immunity, results in people at risk likely having been pre-exposed, and thus having some level of underlying non-protective immunity. Like human Ct, Cs is highly prevalent in outbred pigs. Therefore, the goal of this study was to model a trial in pre-exposed humans, and to determine the immunogenicity and efficacy of intranasal Cs vaccination in pre-exposed outbred pigs. The vaccine candidates consisted of UV-inactivated Cs particles in the presence or absence of an adjuvant (TriAdj). In this study, both groups of vaccinated pigs had a lower Cs burden compared to the non-vaccinated group; especially the TriAdj group induced the differentiation of CD4+ cells into tissue-trafficking CCR7- IFN-γ-producing effector memory T cells. These results indicate that Cs vaccination of pre-exposed pigs effectively boosts a non-protective immune response induced by natural infection; moreover, they suggest that a similar approach could be applied to human vaccine trials.