Faculty Bibliography

BACKGROUND:African Americans (AAs) compared to Caucasian Americans (CAs) with colorectal cancer (CRC) have lower stage-specific survival. CRC patients often present with several hematopathologies (such as thrombocytosis, thrombocytopenia, anemia) at diagnosis, which is associated with poorer survival. However, whether these measures impact the racial disparity in survival is not known. METHODS:The study population was composed of 581 histologically confirmed CRCs at the Medical University of South Carolina (393 CA, 188 AA) diagnosed between 01/01/2000 and 06/30/2013. We used Cox proportional hazards regression to estimate the association between thrombocytosis, thrombocytopenia, or anemia at diagnosis and risk of death by race. This analysis was adjusted for age, sex, stage and first-line treatment. RESULTS:In all patients combined, thrombocytosis, thrombocytopenia, and anemia (vs. the normal ranges) were associated with significantly higher risks of death. In the race-specific analyses, AAs (HR 2.51 [95 % CI: 1.52-4.15]) vs. CAs (HR 1.15 [95 % CI: 0.75-1.75]) with thrombocytosis compared to normal had a higher risk of death (p for difference = 0.03). CONCLUSIONS:Abnormal thrombocyte and hemoglobin levels at diagnosis were associated with poorer survival. AAs compared to CAs with elevated platelets at diagnosis had a higher risk of death. Our study is the first to examine the role of race, hematologic measures at diagnosis, and risk of death in colorectal cancer patients. These results suggest that the racial differences in the immune response may contribute to the racial disparity in survival.

Blood microbiome is important to investigate microbial-host interactions and the effects on systemic immune perturbations. However, this effort has met with major challenges due to low microbial biomass and background artifacts. In the current study, microbial 16S DNA sequencing was applied to analyze plasma microbiome. We have developed a quality-filtering strategy to evaluate and exclude low levels of microbial sequences, potential contaminations, and artifacts from plasma microbial 16S DNA sequencing analyses. Furthermore, we have applied our technique in three cohorts, including tobacco-smokers, HIV-infected individuals, and individuals with systemic lupus erythematosus (SLE), as well as corresponding controls. More than 97% of total sequence data was removed using stringent quality-filtering strategy analyses; those removed amplicon sequence variants (ASVs) were low levels of microbial sequences, contaminations, and artifacts. The specifically enriched pathobiont bacterial ASVs have been identified in plasmas from tobacco-smokers, HIV-infected individuals, and individuals with SLE but not from control subjects. The associations between these ASVs and disease pathogenesis were demonstrated. The pathologic activities of some identified bacteria were further verified in vitro. We present a quality-filtering strategy to identify pathogenesis-associated plasma microbiome. Our approach provides a method for studying the diagnosis of subclinical microbial infection as well as for understanding the roles of microbiome-host interaction in disease pathogenesis.

BACKGROUND:Host-microbiome interactions (HMIs) are critical for the modulation of biological processes and are associated with several diseases. Extensive HMI studies have generated large amounts of data. We propose that the logical representation of the knowledge derived from these data and the standardized representation of experimental variables and processes can foster integration of data and reproducibility of experiments and thereby further HMI knowledge discovery. METHODS:Through a multi-institutional collaboration, a community-based Ontology of Host-Microbiome Interactions (OHMI) was developed following the Open Biological/Biomedical Ontologies (OBO) Foundry principles. As an OBO library ontology, OHMI leverages established ontologies to create logically structured representations of (1) microbiomes, microbial taxonomy, host species, host anatomical entities, and HMIs under different conditions and (2) associated study protocols and types of data analysis and experimental results. RESULTS:Aligned with the Basic Formal Ontology, OHMI comprises over 1000 terms, including terms imported from more than 10 existing ontologies together with some 500 OHMI-specific terms. A specific OHMI design pattern was generated to represent typical host-microbiome interaction studies. As one major OHMI use case, drawing on data from over 50 peer-reviewed publications, we identified over 100 bacteria and fungi from the gut, oral cavity, skin, and airway that are associated with six rheumatic diseases including rheumatoid arthritis. Our ontological study identified new high-level microbiota taxonomical structures. Two microbiome-related competency questions were also designed and addressed. We were also able to use OHMI to represent statistically significant results identified from a large existing microbiome database data analysis. CONCLUSION/CONCLUSIONS:OHMI represents entities and relations in the domain of HMIs. It supports shared knowledge representation, data and metadata standardization and integration, and can be used in formulation of advanced queries for purposes of data analysis.

Throughout our lives we are immersed in, and colonized by, immense and complex microbial communities. These microbiota serve as activators and early sparring partners for the progressive construction of the layers within our immune defenses and are essential to immune homeostasis. Yet, at times imbalances within the microbiota may contribute to metabolic and immune regulatory abnormalities that underlie the development of inflammatory and autoimmune diseases. Here, we review recent progress in investigations of the microbiome, with emphasis on the gut microbiota associated with systemic autoimmunity. In particular, these studies are beginning to illuminate aspects of the pathogenesis of Systemic Lupus Erythematosus, and may suggest that interconnections with specific disease-associated patterns of dysbiosis within gut communities are bidirectional and mutually reinforcing.

Many important exposure-response relationships, such as diet and weight, can be influenced by intermediates, such as the gut microbiome. Understanding the role of these intermediates, the mediators, is important in refining cause-effect theories and discovering additional medical interventions (e.g., probiotics, prebiotics). Mediation analysis has been at the heart of behavioral health research, rapidly gaining popularity with the biomedical sciences in the last decade. A specific analytic challenge is being able to incorporate an entire 'omics assay as a mediator. To address this challenge, we propose a hypothesis testing framework for multivariate omnibus distance mediation analysis (MODIMA). We use the power of energy statistics, such as partial distance correlation, to allow for analysis of multivariate exposure-mediator-response triples. Our simulation results demonstrate the favorable statistical properties of our approach relative to the available alternatives. Finally, we demonstrate the application of the proposed methods in two previously published microbiome datasets. Our framework adds a new tool to the toolbox of approaches to the integration of 'omics big data.

BACKGROUND/PURPOSE/OBJECTIVE:To search for a transmissible agent involved in lupus pathogenesis, we investigated the faecal microbiota of patients with systemic lupus erythematosus (SLE) for candidate pathobiont(s) and evaluated them for special relationships with host immunity. METHODS:In a cross-sectional discovery cohort, matched blood and faecal samples from 61 female patients with SLE were obtained. Faecal 16 S rRNA analyses were performed, and sera profiled for antibacterial and autoantibody responses, with findings validated in two independent lupus cohorts. RESULTS:strain-restricted antibodies were detected in those with active nephritis (including Class III and IV) in the discovery cohort, with findings validated in two independent cohorts. CONCLUSION/CONCLUSIONS:These findings suggest a novel paradigm in which specific strains of a gut commensal may contribute to the immune pathogenesis of lupus nephritis.

BACKGROUND:Community-wide analyses provide an essential means for evaluation of the effect of interventions or design variables on the composition of the microbiome. Applications of these analyses are omnipresent in microbiome literature, yet some of their statistical properties have not been tested for robustness towards common features of microbiome data. Recently, it has been reported that PERMANOVA can yield wrong results in the presence of heteroscedasticity and unbalanced sample sizes. FINDINGS:We develop a method for multivariate analysis of variance, [Formula: see text], based on Welch MANOVA that is robust to heteroscedasticity in the data. We do so by extending a previously reported method that does the same for two-level independent factor variables. Our approach can accommodate multi-level factors, stratification, and multiple post hoc testing scenarios. An R language implementation of the method is available at https://github.com/alekseyenko/WdStar . CONCLUSION:Our method resolves potential for confounding of location and dispersion effects in multivariate analyses by explicitly accounting for the differences in multivariate dispersion in the data tested. The methods based on [Formula: see text] have general applicability in microbiome and other 'omics data analyses.

BACKGROUND:Increased autoreactive antibodies have been reported in HIV disease; however, the mechanism accounting for autoantibody induction in HIV remains unknown. RESULTS:Herein, we show that seasonal influenza vaccination induces autoantibody production (e.g., IgG anti-nuclear antibody (ANA) and anti-double-stranded DNA antibody (anti-dsDNA)) in some viral-suppressed antiretroviral therapy (ART)-treated HIV+ subjects, but not in healthy controls. These autoantibodies were not derived from antigen-specific B cells but from activated "bystander" B cells analyzed by single-cell assay and by study of purified polyclonal ANAs from plasma. To explore the mechanism of autoantibody generation in HIV+ subjects, plasma level of microbial products, gene expression profile of B cells, and B cell receptor (BCR) repertoires were analyzed. We found that autoantibody production was associated with increased plasma level of microbial translocation; the patients with high autoantibodies had skewed B cell repertoires and upregulation of genes related to innate immune activation in response to microbial translocation. By analyzing circulating microbial 16S rDNA in plasma, the relative abundance of Staphylococcus was found to be associated with autoantibody production in HIV+ subjects. Finally, we found that injection of heat-killed Staphylococcus aureus promoted germinal center B cell responses and autoantibody production in mice, consistent with the notion that autoantibody production in HIV+ patients is triggered by microbial products. CONCLUSIONS:Our results showed that translocation of Staphylococcus can promote B cell activation through enhancing germinal center response and induces autoantibody production. It uncovers a potential mechanism linking microbial translocation and autoimmunity in HIV+ disease and provides a strong rationale for targeting Staphylococcus to prevent autoantibody production.