Faculty Bibliography

OBJECTIVES/OBJECTIVE:To investigate the cutaneous microbiome spanning the entire psoriatic disease spectrum, and to evaluate distinguishing features of psoriasis (PsO) and psoriatic arthritis (PsA). METHODS:Skin swabs were collected from upper and lower extremities of healthy individuals and patients with PsO and PsA. Psoriatic patients contributed both lesional (L) and contralateral non-lesional (NL) samples. Microbiota were analysed using 16S rRNA sequencing. RESULTS:was higher in NL PsA samples compared with NL PsO samples (p<0.05), potentially serving as a biomarker for disease progression. CONCLUSIONS:These findings show differences in diversity, bacterial composition and microbe-microbe interactions between healthy and psoriatic skin, both L and NL. We further identified bacterial biomarkers that differentiate disease phenotypes, which could potentially aid in predicting the transition from PsO to PsA.

Background/Purpose: Psoriasis (PsO) is an inflammatory, immune-mediated skin disorder affecting ~3% of the population worldwide. It is associated with multiple comorbidities, including psoriatic arthritis (PsA), which occurs in up to a third of patients. While genes contribute to the pathogenesis of psoriatic disease, twin studies demonstrate substantial discordance in PsO and PsA, suggesting that epigenetics and environmental factors play a significant role. In fact, there is increasing evidence that the microbiome has an impact on psoriatic disease pathogenesis. However, prior investigations were performed in populations of unrelated individuals and could not discern environmental from genetic influences. To characterize the host-microbiome relationship, we studied the gut and skin microbiome of monozygotic (MZ) twins discordant for psoriatic disease in order to determine disease-specific microbial perturbations that are independent of host-genes.
Method(s): Stool and skin swabs were collected from subjects with psoriatic disease and their unaffected MZ twin siblings (pairs=9, n=18). Non-lesional (NL) or healthy skin was swabbed at three separate sites: bicep, scalp, and elbow/forearm. Fecal samples underwent shotgun metagenomic sequencing to deeply characterize the gut microbiome taxonomy and functional pathways at high resolution. Sequences were processed with the HUMAnN and MetaPhlAn2 pipelines. Skin swab samples underwent 16S rRNA sequencing to characterize the cutaneous bacterial microbiome. Forward sequences were processed with the QIIME2 pipeline and SILVA reference database. Downstream computational analysis was performed using several libraries in R, including DESeq2.
Result(s): In gut samples, the relative abundance of Ruminococcus bromii species was significantly reduced and two pathways related to tetrahydrofolate biosynthesis were upregulated in psoriatic twins compared to their corresponding unaffected siblings (Fig 1; p< 0.05, Mann-Whitney). In NL skin samples from psoriatic twins, there was a significant reduction in alpha diversity and beta diversity differences in microbial communities of the scalp, but not the bicep or elbow/forearm, compared to healthy samples from unaffected twins (Fig 2A-B; p< 0.05, Mann-Whitney and Permanova). Differential analysis of taxa in the scalp identified a higher abundance of the Bacillales order and related taxa, as well as a lower abundance of the Deinococcus genus and related taxa in psoriatic twins compared to their unaffected siblings (Fig 2C; p< 0.05 with FDR correction).
Conclusion(s): This is the first study exploring microbial differences in MZ twins discordant for psoriatic disease. In agreement with our previous results, we found that Ruminococcus is reduced or virtually absent in the gut of psoriatic patients, and may therefore be associated with psoriatic disease. Additionally, we discovered that even healthyappearing NL skin of psoriatic subjects, particularly in the scalp, exhibited microbial perturbations and decreased diversity compared to unaffected twins. A further understanding of these changes and their downstream effects should shed light into the pathogenesis of psoriatic disease beyond genetic susceptibility

OBJECTIVES/OBJECTIVE:Although oral methotrexate (MTX) remains the anchor drug for RA, up to 50% of patients do not achieve a clinically adequate outcome. Concomitantly, there is a lack of prognostic tools for treatment response prior to drug initiation. Here we study whether inter-individual differences in the human gut microbiome can aid in the prediction of MTX efficacy in new-onset RA (NORA). METHODS:16S rRNA gene and shotgun metagenomic sequencing were performed on the baseline gut microbiomes of drug-naïve, NORA patients (n=26). Results were validated in an additional independent cohort (n=21). To gain insight into potential microbial mechanisms, ex vivo experiments coupled with metabolomics analysis evaluated the association between microbiome-driven MTX depletion and clinical response. RESULTS:Our analysis revealed significant associations between the abundance of gut bacterial taxa and their genes with future clinical response, including orthologs related to purine and methotrexate metabolism. Machine learning techniques were applied to the metagenomic data, resulting in a microbiome-based model that predicts lack of response to MTX in an independent group of patients. Finally, MTX levels remaining after ex vivo incubation with distal gut samples from pre-treatment RA patients significantly correlated with the magnitude of future clinical response, suggesting a possible direct effect of the gut microbiome on MTX metabolism and treatment outcomes. CONCLUSIONS:Together, these results provide the first step towards predicting lack of response to oral MTX in NORA patients and support the value of the gut microbiome as a possible prognostic tool and as a potential target in RA therapeutics.

We engineered a machine learning approach, MSHub, to enable auto-deconvolution of gas chromatography-mass spectrometry (GC-MS) data. We then designed workflows to enable the community to store, process, share, annotate, compare and perform molecular networking of GC-MS data within the Global Natural Product Social (GNPS) Molecular Networking analysis platform. MSHub/GNPS performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization and quantifies the reproducibility of fragmentation patterns across samples.

From birth, humans coexist and coevolve with trillions of micro-organisms inhabiting most body surfaces and cavities, referred to as the human microbiome. Advances in sequencing technologies and computational methods have propelled the exploration of the microbiome's contribution to human health and disease, spearheaded by massive efforts such as the Human Microbiome Project and the Europe-based MetaHit Consortium. Yet, despite the accumulated body of literature and a growing awareness among patients, microbiome research in rheumatology has not had a key impact on clinical practice. Herein, we describe some of the landmark microbiome studies in autoimmunity and rheumatology, the challenges and opportunities of microbiome research and how to navigate them, advances in related fields that have overcome these pitfalls, and future directions of harnessing the microbiome for diagnostic and therapeutic purposes.

OBJECTIVE:To characterize the ecological effects of biologic therapies on the gut bacterial and fungal microbiome of psoriatic arthritis (PsA)/spondyloarthritis (SpA) patients. METHODS:Fecal samples from PsA/SpA patients pre- and post-treatment with tumor necrosis factor inhibitors (TNFi; n=15) or an anti-interleukin (IL)-17A monoclonal antibody inhibitor (IL-17i; n=14) underwent sequencing (16S, ITS and shotgun metagenomics) and computational microbiome analysis. Fecal levels of fatty acid metabolites and cytokines/proteins implicated in PsA/SpA pathogenesis or intestinal inflammation were correlated with sequence data. Additionally, ileal biopsies obtained from SpA patients who developed clinically overt Crohn's disease (CD) after treatment with IL-17i (n=5) were analyzed for expression of IL-23/Th-17 related cytokines, IL-25/IL-17E-producing cells and type-2 innate lymphoid cells (ILC2s). RESULTS:There were significant shifts in abundance of specific taxa after treatment with IL-17i compared to TNFi, particularly Clostridiales (p=0.016) and Candida albicans (p=0.041). These subclinical alterations correlated with changes in bacterial community co-occurrence, metabolic pathways, IL-23/Th17-related cytokines and various fatty acids. Ileal biopsies showed that clinically overt CD was associated with expansion of IL-25/IL-17E-producing tuft cells and ILC2s (p<0.05) compared to pre-IL-17i treatment levels. CONCLUSION/CONCLUSIONS:In a subgroup of SpA patients, the initiation of IL-17A blockade correlated with features of subclinical gut inflammation and intestinal dysbiosis of certain bacterial and fungal taxa, most notably C. albicans. Further, IL-17i-related CD was associated with overexpression of IL-25/IL-17E-producing tuft cells and ILC2s. These results may help to explain the potential link between inhibition of a specific IL-17 pathway and the (sub)clinical gut inflammation observed in SpA.

Background/Purpose : Although a number of genetic factors, including susceptibility alleles, have been identified in rheumatoid arthritis (RA), the concordance rate in monozygotic (MZ) twins is only 15% (Silman AJ et al, Br J Rheumatol 1993). Given this relatively low rate, environmental factors (including smoking, the microbiome and others) likely play a significant role in disease pathogenesis. Several prior studies have highlighted this relationship. Two examples include oral Porphyromonas gingivalis , which has been implicated in the citrullination of peptides (Hitchon CA et al, J Rheumatol 2010), and intestinal Prevotella copri , which is significantly increased in new-onset RA (Scher JU et al, Elife, 2013). In our study, we sought to further understand this relationship by exploring the microbial, metabolomic and cytokine differences in MZ twins with discordant disease. Methods : Fecal and blood samples were collected from nine pairs of MZ twins where one sibling was diagnosed with RA and the other unaffected. Fecal samples underwent bacterial DNA extraction, amplification and 16S rRNA gene sequencing. Additionally, gas chromatography mass spectrometry (GC-MS) was used to quantify fecal metabolites and multiplex assays were used to quantify fecal and plasma ACPA autoantigens as well as other cytokines (Sokolove J et al, PLoS One, 2012.). Analysis was performed using R, Quantitative Insights into Microbial Ecology (QIIME) and Linear discriminant analysis Effect Size (LEfSe). Results : Microbiome analysis revealed no significant differences in overall bacterial alpha or beta diversity between unaffected and RA twins. On average, RA twins had higher relative abundance of Bacteroidales (RA 56.6% vs Unaffected 47.9%) and lower abundance of Clostridiales (RA 34.5% vs Unaffected 42.8%). LEfSe analysis showed differentially higher abundance of Unclassified Veillonellaceae in unaffected twins (p=0.042). Unaffected twins also demonstrated higher levels of fecal octanoate (p=0.008), a medium-chain fatty acid with beneficial immune effects, as well as significantly increased levels of plasma IL-3 (p=0.039). Conversely, several fecal and plasma citrullinated/ non-citrullinated peptides were significantly higher in RA twins compared to their unaffected siblings (p< 0.05). Conclusion : We characterized differences in the bacterial microbiome, metabolites, cytokines and citrullinated/noncitrullinated peptides in MZ twins discordant for RA. We found changes in the abundance of particular taxa, as well as higher levels of octanoate and IL-3 in unaffected twins, further suggesting the possibility that the initial citrullination and autoimmune events in this disease may occur in the intestinal mucosa. Understanding the immune mechanisms Figure 2 Unaffected twins demonstrate higher levels of octanoate and IL-3, whereas RA twins have significantly higher levels of fecal and plasma citrullinated/non-citrullinated peptides. (A-B) Boxplots of fatty acid metabolites and IL-3 abundance comparing unaffected and RA twins. (C) Tables of plasma and fecal citrullinated/non-citrullinated peptides comparing unaffected and RA twins. Only the top ten peptides by p-value are listed. For all panels, statistical significance calculated using the Wilcoxon signed-rank test. orchestrated by the gut microbiota and their downstream effects may ultimately shed light on the pathogenesis of RA beyond genetic susceptibility and allow us to potentially alter the course of disease development and progression

INTRODUCTION/BACKGROUND:The information content of multiparametric flow cytometry experiments is routinely underexploited given the paucity of adequate tools for unbiased comprehensive data analysis that can be applied successfully and independently by immunologists without computational training. METHODS:We aimed to develop a tool that allows straightforward access to the entire information content of any given flow cytometry panel for immunologists without special computational expertise. We used a data analysis approach which accounts for all mathematically possible combinations of markers in a given panel, coded the algorithm and applied the method to mined and self-generated data sets. RESULTS:We developed Flow Plex, a straightforward computational tool that allows unrestricted access to the information content of a given flow cytometry panel, enables classification of human samples according to distinct immune phenotypes, such as different forms of autoimmune uveitis, acute myeloid leukemia vs "healthy", "old" vs "young", and facilitates the identification of cell populations with potential biologic relevance to states of disease and health. CONCLUSIONS:We provide a tool that allows immunologists and other flow cytometry users with limited bioinformatics skills to extract comprehensive, unbiased information from flow cytometry data sets.