Faculty Bibliography

Background/Purpose : Early treatment initiation in rheumatoid arthritis (RA) is fundamental to avoid chronic joint destruction and disability. Despite remarkable advances in RA therapeutics, oral methotrexate (MTX) remains the anchor drug and mainstay of treatment worldwide. However, MTX bioavailability has a wide inter-individual variability and >50% of patients with moderate or severe RA show no or suboptimal improvement in their symptoms in response to MTX. The reasons for these disparities in treatment response remain unclear. Prior studies have shown that the biotransformation of MTX is altered in germ-free and microbiome-depleted mice, prompting us to hypothesize that inter-individual differences in the human gut microbiome could impact drug bioavailability and thus clinical efficacy. We sought to determine differences in the microbiome of drug-naive, new onset RA (NORA) patients that could predict response to MTX therapy. Methods : We enrolled 27 drug-naive, NORA patients priori to MTX initiation (test cohort), and classified them as either MTX-responders (MTX-R; 39% of the cohort) or non-responders (MTX-NR; 61%) based on a stringent definition of clinical response (delta improvement of DAS28 >1.8 by month 4). We performed 16S rRNA gene and Shotgun Metagenomic sequencing on the baseline gut microbiomes of these NORA patients and confirmed the results in an independent validation cohort (n=31). NMR and LC-MS were performed in ex vivo incubations to measure the capacity of each NORA microbiome to metabolize MTX. Results : Our analysis revealed significant associations between the abundance of gut bacterial taxa and future MTX response. Patients that responded to therapy had significantly lower microbial diversity (p< 0.05). A significant difference in overall gut microbial community structure was also observed between groups (Bray-Curtis distance; PERMANOVA < 0.05). At the class level, we observed statistically higher abundance of Clostridia and lower abundance of Bacteroidia in MTX-NR (p< 0.05; q< 0.2). Furthermore, the baseline metagenome separated most MTX-R from MTX-NR (PCoA; PERMANOVA p< 0.05). We identified 8 microbial modules and 23 pathways, whose abundance significantly differed between groups (p< 0.05, q< 0.2), including genes related with purine and MTX metabolism, indicating a major difference in metabolic and biosynthetic potential between the microbiome of MTX-R and MTX-NR patients. Machine learning techniques were applied to this metagenomic data, resulting in a robust model based on bacterial gene abundance that accurately predicted response to MTX in an independent cohort. Finally, MTX available 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 direct effect of the gut microbiome on MTX bioavailability and response to therapy. Conclusion : Together, these results provide the first step towards predicting response to oral MTX in NORA patients and support the utility of the gut microbiome as a prognostic tool and perhaps even as a target for manipulation in the treatment of rheumatic and autoimmune disease

OBJECTIVE:To develop new classification criteria for systemic lupus erythematosus (SLE) jointly supported by the European League Against Rheumatism (EULAR) and the American College of Rheumatology (ACR). METHODS:This international initiative had four phases. (1) Evaluation of antinuclear antibody (ANA) as an entry criterion through systematic review and meta-regression of the literature and criteria generation through an international Delphi exercise, an early patient cohort and a patient survey. (2) Criteria reduction by Delphi and nominal group technique exercises. (3) Criteria definition and weighting based on criterion performance and on results of a multi-criteria decision analysis. (4) Refinement of weights and threshold scores in a new derivation cohort of 1001 subjects and validation compared with previous criteria in a new validation cohort of 1270 subjects. RESULTS:The 2019 EULAR/ACR classification criteria for SLE include positive ANA at least once as obligatory entry criterion; followed by additive weighted criteria grouped in seven clinical (constitutional, haematological, neuropsychiatric, mucocutaneous, serosal, musculoskeletal, renal) and three immunological (antiphospholipid antibodies, complement proteins, SLE-specific antibodies) domains, and weighted from 2 to 10. Patients accumulating ≥10 points are classified. In the validation cohort, the new criteria had a sensitivity of 96.1% and specificity of 93.4%, compared with 82.8% sensitivity and 93.4% specificity of the ACR 1997 and 96.7% sensitivity and 83.7% specificity of the Systemic Lupus International Collaborating Clinics 2012 criteria. CONCLUSION/CONCLUSIONS:These new classification criteria were developed using rigorous methodology with multidisciplinary and international input, and have excellent sensitivity and specificity. Use of ANA entry criterion, hierarchically clustered and weighted criteria reflect current thinking about SLE and provide an improved foundation for SLE research.

OBJECTIVE:To develop new classification criteria for systemic lupus erythematosus (SLE) jointly supported by the European League Against Rheumatism (EULAR) and the American College of Rheumatology (ACR). METHODS:This international initiative had four phases. 1) Evaluation of antinuclear antibody (ANA) as an entry criterion through systematic review and meta-regression of the literature and criteria generation through an international Delphi exercise, an early patient cohort, and a patient survey. 2) Criteria reduction by Delphi and nominal group technique exercises. 3) Criteria definition and weighting based on criterion performance and on results of a multi-criteria decision analysis. 4) Refinement of weights and threshold scores in a new derivation cohort of 1,001 subjects and validation compared with previous criteria in a new validation cohort of 1,270 subjects. RESULTS:The 2019 EULAR/ACR classification criteria for SLE include positive ANA at least once as obligatory entry criterion; followed by additive weighted criteria grouped in 7 clinical (constitutional, hematologic, neuropsychiatric, mucocutaneous, serosal, musculoskeletal, renal) and 3 immunologic (antiphospholipid antibodies, complement proteins, SLE-specific antibodies) domains, and weighted from 2 to 10. Patients accumulating ≥10 points are classified. In the validation cohort, the new criteria had a sensitivity of 96.1% and specificity of 93.4%, compared with 82.8% sensitivity and 93.4% specificity of the ACR 1997 and 96.7% sensitivity and 83.7% specificity of the Systemic Lupus International Collaborating Clinics 2012 criteria. CONCLUSION/CONCLUSIONS:These new classification criteria were developed using rigorous methodology with multidisciplinary and international input, and have excellent sensitivity and specificity. Use of ANA entry criterion, hierarchically clustered, and weighted criteria reflects current thinking about SLE and provides an improved foundation for SLE research.

Nonadherence to treatment is a major cause of lupus flares. Hydroxychloroquine (HCQ), a major medication in systemic lupus erythematosus, has a long half-life and can be quantified by high-performance liquid chromatography. This international study evaluated nonadherence in 305 lupus patients with flares using drug levels (HCQ < 200 ng/ml or undetectable desethylchloroquine), and self-administered questionnaires (MASRI < 80%). Drug levels defined 18.4% of the patients as severely nonadherent. In multivariate analyses, younger age, nonuse of steroids, higher body mass index, and unemployment were associated with nonadherence by drug level. Questionnaires classified 23.4% of patients as nonadherent. Correlations between adherence measured by questionnaires, drug level, and physician assessment were moderate. Both methods probably measured two different patterns of nonadherence: self-administered questionnaires mostly captured relatively infrequently missed tablets, while drug levels identified severe nonadherence (i.e., interruption or erratic tablet intake). The frequency with which physicians miss nonadherence, together with underreporting by patients, suggests that therapeutic drug monitoring is useful in this setting. (Trial registration: ClinicalTrials.gov: NCT01509989.).

OBJECTIVE:Extant epidemiologic data of primary Sjögren's Syndrome (pSS) remains limited, particularly for racial/ethnic populations in the United States (US). The Manhattan Lupus Surveillance Program (MLSP), a population-based retrospective registry of cases with Systemic Lupus Erythematosus and related diseases including pSS in Manhattan, was used to provide estimates of the incidence and prevalence of pSS across major racial/ethnic populations. METHODS:MLSP cases were identified from hospitals, rheumatologists, and population databases. Three case definitions were used for pSS: physician diagnosis, rheumatologist diagnosis, and modified pSS criteria. Rates among Manhattan residents were age-adjusted, and capture-recapture analyses were conducted to assess case under-ascertainment. RESULTS:By physician diagnosis, age-adjusted overall incidence and prevalence rates of pSS among adult Manhattan residents were 3.5 and 13.1 per 100,000 person-years. Capture-recapture adjustment increased incidence and prevalence rates (4.1 and 14.2). Based on physician diagnosis, incidence and prevalence rates were approximately 6 times higher among women than men (p<0.01). Incidence of pSS was statistically higher among non-Latina Asian (10.5) and non-Latina White women (6.2) compared with Latina women (3.2). Incidence was also higher among non-Latina Asian women compared with non-Latina Black women (3.3). Prevalence of pSS did not differ by race/ethnicity. Similar trends were observed when more restrictive case definitions were applied. CONCLUSION/CONCLUSIONS:Data from the MLSP revealed disparities in pSS incidence and prevalence by sex among Manhattan residents and differences in pSS incidence by race/ethnicity among women. These data also provided epidemiologic estimates for the major racial/ethnic populations in the US.

The molecular and cellular processes that lead to renal damage and to the heterogeneity of lupus nephritis (LN) are not well understood. We applied single-cell RNA sequencing (scRNA-seq) to renal biopsies from patients with LN and evaluated skin biopsies as a potential source of diagnostic and prognostic markers of renal disease. Type I interferon (IFN)-response signatures in tubular cells and keratinocytes distinguished patients with LN from healthy control subjects. Moreover, a high IFN-response signature and fibrotic signature in tubular cells were each associated with failure to respond to treatment. Analysis of tubular cells from patients with proliferative, membranous and mixed LN indicated pathways relevant to inflammation and fibrosis, which offer insight into their histologic differences. In summary, we applied scRNA-seq to LN to deconstruct its heterogeneity and identify novel targets for personalized approaches to therapy.

Background Classification and treatment decisions in lupus nephritis (LN) are largely based on renal histology. Single-cell RNA sequencing (scRNAseq) analysis may accurately differentiate types of renal involvement at the transcriptomic level, and better inform treatment decisions and prognosis. Methods scRNAseq was performed on kidney and non-lesional skin tissue collected from 20 SLE patients undergoing a clinically indicated renal biopsy. Cell types were determined using principal component analysis and t-distributed stochastic neighbor embedding (tSNE) plotting, resulting in the definitive identification of keratinocytes, tubular cells, mesangial cells, fibroblasts, endothelial cells, and leukocytes. Results LN patients expressed upregulated IFN response genes in both their tubular cells and keratinocytes. This IFN response signature in tubular cells predicted poor response to therapy 6 months post-biopsy. Tubular cells of non-responder patients also expressed upregulated extracellular matrix proteins and fibrotic markers (figure 1A and 1B). Using logistic regression analysis, a 4-gene tubular fibrosis score was created and able to predict response to treatment with an area under curve of 0.9 (figure 1C). Keratinocytes of non-responders also upregulated certain extracellular matrix genes and this response was not observed in peripheral blood mononuclear cells. Differential expression analysis between histology classes indicated an upregulation of IFN and TNF signaling in the tubular cells of patients with proliferative LN compared with membranous. Conclusions scRNAseq from 2-10 mm of renal biopsy tissue in SLE can differentiate between the different classes of LN, and provide important insights into potential pathogenic mechanisms. Further, changes in the skin of LN patients can provide a useful source of biomarkers and may reflect important information concerning concurrent kidney pathological events

Background The impact of renal injury in lupus nephritis is widespread with consequences to resident cells in other tissue beds, even non-lesional non-sun exposed skin. Faithful reflection of a relevant renal tissue pathway in a more readily accessible compartment would allow for less invasive diagnostic alternatives. Single-cell transcriptional states as performed in this study may provide a framework for understanding how in vivo biological function emerges from complex cell ensembles, thus allowing for a clearer understanding of potential mutual pathways. Methods Patients with proteinuria and known ISN/RPS Class and controls were recruited to discovery 1 and 2 cohorts. Single cell RNAseq was performed on cell suspensions prepared from ~2 mm punch biopsies of non-lesional non sun-exposed skin from the buttocks. The libraries were prepared on the Fluidigm C1 platform (discovery 1) and 10X Genomics platform (discovery 2) along with Illumina HiSeq 2500 sequencing. Results Sorting based on COL1A1, COL1A2, COL3A1, MFAP5 and MFAP4 expression yielded 12 fibroblasts from 3 patients. The 1 Class II subject yielded 5 single-cell transcriptomes. The other 2 subjects (1 Class IV,V; 1 Class III,V) yielded 7 single-cell transcriptomes. 22 transcriptomes were derived from 3 controls. The aggregate data were used to determine the top upregulated genes in cases versus controls, most of which belonged to the interferon-stimulated gene category and the extracellular matrix category (DAVID databases). Fewer cells were obtained using Fluidigm C1 (36 single-cell) than 10X Genomics (7280 single-cell). For the latter, the major biopsy classes were represented (Class III, III/IV, III/V, V and no LN). We applied graph-based clustering and identified 12 major clusters of cells from the patient skin as visualized by t-distributed stochastic neighbor embedding (t-SNE; figure 1). Differential gene expression analysis guided by established lineage markers revealed three keratinocyte clusters (KC1- KC3), two fibroblast clusters (FB1, FB2), smooth muscle cells (SMC), two endothelial cell clusters (VEC, LEC), melanocytes (MEL), sweat gland cells (SG), macrophages/dendritic cells (MAC-DC) and T cells (TC). Ranked by abundance, patient skin exhibited KC>FB>EC>MAC-DC>SMC>TC>SG> MEL. Conclusions Single-cell RNAseq is both feasible and informative in cell-specific transcriptome analysis of fresh non-lesional non-sun exposed LN skin biopsies. 10X genomics significantly increases cell numbers and facilitates identification of major cell clusters compared to Fluidigm C1. The expression of fibroblasts and genes reflective of fibrotic and interferonrelated pathways support the application of this approach to study readily accessible tissue for biomarkers related to disease progression. (Figure Presented)

Background Autoantibody and laboratory testing is essential for SLE diagnosis. ANA by indirect immunofluorescence (ANA IIF) remains the gold standard to screen for lupus and studies demonstrate preclinical phase during which autoantibodies accumulate. Prevalence of ANA IIF alone without more specific autoantibodies and the accompanying clinical phenotype of these patients uncertain. Methods Queried 602 patients in the NYU Lupus Registry with 4 or more ACR or SLICC criteria as adjudicated by the authors for prevalence of ANA IIF, dsDNA, Sm, Ro, La, RNP, aPL, C3, C4. Compared clinical features of isolated ANA (ANA IIF alone) with the ANA IIF plus one or more lupus associated abnormalities (ANA IIF +). Results 590/602 (98%) ANA IIF positive. 548/590 (93%) patients at least one of associated tests compared to only 42/ 590 (7%) ANA IIF alone. SLE nephritis significantly more prevalent in ANA IIF+254/548 (46%), compared to 13/42 (31%) recorded with renal criteria ANA IIF alone. ANA IIF +, 158/254 (62%) biopsy proven nephritis (LN), rather than relying on proteinuria for diagnosis, compared to 5/13 (38%) of ANA IIF alone biopsy proven LN. Remaining 8 ANA IIF alone, uPCR exceeded 0.5 g in 1 of 44 (2%) encounters. Low incidence of proteinuria explained by complete renal response or prior proteinuria misconstrued as evidence of LN. In comparison, uPCR >0.5 g was present in 694 of 1157 (60%) encounters in ANA IIF +, casting doubt on validity of LN diagnosis in 8 ANA IIF alone without biopsy. Leucopenia, lymphocytopenia, AITP, AIHA statistically less ANA IIF alone compared to ANA IIF + ; 24% vs 36%, 29% vs 40%, 7% vs 15% and 0% vs 7%, respectively. 42 patients with ANA IIF alone prevalence of potentially misattributed (e.g. not result of IMID) clinical criteria such as photosensitivity (64%) and malar rash (60%) greater compared to ANA IIF +, 38% and 44%, respectively. Prevalence oral ulcers, DLE, arthritis, serositis, seizures and psychosis equivalent in both. Conclusions ANA IIF alone rare and patients infrequently develop nephritis, leucopenia, lymphocytopenia, AITP, AIHA. In patients ANA IIF alone attribution of ACR/SLICC clinical criteria needs to be point of emphasis and unless biopsy proven, alternative explanation for proteinuria should be considered. Data argues inclusion criteria for clinical trials, rather than allowing ANA IIF alone or dsDNA, may need to require ANA IIF and at least one of the following (dsDNA, Sm, Ro, La, aPL, or hypocomplementemia) to avoid enrolling patients that do not have SLE