Small Extracellular Vesicle Enrichment of a Retrotransposon-Derived Double-Stranded RNA: A Means to Avoid Autoinflammation?

Small extracellular vesicles (SEVs) such as exosomes are released by multiple cell types. Originally believed to be a mechanism for selectively removing unwanted cellular components, SEVs have received increased attention in recent years for their ability to mediate intercellular communication. Apart from proteins and lipids, SEVs contain RNAs, but how RNAs are selectively loaded into SEVs remains poorly understood. To address this question, we profiled SEV RNAs from mouse dendritic cells using RNA-Seq and identified a long noncoding RNA of retroviral origin, VL30, which is highly enriched (>200-fold) in SEVs compared to parental cells. Bioinformatic analysis revealed that exosome-enriched isoforms of VL30 RNA contain a repetitive 26-nucleotide motif. This repeated motif is itself efficiently incorporated into SEVs, suggesting the likelihood that it directly promotes SEV loading. RNA folding analyses indicate that the motif is likely to form a long double-stranded RNA hairpin and, consistent with this, its overexpression was associated with induction of a potent type I interferon response. Taken together, we propose that preferential loading into SEVs of the VL30 RNA containing this immunostimulatory motif enables cells to remove a potentially toxic RNA and avoid autoinflammation. In this way, the original notion of SEVs as a cellular garbage bin should not be entirely discounted.

Extracellular Vesicles in Synovial Fluid from Rheumatoid Arthritis Patients Contain miRNAs with Capacity to Modulate Inflammation.

In rheumatoid arthritis (RA), extracellular vesicles (EVs) are associated with both the propagation and attenuation of joint inflammation and destruction. However, the specific EV content responsible for these processes is largely unknown. Investigations into identifying EV content are confounded by the challenges in obtaining high-quality EV preparations from synovial fluid. Implementing a size exclusion chromatography-based method of EV isolation, coupled with small RNA sequencing, we accurately characterised EV miRNAs in synovial fluid obtained from RA patients and investigated the differences between joints with high- and low-grade inflammation. Synovial fluid was obtained from the joints of 12 RA patients and, based on leukocyte counts, classified as either high (n = 7)- or low (n = 5)-grade inflammation. Using size exclusion chromatography, EVs were purified and small RNA was extracted and sequenced on a NextSeq 500. Sequencing reads were aligned to miRBase v21, and differences in miRNA profiles between RA patients with high- and low-grade joint inflammation were analysed. In total, 1972 distinct miRNAs were identified from RA synovial fluid EVs. miRNAs with less than five reads in fewer than five patients were filtered out, leaving 318 miRNAs for analysis. Analysis of the most abundant miRNAs suggested that they negatively regulate multiple genes relevant to inflammation, including signal transducer and activator of transcription 3 (STAT3), which lies downstream of IL-6 and has a pro-inflammatory role in RA. Synovial fluid from joints with high-grade inflammation contained 3.5-fold more EV miRNA per mL of synovial fluid (p = 0.0017). Seventy-eight EV miRNAs were differentially expressed between RA joints with high- and low-grade inflammation, and pathway analysis revealed that their target genes were commonly involved a variety of processes, including cellular apoptosis, proliferation and migration. Of the 49 miRNAs that were elevated in joints with high-grade inflammation, pathway analysis revealed that genes involved in cytokine-mediated signalling pathways were significantly enriched targets. In contrast, genes associated with reactive oxygen species signalling were significantly enriched as targets of the 29 miRNAs elevated in joints with low-grade inflammation. Our study identified an abundance of EV miRNAs from the synovial fluid of RA patients with the potential to modulate inflammation. In doing so, we defined potential mechanisms by which synovial fluid EVs may contribute to RA pathophysiology.

Use of machine learning to identify a T cell response to SARS-CoV-2.

The identification of SARS-CoV-2-specific T cell receptor (TCR) sequences is critical for understanding T cell responses to SARS-CoV-2. Accordingly, we reanalyze publicly available data from SARS-CoV-2-recovered patients who had low-severity disease (n = 17) and SARS-CoV-2 infection-naive (control) individuals (n = 39). Applying a machine learning approach to TCR beta (TRB) repertoire data, we can classify patient/control samples with a training sensitivity, specificity, and accuracy of 88.2%, 100%, and 96.4% and a testing sensitivity, specificity, and accuracy of 82.4%, 97.4%, and 92.9%, respectively. Interestingly, the same machine learning approach cannot separate SARS-CoV-2 recovered from SARS-CoV-2 infection-naive individual samples on the basis of B cell receptor (immunoglobulin heavy chain; IGH) repertoire data, suggesting that the T cell response to SARS-CoV-2 may be more stereotyped and longer lived. Following validation in larger cohorts, our method may be useful in detecting protective immunity acquired through natural infection or in determining the longevity of vaccine-induced immunity.

Classification of intestinal T-cell receptor repertoires using machine learning methods can identify patients with coeliac disease regardless of dietary gluten status.

In coeliac disease (CeD), immune-mediated small intestinal damage is precipitated by gluten, leading to variable symptoms and complications, occasionally including aggressive T-cell lymphoma. Diagnosis, based primarily on histopathological examination of duodenal biopsies, is confounded by poor concordance between pathologists and minimal histological abnormality if insufficient gluten is consumed. CeD pathogenesis involves both CD4+ T-cell-mediated gluten recognition and CD8+ and γδ T-cell-mediated inflammation, with a previous study demonstrating a permanent change in γδ T-cell populations in CeD. We leveraged this understanding and explored the diagnostic utility of bulk T-cell receptor (TCR) sequencing in assessing duodenal biopsies in CeD. Genomic DNA extracted from duodenal biopsies underwent sequencing for TCR-δ (TRD) (CeD, n = 11; non-CeD, n = 11) and TCR-γ (TRG) (CeD, n = 33; non-CeD, n = 21). We developed a novel machine learning-based analysis of the TCR repertoire, clustering samples by diagnosis. Leave-one-out cross-validation (LOOCV) was performed to validate the classification algorithm. Using TRD repertoire, 100% (22/22) of duodenal biopsies were correctly classified, with a LOOCV accuracy of 91%. Using TCR-γ (TRG) repertoire, 94.4% (51/54) of duodenal biopsies were correctly classified, with LOOCV of 87%. Duodenal biopsy TRG repertoire analysis permitted accurate classification of biopsies from patients with CeD following a strict gluten-free diet for at least 6 months, who would be misclassified by current tests. This result reflects permanent changes to the duodenal γδ TCR repertoire in CeD, even in the absence of gluten consumption. Our method could complement or replace histopathological diagnosis in CeD and might have particular clinical utility in the diagnostic testing of patients unable to tolerate dietary gluten, and for assessing duodenal biopsies with equivocal features. This approach is generalisable to any TCR/BCR locus and any sequencing platform, with potential to predict diagnosis or prognosis in conditions mediated or modulated by the adaptive immune response. © 2020 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.

Proteomic analysis of extracellular vesicles reveals an immunogenic cargo in rheumatoid arthritis synovial fluid.

OBJECTIVES: Extracellular vesicles (EVs) from rheumatoid arthritis (RA) synovial fluid (SF) have been reported to stimulate the release of pro-inflammatory mediators from recipient cells. We recently developed a size exclusion chromatography (SEC)-based method for EV isolation capable of high-quality enrichments from human SF. Here, we employed this method to accurately characterise the SF EV proteome and investigate potential contributions to inflammatory pathways in RA. METHODS: Using our SEC-based approach, SF EVs were purified from the joints of RA patients classified as having high-level (n = 7) or low-level inflammation (n = 5), and from osteoarthritis (OA) patients (n = 5). Protein profiles were characterised by mass spectrometry. Potential contributions of EV proteins to pathological pathways and differences in protein expression between disease groups were investigated. RESULTS: Synovial fluid EVs were present at higher concentrations in RA joints with high-level inflammation (P-value = 0.004) but were smaller in diameter (P-value = 0.03) than in low-level inflammation. In total, 1058 SF EV proteins were identified by mass spectrometry analysis. Neutrophil and fibroblast markers were overrepresented in all disease groups. Numerous proteins with potential to modulate inflammatory and immunological processes were detected, including nine citrullinated peptides. Forty-five and 135 EV-associated proteins were significantly elevated in RA joints with high-level inflammation than in RA joints with low-level inflammation and OA joints, respectively. Gene ontology analysis revealed significant enrichment for proteins associated with 'neutrophil degranulation' within SF EVs from RA joints with high-level inflammation. CONCLUSION: Our results provide new information about SF EVs and insight into how EVs might contribute to the perpetuation of RA.

Circulating Small Noncoding RNA Biomarkers of Response to Triple Disease-modifying Antirheumatic Drug Therapy in White Women With Early Rheumatoid Arthritis.

OBJECTIVE: To identify small noncoding RNA (sncRNA) serum biomarkers that predict response to triple disease-modifying antirheumatic drug (DMARD) therapy in patients with early rheumatoid arthritis (RA). METHODS: Early RA patients entered into a treat-to-target management algorithm, with triple DMARD therapy (methotrexate, sulfasalazine, hydroxychloroquine). Patients were assessed following 6 months of therapy and classified as European League Against Rheumatism responders or nonresponders. RNA was isolated from 42 archived serum samples, collected prior to commencement of triple DMARD therapy. Small RNA sequencing was performed and the reads mapped to annotations in a database of human sncRNA. Differential expression analysis was performed, comparing responders (n = 24) and nonresponders (n = 18). RESULTS: Pretreatment levels of 4 sncRNA were significantly increased in nonresponders: chr1. tRNA131-GlyCCC (4.1-fold, adjusted P = 0.01), chr2.tRNA13-AlaCGC (2.2-fold, adjusted P = 0.02), U2-L166 (6.6-fold, adjusted P = 0.02), and piR-35982 (2.4-fold, adjusted P = 0.03). 5S-L612 was the only sncRNA significantly increased in responders (3.3-fold; adjusted P = 0.01). Reads for chr1. tRNA131-GlyCCC and chr2.tRNA13-AlaCGC mapped to the 5' end of each tRNA gene and were truncated at the anticodon loop, consistent with these sncRNA having roles as 5' translation interfering tRNA halves (tiRNA). CONCLUSION: Pretreatment levels of specific serum sncRNA might facilitate identification of patients more likely to respond to triple DMARD therapy.

Enrichment of extracellular vesicles from human synovial fluid using size exclusion chromatography.

As a complex biological fluid, human synovial fluid (SF) presents challenges for extracellular vesicle (EV) enrichment using standard methods. In this study of human SF, a size exclusion chromatography (SEC)-based method of EV enrichment is shown to deplete contaminants that remain after standard ultracentrifugation-based enrichment methods. Specifically, considerable levels of serum albumin, the high-density lipoprotein marker, apolipoprotein A-I, fibronectin and other extracellular proteins and debris are present in EVs prepared by differential ultracentrifugation. While the addition of a sucrose density gradient purification step improved purification quality, some contamination remained. In contrast, using a SEC-based approach, SF EVs were efficiently separated from serum albumin, apolipoprotein A-I and additional contaminating proteins that co-purified with high-speed centrifugation. Finally, using high-resolution mass spectrometry analysis, we found that residual contaminants which remain after SEC, such as fibronectin and other extracellular proteins, can be successfully depleted by proteinase K. Taken together, our results highlight the limitations of ultracentrifugation-based methods of EV isolation from complex biological fluids and suggest that SEC can be used to obtain higher purity EV samples. In this way, SEC-based methods are likely to be useful for identifying EV-enriched components and improving understanding of EV function in disease.

EV-TRACK: transparent reporting and centralizing knowledge in extracellular vesicle research.

We argue that the field of extracellular vesicle (EV) biology needs more transparent reporting to facilitate interpretation and replication of experiments. To achieve this, we describe EV-TRACK, a crowdsourcing knowledgebase (http://evtrack.org) that centralizes EV biology and methodology with the goal of stimulating authors, reviewers, editors and funders to put experimental guidelines into practice.