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There are thousands of survivors of the 2014 Ebola outbreak in west Africa. Ebola virus can persist in survivors for months in immune-privileged sites; however, viral relapse causing life-threatening and potentially transmissible disease has not been described. We report a case of late relapse in a patient who had been treated for severe Ebola virus disease with high viral load (peak cycle threshold value 13·2). A 39-year-old female nurse from Scotland, who had assisted the humanitarian effort in Sierra Leone, had received intensive supportive treatment and experimental antiviral therapies, and had been discharged with undetectable Ebola virus RNA in peripheral blood. The patient was readmitted to hospital 9 months after discharge with symptoms of acute meningitis, and was found to have Ebola virus in cerebrospinal fluid (CSF). She was treated with supportive therapy and experimental antiviral drug GS-5734 (Gilead Sciences, San Francisco, Foster City, CA, USA). We monitored Ebola virus RNA in CSF and plasma, and sequenced the viral genome using an unbiased metagenomic approach. On admission, reverse transcriptase PCR identified Ebola virus RNA at a higher level in CSF (cycle threshold value 23·7) than plasma (31·3); infectious virus was only recovered from CSF. The patient developed progressive meningoencephalitis with cranial neuropathies and radiculopathy. Clinical recovery was associated with addition of high-dose corticosteroids during GS-5734 treatment. CSF Ebola virus RNA slowly declined and was undetectable following 14 days of treatment with GS-5734. Sequencing of plasma and CSF viral genome revealed only two non-coding changes compared with the original infecting virus. Our report shows that previously unanticipated, late, severe relapses of Ebola virus can occur, in this case in the CNS. This finding fundamentally redefines what is known about the natural history of Ebola virus infection. Vigilance should be maintained in the thousands of Ebola survivors for cases of relapsed infection. The potential for these cases to initiate new transmission chains is a serious public health concern. Royal Free London NHS Foundation Trust.

Protein sequences preserved in two Quaternary taxa, Macrauchenia and Toxodon , resolve the evolutionary history of South American native ungulates. South American ungulates demystified When Charles Darwin first unearthed the remains of the South American native ungulates — herbivorous mammals that evolved in isolation on the South American continent for tens of millions of years — he declared them “the strangest animals ever discovered”. Their origins are obscured by rampant convergence of morphology, and work with ancient DNA has been similarly frustrating. Ian Barnes and colleagues have circumvented these problems by screening protein sequences preserved in two Quaternary taxa, Macrauchenia and Toxodon , to show that their closest relatives among other mammals are the Perissodactyls (odd-toed ungulates such as horses and rhinos) rather than among the Afrotheres (elephants, sirenians and allies). No large group of recently extinct placental mammals remains as evolutionarily cryptic as the approximately 280 genera grouped as ‘South American native ungulates’. To Charles Darwin 1 , 2 , who first collected their remains, they included perhaps the ‘strangest animal[s] ever discovered’. Today, much like 180 years ago, it is no clearer whether they had one origin or several, arose before or after the Cretaceous/Palaeogene transition 66.2 million years ago 3 , or are more likely to belong with the elephants and sirenians of superorder Afrotheria than with the euungulates (cattle, horses, and allies) of superorder Laurasiatheria 4 , 5 , 6 . Morphology-based analyses have proved unconvincing because convergences are pervasive among unrelated ungulate-like placentals. Approaches using ancient DNA have also been unsuccessful, probably because of rapid DNA degradation in semitropical and temperate deposits. Here we apply proteomic analysis to screen bone samples of the Late Quaternary South American native ungulate taxa Toxodon (Notoungulata) and Macrauchenia (Litopterna) for phylogenetically informative protein sequences. For each ungulate, we obtain approximately 90% direct sequence coverage of type I collagen α1- and α2-chains, representing approximately 900 of 1,140 amino-acid residues for each subunit. A phylogeny is estimated from an alignment of these fossil sequences with collagen (I) gene transcripts from available mammalian genomes or mass spectrometrically derived sequence data obtained for this study. The resulting consensus tree agrees well with recent higher-level mammalian phylogenies 7 , 8 , 9 . Toxodon and Macrauchenia form a monophyletic group whose sister taxon is not Afrotheria or any of its constituent clades as recently claimed 5 , 6 , but instead crown Perissodactyla (horses, tapirs, and rhinoceroses). These results are consistent with the origin of at least some South American native ungulates 4 , 6 from ‘condylarths’, a paraphyletic assembly of archaic placentals. With ongoing improvements in instrumentation and analytical procedures, proteomics may produce a revolution in systematics such as that achieved by genomics, but with the possibility of reaching much further back in time.

Tumour necrosis factor (TNF) inhibition and B-cell depletion are highly effective treatments for active rheumatoid arthritis, but so far no randomised controlled trials have directly compared their safety, efficacy, and cost-effectiveness. This study was done to test the hypothesis that using rituximab would be clinically non-inferior and cheaper compared with TNF inhibitor treatment in biological-treatment naive patients with rheumatoid arthritis. This open-label, randomised controlled, non-inferiority trial enrolled patients with active, seropositive rheumatoid arthritis and an inadequate response to synthetic disease modifying anti-rheumatic drugs (DMARDs) from 35 rheumatology departments in the UK. Patients were randomly assigned 1:1 to the rituximab or TNF inhibitor groups with minimisation to account for methotrexate intolerance using a web-based randomisation system. Patients were given intravenous rituximab 1 g on days 1 and 15, and after 26 weeks if they responded to treatment but had persistent disease activity (28 joint count disease activity score [DAS28-ESR] >3·2; rituximab group) or a TNF inhibitor—adalimumab (40 mg subcutaneously every other week) or etanercept (50 mg per week subcutaneously) according to the patient's and rheumatologist's choice (TNF inhibitor group). Patients could switch treatment in the case of drug-related toxic effects or absence or loss of response. The primary outcome measure was the change in DAS28-ESR between 0 and 12 months in the per-protocol population of patients who were assigned to treatment and remained in follow-up to 1 year. We assessed safety in all patients who received at least one dose of study drug. We also assessed the cost-effectiveness of each strategy. The non-inferiority margin was specified as 0·6 DAS28-ESR units. This study is registered with ClinicalTrials.gov, number NCT01021735. Between April 6, 2009, and Nov 11, 2013, 295 patients were randomly assigned and given either rituximab (n=144) or TNF inhibitor (n=151) treatment. After 12 months, the change in DAS28-ESR for patients assigned to rituximab was −2·6 (SD 1·4) and TNF inhibitor was −2·4 (SD 1·5), with a difference within the prespecified non-inferiority margin of −0·19 (95% CI −0·51 to 0·13; p=0·24). The health-related costs associated with the rituximab strategy were lower than the TNF inhibitor strategy (£9405 vs £11 523 per patient, p<0·0001). 137 (95%) of 144 patients in the rituximab group and 143 (95%) of 151 patients in the TNF inhibitor group had adverse events. 37 serious adverse events occurred in patients receiving rituximab compared with 26 in patients receiving TNF inhibitors, of which 27 were deemed to be possibly, probably, or definitely related to the treatment (15 vs 12, p=0·5462). One patient in each group died during the study. Initial treatment with rituximab is non-inferior to initial TNF inhibitor treatment in patients seropositive for rheumatoid arthritis and naive to treatment with biologicals, and is cost saving over 12 months. Arthritis Research UK, Roche.

Background Rheumatoid arthritis (RA) is an autoimmune disease characterized by the presence of autoantibodies against modified proteins, known as anti-modified protein autoantibodies (AMPAs). While the relationship between different AMPA isotypes and various risk factors remains poorly understood, investigating this association is important for a deeper understanding of RA pathophysiology. Smoking, has its primary effects in the lungs, and it remains unclear whether smoking is preferentially linked to specific AMPA isotypes, such as IgA, which could suggest a mucosal origin. Therefore, we set out to investigate the association between smoking, genetic risk factors for RA, and the presence of specific AMPA isotypes, particular IgA. Methods In 618 RA patients, anti-citrullinated protein antibodies (ACPA-) and anti-acetylated protein antibodies (AAPA-) IgA, -IgG and -IgM and RF-IgA and -IgM were measured by ELISA. Associations with genetic risk factors, smoking and autoantibodies were assessed with logistic regression analysis. For replication, a comprehensive meta-analysis incorporating 3309 RA patients was performed. Results Smoking was primarily associated with IgA AMPA, with associations that prevailed after correcting for the concurrent presence of AMPA IgG (ACPA-IgA OR 1.89 [1.14–3.12], AAPA-IgA 2.30 [1.35–3.94]). To further substantiate these results, we performed a meta-analysis of 3309 RA patients and observed that smoking was again predominantly associated with the combined presence of ACPA-IgA in addition to ACPA-IgG (OR 2.05 [1.69–2.49], p  < 0.001) versus the single presence of ACPA-IgG (OR 1.18 [0.97–1.44], p  = 0.11). A gene-environment interaction between the most important genetic risk factor for RA (the HLA shared epitope alleles) and smoking was only seen in patients that were both ACPA-IgG and ACPA-IgA positive, but not in patients who were only positive for ACPA-IgG. Conclusion These data provide a pivotal refinement of existing knowledge regarding risk factor associations for RA and lend novel support to the hypothesis that smoking may exert its effect on RA by the induction of local (auto)immune responses at mucosal sites.

Current treatments for rheumatoid arthritis (RA) do not reverse underlying aberrant immune function. A genetic predisposition to RA, such as HLA-DR4 positivity, indicates that dendritic cells (DC) are of crucial importance to pathogenesis by activating auto-reactive lymphocytes. Here we show that microRNA-34a provides homoeostatic control of CD1c + DC activation via regulation of tyrosine kinase receptor AXL, an important inhibitory DC auto-regulator. This pathway is aberrant in CD1c + DCs from patients with RA, with upregulation of miR-34a and lower levels of AXL compared to DC from healthy donors. Production of pro-inflammatory cytokines is reduced by ex vivo gene-silencing of miR-34a. miR-34a-deficient mice are resistant to collagen-induced arthritis and interaction of DCs and T cells from these mice are reduced and do not support the development of Th17 cells in vivo . Our findings therefore show that miR-34a is an epigenetic regulator of DC function that may contribute to RA. Axl is a TAM receptor that can inhibit Toll-like receptor (TLR) -induced pro-inflammatory production by dendritic cells (DC). Here the authors show that miR-34a targets Axl to control CD1c+ DC activity in mice, and that miR-34a-deficient mice are resistant to collagen-induced arthritis, whereas DCs from patients with rheumatoid arthritis have high levels of miR- 34a.

Background Gene expression analysis is a crucial tool for uncovering the biological mechanisms that underlie differences between patient subgroups, offering insights that can inform clinical decisions. However, despite its potential, gene expression analysis remains challenging for clinicians due to the specialised skills required to access, integrate, and analyse large datasets. Existing tools primarily focus on RNA-Seq data analysis, providing user-friendly interfaces but often falling short in several critical areas: they typically do not integrate clinical data, lack support for patient-specific analyses, and offer limited flexibility in exploring relationships between gene expression and clinical outcomes in disease cohorts. Users, including clinicians with a general knowledge of transcriptomics, however, who may have limited programming experience, are increasingly seeking tools that go beyond traditional analysis. To overcome these issues, computational tools must incorporate advanced techniques, such as machine learning, to better understand how gene expression correlates with patient symptoms of interest. Results Our RNAcare platform, addresses these limitations by offering an interactive and reproducible solution specifically designed for analysing transcriptomic data from patient samples in a clinical context. This enables researchers to directly integrate gene expression data with clinical features, perform exploratory data analysis, and identify patterns among patients with similar diseases. By enabling users to integrate transcriptomic and clinical data, and customise the target label, the platform facilitates the analysis of the relationships between gene expression and clinical symptoms like pain and fatigue. This allows users to generate hypotheses and illustrative visualisations/reports to support their research. As proof of concept, we use RNAcare to link inflammation-related genes to pain and fatigue in rheumatoid arthritis (RA) and detect signatures in the drug response group, confirming previous findings. Conclusion We present a novel computational platform allowing the interpretation of clinical and transcriptomics data in real-time. The platform can be used for data generated by the user, such as the patient data presented here or using published datasets. The platform is available at https://rna-care.mvls.gla.ac.uk/ , and its source code is https://github.com/sii-scRNA-Seq/RNAcare/ .

ObjectiveDepression and anxiety are not uncommon in Rheumatoid arthritis (RA). It is increasingly recognised that they are associated with high disease activity and worse disease outcomes. We aimed to examine the frequency of depression and anxiety in an early RA inception cohort and to explore associations with disease-related measures.MethodsThe Scottish Early Rheumatoid Arthritis inception cohort recruited newly diagnosed RA patients followed-up 6-monthly. Anxiety and depression were assessed using the hospital anxiety and depression scale. Associations with demographic characteristics and disease-related measures were examined at baseline, 6 months and 12 months.Results848 RA patients were included. The prevalence of anxiety and depression at baseline was 19.0% and 12.2%, respectively. Depression and anxiety scores correlated with DAS28 at all time-points (all p<0.0001). In multivariable linear regression, anxiety score at baseline was associated with younger age and Health Assessment Questionnaire (HAQ) score. Anxiety scores at 6 months and 12 months were associated with low body mass index (BMI), baseline anxiety score and current patient global score and HAQ. Depression score at baseline was associated with younger age, being single and HAQ, while depression scores at 6 months and 12 months were associated with male gender (only at 6 months), baseline anxiety and depression scores and current patient global score, HAQ and C-reactive protein (CRP) levels.ConclusionDepression and anxiety are associated with disease activity, worse functional status and other variables in early RA. There is a close relationship between CRP and depression but not anxiety.

Personalised medicine, new discoveries and studies on rare exposures or outcomes require large samples that are increasingly difficult for any single investigator to obtain. Collaborative work is limited by heterogeneities, both what is being collected and how it is defined. To develop a core set for data collection in rheumatoid arthritis (RA) research which (1) allows harmonisation of data collection in future observational studies, (2) acts as a common data model against which existing databases can be mapped and (3) serves as a template for standardised data collection in routine clinical practice to support generation of research-quality data. A multistep, international multistakeholder consensus process was carried out involving voting via online surveys and two face-to-face meetings. A core set of 21 items (‘what to collect’) and their instruments (‘how to collect’) was agreed: age, gender, disease duration, diagnosis of RA, body mass index, smoking, swollen/tender joints, patient/evaluator global, pain, quality of life, function, composite scores, acute phase reactants, serology, structural damage, treatment and comorbidities. The core set should facilitate collaborative research, allow for comparisons across studies and harmonise future data from clinical practice via electronic medical record systems.

Current diagnostic tests applied to inflammatory arthritis lack the necessary specificity to appropriately categorise patients. There is a need for novel approaches to classify patients with these conditions. Herein we explored whether urinary proteomic biomarkers specific for different forms of arthritis (rheumatoid arthritis (RA), psoriatic arthritis (PsA), osteoarthritis (OA)) or chronic inflammatory conditions (inflammatory bowel disease (IBD)) can be identified. Fifty subjects per group with RA, PsA, OA or IBD and 50 healthy controls were included in the study. Two-thirds of these populations were randomly selected to serve as a training set, while the remaining one-third was reserved for validation. Sequential comparison of one group to the other four enabled identification of multiple urinary peptides significantly associated with discrete pathological conditions. Classifiers for the five groups were developed and subsequently tested blind in the validation test set. Upon unblinding, the classifiers demonstrated excellent performance, with an area under the curve between 0.90 and 0.97 per group. Identification of the peptide markers pointed to dysregulation of collagen synthesis and inflammation, but also novel inflammatory markers. We conclude that urinary peptide signatures can reliably differentiate between chronic arthropathies and inflammatory conditions with discrete pathogenesis.