Summary There is a current imperative to unravel the hierarchy of molecular pathways that drive the transition of early to established disease in rheumatoid arthritis (RA). Herein, we report a comprehensive RNA sequencing analysis of the molecular pathways that drive early RA progression in the disease tissue (synovium), comparing matched peripheral blood RNA-seq in a large cohort of early treatment-naive patients, namely, the Pathobiology of Early Arthritis Cohort (PEAC). We developed a data exploration website ( https://peac.hpc.qmul.ac.uk/ ) to dissect gene signatures across synovial and blood compartments, integrated with deep phenotypic profiling. We identified transcriptional subgroups in synovium linked to three distinct pathotypes: fibroblastic pauci-immune pathotype, macrophage-rich diffuse-myeloid pathotype, and a lympho-myeloid pathotype characterized by infiltration of lymphocytes and myeloid cells. This is suggestive of divergent pathogenic pathways or activation disease states. Pro-myeloid inflammatory synovial gene signatures correlated with clinical response to initial drug therapy, whereas plasma cell genes identified a poor prognosis subgroup with progressive structural damage.

Leaves of almost all C 4 lineages separate the reactions of photosynthesis into the mesophyll (M) and bundle sheath (BS). The extent to which messenger RNA profiles of M and BS cells from independent C 4 lineages resemble each other is not known. To address this, we conducted deep sequencing of RNA isolated from the M and BS of Setaria viridis and compared these data with publicly available information from maize (Zea mays). This revealed a high correlation (r = 0.89) between the relative abundance of transcripts encoding proteins of the core C 4 pathway in M and BS cells in these species, indicating significant convergence in transcript accumulation in these evolutionarily independent C 4 lineages. We also found that the vast majority of genes encoding proteins of the C 4 cycle in S. viridis are syntenic to homologs used by maize. In both lineages, 122 and 212 homologous transcription factors were preferentially expressed in the M and BS, respectively. Sixteen shared regulators of chloroplast biogenesis were identified, 14 of which were syntenic homologs in maize and S. viridis. In sorghum (Sorghum bicolor), a third C 4 grass, we found that 82% of these trans-factors were also differentially expressed in either M or BS cells. Taken together, these data provide, to our knowledge, the first quantification of convergence in transcript abundance in the M and BS cells from independent lineages of C 4 grasses. Furthermore, the repeated recruitment of syntenic homologs from large gene families strongly implies that parallel evolution of both structural genes and trans-factors underpins the polyphyletic evolution of this highly complex trait in the monocotyledons.

Genome-wide data is used to stratify patients into classes for precision medicine using clustering algorithms. A common problem in this area is selection of the number of clusters (K). The Monti consensus clustering algorithm is a widely used method which uses stability selection to estimate K. However, the method has bias towards higher values of K and yields high numbers of false positives. As a solution, we developed Monte Carlo reference-based consensus clustering (M3C), which is based on this algorithm. M3C simulates null distributions of stability scores for a range of K values thus enabling a comparison with real data to remove bias and statistically test for the presence of structure. M3C corrects the inherent bias of consensus clustering as demonstrated on simulated and real expression data from The Cancer Genome Atlas (TCGA). For testing M3C, we developed clusterlab, a new method for simulating multivariate Gaussian clusters.

No abstract

Patients with rheumatoid arthritis (RA) receive highly targeted biologic therapies without previous knowledge of target expression levels in the diseased tissue. Approximately 40% of patients do not respond to individual biologic therapies and 5–20% are refractory to all. In a biopsy-based, precision-medicine, randomized clinical trial in RA (R4RA; n = 164), patients with low/absent synovial B cell molecular signature had a lower response to rituximab (anti-CD20 monoclonal antibody) compared with that to tocilizumab (anti-IL6R monoclonal antibody) although the exact mechanisms of response/nonresponse remain to be established. Here, in-depth histological/molecular analyses of R4RA synovial biopsies identify humoral immune response gene signatures associated with response to rituximab and tocilizumab, and a stromal/fibroblast signature in patients refractory to all medications. Post-treatment changes in synovial gene expression and cell infiltration highlighted divergent effects of rituximab and tocilizumab relating to differing response/nonresponse mechanisms. Using ten-by-tenfold nested cross-validation, we developed machine learning algorithms predictive of response to rituximab (area under the curve (AUC) = 0.74), tocilizumab (AUC = 0.68) and, notably, multidrug resistance (AUC = 0.69). This study supports the notion that disease endotypes, driven by diverse molecular pathology pathways in the diseased tissue, determine diverse clinical and treatment–response phenotypes. It also highlights the importance of integration of molecular pathology signatures into clinical algorithms to optimize the future use of existing medications and inform the development of new drugs for refractory patients.

Motivation Clustering patient omic data is integral to developing precision medicine because it allows the identification of disease subtypes. A current major challenge is the integration multi-omic data to identify a shared structure and reduce noise. Cluster analysis is also increasingly applied on single-omic data, for example, in single cell RNA-seq analysis for clustering the transcriptomes of individual cells. This technology has clinical implications. Our motivation was therefore to develop a flexible and effective spectral clustering tool for both single and multi-omic data. Results We present Spectrum, a new spectral clustering method for complex omic data. Spectrum uses a self-tuning density-aware kernel we developed that enhances the similarity between points that share common nearest neighbours. It uses a tensor product graph data integration and diffusion procedure to reduce noise and reveal underlying structures. Spectrum contains a new method for finding the optimal number of clusters (K) involving eigenvector distribution analysis. Spectrum can automatically find K for both Gaussian and non-Gaussian structures. We demonstrate across 21 real expression datasets that Spectrum gives improved runtimes and better clustering results relative to other methods. Availability and implementation Spectrum is available as an R software package from CRAN https://cran.r-project.org/web/packages/Spectrum/index.html. Supplementary information Supplementary data are available at Bioinformatics online.

and the PEAC-R4RA Investigators Objective. To define the relationship of synovial B cells to clinical phenotypes at different stages of disease evolution and drug exposure in rheumatoid arthritis (RA).Methods. Synovial biopsy specimens and demographic and clinical data were collected from 2 RA cohorts (n = 329), one of patients with untreated early RA (n = 165) and one of patients with established RA with an inadequate response to tumor necrosis factor inhibitors (TNFi-IR; n = 164). Synovial tissue was subjected to hematoxylin and eosin and immunohistochemical staining and semiquantitative assessment for the degree of synovitis (on a scale of 0-9) and of CD20+ B cell infiltrate (on a scale of 0-4). B cell scores were validated by digital image analysis and B cell lineage-specific transcript analysis (RNA-Seq) in the early RA (n = 91) and TNFi-IR (n = 127) cohorts. Semiquantitative CD20 scores were used to classify patients as B cell rich (≥2) or B cell poor (<2).Results. Semiquantitative B cell scores correlated with digital image analysis quantitative measurements and B cell lineage-specific transcripts. B cell-rich synovitis was present in 35% of patients in the early RA cohort and 47.7% of patients in the TNFi-IR cohort (P = 0.025). B cell-rich patients showed higher levels of disease activity and seropositivity for rheumatoid factor and anti-citrullinated protein antibody in early RA but not in established RA, while significantly higher histologic synovitis scores in B cell-rich patients were demonstrated in both cohorts.Conclusion. We describe a robust semiquantitative histologic B cell score that closely replicates the quantification of B cells by digital or molecular analyses. Our findings indicate an ongoing B cell-rich synovitis, which does not seem to be captured by standard clinimetric assessment, in a larger proportion of patients with established RA than early RA.

No abstract