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.