Global S&T Development Trend Analysis Platform of Resources and Environment
NOTCH3 signalling is shown to be the underlying driver of the differentiation and expansion of a subset of synovial fibroblasts implicated in the pathogenesis of rheumatoid arthritis.
The synovium is a mesenchymal tissue composed mainly of fibroblasts, with a lining and sublining that surround the joints. In rheumatoid arthritis the synovial tissue undergoes marked hyperplasia, becomes inflamed and invasive, and destroys the joint(1,2). It has recently been shown that a subset of fibroblasts in the sublining undergoes a major expansion in rheumatoid arthritis that is linked to disease activity(3-5)
Microbial nucleic acids are detected in samples of tissues and blood from more than 10,000 patients with cancer, and machine learning is used to show that these can be used to discriminate between and among different types of cancer, suggesting a new microbiome-based diagnostic approach.
Systematic characterization of the cancer microbiome provides the opportunity to develop techniques that exploit non-human, microorganism-derived molecules in the diagnosis of a major human disease. Following recent demonstrations that some types of cancer show substantial microbial contributions(1-10), we re-examined whole-genome and whole-transcriptome sequencing studies in The Cancer Genome Atlas(11) (TCGA) of 33 types of cancer from treatment-naive patients (a total of 18,116 samples) for microbial reads, and found unique microbial signatures in tissue and blood within and between most major types of cancer. These TCGA blood signatures remained predictive when applied to patients with stage Ia-IIc cancer and cancers lacking any genomic alterations currently measured on two commercial-grade cell-free tumour DNA platforms, despite the use of very stringent decontamination analyses that discarded up to 92.3% of total sequence data. In addition, we could discriminate among samples from healthy, cancer-free individuals (n = 69) and those from patients with multiple types of cancer (prostate, lung, and melanoma
Protein structure prediction can be used to determine the three-dimensional shape of a protein from its amino acid sequence(1). This problem is of fundamental importance as the structure of a protein largely determines its function(2)
