NEW YORK (GenomeWeb News) – An international team of researchers led by Robert Plenge from Brigham and Women's Hospital uncovered nearly four dozen loci linked to risk of rheumatoid arthritis, bringing the total number of known risk loci for the disease to 101.
Rheumatoid arthritis, an autoimmune disease, affects some 1 percent of the adult population and leads to joint inflammation.
As they reported in Nature yesterday, the team of investigators conducted a three-stage genome-wide association study meta-analysis that drew upon 29,880 rheumatoid arthritis cases and 73,758 controls of European and Asian descent. They examined about 10 million SNPs in that population to find 42 risk loci.
Using a bioinformatics pipeline, the investigators homed in on 98 biologically plausible candidate genes based on the total 101 risk loci.
These candidate genes, the researchers noted, could reflect possible drug targets to treat the disease. Indeed, 27 drug target genes for drugs approved to treat rheumatoid arthritis overlapped with those 98 candidate genes. Drugs approved for other conditions may also be re-purposed as rheumatoid arthritis therapeutics, the researchers noted. For instance, they uncovered risk loci located near the genes CDK6 and CDK4, which are already targets of three approved drugs for cancer.
"We can use this knowledge to figure out the molecular pathways of disease, and which drugs we already have (for treating cardiovascular disease, for instance) that might also be effective against rheumatoid arthritis," said author Kathy Siminovitch, the director of the Office of Personalized Genomics and Innovative Medicine at Mount Sinai Hospital in Toronto, in a statement. "There is also the potential to develop new therapies targeted to some of the specific disease processes that are suggested by these genetic findings."
Using a CRISPR-Cas9 system, researchers from the Wellcome Trust Sanger Institute developed a library of mutations in mouse embryonic stem cells, as they reported in Nature Biotechnology this week.
Genome-wide RNAi screens, the researchers noted, can be used to identify genes and phenotypes of interest, but they can also lead to off-target effects and don't always lead to full suppression of gene activity. A CRISPR-based genome-editing tool, they said, could generate genome-wide knockout libraries.
The investigators drew upon nearly 88,000 guide RNAs targeting 19,150 mouse protein-coding genes and, using a lentiviral vector, expressed those guide RNAs in embryonic stem cells constitutively expressing Cas9. They then screened the libraries they made for resistance to Clostridium septicum alpha-toxin or to 6-thioguanine and found 27 known and four novel genes associated with resistance.
"CRISPR technology is revolutionizing how we study the behavior of cells," the Wellcome Trust's Kosuke Yusa said in a statement. "We've developed a thorough library that can be used by other researchers to study the role of any gene. We can create a library of this type for any cell or any species."
A team of researchers from Italy and the US appraised nine read-trimming algorithms and their effects on sequencing data analysis, as it reported in PLOS One this week.
Before raw sequenced reads are analyzed, they typically go through processing steps such as read trimming, which removes low-quality nucleotides from the reads.
Here, the researchers examined nine algorithms, including ERNE-FILTER, FASTX quality trimmer, and SolexaQA, among others, and how they faired dealing with four datasets and three sequencing applications — RNA-seq, SNP calling, and genome assembly.
While read trimming can improve downstream analyses and reduce computational requirements, the team also reported that the different algorithms behave differently and that that also depends on the parameters used. Additionally, five algorithms — Cutadapt, ERNEFILTER, FASTX, PRINSEQ, and SolexaQA-BWA — have some behavioral similarities while ConDeTri, Sickle, SolexaQA, and Trimmomatic act a bit differently.
"As for the generic question 'what is the best trimming algorithm?' no generic answer can be given, since this is highly dependent on the dataset, downstream analysis and user-decided parameter-dependent tradeoffs," the researchers added.
Copy-number variations in circulating tumor cells from lung cancer patients are cancer-type specific and are reproducible between cells as well as between patients, Peking University and Peking University Cancer Hospital and Institute researchers reported in the Proceedings of the National Academy of Sciences this week.
Researchers led by Peking's Jie Wang used a single-cell whole-genome amplification approach along with multiple annealing and looping-based amplification cycles to analyze the whole genomes of circulating tumor cells from 11 patients with lung cancer. They further analyzed the whole exomes of 24 individual CTCs from four lung adenocarcinoma patients and compared those exomes to those from the patients' primary and metastatic tumors.
Overall, the researchers found that the CTC genes harbored characteristic disease-associated SNVs and indels, though those mutations were heterogeneous from cell to cell. Within patients, though, the researchers found that the CTCs had reproducible CNV patterns that were similar to the patterns seen in their metastatic tumor cells. In addition, different patients with adenocarcinoma had similar CNV patterns, and that pattern was distinct from the CNV pattern seen in small-cell lung cancer patients.
"The reproducible CNV patterns that are characteristic of different cancers might allow noninvasive cancer diagnostics and classification through sequencing of CTCs," the researchers wrote.