The secretome atlas of two mouse models of progeria.

Hutchinson-Gilford progeria syndrome (HGPS) is a rare genetic disease caused by nuclear envelope alterations that lead to accelerated aging and premature death. Several studies have linked health and longevity to cell-extrinsic mechanisms, highlighting the relevance of circulating factors in the aging process as well as in age-related diseases. We performed a global plasma proteomic analysis in two preclinical progeroid models (LmnaG609G/G609G and Zmpste24-/- mice) using aptamer-based proteomic technology. Pathways related to the extracellular matrix, growth factor response and calcium ion binding were among the most enriched in the proteomic signature of progeroid samples compared to controls. Despite the global downregulation trend found in the plasma proteome of progeroid mice, several proteins associated with cardiovascular disease, the main cause of death in HGPS, were upregulated. We also developed a chronological age predictor using plasma proteome data from a cohort of healthy mice (aged 1-30 months), that reported an age acceleration when applied to progeroid mice, indicating that these mice exhibit an "old" plasma proteomic signature. Furthermore, when compared to naturally-aged mice, a great proportion of differentially expressed circulating proteins in progeroid mice were specific to premature aging, highlighting secretome-associated differences between physiological and accelerated aging. This is the first large-scale profiling of the plasma proteome in progeroid mice, which provides an extensive list of candidate circulating plasma proteins as potential biomarkers and/or therapeutic targets for further exploration and hypothesis generation in the context of both physiological and premature aging.

RFcaller: a machine learning approach combined with read-level features to detect somatic mutations.

The cost reduction in sequencing and the extensive genomic characterization of a wide variety of cancers are expanding tumor sequencing to a wide number of research groups and the clinical practice. Although specific pipelines have been generated for the identification of somatic mutations, their results usually differ considerably, and a common approach is to use several callers to achieve a more reliable set of mutations. This procedure is computationally expensive and time-consuming, and it suffers from the same limitations in sensitivity and specificity as other approaches. Expert revision of mutant calls is therefore required to verify calls that might be used for clinical diagnosis. This step could take advantage of machine learning techniques, as they provide a useful approach to incorporate expert-reviewed information for the identification of somatic mutations. Here we present RFcaller, a pipeline based on machine learning algorithms, for the detection of somatic mutations in tumor-normal paired samples that does not require large computing resources. RFcaller shows high accuracy for the detection of substitutions and insertions/deletions from whole genome or exome data. It allows the detection of mutations in driver genes missed by other approaches, and has been validated by comparison to deep and Sanger sequencing.

Molecular map of chronic lymphocytic leukemia and its impact on outcome.

Recent advances in cancer characterization have consistently revealed marked heterogeneity, impeding the completion of integrated molecular and clinical maps for each malignancy. Here, we focus on chronic lymphocytic leukemia (CLL), a B cell neoplasm with variable natural history that is conventionally categorized into two subtypes distinguished by extent of somatic mutations in the heavy-chain variable region of immunoglobulin genes (IGHV). To build the 'CLL map,' we integrated genomic, transcriptomic and epigenomic data from 1,148 patients. We identified 202 candidate genetic drivers of CLL (109 new) and refined the characterization of IGHV subtypes, which revealed distinct genomic landscapes and leukemogenic trajectories. Discovery of new gene expression subtypes further subcategorized this neoplasm and proved to be independent prognostic factors. Clinical outcomes were associated with a combination of genetic, epigenetic and gene expression features, further advancing our prognostic paradigm. Overall, this work reveals fresh insights into CLL oncogenesis and prognostication.

PanCancer analysis of somatic mutations in repetitive regions reveals recurrent mutations in snRNA U2.

Current somatic mutation callers are biased against repetitive regions, preventing the identification of potential driver alterations in these loci. We developed a mutation caller for repetitive regions, and applied it to study repetitive non protein-coding genes in more than 2200 whole-genome cases. We identified a recurrent mutation at position c.28 in the gene encoding the snRNA U2. This mutation is present in B-cell derived tumors, as well as in prostate and pancreatic cancer, suggesting U2 c.28 constitutes a driver candidate associated with worse prognosis. We showed that the GRCh37 reference genome is incomplete, lacking the U2 cluster in chromosome 17, preventing the identification of mutations in this gene. Furthermore, the 5'-flanking region of WDR74, previously described as frequently mutated in cancer, constitutes a functional copy of U2. These data reinforce the relevance of non-coding mutations in cancer, and highlight current challenges of cancer genomic research in characterizing mutations affecting repetitive genes.

No genome-wide DNA methylation changes found associated with medium-term reduced graphene oxide exposure in human lung epithelial cells.

The presence of nanomaterials in our everyday life is ever increasing, and so too are concerns about the possible health consequences of exposure to them. While evidence of their biological activity is growing, there is still scant knowledge of the epigenetic mechanisms that could be at play in these processes. Moreover, the great variability in the chemical and physical structures of these compounds handicaps the study of their possible health risks. Here we have synthesized reduced graphene oxide (rGO) through the thermal exfoliation/reduction of graphite oxide, and characterized the resulting material. We have then made use of Illumina's MethylationEPIC arrays and bisulphite pyrosequencing to analyse the genome-wide and global DNA methylation dynamics associated with the medium-term exposure of human lung epithelial cells to rGO at concentrations of 1 and 10 µg/mL. The results show no genome-wide or global DNA methylation changes associated with either condition. Our observations thus suggest that medium-term rGO exposure does not have significant effects on the DNA methylation patterns of human lung epithelial cells.