Peroxisomes are highly metabolic, autonomously replicating organelles that generate ROS as a by product of fatty acid β-oxidation. Consequently, cells must maintain peroxisome homeostasis, or risk pathologies associated with too few peroxisomes, such as peroxisome biogenesis disorders, or too many peroxisomes, inducing oxidative damage and promoting diseases such as cancer. We report that the PEX5 peroxisome import receptor binds ataxia-telangiectasia mutated (ATM) and localizes this kinase to the peroxisome. In response to reactive oxygen species (ROS), ATM signaling activates ULK1 and inhibits mTORC1 to induce autophagy. Specificity for autophagy of peroxisomes (pexophagy) is provided by ATM phosphorylation of PEX5 at Ser141, which promotes PEX5 mono-ubiquitination at K209, and recognition of ubiquitinated PEX5 by the autophagy adapter protein p62, directing the autophagosome to peroxisomes to induce pexophagy. These data reveal an important new role for ATM in metabolism as a sensor of ROS that regulates pexophagy.

SUMMARY Posttranslational modifications (PTMs) of tubulin specify microtubules for specialized cellular functions and comprise what is termed a “tubulin code”. PTMs of histones comprise an analogous “histone code”, although the “readers, writers and erasers” of the cytoskeleton and epigenome have heretofore been distinct. We show that methylation is a PTM of dynamic microtubules, and that the histone methytransferase, SETD2, which is responsible for H3 lysine 36 trimethylation (H3K36me3) of histones, also methylates α-tubulin at lysine 40, the same lysine that is marked by acetylation on microtubules. Methylation of microtubules occurs during mitosis and cytokinesis, and can be ablated by SETD2 deletion, which causes mitotic spindle and cytokinesis defects, micronuclei and polyploidy. These data now identify SETD2 as a dual function methyltransferase for both chromatin and the cytoskeleton, and show a requirement for methylation in maintenance of genomic stability and the integrity of both the tubulin and histone codes.

The Cancer Genome Atlas (TCGA) represents one of several international consortia dedicated to performing comprehensive genomic and epigenomic analyses of selected tumour types to advance our understanding of disease and provide an open-access resource for worldwide cancer research. Thirty-three tumour types (selected by histology or tissue of origin, to include both common and rare diseases), comprising >11 000 specimens, were subjected to DNA sequencing, copy number and methylation analysis, and transcriptomic, proteomic and histological evaluation. Each cancer type was analysed individually to identify tissue-specific alterations, and make correlations across different molecular platforms. The final dataset was then normalized and combined for the PanCancer Initiative, which seeks to identify commonalities across different cancer types or cells of origin/lineage, or within anatomically or morphologically related groups. An important resource generated along with the rich molecular studies is an extensive digital pathology slide archive, composed of frozen section tissue directly related to the tissues analysed as part of TCGA, and representative formalin-fixed paraffin-embedded, haematoxylin and eosin (H&E)-stained diagnostic slides. These H&E image resources have primarily been used to verify diagnoses and histological subtypes with some limited extraction of standard pathological variables such as mitotic activity, grade, and lymphocytic infiltrates. Largely overlooked is the richness of these scanned images for more sophisticated feature extraction approaches coupled with machine learning, and ultimately correlation with molecular features and clinical endpoints. Here, we document initial attempts to exploit TCGA imaging archives, and describe some of the tools, and the rapidly evolving image analysis/feature extraction landscape. Our hope is to inform, and ultimately inspire and challenge, the pathology and cancer research communities to exploit these imaging resources so that the full potential of this integral platform of TCGA can be used to complement and enhance the insightful integrated analyses from the genomic and epigenomic platforms.

Summary Environmental exposures to chemically heterogeneous endocrine disrupting chemicals (EDCs) mimic or interfere with hormone actions, and negatively impact human health. Despite public interest and the prevalence of EDCs in the environment, methods to mechanistically classify these diverse chemicals in a high throughput (HT) manner have not been actively explored. Here, we describe the use of multi-parametric, HT microscopy-based platforms to examine how a prototypical EDC, Bisphenol A (BPA), and eighteen poorly studied analogs (BPXs), affect estrogen receptor (ER). We show that short exposure to BPA and most BPXs induce ERα and/or ERβ and change levels of target gene transcription. Many BPXs exhibit higher affinity for ERβ and act as ERβ antagonists, while they act largely as agonists or mixed agonists/antagonists on ERα. Finally, despite binding to ERs, some BPXs exhibit lower levels of activity. Our comprehensive view of BPXs activities allows their classification and evaluation of potential harmful effects. The strategy described here used on a large scale basis likely offers a faster, more cost-effective way to identify safer BPA alternatives.