Schwann Cell O-GlcNAc Glycosylation Is Required for Myelin Maintenance and Axon Integrity

Schwann cells (SCs), ensheathing glia of the peripheral nervous system, support axonal survival and function. Abnormalities in SC metabolism affect their ability to provide this support and maintain axon integrity. To further interrogate this metabolic influence on axon–glial interactions, we generated OGT-SCKO mice with SC-specific deletion of the metabolic/nutrient sensing protein O-GlcNAc transferase that mediates the O-linked addition of N-acetylglucosamine (GlcNAc) moieties to Ser and Thr residues. The OGT-SCKO mice develop tomaculous demyelinating neuropathy characterized by focal thickenings of the myelin sheath (tomacula), progressive demyelination, axonal loss, and motor and sensory nerve dysfunction. Proteomic analysis identified more than 100 O-GlcNAcylated proteins in rat sciatic nerve, including Periaxin (PRX), a myelin protein whose mutation causes inherited neuropathy in humans. PRX lacking O-GlcNAcylation is mislocalized within the myelin sheath of these mutant animals. Furthermore, phenotypes of OGT-SCKO and Prx-deficient mice are very similar, suggesting that metabolic control of PRX O-GlcNAcylation is crucial for myelin maintenance and axonal integrity. SIGNIFICANCE STATEMENT The nutrient sensing protein O-GlcNAc transferase (OGT) mediates post-translational O-linked N-acetylglucosamine (GlcNAc) modification. Here we find that OGT functions in Schwann cells (SCs) to maintain normal myelin and prevent axonal loss. SC-specific deletion of OGT (OGT-SCKO mice) causes a tomaculous demyelinating neuropathy accompanied with progressive axon degeneration and motor and sensory nerve dysfunction. We also found Periaxin (PRX), a myelin protein whose mutation causes inherited neuropathy in humans, is O-GlcNAcylated. Importantly, phenotypes of OGT-SCKO and Prx mutant mice are very similar, implying that compromised PRX function contributes to the neuropathy of OGT-SCKO mice. This study will be useful in understanding how SC metabolism contributes to PNS function and in developing new strategies for treating peripheral neuropathy by targeting SC function

Social and Physical Environments and Disparities in Risk for Cardiovascular Disease: The Healthy Environments Partnership Conceptual Model

The Healthy Environments Partnership (HEP) is a community-based participatory research effort investigating variations in cardiovascular disease risk, and the contributions of social and physical environments to those variations, among non-Hispanic black, non-Hispanic white, and Hispanic residents in three areas of Detroit, Michigan. Initiated in October 2000 as a part of the National Institute of Environmental Health Sciences’ Health Disparities Initiative, HEP is affiliated with the Detroit Community–Academic Urban Research Center. The study is guided by a conceptual model that considers race-based residential segregation and associated concentrations of poverty and wealth to be fundamental factors influencing multiple, more proximate predictors of cardiovascular risk. Within this model, physical and social environments are identified as intermediate factors that mediate relationships between fundamental factors and more proximate factors such as physical activity and dietary practices that ultimately influence anthropomorphic and physiologic indicators of cardiovascular risk. The study design and data collection methods were jointly developed and implemented by a research team based in community-based organizations, health service organizations, and academic institutions. These efforts include collecting and analyzing airborne particulate matter over a 3-year period; census and administrative data; neighborhood observation checklist data to assess aspects of the physical and social environment; household survey data including information on perceived stressors, access to social support, and health-related behaviors; and anthropometric, biomarker, and self-report data as indicators of cardiovascular health. Through these collaborative efforts, HEP seeks to contribute to an understanding of factors that contribute to racial and socioeconomic health inequities, and develop a foundation for efforts to eliminate these disparities in Detroit

Ancient Lowland Maya neighborhoods: Average Nearest Neighbor analysis and kernel density models, environments, and urban scale

Many humans live in large, complex political centers, composed of multi-scalar communities including neighborhoods and districts. Both today and in the past, neighborhoods form a fundamental part of cities and are defined by their spatial, architectural, and material elements. Neighborhoods existed in ancient centers of various scales, and multiple methods have been employed to identify ancient neighborhoods in archaeological contexts. However, the use of different methods for neighborhood identification within the same spatiotemporal setting results in challenges for comparisons within and between ancient societies. Here, we focus on using a single method—combining Average Nearest Neighbor (ANN) and Kernel Density (KD) analyses of household groups—to identify potential neighborhoods based on clusters of households at 23 ancient centers across the Maya Lowlands. While a one-size-fits all model does not work for neighborhood identification everywhere, the ANN/KD method provides quantifiable data on the clustering of ancient households, which can be linked to environmental zones and urban scale. We found that centers in river valleys exhibited greater household clustering compared to centers in upland and escarpment environments. Settlement patterns on flat plains were more dispersed, with little discrete spatial clustering of households. Furthermore, we categorized the ancient Maya centers into discrete urban scales, finding that larger centers had greater variation in household spacing compared to medium-sized and smaller centers. Many larger political centers possess heterogeneity in household clustering between their civic-ceremonial cores, immediate hinterlands, and far peripheries. Smaller centers exhibit greater household clustering compared to larger ones. This paper quantitatively assesses household clustering among nearly two dozen centers across the Maya Lowlands, linking environment and urban scale to settlement patterns. The findings are applicable to ancient societies and modern cities alike; understanding how humans form multi-scalar social groupings, such as neighborhoods, is fundamental to human experience and social organization

Sushi in the United States, 1945-1970

Sushi first achieved widespread popularity in the United States in the mid-1960s. Many accounts of sushi’s US establishment foreground the role of a small number of key actors, yet underplay the role of a complex web of large-scale factors that provided the context in which sushi was able to flourish. This article critically reviews existing literature, arguing that sushi’s US popularity arose from contingent, long-term, and gradual processes. It examines US newspaper accounts of sushi during 1945–1970, which suggest the discursive context for US acceptance of sushi was considerably more propitious than generally acknowledged. Using California as a case study, the analysis also explains conducive social and material factors, and directs attention to the interplay of supply- and demand-side forces in the favorable positioning of this “new” food. The article argues that the US establishment of sushi can be understood as part of broader public acceptance of Japanese cuisine

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment