During successful pregnancy, a woman is immunologically tolerant of her genetically and antigenically disparate fetus, a state known as maternal-fetal tolerance. How this state is maintained has puzzled investigators for more than half a century. Diverse, immune and nonimmune mechanisms have been proposed; however, these mechanisms appear to be unrelated and to act independently. A population of immune suppressive cells called myeloid-derived suppressor cells (MDSCs) accumulates in pregnant mice and women. Given the profound immune suppressive function of MDSCs, it has been suggested that this cell population may facilitate successful pregnancy by contributing to maternal-fetal tolerance. We now report that myeloid cells with the characteristics of MDSCs not only accumulate in the circulation and uterus of female mice following mating but also suppress T cell activation and function in pregnant mice. Depletion of cells with the phenotype and function of MDSCs from gestation d 0.5 through d 7.5 resulted in implantation failure, increased T cell activation, and increased T cell infiltration into the uterus, whereas induction of MDSCs restored successful pregnancy and reduced T cell activation. MDSC-mediated suppression during pregnancy was accompanied by the down-regulation of L-selectin on naïve T cells and a reduced ability of naïve T cells to enter lymph nodes and become activated. Because MDSCs regulate many of the immune and nonimmune mechanisms previously attributed to maternal-fetal tolerance, MDSCs may be a unifying mechanism promoting maternal-fetal tolerance, and their induction may facilitate successful pregnancy in women who spontaneously abort or miscarry because of dysfunctional maternal-fetal tolerance.

The purpose of this study was to assess effects of a mobile coaching system on glycated hemoglobin (HbA1c) levels in younger versus older patients over 1 year. Participants (n = 118) included adult patients with Type 2 diabetes cared for by community physicians. Intervention patients received mobile phone coaching and individualized web portal. Control patients received usual care. Patients were stratified into two age groups: younger (<55 years) and older (≥ 55 years). The intervention resulted in greater 12-month declines in HbA1c, compared with usual care, for patients in both age groups (p < .0001). Among older patients, HbA1c changed by -1.8% (95% confidence interval [CI] = [-2.4, -1.1]) in the intervention group and -0.3% (95% CI = [-0.9, +0.3]) in the control group. Among younger patients, HbA1c changed by -2.0% (95% CI = [-2.5, -1.5]) in the intervention group and -1.0% (95% CI = [-1.6, -0.4]) in the control group. The mobile health intervention was as effective at managing Type 2 diabetes in older adults as younger persons.

Genes adjacent to telomeres are subject to transcriptional repression mediated by an integrated set of chromatin modifying and remodeling factors. The telomeres of Saccharomyces cerevisiae have served as a model for dissecting the function of diverse chromatin proteins in gene silencing, and their study has revealed overlapping roles for many chromatin proteins in either promoting or antagonizing gene repression. The H3K4 methyltransferase Set1, which is commonly linked to transcriptional activation, has been implicated in telomere silencing. Set5 is an H4 K5, K8, and K12 methyltransferase that functions with Set1 to promote repression at telomeres. Here, we analyzed the combined role for Set1 and Set5 in gene expression control at native yeast telomeres. Our data reveal that Set1 and Set5 promote a Sir proteinindependent mechanism of repression that may primarily rely on regulation of H4K5ac and H4K8ac at telomeric regions. Furthermore, cells lacking both Set1 and Set5 have highly correlated transcriptomes to mutants in telomere maintenance pathways and display defects in telomere stability, linking their roles in silencing to protection of telomeres. Our data therefore provide insight into and clarify potential mechanisms by which Set1 contributes to telomere silencing and shed light on the function of Set5 at telomeres.

BackgroundLarge-scale tumor sequencing projects are now underway to identify genetic mutations that drive tumor initiation and development. Most studies take a gene-based approach to identifying driver mutations, highlighting genes mutated in a large percentage of tumor samples as those likely to contain driver mutations. However, this gene-based approach usually does not consider the position of the mutation within the gene or the functional context the position of the mutation provides. Here we introduce a novel method for mapping mutations to distinct protein domains, not just individual genes, in which they occur, thus providing the functional context for how the mutation contributes to disease. Furthermore, aggregating mutations from all genes containing a specific protein domain enables the identification of mutations that are rare at the gene level, but that occur frequently within the specified domain. These highly mutated domains potentially reveal disruptions of protein function necessary for cancer development.ResultsWe mapped somatic mutations from the protein coding regions of 100 colon adenocarcinoma tumor samples to the genes and protein domains in which they occurred, and constructed topographical maps to depict the “mutational landscapes” of gene and domain mutation frequencies. We found significant mutation frequency in a number of genes previously known to be somatically mutated in colon cancer patients including APC, TP53 and KRAS. In addition, we found significant mutation frequency within specific domains located in these genes, as well as within other domains contained in genes having low mutation frequencies. These domain “peaks” were enriched with functions important to cancer development including kinase activity, DNA binding and repair, and signal transduction.ConclusionsUsing our method to create the domain landscapes of mutations in colon cancer, we were able to identify somatic mutations with high potential to drive cancer development. Interestingly, the majority of the genes involved have a low mutation frequency. Therefore, themethod shows good potential for identifying rare driver mutations in current, large-scale tumor sequencing projects. In addition, mapping mutations to specific domains provides the necessary functional context for understanding how the mutations contribute to the disease, and may reveal novel or more refined gene and domain target regions for drug development.

SIGNIFICANCE:This study reports prevalence data combined independently for accommodative dysfunction, convergence insufficiency, visual field loss, and visual acuity loss in patients with traumatic brain injury in the absence of eye injury. OBJECTIVE:The objective of this study was to conduct a systematic review and meta-analysis to determine the prevalence rates of accommodative dysfunction, convergence insufficiency, visual field loss, and visual acuity loss in TBI patients without concomitant eye injury.

By aggregating mutations with known disease association at the domain level, the method was able to discover domain positions enriched with multiple occurrences of deleterious mutations while incorporating relevant functional annotations. The method can be incorporated into translational bioinformatics tools to characterize rare and novel variants within large-scale sequencing studies.

Summary In recent mutation studies, analyses based on protein domain positions are gaining popularity over gene-centric approaches since the latter have limitations in considering the functional context that the position of the mutation provides. This presents a large-scale simultaneous inference problem, with hundreds of hypothesis tests to consider at the same time. This paper aims to select significant mutation counts while controlling a given level of Type I error via False Discovery Rate (FDR) procedures. One main assumption is that the mutation counts follow a zero-inflated model in order to account for the true zeros in the count model and the excess zeros. The class of models considered is the Zero-inflated Generalized Poisson (ZIGP) distribution. Furthermore, we assumed that there exists a cut-off value such that smaller counts than this value are generated from the null distribution. We present several data-dependent methods to determine the cut-off value. We also consider a two-stage procedure based on screening process so that the number of mutations exceeding a certain value should be considered as significant mutations. Simulated and protein domain data sets are used to illustrate this procedure in estimation of the empirical null using a mixture of discrete distributions. Overall, while maintaining control of the FDR, the proposed two-stage testing procedure has superior empirical power.

The fight against cancer is hindered by its highly heterogeneous nature. Genome-wide sequencing studies have shown that individual malignancies contain many mutations that range from those commonly found in tumor genomes to rare somatic variants present only in a small fraction of lesions. Such rare somatic variants dominate the landscape of genomic mutations in cancer, yet efforts to correlate somatic mutations found in one or few individuals with functional roles have been largely unsuccessful. Traditional methods for identifying somatic variants that drive cancer are ‘gene-centric’ in that they consider only somatic variants within a particular gene and make no comparison to other similar genes in the same family that may play a similar role in cancer. In this work, we present oncodomain hotspots, a new ‘domain-centric’ method for identifying clusters of somatic mutations across entire gene families using protein domain models. Our analysis confirms that our approach creates a framework for leveraging structural and functional information encapsulated by protein domains into the analysis of somatic variants in cancer, enabling the assessment of even rare somatic variants by comparison to similar genes. Our results reveal a vast landscape of somatic variants that act at the level of domain families altering pathways known to be involved with cancer such as protein phosphorylation, signaling, gene regulation, and cell metabolism. Due to oncodomain hotspots’ unique ability to assess rare variants, we expect our method to become an important tool for the analysis of sequenced tumor genomes, complementing existing methods.