Structural and Functional Brain Connectivity in Middle-Aged Carriers of Risk Alleles for Alzheimer\u27s Disease

Single nucleotide polymorphisms (SNPs) in APOE, COMT, BDNF, and KIBRA have been associated with age-related memory performance and executive functioning as well as risk for Alzheimer’s disease (AD). The purpose of the present investigation was to characterize differences in brain functional and structural integrity associated with these SNPs as potential endophenotypes of age-related cognitive decline. I focused my investigation on healthy, cognitively normal middle-aged adults, as disentangling the early effects of healthy versus pathological aging in this group may aid early detection and prevention of AD. The aims of the study were 1) to characterize SNP-related differences in functional connectivity within two resting state networks (RSNs; default mode network [DMN] and executive control network [ECN]) associated with memory and executive functioning, respectively; 2) to identify differences in the white matter (WM) microstructural integrity of tracts underlying these RSNs; and 3) to characterize genotype differences in the graph properties of an integrated functional-structural network. Participants (age 40-60, N = 150) underwent resting state functional magnetic resonance imaging (rs-fMRI), diffusion tensor imaging (DTI), and genotyping. Independent components analysis (ICA) was used to derive RSNs, while probabilistic tractography was performed to characterize tracts connecting RSN subregions. A technique known as functional-by-structural hierarchical (FSH) mapping was used to create the integrated, whole brain functional-structural network, or resting state structural connectome (rsSC). I found that BDNF risk allele carriers had lower functional connectivity within the DMN, while KIBRA risk allele carriers had poorer WM microstructural integrity in tracts underlying the DMN and ECN. In addition to these differences in the connectivity of specific RSNs, I found significant impairments in the global and local topology of the rsSC across all evaluated SNPs. Collectively, these findings suggest that integrating multiple neuroimaging modalities and using graph theoretical analysis may reveal network-level vulnerabilities that may serve as biomarkers of age-related cognitive decline in middle age, decades before the onset of overt cognitive impairment

Cyclists' experiences in urban longitudinal traffic scenarios and their requirements for designing interactions with highly automated vehicles

As cycling becomes more popular and automated driving is on the rise, it can be assumed that in the city of the future highly automated vehicles (HA Vs) and cyclists will share the same roads. Yet only little is known about how cyclists announce their maneuvers to motorized vehicles or how they communicate and interact with them. Knowledge on these aspects is currently missing to guide the design of cyclist-HA V interactions. Situations where a cyclist rides upfront a vehicle, will be especially challenging for HA Vs, such as when a cyclist (A) avoids an obstacle on the road section ahead, (B) merges onto the road from an ending cycling path, or (C) leaves the road turning into a driveway {see Figure 1) [1 ]. Based on the cyclist's intention, the HA V will have to pass or keep following with only limited options to communicate to the cyclist ahead. Design solutions derived from the well-studied field of pedestrian-HA V interactions cannot simply be transferred to the here considered cyclist-HA V interactions, since in past research successful design concepts for pedestrians were not beneficial for cyclists [2]. Hence, it is vital to investigate the behavior and experiences of cyclists in more detail and to explore possible design solutions for HA V interaction behavior in these situations. With this study we aim to get more insights into the subjective experience of cyclists travelling in longitudinal traffic, especially during cyclist-vehicle interactions, as well as to derive cyclists' requirements to design safe and desirable cyclist-HA V interactions

Ribp, a Novel Rlk/Txk- and Itk-Binding Adaptor Protein That Regulates T Cell Activation

A novel T cell–specific adaptor protein, RIBP, was identified based on its ability to bind Rlk/Txk in a yeast two-hybrid screen of a mouse T cell lymphoma library. RIBP was also found to interact with a related member of the Tec family of tyrosine kinases, Itk. Expression of RIBP is restricted to T and natural killer cells and is upregulated substantially after T cell activation. RIBP-disrupted knockout mice displayed apparently normal T cell development. However, proliferation of RIBP-deficient T cells in response to T cell receptor (TCR)-mediated activation was significantly impaired. Furthermore, these activated T cells were defective in the production of interleukin (IL)-2 and interferon γ, but not IL-4. These data suggest that RIBP plays an important role in TCR-mediated signal transduction pathways and that its binding to Itk and Rlk/Txk may regulate T cell differentiation

Affinity maturation of antibodies requires integrity of the adult thymus

The generation of B‐cell responses to proteins requires a functional thymus to produce CD4 + T cells which helps in the activation and differentiation of B cells. Because the mature T‐cell repertoire has abundant cells with the helper phenotype, one might predict that in mature individuals, the generation of B‐cell memory would proceed independently of the thymus. Contrary to that prediction, we show here that the removal of the thymus after the establishment of the T‐cell compartment or sham surgery without removal of the thymus impairs the affinity maturation of antibodies. Because removal or manipulation of the thymus did not decrease the frequency of mutation of the Ig variable heavy chain exons encoding antigen‐specific antibodies, we conclude that the thymus controls affinity maturation of antibodies in the mature individual by facilitating the selection of B cells with high‐affinity antibodies.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90153/1/eji_201141889_sm_SupplInfo.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/90153/2/500_ftp.pd

A statistical approach for identifying differential distributions in single-cell RNA-seq experiments

The ability to quantify cellular heterogeneity is a major advantage of single-cell technologies. However, statistical methods often treat cellular heterogeneity as a nuisance. We present a novel method to characterize differences in expression in the presence of distinct expression states within and among biological conditions. We demonstrate that this framework can detect differential expression patterns under a wide range of settings. Compared to existing approaches, this method has higher power to detect subtle differences in gene expression distributions that are more complex than a mean shift, and can characterize those differences. The freely available R package scDD implements the approach. Electronic supplementary material The online version of this article (doi:10.1186/s13059-016-1077-y) contains supplementary material, which is available to authorized users

Detecting Neuroendocrine Prostate Cancer Through Tissue-Informed Cell-Free DNA Methylation Analysis

Purpose: Neuroendocrine prostate cancer (NEPC) is a resistance phenotype that emerges in men with metastatic castration-resistant prostate adenocarcinoma (CR-PRAD) and has important clinical implications, but is challenging to detect in practice. Herein, we report a novel tissue-informed epigenetic approach to noninvasively detect NEPC. Experimental Design: We first performed methylated immunoprecipitation and high-throughput sequencing (MeDIP-seq) on a training set of tumors, identified differentially methylated regions between NEPC and CR-PRAD, and built a model to predict the presence of NEPC (termed NEPC Risk Score). We then performed MeDIP-seq on cell-free DNA (cfDNA) from two independent cohorts of men with NEPC or CR-PRAD and assessed the accuracy of the model to predict the presence NEPC. Results: The test cohort comprised cfDNA samples from 48 men, 9 with NEPC and 39 with CR-PRAD. NEPC Risk Scores were significantly higher in men with NEPC than CR-PRAD (P = 4.3 × 10-7) and discriminated between NEPC and CR-PRAD with high accuracy (AUROC 0.96). The optimal NEPC Risk Score cutoff demonstrated 100% sensitivity and 90% specificity for detecting NEPC. The independent, multi-institutional validation cohort included cfDNA from 53 men, including 12 with NEPC and 41 with CR-PRAD. NEPC Risk Scores were significantly higher in men with NEPC than CR-PRAD (P = 7.5×10-12) and perfectly discriminated NEPC from CR-PRAD (AUROC 1.0). Applying the predefined NEPC Risk Score cutoff to the validation cohort resulted in 100% sensitivity and 95% specificity for detecting NEPC. Conclusions: Tissue-informed cfDNA methylation analysis is a promising approach for noninvasive detection of NEPC in men with advanced prostate cancer

Update on the hyper immunoglobulin M syndromes

The Hyper-immunoglobulin M syndromes (HIGM) are a heterogeneous group of genetic disorders resulting in defects of immunoglobulin class switch recombination (CSR), with or without defects of somatic hypermutation (SHM). They can be classified as defects of signalling through CD40 causing both a humoral immunodeficiency and a susceptibility to opportunistic infections, or intrinsic defects in B cells of the mechanism of CSR resulting in a pure humoral immunodeficiency. A HIGM picture can also be seen as part of generalized defects of DNA repair and in antibody deficiency syndromes, such as common variable immunodeficiency. CD40 signalling defects may require corrective therapy with bone marrow transplantation. Gene therapy, a potential curative approach in the future, currently remains a distant prospect. Those with a defective CSR mechanism generally do well on immunologoblulin replacement therapy. Complications may include autoimmunity, lymphoid hyperplasia and, in some cases, a predisposition to lymphoid malignancy

CD27− B-Cells Produce Class Switched and Somatically Hyper-Mutated Antibodies during Chronic HIV-1 Infection

Class switch recombination and somatic hypermutation occur in mature B-cells in response to antigen stimulation. These processes are crucial for the generation of functional antibodies. During HIV-1 infection, loss of memory B-cells, together with an altered differentiation of naïve B-cells result in production of low quality antibodies, which may be due to impaired immunoglobulin affinity maturation. In the current study, we evaluated the effect of HIV-1 infection on class switch recombination and somatic hypermutation by studying the expression of activation-induced cytidine deaminase (AID) in peripheral B-cells from a cohort of chronically HIV-1 infected patients as compared to a group of healthy controls. In parallel, we also characterized the phenotype of B-cells and their ability to produce immunoglobulins in vitro. Cells from HIV-1 infected patients showed higher baseline levels of AID expression and increased IgA production measured ex-vivo and upon CD40 and TLR9 stimulation in vitro. Moreover, the percentage of CD27−IgA+ and CD27−IgG+ B-cells in blood was significantly increased in HIV-1 infected patients as compared to controls. Interestingly, our results showed a significantly increased number of somatic hypermutations in the VH genes in CD27− cells from patients. Taken together, these results show that during HIV-1 infection, CD27− B-cells can also produce class switched and somatically hypermutated antibodies. Our data add important information for the understanding of the mechanisms underlying the loss of specific antibody production observed during HIV-1 infection

Computational approaches for interpreting scRNA-seq data.

The recent developments in high-throughput single-cell RNA sequencing technology (scRNA-seq) have enabled the generation of vast amounts of transcriptomic data at cellular resolution. With these advances come new modes of data analysis, building on high-dimensional data mining techniques. Here, we consider biological questions for which scRNA-seq data is used, both at a cell and gene level, and describe tools available for these types of analyses. This is an exciting and rapidly evolving field, where clustering, pseudotime inference, branching inference and gene-level analyses are particularly informative areas of computational analysis