The progress and potential of proteomic biomarkers for type 1 diabetes in children

Introduction: Although it is possible to identify the genetic risk for type 1 diabetes (T1D), it is not possible to predict who will develop the disease. New biomarkers are needed that would help understand the mechanisms of disease onset and when to administer targeted therapies and interventions.  Areas Covered: An overview is presented of international study efforts towards understanding the cause of T1D, including the collection of several extensive temporal sample series that follow the development of T1D in at risk children. The results of the proteomics analysis of these material are presented, which have included bodily fluids, such as serum or plasma and urine, as well as tissue samples from the pancreas. Expert Commentary: Promising recent reports have indicated detection of early proteomic changes in the serum of patients prior to diagnosis, potentially providing new measures for risk assessment.  Similarly, there has been evidence that post-translationally modifications may result in the recognition of islet cell proteins as autoantigens; proteins thus modified could be used as targets for immunomodulation to overcome the threat of autoimmune response.</p

Talking Points on Publicly Engaged Scholarship at IUPUI

Talking Points on Publicly Engaged Scholarship at IUPUI Informed by Public Scholarship at Indiana University-Purdue University Indianapolis, a concept paper written by the Faculty Learning Community (FLC) on Public Scholarship and refined through ongoing FLC work between 2015-18 in collaboration with faculty across the campus and with nationally-recognized scholars

Stromal mesenchyme cell genes of the human prostate and bladder

BACKGROUND: Stromal mesenchyme cells play an important role in epithelial differentiation and likely in cancer as well. Induction of epithelial differentiation is organ-specific, and the genes responsible could be identified through a comparative genomic analysis of the stromal cells from two different organs. These genes might be aberrantly expressed in cancer since cancer could be viewed as due to a defect in stromal signaling. We propose to identify the prostate stromal genes by analysis of differentially expressed genes between prostate and bladder stromal cells, and to examine their expression in prostate cancer. METHODS: Immunohistochemistry using antibodies to cluster designation (CD) cell surface antigens was first used to characterize the stromas of the prostate and bladder. Stromal cells were prepared from either prostate or bladder tissue for cell culture. RNA was isolated from the cultured cells and analyzed by DNA microarrays. Expression of candidate genes in normal prostate and prostate cancer was examined by RT-PCR. RESULTS: The bladder stroma was phenotypically different from that of the prostate. Most notable was the presence of a layer of CD13(+ )cells adjacent to the urothelium. This structural feature was also seen in the mouse bladder. The prostate stroma was uniformly CD13(-). A number of differentially expressed genes between prostate and bladder stromal cells were identified. One prostate gene, proenkephalin (PENK), was of interest because it encodes a hormone. Secreted proteins such as hormones and bioactive peptides are known to mediate cell-cell signaling. Prostate stromal expression of PENK was verified by an antibody raised against a PENK peptide, by RT-PCR analysis of laser-capture microdissected stromal cells, and by database analysis. Gene expression analysis showed that PENK expression was down-regulated in prostate cancer. CONCLUSION: Our findings show that the histologically similar stromas of the prostate and bladder are phenotypically different, and express organ-specific genes. The importance of these genes in epithelial development is suggested by their abnormal expression in cancer. Among the candidates is the hormone PENK and the down-regulation of PENK expression in cancer suggests a possible association with cancer development

A Widespread Bacterial Type VI Secretion Effector Superfamily Identified Using a Heuristic Approach

SummarySophisticated mechanisms are employed to facilitate information exchange between interfacing bacteria. A type VI secretion system (T6SS) of Pseudomonas aeruginosa was shown to deliver cell wall-targeting effectors to neighboring cells. However, the generality of bacteriolytic effectors and, moreover, of antibacterial T6S remained unknown. Using parameters derived from experimentally validated bacterial T6SS effectors we identified a phylogenetically disperse superfamily of T6SS-associated peptidoglycan-degrading effectors. The effectors separate into four families composed of peptidoglycan amidase enzymes of differing specificities. Effectors strictly co-occur with cognate immunity proteins, indicating that self-intoxication is a general property of antibacterial T6SSs and effector delivery by the system exerts a strong selective pressure in nature. The presence of antibacterial effectors in a plethora of organisms, including many that inhabit or infect polymicrobial niches in the human body, suggests that the system could mediate interbacterial interactions of both environmental and clinical significance

Screen-printed digital microfluidics combined with surface acoustic wave nebulization for hydrogen-deuterium exchange measurements

International audienc

Deep-sea microbes as tools to refine the rules of innate immune pattern recognition.

The assumption of near-universal bacterial detection by pattern recognition receptors is a foundation of immunology. The limits of this pattern recognition concept, however, remain undefined. As a test of this hypothesis, we determined whether mammalian cells can recognize bacteria that they have never had the natural opportunity to encounter. These bacteria were cultivated from the deep Pacific Ocean, where the genus Moritella was identified as a common constituent of the culturable microbiota. Most deep-sea bacteria contained cell wall lipopolysaccharide (LPS) structures that were expected to be immunostimulatory, and some deep-sea bacteria activated inflammatory responses from mammalian LPS receptors. However, LPS receptors were unable to detect 80% of deep-sea bacteria examined, with LPS acyl chain length being identified as a potential determinant of immunosilence. The inability of immune receptors to detect most bacteria from a different ecosystem suggests that pattern recognition strategies may be defined locally, not globally.R01 AI093589 - NIAID NIH HHS; P30 DK034854 - NIDDK NIH HHS; U19 AI133524 - NIAID NIH HHS; R01 AI147314 - NIAID NIH HHS; R01 AI116550 - NIAID NIH HHS; R37 AI116550 - NIAID NIH HHS; R01 AI123820 - NIAID NIH HHSAccepted manuscrip

Quantitative mass spectrometry reveals a role for the GTPase Rho1p in actin organization on the peroxisome membrane

We have combined classical subcellular fractionation with large-scale quantitative mass spectrometry to identify proteins that enrich specifically with peroxisomes of Saccharomyces cerevisiae. In two complementary experiments, isotope-coded affinity tags and tandem mass spectrometry were used to quantify the relative enrichment of proteins during the purification of peroxisomes. Mathematical modeling of the data from 306 quantified proteins led to a prioritized list of 70 candidates whose enrichment scores indicated a high likelihood of them being peroxisomal. Among these proteins, eight novel peroxisome-associated proteins were identified. The top novel peroxisomal candidate was the small GTPase Rho1p. Although Rho1p has been shown to be tethered to membranes of the secretory pathway, we show that it is specifically recruited to peroxisomes upon their induction in a process dependent on its interaction with the peroxisome membrane protein Pex25p. Rho1p regulates the assembly state of actin on the peroxisome membrane, thereby controlling peroxisome membrane dynamics and biogenesis

Unsupervised White Matter Fiber Clustering and Tract Probability Map Generation: Applications of a Gaussian Process framework for White Matter Fibers

With the increasing importance of fiber tracking in diffusion tensor images for clinical needs, there has been a growing demand for an objective mathematical framework to perform quantitative analysis of white matter fiber bundles incorporating their underlying physical significance. This paper presents such a novel mathematical framework that facilitates mathematical operations between tracts using an inner product based on Gaussian processes, between fibers which span a metric space. This metric facilitates combination of fiber tracts, rendering operations like tract membership to a bundle or bundle similarity simple. Based on this framework, we have designed an automated unsupervised atlas-based clustering method that does not require manual initialization nor an a priori knowledge of the number of clusters. Quantitative analysis can now be performed on the clustered tract volumes across subjects thereby avoiding the need for point parametrization of these fibers, or the use of medial or envelope representations as in previous work. Experiments on synthetic data demonstrate the mathematical operations. Subsequently, the applicability of the unsupervised clustering framework has been demonstrated on a 21 subject dataset

The human diabetes proteome project (HDPP): The 2014 update

Diabetes is an increasing worldwide problem leading to major associated health issues and increased health care costs. In 2012, 9.3% of the American population was affected by diabetes, according to the American Diabetes Association, with 1.7 million of new cases since during the year (www.diabetes.org). Proteome initiatives can provide a deeper understanding of the biology of this disease and help develop more effective treatments. The collaborative effort of the Human Diabetes Proteome Project (HDPP) brings together a wide variety of complementary resources to increase the existing knowledge about both type 1 and type 2 diabetes and their related complications. The goals are to identify proteins and protein isoforms associated with the pathology and to characterize underlying disease-related pathways and mechanisms. Moreover, a considerable effort is being made on data integration and network biology. Sharing these data with the scientific community will be an important part of the consortium. Here we report on: the content of the HDPP session held at the 12th HUPO meeting in Yokohama; recent achievements of the consortium; discussions of several HDPP workshops; as well as future HDPP directions as discussed at the 13th HUPO congress in Madrid, with a special attention given to the lists of prioritized, diabetes-related proteins and the proteomic means to study them.</p