Comprehensive description of the N-glycoproteome of mouse pancreatic ß-cells and human islets.

Cell surface N-glycoproteins provide a key interface of cells to their environment and therapeutic entry points for drug and biomarker discovery. Their comprehensive description denotes therefore a formidable challenge. The ß-cells of the pancreas play a crucial role in blood glucose homeostasis, and disruption of their function contributes to diabetes. By combining cell surface and whole cell capturing technologies with high-throughput quantitative proteomic analysis, we report on the identification of a total of 956 unique N-glycoproteins from mouse MIN6 ß-cells and human islets. Three-hundred-forty-nine of these proteins encompass potential surface N-glycoproteins and include orphan G-protein-coupled receptors, novel proteases, receptor protein kinases, and phosphatases. Interestingly, stimulation of MIN6 ß-cells with glucose and the hormone GLP1, known stimulators of insulin secretion, causes significant changes in surface N-glycoproteome expression. Taken together, this ß-cell N-glycoproteome resource provides a comprehensive view on the composition of ß-cell surface proteins and expands the scope of signaling systems potentially involved in mediating responses of ß-cells to various forms of (patho)physiologic stress and the extent of dynamic remodeling of surface N-glycoprotein expression associated with metabolic and hormonal stimulation. Moreover, it provides a foundation for the development of diabetes medicines that target or are derived from the ß-cell surface N-glycoproteome.

Bioinformatics prediction of overlapping frameshifted translation products in mammalian transcripts.

BACKGROUND: Exceptionally, a single nucleotide sequence can be translated in vivo in two different frames to yield distinct proteins. In the case of the G-protein alpha subunit XL-alpha-s transcript, a frameshifted open reading frame (ORF) in exon 1 is translated to yield a structurally distinct protein called Alex, which plays a role in platelet aggregation and neurological processes. We carried out a novel bioinformatics screen for other possible dual-frame translated sequences, based on comparative genomics. RESULTS: Our method searched human, mouse and rat transcripts in frames +1 and -1 for ORFs which are unusually well conserved at the amino acid level. We name these conserved frameshifted overlapping ORFs 'matreshkas' to reflect their nested character. Select findings of our analysis revealed that the G-protein coupled receptor GPR27 is entirely contained within a frame -1 matreshka, thrombopoietin contains a matreshka which spans ~70% of its length, platelet glycoprotein IIIa (ITGB3) contains a matreshka with the predicted characteristics of a secreted peptide hormone, while the potassium channel KCNK12 contains a matreshka spanning >400 amino acids. CONCLUSION: Although the in vivo existence of translated matreshkas has not been experimentally verified, this genome-wide analysis provides strong evidence that substantial overlapping coding sequences exist in a number of human and rodent transcripts.

Considerations for Optimization of High-Throughput Sequencing Bioinformatics Pipelines for Virus Detection.

High-throughput sequencing (HTS) has demonstrated capabilities for broad virus detection based upon discovery of known and novel viruses in a variety of samples, including clinical, environmental, and biological. An important goal for HTS applications in biologics is to establish parameter settings that can afford adequate sensitivity at an acceptable computational cost (computation time, computer memory, storage, expense or/and efficiency), at critical steps in the bioinformatics pipeline, including initial data quality assessment, trimming/cleaning, and assembly (to reduce data volume and increase likelihood of appropriate sequence identification). Additionally, the quality and reliability of the results depend on the availability of a complete and curated viral database for obtaining accurate results; selection of sequence alignment programs and their configuration, that retains specificity for broad virus detection with reduced false-positive signals; removal of host sequences without loss of endogenous viral sequences of interest; and use of a meaningful reporting format, which can retain critical information of the analysis for presentation of readily interpretable data and actionable results. Furthermore, after alignment, both automated and manual evaluation may be needed to verify the results and help assign a potential risk level to residual, unmapped reads. We hope that the collective considerations discussed in this paper aid toward optimization of data analysis pipelines for virus detection by HTS.