Role of a Contactin multi-molecular complex secreted by oligodendrocytes in nodal protein clustering in the CNS.

The fast and reliable propagation of action potentials along myelinated fibers relies on the clustering of voltage-gated sodium channels at nodes of Ranvier. Axo-glial communication is required for assembly of nodal proteins in the central nervous system, yet the underlying mechanisms remain poorly understood. Oligodendrocytes are known to support node of Ranvier assembly through paranodal junction formation. In addition, the formation of early nodal protein clusters (or prenodes) along axons prior to myelination has been reported, and can be induced by oligodendrocyte conditioned medium (OCM). Our recent work on cultured hippocampal neurons showed that OCM-induced prenodes are associated with an increased conduction velocity (Freeman et al., 2015). We here unravel the nature of the oligodendroglial secreted factors. Mass spectrometry analysis of OCM identified several candidate proteins (i.e., Contactin-1, ChL1, NrCAM, Noelin2, RPTP/Phosphacan, and Tenascin-R). We show that Contactin-1 combined with RPTP/Phosphacan or Tenascin-R induces clusters of nodal proteins along hippocampal GABAergic axons. Furthermore, Contactin-1-immunodepleted OCM or OCM from Cntn1-null mice display significantly reduced clustering activity, that is restored by addition of soluble Contactin-1. Altogether, our results identify Contactin-1 secreted by oligodendrocytes as a novel factor that may influence early steps of nodal sodium channel cluster formation along specific axon populations.

Quantitative RNAseq analysis of Ugandan KS tumors reveals KSHV gene expression dominated by transcription from the LTd downstream latency promoter.

KSHV is endemic in Uganda and the HIV epidemic has dramatically increased the incidence of Kaposi sarcoma (KS). To investigate the role of KSHV in the development of KS, we obtained KS biopsies from ART-naïve, HIV-positive individuals in Uganda and analyzed the tumors using RNAseq to globally characterize the KSHV transcriptome. Phylogenetic analysis of ORF75 sequences from 23 tumors revealed 6 distinct genetic clusters with KSHV strains exhibiting M, N or P alleles. RNA reads mapping to specific unique coding sequence (UCDS) features were quantitated using a gene feature file previously developed to globally analyze and quantitate KSHV transcription in infected endothelial cells. A pattern of high level expression was detected in the KSHV latency region that was common to all KS tumors. The clear majority of transcription was derived from the downstream latency transcript promoter P3(LTd) flanking ORF72, with little evidence of transcription from the P1(LTc) latency promoter, which is constitutive in KSHV-infected lymphomas and tissue-culture cells. RNAseq data provided evidence of alternate P3(LTd) transcript editing, splicing and termination resulting in multiple gene products, with 90% of the P3(LTd) transcripts spliced to release the intronic source of the microRNAs K1-9 and 11. The spliced transcripts encode a regulatory uORF upstream of Kaposin A with alterations in intervening repeat sequences yielding novel or deleted Kaposin B/C-like sequences. Hierarchical clustering and PCA analysis of KSHV transcripts revealed three clusters of tumors with different latent and lytic gene expression profiles. Paradoxically, tumors with a latent phenotype had high levels of total KSHV transcription, while tumors with a lytic phenotype had low levels of total KSHV transcription. Morphologically distinct KS tumors from the same individual showed similar KSHV gene expression profiles suggesting that the tumor microenvironment and host response play important roles in the activation level of KSHV within the infected tumor cells.

Software platform for rapidly creating computational tools for mass spectrometry-based proteomics.

We describe and demonstrate the proteomics computational toolkit provided in the open-source msInspect software distribution. The toolkit includes modules written in Java and in the R statistical programming language to aid the rapid development of proteomics software applications. It contains tools for processing and manipulating standard MS data files, including signal processing of LC-MS data and parsing of MS/MS search results, as well as for modeling proteomics data structures, creating charts, and other common tasks. We present this toolkit's capability to rapidly develop new computational tools by presenting an example application, Qurate, a graphical tool for manually curating isotopically labeled peptide quantitative events.

Peptide sequence confidence in accurate mass and time analysis and its use in complex proteomics experiments.

We present new algorithms and a software implementation for assigning confidence to peptide sequence assignments obtained through classic accurate mass and retention time (AMT) matching techniques, as well as methods for integrating these assignments with standard proteomics workflows. The algorithms are intended to increase the number of peptides and proteins identified (and, when applicable, quantitated by isotopic labeling) among related proteomics experiments that use high-resolution mass spectrometry instrumentation. The motivations for our extensions include the need to exploit high-resolution data to support highly complex proteomics experiments, especially those involving extensive off-line fractionation, to which recent label-free workflows might not easily generalize.

Modes of inference for evaluating the confidence of peptide identifications.

Several modes of inference are currently used in practice to evaluate the confidence of putative peptide identifications resulting from database scoring algorithms such as Mascot, SEQUEST, or X!Tandem. The approaches include parametric methods, such as classic PeptideProphet, and distribution free methods, such as methods based on reverse or decoy databases. Because of its parametric nature, classic PeptideProphet, although highly robust, was not highly flexible and was difficult to apply to new search algorithms or classification scores. While commonly applied, the decoy approach has not yet been fully formalized and standardized. And, although they are distribution-free, they like other approaches are not free of assumptions. Recent manuscripts by Kall et al., Choi and Nesvizhskii, and Choi et al. help advance these methods, specifically by formalizing an alternative formulation of decoy databases approaches and extending the PeptideProphet methods to make explicit use of decoy databases, respectively. Taken together with standardized decoy database methods, and expectation scores computed by search engines like Tandem, there exist at least four different modes of inference used to assign confidence levels to individual peptides or groups of peptides. We overview and compare the assumptions of each of these approaches and summarize some interpretation issues. We also discuss some suggestions, which may make the use of decoy databases more computationally efficient in practice.

Contribution of protein fractionation to depth of analysis of the serum and plasma proteomes.

In-depth analysis of the serum and plasma proteomes by mass spectrometry is challenged by the vast dynamic range of protein abundance and substantial complexity. There is merit in reducing complexity through fractionation to facilitate mass spectrometry analysis of low-abundance proteins. However, fractionation reduces throughput and has the potential of diluting individual proteins or inducing their loss. Here, we have investigated the contribution of extensive fractionation of intact proteins to depth of analysis. Pooled serum depleted of abundant proteins was fractionated by an orthogonal two-dimensional system consisting of anion-exchange and reversed-phase chromatography. The resulting protein fractions were aliquotted; one aliquot was analyzed by shotgun LC-MS/MS, and another was further resolved into protein bands in a third dimension using SDS-PAGE. Individual gel bands were excised and subjected to in situ digestion and mass spectrometry. We demonstrate that increased fractionation results in increased depth of analysis based on total number of proteins identified in serum and based on representation in individual fractions of specific proteins identified in gel bands following a third-dimension SDS gel analysis. An intact protein analysis system (IPAS) based on a two-dimensional plasma fractionation schema was implemented that resulted in identification of 1662 proteins with high confidence with representation of protein isoforms that differed in their chromatographic mobility. Further increase in depth of analysis was accomplished by repeat analysis of aliquots from the same set of two-dimensional fractions resulting in overall identification of 2254 proteins. We conclude that substantial depth of analysis of proteins from milliliter quantities of serum or plasma and detection of isoforms are achieved with depletion of abundant proteins followed by two-dimensional protein fractionation and MS analysis of individual fractions.

A platform for accurate mass and time analyses of mass spectrometry data.

We describe an integrated suite of algorithms and software for general accurate mass and time (AMT) tagging data analysis of mass spectrometry data. The AMT approach combines identifications from liquid chromatography (LC) tandem mass spectrometry (MS/MS) data with peptide accurate mass and retention time locations from high-resolution LC-MS data. Our workflow includes the traditional AMT approach, in which MS/MS identifications are located in external databases, as well as methods based on more recent hybrid instruments such as the LTQ-FT or Orbitrap, where MS/MS identifications are embedded with the MS data. We demonstrate our AMT workflow's utility for general data synthesis by combining data from two dissimilar biospecimens. Specifically, we demonstrate its use relevant to serum biomarker discovery by identifying which peptides sequenced by MS/MS analysis of tumor tissue may also be present in the plasma of tumor-bearing and control mice. The analysis workflow, referred to as msInspect/AMT, extends and combines existing open-source platforms for LC-MS/MS (CPAS) and LC-MS (msInspect) data analysis and is available in an unrestricted open-source distribution.