In multiple sclerosis, oligoclonal bands connect to peripheral B-cell responses.

OBJECTIVE: To determine to what extent oligoclonal band (OCB) specificities are clonally interrelated and to what degree they are associated with corresponding B-cell responses in the peripheral blood (PB) of multiple sclerosis (MS) patients. METHODS: Mass-spectrometric proteomic analysis of isoelectric focused (IEF) cerebrospinal fluid (CSF) immunoglobulin G (IgG) was used in combination with next-generation deep-immune repertoire sequencing of PB and CSF IgG heavy chain variable regions from MS patients. RESULTS: We find evidence for ongoing stimulation and maturation to antibody-expressing B cells to occur primarily inside the central nervous system (CNS) compartment. B cells participating in OCB production can also be identified in PB; these cells appear to migrate across the blood-brain barrier and may also undergo further antigen stimulation in the periphery. In individual patients, different bands comprising OCBs are clonally related. INTERPRETATION: Our data provide a high-resolution molecular analysis of OCBs and strongly support the concept that OCBs are not merely the terminal result of a targeted immune response in MS but represent a component of active B cell immunity that is dynamically supported on both sides of the blood-brain barrier.

Improving the quality of protein similarity network clustering algorithms using the network edge weight distribution.

MOTIVATION: Clustering protein sequence data into functionally specific families is a difficult but important problem in biological research. One useful approach for tackling this problem involves representing the sequence dataset as a protein similarity network, and afterwards clustering the network using advanced graph analysis techniques. Although a multitude of such network clustering algorithms have been developed over the past few years, comparing algorithms is often difficult because performance is affected by the specifics of network construction. We investigate an important aspect of network construction used in analyzing protein superfamilies and present a heuristic approach for improving the performance of several algorithms. RESULTS: We analyzed how the performance of network clustering algorithms relates to thresholding the network prior to clustering. Our results, over four different datasets, show how for each input dataset there exists an optimal threshold range over which an algorithm generates its most accurate clustering output. Our results further show how the optimal threshold range correlates with the shape of the edge weight distribution for the input similarity network. We used this correlation to develop an automated threshold selection heuristic in order to most optimally filter a similarity network prior to clustering. This heuristic allows researchers to process their protein datasets with runtime efficient network clustering algorithms without sacrificing the clustering accuracy of the final results. AVAILABILITY: Python code for implementing the automated threshold selection heuristic, together with the datasets used in our analysis, are available at http://www.rbvi.ucsf.edu/Research/cytoscape/threshold_scripts.zip.

clusterMaker: a multi-algorithm clustering plugin for Cytoscape.

BACKGROUND: In the post-genomic era, the rapid increase in high-throughput data calls for computational tools capable of integrating data of diverse types and facilitating recognition of biologically meaningful patterns within them. For example, protein-protein interaction data sets have been clustered to identify stable complexes, but scientists lack easily accessible tools to facilitate combined analyses of multiple data sets from different types of experiments. Here we present clusterMaker, a Cytoscape plugin that implements several clustering algorithms and provides network, dendrogram, and heat map views of the results. The Cytoscape network is linked to all of the other views, so that a selection in one is immediately reflected in the others. clusterMaker is the first Cytoscape plugin to implement such a wide variety of clustering algorithms and visualizations, including the only implementations of hierarchical clustering, dendrogram plus heat map visualization (tree view), k-means, k-medoid, SCPS, AutoSOME, and native (Java) MCL. RESULTS: Results are presented in the form of three scenarios of use: analysis of protein expression data using a recently published mouse interactome and a mouse microarray data set of nearly one hundred diverse cell/tissue types; the identification of protein complexes in the yeast Saccharomyces cerevisiae; and the cluster analysis of the vicinal oxygen chelate (VOC) enzyme superfamily. For scenario one, we explore functionally enriched mouse interactomes specific to particular cellular phenotypes and apply fuzzy clustering. For scenario two, we explore the prefoldin complex in detail using both physical and genetic interaction clusters. For scenario three, we explore the possible annotation of a protein as a methylmalonyl-CoA epimerase within the VOC superfamily. Cytoscape session files for all three scenarios are provided in the Additional Files section. CONCLUSIONS: The Cytoscape plugin clusterMaker provides a number of clustering algorithms and visualizations that can be used independently or in combination for analysis and visualization of biological data sets, and for confirming or generating hypotheses about biological function. Several of these visualizations and algorithms are only available to Cytoscape users through the clusterMaker plugin. clusterMaker is available via the Cytoscape plugin manager.

Linking Cytoscape and the corynebacterial reference database CoryneRegNet.

BACKGROUND: Recently, the research community has seen an influx of data relating to transcriptional regulatory interactions of Corynebacteria, organisms that are highly relevant to fields of systems biology, biotechnology, and human medicine. Information derived from DNA microarray experiments, computational predictions, and literature has opened the way for the graph-based analysis, visualization, and reconstruction of transcriptional regulatory networks across entire organisms. The reference database for corynebacterial gene regulatory networks CoryneRegNet provides methods for data storage and data exchange in a well-structured manner. Additional information on the model organism Escherichia coli K12 obtained from RegulonDB has been integrated. Generally, gene regulatory networks can be visualized as graphs by drawing directed edges between nodes, where a node represents a gene and an edge corresponds to a typed regulatory interaction. Cytoscape is an open-source software project whose aim is to provide graph-based visualization and analysis for biological networks. Its architecture allows the development and integration of user-made plugins to enhance core functionalities. RESULTS: We introduce two novel plugins for the Cytoscape environment designed to enhance in silico studies of procaryotic transcriptional regulatory networks. Our plugins leverage the information from the cornyebacterial reference database CoryneRegNet with the graph analysis capabilities of Cytoscape. CoryneRegNetLoader queries the CoryneRegNet database to extract a gene regulatory network represented as a directed graph. Additional information is stored as node/edge attributes within the graph. COMA facilitates consistency checks for gene expression studies given a gene regulatory network. COMA tests whether all gene expression levels correlate properly with the given network topology. CONCLUSION: The plugins facilitate in silico studies of procaryotic transcriptional gene regulation, particularly in Corynebacteria and E. coli, by combining the knowledge from the corynebacterial reference database with the graph analysis capabilities of Cytoscape, which is one of the most-widely used tools for biological network analyses.

A Haystack Heuristic for Autoimmune Disease Biomarker Discovery Using Next-Gen Immune Repertoire Sequencing Data.

Large-scale DNA sequencing of immunological repertoires offers an opportunity for the discovery of novel biomarkers for autoimmune disease. Available bioinformatics techniques however, are not adequately suited for elucidating possible biomarker candidates from within large immunosequencing datasets due to unsatisfactory scalability and sensitivity. Here, we present the Haystack Heuristic, an algorithm customized to computationally extract disease-associated motifs from next-generation-sequenced repertoires by contrasting disease and healthy subjects. This technique employs a local-search graph-theory approach to discover novel motifs in patient data. We apply the Haystack Heuristic to nine million B-cell receptor sequences obtained from nearly 100 individuals in order to elucidate a new motif that is significantly associated with multiple sclerosis. Our results demonstrate the effectiveness of the Haystack Heuristic in computing possible biomarker candidates from high throughput sequencing data and could be generalized to other datasets.

Immunoglobulin class-switched B cells form an active immune axis between CNS and periphery in multiple sclerosis.

In multiple sclerosis (MS), lymphocyte--in particular B cell--transit between the central nervous system (CNS) and periphery may contribute to the maintenance of active disease. Clonally related B cells exist in the cerebrospinal fluid (CSF) and peripheral blood (PB) of MS patients; however, it remains unclear which subpopulations of the highly diverse peripheral B cell compartment share antigen specificity with intrathecal B cell repertoires and whether their antigen stimulation occurs on both sides of the blood-brain barrier. To address these questions, we combined flow cytometric sorting of PB B cell subsets with deep immune repertoire sequencing of CSF and PB B cells. Immunoglobulin (IgM and IgG) heavy chain variable (VH) region repertoires of five PB B cell subsets from MS patients were compared with their CSF Ig-VH transcriptomes. In six of eight patients, we identified peripheral CD27(+)IgD(-) memory B cells, CD27(hi)CD38(hi) plasma cells/plasmablasts, or CD27(-)IgD(-) B cells that had an immune connection to the CNS compartment. Pinpointing Ig class-switched B cells as key component of the immune axis thought to contribute to ongoing MS disease activity strengthens the rationale of current B cell-targeting therapeutic strategies and may lead to more targeted approaches.

B cell exchange across the blood-brain barrier in multiple sclerosis.

In multiple sclerosis (MS) pathogenic B cells likely act on both sides of the blood-brain barrier (BBB). However, it is unclear whether antigen-experienced B cells are shared between the CNS and the peripheral blood (PB) compartments. We applied deep repertoire sequencing of IgG heavy chain variable region genes (IgG-VH) in paired cerebrospinal fluid and PB samples from patients with MS and other neurological diseases to identify related B cells that are common to both compartments. For the first time to our knowledge, we found that a restricted pool of clonally related B cells participated in robust bidirectional exchange across the BBB. Some clusters of related IgG-VH appeared to have undergone active diversification primarily in the CNS, while others have undergone active diversification in the periphery or in both compartments in parallel. B cells are strong candidates for autoimmune effector cells in MS, and these findings suggest that CNS-directed autoimmunity may be triggered and supported on both sides of the BBB. These data also provide a powerful approach to identify and monitor B cells in the PB that correspond to clonally amplified populations in the CNS in MS and other inflammatory states.

Comparison of human solute carriers.

Solute carriers are eukaryotic membrane proteins that control the uptake and efflux of solutes, including essential cellular compounds, environmental toxins, and therapeutic drugs. Solute carriers can share similar structural features despite weak sequence similarities. Identification of sequence relationships among solute carriers is needed to enhance our ability to model individual carriers and to elucidate the molecular mechanisms of their substrate specificity and transport. Here, we describe a comprehensive comparison of solute carriers. We link the proteins using sensitive profile-profile alignments and two classification approaches, including similarity networks. The clusters are analyzed in view of substrate type, transport mode, organism conservation, and tissue specificity. Solute carrier families with similar substrates generally cluster together, despite exhibiting relatively weak sequence similarities. In contrast, some families cluster together with no apparent reason, revealing unexplored relationships. We demonstrate computationally and experimentally the functional overlap between representative members of these families. Finally, we identify four putative solute carriers in the human genome. The solute carriers include a biomedically important group of membrane proteins that is diverse in sequence and structure. The proposed classification of solute carriers, combined with experiment, reveals new relationships among the individual families and identifies new solute carriers. The classification scheme will inform future attempts directed at modeling the structures of the solute carriers, a prerequisite for describing the substrate specificities of the individual families.

Understanding actin organization in cell structure through lattice based Monte Carlo simulations.

Understanding the connection between mechanics and cell structure requires the exploration of the key molecular constituents responsible for cell shape and motility. One of these molecular bridges is the cytoskeleton, which is involved with intracellular organization and mechanotransduction. In order to examine the structure in cells, we have developed a computational technique that is able to probe the self-assembly of actin filaments through a lattice based Monte Carlo method. We have modeled the polymerization of these filaments based upon the interactions of globular actin through a probabilistic model encompassing both inert and active proteins. The results show similar response to classic ordinary differential equations at low molecular concentrations, but a bi-phasic divergence at realistic concentrations for living mammalian cells. Further, by introducing localized mobility parameters, we are able to simulate molecular gradients that are observed in nonhomogeneous protein distributions in vivo. The method and results have potential applications in cell and molecular biology as well as self-assembly for organic and inorganic systems.