Matthew A. Care

Whole genome expression profiling based on paraffin embedded tissue can be used to classify diffuse large B‐cell lymphoma and predict clinical outcome

This study tested the validity of whole‐genome expression profiling (GEP) using RNA from formalin‐fixed, paraffin‐embedded (FFPE) tissue to sub‐classify Diffuse Large B‐cell Lymphoma (DLBCL), in a population based cohort of 172 patients. GEP was performed using Illumina Whole Genome cDNA‐mediated Annealing, Selection, extension & Ligation, and tumours were classified into germinal centre (GCB), activated B‐cell (ABC) and Type‐III subtypes. The method was highly reproducible and reliably classified cell lines of known phenotype. GCB and ABC subtypes were each characterized by unique gene expression signatures consistent with previously published data. A significant relationship between subtype and survival was observed, with ABC having the worst clinical outcome and in a multivariate survival model only age and GEP class remained significant. This effect was not seen when tumours were classified by immunohistochemistry. There was a significant association between age and subtype (mean ages ABC – 72·8 years, GC – 68·4 years, Type‐III – 64·5 years). Older patients with ABC subtype were also over‐represented in patients who died soon after diagnosis. The relationship between prognosis and subtype improved when only patients assigned to the three categories with the highest level of confidence were analysed. This study demonstrates that GEP‐based classification of DLBCL can be applied to RNA extracted from routine FFPE samples and has potential for use in stratified medicine trials and clinical practice.

An extended set of PRDM1/BLIMP1 target genes links binding motif type to dynamic repression

The transcriptional repressor B lymphocyte-induced maturation protein-1 (BLIMP1) regulates gene expression and cell fate. The DNA motif bound by BLIMP1 in vitro overlaps with that of interferon regulatory factors (IRFs), which respond to inflammatory/immune signals. At such sites, BLIMP1 and IRFs can antagonistically regulate promoter activity. In vitro motif selection predicts that only a subset of BLIMP1 or IRF sites is subject to antagonistic regulation, but the extent to which antagonism occurs is unknown, since an unbiased assessment of BLIMP1 occupancy in vivo is lacking. To address this, we identified an extended set of promoters occupied by BLIMP1. Motif discovery and enrichment analysis demonstrate that multiple motif variants are required to capture BLIMP1 binding specificity. These are differentially associated with CpG content, leading to the observation that BLIMP1 DNA-binding is methylation sensitive. In occupied promoters, only a subset of BLIMP1 motifs overlap with IRF motifs. Conversely, a distinct subset of IRF motifs is not enriched amongst occupied promoters. Genes linked to occupied promoters containing overlapping BLIMP1/IRF motifs (e.g. AIM2, SP110, BTN3A3) are shown to constitute a dynamic target set which is preferentially activated by BLIMP1 knock-down. These data confirm and extend the competitive model of BLIMP1 and IRF interaction.

In Vitro Generation of Long-lived Human Plasma Cells

Plasma cells (PCs), the terminal effectors of humoral immunity, are short-lived unless supported by niche environments in which they may persist for years. No model system has linked B cell activation with niche function to allow the in vitro generation of long-lived PCs. Thus, the full trajectory of B cell terminal differentiation has yet to be investigated in vitro. In this article, we describe a robust model for the generation of polyclonal long-lived human PCs from peripheral blood B cells. After a proliferative plasmablast phase, PCs persist in the absence of cell division, with viability limited only by elective culture termination. Conservative predictions for PC life expectancy are 300 d, but with the potential for significantly longer life spans for some cells. These long-lived PCs are preferentially derived from memory B cells, and acquire a CD138high phenotype analogous to that of human bone marrow PCs. Analysis of gene expression across the system defines clusters of genes with related dynamics and linked functional characteristics. Importantly, genes in these differentiation clusters demonstrate a similar overall pattern of expression for in vitro and ex vivo PCs. In vitro PCs are fully reprogrammed to a secretory state and are adapted to their secretory load, maintaining IgG secretion of 120 pg/cell/day in the absence of XBP1 mRNA splicing. By establishing a set of conditions sufficient to allow the development and persistence of mature human PCs in vitro, to our knowledge, we provide the first platform with which to sequentially explore and manipulate each stage of human PC differentiation.

A sequence-anchored genetic linkage map for the moss, Physcomitrella patens

The moss Physcomitrella patens is a model for the study of plant cell biology and, by virtue of its basal position in land plant phylogeny, for comparative analysis of the evolution of plant gene function and development. It is ideally suited for ‘reverse genetic’ analysis by virtue of its outstanding ability to undertake targeted transgene integration by homologous recombination. However, gene identification through mutagenesis and map-based cloning has hitherto not been possible, due to the lack of a genetic linkage map. Using molecular markers [amplified fragment length polymorphisms (AFLP) and simple sequence repeats (SSR)] we have generated genetic linkage maps for Physcomitrella. One hundred and seventy-nine gene-specific SSR markers were mapped in 46 linkage groups, and 1574 polymorphic AFLP markers were identified. Integrating the SSR- and AFLP-based maps generated 31 linkage groups comprising 1420 markers. Anchorage of the integrated linkage map with gene-specific SSR markers coupled with computational prediction of AFLP loci has enabled its correspondence with the newly sequenced Physcomitrella genome. The generation of a linkage map densely populated with molecular markers and anchored to the genome sequence now provides a resource for forward genetic interrogation of the organism and for the development of a pipeline for the map-based cloning of Physcomitrella genes. This will radically enhance the potential of Physcomitrella for determining how gene function has evolved for the acquisition of complex developmental strategies within the plant kingdom.

A Microarray Platform-Independent Classification Tool for Cell of Origin Class Allows Comparative Analysis of Gene Expression in Diffuse Large B-cell Lymphoma

Cell of origin classification of diffuse large B-cell lymphoma (DLBCL) identifies subsets with biological and clinical significance. Despite the established nature of the classification existing studies display variability in classifier implementation, and a comparative analysis across multiple data sets is lacking. Here we describe the validation of a cell of origin classifier for DLBCL, based on balanced voting between 4 machine-learning tools: the DLBCL automatic classifier (DAC). This shows superior survival separation for assigned Activated B-cell (ABC) and Germinal Center B-cell (GCB) DLBCL classes relative to a range of other classifiers. DAC is effective on data derived from multiple microarray platforms and formalin fixed paraffin embedded samples and is parsimonious, using 20 classifier genes. We use DAC to perform a comparative analysis of gene expression in 10 data sets (2030 cases). We generate ranked meta-profiles of genes showing consistent class-association using ≥6 data sets as a cut-off: ABC (414 genes) and GCB (415 genes). The transcription factor ZBTB32 emerges as the most consistent and differentially expressed gene in ABC-DLBCL while other transcription factors such as ARID3A, BATF, and TCF4 are also amongst the 24 genes associated with this class in all datasets. Analysis of enrichment of 12323 gene signatures against meta-profiles and all data sets individually confirms consistent associations with signatures of molecular pathways, chromosomal cytobands, and transcription factor binding sites. We provide DAC as an open access Windows application, and the accompanying meta-analyses as a resource.

Predicting the effect of missense mutations on protein function: analysis with Bayesian networks

BackgroundA number of methods that use both protein structural and evolutionary information are available to predict the functional consequences of missense mutations. However, many of these methods break down if either one of the two types of data are missing. Furthermore, there is a lack of rigorous assessment of how important the different factors are to prediction.ResultsHere we use Bayesian networks to predict whether or not a missense mutation will affect the function of the protein. Bayesian networks provide a concise representation for inferring models from data, and are known to generalise well to new data. More importantly, they can handle the noisy, incomplete and uncertain nature of biological data. Our Bayesian network achieved comparable performance with previous machine learning methods. The predictive performance of learned model structures was no better than a naïve Bayes classifier. However, analysis of the posterior distribution of model structures allows biologically meaningful interpretation of relationships between the input variables.ConclusionThe ability of the Bayesian network to make predictions when only structural or evolutionary data was observed allowed us to conclude that structural information is a significantly better predictor of the functional consequences of a missense mutation than evolutionary information, for the dataset used. Analysis of the posterior distribution of model structures revealed that the top three strongest connections with the class node all involved structural nodes. With this in mind, we derived a simplified Bayesian network that used just these three structural descriptors, with comparable performance to that of an all node network.

Combining the interactome and deleterious SNP predictions to improve disease gene identification

A method has been developed for the prediction of proteins involved in genetic disorders. This involved combining deleterious SNP prediction with a system based on protein interactions and phenotype distances; this is the first time that deleterious SNP prediction has been used to make predictions across linkage‐intervals. At each step we tested and selected the best procedure, revealing that the computationally expensive method of assigning medical meta‐terms to create a phenotype distance matrix was outperformed by a simple word counting technique. We carried out in‐depth benchmarking with increasingly stringent data sets, reaching precision values of up to 75% (19% recall) for 10‐Mb linkage‐intervals (averaging 100 genes). For the most stringent (worst‐case) data we attained an overall recall of 6%, yet still achieved precision values of up to 90% (4% recall). At all levels of stringency and precision the addition of predicted deleterious SNPs was shown to increase recall. Hum Mutat 0, 1–9, 2009.

Network Analysis Identifies Proinflammatory Plasma Cell Polarization for Secretion of ISG15 in Human Autoimmunity.

Plasma cells (PCs) as effectors of humoral immunity produce Igs to match pathogenic insult. Emerging data suggest more diverse roles exist for PCs as regulators of immune and inflammatory responses via secretion of factors other than Igs. The extent to which such responses are preprogrammed in B-lineage cells or can be induced in PCs by the microenvironment is unknown. In this study, we dissect the impact of IFNs on the regulatory networks of human PCs. We show that core PC programs are unaffected, whereas PCs respond to IFNs with distinctive transcriptional responses. The IFN-stimulated gene 15 (ISG15) system emerges as a major transcriptional output induced in a sustained fashion by IFN-α in PCs and linked both to intracellular conjugation and ISG15 secretion. This leads to the identification of ISG15-secreting plasmablasts/PCs in patients with active systemic lupus erythematosus. Thus, ISG15-secreting PCs represent a distinct proinflammatory PC subset providing an Ig-independent mechanism of PC action in human autoimmunity.

Biallelic mutations in IRF8 impair human NK cell maturation and function

Human NK cell deficiencies are rare yet result in severe and often fatal disease, particularly as a result of viral susceptibility. NK cells develop from hematopoietic stem cells, and few monogenic errors that specifically interrupt NK cell development have been reported. Here we have described biallelic mutations in IRF8, which encodes an interferon regulatory factor, as a cause of familial NK cell deficiency that results in fatal and severe viral disease. Compound heterozygous or homozygous mutations in IRF8 in 3 unrelated families resulted in a paucity of mature CD56dim NK cells and an increase in the frequency of the immature CD56bright NK cells, and this impairment in terminal maturation was also observed in Irf8–/–, but not Irf8+/–, mice. We then determined that impaired maturation was NK cell intrinsic, and gene expression analysis of human NK cell developmental subsets showed that multiple genes were dysregulated by IRF8 mutation. The phenotype was accompanied by deficient NK cell function and was stable over time. Together, these data indicate that human NK cells require IRF8 for development and functional maturation and that dysregulation of this function results in severe human disease, thereby emphasizing a critical role for NK cells in human antiviral defense.

SPIB and BATF provide alternate determinants of IRF4 occupancy in diffuse large B-cell lymphoma linked to disease heterogeneity.

Interferon regulatory factor 4 (IRF4) is central to the transcriptional network of activated B-cell-like diffuse large B-cell lymphoma (ABC-DLBCL), an aggressive lymphoma subgroup defined by gene expression profiling. Since cofactor association modifies transcriptional regulatory input by IRF4, we assessed genome occupancy by IRF4 and endogenous cofactors in ABC-DLBCL cell lines. IRF4 partners with SPIB, PU.1 and BATF genome-wide, but SPIB provides the dominant IRF4 partner in this context. Upon SPIB knockdown IRF4 occupancy is depleted and neither PU.1 nor BATF acutely compensates. Integration with ENCODE data from lymphoblastoid cell line GM12878, demonstrates that IRF4 adopts either SPIB- or BATF-centric genome-wide distributions in related states of post-germinal centre B-cell transformation. In primary DLBCL high-SPIB and low-BATF or the reciprocal low-SPIB and high-BATF mRNA expression links to differential gene expression profiles across nine data sets, identifying distinct associations with SPIB occupancy, signatures of B-cell differentiation stage and potential pathogenetic mechanisms. In a population-based patient cohort, SPIBhigh/BATFlow-ABC-DLBCL is enriched for mutation of MYD88, and SPIBhigh/BATFlow-ABC-DLBCL with MYD88-L265P mutation identifies a small subgroup of patients among this otherwise aggressive disease subgroup with distinct favourable outcome. We conclude that differential expression of IRF4 cofactors SPIB and BATF identifies biologically and clinically significant heterogeneity among ABC-DLBCL.