Background The highest risk of tuberculosis arises in the first few months after exposure. We reasoned that this risk reflects incipient disease among tuberculosis contacts. Blood transcriptional biomarkers of tuberculosis may predate clinical diagnosis, suggesting they offer improved sensitivity to detect subclinical incipient disease. Therefore, we sought to test the hypothesis that refined blood transcriptional biomarkers of active tuberculosis will improve stratification of short-term disease risk in tuberculosis contacts. Methods We combined analysis of previously published blood transcriptomic data with new data from a prospective human immunodeficiency virus (HIV)–negative UK cohort of 333 tuberculosis contacts. We used stability selection as an alternative computational approach to identify an optimal signature for short-term risk of active tuberculosis and evaluated its predictive value in independent cohorts. Results In a previously published HIV-negative South African case-control study of patients with asymptomatic Mycobacterium tuberculosis infection, a novel 3-gene transcriptional signature comprising BATF2, GBP5, and SCARF1 achieved a positive predictive value (PPV) of 23% for progression to active tuberculosis within 90 days. In a new UK cohort of 333 HIV-negative tuberculosis contacts with a median follow-up of 346 days, this signature achieved a PPV of 50% (95% confidence interval [CI], 15.7–84.3) and negative predictive value of 99.3% (95% CI, 97.5–99.9). By comparison, peripheral blood interferon gamma release assays in the same cohort achieved a PPV of 5.6% (95% CI, 2.1–11.8). Conclusions This blood transcriptional signature provides unprecedented opportunities to target therapy among tuberculosis contacts with greatest risk of incident disease.

Fungi are an exceptional source of diverse and novel cytochrome P450 monooxygenases (P450s), heme-thiolate proteins, with catalytic versatility. Agaricomycotina saprophytes have yielded most of the available information on basidiomycete P450s. This resulted in observing similar P450 family types in basidiomycetes with few differences in P450 families among Agaricomycotina saprophytes. The present study demonstrated the presence of unique P450 family patterns in basidiomycete biotrophic plant pathogens that could possibly have originated from the adaptation of these species to different ecological niches (host influence). Systematic analysis of P450s in basidiomycete biotrophic plant pathogens belonging to three different orders, Agaricomycotina (Armillaria mellea), Pucciniomycotina (Melampsora laricis-populina, M. lini, Mixia osmundae and Puccinia graminis) and Ustilaginomycotina (Ustilago maydis, Sporisorium reilianum and Tilletiaria anomala), revealed the presence of numerous putative P450s ranging from 267 (A. mellea) to 14 (M. osmundae). Analysis of P450 families revealed the presence of 41 new P450 families and 27 new P450 subfamilies in these biotrophic plant pathogens. Order-level comparison of P450 families between biotrophic plant pathogens revealed the presence of unique P450 family patterns in these organisms, possibly reflecting the characteristics of their order. Further comparison of P450 families with basidiomycete non-pathogens confirmed that biotrophic plant pathogens harbour the unique P450 families in their genomes. The CYP63, CYP5037, CYP5136, CYP5137 and CYP5341 P450 families were expanded in A. mellea when compared to other Agaricomycotina saprophytes and the CYP5221 and CYP5233 P450 families in P. graminis and M. laricis-populina. The present study revealed that expansion of these P450 families is due to paralogous evolution of member P450s. The presence of unique P450 families in these organisms serves as evidence of how a host/ecological niche can influence shaping the P450 content of an organism. The present study initiates our understanding of P450 family patterns in basidiomycete biotrophic plant pathogens.

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MotivationSomatic DNA recombination, the hallmark of vertebrate adaptive immunity, has the potential to generate a vast diversity of antigen receptor sequences. How this diversity captures antigen specificity remains incompletely understood. In this study we use high throughput sequencing to compare the global changes in T cell receptor β chain complementarity determining region 3 (CDR3β) sequences following immunization with ovalbumin administered with complete Freund’s adjuvant (CFA) or CFA alone.ResultsThe CDR3β sequences were deconstructed into short stretches of overlapping contiguous amino acids. The motifs were ranked according to a one-dimensional Bayesian classifier score comparing their frequency in the repertoires of the two immunization classes. The top ranking motifs were selected and used to create feature vectors which were used to train a support vector machine. The support vector machine achieved high classification scores in a leave-one-out validation test reaching >90% in some cases.SummaryThe study describes a novel two-stage classification strategy combining a one-dimensional Bayesian classifier with a support vector machine. Using this approach we demonstrate that the frequency of a small number of linear motifs three amino acids in length can accurately identify a CD4 T cell response to ovalbumin against a background response to the complex mixture of antigens which characterize Complete Freund’s Adjuvant.Availability and implementationThe sequence data is available at www.ncbi.nlm.nih.gov/sra/?term¼SRP075893. The Decombinator package is available at github.com/innate2adaptive/Decombinator. The R package e1071 is available at the CRAN repository https://cran.r-project.org/web/packages/e1071/index.html.Supplementary information Supplementary data are available at Bioinformatics online.

T cells recognize antigen using a large and diverse set of antigen-specific receptors created by a complex process of imprecise somatic cell gene rearrangements. In response to antigen-/receptor-binding-specific T cells then divide to form memory and effector populations. We apply high-throughput sequencing to investigate the global changes in T cell receptor sequences following immunization with ovalbumin (OVA) and adjuvant, to understand how adaptive immunity achieves specificity. Each immunized mouse contained a predominantly private but related set of expanded CDR3β sequences. We used machine learning to identify common patterns which distinguished repertoires from mice immunized with adjuvant with and without OVA. The CDR3β sequences were deconstructed into sets of overlapping contiguous amino acid triplets. The frequencies of these motifs were used to train the linear programming boosting (LPBoost) algorithm LPBoost to classify between TCR repertoires. LPBoost could distinguish between the two classes of repertoire with accuracies above 80%, using a small subset of triplet sequences present at defined positions along the CDR3. The results suggest a model in which such motifs confer degenerate antigen specificity in the context of a highly diverse and largely private set of T cell receptors.