A Novel Repertoire of Blood Transcriptome Modules Based on Co-expression Patterns across sixteen Disease and Physiological States

Blood transcriptomics measures the abundance of circulating leukocyte RNA on a genome-wide scale. Dimension reduction is an important analytic step which condenses the number of variables and permits to enhance the robustness of data analyses and functional interpretation. An approach consisting in the construction of modular repertoires based on differential co-expression observed across multiple biological states of a given system has been described before. In this report, a new blood transcriptome modular repertoire is presented based on an expended range of disease and physiological states (16 in total, encompassing 985 unique transcriptome profiles). The input datasets have been deposited in NCBI public repository, GEO. The composition of the set of 382 modules constituting the repertoire is shared, along with extensive functional annotations and a custom fingerprint visualization scheme. Finally, the similarities and differences between the blood transcriptome profiles of this wide range of biological states are presented and discussed

Immune control of Burkholderia pseudomallei––common, high-frequency T-cell responses to a broad repertoire of immunoprevalent epitopes

Burkholderia pseudomallei (Bp) is an environmental bacterial pathogen that causes potentially lethal sepsis in susceptible individuals and is considered a Category B, Tier-1 biothreat agent. As such, it is crucial to gain an improved understanding of protective immunity and potential vaccine candidates. The nature of immune correlates dictating why most exposed individuals in endemic regions undergo asymptomatic seroconversion while others succumb to life-threatening sepsis is largely uncharted. Bp seroreactive, immunogenic proteins have previously been identified by antigen microarray. We here set out to conduct an analysis of T-cell recognition of the Bp immunome using serodominant antigens represented in the original antigen microarray, examining immune correlates of disease in healthy seropositive individuals and those with acute disease or in convalescence. By screening a library of 739 overlapping peptides representing the sequences of 20 different Bp antigens, we aimed to define immune correlates of protection at the level of immunoprevalent T-cell epitopes. Responses to a large number of epitopes were common in healthy seropositive individuals: we found remarkably broad responsiveness to Bp epitopes, with 235 of 739 peptides recognized by ≥80% of all tested donors. The cumulative response to Bp epitopes in healthy, seropositive, donors from this endemic region were of the order of thousands of spot forming cells per million cells, making Bp recognition a significant component of the T-cell repertoire. Noteworthy among our findings, analysis revealed 10 highly immunoprevalent T-cell epitopes, able to induce Bp-specific IFNγ responses that were high in responding T-cell frequency within the repertoire, and also common across individuals with different human leukocyte antigen types. Acute melioidosis patients showed poor T-cell responses to the immunoprevalent epitopes, but acquired responsiveness following recovery from infection. Our findings suggest that a large repertoire of CD4 T cells, high in frequency and with broad coverage of antigens and epitopes, is important in controlling Bp infection. This offers an attractive potential strategy for subunit or epitope-based vaccines

Development of a fixed module repertoire for the analysis and interpretation of blood transcriptome data

As the capacity for generating large-scale molecular profiling data continues to grow, the ability to extract meaningful biological knowledge from it remains a limitation. Here, we describe the development of a new fixed repertoire of transcriptional modules, BloodGen3, that is designed to serve as a stable reusable framework for the analysis and interpretation of blood transcriptome data. The construction of this repertoire is based on co-clustering patterns observed across sixteen immunological and physiological states encompassing 985 blood transcriptome profiles. Interpretation is supported by customized resources, including module-level analysis workflows, fingerprint grid plot visualizations, interactive web applications and an extensive annotation framework comprising functional profiling reports and reference transcriptional profiles. Taken together, this well-characterized and well-supported transcriptional module repertoire can be employed for the interpretation and benchmarking of blood transcriptome profiles within and across patient cohorts. Blood transcriptome fingerprints for the 16 reference cohorts can be accessed interactively via: https://drinchai.shinyapps.io/BloodGen3Module/