Biology subjects

Lipman, M.

Publications and source records attributed to Lipman, M..

3 recordsLinked to original sources

bioRxiv2019

Concise whole blood transcriptional signatures for incipient tuberculosis: A systematic review and patient-level pooled meta-analysis

Blood transcriptional signatures may predict risk of tuberculosis (TB). We compared the performance of 17 mRNA signatures in a pooled dataset comprising 1,026 samples, including 183 samples from 127 incipient TB cases, from four studies conducted in South Africa, Ethiopia, The Gambia and the UK. We show that eight signatures (comprising 1-25 transcripts) that predominantly reflect interferon inducible gene expression, have equivalent diagnostic accuracy for incipient TB over a two-year period with areas under the receiver operating characteristic curves ranging from 0.70 (95% confidence interval 0.64-0.76) to 0.77 (0.71-0.82). The sensitivity of all eight signatures declined with increasing disease-free time interval. Using a threshold derived from two standard deviations above the mean of uninfected controls giving specificities of >90%, the eight signatures achieved sensitivities ranging 24.7-39.9% over a 24 month interval, rising to 47.1-81.0% over 3 months. Based on pre-test probability of 2%, the eight signatures achieved positive predictive value ranging from 6.8-9.4% over 24 months, rising to 11.1-14.3% over 3 months. When using biomarker thresholds maximising sensitivity and specificity with equal weighting to both, no signature met the minimum World Health Organization (WHO) Target Product Profile parameters for incipient TB biomarkers over a two-year period. Blood transcriptional biomarkers reflect short-term risk of TB and only exceed WHO benchmarks if applied to 3-6 month intervals.

microbiology↗

bioRxiv2019

A Transcriptome Fingerprinting Assay for Clinical Immune Monitoring

BackgroundWhile our understanding of the role that the immune system plays in health and disease is growing at a rapid pace, available clinical tools to capture this complexity are lagging. We previously described the construction of a third-generation modular transcriptional repertoire derived from genome-wide transcriptional profiling of blood of 985 subjects across 16 diverse immunologic conditions, which comprises 382 distinct modules.\n\nResultsHere we describe the use of this modular repertoire framework for the development of a targeted transcriptome fingerprinting assay (TFA). The first step consisted in down-selection of the number of modules to 32, on the basis of similarities in changes in transcript abundance and functional interpretation. Next down-selection took place at the level of each of the 32 modules, with each one of them being represented by four transcripts in the final 128 gene panel. The assay was implemented on both the Fluidigm high throughput microfluidics PCR platform and the Nanostring platform, with the list of assays target probes being provided for both. Finally, we provide evidence of the versatility of this assay to assess numerous immune functions in vivo by demonstrating applications in the context of disease activity assessment in systemic lupus erythematosus and longitudinal immune monitoring during pregnancy.\n\nConclusionsThis work demonstrates the utility of data-driven network analysis applied to large-scale transcriptional profiling to identify key markers of immune responses, which can be downscaled to a rapid, inexpensive, and highly versatile assay of global immune function applicable to diverse investigations of immunopathogenesis and biomarker discovery.

immunology↗

bioRxiv2019

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

As the capacity for generating large scale 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. It is meant to serve as a stable reusable framework for the analysis and interpretation of blood transcriptome profiling data. It is supported by customized resources, which include analysis workflows, fingerprint grid plots data visualizations, interactive web applications providing access to a vast number of module-specific functional profiling reports, reference transcriptional profiles and give users the ability to visualize of changes in transcript abundance across the modular repertoire at different granularity levels. A use case focusing on a set of six modules comprising interferon-inducible genes is also provided. Altogether we hope that this resource will also serve as a framework for improving over time our collective understanding of the immunobiology underlying blood transcriptome profiling data.

immunology↗