Impact of in vitro evolution on antigenic diversity of Mycobacterium bovis bacillus Calmette-Guerin (BCG).

Mycobacterium bovis bacillus Calmette-Guerin (BCG), the only vaccine currently used against tuberculosis, is an attenuated derivative of M. bovis that has been propagated in vitro for more than 40 years. We have previously reported that the experimentally-verified human T cell epitopes of the M. tuberculosis complex (MTBC) are the most conserved elements of the genome; whether immune recognition is the force driving the conservation of epitopes in the MTBC is unknown. Therefore, we sequenced the genomes of 12 BCG strains to determine whether T cell epitopes were under selection pressure during BCG in vitro evolution. We constructed a genome-wide phylogeny and refined the previously-determined BCG phylogeny. Notably, we identified a new cluster between BCG Japan and BCG Russia, and repositioned the relationships of several strains within the lineage. We also compared the sequence diversity of 1530 experimentally verified human T cell epitopes in the BCG vaccines with those in the MTBC. We found 23% of the known T cell epitopes are absent, and that the majority (82%) of the absent epitopes in BCG are contained in 6 proteins encoded in 2 regions of difference (RD) unique to BCG strains. We also found that T cell epitope sequences in BCG are more conserved than non-epitope sequences in the same gene. Finally, we find evidence that epitope sequence variation in BCG potentially affects human T cell recognition. These findings provide new insight into sequence variation in a slow-growing bacterium closely related to the MTBC that has been subjected to prolonged passage outside of a mammalian host, and indicate little difference in the extent of variation in vivo and in vitro.

Information content and analysis methods for multi-modal high-throughput biomedical data.

The spectrum of modern molecular high-throughput assaying includes diverse technologies such as microarray gene expression, miRNA expression, proteomics, DNA methylation, among many others. Now that these technologies have matured and become increasingly accessible, the next frontier is to collect "multi-modal" data for the same set of subjects and conduct integrative, multi-level analyses. While multi-modal data does contain distinct biological information that can be useful for answering complex biology questions, its value for predicting clinical phenotypes and contributions of each type of input remain unknown. We obtained 47 datasets/predictive tasks that in total span over 9 data modalities and executed analytic experiments for predicting various clinical phenotypes and outcomes. First, we analyzed each modality separately using uni-modal approaches based on several state-of-the-art supervised classification and feature selection methods. Then, we applied integrative multi-modal classification techniques. We have found that gene expression is the most predictively informative modality. Other modalities such as protein expression, miRNA expression, and DNA methylation also provide highly predictive results, which are often statistically comparable but not superior to gene expression data. Integrative multi-modal analyses generally do not increase predictive signal compared to gene expression data.

A streamlined method for detecting structural variants in cancer genomes by short read paired-end sequencing.

Defining the architecture of a specific cancer genome, including its structural variants, is essential for understanding tumor biology, mechanisms of oncogenesis, and for designing effective personalized therapies. Short read paired-end sequencing is currently the most sensitive method for detecting somatic mutations that arise during tumor development. However, mapping structural variants using this method leads to a large number of false positive calls, mostly due to the repetitive nature of the genome and the difficulty of assigning correct mapping positions to short reads. This study describes a method to efficiently identify large tumor-specific deletions, inversions, duplications and translocations from low coverage data using SVDetect or BreakDancer software and a set of novel filtering procedures designed to reduce false positive calls. Applying our method to a spontaneous T cell lymphoma arising in a core RAG2/p53-deficient mouse, we identified 40 validated tumor-specific structural rearrangements supported by as few as 2 independent read pairs.