Clusternomics: Integrative context-dependent clustering for heterogeneous datasets.

Integrative clustering is used to identify groups of samples by jointly analysing multiple datasets describing the same set of biological samples, such as gene expression, copy number, methylation etc. Most existing algorithms for integrative clustering assume that there is a shared consistent set of clusters across all datasets, and most of the data samples follow this structure. However in practice, the structure across heterogeneous datasets can be more varied, with clusters being joined in some datasets and separated in others. In this paper, we present a probabilistic clustering method to identify groups across datasets that do not share the same cluster structure. The proposed algorithm, Clusternomics, identifies groups of samples that share their global behaviour across heterogeneous datasets. The algorithm models clusters on the level of individual datasets, while also extracting global structure that arises from the local cluster assignments. Clusters on both the local and the global level are modelled using a hierarchical Dirichlet mixture model to identify structure on both levels. We evaluated the model both on simulated and on real-world datasets. The simulated data exemplifies datasets with varying degrees of common structure. In such a setting Clusternomics outperforms existing algorithms for integrative and consensus clustering. In a real-world application, we used the algorithm for cancer subtyping, identifying subtypes of cancer from heterogeneous datasets. We applied the algorithm to TCGA breast cancer dataset, integrating gene expression, miRNA expression, DNA methylation and proteomics. The algorithm extracted clinically meaningful clusters with significantly different survival probabilities. We also evaluated the algorithm on lung and kidney cancer TCGA datasets with high dimensionality, again showing clinically significant results and scalability of the algorithm.

A Class of Environmental and Endogenous Toxins Induces BRCA2 Haploinsufficiency and Genome Instability.

Mutations truncating a single copy of the tumor suppressor, BRCA2, cause cancer susceptibility. In cells bearing such heterozygous mutations, we find that a cellular metabolite and ubiquitous environmental toxin, formaldehyde, stalls and destabilizes DNA replication forks, engendering structural chromosomal aberrations. Formaldehyde selectively depletes BRCA2 via proteasomal degradation, a mechanism of toxicity that affects very few additional cellular proteins. Heterozygous BRCA2 truncations, by lowering pre-existing BRCA2 expression, sensitize to BRCA2 haploinsufficiency induced by transient exposure to natural concentrations of formaldehyde. Acetaldehyde, an alcohol catabolite detoxified by ALDH2, precipitates similar effects. Ribonuclease H1 ameliorates replication fork instability and chromosomal aberrations provoked by aldehyde-induced BRCA2 haploinsufficiency, suggesting that BRCA2 inactivation triggers spontaneous mutagenesis during DNA replication via aberrant RNA-DNA hybrids (R-loops). These findings suggest a model wherein carcinogenesis in BRCA2 mutation carriers can be incited by compounds found pervasively in the environment and generated endogenously in certain tissues with implications for public health.

Non-replicating Mycobacterium tuberculosis elicits a reduced infectivity profile with corresponding modifications to the cell wall and extracellular matrix.

A key feature of Mycobacterium tuberculosis is its ability to become dormant in the host. Little is known of the mechanisms by which these bacilli are able to persist in this state. Therefore, the focus of this study was to emulate environmental conditions encountered by M. tuberculosis in the granuloma, and determine the effect of such conditions on the physiology and infectivity of the organism. Non-replicating persistent (NRP) M. tuberculosis was established by the gradual depletion of nutrients in an oxygen-replete and controlled environment. In contrast to rapidly dividing bacilli, NRP bacteria exhibited a distinct phenotype by accumulating an extracellular matrix rich in free mycolate and lipoglycans, with increased arabinosylation. Microarray studies demonstrated a substantial down-regulation of genes involved in energy metabolism in NRP bacteria. Despite this reduction in metabolic activity, cells were still able to infect guinea pigs, but with a delay in the development of disease when compared to exponential phase bacilli. Using these approaches to investigate the interplay between the changing environment of the host and altered physiology of NRP bacteria, this study sheds new light on the conditions that are pertinent to M. tuberculosis dormancy and how this organism could be establishing latent disease.