Dissection of Other Biological Phenotypes

Although the majority of our laboratory’s energy is focused on improving understanding of human cancer, the systems biology methodologies that we have developed are not disease-specific, and offer a general paradigm for the investigation of the network-based origins of biological phenotypes. Our work is suggesting that the bottleneck hypothesis of master regulator activity that we have identified in cancer may be a general principle for explaining how phenotypes arise and can be abrogated. In other collaborations, for example, we have been investigating this concept within the context of neurodegenerative disease and stem cell lineage differentiation.

Neurodegenerative disease

Many attempts to elucidate the molecular determinants underlying progressive cell loss in neurodegenerative disorders have relied exclusively on the comparison of gene expression levels in normal and pathological tissue. Unfortunately, this approach typically does not go beyond the generation of comprehensive lists of differentially expressed genes that are ranked based on the degree of change of their transcript levels. In particular, this analysis does not support the discovery of key upstream regulators that cause the observed gene expression changes.

Using a similar approach to the one we have used to identify master regulators of cancer phenotypes, we have participated in several collaborations aimed at elucidating the context-specific regulatory networks that give rise to neurodegenerative diseases. These efforts have enabled us to identify a repertoire of upstream regulators that drive the dopaminergic stress response seen in neurodegeneration, identified causal master regulators of neurodegeneration in an in vitro model of amyotrophic lateral sclerosis (ALS), and revealed an unbiased set of candidate MRs causally responsible for regulating the transcriptional signature of Alzheimer’s disease progression.

Stem cell differentiation and lineage commitment

During mammalian development, stem cells undergo a variety of changes as they transition from a pluripotent state into cell lineages with specific properties and functions. Although much research in the past has studied the roles that individual genes and pathways play in these transformations, relatively little has been done to investigate the origins of cell identity at a systems level. Just as our lab has shown that interaction networks define specific cellular phenotypes related to human disease, we have hypothesized that network rewiring plays important roles in cell type differentiation and tissue development. We are thus applying our computational methods to characterize regulatory networks in cells at various developmental stages in order to gain a more precise, mechanistic understanding of stem cell lineage commitment.

In collaborations with the laboratory of Michael Shen (Columbia University Medical Center) we have explored how gene regulatory networks function during embryonic development. In work published in 2013 we used ARACNe to generate interactomes for mouse embryonic stem cells and mouse epiblast stem cells. Combined with experiments done in the Shen Lab, the project confirmed a previously disputed model suggesting that the type of cell in which a tumor originates determines tumor subtype in prostate cancer. As this collaboration continues to evolve, our goal is to use network models to learn more about molecular drivers of tissue programming.

More recently we have also begun collaborating with Maria Pia Cosma (Center for Genomic Regulation, Barcelona), whose laboratory is investigating the mechanisms that control the reprogramming of somatic cells into a pluripotent stem-like state. In contrast with other methods such as direct reprogramming and somatic cell nuclear transfer (SCNT), the Cosma lab uses cell-fusion mediated reprogramming, an approach that is more efficient and makes it possible to track gene expression changes in somatic cells and embryonic stem (ES) cells simultaneously in two different species. In our case we fuse a mouse ES cell and a human B cell and track the human and mouse genes separately. Using reverse engineering approaches developed in our laboratory, we can then analyze such data to elucidate the networks responsible for the reprogramming. Ultimately, the Cosma Lab anticipates that this knowledge could reveal therapeutic strategies that would enable the regeneration of damaged tissue.

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Selected related publications

Ikiz B, Alvarez MJ, Ré DB, Le Verche V, Politi K, Lotti F, Phani S, Pradhan R, Yu C, Croft GF, Jacquier A, Henderson CE, Califano A, Przedborski S. The regulatory machinery of neurodegeneration in in vitro models of amyotrophic lateral sclerosis. Cell Rep. 2015 Jul 14;12(2):335-45.

Aubry S, Shin W, Crary JF, Lefort R, Qureshi YH, Lefebvre C, Califano A, Shelanski ML. Assembly and interrogation of Alzheimer's disease genetic networks reveal novel regulators of progression. PLoS One. 2015 Mar 17;10(3):e0120352.

Kushwaha R, Jagadish N, Kustagi M, Tomishima MJ, Mendiratta G, Bansal M, Kim HR, Sumazin P, Alvarez MJ, Lefebvre C, Villagrasa-Gonzalez P, Viale A, Korkola JE, Houldsworth J, Feldman DR, Bosl GJ, Califano A, Chaganti RS. Interrogation of a context-specific transcription factor network identifies novel regulators of pluripotency. Stem Cells. 2015 Feb;33(2):367-77.

Wang ZA, Mitrofanova A, Bergren SK, Abate-Shen C, Cardiff RD, Califano A, Shen MM. Lineage analysis of basal epithelial cells reveals their unexpected plasticity and supports a cell-of-origin model for prostate cancer heterogeneity. Nat Cell Biol. 2013 Mar;15(3):274-83.