Biology subjects

Hausser, J.

Publications and source records attributed to Hausser, J..

3 recordsLinked to original sources

bioRxiv2018

Universal cancer tasks, evolutionary tradeoffs, and the functions of driver mutations

Recent advances have led to an appreciation of the vast molecular diversity of cancer. Detailed data has enabled powerful methods to sort tumors into groups with benefits for prognosis and treatment. We are still missing, however, a general theoretical framework to understand the diversity of tumor gene-expression and mutations. To address this, we present a framework based on multi-task evolution theory, using the fact that tumors evolve in the body, and that tumors are faced with multiple tasks that contribute to their fitness. In accordance with the theory, we find that tradeoff between tasks constrains tumor gene-expression to a continuum bounded by a polyhedron. The vertices of the polyhedron are gene-expression profiles each specializing in one task, allowing the tasks to be identified. We find five universal cancer tasks across tissue-types: cell-division, biomass & energy, lipogenesis, immune-interaction and invasion & tissue remodeling. Tumors whose gene-expression lies close to a vertex are task specialists. We find evidence that such specialists are more sensitive to drugs that interfere with this task. We find that driver mutations, but not passenger mutations, tune gene-expression towards specialization in specific tasks. This approach can integrate additional types of molecular data into a theoretically-based framework for understanding tumor diversity.

cancer biology↗

bioRxiv2018

Central dogma rates and the trade-off between precision and economy

Steady-state protein abundance is set by four rates: transcription, translation, mRNA decay and protein decay. A given protein abundance can be obtained from infinitely many combinations of these rates. This raises the question of whether the natural rates for each gene result from historical accidents, or are there rules that give certain combinations a selective advantage? We address this question using high-throughput measurements in rapidly growing cells from diverse organisms to find that about half of the rate combinations do not exist: genes that combine high transcription with low translation are strongly depleted. This depletion is due to a trade-off between precision and economy: high transcription decreases stochastic fluctuations but increases transcription costs. Our theory quantitatively explains which rate combinations are missing, and predicts the curvature of the fitness function for each gene. It may guide the design of gene circuits with desired expression levels and noise.

systems biology↗

bioRxiv2018

Reprogramming protein abundance fluctuations in single cells by degradation

Isogenic cells living in the same environment show a natural heterogeneity associated with fluctuations in gene expression. When these fluctuations propagate through cellular regulatory networks, they can give rise to noise regulons, whereby multiple genes fluctuate in a coordinated fashion in single cells. The propagation of these fluctuations has been extensively characterized at the transcriptional level. For example, variations in transcription factor concentration induce correlated fluctuations in the abundance of target gene products. Here, we find that such noise regulons can also stem from protein degradation. We expressed pairs of yellow and red fluorescent proteins, subjected them to differential translation or degradation, and analyzed their fluctuations in single cells. While differential translation had little impact on fluctuations, protein degradation was found to be a dominant contributor. A mathematical model to decompose fluctuations arising from multiple sources of regulation revealed that cells with higher protein production capacity also exhibited higher protein degradation capacity. This association uncouples fluctuations in protein abundance from fluctuations in production rate, and can generate orthogonal noise regulons even for proteins relying on the same transcriptional program.

systems biology↗