Kornai, D. | Search bioRxiv

bioRxiv2023

Hierarchical heuristic species delimitation under the multispecies coalescent model with migration

The multispecies coalescent (MSC) model accommodates genealogical fluctuations across the genome and provides a natural framework for comparative analysis of genomic sequence data to infer the history of species divergence and gene flow. Given a set of populations, hypotheses of species delimitation (and species phylogeny) may be formulated as instances of MSC models (e.g., MSC for one species versus MSC for two species) and compared using Bayesian model selection. This approach, implemented in the program O_SCPLOWBPPC_SCPLOW, has been found to be prone to over-splitting. Alternatively heuristic criteria based on population parameters under the MSC model (such as population/species divergence times, population sizes, and migration rates) estimated from genomic sequence data may be used to delimit species. Here we extend the approach of species delimitation using the genealogical divergence index (gdi) to develop hierarchical merge and split algorithms for heuristic species delimitation, and implement them in a python pipeline called O_SCPLOWHHSDC_SCPLOW. Applied to data simulated under a model of isolation by distance, the approach was able to recover the correct species delimitation, whereas model comparison by O_SCPLOWBPPC_SCPLOW failed. Analyses of empirical datasets suggest that the procedure may be less prone to over-splitting. We discuss possible strategies for accommodating paraphyletic species in the procedure, as well as the challenges of species delimitation based on heuristic criteria.

bioinformatics↗

bioRxiv2021

Genomic hallmarks of cellular dormancy in cancer and therapeutic implications

Therapy resistance in cancer is often driven by a subpopulation of cells that are temporarily arrested in a non-proliferative G0 state, which is difficult to capture and whose mutational drivers remain largely unknown. We developed methodology to robustly identify this state from transcriptomic signals and characterised its prevalence and genomic constraints in solid primary tumours. We show that G0 arrest preferentially emerges in the context of more stable, less mutated genomes which maintain TP53 integrity and lack the hallmarks of DNA damage repair deficiency, while presenting increased APOBEC mutagenesis. We employ machine learning to uncover novel genomic dependencies of this process and validate the role of the centrosomal gene CEP89 as a modulator of proliferation/G0 arrest capacity. Lastly, we demonstrate that G0 arrest underlies unfavourable responses to various therapies exploiting cell cycle, kinase signalling and epigenetic mechanisms in single cell data, and propose a G0 arrest transcriptional signature that is linked with therapeutic resistance and can be used to further study and clinically track this state.

cancer biology↗