Farina, G. | bioRxiv Science

bioRxiv2023

Molecular and phenotypic blueprint of the hematopoietic compartment reveals proliferation stress as a driver of age-associated human stem cell dysfunctions

Hematopoietic stem/progenitor cell (HSPC) aging studies have been associated with myeloid skewing, reduced clonal output, and impaired regenerative capacity, but quantitative immunophenotypic and functional analysis across human aging is lacking. Here, we provide a comprehensive phenotypic, transcriptional, and functional dissection of human hematopoiesis across the lifespan. Although primitive HSPC numbers were stable during aging, overall cellularity was reduced, especially for erythroid and lymphoid lineages. Notably, HSPC from aged individuals had superior repopulating frequency than younger counterparts in xenografts; yet aged HSPC displayed epigenetic dysregulation of cell cycle, inflammatory signatures, and a reduced capacity to counteract activation-induced proliferative stress with concomitant accumulation of DNA damage and senescence-like features upon xenotransplantation. This phenotype was recapitulated by enforcing proliferative stress in vivo on cord blood (CB) HSPC. Overall, our work sheds light on dysregulated responses to activation-induced proliferation underlying HSPC aging and establishes CB xenotransplantation-based models as suitable for studying age-associated hematopoietic defects.

cell biology↗

bioRxiv2020

Combination Treatment Optimization Using aPan-Cancer Pathway Model

The design of efficient combination therapies is a difficult key challenge in the treatment of complex diseases such as cancers. The large heterogeneity of cancers and the large number of available drugs renders exhaustive in vivo or even in vitro investigation of possible treatments impractical. In recent years, sophisti-cated mechanistic, ordinary differential equation-based pathways models that can predict treatment responses at a molecular level have been developed. However, surprisingly little effort has been put into leveraging these models to find novel therapies. In this paper we use for the first time, to our knowledge, a large-scale state-of-the-art pan-cancer signaling pathway model to identify potentially novel combination therapies to treat individual cancer cell lines from various tissues (e.g., minimizing proliferation while keeping dosage low to avoid adverse side effects) and populations of cancer cell lines (e.g., minimizing the maximum or average proliferation across the cell lines while keeping dosage low). We also show how our method can be used to optimize the mixtures and dosages used in sequential treatment plans--that is, optimized sequences of potentially different drug combinations--providing additional benefits. In order to solve the treatment optimization problems, we combine the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm with a significantly more scalable sampling scheme for truncated Gaussian distributions, based on a Hamiltonian Monte-Carlo method. These optimization techniques are independent of the signaling pathway model, and can thus be used for other signaling pathway models also, provided that a suitable predictive model is available.

systems biology↗