Hybrid computational modeling highlights reverse warburg effect in breast cancer-associated fibroblasts

Unlocking mitochondrial dysfunction-associated senescence (MiDAS) with NAD+ - A Boolean model of mitochondrial dynamics and cell cycle control

The steady accumulation of senescent cells with aging creates tissue environments that aid cancer evolution. Aging cell states are highly heterogeneous. 'Deep senescent' cells rely on healthy mitochondria to fuel a strong proinflammatory secretome, including cytokines, growth and transforming signals. Yet, the physiological triggers of senescence such as reactive oxygen species (ROS) can also trigger mitochondrial dysfunction, and sufficient energy deficit to alter their secretome and cause chronic oxidative stress - a state termed Mitochondrial Dysfunction-Associated Senescence (MiDAS). Here, we offer a mechanistic hypothesis for the molecular processes leading to MiDAS, along with testable predictions. To do this we have built a Boolean regulatory network model that qualitatively captures key aspects of mitochondrial dynamics during cell cycle progression (hyper-fusion at the G1/S boundary, fission in mitosis), apoptosis (fission and dysfunction) and glucose starvation (reversible hyper-fusion), as well as MiDAS in response to SIRT3 knockdown or oxidative stress. Our model reaffirms the protective role of NAD+ and external pyruvate. We offer testable predictions about the growth factor- and glucose-dependence of MiDAS and its reversibility at different stages of reactive oxygen species (ROS)-induced senescence. Our model provides mechanistic insights into the distinct stages of DNA-damage induced senescence, the relationship between senescence and epithelial-to-mesenchymal transition in cancer and offers a foundation for building multiscale models of tissue aging.

Targeting senescent cells to reshape the tumor microenvironment and improve anticancer efficacy

Cancer is daunting pathology with remarkable breadth and scope, spanning genetics, epigenetics, proteomics, metalobomics and cell biology. Cellular senescence represents a stress-induced and essentially irreversible cell fate associated with aging and various age-related diseases, including malignancies. Senescent cells are characterized of morphologic alterations and metabolic reprogramming, and develop a highly active secretome termed as the senescence-associated secretory phenotype (SASP). Since the first discovery, senescence has been understood as an important barrier to tumor progression, as its induction in pre-neoplastic cells limits carcinogenesis. Paradoxically, senescent cells arising in the tumor microenvironment (TME) contribute to tumor progression, including augmented therapeutic resistance. In this article, we define typical forms of senescent cells commonly observed within the TME and how senescent cells functionally remodel their surrounding niche, affect immune responses and promote cancer evolution. Furthermore, we highlight the recently emerging pipelines of senotherapies particularly senolytics, which can selectively deplete senescent cells from affected organs in vivo and impede tumor progression by restoring therapeutic responses and securing anticancer efficacies. Together, co-targeting cancer cells and their normal but senescent counterparts in the TME holds the potential to achieve increased therapeutic benefits and restrained disease relapse in future clinical oncology.

Hypoxia-inducible factor in breast cancer: role and target for breast cancer treatment

Globally, breast cancer stands as the most prevalent form of cancer among women. The tumor microenvironment of breast cancer often exhibits hypoxia. Hypoxia-inducible factor 1-alpha, a transcription factor, is found to be overexpressed and activated in breast cancer, playing a pivotal role in the anoxic microenvironment by mediating a series of reactions. Hypoxia-inducible factor 1-alpha is involved in regulating downstream pathways and target genes, which are crucial in hypoxic conditions, including glycolysis, angiogenesis, and metastasis. These processes significantly contribute to breast cancer progression by managing cancer-related activities linked to tumor invasion, metastasis, immune evasion, and drug resistance, resulting in poor prognosis for patients. Consequently, there is a significant interest in Hypoxia-inducible factor 1-alpha as a potential target for cancer therapy. Presently, research on drugs targeting Hypoxia-inducible factor 1-alpha is predominantly in the preclinical phase, highlighting the need for an in-depth understanding of HIF-1α and its regulatory pathway. It is anticipated that the future will see the introduction of effective HIF-1α inhibitors into clinical trials, offering new hope for breast cancer patients. Therefore, this review focuses on the structure and function of HIF-1α, its role in advancing breast cancer, and strategies to combat HIF-1α-dependent drug resistance, underlining its therapeutic potential.

MetaLo: metabolic analysis of Logical models extracted from molecular interaction maps

Molecular interaction maps (MIMs) are static graphical representations depicting complex biochemical networks that can be formalized using one of the Systems Biology Graphical Notation languages. Regardless of their extensive coverage of various biological processes, they are limited in terms of dynamic insights. However, MIMs can serve as templates for developing dynamic computational models. We present MetaLo, an open-source Python package that enables the coupling of Boolean models inferred from process description MIMs with generic core metabolic networks. MetaLo provides a framework to study the impact of signaling cascades, gene regulation processes, and metabolic flux distribution of central energy production pathways. MetaLo computes the Boolean model's asynchronous asymptotic behavior, through the identification of trap-spaces, and extracts metabolic constraints to contextualize the generic metabolic network. MetaLo is able to handle large-scale Boolean models and genome-scale metabolic models without requiring kinetic information or manual tuning. The framework behind MetaLo enables in depth analysis of the regulatory model, and may allow tackling a lack of omics data in poorly addressed biological fields to contextualize generic metabolic networks along with improper automatic reconstructions of cell- and/or disease-specific metabolic networks. MetaLo is available at https://pypi.org/project/metalo/ under the terms of the GNU General Public License v3.