Identification of a Proteomic Signature of Senescence in Primary Human Mammary Epithelial Cells

Puerarin Delays Mammary Gland Aging by Regulating Gut Microbiota and Inhibiting the p38MAPK Signaling Pathway

Mammary gland aging is one of the most important problems faced by humans and animals. How to delay mammary gland aging is particularly important. Puerarin is a kind of isoflavone substance extracted from Pueraria lobata, which has anti-inflammatory, antioxidant, and other pharmacological effects. However, the role of puerarin in delaying lipopolysaccharide (LPS)-induced mammary gland aging and its underlying mechanism remains unclear. On the one hand, we found that puerarin could significantly downregulate the expression of senescence-associated secretory phenotype (SASP) and age-related indicators (SA-β-gal, p53, p21, p16) in mammary glands of mice. In addition, puerarin mainly inhibited the p38MAPK signaling pathway to repair mitochondrial damage and delay mammary gland aging. On the other hand, puerarin could also delay the cellular senescence of mice mammary epithelial cells (mMECs) by targeting gut microbiota and promoting the secretion of gut microbiota metabolites. In conclusion, puerarin could not only directly act on the mMECs but also regulate the gut microbiota, thus, playing a role in delaying the aging of the mammary gland. Based on the above findings, we have discovered a new pathway for puerarin to delay mammary gland aging.

Subcellular structure, heterogeneity, and plasticity of senescent cells

Cellular senescence is a state of permanent growth arrest. It can be triggered by telomere shortening (replicative senescence) or prematurely induced by stresses such as DNA damage, oncogene overactivation, loss of tumor suppressor genes, oxidative stress, tissue factors, and others. Advances in techniques and experimental designs have provided new evidence about the biology of senescent cells (SnCs) and their importance in human health and disease. This review aims to describe the main aspects of SnCs phenotype focusing on alterations in subcellular compartments like plasma membrane, cytoskeleton, organelles, and nuclei. We also discuss the heterogeneity, dynamics, and plasticity of SnCs' phenotype, including the SASP, and pro-survival mechanisms. We advance on the multiple layers of phenotypic heterogeneity of SnCs, such as the heterogeneity between inducers, tissues and within a population of SnCs, discussing the relevance of these aspects to human health and disease. We also raise the main challenges as well alternatives to overcome them. Ultimately, we present open questions and perspectives in understanding the phenotype of SnCs from the perspective of basic and applied questions.

Senescence suppresses the integrated stress response and activates a stress-enhanced secretory phenotype

Senescence is a state of indefinite cell cycle arrest associated with aging, cancer, and age-related diseases. Here, using label-based mass spectrometry, ribosome profiling and nanopore direct RNA sequencing, we explore the coordinated interaction of translational and transcriptional programs of human cellular senescence. We find that translational deregulation and a corresponding maladaptive integrated stress response (ISR) is a hallmark of senescence that desensitizes senescent cells to stress. We present evidence that senescent cells maintain high levels of eIF2α phosphorylation, typical of ISR activation, but translationally repress production of the stress response transcription factor 4 (ATF4) by ineffective bypass of the inhibitory upstream open reading frames. Surprisingly, ATF4 translation remains inhibited even after acute proteotoxic and amino acid starvation stressors, resulting in a highly diminished stress response. Furthermore, absent a response, stress augments the senescence secretory phenotype, thus intensifying a proinflammatory state that exacerbates disease. Our results reveal a novel mechanism that senescent cells exploit to evade an adaptive stress response and remain viable.

Yearning for machine learning: applications for the classification and characterisation of senescence

Senescence is a widely appreciated tumour suppressive mechanism, which acts as a barrier to cancer development by arresting cell cycle progression in response to harmful stimuli. However, senescent cell accumulation becomes deleterious in aging and contributes to a wide range of age-related pathologies. Furthermore, senescence has beneficial roles and is associated with a growing list of normal physiological processes including wound healing and embryonic development. Therefore, the biological role of senescent cells has become increasingly nuanced and complex. The emergence of sophisticated, next-generation profiling technologies, such as single-cell RNA sequencing, has accelerated our understanding of the heterogeneity of senescence, with distinct final cell states emerging within models as well as between cell types and tissues. In order to explore data sets of increasing size and complexity, the senescence field has begun to employ machine learning (ML) methodologies to probe these intricacies. Most notably, ML has been used to aid the classification of cells as senescent, as well as to characterise the final senescence phenotypes. Here, we provide a background to the principles of ML tasks, as well as some of the most commonly used methodologies from both traditional and deep ML. We focus on the application of these within the context of senescence research, by addressing the utility of ML for the analysis of data from different laboratory technologies (microscopy, transcriptomics, proteomics, methylomics), as well as the potential within senolytic drug discovery. Together, we aim to highlight both the progress and potential for the application of ML within senescence research.

Drug mechanism enrichment analysis improves prioritization of therapeutics for repurposing

BACKGROUND

There is a pressing need for improved methods to identify effective therapeutics for diseases. Many computational approaches have been developed to repurpose existing drugs to meet this need. However, these tools often output long lists of candidate drugs that are difficult to interpret, and individual drug candidates may suffer from unknown off-target effects. We reasoned that an approach which aggregates information from multiple drugs that share a common mechanism of action (MOA) would increase on-target signal compared to evaluating drugs on an individual basis. In this study, we present drug mechanism enrichment analysis (DMEA), an adaptation of gene set enrichment analysis (GSEA), which groups drugs with shared MOAs to improve the prioritization of drug repurposing candidates.

RESULTS

First, we tested DMEA on simulated data and showed that it can sensitively and robustly identify an enriched drug MOA. Next, we used DMEA on three types of rank-ordered drug lists: (1) perturbagen signatures based on gene expression data, (2) drug sensitivity scores based on high-throughput cancer cell line screening, and (3) molecular classification scores of intrinsic and acquired drug resistance. In each case, DMEA detected the expected MOA as well as other relevant MOAs. Furthermore, the rankings of MOAs generated by DMEA were better than the original single-drug rankings in all tested data sets. Finally, in a drug discovery experiment, we identified potential senescence-inducing and senolytic drug MOAs for primary human mammary epithelial cells and then experimentally validated the senolytic effects of EGFR inhibitors.

CONCLUSIONS

DMEA is a versatile bioinformatic tool that can improve the prioritization of candidates for drug repurposing. By grouping drugs with a shared MOA, DMEA increases on-target signal and reduces off-target effects compared to analysis of individual drugs. DMEA is publicly available as both a web application and an R package at https://belindabgarana.github.io/DMEA .

Can 3D bioprinting solve the mystery of senescence in cancer therapy?

Tumor dormancy leading to cancer relapse is still a poorly understood mechanism. Several cell states such as quiescence and diapause can explain the persistence of tumor cells in a dormant state, but the potential role of tumor cell senescence has been met with hesitance given the historical understanding of the senescent growth arrest as irreversible. However, recent evidence has suggested that senescence might contribute to dormancy and relapse, although its exact role is not fully developed. This limited understanding is largely due to the paucity of reliable study models. The current 2D cell modeling is overly simplistic and lacks the appropriate representation of the interactions between tumor cells (senescent or non-senescent) and the other cell types within the tumor microenvironment (TME), as well as with the extracellular matrix (ECM). 3D cell culture models, including 3D bioprinting techniques, offer a promising approach to better recapitulate the native cancer microenvironment and would significantly improve our understanding of cancer biology and cellular response to treatment, particularly Therapy-Induced Senescence (TIS), and its contribution to tumor dormancy and cancer recurrence. Fabricating a novel 3D bioprinted model offers excellent opportunities to investigate both the role of TIS in tumor dormancy and the utility of senolytics (drugs that selectively eliminate senescent cells) in targeting dormant cancer cells and mitigating the risk for resurgence. In this review, we discuss literature on the possible contribution of TIS in tumor dormancy, provide examples on the current 3D models of senescence, and propose a novel 3D model to investigate the ultimate role of TIS in mediating overall response to therapy.