Clinicopathological and cellular senescence biomarkers in chronic stalled wounds.

BACKGROUND: Chronic wounds have been associated with an elevated burden of cellular senescence, a state of essentially irreversible cell cycle arrest, resistance to apoptosis, and a secretory phenotype. However, whether senescent cells contribute to wound chronicity in humans remains unclear. The objective of this article is to assess the role of clinicopathological characteristics and cellular senescence in the time-to-healing of chronic wounds. METHODS: A cohort of 79 patients with chronic wounds was evaluated in a single-center academic practice from February 1, 2005, to February 28, 2015, and followed for up to 36 months. Clinical characteristics and wound biopsies were obtained at baseline, and time-to-healing was assessed. Wound biopsies were analyzed histologically for pathological characteristics and molecularly for markers of cellular senescence. In addition, biopsy slides were stained for p16INK4a expression. RESULTS: No clinical or pathological characteristics were found to have significant associations with time-to-healing. A Cox proportional hazard ratio model revealed increased CDKN1A (p21CIP1/WAF1 ) expression to predict longer time-to-healing, and a model adjusted for gender and epidermal hyperplasia revealed increased CDKN1A expression and decreased PAPPA expression to predict longer time-to-healing. Increased p16INK4a staining was observed in diabetic wounds compared to non-diabetic wounds, and the same association was observed in the context of high dermal fibrosis. CONCLUSIONS: The findings of this pilot study suggest that senescent cells contribute to wound chronicity in humans, especially in diabetic wounds.

Mapping cellular senescence networks in human diabetic foot ulcers.

Cellular senescence, a cell fate defined by irreversible cell cycle arrest, has been observed to contribute to chronic age-related conditions including non-healing wounds, such as diabetic foot ulcers. However, the role of cellular senescence in the pathogenesis of diabetic foot ulcers remains unclear. To examine the contribution of senescent phenotypes to these chronic wounds, differential gene and network analyses were performed on publicly available bulk RNA sequencing of whole skin biopsies of wound edge diabetic foot ulcers and uninvolved diabetic foot skin. Wald tests with Benjamini-Hochberg correction were used to evaluate differential gene expression. Results showed that cellular senescence markers, CDKN1A, CXCL8, IGFBP2, IL1A, MMP10, SERPINE1, and TGFA, were upregulated, while TP53 was downregulated in diabetic foot ulcers compared to uninvolved diabetic foot skin. NetDecoder was then used to identify and compare context-specific protein-protein interaction networks using known cellular senescence markers as pathway sources. The diabetic foot ulcer protein-protein interaction network demonstrated significant perturbations with decreased inhibitory interactions and increased senescence markers compared to uninvolved diabetic foot skin. Indeed, TP53 (p53) and CDKN1A (p21) appeared to be key regulators in diabetic foot ulcer formation. These findings suggest that cellular senescence is an important mediator of diabetic foot ulcer pathogenesis.

A Knowledge-Based Discovery Approach Couples Artificial Neural Networks With Weight Engineering to Uncover Immune-Related Processes Underpinning Clinical Traits of Breast Cancer.

Immune-related processes are important in underpinning the properties of clinical traits such as prognosis and drug response in cancer. The possibility to extract knowledge learned by artificial neural networks (ANNs) from omics data to explain cancer clinical traits is a very attractive subject for novel discovery. Recent studies using a version of ANNs called autoencoders revealed their capability to store biologically meaningful information indicating that autoencoders can be utilized as knowledge discovery platforms aside from their initial assigned use for dimensionality reduction. Here, we devise an innovative weight engineering approach and ANN platform called artificial neural network encoder (ANNE) using an autoencoder and apply it to a breast cancer dataset to extract knowledge learned by the autoencoder model that explains clinical traits. Intriguingly, the extracted biological knowledge in the form of gene-gene associations from ANNE shows immune-related components such as chemokines, carbonic anhydrase, and iron metabolism that modulate immune-related processes and the tumor microenvironment play important roles in underpinning breast cancer clinical traits. Our work shows that biological "knowledge" learned by an ANN model is indeed encoded as weights throughout its neuronal connections, and it is possible to extract learned knowledge via a novel weight engineering approach to uncover important biological insights.

The Trifecta of Single-Cell, Systems-Biology, and Machine-Learning Approaches.

Together, single-cell technologies and systems biology have been used to investigate previously unanswerable questions in biomedicine with unparalleled detail. Despite these advances, gaps in analytical capacity remain. Machine learning, which has revolutionized biomedical imaging analysis, drug discovery, and systems biology, is an ideal strategy to fill these gaps in single-cell studies. Machine learning additionally has proven to be remarkably synergistic with single-cell data because it remedies unique challenges while capitalizing on the positive aspects of single-cell data. In this review, we describe how systems-biology algorithms have layered machine learning with biological components to provide systems level analyses of single-cell omics data, thus elucidating complex biological mechanisms. Accordingly, we highlight the trifecta of single-cell, systems-biology, and machine-learning approaches and illustrate how this trifecta can significantly contribute to five key areas of scientific research: cell trajectory and identity, individualized medicine, pharmacology, spatial omics, and multi-omics. Given its success to date, the systems-biology, single-cell omics, and machine-learning trifecta has proven to be a potent combination that will further advance biomedical research.