Beta adrenergic receptor antagonist can modify Pseudomonas aeruginosa biofilm formation in vitro: Implications for chronic wounds.

Non-healing wounds are a major medical challenge, affecting over 6.5 million people in the US alone, with associated healthcare costs of about $16 billion annually. They can result in prolonged hospitalizations, work loss, disability, poor quality of life, and in diabetic patients with foot ulcers, amputation of the affected limb in 25% of patients. Though chronic ulcers may arise from different underlying diseases, the unifying feature is chronic infection, driving persistent inflammation that prolongs the healing process. One of the most frequently cultured or genetically identified pathogens in skin wounds is Pseudomonas aeruginosa. This species avidly forms biofilms in the wound that impede bacterial eradication by the host's immune mechanisms and limit efficacy of systemic antibiotics. Thus, non-antibiotic approaches to limit the growth and biofilm formation of this wound pathogen would be an advance in the treatment of chronic wounds. Prior work has demonstrated that the growth of other microbial species can be modulated by catecholamine agonists and antagonists of the adrenergic receptors (ARs). Here, we demonstrate that not only can the growth of this common wound pathogen be modulated by catecholamines, but also that the beta-AR antagonists can significantly decrease their growth, and importantly, limit their ability to form biofilms. These findings suggest that beta adrenergic antagonists may have a therapeutic role in the treatment of chronic skin wounds.

Microbiome-skin-brain axis: A novel paradigm for cutaneous wounds.

Chronic wounds cause a significant burden on society financially, medically, and psychologically. Unfortunately, patients with nonhealing wounds often suffer from comorbidities that further compound their disability. Given the high rate of depressive symptoms experienced by patients with chronic wounds, further studies are needed to investigate the potentially linked pathophysiological changes in wounds and depression in order to improve patient care. The English literature on wound healing, inflammatory and microbial changes in chronic wounds and depression, and antiinflammatory and probiotic therapy was reviewed on PubMed. Chronic wound conditions and depression were demonstrated to share common pathologic features of dysregulated inflammation and altered microbiome, indicating a possible relationship. Furthermore, alternative treatment strategies such as immune-targeted and probiotic therapy showed promising potential by addressing both pathophysiological pathways. However, many existing studies are limited to a small study population, a cross-sectional design that does not establish temporality, or a wide range of confounding variables in the context of a highly complex and multifactorial disease process. Therefore, additional preclinical studies in suitable wound models, as well as larger clinical cohort studies and trials are necessary to elucidate the relationship between wound microbiome, healing, and depression, and ultimately guide the most effective therapeutic and management plan for chronic wound patients.

A tractable, simplified ex vivo human skin model of wound infection.

The prevalence of infection in chronic wounds is well documented in the literature but not optimally studied due to the drawbacks of current methodologies. Here, we describe a tractable and simplified ex vivo human skin model of infection that addresses the critical drawbacks of high costs and limited translatability. Wounds were generated from excised abdominal skin from cosmetic procedures and cultured, inoculated with Staphylococcus aureus strain UAMS-1, or under aseptic conditions. After three days, the infected wounds exhibited biofilm formation and significantly impaired reepithelialization compared to the control. Additionally, promigratory and proreparative genes were significantly downregulated, while proinflammatory genes were significantly upregulated, demonstrating molecular characterizations of impaired healing as in chronic wounds. This model allows for a simplified and versatile tool for the study of wound infection and subsequent development of novel therapies.

Deep learning classification for macrophage subtypes through cell migratory pattern analysis.

Macrophages can exhibit pro-inflammatory or pro-reparatory functions, contingent upon their specific activation state. This dynamic behavior empowers macrophages to engage in immune reactions and contribute to tissue homeostasis. Understanding the intricate interplay between macrophage motility and activation status provides valuable insights into the complex mechanisms that govern their diverse functions. In a recent study, we developed a classification method based on morphology, which demonstrated that movement characteristics, including speed and displacement, can serve as distinguishing factors for macrophage subtypes. In this study, we develop a deep learning model to explore the potential of classifying macrophage subtypes based solely on raw trajectory patterns. The classification model relies on the time series of x-y coordinates, as well as the distance traveled and net displacement. We begin by investigating the migratory patterns of macrophages to gain a deeper understanding of their behavior. Although this analysis does not directly inform the deep learning model, it serves to highlight the intricate and distinct dynamics exhibited by different macrophage subtypes, which cannot be easily captured by a finite set of motility metrics. Our study uses cell trajectories to classify three macrophage subtypes: M0, M1, and M2. This advancement holds promising implications for the future, as it suggests the possibility of identifying macrophage subtypes without relying on shape analysis. Consequently, it could potentially eliminate the necessity for high-quality imaging techniques and provide more robust methods for analyzing inherently blurry images.

Quantifying innervation facilitated by deep learning in wound healing.

The peripheral nerves (PNs) innervate the dermis and epidermis, which have been suggested to play an important role in wound healing. Several methods to quantify skin innervation during wound healing have been reported. Those usually require multiple observers, are complex and labor-intensive, and noise/background associated with the Immunohistochemistry (IHC) images could cause quantification errors/user bias. In this study, we employed the state-of-the-art deep neural network, DnCNN, to perform pre-processing and effectively reduce the noise in the IHC images. Additionally, we utilized an automated image analysis tool, assisted by Matlab, to accurately determine the extent of skin innervation during various stages of wound healing. The 8mm wound is generated using a circular biopsy punch in the wild-type mouse. Skin samples were collected on days 3,7,10 and 15, and sections from paraffin-embedded tissues were stained against pan-neuronal marker- protein-gene-product 9.5 (PGP 9.5) antibody. On day 3 and day 7, negligible nerve fibers were present throughout the wound with few only on the lateral boundaries of the wound. On day 10, a slight increase in nerve fiber density appeared, which significantly increased on day 15. Importantly we found a positive correlation (R- 2 = 0.933) between nerve fiber density and re-epithelization, suggesting an association between re-innervation and re-epithelization. These results established a quantitative time course of re-innervation in wound healing, and the automated image analysis method offers a novel and useful tool to facilitate the quantification of innervation in the skin and other tissues.

Quantifying innervation facilitated by deep learning in wound healing.

The peripheral nerves (PNs) innervate the dermis and epidermis, which have been suggested to play an important role in wound healing. Several methods to quantify skin innervation during wound healing have been reported. Those usually require multiple observers, are complex and labor-intensive, and noise/background associated with the Immunohistochemistry (IHC) images could cause quantification errors/user bias. In this study, we employed the state-of-the-art deep neural network, DnCNN, to perform pre-processing and effectively reduce the noise in the IHC images. Additionally, we utilized an automated image analysis tool, assisted by Matlab, to accurately determine the extent of skin innervation during various stages of wound healing. The 8mm wound is generated using a circular biopsy punch in the wild-type mouse. Skin samples were collected on days 3,7,10 and 15, and sections from paraffin-embedded tissues were stained against pan-neuronal marker- protein-gene-product 9.5 (PGP 9.5) antibody. On day 3 and day 7, negligible nerve fibers were present throughout the wound with few only on the lateral boundaries of the wound. On day 10, a slight increase in nerve fiber density appeared, which significantly increased on day 15. Importantly we found a positive correlation (R 2 = 0.933) between nerve fiber density and re-epithelization, suggesting an association between re-innervation and re-epithelization. These results established a quantitative time course of re-innervation in wound healing, and the automated image analysis method offers a novel and useful tool to facilitate the quantification of innervation in the skin and other tissues.

Quantifying innervation facilitated by deep learning in wound healing.

The peripheral nerves (PNs) innervate the dermis and epidermis, and are suggested to play an important role in wound healing. Several methods to quantify skin innervation during wound healing have been reported. Those usually require multiple observers, are complex and labor-intensive, and the noise/background associated with the immunohistochemistry (IHC) images could cause quantification errors/user bias. In this study, we employed the state-of-the-art deep neural network, Denoising Convolutional Neural Network (DnCNN), to perform pre-processing and effectively reduce the noise in the IHC images. Additionally, we utilized an automated image analysis tool, assisted by Matlab, to accurately determine the extent of skin innervation during various stages of wound healing. The 8 mm wound is generated using a circular biopsy punch in the wild-type mouse. Skin samples were collected on days 3, 7, 10 and 15, and sections from paraffin-embedded tissues were stained against pan-neuronal marker- protein-gene-product 9.5 (PGP 9.5) antibody. On day 3 and day 7, negligible nerve fibers were present throughout the wound with few only on the lateral boundaries of the wound. On day 10, a slight increase in nerve fiber density appeared, which significantly increased on day 15. Importantly, we found a positive correlation (R2 = 0.926) between nerve fiber density and re-epithelization, suggesting an association between re-innervation and re-epithelization. These results established a quantitative time course of re-innervation in wound healing, and the automated image analysis method offers a novel and useful tool to facilitate the quantification of innervation in the skin and other tissues.

Combination product of dermal matrix, preconditioned human mesenchymal stem cells and timolol promotes wound healing in the porcine wound model.

A combination product of human mesenchymal stem/stromal cells (MSCs) embedded in an extracellular matrix scaffold and preconditioned with hypoxia and the beta-adrenergic receptor antagonist, timolol, combined with sustained timolol application post implantation, has shown promising results for improving wound healing in a diabetic mouse model. In the present study, we extend those findings to the more translatable large animal porcine wound model and show that the combined treatment promotes wound reepithelialization in these excisional wounds by 40.2% and increases the CD31 immunostaining marker of angiogenesis compared with the matrix control, while maintaining an accumulated timolol plasma concentration below the clinically safe level of 0.3 ng/mL after the 15-day course of topical application. Human GAPDH was not elevated in the day 15 wounds treated with MSC-containing device relative to wounds treated with matrix alone, indicating that the xenografted human MSCs in the treatment do not persist in these immune-competent animals after 15 days. The work demonstrates the efficacy and safety of the combined treatment for improving healing in the clinically relevant porcine wound model.

Skin-brain axis signaling mediates behavioral changes after skin wounding.

Patients with chronic wounds often have associated cognitive dysfunction and depression with an as yet unknown mechanism for this association. To address the possible causality of skin wounding inducing these changes, behavior and cognitive functions of female C57BL/6 mice with an excisional skin wound were compared to unwounded animals. At six days post wounding, animals exhibited anxiety-like behaviors, impaired recognition memory, and impaired coping behavior. Wounded animals also had concomitant increased hippocampal expression of Tnfa, the pattern recognition receptor (PRR) Nod2, the glucocorticoid receptors GR/Nr3c1 and Nr3c2. Prefrontal cortex serotonin and dopamine turnover were increased on day six post-wounding. In contrast to the central nervous system (CNS) findings, day six post -wounding serum catecholamines did not differ between wounded and unwounded animals, nor did levels of the stress hormone corticosterone, TNFα, or TGFß. Serum IL6 levels were, however elevated in the wounded animals. These findings provide evidence of skin-to-brain signaling, mediated either by elevated serum IL6 or a direct neuronal signaling from the periphery to the CNS, independent of systemic mediators. Wounding in the periphery is associated with an altered expression of inflammatory mediators and PRR genes in the hippocampus, which may be responsible for the observed behavioral deficits.

Validated limited gene predictor for cervical cancer lymph node metastases.

PURPOSE: Recognizing the prognostic significance of lymph node (LN) involvement for cervical cancer, we aimed to identify genes that are differentially expressed in LN+ versus LN- cervical cancer and to potentially create a validated predictive gene signature for LN involvement. MATERIALS AND METHODS: Primary tumor biopsies were collected from 74 cervical cancer patients. RNA was extracted and RNA sequencing was performed. The samples were divided by institution into a training set (n = 57) and a testing set (n = 17). Differentially expressed genes were identified among the training cohort and used to train a Random Forest classifier. RESULTS: 22 genes showed > 1.5 fold difference in expression between the LN+ and LN- groups. Using forward selection 5 genes were identified and, based on the clinical knowledge of these genes and testing of the different combinations, a 2-gene Random Forest model of BIRC3 and CD300LG was developed. The classification accuracy of lymph node (LN) status on the test set was 88.2%, with an Area under the Receiver Operating Characteristic curve (ROC-AUC) of 98.6%. CONCLUSIONS: We identified a 2 gene Random Forest model of BIRC3 and CD300LG that predicted lymph node involvement in a validation cohort. This validated model, following testing in additional cohorts, could be used to create a reverse transcription-quantitative polymerase chain reaction (RT-qPCR) tool that would be useful for helping to identify patients with LN involvement in resource-limited settings.

Collagen Remodeling in the Hypoxic Tumor-Mesothelial Niche Promotes Ovarian Cancer Metastasis.

Peritoneal metastases are the leading cause of morbidity and mortality in high-grade serous ovarian cancer (HGSOC). Accumulating evidence suggests that mesothelial cells are an important component of the metastatic microenvironment in HGSOC. However, the mechanisms by which mesothelial cells promote metastasis are unclear. Here, we report that the HGSOC tumor-mesothelial niche was hypoxic, and hypoxic signaling enhanced collagen I deposition by mesothelial cells. Specifically, hypoxic signaling increased expression of lysyl oxidase (LOX) in mesothelial and ovarian cancer cells to promote collagen crosslinking and tumor cell invasion. The mesothelial niche was enriched with fibrillar collagen in human and murine omental metastases. Pharmacologic inhibition of LOX reduced tumor burden and collagen remodeling in murine omental metastases. These findings highlight an important role for hypoxia and mesothelial cells in the modification of the extracellular matrix and tumor invasion in HGSOC. SIGNIFICANCE: This study identifies HIF/LOX signaling as a potential therapeutic target to inhibit collagen remodeling and tumor progression in HGSOC.Graphical Abstract: http://cancerres.aacrjournals.org/content/canres/79/9/2271/F1.large.jpg.

Topical Fluoxetine as a Novel Therapeutic That Improves Wound Healing in Diabetic Mice.

Diabetic foot ulcers represent a significant source of morbidity in the U.S., with rapidly escalating costs to the health care system. Multiple pathophysiological disturbances converge to result in delayed epithelialization and persistent inflammation. Serotonin (5-hydroxytryptamine [5-HT]) and the selective serotonin reuptake inhibitor fluoxetine (FLX) have both been shown to have immunomodulatory effects. Here we extend their utility as a therapeutic alternative for nonhealing diabetic wounds by demonstrating their ability to interact with multiple pathways involved in wound healing. We show that topically applied FLX improves cutaneous wound healing in vivo. Mechanistically, we demonstrate that FLX not only increases keratinocyte migration but also shifts the local immune milieu toward a less inflammatory phenotype in vivo without altering behavior. By targeting the serotonin pathway in wound healing, we demonstrate the potential of repurposing FLX as a safe topical for the challenging clinical problem of diabetic wounds.