Jeffrey E. Thatcher

Dysregulation of microRNAs after myocardial infarction reveals a role of miR-29 in cardiac fibrosis

Acute myocardial infarction (MI) due to coronary artery occlusion is accompanied by a pathological remodeling response that includes hypertrophic cardiac growth and fibrosis, which impair cardiac contractility. Previously, we showed that cardiac hypertrophy and heart failure are accompanied by characteristic changes in the expression of a collection of specific microRNAs (miRNAs), which act as negative regulators of gene expression. Here, we show that MI in mice and humans also results in the dysregulation of specific miRNAs, which are similar to but distinct from those involved in hypertrophy and heart failure. Among the MI-regulated miRNAs are members of the miR-29 family, which are down-regulated in the region of the heart adjacent to the infarct. The miR-29 family targets a cadre of mRNAs that encode proteins involved in fibrosis, including multiple collagens, fibrillins, and elastin. Thus, down-regulation of miR-29 would be predicted to derepress the expression of these mRNAs and enhance the fibrotic response. Indeed, down-regulation of miR-29 with anti-miRs in vitro and in vivo induces the expression of collagens, whereas over-expression of miR-29 in fibroblasts reduces collagen expression. We conclude that miR-29 acts as a regulator of cardiac fibrosis and represents a potential therapeutic target for tissue fibrosis in general.

Myocardin-Related Transcription Factor-A Controls Myofibroblast Activation and Fibrosis in Response to Myocardial Infarction

Rationale: Myocardial infarction (MI) results in loss of cardiac myocytes in the ischemic zone of the heart, followed by fibrosis and scar formation, which diminish cardiac contractility and impede angiogenesis and repair. Myofibroblasts, a specialized cell type that switches from a fibroblast-like state to a contractile, smooth muscle-like state, are believed to be primarily responsible for fibrosis of the injured heart and other tissues, although the transcriptional mediators of fibrosis and myofibroblast activation remain poorly defined. Myocardin-related transcription factors (MRTFs) are serum response factor (SRF) cofactors that promote a smooth muscle phenotype and are emerging as components of stress-responsive signaling. Objective: We aimed to examine the effect of MRTF-A on cardiac remodeling and fibrosis. Methods and Results: Here, we show that MRTF-A controls the expression of a fibrotic gene program that includes genes involved in extracellular matrix production and smooth muscle cell differentiation in the heart. In MRTF-A–null mice, fibrosis and scar formation following MI or angiotensin II treatment are dramatically diminished compared with wild-type littermates. This protective effect of MRTF-A deletion is associated with a reduction in expression of fibrosis-associated genes, including collagen 1a2, a direct transcriptional target of SRF/MRTF-A. Conclusions: We conclude that MRTF-A regulates myofibroblast activation and fibrosis in response to the renin–angiotensin system and post-MI remodeling.

C/EBP Transcription Factors Mediate Epicardial Activation During Heart Development and Injury

Enhancing Heart Function The epicardium, a protective layer of tissue surrounding the mammalian heart, plays a critical role during embryogenesis because it supplies growth factors and multipotent progenitor cells essential for heart development. In adults, the epicardium is dormant but it becomes reactivated when the heart is injured, a response that leads to re-expression of developmental genes. Studying mouse models, Huang et al. (p. 1599, published online 15 November; see the Perspective by Rosenzweig) found that the C/EBP transcription factors activated the epicardium during development and injury. Blockade of C/EBP signaling in the epicardium of injured (ischemic) hearts reduced inflammation and improved heart function, a finding that could ultimately lead to new strategies for the repair of heart damage. Transcriptional mechanisms controlling gene expression in the heart’s outer layer are exploited for cardiac repair. The epicardium encapsulates the heart and functions as a source of multipotent progenitor cells and paracrine factors essential for cardiac development and repair. Injury of the adult heart results in reactivation of a developmental gene program in the epicardium, but the transcriptional basis of epicardial gene expression has not been delineated. We established a mouse embryonic heart organ culture and gene expression system that facilitated the identification of epicardial enhancers activated during heart development and injury. Epicardial activation of these enhancers depends on a combinatorial transcriptional code centered on CCAAT/enhancer binding protein (C/EBP) transcription factors. Disruption of C/EBP signaling in the adult epicardium reduced injury-induced neutrophil infiltration and improved cardiac function. These findings reveal a transcriptional basis for epicardial activation and heart injury, providing a platform for enhancing cardiac regeneration.

Outlier detection and removal improves accuracy of machine learning approach to multispectral burn diagnostic imaging.

Abstract. Multispectral imaging (MSI) was implemented to develop a burn tissue classification device to assist burn surgeons in planning and performing debridement surgery. To build a classification model via machine learning, training data accurately representing the burn tissue was needed, but assigning raw MSI data to appropriate tissue classes is prone to error. We hypothesized that removing outliers from the training dataset would improve classification accuracy. A swine burn model was developed to build an MSI training database and study an algorithm’s burn tissue classification abilities. After the ground-truth database was generated, we developed a multistage method based on Z-test and univariate analysis to detect and remove outliers from the training dataset. Using 10-fold cross validation, we compared the algorithm’s accuracy when trained with and without the presence of outliers. The outlier detection and removal method reduced the variance of the training data. Test accuracy was improved from 63% to 76%, matching the accuracy of clinical judgment of expert burn surgeons, the current gold standard in burn injury assessment. Given that there are few surgeons and facilities specializing in burn care, this technology may improve the standard of burn care for patients without access to specialized facilities.

Surgical wound debridement sequentially characterized in a porcine burn model with multispectral imaging.

INTRODUCTION Multispectral imaging (MSI) is an optical technique that measures specific wavelengths of light reflected from wound site tissue to determine the severity of burn wounds. A rapid MSI device to measure burn depth and guide debridement will improve clinical decision making and diagnoses. METHODOLOGY We used a porcine burn model to study partial thickness burns of varying severity. We made eight 4 × 4 cm burns on the dorsum of one minipig. Four burns were studied intact, and four burns underwent serial tangential excision. We imaged the burn sites with 400-1000 nm wavelengths. RESULTS Histology confirmed that we achieved various partial thickness burns. Analysis of spectral images show that MSI detects significant variations in the spectral profiles of healthy tissue, superficial partial thickness burns, and deep partial thickness burns. The absorbance spectra of 515, 542, 629, and 669 nm were the most accurate in distinguishing superficial from deep partial thickness burns, while the absorbance spectra of 972 nm was the most accurate in guiding the debridement process. CONCLUSION The ability to distinguish between partial thickness burns of varying severity to assess whether a patient requires surgery could be improved with an MSI device in a clinical setting.

Multispectral and Photoplethysmography Optical Imaging Techniques Identify Important Tissue Characteristics in an Animal Model of Tangential Burn Excision.

Burn excision, a difficult technique owing to the training required to identify the extent and depth of injury, will benefit from a tool that can cue the surgeon as to where and how much to resect. We explored two rapid and noninvasive optical imaging techniques in their ability to identify burn tissue from the viable wound bed using an animal model of tangential burn excision. Photoplethysmography (PPG) imaging and multispectral imaging (MSI) were used to image the initial, intermediate, and final stages of burn excision of a deep partial-thickness burn. PPG imaging maps blood flow in the skin’s microcirculation, and MSI collects the tissue reflectance spectrum in visible and infrared wavelengths of light to classify tissue based on a reference library. A porcine deep partial-thickness burn model was generated and serial tangential excision accomplished with an electric dermatome set to 1.0 mm depth. Excised eschar was stained with hematoxylin and eosin to determine the extent of burn remaining at each excision depth. We confirmed that the PPG imaging device showed significantly less blood flow where burn tissue was present, and the MSI method could delineate burn tissue in the wound bed from the viable wound bed. These results were confirmed independently by a histological analysis. We found these devices can identify the proper depth of excision, and their images could cue a surgeon as to the preparedness of the wound bed for grafting. These image outputs are expected to facilitate clinical judgment in the operating room.

C-terminal variable AGES domain of Thymosin β4: the molecule's primary contribution in support of post-ischemic cardiac function and repair

Repairing defective cardiac cells is important towards improving heart function. Due to the frequency and severity of ischemic heart disease, management of patients featuring this type of cardiac failure receives significant interest. Previously we discovered that Thymosin β4 (TB4), a 43 amino-acid secreted actin sequestering peptide, is beneficial for myocardial cell survival and coronary re-growth after infarction in adult mammals. Considering the regenerative potential of full-length TB4 in the heart, and that minimal structural variations alter TB4s influence on actin assembly and cell movement, we investigated how various TB4 domains affect cardiac cell behavior and post-ischemic mammalian heart function. We synthesized 17 domain combinations of full-length TB4 and analyzed their impact on embryonic cardiac cells in vitro, and after cardiac infarction in vivo. We discovered the domains of TB4 affect cardiac cell behavior distinctly. We revealed TB4 specific C-terminal tetrapeptide, AGES, increases embryonic cardiac cell migration and myocyte beating in culture, and improves adult mammalian heart function following ischemia. Investigating the molecular background and mechanism we discovered systemic injection of AGES enhances early myocyte survival by activating Akt-mediated signaling mechanisms, increases coronary vessel growth and inhibits inflammation in mice and pigs. Biodistribution analyses revealed cardiomyocytes uptake AGES efficiently in vitro and in vivo projecting a potential independent clinical utilization for the tetrapeptide. Our comprehensive domain investigations also suggest, preservation and/or restoration of cardiomyocyte communication is a target of TB4 and AGES, and critical to improve post-ischemic heart function in pigs. In summary, we identified the C-terminal four amino-acid variable end of TB4 as the essential and responsible domain for the molecules full benefits in the hypoxic heart. Additionally, we introduced AGES as a novel, systemically applicable drug candidate to aid cardiac infarction in adult mammals.

Multispectral imaging burn wound tissue classification system: a comparison of test accuracies between several common machine learning algorithms

The clinical judgment of expert burn surgeons is currently the standard on which diagnostic and therapeutic decisionmaking regarding burn injuries is based. Multispectral imaging (MSI) has the potential to increase the accuracy of burn depth assessment and the intraoperative identification of viable wound bed during surgical debridement of burn injuries. A highly accurate classification model must be developed using machine-learning techniques in order to translate MSI data into clinically-relevant information. An animal burn model was developed to build an MSI training database and to study the burn tissue classification ability of several models trained via common machine-learning algorithms. The algorithms tested, from least to most complex, were: K-nearest neighbors (KNN), decision tree (DT), linear discriminant analysis (LDA), weighted linear discriminant analysis (W-LDA), quadratic discriminant analysis (QDA), ensemble linear discriminant analysis (EN-LDA), ensemble K-nearest neighbors (EN-KNN), and ensemble decision tree (EN-DT). After the ground-truth database of six tissue types (healthy skin, wound bed, blood, hyperemia, partial injury, full injury) was generated by histopathological analysis, we used 10-fold cross validation to compare the algorithms’ performances based on their accuracies in classifying data against the ground truth, and each algorithm was tested 100 times. The mean test accuracy of the algorithms were KNN 68.3%, DT 61.5%, LDA 70.5%, W-LDA 68.1%, QDA 68.9%, EN-LDA 56.8%, EN-KNN 49.7%, and EN-DT 36.5%. LDA had the highest test accuracy, reflecting the bias-variance tradeoff over the range of complexities inherent to the algorithms tested. Several algorithms were able to match the current standard in burn tissue classification, the clinical judgment of expert burn surgeons. These results will guide further development of an MSI burn tissue classification system. Given that there are few surgeons and facilities specializing in burn care, this technology may improve the standard of burn care for patients without access to specialized facilities.

Dynamic tissue phantoms and their use in assessment of a noninvasive optical plethysmography imaging device

Non-contact photoplethysmography (PPG) has been studied as a method to provide low-cost and non-invasive medical imaging for a variety of near-surface pathologies and two dimensional blood oxygenation measurements. Dynamic tissue phantoms were developed to evaluate this technology in a laboratory setting. The purpose of these phantoms was to generate a tissue model with tunable parameters including: blood vessel volume change; pulse wave frequency; and optical scattering and absorption parameters. A non-contact PPG imaging system was evaluated on this model and compared against laser Doppler imaging (LDI) and a traditional pulse oximeter. Results indicate non-contact PPG accurately identifies pulse frequency and appears to identify signals from optically dense phantoms with significantly higher detection thresholds than LDI.

Quantifying regional left ventricular contractile function: Leave it to the machines?

In this issue of the Journal, Henn and colleagues from Washington University in St Louis present a novel method to quantify and localize regional left ventricle (LV) contractile function in coronary artery disease by means of cardiac magnetic resonance imaging with radiofrequency tissue tagging. They have developed a mechanism by which the quantified LV function can be compared against a normalized standard to determine the presence and severity of an individual patient’s pathologic contractile dysfunction. Henn and colleagues deserve praise for developing an automated, quantitative solution for a problem that has only been addressed qualitatively in the past. The ability to both quantify and localize LV contractile dysfunction should improve clinical outcomes in several ways by addressing limitations of the current nonquantitative metrics of regional LV function. First, this method creates an objective standard that will increase consistency and accuracy across the board by reducing the interuser and intertemporal variability that has plagued echocardiographic interpretation of LV function in the past. Furthermore, the simplicity of interpreting and displaying these results may increase patients’ understanding of their coronary artery disease. A key component of this study is the methodology to extrapolate a single measurement of cardiac wall motion (z score) from detailed and multidimensional data and to construct a reference range model by ‘‘normalizing’’ the LV function z score of a healthy cohort, a process similar to what is known as machine learning in engineering circles. Machine learning typifies a ubiquitous engineering approach to solving complex biological questions—and its application is only increasing in frequency within the biomedical realm. Machine learning takes advantage of computer models that can be taught to perform a desired task. Training data are collected to develop and