Developing an eco-driving strategy in a hybrid traffic network using reinforcement learning.

Eco-driving has garnered considerable research attention owing to its potential socio-economic impact, including enhanced public health and mitigated climate change effects through the reduction of greenhouse gas emissions. With an expectation of more autonomous vehicles (AVs) on the road, an eco-driving strategy in hybrid traffic networks encompassing AV and human-driven vehicles (HDVs) with the coordination of traffic lights is a challenging task. The challenge is partially due to the insufficient infrastructure for collecting, transmitting, and sharing real-time traffic data among vehicles, facilities, and traffic control centers, and the following decision-making of agents involved in traffic control. Additionally, the intricate nature of the existing traffic network, with its diverse array of vehicles and facilities, contributes to the challenge by hindering the development of a mathematical model for accurately characterizing the traffic network. In this study, we utilized the Simulation of Urban Mobility (SUMO) simulator to tackle the first challenge through computational analysis. To address the second challenge, we employed a model-free reinforcement learning (RL) algorithm, proximal policy optimization, to decide the actions of AV and traffic light signals in a traffic network. A novel eco-driving strategy was proposed by introducing different percentages of AV into the traffic flow and collaborating with traffic light signals using RL to control the overall speed of the vehicles, resulting in improved fuel consumption efficiency. Average rewards with different penetration rates of AV (5%, 10%, and 20% of total vehicles) were compared to the situation without any AV in the traffic flow (0% penetration rate). The 10% penetration rate of AV showed a minimum time of convergence to achieve average reward, leading to a significant reduction in fuel consumption and total delay of all vehicles.

Transcriptional profiles of non-neuronal and immune cells in mouse trigeminal ganglia.

Non-neuronal cells constitute 90%-95% of sensory ganglia. These cells, especially glial and immune cells, play critical roles in the modulation of sensory neurons. This study aimed to identify, profile, and summarize the types of trigeminal ganglion (TG) non-neuronal cells in naïve male mice using published and our own data generated by single-cell RNA sequencing, flow cytometry, and immunohistochemistry. TG has five types of non-neuronal cells, namely, glial, fibroblasts, smooth muscle, endothelial, and immune cells. There is an agreement among publications for glial, fibroblasts, smooth muscle, and endothelial cells. Based on gene profiles, glial cells were classified as myelinated and non-myelinated Schwann cells and satellite glial cells. Mpz has dominant expression in Schwann cells, and Fabp7 is specific for SCG. Two types of Col1a2+ fibroblasts located throughout TG were distinguished. TG smooth muscle and endothelial cells in the blood vessels were detected using well-defined markers. Our study reported three types of macrophages (Mph) and four types of neutrophils (Neu) in TG. Mph were located in the neuronal bodies and nerve fibers and were sub-grouped by unique transcriptomic profiles with Ccr2, Cx3cr1, and Iba1 as markers. A comparison of databases showed that type 1 Mph is similar to choroid plexus-low (CPlo) border-associated Mph (BAMs). Type 2 Mph has the highest prediction score with CPhi BAMs, while type 3 Mph is distinct. S100a8+ Neu were located in the dura surrounding TG and were sub-grouped by clustering and expressions of Csf3r, Ly6G, Ngp, Elane, and Mpo. Integrative analysis of published datasets indicated that Neu-1, Neu-2, and Neu-3 are similar to the brain Neu-1 group, while Neu-4 has a resemblance to the monocyte-derived cells. Overall, the generated and summarized datasets on non-neuronal TG cells showed a unique composition of myeloid cell types in TG and could provide essential and fundamental information for studies on cell plasticity, interactomic networks between neurons and non-neuronal cells, and function during a variety of pain conditions in the head and neck regions.

Transformer for Gene Expression Modeling (T-GEM): An Interpretable Deep Learning Model for Gene Expression-Based Phenotype Predictions.

Deep learning has been applied in precision oncology to address a variety of gene expression-based phenotype predictions. However, gene expression data's unique characteristics challenge the computer vision-inspired design of popular Deep Learning (DL) models such as Convolutional Neural Network (CNN) and ask for the need to develop interpretable DL models tailored for transcriptomics study. To address the current challenges in developing an interpretable DL model for modeling gene expression data, we propose a novel interpretable deep learning architecture called T-GEM, or Transformer for Gene Expression Modeling. We provided the detailed T-GEM model for modeling gene-gene interactions and demonstrated its utility for gene expression-based predictions of cancer-related phenotypes, including cancer type prediction and immune cell type classification. We carefully analyzed the learning mechanism of T-GEM and showed that the first layer has broader attention while higher layers focus more on phenotype-related genes. We also showed that T-GEM's self-attention could capture important biological functions associated with the predicted phenotypes. We further devised a method to extract the regulatory network that T-GEM learns by exploiting the attributions of self-attention weights for classifications and showed that the network hub genes were likely markers for the predicted phenotypes.

Deep learning tackles single-cell analysis-a survey of deep learning for scRNA-seq analysis.

Since its selection as the method of the year in 2013, single-cell technologies have become mature enough to provide answers to complex research questions. With the growth of single-cell profiling technologies, there has also been a significant increase in data collected from single-cell profilings, resulting in computational challenges to process these massive and complicated datasets. To address these challenges, deep learning (DL) is positioned as a competitive alternative for single-cell analyses besides the traditional machine learning approaches. Here, we survey a total of 25 DL algorithms and their applicability for a specific step in the single cell RNA-seq processing pipeline. Specifically, we establish a unified mathematical representation of variational autoencoder, autoencoder, generative adversarial network and supervised DL models, compare the training strategies and loss functions for these models, and relate the loss functions of these models to specific objectives of the data processing step. Such a presentation will allow readers to choose suitable algorithms for their particular objective at each step in the pipeline. We envision that this survey will serve as an important information portal for learning the application of DL for scRNA-seq analysis and inspire innovative uses of DL to address a broader range of new challenges in emerging multi-omics and spatial single-cell sequencing.

Prediction and interpretation of cancer survival using graph convolution neural networks.

The survival rate of cancer has increased significantly during the past two decades for breast, prostate, testicular, and colon cancer, while the brain and pancreatic cancers have a much lower median survival rate that has not improved much over the last forty years. This has imposed the challenge of finding gene markers for early cancer detection and treatment strategies. Different methods including regression-based Cox-PH, artificial neural networks, and recently deep learning algorithms have been proposed to predict the survival rate for cancers. We established in this work a novel graph convolution neural network (GCNN) approach called Surv_GCNN to predict the survival rate for 13 different cancer types using the TCGA dataset. For each cancer type, 6 Surv_GCNN models with graphs generated by correlation analysis, GeneMania database, and correlation + GeneMania were trained with and without clinical data to predict the risk score (RS). The performance of the 6 Surv_GCNN models was compared with two other existing models, Cox-PH and Cox-nnet. The results showed that Cox-PH has the worst performance among 8 tested models across the 13 cancer types while Surv_GCNN models with clinical data reported the best overall performance, outperforming other competing models in 7 out of 13 cancer types including BLCA, BRCA, COAD, LUSC, SARC, STAD, and UCEC. A novel network-based interpretation of Surv_GCNN was also proposed to identify potential gene markers for breast cancer. The signatures learned by the nodes in the hidden layer of Surv_GCNN were identified and were linked to potential gene markers by network modularization. The identified gene markers for breast cancer have been compared to a total of 213 gene markers from three widely cited lists for breast cancer survival analysis. About 57% of gene markers obtained by Surv_GCNN with correlation + GeneMania graph either overlap or directly interact with the 213 genes, confirming the effectiveness of the identified markers by Surv_GCNN.

Classification of Cancer Types Using Graph Convolutional Neural Networks.

BACKGROUND: Cancer has been a leading cause of death in the United States with significant health care costs. Accurate prediction of cancers at an early stage and understanding the genomic mechanisms that drive cancer development are vital to the improvement of treatment outcomes and survival rates, thus resulting in significant social and economic impacts. Attempts have been made to classify cancer types with machine learning techniques during the past two decades and deep learning approaches more recently. RESULTS: In this paper, we established four models with graph convolutional neural network (GCNN) that use unstructured gene expressions as inputs to classify different tumor and non-tumor samples into their designated 33 cancer types or as normal. Four GCNN models based on a co-expression graph, co-expression+singleton graph, protein-protein interaction (PPI) graph, and PPI+singleton graph have been designed and implemented. They were trained and tested on combined 10,340 cancer samples and 731 normal tissue samples from The Cancer Genome Atlas (TCGA) dataset. The established GCNN models achieved excellent prediction accuracies (89.9-94.7%) among 34 classes (33 cancer types and a normal group). In silico gene-perturbation experiments were performed on four models based on co-expression graph, co-expression+singleton, PPI graph, and PPI+singleton graphs. The co-expression GCNN model was further interpreted to identify a total of 428 markers genes that drive the classification of 33 cancer types and normal. The concordance of differential expressions of these markers between the represented cancer type and others are confirmed. Successful classification of cancer types and a normal group regardless of normal tissues' origin suggested that the identified markers are cancer-specific rather than tissue-specific. CONCLUSION: Novel GCNN models have been established to predict cancer types or normal tissue based on gene expression profiles. We demonstrated the results from the TCGA dataset that these models can produce accurate classification (above 94%), using cancer-specific markers genes. The models and the source codes are publicly available and can be readily adapted to the diagnosis of cancer and other diseases by the data-driven modeling research community.

Deriving a Boolean dynamics to reveal macrophage activation with in vitro temporal cytokine expression profiles.

BACKGROUND: Macrophages show versatile functions in innate immunity, infectious diseases, and progression of cancers and cardiovascular diseases. These versatile functions of macrophages are conducted by different macrophage phenotypes classified as classically activated macrophages and alternatively activated macrophages due to different stimuli in the complex in vivo cytokine environment. Dissecting the regulation of macrophage activations will have a significant impact on disease progression and therapeutic strategy. Mathematical modeling of macrophage activation can improve the understanding of this biological process through quantitative analysis and provide guidance to facilitate future experimental design. However, few results have been reported for a complete model of macrophage activation patterns. RESULTS: We globally searched and reviewed literature for macrophage activation from PubMed databases and screened the published experimental results. Temporal in vitro macrophage cytokine expression profiles from published results were selected to establish Boolean network models for macrophage activation patterns in response to three different stimuli. A combination of modeling methods including clustering, binarization, linear programming (LP), Boolean function determination, and semi-tensor product was applied to establish Boolean networks to quantify three macrophage activation patterns. The structure of the networks was confirmed based on protein-protein-interaction databases, pathway databases, and published experimental results. Computational predictions of the network evolution were compared against real experimental results to validate the effectiveness of the Boolean network models. CONCLUSION: Three macrophage activation core evolution maps were established based on the Boolean networks using Matlab. Cytokine signatures of macrophage activation patterns were identified, providing a possible determination of macrophage activations using extracellular cytokine measurements.

State feedback control design for Boolean networks.

BACKGROUND: Driving Boolean networks to desired states is of paramount significance toward our ultimate goal of controlling the progression of biological pathways and regulatory networks. Despite recent computational development of controllability of general complex networks and structural controllability of Boolean networks, there is still a lack of bridging the mathematical condition on controllability to real boolean operations in a network. Further, no realtime control strategy has been proposed to drive a Boolean network. RESULTS: In this study, we applied semi-tensor product to represent boolean functions in a network and explored controllability of a boolean network based on the transition matrix and time transition diagram. We determined the necessary and sufficient condition for a controllable Boolean network and mapped this requirement in transition matrix to real boolean functions and structure property of a network. An efficient tool is offered to assess controllability of an arbitrary Boolean network and to determine all reachable and non-reachable states. We found six simplest forms of controllable 2-node Boolean networks and explored the consistency of transition matrices while extending these six forms to controllable networks with more nodes. Importantly, we proposed the first state feedback control strategy to drive the network based on the status of all nodes in the network. Finally, we applied our reachability condition to the major switch of P53 pathway to predict the progression of the pathway and validate the prediction with published experimental results. CONCLUSIONS: This control strategy allowed us to apply realtime control to drive Boolean networks, which could not be achieved by the current control strategy for Boolean networks. Our results enabled a more comprehensive understanding of the evolution of Boolean networks and might be extended to output feedback control design.

Myocardial Infarction Superimposed on Aging: MMP-9 Deletion Promotes M2 Macrophage Polarization.

In this study, we examined the combined effect of aging and myocardial infarction on left ventricular remodeling, focusing on matrix metalloproteinase (MMP)-9-dependent mechanisms. We enrolled 55 C57BL/6J wild type (WT) and 85 MMP-9 Null (Null) mice of both sexes at 11-36 months of age and evaluated their response at Day 7 post-myocardial infarction. Plasma MMP-9 levels positively linked to age in WT mice (r = .46, p = .001). MMP-9 deletion improved survival (76% for WT vs 88% for Null, p = .021). Post-myocardial infarction, there was a progressive increase in left ventricular dilation with age in WT but not in Null mice. By inflammatory gene array analysis, WT mice showed linear age-dependent increases in three different proinflammatory genes (C3, CCl4, and CX3CL1; all p < .05), whereas Null mice showed increases in three proinflammatory genes (CCL5, CCL9, and CXCL4; all p < .05) and seven anti-inflammatory genes (CCL1, CCL6, CCR1, IL11, IL1r2, IL8rb, and Mif; all p < .05). Compared with WT, macrophages isolated from Null left ventricle infarct demonstrated enhanced expression of anti-inflammatory M2 markers CD163, MRC1, TGF-ß1, and YM1 (all p < .05), without affecting proinflammatory M1 markers. In conclusion, MMP-9 deletion stimulated anti-inflammatory polarization of macrophages to attenuate left ventricle dysfunction in the aging post-myocardial infarction.

CD36 Is a Matrix Metalloproteinase-9 Substrate That Stimulates Neutrophil Apoptosis and Removal During Cardiac Remodeling.

BACKGROUND: After myocardial infarction, the left ventricle undergoes a wound healing response that includes the robust infiltration of neutrophils and macrophages to facilitate removal of dead myocytes as well as turnover of the extracellular matrix. Matrix metalloproteinase (MMP)-9 is a key enzyme that regulates post-myocardial infarction left ventricular remodeling. METHODS AND RESULTS: Infarct regions from wild-type and MMP-9 null mice (n=8 per group) analyzed by glycoproteomics showed that of 541 N-glycosylated proteins quantified, 45 proteins were at least 2-fold upregulated or downregulated with MMP-9 deletion (all P<0.05). Cartilage intermediate layer protein and platelet glycoprotein 4 (CD36) were identified as having the highest fold increase in MMP-9 null mice. By immunoblotting, CD36 but not cartilage intermediate layer protein decreased steadily during the time course post-myocardial infarction, which identified CD36 as a candidate MMP-9 substrate. MMP-9 was confirmed in vitro and in vivo to proteolytically degrade CD36. In vitro stimulation of day 7 post-myocardial infarction macrophages with MMP-9 or a CD36-blocking peptide reduced phagocytic capacity. Dual immunofluorescence revealed concomitant accumulation of apoptotic neutrophils in the MMP-9 null group compared with wild-type group. In vitro stimulation of isolated neutrophils with MMP-9 decreased neutrophil apoptosis, indicated by reduced caspase-9 expression. CONCLUSIONS: Our data reveal a new cell-signaling role for MMP-9 through CD36 degradation to regulate macrophage phagocytosis and neutrophil apoptosis.

A Novel Collagen Matricryptin Reduces Left Ventricular Dilation Post-Myocardial Infarction by Promoting Scar Formation and Angiogenesis.

BACKGROUND: Proteolytically released extracellular matrix (ECM) fragments, matricryptins, are biologically active and play important roles in wound healing. Following myocardial infarction (MI), collagen I, a major component of cardiac ECM, is cleaved by matrix metalloproteinases (MMPs). OBJECTIVES: This study identified novel collagen-derived matricryptins generated post-MI that mediate remodeling of the left ventricle (LV). METHODS: Recombinant collagen Ia1 was used in MMPs cleavage assays, the products were analyzed by mass spectrometry for identification of cleavage sites. C57BL6/J mice were given MI and animals were treated either with vehicle control or p1158/59 matricryptin. Seven days post-MI, LV function and parameters of LV remodeling were measured. Levels of p1158/59 were also measured in plasma of MI patients and healthy controls. RESULTS: In situ, MMP-2 and -9 generate a collagen Iα1 C-1158/59 fragment, and MMP-9 can further degrade it. The C-1158/59 fragment was identified post-MI, both in human plasma and mouse LV, at levels that inversely correlated to MMP-9 levels. We synthesized a peptide beginning at the cleavage site (p1158/59, amino acids 1159 to 1173) to investigate its biological functions. In vitro, p1158/59 stimulated fibroblast wound healing and robustly promoted angiogenesis. In vivo, early post-MI treatment with p1158/59 reduced LV dilation at day 7 post-MI by preserving LV structure (p < 0.05 vs. control). The p1158/59 stimulated both in vitro and in vivo wound healing by enhancing basement membrane proteins, granulation tissue components, and angiogenic factors. CONCLUSIONS: Collagen Iα1 matricryptin p1158/59 facilitates LV remodeling post-MI by regulating scar formation through targeted ECM generation and stimulation of angiogenesis.

Transformative Impact of Proteomics on Cardiovascular Health and Disease: A Scientific Statement From the American Heart Association.

The year 2014 marked the 20th anniversary of the coining of the term proteomics. The purpose of this scientific statement is to summarize advances over this period that have catalyzed our capacity to address the experimental, translational, and clinical implications of proteomics as applied to cardiovascular health and disease and to evaluate the current status of the field. Key successes that have energized the field are delineated; opportunities for proteomics to drive basic science research, facilitate clinical translation, and establish diagnostic and therapeutic healthcare algorithms are discussed; and challenges that remain to be solved before proteomic technologies can be readily translated from scientific discoveries to meaningful advances in cardiovascular care are addressed. Proteomics is the result of disruptive technologies, namely, mass spectrometry and database searching, which drove protein analysis from 1 protein at a time to protein mixture analyses that enable large-scale analysis of proteins and facilitate paradigm shifts in biological concepts that address important clinical questions. Over the past 20 years, the field of proteomics has matured, yet it is still developing rapidly. The scope of this statement will extend beyond the reaches of a typical review article and offer guidance on the use of next-generation proteomics for future scientific discovery in the basic research laboratory and clinical settings.

Plasma Glycoproteomics Reveals Sepsis Outcomes Linked to Distinct Proteins in Common Pathways.

OBJECTIVE: Sepsis remains a predominant cause of mortality in the ICU, yet strategies to increase survival have proved largely unsuccessful. This study aimed to identify proteins linked to sepsis outcomes using a glycoproteomic approach to target extracellular proteins that trigger downstream pathways and direct patient outcomes. DESIGN: Plasma was obtained from the Lactate Assessment in the Treatment of Early Sepsis cohort. N-linked plasma glycopeptides were quantified by solid-phase extraction coupled with mass spectrometry. Glycopeptides were assigned to proteins using RefSeq (National Center of Biotechnology Information, Bethesda, MD) and visualized in a heat map. Protein differences were validated by immunoblotting, and proteins were mapped for biological processes using Database for Annotation, Visualization and Integrated Discovery (National Institute of Allergy and Infectious Diseases, National Institutes of Health; Bethesda, MD) and for functional pathways using Kyoto Encyclopedia of Genes and Genomes (Kanehisa Laboratories, Kyoto, Japan) databases. SETTING: Hospitalized care. PATIENTS: Patients admitted to the emergency department were enrolled in the study when the diagnosis of sepsis was made, within 6 hours of presentation. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A total of 501 glycopeptides corresponding to 234 proteins were identified. Of these, 66 glycopeptides were unique to the survivor group and corresponded to 54 proteins, 60 were unique to the nonsurvivor group and corresponded to 43 proteins, and 375 were common responses between groups and corresponded to 137 proteins. Immunoblotting showed that nonsurvivors had increased total kininogen; decreased total cathepsin-L1, vascular cell adhesion molecule, periostin, and neutrophil gelatinase-associated lipocalin; and a two-fold decrease in glycosylated clusterin (all p < 0.05). Kyoto Encyclopedia of Genes and Genomes analysis identified six enriched pathways. Interestingly, survivors relied on the extrinsic pathway of the complement and coagulation cascade, whereas nonsurvivors relied on the intrinsic pathway. CONCLUSION: This study identifies proteins linked to patient outcomes and provides insight into unexplored mechanisms that can be investigated for the identification of novel therapeutic targets.

Systems analysis of gene ontology and biological pathways involved in post-myocardial infarction responses.

BACKGROUND: Pathway analysis has been widely used to gain insight into essential mechanisms of the response to myocardial infarction (MI). Currently, there exist multiple pathway databases that organize molecular datasets and manually curate pathway maps for biological interpretation at varying forms of organization. However, inconsistencies among different databases in pathway descriptions, frequently due to conflicting results in the literature, can generate incorrect interpretations. Furthermore, although pathway analysis software provides detailed images of interactions among molecules, it does not exhibit how pathways interact with one another or with other biological processes under specific conditions. METHODS: We propose a novel method to standardize descriptions of enriched pathways for a set of genes/proteins using Gene Ontology terms. We used this method to examine the relationships among pathways and biological processes for a set of condition-specific genes/proteins, represented as a functional biological pathway-process network. We applied this algorithm to a set of 613 MI-specific proteins we previously identified. RESULTS: A total of 96 pathways from Biocarta, KEGG, and Reactome, and 448 Gene Ontology Biological Processes were enriched with these 613 proteins. The pathways were represented as Boolean functions of biological processes, delivering an interactive scheme to organize enriched information with an emphasis on involvement of biological processes in pathways. We extracted a network focusing on MI to demonstrate that tyrosine phosphorylation of Signal Transducer and Activator of Transcription (STAT) protein, positive regulation of collagen metabolic process, coagulation, and positive/negative regulation of blood coagulation have immediate impacts on the MI response. CONCLUSIONS: Our method organized biological processes and pathways in an unbiased approach to provide an intuitive way to identify biological properties of pathways under specific conditions. Pathways from different databases have similar descriptions yet diverse biological processes, indicating variation in their ability to share similar functional characteristics. The coverages of pathways can be expanded with the incorporation of more biological processes, predicting involvement of protein members in pathways. Further, detailed analyses of the functional biological pathway-process network will allow researchers and scientists to explore critical routes in biological systems in the progression of disease.

Deriving a cardiac ageing signature to reveal MMP-9-dependent inflammatory signalling in senescence.

AIMS: Cardiac ageing involves the progressive development of cardiac fibrosis and diastolic dysfunction coordinated by MMP-9. Here, we report a cardiac ageing signature that encompasses macrophage pro-inflammatory signalling in the left ventricle (LV) and distinguishes biological from chronological ageing. METHODS AND RESULTS: Young (6-9 months), middle-aged (12-15 months), old (18-24 months), and senescent (26-34 months) mice of both C57BL/6J wild type (WT) and MMP-9 null were evaluated. Using an identified inflammatory pattern, we were able to define individual mice based on their biological, rather than chronological, age. Bcl6, Ccl24, and Il4 were the strongest inflammatory markers of the cardiac ageing signature. The decline in early-to-late LV filling ratio was most strongly predicted by Bcl6, Il1r1, Ccl24, Crp, and Cxcl13 patterns, whereas LV wall thickness was most predicted by Abcf1, Tollip, Scye1, and Mif patterns. With age, there was a linear increase in cardiac M1 macrophages and a decrease in cardiac M2 macrophages in WT mice; of which, both were prevented by MMP-9 deletion. In vitro, MMP-9 directly activated young macrophage polarization to an M1/M2 mid-transition state. CONCLUSION: Our results define the cardiac ageing inflammatory signature and assign MMP-9 roles in mediating the inflammaging profile by indirectly and directly modifying macrophage polarization. Our results explain early mechanisms that stimulate ageing-induced cardiac fibrosis and diastolic dysfunction.

P. gingivalis lipopolysaccharide intensifies inflammation post-myocardial infarction through matrix metalloproteinase-9.

Periodontal disease (PD) strongly correlates with increased mortality post-myocardial infarction (MI); however, the underlying mechanisms are unknown. Matrix metalloproteinase (MMP)-9 levels directly correlate with dysfunction and remodeling of the left ventricle (LV) post-MI. Post-MI, MMP-9 is produced by leukocytes and modulates inflammation. We have shown that exposure to Porphyromonas gingivalis lipopolysaccharide (PgLPS), an immunomodulatory molecule identified in PD patients, increases LV MMP-9 levels in mice and leads to cardiac inflammation and dysfunction. The aim of the study was to determine if circulating PgLPS exacerbates the LV inflammatory response post-MI through MMP-9 dependent mechanisms. We exposed wild type C57BL/6J and MMP-9(-/-) mice to PgLPS (ATCC 33277) for a period of 28 days before performing MI, and continued to deliver PgLPS for up to 7 days post-MI. We found systemic levels of PgLPS 1) increased MMP-9 levels in both plasma and infarcted LV resulting in reduced wall thickness and increased incidence of LV rupture post-MI and 2) increased systemic and local macrophage chemotaxis leading to accelerated M1 macrophage infiltration post-MI and decreased LV function. MMP-9 deletion played a protective role by attenuating the inflammation induced by systemic delivery of PgLPS. In conclusion, MMP-9 deletion has a cardioprotective role against PgLPS exposure, by attenuating macrophage mediated inflammation.

Aliskiren and valsartan mediate left ventricular remodeling post-myocardial infarction in mice through MMP-9 effects.

We evaluated whether aliskiren, valsartan, or a combination of both was protective following myocardial infarction (MI) through effects on matrix metalloproteinase (MMP)-9. C57BL/6J wild type (WT, n=94) and MMP-9 null (null, n=85) mice were divided into 4 groups at 3h post-MI: saline (S), aliskiren (A; 50mg/kg/day), valsartan (V; 40mg/kg/day), or A+V and compared to no MI controls at 28days post-MI. All groups had similar infarct areas, and survival rates were higher in the null mice. The treatments influenced systolic function and hypertrophy index, as well as extracellular matrix (ECM) and inflammatory genes in the remote region, indicating that primary effects were on the viable myocardium. Saline treated WT mice showed increased end systolic and diastolic volumes and hypertrophy index, along with reduced ejection fraction. MMP-9 deletion improved LV function post-MI. Aliskiren attenuated the increase in end systolic volume and hypertrophy index, while valsartan improved end diastolic volumes and aliskiren+valsartan improved the hypertrophy index only when MMP-9 was absent. Extracellular matrix and inflammatory gene expression showed distinct patterns among the treatment groups, indicating a divergence in mechanisms of remodeling. This study shows that MMP-9 regulates aliskiren and valsartan effects in mice. These results in mice provide mechanistic insight to help translate these findings to post-MI patients.

Using systems biology approaches to understand cardiac inflammation and extracellular matrix remodeling in the setting of myocardial infarction.

Inflammation and extracellular matrix (ECM) remodeling are important components regulating the response of the left ventricle to myocardial infarction (MI). Significant cellular- and molecular-level contributors can be identified by analyzing data acquired through high-throughput genomic and proteomic technologies that provide expression levels for thousands of genes and proteins. Large-scale data provide both temporal and spatial information that need to be analyzed and interpreted using systems biology approaches in order to integrate this information into dynamic models that predict and explain mechanisms of cardiac healing post-MI. In this review, we summarize the systems biology approaches needed to computationally simulate post-MI remodeling, including data acquisition, data analysis for biomarker classification and identification, data integration to build dynamic models, and data interpretation for biological functions. An example for applying a systems biology approach to ECM remodeling is presented as a reference illustration.

Integrative computational and experimental approaches to establish a post-myocardial infarction knowledge map.

Vast research efforts have been devoted to providing clinical diagnostic markers of myocardial infarction (MI), leading to over one million abstracts associated with "MI" and "Cardiovascular Diseases" in PubMed. Accumulation of the research results imposed a challenge to integrate and interpret these results. To address this problem and better understand how the left ventricle (LV) remodels post-MI at both the molecular and cellular levels, we propose here an integrative framework that couples computational methods and experimental data. We selected an initial set of MI-related proteins from published human studies and constructed an MI-specific protein-protein-interaction network (MIPIN). Structural and functional analysis of the MIPIN showed that the post-MI LV exhibited increased representation of proteins involved in transcriptional activity, inflammatory response, and extracellular matrix (ECM) remodeling. Known plasma or serum expression changes of the MIPIN proteins in patients with MI were acquired by data mining of the PubMed and UniProt knowledgebase, and served as a training set to predict unlabeled MIPIN protein changes post-MI. The predictions were validated with published results in PubMed, suggesting prognosticative capability of the MIPIN. Further, we established the first knowledge map related to the post-MI response, providing a major step towards enhancing our understanding of molecular interactions specific to MI and linking the molecular interaction, cellular responses, and biological processes to quantify LV remodeling.

Translating Koch's postulates to identify matrix metalloproteinase roles in postmyocardial infarction remodeling: cardiac metalloproteinase actions (CarMA) postulates.

The first matrix metalloproteinase (MMP) was described in 1962; and since the 1990s, cardiovascular research has focused on understanding how MMPs regulate many aspects of cardiovascular pathology from atherosclerosis formation to myocardial infarction and stroke. Although much information has been gleaned by these past reports, to a large degree MMP cardiovascular biology remains observational, with few studies homing in on cause and effect relationships. Koch's postulates were first developed in the 19th century as a way to establish microorganism function and were modified in the 20th century to include methods to establish molecular causality. In this review, we outline the concept for establishing a similar approach to determine causality in terms of MMP functions. We use left ventricular remodeling postmyocardial infarction as an example, but this approach will have broad applicability across both the cardiovascular and the MMP fields.