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.

[Epidemiological investigation on the local epidemic situation in Zhengzhou High-Tech Zone caused by SARS-CoV-2 Delta variant]

This study collected epidemic data of COVID-19 in Zhengzhou from January 1 to January 20 in 2022. The epidemiological characteristics of the local epidemic in Zhengzhou High-tech Zone caused by the SARS-CoV-2 Delta variant were analyzed through epidemiological survey and big data analysis, which could provide a scientific basis for the prevention and control of the Delta variant. In detail, a total of 276 close contacts and 599 secondary close contacts were found in this study. The attack rate of close contacts and secondary close contacts was 5.43% (15/276) and 0.17% (1/599), respectively. There were 10 confirmed cases associated with the chain of transmission. Among them, the attack rates in close contacts of the first, second, third, fourth and fifth generation cases were 20.00% (5/25), 17.86% (5/28), 0.72% (1/139) and 14.81% (4/27), 0 (0/57), respectively. The attack rates in close contacts after sharing rooms/beds, having meals, having neighbor contacts, sharing vehicles with the patients, having same space contacts, and having work contacts were 26.67%, 9.10%, 8.33%, 4.55%, 1.43%, and 0 respectively. Collectively, the local epidemic situation in Zhengzhou High-tech Zone has an obvious family cluster. Prevention and control work should focus on decreasing family clusters of cases and community transmission.

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.

[Effects of ambient particulate matter (PM10) on prevalence of diabetes and fasting plasma glucose]

Objective: To explore the effect of long-term exposure to ambient particulate matter (PM₁₀) on the prevalence of diabetes and fasting plasma glucose (FPG). Methods: The subjects of the study were from the baseline population of "Jinchang Cohort", and 24 285 subjects were finally included after excluding incomplete home address information and diabetic diagnosis information. The demographic characteristics, lifestyle and health status of the survey subjects were collected through questionnaire, physical examination and laboratory tests. ArcGIS software was used to match the nearest environmental monitoring stations for each subject according to residential address. Two-year average concentrations of PM₁₀ were calculated to estimate exposure level. The logistic regression and the multiple linear regression were conducted to assess the effects of ambient PM₁₀ on the prevalence of diabetes and FPG. The restricted cubic spline was used to quantify the dose-response relationship. Stratified analysis and effect modification analysis were also performed. Results: The age of 24 285 participants was (49.32±8.60) years, and the BMI was (24.22±6.09) kg/m². There were 13 950 (57.44%) males and 2 066 (8.51%) diabetic patients. After adjusting for confounders, for every 10 μg/m³ increase in the average PM₁₀ concentration in the first two years of the survey, the prevalence of diabetes increased [OR (95%CI) =1.05 (1.01-1.09)]and the FPG level elevated [β (95%CI) = 0.061 (0.047-0.076) mmol/L]. The results of the restricted cubic spline analysis showed a nonlinear relationship between PM₁₀ concentration and FPG level (P<0.001). Further subgroup analysis showed that female [OR (95%CI) =1.10 (1.03-1.18)], people over 50 years old [OR (95%CI) =1.06 (1.02-1.11) ], subjects with family history of diabetes [OR (95%CI) = 1.13 (1.04-1.23) ], and with hypertension [OR (95%CI) = 1.07 (1.02-1.12) ] had a stronger association between the prevalence of diabetes and PM₁₀ exposure (all P interaction values were<0.05). The effects of PM₁₀ on FPG were more significant in people older than 50 years[β (95%CI) = 0.080 (0.050-0.109) mmol/L], with family history of diabetes [β (95%CI) = 0.087 (0.036-0.137) mmol/L], and hypertension [β (95%CI) = 0.077 (0.046-0.108) mmol/L] (all P interaction values were<0.05). Conclusions: Long-term exposure to ambient PM₁₀ increases the diabetes prevalence and FPG. People older than 50 years old, with family history of diabetes and hypertension could be more sensitive to the effects of PM₁₀ exposure.

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.

[Preliminary analysis on COVID-19 case spectrum and spread intensity in different provinces in China except Hubei province]

Objective: To analyze the characteristics of COVID-19 case spectrum and spread intensity in different provinces in China except Hubei province. Methods: The daily incidence data and case information of COVID-19 were collected from the official websites of provincial and municipal health commissions. The morbidity rate, severity rate, case-fatality rate, and spread ratio of COVID-19 were calculated. Results: As of 20 March, 2020, a total of 12 941 cases of COVID-19 had been conformed, including 116 deaths, and the average morbidity rate, severity rate and case-fatality rate were 0.97/100 000, 13.5% and 0.90%, respectively. The morbidity rates in Zhejiang (2.12/100 000), Jiangxi (2.01/100 000) and Beijing (1.93/100 000) ranked top three. The characteristics of COVID-19 case spectrum varied from province to province. The first three provinces (autonomous region, municipality) with high severity rates were Tianjin (45.6%), Xinjiang (35.5%) and Heilongjiang (29.5%). The case-fatality rate was highest in Xinjiang (3.95%), followed by Hainan (3.57%) and Heilongjiang (2.70%). The average spread ratio was 0.98 and the spread intensity varied from province to province. Tibet had the lowest spread ratio (0), followed by Qinghai (0.20) and Guangdong (0.23). Conclusion: The intervention measures were effective in preventing the spread of COVID-19 and improved treatment effect in China. However, there were significant differences among different regions in severity, case-fatality rate and spread ratio.

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.

[The preliminary analysis on the characteristics of the cluster for the COVID-19]

Since December 2019, COVID-19, a new emerging infection disease, has spread in 27 countries and regions. The clusters of many cases were reported with the epidemic progresses. We collected currently available information for 377 COVID-19 clusters (1 719 cases), excluded the hospital clusters and Hubei cases, during the period from January 1 to February 20, 2020. There were 297 family clusters (79%), case median was 4; 39 clusters of dining (10%), case median was 5; 23 clusters of shopping malls or supermarkets (6%), case median was 13; 12 clusters of work units (3%), case median was 6, and 6 clusters of transportation. We selected 325 cases to estimate the incubation period and its range was 1 to 20 days, median was 7 days, and mode was 4 days. The analysis of the epidemic situation in a department store in China indicated that there was a possibility of patients as the source of infection during the incubation period of the epidemic. From February 5 to 21, 2020, 634 persons were infected on the Diamond Princess Liner. All persons are susceptible to the 2019 coronavirus. Age, patients during the incubation period and the worse environment might be the cause of the cases rising. The progress of the two typical outbreaks clearly demonstrated the spread of the early cases in Wuhan. In conclusion, screening and isolating close contacts remained essential other than clinical treatment during the epidemic. Especially for the healthy people in the epidemic area, isolation was the key.

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.

[Bilateral laryngocele:a case report]

A 71 years old male with throat discomfort, shortness of breath, irritating cough admission. Fiberoptic laryngoscope: bilateral glottis ventricular zone with about quail egg size smooth cystic masses. Throat enhanced CT: infrahyoid margin level about bilateral aryepiglottic fold inside have package containing gas shadow, communicated with the laryngeal chamber. Support laryngoscope under coblation radiofrequency ablation assisted laryngeal cyst excision were done and postoperative pathology consistent with laryngocele.

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.

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.

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.

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.

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.

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-β1, 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.

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.

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.

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.

Cardiac aging is initiated by matrix metalloproteinase-9-mediated endothelial dysfunction

Aging is linked to increased matrix metalloproteinase-9 (MMP-9) expression and extracellular matrix turnover, as well as a decline in function of the left ventricle (LV). Previously, we demonstrated that C57BL/6J wild-type (WT) mice > 18 mo of age show impaired diastolic function, which was attenuated by MMP-9 deletion. To evaluate mechanisms that initiate the development of cardiac dysfunction, we compared the LVs of 6-9- and 15-18-mo-old WT and MMP-9 null (Null) mice. All groups showed similar LV function by echocardiography, indicating that dysfunction had not yet developed in the older group. Myocyte nuclei numbers and cross-sectional areas increased in both WT and Null 15-18-mo mice compared with young controls, indicating myocyte hypertrophy. Myocyte hypertrophy leads to an increased oxygen demand, and both WT and Null 15-18-mo mice showed an increase in angiogenic signaling. Plasma proteomic profiling and LV analysis revealed a threefold increase in von Willebrand factor and fivefold increase in vascular endothelial growth factor in WT 15-18-mo mice, which were further elevated in Null mice. In contrast to the upregulation of angiogenic stimulating factors, actual LV vessel numbers increased only in the 15-18-mo Null LV. The 15-18-mo WT showed amplified expression of inflammatory genes related to angiogenesis, including C-C chemokine receptor (CCR)7, CCR10, interleukin (IL)-1f8, IL-13, and IL-20 (all, P < 0.05), and these increases were blunted by MMP-9 deletion (all, P < 0.05). To measure vascular permeability as an index of endothelial function, we injected mice with FITC-labeled dextran. The 15-18-mo WT LV showed increased vascular permeability compared with young WT controls and 15-18-mo Null mice. Combined, our findings revealed that MMP-9 deletion improves angiogenesis, attenuates inflammation, and prevents vascular leakiness in the setting of cardiac aging.

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.

Heart attack, known medically as myocardial infarction, often occurs as a result of partial shortage of blood supply to a portion of the heart, leading to the death of heart muscle cells. Following myocardial infarction, complications might arise, including arrhythmia, myocardial rupture, left ventricular dysfunction, and heart failure. Although myocardial infarction can be quickly diagnosed using a various number of tests, including blood tests and electrocardiography, there have been no available prognostic tests to predict the long-term outcome in response to myocardial infarction. Here, we present a framework to analyze how the left ventricle responds to myocardial infarction by combining protein interactome and experimental results retrieved from published human studies. The framework organized current understanding of molecular interactions specific to myocardial infarction, cellular responses, and biological processes to quantify left ventricular remodeling process. Specifically, our knowledge map showed that transcriptional activity, inflammatory response, and extracellular matrix remodeling are the main functional themes post myocardial infarction. In addition, text analytics of relevant abstracts revealed differentiated protein expressions in plasma or serum expressions from patients with myocardial infarction. Using this data, we predicted expression levels of other proteins following myocardial infarction.

Reduced BDNF attenuates inflammation and angiogenesis to improve survival and cardiac function following myocardial infarction in mice

Brain-derived neurotrophic factor (BDNF) increases in failing hearts, but BDNF roles in cardiac remodeling following myocardial infarction (MI) are unclear. Male BDNF(+/+) [wild-type (WT)] and BDNF(+/-) heterozygous (HET) mice at 6-9 mo of age were subjected to MI and evaluated at days 1, 3, 5, 7, or 28 post-MI. At day 28 post-MI, 76% of HET versus 40% of WT survived, whereas fractional shortening improved and neovascularization levels were reduced in the HET (all, P < 0.05). At day 1, post-MI, matrix metalloproteinase-9, and myeloperoxidase (MPO) increased in WT, but not in HET. Concomitantly, monocyte chemotactic protein-1 and -5 levels increased and vascular endothelial growth factor (VEGF)-A decreased in HET. Neutrophil infiltration peaked at days 1-3 in WT mice, and this increase was blunted in HET. To determine if MPO administration could rescue the HET phenotype, MPO was injected at 3 h post-MI. MPO restored VEGF-A levels without altering matrix metalloproteinase-9 or neutrophil content. In conclusion, reduced BDNF levels modulated the early inflammatory and neovascularization responses, leading to improved survival and reduced cardiac remodeling at day 28 post-MI. Thus reduced BDNF attenuates early inflammation following MI by modulating MPO and angiogenic response through VEGF-A.

Matrix metalloproteinase (MMP)-9: a proximal biomarker for cardiac remodeling and a distal biomarker for inflammation

Adverse cardiac remodeling following myocardial infarction (MI) remains a significant cause of congestive heart failure. Additional and novel strategies that improve our ability to predict, diagnose, or treat remodeling are needed. Numerous groups have explored single and multiple biomarker strategies to identify diagnostic prognosticators of remodeling progression, which will improve our ability to promptly and accurately identify high-risk individuals. The identification of better clinical indicators should further lead to more effective prediction and timely treatment. Matrix metalloproteinase (MMP-9) is one potential biomarker for cardiac remodeling, as demonstrated by both animal models and clinical studies. In animal MI models, MMP-9 expression significantly increases and is linked with inflammation, diabetic microvascular complications, extracellular matrix degradation and synthesis, and cardiac dysfunction. Clinical studies have also established a relationship between MMP-9 and post-MI remodeling and mortality, making MMP-9 a viable candidate to add to the multiple biomarker list. By definition, a proximal biomarker shows a close relationship with its target disease, whereas a distal biomarker exhibits non-targeted disease modifying outcomes. In this review, we explore the ability of MMP-9 to serve as a proximal biomarker for cardiac remodeling and a distal biomarker for inflammation. We summarize the current molecular basis and clinical platform that allow us to include MMP-9 as a biomarker in both categories.

Matrix metalloproteinase-28 deletion exacerbates cardiac dysfunction and rupture after myocardial infarction in mice by inhibiting M2 macrophage activation

RATIONALE

Matrix metalloproteinase (MMP)-28 regulates the inflammatory and extracellular matrix responses in cardiac aging, but the roles of MMP-28 after myocardial infarction (MI) have not been explored.

OBJECTIVE

To determine the impact of MMP-28 deletion on post-MI remodeling of the left ventricle (LV).

METHODS AND RESULTS

Adult C57BL/6J wild-type (n=76) and MMP null (MMP-28((-/-)), n=86) mice of both sexes were subjected to permanent coronary artery ligation to create MI. MMP-28 expression decreased post-MI, and its cell source shifted from myocytes to macrophages. MMP-28 deletion increased day 7 mortality because of increased cardiac rupture post-MI. MMP-28(-/-) mice exhibited larger LV volumes, worse LV dysfunction, a worse LV remodeling index, and increased lung edema. Plasma MMP-9 levels were unchanged in the MMP-28((-/-)) mice but increased in wild-type mice at day 7 post-MI. The mRNA levels of inflammatory and extracellular matrix proteins were attenuated in the infarct regions of MMP-28(-/-) mice, indicating reduced inflammatory and extracellular matrix responses. M2 macrophage activation was impaired when MMP-28 was absent. MMP-28 deletion also led to decreased collagen deposition and fewer myofibroblasts. Collagen cross-linking was impaired as a result of decreased expression and activation of lysyl oxidase in the infarcts of MMP-28(-/-) mice. The LV tensile strength at day 3 post-MI, however, was similar between the 2 genotypes.

CONCLUSIONS

MMP-28 deletion aggravated MI-induced LV dysfunction and rupture as a result of defective inflammatory response and scar formation by suppressing M2 macrophage activation.

Mathematical modeling of left ventricular dimensional changes in mice during aging

Cardiac aging is characterized by diastolic dysfunction of the left ventricle (LV), which is due in part to increased LV wall stiffness. In the diastolic phase, myocytes are relaxed and extracellular matrix (ECM) is a critical determinant to the changes of LV wall stiffness. To evaluate the effects of ECM composition on cardiac aging, we developed a mathematical model to predict LV dimension and wall stiffness changes in aging mice by integrating mechanical laws and our experimental results. We measured LV dimension, wall thickness, LV mass, and collagen content for wild type (WT) C57/BL6J mice of ages ranging from 7.3 months to those of 34.0 months. The model was established using the thick wall theory and stretch-induced tissue growth to an isotropic and homogeneous elastic composite with mixed constituents. The initial conditions of the simulation were set based on the data from the young mice. Matlab simulations of this mathematical model demonstrated that the model captured the major features of LV remodeling with age and closely approximated experimental results. Specifically, the temporal progression of the LV interior and exterior dimensions demonstrated the same trend and order-of-magnitude change as our experimental results. In conclusion, we present here a validated mathematical model of cardiac aging that applies the thick-wall theory and stretch-induced tissue growth to LV remodeling with age.

Mathematical modeling and stability analysis of macrophage activation in left ventricular remodeling post-myocardial infarction

Background

About 6 million Americans suffer from heart failure and 70% of heart failure cases are caused by myocardial infarction (MI). Following myocardial infarction, increased cytokines induce two major types of macrophages: classically activated macrophages which contribute to extracellular matrix destruction and alternatively activated macrophages which contribute to extracellular matrix construction. Though experimental results have shown the transitions between these two types of macrophages, little is known about the dynamic progression of macrophages activation. Therefore, the objective of this study is to analyze macrophage activation patterns post-MI.

Results

We have collected experimental data from adult C57 mice and built a framework to represent the regulatory relationships among cytokines and macrophages. A set of differential equations were established to characterize the regulatory relationships for macrophage activation in the left ventricle post-MI based on the physical chemistry laws. We further validated the mathematical model by comparing our computational results with experimental results reported in the literature. By applying Lyaponuv stability analysis, the established mathematical model demonstrated global stability in homeostasis situation and bounded response to myocardial infarction.

Conclusions

We have established and validated a mathematical model for macrophage activation post-MI. The stability analysis provided a possible strategy to intervene the balance of classically and alternatively activated macrophages in this study. The results will lay a strong foundation to understand the mechanisms of left ventricular remodelling post-MI.

Matrix metalloproteinase-9 deletion attenuates myocardial fibrosis and diastolic dysfunction in ageing mice

AIMS

Age-related diastolic dysfunction has been attributed to an increased passive stiffness, which is regulated by extracellular matrix (ECM). We recently showed that matrix metalloproteinase (MMP)-9, an ECM mediator, increases in the left ventricle (LV) with age. The aim of this study, accordingly, was to determine the role of MMP-9 in cardiac ageing.

METHODS AND RESULTS

We compared LV function in young (6-9 months), middle-aged (12-15 months), old (18-24 months) and senescent (26-34 months) wild-type (WT) and MMP-9 null mice (n ≥ 12/group). All groups had similar fractional shortenings and aortic peak velocities, indicating that systolic function was not altered by ageing or MMP-9 deletion. The mitral ratios of early to late diastolic filling velocities were reduced in old and senescent WT compared with young controls, and this reduction was attenuated in MMP-9 null mice. Concomitantly, the increase in LV collagen content was reduced in MMP-9 null mice (n = 5-6/group). To dissect the mechanisms of these changes, we evaluated the mRNA expression levels of 84 ECM and adhesion molecules by real-time qPCR (n = 6/group). The expression of pro-fibrotic periostin and connective tissue growth factor (CTGF) increased with senescence, as did transforming growth factor-β (TGF-β)-induced protein levels and Smad signalling, and these increases were blunted by MMP-9 deletion. In senescence, MMP-9 deletion also resulted in a compensatory increase in MMP-8.

CONCLUSION

MMP-9 deletion attenuates the age-related decline in diastolic function, in part by reducing TGF-β signalling-induced periostin and CTGF expression and increasing MMP-8 expression to regulate myocardial collagen turnover and deposition.

Transgenic overexpression of matrix metalloproteinase-9 in macrophages attenuates the inflammatory response and improves left ventricular function post-myocardial infarction

Following myocardial infarction (MI), activated macrophages infiltrate into the necrotic myocardium as part of a robust pro-inflammatory response and secrete matrix metalloproteinase-9 (MMP-9). Macrophage activation, in turn, modulates the fibrotic response, in part by stimulating fibroblast extracellular matrix (ECM) synthesis. We hypothesized that overexpression of human MMP-9 in mouse macrophages would amplify the inflammatory and fibrotic responses to exacerbate left ventricular dysfunction. Unexpectedly, at day 5 post-MI, ejection fraction was improved in transgenic (TG) mice (25±2%) compared to the wild type (WT) mice (18±2%; p<0.05). By gene expression profiling, 23 of 84 inflammatory genes were decreased in the left ventricle infarct (LVI) region from the TG compared to WT mice (all p<0.05). Concomitantly, TG macrophages isolated from the LVI, as well as TG peritoneal macrophages stimulated with LPS, showed decreased inflammatory marker expression compared to WT macrophages. In agreement with attenuated inflammation, only 7 of 84 cell adhesion and ECM genes were increased in the TG LVI compared to WT LVI, while 43 genes were decreased (all p<0.05). These results reveal a novel role for macrophage-derived MMP-9 in blunting the inflammatory response and limiting ECM synthesis to improve left ventricular function post-MI.

Matrix metalloproteinase-28 deletion amplifies inflammatory and extracellular matrix responses to cardiac aging

To determine if matrix metalloproteinase (MMP)-28 mediates cardiac aging, wild-type (WT) and MMP-28-/- young (7 ± 1 months, n = 9 each) and old (20 ± 2 months, n = 7 each) female mice were evaluated. MMP-28 expression in the left ventricle (LV) increased 42% in old WT mice compared to young controls (p < 0.05). By Doppler echocardiography, LV function declined at 20 ± 2 months of age for both groups. However, dobutamine stress responses were similar, indicating that cardiac reserve was maintained. Plasma proteomic profiling revealed that macrophage inflammatory protein (MIP)-1 α, MIP-1β and MMP-9 plasma levels did not change in WT old mice but were significantly elevated in MMP-28-/- old mice (all p < 0.05), suggestive of a higher inflammatory status when MMP-28 is deleted. RT2-PCR gene array and immunoblotting analyses demonstrated that MIP-1α and MMP-9 gene and protein levels in the LV were also higher in MMP-28-/- old mice (all p < 0.05). Macrophage numbers in the LV increased similarly in WT and MMP-28-/- old mice, compared to respective young controls (both p < 0.05). Collagen content was not different among the WT and MMP-28-/- young and old mice. In conclusion, LV inflammation increases with age, and MMP-28 deletion further elevates inflammation and extracellular matrix responses, without altering macrophage numbers or collagen content.

Bayesian parameter estimation for nonlinear modelling of biological pathways

BACKGROUND

The availability of temporal measurements on biological experiments has significantly promoted research areas in systems biology. To gain insight into the interaction and regulation of biological systems, mathematical frameworks such as ordinary differential equations have been widely applied to model biological pathways and interpret the temporal data. Hill equations are the preferred formats to represent the reaction rate in differential equation frameworks, due to their simple structures and their capabilities for easy fitting to saturated experimental measurements. However, Hill equations are highly nonlinearly parameterized functions, and parameters in these functions cannot be measured easily. Additionally, because of its high nonlinearity, adaptive parameter estimation algorithms developed for linear parameterized differential equations cannot be applied. Therefore, parameter estimation in nonlinearly parameterized differential equation models for biological pathways is both challenging and rewarding. In this study, we propose a Bayesian parameter estimation algorithm to estimate parameters in nonlinear mathematical models for biological pathways using time series data.

RESULTS

We used the Runge-Kutta method to transform differential equations to difference equations assuming a known structure of the differential equations. This transformation allowed us to generate predictions dependent on previous states and to apply a Bayesian approach, namely, the Markov chain Monte Carlo (MCMC) method. We applied this approach to the biological pathways involved in the left ventricle (LV) response to myocardial infarction (MI) and verified our algorithm by estimating two parameters in a Hill equation embedded in the nonlinear model. We further evaluated our estimation performance with different parameter settings and signal to noise ratios. Our results demonstrated the effectiveness of the algorithm for both linearly and nonlinearly parameterized dynamic systems.

CONCLUSIONS

Our proposed Bayesian algorithm successfully estimated parameters in nonlinear mathematical models for biological pathways. This method can be further extended to high order systems and thus provides a useful tool to analyze biological dynamics and extract information using temporal data.

Multi-analyte profiling reveals matrix metalloproteinase-9 and monocyte chemotactic protein-1 as plasma biomarkers of cardiac aging

BACKGROUND

We have previously shown that cardiac sarcopenia occurs with age in C57/BL6J mice. However, underlying mechanisms and plasma biomarkers of cardiac aging have not been identified. Accordingly, the objective of this study was to identify and evaluate plasma biomarkers that reflect cardiac aging phenotypes.

METHODS AND RESULTS

Plasma from adult (7.5±0.5 months old, n=27) and senescent (31.7±0.5 months old, n=25) C57/BL6J mice was collected, and levels of 69 markers were measured by multi-analyte profiling. Of these, 26 analytes were significantly increased and 3 were significantly decreased in the senescent group compared with the adult group. The majority of analytes that increased in the senescent group were inflammatory markers associated with macrophage functions, including matrix metalloproteinase-9 (MMP-9) and monocyte chemotactic protein-1 (MCP-1/CCL-2). Immunoblotting (n=12/group) showed higher MMP-9 and MCP-1 levels in the left ventricle (LV) of senescent mice (P<0.05), and their expression levels in the LV correlated with plasma levels (ρ=0.50 for MMP-9 and ρ =0.62 for MCP1, P<0.05). Further, increased plasma MCP-1 and MMP-9 levels correlated with the increase in end-diastolic dimensions that occurs with senescence. Immunohistochemistry (n=3/group) for Mac-3, a macrophage marker, showed increased macrophage densities in the senescent LV, and dual-labeling immunohistochemistry of Mac-3 and MMP-9 revealed robust colocalization of MMP-9 to the macrophages in the senescent LV sections, indicating that the macrophage is a major contributor of MMP-9 in the senescent LV.

CONCLUSIONS

Our results suggest that MCP-1 and MMP-9 are potential plasma markers for cardiac aging and that augmented MCP-1 and MMP-9 levels and macrophage content in the LV could provide an underlying inflammatory mechanism of cardiac aging.

SPARC mediates early extracellular matrix remodeling following myocardial infarction

Secreted protein, acidic, and rich in cysteine (SPARC) is a matricellular protein that functions in the extracellular processing of newly synthesized collagen. Collagen deposition to form a scar is a key event following a myocardial infarction (MI). Because the roles of SPARC in the early post-MI setting have not been defined, we examined age-matched wild-type (WT; n=22) and SPARC-deficient (null; n=25) mice at day 3 post-MI. Day 0 WT (n=28) and null (n=20) mice served as controls. Infarct size was 52 ± 2% for WT and 47 ± 2% for SPARC null (P=NS), indicating that the MI injury was comparable in the two groups. By echocardiography, WT mice increased end-diastolic volumes from 45 ± 2 to 83 ± 5 μl (P < 0.05). SPARC null mice also increased end-diastolic volumes but to a lesser extent than WT (39 ± 3 to 63 ± 5 μl; P < 0.05 vs. day 0 controls and vs. WT day 3 MI). Ejection fraction fell post-MI in WT mice from 57 ± 2 to 19 ± 1%. The decrease in ejection fraction was attenuated in the absence of SPARC (65 ± 2 to 28 ± 2%). Fibroblasts isolated from SPARC null left ventricle (LV) showed differences in the expression of 22 genes encoding extracellular matrix and adhesion molecule genes, including fibronectin, connective tissue growth factor (CTGF; CCN2), matrix metalloproteinase-3 (MMP-3), and tissue inhibitor of metalloproteinase-2 (TIMP-2). The change in fibroblast gene expression levels was mirrored in tissue protein extracts for fibronectin, CTGF, and MMP-3 but not TIMP-2. Combined, the results of this study indicate that SPARC deletion preserves LV function at day 3 post-MI but may be detrimental for the long-term response due to impaired fibroblast activation.

Combining experimental and mathematical modeling to reveal mechanisms of macrophage-dependent left ventricular remodeling

Background

Progressive remodeling of the left ventricle (LV) following myocardial infarction (MI) can lead to congestive heart failure, but the underlying initiation factors remain poorly defined. The objective of this study, accordingly, was to determine the key factors and elucidate the regulatory mechanisms of LV remodeling using integrated computational and experimental approaches.

Results

By examining the extracellular matrix (ECM) gene expression and plasma analyte levels in C57/BL6J mice LV post-MI and ECM gene responses to transforming growth factor (TGF-β₁) in cultured cardiac fibroblasts, we found that key factors in LV remodeling included macrophages, fibroblasts, transforming growth factor-β₁, matrix metalloproteinase-9 (MMP-9), and specific collagen subtypes. We established a mathematical model to study LV remodeling post-MI by quantifying the dynamic balance between ECM construction and destruction. The mathematical model incorporated the key factors and demonstrated that TGF-β₁ stimuli and MMP-9 interventions with different strengths and intervention times lead to different LV remodeling outcomes. The predictions of the mathematical model fell within the range of experimental measurements for these interventions, providing validation for the model.

Conclusions

In conclusion, our results demonstrated that the balance between ECM synthesis and degradation, controlled by interactions of specific key factors, determines the LV remodeling outcomes. Our mathematical model, based on the balance between ECM construction and destruction, provides a useful tool for studying the regulatory mechanisms and for predicting LV remodeling outcomes.

Effect of hydrophobicity and molecular mass on the accumulation of fluoroquinolones by Staphylococcus aureus

Ten novel fluoroquinolones, with similar chemical structures but differing antibacterial activities and hydrophobicities, were studied to evaluate the role of the physical properties of quinolones on their accumulation and antibacterial activity for Staphylococcus aureus. Six of the 10 agents and tosufloxacin were more active against quinolone-susceptible and -resistant S. aureus than the remaining four agents and several piperazinyl fluoroquinolones. Changes to the side chains of the pyrollidinyl substituent at the R7 position alone made little difference to the MICs. Comparison of MICs of agents that were structurally identical apart from the R1 substituents, confirmed that a t-butyl group confers enhanced activity against S. aureus over a cyclopropyl or ethyl group at this position. The steady-state concentrations (SSCs) of the 10 novel quinolones accumulated by wild-type S. aureus did not correlate with their MICs or chemical structures. There was no apparent relationship between logP of the quinolone and accumulation by S. aureus F77; however, accumulation was positively correlated with molecular mass for 9/10 agents (r = 0.745) confirming that high molecular mass is not a barrier to accumulation in S. aureus. For all 10 agents, the presence of carbonyl cyanide m-chlorophenylhydrazone (CCCP) increased the concentration of quinolone accumulated by SA-1199, suggesting that NorA was inhibited. The fold increase of the SSC in the presence of CCCP did not correlate with hydrophobicity, but the SSC of agents with either an ethyl or cyclopropyl group at R1 was increased two- to three-fold in the presence of CCCP, suggesting that affinity for the NorA efflux pump may be influenced by quinolone structure.

Antimicrobial activity and accumulation of moxifloxacin in quinolone-susceptible bacteria

The antibacterial activity of moxifloxacin, compared with that of ciprofloxacin, was determined for five strains of Staphylococcus aureus, including one NorA-overproducing strain, two quinolone-susceptible strains of Streptococcus pneumoniae, four quinolone-susceptible strains of Haemophilus influenzae, and one strain each of quinolone-susceptible Escherichia coli, Pseudomonas aeruginosa and Moraxella catarrhalis. In addition, the accumulation of moxifloxacin and ciprofloxacin by the NCTC type strain of S. pneumoniae, H. influenzae, S. aureus, E. coli and P. aeruginosa was determined by a fluorescence method. For all strains, moxifloxacin accumulated to a lower concentration than ciprofloxacin. The concentrations of moxifloxacin accumulated ranged from 12 to 44 ng/mg dry cells. The lowest concentration was accumulated by S. pneumoniae NCTC 7465 and the highest concentration by S. aureus NCTC 8532. Increased expression of norA in S. aureus had no effect on the accumulation of moxifloxacin. Despite differences in the concentration of moxifloxacin accumulated by the different species, there was little difference between the MICs of this agent for each strain (0.06-0.5 mg/L), suggesting that the concentration accumulated by wild-type bacteria has little effect on the MIC.

Quinolone accumulation by Pseudomonas aeruginosa, Staphylococcus aureus and Escherichia coli

The accumulation of nalidixic acid and 14 fluoroquinolones over a range of external drug concentrations (10-100 mg/L; c. 25-231 microM) into intact cells of Escherichia coli KL-16, Staphylococcus aureus NCTC 8532, Pseudomonas aeruginosa NCTC 10662 and spheroplasts of E. coli was investigated. The effect of 100 microM carbonyl cyanide m-chlorophenyl hydrazone (CCCP) upon the concentration of quinolone accumulated by intact cells and spheroplasts of E. coli was also determined. Except for pefloxacin, there was an increase in the concentration of the six quinolones examined accumulated by E. coli, despite a reduction in fluorescence at alkaline pH. For ciprofloxacin the partition coefficient (P(app)) was constant despite an increase in the pH; however, the P(app) for nalidixic acid decreased significantly with an increase in pH. The concentration of nalidixic acid, ciprofloxacin and enrofloxacin accumulated by E. coli and S. aureus increased with an increase in temperature up to 40 degrees C and 50 degrees C, respectively. Above these temperatures the cell viability decreased. With an increase in drug concentration there was, for intact E. coli and 12/15 agents, and for S. aureus and 10/15 agents, a linear increase in the concentration of drug accumulated. However, for P. aeruginosa and 13/15 agents there was apparent saturation of an accumulation pathway. Assuming 100% accumulation into intact cells of E. coli, for 10/14 fluoroquinolones < or = 40% was accumulated by spheroplasts. CCCP increased the concentration of quinolone accumulated but the increase varied with the agent and the bacterial species. The variation in the effect of CCCP upon accumulation of the different quinolones into E. coli could result from chemical interactions or from different affinities of the proposed efflux transporter for each quinolone. Overall, these data suggest that accumulation of most quinolones into E. coli and S. aureus proceeds by simple diffusion, but that P. aeruginosa behaves differently.

Non-gyrA-mediated ciprofloxacin resistance in laboratory mutants of Streptococcus pneumoniae

Two strains of Streptococcus pneumoniae, M4 (NCTC 7465, type strain) and M5 (clinical isolate), and their respective ciprofloxacin-resistant mutants, M4/C1, M5/C1 and M5/C3, were evaluated. All mutants were stable after one year's storage and all grew more slowly in Brain Heart Infusion broth than the parent. The MICs of ciprofloxacin, sparfloxacin and tosufloxacin were increased for the mutants of M4, whereas the mutants of M5 were less susceptible to ciprofloxacin only. The optimal bactericidal concentration (OBC) of each quinolone for all the strains was approximately ten-fold greater than the MIC. The OBCs for the mutants were increased for ciprofloxacin, but not for the other two quinolones. The DNA synthesis IC50 values of all quinolones correlated well with the MIC of each drug. All quinolones accumulated rapidly within all five strains; 10 mM magnesium chloride decreased the concentration of quinolone accumulated, but carbonyl cyanide m-chlorophenyl hydrazone had no effect. Mutant strains M4/C1, M5/C1 and M5/C3 accumulated less quinolone than their respective parent strains. DNA sequencing of those regions of gyrA and gyrB corresponding to the quinolone resistance-determining region in other bacteria did not reveal any differences between the parent and mutant strains.

Prevalence and mechanism of resistance to 'third-generation' cephalosporins in clinically relevant isolates of Enterobacteriaceae from 43 hospitals in the UK, 1990-1991

In a UK survey of the occurrence of extended spectrum beta-lactamases, 96 hospitals submitted a total of 3951 non-selected, non-duplicate isolates of Enterobacteriaceae from 100 patients in each hospital, 206 of these cultures being mixed and, therefore, discarded. These isolates were initially screened for strains likely to produce extended-spectrum beta-lactamases (ESBLs) by MIC determination of beta-lactams followed by a bioassay, then disc approximation test and isoelectric focusing (IEF). Isolates were further examined using two pairs of PCR primers for both blaTEM and blaSHV genes. The ability of isolates to transfer resistance to both cefotaxime and ceftazidime by conjugation and transformation were examined. Four hundred and nine cefotaxime/ceftazidime-resistant isolates (10.9%) were identified from the 3745 submitted isolates, of which 338 (9.0%) were Enterobacteriaceae, 29 Escherichia coli, 35 Klebsiella spp. and seven Hafnia alveii. IEF suggested that 17 isolates produced an ESBL, which was confirmed in most cases by PCR and hydrolysis, five isolates produced an SHV enzyme by IEF, but not confirmed by PCR, and 11 had isoelectric points in the range 8-9 suggesting a possible AmpC enzyme. Only two isolates transferred the determinants. In the case of the Klebsiella spp., 19 of the 24 ceftazidime-resistant/clavulanate-sensitive isolates were positive by PCR for a blaSHV gene. No isolates were identified as carrying blaTEM, although eight isolates had isoelectric points of 5-6.3, suggesting the presence of a possible TEM beta-lactamase. The results for the H. alveii isolates suggest that either an AmpC-like enzyme or a transferable beta-lactamase which is not TEM/SHV is present. This study shows that a wide range of genotypically and phenotypically different isolates of Enterobacteriaceae producing ESBL-like enzymes is present throughout the UK at a frequency of about 1% of unselected isolates. It is important that surveillance of resistance to these clinically important antibiotics is maintained as the occurrence of localized or more widespread outbreaks caused by bacteria producing ESBLs is to be expected.

Contributions of individual mechanisms to fluoroquinolone resistance in 36 Escherichia coli strains isolated from humans and animals

Twenty-eight human isolates of Escherichia coli from Argentina and Spain and eight veterinary isolates received from the Ministry of Agriculture Fisheries and Foods in the United Kingdom required 2 to > 128 micrograms of ciprofloxacin per ml for inhibition. Fragments of gyrA and parC encompassing the quinolone resistance-determining region were amplified by PCR, and the DNA sequences of the fragments were determined. All isolates contained a mutation in gyrA of a serine at position 83 (Ser83) to an Leu, and 26 isolates also contained a mutation of Asp87 to one of four amino acids: Asn (n = 14), Tyr (n = 6), Gly (n = 5), or His (n = 1). Twenty-four isolates contained a single mutation in parC, either a Ser80 to Ile (n = 17) or Arg (n = 2) or a Glu84 to Lys (n = 3). The role of a mutation in gyrB was investigated by introducing wild-type gyrB (pBP548) into all isolates; for three transformants MICs of ciprofloxacin were reduced; however, sequencing of PCR-derived fragments containing the gyrB quinolone resistance-determining region revealed no changes. The analogous region of parE was analyzed in 34 of 36 isolates by single-strand conformational polymorphism analysis and sequencing; however, no amino acid substitutions were discovered. The outer membrane protein and lipopolysaccharide profiles of all isolates were compared with those of reference strains, and the concentration of ciprofloxacin accumulated (with or without 100 microM carbony cyanide m-chlorophenylhydrazone [CCCP] was determined. Twenty-two isolates accumulated significantly lower concentrations of ciprofloxacin than the wild-type E. coli isolate; nine isolates accumulated less then half the concentration. The addition of CCCP increased the concentration of ciprofloxacin accumulated, and in all but one isolate the percent increase was greater than that in the control strains. The data indicate that high-level fluoroquinolone resistance in E. coli involves the acquisition of mutations at multiple loci.

Activity of biapenem (LJC 10627) against 51 imipenem-resistant bacteria and selection and characterisation of biapenem-resistant mutants

For wild-type bacteria the activity of biapenem was similar to that of imipenem, but for 51 imipenem-resistant strains meropenem was more active than either. When penicillin-binding protein 2a (PBP 2a) was expressed in Staphylococcus aureus biapenem had reduced activity. Mutant bacteria with decreased susceptibility to biapenem were selected in agar. Most of the mutant Gram negative bacteria were unstable and readily reverted to susceptible. The mutant Proteus vulgaris and Pseudomonas aeruginosa lacked an outer membrane protein. Biapenem-resistant S. aureus could be selected only from MRSA.

A novel gyrB mutation in a fluoroquinolone-resistant clinical isolate of Salmonella typhimurium

In order to study the role of gyrB in antibiotic resistance in post-ciprofloxacin therapy fluoroquinolone-resistant clinical isolates of Salmonella typhimurium, plasmid pBP548, which contains the Escherichia coli gyrB gene, was used in complementation studies. In a heterodiploid strain, the wild-type (quinolone sensitive) allele is dominant over the resistant allele therefore, eleven clinical isolates were complemented with gyrB encoded on pBP548. Only one transformant, L18pBP548, exhibited increased susceptibility to the quinolones nalidixic acid, ciprofloxacin and sparfloxacin. The amino acid sequence of the gyrase B protein from a wild-type and the pre-therapy S. typhimurium (deduced from the nucleotide sequence) was identical to that of E. coli from codons 436 to 470; however, a point mutation was identified in codon 463 of gyrB of the quinolone-resistant post-therapy isolate L18, giving rise to an amino acid substitution of serine to tyrosine.

Quinolone resistance in veterinary isolates of Salmonella

Twenty-seven nalidixic acid-resistant (MIC > or = 256 mg/L) isolates of salmonella from veterinary sources were also less susceptible to fluoroquinolones (range of MICs of ciprofloxacin, 0.12-2 mg/L). Six isolates were cross-resistant to one or more chemically unrelated antibacterial agents. The concentrations of enrofloxacin that inhibited DNA synthesis by 50% were similar to the MIC values for 23 of 27 isolates, suggesting a mutation in gyrA. Insertion of pNJR3-2 (gyrA) in nine of 20 isolates increased susceptibility to quinolones, suggesting that resistance was due to mutation in gyrA. Five of 27 isolates had reduced levels of accumulation of enrofloxacin. Two of the five also had increased susceptibility to quinolones when pNJR3-2 was introduced. None of the outer membrane protein profiles of the resistant isolates differed from those of sensitive control strains. Three of 27 isolates had differences in lipopolysaccharide profiles compared to control strains. Although the MIC of ciprofloxacin was less than the recommended UK break point concentrations for most isolates, the increasing incidence of quinolone-resistance in salmonella from veterinary sources is a matter of concern.

Activity of Bay y3118 against quinolone-susceptible and -resistant gram-negative and gram-positive bacteria

The activity of Bay y3118 against laboratory strains of bacteria, including those with mutations in gyrA, with decreased expression of outer membrane proteins, and/or that are multiply resistant, and 121 selected clinical isolates, including highly fluoroquinolone-resistant bacteria from Spain and Argentina, was determined. Bay y3118 was extremely active (MICs, < or = 1 microgram/ml) against all bacteria, including quinolone-resistant laboratory strains. However, Bay y3118 was less active against 46 of 121 quinolone-resistant clinical isolates, such that > or = 16 micrograms of Bay y3118 per ml was required to inhibit 3 isolates. The concentration of Bay y3118 required to inhibit DNA synthesis by 50% correlated well with the MIC. Bay y3118 had accumulation kinetics similar to those of previously studied fluoroquinolones, e.g., ciprofloxacin, and there was a 50% decrease in the steady-state concentration in those members of the family Enterobacteriaceae that lacked porin proteins. Magnesium chloride at 20 mM apparently abolished the accumulation of Bay y3118 into Escherichia coli and reduced the level of accumulation into other gram-negative bacteria and Staphylococcus aureus. Carbonyl cyanide m-chlorophenylhydrazone at 100 microM enhanced the accumulation of Bay y3118 into E. coli, but it had a minimal effect on accumulation into S. aureus.