More than 20 years ago, Seta and colleagues hypothesized that cytokines, which are activated by myocardial injury, significantly drive heart failure progression and would therefore be effective targets to treat cardiac dysfunction. Unfortunately, several clinical trials inhibiting key cytokines like tumor necrosis factor alpha (TNF-α) and interleukin 1 beta (Il-1β) turned out negative or even revealed adverse clinical effects. Providing a potential mechanistic explanation for the ineffectiveness of TNF-α blockade in heart failure, novel findings demonstrate that the membrane-bound precursor form of TNF-α, transmembrane TNF-α (tmTNF-α), mediates cardioprotective effects during pressure overload-induced cardiac remodeling. This study suggests that preventing tmTNF-α cleavage by targeting the TNF-α converting enzyme (TACE) rather than inhibiting TNF-α signaling altogether might be a valuable therapeutic approach. The Yin and Yang of TNF-α signaling in heart failure: this Primer explores the history of failure of TNF-α antagonistic therapy in heart failure and how a recent study suggests that inhibition of TNF-α converting enzyme TACE might be a way out of this dilemma. Acute or chronic injuries of the heart initiate cellular signaling cascades causing a variety of adaptive and maladaptive processes . EnhanceWhile several preclinical studies have shown a direct negative effect of inflammatory cytokines on cardiomyocyte contractility, hypertrophy, and survival, answering the question whether inflammatory cytokines directly cause heart failure progression in an in vivo setup is certainly more difficult ,5. AlthoThe pro-inflammatory cytokine TNF-α signals through 2 different cell surface receptors, tumor necrosis factor receptor 1 (TNFR1) and tumor necrosis factor receptor 2 (TNFR2). Immunofluorescence stainings of uninjured human myocardium revealed localization of TNFR1 predominantly on cardiomyocytes and vascular endothelial cells and rarely on fibroblasts and leukocytes, while TNFR2 was mostly found on vascular endothelial cells and some leukocytes, but not on cardiomyocytes or fibroblasts. However, during inflammation, the mRNA expression and protein levels of TNFR2 in cardiomyocytes were strongly up-regulated. Production of TNF-α in the heart was restricted to microvessels and leukocytes . Upon liIn the issued study, the authors investigated the effect of TNF-α signaling on pressure overload-induced cardiac hypertrophy . Using mWhile previous studies had already indicated a protective role for TNFR2-mediated signaling, whereas negative effects were predominantly associated with activation of TNFR1, the specific ligands for the receptors in the heart were previously less clear . The curBefore these interesting new findings could be considered for clinical application, further questions are still open that will need to be investigated. One critical point to address is which cell type serves as primary provider of tmTNF-α and whether cardiac fibroblasts, endothelial cells, or leukocytes thereby contribute to the termination of soluble TNF-α–triggered cardiac tissue inflammation. Moreover, it is necessary to clarify whether TNF receptor expression on other cardiac cell types (apart from cardiomyocytes) might also  mediate the effects of TNF-α in the heart. Studies with cell type–specific knockout of TNFR1 and TNFR2 or TNF-α would therefore be very interesting in the context of pressure overload or myocardial infarction. Additionally, the downstream signaling of TNFR2 requires further attention in order to understand its protective signaling response. It is so far unclear how TNFR2-mediated activation of Akt protects the heart from pressure overload or isoproterenol-induced hypertrophy, as Akt is well known to promote adaptive, but, upon prolonged activation, also maladaptive cardiomyocyte growth. Which other pathways become activated in cardiomyocytes upon TNFR2 activation? Investigation of isolated TNFR1 knockout cardiomyocytes after stimulation with tmTNF-α in a phospho-proteomics screen could, for instance, elucidate potentially protective signaling pathways. Moreover, the role of TACE deserves additional investigation, as this might be a potentially promising therapeutic target. Which cells are mainly mediating TNF-α cleavage by delivering TACE? What are the  mechanisms that induce its expression upon mechanical loading? Is it also induced in response to inflammatory stimuli? Answering these questions might open additional options for therapeutic intervention to prevent excessive TNF-α signaling without abolishing the protective effects of tmTNF-α on cardiac remodeling.


The current research aimed to expound the genes and pathways that are involved in coronary artery disease (CAD) and ischaemic stroke (IS) and the related mechanisms.david.abcc.ncifcrf.gov) was used to annotate the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment analyses of the DEGs. A protein-protein interaction (PPI) network was constructed by Cytoscape software, and then Molecular Complex Detection (MCODE) analysis was used to screen for hub genes. The hub genes were also confirmed by RT-qPCR and unconditional logistic regression analysis in our CAD and IS patients.Two array CAD datasets of (GSE66360 and GSE97320) and an array IS dataset (GSE22255) were downloaded. Differentially expressed genes (DEGs) were identified using the limma package. The online tool Database for Annotation, Visualization and Integrated Discovery (DAVID) , C-X-C motif chemokine ligand 8 , tumour necrosis factor , suppressor of cytokine signalling 3  and TNF alpha induced protein 3  were noted. RT-qPCR results demonstrated that the expression levels of CXCL8 were increased in IS patients than in normal participants and the expression levels of SOCS3, TNF and TNFAIP were higher in CAD/IS patients than in normal participants. Meanwhile, unconditional logistic regression analysis revealed that the incidence of CAD or IS was positively correlated with the CXCL8, SOCS3, TNF and TNFAIP3.A total of 20 common DEGs  were identified between the CAD/IS and control groups. Eleven molecular functions, 3 cellular components, and 49 biological processes were confirmed by GO enrichment analysis, and the 20 common upregulated DEGs were enriched in 21 KEGG pathways. A PPI network including 24 nodes and 68 edges was constructed with the STRING online tool. After MCODE analysis, the top 5 high degree genes, including Jun proto-oncogene (CXCL8, TNF, SOCS3 and TNFAIP3 associated with inflammation may serve as biomarkers for the diagnosis of CAD or IS. The possible mechanisms may involve the Toll-like receptor, TNF, NF-kappa B, cytokine-cytokine receptor interactions and the NOD-like receptor signalling pathways.The  Coronary artery disease (CAD) and ischaemic stroke (IS) are prominent causes of disability, mortality, morbidity, functional deterioration and healthcare expenses and account for approximately 30% of all deaths worldwide –4. Twinshttp://www.ncbi.nlm.nih.gov/geo/) [As a novel and practical approach for identifying CAD and IS susceptibility genes, a microarray analysis may be helpful for the early diagnosis of CAD and IS , 15. Howov/geo/)  is an inTwo CAD datasets (GSE66360 and GSE97320) and another IS dataset pf IS (GSE22255) were obtained from the GPL570 Affymetrix Human Genome U133 Plus 2.0 array. The GSE22255 dataset included 20 normal samples and 20 IS samples. An integrated analysis of 53 normal samples and 52 CAD samples from the two CAD datasets was performed. The original files in CEL format were transformed into an expression value matrix using the Affy package in R with the RMA method to normalize the expression values and the SVA method to remove batch differences . Then, tP < 0.05 and |log fold change (FC)| > 1. To visualize the shared DEGs between the CAD datasets and the IS dataset, an online tool (bioinformatics.psb.ugent.be/webtools/Venn) was used to draw a Venn diagram.The DEGs between patients suffering from CAD or IS and healthy participants were identified using the limma package . The thrdavid.abcc.ncifcrf.gov) was used to annotate the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway [P < 0.05 was defined as the threshold for significant enrichment for KEGG and GO analyses.The online tool Database for Annotation, Visualization and Integrated Discovery (DAVID)  [www.cytoscape.org) was applied to visualize the PPI network [A PPI interaction network of common DEGs was constructed with the Search Tool for the Retrieval of Interacting Genes database  , and a c network . Degrees network .A total of 420 unrelated participants (202 IS patients and 218 CAD patients) were recruited from the First Affiliated Hospital of Guangxi Medical University from Jan. 1, 2015 to Dec. 31, 2016. CAD was defined as significantly coronary artery stenosis (≥ 50%) in at least anyone of the three main coronary vessels or their main branches (branch diameter ≥ 2 mm) . All patA total of 203 healthy controls matched by ethnic group (Han Chinese), age, and gender were also recruited. All subjects were healthy, and none of them had a history of CAD, myocardial infarction, IS or type 2 diabetes mellitus (T2DM), as determined by history-taking, questionnaires, or critical clinical examination. All participants were randomly recruited from the Physical Examination Center of the First Affiliated Hospital, Guangxi Medical University in the same period. Before the beginning of the study, all participants signed a written informed consent form. The research proposal was approved by the Ethics Committee of the First Affiliated Hospital, Guangxi Medical University .RT-qPCR was used to validate the four significantly dysregulated mRNAs identified by the microarray results in the 603 subjects. Total RNA was extracted from peripheral blood mononuclear cells (PBMCs) that were separated from blood samples using TRIzol reagent and reverse transcribed into cDNA using the PrimeScript RT reagent kit  according to the manufacturer’s instructions. The resulting cDNA was used as a template for RT-qPCR. Supplementary Table In our Clinical Science Experiment Center, 0.56–1.70 mmol/L serum triglyceride (TG), 3.10–5.17 mmol/L total cholesterol (TC), 0.80–1.05 g/L apolipoprotein (Apo) B, 2.70–3.10 mmol/L low-density lipoprotein cholesterol (LDL-C), 1.20–1.60 g/L ApoA1, 1.16–1.42 mmol/L high-density lipoprotein cholesterol (HDL-C) and a ApoA1/ApoB ratio of 1.00–2.50 were defined as normal values. The diagnostic criteria of hyperlipidaemia , hypertet test was used to analyse differences in general characteristics between patients and controls. Unconditional logistic regression was used to evaluate the relationship between genes and clinical variables and the incidence of CAD or IS. The pheatmap and ggplot2 packages (https://cran.r-project.org/) were used to draw the volcano plot and heat map.All data were x (Version 22.0). The values are presented as the mean ± SD. The chi-square test was used to calculate the differences in the rates between patients and controls. Independent samples P < 0.05. A total of 29 DEGs, including 2 downregulated genes and 27 upregulated genes were identified between IS patients and healthy controls, and 20 common upregulated DEGs between the CAD patients and controls and between IS patients and controls were identified ; the NF-kappa B signalling pathway ; the TNF signalling pathway , 11 molecular functions, 3 cellular components, and 49 biological processes were enriched in the present study, and GO:0006915~apoptotic process (IL1B and TNFAIP3), GO:0042346~positive regulation of NF-kB import into nucleus (TNF and IL1B), GO:0045429~positive regulation of nitric oxide biosynthetic process (TNF and IL1B), GO:0006954~inflammatory response , GO:0050995~negative regulation of lipid catabolic process (TNF and IL1B), GO:0034116~positive regulation of heterotypic cell-cell adhesion (TNF and IL1B), GO:0048661~positive regulation of smooth muscle cell proliferation (TNF and JUN), GO:0010803~regulation of tumour necrosis factor-mediated signalling pathway (TNF and TNFAIP3), GO:0001525~angiogenesis (JUN and CXCL8) and GO:0043122~regulation of I-kappaB kinase/NF-kB signalling (TNF and IL1B) were selected for further analysis, as presented in Fig. The online tool DAVID was used to predict the potential biological functions of the DEGs. A total of 21 KEGG pathways, including the Toll-like receptor signalling pathway , Jun proto-oncogene , tumour necrosis factor , suppressor of cytokine signalling 3 , and TNF alpha induced protein 3 , were identified in the present study . However, when we verified the above results in our experiment, we found that the expression of CXCL8, TNF, SOCS3, and TNFAIP3 was higher in patients with CAD or IS than in healthy controls and that there was no significant difference in the expression of JUN between CAD or IS patients and the control group.Currently, the diagnosis of CAD is based on ischaemia-related symptoms, detailed physical examination, electrocardiogram changes, elevated biomarkers of myocardial injury and coronary angiography , 33. MeaTNF, CXCL8 and IL1B genes may be involved in the Toll-like receptor signalling pathway. Thus, we speculated that these genes might exert their biological functions through the Toll-like receptor signalling pathway.A recent study showed that CAD and IS result from various factors and can be influenced by genomic background, lifestyle, environmental factors, alterations in plasma lipid levels and the interactions of these factors . AtherosTNF, SOCS3, JUN, and TNFAIP3 genes may be involved in the TNF signalling pathway and that TNF, CXCL8 and TNFAIP3 may be involved in the NF-kB signalling pathway. Meanwhile, cytokine-cytokine receptor interactions  and the NOD-like receptor signalling pathway  were also identified in our study.The TNF signalling pathway plays a crucial role in inflammatory and autoimmune diseases. TNF-α is one of the most important members of the TNF superfamily is mainly secreted by macrophages and participates in the regulation of a wide spectrum of biological processes, including cell proliferation, lipid metabolism, apoptosis and differentiation. All of the above biological processes can lead to chronic immunoinflammatory lesions that eventually result in atherosclerosis . PreviouTNFAIP3), GO:0042346~positive regulation of NF-kB import into nucleus (TNF), GO:0045429~positive regulation of nitric oxide biosynthetic process (TNF), GO:0006954~inflammatory response , GO:0050995~negative regulation of lipid catabolic process (TNF), GO:0048661~positive regulation of smooth muscle cell proliferation (TNF), GO:0010803~regulation of tumour necrosis factor-mediated signalling pathway (TNF and TNFAIP3), GO:0001525~angiogenesis (CXCL8), GO:0042517~positive regulation of tyrosine phosphorylation of Stat3 protein (SOCS3) and GO:0043122~regulation of I-kappaB kinase/NF-kB signalling (TNF). This information about the relevant pathways is not novel; however, the analytical approach is different from that of previous studies. Thus, the combination of previous and current research results revealed that TNF, CXCL8, SOCS3 and TNFAIP3 may be involved in chronic inflammatory lesions that eventually result in atherosclerosis, CAD or IS. Furthermore, RT-qPCR and unconditional logistic regression also validated the above results in our CAD and IS patients. We obtained results that were consistent with those of the microarray analysis, which might increase the credibility of the conclusions.In addition, several main biological processes that may be involved in chronic inflammatory lesions that eventually result in atherosclerosis were identified by GO functional enrichment analysis of DEGs, such as GO:0006915~apoptotic process  were identified following GO functional enrichment analysis, KEGG pathway enrichment analysis, PPI network construction and MCODE analysis, but only four genes  were verified by RT-qPCR in our CAD or IS patients. The CXCL8, TNF, SOCS3, TNFAIP3 genes, which are associated with inflammation, may serve as biomarkers for the diagnosis of CAD or IS. The mechanism may involve the TNF signalling pathway, the Toll-like receptor signalling pathway, the NF-kappa B signalling pathway, cytokine-cytokine receptor interactions and the NOD-like receptor signalling pathway.Two microarray CAD datasets and an IS dataset were integrated and analysed in the present study. Five hub genes (Additional file 1 : Table S1 KEGG pathways and GO function enrichment analyses of twenty common upregulated-DEGs. Table S2 PCR primers for quantitative real-time PCR


Type 2 diabetes mellitus (T2DM) is believed to be associated with low-grade chronic inflammation. The current study aims to investigate the involvement of leptin and TNF-α) is a cytokine that is released by adipocytes and inflammatory cells in response to chronic inflammation. Type 2 diabetes mellitus (T2DM) is believed to be associated with low-grade chronic inflammation. The current study aims to investigate the involvement of leptin and TNF-Methodology. This is a cross-sectional study involving 63 healthy volunteers and 65 patients with T2DM. Body composition was measured, and fasting venous blood samples were analyzed for blood glucose, glycosylated hemoglobin (HbA1c), basal insulin, leptin, and TNF-α) is a cytokine that is released by adipocytes and inflammatory cells in response to chronic inflammation. Type 2 diabetes mellitus (T2DM) is believed to be associated with low-grade chronic inflammation. The current study aims to investigate the involvement of leptin and TNF-α) is a cytokine that is released by adipocytes and inflammatory cells in response to chronic inflammation. Type 2 diabetes mellitus (T2DM) is believed to be associated with low-grade chronic inflammation. The current study aims to investigate the involvement of leptin and TNF- Diabetes and obesity are very common associated metabolic disorders that are linked to chronic inflammation. Leptin is one of the important adipokines released from adipocytes, and its level increases with increasing body mass index (BMI). Tumor necrosis factor alpha (TNF-α) is a cytokine that is released by adipocytes and inflammatory cells in response to chronic inflammation. Type 2 diabetes mellitus (T2DM) is believed to be associated with low-grade chronic inflammation. The current study aims to investigate the involvement of leptin and TNF-p=0.008). In obese diabetic patients, the serum level of TNF-α) is a cytokine that is released by adipocytes and inflammatory cells in response to chronic inflammation. Type 2 diabetes mellitus (T2DM) is believed to be associated with low-grade chronic inflammation. The current study aims to investigate the involvement of leptin and TNF-p=0.008). In obese diabetic patients, the serum level of TNF-p=0.008). In obese diabetic patients, the serum level of TNF-α) is a cytokine that is released by adipocytes and inflammatory cells in response to chronic inflammation. Type 2 diabetes mellitus (T2DM) is believed to be associated with low-grade chronic inflammation. The current study aims to investigate the involvement of leptin and TNF-r = 0.361, p=0.008). In obese diabetic patients, the serum level of TNF-r = 0.361, p=0.008). In obese diabetic patients, the serum level of TNF- Our study showed a significantly higher level of TNF-α is associated with concurrent obesity and T2DM and correlates with HbA1c. This suggests that TNF-α needs further investigation to explore if it has a role in monitoring the effectiveness of management in individuals with obesity and T2DM.α) is a cytokine that is released by adipocytes and inflammatory cells in response to chronic inflammation. Type 2 diabetes mellitus (T2DM) is believed to be associated with low-grade chronic inflammation. The current study aims to investigate the involvement of leptin and TNF-α) is a cytokine that is released by adipocytes and inflammatory cells in response to chronic inflammation. Type 2 diabetes mellitus (T2DM) is believed to be associated with low-grade chronic inflammation. The current study aims to investigate the involvement of leptin and TNF- TNF- Among the most fundamental systems required for survival are metabolic and immune systems, and there is a strong integration between regulation of metabolism and the immune responses. Chronic inflammation due to abnormal productions of cytokines and activation of inflammatory signaling pathways is closely associated with metabolic disorders such as obesity, insulin resistance, and T2DM . Obesityα. It has been reported that TNF-α reduces the expression of insulin-regulated glucose transporter type 4 (GLUT4) which is located mainly in adipocytes and skeletal and cardiac muscles [α by inducing serine phosphorylation of insulin receptor substrate-1 can act as an inhibitor of peripheral insulin action which leads to insulin resistance [α which showed a positive correlation with insulin resistance [α plasma level is related to the amount of visceral fat and is not instantly affected in poorly controlled diabetic patients by acute lowering of blood glucose level [The first proinflammatory cytokine recognized for its involvement in pathogenesis of insulin resistance and T2DM was TNF-sistance , 6. In ose level .Research about the role of TNF-alpha in obesity and diabetes is not settled yet and is still a subject of active research work. Prior studies showed conflicting results about the correlation of obesity with TNF-alpha level. A recent systematic review and meta-analysis investigated the association between TNF-alpha and type 2 diabetes mellitus and concluded that there is considerable heterogeneity between studies and further work is needed . Additio2.In this study, we aim to look into the correlation between TNF-alpha, insulin resistance, and HBA1c level in addition to the association between TNF-alpha, obesity, and diabetes. Specifically, we will investigate the effect of obesity and diabetes separately on the level of TNF-alpha, and we aim to recruit participants with BMI higher than 30 kg/mIn this cross-sectional study, we recruited healthy employees and patients with T2DM from the primary care clinics at King Khalid University Hospital, Riyadh, Saudi Arabia. The control group was evaluated by detailed history, clinical examination, and investigations. Patients with complications of T2DM such as nephropathy, neuropathy, and cardiovascular incidents were excluded from the study. All T2DM patients included in the study were receiving oral hypoglycemic agents, and 18 (24%) patients were on lipid lowering agents. Any patient with pregnancy and using glucocorticoids or oral contraceptive pills was excluded.Body composition was measured using the body composition analyzer . The following measurements were taken for all subjects: body mass index (BMI), percentage of body fat (BF%), lean body mass, and waist-hip ratio (WHR). Before those measurements were taken, the subjects were advised to fast for 10 hours and allowed to rest for 30 minutes. Palms and soles were cleaned with electrolytes tissue, and information about subjects' height, sex, and age was fed to the machine. The subject was asked to stand with barefoot on the platform of the machine.Fasting venous blood samples were analyzed for blood glucose, glycosylated hemoglobin (HbA1c), basal insulin, leptin, and tumor necrosis factor alpha (TNF). HbA1c was measured by the Helena Glyco-Tek Affinity Column method .α immunoassays were performed by the quantitative standard sandwich ELISA technique using monoclonal antibody specific for these parameters with kits supplied by R&D Systems . The indices of basal insulin resistance and beta-cell function were assessed using the homeostasis model assessment (HOMA-IR and HOMA-B) in which HOMA-IR (mmol/L × μIU/mL) = fasting glucose (mmol/L) × fasting insulin (μIU/mL)/22.5 and HOMA-B = fasting insulin (μIU/mL × 20)/(fasting glucose (mmol/L) − 3.5).Insulin, leptin, and TNF-a and Shapiro–Wilk tests were used to see whether data follow normal distribution or not. Those parameters which were not following normal distribution were analyzed by nonparametric Mann–Whitney U test. For continuous data with normal distribution, Student's t-test was used. Correlation between TNF-α, HbA1c, and markers of insulin resistance was determined by simple regression analysis.The data were analyzed by the computer software program Statistical Package for Social Sciences . Descriptive characteristics of the study patients were expressed as mean ± SD (standard deviation). Kolmogorov–SmirnovThe study was approved by the ethical committee of institutional review board of College of Medicine, and it was conducted at the Department of Physiology, College of Medicine, King Saud University, Riyadh. All participants signed a consent form after the study protocol was explained to them, and it was made clear to all subjects that they can withdraw at any time if they want to do so.p=0.005). Patients with T2DM have a higher value of waist to hip ratio when compared to healthy controls . TNF-α level was significantly higher in T2DM patients than in controls , while the difference was not significant for leptin   and sixty-three were healthy control subjects . Clinical and demographic characteristics were compared between controls and T2DM patients . Age ranp=0.331) .α levels in obese diabetic patients were significantly higher than in nonobese diabetic patients (p < 0.018). The obese diabetic patients have significant higher serum TNF-α levels than the obese nondiabetic group (p < 0.001) as shown in Serum TNF-α correlated positively with HbA1c  and HOMA-IR , indicating a significant relationship with glycemic control and insulin resistance (In patients with T2DM, the level of TNF-sistance .α level was significantly higher in T2DM patients compared to healthy subjects while the difference between the two groups was not significant for leptin. The level of TNF-α has a strong positive correlation with HbA1c and was positively associated with insulin resistance. These findings suggest that TNF-α plays an important role in the pathogenesis of T2DM via mechanisms related to insulin peripheral action independent of leptin. Our patients have higher insulin resistance indices and a lower beta-cell function than control subjects.Our study demonstrated that the serum TNF-α on adipose tissue and increased peripheral glucose uptake after neutralizing TNF-α in obese rats indicate its important role in development of insulin resistance and diabetes as a consequence of obesity [α contributes to insulin resistance and T2DM that is associated with obesity [α levels in obese diabetic patients compared to nonobese diabetic patients. Additionally, we reported a higher TNF-α level in obese diabetic patients in comparison with obese nondiabetic healthy subjects.The catabolic effect of TNF- obesity . Previou obesity –13; howe obesity . In our α levels showed a significant positive correlation with insulin resistance. On the other hand, leptin level was not higher in our patients than in control subjects and its correlation with insulin resistance is weak. Owecki and coworkers performed a linear regression test for BMI and leptin to find the threshold of BMI at which serum leptin level starts increasing, and they reported a sudden increase in leptin levels at a BMI of 24.6 kg/m2 which indicates that some metabolic changes occur before reaching obesity levels [The majority of diabetic patients in the current study have BMI above 30, and their elevated TNF-α, the higher their HbA1c value, which means that TNF-α level can be used to predict glycemic control in obese diabetic patients. Mirza et al. showed that diabetes was strongly associated with elevated levels of TNF-α, which was most significantly elevated in the group of patients with HbA1c values higher than 6.5% [α level. Since HbA1c is not a direct measure of glycemia, there is a chance that its level might change due to factors unrelated to blood glucose levels such as rate of glycation and turnover of erythrocytes [α, were strongly associated with increased risk to occurrence of T2DM [α level decreases with good glycemic control would confirm this hypothesis. Interestingly, a previous study showed a significant reduction in TNF-α serum level in obese T2DM patients following 4 weeks of treatment with diet and exercise [α level and HbA1c value after regular exercise in healthy women [α and HbA1c might be useful clinically to follow up the level of glycemic control in patients with T2DM after medical treatment or changed lifestyle or in cases where HbA1c cannot be trusted due to defect in glycation or severe anemia.We found that in patients with T2DM, the higher the level of TNF-han 6.5% . In our hrocytes . Recentlexercise . Tsukui hy women . TheoretLimitations. This is a cross-sectional study with a limited number of patients. A longitudinal study with categorization of patients based on HbA1c and BMI would help in confirming our findings. Follow-up studies after good glycemic control to measure the improvement in beta-cell function and its association with decreased TNF-α level are our future plan.α in diabetic patients correlates with the level of insulin resistance and the glucose metabolism parameters such as HbA1c. The increased TNF-α level is related to the combined effect of obesity and diabetes while increased leptin level is caused by obesity unescorted by diabetes. Our findings suggest that further research should investigate the role of TNF-α as a tool to monitor the effectiveness of diabetes management, particularly in individuals with obesity.Independent of leptin level, serum level of TNF-


Huang HY, Fang Y, Fang H, Wu DW, Wang SH,Bai Y, Yu AQ, Wang H, Sun C, Yu Y, Fan Y, Yang C, Shi JF,Li N performed the experiments. Huang HY, Fang Y, Shi JF,Li N analyzed the data. Huang HY, Yang C, Shi JF, Shi JFcontributed analysis tools. Huang HY, Fang Y, Shi JF, Li Nprovided critical inputs on design, analysis, and interpretationof the study. All the authors had access to the data. All authorsread and approved the final manuscript as submitted.


The package ggroups is introduced in this article. The purpose of this package is providing functions for checking and processing the pedigree, calculation of the additive genetic relationship matrix and its inverse, which are used to study the population structure and predicting the genetic merit of animals. Calculation of the dominance relationship matrix and its inverse are also covered. A concept in animal breeding is genetic groups, which is about the inequality of the average genetic merits for groups of unknown parents. The package provides functions for the calculation of the matrix of genetic group contributions (Q). Calculating Q is computationally demanding, and depending on the size of the pedigree and the number of genetic groups, it might not be feasible using personal computers. Therefore, a computationally optimised function and its parallel processing alternative are provided in the package.R is a multi-platform statistical software and an object oriented programming language. The package archive network for R provides CRAN repository that features over 15,000 free open source packages, at the time of writing this article (Using sample data, outputs from different functions of the package were presented to illustrate a real experience of working with the package.The presented R package is a free and open source tool mainly for quantitative geneticists and ecologists, who deal with pedigree data. It provides numerous functions for handling pedigree data, and calculating various pedigree-based matrices. Some of the functions are computationally optimised for large-scale data. A) is used in the best linear unbiased prediction (BLUP) animal models  0.25. The same result can be obtained using function pedigree::makeA, but with less convenience.Calculating relationship coefficient between a pair of individuals using rg, information from their common parents are used only. Runtime of 100 reiterations of rg on random pairs of individuals is provided in Table If the inbreeding coefficient of an individual is of interest, using functions buildA and tabA is computationally expensive, especially in a large pedigree. Instead, function inb can be used. For example, to calculate the inbreeding coefficient of individual 6 in ped2, inb results in: 1] 0.25. Calculating inbreeding coefficient for an individual, information from parents’ common ancestors are used only. Runtime of 100 reiterations of inb on randomly chosen individuals is provided in Table  0.25. CaUsing package ggroups , there aFunctions pedigree::calcInbreeding  and pediA−1 in a tabular-sparse format. Compared to matrix data, handling tabular data takes a longer runtime. However, it is more memory-efficient for large pedigree. The pedigree object and inbreeding coefficients are required for tabAinv. None of the animals in ped4 were inbred. For example,Function tabAinv builds Function tabAinv performs similar to functions pedigree::makeAinv  and pediPlease note that the first two columns in Ainv.txt written by pedigree::makeAinv are animal orders in the pedigree, not animal IDs. Table A−1 created in the previous example is converted to matrix format:Function tab2mat converts matrices from tabular-sparse format to matrix format. For example, vice versa (tab2mat) are provided in Table Function mat2tab converts matrices from matrix format to tabular-sparse format. For example, mat2tab(tab2mat(tabA(ped4))) returns tabA(ped4). Runtime records for converting a pedigree relationship matrix to tabular-sparse format (mat2tab) and R package reshape2  is a popor to transform a tabular data.frame to a matrix:Unlike reshape2::melt and reshape2::dcast functions used above, mat2tab and tab2mat functions are specifically designed for symmetric matrices and tabular-sparse data, which reduces the number of rows in the data.frame to be read/written and kept in the memory.Q, which is the matrix of genetic group contributions to animals. According to Quaas [Q=(I−P)−1PbQb, where (I−P)−1Pb is equal to the genetic relationship matrix between animals (rows) and unknown parents (columns), and Qb is the incidence matrix of unknown parents (rows) and genetic groups (columns). Function qmatL is the computationally optimised version of qmat. It calculates each column of Q (each genetic group) separately, by calculating relationship coefficients between the genetic group of interest with animals that receive contribution from the genetic group .Function qmat creates matrix to Quaas , Q=(I−P)The input pedigree should contain genetic groups (not unknown parents) as the only IDs with unknown parents, similar to the output from ped2 = gghead(ped). For example,Q for pedigree and genetic groups. Using the Mexican Braunvieh pedigree on Sys.i7-8650U.16, both qmat and nadiv::ggcontrib aborted with "Error: cannot allocate vector of size 25.6 Gb" message. Function qmatL could even return Q on a machine with 4 Gb RAM . Functions qmat and nadiv::ggcontrib were tested on a server with 526 Gb of RAM. Function qmat returned Q after 17m:51s, and function nadiv::ggcontrib aborted with the following message:Function nadiv::ggcontrib  also retQ on Sys.i7-8650U.16. Runtimes of qmat, qmatL, and nadiv::ggcontrib are provided in Table With the pedigree subset of 3,000 animals, function nadiv::ggcontrib successfully returned matrix Q, the process can take advantage of multicore processors and parallel computing. Function qmatXL is the parallelised version of qmatL. It requires a pedigree object as described for ped2, and the number of user-defined computational nodes. If the number of user-defined nodes was greater than the number of genetic groups, the number of genetic groups is considered for the number of nodes. This function requires R packages doParallel [In order to further speed up the calculation of Parallel  and foreParallel , otherwiTable There are two main alternatives of introducing genetic groups in BLUP :1\documX, Z, and ZQ. Matrix R, and vector y correspond to the residuals and phenotypes. Using Quaas and Pollak [where d Pollak  transforQ matrix and a data.frame with 2 columns for IDs and solutions [Q. For example,If the genetic evaluation software applies Eq.   are provided in Table D−1 in a tabular-sparse format from a pedigree object. For example,Function tabDinv is used for obtaining Runtime record of tabDinv is provided in Table There are other functions included in package ggroups, and possibly more functions will be added in the future. Some of the existing functions are offspring, pedup, and peddown. Function offspring reports the number of descendants from an individual, in each generation following that individual. Providing a list of animals such as the list of phenotyped or genotyped animals, the number of descendants from that list per generation is also provided. Function pedup extracts the pedigree for one or a group of individuals, by tracing their ancestors, for a defined or maximum possible number of generations. Similarly, function peddown extracts the pedigree for a group of individuals, by tracing their descendants down the pedigree.vice versa. The results showed that functions dealing with data in matrix format are faster than functions dealing with data in tabular-sparse format. However, where memory of most personal computers fail forming large matrices for large pedigree, dealing with tabular-sparse data reduces the memory demand.The R package ggroups is a useful, free and open source tool for animal breeders, researchers, and students, who work with pedigree data and R. It provides functions for the most important tasks related to working with pedigree, including creating the additive genetic relationship matrix and its inverse, and the matrix of genetic group contributions. The advantage of this package over other packages that also calculate the matrix of genetic group contributions, is that it has computationally optimised functions for large pedigree, which also can take advantage of multicore processors. In addition, R package ggroups provides other helpful functions such as functions for pedigree checking, renumbering and extraction, forming the dominance relationship matrix and its inverse, and converting tabular-sparse data to matrix, and Project name: R package ggroupsProject home page:https://cran.r-project.org/package=ggroupsOperating system(s): Platform independentProgramming language: ROther requirements:doParallel (≥1.0.14) and foreach (≥1.4.4) are required for function qmatXL.License: GPL-3


Increasing evidence suggests that NODs are involved in liver diseases; however, the underlying mechanisms remain obscure. In the present study, we analyzed the effect of NOD1 agonist pretreatment on acute liver failure induced by lipopolysaccharide (LPS) in D-galactosamine -sensitized mice. We found that pretreatment with the NOD1 agonist markedly reduced LPS/D-GalN-induced mortality, elevation of serum ALT levels, and hepatocyte apoptosis. The protective effect of NOD1 agonist was independent of tumor necrosis factor (TNF)-α inhibition. NOD1 agonist pretreatment also attenuated TNF-α/D-GalN-induced apoptotic liver damage. The anti-apoptotic protein A20 expression was more pronounced in NOD1 agonist pretreated mice than in controls, and knockdown of A20 abrogated the protective effect of NOD1 agonist on LPS/D-GalN-induced liver injury and hepatocyte apoptosis. Further experiments showed that NOD1 agonist-induced A20 upregulation required the presence of kupffer cells and TNF-α. Taken together, our data strongly indicate that NOD1 is involved in the regulation of liver injury and could be a potential therapeutic target for liver diseases. Acute liver failure (ALF), caused by massive destruction of hepatocytes with resultant severe impairment of liver function, is a serious problem in clinical practice . ApoptosThe LPS/D-GalN model has been widely used to explore the mechanisms of ALF and screen for potential hepatoprotective drugs , 6. In tNucleotide oligomerization domain receptor 1 (NOD1) is a cytoplasmic pattern recognition receptor that recognizes bacterial cell wall components and has been associated with many inflammatory diseases in humans, highlighting their significant immunologic role . NOD1 isIn the present study, we sought to determine the effect of NOD1 activation by its agonist on ALF induced by LPS/D-GalN. Our data show that NOD1 agonist treatment prevented LPS/D-GalN-induced fatal hepatitis through the upregulation of A20 expression in hepatocytes.Escherichia coli, O55:B5) and D-GalN were purchased from Sigma-Aldrich . Two NOD1 agonists, C14-Tri-LAN-Gly, and C12-iE-DAP, were obtained from InvivoGen . Recombinant mouse TNF-α was purchased from Biolegend . Clodronate-liposomes was purchased from FormuMax .LPS  were provided by the Institute of Laboratory Animal Sciences, Chongqing Medical University , while TNF-α KO mice in C57BL/6J background were provided by Xuemei Zhang . Mice were housed under a 12-h light-dark cycle in a barrier housing facility. The house facility complies with the national standard of Laboratory Animal-Requirements of Environment and Housing Facilities (GB 14925-2010), and the facility permit number is SYXK 2018-003. The animals were treated according to the guidelines of the Institutional Animal Care and Use Committee of Chongqing Medical University. Ethical approval was obtained from the Ethics Committee of the The Second Affiliated Hospital of Chongqing Medical University.Two mouse models of LPS/D-GalN and TNF-α/D-GalN were used to induce liver injury. For LPS-induced liver injury, mice were co-injected i.p., with LPS (7.5 μg/kg) and D-GalN (500 mg/kg). In the TNF-α/D-GalN model, 0.4 μg/mouse TNF-α and 750 mg/kg D-GalN were co-injected i.p., into mice. C14-Tri-LAN-Gly was administered i.p., at 5 μg/mouse at 6 h before LPS/D-GalN or TNF-α/D-GalN co-injection. ShRNA was purchased from Genechem . The target sequences for shRNA design were CAAAGCACTTATTGACAGA for A20 and TTCTCCGAACGTGTCACGT for GFP (shControl). Mice were hydrodynamically injected with shRNA (30 μg/mouse) at 72 h before C14-Tri-LAN-Gly challenge. For Kupffer cell depletion, mice were injected with clodronate-liposomes i.v., containing 200 and 100 μl/mouse at 48 and 24 h, respectively, before C14-Tri-LAN-Gly challenge. The vehicle control groups received the same volume PBS.To assay for serum ALT levels, we slightly anesthetized the mice with ether and obtained the blood sample from the eyepit. Serum was mixed with ALT assay solution  and analyzed using a spectrophotometer following the supplier's protocol.The serum samples were kept at −20°C until ready for cytokine measurement. Liver tissues homogenates were prepared following the protocol of the ELISA kits. The levels of TNF-α and IL-6 were measured using commercially available ELISA kits from DAKEWE .in situ Cell Death Detection Kit, POD .Liver samples were excised and immediately fixed within 10% neutral-buffered formalin. Samples were embedded in paraffin and cut into 5 μm sections. Livers sections were affixed to slides, deparaffinized, and stained with hematoxylin and eosin (H&E) to determine morphologic changes. Apoptotic hepatocytes were detected with the terminal deoxynucleotidyl transferase-mediated nick end labeling (TUNEL) assay by using the Whole-cell protein and liver tissue homogenates were extracted for Western blot analysis. Proteins were desaturated with loading buffer and boiled for 5 min. Exactly 50 μg of total protein was loaded in SDS-PAGE. After electrophoresis, proteins were transferred onto PVDF membrane, and blotted against cleaved caspase-3, A20, and GAPDH  primary antibodies overnight at 4°C. Membranes were washed with 0.05% Tween-20 in TBS and incubated with a 1:10000 dilution horseradish peroxidase-conjugated secondary antibodies, and chemiluminescence was performed according to the manufacturer's protocol .Total RNA was extracted from liver tissue and cells by using TRIzol reagent . Cellular RNA (1 μg) was reverse-transcribed into cDNA by using the PrimeScriptTM RT reagent kit with gDNA Eraser . Quantitative PCR was performed using a sequence detector  and a SYBR Premix Ex Taq , according to the manufacturer's instructions. The primer sequences used were as follows: GAPDH, CATCACTGCCACCCAGAAGACTG (forward) and ATGCCAGTGAGCTTCCCGTTCAG (reverse); A20, AGCAAGTGCAGGAAAGCTGGCT (forward) and GCTTTCGCAGAGGCAGTAACAG (reverse); c-IAP1, GATACGGATGAAGGGTCAGGAG (forward) and GGGTCAGCATTTTCTTCTCCTGG XIAP, GGCAGAATATGAAGCACGGATCG (forward) and CACTTGGCTTCCAATCCGTGAG (reverse); and Flip, GCTCTACAGAGTGAGGCGGTTT (forward) and CACCAATCTCCATCAGCAGGAC (reverse). The relative mRNA levels of specific genes were normalized to those of GAPDH. Gene expression values were then calculated using the ΔΔCt method, as previously described .Liver mononuclear cells (MNCs) were isolated, as described previously . The liv2HPO4, EGTA, and Tricine. Solution B was composed of DMEM and 0.075% collagenase I . Hepatocytes were then separated with 40% Percoll by centrifugation at 400 ×g for 10 min. The isolated primary hepatocytes were cultured in Dulbecco's Modified Eagle's Medium supplemented with 10% fetal bovine serum overnight prior to treatments.Primary hepatocytes were isolated using the collagenase perfusion method, as described previously . The livThe cell surface expression of TLR4 and TNFR1 was assessed using FITC-conjugated anti-mouse TLR4  and PE-conjugated anti-mouse TNFR1 . After blocking with anti-FcrR, cells were incubated with saturation mounts of indicated fluorescence-labeled mAbs at 4°C for 30 min in darkness and then washed twice with PBS. Date were recorded from the stained cells using a FACSCanto II flow cytometer  and analyzed using the FlowJo software.P-values <0.05 were considered as statistically significant.Date were expressed as the mean ± SEM. The results were analyzed using log-rank test  and Mann-Whitney test. Co-injection with LPS and D-GalN in mice induced severe liver injury with a high mortality in a short time. C14-Tri-LAN-Gly, a highly selective and potent NOD1 agonist, was used to activate NOD1. To investigate the effects of NOD1 agonist on LPS/D-GalN-induced liver injury, we treated the mice with C14-Tri-LAN-Gly (5 or 10 μg/mouse) 6 h before LPS/D-GalN challenge. As shown in TUNEL assay showed significant hepatocyte apoptosis in the livers of mice treated with LPS/D-GalN, which was markedly prevented by C14-Tri-LAN-Gly pretreatment . MoreoveBecause TNF-α is a key mediator in LPS/D-GalN-induced acute liver injury , we deteDown-regulation of TLR4 has been suggested as a mechanism for LPS tolerance induction . HoweverConsidering that the protective effect of C14-Tri-LAN-Gly on LPS/D-GalN induced liver injury is not attributable to decreased TNF-α production, we speculate that C14-Tri-LAN-Gly may influence TNF-α-induced hepatocyte apoptosis. To test this hypothesis, we investigated the effect of C14-Tri-LAN-Gly on TNF-α/D-GalN-induced liver failure. Similarly, C14-Tri-LAN-Gly pretreatment significantly increased the survival rate of TNF-α/D-GalN-treated mice . The proThe TUNEL assay further revealed that C14-Tri-LAN-Gly reduced the frequency of apoptosis induced by TNF-α/D-GalN to a significant extent . WesternTNF-α-induced apoptosis is mediated by the TNF receptor-1 (TNFR1) . To clarTo further clarify the mechanism by which C14-Tri-LAN-Gly protects hepatocytes from apoptosis, we determined the expression of some potential anti-apoptosis genes in livers of TNF-α/D-GalN or LPS/D-GalN mice with real-time PCR assay. Notably, the expression of A20 was markedly enhanced in the C14-Tri-LAN-Gly-pretreated mice compared with control mice . In addiWe also examined the effect of C14-Tri-LAN-Gly treatment alone on the expression of A20. As shown in in vivo by ~70% . For hepatocyte-specific knockdown of A20 , mice we by ~70% . A20 kno by ~70% . Restora by ~70% . These eTo further investigate the mechanism of NOD1 agonist-induced A20 upregulation, we isolated primary hepatocytes and treated them with C14-Tri-LAN-Gly, and total RNA and protein were extracted to analyze A20 expression. As shown in −/− (TNF-α knockout) mice with C14-Tri-LAN-Gly for 8 h, then the expression levels of A20 mRNA and protein were measured by real-time PCR and western blot assay. As shown in −/− mice. These results indicate that TNF-α produced by kupffer cells induced A20 upregulation in hepatocytes following C14-Tri-LAN-Gly treatment.A20 was originally identified as a TNF-inducible gene . C14-TriTreatment of mice with LPS/D-GalN induces fulminant hepatitis, which is mediated by TNF-α and characterized by massive hepatic apoptosis. In this study, we defined a protective role of C14-Tri-LAN-Gly, a NOD1 agonist, in LPS/GalN-induced (TNF-α-mediated) hepatocyte killing. This protection was associated with the upregulation of A20 in hepatocytes.The liver is a sentinel organ in a unique position to monitor pathogen-associated molecules in the portal and systemic circulations. NOD1 is highly expressed in liver and is stimulated mainly by bacterial cell wall components from gram-negative bacteria , which mIn the LPS/D-GalN-induced liver injury model, neutralization of TNF-α or deletion of its receptor TNFR1 offers protection from lethal liver damage , 28. IntA20, also known as tumor necrosis factor α-induced protein 3 (TNFAIP3), is essential for the termination of NF-κB signaling in response to TNF and microbial products such as LPS and muramyl dipeptide, but also negatively regulates TNF-induced apoptosis . A20 hasin vitro data showed that treatment of primary hepatocytes with C14-Tri-LAN-Gly could not directly upregulate A20 expression. Instead, we found that TNF-α produced by kupffer cells induced A20 upregulation in hepatocytes following C14-Tri-LAN-Gly treatment. When kupffer cells were depleted or TNF-α was deficient, C14-Tri-LAN-Gly-induced A20 upregulation was impaired. In inflammatory conditions, TNF-α has both a beneficial and a deleterious effect on hepatocytes (A previous study has shown that hepatocytes express high levels of NOD1(17). However, our atocytes . TNF-α iatocytes . Meanwhiatocytes , 34. OurAlthough we and others have used clodronate liposomes to deplete kupffer cells , it shouIn summary, the present study showed that NOD1 agonist administration ameliorated liver injury and the high lethality induced by either LPS/D-GalN or TNF-α/D-GalN. NOD1 agonist prevented TNF-α-induced hepatocyte apoptosis through the upregulation of A20 expression. NOD1 agonist induced A20 upregulation by Kupffer cell- and TNF-α-dependent mechanisms. The present study also suggests NOD1 as a therapeutic target for liver diseases.The original contributions presented in the study are included in the article/The animal study was reviewed and approved by Institutional Animal Care and Use Committee of Chongqing medical university.FJ performed the experiments. FJ, ST, and WY designed the experiments and wrote the paper. FJ, FD, PX, SL, XW, PH, HR, ST, and WY analyzed the data. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Matrix metalloproteinases (MMPs) and tissue inhibitors of metalloproteinases (TIMPs) are pivotal regulators of extracellular matrix (ECM) composition and could, due to their dynamic activity, function as prognostic tools for fibrosis and cardiac function in left ventricular diastolic dysfunction (LVDD) and heart failure with preserved ejection fraction (HFpEF). We conducted a systematic review on experimental animal models of LVDD and HFpEF published in MEDLINE or Embase. Twenty-three studies were included with a total of 36 comparisons that reported established LVDD, quantification of cardiac fibrosis and cardiac MMP or TIMP expression or activity. LVDD/HFpEF models were divided based on underlying pathology: hemodynamic overload (17 comparisons), metabolic alteration (16 comparisons) or ageing (3 comparisons). Meta-analysis showed that echocardiographic parameters were not consistently altered in LVDD/HFpEF with invasive hemodynamic measurements better representing LVDD. Increased myocardial fibrotic area indicated comparable characteristics between hemodynamic and metabolic models. Regarding MMPs and TIMPs; MMP2 and MMP9 activity and protein and TIMP1 protein levels were mainly enhanced in hemodynamic models. In most cases only mRNA was assessed and there were no correlations between cardiac tissue and plasma levels. Female gender, a known risk factor for LVDD and HFpEF, was underrepresented. Novel studies should detail relevant model characteristics and focus on MMP and TIMP protein expression and activity to identify predictive circulating markers in cardiac ECM remodeling. Left ventricular diastolic dysfunction (LVDD) is an early common alteration in many cardiovascular diseases (CVDs) and highly prevalent in the general population, with reported incidence ranging from 3% to 39% ,2. LVDD LVDD and HFpEF are characterized by systemic inflammation, endothelial (microvascular) dysfunction, impaired intracellular cardiomyocyte calcium handling, cardiac hypertrophy and interstitial fibrosis ,23. FibrDespite diagnostic advances, therapeutic approaches known to benefit HFrEF patients have not proven as clinically efficacious for LVDD and HFpEF patients. HFpEF management primarily consists of treatment of co-morbidities, blood pressure control and diuretic treatment but overall, there is poor control of symptoms ,32. The In this systematic review, our aim was to report cardiac MMP and TIMP expression or activity in relation to both LVDD/HFpEF and fibrosis in adequately controlled animal models, e.g., established diastolic dysfunction in absence of systolic dysfunction. Besides providing insights into overall ECM dynamics and patterns of fibrosis, this information may be further used to critically assess (a combination of) novel interesting MMPs and TIMPs as prognostic tools in future studies.Our systematic search resulted in 4868 articles. As described in the Materials and Methods min) (7/23), time constant of relaxation Tau (6/23) and end diastolic pressure volume relationship (EDPVR) (5/23) were reported less frequently (LVDD was primarily examined using (tissue Doppler) echocardiography E/A ratio (15/23), followed by changes in peak E-wave velocity (8/23), E/e’ ratio (6/23) or isovolumic relaxation time . Invasive hemodynamic measurements end diastolic pressure (EDP) (11/23), minimum derivative of pressure over time  in the experimental model and control, as defined in the exclusion criteria. Studies were first divided based on underlying pathophysiology; ageing (3 comparisons), hemodynamic alterations (17 comparisons) and metabolic alterations (16 comparisons) , fractional shortening (FS) and/or peak derivative of pressure over time (dP/dtarisons) . Due to p = 0.03). For E/A, E-wave and IVRT, there were no subgroup differences.Pooled analysis of E/e’ A but notmin (p = 0.89 and p = 0.76). EDP remained unchanged in subgroup analysis and was similar in both models. dP/dtmin decreased in metabolic models, without subgroup differences (p = 0.39). Thus hemodynamic and metabolic models generally display similar changes in cardiodynamics , resulting from increased collagen type I expression (mRNA and protein) and increased collagen type III on mRNA but not protein level  guidelines . While Liography ,61,62. Aiography ,64. In ciography ,5. To thiography ,66 and tiography . In geneiography . Among oiography  but chaniography . Almost iography .Given the pooled and separate effect of hemodynamic models on increased E/e’ ratio but lack of effect on E/A and E-wave, e’ seems to represent the most reliable change in LVDD/HFpEF. Indeed, Zhong et al. , Pagan emin, which were both identified in metabolic alterations, in accordance with literature [Invasive hemodynamics were less frequently applied in the included articles, probably due to practical constraints, especially in small animals. In pooled data, we did find prolonged Tau and decreased dP/dtterature ,73.The cardiac ECM mainly comprises fibrillar collagen, specifically collagen type I and III  . MyocardSeveral clinical studies have previously tried to improve LVDD and HFpEF diagnosis by incorporating plasma markers of collagen turnover ,81,82. TDifferences in MMP and TIMP expression and activity may also be relevant in relation to underlying co-morbidities and severity of HFpEF. While our meta-analysis had a low power concerning subgroup analysis, we did identify higher MMP2 and MMP9 protein in hemodynamic and lower TIMP4 gene expression in metabolic alterations. Sakamuri et al. previously studied high-fat diet changes in TIMP4 knock-out (KO) mice compared to wild-type. TIMP4 KO mice showed reduced cardiac fibrosis and systemic protection from dyslipidemia, indicating a protective mechanism in the context of metabolic changes . In chroWe retrieved 23 relevant studies via our systematic search, complemented by cross-referencing. In order to exclusively include models with well-established phenotypic characterization, we applied stringent inclusion criteria. These included established echocardiographic measurements of diastolic function in absence of systolic dysfunction, combined with quantification of fibrosis and cardiac tissue quantification of at least one MMP or TIMP, and only in pre-determined experimental models known to represent co-morbidities in human HFpEF. Inclusion of stable LVDD/HFpEF models came at the cost of the relatively low power of our meta-analysis. Our broad search strategy was performed in two biomedical databases, leading to a large number of references. Several papers did not explicitly mention either LVDD/HFpEF or MMP/TIMP expression while focusing on disease development or only retrieved MMPs/TIMPs by applying an mRNA-sequencing protocol. Consequently, these studies could not be identified by our search, but we have resolved this by cross-reference searching.max, compared to controls. On the other hand, clinical diagnosis of LVDD or HFpEF is described in detailed guidelines and depends on specific alterations in cardiac parameters that are not well-defined in animal models. We therefore included all studies that showed a significant difference in at least one measured clinically relevant diastolic parameter, e.g., E/e’, E/A, Tau and dP/dtmin, compared to control, irrespective of the direction of the change. We also identified significant heterogeneity (>75%) between several comparisons. This can be largely explained by differences in study design, cardiac, fibrotic and MMP and TIMP outcome as well as the differences between underlying pathology, animal species and strains. Creating a division between hemodynamic and metabolic-driven pathologies allowed us to analyze both overall data and individual underlying pathologies, in line with the heterogeneity of co-morbidities found in HFpEF patients [Several studies including relevant co-morbidities were excluded based on a decrease in systolic function. While a threshold to discern HFrEF from HFpEF is routine in clinical practice , this dopatients ,93. By iWe registered the systematic review protocol in PROSPERO (CRD4202018315) on 27 May 2020.A systematic search MEDLINE and Embase was conducted from database inception up to March 2020. Medical Subject Headings (MeSH) terms and free text terms in title and abstract were used to identify all possible studies regarding HFpEF and LVDD with measured (diastolic) heart function, fibrosis and MMP or TIMP measurements. The search syntax can be found in Titles and abstracts were evaluated independently by two researchers (C.G.M.v.D and M.M.K.). Duplicates, non-English, editorials, poster presentations, letters or abstracts only were excluded prior to full text assessment. Consequently, all articles deemed eligible in the title and abstract screening phase were reviewed in the full-text screening phase, independently and in duplicate. The two reviewers resolved disagreements by discussion and, if needed, by third-party adjudication. Only animal studies focusing on stable HFpEF or LVDD and not progressive models leading to HFrEF were included. Inclusion and exclusion criteria for all different animal models and cardiac parameters were predefined and listed below:Pathologies eligible for inclusion: (1) amyloid (non-hereditary) cardiomyopathy, (2) hypertrophic cardiomyopathy independent of coronary artery disease (CAD) and myocardial infarction (MI), (3) all models of trans-aortic constriction (TAC) in absence of effects on ejection fraction (EF) and fractional shortening (FS), e.g., 2-kidney-1-clip (2K1C) and abdominal-aortic banding, (4) aortic stenosis in absence of CAD or MI, (5) atrial fibrillation in exercise in absence of CAD or MI, (6) pulmonary hypertension, (7) chronic  angiotensin II (AngII), (8) chronic  deoxycorticosterone acetate (DOCA)-salt, (9) chronically induced isoproterenol, (10) mitral (non-hereditary) regurgitation, 11) arterio-venous fistula (AVF), (12) natural ageing and (13) (genetic) models not restricted to rodents described by Valero-Muñoz et al. [ arterio-Pathologies that were excluded: (1) stenotic or hypertensive models where the underlying cause is based on systemic atherosclerosis and/or atherosclerotic coronary artery disease (CAD) since CAD is seen as a macrovascular disease and onset mechanisms may deviate from true LVDD and HFpEF, (2) unstable HFpEF of LVDD models that eventually progress into HFrEF , (3) trained ischemia models such as ischemia-reperfusion (I/R), (4) genetic models of dilated cardiomyopathy (DCM), (5) Homocysteine-enriched diets, 6) exclusion criteria in accordance with HELPFUL protocol  exclusio as well Studies that met the criteria were further assessed and only included if; (1) HFpEF or LVDD was confirmed with at least one parameter of diastolic function in accordance with the ASE/EACVI guidelines , (2) fibMethodological quality assessment of the included studies was performed by a risk of bias tool adapted from Papazova et al. . We sepahttps://automeris.io/WebPlotDigitizer/) by one researcher (C.G.M.v.D) and validated using PlotDigitizer (http://plotdigitizer.sourceforge.net/) by a second researcher (M.M.K.).Using standardized piloted data-extraction forms, pair of reviewers independently extracted data on study characteristics including species, strain, sex, age, weight, number of animals and experimental model. The total duration of the experiment was reported as end time point. Cardiac parameters from either echocardiography, invasive hemodynamics or tissue Doppler were extracted for (1) diastolic function and, when applicable, (2) systolic function. Fibrotic outcomes and MMP and/or TIMP outcomes were extracted from all parameters measured. Studies that only showed representative images of a staining or WB related to fibrosis or collagen or zymography but no quantitative data were excluded. For each outcome, the sample size and standard deviation (SD) or standard error (SEM) were extracted. When the sample size was described as a range, the lowest number of replicates was used. When data was not present in text or tables, graphical data was extracted using WebPlotDigitizer , defined as the between-group difference in mean values divided by the pooled SD, with their corresponding 95% confidence interval using Review Manager (version 5.3.5). Studies were divided based on underlying pathophysiology; ageing, hemodynamic alterations and metabolic alterations.2 and I2 statistics as a measure of between-study heterogeneity. The I2 described a percentage of variation across the studies attributable to heterogeneity with values of <25%, 25–75%, and >75% interpreted as, respectively, low, moderate, and high between-trial heterogeneity. We used standard inverse-variance random-effect meta-analysis to combine outcome data across studies on predetermined parameters [We examined the heterogeneity by visually inspecting forest plots for the presence of heterogeneity and the taurameters  in RevieThis systematic review followed the PRISMA guidelines .Our study shows that when MMPs and TIMPs are studied in relation to LVDD/HFpEF, cardiac mRNA expression is still most frequently measured while this does not seem to resemble cardiac ECM dynamics in these experimental models. Since post-transcriptional and post-translational activation of both MMPs and TIMPs takes place, future studies should focus on MMP and TIMP protein levels and enzyme activity. Changes in active MMPs seem to most closely resemble serum values. Besides increased enzymatic activity of MMP2 and MMP9 and TIMP1 mRNA, we propose MMP15 as an interesting novel candidate in HFpEF-driven cardiac fibrosis, as MMP15 mRNA was downregulated in HFpEF compared to controls. Ideally, a combination of tissue and plasma concentration should be measured to correlate MMP and TIMP dynamics for a better clinical translatability. Furthermore, MMP and TIMP protein expression and enzymatic activity may differ in underlying co-morbidities associated with LVDD/HFpEF; we identified TIMP4 mRNA as a relevant candidate since it was downregulated in metabolic compared to hemodynamic models.Besides these conclusions related to MMPs and TIMPs, a number of general recommendations related to experimental LVDD/HFpEF studies can be put forward. These are listed below.We recommend future studies to focus on experimental LVDD/HFpEF models in which female gender is separately represented, on models that include pure volume overload and atrial fibrillation and on models of ageing and ageing in combination with either hemodynamic or metabolic models. Perform adequate hemodynamic and metabolic phenotyping to more clearly discern differences between LVDD/HFpEF associated sub-groups. Focus should be on measuring invasive hemodynamic parameters instead of, or in addition to, (speckle tracking) echocardiography, since these appear to be more reliable across species and will decrease the translation bias to the clinic. Include a systolic parameter, in addition to establishing diastolic dysfunction, to ascertain pure LVDD/HFpEF. Lastly, we recommend a focus on spatiotemporal patterns of diastolic dysfunction and fibrosis, to ascertain whether clinical stages of LVDD/HFpEF are translatable to experimental models.


Virus-like particles (VLPs) have become a key tool for vaccine developers and manufacturers. They can be broadly used to develop prophylactic as well as therapeutic vaccines in a vast number of indications for human as well as companion animals and animals for food production. An additional use of VLPs is to tune the type and duration of immune responses. In this Special Issue “Virus-like Particles Vaccines”, essentially all of these aspects and applications are discussed and various aspects of VLP vaccinology are highlighted, including VLP characterization. The manuscript by Irene Gonzales-Dominguez et al. is an interesting example, where six different biophysical methods were assessed and compared for the characterization of HIV-1-based VLPs produced in mammalian and insect cell platforms  as well


Influenza viruses infect millions of humans every year causing an estimated 400,000 deaths globally. Due to continuous virus evolution current vaccines provide only limited protection against the flu. Several antiviral drugs are available to treat influenza infection, and one of the most commonly used drugs is oseltamivir (Tamiflu). While the mechanism of action of oseltamivir as a neuraminidase inhibitor is well-understood, the impact of oseltamivir on influenza virus dynamics in humans has been controversial. Many clinical trials with oseltamivir have been done by pharmaceutical companies such as Roche but the results of these trials until recently have been provided as summary reports or papers. Typically, such reports included median virus shedding curves for placebo and drug-treated influenza virus infected volunteers often indicating high efficacy of the early treatment. However, median shedding curves may be not accurately representing drug impact in individual volunteers. Importantly, due to public pressure clinical trials data testing oseltamivir efficacy has been recently released in the form of redacted PDF documents. We digitized and re-analyzed experimental data on influenza virus shedding in human volunteers from three previously published trials: on influenza A  or B viruses . Given that not all volunteers exposed to influenza viruses actually start virus shedding we found that impact of oseltamivir on the virus shedding dynamics was dependent on (i) selection of volunteers that were infected with the virus, and (ii) the detection limit in the measurement assay; both of these details were not well-articulated in the published studies. By assuming that any non-zero viral measurement is above the limit of detection we could match previously published data on median influenza A virus (flu A study) shedding but not on influenza B virus shedding (flu B study B) in human volunteers. Additional analyses confirmed that oseltamivir had an impact on the duration of shedding and overall shedding (defined as area under the curve) but this result varied by the trial. Interestingly, treatment had no impact on the rates at which shedding increased or declined with time in individual volunteers. Additional analyses showed that oseltamivir impacted the kinetics of the end of viral shedding, and in about 20–40% of volunteers that shed the virus treatment had no impact on viral shedding duration. Our results suggest an unusual impact of oseltamivir on influenza viruses shedding kinetics and caution about the use of published median data or data from a few individuals for inferences. Furthermore, we call for the need to publish raw data from critical clinical trials that can be independently analyzed. Influenza is a respiratory infection caused by different strains of the influenza virus. Influenza A viruses originate from animals such as birds and pigs while influenza B viruses have no known animal origin  or influenza B virus  were done using volunteers . Data from three trials involving treatment of volunteers with oseltamivir (Tamiflu) 24 h after controlled exposure to influenza viruses were chosen for further analysis: study on infection with influenza A virus , and two studies on infection with influenza B virus   and are given in log10 units. The data were digitized by KLH into a spreadsheet format with time given in minutes or days since infection. Accuracy of digitization was confirmed by checking the correspondence between pdf files by another student for a set of randomly chosen volunteers.Redacted PDF files describing in detail clinical trials have been downloaded in 2014 from dryad.org  were excluded from the data analysis  we did the following. First, for each volunteer we converted the shedding data into 0 or 1 with 0 values being assigned for times when viral shedding was at or below the LOD and 1 values assigned for all times when viral shedding exceeded LOD or the time was later than the first time point of the positive viral shedding event. Then we used equally spaced time points  and we counted the number of 0 or 1 for all volunteers in the cohort. The resulting data are given as the number of volunteers that started shedding the virus at time To compare median shedding curves calculated in our datasets with published values we digitized data for influenza A virus shedding . Value 0.24 was chosen so there is no overlap in viral titers used to calculate median for different t.Because measurements of viral shedding by individual volunteers was done at different times after infection to calculate median shedding curves per cohort we did the following. We defined times since infection as r) in the viral shedding we selected shedding data which are above the LOD and up to the peak of shedding. The growth rate was calculated as the slope of a linear function fitted to these (log10) viral titer data multiplied by ln(10) [r = slope × ln(10)]. In a similar fashion, for calculating the exponential decline rate (δ) in the viral shedding we selected data after the peak of viral shedding  until the last shedding value above the LOD. The early growth rate was calculated using the last measurement at the LOD and all measurements above the LOD including before the maximum viral titer measurement. In a similar fashion, the late decline rates included all points after the maximum viral titer that were above the LOD and the first measurement at the LOD.To calculate the exponential increase  = Γ = (k−1)!. Assuming that shedding starts (or stops) after a delay τ the proportion of volunteers that start shedding by time t after infection is given by the formulaTo quantify the rates at which volunteers start or stop shedding we used a novel mathematical model. In the model we assume that the population of shedders may consist of two sub-populations with fraction t after infection we used formulaTo describe how volunteers stop shedding the virus at time Sstart(t) being defined in Equation (2). To characterize the speed at which volunteers start or stop shedding we used average time defined as Ti = k/si. To fit the models to experimental data we used a likelihood approach that had been used previously to describe viral escape from T cell immunity  is given in Equation (2), and we ignored the constant terms that are irrelevant when maximizing likelihood. Alternative models in this analysis also included one population model (f = 1 and s2 = 0), no delay (τ = 0), or exponentially distributed shedding times (k = 1). Confidence intervals for the model parameters were estimated using bootstrap approach  or Mathematica 11.3. Fitting the mathematical models to data was done in Mathematica 11.3. Statistical comparisons for various parameters estimated for placebo and treated volunteers were done using non-parameteric unpaired Wilcoxon test . Nested models were compared using likelihood ratio test (LRT). To compare similarity in kinetics of start or end of virus shedding between placebo- and drug-treated volunteers we fitted the data from two cohorts with either individual parameters per cohort/dataset or with the same parameters for both cohorts/datasets. We then used likelihood ratio test to determine if parameters for virus shedding start/end were different between the two cohorts/datasets.In clinical trials testing efficacy of oseltamivir, healthy volunteers were inoculated with a defined dose of the influenza A or B viruses ;Flu A: placebo 4/16 (25%) and treated 10/64 (15%) or 17.5% overall (p = 0.27);Flu B study A: placebo 7/20 (35%) and treated 20/40 (50%) or 45% overall (p = 0.49).Flu B study B: placebo 12/39 (30%) and treated 29/78 (37%) or 35% overall  shedding only at one time point. It was unclear from the study descriptions whether such individuals should be counted as infected or if such viral blips are false positives. We investigated how changing the limit of detection (LOD) might impact the number of volunteers classified as infected for these clinical trials, and thus, may influence the estimated percent of infected volunteers.10TCID50/ml significantly impact the frequency of infection between placebo and treated volunteers. Interestingly, changes in the number of volunteers classified as infected at different LOD also impacted the average/median viral shedding curves as well as parameters inferred from such curves (results not shown) suggesting that LOD value is critical for analysis of such data. Because increasing LOD reduced the number of volunteers shedding the virus above LOD , the overall viral shedding , the virus growth rate prior to peak r, and the virus decline rate after the peak δ. Treatment impacted these parameters for the median shedding curves differently. In particular, oseltamivir treatment reduced the median duration of infection from 5 to 3 days and reduced the overall viral shedding over 10-fold (AUC change from 4.2 to 2.9) in flu A clinical trial as judged by the median shedding curves  for individual volunteers and compared these parameters between placebo- and drug-treated cohorts . Therefore, we also calculated two additional rates - the rate at which virus shedding increases early during the infection  and the late rate of virus decline . Interestingly, there was no evidence that the rate at which virus shedding increased early was changing over time either in placebo- or drug-treated volunteers . In contBoth flu A and flu B study A trials tested the impact of different drug dosing on viral control (see section 2). Additional analysis showed that interestingly the oseltamivir's dose had no measurable impact on the basic parameters for virus shedding kinetics which further justifies considering drug-treated volunteers as a single group , 14.Original papers of the analyzed clinical trials included median viral shedding curves for the influenza A trial  varied. We therefore investigated if the rate at which volunteers started or stopped shedding was dependent on the virus type and, more importantly, on the oseltamivir treatment. We developed a novel mathematical model predicting start (or end) of shedding in the cohort of patients, based on a mixture of two gamma distributions, and estimated the model parameters by fitting a series of nested models to the data. Specifically, placebo- or drug-treated volunteers were followed as a cohort and the fraction of volunteers that started virus shedding by time nfection  or stoppction  are exposed to well-defined dose and type of virus , and more frequent measurements of viral shedding are likely to provide more accurate estimates of these rates.Our work generates several recommendations that should be implemented for clinical trial data on human challenge studies with influenza viruses. We believe that original shedding data should be shared following publication of the work. The data can be anonymized and redacted if needed, but the critical numeric data must be provided in the proper digital format . For example, recent clinical trial data for baloxavir have not been made available (Hayden et al., The original contributions presented in the study are included in the article/Data from redacted documents provided publicly by Roche and published previously.KH digitized the data from original publications into spreadsheet format. KH and VG have analyzed the data and wrote the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Neorickettsia species DNA sequences in various trematode species and their hosts, only three Neorickettsia species have been cell culture isolated and whole-genome sequenced and are known to infect mammals and/or cause disease. The molecular mechanisms that enable the obligatory intracellular bacterium Neorickettsia to colonize trematodes and to horizontally transmit from trematodes to mammals, as well as the virulence factors associated with specific mammalian hosts, are unknown. Potomac horse fever (PHF) is a severe and acute systemic infectious disease of horses, with clinical signs that include diarrhea. Neorickettsia risticii is the only known bacterial species that causes PHF. Ingestion of insects harboring N. risticii-infected trematodes by horses leads to PHF. Our discovery of a new Neorickettsia species that causes PHF and whole-genome sequence analysis of this bacterium will improve laboratory diagnosis and vaccine development for PHF and will contribute to our understanding of Neorickettsia ecology, pathogenesis, and biology.Despite the detection of  Neorickettsia risticii, an endosymbiont of digenean trematodes. Here, we report the cell culture isolation of a new Neorickettsia species found in two locations in eastern Ontario, Canada, in 2016 and 2017 (in addition to 10 variable strains of N. risticii) from N. risticii PCR-negative horses with clinical signs of PHF. Gene sequences of 16S rRNA and the major surface antigen P51 of this new Neorickettsia species were distinct from those of all previously characterized N. risticii strains and Neorickettsia species, except for those from an uncharacterized Neorickettsia species culture isolate from a horse with PHF in northern Ohio in 1991. The new Neorickettsia species nonetheless had the characteristic intramolecular repeats within strain-specific antigen 3 (Ssa3), which were found in all sequenced Ssa3s of N. risticii strains. Experimental inoculation of two naive ponies with the new Neorickettsia species produced severe and subclinical PHF, respectively, and the bacteria were reisolated from both of them, fulfilling Koch’s postulates. Serological assay titers against the new Neorickettsia species were higher than those against N. risticii. Whole-genome sequence analysis of the new Neorickettsia species revealed unique features of this bacterium compared with N. risticii. We propose to classify this new bacterium as Neorickettsia finleia sp. nov. This finding will improve the laboratory diagnosis of and vaccine for PHF, environmental risk assessment of PHF, and understanding of PHF pathogenesis and Neorickettsia biology in general.Potomac horse fever (PHF), a severe and frequently fatal febrile diarrheal disease, has been known to be caused only by  Neorickettsia spp. are obligatory intracellular bacteria of digenean trematodes, a kind of fluke, which are transmitted through all developmental stages of the trematodes and vertically through generations of trematodes , N. sennetsu (formerly Ehrlichia sennetsu and Rickettsia sennetsu), and N. helminthoeca (type species), and one additional Neorickettsia species denoted as the fluke Stellantchasmus falcatus agent (SF agent) have been stably culture isolated  in North and South America, N. helminthoeca causes salmon-poisoning disease in North and South American canids, and N. sennetsu causes human Sennetsu neorickettsiosis in Japan and Southeast Asia , free-swimming cercariae, metacercariae in aquatic insects (caddisflies and mayflies), and adults that lay eggs in the intestinal lumen of insectivorous bats  for our 12 newly cultured specimens were aligned with those of several N. risticii strains from horses that had resided in Minnesota, Kentucky, Pennsylvania, and Maryland. The aligned sequences were used for phylogenetic analysis. The Ohio 081 strain (Neorickettsia sp. 081) isolated from a horse with PHF in Finley, OH, in 1991  and the United States (Oregon); and the Neorickettsia sp. from F. hepatica were also included in the analysis as they are closely related to N. risticii. Surprisingly, for Fin17 and Tom16, their P51 loop 2 amino acid sequences were identical to that of Neorickettsia sp. 081 but were distinct from those of the N. risticii strains  and Maryland (Illinois) isolates, and three strains clustered with TW2-1, an N. risticii isolate from an A. oregonense adult gravid trematode isolated from the intestines of Eptesicus fuscus (bat) in Pennsylvania Neoricket10.1128/mBio.03429-19.1FIG S1Neorickettsia finleia, 15 strains of N. risticii, 3 N. sennetsu strains, 2 SF agents, and 1 Neorickettsia strain from F. hepatica. Dashes, sequence not available or deletion; dots, identical to the majority of amino acids. Download FIG S1, PDF file, 0.9 MB.P51 external loop 2 amino acid sequence alignment (A) and percent identity and divergence (B) of 3 strains of Copyright © 2020 Teymournejad et al.2020Teymournejad et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mBio.03429-19.6TABLE S1Table S1, PDF file, 0.2 MB.Primers and sequences utilized for PCR amplification. Download Copyright © 2020 Teymournejad et al.2020Teymournejad et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Neorickettsia sp. 081, were found to be identical (N. risticii Illinois P51 and includes deletions) that were available in the GenBank database revealed that the Fin17 and Tom16 P51 amino acid sequences were almost identical . However, they were distinct from the N. risticii, N. sennetsu, and SF agent sequences  of P51 amino acid sequences among N. risticii isolates is 1.3 to 3.7  of three strains, Fin17, Tom16, and dentical . Compariequences  and B. IF agents . The N.  strains   Percent identity and divergence of two strains of Neorickettsia finleia and available long P51 amino acid sequences of other Neorickettsia species. Download FIG S2, PDF file, 0.5 MB.(A) Long P51 amino acid sequence alignment of three strains of Copyright © 2020 Teymournejad et al.2020Teymournejad et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Neorickettsia sp. 081 at only 3 nucleotide positions (Neorickettsia sp. 081 clustered near that of the Neorickettsia sp. from F. hepatica and were divergent from those of the SF agent strains (N. risticii strains that included the 16S rRNA gene of Neorickettsia sp. from Deropegus aspina cercaria (family Lecithodendriidae) collected in Corvallis, OR . The divergence of 16S rRNA gene sequences of N. risticii isolates from N. sennetsu isolates was 0.8. The divergence of 16S rRNA gene sequences of N. risticii isolates from Fin17, Tom16, and Neorickettsia sp. 081 was 0.8. Thus, N. risticii divergence from Fin17, Tom16, and Neorickettsia sp. 081 was on par with that from N. sennetsu  that were unambiguously aligned, we found that those of Fin17 and Tom16 were identical, both of which differed from ositions . Using t strains , unlike  strains . These tllis, OR  9). The. TheNeorhepatica . The divsennetsu . Togethe10.1128/mBio.03429-19.3FIG S3Neorickettsia finleia and other Neorickettsia species. (B) Percent identity and divergence of two strains of Neorickettsia finleia and other Neorickettsia species. Download FIG S3, PDF file, 0.5 MB.(A) 16S rRNA gene-based sequence alignment of three strains of Copyright © 2020 Teymournejad et al.2020Teymournejad et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the ssa3 gene from the 12 Neorickettsia strains using three different sets of primer pairs that anneal to the conserved sequences within ssa3 of N. risticii strains . When amino acid sequences were aligned according to the deduced N. risticii Illinois Ssa3 sequence between positions 72 and 250, it was found that the Fin17, Tom16, and 081 Neorickettsia species Ssa3s contain two tandem 52-aa repeats and a 40-aa terminal peptide (Neorickettsia strains are similar to those of the N. risticii strains and distinct from those of the N. sennetsu strain and the SF agents. The Ssa3 sequences from the 10 N. risticii isolates have two or three 52-aa residue repeats (N. risticii Illinois Ssa3) showed that those of Fin17, Tom16, and 081 were clustered and distinct from those of the N. risticii strains  for 8 of the N. risticii isolates but not for the new Neorickettsia sp. (Fin17 or Tom16) or the N. risticii isolates (Dai17 and Dun17). Phylogenetic analysis showed that the 8 deduced aa sequences clustered with known Ssa1 sequences from N. risticii strains isolated in the eastern and Midwestern regions of the United States  in the peripheral blood specimens from both ponies starting at day 9 postinoculation and peaked at day 12  (Neorickettsia surface proteins (Nsps), which are found in all Neorickettsia spp. sequenced so far  of four conserved housekeeping genes (Eno/GltA/GroEL/DsbB) and three surface proteins (P51/Nsp1/Ssa3). By this analysis, N. risticii is more divergent from Fin17 than from N. sennetsu  , 46, 48 nthoeca) . In addid so far . To moresennetsu , in agresennetsu  to 9. Tosia spp. . Two-wayNeorickettsia strains from the blood of horses with typical PHF clinical signs that resided at various locations in Ontario, Canada, during 2016 and 2017. As both blood and fecal samples were real-time N. risticii PCR  were a previously uncharacterized Neorickettsia sp. The results were corroborated by whole-genome sequencing of Fin17 and genomic comparison with N. risticii, N. sennetsu, and N. helminthoeca  tandemly repeated ssa genes, respectively , were included with this new species. Including this new species in a newly designed PHF vaccine could improve vaccine efficacy.If therapeutic intervention does not occur early, the course of PHF is usually 5 to 10 days, with the mortality rate being 17 to 36%. Owing to the short diagnostic window and dire emotional and economic impacts on horse owners and the equine industry, an effective PHF vaccine is much needed. Currently, the inactivated whole-cell vaccines that are commercially available provide only limited protection . This laNeorickettsia spp. are only 0.86 to 0.88 Mb, and ∼60% of their genomes encode housekeeping proteins  was culture isolated , the secondary intermediate host, and the definitive host  of the new Canadian Neorickettsia sp. and the life cycle of its encysted trematode remain to be studied. The results reported here should help find the natural reservoirs of this Neorickettsia sp.The occurrence of PHF is associated with the presence of a specific type of freshwater snail, which is the first intermediate host of trematodes that harbor ckettsia , 58. Altckettsia , includiisolated . The gen ecotype . Each tytrain Ont5 was culive host –9, 35. Sve host –. The Neoc plants , is closNeorickettsia spp. are obligatory intracellular bacteria, their survival, distribution, and population density are dependent on the host trematode population. In turn, the survival and population density of the trematode (the obligatory endoparasite) are dependent on their two intermediate and definitive hosts, of which mollusks are the first and essential intermediate hosts. Because of this double parasitic relationship, Neorickettsia spp. are expected to be very sensitive to changes in their natural environments, which include both pollution and climate effects. Consequently, the emergence of a Neorickettsia sp. population and its associated disease state as found in this study may reflect environmental changes.Because Neorickettsia sp. strain is closely related to Neorickettsia sp. 081 isolated ∼26 years ago in Findley, OH. Given the molecular evidence, we suggest that this new species should be named Neorickettsia finleia, because it was first found in Findley, OH, the birthplace of Howard Ricketts, the discoverer of Rickettsia rickettsii. However, the type strain will be Fin17, as 081 was recently lost due to accidental liquid nitrogen dry-out.This newly isolated Canadian Neorickettsia finleia .N. finleia causes an illness characterized by fever, anorexia, depression, and diarrhea. N. finleia and N. risticii are serologically cross-reactive. N. finleia grows well in the P388D1 murine macrophage cell line. The type strain, Fin17T, is available through the Centers for Disease Control and Prevention Rickettsial Isolate Reference Collection (CRIRC number NFI001T) and through the Collection de Souches de I’Unite des Rickettsies (CSUR deposit identification number Q1925).To date, all infected horses have originated from Findley, OH, and Ontario, Canada. Horse infection with All animal experiments were performed in accordance with The Ohio State University Institutional Animal Care and Use Committee guidelines and approved e-protocol number 2008A0066. The University program has full continued accreditation by the Association for Assessment and Accreditation of Laboratory Animal Care International (AAALAC), number 000028, dated 18 April 1966, and has Public Health Service assurance renewal number A3261-01, dated 7 March 2019 through 28 February 2023. The program is licensed by the USDA, under license number 31-R-014, and is in full compliance with animal welfare regulations.Neorickettsia species isolation . The following clinical signs were recorded: depression, anorexia, rectal temperature, heart rates, color of mucous membranes , dehydration, gastrointestinal sounds and colic signs, nature of diarrhea , and laminitis (hoof pain).Twelve horses clinically diagnosed with PHF between 12 July and 16 September 2016 and between 26 June and 17 August 2017 were assessed. Horses resided near Lake St. Clair, Lake Erie, Lake Ontario, and/or Lake Simcoe in Ontario, Canada . The numN. risticii, using primers ER.133f (5-GTTATTCCCTACTACCAGGCAAGTTC-3′) and ER.54r (5′-AACGGAATCAGGGCTGCTT-3′), which amplified an 85-bp fragment of the 16S rRNA gene  causing diarrhea in horses were excluded.Blood and fecal samples were tested by real-time PCR, which detects 85 bp of the 16S rRNA gene of RNA gene , at the g for 10 min to obtain the buffy coat. By lysing the remaining red blood cells in the buffy coat with an ammonium chloride solution, peripheral blood leukocytes were obtained  preparations (l-glutamine (Gibco) , for culture. Blood from each horse was centrifuged at 500 × obtained . The leuarations , which w (Gibco) . Samples (Gibco) . When insequence  . PCR amplification was performed using Taq DNA polymerase , extracted genomic DNA as the template, and primers designed for conserved regions identified by alignment of multiple Neorickettsia strains and/or N. risticii strain DNA sequences of proteins , as previously described  and centrifuged at 1,500 × g for 25 min. The interface containing mononuclear cells between Histopaque 1077 and remaining plasma was collected and resuspended in 1 ml of RPMI 1640 medium. The mononuclear cell suspension was added to a well of a 6-well plate containing a monolayer of P388D1 cells in 1 ml of RPMI 1640 medium containing 5% heat-inactivated FBS and 2 mM l-glutamine. Floating neutrophils and lymphocytes were removed after 2 days of culturing. Cultured cell samples were taken and examined every 3 to 4 days after centrifugation by using a Cytospin 3 centrifuge  and Diff-Quik staining . For positive cultures, the number of days required for infection of more than 3% of P388D1 cells with more than 5 to 10 clearly identifiable organisms or a morula (inclusion) was recorded. The culture was considered negative when Neorickettsia was not found for >29 days of culture.Citrated blood samples (∼8 ml) were centrifuged at 1,500 × N. risticii PA-1 strain- and Neorickettsia Fin17-infected P388D1 cells as the antigens, as previously described . The cells were washed three times with RPMI 1640 medium, and total RNA was extracted using an RNeasy kit . RNA concentrations and quality were determined by using a NanoDrop spectrophotometer (Thermo Fisher). Total RNA (1 μg) was reverse transcribed using a Maxima H-Minus first-strand cDNA synthesis kit and random hexamers (Thermo Fisher). The qPCR mixture (25 μl) included 2 μl cDNA , 0.25 μM each primer, and 12.5 μl SYBR green qPCR master mix (Thermo Fisher). PCR was performed in an Mx3000P instrument (Stratagene) using equine actin and equine interferon gamma primers .8 cells) of infected P388D1 cells as previously described, with slight modifications  with a genomic DNA buffer set . Whole-genome sequencing, assembly, genome component analysis, gene function annotation, and circular genome data visualization were performed by Novogene Corporation, Inc. . The coding genes, including the protein-coding open reading frames (ORFs), rRNAs, tRNAs, and small RNA (sRNA), were predicted by the NCBI Prokaryotic Annotation Pipeline (PGAP).Host-cell-free Fin17 was purified from 13 T175 flasks  and 3 divergent outer membranes proteins (P51/Nsp1/Ssa3) from the four Neorickettsia spp. with completed genome sequences, were aligned individually using MegAlign. The aligned sequences were concatenated  and converted to PHYLIP format using the EMBOSS seqret program (https://www.ebi.ac.uk/Tools/sfc/emboss_seqret/). Phylogenetic tree and evolutionary distance analyses of the concatenated proteins were performed with CLUSTAL MUSCLE in the MegAlign Pro program (DNAStar). Bootstrap values for 1,000 replicates were obtained using PHYLIP as described above.To calculate the numbers of conserved and unique proteins among N. risticii strains and the new Neorickettsia strain isolated in this study are shown in N. finleia Fin17 was deposited in GenBank (GenBank accession number NZ_CP047224).GenBank accession numbers for the P51, Ssa3, and Ssa1 sequences of the Canadian 10.1128/mBio.03429-19.7TABLE S2Neorickettsia sequences obtained from GenBank and used for phylogenetic analysis. Download Table S2, PDF file, 0.1 MB.Copyright © 2020 Teymournejad et al.2020Teymournejad et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mBio.03429-19.8TABLE S3Neorickettsia spp. Download Table S3, DOCX file, 0.02 MB.Putative outer membrane proteins of Copyright © 2020 Teymournejad et al.2020Teymournejad et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the