Potential molecular patterns for tuberculosis susceptibility in diabetic patients with poor glycaemic control: a pilot study.

Type 2 diabetes (DM2) is an increasingly prevalent disease that challenges tuberculosis (TB) control strategies worldwide. It is significant that DM2 patients with poor glycemic control (PDM2) are prone to developing tuberculosis. Furthermore, elucidating the molecular mechanisms that govern this susceptibility is imperative to address this problem. Therefore, a pilot transcriptomic study was performed. Human blood samples from healthy controls (CTRL, HbA1c < 6.5%), tuberculosis (TB), comorbidity TB-DM2, DM2 (HbA1c 6.5-8.9%), and PDM2 (HbA1c > 10%) groups (n = 4 each) were analyzed by differential expression using microarrays. We use a network strategy to identify potential molecular patterns linking the differentially expressed genes (DEGs) specific for TB-DM2 and PDM2 (p-value < 0.05, fold change > 2). We define OSM, PRKCD, and SOCS3 as key regulatory genes (KRGs) that modulate the immune system and related pathways. RT-qPCR assays confirmed upregulation of OSM, PRKCD, and SOCS3 genes (p < 0.05) in TB-DM2 patients (n = 18) compared to CTRL, DM2, PDM2, or TB groups (n = 17, 19, 15, and 9, respectively). Furthermore, OSM, PRKCD, and SOCS3 were associated with PDM2 susceptibility pathways toward TB-DM2 and formed a putative protein-protein interaction confirmed in STRING. Our results reveal potential molecular patterns where OSM, PRKCD, and SOCS3 are KRGs underlying the compromised immune response and susceptibility of patients with PDM2 to develop tuberculosis. Therefore, this work paved the way for fundamental research of new molecular targets in TB-DM2. Addressing their cellular implications, and the impact on the diagnosis, treatment, and clinical management of TB-DM2 could help improve the strategy to end tuberculosis for this vulnerable population.

Genomic epidemiology analysis of drug-resistant Mycobacterium tuberculosis distributed in Mexico.

Genomics has significantly revolutionized pathogen surveillance, particularly in epidemiological studies, the detection of drug-resistant strains, and disease control. Despite its potential, the representation of Latin American countries in the genomic catalogues of Mycobacterium tuberculosis (Mtb), the bacteria responsible for Tuberculosis (TB), remains limited. In this study, we present a whole genome sequencing (WGS)-based analysis of 85 Mtb clinical strains from 17 Mexican states, providing insights into local adaptations and drug resistance signatures in the region. Our results reveal that the Euro-American lineage (L4) accounts for 94% of our dataset, showing 4.1.2.1 (Haarlem, n = 32), and 4.1.1.3 (X-type, n = 34) sublineages as the most prevalent. We report the presence of the 4.1.1.3 sublineage, which is endemic to Mexico, in six additional locations beyond previous reports. Phenotypic drug resistance tests showed that 34 out of 85 Mtb samples were resistant, exhibiting a variety of resistance profiles to the first-line antibiotics tested. We observed high levels of discrepancy between phenotype and genotype associated with drug resistance in our dataset, including pyrazinamide-monoresistant Mtb strains lacking canonical variants of drug resistance. Expanding the Latin American Mtb genome databases will enhance our understanding of TB epidemiology and potentially provide new avenues for controlling the disease in the region.

Fluoroquinolone Analogs, SAR Analysis, and the Antimicrobial Evaluation of 7-Benzimidazol-1-yl-fluoroquinolone in In Vitro, In Silico, and In Vivo Models.

Structure-activity relationship (SAR) studies allow the evaluation of the relationship between structural chemical changes and biological activity. Fluoroquinolones have chemical characteristics that allow their structure to be modified and new analogs with different therapeutic properties to be generated. The objective of this research is to identify and select the C-7 heterocycle fluoroquinolone analog (FQH 1-5) with antibacterial activity similar to the reference fluoroquinolone through in vitro, in silico, and in vivo evaluations. First, SAR analysis was conducted on the FQH 1-5, using an in vitro antimicrobial sensibility model in order to select the best compound. Then, an in silico model mechanism of action analysis was carried out by molecular docking. The non-bacterial cell cytotoxicity was evaluated, and finally, the antimicrobial potential was determined by an in vivo model of topical infection in mice. The results showed antimicrobial differences between the FQH 1-5 and Gram-positive and Gram-negative bacteria, identifying the 7-benzimidazol-1-yl-fluoroquinolone (FQH-2) as the most active against S. aureus. Suggesting the same mechanism of action as the other fluoroquinolones; no cytotoxic effects on non-bacterial cells were found. FQH-2 was demonstrated to decrease the amount of bacteria in infected wound tissue.

Bioinformatic prediction of the molecular links between Alzheimer's disease and diabetes mellitus.

Background: Alzheimer's disease (AD) and type 2 diabetes mellitus (DM2) are chronic degenerative diseases with complex molecular processes that are potentially interconnected. The aim of this work was to predict the potential molecular links between AD and DM2 from different sources of biological information. Materials and Methods: In this work, data mining of nine databases (DisGeNET, Ensembl, OMIM, Protein Data Bank, The Human Protein Atlas, UniProt, Gene Expression Omnibus, Human Cell Atlas, and PubMed) was performed to identify gene and protein information that was shared in AD and DM2. Next, the information was mapped to human protein-protein interaction (PPI) networks based on experimental data using the STRING web platform. Then, gene ontology biological process (GOBP) and pathway analyses with EnrichR showed its specific and shared biological process and pathway deregulations. Finally, potential biomarkers and drug targets were predicted with the Metascape platform. Results: A total of 1,551 genes shared in AD and DM2 were identified. The highest average degree of nodes within the PPI was for DM2 (average = 2.97), followed by AD (average degree = 2.35). GOBP for AD was related to specific transcriptional and translation genetic terms occurring in neurons cells. The GOBP and pathway information for the association AD-DM2 were linked mainly to bioenergetics and cytokine signaling. Within the AD-DM2 association, 10 hub proteins were identified, seven of which were predicted to be present in plasma and exhibit pharmacological interaction with monoclonal antibodies in use, anticancer drugs, and flavonoid derivatives. Conclusion: Our data mining and analysis strategy showed that there are a plenty of biological information based on experiments that links AD and DM2, which could provide a rational guide to design further diagnosis and treatment for AD and DM2.

Transcriptome Analysis Identifies Oxidative Stress Injury Biomarkers for Diabetic Nephropathy.

BACKGROUND: The early diagnosis of diabetic nephropathy (DN) is essential for improving the prognosis and effectively manage patients affected with this disease. The standard biomarkers, including albuminuria and glomerular filtration rate, are not very precise. New molecular biomarkers are needed to more accurately identify DN and better predict disease progression. Characteristic DN biomarkers can be identified using transcriptomic analysis. AIM OF THE STUDY: To evaluate the transcriptomic profile of controls (CTRLs, n = 15), patients with prediabetes (PREDM, n = 15), patients with type-2 diabetes mellitus (DM2, n = 15), and patients with DN (n = 15) by microarray analysis to find new biomarkers. RT-PCR was then used to confirm gene biomarkers specific for DN. MATERIALS AND METHODS: Blood samples were used to isolate RNA for microarray expression analysis. 26,803 unique gene sequences and 30,606 LncRNA sequences were evaluated-Selected gene biomarkers for DN were validated using qPCR assays. Sensitivity, specificity, and area under the curve (AUC) were calculated as measures of diagnostic accuracy. RESULTS: The DN transcriptome was composed of 300 induced genes, compared to CTRLs, PREDM, and DM-2 groups. RT-qPCR assays validated that METLL22, PFKL, CCNB1 and CASP2 genes were induced in the DN group compared to CTRLs, PREDM, and DM-2 groups. The ROC analysis for these four genes showed 0.9719, 0.8853, 0.8533 and 0.7748 AUC values, respectively. CONCLUSION: Among induced genes in the DN group, we found that CASP2, PFKL and CCNB1 may potentially be used as biomarkers to diagnose DN. Of these, METLL22 had the highest AUC score, at 0.9719.

COVID-19 Outcome Prediction by Integrating Clinical and Metabolic Data using Machine Learning Algorithms.

Background: The coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus and is responsible for nearly 6 million deaths worldwide in the past 2 years. Machine learning (ML) models could help physicians in identifying high-risk individuals. Objectives: To study the use of ML models for COVID-19 prediction outcomes using clinical data and a combination of clinical and metabolic data, measured in a metabolomics facility from a public university. Methods: A total of 154 patients were included in the study. "Basic profile" was considered with clinical and demographic variables (33 variables), whereas in the "extended profile," metabolomic and immunological variables were also considered (156 characteristics). A selection of features was carried out for each of the profiles with a genetic algorithm (GA) and random forest models were trained and tested to predict each of the stages of COVID-19. Results: The model based on extended profile was more useful in early stages of the disease. Models based on clinical data were preferred for predicting severe and critical illness and death. ML detected trimethylamine N-oxide, lipid mediators, and neutrophil/lymphocyte ratio as important variables. Conclusions: ML and GAs provided adequate models to predict COVID-19 outcomes in patients with different severity grades.

COVID-19 Outcome Prediction by Integrating Clinical and Metabolic Data using Machine Learning Algorithms

ABSTRACT Background: The coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus and is responsible for nearly 6 million deaths worldwide in the past 2 years. Machine learning (ML) models could help physicians in identifying high-risk individuals. Objectives: To study the use of ML models for COVID-19 prediction outcomes using clinical data and a combination of clinical and metabolic data, measured in a metabolomics facility from a public university. Methods: A total of 154 patients were included in the study. "Basic profile" was considered with clinical and demographic variables (33 variables), whereas in the "extended profile," metabolomic and immunological variables were also considered (156 characteristics). A selection of features was carried out for each of the profiles with a genetic algorithm (GA) and random forest models were trained and tested to predict each of the stages of COVID-19. Results: The model based on extended profile was more useful in early stages of the disease. Models based on clinical data were preferred for predicting severe and critical illness and death. ML detected trimethylamine N-oxide, lipid mediators, and neutrophil/lymphocyte ratio as important variables. Conclusion: ML and GAs provided adequate models to predict COVID-19 outcomes in patients with different severity grades.

A pseudovirus-based platform to measure neutralizing antibodies in Mexico using SARS-CoV-2 as proof-of-concept.

The gold-standard method to evaluate a functional antiviral immune response is to titer neutralizing antibodies (NAbs) against a viral pathogen. This is historically performed using an in vitro assay of virus-mediated infection, which requires BSL-3 facilities. As these are insufficient in Latin American countries, including Mexico, scant information is obtained locally about viral pathogens NAb, using a functional assay. An alternative solution to using a BSL-3 assay with live virus is to use a BSL-2-safe assay with a non-replicative pseudovirus. Pseudoviral particles can be engineered to display a selected pathogen's entry protein on their surface, and to deliver a reporter gene into target cells upon transduction. Here we comprehensively describe the first development of a BSL-2 safe NAbs-measuring functional assay in Mexico, based on the production of pseudotyped lentiviral particles. As proof-of-concept, the assay is based on Nanoluc luciferase-mediated luminescence measurements from target cells transduced with SARS-CoV-2 Spike-pseudotyped lentiviral particles. We applied the optimized assay in a BSL-2 facility to measure NAbs in 65 serum samples, which evidenced the assay with 100% sensitivity, 86.6% specificity and 96% accuracy. Overall, this is the first report of a BSL-2 safe pseudovirus-based functional assay developed in Mexico to measure NAbs, and a cornerstone methodology necessary to measure NAbs with a functional assay in limited resources settings.

GPR15 expressed in T lymphocytes from RA patients is involved in leukocyte chemotaxis to the synovium.

The rheumatoid arthritis (RA) inflammatory process occurs in the joints where immune cells are attracted into the synovium to promote remodeling and tissue damage. GPR15 is a G protein-coupled receptor (GPCR) located on chromosome 3 and has similarity in its sequence with chemokine receptors. Recent evidence indicates that GPR15 may be associated with modulation of the chronic inflammatory response. We evaluated the expression of GPR15 and GPR15L in blood and synovial tissue samples from RA patients, as well as to perform a functional migration assay in response to GPR15L. The expression of GPR15 and c10orf99/gpr15l mRNA was analyzed by RT-qPCR. Samples of synovial fluid and peripheral blood were analyzed for CD45+CD3+CD4+GPR15+ and CD45+CD3+CD8+GPR15+ T cell frequency comparing RA patients versus control subjects by flow cytometry. Migration assays were performed using PBMCs isolated from these individuals in response to the synthetic GPR15 ligand. Statistical analysis included Kruskal-Wallis test, T-test, or Mann-Whitney U test, according to data distribution. A higher expression in the mRNA for GPR15 was identified in early RA subjects. The frequencies of CD4+/CD8+ GPR15+ T lymphocytes are higher in RA patients comparing with healthy subjects. Also, the frequency CD4+/CD8+ GPR15+ T lymphocytes are higher in synovial fluid of established RA patients comparing with OA patients. GPR15 and GPR15L are present in the synovial tissue of RA patients and GPR15L promotes migration of PBMCs from RA patients and healthy subjects. Our results suggest that GPR15/GPR15L have a pathogenic role in RA and their antagonizing could be a therapeutic approach in RA.

Non-Coding RNAs in Rheumatoid Arthritis: Implications for Biomarker Discovery.

Recent advances in gene expression analysis techniques and increased access to technologies such as microarrays, qPCR arrays, and next-generation sequencing, in the last decade, have led to increased awareness of the complexity of the inflammatory responses that lead to pathology. This finding is also the case for rheumatic diseases, importantly and specifically, rheumatoid arthritis (RA). The coincidence in major genetic and epigenetic regulatory events leading to RA's inflammatory state is now well-recognized. Research groups have characterized the gene expression profile of early RA patients and identified a group of miRNAs that is particularly abundant in the early stages of the disease and miRNAs associated with treatment responses. In this perspective, we summarize the current state of RNA-based biomarker discovery and the context of technology adoption/implementation due to the COVID-19 pandemic. These advances have great potential for clinical application and could provide preclinical disease detection, follow-up, treatment targets, and biomarkers for treatment response monitoring.

Overexpression of TLR7 and TLR9 Occurs Before Onset Symptoms In First-Degree Relatives of Rheumatoid Arthritis Patients.

BACKGROUND: Autoantibodies have a central role in the physiopathology of Rheumatoid Arthritis (RA). However, the responsible factors that trigger and perpetuate the autoantibodies production are unknown. Toll-like receptors (TLRs) have been considered as promotors of autoantibodies production to break down the immunotolerance in RA. AIM OF THE STUDY: Evaluate the expression levels of TLR7 and TLR9 as well as their correlation with autoantibodies in first-degree relatives (FDR) of RA patients (seropositive and seronegative to ACPA), respect to early RA (eRA) and chronic RA (cRA) patients. METHODS: We selected 32 RA patients (16 as eRA and 16 as cRA) and 32 FDR of RA patients (16 seropositive and 16 seronegative to ACPA). Expression levels of TLR7 and TLR9 in whole blood samples from each group were measured by real-time PCR using total RNA extracted from each subject. Also, correlation analysis between TLRs expression and autoantibodies was performed. RESULTS: The expression of TLR7 and TLR9 was diminished in RA patients (p <0.01) but elevated in ACPA- FDR (p <0.0001) and ACPA+ FDR (p <0.05) with a positive correlation between them (r = 0.749, p <0.000). Moreover, the expression levels of TLR7 correlate positively with ACPA levels in both seropositive ACPA+ FDR subjects (r = 0.582, p = 0.018) and eRA patients (r = 0.593, p = 0.020). CONCLUSIONS: Our results showed overexpression of TLR7 and TLR9 may occur in preclinical RA subjects. TLR7 overexpression correlated with ACPA levels' production, suggesting TLR7 may play a role in ACPA development.

Kynurenine and Hemoglobin as Sex-Specific Variables in COVID-19 Patients: A Machine Learning and Genetic Algorithms Approach.

Differences in clinical manifestations, immune response, metabolic alterations, and outcomes (including disease severity and mortality) between men and women with COVID-19 have been reported since the pandemic outbreak, making it necessary to implement sex-specific biomarkers for disease diagnosis and treatment. This study aimed to identify sex-associated differences in COVID-19 patients by means of a genetic algorithm (GALGO) and machine learning, employing support vector machine (SVM) and logistic regression (LR) for the data analysis. Both algorithms identified kynurenine and hemoglobin as the most important variables to distinguish between men and women with COVID-19. LR and SVM identified C10:1, cough, and lysoPC a 14:0 to discriminate between men with COVID-19 from men without, with LR being the best model. In the case of women with COVID-19 vs. women without, SVM had a higher performance, and both models identified a higher number of variables, including 10:2, lysoPC a C26:0, lysoPC a C28:0, alpha-ketoglutaric acid, lactic acid, cough, fever, anosmia, and dysgeusia. Our results demonstrate that differences in sexes have implications in the diagnosis and outcome of the disease. Further, genetic and machine learning algorithms are useful tools to predict sex-associated differences in COVID-19.

Immunometabolic signatures predict risk of progression to sepsis in COVID-19.

Viral sepsis has been proposed as an accurate term to describe all multisystemic dysregulations and clinical findings in severe and critically ill COVID-19 patients. The adoption of this term may help the implementation of more accurate strategies of early diagnosis, prognosis, and in-hospital treatment. We accurately quantified 110 metabolites using targeted metabolomics, and 13 cytokines/chemokines in plasma samples of 121 COVID-19 patients with different levels of severity, and 37 non-COVID-19 individuals. Analyses revealed an integrated host-dependent dysregulation of inflammatory cytokines, neutrophil activation chemokines, glycolysis, mitochondrial metabolism, amino acid metabolism, polyamine synthesis, and lipid metabolism typical of sepsis processes distinctive of a mild disease. Dysregulated metabolites and cytokines/chemokines showed differential correlation patterns in mild and critically ill patients, indicating a crosstalk between metabolism and hyperinflammation. Using multivariate analysis, powerful models for diagnosis and prognosis of COVID-19 induced sepsis were generated, as well as for mortality prediction among septic patients. A metabolite panel made of kynurenine/tryptophan ratio, IL-6, LysoPC a C18:2, and phenylalanine discriminated non-COVID-19 from sepsis patients with an area under the curve (AUC (95%CI)) of 0.991 (0.986-0.995), with sensitivity of 0.978 (0.963-0.992) and specificity of 0.920 (0.890-0.949). The panel that included C10:2, IL-6, NLR, and C5 discriminated mild patients from sepsis patients with an AUC (95%CI) of 0.965 (0.952-0.977), with sensitivity of 0.993(0.984-1.000) and specificity of 0.851 (0.815-0.887). The panel with citric acid, LysoPC a C28:1, neutrophil-lymphocyte ratio (NLR) and kynurenine/tryptophan ratio discriminated severe patients from sepsis patients with an AUC (95%CI) of 0.829 (0.800-0.858), with sensitivity of 0.738 (0.695-0.781) and specificity of 0.781 (0.735-0.827). Septic patients who survived were different from those that did not survive with a model consisting of hippuric acid, along with the presence of Type II diabetes, with an AUC (95%CI) of 0.831 (0.788-0.874), with sensitivity of 0.765 (0.697-0.832) and specificity of 0.817 (0.770-0.865).

Impact of type 2 diabetes on the capacity of human macrophages infected with Mycobacterium tuberculosis to modulate monocyte differentiation through a bystander effect.

Type 2 diabetes mellitus (T2D) is a risk factor for the development of tuberculosis (TB) through mechanisms poorly understood. Monocytes and macrophages are key effector cells to control TB, but they are also subverted by Mycobacterium tuberculosis (Mtb). Specifically, Mtb can induce a bystander effect that skews monocyte differentiation towards macrophages with a permissive phenotype to infection. Here, we evaluated whether T2D impacts this TB aspect. Our approach was to differentiate monocytes from healthy control (HC) subjects and T2D patients into macrophages (MDM), and then assess their response to Mtb infection, including their secretome content and bystander effect capacity. Through flow cytometric analyses, we found a lower level of activation markers in MDM from T2D patients than from HC in response to mock (HLA-DR, CD86 and CD163) or Mtb challenge (CD14 and CD80). In spite of high TGF-ß1 levels in mock-infected MDM from T2D patients, cytometric bead arrays indicated that there were no major differences in the secretome cytokine content in these cells relative to HC-MDM, even in response to Mtb. Mimicking a bystander effect, the secretome of Mtb-infected HC-MDM drove HC monocytes towards MDM with a permissive phenotype for Mtb intracellular growth. However, the secretome from Mtb-infected T2D-MDM did not exacerbate the Mtb load compared to secretome from Mtb-infected HC-MDM, possibly due to the high IL-1ß production relative to Mtb-infected HC-MDM. Collectively, despite T2D affecting the basal MDM activation, our approach revealed that it has no major consequence on their response to Mtb or capacity to generate a bystander effect influencing monocyte differentiation.

Clinical Factors Associated with COVID-19 Severity in Mexican Patients: Cross-Sectional Analysis from a Multicentric Hospital Study.

(1) Background: Latin America has been harshly hit by SARS-CoV-2, but reporting from this region is still incomplete. This study aimed at identifying and comparing clinical characteristics of patients with COVID-19 at different stages of disease severity. (2) Methods: Cross-sectional multicentric study. Individuals with nasopharyngeal PCR were categorized into four groups: (1) negative, (2) positive, not hospitalized, (3) positive, hospitalized with/without supplementary oxygen, and (4) positive, intubated. Clinical and laboratory data were compared, using group 1 as the reference. Multivariate multinomial logistic regression was used to compare adjusted odds ratios. (3) Results: Nine variables remained in the model, explaining 76% of the variability. Men had increased odds, from 1.90 (95%CI 0.87-4.15) in the comparison of 2 vs. 1, to 3.66 (1.12-11.9) in 4 vs. 1. Diabetes and obesity were strong predictors. For diabetes, the odds for groups 2, 3, and 4 were 1.56 (0.29-8.16), 12.8 (2.50-65.8), and 16.1 (2.87-90.2); for obesity, these were 0.79 (0.31-2.05), 3.38 (1.04-10.9), and 4.10 (1.16-14.4), respectively. Fever, myalgia/arthralgia, cough, dyspnea, and neutrophilia were associated with the more severe COVID-19 group. Anosmia/dysgeusia were more likely to occur in group 2 (25.5; 2.51-259). (4) Conclusion: The results point to relevant differences in clinical and laboratory features of COVID-19 by level of severity that can be used in medical practice.

UVB Inhibits Proliferation, Cell Cycle and Induces Apoptosis via p53, E2F1 and Microtubules System in Cervical Cancer Cell Lines.

Ultraviolet (UV) exposure has been linked to skin damage and carcinogenesis, but recently UVB has been proposed as a therapeutic approach for cancer. Herein, we investigated the cellular and molecular effects of UVB in immortal and tumorigenic HPV positive and negative cells. Cells were irradiated with 220.5 to 1102.5 J/m2 of UVB and cell proliferation was evaluated by crystal violet, while cell cycle arrest and apoptosis analysis were performed through flow cytometry. UVB effect on cells was recorded at 661.5 J/m2 and it was exacerbated at 1102.5 J/m2. All cell lines were affected by proliferation inhibition, cell cycle ablation and apoptosis induction, with different degrees depending on tumorigenesis level or HPV type. Analysis of the well-known UV-responsive p53, E2F1 and microtubules system proteins was performed in SiHa cells in response to UVB through Western-blotting assays. E2F1 and the Microtubule-associated protein 2 (MAP2) expression decrease correlated with cellular processes alteration while p53 and Microtubule-associated Protein 1S (MAP1S) expression switch was observed since 882 J/m2, suggesting they were required under more severe cellular damage. However, expression transition of α-Tubulin3C and ß-Tubulin was abruptly noticed until 1102.5 J/m2 and particularly, γ-Tubulin protein expression remained without alteration. This study provides insights into the effect of UVB in cervical cancer cell lines.

Calpain Participates in Cortical Cytoskeleton Modification and DNA Release during Neutrophil Extracellular Trap Formation.

INTRODUCTION: The formation of neutrophil extracellular traps (NETs) is a process in which several kinds of enzymes participate generating posttranslational modifications of proteins. NETs have been associated with infectious, autoimmune, and inflammatory diseases. Inhibition of several proteases reduces the formation of NETs. In the present work, we analyzed the role of several broad-acting and specific inhibitors of proteases in the formation of NETs. METHODS: Neutrophils were isolated from peripheral blood of healthy individuals by density gradient. The neutrophils were quantified and seeded into cell culture plates. Phorbol myristate acetate and A23187 were used as NETs inducers, and several specific inhibitors of proteases were used. The cells were stained for cytoskeleton or DNA. The cell-free supernatants were used to assess DNA release. Statistical analysis was carried out by a Kruskal-Wallis or ANOVA test. RESULTS: We observed marked changes in actin organization after the induction of NETs, suggesting that the cytoskeleton is being actively regulated. When we used protease inhibitors, the release of DNA was reduced, suggesting the participation of actin remodeling in the process. Further characterization of the specific proteases revealed that calpain modulates the reorganization of actin cytoskeleton and DNA release. Preservation of part of the actin cytoskeleton suggests that DNA release is not only a mechanic process associated to the chromatin decondensation; rather the process is highly regulated by active proteases that promote cytoskeleton reorganization and chromatin decondensation that culminates in DNA release. CONCLUSION: Calpain mediates the DNA release in the NET formation process by the modification of cortical actin cytoskeleton in a calcium-dependent manner.

The analysis on the human protein domain targets and host-like interacting motifs for the MERS-CoV and SARS-CoV/CoV-2 infers the molecular mimicry of coronavirus.

The MERS-CoV, SARS-CoV, and SARS-CoV-2 are highly pathogenic viruses that can cause severe pneumonic diseases in humans. Unfortunately, there is a non-available effective treatment to combat these viruses. Domain-motif interactions (DMIs) are an essential means by which viruses mimic and hijack the biological processes of host cells. To disentangle how viruses achieve this process can help to develop new rational therapies. Data mining was performed to obtain DMIs stored as regular expressions (regexp) in 3DID and ELM databases. The mined regexp information was mapped on the coronaviruses' proteomes. Most motifs on viral protein that could interact with human proteins are shared across the coronavirus species, indicating that molecular mimicry is a common strategy for coronavirus infection. Enrichment ontology analysis for protein domains showed a shared biological process and molecular function terms related to carbon source utilization and potassium channel regulation. Some of the mapped motifs were nested on B, and T cell epitopes, suggesting that it could be as an alternative way for reverse vaccinology. The information obtained in this study could be used for further theoretic and experimental explorations on coronavirus infection mechanism and development of medicines for treatment.

Methotrexate reduces keratinocyte proliferation, migration and induces apoptosis in HaCaT keratinocytes in vitro and reduces wound closure in Skh1 mice in vivo.

The aim of the present work was to evaluate MTX treatment (0.1, 1 and 10 µg mL-1) in vitro in order to characterize its effects on cell proliferation alterations in cell cycle of HaCaT keratinocytes and wound healing in a Skh1 mice treated with MTX (low doses 30 mg kg-1, high doses 200 mg kg-1 and repeated doses at 1.5 mg kg-1). We analyzed the cytotoxic effect of methotrexate by a resazurin assay. The effects in the proliferation, cell cycle and apoptosis of HaCaT cells were analyzed by flow cytometry. The effects of MTX on wound healing in vivo were also analyzed. A trend toward reduction in the resazurin assay was found (p > 0.05). Reduced proliferation was also identified in a clonogenic assay and a CFSE assay (p < 0.05) due to the MTX treatment. A reduction in the G2/M and S phases was observed accompanied by apoptosis induction with increased sub G0 phase and annexin V FITC staining. Effect of MTX was evidenced in vivo on the wound closure process after day 10 (p < 0.05) with alterations in tissue architecture and remodeling. There is a marked effect of MTX on wound healing in vivo in Skh1 mice with implications for long-term therapy and surgical interventions.

Innate Immunity Alterations in Type 2 Diabetes Mellitus: Understanding Infection Susceptibility.

Diabetes is a chronic disease characterized by marked alterations in the metabolism of glucose and by high concentrations of glucose in the blood due to a decreased insulin production or resistance to the action of this hormone in peripheral tissues. The International Diabetes Federation estimates a global incidence of diabetes of about 10% in the adult population (20 - 79 years old), some 430 million cases reported worldwide in 2018. It is well documented that people with diabetes have a higher susceptibility to infectious diseases and therefore show higher morbidity and mortality compared to the non-diabetic population. Given that the innate immune response plays a fundamental role in protecting against invading pathogens through a myriad of humoral and cellular mechanisms, the present work makes a comprehensive review of the innate immune alterations in patients with type 2 diabetes mellitus (T2D) as well as a brief description of the molecular events leading or associated to such conditions. We show that in these patients a compromised innate immune response increases susceptibility to infections.