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.

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.

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.

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).

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.

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.

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.

Glycerophospholipid Metabolism Alterations in Patients with Type 2 Diabetes Mellitus and Tuberculosis Comorbidity.

Type-2 Diabetes (T2D) is a predisposing cause for developing tuberculosis (TB) in low- and middle-income countries. TB-T2D comorbidity worsens clinical control and prognosis of the affected individuals. The underlying metabolic alterations for this infectious-metabolic disease are still largely unknown. Possible mediators of the increased susceptibility to TB in diabetic patients are lipids levels, which are altered in individuals with T2D. To evaluate the modulation of glycerophospholipids in patients with TB-T2D, an untargeted lipidomic approach was developed by means of ultra-performance liquid chromatography (UPLC) coupled to electrospray ionization/quadrupole time-of-flight mass spectrometry (ESI-QToF). In addition, tandem mass spectrometry was performed to determine the identity of the differentially expressed metabolites. We found that TB infected individuals with or without T2D share a common glycerophospholipid profile characterized by a decrease in phosphatidylcholines. A total of 14 glycerophospholipids were differentially deregulated in TB and TB-T2D patients and could potentially be considered biomarkers. It is necessary to further validate these identified lipids as biomarkers, focusing on the anticipate diagnosis for TB development in T2D predisposed individuals.

Medicinal Plant Extracts and Their Use As Wound Closure Inducing Agents.

Skin insult and damage start a complex healing process that involves a myriad of coordinated reactions at both the cellular and molecular level occurring simultaneously. These processes can be divided into that of cell migration and tissue remodeling of the wound. In addition, it is well known that deep wounds that derive from surgical procedures need a multidisciplinary approach to have a successful wound healing process. Recently, there has been a renowned interest in the identification of active compounds derived from ornamental, edible, and wild plants being used in the cosmetic and skin product industry. Recent reports suggest that active components of several plants such as Propolis and Aloe vera could be used to induce the process of wound healing and tissue regeneration and reducing therefore the time to complete wound closure. Other plant species such as Achillea millefolium or Salvia officinalis have anti-inflammatory properties and promote cellular proliferation contributing to faster tissue regeneration. It has been described that Malva sylvestris influences the formation of fibrosis-free granulation tissue in the skin. Recent observations suggest that Casearia sylvestris induces the angiogenic process. These effects have been evaluated in cell lines, different animal models, and some in randomized clinical trials. In this review we summarize the evidence of plant extracts and their active components (when known) in the acceleration of the wound closure process and tissue repair.

Myeloid-Derived Suppressor Cells Show Different Frequencies in Diabetics and Subjects with Arterial Hypertension.

Type 2 diabetes mellitus (DM2) is strongly associated with other comorbidities such as obesity, atherosclerosis, and hypertension. Obesity is associated with sustained low-grade inflammatory response due to the production of proinflammatory cytokines. This inflammatory process promotes the differentiation of some myeloid cells, including myeloid-derived suppressor cells (MDSCs). In this study, two groups of individuals were included: DM2 patients and non-DM2 individuals with similar characteristics. Immunolabeling of CD15+ CD14- and CD33+ HLA-DR-/low was performed from whole peripheral blood, and samples were analyzed by flow cytometry, and frequencies of MDSCs and the relationship of these with clinical variables, cytokine profile (measured by cytometric bead array), and anthropometric variables were analyzed. The frequency of CD33+ HLA-DR-/low MDSCs (that produce IL-10 and TGF-ß, according to an intracellular detection) is higher in patients with DM2 (P < 0.05), and there is a positive correlation between the frequency of CD15+ CD14- and CD33+ HLA-DR-/low MDSC phenotypes. DM2 patients have an increased concentration of serum IL-5 (P < 0.05). Also, a negative correlation between the frequency of CD15+ CD14- MDSCs and LDL cholesterol was found. Our group of DM2 patients have an increased frequency of mononuclear MDSC CD33+ HLA-DR-/low that produce TGF-ß and IL-10. These cytokines have been associated with immune modulation and reduced T cell responses. DM2 and non-DM2 subjects show a similar cytokine profile, but the DM2 patients have an increased concentration of IL-5.

Analysis of miRNA expression in patients with rheumatoid arthritis during remission and relapse after a 5-year trial of tofacitinib treatment.

The physiopathology of rheumatoid arthritis (RA) is mediated by proinflammatory cytokines, some of which are regulated by the JAK/STAT pathway. Tofacitinib is a JAK inhibitor, but its role in the regulation of microRNAs (miRNAs) is unknown. There is also no information regarding the role of miRNAs in the clinical relapse/remission of RA. The present project aims to identify a signature profile of miRNA expression in a subgroup of RA patients who had to discontinue tofacitinib treatment (because of the ending of a 5-year open-label clinical trial) and to describe the expression of miRNAs during RA remission or flare-up. The relative expression of 61 miRNAs was determined in serum samples with the Firefly™ BioWorks assay. Statistical analysis was performed by means of Student's t-test and heatmap analysis was performed with Firefly™ Analysis Workbench software and in the software GraphPad® Prism v5.0. Target prediction and Gene Ontology analysis were carried out using bioinformatic tools. We found a distinctive signature of miRNA expression associated with relapse, featuring upregulated expression of hsa­miR­432­5p (p < 0.05). We also found upregulation of hsa­miR­194­5p (p < 0.05) in samples of patients with RA flare-up. Gene Ontology analysis of the target genes for hsa­miR­432­5p was performed to identify relevant pathways associated with relapse; the implications of these pathways in the physiopathology of RA are discussed. Tofacitinib treatment does not have a direct effect on the expression of measured miRNAs. The changes in hsa­miR­432­5p and hsa­miR­194­5p are associated with the regulation of proinflammatory pathways and RA flare-up.

Improvement in the Diagnosis of Tuberculosis Combining Mycobacterium Tuberculosis Immunodominant Peptides and Serum Host Biomarkers.

BACKGROUND: Pulmonary tuberculosis (PTB) is a public health problem with 10.4 million new cases reported in 2017 (1). According to the World Health Organization (WHO), accurate diagnostic tests based in serum biomarkers to detect new cases of tuberculosis are necessary. AIM OF THE STUDY: To evaluate antibodies against Mycobacterium. tuberculosis (Mtb) peptides (Ab-Mtb) and three soluble host biomarkers by ELISA serial multiple test in sera from non-infected controls (NIC, n = 31), latent tuberculosis (LTB, n = 37) and PTB (n = 28) patients in a diagnosis tuberculosis assay. MATERIALS AND METHODS: Levels of four Ab-Mtb peptides derived from Mtb and three host response molecules in serum from NIC, LTB and PTB were evaluated by ELISA as tuberculosis biomarkers. Multiple comparisons tests, determination of diagnostic values and ROC curves were performed. Serial and parallel multiple tests were performed with the biomarkers with the highest discriminatory capacity to improve diagnostic values of the test. RESULTS: We found significant differences between biomarkers levels in PTB comparing LTB and NIC to all candidate biomarkers; peptides P12033, P12037, and serum biomarkers such as sCD14 and chemokine CXCL9 showed the best sensitivity and specificity, the highest discriminatory power, and the best area under the curve (AUC) individually. In serial multiple tests, P12037 and sCD14 together have 92% of sensitivity and 91% of specificity, with positive and negative likelihood ratios greater than 10. CONCLUSIONS: Ab-Mtb peptide P12037 and sCD14 could be applied in a diagnostic test for suspected PTB to improve accuracy and time to diagnosis and could be implemented in a POCT device which can be affordable.

Type I Interferon Gene Response Is Increased in Early and Established Rheumatoid Arthritis and Correlates with Autoantibody Production.

BACKGROUND: Rheumatoid arthritis (RA) is an inflammatory debilitating disease that affects the joints in the early and productive phases of an individual's life. Several cytokines have been linked to the disease pathogenesis and are known to contribute to the inflammatory state characteristic of RA. The participation of type I interferon (IFN) in the pathogenesis of the disease has been already described as well as the identity of the genes that are regulated by this molecule, which are collectively known as the type I IFN signature. These genes have several functions associated with apoptosis, transcriptional regulation, protein degradation, Th2 cell induction, B cell proliferation, etc. This article evaluated the expression of several genes of the IFN signature in different stages of disease and their correlation with the levels of anticitrullinated protein antibodies (ACPA) anticarbamylated protein (Anti-CarP) antibodies. METHODS: Samples from individuals with early and established RA, high-risk individuals (ACPA+ and ACPA-), and healthy controls were recruited at "Unidad de Artritis y Rheumatismo" (Rheumatism and Arthritis Unit) in Guadalajara Jalisco Mexico. Determinations of ACPA were made with Eurodiagnostica ACPA plus kit. Anti-CarP determinations were made according to previously described protocols. RNA was isolated, and purity and integrity were determined according to RNA integrity number >6. Gene expression analysis was made by RT-qPCR using specific primers for mRNAs of the type I IFN signature. Relative gene expression was calculated according to Livak and Schmitgen. RESULTS: Significant differences in gene expression were identified when comparing the different groups for MXA and MXB (P < 0.05), also when comparing established RA and ACPA- in both IFIT 1 and G15. An increased expression of ISG15 was identified (P < 0.05), and a clear tendency toward increase was identified for HERC5. EPSTRI1, IFI6, and IFI35 were found to be elevated in the chronic/established RA and early RA (P < 0.05). Significant correlations were identified for the IFN signature genes with the levels of ACPA and anti-CarP (P < 0.05). CONCLUSION: Our data confirm previous observations in the role of IFN signature and the pathogenesis of RA. Also, we provide evidence of an association between several genes of the IFN signature (that regulate Th2 cells and B cell proliferation) with the levels of anti-CarP antibodies and ACPA.

Differences in Cytokine Production during Aging and Its Relationship with Antimicrobial Peptides Production.

Aging is a major health issue due to the increased susceptibility of elderly people to infectious, autoimmune, and cardiovascular diseases. Innate immunity is an important mechanism to avoid primary infections; therefore, decreasing of its activity may lead to development of infections. Antimicrobial peptides (AMPs) are effector molecules of innate immunity that can eliminate microbial invaders. The role that cytokines play in the regulation of these innate immune mechanisms needs to be explored. Serum determinations of Th1, Th2, and Th17 cytokines were performed in order to evaluate their association with AMPs human beta-defensin (HBD)-2 and LL-37 in young adults, elder adults, and elder adults with recurrent infections. Our results showed differences in interleukin (IL)-10 and IL-6 among the different groups. Inverse correlations in serum cytokine levels and HBD-2 production were identified for IL-10, IL-2, IL-4, tumor necrosis factor-α, and IL-6. Also inverse correlations were identified for IL-10, IL-4, and cathelicidin (LL-37). Such results could impact the development of immunomodulators that promote AMP production to prevent and/or contain infectious diseases in this population.

Correction: Metformin Induces Cell Cycle Arrest, Reduced Proliferation, Wound Healing Impairment In Vivo and Is Associated to Clinical Outcomes in Diabetic Foot Ulcer Patients.

[This corrects the article DOI: 10.1371/journal.pone.0150900.].

Metformin Induces Cell Cycle Arrest, Reduced Proliferation, Wound Healing Impairment In Vivo and Is Associated to Clinical Outcomes in Diabetic Foot Ulcer Patients.

BACKGROUND: Several epidemiological studies in diabetic patients have demonstrated a protective effect of metformin to the development of several types of cancer. The underlying mechanisms of such phenomenon is related to the effect of metformin on cell proliferation among which, mTOR, AMPK and other targets have been identified. However, little is known about the role that metformin treatment have on other cell types such as keratinocytes and whether exposure to metformin of these cells might have serious repercussions in wound healing delay and in the development of complications in diabetic patients with foot ulcers or in their exacerbation. MATERIAL AND METHODS: HaCaT Cells were exposed to various concentrations of metformin and cell viability was evaluated by a Resazurin assay; Proliferation was also evaluated with a colony formation assay and with CFSE dilution assay by flow cytometry. Cell cycle was also evaluated by flow cytometry by PI staining. An animal model of wound healing was used to evaluate the effect of metformin in wound closure. Also, an analysis of patients receiving metformin treatment was performed to determine the effect of metformin treatment on the outcome and wound area. Statistical analysis was performed on SPSS v. 18 and GraphPad software v.5. RESULTS: Metformin treatment significantly reduces cell proliferation; colony formation and alterations of the cell cycle are observed also in the metformin treated cells, particularly in the S phase. There is a significant increase in the area of the wound of the metformin treated animals at different time points (P<0.05). There is also a significant increase in the size and wound area of the patients with diabetic foot ulcers at the time of hospitalization. A protective effect of metformin was observed for amputation, probably associated with the anti inflammatory effects reported of metformin. CONCLUSIONS: Metformin treatment reduces cell proliferation and reduces wound healing in an animal model and affects clinical outcomes in diabetic foot ulcer patients. Chronic use of this drug should be further investigated to provide evidence of their security in association with DFU.

Vitamin D and L-Isoleucine Promote Antimicrobial Peptide hBD-2 Production in Peripheral Blood Mononuclear Cells from Elderly Individuals.

Elderly individuals are susceptible to develop infectious diseases; promoting innate immunity to prevent infections is a key issue. Human ß-defensin-2 (hBD-2) is an antimicrobial peptide with antimicrobial and immunomodulatory properties. L-isoleucine and vitamin D are important molecules that induce hBD-2. The Aim of this study was to determine the use L-isoleucine and Vitamin D to induce hBD-2 in cells from healthy elderly individuals and elderly individuals with recurrent infections. We explored three groups: young adults (n = 20) used as control group, elderly adults (n = 18) and elderly with recurrent infections (n = 11). PBMCs (peripheral blood mononuclear cells) were isolated from the different groups and then were treated with L-isoleucine or vitamin D3. hBD-2 concentration was assessed with a sandwich enzyme Immunosorbent assay by triplicate. Using the vehicle as a mock control. Our results showed that a percentage of the individuals responded to the treatments producing hBD-2 (p < 0.05). These results showed that both molecules induced hBD-2 in elderly individuals and can be potentially used as prophylactic therapy to decrease infection diseases rates in this vulnerable group.

Human neutrophil peptide-1 decreases during ageing in selected Mexican population.

Antimicrobial peptide innate immunity plays a central role in the susceptibility to infectious diseases, as has been described extensively in different settings. However, the role that these molecules play in the immunity mediated by polymorphonuclear phagocytes as part of the innate immunity of ageing individuals has not been described. In the present study, we addressed the question whether antimicrobial activity in polymorphonuclear cells from elderly individuals was altered in comparison with young adults. We compared phagocytosis index, bacterial killing efficiency, myeloperoxidase activity and cathelicidin expression. Results showed that there were no statistical differences among groups. However, human neutrophil peptide-1 (HNP-1) was decreased in the elderly individuals group. Results suggest that the decreased HNP-1 production in the polymorphonuclear phagocytes form elderly individuals might have an important participation in the increased susceptibility to infectious diseases.