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Projected to impact 310 million children by the next decade, childhood obesity is linked to serious health issues like metabolic disturbance and cardiovascular diseases. This study introduces a novel approach for the integrated assessment of inflammatory, glycemic and lipid disorders in obese children in resources-limited settings and also identifies key factors contributing to these changes. Conducting a cross-sectional analysis of 231 children aged 5-12 years from public schools in Brazil's semi-arid region, the research involved collecting medical history, anthropometric measurements, and blood samples to analyze glycemic and lipid profiles, along with C-reactive protein levels. We used an adapted the Molecular Degree of Perturbation model to analyze deviations in metabolic markers from a healthy control group. Statistical analyses included Mann-Whitney and Fisher exact tests, backward logistic regression, and hierarchical cluster analysis. The study identified a direct and independent association between elevated Metabolic Disturbance Degree and both overweight and obesity in children, with significant differences in CRP, Triglycerides, and HDL levels noted between obese and healthy-weight groups. The findings highlight the critical need for early detection and comprehensive understanding of obesity-related changes to mitigate the severe health risks associated with childhood obesity.

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BACKGROUND: Identifying patients at increased risk of loss to follow-up (LTFU) is key to developing strategies to optimize the clinical management of tuberculosis (TB). The use of national registry data in prediction models may be a useful tool to inform healthcare workers about risk of LTFU. Here we developed a score to predict the risk of LTFU during anti-TB treatment (ATT) in a nationwide cohort of cases using clinical data reported to the Brazilian Notifiable Disease Information System (SINAN). METHODS: We performed a retrospective study of all TB cases reported to SINAN between 2015 and 2022; excluding children (< 18 years-old), vulnerable groups or drug-resistant TB. For the score, data before treatment initiation were used. We trained and internally validated three different prediction scoring systems, based on Logistic Regression, Random Forest, and Light Gradient Boosting. Before applying our models we splitted our data into training (~ 80% data) and test (~ 20%) sets, and then compared the model metrics using the test data set. RESULTS: Of the 243,726 cases included, 41,373 experienced LTFU whereas 202,353 were successfully treated. The groups were different with regards to several clinical and sociodemographic characteristics. The directly observed treatment (DOT) was unbalanced between the groups with lower prevalence in those who were LTFU. Three models were developed to predict LTFU using 8 features (prior TB, drug use, age, sex, HIV infection and schooling level) with different score composition approaches. Those prediction scoring systems exhibited an area under the curve (AUC) ranging between 0.71 and 0.72. The Light Gradient Boosting technique resulted in the best prediction performance, weighting specificity and sensitivity. A user-friendly web calculator app was developed ( https://tbprediction.herokuapp.com/ ) to facilitate implementation. CONCLUSIONS: Our nationwide risk score predicts the risk of LTFU during ATT in Brazilian adults prior to treatment commencement utilizing schooling level, sex, age, prior TB status, and substance use (drug, alcohol, and/or tobacco). This is a potential tool to assist in decision-making strategies to guide resource allocation, DOT indications, and improve TB treatment adherence.

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Zika virus (ZIKV) outbreak caused one of the most significant medical emergencies in the Americas due to associated microcephaly in newborns. To evaluate the impact of ZIKV infection on neuronal cells over time, we retrieved gene expression data from several ZIKV-infected samples obtained at different time point post-infection (pi). Differential gene expression analysis was applied at each time point, with more differentially expressed genes (DEG) identified at 72h pi. There were 5 DEGs (PLA2G2F, TMEM71, PKD1L2, UBD, and TNFAIP3 genes) across all timepoints, which clearly distinguished between infected and healthy samples. The highest expression levels of all five genes were identified at 72h pi. Taken together, our results indicate that ZIKV infection greatly impacts human neural cells at early times of infection, with peak perturbation observed at 72h pi. Our analysis revealed that all five DEGs, in samples of ZIKV-infected human neural stem cells, remained highly upregulated across the timepoints evaluated. Moreover, despite the pronounced inflammatory host response observed throughout infection, the impact of ZIKV is variable over time. Finally, the five DEGs identified herein play prominent roles in infection, and could serve to guide future investigations into virus-host interaction, as well as constitute targets for therapeutic drug development.

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Tuberculosis-diabetes mellitus (TB-DM) is linked to a distinct inflammatory profile, which can be assessed using multi-omics analyses. Here, a machine learning algorithm was applied to multi-platform data, including cytokines and gene expression in peripheral blood and eicosanoids in urine, in a Brazilian multi-center TB cohort. There were four clinical groups: TB-DM(n = 24), TB only(n = 28), DM(HbA1c ≥ 6.5%) only(n = 11), and a control group of close TB contacts who did not have TB or DM(n = 13). After cross-validation, baseline expression or abundance of MMP-28, LTE-4, 11-dTxB2, PGDM, FBXO6, SECTM1, and LINCO2009 differentiated the four patient groups. A distinct multi-omic-derived, dimensionally reduced, signature was associated with TB, regardless of glycemic status. SECTM1 and FBXO6 mRNA levels were positively correlated with sputum acid-fast bacilli grade in TB-DM. Values of the biomarkers decreased during the course of anti-TB therapy. Our study identified several markers associated with the pathophysiology of TB-DM that could be evaluated in future mechanistic investigations.

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IMPORTANCE: Some tick species are competent to transmit more than one pathogen while other species are, until now, known to be competent to transmit only one single or any pathogen. Such a difference in vector competence for one or more pathogens might be related to the microbiome, and understanding what differentiates these two groups of ticks could help us control several diseases aiming at the bacteria groups that contribute to such a broad vector competence. Using 16S rRNA from tick species that could be classified into these groups, genera such as Rickettsia and Staphylococcus seemed to be associated with such a broad vector competence. Our results highlight differences in tick species when they are divided based on the number of pathogens they are competent to transmit. These findings are the first step into understanding the relationship between one single tick species and the pathogens it transmits.

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Diabetes mellitus (DM) increases tuberculosis (TB) severity. We compared blood gene expression in adults with pulmonary TB, with or without diabetes mellitus (DM) from sites in Brazil and India. RNA sequencing (RNAseq) performed at baseline and during TB treatment. Publicly available baseline RNAseq data from South Africa and Romania reported by the TANDEM Consortium were also analyzed. Across the sites, differentially expressed genes varied for each condition (DM, TB, and TBDM) and no pattern classified any one group across all sites. A concise signature of TB disease was identified but this was expressed equally in TB and TBDM. Pathway enrichment analysis failed to distinguish TB from TBDM, although there was a trend for greater neutrophil and innate immune pathway activation in TBDM participants. Pathways associated with insulin resistance, metabolic dysfunction, diabetic complications, and chromosomal instability were positively correlated with glycohemoglobin. The immune response to pulmonary TB as reflected by whole blood gene expression is substantially similar with or without comorbid DM. Gene expression pathways associated with the microvascular and macrovascular complications of DM are upregulated during TB, supporting a syndemic interaction between these coprevalent diseases.

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Diabetes (DM) has a significant impact on public health. We performed an in silico study of paired datasets of messenger RNA (mRNA) micro-RNA (miRNA) transcripts to delineate potential biosignatures that could distinguish prediabetes (pre-DM), type-1DM (T1DM) and type-2DM (T2DM). Two publicly available datasets containing expression values of mRNA and miRNA obtained from individuals diagnosed with pre-DM, T1DM or T2DM, and normoglycemic controls (NC), were analyzed using systems biology approaches to define combined signatures to distinguish different clinical groups. The mRNA profile of both pre-DM and T2DM was hallmarked by several differentially expressed genes (DEGs) compared to NC. Nevertheless, T1DM was characterized by an overall low number of DEGs. The miRNA signature profiles were composed of a substantially lower number of differentially expressed targets. Gene enrichment analysis revealed several inflammatory pathways in T2DM and fewer in pre-DM, but with shared findings such as Tuberculosis. The integration of mRNA and miRNA datasets improved the identification and discriminated the group composed by pre-DM and T2DM patients from that constituted by normoglycemic and T1DM individuals. The integrated transcriptomic analysis of mRNA and miRNA expression revealed a unique biosignature able to characterize different types of DM.

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There is currently no system to track the emergence of Zika virus (ZIKV) subtypes. We developed a surveillance system able to retrieve sequence submissions and further classify distinct ZIKV genotypes in the world. This approach was able to detect a new occurrence of ZIKV from an African lineage in Brazil in 2019.

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BACKGROUND: Cigarette smoking is associated with an increased risk of developing respiratory diseases and various types of cancer. Early identification of such unfavorable outcomes in patients who smoke is critical for optimizing personalized medical care. METHODS: Here, we perform a comprehensive analysis using Systems Biology tools of publicly available data from a total of 6 transcriptomic studies, which examined different specimens of lung tissue and/or cells of smokers and nonsmokers to identify potential markers associated with lung cancer. RESULTS: Expression level of 22 genes was capable of classifying smokers from non-smokers. A machine learning algorithm revealed that AKR1B10 was the most informative gene among the 22 differentially expressed genes (DEGs) accounting for the classification of the clinical groups. AKR1B10 expression was higher in smokers compared to non-smokers in datasets examining small and large airway epithelia, but not in the data from a study of sorted alveolar macrophages. Moreover, AKR1B10 expression was relatively higher in lung cancer specimens compared to matched healthy tissue obtained from nonsmoking individuals. Although the overall accuracy of AKR1B10 expression level in distinction between cancer and healthy lung tissue was 76%, with a specificity of 98%, our results indicated that such marker exhibited low sensitivity, hampering its use for cancer screening such specific setting. CONCLUSION: The systematic analysis of transcriptomic studies performed here revealed a potential critical link between AKR1B10 expression, smoking and occurrence of lung cancer.

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Pathogenic bacteria, such as Streptococcus pneumoniae, Staphylococcus aureus, Haemophilus influenzae and Moraxella catarrhalis, are important vaccine targets. The 10-valent pneumococcal conjugate vaccine (PCV10) acts on 10 differents S. pneumoniae serovars. However, this vaccine could also act on other bacteria genera, leading to dysbiosis. Moreover, the vaccination has also been associated with imbalances in the ratio between commensal and potentially pathogenic bacteria. Despite the wealth of studies assessing the influence of the microbiome on vaccine effects, how vaccination can influence the microbiome remains poorly understood. Herein, we assessed the effects of PCV10 on infant nasopharyngeal microbiome composition. Nasopharyngeal aspirates were collected from children with acute respiratory infection (ARI) aged 6-23 months. Two groups were composed of 48 vaccinated and 36 unvaccinated subjects. 16S ribosomal RNA sequencing was performed to assess bacterial composition and results were analyzed with QIIME. Similar bacterial compositions were observed in the unvaccinated and vaccinated samples. Principal component analysis also indicated a similar bacterial composition between the groups. In addition, bacterial diversity was not different between the vaccinated and unvaccinated samples. Accordingly, our results suggest that PCV10 vaccination promotes a specific response against its targets, thereby preserving the nosocomial microbiome. Although not statistically significant, Streptococcus and Haemophilus genera were increased in the vaccinated group, while Moraxella was decreased. Increases in Streptococcus may be associated with vaccine-target taxa replacement by non-pathogenic species. In sum, we observed that PCV10 vaccination acts by promoting a target-specific action against pathogenic bacteria and also induces commensal bacteria colonization without substantially changing the nasopharyngeal microbiome.

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Human T-lymphotropic virus 1 (HTLV-1) was the first recognized human retrovirus. Infection can lead to two main symptomatologies: adult T-cell lymphoma/leukemia (ATLL) and HTLV-1 associated myelopathy/tropical spastic paraparesis (HAM/TSP). Each manifestation is associated with distinct characteristics, as ATLL presents as a leukemia-like disease, while HAM/TSP presents as severe inflammation in the central nervous system, leading to paraparesis. Previous studies have identified molecules associated with disease development, e.g., the downregulation of Foxp3 in Treg cells was associated with increased risk of HAM/TSP. In addition, elevated levels of CXCL10, CXCL9, and Neopterin in cerebrospinal fluid also present increased risk. However, these molecules were only associated with specific patient groups or viral strains. Furthermore, the majority of studies did not jointly compare all clinical manifestations, and robust analysis entails the inclusion of both ATLL and HAM/TSP. The low numbers of samples also pose difficulties in conducting gene expression analysis to identify specific molecular relationships. To address these limitations and increase the power of manifestation-specific gene associations, meta-analysis was performed using publicly available gene expression data. The application of supervised learning techniques identified alterations in two genes observed to act in tandem as potential biomarkers: GBP2 was associated with HAM/TSP, and CD40LG with ATLL. Together, both molecules demonstrated high sample-classification accuracy (AUC values: 0.88 and 1.0, respectively). Next, other genes with expression correlated to these genes were identified, and we attempted to relate the enriched pathways identified with the characteristic of each clinical manifestation. The present findings contribute to knowledge surrounding viral progression and suggest a potentially powerful new tool for the molecular classification of HTLV-associated diseases.

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The mosquito Aedes aegypti is the main vector of several arthropod-borne diseases that have global impacts. In a previous meta-analysis, our group identified a vector gene set containing 110 genes strongly associated with infections of dengue, West Nile and yellow fever viruses. Of these 110 genes, four genes allowed a highly accurate classification of infected status. More recently, a new study of Ae. aegypti infected with Zika virus (ZIKV) was published, providing new data to investigate whether this "infection" gene set is also altered during a ZIKV infection. Our hypothesis is that the infection-associated signature may also serve as a proxy to classify the ZIKV infection in the vector. Raw data associated with the NCBI/BioProject were downloaded and re-analysed. A total of 18 paired-end replicates corresponding to three ZIKV-infected samples and three controls were included in this study. The nMDS technique with a logistic regression was used to obtain the probabilities of belonging to a given class. Thus, to compare both gene sets, we used the area under the curve and performed a comparison using the bootstrap method. Our meta-signature was able to separate the infected mosquitoes from the controls with good predictive power to classify the Zika-infected mosquitoes.

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The mosquito Aedes aegypti is the main vector of several arthropod-borne diseases that have global impacts. In a previous meta-analysis, our group identified a vector gene set containing 110 genes strongly associated with infections of dengue, West Nile and yellow fever viruses. Of these 110 genes, four genes allowed a highly accurate classification of infected status. More recently, a new study of Ae. aegypti infected with Zika virus (ZIKV) was published, providing new data to investigate whether this "infection" gene set is also altered during a ZIKV infection. Our hypothesis is that the infection-associated signature may also serve as a proxy to classify the ZIKV infection in the vector. Raw data associated with the NCBI/BioProject were downloaded and re-analysed. A total of 18 paired-end replicates corresponding to three ZIKV-infected samples and three controls were included in this study. The nMDS technique with a logistic regression was used to obtain the probabilities of belonging to a given class. Thus, to compare both gene sets, we used the area under the curve and performed a comparison using the bootstrap method. Our meta-signature was able to separate the infected mosquitoes from the controls with good predictive power to classify the Zika-infected mosquitoes.

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