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Leprosy is a chronic bacterial infection mainly caused by Mycobacterium leprae that primarily affects skin and peripheral nerves. Due to its ability to absorb carbon from the host cell, the bacillus became dependent on energy production, mainly through oxidative phosphorylation. In fact, variations in genes of Complex I of oxidative phosphorylation encoded by mtDNA have been associated with several diseases in humans, including bacterial infections, which are possible influencers in the host response to leprosy. Here, we investigated the presence of variants in the mtDNA genes encoding Complex I regarding leprosy, as well as the analysis of their pathogenicity in the studied cohort. We found an association of 74 mitochondrial variants with either of the polar forms, Pole T (Borderline Tuberculoid) or Pole L (Borderline Lepromatous and Lepromatous) of leprosy. Notably, six variants were exclusively found in both clinical poles of leprosy, including m.4158A>G and m.4248T>C in MT-ND1, m.13650C>A, m.13674T>C, m.12705C>T and m.13263A>G in MT-ND5, of which there are no previous reports in the global literature. Our observations reveal a substantial number of mutations among different groups of leprosy, highlighting a diverse range of consequences associated with mutations in genes across these groups. Furthermore, we suggest that the six specific variants exclusively identified in the case group could potentially play a crucial role in leprosy susceptibility and its clinical differentiation. These variants are believed to contribute to the instability and dysregulation of oxidative phosphorylation during the infection, further emphasizing their significance.

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This pilot study aimed to investigate genetic factors that may have contributed to the milder clinical outcomes of COVID-19 in Brazilian indigenous populations. 263 Indigenous from the Araweté, Kararaô, Parakanã, Xikrin do Bacajá, Kayapó and Munduruku peoples were analyzed, 55.2% women, ages ranging from 10 to 95 years (average 49.5 ± 20.7). Variants in genes involved in the entry of SARS-CoV-2 into the host cell (ACE1 rs1799752 I/D, ACE2 rs2285666 C/T, ACE2 rs73635825 A/G and TMPRSS2 rs123297605 C/T), were genotyped in indigenous peoples from the Brazilian Amazon, treated during the SARS-CoV-2 pandemic between 2020 and 2021. The distribution of genotypes did not show any association with the presence or absence of IgG antibodies. Additionally, the influence of genetic variations on the severity of the disease was not examined extensively because a significant number of indigenous individuals experienced the disease with either mild symptoms or no symptoms. It is worth noting that the frequencies of risk alleles were found to be lower in Indigenous populations compared to both continental populations and Brazilians. Indigenous Brazilian Amazon people exhibited an ethnic-specific genetic profile that may be associated with a milder disease, which could explain the unexpected response they demonstrated to COVID-19, being less impacted than Brazilians.

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BACKGROUND: In recent years, the mitochondria/immune system interaction has been proposed, so that variants of mitochondrial genome and levels of heteroplasmy might deregulate important metabolic processes in fighting infections, such as leprosy. METHODS: We sequenced the whole mitochondrial genome to investigate variants and heteroplasmy levels, considering patients with different clinical forms of leprosy and household contacts. After sequencing, a specific pipeline was used for preparation and bioinformatics analysis to select heteroplasmic variants. RESULTS: We found 116 variants in at least two of the subtypes of the case group (Borderline Tuberculoid, Borderline Lepromatous, Lepromatous), suggesting a possible clinical significance to these variants. Notably, 15 variants were exclusively found in these three clinical forms, of which five variants stand out for being missense (m.3791T > C in MT-ND1, m.5317C > A in MT-ND2, m.8545G > A in MT-ATP8, m.9044T > C in MT-ATP6 and m.15837T > C in MT-CYB). In addition, we found 26 variants shared only by leprosy poles, of which two are characterized as missense (m.4248T > C in MT-ND1 and m.8027G > A in MT-CO2). CONCLUSION: We found a significant number of variants and heteroplasmy levels in the leprosy patients from our cohort, as well as six genes that may influence leprosy susceptibility, suggesting for the first time that the mitogenome might be involved with the leprosy process, distinction of clinical forms and severity. Thus, future studies are needed to help understand the genetic consequences of these variants.

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Alzheimer's Disease (AD) is an irreversible neurodegenerative disease clinically characterized by the presence of ß-amyloid plaques and tau deposits in various regions of the brain. However, the underlying factors that contribute to the development of AD remain unclear. Recently, the fusiform gyrus has been identified as a critical brain region associated with mild cognitive impairment, which may increase the risk of AD development. In our study, we performed gene co-expression and differential co-expression network analyses, as well as gene-expression-based prediction, using RNA-seq transcriptome data from post-mortem fusiform gyrus tissue samples collected from both cognitively healthy individuals and those with AD. We accessed differential co-expression networks in large cohorts such as ROSMAP, MSBB, and Mayo, and conducted over-representation analyses of gene pathways and gene ontology. Our results comprise four exclusive gene hubs in co-expression modules of Alzheimer's Disease, including FNDC3A, MED23, NRIP1, and PKN2. Further, we identified three genes with differential co-expressed links, namely FAM153B, CYP2C8, and CKMT1B. The differential co-expressed network showed moderate predictive performance for AD, with an area under the curve ranging from 0.71 to 0.76 (+/- 0.07). The over-representation analysis identified enrichment for Toll-Like Receptors Cascades and signaling pathways, such as G protein events, PIP2 hydrolysis and EPH-Epherin mechanism, in the fusiform gyrus. In conclusion, our findings shed new light on the molecular pathophysiology of AD by identifying new genes and biological pathways involved, emphasizing the crucial role of gene regulatory networks in the fusiform gyrus.

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Emerging deep learning-based applications in precision medicine include computational histopathological analysis. However, there is a lack of the required training image datasets to generate classification and detection models. This phenomenon occurs mainly due to human factors that make it difficult to obtain well-annotated data. The present study provides a curated public collection of histopathological images (DeepHP) and a convolutional neural network model for diagnosing gastritis. Images from gastric biopsy histopathological exams were used to investigate the performance of the proposed model in detecting gastric mucosa with Helicobacter pylori infection. The DeepHP database comprises 394,926 histopathological images, of which 111 K were labeled as Helicobacter pylori positive and 283 K were Helicobacter pylori negative. We investigated the classification performance of three Convolutional Neural Network architectures. The models were tested and validated with two distinct image sets of 15% (59K patches) chosen randomly. The VGG16 architecture showed the best results with an Area Under the Curve of 0.998%. The results showed that CNN could be used to classify histopathological images from gastric mucosa with marked precision. Our model evidenced high potential and application in the computational pathology field.

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Given the role of pharmacogenomics in the large variability observed in drug efficacy/safety, an assessment about the pharmacogenomic profile of patients prior to drug prescription or dose adjustment is paramount to improve adherence to treatment and prevent adverse drug reaction events. A population commonly underrepresented in pharmacogenomic studies is the Native American populations, which have a unique genetic profile due to a long process of geographic isolation and other genetic and evolutionary processes. Here, we describe the pharmacogenetic variability of Native American populations regarding 160 pharmacogenes involved in absorption, distribution, metabolism, and excretion processes and biological pathways of different therapies. Data were obtained through complete exome sequencing of individuals from 12 different Amerindian groups of the Brazilian Amazon. The study reports a total of 3311 variants; of this, 167 are exclusive to Amerindian populations, and 1183 are located in coding regions. Among these new variants, we found non-synonymous coding variants in the DPYD and the IFNL4 genes and variants with high allelic frequencies in intronic regions of the MTHFR, TYMS, GSTT1, and CYP2D6 genes. Additionally, 332 variants with either high or moderate (disruptive or non-disruptive impact in protein effectiveness, respectively) significance were found with a minimum of 1% frequency in the Amazonian Amerindian population. The data reported here serve as scientific basis for future design of specific treatment protocols for Amazonian Amerindian populations as well as for populations admixed with them, such as the Northern Brazilian population.

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Genetic and omics analyses frequently require independent observations, which is not guaranteed in real datasets. When relatedness cannot be accounted for, solutions involve removing related individuals (or observations) and, consequently, a reduction of available data. We developed a network-based relatedness-pruning method that minimizes dataset reduction while removing unwanted relationships in a dataset. It uses node degree centrality metric to identify highly connected nodes (or individuals) and implements heuristics that approximate the minimal reduction of a dataset to allow its application to complex datasets. When compared with two other popular population genetics methodologies (PLINK and KING), NAToRA shows the best combination of removing all relatives while keeping the largest possible number of individuals in all datasets tested and also, with similar effects on the allele frequency spectrum and Principal Component Analysis than PLINK and KING. NAToRA is freely available, both as a standalone tool that can be easily incorporated as part of a pipeline, and as a graphical web tool that allows visualization of the relatedness networks. NAToRA also accepts a variety of relationship metrics as input, which facilitates its use. We also release a genealogies simulator software used for different tests performed in this study.

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This study was carried out to investigate the frequency of genetic variants related to body mass index (BMI) and type 2 diabetes (T2D) and evaluating the potential impact of risk alleles on susceptibility to these disorders in six indigenous peoples from Brazilian Amazon region. The majority of Fst values for pairwise population comparisons among the indigenous groups are low or moderate. The indigenous people show high values of differentiation with Africans, Europeans and Southeast Asians and moderate values with East Asian and American populations, as expected. The allelic frequencies among indigenous indicate that the majority of associations observed with T2D in continental populations can be replicated in native Amazonians. The genetic risk scores calculated for T2D in indigenous are high and similar to those calculated for Americans and East Asians, while the estimates obtained for obesity are low, probably due to the low frequencies of the risk allele of the FTO gene found in our samples. ADRB3-rs4994 and ABCC8-rs1799854 genes showed a significant association with BMI and waist circumference, and the KCNJ11-rs5219 gene with hyperglycemia. These results emphasize the importance of knowing the genetic variability underlying complex genetic diseases in indigenous peoples and the search for particular or rare variants.

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Nuclear DNA has been the main source of genome-wide loci association in neurodegenerative diseases, only partially accounting for the heritability of Alzheimer's Disease (AD). In this context, mitochondrial DNA (mtDNA) is gaining more attention. Here, we investigated mitochondrial genes and genetic variants that may influence mild cognitive impairment and AD, through an integrative analysis including differential gene expression and mitochondrial genome-wide epistasis. We assessed the expression of mitochondrial genes in different brain tissues from two public RNA-Seq databases (GEO and GTEx). Then, we analyzed mtDNA from the ADNI Cohort and investigated epistasis regarding mitochondrial variants and levels of Aß1-42, TAU, and Phosphorylated TAU (PTAU) from cognitively healthy controls, and both mild cognitive impairment (MCI) and AD cases. We identified multiple differentially expressed mitochondrial genes in the comparisons between cognitively healthy individuals and AD patients. We also found increased protein levels in MCI and AD patients when compared to healthy controls, as well as novel candidate networks of mtDNA epistasis, which included variants in all mitochondrially-encoded oxidative phosphorylation complexes, 12S rRNA and MT-DLOOP. Our results highlight layers of potential interactions involving mitochondrial genetics and suggest specific molecular alterations as potential biomarkers for AD.

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ClinVar is a web platform that stores ∼789,000 genetic associations with complex diseases. A partial set of these cataloged genetic associations has challenged clinicians and geneticists, often leading to conflicting interpretations or uncertain clinical impact significance. In this study, we addressed the (re)classification of genetic variants by AmazonForest, which is a random-forest-based pathogenicity metaprediction model that works by combining functional impact data from eight prediction tools. We evaluated the performance of representation learning algorithms such as autoencoders to propose a better strategy. All metaprediction models were trained with ClinVar data, and genetic variants were annotated with eight functional impact predictors cataloged with SnpEff/SnpSift. AmazonForest implements the best random forest model with a one hot data-encoding strategy, which shows an Area Under ROC Curve of ≥0.93. AmazonForest was employed for pathogenicity prediction of a set of ∼101,000 genetic variants of uncertain significance or conflict of interpretation. Our findings revealed ∼24,000 variants with high pathogenic probability (RFprob≥0.9). In addition, we show results for Alzheimer's Disease as a demonstration of its application in clinical interpretation of genetic variants in complex diseases. Lastly, AmazonForest is available as a web tool and R object that can be loaded to perform pathogenicity predictions.