SARS-CoV-2 Research in Dogs (Canis lupus familiaris) and Felines (Felis silvestris catus) Domiciled in an International Border Region (Paraguay and Brazil).

Introduction: COVID-19 is an infectious disease caused by SARS-CoV-2 that has become a serious threat to public health owing to its rapid spread from aerosols from infected people. Despite being considered a strictly human disease, there are reports in the literature about animals with confirmed presence of the virus. Aim: Owing to the scarcity of scientific literature on the potential for infection of animals and their importance for One Health, the objective of this work was to research SARS-CoV-2 RNA in felines (Felis silvestris catus) and dogs (Canis lupus familiaris) domiciled. Materials and Methods: Oropharyngeal swabs were collected from domestic dogs and cats belonging to patients diagnosed with COVID-19 from August to October 2021 and residents of the northwest and west regions of Paraná, Brazil. Results: Of the 34 samples collected, 14 were from dogs and 20 from cats. Three of these samples tested positive in real-time PCR, and two of them were also positive in the immunochromatographic test. After testing positive in real-time PCR, the samples underwent genetic sequencing using the Illumina COVIDSeq test. Of the 34 samples collected, three (9%), all of them female and from the feline species, tested positive in real-time PCR, with two of these (67%) also testing positive in the immunochromatographic test. Regarding sequencing, it was possible to sequence the three samples aligned with the AY.101 lineage, corresponding to the Delta variant. Conclusion: The occurrence of SARS-CoV-2 infection in dogs and cats is seen as an unintended event with significant implications for public health, including its potential transmission to other animal species. Further research is required to enhance our understanding of how this disease spreads among these animals and its broader impact on One Health initiatives.

Co-occurrence of PRKN and SYNJ1 variants in Early-Onset Parkinson's disease.

Parkinson's disease (PD) is the second most prevalent neurodegenerative disease globally, with a fast-growing prevalence. The etiology of PD exhibits a multifactorial complex nature and remains challenging. Herein, we described clinical, molecular, and integrative bioinformatics findings from a Brazilian female affected by Early-Onset PD (EOPD) harboring a recurrent homozygous pathogenic deletion in the parkin RBR E3 ubiquitin protein ligase gene (PRKN; NM_004562.3:c.155delA; p.Asn52Metfs*29; rs754809877), along with a novel heterozygous variant in the synaptojanin 1 gene (SYNJ1; NM_003895.3:c.62G > T; p.Cys21Phe; rs1486511197) found by Whole Exome Sequencing. Uncommon or unreported PRKN-related clinical features in the patient include cognitive decline, auditory and visual hallucinations, REM sleep disorder, and depression, previously observed in SYNJ1-related conditions. Moreover, PRKN interacts with endophilin A1, which is a major binding partner of SYNJ1. This protein plays a pivotal role in regulating the dynamics of synaptic vesicles, particularly in the context of endocytosis and recycling processes. Altogether, our comprehensive analyses underscore a potential synergistic effect between the PRKN and SYNJ1 variants over the pathogenesis of EOPD.

Understanding the (epi)genetic dysregulation in Parkinson's disease through an integrative brain competitive endogenous RNA network.

Parkinson's disease (PD) is a rapidly growing neurodegenerative disorder characterized by dopaminergic neuron loss in the substantia nigra pars compacta (SN) and aggregation of α-synuclein. Its aetiology involves a multifaceted interplay among genetic, environmental, and epigenetic factors. We integrated brain gene expression data from PD patients to construct a comprehensive regulatory network encompassing messenger RNAs (mRNAs), microRNAs (miRNAs), circular RNAs (circRNAs) and, for the first time, RNA binding proteins (RBPs). Expression data from the SN of PD patients and controls were systematically selected from public databases to identify combined differentially expressed genes (DEGs). Brain co-expression analysis revealed modules comprising significant DEGs that function cooperatively. The relationships among co-expressed DEGs, miRNAs, circRNAs, and RBPs revealed an intricate competitive endogenous RNA (ceRNA) network responsible for post-transcriptional dysregulation in PD. Many genes in the ceRNA network, including the TOMM20 and HMGCR genes, overlap with the most relevant genes in our previous Alzheimer's disease-associated ceRNA network, suggesting common underlying mechanisms between both conditions. Moreover, in the ceRNA subnetwork, the RBP Aly/REF export factor (ALYREF), which acts as an RNA 5-methylcytosine(m5C)-binding protein, stood out. Our data sheds new light on the potential role of brain ceRNA networks in PD pathogenesis.

Whole genomes show contrasting trends of population size changes and genomic diversity for an Amazonian endemic passerine over the late quaternary.

The "Amazon tipping point" is a global change scenario resulting in replacement of upland terra-firme forests by large-scale "savannization" of mostly southern and eastern Amazon. Reduced rainfall accompanying the Last Glacial Maximum (LGM) has been proposed to have acted as such a tipping point in the past, with the prediction that terra-firme inhabiting species should have experienced reductions in population size as drier habitats expanded. Here, we use whole-genomes of an Amazonian endemic organism (Scale-backed antbirds - Willisornis spp.) sampled from nine populations across the region to test this historical demography scenario. Populations from southeastern Amazonia and close to the Amazon-Cerrado ecotone exhibited a wide range of demographic patterns, while most of those from northern and western Amazonia experienced uniform expansions between 400 kya and 80-60 kya, with gradual declines toward 20 kya. Southeastern populations of Willisornis were the last to diversify and showed smaller heterozygosity and higher runs of homozygosity values than western and northern populations. These patterns support historical population declines throughout the Amazon that affected more strongly lineages in the southern and eastern areas, where historical "tipping point" conditions existed due to the widespread replacement of humid forest by drier and open vegetation during the LGM.

Immune response stability to the SARS-CoV-2 mRNA vaccine booster is influenced by differential splicing of HLA genes.

Many molecular mechanisms that lead to the host antibody response to COVID-19 vaccines remain largely unknown. In this study, we used serum antibody detection combined with whole blood RNA-based transcriptome analysis to investigate variability in vaccine response in healthy recipients of a booster (third) dose schedule of the mRNA BNT162b2 vaccine against COVID-19. The cohort was divided into two groups: (1) low-stable individuals, with antibody concentration anti-SARS-CoV IgG S1 below 0.4 percentile at 180 days after boosting vaccination; and (2) high-stable individuals, with antibody values greater than 0.6 percentile of the range in the same period (median 9525 [185-80,000] AU/mL). Differential gene expression, expressed single nucleotide variants and insertions/deletions, differential splicing events, and allelic imbalance were explored to broaden our understanding of the immune response sustenance. Our analysis revealed a differential expression of genes with immunological functions in individuals with low antibody titers, compared to those with higher antibody titers, underscoring the fundamental importance of the innate immune response for boosting immunity. Our findings also provide new insights into the determinants of the immune response variability to the SARS-CoV-2 mRNA vaccine booster, highlighting the significance of differential splicing regulatory mechanisms, mainly concerning HLA alleles, in delineating vaccine immunogenicity.

Finding microbial composition and biological processes as predictive signature to access the ongoing status of mangrove preservation.

Mangroves are complex land-sea transition ecosystems whose microbiota are essential for their nutrient recycling and conservation. Brazil is the third-largest estuarine area in the world and "Baía de Todos os Santos" (BTS) is one of the largest bays of the country, with wide anthropogenic exploration. Using a metagenomic approach, we investigated composition and functional adaptability as signatures of the microbiome of pristine and anthropized areas of BTS, including those under petroleum refinery influence. The taxonomic analysis showed dominance of sulfate-reducing Desulfobacteraceae, Rhodobacteraceae, and Flavobacteriaceae. Taxa were significantly diverse between pristine and disturbed areas. Disturbed mangroves showed a notary increase in abundance of halophilic, sulfur-related, and hydrocarbon-degrading genera and a decrease in diatoms compared to pristine area. The metabolic profile of BTS mangroves was correlated with the differentially abundant microbiota. Two ecological scenarios were observed: one marked by functions of central metabolism associated with biomass degradation and another by mechanisms of microbial adaptability to pollution conditions and environmental degradation. Part of the microbiome was distinct and not abundant in Brazilian estuarine soils. The microbiome signature observed in each BTS mangrove reflects how human actions impact the diversity of these ecosystems and also emphasize their role in attempting to restore disturbed mangroves. The microbiome may act as a potential biological indicator of the preservation status of these soils, despite the limitation of soil property conditions. Additionally, our data pointed to metagenomics as an additional tool for environmental assessment and reinforced the need for protective measures for the mangroves under study.

Mitochondrial genome structure and composition in 70 fishes: a key resource for fisheries management in the South Atlantic.

BACKGROUND: Phylogenetic gaps of public databases of reference sequences are a major obstacle for comparative genomics and management of marine resources, particularly in the Global South, where economically important fisheries and conservation flagship species often lack closely-related references. We applied target-enrichment to obtain complete mitochondrial genomes of marine ichthyofauna from the Brazilian coast selected based on economic significance, conservation status and lack of phylogenetically-close references. These included sardines (Dorosomatidae, Alosidae), mackerels (Scombridae) croakers (Sciaenidae), groupers (Epinephelidae) and snappers (Lutjanidae). RESULTS: Custom baits were designed to enrich mitochondrial DNA across a broad phylogenetic range of fishes. Sequencing generated approximately 100k reads per sample, which were assembled in a total of 70 complete mitochondrial genomes and include fifty-two new additions to GenBank, including five species with no previous mitochondrial data. Departures from the typical gene content and order occurred in only three taxa and mostly involved tRNA gene duplications. Start-codons for all genes, except Cytochrome C Oxidase subunit I (COI), were consistently ATG, whilst a wide range of stop-codons deviated from the prevailing TAA. Phylogenetic analysis confirmed assembly accuracy and revealed signs of cryptic diversification within the Mullus genus. Lineage delimitation methods using Sardinella aurita and S. brasiliensis mitochondrial genomes support a single Operational Taxonomic Unit. CONCLUSIONS: Target enrichment was highly efficient, providing complete novel mitochondrial genomes with little sequencing effort. These sequences are deposited in public databases to enable subsequent studies in population genetics and adaptation of Latin American fish species and serve as a vital resource for conservation and management programs that rely on molecular data for species and genus-level identification.

Differential Type-I Interferon Response in Buffy Coat Transcriptome of Individuals Infected with SARS-CoV-2 Gamma and Delta Variants.

The innate immune system is the first line of defense against pathogens such as the acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The type I-interferon (IFN) response activation during the initial steps of infection is essential to prevent viral replication and tissue damage. SARS-CoV and SARS-CoV-2 can inhibit this activation, and individuals with a dysregulated IFN-I response are more likely to develop severe disease. Several mutations in different variants of SARS-CoV-2 have shown the potential to interfere with the immune system. Here, we evaluated the buffy coat transcriptome of individuals infected with Gamma or Delta variants of SARS-CoV-2. The Delta transcriptome presents more genes enriched in the innate immune response and Gamma in the adaptive immune response. Interactome and enriched promoter analysis showed that Delta could activate the INF-I response more effectively than Gamma. Two mutations in the N protein and one in the nsp6 protein found exclusively in Gamma have already been described as inhibitors of the interferon response pathway. This indicates that the Gamma variant evolved to evade the IFN-I response. Accordingly, in this work, we showed one of the mechanisms that variants of SARS-CoV-2 can use to avoid or interfere with the host Immune system.

Multi-layered transcriptomic analysis reveals a pivotal role of FMR1 and other developmental genes in Alzheimer's disease-associated brain ceRNA network.

Alzheimer's disease (AD) is an increasingly neurodegenerative disorder that causes progressive cognitive decline and memory impairment. Despite extensive research, the underlying causes of late-onset AD (LOAD) are still in progress. This study aimed to establish a network of competing regulatory interactions involving circular RNAs (circRNAs), microRNAs (miRNAs), RNA-binding proteins (RBPs), and messenger RNAs (mRNAs) connected to LOAD. A systematic analysis of publicly available expression data was conducted to identify integrated differentially expressed genes (DEGs) from the hippocampus of LOAD patients. Subsequently, gene co-expression analysis identified modules comprising highly expressed DEGs that act cooperatively. The competition between co-expressed DEGs and miRNAs/RBPs and the simultaneous interactions between circRNA and miRNA/RBP revealed a complex ceRNA network responsible for post-transcriptional regulation in LOAD. Hippocampal expression data for miRNAs, circRNAs, and RBPs were used to filter relevant relationships for AD. An integrated topological score was used to identify the highly connected hub gene, from which a brain core ceRNA subnetwork was generated. The Fragile X Messenger Ribonucleoprotein 1 (FMR1) coding for the RBP FMRP emerged as the prominent driver gene in this subnetwork. FMRP has been previously related to AD but not in a ceRNA network context. Also, the substantial number of neurodevelopmental genes in the ceRNA subnetwork and their related biological pathways strengthen that AD shares common pathological mechanisms with developmental conditions. Our results enhance the current knowledge about the convergent ceRNA regulatory pathways underlying AD and provide potential targets for identifying early biomarkers and developing novel therapeutic interventions.

Genetic screening in a Brazilian cohort with inborn errors of immunity.

BACKGROUND: Inherited genetic defects in immune system-related genes can result in Inborn Errors of Immunity (IEI), also known as Primary Immunodeficiencies (PID). Diagnosis of IEI disorders is challenging due to overlapping clinical manifestations. Accurate identification of disease-causing germline variants is crucial for appropriate treatment, prognosis, and genetic counseling. However, genetic sequencing is challenging in low-income countries like Brazil. This study aimed to perform genetic screening on patients treated within Brazil's public Unified Health System to identify candidate genetic variants associated with the patient's phenotype. METHODS: Thirteen singleton unrelated patients from three hospitals in Rio de Janeiro were enrolled in this study. Genomic DNA was extracted from the peripheral blood lymphocytes of each patient, and whole exome sequencing (WES) analyses were conducted using Illumina NextSeq. Germline genetic variants in IEI-related genes were prioritized using a computational framework considering their molecular consequence in coding regions; minor allele frequency ≤ 0.01; pathogenicity classification based on American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) guidelines gathered from the VarSome clinical database; and IEI-related phenotype using the Franklin tool. The genes classification into IEI categories follows internationally recognized guidelines informed by the International Union of Immunological Societies Expert Committee. Additional methods for confirmation of the variant included Sanger sequencing, phasing analysis, and splice site prediction. RESULTS: A total of 16 disease-causing variants in nine genes, encompassing six different IEI categories, were identified. X-Linked Agammaglobulinemia, caused by BTK variations, emerged as the most prevalent IEI disorder in the cohort. However, pathogenic and likely pathogenic variants were also reported in other known IEI-related genes, namely CD40LG, CARD11, WAS, CYBB, C6, and LRBA. Interestingly, two patients with suspected IEI exhibited pathogenic variants in non-IEI-related genes, ABCA12 and SLC25A13, potentially explaining their phenotypes. CONCLUSIONS: Genetic screening through WES enabled the detection of potentially harmful variants associated with IEI disorders. These findings contribute to a better understanding of patients' clinical manifestations by elucidating the genetic basis underlying their phenotypes.

(m, n)-mer-a simple statistical feature for sequence classification.

Summary: The (m, n)-mer is a simple alternative classification feature based on conditional probability distributions. In this application note, we compared k-mer and (m, n)-mer frequency features in 11 distinct datasets used for binary, multiclass and clustering classifications. Our findings show that the (m, n)-mer frequency features are related to the highest performance metrics and often statistically outperformed the k-mers. Here, the (m, n)-mer frequencies improved performance for classifying smaller sequence lengths (as short as 300 bp) and yielded higher metrics when using short values of k (ranging from 2 to 4). Therefore, we present the (m, n)-mers frequencies to the scientific community as a feature that seems to be quite effective in identifying complex discriminatory patterns and classifying polyphyletic sequence groups. Availability and implementation: The (m, n)-mer algorithm is released as an R package within the CRAN project (https://cran.r-project.org/web/packages/mnmer) and is also available at https://github.com/labinfo-lncc/mnmer. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Assessing whole-exome sequencing data from undiagnosed Brazilian patients to improve the diagnostic yield of inborn errors of immunity.

OBJECTIVES: Inborn error of immunity (IEI) comprises a broad group of inherited immunological disorders that usually display an overlap in many clinical manifestations challenging their diagnosis. The identification of disease-causing variants from whole-exome sequencing (WES) data comprises the gold-standard approach to ascertain IEI diagnosis. The efforts to increase the availability of clinically relevant genomic data for these disorders constitute an important improvement in the study of rare genetic disorders. This work aims to make available WES data of Brazilian patients' suspicion of IEI without a genetic diagnosis. We foresee a broad use of this dataset by the scientific community in order to provide a more accurate diagnosis of IEI disorders. DATA DESCRIPTION: Twenty singleton unrelated patients treated at four different hospitals in the state of Rio de Janeiro, Brazil were enrolled in our study. Half of the patients were male with mean ages of 9 ± 3, while females were 12 ± 10 years old. The WES was performed in the Illumina NextSeq platform with at least 90% of sequenced bases with a minimum of 30 reads depth. Each sample had an average of 20,274 variants, comprising 116 classified as rare pathogenic or likely pathogenic according to American College of Medical Genetics and Genomics and the Association (ACMG) guidelines. The genotype-phenotype association was impaired by the lack of detailed clinical and laboratory information, besides the unavailability of molecular and functional studies which, comprise the limitations of this study. Overall, the access to clinical exome sequencing data is limited, challenging exploratory analyses and the understanding of genetic mechanisms underlying disorders. Therefore, by making these data available, we aim to increase the number of WES data from Brazilian samples despite contributing to the study of monogenic IEI-disorders.

PPIntegrator: semantic integrative system for protein-protein interaction and application for host-pathogen datasets.

Summary: Semantic web standards have shown importance in the last 20 years in promoting data formalization and interlinking between the existing knowledge graphs. In this context, several ontologies and data integration initiatives have emerged in recent years for the biological area, such as the broadly used Gene Ontology that contains metadata to annotate gene function and subcellular location. Another important subject in the biological area is protein-protein interactions (PPIs) which have applications like protein function inference. Current PPI databases have heterogeneous exportation methods that challenge their integration and analysis. Presently, several initiatives of ontologies covering some concepts of the PPI domain are available to promote interoperability across datasets. However, the efforts to stimulate guidelines for automatic semantic data integration and analysis for PPIs in these datasets are limited. Here, we present PPIntegrator, a system that semantically describes data related to protein interactions. We also introduce an enrichment pipeline to generate, predict and validate new potential host-pathogen datasets by transitivity analysis. PPIntegrator contains a data preparation module to organize data from three reference databases and a triplification and data fusion module to describe the provenance information and results. This work provides an overview of the PPIntegrator system applied to integrate and compare host-pathogen PPI datasets from four bacterial species using our proposed transitivity analysis pipeline. We also demonstrated some critical queries to analyze this kind of data and highlight the importance and usage of the semantic data generated by our system. Availability and implementation: https://github.com/YasCoMa/ppintegrator, https://github.com/YasCoMa/ppi_validation_process and https://github.com/YasCoMa/predprin.

The Omicron Lineages BA.1 and BA.2 (Betacoronavirus SARS-CoV-2) Have Repeatedly Entered Brazil through a Single Dispersal Hub.

Brazil currently ranks second in absolute deaths by COVID-19, even though most of its population has completed the vaccination protocol. With the introduction of Omicron in late 2021, the number of COVID-19 cases soared once again in the country. We investigated in this work how lineages BA.1 and BA.2 entered and spread in the country by sequencing 2173 new SARS-CoV-2 genomes collected between October 2021 and April 2022 and analyzing them in addition to more than 18,000 publicly available sequences with phylodynamic methods. We registered that Omicron was present in Brazil as early as 16 November 2021 and by January 2022 was already more than 99% of samples. More importantly, we detected that Omicron has been mostly imported through the state of São Paulo, which in turn dispersed the lineages to other states and regions of Brazil. This knowledge can be used to implement more efficient non-pharmaceutical interventions against the introduction of new SARS-CoV variants focused on surveillance of airports and ground transportation.

Inference of differentially expressed genes using generalized linear mixed models in a pairwise fashion.

Background: Technological advances involving RNA-Seq and Bioinformatics allow quantifying the transcriptional levels of genes in cells, tissues, and cell lines, permitting the identification of Differentially Expressed Genes (DEGs). DESeq2 and edgeR are well-established computational tools used for this purpose and they are based upon generalized linear models (GLMs) that consider only fixed effects in modeling. However, the inclusion of random effects reduces the risk of missing potential DEGs that may be essential in the context of the biological phenomenon under investigation. The generalized linear mixed models (GLMM) can be used to include both effects. Methods: We present DEGRE (Differentially Expressed Genes with Random Effects), a user-friendly tool capable of inferring DEGs where fixed and random effects on individuals are considered in the experimental design of RNA-Seq research. DEGRE preprocesses the raw matrices before fitting GLMMs on the genes and the derived regression coefficients are analyzed using the Wald statistical test. DEGRE offers the Benjamini-Hochberg or Bonferroni techniques for P-value adjustment. Results: The datasets used for DEGRE assessment were simulated with known identification of DEGs. These have fixed effects, and the random effects were estimated and inserted to measure the impact of experimental designs with high biological variability. For DEGs' inference, preprocessing effectively prepares the data and retains overdispersed genes. The biological coefficient of variation is inferred from the counting matrices to assess variability before and after the preprocessing. The DEGRE is computationally validated through its performance by the simulation of counting matrices, which have biological variability related to fixed and random effects. DEGRE also provides improved assessment measures for detecting DEGs in cases with higher biological variability. We show that the preprocessing established here effectively removes technical variation from those matrices. This tool also detects new potential candidate DEGs in the transcriptome data of patients with bipolar disorder, presenting a promising tool to detect more relevant genes. Conclusions: DEGRE provides data preprocessing and applies GLMMs for DEGs' inference. The preprocessing allows efficient remotion of genes that could impact the inference. Also, the computational and biological validation of DEGRE has shown to be promising in identifying possible DEGs in experiments derived from complex experimental designs. This tool may help handle random effects on individuals in the inference of DEGs and presents a potential for discovering new interesting DEGs for further biological investigation.

Clinical markers of post-Chikungunya chronic inflammatory joint disease: A Brazilian cohort.

BACKGROUND: Chikungunya-fever (CHIKF) remains a public health major issue. It is clinically divided into three phases: acute, post-acute and chronic. Chronic cases correspond to 25-40% individuals and, though most of them are characterized by long-lasting arthralgia alone, many of them exhibit persistent or recurrent inflammatory signs that define post-Chikungunya chronic inflammatory joint disease (pCHIKV-CIJD). We aimed to identify early clinical markers of evolution to pCHIKV-CIJD during acute and post-acute phases. METHODOLOGY/PRINCIPAL FINDINGS: We studied a prospective cohort of CHIKF-confirmed volunteers with longitudinal clinical data collection from symptoms onset up to 90 days, including a 21-day visit (D21). Of 169 patients with CHIKF, 86 (50.9%) completed the follow-up, from whom 39 met clinical criteria for pCHIKV-CIJD (45.3%). The relative risk of chronification was higher in women compared to men (RR = 1.52; 95% CI = 1.15-1.99; FDR = 0.03). None of the symptoms or signs presented at D0 behaved as an early predictor of pCHIKV-CIJD, while being symptomatic at D21 was a risk factor for chronification (RR = 1.31; 95% CI = 1.09-1.55; FDR = 0.03). Significance was also observed for joint pain (RR = 1.35; 95% CI = 1.12-1.61; FDR = 0.02), reported edema (RR = 3.61; 95% CI = 1.44-9.06; FDR = 0.03), reported hand and/or feet small joints edema (RR = 4.22; 95% CI = 1.51-11.78; FDR = 0.02), and peri-articular edema observed during physical examination (RR = 2.89; 95% CI = 1.58-5.28; FDR = 0.002). Furthermore, patients with no findings in physical examination at D21 were at lower risk of chronic evolution (RR = 0.41, 95% CI = 0.24-0.70, FDR = 0.01). Twenty-nine pCHIKV-CIJD patients had abnormal articular ultrasonography (90.6% of the examined). The most common findings were synovitis (65.5%) and joint effusion (58.6%). CONCLUSION: This cohort has provided important insights into the prognostic evaluation of CHIKF. Symptomatic sub-acute disease is a relevant predictor of evolution to chronic arthritis with synovitis, drawing attention to joint pain, edema, multiple articular involvement including small hand and feet joints as risk factors for chronification beyond three months, especially in women. Future studies are needed to accomplish the identification of accurate and early biomarkers of poor clinical prognosis, which would allow better understanding of the disease's evolution and improve patients' management, modifying CHIKF burden on global public health.

Host genetic susceptibility underlying SARS-CoV-2-associated Multisystem Inflammatory Syndrome in Brazilian Children.

BACKGROUND: Multisystem Inflammatory Syndrome in Children (MIS-C) is a life-threatening complication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, which manifests as a hyper inflammatory process with multiorgan involvement in predominantly healthy children in the weeks following mild or asymptomatic coronavirus disease 2019 (COVID-19). However, host monogenic predisposing factors to MIS-C remain elusive. METHODS: Herein, we used whole exome sequencing (WES) on 16 MIS-C Brazilian patients to identify single nucleotide/InDels variants as predisposition factors associated with MIS-C. RESULTS: We identified ten very rare variants in eight genes (FREM1, MPO, POLG, C6, C9, ABCA4, ABCC6, and BSCL2) as the most promising candidates to be related to a higher risk of MIS-C development. These variants may propitiate a less effective immune response to infection or trigger the inflammatory response or yet a delayed hyperimmune response to SARS-CoV-2. Protein-Protein Interactions (PPIs) among the products of the mutated genes revealed an integrated network, enriched for immune and inflammatory response mechanisms with some of the direct partners representing gene products previously associated with MIS-C and Kawasaki disease (KD). In addition, the PPIs direct partners are also enriched for COVID-19-related gene sets. HLA alleles prediction from WES data allowed the identification of at least one risk allele in 100% of the MIS-C patients. CONCLUSIONS: This study is the first to explore host MIS-C-associated variants in a Latin American admixed population. Besides expanding the spectrum of MIS-C-associated variants, our findings highlight the relevance of using WES for characterising the genetic interindividual variability associated with COVID-19 complications and ratify the presence of overlapping/convergent mechanisms among MIS-C, KD and COVID-19, crucial for future therapeutic management.

Buffy Coat Transcriptomic Analysis Reveals Alterations in Host Cell Protein Synthesis and Cell Cycle in Severe COVID-19 Patients.

Transcriptome studies have reported the dysregulation of cell cycle-related genes and the global inhibition of host mRNA translation in COVID-19 cases. However, the key genes and cellular mechanisms that are most affected by the severe outcome of this disease remain unclear. For this work, the RNA-seq approach was used to study the differential expression in buffy coat cells of two groups of people infected with SARS-CoV-2: (a) Mild, with mild symptoms; and (b) SARS (Severe Acute Respiratory Syndrome), who were admitted to the intensive care unit with the severe COVID-19 outcome. Transcriptomic analysis revealed 1009 up-regulated and 501 down-regulated genes in the SARS group, with 10% of both being composed of long non-coding RNA. Ribosome and cell cycle pathways were enriched among down-regulated genes. The most connected proteins among the differentially expressed genes involved transport dysregulation, proteasome degradation, interferon response, cytokinesis failure, and host translation inhibition. Furthermore, interactome analysis showed Fibrillarin to be one of the key genes affected by SARS-CoV-2. This protein interacts directly with the N protein and long non-coding RNAs affecting transcription, translation, and ribosomal processes. This work reveals a group of dysregulated processes, including translation and cell cycle, as key pathways altered in severe COVID-19 outcomes.

Global disparities in SARS-CoV-2 genomic surveillance.

Genomic sequencing is essential to track the evolution and spread of SARS-CoV-2, optimize molecular tests, treatments, vaccines, and guide public health responses. To investigate the global SARS-CoV-2 genomic surveillance, we used sequences shared via GISAID to estimate the impact of sequencing intensity and turnaround times on variant detection in 189 countries. In the first two years of the pandemic, 78% of high-income countries sequenced >0.5% of their COVID-19 cases, while 42% of low- and middle-income countries reached that mark. Around 25% of the genomes from high income countries were submitted within 21 days, a pattern observed in 5% of the genomes from low- and middle-income countries. We found that sequencing around 0.5% of the cases, with a turnaround time <21 days, could provide a benchmark for SARS-CoV-2 genomic surveillance. Socioeconomic inequalities undermine the global pandemic preparedness, and efforts must be made to support low- and middle-income countries improve their local sequencing capacity.

Modulation of HERV Expression by Four Different Encephalitic Arboviruses during Infection of Human Primary Astrocytes.

Human retroelements (HERVs) are retroviral origin sequences fixed in the human genome. HERVs induction is associated with neurogenesis, cellular development, immune activation, and neurological disorders. Arboviruses are often associated with the development of encephalitis. The interplay between these viruses and HERVs has not been fully elucidated. In this work, we analyzed RNAseq data derived from infected human primary astrocytes by Zika (ZikV), Mayaro (MayV), Oropouche (OroV) and Chikungunya (ChikV) viruses, and evaluated the modulation of HERVs and their nearby genes. Our data show common HERVs expression modulation by both alphaviruses, suggesting conserved evolutionary routes of transcription regulation. A total of 15 HERVs were co-modulated by the four arboviruses, including the highly upregulated HERV4_4q22. Data on the upregulation of genes nearby to these elements in ChikV, MayV and OroV infections were also obtained, and interaction networks were built. The upregulation of 14 genes common among all viruses was observed in the networks, and 93 genes between MayV and ChikV. These genes are related to cellular processes such as cellular replication, cytoskeleton, cell vesicle traffic and antiviral response. Together, our results support the role of HERVs induction in the transcription regulation process of genes during arboviral infections.