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1.
BMC Bioinformatics ; 21(Suppl 14): 368, 2020 Sep 30.
Artigo em Inglês | MEDLINE | ID: mdl-32998690

RESUMO

BACKGROUND: Lung cancer is the leading cause of the largest number of deaths worldwide and lung adenocarcinoma is the most common form of lung cancer. In order to understand the molecular basis of lung adenocarcinoma, integrative analysis have been performed by using genomics, transcriptomics, epigenomics, proteomics and clinical data. Besides, molecular prognostic signatures have been generated for lung adenocarcinoma by using gene expression levels in tumor samples. However, we need signatures including different types of molecular data, even cohort or patient-based biomarkers which are the candidates of molecular targeting. RESULTS: We built an R pipeline to carry out an integrated meta-analysis of the genomic alterations including single-nucleotide variations and the copy number variations, transcriptomics variations through RNA-seq and clinical data of patients with lung adenocarcinoma in The Cancer Genome Atlas project. We integrated significant genes including single-nucleotide variations or the copy number variations, differentially expressed genes and those in active subnetworks to construct a prognosis signature. Cox proportional hazards model with Lasso penalty and LOOCV was used to identify best gene signature among different gene categories. We determined a 12-gene signature (BCHE, CCNA1, CYP24A1, DEPTOR, MASP2, MGLL, MYO1A, PODXL2, RAPGEF3, SGK2, TNNI2, ZBTB16) for prognostic risk prediction based on overall survival time of the patients with lung adenocarcinoma. The patients in both training and test data were clustered into high-risk and low-risk groups by using risk scores of the patients calculated based on selected gene signature. The overall survival probability of these risk groups was highly significantly different for both training and test datasets. CONCLUSIONS: This 12-gene signature could predict the prognostic risk of the patients with lung adenocarcinoma in TCGA and they are potential predictors for the survival-based risk clustering of the patients with lung adenocarcinoma. These genes can be used to cluster patients based on molecular nature and the best candidates of drugs for the patient clusters can be proposed. These genes also have a high potential for targeted cancer therapy of patients with lung adenocarcinoma.


Assuntos
Adenocarcinoma de Pulmão/patologia , Genômica/métodos , Neoplasias Pulmonares/patologia , Transcriptoma , Adenocarcinoma de Pulmão/genética , Adenocarcinoma de Pulmão/mortalidade , Área Sob a Curva , Análise por Conglomerados , Variações do Número de Cópias de DNA , Bases de Dados Genéticas , Regulação Neoplásica da Expressão Gênica , Humanos , Neoplasias Pulmonares/genética , Neoplasias Pulmonares/mortalidade , Estadiamento de Neoplasias , Prognóstico , Modelos de Riscos Proporcionais , Mapas de Interação de Proteínas/genética , Curva ROC , Fatores de Risco , Taxa de Sobrevida
2.
BMC Bioinformatics ; 21(Suppl 14): 364, 2020 Sep 30.
Artigo em Inglês | MEDLINE | ID: mdl-32998700

RESUMO

BACKGROUND: Machine learning has been utilized to predict cancer drug response from multi-omics data generated from sensitivities of cancer cell lines to different therapeutic compounds. Here, we build machine learning models using gene expression data from patients' primary tumor tissues to predict whether a patient will respond positively or negatively to two chemotherapeutics: 5-Fluorouracil and Gemcitabine. RESULTS: We focused on 5-Fluorouracil and Gemcitabine because based on our exclusion criteria, they provide the largest numbers of patients within TCGA. Normalized gene expression data were clustered and used as the input features for the study. We used matching clinical trial data to ascertain the response of these patients via multiple classification methods. Multiple clustering and classification methods were compared for prediction accuracy of drug response. Clara and random forest were found to be the best clustering and classification methods, respectively. The results show our models predict with up to 86% accuracy; despite the study's limitation of sample size. We also found the genes most informative for predicting drug response were enriched in well-known cancer signaling pathways and highlighted their potential significance in chemotherapy prognosis. CONCLUSIONS: Primary tumor gene expression is a good predictor of cancer drug response. Investment in larger datasets containing both patient gene expression and drug response is needed to support future work of machine learning models. Ultimately, such predictive models may aid oncologists with making critical treatment decisions.


Assuntos
Antineoplásicos/farmacologia , Regulação Neoplásica da Expressão Gênica/efeitos dos fármacos , Aprendizado de Máquina , Antineoplásicos/uso terapêutico , Área Sob a Curva , Análise por Conglomerados , Bases de Dados Genéticas , Desoxicitidina/análogos & derivados , Desoxicitidina/farmacologia , Desoxicitidina/uso terapêutico , Fluoruracila/uso terapêutico , Humanos , Neoplasias/tratamento farmacológico , Curva ROC
3.
Mol Med Rep ; 22(5): 4221-4226, 2020 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-33000221

RESUMO

Infection by the severe acute respiratory syndrome (SARS) coronavirus­2 (SARS­CoV­2) is the cause of the new viral infectious disease (coronavirus disease 2019; COVID­19). Emerging evidence indicates that COVID­19 may be associated with a wide spectrum of neurological symptoms and complications with central nervous system (CNS) involvement. It is now well­established that entry of SARS­CoV­2 into host cells is facilitated by its spike proteins mainly through binding to the angiotensin­converting enzyme 2 (ACE­2). Preclinical studies have suggested that neuropilin­1 (NRP1), which is a transmembrane receptor that lacks a cytosolic protein kinase domain and exhibits high expression in the respiratory and olfactory epithelium, may also be implicated in COVID­19 by enhancing the entry of SARS­CoV­2 into the brain through the olfactory epithelium. In the present study, we expand on these findings and demonstrate that the NRP1 is also expressed in the CNS, including olfactory­related regions such as the olfactory tubercles and paraolfactory gyri. This furthers supports the potential role of NRP1 as an additional SARS­CoV­2 infection mediator implicated in the neurologic manifestations of COVID­19. Accordingly, the neurotropism of SARS­CoV­2 via NRP1­expressing cells in the CNS merits further investigation.


Assuntos
Sistema Nervoso Central/metabolismo , Infecções por Coronavirus/metabolismo , Neuropilina-1/metabolismo , Pneumonia Viral/metabolismo , Receptores Virais/metabolismo , Betacoronavirus/fisiologia , Encéfalo/metabolismo , Encéfalo/virologia , Sistema Nervoso Central/virologia , Bases de Dados Genéticas , Humanos , Pandemias
4.
PLoS One ; 15(10): e0237689, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33006981

RESUMO

Genomes of tens of thousands of SARS-CoV2 isolates have been sequenced across the world and the total number of changes (predominantly single base substitutions) in these isolates exceeds ten thousand. We compared the mutational spectrum in the new SARS-CoV-2 mutation dataset with the previously published mutation spectrum in hypermutated genomes of rubella-another positive single stranded (ss) RNA virus. Each of the rubella virus isolates arose by accumulation of hundreds of mutations during propagation in a single subject, while SARS-CoV-2 mutation spectrum represents a collection events in multiple virus isolates from individuals across the world. We found a clear similarity between the spectra of single base substitutions in rubella and in SARS-CoV-2, with C to U as well as A to G and U to C being the most prominent in plus strand genomic RNA of each virus. Of those, U to C changes universally showed preference for loops versus stems in predicted RNA secondary structure. Similarly, to what was previously reported for rubella virus, C to U changes showed enrichment in the uCn motif, which suggested a subclass of APOBEC cytidine deaminase being a source of these substitutions. We also found enrichment of several other trinucleotide-centered mutation motifs only in SARS-CoV-2-likely indicative of a mutation process characteristic to this virus. Altogether, the results of this analysis suggest that the mutation mechanisms that lead to hypermutation of the rubella vaccine virus in a rare pathological condition may also operate in the background of the SARS-CoV-2 viruses currently propagating in the human population.


Assuntos
Betacoronavirus/genética , Genoma Viral , RNA Viral/genética , Vírus da Rubéola/genética , Infecções por Coronavirus/virologia , Citidina Desaminase/genética , Bases de Dados Genéticas , Evolução Molecular , Humanos , Mutação , Pandemias , Pneumonia Viral/virologia
5.
Artigo em Inglês | MEDLINE | ID: mdl-33017933

RESUMO

Early prediction of sepsis is essential to give the patient timely treatment since each hour of delayed treatment has been associated with an increase in mortality. Current sepsis detection systems rely on empirical Clinical Decision Rules(CDR)s, which are based on vital signs that can be collected from the bedside. The main disadvantages of CDRs include questions of generalizability and performance variance when applied to the populations different from the groups used for derivation and often take years to develop and validate. This paper proposes a deep learning model using Bi-Directional Gated Recurrent Units(GRU), which uses a wide range of parameters that are associated with vitals, laboratory, and demographics of patients. The proposed model has an area under the receiver operating characteristic (AUROC) of 0.97, outperforming all the existing systems in the current literature. The model can handle the missing data, and irregular sampling intervals frequently present in medical records.Clinical relevance-The proposed model can be used to predict the onset of sepsis 6 hours ahead of time by the use of a machine learning algorithm. This proposed method outperforms the sepsis prediction machine learning models found in the current literature.


Assuntos
Sepse , Bases de Dados Genéticas , Diagnóstico Precoce , Humanos , Aprendizado de Máquina , Sepse/diagnóstico , Sinais Vitais
6.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 184-187, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33017960

RESUMO

Recent advances in deep learning have enabled the development of automated frameworks for analysing medical images and signals. For analysis of physiological recordings, models based on temporal convolutional networks and recurrent neural networks have demonstrated encouraging results and an ability to capture complex patterns and dependencies in the data. However, representations that capture the entirety of the raw signal are suboptimal as not all portions of the signal are equally important. As such, attention mechanisms are proposed to divert focus to regions of interest, reducing computational cost and enhancing accuracy. Here, we evaluate attention-based frameworks for the classification of physiological signals in different clinical domains. We evaluated our methodology on three classification scenarios: neurogenerative disorders, neurological status and seizure type. We demonstrate that attention networks can outperform traditional deep learning models for sequence modelling by identifying the most relevant attributes of an input signal for decision making. This work highlights the benefits of attention-based models for analysing raw data in the field of biomedical research.


Assuntos
Atenção , Redes Neurais de Computação , Bases de Dados Genéticas , Humanos , Convulsões
7.
Stud Health Technol Inform ; 273: 129-135, 2020 Sep 04.
Artigo em Inglês | MEDLINE | ID: mdl-33087602

RESUMO

In this paper, we describe a strategy for the development of a genetic analysis comprehensive representation. The primary intention is to ensure the available utilization of genetic analysis results in clinical practice. The system is called Personnel Genetic Card (PGC), and it is developed in cooperation of CIIRC CTU in Prague and the Mediware company. Nowadays, genetic information is more and more part of medicine and life quality services (e.g. nutritional consulting). Therefore, there is necessary to bind genetic information with the clinical phenotype, such as drug metabolism or intolerance to various substances. We proposed a structured form of the record, where we utilize the LOINC® standard to identify genetic test parameters, and several terminology databases for representing specific genetic information (e.g. HGNC, NCBI RefSeq, NCBI dbNSP, HGVS). Further, there are also several knowledge databases (PharmGKB, SNPedia, ClinVar) that collect interpretation for genetic analysis results. In the results of this paper, we describe our idea in the structure and process perspective. The structural perspective includes the representation of the analysis record and its binding with the interpretations. The process perspective describes roles and activities within the PGC system use.


Assuntos
Testes Genéticos , Informações Pessoalmente Identificáveis , Bases de Dados Genéticas , Logical Observation Identifiers Names and Codes , Fenótipo
8.
PLoS Comput Biol ; 16(9): e1008182, 2020 09.
Artigo em Inglês | MEDLINE | ID: mdl-32931516

RESUMO

Recent advances in experimental biology allow creation of datasets where several genome-wide data types (called omics) are measured per sample. Integrative analysis of multi-omic datasets in general, and clustering of samples in such datasets specifically, can improve our understanding of biological processes and discover different disease subtypes. In this work we present MONET (Multi Omic clustering by Non-Exhaustive Types), which presents a unique approach to multi-omic clustering. MONET discovers modules of similar samples, such that each module is allowed to have a clustering structure for only a subset of the omics. This approach differs from most existent multi-omic clustering algorithms, which assume a common structure across all omics, and from several recent algorithms that model distinct cluster structures. We tested MONET extensively on simulated data, on an image dataset, and on ten multi-omic cancer datasets from TCGA. Our analysis shows that MONET compares favorably with other multi-omic clustering methods. We demonstrate MONET's biological and clinical relevance by analyzing its results for Ovarian Serous Cystadenocarcinoma. We also show that MONET is robust to missing data, can cluster genes in multi-omic dataset, and reveal modules of cell types in single-cell multi-omic data. Our work shows that MONET is a valuable tool that can provide complementary results to those provided by existent algorithms for multi-omic analysis.


Assuntos
Algoritmos , Genômica/métodos , Análise por Conglomerados , Bases de Dados Genéticas , Humanos , Neoplasias/genética , Neoplasias/metabolismo , Análise de Célula Única
9.
Nat Commun ; 11(1): 4748, 2020 09 21.
Artigo em Inglês | MEDLINE | ID: mdl-32958763

RESUMO

The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts.


Assuntos
Genoma Humano/genética , Mutação , Neoplasias/genética , Composição de Bases , DNA Intergênico , Bases de Dados Genéticas , Exoma/genética , Éxons , Humanos , Estudos Retrospectivos , Sequenciamento Completo do Exoma , Sequenciamento Completo do Genoma
10.
Medicine (Baltimore) ; 99(35): e21902, 2020 Aug 28.
Artigo em Inglês | MEDLINE | ID: mdl-32871922

RESUMO

The function of miR-9 in osteosarcoma is not well-investigated and controversial. Therefore, we conducted meta-analysis to explore the role of miR-9 in osteosarcoma, and collected relevant TCGA data to further testify the result. In addition, bioinformatics analysis was conducted to investigate the mechanism and related pathways of miR-9-3p in osteosarcoma.Literature search was operated on databases up to February 19, 2020, including PubMed, Web of Science, Science Direct, Cochrane Central Register of Controlled Trials, and Wiley Online Library, China National Knowledge Infrastructure, China Biology Medicine disc, Chongqing VIP, and Wan Fang Data. The relation of miR-9 expression with survival outcome was estimated by hazard ratio (HRs) and 95% CIs. Meta-analysis was conducted on the Stata 12.0 (Stata Corporation, TX). To further assess the function of miR-9 in osteosarcoma, relevant data from the TCGA database was collected. Three databases, miRDB, miRPathDB 2.0, and Targetscan 7.2, were used for prediction of target genes. Genes present in these 3 databases were considered as predicted target genes of miR-9-3p. Venny 2.1 were used for intersection analysis. Subsequently, GO, KEGG, and PPI network analysis were conducted based on the overlapping target genes of miR-9-3p to explore the possible molecular mechanism in osteosarcoma.Meta-analysis shown that overexpression of miR-9 was associated with worse overall survival (OS) (HR = 4.180, 95% CI: 2.880-6.066, P < .001, I = 23.5%). Based on TCGA data, osteosarcoma patients with overexpression of miR-9-3p (HR = 1.603, 95% CI: 1.028-2.499, P = .037) and miR-9-5p (HR = 1.698, 95% CI: 1.133-2.545, P = .01) also suffered poor OS. In bioinformatics analysis, 2 significant and important pathways were enriched: Wnt signaling pathway from gene ontology analysis (gene ontology:0016055, P-adjust = .008); hippo signaling pathway from Kyoto Encyclopedia of Genes and Genomes analysis (P-adjust = .007). Moreover, network analysis relevant protein-protein interaction was visualized, revealing 117 nodes and 161 edges.High miR-9 expression was associated with poor prognosis. Based on bioinformatics analysis, this study enhanced the understanding of the mechanism and related pathways of miR-9 in osteosarcoma.


Assuntos
MicroRNAs/genética , Osteossarcoma/genética , Biologia Computacional , Bases de Dados Genéticas , Perfilação da Expressão Gênica , Humanos , Prognóstico , Transdução de Sinais/genética
11.
PLoS Comput Biol ; 16(9): e1008108, 2020 09.
Artigo em Inglês | MEDLINE | ID: mdl-32898133

RESUMO

Existing models for assessing microbiome sequencing such as operational taxonomic units (OTUs) can only test predictors' effects on OTUs. There is limited work on how to estimate the correlations between multiple OTUs and incorporate such relationship into models to evaluate longitudinal OTU measures. We propose a novel approach to estimate OTU correlations based on their taxonomic structure, and apply such correlation structure in Generalized Estimating Equations (GEE) models to estimate both predictors' effects and OTU correlations. We develop a two-part Microbiome Taxonomic Longitudinal Correlation (MTLC) model for multivariate zero-inflated OTU outcomes based on the GEE framework. In addition, longitudinal and other types of repeated OTU measures are integrated in the MTLC model. Extensive simulations have been conducted to evaluate the performance of the MTLC method. Compared with the existing methods, the MTLC method shows robust and consistent estimation, and improved statistical power for testing predictors' effects. Lastly we demonstrate our proposed method by implementing it into a real human microbiome study to evaluate the obesity on twins.


Assuntos
Biologia Computacional/métodos , DNA Bacteriano , Microbioma Gastrointestinal/genética , Modelos Estatísticos , Análise de Sequência de DNA/métodos , DNA Bacteriano/classificação , DNA Bacteriano/genética , Bases de Dados Genéticas , Humanos
15.
Medicine (Baltimore) ; 99(39): e22257, 2020 Sep 25.
Artigo em Inglês | MEDLINE | ID: mdl-32991423

RESUMO

Cutaneous squamous cell carcinoma (cSCC) is a common skin cancer with an increasing incidence. As a pre-cancerous condition, actinic keratosis (AK) has an up to 20% risk of progression to cSCC. This study aims to define the potential genes that associated with genesis and progression of cSCC, thereby further identify critical biomarkers for the prevention, early diagnosis, and effective treatment of cSCC.Two datasets GSE42677 and GSE45216 were downloaded from the GEO. Microarray data analysis was applied to explore the differentially expressed genes (DEGs) between cSCC samples and AK samples. Then functional enrichment analysis, protein-protein interaction (PPI) network, and drug-gene interaction analysis were performed to screen key genes.A total of 711 DEGs, including 238 upregulated genes and 473 downregulated genes, were screened out. DEGs mainly involved in pathways as extracellular matrix (ECM)-receptor interaction, hematopoietic cell lineage, phosphatidylinositol 3-kinase (PI3K-Akt) signaling pathway, and focal adhesion. Candidate genes, including upregulated genes as JUN, filamin A (FLNA), casein kinase 1 delta (CSNK1D), and histone cluster 1 H3 family member f (HIST1H3F), and downregulated genes as androgen receptor (AR), heat shock protein family H member 1 (HSPH1), tropomyosin 1 (TPM1), pyruvate kinase, muscle (PKM), LIM domain and actin binding 1 (LIMA1), and synaptopodin (SYNPO) were screened out. In drug-gene interaction analysis, 13 genes and 44 drugs were identified.This study demonstrates that genes JUN, FLNA, AR, HSPH1, and CSNK1D have the potential to function as targets for diagnosis and treatment of cSCC.


Assuntos
Carcinoma de Células Escamosas/genética , Análise em Microsséries/normas , Neoplasias Cutâneas/genética , Biomarcadores Tumorais/genética , Bases de Dados Genéticas , Perfilação da Expressão Gênica , Regulação Neoplásica da Expressão Gênica , Humanos , Ceratose Actínica/genética , Mapas de Interação de Proteínas , Melhoria de Qualidade
16.
Anticancer Res ; 40(10): 5417-5421, 2020 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-32988862

RESUMO

BACKGROUND: Type II diabetes agents have anticancer effects on head and neck squamous cell carcinoma (HNSCC). The mechanistic target of rapamycin (MTOR) pathway represents a putative target. MATERIALS AND METHODS: We interrogated an Affymetrix HNSCC dataset for MTOR-related gene expression. RESULTS: MTOR expression itself was unchanged, but various related genes demonstrated differential expression. Pathway promoters ras homolog (RHEB), MTOR-associated protein (MLST8), and ribosomal protein S6 kinase B1 (RPS6KB1) were up-regulated. Expression of growth suppressors tuberous sclerosis complex 2 (TSC2), programmed cell death 4 (PDCD4), and BCL2 apoptosis regulator-associated agonist of cell death (BAD) were reduced in HNSCC. Upstream, phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA), AKT serine/threonine kinase 1 (AKT1), and phosphatase and tensin homolog (PTEN) were up-regulated in cancer. CONCLUSION: Several MTOR pathway promoters and tumor suppressors were found to be differentially expressed, favoring MTOR pathway up-regulation in HNSCC. Genomic databases can be interrogated to identify intervention targets and endpoints in HNSCC trials.


Assuntos
Bases de Dados Genéticas , Neoplasias de Cabeça e Pescoço/genética , Proteínas de Neoplasias/genética , Serina-Treonina Quinases TOR/genética , Proteínas Reguladoras de Apoptose/genética , Classe I de Fosfatidilinositol 3-Quinases/genética , Regulação Neoplásica da Expressão Gênica/genética , Neoplasias de Cabeça e Pescoço/classificação , Neoplasias de Cabeça e Pescoço/patologia , Humanos , PTEN Fosfo-Hidrolase/genética , Proteínas Proto-Oncogênicas c-akt/genética , Proteínas Proto-Oncogênicas c-bcl-2/genética , Proteínas de Ligação a RNA/genética , Proteína Enriquecida em Homólogo de Ras do Encéfalo/genética , Proteínas Quinases S6 Ribossômicas 70-kDa/genética , Transdução de Sinais/genética , Proteína 2 do Complexo Esclerose Tuberosa/genética , Proteína de Morte Celular Associada a bcl/genética , Homólogo LST8 da Proteína Associada a mTOR/genética
17.
BMC Bioinformatics ; 21(1): 397, 2020 Sep 09.
Artigo em Inglês | MEDLINE | ID: mdl-32907531

RESUMO

BACKGROUND: Ion Torrent is one of the major next generation sequencing (NGS) technologies and it is frequently used in medical research and diagnosis. The built-in software for the Ion Torrent sequencing machines delivers the sequencing results in the BAM format. In addition to the usual SAM/BAM fields, the Ion Torrent BAM file includes technology-specific flow signal data. The flow signals occupy a big portion of the BAM file (about 75% for the human genome). Compressing SAM/BAM into CRAM format significantly reduces the space needed to store the NGS results. However, the tools for generating the CRAM formats are not designed to handle the flow signals. This missing feature has motivated us to develop a new program to improve the compression of the Ion Torrent files for long term archiving. RESULTS: In this paper, we present IonCRAM, the first reference-based compression tool to compress Ion Torrent BAM files for long term archiving. For the BAM files, IonCRAM could achieve a space saving of about 43%. This space saving is superior to what achieved with the CRAM format by about 8-9%. CONCLUSIONS: Reducing the space consumption of NGS data reduces the cost of storage and data transfer. Therefore, developing efficient compression software for clinical NGS data goes beyond the computational interest; as it ultimately contributes to the overall cost reduction of the clinical test. The space saving achieved by our tool is a practical step in this direction. The tool is open source and available at Code Ocean, github, and http://ioncram.saudigenomeproject.com .


Assuntos
Interface Usuário-Computador , Algoritmos , Bases de Dados Genéticas , Genoma Humano , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Humanos
18.
PLoS One ; 15(9): e0237721, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32915809

RESUMO

The number of national reference populations that are whole-genome sequenced are rapidly increasing. Partly driving this development is the fact that genetic disease studies benefit from knowing the genetic variation typical for the geographical area of interest. A whole-genome sequenced Swedish national reference population (n = 1000) has been recently published but with few samples from northern Sweden. In the present study we have whole-genome sequenced a control population (n = 300) (ACpop) from Västerbotten County, a sparsely populated region in northern Sweden previously shown to be genetically different from southern Sweden. The aggregated variant frequencies within ACpop are publicly available (DOI 10.17044/NBIS/G000005) to function as a basic resource in clinical genetics and for genetic studies. Our analysis of ACpop, representing approximately 0.11% of the population in Västerbotten, indicates the presence of a genetic substructure within the county. Furthermore, a demographic analysis showed that the population from which samples were drawn was to a large extent geographically stationary, a finding that was corroborated in the genetic analysis down to the level of municipalities. Including ACpop in the reference population when imputing unknown variants in a Västerbotten cohort resulted in a strong increase in the number of high-confidence imputed variants (up to 81% for variants with minor allele frequency < 5%). ACpop was initially designed for cancer disease studies, but the genetic structure within the cohort will be of general interest for all genetic disease studies in northern Sweden.


Assuntos
Genoma Humano , Polimorfismo Genético , População/genética , Idoso , Idoso de 80 Anos ou mais , Bases de Dados Genéticas , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Suécia , Sequenciamento Completo do Genoma
19.
J Med Internet Res ; 22(10): e22299, 2020 10 02.
Artigo em Inglês | MEDLINE | ID: mdl-32931441

RESUMO

BACKGROUND: COVID-19 became a global pandemic not long after its identification in late 2019. The genomes of SARS-CoV-2 are being rapidly sequenced and shared on public repositories. To keep up with these updates, scientists need to frequently refresh and reclean data sets, which is an ad hoc and labor-intensive process. Further, scientists with limited bioinformatics or programming knowledge may find it difficult to analyze SARS-CoV-2 genomes. OBJECTIVE: To address these challenges, we developed CoV-Seq, an integrated web server that enables simple and rapid analysis of SARS-CoV-2 genomes. METHODS: CoV-Seq is implemented in Python and JavaScript. The web server and source code URLs are provided in this article. RESULTS: Given a new sequence, CoV-Seq automatically predicts gene boundaries and identifies genetic variants, which are displayed in an interactive genome visualizer and are downloadable for further analysis. A command-line interface is available for high-throughput processing. In addition, we aggregated all publicly available SARS-CoV-2 sequences from the Global Initiative on Sharing Avian Influenza Data (GISAID), National Center for Biotechnology Information (NCBI), European Nucleotide Archive (ENA), and China National GeneBank (CNGB), and extracted genetic variants from these sequences for download and downstream analysis. The CoV-Seq database is updated weekly. CONCLUSIONS: We have developed CoV-Seq, an integrated web service for fast and easy analysis of custom SARS-CoV-2 sequences. The web server provides an interactive module for the analysis of custom sequences and a weekly updated database of genetic variants of all publicly accessible SARS-CoV-2 sequences. We believe CoV-Seq will help improve our understanding of the genetic underpinnings of COVID-19.


Assuntos
Betacoronavirus/genética , Infecções por Coronavirus/virologia , Visualização de Dados , Bases de Dados Genéticas , Genoma Viral/genética , Pneumonia Viral/virologia , Software , Biologia Computacional , Infecções por Coronavirus/epidemiologia , Humanos , Pandemias , Pneumonia Viral/epidemiologia
20.
Toxicol Appl Pharmacol ; 406: 115237, 2020 11 01.
Artigo em Inglês | MEDLINE | ID: mdl-32920000

RESUMO

Improvement of COVID-19 clinical condition was seen in studies where combination of antiretroviral drugs, lopinavir and ritonavir, as well as immunomodulant antimalaric, chloroquine/hydroxychloroquine together with the macrolide-type antibiotic, azithromycin, was used for patient's treatment. Although these drugs are "old", their pharmacological and toxicological profile in SARS-CoV-2 - infected patients are still unknown. Thus, by using in silico toxicogenomic data-mining approach, we aimed to assess both risks and benefits of the COVID-19 treatment with the most promising candidate drugs combinations: lopinavir/ritonavir and chloroquine/hydroxychloroquine + azithromycin. The Comparative Toxicogenomics Database (CTD; http://CTD.mdibl.org), Cytoscape software (https://cytoscape.org) and ToppGene Suite portal (https://toppgene.cchmc.org) served as a foundation in our research. Our results have demonstrated that lopinavir/ritonavir increased the expression of the genes involved in immune response and lipid metabolism (IL6, ICAM1, CCL2, TNF, APOA1, etc.). Chloroquine/hydroxychloroquine + azithromycin interacted with 6 genes (CCL2, CTSB, CXCL8, IL1B, IL6 and TNF), whereas chloroquine and azithromycin affected two additional genes (BCL2L1 and CYP3A4), which might be a reason behind a greater number of consequential diseases. In contrast to lopinavir/ritonavir, chloroquine/hydroxychloroquine + azithromycin downregulated the expression of TNF and IL6. As expected, inflammation, cardiotoxicity, and dyslipidaemias were revealed as the main risks of lopinavir/ritonavir treatment, while chloroquine/hydroxychloroquine + azithromycin therapy was additionally linked to gastrointestinal and skin diseases. According to our results, these drug combinations should be administrated with caution to patients suffering from cardiovascular problems, autoimmune diseases, or acquired and hereditary lipid disorders.


Assuntos
Betacoronavirus , Simulação por Computador , Mineração de Dados/métodos , Toxicogenética/métodos , Antivirais/administração & dosagem , Antivirais/efeitos adversos , Azitromicina/administração & dosagem , Azitromicina/efeitos adversos , Cloroquina/administração & dosagem , Cloroquina/efeitos adversos , Infecções por Coronavirus/tratamento farmacológico , Infecções por Coronavirus/genética , Bases de Dados Genéticas , Quimioterapia Combinada , Redes Reguladoras de Genes/efeitos dos fármacos , Redes Reguladoras de Genes/genética , Humanos , Hidroxicloroquina/administração & dosagem , Hidroxicloroquina/efeitos adversos , Lopinavir/administração & dosagem , Lopinavir/efeitos adversos , Pandemias , Pneumonia Viral/tratamento farmacológico , Pneumonia Viral/genética , Ritonavir/administração & dosagem , Ritonavir/efeitos adversos
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