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2.
Gene ; 806: 145922, 2022 Jan 05.
Artigo em Inglês | MEDLINE | ID: mdl-34454032

RESUMO

Gastric cancer (GC)-derived cell lines were generally used in basic cancer research and drug screening. However, it is always concerned about the difference between cultured cells and primary tumor by oncologists. To address this question, we compared differentially expressed genes (DEGs) in primary cancers, healthy tissues, and cell lines both in vitro and in silico. Seven reported genes with decreased expression in GCs by DNA methylation were analyzed in our cohort studies and experimentally validation. Selected datasets from TCGA (The Cancer Genome Atlas), CCLE (The Broad Institute Cancer Cell Line Encyclopedia), and GTEx (The Genotype-Tissue Expression project) were used to represent GCs, GC-derived cell lines, and healthy tissues respectively in the in silico analysis. Thirty gastric tissues together with six cell lines were used for validations. Unexpectedly, we experimentally found that reported cancer-related downregulated genes were only found in cancer cell lines but not in biopsies. The unchanged gene expressions in primary GCs were generally consistent with our cohort study, using information from cancerous (TCGA) and healthy tissues (GETx). Substantial differences were also found between DEGs of cancer tissues (TGCA)/ healthy tissues (GTEx) pair and cell lines (CCLE)/ healthy tissues (GTEx) pair, which confirmed the significant differences between primary cancer and cancer cell lines. Moreover, elevated expression of YWHAQ (14-3-3 δ) and THBS1 were observed in the GC biopsies, which might be potential biomarkers for GC diagnosis, considering the increased YWHAQ and THBS1 associated with poor survival rates in gastric cancer patients. In sum, it is suggested that cautions should be taken when using GC cell lines to study genes that show great differences between cell lines and tissues.


Assuntos
Proteínas 14-3-3/genética , Regulação Neoplásica da Expressão Gênica , Proteínas de Neoplasias/genética , Neoplasias Gástricas/genética , Trombospondinas/genética , Proteínas 14-3-3/metabolismo , Idoso , Atlas como Assunto , Biomarcadores Tumorais/genética , Biomarcadores Tumorais/metabolismo , Linhagem Celular Tumoral , Metilação de DNA , Conjuntos de Dados como Assunto , Epigênese Genética , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Modelos Biológicos , Proteínas de Neoplasias/metabolismo , Cultura Primária de Células , Prognóstico , Neoplasias Gástricas/diagnóstico , Neoplasias Gástricas/mortalidade , Neoplasias Gástricas/patologia , Análise de Sobrevida , Trombospondinas/metabolismo , Células Tumorais Cultivadas
3.
PLoS One ; 16(12): e0260440, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34919543

RESUMO

Phosphorylation by serine-threonine and tyrosine kinases is critical for determining protein function. Array-based platforms for measuring reporter peptide signal levels allow for differential phosphorylation analysis between conditions for distinct active kinases. Peptide array technologies like the PamStation12 from PamGene allow for generating high-throughput, multi-dimensional, and complex functional proteomics data. As the adoption rate of such technologies increases, there is an imperative need for software tools that streamline the process of analyzing such data. We present Kinome Random Sampling Analyzer (KRSA), an R package and R Shiny web-application for analyzing kinome array data to help users better understand the patterns of functional proteomics in complex biological systems. KRSA is an All-In-One tool that reads, formats, fits models, analyzes, and visualizes PamStation12 kinome data. While the underlying algorithm has been experimentally validated in previous publications, we demonstrate KRSA workflow on dorsolateral prefrontal cortex (DLPFC) in male (n = 3) and female (n = 3) subjects to identify differential phosphorylation signatures and upstream kinase activity. Kinase activity differences between males and females were compared to a previously published kinome dataset (11 female and 7 male subjects) which showed similar global phosphorylation signals patterns.


Assuntos
/enzimologia , Família Multigênica , Fosfoproteínas/metabolismo , Proteínas Tirosina Quinases/metabolismo , Software , Algoritmos , Autopsia , Benchmarking , Conjuntos de Dados como Assunto , Feminino , Expressão Gênica , Humanos , Masculino , Fosfoproteínas/classificação , Fosfoproteínas/genética , Fosforilação , Análise de Componente Principal , Análise Serial de Proteínas , /genética , Proteínas Tirosina Quinases/classificação , Proteínas Tirosina Quinases/genética , Proteômica/métodos
4.
BMC Plant Biol ; 21(1): 604, 2021 Dec 22.
Artigo em Inglês | MEDLINE | ID: mdl-34937558

RESUMO

BACKGROUND: Picrorhiza kurroa Royle ex Benth. being a rich source of phytochemicals, is a promising high altitude medicinal herb of Himalaya. The medicinal potential is attributed to picrosides i.e. iridoid glycosides, which synthesized in organ-specific manner through highly complex pathways. Here, we present a large-scale proteome reference map of P. kurroa, consisting of four morphologically differentiated organs and two developmental stages. RESULTS: We were able to identify 5186 protein accessions (FDR < 1%) providing a deep coverage of protein abundance array, spanning around six orders of magnitude. Most of the identified proteins are associated with metabolic processes, response to abiotic stimuli and cellular processes. Organ specific sub-proteomes highlights organ specialized functions that would offer insights to explore tissue profile for specific protein classes. With reference to P. kurroa development, vegetative phase is enriched with growth related processes, however generative phase harvests more energy in secondary metabolic pathways. Furthermore, stress-responsive proteins, RNA binding proteins (RBPs) and post-translational modifications (PTMs), particularly phosphorylation and ADP-ribosylation play an important role in P. kurroa adaptation to alpine environment. The proteins involved in the synthesis of secondary metabolites are well represented in P. kurroa proteome. The phytochemical analysis revealed that marker compounds were highly accumulated in rhizome and overall, during the late stage of development. CONCLUSIONS: This report represents first extensive proteomic description of organ and developmental dissected P. kurroa, providing a platform for future studies related to stress tolerance and medical applications.


Assuntos
Organogênese Vegetal , Picrorhiza/química , Proteínas de Plantas/análise , Conjuntos de Dados como Assunto , Espectrometria de Massas , Redes e Vias Metabólicas , Mapeamento de Peptídeos , Proteoma , Estresse Fisiológico
5.
PLoS One ; 16(12): e0261083, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34928943

RESUMO

Web-based data analysis and visualization tools are mostly designed for specific purposes, such as the analysis of data from whole transcriptome RNA sequencing or single-cell RNA sequencing. However, generic tools designed for the analysis of common laboratory data for noncomputational scientists are also needed. The importance of such web-based tools is emphasized by the continuing increases in the sample capacity of conventional laboratory tools such as quantitative PCR, flow cytometry or ELISA instruments. We present a web-based application FaDA, developed with the R Shiny package that provides users with the ability to perform statistical group comparisons, including parametric and nonparametric tests, with multiple testing corrections suitable for most standard wet-laboratory analyses. FaDA provides data visualizations such as heatmaps, principal component analysis (PCA) plots, correlograms and receiver operating curves (ROCs). Calculations are performed through the R language. The FaDA application provides a free and intuitive interface that allows biologists without bioinformatic skill to easily and quickly perform common laboratory data analyses. The application is freely accessible at https://shiny-bird.univ-nantes.fr/app/Fada.


Assuntos
Análise de Dados , Internet , Software , Interpretação Estatística de Dados , Visualização de Dados , Conjuntos de Dados como Assunto , Citometria de Fluxo/instrumentação , Humanos , Transplante de Rim , Laboratórios
6.
Nat Commun ; 12(1): 5915, 2021 10 08.
Artigo em Inglês | MEDLINE | ID: mdl-34625565

RESUMO

Automatic medical image segmentation plays a critical role in scientific research and medical care. Existing high-performance deep learning methods typically rely on large training datasets with high-quality manual annotations, which are difficult to obtain in many clinical applications. Here, we introduce Annotation-effIcient Deep lEarning (AIDE), an open-source framework to handle imperfect training datasets. Methodological analyses and empirical evaluations are conducted, and we demonstrate that AIDE surpasses conventional fully-supervised models by presenting better performance on open datasets possessing scarce or noisy annotations. We further test AIDE in a real-life case study for breast tumor segmentation. Three datasets containing 11,852 breast images from three medical centers are employed, and AIDE, utilizing 10% training annotations, consistently produces segmentation maps comparable to those generated by fully-supervised counterparts or provided by independent radiologists. The 10-fold enhanced efficiency in utilizing expert labels has the potential to promote a wide range of biomedical applications.


Assuntos
Neoplasias da Mama/diagnóstico por imagem , Aprendizado Profundo , Processamento de Imagem Assistida por Computador/métodos , Imageamento por Ressonância Magnética/estatística & dados numéricos , Neoplasias da Mama/patologia , Conjuntos de Dados como Assunto , Feminino , Humanos , Estudos Retrospectivos
7.
Int J Mol Sci ; 22(19)2021 Oct 05.
Artigo em Inglês | MEDLINE | ID: mdl-34639124

RESUMO

Bisphenols are important environmental pollutants that are extensively studied due to different detrimental effects, while the molecular mechanisms behind these effects are less well understood. Like other environmental pollutants, bisphenols are being tested in various experimental models, creating large expression datasets found in open access storage. The meta-analysis of such datasets is, however, very complicated for various reasons. Here, we developed an integrating statistical and machine-learning model approach for the meta-analysis of bisphenol A (BPA) exposure datasets from different mouse tissues. We constructed three joint datasets following three different strategies for dataset integration: in particular, using all common genes from the datasets, uncorrelated, and not co-expressed genes, respectively. By applying machine learning methods to these datasets, we identified genes whose expression was significantly affected in all of the BPA microanalysis data tested; those involved in the regulation of cell survival include: Tnfr2, Hgf-Met, Agtr1a, Bdkrb2; signaling through Mapk8 (Jnk1)); DNA repair (Hgf-Met, Mgmt); apoptosis (Tmbim6, Bcl2, Apaf1); and cellular junctions (F11r, Cldnd1, Ctnd1 and Yes1). Our results highlight the benefit of combining existing datasets for the integrated analysis of a specific topic when individual datasets are limited in size.


Assuntos
Apoptose , Compostos Benzidrílicos/toxicidade , Biomarcadores/metabolismo , Regulação da Expressão Gênica/efeitos dos fármacos , Fígado/metabolismo , Aprendizado de Máquina , Modelos Estatísticos , Fenóis/toxicidade , Poluentes Ocupacionais do Ar/toxicidade , Animais , Sobrevivência Celular , Conjuntos de Dados como Assunto , Perfilação da Expressão Gênica , Fígado/efeitos dos fármacos , Masculino , Metanálise como Assunto , Camundongos
8.
Nature ; 598(7880): 364-367, 2021 10.
Artigo em Inglês | MEDLINE | ID: mdl-34616041

RESUMO

The enzymes of the mitochondrial electron transport chain are key players of cell metabolism. Despite being active when isolated, in vivo they associate into supercomplexes1, whose precise role is debated. Supercomplexes CIII2CIV1-2 (refs. 2,3), CICIII2 (ref. 4) and CICIII2CIV (respirasome)5-10 exist in mammals, but in contrast to CICIII2 and the respirasome, to date the only known eukaryotic structures of CIII2CIV1-2 come from Saccharomyces cerevisiae11,12 and plants13, which have different organization. Here we present the first, to our knowledge, structures of mammalian (mouse and ovine) CIII2CIV and its assembly intermediates, in different conformations. We describe the assembly of CIII2CIV from the CIII2 precursor to the final CIII2CIV conformation, driven by the insertion of the N terminus of the assembly factor SCAF1 (ref. 14) deep into CIII2, while its C terminus is integrated into CIV. Our structures (which include CICIII2 and the respirasome) also confirm that SCAF1 is exclusively required for the assembly of CIII2CIV and has no role in the assembly of the respirasome. We show that CIII2 is asymmetric due to the presence of only one copy of subunit 9, which straddles both monomers and prevents the attachment of a second copy of SCAF1 to CIII2, explaining the presence of one copy of CIV in CIII2CIV in mammals. Finally, we show that CIII2 and CIV gain catalytic advantage when assembled into the supercomplex and propose a role for CIII2CIV in fine tuning the efficiency of electron transfer in the electron transport chain.


Assuntos
Respiração Celular , Mitocôndrias/enzimologia , Complexos Multienzimáticos/química , Complexos Multienzimáticos/metabolismo , Ovinos , Animais , Sítios de Ligação , Conjuntos de Dados como Assunto , Transporte de Elétrons , Camundongos , Mitocôndrias/metabolismo , Modelos Moleculares , NAD/metabolismo , Ácido Succínico/metabolismo
9.
Nature ; 598(7879): 120-128, 2021 10.
Artigo em Inglês | MEDLINE | ID: mdl-34616061

RESUMO

Mammalian brain cells show remarkable diversity in gene expression, anatomy and function, yet the regulatory DNA landscape underlying this extensive heterogeneity is poorly understood. Here we carry out a comprehensive assessment of the epigenomes of mouse brain cell types by applying single-nucleus DNA methylation sequencing1,2 to profile 103,982 nuclei (including 95,815 neurons and 8,167 non-neuronal cells) from 45 regions of the mouse cortex, hippocampus, striatum, pallidum and olfactory areas. We identified 161 cell clusters with distinct spatial locations and projection targets. We constructed taxonomies of these epigenetic types, annotated with signature genes, regulatory elements and transcription factors. These features indicate the potential regulatory landscape supporting the assignment of putative cell types and reveal repetitive usage of regulators in excitatory and inhibitory cells for determining subtypes. The DNA methylation landscape of excitatory neurons in the cortex and hippocampus varied continuously along spatial gradients. Using this deep dataset, we constructed an artificial neural network model that precisely predicts single neuron cell-type identity and brain area spatial location. Integration of high-resolution DNA methylomes with single-nucleus chromatin accessibility data3 enabled prediction of high-confidence enhancer-gene interactions for all identified cell types, which were subsequently validated by cell-type-specific chromatin conformation capture experiments4. By combining multi-omic datasets (DNA methylation, chromatin contacts, and open chromatin) from single nuclei and annotating the regulatory genome of hundreds of cell types in the mouse brain, our DNA methylation atlas establishes the epigenetic basis for neuronal diversity and spatial organization throughout the mouse cerebrum.


Assuntos
Encéfalo/citologia , Metilação de DNA , Epigenoma , Epigenômica , Neurônios/classificação , Neurônios/metabolismo , Análise de Célula Única , Animais , Atlas como Assunto , Encéfalo/metabolismo , Cromatina/química , Cromatina/genética , Cromatina/metabolismo , Citosina/química , Citosina/metabolismo , Conjuntos de Dados como Assunto , Giro Denteado/citologia , Elementos Facilitadores Genéticos/genética , Perfilação da Expressão Gênica , Hipocampo/citologia , Hipocampo/metabolismo , Masculino , Camundongos , Camundongos Endogâmicos C57BL , Modelos Biológicos , Vias Neurais , Neurônios/citologia
10.
Nature ; 598(7879): 103-110, 2021 10.
Artigo em Inglês | MEDLINE | ID: mdl-34616066

RESUMO

Single-cell transcriptomics can provide quantitative molecular signatures for large, unbiased samples of the diverse cell types in the brain1-3. With the proliferation of multi-omics datasets, a major challenge is to validate and integrate results into a biological understanding of cell-type organization. Here we generated transcriptomes and epigenomes from more than 500,000 individual cells in the mouse primary motor cortex, a structure that has an evolutionarily conserved role in locomotion. We developed computational and statistical methods to integrate multimodal data and quantitatively validate cell-type reproducibility. The resulting reference atlas-containing over 56 neuronal cell types that are highly replicable across analysis methods, sequencing technologies and modalities-is a comprehensive molecular and genomic account of the diverse neuronal and non-neuronal cell types in the mouse primary motor cortex. The atlas includes a population of excitatory neurons that resemble pyramidal cells in layer 4 in other cortical regions4. We further discovered thousands of concordant marker genes and gene regulatory elements for these cell types. Our results highlight the complex molecular regulation of cell types in the brain and will directly enable the design of reagents to target specific cell types in the mouse primary motor cortex for functional analysis.


Assuntos
Epigenômica , Perfilação da Expressão Gênica , Córtex Motor/citologia , Neurônios/classificação , Análise de Célula Única , Transcriptoma , Animais , Atlas como Assunto , Conjuntos de Dados como Assunto , Epigênese Genética , Feminino , Masculino , Camundongos , Córtex Motor/anatomia & histologia , Neurônios/citologia , Neurônios/metabolismo , Especificidade de Órgãos , Reprodutibilidade dos Testes
11.
Int J Public Health ; 66: 584916, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34616240

RESUMO

Objective: Much of the extensive quantitative research linking socio-economic position (SEP) and health utilizes three common indicators: income, occupation and education. Existing survey data may enable researchers to include indicators of additional forms of capital in their analyses, permitting more nuanced consideration of the relationship between SEP and health. Our objective was to identify the breadth of survey questions related to economic, cultural, and social capital available through Statistics Canada surveys, and the extent to which those surveys also include health measures. Methods: We compiled a list of all population-based Statistics Canada surveys, and developed a broad list of potential indicators of forms of capital. We systematically searched the surveys for those indicators and health measures, analyzing their co-occurrence. Results: Traditional SEP indicators were present in 73% of surveys containing health measures, while additional indicators of social and cultural capital were available in 57%. Conclusion: Existing national survey data represent an under-exploited opportunity for research examining the relationship between various forms of capital and health in Canada. Future empirical explorations of these data could enrich our theoretical understanding of health inequities.


Assuntos
Disparidades nos Níveis de Saúde , Canadá , Conjuntos de Dados como Assunto , Humanos , Capital Social , Fatores Socioeconômicos , Inquéritos e Questionários
12.
Int J Lab Hematol ; 43(6): 1325-1333, 2021 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-34623759

RESUMO

BACKGROUND: Multiple myeloma (MM) is a hematological malignancy. Coronavirus disease 2019 (COVID-19) infection correlates with MM features. This study aimed to identify MM prognostic biomarkers with potential association with COVID-19. METHODS: Differentially expressed genes (DEGs) in five MM data sets (GSE47552, GSE16558, GSE13591, GSE6477, and GSE39754) with the same expression trends were screened out. Functional enrichment analysis and the protein-protein interaction network were performed for all DEGs. Prognosis-associated DEGs were screened using the stepwise Cox regression analysis in the cancer genome atlas (TCGA) MMRF-CoMMpass cohort and the GSE24080 data set. Prognosis-associated DEGs associated with COVID-19 infection in the GSE164805 data set were also identified. RESULTS: A total of 98 DEGs with the same expression trends in five data sets were identified, and 83 DEGs were included in the protein-protein interaction network. Cox regression analysis identified 16 DEGs were associated with MM prognosis in the TCGA cohort, and only the cytochrome c oxidase subunit 6C (COX6C) gene (HR = 1.717, 95% CI 1.231-2.428, p = .002) and the nucleotide-binding oligomerization domain containing 2 (NOD2) gene (HR = 0.882, 95% CI 0.798-0.975, p = .014) were independent factors related to MM prognosis in the GSE24080 data set. Both of them were downregulated in patients with mild COVID-19 infection compared with controls but were upregulated in patients with severe COVID-19 compared with patients with mild illness. CONCLUSIONS: The NOD2 and COX6C genes might be used as prognostic biomarkers in MM. The two genes might be associated with the development of COVID-19 infection.


Assuntos
COVID-19/genética , Biologia Computacional/métodos , Perfilação da Expressão Gênica , Mieloma Múltiplo/genética , SARS-CoV-2 , COVID-19/mortalidade , Conjuntos de Dados como Assunto , Complexo IV da Cadeia de Transporte de Elétrons/genética , Regulação Neoplásica da Expressão Gênica , Regulação Viral da Expressão Gênica , Ontologia Genética , Humanos , Estimativa de Kaplan-Meier , Análise em Microsséries , Proteínas de Neoplasias/biossíntese , Proteínas de Neoplasias/genética , Proteína Adaptadora de Sinalização NOD2/genética , Prognóstico , Modelos de Riscos Proporcionais , Mapas de Interação de Proteínas/genética
13.
Nat Commun ; 12(1): 5728, 2021 09 30.
Artigo em Inglês | MEDLINE | ID: mdl-34593791

RESUMO

Our thoughts arise from coordinated patterns of interactions between brain structures that change with our ongoing experiences. High-order dynamic correlations in neural activity patterns reflect different subgraphs of the brain's functional connectome that display homologous lower-level dynamic correlations. Here we test the hypothesis that high-level cognition is reflected in high-order dynamic correlations in brain activity patterns. We develop an approach to estimating high-order dynamic correlations in timeseries data, and we apply the approach to neuroimaging data collected as human participants either listen to a ten-minute story or listen to a temporally scrambled version of the story. We train across-participant pattern classifiers to decode (in held-out data) when in the session each neural activity snapshot was collected. We find that classifiers trained to decode from high-order dynamic correlations yield the best performance on data collected as participants listened to the (unscrambled) story. By contrast, classifiers trained to decode data from scrambled versions of the story yielded the best performance when they were trained using first-order dynamic correlations or non-correlational activity patterns. We suggest that as our thoughts become more complex, they are reflected in higher-order patterns of dynamic network interactions throughout the brain.


Assuntos
Percepção Auditiva/fisiologia , Encéfalo/fisiologia , Cognição/fisiologia , Modelos Neurológicos , Encéfalo/diagnóstico por imagem , Conectoma , Conjuntos de Dados como Assunto , Imageamento por Ressonância Magnética , Rede Nervosa/fisiologia , Neuroimagem/métodos , Fatores de Tempo
14.
Nat Commun ; 12(1): 5732, 2021 09 30.
Artigo em Inglês | MEDLINE | ID: mdl-34593797

RESUMO

Although alterations in chromatin structure are known to exist in tumors, how these alterations relate to molecular phenotypes in cancer remains to be demonstrated. Multi-omics profiling of human tumors can provide insight into how alterations in chromatin structure are propagated through the pathway of gene expression to result in malignant protein expression. We applied multi-omics profiling of chromatin accessibility, RNA abundance, and protein abundance to 36 human thyroid cancer primary tumors, metastases, and patient-match normal tissue. Through quantification of chromatin accessibility associated with active transcription units and global protein expression, we identify a local chromatin structure that is highly correlated with coordinated RNA and protein expression. In particular, we identify enhancers located within gene-bodies as predictive of correlated RNA and protein expression, that is independent of overall transcriptional activity. To demonstrate the generalizability of these findings we also identify similar results in an independent cohort of human breast cancers. Taken together, these analyses suggest that local enhancers, rather than distal enhancers, are likely most predictive of cancer gene expression phenotypes. This allows for identification of potential targets for cancer therapeutic approaches and reinforces the utility of multi-omics profiling as a methodology to understand human disease.


Assuntos
Neoplasias da Mama/genética , Cromatina/metabolismo , Regulação Neoplásica da Expressão Gênica , Câncer Papilífero da Tireoide/genética , Neoplasias da Glândula Tireoide/genética , Neoplasias da Mama/patologia , Sequenciamento de Cromatina por Imunoprecipitação , Estudos de Coortes , Conjuntos de Dados como Assunto , Elementos Facilitadores Genéticos , Epigênese Genética , Feminino , Redes Reguladoras de Genes , Humanos , Masculino , Regiões Promotoras Genéticas , Proteômica , RNA/metabolismo , RNA-Seq , Câncer Papilífero da Tireoide/patologia , Câncer Papilífero da Tireoide/cirurgia , Glândula Tireoide/patologia , Glândula Tireoide/cirurgia , Neoplasias da Glândula Tireoide/patologia , Neoplasias da Glândula Tireoide/cirurgia , Tireoidectomia , Fatores de Transcrição/metabolismo
15.
Nat Commun ; 12(1): 5743, 2021 09 30.
Artigo em Inglês | MEDLINE | ID: mdl-34593817

RESUMO

Machine learning has been increasingly used for protein engineering. However, because the general sequence contexts they capture are not specific to the protein being engineered, the accuracy of existing machine learning algorithms is rather limited. Here, we report ECNet (evolutionary context-integrated neural network), a deep-learning algorithm that exploits evolutionary contexts to predict functional fitness for protein engineering. This algorithm integrates local evolutionary context from homologous sequences that explicitly model residue-residue epistasis for the protein of interest with the global evolutionary context that encodes rich semantic and structural features from the enormous protein sequence universe. As such, it enables accurate mapping from sequence to function and provides generalization from low-order mutants to higher-order mutants. We show that ECNet predicts the sequence-function relationship more accurately as compared to existing machine learning algorithms by using ~50 deep mutational scanning and random mutagenesis datasets. Moreover, we used ECNet to guide the engineering of TEM-1 ß-lactamase and identified variants with improved ampicillin resistance with high success rates.


Assuntos
Aprendizado Profundo , Evolução Molecular , Engenharia de Proteínas/métodos , Sequência de Aminoácidos/genética , Conjuntos de Dados como Assunto , Aptidão Genética , Ensaios de Triagem em Larga Escala , Mutação , Homologia de Sequência de Aminoácidos , Resistência beta-Lactâmica/genética , beta-Lactamases/genética
16.
J Immunol ; 207(9): 2195-2202, 2021 11 01.
Artigo em Inglês | MEDLINE | ID: mdl-34663591

RESUMO

Sepsis develops after a dysregulated host inflammatory response to a systemic infection. Identification of sepsis biomarkers has been challenging because of the multifactorial causes of disease susceptibility and progression. Public transcriptomic data are a valuable resource for mechanistic discoveries and cross-studies concordance of heterogeneous diseases. Nonetheless, the approach requires structured methodologies and effective visualization tools for meaningful data interpretation. Currently, no such database exists for sepsis or systemic inflammatory diseases in human. Hence we curated SysInflam HuDB (http://sepsis.gxbsidra.org/dm3/geneBrowser/list), a unique collection of human blood transcriptomic datasets associated with systemic inflammatory responses to sepsis. The transcriptome collection and the associated clinical metadata are integrated onto a user-friendly and Web-based interface that allows the simultaneous exploration, visualization, and interpretation of multiple datasets stemming from different study designs. To date, the collection encompasses 62 datasets and 5719 individual profiles. Concordance of gene expression changes with the associated literature was assessed, and additional analyses are presented to showcase database utility. Combined with custom data visualization at the group and individual levels, SysInflam HuDB facilitates the identification of specific human blood gene signatures in response to infection (e.g., patients with sepsis versus healthy control subjects) and the delineation of major genetic drivers associated with inflammation onset and progression under various conditions.


Assuntos
Células Sanguíneas/fisiologia , Inflamação/imunologia , Sepse/imunologia , Mineração de Dados , Bases de Dados como Assunto , Conjuntos de Dados como Assunto , Perfilação da Expressão Gênica , Humanos , Internet , Software , Transcriptoma , Interface Usuário-Computador
17.
Nat Commun ; 12(1): 5929, 2021 10 12.
Artigo em Inglês | MEDLINE | ID: mdl-34642339

RESUMO

Arab populations are largely understudied, notably their genetic structure and history. Here we present an in-depth analysis of 6,218 whole genomes from Qatar, revealing extensive diversity as well as genetic ancestries representing the main founding Arab genealogical lineages of Qahtanite (Peninsular Arabs) and Adnanite (General Arabs and West Eurasian Arabs). We find that Peninsular Arabs are the closest relatives of ancient hunter-gatherers and Neolithic farmers from the Levant, and that founder Arab populations experienced multiple splitting events 12-20 kya, consistent with the aridification of Arabia and farming in the Levant, giving rise to settler and nomadic communities. In terms of recent genetic flow, we show that these ancestries contributed significantly to European, South Asian as well as South American populations, likely as a result of Islamic expansion over the past 1400 years. Notably, we characterize a large cohort of men with the ChrY J1a2b haplogroup (n = 1,491), identifying 29 unique sub-haplogroups. Finally, we leverage genotype novelty to build a reference panel of 12,432 haplotypes, demonstrating improved genotype imputation for both rare and common alleles in Arabs and the wider Middle East.


Assuntos
Cromossomos Humanos Y , Genoma Humano , Haplótipos , Migração Humana/história , Filogenia , África , Alelos , Árabes/genética , Ásia , DNA Mitocondrial/genética , Conjuntos de Dados como Assunto , Europa (Continente) , Feminino , Fluxo Gênico , Frequência do Gene , História do Século XXI , História Antiga , História Medieval , Humanos , Masculino , Filogeografia , Catar , Análise de Sequência de DNA , Sequenciamento Completo do Genoma
19.
Mol Syst Biol ; 17(11): e10260, 2021 11.
Artigo em Inglês | MEDLINE | ID: mdl-34709707

RESUMO

Tremendous progress has been made to control the COVID-19 pandemic caused by the SARS-CoV-2 virus. However, effective therapeutic options are still rare. Drug repurposing and combination represent practical strategies to address this urgent unmet medical need. Viruses, including coronaviruses, are known to hijack host metabolism to facilitate viral proliferation, making targeting host metabolism a promising antiviral approach. Here, we describe an integrated analysis of 12 published in vitro and human patient gene expression datasets on SARS-CoV-2 infection using genome-scale metabolic modeling (GEM), revealing complicated host metabolism reprogramming during SARS-CoV-2 infection. We next applied the GEM-based metabolic transformation algorithm to predict anti-SARS-CoV-2 targets that counteract the virus-induced metabolic changes. We successfully validated these targets using published drug and genetic screen data and by performing an siRNA assay in Caco-2 cells. Further generating and analyzing RNA-sequencing data of remdesivir-treated Vero E6 cell samples, we predicted metabolic targets acting in combination with remdesivir, an approved anti-SARS-CoV-2 drug. Our study provides clinical data-supported candidate anti-SARS-CoV-2 targets for future evaluation, demonstrating host metabolism targeting as a promising antiviral strategy.


Assuntos
Monofosfato de Adenosina/análogos & derivados , Alanina/análogos & derivados , Antivirais/uso terapêutico , COVID-19/metabolismo , Redes e Vias Metabólicas/genética , Pandemias , SARS-CoV-2/fisiologia , Monofosfato de Adenosina/uso terapêutico , Alanina/uso terapêutico , Animais , COVID-19/tratamento farmacológico , COVID-19/virologia , Células CACO-2 , Chlorocebus aethiops , Conjuntos de Dados como Assunto , Desenvolvimento de Medicamentos , Reposicionamento de Medicamentos , Interações Hospedeiro-Patógeno , Humanos , RNA Interferente Pequeno , Análise de Sequência de RNA , Células Vero
20.
Nat Biotechnol ; 39(9): 1151-1160, 2021 09.
Artigo em Inglês | MEDLINE | ID: mdl-34504347

RESUMO

The lack of samples for generating standardized DNA datasets for setting up a sequencing pipeline or benchmarking the performance of different algorithms limits the implementation and uptake of cancer genomics. Here, we describe reference call sets obtained from paired tumor-normal genomic DNA (gDNA) samples derived from a breast cancer cell line-which is highly heterogeneous, with an aneuploid genome, and enriched in somatic alterations-and a matched lymphoblastoid cell line. We partially validated both somatic mutations and germline variants in these call sets via whole-exome sequencing (WES) with different sequencing platforms and targeted sequencing with >2,000-fold coverage, spanning 82% of genomic regions with high confidence. Although the gDNA reference samples are not representative of primary cancer cells from a clinical sample, when setting up a sequencing pipeline, they not only minimize potential biases from technologies, assays and informatics but also provide a unique resource for benchmarking 'tumor-only' or 'matched tumor-normal' analyses.


Assuntos
Benchmarking , Neoplasias da Mama/genética , Análise Mutacional de DNA/normas , Sequenciamento de Nucleotídeos em Larga Escala/normas , Sequenciamento Completo do Genoma/normas , Linhagem Celular Tumoral , Conjuntos de Dados como Assunto , Células Germinativas , Humanos , Mutação , Padrões de Referência , Reprodutibilidade dos Testes
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