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The existing treatment modalities for skin injuries mainly include dressings, negative-pressure wound treatment, autologous skin grafting, and high-pressure wound treatment. All of these therapies have limitations such as high time cost, the inability to remove inactivated tissue in a timely manner, surgical debridement, and oxygen toxicity. Mesenchymal stem cells have a unique self-renewal ability and wide differentiation potential, and they are one of the most promising stem cell types in cell therapy and have great application prospects in the field of regenerative medicine. Collagen exerts structural roles by promoting the molecular structure, shape, and mechanical properties of cells, and adding it to cell cultures can also promote cell proliferation and shorten the cell doubling time. The effects of collagen on MSCs were examined using Giemsa staining, EdU staining, and growth curves. Mice were subjected to allogeneic experiments and autologous experiments to reduce individual differences; all animals were separated into four groups. Neonatal skin sections were detected by HE staining, Masson staining, immunohistochemical staining, and immunofluorescence staining. We found that the MSCs pretreated with collagen accelerated the healing of skin wounds in mice and canines by promoting epidermal layer repair, collagen deposition, hair follicle angiogenesis, and an inflammatory response. Collagen promotes the secretion of the chemokines and growth factors associated with skin healing by MSCs, which positively influences skin healing. This study supports the treatment of skin injuries with MSCs cultured in medium with collagen added.
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Células Madre Mesenquimatosas , Cicatrización de Heridas , Ratones , Animales , Perros , Cicatrización de Heridas/fisiología , Piel/lesiones , Colágeno , Proliferación CelularRESUMEN
BACKGROUND: MicroRNAs are a class of important small noncoding RNAs, which have been reported to be involved in the processes of tumorigenesis and development by targeting a few genes. Existing studies show that the imbalance between cell proliferation and apoptosis is closely related to the initiation and development of cancers. However, the impact of miRNAs on this imbalance has not been studied systematically. RESULTS: In this study, we first construct a cell fate miRNA-gene regulatory network. Then, we propose a systematical method for calculating the global impact of miRNAs on cell fate genes based on the shortest path. Results on breast cancer and liver cancer datasets show that most of the cell fate genes are perturbed by the differentially expressed miRNAs. Most of the top-identified miRNAs are verified in the Human MicroRNA Disease Database (HMDD) and are related to breast and liver cancers. Function analysis shows that the top 20 miRNAs regulate multiple cell fate related function modules and interact tightly based on their functional similarity. Furthermore, more than half of them can promote sensitivity or induce resistance to some anti-cancer drugs. Besides, survival analysis demonstrates that the top-ranked miRNAs are significantly related to the overall survival time in the breast and liver cancers group. CONCLUSION: In sum, this study can help to systematically study the important role of miRNAs on proliferation and apoptosis and thereby uncover the key miRNAs during the process of tumorigenesis. Furthermore, the results of this study will contribute to the development of clinical therapy based miRNAs for cancers.
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Apoptosis/genética , Proliferación Celular/genética , MicroARNs/metabolismo , Biomarcadores/metabolismo , Neoplasias de la Mama/genética , Neoplasias de la Mama/mortalidad , Neoplasias de la Mama/patología , Bases de Datos Genéticas , Femenino , Redes Reguladoras de Genes , Humanos , Neoplasias Hepáticas/genética , Neoplasias Hepáticas/mortalidad , Neoplasias Hepáticas/patología , MicroARNs/genética , ARN Mensajero/metabolismo , Análisis de SupervivenciaRESUMEN
BACKGROUND: Avian influenza virus can directly cross species barriers and infect humans with high fatality. As antigen novelty for human host, the public health is being challenged seriously. The pandemic risk of avian influenza viruses should be analyzed and a prediction model should be constructed for virology applications. RESULTS: The 178 signature positions in 11 viral proteins were firstly screened as features by the scores of five amino acid factors and their random forest rankings. The Supporting Vector Machine algorithm achieved well performance. The most important amino acid factor (Factor 5) and the minimal range of signature positions (63 amino acid residues) were also explored. Moreover, human-origin avian influenza viruses with three or four genome segments from human virus had pandemic risk with high probability. CONCLUSION: Using machine learning methods, the present paper scores the amino acid mutations and predicts pandemic risk with well performance. Although long evolution distances between avian and human viruses suggest that avian influenza virus in nature still need time to fix among human host, it should be notable that there are high pandemic risks for H7N9 and H9N2 avian viruses.
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Aminoácidos/genética , Aves/virología , Gripe Aviar/epidemiología , Gripe Aviar/virología , Mutación/genética , Pandemias , Algoritmos , Animales , Simulación por Computador , Bases de Datos como Asunto , Genoma Viral , Aprendizaje Automático , Virus Reordenados/genética , Factores de RiesgoRESUMEN
Avian influenza virus (AIV) can directly cross species barriers and infect humans with high fatality. Using machine learning methods, the present paper scores the amino acid mutations and predicts interspecies transmission. Initially, 183 signature positions in 11 viral proteins were screened by the scores of five amino acid factors and their random forest rankings. The most important amino acid factor (Factor 3) and the minimal range of signature positions (50 amino acid residues) were explored by a supporting vector machine (the highest-performing classifier among four tested classifiers). Based on these results, the avian-to-human transmission of AIVs was analyzed and a prediction model was constructed for virology applications. The distributions of human-origin AIVs suggested that three molecular patterns of interspecies transmission emerge in nature. The novel findings of this paper provide important clues for future epidemic surveillance.
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Sustitución de Aminoácidos , Virus de la Influenza A/genética , Gripe Aviar/virología , Gripe Humana/transmisión , Gripe Humana/virología , Mutación , Animales , Animales Salvajes , Aves , Humanos , Posición Específica de Matrices de Puntuación , Reproducibilidad de los ResultadosRESUMEN
We characterized two newly isolated myoviruses, Bp8p-C and Bp8p-T, infecting the ginger rhizome rot disease pathogen Bacillus pumilus GR8. The plaque of Bp8p-T exhibited a clear center with a turbid rim, suggesting that Bp8p-T could transform into latent phage. Lysogeny assays showed that both the two phages could form latent states, while Bp8p-T could form latent phage at a higher frequency and stability than Bp8p-C. The genomes of Bp8p-C and Bp8p-T were 151,417 and 151,419 bp, respectively; both encoded 212 putative proteins, and only differed by three nucleotides. Moreover, owing to this difference, Bp8p-C encoded a truncated, putative actin-like plasmid segregation protein Gp27-C. Functional analysis of protein Gp27 showed that Gp27-T encoded by Bp8p-T exhibited higher ATPase activity and assembly ability than Gp27-C. The results indicate that the difference in Gp27 affected the phage lysogenic ability. Structural proteome analysis of Bp8p-C virion resulted in the identification of 14 structural proteins, among which a pectin lyase-like protein, a putative poly-gamma-glutamate hydrolase, and three proteins with unknown function, were firstly identified as components of the phage virion. Both phages exhibited specific lytic ability to the host strain GR8. Bp8p-C showed better control effect on the pathogen in ginger rhizome slices than Bp8p-T, suggesting that Bp8p-C has a potential application in bio-control of ginger rhizome rot disease.
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Actinas/metabolismo , Fagos de Bacillus/fisiología , Bacillus/virología , Lisogenia , Proteínas Virales/metabolismo , Actinas/genética , Adenosina Trifosfatasas/genética , Adenosina Trifosfatasas/metabolismo , Fagos de Bacillus/genética , Fagos de Bacillus/aislamiento & purificación , Genoma Viral , Zingiber officinale/microbiología , Datos de Secuencia Molecular , Myoviridae/genética , Myoviridae/aislamiento & purificación , Myoviridae/fisiología , Enfermedades de las Plantas/microbiología , Análisis de Secuencia de ADN , Ensayo de Placa Viral , Proteínas Virales/genéticaRESUMEN
Infectious diseases have long been a shaping force in human history, necessitating a comprehensive understanding of their dynamics. This study introduces a co-evolution model that integrates both epidemiological and evolutionary dynamics. Utilizing a system of differential equations, the model represents the interactions among susceptible, infected, and recovered populations for both ancestral and evolved viral strains. Methodologically rigorous, the model's existence and uniqueness have been verified, and it accommodates both deterministic and stochastic cases. A myriad of graphical techniques have been employed to elucidate the model's dynamics. Beyond its theoretical contributions, this model serves as a critical instrument for public health strategy, particularly predicting future outbreaks in scenarios where viral mutations compromise existing interventions.
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Procesos Estocásticos , Humanos , Sistema Inmunológico/virología , Evolución Molecular , Virus/genética , Virus/inmunología , Evolución BiológicaRESUMEN
The current circulating influenza B viruses can be divided into two major phylogenetic lineages: the Victoria and Yamagata lineages. We conducted a survey of influenza B viruses in Hubei and Zhejiang provinces during 2009-2010. Out of 341 throat swabs, 18 influenza B viruses were isolated. Five isolates were selected for genetic and phylogenetic analysis. The molecular analyses revealed that all the isolates had similar antigenic characteristics to B/Brisbane/60/2008. However, in the three viruses isolated from Zhejiang, a single asparagine to aspartic acid substitution in position 197 was observed, thereby eliminating the glycosylation at that site and possibly causing an antigenic change. None of the viruses had amino acid mutations at positions 116, 149, 152, 198, 222, 250, 291, and 402 of the neuraminidase (NA) gene, predicting that the viruses would still be sensitive to NA inhibitors. Phylogenetic analyses revealed that all five isolates were closely related to B/Brisbane/60/2008-the 2010 vaccine strain-and contained Victoria-like hemagglutinin and Yamagata-like NA genes, suggesting that reassortment may had occurred. In addition, similar phylogenetic patterns among the acidic polymerase, nucleoprotein and matrix protein genes, as well as between the basic polymerase 1 and basic polymerase 2 genes, were observed, suggesting possible functional interactions among these proteins. All the results highlighted the importance of molecular monitoring of influenza B viruses for reassortment and antigenic drift.
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Virus de la Influenza B/clasificación , Virus de la Influenza B/genética , Gripe Humana/epidemiología , Gripe Humana/virología , China/epidemiología , Genotipo , Humanos , Virus de la Influenza B/aislamiento & purificación , Epidemiología Molecular , Datos de Secuencia Molecular , Mutación Missense , Faringe/virología , Filogenia , ARN Viral/genética , Virus Reordenados , Análisis de Secuencia de ADN , Proteínas Virales/genéticaRESUMEN
DNA, as the storage medium in organisms, can address the shortcomings of existing electromagnetic storage media, such as low information density, high maintenance power consumption, and short storage time. Current research on DNA storage mainly focuses on designing corresponding encoders to convert binary data into DNA base data that meets biological constraints. We have created a new Chinese character code table that enables exceptionally high information storage density for storing Chinese characters (compared to traditional UTF-8 encoding). To meet biological constraints, we have devised a DNA shift coding scheme with low algorithmic complexity, which can encode any strand of DNA even has excessively long homopolymer. The designed DNA sequence will be stored in a double-stranded plasmid of 744bp, ensuring high reliability during storage. Additionally, the plasmid's resistance to environmental interference ensuring long-term stable information storage. Moreover, it can be replicated at a lower cost.
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In this paper, the POI data of 736 Cainiao stations in Nanjing is taken as the research sample. With the help of ArcGIS software, the standard deviation ellipse, spatial autocorrelation, average nearest neighbor, cold and hot spot analysis, nuclear density estimation, and other spatial analysis models are used to quantitatively characterize its business mode, spatial distribution characteristics, and equilibrium. Based on DNN, the spatial agglomeration characteristics and distribution directions of the Cainiao station in Nanjing were sorted out, the cold spots and hot spots of the spatial layout were identified, and the spatial differentiation rules and agglomeration patterns were revealed. Finally, the geographically weighted regression analysis model is used to analyze the influencing factors of the spatial agglomeration of the Cainiao station in Nanjing. The research found that Firstly, the proportion of Nanjing Cainiao station operating mode adopting the exclusive mode is 59.1%, the proportion adopting the concurrent operation mode is 33.7%, and the rest adopting the joint operation mode of cooperation with other logistics enterprises. Secondly, Nanjing Cainiao Station gathers in the central city area, forming a "central hot spot." The urban fringe area does not form a "peripheral cold spot area," and the whole presents a "1 + 4" five-core agglomeration model across the river. Thirdly, Regional GDP, population density, and the number of convenience stores/supermarkets are the main factors affecting the spatial agglomeration of the Cainiao station in Nanjing.
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Comercio , Descanso , Análisis por Conglomerados , Transporte de Proteínas , Programas InformáticosRESUMEN
BACKGROUND: Influenza B virus can cause epidemics with high pathogenicity, so it poses a serious threat to public health. A feature representation algorithm is proposed in this paper to identify the pathogenicity phenotype of influenza B virus. METHODS: The dataset included all 11 influenza virus proteins encoded in eight genome segments of 1724 strains. Two types of features were hierarchically used to build the prediction model. Amino acid features were directly delivered from 67 feature descriptors and input into the random forest classifier to output informative features about the class label and probabilistic prediction. The sequential forward search strategy was used to optimize the informative features. The final features for each strain had low dimensions and included knowledge from different perspectives, which were used to build the machine learning model for pathogenicity identification. RESULTS: The 40 signature positions were achieved by entropy screening. Mutations at position 135 of the hemagglutinin protein had the highest entropy value (1.06). After the informative features were directly generated from the 67 random forest models, the dimensions for class and probabilistic features were optimized as 4 and 3, respectively. The optimal class features had a maximum accuracy of 94.2% and a maximum Matthews correlation coefficient of 88.4%, while the optimal probabilistic features had a maximum accuracy of 94.1% and a maximum Matthews correlation coefficient of 88.2%. The optimized features outperformed the original informative features and amino acid features from individual descriptors. The sequential forward search strategy had better performance than the classical ensemble method. CONCLUSIONS: The optimized informative features had the best performance and were used to build a predictive model so as to identify the phenotype of influenza B virus with high pathogenicity and provide early risk warning for disease control.
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Aminoácidos , Virus de la Influenza B , Algoritmos , Aminoácidos/genética , Virus de la Influenza B/genética , Aprendizaje Automático , VirulenciaRESUMEN
BACKGROUND: Mesenchymal stem cells (MSCs) are promising candidates for tissue regeneration and disease treatment. However, long-term in vitro passaging leads to stemness loss of MSCs, resulting in failure of MSC therapy. This study investigated whether the combination of melatonin and human umbilical cord mesenchymal stem cells (hUC-MSCs) was superior to hUC-MSCs alone in ameliorating high-fat diet and streptozocin (STZ)-induced type II diabetes mellitus (T2DM) in a mouse model. METHODS: Mice were divided into four groups: normal control (NC) group; T2DM group; hUC-MSCs treatment alone (UCMSC) group and pretreatment of hUC-MSCs with melatonin (UCMSC/Mel) group. RESULTS: RNA sequence analysis showed that certain pathways, including the signaling pathway involved in the regulation of cell proliferation signaling pathway, were regulated by melatonin. The blood glucose levels of the mice in the UCMSC and UCMSC/Mel treatment groups were significantly reduced compared with the T2DM group without treatment (P < 0.05). Furthermore, hUC-MSCs enhance the key factor in the activation of the PI3K/Akt pathway in T2DM mouse hepatocytes. CONCLUSION: The pretreatment of hUC-MSCs with melatonin partly boosted cell efficiency and thereby alleviated impaired glycemic control and insulin resistance. This study provides a practical strategy to improve the application of hUC-MSCs in diabetes mellitus and cytotherapy. Overview of the PI3K/AKT signaling pathway. (A) Underlying mechanism of UCMSC/Mel inhibition of hyperglycemia and insulin resistance T2DM mice via regulation of PI3K/AKT pathway. hUC-MSCs stimulates glucose uptake and improves insulin action thus should inhibition the clinical signs of T2DM, through activation of the p-PI3K/Akt signaling pathway and then regulates glucose transport through activating AS160. UCMSC/Mel increases p53-dependent expression of BCL2, and inhibit BAX and Capase3 protein activation. Leading to the decrease in apoptosis. (B) Melatonin modulated PI3K/AKT signaling pathway. Melatonin activated PI3K/AKT response pathway through binding to MT1and MT2 receptor. Leading to the increase in hUC-MSCs proliferation, migration and differentiation. â (Direct stimulatory modification); â´ ( Direct Inhibitory modification); â ⤠(Multistep inhibitory modification); ↑ (Up regulate); ↓ (Down regulate); PI3K (Phosphoinositide 3-Kinase); AKT ( protein kinase B); PDK1 (Phosphoinositide-dependent protein kinase 1); IR, insulin receptor; GLUT4 ( glucose transporter type 4); ROS (reactive oxygen species); BCL-2 (B-cell lymphoma-2); PDK1 (phosphoinositide-dependent kinase 1) BAX (B-cell lymphoma-2-associated X protein); PCNA (Proliferating cell nuclear antigen); Cell cycle-associated proteins (KI67, cyclin A, cyclin E).
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Diabetes Mellitus Tipo 2 , Resistencia a la Insulina , Melatonina , Trasplante de Células Madre Mesenquimatosas , Animales , Diabetes Mellitus Tipo 2/terapia , Humanos , Melatonina/farmacología , Melatonina/uso terapéutico , Trasplante de Células Madre Mesenquimatosas/métodos , Ratones , Fosfatidilinositol 3-Quinasas/metabolismo , Fosfatidilinositoles/uso terapéutico , Proteínas Proto-Oncogénicas c-akt/metabolismo , Transducción de Señal , Cordón Umbilical , Proteína X Asociada a bcl-2RESUMEN
Swine influenza viruses (SIVs) can unforeseeably cross the species barriers and directly infect humans, which pose huge challenges for public health and trigger pandemic risk at irregular intervals. Computational tools are needed to predict infection phenotype and early pandemic risk of SIVs. For this purpose, we propose a feature representation algorithm to predict cross-species infection of SIVs. We built a high-quality dataset of 1902 viruses. A feature representation learning scheme was applied to learn feature representations from 64 well-trained random forest models with multiple feature descriptors of mutant amino acid in the viral proteins, including compositional information, position-specific information, and physicochemical properties. Class and probabilistic information were integrated into the feature representations, and redundant features were removed by feature space optimization. High performance was achieved using 20 informative features and 22 probabilistic information. The proposed method will facilitate SIV characterization of transmission phenotype.
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Virus de la Influenza A/genética , Virus de la Influenza A/patogenicidad , Infecciones por Orthomyxoviridae/veterinaria , Enfermedades de los Porcinos/virología , Algoritmos , Secuencia de Aminoácidos , Aminoácidos/análisis , Aminoácidos/genética , Animales , Biología Computacional , Especificidad del Huésped , Humanos , Subtipo H1N1 del Virus de la Influenza A/genética , Subtipo H1N2 del Virus de la Influenza A/genética , Subtipo H3N2 del Virus de la Influenza A/genética , Virus de la Influenza A/clasificación , Gripe Humana/epidemiología , Gripe Humana/transmisión , Gripe Humana/virología , Aprendizaje Automático , Modelos Estadísticos , Mutación , Infecciones por Orthomyxoviridae/virología , Pandemias , Factores de Riesgo , Porcinos , Enfermedades de los Porcinos/transmisión , Proteínas Virales/química , Proteínas Virales/genéticaRESUMEN
BACKGROUND: Coronaviruses can be isolated from bats, civets, pangolins, birds and other wild animals. As an animal-origin pathogen, coronavirus can cross species barrier and cause pandemic in humans. In this study, a deep learning model for early prediction of pandemic risk was proposed based on the sequences of viral genomes. METHODS: A total of 3257 genomes were downloaded from the Coronavirus Genome Resource Library. We present a deep learning model of cross-species coronavirus infection that combines a bidirectional gated recurrent unit network with a one-dimensional convolution. The genome sequence of animal-origin coronavirus was directly input to extract features and predict pandemic risk. The best performances were explored with the use of pre-trained DNA vector and attention mechanism. The area under the receiver operating characteristic curve (AUROC) and the area under precision-recall curve (AUPR) were used to evaluate the predictive models. RESULTS: The six specific models achieved good performances for the corresponding virus groups (1 for AUROC and 1 for AUPR). The general model with pre-training vector and attention mechanism provided excellent predictions for all virus groups (1 for AUROC and 1 for AUPR) while those without pre-training vector or attention mechanism had obviously reduction of performance (about 5-25%). Re-training experiments showed that the general model has good capabilities of transfer learning (average for six groups: 0.968 for AUROC and 0.942 for AUPR) and should give reasonable prediction for potential pathogen of next pandemic. The artificial negative data with the replacement of the coding region of the spike protein were also predicted correctly (100% accuracy). With the application of the Python programming language, an easy-to-use tool was created to implements our predictor. CONCLUSIONS: Robust deep learning model with pre-training vector and attention mechanism mastered the features from the whole genomes of animal-origin coronaviruses and could predict the risk of cross-species infection for early warning of next pandemic.
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Infecciones por Coronavirus , Coronavirus , Pandemias , Animales , Coronavirus/aislamiento & purificación , Infecciones por Coronavirus/epidemiología , Infecciones por Coronavirus/veterinaria , Aprendizaje Profundo , Humanos , Modelos Estadísticos , Medición de Riesgo/métodosRESUMEN
Abundant evidence proves the therapeutic effect of adipose-derived mesenchymal stem cells (ADMSCs) in the treatment of diabetes mellitus. However, the problems have not been solved that viability of ADMSCs were inconsistent and the cells quickly undergo senescence after in vitro cell culture. In addition, the therapeutic effect of ADMSCs is still not satisfactory. In this study, melatonin (MLT) was added to canine ADMSC culture medium, and the treated cells were used to treat type 2 diabetes mellitus (T2DM). Our research reveals that adding MLT to ADMSC culture medium can promote the viability of ADMSCs. This effect depends on the binding of MLT and MLT receptors, which activates the transforming growth factor ß (TGF-ß) pathway and then changes the cell cycle of ADMSCs and improves the viability of ADMSCs. Since ADMSCs were found to be used to treat T2DM by anti-inflammatory and anti-endoplasmic reticulum (ER) stress capabilities, our data demonstrate that MLT augment several effects of ADMSCs in remission hyperglycemia, insulin resistance, and liver glycogen metabolism in T2DM patients. This suggest that ADMSCs and MLT-ADMSCs is safe and vabulable for pet clinic.
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[This corrects the article DOI: 10.3389/fcell.2021.722365.].
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To trace the endemic situation of highly pathogenic influenza H5N1 virus in wildlife in the Lake Qinghai area of northwest China, a continuous surveillance program was implemented from September 2005 to September 2007. A total of 2699 field samples were collected, and 95 of the samples were positive by a reverse transcription-polymerase chain reaction (RT-PCR) test of subtype H5 influenza virus. To some extent, the detection rate correlated with the species, the location, and the seasons of collection. In 2007, two H5N1 isolates were identified from two species of migratory birds. The two 2007 isolates showed high similarities of the hemagglutinin and neuraminidase genes with those of the 2005 and 2006 Qinghai isolates (98.6-99.8% for HA and 98.7-98.9% for NA at the nucleotide level). Both isolates fell into clade 2.2.2 and were experimentally highly pathogenic to chickens and mice. According to our surveillance results, HPAI H5N1 viruses still exist in this region.
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Subtipo H5N1 del Virus de la Influenza A , Gripe Aviar/epidemiología , Migración Animal , Animales , Aves , China/epidemiología , Femenino , Subtipo H5N1 del Virus de la Influenza A/genética , Gripe Aviar/virología , Ratones , Ratones Endogámicos BALB C , Filogenia , Factores de TiempoRESUMEN
BACKGROUND: Coronavirus can cross the species barrier and infect humans with a severe respiratory syndrome. SARS-CoV-2 with potential origin of bat is still circulating in China. In this study, a prediction model is proposed to evaluate the infection risk of non-human-origin coronavirus for early warning. METHODS: The spike protein sequences of 2666 coronaviruses were collected from 2019 Novel Coronavirus Resource (2019nCoVR) Database of China National Genomics Data Center on Jan 29, 2020. A total of 507 human-origin viruses were regarded as positive samples, whereas 2159 non-human-origin viruses were regarded as negative. To capture the key information of the spike protein, three feature encoding algorithms (amino acid composition, AAC; parallel correlation-based pseudo-amino-acid composition, PC-PseAAC and G-gap dipeptide composition, GGAP) were used to train 41 random forest models. The optimal feature with the best performance was identified by the multidimensional scaling method, which was used to explore the pattern of human coronavirus. RESULTS: The 10-fold cross-validation results showed that well performance was achieved with the use of the GGAP (g = 3) feature. The predictive model achieved the maximum ACC of 98.18% coupled with the Matthews correlation coefficient (MCC) of 0.9638. Seven clusters for human coronaviruses (229E, NL63, OC43, HKU1, MERS-CoV, SARS-CoV, and SARS-CoV-2) were found. The cluster for SARS-CoV-2 was very close to that for SARS-CoV, which suggests that both of viruses have the same human receptor (angiotensin converting enzyme II). The big gap in the distance curve suggests that the origin of SARS-CoV-2 is not clear and further surveillance in the field should be made continuously. The smooth distance curve for SARS-CoV suggests that its close relatives still exist in nature and public health is challenged as usual. CONCLUSIONS: The optimal feature (GGAP, g = 3) performed well in terms of predicting infection risk and could be used to explore the evolutionary dynamic in a simple, fast and large-scale manner. The study may be beneficial for the surveillance of the genome mutation of coronavirus in the field.
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Betacoronavirus/inmunología , Infecciones por Coronavirus , Coronavirus/inmunología , Reservorios de Enfermedades/virología , Pandemias , Peptidil-Dipeptidasa A/metabolismo , Neumonía Viral , Receptores Virales/genética , Glicoproteína de la Espiga del Coronavirus/inmunología , Algoritmos , Aminoácidos/genética , Enzima Convertidora de Angiotensina 2 , Animales , Betacoronavirus/genética , COVID-19 , China , Chlorocebus aethiops , Coronavirus/genética , Coronavirus/aislamiento & purificación , Infecciones por Coronavirus/genética , Infecciones por Coronavirus/transmisión , Infecciones por Coronavirus/virología , Endopeptidasas/genética , Endopeptidasas/metabolismo , Genoma/genética , Genoma Viral/genética , Humanos , Pandemias/prevención & control , Peptidil-Dipeptidasa A/genética , Filogenia , Neumonía Viral/genética , Neumonía Viral/transmisión , Neumonía Viral/virología , Receptores Virales/metabolismo , Medición de Riesgo , SARS-CoV-2RESUMEN
MicroRNAs (miRNAs) are a class of important non-coding RNAs, which play important roles in tumorigenesis and development by targeting oncogenes or tumor suppressor genes. One miRNA can regulate multiple genes, and one gene can be regulated by multiple miRNAs. To promote the clinical application of miRNAs, two fundamental questions should be answered: what's the regulatory mechanism of a miRNA to a gene, and which miRNAs are important for a specific type of cancer. In this study, we propose a miRNA influence capturing (miRNAInf) to decipher regulation relations of miRNAs on target genes and identify critical miRNAs in cancers in a systematic approach. With the pair-wise miRNA/gene expression profiles data, we consider the assigning problem of a miRNA on target genes and determine the regulatory mechanisms by computing the Pearson correlation coefficient between the expression changes of a miRNA and that of its target gene. Furthermore, we compute the relative local influence strength of a miRNA on its target gene. Finally, integrate the local influence strength and target gene's importance to determine the critical miRNAs involved in specific cancer. Results on breast, liver and prostate cancers show that positive regulations are as common as negative regulations. The top-ranked miRNAs show great potential as therapeutic targets driving cancer to a normal state, and they are demonstrated to be closely related to cancers based on biological functional analysis, drug sensitivity/resistance analysis and survival analysis. This study will be helpful for the discovery of critical miRNAs and development of miRNAs-based clinical therapeutics.
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Using wavelet packet decomposition, the energy coefficients in the fifth level of viral protein sequences were achieved to predict interspecies transmission. Since avian-origin influenza viruses could have high sequence similarities with human-origin avian influenza virus and could have the phenotype of interspecies transmission, viral data should be filtered to prevent the misconduct of feature selection and false performance of predicting models. Considering the balance of data size, the empirical cut-off value 97% was used to screen avian-origin influenza virus with high sequence similarity. The excellent performances of cross validation show that the SVM model has the best capability of predicting transmission and evaluating the contribution of five amino acid factors. The robust model was finally used to evaluate the filtered data of avian-origin virus and the results confirmed that double check for ambiguous phenotype of avian-origin virus with high sequence similarity was necessary and part of them have the ability to across species barriers.
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Algoritmos , Virus de la Influenza A/genética , Procesamiento de Señales Asistido por Computador , Bases de Datos Genéticas , Humanos , FenotipoRESUMEN
Identifying stable gene markers at an individual level can help to understand the genetic mechanisms of each individual patient and accomplish personalized medicine. In this paper, we propose an efficient framework to identify sample-specific markers. Gene expression data first is transformed to a corresponding likelihood matrix to alleviate inherent noise besides adding population information to each sample. Then those significantly differential genes or gene pairs are further mapped to a STRING network for analysis by assuming that the likelihood of each gene or gene pairs in the control group follows a Gaussian distribution. The proposed method is applied to three benchmark datasets including lung adenocarcinoma, kidney renal clear cell carcinoma, and uterine corpus endometrial carcinoma. It is found that disease gene markers identified by the proposed methods outperform the previous sample-specific network (SSN) method in both subtyping and survival analysis. Furthermore, we exploit the application of the subtype markers in following drug selection. The difference of the enriched drug set may reflect some underlying mechanisms of the subtypes and shed light on selecting appropriate drugs for each cancer subtype.