A stochastic approach for co-evolution process of virus and human immune system.

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.

Towards Chinese text and DNA shift encoding scheme based on biomass plasmid storage.

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.

Mesenchymal Stem Cells Pretreated with Collagen Promote Skin Wound-Healing.

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.

Research on the Spatial Agglomeration Characteristics and Influencing Factors of Express Delivery Station Based on DNN.

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.

Using amino acid features to identify the pathogenicity of influenza B virus.

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.

Melatonin treatment improves human umbilical cord mesenchymal stem cell therapy in a mouse model of type II diabetes mellitus via the PI3K/AKT signaling pathway.

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. → (Direct stimulatory modification); ┴ ( Direct Inhibitory modification); → ┤ (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).

Corrigendum: Melatonin Promotes the Therapeutic Effect of Mesenchymal Stem Cells on Type 2 Diabetes Mellitus by Regulating TGF-ß Pathway.

[This corrects the article DOI: 10.3389/fcell.2021.722365.].

Melatonin Promotes the Therapeutic Effect of Mesenchymal Stem Cells on Type 2 Diabetes Mellitus by Regulating TGF-ß Pathway.

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.

Prediction of pandemic risk for animal-origin coronavirus using a deep learning method.

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.

Predicting Cross-Species Infection of Swine Influenza Virus with Representation Learning of Amino Acid Features.

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.

A systematic study of critical miRNAs on cells proliferation and apoptosis by the shortest path.

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.

A Systematic Way to Infer the Regulation Relations of miRNAs on Target Genes and Critical miRNAs in Cancers.

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.

Using the spike protein feature to predict infection risk and monitor the evolutionary dynamic of coronavirus.

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.

Efficient Gaussian sample specific network marker discovery and drug enrichment analysis validation.

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.

Scoring amino acid mutation to predict pandemic risk of avian influenza virus.

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.

Predicting interspecies transmission of avian influenza virus based on wavelet packet decomposition.

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.

Scoring Amino Acid Mutations to Predict Avian-to-Human Transmission of Avian Influenza Viruses.

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.

Genome Sequencing and Analysis of Catopsilia pomona nucleopolyhedrovirus: A Distinct Species in Group I Alphabaculovirus.

The genome sequence of Catopsilia pomona nucleopolyhedrovirus (CapoNPV) was determined by the Roche 454 sequencing system. The genome consisted of 128,058 bp and had an overall G+C content of 40%. There were 130 hypothetical open reading frames (ORFs) potentially encoding proteins of more than 50 amino acids and covering 92% of the genome. Among all the hypothetical ORFs, 37 baculovirus core genes, 23 lepidopteran baculovirus conserved genes and 10 genes conserved in Group I alphabaculoviruses were identified. In addition, the genome included regions of 8 typical baculoviral homologous repeat sequences (hrs). Phylogenic analysis showed that CapoNPV was in a distinct branch of clade "a" in Group I alphabaculoviruses. Gene parity plot analysis and overall similarity of ORFs indicated that CapoNPV is more closely related to the Group I alphabaculoviruses than to other baculoviruses. Interesting, CapoNPV lacks the genes encoding the fibroblast growth factor (fgf) and ac30, which are conserved in most lepidopteran and Group I baculoviruses, respectively. Sequence analysis of the F-like protein of CapoNPV showed that some amino acids were inserted into the fusion peptide region and the pre-transmembrane region of the protein. All these unique features imply that CapoNPV represents a member of a new baculovirus species.

[Recent Advances in Vaccines and Drugs Against the Ebola Virus].

The Ebola virus belongs to the Filovirus family, which causes Ebola hemorrhagic fever (mortality, 25%-90%). An outbreak of infection by the Ebola virus is sweeping across West Africa, leading to high mortality and worldwide panic. The Ebola virus has caused a serious threat to public health, so intensive scientific studies have been carried out. Several vaccines (e.g., rVSV-ZEBOV, ChAd3-ZEBOV) have been put into clinical trials and antiviral drugs (e.g., TKM-Ebola, ZMAPP) have been administered in the emergency setting to patients infected by the Ebola virus. Here, recent advances in vaccines and drugs against the Ebola virus are reviewed.

The Complete Genome of a New Betabaculovirus from Clostera anastomosis.

Clostera anastomosis (Lepidoptera: Notodontidae) is a defoliating forest insect pest. Clostera anastomosis granulovirus-B (ClasGV-B) belonging to the genus Betabaculovirus of family Baculoviridae has been used for biological control of the pest. Here we reported the full genome sequence of ClasGV-B and compared it to other previously sequenced baculoviruses. The circular double-stranded DNA genome is 107,439 bp in length, with a G+C content of 37.8% and contains 123 open reading frames (ORFs) representing 93% of the genome. ClasGV-B contains 37 baculovirus core genes, 25 lepidopteran baculovirus specific genes, 19 betabaculovirus specific genes, 39 other genes with homologues to baculoviruses and 3 ORFs unique to ClasGV-B. Hrs appear to be absent from the ClasGV-B genome, however, two non-hr repeats were found. Phylogenetic tree based on 37 core genes from 73 baculovirus genomes placed ClasGV-B in the clade b of betabaculoviruses and was most closely related to Erinnyis ello GV (ErelGV). The gene arrangement of ClasGV-B also shared the strongest collinearity with ErelGV but differed from Clostera anachoreta GV (ClanGV), Clostera anastomosis GV-A (ClasGV-A, previously also called CaLGV) and Epinotia aporema GV (EpapGV) with a 20 kb inversion. ClasGV-B genome contains three copies of polyhedron envelope protein gene (pep) and phylogenetic tree divides the PEPs of betabaculoviruses into three major clades: PEP-1, PEP-2 and PEP/P10. ClasGV-B also contains three homologues of P10 which all harbor an N-terminal coiled-coil domain and a C-terminal basic sequence. ClasGV-B encodes three fibroblast growth factor (FGF) homologues which are conserved in all sequenced betabaculoviruses. Phylogenetic analysis placed these three FGFs into different groups and suggested that the FGFs were evolved at the early stage of the betabaculovirus expansion. ClasGV-B is different from previously reported ClasGV-A and ClanGV isolated from Notodontidae in sequence and gene arrangement, indicating the virus is a new notodontid betabaculovirus.