Microvessel changes in the gerbil hippocampus after cerebral ischemia and reperfusion by Buyang Huanwu decoction pretreatment

Buyang Huanwu decoction （BYHWD） is a classic recipe for the prevention and treatment of ischemic cerebrovascular disease. Gerbils were pretreated with BYHWD, and then subjected to cerebral ischemia and reperfusion. Microvascular changes were determined with laser Doppler monitoring, tannic acid-ferric chloride mordant, and electron microscopy. Results showed that BYHWD pretreatment could enhance the function of hippocampal microvessels, prevent injury, and increase microvasular density and microvasular area density. Thus, these results suggest that BYHWD pretreatment could prevent microvascular occlusion, enhance the capacity of microvascular reperfusion, increase cerebral blood flow, and inhibit neuronal damage, and may be an effective therapy against brain ischemic injury.

Identifying cooperative transcription factors by combining ChiP-chip data and knockout data

Dear Editor, Eukaryotic transcriptional regulation networks are extremely complex. Usually, multiple transcription factors (TFs) bind to the promoter region of a gene and cooperate to control gene expression precisely. Identifying cooperative TFs remains a major challenge in modem biological research. Various types of data, including genomic sequences, expression profiles, ChiP-chip data and protein-protein interactions, have been used to identify mechanisms of cooperative transcriptional regulation.

Predicting DNA methylation status using word composition

Background: DNA methylation will influence the gene expression pattern and cause the changes of the genetic functions. Computational analysis of the methylation status for nucleotides can help to explore the underlying reasons for developing methylations. Results: We present a DNA sequence based method to analyze the methylation status of CpG dinucleotides using 5bp (5-mer) DNA fragments – named as the word composition encoding method. The prediction accuracy is 75.16% when all 5bp word compositions are used (totally 45 = 1024). Furthermore, 5-bp DNA fragments/words having the most impact on the methylation status are identified by mRMR (Maximum-Relevant-Minimum-Redundancy) feature selection method. As a result, 58 words are selected, and they are used to build a compact predictor, which achieves 77.45% prediction accuracy. When the word composition encoding method and the feature selection strategy are coupled together, the meaning of these words can be analyzed through their contribution towards the prediction. The biological evidence in the literature supports that the surrounding DNA sequence of the CpG dinucleotides will affect the methylation of the CpG dinucleotides. Conclusions: The main contribution of this paper is to find out and analyze the key DNA words taken from the neighbor-hood of the CpG dinucleotides that are inducing the DNA methylation.

Urban Configuration Analysis of Idle Land Market Based on Game Model

In recent years, the speed of urban development becomes faster and faster with expanding of land construction scale, and a lot of idle lands lead to serious land waste. This paper builds game model by carrying out a market allocation analysis and applying economic game theory to the analysis of current idle land problem; it gets six kinds of results through analyzing the game model of idle land market, and the final Nash equilibrium is(system innovation, publicly traded) through contrastive to help balance the game relationship between government and the user of idle land and raise some new scientific and rational institutions to serve as future references for effective usage of idle land.

Forecast the potential SARS-CoV-2 variants in the future and predict their biological properties and social impacts from bioinformatics and public health perspectives

The evolutionary nature of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)is a topic of concern,as it is anticipated that additional variants will emerge in the future.The likelihood of these variants and their biological properties are difficult to predict,as are the rates of transmission,incidence,and mortality.The current vaccines may not be sufficient in protecting against these new variants,as demonstrated by the recent delta and omicron strains.It is imperative that we prepare for future outbreaks by forecasting the evolution of SARS-CoV-2,identifying potential variants,and predicting their biological properties.In this special issue,we aim to provide a comprehensive view on the effects of new SARS-CoV-2 variants on transmission,pathogenicity,and fatality.A wide range of multimodal data have been analyzed,including genomics of SARS-CoV-2 variants,transcriptomics of SARS-CoV-2 infection,single cell omics during vaccination and drug treatment,and public health data.Our objective is to support epidemic prevention and control efforts.

Building and sharing medical cohorts for research

Cohort studies are costly and time consuming.They require not only laboratory equipment and assays but also collaboration from participants and health agencies.Due to cost constraints,they are often confined to a specific population.Nevertheless,they play a crucial role in providing fundamental insights for medical advancements,shedding light on the origins of diseases,and acting in socioeconomic influence in policy making.

Cell type specificity of signaling:view from membrane receptors distribution and their downstream transduction networks

Studies on cell signaling pay more attention to spatial dynamics and how such diverse organization can relate to high order of cellular capabilities.To overview the specificity of cell signaling,we integrated human receptome data with proteome spatial expression profiles to systematically investigate the specificity of receptors and receptor-triggered transduction networks across 62 normal cell types and 14 cancer types.Six percent receptors showed cell-type-specific expression,and 4% signaling networks presented enriched cell-specific proteins induced by the receptors.We introduced a concept of“response context”to annotate the cell-type dependent signaling networks.We found that most cells respond similarly to the same stimulus,as the“response contexts”presented high functional similarity.Despite this,the subtle spatial diversity can be observed from the difference in network architectures.The architecture of the signaling networks in nerve cells displayed less completeness than that in glandular cells,which indicated cellular-context dependent signaling patterns are elaborately spatially organized.Likewise,in cancer cells most signaling networks were generally dysfunctional and less complete than that in normal cells.However,glioma emerged hyper-activated transduction mechanism in malignant state.Receptor ATP6AP2 and TNFRSF21 induced rennin-angiotensin and apoptosis signaling were found likely to explain the glioma-specific mechanism.This work represents an effort to decipher context-specific signaling network from spatial dimension.Our results indicated that although a majority of cells engage general signaling response with subtle differences,the spatial dynamics of cell signaling can not only deepen our insights into different signaling mechanisms,but also help understand cell signaling in disease.

Cell-cell communication analysis for single-cell RNA sequencing and its applications in carcinogenesis and COVID-19

Cell-cell communication is the basis of physiological processes and cell signals.The disease occurs when the cells do not adequately communicate and the messages are blocked.With ligand-receptor interaction databases and single-cell RNA sequencing(scRNA-seq)databases,we can detect intercellular signaling and reconstruct the cell-cell communications among different cell types.This review summarized the computational approaches for analyzing the cell-cell communication based on scRNA-seq data and discussed its applications in carcinogenesis and COVID-19.We believe that this review will accelerate the scRNA-seq data deciphering and facilitate the cell-cell communication studies for complex physiological processes,such as carcinogenesis and SARS-CoV-2 infection.

An interactive viral genome evolution network analysis system enabling rapid large-scale molecular tracing of SARS-CoV-2

The comprehensive analyses of the SARS-CoV-2 genomes could provide a global picture of how the virus was transmitted among different populations,which may help predict the oncoming trends of the pandemic.The main approach for the molecular tracing of viral transmission is to thoroughly compare the genomes of different viral strains.

Characterization of heavy-chain antibody gene repertoires in Bactrian camels

Camelids are the only mammals that can produce functional heavy-chain antibodies(HCAbs).Although HCAbs were discovered over 30 years ago,the antibody gene repertoire of Bactrian camels remains largely underexplored.To characterize the diversity of variable genes of HCAbs(VHHs),germline and rearranged VHH repertoires are constructed.Phylogenetics analysis shows that all camelid VHH genes are derived from a common ancestor and the nucleotide diversity of VHHs is similar across all camelid species.While species-specific hallmark sites are identified,the non-canonical cysteines specific to VHHs are distinct in Bactrian camels and dromedaries compared with alpacas.Though low divergence at the germline repertoire between wild and domestic Bactrian camels,higher expression of VHHs is observed in some wild Bactrian camels than that of domestic ones.This study not only adds our understanding of VHH repertoire diversity across camelids,but also provides useful resources for HCAb engineering.

植物源性天然产物在口腔细菌感染性疾病中的研究进展

天然产物是来源于自然界中具有药理生物活性的化合物。其中,来源于植物的天然产物具有抗菌作用广、细胞毒性小和生物相容性好等特点而受到关注。植物源性天然产物主要包括多酚类、生物碱类、萜类、醌类和醛类化合物,它们可以通过损伤细菌膜结构、抑制细菌酶活性、诱导氧化应激反应、促进铁离子的螯合和抑制细菌二元信号通路等途径抗菌。研究发现,植物源性天然产物可以抑制变异链球菌、牙龈卟啉单胞菌和粪肠球菌等常见的口腔致病菌。本文就植物源性天然产物的抗菌机制及其在口腔感染性疾病中的相关应用作一综述。

Towards precision medicine:Omics approach for COVID-19

The coronavirus disease 2019(COVID-19)pandemic had a devastating impact on human society.Beginning with genome surveillance of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),the development of omics technologies brought a clearer understanding of the complex SARS-CoV-2 and COVID-19.Here,we reviewed how omics,including genomics,proteomics,single-cell multi-omics,and clinical phenomics,play roles in answering biological and clinical questions about COVID-19.Large-scale sequencing and advanced analysis methods facilitate COVID-19 discovery from virus evolution and severity risk prediction to potential treatment identification.Omics would indicate precise and globalized prevention and medicine for the COVID-19 pandemic under the utilization of big data capability and phenotypes refinement.Furthermore,decoding the evolution rule of SARS-CoV-2 by deep learning models is promising to forecast new variants and achieve more precise data to predict future pandemics and prevent them on time.

Early warning of emerging infectious diseases based on multimodal data

The coronavirus disease 2019 (COVID-19) pandemic has dramatically increased the awareness of emerging infectious diseases. The advancement of multiomics analysis technology has resulted in the development of several databases containing virus information. Several scientists have integrated existing data on viruses to construct phylogenetic trees and predict virus mutation and transmission in different ways, providing prospective technical support for epidemic prevention and control. This review summarized the databases of known emerging infectious viruses and techniques focusing on virus variant forecasting and early warning. It focuses on the multi-dimensional information integration and database construction of emerging infectious viruses, virus mutation spectrum construction and variant forecast model, analysis of the affinity between mutation antigen and the receptor, propagation model of virus dynamic evolution, and monitoring and early warning for variants. As people have suffered from COVID-19 and repeated flu outbreaks, we focused on the research results of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza viruses. This review comprehensively viewed the latest virus research and provided a reference for future virus prevention and control research.

Back to Science in Searching for SARS-CoV-2 Origins

In recent decades,emerging and re-emerging human-infecting pathogens have been represented as huge threats to public health and have become a global concern(1).After outbreaks of two coronaviruses(CoVs),severe acute respiratory syndrome coronavirus(SARS-CoV)and Middle East respiratory syndrome coronavirus(MERS-CoV),severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)became the first-known pandemic hastening CoV with tremendous wrecking to the world(2).The origin tracing of these emerging pathogens is of great significance in infectious disease prevention and control(3–4).The origin of SARS-CoV-2 remains elusive after the more than 3-year pandemic,though scientists around the world are making great efforts.From the experience of studying many other infectious pathogens,origin tracing is systematic and time-consuming work.The supposed origins of many infectious pathogens are still in debate,including SARS-CoV and human immunodeficiency virus,etc(5).

Current progress,challenges,and future perspectives of language models for protein representation and protein design

The sequence-structure-function paradigm of protein is the basis of molecular biology.What is the underlying mechanism of such sequence and structure/function corresponding relationship?We reviewed the methods for protein representation and protein design.With these protein representation models,we can accurately predict many properties of proteins,such as stability and binding affinity.

Big Biological Data:Challenges and Opportunities

In ＂Omics＂ era of the life sciences, data is presented in many forms, which represent the information at various levels of bio- logical systems, including data about genome, transcriptome, epigenome, proteome, metabolome, molecular imaging, molec- ular pathways, different population of people and clinical/med- ical records. The biological data is big, and its scale has already been well beyond petabyte （PB） even exabyte （EB）. Nobody doubts that the biological data will create huge amount of val- ues, if scientists can overcome many challenges, e.g., how to handle the complexity of information, how to integrate the data from very heterogeneous resources, what kind of principles or standards to be adopted when facing with the big data. Tools and techniques for analyzing big biological data enable us to translate massive amount of information into a better under- standing of the basic biomedical mechanisms, which can be fur- ther applied to translational or personalized medicine.

Kinase-substrate Edge Biomarkers Provide a More Accurate Prognostic Prediction in ER-negative Breast Cancer

The estrogen receptor(ER)-negative breast cancer subtype is aggressive with few treatment options available.To identify specific prognostic factors for ER-negative breast cancer,this study included 705,729 and 1034 breast invasive cancer patients from the Surveillance,Epidemiology,and End Results(SEER)and The Cancer Genome Atlas(TCGA)databases,respectively.To identify key differential kinase-substrate node and edge biomarkers between ER-negative and ERpositive breast cancer patients,we adopted a network-based method using correlation coefficients between molecular pairs in the kinase regulatory network.Integrated analysis of the clinical and molecular data revealed the significant prognostic power of kinase-substrate node and edge features for both subtypes of breast cancer.Two promising kinase-substrate edge features,CSNK1A1-NFATC3 and SRC-OCLN,were identified for more accurate prognostic prediction in ERnegative breast cancer patients.

Genome assembly and transcriptome analysis provide insights into the antischistosome mechanism of Microtus fortis

Microtus fortis is the only mammalian host that exhibits intrinsic resistance against Schistosoma japonicum infection.However,the underlying molecular mechanisms of this resistance are not yet known.Here,we perform the first de novo genome assembly of M.fortis,comprehensive gene annotation analysis,and evolution analysis.Furthermore,we compare the recovery rate of schistosomes,pathological changes,and liver transcriptomes between M.fortis and mice at different time points after infection.We observe that the time and type of immune response in M.fortis are different from those in mice.M.fortis activates immune and inflammatory responses on the 10th day post infection,such as leukocyte extravasation,antibody activation,Fc-gamma receptor-mediated phagocytosis,and the interferon signaling cascade,which play important roles in preventing the development of schistosomes.In contrast,an intense immune response occurrs in mice at the late stages of infection and could not eliminate schistosomes.Infected mice suffer severe pathological injury and continuous decreases in cell cycle,lipid metabolism,and other functions.Our findings offer new insights into the intrinsic resistance mechanism of M.fortis against schistosome infection.The genome sequence also provides the basis for future studies of other important traits in M.fortis.

Analysis of the origin of peak aerosol optical depth in springtime over the Gulf of Tonkin

By aggregating MODIS（moderate-resolution imaging spectroradiometer） AOD（aerosol optical depth） and OMI（ozone monitoring instrument） UVAI（ultra violet aerosol index）datasets over 2010–2014, it was found that peak aerosol loading in seasonal variation occurred annually in spring over the Gulf of Tonkin（17–23°N, 105–110°E）. The vertical structure of the aerosol extinction coefficient retrieved from the spaceborne lidar CALIOP（cloud-aerosol lidar with orthogonal polarization） showed that the springtime peak AOD could be attributed to an abrupt increase in aerosol loading between altitudes of 2 and 5 km.In contrast, aerosol loading in the low atmosphere（below 1 km） was only half of that in winter. Wind fields in the low and high atmosphere exhibited opposite transportation patterns in spring over the Gulf of Tonkin, implying different sources for each level. By comparing the emission inventory of anthropogenic sources with biomass burning, and analyzing the seasonal variation of the vertical structure of aerosols over the Northern Indo-China Peninsula（NIC）, it was concluded that biomass burning emissions contributed to high aerosol loading in spring. The relatively high topography and the high surface temperature in spring made planetary boundary layer height greater than 3 km over NIC. In addition, small-scale cumulus convection frequently occurred, facilitating pollutant rising to over 3 km, which was a height favoring long-range transport. Thus, pollutants emitted from biomass burning over NIC in spring were raised to the high atmosphere, then experienced long-range transport, leading to the increase in aerosol loading at high altitudes over the Gulf of Tonkin during spring.

Highly efficient base editing in human tripronuclear zygotes

There are hundreds of disease-causing single-gene mutations, mainly caused by single-nucleotide substitutions or point mutations rather than small insertions/deletions （in- dels）, and often there are no cures for these diseases.