Computational and structural biotechnology journal最新文献

{"title":"Integrative analysis of miRNA expression data reveals a minimal signature for tumour cells classification","authors":"Sabrina Napoletano ,&nbsp;David Dannhauser ,&nbsp;Paolo Antonio Netti ,&nbsp;Filippo Causa","doi":"10.1016/j.csbj.2024.12.023","DOIUrl":"10.1016/j.csbj.2024.12.023","url":null,"abstract":"<div><div>MicroRNAs (miRNAs) are pivotal biomarkers for cancer screening. Identifying distinctive expression patterns of miRNAs in specific cancer types can serve as an effective strategy for classification and characterization. However, the development of a minimal signature of miRNAs for accurate cancer classification remains challenging, hindered by the lack of integrated approaches that systematically analyse miRNA expression levels of miRNAs alongside their associated biological pathways. In this study, we present a comprehensive integrative approach that utilizes transcriptomic data from lung, breast, and melanoma cancer cell lines to identify specific expression patterns. By combining bioinformatics, dimensionality reduction techniques, machine learning, and experimental validation, we pinpoint miRNAs linked to critical biological pathways. Our results demonstrate a highly significant differentiation of cancer types, achieving 100 % classification accuracy with minimal training time using a streamlined miRNA signature. Validation of the miRNA profile confirms that each of the three identified miRNAs regulates distinct biological pathways with minimal overlap. This specificity highlights their unique roles in tumour biology and set the stage for further exploration of miRNAs interactions and their contributions to tumourigenesis across diverse cancer types. Our work paves the way for multi-cancer classification, emphasizing the transformative potential of miRNA research in oncology. Beyond advancing the understanding of tumour biology, our step-by-step guide offers a robust tool for a wide range of users to investigate precise diagnostics and promising therapeutic strategies.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"Pages 233-242"},"PeriodicalIF":4.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11760817/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143045308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterizing the pan-cancer role of exosomal miRNAs in metastasis across cancers","authors":"Piyush Agrawal ,&nbsp;Gulden Olgun ,&nbsp;Arashdeep Singh ,&nbsp;Vishaka Gopalan ,&nbsp;Sridhar Hannenhalli","doi":"10.1016/j.csbj.2024.12.025","DOIUrl":"10.1016/j.csbj.2024.12.025","url":null,"abstract":"<div><div>Exosomal microRNAs (exomiRs) play a critical role in intercellular communication, especially in cancer, where they regulate key cellular processes like proliferation, angiogenesis, and metastasis, highlighting their significance as potential diagnostic and therapeutic targets. Here, we aimed to characterize the role of exomiRs, derived from seven cancer types (four cell lines and three tumors), in influencing the pre-metastatic niche (PMN). In each cancer type we extracted high confidence exomiRs (LogFC &gt;= 2 in exosomes relative to control), their experimentally validated targets, and the enriched pathways among those targets. We then selected the top100 high-confidence targets based on their frequency of appearance in the enriched pathways. We observed significantly higher GC content in exomiRs relative to genomic background. Gene Ontology analysis revealed both general cancer processes, such as wound healing and epithelial cell proliferation, as well as cancer-specific processes, such as “angiogenesis” in the kidney and “ossification” in the lung. ExomiR targets were enriched for cancer-specific tumor suppressor genes and downregulated in PMN formed in lungs compared to normal. Motif analysis showed high inter-cancer similarity among motifs enriched in exomiRs. Our analysis recapitulated exomiRs associated with M2 macrophage differentiation and chemoresistance, such as miR-21 and miR-222–3p, regulating signaling pathways like PTEN/PI3/Akt, NF-kB, etc. Additionally, Cox regression analysis in TCGA indicated that exomiR targets are significantly associated with better overall survival of patients. Lastly, support vector machine model using exomiR targets gene expression classified responders and non-responders to therapy with an AUROC ranging from 0.72 to 0.96, higher than previously reported gene signatures.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"Pages 252-264"},"PeriodicalIF":4.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11763893/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143045964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Network-based estimation of therapeutic efficacy and adverse reaction potential for prioritisation of anti-cancer drug combinations","authors":"Arindam Ghosh,&nbsp;Vittorio Fortino","doi":"10.1016/j.csbj.2024.12.003","DOIUrl":"10.1016/j.csbj.2024.12.003","url":null,"abstract":"<div><div>Drug combinations, although a key therapeutic agent against cancer, are yet to reach their full applicability potential due to the challenges involved in the identification of effective and safe drug pairs. <em>In vitro</em> or in vivo screening would have been the optimal approach if combinatorial explosion was not an issue. <em>In silico</em> methods, on the other hand, can enable rapid screening of drug pairs to prioritise for experimental validation. Here we present a novel network medicine approach that systematically models the proximity of drug targets to disease-associated genes and adverse effect-associated genes, through the combination of network propagation algorithm and gene set enrichment analysis. The proposed approach is applied in the context of identifying effective drug combinations for cancer treatment starting from a training set of drug combinations curated from DrugComb and DrugBank databases. We observed that effective drug combinations usually enrich disease-related gene sets while adverse drug combinations enrich adverse-effect gene sets. We use this observation to systematically train classifiers distinguishing drug combinations with higher therapeutic effects and no known adverse reaction from combinations with lower therapeutic effects and potential adverse reactions in six cancer types. The approach is tested and validated using drug combinations curated from in vitro screening data and clinical reports. Trained classification models are also used to identify novel potential anti-cancer drug combinations for experimental validation. We believe our framework would be a key addition to the anti-cancer drug combination identification pipeline by enabling rapid yet robust estimation of therapeutic efficacy or adverse reaction potential.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"Pages 65-77"},"PeriodicalIF":4.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143098046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IGN: Invariable gene set-based normalization for chromatin accessibility profile data analysis","authors":"Shengen Shawn Hu ,&nbsp;Hai-Hui Xue ,&nbsp;Chongzhi Zang","doi":"10.1016/j.csbj.2025.01.018","DOIUrl":"10.1016/j.csbj.2025.01.018","url":null,"abstract":"<div><div>Chromatin accessibility profiles generated using ATAC-seq or DNase-seq carry functional information of the regulatory genome that controls gene expression. Appropriate normalization of ATAC-seq and DNase-seq data is essential for accurate differential analysis when studying chromatin dynamics. Existing normalization methods usually assume the same distribution of genomic signals across samples; however, this assumption may not be appropriate when there are global changes in chromatin accessibility levels between experimental conditions/samples. We present IGN (Invariable Gene Normalization), a method for ATAC-seq and DNase-seq data normalization. IGN normalizes the promoter chromatin accessibility signals for a set of genes that are unchanged in expression, usually obtained from accompanying RNA-seq data, and extrapolating to normalize the genome-wide chromatin accessibility profile. We demonstrate the effectiveness of IGN in analyzing central memory CD8⁺ T cell activation, a system with anticipated global reprogramming of chromatin and gene expression, and show that IGN outperforms existing methods. As the first chromatin accessibility normalization method that accounts for global differences, IGN can be widely applied to differential ATAC-seq and DNase-seq analysis. The package and source code are available on GitHub at <span><span>https://github.com/zang-lab/IGN</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"Pages 501-507"},"PeriodicalIF":4.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143098822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An ecological and stochastic perspective on persisters resuscitation","authors":"Tania Alonso-Vásquez ,&nbsp;Michele Giovannini ,&nbsp;Gian Luigi Garbini ,&nbsp;Mikolaj Dziurzynski ,&nbsp;Giovanni Bacci ,&nbsp;Ester Coppini ,&nbsp;Donatella Fibbi ,&nbsp;Marco Fondi","doi":"10.1016/j.csbj.2024.12.002","DOIUrl":"10.1016/j.csbj.2024.12.002","url":null,"abstract":"<div><div>Resistance, tolerance, and persistence to antibiotics have mainly been studied at the level of a single microbial isolate. However, in recent years it has become evident that microbial interactions play a role in determining the success of antibiotic treatments, in particular by influencing the occurrence of persistence and tolerance within a population. Additionally, the challenge of resuscitation (the capability of a population to revive after antibiotic exposure) and pathogen clearance are strongly linked to the small size of the surviving population and to the presence of fluctuations in cell counts. Indeed, while large population dynamics can be considered deterministic, small populations are influenced by stochastic processes, making their behaviour less predictable. Our study argues that microbe-microbe interactions within a community affect the mode, tempo, and success of persister resuscitation and that these are further influenced by noise. To this aim, we developed a theoretical model of a three-member microbial community and analysed the role of cell-to-cell interactions on pathogen clearance, using both deterministic and stochastic simulations. Our findings highlight the importance of ecological interactions and population size fluctuations (and hence the underlying cellular mechanisms) in determining the resilience of microbial populations following antibiotic treatment.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"Pages 1-9"},"PeriodicalIF":4.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11697298/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142930755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Structure-based identification of HNF4α agonists: Rosmarinic acid as a promising candidate for NAFLD treatment","authors":"Xi Chen ,&nbsp;Xinqi Zhu ,&nbsp;Gang Wu ,&nbsp;Xiaobo Wang ,&nbsp;Yu Zhang ,&nbsp;Nan Jiang","doi":"10.1016/j.csbj.2024.12.014","DOIUrl":"10.1016/j.csbj.2024.12.014","url":null,"abstract":"<div><div>The prevention and treatment of metabolic disorders, such as non-alcoholic fatty liver disease (NAFLD), have emerged as critical global health challenges. Current lipid-lowering pharmacotherapies are associated with side effects, including hepatotoxicity, rhabdomyolysis, and decreased erythrocyte counts, underscoring the urgent need for safer therapeutic alternatives. Hepatocyte nuclear factor 4α (HNF4α) has been identified as a pivotal regulator of lipid metabolism, making it an attractive target for drug development. In this study, we investigated the structural characteristics and binding interactions of four HNF4α agonists: Alverine, Benfluorex, N-trans caffeoyltyramine (NCT), and N-trans feruloyltyramine (NFT). Our results indicate that the conjugated structure formed by the amide bond and the aromatic ring in NCT and NFT enhances electron density, potentially contributing to their increased specificity for HNF4α relative to Alverine and Benfluorex. Additionally, electrostatic interactions between the aromatic moieties of the compounds and HNF4α residues were found to play a crucial role in ligand binding. Leveraging these insights, we performed a high-throughput virtual screening of 2131 natural compounds, using the binding modes of NCT and NFT as reference templates. Rosmarinic acid emerged as a promising HNF4α agonist, exhibiting a high consensus score and favorable binding affinity. Subsequent biological assays demonstrated that rosmarinic acid significantly inhibited HepG2 cell proliferation which related to the enhancement of autophagy. After the knockdown of P2 isoform of HNF4α, HepG2 was more sensitive to the administration of NCT and rosmarinic acid. Furthermore, the proliferation of DLD-1 cell, which only expresses the P2 isoform of HNF4α, was not significantly inhibited by the administration of NCT and rosmarinic acid. Collectively, these findings suggest that rosmarinic acid is a promising HNF4α agonist which is more effective to activate the P1 isoform of HNF4α and holds potential as an effective treatment for NAFLD, providing a foundation for the development of novel lipid-lowering drugs with enhanced efficacy and reduced side effect.</div></div><div><h3>Data Availability</h3><div>Data will be made available on request.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"Pages 171-183"},"PeriodicalIF":4.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11755020/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143028119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamical features of smooth muscle actin pathological mutants: The arginine-257(258)-Cysteine cases","authors":"F. Chiappori ,&nbsp;F. Di Palma ,&nbsp;A. Cavalli ,&nbsp;M. de Rosa ,&nbsp;F. Viti","doi":"10.1016/j.csbj.2025.02.010","DOIUrl":"10.1016/j.csbj.2025.02.010","url":null,"abstract":"<div><div>The R257(8)C mutation in smooth muscle actins, ACTG2 and ACTA2, is the most frequent cause of severe genetic diseases: namely, visceral myopathy, and familial thoracic aortic aneurysms and dissections, which respectively, stem from impairment of the visceral and vascular muscle. The molecular mechanisms underlying such pathologies are not fully elucidated. In the absence of experimental data of WT and mutated actins in their monomeric (g-) and filamentous (f-) form, molecular dynamics can shed light on the role of the R257(8)C in protein structure and dynamics. Analysis of g-actins does not show significant differences between WT and mutated proteins suggesting the correct monomers folding. On the contrary, mutated filaments are destabilized. Subunits of R257C f-ACTG2 adopt non optimal angles and in R258C f-ACTA2 we observe depolymerization already in the simulated time frame. Overall, our data points to a crucial role of residue R257(8) in actin structure and dynamics, in particular when the protein assembles into the filament.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"Pages 753-764"},"PeriodicalIF":4.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative analysis of amino acid auxotrophies and peptidase profiles in non-dysbiotic and dysbiotic small intestinal microbiomes","authors":"Svenja Starke ,&nbsp;Danielle M.M. Harris ,&nbsp;Amandine Paulay ,&nbsp;Konrad Aden ,&nbsp;Silvio Waschina","doi":"10.1016/j.csbj.2025.02.004","DOIUrl":"10.1016/j.csbj.2025.02.004","url":null,"abstract":"<div><div>Small Intestinal Bacterial Overgrowth (SIBO) is linked to various diseases and has been associated with altered serum amino acid levels. However, the direct role of the gut microbiome in these changes remains unconfirmed. This study employs genome-scale metabolic modeling to predict amino acid auxotrophy and peptidase gene profiles in the small intestinal microbiomes of SIBO and non-SIBO subjects. Auxotrophy and peptidase gene profiles were further examined in the large intestinal microbiome under non-dysbiotic conditions to assess their similarity to the microbial SIBO profile. Our analysis revealed that the abundance of auxotrophic bacteria is higher in the microbiota of the small intestine than in the large intestine in non-dysbiotic controls. In patients with SIBO, the abundance of auxotrophies in the small intestine decreased compared to non-SIBO subjects. Peptidase gene profiles in non-dysbiotic individuals were distinct between small and large intestinal microbiomes, with fewer extracellular peptidase genes in small intestine microbiomes. In SIBO, extracellular peptidase genes increased compared to non-SIBO subjects. Further, there were more significant associations between the abundance of auxotrophies and peptidase genes in microbiomes of the small intestine compared to the large intestine. In conclusion, the auxotrophy and peptidase gene profiles of the small and large intestinal microbiomes were distinct. In SIBO, the small intestinal microbiome shifts towards a metabolic state resembling that of the large intestine, particularly in its increased abundance of extracellular peptidase genes. This highlights the potential of genome-scale metabolic modeling in identifying metabolic disruptions associated with SIBO, which could inform the development of targeted interventions.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"Pages 821-831"},"PeriodicalIF":4.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143520395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Altered heme metabolism and hemoglobin concentration due to empirical antibiotics-induced gut dysbiosis in preterm infants","authors":"Seung Hyun Kim ,&nbsp;Min‑Jin Kwak ,&nbsp;Jae Kyoon Hwang ,&nbsp;Jihyun Keum ,&nbsp;Hee Yeon Jin ,&nbsp;Chan-Yeong Lee ,&nbsp;Rahul Sadashiv Tanpure ,&nbsp;Yong Joo Kim ,&nbsp;Jeong-Kyu Hoh ,&nbsp;Jae Yong Park ,&nbsp;Woojin Chung ,&nbsp;Byong-Hun Jeon ,&nbsp;Hyun-Kyung Park","doi":"10.1016/j.csbj.2025.03.009","DOIUrl":"10.1016/j.csbj.2025.03.009","url":null,"abstract":"<div><h3>Background</h3><div>High-risk infants are usually treated with empirical antibiotics after birth, regardless of the evidence of infection; however, their gut microbiome and metabolome have seldom been studied. This study investigated the influence of antibiotic exposure on the gut microbiome and associated metabolic pathways in term and preterm infants.</div></div><div><h3>Methods</h3><div>Thirty-six infants within 10 days of birth who were admitted to a neonatal intensive care unit/newborn nursery unit were divided into four groups based on maturity (gestational age) and use of empirical antibiotics. Genomic DNA was extracted from the fecal samples and underwent high-throughput 16S rRNA amplicon sequencing using the Illumina platforms. Taxonomic classification, diversity analysis, and metagenomic function prediction were performed.</div></div><div><h3>Results</h3><div>Preterm infants with empirical antibiotics showed a significantly decreased population of <em>Firmicutes</em> (p = 0.003) and an increased population of <em>Proteobacteria</em> (p &lt; 0.001) compared to other groups. At the genus level, the populations of <em>Raoultella</em> (<em>p</em> = 0.065) and <em>Escherichia</em> (<em>p</em> = 0.052) showed an increased trend. The change in microbial composition was correlated with increased heme biosynthesis and decreased hemoglobin levels.</div></div><div><h3>Conclusion</h3><div>Collectively, our finding suggested that empirical antibiotic exposure in preterm infants alters the gut microbiome, potentially leading to adverse health outcomes. This dysbiosis may affect heme metabolism, increasing the risk of anemia in these vulnerable infants. Therefore, antibiotic use should be carefully tailored to minimize potential harm.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"Pages 937-945"},"PeriodicalIF":4.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143578930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fast detection of unique genomic regions","authors":"Beatriz Vieira Mourato,&nbsp;Bernhard Haubold","doi":"10.1016/j.csbj.2025.02.025","DOIUrl":"10.1016/j.csbj.2025.02.025","url":null,"abstract":"<div><div>Unique genomic regions are of particular interest in two scenarios: When extracted from a single mammalian target genome, they are highly enriched for developmental genes. When extracted from target genomes compared to closely related neighbor genomes, they are highly enriched for diagnostic markers. Despite their biological importance and potential economic value, unique regions remain difficult to detect from whole genome sequences. In this review we survey three efficient programs for the detection of unique regions at scale, <span>genmap</span>, <span>macle</span>, and <span>fur</span>. We explain these programs and demonstrate their application by analyzing simulated and real data. Example scripts for searching for unique regions are available from the Github repository <span>evolbioinf/sure</span> as part of a detailed tutorial.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"Pages 843-850"},"PeriodicalIF":4.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}