Journal of bioinformatics and systems biology : Open access最新文献

{"title":"CDKs Functional Analysis in Low Proliferating Early-Stage Pancreatic Ductal Adenocarcinoma.","authors":"Shikai Zhu, Huining Yang, Lingling Liu, Zhilin Jiang, Juanjuan Ji, Xiao Wang, Lin Zhong, Fulin Liu, Xueliang Gao, Haizhen Wang, Yu Zhou","doi":"10.26502/jbsb.5107060","DOIUrl":"10.26502/jbsb.5107060","url":null,"abstract":"<p><p>Pancreatic ductal adenocarcinoma (PDAC) is a highly devastating disease with a poor prognosis and growing incidence. In this study, we explored the potential roles of CDK1, CDK2, CDK4, and CDK6 in the progression of early-stage PDAC. Clinicopathologic and mRNA expression data and treatment information of 140 patients identified with stage I/II PDAC who underwent pancreaticoduodenectomy were obtained from the Cancer Genome Atlas data set. Our bioinformatic analysis showed that higher CDK1, CDK2, CDK4, or CDK6 expression was associated with a shorter median survival of the early-stage PDAC patients. Of note, in the low-proliferating pancreatic cancer group, CDKs expressions were significantly associated with proteins functioning in apoptosis, metastasis, immunity, or stemness. Among the low-proliferating PDAC, higher expression of CDK1 was associated with the shorter survival of patients, suggesting that CDK1 may regulate PDAC progression through cell cycle-independent mechanisms. Our experimental data showed that CDK1 knockdown/inhibition significantly suppressed the expression levels of AHR and POU5F1, two critical proteins functioning in cancer cell metastasis and stemness, in low-proliferating, but not in high-proliferating pancreatic cancer cells. In all, our study suggests that CDKs regulate PDAC progression not only through cell proliferation but also through apoptosis, metastasis, immunity, and stemness.</p>","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"6 3","pages":"187-200"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10516534/pdf/nihms-1926474.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41156658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SMS: A Novel Approach for Bacterial Strain Analysis in Multiple Samples","authors":"Saidi Wang, Minerva Fatimae Ventolero, Haiyan Hu, Xiaoman Li","doi":"10.26502/jbsb.5107065","DOIUrl":"https://doi.org/10.26502/jbsb.5107065","url":null,"abstract":"The analysis of the bacterial strains is important for understanding drug resistance. Despite the existence of dozens of computational tools for bacterial strain studies, most of them are for known bacterial strains. Almost all remaining tools are designed to analyze individual samples or local strain regions. With multiple shotgun metagenomic samples routinely generated in a project, it is necessary to create methods to infer novel bacterial strain genomes in multiple samples. To fill this gap, we developed a novel computational approach called SMS to de novo reconstruct bacterial Strain genomes in Multiple Samples. Tested on 702 simulated and 195 experimental datasets, SMS reliably identified the strain number, abundance, and polymorphisms. Compared with two existing approaches, SMS showed superior performance.","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136260003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Computational Pipeline to Control the Quality and Reduce Contamination in Single Retinal Ganglion Cells","authors":"Yeganeh Madadi, Hao Chen, Lu Lu, Monica M Jablonski, Robert W Williams, Siamak Yousefi","doi":"10.26502/jbsb.5107061","DOIUrl":"https://doi.org/10.26502/jbsb.5107061","url":null,"abstract":"Single-cell transcriptome profiling has transformed our understanding of cellular heterogeneity. However, single-cell data with poor quality can impede proper identification of distinct cell populations and subsequent biological interpretations. In this study, we present a customized computational approach to control the quality and reduce contaminations in single-cell transcriptome profiling of retinal ganglion cells (RGCs). We leverage domain knowledge and statistical methods to effectively eliminate various sources of contaminants for identification of RGC types and subtypes. We show that our end-to-end computational pipeline improves the accuracy and reliability of single-cell transcriptome profiling of RGCs and enhances the biological interpretations. To show the effectiveness of our pipeline, we use 5,994 RGCs captured from retinas of mouse using Fluidigm technology as a benchmark dataset and compare with widely used quality control tools. Further, we introduce seven candidate F-RGC subtype markers that we identified after applying our introduced pipeline on the benchmark dataset. Our customized quality control pipeline could enable retinal single RGC probing with more granularity, leading to new insights into RGC-related visual diseases and development of therapeutic approaches.","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69367526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NMF Clustering: Accessible NMF-based Clustering Utilizing GPU Acceleration.","authors":"Ted Liefeld, Edwin Huang, Alexander T Wenzel, Kenneth Yoshimoto, Ashwyn K Sharma, Jason K Sicklick, Jill P Mesirov, Michael Reich","doi":"10.26502/jbsb.5107072","DOIUrl":"10.26502/jbsb.5107072","url":null,"abstract":"<p><p>Non-negative Matrix Factorization (NMF) is an algorithm that can reduce high dimensional datasets of tens of thousands of genes to a handful of metagenes which are biologically easier to interpret. Application of NMF on gene expression data has been limited by its computationally intensive nature, which hinders its use on large datasets such as single-cell RNA sequencing (scRNA-seq) count matrices. We have implemented NMF based clustering to run on high performance GPU compute nodes using CuPy, a GPU backed python library, and the Message Passing Interface (MPI). This reduces the computation time by up to three orders of magnitude and makes the NMF Clustering analysis of large RNA-Seq and scRNA-seq datasets practical. We have made the method freely available through the GenePattern gateway, which provides free public access to hundreds of tools for the analysis and visualization of multiple 'omic data types. Its web-based interface gives easy access to these tools and allows the creation of multi-step analysis pipelines on high performance computing (HPC) clusters that enable reproducible <i>in silico</i> research for non-programmers.</p>","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"6 4","pages":"379-383"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10883375/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139934481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Conservation Genomics and Species Distribution Models Motivate Proactive and Collaborative Conservation in an Era of Rapid Change","authors":"Maria Sagatelova, Rosa A Rodriguez- Pena, Thomas J Rodhouse, Jeffrey Lonneker, Kirk R Sherrill, Andrea D Wolfe","doi":"10.26502/jbsb.5107063","DOIUrl":"https://doi.org/10.26502/jbsb.5107063","url":null,"abstract":"Small, fragmented plant populations with low genetic diversity are susceptible to deterministic and stochastic events that can affect long-term persistence of species. Penstemon lemhiensis Keck (Plantaginaceae) is a rare endemic with small, scattered populations across Idaho and Montana threatened by cumulative impacts of biological invasion, drought, and altered fire regimes. When contextualized by an understanding of rangewide distributions under different environmental change scenarios, conservation genetics can be leveraged to motivate proactive conservation action among collaborating stakeholder groups. We applied a genotypingby- sequencing (GBS) approach across eight populations and 93 individuals of P. lemhiensis. Genetic differentiation among populations followed an isolation-by-distance pattern and ranged from low to moderate (FST = 0.095-0.280). Values of inbreeding were low, and often negative (FIS = -0.039-0.032), indicating outbreeding within populations. Population structure analyses identified six ancestral populations and admixture across all individuals. We contextualized these findings by fitting bioclimatic niche models to past, present, and future climate regime scenarios. Habitat connectivity peaked mid-Holocene and nearly disappeared in the future scenario. Genetic analyses and species distribution models indicated that the species may experience drastic range contraction and accelerated isolation and inbreeding in future. We identified a core area in the Upper Big Hole Valley, Montana most likely to persist as suitable habitat. The National Park Service, Bureau of Land Management, and US Forest Service were identified as key stakeholders in that valley. We outline a proactive collaborative conservation strategy that aim to maintain wild P. lemhiensis populations.","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135952823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Acyclovir Improves the Efficacy of Chemoradiation in Nasopharyngeal Cancer Containing the Epstein Barr Virus Genome","authors":"Aditya Thandoni, Andrew Zloza, Devora Schiff, Malay Rao, Kwok-wai Lo, Bruce G Haffty, Sung Kim, Sachin R Jhawar","doi":"10.26502/jbsb.5107064","DOIUrl":"https://doi.org/10.26502/jbsb.5107064","url":null,"abstract":"Nasopharyngeal carcinoma (NPC) is a malignancy endemic to East Asia and is caused by Epstein-Barr Virus (EBV)-mediated cancerous transformation of epithelial cells. The standard of care treatment for NPC involves radiation and chemotherapy. While treatment outcomes continue to improve, up to 50% of patients can be expected to recur by five years, and additional innovative treatment options are needed. We posit that a potential way to do this is by targeting the underlying cause of malignant transformation, namely EBV. One method by which EBV escapes immune surveillance is by undergoing latent phase replication, during which EBV expression of immunogenic proteins is reduced. However, chemoradiation is known to drive conversion of EBV from a latent to a lytic phase. This creates an opportunity for the targeting of EBV-infected cells utilizing antiviral drugs. Indeed, we found that combining acyclovir with cisplatin and radiation significantly decreases the viability of the EBV-infected C666- 1 cell line. Western blot quantification revealed a resultant increase of thymidine kinase (TK) and apoptosis-inducing mediators, cleaved PARP (cPARP) and phosphorylated ERK (pERK). These studies suggest that the addition of anti-viral drugs to frontline chemoradiation may improve outcomes in patients treated for EBV-related NPC and future in vivo and clinical studies are needed.","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135954929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Machine Learning to identify microRNA biomarkers for predisposition to Huntington’s Disease","authors":"P. K, Sheridan C, Chandrasegaran S, Shanley Dp","doi":"10.26502/jbsb.5107046","DOIUrl":"https://doi.org/10.26502/jbsb.5107046","url":null,"abstract":"","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69367931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LDBPR: Latest Database of Protein Research","authors":"Shahid Ullah, Tianshun Gao, W. Rahman, F. Ullah, R. Jahan, Anees Ullah, Gulzar Ahmad, Muhammad Ijaz, Yihang Pan","doi":"10.26502/jbsb.5107032","DOIUrl":"https://doi.org/10.26502/jbsb.5107032","url":null,"abstract":"With the vast and rapid growth of protein research data, a large number of databases are produced to annotate proteins. How to use these databases is becoming a crucial part of modern biology. Database research is usually the first step in the analysis of a new protein. The combined utilization of multiple databases could help researchers to understand the evolution, structure, and function of proteins. Therefore, a well comprehensive and large-scale resource integrated with most of databases is urgently desirable for systematic and precise studies of proteins. Here we designed a platform LDBPR with a collection of 564 latest scientific protein databases. It fully covered physical, chemical, and biological information of Protein sequence, structure, and model, domain, function, and protein‐ protein interactions. Furthermore, The LDBPR can be explored by three ways: (i) single database can be browsed by typing the name in the given search bar; (ii) all protein categories can be browsed by clicking on the name of the category; (iii) the image icon, could give all categorized protein databases on single click. Moreover, the programming languages including PHP, HTML, CSS, and MySQL were used to construct LDBPR for the protein scientific community that can be freely searched by clicking http://www.habdsk.org/ldbpr.php and will be updated timely.","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69367509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transcriptome Dedifferentiation Observed in Animal Primary Cultures is Essential to Plant Reprogramming.","authors":"Norichika Ogata","doi":"10.26502/jbsb.5107039","DOIUrl":"https://doi.org/10.26502/jbsb.5107039","url":null,"abstract":"<p><p>Tissue culture environment liberate cells from ordinary laws of multi-cellular organisms. This liberation enables cells several behaviors, such as growth, dedifferentiation, acquisition of pluripotency, immortalization and reprogramming. Each phenomenon is relating to each other and hardly to determine. Recently, dedifferentiation of animal cell was quantified as increasing liberality which is information entropy of transcriptome. The increasing liberality induced by tissue culture may reappear in plant cells too. Here we corroborated it. Measuring liberality during reprogramming of plant cells suggested that reprogramming is a combined phenomenon of dedifferentiation and re-differentiation.</p>","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":" ","pages":"116-118"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9668052/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40483821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computer-Aided Molecular Design of CCR2 - CCR5 Dual Antagonists for the Treatment of NASH","authors":"S. Kumari, Elizabeth Sobhia M","doi":"10.26502/jbsb.5107035","DOIUrl":"https://doi.org/10.26502/jbsb.5107035","url":null,"abstract":"Non-alcoholic fatty liver disease (NAFLD), one of the most common liver diseases, is caused by the disruption of hepatic lipid homeostasis by various metabolic disorders. The progression of NAFLD into Non-alcoholic steatohepatitis (NASH) is mediated by inflammatory chemokines, cytokines, mitochondrial dysfunction, and oxidative stress resulting in hepatocyte inflammation, ballooning, apoptosis, and activation of hepatic stellate cells (HSC). NASH can further lead to cirrhosis, hepatic carcinoma, and also it is predicted to be a major cause of liver transplantation over the next 10 years. Chemokine receptors are majorly involved in recruiting the monocytes in the liver where they are converted into pro-inflammatory macrophages, which further activate the hepatic stellate cell (HSCs) to promote their survival while activating collagen production and fibrogenesis. Thus, chemokines and their receptor play a vital role in the pathogenesis of NASH and can be a potential target for the treatment of NASH. Herein, in this study, we have carried out a structure-based design of CCR2 and CCR5 dual antagonists. We performed pharmacophore mapping studies followed by virtual screening of commercial database to obtain novel molecules which can potentially act as CCR2 and CCR5 dual antagonists. We also performed molecular docking studies of newly obtained hits molecules to see their interactions with both CCR2 and CCR5 receptors. Non-alcoholic By evaluating the chemical structures of the top five molecules, it was observed that all five molecules possess C2 symmetry. The docking results of the top five molecules showed that Thr284, Trp86, Tyr89, and Glu283 (in CCR5) and Asp283, Val37, Asn286, His202, and Gln288 (in CCR2) residues were involved in hydrogen bond interactions. The molecules also showed π-π stacking interaction with key residues Phe112, Tyr108, Phe109, Trp86, and Tyr89 (in CCR5) and HIE121, Trp98, Tyr120 (in CCR2). Additionally, in some molecule’s halogen bond was also observed. The residues which formed the halogen bond include Phe182, Thr195 (in CCR5), and Lys38 (in CCR2). The screened molecules showed the interactions with some key residues i.e., Phe112, Tyr108, Phe109 (in CCR5) and Trp98, Tyr120 (in CCR2) as same that of CVC interactions with CCR5 and CCR2 receptor.","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69367855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}