Biology subjects

Jensen, L. J.

Publications and source records attributed to Jensen, L. J..

At least 37 records · Page 2Linked to original sources

bioRxiv2022

The impact of PrsA over-expression on the Bacillus subtilis transcriptome during fed-batch fermentation of alpha-amylase production

The production of the alpha-amylase (AMY) enzyme in Bacillus subtilis at a high rate leads to the accumulation of unfolded AMY, which causes secretion stress. The over-expression of the PrsA chaperone aids the enzyme folding and reduces stress. To identify affected pathways and potential mechanisms involved in the reduced growth, we analyzed the transcriptomic differences during fed-batch fermentation between a PrsA over-expressing strain and a control in a time-series RNA-seq experiment. We observe transcription in 542 previously un-annotated regions, of which 234 had significant changes in expression levels between the samples. Moreover, 1,791 protein-coding sequences, 80 non-coding genes, and 20 riboswitches overlapping UTR regions of coding genes had significant changes in expression. Via gene-set over-representation analysis of the differentially expressed genes, we identified putatively regulated biological processes; overall the analysis suggests that the PrsA over-expression affects ATP biosynthesis activity, amino acid metabolism, and cell wall stability. The investigation of the protein interaction network points to a potential impact on cell motility signaling. We discuss the impact of these highlighted mechanisms for reducing secretion stress or detrimental aspects of PrsA over-expression during AMY production.

microbiology↗

bioRxiv2022

Identifying the genes impacted by cell proliferation in proteomics and transcriptomics studies

Today, hypothesis-free high-throughput profiling allows relative quantification of thousands of proteins or transcripts across samples and thereby identification of differentially expressed genes. It is used in many biological contexts to characterize differences between cell lines and tissues, identify drug mode of action or drivers of drug resistance, among others. Genes can also be differentially regulated because of confounding factors that were not accounted for in the experimental plan, such as change in cell proliferation. Here, we identified genes for which expression consistently correlates with cell proliferation rates in proteomics and transcriptomics high-throughput data sets to determine the overall impact of cell growth rate on these data. We combined the analysis of 449 cell lines and 1,040 cell lines in five proteomics and three transcriptomics data sets to generate a refined list of 223 confounding genes that correlate with cell proliferation rates. These include many actors in DNA replication and mitosis, and genes periodically expressed during the cell cycle. It constitutes a valuable resource when analyzing high-throughput datasets showing changes in proliferation across conditions. We show how to use this resource to analyze in vitro drug screens and tumor samples. By disregarding the proliferation confounders, one can instead focus on the experiment-specific regulation events otherwise buried in the statistical analysis.

cell biology↗

bioRxiv2021

DISEASES 2.0: a weekly updated database of disease-gene associations from text mining and data integration

The scientific knowledge about which genes are involved in which diseases grows rapidly, which makes it difficult to keep up with new publications and genetics datasets. The DISEASES database aims to provide a comprehensive overview by systematically integrating and assigning confidence scores to evidence for disease-gene associations from curated databases, genome-wide association studies (GWAS), and automatic text mining of the biomedical literature. Here, we present a major update to this resource, which greatly increases the number of associations from all these sources. This is especially true for the text-mined associations, which have increased by at least 9-fold at all confidence cutoffs. We show that this dramatic increase is primarily due to adding full-text articles to the text corpus, secondarily due to improvements to both the disease and gene dictionaries used for named entity recognition, and only to a very small extent due to the growth in number of PubMed abstracts. DISEASES now also makes use of a new GWAS database, TIGA, which considerably increased the number of GWAS-derived disease-gene associations. DISEASES itself is also integrated into several other databases and resources, including GeneCards/MalaCards, Pharos/TCRD, and the Cytoscape stringApp. All data in DISEASES is updated on a weekly basis and is available via a web interface at https://diseases.jensenlab.org, from where it can also be downloaded under open licenses.

bioinformatics↗

bioRxiv2021

U-CIE : Color encoding of high-dimensional data

Data visualization is essential to discover patterns and anomalies in large high-dimensional datasets. New dimensionality reduction techniques have thus been developed for visualizing omics data, in particular from single-cell studies. However, jointly showing several types of data, e.g. single-cell expression and gene networks, remains a challenge. Here, we present U-CIE, a visualization method that encodes arbitrary high-dimensional data as colors using a combination of dimensionality reduction and the CIELAB color space to retain the original structure to the extent possible. U-CIE first uses UMAP to reduce high-dimensional data to three dimensions, partially preserving distances between entities. Next, it embeds the resulting three-dimensional representation within the CIELAB color space. This color model was designed to be perceptually uniform, meaning that the Euclidean distance between any two points should correspond to their relative perceptual difference. Therefore, the combination of UMAP and CIELAB thus results in a color encoding that captures much of the structure of the original high-dimensional data. We illustrate its broad applicability by visualizing single-cell data on a protein network and metagenomic data on a world map and on scatter plots.

bioinformatics↗

bioRxiv2021

Light-induced asymmetries in the embryonic retina are mediated by the vascular system and extracellular matrix

Left-right asymmetries in the nervous system (lateralisation) influence a broad range of behaviours, from social responses to navigation and language. The role and pathways of endogenous and environmental mechanisms in the ontogeny of lateralisation remains to be established. The domestic chick is a model of both endogenous and experience-induced lateralisation driven by light exposure. Following the endogenous rightward rotation of the embryo, the asymmetrical position in the egg results in a greater exposure of the right eye to environmental light. To identify the genetic pathways activated by asymmetric light stimulation, and their time course, we exposed embryos to different light regimes: darkness, 6 hours of light and 24 hours of light. We used RNA-seq to compare gene expression in the right and left retinas and telencephalon. As expected, no differential expression between left and right was present in darkness. We detected differential gene expression in right vs left retina after 6 hours of light exposure. This difference disappeared before 24 hours of light exposure, suggesting that light-induced activation is a self-terminating phenomenon. This transient effect of light exposure was associated with a downregulation of the sensitive-period mediator gene DIO2 (iodothyronine deiodinase 2) in the right retina. No differences between genes expressed in the right vs. left telencephalon were detected. Gene networks associated with lateralisation were connected to vascularisation, cell motility, and the extracellular matrix. Interestingly, we know that the extracellular matrix- including the differentially expressed PDGFRB (platelet-derived growth factor receptor {beta}) gene - is involved in both sensitive periods and in the endogenous chiral mechanism of primary cilia, that drives lateralisation. Our data show a similarity between endogenous and experience-driven lateralisation, identifying functional gene networks that affect lateralisation in a specific time window.

neuroscience↗

bioRxiv2021

OnTheFly2.0: a text-mining web application for automated biomedical entity recognition, document annotation, network and functional enrichment analysis

Extracting and processing information from documents is of great importance as lots of experimental results and findings are stored in local files. Therefore, extracting and analysing biomedical terms from such files in an automated way is absolutely necessary. In this article, we present OnTheFly2.0, a web application for extracting biomedical entities from individual files such as plain texts, Office documents, PDF files or images. OnTheFly2.0 can generate informative summaries in popup windows containing knowledge related to the identified terms along with links to various databases. It uses the EXTRACT tagging service to perform Named Entity Recognition (NER) for genes/proteins, chemical compounds, organisms, tissues, environments, diseases, phenotypes and Gene Ontology terms. Multiple files can be analysed, whereas identified terms such as proteins or genes can be explored through functional enrichment analysis or be associated with diseases and PubMed entries. Finally, protein-protein and protein-chemical networks can be generated with the use of STRING and STITCH services. To demonstrate its capacity for knowledge discovery, we interrogated published meta-analyses of clinical biomarkers of severe COVID-19 and uncovered inflammatory and senescence pathways that impact disease pathogenesis. OnTheFly2.0 currently supports 197 species and is available at http://onthefly.pavlopouloslab.info.

bioinformatics↗

bioRxiv2020

TIGA: Target illumination GWAS analytics

Genome wide association studies (GWAS) can reveal important genotype-phenotype associations, however, data quality and interpretability issues must be addressed. For drug discovery scientists seeking to prioritize targets based on the available evidence, these issues go beyond the single study. Here, we describe rational ranking, filtering and interpretation of inferred gene-trait associations and data aggregation across studies by leveraging existing curation and harmonization efforts. Each gene-trait association is evaluated for confidence, with scores derived solely from aggregated statistics, linking a protein-coding gene and phenotype. We propose a method for assessing confidence in gene-trait associations from evidence aggregated across studies, including a bibliometric assessment of scientific consensus based on the iCite Relative Citation Ratio, and meanRank scores, to aggregate multivariate evidence. This method, intended for drug target hypothesis generation, scoring and ranking, has been implemented as an analytical pipeline, available as open source, with public datasets of results, and a web application designed for usability by drug discovery scientists, at https://unmtid-shinyapps.net/tiga/.

bioinformatics↗

bioRxiv2020

A Workflow of Integrated Resources to Catalyze Network Pharmacology Driven COVID-19 Research

MotivationIn the event of an outbreak due to an emerging pathogen, time is of the essence to contain or to mitigate the spread of the disease. Drug repositioning is one of the strategies that has the potential to deliver therapeutics relatively quickly. The SARS-CoV-2 pandemic has shown that integrating critical data resources to drive drug-repositioning studies, involving host-host, hostpathogen and drug-target interactions, remains a time-consuming effort that translates to a delay in the development and delivery of a life-saving therapy. ResultsHere, we describe a workflow we designed for a semi-automated integration of rapidly emerging datasets that can be generally adopted in a broad network pharmacology research setting. The workflow was used to construct a COVID-19 focused multimodal network that integrates 487 host-pathogen, 74,805 host-host protein and 1,265 drug-target interactions. The resultant Neo4j graph database named "Neo4COVID19" is accessible via a web interface and via API calls based on the Bolt protocol. We believe that our Neo4COVID19 database will be a valuable asset to the research community and will catalyze the discovery of therapeutics to fight COVID-19. Availabilityhttps://neo4covid19.ncats.io

systems biology↗

bioRxiv2020

Transcriptome profiling of ulcerative colitis mouse model suggests biomarkers and therapeutic targets for human colitis

1.BACKGROUND & AIMSUlcerative colitis (UC) is an inflammatory bowel disorder with unknown etiology. Given its complex nature, in vivo studies to investigate its pathophysiology is vital. Animal models play an important role in molecular profiling necessary to pinpoint mechanisms that contribute to human disease. Thus, we aim to identify common conserved gene expression signatures and differentially regulated pathways between human UC and a mouse model hereof, which can be used to identify UC patients from healthy individuals and to suggest novel treatment targets and biomarker candidates. METHODSTherefore, we performed high-throughput total and small RNA sequencing to comprehensively characterize the transcriptome landscape of the most widely used UC mouse model, the dextran sodium sulfate (DSS) model. We used this data in conjunction with publicly available human UC transcriptome data to compare gene expression profiles and pathways. RESULTSWe identified differentially regulated protein-coding genes, long non-coding RNAs and microRNAs from colon and blood of UC mice and further characterized the involved pathways and biological processes through which these genes may contribute to disease development and progression. By integrating human and mouse UC datasets, we suggest a set of 51 differentially regulated genes in UC colon and blood that may improve molecular phenotyping, aid in treatment decisions, drug discovery and the design of clinical trials. CONCLUSIONGlobal transcriptome analysis of the DSS-UC mouse model supports its use as an efficient high-throughput tool to discover new targets for therapeutic and diagnostic applications in human UC through identifying relationships between gene expression and disease phenotype.

molecular biology↗

bioRxiv2020

Clinical Knowledge Graph Integrates Proteomics Data into Clinical Decision-Making

The promise of precision medicine is to deliver personalized treatment based on the unique physiology of each patient. This concept was fueled by the genomic revolution, but it is now evident that integrating other types of omics data, like proteomics, into the clinical decision-making process will be essential to accomplish precision medicine goals. However, quantity and diversity of biomedical data, and the spread of clinically relevant knowledge across myriad biomedical databases and publications makes this exceptionally difficult. To address this, we developed the Clinical Knowledge Graph (CKG), an open source platform currently comprised of more than 16 million nodes and 220 million relationships to represent relevant experimental data, public databases and the literature. The CKG also incorporates the latest statistical and machine learning algorithms, drastically accelerating analysis and interpretation of typical proteomics workflows. We use several biomarker studies to illustrate how the CKG may support, enrich and accelerate clinical decision-making. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=114 SRC="FIGDIR/small/084897v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@433689org.highwire.dtl.DTLVardef@b18068org.highwire.dtl.DTLVardef@1656084org.highwire.dtl.DTLVardef@a38201_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2020

Proteomics dissection of cardiac protein profiles of humans and model organisms

The study of human cardiac pathologies often relies on research conducted in model organisms to gain molecular insight into disease and to develop novel treatment strategies; however, translating findings from model organisms back to human can present a significant challenge, in part due to a lack of knowledge about the differences across species in cardiac protein abundances and their interactions. Here we set out to bridge this knowledge gap by presenting a global analysis of cardiac protein expression profiles in humans and commonly used model organisms. Using quantitative mass spectrometry-based proteomics, we measured the abundance of ~7,000 proteins in samples from the separate chambers of human, pig, horse, rat, mouse and zebrafish hearts. This knowledgebase of cardiac protein signatures is accessible through an online database at: atlas.cardiacproteomics.com. Quantitative comparison of the protein profiles support the pig as model organism of choice for arrhythmogenic right ventricular cardiomyopathy whereas comparison of profiles from the two-chambered zebrafish heart suggests a better resemblance to the right side of mammalian hearts. This proteomics resource facilitates translational prospect of cardiac studies from model organisms to humans by enabling direct comparison of disease-linked protein networks across species.

systems biology↗

bioRxiv2018

CoCoScore: Context-aware co-occurrence scoring for text mining applications using distant supervision

Information extraction by mining the scientific literature is key to uncovering relations between biomedical entities. Most existing approaches based on natural language processing extract relations from single sentence-level co-mentions, ignoring co-occurrence statistics over the whole corpus. Existing approaches counting entity co-occurrences ignore the textual context of each co-occurrence. We propose a novel corpus-wide co-occurrence scoring approach to relation extraction that takes the textual context of each co-mention into account. Our method, called CoCoScore, scores the certainty of stating an association for each sentence that co-mentions two entities. CoCoScore is trained using distant supervision based on a gold-standard set of associations between entities of interest. Instead of requiring a manually annotated training corpus, co-mentions are labeled as positives/negatives according to their presence/absence in the gold standard. We show that CoCoScore outperforms previous approaches in identifying human disease-gene and tissue-gene associations as well as in identifying physical and functional protein-protein associations in different species. CoCoScore is a versatile text-mining tool to uncover pairwise associations via co-occurrence mining, within and beyond biomedical applications. CoCoScore is available at: https://github.com/JungeAlexander/cocoscore

bioinformatics↗

bioRxiv2018

Cytoscape stringApp: Network analysis and visualization of proteomics data

Protein networks have become a popular tool for analyzing and visualizing the often long lists of proteins or genes obtained from proteomics and other high-throughput technologies. One of the most popular sources of such networks is the STRING database, which provides protein networks for more than 2000 organisms, including both physical interactions from experimental data and functional associations from curated pathways, automatic text mining, and prediction methods. However, its web interface is mainly intended for inspection of small networks and their underlying evidence. The Cytoscape software, on the other hand, is much better suited for working with large networks and offers greater flexibility in terms of network analysis, import and visualization of additional data. To include both resources in the same workflow, we created stringApp, a Cytoscape app that makes it easy to import STRING networks into Cytoscape, retains the appearance and many of the features of STRING, and integrates data from associated databases. Here, we introduce many of the stringApp features and show how they can be used to carry out complex network analysis and visualization tasks on a typical proteomics dataset, all through the Cytoscape user interface. stringApp is freely available from the Cytoscape app store: http://apps.cytoscape.org/apps/stringapp.

bioinformatics↗

bioRxiv2018

Viruses.STRING: A virus-host protein-protein interaction database

As viruses continue to pose risks to global health, having a better un-derstanding of virus-host protein-protein interactions aids in the development of treatments and vaccines. Here, we introduce Viruses.STRING, a protein-protein interaction database specifically catering to virus-virus and virus-host interactions. This database combines evidence from experimental and text-mining channels to provide combined probabilities for interactions between viral and host proteins. The database contains 177,425 interactions between 239 viruses and 319 hosts. The database is publicly available at viruses.string-db.org, and the interaction data can also be accessed through the latest version of the Cytoscape STRING app.

bioinformatics↗

bioRxiv2018

A Comprehensive Approach Characterizing Fusion Proteins and Their Interactions Using Biomedical Literature

Todays increase in scientific literature requires the efficient methods of data mining for improving the extraction of the useful information from texts. In this manuscript, we used a data and text mining method to identify fusions and their protein-protein interactions from published biomedical text. The extracted fusion proteins and their protein-protein interactions are used as a training set for a Naive Bayes classifier that is further used for final identification of testing dataset, consisting of 1817 fusions. Our method has a literature corpus, text and annotation mappers; keywords, rule bases, negative tokens, and pattern extractor; synonym tagger, normalization, regular expression mapper; and Naive Bayes classifier. We classified 1817 unique fusion proteins and their corresponding 2908 protein-protein interactions for 18 cancer types. Therefore, it can be used for screening literature for identifying mentions unique cases of fusions that can be further used for downstream analysis. It is available at http://protfus.md.biu.ac.il/.

bioinformatics↗

bioRxiv2017

Text mining of disease-lifestyle associations to explain comorbidities in electronic health registries

Mining of electronic health registries can reveal vast numbers of disease correlations (from hereon referred to as comorbidities for simplicity). However, the underlying causes can be hard to identify, in part because health registries usually do not record important lifestyle factors such as diet, substance consumption, and physical activity. To address this challenge, I developed a text-mining approach that uses dictionaries of diseases and lifestyle factors for named entity recognition and subsequently for co-occurrence extraction of disease-lifestyle associations from Medline. I show that this approach is able to extract many correct associations and provide proof-of-concept that these can provide plausible explanations for comorbidities observed in Swedish and Danish health registry data.

bioinformatics↗

bioRxiv2017

Text mining of 15 million full-text scientific articles

Across academia and industry, text mining has become a popular strategy for keeping up with the rapid growth of the scientific literature. Text mining of the scientific literature has mostly been carried out on collections of abstracts, due to their availability. Here we present an analysis of 15 million English scientific full-text articles published during the period 1823-2016. We describe the development in article length and publication sub-topics during these nearly 250 years. We showcase the potential of text mining by extracting published protein-protein, disease-gene, and protein subcellular associations using a named entity recognition system, and quantitatively report on their accuracy using gold standard benchmark data sets. We subsequently compare the findings to corresponding results obtained on 16.5 million abstracts included in MEDLINE and show that text mining of full-text articles consistently outperforms using abstracts only.

bioinformatics↗

bioRxiv2017

An integrative method to unravel the host-parasite interactome: An orthology-based approach

The study of molecular host-parasite interactions is essential to understand parasitic infection and adaptation within the host system. As well, prevention and treatment of infectious diseases require clear understanding of the molecular crosstalk between parasites and their hosts. As yet, experimental large-scale identification of host-parasite molecular interactions remains challenging and the use of in silico predictions becomes then necessary. Here, we propose a computational integrative approach to predict host-parasite protein-protein interaction (PPI) networks resulting from the infection of human by 12 different parasites. We used an orthology-based method to transfer high-confidence intra-species interactions obtained from the STRING database to the corresponding inter-species protein pairs in the host-parasite system. To reduce the number of spurious predictions, our approach uses either the parasites predicted secretome and membrane proteins or only the secretome depending on whether they are uni- or multicellular respectively. Besides, the host proteome is filtered for proteins expressed in selected cellular localizations and tissues supporting the parasites growth. We evaluated the inferred interactions by analyzing the enriched biological processes and pathways in the predicted networks and their association to known parasitic invasion and evasion mechanisms. The resulting PPI networks were compared across parasites to identify common mechanisms that may define a global pathogenic hallmark. The predicted PPI networks can be visualized and downloaded at http://orthohpi.jensenlab.org.\n\nAuthor SummaryA protein-protein interaction (PPI) network is a collection of interactions between proteins from one or more organisms. Host-parasite PPIs are key to understanding the biology of different parasitic diseases, since predicting PPIs enable to know more about the parasite invasion, infection and persist. Our understanding of PPIs between host and parasites is still very limited, as no many systematic experimental studies have so far been performed. Efficacy of treatments for parasitic diseases is limited and in many cases parasites evolve resistance. Thus, there is an urgent need to develop novel drugs or vaccines for these neglected diseases, and thus interest in the functions and interactions of proteins associated with parasitism processes. Here we developed an in silico method to shed light on the interactome in twelve human parasites by combining an orthology based strategy and integrating, domain-domain interaction data, sub-cellular localization and to give a spatial context, we only took in to account those human tissues that support the parasites tropism. Here we show that is possible to identify relevant interactions across different parasites and their human host and that these interactions are well supported based on the biology of the parasites.

systems biology↗