Biology subjects

Holsbo, E.

Publications and source records attributed to Holsbo, E..

4 recordsLinked to original sources

bioRxiv2025

Machine learning based lineage prediction from AMR phenotypes for Escherichia coli ST131 clade C surveillance across infection types

2.Rising antimicrobial resistance (AMR) in Escherichia coli bloodstream infections (BSIs) in high-income settings has typically been dominated by one clone, the sequence type (ST) 131. More specifically, ST131 clade C (ST131-C) is associated with fluoroquinolone resistance and extended-spectrum {beta}-lactamases (ESBLs). Even though urinary tract infections (UTIs) are a known common precursor to BSIs, there is currently limited knowledge on the longitudinal prevalence of ST131-C in UTIs and, therefore, the temporal link between the two infection types. Leveraging available genomic and antimicrobial susceptibility test (AST) data for ciprofloxacin, gentamicin, and ceftazidime in 2790 E. coli BSI isolates, we trained random forest and Extreme Gradient Boosting (XGBoost) classifiers to predict if an E. coli isolate belongs to ST131-C using only AST data. These models were used to predict the yearly prevalence of ST131-C in 22942 UTI and 24866 BSI isolates from Norway. The XGBoost classifier achieved a prediction F1-score of over 70% on a highly unbalanced dataset where only 4.3% of the genomic BSI isolates belonged to ST131-C. The predicted prevalence of ST131-C in UTIs exhibited a similar annual trend to that of BSIs, with a stable infection burden for eight years after its rapid expansion, confirming that the persistence of ST131-C in BSIs is largely driven by ST131-C UTIs. However, a higher prevalence of ST131-C in BSIs ([~]7%) compared to UTIs ([~]4%) suggests a subsequent enrichment of ST131-C. Our study highlights how existing epidemiological knowledge can be supplemented by utilising extensive data from AMR surveillance efforts without genomic markers. 3. Impact statementThis study proposes a potential analysis method that leverages AST data, which is already regularly collected for AMR surveillance purposes. Using such data to approximate the population-wide prevalence of MDR clones, such as ST131-C, could allow for larger-scale, retrospective studies of its prevalence in a population than genomic-based methods at a significantly lower cost. Such a method could supplement existing knowledge and epidemiology study practices. We use the proposed method to find relationships between the prevalence of the important MDR E. coli clone ST131-C in UTIs and BSIs in Norway. These results suggest that monitoring and reducing MDR in UTIs could reduce the burden of this invasive clone in hard-to-treat BSIs. 4. Data summaryAll AST data, clonal information for isolates with genomic data, and code used in this study can be found in the following repository: https://github.com/theodorross/EColi-UTI-Predictions. Only the clone and published metadata information is shown for the UTI data shared by Handal, Kaspersen et al.

bioinformatics↗

bioRxiv2019

Reproducible data management and analysis using R

BackgroundStandardizing and documenting computational analyses are necessary to ensure reproducible results. It is especially important for large and complex projects where data collection, analysis, and interpretation may span decades. Our objective is therefore to provide methods, tools, and best practice guidelines adapted for analyses in epidemiological studies that use -omics data.\n\nResultsWe describe an R-based implementation of data management and preprocessing. The method is well-integrated with the analysis tools typically used for statistical analysis of -omics data. We document all datasets thoroughly and use version control to track changes to both datasets and code over time. We provide a web application to perform the standardized preprocessing steps for gene expression datasets. We provide best practices for reporting data analysis results and sharing analyses.\n\nConclusionWe have used these tools to organize data storage and documentation, and to standardize the analysis of gene expression data, in the Norwegian Women and Cancer (NOWAC) system epidemiology study. We believe our approach and lessons learned are applicable to analyses in other large and complex epidemiology projects.

bioinformatics↗

bioRxiv2017

A Standard Operating Procedure For Outlier Removal In Large-Sample Epidemiological Transcriptomics Datasets

Transcriptome measurements and other -omics type data are increasingly more used in epidemiological studies. Most of omics studies to date are small with samples sizes in the tens, or sometimes low hundreds, but this is changing. Our Norwegian Woman and Cancer (NOWAC) datasets are to date one or two orders of magnitude larger. The NOWAC biobank contains about 50000 blood samples from a prospective study. Around 125 breast cancer cases occur in this cohort each year. The large biological variation in gene expression means that many observations are needed to draw scientific conclusions. This is true for both microarray and RNA-seq type data. Hence, larger datasets are likely to become more common soon.\n\nTechnical outliers are observations that somehow were distorted at the lab or during sampling. If not removed these observations add bias and variance in later statistical analyses, and may skew the results. Hence, quality assessment and data cleaning are important. We find common quality assessment libraries difficult to work with for large datasets for two reasons: slow execution speed and unsuitable visualizations.\n\nIn this paper, we present our standard operating procedure (SOP) for large-sample transcriptomics datasets. Our SOP combines automatic outlier detection with manual evaluation to avoid removing valuable observations. We use laboratory quality measures and statistical measures of deviation to aid the analyst. These are available in the nowaclean R package, currently available on GitHub (https://github.com/3inar/nowaclean). Finally, we evaluate our SOP on one of our larger datasets with 832 observations.

epidemiology↗

bioRxiv2017

Curve Selection For Predicting Breast Cancer Metastasis From Prospective Gene Expression In Blood

We investigate whether there is information in gene expression levels in blood that predicts breast cancer metastasis. Our data comes from the NOWAC epidemiological cohort study where blood samples were provided at enrollment. This could be anywhere from years to weeks before any cancer diagnosis. When and if a cancer is diagnosed, it could be so in different ways: at a screening, between screenings, or in the clinic, outside of the screening program. To build predictive models we propose that variable selection should include followup time and stratify by detection method. We show by simulations that this improves the probability of selecting relevant predictor genes. We also demonstrate that it leads to improved predictions and more stable gene signatures in our data. There is some indication that blood gene expression levels hold predictive information about metastasis. With further development such information could be used for early detection of metastatic potential and as such aid in cancer treatment.

bioinformatics↗