Biology subjects

McLaren, M. R.

Publications and source records attributed to McLaren, M. R..

6 recordsLinked to original sources

bioRxiv2023

Virus-like DNA tracers for characterizing wastewater-based pathogen monitoring systems

Wastewater-based pathogen monitoring has recently become a ubiquitous method for tracking the abundance and evolution of pathogens in a community or port of entry. But accurate inference of disease incidence from wastewater remains challenging, in large part due to noise and bias arising from wastewater transport and measurement. We developed a new system of tracer particles designed to enhance wastewater monitoring of viral pathogens. These tracers, which consist of synthetic DNA barcodes packaged into non-infectious viral capsids, can be deposited at known amounts and times into the sewer system and measured in wastewater samples to assess features such as assay sensitivity, and the decay and delay in viral signal arising from a shedding event. Here we describe the design, construction, and initial characterization of a 25-plex tracer library. Our tracer library supports simultaneous measurement of up to 5 distinct tracers by qPCR and up to 25 distinct tracers by amplicon or metagenomic sequencing. We demonstrate experimentally that the tracers are biologically inert and exhibit good stability and qPCR-assay performance in wastewater. These features collectively suggest that barcoded, virus-like tracers have the required properties for high-accuracy, high-resolution characterization of wastewater monitoring systems.

synthetic biology↗

bioRxiv2022

Implications of taxonomic bias for microbial differential-abundance analysis

Differential-abundance (DA) analyses enable microbiome researchers to assess how microbial species vary in relative or absolute abundance with specific host or environmental conditions, such as health status or pH. These analyses typically use sequencing-based community measurements that are taxonomically biased to measure some species more efficiently than others. Understanding the effects that taxonomic bias has on the results of a DA analysis is essential for achieving reliable and translatable findings; yet currently, these effects are unknown. Here, we characterized these effects for DA analyses of both relative and absolute abundances, using a combination of mathematical theory and data analysis of real and simulated case studies. We found that, for analyses based on species proportions, taxonomic bias can cause significant errors in DA results if the average measurement efficiency of the community is associated with the condition of interest. These errors can be avoided by using more robust DA methods (based on species ratios) or quantified and corrected using appropriate controls. Wide adoption of our recommendations can improve the reproducibility, interpretability, and translatability of microbiome DA studies. This manuscript was rendered from commit 7412a36 of https://github.com/mikemc/differential-abundance-theory. Supporting data analyses can be found in the accompanying computational research notebook. Please post comments or questions on GitHub. The manuscript is licensed under a CC BY 4.0 License. See the GitHub Releases or Zenodo record for earlier versions.

bioinformatics↗

bioRxiv2020

Clostridioides difficile exploits toxin-mediated inflammation to alter the host nutritional landscape and exclude competitors from the gut microbiota

Introductory paragraphClostridioides difficile is a bacterial pathogen that causes a range of clinical disease from mild to moderate diarrhea, pseudomembranous colitis, and toxic megacolon. Typically, C. difficile infections (CDIs) occur after antibiotic treatment, which alters the gut microbiota, decreasing colonization resistance against C. difficile. Disease is mediated by two large toxins and the expression of their genes is induced upon nutrient depletion via the alternative sigma factor TcdR. Using tcdR mutants in two strains of C. difficile, we defined how toxin-induced inflammation alters C. difficile metabolism, tissue gene expression, and the gut microbiota to determine how inflammation by the host may be beneficial to C. difficile. Here we show that C. difficile metabolism is significantly different in the face of inflammation, with changes in many carbohydrate and amino acid uptake and utilization pathways. Host gene expression signatures suggest that degradation of collagen and other components of the extracellular matrix by matrix metalloproteinases is a major source of peptides and amino acids that supports C. difficile growth in vivo. Lastly, the inflammation induced by C. difficile toxin activity alters the gut microbiota, excluding members from the genus Bacteroides that are able to compete against C. difficile for the same essential nutrients released from collagen degradation.

microbiology↗

bioRxiv2019

Characterization of C. difficile strains isolated from companion animals and the associated changes in the host fecal microbiota

BackgroundClostridioides difficile is an enteric pathogen historically known to cause hospital associated (HA)-infections in humans. A major risk factor for CDI in humans is antibiotic usage as it alters the gut microbiota and there is a loss of colonization resistance against C. difficile. In recent years there has been an increase in community associated (CA)-C. difficile infection that does not have the same risk factors as HA-CDI. Potential sources of CA-CDI have been proposed and include animals, food, water, and the environment, however these sources remain poorly investigated. Here, we define the prevalence of C. difficile strains found in different companion animals (canines, felines, and equines) to investigate a potential zoonotic link. C. difficile strains were identified by toxin gene profiling, fluorescent PCR ribotyping, and antimicrobial susceptibility testing. 16s rRNA gene sequencing was done on animal feces to investigate the relationship between the presence of C. difficile and the gut microbiota in different hosts.\n\nResultsHere, we show that C. difficile was recovered from 20.9% of samples (42/201), which included 33 canines, 2 felines, and 7 equines. Over 69% (29/42) of the isolates were toxigenic and belonged to 14 different ribotypes, with overlap between HA- and CA-CDI cases in humans. The presence of C. difficile results in a shift in the fecal microbial community structure in both canines and equines. Commensal Clostridia C. hiranonis was negatively associated with C. difficile in canines. Further experimentation showed a clear antagonistic relationship between the two strains in vitro, suggesting that commensal Clostridia might play a role in colonization resistance against C. difficile in different hosts.\n\nConclusionsIn this study we investigated a potentially important source of C. difficile transmission: the companion animal population. C. difficile carriage was common in dogs, cats, and horses. C. difficile isolates from companion animals included many of the same ribotypes known to cause HA- and CA-CDI in humans, and had similar antimicrobial resistance profiles as those isolated from human populations. These data contribute to our understanding of non-hospital exposure to C. difficile in the human population and suggest new avenues for reducing C. difficile prevalence in companion animals and, perhaps, thereby reducing CA-CDI in humans.

microbiology↗

bioRxiv2019

Reconsidering the management paradigm of fragmented populations

Habitat fragmentation and population declines call for informed management of many endangered species. The dominant paradigm for such management focuses on avoiding deleterious inbreeding effects in separated populations, by facilitating migration to maintain connectivity between them, an approach epitomized by the \"one migrant per generation\" rule. We show that this paradigm fails to take into account two important factors. First, it ignores an inherent trade-off: maintaining within-population genetic diversity is at the expense of maintaining global diversity. Migration increases local within-population genetic diversity, but also homogenizes the meta-population, which may lead to erosion of global genetic diversity. Second, this paradigm does not consider that because many fragmented species have declined in numbers only within the last century, they still carry much of the high genetic diversity characteristic of the historically large population. The conservation of a species global diversity, crucial for evolutionarily adaptation to ecological challenges such as epidemics, industrial pollutants, or climate change, is paramount to a species long-term survival. Here we discuss how consideration of these factors can inform management of fragmented populations and provide a framework to assess the impact on genetic diversity of different management strategies. We also propose two alternative management strategies to replace the \"one migrant per generation\" dogma.

ecology↗

bioRxiv2019

Consistent and correctable bias in metagenomic sequencing measurements

Measurements of biological communities by marker-gene and metagenomic sequencing are biased: The measured relative abundances of taxa or their genes are systematically distorted from their true values because each step in the experimental workflow preferentially detects some taxa over others. Bias can lead to qualitatively incorrect conclusions and makes measurements from different protocols quantitatively incomparable. A rigorous understanding of bias is therefore essential. Here we propose, test, and apply a simple mathematical model of how bias distorts marker-gene and metagenomics measurements: Bias multiplies the true relative abundances within each sample by taxon-and protocol-specific factors that describe the different efficiencies with which taxa are detected by the workflow. Critically, these factors are consistent across samples with different compositions, allowing bias to be estimated and corrected. We validate this model in 16S rRNA gene and shotgun metagenomics data from bacterial communities with defined compositions. We use it to reason about the effects of bias on downstream statistical analyses, finding that analyses based on taxon ratios are less sensitive to bias than analyses based on taxon proportions. Finally, we demonstrate how this model can be used to quantify bias from samples of defined composition, partition bias into steps such as DNA extraction and PCR amplification, and to correct biased measurements. Our model improves on previous models by providing a better fit to experimental data and by providing a composition-independent approach to analyzing, measuring, and correcting bias.

bioinformatics↗