Publications by authors named "Cresten Mansfeldt"

Article Synopsis
  • Wastewater-based surveillance has emerged since 2020 as a valuable and economical method for tracking public health, but must prioritize justice to ensure global health equity.
  • Current ethics discussions on this topic have mainly focused on privacy and legality in North America and Western Europe, neglecting broader global implications.
  • The authors propose a justice-centered framework using Nancy Fraser's theories to address issues like community definition, transparency, and inequalities, advocating for fair outcomes rather than strict regulations in varying contexts.
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The expanding field of synthetic biology (synbio) supports new opportunities in the design of targeted bioproducts or modified microorganisms. However, this rapid development of synbio products raises concerns surrounding the potential risks of modified microorganisms contaminating unintended environments. These potential invasion risks require new bioinformatic tools to inform the design phase.

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Article Synopsis
  • * A workshop held in April 2023, funded by the National Science Foundation, focused on identifying the current challenges and research gaps in bacterial wastewater surveillance, leading to discussions about methods, data standardization, and the importance of correlating wastewater data with human disease.
  • * To enhance bacterial monitoring in wastewater, experts suggested the need for better data reporting standards, method optimization, and a deeper understanding of bacterial shedding patterns to link wastewater findings to infection rates in communities.
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  • Wastewater-based epidemiology (WBE) is essential for public health, but participation from wastewater operators is declining due to concerns like resource limitations and ethical issues.
  • WBE efforts face challenges from operator fatigue, especially post-COVID, leading to hesitance in engaging with WBE programs.
  • The study introduces a decision-making tool to help WBE teams address operators' needs and risks, improving collaboration and data quality across public health initiatives.
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Wastewater-based testing (WBT) for SARS-CoV-2 has rapidly expanded over the past three years due to its ability to provide a comprehensive measurement of disease prevalence independent of clinical testing. The development and simultaneous application of WBT measured biomarkers for research activities and for the pursuit of public health goals, both areas with well-established ethical frameworks. Currently, WBT practitioners do not employ a standardized ethical review process, introducing the potential for adverse outcomes for WBT professionals and community members.

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Unlabelled: Wastewater-based testing (WBT) for SARS-CoV-2 has rapidly expanded over the past three years due to its ability to provide a comprehensive measurement of disease prevalence independent of clinical testing. The development and simultaneous application of the field blurred the boundary between measuring biomarkers for research activities and for pursuit of public health goals, both areas with well-established ethical frameworks. Currently, WBT practitioners do not employ a standardized ethical review process (or associated data management safeguards), introducing the potential for adverse outcomes for WBT professionals and community members.

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Pathogen surveillance within wastewater rapidly progressed during the SARS-CoV-2 pandemic and informed public health management. In addition to the successful monitoring of entire sewer catchment basins at the treatment facility scale, subcatchment or building-level monitoring enabled targeted support of resource deployment. However, optimizing the temporal and spatial resolution of these monitoring programs remains complex due to population dynamics and within-sewer physical, chemical, and biological processes.

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Wastewater microbial communities are not static and can vary significantly across time and space, but this variation and the factors driving the observed spatiotemporal variation often remain undetermined. We used a shotgun metagenomic approach to investigate changes in wastewater microbial communities across 17 locations in a sewer network, with samples collected from each location over a 3-week period. Fecal material-derived bacteria constituted a relatively small fraction of the taxa found in the collected samples, highlighting the importance of environmental sources to the sewage microbiome.

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Much of what is known and theorized concerning passive sampling techniques has been developed considering chemical analytes. Yet, historically, biological analytes, such as Salmonella typhi, have been collected from wastewater via passive sampling with Moore swabs. In response to the COVID-19 pandemic, passive sampling is re-emerging as a promising technique to monitor SARS-CoV-2 RNA in wastewater.

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SARS-CoV-2 RNA detection in wastewater is being rapidly developed and adopted as a public health monitoring tool worldwide. With wastewater surveillance programs being implemented across many different scales and by many different stakeholders, it is critical that data collected and shared are accompanied by an appropriate minimal amount of metainformation to enable meaningful interpretation and use of this new information source and intercomparison across datasets. While some databases are being developed for specific surveillance programs locally, regionally, nationally, and internationally, common globally-adopted data standards have not yet been established within the research community.

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Article Synopsis
  • Daily composite wastewater samples were collected from 20 locations on the University of Colorado Boulder campus to monitor SARS-CoV-2 levels as part of COVID-19 public health guidance.
  • Low-cost autosamplers were created in-house, allowing for the efficient collection of 1512 samples from August 25 to November 23, 2020, and SARS-CoV-2 concentrations were analyzed using two reverse transcription quantitative polymerase chain reaction methods.
  • The results showed that raw wastewater concentrations accurately reflected campus infection trends, enabling timely data delivery to stakeholders to aid in decision-making and assess the effectiveness of interventions during the pandemic.
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Wastewater treatment plant (WWTP) effluents release not only chemical constituents in watersheds, but also contain microorganisms. Thus, an understanding of what microorganisms are released and how they change microbial communities within natural streams is needed. To characterize the community shifts in streams receiving WWTP effluent, we sampled water-column microorganisms from upstream, downstream, and the effluent of WWTPs located on 23 headwater streams in which no other effluent was released upstream.

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Biotransformation of chemical contaminants is of importance in various natural and engineered systems. However, in complex microbial communities and with chemical contaminants at low concentrations, our current understanding of biotransformation at the level of enzyme-chemical interactions is limited. Here, we explored an approach to identify associations between micropollutant biotransformation and specific gene products in complex microbial communities, using association mining between chemical and metatranscriptomic data obtained from experiments with activated sludge grown at different solid retention times.

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RNA-based biomarkers have been successfully detected at field sites undergoing bioremediation, but the detection of expressed enzymes is a more direct way to prove activity for a particular biocatalytic process of interest since they provide evidence of potential activity rather than simply confirming presence and abundance of genes in a given population by measurement of DNA copies using qPCR. Here we successfully applied shotgun proteomics to field samples from a trichloroethene (TCE)-contaminated industrial site in southern Ontario, Canada that had been bio-augmented with the commercially available KB-1 microbial culture. The KB-1 culture contains multiple strains of () as well as an organohalide respiring species.

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A remaining challenge within microbial ecology is to understand the determinants of richness and diversity observed in environmental microbial communities. In a range of systems, including activated sludge bioreactors, the microbial residence time (MRT) has been previously shown to shape the microbial community composition. However, the physiological and ecological mechanisms driving this influence have remained unclear.

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For many polar organic micropollutants, biotransformation by activated sludge microorganisms is a major removal process during wastewater treatment. However, our current understanding of how wastewater treatment operations influence microbial communities and their micropollutant biotransformation potential is limited, leaving major parts of observed variability in biotransformation rates across treatment facilities unexplained. Here, we present biotransformation rate constants for 42 micropollutants belonging to different chemical classes along a gradient of solids retention time (SRT).

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Given the challenges facing the economically favorable production of products from microalgae, understanding factors that might impact productivity rates including growth rates and accumulation of desired products, for example, triacylglycerols (TAG) for biodiesel feedstock, remains critical. Although operational parameters such as media composition and reactor design can clearly effect growth rates, the role of microbe-microbe interactions is just beginning to be elucidated. In this study an oleaginous marine algae Chlorella spp.

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The presence of antibiotics in treated wastewater and consequently in surface and groundwater resources raises concerns about the formation and spread of antibiotic resistance. Improving the removal of antibiotics during wastewater treatment therefore is a prime objective of environmental engineering. Here we obtained a detailed picture of the fate of sulfonamide antibiotics during activated sludge treatment using a combination of analytical methods.

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Article Synopsis
  • The study investigates using mRNA levels of key enzymes as indicators for monitoring organohalide respiration (OHR) rates in bioaugmentation cultures at chlorinated solvent-contaminated sites.
  • Genome-wide data identified crucial enzymes in the KB-1 bioaugmentation culture, allowing differentiation between strains based on specific enzyme expressions.
  • Quantitative analysis revealed that certain transcripts corresponded well with OHR rates, highlighting HupL as a promising biomarker, while adding oxygen caused significant drops in respiration rates.
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The interpretation of high-throughput gene expression data for non-model microorganisms remains obscured because of the high fraction of hypothetical genes and the limited number of methods for the robust inference of gene networks. Therefore, to elucidate gene-gene and gene-condition linkages in the bioremediation-important genus Dehalococcoides, we applied a Bayesian inference strategy called Reverse Engineering/Forward Simulation (REFS™) on transcriptomic data collected from two organohalide-respiring communities containing different Dehalococcoides mccartyi strains: the Cornell University mixed community D2 and the commercially available KB-1® bioaugmentation culture. In total, 49 and 24 microarray datasets were included in the REFS™ analysis to generate an ensemble of 1,000 networks for the Dehalococcoides population in the Cornell D2 and KB-1® culture, respectively.

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Article Synopsis
  • The study focuses on a subtropical strain of Chlorella, which is a type of unicellular algae known for its ability to produce high lipid levels for biodiesel production, evaluating its growth in different conditions.
  • The researchers sequenced the RNA of Chlorella strain C596 to identify important metabolic genes and assess how the algae respond to changes in nutrient availability, noting an increase in lipid and starch biosynthesis genes as nutrients decreased.
  • Findings will enhance the understanding and optimization of Chlorella for biofuel production by confirming specific gene expressions related to lipid accumulation and metabolism.
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We present a statistical model designed to identify the effect of experimental perturbations on the aggregate behavior of the transcriptome expressed by the bacterium Dehalococcoides mccartyi strain 195. Strains of Dehalococcoides are used in sub-surface bioremediation applications because they organohalorespire tetrachloroethene and trichloroethene (common chlorinated solvents that contaminate the environment) to non-toxic ethene. However, the biochemical mechanism of this process remains incompletely described.

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Article Synopsis
  • The study investigates the relationship between mRNA and protein levels in a specific anaerobic community involved in bioremediation.
  • mRNA levels of certain genes in Dehalococcoides and Methanospirillum show varying abundances and regulation, but there are discrepancies between mRNA abundance and corresponding protein levels.
  • These findings emphasize the complex nature of gene expression in microbes and the need for direct measurements to accurately assess biomarker levels in environmental contexts.
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A cDNA-microarray was designed and used to monitor the transcriptomic profile of Dehalococcoides mccartyi strain 195 (in a mixed community) respiring various chlorinated organics, including chloroethenes and 2,3-dichlorophenol. The cultures were continuously fed in order to establish steady-state respiration rates and substrate levels. The organization of array data into a clustered heat map revealed two major experimental partitions.

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Article Synopsis
  • Bioremediation of chlorinated ethenes is driven by specific microbes, notably Dehalococcoides strains, which thrive best in mixed communities with fermenters and methanogens.
  • Researchers are developing molecular biomarkers to improve diagnostics for these microbial communities, aiming to enhance modeling of anaerobic reductive dechlorination processes.
  • A biokinetic model incorporating quantitative PCR data was calibrated with various experiments, revealing that mRNA levels provide better insights into microbial activity compared to 16S rRNA, especially under competitive conditions at higher feeding rates.
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