Publications by authors named "Kristen L Beck"

12 Publications

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DNA Extraction and Host Depletion Methods Significantly Impact and Potentially Bias Bacterial Detection in a Biological Fluid.

mSystems 2021 Jun 15;6(3):e0061921. Epub 2021 Jun 15.

Department of Food Science, Cornell University, Ithaca, New York, USA.

Untargeted sequencing of nucleic acids present in food can inform the detection of food safety and origin, as well as product tampering and mislabeling issues. The application of such technologies to food analysis may reveal valuable insights that are simply unobtainable by targeted testing, leading to the efforts of applying such technologies in the food industry. However, before these approaches can be applied, it is imperative to verify that the most appropriate methods are used at every step of the process: gathering of primary material, laboratory methods, data analysis, and interpretation. The focus of this study is on gathering the primary material, in this case, DNA. We used bovine milk as a model to (i) evaluate commercially available kits for their ability to extract nucleic acids from inoculated bovine milk, (ii) evaluate host DNA depletion methods for use with milk, and (iii) develop and evaluate a selective lysis-propidium monoazide (PMA)-based protocol for host DNA depletion in milk. Our results suggest that magnetically based nucleic acid extraction methods are best for nucleic acid isolation of bovine milk. Removal of host DNA remains a challenge for untargeted sequencing of milk, highlighting the finding that the individual matrix characteristics should always be considered in food testing. Some reported methods introduce bias against specific types of microbes, which may be particularly problematic in food safety, where the detection of Gram-negative pathogens and hygiene indicators is essential. Continuous efforts are needed to develop and validate new approaches for untargeted metagenomics in samples with large amounts of DNA from a single host. Tracking the bacterial communities present in our food has the potential to inform food safety and product origin. To do so, the entire genetic material present in a sample is extracted using chemical methods or commercially available kits and sequenced using next-generation platforms to provide a snapshot of the microbial composition. Because the genetic material of higher organisms present in food (e.g., cow in milk or beef, wheat in flour) is around 1,000 times larger than the bacterial content, challenges exist in gathering the information of interest. Additionally, specific bacterial characteristics can make them easier or harder to detect, adding another layer of complexity to this issue. In this study, we demonstrate the impact of using different methods for the ability to detect specific bacteria and highlight the need to ensure that the most appropriate methods are being used for each particular sample.
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http://dx.doi.org/10.1128/mSystems.00619-21DOI Listing
June 2021

SARS-CoV-2 detection status associates with bacterial community composition in patients and the hospital environment.

Microbiome 2021 06 8;9(1):132. Epub 2021 Jun 8.

Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA.

Background: SARS-CoV-2 is an RNA virus responsible for the coronavirus disease 2019 (COVID-19) pandemic. Viruses exist in complex microbial environments, and recent studies have revealed both synergistic and antagonistic effects of specific bacterial taxa on viral prevalence and infectivity. We set out to test whether specific bacterial communities predict SARS-CoV-2 occurrence in a hospital setting.

Methods: We collected 972 samples from hospitalized patients with COVID-19, their health care providers, and hospital surfaces before, during, and after admission. We screened for SARS-CoV-2 using RT-qPCR, characterized microbial communities using 16S rRNA gene amplicon sequencing, and used these bacterial profiles to classify SARS-CoV-2 RNA detection with a random forest model.

Results: Sixteen percent of surfaces from COVID-19 patient rooms had detectable SARS-CoV-2 RNA, although infectivity was not assessed. The highest prevalence was in floor samples next to patient beds (39%) and directly outside their rooms (29%). Although bed rail samples more closely resembled the patient microbiome compared to floor samples, SARS-CoV-2 RNA was detected less often in bed rail samples (11%). SARS-CoV-2 positive samples had higher bacterial phylogenetic diversity in both human and surface samples and higher biomass in floor samples. 16S microbial community profiles enabled high classifier accuracy for SARS-CoV-2 status in not only nares, but also forehead, stool, and floor samples. Across these distinct microbial profiles, a single amplicon sequence variant from the genus Rothia strongly predicted SARS-CoV-2 presence across sample types, with greater prevalence in positive surface and human samples, even when compared to samples from patients in other intensive care units prior to the COVID-19 pandemic.

Conclusions: These results contextualize the vast diversity of microbial niches where SARS-CoV-2 RNA is detected and identify specific bacterial taxa that associate with the viral RNA prevalence both in the host and hospital environment. Video Abstract.
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http://dx.doi.org/10.1186/s40168-021-01083-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8186369PMC
June 2021

Analysis and forecasting of global real time RT-PCR primers and probes for SARS-CoV-2.

Sci Rep 2021 04 26;11(1):8988. Epub 2021 Apr 26.

IBM Research, San Jose, 95120, USA.

Rapid tests for active SARS-CoV-2 infections rely on reverse transcription polymerase chain reaction (RT-PCR). RT-PCR uses reverse transcription of RNA into complementary DNA (cDNA) and amplification of specific DNA (primer and probe) targets using polymerase chain reaction (PCR). The technology makes rapid and specific identification of the virus possible based on sequence homology of nucleic acid sequence and is much faster than tissue culture or animal cell models. However the technique can lose sensitivity over time as the virus evolves and the target sequences diverge from the selective primer sequences. Different primer sequences have been adopted in different geographic regions. As we rely on these existing RT-PCR primers to track and manage the spread of the Coronavirus, it is imperative to understand how SARS-CoV-2 mutations, over time and geographically, diverge from existing primers used today. In this study, we analyze the performance of the SARS-CoV-2 primers in use today by measuring the number of mismatches between primer sequence and genome targets over time and spatially. We find that there is a growing number of mismatches, an increase by 2% per month, as well as a high specificity of virus based on geographic location.
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http://dx.doi.org/10.1038/s41598-021-88532-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8076216PMC
April 2021

EMPress Enables Tree-Guided, Interactive, and Exploratory Analyses of Multi-omic Data Sets.

mSystems 2021 Mar 16;6(2). Epub 2021 Mar 16.

Department of Computer Science, Jacobs School of Engineering, University of California, San Diego, California, USA

Standard workflows for analyzing microbiomes often include the creation and curation of phylogenetic trees. Here we present EMPress, an interactive web tool for visualizing trees in the context of microbiome, metabolome, and other community data scalable to trees with well over 500,000 nodes. EMPress provides novel functionality-including ordination integration and animations-alongside many standard tree visualization features and thus simplifies exploratory analyses of many forms of 'omic data. Phylogenetic trees are integral data structures for the analysis of microbial communities. Recent work has also shown the utility of trees constructed from certain metabolomic data sets, further highlighting their importance in microbiome research. The ever-growing scale of modern microbiome surveys has led to numerous challenges in visualizing these data. In this paper we used five diverse data sets to showcase the versatility and scalability of EMPress, an interactive web visualization tool. EMPress addresses the growing need for exploratory analysis tools that can accommodate large, complex multi-omic data sets.
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http://dx.doi.org/10.1128/mSystems.01216-20DOI Listing
March 2021

Microbiome Metadata Standards: Report of the National Microbiome Data Collaborative's Workshop and Follow-On Activities.

mSystems 2021 02 23;6(1). Epub 2021 Feb 23.

Lawrence Berkeley National Laboratory, Berkeley, California, USA.

Microbiome samples are inherently defined by the environment in which they are found. Therefore, data that provide context and enable interpretation of measurements produced from biological samples, often referred to as metadata, are critical. Important contributions have been made in the development of community-driven metadata standards; however, these standards have not been uniformly embraced by the microbiome research community. To understand how these standards are being adopted, or the barriers to adoption, across research domains, institutions, and funding agencies, the National Microbiome Data Collaborative (NMDC) hosted a workshop in October 2019. This report provides a summary of discussions that took place throughout the workshop, as well as outcomes of the working groups initiated at the workshop.
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http://dx.doi.org/10.1128/mSystems.01194-20DOI Listing
February 2021

Monitoring the microbiome for food safety and quality using deep shotgun sequencing.

NPJ Sci Food 2021 Feb 8;5(1). Epub 2021 Feb 8.

Consortium for Sequencing the Food Supply Chain, San Jose, CA, USA.

In this work, we hypothesized that shifts in the food microbiome can be used as an indicator of unexpected contaminants or environmental changes. To test this hypothesis, we sequenced the total RNA of 31 high protein powder (HPP) samples of poultry meal pet food ingredients. We developed a microbiome analysis pipeline employing a key eukaryotic matrix filtering step that improved microbe detection specificity to >99.96% during in silico validation. The pipeline identified 119 microbial genera per HPP sample on average with 65 genera present in all samples. The most abundant of these were Bacteroides, Clostridium, Lactococcus, Aeromonas, and Citrobacter. We also observed shifts in the microbial community corresponding to ingredient composition differences. When comparing culture-based results for Salmonella with total RNA sequencing, we found that Salmonella growth did not correlate with multiple sequence analyses. We conclude that microbiome sequencing is useful to characterize complex food microbial communities, while additional work is required for predicting specific species' viability from total RNA sequencing.
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http://dx.doi.org/10.1038/s41538-020-00083-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7870667PMC
February 2021

Microbial context predicts SARS-CoV-2 prevalence in patients and the hospital built environment.

medRxiv 2020 Nov 22. Epub 2020 Nov 22.

Synergistic effects of bacteria on viral stability and transmission are widely documented but remain unclear in the context of SARS-CoV-2. We collected 972 samples from hospitalized ICU patients with coronavirus disease 2019 (COVID-19), their health care providers, and hospital surfaces before, during, and after admission. We screened for SARS-CoV-2 using RT-qPCR, characterized microbial communities using 16S rRNA gene amplicon sequencing, and contextualized the massive microbial diversity in this dataset in a meta-analysis of over 20,000 samples. Sixteen percent of surfaces from COVID-19 patient rooms were positive, with the highest prevalence in floor samples next to patient beds (39%) and directly outside their rooms (29%). Although bed rail samples increasingly resembled the patient microbiome throughout their stay, SARS-CoV-2 was less frequently detected there (11%). Despite surface contamination in almost all patient rooms, no health care workers providing COVID-19 patient care contracted the disease. SARS-CoV-2 positive samples had higher bacterial phylogenetic diversity across human and surface samples, and higher biomass in floor samples. 16S microbial community profiles allowed for high classifier accuracy for SARS-CoV-2 status in not only nares, but also forehead, stool and floor samples. Across these distinct microbial profiles, a single amplicon sequence variant from the genus Rothia was highly predictive of SARS-CoV-2 across sample types, and had higher prevalence in positive surface and human samples, even when comparing to samples from patients in another intensive care unit prior to the COVID-19 pandemic. These results suggest that bacterial communities contribute to viral prevalence both in the host and hospital environment.
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http://dx.doi.org/10.1101/2020.11.19.20234229DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7685343PMC
November 2020

IBM Functional Genomics Platform, A Cloud-Based Platform for Studying Microbial Life at Scale.

IEEE/ACM Trans Comput Biol Bioinform 2020 Sep 2;PP. Epub 2020 Sep 2.

The rapid growth in biological sequence data is revolutionizing our understanding of genotypic diversity and challenging conventional approaches to informatics. Due to the increasing available genomic data, traditional bioinformatic tools require substantial computational time and the creation of ever-larger indices each time a researcher seeks to gain insight from the data. To address this, we pre-computed important relationships between biological entities spanning the Central Dogma of Molecular Biology and captured this information in a relational database. The database can be queried across hundreds of millions of entities and returns results in a fraction of the time required by traditional methods. We describe IBM Functional Genomics Platform, a comprehensive database relating genotype to phenotype for bacterial life. Continually updated, the platform contains data derived from 200,000 curated, self-consistently assembled genomes. The database stores functional data for over 68 million genes, 52 million proteins, and 239 million domains with associated biological activity annotations from Gene Ontology, KEGG, MetaCyc, and Reactome. It maps the connections between each biological entity including the originating genome, gene, protein, and protein domain. We describe the data selection, the pipeline to create and update, and the developer tools.
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http://dx.doi.org/10.1109/TCBB.2020.3021231DOI Listing
September 2020

Food authentication from shotgun sequencing reads with an application on high protein powders.

NPJ Sci Food 2019 19;3:24. Epub 2019 Nov 19.

Consortium for Sequencing the Food Supply Chain, San Jose, CA USA.

Here we propose that using shotgun sequencing to examine food leads to accurate authentication of ingredients and detection of contaminants. To demonstrate this, we developed a bioinformatic pipeline, FASER (Food Authentication from SEquencing Reads), designed to resolve the relative composition of mixtures of eukaryotic species using RNA or DNA sequencing. Our comprehensive database includes >6000 plants and animals that may be present in food. FASER accurately identified eukaryotic species with 0.4% median absolute difference between observed and expected proportions on sequence data from various sources including sausage meat, plants, and fish. FASER was applied to 31 high protein powder raw factory ingredient total RNA samples. The samples mostly contained the expected source ingredient, chicken, while three samples unexpectedly contained pork and beef. Our results demonstrate that DNA/RNA sequencing of food ingredients, combined with a robust analysis, can be used to find contaminants and authenticate food ingredients in a single assay.
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http://dx.doi.org/10.1038/s41538-019-0056-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6863864PMC
November 2019

Insular Microbiogeography: Three Pathogens as Exemplars.

Curr Issues Mol Biol 2020 9;36:89-108. Epub 2019 Oct 9.

University of California Davis, School of Veterinary Medicine, 100K Pathogen Genome Project, Davis, CA, USA.

Traditional taxonomy in biology assumes that life is organized in a simple tree. Attempts to classify microorganisms in this way in the genomics era led microbiologists to look for finite sets of 'core' genes that uniquely group taxa as clades in the tree. However, the diversity revealed by large-scale whole genome sequencing is calling into question the long-held model of a hierarchical tree of life, which leads to questioning of the definition of a species. Large-scale studies of microbial genome diversity reveal that the cumulative number of new genes discovered increases with the number of genomes studied as a power law and subsequently leads to the lack of evidence for a unique core genome within closely related organisms. Sampling 'enough' new genomes leads to the discovery of a replacement or alternative to any gene. This power law behaviour points to an underlying self-organizing critical process that may be guided by mutation and niche selection. Microbes in any particular niche exist within a local web of organism interdependence known as the microbiome. The same mechanism that underpins the macro-ecological scaling first observed by MacArthur and Wilson also applies to microbial communities. Recent metagenomic studies of a food microbiome demonstrate the diverse distribution of community members, but also genotypes for a single species within a more complex community. Collectively, these results suggest that traditional taxonomic classification of bacteria could be replaced with a quasispecies model. This model is commonly accepted in virology and better describes the diversity and dynamic exchange of genes that also hold true for bacteria. This model will enable microbiologists to conduct population-scale studies to describe microbial behaviour, as opposed to a single isolate as a representative.
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http://dx.doi.org/10.21775/cimb.036.089DOI Listing
September 2020

Comparative Proteomics of Human and Macaque Milk Reveals Species-Specific Nutrition during Postnatal Development.

J Proteome Res 2015 May 6;14(5):2143-57. Epub 2015 Apr 6.

⊥Department of Human Evolutionary Biology, Harvard University, 11 Divinity Avenue, Cambridge, Massachusetts 02138, United States.

Milk has been well established as the optimal nutrition source for infants, yet there is still much to be understood about its molecular composition. Therefore, our objective was to develop and compare comprehensive milk proteomes for human and rhesus macaques to highlight differences in neonatal nutrition. We developed a milk proteomics technique that overcomes previous technical barriers including pervasive post-translational modifications and limited sample volume. We identified 1606 and 518 proteins in human and macaque milk, respectively. During analysis of detected protein orthologs, we identified 88 differentially abundant proteins. Of these, 93% exhibited increased abundance in human milk relative to macaque and include lactoferrin, polymeric immunoglobulin receptor, alpha-1 antichymotrypsin, vitamin D-binding protein, and haptocorrin. Furthermore, proteins more abundant in human milk compared with macaque are associated with development of the gastrointestinal tract, the immune system, and the brain. Overall, our novel proteomics method reveals the first comprehensive macaque milk proteome and 524 newly identified human milk proteins. The differentially abundant proteins observed are consistent with the perspective that human infants, compared with nonhuman primates, are born at a slightly earlier stage of somatic development and require additional support through higher quantities of specific proteins to nurture human infant maturation.
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http://dx.doi.org/10.1021/pr501243mDOI Listing
May 2015