A microbial clock provides an accurate estimate of the postmortem interval in a mouse model system
TL;DR: It is shown that postmortem microbial community changes are dramatic, measurable, and repeatable in a mouse model system, allowing PMI to be estimated within approximately 3 days over 48 days, and suggested that microbial community data can be developed into a forensic tool for estimating PMI.
Abstract: Our bodies—especially our skin, our saliva, the lining of our mouth and our gastrointestinal tract—are home to a diverse collection of bacteria and other microorganisms called the microbiome. While the roles played by many of these microorganisms have yet to be identified, it is known that they contribute to the health and wellbeing of their host by metabolizing indigestible compounds, producing essential vitamins, and preventing the growth of harmful bacteria. They are important for nutrient and carbon cycling in the environment. The advent of advanced sequencing techniques has made it feasible to study the composition of this microbial community, and to monitor how it changes over time or how it responds to events such as antibiotic treatment. Sequencing studies have been used to highlight the significant differences between microbial communities found in different parts of the body, and to follow the evolution of the gut microbiome from birth. Most of these studies have focused on live animals, so little is known about what happens to the microbiome after its host dies. In particular, it is not known if the changes that occur after death are similar for all individuals. Moreover, the decomposing animal supplies nutrients and carbon to the surrounding ecosystem, but its influence on the microbial community of its immediate environment is not well understood. Now Metcalf et al. have used high-throughput sequencing to study the bacteria and other microorganisms (such as nematodes and fungi) in dead and decomposing mice, and also in the soil beneath them, over the course of 48 days. The changes were significant and also consistent across the corpses, with the microbial communities in the corpses influencing those in the soil, and vice versa. Metcalf et al. also showed that these measurements could be used to estimate the postmortem interval (the time since death) to within approximately 3 days, which suggests that the work could have applications in forensic science.
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25 Feb 2016
TL;DR: Modification of modified 16S rRNA gene and internal transcribed spacer (ITS) primers for archaea/bacteria and fungi with nonaquatic samples demonstrated that two recently modified primer pairs that target taxonomically discriminatory regions of bacterial and fungal genomic DNA do not introduce new biases when used on a variety of sample types.
Abstract: Designing primers for PCR-based taxonomic surveys that amplify a broad range of phylotypes in varied community samples is a difficult challenge, and the comparability of data sets amplified with varied primers requires attention. Here, we examined the performance of modified 16S rRNA gene and internal transcribed spacer (ITS) primers for archaea/bacteria and fungi, respectively, with nonaquatic samples. We moved primer bar codes to the 5' end, allowing for a range of different 3' primer pairings, such as the 515f/926r primer pair, which amplifies variable regions 4 and 5 of the 16S rRNA gene. We additionally demonstrated that modifications to the 515f/806r (variable region 4) 16S primer pair, which improves detection of Thaumarchaeota and clade SAR11 in marine samples, do not degrade performance on taxa already amplified effectively by the original primer set. Alterations to the fungal ITS primers did result in differential but overall improved performance compared to the original primers. In both cases, the improved primers should be widely adopted for amplicon studies. IMPORTANCE We continue to uncover a wealth of information connecting microbes in important ways to human and environmental ecology. As our scientific knowledge and technical abilities improve, the tools used for microbiome surveys can be modified to improve the accuracy of our techniques, ensuring that we can continue to identify groundbreaking connections between microbes and the ecosystems they populate, from ice caps to the human body. It is important to confirm that modifications to these tools do not cause new, detrimental biases that would inhibit the field rather than continue to move it forward. We therefore demonstrated that two recently modified primer pairs that target taxonomically discriminatory regions of bacterial and fungal genomic DNA do not introduce new biases when used on a variety of sample types, from soil to human skin. This confirms the utility of these primers for maintaining currently recommended microbiome research techniques as the state of the art.
1,222 citations
TL;DR: A suite of bacterial and fungal groups that contribute to nitrogen cycling and a reproducible network of decomposers that emerge on predictable time scales are found.
Abstract: Vertebrate corpse decomposition provides an important stage in nutrient cycling in most terrestrial habitats, yet microbially mediated processes are poorly understood. Here we combine deep microbial community characterization, community-level metabolic reconstruction, and soil biogeochemical assessment to understand the principles governing microbial community assembly during decomposition of mouse and human corpses on different soil substrates. We find a suite of bacterial and fungal groups that contribute to nitrogen cycling and a reproducible network of decomposers that emerge on predictable time scales. Our results show that this decomposer community is derived primarily from bulk soil, but key decomposers are ubiquitous in low abundance. Soil type was not a dominant factor driving community development, and the process of decomposition is sufficiently reproducible to offer new opportunities for forensic investigations.
359 citations
TL;DR: A review of bone and tooth diagenesis using the most comprehensive collection of literature to date can be found in this article, which illustrates that researchers must examine multiple diagenetic pathways to fully understand the post-mortem interactions of archaeological skeletal material and the burial environment.
193 citations
TL;DR: It is shown that statistical analysis of DNA from natural microbial communities can be used to accurately identify environmental contaminants, including uranium and nitrate at a nuclear waste site, and the bacterial strains that are most useful for detecting oil and uranium are known to interact with these substrates.
Abstract: Biological sensors can be engineered to measure a wide range of environmental conditions. Here we show that statis- tical analysis of DNA from natural microbial communities can be used to accurately identify environmental contaminants, in- cluding uranium and nitrate at a nuclear waste site. In addition to contamination, sequence data from the 16S rRNA gene alone can quantitatively predict a rich catalogue of 26 geochemical features collected from 93 wells with highly differing geochemistry characteristics. We extend this approach to identify sites contaminated with hydrocarbons from the Deepwater Horizon oil spill, finding that altered bacterial communities encode a memory of prior contamination, even after the contaminants themselves have been fully degraded. We show that the bacterial strains that are most useful for detecting oil and uranium are known to in- teract with these substrates, indicating that this statistical approach uncovers ecologically meaningful interactions consistent with previous experimental observations. Future efforts should focus on evaluating the geographical generalizability of these associations. Taken as a whole, these results indicate that ubiquitous, natural bacterial communities can be used as in situ envi- ronmental sensors that respond to and capture perturbations caused by human impacts. These in situ biosensors rely on envi- ronmental selection rather than directed engineering, and so this approach could be rapidly deployed and scaled as sequencing technology continues to become faster, simpler, and less expensive. IMPORTANCE Here we show that DNA from natural bacterial communities can be used as a quantitative biosensor to accurately distinguish unpolluted sites from those contaminated with uranium, nitrate, or oil. These results indicate that bacterial commu- nities can be used as environmental sensors that respond to and capture perturbations caused by human impacts.
168 citations
TL;DR: Correlations between microbial community sources and sinks allow for inference of the interactions between humans and their environment.
Abstract: Microbial interaction between human-associated objects and the environments we inhabit may have forensic implications, and the extent to which microbes are shared between individuals inhabiting the same space may be relevant to human health and disease transmission. In this study, two participants sampled the front and back of their cell phones, four different locations on the soles of their shoes, and the floor beneath them every waking hour over a 2-day period. A further 89 participants took individual samples of their shoes and phones at three different scientific conferences. Samples taken from different surface types maintained significantly different microbial community structures. The impact of the floor microbial community on that of the shoe environments was strong and immediate, as evidenced by Procrustes analysis of shoe replicates and significant correlation between shoe and floor samples taken at the same time point. Supervised learning was highly effective at determining which participant had taken a given shoe or phone sample, and a Bayesian method was able to determine which participant had taken each shoe sample based entirely on its similarity to the floor samples. Both shoe and phone samples taken by conference participants clustered into distinct groups based on location, though much more so when an unweighted distance metric was used, suggesting sharing of low-abundance microbial taxa between individuals inhabiting the same space. Correlations between microbial community sources and sinks allow for inference of the interactions between humans and their environment.
136 citations
Cites background from "A microbial clock provides an accur..."
...Recent work has shown that postmortem, the microbiome of animal hosts changes dramatically, but in a predictable manner [11]....
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References
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TL;DR: A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score.
88,255 citations
"A microbial clock provides an accur..." refers methods in this paper
...Initial taxonomy assignment was done by BLAST (Altschul et al., 1990) with an e-value threshold of e-10....
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TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Abstract: Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, aaa, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.
79,257 citations
"A microbial clock provides an accur..." refers methods in this paper
...We regressed PMI directly on the taxon relative abundances (derived from the Illumina HiSeq data) using the Random Forests model (Breiman, 2001) with version 4....
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...We regressed PMI directly on the taxon relative abundances (derived from the Illumina HiSeq data) using the Random Forests model (Breiman, 2001) with version 4.6–7 of the randomForest package in R (Liaw and Wiener, 2002) with default settings....
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01 Jan 2007
TL;DR: random forests are proposed, which add an additional layer of randomness to bagging and are robust against overfitting, and the randomForest package provides an R interface to the Fortran programs by Breiman and Cutler.
Abstract: Recently there has been a lot of interest in “ensemble learning” — methods that generate many classifiers and aggregate their results. Two well-known methods are boosting (see, e.g., Shapire et al., 1998) and bagging Breiman (1996) of classification trees. In boosting, successive trees give extra weight to points incorrectly predicted by earlier predictors. In the end, a weighted vote is taken for prediction. In bagging, successive trees do not depend on earlier trees — each is independently constructed using a bootstrap sample of the data set. In the end, a simple majority vote is taken for prediction. Breiman (2001) proposed random forests, which add an additional layer of randomness to bagging. In addition to constructing each tree using a different bootstrap sample of the data, random forests change how the classification or regression trees are constructed. In standard trees, each node is split using the best split among all variables. In a random forest, each node is split using the best among a subset of predictors randomly chosen at that node. This somewhat counterintuitive strategy turns out to perform very well compared to many other classifiers, including discriminant analysis, support vector machines and neural networks, and is robust against overfitting (Breiman, 2001). In addition, it is very user-friendly in the sense that it has only two parameters (the number of variables in the random subset at each node and the number of trees in the forest), and is usually not very sensitive to their values. The randomForest package provides an R interface to the Fortran programs by Breiman and Cutler (available at http://www.stat.berkeley.edu/ users/breiman/). This article provides a brief introduction to the usage and features of the R functions.
14,830 citations
"A microbial clock provides an accur..." refers methods in this paper
...6–7 of the randomForest package in R (Liaw and Wiener, 2002) with default settings....
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...We regressed PMI directly on the taxon relative abundances (derived from the Illumina HiSeq data) using the Random Forests model (Breiman, 2001) with version 4.6–7 of the randomForest package in R (Liaw and Wiener, 2002) with default settings....
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TL;DR: Improvements to FastTree are described that improve its accuracy without sacrificing scalability, and FastTree 2 allows the inference of maximum-likelihood phylogenies for huge alignments.
Abstract: Background
We recently described FastTree, a tool for inferring phylogenies for alignments with up to hundreds of thousands of sequences. Here, we describe improvements to FastTree that improve its accuracy without sacrificing scalability.
10,010 citations
"A microbial clock provides an accur..." refers methods in this paper
..., 2010) and this alignment was used to construct a phylogenetic tree using FastTree (Price et al., 2010) within QIIME....
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...The representative sequences of all OTUs were then aligned to the Greengenes reference alignment using PyNAST (Caporaso et al., 2010) and this alignment was used to construct a phylogenetic tree using FastTree (Price et al., 2010) within QIIME....
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TL;DR: A 16S rRNA gene database (http://greengenes.lbl.gov) was used to provide chimera screening, standard alignment, and taxonomic classification using multiple published taxonomies as mentioned in this paper.
Abstract: A 16S rRNA gene database (http://greengenes.lbl.gov) addresses limitations of public repositories by providing chimera screening, standard alignment, and taxonomic classification using multiple published taxonomies. It was found that there is incongruent taxonomic nomenclature among curators even at the phylum level. Putative chimeras were identified in 3% of environmental sequences and in 0.2% of records derived from isolates. Environmental sequences were classified into 100 phylum-level lineages in the Archaea and Bacteria.
9,593 citations