Random whole metagenomic sequencing for forensic discrimination of soils

Forensic Microbiology: When, Where and How

Forensic microbiology is a relatively new discipline, born in part thanks to the development of advanced methodologies for the detection, identification and characterization of microorganisms, and also in relation to the growing impact of infectious diseases of iatrogenic origin. Indeed, the increased application of medical practices, such as transplants, which require immunosuppressive treatments, and the growing demand for prosthetic installations, associated with an increasing threat of antimicrobial resistance, have led to a rise in the number of infections of iatrogenic origin, which entails important medico-legal issues. On the other hand, the possibility of detecting minimal amounts of microorganisms, even in the form of residual traces (e.g., their nucleic acids), and of obtaining gene and genomic sequences at contained costs, has made it possible to ask new questions of whether cases of death or illness might have a microbiological origin, with the possibility of also tracing the origin of the microorganisms involved and reconstructing the chain of contagion. In addition to the more obvious applications, such as those mentioned above related to the origin of iatrogenic infections, or to possible cases of infections not properly diagnosed and treated, a less obvious application of forensic microbiology concerns its use in cases of violence or violent death, where the characterization of the microorganisms can contribute to the reconstruction of the case. Finally, paleomicrobiology, e.g., the reconstruction and characterization of microorganisms in historical or even archaeological remnants, can be considered as a sister discipline of forensic microbiology. In this article, we will review these different aspects and applications of forensic microbiology.

Mobility of antibiotic resistance and its co-occurrence with metal resistance in pathogens under oxidative stress

The bacterial communities are challenged with oxidative stress during their exposure to bactericidal antibiotics, metals, and different levels of dissolved oxygen (DO) encountered in diverse environmental habitats. The frequency of antibiotic resistance genes (ARGs) and metal resistance genes (MRGs) co-selection is increased by selective pressure posed by oxidative stress. Hence, study of resistance acquisition is important from an evolutionary perspective. To understand the dependence of oxidative stress on the dissemination of ARGs and MRGs through a pathogenic bacterial population, 12 metagenomes belonging to gut, water and soil habitats were evaluated. The metagenome-wide analysis showed the chicken gut to pose the most diverse pool of ARGs (30.4 ppm) and pathogenic bacteria (Simpson diversity = 0.98). The most common types of resistances found in all the environmental samples were efflux pumps (13.22 ppm) and genes conferring resistance to vancomycin (12.4 ppm), tetracycline (12.1 ppm), or beta-lactam (9.4 ppm) antibiotics. Additionally, limiting DO level in soil was observed to increase the abundance of excision nucleases (uvrA and uvrB), DNA polymerase (polA), catalases (katG), and other oxidative stress response genes (OSGs). This was further evident from major variations occurred in antibiotic efflux genes due to the effect of DO concentration on two human pathogens, namely Salmonella enterica and Shigella sonnei found in all the selected habitats. In conclusion, the microbial community, when challenged with oxidative stress caused by environmental variations in oxygen level, tends to accumulate higher amounts of ARGs with increased dissemination potential through triggering non-lethal mutagenesis. Furthermore, the genetic linkage or co-occurrence of ARGs and MRGs provides evidence for selecting ARGs under high concentrations of heavy metals.

Biological Ingredient Analysis of Traditional Herbal Patent Medicine Fuke Desheng Wan Using the Shotgun Metabarcoding Approach

With the widespread use of traditional medicine around the world, the safety and efficacy of traditional herbal patent medicine have become an increasing concern to the public. However, it is difficult to supervise the authenticity of herbal materials in mixed herbal products according to the current quality standards, especially for traditional herbal patent medicine, with a distinct variance in the dosage of herbal materials. This study utilized the shotgun metabarcoding approach to analyze the biological ingredients of Fuke Desheng Wan (FKDSW), which is an effective traditional herbal product for the treatment of dysmenorrhea. Six herbal materials were collected, and a lab-made mock FKDSW sample was produced to establish a method for the authentication assessment of biological ingredients in traditional herbal patent medicine based on shotgun metabarcoding. Furthermore, four commercial FKDSW samples were collected to verify the practicality of the shotgun metabarcoding approach. Then, a total of 52.16 Gb raw data for 174 million paired-end reads was generated using the Illumina NovaSeq sequencing platform. Meanwhile, 228, 23, and 14 operational taxonomic units (OTUs) were obtained for the ITS2, matK, and rbcL regions, respectively, after bioinformatic analysis. Moreover, no differences were evident between the assembly sequences obtained via shotgun metabarcoding and their corresponding reference sequences of the same species obtained via Sanger sequencing, except for part of the ITS2 and matK assembly sequences of Paeonia lactiflora Pall., Saussurea costus (Falc.) Lipsch. and Bupleurum chinense DC. with 1–6 different bases. The identification results showed that all six prescribed ingredients were successfully detected and that the non-authentic ingredient of Bupleuri Radix (Chaihu, Bupleurum chinense DC. or Bupleurum scorzonerifolium Willd.) was found in all the commercial samples, namely Bupleurum falcatum L. Here, 25 weed species representing 16 genera of ten families were detected. Moreover, 26 fungal genera belonging to 17 families were found in both lab-made and commercial FKDSW samples. This study demonstrated that the shotgun metabarcoding approach could overcome the biased PCR amplification and authenticate the biological ingredients of traditional herbal patent medicine with a distinct variance in the dosage of the herbal materials. Therefore, this provides an appropriate evaluation method for improving the safety and efficacy of traditional herbal patent medicine.

A walk on the dirt: soil microbial forensics from ecological theory to the crime lab

Forensics aims at using physical evidence to solve investigations with science-based principles, thus operating within a theoretical framework. This however is often rather weak, the exception being DNA-based human forensics that is well anchored in theory. Soil is a most commonly encountered, easily and unknowingly transferred evidence but it is seldom employed as soil analyses require extensive expertise. In contrast, comparative analyses of soil bacterial communities using nucleic acid technologies can efficiently and precisely locate the origin of forensic soil traces. However, this application is still in its infancy, and is very rarely used. We posit that understanding the theoretical bases and limitations of their uses is essential for soil microbial forensics to be judiciously implemented. Accordingly, we review the ecological theory and experimental evidence explaining differences between soil microbial communities, i.e. the generation of beta diversity, and propose to integrate a bottom-up approach of interactions at the microscale, reflecting historical contingencies with top-down mechanisms driven by the geographic template, providing a potential explanation as to why bacterial communities map according to soil types. Finally, we delimit the use of soil microbial forensics based on the present technologies and ecological knowledge, and propose possible venues to remove existing bottlenecks.

Bacterial Community of Grana Padano PDO Cheese and Generical Hard Cheeses: DNA Metabarcoding and DNA Metafingerprinting Analysis to Assess Similarities and Differences

The microbiota of Protected Designation of Origin (PDO) cheeses plays an essential role in defining their quality and typicity and could be applied to protect these products from counterfeiting. To study the possible role of cheese microbiota in distinguishing Grana Padano (GP) cheese from generical hard cheeses (HC), the microbial structure of 119 GP cheese samples was studied by DNA metabarcoding and DNA metafingerprinting and compared with 49 samples of generical hard cheeses taken from retail. DNA metabarcoding highlighted the presence, as dominant taxa, of Lacticaseibacillus rhamnosus, Lactobacillus helveticus, Streptococcus thermophilus, Limosilactobacillus fermentum, Lactobacillus delbrueckii, Lactobacillus spp., and Lactococcus spp. in both GP cheese and HC. Differential multivariate statistical analysis of metataxonomic and metafingerprinting data highlighted significant differences in the Shannon index, bacterial composition, and species abundance within both dominant and subdominant taxa between the two cheese groups. A supervised Neural Network (NN) classification tool, trained by metagenotypic data, was implemented, allowing to correctly classify GP cheese and HC samples. Further implementation and validation to increase the robustness and improve the predictive capacity of the NN classifier will be needed. Nonetheless, the proposed tool opens interesting perspectives in helping protection and valorization of GP and other PDO cheeses.

Unraveling City-Specific Microbial Signatures and Identifying Sample Origins for the Data From CAMDA 2020 Metagenomic Geolocation Challenge

The composition of microbial communities has been known to be location-specific. Investigating the microbial composition across different cities enables us to unravel city-specific microbial signatures and further predict the origin of unknown samples. As part of the CAMDA 2020 Metagenomic Geolocation Challenge, MetaSUB provided the whole genome shotgun (WGS) metagenomics data from samples across 28 cities along with non-microbial city data for 23 of these cities. In our solution to this challenge, we implemented feature selection, normalization, clustering and three methods of machine learning to classify the cities based on their microbial compositions. Of the three methods, multilayer perceptron obtained the best performance with an error rate of 19.60% based on whether the correct city received the highest or second highest number of votes for the test data contained in the main dataset. We then trained the model to predict the origins of samples from the mystery dataset by including these samples with the additional group label of "mystery." The mystery dataset compromised of samples collected from a subset of the cities in the main dataset as well as samples collected from new cities. For samples from cities that belonged to the main dataset, error rates ranged from 18.18 to 72.7%. For samples from new cities that did not belong to the main dataset, 57.7% of the test samples could be correctly labeled as "mystery" samples. Furthermore, we also predicted some of the non-microbial features for the mystery samples from the cities that did not belong to main dataset to draw inferences and narrow the range of the possible sample origins using a multi-output multilayer perceptron algorithm.

Mechanistic models of microbial community metabolism

Microbial communities affect many facets of human health and well-being. Naturally occurring bacteria, whether in nature or the human body, rarely exist in isolation. A deeper understanding of the metabolic functions of these communities is now possible with emerging computational models. In this review, we summarize frameworks for constructing mechanistic models of microbial community metabolism and discuss available algorithms for model analysis. We highlight essential decision points that greatly influence algorithm selection, as well as model analysis. Polymicrobial metabolic models can be utilized to gain insights into host-pathogen interactions, bacterial engineering, and many more translational applications.

Lampenflora in a Show Cave in the Great Basin Is Distinct from Communities on Naturally Lit Rock Surfaces in Nearby Wild Caves

In show caves, artificial lighting is intended to illuminate striking cave formations for visitors. However, artificial lighting also promotes the growth of novel and diverse biofilm communities, termed lampenflora, that obtain their energy from these artificial light sources. Lampenflora, which generally consist of cyanobacteria, algae, diatoms, and bryophytes, discolor formations and introduce novel ecological interactions in cave ecosystems. The source of lampenflora community members and patterns of diversity have generally been understudied mainly due to technological limitations. In this study, we investigate whether members of lampenflora communities in an iconic show cave—Lehman Caves—in Great Basin National Park (GRBA) in the western United States also occur in nearby unlit and rarely visited caves. Using a high-throughput environmental DNA metabarcoding approach targeting three loci—the ITS2 (fungi), a fragment of the 16S (bacteria), and a fragment of 23S (photosynthetic bacteria and eukaryotes)—we characterized diversity of lampenflora communities occurring near artificial light sources in Lehman Caves and rock surfaces near the entrances of seven nearby “wild” caves. Most caves supported diverse and distinct microbial-dominated communities, with little overlap in community members among caves. The lampenflora communities in the show cave were distinct, and generally less diverse, from those occurring in nearby unlit caves. Our results suggest an unidentified source for a significant proportion of lampenflora community members in Lehman Caves, with the majority of community members not found in nearby wild caves. Whether the unique members of the lampenflora communities in Lehman Caves are related to distinct abiotic conditions, increased human visitation, or other factors remains unknown. These results provide a valuable framework for future research exploring lampenflora community assemblies in show caves, in addition to a broad perspective into the range of microbial and lampenflora community members in GRBA. By more fully characterizing these communities, we can better monitor the establishment of lampenflora and design effective strategies for their management and removal.

Machine learning applications in microbial ecology, human microbiome studies, and environmental monitoring

Advances in nucleic acid sequencing technology have enabled expansion of our ability to profile microbial diversity. These large datasets of taxonomic and functional diversity are key to better understanding microbial ecology. Machine learning has proven to be a useful approach for analyzing microbial community data and making predictions about outcomes including human and environmental health. Machine learning applied to microbial community profiles has been used to predict disease states in human health, environmental quality and presence of contamination in the environment, and as trace evidence in forensics. Machine learning has appeal as a powerful tool that can provide deep insights into microbial communities and identify patterns in microbial community data. However, often machine learning models can be used as black boxes to predict a specific outcome, with little understanding of how the models arrived at predictions. Complex machine learning algorithms often may value higher accuracy and performance at the sacrifice of interpretability. In order to leverage machine learning into more translational research related to the microbiome and strengthen our ability to extract meaningful biological information, it is important for models to be interpretable. Here we review current trends in machine learning applications in microbial ecology as well as some of the important challenges and opportunities for more broad application of machine learning to understanding microbial communities.

The signatures of microorganisms and of human and environmental biomes can now be used to provide evidence in legal cases

The microorganisms with which we share our world go largely unnoticed. We are, however, beginning to be able to exploit their apparently silent presence as witnesses to events that are of legal concern. This information can be used to link forensic samples to criminal events and even perpetrators. Once dead, our bodies are rapidly colonised, internally and externally. The progress of these events can be charted to inform how long and even by what means a person has died. A small number of microbial species could actually be the cause of such deaths as a result of biocrime or bioterrorism. The procedures and techniques to respond to such attacks have matured in the last 20 years. The capability now exists to identify malicious intent, characterise the threat agent to isolate level and potentially link it to perpetrators with a high level of confidence.

What we can see from very small size sample of metagenomic sequences

BACKGROUND

Since the analysis of a large number of metagenomic sequences costs heavy computing resources and takes long time, we examined a selected small part of metagenomic sequences as "sample"s of the entire full sequences, both for a mock community and for 10 different existing metagenomics case studies. A mock community with 10 bacterial strains was prepared, and their mixed genome were sequenced by Hiseq. The hits of BLAST search for reference genome of each strain were counted. Each of 176 different small parts selected from these sequences were also searched by BLAST and their hits were also counted, in order to compare them to the original search results from the full sequences. We also prepared small parts of sequences which were selected from 10 publicly downloadable research data of MG-RAST service, and analyzed these samples with MG-RAST.

RESULTS

Both the BLAST search tests of the mock community and the results from the publicly downloadable researches of MG-RAST show that sampling an extremely small part from sequence data is useful to estimate brief taxonomic information of the original metagenomic sequences. For 9 cases out of 10, the most annotated classes from the MG-RAST analyses of the selected partial sample sequences are the same as the ones from the originals.

CONCLUSIONS

When a researcher wants to estimate brief information of a metagenome's taxonomic distribution with less computing resources and within shorter time, the researcher can analyze a selected small part of metagenomic sequences. With this approach, we can also build a strategy to monitor metagenome samples of wider geographic area, more frequently.

Molecular markers used in forensic genetics

Forensic genetics is a field that has become subject to increasing interest in recent years. Both the technology and the markers used for forensic purposes have changed since the 1980s. The minisatellite sequences used in the famous Pitchfork case introduced genetics to the forensic sciences. Minisatellite sequences have now been replaced by more sensitive microsatellite markers, which have become the basis for the creation of genetic profile databases. Modern molecular methods also exploit single nucleotide polymorphisms, which are often the only way to identify degraded DNA samples. The same type of variation is taken into consideration in attempting to establish the ethnicity of a perpetrator and to determine phenotypic traits such as the eye or hair colour of the individual who is the source of the genetic material. This paper contains a review of the techniques and molecular markers used in human and animal forensic genetics, and also presents the potential trends in forensic genetics such as phenotyping.

Arbitrarily Primed PCR for Comparison of Meta Genomes and Extracting Useful Loci from Them

A method is described that uses arbitrarily primed PCR followed by many cycles of amplification under stringent conditions and selection by computational means to obtain a set of sequence tags that can be used for the comparison of metagenomes. Relative to unselective shot-gun sequencing, the results are small data sets that can be csompared electronically or plotted as scattergrams that are simple to interpret. The method can be used to compare groups of samples of any size to build in-house databases from which, for example, the provenance of trace soil samples may be inferred. The method also allows for selection of primers with locus-specificity and an example is given in which a South Australian sequence-related to a Portuguese thermophile (Rubrobacter radiotolerans) is extracted and tested on a set of soils.

A protocol for obtaining DNA barcodes from plant and insect fragments isolated from forensic-type soils

Soil is often collected from a suspect's tire, vehicle, or shoes during a criminal investigation and subsequently submitted to a forensic laboratory for analysis. Plant and insect material recovered in such samples is rarely analyzed, as morphological identification is difficult. In this study, DNA barcoding was used for taxonomic identifications by targeting the gene regions known to permit discrimination in plants [maturase K (matK) and ribulose 1,5-biphosphate carboxylase (rbcL)] and insects [cytochrome oxidase subunit I (COI)]. A DNA barcode protocol suitable for processing forensic-type biological fragments was developed and its utility broadly tested with forensic-type fragments (e.g., seeds, leaves, bark, head, legs; n, 213) isolated from soils collected within Virginia, USA (n, 11). Difficulties with PCR inhibitors in plant extracts and obtaining clean Sanger sequence data from insect amplicons were encountered during protocol development; however, the final protocol produced sequences specific to the expected locus and taxa. The overall quantity and quality of DNA extracted from the 213 forensic-type biological fragments was low (< 15 ng/μL). For plant fragments, only the rbcL sequence data was deemed reliable; thus, taxonomic identifications were limited to the family level. The majority of insect sequences matched COI in both GenBank and Barcode of Life DataSystems; however, they were identified as an undescribed environmental contaminant. Although limited taxonomic information was gleaned from the forensic-type fragments processed in this study, the new protocol shows promise for obtaining reliable and specific identifications through DNA barcoding, which could ultimately enhance the information gleaned from soil examinations.

Bioinformatics Approach to Assess the Biogeographical Patterns of Soil Communities: The Utility for Soil Provenance

Soil DNA profiling has potential as a forensic tool to establish a link between soil collected at a crime scene and soil recovered from a suspect. However, a quantitative measure is needed to investigate the spatial/temporal variability across multiple scales prior to their application in forensic science. In this study, soil DNA profiles across Miami-Dade, FL, were generated using length heterogeneity PCR to target four taxa. The objectives of this study were to (i) assess the biogeographical patterns of soils to determine whether soil biota is spatially correlated with geographic location and (ii) evaluate five machine learning algorithms for their predictive ability to recognize biotic patterns which could accurately classify soils at different spatial scales regardless of seasonal collection. Results demonstrate that soil communities have unique patterns and are spatially autocorrelated. Bioinformatic algorithms could accurately classify soils across all scales with Random Forest significantly outperforming all other algorithms regardless of spatial level.

A review of bioinformatic methods for forensic DNA analyses

Short tandem repeats, single nucleotide polymorphisms, and whole mitochondrial analyses are three classes of markers which will play an important role in the future of forensic DNA typing. The arrival of massively parallel sequencing platforms in forensic science reveals new information such as insights into the complexity and variability of the markers that were previously unseen, along with amounts of data too immense for analyses by manual means. Along with the sequencing chemistries employed, bioinformatic methods are required to process and interpret this new and extensive data. As more is learnt about the use of these new technologies for forensic applications, development and standardization of efficient, favourable tools for each stage of data processing is being carried out, and faster, more accurate methods that improve on the original approaches have been developed. As forensic laboratories search for the optimal pipeline of tools, sequencer manufacturers have incorporated pipelines into sequencer software to make analyses convenient. This review explores the current state of bioinformatic methods and tools used for the analyses of forensic markers sequenced on the massively parallel sequencing (MPS) platforms currently most widely used.

Microbial composition analyses by 16S rRNA sequencing: A proof of concept approach to provenance determination of archaeological ochre

Many archaeological science studies use the concept of “provenance”, where the origins of cultural material can be determined through physical or chemical properties that relate back to the origins of the material. Recent studies using DNA profiling of bacteria have been used for the forensic determination of soils, towards determination of geographic origin. This manuscript presents a novel approach to the provenance of archaeological minerals and related materials through the use of 16S rRNA sequencing analysis of microbial DNA. Through the microbial DNA characterization from ochre and multivariate statistics, we have demonstrated the clear discrimination between four distinct Australian cultural ochre sites.

Resemblance profiles as clustering decision criteria: Estimating statistical power, error, and correspondence for a hypothesis test for multivariate structure

Clustering data continues to be a highly active area of data analysis, and resemblance profiles are being incorporated into ecological methodologies as a hypothesis testing‐based approach to clustering multivariate data. However, these new clustering techniques have not been rigorously tested to determine the performance variability based on the algorithm's assumptions or any underlying data structures. Here, we use simulation studies to estimate the statistical error rates for the hypothesis test for multivariate structure based on dissimilarity profiles (DISPROF). We concurrently tested a widely used algorithm that employs the unweighted pair group method with arithmetic mean (UPGMA) to estimate the proficiency of clustering with DISPROF as a decision criterion. We simulated unstructured multivariate data from different probability distributions with increasing numbers of objects and descriptors, and grouped data with increasing overlap, overdispersion for ecological data, and correlation among descriptors within groups. Using simulated data, we measured the resolution and correspondence of clustering solutions achieved by DISPROF with UPGMA against the reference grouping partitions used to simulate the structured test datasets. Our results highlight the dynamic interactions between dataset dimensionality, group overlap, and the properties of the descriptors within a group (i.e., overdispersion or correlation structure) that are relevant to resemblance profiles as a clustering criterion for multivariate data. These methods are particularly useful for multivariate ecological datasets that benefit from distance‐based statistical analyses. We propose guidelines for using DISPROF as a clustering decision tool that will help future users avoid potential pitfalls during the application of methods and the interpretation of results.

Microbiome Tools for Forensic Science

Microbes are present at every crime scene and have been used as physical evidence for over a century. Advances in DNA sequencing and computational approaches have led to recent breakthroughs in the use of microbiome approaches for forensic science, particularly in the areas of estimating postmortem intervals (PMIs), locating clandestine graves, and obtaining soil and skin trace evidence. Low-cost, high-throughput technologies allow us to accumulate molecular data quickly and to apply sophisticated machine-learning algorithms, building generalizable predictive models that will be useful in the criminal justice system. In particular, integrating microbiome and metabolomic data has excellent potential to advance microbial forensics.

Metagenomics and Bioinformatics in Microbial Ecology: Current Status and Beyond

Metagenomic approaches are now commonly used in microbial ecology to study microbial communities in more detail, including many strains that cannot be cultivated in the laboratory. Bioinformatic analyses make it possible to mine huge metagenomic datasets and discover general patterns that govern microbial ecosystems. However, the findings of typical metagenomic and bioinformatic analyses still do not completely describe the ecology and evolution of microbes in their environments. Most analyses still depend on straightforward sequence similarity searches against reference databases. We herein review the current state of metagenomics and bioinformatics in microbial ecology and discuss future directions for the field. New techniques will allow us to go beyond routine analyses and broaden our knowledge of microbial ecosystems. We need to enrich reference databases, promote platforms that enable meta- or comprehensive analyses of diverse metagenomic datasets, devise methods that utilize long-read sequence information, and develop more powerful bioinformatic methods to analyze data from diverse perspectives.