Microbial Similarity between Students in a Common Dormitory Environment Reveals the Forensic Potential of Individual Microbial Signatures

Exploring the role of the human microbiome in forensic identification: opportunities and challenges

Forensic microbiology is rapidly emerging as a novel tool for human identification. The human microbiome, comprising diverse microbial communities including fungi, bacteria, protozoa, and viruses, is unique to each individual, offering a new dimension to forensic investigations. While traditional identification methods primarily rely on DNA profiling and fingerprint analysis, they face limitations when complete DNA or fingerprints profiles are unattainable or degraded. In this context, the microbial signatures of the human skin microbiome present a promising alternative due to their resilience to environmental stresses and individual-specific composition. This review explores the potential of microbiome analysis in forensic human identification, evaluating its applications, advantages, limitations, and future prospects. The uniqueness of an individual's microbial community, particularly the skin microbiota, can provide distinctive biological markers for identification purposes, while technological advancements like 16 S rRNA sequencing and metagenomic shotgun sequencing are enhancing the specificity of microbial identification, enabling detailed analysis of these complex ecological communities. Despite these promising findings, current research has not yet achieved a level of identification probability that could establish microbial analysis as a stand-alone evidence tool. Therefore, it is presently considered ancillary to traditional methods, contributing to a more comprehensive biological profile of individuals.

The indoors microbiome and human health

Indoor environments serve as habitat for humans and are replete with various reservoirs and niches for microorganisms. Microorganisms enter indoor spaces with their human and non-human hosts, as well as via exchange with outdoor sources, such as ventilation and plumbing. Once inside, many microorganisms do not survive, especially on dry, barren surfaces. Even reduced, this microbial biomass has critical implications for the health of human occupants. As urbanization escalates, exploring the intersection of the indoor environment with the human microbiome and health is increasingly vital. The indoor microbiome, a complex ecosystem of microorganisms influenced by human activities and environmental factors, plays a pivotal role in modulating infectious diseases and fostering healthy immune development. Recent advancements in microbiome research shed light on this unique ecological system, highlighting the need for innovative approaches in creating health-promoting living spaces. In this Review, we explore the microbial ecology of built environments - places where humans spend most of their lives - and its implications for immune, endocrine and neurological health. We further propose strategies to harness the indoor microbiome for better health outcomes.

Combined analysis of the microbiome and metabolome to reveal the characteristics of saliva from different diets: a comparison among vegans, seafood-based omnivores, and red meat (beef and lamb) omnivores

Introduction

Revealing individual characteristics is supportive for identifying individuals in forensic crime. As saliva is one of the most common biological samples used in crime scenes, it is important to make full use of the rich individual information contained in saliva. The aim of this study was to explore the application of the microbiome in forensic science by analysing differences in the salivary microbiome and metabolome of healthy individuals with different dietary habits.

Methods

We performed 16S rDNA sequencing analysis based on oral saliva samples collected from 12 vegetarians, 12 seafood omnivores and 12 beef and lamb omnivores. Non-targeted metabolomics analyses were also performed based on saliva samples from healthy individuals.

Results

The results showed that the dominant flora of vegetarians was dominated by Neisseria (belonging to the phylum Proteobacteria), while seafood omnivores and beef and lamb omnivores were dominated by Streptococcus (belonging to the phylum Firmicutes). NDMS-based and cluster analyses showed that vegetarian dieters were significantly differentiated from meat dieters (seafood omnivores and beef and lamb omnivores), which may be related to the fact that high-fiber diets can create a different salivary flora structure. Variants were also detected in salivary metabolic pathways, including positive correlations with Lipid metabolism, Amino acid metabolism, Carbohydrate metabolism, and Nucleotide metabolism in vegetarians, and correlations in seafood omnivores. In order to select salivary microorganisms and metabolic markers that can distinguish different dietary profiles, a random forest classifier model was constructed in this study, and the results showed that individuals with different dietary profiles could be successfully distinguished based on the core genera and metabolites such as Streptococcus, Histidinyl-Valine.

Conclusion

Our study provides a supportive basis for the application of salivary polyomics in order to reveal the dietary characteristics of individuals for forensic investigation and crime solving.

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.

Response of salivary microbiome to temporal, environmental, and surface characteristics under in vitro exposure

The microbiome of saliva stains deposited at crime scenes and in everyday settings is valuable for forensic investigations and environmental ecology. However, the dynamics and applications of microbial communities in these saliva stains have not been fully explored. In this study, we analyzed saliva samples that were exposed to indoor conditions for up to 1 year and to different carriers (cotton, sterile absorbent cotton swab, woolen, dacron) in both indoor and outdoor environments for 1 month using high-throughput sequencing. The analysis of microbial composition and Mfuzz clustering showed that the salivary flora, specifically Streptococcus (cluster7), which was associated with microbial contamination, remained stable over short periods of time. However, prolonged exposure led to significant differences due to the invasion of environmental bacteria such as Pseudomonas and Achromobacter. The growth and colonization of environmental flora were promoted by humidity. The neutral model predictions indicated that the assembly of salivary microbial communities in outdoor environments was significantly influenced by stochastic processes, with environmental characteristics having a greater impact on community change compared to surface characteristics. By incorporating data from previous studies on fecal and vaginal secretion microbiology, we developed RF and XGBoost classification models that achieved high accuracy (>98 %) and AUC (>0.8). Additionally, a RF regression model was created to determine the time since deposition (TsD) of the stains. Time inference models yielded a mean absolute error (MAE) of 7.1 days for stains exposed for 1 year and 14.2 h for stains exposed for 14 days. These findings enhance our understanding of the changes in the microbiome of saliva stains over time, in different environments, and on different surfaces. They also have potential applications in assessing potential microbial contamination, identifying body fluids, and inferring the time of deposition.

Preliminary exploratory research on the application value of oral and intestinal meta-genomics in predicting subjects' occupations-A case study of the distinction between students and migrant workers

Background

In the field of forensic science, accurately determining occupation of an individual can greatly assist in resolving cases such as criminal investigations or disaster victim identifications. However, estimating occupation can be challenging due to the intricate relationship between occupation and various factors, including gender, age, living environment, health status, medication use, and lifestyle habits such as alcohol consumption and smoking. All of these factors can impact the composition of oral or gut microbial community of an individual.

Methods and results

In this study, we collected saliva and feces samples from individuals representing different occupational sectors, specifically students and manual laborers. We then performed metagenomic sequencing on the DNA extracted from these samples to obtain data that could be analyzed for taxonomic and functional annotations in five different databases. The correlation between occupation with microbial information was assisted from the perspective of α and β diversity, showing that individuals belonging to the two occupations hold significantly different oral and gut microbial communities, and that this correlation is basically not affected by gender, drinking, and smoking in our datasets. Finally, random forest (RF) models were built with recursive feature elimination (RFE) processes. Models with 100% accuracy in both training and testing sets were constructed based on three species in saliva samples or on a single pathway annotated by the KEGG database in fecal samples, namely, "ko04145" or Phagosome.

Conclusion

Although this study may have limited representativeness due to its small sample size, it provides preliminary evidence of the potential of using microbiome information for occupational inference.

Contamination source modeling with SCRuB improves cancer phenotype prediction from microbiome data

Sequencing-based approaches for the analysis of microbial communities are susceptible to contamination, which could mask biological signals or generate artifactual ones. Methods for in silico decontamination using controls are routinely used, but do not make optimal use of information shared across samples and cannot handle taxa that only partially originate in contamination or leakage of biological material into controls. Here we present Source tracking for Contamination Removal in microBiomes (SCRuB), a probabilistic in silico decontamination method that incorporates shared information across multiple samples and controls to precisely identify and remove contamination. We validate the accuracy of SCRuB in multiple data-driven simulations and experiments, including induced contamination, and demonstrate that it outperforms state-of-the-art methods by an average of 15-20 times. We showcase the robustness of SCRuB across multiple ecosystems, data types and sequencing depths. Demonstrating its applicability to microbiome research, SCRuB facilitates improved predictions of host phenotypes, most notably the prediction of treatment response in melanoma patients using decontaminated tumor microbiome data.

Applications of microbiology to different forensic scenarios - A narrative review

In contrast to other forensic disciplines, forensic microbiology is still too often considered a "side activity" and is not able to make a real and concrete contribution to forensic investigations. Indeed, the various application aspects of this discipline still remain a niche activity and, as a result, microbiological investigations are often omitted or only approximated, in part due to poor report in the literature. However, in certain situations, forensic microbiology can prove to be extremely effective, if not crucial, when all other disciplines fail. Precisely because microorganisms can represent forensic evidence, in this narrative review all the major pathological forensic applications described in the literature have been presented. The goal of our review is to highlight the versatility and transversality of microbiology in forensic science and to provide a comprehensive source of literature to refer to when needed.

The Microbiome of Things: Appliances, Machines, and Devices Hosting Artificial Niche-Adapted Microbial Communities

As it is the case with natural substrates, artificial surfaces of man-made devices are home to a myriad of microbial species. Artificial products are not necessarily characterized by human-associated microbiomes; instead, they can present original microbial populations shaped by specific environmental-often extreme-selection pressures. This review provides a detailed insight into the microbial ecology of a range of artificial devices, machines, and appliances, which we argue are specific microbial niches that do not necessarily fit in the "build environment" microbiome definition. Instead, we propose here the Microbiome of Things (MoT) concept analogous to the Internet of Things (IoT) because we believe it may be useful to shed light on human-made, but not necessarily human-related, unexplored microbial niches.

The dynamic balance of the skin microbiome across the lifespan

For decades research has centered on identifying the ideal balanced skin microbiome that prevents disease and on developing therapeutics to foster this balance. However, this single idealized balance may not exist. The skin microbiome changes across the lifespan. This is reflected in the dynamic shifts of the skin microbiome's diverse, inter-connected community of microorganisms with age. While there are core skin microbial taxa, the precise community composition for any individual person is determined by local skin physiology, genetics, microbe-host interactions, and microbe-microbe interactions. As a key interface with the environment, the skin surface and its appendages are also constantly exchanging microbes with close personal contacts and the environment. Hormone fluctuations and immune system maturation also drive age-dependent changes in skin physiology that support different microbial community structures over time. Here, we review recent insights into the factors that shape the skin microbiome throughout life. Collectively, the works summarized within this review highlight how, depending on where we are in lifespan, our skin supports robust microbial communities, while still maintaining microbial features unique to us. This review will also highlight how disruptions to this dynamic microbial balance can influence risk for dermatological diseases as well as impact lifelong health.

Dynamics of Microbial Community and Potential Microbial Pollutants in Shopping Malls

Shopping malls offer various niches for microbial populations, potentially serving as sources and reservoirs for the spread of microorganisms of public health concern. However, knowledge about the microbiome and the distribution of human pathogens in malls is largely unknown. Here, we examine the microbial community dynamics and genotypes of potential pathogens from floor and escalator surfaces in shopping malls and adjacent road dusts and greenbelt soils. The distribution pattern of microbial communities is driven primarily by habitats and seasons. A significant enrichment of human-associated microbiota in the indoor environment indicates that human interactions with surfaces might be another strong driver for mall microbiomes. Neutral community models suggest that the microbial community assembly is strongly driven by stochastic processes. Distinct performances of microbial taxonomic signatures for environmental classifications indicate the consistent differences of microbial communities of different seasons/habitats and the strong anthropogenic effect on homogenizing microbial communities of shopping malls. Indoor environments harbored higher concentrations of human pathogens than outdoor samples, also carrying a high proportion of antimicrobial resistance-associated multidrug efflux genes and virulence genes. These findings enhanced the understanding of the microbiome in the built environment and the interactions between humans and the built environment, providing a basis for tracking biothreats and communicable diseases and developing sophisticated early warning systems. IMPORTANCE Shopping malls are distinct microbial environments which can facilitate a constant transmission of microorganisms of public health concern between humans and the built environment or between human and human. Despite extensive investigation of the natural environmental microbiome, no comprehensive profile of microbial ecology has been reported in malls. Characterizing microbial distribution, potential pathogens, and antimicrobial resistance will enhance our understanding of how these microbial communities are formed, maintained, and transferred and help establish a baseline for biosurveillance of potential public health threats in malls.

Microbiome-Based Stain Analyses in Crime Scenes

Recent advances in next-generation sequencing technologies (NGS) coupled with machine learning have demonstrated the potential of microbiome-based analyses in applied areas such as clinical diagnostics and forensic sciences. Particularly in forensics, microbial markers in biological stains left at a crime scene can provide valuable information for the reconstruction of crime scene cases, as they contain information on bodily origin, the time since deposition, and donor(s) of the stain. Importantly, microbiome-based analyses provide a complementary or an alternative approach to current methods when these are limited or not feasible. Despite the promising results from recent research, microbiome-based stain analyses are not yet employed in routine casework. In this review, we highlight the two main gaps that need to be addressed before we can successfully integrate microbiome-based analyses in applied areas with a special focus on forensic casework: one is a comprehensive assessment of the method's strengths and limitations, and the other is the establishment of a standard operating procedure. For the latter, we provide a roadmap highlighting key decision steps and offering laboratory and bioinformatic workflow recommendations, while also delineating those aspects that require further testing. Our goal is to ultimately facilitate the streamlining of microbiome-based analyses within the existing forensic framework to provide alternate lines of evidence, thereby improving the quality of investigations.

A Pilot Study: the Development of a Facility-Associated Microbiome and Its Association with the Presence of Listeria Spp. in One Small Meat Processing Facility

Microbial communities which persist in food processing facilities may have a detrimental impact on food safety and spoilage. In meat processing, Listeria monocytogenes is an organism of concern due to its ability to cause significant human illnesses and persist in refrigerated environments. The microbial ecology of Listeria spp. in small meat processing facilities has not been well characterized. Therefore, we collected samples from a newly constructed meat processing facility as an opportunity to investigate several research objectives: (i) to determine whether a stable, consistent microbiome develops in a small meat processing facility during the first 18 months of operation, (ii) to evaluate the environmental factors that drive microbial community formation, and (iii) to elucidate the relationship between microbial communities and the presence of Listeria species. We evaluated microbiomes using 16S rRNA gene sequencing and Listeria presence using quantitative PCR. We demonstrated that microbial communities differentiate by the functional room type, which is representative of several environmental differences such as temperature, sources of microbes, and activity. Temperature was an especially important factor; in rooms with low temperatures, communities were dominated by psychotrophs, especially Pseudomonas, while warmer rooms supported greater diversity. A stable core community formed in facility drains, indicating that mechanisms which cause persistence are present in the communities. The overall presence of Listeria in the facility was low but could be tied to specific organisms within a room, and the species of Listeria could be stratified by room function. IMPORTANCE This study provides critical knowledge to improve meat safety and quality from small meat processing facilities. Principally, it demonstrates the importance of facility design and room condition to the development of important microbial communities; temperature, sanitation regimen, and physical barriers all influence the ability of microorganisms to join the stable core community. It also demonstrates a relationship between the microbial community and Listeria presence in the facility, showing the importance of managing facility sanitation plans for not only pathogens, but also the general facility microbiome.

Microbiome analysis: An emerging forensic investigative tool

Microbial diversity's potential has been investigated in medical and therapeutic studies throughout the last few decades. However, its usage in forensics is increasing due to its effectiveness in circumstances when traditional approaches fail to provide a decisive opinion or are insufficient in forming a concrete opinion. The application of human microbiome may serve in detecting the type of stains of saliva and vaginal fluid, as well as in attributing the stains to the individual. Similarly, the microbiome makeup of a soil sample may be utilised to establish geographic origin or to associate humans, animals, or things with a specific area, additionally microorganisms influence the decay process which may be used in depicting the Time Since death. Further in detecting the traces of the amount and concentration of alcohol, narcotics, and other forensically relevant compounds in human body or visceral tissues as they also affect the microbial community within human body. Beside these, there is much more scope of microbiomes to be explored in terms of forensic investigation, this review focuses on multidimensional approaches to human microbiomes from a forensic standpoint, implying the potential of microbiomes as an emerging tool for forensic investigations such as individual variability via skin microbiomes, reconstructing crime scene, and linking evidence to individual.

Determining Informative Microbial Single Nucleotide Polymorphisms for Human Identification

The skin microbiome is a highly abundant and relatively stable source of DNA that may be utilized for human identification (HID). In this study, a set of single nucleotide polymorphisms (SNPs) with a high mean estimated Wright's fixation index (F_ST) (>0.1) and widespread abundance (found in ≥75% of samples compared) were selected from a diverse set of markers in the hidSkinPlex panel. The least absolute shrinkage and selection operator (LASSO) was used in a novel machine learning framework to generate a SNP panel and predict the human host from skin microbiome samples collected from the hand, manubrium, and foot. The framework was devised to emulate a new unknown person introduced to the algorithm and to match samples from that person against a population database. Unknown samples were classified with 96% accuracy (Matthews correlation coefficient [MCC], 0.954) in the test (n = 225 samples) data set. A final panel of informative SNPs was determined for HID (hidSkinPlex+) using all 51 individuals sampled at three body sites in triplicate. The hidSkinPlex+ panel comprises 365 SNPs and yielded prediction accuracy for the correct host of 95% (MCC = 0.949). The accuracy of the hidSkinPlex+ panel may be somewhat overestimated due to using 26 individuals from the training data set for the selection of the final panel. However, this accuracy still provides an indication of performance when tested on new samples. IMPORTANCE One of the fundamental goals in forensic genetics is to identify the source of biological evidence. Methods for detecting human DNA have advanced and can be quite sensitive, but not all DNA samples are amenable to current methods. However, the human skin microbiome is a source of DNA with high copy numbers, and it has the potential for high discriminatory power. The hidSkinPlex panel has been used for HID; however, some aspects of it could be improved. Missing information is ambiguous, as it is unclear if marker drop-out is a by-product of a low-template sample or if the reasons for not observing a marker are biological. Such ambiguity may confound methods for HID, and as such, an improved marker set (hidSkinPlex+) was designed that is considerably smaller and more robust to drop-out (365 SNPs contained in 135 markers) yet still can be used to accurately predict the human host.

Forensic Analysis of Human Microbiome in Skin and Body Fluids Based on Geographic Location

High-throughput DNA sequencing technologies have facilitated the in silico forensic analysis of human microbiome. Specific microbial species or communities obtained from the crime scene provide evidence of human contacts and their body fluids. The microbial community is influenced by geographic, ethnic, lifestyle, and environmental factors such as urbanization. An understanding of the effects of these external stressors on the human microbiome and determination of stable and changing elements are important in selecting appropriate targets for investigation. In this study, the Forensic Microbiome Database (FMD) (http://www.fmd.jcvi.org) containing the microbiome data of various locations in the human body in 35 countries was used. We focused on skin, saliva, vaginal fluid, and stool and found that the microbiome distribution differed according to the body part as well as the geographic location. In the case of skin samples, Staphylococcus species were higher than Corynebacterium species among Asians compared with Americans. Holdemanella and Fusobacterium were specific in the saliva of Koreans and Japanese populations. Lactobacillus was found in the vaginal fluids of individuals in all countries, whereas Serratia and Enterobacter were endemic to Bolivia and Congo, respectively. This study is the first attempt to collate and describe the observed variation in microbiomes from the forensic microbiome database. As additional microbiome databases are reported by studies worldwide, the diversity of the applications may exceed and expand beyond the initial identification of the host.

Population informative markers selected using Wright's fixation index and machine learning improves human identification using the skin microbiome

Microbial DNA, shed from human skin, can be distinctive to its host and thus help individualize donors of forensic biological evidence. Previous studies have utilized single locus microbial DNA markers (e.g., 16S rRNA) to assess the presence/absence of personal microbiota to profile human hosts. However, since the taxonomic composition of the microbiome is in constant fluctuation, this approach may not be sufficiently robust for human identification (HID). Multi-marker approaches may be more powerful. Additionally, genetic differentiation, rather than taxonomic distinction, may be more individualizing. To this end, the non-dominant hands of 51 individuals were sampled in triplicate (n = 153). They were analyzed for markers in the hidSkinPlex, a multiplex panel comprising candidate markers for skin microbiome profiling. Single nucleotide polymorphisms (SNPs) with the highest Wright's fixation index (F_ST) estimates were then selected for predicting donor identity using a support vector machine (SVM) learning model. F_ST is an estimate of the genetic differences within and between populations. Three different SNP selection criteria were employed: SNPs with the highest-ranking F_ST estimates 1) common between any two samples regardless of markers present (termed overall); 2) each marker common between samples (termed per marker); and 3) common to all samples used to train the SVM algorithm for HID (termed selected). The SNPs chosen based on criteria for overall, per marker, and selected methods resulted in an accuracy of 92.00%, 94.77%, and 88.00%, respectively. The results support that estimates of F_ST, combined with SVM, can notably improve forensic HID via skin microbiome profiling. IMPORTANCE There is a need for additional genetic information to help identify the source of biological evidence found at a crime scene. The human skin microbiome is a potentially abundant source of DNA that can enable the identification of a donor of biological evidence. With microbial profiling for human identification, there will be an additional source of DNA to identify individuals as well as to exclude individuals wrongly associated with biological evidence, thereby improving the utility of forensic DNA profiling to support criminal investigations.

Diurnal variation in the human skin microbiome affects accuracy of forensic microbiome matching

BACKGROUND

The human skin microbiome has been recently investigated as a potential forensic tool, as people leave traces of their potentially unique microbiomes on objects and surfaces with which they interact. In this metagenomic study of four people in Hong Kong, their homes, and public surfaces in their neighbourhoods, we investigated the stability and identifiability of these microbiota traces on a timescale of hours to days.

RESULTS

Using a Canberra distance-based method of comparing skin and surface microbiomes, we found that a person could be accurately matched to their household in 84% of tests and to their neighbourhood in 50% of tests, and that matching accuracy did not decay for household surfaces over the 10-day study period, although it did for public surfaces. The time of day at which a skin or surface sample was taken affected matching accuracy, and 160 species across all sites were found to have a significant variation in abundance between morning and evening samples. We hypothesised that daily routines drive a rhythm of daytime dispersal from the pooled public surface microbiome followed by normalisation of a person's microbiome by contact with their household microbial reservoir, and Dynamic Bayesian Networks (DBNs) supported dispersal from public surfaces to skin as the major dispersal route among all sites studied.

CONCLUSIONS

These results suggest that in addition to considering the decay of microbiota traces with time, diurnal patterns in microbiome exposure that contribute to the human skin microbiome assemblage must also be considered in developing this as a potential forensic method. Video Abstract.

Microbial, environmental and anthropogenic factors influencing the indoor microbiome of the built environment

A built environment is a human-made environment providing surroundings for human occupancy, activities, and settlement. It is supposed to safeguard humans from all undesirable and harmful pollutants; however, indoor concentrations of some pollutants are much greater than that of the outdoors. Bioaerosols infiltrate from the outdoors in addition to many indoor sources of bioaerosols including the use of various chemicals as well as activities like cooking, smoking, cleaning, or even normal movement. They are also associated with a number of serious health concerns. Various ecological factors associated with the generation, the persistence as well as the dispersal of these microbial components of indoor bioaerosols, are discussed in this review, that have not been considered all together till now. The factors like microbial taxa, environmental factors, and anthropogenic activities (human occupancy, activities, and impact of urbanization) are addressed in the review. Effects of both indoor environmental factors like architectural design, lighting, ventilation, temperature, humidity, indoor/outdoor ratio, particulate matter, indoor chemistry as well as outdoor environmental factors like geography, seasons, and meteorology on the microbial concentrations have been discussed. Efforts are underway to design selective pressures for microbes to create a healthy symbiotic built microbiome as the "right" indoor microbiome is a "healthy" indoor microbiome.

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.

Feasibility of using alternative swabs and storage solutions for paired SARS-CoV-2 detection and microbiome analysis in the hospital environment

BACKGROUND

Determining the role of fomites in the transmission of SARS-CoV-2 is essential in the hospital setting and will likely be important outside of medical facilities as governments around the world make plans to ease COVID-19 public health restrictions and attempt to safely reopen economies. Expanding COVID-19 testing to include environmental surfaces would ideally be performed with inexpensive swabs that could be transported safely without concern of being a source of new infections. However, CDC-approved clinical-grade sampling supplies and techniques using a synthetic swab are expensive, potentially expose laboratory workers to viable virus and prohibit analysis of the microbiome due to the presence of antibiotics in viral transport media (VTM). To this end, we performed a series of experiments comparing the diagnostic yield using five consumer-grade swabs (including plastic and wood shafts and various head materials including cotton, synthetic, and foam) and one clinical-grade swab for inhibition to RNA. For three of these swabs, we evaluated performance to detect SARS-CoV-2 in twenty intensive care unit (ICU) hospital rooms of patients including COVID-19+ patients. All swabs were placed in 95% ethanol and further evaluated in terms of RNase activity. SARS-CoV-2 was measured both directly from the swab and from the swab eluent.

RESULTS

Compared to samples collected in VTM, 95% ethanol demonstrated significant inhibition properties against RNases. When extracting directly from the swab head as opposed to the eluent, RNA recovery was approximately 2-4× higher from all six swab types tested as compared to the clinical standard of testing the eluent from a CDC-approved synthetic (SYN) swab. The limit of detection (LoD) of SARS-CoV-2 from floor samples collected using the consumer-grade plastic (CGp) or research-grade plastic The Microsetta Initiative (TMI) swabs was similar or better than the SYN swab, further suggesting that swab type does not impact RNA recovery as measured by the abundance of SARS-CoV-2. The LoD for TMI was between 0 and 362.5 viral particles, while SYN and CGp were both between 725 and 1450 particles. Lastly microbiome analyses (16S rRNA gene sequencing) of paired samples (nasal and floor from same patient room) collected using different swab types in triplicate indicated that microbial communities were not impacted by swab type, but instead driven by the patient and sample type.

CONCLUSIONS

Compared to using a clinical-grade synthetic swab, detection of SARS-CoV-2 from environmental samples collected from ICU rooms of patients with COVID was similar using consumer-grade swabs, stored in 95% ethanol. The yield was best from the swab head rather than the eluent and the low level of RNase activity and lack of antibiotics in these samples makes it possible to perform concomitant microbiome analyses. Video abstract.

Forensic Applications of Microbiomics: A Review

The rise of microbiomics and metagenomics has been driven by advances in genomic sequencing technology, improved microbial sampling methods, and fast-evolving approaches in bioinformatics. Humans are a host to diverse microbial communities in and on their bodies, which continuously interact with and alter the surrounding environments. Since information relating to these interactions can be extracted by analyzing human and environmental microbial profiles, they have the potential to be relevant to forensics. In this review, we analyzed over 100 papers describing forensic microbiome applications with emphasis on geolocation, personal identification, trace evidence, manner and cause of death, and inference of the postmortem interval (PMI). We found that although the field is in its infancy, utilizing microbiome and metagenome signatures has the potential to enhance the forensic toolkit. However, many of the studies suffer from limited sample sizes and model accuracies, and unrealistic environmental settings, leaving the full potential of microbiomics to forensics unexplored. It is unlikely that the information that can currently be elucidated from microbiomics can be used by law enforcement. Nonetheless, the research to overcome these challenges is ongoing, and it is foreseeable that microbiome-based evidence could contribute to forensic investigations in the future.

DNA Sequence-Based Approach for Classifying the Mold Status of Buildings

Dampness or water damage in buildings and human exposure to the resultant mold growth is an ever-present public health concern. This study provides quantitative evidence that the airborne fungal ecology of homes with known mold growth ("moldy") differs from the normal airborne fungal ecology of homes with no history of dampness, water damage, or visible mold ("no mold"). Settled dust from indoor air and outdoor air and direct samples from building materials with mold growth were examined in homes from 11 cities across dry, temperate, and continental climate regions within the United States. Community analysis based on the sequence of the internal transcribed spacer region of fungal ribosomal RNA encoding genes demonstrated consistent and quantifiable differences between the fungal ecology of settled dust in homes with inspector-verified water damage and visible mold versus the settled dust of homes with no history of dampness, water damage, or visible mold. These differences include lower community richness (p_adj = 0.01) in the settled dust of moldy homes versus no mold homes, as well as distinct community taxonomic structures between moldy and no mold homes (ANOSIM, R = 0.15, p = 0.001). We identified 11 Ascomycota taxa that were more highly enriched in moldy homes and 14 taxa from Ascomycota, Basidiomycota, and Zygomycota that were more highly enriched in no mold homes. The indoor air differences between moldy versus no mold homes were significant for all three climate regions considered. These distinct but complex differences between settled dust samples from moldy and no homes were used to train a machine learning-based model to classify the mold status of a home. The model was able to accurately classify 100% of moldy homes and 90% of no mold homes. The integration of DNA-based fungal ecology with advanced computational approaches can be used to accurately classify the presence of mold growth in homes, assist with inspection and remediation decisions, and potentially lead to reduced exposure to hazardous microbes indoors.

Feasibility of using alternative swabs and storage solutions for paired SARS-CoV-2 detection and microbiome analysis in the hospital environment

Background

Determining the role of fomites in the transmission of SARS-CoV-2 is essential in the hospital setting and will likely be important outside of medical facilities as governments around the world make plans to ease COVID-19 public health restrictions and attempt to safely reopen economies. Expanding COVID-19 testing to include environmental surfaces would ideally be performed with inexpensive swabs that could be transported safely without concern of being a source of new infections. However, CDC-approved clinical-grade sampling supplies and techniques using a synthetic swab are expensive, potentially expose laboratory workers to viable virus and prohibit analysis of the microbiome due to the presence of antibiotics in viral transport media (VTM). To this end, we performed a series of experiments comparing the diagnostic yield using five consumer-grade swabs (including plastic and wood shafts and various head materials including cotton, synthetic, and foam) and one clinical grade swab for inhibition to RNA. For three of these swabs, we evaluated performance to detect SARS-CoV-2 in twenty intensive care unit (ICU) hospital rooms of patients including COVID-19+ patients. All swabs were placed in 95% ethanol and further evaluated in terms of RNase activity. SARS-CoV-2 was measured both directly from the swab and from the swab eluent.

Results

Compared to samples collected in VTM, 95% ethanol demonstrated significant inhibition properties against RNases. When extracting directly from the swab head as opposed to the eluent, RNA recovery was approximately 2-4x higher from all six swab types tested as compared to the clinical standard of testing the eluent from a CDC-approved synthetic (SYN) swab. The limit of detection (LoD) of SARSSARS-CoV-2 from floor samples collected using the consumer-grade plastic (CGp) or research-grade plastic The Microsetta Initiative (TMI) swabs was similar or better than the SYN swab, further suggesting that swab type does not impact RNA recovery as measured by the abundance of SARSSARS-CoV-2. The LoD for TMI was between 0-362.5 viral particles while SYN and CGp were both between 725-1450 particles. Lastly microbiome analyses (16S rRNA gene sequencing) of paired samples (nasal and floor from same patient-room) collected using different swab types in triplicate indicated that microbial communities were not impacted by swab type, but instead driven by the patient and sample type.

Conclusions

Compared to using a clinical-grade synthetic swab, detection of SARS-CoV-2 from environmental samples collected from ICU rooms of patients with COVID was similar using consumer grade swabs, stored in 95% ethanol. The yield was best from the swab head rather than the eluent and the low level of RNase activity and lack of antibiotics in these samples makes it possible to perform concomitant microbiome analyses.

Effect of the similarity of gut microbiota composition between donor and recipient on graft function after living donor kidney transplantation

Graft outcomes of unrelated donor kidney transplant are comparable with those of related donor kidney transplant despite their genetic distance. This study aimed to identify whether the similarity of donor–recipient gut microbiota composition affects early transplant outcomes. Stool samples from 67 pairs of kidney transplant recipients and donors were collected. Gut microbiota differences between donors and recipients were determined using weighted UniFrac distance. Among the donor–recipient pairs, 30 (44.8%) pairs were related, while 37 (55.2%) were unrelated. The unrelated pairs, especially spousal pairs, had similar microbial composition, and they more frequently shared their meals than related pairs did. The weighted UniFrac distance showed an inverse correlation with the 6-month allograft function (p = 0.034); the correlation was significant in the unrelated pairs (p = 0.003). In the unrelated pairs, the microbial distance showed an excellent accuracy in predicting the estimated glomerular filtration rate of < 60 mL/min/1.73 m² at 6-months post-transplantation and was better than human leukocyte antigen incompatibility and rejection. The incidence of infection within 6 months post-transplantation increased in the recipients having dissimilar microbiota with donors compared to the other recipients. Thus, pre-transplantation microbial similarity in unrelated donors and recipients may be associated with 6-month allograft function.

Impact of the Human Microbiome in Forensic Sciences: a Systematic Review

Numerous studies relate differences in microbial communities to human health and disease; however, little is known about microbial changes that occur postmortem or the possible applications of microbiome analysis in the field of forensic science. The aim of this review was to study the microbiome and its applications in forensic sciences and to determine the main lines of investigation that are emerging, as well as its possible contributions to the forensic field. A systematic review of the human microbiome in relation to forensic science was carried out by following PRISMA guidelines. This study sheds light on the role of microbiome research in the postmortem interval during the process of decomposition, identifying death caused by drowning or sudden death, locating the geographical location of death, establishing a connection between the human microbiome and personal items, sexual contact, and the identification of individuals. Actinomycetaceae, Bacteroidaceae, Alcaligenaceae, and Bacilli play an important role in determining the postmortem interval. Aeromonas can be used to determine the cause of death, and Corynebacterium or Helicobacter pylori can be used to ascertain personal identity or geographical location. Several studies point to a promising future for microbiome analysis in the different fields of forensic science, opening up an important new area of research.

Associations between respiratory infections and bacterial microbiome in student dormitories in Northern China

Recent studies reveal that the microbial diversity and composition in the respiratory tract are related to the susceptibility, development, and progression of respiratory infections. Indoor microorganisms can transmit into the respiratory tract through breathing, but their role in infections is unclear. Here, we present the first association study between the indoor microbiome and respiratory infections. In total, 357 students living in 86 dormitory rooms in Shanxi University were randomly selected to survey symptoms of infections. Settled air dust was collected to characterize bacterial compositions by 16S rRNA sequencing. The overall microbial richness was not associated with respiratory infections, but microorganisms from specific phylogenetic classes showed various associations. Taxa richness and abundance of Actinobacteria were protectively associated with infections (P < .05). The abundance of several genera in Gammaproteobacteria, including Haemophilus, Klebsiella, Buttiauxella, and Raoultella, was positively associated with infections (P < .005). The role of these microorganisms was consistent with previous human microbiota studies. Building age was associated with the overall microbial composition variation in dormitories and negatively associated with three potential risk genera in Proteobacteria (P < .05). The weight of vacuum dust was positively associated with a protective genus, Micrococcus in Actinobacteria (P < .05).

Challenges in Human Skin Microbial Profiling for Forensic Science: A Review

The human microbiome is comprised of the microbes that live on and within an individual, as well as immediately surrounding them. Microbial profiling may have forensic utility in the identification or association of individuals with criminal activities, using microbial signatures derived from a personal microbiome. This review highlights some important aspects of recent studies, many of which have revealed issues involving the effect of contamination of microbial samples from both technical and environmental sources and their impacts on microbiome research and the potential forensic applications of microbial profiling. It is imperative that these challenges be discussed and evaluated within a forensic context to better understand the future directions and potential applications of microbial profiling for human identification. It is necessary that the limitations identified be resolved prior to the adoption of microbial profiling, or, at a minimum, acknowledged by those applying this new approach.

Feasibility of using alternative swabs and storage solutions for paired SARS-CoV-2 detection and microbiome analysis in the hospital environment

Background Determining the role of fomites in the transmission of SARS-CoV-2 is essential in the hospital setting and will likely be important outside of medical facilities as governments around the world make plans to ease COVID-19 public health restrictions and attempt to safely reopen economies. Expanding COVID-19 testing to include environmental surfaces would ideally be performed with inexpensive swabs that could be transported safely without concern of being a source of new infections. However, CDC-approved clinical-grade sampling supplies and techniques using a synthetic swab are expensive, potentially expose laboratory workers to viable virus and prohibit analysis of the microbiome due to the presence of antibiotics in viral transport media (VTM). To this end, we performed a series of experiments comparing the diagnostic yield using five consumer-grade swabs (including plastic and wood shafts and various head materials including cotton, synthetic, and foam) and one clinical grade swab for inhibition to RNA. For three of these swabs, we evaluated performance to detect SARS-CoV-2 in twenty intensive care unit (ICU) hospital rooms of patients with 16 COVID-19+. All swabs were placed in 95% ethanol and further evaluated in terms of RNase activity. SARS-CoV-2 was measured both directly from the swab and from the swab eluent. Results Compared to samples collected in VTM, 95% ethanol demonstrated significant inhibition properties against RNases. When extracting directly from the swab head as opposed to the eluent, RNA recovery was approximately 2-4x higher from all six swab types tested as compared to the clinical standard of testing the eluent from a CDC-approved synthetic swab. The limit of detection (LoD) of SARs-CoV-2 from floor samples collected using the CGp or TMI swabs was similar or better than the CDC standard, further suggesting that swab type does not impact RNA recovery as measured by SARs-CoV-2. The LoD for TMI was between 0-362.5 viral particles while SYN and CGp were both between 725-1450 particles. Lastly microbiome analyses (16S rRNA) of paired samples (e.g., environment to host) collected using different swab types in triplicate indicated that microbial communities were not impacted by swab type but instead driven by the patient and sample type (floor or nasal). Conclusions Compared to using a clinical-grade synthetic swab, detection of SARS-CoV-2 from environmental samples collected from ICU rooms of patients with COVID was similar using consumer grade swabs, stored in 95% ethanol. The yield was best from the swab head rather than the eluent and the low level of RNase activity in these samples makes it possible to perform concomitant microbiome analysis. Keywords: COVID-19, SARS-CoV-2, RT-qPCR, swab, global health.

Skin Microbiome Analysis for Forensic Human Identification: What Do We Know So Far?

Microbiome research is a highly transdisciplinary field with a wide range of applications and methods for studying it, involving different computational approaches and models. The fact that different people host radically different microbiota highlights forensic perspectives in understanding what leads to this variation and what regulates it, in order to effectively use microbes as forensic evidence. This narrative review provides an overview of some of the main scientific works so far produced, focusing on the potentiality of using skin microbiome profiling for human identification in forensics. This review was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The examined literature clearly ascertains that skin microbial communities, although personalized, vary systematically across body sites and time, with intrapersonal differences over time smaller than interpersonal ones, showing such a high degree of spatial and temporal variability that the degree and nature of this variability can constitute in itself an important parameter useful in distinguishing individuals from one another. Even making the effort to organically synthesize all results achieved until now, it is quite evident that these results are still the pieces of a puzzle, which is not yet complete.