Search

After death, microbes (including bacteria and fungi) colonize carrion from a variety of sources during the decomposition process. The predictable succession of microbes could be useful for forensics, such as postmortem submersion interval estimation (PMSI) for aquatic deaths. However, gaps exist in our understanding of microbial succession on submerged bone, particularly regarding longer‐term decomposition (>1 year), fungal composition, and differences between internal and external microbial communities. To further explore this potential forensic tool, we described the postmortem microbial communities (bacteria and fungi) on and within submerged bones using targeted amplicon sequencing. We hypothesized predictable successional patterns of microbial colonization would be detected on the surface and within submerged bones, which would eventually converge to a similar microbial community. To best replicate forensic contexts, we sampled bones from replicate swine (Sus scrofa domesticus) carcasses submerged in a freshwater pond, every three months for nearly two years. Microbial bone (internal vs. external) community structure (taxa abundance and diversity) of bones differed for both bacteria and fungi, but internal and external communities did not converge to a similar structure. PMSI estimation models built with random forest regression of postmortem microbiomes were highly accurate (>80% variation explained in PMSI) and showed promise for forensic purposes. Overall, we provide further evidence that internal and external bone microbial communities submerged in an aquatic habitat are distinct and each community undergoes predictable succession, demonstrating potential utility in forensics for modeling PMSI in unattended deaths and/or cold cases. [ABSTRACT FROM AUTHOR]

Death investigations in aquatic ecosystems are challenging due to abiotic and biotic factors that may influence the estimation of a postmortem submersion interval (PMSI). In this study, we examined bacterial changes throughout the decomposition process on porcine carcasses submerged in a tidal‐influenced river and identified predictors of epinecrotic community succession. Fetal porcine (Sus scrofa) carcasses (N = 6) were submerged with epinecrotic samples collected every 3 days (6 collections) over a period of 19 days (~7415 accumulated degree hours (ADH)). Amplicon sequencing was performed using the Illumina MiSeq platform (16S V4 region, 2 × 250 bp format) to identify changes in bacterial relative abundance and diversity. To match bacterial succession with rough taphonomy, carcasses were visually assessed at each sampling time point to determine the decomposition stage. Notably, the three most abundant families were Moraxellaceae, Burkholderiaceae (Proteobacteria), and Clostridiaceae (Firmicutes), though communities composition varied significantly across decomposition stages. Greater bacterial phylogenetic diversity was observed in in latter decomposition stages (advanced floating decay, sunken remains). Random Forest Models were built to predict ADH and explained 77%–80.8% of variation in ADH with an error rate of +/−1943.2 ADH (Root Mean Square Error) or approx. ±2.7 days at the mean water temperature of this study. This study provided a useful model that could be used to estimate a PMSI in this river system utilizing bacterial community succession, and thus, potentially improve the accuracy of such estimations to be used in the court of law. [ABSTRACT FROM AUTHOR]

The postmortem microbiome plays an important functional role in host decomposition after death. Postmortem microbiome community successional patterns are specific to body site, with a significant shift in composition 48 h after death. While the postmortem microbiome has important forensic applications for postmortem interval estimation, it also has the potential to aid in manner of death (MOD) and cause of death (COD) determination as a reflection of antemortem health status. To further explore this association, we tested beta-dispersion, or the variability of microbiomes within the context of the "Anna Karenina Principle" (AKP). The foundational principle of AKP is that stressors affect microbiomes in unpredictable ways, which increases community beta-dispersion. We hypothesized that cases with identified M/CODs would have differential community beta-dispersion that reflected antemortem conditions, specifically that cardiovascular disease and/or natural deaths would have higher beta-dispersion compared to other deaths (e.g., accidents, drug-related deaths). Using a published microbiome data set of 188 postmortem cases (five body sites per case) collected during routine autopsy in Wayne County (Detroit), MI, we modeled beta-dispersion to test for M/COD associations a priori. Logistic regression models of beta-dispersion and case demographic data were used to classify M/COD. We demonstrated that beta-dispersion, along with case demographic data, could distinguish among M/COD – especially cardiovascular disease and drug related deaths, which were correctly classified in 79% of cases. Binary logistic regression models had higher correct classifications than multinomial logistic regression models, but changing the defined microbial community (e.g., full vs. non-core communities) used to calculate beta-dispersion overall did not improve model classification or M/COD. Furthermore, we tested our analytic approach on a case study that predicted suicides from other deaths, as well as distinguishing MOD (e.g., homicides vs. suicides) within COD (e.g., gunshot wound). We propose an analytical workflow that combines postmortem microbiome indicator taxa, beta-dispersion, and case demographic data for predicting MOD and COD classifications. Overall, we provide further evidence the postmortem microbiome is linked to the host's antemortem health condition(s), while also demonstrating the potential utility of including beta-dispersion (a non-taxon dependent approach) coupled with case demographic data for death determination. [ABSTRACT FROM AUTHOR]

Multiple methods have been proposed to provide accurate time since death estimations, and recently, the discovery of bacterial community turnover during decomposition has shown itself to have predictable patterns that may prove useful. In this study, we demonstrate the use of metatranscriptomics from the postmortem microbiome to simultaneously obtain community structure and functional data across postmortem intervals (PMIs). We found that bacterial succession patterns reveal similar trends as detected through DNA analysis, such as increasing Clostridiaceae as decomposition occurs, strengthening the reliability of total RNA community analyses. We also provide one of the first analyses of RNA transcripts to characterize bacterial metabolic pathways during decomposition. We found distinct pathways, such as amino acid metabolism, to be strongly up‐regulated with increasing PMIs. Elucidating the metabolic activity of postmortem microbial communities provides the first steps to discovering postmortem functional biomarkers since functional redundancy across bacteria may reduce host individual microbiome variability. [ABSTRACT FROM AUTHOR]

Humans have increased the amount of reactive N available in the environment by over an order of magnitude since the industrial revolution. Most studies have been conducted in ecosystems with pervasive anthropogenic nutrient inputs, so little is understood about how naïve biofilm communities respond to elevated nutrients. Our nutrient-diffusing substrate (NDS) experiments, which were conducted in Alaskan freshwater ponds with very little anthropogenic nutrient inputs, suggest that P limits biofilm photoautotrophs. However, despite low water-column nutrient concentrations, overall biofilm biomass was not enhanced by the addition of N or P. Rather, we observed an ~60% biomass reduction with NO3- amendment in 15 oligotrophic ponds across 2 y. This widespread biomass reduction was accompanied by changes in microbial communities, but these trends were not observed with NH4+ or P amendment. Nonamended communities (i.e., no nutrient amendment other than lysogeny broth agar) were characterized by anaerobic heterotrophs and purple nonsulfur bacteria, whereas NO3--amended communities were characterized by aerobic heterotrophs and facultatively aerobic heterotrophs (e.g., denitrifiers). These community patterns suggest that NO3- can strongly affect microbial interactions during biofilm formation by altering redox conditions. The effect of NO3- on microbial biomass may be caused by an NO3- toxicity effect or competitive shifts in taxa, both of which may shape biofilm formation and community assembly. Our results reveal possible consequences for low-NO3-, aquatic environments after novel exposure to anthropogenic NO3- inputs, suggesting that a legacy of anthropogenic NO3- inputs may have fundamentally changed microbial community assembly and biogeochemical cycling in aquatic ecosystems. [ABSTRACT FROM AUTHOR]

While emerging research suggests urban green space revegetation increases soil microbiota diversity and native plant species affect skin microbiome diversity, there is still a paucity of knowledge on relationships between neighborhood environmental conditions and the human microbiome. This study leveraged data on human microbiome samples (nose, mouth, rectum) taken at autopsy at the Wayne County Medical Examiner's Office (2014–2015). We evaluated relationships between the microbiome and five measures of environmental conditions (NDVI standard deviation, NDVI mean, percent trees, percent grassland and soil type) near the home of 126 decedents. For the rectum microbiome, NDVI sd had negative, significant associations with diversity (ASVs β = −0.20, p = 0.045; Faith PD β = −0.22, p = 0.026). In contrast, while insignificant, there were consistent, positive associations between diversity and NDVI sd for the mouth microbiome (ASVs β = 0.09, p = 0.337, Faith PD β = 0.14, p = 0.149, Shannon diversity β = 0.14, p = 0.159, Heip's evenness β = 0.11, p = 0.259) and a significant association for the nose microbiome (eigenvalues 3 β = 0.18, p = 0.057). We found consistent, significant, negative associations between percent grassland and diversity of the nose microbiome (ASVs β = −0.25, p = 0.008, Faith PD β = −0.25, p = 0.009, Shannon diversity β = −0.17, p = 0.062). For the mouth microbiome, we found a small effect of percent trees on diversity (eigenvalues 1 β = −0.08, p = 0.053). Clay loam soil was negatively (eigenvalues 2 β = −0.47, p = 0.053) and positively associated (eigenvalues 3 β = 0.65, p = 0.008) with rectum microbiome diversity, compared to loam soil. There was no potential indicator taxon among NDVI quartiles. These findings may be relevant for urban planning and management of urban outdoor spaces in ways that may support healthy human microbiomes. Still, future research is needed to link variation in NDVI, vegetation, plant and/or soil microbe diversity and to confirm or negate our findings that environmental conditions may have contrasting influence on the microbiome of the rectum versus the nose and mouth and that grasslands affect the nose microbiome. Unlabelled Image • This study tests how neighborhood environments may affect the human microbiome. • For the rectum microbiome, NDVI sd had negative associations with diversity. • There were positive links between NDVI sd and mouth & nose microbiome diversity. • For the mouth microbiome, we found an effect of percent trees on diversity. • Consistently, percent grassland was negatively related to nose microbiome diversity. [ABSTRACT FROM AUTHOR]