Microbial community succession of submerged bones in an aquatic habitat

Abstract

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.

Trends in forensic microbiology: From classical methods to deep learning

“…In addition, machine learning can be used for PMI estimation in microbial forensics (Johnson et al, 2016 ; Liu et al, 2020 , 2021 ; Cao et al, 2021 ; Cartozzo et al, 2021 ; Randall et al, 2021 ; Zhang et al, 2021 ; Kaszubinski et al, 2022 ; Zhao et al, 2022 ). RF models are widely used for PMI estimation and postmortem submersion interval (PMSI) diagnosis (Metcalf et al, 2013 ; Pechal et al, 2014 ; Cartozzo et al, 2021 ; Randall et al, 2021 ; Zhang et al, 2021 ; Kaszubinski et al, 2022 ; Zhao et al, 2022 ). In 2013, Metcalf et al ( 2013 ) first proposed the concept of a “microbial clock” to infer PMI by setting up regression models using an RF.…”

Advances in artificial intelligence-based microbiome for PMI estimation

“…The model using rib microbiota performed best within 353 days, with a root mean square error (RMSE) of ±27 days, while the model using scapula microbiota performed best within 579 days with a RMSE of ±63 days ( Cartozzo et al, 2021 ; Randall et al, 2021 ). Kaszubinski et al (2022) constructed a RF regression model using microbial communities from pig bone within 547 days, and the model exhibited high accuracy, explaining more than 80% of the variation in PMSI. Recently, Liu et al (2020 , 2021) compared the performance of RF, SVM, and ANN models using microbial communities in cecum and concluded that the ANN model performed best, with a MAE of 1.5 ± 0.8 h within 24 h and 14.5 ± 4.4 h within 15 days after death for PMI prediction.…”

Exploring the Potential of Microbial Communities: Understanding their Role in PMI estimation

“…Although decomposition in aquatic environments has received less study, it is not a rare occurrence in investigations, with a primary focus on detecting the presence of diatoms, algae, and scavenging activity of aquatic species [16][17][18]. Only a few authors have discussed the utility of microbial communities in estimating PMI [16,19,20]. In a study conducted by Cartozzo et al [20], they estimated PMI in freshwater by studying the successional pattern and associated changes in the bacterial community within submerged skeletal remains of pigs.…”