A preliminary study characterizing temporal changes in soil bacterial communities after dismembered bones were buried

Determining the burial time of skeletal remains is one of the most important issues of forensic medicine. We speculated that the microbiome of gravesoil may be a promising method to infer burial time by virtue of time‐dependent. As we know, forensic scientists have established various models to pred...

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Veröffentlicht in:Electrophoresis 2024-08, Vol.45 (15-16), p.1370-1378
Hauptverfasser: Wang, Jiaqi, Liu, Zidong, Ren, Jianbo, Zhang, Mingming, Guan, Zimeng, Zhao, Xingchun, Gao, Cairong, Zhang, Gengqian
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container_end_page 1378
container_issue 15-16
container_start_page 1370
container_title Electrophoresis
container_volume 45
creator Wang, Jiaqi
Liu, Zidong
Ren, Jianbo
Zhang, Mingming
Guan, Zimeng
Zhao, Xingchun
Gao, Cairong
Zhang, Gengqian
description Determining the burial time of skeletal remains is one of the most important issues of forensic medicine. We speculated that the microbiome of gravesoil may be a promising method to infer burial time by virtue of time‐dependent. As we know, forensic scientists have established various models to predict the postmortem interval of a decedent based on the changes in body and soil microbiome communities. However, limited data are available on the burial time prediction for bones, especially dismembered bones. In this exploratory study, we initially conducted 16S rRNA amplicon high‐throughput sequencing on the burial soil of 10 porcine femurs within a 120‐day period and analyzed the changes in soil microbial communities. Compared with the control soil, a higher Shannon index in the microbial diversity of burial soil containing bones was observed. Correlation analysis identified 61 time‐related bacterial families and the best subset selection method obtained best subset, containing Thermomonosporaceae, Clostridiaceae, 0319‐A21, and Oxalobacteraceae, which were used to construct a simplified multiple linear regression model with a mean absolute error (MAE) of 56.69 accumulated degree day (ADD). An additional random forest model was established based on indicators for the minimum cross‐validation error of Thermomonosporaceae, Clostridiaceae, 0319‐A21, Oxalobacteraceae, and Syntrophobacteraceae, with an MAE of 55.65 ADD. The produced empirical data in this pilot study provided the evidence of feasibility that the microbial successional changes of burial soil will predict the burial time of dismembered bones and may also expand the current knowledge of the effects of bone burial on soil bacterial communities.
doi_str_mv 10.1002/elps.202300274
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We speculated that the microbiome of gravesoil may be a promising method to infer burial time by virtue of time‐dependent. As we know, forensic scientists have established various models to predict the postmortem interval of a decedent based on the changes in body and soil microbiome communities. However, limited data are available on the burial time prediction for bones, especially dismembered bones. In this exploratory study, we initially conducted 16S rRNA amplicon high‐throughput sequencing on the burial soil of 10 porcine femurs within a 120‐day period and analyzed the changes in soil microbial communities. Compared with the control soil, a higher Shannon index in the microbial diversity of burial soil containing bones was observed. Correlation analysis identified 61 time‐related bacterial families and the best subset selection method obtained best subset, containing Thermomonosporaceae, Clostridiaceae, 0319‐A21, and Oxalobacteraceae, which were used to construct a simplified multiple linear regression model with a mean absolute error (MAE) of 56.69 accumulated degree day (ADD). An additional random forest model was established based on indicators for the minimum cross‐validation error of Thermomonosporaceae, Clostridiaceae, 0319‐A21, Oxalobacteraceae, and Syntrophobacteraceae, with an MAE of 55.65 ADD. 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Correlation analysis identified 61 time‐related bacterial families and the best subset selection method obtained best subset, containing Thermomonosporaceae, Clostridiaceae, 0319‐A21, and Oxalobacteraceae, which were used to construct a simplified multiple linear regression model with a mean absolute error (MAE) of 56.69 accumulated degree day (ADD). An additional random forest model was established based on indicators for the minimum cross‐validation error of Thermomonosporaceae, Clostridiaceae, 0319‐A21, Oxalobacteraceae, and Syntrophobacteraceae, with an MAE of 55.65 ADD. 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Correlation analysis identified 61 time‐related bacterial families and the best subset selection method obtained best subset, containing Thermomonosporaceae, Clostridiaceae, 0319‐A21, and Oxalobacteraceae, which were used to construct a simplified multiple linear regression model with a mean absolute error (MAE) of 56.69 accumulated degree day (ADD). An additional random forest model was established based on indicators for the minimum cross‐validation error of Thermomonosporaceae, Clostridiaceae, 0319‐A21, Oxalobacteraceae, and Syntrophobacteraceae, with an MAE of 55.65 ADD. 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subjects Animals
Bacteria - classification
Bacteria - genetics
Bacteria - isolation & purification
Bone and Bones - chemistry
Bone and Bones - microbiology
Bones
Burial
burial soil microbial community
burial time period prediction
Correlation analysis
dismembering bones
Feasibility studies
Femur - microbiology
High-Throughput Nucleotide Sequencing
Human remains
Microbiota
Microorganisms
Postmortem Changes
Regression models
RNA, Ribosomal, 16S - analysis
RNA, Ribosomal, 16S - genetics
Soil - chemistry
Soil analysis
Soil Microbiology
Swine
Time dependence
title A preliminary study characterizing temporal changes in soil bacterial communities after dismembered bones were buried
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