Dissecting the Microbial Community Structure of Internal Organs During the Early Postmortem Period in a Murine Corpse Model

doi:10.21203/rs.3.rs-1099733/v1

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Dissecting the Microbial Community Structure of Internal Organs During the Early Postmortem Period in a Murine Corpse Model

https://doi.org/10.21203/rs.3.rs-1099733/v1

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Background

Microorganisms inhabit and proliferate throughout the body both externally and internally, which are the primary mediators of putrefaction after death. However, limited information is available about the changes in the postmortem microbiota of extraintestinal body sites in the early decomposition stage of mammalian corpses.

Results

This study applied 16S rRNA barcoding to investigate microbial composition variations among different organs and the relationship between microbial communities and time since death over 1 day of decomposition. During 1 day of decomposition, Agrobacterium, Prevotella, Bacillus, and Turicibacter were regarded as time-relevant genera in internal organs at different timepoints. Pathways associated with lipid, amino acid, carbohydrate and terpenoid and polyketide metabolism were significantly enriched at 8 hours than that at 0.5 or 4 hours. The microbiome compositions and postmortem metabolic pathways differed by time since death, and more importantly, these alterations were organ specific.

Conclusion

The dominant microbes differed by organ, while they tended toward similarity as decomposition progressed. The observed thanatomicrobiome variation by body site provides new knowledge into decomposition ecology and forensic microbiology. Additionally, the microbes detected at 0.5 hours in internal organs may inform a new direction for organ transplantation.

General Microbiology

Microorganisms

microbial community

internal organs

early postmortem period

murine corpse model

Decomposition involves a mosaic ecosystem with an intimate association of biotic factors (such as the corpse, intrinsic and extrinsic bacteria and insects), and abiotic factors (such as weather and climate). This process mobilizes nutrients bound to once-living organisms into the surrounding environment, yielding a group of assembled decomposers to realize nutrient recycling [1]. Exploring the action of these organisms, especially microbes, in the maintenance of intrinsic ecosystems is important. We propose that the microbial composition differences seen during decomposition can be directly traced to the status of bacteria present in the earliest stages of decomposition. Microorganisms, important decomposers, are ubiquitous and come from the environment, are scavengers, or were part of the existing microflora of once-living organisms. After an initial lag phase following organismal death, decomposers begin to proliferate, transmigrate and create specialized proteins that digest host tissues [2]. Moreover, volatile organic compounds associated with the process of bacterial metabolism recruit (or repel) insects [3], which, if given the opportunity, can subsequently eliminate any remaining flesh. Thus, understanding the bacterial basis of decomposition is crucial for understanding decomposition ecology. Additionally, the processes and dynamics of these microbes may be applied in forensic science, particularly concerning estimating the postmortem interval (PMI) [4–6].

Recently, several studies have utilized culture-independent, high-throughput sequencing technologies and bioinformatic analysis to determine the microbial community composition during decomposition associated with cadaver skin [1, 4, 7, 8], the oral cavity [7–9], and the intestinal tract [1, 4, 7, 9–11]. Many of these studies have mainly focused mainly on microbial behaviors within the long-term decomposition process [1, 5, 6, 12] rather than the immediate change after death when there is no obvious putrefaction of the corpse [13]. These studies mainly collected samples from body sites that have indigenous microbes, such as the skin, intestine, and oral cavity. Postmortem microbial succession in internal organs, which are presumed to be sterile, has rarely been reported. Studying microorganisms associated with internal organs is important, as the presence/absence and/or abundance of certain bacteria might be indicative of an early PMI (EPMI). Compared to a long PMI (LPMI), the EPMI estimation is more significant in forensic practice, as it improves the efficiency of case detection [14–16].

Studies have indicated rapid microbial succession during early decomposition [17], from aerobic bacteria to anaerobic bacteria in most body sites [18]. In addition to changes in the microbiota in specific topographic locations within the body, it has been suggested that bacteria translocate from one organ to other organs after death, whether locally to adjacent organs or to distant sites via vascular channels or other unknown mechanisms [13]. Burcham et al found significant increases in relative abundance of Clostridium during long-term decomposition in the brain, heart, spleen, and liver samples, as fluids from internal organs begin to emulsify within the body cavity [2].

Their study also demonstrated that gene transcripts for multiple metabolic pathways were highly abundant in these organs, while transcripts associated with the stress response and dormancy increased as decomposition progressed [2]. Some studies have utilized intranasal inoculation of Staphylococcus aureus and Clostridium perfringens to investigate their postmortem structure and functional dynamics [14]. The detection rate of S. aureus KUB7 in previously sterile organs, such as the kidney, liver, spleen, and heart, showed undulating positivity over time. For instance, S. aureus KUB7 was detected in the kidneys at 1 hour post sacrifice. However, limited information is available about the early changes in the postmortem microbiota in mammalian animals, especially about the microbiome of extraintestinal body sites [15, 16]. Thus, there is a need for systematic studies that investigate postmortem changes in the microbiota of internal organs in healthy individuals to provide basic data. In the current study, we characterized and compared the microbial composition of postmortem internal organs (brain, heart, liver, and kidney). In addition, we characterized the shift in microbial assemblages through the EPMI in a mouse decomposition model. Understanding the shifts in microbial community composition or density and functional-associated pathway levels will benefit studies in decomposition ecology, forensic science, and even organ transplantation.

Sample collection protocol

Adult male C57BL/6J mice (18-25 g, 6-10 weeks, n = 30) were housed under standard lighting (light/dark periods of 12 hours), temperature (25.0 ± 1.5°C), and relative humidity (50.0 ±7.0%) conditions at the animal centre of Xian Jiaotong University. They were housed and handled daily under laboratory conditions for at least 7 days before the study. Water and food were available ad libitum. All animal experiments were approved by the Committee of Laboratory Care and Use of Xian Jiaotong University (approval No: 2017-288). The experiment was performed under the same conditions of light, temperature, and relative humidity described above. After cervical dislocation, mice were placed in a controlled-environment chamber (PRX-350D, ZuoLe, ShangHai) with constant temperature (Ta, 25±1.5℃) and relative humidity (RH, 50±7%) at Xi’an Jiaotong University, College of Forensic Medicine. Internal organic samples (brain, heart, liver, and kidneys) were harvested from mouse corpses following aseptic principles at half an hour, 4 hours, 8 hours, 12 hours, and 1 day (6 mice per timepoint). Concretely, every surgical instrument was autoclaved before used. The clean bench was sterilized under ultraviolet light for at least 2 hours before operation. Operators wore sterile surgical clothes during operation. In addition, we swabbed surgical instruments with sterile swabs and set them as negative controls. All samples were then immediately stored at −80°C until further experimentation.

Postmortem microbiota analysis by 16S rRNA gene sequencing

Total genomic DNA was extracted from samples using an OMEGA Soil DNA Kit (D5625-01) (Omega Bio-Tek, Norcross, GA, USA) following the manufacturer’s recommendations and stored at -80°C before further analysis. The quantity and quality of extracted DNA were measured using a NanoDrop ND-1000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) and agarose gel electrophoresis, respectively. The microbial 16S rRNA gene V3 and V4 regions were amplified by polymerase chain reaction (PCR) with the universal primers 338F (5'-ACTCCTACGGGAGCAGCAGCA-3') and 806R (5'-GGACTACVSGTATCTAAT-3'). The PCR components consisted of 5 µl of buffer (5×), 0.25 µl of Fast Pfu DNA Polymerase (5 U/µl), 2 µl (2.5 mM) of dNTPs, 1 µl (10 µM) of each forward and reverse primer, 1 µl of DNA template, and 14.75 µl of ddH₂O. The PCR cycling consisted of an initial denaturation for 30 s at 98°C, followed by 30 cycles of 15 s at 98°C, 30 s at 50°C, and 30 s at 72°C, and an extension for 5 min at 72°C. PCR amplicons were purified with Vazyme VAHTSTM DNA Clean Beads (Vazyme, Nanjing, China) and quantified using a Quant-iT PicoGreen dsDNA Assay Kit (Invitrogen, Carlsbad, CA, USA). After the individual quantification step, amplicons were pooled in equal amounts, and paired-end 2×250 bp sequencing was performed using the Illumina MiSeq platform with a MiSeq Reagent Kit v3 at Shanghai Personal Biotechnology Co., Ltd. (Shanghai, China).

Sequence bioinformatics analysis

Microbiome bioinformatic analysis was performed with QIIME2 [19] according to the official tutorials (https://docs.qiime2.org/2019.4/tutorials/), with slight modification. Briefly, raw sequence data were demultiplexed using the demux plugin followed by primer trimming with the cutadapt plugin [20]. Sequences were then quality filtered, denoised, and merged, and chimeras were removed using the DADA2 plugin [21]. Nonsingleton amplicon sequence variants (ASVs) were aligned with mafft [22] and used to construct a phylogeny with fasttree2 [23]. ASV-level ranked abundance curves were generated to compare the richness and evenness of ASVs among samples. Taxonomy was assigned to ASVs using the classify-sklearn naïve Bayes taxonomy classifier in the feature-classifier plugin [24] against the SILVA Release 132 Database.

Sequencing data analyses were mainly performed using QIIME2 and R packages (v3.2.0). Alpha diversity metrics included Chao1 [25], observed species, Shannon [26], Simpson [27], Faith’s phylogenetic diversity (PD) [28], Pielou’s evenness [29] and Good’s coverage [30]. Beta diversity analysis was performed to investigate the structural variation in microbial communities across samples using Jaccard metrics, Bray-Curtis metrics, and UniFrac distance metrics [31, 32], and visualized via principal coordinates analysis (PCoA), nonmetric multidimensional scaling (NMDS) and unweighted paired-group method with arithmetic means (UPGMA) hierarchical clustering [33]. The significance of microbiota structure differentiation among groups was assessed by permutational multivariate analysis of variance (PERMANOVA) [34], analysis of similarities (ANOSIM), and Permdisp using QIIME2. The taxonomy compositions and abundances were visualized using MEGAN [35] and GraPhlAn [36]. A Venn diagram was generated to visualize the shared and unique ASVs among samples or groups using the R package Venn Diagram based on the occurrence of ASVs across samples/groups regardless of their relative abundance. Linear discriminant analysis (LDA) effect size (LEfSe) was performed to detect differentially abundant taxa as potential biomarkers across groups using the default parameters [37]. An LDA threshold score of 3 and a significant alpha of 0.05 were used to detect biomarkers. Microbial functions were predicted by Phylogenetic Investigation of Communities by Reconstruction of Unobserved States 2 (PICRUSt2) [38] with the Kyoto Encyclopedia of Genes and Genomes (KEGG) (https://www.kegg.jp/) databases.

Statistical analysis

To compare microbial succession differences in different organs, the relative abundances of ASVs were summed in every sample. Based on the microbial relative abundance data of the adjacent death timepoints of the same organ, the dataset for the relative change rate in this period was obtained; please see supplementary material 1 for the detailed calculation. We compared the rates of different organs within the same PMI period. The statistical analyses in this work were conducted using SPSS software (v18.0, Chicago, IL, USA), and the statistically significant results are shown in the relevant figures. Statistical analyses used in this study were Student’s t-test, least significant difference (LSD), Kruskal-Wallis rank-sum test (KW-test), analysis of variance (ANOVA), and Dunnett’s T3 test according to the applicable conditions.

1. Microbial variation in internal organs after death

1.1 Relative abundance profiles of the microbial community during 1 day of decomposition

A total of 120 organ samples, including those from the brain, heart, liver, and kidneys, were collected from 30 mouse remains over 5 timepoints over 1 day of decomposition during 1 day of decomposition. In our study, no valid bacterial sequence was detected in the negative controls, suggesting that the sequencing data are reliable. To analyze microbial community succession in every organ, the microbial community composition profiles in the four organs were described separately.

In the brain samples, at the genus level (Fig. 1A and Fig. S1A), Ochrobactrum (10.37%~18.54%) and Sediminibacterium (3.23%~17.74%) were dominant and showed a similar decreasing relative abundance profile along PMI progression. Acinetobacter, Cupriavidus, and Agrobacterium showed increasing abundance profiles. In particular, the relative abundance of Agrobacterium significantly increased (11.99% ± 0.41%) at 8 hours compared with 0.5 hours (4.32% ± 0.28%, P = 0.004) and 4 hours (4.02% ± 0.79%, P = 0.004) (P < 0.01, KW-test, Fig. 2A). At the phylum level (Fig. S2A), Proteobacteria (54.06%~87.80%) was the most prevalent in all brain samples. The relative abundance of Bacteroidetes was higher in the H0.5Brain (19.31%±0.81%) and H4Brain (19.63%±3.27%) samples than in the samples from the other three groups (the P values of the six paired comparison groups were all 0.000, P<0.001, LSD t- test). Proteobacteria showed different succession structures during decomposition. In addition, the relative abundances of Cyanobacteria and Thermi illustrated an increase during 12 hours of decomposition. At the order level (Fig. S2A), Rhizobiales (21.97%~31.65%) was dominant in all brain samples. The relative abundances of Saprospirales, Caulobacterales, and Thermales decreased, while those of Burkholderiales and Pseudomonadales showed increasing profiles during 1 day of decomposition. At the species level (Fig. S2A), the relative abundance of Bifidobacterium longum showed a peak value at hour 4 after death. Acinetobacter johnsonii showed a peak value at hour 12 after death. The abundances of Deinococcus geothermalis and Sphingomonas azotifigens abundances increased during decomposition.

In the heart samples, at the genus level (Fig. 1B and Fig. S1B), Thermus (14.52%~25.12%) was more abundant than the other genera in all the heart sample groups. The relative abundances of Enhydrobacter, Caulobacter, and Methyloversatilis gradually decreased during 1 day of decomposition. However, the relative abundance of Pseudomonas increased to 11.38% at 8 hours after death. The relative abundances of Sphingomonas and Cupriavidus increased to peak values of 7.93% and 12.58%, respectively, at 12 hours after death. At the phylum level (Fig. S2), Proteobacteria (47.49%~67.28%) and Thermi (16.70%~27.90%) were dominant in the early postmortem heart samples. The relative abundance of Firmicutes gradually increased during 1 day of decomposition, while that of Actinobacteria decreased. At the order level (Fig. S2B), Pseudomonadales (15.21%~27.57%), Thermales (14.52%~25.12%), and Burkholderiales (15.41%~20.26%) were dominant in all heart samples. The relative abundance of Sphingomonadales increased to a peak value of 8.96% at 12 hours after death. Rhizobiales showed a gradual increasing abundance profile during 1 day of decomposition. Furthermore, the relative abundance of Deinococcales increased to 4.72% at 12 hours after death. However, the relative abundance of Rhodocyclales, Rhodospirillales, and Caulobacterales decreased during 1 day of decomposition. At the species level (Fig. S2B), Pseudomonas viridiflava, Sphingomonas azotifigens, and Deinococcus geothermalis were dominant in all the postmortem heart samples.

In the liver samples, at the genus level (Fig. 1C and Fig. S1C), Thermus (16.83%~24.22%) and Cupriavidus (11.40%~14.35) were dominant in all the postmortem liver sample groups. The relative abundance of Microbacterium gradually decreased to zero percent at 24 hours after death. In contrast, the relative abundances of Acinetobacter, Cupriavidus, and Pseudomonas gradually increased during decomposition. The genera Paracoccus and Cryocola were detected only half an hour after death. Prevotella showed a significant increase in relative abundance at 4 hours (1.14% ± 0.57%) compared with that at 0.5 hours (0.44% ± 0.38%) (P=0.037, P < 0.05, KW-test, Fig. 2B). At the phylum level (Fig. S2C), Proteobacteria (48.29%~62.36%) and Thermi (18.79%~25.39%) were dominant in all the liver sample groups. Actinobacteria, Firmicutes, Bacteroidetes, and Cyanobacteria showed relative abundances of more than 1% in all the liver sample groups. Among these phyla, Actinobacteria gradually decreased in relative abundance during 1 day of decomposition. At the order level (Fig. S2C), Burkholderiales (15.25%~20.72%), Pseudomonadales (14.59%~27.66%), and Thermales (16.83%~24.22%) were dominant in all the liver sample groups. The relative abundance of Clostridiales gradually increased during 1 day of decomposition, while that of Actinomycetales decreased during decomposition. Rhodobacterales immediately decreased in relative abundance before 4 hours after death. At the species level (Fig. S2C), Pseudomonas viridiflava, Sphingomonas azotifigens, and Sphingomonas azotifigens were dominant in all the liver sample groups. Paracoccus marcusii decreased in relative abundance before 4 hours after death.

In the kidney samples, at the genus level (Fig. 1D and Fig. S1D), Thermus (7.40%~31.59%) was dominant. The relative abundances of Acinetobacter and Pseudomonas increased during 8 hours of decomposition. The relative abundance of Methyloversatilis decreased during 1 day of decomposition. Bacillus was significantly increased at 4 hours compared with 0.5 hours (P=0.045, P <0.05, KW-test, Fig. 2C). Turicibacter exhibited a significantly higher relative abundance in the H24Kidney group (1.33% ± 0.51%) than in the other kidney sample groups (v.s. H0.5; P = 0.038; v.s. H4, P=0.031; v.s. H8, P=0.013; v.s. H12, P=0.021, P <0.05, KW-test, Fig. 2D). At the phylum level (Fig. S2D), Proteobacteria (41.37%~60.51%), Thermi (8.00%~32.14%), and Firmicutes (7.35%~10.79%) were dominant in all the postmortem kidney sample groups. The relative abundances of Fusobacteria and Cyanobacteria gradually decreased during 1 day of decomposition, while those of Proteobacteria and Actinobacteria gradually increased during decomposition. At the order level (Fig. S2D), Pseudomonadales (11.94%~22.23%) and Thermales (7.40%~31.59%) were dominant orders in all the kidney sample groups. During 1 day of decomposition, the relative abundances of Streptophyta, Clostridiales, and Rhodocyclales gradually decreased. However, the abundances of Burkholderiales, Rhizobiales, Bacteroidales, and Actinomycetales gradually increased during this decomposition period. At the species level (Fig. S2D), Paracoccus marcusii and Lactobacillus reuteri had an increasing abundance profile across the 12 hours after death.

1.2 Comparison of alpha and beta diversity at different timepoints

Alpha diversity was measured by the Shannon index, Pielou’s evenness, Good’s coverage index, observed species index, Faith’s PD index, and Chao1 index in this work. Samples in the H8Brain and H24Brain groups had significantly lower alpha diversity (measured as Shannon diversity, H8 v.s. H0.5, P=0.035; H24 v.s. H0.5, P=0.003 (Fig. 4A), Pielou’s evenness, H8 v.s. H0.5, P=0.009; H24 v.s. H0.5, P=0.046 (Fig. S3A), and Good’ s coverage index H8 v.s. H0.5, P=0.028; H24 v.s. H0.5, P=0.035 (Fig. S3C)) than those in the H0.5Brain group (P<0.05, KW-test). Faith’s PD index, indicating the phylogenetic distances of OTUs in a sample, had a significantly lower value in the H0.5Brain group than in the H8Brain (P=0.0015), H12Brain (P=0.014), and H24Brain (P=0.0024) groups (P<0.05, LSD t-test, Fig. S3B). Faith’s PD index was significantly lower in the H24Liver group than in the H4Liver (P=0.009) and H8Liver (P=0.003) groups (P<0.01, Dunnett T3 test, Fig. S3D). According to a comparison of alpha diversity in the kidney groups, the observed species index demonstrated a significantly lower value in the H12Kidney group than in the H4Kidney (P=0.01) and H8Kidney (P=0.000) groups (P < 0.01, Dunnett T3 test, Fig. S3E). The samples in the H12Kidney group showed a significantly lower value of Chao 1 index than those in the H4Kidney (P=0.01) and H8Kidney (P=0.000) groups (P < 0.01, Dunnett T3 test, Fig. S3F).

To visualize the similarities and dissimilarities in community composition between samples, as a measure of beta diversity, PCoA plots were calculated based on the weighted UniFrac dissimilarity index. Overall, PCoA of brain samples revealed two distinct clusters (the PCoA explained 42.8% of the variation): one including samples of the H0.5Brain and H4Brain groups and another including samples from the H8Brain, H12Brain, and H24Brain groups (Fig. 3E). PCoA of kidney samples showed three clusters: a cluster of samples from the H0.5Kidney, H4Kidney, and H8Kidney groups; a cluster of samples from the H12Kidney group; and a cluster of samples from the H24Kidney group (Fig. 3H). Liver and heart samples did not show significant clusters according to beta diversity analysis (Fig. 3).

To determine the classified bacterial taxa with significant abundance differences among different PMIs for internal organs, we performed biomarker analysis using the LEfSe method (Fig. 4). As shown in Fig. 4A, Sediminibacterium and Ochrobactrum were representative genera in the H0.5Brain group. Lactobacillales, and Mycoplana were significant microbes in the H4Brain group. Agrobacterium was the significant genus in the H8Brain group. Deinococcus and Acinetobacter, were representative microbes in the H12Brain group. Cupriavidus and Cryocola were representative microbes in the H24Brain group.

When the taxonomic composition was compared within different decomposition stages in the heart (Fig. 4B), Phascolarctobacterium, Xanthomonadaceae, Sphingobium, and Ellin6529 were significant microbes in the H0.5Heart group; Enterobacteriaceae, Lysinibacillus, and Steroidobacter were detected as representatives in the H4Heart group; Proteobacteria was significant in the H8Heart group; Cupriavidus was regarded as a significant microbe in the H12Heart group; and Lactococcus showed significance in the H24Heart group. In regard to the liver groups, Microbacteriaceae, Comamonadaceae, and Caulobacter were significant microbes in the H0.5Liver group (Fig. 4C); Actinomycetales, PRR_12, and Solibacterales were representative microbes in the H4Liver group; Myroides, Kocuria, Citrobacter, and Streptomycetaceae were significant microbes in the H8Liver group; Gemmatimonadetes and Raphanus were representatives in the H12Liver group; and mitochondria were regarded as significant microbes in the H24Liver group. For the kidney groups (Fig. 4D), Enterobacteriaceae and Streptophyta were significant microbes in the H0.5Kidney group; Limnobacter, Sphingomonadaceae, Clostridiales, Sphingomonadaceae, Hyphomonadaceae, and Planococcaceae were significant in the H4Kidney group; Thermus, Bacteriovoracaceae, and Luteimonas were significant in the H8Kidney group; Rhodobacterales, Perlucidibaca, Bacillales, Flavobacterium, Anoxybacillus, Comamonas, and Rhodospirillaceae were significant in the H12Kidney group; and S24_7, Oxalobacteraceae, Turicibacter, Bacteroides, Akkermansia, Cupriavidus, and Deltaproteobacteria were significant microbes in the H24Kidney group.

2. Differential postmortem microbial community structure and diversity between different organs

Alpha diversity was significantly different between the brain and the other organs (heart, kidney, and liver) (P < 0.05, Student’s t-test (LSD), KW-test, Fig. 5). The brain groups exhibited lower observed amplicon sequence variant (ASV) richness values than the other organs at half an hour, 4 hours, 8 hours, and 24 hours after death. Kidney samples showed the lowest observed ASV richness values among the other organ groups at 12 hours after death (Fig. 5). Beta diversity based on weighted UniFrac distance showed obvious clustering at the early decomposition stage according to the organ, while the distinction decreased with the decomposition process (Fig. 5). In general, the brain samples were dominated by the bacterial genera Acinetobacter, Cupriavidus, Ochrobactrum, and Sediminibacterium, while the other organs were dominated by Thermus, Enhydrobacter, and Pseudomonas (Fig. 1).

LEfSe analyses were used to detect significant biomarkers among the different sample sites during decomposition at different levels (Fig. 6). The number of significant taxa in each organ increased before 8 hours and decreased from then on, leading to no significantly differentially abundant taxa in any organ at 24 hours after death, with a linear discriminant analysis (LDA) threshold of 4 (Fig. 6). Half an hour after death (Fig. 6A), Agrobacterium, Sediminibacterium, and Ochrobactrum were detected as significantly differentially abundant genera in the H0.5Brain group; Comamonadaceae, Sphingomonas, and Sphingomonas azotifigens were detected in the H0.5Heart group at multiple levels; Pseudomonas, Cupriavidus, Deinococcus, and Cryocola were significantly differentially abundant genera in the H0.5Liver group; Pseudomonas viridiflava and Deinococcus geothermalis were detected as significantly differentially abundant species in the H0.5Liver group; and Enterobacteriaceae, Streptophyta and Methyloversatilis were significantly differentially abundant bacteria in the H0.5Kidney group. After 4 hours of decomposition (Fig. 6B), Bradyrhizobiaceae and Agrobacterium were significantly differentially abundant microbes in the H4Brain group; Pseudomonas, Acinetobacter, and Sphingomonas were detected as representative genera in the H4Heart group. In addition, Sphingomonas azotifigens, Pseudomonas viridiflava, and Deinococcus geothermalis were significantly differentially abundant species in the H4Heart group. Thermus, Limnobacter, Perlucidibaca, Methyloversatilis, and Flavobacterium were significantly differentially abundant genera in the H4Kidney group. Moraxellaceae and Cupriavidus were detected as significantly differentially abundant microbes in the H4Liver group. After 8 hours of decomposition (Fig. 6C), Cupriavidus, Ochrobactrum, Agrobacterium, Sediminibacterium, and Acinetobacter were significantly differentially abundant genera in the H8Brain group; Sphingomonas, Pseudomonas, and Deinococcus were significantly differentially abundant genera in the H8Heart group. Pseudomonas viridiflava, Sphingomonas azotifigens, and Deinococcus geothermalis were significantly differentially abundant species in the H8Heart group. In the H8Kidney group, Thermus and Methylobacterium were significantly differentially abundant genera. After 12 hours of decomposition (Fig. 6D), Acinetobacter, Cupriavidus, Acinetobacter, and Agrobacterium were detected as significantly differentially abundant genera in the H12Brain group; Thermus, Pseudomonas, Sphingomonas, and Pseudomonas were representative genera in the H12Heart group. Sphingomonas azotifigens and Pseudomonas viridiflava were detected as significantly differentially abundant species in the H12Heart group. Perlucidibaca and Flavobacterium were significantly differentially abundant genera in the H12Kidney group. We also counted the genera with continuous difference in the comparison of four organs based on the LDA effect size (LEfSe) analysis results (Table 1).

Table 1

Differential genera associated with different organs during decomposition based on Lefse analysis.
Group	Genera	P value/ LDA score at hour 0.5	P value/ LDA score at hour 4	P value/ LDA score at hour 8	P value/ LDA score at hour 12	trend^a
Brain	Ochrobactrum	0.0041/ 4.9100	0.0402/ 4.8934	0.0042/ 4.7879	0.0100/ 4.6123	up
	Agrobacterium	0.0018/ 4.2848	0.0007/ 4.2821	0.0004/ 4.7662	0.0007/ 4.5610	up
	Leptothrix	0.0012/ 3.5893	0.0013/ 3.6662	0.0001/ 3.8906	0.0009/ 3.7786	up
	Aminobacter	0.0032/ 3.5617	0.0354/ 3.6697	0.0041/ 3.6722	0.0124/ 3.4371	up
	Bradyrhizobium	0.0024/ 3.5432	0.0014/ 3.5230	0.0034/ 3.5848	0.0228/ 3.3360	up
	Phyllobacterium	0.0029/ 3.3562	0.0009/ 3.4450	0.0038/ 3.5664	0.0005/ 3.2525	up
Heart	Sphingomonas	0.0033/ 4.2528	0.0022/ 4.4560	0.0003/ 4.4591	0.0006/ 4.5032	up
Kidney	Lysinibacillus	0.0040/ 3.3069	0.0008/ 3.4894	0.0021/ 3.2596	0.0007/ 3.1509	up
	Perlucidibaca	0.0035/ 3.6762	0.0007/ 4.1782	0.0002/ 3.8716	0.0003/ 4.2633	up
	Limnobacter	0.0004/ 3.7829	0.0024/ 4.2050	0.0029/ 3.7611	0.0159/ 4.1156	up
^a The trend means that the relative abundance of the corresponding genus was increased or decreased compared to other organ group at the same PMI.

3. Metabolically active members in postmortem organs during decomposition

The metabolic pathways based on the KEGG database were used to link microbial genomic information by the PICRUSt2 algorithm with higher-order functions that were significantly altered during 1-day decomposition. To facilitate writing, the organ groups harvested before 4 hours after death were called “before 4 h”, while those harvested after 4 hours of decomposition were called as “after 4 h”. Broad classes of metabolic pathways within the samples are presented in Fig. 7. The enriched pathways of the current study were amino acid metabolism, carbohydrate metabolism, cofactor and vitamin metabolism, xenobiotic biodegradation and metabolism, other amino acid metabolism, terpenoid and polyketide metabolism, lipid metabolism, and energy metabolism, which are summarized in Fig. 7A. The top 5 relative abundance pathways were similar in the four organs. Certain pathways, including ketone body synthesis and degradation; ansamycin biosynthesis; valine, leucine, and isoleucine biosynthesis; C5-branched dibasic acid metabolism; and fatty acid biosynthesis, were dramatically enriched in “after 4 h” samples, compared to “before 4 h” samples of the brains and hearts (Fig. S4, P < 0.05, KW-test). A heatmap shows particular pathways that were differentially abundant according to the organ (Fig. 7B). According to comparisons of different organs at 0.5 hours, selenocompound metabolism, histidine metabolism, and several amino acid metabolism pathways were obviously more enriched in the brain than in the other organs. The bacterial chemotaxis and flagellar assembly pathways were depleted in kidney samples. After 4 hours of decomposition, pantothenate and CoA biosynthesis, selenocompound metabolism, and amino acid metabolism were enriched in the H4Brain group compared with in the other organ groups. After 8 hours of decomposition, the biotin, lipoic acid metabolism, carbon fixation in prokaryotes and terpenoid backbone biosynthesis pathways were depleted in brain samples compared to those in samples from the other organs. After 12 hours of decomposition, the pyruvate metabolism pathway was enriched in the kidney compared with in the other organs.

The common thanatomicrobiome sampling method in previous studies was continuous swabbing in the same bodies. Our study was designed such that we sampled different individuals at each time rather than repeatedly sampling the same bodies, with the explicit intent of preventing exposure of internal organs to environmental oxygen and causing potentially associated shifts in the microbiota. We investigated the temporal dynamics of decomposition on microbial community composition and function directly inside the cadaver with 16S rRNA MiSeq sequencing. A significant difference in microbial community structure was observed among different stages of decomposition. As posited, the bacterial communities appeared to notably shift along with PMI timepoints in different organs. The thanatomicrobiome abundance and composition differed across PMI timepoints in the internal organs. The differences were most striking in the brain (Fig. 3 and Fig. S3). Acinetobacter immediately increased in abundance at hour 8 in brain samples (Fig. S1); Acinetobacter is an aerobic, nonfermentative, gram-negative pathogenic bacilli. Howard et al. detected Acinetobacter in a swine carcass using fluorescence in situ hybridization (FISH) [39]. Cupriavidus and Agrobacterium were increased after 8 hours of decomposition in brain samples. A previous study reported the decomposition ability of Cupriavidus basilens in organic matter since it accelerates organic phosphate decomposition, which could acidify microbial cells and cause the release of orthophosphate from mineral phosphate [40]. At the phylum level, Bacteroidetes and Proteobacteria showed different succession structures during decomposition, which could be important information in PMI estimation. The phyla Proteobacteria and Bacteroidetes are believed to play a major role in organic matter degradation and in carbon turnover [41]. At the species level, Bifidobacterium longum has a peak relative abundance at hour 4 and is considered to be a scavenger, possessing multiple catabolic pathways to scavenge a large variety of nutrients from extracellular polymers. [42]. The relative abundance of Deinococcus geothermalis increased, which can fight oxidative stress caused by an aerobic lifestyle and by oxidizing biocides, suggesting that exacting this species from corpses may facilitate in industrial usage. [43]. Sphingomonas azotifigens, a nitrogen-fixing bacterium [44], also increased in abundance during decomposition.

When analyzing microbial succession of internal organs during decomposition, we observed some interesting findings. The microbial composition of brain samples shifted from facultative anaerobes to obligate aerobes, while that of heart samples shifted from obligate aerobes to facultative anaerobes (Fig. 4). Proteobacteria and Cupriavidus were differentially abundant in the H8Heart and H12Heart groups, which was similarly observed in the brain sample groups. In the heart samples, Enhydrobacter and Caulobacter, belonging to Alphaproteobacteria, showed a decreasing abundance profile. Caulobacter uses energy-dependent proteases to control protein destruction in a highly specific manner [45]. Methyloversatilis and Sphingomonas are reportedly decomposers that have been isolated from a wide variety of environments and were also detected in postmortem heart samples in this work [46]. In the liver groups, Microbacterium, which gradually disappeared with body degradation and includes aerobiotic bacteria that utilize riboflavin (vitamin B2) as a carbon source [47], was identified and has been reported previously in murine decomposition[48]. Paracoccus and Cryocola were observed in the H0.5Liver group. Paracoccus is a biochemically versatile genus possessing a variety of metabolic pathways through which a wide range of diverse compounds can be degraded. Cryocola was found in the co-composting of organic wastes [49]. Comamonadaceae, a family of Betaproteobacteria, was also significantly enriched in the H0.5Liver group, meaning that nutrition of the liver was sufficient at the beginning of death. With oxygen consumption, Actinomycetales, gram-positive and anaerobic bacteria, were dominant in the H4Liver group. This order can be found mostly in soil and decaying organic matter. We detected Rickettsiales as a representative the H24Liver group. However, all bacteria of this order are short coccobacillary organisms that are strictly intracellular bacteria and thus require survival inside an animal host (most important animal hosts are arthropods). This result surprised us since, even though the body did not encounter arthropods, the microbial community could provide some important information on the appearance of arthropods. The representative microbes shift from facultative anaerobes, such as Enterobacteriaceae, to aerobic bacteria (Limnobacter, Deinococcus, and Rhodobacterales), and then to obligate anaerobes, such as Bacteroides, in H24Kidney group (Fig. 4D). Leptothrix, observed in the H24Kidney group, is a sheathed filamentous bacterium that can generally be found in environments with sufficient organic matter. The relative abundance of Turicibacter, which has most commonly been found in the gut of animals, showed a significant increase in the H24 group compared with the other kidney sample groups (Fig. 2D). Meanwhile, Lactobacillus reuteri, one of the most ubiquitous species of mammalian gut microbes, had an increasing abundance profile during 12 hours of decomposition in the kidney samples. These results suggested the possibility of gut microbes participating in body decomposition after death.

Microbial alpha diversity showed a slightly fluctuating decrease during 1 day of decomposition, as was observed previously for bacterial alpha diversity in human [1], porcine [8], and murine [5] remains. The results of this study demonstrated that the microbial community evenness of the H0.5Brain group was higher than that of the other brain sample groups according to a higher Pielou’s evenness value. Faith’s PD index comparison among the brain sample groups indicated that the microbial species affinity in postmortem organs decreased with time. Additionally, the decline in Good’s coverage index showed that the proportion of species detected decreased with brain decomposition. The alpha diversity change trends in other organs were not as obvious as those in the brain groups. We supposed that the incongruence of decay stages among different organs occurred mainly because of their microbial composition at the moment of death. The brain samples formed two clusters in a PCoA plot based on weighted UniFrac distance according to the decomposition stage. In addition, the groups separated in the PCo1 direction that explained 42.8% of the variance, which was higher than that for the PCoA of the other organs. These results all illustrated obvious microbial composition succession in postmortem brain samples. We also detected some marked pathways predicted to be associated with metabolism that were enriched in our samples, including lipid metabolism, terpenoid and polyketide metabolism, amino acid metabolism, and carbohydrate metabolism (Fig. S4). The significant increase in the relative abundance of these pathways suggested that organic synthesis and degradation processes were present after 8 hours of decomposition. This result explained the increase in the relative abundances of some microbes during decomposition. These findings provide future research directions for the related analysis of microbial communities and metabolic pathways.

The microbiome changes after death, as previously reported, and more importantly, these alterations are organ specific[50]. Specifically, the microbial composition in postmortem organs (Fig. 1) differed. Acinetobacter, Cupriavidus, and Ochrobactrum, dominant in postmortem brain samples, are environmental organisms, especially in soil, and are considered opportunistic pathogens in humans in the hospital. Enhydrobacter is reportedly a member of the mammalian intestinal microbiome and was dominant in postmortem kidney and liver samples. The possible reason may be that the locations of the liver and kidney are near the intestine of the body. Thermus, dominant in the heart, liver, and kidney samples, was found in the human postmortem skeleton, which was due to the greater duration of exposure to environmental fluctuations [51]. Based on the comparison of alpha diversity, a lower observed ASV value was observed in the brain than in the other organs (P < 0.05, Fig. 5), which suggested that postmortem microbial diversity depends on the distance between the organ and the gut. The kidney and liver are close to the gut and immediately begin to putrefy in the particular natural order of decomposition [50]. PCoA based on the beta diversity index showed a tendency to separate all the organ groups at the very beginning of decomposition, while the tendency was not clear over time (Fig. 5). Likewise, LEfSe analysis demonstrated that no difference remained after 1 day of decomposition (Fig. 6). All of the results confirmed that the compositions of the microbes participating in organ degradation became much more similar over time [52]. To further detected significant microbes of different organs, we summarized the continuous significant genera in different organ based on the P-value and LDA score of LEfSe analysis (Table 1). Ochrobactrum, Agrobacterium, Leptothrix, Aminobacter, Bradyrhizobium, and Phyllobacterium had significant higher relative abundances in postmortem brain samples than in other organs during 1 day of decomposition. Ochrobactrum [53] and Agrobacterium [54] were reported to be associated with corpse decomposition. Leptothrix can be found in environments with sufficient amounts of organic matter, which may suggest its decomposition capacity. Bradyrhizobium has been reported to have the capacity to oxidize sulfur [55], carry out denitrification, and fix nitrogen. Phyllobacterium was reported to exhibit obvious changes in postmortem oral microbiota community [56], which may suggest that it invades the brain from the mouth after death. Sphingomonas was found to be a continuously continuous significant genus of heart compared to other organs, and was also reported in internal organs of human corpses [50]. This genus is considered to comprise ammonia-oxidizing bacteria that can decompose polycyclic aromatic hydrocarbons positive for nitrogenase [57]. In addition, it has been reported produce NH₃ using pig carcass proteins as a substrate [58]. We identified Lysinibacillus and Perlucidibaca as significant differential genera in kidney compared to other organs, and these genera have been reported in other decomposition studies [59].

The differences among organs lie not only in the microbial composition but also in the relative abundance of some metabolic active pathways. Selenium is widely distributed throughout the body and is particularly well maintained in the brain, even under dietary selenium deficiency. When the body dies, selenocompounds in the brain degrade, as the relative abundance of selenocompound metabolism was higher in the H0.5Brain group than in the other organ groups [60]. The histidine metabolism pathway was activated in the H0.5Brain group but not in other organs. Histamine in the brain is a neurotransmitter formed by the decarboxylation of histidine and regulates diverse physiological functions. Histamine N-methyltransferase (HNMT) is a histamine-metabolizing enzyme expressed in the brain. These results all explained the differential abundance of pathways among different organs [61]. Several amino acid metabolism pathways, including those for D-alanine, glycine, serine, and threonine metabolism, were activated in the brain compared to those of the other organs, possibly because the protein content is highest in the brain. In the H0.5Kidney samples, the relative abundances of the genes associated with the bacterial chemotaxis and flagellar assembly pathways were lower than those in the other organs. This result means that microbial chemotactic activity was not active in kidney samples immediately after death because chemicals such as nutrients and toxins were not yet released [62]. After 4 hours of decomposition, the microbial behaviour was continually active in brain samples. The relative abundances of the pantothenate and CoA biosynthesis, selenocompound metabolism, and amino acid metabolism pathways were higher in the H4Brain group than in the other organ groups. In some bacteria, the production of phosphopantothenate by pantothenate kinase is the rate limiting and most regulated step in the biosynthetic pathway [63]. Similar to those in the H0.5Brain group, some amino acid metabolism pathways, such as those for glycine, serine, threonine, histidine, alanine, aspartate, glutamate, and lysine, were activated because of some bacterial behaviour. For example, the lysine biosynthetic pathway is closely associated with bacterial activity. One of the products of this pathway, lysine, is used in the peptidoglycan layer of gram-positive bacterial cell walls. A second product, mesodiaminopimelate (m-DAP), is an essential component of the peptidoglycan cell wall for gram-negative bacteria. Lysine, an essential amino acid, is not synthesized by mammals. However, most bacteria, plants, and algae synthesize lysine and m-DAP from aspartic acid through three related pathways that diverge after the production of L-tetrahydrodipicolinate. The presence of multiple biosynthetic pathways in bacteria for the synthesis of m-DAP/lysine highlights the importance of m-DAP/lysine for bacterial cell survival [64]. After 8 hours of decomposition, the relative abundances of biotin, lipoic acid metabolism, carbon fixation pathways in prokaryotes and the terpenoid backbone biosynthesis pathway were lower in brain samples than in the other organ samples. Biotin, a water-soluble vitamin, is used as a cofactor of enzymes involved in carboxylation reactions. Thus, it plays an essential role in maintaining metabolic homeostasis [65]. Lipoic acid is an enzyme cofactor required for intermediate metabolism in free-living cells. Cells can acquire lipoate through either scavenging or de novo synthesis. Microbial pathogens implement these basic lipoylation strategies with a surprising variety of adaptations that can affect pathogenesis and virulence [66]. Phototrophic bacteria, such as green sulfur bacteria, cyanobacteria, filamentous anoxygenic phototrophs, heliobacteria, and the recently discovered chloroacidobacteria, use light as the energy source to produce phosphate bond energy (ATP) and reductants [e.g., NAD(P)H and reduced ferredoxin] through photosynthetic electron transport [67]. After 12 hours of decomposition, pyruvate metabolism pathway was enriched in the kidney compared with the other organs. Previous studies have indicated that lactic acid bacteria can produce pyruvate and lactate from some substrates, including carbohydrates, organic acids, and amino acids. The LEfSe analysis results also revealed Lactobacillales as a significant bacterium in H12Kidney samples in contrast to in the other organs at 12 hours.

The results of this study provide a detailed description of the microbial composition within a 1-day decomposing cadaver system and a thorough comparison among different organs. The functional prediction of the microbial community in early decomposition body was also analyzed in this work. This work provided basic information on the microbial composition of internal organs immediately after death, which may facilitate organ transplantation. However, additional research is required to better comprehend this process, such as the shifts in and role of fungi during carcass decomposition.

In conclusion, postmortem internal organs contained bacteria from 0.5 hours to 24 hours after death but had a relatively low species richness and abundance of bacteria. Interestingly, the microbiome changed after death, and more importantly, these alterations were organ specific. The dominant microbes differed from one organ to another, while they tended to become more similar over time. Substantial differences in the microbial relative change rate between different organs over time due to the initial microbial composition differed. The thanatomicrobiome variation by body site provides new knowledge into decomposition ecology. These findings may also be valuable in early PMI estimation and organ transplantation.

PMI, postmortem interval; PCoAs, principal coordinate analyses; PERMANOVA, permutational analyses of variance; 16S rRNA, 16S ribosomal RNA; EPMI, early postmortem interval (within 1 day); LPMI, long postmortem interval (greater than 1 day); PCR, polymerase chain reaction; ASVs, nonsingleton amplicon sequence variants; Faith’s PD, Faith’s phylogenetic diversity; NMDS, non-metric multidimensional scaling; UPGMA, unweighted paired-group method with arithmetic means; ANOSIM, analysis of similarities; LEfSe, linear discriminant analysis effect size; PICRUSt2, phylogenetic investigation of communities by reconstruction of unobserved states 2; KEGG, Kyoto Encyclopedia of Genes and Genomes; LSD, least significant difference; KW-test, Kruskal-Wallis rank-sum test; ANOVA, analysis of variance; FISH, fluorescence in situ hybridization; RCR, the relative change rate.

Consent for publication

Not applicable

Availability of data and materials

All raw sequence data used for our analyses have been uploaded to the National Center for Biotechnology Information Sequence Read Archive (NCBI-SRA) under BioProject number PRJNA746418: www.ncbi.nlm.nih.gov/bioproject/PRJNA746418. Four supplementary figures are available with the online version of this article.

Competing interests

The authors declare no conflict of interest. The funders or supporters had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Funding

This work was supported by the Council of National Natural Science Foundation of China (No. 81730056) and the Key Research and Development Program of Shaanxi (2020SF-133).

Author contributions

Liu Ruina: conceive the study and collect the samples. Liu Ruina and Li Huan: perform laboratory assays. Liu ruina: analyse the data. Liu ruina: write the manuscript with the assistance of Zhang Kai, Wei Xin. Zhang Siruo and Li Huiyu: investigate the study process. Sun Qinru, Wang Zhenyuan and Fan Shuanliang: review and edit the article. Wang Zhenyuan: acquire the financial support for the project. All authors edited the manuscript and approved the final draft.

Acknowledgments

The authors thank Shanghai Personal Biotechnology Co., Ltd (Shanghai, China) for insightful assistance with the DNA sequencing and bioinformatics analysis.

Animal experimentation

This work used only dead animals that had been sacrificed as a byproduct of other research, and was deemed not to be animal research by the Institutional Animal Use and Care Committee of Xi’an Jiaotong University. Mice were euthanized humanely under approved approval No: 2017-288.

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Dissecting the Microbial Community Structure of Internal Organs During the Early Postmortem Period in a Murine Corpse Model

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