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Components of a Neanderthal gut microbiome recovered from fecal sediments from El Salt


A comprehensive view of our evolutionary history cannot ignore the ancestral features of our gut microbiota. To provide some glimpse into the past, we searched for human gut microbiome components in ancient DNA from 14 archeological sediments spanning four stratigraphic units of El Salt Middle Paleolithic site (Spain), including layers of unit X, which has yielded well-preserved Neanderthal occupation deposits dating around 50 kya. According to our findings, bacterial genera belonging to families known to be part of the modern human gut microbiome are abundantly represented only across unit X samples, showing that well-known beneficial gut commensals, such as Blautia, Dorea, Roseburia, Ruminococcus, Faecalibacterium and Bifidobacterium already populated the intestinal microbiome of Homo since as far back as the last common ancestor between humans and Neanderthals.


Over the past decade, microbiome research has highlighted the crucial role that the gut microbiome plays in human biology through its pleiotropic influence on many physiological functions, such as human development, immunity, metabolism and neurogenerative processes1. This body of knowledge has catalyzed interest in incorporating the gut microbiome into our evolutionary history, as an adaptive partner providing the necessary phenotypic plasticity to buffer dietary and environmental changes. Studies aimed at exploring the ancestral traits of the human gut microbiome are therefore encouraged, as a unique evolutionary perspective to improve our knowledge of gut microbiome assembly and interactions with the human host2.

The ancestral configuration of the human gut microbiome has generally been inferred by microbiome data stemming from contemporary populations found across all six human occupied continents who adhere to traditional lifestyles, such as the Hadza hunter-gatherers from Tanzania, the rural Bassa from Nigeria and rural Papuans from Papua New Guinea, among others3,4,5,6,7,8,9,10,11. However, since this research involved modern populations, no direct information on the ancient human gut microbiome structure can actually be provided.

Alternatively, ancient DNA (aDNA) analysis based on shotgun metagenomic sequencing is emerging as an attractive and reliable opportunity to directly investigate the microbial ecology of our ancestors12,13,14,15. Paleomicrobiological aDNA studies have traditionally been conducted on dental calculus and bones15,16,17,18, providing landmark information on ancient pathogens and oral microbial communities. However, given that stools are widely acknowledged as a proxy of the gut microbiome structure19, the metagenomics of aDNA from paleofeces, also known as coprolites, represents the only way to gain insight into the ancient human gut microbiome2. Pioneering studies in this direction have been carried out, providing next-generation sequencing data from modern human mummified intestinal contents and paleofeces20,21,22,23,24. Nevertheless, to the best of our knowledge, paleofecal samples older than 8,000 years have never being analyzed, leaving an important gap on the pre-historical human gut microbiome configuration.

In this scenario, we attempted to identify ancient human gut microbiome components by shotgun metagenomic analysis of aDNA extracted from archeological sedimentary samples (ES1 to ES7) from the stratigraphic unit (SU) X (subunit Xb-H44) of the Middle Paleolithic open-air site, El Salt (Alicante, Spain)25 (Fig. 1). The archeological setting of El Salt yielded evidence of recurrent occupation by Neanderthals, our closest evolutionary relatives, dated between 60.7 ± 8.9 and 45.2 ± 3.4 kya26,27. In particular, the sedimentary samples ES1-7 have been previously shown to include several millimetric phosphatic coprolites and fecal lipid biomarkers, namely coprostanol and 5ß-stigmastanol, with proportions suggesting a human origin25. These samples therefore represent, to our knowledge, the oldest known positive identification of human fecal matter. The present work also includes an additional seven new archeological sediments collected in 2018 as a control. Two were from SU X (subunit Xa and Xb, respectively) and the others from surrounding SUs, i.e., upper V (three samples), IX and XI (one sample each) (Fig. 2a). While SUs IX to XI are associated with rich archaeological assemblages, upper SU V yielded very few archaeological remains26. We found that samples positive for the presence of fecal biomarkers showed traces of both ancient human mtDNA and ancient components of the modern human gut microbiome. These components included so-called “old friends” and beneficial commensal inhabitants of modern human guts, providing unique insights into their relevance to the biology of the Homo lineage.

Fig. 1: The Middle Paleolithic site of El Salt (Spain).
figure 1

a, b General site setting. The yellow star marks the location of the excavated area. As can be seen in the photograph, the sedimentary deposit rests against a tall limestone wall. c, d Different views of the excavation area indicating the zones sampled for this study. Zone 1 includes samples V1-3 and Zone 2 all the rest.

Fig. 2: Pleistocene stratigraphic sequence of El Salt (Units V-XI), chronometric dates of the sampled units, and evidence of traces of ancient human mitochondrial DNA.
figure 2

a The 14 sediment samples included in this study are shown in red. Samples ES1 to ES7 (subunit Xb-H44) are from Sistiaga et al.25. b Red boxes, human mtDNA fragments as recovered from metagenomic sequencing data; red circles, confirmation of the presence of ancient human mtDNA through target capture and sequencing (please, see Methods for further details).

Results and discussion

Ancient DNA sequencing and damage assessment

DNA was extracted from 14 archeological sedimentary samples and prepared for shotgun metagenomics in a dedicated aDNA facility at the Laboratories of Molecular Anthropology and Microbiome Research in Norman (OK, USA) (see Methods). A total of 124,592,506 high-quality paired-end sequences were obtained by Illumina NextSeq sequencing and analyzed for bacterial aDNA. To remove contamination by modern DNA, which is one of the major complications in studies of ancient samples14,28,29, we evaluated the DNA damage pattern as compared with present-day DNA references. In particular, Skoglund and colleagues12 translated the pattern of cytosine deamination into a postmortem degradation score (PMDS), which provides information on whether a given sequence is likely to derive from a degraded aDNA molecule. Reads were aligned against all bacterial genomes of the NCBI database, and ancient bacterial reads were recovered by setting PMDS > 5, to minimize the probability of a sequence being from a present-day contaminating source12. An average of 6,836 sequences per sample (range, 279–17,901) were retained, corresponding to a small but consistent fraction of DNA being ancient and derived from bacteria (mean ± SD, 0.069% ± 0.029%) (Supplementary Table 1 and Supplementary Fig. 1). The same procedure was applied to extraction, library and PCR blanks, resulting in the retrieval of a minimal number of 144, 1, and 42 ancient bacterial sequences, respectively. Ancient reads from blanks were assigned to 116 bacterial species that showed no overlap with the sample dataset (Supplementary Data 1). When comparing the fraction of reads with PMDS > 5 per million reads between samples IX, Xa, ES1-7, Xb and XI (i.e., those positive for the presence of fecal biomarkers and/or associated with rich archaeological assemblages), and samples from SU V (i.e., with no or very few archeological remains), the first showed a greater abundance of PMDS > 5 reads (p-value = 0.01, Wilcoxon test) (Supplementary Fig. 2), possibly as a result of the presence of human fecal sediment.

Detection of ancient human mitochondrial DNA

In order to detect human aDNA traces in our sample set, we searched for human mitochondrial DNA (mtDNA) sequences in PMDS-filtered metagenomes obtained from the 14 archeological sedimentary samples. Ancient human mtDNA was detected in almost all ES1 to ES7 samples from SU X (Fig. 2b). No traces of mtDNA from other animals were detected. To strengthen these findings, all samples were subjected to target capture of mtDNA with a Neanderthal bait panel (Arbor Biosciences; see Methods), and subsequent sequencing on Illumina NextSeq platform. Based on this analysis, ES1, ES2, ES5 and Xb samples tested positive for the presence of ancient human mtDNA, showing >1000 human mtDNA reads with PMDS > 1, breath of coverage >10%, −Δ % ≥ 0.9 and modern contamination less than 2% (Fig. 2b and Supplementary Table 2)30. Taken together, this evidence strongly supports human origin for the El Salt samples, particularly those from SU X25.

Profiling of ancient prokaryotic DNA

As for prokaryotic aDNA, seventeen bacterial and one archaeal phyla were identified in the aDNA sedimentary record of El Salt, with different representation across SUs (Fig. 3). As expected for its wide distribution in nature31,32, Actinobacteria is the most represented phylum, with environmental species from Streptomycetaceae, Pseudonocardiaceae, Micromonosporaceae, Nocardiaceae, Mycobacteriaceae, Microbacteriaceae and Nocardioidaceae families being detected in almost all the SUs. Similarly, the vast majority of sediment samples share a number of ancient sequences assigned to Bacillaceae members, which are known to play fundamental roles in soil ecology, where they can persist up to thousands of years, if not longer, due to their ability to form resistant endospores33,34. Another large fraction of aDNA shared by most SUs includes Proteobacteria constituents, especially from Alphaproteobacteria (mainly Rhodobacteraceae, Rhodospirillaceae and Sphingomonadaceae families), Betaproteobacteria (mainly Comamonadaceae and Burkholderiaceae) and Gammaproteobacteria (with Xanthomonadaceae) classes. Again, these are cosmopolitan bacteria commonly found in both terrestrial and aquatic environments, as free-living organisms or symbionts in different hosts35,36. In light of their DNA damage pattern, it is reasonable to assume that these are truly ancient environmental bacteria that populated archeological sediments. The contamination of archeological remains by environmental bacteria is indeed well expected, as already documented in previous paleomicrobiological aDNA studies15,18,37. For the relative abundances of bacterial families detected across the samples, please see Supplementary Data 2.

Fig. 3: Ancient bacteria in sediment samples from El Salt.
figure 3

The phylogenetic tree was built with representative sequences of bacterial genera for which at least one species was present with more than four hits in one sample. Different colors indicate different phyla (or classes for Proteobacteria) as follows: cyan, Actinobacteria; blue, Bacteroidetes; red, Alphaprotebacteria; brown, Gammaproteobacteria; pink, Betaproteobacteria; purple, Deltaproteobacteria; yellow, Acidobacteria; green, Firmicutes; light-yellow, Planctomycetes; orange, Euryarchaeota; grey, others (including Armatimonadetes, Chlorobi, Chloroflexi, Cyanobacteria, Deinococcus-Thermus, Fusobacteria, Gemmatimonadetes, Nitrospirae, Spirochaetes, Synergistetes and Verrucomicrobia). Bacterial taxa belonging to families common to the gut microbiome of hominids are highlighted at different taxonomic level. A, Alistipes; P, Prevotella; B, Bacteroides; PB, Parabacteroides.

Putative components of the Neanderthal gut microbiome

Next, following an approach similar to Weyrich et al.18, who first characterized the oral microbiome from Neanderthal dental calculus, we focused our analysis on intestinal microorganisms. Specifically, in order to identify potential ancient human gut microbiome components, we searched for bacterial genera belonging to the 24 families that have recently been indicated as being common to the gut microbiome of hominids (i.e., Methanobacteriaceae, Bifidobacteriaceae, Coriobacteriaceae, Bacteroidaceae, Porphyromonadaceae, Prevotellaceae, Rikenellaceae, Tannerellaceae, Enterococcaceae, Lactobacillaceae, Streptococcaceae, Christensenellaceae, Clostridiaceae, Eubacteriaceae, Lachnospiraceae, Oscillospiraceae, Peptostreptococcaceae, Ruminococcaceae, Erysipelotrichaceae, Veillonellaceae, Desulfovibrionaceae, Succinivibrionaceae, Enterobacteriaceae and Spirochaetaceae)38,39,40,41,42,43,44,45. Accordingly, while harboring similar family-level gut microbiome profiles, humans and non-human hominids, including our closest living relatives—chimpanzees, can be differentiated on the basis of the particular pattern of associated gut microbiome genera (as well as species and strains) represented within these families45. This strong association between gut microbiome composition and host physiology—known as phylosymbiosis—is believed to be universal in mammals, essentially as a result of all the physical, chemical and immunological factors that differentiate the intestine of the host species (e.g., type of digestive organs, pH, oxygen level, host-derived molecules and immune system)46. According to our findings, 210 bacterial species belonging to hominid-associated gut microbiome families, as listed above, are represented in the aDNA from El Salt SU IX, X and XI, with the highest detection rate in samples from SU X and, particularly, in ES1 to ES7 (Fig. 4), for which a human-like host origin had been previously suggested25. In Supplementary Fig. 3, we provide the overall compositional profile of the El Salt samples from SU IX, X and XI restricted to the hominid-associated gut microbiome families, while in Supplementary Fig. 4, the proportions of these families are compared with those of the samples with no or very few archeological remains (i.e., from SU V). The compositional profile of samples from SU IX, X and XI was next compared to publicly available gut microbiomes from contemporary human populations as representative of different subsistence practices, such as Hadza and Matses hunter-gatherers, Tunapuco rural agriculturalists and western urbans from Italy and the US7,47. As shown by the Principal Coordinates Analysis of Bray-Curtis distances between the family-level profiles (Supplementary Fig. 5), the El Salt samples from SU IX, X and XI tend to cluster closer to Tunapuco and Matses, resembling more the “ancestral” human gut microbiome of rural agriculturalists and hunter-gatherers than the urban western gut microbiome7. However, as the degree of degradation of microbial DNA in ancient samples might be different for various gut microbiome components, any conclusions from these compositional data must be taken with due caution.

Fig. 4: Ancient components of the human gut microbiome in sediment samples from El Salt.
figure 4

The heat map shows the hit number distribution across sediment samples from the different stratigraphic units. Only ancient bacterial species with ≥ 2 hits in at least one sample were kept. See also Supplementary Data 3.

Further supporting a human-host origin of the bacterial species belonging to the hominid-associated gut microbiome families detected in the El Salt samples from SU IX, X and XI, feces or gastrointestinal tract are the first documented isolation source for 91 species out of 210 (43.3%), with 60 of these being classifiable as closely related to the human gut (Supplementary Data 3). In the latter subgroup, we can count several species from Lachnospiraceae (including well-known (beneficial) commensal inhabitants of modern human guts, such as Blautia, Coprococcus, Dorea, Fusicatenibacter and Roseburia spp.) and Ruminococcaeae families. Particularly, within Ruminococcaceae, we detected members of Anaerotruncus, Ruminococcus and Subdoligranulum genera, and the butyrate producer Faecalibacterium, one of the human commensal bacteria of greatest current interest, due to its very promising potential as a biomarker of a healthy gut microbiome48. Most of the aforementioned bacterial genera have been reported to account for the phylotypic diversity between human and non-human hominids, showing strong bias towards a human-host45. It is also worth remembering that most of these bacteria are able to produce short-chain fatty acids (mainly acetate and butyrate) from the fermentation of indigestible carbohydrates, through the establishment of complex syntrophic networks. Short-chain fatty acids are today considered metabolic and immunological gut microbiome players with a leading role in human physiology49. In addition, the Xb-H44 samples showed a high number of hits for Bacteroides, Parabacteroides, Alistipes and Bifidobacterium spp., other genera known to prevail in the human gut microbiome39,45. Interestingly, Bacteroides and Bifidobacterium have been shown to exhibit patterns of co-speciation with hominids45. For Bifidobacterium, this is particularly consistent with the propensity of this genus to be maternally inherited across generations. Being capable of metabolizing milk oligosaccharides and acting as a potent immunomodulator, the presence of vertically transmitted Bifidobacterium spp. in the infant gut could have provided important growth benefits to infant Hominidae50,51,52.

To further characterize the ancient microbial taxa detected in the El Salt samples, we applied the HOPS53-based approach recently used by Jensen et al.30. In short, all the reads were first annotated and, subsequently, the ancient origin of each taxon was authenticated by computing three indicators: (i) the fraction of reads with PMDS > 1, (ii) the negative difference proportion (−Δ %) of PMDS > 1 reads, and (iii) their deamination rate at 5′. Taxa showing more than 200 assigned reads, more than 50 reads with PMDS > 1, −Δ % = 1 and C-T transition at 5′ >10% were considered to be of ancient origin (see Table 1 and Supplementary Figs. 68 for MapDamage plots, coverage plots and edit distance distribution)30. This in-depth characterization of the microbial metagenomic reads from the El Salt samples allowed us to confirm the presence of several species belonging to the gut microbiome families of hominids (including, among others, Alistipes, Bifidobacterium, Desulfovibrio and Prevotella spp., and Faecalibacterium prausnitzii), showing a read profile consistent with their ancient origin.

Table 1 List of the 36 most abundant microbial taxa identified in the El Salt sediments, belonging to the hominid gut microbiome families.

As mentioned above, high amounts of coprostanol, a metabolite formed through hydrogenation of cholesterol by specific bacteria in the intestine of higher mammals, were found in some of the El Salt sediments from SU X, with proportions consistent with the presence of human fecal matter25. We therefore specifically looked for microorganisms capable of this metabolism in the aDNA from El Salt samples. To date, cholesterol-reducing capabilities associated with coprostanol conversion in feces have been suggested for Bifidobacterium, Collinsella, Bacteroides, Prevotella, Alistipes, Parabacteroides, Enterococcus, Lactobacillus, Streptococcus, Eubacterium, Lachnospiraceae (e.g., Coprococcus and Roseburia) and Ruminococcaceae (e.g., Anaerotruncus, Faecalibacterium, Ruminococcus and Subdoligranulum)54,55,56,57,58, which were all detected, at variable but substantial abundances, within the species belonging to the 24 gut microbiome families (as defined above) in Xa and Xb-H44 subunits. While lending support to the presence of coprostanol in the same layer as reported by Sistiaga et al.25, our findings on the representation of potential cholesterol-reducing bacteria in Neanderthal feces point to the microbial metabolism of cholesterol as an important function of the human gut microbiome for both modern and ancient humans, and suggest that relatively higher cholesterol intake has been a feature of the human diet at least since the Middle Pleistocene.

Finally, the remaining bacterial species belonging to the hominid gut microbiome families identified in El Salt sediments from SUs IX, X and XI could be sorted into two major source categories: human (or animal) oral and/or pathobiont, and environmental (see Supplementary Fig. 9). In particular, possibly consistent with evidence of dental caries and periodontal disease in Neanderthals18, we found traces of potential opportunistic pathogens (e.g., Methanobrevibacter oralis, Scardovia inopinata, Streptococcus parasanguinis, Streptococcus sanguinis, Pseudoramibacter alactolyticus, Catonella morbi, Johnsonella ignava, Lachnoanaerobaculum saburreum, Shuttleworthia satelles, Stomatobaculum longum, Treponema maltophilum, Treponema medium, Treponema socranskii and Treponema vincentii), which have been associated with modern oral and dental diseases in humans59,60,61,62,63,64,65,66,67,68.

Expectedly, the samples from the upper part of SU V (which are poor in archaeological remains) showed scarce and inconsistent presence of aDNA related to hominid-associated gut microbiome bacterial families. The highest hit counts were found for Clostridium perfringens, Paeniclostridium sordellii and Turicibacter sanguinis, with the first two being environmental opportunistic microorganisms historically associated with human gangrene and the last with acute appendicitis69,70,71. These findings further support the presence of potential human-like gut microbiome components as being unique to the samples from Xa and Xb, the only sedimentary layers that to date have shown traces of microscopic coprolites and fecal lipid biomarkers of presumed archaic human origin.

In conclusion, by reconstructing ancient bacterial profiles from El Salt Neanderthal feces-containing sediments, we propose the existence of a core human gut microbiome with recognizable coherence between Neanderthals and modern humans, whose existence would pre-date the split between these two lineages, i.e., in the early Middle Pleistocene72. Although the risk of fractional contamination by modern DNA can never be ruled out and our data must be taken with some caution, the identification of this ancient human gut microbiome core supports the existence of evolutionary symbioses with strong potential to have a major impact on our health. In particular, the presence of known short-chain fatty acid producers, such as Blautia, Dorea, Roseburia, Rumunicoccus, Subdoligranulum, Faecalibacterium and Bifidobacterium, among the gut microbiome of Neanderthals, provides a unique perspective on their relevance as keystone taxa to the biology and health of the Homo lineage. While the former are known to allow extra energy to be extracted from dietary fiber73, strengthening the relevance of plant foods in human evolution, Bifidobacterium could have provided benefits to archaic human mothers and infants as a protective and immunomodulatory microorganism. Furthermore, the detection of so-called “old friend” microorganisms74 as putative components of Neanderthal gut microbiome (e.g., Spirochaetaceae, Prevotella and Desulfovibrio) further supports the hypothesized ancestral nature of these human gut microbiome members, which are now disappearing in westernized populations3,4,5,6,7,8,9,10,11. In the current scenario where we are witnessing a wholescale loss of bacterial diversity in the gut microbiome of the cultural “west”, with the parallel rise in dysbiosis-related autoimmune and inflammatory disorders75, the identification of evolutionarily integral taxa of the human holobiont may benefit practical applications favoring their retention among populations living in or transitioning to increasingly microbially deplete contexts. Such therapeutic applications may in the near future include next-generation probiotics, prebiotics or other gut microbiome-tailored dietary interventions.


Site and sampling

All samples used for this study were collected from the archaeological site of El Salt, Alicante, Spain. The archaeological team led by B. Galván conducted the excavations under a government permit and following the Spanish heritage law (No. 16/1985, 25 June). All excavated material including the sedimentary material is interpreted as archaeological material so no further permits are required for the presented study. Loose sediment samples (5–10 g) were collected in plastic vials using sterilized spoons (one per sample) after thoroughly cleaning the excavation surface with a vacuum cleaner in order to guarantee removal of any recent dust or sediment blown in from a different location. Lab safety masks and nitrile gloves were used at all times. The samples were collected from two different zones of the current El Salt excavation area (see Fig. 1):

  1. 1.

    Zone 1. This is the upper excavation zone. Samples were collected from SU V, Facies 23 (one sample, V1) and Facies 24 (two samples, V2 and V3). This unit has been dated by OSL to 44.7 ± 3.2 ky BP76. Lithologically, it is composed of massive, loose yellowish-brown calcareous silt with coarse sand and isolated larger limestone and travertine clasts. Facies 23 is fine-grained, while Facies 24 (overlying Facies 23) also contains gravel. Unit V has yielded very few archaeological remains (bone fragments and technologically undiagnostic flint flakes).

  2. 2.

    Zone 2. This is the lower excavation zone. Samples were collected from SUs IX, Xa, Xb and XI, which are a stratified succession of sedimentary layers rich in Middle Paleolithic archaeological remains (charcoal, combustion features and burnt and unburnt bone and flint artifacts). From top to base:

    • Unit IX (one sample): is the uppermost layer in this succession. It is discontinuous across the excavation area, comprising a series of dark brown-black sandy silt lenses.

    • Unit Xa (one sample): dated by TL to 52.3 ± 4.6 ky BP76, this is a microstratified brownish-yellow deposit of loose calcareous silt sands with few larger clasts.

    • Unit Xb (eight samples): similar to Xa, also microstratified but darker (brown) and finer-grained (sandy silts). Seven samples from this unit (ES1-7) were collected from a microstratified combustion structure (H44) at the top of this layer that yielded human fecal biomarkers25. The other sample was collected from underlying sediment.

    • Unit XI (one sample): this is a layer of loose brown silty sand.

Ancient DNA extraction

All work was conducted at University of Oklahoma LMAMR ancient DNA laboratory according to the following protocols for coprolite-derived materials.

For DNA extraction, approximately 200 mg were subsampled from each sample material and incubated on a rotator with 400 µl of 0.5 M EDTA and 100 µl of proteinase K (QIAGEN) for 4 h. After that, the samples were subjected to bead-beating with 750 µl of PowerBead solution (QIAGEN) and then extracted using the MinElute PCR Purification kit (QIAGEN) with a modified protocol (method B) described in Hagan et al.77 and based on Dabney et al.78, including two cleaning steps before final elution into two 30 μl of EB buffer (QIAGEN).

Library preparation and sequencing

Shotgun sequencing indexing libraries were constructed using the NEBNext DNA Library Prep Master Mix Set for 454 (New England Biolabs), following the “BEST” (Blunt-End-Single-Tube) method79, with the hybridization of adapter oligos as per Meyer and Kircher80. Briefly, deaminated (C to U) bases were first partially removed (UDG-half) by uracil DNA glycosylase treatment using USER enzyme81. End overhangs were repaired, creating blunt-end phosphorylated regions for adapter ligation. Oligo adapters were ligated directly to blunt ends and filled in to create priming sites for index primers. After purification with a MinElute column (QIAGEN), indexed libraries were generated in triplicate for each sample using unique forward and reverse barcoded primers. See Supplementary Table 3 for adapter and oligo sequences. The triplicates were pooled, cleaned using Agencourt AMPure XP magnetic beads (Beckman Coulter), and then run on a Fragment Analyzer (Advanced Analytical) using the high sensitivity NGS standard protocol. Samples containing adapter dimers below the main peak for putative authentic endogenous DNA (i.e., 200–250 bp)82, were further cleaned using AMPure XP magnetic beads in a PEG/NaCl buffer83. Cleaned samples were sequenced on Illumina NextSeq 500 platform (Illumina) at University of Bologna (Bologna, Italy), using paired-end 2 × 75 bp chemistry in order to obtain >1 Gbp of sequences per sample. Quality score exceeded Q30 for more than 95% of the sequenced bases. Sequencing data was pre-processed by retaining only merged reads matching the forward and reverse barcodes with no mismatches using AdapterRemoval84.

Bioinformatics analysis

Sequences were analyzed using Burrows-Wheeler Aligner (BWA) aln algorithm and the entire set of bacterial and archaeal genomes available through NCBI RefSeq (downloaded on November 15th, 2017). In particular, we reduced the maximum accepted edit distance (i.e., the threshold of the maximum number of deletions, insertions, and substitutions needed to transform the reference sequence into the read sequence) to 1% (-n 0.01) and set the maximum number of gap opens (i.e., the threshold of the maximum number of gaps that can be initiated to match a given read to the reference) to 2, with long gap and seed length disabled (-e-1 -l 16500). These parameters are optimized for the specific types of errors generated by postmortem DNA damage during the alignment of ancient DNA to modern references, as indicated by Schubert and colleagues85. The aligned reads were further filtered for mapping quality >20, and only the hits with the best unique match (X0 = 1) were considered for analysis in order to minimize the number of false positives. In order to retrieve the entire phylogeny of the assignment, database sequences were previously annotated with the “Tax” tags of the NCBI database using the reference-annotator tool of the MEGAN utils package86. We then used the calmd program of the samtools suite to recompute the MD tags (containing alignment information, such as mismatches) for all datasets.

To discriminate ancient DNA from modern-day contamination, we calculated the postmortem degradation score (PMDS) distributions12. Sequences with PMDS > 5 were considered ancient (over 5,000 years ago), as reported by Skoglund et al.12, and used for further analysis. The outputs were transformed in sequence (.fasta) and annotation (.txt) files compatible with the QIIME command “”, in order to create a table that contained the phylogenetic classification and the abundance as number of reads for each specific taxon. This table was then collapsed at family, genus and species level using the command “”. The family-level relative abundance profiles of samples IX, Xa, ES1-7, Xb and XI were compared with publicly available data of the gut microbiota of human populations adhering to different subsistence strategies: urban Italians and Hadza hunter-gatherers from Tanzania (NCBI SRA, Bioproject ID PRJNA278393)47, urban US residents, Matses hunter-gatherers and Tunapuco rural agriculturalists from Peru (NCBI SRA, BioProject ID PRJNA268964)7. Shotgun sequence datasets were downloaded and processed as El Salt samples, without applying the PMDS filter. 16S rRNA gene representative sequences of bacterial genera for which at least one species was present with more than 4 hits in one El Salt sample, were downloaded from the SILVA repository and used to build a phylogenetic tree by MUSCLE87 and FastTree88. The tree was visualized using the GraPhlAn software89. Finally, bacterial species belonging to families that have recently been indicated as being common to the gut microbiome of hominids38,39,40,41,42,43,44,45, were specifically sought and visualized for their abundance across El Salt samples by a heat map using the R software. Species membership in other source categories (i.e., human (or animal) oral and/or pathobiont, and environmental) was inferred by searching in PubMed the original article in which the taxonomy was first assigned to that organism, as well as more recent articles reporting its habitat description.

Independent validation of taxonomic assignments

To validate the taxonomic assignments of the metagenomic reads recovered from the El Salt samples, we used the same procedure adopted by Jensen and colleagues30. Specifically, we combined results from samples IX, Xa, ES1-7, Xb and XI (i.e., those positive for the presence of fecal biomarkers and/or associated with rich archaeological assemblages), then aligned the assigned reads to their respective reference genomes and examined edit distances, coverage distributions, and postmortem DNA damage patterns14,53. For the 24 bacterial families identified as common to hominid gut microbiome, we chose to further investigate bacterial species with ≥200 assigned reads (including strain-specific reads), for which at least 50 reads showed PMDS > 1 and at least one mismatch in the first 10 bases with respect to the reference genome. We then aligned the taxon-specific reads to the respective reference genome from the NCBI RefSeq database using bwa aln. MapDamage was used to estimate deamination rates (Supplementary Fig. 6)90. The breadth and depth of coverage were calculated with bedtools91 and visualized with Circos92 (Supplementary Fig. 7). Edit distances for all reads and filtered for PMDS > 1 were extracted from the bam files with the samtools view93 and plotted in R (Supplementary Fig. 8). The negative difference proportion (−Δ %) was calculated considering the first 10 bases of reads with PMDS > 1. This metric was proposed by Hübler et al.53 as a measure of decline in the edit distance distribution, with a −Δ % value of 1 indicating a declining distribution associated with an ancient DNA profile. Indeed, correct taxonomic assignments generally show a continuously declining edit distance distribution with only a few mismatches, mostly resulting from aDNA damage or divergence of the ancient genome from the modern reference. On the other hand, the mapping to an incorrect reference is associated with an increased number of mismatches, highlighted by the analysis of the edit distance distribution.

mtDNA analysis and contamination estimate

In order to detect human mtDNA, a similar procedure combining BWA (same parameters as above) and the reference-annotator tool of the MEGAN utils package, was applied to the entire set of mitochondrial sequences listed at the MitoSeqs website (, including all the eukaryotic mitochondrial sequences available at the NCBI database. Only taxa detected in ancient sequences (i.e., with PMDS > 5) with more than 2 hits and not present in the control samples were retained. This procedure allowed us to detect ancient human traces beyond any reasonable doubt, eventually discarding more sequences than necessary.

In parallel, capture-enrichment for mtDNA sequencing was performed on the indexed libraries with a Neanderthal bait panel, as per the manufacturer’s protocol (version 4.01, Arbor Biosciences). In short, libraries were denatured, blocked and incubated with baits for 48 h. After purification with streptavidin-coated magnetic beads, enriched libraries were amplified and concentrated, before being subjected to a second round of capture. Final libraries were sequenced on an Illumina NextSeq 500 platform (Illumina) at University of Bologna (Bologna, Italy), as described above. As for read processing, we used Schmutzi94 to determine the endogenous consensus mtDNA sequence and to estimate present-day human contamination. Reads were mapped to the mt-Neanderthal reference sequence (NC_011137) and filtered for MAPQ ≥ 30. Haploid variants were called using the endoCaller program implemented in Schmutzi and only the variants with a posterior probability exceeding 50 on the PHRED scale (probability of error: 1/100,000) and breadth of coverage >10% of the total mitochondrial length were retained for further analysis. The PMDS profile of the reads was computed by PMDtools12. The negative difference proportion (−Δ %) was calculated using only reads with PMDS > 153. Contamination estimates were obtained using Schmutzi’s mtCont program and a database of putative modern contaminant mtDNA sequences. Samples with >1,000 PMDS > 1 reads, breadth of coverage >10%, −Δ % ≥ 0.9 and mtCont contamination less than 2% were considered to contain ancient human mtDNA.

Statistics and reproducibility

No replicates are included, all samples herein analyzed are unique.

Wilcoxon test was used to assess differences between samples IX, Xa, ES1-7, Xb and XI (i.e., those positive for the presence of fecal biomarkers and/or associated with rich archaeological assemblages), and samples from SU V (i.e., with no or very few archeological remains) in the number of PMDS > 5 reads, as well as in the relative abundances of the 24 families common to the gut microbiome of hominids38,39,40,41,42,43,44,45.

The significance of data separation in the Bray-Curtis-based Principal Coordinates Analysis between the family-level relative abundance profiles of samples IX, Xa, ES1-7, Xb and XI, and the gut microbiota of urban Italians and Hadza hunter-gatherers from Tanzania47, urban US residents, Matses hunter-gatherers and Tunapuco rural agriculturalists from Peru7 was tested using a permutation test with pseudo-F ratio.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

Sequencing data are accessible at the European Nucleotide Archive (ENA; project ID PRJEB41665). Source data are available as Supplementary Data. All sediment samples are readily available from the authors, subject to exhaustion.


  1. 1.

    Lynch, S. V. & Pedersen, O. The human intestinal microbiome in health and disease. N. Engl. J. Med. 375, 2369–2379 (2016).

    CAS  PubMed  Article  Google Scholar 

  2. 2.

    Davenport, E. R. et al. The human microbiome in evolution. BMC Biol. 15, 127 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  3. 3.

    De Filippo, C. et al. Impact of diet in shaping gut microbiota revealed by a comparative study in children from Europe and rural Africa. Proc. Natl Acad. Sci. USA 107, 14691–14696 (2010).

    PubMed  Article  PubMed Central  Google Scholar 

  4. 4.

    Tyakht, A. V. et al. Human gut microbiota community structures in urban and rural populations in Russia. Nat. Commun. 4, 2469 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  5. 5.

    Schnorr, S. L. et al. Gut microbiome of the Hadza hunter-gatherers. Nat. Commun. 5, 3654 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  6. 6.

    Martínez, I. et al. The gut microbiota of rural Papua New Guineans: composition, diversity patterns, and ecological processes. Cell Rep. 11, 527–538 (2015).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  7. 7.

    Obregon-Tito, A. J. et al. Subsistence strategies in traditional societies distinguish gut microbiomes. Nat. Commun. 6, 6505 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  8. 8.

    Sankaranarayanan, K. et al. Gut microbiome diversity among Cheyenne and Arapaho individuals from Western Oklahoma. Curr. Biol. 25, 3161–3169 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  9. 9.

    Girard, C., Tromas, N., Amyot, M. & Shapiro, B. J. Gut microbiome of the Canadian Arctic Inuit. mSphere 2, e00297–e00316 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  10. 10.

    Ayeni, F. A. et al. Infant and adult gut microbiome and metabolome in rural Bassa and urban settlers from Nigeria. Cell Rep. 23, 3056–3067 (2018).

    CAS  PubMed  Article  Google Scholar 

  11. 11.

    Jha, A. R. et al. Gut microbiome transition across a lifestyle gradient in Himalaya. PLoS Biol. 16, e2005396 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  12. 12.

    Skoglund, P. et al. Separating endogenous ancient DNA from modern day contamination in a Siberian Neandertal. Proc. Natl Acad. Sci. USA 111, 2229–2234 (2014).

    CAS  PubMed  Article  Google Scholar 

  13. 13.

    Moeller, A. H. et al. Cospeciation of gut microbiota with hominids. Science 353, 380–382 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. 14.

    Key, F. M., Posth, C., Krause, J., Herbig, A. & Bos, K. I. Mining metagenomic data sets for ancient DNA: recommended protocols for authentication. Trends Genet. 33, 508–520 (2017).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  15. 15.

    Philips, A. et al. Comprehensive analysis of microorganisms accompanying human archaeological remains. Gigascience 6, 1–13 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  16. 16.

    Warinner, C. et al. Pathogens and host immunity in the ancient human oral cavity. Nat. Genet. 46, 336–344 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  17. 17.

    Rasmussen, S. et al. Early divergent strains of Yersinia pestis in Eurasia 5,000 years ago. Cell 163, 571–582 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. 18.

    Weyrich, L. S. et al. Neanderthal behaviour, diet, and disease inferred from ancient DNA in dental calculus. Nature 544, 357–361 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  19. 19.

    Eckburg, P. B. et al. Diversity of the human intestinal microbial flora. Science 308, 1635–1638 (2005).

    PubMed  PubMed Central  Article  Google Scholar 

  20. 20.

    Tito, R. Y. et al. Phylotyping and functional analysis of two ancient human microbiomes. PLoS ONE 3, e3703 (2008).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  21. 21.

    Tito, R. Y. et al. Insights from characterizing extinct human gut microbiomes. PLoS ONE 7, e51146 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  22. 22.

    Lugli, G. A. et al. Ancient bacteria of the Ötzi’s microbiome: a genomic tale from the Copper Age. Microbiome 5, 5 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  23. 23.

    Santiago-Rodriguez, T. M. et al. Taxonomic and predicted metabolic profiles of the human gut microbiome in pre-Columbian mummies. FEMS Microbiol. Ecol. 92, fiw182 (2016).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  24. 24.

    Søe, M. J. et al. Ancient DNA from latrines in Northern Europe and the Middle East (500 BC-1700 AD) reveals past parasites and diet. PLoS ONE 13, e0195481 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  25. 25.

    Sistiaga, A., Mallol, C., Galván, B. & Summons, R. E. The Neanderthal meal: a new perspective using faecal biomarkers. PLoS ONE 9, e101045 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  26. 26.

    Galván, B. et al. “El Salt. The Last Neanderthals Of The Alicante Mountains (Alcoy, Spain)” in Pleistocene and Holocene hunter-gatherers in Iberia and the Gibraltar Strait. The current archaeological record, 380–388 (R. Sala Ramos, Ed., Burgos, Univ. de Burgos y Fundación Atapuerca, 2014).

  27. 27.

    Garralda, M. D. et al. Neanderthals from El Salt (Alcoy, Spain) in the context of the latest Middle Palaeolithic populations from the southeast of the Iberian Peninsula. J. Hum. Evol. 75, 1–15 (2014).

    PubMed  Article  PubMed Central  Google Scholar 

  28. 28.

    Green, R. E. et al. Analysis of one million base pairs of Neanderthal DNA. Nature 444, 330–336 (2006).

    CAS  PubMed  Article  Google Scholar 

  29. 29.

    Sampietro, M. L. et al. Tracking down human contamination in ancient human teeth. Mol. Biol. Evol. 23, 1801–1807 (2006).

    CAS  PubMed  Article  Google Scholar 

  30. 30.

    Jensen, T. Z. T. et al. A 5700 year-old human genome and oral microbiome from chewed birch pitch. Nat. Commun. 10, 5520 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  31. 31.

    Barka, E. A. et al. Taxonomy, physiology, and natural products of Actinobacteria. Microbiol. Mol. Biol. Rev. 80, 1–43 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  32. 32.

    Lewin, G. R. et al. Evolution and ecology of Actinobacteria and their bioenergy applications. Annu. Rev. Microbiol. 70, 235–254 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  33. 33.

    Vreeland, R. H., Rosenzweig, W. D. & Powers, D. W. Isolation of a 250 million-year-old halotolerant bacterium from a primary salt crystal. Nature 407, 897–900 (2000).

    CAS  PubMed  Article  Google Scholar 

  34. 34.

    Mandic-Mulec, I., Stefanic, P. & van Elsas, J. D. Ecology of Bacillaceae. Microbiol. Spectr. 3, TBS-0017-2013 (2015).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  35. 35.

    Aylward, F. O. et al. Comparison of 26 sphingomonad genomes reveals diverse environmental adaptations and biodegradative capabilities. Appl. Environ. Microbiol. 79, 3724–3733 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  36. 36.

    Simon, M. et al. Phylogenomics of Rhodobacteraceae reveals evolutionary adaptation to marine and non-marine habitats. ISME J. 11, 1483–1499 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  37. 37.

    Mann, A. E. et al. Differential preservation of endogenous human and microbial DNA in dental calculus and dentin. Sci. Rep. 8, 9822 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  38. 38.

    Bittar, F. et al. Gorilla gorilla gorilla gut: a potential reservoir of pathogenic bacteria as revealed using culturomics and molecular tools. Sci. Rep. 4, 7174 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  39. 39.

    Moeller, A. H. et al. Rapid changes in the gut microbiome during human evolution. Proc. Natl Acad. Sci. USA 111, 16431–16435 (2014).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  40. 40.

    Zhang, J. et al. A phylo-functional core of gut microbiota in healthy young Chinese cohorts across lifestyles, geography and ethnicities. ISME J. 9, 1979–1990 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  41. 41.

    Lloyd-Price, J., Abu-Ali, G. & Huttenhower, C. The healthy human microbiome. Genome Med. 8, 51 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  42. 42.

    Clayton, J. B. et al. The gut microbiome of nonhuman primates: Lessons in ecology and evolution. Am. J. Primatol. 80, e22867 (2018).

    PubMed  Article  Google Scholar 

  43. 43.

    Hicks, A. L. et al. Gut microbiomes of wild great apes fluctuate seasonally in response to diet. Nat. Commun. 9, 1786 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  44. 44.

    Nishida, A. H. & Ochman, H. Rates of gut microbiome divergence in mammals. Mol. Ecol. 27, 1884–1897 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  45. 45.

    Nishida, A. H. & Ochman, H. A great-ape view of the gut microbiome. Nat. Rev. Genet. 20, 195–206 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  46. 46.

    Amato, K. R. et al. Evolutionary trends in host physiology outweigh dietary niche in structuring primate gut microbiomes. ISME J. 13, 576–587 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  47. 47.

    Rampelli, S. et al. Metagenome sequencing of the Hadza Hunter-Gatherer gut microbiota. Curr. Biol. 25, 1682–1693 (2015).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  48. 48.

    Martín, R. et al. Functional characterization of novel Faecalibacterium prausnitzii strains isolated from healthy volunteers: a step forward in the use of F. prausnitzii as a next-generation probiotic. Front. Microbiol. 8, 1226 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  49. 49.

    Koh, A., De Vadder, F., Kovatcheva-Datchary, P. & Bäckhed, F. From dietary fiber to host physiology: short-chain fatty acids as key bacterial metabolites. Cell 165, 1332–1345 (2016).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  50. 50.

    O’Callaghan, A. & van Sinderen, D. Bifidobacteria and their role as members of the human gut microbiota. Front. Microbiol. 7, 925 (2016).

    PubMed  PubMed Central  Google Scholar 

  51. 51.

    Milani, C. et al. Genomics of the genus Bifidobacterium reveals species-specific adaptation to the glycan-rich gut environment. Appl. Environ. Microbiol. 82, 980–991 (2015).

    PubMed  Article  CAS  Google Scholar 

  52. 52.

    Lugli, G. A. et al. Reconstruction of the bifidobacterial pan-secretome reveals the network of extracellular interactions between bifidobacteria and the infant gut. Appl. Environ. Microbiol. 84, e00796–e00818 (2018).

    Article  Google Scholar 

  53. 53.

    Hübler, R. et al. HOPS: automated detection and authentication of pathogen DNA in archaeological remains. Genome Biol. 20, 280 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  54. 54.

    Lye, H. S., Rusul, G. & Liong, M. T. Removal of cholesterol by lactobacilli via incorporation and conversion to coprostanol. J. Dairy Sci. 93, 1383–1392 (2010).

    CAS  PubMed  Article  Google Scholar 

  55. 55.

    Gérard, P. Metabolism of cholesterol and bile acids by the gut microbiota. Pathogens 3, 14–24 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  56. 56.

    Shimizu, M., Hashiguchi, M., Shiga, T., Tamura, H. O. & Mochizuki, M. Meta-analysis: effects of probiotic supplementation on lipid profiles in normal to mildly hypercholesterolemic individuals. PLoS ONE 10, e0139795 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  57. 57.

    Zanotti, I. et al. Evidence for cholesterol-lowering activity by Bifidobacterium bifidum PRL2010 through gut microbiota modulation. Appl. Microbiol. Biotechnol. 99, 6813–6829 (2015).

    CAS  PubMed  Article  Google Scholar 

  58. 58.

    Antharam, V. C. et al. An integrated metabolomic and microbiome analysis identified specific gut microbiota associated with fecal cholesterol and coprostanol in Clostridium difficile infection. PLoS ONE 11, e0148824 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  59. 59.

    Moore, L. V. & Moore, W. E. Oribaculum catoniae gen. nov., sp. nov.; Catonella morbi gen. nov., sp. nov.; Hallella seregens gen. nov., sp. nov.; Johnsonella ignava gen. nov., sp. nov.; and Dialister pneumosintes gen. nov., comb. nov., nom. rev., anaerobic gram-negative bacilli from the human gingival crevice. Int. J. Syst. Bacteriol. 44, 187–192 (1994).

    CAS  PubMed  Article  Google Scholar 

  60. 60.

    Crociani, F., Biavati, B., Alessandrini, A., Chiarini, C. & Scardovi, V. Bifidobacterium inopinatum sp. nov. and Bifidobacterium denticolens sp. nov., two new species isolated from human dental caries. Int. J. Syst. Bacteriol. 46, 564–571 (1996).

    CAS  PubMed  Article  Google Scholar 

  61. 61.

    Willems, A. & Collins, M. D. Phylogenetic relationships of the genera Acetobacterium and Eubacterium sensu stricto and reclassification of Eubacterium alactolyticum as Pseudoramibacter alactolyticus gen. nov., comb. nov. Int. J. Syst. Bacteriol. 46, 1083–1087 (1996).

    CAS  PubMed  Article  Google Scholar 

  62. 62.

    Willis, S. G. et al. Identification of seven Treponema species in health- and disease-associated dental plaque by nested PCR. J. Clin. Microbiol. 37, 867–869 (1999).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  63. 63.

    Becker, M. R. et al. Molecular analysis of bacterial species associated with childhood caries. J. Clin. Microbiol. 40, 1001–1009 (2002).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  64. 64.

    Downes, J., Munson, M. A., Radford, D. R., Spratt, D. A. & Wade, W. G. Shuttleworthia satelles gen. nov., sp. nov., isolated from the human oral cavity. Int. J. Syst. Evol. Microbiol. 52, 1469–1475 (2002).

    PubMed  Google Scholar 

  65. 65.

    Xu, P. et al. Genome of the opportunistic pathogen Streptococcus sanguinis. J. Bacteriol. 189, 3166–3175 (2007).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  66. 66.

    Horz, H. P. & Conrads, G. Methanogenic Archaea and oral infections - ways to unravel the black box. J. Oral Microbiol. 3; (2011).

  67. 67.

    Hedberg, M. E. et al. Lachnoanaerobaculum gen. nov., a new genus in the Lachnospiraceae: characterization of Lachnoanaerobaculum umeaense gen. nov., sp. nov., isolated from the human small intestine, and Lachnoanaerobaculum orale sp. nov., isolated from saliva, and reclassification of Eubacterium saburreum (Prevot 1966) Holdeman and Moore 1970 as Lachnoanaerobaculum saburreum comb. nov. Int. J. Syst. Evol. Microbiol. 62, 2685–2690 (2012).

    PubMed  PubMed Central  Article  Google Scholar 

  68. 68.

    Sizova, M. V. et al. Stomatobaculum longum gen. nov., sp. nov., an obligately anaerobic bacterium from the human oral cavity. Int. J. Syst. Evol. Microbiol. 63, 1450–1456 (2013).

    PubMed  PubMed Central  Article  Google Scholar 

  69. 69.

    Welch, W. H. & Nuttall, G. H. F. A gas-producing bacillus (Bacillus aerogenes capsulatus, Nov, Spec.) capable of rapid development in the body after death. Bull. John Hopkins Hosp. Baltim. 3, 81–91 (1891).

    Google Scholar 

  70. 70.

    Bosshard, P. P., Zbinden, R. & Altwegg, M. Turicibacter sanguinis gen. nov., sp. nov., a novel anaerobic, Gram-positive bacterium. Int. J. Syst. Evol. Microbiol. 52, 1263–1266 (2002).

    CAS  PubMed  Google Scholar 

  71. 71.

    Aldape, M. J., Bryant, A. E. & Stevens, D. L. Clostridium sordellii infection: epidemiology, clinical findings, and current perspectives on diagnosis and treatment. Clin. Infect. Dis. 43, 1436–1446 (2006).

    CAS  PubMed  Article  Google Scholar 

  72. 72.

    Prüfer, K. et al. A high-coverage Neandertal genome from Vindija Cave in Croatia. Science 358, 655–658 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  73. 73.

    Kolodziejczyk, A. A., Zheng, D. & Elinav, E. Diet-microbiota interactions and personalized nutrition. Nat. Rev. Microbiol. 7, 742–753 (2019).

    Article  CAS  Google Scholar 

  74. 74.

    Rook, G. A. 99th Dahlem conference on infection, inflammation and chronic inflammatory disorders: darwinian medicine and the ‘hygiene’ or ‘old friends’ hypothesis. Clin. Exp. Immunol. 160, 70–79 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  75. 75.

    Duvallet, C., Gibbons, S. M., Gurry, T., Irizarry, R. A. & Alm, E. J. Meta-analysis of gut microbiome studies identifies disease-specific and shared responses. Nat. Commun. 8, 1784 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  76. 76.

    Galván, B. et al. New evidence of early Neanderthal disappearance in the Iberian Peninsula. J. Hum. Evol. 75, 16–27 (2014).

    PubMed  Article  Google Scholar 

  77. 77.

    Hagan, R. W. et al. Comparison of extraction methods for recovering ancient microbial DNA from paleofeces. Am. J. Phys. Anthropol. 171, 275–284 (2019).

    PubMed  Article  Google Scholar 

  78. 78.

    Dabney, J. et al. Complete mitochondrial genome sequence of a Middle Pleistocene cave bear reconstructed from ultrashort DNA fragments. Proc. Natl Acad. Sci. USA 110, 15758–15763 (2013).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  79. 79.

    Carøe, C. et al. Single-tube library preparation for degraded DNA. Methods Ecol. Evol. 9, 410–419 (2018).

    Article  Google Scholar 

  80. 80.

    Meyer, M. & Kircher, M. Illumina sequencing library preparation for highly multiplexed target capture and sequencing. Cold Spring Harb. Protoc. 2010, pdb.prot5448 (2010).

    PubMed  Article  PubMed Central  Google Scholar 

  81. 81.

    Rohland, N., Harney, E., Mallick, S., Nordenfelt, S. & Reich, D. Partial uracil-DNA-glycosylase treatment for screening of ancient DNA. Philos. Trans. R. Soc. Lond. B Biol. Sci. 370, 20130624 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  82. 82.

    Ziesemer, K. A. et al. Intrinsic challenges in ancient microbiome reconstruction using 16S rRNA gene amplification. Sci. Rep. 5, 16498 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  83. 83.

    Rohland, N. & Reich, D. Cost-effective, high-throughput DNA sequencing libraries for multiplexed target capture. Genome Res. 22, 939–946 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  84. 84.

    Lindgreen, S. AdapterRemoval: easy cleaning of next-generation sequencing reads. BMC Res. Notes 5, 337 (2012).

    PubMed  PubMed Central  Article  Google Scholar 

  85. 85.

    Schubert, M. et al. Improving ancient DNA read mapping against modern reference genomes. BMC Genomics 13, 178 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  86. 86.

    Huson, D. H. et al. MEGAN community edition - interactive exploration and analysis of large-scale microbiome sequencing data. PLoS Comput. Biol. 12, e1004957 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  87. 87.

    Edgar, R. C. MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinformatics 5, 113 (2004).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  88. 88.

    Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree: computing large minimum evolution trees with profiles instead of a distance matrix. Mol. Biol. Evol. 26, 1641–1650 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  89. 89.

    Asnicar, F., Weingart, G., Tickle, T. L., Huttenhower, C. & Segata, N. Compact graphical representation of phylogenetic data and metadata with GraPhlAn. PeerJ 3, e1029 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  90. 90.

    Jónsson, H., Ginolhac, A., Schubert, M., Johnson, P. L. & Orlando, L. mapDamage2.0: fast approximate Bayesian estimates of ancient DNA damage parameters. Bioinformatics 29, 1682–1684 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  91. 91.

    Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  92. 92.

    Krzywinski, M. et al. Circos: an information aesthetic for comparative genomics. Genome Res. 19, 1639–1645 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  93. 93.

    Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  94. 94.

    Renaud, G., Slon, V., Duggan, A. T. & Kelso, J. Schmutzi: estimation of contamination and endogenous mitochondrial consensus calling for ancient DNA. Genome Biol. 16, 224 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

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We thank F. D’Amico (Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy) and E. Cilli (Department of Cultural Heritage, University of Bologna, Ravenna, Italy) for their valuable help in library preparation. This research was supported by the US National Institutes of Health, grant number R01GM089886 (C.W. and C.L.). S.B. was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 724046 - SUCCESS). Archaeological research at El Salt is funded by Spanish I+D Project HAR2008-06117/HIST (C.M., C.H. and B.G.).

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M.C., S.T., S.R., S.L.S. and C.W.: conceptualization; C.M., C.H., B.G. and A.S.: field work, excavation, and sampling; S.L.S., C.A.H. and S.T.: DNA extraction and library preparation; A.A.: sequencing; S.R.: bioinformatics analysis; P.B., M.C., C.L., C.W. and S.B.: resources; M.C. and S.L.S.: supervision; M.C., S.T. and S.R.: writing—original draft; C.W., C.L., S.B., C.M., A.S., E.B., A.A., C.A.H. and S.L.S.: writing—review & editing. All authors gave final approval for publication.

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Correspondence to Stephanie L. Schnorr or Marco Candela.

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Rampelli, S., Turroni, S., Mallol, C. et al. Components of a Neanderthal gut microbiome recovered from fecal sediments from El Salt. Commun Biol 4, 169 (2021).

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