Assessing Metagenomic Signals Recovered from Lyuba, a 42,000-Year-Old Permafrost-Preserved Woolly Mammoth Calf

Abstract

The reconstruction of ancient metagenomes from archaeological material, and their implication in human health and evolution, is one of the most recent advances in paleomicrobiological studies. However, as for all ancient DNA (aDNA) studies, environmental and laboratory contamination need to be specifically addressed. Here we attempted to reconstruct the tissue-specific metagenomes of a 42,000-year-old, permafrost-preserved woolly mammoth calf through shotgun high-throughput sequencing. We analyzed the taxonomic composition of all tissue samples together with environmental and non-template experimental controls and compared them to metagenomes obtained from permafrost and elephant fecal samples. Preliminary results suggested the presence of tissue-specific metagenomic signals. We identified bacterial species that were present in only one experimental sample, absent from controls, and consistent with the nature of the samples. However, we failed to further authenticate any of these signals and conclude that, even when experimental samples are distinct from environmental and laboratory controls, this does not necessarily indicate endogenous presence of ancient host-associated microbiomic signals.

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G C A T
T A C G
G C A T
Article
Assessing Metagenomic Signals Recovered from
Lyuba, a 42,000-Year-Old Permafrost-Preserved
Woolly Mammoth Calf
Giada Ferrari 1, 2, *ID , Heidi E. L. Lischer 3,4, Judith Neukamm 1,5 ID , Enrique Rayo 1,
Nicole Borel 6, Andreas Pospischil 6, Frank Rühli 1, Abigail S. Bouwman 1
and Michael G. Campana 1, 7, *ID
1Institute of Evolutionary Medicine, University of Zurich, 8057 Zurich, Switzerland;
judith.neukamm@iem.uzh.ch (J.N.); enrique.rayo@iem.uzh.ch (E.R.); frank.ruehli@iem.uzh.ch (F.R.);
abigail.bouwman@iem.uzh.ch (A.S.B.)
2Centre for Ecological and Evolutionary Synthesis (CEES), Department of Biosciences, University of Oslo,
0316 Oslo, Norway
3Institute of Evolutionary Biology and Environmental Studies, University of Zurich,
8057 Zurich, Switzerland; heidi.lischer@ieu.uzh.ch
4Swiss Institute of Bioinformatics (SIB), 1015 Lausanne, Switzerland
5Institute for Archaeological Sciences, University of Tübingen, 72070 Tübingen, Germany
6Institute of Veterinary Pathology, University of Zurich, 8057 Zurich, Switzerland;
n.borel@access.uzh.ch (N.B.); apos@vetpath.uzh.ch (A.P.)
7Center for Conservation Genomics, Smithsonian Conservation Biology Institute, Washington,
DC 20008, USA
*Correspondence: giada.ferrari@ibv.uio.no (G.F.); campanam@si.edu (M.G.C.); Tel.: +47-22-85-50-65 (G.F.);
+1-202-633-4183 (M.G.C.)
Received: 2 August 2018; Accepted: 30 August 2018; Published: 31 August 2018


Abstract:
The reconstruction of ancient metagenomes from archaeological material, and their
implication in human health and evolution, is one of the most recent advances in paleomicrobiological
studies. However, as for all ancient DNA (aDNA) studies, environmental and laboratory
contamination need to be specifically addressed. Here we attempted to reconstruct the tissue-specific
metagenomes of a 42,000-year-old, permafrost-preserved woolly mammoth calf through shotgun
high-throughput sequencing. We analyzed the taxonomic composition of all tissue samples together
with environmental and non-template experimental controls and compared them to metagenomes
obtained from permafrost and elephant fecal samples. Preliminary results suggested the presence of
tissue-specific metagenomic signals. We identified bacterial species that were present in only one
experimental sample, absent from controls, and consistent with the nature of the samples. However,
we failed to further authenticate any of these signals and conclude that, even when experimental
samples are distinct from environmental and laboratory controls, this does not necessarily indicate
endogenous presence of ancient host-associated microbiomic signals.
Keywords:
ancient DNA; Mammuthus primigenius; microbiome; environmental DNA; DNA contamination
1. Introduction
The field of paleomicrobiology has experienced significant advances thanks to the development of
high-throughput DNA sequencing techniques: From the first attempts to extract ancient DNA (aDNA)
from mummified tissues [
1
], to validating the presence of pathogens [
2
–
4
], to reconstructing ancient
bacterial genomes [
5
,
6
], to recovering entire ancient microbial communities [
7
,
8
]. Given the importance
Genes 2018,9, 436; doi:10.3390/genes9090436 www.mdpi.com/journal/genes

Genes 2018,9, 436 2 of 17
of commensal microbes in human health [
9
–
11
], increasing numbers of microbiome studies have
been conducted in recent years. Consequently, much attention has also been given to the taxonomic
reconstruction of ancient microbiomes, with a particular focus on human fecal and oral microbiomes
using coprolites [
12
] and dental calculus [
7
,
8
,
13
]. Such approaches offer an ideal opportunity to observe
the evolution of these site-specific microbial communities over time.
Since the majority of ancient microbiome studies rely on archaeological material, particular
attention needs to be given to the issue of environmental exposure to closely related soil taxa.
The presence of DNA from unsequenced or poorly characterized environmental microorganisms
potentially, related to pathogens can confound paleomicrobiological studies [
14
,
15
]. Such issues are
even more prominent when attempting to reconstruct entire ancient microbial communities and
authentication procedures need therefore to address environmental signals [
16
,
17
]. Additionally,
the same issues can be caused by contaminants in laboratory reagents [18–20].
Here, we illustrate the issue of environmental and laboratory contamination by attempting
to reconstruct tissue-specific ancient metagenomes from a permafrost-preserved woolly mammoth
(Mammuthus primigenius, Blumenbach 1799) calf. Asian elephants (Elephas maximus), the closest living
relatives of woolly mammoths [
21
], are known to be affected by a number of microbial diseases, such as
elephant endotheliotropic herpesviruses [
22
] or tuberculosis [
23
]. We therefore set out to investigate
whether this was the case for woolly mammoths too, and chose a shotgun metagenomics approach,
in order to capture the entire microbial diversity.
The calf, originally named “Lyuba”, was discovered by Nenets reindeer herders in 2007,
exposed on a point bar along the Yuribey river on the Yamal peninsula, northwestern Siberia
(68◦380N, 71◦400E)
[
24
,
25
]. Lyuba (Figure 1) is the best preserved mummified woolly mammoth
ever found, presenting only slight skin lesions [
24
]. Radioisotopic analyses of bone collagen indicate
that it lived approximately 41,800
14
C years BP [
25
], and anatomical investigations revealed that
the calf was female and in a good nutritional state [
24
,
25
]. The age at death was estimated at
30–35 days, based on a dentin neonatal line and postnatal dentin increments on its deciduous teeth [
26
].
Vivianite (hydrated iron phosphate), was found in Lyuba’s trachea and bronchi, suggesting the cause
of death to be suffocation after inhalation of mud [
25
]. Due to Lyuba’s accidental cause of death, good
health, and excellent preservation, several studies have been performed on its remains, since findings
can potentially be extended to juvenile woolly mammoths in general.
Genes 2018, 9, x FOR PEER REVIEW 2 of 17
the taxonomic reconstruction of ancient microbiomes, with a particular focus on human fecal and
oral microbiomes using coprolites [12] and dental calculus [7,8,13]. Such approaches offer an ideal
opportunity to observe the evolution of these site-specific microbial communities over time.
Since the majority of ancient microbiome studies rely on archaeological material, particular
attention needs to be given to the issue of environmental exposure to closely related soil taxa. The
presence of DNA from unsequenced or poorly characterized environmental microorganisms
potentially, related to pathogens can confound paleomicrobiological studies [14,15]. Such issues are
even more prominent when attempting to reconstruct entire ancient microbial communities and
authentication procedures need therefore to address environmental signals [16,17]. Additionally, the
same issues can be caused by contaminants in laboratory reagents [18–20].
Here, we illustrate the issue of environmental and laboratory contamination by attempting to
reconstruct tissue-specific ancient metagenomes from a permafrost-preserved woolly mammoth
(Mammuthus primigenius, Blumenbach 1799) calf. Asian elephants (Elephas maximus), the closest living
relatives of woolly mammoths [21], are known to be affected by a number of microbial diseases, such
as elephant endotheliotropic herpesviruses [22] or tuberculosis [23]. We therefore set out to
investigate whether this was the case for woolly mammoths too, and chose a shotgun metagenomics
approach, in order to capture the entire microbial diversity.
The calf, originally named “Lyuba”, was discovered by Nenets reindeer herders in 2007, exposed
on a point bar along the Yuribey river on the Yamal peninsula, northwestern Siberia (68°38′ N, 71°40′ E)
[24,25]. Lyuba (Figure 1) is the best preserved mummified woolly mammoth ever found, presenting
only slight skin lesions [24]. Radioisotopic analyses of bone collagen indicate that it lived
approximately 41,800
14
C years BP [25], and anatomical investigations revealed that the calf was
female and in a good nutritional state [24,25]. The age at death was estimated at 30–35 days, based
on a dentin neonatal line and postnatal dentin increments on its deciduous teeth [26]. Vivianite
(hydrated iron phosphate), was found in Lyuba’s trachea and bronchi, suggesting the cause of death
to be suffocation after inhalation of mud [25]. Due to Lyuba’s accidental cause of death, good health,
and excellent preservation, several studies have been performed on its remains, since findings can
potentially be extended to juvenile woolly mammoths in general.
Figure 1. Lateral right view of Lyuba (photo credit: Daniel C. Fisher. Museum of Paleontology,
University of Michigan, Ann Arbor, MI, USA). From Reference [27], reprinted by permission of John
Wiley & Sons, Inc. (Hoboken, NJ, USA).
Figure 1.
Lateral right view of Lyuba (photo credit: Daniel C. Fisher. Museum of Paleontology,
University of Michigan, Ann Arbor, MI, USA). From Reference [
27
], reprinted by permission of
John Wiley & Sons, Inc. (Hoboken, NJ, USA).

Genes 2018,9, 436 3 of 17
Fisher and colleagues [
25
] argue that the point bar on which Lyuba was found was not likely to
represent the site of death or preservation. They conclude that an ice-out flooding during the previous
summer is the most likely natural process to have moved the calf to its discovery location. Therefore,
Lyuba must have endured at least one cycle of thawing and freezing before discovery, a finding that
has also been corroborated by histological analysis [27].
Dissections were performed on Lyuba in 2008 and 2009 [
25
], and several samples, including
intestinal, peritoneal, muscle, and fat tissues, and vivianite, were obtained for further analysis. Here,
we attempted to reconstruct these samples’ metagenomes through shotgun high-throughput DNA
sequencing. Despite displaying a large component derived from environmental and laboratory
contamination, the tissue-specific metagenomes were taxonomically different from non-template and
environmental controls. Furthermore, some bacterial species were only associated with one specific
tissue, consistent with a potential bacterial infection or a tissue-specific microbiomic signal. However,
we failed to further authenticate any of these signals and conclude that, in this case, all detected signals
were the result of laboratory or environmental contamination. Caution and strict validation procedures
are therefore recommended even under ideal morphological preservation circumstances and when
comparison with laboratory and environmental control samples suggest endogenous presence of
microbial species.
2. Materials and Methods
2.1. Samples
The samples used in this study (donated by Daniel C. Fisher, Museum of Paleontology,
University of Michigan,
1109 Geddes Ave., Ann Arbor, MI 48109-1079, USA) were obtained during
endoscopical examination, and partial dissections performed in 2008 and 2009, and described in
References [
25
,
27
]. The authors were not present during these procedures, but received the samples
at a later stage. This study was conducted with written permission of the sample donors and of
the International Mammoth Committee. Lyuba was kept frozen during the time intervals between
discovery and the two dissections. The samples used in this study were previously labeled as follows:
Abdominal oblique muscle, abdominal subcutaneous fat and muscle, intestinal tissue (two samples),
caecum, omentum, peritoneum, cheek fat, and vivianite.
2.2. DNA Extraction
The samples’ surfaces were cleaned with a 1% NaOCl solution and UV-irradiated for 10 min
to reduce contamination. One hundred milligrams of homogenized tissue were digested at 55
◦
C
for 18 h, followed by three days rotating at room temperature, in 1 mL of extraction buffer (50 mM
Tris-HCl, 100 mM NaCl, 25 mM EDTA, 2% SDS and 0.5 mg/mL proteinase K). Following centrifugation,
the supernatant was extracted twice with a 25:24:1 phenol, chloroform, and isoamyl alcohol mixture,
followed by a final chloroform step. DNA was isolated using QIAquick spin columns (QIAGEN,
Hombrechtikon, Switzerland), with two elutions in 30
µ
L EB buffer and reduced centrifugation speed
(6–10 krpm) to prevent loss of short DNA fragments. Extractions were performed in a dedicated aDNA
clean laboratory at the University of Zurich [
28
], which has an independent HEPA (High Efficiency
Particulate Air) air filtration system, is physically separated from all laboratories in which PCR is
performed, and follows established anti-contamination protocols [
29
,
30
], including, unidirectional
work flows, overhead UV lights, regular sterilization of all work surfaces, the use of full body suits,
masks, and gloves by all researchers, and the parallel processing of non-template controls.
2.3. Library Preparation and Sequencing
Ten microliters of extract or extraction blank were converted into double-indexed Illumina
sequencing libraries following a protocol optimized for aDNA [
31
,
32
]. While all samples had unique
index pairs, some shared a single index. Sample-specific indexes were added to each library via

Genes 2018,9, 436 4 of 17
10 cycles of PCR amplification [
32
]. Blunt-end repair, adapter ligation and set up of indexing PCRs
were performed in an aDNA clean laboratory and non-template library blanks were generated in
parallel. In order to increase library concentrations to 10
13
copies/
µ
L, a re-amplification step was
performed on all indexed libraries in 100
µ
L reactions containing 1 unit AccuPrime
™
Pfx DNA
polymerase (Thermo Fisher Scientific, Reinach, Switzerland), 1
×
AccuPrime
™
Pfx reaction mix,
0.3
µ
M primers IS5 and IS6 [
31
] and 0.5–4
µ
L library template with the following thermal profile:
Initial denaturation at 95
◦
C for 2 min, 9–18 cycles of denaturation at 95
◦
C for 15 s, annealing at 60
◦
C
for 30 s and elongation at 68
◦
C for 1 min, followed by a final elongation at 65
◦
C for 5 min. Libraries
were purified with MinElute spin columns (QIAGEN, Hombrechtikon, Switzerland) following the
manufacturer’s instructions. Quantitative PCR and analysis on an Agilent 2100 Bioanalyzer DNA 1000
chip were used to assess library qualities. Libraries were pooled equally and sequenced on a MiSeq
(Illumina, San Diego, CA, USA) with paired-end 150 bp reads, as well as on 1.5 lanes of a HiSeq2500
(Illumina) with paired-end 125 bp reads and v4 chemistry by the Functional Genomics Center Zurich
(Zurich, Switzerland). While all samples were sequenced on the same MiSeq lane, individuals which
shared an index were separated across the two HiSeq lanes to limit index switching [32].
2.4. Sequence Quality Control and Filtering
All Illumina paired-end reads (MiSeq and HiSeq) were processed using the same pipelines. Raw reads
were demultiplexed using DeML [
33
] with default settings. Due to low-quality p5 sequences, the HiSeq
data were demultiplexed using only the p7 adapter sequence. Trimmomatic 0.33 [
34
] was used to remove
adapter sequences and artifacts with the followingparameters: Maximum seed mismatches = 2, palindrome
clip threshold = 30, simple clip threshold = 7, minimum adapter
length = 5,
and retaining reverse reads.
Leading and trailing bases below Phred quality score 3 were removed, reads were scanned using 4 bp
sliding windows and trimmed when average Phred quality score fell below 15. Trimmed reads shorter
than 25 bp were discarded. Surviving paired reads were merged with SeqPrep [
35
] as described in
Reference [
36
] to increase sequence quality. Merged reads shorter than 25 bp and unmerged reads were
discarded. Finally reads qualities were assessed using FastQC 0.10.1 [37].
2.5. Mapping to Reference Genomes and Deamination Pattern Analysis
Reads were mapped to the African elephant (Loxodonta africana) nuclear genome (NW_003573420.1),
the woolly mammoth (M. primigenius) mitochondrial genome (EU155210.1), and the human reference
genome (GRCh38.p2) using the Burrows-Wheeler Aligner (BWA) 0.7.12 aln algorithm [
38
], with disabled
seeding, increased gap open (-o 2), and reduced edit distance (-i 0) as recommended in Reference [
39
].
Duplicates were removed with Picard 2.1.0 [
40
] with validation stringency set to lenient, and filtering for
a minimal mapping quality of 25 was performed with SAMtools 1.3.1 [
41
]. DNA deamination patterns of
mapped reads were analyzed with mapDamage2 2.0.6 [42].
2.6. Mitogenome Analysis
MiSeq and HiSeq reads, that mapped to the woolly mammoth mitochondrial genome for all
samples, excluding vivianite, were merged into a single BAM file, duplicates were marked as described
above and a consensus sequence was built using SAMtools 1.3.1 [
41
]. LASTZ [
43
] was used to
compare the obtained consensus sequence with the Lyuba mitochondrial genome published by
Enk and colleagues [44] (KX027526.1).
2.7. Shotgun Metagenomic Analysis
From this step onwards, published Illumina shotgun metagenomes from two Asian elephant
(E. maximus) fecal samples (SRP040073) [
45
] and two Russian permafrost samples (SRP049520) [
46
]
obtained from the Sequence Read Archive were included. The elephant fecal samples were
obtained from three-week-old and six-year-old Asian elephants (E. maximus). No other comparable
elephant metagenomic datasets are currently available. The permafrost samples originated from

Genes 2018,9, 436 5 of 17
lake sediment (alluvium) from the Panteleikha River floodplain and from the late Pleistocene Ice
Complex on the Omolon River, both located in northeast Siberia. These reads were processed
using the same parameters as for the Lyuba samples. Quality-controlled, merged reads from the
libraries generated here and the reference data were deduplicated by clustering and removing all
exact matches using CD-HIT-EST 4.6 [
47
]. Deduplicated reads were compared to the National
Center for Biotechnology Information (NCBI) nucleotide collection (downloaded January 2018) using
the megablast algorithm
(BLAST 2.6.0+ [48])
with default parameters. To verify megablast species
identifications, we additionally analyzed the Lyuba, vivianite, and laboratory controls with MALT [
49
]
using a curated reference database consisting of 5242 complete bacterial genomes available in NCBI
RefSeq (December 2015). The BlastN algorithm with SemiGlobal alignment was used. Megablast and
MALT results were visualized with MEGAN6 (builds 6.11.6 or 6.10.6, respectively) [
50
]. The megablast
results were analyzed using MEGAN default Lowest Common Ancestor (LCA) parameters, while the
MALT results used the following options: –topPercent 1.0, –minSupportPercent 0, and –minSupport 5.
Sample comparisons used normalized counts to control for variation in sequencing depth.
2.8. 16S Metagenomic Analysis
16S metagenomic analysis was performed using the QIIME2 (version 2018.4) [
51
] package and
its associated plugins. All analyses were performed using default settings unless otherwise specified.
Deduplicated reads from the novel and published shotgun libraries were closed-reference clustered at
99% sequence identity to the SILVA database release 132 [
52
] using VSEARCH [
53
]. Retained sequences
were aligned and a phylogenetic tree was constructed using MAFFT [
54
] and FastTree [
55
]. The library
blank was excluded from diversity metric calculations because only nine sequences were retained after
clustering. Phylogenetic and non-phylogenetic diversity metrics were calculated with rarefication to
149 and 945 sequences, corresponding to the next two smallest sample sizes (extraction blank and
intestinal tissue 2, respectively) from our novel Lyuba dataset. Principal Coordinate Analyses of
Bray-Curtis, Jaccard, and Unweighted UniFrac distances were visualized using EMPeror [56,57].
Clustered sequences were classified according to the SILVA consensus taxonomy (all levels) using
VSEARCH [
53
] (99% nucleotide identity). Clustered sequences were exported from QIIME 2 and analyzed
using SourceTracker 2 [
58
]. All mammoth tissue samples were classified as ‘sinks’, while the permafrost,
blanks, feces and vivianite samples were treated as ‘sources’. Due to the very small sample sizes, we did
not rarefy the sources and rarefied the sinks to 100. All other parameters used their default values.
2.9. MetaPhlAn2 Analysis
We also analyzed the deduplicated metagenomes using MetaPhlAn2 (version 2.7.7) under default
settings [
59
]. We generated heat maps clustering the individuals and observed species-level taxa using
Euclidean distances.
2.10. Authentication of Bacterial Reads
In order to test the authenticity of overrepresented bacterial species in the omentum and cheek fat
samples, reads were mapped against the reference genomes of several Yersinia species for the omentum
sample (Y. aldovae, NZ_CP009781.1; Y. aleksiciae, NZ_CP011975.1; Y. enterocolitica, NC_008800.1;
Y. entomophaga, NZ_CP010029.1; Y. fredericksenii, NZ_CP009364.1; Y. intermedia, NZ_CP009801.1;
Y. kristensenii, NZ_CP008955.1; Y. pestis, NC_003143.1; Y. pseudotuberculosis, NZ_CP008943.1; Y. rohdei,
NZ_CP009787.1; Y. ruckeri, NZ_CP011078.1; and Y. similis NZ_CP007230.1), as well as to the
Alcaligenes faecalis reference genome (NZ_CP013119.1) and the Carnobacterium sp. CP1 genome
(NZ_CP010796.1) for the cheek fat sample, using the same parameters as described above.
DNA deamination patterns of mapped reads were analyzed with mapDamage2 2.0.6 [42].

Genes 2018,9, 436 6 of 17
2.11. Data Availability
Raw sequencing data have been deposited in the NCBI Sequence Read Archive (accession
number SRP113695).
3. Results
3.1. Data Quality Control and Authentication
We obtained 17–47 million reads per sample and, with the exception of the vivianite sample
(39% reads surviving), 81–93% of the raw reads survived quality filtering and merging (Table 1).
Average read lengths ranged from 34 to 56 bp, with only few reads longer than 100 bp (Figure 2),
consistent with fragmentation typical for aDNA [60].
Genes 2018, 9, x FOR PEER REVIEW 6 of 17
2.11. Data Availability
Raw sequencing data have been deposited in the NCBI Sequence Read Archive (accession
number SRP113695).
3. Results
3.1. Data Quality Control and Authentication
We obtained 17–47 million reads per sample and, with the exception of the vivianite sample
(39% reads surviving), 81–93% of the raw reads survived quality filtering and merging (Table 1).
Average read lengths ranged from 34 to 56 bp, with only few reads longer than 100 bp (Figure 2),
consistent with fragmentation typical for aDNA [60].
(a)
(b)
Figure 2. Ancient DNA (aDNA) fragmentation and misincorporation patterns. (a) Read length
distribution of quality-filtered reads is shown for all samples and controls. Average read length
ranges from 34 to 56 bp. (b) Quality-filtered reads were mapped to the African elephant nuclear
genome with a minimum mapping quality of 25 and nucleotide misincorporation rates were
calculated using mapDamage. Increased cytosine deamination rates at 5′-overhangs are visible (C to
T and G to A transitions), consistent with aDNA.
Figure 2.
Ancient DNA (aDNA) fragmentation and misincorporation patterns. (
a
) Read length
distribution of quality-filtered reads is shown for all samples and controls. Average read length ranges
from 34 to 56 bp. (
b
) Quality-filtered reads were mapped to the African elephant nuclear genome
with a minimum mapping quality of 25 and nucleotide misincorporation rates were calculated using
mapDamage. Increased cytosine deamination rates at 5
0
-overhangs are visible (C to T and G to A
transitions), consistent with aDNA.

Genes 2018,9, 436 7 of 17
Quality-filtered, merged reads were mapped to the African elephant (L. africana) nuclear genome
and the human reference genome, as well as the woolly mammoth (M. primigenius) mitogenome.
Tissue samples showed a highly variable number of reads mapping to the elephant and mammoth
references (2.1–43% and 0.1–0.8%, respectively), indicating different levels of endogenous mammoth
DNA preservation depending on the tissue of origin (Table 1). The abdominal oblique muscle and
cheek fat samples yielded the highest proportions of reads mapping to the elephant nuclear genome
(42–43% of total unique reads), whereas for the vivianite sample less than 2500 reads mapped to the
elephant nuclear genome (0.28% of total unique reads). Since the vivianite sample did not differ
significantly from negative controls in terms of raw data quality and endogenous DNA content,
we treated it as an environmental control sample. mapDamage analysis of reads mapped to the African
elephant nuclear genome (Figure 2) and woolly mammoth mitochondrial genome (Figure S1a) yielded
DNA damage patterns consistent with authentic aDNA, showing increased cytosine deamination rates
at 50-overhangs for all tissue samples [42].
Table 1. Quality filtering and mapping statistics for reads obtained from shotgun sequencing.
Raw
Read Pairs
Merged Reads
(% of Total)
Unique Reads
(Clonality) Unique Mapped Reads (% of Unique Reads)
Loxodonta
africana
Mammuthus
primigenius Homo
sapiens
Nuclear Genome Mitogenome
Abdominal 35,511,605 31,176,532 102,34,165 (3.05×)4,371,482 55532 78047
oblique muscle −87.80% −42.71% −0.54% −0.76%
Abd. subcut. 22,977,668 21,176,646 6,834,930 (3.10×)143221 16216 20252
fat and muscle −92.20% −2.10% −0.24% −0.29%
Intestinal 25,053,246 23,305,474 7,496,017 (3.11×)605598 32548 16223
tissue 1 −93.00% −8.08% −0.43% −0.22%
Intestinal 21,130,716 17,066,995 2,829,012 (6.03×)270976 12162 6882
tissue 2 −80.80% −9.58% −0.43% −0.24%
Caecum 17,386,802 16,168,853 5,819,269 (2.78×)634528 46103 12498
−93.00% −10.90% −0.79% −0.21%
Omentum 22,924,111 21,390,754 6,909,388 (3.10×)186132 9453 7581
−93.30% −2.69% −0.14% −0.11%
Peritoneum 18,925,818 17,416,113 5,994,320 (2.91×)405067 6093 14650
−92.00% −6.76% −0.10% −0.24%
Cheek 47,716,128 42,628,440 11,029,081 (3.87×)1947669 8582 37331
fat −89.30% −42.08% −0.19% −0.81%
Vivianite 21,888,799 8,481,750 824,368 (10.29×)2334 194 6298
−38.80% −0.28% −0.02% −0.76%
Extraction 7,036,101 3,224,382 711,189 (4.53×)22774 227 5038
blank −45.80% −3.20% −0.03% −0.71%
Library 4,820,063 285,265 33387 (8.54×)8417 151 4479
blank −5.90% −25.21% −0.45% −13.42%
We also observed a low degree of sample contamination with human DNA (0.11–0.81%, Table 1).
DNA damage patterns showed a slight increase in cytosine deamination rates at 5
0
-overhangs
(Figure S1b), suggesting that contamination with human DNA happened prior to this study and is
consistent with the extensive handling of Lyuba at discovery and recovery. We detected small numbers
of unique reads that matched elephant, mammoth and humans in the library and extraction blanks.
Since some samples shared indexes with the blanks, these likely derive from sample mis-assignment
due to index switching [32].
In order to further validate the authenticity of the sequencing data, we pooled all reads mapping
to the woolly mammoth mitogenome together, remapped them to the reference, and built a consensus
sequence for the mitogenome of Lyuba. We obtained a sequence with an average per-base coverage of
227×, which was consistent with the previously published Lyuba mitogenome [44].

Genes 2018,9, 436 8 of 17
3.2. Shotgun Metagenomic Analysis
In order to reconstruct the taxonomic composition of Lyuba’s tissue-specific metagenomes,
we compared quality-filtered, deduplicated reads to the NCBI nucleotide database with megablast and
visualized the results with MEGAN6 [
50
]. Additionally, we included published metagenomes obtained
from Asian elephant fecal samples [45] and Russian permafrost sediments [46] for comparison.
For all Lyuba tissue samples, reads could be assigned to the genera Loxodonta,Elephas,
or Mammuthus (25,000–600,000 reads per sample), with the abdominal oblique muscle sample yielding
the most reads (Figure 3b). Furthermore, as expected for this kind of material, we observed a large
environmental DNA component in Lyuba’s tissues, with the most commonly occurring bacterial
species being known environmental, soil- or water-dwelling, bacteria (Figure 3a,b). For example,
roughly 50-80% of the metagenome of all tissue samples, excluding the cheek fat sample, is composed of
bacterial species belonging to the genera Pseudomonas,Janthinobacterium,Caulobacter, and Brevundimonas.
All these taxa are present in the vivianite control, and in at least one of the laboratory non-template
controls, suggesting contamination from laboratory reagents or workflows, the environment, or both.
Despite this, the remaining fraction of the tissue samples’ metagenomes appear to have overall
different taxonomic compositions and to differ from the non-template controls as well as from the
vivianite control (Figure 3b). This can also be seen in the Principal Coordinate Analysis (PCoA) of
taxonomic profiles at the species level (Figure 3c), where the tissue samples cluster separately from the
laboratory and vivianite controls and the cheek fat sample is most distant from the other tissue samples.
Contrary to expectations, while mammoth samples of intestinal origin cluster together, they are not
taxonomically similar to the elephant fecal samples. Similarly, the vivianite sample is taxonomically
different from the permafrost samples.
Interestingly, we also observed a few bacterial taxa that were particularly abundant in the
metagenome of one of Lyuba’s tissue samples and nearly or completely absent from the other tissues
and controls. In particular, we detected the presence of Yersinia (354,641 summed reads) in the
omentum sample and of Carnobacterium (307,572 summed reads) and Alcaligenes (272,401 summed
reads) in the cheek fat sample. While species of the genus Yersinia are widely found in the environment,
mostly in fresh water and soil, some are important pathogens for humans and other animals (Y. pestis,
Y. pseudotuberculosis and Y. enterocolitica) and yet others are capable of opportunistic infections [
61
].
The majority of reads (330,049) were assigned at the genus level rather than to one or more species in
particular. Similarly, A. faecalis, to which the majority of the Alcaligenes reads (271,878) were assigned
to, is a common soil bacterium and human opportunistic pathogen [
62
]. In contrast, there are no
known pathogens in the genus Carnobacterium [
63
]. However, its presence in the cheek fat sample
is interesting, since acidification of Lyuba’s tissues through lactic-acid-producing bacteria has been
suggested as an explanation for its exceptional preservation and lack of scavenging during the time
the calf was exposed between the ice-out flooding and its recovery [
25
]. The majority of reads (201,132)
were assigned to Carnobacterium sp. CP1.
When ranking bacterial taxa in Lyuba’s tissues at the genus and species level and ignoring all taxa
that were present in the laboratory or environmental vivianite controls (with a minimum of 50 assigned
reads), we observed a much lower diversity in taxonomic composition (Figure S2). Furthermore,
all detected species and genera are common soil- or water-dwelling bacteria. The only exception is
the marked presence of Yersinia in the omentum sample. Because the laboratory controls contained
80–160 reads that were assigned to Alcaligenes or Carnobacterium, we could not observe these taxa in
the cheek fat sample in this analysis. However, given the high number of reads in the tissue sample
(272,401 and 307,572 summed reads, respectively), this could also be due to index switching rather
than to laboratory contamination.
Finally, we also analyzed all quality-filtered reads obtained from Lyuba’s tissue samples and
controls with MALT [
49
] and visualized the results with MEGAN6 [
50
]. Results were generally
consistent with the outcome of the megablast analysis (Figure S3), but we did observe some
discrepancies. For example, the MALT analysis did not detect Janthinobacterium as a major component

Genes 2018,9, 436 9 of 17
of most of Lyuba’s tissues. We also obtained fewer reads assigned to the genus Yersinia (95,021 summed
reads) in the omentum sample. Furthermore, we did not observe the presence of A. faecalis in the cheek
fat sample. These discrepancies are due to the fact that the reference database we utilized for the MALT
analysis contains fewer Janthinobacterium and Yersinia spp. genomes and lacks the A. faecalis genome.
Genes 2018, 9, x FOR PEER REVIEW 9 of 17
summed reads) in the omentum sample. Furthermore, we did not observe the presence of A. faecalis
in the cheek fat sample. These discrepancies are due to the fact that the reference database we utilized
for the MALT analysis contains fewer Janthinobacterium and Yersinia spp. genomes and lacks the A.
faecalis genome.
(a) (b)
(c)
Figure 3. Taxonomic composition of tissue-specific metagenomes. Quality-filtered, deduplicated data
were compared to the National Center for Biotechnology Information (NCBI) nucleotide collection
with megablast and results were visualized with MEGAN6. Taxa are displayed at the phylum (a) and
genus (b) level. The presence of Yersinia in the omentum sample and of Carnobacterium and Alcaligenes
in the cheek fat sample characterize these tissues. For visualization purposes only the most abundant
taxa are listed. (c) The Bray-Curtis Principal Coordinate Analysis (PCoA) of taxonomic profiles ranked
by species shows samples to differ in their taxonomic composition from the non-template controls
Figure 3.
Taxonomic composition of tissue-specific metagenomes. Quality-filtered, deduplicated data
were compared to the National Center for Biotechnology Information (NCBI) nucleotide collection
with megablast and results were visualized with MEGAN6. Taxa are displayed at the phylum (
a
) and
genus (
b
) level. The presence of Yersinia in the omentum sample and of Carnobacterium and Alcaligenes
in the cheek fat sample characterize these tissues. For visualization purposes only the most abundant
taxa are listed. (
c
) The Bray-Curtis Principal Coordinate Analysis (PCoA) of taxonomic profiles ranked
by species shows samples to differ in their taxonomic composition from the non-template controls
and the vivianite environmental control, as well as from the permafrost and elephant fecal samples.
EB = extraction blank; LB = library blank.

Genes 2018,9, 436 10 of 17
3.3. 16S Metagenomic Analysis
The 16S metagenomic analysis retained between 9 (library blank) and 8037 (elephant 3 weeks)
unique sequences (mean: 1739 sequences per sample; standard deviation: 1988 sequences) after
clustering to the SILVA database. We identified Yersinia in the omentum sample at a 0.84% relative
frequency and Alcaligenes and Carnobacterium in the cheek fat sample at 1.77% and 1.56% relative
frequencies, respectively (Figure S4). All PCoA analyses produced concordant results: With the
exception of the cheek fat sample, the Lyuba tissues clustered with themselves near the controls
(Figure 4a and Figure S5). The cheek fat sample was the most similar to the elephant feces. The tissue
samples did not cluster with the permafrost sample. These results are concordant with those of
the SourceTracker analysis (Figure 4b): ~10% of tissue reads derived from the local environment
(represented by vivianite) and ~5% derive from laboratory contamination. The cheek fat sample had
the lowest laboratory contamination level (2%) which may explain its differentiation in the PCoAs.
Interestingly, the omentum had the highest proportion of reads that correspond to the elephant fecal
microbiome (11%), but still clustered with the other Lyuba tissue samples.
Genes 2018, 9, x FOR PEER REVIEW 10 of 17
and the vivianite environmental control, as well as from the permafrost and elephant fecal samples.
EB = extraction blank; LB = library blank.
3.3. 16S Metagenomic Analysis
The 16S metagenomic analysis retained between 9 (library blank) and 8037 (elephant 3 weeks)
unique sequences (mean: 1739 sequences per sample; standard deviation: 1988 sequences) after
clustering to the SILVA database. We identified Yersinia in the omentum sample at a 0.84% relative
frequency and Alcaligenes and Carnobacterium in the cheek fat sample at 1.77% and 1.56% relative
frequencies, respectively (Figure S4). All PCoA analyses produced concordant results: With the
exception of the cheek fat sample, the Lyuba tissues clustered with themselves near the controls
(Figure 4a and Figure S5). The cheek fat sample was the most similar to the elephant feces. The tissue
samples did not cluster with the permafrost sample. These results are concordant with those of the
SourceTracker analysis (Figure 4b): ~10% of tissue reads derived from the local environment
(represented by vivianite) and ~5% derive from laboratory contamination. The cheek fat sample had
the lowest laboratory contamination level (2%) which may explain its differentiation in the PCoAs.
Interestingly, the omentum had the highest proportion of reads that correspond to the elephant fecal
microbiome (11%), but still clustered with the other Lyuba tissue samples.
(a)
Figure 4. Cont.

Genes 2018,9, 436 11 of 17
Genes 2018, 9, x FOR PEER REVIEW 11 of 17
(b)
Figure 4. QIIME2 16S metagenomic analysis. (a) PCoA of Unweighted UniFrac distances.
Phylogenetic diversity metrics were calculated with rarefication to 149 sequences. (b) SourceTracker
analysis. Laboratory blank controls, the vivianite environmental control, the elephant fecal samples,
and the Russian permafrost samples were set as possible sources of microbial communities.
3.4. MetaPhlAn2 Analysis
The MetaPhlAn2 analysis results largely replicated the results of the megablast and MALT
analyses (Figure S6). With the exception of the cheek fat sample, the Lyuba tissue metagenomes were
dominated by Pseudomonas, Janthinobacterium, Caulobacter, Pedobacter, and Brevundimonas. Yersinia
intermedia was detected in the omentum and intestinal tissue 1 sample. Alcaligenes, Carnobacterium sp.
17.4, and Arthrobacter gangostriensis were found in the cheek fat. Only Brevundimonas was identified
in the vivianite. With the exception of the cheek fat, the Lyuba tissue samples cluster together as in
the other analyses.
3.5. Authentication of Potential Ancient Bacterial Signals
While the taxonomic composition of Lyuba’s tissue-specific metagenomes was overall similar,
the presence of Yersinia, Carnobacterium sp. CP1, and A. faecalis as prominent components in the
omentum and cheek fat samples’ metagenomes did set these two tissues apart. In order to test
whether the presence of these bacteria represented authentic ancient host-associated microbiomic
signatures, we mapped all reads obtained from the omentum sample against the reference genomes
of various Yersinia species, and the reads obtained from the cheek fat sample to the A. faecalis reference
Figure 4.
QIIME2 16S metagenomic analysis. (
a
) PCoA of Unweighted UniFrac distances. Phylogenetic
diversity metrics were calculated with rarefication to 149 sequences. (
b
) SourceTracker analysis.
Laboratory blank controls, the vivianite environmental control, the elephant fecal samples, and the
Russian permafrost samples were set as possible sources of microbial communities.
3.4. MetaPhlAn2 Analysis
The MetaPhlAn2 analysis results largely replicated the results of the megablast and MALT
analyses (Figure S6). With the exception of the cheek fat sample, the Lyuba tissue metagenomes
were dominated by Pseudomonas,Janthinobacterium,Caulobacter,Pedobacter, and Brevundimonas.
Yersinia intermedia was detected in the omentum and intestinal tissue 1 sample. Alcaligenes,
Carnobacterium sp. 17.4, and Arthrobacter gangostriensis were found in the cheek fat.
Only Brevundimonas was identified in the vivianite. With the exception of the cheek fat, the Lyuba
tissue samples cluster together as in the other analyses.
3.5. Authentication of Potential Ancient Bacterial Signals
While the taxonomic composition of Lyuba’s tissue-specific metagenomes was overall similar,
the presence of Yersinia,Carnobacterium sp. CP1, and A. faecalis as prominent components in the
omentum and cheek fat samples’ metagenomes did set these two tissues apart. In order to test whether
the presence of these bacteria represented authentic ancient host-associated microbiomic signatures,
we mapped all reads obtained from the omentum sample against the reference genomes of various

Genes 2018,9, 436 12 of 17
Yersinia species, and the reads obtained from the cheek fat sample to the A. faecalis reference genome
and the Carnobacterium sp. CP1 genome. We then analyzed the DNA damage patterns of all mapped
reads to test whether we could detect typical aDNA deamination.
For the omentum sample we obtained the most reads when mapping against the reference
genome of Y. intermedia (751,425 reads mapping with a quality of 25 or higher, 10.9% of unique reads).
While Y. intermedia has been associated with opportunistic gastrointestinal infections, this bacterium is
widely found in the environment, mostly in fresh water [
61
]. Furthermore, a mapDamage analysis
showed no increase of cytosine deamination rates at 5
0
-overhangs (Figure 5), which is inconsistent
with authentic aDNA [42].
Similarly, while we obtained 519,810 reads (4.7% of unique reads) mapping to the Carnobacterium
reference genome and 549,975 reads (5% of unique reads) mapping to the A. faecalis reference genome
(with a quality of 25 or higher) we failed to detect DNA damage patterns consistent with aDNA (Figure 5).
Genes 2018, 9, x FOR PEER REVIEW 12 of 17
genome and the Carnobacterium sp. CP1 genome. We then analyzed the DNA damage patterns of all
mapped reads to test whether we could detect typical aDNA deamination.
For the omentum sample we obtained the most reads when mapping against the reference
genome of Y. intermedia (751,425 reads mapping with a quality of 25 or higher, 10.9% of unique reads).
While Y. intermedia has been associated with opportunistic gastrointestinal infections, this bacterium
is widely found in the environment, mostly in fresh water [61]. Furthermore, a mapDamage analysis
showed no increase of cytosine deamination rates at 5′-overhangs (Figure 5), which is inconsistent with
authentic aDNA [42].
Similarly, while we ob tained 519,81 0 reads (4 .7% of u nique r eads) map ping to the Carnobacterium
reference genome and 549,975 reads (5% of unique reads) mapping to the A. faecalis reference genome
(with a quality of 25 or higher) we failed to detect DNA damage patterns consistent with aDNA
(Figure 5).
Figure 5. DNA damage pattern analysis of omentum reads mapped to Y. intermedia and cheek fat
reads mapped to Carnobacterium sp. CP1 and Alcaligenes faecalis. Quality-filtered reads were mapped
to the reference genomes with a minimum mapping quality of 25 and DNA damage plots were
generated using mapDamage. No increase in cytosine deamination rates at 5′-overhangs is visible (C
to T and G to A transitions), which is inconsistent with aDNA.
4. Discussion
Ancient microbiome studies are of great value in the context of human health and evolution.
However, microbial communities reconstructed from archaeological material typically have a large
environmental component. This can lead to false positive signals [14,15] and strict validation
procedures are necessary. Additionally, the introduction of microbial contaminants via laboratory
reagents also needs to be considered [18–20]. Excellent reviews and guidelines on these issues have
already been provided by others (e.g., References [16,17]). Based on these guidelines, we attempted
to reconstruct the tissue-specific metagenomes of Lyuba, a 42,000-year-old, permafrost-preserved
woolly mammoth calf, together with environmental and non-template experimental controls and
compared them to metagenomes obtained from permafrost and elephant fecal samples. The
endogenous mammoth DNA content was very variable across samples (2–43%), but we obtained
DNA deamination patterns consistent with authentic aDNA for all tissue samples [42]. Furthermore,
the mitogenome consensus sequence we obtained was consistent with the previously published
Lyuba mitogenome [44].
A preliminary taxonomic composition analysis suggested that, despite a large component
derived from environmental or laboratory contamination, metagenomes reconstructed from Lyuba’s
tissue samples were distinct from those obtained from controls. We paid particular attention to the
presence of Yersinia in the omentum sample, and of A. faecalis and Carnobacterium sp. CP1 in the cheek
Figure 5.
DNA damage pattern analysis of omentum reads mapped to Y. intermedia and cheek fat reads
mapped to Carnobacterium sp. CP1 and Alcaligenes faecalis. Quality-filtered reads were mapped to the
reference genomes with a minimum mapping quality of 25 and DNA damage plots were generated
using mapDamage. No increase in cytosine deamination rates at 5
0
-overhangs is visible (C to T and G
to A transitions), which is inconsistent with aDNA.
4. Discussion
Ancient microbiome studies are of great value in the context of human health and evolution.
However, microbial communities reconstructed from archaeological material typically have a large
environmental component. This can lead to false positive signals [
14
,
15
] and strict validation
procedures are necessary. Additionally, the introduction of microbial contaminants via laboratory
reagents also needs to be considered [
18
–
20
]. Excellent reviews and guidelines on these issues have
already been provided by others (e.g., References [
16
,
17
]). Based on these guidelines, we attempted to
reconstruct the tissue-specific metagenomes of Lyuba, a 42,000-year-old, permafrost-preserved woolly
mammoth calf, together with environmental and non-template experimental controls and compared
them to metagenomes obtained from permafrost and elephant fecal samples. The endogenous
mammoth DNA content was very variable across samples (2–43%), but we obtained DNA deamination
patterns consistent with authentic aDNA for all tissue samples [
42
]. Furthermore, the mitogenome
consensus sequence we obtained was consistent with the previously published Lyuba mitogenome [
44
].
A preliminary taxonomic composition analysis suggested that, despite a large component derived
from environmental or laboratory contamination, metagenomes reconstructed from Lyuba’s tissue
samples were distinct from those obtained from controls. We paid particular attention to the presence

Genes 2018,9, 436 13 of 17
of Yersinia in the omentum sample, and of A. faecalis and Carnobacterium sp. CP1 in the cheek
fat sample. These taxa were absent or nearly absent from all other tissues and from laboratory
and environmental controls. Therefore, the detection of several hundred thousand of reads in one
particular tissue in an otherwise rather uniform taxonomic background, led us to think that these
could represent ancient microbiomic signals originating, for example, from an infection in Lyuba’s
tissues. Further corroborating this hypothesis, several Yersinia species are known pathogens or are
at least capable of opportunistic infections [
61
]. A. faecalis is an opportunistic pathogen as well [
62
]
and while there are no known pathogens in the genus Carnobacterium [
63
], the presence of this
lactic-acid-producing bacterium is consistent with the proposed explanation that acidification of
Lyuba’s tissues contributed to its exceptional preservation [25].
However, these hypotheses were not corroborated by aDNA authentication criteria. In order to
identify which Yersinia species was most likely to be present in the omentum sample, we mapped
all reads against several Yersinia reference genomes and obtained the most reads for Y. intermedia,
a species that is widely distributed in the environment [
61
]. Additionally, we analyzed deamination
rates at 5
0
-overhangs of omentum reads mapped to Y. intermedia, as well as of cheek fat reads mapped
to A. faecalis and Carnobacterium sp. CP1 and found no increase of cytosine deamination, which is
inconsistent with authentic aDNA [
42
]. For these reasons, we cannot conclude that the presence of
these bacteria in the omentum and cheek fat samples represents authentic ancient microbiomic signals.
We believe that these signals are much more likely to have originated from contamination instead.
We observed several bacterial taxa that were present in almost all of Lyuba’s tissues as well as in
one or more of the laboratory and vivianite environmental controls (e.g., Pseudomonas,Janthinobacterium,
Caulobacter, and Brevundimonas). In this case, the source of contamination can easily be attributed to
the environment or to laboratory reagents or workflows. The 16S metagenomic and SourceTracker
analyses indicate that our results are affected by these contaminants. However, Yersinia,A. faecalis,
and Carnobacterium sp. CP1 were unique to the omentum and cheek fat samples, respectively,
with the exception of very few reads that can be explained by index switching. While environmental
or laboratory contamination cannot be excluded, they are less likely. Other possible sources of
contamination that consider the tissue specificity we observed are contamination during the sampling
procedure or a non-uniform colonization of Lyuba’s tissues by environmental bacteria. Lyuba likely
endured at least one cycle of thawing and freezing before discovery [
25
,
27
]. This may have affected
DNA recovery and the fact that we observed very variable amounts of endogenous DNA in the
different tissues could reflect variations in DNA preservation. Furthermore, thawing may have offered
an opportunity for colonization of Lyuba’s tissues by environmental microorganisms, and if this
process did not take place in a homogenous way, it could explain different contaminants among tissues.
Moreover, part of the investigated tissues, such as fat and muscle, should be free of bacterial signals,
which also indicates post-mortem contamination as a source of the detected bacterial signals.
We conclude that, even in ideal morphological preservation circumstances, host-endogenous
microbiome signals can be swamped out by contaminating signals. Therefore, microbiome analyses
from ancient tissues must be done with the utmost care, because even when environmental and
laboratory controls are not compositionally similar to experimental samples, this does not indicate that
microbes are necessarily endogenous.
Supplementary Materials:
The following are available online at http://www.mdpi.com/2073-4425/9/9/436/s1,
Figure S1: DNA damage pattern analysis of reads mapped to the woolly mammoth mitogenome and the
human reference genome, Figure S2: Taxonomic composition of Lyuba’s tissue-specific metagenomes., Figure S3:
MALT analysis., Figure S4: QIIME2 16S metagenomic analysis, Figure S5: QIIME2 16S Principal Coordinate
Analyses, Figure S6: Heat map of MetaPhlAn2 results based on Euclidean distances.
Author Contributions:
Conceptualization, N.B., A.P. and F.R.; Data curation, G.F. and M.G.C.; Formal analysis,
G.F., H.E.L.L., J.N. and M.G.C.; Investigation, A.S.B. and M.G.C.; Methodology, G.F., A.S.B. and M.G.C.;
Project administration, F.R.; Resources, H.E.L.L., N.B., A.P., F.R. and M.C.; Supervision, F.R., A.S.B. and M.G.C.;
Visualization, G.F. and J.N.; Writing—original draft, G.F., E.R. and M.G.C.; Writing—review & editing, G.F., J.N.,
A.S.B. and M.G.C.

Genes 2018,9, 436 14 of 17
Funding:
We would like to acknowledge the University of Zurich’s University Research Priority Program
“Evolution in Action: From Genomes to Ecosystems”, and the Mäxi Foundation Zurich for financial support.
M.G.C. was supported by the Smithsonian Institution.
Acknowledgments:
We thank Daniel Fisher at the University of Michigan for providing the samples used in
this study. The International Mammoth Committee graciously approved this research. We are grateful to Sirisha
Aluri, Jelena Kühn-Georgijevic, Catharine Aquino and Lennart Opitz at the Functional Genomics Center Zurich
for assistance with sequencing. The Smithsonian Institution High Performance Cluster (“Hydra”) was used for
computational analyses.
Conflicts of Interest:
The authors declare no conflict of interest. The founding sponsors had no role in the design
of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the
decision to publish the results.
References
1.
Paabo, S. Molecular cloning of Ancient Egyptian mummy DNA. Nature
1985
,314, 644–645. [CrossRef]
[PubMed]
2.
Taylor, G.M.; Crossey, M.; Saldanha, J.; Waldron, T. DNA from Mycobacterium tuberculosis identified in
Mediaeval human skeletal remains using polymerase chain reaction. J. Archaeol. Sci.
1996
,23, 789–798.
[CrossRef]
3.
Nerlich, A.G.; Haas, C.J.; Zink, A.; Szeimies, U.; Hagedorn, H.G. Molecular evidence for tuberculosis in an
ancient Egyptian mummy. Lancet 1997,350, 1404. [CrossRef]
4.
Zink, A.; Reischl, U.; Wolf, H.; Nerlich, A.G. Molecular evidence of bacteremia by gastrointestinal pathogenic
bacteria in an infant mummy from ancient Egypt. Arch. Pathol. Lab. Med. 2000,124, 1614–1618. [PubMed]
5.
Bos, K.I.; Schuenemann, V.J.; Golding, G.B.; Burbano, H.A.; Waglechner, N.; Coombes, B.K.; McPhee, J.B.;
DeWitte, S.N.; Meyer, M.; Schmedes, S.; et al. A draft genome of Yersinia pestis from victims of the Black
Death. Nature 2011,478, 506–510. [CrossRef] [PubMed]
6.
Schuenemann, V.J.; Singh, P.; Mendum, T.A.; Krause-Kyora, B.; Jager, G.; Bos, K.I.; Herbig, A.; Economou, C.;
Benjak, A.; Busso, P.; et al. Genome-wide comparison of medieval and modern Mycobacterium leprae.Science
2013,341, 179–183. [CrossRef] [PubMed]
7.
Adler, C.J.; Dobney, K.; Weyrich, L.S.; Kaidonis, J.; Walker, A.W.; Haak, W.; Bradshaw, C.J.A.; Townsend, G.;
Sołtysiak, A.; Alt, K.W.; et al. Sequencing ancient calcified dental plaque shows changes in oral microbiota
with dietary shifts of the Neolithic and Industrial revolutions. Nat. Genet.
2013
,45, 450–455. [CrossRef]
[PubMed]
8.
Warinner, C.; Rodrigues, J.F.M.; Vyas, R.; Trachsel, C.; Shved, N.; Grossmann, J.; Radini, A.; Hancock, Y.;
Tito, R.Y.; Fiddyment, S.; et al. Pathogens and host immunity in the ancient human oral cavity. Nat. Genet.
2014,46, 336–344. [CrossRef] [PubMed]
9.
Hooper, L.V.; Gordon, J.I. Commensal host-bacterial relationships in the gut. Science
2001
,292, 1115–1118.
[CrossRef] [PubMed]
10.
Tremaroli, V.; Bäckhed, F. Functional interactions between the gut microbiota and host metabolism. Nature
2012,489, 242–249. [CrossRef] [PubMed]
11.
Yatsunenko, T.; Rey, F.E.; Manary, M.J.; Trehan, I.; Dominguez-Bello, M.G.; Contreras, M.; Magris, M.;
Hidalgo, G.; Baldassano, R.N.; Anokhin, A.P.; et al. Human gut microbiome viewed across age and
geography. Nature 2012,486, 222. [CrossRef] [PubMed]
12.
Tito, R.Y.; Knights, D.; Metcalf, J.; Obregon-Tito, A.J.; Cleeland, L.; Najar, F.; Roe, B.; Reinhard, K.; Sobolik, K.;
Belknap, S.; et al. Insights from characterizing extinct human gut microbiomes. PLoS ONE
2012
,7, e51146.
[CrossRef] [PubMed]
13.
Weyrich, L.S.; Duchene, S.; Soubrier, J.; Arriola, L.; Llamas, B.; Breen, J.; Morris, A.G.; Alt, K.W.; Caramelli, D.;
Dresely, V.; et al. Neanderthal behaviour, diet, and disease inferred from ancient DNA in dental calculus.
Nature 2017,544, 357–361. [CrossRef] [PubMed]
14.
Campana, M.G.; Robles Garcia, N.; Ruhli, F.J.; Tuross, N. False positives complicate ancient pathogen
identifications using high-throughput shotgun sequencing. BMC Res. Notes
2014
,7, 111. [CrossRef]
[PubMed]

Genes 2018,9, 436 15 of 17
15.
Bos, K.I.; Jager, G.; Schuenemann, V.J.; Vagene, A.J.; Spyrou, M.A.; Herbig, A.; Nieselt, K.; Krause, J.
Parallel detection of ancient pathogens via array-based DNA capture. Philos. Trans. R. Soc. Lond. B Biol. Sci.
2015,370, 20130375. [CrossRef] [PubMed]
16.
Warinner, C.; Herbig, A.; Mann, A.; Fellows Yates, J.A.; Weiß, C.L.; Burbano, H.A.; Orlando, L.; Krause, J.
A robust framework for microbial archaeology. Annu. Rev. Genom. Hum. Genet.
2017
,18, 321–356. [CrossRef]
[PubMed]
17.
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. 2017,33, 508–520. [CrossRef] [PubMed]
18.
Salter,S.J.; Cox, M.J.; Turek, E.M.; Calus, S.T.; Cookson, W.O.; Moffatt, M.F.; Turner, P.; Parkhill, J.; Loman, N.J.;
Walker, A.W. Reagent and laboratory contamination can critically impact sequence-based microbiome
analyses. BMC Biol. 2014,12. [CrossRef] [PubMed]
19.
Glassing, A.; Dowd, S.E.; Galandiuk, S.; Davis, B.; Chiodini, R.J. Inherent bacterial DNA contamination of
extraction and sequencing reagents may affect interpretation of microbiota in low bacterial biomass samples.
Gut Pathog. 2016,8, 24. [CrossRef] [PubMed]
20.
Lauder, A.P.; Roche, A.M.; Sherrill-Mix, S.; Bailey, A.; Laughlin, A.L.; Bittinger, K.; Leite, R.; Elovitz, M.A.;
Parry, S.; Bushman, F.D. Comparison of placenta samples with contamination controls does not provide
evidence for a distinct placenta microbiota. Microbiome 2016,4, 29. [CrossRef] [PubMed]
21.
Rohland, N.; Malaspinas, A.-S.; Pollack, J.L.; Slatkin, M.; Matheus, P.; Hofreiter, M.
Proboscidean mitogenomics: Chronology and mode of elephant evolution using mastodon as outgroup.
PLoS Biol. 2007,5, e207. [CrossRef] [PubMed]
22.
Long, S.Y.; Latimer, E.M.; Hayward, G.S. Review of elephant endotheliotropic Herpesviruses and acute
hemorrhagic disease. ILAR J. 2015,56, 283–296. [CrossRef] [PubMed]
23.
Zlot, A.; Vines, J.; Nystrom, L.; Lane, L.; Behm, H.; Denny, J.; Finnegan, M.; Hostetler, T.; Matthews, G.;
Storms, T.; et al. Diagnosis of tuberculosis in three zoo elephants and a human contact—Oregon, 2013.
Centers Dis. Control Prev. Morb. Mortal. Wkly. Rep. 2016,64, 1398–1402. [CrossRef] [PubMed]
24.
Kosintsev, P.A.; Lapteva, E.G.; Trofimova, S.S.; Zanina, O.G.; Tikhonov, A.N.; van der Plicht, J. The intestinal
contents of a baby woolly mammoth (Mammuthus primigenius Blumenbach, 1799) from the Yuribey River
(Yamal Peninsula). Dokl. Biol. Sci. 2010,432, 209–211. [CrossRef] [PubMed]
25.
Fisher, D.C.; Tikhonov, A.N.; Kosintsev, P.A.; Rountrey, A.N.; Buigues, B.; van der Plicht, J. Anatomy, death,
and preservation of a woolly mammoth (Mammuthus primigenius) calf, Yamal Peninsula, northwest Siberia.
Quat. Int. 2012,255, 94–105. [CrossRef]
26.
Rountrey, A.N.; Fisher, D.C.; Tikhonov, A.N.; Kosintsev, P.A.; Lazarev, P.A.; Boeskorov, G.; Buigues, B.
Early tooth development, gestation, and season of birth in mammoths. Quat. Int.
2012
,255, 196–205.
[CrossRef]
27.
Papageorgopoulou, C.; Link, K.; Rühli, F.J. Histology of a woolly mammoth (Mammuthus primigenius)
preserved in permafrost, Yamal Peninsula, Northwest Siberia. Anat. Rec.
2015
,298, 1059–1071. [CrossRef]
[PubMed]
28.
Krüttli, A.; Bouwman, A.; Akgül, G.; Della Casa, P.; Rühli, F.; Warinner, C. Ancient DNA analysis reveals
high frequency of European lactase persistence allele (T-13910) in medieval central Europe. PLoS ONE
2014
,
9, e86251. [CrossRef] [PubMed]
29.
Cooper, A.; Poinar, H.N. Ancient DNA: Do it right or not at all. Science
2000
,289, 1139. [CrossRef] [PubMed]
30.
Llamas, B.; Valverde, G.; Fehren-Schmitz, L.; Weyrich, L.S.; Cooper, A.; Haak, W. From the field to the
laboratory: Controlling DNA contamination in human ancient DNA research in the high-throughput
sequencing era. STAR Sci. Technol. Archaeol. Res. 2017,3, 1–14. [CrossRef]
31.
Meyer, M.; Kircher, M. Illumina sequencing library preparation for highly multiplexed target capture and
sequencing. Cold Spring Harb. Protoc. 2010,2010. [CrossRef] [PubMed]
32.
Kircher, M.; Sawyer, S.; Meyer, M. Double indexing overcomes inaccuracies in multiplex sequencing on the
Illumina platform. Nucleic Acids Res. 2012,40, e3. [CrossRef] [PubMed]
33.
Renaud, G.; Stenzel, U.; Maricic, T.; Wiebe, V.; Kelso, J. deML: Robust demultiplexing of Illumina sequences
using a likelihood-based approach. Bioinformatics 2015,31, 770–772. [CrossRef] [PubMed]
34.
Bolger, A.M.; Lohse, M.; Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data.
Bioinformatics 2014,30, 2114–2120. [CrossRef] [PubMed]
35. SeqPrep. Available online: https://github.com/jstjohn/SeqPrep (accessed on 19 February 2016).

Genes 2018,9, 436 16 of 17
36.
Maixner, F.; Krause-Kyora, B.; Turaev, D.; Herbig, A.; Hoopmann, M.R.; Hallows, J.L.; Kusebauch, U.;
Vigl, E.E.; Malfertheiner, P.; Megraud, F.; et al. The 5300-year-old Helicobacter pylori genome of the Iceman.
Science 2016,351, 162–165. [CrossRef] [PubMed]
37.
Andrews, S. FastQC: A Quality Control Tool for High Throughput Sequence Data. Available online:
http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (accessed on 8 April 2014).
38.
Li, H.; Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics
2009,25, 1754–1760. [CrossRef] [PubMed]
39.
Schubert, M.; Ginolhac, A.; Lindgreen, S.; Thompson, J.F.; AL-Rasheid, K.A.; Willerslev, E.; Krogh, A.;
Orlando, L. Improving ancient DNA read mapping against modern reference genomes. BMC Genom.
2012
,
13, 178. [CrossRef] [PubMed]
40. Picard. Available online: http://broadinstitute.github.io/picard/ (accessed on 5 February 2016).
41.
Li, H.; Handsaker, B.; Wysoker, A.; Fennell, T.; Ruan, J.; Homer, N.; Marth, G.; Abecasis, G.; Durbin, R.
1000 Genome Project Data Processing Subgroup. The Sequence Alignment/Map format and SAMtools.
Bioinformatics 2009,25, 2078–2079. [CrossRef] [PubMed]
42.
Jónsson, H.; Ginolhac, A.; Schubert, M.; Johnson, P.; Orlando, L. mapDamage2.0: Fast approximate Bayesian
estimates of ancient DNA damage parameters. Bioinformatics 2013,29, 1682–1684. [CrossRef] [PubMed]
43.
Harris, R.S. Improved Pairwise Alignment of Genomic DNA. Ph.D. Thesis, The Pennsylvania State University,
State College, PA, USA, 2007.
44.
Enk, J.; Devault, A.; Widga, C.; Saunders, J.; Szpak, P.; Southon, J.; Rouillard, J.-M.; Shapiro, B.; Golding, G.B.;
Zazula, G.; et al. Mammuthus population dynamics in Late Pleistocene North America: Divergence,
phylogeography and introgression. Front. Ecol. Evol. 2016,4. [CrossRef]
45.
Ilmberger, N.; Güllert, S.; Dannenberg, J.; Rabausch, U.; Torres, J.; Wemheuer, B.; Alawi, M.; Poehlein, A.;
Chow, J.; Turaev, D.; et al. A comparative metagenome survey of the fecal microbiota of a breast- and
a plant-fed Asian elephant reveals an unexpectedly high diversity of glycoside hydrolase family enzymes.
PLoS ONE 2014,9, e106707. [CrossRef] [PubMed]
46.
Krivushin, K.; Kondrashov, F.; Shmakova, L.; Tutukina, M.; Petrovskaya, L.; Rivkina, E. Two metagenomes
from late Pleistocene northeast Siberian permafrost. Genome Announc.
2015
,3, e01380-14. [CrossRef]
[PubMed]
47.
Li, W.; Godzik, A. Cd-hit: A fast program for clustering and comparing large sets of protein or nucleotide
sequences. Bioinformatics 2006,22, 1658–1659. [CrossRef] [PubMed]
48.
Zhang, Z.; Schwartz, S.; Wagner, L.; Miller, W. A greedy algorithm for aligning DNA sequences. J. Comput. Biol.
2000,7, 203–214. [CrossRef] [PubMed]
49.
Vågene, Å.J.; Herbig, A.; Campana, M.G.; Robles García, N.M.; Warinner, C.; Sabin, S.; Spyrou, M.A.;
Andrades Valtueña, A.; Huson, D.; Tuross, N.; et al. Salmonella enterica genomes from victims of a major
sixteenth-century epidemic in Mexico. Nat. Ecol. Evol. 2018,2, 520–528. [CrossRef] [PubMed]
50.
Huson, D.H.; Beier, S.; Flade, I.; Górska, A.; El-Hadidi, M.; Mitra, S.; Ruscheweyh, H.-J.; Tappu, R.
MEGAN Community edition—interactive exploration and analysis of large-scale microbiome sequencing
data. PLoS Comput. Biol. 2016,12, e1004957. [CrossRef] [PubMed]
51.
Caporaso, J.G.; Kuczynski, J.; Stombaugh, J.; Bittinger, K.; Bushman, F.D.; Costello, E.K.; Fierer, N.; Peña, A.G.;
Goodrich, J.K.; Gordon, J.I.; et al. QIIME allows analysis of high-throughput community sequencing data.
Nat. Methods 2010,7, 335–336. [CrossRef] [PubMed]
52.
Pruesse, E.; Quast, C.; Knittel, K.; Fuchs, B.M.; Ludwig, W.; Peplies, J.; Glöckner, F.O. SILVA: A comprehensive
online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB.
Nucleic Acids Res. 2007,35, 7188–7196. [CrossRef] [PubMed]
53.
Rognes, T.; Flouri, T.; Nichols, B.; Quince, C.; Mahé, F. VSEARCH: A versatile open source tool for
metagenomics. PeerJ 2016,4, e2584. [CrossRef] [PubMed]
54.
Katoh, K.; Standley, D.M. MAFFT multiple sequence alignment software version 7: Improvements in
performance and usability. Mol. Biol. Evol. 2013,30, 772–780. [CrossRef] [PubMed]
55.
Price, M.N.; Dehal, P.S.; Arkin, A.P. FastTree 2—Approximately maximum-likelihood trees for large
alignments. PLoS ONE 2010,5, e9490. [CrossRef] [PubMed]
56.
Vázquez-Baeza, Y.; Pirrung, M.; Gonzalez, A.; Knight, R. EMPeror: A tool for visualizing high-throughput
microbial community data. Gigascience 2013,2, 16. [CrossRef] [PubMed]

Genes 2018,9, 436 17 of 17
57.
Vázquez-Baeza, Y.; Gonzalez, A.; Smarr, L.; McDonald, D.; Morton, J.T.; Navas-Molina, J.A.; Knight, R.
Bringing the dynamic microbiome to life with animations. Cell Host Microbe
2017
,21, 7–10. [CrossRef]
[PubMed]
58.
Knights, D.; Kuczynski, J.; Charlson, E.S.; Zaneveld, J.; Mozer, M.C.; Collman, R.G.; Bushman, F.D.; Knight, R.;
Kelley, S.T. Bayesian community-wide culture-independent microbial source tracking. Nat. Methods
2011
,8,
761–763. [CrossRef] [PubMed]
59.
Truong, D.T.; Franzosa, E.A.; Tickle, T.L.; Scholz, M.; Weingart, G.; Pasolli, E.; Tett, A.; Huttenhower, C.;
Segata, N. MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nat. Methods
2015
,12, 902–903.
[CrossRef] [PubMed]
60.
Dabney, J.; Meyer, M.; Pääbo, S. Ancient DNA Damage. Cold Spring Harb. Perspect. Biol.
2013
,5, a012567.
[CrossRef] [PubMed]
61.
Sulakvelidze, A. Yersiniae other than Y. enterocolitica,Y. pseudotuberculosis, and Y. pestis: The ignored species.
Microbes Infect. 2000,2, 497–513. [CrossRef]
62.
Phung, L.T.; Trimble, W.L.; Meyer, F.; Gilbert, J.A.; Silver, S. Draft genome sequence of Alcaligenes faecalis
subsp. faecalis NCIB 8687 (CCUG 2071). J. Bacteriol. 2012,194, 5153. [CrossRef] [PubMed]
63.
Leisner, J.J.; Laursen, B.G.; Prévost, H.; Drider, D.; Dalgaard, P. Carnobacterium: Positive and negative effects
in the environment and in foods. FEMS Microbiol. Rev. 2007,31, 592–613. [CrossRef] [PubMed]
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