Assessing alignment-based taxonomic classification of ancient microbial DNA

Ancient environmental microbiomes and the cryosphere

In this review, we delineate the unique set of characteristics associated with cryosphere environments (namely, ice and permafrost) which present both challenges and opportunities for studying ancient environmental microbiomes (AEMs). In a field currently reliant on several assumptions, we discuss the theoretical and empirical feasibility of recovering microbial nucleic acids (NAs) from ice and permafrost with varying degrees of antiquity. We also summarize contamination control best practices and highlight considerations for the latest approaches, including shotgun metagenomics, and downstream bioinformatic authentication approaches. We review the adoption of existing software and provide an overview of more recently published programs, with reference to their suitability for AEM studies. Finally, we summarize outstanding challenges and likely future directions for AEM research.

Filtering out the noise: metagenomic classifiers optimize ancient DNA mapping

Contamination with exogenous DNA presents a significant challenge in ancient DNA (aDNA) studies of single organisms. Failure to address contamination from microbes, reagents, and present-day sources can impact the interpretation of results. Although field and laboratory protocols exist to limit contamination, there is still a need to accurately distinguish between endogenous and exogenous data computationally. Here, we propose a workflow to reduce exogenous contamination based on a metagenomic classifier. Unlike previous methods that relied exclusively on DNA sequencing reads mapping specificity to a single reference genome to remove contaminating reads, our approach uses Kraken2-based filtering before mapping to the reference genome. Using both simulated and empirical shotgun aDNA data, we show that this workflow presents a simple and efficient method that can be used in a wide range of computational environments-including personal machines. We propose strategies to build specific databases used to profile sequencing data that take into consideration available computational resources and prior knowledge about the target taxa and likely contaminants. Our workflow significantly reduces the overall computational resources required during the mapping process and reduces the total runtime by up to ~94%. The most significant impacts are observed in low endogenous samples. Importantly, contaminants that would map to the reference are filtered out using our strategy, reducing false positive alignments. We also show that our method results in a negligible loss of endogenous data with no measurable impact on downstream population genetics analyses.

Methanobrevibacter oralis: a comprehensive review

Methanobrevibacter oralis (M. oralis) has predominated human oral microbiota methanogenic archaea as far back as the Palaeolithic era in Neanderthal populations and gained dominance from the 18th century onwards. M. oralis was initially isolated from dental plaque samples collected from two apparently healthy individuals allowing its first characterization. The culture of M. oralis is fastidious and has been the subject of several studies to improve its laboratory growth. Various PCR methods are used to identify M. oralis, targeting either the 16S rRNA gene or the mcrA gene. However, only one RTQ-PCR system, based on a chaperonin gene, offers specificity, and allows for microbial load quantification. Next-generation sequencing contributed five draft genomes, each approximately 2.08 Mb (±0.052 Mb) with a 27.82 (±0.104) average GC%, and two ancient metagenomic assembled genomes. M. oralis was then detected in various oral cavity sites in healthy individuals and those diagnosed with oral pathologies, notably periodontal diseases, and endodontic infections. Transmission pathways, possibly involving maternal milk and breastfeeding, remain to be clarified. M. oralis was further detected in brain abscesses and respiratory tract samples, bringing its clinical significance into question. This review summarizes the current knowledge about M. oralis, emphasizing its prevalence, associations with dysbiosis and pathologies in oral and extra-oral situations, and symbiotic relationships, with the aim of paving the way for further investigations.

Benchmarking bacterial taxonomic classification using nanopore metagenomics data of several mock communities

Taxonomic classification is crucial in identifying organisms within diverse microbial communities when using metagenomics shotgun sequencing. While second-generation Illumina sequencing still dominates, third-generation nanopore sequencing promises improved classification through longer reads. However, extensive benchmarking studies on nanopore data are lacking. We systematically evaluated performance of bacterial taxonomic classification for metagenomics nanopore sequencing data for several commonly used classifiers, using standardized reference sequence databases, on the largest collection of publicly available data for defined mock communities thus far (nine samples), representing different research domains and application scopes. Our results categorize classifiers into three categories: low precision/high recall; medium precision/medium recall, and high precision/medium recall. Most fall into the first group, although precision can be improved without excessively penalizing recall with suitable abundance filtering. No definitive 'best' classifier emerges, and classifier selection depends on application scope and practical requirements. Although few classifiers designed for long reads exist, they generally exhibit better performance. Our comprehensive benchmarking provides concrete recommendations, supported by publicly available code for reassessment and fine-tuning by other scientists.

Application and Comparison of Machine Learning and Database-Based Methods in Taxonomic Classification of High-Throughput Sequencing Data

The advent of high-throughput sequencing technologies has not only revolutionized the field of bioinformatics but has also heightened the demand for efficient taxonomic classification. Despite technological advancements, efficiently processing and analyzing the deluge of sequencing data for precise taxonomic classification remains a formidable challenge. Existing classification approaches primarily fall into two categories, database-based methods and machine learning methods, each presenting its own set of challenges and advantages. On this basis, the aim of our study was to conduct a comparative analysis between these two methods while also investigating the merits of integrating multiple database-based methods. Through an in-depth comparative study, we evaluated the performance of both methodological categories in taxonomic classification by utilizing simulated data sets. Our analysis revealed that database-based methods excel in classification accuracy when backed by a rich and comprehensive reference database. Conversely, while machine learning methods show superior performance in scenarios where reference sequences are sparse or lacking, they generally show inferior performance compared with database methods under most conditions. Moreover, our study confirms that integrating multiple database-based methods does, in fact, enhance classification accuracy. These findings shed new light on the taxonomic classification of high-throughput sequencing data and bear substantial implications for the future development of computational biology. For those interested in further exploring our methods, the source code of this study is publicly available on https://github.com/LoadStar822/Genome-Classifier-Performance-Evaluator. Additionally, a dedicated webpage showcasing our collected database, data sets, and various classification software can be found at http://lab.malab.cn/~tqz/project/taxonomic/.

Benchmarking a targeted 16S ribosomal RNA gene enrichment approach to reconstruct ancient microbial communities

The taxonomic characterization of ancient microbiomes is a key step in the rapidly growing field of paleomicrobiology. While PCR amplification of the 16S ribosomal RNA (rRNA) gene is a widely used technique in modern microbiota studies, this method has systematic biases when applied to ancient microbial DNA. Shotgun metagenomic sequencing has proven to be the most effective method in reconstructing taxonomic profiles of ancient dental calculus samples. Nevertheless, shotgun sequencing approaches come with inherent limitations that could be addressed through hybridization enrichment capture. When employed together, shotgun sequencing and hybridization capture have the potential to enhance the characterization of ancient microbial communities. Here, we develop, test, and apply a hybridization enrichment capture technique to selectively target 16S rRNA gene fragments from the libraries of ancient dental calculus samples generated with shotgun techniques. We simulated data sets generated from hybridization enrichment capture, indicating that taxonomic identification of fragmented and damaged 16S rRNA gene sequences was feasible. Applying this enrichment approach to 15 previously published ancient calculus samples, we observed a 334-fold increase of ancient 16S rRNA gene fragments in the enriched samples when compared to unenriched libraries. Our results suggest that 16S hybridization capture is less prone to the effects of background contamination than 16S rRNA amplification, yielding a higher percentage of on-target recovery. While our enrichment technique detected low abundant and rare taxa within a given sample, these assignments may not achieve the same level of specificity as those achieved by unenriched methods.

Benchmarking Metagenomic Classifiers on Simulated Ancient and Modern Metagenomic Data

Taxonomic profiling of ancient metagenomic samples is challenging due to the accumulation of specific damage patterns on DNA over time. Although a number of methods for metagenome profiling have been developed, most of them have been assessed on modern metagenomes or simulated metagenomes mimicking modern metagenomes. Further, a comparative assessment of metagenome profilers on simulated metagenomes representing a spectrum of degradation depth, from the extremity of ancient (most degraded) to current or modern (not degraded) metagenomes, has not yet been performed. To understand the strengths and weaknesses of different metagenome profilers, we performed their comprehensive evaluation on simulated metagenomes representing human dental calculus microbiome, with the level of DNA damage successively raised to mimic modern to ancient metagenomes. All classes of profilers, namely, DNA-to-DNA, DNA-to-protein, and DNA-to-marker comparison-based profilers were evaluated on metagenomes with varying levels of damage simulating deamination, fragmentation, and contamination. Our results revealed that, compared to deamination and fragmentation, human and environmental contamination of ancient DNA (with modern DNA) has the most pronounced effect on the performance of each profiler. Further, the DNA-to-DNA (e.g., Kraken2, Bracken) and DNA-to-marker (e.g., MetaPhlAn4) based profiling approaches showed complementary strengths, which can be leveraged to elevate the state-of-the-art of ancient metagenome profiling.

Microbially mediated fossil concretions and their characterization by the latest methodologies: a review

The study of well-preserved organic matter (OM) within mineral concretions has provided key insights into depositional and environmental conditions in deep time. Concretions of varied compositions, including carbonate, phosphate, and iron-based minerals, have been found to host exceptionally preserved fossils. Organic geochemical characterization of concretion-encapsulated OM promises valuable new information of fossil preservation, paleoenvironments, and even direct taxonomic information to further illuminate the evolutionary dynamics of our planet and its biota. Full exploitation of this largely untapped geochemical archive, however, requires a sophisticated understanding of the prevalence, formation controls and OM sequestration properties of mineral concretions. Past research has led to the proposal of different models of concretion formation and OM preservation. Nevertheless, the formation mechanisms and controls on OM preservation in concretions remain poorly understood. Here we provide a detailed review of the main types of concretions and formation pathways with a focus on the role of microbes and their metabolic activities. In addition, we provide a comprehensive account of organic geochemical, and complimentary inorganic geochemical, morphological, microbial and paleontological, analytical methods, including recent advancements, relevant to the characterization of concretions and sequestered OM. The application and outcome of several early organic geochemical studies of concretion-impregnated OM are included to demonstrate how this underexploited geo-biological record can provide new insights into the Earth's evolutionary record. This paper also attempts to shed light on the current status of this research and major challenges that lie ahead in the further application of geo-paleo-microbial and organic geochemical research of concretions and their host fossils. Recent efforts to bridge the knowledge and communication gaps in this multidisciplinary research area are also discussed, with particular emphasis on research with significance for interpreting the molecular record in extraordinarily preserved fossils.

MT-MAG: Accurate and interpretable machine learning for complete or partial taxonomic assignments of metagenomeassembled genomes

We propose MT-MAG, a novel machine learning-based software tool for the complete or partial hierarchically-structured taxonomic classification of metagenome-assembled genomes (MAGs). MT-MAG is alignment-free, with k-mer frequencies being the only feature used to distinguish a DNA sequence from another (herein k = 7). MT-MAG is capable of classifying large and diverse metagenomic datasets: a total of 245.68 Gbp in the training sets, and 9.6 Gbp in the test sets analyzed in this study. In addition to complete classifications, MT-MAG offers a "partial classification" option, whereby a classification at a higher taxonomic level is provided for MAGs that cannot be classified to the Species level. MT-MAG outputs complete or partial classification paths, and interpretable numerical classification confidences of its classifications, at all taxonomic ranks. To assess the performance of MT-MAG, we define a "weighted classification accuracy," with a weighting scheme reflecting the fact that partial classifications at different ranks are not equally informative. For the two benchmarking datasets analyzed (genomes from human gut microbiome species, and bacterial and archaeal genomes assembled from cow rumen metagenomic sequences), MT-MAG achieves an average of 87.32% in weighted classification accuracy. At the Species level, MT-MAG outperforms DeepMicrobes, the only other comparable software tool, by an average of 34.79% in weighted classification accuracy. In addition, MT-MAG is able to completely classify an average of 67.70% of the sequences at the Species level, compared with DeepMicrobes which only classifies 47.45%. Moreover, MT-MAG provides additional information for sequences that it could not classify at the Species level, resulting in the partial or complete classification of 95.13%, of the genomes in the datasets analyzed. Lastly, unlike other taxonomic assignment tools (e.g., GDTB-Tk), MT-MAG is an alignment-free and genetic marker-free tool, able to provide additional bioinformatics analysis to confirm existing or tentative taxonomic assignments.

A Case Study for the Recovery of Authentic Microbial Ancient DNA from Soil Samples

High Throughput DNA Sequencing (HTS) revolutionized the field of paleomicrobiology, leading to an explosive growth of microbial ancient DNA (aDNA) studies, especially from environmental samples. However, aDNA studies that examine environmental microbes routinely fail to authenticate aDNA, examine laboratory and environmental contamination, and control for biases introduced during sample processing. Here, we surveyed the available literature for environmental aDNA projects—from sample collection to data analysis—and assessed previous methodologies and approaches used in the published microbial aDNA studies. We then integrated these concepts into a case study, using shotgun metagenomics to examine methodological, technical, and analytical biases during an environmental aDNA study of soil microbes. Specifically, we compared the impact of five DNA extraction methods and eight bioinformatic pipelines on the recovery of microbial aDNA information in soil cores from extreme environments. Our results show that silica-based methods optimized for aDNA research recovered significantly more damaged and shorter reads (<100 bp) than a commercial kit or a phenol−chloroform method. Additionally, we described a stringent pipeline for data preprocessing, efficiently decreasing the representation of low-complexity and duplicated reads in our datasets and downstream analyses, reducing analytical biases in taxonomic classification.

Improvement of eukaryotic protein predictions from soil metagenomes

During the last decades, metagenomics has highlighted the diversity of microorganisms from environmental or host-associated samples. Most metagenomics public repositories use annotation pipelines tailored for prokaryotes regardless of the taxonomic origin of contigs. Consequently, eukaryotic contigs with intrinsically different gene features, are not optimally annotated. Using a bioinformatics pipeline, we have filtered 7.9 billion contigs from 6,872 soil metagenomes in the JGI's IMG/M database to identify eukaryotic contigs. We have re-annotated genes using eukaryote-tailored methods, yielding 8 million eukaryotic proteins and over 300,000 orphan proteins lacking homology in public databases. Comparing the gene predictions we made with initial JGI ones on the same contigs, we confirmed our pipeline improves eukaryotic proteins completeness and contiguity in soil metagenomes. The improved quality of eukaryotic proteins combined with a more comprehensive assignment method yielded more reliable taxonomic annotation. This dataset of eukaryotic soil proteins with improved completeness, quality and taxonomic annotation reliability is of interest for any scientist aiming at studying the composition, biological functions and gene flux in soil communities involving eukaryotes.

Benchmarking metagenomics classifiers on ancient viral DNA: a simulation study

Owing to technological advances in ancient DNA, it is now possible to sequence viruses from the past to track down their origin and evolution. However, ancient DNA data is considerably more degraded and contaminated than modern data making the identification of ancient viral genomes particularly challenging. Several methods to characterise the modern microbiome (and, within this, the virome) have been developed; in particular, tools that assign sequenced reads to specific taxa in order to characterise the organisms present in a sample of interest. While these existing tools are routinely used in modern data, their performance when applied to ancient microbiome data to screen for ancient viruses remains unknown. In this work, we conducted an extensive simulation study using public viral sequences to establish which tool is the most suitable to screen ancient samples for human DNA viruses. We compared the performance of four widely used classifiers, namely Centrifuge, Kraken2, DIAMOND and MetaPhlAn2, in correctly assigning sequencing reads to the corresponding viruses. To do so, we simulated reads by adding noise typical of ancient DNA to a set of publicly available human DNA viral sequences and to the human genome. We fragmented the DNA into different lengths, added sequencing error and C to T and G to A deamination substitutions at the read termini. Then we measured the resulting sensitivity and precision for all classifiers. Across most simulations, more than 228 out of the 233 simulated viruses were recovered by Centrifuge, Kraken2 and DIAMOND, in contrast to MetaPhlAn2 which recovered only around one third. Overall, Centrifuge and Kraken2 had the best performance with the highest values of sensitivity and precision. We found that deamination damage had little impact on the performance of the classifiers, less than the sequencing error and the length of the reads. Since Centrifuge can handle short reads (in contrast to DIAMOND and Kraken2 with default settings) and since it achieve the highest sensitivity and precision at the species level across all the simulations performed, it is our recommended tool. Regardless of the tool used, our simulations indicate that, for ancient human studies, users should use strict filters to remove all reads of potential human origin. Finally, we recommend that users verify which species are present in the database used, as it might happen that default databases lack sequences for viruses of interest.

Ancient Metagenomic Studies: Considerations for the Wider Scientific Community

Like modern metagenomics, ancient metagenomics is a highly data-rich discipline, with the added challenge that the DNA of interest is degraded and, depending on the sample type, in low abundance. This requires the application of specialized measures during molecular experiments and computational analyses. Furthermore, researchers often work with finite sample sizes, which impedes optimal experimental design and control of confounding factors, and with ethically sensitive samples necessitating the consideration of additional guidelines. In September 2020, early career researchers in the field of ancient metagenomics met (Standards, Precautions & Advances in Ancient Metagenomics 2 [SPAAM2] community meeting) to discuss the state of the field and how to address current challenges. Here, in an effort to bridge the gap between ancient and modern metagenomics, we highlight and reflect upon some common misconceptions, provide a brief overview of the challenges in our field, and point toward useful resources for potential reviewers and newcomers to the field.

Uncovering the genomic and metagenomic research potential in old ethanol-preserved snakes

Natural history museum collections worldwide represent a tremendous resource of information on past and present biodiversity. Fish, reptiles, amphibians and many invertebrate collections have often been preserved in ethanol for decades or centuries and our knowledge on the genomic and metagenomic research potential of such material is limited. Here, we use ancient DNA protocols, combined with shotgun sequencing to test the molecular preservation in liver, skin and bone tissue from five old (1842 to 1964) museum specimens of the common garter snake (Thamnophis sirtalis). When mapping reads to a T. sirtalis reference genome, we find that the DNA molecules are highly damaged with short average sequence lengths (38-64 bp) and high C-T deamination, ranging from 9% to 21% at the first position. Despite this, the samples displayed relatively high endogenous DNA content, ranging from 26% to 56%, revealing that genome-scale analyses are indeed possible from all specimens and tissues included here. Of the three tested types of tissue, bone shows marginally but significantly higher DNA quality in these metrics. Though at least one of the snakes had been exposed to formalin, neither the concentration nor the quality of the obtained DNA was affected. Lastly, we demonstrate that these specimens display a diverse and tissue-specific microbial genetic profile, thus offering authentic metagenomic data despite being submerged in ethanol for many years. Our results emphasize that historical museum collections continue to offer an invaluable source of information in the era of genomics.

Effectiveness of decontamination protocols when analyzing ancient DNA preserved in dental calculus

Ancient DNA analysis of human oral microbial communities within calcified dental plaque (calculus) has revealed key insights into human health, paleodemography, and cultural behaviors. However, contamination imposes a major concern for paleomicrobiological samples due to their low endogenous DNA content and exposure to environmental sources, calling into question some published results. Decontamination protocols (e.g. an ethylenediaminetetraacetic acid (EDTA) pre-digestion or ultraviolet radiation (UV) and 5% sodium hypochlorite immersion treatments) aim to minimize the exogenous content of the outer surface of ancient calculus samples prior to DNA extraction. While these protocols are widely used, no one has systematically compared them in ancient dental calculus. Here, we compare untreated dental calculus samples to samples from the same site treated with four previously published decontamination protocols: a UV only treatment; a 5% sodium hypochlorite immersion treatment; a pre-digestion in EDTA treatment; and a combined UV irradiation and 5% sodium hypochlorite immersion treatment. We examine their efficacy in ancient oral microbiota recovery by applying 16S rRNA gene amplicon and shotgun sequencing, identifying ancient oral microbiota, as well as soil and skin contaminant species. Overall, the EDTA pre-digestion and a combined UV irradiation and 5% sodium hypochlorite immersion treatment were both effective at reducing the proportion of environmental taxa and increasing oral taxa in comparison to untreated samples. This research highlights the importance of using decontamination procedures during ancient DNA analysis of dental calculus to reduce contaminant DNA.

The evolutionary history of the human oral microbiota and its implications for modern health

Numerous biological and cultural factors influence the microbial communities (microbiota) that inhabit the human mouth, including diet, environment, hygiene, physiology, health status, genetics, and lifestyle. As oral microbiota can underpin oral and systemic diseases, tracing the evolutionary history of oral microbiota and the factors that shape its origins will unlock information to mitigate disease today. Despite this, the origins of many oral microbes remain unknown, and the key factors in the past that shaped our oral microbiota are only now emerging. High throughput DNA sequencing of oral microbiota using ancient DNA and comparative anthropological methodologies has been employed to investigate oral microbiota origins, revealing a complex, rich history. Here, I review the current literature on the factors that shaped and guided oral microbiota evolution, both in Europe and globally. In Europe, oral microbiota evolution was shaped by interactions with Neandertals, the adaptation of farming, widespread integration of industrialization, and postindustrial lifestyles that emerged after World War II. Globally, evidence for a multitude of different oral microbiota histories is emerging, likely supporting dissimilarities in modern oral health across discrete human populations. I highlight how these evolutionary changes are linked to the development of modern oral diseases and discuss the remaining factors that need to be addressed to improve this embryonic field of research. I argue that understanding the evolutionary history of our oral microbiota is necessary to identify new treatment and prevention options to improve oral and systemic health in the future.

Investigating the demographic history of Japan using ancient oral microbiota

While microbial communities in the human body (microbiota) are now commonly associated with health and disease in industrialised populations, we know very little about how these communities co-evolved and changed with humans throughout history and deep prehistory. We can now examine these communities by sequencing ancient DNA preserved within calcified dental plaque (calculus), providing insights into the origins of disease and their links to human history. Here, we examine ancient DNA preserved within dental calculus samples and their associations with two major cultural periods in Japan: the Jomon period hunter–gatherers approximately 3000 years before present (BP) and the Edo period agriculturalists 400–150 BP. We investigate how human oral microbiomes have changed in Japan through time and explore the presence of microorganisms associated with oral diseases (e.g. periodontal disease, dental caries) in ancient Japanese populations. Finally, we explore oral microbial strain diversity and its potential links to ancient demography in ancient Japan by performing phylogenomic analysis of a widely conserved oral species—Anaerolineaceae oral taxon 439. This research represents, to our knowledge, the first study of ancient oral microbiomes from Japan and demonstrates that the analysis of ancient dental calculus can provide key information about the origin of non-infectious disease and its deep roots with human demography.

This article is part of the theme issue ‘Insights into health and disease from ancient biomolecules’.

Dental Calculus as a Tool to Study the Evolution of the Mammalian Oral Microbiome

Dental calculus, the calcified form of the mammalian oral microbial plaque biofilm, is a rich source of oral microbiome, host, and dietary biomolecules and is well preserved in museum and archaeological specimens. Despite its wide presence in mammals, to date, dental calculus has primarily been used to study primate microbiome evolution. We establish dental calculus as a valuable tool for the study of nonhuman host microbiome evolution, by using shotgun metagenomics to characterize the taxonomic and functional composition of the oral microbiome in species as diverse as gorillas, bears, and reindeer. We detect oral pathogens in individuals with evidence of oral disease, assemble near-complete bacterial genomes from historical specimens, characterize antibiotic resistance genes, reconstruct components of the host diet, and recover host genetic profiles. Our work demonstrates that metagenomic analyses of dental calculus can be performed on a diverse range of mammalian species, which will allow the study of oral microbiome and pathogen evolution from a comparative perspective. As dental calculus is readily preserved through time, it can also facilitate the quantification of the impact of anthropogenic changes on wildlife and the environment.

Classification of COVID-19 and Other Pathogenic Sequences: A Dinucleotide Frequency and Machine Learning Approach

The world is grappling with the COVID-19 pandemic caused by the 2019 novel SARS-CoV-2. To better understand this novel virus and its relationship with other pathogens, new methods for analyzing the genome are required. In this study, intrinsic dinucleotide genomic signatures were analyzed for whole genome sequence data of eight pathogenic species, including SARS-CoV-2. The genome sequences were transformed into dinucleotide relative frequencies and classified using the extreme gradient boosting (XGBoost) model. The classification models were trained to a) distinguish between the sequences of all eight species and b) distinguish between sequences of SARS-CoV-2 that originate from different geographic regions. Our method attained 100% in all performance metrics and for all tasks in the eight-species classification problem. Moreover, the models achieved 67% balanced accuracy for the task of classifying the SARS-CoV-2 sequences into the six continental regions and achieved 86% balanced accuracy for the task of classifying SARS-CoV-2 samples as either originating from Asia or not. Analysis of the dinucleotide genomic profiles of the eight species revealed a similarity between the SARS-CoV-2 and MERS-CoV viral sequences. Further analysis of SARS-CoV-2 viral sequences from the six continents revealed that samples from Oceania had the highest frequency of TT dinucleotides as well as the lowest CG frequency compared to the other continents. The dinucleotide signatures of AC, AG,CA, CT, GA, GT, TC, and TG were well conserved across most genomes, while the frequencies of other dinucleotide signatures varied considerably. Altogether, the results from this study demonstrate the utility of dinucleotide relative frequencies for discriminating and identifying similar species.

Oral microbiome diversity in chimpanzees from Gombe National Park

Historic calcified dental plaque (dental calculus) can provide a unique perspective into the health status of past human populations but currently no studies have focused on the oral microbial ecosystem of other primates, including our closest relatives, within the hominids. Here we use ancient DNA extraction methods, shotgun library preparation, and next generation Illumina sequencing to examine oral microbiota from 19 dental calculus samples recovered from wild chimpanzees (Pan troglodytes schweinfurthii) who died in Gombe National Park, Tanzania. The resulting sequences were trimmed for quality, analyzed using MALT, MEGAN, and alignment scripts, and integrated with previously published dental calculus microbiome data. We report significant differences in oral microbiome phyla between chimpanzees and anatomically modern humans (AMH), with chimpanzees possessing a greater abundance of Bacteroidetes and Fusobacteria, and AMH showing higher Firmicutes and Proteobacteria. Our results suggest that by using an enterotype clustering method, results cluster largely based on host species. These clusters are driven by Porphyromonas and Fusobacterium genera in chimpanzees and Haemophilus and Streptococcus in AMH. Additionally, we compare a nearly complete Porphyromonas gingivalis genome to previously published genomes recovered from human gingiva to gain perspective on evolutionary relationships across host species. Finally, using shotgun sequence data we assessed indicators of diet from DNA in calculus and suggest exercising caution when making assertions related to host lifestyle. These results showcase core differences between host species and stress the importance of continued sequencing of nonhuman primate microbiomes in order to fully understand the complexity of their oral ecologies.