A 14th century CE Brucella melitensis genome and the recent expansion of the Western Mediterranean clade.

Brucellosis is a disease caused by the bacterium Brucella and typically transmitted through contact with infected ruminants. It is one of the most common chronic zoonotic diseases and of particular interest to public health agencies. Despite its well-known transmission history and characteristic symptoms, we lack a more complete understanding of the evolutionary history of its best-known species-Brucella melitensis. To address this knowledge gap we fortuitously found, sequenced and assembled a high-quality ancient B. melitensis draft genome from the kidney stone of a 14th-century Italian friar. The ancient strain contained fewer core genes than modern B. melitensis isolates, carried a complete complement of virulence genes, and did not contain any indication of significant antimicrobial resistances. The ancient B. melitensis genome fell as a basal sister lineage to a subgroup of B. melitensis strains within the Western Mediterranean phylogenetic group, with a short branch length indicative of its earlier sampling time, along with a similar gene content. By calibrating the molecular clock we suggest that the speciation event between B. melitensis and B. abortus is contemporaneous with the estimated time frame for the domestication of both sheep and goats. These results confirm the existence of the Western Mediterranean clade as a separate group in the 14th CE and suggest that its divergence was due to human and ruminant co-migration.

Examining pathogen DNA recovery across the remains of a 14th century Italian friar (Blessed Sante) infected with Brucella melitensis.

OBJECTIVE: To investigate variation in ancient DNA recovery of Brucella melitensis, the causative agent of brucellosis, from multiple tissues belonging to one individual MATERIALS: 14 samples were analyzed from the mummified remains of the Blessed Sante, a 14 th century Franciscan friar from central Italy, with macroscopic diagnosis of probable brucellosis. METHODS: Shotgun sequencing data from was examined to determine the presence of Brucella DNA. RESULTS: Three of the 14 samples contained authentic ancient DNA, identified as belonging to B. melitensis. A genome (23.81X depth coverage, 0.98 breadth coverage) was recovered from a kidney stone. Nine of the samples contained reads classified as B. melitensis (7-169), but for many the data quality was insufficient to withstand our identification and authentication criteria. CONCLUSIONS: We identified significant variation in the preservation and abundance of B. melitensis DNA present across multiple tissues, with calcified nodules yielding the highest number of authenticated reads. This shows how greatly sample selection can impact pathogen identification. SIGNIFICANCE: Our results demonstrate variation in the preservation and recovery of pathogen DNA across tissues. This study highlights the importance of sample selection in the reconstruction of infectious disease burden and highlights the importance of a holistic approach to identifying disease. LIMITATIONS: Study focuses on pathogen recovery in a single individual. SUGGESTIONS FOR FURTHER RESEARCH: Further analysis of how sampling impacts aDNA recovery will improve pathogen aDNA recovery and advance our understanding of disease in past peoples.

A 16th century Escherichia coli draft genome associated with an opportunistic bile infection.

Escherichia coli - one of the most characterized bacteria and a major public health concern - remains invisible across the temporal landscape. Here, we present the meticulous reconstruction of the first ancient E. coli genome from a 16th century gallstone from an Italian mummy with chronic cholecystitis. We isolated ancient DNA and reconstructed the ancient E. coli genome. It consisted of one chromosome of 4446 genes and two putative plasmids with 52 genes. The E. coli strain belonged to the phylogroup A and an exceptionally rare sequence type 4995. The type VI secretion system component genes appears to be horizontally acquired from Klebsiella aerogenes, however we could not identify any pathovar specific genes nor any acquired antibiotic resistances. A sepsis mouse assay showed that a closely related contemporary E. coli strain was avirulent. Our reconstruction of this ancient E. coli helps paint a more complete picture of the burden of opportunistic infections of the past.

Collapse of the mammoth-steppe in central Yukon as revealed by ancient environmental DNA.

The temporal and spatial coarseness of megafaunal fossil records complicates attempts to to disentangle the relative impacts of climate change, ecosystem restructuring, and human activities associated with the Late Quaternary extinctions. Advances in the extraction and identification of ancient DNA that was shed into the environment and preserved for millennia in sediment now provides a way to augment discontinuous palaeontological assemblages. Here, we present a 30,000-year sedimentary ancient DNA (sedaDNA) record derived from loessal permafrost silts in the Klondike region of Yukon, Canada. We observe a substantial turnover in ecosystem composition between 13,500 and 10,000 calendar years ago with the rise of woody shrubs and the disappearance of the mammoth-steppe (steppe-tundra) ecosystem. We also identify a lingering signal of Equus sp. (North American horse) and Mammuthus primigenius (woolly mammoth) at multiple sites persisting thousands of years after their supposed extinction from the fossil record.

Identifying genetic determinants of complex phenotypes from whole genome sequence data.

BACKGROUND: A critical goal in biology is to relate the phenotype to the genotype, that is, to find the genetic determinants of various traits. However, while simple monofactorial determinants are relatively easy to identify, the underpinnings of complex phenotypes are harder to predict. While traditional approaches rely on genome-wide association studies based on Single Nucleotide Polymorphism data, the ability of machine learning algorithms to find these determinants in whole proteome data is still not well known. RESULTS: To better understand the applicability of machine learning in this case, we implemented two such algorithms, adaptive boosting (AB) and repeated random forest (RRF), and developed a chunking layer that facilitates the analysis of whole proteome data. We first assessed the performance of these algorithms and tuned them on an influenza data set, for which the determinants of three complex phenotypes (infectivity, transmissibility, and pathogenicity) are known based on experimental evidence. This allowed us to show that chunking improves runtimes by an order of magnitude. Based on simulations, we showed that chunking also increases sensitivity of the predictions, reaching 100% with as few as 20 sequences in a small proteome as in the influenza case (5k sites), but may require at least 30 sequences to reach 90% on larger alignments (500k sites). While RRF has less specificity than random forest, it was never <50%, and RRF sensitivity was significantly higher at smaller chunk sizes. We then used these algorithms to predict the determinants of three types of drug resistance (to Ciprofloxacin, Ceftazidime, and Gentamicin) in a bacterium, Pseudomonas aeruginosa. While both algorithms performed well in the case of the influenza data, results were more nuanced in the bacterial case, with RRF making more sensible predictions, with smaller errors rates, than AB. CONCLUSIONS: Altogether, we demonstrated that ML algorithms can be used to identify genetic determinants in small proteomes (viruses), even when trained on small numbers of individuals. We further showed that our RRF algorithm may deserve more scrutiny, which should be facilitated by the decreasing costs of both sequencing and phenotyping of large cohorts of individuals.