Alexander Seitz

EAGER: efficient ancient genome reconstruction.

BackgroundThe automated reconstruction of genome sequences in ancient genome analysis is a multifaceted process.ResultsHere we introduce EAGER, a time-efficient pipeline, which greatly simplifies the analysis of large-scale genomic data sets. EAGER provides features to preprocess, map, authenticate, and assess the quality of ancient DNA samples. Additionally, EAGER comprises tools to genotype samples to discover, filter, and analyze variants.ConclusionsEAGER encompasses both state-of-the-art tools for each step as well as new complementary tools tailored for ancient DNA data within a single integrated solution in an easily accessible format.

Origin of modern syphilis and emergence of a pandemic Treponema pallidum cluster

The abrupt onslaught of the syphilis pandemic that started in the late fifteenth century established this devastating infectious disease as one of the most feared in human history1. Surprisingly, despite the availability of effective antibiotic treatment since the mid-twentieth century, this bacterial infection, which is caused by Treponema pallidum subsp. pallidum (TPA), has been re-emerging globally in the last few decades with an estimated 10.6 million cases in 2008 (ref. 2). Although resistance to penicillin has not yet been identified, an increasing number of strains fail to respond to the second-line antibiotic azithromycin3. Little is known about the genetic patterns in current infections or the evolutionary origins of the disease due to the low quantities of treponemal DNA in clinical samples and difficulties in cultivating the pathogen4. Here, we used DNA capture and whole-genome sequencing to successfully interrogate genome-wide variation from syphilis patient specimens, combined with laboratory samples of TPA and two other subspecies. Phylogenetic comparisons based on the sequenced genomes indicate that the TPA strains examined share a common ancestor after the fifteenth century, within the early modern era. Moreover, most contemporary strains are azithromycin-resistant and are members of a globally dominant cluster, named here as SS14-Ω. The cluster diversified from a common ancestor in the mid-twentieth century subsequent to the discovery of antibiotics. Its recent phylogenetic divergence and global presence point to the emergence of a pandemic strain cluster.

Inferring disease-related metabolite dependencies with a bayesian optimization algorithm

Understanding disease-related metabolite interactions is a key issue in computational biology. We apply a modified Bayesian Optimization Algorithm to targeted metabolomics data from plasma samples of insulin-sensitive and -resistant subjects both suffering from non-alcoholic fatty liver disease. In addition to improving the classification accuracy by selecting relevant features, we extract the information that led to their selection and reconstruct networks from detected feature dependencies. We compare the influence of a variety of classifiers and different scoring metrics and examine whether the reconstructed networks represent physiological metabolite interconnections. We find that the presented method is capable of significantly improving the classification accuracy of otherwise hardly classifiable metabolomics data and that the detected metabolite dependencies can be mapped to physiological pathways, which in turn were affirmed by literature from the domain.

Ancient genomes reveal a high diversity of Mycobacterium leprae in medieval Europe

Studying ancient DNA allows us to retrace the evolutionary history of human pathogens, such as Mycobacterium leprae, the main causative agent of leprosy. Leprosy is one of the oldest recorded and most stigmatizing diseases in human history. The disease was prevalent in Europe until the 16th century and is still endemic in many countries with over 200,000 new cases reported annually. Previous worldwide studies on modern and European medieval M. leprae genomes revealed that they cluster into several distinct branches of which two were present in medieval Northwestern Europe. In this study, we analyzed 10 new medieval M. leprae genomes including the so far oldest M. leprae genome from one of the earliest known cases of leprosy in the United Kingdom—a skeleton from the Great Chesterford cemetery with a calibrated age of 415–545 C.E. This dataset provides a genetic time transect of M. leprae diversity in Europe over the past 1500 years. We find M. leprae strains from four distinct branches to be present in the Early Medieval Period, and strains from three different branches were detected within a single cemetery from the High Medieval Period. Altogether these findings suggest a higher genetic diversity of M. leprae strains in medieval Europe at various time points than previously assumed. The resulting more complex picture of the past phylogeography of leprosy in Europe impacts current phylogeographical models of M. leprae dissemination. It suggests alternative models for the past spread of leprosy such as a wide spread prevalence of strains from different branches in Eurasia already in Antiquity or maybe even an origin in Western Eurasia. Furthermore, these results highlight how studying ancient M. leprae strains improves understanding the history of leprosy worldwide.

Improving ancient DNA genome assembly

Most reconstruction methods for genomes of ancient origin that are used today require a closely related reference. In order to identify genomic rearrangements or the deletion of whole genes, de novo assembly has to be used. However, because of inherent problems with ancient DNA, its de novo assembly is highly complicated. In order to tackle the diversity in the length of the input reads, we propose a two-layer approach, where multiple assemblies are generated in the first layer, which are then combined in the second layer. We used this two-layer assembly to generate assemblies for two different ancient samples and compared the results to current de novo assembly approaches. We are able to improve the assembly with respect to the length of the contigs and can resolve more repetitive regions.

Origin of modern syphilis and emergence of a contemporary pandemic cluster

Syphilis swept across the world in the 16th century as one of most prominent documented pandemics and is re-emerging worldwide despite the availability of effective antibiotics. Little is known about the genetic patterns in current infections or the evolutionary origins of the disease due to the non-cultivable and clonal nature of the causative bacterium Treponema pallidum subsp. pallidum. In this study, we used DNA capture and next generation sequencing to obtain whole genome data from syphilis patient specimens and from treponemes propagated in laboratory settings. Phylogenetic analyses indicate that the syphilis strains examined here share a common ancestor after the 15th century. Moreover, most contemporary strains are azithromycin resistant and members of a globally dominant cluster named here as SS14-Ω. This cluster diversified from a common ancestor in the mid-20th century and has the population genetic and epidemiological features indicative of the emergence of a pandemic strain cluster.

DACCOR–Detection, characterization, and reconstruction of repetitive regions in bacterial genomes

The reconstruction of genomes using mapping-based approaches with short reads experiences difficulties when resolving repetitive regions. These repetitive regions in genomes result in low mapping qualities of the respective reads, which in turn lead to many unresolved bases. Currently, the reconstruction of these regions is often based on modified references in which the repetitive regions are masked. However, for many references, such masked genomes are not available or are based on repetitive regions of other genomes. Our idea is to identify repetitive regions in the reference genome de novo. These regions can then be used to reconstruct them separately using short read sequencing data. Afterward, the reconstructed repetitive sequence can be inserted into the reconstructed genome. We present the program detection, characterization, and reconstruction of repetitive regions, which performs these steps automatically. Our results show an increased base pair resolution of the repetitive regions in the reconstruction of Treponema pallidum samples, resulting in fewer unresolved bases.

The impact of winter feed type on intestinal microbiota and parasites in honey bees

The intestinal microbiota of honey bees consists of only few bacterial species and may have effects on health and pathogen resilience. Honey is usually harvested and replaced by sugar syrup. We hypothesized that replacing honey may change the composition of the intestinal microbiota, and therefore compromise pathogen resilience. Fifteen colonies were fed with wheat starch syrup, sucrose syrup, or blossom honey. 16S-based bacterial community analysis was performed on three individuals per hive in summer and winter, and Nosema ceranae and Crithidia/Lotmaria levels were assessed by qPCR. Seasonal differences in the intestinal microbiota and N. ceranae were found; however, microbiota and parasite levels were very similar between the feed types. Rhizobiales and Bifidobacteria were found to be increased in the bees that had received honey or wheat starch syrup, as compared to sucrose syrup. In conclusion, intestinal microbiota and parasites were found to be largely unaffected by the winter feed type.