Final Report Summary - APGREID (Ancient Pathogen Genomics of Re-Emerging Infectious Disease)
The APGREID project focused on pathogen evolution by studying microbial DNA of ancient specimens from historical disease contexts. In these studies we pioneered a novel application of ancient DNA by establishing the research field of “ancient pathogen genomics”, which permits the retrieval of empirical data relating to the evolutionary history of pathogens. Our team demonstrated for the first time the application of DNA capture techniques and high-throughput DNA sequencing to reconstruct complete pathogen genomes from ancient skeletons.
During the APGREID project we were able to study complete genomes from multiple causative agents of infectious diseases from historical and prehistorical skeletons, those include Yersinia pestis, medieval Mycobacterium leprae, pre-Columbian Mycobacterium tuberculosis from Peru, Salmonella enterica from 16th century early contact period in Mexico and Heliobacter pylori from the Tyrolean iceman. We applied the same techniques to reconstruct the plant pathogen Phytophtora infestans from the 19th century potato leaves from the time of the Irish potatoe famine in a collaboration project, establishing the field of ancient plant pathogen genomics. In all those cases we were able to calculate mutation rates and evolutionary relationships for those pathogens that provided new insights into the evolutionary trajectory of those disease-causing agents. We were furthermore able to demonstrate when during their evolution virulence factors emerged and how they evolved and point out the existence of likely now extinct rodent reservoir populations for Y.pestis in Europe, that fed into the domestic animals that passed the plague on to humans.
In order to retrieve and reconstruct the ancient pathogen genomes we applied and designed a number of targeted capture techniques. To screen a large number of skeletal samples in parallel for ancient pathogens we developed the ancient pathogen screening array (APSA) (Bos et al. 2015, Proc.Roy.Soc.London). The array is capable of detecting nearly 100 pathogens that could have potentially left behind molecular signatures in preserved ancient tissues. We were able to demonstrate the sensitivity of this method through evaluation of its performance in comparison with previously tested control samples. This rapid and economical technique is highly useful for the identification of historical diseases that are difficult to characterize based on archaeological information alone.
We furthermore developed in collaboration with Prof. Huson at the University of Tuebingen an algorithm that can be used to sort billions of next generation DNA sequencing reads and bin them by taxon. The algorithm called MALT is orders of magnitude faster than classical approaches to identify DNA reads such as BLAST. This approach is an alternative to the APSA-array as it can allow identifying pathogens from ancient skeletons using direct shotgun sequencing data. This is more economical and allows a higher throughput of samples. It furthermore allows detecting bacteria that are not part of the APSA design. Furthermore an array capture bias will be avoided. This algorithm is now routinely used in the lab and has largely replaced the APSA detection of pathogens during the second part of the APGREID project. The algorithm has also major potential to be applied within the medical field in particular in metagenomics and microbiome projects that have gained a tremendous interest in the last few years in the medical community.
During the APGREID project we were able to study complete genomes from multiple causative agents of infectious diseases from historical and prehistorical skeletons, those include Yersinia pestis, medieval Mycobacterium leprae, pre-Columbian Mycobacterium tuberculosis from Peru, Salmonella enterica from 16th century early contact period in Mexico and Heliobacter pylori from the Tyrolean iceman. We applied the same techniques to reconstruct the plant pathogen Phytophtora infestans from the 19th century potato leaves from the time of the Irish potatoe famine in a collaboration project, establishing the field of ancient plant pathogen genomics. In all those cases we were able to calculate mutation rates and evolutionary relationships for those pathogens that provided new insights into the evolutionary trajectory of those disease-causing agents. We were furthermore able to demonstrate when during their evolution virulence factors emerged and how they evolved and point out the existence of likely now extinct rodent reservoir populations for Y.pestis in Europe, that fed into the domestic animals that passed the plague on to humans.
In order to retrieve and reconstruct the ancient pathogen genomes we applied and designed a number of targeted capture techniques. To screen a large number of skeletal samples in parallel for ancient pathogens we developed the ancient pathogen screening array (APSA) (Bos et al. 2015, Proc.Roy.Soc.London). The array is capable of detecting nearly 100 pathogens that could have potentially left behind molecular signatures in preserved ancient tissues. We were able to demonstrate the sensitivity of this method through evaluation of its performance in comparison with previously tested control samples. This rapid and economical technique is highly useful for the identification of historical diseases that are difficult to characterize based on archaeological information alone.
We furthermore developed in collaboration with Prof. Huson at the University of Tuebingen an algorithm that can be used to sort billions of next generation DNA sequencing reads and bin them by taxon. The algorithm called MALT is orders of magnitude faster than classical approaches to identify DNA reads such as BLAST. This approach is an alternative to the APSA-array as it can allow identifying pathogens from ancient skeletons using direct shotgun sequencing data. This is more economical and allows a higher throughput of samples. It furthermore allows detecting bacteria that are not part of the APSA design. Furthermore an array capture bias will be avoided. This algorithm is now routinely used in the lab and has largely replaced the APSA detection of pathogens during the second part of the APGREID project. The algorithm has also major potential to be applied within the medical field in particular in metagenomics and microbiome projects that have gained a tremendous interest in the last few years in the medical community.