The rise and fall of the Phytophthora infestans lineage that triggered the Irish potato famine

Yoshida, Kentaro; Schuenemann, Verena J.; Cano, Lililana M.; Pais, Marina; Mishra, Bagdevi; Sharma, Rahul; Lanz, Christa; Martin, Frank N.; Kamoun, Sophien; Krause, Johannes; Thines, Marco; Weigel, Detlef; Burbano, Hernán A.

doi:10.7554/elife.00731

Cited by 335 publications

(418 citation statements)

References 66 publications

(153 reference statements)

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“…In a potato sample, in which the pathogenic oomycete Phytophthora infestans was previously identified [6], we found a large portion of reads assigned to the bacterial genus Pseudomonas. Reads were assigned in particular to the species Pseudomonas syringae and Pseudomonas rhizosphere in different proportions ( Figure S5).…”

mentioning

confidence: 93%

“…S. tuberosum herbarium specimens were documented to be infected by Phytophthora infestans [6]. We used also Zea mays archaeological remains excavated in the Turkey Penn shelter in Utah, USA [24].…”

Section: Plant Samplesmentioning

confidence: 99%

“…DNA from all herbarium specimens and plant archaeological remains was performed as previously described [6].…”

Section: Dna Extraction and Library Preparationmentioning

confidence: 99%

“…As a proof-of-principle experiment, we used here U-selection in combination with taxonomic binning of Illumina sequenced reads to characterize the microbiomes of Neandertal bones (~39,000 years old), herbaria specimens (between 41 and 279 years old) and plant archaeological remains (~2,000 years old) ( Table 1). 4 University of Connecticut Herbarium; 5 New York Botanical Garden; 6 Turkey Pen Shelter, UTAH, USA; 7 El Sidrón Cave, Spain; 8 Vindija Cave, Croatia.…”

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confidence: 99%

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Mining ancient microbiomes using selective enrichment of damaged DNA molecules

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Gansauge

Aximu‐Petri

et al. 2018

Preprint

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The identification of bona fide microbial taxa in microbiomes derived from historic samples is complicated by the unavoidable mixture between DNA from ante-and post-mortem microbial colonizers. One possibility to distinguish between these sources of microbial DNA is querying for the presence of age-associated degradation patterns typical of ancient DNA (aDNA). The presence of uracils, resulting from cytosine deamination, has been detected ubiquitously in aDNA retrieved from diverse sources, and used as an authentication criterion. Here, we employ a library preparation method that separates molecules that carry uracils from those that do not in a set of samples that includes Neandertal remains, herbaria specimens and archaeological plant remains. We show that this method facilitates the discovery of authentic ancient microbial taxa, as it amplifies degradation patterns that would otherwise be difficult to detect in sequences from diverse microbial mixtures. Main textDNA retrieved from historical or ancient samples is a complex mixture of molecules that contains not only endogenous host DNA, but also DNA from microorganisms that were present ante-mortem or that colonized the tissue post-mortem [1]. Therefore, all ancient DNA (aDNA) shotgun sequencing projects are metagenomic in nature. While earlier aDNA research has mostly focused on the evolution of animals and plants [2,3], a growing number of studies are now centering on the identification and characterization of ancient pathogens and microbiomes . CC-BY-NC 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

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mentioning

confidence: 93%

Section: Plant Samplesmentioning

confidence: 99%

“…DNA from all herbarium specimens and plant archaeological remains was performed as previously described [6].…”

Section: Dna Extraction and Library Preparationmentioning

confidence: 99%

mentioning

confidence: 99%

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Mining ancient microbiomes using selective enrichment of damaged DNA molecules

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Gansauge

Aximu‐Petri

et al. 2018

Preprint

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“…Исследователям удалось восстановить геномную информацию из самых разнообразных типов древнего биологического материала: волос [4,5], мумифицированных тканей [6], кальцинированных зубов и костей [7][8][9], а также растительных останков [10,11]. С появлением новых технических возможностей миллионы и миллиарды последовательностей ДНК могут быть получены из древних биологических образцов, благодаря массивной пропускной способности современных платформ для секвенирования следующего поколения.…”

Section: Introductionunclassified

Integration of Heterogeneous Computing Infrastructures for Genome Sequencing Data Analysis

Aulov¹

2016

Math.Biol.Bioinf.

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Аннотация. Недавние достижения технологий в области геномного секвенирования нового поколения (NGS -Next Generation Genome Sequencing) привели к значительному увеличению объема данных, которые должны быть обработаны, проанализированы и доступны заинтересованным исследователям. Это, в свою очередь, привело к повышению требований к вычислительным платформам обработки данных: потребовалось больше оперативной памяти и более мощные процессоры. Для эффективной обработки данных необходимы принципиально новые подходы в организации вычислений. Авторами статьи было проведено исследование возможности применения методов и подходов, используемых в физике высоких энергий, для объединения гетерогенных вычислительных ресурсов в единую вычислительную платформу. Была разработана полномасштабная система управления данными и заданиями на базе вычислительных мощностей Национального исследовательского центра «Курчатовский институт». В разработанную систему был интегрирован рабочий поток для обработки данных геномного секвенирования с использованием пакета PALEOMIX. Результаты апробации разработанной системы на задаче анализа древней ДНК мамонта показали существенное уменьшение общего времени выполнения задачи.Ключевые слова: распределенные вычисления, суперкомпьютеры, геномное секвенирование.

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Population Biology of Plant Pathogens

Shaw

2014

Encyclopedia of Life Sciences

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Population change in plant pathogens is driven by environmental factors, especially wetness and temperature, and by intrinsic and induced host resistance. For pathogens except those of woody tissue, turnover of host tissue, and decline in pathogen population during periods when the host is absent or conditions unsuitable for infection produce intrinsic fluctuations in populations. Movement of propagules to new hosts is much more efficient in denser host stands, introducing density dependence at a larger scale. The reduction of host populations by a successful pathogen both allows space for other plants to grow, and introduces selective forces increasing resistance in the host. This gives rise to a coevolutionary race between host and pathogen, resulting in both diversity and turnover of genetic elements involved in defence signalling. Rare movement of pathogens between subpopulations of a metapopulation introduces a further birth–death process causing fluctuations in disease and selection pressures on relevant genetic elements. Key Concepts: Population densities of plant pathogens are set by the balance between pathogen infection and host turnover. Populations of plant pathogens are rarely stable, but cycle from high to low across seasons. Environmental factors modulate or limit but do not regulate pathogen abundance. Asexual reproduction is often associated with shorter distance dispersal mechanisms and sexual reproduction with longer distance mechanisms. Density‐dependence acts at the level of the individual host, the host population and within metapopulations. Pathogens increase biodiversity by disadvantaging common hosts. Coevolution between host and pathogen leads to continuing change and diversity in pathogenesis related parts of the genome. When a pathogen encounters a susceptible host not coevolved with it – for example, through trade – a dramatic drop in host density may occur.

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The rise and fall of the Phytophthora infestans lineage that triggered the Irish potato famine

Cited by 335 publications

References 66 publications

Mining ancient microbiomes using selective enrichment of damaged DNA molecules

Mining ancient microbiomes using selective enrichment of damaged DNA molecules

Integration of Heterogeneous Computing Infrastructures for Genome Sequencing Data Analysis

Population Biology of Plant Pathogens

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