abstractinhalt 2019 cv - systems biology · 2 // 5th international symposium systems biology of...

5th International Symposium Systems Biology of Microbial Infection // 1

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Dear participants of SBMI 2019,dear scientists, dear colleagues,

It is our great pleasure to welcome you to the 5th International Symposium on Systems Biology of Microbial Infections in Jena, which is taking place biennially

since the year 2011.

Last year, Friedrich Schiller University Jena (Germany) was granted funding for the Cluster of Excellence ’Balance of the Microverse’. This Cluster is among the 57 selected consortia, which will be funded by the German Excellence Strategy for the following seven years. In line with the thematic orientation of the Microverse cluster on the formation and balance of microbial consortia, SBMI 2019 brings together international top scientists working at the interface of microbiota and the hosts they inhabit. Talks will highlight exciting research in the fields of immunology, microbiology, ecology and evolution. Participants will learn about how the microbiota shapes the host immune response and how alterations to the microbiota through infection, medication or lifestyle can influence susceptibility to a variety of diseases and discuss the challenges of big data in microbiome research.

A second focus of the symposium is on the interaction between the immune systems of humans or animals and bacterial or fungal pathogens. Participants will learn about methods, such as machine learning, virtual infection modeling and metagenomics to decipher pathogenicity mechanisms, analyze spatio-temporal aspects of infection processes and reconstruct molecular and cellular networks based on time series data. Thereby, diagnostic biomarkers and potential drug targets are identified and novel strategies for personalized therapy are discussed.

The program of SBMI 2019 covers nine plenary lectures with excellent international speakers from different fields together with many talks from young engaged scientists. During an exciting poster session, we hope to provide you with the great opportunity to have scientific discussions and to make steps into future collaborations that will advance the field of Systems Biology of Microbial Infections.

We would like to take this opportunity to thank you all for being an important part of SBMI 2019 and we hope you enjoy the meeting!

The organizers

Gianni Panagiotou Marc Thilo Figge

2 // 5th International Symposium Systems Biology of Microbial Infection

SBMI PROGRAM

Thursday, September 26, 2019

12:00 Registration & Light Lunch

12:30 Opening Gianni Panagiotou (Leibniz HKI, Jena, Germany)

Session I: Microbiota Evolution, Development and Metabolism (Chair: Howell Leung)

12:40 Bacteriophages as architects of bacteriome structure and functionColin Hill (University College Cork, Ireland)

13:20 High-throughput interaction profiling in bacteriaNassos Typas (EMBL Heidelberg, Germany)

14:00 Gut microbiome fermentation determines the therapeutic efficacy of exercise on insulin resistance in individuals with prediabetesYueqiong Ni (Leibniz HKI, Jena, Germany)

14:15 Break

Session II: Current Innovations in Metagenomics Analysis (Chair: Daniel Loos)

15:00 High-resolution large-scale profiling of pathogens and commensals frommetagenomicsNicola Segata (University of Trento, Italy)

15:40 Keeping up with exponentially growing databases and time constraints in metagenomic pathogen discoveryBernhard Renard (Robert Koch Institute, Berlin, Germany)

16:20 Gut fungi – How antibiotics administration shapes our mycobiomeBastian Seelbinder (Leibniz HKI, Jena, Germany)

16:35 Break

Session III: Imaging and Organ-on-Chip Models (Chair: Teresa Lehnert)

17:00 Microphysiological systems as platform for studies on host-microbiota interactionAlexander Mosig (Jena University Hospital, Germany)

17:15 "Invasive aspergillosis-on-a-chip” – a novel disease model to study Aspergillus fumigatus infection in the human lungSusann Hartung & Zoltan Cseresnyes (Jena University Hospital, Germany & Leibniz HKI, Jena, Germany)


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17:40 Describing neutrophil behavior following influenza vaccination from intravital imaging dataDiego U. Pizzagalli & Santiago Fernando Gonzalez (USI, Lugano, Switzerland & Institute for Research in Biomedicine, Bellinzona, Switzerland)

18:05 Dinner

19:00 Poster Session

Friday, September 27, 2019

Session IV: Systems Biology of Blood Infection and Sepsis (Chair: Sandra Timme)

9:00 Immune recognition of fungal pathogens in human blood Oliver Kurzai (University of Würzburg, Germany)

9:40 Unraveling innate immune mechanisms against pathogens in avian whole blood by combining experimental and mathematical infection modelsTeresa Lehnert & Ilse D. Jacobsen (Leibniz HKI, Jena, Germany)

10:05 Reprogramming of macrophages employing gene-regulatory and metabolicnetwork modelsRainer König (Jena University Hospital, Germany)

10:20 High throughput Raman spectroscopy for leukocytes investigationsJan Rüger & Anuradha Ramoji (Leibniz IPHT, Jena, Germany & Friedrich Schiller University Jena, Germany)

10:45 Break

Session V: Systems Biology of Fungal and Bacterial Infections I (Chair: Alexander Tille)

11:05 Sensing and signalling during directional growth in Candida albicans hyphaeAlexandra Brand (University of Aberdeen, UK)

11:45 Multiple perspectives of the glyoxylate shunt as antifungal drug targetin Candida albicansJan Ewald & Sascha Brunke (Friedrich Schiller University Jena, Germany & Leibniz HKI, Jena, Germany)

12:10 HOPS: A pipeline for screening archaeological remains for pathogen DNARon Hübler (MPI for the Science of Human History, Jena, Germany)


12:25 Effects of 1-Methyltryptophan on the kynurenine pathway in pigsDana Kleimeier (University Medicine Greifswald, Germany)

12:40 Break

Session VI: Systems Biology of Fungal and Bacterial Infections II (Chair: Albert Garcia Lopez)

13:30 The Cryptococcus neoformans Titan cell is an inducible and regulated morphotype underlying pathogenesisLiz Ballou (University of Birmingham, UK)

14:10 Fighting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnosticsAlice C. McHardy (Helmholz Center for Infection Research, Braunschweig, Germany)

14:50 Impact of genetic variability on fungal and bacterial infectionAntje Header & Sascha Schäuble (Leibniz HKI, Jena, Germany)

15:15 Concluding remarksMarc Thilo Figge (Leibniz HKI, Jena, Germany)

SBMI PROGRAM


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Talks

Hill C.: Bacteriophages as architects of bacteriome structure and function 08

Typas N.: High-throughput interaction profiling in bacteria 09

Ni Y.: Gut microbiome fermentation determines the therapeutic efficacy of exercise on insulin resistance in individuals with prediabetes

10

Segata N.: High-resolution large-scale profiling of pathogens and commensals from metag-enomics

11

Renard B.: Keeping up with exponentially growing databases and time constraints in metag-enomic pathogen discovery

12

Seelbinder B.: Gut fungi – How antibiotics administration shapes our mycobiome 13

Mosig A.: Microphysiological systems as platform for studies on host-microbiota interaction 14

Hartung S. & Cseresnyes Z.: “Invasive aspergillosis-on-a-chip” – a novel disease model to study Aspergillus fumigatus infection in the human lung

15

Pizzagalli D. U., Gonzalez S. F.: Describing neutrophil behavior following influenza vaccination from intravital imaging data

16

Kurzai O.: Immune recognition of fungal pathogens in human blood 17

Lehnert T., Jacobsen I. D.: Unraveling innate immune mechanisms against pathogens in avian whole blood by combining experimental and mathematical infection models

18

Koenig R.: Reprogramming of macrophages employing gene-regulatory and metabolic net-work models

19

Ruger J., Ramoji A.: High throughput Raman spectroscopy for leukocytes investigations 20

Brand A.: Sensing and signalling during directional growth in Candida albicans hyphae 21

Ewald J., Brunke S.: Multiple perspectives of the glyoxylate shunt as antifungal drug target in Candida albicans

22

Hubler R.: HOPS: A pipeline for screening archaeological remains for pathogen DNA 23

Kleimeier D.: Effects of 1-Methyltryptophan on the kynurenine pathway in pigs 24

Ballou E.: The Cryptococcus neoformans Titan cell is an inducible and regulated morphotype underlying pathogenesis

25

McHardy A. C.: Fighting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics

26

Haeder A., Schäuble S.: Impact of genetic variability on fungal and bacterial infection 27

Posters

ABSTRACTS


Belyaev I.: Morphokinetic analysis of live-cell imaging data 30

Blickensdorf M.: Comparative assessment of aspergillosis by virtual infection modeling in murine and human lung

31

Borry M.: Metagenomic samples source prediction using machine learning 32

Chen J.: A Pollution gradient contributes to the taxonomic, functional and resistome diversity of microbial communities in marine sediments

33

Falola D.: A supervised machine learning approach for predicting transcription factor target gene interactions

34

Lopez A. G.: Early detection of sepsis through the utilization of effective biomarkers 35

Moreno M. G.: Bone Biochip: The alternative model to study bone infection 36

Hoang M. T. N.: “Invasive aspergillosis-on-a-chip” – a novel disease model to study Aspergillus fumigatus infection in the human lung

37

Tetteh J.: Antibiotic resistance: a view from control theory 38

Lehnert T.: Quantification of the innate immune function in human whole-blood infection as-says reveals pathogen-dependent immune defence of different sepsis phases.

39

Lehnert T.: Verifying hypotheses on immune evasion by pathogens in human whole blood by state-based virtual infection models.

40

Loos D.: A user friendly web application for ITS sequencing data to analyze abundance, diver-sity, interactions, and disease associations of fungal communities

41

Mirhakkak Esfahani M.: Metabolic interactions with specific gut bacteria are key determinants for colonization levels of Candida albicans

42

Pistiki A.: Indirect identification of pathogens via Raman spectroscopy on monocytes in an in vitro infection model

43

Praetorius J.-P.: Automatic analysis of fungal-infected tissue using deep learning 44

Sae-Ong T.: Genomics insights into azole resistance in Aspergillus fumigatus 45

Santhanam R.: Gut bacterial dysbiosis leads to overgrowth of opportunistic pathogen C. albicans in lung cancer patients.

46

Schäuble S.: Insights into invasive aspergillosis by OMICS data from a longitudinal patient cohort 47

Siegmund A: S. aureus uses autophagic cell compartments to adapt for long-term intracel-lular persistence

48

Svensson C.-M.: Bayesian analysis of pseudo-time resolved somatic hypermutation in Brca2-deficient B cells

49

Tille A.: Mathematical model of the factor H mediated self and non-self discrimination by the complement system

50

Timme S.: Virtual phagocytosis assays reveal strain-specific differences in the microscopic parameters of the interaction between alveolar macrophages and two A. fumigatus strains

51

Velsko I. M.: Ancient human microbiomes for the study of microbe-microbe and host-micro-biome interactions

52

ABSTRACTS


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TALKS

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TALKS

Bacteriophages as architects of bacteriome structure and function Colin Hill1

1 APC Microbiome Ireland, University College Cork, Cork Ireland

Bacteriophage are the most abundant biological entities on earth, and play a role in shaping bacterial communities. This is also the case in the human gut. We have performed a combination of wet-lab and bioinformatics approaches to try to understand the role of bacteriophage in the gut microbiome, in the process identifying tens of thousands of novel phage genomes of extraordinary diversity. We have also isolated in culture some phage which had only been seen previously ‘in silico’. These include the crAss-phage, the most abundant phage in the human microbiome, which can represent 95% of all of the phage in some individuals. I will describe some of the challenges and rewards of studying this complex community, including an unpublished longitudinal study which provides insights into the stability and uniqueness of individual human ‘phageomes’.


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High-throughput interaction profiling in bacteria Nassos Typas1

1 Structural and Computational Biology, European Molecular Biology Laboratory, Heidelberg, Germany

Systematic and quantitative profiling of functional interactions at a genome-wide level provides unique insights into cellular behaviors and the overall cellular network architecture. In my lab, we develop new tools and strategies for such systematic approaches in bacteria and then use them to study underlying mechanisms. Here, I will present recent developments on this front, and provide insights into how we use these approaches to uncover new biology in different bacterial species, and their interfaces with other microbes, the host and the environment. As highlights, I will zoom in a recent effort to identify general prin-ciples behind drug-drug interactions in bacteria, and present our advances in establishing experimental frameworks for high-throughput microbiomics.


Gut microbiome fermentation determines the therapeutic efficacy of exercise on insulin resistance in individuals with prediabetesYueqiong Ni1,2,*, Yan Liu4,5,*, Yao Wang4,5,*, Michael Andrew Tse6, Gianni Panagiotou1,2,3, Aimin Xu4,5

1 Leibniz Institute for Natural Product Research and Infection Biology, Hans Knoll Institute, Jena, Germany2 Systems Biology & Bioinformatics Group, School of Biological Sciences, Faculty of Science, The University of Hong Kong, Hong Kong S.A.R., China3 Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong S.A.R., China4 State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong S.A.R., China5 Department of Medicine, The University of Hong Kong, Hong Kong S.A.R., China6 Active Health Clinic, Centre for Sports and Exercise, The University of Hong Kong, Hong Kong S.A.R., China* These authors contributed equally to this work

Exercise is an effective strategy for diabetes management. However, the phenomenon of exercise resis-tance has hindered its clinical implementation. Here, we recruited 39 medication-naïve men with predi-abetes and showed that exercise-induced alterations in gut microbiota correlated closely with improve-ments in glucose homeostasis and insulin sensitivity. The microbiome of exercise responders exhibited enhanced capacity for biosynthesis of short-chain fatty acids and also for catabolism of branched-chain amino acids, whereas those of non-responders were characterized by increased production of metabol-ically-detrimental compounds. Fecal microbial transplantation from responders, but not non-respond-ers, mimicked the effects of exercise on alleviation of insulin resistance in obese mice. Furthermore, a machine-learning algorithm integrating baseline microbial signatures accurately predicted personalized glycemic response to exercise in additional 30 subjects. These findings uncover gut microbiota as an important molecular transducer to mediate the heterogeneous metabolic effects of exercise, raising the possibility to maximize the benefits of exercise by targeting gut microbiota.

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High-resolution large-scale profiling of pathogens and commensals from metagenomicsNicola Segata1

1 Department CIBIO, University of Trento, Italy

Shotgun metagenomics has uncovered a substantial amount of diversity in the human microbiome and can be used for pathogen detection and characterization. In my talk I will show how the combination of multiple reference-based and assembly-based computational approaches can be used for strain-level profiling of the organisms present in a metagenome. Successful examples of pathogen profiling from metagenomics include the detection of pathogenic Escherichia coli in intestinal samples, and of Pseudo-monas aeruginosa and Staphylococcus aureus in sputum samples from cystic fibrosis patients. In these cases, the genome of the pathogen could be reconstructed and analyzed to identify virulence genes and antibiotic resistance factors. The same framework can be used to profile commensal members of the human microbiome and uncover strain-level variability. I will thus focus on two key but under-investigat-ed gut microbial commensals, Prevotella copri and Eubacteriun rectale, showing that it is now possible to perform strain-level population genomics studies directly on metagenomic samples with a resolution comparable to that of isolate genome sequencing. Some of the human-associated microbes are also found in non-human primates, opening intriguing scenarios on how the microbiome co-evolved with their primate host. I will finally describe out effort in cataloging unknown species in the human microbiome by reconstructing 154,723 microbial genomes from 9,428 metagenomes spanning body sites, ages, coun-tries, and lifestyles. I will conclude discussing what are the challenges in performing comparative micro-bial genomics for pathogens and commensals considering the vast catalogs of human-associated and metagenomically reconstructed microbial genomes.


Keeping up with exponentially growing databases and time constraints in metagenomic pathogen discovery Bernhard Renard1

1 Robert Koch Institute

With the continuously increasing use of next-generation sequencing in time-critical applications such as disease outbreak analysis, there is a high demand for novel concepts to overcome the limitations of tra-ditional approaches for data analysis. We observe exponential growth of commonly used reference data-bases for metagenomics. Daily or weekly updates of computational indices from these references become almost impossible even on large scale computing resources. At the same time, while reference collections continue to grow, there is strong bias towards few overrepresented species and resulting incompleteness of these collections. While runtime of data analysis software, e.g. for read alignment, have significantly de-creased and more powerful computational resources became available, the overall turnaround time from sample arrival to analysis results remained nearly the same due to the sequential paradigm of data pro-duction and analysis. As part of our work at Robert Koch Institute, the German national public health insti-tute, we developed and have routinely applied a collection of tools for sequence analyses. These include (i) steps to modularize reference genome indices, allowing updates and recomputations within few minutes, (ii) deep learning approaches to predict phenotypes such as pathogenic potential for sequences which are not assessable via sequence homology and (iii) approaches for analyzing Illumina data, while the sequenc-er is still running. In doing so, intermediate results can be obtained for four crucial steps in time-critical analysis workflows: read alignment, read filtering, metagenomic classification and viral diagnostics.

TALKS


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Gut fungi – How antibiotics administration shapes our mycobiomeBastian Seelbinder1, Jiarui Chen3, Morten Sommer2, Gianni Panagiotou1,*

1 Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute (HKI), 07745 Jena, Germany 2 Technical University of Denmark, 2800 Kgs. Lyngby, Denmark 3 University of Hong Kong, Pok Fu Lam Road, Hong Kong SAR, China* [email protected]

The human gut microbiome is a complex ecosystem composed of bacteria, fungi, archaea and phag-es, but only a handful of studies have investigated concomitantly bacteria and fungi in the human gut and their interactions. It is well established that antibiotic treatment has a detrimental effect on the gut bacteria composition[1], but the effect on the fungal community is far less clear. In this project, we have integrated shotgun metagenomics, metatranscriptomics, metabolomics and ITS sequencing[2,3,4] in stool samples obtained longitudinally from 14 human subjects before, during and after antibiotic treatment. Interestingly, antibiotic treatment reduces interactions between fungi and bacteria and create a shift from mutualistic interactions within specific phyla to between fungal phyla competition. We show here the first data that follows the bacterial and fungal communities of human subjects over a time period of 3 months after antibiotic treatment, which allowed us to investigate disturbance and resilience in cross-kingdom interactions.

[1] Nature. 2018. doi: 10.1038/s41564-018-0257-9[2] Cell. 2018. doi: 10.1016/j.celrep.2018.06.109[3] PNAS. 2016. doi: 10.1073/pnas.1518189113[4] mBIO. 2015. doi: 10.1128/mBio.01263-15


Microphysiological systems as platform for studies on host-microbiota InteractionAlexander Mosig1,2, Zoltan Cseresnyes3, Michelle Maurer2, Antonia Last2,3, Mark S. Gresnigt3, Stefanie Deinhardt-Emmer2,4,7, Susann Hartung3,5, Fatina Siwczak2, Ilse D. Jacobsen2,3,6, Christina Ehrhardt4,7, Marie von Lillienfeld-Toal3,5, Bettina Löffler2,4, Bernhard Hube2,3,6, Marc Thilo Figge3

1 Institute of Biochemistry II, Jena University Hospital, Jena, Germany2 Jena University Hospital, Center for Sepsis Control and Care, Jena, Germany 3 Leibniz Institute for Natural Product Research and Infection Biology, Hans-Knöll-Institute, Jena, Germany 4 Institute of Medical Microbiology, Jena University Hospital, Jena, Germany 5 Clinic of Internal Medicine II, Jena University Hospital, Jena, Germany 6 Institute of Microbiology, Friedrich Schiller University, Jena, Germany 7 Section of Experimental Virology, Jena University Hospital, Jena, Germany

The interaction of the microbiota with its host is crucial for the maintenance of physiological conditions within the human body. Thus, deregulation and disbalance of these interactions can be directly associated with the development of a variety of diseases, including infection and sepsis. However, much of our knowl-edge about the microbiome and its impact on human health is based solely on descriptive and correlative studies. One reason for this is the lack of suitable experimental models that allow detailed mechanistic studies on microbiota-host interaction. Current in vitro models lack the required complexity with major limitation for long-term coculture of living microbiota, whilst animal models have limitations in the trans-ferability to the human situation.

Microphysiological systems (MPS) now emerge as an attractive new platform for the coculture of defined live microbiota with bioengineered tissue models, emulating organotypic functions under well-controlled conditions. These models are able to reflect the human immune response and even allow studies on cross communication between multiple organ models by combining individual MPS. We developed immunocom-petent organ models emulating the human lung, liver and intestine, and already established viral, bacterial and fungal infection/co-culture models that demonstrate agonistic and antagonistic effects of microbial colonization to the human host fitness. We will present the current progress in the development of next generation MPS equipped with miniaturized sensor arrays for culture of tissue models differentiated from human induced pluripotent stem cells. These MPS should be able to reflect a patient-specific background with an individualized immune response to the colonization or infection with microorganisms. Ultimately, these systems should allow a better prediction of an individualized susceptibility to infections and help in the development of tailored treatment strategies.

TALKS


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“Invasive aspergillosis-on-a-chip” – a novel disease model to study Aspergillus fumigatus infection in the human lungSusann Hartung1,6, Zoltan Cseresnyes2, Mai T.N. Hoang1,6, Knut Rennert4,5, Marc Thilo Figge2,3, Alexander S. Mosig4,5, Marie von Lilienfeld-Toal1,6

1 Research group “Infections in Hematology and Oncology”, Leibniz Institute for Natural Product Research and Infection Biology – Hans Knöll Institute (HKI), Jena, Germany2 Applied Systems Biology, Leibniz Institute for Natural Product Research and Infection Biology – Hans Knöll Institute (HKI), Jena, Germany3 Faculty of Biological Sciences, Friedrich Schiller University Jena, Jena, Germany.4 Research group “INSPIRE”, Center for Sepsis Control and Care (CSCC), Jena University Hospital, Jena, Germany5 Institute of Biochemistry II, Jena University Hospital, Jena, Germany6 Department for Internal Medicine II Haematology and Medical Oncology, Jena University Hospital, Jena, Germany

Invasive pulmonary aspergillosis is a great threat to immunocompromised patients because treatment options are limited and only successful upon early diagnosis, which leads to high mortality rates. Infec-tious agents are conidia of the mold Aspergillus fumigatus that easily enter the lung alveoli but are im-mediately cleared by innate immune cells such as macrophages in immunocompetent humans. In im-munocompromised patients, however, conidia can germinate and grow into filamentous bodies (hyphae) leading to tissue destruction and invasion of blood vessels. To date, complex human cell-based models to investigate invasive aspergillosis are rare and often lack the quantification of hyphal growth parameters.

Our novel “Invasive aspergillosis-on-a-chip” model is based on a microfluidic “lung-on-a-chip” consist-ing of human lung epithelial cells at an air-liquid interface and human endothelial cells separated by a porous membrane. Models were infected by FITC-labelled A. fumigatus conidia on the epithelial side and fungal growth was monitored by confocal microscopy. Three-dimensional (3D) and four-dimensional (4D; 3D plus time) image data were analysed using automated systems biology methods. The structure of the organ model was reconstructed by 1) the blue fluorescence of calcofluor white labelling the hyphae and membranes; 2) the reflected light image identifying the membrane pores and the epithelial cells; 3) the FITC fluorescence identifying the conidia. These components allowed us to compute the morphometric measures of the hyphae (volume, length, branching levels and angles, etc.), as well as to characterize the hyphal behaviour regarding the piercing of the membrane through the pores. The 4D data were acquired in live chips containing macrophages, followed by long-term confocal time series microscopy to reveal the morphokinetic behaviour of the immune cells in the presence and absence of fungi.

The development of this versatile “invasive aspergillosis on a chip” system is very promising in respect to its potential applications in understanding pathogenicity and pathophysiology in invasive aspergillosis and providing a much-needed tool for animal-free drug screening.


Describing neutrophil behavior following influenza vaccination from intravital imaging data Diego Ulisse Pizzagalli1,2, Alain Pulfer1, Chatziandreou Nikolaos1, Tommaso Virgilio1, Miguel Palomino-Segura1, Yagmur Farsakoglu3, Rolf Krause2, Santiago Fernandez Gonzalez1

1 Institute for Research in Biomedicine, Università della Svizzera italiana, Bellinzona, –Switzerland2 Institute of Computational Sciences, Università della Svizzera italiana, Lugano - Switzerland3 SALK Institute, San Diego CA

In their wild life, neutrophils experience diverse activities. For instance, they can run towards a target, patrol a specific area, form contacts and undergo apoptosis. These activities often result in specific move-ment patterns.

Thanks to intravital imaging techniques, such as 2-photon video microscopy, it is possible to record the movement of immune cells in organs of living organisms. However, describing and quantifying what is happening in the microscopy scene is challenging. Indeed, widely used measures of cell motility such as mean velocity and confinement ratio, summarize the behavior of individual cells in a scalar but cancel information during the averaging process.

Therefore, we propose to model the microscopy scene as a hierarchical graph. This graph links activities (highest level), to cells (intermediate level) and pixels (lowest level). Thus, it links images to knowledge. Amongst the simplest methods to link activities to cell, we consider a function that maps a movement pattern to a biological meaningful name. We try to define this function as a lookup table, by reviewing from the literature how different cell behaviors were described. However, not all the possible activities of neutrophils are known. Moreover, two activities with different biological meanings might look similar in movement. Hence, we additionally propose a method to identify the generic activity of changing activity. This applies even if not all the possible activities are known. We do this relying on the intuition that the future movement pattern of a cell can be predicted from the past if the cell keeps moving in the same way. Hence, we detect changes in behavior when future patterns cannot be predicted accurately.

We applied the proposed graph structure and methods to study the dynamics of neutrophils in response to influenza vaccination in the draining lymph node. This allowed us to identify phases and areas with distinct activities in the early response to vaccination (< 12h).

TALKS


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Immune recognition of fungal pathogens in human bloodOliver Kurzai1,2, Kerstin Hünniger1,2, Ines Leonhardt1,2, Antje Häder1,2, Teresa Lehnert3, Sandra Timme3, Marc Thilo Figge3, Sascha Schäuble4, Tongta-Sae-Ong4, Gianni Panagiotou4, Vitalia Schüler5, Benedikt Bürfent5, Johannes Schumacher5

1 Institut für Hygiene und Mikrobiologie, Univ. Würzburg2 Fungal Septomics, HKI Jena 3 Applied Systems Biology, HKI Jena4 Systems Biology and Bioinformatics, HKI Jena5 Zentrum für Humangenetik, Universitätsklinikum Marburg

Human blood is a central niche for some pathogens that disseminate via hematogenous spread during systemic infection. This group of pathogens includes fungi like Candida spp. and Cryptococcus spp. Oth-er pathogens are rarely found in blood but will be confronted with circulating immune cells when those extravasate and migrate to foci of infection. This group includes fungal pathogens like Aspergillus spp. or Mucorales. Using a human whole blood model of infection we analyze host pathogen interactions in a situation close to the in vivo relevant host niche “blood”. Our assay provides time-resolved data on cell activation, localization and physiological state of the pathogen and immune activation using flow-cyto-metric, transcriptomic or proteomic analysis tools and require minimal pre-analytical handling of the cells. Biomathematical modeling allows quantification of parameters that remain inaccessible to direct experi-mental quantification. Based on these analyses, we show that (i) immune activation in human blood differs considerably from activation patterns found in isolated immune cells and (ii) the host response in blood differs between fungal and bacterial pathogens. As early transcriptional responses in human blood are largely determined by monocytes we have additionally studied individual variation in monocyte activation in more detail. By parallel assessment of transcriptional patterns and genetic polymorphisms expres-sion quantitative trait loci (eQTLs) that govern transcriptional responses to infection in human monocytes could be identified.


Unraveling innate immune mechanisms against pathogens in avian whole blood by combining experimental and mathematical infection models Teresa Lehnert1, Ilse D. Jacobsen2,3, Sravya Sreekantapuram2,4, Maria E. T. Prauße1,4, Angela Berndt5, Christian Berens5, Steffen Weigend6, Marc Thilo Figge1,3

1 Research Group Applied Systems Biology, Leibniz Institute for Natural Product Research and Infection Biology – Hans Knoell Institute, Jena, Germany2 Research Group Microbial Immunology, Leibniz Institute for Natural Product Research and Infection Biology – Hans Knoell Institute, Jena, Germany3 Institute of Microbiology, Faculty of Biological Sciences, Friedrich Schiller University Jena, Jena, Germany4 Faculty of Biological Sciences, Friedrich Schiller University Jena, Jena, Germany5 Institute of Molecular Pathogenesis, Friedrich-Loeffler-Institut,Jena, Germany6 Institute of Farm animal Genetics, Friedrich-Loeffler-Institut, Neustadt, Germany

Bacterial and fungal infections constitute severe threats to the health of chicken. Colibacillosis is an in-fection that is caused by the bacterium Escherichia coli and often affects the respiratory tract of chicken. However, bacteria can also enter the blood stream causing colisepticaemia and organ infection. Infections by the gram-positive bacterium Staphylococcus aureus and the fungal pathogen Candida albicans com-monly affect various organ systems like the skin and mucosal surfaces and can also spread into the blood stream. So far, the immune response to these pathogens in avian blood has not been studied in detail and it remains unclear, which immune components are essential for pathogen elimination from the blood.

In order to investigate the immune mechanisms in chicken blood, we applied a systems biology approach, by combining an experimental avian whole-blood infection assay and mathematical infection modeling. We adapted the human whole-blood infection assay[1] and performed flow cytometry analysis and survival assays in order to measure the immune cell number, the immune cell association to pathogens as well as the viability of these pathogens during the time course of four hours. Based on the mathematical model of human whole-blood infection[1,2], we developed a state-based infection model that represents the spe-cific immune reactions in avian whole blood. The immune reaction rates were quantified by calibrating this model to the experimental data. We analyzed whole-blood samples from two chicken lines that were infected with one of the three aforementioned pathogens and found massive killing of pathogens and pathogen association to heterophils and monocytes. Furthermore, we observed clear differences between the pathogens regarding killing and association kinetics as well as the reaction rates. Eventually, we could identify and quantify the essential immune reactions during the infection in chicken and predict novel immune reactions.[1] Hünniger K. et al. (2014) PLoS Computational Biology 10(2), e1003479.[2] Lehnert T. et al. (2015) Frontiers in Microbiology 6, 608.

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Reprogramming of macrophages employing gene-regulatory and metabolic network models Rainer Koenig1, Franziska Hörhold1, David Eisel1, Marcus Oswald1, Amol Kolte1, Daniela Röll1,Wolfram Osen1, Stefan Eichmüller1

1 Integrated Research and Treatment Center, Center for Sepsis Control and Care (CSCC), Jena University Hospital, Am Klinikum 1, 07747 Jena, Germany

Upon exposure to different stimuli, resting macrophages undergo classical or alternative polarization into distinct phenotypes which can cause fatal dysfunction in a large range of diseases such as systemic infec-tion leading to sepsis or the generation of an immunosuppressive tumor microenvironment. Investigating gene regulatory and metabolic networks, we observed two metabolic switches during polarization. Most prominently, anaerobic glycolysis was utilized by M1-polarized macrophages, while biosynthesis of inosine monophosphate was upregulated in M2-polarized macrophages. Moreover, we observed a switch in the urea cycle. Gene regulatory network models revealed E2f1, Myc, Pparγ and Stat6 as the major players for the distinct signatures of these polarization events. Employing functional assays targeting these regu-lators we observed repolarization of M2- into M1-like cells, evidenced by their specific gene expression signature and cytokine secretion profiles. The predicted regulators are essential to maintain M2-like phe-notype and function, thus representing potential targets for therapeutic reprogramming of immunosup-pressive M2-like macrophages.


High throughput Raman spectroscopy for leukocytes investigationsJan Rüger1, Anuradha Ramoji1,2,3, Daniel Thomas-Rüddel2,4, Oleg Rybachkov1,3, Aikaterini Pistiki1, Natalie Töpfer1,2, Abdullah S. Mondol1, Evangelos J. Giamarellos-Bourboulis5, Michael Bauer2,4,7, Thomas W. Bocklitz1,3, Iwan W. Schie1, Ute Neugebauer1,2,3,6,7, Jürgen Popp1,2,3,6,7

1 Leibniz Institute of Photonic Technology, Jena, Germany2 Center for Sepsis Control and Care, Jena University Hospital, Germany3 Institute of Physical Chemistry and Abbe Center of Photonics, University Jena, Germany4 Department of Anaesthesiology and Intensive Care Medicine, University Hospital Jena, Germany5 44th department of internal medicine, National and Kapodistrian University of Athens, Medical School, Athens, Greece6 Research Campus InfectoGnostics Jena, Germany7 Jena Biophotonics and Imaging Laboratory, Jena, Germany

Leukocytes display an important part of the immune system and are responsible for maintaining homeostasis in the

host system. The leukocytes comprise of different cell types and the major subtypes are neutrophils, monocytes and

lymphocytes. It is well known that leukocytes opt for different chemical strategies, where different subtypes play a

selective role to combat against the disease causing agents, e.g. pathogens, allergens, chemical agents etc. Current-

ly, in routine clinical diagnostic immediately available parameters of leukocytes are mainly the absolute leukocytes

count and their differential count. Further, the protein biomarkers (IL6, CRP, PCT etc.), released from the leukocytes

are utilized for diagnosis upon suspicion. However, these tests do not provide complete disease characterization

and sometimes lack in providing precise information on hosts’ condition. Understanding the leukocyte response and

early recognition of the intracellular biochemical changes will allow timely diagnosis of the disease condition. In this

study, exemplarily, we have focused on the infection relevant leukocyte activation. Previously, Raman spectrosco-

py has been demonstrated as an upcoming diagnostic method, enabling leukocyte phenotyping and capturing the

activation relevant biochemical changes in cells. Conventionally, Raman spectra of the leukocytes are collected via

point-by-point imaging of the cell surface and collecting Raman spectra from the entire cell surface. The acquisition

speed and hence cell screening is limited by the configuration of the commercial device. In most cases only few cells

can be screened. However, for investigation of the leukocytes, screening of large number of cells is desired given the

vast number of their subtypes. To address this issue, in this collaborative work, for fast and high-throughput screen-

ing of cells an in-house built Raman spectrometer was developed which allows fast recording of Raman spectra

from 100,000 cells within a time span of a few hours yielding spectra with high S/N ratio and retaining cell specific

information. This newly built platform has been validated for phenotyping of the leukocytes isolated from healthy

volunteers and patients. Further, the device was employed in a clinical trial for screening of sepsis patients. Here we

present the development to validation and application of our newly developed high-throughput Raman device along

with our recent results on different activation states of leukocytes.

Acknowledgements: Financial support by the EU “HemoSpec” (FP7, CN611682), BMBF (CSCC, FKZ 01EO1502), DF-

G(JSMC), DFG(JBIL), InfectoGnostics-FKZ 13GW0096F and Leibniz (SAS-2015-HKI-LWC), are highly acknowledged.

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Sensing and signalling during directional growth in Candida albicans hyphae Alexandra Brand1, Tina Bedekovic1, Claudiu Giuraniuc2, Mariana Almeida2, Angela Lopez2, Darren Thomson2

1 University of Exeter, UK2 University of Aberdeen, UK

Background: The fungus, Candida albicans, grows as a commensal yeast in humans. In some, it can cause irritating mucosal infections but, in severely immunocompromised patients, it can escape into the blood-stream to cause life-threatening systemic disease. A primary virulence trait of C. albicans is the morpho-logical switch to hyphal filaments that penetrate barrier-cell layers and disrupt underlying tissue, causing sepsis and organ failure.

Objectives: Our aim is to understand how C. albicans hyphae regulate their direction of growth in response to physical aspects of the host environment. Our objectives are to define the sensing and signalling path-ways that regulate the positioning of cell polarity machinery during constitutively polarised hyphal growth.

Methods: We have developed a microfabricated, live-cell imaging system in which we have quantified re-al-time directional responses and growth behaviours in C. albicans. By generating gene deletion mutants and strains expressing GFP-tagged proteins, we have observed the temporal and spatial reorganisation of polarity and cell-growth proteins as the hyphal tip encounters various topographical cues.

Results: We have quantified the force applied by C. albicans hyphae as 8.7 µN, but this carbon-source dependent. The key regulators of directional responses, such as Cdc42, Rap1/Rsr1 and Paxillin, appear to be conserved across eukaryotic cells, but contact-dependent growth responses are an emergent prop-erty of fungus-specific biology and lifestyle. Using live-cell imaging and proteomics, we find that polarity proteins direct multiple processes that require the spatial organisation of cellular components, including nuclear division, nuclear migration, vacuole fusion and septum formation.


Multiple perspectives of the glyoxylate shunt as antifungal drug target in Candida albicansJan Ewald1, Sascha Brunke2

1 Department of Bioinformatics, Matthias Schleiden Institute, Friedrich Schiller University Jena2 Department of Microbial Pathogenicity Mechanisms, Leibniz Institute for Natural Product Research and Infection Biology – Hans Knoell Institute Jena (HKI), Jena

Due to limited treatment options and an increasing number of susceptible immunocompromised patients in medical care, Candida albicans and other fungi are important causes of infections with high mortality rates. A key virulence factor for Candida albicans, a common commensal of the gut, is their flexible metab-olism. This allows them to evade aspects of the immune system and switch to filamentous growth during tissue invasion. Therefore, fungal metabolism is a valuable resource to identify antifungal targets.

To study pathway regulation and the potential toxicity of pathway intermediates in silico, dynamic optimi-zation and machine learning is used here. This pursues the idea that toxic intermediates are weak points in fungal metabolism which can be used as endogenous self-poisoning agents. As an result of this in silico screening we identified the glyoxylate shunt and its enzymes as antifungal targets, which could accumu-late glyoxylate to toxic levels in infection scenarios. This is explored experimentally by the generation of mutant strains lacking glyoxylate shunt enzymes as well as other enzymes potentially detoxifying glyoxyl-ate. The mutants are assayed on different media and in confrontation with macrophages.

In parallel we reviewed host-pathogen interactions and counter defenses of macrophages against fungal and bacterial enzymes of the glyoxylate shunt. It is known that macrophages are able to produce itaconate as an inhibitor of the glyoxylate shunt in confrontation especially with bacteria, and we identified, based on a homology searches, degradation pathways that likely enable C. albicans to evade itaconate-based growth inhibition within macrophages. In close collaboration between dry and wet lab, growth inhibition by itaconate as well as degradation mechanisms of C. albicans are currently investigated. Our work shows how theoretical and experimental efforts combined help to identify urgently needed antifungal drug tar-gets.

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HOPS: A pipeline for screening archaeological remains for pathogen DNA Ron Hübler1, Felix M Key1,2, Christina Warinner1, Kirsten Bos1, Johannes Krause1, Alexander Herbig1

1 Max-Planck Institute for the Science of Human History2 Massachusetts Institute of Technology

Understanding the relationship between human hosts and bacteria throughout history can provide un-precedented insights into prehistoric disease outbreaks and bacterial evolution. Metagenomic data from archaeological remains can be mined for the presence of ancient bacterial pathogens, a process required to adhere to an established set of authenticity criteria specific to ancient DNA (aDNA). In order to facilitate the analysis of large-scale datasets, an automated tool tailored towards the specific requirements for aDNA is necessary.

Here we present HOPS (Heuristic Operations for Pathogen Screening), a pathogen screening pipeline for aDNA sequence data that provides straightforward and reproducible information on bacterial species identification and authentication of its ancient origin. HOPS consists of a customized version of (1) MALT (Megan ALignment Tool), (2) MaltExtract, a Java tool that evaluates a series of authenticity criteria for a list of target species, and (3) customizable post-processing scripts to identify, filter, and visualize candidates.

In this study we show the overall performance of HOPS and compare HOPS to four other common meth-odologies for metagenomic assessment, which are Kraken (k-mer matching), MIDAS (marker gene-based), Metabit (marker gene-based for aDNA) and SPARSE (cluster-based species identification).

We evaluate HOPS and all other tools using publicly available ancient metagenomics data containing known bacterial pathogens as well as simulated data from 33 bacterial pathogens spiked into diverse metagenomics backgrounds (soil, archaeological bone, dentine, and dental calculus).

HOPS outperforms available tools and successfully confirms all experimental and simulated candidates with as little as 50 sequencing reads present in an aDNA metagenomic dataset.

With HOPS we provide a versatile and fast pipeline for pathogen screening of archaeological material that aids in the identification of candidate samples for further analysis.


Effects of 1-Methyltryptophan on the kynurenine pathway in pigs Dana Kleimeier1, Grazyna Domanska2, Ellen Kanitz3, Winfried Otten3, Elisa Wirthgen4, Barbara M. Bröker2, Lars Kaderali1

1 Institute of Bioinformatics, University Medicine Greifswald, Greifswald, Germany2 Institute of Immunology, University Medicine Greifswald, Greifswald, Germany3 Institute of Behavioural Physiology, Leibniz Institute for Farm Animal Biology (FBN), Dummerstorf, Germany4 University Children’s Hospital, Rostock University Medical Center, Rostock, Germany

Indolamine 2,3-dioxygenase 1 (IDO1) is of special interest as a target for anti-cancer therapy or pre-vention of immunoparalysis. The application of IDO1 inhibitors should prevent both the depletion of tryptophan (TRP) and the production of immunomodulatory TRP metabolites such as kynurenine (KYN) or kynurenic acid (KYNA) contributing to a prevention of IDO1-induced immunosuppression. However, in pigs and mice the application of the IDO inhibitor 1-methyltryptophan (1-MT) induced an elevation of blood TRP and KYNA levels but not of the intermediate metabolite KYN [1, 2]. However, it is unclear, whether KYNA is produced directly from TRP or via KYN. This motivates us to develop a system of ordinary differential equations to explain tryptophan metabolism (Matlab, Data 2 Dynamics [3]) and degradation of 1-MT in a domestic pigs model, applied as non linear mixed effects model (Monolix [4]). Two possible degradation pathways were investigated: 1. Tryptophan is degraded via kynurenine to kynurenine acid. 2. Tryptophan is directly degraded to kynurenine acid. Our calculations resulting in loglikelihood values sug-gesting that the most probable metabolic pathway is a direct degradation of TRP to KYNA. Similar con-clusion was drawn by Qian et al., which included kinetic measurements, revealing that TRP is a substrate for kynurenine aminotransferase II (KAT II) with a similar Km-value as KYN [5]. Our results suggests, that after 1-MT application, tryptophan is metabolized directly to kynurenic acid, which should be confirmed in further experiments. Besides 1-MT has only little inhibitory effects on IDO1.

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The Cryptococcus neoformans Titan cell is an inducible and regulated morphotype underlying pathogenesis Elizabeth Ballou1

1 Institute of Microbiology and Infection, School of Biosciences, University of Birmingham, Birmingham, UK

Fungal cells change shape in response to environmental stimuli, and these morphogenic transitions drive pathogenesis and niche adaptation. For example, dimorphic fungi switch between yeast and hyphae in response to changing temperature. The basidiomycete Cryptococcus neoformans undergoes an unusual morphogenetic transition in the lung from haploid yeast to large, highly polyploid cells termed Titans. Titans influence fungal interaction with host cells, including through increased drug resistance, altered cell size, and altered Pathogen Associated Molecular Pattern exposure. Despite the important role these cells play in pathogenesis, understanding the environmental stimuli that drive the morphological transi-tion, and the molecular mechanisms underlying their unique biology, has been hampered by the lack of a reproducible in vitro induction system. We recently demonstrated that Titan cells can be induced in vitro in response to environmental stimuli consistent with the host lung. Significantly, we showed that bacte-rial cell wall serves as a key component in this process. In vitro Titans exhibit all the properties of in vivo Titans, including altered capsule, cell wall, size, high mother cell ploidy, and aneuploid progeny. Using this model, we begin to describe molecular mechanisms underlying the yeast-to-Titan transition, investigate Titanisation in clinical isolates, and characterize their impact on host interaction and disease outcome.


Fighting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics Alice C. McHardy3,4,$, Ariane Khaledi1,2,*, Aaron Weimann2,3,4,*, Monika Schniederjans1,2,*, Ehsaneddin Asgari3,8, Tzu-Hao Kuo3, Antonio Oliver5, Gabriel Cabot5, Axel Kola6, Petra Gastmeier6, Michael Hogardt7, Daniel Jonas10, Mohammad R.K. Mofrad8,9, Andreas Bremges3,4, Susanne Häussler1,2,$

1 Molecular Bacteriology, Helmholtz Centre for Infection Research, Braunschweig, Germany;2 Molecular Bacteriology, TWINCORE, Centre for Experimental and Clinical Infection Research, Hannover, Germany;3 Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Braunschweig, Germany;4 German Center for Infection Research (DZIF), Braunschweig, Germany;5 Servicio de Microbiología and Unidad de Investigación Hospital Universitario Son Espases,Instituto de Investigación Sanitaria Illes Balears (IdISPa), Palma de Mallorca, Spain;6 Charité-Universitätsmedizin Berlin, Institute of Hygiene and Environmental Medicine, Berlin, Germany;7 Institute of Medical Microbiology and Infection Control, University Hospital Frankfurt, Frankfurt/Main,Germany;8 Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California, Berkeley, USA;9 Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Lab, Berkeley, USA;10 Institute for Infection Prevention and Hospital Epidemiology, Faculty of Medicine, Medical Center -University of Freiburg, Freiburg, Germany* These authors contributed equally to this work. $ Shared last authors

The growing importance of antibiotic resistance on clinical outcomes and cost of care underscores the need for optimization of current diagnostics. For a number of bacterial species antimicrobial resistance can be unambiguously predicted based on their genome sequence. I will speak about our joint work with Susanne Häussler’s lab, who sequenced the genomes and transcriptomes of 414 drug-resistant clini-cal Pseudomonas aeruginosa isolates. By training machine learning classifiers on information about the presence or absence of genes, their sequence variation, and gene expression profiles, we generated pre-dictive models and identified biomarkers of susceptibility or resistance to four commonly administered antimicrobial drugs. Using these data types alone or in combination resulted in high (0.8-0.9) or very high (>0.9) sensitivity and predictive values, where the relative contribution of the different categories of biomarkers strongly depended on the antibiotic. For all drugs except for ciprofloxacin, gene expression information substantially improved diagnostic performance. Our results pave the way for the development of a molecular resistance profiling tool that reliably predicts antimicrobial susceptibility based on genomic and transcriptomic markers. The implementation of a molecular susceptibility test system in routine clin-ical microbiology diagnostics holds promise to provide earlier and more detailed information on antibiotic resistance profiles of bacterial pathogens and thus could change how physicians treat bacterial infections.

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Impact of genetic variability on fungal and bacterial infection Antje Haeder1, Sascha Schäuble2, Gianni Panagiotou2, Johannes Schumacher3, Oliver Kurzai1,4

1 Fungal Septomics, Leibniz Institute for Natural Product Research and Infection Biology - Hans-Knoell-Institute, Jena, Germany2 Research group PiDOMICS, Systems Biology and Bioinformatics Unit, Leibniz Institute for Natural Product Research and Infection Biology - Hans-Knoell-Institute, Jena, Germany 3 Centre for Human Genetics, University Marburg, Marburg, Germany4 Institute for Hygiene and Microbiology, University of Würzburg, Würzburg, Germany

Genetic variability has been shown to play a crucial role in the immune response. Blood monocytes are an important part of the innate immune response that cover a wide range of defense mechanisms including phagocytosis or release of cytokines against systemic fungal or bacterial infections. To identify genome wide single nucleotide polymorphisms that influence the gene expression of immune cells we genotyped a healthy caucasian male cohort (n = 230, non-smoker, age = 18-40).

Additionally, we generated transcriptome and cytokine release data for human monocytes derived from all donors after stimulation with Aspergillus fumigatus, Neisseria meningitidis and Staphylococcus aureus for three and six hours.

We used linear regression calculations to determine response expression quantitative trait loci (reQTL) that allow the identification of genes whose expression is influenced by specific genotypes. Further tran-scriptomic studies reveal infection specific enriched pathways with reQTL present at key regulatory points. Among others, these pathways comprise immune responsive targets such as a concerted cytokine re-sponse or various cell signaling pathways such as lectin- or toll like receptor signaling. We identified in-fection specific reQTL gene expression that is notably distinct for infection type and time points. Finally, we investigated the influence of reQTL on cytokine secretion by analysing specific cytokines via multiplex arrays, which shows distinct differences for fungal and bacterial infection.

Our study contributes to distinguish fungal from bacterial infection and bridges genetic variability, tran-scriptomic expression and cytokine release in a large study cohort.


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Morphokinetic analysis of live-cell imaging data Ivan Belyaev1,2, Anna Medyukhina1, Marc Thilo Figge1,2

1 Applied Systems Biology, Leibniz Institute for Natural Product Research and Infection Biology – Hans Knöll Institute, Jena, Germany2 Faculty of Biological Sciences, Friedrich Schiller University Jena, Jena, Germany

The morphology of a cell is a macroscopic (relative to cell-scale) manifestation of intracellular processes orchestrated by plenty of factors. The time-correlated cell shapes repertoire (cell morphokinetics) can be used as a marker for an analysis of cells population behavior. In this work we demonstrate, how to use these characteristics for semi-automatic identification of human polymorphonuclear neutrophil (PMN) populations affected by Candida albicans (C.a.) or Candida glabrata (C.g.). To identify changes in PMNs behavior induced by fungi of different species, their cells were added to human whole blood in vitro and compared with mock-infected control samples. Following a one-hour confrontation in human whole blood, PMNs were purified and monitored by time-lapse microscopy. These experiments were done by Fungal Septomics group (Hans Knöll Institute, Jena). The visual analysis of live cell imaging data revealed two types of morphological appearances: spreading (S) and non-spreading (N) cells. Additionally, it was found that for PMNs from C.a.-infected samples the time intervals in S-regime were shorter than for those from C.g.-infected ones. In the mock-infected scenario, the cells of N-type dominate. This fact allowed us to create a PCA-based one-class classifier for static N-cells using simple shape (area) and membrane rough-ness (image gradient-based) descriptors extracted from mock-infected experiments data. As the result, the fraction of S-cells for each time-lapse video was estimated, which made possibility to distinguish mock-infected and infected samples in a majority of cases. The pathogen identification was done by SVM classifier which utilized frequency spectra of S-type episodes duration derived from the combination of cells track data and the static cells classifier outcome. Obtained results indicate, that morphokinetic infor-mation can potentially be used as a biomarker for blood stream fungal infection diagnostics.


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Comparative assessment of aspergillosis by virtual infection modeling in murine and human lung Marco Blickensdorf1, Sandra Timme1, Marc Thilo Figge1,2

1 Research Group Applied Systems Biology, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute, Jena, Germany 2 Faculty of Biological Sciences, Friedrich Schiller University Jena, Germany

Aspergillus fumigatus is an airborne ubiquitous pathogen which is frequently inhaled every day. Once the conidia reach the lung, they are able to quickly adapt to the humid environment and, if not attacked by the immune system, can cause severe damage by germination and invasive growth within hours, leading to mortality rates of up to 95%. Research on A. fumigatus often involves mice models for studies of the infection dynamics. However, relatively little is known about the effect of differences in lung morphology or infection dose with regard to naturally occurring infections in humans and experimental infections in mouse models. We implemented a spatio-temporal agent-based software framework to investigate the dynamics of early A. fumigatus infections in the human lung. It simulates a realistic morphological 3D environment of the human alveolus, the fungal conidia and phagocytes of the early immune response. We demonstrated the predictive power of computer simulations in previous studies[1,2], while here we extend this model to alveoli of mice to compare differences due to various parameters and infection dynamics between humans and mice. Our computer simulations enable comparative quantification of A. fumigatus infection clearance in the two hosts to elucidate the complex interplay between alveolar morphometry and the fungal burden as well as the dynamics of infection clearance, which for realistic fungal burdens is found to be more efficiently realized in mice compared to humans.[1] Blickensdorf M, Timme S and Figge MT (2019) Comparative Assessment of Aspergillosis by Virtual Infection Modeling in Murine and Human Lung. Front. Immunol. 10:142. doi: 10.3389/fimmu.2019.00142[2] Pollmächer J, Figge MT. Agent-Based Model of Human Alveoli Predicts Chemotactic Signaling by Epithelial Cells during Early Aspergillus fumigatus Infection. Sturtevant J, ed. PLoS ONE. 2014;9(10): e111630. doi:10.1371/journal.pone.0111630.[3] Pollmächer J, Figge MT. Deciphering chemokine properties by a hybrid agent-based model of Aspergillus fumigatus infection in human alveoli. Frontiers in Microbiology. 2015;6:503. doi: 10.3389/fmicb.2015.00503.


POSTERS

Metagenomic samples source prediction using machine learning Maxime Borry1

1 Department of Archaeogenetics, Max Planck Institute for the Science of Human History, Jena, 07745,Germany

Metagenomics sequencing of human, animal, and environmental sample opened up new doors to explore the diversity of these environments. With tools such as taxonomic classifiers, scientists now have access to the organism composition of each these metagenomic sequencing samples. Taxonomic classifiers, like Kraken for example, will compute the organism taxonomic composition at different taxonomic level, from the DNA sequencing data. In cases where the origin of a metagenomic sample, its source, is unknown or uncertain, it is often part of the research question to predict or confirm it. Using samples of known sourc-es, a reference dataset can be established with the sample’s taxonomic composition, i.e. the organisms identified in the sample, as features, and the source of the sample as class labels. With this reference dataset, one can train a machine learning algorithm to predict the source of unknown samples from their taxonomic composition. Here I present Sourcepredict which uses a non-linear dimension reduction algo-rithm, followed by K-Nearest-Neighbors (KNN) classification to perform source prediction. Compared to other tools doing source prediction, Sourcepredict makes the interpretation of the classification results relatively straightforward thanks to the visualization of the samples in a low dimensional space.


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A pollution gradient contributes to the taxonomic, functional and resistome diversity of microbial communities in marine sedimentsJiarui Chen1, Shelby E. Mcllroy2, Anand Archana3, David M. Maker4, Gianni Panagiotou5

1 Systems Biology & Bioinformatics Group, School of Biological Sciences, Faculty of Science, The University of Hong Kong, Hong Kong S.A.R., China2 Swire Institute of Marine Science, The University of Hong Kong, Hong Kong S.A.R., China3 School of Biological Sciences, Faculty of Sciences, The University of Hong Kong, Hong Kong S.A.R., China4 Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong S.A.R., China5 Leibniz Institute for Natural Product Research and Infection Biology, Hans Knoll Institute, Jena, Germany

Background: Coastal marine environments are one of the most productive ecosystems on Earth. However, anthropogenic impacts exert significant pressure on coastal marine biodiversity, contributing to function-al shifts in microbial communities and human health risk factors. However, relatively little is known about the impact of eutrophication -human derived nutrient pollution- on the marine microbial biosphere.

Results: Here, we tested the hypothesis that benthic microbial diversity and function varies along a pollu-tion gradient, with a focus on human pathogens and antibiotic resistance genes. Comprehensive metag-enomic analysis including taxonomic investigation, functional detection and ARG annotation revealed that zinc, lead, total volatile solids and ammonia nitrogen were correlated with microbial diversity and function. We propose several microbes, including Planctomycetes and sulfate-reducing microbes as candidates to reflect pollution concentration. Annotation of antibiotic resistance genes showed that the highest abun-dance of efflux pumps was found at the most polluted site, corroborating the relationship between pollu-tion and human health risk factors. This result suggests that sediments at polluted sites harbor microbes with a higher capacity to reduce intracellular levels of antibiotics, heavy metals or other environmental contaminants.

Conclusions: Our findings suggest a correlation between pollution and the marine sediment microbiome, provide insight into the role of high-turnover microbial communities as well as potential pathogenic or-ganisms as real-time indicators of water quality, with implications for human health and demonstrate the inner functional shifts contributed by the microcommunities.


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A supervised machine learning approach for predicting transcription factor target gene interactions Dare Falola1, Thomas Beder1, Ezekiel Adebiyi2,3, Rainer Koenig1

1 Integrated Research and Treatment Center, Center for Sepsis Control and Care (CSCC), Jena University Hospital, D-07747 Jena, Erlanger Allee 101, Germany2 Covenant University Bioinformatics Research (CUBRe), Ota, Ogun State, Nigeria, P.M.B. 10233 Applied Bioinformatics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany

A detailed knowledge of gene regulatory networks can help to explain transcriptional manifestations. Transcription factors (TF) and the target genes (TG) they regulate are crucial to building these networks. Anopheles gambiae is the major Plasmodium falciparum vector and analyzing its gene regulatory net-work can help to understand its transmission capacity. However, little is known about gene regulation in Anopheles so we want to infer information from the model organism Drosophila melanogaster. To predict interactions between TFs and TGs, in a condition independent manner we apply a supervised machine learning approach. Gold standard data was collected from different experimentally validated and curated interaction databases (Redfly, FlyReg and GTRD). Moreover, the total binding affinity (TBA) of a TF to the promoter of a TG was calculated and integrated in the gold standard. Features for the machine learning approach were extracted from gene expression data by correlation meta-analysis, mutual information network models, TBA scans utilizing position weight matrices and network topological features from the gold standard. Using support vector machine we predicted TF-TG interactions with high reliability (AUC = 84.35, sensitivity = 91.35 and specificity = 77.35), which is substantially better than utilizing TBA (AUC= 38.98) and mutual information models (AUC = 49.44) alone. We also discovered a significant overlap of our predictions with experimental and curated interactions by Fisher’s exact test (p-value: 0.00112). Using our approach we predicted 7904 new TGs for 18 TFs in Drosophila melanogaster and the pathway en-richment of the TF targets in KEGG pointed to several signaling pathways like MAPK, FoxO, Hedgehog. In the future, we intend to extend this method to Anopheles gambiae in order to predict TF-TG interactions and build gene regulatory network models inspecting scenarios when Anopheles is infected Plasmodium falciparum.


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Early detection of sepsis through the utilization of effective biomarkers Albert Garcia Lopez1, Wolfgang Schmidt-Heck1, Gianni Panagiotou1

1 Leibniz Institute for Natural Product Research and Infection Biology – Hans Knöll Institute (HKI)

Sepsis, defined by the most recent paradigm (Sepsis-3, 2016) as a life-threatening organ dysfunction caused by a dysregulated host immune response to infection, which varies according to multiple factors, such as the host’s genotype, immune system status, and comorbidities, along with the type, site and extent of the infection. Considered one of the major sources of mortality and morbidity among patients worldwide, affecting more than 30 million people every year, there is an urge for an improved diagnostic and better individualized management strategies matched to a patient’s biochemical and genetic make-up. The aim of the current work is based on ensuring the early detection of infection and organ dysfunction before a patient becomes gravely ill. Blood samples from patients were collected before and for up to a week after major elective surgery. Samples were analyzed from individual patients longitudinally before a clinical diagnosis of infection was made, either uncomplicated or complicated by organ dysfunction (sep-sis). Matched samples were taken from age, gender and surgery-type patients who developed a non-in-fectious postoperative inflammatory response, or from uncomplicated postoperative control patients. Accuracy of diagnosis was obtained by clear consensus guided by a panel of clinicians independently assessing clinical and microbiological data available. Through the combined use of data and hypothesis driven approaches with state-of-the-art technologies, such as microarray and RNA-Seq analysis, along-side with machine learning algorithms we predicted with more than 85% accuracy the outcome of the patients before the clinical diagnosis. Therefore, the proposed research on the pathogenesis of sepsis could have a significant impact on the management and treatment of sepsis.


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Bone Biochip: The alternative model to study bone infectionMarina Garcia Moreno1,3, Fatina Siwczak2,3, Bettina Löffler1,3, Alexander Mosig2,3, Lorena Tuchscherr1,3

1 Institute of Medical Microbiology, Jena University Hospital, Jena, Germany2 Institute of Biochemistry II, Jena University Hospital, Jena, Germany3 Center for Sepsis Control and Care (CSCC)

Staphylococcus aureus is an opportunistic pathogen and a frequent cause of infection. Bone infections can develop from an acute to a chronic stage, where they become longsome and difficult to treat and often requires surgical interventions. The ability of S. aureus to establish chronic infections most likely originates in the complex interaction of the bacteria with bone tissue that is not fully understood. To in-vestigate the interaction between S. aureus and bone tissue, we established a mouse biochip organoid to analyse the bacterial localisation and the bacterial-host cell communication that might trigger bacterial adaptation processes in bone tissue.Mouse bone biochip was established by seeding MC3T3-E1 osteo-blast and MLO-Y4 osteocytes, murine non-phagocytic cell lines. We standardised several conditions such as cell quantity, the time of cells in culture to get the right confluence, the possible impact of position for cells, possible coatings, the viability of the cells and the 3D cell structure in the biochip. Following, we have adapted our cell culture infection protocol into the chip model with S. aureus-GFP-labelled. Our results showed that the biochip was successfully infected by S. aureus by fluorescence microscope. The biochip-organoid bone is an alternative model to study the localisation of S. aureus during infection by closely simulating osteomyelitis in patients. This alternative ex vivo model will provide critical real-time information needed for developing novel therapeutics and investigate possible antimicrobial treatments for chronic infections.


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“Invasive aspergillosis-on-a-chip” – a novel disease model to study Aspergillus fumigatus infection in the human lung Albert Garcia Lopez1,6, Susann Hartung1,6, Zoltan Cseresnyes2, Knut Rennert4,5, Marc Thilo Figge2,3, Alexander S. Mosig4,5, Marie von Lilienfeld-Toal1,6

1 Research group “Infections in Hematology and Oncology”, Leibniz Institute for Natural Product Research and Infection Biology – Hans Knöll Institute (HKI), Jena, Germany2 Applied Systems Biology, Leibniz Institute for Natural Product Research and Infection Biology – Hans Knöll Institute (HKI), Jena, Germany3 Faculty of Biological Sciences, Friedrich Schiller University Jena, Jena, Germany.4 Research group “INSPIRE”, Center for Sepsis Control and Care (CSCC), Jena University Hospital, Jena, Germany5 Institute of Biochemistry II, Jena University Hospital, Jena, Germany6 Department for Internal Medicine II Haematology and Medical Oncology, Jena University Hospital, Jena, Germany

Invasive pulmonary aspergillosis is a great threat to immunocompromised patients because treatment options are limited and only successful upon early diagnosis, which leads to high mortality rates. Infec-tious agents are conidia of the mold Aspergillus fumigatus that easily enter the lung alveoli but are im-mediately cleared by innate immune cells such as macrophages in immunocompetent humans. In im-munocompromised patients, however, conidia can germinate and grow into filamentous bodies (hyphae) leading to tissue destruction and invasion of blood vessels. To date, complex human cell-based models to investigate invasive aspergillosis are rare and often lack the quantification of hyphal growth parameters.

Our novel “Invasive aspergillosis-on-a-chip” model is based on a microfluidic “lung-on-a-chip” consist-ing of human lung epithelial cells at an air-liquid interface and human endothelial cells separated by a porous membrane. Models were infected by FITC-labelled A. fumigatus conidia on the epithelial side and fungal growth was monitored by confocal microscopy. Three-dimensional (3D) and four-dimensional (4D; 3D plus time) image data were analysed using automated systems biology methods. The structure of the organ model was reconstructed by 1) the blue fluorescence of calcofluor white labelling the hyphae and membranes; 2) the reflected light image identifying the membrane pores and the epithelial cells; 3) the FITC fluorescence identifying the conidia. These components allowed us to compute the morphometric measures of the hyphae (volume, length, branching levels and angles, etc.), as well as to characterize the hyphal behaviour regarding the piercing of the membrane through the pores. The 4D data were acquired in live chips containing macrophages, followed by long-term confocal time series microscopy to reveal the morphokinetic behaviour of the immune cells in the presence and absence of fungi.

The development of this versatile “invasive aspergillosis on a chip” system is very promising in respect to its potential applications in understanding pathogenicity and pathophysiology in invasive aspergillosis and providing a much-needed tool for animal-free drug screening.


POSTERS

Antibiotic resistance: a view from control theoryJosephine Tetteh1, Esteban Hernandez-Vargas2

1 Frankfurt Institute for Advanced Studies, Goethe University2 Frankfurt Institute for Advanced Studies, Frankfurt

Combating antibiotic resistance, a global threat to public health, is a matter of concern to many clinicians, pharmaceutical companies, researchers and policy makers. Bacteria have developed augmented abilities to resist antibiotic treatment. The evolution that leads to resistance is somewhat unclear as very few stud-ies have aimed to uncover such mechanisms. Recent genome sequencing studies have uncovered inter-actions between bacteria and various antibiotic classes in terms of collateral sensitivities and cross-resis-tance. Exploiting these phenomena can be useful in gaining insight into drug scheduling techniques that could lead to a reduction in resistance development. Here, we utilize the concept of collateral sensitivity and collateral resistance and propose a mathematical model for antibiotic resistance which considers cycling of therapies whilst taking into account the effect of mutation of bacterial strain. Computational simulations show that periodic cycling therapies are beneficial only for limit conditions, otherwise, tailored cycling strategies are needed.


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Quantification of the innate immune function in human whole-blood infection assays reveals pathogen-dependent immune defence of different sepsis phases. Teresa Lehnert1,2, Ines Leonhardt2,3, Kerstin Hünniger3,4, Oliver Kurzai2,3,4, Marc Thilo Figge1,2,5

1 Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute, Applied Systems Biology, Jena, Germany 2 Center for Sepsis Control and Care (CSCC), University Hospital Jena, Jena, Germany 3 Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute, Fungal Septomics, Jena, Germany 4 Institute for Hygiene and Microbiology, University of Würzburg, Würzburg, Germany 5 Friedrich Schiller University Jena, Faculty of Biological Sciences, Jena, Germany

Sepsis is a major cause of death and morbidity worldwide and known as a heterogeneous clinical syn-drome described by marked inhomogeneous changes of the immune state in terms of diverse patholog-ical conditions and variable disease kinetics in individual patients. Therefore, it is of major importance to develop tools that enable stratification of septic patients and predict the efficacies of tailor-made thera-peutic interventions. In this study, we apply mathematical modeling combined with human whole-blood infection assays to quantify the immune response to infections in donors with distinct immune states. We developed a state-based virtual infection model[1,2] representing essential innate immune mechanisms during whole-blood infection. In order to quantify the a priori unknown rates of immune mechanisms, the model was calibrated to whole-blood infection assays using the parameter estimation algorithm simulat-ed annealing. First, we quantified the immune response in blood samples of healthy donors that were in-fected with either the bacterial pathogen Staphylococcus aureus or the fungal pathogen Candida albicans. Thereby, we identified clear differences in the immune response to these pathogens, i.e. in the parameter values of immune cell reaction rates. Building on this work, we are currently applying the state-based model to quantify the immune response to different pathogens in blood samples of patients who under-went cardiac surgery with extracorporeal circulation. This surgery provides an inflammatory stimulus that is both time-defined and relatively homogeneous. With the ability to investigate the blood of the same patient at defined time points before and after surgery, inter-individual differences and the effects of inflammation could be clearly distinguished. The analysis of several patients revealed an increase in white blood cell count after surgery as well as variations in immune cell reaction rates. Additionally, we found that pathogen-specific patterns of the immune responses before surgery diminish after the surgery. In order to bring this study closer to the clinical situation, in the future we plant to quantify blood samples from sepsis patients. This will allow identifying patterns of the dysregulated immune homeostasis provid-ing functional classifiers for the differentiation and categorization of sepsis patients.[1] Hüniger and Lehnert et al. (2014) PLOS Comp. Biol. 10(2), e1003479[2] Lehnert and Timme et al. (2015) Front. Microbiol. 6(608)


POSTERS

Verifying hypotheses on immune evasion by pathogens in human whole blood by state-based virtual infection modelsTeresa Lehnert1, Maria T. E. Prauße1,3, Jan-Philipp Praetorius1,3, Kerstin Hünniger4,5, Ines Leonhardt3,5, Oliver Kurzai3,4,5, Marc Thilo Figge1,2,3

1 Applied Systems Biology, Leibniz Institute for Natural Product Research and Infection Biology – Hans Knöll Institute, Jena, Germany2 Center for Sepsis Control and Care (CSCC), Jena University Hospital, Jena, Germany3 Faculty of Biological Sciences, Friedrich Schiller University, Jena, Germany4 Fungal Septomics, Leibniz Institute for Natural Product Research and Infection Biology – Hans Knöll Institute (HKI), Jena, Germany5 Institute of Hygiene and Microbiology, University of Würzburg, Germany

The immune system protects us constantly against the harm of pathogens which we inhale on a daily basis or which

colonize our skin. If the immune response is impaired and the blood stream is reached, life-threatening systemic in-

fections develop, which attribute to high morbidity and mortality. Pathogens which are most commonly associated to

such severe diseases are the bacterial pathogen Staphylococcus aureus, and the fungal pathogens Candida albicans

and Candida glabrata. As the early clearance of these pathogens from the blood stream is of fundamental importance,

the innate immune response in human whole-blood was investigated in a systems biology study combining experi-

mental whole-blood infection assays and state-based modelling[1,2]. In this study, we found a population of pathogens

that were not cleared by immune cells, but evade the immune response in whole blood. So far, the underlying mecha-

nism causing these immune evasive pathogens could not be identified.

By the means of biomathematical models and the acquired data from whole-blood assays we tested biologically

reasonable hypotheses. The established state-based model of whole-blood infection comprises three different im-

mune-evasion mechanisms: The spontaneous mechanism defines the immune-evasion rate with a constant proba-

bility[1,2]. The second immune-evasion mechanism is dependent on PMN which phagocytose microbes and it presents

a time-dependent probability[3]. The third immune-evasion mechanism entails that a subpopulation of immune-eva-

sive cells already exists prior to the infection. Furthermore, we test all combinations of these immune-evasion mech-

anisms. We fit these seven models to the data from whole-blood assays which were conducted with blood samples

from healthy donors and were then infected with either C. albicans, C. glabrata or S. aureus.

We found that all immune-evasion models are in agreement with the experimental data from whole-blood infection

assays. Nevertheless, we could detect significant differences in the simulation results, like the time course of extracel-

lular killing by antimicrobial peptides and the immune evasion effect. Based on these findings we are able to propose

new experimental measurements that could provide further insights into the underlying mechanism of pathogenic

immune evasion in human blood.

[1]Hünniger and Lehnert et al. (2014) PLoS Comput Biol.[2]Lehnert and Timme et al. (2015) Frontiers in Microbiology.[3]Prauße et al. (2018) Frontiers in Immunology.


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A user friendly web application for ITS sequencing data to analyze abundance, diversity, interactions, and disease associations of fungal communities Daniel Loos1, Gianni Panagiotou1

1 Leibniz Institute for Natural Product Research and Infection Biology – Hans Knöll Institute (HKI) -Systems Biology and Bioinformatics

Fungi play an important role in modulating the immune response of their hosts. Abundance, Diversity, and interaction patterns between the fungal species are altered in various diseases including IBD and HIV. These communities can be investigated using Next-Generation Sequencing of ITS markers without the need for cultivation. However, bioinformatical analysis of these heterogenic data sets is still challenging and no gold standard exists. We report here a user friendly web application to elucidate any mycobiome beginning from raw reads to publication ready plots. The pipeline is customizable and allows the use of different techniques for profiling, normalization, and statistics. Furthermore, existing projects covering several diseases can be easily integrated to the uploaded cohort. A knowledge base will be provided con-taining references for the pathogenicity of a particular fungus.


POSTERS

Metabolic interactions with specific gut bacteria are key determinants for colonization levels of Candida albicansMohammadhassan Mirhakkak Esfahani1, Sascha Schaeuble1, Tilman Klassert3, Ruben Vazquez Uribe2, Felipe Lino2, Daniel Loos1, Hortense Slevogt3, Glen J. Weiss4, Morten Sommer2, Gianni Panagiotou1

1 Department of Systems Biology and Bioinformatics, Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute (HKI), Jena, Germany2 Bacterial Synthetic Biology Section, Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Denmark3 Septomics Research Center, Jena University Hospital, Jena, Germany4 Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts

C. albicans is found in the mucosal surfaces of at least 50-70% of healthy adults and is a classical oppor-tunistic pathogen. C. albicans resides as a harmless commensal, however, it can become pathogenic in immunocompromised patients or hosts that develop microbial dysbiosis. Alterations in composition and functionality of the gut microbiota are hypothesized to be linked to the development of local or systemic C. albicans infections. In this study we studied the interactions between C. albicans and gut microbial species by performing in silico predictions using Genome-scale metabolic models (GSMMs) as promising tool. First, we developed a C. albicans GSMM by manual curation and adaptation to C. albicans biolog growth experiments. We showed that the model is over 90% accurate to predict the growth of C. albicans on wide variety of substrates. We then generated in silico pairwise interaction experiments with 910 gut bacteria models collected from different sources and we challenged our predictions by performing pair-wise competition experiments as well as in a cohort of 26 patients where we performed metagenomics and ITS sequencing to predict bacteria species that can significantly affect the growth of C. albicans. Our results show that GSMMs can provide significant insight in the metabolic interdependencies taking place in the human gut between C. albicans and gut bacteria and reveals potential vulnerable pathways of this opportunistic fungi for drug discovery.


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Indirect identification of pathogens via Raman spectroscopy on monocytes in an in vitro infection model Aikaterini Pistik1,*, Anuradha Ramoji1,2,3,*, Oleg Ryabchykov1,3, Adrian T. Press2,6, Thomas Bocklitz1,3, Michael Bauer2,4,6, Juergen Popp1,2,3,4,5, Ute Neugebauer1,2,3,4,5

1 Leibniz Institute of Photonic Technology Jena (a member of Leibniz Health Technologies), Albert-Einstein-str. 9, 07745 Jena, Germany2 Center for Sepsis Control and Care, Jena University Hospital, Am Klinikum 1, 07747 Jena, Germany3 Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Lessingstraße 10, 07743 Jena, Germany 4 Jena Biophotonics and Imaging Laboratory, Albert-Einstein-str. 9, 07745 Jena, Germany5 InfectoGnostics Research Campus Jena, Philosophenweg 7, 07743 Jena, Germany6 Department of Anesthesiology and Intensive Care, Jena University Hospital, Am Klinikum 1, 07747 Jena, Germany* Equal contribution

Microbiological documentation of infections is not always possible. Undocumented infections require the use of, often inappropriate, empirical antibiotic treatment. It has been shown that bacterial pathogens can be identified using Raman spectroscopy. Monocytes are phagocytes intending the engulfment, degrada-tion and presentation of the pathogens to other cells of the immune system. Here we investigate whether it is possible to differentiate between pathogen by applying Raman spectroscopy on infected monocytes. PBMCs from 5 healthy donors were incubated for 24h with medium and the clinical isolates C. albicans, K. pneumoniae, S. aureus. TNF-a concentrations in the supernatants were 7.8±0.0, 14831.45±2334.5, 5814.79±819.96, 9669.65±2690 pg/ml respectively. Monocytes were isolated using trypsin/EDTA solution and Raman spectroscopy was applied. Three models were built using PCA/LDA analysis. Infected from non-infected monocytes could be differentiated from the important peaks of their Raman mean spectra (Infected: 795/791, 2852/2856, 2886/2893 cm-1; non-infected: 1005, 1235, 1340/1344, 1443, 1589/1597, 2947/2951, 2974cm-1). Cells infected by C. albicans could be differentiated from those infected by bacteria (C. albicans: no important peaks; bacteria: 791, 1010/1014, 1100, 1302, 1451, 1593 cm-1). Cells infected with Gram- could be differentiated from those infected by Gram+ bacteria (K. pneumoniae: 791, 1104, 1447, 1492, 2856, 2897 cm-1; S. aureus: 1010, 1633, 1645, 2940 cm-1). The pathogens could be differen-tiated using Raman spectra from infected monocytes. This could lead to the development of a diagnostic system for detection of pathogens in patients with undocumented infections. Financial support of the COST Action (BM 1401), the project HemoSpec (CN 611682), CarbaTech (FKZ 01EI1701) by the EU and fund-ing of the Center for Sepsis Control and Care (FKZ 01EO1502) and the research campus InfectoGnostics (FKZ 13GW0096F) by the BMBF is gratefully acknowledged.


POSTERS

Automatic analysis of fungal-infected tissue using deep learning Jan-Philipp Praetorius1, Carl-Magnus Svensson1, Franziska Hoffmann6,7, Ferdinand von Eggeling6,7,8,9, Oliver Kurzai3,4,5, Marc Thilo Figge1,2,3

1 Applied Systems Biology, Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute (HKI), Jena, Germany2 Faculty of Biological Sciences, Friedrich Schiller University Jena, Jena, Germany3 Center for Sepsis Control and Care (CSCC), Jena University Hospital, Jena, Germany4 Fungal Septomics, Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute (HKI), Jena, Germany5 Institute of Hygiene and Microbiology, University of Würzburg, Würzburg, Germany6 Institute of Physical Chemistry, Friedrich Schiller University, Jena, Germany7 ENT Department, University Hospital Jena, Germany8 Leibniz Institute of Photonic Technology (IPHT), Jena, Germany9 Jena Center for Soft Matter (JCSM), Friedrich Schiller University Jena, Jena, Germany

If the human immune system is weakened, fungal infections can lead to life-threatening conditions. One method of detecting fungus is to manually inspect immunohistologically stained tissue slices. This is a time consuming and labor intensive task where the identification is highly dependent on the subjective judgment of the individual pathologist. Regardless of which images are used for the pathologist’s exam-ination, the pathologist needs a lot of time to examine the entire tissue due to the far above-average size of the image. An alternative and relatively new method is MALDI-imaging (matrix-assisted laser desorption ionization), in which mass spectroscopy of the tissue is obtained at multiple positions to acquire an “im-age” of the molecular composition. We will present a framework to automate the process of fungal identi-fication in immunohistologically stained tissue slices with the ultimate goal of combining this information with the MALDI spectra of the same tissue sample. We have immunohistologically stained slices of tissue from mouse and humans that are infected by different fungal species, including Aspergillus fumigatus and Mucorales. Recently, deep learning has become increasingly important in the field of image-based sys-tems biology. Here we train a convolutional neuronal network (CNN) that is able to learn features of images to distinguish between normal tissue and fungal-infected tissue. Preliminary results on the stained tissue show that CNNs are capable of segmenting fungal-infected tissue and even individual fungi. We provide a framework that is able to examine these images to automatically segment and classify various types of fungi in various organs. We will also present initial results of the analysis of the MALDI data and how it can be combined with the CNN output through Bayesian inference.


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Genomics insights into azole resistance in Aspergillus fumigatusTongta Sae-Ong1, Amelia E. Barber2, Kang Kang1, Oliver Kurzai2,3, Gianni Panagiotou1,4

1 Systems Biology and Bioinformatics, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute, Jena, Germany, 077452 Fungal Septomics, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute, Jena, Germany, 077453 Institute of Hygiene and Microbiology, Faculty of Medicine, University of Würzburg, Würzburg, Germany, 970804 Systems Biology and Bioinformatics Group, School of Biological Sciences, Faculty of Sciences, The University of Hong Kong, Hong Kong S.A.R., China

Aspergillus fumigatus is an environmental ubiquitous human pathogen. Azole-resistant A. fumigatus have been isolated from long-term treatment patients and azole-exposed agricultural farms. To understand the azole-resistant mechanisms, we sequenced here whole-genomes of 333 A. fumigatus isolates from conventional (before and after azole exposure) and organic farms in Germany. We tested the resistant sta-tus of A. fumigatus to 4 triazoles which were Itraconazole, Posaconazole, Voriconazole, and Tebuconazole. We applied two main bioinformatics approaches which are reference-based comparative genomics and de novo assembly to study the correlation between agricultural A. fumigatus and azole-resistance. The preliminary results revealed that genomic variation of A. fumigatus has stronger correlation with azole-ex-posure than geographical location. Genome-wide association study (GWAS) analysis reported a number of significant azole-associated variants. Four hypothetical genes were found as the common mechanism associated with the resistance to the 4 triazoles. We are currently investigating more novel genes which are associated to triazole resistance from the de novo assembled analysis.


POSTERS

Gut bacterial dysbiosis leads to overgrowth of opportunistic pathogen C. albicans in lung cancer patients. Rakesh Santhanam1, Tingting Zheng2, Daniel Loos1, Morten Sommer3, Gianni Panagiotou1,2

1 Leibniz Institute for Natural Product Research and Infection Biology e.V. (HKI), Research Group Systems Biology and Bioinformatics. 07745 Jena, Germany.2 School of Biological Sciences, the University of Hong Knog, HKSAR, China.3 Technical University of Denmark, 2800 Kgs, Lyngby, Denmark.

The gut bacterial community maintains homeostasis and confers colonization resistance against patho-gens while gut dysbiosis could lead to overgrowth of pathogens. The role of gut bacterial communities in prevention of C. albicans infections only recently started to be explored. Here, we performed shotgun metagenomics and amplicon sequencing to characterize bacterial and fungal communities of 64 lung cancer patients using stool samples before or/and after chemotherapy treatment. Our analysis revealed that, despite no major changes in the mycobiome structure induced by chemotherapy, there are signifi-cant changes in the fungal and bacterial specie-species interactions. Furthermore, bacterial species be-longing to Bacteroides and Bifidobacterium genera along with certain functional pathways related to SCFA production and antifungal BGCs (T1PKS, terpenes) were revealed as possible critical factors for preventing C. albicans overgrowth in the human gut. Our findings suggest potential biotherapeutic interventions to restore the gut bacterial community in cancer patients or other high risk for C. albicans infections patient groups.


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Insights into invasive aspergillosis by OMICS Data from a longitudinal patient cohort Sascha Schäuble1, Tamara Zoran1,2, Bastian Seelbinder1, Patricia Sieber1, Gianni Panagiotou1, Juergen Loeffler2

1 Research group PiDOMICS, Systems Biology and Bioinformatics Unit, Leibniz Institute for Natural Product Research and Infection Biology - Hans-Knoell-Institute, Jena, Germany2 Department of Internal Medicine II, WÜ4i, University Hospital Wuerzburg, Wuerzburg, Germany

Invasive aspergillosis (IA) is a life-threatening disease affecting immunocompromised patients. Despite known risk factors, due to unspecific symptoms of IA and limited methods for detecting fungal biomarkers, diagnosis of IA remains challenging.

Within PiDOMICS we conducted a longitudinal study comprising patients suffering from a multitude of diseases that led to an allogeneic stem cell transplantation (alloSCT). We followed patients after receiving an alloSCT for at least 100 days and gathered blood samples up to twice a week. Our cohort comprises patients of all ages ranging from 17 to 77 years and from both sexes. We collected approximately 1400 samples from 93 patients, of which four developed a probable IA according to the current EORTC/MSG classification.

In contrast to fungal biomarkers, within PiDOMICS we aimed to study human markers with the goal to identify early onset markers of IA as well as markers that might improve monitoring the progress of IA. We matched individual patients that developed an IA to best fitting control patients, making sure that paired patients had compatible gender, age and underlying disease.

Sequencing of mRNA and isolation of exosomal miRNA from patient blood, enabled us to study IA on a molecular level using next generation sequencing techniques. We analysed miRNA and mRNA levels in-dividually and in combination to identify meaningful signals based on differentially expressed gene (DEG) analysis alongside onset or process of IA in patients. DEGs were used to optimize classification algorithms, which additionally provide clues for the most important features defining the status of IA. Moreover, we computed data optimized metabolic models to shed light on altered system wide metabolic functionality with respect to the fungal infection. Finally, we compare our results to in vitro studies of monocyte-derived dendritic cells challenged with A. fumigatus and observe overlapping targets with genes derived from the longitudinal study.


POSTERS

S. aureus uses autophagic cell compartments to adapt for long-term intracellular persistence Anke Siegmund1, Muhammad Awais Afzal5, Daniela Keinhörster3, Christiane Wolz3, Martin Fraunholz4, Christian Hübner5, Bettina Löffler1,2, Lorena Tuchscherr1,2

1 Institute of Medical Microbiology, Jena University Hospital, Jena, Germany2 Center for Sepsis Control and Care, Jena University Hospital, Jena, Germany3 Department of Medical Microbiology and Hygiene, Interfaculty Institute for Microbiology and Infection Medicine Tübingen, Tübingen, Germany4 Biocenter, Chair of Microbiology, University of Würzburg, Würzburg, Germany5 Institute of Human Genetics, Jena University Hospital, Friedrich Schiller Universität, Jena, Germany

Staphylococcus aureus (S. aureus) is responsible of chronic and difficult-to-treat infections which repre-sent a severe clinical problem. S. aureus can act as an intracellular pathogen capable of invading multiple types of host cells and remaining in the intracellular environment for long time-periods. The intracellular survival is associated with a phenotype switch to a low metabolic subpopulation, called small colony vari-ants (SCVs) that largely avoid activation of the immune system and are tolerant to antibiotics. Yet, the sig-nals, stress factors and mechanisms that induce phenotype switching to SCVs and subsequent bacterial persistence remain largely unknown. Many bacterial factors have been discussed to be involved in bacte-rial adaptation and virulence. Important regulatory and stress coping systems include the global regulator Agr and the stringent stress response system. It is well known that both, Agr and stringent response, are involved in the expression of phenol soluble modulins (PSMs) which are small staphylococcal peptides with a strong cytolytic activity and can mediate the intracellular phagosome escape. In this study, we investigated the impact of staphylococcal factors on the intracellular SCV-formation, phagosomal escape and persistence in non-professional phagocytes. To this end, we generated different S. aureus knock out strains in LS1 and USA300 backgrounds in factors involved in the stringent response (Δrsh/relP/relQ), in PSMs (Δpsmαβ and Δpsmαβ/rsh/relP/relQ) and in Agr expression (Δagr). We performed several functional assays in cell culture systems and an in vivo footpad infection model in mice to analyse the staphylococcal persistence. We found that the absence of PSMs regardless of stringent response increased significantly SCV formation and S. aureus long-term survival by manipulation of the autophagy pathways. Collectively, our results demonstrate that the expression of PSMs by S. aureus is essential for the bacterial spreading during the acute phase of infection, but in the chronic phase PSMs need to be downregulated for bacterial persistence.


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Bayesian analysis of pseudo-time resolved somatic hypermutation in Brca2-deficient B cells Carl-Magnus Svensson1, Gianna Hirth2, Katrin Böttcher2, Steffen Ullrich2, Marc Thilo Figge1,3, Berit Jungnickel2

1 Research Group Applied Systems Biology, Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute, Jena, Germany2 Department of Cell Biology, Institute of Biochemistry and Biophysics, Faculty of Biological Sciences, Friedrich Schiller University, Jena, Germany 3 Faculty of Biological Sciences, Friedrich Schiller University, Jena, Germany

Somatic hypermutation (SHM) of immunoglobulin genes in germinal centre B cells is a key mechanism in the response of the immune system against a vast array of microbes. It relies on error-prone DNA repair processes leading to different types of mutations. Homologous recombination (HR), which in general is a crucial mechanism in the error-free repair of DNA double-strand breaks, may thus play an important role in the regulation of SHM. By inactivation of the critical HR factor Brca2 in murine B cells, we detected decreased proliferation and survival of ex vivo activated B cells. In vivo, it was shown that the germinal centre reactions still took place, albeit reduced. Going beyond the static analysis of counting the number of mutations of a certain type, we introduced a pseudo-timing of the data by looking at the total number of mutations in each sequence. If a sequence has many mutations, it can be assumed to come from a B cell that has been through the selection process numerous times. These data were analysed using Bayesian statistics to investigate if the relative frequency of a certain base change varied with the total number of mutations in the sequence. To analyse the pseudo-time resolved data, a combination of Bayesian in-ference, Monte Carlo simulations and a linear model was used to determine if the probability for specific mutational events were equal for sequences with different number of overall mutations. We found that the Polη hallmark mutation A>G for controls became more common as the overall number of mutations in-creased, however this increase was not present in the Brca2 mutant. This suggests that there is a specific need for HR to survive SHM, especially to achieve sufficient A>G mutations.


POSTERS

Mathematical model of the factor H mediated self and non-self discrimination by the complement systemAlexander Tille1, Teresa Lehnert1, Nadine Reiher2,3, Peter Zipfel2,3, Marc Thilo Figge1,2

1 Applied Systems Biology, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute, Jena, Germany2 Faculty of Biological Sciences, Friedrich-Schiller-University Jena, Jena, Germany3 Infection Biology, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute, Jena, Germany

The complement system is part of the innate immune system and plays an important role in host defense against pathogenic infections. By mediating immunological and inflammatory processes it plays a key role in coordinating innate and adaptive immune response. Its main task is the recognition and subsequent opsonization of foreign particles or dysfunctional cells.

In this study, we focus on the alternative pathway of the human complement system. The alternative path-way is activated spontaneously, which leads to a basal level of active complement molecules. Thus a tight regulation mechanism is needed to protect the body’s own cells (self-cells) from opsonization. The major regulator of the alternative pathway is the protein factor H. It acts in the fluid phase and can attach to cell surfaces where it controls complement activation effectively. Besides the body’s own cells, pathogens like C. albicans also have acquired the ability to bind factor H and thus escape opsonization and cause severe infections.

In order to understand the opsonization process better, we developed a mathematical model of the alter-native pathway using ordinary differential equation for surface-bound molecules and partial differential equations for the concentration profiles of fluid phase molecules around a cell. The model focuses on the most important components of the complement cascade: C3b in the fluid phase and on the cell surface as well as inactivated C3b on the cell surface. The other components of the complement system are com-bined in effective rates that represent the dynamics of the formation of several intermediate products of the cascade.

Using steady state analysis we investigated driving processes of the complement activation and regula-tion on the cell surface. The model enables a clear distinction between pathogens and self-cells. Based on these results we propose treatment strategies to enhance the opsonization of pathogens while protecting the body’s own cells.


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Virtual phagocytosis assays reveal strain-specific differences in the microscopic parameters of the interaction between alveolar macrophages and two A. fumigatus strains Sandra Timme1, Jan-Philipp Praetorius1,2, Mohamed Hassan1,2, Zoltán Cseresnyés1, Marc Thilo Figge1,2

1 Research Group Applied Systems Biology, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute, Jena, Germany2 Faculty of Biological Sciences, Friedrich Schiller University Jena, Germany

Phagocytosis is a major immune response mechanism of professional phagocytes to combat invading pathogens. Therefore, the quantification and characterization of phagocytosis is essential for a better un-derstanding of host-pathogen interactions. Typically, various phagocytosis measures, such as the phago-cytosis ratio, the uptake ratio or the symmetrized phagocytic index are used for this characterization. However, these measures are limited in several ways: (i) they can give the same result despite different experimental results, (ii) different measures can give contradictory results, and (iii) they do not allow to obtain microscopic cell-based interaction rates. To overcome these limitations we developed a general-ized C++ framework - CellRain - for modeling based on endpoint images. The saprophyte A. fumigatus is a ubiquitous human pathogen. Its spores that are distributed via air are inhaled hundreds of times every day by most humans. If not efficiently cleared by the resident immune cells in the lung - the alveolar macro-phages (AM) - they can cause severe pulmonary infections in immunocompromised patients, resulting in high mortality rates. Previously, phagocytosis assays with AM and two A. fumigatus wild-type strains, ATCC 46645 and CEA10, have been performed using differential staining. After one hour of co-incubation fluo-rescence microscopy images were taken and subsequent image analysis allowed for the quantification of phagocytosed, adherent and non-associated conidia[1,2]. Based on these data we simulated virtual phago-cytosis assays using CellRain. First, virtual conidia were distributed randomly on images with segmented AM. Subsequently, experimental steps, such as co-incubation and washing, were simulated. During the co-incubation, the conidia that are located on or within the operating distance of an AM can become ad-herent or phagocytosed with certain probabilities. During the washing step non-associated conidia are removed with the respective probability. To estimate these microscopic parameters, we performed a grid-based screening and compared in vitro and in silico data. For statistically sound results we performed sev-eral repetitions for each parameter set. This image-based systems biology approach enables prediction of microscopic parameters and reveals strain-specific differences in the phagocytosis and adherence by AM.[1] Kraibooj et al. Front Microbiol 2015[2] Cseresnyes et al. Cytometry A 2018


POSTERS

Ancient human microbiomes for the study of microbe-microbe and host-microbiome interactionsIrina M. Velsko1, James A. Fellows Yates1, OMEC Consortium, Christina Warinner1

1 Max Planck Institute for the Science of Human History, Jena, 07745, Germany

The human oral microbiome is a highly structured and spatially organized biofilm. Dental plaque has a high microbial diversity that varies within and across individuals, yet maintains a core set of taxa across hu-mans. Today, biofilm functionality to thrive on the tooth surface is highly conserved, shows little variation with diet, and is altered at sites of oral diseases such as caries and periodontal disease. Ancient dental calculus, a calcified dental plaque biofilm, captures temporal variation of the oral microbiome and is a medium with which to study deep-time evolutionary impact on interactions between biofilm species and between the biofilm and the host. We investigated functional profiles of dental calculus from Neanderthals (n=10), ancient humans (n=28), contemporary humans (n=18), chimpanzees (n=18), gorillas (n=17), and howler monkeys (n=5), as well as modern dental plaque (n=20) using a shotgun metagenomics approach. DNA sequences were assigned functional annotations by HUMAnN2 and by AADDER. Both programs as-signed function to a substantially higher proportion of sequences from modern human dental calculus and plaque than to ancient samples or to non-human primates, although ancient humans exhibited high variation. AADDER assigned a higher proportion of reads in ancient human, chimpanzee, gorilla, and howler monkey samples than did HUMANn2. Principal components analysis of AADDER assignments reveal the functional profiles of oral samples cluster by host genus. The 10 enzymes with strongest loadings separat-ing humans from non-human primates are predominantly assigned to Streptococcus species. Differences in the taxonomic and functional profiles of hominids through time may be interdependent and potentially reflect major shifts in primate in dietary practices.


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abstractinhalt 2019 cv - systems biology · 2 // 5th international symposium systems biology of...

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