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Bioinformatics (van Nimwegen)

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Bacterial stationary phase: interlacing of variability and active responses

Research Project  | 3 Project Members

An organism's evolutionary viability depends on its ability to respond to various environmental challenges. These challenges can range from highly complex ecological rearrangements to seemingly simple changes such as the lack of a particular nutrient. Even for these "simpler" changes, the responses of an organism often involve profound physiological reorganization. In bacteria, for instance, shifting from a nutrient-rich to a nutrient-deprived environment causes dramatic changes in their physiology. In such a shift, machinery that supports exponential growth becomes poorly suited for survival in a non-growing state. Thus, bacteria must reshape their proteome, condense their DNA, etc., to better cope with the new environment. These responses are inherently variable yet reflective of bacterial regulatory programs shaped by evolution. Variability underlies bet-hedging strategies, but it is unclear how it arises and determines the survival of bacteria in a stationary phase. During my postdoc in the van Nimwegen group, I aim to identify quantitative rules governing physiological rearrangements and gene expression variability at the entry into the stationary phase. By combining experimental and modeling work, I will investigate the relative contribution of both passive processes (e.g., exhaustion of intracellular resources) and active regulatory responses (e.g., targeted protein degradation, up- and down-regulation of gene expression) during the transition into the stationary phase. Besides ecology and evolution, the physiology of bacteria in the stationary phase shapes bacterial responses to antibiotics, which makes understanding the mechanisms of growth arrest clinically relevant as well.

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SIMBioData: Standardized integration of multi-omics biomedical data

Research Project  | 2 Project Members

As technological advances enable the collection of vast datasets of biomedical measurements, many ongoing studies attempt to decipher various aspects of human health from such data. Although the focus has been primarily on genetic information, other data modalities, such as the abundances of RNAs and proteins within cells and tissues, relate more directly to phenotypes. However, these latter modalities raise significantly more data analysis challenges, and so far, the emphasis in large consortia has been almost exclusively on data production, curation and storage. Efforts to standardize analysis methods so as to allow application on a large scale without the need for subjective choices, are virtually nonexistent. Moreover, while measures have been put in place to ensure that the data generated in scientific studies satisfies the FAIR principles, FAIRification of methods does not help in addressing issues of data quality, internal consistency, and interpretability of analysis outputs. We propose that to really harness the potential of the wealth of omics data for biomedical research, it is essential to establish a standardized, sustainable and evolvable method infrastructure for extracting biophysically-meaningful quantities and underlying regulatory information. In particular, only by providing standardized methods that extract biophysically-meaningful quantities in a transparent manner, will it become possible to quantitatively compare and integrate results from omics data across different modalities and experimental approaches. In addition, we feel that our project will provide an ideal prototype for the analysis component of the SwissBioData initiative, which is scheduled to start after the completion of our project.

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NCCR AntiResist: New approaches to combat antibiotic-resistant bacteria

Umbrella Project  | 32 Project Members

Antibiotics are powerful and indispensable drugs to treat life threatening bacterial infections such as sepsis or pneumonia. Antibiotics also play a central role in many other areas of modern medicine, in particular to protect patients with compromised immunity during cancer therapies, transplantations or surgical interventions. These achievements are now at risk, with the fraction of bacterial pathogens that are resistant to one or more antibiotics steadily increasing. In addition, development of novel antimicrobials lags behind, suffering from inherently high attrition rates in particular for drug candidates against the most problematic Gram-negative bacteria. Together, these factors increasingly limit the options clinicians have for treating bacterial infections. The overarching goal of NCCR AntiResist is to elucidate the physiological properties of bacterial pathogens in infected human patients in order to find new ways of combatting superbugs. Among the many societal, economic, and scientific factors that impact on the development of alternative strategies for antibiotic discovery, our limited understanding of the physiology and heterogeneity of bacterial pathogens in patients ranks highly. Bacteria growing in tissues of patients experience environments very different from standard laboratory conditions, resulting in radically different microbial physiology and population heterogeneity compared to conditions generally used for antibacterial discovery. There is currently no systematic strategy to overcome this fundamental problem. This has resulted in: (i) suboptimal screens that identify new antibiotics, which do not target the special properties of bacteria growing within the patient; (ii) an inability to properly evaluate the efficacy of non-conventional antibacterial strategies; (iii) missed opportunities for entirely new treatment strategies. This NCCR utilizes patient samples from ongoing clinical studies and establishes a unique multidisciplinary network of clinicians, biologists, engineers, chemists, computational scientists and drug developers that will overcome this problem. We are excited to merge these disciplines in order to determine the properties of pathogens infecting patients, establish conditions in the lab that reproduce these properties and utilize these in-vitro models for antimicrobial discovery and development. In addition, clinical-trial networks and the pharmaceutical industry have major footprints in antimicrobial R&D. Exploiting synergies between these players has great potential for making transformative progress in this critical field of human health. This NCCR maintains active collaborations with Biotech SMEs and large pharmaceutical companies with the goal to: accelerate antibiotic discovery by providing relevant read-outs for early prioritization of compounds; enable innovative screens for non-canonical strategies such as anti-virulence inhibitors and immunomodulators; identify new antibacterial strategies that effectively combat bacteria either by targeting refractory subpopulations or by synergizing with bacterial stresses imposed by the patients' own immune system. This NCCR proposes a paradigm shift in antibiotic discovery by investigating the physiology of bacterial pathogens in human patients. This knowledge will be used to develop assays for molecular analyses and drug screening under relevant conditions and to accelerate antibacterial discovery, improve treatment regimens, and uncover novel targets for eradicating pathogens. Through this concerted effort, this NCCR will make a crucial and unique contribution to winning the race against superbugs.

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A quantitative approach to transcriptional network dynamics

Research Project  | 4 Project Members

Transkriptionsfaktoren (TFs) orchestrieren die Genexpression und spielen somit eine zentrale Rolle in praktisch jedem zellulären Prozess, einschließlich der Kontrolle der Zellidentität. FTs sind im Allgemeinen in Netzwerken organisiert, innerhalb derer sich verschiedene FTs gegenseitig aktivieren oder hemmen. die Richtung dieser Interaktionen kennen wir zwar sehr gut, aber wir haben keine quantitativen Informationen über die Konzentration und das dynamische Verhalten von FTs. Diese quantitativen Informationen sind unerlässlich, um zu verstehen, welche Einschränkungen die FT-Netze aktiv halten und unter welchen Bedingungen sie inaktiviert werden. Dies ist besonders wichtig im Zusammenhang mit der Regulierung der zellulären Identität, die in erster Linie durch FT-Netze kontrolliert wird. In diesem Sinergia - Projekt schlagen wir vor, eine breite Palette von quantitativen in vitro- und in vivo-Methoden zur Bestimmung der biophysikalischen, Konzentrations- und DNA-Bindungseigenschaften von 7 verschiedenen FTs, die an der Erneuerung embryonaler Stammzellen der Maus beteiligt sind, einzusetzen. Wir werden Methoden zur Abbildung einzelner Moleküle von FTs, zur Überwachung von FT-Konzentrationen in lebenden Zellen durch Bildgebung, zur Synthese künstlicher FTs, zu genomischen Methoden zur Kartierung der FT-Bindung im Genom und zur mathematischen Modellierung verwenden. Dieses Projekt wird es uns ermöglichen, das dynamische Verhalten dieses Netzwerks von FTs vorherzusagen, was uns helfen wird, besser zu verstehen, wie FTs die Zellidentität regulieren.

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Inferring gene regulatory landscapes from single-cell omics data

Research Project  | 9 Project Members

In multi-cellular organisms such as animals and humans, a single genome sequence is expressed into a complex organization of hundreds of different cell types. Although it is well understood that this process is controlled by regulatory proteins that can switch genes on and off by binding to short sequences in the DNA, there is still an enormous gap in our understanding between experimentally determined lists of components on the one hand, and idealized mathematical models on the other hand. In a major development, it has become possible over the last few years to experimentally quantitatively track the states of individual cells across tissues and embryos as they are developing, measuring how much each gene is expressed, the state of the DNA, which cells derived from the same ancestor cell, and so on. These methods promise to revolutionize our ability to understand the ways in which a single genome can be expressed into a complex organism. However, there are many challenges on the way to realizing this promise. For example, single cell measurements are very noisy, with many missing values. Most importantly, it is currently unclear how the measured state of a single cell can be related to the actions of the regulatory proteins that guide the process. Therefore, novel computational methods are needed that allow bridging the gap between mathematical models and single-cell measurements. The goals of the project are to develop new mathematical and computational methods for the analysis of genome-wide gene expression and DNA state measurements in single cells. In particular, we propose to develop new statistical models for carefully distinguishing true biological variability from measurement noise. Moreover, we propose to adapt a previously developed method for relating the measured state of a cell to the activities of regulatory proteins to be applied to single cells. The methods we propose should make it possible to precisely quantify the activities of regulatory proteins in single cells, and to map the landscapes that guide the states of cells. The results of our work will consist of a suite of tools that can be used by researchers worldwide. Our project involves several collaborations with experimentalists at the forefront of developing these single cell measurements, and application of our methods to data generated in the labs will guide further refinement of our methods. The methods we propose will make a major contribution to realizing the promise of single-cell methodologies for unraveling how a single genome is expressed into a complex multi-cellular organization. Understanding this will eventually have innumerable applications in human health and medicine.

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High-throughput multiplexed microfluidics for antimicrobial drug discovery

Research Project  | 4 Project Members

This project ist part of the PhD program of the SNI Swiss Nanoscience Institute. The project is a collaboration between the van Nimwegen research group at the Biozentrum and the Laboratory for Micro- and Nanotechnology at the PSI. The wet lab of the van Nimwegen group, led by Dr. Thomas Julou, is at the forefront of method development for quantitatively tracking bacteria at the single-cell level in dynamically controlled environmental conditions (Kaiser, et al. 2018), whereas the PSI group focuses on microfabrication and prototyping. The main goal of the project is to develop a new method for high-throughput quantification of the effects of antimicrobial compounds on single cells as a function of their physiological state. In the first phase of the project the student will develop new microfluic designs that allow arrays of strains and treatments to be assayed in parallel, building on existing prototypes that have already been developed in the van Nimwegen lab (e.g. the figure shows the response of a lineage of single E. coli cells to a sudden exposure to ceftriaxone). These designs will involve fabrication of channels with sub-micrometer dimensions and thus the use of electron beam lithography. The fabrication will be carried out at the PSI where, besides optical UV lithography, high resolution e-beam direct writing tools are available for defining high aspect ratio micro- and nanometer structures of arbitrary shape (Vila-Comamala, et al. 2011).

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Measuring single-cell pharmacodynamics with deep learning

Research Project  | 3 Project Members

The pharmacodynamics of antibiotics are currently almost exclusively defined at the population level. However, recent studies have highlighted that microbial pathogens diversify into different physiological states within their hosts, and that the action of antibiotics can vary dramatically with the physiological state of single cells. Thus, a comprehensive approach to quantifying pharmacodynamics at the single-cell level, across bacterial strains and growth conditions, will likely have a profound impact on the development of novel antimicrobial therapies. Recently developed microfluidic setups, when used in combination with time-lapse microscopy, allow long-term monitoring of growth and gene expression in single bacterial cells exposed to precisely controlled environments. However, the throughput of such methods is currently highly constrained by the image analysis, which still requires manual curation. We here propose to harness recent progress in deep learning image analysis methods to develop a fully automated image analysis tool for time-lapse microfluidic data. As a proof-of-principle, we will use our tool in combination with downstream Bayesian probabilistic methods to infer detailed single-cell pharmacodynamics of several antibiotics from measurements of growth inhibition and killing of individual bacteria exposed to antibiotics with different growth conditions and treatment protocols.

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The role of gene expression noise in the evolution of gene regulation

Research Project  | 12 Project Members

Eines der auffälligsten Merkmale, welches uns Lebewesen von den unbelebten Objekten unterscheidet, die die Physik und die Chemie untersucht, ist die Eigenschaft, auf die Umwelt zu reagieren und sich anzupassen. Zellen können Chemikalien in ihrer Umgebung wahrnehmen und darauf reagieren, da sie Mechanismen besitzen, um die Expression ihrer Gene entsprechend anzupassen. Über die molekularen Mechanismen der Genregulierung ist schon viel bekannt; regulatorische Proteine wie z.B. Transkriptionsfaktoren binden in einer sequenz-abhängigen Weise an kurze DNA Stücke, und die Bindungsmuster dieser regulatorischen Proteine sind ziemlich gut erforscht, aber beinahe nichts ist bekannt über die Art und Weise, in der die Genregulation evolutionär entstanden ist. Neuere Arbeit aus unserem Labor hat angedeutet, dass es eine grundlegende, starke Verbindung gibt zwischen der Evolution der Genregulierung und dem Rauschen in dem Prozess der Genexpression. Wie jeder physikalische Prozess hängt auch die Genexpression von thermischen und anderen Fluktuationen ab, welche bewirken, dass sogar identische Zellen in einer homogenen Umgebung Schwankungen in ihrem Verhalten zeigen. Ein Teil dieses Genexpressions-Rauschens wird durch die Ausbreitung des Rauschens der regulatorischen Protein an ihre Zielgene verursacht, und aus unserer jüngsten Arbeit lässt sich schliessen, dass die Weiterleitung des Rauschen eine wichtige Rolle spielt bei der Entwicklung der Genregulation. In diesem Projekt werden wir mit einer Kombination aus experimentellen und theoretischen Ansätzen die Rolle des Rauschens der Genexpression in der Evolution der Genregulierung am Beispiel des Bakteriums Escherichia coli untersuchen. Wir erwarten, dass diese Arbeit neue grundlegende Erkenntnisse liefert, wie Organismen lernen können, sich an ihre Umgebung anzupassen. Diese Erkenntnisse könnten wichtige Auswirkungen auf alle Gebiete der Biotechnologie haben.