Biology subjects

Guenther, P.

Publications and source records attributed to Guenther, P..

6 recordsLinked to original sources

bioRxiv2021

Structure of a bacterial Rhs effector exported by the type VI secretion system

The type VI secretion system (T6SS) is a widespread protein export apparatus found in Gram-negative bacteria. The majority of T6SSs deliver toxic effector proteins into competitor bacteria. Yet, the structure, function, and activation of many of these effectors remains poorly understood. Here, we present the structures of the T6SS effector RhsA from Pseudomonas protegens and its cognate T6SS spike protein, VgrG1, at 3.3 [A] resolution. The structures reveal that the rearrangement hotspot (Rhs) repeats of RhsA assemble into a closed anticlockwise {beta}-barrel spiral similar to that found in bacterial insecticidal Tc toxins and in metazoan teneurin proteins. We find that the C-terminal toxin domain of RhsA is autoproteolytically cleaved but remains inside the Rhs cocoon where, with the exception of three ordered structural elements, most of the toxin is disordered. The N-terminal plug domain is unique to T6SS Rhs proteins and resembles a champagne cork that seals the Rhs cocoon at one end while also mediating interactions with VgrG1. Interestingly, this domain is also autoproteolytically cleaved inside the cocoon but remains associated with it. We propose that mechanical force is required to remove the cleaved part of the plug, resulting in the release of the toxin domain as it is delivered into a susceptible bacterial cell by the T6SS.

biochemistry↗

bioRxiv2020

Alterations of multiple alveolar macrophage states in chronic obstructive pulmonary disease

Despite the epidemics of chronic obstructive pulmonary disease (COPD), the cellular and molecular mechanisms of this disease are far from being understood. Here, we characterize and classify the cellular composition within the alveolar space and peripheral blood of COPD patients and control donors using a clinically applicable single-cell RNA-seq technology corroborated by advanced computational approaches for: machine learning-based cell-type classification, identification of differentially expressed genes, prediction of metabolic changes, and modeling of cellular trajectories within a patient cohort. These high-resolution approaches revealed: massive transcriptional plasticity of macrophages in the alveolar space with increased levels of invading and proliferating cells, loss of MHC expression, reduced cellular motility, altered lipid metabolism, and a metabolic shift reminiscent of mitochondrial dysfunction in COPD patients. Collectively, single-cell omics of multi-tissue samples was used to build the first cellular and molecular framework for COPD pathophysiology as a prerequisite to develop molecular biomarkers and causal therapies against this deadly disease.

systems biology↗

bioRxiv2019

A rule-based data-informed cellular consensus map of the human mononuclear phagocyte cell space

Single-cell genomic techniques are opening new avenues to understand the basic units of life. Large international efforts, such as those to derive a Human Cell Atlas, are driving progress in this area; here, cellular map generation is key. To expedite the inevitable iterations of these underlying maps, we have developed a rule-based data-informed approach to build next generation cellular consensus maps. Using the human dendritic-cell and monocyte compartment in peripheral blood as an example, we performed computational integration of previous, partially overlapping maps using an approach we termed backmapping, combined with multi-color flow-cytometry and index sorting-based single-cell RNA-sequencing. Our general strategy can be applied to any atlas generation for humans and other species.\n\nGraphical Abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC=\"FIGDIR/small/658179v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (18K):\norg.highwire.dtl.DTLVardef@c1b7b2org.highwire.dtl.DTLVardef@32b68org.highwire.dtl.DTLVardef@16e5fcorg.highwire.dtl.DTLVardef@1552028_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIDefining a consensus of the human myeloid cell compartment in peripheral blood\nC_LIO_LI3 monocytes subsets, pDC, cDC1, DC2, DC3 and precursor DC make up the compartment\nC_LIO_LIDistinguish myeloid cell compartment from other cell spaces, e.g. the NK cell space\nC_LIO_LIProviding a generalizable method for building consensus maps for the life sciences\nC_LI

immunology↗

bioRxiv2019

Memory-driven computing accelerates genomic data processing

Next generation sequencing (NGS) is the driving force behind precision medicine and is revolutionizing most, if not all, areas of the life sciences. Particularly when targeting the major common diseases, an exponential growth of NGS data is foreseen for the next decades. This enormous increase of NGS data and the need to process the data quickly for real-world applications requires to rethink our current compute infrastructures. Here we provide evidence that memory-driven computing (MDC), a novel memory-centric hardware architecture, is an attractive alternative to current processor-centric compute infrastructures. To illustrate how MDC can change NGS data handling, we used RNA-seq assembly and pseudoalignment followed by quantification as two first examples. Adapting transcriptome assembly pipelines for MDC reduced compute time by 5.9-fold for the first step (SAMtools). Even more impressive, pseudoalignment by near-optimal probabilistic RNA-seq quantification (kallisto) was accelerated by more than two orders of magnitude with identical accuracy and indicated 66% reduced energy consumption. One billion RNA-seq reads were processed in just 92 seconds. Clearly, MDC simultaneously reduces data processing time and energy consumption. Together with the MDC-inherent solutions for local data privacy, a new compute model can be projected pushing large scale NGS data processing and primary data analytics closer to the edge by directly combining high-end sequencers with local MDC, thereby also reducing movement of large raw data to central cloud storage. We further envision that other data-rich areas will similarly benefit from this new memory-centric compute architecture.

bioinformatics↗

bioRxiv2018

Diagnostic value of blood gene expression-based classifiers as exemplified for acute myeloid leukemia

ABSTRACTAcute Myeloid Leukemia (AML) is a severe, mostly fatal hematopoietic malignancy. Despite nearly two decades of promising results using gene expression profiling, international recommendations for diagnosis and differential diagnosis of AML remain based on classical approaches including assessment of morphology, immunophenotyping, cytochemistry, and cytogenetics. Concerns about the translation of whole transcriptome profiling include the robustness of derived predictors when taking into account factors such as study- and site-specific effects and whether achievable levels of accuracy are sufficient for practical use. In the present study, we sought to shed light on these issues via a large-scale analysis using machine learning methods applied to a total of 12,029 samples from 105 different studies. Taking advantage of the breadth of data and the now much improved understanding of high-dimensional modeling, we show that AML can be predicted with high accuracy. High-dimensional approaches - in which multivariate signatures are learned directly from genome-wide data with no prior biological knowledge - are highly effective and robust. We explore also the relationship between predictive signatures, differential expression and known AML-related genes. Taken together, our results support the notion that transcriptome assessment could be used as part of an integrated genomic approach in cancer diagnosis and treatment to be implemented early on for diagnosis and differential diagnosis of AML.\n\nOne Sentence SummaryBlood gene expression data and machine learning were used to develop robust and accurate classifiers for diagnosis and differential diagnosis of acute myeloid leukemia based on analysis of more than 12,000 samples derived from more than 100 individual studies

genomics↗

bioRxiv2017

Cellular reprogramming of human monocytes is regulated by time-dependent IL4 signalling and NCOR2

The clinical and therapeutic value of human in vitro generated monocyte-derived dendritic cell (moDC) and macrophages is well established. However, in line with recent findings regarding myeloid cell ontogeny and due to our limited understanding of their physiological counterparts, transcriptional regulation and heterogeneity, the full potential of these important cellular systems is still underestimated.\n\nIn this study, we use cutting edge high-dimensional analysis methods to better understand the transcriptional organization, phenotypic heterogeneity and functional differences between human ex vivo isolated and in vitro generated mononuclear phagocytes with the aim to better realize their full potential in the clinic.\n\nWe demonstrate that human monocytes activated by MCSF or GMCSF most closely resemble inflammatory macrophages identified in vivo, while IL4 signalling in the presence of GMCSF generates moDCs resembling inflammatory DCs in vivo, but not steady state cDC1 or cDC2. Moreover, these reprogramming regimes lead to activated monocytes that present with profoundly different transcriptomic, metabolic, phenotypic and functional profiles. Furthermore, we demonstrate that CD14+ monocytes are integrating multiple exogenous activation signals such as GMCSF and IL4 in a combinatorial and temporal fashion, resulting in a high-dimensional cellular continuum of reprogrammed monocytes dependent on the mode and timing of cytokine exposure. Utilizing nanostraw-based knockdown technology, we demonstrate that the IL4-dependent generation of moDCs relies on the induction, nuclear localization and function of the transcriptional regulator NCOR2.\n\nFinally, we unravel unappreciated heterogeneity within the clinically moDCs population and propose a novel high-dimensional phenotyping strategy to better tailor clinical quality control strategies for patient need and culture conditions to enhance therapeutic outcome.

immunology↗