Biology subjects

Schultze, J. L.

Publications and source records attributed to Schultze, J. L..

27 records · Page 2Linked to original sources

bioRxiv2020

Soluble mannose receptor induces pro-inflammatory macrophage activation andobesity-associated metaflammation

Pro-inflammatory activation of macrophages in metabolic tissues is critically important in induction of obesity-induced metaflammation. Here, we demonstrate that the soluble mannose receptor (sMR) plays a direct, functional role in both macrophage activation and metaflammation. We show that sMR binds CD45 on macrophages and inhibits its phosphatase activity, leading to a Src/Akt/NF-{kappa}B-mediated cellular reprogramming towards an inflammatory phenotype both in vitro and in vivo. Remarkably, increased serum sMR levels were observed in obese mice and humans and directly correlated with body weight. Additionally, MR deficiency lowers pro-inflammatory macrophages in metabolic tissues and protects against hepatic steatosis and whole-body metabolic dysfunctions in high-fat diet-induced obese mice. Conversely, administration of sMR in lean mice increases serum pro-inflammatory cytokines, activates tissue macrophages and promotes insulin resistance. Altogether, our results reveal sMR as novel regulator of pro-inflammatory macrophage activation which could constitute a new therapeutic target for metaflammation and other hyperinflammatory diseases.

immunology↗

bioRxiv2020

De novo DNA methylation controls neuronal maturation during adult hippocampal neurogenesis

Dynamic DNA methylation controls gene-regulatory networks underlying cell fate specification. How DNA methylation patterns change during adult hippocampal neurogenesis and their relevance for adult neural stem cell differentiation and related brain function has, however, remained unknown. Here, we show that neurogenesis-associated de novo DNA methylation is critical for maturation and functional integration of adult-born hippocampal neurons. Cell stage-specific bisulfite sequencing revealed a pronounced gain of DNA methylation at neuronal enhancers, gene bodies and binding sites of pro-neuronal transcription factors during adult neurogenesis, which mostly correlated with transcriptional up-regulation of the associated loci. Inducible deletion of both de novo DNA methyltransferases Dnmt3a and Dnmt3b in adult neural stem cells specifically impaired dendritic outgrowth and synaptogenesis of new-born neurons, resulting in reduced hippocampal excitability and specific deficits in hippocampus-dependent learning and memory. Our results highlight that, during adult neurogenesis, remodeling of neuronal methylomes is fundamental for proper hippocampal function.

neuroscience↗

bioRxiv2020

Swarm Learning as a privacy-preserving machine learning approach for disease classification

Identification of patients with life-threatening diseases including leukemias or infections such as tuberculosis and COVID-19 is an important goal of precision medicine. We recently illustrated that leukemia patients are identified by machine learning (ML) based on their blood transcriptomes. However, there is an increasing divide between what is technically possible and what is allowed because of privacy legislation. To facilitate integration of any omics data from any data owner world-wide without violating privacy laws, we here introduce Swarm Learning (SL), a decentralized machine learning approach uniting edge computing, blockchain-based peer-to-peer networking and coordination as well as privacy protection without the need for a central coordinator thereby going beyond federated learning. Using more than 14,000 blood transcriptomes derived from over 100 individual studies with non-uniform distribution of cases and controls and significant study biases, we illustrate the feasibility of SL to develop disease classifiers based on distributed data for COVID-19, tuberculosis or leukemias that outperform those developed at individual sites. Still, SL completely protects local privacy regulations by design. We propose this approach to noticeably accelerate the introduction of precision medicine.

bioinformatics↗

bioRxiv2020

Creld2 function during unfolded protein response is essential for liver metabolism homeostasis

The unfolded protein response (UPR) is associated with the hepatic metabolic function, yet it is not well understood how endoplasmic reticulum (ER) disturbance might influence metabolic homeostasis. Here, we describe the physiological function of Cysteine-rich with EGF-like domains 2 (Creld2), previously characterized as a downstream target of the ER-stress signal transducer Atf6. To this end we generated Creld2-deficient mice and induced UPR by injection of tunicamycin. Creld2 augments protein folding and creates an interlink between the UPR axes through its interaction with proteins involved in UPR. Thereby, Creld2 promotes tolerance to ER stress and recovery from acute stress. Creld2-deficiency leads to a dysregulated UPR, and causes the development of hepatic steatosis during ER stress conditions. Moreover, Creld2 enhancement of the UPR assists in the regulation of energy expenditure. Furthermore, we observed a sex dimorphism in humans with fatty liver disease, with only males showing an accumulation of CRELD2 protein in the liver. These results reveal a Creld2 function at the intersection between UPR and metabolic homeostasis and suggest a mechanism in which chronic ER stress underlies fatty liver disease in males.

molecular 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↗

bioRxiv2018

FASTGenomics: An analytical ecosystem for single-cell RNA sequencing data

Recent technological advances enable genomics of individual cells, the building blocks of all living organisms. Single cell data characteristics differ from those of bulk data, which led to a plethora of new analytical strategies. However, solutions are only useful for experts and currently, there are no widely accepted gold standards for single cell data analysis. To meet the requirements of analytical flexibility, ease of use and data security, we developed FASTGenomics (https://fastgenomics.org) as a powerful, efficient, versatile, robust, safe and intuitive analytical ecosystem for single-cell transcriptomics.

bioinformatics↗

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↗