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A kinetics-based model of hematopoiesis reveals extrinsic regulation of skewed lineage output from stem cells - data repository
No full textSingle-cell RNA sequencing data from five single-HSC-derived murine hematopoietic systems, as well as two polyclonal controls. The resulting clonally-resolved hematopoietic bone marrow atlas (scBM_atlas.rds) consists of 76,863 high-quality cells and covers all major hematopoietic cell types, including differentiation tracks (slingshot.rds) from the most immature HSCs to all lineage-committed progenitors (HSPCs.rds) and their continued maturation into blood and immune cells
Lamin A/C-regulated cysteine catabolic flux modulates stem cell fate through epigenome reprogramming [bulkPolyA_RNA_seq_LA_ES]
No full textSpatiotemporal changes in the nuclear lamina and cell metabolism shape cell fate, yet their interplay is poorly understood. Here, we identify lamin A/C as a key regulator of cysteine catabolic flux essential for proper cell fate and longevity. Its loss in naïve mouse pluripotent stem cells leads to upregulation of the cysteine generating and catabolizing enzymes, cystathionine γ-lyase (CTH) and cystathionine β-synthase (CBS), thereby promoting de novo cysteine synthesis. Increased cysteine flux into acetyl-CoA fosters histone H3K9 and H3K27 acetylation, triggering a transition from naïve to primed pluripotency and abnormal cell fate and function. Conversely, the toxic gain-of-function mutation of Lmna, encoding lamin A/C and associated with premature aging, reduces CTH and CBS levels. This reroutes cysteine catabolic flux and alters the balance between H3K9 acetylation and methylation, crucially impacting germ layer formation and genome stability. Importantly, modulation of Cth and Cbs rescues the abnormal cell fate and function, restores the DNA damage repair capacity, and alleviates the senescent phenotype caused by lamin A/C mutations, highlighting the potential of modulating cell metabolism to mitigate epigenetic diseases
EMBARK - Establishing a monitoring baseline for antimicrobial resistance in key environments
No full textIn this project, we establish baseline ranges for background antibiotic resistance abundances and diversity in different environments, standardize different methods for monitoring antibiotic resistance in the environment, identify sets of priority targets for environmental antibiotic resistance monitoring, and develop methods to detect novel resistance threats to develop an early-warning system for emerging forms of resistance. The end goal of the project is a monitoring scheme that can be used in a modular fashion depending on the available resources. Establishing a coherent monitoring scheme is imperative for efficient monitoring, which in turn is essential to limit resistance development in the future
Protein intake and cardiovascular diseases: an umbrella review of systematic reviews for the evidence-based guideline on protein intake of the German Nutrition Society
Get PDFPURPOSE: This umbrella review aimed to investigate the evidence for an association of dietary intake of total protein as well as animal and plant protein with the incidence of coronary heart disease (CHD), stroke and total cardiovascular diseases (CVD). METHODS: PubMed, Embase and Cochrane Database were systematically searched for systematic reviews (SRs) of prospective studies with or without meta-analysis (MA) published between January 2012 and April 2024. Methodological quality, outcome-specific certainty of evidence, and overall certainty of evidence were assessed using established tools and predefined criteria. RESULTS: Ten SRs were considered eligible for the umbrella review; all were based on prospective cohort studies, and six conducted a MA. Dietary intakes of total, animal and plant protein were not associated with the risk of CHD or stroke. For CHD, the overall certainty of evidence for the absence of an association was "probable" for total, animal and plant protein. For stroke and total CVD, the overall certainty of evidence was rated as "possible" for the absence of an association with the intake of total protein and plant protein and insufficient for animal protein intake. CONCLUSION: Given that most SRs on dietary protein intake did not indicate an association, it seems that protein intake plays no major role in the development of CVD. This investigation was registered at PROSPERO as CRD42018082395
Membrane-distal part of extracellular domain of the Fap2 autotransporter adhesin from Fusobacterium nucleatum ATCC23726
No full textSlice-PASEF: maximising ion utilisation in LC-MS proteomics
Get PDFQuantitative mass spectrometry (MS)-based proteomics has become a streamlined technology with a wide range of usage. Many emerging applications, such as single-cell proteomics, spatial proteomics of tissue sections and the profiling of low-abundant posttranslational modifications, require the analysis of minimal sample amounts and are thus constrained by the sensitivity of the workflow. Here, we present Slice-PASEF, a mass spectrometry technology that leverages trapped ion mobility separation of ions to attain the theoretical maximum of tandem MS sensitivity. We implement Slice-PASEF using a new module in our DIA-NN software and show that Slice-PASEF uniquely enables precise quantitative proteomics of low sample amounts. We further demonstrate its utility towards a range of applications, including single cell proteomics and degrader drug screens via ubiquitinomics
Deep learning modeling to differentiate multiple Sclerosis from MOG antibody-associated disease
Get PDFBACKGROUND AND OBJECTIVES: Multiple sclerosis (MS) is common in adults while myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) is rare. Our previous machine-learning algorithm, using clinical variables, ≤6 brain lesions, and no Dawson fingers, achieved 79% accuracy, 78% sensitivity, and 80% specificity in distinguishing MOGAD from MS but lacked validation. The aim of this study was to (1) evaluate the clinical/MRI algorithm for distinguishing MS from MOGAD, (2) develop a deep learning (DL) model, (3) assess the benefit of combining both, and (4) identify key differentiators using probability attention maps (PAMs). METHODS: This multicenter, retrospective, cross-sectional MAGNIMS study included scans from 19 centers. Inclusion criteria were as follows: adults with non-acute MS and MOGAD, with high-quality T2-fluid-attenuated inversion recovery and T1-weighted scans. Brain scans were scored by 2 readers to assess the performance of the clinical/MRI algorithm on the validation data set. A DL-based classifier using a ResNet-10 convolutional neural network was developed and tested on an independent validation data set. PAMs were generated by averaging correctly classified attention maps from both groups, identifying key differentiating regions. RESULTS: We included 406 MRI scans (218 with relapsing remitting MS [RRMS], mean age: 39 years ±11, 69% F; 188 with MOGAD, age: 41 years ±14, 61% F), split into 2 data sets: a training/testing set (n = 265: 150 with RRMS, age: 39 years ±10, 72% F; 115 with MOGAD, age: 42 years ±13, 61% F) and an independent validation set (n = 141: 68 with RRMS, age: 40 years ±14, 65% F; 73 with MOGAD, age: 40 years ±15, 63% F). The clinical/MRI algorithm predicted RRMS over MOGAD with 75% accuracy (95% CI 67-82), 96% sensitivity (95% CI 88-99), and specificity 56% (95% CI 44-68) in the validation cohort. The DL model achieved 77% accuracy (95% CI 64-89), 73% sensitivity (95% CI 57-89), and 83% specificity (95% CI 65-96) in the training/testing cohort, and 70% accuracy (95% CI 63-77), 67% sensitivity (95% CI 55-79), and 73% specificity (95% CI 61-83) in the validation cohort without retraining. When combined, the classifiers reached 86% accuracy (95% CI 81-92), 84% sensitivity (95% CI 75-92), and 89% specificity (95% CI 81-96). PAMs identified key region volumes: corpus callosum (1872 mm(3)), left precentral gyrus (341 mm(3)), right thalamus (193 mm(3)), and right cingulate cortex (186 mm(3)) for identifying RRMS and brainstem (629 mm(3)), hippocampus (234 mm(3)), and parahippocampal gyrus (147 mm(3)) for identifying MOGAD. DISCUSSION: Both classifiers effectively distinguished RRMS from MOGAD. The clinical/MRI model showed higher sensitivity while the DL model offered higher specificity, suggesting complementary roles. Their combination improved diagnostic accuracy, and PAMs revealed distinct damage patterns. Future prospective studies should validate these models in diverse, real-world settings. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that both a clinical/MRI algorithm and an MRI-based DL model accurately distinguish RRMS from MOGAD
Rapid UPF1 depletion illuminates the temporal dynamics of the NMD-regulated human transcriptome
No full textThe RNA helicase UPF1 shapes the transcriptome as the core factor of nonsense-mediated mRNA decay (NMD). The essential role of UPF1 in human cells has impeded efforts to delineate its directly regulated transcripts and molecular function. To investigate the effects of rapid UPF1 depletion, we engineered human cell lines with endogenous UPF1 fused to conditional degron tags. Temporal-resolution transcriptomic analyses identified direct target mRNAs, consisting predominantly of NMD substrates that are mostly stabilized within hours of UPF1 depletion. By integrating long-read sequencing and ribosome profiling data, we defined the consolidated NMD-regulated human transcriptome (NMDRHT), uncovering previously unannotated transcripts and establishing alternative splicing as a major contributor of NMD-targeted mRNAs. Additionally, we identified non-canonical NMD events that lack indication of being driven by other UPF1-dependent degradation routes. Our work refines the role of the post-transcriptional regulator UPF1 and introduces an experimentally validated NMD-regulated transcriptome as a navigable resource at https://nmdrht.uni-koeln.de
Shortcomings of silhouette in single-cell integration benchmarking
Get PDFSingle-cell studies rely on advanced integration methods for complex datasets affected by batch effects from technical factors alongside meaningful biological variation. Silhouette is an established metric for assessing unsupervised clustering results, comparing within-cluster cohesion to between-cluster separation. However, silhouette’s assumptions are typically violated in single-cell data integration scenarios. We demonstrate that silhouette-based metrics cannot reliably assess batch effect removal or biological signal conservation and propose more robust evaluation strategies
Extended supplementary data for manuscript: Specialised super-enhancer networks in stem cells and neurons
No full textSuper-enhancers (SEs) are clusters of enhancers with high transcriptional activity that play essential roles in defining cell identity through regulation of nearby genes. SEs are also known to form extensive multiway chromatin interactions in embryonic stem cells, which span tens of megabases with other SEs and highly transcribed regions. However, the properties and contributions of SEs in shaping complex regulatory interactions, and how they differ between dividing and in terminally differentiated cells remain poorly understood. Here, we study the structural and functional properties of SEs in embryonic stem cells and dopaminergic neurons by combining Genome Architecture Mapping (GAM), chromatin accessibility, histone modification, and transcriptome data. We find that most SEs are cell-type specific and form extensive pairwise and multiway chromatin interactions with differentially expressed genes and other SEs. SE interactions frequently connect topologically associating domains across megabase genomic distances. Cell-type specific SEs establish complex chromatin interactions with other SEs and highly transcribed regions. SE network analysis identifies important SEs, with highest centrality. Central SEs contain binding motifs for cell-type specific transcription factors, and may act as regulatory hubs. The functional heterogeneity of SEs is also highlighted by their organization into modular sub-networks that differ in structure and spatial scale between ESCs and DNs, with more specific and strongly connected SE modules in post-mitotic neurons. Our results uncover the complexity and specificity of SE-based 3D regulatory networks and provide a resource for prioritizing SEs with potential roles in transcriptional regulation and disease