Biology subjects

Mann, M.

Publications and source records attributed to Mann, M..

At least 109 records · Page 6Linked to original sources

bioRxiv2021

Unscheduled DNA replication in G1 causes genome instability through head-to-tail replication fork collisions

DNA replicates once per cell cycle. Interfering with the regulation of DNA replication initiation generates genome instability through over-replication and has been linked to early stages of cancer development. Here, we engineered genetic systems in budding yeast to induce unscheduled replication in the G1-phase of the cell cycle. Unscheduled G1 replication initiated at canonical S-phase origins across the genome. We quantified differences in replisomes in G1- and S-phase and identified firing factors, polymerase , and histone supply as factors that limit replication outside S-phase. G1 replication per se did not trigger cellular checkpoints. Subsequent replication during S-phase, however, resulted in over-replication and led to chromosome breaks via head-to-tail replication fork collisions that are marked by chromosome-wide, strand-biased occurrence of RPA-bound single-stranded DNA. Low-level, sporadic induction of G1 replication induced an identical response, indicating findings from synthetic systems are applicable to naturally occurring scenarios of unscheduled replication initiation by G1/S deregulation.

molecular biology↗

bioRxiv2021

A GID E3 ligase assembly ubiquitinates an Rsp5 E3 adaptor and regulates plasma membrane transporters

Cells rapidly remodel their proteomes to align their cellular metabolism to environmental conditions. Ubiquitin E3 ligases enable this response, by facilitating rapid and reversible changes to protein stability, localization, or interaction partners. In S. cerevisiae, the GID E3 ligase regulates the switch from gluconeogenic to glycolytic conditions through induction and incorporation of the substrate receptor subunit Gid4, which promotes the degradation of gluconeogenic enzymes. Here, we show an alternative substrate receptor, Gid10, which is induced in response to changes in temperature, osmolarity and nutrient availability, and regulates the ART-Rsp5 pathway. Art2 levels are elevated upon GID10 deletion, a crystal structure shows the basis for Gid10-Art2 interactions, and Gid10 directs a GID E3 ligase complex to ubiquitinate Art2. We also find that the GID E3 ligase affects the flux of plasma membrane nutrient transporters during heat stress. The data reveal GID as a system of E3 ligases with metabolic regulatory functions outside of glycolysis and gluconeogenesis, controlled by distinct stress-specific substrate receptors.

cell biology↗

bioRxiv2021

AlphaMap: An open-source Python package for the visual annotation of proteomics data with sequence specific knowledge

SummaryIntegrating experimental information across proteomic datasets with the wealth of publicly available sequence annotations is a crucial part in many proteomic studies that currently lacks an automated analysis platform. Here we present AlphaMap, a Python package that facilitates the visual exploration of peptide-level proteomics data. Identified peptides and post-translational modifications in proteomic datasets are mapped to their corresponding protein sequence and visualized together with prior knowledge from UniProt and with expected proteolytic cleavage sites. The functionality of AlphaMap can be accessed via an intuitive graphical user interface or - more flexibly - as a Python package that allows its integration into common analysis workflows for data visualization. AlphaMap produces publication-quality illustrations and can easily be customized to address a given research question. Availability and implementationAlphaMap is implemented in Python and released under an Apache license. The source code and one-click installers are freely available at https://github.com/MannLabs/alphamap. Supplementary informationA detailed user guide for AlphaMap is provided as supplementary data.

bioinformatics↗

bioRxiv2021

AlphaTims: Indexing trapped ion mobility spectrometry - time of flight data for fast and easy accession and visualization

High resolution mass spectrometry-based proteomics generates large amounts of data, even in the standard liquid chromatography (LC) - tandem mass spectrometry configuration. Adding an ion mobility dimension vastly increases the acquired data volume, challenging both analytical processing pipelines and especially data exploration by scientists. This has necessitated data aggregation, effectively discarding much of the information present in these rich data sets. Taking trapped ion mobility spectrometry (TIMS) on a quadrupole time-of-flight platform (Q-TOF) as an example, we developed an efficient indexing scheme that represents all data points as detector arrival times on scales of minutes (LC), milliseconds (TIMS) and microseconds (TOF). In our open source AlphaTims package, data are indexed, accessed and visualized by a combination of tools of the scientific Python ecosystem. We interpret unprocessed data as a sparse 4D matrix and use just-in-time compilation to machine code with Numba, accelerating our computational procedures by several orders of magnitude while keeping to familiar indexing and slicing notations. For samples with more than six billion detector events, a modern laptop can load and index raw data in about a minute. Loading is even faster when AlphaTims has already saved indexed data in a HDF5 file, a portable scientific standard used in extremely large-scale data acquisition. Subsequently, data accession along any dimension and interactive visualization happen in milliseconds. We have found AlphaTims to be a key enabling tool to explore high dimensional LC-TIMS-QTOF data and have made it freely available as an open-source Python package with a stand-alone graphical user interface at https://github.com/MannLabs/alphatims or as part of the AlphaPept ecosystem. HighlightsO_LIEasy visualization and fast accession of LC-TIMS-QTOF data C_LIO_LIFreely available graphical user interface, command-line interface and Python module on Windows, Linux and macOS. C_LI

bioinformatics↗

bioRxiv2021

AlphaPept, a modern and open framework for MS-based proteomics

In common with other omics technologies, mass spectrometry (MS)-based proteomics produces ever-increasing amounts of raw data, making their efficient analysis a principal challenge. There is a plethora of different computational tools that process the raw MS data and derive peptide and protein identification and quantification. During the last decade, there has been dramatic progress in computer science and software engineering, including collaboration tools that have transformed research and industry. To leverage these advances, we developed AlphaPept, a Python-based open-source framework for efficient processing of large high-resolution MS data sets. Using Numba for just-in-time machine code compilation on CPU and GPU, we achieve hundred-fold speed improvements while maintaining clear syntax and rapid development speed. AlphaPept uses the Python scientific stack of highly optimized packages, reducing the code base to domain-specific tasks while providing access to the latest advances in machine learning. We provide an easy on-ramp for community validation and contributions through the concept of literate programming, implemented in Jupyter Notebooks of the different modules. A framework for continuous integration, testing, and benchmarking enforces solid software engineering principles. Large datasets can rapidly be processed as shown by the analysis of hundreds of cellular proteomes in minutes per file, many-fold faster than the data acquisiton. The AlphaPept framework can be used to build automated processing pipelines using efficient HDF5 based file formats, web-serving functionality and compatibility with downstream analysis tools. Easy access for end-users is provided by one-click installation of the graphical user interface, for advanced users via a modular Python library, and for developers via a fully open GitHub repository.

bioinformatics↗

bioRxiv2021

Proteome Profiling of Cerebrospinal Fluid Reveals Novel Biomarker Candidates for Parkinson's Disease

Parkinsons disease (PD) is a growing burden worldwide, and despite ongoing efforts to find reliable biomarkers for early and differential diagnosis, prognosis and disease monitoring, there is no biofluid biomarker used in clinical routine to date. Cerebrospinal fluid (CSF) is collected often and should closely reflect structural and functional alterations in PD patients brains. Here we describe a scalable and sensitive mass spectrometry (MS)-based proteomics workflow for CSF proteome profiling to find specific biomarkers and identify disease-related changes in CSF protein levels in PD. From two independent cohorts consisting of more than 200 individuals, our workflow reproducibly quantified over 1,700 proteins from minimal sample amounts. Combined with machine learning, this identified a group of several proteins, including OMD, CD44, VGF, PRL, and MAN2B1 that were altered in PD patients or significantly correlate with clinical scores, indicative of disease progression. Interestingly, we uncovered signatures of enhanced neuroinflammation in patients with familial PD (LRRK2 G2019S carriers) as indicated by increased levels of CTSS, PLD4, HLA-DRA, HLA-DRB1, and HLA-DPA1. A comparison with urinary proteome changes in PD patients revealed a large overlap in protein composition PD-associated changes in these body fluids, including lysosomal factors like CTSS. Our results validate MS-based proteomics of CSF as a valuable strategy for biomarker discovery and patient stratification in a neurodegenerative disease like PD. Consistent proteomic signatures across two independent CSF cohorts and previously acquired urinary proteome profiles open up new avenues to improve our understanding of PD pathogenesis.

systems biology↗

bioRxiv2021

Integrated phospho-proteogenomic and single-cell transcriptomic analysis of meningiomas establishes robust subtyping and reveals subtype-specific immune invasion

Meningiomas are the most frequent primary intracranial tumors. They can follow a wide clinical spectrum from benign to highly aggressive clinical course. No specific therapy exists for refractory cases or cases not amenable to resection and radiotherapy. Identification of risk of recurrence and malignant transformation for the individual patients is challenging. However, promising molecular markers and prognostic subgrouping by DNA methylation are emerging. Still, the biological underpinnings of these diagnostic subgroups are elusive, and, consequently, no novel therapeutic options arise thereof. Here we establish robust subgroups across the full landscape of meningiomas, consistent through DNA methylation, mutations, the transcriptomic, proteomic and phospho-proteomic level. Pronounced proliferative stress and DNA damage repair signals in malignant cells and in clusters exclusive to recurrent tumors are in line with their higher mitotic activity, but also provide an explanation for the accumulation of genomic instability in anaplastic meningiomas. Although homozygous deletion of CDKN2A/B is a diagnostic marker of high-grade meningioma, the expression of its gene product increased from low to non-deleted high-grade cases. Differences between subgroups in lymphocyte and myeloid cell infiltration, representing a majority of tumor mass in low-grade NF2 tumors, could be assigned to cluster-specific interaction with tumor cells. Activation to a more proinflammatory phenotype and decreased infiltration of myeloid cells in high-grade cases correlated with lower expression of CSF1, located on chromosome arm 1p, whose deletion is known as prognostic marker, with no proposed mechanism before. Our results demonstrate a robust molecular subclassification of a tumor type across multiple layers, provide insight into heterogeneous growth dynamics despite shared pathognomonic mutations, and highlight immune infiltration modulation as a novel target for meningioma therapy.

genomics↗

bioRxiv2021

Organ-specific metabolic pathways distinguish prediabetes, type 2 diabetes and normal tissues

Defects in pancreatic islets and the progression of multi-tissue insulin resistance in combination with environmental factors are the main causes of type 2 diabetes (T2D). Mass spectrometry-based proteomics of five key-metabolic tissues on a cohort of 42 multi-organ donors provided deep coverage of the proteomes of pancreatic islets, visceral adipose tissue (VAT), liver, skeletal muscle and serum. Enrichment analysis of gene ontology (GO) terms built a tissue-specific map of the chronological order of altered biological processes across healthy controls (CTRL), pre-diabetes (PD) and T2D subjects. This unique dataset allowed us to explore alterations of entire biological pathways and individual proteins in multiple tissues. We confirmed the significant decrease of the citric acid cycle and the respiratory electron transport in VAT and muscle of T2D and we provided a thorough visual representation of the complete set of downregulated proteins. Importantly, we found widespread novel alterations in relevant biological pathways including the increase in hemostasis in pancreatic islets of PD, the increase in the complement cascade in liver and pancreatic islets of PD and the elevation in cholesterol biosynthesis in liver of T2D. Overall, our findings suggest inflammatory, immune and vascular impairments in pancreatic islets as potentially causal factors of insufficient insulin production and increased glucagon levels in the early stages of T2D. In contrast alterations in lipid metabolism and mitochondrial function in the liver and VAT/muscle, respectively, became evident later in manifest T2D. This first multi-tissue proteomic map indicates the temporal order of tissue-specific metabolic dysregulation in T2D development.

cell biology↗

bioRxiv2021

Scaling of cellular proteome with ploidy

Ploidy changes are frequent in nature and contribute to evolution, functional specialization and tumorigenesis (1,2). Analysis of model organisms of different ploidies revealed that increased ploidy leads to an increase in cell and nuclear volume, reduced proliferation (2-4), metabolic changes (5), lower fitness (6,7), and increased genomic instability (8,9), but the underlying mechanisms remain poorly understood. To investigate how the gene expression changes with cellular ploidy, we analyzed isogenic series of budding yeasts from 1N to 4N. We show that mRNA and protein abundance scales allometrically with ploidy, with tetraploid cells showing only threefold increase in proteins compared to haploids. This ploidy-specific scaling occurs via decreased rRNA and ribosomal protein abundance and reduced translation. We demonstrate that the Tor1 activity is reduced with increasing ploidy, which leads to rRNA gene repression via a novel Tor1-Sch9-Tup1 signaling pathway. mTORC1 and S6K activity are also reduced in human tetraploid cells and the concomitant increase of the Tup1 homolog Tle1 downregulates the rDNA transcription. Our results revealed a novel conserved mTORC1-S6K-Tup1/Tle1 pathway that ensures proteome remodeling in response to increased ploidy.

cell biology↗

bioRxiv2021

Sequential defects in cardiac lineage commitment and maturation cause hypoplastic left heart syndrome

BackgroundComplex molecular programs in specific cell lineages govern human heart development. Hypoplastic left heart syndrome (HLHS) is the most common and severe manifestation within the spectrum of left ventricular outflow tract obstruction defects occurring in association with ventricular hypoplasia. The pathogenesis of HLHS is unknown, but hemodynamic disturbances are assumed to play a prominent role. MethodsTo identify perturbations in gene programs controlling ventricular muscle lineage development in HLHS, we performed: i) whole-exome sequencing of 87 HLHS parent-offspring trios, ii) nuclear transcriptomics of cardiomyocytes from ventricles of 4 patients with HLHS and 15 controls at different stages of heart development, iii) single cell RNA sequencing and iv) 3D modeling in iPSCs from 3 patients with HLHS and 3 controls. ResultsGene set enrichment and protein network analyses of damaging de-novo mutations and dysregulated genes from ventricles of patients with HLHS suggested alterations in specific gene programs and cellular processes critical during fetal ventricular cardiogenesis, including cell-cycle and cardiomyocyte maturation. Single-cell and 3D modeling with iPSCs demonstrated intrinsic defects in the cell-cycle/UPR/autophagy hub resulting in disrupted differentiation of early cardiac progenitor lineages leading to defective cardiomyocyte-subtype differentiation/maturation in HLHS. Additionally, premature cell-cycle exit of ventricular cardiomyocytes from HLHS patients prevented normal tissue responses to developmental signals for growth leading to multinucleation/polyploidy, accumulation of DNA damage, and exacerbated apoptosis, all potential drivers of left ventricular hypoplasia in absence of hemodynamic cues. ConclusionsOur results highlight that despite genetic heterogeneity in HLHS, many mutations converge on sequential cellular processes primarily driving cardiac myogenesis, suggesting novel therapeutic approaches.

developmental biology↗

bioRxiv2021

Large scale discovery of coronavirus-host factor protein interaction motifs reveals SARS-CoV-2 specific mechanisms and vulnerabilities

Viral proteins make extensive use of short peptide interaction motifs to hijack cellular host factors. However, current methods do not identify this important class of protein-protein interactions. Uncovering peptide mediated interactions provides both a molecular understanding of viral interactions with their host and the foundation for developing novel antiviral reagents. Here we describe a scalable viral peptide discovery approach covering 229 RNA viruses that provides high resolution information on direct virus-host interactions. We identify 269 peptide-based interactions for 18 coronaviruses including a specific interaction between the human G3BP1/2 proteins and an {Phi}xFG peptide motif in the SARS-CoV-2 nucleocapsid (N) protein. This interaction supports viral replication and through its {Phi}xFG motif N rewires the G3BP1/2 interactome to disrupt stress granules. A peptide-based inhibitor disrupting the G3BP1/2-N interaction blocks SARS-CoV-2 infection showing that our results can be directly translated into novel specific antiviral reagents.

biochemistry↗

bioRxiv2021

GID E3 ligase supramolecular chelate assembly configures multipronged ubiquitin targeting of an oligomeric metabolic enzyme

To achieve precise cellular regulation, E3 ubiquitin ligases must be configured to match substrate quaternary structures. Here, by studying the yeast GID complex, mutation of which is Glucose-Induced Degradation deficient, we discover supramolecular chelate assembly as an E3 ligase strategy for targeting an oligomeric substrate. Cryo EM structures show that to bind the tetrameric substrate fructose-1,6-bisphosphatase (Fbp1), two otherwise functional GID E3s assemble into a 20-protein Chelator-GIDSR4, which resembles an organometallic supramolecular chelate. The Chelator-GIDSR4 assembly avidly binds multiple Fbp1 degrons and positions Fbp1 so that its protomers are simultaneously ubiquitylated at lysines near its allosteric and substrate binding sites. Significantly, key structural and biochemical features -including capacity for supramolecular assembly - are preserved in the human ortholog, the CTLH E3. Based on our integrative structural, biochemical and cell biological data, we propose that higher-order E3 ligase assembly generally underlies multipronged targeting, capable of simultaneously incapacitating multiple protomers and functionalities of oligomeric substrates.

biochemistry↗

bioRxiv2021

OpenCell: proteome-scale endogenous tagging enables the cartography of human cellular organization

Elucidating the wiring diagram of the human cell is a central goal of the post-genomic era. We combined genome engineering, confocal live-cell imaging, mass spectrometry and data science to systematically map the localization and interactions of human proteins. Our approach provides a data-driven description of the molecular and spatial networks that organize the proteome. Unsupervised clustering of these networks delineates functional communities that facilitate biological discovery, and uncovers that RNA-binding proteins form a specific sub-group defined by unique interaction and localization properties. Furthermore, we discover that remarkably precise functional information can be derived from protein localization patterns, which often contain enough information to identify molecular interactions. Paired with a fully interactive website opencell.czbiohub.org, we provide a resource for the quantitative cartography of human cellular organization.

cell biology↗

bioRxiv2021

Gel-like inclusions of C-terminal fragments of TDP-43 sequester and inhibit proteasomes in neurons

TDP-43 inclusions enriched in C-terminal fragments of ~25kDa ("TDP-25") are associated with neurodegeneration in amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). Here, we analyzed gain-of-function mechanisms of TDP-25 combining cryo-electron tomography, proteomics and functional assays. TDP-25 inclusions are amorphous with gel-like biophysical properties and sequester proteasomes adopting exclusively substrate-processing conformations. This leads to proteostasis impairment, further enhanced by pathogenic mutations. These findings bolster the importance of proteasome dysfunction in ALS/FTD.

neuroscience↗

bioRxiv2021

Molecular Response to PARP1 Inhibition in Ovarian Cancer Cells as Determined by Mass Spectrometry Based Proteomics

Poly (ADP)-ribose polymerase (PARP) inhibitors have entered routine clinical practice for the treatment of high-grade serous ovarian cancer (HGSOC), yet the molecular mechanisms underlying treatment response to PARP1 inhibition (PARP1i) are not fully understood. Here, we used unbiased mass spectrometry based proteomics with data-driven protein network analysis to systematically characterize how HGSOC cells respond to PARP1i treatment. We found that PARP1i leads to pronounced proteomic changes in a diverse set of cellular processes in HGSOC cancer cells, consistent with transcript changes in an independent perturbation dataset. We interpret decreases in the levels of the pro-proliferative transcription factors SP1 and {beta}-catenin and in growth factor signaling as reflecting the anti-proliferative effect of PARP1i; and the strong activation of pro-survival processes NF-{varkappa}B signaling and lipid metabolism as PARPi-induced adaptive resistance mechanisms. Based on these observations, we nominate several protein targets for therapeutic inhibition in combination with PARP1i. When tested experimentally, the combination of PARPi with an inhibitor of fatty acid synthase (TVB-2640) has a 3-fold synergistic effect and is therefore of particular pre-clinical interest. Our study improves the current understanding of PARP1 function, highlights the potential that the anti-tumor efficacy of PARP1i may not only rely on DNA damage repair mechanisms and informs on the rational design of PARP1i combination therapies in ovarian cancer.

cancer biology↗

bioRxiv2021

A new parallel high-pressure packing system enables rapid multiplexed production of capillary columns

Reversed-phase high performance liquid chromatography (HPLC) is the most commonly applied peptide separation technique in mass spectrometry (MS)-based proteomics. Particle-packed capillary columns are predominantly used in nano-flow HPLC systems. Despite being the broadly applied standard for many years capillary columns are still expensive and suffer from short lifetimes, particularly in combination with ultra-high-pressure chromatography systems. For this reason, and to achieve maximum performance, many laboratories produce their own in-house packed columns. This typically requires a considerable amount of time and trained personnel. Here, we present a new packing system for capillary columns enabling rapid, multiplexed column production with pressures reaching up to 3000 bar. Requiring only a conventional gas pressure supply and methanol as driving fluid, our system replaces the traditional setup of helium pressured packing bombs. By using 10x multiplexing, we have reduced the production time to just under 2 minutes for several 50 cm columns with 1.9 {micro}m particle size, speeding up the process of column production 40 to 800 times. We compare capillary columns with various inner diameters (ID) and length packed under different pressure conditions with our newly designed, broadly accessible high-pressure packing station. One sentence summaryA newly constructed parallel high-pressure packing system enables the rapid multiplexed production of capillary columns. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=162 SRC="FIGDIR/small/433033v2_ufig1.gif" ALT="Figure 1"> View larger version (50K): org.highwire.dtl.DTLVardef@4d224forg.highwire.dtl.DTLVardef@79dd3org.highwire.dtl.DTLVardef@aa047org.highwire.dtl.DTLVardef@1a0b837_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2021

Furin cleavage of the SARS-CoV-2 spike is modulated by O-glycosylation

The SARS-CoV-2 coronavirus responsible for the global pandemic contains a unique furin cleavage site in the spike protein (S) that increases viral infectivity and syncytia formation. Here, we show that O-glycosylation near the furin cleavage site is mediated by specific members of the GALNT enzyme family and is dependent on the novel proline at position 681 (P681). We further demonstrate that O-glycosylation of S decreases furin cleavage. Finally, we show that GALNT family members capable of glycosylating S are expressed in human respiratory cells that are targets for SARS-CoV-2 infection. Our results suggest that O-glycosylation may influence viral infectivity/tropism by modulating furin cleavage of S and provide mechanistic insight into the potential role of P681 mutations in the recently identified, highly transmissible B.1.1.7 variant.

biochemistry↗

bioRxiv2021

AI-driven Deep Visual Proteomics defines cell identity and heterogeneity

The systems-wide analysis of biomolecules in time and space is key to our understanding of cellular function and heterogeneity in health and disease1. Remarkable technological progress in microscopy and multi-omics technologies enable increasingly data-rich descriptions of tissue heterogeneity2,3,4,5. Single cell sequencing, in particular, now routinely allows the mapping of cell types and states uncovering tremendous complexity6. Yet, an unaddressed challenge is the development of a method that would directly connect the visual dimension with the molecular phenotype and in particular with the unbiased characterization of proteomes, a close proxy for cellular function. Here we introduce Deep Visual Proteomics (DVP), which combines advances in artificial intelligence (AI)-driven image analysis of cellular phenotypes with automated single cell laser microdissection and ultra-high sensitivity mass spectrometry7. DVP links protein abundance to complex cellular or subcellular phenotypes while preserving spatial context. Individually excising nuclei from cell culture, we classified distinct cell states with proteomic profiles defined by known and novel proteins. AI also discovered rare cells with distinct morphology, whose potential function was revealed by proteomics. Applied to archival tissue of salivary gland carcinoma, our generic workflow characterized proteomic differences between normal-appearing and adjacent cancer cells, without admixture of background from unrelated cells or extracellular matrix. In melanoma, DVP revealed immune system and DNA replication related prognostic markers that appeared only in specific tumor regions. Thus, DVP provides unprecedented molecular insights into cell and disease biology while retaining spatial information.

systems biology↗