Nucleic acids (DNA and RNA) are widely used to construct nanometre-scale structures with ever increasing complexity, with possible application in fields such as structural biology, biophysics, ...synthetic biology and photonics. The nanostructures are formed through one-pot self-assembly, with early kilodalton-scale examples containing typically tens of unique DNA strands. The introduction of DNA origami, which uses many staple strands to fold one long scaffold strand into a desired structure, has provided access to megadalton-scale nanostructures that contain hundreds of unique DNA strands. Even larger DNA origami structures are possible, but manufacturing and manipulating an increasingly long scaffold strand remains a challenge. An alternative and more readily scalable approach involves the assembly of DNA bricks, which each consist of four short binding domains arranged so that the bricks can interlock. This approach does not require a scaffold; instead, the short DNA brick strands self-assemble according to specific inter-brick interactions. First-generation bricks used to create three-dimensional structures are 32 nucleotides long, consisting of four eight-nucleotide binding domains. Protocols have been designed to direct the assembly of hundreds of distinct bricks into well formed structures, but attempts to create larger structures have encountered practical challenges and had limited success. Here we show that DNA bricks with longer, 13-nucleotide binding domains make it possible to self-assemble 0.1-1-gigadalton, three-dimensional nanostructures from tens of thousands of unique components, including a 0.5-gigadalton cuboid containing about 30,000 unique bricks and a 1-gigadalton rotationally symmetric tetramer. We also assembled a cuboid that contains around 10,000 bricks and about 20,000 uniquely addressable, 13-base-pair 'voxels' that serves as a molecular canvas for three-dimensional sculpting. Complex, user-prescribed, three-dimensional cavities can be produced within this molecular canvas, enabling the creation of shapes such as letters, a helicoid and a teddy bear. We anticipate that with further optimization of structure design, strand synthesis and assembly procedure even larger structures could be accessible, which could be useful for applications such as positioning functional components.
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IJS, KISLJ, NUK, SBMB, UL, UM, UPUK
Major Depression is mainly related to structural and functional alterations in brain networks involving limbic and prefrontal regions. Reduced olfactory sensitivity in depression is associated with ...reduced olfactory bulb (OB) volume. We determined if the OB volume reduction is a specific biomarker for depression and whether its diagnostic accuracy allows its use as a valid biomarker to support its diagnosis.
84 in-patients with mixed mental disorders and 51 age-matched healthy controls underwent structural MR imaging with a spin-echo T2-wheighted sequence. Individual OB volume was calculated manually (interrater-reliability = .81, p < .001) and compared between groups. Multiple regression analysis with OB volume as dependent variable and Receiver Operator Characteristic analysis to obtain its diagnostic accuracy for depression were ruled out.
Patients exhibited a 13.5% reduced OB volume. Multiple regression analysis showed that the OB volume variation was best explained by depression (β = −.19), sex (β = −.31) and age (β = −.29), but not by any other mental disorder. OB volume attained a diagnostic accuracy of 68.1% for depression.
The patient group mainly contained highly comorbid patients with mostly internalizing disorders which limits the generalisability of the results of the regression analysis.
The OB may serve as a marker for depression. We assume that reduced neural olfactory input to subsequent limbic and salience processing structures moderates this relation. However, the OB was in an inferior position compared to conventional questionnaires for diagnosis of depression. Combination with further structural or functional measurements is suggested.
•Olfactory bulb (OB) volume reduction in psychiatric patients averaged out at 13.5%.•OB reduction was best predicted by sex, age and diagnosis of depression.•OB volume attained a diagnostic accuracy of 68.1% for major depression.•OB may be a biomarker for depression, but is insufficient as a solitary biomarker.•Hence, a combination with further structural or functional parameters is suggested.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Self-assembled DNA nanostructures feature an unprecedented addressability with sub-nanometer precision and accuracy. This addressability relies on the ability to attach functional entities to single ...DNA strands in these structures. The efficiency of this attachment depends on two factors: incorporation of the strand of interest and accessibility of this strand for downstream modification. Here we use DNA-PAINT super-resolution microscopy to quantify both incorporation and accessibility of all individual strands in DNA origami with molecular resolution. We find that strand incorporation strongly correlates with the position in the structure, ranging from a minimum of 48% on the edges to a maximum of 95% in the center. Our method offers a direct feedback for the rational refinement of the design and assembly process of DNA nanostructures and provides a long sought-after quantitative explanation for efficiencies of DNA-based nanomachines.
Machine learning and in particular deep learning (DL) are increasingly important in mass spectrometry (MS)-based proteomics. Recent DL models can predict the retention time, ion mobility and fragment ...intensities of a peptide just from the amino acid sequence with good accuracy. However, DL is a very rapidly developing field with new neural network architectures frequently appearing, which are challenging to incorporate for proteomics researchers. Here we introduce AlphaPeptDeep, a modular Python framework built on the PyTorch DL library that learns and predicts the properties of peptides ( https://github.com/MannLabs/alphapeptdeep ). It features a model shop that enables non-specialists to create models in just a few lines of code. AlphaPeptDeep represents post-translational modifications in a generic manner, even if only the chemical composition is known. Extensive use of transfer learning obviates the need for large data sets to refine models for particular experimental conditions. The AlphaPeptDeep models for predicting retention time, collisional cross sections and fragment intensities are at least on par with existing tools. Additional sequence-based properties can also be predicted by AlphaPeptDeep, as demonstrated with a HLA peptide prediction model to improve HLA peptide identification for data-independent acquisition ( https://github.com/MannLabs/PeptDeep-HLA ).
Patients with cancer have an increased risk of malnutrition which is associated with poor outcome. The Mini Nutritional Assessment (MNA®) is often used in older patients with cancer but its relation ...to outcome is not known.
Four databases were systematically searched for studies relating MNA-results with any reported outcome. Two reviewers screened titles/abstracts and full-texts, extracted data and rated the risk of bias (RoB) independently.
We included 56 studies which varied widely in patient and study characteristics. In multivariable analyses, (risk of) malnutrition assessed by MNA significantly predicts a higher chance for mortality/poor overall survival (22/27 studies), shorter progression-free survival/time to progression (3/5 studies), treatment maintenance (5/8 studies) and (health-related) quality of life (2/2 studies), but not treatment toxicity/complications (1/7 studies) or functional status/decline in (1/3 studies). For other outcomes - length of hospital stay (2 studies), falls, fatigue and unplanned (hospital) admissions (1 study each) - no adjusted results were reported. RoB was rated as moderate to high.
MNA®-result predicts mortality/survival, cancer progression, treatment maintenance and (health-related) quality of life and did not predict adverse treatment outcomes and functional status/ decline in patients with cancer. For other outcomes results are less clear. The moderate to high RoB calls for studies with better control of potential confounders.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The size and shape of peptide ions in the gas phase are an under-explored dimension for mass spectrometry-based proteomics. To investigate the nature and utility of the peptide collisional cross ...section (CCS) space, we measure more than a million data points from whole-proteome digests of five organisms with trapped ion mobility spectrometry (TIMS) and parallel accumulation-serial fragmentation (PASEF). The scale and precision (CV < 1%) of our data is sufficient to train a deep recurrent neural network that accurately predicts CCS values solely based on the peptide sequence. Cross section predictions for the synthetic ProteomeTools peptides validate the model within a 1.4% median relative error (R > 0.99). Hydrophobicity, proportion of prolines and position of histidines are main determinants of the cross sections in addition to sequence-specific interactions. CCS values can now be predicted for any peptide and organism, forming a basis for advanced proteomics workflows that make full use of the additional information.
The recent revolution in computational protein structure prediction provides folding models for entire proteomes, which can now be integrated with large-scale experimental data. Mass spectrometry ...(MS)-based proteomics has identified and quantified tens of thousands of posttranslational modifications (PTMs), most of them of uncertain functional relevance. In this study, we determine the structural context of these PTMs and investigate how this information can be leveraged to pinpoint potential regulatory sites. Our analysis uncovers global patterns of PTM occurrence across folded and intrinsically disordered regions. We found that this information can help to distinguish regulatory PTMs from those marking improperly folded proteins. Interestingly, the human proteome contains thousands of proteins that have large folded domains linked by short, disordered regions that are strongly enriched in regulatory phosphosites. These include well-known kinase activation loops that induce protein conformational changes upon phosphorylation. This regulatory mechanism appears to be widespread in kinases but also occurs in other protein families such as solute carriers. It is not limited to phosphorylation but includes ubiquitination and acetylation sites as well. Furthermore, we performed three-dimensional proximity analysis, which revealed examples of spatial coregulation of different PTM types and potential PTM crosstalk. To enable the community to build upon these first analyses, we provide tools for 3D visualization of proteomics data and PTMs as well as python libraries for data accession and processing.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Proteins carry out the vast majority of functions in all biological domains, but for technological reasons their large-scale investigation has lagged behind the study of genomes. Since the first ...essentially complete eukaryotic proteome was reported
, advances in mass-spectrometry-based proteomics
have enabled increasingly comprehensive identification and quantification of the human proteome
. However, there have been few comparisons across species
, in stark contrast with genomics initiatives
. Here we use an advanced proteomics workflow-in which the peptide separation step is performed by a microstructured and extremely reproducible chromatographic system-for the in-depth study of 100 taxonomically diverse organisms. With two million peptide and 340,000 stringent protein identifications obtained in a standardized manner, we double the number of proteins with solid experimental evidence known to the scientific community. The data also provide a large-scale case study for sequence-based machine learning, as we demonstrate by experimentally confirming the predicted properties of peptides from Bacteroides uniformis. Our results offer a comparative view of the functional organization of organisms across the entire evolutionary range. A remarkably high fraction of the total proteome mass in all kingdoms is dedicated to protein homeostasis and folding, highlighting the biological challenge of maintaining protein structure in all branches of life. Likewise, a universally high fraction is involved in supplying energy resources, although these pathways range from photosynthesis through iron sulfur metabolism to carbohydrate metabolism. Generally, however, proteins and proteomes are remarkably diverse between organisms, and they can readily be explored and functionally compared at www.proteomesoflife.org.
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IJS, KISLJ, NUK, SBMB, UL, UM, UPUK
Super‐resolution microscopy allows optical imaging below the classical diffraction limit of light with currently up to 20× higher spatial resolution. However, the detection of multiple targets ...(multiplexing) is still hard to implement and time‐consuming to conduct. Here, we report a straightforward sequential multiplexing approach based on the fast exchange of DNA probes which enables efficient and rapid multiplexed target detection with common super‐resolution techniques such as (d)STORM, STED, and SIM. We assay our approach using DNA origami nanostructures to quantitatively assess labeling, imaging, and washing efficiency. We furthermore demonstrate the applicability of our approach by imaging multiple protein targets in fixed cells.
Many happy returns: A straightforward sequential multiplexing approach based on the fast exchange of DNA probes has been developed that enables efficient and rapid multiplexed target detection with common super‐resolution techniques such as (d)STORM, STED, and SIM.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
The prevalence of Parkinson's disease (PD) is increasing but the development of novel treatment strategies and therapeutics altering the course of the disease would benefit from specific, sensitive, ...and non‐invasive biomarkers to detect PD early. Here, we describe a scalable and sensitive mass spectrometry (MS)‐based proteomic workflow for urinary proteome profiling. Our workflow enabled the reproducible quantification of more than 2,000 proteins in more than 200 urine samples using minimal volumes from two independent patient cohorts. The urinary proteome was significantly different between PD patients and healthy controls, as well as between LRRK2 G2019S carriers and non‐carriers in both cohorts. Interestingly, our data revealed lysosomal dysregulation in individuals with the LRRK2 G2019S mutation. When combined with machine learning, the urinary proteome data alone were sufficient to classify mutation status and disease manifestation in mutation carriers remarkably well, identifying VGF, ENPEP, and other PD‐associated proteins as the most discriminating features. Taken together, our results validate urinary proteomics as a valuable strategy for biomarker discovery and patient stratification in PD.
Synopsis
This study presents a scalable, sensitive and reproducible mass spectrometry‐based proteomics workflow for urinary proteome profiling, and demonstrates it as a promising strategy for urine biomarker discovery for Parkinson’s disease (PD).
The presented workflow allows quantification of more than 2,000 proteins in urine.
Lysosomal dysregulation is reflected in the urinary proteomes of individuals with the pathogenic LRRK2 G2019S mutation.
Machine learning on the urinary proteome classifies LRRK2 mutation and PD disease states with sensitivities of 78% and 74% and specificities of 73% and 84%, respectively.
The neurotrophic factor VGF was identified as the most important feature to discriminate manifesting from non‐manifesting LRRK2 G2019S carriers.
This study presents a scalable, sensitive and reproducible mass spectrometry‐based proteomics workflow for urinary proteome profiling, and demonstrates it as a promising strategy for urine biomarker discovery for Parkinson’s disease (PD).
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
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