Biology subjects

Jensen, M.

Publications and source records attributed to Jensen, M..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Femtosecond X-ray snapshots reveal correlated displacements of specific distal atoms in a protein crystal

Protein dynamics is shaped by interactions between thermal fluctuations, external forces, and molecular structures. Thermal fluctuations have high frequencies and are hence very challenging to quantify. We were able to record femtosecond X-ray diffraction snapshots and determine atomic displacements of crystalline bovine trypsin atoms either in the native steady state or in a transient state initiated by a short THz pulse, the results of which were interpreted with numerical simulations. In the absence of THz pulses, specific distal atoms exhibited correlated movements. Under the influence of THz fields, numerical simulations demonstrated a slight enhancement of displacement correlations. The experimental results revealed a contrasting response to short THz pulses, where the measured displacements were not solely determined by the atom position, but also influenced by the atomic type. These findings call for a reinterpretation of the dynamic properties inherent in folded protein structures and the physical principles behind the formation of protein assemblies. To this end, a theoretical model was developed for lattice deformations and local excitations entraining a (squeezed) coherent steady state, explaining the emergence of atom correlations. The model accounts for the magnitudes of atomic displacements and fluctuations through a balance between harmonic and anharmonic coupling forces.

biophysics↗

bioRxiv2024

Homomorphic Encryption: An Application to Polygenic Risk Scores

1BackgroundPolygenic risk scores (PRSs) have emerged as a powerful tool in precision medicine, enabling personalized risk assessments for complex diseases. However, the use of sensitive genomic data in PRS calculations raises concerns about privacy and security. Fully homomorphic encryption (FHE) offers a promising solution by allowing computations on encrypted data, preserving the privacy of both genomic information and PRS models. MethodsHere, we present an application of FHE for encrypted PRS calculations using a particular protocol (CKKS) within the Lattigo library. Our approach involves a three-party system: clients (clinicians handling sensitive genetic data), modelers developing a PRS (academics or companies), and evaluators (a local hospital running the models while maintaining data confidentiality). We demonstrate the feasibility and accuracy of our approach by applying it to synthetic datasets of various sizes and to a robust 110k-single-nucleotide polymorphism (SNP) model for schizophrenia. The complete codebase and a sample dataset are available at https://github.com/gersteinlab/HEPRS. ResultsThe difference between traditional plaintext and encrypted PRS calculation results is negligible: the R2 is 0.999 and the mean squared error is 2.27 x 10-6. Moreover, while the encrypted calculation is roughly 1,000 times slower than conventional non-encrypted ones (when considering only the core PRS calculation), the computation remains feasible on a single-CPU node. For example, processing [~]1,100 individuals with [~]110k SNPs took six minutes and [~]65 GB of memory on a laptop computer. In addition, we investigated the impact of the encryption parameters on the computational time and accuracy in detail, showing the expected slowdown with higher security settings. ConclusionOur approach showcases the applicability and feasibility of using FHE on real-world PRS models. With the pressing need for privacy-preserving solutions in the era of precision medicine, our work serves as a pilot application, offering a simple use case and providing a detailed comparison and evaluation in terms of accuracy, cost, and scalability.

bioinformatics↗

bioRxiv2024

Single-cell genomics and regulatory networks for 388 human brains

Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet, little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multi-omics datasets into a resource comprising >2.8M nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550K cell-type-specific regulatory elements and >1.4M single-cell expression-quantitative-trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized [~]250 disease-risk genes and drug targets with associated cell types. Summary Figure O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=95 SRC="FIGDIR/small/585576v2_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@70b24org.highwire.dtl.DTLVardef@6c9eddorg.highwire.dtl.DTLVardef@131fae2org.highwire.dtl.DTLVardef@4e0f1e_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2024

Meningioma transcriptomic landscape demonstrates novel subtypes with regional associated biology and patient outcome.

Meningiomas, the most common intracranial tumor, though mostly benign can be recurrent and fatal. WHO grading does not always identify high risk meningioma and better characterizations of their aggressive biology is needed. To approach this problem, we combined 13 bulk RNA-Seq datasets to create a dimension-reduced reference landscape of 1298 meningiomas. Clinical and genomic metadata effectively correlated with landscape regions which led to the identification of meningioma subtypes with specific biological signatures. Time to recurrence also correlated with the map location. Further, we developed an algorithm that maps new patients onto this landscape where nearest neighbors predict outcome. This study highlights the utility of combining bulk transcriptomic datasets to visualize the complexity of tumor populations. Further, we provide an interactive tool for understanding the disease and predicting patient outcome. This resource is accessible via the online tool Oncoscape, where the scientific community can explore the meningioma landscape.

cancer biology↗

bioRxiv2024

Deciphering Peptide-Protein Interactions via Composition-Based Prediction: A Case Study with Survivin/BIRC5

In the realm of atomic physics and chemistry, composition emerges as the most powerful means of describing matter. Mendeleevs periodic table and chemical formulas, while not entirely free from ambiguities, provide robust approximations for comprehending the properties of atoms, chemicals, and their collective behaviours, which stem from the dynamic interplay of their constituents. Our study illustrates that protein-protein interactions follow a similar paradigm, wherein the composition of peptides plays a pivotal role in predicting their interactions with the protein survivin, using an elegantly simple model. An analysis of these predictions within the context of the human proteome not only illuminates the known cellular locations of survivin and its interaction partners, but also introduces novel insights into biological functionality. It becomes evident that an electrostatic- and primary structure-based descriptions fall short in predictive power, leading us to speculate that protein interactions are orchestrated by the collective dynamics of functional groups.

bioinformatics↗

bioRxiv2023

Leveraging a large language model to predict protein phase transition: a physical, multiscale and interpretable approach

Protein phase transitions (PPTs) from the soluble state to a dense liquid phase (forming droplets via liquid-liquid phase separation) or to solid aggregates (such as amyloids) play key roles in pathological processes associated with age-related diseases such as Alzheimers disease. Several computational frameworks are capable of separately predicting the formation of droplets or amyloid aggregates based on protein sequences, yet none have tackled the prediction of both within a unified framework. Recently, large language models (LLMs) have exhibited great success in protein structure prediction; however, they have not yet been used for PPTs. Here, we fine-tune a LLM for predicting PPTs and demonstrate its usage in evaluating how sequence variants affect PPTs, an operation useful for protein design. In addition, we show its superior performance compared to suitable classical benchmarks. Due to the "black-box" nature of the LLM, we also employ a classical random forest model along with biophysical features to facilitate interpretation. Finally, focusing on Alzheimers disease-related proteins, we demonstrate that greater aggregation is associated with reduced gene expression in AD, suggesting a natural defense mechanism. Significance StatementProtein phase transition (PPT) is a physical mechanism associated with both physiological processes and age-related diseases. We present a modeling approach for predicting the protein propensity to undergo PPT, forming droplets or amyloids, directly from its sequence. We utilize a large language model (LLM) and demonstrate how variants within the protein sequence affect PPT. Because the LLM is naturally domain-agnostic, to enhance interpretability, we compare it with a classical knowledge-based model. Furthermore, our findings suggest the possible regulation of PPT by gene expression and transcription factors, hinting at potential targets for drug development. Our approach demonstrates the usefulness of fine-tuning a LLM for downstream tasks where only small datasets are available.

biophysics↗

bioRxiv2023

Label-free 3-D molecular imaging of living tissues using Raman Spectral Projection Tomography

The ability to image tissues in three-dimensions (3-D) with label-free molecular contrast at mesoscale would be a valuable capability in biology and biomedicine. Here, we introduce Raman spectral projection tomography (RSPT) for volumetric molecular imaging with sub-millimeter spatial resolution. We have developed a RSPT imaging instrument capable of providing 3-D molecular contrast in transparent and semi-transparent samples. A computational pipeline for multivariate reconstruction was established to extract label-free spatial molecular information from Raman projection data. We demonstrate imaging and visualization of phantoms of various complex shapes with label-free molecular contrast. Finally, we apply RSPT as a novel tool for imaging of molecular gradients and extracellular matrix heterogeneities in fixed and live tissue-engineered constructs and explanted native tissues. RSPT imaging opens new possibilities for label-free molecular monitoring of tissues.

bioengineering↗

bioRxiv2023

Opto-lipidomics of tissues

Lipid metabolism and signalling play pivotal functions in biology and disease development. Despite this, there is currently no optical technique available that can directly visualise the lipidome in tissues. In this study, we introduce opto-lipidomics, a new approach to optical molecular tissue imaging. We expand the capability of vibrational Raman spectroscopy to identify individual lipids in complex tissue matrices through correlation with desorption electrospray ionisation (DESI) - mass spectrometry imaging in an integrated instrument. A computational pipeline of inter-modality regression analysis is established to extract lipidomic information from optical vibrational spectra. Opto-lipidomic imaging of transient cerebral ischemia-reperfusion injury in a murine model of ischemic stroke demonstrates the visualisation and identification of lipids in disease with unprecedented molecular specificity using light. Furthermore, we deploy opto-lipidomics in a handheld fiber-optic Raman probe and demonstrate real-time classification of bulk brain tissues based on specific lipid abundances. Opto-lipidomics opens a host of opportunities to study lipid biomarkers for diagnostics, prognostics, and novel therapeutic targets.

bioengineering↗

bioRxiv2023

Nondestructive Spatial Lipidomics for Glioma Classification

Mapping the molecular composition of tissues using spatial biology provides high-content information for molecular diagnostics. However, spatial biology approaches require invasive procedures to collect samples and destroy the investigated tissue, limiting the extent of analysis, particularly for highly functional tissues such as those of the brain. To address these limitations, we developed a workflow to harvest biomolecules from brain tissues using nanoneedles and characterise the distribution of lipids using desorption electrospray ionization mass spectrometry imaging. The nanoneedles preserved the original tissue while harvesting a reliable molecular profile and retaining the original lipid distribution for mouse and human brain samples, accurately outlining the morphology of key regions within the brain and tumour lesions. The deep neural network analysis of a cohort containing 23 human glioma biopsies showed that nanoneedle samples maintain the molecular signatures required to accurately classify disease state. Thus, nanoneedles provide a route for tissue-preserving spatial lipidomic and molecular diagnostics.

bioengineering↗

bioRxiv2023

An RNA seq-based reference landscape of human normal and neoplastic brain

In order to better understand the relationship between normal and neoplastic brain, we combined five publicly available large-scale datasets, correcting for batch effects and applying Uniform Manifold Approximation and Projection (UMAP) to RNA-seq data. We assembled a reference Brain-UMAP including 702 adult gliomas, 802 pediatric tumors and 1409 healthy normal brain samples, which can be utilized to investigate the wealth of information obtained from combining several publicly available datasets to study a single organ site. Normal brain regions and tumor types create distinct clusters and because the landscape is generated by RNA seq, comparative gene expression profiles and gene ontology patterns are readily evident. To our knowledge, this is the first meta-analysis that allows for comparison of gene expression and pathways of interest across adult gliomas, pediatric brain tumors, and normal brain regions. We provide access to this resource via the open source, interactive online tool Oncoscape, where the scientific community can readily visualize clinical metadata, gene expression patterns, gene fusions, mutations, and copy number patterns for individual genes and pathway over this reference landscape.

bioinformatics↗

bioRxiv2022

Reducing the foreign body response on human cochlear implants and their materials in vivo with photografted zwitterionic hydrogel coatings

The foreign body response to implanted materials often complicates the functionality of sensitive biomedical devices. For cochlear implants, this response can reduce device performance, battery life and preservation of residual acoustic hearing. As a permanent and passive solution to the foreign body response, this work investigates ultra-low-fouling poly(carboxybetaine methacrylate) (pCBMA) thin film hydrogels that are simultaneously photo-grafted and photo-polymerized onto polydimethylsiloxane (PDMS). The cellular anti-fouling properties of these coatings are robustly maintained even after six-months subcutaneous incubation and over a broad range of cross-linker compositions. On pCBMA-coated PDMS sheets implanted subcutaneously, capsule thickness and inflammation are reduced significantly in comparison to uncoated PDMS or coatings of polymerized poly(ethylene glycol dimethacrylate) (pPEGDMA) or poly(hydroxyethyl methacrylate) (pHEMA). Further, capsule thickness is reduced over a wide range of pCBMA cross-linker compositions. On cochlear implant electrode arrays implanted subcutaneously for one year, the coating bridges over the exposed platinum electrodes and dramatically reduces the capsule thickness over the entire implant. Coated cochlear implant electrode arrays could therefore lead to persistent improved performance and reduced risk of residual hearing loss. More generally, the in vivo anti-fibrotic properties of pCBMA coatings also demonstrate potential to mitigate the fibrotic response on a variety of sensing/stimulating implants. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=75 SRC="FIGDIR/small/518125v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@1d92121org.highwire.dtl.DTLVardef@e2ca3org.highwire.dtl.DTLVardef@948783org.highwire.dtl.DTLVardef@14ce647_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2022

Survivin prevents the Polycomb Repressor Complex 2 from methylating Histone 3 lysine 27

Survivin is a small protein that belongs to the inhibitor of apoptosis protein family and participates in cell division and apoptosis. It was actively studied in human cancers, inflammatory diseases and in autoimmune diseases. Here, we reveal that survivin takes part in epigenetic gene silencing by interaction with the polycomb repressive complex 2 (PRC2). PRC2 silences gene expression through tri-methylation of lysine 27 on histone 3 (H3K27). We detected differential expression of PRC2 core subunits in CD4+T cells with different survivin expression. ChIP-seq experiments indicated that survivin binds chromatin that overlap with the regions occupied by PRC2. ChIP-seq of H3K27 in CD4+T cells indicate that inhibition of survivin leads to a substantial increase in H3K27 tri-methylation by PRC2 in contrast to other histone modifications, which lends support to that survivin prevents PRC2 from functioning. Survivin binds peptides derived from PRC2 subunits EZH2, EED, SUZ12 and JARID2 in a peptide microarray that cover intersubunit interfaces, catalytic residues, and present binding sites for substrates, DNA, and regulatory proteins. Amino acid composition of the peptides has substantial predictive power for survivin interaction in the peptide microarray as determined by multilayer perceptron classification analysis. NMR experiments with 15N labelled survivin indicate that peptide colocalization does not entirely depend on binding mediated by short range interactions. These results indicate that survivin interacts with PRC2, preventing the methylation of H3K27 and specific gene silencing. This has transcriptional consequences and specific gene silencing.

molecular biology↗

bioRxiv2022

De novo phytosterol synthesis in animals

Sterols are lipids that regulate multiple processes in eukaryotic cells, and are essential components of cellular membranes. Sterols are currently assumed to be kingdom specific, with phytosterol synthesis restricted to plants while animals are only able to synthesize cholesterol. Here, we challenge this assumption by demonstrating that the marine annelids Olavius and Inanidrilus synthesize the phytosterol sitosterol de novo. Using multi-omics, high-resolution metabolite imaging, heterologous gene expression and enzyme assays, we show that sitosterol is the most abundant (60%) sterol in these animals and characterize its biosynthetic pathway. We show that phytosterol synthesis partially overlaps with cholesterol synthesis and involves a non-canonical C-24 sterol methyltransferase (C24-SMT). C24-SMT is an essential enzyme for sitosterol synthesis in plants, but not known from animals with bilateral symmetry (bilaterians). Our comparative phylogenetic analyses of C24-SMT homologs revealed that these are widely distributed across annelids and other animal phyla, including sponges and rotifers. Our findings show that phytosterol synthesis and use is not restricted to the plant kingdom, and indicate that the evolution of sterols in animals is more complex than previously assumed.

biochemistry↗

bioRxiv2021

Neural language processing across time, space, frequency and age: MEG-MVPA classification of intertrial phase coherence

Language is a key part of human cognition. Whereas many neurocognitive abilities decline with age, for language the picture is much less clear and how exactly language processing changes with aging is still unknown. To investigate this, we employed magnetoencephalography (MEG) and recorded neuromagnetic brain responses to auditory linguistic stimuli in healthy participants of younger and older age using a passive task-free paradigm and a range of different linguistic stimulus contrasts, which enabled us to assess neural language processes at multiple levels (lexical, semantic, morphosyntactic). By using machine learning-based classification algorithms to scrutinise intertrial phase coherence of MEG responses in source space, we found significant differences between younger and older participants across several frequency bands and for all tested processing types, which shows multiple changes in the brains neurolinguistic circuits which may be due to both healthy aging in general and compensatory processes in particular.

neuroscience↗

bioRxiv2021

Evaluation of RNAlater™ as a field-compatible preservation method for metaproteomic analyses of bacteria-animal symbioses

Field studies are central to environmental microbiology and microbial ecology as they enable studies of natural microbial communities. Metaproteomics, the study of protein abundances in microbial communities, allows to study these communities in situ which requires protein preservation directly in the field as protein abundance patterns can change rapidly after sampling. Ideally, a protein preservative for field deployment works rapidly and preserves the whole proteome, is stable in long-term storage, is non-hazardous and easy to transport, and is available at low cost. Although these requirements might be met by several protein preservatives, an assessment of their suitability in field conditions when targeted for metaproteomics is currently lacking. Here, we compared the protein preservation performance of flash freezing and the preservation solution RNAlater using the marine gutless oligochaete Olavius algarvensis and its symbiotic microbes as a test case. In addition, we evaluated long-term RNAlater storage after 1 day, 1 week and 4 weeks at room temperature (22-23 {degrees}C). We evaluated protein preservation using one dimensional liquid chromatography tandem mass spectrometry (1D-LC-MS/MS). We found that RNAlater and flash freezing preserved proteins equally well in terms of total number of identified proteins or relative abundances of individual proteins and none of the test time points were altered compared to t0. Moreover, we did not find biases against specific taxonomic groups or proteins with particular biochemical properties. Based on our metaproteomics data and the logistical requirements for field deployment we recommend RNAlater for protein preservation of field-collected samples when targeted for metaproteomcis. ImportanceMetaproteomics, the large-scale identification and quantification of proteins from microbial communities, provides direct insights into the phenotypes of microorganisms on the molecular level. To ensure the integrity of the metaproteomic data, samples need to be preserved immediately after sampling to avoid changes in protein abundance patterns. In laboratory set-ups samples for proteomic analyses are most commonly preserved by flash freezing; however, liquid nitrogen or dry ice is often unavailable at remote field locations due to its hazardous nature and transport restrictions. Our study shows that RNAlater can serve as a low hazard, easy to transport alternative to flash freezing for field preservation of samples for metaproteomics. We show that RNAlater preserves the metaproteome equally well as compared to flash freezing and protein abundance patterns remain stable during long-term storage for at least 4 weeks at room temperature.

microbiology↗

bioRxiv2021

Raman Needle Arthroscopy for In Vivo Molecular Assessment of Cartilage

The development of treatments for osteoarthritis (OA) is burdened by the lack of standardized biomarkers of cartilage health that can be applied in clinical trials. We present a novel arthroscopic Raman probe that can "optically biopsy" cartilage and quantify key ECM biomarkers for determining cartilage composition, structure, and material properties in health and disease. Technological and analytical innovations to optimize Raman analysis include: 1) multivariate decomposition of cartilage Raman spectra into ECM-constituent-specific biomarkers (glycosaminoglycan [GAG], collagen [COL], water [H2O] scores), and 2) multiplexed polarized Raman spectroscopy to quantify superficial zone collagen anisotropy via a PLS-DA-derived Raman collagen alignment factor (RCAF). Raman measurements were performed on a series of ex vivo cartilage models: 1) chemically GAG-depleted bovine cartilage explants (n=40), 2) mechanically abraded bovine cartilage explants (n=30), 3) aging human cartilage explants (n=14), and 4) anatomical-site-varied ovine osteochondral explants (n=6). Derived Raman GAG score biomarkers predicted 95%, 66%, and 96% of the variation in GAG content of GAG-depleted bovine explants, human explants, and ovine explants, respectively (p<0.001). RCAF values were significantly different for explants with abrasion-induced superficial zone collagen loss (p<0.001). The multivariate linear regression of Raman-derived ECM biomarkers (GAG and H2O scores) predicted 94% of the variation in elastic modulus of ovine explants (p<0.001). Finally, we demonstrated the first in vivo Raman arthroscopy assessment of an ovine femoral condyle through intraarticular entry into the synovial capsule. This work advances Raman arthroscopy towards a transformative low cost, minimally invasive diagnostic platform for objective monitoring of treatment outcomes from emerging OA therapies.

bioengineering↗

bioRxiv2021

Contactless recordings of retinal activity using optically pumped magnetometers

Optically pumped magnetometers (OPMs) have been adopted for the recording of brain activity. Without the need to be cooled to cryogenic temperatures, an array of these sensors can be placed more flexibly, which allows for the recording of neuronal structures other than neocortex. Here we use eight OPM sensors to record human retinal activity following flash stimulation. We compare this magnetoretinographic (MRG) activity to the simultaneously recorded electroretinogram of the eight participants. The MRG shows the familiar flash-evoked potentials (a-wave and b-wave) and shares a highly significant amount of information with the electroretinogram recording (both in a simultaneous and separate recording). We conclude that OPM sensors have the potential to become a contactless alternative to fiber electrodes for the recording of retinal activity. Such a contactless solution can benefit both clinical and neuroscientific settings.

neuroscience↗

bioRxiv2021

Ultra-sensitive Protein-SIP to quantify activity and substrate uptake in microbiomes with stable isotopes

Stable isotope probing (SIP) approaches are a critical tool in microbiome research to determine associations between species and substrates. The application of these approaches ranges from studying microbial communities important for global biogeochemical cycling to host-microbiota interactions in the intestinal tract. Current SIP approaches, such as DNA-SIP or nanoSIMS, are limited in terms of sensitivity, resolution or throughput. Here we present an ultra-sensitive, high-throughput protein-based stable isotope probing approach (Protein-SIP), which cuts cost for labeled substrates by [~]90% as compared to other SIP and Protein-SIP approaches and thus enables isotope labeling experiments on much larger scales and with higher replication. It allows for the determination of isotope incorporation into microbiome members with species level resolution using standard metaproteomics LC-MS/MS measurements. The analysis has been implemented as an open-source application (https://sourceforge.net/projects/calis-p/). We demonstrate sensitivity, precision and accuracy using bacterial cultures and mock communities with different labeling schemes. Furthermore, we benchmark our approach against two existing Protein-SIP approaches and show that in the low labeling range used our approach is the most sensitive and accurate. Finally, we measure translational activity using 18O heavy water labeling in a 63-species community derived from human fecal samples grown on media simulating two different diets. Activity could be quantified on average for 27 species per sample, with 9 species showing significantly higher activity on a high protein diet, as compared to a high fiber diet. Surprisingly, among the species with increased activity on high protein were several Bacteroides species known as fiber consumers. Apparently, protein supply is a critical consideration when assessing growth of intestinal microbes on fiber, including fiber based prebiotics. In summary, we demonstrate that our Protein-SIP approach allows for the ultra-sensitive (0.01% to 10% label) detection of stable isotopes of elements found in proteins, using standard metaproteomics data.

microbiology↗