Biology subjects

Sorensen, P.

Publications and source records attributed to Sorensen, P..

14 recordsLinked to original sources

bioRxiv2026

GALE-dependent glycoproteome remodelling is a determinant of oncogenic RAS transformation

Oncogenic transformation is accompanied by extensive remodelling of the cellular glycosylation landscape, yet the mechanisms linking oncogenic signalling to glycoproteome reprogramming remain poorly defined. We used site-resolved intact glycoproteomics to systematically map oncogenic RAS-dependent changes across the N- and O-linked glycoproteome. Integrating multiomics profiling with functional approaches, we identify UDP-glucose 4-epimerase (GALE) as a transcriptional target of oncogenic RAS that is induced through MAPK signalling and MYC-dependent transcription. GALE depletion selectively disrupted RAS-dependent glycoproteome remodelling, preferentially impairing sialylation of N-linked glycans and O-linked GalNAc-type glycosylation. Functionally, these glycosylation defects were accompanied by suppressed anchorage-independent growth and reduced in vivo tumorigenicity of KRAS-mutant cells, establishing GALE-dependent nucleotide-sugar interconversion as a requirement for malignant growth. Clinically, GALE expression was elevated in pancreatic ductal adenocarcinoma, wherein KRAS mutations are highly prevalent, and across a broad spectrum of human cancers. Collectively, our findings define a regulatory axis that connects oncogenic signalling to nucleotide-sugar precursor availability and diversification of the cellular glycan repertoire, revealing GALE as a metabolic dependency in RAS-driven cancer.

Cancer Biology↗

bioRxiv2025

Earlier snowmelt reduces the strength of carbon sink in montane meadows

O_LIWarming temperatures are changing winters, leading to earlier snowmelt. This shift can lead to an earlier and potentially longer growing season, which in turn may affect various plant-mediated ecosystem functions. Despite its relevance in the carbon cycle, we still know little about how earlier snowmelt impacts the carbon balance in ecosystems over the growing season, e.g., does it only shift phenology, or does it affect the overall carbon uptake? Most studies rely on interannual variability in snowmelt timing, making it difficult to isolate snowmelt effects from other confounding variables, e.g, temperature and moisture anomalies. To address this uncertainty, we investigated how experimentally advancing snowmelt affects the carbon cycling of montane meadows across the growing season. C_LIO_LIWe experimentally advanced the snowmelt date in a montane meadow by approximately 12 days and collected data every two weeks throughout the growing season, including net ecosystem exchange (NEE), gross primary productivity (GPP), ecosystem respiration (ER), plant composition, and shrub, graminoid, and forb biomass. C_LIO_LIEarly in the growing season, GPP was higher in the early snowmelt plots, though this effect decreased as the growing season progressed. Our modeling of cumulative NEE showed that earlier snowmelt increased the carbon sink strength by 22%, with the strongest effect in the early spring and the effect diminishing as the growing season progressed, with control plots being a greater carbon sink in the later season. Graminoid biomass was 47% higher in plots with earlier snowmelt, but there was no change in total biomass. C_LIO_LISynthesis: As winters warm and snowmelt occurs earlier, plant productivity will shift earlier in the growing season, and montane meadows will become a stronger carbon sink. However, this effect will differ seasonally, altering the carbon balance in montane meadows. C_LI

ecology↗

bioRxiv2024

Coordinated organic and inorganic nitrogen transformations fuel soil microbial blooms and increase nitrogen retention during snowmelt

Snowmelt in high-elevation watersheds triggers a microbial bloom and crash that affects nitrogen (N) export. Predicting watershed N dynamics as snowpack declines is a challenge because the mechanisms that underlie this microbial bloom and crash are uncertain. Using a multi-omic approach, we show that the dynamic molecular properties of dissolved organic N, plus high gene expression for peptidases that recycle microbial biomass, suggested that microbial turnover provided N for biosynthesis during the microbial bloom. Amino acid fermentation by Bradyrhizobia produced organic acids that also fueled denitrification and dissimilatory nitrate reduction to ammonia (DNRA) during snowmelt. Nitrification in spring was driven by Spring-Adapted Nitrososphaerales, which utilized ammonium derived from osmolyte degradation by Winter-Adapted Solirubrobacteraceae. High DNRA gene expression after snowmelt suggested significant nitrate retention, increasing watershed N retention potential. However, declining snowfall may compromise microbial regulation of soil N retention, with implications for watershed N export.

microbiology↗

bioRxiv2024

Molecular signatures of altered energy metabolism and circadian rhythm perturbations in a model of extra-nigral Synucleinopathy.

A pathological role of alpha-Synuclein (aSyn) aggregation in the central nervous system (CNS) is a recognized feature in Parkinson disease (PD) and related neurodegenerative conditions termed synucleinopathies. In order to characterize the cellular response in CNS to incipient and advanced aSyn pathology, we applied spatial transcriptomics on brain sections derived from a transgenic mouse model (M83+/+ line, Prnp-SNCA*A53T) in which aSyn aggregation was induced in a prion-like fashion through hindlimb intramuscular delivery of pre-formed fibrillar (PFF) murine aSyn. Our spatially-resolved transcriptomics (ST) data point to unique perturbations in brain energy metabolism during the progression of aSyn pathology, such that the early stage of aSyn aggregate pathology activates molecular pathways controlling metabolic flux through glycolysis, oxidative phosphorylation and fatty acid metabolism. In contrast, the ST data indicate a profound decline in mitochondrial metabolism in the brains of symptomatic animals with advanced aSyn pathology. The latter stage was also associated with drastic reduction in mRNA translation machinery, along with aberrant expression of molecular drivers involved in RNA splicing and inflammatory response. Intriguingly, our ST data also point to perturbed regulation of circadian rhythm, was corroborated by increased immunodetection of CREB-binding protein (a modulator of core clock machinery) in the brains of symptomatic animals, and transcriptional upregulation of CREBBP in 4 independent PD microarray datasets. Collectively, we anticipate that our findings offer novel opportunities in knowledge translation for mechanism-based drug discovery and biomarkers in neurodegenerative synucleinopathies.

neuroscience↗

bioRxiv2024

inSequio: A Programmable 3D CAD Application for Designing DNA Nanostructures

DNA nanotechnology is evolving rapidly, paralleling the historic trajectory of the 1970s electronics industry. However, current DNA nanostructure (DN) design software limits users to either manual design with minimal automation or a constrained range of automated designs. inS[e]quio Design Studio, developed by Parabon(R) NanoLabs, bridges this gap as a programmable 3D computer-aided design (CAD) application, integrating a domain-specific graphical editor with a Python API for versatile DN design. Developed in C++ for Windows(R) and Macintosh(R) systems, inS[e]quio features a user-friendly GUI with extensive CAD tools, capable of managing complex designs and offloading computational tasks to the cloud. It supports various DNA design formats, PDB molecule integration, residue modifications, and includes preloaded designs and thorough documentation. With its combination of features, inS[e]quio enables a code-centric design (CCD) approach, enhancing DN construction with improved precision, scalability, and efficiency. This approach is elucidated through a streptavidin barrel cage designed via Python notebook and a spheroid origami case study. Marking a significant advance in DN design automation, inS[e]quio, the first fully programmable 3D CAD tool for DN design, enables both manual and programmatic 3D editing. This fusion of features establishes inS[e]quio as a transformative tool, poised to significantly enhance designer productivity and expand the scope of possible designs. Extended AbstractAdvances in DNA nanotechnology have positioned the field at a juncture reminiscent of the pivotal growth phase of the electronics industry in the 1970s. The evolution of software for designing DNA nanostructures (DNs) is following a similar historical trajectory and dozens of software packages have been developed for creating them. Existing software options, however, require users to choose between manual design with minimal automation support or selecting from a limited set of designs, typically wireframe, that can be generated from a high-level structural description. Here, we introduce the inS[e]quio Design Studio, a programmable 3D computer-aided design (CAD) application that effectively bridges this gap. By integrating a domain-specific, freeform graphical editor with a Python application programming interface (API), inS[e]quio provides a comprehensive and extensible platform for designing complex nucleic acid (NA) nanostructures. The inS[e]quio desktop application, developed in C++, runs on Windows(R) and Macintosh(R) operating systems. Its graphical user interface (GUI) features multiple synchronized view panels and a diverse set of CAD and NA-specific editing tools. Its optimized graphics pipeline enables editing of designs with >2M nucleotides, and it includes an integrated service infrastructure for offloading heavy computations to cloud servers. The software also supports import and export of various DNA design file formats, integration of arbitrary PDB molecules, and specification of residue modifications. Additionally, it includes preloaded sample designs, scripts, and comprehensive documentation. Parabon has used evolving versions of inS[e]quio for over a decade to design a variety of proprietary DNs and have now transitioned it into a commercially available product. This paper summarizes inS[e]quios features, discusses its strengths and limitations, and outlines planned enhancements. Although freeform 3D design is well supported in inS[e]quio, the integration of its CAD environment with its API facilitates a code-centric design (CCD) approach for DN construction that offers notable productivity advantages over traditional methods, including enhanced precision, scalability, and efficiency. Here we describe CCD, outline its benefits and demonstrate its use through a well-documented Python notebook, included with the product, which generates a sample design within the inS[e]quio application. A spheroid origami created using CCD is also presented. As the first commercial fully programmable 3D CAD application specifically created for DN design, the release of inS[e]quio represents a milestone in the field of DN design automation. It introduces a new dimension to the discipline by enabling both manual and programmatic 3D editing, thereby facilitating an innovative CCD approach. The availability of extensive documentation and technical support enables designers to efficiently adopt and utilize these capabilities. This combination of features establishes inS[e]quio as a noteworthy addition to the tools available for DN design, with the potential to significantly increase designer productivity and broaden the scope of designs that can be developed by practitioners of all skill levels. Windows and Mac versions of the inS[e]quio desktop application are available for download at https://parabon.com/insequio. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=119 SRC="FIGDIR/small/586810v1_ufig1.gif" ALT="Figure 1"> View larger version (64K): org.highwire.dtl.DTLVardef@16a2714org.highwire.dtl.DTLVardef@2bc610org.highwire.dtl.DTLVardef@1d852e2org.highwire.dtl.DTLVardef@129b3fa_HPS_FORMAT_FIGEXP M_FIG C_FIG An illustration of the inS[e]quio Design Studio desktop application interoperating with a Python Jupyter notebook and molecular dynamics (MD) simulation tools to support an iterative code-centric design (CCD) process. The design cycle includes (a) programmatic and/or manual creation of objects in the inS[e]quio editors; (b) visual inspection and manipulation of objects via user interface; (c) in silico evaluation of designs via MD simulation using native or external tools; repeating a-c as necessary; and (d) procurement of strands and synthesis of DNA nanostructures (DNs).

bioengineering↗

bioRxiv2024

Evaluation of Bayesian Linear Regression Models for Gene Set Prioritization in Complex Diseases

Genome-wide association studies (GWAS) provide valuable insights into the genetic architecture of complex traits, yet interpreting their results remains challenging due to the polygenic nature of most traits. Gene set analysis offers a solution by aggregating genetic variants into biologically relevant pathways, enhancing the detection of coordinated effects across multiple genes. In this study, we present and evaluate a gene set prioritization approach utilizing Bayesian Linear Regression (BLR) models to uncover shared genetic components among different phenotypes and facilitate biological interpretation. Through extensive simulations and analyses of real traits, we demonstrate the efficacy of the BLR model in prioritizing pathways for complex traits. Simulation studies reveal insights into the models performance under various scenarios, highlighting the impact of factors such as the number of causal genes, proportions of causal variants, heritability, and disease prevalence. Application of both single-trait and multi-trait BLR models to real data, specifically GWAS summary data for type 2 diabetes (T2D) and related phenotypes, identifies significant associations with T2D-related pathways. Furthermore, comparison between single- and multi-trait BLR analyses highlights the superior performance of the multi-trait approach in identifying associated pathways, showcasing increased statistical power when analyzing multiple traits jointly. Additionally, enrichment analysis with integrated data from various public resources supports our results, confirming significant enrichment of diabetes-related genes within the top T2D pathways resulting from the multi-trait analysis. The BLR models ability to handle diverse genomic features, perform regularization, conduct variable selection, and integrate information from multiple traits, genders, and ancestries demonstrates its utility in understanding the genetic architecture of complex traits. Our study provides insights into the potential of the BLR model to prioritize gene sets, offering a flexible framework applicable to various datasets. This model presents opportunities for advancing personalized medicine by exploring the genetic underpinnings of multifactorial traits, potentially leading to tailored therapeutic interventions.

genomics↗

bioRxiv2024

Evaluation of Bayesian Linear Regression Derived Gene Set Test Methods

Gene set tests can pinpoint genes and biological pathways that exert small to moderate effects on complex diseases like Type 2 Diabetes (T2D). By aggregating genetic markers based on biological information, these tests can enhance the statistical power needed to detect genetic associations. Our goal was to develop a gene set test utilizing Bayesian Linear Regression (BLR) models, which account for both linkage disequilibrium (LD) and the complex genetic architectures intrinsic to diseases, thereby increasing the detection power of genetic associations. Through a series of simulation studies, we demonstrated how the efficacy of BLR derived gene set tests is influenced by several factors, including the proportion of causal markers, the size of gene sets, the percentage of genetic variance explained by the gene set, and the genetic architecture of the traits. Comparing our method with other approaches, such as the gold standard MAGMA (Multi-marker Analysis of Genomic Annotation) approach, our BLR gene set test showed superior performance. This suggests that our BLR-based approach could more accurately identify genes and biological pathways underlying complex diseases.

genomics↗

bioRxiv2023

Evaluation of Bayesian Linear Regression Models as a Fine Mapping tool

Our aim was to evaluate Bayesian Linear Regression (BLR) models with BayesC and BayesR priors as a fine mapping tool and compare them to the state-of-the-art external models: FINEMAP, SuSIE-RSS, SuSIE-Inf and FINEMAP-Inf. Based on extensive simulations, we evaluated the different models based on F1 classification score. The different models were applied on quantitative and binary UK Biobank (UKB) phenotypes and evaluated based upon predictive accuracy and features of credible sets (CSs). We used over 533K genotyped and 6.6 million imputed single nucleotide polymorphisms (SNPs) for simulations and UKB phenotypes respectively, from over 335K UKB White British Unrelated samples. We simulated phenotypes from low (GA1) to moderate (GA2) polygenicity, heritability (h2) of 10% and 30%, causal SNPs ({pi}) of 0.1% and 1% sampled genome-wide, and disease prevalence (PV) of 5% and 15%. Single marker summary statistics and in-sample linkage disequilibrium were used to fit models in regions defined by lead SNPs. BayesR improved the F1 score, averaged across all simulations, between 27.26% and 13.32% relative to the external models. Predictive accuracy quantified as variance explained (R2), averaged across all the UKB quantitative phenotypes, with BayesR was decreased by 5.32% (SuSIE-Inf) and 3.71% (FINEMAP-Inf), and was increased by 7.93% (SuSIE-RSS) and 8.3% (BayesC). Area under the receiver operating characteristic curve averaged across all the UKB binary phenotypes, with BayesR was increased between 0.40% and 0.05% relative to the external models. SuSIE-RSS and BayesR, demonstrated the highest number of CSs, with BayesC and BayesR exhibiting the smallest average median size CSs in the UKB phenotypes. The BLR models performed similar to the external models. Specifically, BayesRs performance closely aligned with SuSIE-Inf and FINEMAP-Inf models. Collectively, our findings from both simulations and application of the models in the UKB phenotypes support that the BLR models are efficient fine mapping tools.

genomics↗

bioRxiv2022

Expanded utility of the R package qgg with applications within genomic medicine

SummaryHere, we present an expanded utility of the R package qgg for quantitative genetic and genomic analyses of complex traits and diseases. One of the major updates of the package is, that it now includes five different Bayesian Linear Regression (BLR) models, which provide a unified framework for mapping of genetic variants, estimation of heritability and genomic prediction from either individual level data or from genome-wide association study (GWAS) summary statistics. To showcase some of the novel implementations, we analysed two quantitative trait phenotypes, body mass index and standing height from United Kingdom Biobank (UKB). We compared genomic prediction accuracies from single and multiple trait models, showed accurate estimation of genomic parameters, illustrate how a BLR model can be used to fine map potential causal loci, and finally, provide an extension of gene set enrichment analyses based on the BLR framework. With this release, the qgg package now provides a wealth of the commonly used methods in analysis of complex traits and diseases, without the need to switch between software tools and data formats. AvailabilityOur methodology is implemented in the publicly available R software package qgg using fast and memory efficient algorithms in C++ and is available from CRAN or as a developer version at our GitHub page (https://github.com/psoerensen/qgg). Notes on the implemented statistical genetic models, tutorials and example scripts are available from our accompanied homepage https://qganalytics.com/. Contactpalledr@hst.aau.dk and pso@qgg.au.dk Supplementary informationSupplementary data are available online.

genetics↗

bioRxiv2021

Optineurin links Hace1-dependent Rac ubiquitylation to integrin-mediated mechanotransduction to control bacterial invasion and cell division

Extracellular matrix (ECM) elasticity is perceived by cells via focal adhesion structures, which transduce mechanical cues into chemical signalling to conform cell behaviour. Although the contribution of ECM compliance to the control of cell migration or division has been extensively studied, little has been reported regarding infectious processes. We have studied how mechanical properties of the ECM impact invasion of cells by the extraintestinal Escherichia coli pathogen UTI89. We show that UTI89 takes advantage, via its CNF1 toxin, of integrin mechanoactivation to trigger its invasion into cells. We identified OPTN as a protein regulated by ECM stiffness whose function is required for bacterial invasion and integrin mechanical coupling and for stimulation of HACE1 E3 ligase activity towards the Rac1 GTPase. We showed that OPTN knockdown cells display enhanced Rac1 activation, strong mechanochemical adhesion signalling and increased cyclin D1 translation, together with enhanced cell proliferation independent of ECM stiffness. Despite such features, OPTN knockdown cells displayed defective traction force buildup associated with limited cellular invasion by UTI89. Together, our data indicate that OPTN, through a new role in mechanobiology, supports CNF1-producing uropathogenic E. coli invasion and links HACE1-mediated ubiquitylation of Rac1 to ECM mechanical properties and integrin mechanotransduction.

cell biology↗

bioRxiv2021

Anti-GD2 antibody disrupts GD2:Siglec-7 interactions and synergizes with CD47 blockade to mediate tumor eradication

The disialoganglioside GD2 is consistently overexpressed in neuroblastoma and osteosarcoma, and is variably expressed in other sarcomas, gliomas, neuroendocrine tumors, and epithelial cancers. Anti-GD2 antibodies have improved the survival rates of patients with neuroblastoma only when administered as part of intense chemotherapy-based cytotoxic regimens, which are associated with debilitating late effects including hearing loss, growth retardation, and secondary leukemias. Despite broad expression of GD2 on osteosarcoma, anti-GD2 antibody has not mediated significant antitumor activity in that disease or any other GD2+ cancers. CD47 is a checkpoint molecule overexpressed on tumor cells that inhibits macrophage activity, and CD47 blockade has demonstrated promising clinical activity in early human trials. We investigated whether anti-CD47 antibody could enhance the efficacy of anti-GD2 antibody in neuroblastoma and other GD2+ malignancies. We demonstrate substantial synergy of these two agents, resulting in the recruitment of tumor associated macrophages (TAMs) to mediate robust and durable anti-tumor responses. The responses are driven by GD2-specific factors that reorient the balance of macrophage activity towards phagocytosis of tumor cells, including disruption of a newly described GD2:Siglec-7 axis. These results demonstrate the unique synergy of combining anti-GD2 with anti-CD47, which has the potential to significantly enhance outcomes for children with neuroblastoma and osteosarcoma and will soon be investigated in a first-in-human clinical trial.

immunology↗

bioRxiv2020

The snowmelt niche differentiates three microbial life strategies that influence soil nitrogen availability during and after winter

Soil microbial biomass can reach its annual maximum pool size beneath the winter snowpack and is known to decline abruptly following snowmelt in seasonally snow-covered ecosystems. Observed differences in winter versus summer microbial taxonomic composition also suggests that phylogenetically conserved traits may permit winter-versus summer-adapted microorganisms to occupy distinct niches. In this study, we sought to identify archaea, bacteria, and fungi that are associated with the soil microbial bloom overwinter and the subsequent biomass collapse following snowmelt at a high-altitude watershed in central Colorado, USA. Archaea, bacteria, and fungi were categorized into three life strategies (Winter-Adapted, Snowmelt-Specialist, Spring-Adapted) based on changes in abundance during winter, the snowmelt period, and after snowmelt in spring. We calculated indices of phylogenetic relatedness (archaea and bacteria) or assigned functional attributes (fungi) to organisms within life strategies to infer whether phylogenetically conserved traits differentiate Winter-Adapted, Snowmelt-Specialist, and Spring-Adapted groups. We observed that the soil microbial bloom was correlated in time with a pulse of snowmelt infiltration, which commenced 65 days prior to soils becoming snow-free. A pulse of nitrogen (N, as nitrate) occurred after snowmelt, along with a collapse in the microbial biomass pool size, and an increased abundance of nitrifying archaea and bacteria (e.g., Thaumarchaeota, Nitrospirae). Winter- and Spring-Adapted archaea and bacteria were phylogenetically clustered, suggesting that phylogenetically conserved traits allow Winter- and Spring-Adapted archaea and bacteria to occupy distinct niches. In contrast, Snowmelt-Specialist archaea and bacteria were phylogenetically overdispersed, suggesting that the key mechanism(s) of the microbial biomass crash are likely to be density-dependent (e.g., trophic interactions, competitive exclusion) and affect organisms across a broad phylogenetic spectrum. Saprotrophic fungi were the dominant functional group across fungal life strategies, however, ectomycorrhizal fungi experienced a large increase in abundance in spring. If well-coupled plant-mycorrhizal phenology currently buffers ecosystem N losses in spring, then changes in snowmelt timing may alter ecosystem N retention potential. Overall, we observed that the snowmelt separates three distinct soil niches that are occupied by ecologically distinct groups of microorganisms. This ecological differentiation is of biogeochemical importance, particularly with respect to the mobilization of nitrogen during winter, before and after snowmelt.

microbiology↗

bioRxiv2019

Leveraging multiple layers of data to predict Drosophila complex traits

An important challenge in genetics is to be able to predict complex traits accurately. Despite recent advances, prediction accuracy for most complex traits remains low. Here, we used the Drosophila Genetic Reference Panel (DGRP), a collection of 200 lines with whole-genome sequences and deep RNA sequencing data, to evaluate the usefulness of using high-quality gene expression levels compared to relying on genotypes for predicting three complex traits. We found that expression levels provided higher accuracy than genotypes for starvation resistance, similar accuracy for chill coma recovery, and lower accuracy for startle response. Models including both genotype and expressions levels did not outperform the best single component model. However, accuracy increased considerably for all the three traits when we included another layer of information, i.e., gene ontology (GO). We found that a limited number of GO terms, some of which had a clear biological interpretation, were strongly predictive of the traits. In summary, this study shows that integrating different sources of information can improve prediction accuracy, especially when large samples are not available.

genetics↗

bioRxiv2018

High-flow Nano-chromatography Columns Facilitate Rapid and High-quality Proteome Analysis on Standard Nano-LC Hardware

Optimizing the acquisition of proteomics data collected from a mass spectrometer (MS) requires careful selection of processed material quantities, liquid-chromatography (LC) setup, and data acquisition parameters. The small internal diameter (ID) columns standardly used in nano-chromatography coupled MS result in long per injection overhead times that require sacrifices in design of offline-fractionation and data acquisition schemes. As cohort sizes and the numbers of samples to be analyzed continue to increase, there is a need to investigate methods for improving the efficiency and time of an acquisition (LC + MS). In this work, the ability to improve throughput in single runs or as part of an in-depth proteome analysis of a fractionated sample using standard LC hardware is investigated. Capitalizing on the increased loading capacity of nanochromatography columns with larger IDs, substantially improved throughput with no reduction in detection sensitivity is achieved in single-injection proteome analyses. An optimized 150 m ID column setup is paired with an offline fractionation-concatenation scheme to demonstrate the ability to perform in-depth proteome analysis on-par with current state-of-the-art studies, while minimizing sample loading overhead. Together, these data demonstrate an easy and effective means to improve sample analysis throughput with no reduction in data quality using an approach that is applicable to any standard nano-LC hardware.

biochemistry↗