Biology subjects

Hansen, P.

Publications and source records attributed to Hansen, P..

15 recordsLinked to original sources

bioRxiv2026

Soil microbiome effects on quantitative floral traits of the annual plant Mimulus guttatus

Plant flowering is a critical determinant of fitness and supports pollination services to ecosystems. Flowers are energetically expensive and require nutrients that plants might otherwise use for growth. Soil microorganisms can alter plant development in a variety of ways, but the quantitative importance of microbiomes in determining floral variation has yet to be established. In particular, it is unclear the extent to which genotypic differences in flowering phenotypes are mediated through the microbiome. We used a two-generation plant-soil feedback design to test whether flowering traits in a short-lived annual plant Mimulus guttatus drive differentiation in rhizosphere microbiomes. Second, we tested whether these microbiomes subsequently affected flowering traits of progeny plants. We found that different plant genotypes did not generate differences in rhizosphere microbiome structure or diversity after eight weeks of growth. When these microbiomes were inoculated onto new plants, flowering traits were mainly driven by the progeny plants genotype and not the genotype of the parental plant that trained the inoculated microbiome. Longer microbial training, as would necessarily happen in perennial plants, may produce stronger effects, but our results suggest microbiome effects contribute mainly to the environmental noise in flower traits in the short-lived annual Mimulus guttatus.

plant biology↗

bioRxiv2026

Temporal changes in allele frequency facilitate detection of adaptive variants in winter wheat (Triticum aestivum L.) breeding programs

In quantitative genetics, candidate SNPs are identified through genotype-phenotype associations inferred with genome-wide association studies (GWAS). In this study, we explore an alternative approach to detect genetic variants with non-neutral effects by tracking temporal trends in allele frequency in a winter wheat (Triticum aestivum L.) breeding population over an eight-year period, from which signals of selection may be inferred. Selection signatures were inferred with a generalized linear model, where we modeled trends in allele frequency as a function of time (crossing year). These signatures of selection were used to prioritize variants. Associations between phenotypic performance and individual load of prioritized variants were then investigated. Furthermore, we assessed whether incorporating selection information into a genomic best linear unbiased prediction (GBLUP) model improves model performance in terms of quality of fit and prediction ability. Our findings indicate that the inferred signals of selection are effective in identifying non-neutral variants. Variants under strong negative selection were associated with a decrease in protein content adjusted for grain yield (p-value < 0.01), while genetic variants that had been under moderate to high levels of positive selection were associated with increased grain yield (p-value < 0.01). However, incorporating selection information did not improve prediction accuracy. In conclusion, temporal trends in allele frequency can be used to detect non-neutral variants. The proposed approach may hence complement traditional quantitative genetic methods for detecting non-neutral genetic variation. This approach may allow breeders to detect non-neutral variants earlier in the breeding cycle, without resorting to phenotypic data.

genetics↗

bioRxiv2025

Microbial community regulation of extracellular enzyme production can mediate patterns of particulate and mineral-associated organic matter accumulation in undersaturated soils

Dissolved low molecular weight (LMW) compounds in soil can either be assimilated by microbes or sorb onto mineral surfaces, forming mineral-associated organic matter (MAOM). This creates potential competition between microbes and mineral surfaces for LMW compounds, potentially influencing whether particulate organic matter (POM) is retained or depolymerized by microbes to produce LMW substrates. Therefore, microscale interactions between unoccupied mineral surfaces and microbial enzymes may mediate patterns of POM and MAOM storage, particularly in soils varying in MAOM saturation. To explore this, we adapted an individual-based microscale model to simulate POM retention and new MAOM formation under different initial POM qualities (carbon:nitrogen ratio; C:N) and MAOM saturation levels, while also considering microbial social-like dynamics, which emerge from interactions between microbes with different capacities to produce and share public goods (in this case, extracellular enzymes). Consistent with prior findings, the presence of these dynamics slowed decomposition of initial POM pools. Additionally, MAOM saturation affected microbial community properties, new MAOM formation, and POM decomposition only when social dynamics were included. The patterns of POM decomposition and MAOM formation identified in our work align with observations of simultaneous POM and MAOM formation in undersaturated soils from prior field studies, suggesting that regulation of enzyme production via microbial interactions may be an additional driver of POM and MAOM storage in such soils. Overall, this highlights the importance of explicitly incorporating microbial ecology into our conceptual understanding of C and N cycling, particularly to improve the predictive capacity of ecosystem models and inform soil management strategies that enhance global change mitigation, especially in degraded soils likely to be undersaturated.

ecology↗

bioRxiv2024

Size-dependent temporal decoupling of morphogenesis and transcriptional programs in gastruloids

Understanding the interplay between cell fate specification and morphogenetic changes remains a central challenge in developmental biology. Gastruloids, self-organizing stem cell-based models of post-implantation mammalian development, provide a powerful platform to address this question. Here, we show that physical parameters, particularly system size, critically influence the timing and outcomes of morphogenetic processes. Larger gastruloids exhibit delayed symmetry breaking, increased multipolarity, and prolonged axial elongation, with morphogenesis driven by system size. Despite these variations, transcriptional programs and cell fate composition remain remarkably stable across a broad size range. Notably, extreme sizes show distinct transcriptional modules and clear shifts in gene expression patterns. Intriguingly, size perturbation experiments rescued the morphogenetic and pattern phenotypes observed in extreme sizes, demonstrating the remarkable adaptability of gastruloids to their effective system size. These findings establish gastruloids as versatile models for studying spatiotemporal dynamics in mammalian embryogenesis and reveal how physical constraints decouple transcriptional from morphogenetic programs.

developmental biology↗

bioRxiv2024

The Unified Phenotype Ontology (uPheno): A framework for cross-species integrative phenomics

Phenotypic data are critical for understanding biological mechanisms and consequences of genomic variation, and are pivotal for clinical use cases such as disease diagnostics and treatment development. For over a century, vast quantities of phenotype data have been collected in many different contexts covering a variety of organisms. The emerging field of phenomics focuses on integrating and interpreting these data to inform biological hypotheses. A major impediment in phenomics is the wide range of distinct and disconnected approaches to recording the observable characteristics of an organism. Phenotype data are collected and curated using free text, single terms or combinations of terms, using multiple vocabularies, terminologies, or ontologies. Integrating these heterogeneous and often siloed data enables the application of biological knowledge both within and across species. Existing integration efforts are typically limited to mappings between pairs of terminologies; a generic knowledge representation that captures the full range of cross-species phenomics data is much needed. We have developed the Unified Phenotype Ontology (uPheno) framework, a community effort to provide an integration layer over domain-specific phenotype ontologies, as a single, unified, logical representation. uPheno comprises (1) a system for consistent computational definition of phenotype terms using ontology design patterns, maintained as a community library; (2) a hierarchical vocabulary of species-neutral phenotype terms under which their species-specific counterparts are grouped; and (3) mapping tables between species-specific ontologies. This harmonized representation supports use cases such as cross-species integration of genotype-phenotype associations from different organisms and cross-species informed variant prioritization.

bioinformatics↗

bioRxiv2024

Systemic coordination of whole-body tissue remodeling in sea anemone local regeneration

The complexity of regeneration extends beyond local wound responses (1,2), eliciting systemic processes that engage the entire organism. However, their functional relevance, and the spatial and temporal orchestration of the underlying molecular processes distant from the injury site remain unknown. Here, we demonstrate that local regeneration in the cnidarian Nematostella vectensis involves a systemic homeostatic response, leading to coordinated whole-body remodeling. Leveraging spatial transcriptomics, endogenous protein tagging, and live imaging, we comprehensively dissect this systemic response at the organismal scale. We identify proteolysis as a critical process driven by both local and systemic upregulation of metalloproteases. We show that metalloproteinase expression levels and activity scale with the extent of tissue loss, leading to proportional long-range movement of tissue and its associated extracellular matrix. Our findings illuminate the adaptive nature of the systematic response in regeneration. We propose that this integrated regenerative mechanism, shifting the system from a steady to a dynamic homeostatic state, allows the organism to cope with a wide range of injuries.

developmental biology↗

bioRxiv2024

Lysis buffer selection guidance for mass spectrometry-based global proteomics including studies on the intersection of signal transduction and metabolism

Prerequisite for a successful proteomics experiment is a high-quality lysis of the sample of interest, resulting in a large number of identified proteins as well as a high coverage of protein sequences. Therefore, the choice of suitable lysis conditions is crucial. Many buffers were previously employed in proteomics studies, yet a comprehensive comparison of lysate preparation conditions was so far missing. In this study, we compared the efficiency of four commonly used lysis buffers, containing the agents NP40, SDS, urea or GdnHCl, in four different types of biological samples (suspension and adherent cell lines, primary mouse cells and mouse liver tissue). After liquid chromatography-mass spectrometry (LC-MS) measurement and MaxQuant analysis, we compared chromatograms, intensities, number of identified proteins and the localization of the identified proteins. Overall, SDS emerged as the most reliable reagent, ensuring stable performance and reproducibility across diverse samples. Furthermore, our data advocated for a dual-sample lysis approach, including that the resulting pellet is lysed again after the initial lysis with a urea lysis buffer and subsequently both lysates are combined for a single LC-MS run to maximize the proteome coverage. However, none of the investigated lysis buffers proved to be superior in every category, indicating that the lysis buffer of choice depends on the proteins of interest and on the biological question. Further, we demonstrated with our systematic studies the establishment of conditions that allows to perform global proteomics and affinity purification-based interactome characterization from the same lysate. In sum our results provide guidance for the best-suited lysis buffer for mass spectrometry-based proteomics depending on the question of interest.

systems biology↗

bioRxiv2023

SIRPα controls CD47-dependent platelet clearance in mice and humans

Over the last decade, more data has revealed that increased surface expression of the "dont eat me" CD47 protein on cancer cells plays a role in immune evasion and tumor progression, with CD47 blockade emerging as a new therapy in immuno-oncology. CD47 is critical in regulating cell homeostasis and clearance, as binding of CD47 to the inhibitory receptor SIRP can prevent phagocytosis and macrophage-mediated cell clearance. The purpose of this study was to examine the role of the CD47-SIRP signal in platelet homeostasis and clearance. Therapeutic reagents targeting the CD47-SIRP axis are very promising for treatment of hematologic malignancies and solid tumors, but lead to transient anemia or thrombocytopenia in a subset of patients. We found that platelet homeostatic clearance is regulated through the CD47-SIRP axis and that therapeutic blockade to disrupt this interaction in mice and in humans has a significant impact on platelet levels. Furthermore, we identified genetic variations at the SIRPA locus that impact platelet levels in humans such that higher SIRPA gene expression is associated with higher platelet levels. SIRPA expression at either end of the normal range may affect clinical outcomes of treatment with anti-CD47 therapy. Key pointsO_LIPlatelet homeostasis is regulated through the CD47-SIRP axis and therapeutic blockade to disrupt this interaction impacts platelet levels C_LIO_LICommon genetic variants at SIRPA locus associate with platelet levels C_LI

immunology↗

bioRxiv2022

Node-degree aware edge sampling mitigates inflated classification performance in biomedical graph representation learning

Graph representation learning is a family of related approaches that learn low-dimensional vector representations of nodes and other graph elements called embeddings. Embeddings approximate characteristics of the graph and can be used for a variety of machine-learning tasks such as novel edge prediction. For many biomedical applications, partial knowledge exists about positive edges that represent relationships between pairs of entities, but little to no knowledge is available about negative edges that represent the explicit lack of a relationship between two nodes. For this reason, classification procedures are forced to assume that the vast majority of unlabeled edges are negative. Existing approaches to sampling negative edges for training and evaluating classifiers do so by uniformly sampling pairs of nodes. We show here that this sampling strategy typically leads to sets of positive and negative edges with imbalanced edge degree distributions. Using representative homogeneous and heterogeneous biomedical knowledge graphs, we show that this strategy artificially inflates measured classification performance. We present a degree-aware node sampling approach for sampling negative edge examples that mitigates this effect and is simple to implement.

bioinformatics↗

bioRxiv2022

Secretoglobin family 1D member 2 (SCGB1D2) protein inhibits growth of Borrelia burgdorferi and affects susceptibility to Lyme disease

Lyme disease is a tick-borne disease caused by bacteria of the genus Borrelia. The disease can initially manifest as an erythema migrans rash and, if able to evade the host immune defenses, can progress into a secondary stage chronic disease with debilitating physical or neurological manifestations1,2. The host factors that modulate susceptibility for Lyme disease have remained mostly unknown. Here we show a novel host defense mechanism against Lyme disease in humans. Using epidemiological and genetic data from FinnGen, we identify a common missense variant at the gene encoding for Secretoglobin family 1D member 2 (SCGB1D2) protein that increases the susceptibility for Lyme disease. The genetic variant changes proline at position 53 to leucine and is predicted as deleterious. Consequently, we validate the dysfunction of this protein variant using live Borrelia burgdorferi (Bb). Recombinant reference SCGB1D2 protein inhibits the growth of Bb twice as effectively as the recombinant SCGB1D2 P53L deleterious missense variant. Together, these data suggest that SCGB1D2 is a host defense factor present in the skin, sweat, and other secretions which protects against Bb infection. This finding provides a novel therapeutic avenue for drug development to prevent and treat Lyme disease.

genetics↗

bioRxiv2022

An algorithmic framework for isoform-specific functional analysis

Gene Ontology (GO) overrepresentation analysis characterizes the biological mechanisms common to sets of differentially expressed genes identified by high-throughput experiments. To date, GO overrepresentation analysis has mainly been used to evaluate differentially expressed genes, but short- and long-read RNA-seq technologies now allow increasingly accurate identification of differential alternative splicing. The function of most splice isoforms remain unknown, but if acccurate predictions could be made, overrepresentation analysis could be applied to differentially spliced isoforms to assess the functional implications of alternative splicing in RNA-seq experiments. We present isopret (Isoform Interpretation), a new paradigm for isoform function prediction based on the expectation-maximization framework. isopret leverages the relationships between sequence and functional isoform similarity to infer isoform specific functions in a highly accurate fashion. This enabled us to adapt GO overrepresentation analysis, which to date has been limited to differential gene expression, to be extended to assess overrepresentation of GO annotations in differentially spliced isoforms. An analysis of 100 RNA-seq studies including investigations of development, cancer, and common disease demonstrated that expression and splicing regulate different sets of biological functions. We make isopret predictions freely available in a desktop application that can be used to analyze differential expression and splicing in any bulk RNA-seq dataset.

bioinformatics↗

bioRxiv2022

Novel Association of Lyme Disease, Age, and Atopic Dermatitis

Borrelia burgdorferi (B. burgdorferi) is a bacterial spirochete that can cause Lyme disease after infecting a susceptible host. Immune responses to the bacteria are highly variable and host specific. The murine substrain, C3H/HeJ, is a frequently utilized mouse model of Lyme disease. In this study, we sought to investigate the correlation of age with onset and severity of dermatitis, both in mice infected with B. burgdorferi as well as humans who have had a diagnosis of Lyme disease. Female C3H/HeJ mice aged 6-8 weeks, 1 year, or 2 years were infected intraperitoneally with 105 B. burgdorferi. Dermatitis of the tail was evaluated by gross examination and histology. Additional female C3H/HeJ and C57BL/6J mice aged 5 weeks were injected intradermally with 105 B. burgdorferi containing the luciferase reporter gene then analyzed under in vivo imaging. Human data via electronic health records of 342,499 Finnish individuals was tested and analyzed for associations between Lyme disease and atopic dermatitis. Dermatitis worsened over the course of untreated infection, with ulceration, hemorrhaging, flaking, hair loss, and dark lesions as well as spongiosis and acanthosis. These features of dermatitis were present in infected mice after 1 year of age. We further confirm the presence of B. burgdorferi in the tail through quantification of bioluminescence and immunohistochemistry of both C3H/HeJ and C57BL/6J mice. This relationship among Lyme disease, atopic dermatitis, and host age seen in the mouse model is consistent with a large pool (342,499) of human epidemiological data from Finland. We identified 5,248 individuals with Lyme disease and 17,233 with atopic dermatitis in FinnGen. Retrospective analysis shows Lyme disease is associated with atopic dermatitis (OR = 1.91 [1.68 -2.37], P < 2e-16). More visits due to Lyme disease complications (3 or more visits versus 1 visit) were associated with atopic dermatitis (OR = 2.19 [1.35-3.55], P = 0.0014) and risk of developing atopic dermatitis over time (HR=2.26 [1.54-3.95], P = 0.0017). Data from mice and humans reveal a novel relationship among Lyme disease, age, and atopic dermatitis. Through defined pathological scoring, we demonstrate the onset of murine atopic dermatitis with B. burgdorferi infection, which is further exacerbated by host age at time of infection. In humans, a diagnosis of Lyme disease in FinnGen was associated with atopic dermatitis and further research is warranted to establish causation.

immunology↗

bioRxiv2019

Accurate and simultaneous identification of differential expression and splicing using hierarchical Bayesian analysis

The regulation of mRNA controls both overall gene expression as well as the distribution of mRNA isoforms encoded by the gene. Current algorithmic approaches focus on characterization of significant differential expression or alternative splicing events or isoform distribution without integrating both events. Here, we present Hierarchical Bayesian Analysis of Differential Expression and ALternative SPlicing (HBA-DEALS), which simultaneously characterizes differential expression and splicing in cohorts. HBA-DEALS attains state of the art or better performance for both expression and splicing, and allows genes to be characterized as having differential gene expression (DGE), differential alternative splicing (DAST), both, or neither. Based on an analysis of Genotype-Tissue Expression (GTEx) data we demonstrate the existence of sets of genes that show predominant DGE or DAST across a comparison of 20 tissue types, and show that these sets have pervasive differences with respect to gene structure, function, membership in protein complexes, and promoter architecture.

bioinformatics↗

bioRxiv2019

SurfaceGenie: A web-based application for integrating predictive and experimental data for rational candidate surface marker prioritization

MotivationCell-type specific surface proteins can be exploited as valuable markers for a range of applications including immunophenotyping live cells, targeted drug delivery, and in vivo imaging. Despite their utility and relevance, the unique combination of molecules present at the cell surface are not yet described for most cell types. A significant challenge in analyzing omic discovery datasets is the selection of candidate markers that are most applicable for downstream applications. ResultsHere, we developed GenieScore, a prioritization metric that integrates a consensus-based prediction of cell surface localization with user-input data to rank-order candidate cell-type specific surface markers. In this report, we demonstrate the utility of GenieScore for analyzing human and rodent data from proteomic and transcriptomic experiments in the areas of cancer, stem cell, and islet biology. We also demonstrate that permutations of GenieScore, termed IsoGenieScore and OmniGenieScore, can efficiently prioritize co-expressed and intracellular cell-type specific markers, respectively. Availability and ImplementationCalculation of GenieScores and lookup of SPC scores is made freely accessible via the SurfaceGenie web-application: www.cellsurfer.net/surfacegenie.

systems biology↗

bioRxiv2018

The impact of sex on alternative splicing

Over 95% of human genes undergo alternative splicing (AS) in a developmental, tissue-specific, or signal transduction-dependent manner. Here, we present a large-scale survey of sex-biased differential alternative splicing (DAS) across 7027 samples of 39 tissues from 532 individuals (351 males and 181 females) from the Genotype-Tissue Expression project. We detected a total of 1278 statistically significant DAS events affecting 888 different genes and 4417 significant differential gene expression (DGE) events in 3221 genes. Only 267 (29.3%) of the differentially spliced genes were also differentially expressed. Genes that displayed sex-biased DGE or DAS across multiple tissues were enriched in functions related to signaling including histone demethylation. The probability of a gene showing significant differential AS varies by chromosome and is highest for the X chromosome, with differentially spliced X chromosomal genes additionally being more likely to escape X chromosomal inactivation. A small but significant association was found between sex-biased AS and transcripts that undergo physiological nonsense-mediated decay (NMD). We show a significant overlap of differential splicing and genes that display estrogen-induced alternative splicing, that are involved in estrogen response pathway. Further, we show overlap of the involved exons with estrogen-receptor bindings sites. Our results provide a comprehensive survey of sex-biased AS and its characteristics across a large collection of human tissues.

genomics↗