Biology subjects

Nilsson, A.

Publications and source records attributed to Nilsson, A..

16 recordsLinked to original sources

bioRxiv2026

Air temperature limits aerobic capacity and scope in active birds

Thermal tolerance limits are an important determinant of species vulnerability to climate change. However, most of our knowledge of thermal tolerance pertains to resting animals, which could yield unrealistic predictions about how air temperature will affect animals when they are active. To gain a better understanding of thermoregulatory contstraints in active birds, we induced zebra finches in captivity to exercise continuously in air temperatures ranging from cool to warm (12- 36 C) while we simultaneously measured their metabolic rate, body temperature, and evaporative water loss. During activity, birds overheated in air temperatures well below their resting thermal tolerance limit, and aerobic scope was reduced by 22% at the warmest temperature. Although zebra finches showed a 8-fold increase in evaporative water loss during exercise in the warmest temperature, this was not sufficiently high to compensate for the substantial increase in heat produced during activity. This could explain why birds became hyperthermic and showed reductions in metabolic rate in the higher air temperatures. Thus, our results provide a mechanistic explanation for how heat could limit aerobic capacity and scope in active birds. These findings support growing evidence that increased air temperature can lead to sub-lethal fitness costs in free-living birds.

ecology↗

bioRxiv2026

Reference-free protein sequencing by consensus assembly of redundant de novo peptide reads

Reading a proteins sequence from tandem mass spectra without a reference is limited by single-spectrum accuracy, most acutely across the hypervariable complementaritydetermining regions of antibodies. Broadly specific proteases tile a protein with long, overlapping peptides, so every residue is covered by many independent de novo reads. borgonovo assembles their per-step probability profiles into a reference-free per-residue consensus, seeding templates from mass-closure-consistent reads and recruiting the rest by substitution- tolerant alignment and per-column voting. Re-decoding each spectrum with a prior from its consensus position lifts amino acid accuracy on placed spectra from 0.80 to 0.87. On the therapeutic antibody trastuzumab, nine proteases cover its heavy and light chains completely at 0.88 fixed-window identity, and 0.93 on the pruned assembly once local indels are accommodated. Applied unchanged to five secretome proteins and trastuzumab with three proteases, it reaches 0.87 mean fixed-window identity over 82% coverage. borgonovo is open source and works with most de novo sequencers, so redundant digestion turns any of them into a protein sequencer where no reference exists.

bioinformatics↗

bioRxiv2026

scLEMBAS: Context-Aware Modeling of Signaling Pathway Activity at Single-Cell Resolution

Cells sense and integrate extracellular cues through intracellular signaling networks that reshape transcription factor activity to dictate cellular responses. Signaling activity is difficult to decipher: it is non-linear, and it contains extensive feedback and crosstalk. Furthermore, the same perturbation can elicit markedly different responses depending on context (e.g., cell type, disease state, and tissue microenvironment) such that identical stimuli produce diverse responses in multicellular populations. Consequently, there is a vast combinatorial space of complex interactions and context-dependent responses that necessitate computational models. Computational models of single-cell perturbation responses are demonstrated to predict cellular responses, but are often limited in mechanistic insight. Prior knowledge networks offer a route to bridge predictive capability and interpretability. Here we present scLEMBAS, a context-aware, gray-box neural network that models signaling pathway activity at single-cell resolution while preserving mechanistic grounding. scLEMBAS encodes a prior-knowledge network of protein-protein interactions as a recurrent neural network whose learnable edge weights correspond to signaling interaction strengths. It also captures context and individual cell variance through compositional bias terms. An adversarial approach allows the model to answer a single-cell counterfactual - what a given cells TF activity would be under a different perturbation or context - while involving mechanistic rather than simply relational information. Across two scRNA-seq datasets spanning single- and multi-perturbation settings, scLEMBAS accurately predicts out-of-distribution combinations of perturbation and context. Capturing population variance across individual cells enables the model to predict cell subtype specific perturbation responses, despite being agnostic to such labels. Beyond prediction, scLEMBAS learned parameters are biologically interpretable: learned edge weights carry information beyond network topology and "self-prune" spurious interactions, while the categorical bias nominates proteins associated with cell-type-specific perturbation states. Overall, scLEMBAS enables quantitative dissection of how signaling pathway activity is reshaped by perturbation within specific cellular contexts.

bioinformatics↗

bioRxiv2025

Biologically informed neural network models are robust to spurious interactions via self-pruning

Computational models of cellular networks hold promise to uncover disease mechanisms and guide therapeutic strategies. Biology-informed neural networks (BINNs) is an emerging approach to create such models by combining the predictive power of deep learning with prior knowledge, a vital aspect of biological research. The architectures of BINNs enforces a network structure from which mechanism can ideally be inferred. However, a key challenge is to evaluate the reliability of these models, as cells are inherently complex, involving intricate and sometimes unknown interactions. Currently, analysis mainly focuses on selected pathways rather than a more comprehensive perspective. In this work we demonstrate an alternative holistic approach: we measure to which extent purposefully introduced spurious interactions are down-weighted by a BINN during training (self-pruning). The metric suggested RRA (Relative Residual Area) allows for direct distribution comparison with perfect self-pruning achieved at zero and a failure to self-prune if above one. To enable rapid testing, we updated LEMBAS (Large-scale knowledge-EMBedded Artificial Signaling-networks), our recurrent neural network framework for intracellular signaling dynamics, with full GPU acceleration. Our implementation achieves a >7-fold speedup compared to the original while preserving predictive accuracy. We evaluated self-pruning in 3 different datasets and found that when spurious interactions are introduced at random, the model prunes these to a larger extent than those from the prior knowledge network (PKN), provided the model is regularized with a sufficiently large L2 norm. This suggests that BINNs can be robust to uncertainty in the PKN. Implementation and applicationOur implementation of LEMBAS is freely available under a MIT license at https://github.com/AvlantNilssonLab/LEMBAS_GPU. The models and results to generate the figures can be downloaded through https://zenodo.org/records/17425598.

systems biology↗

bioRxiv2025

METTL13 Promotes Pre-Leukemic Transformation and the Development of Pediatric Leukemia

Post-transcriptional RNA modifications, such as N6-methyladenosine (m6A) methylation and adenosine to inosine (A-to-I) editing, are critical regulators of hematopoietic stem cell (HSC) self-renewal and differentiation, yet their precise contributions to malignant transformation are not fully elucidated. In this study, we uncovered the epitranscriptomic landscape caused by knockdown of genes from the methyltransferase (METTL)-family in hematopoietic stem and progenitor cells (HSPCs). We identified both converging and distinct roles of METTL3 and METTL14, known members of the m6A writer complex, as well as orphan gene METTL13. Notably, METTL13 was uniquely upregulated by adenosine deaminase acting on RNA 1 (ADAR1) overexpression, while other METTL genes were downregulated. Knockdown of METTL13 altered the expression of multiple genes involved in oncogenic development in HSPCs. Furthermore, METTL13 was associated with a high-risk profile in pediatric T-cell acute lymphoblastic leukemia (T-ALL), and functional studies confirmed that METTL13 is required for T-ALL cell proliferation and survival both in vitro and in vivo. Collectively, our results indicate a previously unrecognized, oncogenic role for METTL13 in pre-leukemic transformation and T-ALL pathogenesis. SignificanceIn this study we uncovered a novel regulatory link between ADAR1 and the METTL-family of RNA methyltransferases in hematopoietic stem cells. Overexpression of ADAR1 uniquely upregulated METTL13 while suppressing other METTL genes. Loss of orphan gene METTL13 affected proliferation, apoptosis and p53 signaling in hematopoietic stem cells. Furthermore, loss of METTL13 suppressed cell proliferation and survival in pediatric T-cell acute lymphoblastic leukemia. Our findings suggest a potential role for METTL13 in pre-leukemia transformation and oncogenic development.

cancer biology↗

bioRxiv2025

Pairwise Attention: Leveraging Mass Differences to Enhance De Novo Sequencing of Mass Spectra

A fundamental challenge in mass spectrometry-based proteomics is determining which peptide generated a given MS2 spectrum. Peptide sequencing typically relies on matching spectra against a known sequence database, which in some applications is not available. Deep learning-based de novo sequencing can address this limitation by directly predicting peptide sequences from MS2 data. We have seen the application of the transformer architecture to de novo sequencing produce state-of-the-art results on the so-called nine-species benchmark. In this study, we propose an improved transformer encoder inspired by the heuristics used in the manual interpretation of spectra. We modify the attention mechanism with a learned bias based on pairwise mass differences, termed Pairwise Attention (PA). Adding PA improves average peptide precision at 100% coverage by 12.7% (5.9 percentage points) over our base transformer on the original nine-species benchmark. We have also achieved a 7.4% increase over the previously published model Casanovo. Our MS2 encoding strategy is largely orthogonal to other transformer-based models encoding MS2 spectra, enabling straightforward integration into existing deep-learning approaches. Our results show that integrating domain-specific knowledge into transformers boosts de novo sequencing performance.

bioinformatics↗

bioRxiv2025

Oxytocin Receptor Expression and Activation in Parasympathetic Brainstem Cardiac Vagal Neurons

Autonomic imbalance, particularly reduced activity from brainstem parasympathetic cardiac vagal neurons (CVNs) is a major characteristic of many cardiorespiratory diseases. Therapeutic approaches to selectively increase CVN activity have been limited by lack of identified selective translational targets. Recent work has shown that there is an important excitatory synaptic pathway from oxytocin (OXT) neurons in the paraventricular nucleus of the hypothalamus (PVN) to brainstem CVNs, and that OXT could provide a key selective excitation of CVNs. In clinical studies, intranasal OXT increases parasympathetic cardiac activity, autonomic balance, and reduces obstructive event durations and oxygen desaturations in obstructive sleep apnea patients. However, the mechanisms by which activation of hypothalamic OXT neurons, or intranasal OXT, increases brainstem parasympathetic cardiac activity is poorly understood. CVNs are located in two cholinergic brainstem nuclei: the nucleus ambiguus (NA) and dorsal motor nucleus of the vagus (DMNX). In this study we characterize the co-localization of OXT receptors in CVNs (OXTR), as well as non-CVN cholinergic neurons, located in the NA and DMNX nuclei. Selective chemogenetic excitation of OXTR+ CVNs was performed by expressing DREADDs with a combination of Cre and flp dependent viruses. We found that OXT receptors are highly expressed in CVNs in the DMNX and OXT increases DMNX CVN activity, but the receptors and responses are absent in CVNs in the NA. Selective chemogenetic activation of OXTR+ CVNs in the DMNX evoked a rapid and sustained bradycardia.

neuroscience↗

bioRxiv2025

Met-ID: An Open-Source Software for Comprehensive Annotation of Multiple On-Tissue Chemical Modifications in MALDI-MSI

Here, we introduce Met-ID, a graphical interface software designed to efficiently identify metabolites from MALDI-MSI datasets. Met-ID enables annotation of m/z features from any type of MALDI-MSI experiment, involving either derivatizing or conventional matrices. It utilizes structural information for derivatizing matrices to generate a subset of targets that contain only functional groups specific to the derivatization agent. The software is able to identify multiple derivatization sites on the same molecule, facilitating identification of the derivatized compound. This ability is exemplified by FMP-10, a reactive matrix that assists the covalent charge-tagging of molecules containing phenolic hydroxyl and/or primary or secondary amine groups. Met-ID also permits users to recalibrate data with known m/z ratios, boosting confidence in mass match results. Furthermore, Met-ID includes a database featuring MS2 spectra of numerous chemical standards, consisting of neurotransmitters and metabolites derivatized with FMP-10, alongside peaks for FMP-10 itself, all accessible directly through the software. The MS2 spectral database supports user-uploaded spectra and enables comparison of these spectra with user-provided tissue MS2 spectra for similarity assessment. Although initially installed with basic data, Met-ID is designed to be customizable, encouraging users to tailor the software to their specific needs. While several MSI-oriented software solutions exist, Met-ID stands out as a pioneer in seamlessly combining both MS1 and MS2 functionalities. Developed in alignment with the FAIR Guiding Principles for scientific software, Met-ID is freely available as an open-source tool on GitHub, ensuring wide accessibility and collaboration.

bioinformatics↗

bioRxiv2024

A Distinct Alternative mRNA Splicing Profile Identifies the Oncogenic CD44 Transcript Variant 3 in KMT2A-Rearranged Pediatric T-cell Acute Lymphoblastic Leukemia Cells

T-cell acute lymphoblastic leukemia (T-ALL), which constitutes of 10-15% of all pediatric ALL cases, is known for its complex pathology due to pervasive genetic and chromosomal abnormalities. Although most children are successfully cured, chromosomal rearrangements involving the KMT2A (KMT2A) gene is considered a poor prognostic factor. In a cohort of 171 pediatric T-ALL samples we have studied differences in gene and splice variant patterns in KMT2A rearranged (KMT2A-r) T-ALL compared to KMT2A negative (KMT2A-wt) T-ALL samples. Our results have identified a distinct gene expression and splice variant expression pattern in pediatric KMT2A-r patient samples including significant expression of splicing regulatory markers ESRP1 and MBNL3. Additionally, the pro-survival long transcript variant of BCL2 were upregulated in KMT2A-r compared to KMT2A-wt T-ALL samples. Lastly, increased levels of activating methylation in the promoter region of CD44 were identified followed by an upregulation of the oncogenic transcript variant CD44v3 in KMT2A-r T-ALL. Together this suggests that CD44v3 could play a potential role as gene expression-based risk stratification of KMT2A-r rearranged T-ALL and could possibly serve as a therapeutic target using splicing modulators.

cancer biology↗

bioRxiv2024

Library-based single-cell analysis of CAR signaling reveals drivers of in vivo persistence

The anti-tumor function of engineered T cells expressing chimeric antigen receptors (CARs) is dependent on signals transduced through intracellular signaling domains (ICDs). Different ICDs are known to drive distinct phenotypes, but systematic investigations into how ICD architectures direct T cell function--particularly at the molecular level--are lacking. Here, we use single-cell sequencing to map diverse signaling inputs to transcriptional outputs, focusing on a defined library of clinically relevant ICD architectures. Informed by these observations, we functionally characterize transcriptionally distinct ICD variants across various contexts to build comprehensive maps from ICD composition to phenotypic output. We identify a unique tonic signaling signature associated with a subset of ICD architectures that drives durable in vivo persistence and efficacy in liquid, but not solid, tumors. Our findings work toward decoding CAR signaling design principles, with implications for the rational design of next-generation ICD architectures optimized for in vivo function.

immunology↗

bioRxiv2023

Inference of drug off-target effects on cellular signaling using Interactome-Based Deep Learning

Many diseases emerge from dysregulated cellular signaling, and drugs are often designed to target specific nodes in cellular networks e.g. signaling proteins, or transcription factors. However, off-target effects are common and may ultimately result in failed clinical trials. Computational modeling of the cells transcriptional response to drugs could improve our understanding of their mechanisms of action. Here we develop such an approach based on ensembles of artificial neural networks, that simultaneously infer drug-target interactions and their downstream effects on intracellular signaling. Applied to gene expression data from different cell lines, it outperforms basic machine learning approaches in predicting transcription factors activity, while recovering most known drug-target interactions and inferring many new, which we validate in an independent dataset. As a case study, we explore the inferred interactions of the drug Lestaurtinib and its effects on downstream signaling. Beyond its intended target FLT3 the model predicts an inhibition of CDK2 that enhances downregulation of the cell cycle-critical transcription factor FOXM1, corroborating literature findings. Our approach can therefore enhance our understanding of drug signaling for therapeutic design.

systems biology↗

bioRxiv2023

The CNS Microenvironment Promotes Leukemia Cell Survival by Disrupting Tumor Suppression and Cell Cycle Regulation in Pediatric T-cell Acute Lymphoblastic Leukemia

A major obstacle in improving survival in pediatric T-cell acute lymphoblastic leukemia is understanding how to predict and treat leukemia relapse in the CNS. Leukemia cells are capable of infiltrating and residing within the CNS, where they interact with the microenvironment and remain sheltered from systemic treatment. These cells can survive in the CNS niche, by hijacking the microenvironment and disrupting normal functions, thus promoting malignant transformation. While the protective effects of the bone marrow niche have been widely studied, the mechanisms behind leukemia infiltration into the CNS and the role of the CNS niche in leukemia cell survival remain unknown. We have identified a dysregulated gene expression profile in CNS infiltrated T-ALL and CNS relapse, promoting cell survival, chemoresistance and disease progression. Furthermore, we discovered that interactions between leukemia cells and CNS microenvironment induce epigenetic alterations, such as changes in gene regulation and histone modifications, including H3K36me3 levels. These findings can be utilized to predict CNS infiltration and CNS relapse, therefore avoiding overtreatment and adverse effects caused by CNS directed therapy. Additionally, the identified genetic drivers of disease progression can serve as a first step towards identifying therapeutic targets, to sensitize the CNS niche to current therapeutic strategies.

cancer biology↗

bioRxiv2023

Multidimensional Characterization of Soft-Tissue Sarcomas with FUS-TFCP2 or EWSR1-TFCP2 Fusions

Linking clinical multi-omics analyses with mechanistic studies provides opportunities to explore the pathogenesis of rare cancers. We leveraged two precision oncology programs to investigate rhabdomyosarcoma with FUS/EWSR1-TFCP2 fusions, an orphan malignancy without effective systemic therapies. All tumors exhibited outlier expression of the ALK receptor tyrosine kinase, which was partly accompanied by intragenic deletions and aberrant splicing, resulting in truncated ALK variants that were oncogenic and sensitive to ALK inhibitors. Additional recurrent alterations included CKDN2A/MTAP co-deletions, providing a rationale for therapies targeting CDK4/6 and PRMT5. Functional studies showed that FUS-TFCP2 blocks myogenic differentiation and induces transcription of ALK and a truncated form of TERT through binding outside their regular promoters. Furthermore, FUS-TFCP2 inhibited DNA double-strand break repair. Consistent with this, and unlike other fusion-driven sarcomas, TFCP2-rearranged tumors exhibited marked genomic instability and signs of defective homologous recombination. DNA methylation profiling indicated a close relationship with undifferentiated sarcomas rather than rhabdomyosarcoma. Finally, we identified patients in whom overt disease was preceded by benign lesions carrying TFCP2 fusions, providing insight into stepwise sarcomagenesis and suggesting new approaches to early detection and interception. SIGNIFICANCEMost rare cancers are poorly understood, and pathogenesis-directed therapies are often lacking, resulting in poor patient outcomes. This study illustrates the potential of linking precision oncology programs with preclinical research to gain insight into the classification, pathogenesis, and therapeutic vulnerabilities of rare cancers that could improve the clinical management of such diseases.

cancer biology↗

bioRxiv2023

Spatial Multimodal Analysis of Transcriptomes and Metabolomes in Tissues

We present a spatial omics approach that merges and expands the capabilities of independently performed in situ assays on a single tissue section. Our spatial multimodal analysis combines histology, mass spectrometry imaging, and spatial transcriptomics to facilitate precise measurements of mRNA transcripts and low-molecular weight metabolites across tissue regions. We demonstrate the potential of our method using murine and human brain samples in the context of dopamine and Parkinsons disease.

genomics↗

bioRxiv2021

Artificial neural networks enable genome-scale simulations of intracellular signaling

Mammalian cells adapt their functional state in response to external signals in form of ligands that bind receptors on the cell-surface. Mechanistically, this involves signal-processing through a complex network of molecular interactions that govern transcription factor (TF) activity patterns. Computer simulations of the information flow through this network could help predict cellular responses in health and disease. Here we develop a recurrent neural network constrained by prior knowledge of the signaling network with ligand concentrations as input, TF activity as output and signaling molecules as hidden nodes. Simulations are assumed to reach steady state, and we regularize the parameters to enforce this. Using synthetic data, we train models that generalize to unseen data and predict the effects of gene knockouts. We also fit models to a small experimental data set from literature and confirm the predictions using cross validation. This demonstrates the feasibility of simulating intracellular signaling at the genome-scale.

systems biology↗

bioRxiv2021

Efficacy of two commercial disinfectants on Paenibacillus larvae spores

SHORT SUMMARYPaenibacillus larvae is a spore-forming bacterium causing American foulbrood (AFB) in honey bee larvae. The remains of a diseased larva contains billions of extremely resilient P. larvae spores viable for decades. Burning clinically symptomatic colonies is widely considered the only workable strategy to prevent further spread of the disease, and the management practices used for decontamination requires high concentrations of chemicals or special equipment. The aim of this study was to test and compare the biocidal effect of two commercially available disinfectants, " Disinfection for beekeeping" and Virkon S on P. larvae. The two products were applied to P. larvae spores in suspension as well as inoculated on two common beehive materials, wood and styrofoam. " Disinfection for beekeeping" had a 100 % biocidal effect on P. larvae spores in suspension compared to 87.0-88.6 % for Virkon S which, however, had a significantly better effect on P. larvae on styrofoam. The two disinfectants had similar effect on infected wood material.

microbiology↗