Biology subjects

Christensen, S.

Publications and source records attributed to Christensen, S..

16 recordsLinked to original sources

bioRxiv2026

Microbiota during early life windows orchestrates intestinal maturation and barrier formation in the offspring

There is a critical window in early life for immune development that prepares a young mammal for postnatal exposure to environmental, dietary and microbial antigens. Both the maternal microbiota and endogenous colonization have emerged as pivotal drivers in this process. Using gnotobiotic mouse models and reversible colonization to model the effects of the maternal microbiota during pregnancy compared with early life colonization on the small intestinal epithelium we show that both microbial metabolites originating from the maternal microbiota and the postnatal endogenous microbiota drive intestinal epithelial maturation by modulating epigenetic signatures. Microbiota-derived signals from both the pregnant mother and from endogenous postnatal colonisation drove the BLIMP1-guided epithelial neonatal-to-adult transition at weaning through epigenetic mechanisms and direct transcriptional effects. These microbial signals also shaped the developmental trajectory of barrier-forming tight junctional claudins in the neonatal intestine, increased aerobic respiration, and increased the gene expression of xenosensors. Thus, both maternal and endogenous microbiota derived drivers of the offspring's epigenetic and transcriptomic intestinal epithelial landscape fine-tuned the timing of epithelial maturation and augmented functional plasticity at weaning.

microbiology↗

bioRxiv2026

Model-based inference of gene expression noise from single-cell RNA-sequencing data

The heterogeneity of expression levels among genetically identical cells, termed gene expression noise, is a property of the gene expression process whose importance in the biology of organisms and their evolution is increasingly recognized. Measuring gene expression noise requires single-cell expression data, as obtained from single-cell RNA sequencing (scRNASeq). Its estimation, however, is challenging owing to (i) the presence of technical noise in addition to biological noise, and (ii) the heterogeneity of cell types in the sampled population. We propose a maximum-likelihood framework to infer biological noise from scRNASeq data, while accounting for technical noise, dropout probabilities, and distinct cell sequencing depths. We demonstrate the parameter identifiability using simulations and that the resulting noise estimates are uncorrelated from the mean gene expression, and therefore do not need extra correction in downstream analyses, easing intra- and inter-genome comparisons. Using two technical replicates of scR-NASeq data from the wild yeast Saccharomyces paradoxus, we show that expression noise can be inferred in a reproducible manner.

bioinformatics↗

bioRxiv2026

A Multidimensional Framework for Behavioral Persistence: Dissociable Dimensions of Effort, Endurance, and Sequence Stability in Mice

Behavioral persistence, the maintenance of goal-directed action despite obstacles, is a fundamental adaptive process, yet its scientific study remains fragmented across disciplines with disparate operational definitions. Here, we introduce the Persistence Spectrum (PERCS) framework, a five-dimensional model deconstructing persistence into Perseverance of Effort (P), Strategic Endurance (E), Resistance to Extinction (R), Temporal Consistency (C), and Repetitive Sequence Stability (S). Using programmable operant schedules via the FED3 system, we induced a continuum of persistent food-seeking in mice across four paradigms: Fixed Ratio (FR), Alternating 2x2 and 5x5, and Random Progressive Ratio (RPR). To objectively identify persistence periods, we developed a session-specific Gaussian mixture model, providing a data-driven alternative to arbitrary frequency thresholds. We found that persistence bouts were fundamentally driven by unrewarded effort, not reward delivery: instantaneous frequency for incorrect pokes significantly exceeded that for correct pokes in FR, 2x2, and 5x5 (all p < 0.05). This pattern was most pronounced in the high-demand RPR schedule, where high-rate poking continued unabated even after successes, directly challenging reinforcement-centric models and providing strong empirical support for frustration theory. Linear mixed-effects modeling revealed that frequency increased with consecutive unrewarded pokes across all paradigms, with a quadratic (inverted-U) relationship specific to FR and 2x2 (p < 0.001), suggesting effort invigoration followed by strategic disengagement in simpler tasks, whereas high-demand schedules maintained linear increases. Notably, the collapse of Sequence Stability (S) in RPR is partly constrained by the tasks environmental contingency: random reward rules mathematically restrict stable sequence learning. Aggregate analysis showed total poke counts and unrewarded effort scaled with task difficulty (all p < 2e-16), yet pellet retrieval rates remained stable, indicating goal achievement despite increased challenge. Crucially, PERCS profiles were robust across independent and continuous training histories, demonstrating they reflect stable phenotypes shaped by current contingencies rather than training artifacts. Application of PERCS revealed distinct fingerprints: FR produced near-zero P, E, R, and C but maximal S, characteristic of an efficient habit; 2x2 and 5x5 elevated P, E, and R while reducing S; and RPR generated highest P and R, lowest S, and marked inter-individual variability. These findings demonstrate that operant schedules dissociably shape distinct persistence dimensions, with unrewarded effort acting as a key motivational trigger, positioning frustrative nonreward as a primary engine of persistent behavior. The PERCS framework provides a unified, quantitative language for characterizing persistent behavior across species and paradigms, offering a powerful tool for linking dimensions to neural circuits and understanding their breakdown in neuropsychiatric disorders.

animal behavior and cognition↗

bioRxiv2024

Binning meets taxonomy: TaxVAMB improves metagenome binning using bi-modal variational autoencoder

A common procedure for studying the microbiome is binning the sequenced contigs into metagenome-assembled genomes. Currently, unsupervised and self-supervised deep learning based methods using co-abundance and sequence based motifs such as tetranucleotide frequencies are state-of-the-art for metagenome binning. Taxonomic labels derived from alignment based classification have not been widely used. Here, we propose TaxVAMB, a metagenome binning tool based on semi-supervised bi-modal variational autoencoders, combining tetranucleotide frequencies and contig co-abundances with contig annotations returned by any taxonomic classifier on any taxonomic rank. TaxVAMB outperforms all other binners on CAMI2 human microbiome datasets, returning on average 40% more near-complete assemblies than the next best binner. On real long-read datasets TaxVAMB recovers on average 13% more near-complete bins and 14% more species. When used in a single-sample setup, TaxVAMB on average returns 83% more high quality bins than VAMB. TaxVAMB bins incomplete genomes drastically better than any other tool, returning 255% more high quality bins of incomplete genomes than the next best binner. Our method has immediate research and industrial applications, as well as methodological novelty which can be translated to other biological problems with semi-supervised multimodal datasets.

bioinformatics↗

bioRxiv2024

Proteolytic Performance is Dependent on Binding Efficiency, Processivity and Turnover: Single Protease Insights

Proteases are essential enzymes for a plethora of biological processes and biotechnological applications, e.g., within the dairy, pharmaceutical, and detergent industries. Decoding the molecular level mechanisms that drive protease performance is key to designing improved biosolutions. However, direct dynamic assessment of the fundamental partial reactions of substrate binding and activity has proven a challenge with conventional ensemble approaches. We developed a single-molecule (SM) assay for the direct and parallel recording of the stochastic binding interaction of Savinase, a serine-type protease broadly employed in biotechnology, with casein synchronously with monitoring proteolytic degradation of the substrate. SM recordings enabled us to determine how the overall activity of Savinase and two mutants relies on binding efficiency, enzymatic turnover and activity per binding event. Analysis of residence times revealed three characteristic binding states. Mutations were found to dominantly alter the likelihood of sampling the long lived state, with lifetimes longer than 30 seconds, indicating this state contributes to overall activity and supporting a level of processivity for Savinase. This observation challenges conventional expectations, as the protease has no characterized substrate binding site, or binding domain, aside from the active site. These insights, inaccessible through conventional assays, offer new perspectives for engineering proteases with improved hydrolytic performance.

biophysics↗

bioRxiv2023

Cross-sector collaboration reduces SARS-CoV-2 risk in deer

One Health helps achieve optimal health outcomes for people, animals, plants, and their shared environments. We describe a multidisciplinary effort to better understand and mitigate SARS-CoV-2 spread in white-tailed deer across One Health sectors. We first framed the risk problem with three governance sectors that manage captive and wild deer and human public health. The framing included the objectives for each sector, interactions that facilitate human-to-deer and deer-to-deer transmission, and alternatives intended to reduce risk. We then developed a dynamic compartmental model that linked wild and captive deer herds and humans and simulated SARS-CoV-2 dynamics. For baseline conditions, we estimated that median SARS-CoV-2 prevalence in wild and captive herds varied between 0.03 - 0.07, incidence between 0.68 - 1.46, and probability of persistence between 0.64 - 0.97 across 120-day simulations. We then tested single-sector alternatives alone and in combination with other sector actions. We found that single sector alternatives varied in their ability to reduce transmission and that the best performing alternative required collaborative actions among wildlife management, agricultural management, and public health agencies.

ecology↗

bioRxiv2022

Distinct super-enhancer elements differentially control Il2ra gene expression in a cell-type specific fashion

The IL-2 receptor -chain (IL-2R/CD25) is constitutively expressed on DN2/DN3 thymocytes and Treg cells but induced by IL-2 on mature T and NK cells. Il2ra expression is regulated by a super-enhancer extensively bound by STAT5 in mature T cells. Here, we demonstrate that STAT5 cooperates with Notch to induce/maintain Il2ra/CD25 expression in DN2/DN3 cells. Moreover, we systematically investigated CD25 regulation using a series of mice with deletions spanning STAT5 binding elements. Deleting the upstream super-enhancer region mainly affected constitutive CD25 expression on DN2/DN3 thymocytes and Tregs, whereas deleting an intronic region primarily decreased IL-2-induced CD25 on peripheral T and NK cells. Thus, distinct elements preferentially control constitutive versus inducible expression in a cell-type-specific manner, with the MED1 coactivator co-localizing with specific STAT5 binding sites. Moreover, the intronic region was a dominant element whose deletion altered the structure throughout the super-enhancer in mature T cells. These results demonstrate differential functions for distinct super-enhancer elements, thereby indicating ways to manipulate CD25 expression in a cell-type specific fashion.

immunology↗

bioRxiv2022

A Unifying Statistical Framework to Discover Disease Genes from GWAS

Genome-wide association studies (GWAS) identify genomic loci associated with complex traits, but it remains an open challenge to identify the genes underlying the association signals. Here, we extend the equations of statistical fine-mapping, to compute the probability that each gene in the human genome is targeted by a causal variant, given a particular trait. Our computations are enabled by several key innovations. First, we partition the genome into optimal linkage disequilibrium blocks, enabling genome-wide detection of trait-associated genes. Second, we unveil a comprehensive mapping that associates genetic variants to the target genes they affect. The combined performance of the map on high-throughput functional genomics and eQTL datasets supersedes the state of the art. Lastly, we describe an algorithm which learns, directly from GWAS data, how to incorporate prior knowledge into the statistical computations, significantly improving their accuracy. We validate each component of the statistical framework individually and in combination. Among methods to identify genes targeted by causal variants, this paradigm rediscovers an unprecedented proportion of known disease genes. Moreover, it establishes human genetics support for many genes previously implicated only by clinical or preclinical evidence, and it discovers an abundance of novel disease genes with compelling biological rationale.

genomics↗

bioRxiv2021

DnaK response to expression of protein mutants is dependent on translation rate and stability

Chaperones play a central part in the quality control system in cells by clearing misfolded and aggregated proteins. The chaperone DnaK acts as a sensor for molecular stress by recognising short hydrophobic stretches of misfolded proteins. As the level of unfolded protein is a function of protein stability, we hypothesised that the level of DnaK response upon overexpression of recombinant proteins would be correlated to stability. Using a set of mutants of the{lambda} -repressor with varying thermal stabilities and a fluorescent reporter system, the effect of stability on DnaK response and protein abundance was investigated. Our results demonstrate that the initial DnaK response is largely dependent on protein synthesis rate but as the recombinantly expressed protein accumulates and homeostasis is approached the response correlates strongly with stability. Furthermore, we observe a large degree of cell-cell variation in protein abundance and DnaK response in more stable proteins.

biochemistry↗

bioRxiv2020

A Role for Maternal Factors in Suppressing Cytoplasmic Incompatibility

Wolbachia are maternally transmitted bacterial endosymbionts, carried by approximately half of all insect species. Wolbachia prevalence in nature stems from manipulation of host reproduction to favor the success of infected females. The best known reproductive modification induced by Wolbachia is referred to as sperm-egg Cytoplasmic Incompatibility (CI). In CI, the sperm of Wolbachia-infected males cause embryonic lethality, attributed to paternal chromatin segregation defects during early mitotic divisions. Remarkably, the embryos of Wolbachia-infected females "rescue" CI lethality, yielding egg hatch rates equivalent to uninfected female crosses. Several models have been discussed as the basis for Rescue, but none have been demonstrated in robust CI models, which are genetically intractable. As such, the extent of host involvement in Rescue remains untested. In this study, we used a chemical feeding approach to assess maternal contributions to CI suppression in Drosophila simulans. We found that uninfected females exhibited significantly higher CI egg hatch rates in response to seven chemical treatments that affect DNA integrity, cell cycle control, and protein turnover. Three of these treatments suppressed CI induced by endogenous wRi Wolbachia, as well as an ectopic wMel Wolbachia infection. When framed in terms of existing literature, the results implicate DNA integrity as a dynamic aspect of CI suppression for different Wolbachia strains. The framework presented here, applied to diverse CI models, will further enrich our understanding of host reproductive manipulation by insect endosymbionts.

microbiology↗

bioRxiv2020

Physical constrains and functional plasticity of cellulases: Linear scaling relationships for a heterogeneous enzyme reaction

Enzyme reactions, both in Nature and technical applications, commonly occur at the interface of immiscible phases. Nevertheless, stringent descriptions of interfacial enzyme catalysis remain sparse, and this is partly due to a shortage of coherent experimental data to guide and assess such work. We have produced and kinetically characterized 83 cellulases, which revealed a conspicuous linear free energy relationship (LFER) between the strength of substrate binding and the activation barrier. This common scaling occurred despite the investigated enzymes were structurally and mechanistically diverse. We suggest that the scaling reflects basic physical restrictions of the hydrolytic process and that evolutionary selection have condensed cellulase phenotypes near the line. One consequence of the LFER is that the activity of a cellulase can be estimated from substrate binding strength, irrespectively of structural and mechanistic details, and this appears promising for in silico selection and design within this industrially important group of enzymes. On a more general note, the LFER may identify a link to inorganic heterogeneous catalysis, and hence open for the implementation of approaches from this field within interfacial enzymology.

biochemistry↗

bioRxiv2020

Advancing Divide-and-Conquer Phylogeny Estimation

One of the Grand Challenges in Science is the construction of the Tree of Life, an evolutionary tree containing several million species, spanning all life on earth. However, the construction of the Tree of Life is enormously computationally challenging, as all the current most accurate methods are either heuristics for NP-hard optimization problems or Bayesian MCMC methods that sample from tree space. One of the most promising approaches for improving scalability and accuracy for phylogeny estimation uses divide-and-conquer: a set of species is divided into overlapping subsets, trees are constructed on the subsets, and then merged together using a "supertree method". Here, we present Exact-RFS-2, the first polynomial-time algorithm to find an optimal supertree of two trees, using the Robinson-Foulds Supertree (RFS) criterion (a major approach in supertree estimation that is related to maximum likelihood supertrees), and we prove that finding the RFS of three input trees is NP-hard. We also present GreedyRFS (a greedy heuristic that operates by repeatedly using Exact-RFS-2 on pairs of trees, until all the trees are merged into a single supertree). We evaluate Exact-RFS-2 and GreedyRFS, and show that they have better accuracy than the current leading heuristic for RFS. Exact-RFS-2 and GreedyRFS are available in open source form on Github at github.com/yuxilin51/GreedyRFS.

bioinformatics↗

bioRxiv2020

Ultra-deep sequencing differentiates patterns of skin clonogenic mutations associated with sun-exposure status

Non-melanoma skin cancer is the most common human malignancy and is primarily caused by exposure to ultraviolet (UV) radiation. The earliest detectable precursor of UV-mediated skin cancer is the growth of cell groups harboring clonal mutation (CM) in clinically normal appearing skin. Systematic evaluation of CMs is crucial to understand early photo-carcinogenesis. Previous studies confirmed the presence of CMs in sun-exposed skin. However, the relationship between UV-exposure and the accumulation of CMs, and the correlation of CMs with skin cancer risk remain poorly understood. To elucidate the exact molecular and clinical effects of long-term UV-exposure on skin, we performed targeted ultra-deep sequencing in 450 individual-matched sun-exposed (SE) and non-sun-exposed (NE) epidermal punch biopsies obtained from clinically normal skin from 13 donors. A total of 638 CMs were identified, including 298 UV-signature mutations (USMs). The numbers of USMs per sample were three times higher in the SE samples and were associated with significantly higher variant allele frequencies (VAFs), compared with the NE samples. We identified genomic regions in TP53, NOTCH1 and GRM3 where mutation burden was significantly associated with UV-exposure. Six mutations were almost exclusively present in SE epidermis and accounted for 42% of the overall difference between SE and NE mutation burden. We defined Cumulative Relative Clonal Area (CRCA), a single metric of UV-damage calculated by the overall relative percentage of the sampled skin area affected by CMs. The CRCA was dramatically elevated by a median of 11.2 fold in SE compared to NE samples. In an extended cohort of SE normal skin samples from patients with a high- or low-burden of cutaneous squamous cell carcinoma (cSCC), the SE samples in high-cSCC patients contained significantly more USMs than SE samples in low-cSCC patients, with the difference mostly conferred by mutations from low-frequency clones (defined by VAF≤1%) but not expanded clones (VAF>1%). Our studies of differential mutational features in normal skin between paired SE/NE body sites and high/low-cSCC patients provide novel insights into the carcinogenic effect of UV exposure, and suggest CMs might be used to develop novel biomarkers for predicting cancer risk.Significance statement In UV radiation exposed skin, mutations fuel clonal cell growth. We established a sequencing-based method to objectively assess the mutational differences between sun-exposed (SE) and non-sun-exposed (NE) areas of normal human skin. Striking differences, in both the numbers of mutations and variant allele frequencies, were found between SE and NE areas. Furthermore, we identified specific genomic regions where mutation burden is significantly associated with UV-exposure status. These findings revealed previously unknown mutational patterns associated with UV-exposure, providing important insights into UV radiation’s early carcinogenic effects. Additionally, in an extended cohort, we identified preliminary association between normal skin mutation burden and cancer risk. These findings pave the road for future development of quantitative measurement of subclinical UV damage and skin cancer risk.Competing Interest StatementThe authors have declared no competing interest.Common AbbreviationsUVUltravioletCMClonogenic mutationNMSCNonmelanoma skin cancerSESun-exposedNENon-sun-exposedUSMUV-signature mutationNUSMNon-UV-signature mutationCRCACumulative Relative Clonal AreacSCCCutaneous squamous cell carcinomaAKActinic keratosisSNVSingle nucleotide variant Indels – Insertions/deletionsDNVDinucleotide variantCSNVCluster of single nucleotide variantMACMulti-Nucleotide Variant Annotation CorrectorVAFVariant allele frequencyView Full Text

cancer biology↗

bioRxiv2019

5-Fluorouracil treatment induces characteristic T>G mutations in human cancer

5-Fluorouracil (5-FU) is a chemotherapeutic drug component that is commonly used for the treatment of solid cancers. It is proposed that 5-FU possesses anticancer properties via the interference with nucleotide synthesis and incorporation into DNA. As both mechanisms may have a mutational impact on both surviving tumor and healthy cells, we treated intestinal organoids with 5-FU followed by whole genome sequencing analysis and uncovered a highly characteristic mutational pattern that is dominated by T>G substitutions in a CTT context. Analysis of tumor whole genome sequencing data confirmed that this signature can also be identified in vivo in colorectal and breast cancer patients that have undergone treatment with 5-FU. We also found that more 5-FU mutations are induced in TP53 null backgrounds which may be of clinical relevance. Taken together, our results demonstrate that 5-FU is mutagenic and may drive tumor evolution and increase the risk of secondary malignancies. Furthermore, the identified signature shows a strong resemblance to COSMIC signature 17, the hallmark signature of treatment-naive esophageal and gastric tumors, which indicates that distinct endogenous and exogenous triggers can converge onto highly similar mutational signatures.

cancer biology↗

bioRxiv2018

Clinical and Functional Characterization of Melanocortin 4 Receptor genetic variants in African American and/or Hispanic children with severe early onset obesity.

ContextMutations in melanocortin receptor (MC4R) are the most frequent cause of monogenic obesity in children of European ancestry, but little is known about their prevalence in children from minority populations in the United States.\n\nObjectiveThis study aims to identify the prevalence of MC4R mutations in children with severe early onset obesity of African-American and/or Latina ancestry.\n\nDesign and SettingIndividuals were recruited from the weight management clinics at two hospitals and from the institutional biobank at a third hospital. Sequencing of the MC4R gene was performed by whole exome and/or Sanger sequencing. Functional testing was performed to establish the surface expression of the receptor and cAMP response to its cognate ligand -melanocyte stimulating hormone.\n\nParticipantsThree hundred and twelve children (1-18 years, 50% girls) with body mass index (BMI) > 120% of 95th percentile of CDC 2000 growth charts at an age < 6 years, with no known pathological cause of obesity were enrolled.\n\nResultsEight rare MC4R mutations (2.6%) were identified in this study (R7S, F202L (n=2), M215I, G252D, V253I, I269N, F284I), three of which have not been previously reported (M215I, G252D, F284I). The pathogenicity of the variants was confirmed either by prior literature reports, or by functional testing. There was no significant difference in the BMI or height trajectories of children with or without MC4R mutations in this cohort.\n\nConclusionsWhile the prevalence of MC4R mutations in this cohort was similar to that reported in obese children of European ancestry, some of the variants were novel.

genetics↗

bioRxiv2018

Investigating the conformational response of the Sortilin receptor upon binding endogenous peptide- and protein ligands by HDX-MS

Sortilin is a multifunctional transmembrane neuronal receptor involved in sorting of neurotrophic factors and apoptosis signalling. So far, structural characterization of Sortilin and its endogenous ligands has been limited to crystallographic studies of Sortilin in complex with the neuropeptide Neurotensin. Here, we use hydrogen/deuterium exchange mass spectrometry to investigate the conformational response of Sortilin to binding biological ligands including the peptides Neurotensin and the Sortilin propeptide and the proteins Progranulin and pro-Nerve growth factor-{beta}. The results show that the ligands employ two binding sites inside the cavity of the {beta}-propeller of Sortilin. However, ligands have distinct differences in their conformational impact on the receptor. Interestingly, the protein ligands induce conformational stabilization in a remote membrane-proximal domain, hinting at an unknown conformational link between the ligand binding region and this membrane-proximal region of Sortilin. Our findings improves our molecular understanding of Sortilin and how it mediates diverse ligand-dependent functions important in neurobiology.

biophysics↗