Biology subjects

Daub, C. O.

Publications and source records attributed to Daub, C. O..

6 recordsLinked to original sources

bioRxiv2026

The DoGA Consortium Atlas of Canine Enhancers and Promoters Across Tissues and Development

The domestic dog is a powerful genetic model for complex traits, disease and behaviours relevant to human biology, yet the canine genome still lacks a systematic transcription-defined atlas of regulatory elements. Existing resources have improved annotation but largely infer regulatory activity from chromatin features rather than directly mapping transcription initiation at promoters and enhancers. Here, we address this gap using 114 CAGE-seq libraries spanning 56 tissues and developmental stages from 9 dogs. We identify 68,446 promoters and 46,661 active enhancers, and define their tissue-enriched activity across the canine body. Regulatory programmes are organized around shared transcription factor motif infrastructures, with prominent enrichment of KLF family motifs across tissues. The cerebellum emerges as a major regulatory hub, showing the highest density of enhancer-promoter interactions among brain regions. During embryogenesis, enhancer activity shifts from early neurodevelopmental patterning programmes, including OTX2 and ZIC1/4, towards regulators of functional maturation and synaptic organization, including AQP4 and NLGN3. Comparative analyses revealed 69 enhancers with highly similar regulatory structures linked to potential orthologous genes in dog and human. Together, these data establish a tissue-resolved transcription-based atlas of the canine regulatory genome that advances functional annotation and improves interpretation of non-coding variation in comparative and disease genomics.

genomics↗

bioRxiv2026

Benchmarking tissue- and cell type-of-origin deconvolution in cell-free transcriptomics

Plasma cell-free RNA (cfRNA) reflects tissue- and cell-type-specific activity across pathological states and is a promising biomarker for organ injury and disease. Computational deconvolution methods are widely used to infer organ and cell-type contributions to cfRNA profiles. However, most were originally developed for single-tissue bulk transcriptomes and their performance in body-wide cfRNA settings, where any tissue or cell type can contribute, remains poorly characterised. Here, we present a systematic benchmarking of tissue- and cell type-of-origin deconvolution for plasma cfRNA that considers both methodological and reference-related sources of variability under realistic cfRNA simulation settings. We evaluated seven commonly used deconvolution methods across distinct algorithmic classes and multi-organ reference configurations derived from bulk and single-cell atlases. We assessed performance using simulation frameworks that model multi-organ mixtures, technical noise, and transcript degradation. We further examined deconvolution methods across multiple previously published clinical cfRNA cohorts spanning diverse disease contexts. Across both tissue- and cell-type-level analyses, deconvolution performance was strongly influenced by both method choice and reference parameters. Tissue-of-origin inference was comparatively robust across simulated and clinical datasets, recovering disease-associated organ signals and concordance with biochemical markers. In contrast, cell type-of-origin inference showed greater variability and reduced consistency across analytical settings, leading to divergent interpretations in both simulations and published clinical cfRNA cohorts. Together, these findings demonstrate that methodological and reference-related variability are major sources of uncertainty in cfRNA deconvolution, with tissue-level inference being more robust than cell-type-level inference. Our benchmarking framework provides guidance for reference selection and comparative interpretation in cfRNA deconvolution.

bioinformatics↗

bioRxiv2026

Impact of single-cell RNA reference selection for the deconvolution of breast cancer spatial transcriptomics datasets

BackgroundSpot-based Spatial transcriptomics (ST) allows for unbiased gene expression analysis within tissue architecture, overcoming the limitations of single-cell RNA sequencing (scRNA-seq) by preserving spatial context. However, the high spatial resolution in ST leads to cellular heterogeneity within spots, requiring computational deconvolution to infer cellular compositions. While scRNA-seq serves as a key reference for deconvolution, the impact of reference composition on its accuracy is still unclear. In this study, we systematically evaluate the impact of reference selection for cellular deconvolution and give helpful guide-lines to researchers to address this task. MethodsWe systematically evaluate the impact of single-cell reference selection for global and cell type-specific deconvolution performances in primary and metastatic breast cancer ST datasets. Focusing on state-of-the-art deconvolution tools Cell2location and Robust Cell Type Decomposition (RCTD), we assess the influence of varying reference sizes, cell-type distributions, reference-ST pairings, as well as the usage of large breast cancer and cross-cancer atlases. ResultsOur findings demonstrate that even small reference datasets can yield accurate deconvolution results, with RCTD and Cell2location exhibiting similar performances across cell-types. Heterogeneous entities, such as myeloids, presented greater challenges for accurate deconvolution compared to more distinct ones, such as epithelial cells. Spatial domains associated with prominent cell types like cancer and stromal cells were detected, although their contributions were systematically under- or over-estimated Additionally, we found that perfect reference-ST matching enhances deconvolution accuracy compared to the usage of cross-patient references. Finally, large breast cancer single-cell atlases com-posed of a diverse set of patients were also able to provide reliable deconvolution results. ConclusionThis study provides important insights into optimizing reference selection for ST deconvolution, highlighting the strengths and limitations of current computational tools in addressing cellular heterogeneity within spatial transcriptomics datasets.

bioinformatics↗

bioRxiv2024

Oxygen level alters energy metabolism in bovine preimplantation embryos

STUDY QUESTIONWhat is the effect of different oxygen (O2) levels on the transcriptomic profile of bovine embryos during the in vitro culture? SUMMARY ANSWEREmbryos grown in hypoxia (6% O2) from zygotes until the blastocyst stage had the highest blastocyst formation rate, whereas normoxia (20% O2) delayed transcriptomic reprogramming and embryonic genome activation, and induced changes in energy metabolism gene expression. WHAT IS KNOWN ALREADYMammalian preimplantation embryo development is a complex sequence of events where, within a week, the zygote is reprogrammed to totipotency and subsequently diverges to embryonic and extraembryonic cell lineages for post-implantation embryo development. This period of development is sensitive to oxygen levels that can affect various cellular processes. STUDY DESIGN, SIZE, DURATIONIn this study, we used triplicate bovine embryos as a model for human embryogenesis to compare the influence of O2 levels on preimplantation embryonic development by culturing embryos either in normoxic (20% O2) or physiological hypoxic (6% O2) conditions, or sequential hypoxia until 16-cell stage and then switching to ultrahypoxic culture (2% O2). PARTICIPANTS/MATERIALS, SETTING, METHODSAs the readout for varied O2 effects, we performed RNA sequencing using 5 targeted STRT-N method on single embryos. We compared zygotes, 4-, 8-, 16-cell and blastocyst stage embryos grown in either normoxic or hypoxic condition, adding ultrahypoxia for blastocyst stage embryos as the third condition. MAIN RESULTS AND THE ROLE OF CHANCEWe found that the initial cleavage rate was not affected by O2 levels but there was a clear difference in blastocyst formation rate. In hypoxia, 36% of embryos reached blastocyst stage while in normoxia the blastocyst formation rate was 13%. In final ultrahypoxia condition only 4.6% of embryos reached blastocyst stage. Transcriptomic profiles showed that normoxic conditions slowed down oocyte transcript degradation and embryonic genome activation. Key metabolic enzyme genes were also altered between hypoxic and normoxic conditions at the blastocyst stage. Both hypoxic and ultrahypoxic conditions induced energy production by upregulating genes involved in glycolysis and lipid metabolism typical to in vivo embryos. In contrast, normoxic conditions failed to upregulate glycolysis genes and only depended on primitive oxidative phosphorylation metabolism. We conclude that constant hypoxia culture of in vitro embryos provided the highest blastocyst formation rate and appropriate energy metabolism. Normoxia altered the energy metabolism and decreased the blastocyst formation rate. Even though ultrahypoxia at blastocyst stage resulted in a drop of blastocyst formation, the transcriptional profile of surviving embryos was normal. LARGE SCALE DATAThe raw data (BCL files) are available at Zenodo: XXXXX FASTQ files generated are available in the EMBLs European Bioinformatics Institute (EMBL-EBI)-BioStudies with accession number X-XXXX. LIMITATIONS, REASONS FOR CAUTIONThe limitation of this study is the use of bovine as an animal model instead of human embryos. Due to this, the direct translation of the results to human should be taken with caution. WIDER IMPLICATIONS OF THE FINDINGSThis study supports previous literature on hypoxic culture conditions being the most suitable for in vitro embryo culture. In addition, we provide new insights on why embryos grown in normoxia do not have the same success rate as embryos grown in hypoxia. We did not observe any benefits of lowering the oxygen levels to 2%, calling for caution of switching to this culture system. STUDY FUNDING/COMPETING INTEREST(S)This project has received funding from the European Unions Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie grant agreement No. 813707.; Work in the JK laboratory is supported by Jane and Aatos Erkko Foundation, Sigrid Juselius Foundation, Liv och Halsa (Finland), Swedish Brain Foundation and Swedish Research Council. The study was also supported by the Estonian Research Council (grant no. PRG1076) and the Horizon Europe NESTOR project (grant no. 101120075). What this means for patients?During assisted reproduction treatment (ART) oocytes are fertilized in vitro by sperm to form an embryo. The embryos are then exposed to environmental effects during the 5 days of culture before implantation. Embryo culture takes place inside the incubator, where different oxygen levels can be used. In this study, we compared 3 different oxygen concentrations for embryo culture: atmospheric or normoxia (20%), physiologic or hypoxia (6%) and combined low and ultra-low concentration with initial culture at 6% oxygen until day 3 followed by 2% (ultrahypoxia) until day 5 of culture. We conducted this study because many ART clinics use different oxygen concentrations for embryo culture, and we aimed to provide insight on how these different concentrations impact embryo development by monitoring gene expression. As using human embryos for this type of experiments is not commonly approved, we used bovine embryos whose early development resembles that of humans. We found that culturing embryos for 5 days at constant low oxygen concentration (hypoxia) gives the highest number of embryos that reach the blastocyst stage (day 5) and have expected gene expression that provides the physiological path of embryo development. Culturing embryos at atmospheric oxygen concentration showed that only 13% of fertilised eggs reached the blastocyst stage at day 5, while culturing at the final ultra-low oxygen concentration had the lowest percentage of matured embryos, and we did not observe any benefits that would justify the use of such a culture system. We suggest that in vitro culturing of embryos at constant 5-6% oxygen gives the best results.

developmental biology↗

bioRxiv2022

Transcriptomic and functional mapping of autism associated environmental factors in developing human neurons

Research continues to identify genetic variation, environmental exposures, and their mixtures underlying different diseases and conditions. There is a need for screening methods to understand the molecular outcomes of such factors. Here, we investigate a highly efficient and multiplexable, fractional factorial experimental design (FFED) to study six environmental factors and four human induced pluripotent stem cell line derived differentiating human neural progenitors. We showcase the FFED coupled with RNA-sequencing to identify the effects of low-grade exposures to these environmental factors and analyse the results in the context of autism spectrum disorder (ASD). We performed this after five-day exposures on differentiating human neural progenitors accompanied by a layered analytical approach and detected several convergent and divergent, gene and pathway level responses. We revealed significant upregulation of pathways related to synaptic function and lipid metabolism following lead and fluoxetine exposure, respectively. The lipid changes were validated using mass spectrometry- based metabolomics after fluoxetine exposure. Our study demonstrates that the FFED can be used for multiplexed transcriptomic analyses to detect relevant pathway-level changes in human neural development caused by low-grade environmental risk factors. Future studies will require multiple cell lines with different genetic backgrounds for characterising the effects of environmental exposures in ASD.

neuroscience↗

bioRxiv2021

Sequence determinants of human gene regulatory elements

DNA determines where and when genes are expressed, but the full set of sequence determinants that control gene expression is not known. To obtain a global and unbiased view of the relative importance of different sequence determinants in gene expression, we measured transcriptional activity of DNA sequences that are in aggregate [~]100 times longer than the human genome in three different cell types. We show that enhancers can be classified to three main types: classical enhancers1, closed chromatin enhancers and chromatin-dependent enhancers, which act via different mechanisms and differ in motif content. Transcription factors (TFs) act generally in an additive manner with weak grammar, with classical enhancers increasing expression from promoters by a mechanism that does not involve specific TF-TF interactions. Few TFs are strongly active in a cell, with most activities similar between cell types. Chromatin-dependent enhancers are enriched in forkhead motifs, whereas classical enhancers contain motifs for TFs with strong transactivator domains such as ETS and bZIP; these motifs are also found at transcription start site (TSS)-proximal positions. However, some TFs, such as NRF1 only activate transcription when placed close to the TSS, and others such as YY1 display positional preference with respect to the TSS. TFs can thus be classified into four non-exclusive subtypes based on their transcriptional activity: chromatin opening, enhancing, promoting and TSS determining factors - consistent with the view that the binding motif is the only atomic unit of gene expression.

genomics↗