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Urban, J.

Publications and source records attributed to Urban, J..

15 recordsLinked to original sources

Mechanisms of resilience to autosomal dominant Alzheimer's disease via oligogenic modulation of rare variants in the entorhinal cortex

Two PSEN1 E280A carriers have presented extreme protection against autosomal dominant Alzheimers disease (ADAD), with over two decades of delay for dementia onset. One of them, a male heterozygous for the RELN-COLBOS protective variant showed increased neuronal density in the entorhinal cortex 1. We conducted a deep phenotyping and genotyping study of the entorhinal cortex in protected and unprotected PSEN1 E280A cases, sporadic AD, and non-demented controls. We used single nuclei and spatial transcriptomics, whole genome sequencing, and candidate genotype-associated expression changes (GAEC) analysis. Our results showed unique neuronal and oligodendrocytic populations in the male RELN-COLBOS patient. Unique RELN positive inhibitory interneurons were enriched in cortical Layer I, while unique abundant ADAMTSL1 positive excitatory neurons were distributed in Layers II/III and Layer Va. These neurons and mature myelinated oligodendrocytes benefitted from increased expression of LRP6 receptor, functioning as a non-canonical receptor for the mutated Reelin protein encoded by RELN-COLBOS. Finally, GAEC and pathway enrichment analyses suggested that other mutations enhanced RELN-COLBOS effects in oligodendrocytes in the male RELN-COLBOS patient, explaining the phenotypic differences with his sister, a RELN-COLBOS carrier with no evident protection from ADAD. Our findings suggest that extreme deviations of the PSEN1 E280A phenotype are more likely attributed to oligogenic effects, including simultaneous mutations occurring in genes including ITGA2, involved in single molecular pathways, such as the Integrins / Focal Adhesion pathway, as potential disease modifiers for Alzheimers disease (AD).

neuroscience↗

Predicting and Elucidating Peptide Retention Mechanisms with Graph Attention Networks

Liquid chromatography (LC) is a key technology in bottom-up proteomics, separating proteolytic peptides to decrease sample complexity, enhance coverage, and increase the robustness of protein identification and quantification. Although high-resolution mass spectrometry has advanced significantly, comparable progress in LC has lagged, primarily due to a limited understanding of peptide-column interactions. To bridge this knowledge gap, we introduce a novel deep learning model (PeptideGNN) based on a Graph Neural Network (GNN) architecture to model and elucidate peptide behaviors across various separation conditions. Trained to accurately predict peptide retention times on ten diverse proteomic datasets, the model subsequently employed a saliency mapping technique to interpret the underlying retention mechanisms. Our model consistently outperformed existing retention-time predictors across multiple datasets, while the saliency mapping, importantly, revealed insights into peptide-stationary phase interactions, highlighting the effects of neighboring amino acids, post-translational modifications (PTMs), chromato-graphic columns, and mobile phase additives on peptide retention.

bioinformatics↗

A bioluminescence resonance energy transfer (BRET) assay to detect telomere length in S. cerevisiae

A method to measure telomere length in S. cerevisiae was developed based on bioluminescence resonance energy transfer (BRET). The system uses energy transfer between a luciferase-Rif2 fusion protein and fluorescently tagged Rap1. The study demonstrates that the BRET ratio correlates with the Rap1/Rif2 complex at the telomeres and thus the availability of telomeric Rap1 binding sites. This enables the measurement of telomere length in living cells. The system was able to reproduce reported deviations in telomere length in mutants lacking telomere length regulators, cells treated with telomere length modifying compounds and strains expressing inducible telomerase. The BRET ratio linearly correlated with the average number of telomeric nucleotides derived from long-read sequencing data using a novel algorithm for telomere length calculation. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/711003v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@105f2c0org.highwire.dtl.DTLVardef@1842e3dorg.highwire.dtl.DTLVardef@f7bc16org.highwire.dtl.DTLVardef@5d5b50_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

Identification of Sample Processing Errors in Microbiome Studies Using Host Genetic Profiles

In microbiome studies, sample processing errors are frequent and difficult to detect, especially in large studies involving multiple sites, personnel, and sample types. We present two complementary approaches to identify such errors using host DNA profiled via metagenomic sequencing of microbiome samples. The first approach compares host SNPs inferred from metagenomics to independently obtained genotypes (e.g., microarray genotypes) to match samples to their donors, while the second method compares metagenomics-inferred SNPs between samples to identify samples supplied by the same donor. Furthermore, we demonstrate that combining these methods with experimental metadata provides greater confidence in the identification of errors. Analyzing a longitudinal vaginal microbiome dataset, we demonstrate the ability of our approach to identify mislabeled samples. Using subsampling, we further show that our methods are robust to low sequencing coverage. Overall, our analysis highlights the frequency of processing errors in microbiome studies. We therefore recommend applying error-detection methods in all studies with suitable data.

bioinformatics↗

Bridging Worlds: Connecting Glycan Representations with Glycoinformatics via Universal Input and a Canonicalized Nomenclature

As the field of glycobiology has developed, so too have different glycan nomenclature systems, reflecting diverse cognitive and practical needs of different scientific uses. While each system serves specific purposes, this multiplicity creates challenges for usability, data integration, and knowledge sharing. Here, we present a practical framework for automated nomenclature conversion, taking any nomenclature as input, without having to declare the specific language, and using a canonicalized IUPAC-condensed format as a standardized output representation. Our implementation handles (i) all common nomenclatures, including common typos, (ii) complex cases including structural ambiguities, modifications, and uncertainty in linkage information, and (iii) different compositional representations. This Universal Input framework can translate more than 10 nomenclatures in less than 1 ms, tested on over 50,000 sequences with 95-100% coverage, enabling seamless integration of existing glycan databases and tools while maintaining the specific advantages of each representation system.

bioinformatics↗

Cell type- and chromosome-specific chromatin landscapes and DNA replication programs of Drosophila testicular stem cells

Stem cells have the unique ability to self-renew and differentiate into specialized cell types. Epigenetic mechanisms, including histones and their post-translational modifications, play a crucial role in regulating programs integral to a cells identity, like gene expression and DNA replication. However, the transcriptional, chromatin, and replication timing profiles of adult stem cells in vivo remain poorly understood. Containing germline stem cells (GSCs) and somatic cyst stem cells (CySCs), the Drosophila testis provides an excellent in vivo model for studying adult stem cells. However, the small number of stem cells and cellular heterogeneity of this tissue have limited comprehensive genomic studies. In this study, we developed cell type-specific genomic techniques to analyze the transcriptome, histone modification patterns, and replication timing of GSC-like and CySC-like cells. Single cell RNA sequencing validated previous findings on GSC-CySC intercellular communication and revealed high expression of chromatin regulators in GSC-like cells. To characterize chromatin landscapes, we developed a cell-type-specific chromatin profiling assay to map H3K4me3-, H3K27me3-, and H3K9me3- enriched regions, corresponding to euchromatic, facultative heterochromatic, and constitutive heterochromatic domains, respectively. Finally, we determined cell type-specific replication timing profiles, integrating our in vivo datasets with published data using cultured cell lines. Our results reveal that GSC-like cells display a distinct replication program compared to somatic lineages, that aligns with chromatin state differences. Collectively, our integrated transcriptomic, chromatin, and replication datasets provide a comprehensive framework for understanding genome regulation differences between these in vivo stem cell populations, demonstrating the power of multi-omics in uncovering cell type- specific regulatory features.

genomics↗

Comparison of morphological and physiological response to drought stress among temperate forest understory forbs and graminoids

O_LIDrought stress can profoundly affect plant growth and physiological vitality, yet there is a notable scarcity of controlled drought experiments focused on herbaceous species of the forest understory. C_LIO_LIIn this study, we collected seeds from five forb and four graminoid species growing in the temperate forest understory of the Czech Republic. These seeds were germinated under controlled glasshouse conditions and subjected to moderate drought stress for five weeks. We assessed biomass partitioning, stomatal and leaf morphology, leaf gas exchange, minimum leaf conductance (gmin), and chlorophyll fluorescence parameters. C_LIO_LIThe comparison of two ecological guilds revealed that graminoids exhibited a higher root-to-shoot ratio, improved water-use efficiency, greater carboxylation efficiency, and enhanced non-photochemical quenching under drought conditions compared to forbs. In contrast, forbs had significantly lower gmin, along with higher total biomass and total leaf area. Despite these differences in morpho-physiological functional traits, both groups experienced a similar relative reduction in biomass during drought stress. Key predictors of biomass accumulation under drought included photochemical quenching, stomatal traits, total leaf area and gmin. A negative correlation between biomass and gmin suggests that plants with lower residual water losses after stomatal closure can accumulate more biomass under drought stress. Additionally, gmin was positively correlated with guard cell length, suggesting that larger stomata contribute to higher residual water loss. C_LIO_LIGraminoids exhibited morpho-physiological modifications that enhanced drought resistance, indicating a greater emphasis on stress tolerance as a survival strategy. In contrast, forbs maintained higher biomass and total leaf area, reflecting a competitive strategy for maximizing resource acquisition. C_LI

plant biology↗

Navigating the Maze of Mass Spectra: A Machine-Learning Guide to Identifying Diagnostic Ions in O-Glycan Analysis

Structural details of oligosaccharides, or glycans, often carry biological relevance, which is why they are typically elucidated using tandem mass spectrometry. Common approaches to distinguish isomers rely on diagnostic glycan fragments for annotating topologies or linkages. Diagnostic fragments are often only known informally among practitioners or stem from individual studies, with unclear validity or generalizability, causing annotation heterogeneity and hampering new analysts. Drawing on a curated set of 237,000 O-glycomics spectra, we here present a rule-based machine learning workflow to uncover quantifiably valid and generalizable diagnostic fragments. This results in fragmentation rules to robustly distinguish common O-glycan isomers. We envision this resource to improve glycan annotation accuracy and concomitantly make annotations more transparent and homogeneous across analysts.

bioinformatics↗

Decoding Glycomics: Differential Expression Reimagined

Glycomics, the comprehensive study of all glycan structures in a sample, is a rapidly expanding field with substantial relevance for understanding physiology and disease mechanisms. However, the complexity of glycan structures and glycomics data interpretation present significant challenges, especially when it comes to differential expression analysis. Here, we present a novel computational framework for differential glycomics expression analysis. Our methodology encompasses specialized and domain-informed methods for data normalization and imputation, glycan motif extraction and quantification, differential expression analysis, motif enrichment analysis, time series analysis, and meta-analytic capabilities, allowing for synthesizing results across multiple studies. All methods are integrated into our open-source glycowork package, facilitating performant workflows and user-friendly access. We demonstrate these methods using dedicated simulations and various glycomics datasets. Our rigorous approach allows for more robust, reliable, and comprehensive differential expression analyses in glycomics, contributing to the advancement of glycomics research and its translation to clinical and diagnostic applications.

bioinformatics↗

Predicting glycan structure from tandem mass spectrometry via deep learning

Glycans constitute the most complicated post-translational modification, modulating protein activity in health and disease. However, structural annotation from tandem mass spectrometry data is a bottleneck in glycomics, preventing high-throughput endeavors and relegating glycomics to a few experts. Trained on a newly curated set of 300,000 annotated MS/MS spectra, we present CandyCrunch, a dilated residual neural network predicting glycan structure from raw LC-MS/MS data in seconds (Top1 Accuracy: 87.7%). We developed an open-access Python-based workflow of raw data conversion and prediction, followed by automated curation and fragment annotation, with predictions recapitulating and extending expert annotation. We demonstrate that this can be used for de novo annotation, diagnostic fragment identification, and high-throughput glycomics. For maximum impact, this entire pipeline is tightly interlaced with our glycowork platform and can be easily tested at https://colab.research.google.com/github/BojarLab/CandyCrunch/blob/main/CandyCru nch.ipynb. We envision CandyCrunch to democratize structural glycomics and the elucidation of biological roles of glycans.

bioinformatics↗

Chromatin and gene expression changes during female Drosophila germline stem cell development illuminate the biology of highly potent stem cells

Highly potent animal stem cells either self renew or launch complex differentiation programs, using mechanisms that are only partly understood. Drosophila female germline stem cells (GSC) perpetuate without change over evolutionary time and generate cystoblast daughters that develop into nurse cells and oocytes. Cystoblasts initiate differentiation by generating a transient syncytial state, the germline cyst, and by increasing pericentromeric H3K9me3 modification, actions likely to suppress transposable element activity. Relatively open GSC chromatin is further restricted by Polycomb repression of testis or somatic cell-expressed genes briefly active in early female germ cells. Subsequently, Neijre/CBP and Myc help upregulate growth and reprogram GSC metabolism by altering mitochondrial transmembrane transport, gluconeogenesis and other processes. In all these respects GSC differentiation resembles development of the totipotent zygote. We propose that the totipotent stem cell state was shaped by the need to resist transposon activity over evolutionary time scales.

developmental biology↗

GlycoDraw: A Python Implementation for Generating High-Quality Glycan Figures

Glycans are essential to all scales of biology, with their intricate structures being crucial for their biological functions. The structural complexity of glycans is communicated through simplified and unified visual representations according to the Symbol Nomenclature for Glycans (SNFG) guidelines adopted by the community. Here, we introduce GlycoDraw, a Python-native implementation for high-throughput generation of high-quality, SNFG-compliant glycan figures with flexible display options. GlycoDraw is released as part of our glycan analysis ecosystem, glycowork, facilitating integration into existing workflows by enabling fully automated annotation of glycan-related figures and thus assisting the analysis of e.g., differential abundance data or glycomics mass spectra.

bioinformatics↗

Sex-specific transcript diversity is regulated by a maternal pioneer factor in early Drosophila embryos

Co-transcriptional splicing coordinates the processes of transcription and splicing and is driven by transcription factors (TFs) and diverse RNA-binding proteins (RBPs). Yet the mechanisms by which specific TFs and RBPs function together in context-specific ways to drive precise co-transcriptional splicing at each of thousands of genomic loci remains unknown. Therefore, we have used sex-specific splicing in Drosophila as a model to understand how the function of TFs and RBPs is coordinated to transcribe and process specific RNA transcripts at the correct genomic locations. We show widespread sex-specific transcript diversity occurs much earlier than previously thought and present a new pipeline called time2splice to quantify splicing changes over time. We define several mechanisms by which the essential and functionally-conserved CLAMP TF functions with specific RBPs to precisely regulate co-transcriptional splicing: 1) CLAMP links the DNA of gene bodies of sex-specifically spliced genes directly to the RNA of target genes and physically interacts with snRNA and protein components of the splicing machinery; 2) In males, CLAMP regulates the distribution of the highly conserved RBP Maleless (MLE) (RNA Helicase A) to prevent aberrant sex-specific splicing; 3) In females, CLAMP modulates alternative splicing by directly binding to target DNA and RNA and indirectly through regulating the splicing of sex lethal, the master regulator of sex determination. Overall, we provide new insight into how TFs function specifically with RBPs to drive alternative splicing.

genomics↗

A High-Quality Genome Assembly from a Single, Field collected Spotted Lanternfly (Lycorma delicatula) using the PacBio Sequel II System

A high-quality reference genome is an essential tool for applied and basic research on arthropods. Long-read sequencing technologies may be used to generate more complete and contiguous genome assemblies than alternate technologies, however, long-read methods have historically had greater input DNA requirements and higher costs than next generation sequencing, which are barriers to their use on many samples. Here, we present a 2.3 Gb de novo genome assembly of a field-collected adult female Spotted Lanternfly (Lycorma delicatula) using a single PacBio SMRT Cell. The Spotted Lanternfly is an invasive species recently discovered in the northeastern United States, threatening to damage economically important crop plants in the region. The DNA from one individual was used to make one standard, size-selected library with an average DNA fragment size of ~20 kb. The library was run on one Sequel II SMRT Cell 8M, generating a total of 132 Gb of long-read sequences, of which 82 Gb were from unique library molecules, representing approximately 36-fold coverage of the genome. The assembly had high contiguity (contig N50 length = 1.5 Mb), completeness, and sequence level accuracy as estimated by conserved gene set analysis (96.8% of conserved genes both complete and without frame shift errors). Further, it was possible to segregate more than half of the diploid genome into the two separate haplotypes. The assembly also recovered two microbial symbiont genomes known to be associated with L. delicatula, each microbial genome being assembled into a single contig. We demonstrate that field-collected arthropods can be used for the rapid generation of high-quality genome assemblies, an attractive approach for projects on emerging invasive species, disease vectors, or conservation efforts of endangered species.

genomics↗

Fin whale (Balaenoptera physalus) mitogenomics: A cautionary tale of defining sub-species from mitochondrial sequence monophyly

HighlightsO_LIMitochondrial monophyly is commonly employed to define evolutionary significant units.\nC_LIO_LIMonophyly may be caused by insufficient sampling or a recent common ancestor.\nC_LIO_LIMitogenomic studies are generally based on few samples and prone to sampling issues.\nC_LIO_LIExpanded mitogenome sampling negates previous monophyly in fin whales.\nC_LI\n\nAbstractThe advent of massive parallel sequencing technologies has resulted in an increase of studies based upon complete mitochondrial genome DNA sequences that revisit the taxonomic status within and among species. Spatially distinct monophyly in mitogenomic genealogies, i.e., the sharing of a recent common ancestor among con-specific samples collected in the same region has been viewed as evidence for subspecies. Several recent studies in cetaceans have employed this criterion to suggest subsequent intraspecific taxonomic revisions. We reason that employing intra-specific, spatially distinct monophyly at non-recombining, clonally inherited genomes is an unsatisfactory criterion for defining subspecies based upon theoretical (genetic drift) and practical (sampling effort) arguments. This point is illustrated by a re-analysis of a global mitogenomic assessment of fin whales, Balaenoptera physalus spp., published by Archer et al. (2013) which proposed to further subdivide the Northern Hemisphere fin whale subspecies, B. p. physalus. The proposed revision was based upon the detection of spatially distinct monophyly among North Atlantic and North Pacific fin whales in a genealogy based upon complete mitochondrial genome DNA sequences. The extended analysis conducted in this study (1,676 mitochondrial control region, 162 complete mitochondrial genome DNA sequences and 20 microsatellite loci genotyped in 358 samples) revealed that the apparent monophyly among North Atlantic fin whales reported by Archer et al. (2013) to be due to low sample sizes. In conclusion, defining sub-species from monophyly (i.e., the absence of para-or polyphyly) can lead to erroneous conclusions due to relatively \"trivial\" aspects, such as sampling. Basic population genetic processes (i.e., genetic drift and migration) also affect the time to most recent common ancestor and hence the probability that individuals in a sample are monophyletic.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=195 SRC=\"FIGDIR/small/488130v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (28K):\norg.highwire.dtl.DTLVardef@1e38e54org.highwire.dtl.DTLVardef@9533eborg.highwire.dtl.DTLVardef@183b5b5org.highwire.dtl.DTLVardef@eae09_HPS_FORMAT_FIGEXP M_FIG C_FIG

evolutionary biology↗