Biology subjects

Jordan, M.

Publications and source records attributed to Jordan, M..

14 recordsLinked to original sources

bioRxiv2026

Histone Variant H2A.J Links Epigenetic Reprogramming to Mitochondrial-dependent Kidney Regeneration under Radiation Stress

The histone variant H2A.J is implicated in radiation-induced senescence by promoting the transcription of inflammatory genes. While H2A.J expression increases in renal tubular epithelial cells (TECs) following ionizing radiation (IR), its functional role remains poorly understood. To investigate this, constitutive H2A.J knock-out (KO) mice and wild-type (WT) controls were subjected to CT-guided IR (fractionated whole-body or localized kidney IR). Kidneys were analyzed at acute, intermediate, and chronic stages using immunofluorescence, histochemistry, automated image analysis, and electron microscopy. In WT TECs, IR induced rapid chromatin incorporation and C-terminal serine phosphorylation of H2A.J. Conversely, KO kidneys exhibited significantly more severe histopathological damage, including tubular dilation, flattened epithelium, associated with increased apoptosis, and premature senescence, characterized by persistent DNA damage with lamin B1 loss. Notably, KO TECs displayed disrupted mitochondrial networks and reduced brush borders even at baseline, which were further exacerbated by IR. Unlike WT controls, KO kidneys developed progressive tubular atrophy and incipient fibrosis, indicating a failure in regenerative capacity. Our findings suggest that H2A.J loss impairs tubular regeneration due to defective mitochondrial activation, resulting in insufficient energy supply for coordinated repair. Collectively, these results identify H2A.J as a critical stress-adaptive histone variant essential for the epigenetic regulation of tissue repair following radiation-induced damage. One Sentence SummaryIn irradiated kidney, the loss of histone variant H2A.J impairs the chromatin-mediated adaptation of mitochondrial function in tubular epithelial cells, thereby exacerbating cellular stress - characterized by increased induction of apoptosis and senescence - and ultimately leading to tubular atrophy. Translational RelevanceAcute and chronic kidney injury are frequent complications of genotoxic cancer therapies. Chemo- and radiotherapy induce DNA lesions that trigger cell death and senescence, often leading to irreversible renal damage. However, renal regeneration can occur through the dedifferentiation, proliferation, and redifferentiation of surviving tubular epithelial cells (TECs). This repair process is governed by epigenetic mechanisms that regulate the DNA damage response (DDR) and adapt gene expression programs. Following ionizing radiation (IR), epigenetic remodeling involves the incorporation of histone variants that modulate chromatin accessibility for stress-responsive transcription factors. We identify the histone variant H2A.J as a constitutive component of renal TECs, significantly upregulated after exposure to ionizing radiation (IR). Using H2A.J knock-out (KO) mice, we demonstrate that its absence disrupts acute damage responses and prevents coordinated repair, severely impairing regeneration. Mechanistically, H2A.J deficiency compromises mitochondrial function under postirradiation metabolic stress, driving the transition from acute injury to chronic kidney disease via persistent inflammation and maladaptive tubulointerstitial repair. Targeting these epigenetic drivers offers a promising strategy for regenerating damaged kidney tissue in oncology.

molecular biology↗

bioRxiv2026

Molecular and phenotypic footprints of climate in native Arabidopsis thaliana

Climate change poses a major threat to humanity by driving biodiversity loss and reducing crop yields1,2. To understand the molecular and developmental impacts of rising temperatures, plant science has relied heavily on the model organism Arabidopsis thaliana. Despite decades of research, its development under fully natural conditions remains poorly understood, and only [~]30% of genes have experimental functional annotations, largely because many functions are subtle or manifest only in specific laboratory or ecological contexts3. Here, we address this gap with a landscape transcriptomic approach that integrates intensive phenotyping and transcriptomic profiling of naturally occurring plants in their native habitats4. Across two contrasting field sites and five growing seasons (2021-2025), we phenotyped more than 3,000 A. thaliana plants and generated >1,600 matching transcriptomes. The resulting >30,000 quantitative trait measurements provide a unique opportunity to link climate fluctuations with plant traits and gene expression. Seasons characterized by extreme temperature anomalies directly influenced plant traits, and climatic variables together explained up to 17% of phenotypic variation. In situ transcriptomes carried clear temperature and local environmental signatures, closely matching temperature-response programs known from the laboratory. Leveraging paired per-plant transcriptomes and phenotypes, we applied machine learning to predict regulators of climate-relevant and other plant traits under natural conditions. The models recovered canonical thermomorphogenesis regulators, including PHYTOCHROME INTERACTING FACTOR 4 (PIF4)5,6, providing ecological evidence that temperature signaling pathways defined in controlled environments operate in the wild, and expanded this regulatory landscape by identifying hormonal receptors, signaling components, and previously uncharacterized genes, some of which we functionally validated. Together, this work demonstrates that landscape transcriptomics, by integrating natural field transcriptomes with phenotypes, and thus, capturing environmental and regulatory states, enables the predictive identification of genetic regulators of temperature responses and broader plant traits. This makes landscape transcriptomics a scalable framework for climate-aware functional genomics in plants.

plant biology↗

bioRxiv2025

Telomerase modRNA offers a novel RNA-based approach to treat human pulmonary fibrosis

Pulmonary Fibrosis (PF) is a life-threatening illness that is characterized by progressive scarring in the lung interstitium. There is an urgent need for new PF therapies because current treatments only slow down the progression of fibrosis and the median life expectancy post-diagnosis is only 4 to 6 years. Since PF patients frequently exhibit telomere attrition, overexpressing telomerase, the enzyme responsible for synthesizing telomeres represents a compelling therapeutic option. In this study, we in vitro transcribed human telomerase reverse transcriptase (hTERT) mRNA using modified nucleosides (modRNA). ModRNA hTERT treatment led to transient activation of telomerase activity in a dose-dependent manner in MRC-5 cells and, importantly, in primary human alveolar type II pneumocytes (ATII cells). Consequently, the proliferative capacity was increased, concomitant with reduced DNA damage and elongated telomere length. Notably, induction of cellular immune response was only detectable at the highest modRNA concentration, and returned to normal levels within 48 hours. Next, we demonstrated that circularized, exonuclease-resistant modRNA hTERT extended the transient expression profile which may be clinically advantageous. Finally, we provided therapeutic proof of concept in organotypic 3D ex vivo human precision-cut lung slices derived from end-stage PF patients. Intriguingly, a single modRNA hTERT treatment inhibited senescence as indicated by significantly lower levels of senescence-associated {beta}-galactosidase, and pro-inflammatory IL6 and IL8. Concurrently, the key fibrosis mediators TGF{beta} and COL1A1 were markedly reduced. In conclusion, the data presented herein provide initial evidence for the potential of RNA-based hTERT therapy for treating human lung fibrosis.

molecular biology↗

bioRxiv2024

Metapipeline-DNA: A Comprehensive Germline & Somatic Genomics Nextflow Pipeline

SummaryThe price, quality and throughout of DNA sequencing continue to improve. Algorithmic innovations have allowed inference of a growing range of features from DNA sequencing data, quantifying nuclear, mitochondrial and evolutionary aspects of both germline and somatic genomes. To automate analyses of the full range of genomic characteristics, we created an extensible Nextflow meta-pipeline called metapipeline-DNA. Metapipeline-DNA analyzes targeted and whole-genome sequencing data from raw reads through pre-processing, feature detection by multiple algorithms, quality-control and data- visualization. Each step can be run independently and is supported robust software engineering including automated failure-recovery, robust testing and consistent verifications of inputs, outputs and parameters. Metapipeline-DNA is cloud-compatible and highly configurable, with options to subset and optimize each analysis. Metapipeline-DNA facilitates high-scale, comprehensive analysis of DNA sequencing data. AvailabilityMetapipeline-DNA is an open-source Nextflow pipeline under the GPLv2 license and is available at https://github.com/uclahs-cds/metapipeline-DNA.

bioinformatics↗

bioRxiv2024

First evidence for the evolution of host manipulation by tumors during the long-term vertical transmission of tumor cells in Hydra oligactis

While host phenotypic manipulation by parasites is a widespread phenomenon, whether tumors - which can be likened to parasite entities, can also manipulate their hosts is not known. Theory predicts that this should nevertheless be the case, especially when tumors (neoplasms) are transmissible. We explored this hypothesis in a cnidarian Hydra model system, in which spontaneous tumors can occur in the lab, and lineages in which such neoplastic cells are vertically transmitted (through host budding) have been maintained for over 15 years. Remarkably, the hydras with long-term transmissible tumors show an unexpected increase in the number of their tentacles, allowing for the possibility that these neoplastic cells can manipulate the host. By experimentally transplanting healthy as well as neoplastic tissues derived from both recent and long-term transmissible tumors, we found that only the long-term transmissible tumors were able to trigger the growth of additional tentacles. Also, supernumerary tentacles, by permitting higher foraging efficiency for the host, were associated with an increased budding rate, thereby favoring the vertical transmission of tumors. To our knowledge, this is the first evidence that, like true parasites, transmissible tumors can evolve strategies to manipulate the phenotype of their host.

evolutionary biology↗

bioRxiv2023

SGLT2 inhibitors attenuate endothelial to mesenchymal transition and cardiac fibroblast activation

Beneficial effects of sodium glucose co-transporter 2 inhibitors (SGLT2is) in cardiovascular diseases have been extensively reported leading to the inclusion of these drugs in the treatment guidelines for heart failure. However, molecular actions especially on non-myocyte cells remain uncertain. We observed dose-dependent inhibitory effects of two SGLT2is, dapagliflozin (DAPA) and empagliflozin (EMPA), on inflammatory signaling in human umbilical vein endothelial cells (HUVECs). Proteomics analyses and subsequent enrichment analyses discovered profound effects of these SGLT2is on proteins involved in mitochondrial respiration and actin cytoskeleton. Validation in functional oxygen consumption measurements as well as tube formation and migration assays revealed strong impacts of DAPA. Considering that most influenced parameters played central roles in endothelial to mesenchymal transition (EndMT), we performed in vitro EndMT assays and identified substantial reduction of mesenchymal and fibrosis marker expression as well as changes in cellular morphology upon treatment with SGLT2is. In line, human cardiac fibroblasts (HCFs) exposed to DAPA showed less proliferation, reduced ATP production, and decelerated migration capacity while less extensive impacts were observed upon EMPA. Mechanistically, sodium proton exchanger 1 (NHE1) as well as sodium-myoinositol cotransporter (SMIT) and sodium-multivitamin cotransporter (SMVT) could be identified as relevant targets of SGLT2is in non-myocyte cardiovascular cells as validated by individual siRNA-knockdown experiments. In summary, we found comprehensive beneficial effects of SGLT2is on human endothelial cells and cardiac fibroblasts. The results of this study therefore support a distinct effect of selected SGLT2i on non-myocyte cardiovascular cells and grant further insights into potential molecular mode of action of these drugs.

pharmacology and toxicology↗

bioRxiv2023

Calibrated Identification of Feature Dependencies in Single-cell Multiomics

Data-driven identification of functional relationships between cellular properties is an exciting promise of single-cell genomics, especially given the increasing prevalence of assays for multiomic and spatial transcriptomic analysis. Major challenges include dealing with technical factors that might introduce or obscure dependencies between measurements, handling complex generative processes that require nonlinear modeling, and correctly assessing the statistical significance of discoveries. VI-VS (Variational Inference for Variable Selection) is a comprehensive framework designed to strike a balance between robustness and interpretability. VI-VS employs nonlinear generative models to identify conditionally dependent features, all while maintaining control over false discovery rates. These conditional dependencies are more stringent and more likely to represent genuine causal relationships. VI-VS is openly available at https://github.com/YosefLab/VIVS, offering a no-compromise solution for identifying relevant feature relationships in multiomic data, advancing our understanding of molecular biology.

bioinformatics↗

bioRxiv2022

Deep generative modeling for quantifying sample-level heterogeneity in single-cell omics

The field of single-cell genomics is now observing a marked increase in the prevalence of cohort-level studies that include hundreds of samples and feature complex designs. These data have tremendous potential for discovering how sample or tissue-level phenotypes relate to cellular and molecular composition. However, current analyses are based on simplified representations of these data by averaging information across cells. We present MrVI, a deep generative model designed to realize the potential of cohort studies at the single-cell level. MrVI tackles two fundamental and intertwined problems: stratifying samples into groups and evaluating the cellular and molecular differences between groups, both without requiring a priori grouping of cells into types or states. Due to its single-cell perspective, MrVI is able to detect clinically relevant stratifications of patients in COVID-19 and inflammatory bowel disease (IBD) cohorts that are only manifested in certain cellular subsets, thus enabling new discoveries that would otherwise be overlooked. Similarly, we demonstrate that MrVI can de-novo identify groups of small molecules with similar biochemical properties and evaluate their effects on cellular composition and gene expression in large-scale perturbation studies. MrVI is available as open source at scvi-tools.org.

bioinformatics↗

bioRxiv2022

Treatment of wound infections in a mouse model using Zn2+-releasing phage bound to gold nanorods

Infections caused by drug-resistant bacteria, particularly gram-negative organisms, are increasingly difficult to treat using antibiotics. A potential alternative is phage therapy, in which phages infect and lyse the bacterial host. However, phage therapy poses serious drawbacks and safety concerns, such as the risk of genetic transduction of antibiotic resistance genes, inconsistent pharmacokinetics, and unknown evolutionary potential. In contrast, metallic nanoparticles possess precise, tunable properties, including efficient conversion of electronic excitation into heat. In this work, we demonstrate that engineered phage-nanomaterial conjugates that target the gram-negative pathogen P. aeruginosa, are highly effective as a treatment of infected wounds in mice. Photothermal heating, performed as a single treatment (15 min) or as two treatments on consecutive days, rapidly reduced the bacterial load and released Zn2+ to promote wound healing. The phage-nanomaterial treatment was significantly more effective than systemic fluoroquinolone antibiotics in reducing both bacterial load and wound size, and was notably effective against a P. aeruginosa strain resistant to polymyxins, a last-line antibiotic therapy. Unlike these antibiotics, the phage-nanomaterial showed no detectable toxicity or systemic effects in mice, consistent with the short duration and localized nature of phage- nanomaterial treatment. Our results demonstrate that phage therapy controlled by inorganic nanomaterials can be a safe and effective antimicrobial strategy in vivo.

bioengineering↗

bioRxiv2020

A Bayesian nonparametric approach to super-resolution single-molecule localization

We consider the problem of single-molecule identification in super-resolution microscopy. Super-resolution microscopy overcomes the diffraction limit by localizing individual fluorescing molecules in a field of view. This is particularly difficult since each individual molecule appears and disappears randomly across time and because the total number of molecules in the field of view is unknown. Additionally, data sets acquired with super-resolution microscopes can contain a large number of spurious fluorescent fluctuations caused by background noise. To address these problems, we present a Bayesian nonparametric framework capable of identifying individual emitting molecules in super-resolved time series. We tackle the localization problem in the case in which each individual molecule is already localized in space. First, we collapse observations in time and develop a fast algorithm that builds upon the Dirichlet process. Next, we augment the model to account for the temporal aspect of fluorophore photo-physics. Finally, we assess the performance of our methods with ground-truth data sets having known biological structure.

bioinformatics↗

bioRxiv2020

Sequence and annotation of 42 cannabis genomes reveals extensive copy number variation in cannabinoid synthesis and pathogen resistance genes

Cannabis is a diverse and polymorphic species. To better understand cannabinoid synthesis inheritance and its impact on pathogen resistance, we shotgun sequenced and assembled a Cannabis trio (sibling pair and their offspring) utilizing long read single molecule sequencing. This resulted in the most contiguous Cannabis sativa assemblies to date. These reference assemblies were further annotated with full-length male and female mRNA sequencing (Iso-Seq) to help inform isoform complexity, gene model predictions and identification of the Y chromosome. To further annotate the genetic diversity in the species, 40 male, female, and monoecious cannabis and hemp varietals were evaluated for copy number variation (CNV) and RNA expression. This identified multiple CNVs governing cannabinoid expression and 82 genes associated with resistance to Golovinomyces chicoracearum, the causal agent of powdery mildew in cannabis. Results indicated that breeding for plants with low tetrahydrocannabinolic acid (THCA) concentrations may result in deletion of pathogen resistance genes. Low THCA cultivars also have a polymorphism every 51 bases while dispensary grade high THCA cannabis exhibited a variant every 73 bases. A refined genetic map of the variation in cannabis can guide more stable and directed breeding efforts for desired chemotypes and pathogen-resistant cultivars. O_FIG O_LINKSMALLFIG WIDTH=160 HEIGHT=200 SRC="FIGDIR/small/894428v1_ufig1.gif" ALT="Figure 1"> View larger version (126K): org.highwire.dtl.DTLVardef@7a1ac3org.highwire.dtl.DTLVardef@731a4dorg.highwire.dtl.DTLVardef@c3e5acorg.highwire.dtl.DTLVardef@11babb4_HPS_FORMAT_FIGEXP M_FIG Sequence and annotation of 42 cannabis genomes reveals extensive copy number variation in cannabinoid synthesis and pathogen resistance genes C_FIG

genomics↗

bioRxiv2019

The tuatara genome: insights into vertebrate evolutionfrom the sole survivor of an ancient reptilian order

The tuatara (Sphenodon punctatus), the only living member of the archaic reptilian order Rhynchocephalia (Sphenodontia) once widespread across Gondwana, is an iconic and enigmatic terrestrial vertebrate endemic to New Zealand. A key link to the now extinct stem reptiles from which dinosaurs, modern reptiles, birds and mammals evolved, the tuatara provides exclusive insights into the ancestral amniotes. The tuatara genome, at [~]5 Gbp, is among the largest vertebrate genomes assembled. Analysis of this genome and comparisons to other vertebrates reinforces the uniqueness of the tuatara. Phylogenetic analyses indicate tuatara diverged from the snakes and lizards [~]250 MYA. This lineage also shows moderate rates of molecular evolution, with instances of punctuated evolution. Genome sequence analysis identifies expansions of protein, non-protein-coding RNA families, and repeat elements, the latter of which show an extraordinary amalgam of reptilian and mammalian features. Sequencing of this genome provides a valuable resource for deep comparative analyses of tetrapods, as well as for tuatara biology and conservation. It also provides important insights into both the technical challenges and the cultural obligations associated with genome sequencing.

genomics↗

bioRxiv2019

Temporal processing of facial expressions of mental states

Faces provide not only cues to an individuals identity, age, gender and ethnicity, but also insight into their mental states. The ability to identify the mental states of others is known as Theory of Mind. Here we present results from a study aimed at extending our understanding of differences in the temporal dynamics of the recognition of expressions beyond the basic emotions at short presentation times ranging from 12.5 to 100 ms. We measured the effect of variations in presentation time on identification accuracy for 36 different facial expressions of mental states based on the Reading the Mind in the Eyes test (Baron-Cohen et al., 2001) and compared these results to those for corresponding stimuli from the McGill Face database, a new set of images depicting mental states portrayed by professional actors. Our results show that subjects are able to identify facial expressions of complex mental states at very brief presentation times. The kind of cognition involved in the correct identification of facial expressions of complex mental states at very short presentation times suggests a fast, automatic Type-1 cognition.

animal behavior and cognition↗

bioRxiv2018

Bayesian Inference for a Generative Model of Transcriptome Profiles from Single-cell RNA Sequencing

Transcriptome profiles of individual cells reflect true and often unexplored biological diversity, but are also affected by noise of biological and technical nature. This raises the need to explicitly model the resulting uncertainty and take it into account in any downstream analysis, such as dimensionality reduction, clustering, and differential expression. Here, we introduce Single-cell Variational Inference (scVI), a scalable framework for probabilistic representation and analysis of gene expression in single cells. Our model uses variational inference, stochastic optimization and deep neural networks to approximate the parameters that govern the distribution of expression values of each gene in every cell, using a non-linear mapping between the observations and a low-dimensional latent space.\n\nBy doing so, scVI pools information between similar cells or genes while taking nuisance factors of variation such as batch effects and limited sensitivity into account. To evaluate scVI, we conducted a comprehensive comparative analysis to existing methods for distributional modeling and dimensionality reduction, all of which rely on generalized linear models. We first show that scVI scales to over one million cells, whereas competing algorithms can process at most tens of thousands of cells. Next, we show that scVI fits unseen data more closely and can impute missing data more accurately, both indicative of a better generalization capacity. We then utilize scVI to conduct a set of fundamental analysis tasks - including batch correction, visualization, clustering and differential expression - and demonstrate its accuracy in comparison to the state-of-the-art tools in each task. scVI is publicly available, and can be readily used as a principled and inclusive solution for multiple tasks of single-cell RNA sequencing data analysis.

bioinformatics↗