Biology subjects

Kim, S.

Publications and source records attributed to Kim, S..

At least 163 records · Page 9Linked to original sources

bioRxiv2025

EphA2 and Ephrin-A1 Use the Same Interface for Both in cis and in trans Interactions That Regulate Distinct Cell Signaling and Function

The 14 members of Eph receptor tyrosine kinases (RTK) bind to membrane-tethered ligand called ephrins and mediate cell contact signaling where the receptors and ligands engage in trans on adjacent cells. Previous studies reveal that some Eph and ephrin pairs are coexpressed on the same cells, including EphA3-ephrin-A3 and EphA4/ephrin-A5, can also interact with each other in cis. However, significant discrepancies persist as to the molecular basis and functional significance of the cis interactions, owning to the difficulties to directly interrogate the interactions. Here, we utilize time-resolved live cell fluorescence spectroscopy to demonstrate direct in cis interactions between EphA2 and ephrin-A1. Structure-guided mutagenesis mapped interactions to two salt bridges between the ligand- and receptor-binding domains of EphA2 and ephrin-A1. Interestingly, the same interface is shared with in trans interaction. Consequently, EphA2-ephrin-A1 interaction in cis competes with their interaction in trans, which leads to attenuation of EphA2 canonical signaling and inhibition of cell rounding when ligand is presented in trans. EphA2 and ephrin-A1 are widely coexpressed in many epithelial tissues, and dysregulation of their expression is known to contribute to tumor initiation and progression. The detailed molecular characterization of the mutually exclusive cis and trans interactions uncovers a new mechanism underpinning their unique roles in oncogenesis. Significance StatementEphA2 exerts dual functions in tumorigenesis, depending on the binding status of its membrane-tethered ephrin-A ligands. Ligands presented in trans on adjacent cells activate EphA2 canonical signaling associated with tumor suppression, whereas loss of ligand expression promotes oncogenic noncanonical signaling of EphA2 via serine 897 phosphorylation. Combining time-resolved spectroscopy in live cells, structure-guided mutagenesis, we show strong in cis interactions between EphA2 and ephrin-A1, which shares the same interface as interaction in trans. Moreover, the cis interaction interferes with ligand binding in trans, attenuates EphA2 canonical signaling. Our results uncover a new mechanism of EphA2 regulation by its co-expressed ligand ephrin-A1 with important implications in its known roles in oncogenesis as well as other disease processes including development of cataract.

biophysics↗

bioRxiv2025

Phagocytosis repurposed: infection strategies in a globalmarine diatom-parasite interaction

Microbial host-parasite interactions shape global biogeochemical cycles, yet their cellular and evolutionary mechanisms remain poorly understood. Here, we developed a novel model pathosystem to explore the interaction between the globally distributed eukaryotic microparasite, Pirsonia, and its host, the bloom-forming diatom, Coscinodiscus. Culture conditions recapitulated Pirsonias rapid life cycle in vitro and live-cell imaging enabled quantitative assessments of infection dynamics and host mortality. To investigate the genetic basis of this interaction, we assembled the Pirsonia genome and identified unique genetic innovations relative to their oomycete relatives. Time-resolved dual RNA-Seq enabled tracking of parasite gene expression through the infection cycle and revealed an upregulation of an expanded multi-gene family of integrin-like proteins in Pirsonia zoospores, analogous to the variable surface proteins in other parasitic lineages. Genes upregulated during infection were associated with cytoskeletal dynamics and drug inhibition assays confirmed that actin is required for host infection, consistent with the presence of parasitic pseudopodia on infected hosts. The dependence on both actin-based phagocytic mechanisms and parasitic-like surface proteins highlights Pirsonias intermediate state between predator and parasite, providing new insights into the evolution of parasite strategies and the complex cellular interactions controlling parasitic shunts in marine trophic networks.

evolutionary biology↗

bioRxiv2025

Optimization of connectome weights for a neural network model generating both forward and backward locomotion in C. elegans.

Previous studies tracking the relationship between manipulations of C. elegans neurons and the resulting behavioral changes have called for the development of a connectome-constrained neural network model that describes the cascade from neurons to behavior. However, the model using anatomical connectome weights as they are did not achieve that. Here, we introduce a concept of learning the synaptic weights in our connectome-constrained neural network model based on the leaky-integrator equation while preserving the structural proportions of anatomical synapses. In this process, the weights of gap junctions and chemical synapses in C. elegans neurons are optimized. As a result, our neural network model generates plausible C. elegans behavior mediated by activity changes in forward and backward command neurons, even without the introduction of pacemaker neurons with intrinsic oscillatory activity. Additionally, we identified necessary or sufficient neurons for maintaining oscillatory patterns on muscular activity that could serve as clues for the central pattern generator in our neural network model. Finally, we provide 10 optimized synaptic weights sets of C. elegans that reproduce the results of manipulation experiments on the SMD neurons. This study will facilitate the future study for unraveling the multiscale relationship of "from synapse to behavior" in nervous system. Significance StatementIn the anatomical connectome of C. elegans, synaptic weights have been considered less important than network topology in designing neural network models for behavior generation. In this study, we introduce an advanced process that optimizes the synaptic weights of C. elegans based on the anatomical connectome weights through machine learning. Our neural network model, incorporating the optimized connectome weights, successfully reproduced both forward and backward locomotion in response to the on/off transitions of command neurons. Through this model, we were able to evaluate how manipulations of specific neurons translate into behavioral changes and to what extent they manifest. This study sets a foundation for exploring the intricate links between neural network structure, neuronal activity, and behavior in living systems.

neuroscience↗

bioRxiv2025

Targeted disruption of Oncogenic Biosphere: A Paradigm Shift Beyond Cancer Cell-Centric Therapies

Traditional cancer therapies have primarily focused on directly targeting malignant cells, yet this paradigm has consistently fallen short in addressing therapeutic resistance and metastasis1. These persistent challenges suggest that the tumors surrounding environment--not just the cancer cells themselves--plays a decisive role in treatment failure. In this study, we present an ecosystem-oriented strategy that targets the pathological microenvironment sustaining cancer progression. We repurposed niclosamide, an FDA-approved anthelmintic agent2,3, and enhanced its bioavailability by formulating Penetrium an orally deliverable nanotherapeutic composed of niclosamide, magnesium oxide, and hydroxypropyl methylcellulose. In syngeneic and xenograft models of triple-negative breast cancer (TNBC) and non-small cell lung cancer (NSCLC), Penetrium showed robust antitumor synergy when combined with standard therapies including anti-PD-1, paclitaxel, and anti-VEGF agents. Notably, Penetrium markedly blocked metastatic spread. Mechanistic analyses revealed that Penetrium remodels the tumor extracellular matrix (ECM), downregulates MMP-9, restores E-cadherin, and enhances immune infiltration-- thereby converting immune-excluded tumors into immune-permissive niches. Furthermore, in patient-derived pancreatic tumor organoids co-cultured with cancer-associated fibroblasts (CAFs), we demonstrated that tumor cell survival is critically dependent on the stromal context. Importantly, Penetrium selectively eliminated CAFs without affecting normal fibroblasts. These findings validate the oncogenic biosphere, and especially the CAF-rich stroma, as a critical therapeutic target. Our results underscore a shift from the conventional cancer cell-centric model to an oncogenic biosphere-disruption strategy that dismantles the structural and immunological defenses of desmoplastic cancers, offering a promising avenue for overcoming resistance and metastasis.

cancer biology↗

bioRxiv2025

Multilayered molecular and cellular mechanisms underlying intestinal epithelial regeneration

Intestinal epithelial cells (IECs) are replenished by intestinal stem cells (ISCs) residing in the intestinal crypts and isthmus. Under homeostatic conditions, Lgr5+ crypt base columnar cells (CBCs) are considered the primary ISCs1,2. The supply of IECs, however, remains continuous even in the absence of Lgr5+ CBCs3, implying the existence of alternative ISC populations. Over the past decade, two contrasting models have emerged to explain the identities of these compensatory cells. The distinct cell population model proposes that slow-cycling, quiescent cells near the +4 position, act as reserve stem cells (rISCs)3-10. The cellular plasticity model suggests a broad spectrum of IECs can dedifferentiate into intermediate fetal-like states, thereby acquiring stemness11,12. It is unclear which of these models is correct because the signaling mechanisms governing crypt regenerative responses remain poorly understood. Here, we demonstrate that Hedgehog (Hh) signaling orchestrates intestinal regeneration following Lgr5+ CBC loss by uniquely driving the emergence of de novo Lgr5+ CBCs from non-Lgr5+ CBCs clones. This regenerative mechanism is fundamentally distinct from both classical self-renewal of existing Lgr5+ CBCs and fetal-like dedifferentiation in intestinal villi. By accelerating the cell cycle and driving clonal competition among stem cell pools, Hh signaling leaves rare dormant stem cells in the crypt and isthmus regions. These rare cells act as damage-resistant "seeds" that can promote damage recovery, thereby conferring damage resistance on the tissue as a whole. Collectively, our findings identify novel molecular and cellular mechanisms underlying ISC regeneration and suggest potential therapeutic strategies for various intestinal epithelial diseases.

cell biology↗

bioRxiv2025

ATOMIC: A graph attention neural network for ATOpic dermatitis prediction on human gut MICrobiome

Atopic dermatitis (AD) is a chronic inflammatory skin disease driven by complex interactions among genetic, environmental, and microbial factors; however, its etiology remains unclear. Recent studies have reported the role of gut microbiota dysbiosis in AD pathogenesis, leading to increased interest in microbiome-targeted therapeutic strategies such as probiotics and fecal microbiota transplantation. Building on these findings, recent advances in computational modeling have introduced machine learning and deep learning-based approaches to capture the nonlinear relationships between gut microbiota and diseases. However, these models focus on diseases other than AD and often fail to capture complex microbial interactions or incorporate microbial genomic information, thereby offering limited interpretability. To address these limitations, we propose ATOMIC, an interpretable graph attention network-based model that incorporates microbial co-expression networks to predict AD. Microbial co-expression networks incorporate microbial genomic information as node features, thereby enhancing their ability to capture functionally relevant microbial patterns. To train and test our model, we collected and processed 99 gut microbiome samples from adult patients with AD and healthy controls at Kangwon National University Hospital (KNUH). As a result, ATOMIC achieved an AUROC of 0.810 and an AUPRC of 0.927 on the KNUH dataset. Furthermore, ATOMIC identified microbes potentially associated with AD prediction and proposed candidate microbial biomarkers that may inform future therapeutic strategies. To facilitate future research, we publicly released a gut microbial abundance dataset from KNUH. The source code and processed abundance data are available from ATOMIC GitHub repository at https://www.github.com/KU-MedAI/ATOMIC. Author summaryAtopic dermatitis (AD) is a chronic inflammatory skin disease affecting approximately 120 million people worldwide, often leading to a reduced quality of life, including sleep disturbances and stress. Current treatment methods rarely achieve complete remission, and the precise cause of AD remains unclear. The association between imbalances in the gut microbiota and the pathogenesis of AD has highlighted the gut microbiota as a promising target for therapeutic strategies. Exploring the potential role of the gut microbiota in modulating host immune responses is likely to have a positive impact on future AD treatment. Consequently, the growing need to develop machine learning and deep learning-based predictive models using gut microbiome data. However, existing models often suffer from limited interpretability owing to their black-box nature and frequently overlook microbial interactions and genomic contexts. To address these limitations, we propose a novel model called ATOMIC, which integrates microbial co-expression networks and genomic information to predict AD. Our model achieved superior performance in terms of AUROC, AUPRC, and F1 scores compared with existing models and provided interpretable insights through an attention mechanism, thereby offering promising potential for identifying relevant biomarkers. Together, these results highlight ATOMICs potential for the accurate prediction of AD and for identifying biologically meaningful microbial signatures.

systems biology↗

bioRxiv2025

Molecular Basis for Asynchronous Chain Elongation During Rifamycin Antibiotic Biosynthesis

The rifamycin synthetase (RIFS) from the bacterium Amycolatopsis mediterranei is a large (3.5 MDa) multienzyme system that catalyzes over 40 chemical reactions to generate a complex precursor to the antibiotic rifamycin B. It is considered a hybrid enzymatic assembly line and consists of an N-terminal nonribosomal peptide synthetase loading module followed by a decamodular polyketide synthase (PKS). While the biosynthetic functions are known for each enzymatic domain of RIFS, structural and biochemical analyses of this system from purified components are relatively scarce. Here, we examine the biosynthetic mechanism of RIFS through complementary crosslinking, kinetic, and structural analyses of its first PKS module (M1). Thiol-selective crosslinking of M1 provided a plausible molecular basis for previously observed conformational asymmetry with respect to ketosynthase (KS)-substrate carrier protein (CP) interactions during polyketide chain elongation. Our data suggest that C-terminal dimeric interfaces--which are ubiquitous in bacterial PKSs--force their adjacent CP domains to co-migrate between two equivalent KS active site chambers. Cryogenic electron microscopy analysis of M1 further supported this observation while uncovering its unique architecture. Single-turnover kinetic analysis of M1 indicated that although removal of C-terminal dimeric interfaces supported 2-fold greater KS-CP interactions, it did not increase the partial product occupancy of the homodimeric protein. Our findings cast light on molecular details of natural antibiotic biosynthesis that will aid in the design of artificial megasynth(et)ases with untold product structures and bioactivities. Significance StatementBacteria use enzymatic assembly lines for the manufacture of complex and often medicinally active organic compounds. Their conserved modular design and biosynthetic logic suggest they evolved to be intrinsically reprogrammable. Yet, strategies to manipulate assembly-line product structures through protein engineering are still met with considerable challenges. This work investigates how a representative bacterial assembly line catalyzes an essential reaction during biosynthesis of the antibiotic rifamycin. Our data provide a molecular rationale for asynchronous C-C bond formation catalyzed by equivalent subunits of the homodimeric system while exposing new aspects of assembly line architecture. These findings represent a step closer towards the design of artificial assembly lines for sustainable production of user-defined chemicals.

biochemistry↗

bioRxiv2025

A deep population stratification and ongoing local adaptations of the range-expanding lovebug Plecia longiforceps

Rapidly range-expanding species provide a superb opportunity to study dynamic demography and ongoing adaptations to novel environments. Plecia longiforceps, a species of lovebug flies native to southeast China and Taiwan, has recently been expanding in Okinawa in Japan and Seoul in South Korea, where massive outbreaks are garnering public attention. Here we analyzed whole-genome sequences of 150 individuals across China, Korea, Okinawa, and Taiwan, including a newly identified northern Chinese population from Qingdao. We found a deep divergence between continental (Korea-China) and insular (Okinawa-Taiwan) populations and specify northern China and Taiwan as sources for Korea and Okinawa, respectively. Severely reduced genetic diversity in Korea and Qingdao suggests serial founder events during their northward expansion. An old split time between Qingdao and Korea ([~]3,500 generations ago) and multiple gene flows into Qingdao reject a simple scenario of a single recent northward migration. Selection signals in northern groups include genes associated with pigmentation, thermal sensing, lipid metabolism, and immunity. We observed a markedly reduced genetic diversity on X chromosome in northern populations, indicating ongoing strong sex-biased selection. Our findings portrait details of the northward range expansion in the species and highlight the role of adaptation for successful invasion at higher latitudes.

evolutionary biology↗

bioRxiv2025

PathCLAST: Pathway-Augmented Contrastive Learning with Attention for Spatial Transcriptomics

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSMotivationC_ST_ABSSpatial transcriptomics provides high-resolution insights into tissue architecture and disease progression. While recent computational methods have advanced spatial domain identification, many focus primarily on gene expression alone, which may limit biological interpretability and underexploit complementary data such as histological images and known gene-pathway associations. ResultsWe present PathCLAST (Pathway-Augmented Contrastive Learning with Attention for Spatial Transcriptomics), a novel framework that integrates gene expression, histopathological image features, and curated pathway graphs through a contrastive learning strategy. By embedding gene expression within biologically grounded pathway-level graphs and aligning them with histological features, PathCLAST enhances spatial domain resolution and provides interpretable attention scores over functional pathways. Across three benchmark datasets, PathCLAST consistently outperforms existing methods in clustering accuracy-achieving a 37% improvement over STAGATE on the IDC dataset and uncovers domain-specific signaling programs and spatial heterogeneity. Additional analyses of spatial autocorrelation and inter-domain crosstalk demonstrate its potential to reveal localized biological processes and tumor microenvironmental dynamics. Our code is available at the following link: https://github.com/sslim-aidrug/PathCLAST.

bioinformatics↗

bioRxiv2025

Integrating Artificial Intelligence-Driven Digital Pathology and Genomics to Establish Patient-Derived Organoids as a Novel Alternative Model for Drug Response in Head and Neck Cancer

Patient-derived organoids (PDOs) are emerging as advanced 3D ex vivo novel alternative method (NAM) preclinical models, offering significant advantages over traditional cell lines and monolayer cultures for therapeutic development. In this study, we established PDOs from surgically resected fresh tissues of human papillomavirus (HPV)-negative head and neck squamous cell carcinoma (HNSCC) across anatomical sites, tumor T-categories, and sample types. These PDOs faithfully recapitulate the tumors pathology, mutational profile, and drug response. To enable rapid classification of PDO identity, we developed a new convolutional neural network (CNN) model, TransferNet-PDO, which accurately distinguished tumor versus normal PDOs in culture using digital histopathology images (AUC[≥]0.88). PDOs maintained stable cultures and were cryopreserved between passages 5 and 12. Immunohistochemistry (IHC) staining (PanCK, p63, Cytokeratin 13, Ki67) confirmed squamous phenotype and histologic aggression of the original tumor. For tumors harboring TP53 mutations by whole-exome sequencing (WES), PDOs retained the corresponding p53 functional status as confirmed by IHC (enhanced or loss of expression). Somatic mutational landscape revealed that PDOs preserved driver somatic mutations, copy number variations (CNVs), and clonal architecture including low-prevalence subclones. Drug sensitivity assessment of PDOs showed that cisplatin reduced cell viability, whereas cetuximab and lenvatinib had minimal effects. Chemoradiation led to greater tumor organoid killing compared to radiation or chemotherapy alone. This study presents an integrated HNSCC PDO platform combining tissue biobanking, organoid establishment, multi-omics characterization, functional drug screening, and AI-driven histopathologic classification, providing a comprehensive and scalable system for translational cancer research.

cancer biology↗

bioRxiv2025

Potential beneficial effects of PD-1/PD-L1 blockade in Alzheimer's disease: A systematic review and meta-analysis of preclinical and clinical studies

Programmed cell death-1 (PD-1) and its ligand (PD-L1) play key roles in cancer immune evasion and in modulating neuroinflammation. This systematic review and meta-analysis investigated the effect of PD-1/PD-L1 blockade on pathology and cognitive function in Alzheimers disease (AD) in preclinical and clinical studies. Relevant studies were systematically identified using the MEDLINE, Embase, CENTRAL, and Web of Science databases from their inception until April 10, 2024. In total, 33 studies were included in this meta-analysis, conducted using R software. Preclinical studies revealed that blockade of PD-1 signaling reduces amyloid-beta plaque burden, tau phosphorylation, and astrocyte reactivity in AD mouse models, accompanied by improvements in cognitive function in behavioral tests. Furthermore, clinical studies demonstrated the beneficial effect of PD-1 signaling inhibitors on cognitive function in cancer patients. This study highlights the necessity for additional research to clarify the exact mechanisms by which PD-1/PD-L1 inhibition impacts AD pathology and cognitive function, opening avenues for potential therapeutic strategies targeting this pathway in AD.

neuroscience↗

bioRxiv2025

CPT1B-Mediated Fatty Acid Oxidation Induces Pigmentation in Solar Lentigo

IntroductionCellular senescence is associated with altered lipid metabolism, including increased cellular lipid uptake, upregulated lipid biosynthesis, and deregulated lipid breakdown. Previous studies have reported that carnitine palmitoyltransferase (CPT), the rate-limiting enzyme in fatty acid oxidation that catalyzes the conversion of acyl-CoA to acylcarnitine, is involved in various senescence-related diseases. Although solar lentigo (SL) is an age-related pigmentary disorder characterized by the accumulation of senescent cells, its role in metabolic dysregulation has rarely been investigated. Methods and ResultsIntegrated transcriptomic profiling of SL skin samples, combining mRNA sequencing, differential gene expression, pathway enrichment analyses, metabolic flux simulations, and protein-protein interaction analysis, was conducted to demonstrate the molecular alterations in SL compared to perilesional normal skin. We found transcriptomic alterations in mitochondrial energy metabolism-associated genes. Metabolic flux simulations revealed that carnitine-associated reactions involved in fatty acid oxidation were upregulated. Using a multi-omics approach, CPT1B was selected as a potential marker for SL, which was confirmed via its overexpression in immunohistochemical studies. Using a zebrafish model, CPT1B was implicated in melanogenesis. DiscussionCPT1B-mediated metabolic alteration is a key driver of SL pathogenesis. Targeting CPT1B and the associated lipid metabolism pathways is a novel therapeutic approach for managing SL and age-related pigmentation disorders. Research HighlightsSolar lentigo (SL) exhibits altered mitochondrial metabolism and fatty acid oxidation. CPT1B activation regulates pigmentation, suggesting that targeting CPT1B may offer a novel therapy for SL and other age-related pigmentary disorders. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=176 HEIGHT=200 SRC="FIGDIR/small/659823v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@81fce3org.highwire.dtl.DTLVardef@1bf3forg.highwire.dtl.DTLVardef@b2c119org.highwire.dtl.DTLVardef@1a38b23_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2025

YAP1 and QSER1 are Key Modulators of Embryonic Signaling Pathways in the Mammalian Epiblast

YAP1 signaling is essential for development but its specific roles in early embryogenesis remain poorly understood. To shed light on this, we analyzed YAP1s role in regulating the pluripotency of the mammalian epiblast, using scRNAseq approaches. Conditional deletion of Yap1 in the mouse epiblast (Sox2-Cre) altered the expression of signaling genes, including Nodal, Wnt3, and Fgf8. Accordingly, Yap1 loss led to enhanced differentiation of the epiblast toward primitive streak lineages, as evidenced by the upregulation of T/Brachyury and Eomes genes. Furthermore, a proximity labeling assay in human pluripotent stem cells, followed by biochemical assays and molecular modeling predictions, revealed that YAP1 cooperates with QSER1 protein to regulate lineage genes. Our analysis shows that YAP1:TEAD4 enhancers recruit QSER1 to prevent RNA Polymerase II recruitment. Accordingly, QSER1 depletion, similar to YAP1, increases NODAL gene expression and leads to hyperactive NODAL signaling in human 2D-gastruloids. Overall, our findings define a role of YAP1 in the epiblast in vivo and uncovered an interplay with QSER1 controlling the activity of developmental signaling pathways in pluripotent cells.

developmental biology↗

bioRxiv2025

Agouti integrates environmental information to regulate natural variation in paternal behavior

Male investment in offspring rearing through paternal care is rare among mammals and the neural mechanisms governing its emergence are poorly understood. We leveraged the natural paternal behavior of African striped mice (Rhabdomys pumilio) in combination with brain-wide cFos quantification, single-nucleus RNA-sequencing, viral-mediated gene manipulation, and environmental manipulation to dissect the neural basis of natural variation in male parenting. We find that socio-environmental conditions drive individual variation in male alloparenting such that post-weaning social isolation increases paternal care while social living in higher density groups increases infanticide. This natural variation in care corresponds to neural activity in the medial preoptic area and changes in correlated activity across brain regions. Within the medial preoptic area, expression of agouti signaling protein (Agouti) in neurons is increased by group housing and is negatively associated with care, and overexpression of Agouti reduces care and enhances infanticide in previously tolerant animals. Naturalistic manipulations further reveal that Agouti integrates long-term housing conditions rather than food availability/hunger. Together, our results demonstrate that Agouti acts as a molecular integrator of socio-environmental information to drive variation in paternal care.

neuroscience↗

bioRxiv2025

X-Atlas/Orion: Genome-wide Perturb-seq Datasets via a Scalable Fix-Cryopreserve Platform for Training Dose-Dependent Biological Foundation Models

The rapid expansion of massively parallel sequencing technologies has enabled the development of foundation models to uncover novel biological findings. While these have the potential to significantly accelerate scientific discoveries by creating AI-driven virtual cell models, their progress has been greatly limited by the lack of large-scale high-quality perturbation data, which remains constrained due to scalability bottlenecks and assay variability. Here, we introduce "Fix-Cryopreserve-ScRNAseq" (FiCS) Perturb-seq, an industrialized platform for scalable Perturb-seq data generation. We demonstrate that FiCS Perturb-seq exhibits high sensitivity and low batch effects, effectively capturing perturbation-induced transcriptomic changes and recapitulating known biological pathways and protein complexes. In addition, we release X-Atlas: Orion edition (X-Atlas/Orion), the largest publicly available Perturb-seq atlas. This atlas, generated from two genome-wide FiCS Perturb-seq experiments targeting all human protein-coding genes, comprises eight million cells deeply sequenced to over 16,000 unique molecular identifiers (UMIs) per cell. Furthermore, we show that single guide RNA (sgRNA) abundance can serve as a proxy for gene knockdown (KD) efficacy. Leveraging the deep sequencing and substantial cell numbers per perturbation, we also show that stratification by sgRNA expression can reveal dose-dependent genetic effects. Taken together, we demonstrate that FiCS Perturb-seq is an efficient and scalable platform for high-throughput Perturb-seq screens. Through the release of X-Atlas/Orion, we highlight the potential of FiCS Perturb-seq to address current scalability and variability challenges in data generation, advance foundation model development that incorporates gene-dosage effects, and accelerate biological discoveries.

genomics↗

bioRxiv2025

Practical considerations for accurate estimation of diffusion parameters from single-particle tracking in living cells

Advances in fluorescence microscopy have enabled high-resolution tracking of individual biomolecules in living cells. However, accurate estimation of diffusion parameters from single-particle trajectories remains challenging due to static and dynamic localization errors inherent in these measurements. While previous studies have characterized how such errors affect mean-squared displacement (MSD) analysis, practical guidelines for minimizing them during data acquisition and correcting them during analysis are still lacking. Here, we combine theoretical modeling and simulations to evaluate how exposure time and sampling rate influence the accuracy of MSD-based inference under fractional Brownian motion (FBM), a canonical model of anomalous diffusion. We demonstrate that decoupling exposure and sampling times enables escape from the error-prone regime, thus improving inference accuracy, and that incorporating an offset in nonlinear MSD fitting substantially improves the estimation of the anomalous diffusion exponent. We validate this framework using trajectories of cytoplasmic particles in Escherichia coli, recovering consistent diffusion parameters across multiple data sets. Our findings offer practical strategies to improve both experimental design and data analysis in single-particle tracking of live or synthetic systems.

biophysics↗

bioRxiv2025

Canagliflozin reprograms the aging hippocampus in genetically diverse UM-HET3 mice and attenuates Alzheimer's-like pathology

Aging is the strongest risk factor for cognitive decline and Alzheimers disease (AD), yet the mechanisms underlying brain aging and their modulation by pharmacological interventions remain poorly defined. The hippocampus, essential for learning and memory, is particularly vulnerable to metabolic stress and inflammation. Canagliflozin (Cana), an FDA-approved sodium-glucose co-transporter 2 inhibitor (SGLT2i) for type 2 diabetes, extends lifespan in male but not female mice, but its impact on brain aging is unknown. Here, we used a multi-omics strategy integrating transcriptomics, proteomics, and metabolomics to investigate how chronic Cana treatment reprograms brain aging in genetically diverse UM-HET3 mice. In males, Cana induced mitochondrial function, insulin and cGMP-PKG signaling, and suppressed neuroinflammatory networks across all molecular layers, resulting in improved hippocampal-dependent learning and memory. In females, transcriptional activation of neuroprotective pathways did not translate to protein or metabolite-level changes and failed to rescue cognition. In the 5xFAD AD model, Cana reduced amyloid plaque burden, microgliosis, and memory deficits in males only, despite comparable peripheral glucose improvements in both sexes. Our study reveals sex-specific remodeling of hippocampal aging by a clinically available SGLT2i, with implications for AD pathology and lifespan extension, and highlights Canas potential to combat brain aging and AD through sex-specific mechanisms.

neuroscience↗

bioRxiv2025

Identification of reactive CpGs and RNA expression in early COVID-19 through cis-eQTM analysis reflecting disease severity and recovery

Multi-omics analyses of severe COVID-19 cases are crucial in deciphering the complex interplay between genetic and epigenetic factors. Here, we present an analysis of Expression Quantitative Trait Methylation (eQTM) to investigate the complex interplay of methylation and gene expression pattern during the acute phase of severe COVID-19. We identified 16 differentially expressed genes and 30 nearby differentially methylated CpG sites. Six key genes--SRXN1, FURIN, IL18RAP, FOXO3, GCNT4, and FKBP5--were either up-regulated or down-regulated near hypomethylated CpG sites. These genes are associated with viral infiltration, immune activation, lung damage, and oxidative stress-related multi-organ failure, which are the hallmarks of severe COVID-19. Interestingly, during the recovery phase, methylation and gene expression levels returned to baseline, underscoring the rapid and reversible nature of these molecular changes. These findings provide insight into the dynamics of epigenetic and transcriptomic shifts according to the infectious stage, supporting potential prognostic and therapeutic approaches for severe COVID-19.

bioinformatics↗