Biology subjects

Ramachandran, S.

Publications and source records attributed to Ramachandran, S..

At least 19 recordsLinked to original sources

bioRxiv2026

Adenosylhomocysteinase drives epigenetic and metabolic reprogramming in ZFTA-RELA ependymoma

Supratentorial ependymomas (ST-EPNs) are aggressive pediatric brain tumors predominantly driven by the recurrent ZFTA-RELA fusion, an oncogenic chromatin regulator that establishes an aberrant transcriptional program. Although metabolic pathways modulate epigenetic regulation by controlling the availability of metabolic cofactors, how ZFTA-RELA coordinates metabolic reprogramming with epigenetic regulation is not well understood. Here, we show that ZFTA-RELA-positive ependymomas exhibit enhanced methionine dependency, elevated adenosyl homocysteinase (AHCY) expression, and epigenetically control aspartate metabolism for tumor growth. Mechanistically, ZFTA-RELA upregulates AHCY, increasing cellular methylation potential which promotes H3K4me3-dependent expression of CAD, the first enzyme complex of de novo pyrimidine biosynthesis, while simultaneously enforcing H3K27me3 mediated repression of ASNS, which encodes asparagine synthetase. Through this coordinated epigenetic program, aspartate utilization is directed toward pyrimidine biosynthesis while endogenous asparagine production is limited. Restricting S-adenosylmethionine availability or genetically or pharmacologically inhibiting AHCY reverses these epigenetic states, leading to CAD downregulation, ASNS upregulation, impaired nucleotide synthesis, and suppression of tumor growth in vitro and in vivo. Similarly, EZH2 inhibition also restores ASNS expression and suppresses tumor proliferation. These findings suggest that ZFTA-RELA EPN maintain ASNS-low phenotype which conditionally make them dependent on extracellular asparagine. We further show that enhanced de novo pyrimidine synthesis not only supports nucleotide production but also provides UDP-sugars required for N-glycosylation and stabilization of the asparagine transporter SLC1A5, thereby promoting extracellular asparagine uptake and tumor proliferation. Collectively, our findings identify AHCY as a critical metabolic regulator of epigenetic and metabolic reprogramming that drives pyrimidine synthesis and establishes extracellular asparagine dependency, revealing actionable metabolic vulnerabilities in ZFTA-RELA-positive ST-EPNs.

cancer biology↗

bioRxiv2026

AI discovery of sequence rules for RNA polymerase II pausing revises the pause-release model of gene activation

The promoter-proximal pause (PPP), at most metazoan genes, is believed to prime transcriptional activation by release of paused RNAPII into the gene. We tested the pause-release model by abolishing the PPP and monitoring gene activation. An LLM-guided AI discovered that upstream G's and a G T/C dinucleotide robustly discriminates pauses from non-pauses. Insertion of G-less sequences into NDRG1 and HSP90AA1 abolished the PPP in an orientation-dependent way, but did not impair activation by hypoxia or heat-shock. Hence pause-release is dispensable for these responses. The 5' peak of RNAPII detected by ChIP persisted even when pausing, detected by NET-seq was absent. Instead of pausing, these promoter-proximal polymerases may undergo abortive initiation. RNAPII C-terminal domain (CTD) deletion suppresses the PPP and shifts it upstream without affecting sequence-dependent pausing, consistent with promoter-tethering by this IDR. We propose that PPP formation is initiated by G-dependent arrest, and stabilized by multiple factors including CTD-dependent tethering.

molecular biology↗

bioRxiv2026

NLRX1 is an essential, druggable regulator of mitochondrial permeability transition

The molecular composition of the mitochondrial permeability transition pore (mPTP) remains contested, and several efficacious mPTP inhibitors act through undefined, cyclophilin D (CypD)-independent targets. Using two structurally distinct chemotypes of optimised, brain-penetrant mPTP inhibitors as chemical probes, we applied affinity-based chemoproteomics to identify the mitochondrial NOD-like receptor NLRX1 as their shared target. Both chemotypes bind NLRX1, and binding potency across a compound series tracks mPTP-inhibitory activity. Using CRISPR-Cas9-edited human cells and Nlrx1-/- mouse tissues, we show that NLRX1 is required for normal calcium-induced mPTP opening: its loss raises the calcium threshold for pore opening and its overexpression lowers it, independently of CypD. NLRX1 associates with postulated mPTP components, including ATP synthase and the adenine nucleotide translocase, in a compound-sensitive manner, and sustains mitochondrial protein homeostasis over longer timescales. The lead compound, GSK900, is orally bioavailable, brain-penetrant, and active in an mPTP-sensitive neurological injury model. These findings, converging with recent genetic studies, establish NLRX1 as an essential, CypD-independent regulator of mitochondrial permeability transition and provide brain-penetrant chemical tools to interrogate this biology.

cell biology↗

bioRxiv2026

From Wikipedia to AI: Measuring 25 years of synthesis of human genetics research in the public-facing information ecosystem

Genetics research frequently intersects with ethnicity, nationality, and race, making it uniquely vulnerable to misrepresentation. Yet, 25 years after the initial sequencing of the human genome, there is little understanding of how human genetics research exists in the public-facing information ecosystem. We analyze 3,050,422 historical revisions from 6,738 Wikipedia pages about ethnicity, nationality, and race spanning 25 years. We find genetics terminology is present in 14.8% of these pages (55.5% in the top 1,000 pages) and in 67.8% of pages about nationalities, suggesting research is synthesized to present a biological element to ethnicity and nationality. We also find that 10.1% of 56,908 discussions from these pages contain genetics terminology. We further analyze responses from three popular chatbots queried about nationalities and find that they commonly reference both genetics and Wikipedia. Lastly, we analyze 133 pages from Grokipedia, an AI-generated encyclopedia, and find it mentions genetics more frequently than Wikipedia and hallucinates or misrepresents human genetics research.

genetics↗

bioRxiv2026

From Nuisance to Signal: Leveraging Close Relatives in Biobank-Scale Demographic Inference

Biobank-scale datasets now routinely include hundreds of thousands to millions of individuals, and as sample sizes grow, close relatives become increasingly prevalent. The convention in population genetics has been to remove close relatives prior to inference, effectively treating them as a nuisance parameter. However, the consequences of this practice for demographic inference, and specifically for estimates of recent effective population size (Ne), have not been rigorously evaluated. Here, we benchmark IBDNe and HapNe-IBD, two widely-used methods for inferring recent Ne from identity-by-descent (IBD) segments, under a range of demographic histories and relative sampling schemes. We show that when individuals are randomly ascertained, retaining all relatives produces the least biased Ne estimates; in contrast, removing even second-degree relatives inflates recent Ne and induces oscillatory artifacts that "ripple", leading to biased estimates up to ten generations into the past. We demonstrate that this ripple effect arises because close relatives contribute IBD segments that are assigned by the model to a range of ancestral ages beyond their true TMRCA, meaning their removal creates signal deficits across multiple generations simultaneously. We further show that deliberately oversampling close relatives produces severe downward bias in recent Ne. To support these analyses, we develop an open-source IBD simulation pipeline using msprime that generates realistic IBD segments under arbitrary demographic histories and Wright-Fisher pedigrees. We provide practical guidelines for IBD simulation schemes incorporating pedigrees and argue that, in the biobank era, retaining close relatives is generally the best practice for IBD-based Ne inference.

genetics↗

bioRxiv2026

Syntrophic microbiomes associated with methane-suppressive irrigation in rice

Rice, a staple crop of nearly half of the world population, is grown predominantly in flooded paddies which are one of the largest contributors to methane emissions. An effective approach is to minimise the anaerobic flooded conditions that favour the growth of methanogenic archaea. Empirical measurements showed that controlled irrigation regime reduces methane emissions by 70% to 90%. The soil microbiomes of both flood and drip irrigated soil were characterised using whole-genome shotgun metagenomics. Controlled irrigation was shown to suppress methanogens and lower methane emissions. While emissions are correlated with mcrA gene abundance, empty flooded fields exhibited relatively high mcrA levels above baseline despite undetectable methane emissions. Rice cultivar genotype had no significant effect on the soil microbiomes. Co-occurrence network analysis indicates that soil microbial communities stratify according to their oxygen preferences along a gradient. Methanogens were increased in flooded paddies, and methane production attributed to the microorganisms involved in the anaerobic decay of organic matter. Controlled irrigation altered the microbiome by raising the soil redox potential by enhancing aeration and promoting ammonia oxidation and nitrification pathways. IMPORTANCEThe temporal dynamics of microbial communities in drip-irrigated rice fields remain poorly characterized to-date. Empirical measurements demonstrate that controlled drip irrigation effectively suppresses methanogens and lowers methane emissions by up to 90%. Statistical analysis further revealed a moderate correlation between methane emissions and the mcrA gene with R = 0.6 and p-value = 2.9e-05. The correlation plot showed that the outliers corresponded to samples from empty flooded fields, where high mcrA gene abundance was observed despite low methane emissions. Methane produced in the soil is likely released into the atmosphere via transport through the aerenchyma of rice plants. Controlled irrigation is shown to be climate friendly as it reduces methane emissions by improving soil aeration and increasing the soil redox potential.

microbiology↗

bioRxiv2026

Fragmentation Patterns of Human Telomeric Chromatin in Plasma cfDNA

The unique chromatin structure at telomeres protects the linear ends of chromosomes from DNA surveillance machineries. Here, we demonstrate that circulating cell-free DNA (cfDNA) from plasma can map chromatin structure at telomeres. We find that the telomeric 6-mer repeats (TTAGGG/CCCTAA) are the most abundant circulating 6-mers in cfDNA. Telomeric sequences in cfDNA contain subnucleosomal footprints distinct from the rest of the genome, arising from specific cleavages in the C-rich strand, and a nucleosome repeat length of 145 bp, markedly shorter than the [~]170 bp observed genome-wide. The abundance of cfDNA telomeric footprints decreases with age, and this decline is exacerbated by Dyskeratosis Congenita (DC), a telomere biology disorder. Promoter subnucleosome enrichment from cfDNA identifies DC-specific gene signatures that reflect disease states and show enrichment towards chromosome ends. In this work, we demonstrate that cfDNA captures telomere chromatin structure and its genome-wide impact non-invasively, including disease-specific signatures in DC.

genomics↗

bioRxiv2026

Clinical grade cryopreservation unlocks transplant ready human pancreatic and stem cell derived islets for diabetes therapy

Pancreatic islet transplantation can restore endogenous insulin production and offers a potential cure for diabetes, but its clinical impact has been limited by the inability to preserve large numbers of functional islets for timely use. Cryopreservation could provide an on-demand supply, yet conventional methods cause ice formation, cell injury, and loss of insulin secretion. We developed CryoMesh, a vitrification platform that combines a low-toxicity cryoprotectant with a thermally conductive, biocompatible mesh to enable ultra-rapid cooling and rewarming for ice-free cryopreservation. This approach supports long-term, clinical-scale preservation of both human pancreatic and stem cell-derived islets while maintaining viability, architecture, mitochondrial integrity, and glucose-responsive insulin secretion. Human islets preserved for up to one year restored normoglycemia in diabetic mice, with complete recovery of function and no increase in immunogenicity or loss of potency. The combination of high viability, recovery, and functional preservation at clinical scale has not been achieved previously. By decoupling islet isolation and manufacture from transplantation, CryoMesh enables extended quality, potency, and safety testing, cost-effective batch production, and global banking and distribution. These capabilities remove a major barrier to curative cell therapy for diabetes and establish a generalizable strategy for preserving complex multicellular therapeutics. One Sentence SummaryA clinical scale vitrification platform enables long-term banking of human pancreatic and stem cell-derived islets for transplantation.

cell biology↗

bioRxiv2026

The Common Fund Data Ecosystem (CFDE)

The NIH Common Fund Data Ecosystem (CFDE) integrates data resources from 18 NIH Common Fund programs for discovery and integrative analysis. These programs generate valuable but heterogeneous datasets that can be difficult to discover, access, and reuse. CFDE aims to provide a collaborative, community-built infrastructure that links and enriches Common Fund programs. We describe the evolution, structure, and core technologies of CFDE, including practical approaches that support submission, integration, visualization, and public release of multimodal data. Training programs and workforce initiatives lower barriers to adoption. CFDE has devised solutions to critical issues facing cross-program initiatives, including data scale and heterogeneity, dataset integration, and long-term sustainability. We demonstrate the utility of linking Common Fund resources through integrative tools and cross-dataset queries to yield insights that would otherwise be infeasible. Collectively, CFDE shows that a standards-driven, federated approach enhances and unifies cross-disciplinary resources, fostering collaboration and data-driven discovery.

scientific communication and education↗

bioRxiv2026

Fiber optical parametric amplification of low-photon-flux microscopy signals

The performance of a laser scanning microscope inevitably depends on the performance of the point detector. As laser scanning approaches aim to penetrate deeper in tissue, there is a commensurate need for detectors that can operate with high sensitivity, bandwidth, and dynamic range at near-infrared wavelengths where scattering is reduced. Here, we demonstrate that fiber optical parametric amplification can be used to boost low-power microscopy signals to levels that can be detected by near-infrared photodiodes without introducing prohibitive noise. We construct amplifiers that achieve >50 dB of parametric gain at wavelengths within the third near-infrared transparency window and have similar sensitivity to near-infrared photomultiplier tubes. Furthermore, these amplifiers outperform detection with a photodiode and subsequent electrical amplification, providing a factor of 10-100-fold improvement in sensitivity. We demonstrate amplifier bandwidths up to ~1.6 GHz, a factor of 10 faster than conventional detectors, including near-infrared photo-multiplier tubes, with sensitivity of ~8 nW (corresponding to ~20 photons/pixel). Finally, the increased performance of the optical amplifier is confirmed in diagnostic imaging experiments where >10x less power is required to achieve the same signal-to-noise ratio and contrast as images using electrical amplification. Accordingly, fiber optical parametric amplification is a new path forward for extending the performance of laser scanning microscopes in the near infrared.

biophysics↗

bioRxiv2026

WINDEX: A hierarchical integration of site- and window-based statistics for characterizing the footprint of positive selection in genome-wide population genetic data

Adaptive mutations, or mutations that confer a fitness benefit, can leave behind distinct signals in genetic data. Computational methods have improved the localization of adaptive mutations in genetic samples using a range of statistical and machine learning classification techniques. However, these methods miss the opportunity to jointly integrate statistics at both the site and window-based level, thus failing to harness all available statistical evidence to detect selection. Our method, WINDEX, combines these different resolutions of statistics to improve the detection of adaptive mutations among hitchhiking signals. Our model simultaneously integrates emissions at different resolutions by defining site-based and window-based latent states corresponding to neutral, linked, and sweep regions, with the site-based states and transition models nested within the window-based states. Using evolutionary simulations with varying selection parameters, we validate the ability of WINDEX to classify positive selective sweeps. Using data from the 1000 Genomes Project, we show that WINDEX is able to identify regions harboring signals of selective sweeps, and provides improved localization within those regions over existing methods. In addition, using WINDEX genome-wide allows for estimation of the proportion of whole genomes that are under positive selective pressures; our estimates of between 9.7-10.5% across different populations provide support for other preliminary estimates of these quantities. Author summaryPopulation geneticists often seek evidence for positive selective sweeps, or an evolutionary event in which a beneficial allele increases in frequency over time in a population, resulting in increased fitness of the individuals that have said allele. Positive selective sweeps, however, are difficult to detect due to varying patterns of linkage disequilibrium (LD), or the nonrandom association of alleles, and detecting these signals reliably among differing LD structures remains a challenge in the field. In this work, we present WINDEX, a probabilistic framework designed to leverage signals of positive selective sweeps at both the site- and window-levels in the form of a hierarchical hidden Markov model (HHMM), to localize regions of positive selective sweeps in aligned haplotype data. We validate WINDEX in evolutionary simulations over varying positive selective sweep scenarios, showcasing the improved resolution that the HHMM structure provides. We apply WINDEX in comparative genomic scans of canonical sites of positive selection as well as whole-genome scans to demonstrate the tools power in localizing functionally-validated signals of selection and to offer insights into the proportion of the human genome currently under positive selective pressures. WINDEX is publicly available and easy to apply to many cases of human genetic data.

evolutionary biology↗

bioRxiv2026

Cell-Type-Specific Bidirectional Modulation of the Cortico-Thalamo-Cortical Sensory Pathway by Transcranial Focused Ultrasound (tFUS)

Transcranial focused ultrasound (tFUS) can noninvasively modulate sensory pathways, but the cell-type-specific mechanisms underlying excitatory or inhibitory effects remain unclear. Here, we investigate how tFUS applied to the somatosensory cortex (S1) influences S1 and posterior medial thalamic nucleus (POm) responses to hind paw vibration-tactile stimulation and which neuronal populations mediate these effects. Vibration-tactile stimulation evoked potentials (TEPs) and multi-unit activities (MUA) in S1 and POm were recorded from male rats. Optogenetic tagging was used to identify S1 CaMKII-positive, PV-positive, and SST-positive neurons, while waveform features were used to classify putative excitatory (i.e., regular-spiking units - RSUs) and inhibitory neurons (i.e., fast-spiking units - FSUs) in POm. We found that only S1 CaMKII-positive neurons and POm RSUs responded robustly to tactile stimulation. When tFUS was applied to S1, high pulse repetition frequency (PRF), high duty cycle, and high-pressure stimulation (etFUS) produced excitatory modulation of the sensory pathway, whereas low PRF, low duty cycle, and low-pressure stimulation (itFUS) induced inhibitory effects. Further analyses revealed that excitatory modulation was mediated by activation of S1 CaMKII-positive neurons, while the inhibitory effect arose from their deactivation. These findings demonstrate that tFUS exerts bidirectional, parameter-dependent modulation of a sensory pathway and highlight the critical role of CaMKII-positive neurons in mediating these effects. This study provides mechanistic insight into cell-type-specific neuromodulation by tFUS, particularly in bidirectional modulation of a sensory pathway, and informs the optimization of stimulation parameters for targeted therapeutic interventions.

neuroscience↗

bioRxiv2026

Systematic clustering alignment and feature characterization for single-cell omics using ACE-OF-Clust

Clustering is widely used to identify cell types in cellular-resolution transcriptomic data, including single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST). Mixed-membership clustering assigns fractional memberships across clusters and captures continuous variation beyond hard clustering, but integrating and interpreting results from either approach is complicated by the "clustering alignment problem," which arises from label switching, multi-modality, and differences in model settings (including differing numbers of clusters). We introduce ACE-OF-Clust, enabling a four-step workflow for single-cell clustering: multiple clustering, clustering alignment, model comparison, and identification of informative features. ACE-OF-Clust introduces direct comparison of clustering solutions, assesses consistency against annotations, and leverages feature-level clustering profiles to prioritize genes discriminating among cell types, as well as to evaluate how gene sets contribute to clustering patterns. We demonstrate its utility on PBMC scRNA-seq and breast cancer ST data, and on multi-omic single-cell data. ACE-OF-Clust quantifies cross-omic clustering variability and suggests putative cross-omic regulatory links. Overall, ACE-OF-Clust increases the interpretability, flexibility, and robustness of single-cell clustering, providing a scalable tool for studying cellular heterogeneity and gene expression dynamics.

genomics↗

bioRxiv2026

Mapping the North American Terrestrial Carbon Cycle: A Process-based Reanalysis Using State Data Assimilation (SDA)

AbstractThe ability to accurately assess ecosystem C budgets across scales from individual sites to continents is essential for C accounting, management, and ultimately mitigating climate change. State data assimilation (SDA) provides a framework for harmonizing observations with models, while robustly accounting for and reducing multiple sources of uncertainty. In this study, we employed a hybrid SDA framework that combines process-based terrestrial biosphere modeling, hierarchical Bayesian inference, and machine learning to harmonize bottom-up and remotely-sensed data streams for 8,000 pre-selected 1km2 locations across North America within a hybrid structure. Combining bottom-up soils data (SoilGrids) with spectral (MODIS and Landsat) and microwave (SMAP) remote sensing helps constrain the major C and water stocks through space and time. Machine learning is used both to identify and correct systematic errors in the process model (SIPNET) and to interpolate the pre-selected locations onto a 1km grid, making it computationally feasible to generate annual ensemble maps of the North American carbon budget. Furthermore, the uncertainties for each variable were reduced compared to those from observations or models alone. Spatiotemporal analysis showed a slight decrease in aboveground biomass (AGB) across the western US, a loss of leaf area across the boreal, and a slight greening of the Alaskan tundra. The uncertainty trends suggest a significant reduction in the uncertainty about soil organic carbon (SOC), the largest C reservoir. Validation results show that we accurately estimate C pools, compared to the assimilated data streams and held-out observations of AGB from GEDI, ICESat-2, and the US FIA, and SOC from the ISCN network. Our ML-debiasing algorithm further improved the accuracy of major C pools (AGB, SOC). In general, our continental SDA framework will facilitate global C MRV (monitoring, reporting, and verification) by providing accurate and precise C-cycle estimates, along with their corresponding spatiotemporal uncertainties.

ecology↗

bioRxiv2026

Divergent condensates tune transcriptional responses during stress

Dynamic reorganization of the transcription machinery within nuclear membrane-less compartments is an emergent feature of mammalian stress response, associated with critical cellular decisions. However, mechanisms governing the subcellular formation of these stress-induced condensates and their role in transcription regulation remain poorly understood. Here, we find that heat shock factor 1 (HSF1), transcriptional mediator of protein and cellular homeostasis, forms condensates during various adverse conditions, but these assemblies exhibit context-dependent divergent transcriptional outcomes. During heat shock, HSF1 orchestrates the coordinated assembly of transcription hubs via canonical activation, including post-translational modifications (PTMs), trimerization, and DNA binding. While HSF1s disordered regions restrict condensate formation in unstressed situations, they promote stress-induced condensate maturation to transcriptionally active states. Strikingly, HSF1 condensates that form during other environmental and chemotherapeutic stresses stall at distinct stages of hub formation, assemble independent of PTMs, and exhibit reduced sub-condensate dynamics. These aspects culminate in attenuated genomic occupancy and transcriptional output at HSF1-associated loci, consistent with functional impairment of HSF1 and the transcription machinery via sequestration. Our work suggests that stress-induced transcription factor condensates drive conserved responses during physiological perturbations, but can be inactivated during pathological insults, rationalizing HSF1 and transcriptional dysfunction across degenerative diseases and toxic exposures.

molecular biology↗

bioRxiv2026

An Explainable Machine Learning Approach to study the positional significance of histone post-translational modifications in gene regulation

Epigenetic mechanisms regulate gene-expression by altering the structure of the chromatin without modifying the underlying DNA sequence. Histone post-translational modifications (PTMs) are critical epigenetic signals that influence transcriptional activity, promoting or repressing gene-expression.Understanding the impact of individual PTMs and the combinatorial effects is essential to deciphering gene regulatory mechanisms.In this study,we analyzed the ChIP-seq data for 26 PTMs in yeast, examining the PTM intensities gene-wise from positions-3 to 8 in each gene.Using XGBoost classifiers, we predicted gene transcription rates and identified key histone modifications and nucleosomal positions that are critical in gene-expression using explainability measures (such as SHAP). Our study provides a comprehensive insight into the histone modifications, their positions and their combinations that are most critical in gene regulation in yeast.The proposed explainable Machine Learning models can be easily extended to other model organisms to provide meaningful insights into gene regulation by epigenetic mechanisms.

bioinformatics↗

bioRxiv2026

BiomarkerKB: FAIR and Integrated Biomarker Knowledge Connecting Biomolecular and Clinical Data Types

Biomarkers are essential tools for disease detection, risk assessment, therapeutic monitoring, and precision medicine. However, biomarker data are dispersed across heterogeneous resources, inconsistently reported in the literature, and rarely standardized for computational use. This fragmentation limits reproducibility, cross-study integration, and the discovery of novel biomarker and disease relationships. We developed BiomarkerKB, a knowledgebase designed to harmonize and integrate biomarker information under a standardized data model. The model follows the FDA-NIH BEST biomarker definition and captures both core fields (biomarker entity, disease/condition, exposure agent) and contextual metadata (specimen, biomarker role, evidence, provenance). Biomarker data and related annotations were either curated from publications or collected from public resources (e.g., OpenTargets, GWAS Catalog, ClinVar, CIViC, OncoMX) and were also contributed by the Common Fund Data Coordinating Centers and the Early Detection Research Network (EDRN). Standardization was achieved using ontologies and reference resources such as Disease Ontology, UBERON, UniProtKB, and HUGO Gene Nomenclature Committee (HGNC) gene symbols. BiomarkerKB data were ingested into a Neo4j-based knowledge graph and integrated with the Common Fund Data Ecosystem (CFDE) Knowledge Graph. The initial release of BiomarkerKB contains over 200,000 biomarker-disease associations spanning genes, proteins, metabolites, glycans, and chemical elements. The knowledge graph comprises more than 300,000 nodes and 1.2 million edges, enabling structured exploration of biomarker relationships within CFDE data as demonstrated through the knowledge graph query-based use cases presented in this study. A publicly accessible web portal (https://biomarkerkb.org) provides keyword search, filtering, data downloads, and access to graph visualization to support both researchers and computational analyses. BiomarkerKB addresses a critical gap in biomarker informatics by providing an integrated, FAIR (Findable, Accessible, Interoperable, and Reusable), and unified framework for biomarker knowledge exploration and discovery.

bioinformatics↗

bioRxiv2025

Rotational settings quantize nucleosome movement by chromatin regulators

Proper nucleosome positioning is essential for gene regulation and genomic integrity. Regulated nucleosome assembly and positioning results from a need to protect DNA sequences genome-wide, constrained by the known intrinsic sequence preferences of histones. Current models posit that chromatin regulators override the intrinsic preferences to establish the nucleosome landscapes observed in vivo, implying minimal roles for DNA sequence in guiding nucleosomal structure in cells. In contrast, we demonstrate that DNA sequence intrinsically guides the structure and remodeling of nucleosomes from yeast to mammals. We demonstrate that nucleosomes with weak translational settings in vitro, in yeast, and in mammalian cells demonstrate a clear preference for inward-facing A/T dinucleotides and outward facing G/C dinucleotides, at 10 bp spacings consistent with established preferences for rotationally positioned nucleosomes. Foreign DNA sequences heterologously inserted into the yeast genome obey similar rules, indicating that DNA sequence itself is causal. Finally, remodelers and transcription elongation change the preference among the alternative translational positions 10 bp apart, retaining the rotational setting. From these results, we propose that DNA sequence creates an energy landscape with preferred rotational settings every [~]10 bp, and that chromatin regulators, rather than overriding these preferences, navigate within them. This "detent" mechanism provides a unifying framework for understanding how diverse cellular processes achieve precise nucleosome positioning while maintaining the same DNA face exposed to regulatory factors.

molecular biology↗