Biology subjects

Lyu, Y.

Publications and source records attributed to Lyu, Y..

At least 19 recordsLinked to original sources

bioRxiv2026

Food perception alone stimulates oviposition through serotonin signaling in Drosophila

Reproduction is strongly shaped by nutrient availability, but whether sensory information about food can regulate reproductive output before nutrients are consumed remains unclear. Here, using Drosophila melanogaster, we show that sensory perception of dietary yeast, a protein-rich food source widely used to stimulate fecundity in the fruit fly, is sufficient to rapidly promote egg laying. Specifically, we found that yeast odor exposure increased the rate of oviposition and the number of eggs laid within hours, independent of yeast intake, and that this response was mediated by olfactory signaling through the odorant receptor co-receptor Orco. We further identified serotonin receptor 2A (5-HT2A), but not 5-HT2B, as necessary for yeast odor-induced egg laying. We then asked whether yeast odor also elicits broader life-history changes and whether its reproductive and longevity effects depend on a shared mechanism. Consistent with previous observations, yeast odor also shortened lifespan. However, this lifespan effect was independent of 5-HT2A signaling, indicating that the reproductive and longevity responses to food perception are separable. Finally, our brain connectomic analyses revealed that yeast-responsive olfactory neurons preferentially influence oviposition descending neurons (OviDNs) compared with other olfactory neurons. OviDNs further connect with endocrine-related circuitry in the corpora allata (CA), and, most interestingly, the interneurons linking OviDNs to CA neurons are enriched for serotonergic and dopaminergic modulation. Taken together, our observations indicate that nutrient acquisition is not a prerequisite for animals to adjust their reproduction to the nutritional environment. Sensory information predicting food availability itself can elicit changes in neural state, likely through olfactory-reproductive circuits, that acutely alter reproductive physiology.

physiology↗

bioRxiv2026

Beyond the Final Layer: Evaluating Single-Cell Foundation Model Representations for Molecular Prediction

Single-cell foundation models (scFMs) are commonly benchmarked using final-layer embeddings, although useful depth may depend on the task. We evaluate Geneformer, scBERT, scGPT, and scFoundation as frozen feature extractors for cell-type and perturbation-response prediction using layer-wise probes, leave-one-dataset-out (LODO) selection, readout controls, representation diagnostics, centered kernel alignment (CKA), and retrieval/collapse analyses. Intermediate layers significantly improve cell-type accuracy for scGPT and scBERT in several settings, while Geneformer and scFoundation generally show performance comparable to their final layers. For perturbation prediction, LODO identifies layer 1 as an earliest-on-par candidate across all four models, without establishing universal superiority. Paired scGPT CLS and mean-pooling analyses show that readout affects preferred depth. CKA and representation metrics reveal model- and task-dependent geometric changes, while retrieval and collapse checks support the robustness of perturbation findings. We recommend reporting extraction depth, readout, selection protocol, and task-specific diagnostics. A validated non-final layer may also reduce computation, although efficiency was not directly benchmarked.

bioinformatics↗

bioRxiv2026

Giant bacteria in gut microbiomes

The advent of sequencing-based omics has transformed our understanding of the gut microbiome, enabling microbial communities to be characterised at unprecedented scale and resolution. However, these approaches provide limited information about cellular traits such as size and morphology. Giant bacteria remain largely overlooked in gut microbiome research, despite their extraordinary cell size, extreme polyploidy and unusual reproductive strategies. Epulopiscium, one of the largest known heterotrophic bacteria, has been studied primarily as an intestinal symbiont of surgeonfish, leaving its broader host range unresolved. Here we show, by a large-scale analysis of publicly available 16S ribosomal RNA amplicon samples, alongside locally collected human samples, that Epulopiscium spp. occur across diverse hosts, including humans and a broad diversity of animals. Full-length 16S rRNA gene phylogenies resolved multiple Epulopiscium-related lineages with distinct host compositions. Among human samples, Epulopiscium sequences were most abundant during the first year of life. In adults, abundance varied across disease states, increasing in inflammatory bowel disease and colorectal cancer while decreasing in neurological conditions compared with a healthy adult baseline. Similar patterns were also observed in mouse models. Our study broadens the known host range of Epulopiscium and establishes giant bacteria as an overlooked component of gut microbiome diversity. These findings highlight the need to combine sequence-based studies with classical microbiological techniques to identify organisms with unique traits, otherwise hidden by taxonomy or phylogeny.

microbiology↗

bioRxiv2026

Reduced Functional Coordination within the Default Mode Network in Schizophrenia During Naturalistic Neuroimaging

BackgroundSocial dysfunction is a major source of disability in schizophrenia, yet the neural mechanisms that contribute to impaired social understanding remain poorly understood. Converging evidence points to the role of the default mode network (DMN) in integrating social information over time to construct interpretations of social behaviors. Here, we tested the hypothesis that individuals with schizophrenia show reduced stimulus-driven coordination between brain regions within the DMN during free viewing of naturalistic social stimuli. MethodsA sample of 124 adults (schizophrenia: n=63; healthy controls: n=61) viewed naturalistic video clips during fMRI. Inter-subject functional connectivity (ISFC) was computed within the two groups. Group differences were identified via permutation testing. We also explored group differences in other brain networks to examine whether effects were specific to the DMN. ResultsIndividuals with schizophrenia showed weaker stimulus-driven coupling within the DMN compared to healthy controls, specifically between areas such as the parahippocampal gyrus, precuneus, and medial prefrontal cortex. Group differences in ISFC were specific to the DMN. Furthermore, no between-group differences emerged for within-participant functional connectivity in the DMN, suggesting that the observed effects reflect reduced stimulus-driven coordination among DMN regions when processing social stimuli rather than a more general decline in DMN connectivity. ConclusionsSchizophrenia is characterized by impaired coordination within the DMN as it dynamically integrates social information over time, which could contribute to difficulties in constructing coherent interpretations of real-world social situations. These findings suggest that disrupted stimulus-driven network coordination might underlie social cognitive impairments in schizophrenia, highlighting the value of naturalistic paradigms for revealing network-level dysfunction under conditions that closely approximate real-world experience.

neuroscience↗

bioRxiv2026

Electrostatic Complementarity at the ClpX Substrate-EntryChannel Governs ATP-Driven Protein Unfolding

AAA+ proteases maintain proteostasis by mechanically unfolding protein substrates before degradation, yet how ATP-driven pulling is converted into productive unfolding remains poorly understood. Here, using ssrA-tagged GFP substrates spanning a range of surface charges, we show that electrostatic complementarity between the positively charged ClpX substrate-entry channel and folded substrate domains governs unfolding efficiency. Positively charged substrates unfolded inefficiently despite substrate recognition, thermal stability, ATPase activation, and pore-loop engagement comparable to negatively charged substrates. Cryo-EM structures of a positively charged substrate revealed multiple substrate-engagement states arising from electrostatic incompatibility at the ClpX substrate-entry channel, whereas negatively charged substrates formed favorable electrostatic interactions that likely stabilize partially unfolded intermediates during successive ATP-driven unfolding attempts. Together, these findings identify electrostatic complementarity as a key determinant of productive ATP-driven protein unfolding and suggest that tuning substrate or substrate-entry electrostatics provides a mechanism for regulating AAA+ protease activity.

biochemistry↗

bioRxiv2026

Integrated ex vivo screening and transcriptomic profiling to prioritize drug combinations for rare cancers

Discovering effective drug combinations requires testing many dose combinations across a diverse panel of tumor models. This approach is limited in rare cancers by a scarcity of cell lines and representative high-throughput models that would make exhaustive screening feasible and predictive. Patient-derived xenograft (PDX) models are genomically representative but too low-throughput for this purpose. Culturing PDX cells ex vivo in three-dimensional (3D) matrices offers a genomically representative and clinically relevant platform for preclinical drug testing, capturing the microenvironmental cues that shape in vivo drug response while requiring only limited tissue per assay. Toward this end, we designed and validated an experimental-computational framework, "ex vivo assessment of combination therapies" (EXACT), to enable drug combination discovery in rare tumors. Using PDX models of malignant peripheral nerve sheath tumors (MPNST), we built a platform to culture PDX cells ex vivo over multiple days, monitoring drug sensitivity and measuring transcriptomic responses to treatment. Computational analysis of these transcriptomic responses then identifies which compensatory pathway creates a unique vulnerability to a second drug. EXACT thus offers a biologically informed, scalable approach for prioritizing drug combinations in rare tumors, nominating drugs alongside biological rationale. Using this methodology, we identified a MEK inhibitor plus HDAC inhibitor combination with enhanced activity in vitro and in vivo, forming the basis of an active clinical trial. This platform could be adapted for real-time use with primary patient specimens, enabling personalized therapeutic discovery. SignificanceEXACT integrates PDX-derived 3D drug screening with biologically informed computational analysis to identify and explain effective combinations, providing a scalable strategy for therapeutic discovery in rare cancers such as MPNST.

cancer biology↗

bioRxiv2026

Prediction of Transcription Factor DNA Binding Affinity with High-Throughput Kd Measurements and Deep Learning

Transcription factors (TFs) regulate gene expression through specific interactions with genomic DNA. While TF binding motifs from public databases describe sequence preferences, quantifying genome-wide affinity (Kd) is highly desirable for a more accurate thermodynamic description. Here, we report ivtFOODIE (in vitro FOOtprinting with DeamInasE), an assay that leverages deaminase-mediated cytosine-to-uracil conversion to measure Kd values for a given TF across accessible genomic regions from human cells. By pre-training on TF binding sites from JASPAR and fine-tuning with our ivtFOODIE data from 46 TFs representing 13 different DNA-binding domains (DBDs), we developed Seq2Kd, a deep learning model capable of predicting a TFs absolute binding affinity on DNA sequences. Seq2Kd enables de novo motif discovery of [~]500 previously uncharacterized human TFs and reveals the effects of genetic variation both in TF-coding regions and DNA-binding sites on gene expression and disease susceptibility. By correlating predicted affinity changes with the sign and magnitude of expression quantitative trait locus (eQTL) effects, we stratified TFs into activator-like and repressor-like groups. Compared to clinically benign variants, pathogenic single-nucleotide variants (SNVs) within regulatory and protein-coding regions show significantly larger predicted shifts in Kd. We provide an interactive web portal, the ENcyclopedia of Transcription-factor Interactions with Regulatory Elements (ENTIRE), which integrates the Seq2Kd model with the ivtFOODIE dataset. This resource offers thermodynamic prediction for TF-DNA interactions for functional genomics and human disease.

biophysics↗

bioRxiv2026

Three-dimensional Virtual Adult Cardiomyocyte Transcriptomics

Obtaining transcriptomes of adult cardiomyocytes at single-cell resolution remains challenging due to their large size, elongated morphology, and frequent multinucleation. Although spatial transcriptomics preserves tissue architecture and captures gene expression in situ, current analytical frameworks largely rely on nuclear-based segmentation and are therefore poorly suited to adult cardiomyocytes. Furthermore, individual tissue sections capture only a fraction of a cardiomyocyte, preventing reconstruction of complete cell-level transcriptomes. Here we present three-dimensional virtual cardiomyocyte (3D-VirtualCM), a membrane-guided framework that integrates cell-contour similarity and optimal transport to reconstruct volumetric cardiomyocyte transcriptomes from consecutive spatial transcriptomic sections. Applying 3D-VirtualCM to infarcted adult mouse hearts, we generated a panoramic transcriptomic atlas spanning 100 m thickness at single-cell resolution. 3D-VirtualCM identified spatially and transcriptionally distinct cardiomyocyte populations within the infarct border zone, enabled high-throughput quantification of cardiomyocytes re-entering the cell cycle together with their associated molecular signatures, and revealed heterogeneous RNA distribution along the longitudinal axis of individual cardiomyocytes. By integrating three-dimensional cellular morphology with in situ transcriptomic data, 3D-VirtualCM provides a scalable approach for resolving cardiomyocyte states and spatial organization in physiological and pathological cardiac remodeling.

bioinformatics↗

bioRxiv2026

SuperSurv: A Unified Framework for Machine Learning Ensembles in Survival Analysis

This paper introduces SuperSurv, a user-friendly R package for building, evaluating, and interpreting ensemble models for right-censored survival data. Although many survival modeling methods are available, existing tools are often model-specific and lack a unified platform for systematically integrating, comparing, and ensembling heterogeneous learners. SuperSurv addresses this gap by providing a unified interface for diverse survival learners, including models that return full survival curves as well as methods that produce only risk scores. All learner outputs are mapped to calibrated survival probability curves on a common evaluation time grid, enabling direct comparison and ensemble construction across heterogeneous model classes. SuperSurv implements stacking of survival models using inverse-probability-of-censoring weighted (IPCW) Brier risk to estimate ensemble weights in the presence of right censoring. The framework integrates hyperparameter tuning, time-dependent benchmarking metrics, and visualization tools for survival model evaluation. In addition, the package provides post-hoc interpretability utilities based on SHAP(SHapley Additive exPlanations) values and supports covariate-adjusted restricted mean survival time (RMST) contrasts through g-computation. By bridging the gap between theoretical rigor and clinical application, SuperSurv offers researchers a comprehensive ecosystem for modern survival analysis. The SuperSurv package is open-source and available on CRAN at https://CRAN.R-project.org/package=SuperSurv and on GitHub at https://github.com/yuelyu21/SuperSurv. An empirical example using the METABRIC breast cancer dataset demonstrates a complete workflow from model training and benchmarking to explainability and clinically interpretable survival contrasts.

bioinformatics↗

bioRxiv2025

Sexually Dimorphic Transdifferentiation of Endothelial Cells in Atherosclerosis

ObjectiveTo elucidate the sex-specific trajectories of endothelial cell (EC) transdifferentiation and their contribution to distinct atherosclerotic plaque phenotypes in males and females. Approach and ResultsWe utilized Cdh5-CreERT2/Apoe-/-/Rosa26-mTmG lineage-tracing mice to perform single-nucleus RNA sequencing (snRNA-seq) of aortic tissues from male, female, and ovariectomized female mice following 12 weeks of high-fat diet. We mapped high-resolution EC fates, identifying distinct transdifferentiation routes into smooth muscle cell (SMC)-like, adipocyte-like, macrophage-like, and fibroblast-like cells. Females exhibited higher overall EC plasticity with a preference for SMC-like and metabolically active adipocyte-like trajectories, contributing to plaque stability. Conversely, male ECs favored inflammatory macrophage-like and stress-responsive fates. Trajectory analysis revealed a novel direct EC-to-adipocyte-like transition that bypasses the canonical endothelial-to-mesenchymal transition (EndoMT). Gene regulatory network analysis, validated by in vitro knockdown assays, identified KLF2 as a homeostatic brake on transdifferentiation, while CREB5 and ESRRG emerged as critical molecular switches governing the bifurcation between adipogenic and myogenic fates. Ovariectomy in females shifted the transcriptomic landscape toward a male-like, lipid-anabolic phenotype. ConclusionsEC plasticity is a sex-dimorphic process driving differential plaque composition. We identify a novel, direct EC-to-adipocyte trajectory and pinpoint KLF2 and CREB5 as key regulators, offering new mechanistic insights into why premenopausal women develop more stable atherosclerotic lesions than men.

pathology↗

bioRxiv2025

Mathematical Modelling of the Mitochondrial Dicarboxylate Carrier (SlC25A10)

1The mitochondrial dicarboxylate carrier SLC25A10 mediates reversible exchange among succinate, malate, and phosphate, contributing to mitochondrial metabolic regulation. Structural studies establish a ping-pong mechanism, but most mathematical models still assume sequential binding, lacking mechanistic justification and overlooking the alternation of a single binding site. Here, we present the first mechanistically derived and thermodynamically consistent model of SLC25A10 based on a ping-pong framework. The model incorporates competitive binding of succinate, malate, and phosphate, heteroexchange, reversibility, and electroneutrality, and is calibrated using experimental datasets from intact mitochondria and reconstituted proteoliposomes. To estimate kinetic parameters and quantify their uncertainty, we employed Bayesian inference, enabling statistically rigorous calibration to uptake and competition assays. The model introduces new terms that quantify which substrate and from which side of the membrane is most likely to start the transport cycle. Beyond reproducing experimentally observed exchange kinetics, the model resolves non-equilibrium transport dynamics that are difficult to access directly in classical uptake assays. In particular, the simulations reveal a two-phase response in which an initial phosphate-driven high-flux uptake regime for malate and succinate is followed by a slower redistribution phase in which the two dicarboxylates continue to readjust primarily against each other. The model also predicts that mitochondrial morphology modulates early transport behaviour, with matrix swelling increasing and matrix condensation decreasing the initial SLC25A10 flux magnitude. More broadly, the framework provides a quantitative basis for studying how substrate competition, thermodynamic driving forces, and compartment geometry shape SLC25A10-mediated exchange, and it offers a transferable modelling strategy for other carriers in the SLC25 family.

systems biology↗

bioRxiv2025

Methamphetamine modulates functional connectivity signatures of sustained attention and arousal

Building on evidence that psychostimulants modulate whole-brain functional connectivity signatures of sustained attention, we examined how a single dose of methamphetamine (MA, 20 mg) changes network-level functional organization and sustained attention in healthy adults. Using a within-subject, placebo-controlled design, we tested whether MA selectively affects behavioral and fMRI connectivity signatures of sustained attention and arousal. Under MA, participants showed improved sustained attention task performance as well as functional connectivity signatures of higher sustained attention and arousal. These network changes emerged consistently across resting-state and task-based fMRI, indicating that MA influences attention- and arousal-related networks regardless of cognitive context. Furthermore, a support vector classifier distinguished functional connectivity patterns observed during the MA and placebo conditions, identifying connections overlapping with networks related to arousal. Together, these findings align with prior work on other psychostimulants like methylphenidate, showing that MA modulates sustained attention and related large-scale brain networks. By revealing how MA modulates attention-relevant brain connectivity patterns, our results highlight the utility of psychostimulants as causal tools for probing the robustness, generalizability, and interpretability of brain-based biomarkers of behavior.

neuroscience↗

bioRxiv2025

Immortalization and Characterization of Schwann Cell Lines Derived from NF1 Associated Cutaneous Neurofibromas

Neurofibromatosis type 1 (NF1) is an autosomal dominant condition in which patients are heterozygous for a disruptive pathogenic variant in the NF1 gene. The most characteristic feature of the condition NF1 is the neurofibroma, a benign, multi-cellular tumor which initiates when a cell of the Schwann cell lineage gains a somatic pathogenic variant of the other NF1 allele. Neurofibromas developing at nerve termini in the skin are termed "cutaneous" neurofibromas (cNFs), while those developing within larger nerves are termed "plexiform." Most patients develop cNFs beginning in late childhood or early adulthood, continuing throughout life at variable rates. Some patients may develop only a few cNFs, while others suffer from thousands. There are no reliably effective physical or pharmaceutical therapies besides surgical removal. Although these are not life-threatening, they are disfiguring and can interfere with normal life functions. To provide a resource for research, we developed short-term cNF Schwann cell cultures from NF1 patients, from which we subsequently established the first semi-immortalized cNF cell lines through transduction with wild-type human telomerase reverse transcriptase (hTERT) and murine cyclin-dependent kinase 4 (mCdk4) genes. Here we present molecular, cellular, and functional characterization of these cell lines, which will be of utility for investigating and developing NF1 cNF therapies.

genomics↗

bioRxiv2025

Transferring functional chromosomes between intergeneric yeast generates monochromosomal hybrids with improved phenotypes and transcriptional responses

Interspecific genetic exchanges caused by natural hybridization or horizontal gene transfer can lead to enhanced phenotypes, which are often of interest for industrial applications and evolutionary research. However, transferring genetic materials between distantly related species, such as intergeneric yeasts, presents technical challenges. In this study, we established a method to transfer individual chromosomes from Saccharomyces cerevisiae (Sc) into Kluyveromyces marxianus (Km), an emerging model for bioproduction. The Sc chromosome of interest was circularized, genetically modified to carry Km centromeres and replication origins, and transformed into Km via protoplast transformation. Using this method, we generated two synthetic strains, each containing a full set of Km chromosomes and either Sc chromosome I or III. The Sc chromosomes exhibited normal replication, segregation, and active transcription after the transfer. The synthetic strains displayed enhanced phenotypes in flocculation and salt tolerance, which were found to be caused by transgressive expression of FLO9 and SPS22 on the transferred Sc chromosomes, respectively. Transcriptomic analysis revealed that transgressive expression was prevalent among the transferred Sc genes, suggesting evolution of lineage-specific cis- and trans-regulatory interactions across a long evolutionary timescale. Our strategy has potential applications in optimizing cell factories, constructing synthetic genomes, and advancing evolutionary research.

synthetic biology↗

bioRxiv2024

Stochasticity in dietary restriction-mediated lifespan outcomes in Drosophila

Dietary restriction (DR) is widely considered to be one of the most potent approaches to extend healthy lifespan across various species, yet it has become increasingly apparent that DR-mediated longevity is influenced by biological and non-biological factors. We propose that current priorities in the field should include understanding the relative contributions of these factors to elucidate the mechanisms underlying the beneficial effects of DR. Our work conducted in two laboratories, represents an attempt to unify DR protocols in Drosophila and to investigate the stochastic effects of DR. Across 64 pairs of survival data (DR/ad libitum, or AL), we find that DR does not universally extend lifespan. Specifically, we observed that DR conferred a significant lifespan extension in only 26.7% (17/64) of pairs. Our pooled data show that the overall lifespan difference between DR and AL groups is statistically significant, but the median lifespan increase under DR (7.1%) is small. The effects of DR were overshadowed by stochastic factors and genotype. Future research efforts directed toward gaining a comprehensive understanding of DR-dependent mechanisms should focus on unraveling the interactions between genetic and environmental factors. This is essential for developing personalized healthspan-extending interventions and optimizing dietary recommendations for individual genetic profiles.

genetics↗

bioRxiv2024

Systemic delivery of murine SOD2 mRNA to experimental abdominal aortic aneurysm mitigates expansion and rupture

BackgroundOxidative stress is implicated in the pathogenesis and progression of abdominal aortic aneurysm (AAA). Antioxidant delivery as a therapeutic for AAA is of substantial interest although clinical translation of antioxidant therapy has met with significant challenges due to limitations in achieving sufficient antioxidant levels at the site of AAA. We posit that nanoparticle-based approaches hold promise to overcome challenges associated with systemic administration of antioxidants. MethodsWe employed a peptide-based nanoplatform to overexpress a key modulator of oxidative stress, superoxide dismutase 2 (SOD2). The efficacy of systemic delivery of SOD2 mRNA as a nanotherapeutic agent was studied in two different murine AAA models. Unbiased mass spectrometry-enabled proteomics and high-dimensional bioinformatics were used to examine pathways modulated by SOD2 overexpression. ResultsThe murine SOD2 mRNA sequence was mixed with p5RHH, an amphipathic peptide capable of delivering nucleic acids in vivo to form self-assembled nanoparticles of [~]55 nm in diameter. We further demonstrated that the nanoparticle was stable and functional up to four weeks following self-assembly when coated with hyaluronic acid. Delivery of SOD2 mRNA mitigated the expansion of small AAA and largely prevented rupture. Mitigation of AAA was accompanied by enhanced SOD2 protein expression in aortic wall tissue. Concomitant suppression of nitric oxide, inducible nitric oxide synthase expression, and cell death was observed. Proteomic profiling of AAA tissues suggests that SOD2 overexpression augments levels of microRNAs that regulate vascular inflammation and cell apoptosis, inhibits platelet activation/aggregation, and downregulates mitogen-activated protein kinase signaling. Gene set enrichment analysis shows that SOD2 mRNA delivery is associated with activation of oxidative phosphorylation, lipid metabolism, respiratory electron transportation, and tricarboxylic acid cycle pathways. ConclusionsThese results confirm that SOD2 is key modulator of oxidative stress in AAA. This nanotherapeutic mRNA delivery approach may find translational application in the medical management of small AAA and the prevention of AAA rupture.

cell biology↗

bioRxiv2024

Chemical Space Localization for Unknown Metabolite Annotation via Semantic Similarity of Mass Spectral Language

Untargeted analysis using liquid chromatography{square}mass spectrometry (LC-MS) allows quantification of known and unknown compounds within biological systems. However, in practical analysis of complex biological system, the majority of compounds often remain unidentified. Here, we developed a novel deep learning-based compound annotation approach via semantic similarity analysis of mass spectral language. This approach enables the prediction of structurally related compounds for unknowns. By considering the chemical space, these structurally related compounds provide valuable information about the potential location of the unknown compounds and assist in ranking candidates obtained from molecular structure databases. Validated with two independent benchmark datasets obtained by chemical standards, our method has consistently demonstrated superior performance compared to existing compound annotation methods. A case study of the tomato ripening process indicates that DeepMASS has significant potential for metabolic biomarker identification in real biological systems. Overall, the presented method shows considerable promise in annotating metabolites, particularly in revealing the "dark matter" in untargeted analysis.

bioinformatics↗

bioRxiv2024

Genomic features of NF1-associated peripheral nerve sheath tumors: a cohort analysis from the Johns Hopkins NF1 biospecimen repository

Neurofibromatosis type 1 (NF1) is an inherited neurocutaneous condition that predisposes to the development of peripheral nerve sheath tumors (PNST) including cutaneous neurofibromas (CNF), plexiform neurofibromas (PNF), atypical neurofibromatous neoplasms with unknown biological potential (ANNUBP), and malignant peripheral nerve sheath tumors (MPNST). The successful advancement of therapeutic development for NF1-associated PNST necessitates the systematic acquisition and analysis of human tumor specimens and their corresponding model systems. RNA sequencing (RNAseq) and whole exome sequencing (WES) data were generated from 73 and 114 primary human tumor samples, respectively. These pre-processed data, standardized for immediate computational analysis, are accessible through the NF Data Portal, allowing immediate interrogation. This analysis combines new and previously released samples, offering a comprehensive view of the entire cohort sequenced. As a dedicated effort to systematically bank tumor samples from people with NF1, in collaboration with molecular geneticists and computational biologists to advance understanding of NF1 biology, the Johns Hopkins NF1 biospecimen repository offers access to samples and genomic data to promote advancement of NF1-related therapies.

bioinformatics↗