Biology subjects

Fu, Z.

Publications and source records attributed to Fu, Z..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

Explicitly nonlinear fMRI networks reveal hidden trajectories of infant brain development

Nonlinearity is a hallmark of brain complexity at multiple scales. However, existing functional magnetic resonance imaging (fMRI) functional connectivity studies typically utilize linear methods. Therefore, links between nonlinear fMRI connectivity patterns and the development of the human brain during critical periods such as infancy remain unclear. To address this gap in knowledge, we developed a data-driven approach to capture brain intrinsic connectivity networks from explicitly nonlinear resting-state fMRI connectivity and profiled their developmental associations in a cohort of typically developing human infants. We identified neurobiologically structured nonlinear fMRI connectivity patterns during early postnatal life, indicating that macroscopic brain ensembles systematically participate in nonlinear relationships at birth. Furthermore, we found that linear and explicitly nonlinear network counterparts are linked to partially overlapping but complementary developmental profiles during this period of rapid brain maturation, with the explicitly nonlinear approach unveiling insights into the development of networks that have been associated with sensorimotor capacities, default mode processes, executive functioning, language production, and stimulus saliency. Our study marks the first comprehensive developmental investigation of whole-brain nonlinear fMRI networks in human infants and deepens contemporary perspectives on neuroimaging data discovery by emphasizing the informational richness of nonlinear relationships at fMRI scales of observation.

neuroscience↗

bioRxiv2026

MoCoO: Momentum Contrast ODE-Regularized VAE for Single-Cell Trajectory Inference and Representation Learning

Characterising cellular differentiation from single-cell RNA sequencing (scRNA-seq) requires representations that capture both discrete cell-type identity and continuous developmental trajectories. We present MOCOO, a modular framework integrating a Variational Autoencoder (VAE), Neural Ordinary Differential Equations (Neural ODE), and Momentum Contrast (MoCo), complemented by a systematic Phase-2 Flow Matching (FM) refinement step applicable to all model variants. Through a systematic six-configuration ablation across 20 scRNA-seq datasets evaluated with a proposed five-metric suite covering clustering geometry (ASW, DAV, CAL) and embedding quality (DRE, DREX), we demonstrate two central findings. First, the ODE+MoCo combination is the core architectural synergy: VAE+ODE+MoCo achieves four of five top-two finishes among base configurations, including the best ASW (0.225) and DAV (1.478), plus second-best DRE (0.640) and CAL. Second, FM refinement systematically improves both embedding quality and clustering geometry across all six base configurations--DREX in 92% and DRE in 88% of 120 dataset- configuration pairs ({Delta}DREX= +0.030, {Delta}DRE= +0.023), CAL in 88%, ASW in 86% ({Delta}ASW= +0.018), and DAV in 80% ({Delta}DAV= -0.072; Fig. 2). Combined, the full MoCoO pipeline (VAE+ODE+MoCo+Proto+FM) achieves the best DRE (0.678), DREX (0.660), and CAL, while VAE+ODE+MoCo+FM achieves the best ASW (0.257) and DAV (1.359). ODE smooths the latent manifold along developmental trajectories; MoCo sharpens cluster geometry; FM recovers and amplifies both embedding quality and cluster separation post-hoc. Downstream validation confirms that MoCoO latent spaces support annotation transfer, uncertainty quantification, differential expression, and branching detection. Pseudotime predictions correlate significantly with canonical marker genes across all five core developmental systems. We publicly release the MoCoO Python package (pip install mocoo) and full benchmark suite. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=65 SRC="FIGDIR/small/714791v1_fig2.gif" ALT="Figure 2"> View larger version (22K): org.highwire.dtl.DTLVardef@59a7feorg.highwire.dtl.DTLVardef@242f8borg.highwire.dtl.DTLVardef@1ad851dorg.highwire.dtl.DTLVardef@fe6b2c_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFig. 2.C_FLOATNO FM systematically improves all model variants across all metrics (Fig. 2). Each subplot shows absolute performance for all 12 configurations (6 base + 6 FM variants) across 20 datasets. Coloured triangle indicators above each pair show FM effect: [Figure 2] improved, [Figure 2] degraded. Subplot titles include FM win-rate (% of 120 config x dataset pairs improved). ODE-containing configurations (V+OM, Full) dominate all metrics, and FM universally raises the median across all 6 base variants (CAL 88%, DREX 92%, DRE 88%, ASW 86%, DAV 80%). C_FIG

bioinformatics↗

bioRxiv2026

CLOP-DiT: Structured-Metadata-Conditioned Single-Cell Latent Generation via Contrastive Language-Omics Pretraining and Diffusion Transformers

Generating realistic single-cell transcriptomic profiles from structured biological descriptions would enable controlled simulation, data augmentation, and hypothesis-driven cell-state creation--yet no existing method combines text-cell alignment with conditional generation. We present CLOP-DiT, a modular three-stage pipeline: (1) a contrastive aligner (CLOP) maps BiomedBERT text embeddings and scGPT cell embeddings into a shared 512-dimensional space; (2) a conditional Diffusion Transformer (DiT) generates scGPT-compatible latent states via flow matching, steered by a five-field biological template (cell type, tissue, organism, marker genes, disease); and (3) a frozen scGPT decoder maps latents to gene expression. Across 69 cell types from 80 GEO datasets (220,304 cells), a high-fidelity regime (CFG = 2.0) achieves 36.9% KNN accuracy (25x chance) and 81.0% steering, while a high-diversity regime (CFG = 1.0) reaches diversity ratio 0.93 at 80.7% steering. Conditioning field ablation and swap-label permutation tests confirm that marker genes are the dominant steering signal (steering accuracy drops from 99.8% to 62.4% when only metadata fields are retained). Key limitations are identified transparently: in-distribution per-gene variance structure is well preserved (r = 0.98) but cross-dataset variance correlation drops to near zero, the discriminator AUC of 0.656 indicates residual distinguishability, and a pilot rare-cell augmentation study was negative. The modular architecture enables targeted remediation of each limitation without full retraining. CLOP-DiT establishes the feasibility of structured-metadata-conditioned single-cell generation and provides a composable framework for iterative improvement. Simple SummaryCLOP-DiT is a computational pipeline that generates synthetic single-cell gene expression profiles from structured biological descriptions (cell type, tissue, organism, marker genes, and disease context). It first learns to align text descriptions with real cell data in a shared mathematical space, then uses a diffusion model to generate new cell states matching a given description. The generated cells capture correct cell-type identity and marker gene patterns, but do not yet reproduce the full cell-to-cell variability seen in real single-cell experiments. This work demonstrates that text-guided single-cell generation is feasible as a proof of concept, opening directions for future simulation and hypothesis-generation tools in biology.

bioinformatics↗

bioRxiv2026

Flow-Matching Refined Dirichlet Process Priors for Structurally Coherent Single-Cell Representation Learning

Standard autoencoders for single-cell transcriptomics learn latent spaces whose cluster structure emerges only post hoc through K-means or community detection, leaving cluster count and boundary quality uncontrolled during training. Here we ask whether imposing an adaptive nonparametric prior can shift this balance. We equip a feedforward autoen-coder with an online Dirichlet Process Mixture Model (DPMM) prior that refits cluster assignments throughout training and directly regularizes latent compactness and separation. Across 56 scRNA-seq datasets the DPMM prior produces a pronounced geometry- concordance trade-off : cluster compactness (ASW) improves by 127% and Davies-Bouldin overlap drops by 47%, but label-recovery metrics decline (NMI -17%, ARI -21%) and downstream kNN accuracy falls from 0.784 to 0.725. Wilcoxon signed-rank tests confirm that the geometry gains are significant with large Cliffs{delta} effects while concordance losses remain bounded and non-significant. A second-stage conditional-flow refinement (DPMM-FM) further improves projection fidelity (DRE 0.751, LSE 0.695, DREX 0.873) at additional concordance cost, revealing a three-tier operating regime: prior-free for label recovery, DPMM for manifold geometry, and DPMM-FM for visualization fidelity. Against 18 external baselines DPMM-Base wins 70.5% of core-metric comparisons (p<0.05). Gene Ontology enrichment confirms that geometry-improved latent components recover coherent biological programs. Rather than claiming universal superiority, this study characterizes the operating envelope of nonparametric mixture priors and identifies the task contexts-- trajectory analysis, manifold visualization, and program-level annotation--where adaptive geometric structure outweighs label-counting accuracy.

bioinformatics↗

bioRxiv2026

Flow-Matching-Refined Dirichlet-Prior Autoencoders for Interpretable and Structurally Balanced Single-Cell Representation Learning

Variational autoencoders for single-cell transcriptomics typically learn Gaussian latent spaces that lack part-based interpretability: individual latent dimensions carry no inherent biological meaning and the decoder provides no explicit gene-program readout. We introduce Topic-FM, a family of neural topic VAEs in which a logistic-normal Dirichlet prior constrains the latent vector to the probability simplex, turning each coordinate into a topic proportion and the decoder weight matrix into a directly readable topic-gene signature. A conditional optimal-transport flow field, trained entirely in pre-softmax [R]K, sharpens posterior geometry without modifying the decoder or breaking simplex validity. Unlike nonparametric mixture priors that improve geometry at the expense of label concordance, Topic-FM improves all core metrics simultaneously: across 56 scRNA-seq datasets, Topic-FM-Transformer raises NMI by 8.2%, ARI by 20.4%, and ASW by 21.7% relative to prior-free Pure-VAE (composite 0.502 vs. 0.434, +15.6%). Wilcoxon signed-rank tests confirm significance with medium-to-large Cliffs{delta} effects on all three metrics--no concordance-geometry trade-off is observed. Downstream kNN classification improves by 13.5% in accuracy and 27.7% in macro-F1. Among four architectural variants, Topic-FM-Contrastive achieves the highest external core win rate (86.4% against 23 baselines), while Topic-FM-Transformer leads on composite score and supervised discrimination. Dual-pathway biological validation--perturbation importance and direct decoder-{beta} readout-- yields convergent GO enrichment, demonstrating that the learned topics correspond to coherent, annotatable gene programs rather than opaque embedding dimensions.

bioinformatics↗

bioRxiv2026

The WalRK two-component system in Streptococcus pneumoniae ensures robustness of secondary wall polymer attachment

Capsular polysaccharide (CPS) is essential for Streptococcus pneumoniae virulence. Yet, the mechanism linking CPS to peptidoglycan (PG) remains unclear. Here, we identified a strong negative genetic interaction between the genes encoding the putative capsule ligase CpsA and the WalK histidine kinase, a component of the WalRK two-component system regulating cell wall homeostasis. In the absence of cpsA, capsule polymers compete with wall teichoic acids for ligase activity to PG. This induces cell wall stress and is sensed by the WalRK system. Overexpression of the PG hydrolase pcsB or disruption of the PG-modifying enzymes pgdA and oatA restored growth of strains lacking cpsA and walK. Furthermore, CpsA overproduction compensates for the loss of other LytR-Cps2A-Psr (LCP) ligases, suggesting it can support capsule and wall teichoic acid syntheses. These findings support the model that LCP ligases are semi-redundant, although they may install secondary polymers on a different residue of PG. This work also suggests that WalRK signaling compensates for reduced capsule and WTA attachment by positively regulating PG hydrolases. SIGNIFICANT STATEMENTStreptococcus pneumoniae causes approximately half a million deaths annually. A powerful public health tool for controlling pneumococcal infections is vaccination against the protective capsule. Yet, the mechanisms by which the capsule layer attaches to the underlying cell wall remain poorly defined. This study shows that the conserved capsule gene CpsA is not strictly required for capsule attachment but instead works together with other LytRIZCpsAIZPsr (LCP) ligases. However, it requires the essential WalRK signaling system to maintain cell envelope integrity. Defects in LCP activity are alleviated by WalRKIZdriven upregulation of peptidoglycan hydrolases, overexpression of PcsB, or inactivating peptidoglycan modifications that limit hydrolysis. These findings reveal coordination among flux to capsule synthesis, secondary wall polymer attachment, and cell wall remodeling. TEASERThe WalRK two-component system responds to cell envelope stress caused by reduced LCP ligase activity.

microbiology↗

bioRxiv2026

Midbrain Tet1 dosage defines inter-individual binge-eating susceptibility

Binge-eating disorder (BED) is the most common eating disorder worldwide and carries life-altering comorbidities. While genetic and environmental risk factors have been identified, the mechanisms that determine inter-individual susceptibility to BED remain largely unknown. Here, we demonstrate that developmental dosage of the DNA hydroxymethylase Tet1 defines stable inter-individual differences in binge-eating susceptibility. In mice, midbrain dopaminergic neurons of the ventral tegmental area (VTADA) are essential for the induction of addictive binge-eating behavior, express high levels of Tet1, and undergo rapid and widespread DNA hydroxymethylation remodeling upon experimental binge eating. Strikingly, Tet1 haploinsufficiency creates pronounced inter-individual variation in binge-eating susceptibility even among genetically identical mice, which we trace to reduced connectivity between the prelimbic medial prefrontal cortex (mPFCPL) and the VTA. Chemogenetic inhibition of mPFCPL[->]VTA projections reduces binge-eating susceptibility, whereas EGR1-guided re-activation of TET1 in VTA dopaminergic neurons restores susceptibility, supporting a causal role for this axis. Importantly, TET1 promoter methylation in patients associates with binge-eating behavior and reward-circuit function, suggesting conservation of this regulatory network in humans. Collectively, these findings identify Tet1 dosage as a novel regulator of binge-eating susceptibility and provide a mechanistic basis for how inter-individual differences in behavior are established.

neuroscience↗

bioRxiv2026

NeuroMark-SZ: A Holistic Resting-State-fMRI-Based Model for Divergent Functional Circuitry in Schizophrenia

BackgroundSchizophrenia is a severe neuropsychiatric disorder. Efforts to describe the underlying biology and establish diagnostic markers through non-invasive neuroimaging methods are ongoing, resulting in a range of theoretical brain-based frameworks. Prominent frameworks for aberrant schizophrenia-associated functional connectivity in resting-state functional magnetic resonance imaging (rsfMRI) include the dysconnectivity hypothesis, theory of cognitive dysmetria, and triple network theory. Although informative, prior work can be improved by increasing sample size, avoiding confirmation bias, and accounting for individual variability and the effects of medication and chronicity. MethodsWith these recommendations in mind, we employed a data-driven, whole-brain approach using a large multi-site rsfMRI dataset (N = 2,656; schizophrenia = 1,248). We used reference-guided independent component analysis to generate subject-specific whole-brain functional network connectivity (FNC) and extract imaging markers of similarity to schizophrenia patterns. We modeled the relationship between medication dosage, age of onset, chronicity, symptom severity, and cognitive performance and FNC. ResultsOur analysis identified a reliable schizophrenia-FNC signature characterized by aberrantly stronger negative cerebellothalamic and positive thalamocortical connectivity, implicating sensory, motor, and associative cortical circuits. While medication and chronicity were significantly associated with these signatures, the core cerebellothalamic disruptions remained a robust marker of schizophrenia. ConclusionsThis work represents the largest schizophrenia-specific rsfMRI study to date, refines existing theoretical frameworks with a more nuanced map of how clinical variables interact with brain connectivity, and provides a high-fidelity template of schizophrenia-related connectivity. We have released this template as an open-access resource to facilitate reproducibility and accelerate the development of reliable rsfMRI-based schizophrenia biomarkers.

neuroscience↗

bioRxiv2026

Synora: vector-based boundary detection for spatial omics

Tumor-stroma boundaries are critical microenvironmental niches where malignant and non-malignant cells exchange signals that shape invasion, immune modulation and therapeutic response. Spatial omics platforms now resolve these interfaces at single-cell scale, but computational boundary detection remains challenging because heterogeneous neighborhoods can arise either from true compartment interfaces or from unstructured immune infiltration. Here we present Synora, a modality-agnostic computational framework that identifies tumor boundaries using only cell coordinates and binary tumor/non-tumor annotations, making it readily applicable across a broad range of spatial omics modalities. Synora introduces orientedness, a novel metric that quantifies directional neighborhood asymmetry and distinguishes true boundary cells, where neighbors are spatially segregated by type, from infiltrated regions where cell types intermingle randomly. By integrating orientedness with traditional diversity measures into a unified BoundaryScore, Synora achieves robust boundary identification across synthetic datasets with ground-truth boundaries, maintaining performance under realistic perturbations including 50% missing cells and 25% infiltration. Application to 15 Visium HD spatial transcriptomic datasets across multiple cancer types reveals consistent boundary-enriched gene signatures and cell-type spatial gradients. Validation on a CODEX multiplexed protein dataset demonstrates that Synoras precise boundary identification enables discovery of clinically relevant cellular neighborhoods and disease-associated spatial patterns missed by frequency-based approaches. Synora enables boundary-aware spatial analyses by making tissue interfaces quantifiable from minimal inputs, helping to standardize interface detection and comparison across spatial omics platforms and biological contexts.

bioinformatics↗

bioRxiv2026

HuR inhibition attenuates hypertension and fibrosis in chronic kidney disease

BackgroundElevated RNA-binding protein HuR has been reported in patients with chronic kidney disease (CKD), but its specific pathogenic role remains unclear. Here, we investigated HuR involvement in progressive CKD induced by deoxycorticosterone acetate (DOCA) plus angiotensin II (Ang II) in mice and evaluated the therapeutic efficacy and mechanisms of the HuR inhibitor KH3. MethodsAdult male mice underwent uninephrectomy and were subjected to DOCA + Ang II infusion with 1% NaCl in drinking water. Mice were then treated with KH3 or vehicle for 3 weeks. Control mice received saline injections without DOCA and Ang II infusion. ResultsDOCA + Ang II infusion markedly increased HuR expression in circulating exosomes and kidney tissues, which was attenuated by KH3. KH3 halted the progression of albuminuria and improved renal function, and reduced kidney hypertrophy and glomerular and tubulointerstitial fibrosis compared with untreated DOCA + Ang II mice. These improvements were associated with reduced podocyte and tubular injury. KH3 also decreased renal macrophage infiltration and suppressed NF-{kappa}Bp65, Nox2, AKT phosphorylation, TGF-{beta}1, and Wisp1, consistent with reduced inflammation, oxidative stress, and fibrosis. In addition, KH3 partially lowered arterial blood pressure in DOCA + Ang II-infused mice, an effect that may involve suppression of SGLT2-associated profibrotic vascular responses, as supported by studies in cultured VSMCs and mesenteric resistance arteries. ConclusionsHuR is upregulated in DOCA + Ang II-induced renal and vascular injury and contributes to hypertensive, inflammatory, oxidative, and fibrotic responses in CKD. Pharmacologic inhibition of HuR-RNA interactions represents a promising therapeutic strategy for CKD. NOVELTY AND RELEVANCEO_ST_ABSWhat Is New?C_ST_ABSThis study identifies the RNA-binding protein HuR (ELAVL1) as a previously unrecognized upstream post-transcriptional regulator of blood pressure in hypertensive chronic kidney disease. We demonstrate for the first time that pharmacologic disruption of HuR-RNA interactions lowers arterial blood pressure in vivo. In addition, we uncover a novel HuR-SGLT2-vascular smooth muscle cell (VSMC) signaling axis, revealing that HuR regulates inducible vascular SGLT2 expression and Ang II-mediated vasoconstrictive responses. What Is Relevant?Hypertension in CKD arises from integrated renal and vascular dysfunction that is incompletely controlled by current therapies. Our findings are highly relevant because we identify HuR as a nodal post-transcriptional regulator that coordinates renal injury, vascular inflammation, and smooth muscle contractility, rather than acting within a single cell type or signaling pathway. Clinical and Pathophysiological ImplicationThese data support a model in which HuR-driven RNA regulatory programs amplify Ang II-dependent vascular hypercontractility and hypertension in CKD. Therapeutic targeting of HuR-RNA interactions represents a novel antihypertensive strategy that may complement renin-angiotensin-aldosterone system (RAAS) blockade and provides mechanistic insight into the blood pressure-lowering and vascular protective effects of SGLT2 inhibitors, including in non-diabetic CKD.

pathology↗

bioRxiv2026

Geoclimatic oscillations and ancient reciprocal adaptive introgression shape the evolutionary trajectories of threatened Coilia

Geoclimatic oscillations have repeatedly reshaped biodiversity, yet how ancient lineages persist and diversify through profound environmental upheavals remains a central question in evolutionary genomics. By integrating chromosome-level genome assemblies, population genomics, and transcriptomic data across the Coilia species complex, we reconstruct deep evolutionary histories of Coilia along the dynamic East Asian margins. Our analyses reveal divergences dating to the Miocene, coinciding with major tectonic and climatic transitions, including Tibetan Plateau uplift-linked reorganization of the Yangtze drainage toward the East China Sea, and the Middle Miocene Climatic Optimum. Despite long-term separation, we uncover evidence for ancient, reciprocal introgression among Coilia lineages, with gene flow confined to discrete genomic regions rather than distributed genome-wide. These introgressed regions are enriched for loci associated with immune function, vascular development, and osmoregulatory processes, and exhibit population genetic signatures consistent with positive selection. Temporal modelling indicates that this window of adaptive exchange preceded Pleistocene glacial intensification, after which gene flow became effectively restricted. Together, our results suggest that transiently permeable species boundaries historically acted as reservoirs of adaptive variation, shaping evolutionary trajectories and facilitating niche differentiation under repeated geoclimatic oscillations. This study highlights ancient adaptive introgression as an important evolutionary process contributing to the long-term persistence of threatened coastal fishes.

evolutionary biology↗

bioRxiv2026

Quantifying Behavioral Structure and Persistence in Open-Field Assays Using Entropy and Spectral Metrics

Traditional open-field assays quantify rodent behavior using low-dimensional scalar metrics that often overlook dynamical and temporally dependent aspects of spontaneous behavior. Here, we establish a high-throughput framework that integrates multi-camera 3D pose estimation from AVATAR-3D (1) with unsupervised autoregressive hidden Markov modeling using Keypoint-MoSeq (2-4) to transform recordings into time sequences of discrete, recurring behavioral units termed syllables. Drawing inspiration from information theory (5) and spectral analysis of Markov systems (6,7), we compute Shannon entropy and the second-largest eigenvalue (Eigen2) of the syllable transition matrix. Entropy and Eigen2 offer complementary lenses through which spontaneous behavior can be understood--one capturing dispersion of behavioral expression, the other reflecting temporal persistence and mixing dynamics. Together, these metrics quantify the organization of behavior as a stochastic dynamical system rather than a collection of isolated actions.

animal behavior and cognition↗

bioRxiv2026

Saline-free preparation for chronic in vivo imaging in adult Drosophila

Longitudinal brain imaging is essential for understanding neural mechanisms. Here, we present a saline-free, chronic preparation for repeated neural recording in adult Drosophila over multiple days. We describe steps for mounting flies, performing manual surgery on the head cuticle without external saline, and resealing the opening to create a transparent optical window. We demonstrate the utility of this approach by tracking single-neuron spiking and neuronal calcium dynamics over 7-10 days. This protocol is potentially applicable to other insect species. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=173 SRC="FIGDIR/small/706199v1_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@abeb34org.highwire.dtl.DTLVardef@deaf93org.highwire.dtl.DTLVardef@1d8fc24org.highwire.dtl.DTLVardef@91a696_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2026

Effects of the Hypomethylating Agent Guadecitabine on Peripheral Blood Mononuclear Cell Methylomes and Immune Cell Populations in Small-Cell Lung Cancer Patients

BackgroundSmall-cell lung cancer (SCLC) represents 15% of lung cancers and with a 5-year survival rate under 7% remains one of the deadliest malignancies. Although initially responsive to chemotherapy, rapid recurrence and resistance are common. Epigenetic modifications, particularly DNA methylation, contribute to tumor progression and therapy resistance. Guadecitabine, a hypomethylating agent (HMA), has shown promising clinical activity when combined with carboplatin in preclinical models. We evaluated the combination of guadecitabine with carboplatin as a second-line treatment for extensive-stage SCLC (NCT03913455). Here we report methylome changes in peripheral blood mononuclear cell (PBMCs) collected at baseline and during treatment from patients on the trial. ResultsPMBC DNA was analyzed using Infinium HumanMethylationEPIC v1.0 bead chips. Data were processed and differentially methylated positions (DMPs) were identified and analyzed for pathway enrichment using bioinformatic approaches and immune deconvolution analyses were conducted to investigate the impact on immune cell composition. Direct comparison of PBMCs between cycle 2 day 5 (C2D5; post-treatment) vs cycle 1 day 1 (C1D1; pre-treatment) revealed a greater number of hypomethylated DMPs (380 DMPs in C2D5 vs C1D1 PBMCs; p < 0.05, |{beta}| > 20%). Moreover, when first compared with normal PBMCs from cancer-free controls, the number of hypomethylated DMPs was even greater in C2D5 than in C1D1 (1,771 vs 237 DMPs, respectively; p < 0.05, |{beta}| > 20%). Long interspersed nucleotide elements-1 (LINE-1) were also significantly hypomethylated in PBMCs after HMA treatment (C2D5), compared to C1D1. Pathway analysis of hypomethylated DMPs revealed significant alterations in key signaling pathways including NF-{kappa}B, Rho GTPase, pulmonary fibrosis, and p75 NTR in C1D1 vs C2D5. When normal PBMCs were compared to C1D1 PBMCs, changes in IL-3 signaling, Fc{gamma} receptor-mediated phagocytosis, and molecular mechanisms of cancer were observed. Deconvolution analysis revealed a significantly higher percentage of monocytes in C1D1 PBMCs vs normal PBMCs. However, after HMA treatment, percentages of monocytes and B cells decreased, while eosinophil percentage increased in C1D1 compared to C2D5 PBMCs. ConclusionIn the first study on the global impact of HMA treatment on PBMC methylomes in SCLC patients, DNA methylation changes associated with biological pathways related to PBMC function reveal shifts in distinct immune cell populations. SummaryMethylome changes in peripheral blood mononuclear cell (PBMCs) from small cell lung cancer (SCLC) patients treated with an epigenetic therapy revealed global hypomethylation and altered cancer signaling processes associated with tumor progression, immune response, therapy resistance and significant change in the proportion of immune cells. Integrating blood-based methylation biomarkers into clinical trials of epigenetic therapy and methylomic analysis of PBMCs provides direct monitoring of treatment effects in cancer patients, which may improve patient selection and enable real-time response assessment in patients receiving hypomethylating agents.

genomics↗

bioRxiv2026

Reciprocal adipose-heart regulation of NRAC

Fatty acids (FAs) are a major energy substrate, and their storage and utilization must be balanced across tissues to maintain metabolic homeostasis. White adipose tissue (WAT) and the heart play opposing roles in FA metabolism, with WAT serving as the primary site of FA storage and the heart relying heavily on FAs as an energy source. However, the molecular factors regulating FA handling between the two tissues remain incompletely understood. We previously identified NRAC as a nutritionally regulated adipose- and cardiac-enriched protein; however, how its expression is regulated between these tissues across nutritional states, and how this relates to systemic FA homeostasis, remains unclear. Here, we integrate analyses of human transcriptomic datasets with nutritional and genetic perturbations in mice to examine the association between NRAC and FA metabolism. Human single-cell transcriptomic data from GTEx show enrichment of NRAC expression in adipocytes and cardiomyocytes, and analysis of subcutaneous adipose tissue from the UK Twins cohort reveals strong associations between NRAC expression and the abundance of multiple adipose lipid species. In mice, NRAC expression in WAT and heart was reciprocally regulated by fasting and refeeding and showed opposing associations with circulating free fatty acid (FFA) levels; moreover, whole-body deletion of NRAC elevated circulating FFA. Together, these findings support a role for NRAC in nutritionally responsive inter-organ regulation of FA between WAT and the heart, with implications for systemic lipid homeostasis.

physiology↗

bioRxiv2026

Starve-Feed Cycles Direct Quiescence to Proliferation Transitions in Drosophila Follicle Stem Cells via Transcriptional Regulation

Stem cell quiescence is a reversible state in which cells temporarily exit the cell cycle but remain poised to reenter, on cue. Robust protection of the balance between stem cell quiescence and proliferation (Q->P) is critical for long-term tissue health. Proliferation without proper resources drives disease states including cancer or birth defects. Conversely, extended periods of quiescence can lead to irreversible senescence, causing stem cell loss, aging symptoms, and vulnerability to oncogenic transformation. Diet is a central regulator of Q->P. Tissue stem cells are particularly impacted, entering periods of quiescence during nutrient restriction, with rapid induction of proliferation upon feeding. We demonstrated previously that the Hedgehog (Hh) signaling pathway is necessary and sufficient for controlling Q->P responses to dietary changes in epithelial Follicle Stem Cells (FSCs) in the fly ovary. The Hh effector, Cubitus Interruptus (Ci), is a transcriptional regulator that mediates the feeding response. To identify Ci-induced Q->P regulators, we labeled transcripts that are induced in FSCs during the 6-hour Q->P timecourse using thiouracil tagging (TU-tagging), and sequenced TU-tagged messages versus Input to prioritize candidates. Unexpectedly, cell cycle regulators were not induced, suggesting that other mechanisms control Q->P in FSCs. We describe a sequential screening approach that uncovered seven novel, feeding-dependent Q->P regulators, including a cholesterol transporter and, surprisingly, glial and neuronal regulators. Our results highlight the importance of dynamic regulation of gene expression for translation of dietary signals by stem cells, uncovering new pathways for mechanistic investigation.

genetics↗

bioRxiv2025

DECODING BRAIN DYNAMICS VIA CROSS-NETWORK NON-LINEAR TRAJECTORIES

Infant brain networks mature rapidly and nonlinearly, yet cross-network comparisons of developmental trajectories remain rare. We introduce cross-network non-linear trajectory characterization (CNTC), which fits cubic polynomials to age-related change in three spatial measures--network-averaged spatial similarity (NASS), network strength, and network size--derived from resting-state fMRI. Using 137 scans from 74 neurotypical infants spanning 0-6 months of age, we estimated networks via group ICA, modeled age trajectories within each network, and then contrasted coefficients across networks. Across seven canonical systems, trajectories showed robust age effects. Cross-network differences were dominated by linear terms, with selective quadratic effects and few cubic effects. Visual and cerebellar systems exhibited the steepest NASS slopes and pronounced increases in strength and size, whereas motor/attention changes were more gradual. CNTC provides a compact, coefficient-level summary of inter-network maturation and a basis for benchmarking atypical development in future work.

neuroscience↗

bioRxiv2025

Mapping the Developmental Trajectory of Functional Brain Networks in Early Infancy: Insights into Typical Maturation

Early infancy is a crucial period for brain development, during which fundamental functional and structural frameworks are established. Understanding the maturation of large-scale brain networks during this stage is essential for characterizing normative neurodevelopment and identifying potential deviations linked to neurodevelopmental disorders. In this study, we investigated developmental changes in the spatial organization of functional brain networks in infants using a longitudinal resting-state fMRI dataset comprising 137 scans from 74 low-likelihood developing infants aged 0-6 months. We applied independent component analysis to extract large-scale brain networks and utilized advanced spatial metrics, including network-averaged spatial similarity (NASS) to assess alignment with group-level patterns, network strength to quantify neural engagement based on voxel intensities, and network size to examine spatial distribution. Our findings reveal significant age-related increases in NASS across multiple networks, indicating greater consistency in functional organization over time. Additionally, most networks demonstrated increased network strength, reflecting heightened neural involvement, while network size exhibited distinct developmental trajectories, with some networks expanding and others remaining stable. These results highlight the dynamic evolution of functional brain architecture during early infancy, providing critical insights into neurodevelopmental processes. Clinical RelevanceThis study provides critical insights into early brain network development, which is essential for identifying biomarkers of neurodevelopmental disorders such as autism and schizophrenia. By mapping typical maturation patterns using advanced spatial metrics, our findings offer a foundation for early detection of atypical development. Deviations in network organization and strength could serve as early indicators, supporting neuroimaging-based screening and intervention strategies to optimize neurodevelopmental outcomes.

neuroscience↗