Biology subjects

Qu, G.

Publications and source records attributed to Qu, G..

10 recordsLinked to original sources

bioRxiv2026

The Drosophila IR20a Integrates L-arginine and Salt Signals via Distinct Subunit Assemblies

Amino acids and sodium salt are essential nutrients that frequently occur together in natural food sources. While animals typically employ distinct receptor subfamilies for specific taste modalities, the molecular mechanisms that integrate these concurrent nutritional signals at the peripheral level remain poorly understood. Here, we identify the Drosophila Ionotropic Receptor IR20a as a multimodal tuning receptor required for the detection of both the amino acid arginine and low NaCl salt. We find that IR20a is expressed in a subset of IR76b-positive gustatory receptor neurons (GRNs) distinct from GRNs dedicated to sweet or bitter taste. Mutation analysis and heterologous expression experiments demonstrate that IR20a functions combinatorially: it pairs with the core receptor IR25a to detect arginine, whereas the additional recruitment of IR76b is required to confer sensitivity to low salt. Crucially, we establish that IR20a/IR25a complex mediates synergistic responses to arginine-salt mixtures, thereby enhancing feeding preference. Furthermore, we reveal that IR20a provides a tonic, state-independent nutrient signal that operates in parallel to the starvation-modulated IR56b pathway to regulate NaCl feeding. Together, our studies uncover a molecular logic in which combinatorial assembly of receptor complexes enables the peripheral integration of complex nutritional signals.

neuroscience↗

bioRxiv2026

Modeling Complex Effects and Individual Variability in Multi-Paradigm fMRI with Nonlinear Mixed Models

Functional magnetic resonance imaging (fMRI) data are inherently complex, characterized by high dimensionality, intricate inter-regional dependencies, and substantial individual variability across experimental paradigms. Traditional linear mixed models (LMMs) provide a principled framework that models population-level fixed effects while estimating variance components arising from subject-level random effects; however, they often fail to adequately capture nonlinear relationships inherent in neuroimaging data. To address these limitations, we introduce the nonlinear mixed model (NMM) approach, an innovative extension of the LMM framework that integrates neural networks to flexibly model complex fixed-effect relationships while preserving the random-effects structure to account for individual differences. NMM advances fMRI analysis by: (1) identifying robust functional connectivity (FC) patterns consistently observed across multiple paradigms; (2) leveraging SHapley Additive exPlanations (SHAP) analysis to provide post-hoc interpretability of the nonlinear fixed effects, quantifying how age, sex, and paradigm contribute to predicted FC and how these effects are distributed across large-scale brain networks; and (3) using subject-specific random effects as neural fingerprints that not only show systematic variability across attention and default mode systems but also predict standardized cognitive scores, demonstrating biological relevance. Applied to the Philadelphia Neurodevelopmental Cohort (PNC) across emotion, n-back, and resting-state paradigms, NMM achieved superior model fit relative to classical LMMs, as evidenced by lower mean squared error (MSE) in predicting FC. This framework offers a statistically rigorous and practically explainable approach for modeling large-scale FC from modest covariates while explicitly separating population-level effects from stable individual variability in functional brain organization.

bioengineering↗

bioRxiv2025

FADVI: disentangled representation learning for robust integration of single-cell and spatial omics data

Integrating single-cell and spatial omics data remains challenging due to strong batch effects across experiments and platforms. Existing methods focus on minimizing these effects but cannot disentangle technical variation from true biological signals. Here, we present FADVI, a variational autoencoder framework partitioning the latent space into batch-specific, label-related, and residual subspaces. By combining supervised classification, adversarial training, and cross-covariance penalty, FADVI disentangles batch from biological representations, preserving biological variation while correcting batch effects. Benchmarking across scRNA-seq, scATAC-seq, and high-resolution spatial transcriptomics datasets, FADVI consistently outperforms state-of-the-art integration methods. FADVI also enables feature attribution for revealing genes associated with cell type identity and batch variation. Together, these results demonstrate that FADVI provides robust, interpretable integration for large-scale single-cell and spatial omics data, offering a powerful framework for downstream analysis and discovery.

bioinformatics↗

bioRxiv2025

A Na+-selective High-salt Taste Receptor Mediates State-Dependent Sodium Aversion

Sodium is an essential ion, but harmful in excess. Animals must suppress sodium aversion during deficiency, yet a cation-nonselective override would expose them to other harmful salts. Whether and how the sensory periphery regulates high-salt taste to achieve Na+-specific tuning remains unknown. Here we identify a Na+-selective, state-tunable high-salt detection mechanism in Drosophila. IR11a and IR25a function in bitter gustatory receptor neurons (GRNs) and are required for aversion specifically to high Na+. Loss of IR11a abolishes responses to Na+ and Li+ while sparing detection of K+, Ca2+, and bitter compounds. Heterologous expression confirms that IR11a and IR25a together confer Na+ responsiveness. During sodium deprivation, Na+ sensitivity in IR11a-expressing neurons is decreased and restored upon satiety, allowing flies to attenuate Na+ aversion specifically. The IR7c-dependent nonselective mechanism remains insensitive to sodium deprivation, thereby preserving aversion to other noxious cations even during sodium deficiency. The IR11a-mediated response is amiloride-sensitive, a functional signature reminiscent of mammalian ENaC despite the absence of sequence homology. This work reveals that peripheral high-salt taste combines a fixed, cation-nonselective defense with a sodium-selective, physiologically tunable mechanisms, offering a candidate logic for aversive high-salt taste in vertebrates, where the molecular basis remains poorly defined.

neuroscience↗

bioRxiv2024

DNA StairLoop: Achieving High Error-correcting and Parallel-processing Capabilities in DNA-based Data Storage

DNA-based data storage is a promising solution to the challenges of large-scale data storage. However, the low throughput of the mainstream inkjet-based DNA synthesis method has hindered its widespread adoption. In contrast, high-throughput electrochemical synthesis provides higher throughput but with more nucleotide insertion, deletion, and substitution errors. Here, we propose an innovative coding scheme with high error correction capabilities, called DNA StairLoop. This coding scheme features a staircase interleaver and allows for component codes such as the convolutional code and the ow-density Parity-check code, allowing for flexible adaptation of the coding scheme. Both the row and the column decoders are soft input and soft output, enabling further improvement in data recovery accuracy through iterative decoding. The staircase interleaver facilitates extensive parallel decoding capabilities while effectively preserving parallelism across a multitude of nodes. In the in vitro experiment, DNA StairLoop successfully recovered the raw information in a staircase block with a nucleotide error rate of more than 8%. The simulations revealed that the DNA StairLoop can correct 10% nucleotide errors. Moreover, in parallel computing processing, the decoding time of our code continues to decrease dramatically.

synthetic biology↗

bioRxiv2024

Multi-View Integrative Approach For Imputing Short-Chain Fatty Acids and Identifying Key factors predicting Blood SCFA

Short-chain fatty acids (SCFAs) are the main metabolites produced by bacterial fermentation of dietary fiber within gastrointestinal tract. SCFAs produced by gut microbiotas (GMs) are absorbed by host, reach bloodstream, and are distributed to different organs, thus influencing host physiology. However, due to the limited budget or the poor sensitivity of instruments, most studies on GMs have incomplete blood SCFA data, limiting our understanding of the metabolic processes within the host. To address this gap, we developed an innovative multi-task multi-view integrative approach (M2AE, Multi-task Multi-View Attentive Encoders), to impute blood SCFA levels using gut metagenomic sequencing (MGS) data, while taking into account the intricate interplay among the gut microbiome, dietary features, and host characteristics, as well as the nuanced nature of SCFA dynamics within the body. Here, each view represents a distinct type of data input (i.e., gut microbiome compositions, dietary features, or host characteristics). Our method jointly explores both view-specific representations and cross-view correlations for effective predictions of SCFAs. We applied M2AE to two in-house datasets, which both include MGS and blood SCFAs profiles, host characteristics, and dietary features from 964 subjects and 171 subjects, respectively. Results from both of two datasets demonstrated that M2AE outperforms traditional regression-based and neural-network based approaches in imputing blood SCFAs. Furthermore, a series of gut bacterial species (e.g., Bacteroides thetaiotaomicron and Clostridium asparagiforme), host characteristics (e.g., race, gender), as well as dietary features (e.g., intake of fruits, pickles) were shown to contribute greatly to imputation of blood SCFAs. These findings demonstrated that GMs, dietary features and host characteristics might contribute to the complex biological processes involved in blood SCFA productions. These might pave the way for a deeper and more nuanced comprehension of how these factors impact human health.

bioinformatics↗

bioRxiv2024

The heme-scavenger, hemopexin, protects against fungal lung injury by mitigating NETosis: an experimental and computational study.

Invasive aspergillosis is characterized by lung hemorrhage and release of extracellular heme, which promotes fungal growth. Heme can also mediate tissue injury directly, and both fungal growth and lung injury may induce hemorrhage. To assimilate these interdependent processes, we hypothesized that, during aspergillosis, heme mediates direct lung injury independent of fungal growth, leading to worse infection outcomes, and the scavenger protein, hemopexin, mitigates these effects. Mice with neutropenic aspergillosis were found to have a time-dependent increase in lung extracellular heme and a corresponding hemopexin induction. Hemopexin deficiency resulted in markedly increased lung injury, fungal growth, and lung hemorrhage. Using a computational model of the interactions of Aspergillus, heme, and the host, we predicted a critical role for heme-mediated generation of neutrophil-extracellular traps in this infection. We tested this prediction using a fungal strain unable to grow at body temperature, and found that extracellular heme and fungal exposure synergize to induce lung injury by promoting NET release, and disruption of NETs was sufficient to attenuate lung injury and fungal burden. These data implicate heme-mediated NETosis in both lung injury and fungal growth during aspergillosis, resulting in a detrimental positive feedback cycle that can be interrupted by scavenging heme or disrupting NETs.

immunology↗

bioRxiv2024

A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds

ObjectivefMRI and derived measures such as functional connectivity (FC) have been used to predict brain age, general fluid intelligence, psychiatric disease status, and preclinical neurodegenerative disease. However, it is not always clear that all demographic confounds, such as age, sex, and race, have been removed from fMRI data. Additionally, many fMRI datasets are restricted to authorized researchers, making dissemination of these valuable data sources challenging. MethodsWe create a variational autoencoder (VAE)-based model, DemoVAE, to decorrelate fMRI features from demographics and generate high-quality synthetic fMRI data based on user-supplied demographics. We train and validate our model using two large, widely used datasets, the Philadelphia Neurodevel-opmental Cohort (PNC) and Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP). ResultsWe find that DemoVAE recapitulates group differences in fMRI data while capturing the full breadth of individual variations. Significantly, we also find that most clinical and computerized battery fields that are correlated with fMRI data are not correlated with DemoVAE latents. An exception are several fields related to schizophrenia medication and symptom severity. ConclusionOur model generates fMRI data that captures the full distribution of FC better than traditional VAE or GAN models. We also find that most prediction using fMRI data is dependent on correlation with, and prediction of, demographics. SignificanceOur DemoVAE model allows for generation of high quality synthetic data conditioned on subject demographics as well as the removal of the confounding effects of demographics. We identify that FC-based prediction tasks are highly influenced by demographic confounds.

neuroscience↗

bioRxiv2022

Osteosarcoma-enriched transcripts paradoxically generate osteosarcoma-suppressing extracellular proteins

Osteosarcoma (OS) is the common primary bone cancer that affects mostly children and young adults. To augment the standard-of-care chemotherapy, we examined the possibility of protein-based therapy using mesenchymal stem cells (MSCs)-derived proteomes and osteosarcoma-elevated proteins. While a conditioned medium (CM), collected from MSCs, did not present tumor-suppressing ability, the activation of PKA converted MSCs into induced tumor-suppressing cells (iTSCs). In a mouse model, the direct and hydrogel-assisted administration of CM inhibited tumor-induced bone destruction, and its effect was additive with Cisplatin. CM was enriched with proteins such as Calreticulin, which acted as an extracellular tumor suppressor by interacting with CD47. Notably, the level of Calr transcripts was elevated in OS tissues, together with other tumor-suppressing proteins, including histone H4, and PCOLCE. PCOLCE acted as an extracellular tumor-suppressing protein by interacting with amyloid precursor protein (APP), a prognostic OS marker with poor survival. The results supported the possibility of employing a paradoxical strategy of utilizing OS transcriptomes for the treatment of OS.

cancer biology↗

bioRxiv2019

Potential function of CbuSPL and gene encoding its interacting protein during flowering in Catalpa bungei

\"Bairihua\", a variety of the Catalpa bungei, has a large amount of flowers and a long flowering period which make it an excellent material for flowering researches in trees. SPL is one of the hub genes that regulate both flowering transition and development. Here, a SPL homologues CbuSPL9 was cloned using degenerate primers with RACE. Expression studies during flowering transition in Bairihua and ectopic expression in Arabidopsis showed that CbuSPL9 was functional similarly with its Arabidopsis homologues. In the next step, we used Y2H to identify the proteins that could interact with CbuSPL9. HMGA, an architectural transcriptional factor, was identified and cloned for further research. BiFC and BLI showed that CbuSPL9 could form a heterodimer with CbuHMGA in the nucleus. The expression analysis showed that CbuHMGA had a similar expression trend to that of CbuSPL9 during flowering in \"Bairihua\". Intriguingly, ectopic expression of CbuHMGA in Arabidopsis would lead to aberrant flowers, but did not effect flowering time. Taken together, our results implied a novel pathway that ChuSPL9 regulated flowering development, but not flowering transition, with the participation of ChuHMGA. Further investments need to be done to verify the details of this pathway.

plant biology↗