Biology subjects

Jiang, F.

Publications and source records attributed to Jiang, F..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Post-weaning social isolation impairs the orbitofrontal cortical circuit subserving contagious pain and prosocial behaviors

Empathic behaviors are sensitive to environmental factors like post-weaning social isolation (SI), yet the mechanisms by which SI affects empathy remain unclear. Here, we show that mice subjected to SI exhibit marked impairments in contagious pain and prosocial behaviors, including allo-grooming and allo-licking toward cagemates experiencing inflammatory pain. Mechanistically, we identify the glutamatergic projection from the ventromedial thalamic nucleus (VM) to the orbitofrontal cortex (OFC) as critical for these empathy-like responses. SI induces hypoexcitability of OFC glutamatergic neurons and attenuates excitatory synaptic transmission within the VM[->]OFC pathway. Remarkably, chemogenetic activation of OFC neurons or the VM[->]OFC projection restores empathic behaviors in SI mice. Furthermore, we uncover a molecular basis for SI-induced OFC hypoexcitability: the downregulation of Grik3, encoding a kainate-type glutamate receptor subunit. These findings reveal a previously uncharacterized thalamocortical mechanism through which early-life social deprivation disrupts empathic behaviors, offering insights into the underpinnings of social-affective dysfunction.

neuroscience↗

bioRxiv2025

RAFTER: a releasing factor tethered RNA editing system for quantifying mRNA translation

Measurement of mRNA translational efficiency has heavily relied on quantifying the amount of polysome-associated RNAs which would not always reflect the efficient translation. To address this, we developed RAFTER, which combined RNA editing enzyme with translation releasing factor to selectively label successfully translated RNAs. The translational efficiency estimated based on RAFTER correlated well with that by Ribo-seq and Polysome-seq, but with greatly simplified workflow, which enables it for single-cell analysis.

genomics↗

bioRxiv2025

High extraversion enhances attentional control through dynamic network reorganization

The extraversion-introversion dimension of personality is hypothesized to differ based on low or high cortical arousal, respectively. Notably, high cortical arousal in introverts is thought to underlie increased distractibility. Here, we assess fMRI while participants meditate (focused attention to their breath) under three levels of auditory distraction: no, low and high. Whereas introverts exhibited worsening attentional focus on their breath with increasing distraction, extraverts retained their ability to focus attention despite distraction. Dynamic functional connectivity analysis indicated that extraverts exhibited less globally efficient and less modular networks, which may prevent distracting stimuli from creating interference. Furthermore, connectivity strengths amongst the default mode, central executive, and salience networks were increased for extraverts and decreased in introverts during high focused attention; potentially indicating distinct cognitive processes that support attentional control. These findings support the hypothesis regarding differing levels of cortical arousal in extraverts and introverts and extend personality theory by linking the extraversion dimension to attentional control and functional connectivity dynamics.

neuroscience↗

bioRxiv2025

Simple Methods to Acutely Measure Multiple Timing Metrics among Sexual Repertoire of Male Drosophila

Male Drosophila courtship behavior is a key model for studying temporal decision-making. While courtship index (CI) is widely used to quantify mating activity, other timing metrics like courtship latency, copulation latency (CL), and mating duration (MD) remain understudied. Traditional methods for quantifying these behaviors are often labor-intensive and prone to human error. In this study, we present a protocol combining a modular chamber system and automated software (DrosoMating) to quantify 6 key timing metrics during male courtship. Our image-based video analysis enables precise identification of courtship and copulation events, as well as quantification of their timing and duration under controlled conditions. Validation shows <0.05% error rates and 98-99% agreement with manual scoring for CL, CI, and MD. The protocol supports genetic and neural circuit manipulations, detecting subtle genetic, social, and environmental behavioral variations. By minimizing manual effort and standardizing data collection, this approach facilitates scalable, reproducible studies on adaptive trade-offs, learning, and neural mechanisms in mating behavior. This method streamlines timing analysis in male courtship, offering reproducible metrics for behavioral genetics. HighlightA high-throughput software pipeline for automated temporal profiling of Drosophila melanogaster mating behavior after brief user-guided calibration. An accompanying, open-hardware platform that can be assembled at minimal cost while maintaining experimental rigor. The system attains near-manual accuracy and outputs Temporal Measurement Parameters data that are readily adaptable to--and quantifiable within--diverse behavioral paradigms.

developmental biology↗

bioRxiv2025

scRetinaDB: A Comprehensive Database of Single-Cell and Spatial Omics from Cross-Species Retinas

The retina is essential for encoding visual signals, and its dysregulation can lead to retinal diseases. Recent advances in single-cell and spatial sequencing technologies have yielded extensive omics data from retinal tissues across species and biological conditions. However, existing retinal omics data are dispersed across various repositories without uniform processing, which limits integrative analysis. To address this we developed scRetinaDB (https://casapp.dnayun.com/scretina/), a comprehensive resource that aggregates single-cell RNA sequencing (scRNA-seq), single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq), and spatial RNA sequencing (spRNA-seq) data from retinas across species and diverse biological conditions. The database comprises over 2.79 million retinal cells collected from 453 scRNA-seq datasets spanning 34 studies, 17 species and 27 biological conditions. For each species, these scRNA-seq datasets were integrated to construct a retinal cell atlas. In addition, scRetinaDB also contains 107 scATAC-seq and 18 spRNA-seq datasets from human and mouse retinas. The scRetinaDB website provides four major modules separately for browsing species-specific omics data, searching cross-species omics profiles, performing analyses of cell type annotation and cell similarity analysis, and downloading preprocessed multi-omics datasets. Overall, scRetinaDB is a valuable resource for retinal single-cell and spatial omics, advancing cross-species studies particularly in vision research.

bioinformatics↗

bioRxiv2025

Bodily Self in Macaque Monkeys and Human Infants

Whether monkeys possess the concept of bodily self remains controversial. We found that macaque monkeys trained for mirror self-recognition (MSR) could spontaneously recognize themselves when viewing video images of their back from a third-person perspective (3PP), but this generalization required a few minutes of "exploration". In contrast, through visual-tactile synchronization training similar to that used in human out-of-body studies, naive monkeys could recognize themselves from 3PP back images and demonstrated MSR within seconds. Interestingly, in human infants, we found that 3PP back self-recognition developed spontaneously several months after MSR. These results demonstrate the existence of hierarchical bodily self in macaques and similar stepwise development of bodily self in human infants, offering insights into the emergence and manifestation of bodily self in natural and artificial intelligence.

neuroscience↗

bioRxiv2025

OpenFISH enables integrated high-resolution spatial transcriptomics and metabolomics on a single tissue section

Spatial transcriptomics enables in situ mapping of gene expression, yet no current platform provides single-cell, same-section integration with metabolomics, limiting direct links between transcriptional programsand metabolic phenotypesin native tissue. We present OpenFISH, a rapid, imaging-based spatial transcriptomics method operable on standard microscopes, requiring no proprietary hardware, and fully compatible with matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). OpenFISH resolves hundreds of transcripts at subcellular resolution within 24 h and can be performed after MALDI-MSI, preserving metabolite distributions for cell-accurate co-registration on the same section. In mouse brain, integration with MALDI-MSI resolved metabolic heterogeneity at the level of individual cells. OpenFISH also quantified cell type-specific transcriptional activation of transposable elements after systemic lipopolysaccharide (LPS) challenge and detected disrupted spatial organization of D1 striatal neurons in Reeler mutants. Benchmarking showed performance comparable to or exceeding commercial platforms at [~]0.5% of per-sample cost. By enabling same-section, near-single-cell co-mapping of transcripts and metabolites in an accessible workflow, OpenFISH provides a scalable framework for high-content spatial multi-omics across neuroscience, immunology, cancer biology, and beyond.

molecular biology↗

bioRxiv2025

CMG2 interaction with actin is required for growth factor-induced chemotaxis in endothelial cells.

Capillary morphogenesis gene 2 (CMG2/ANTXR2) is a cell surface receptor that contributes to corneal angiogenesis and is essential for orienting endothelial cell chemotaxis in growth factor gradients. However, the mechanism by which CMG2 transmits signals to orient endothelial cells is unknown. Here, we use affinity proteomics to identify proteins that interact with CMG2. This led us to investigate CMG2 interaction with F-actin in the context of serum and individual growth factors (bFGF/PDGF/VEGF) in both primary and immortalized endothelial cells. We also measured different matrix-protein-mediated changes in chemotaxis. We find that CMG2 function is matrix dependent, and that CMG2-actin interaction is required for serum- and growth-factor-induced chemotaxis. Inhibiting chemotaxis with CMG2 antagonists (collagen 6, penta-galloyl-glucose, or anthrax toxin protective antigen; PA) leads to the release of actin cytoskeleton from CMG2. Furthermore, CMG2 co-localizes with actin at the leading edge during live migration. This colocalization is disrupted by PA, which also disrupts directional movement. Finally, we demonstrate that the conserved actin binding domain of CMG2 directly binds to F-actin in vitro where it bundles F-actin, suggesting a mechanism by which it enables directional migration.

molecular biology↗

bioRxiv2025

Venus-MAXWELL: Efficient Learning of Protein-Mutation Stability Landscapes using Protein Language Models

In-silico prediction of protein mutant stability, measured by the difference in Gibbs free energy change ({Delta}{Delta}G), is fundamental for protein engineering. Current sequence-to-label methods typically employ the two-stage pipeline: (i) encoding mutant sequences using neural networks (e.g., transformers), followed by (ii) the {Delta}{Delta}G regression from the latent representations. Although these methods have demonstrated promising performance, their dependence on specialized neural network encoders significantly increases the complexity. Additionally, the requirement to individually compute latent representations for each mutant site negatively impacts computational efficiency and poses the risk of overfitting. This work proposes the Venus-MO_SCPLOWAXWELLC_SCPLOW framework, which reformulates mutation {Delta}{Delta}G prediction as a sequence-to-landscape task. In Venus-MO_SCPLOWAXWELLC_SCPLOW, mutations of a protein and their corresponding {Delta}{Delta}G values are organized into a landscape matrix, allowing our framework to learn the {Delta}{Delta}G landscape of a protein with a single forward and backward pass during training. Besides, to facilitate future works, we also curated a large-scale {Delta}{Delta}G dataset with strict controls on data leakage and redundancy to ensure robust evaluation. Venus-MO_SCPLOWAXWELLC_SCPLOW is compatible with multiple protein language models and enables these models for accurate and efficient {Delta}{Delta}G prediction. For example, when integrated with the ESM-IF, Venus-MO_SCPLOWAXWELLC_SCPLOW achieves higher accuracy than ThermoMPNN with 10x faster in inference speed (despite having 50x more parameters than ThermoMPNN). The training codes, model weights, and datasets are publicly available at https://github.com/ai4protein/Venus-MAXWELL.

bioinformatics↗

bioRxiv2025

The RNA splicing factor PRPF8 is required for left-right organiser cilia function and determination of cardiac left-right asymmetry via regulation of Arl13b splicing

Cilia function in the left-right organizer (LRO) is critical for determining internal organ asymmetry in vertebrates. To further understand the genetics of left-right asymmetry, we isolated a mouse mutant with laterality defects, l11Jus27, from a random mutagenesis screen. l11Jus27 mutants carry a missense mutation in the pre-mRNA processing factor, Prpf8. cephaloph[o]nus (cph) mutant zebrafish, carrying a protein truncating mutation in prpf8, phenocopy the laterality defects of l11Jus27 mutants. Prpf8 mutant mouse and fish embryos have increased expression of an alternative transcript encoding the cilium-associated protein, ARL13B, that lacks exon 9. In zebrafish, over-expression of the arl13b transcript lacking exon 9 perturbed cilium formation and caused laterality defects. The shorter ARL13B protein isoform lacked interactions with intraflagellar transport proteins. Our data suggest that PRPF8 plays a prominent role in LRO cilia by through the regulation of alternative splicing of ARL13B, thus uncovering a new mechanism for cilia-linked developmental defects.

developmental biology↗

bioRxiv2025

Upstream open reading frame inactivation augments GATA4 translation and cardiomyocyte hypertrophy in mice

Upstream open reading frames (uORFs) are short peptide-encoding sequences located in the 5 untranslated region (5 UTR) of mRNAs, enabling translational repression of main (m)ORFs. While uORFs are found in [~]50% of mRNAs in humans, our understanding of their biological function remains limited. This study aims to elucidate the role of the uORF in the 5 UTR of the Gata4 (GATA binding protein 4) gene in cardiac biology by inactivating its start codon ({Delta}uORF) in the mouse genome. Our investigation reveals that mice with Gata4 uORF inactivation manifest spontaneous cardiac hypertrophy without apparent fibrosis as they age. Utilizing single-nucleus RNA sequencing (snRNA-seq), we uncovered significant transcriptional variations between wild-type (WT) and {Delta}ORF mice. Notably, mRNAs associated with sarcomeres and contractile functions show heightened expression levels, reflecting the hypertrophic phenotype. Notably, at least nine upregulated genes are GATA4-bound targets in mouse ventricles. Functional assessments of isolated primary adult cardiomyocytes confirmed enhanced hypertrophy and contractility in {Delta}ORF mice. Additionally, we employed single-nucleus transposase-accessible chromatin (snATAC)-seq to investigate changes in chromatin accessibility. Our results indicated increased accessibility within specific transcription-regulatory elements linked to elevated gene transcription. These putative cis-regulatory elements (pCREs) are significantly enriched in MEF2 (myocyte enhancer factor 2) binding motifs. In vitro luciferase reporter assays further supported the regulatory potential of three of these pCREs, highlighting their role in the transcriptional enhancement of three GATA4 target genes bound by MEF2 and GATA4. These findings illuminate the role of uORF in negatively regulating GATA4 protein expression and cardiomyocyte hypertrophy at the organismal level and provide a novel therapeutic target for cardiac pathogenesis.

biochemistry↗

bioRxiv2025

How do bacterial extracellular Contractile Injection Systems bind target cells? A remarkable diversity of receptor binding domains

Extracellular contractile injection systems (eCISs) represent a diverse family of bacteriophage tail-derived toxin delivery complexes in prokaryotes. eCISs are vital for microbial interaction with hosts, and they use tail fiber proteins for target cell binding. However, the identity of the tail fiber proteins and their target cells are unknown. Here, we conducted a comprehensive exploration of eCIS tail fiber genes, providing insights into their remarkable diversity, target cells, functional adaptations, and intriguing evolutionary dynamics. We identified 3,445 eCIS tail fiber proteins found in 2,585 eCIS loci from 1,069 microbes. These fibers can be categorized by five new N-terminal domains responsible for tail fiber attachment to eCIS baseplates. Importantly, we leveraged structure prediction to divide the fibers into 276 structural clusters and dissected 1,177 domain fold families which likely mediate glycan and protein binding on the cell surface of eukaryotes or bacterial targets. These rapidly evolving domains are likely acquired from diverse genomes of eukaryotes, bacteria, and viruses. Finally, we experimentally showed that a candidate tail fiber from Paenibacillus eCIS can bind and direct effector injection into THP-1 human monocyte-like cells, and may bind D-mannose on the cell surface. This study reveals the exceptional diversity of eCIS target specificity determinants, suggests new eCIS target cells in Nature, and provides thousands of proteins that adhere to different cell types.

microbiology↗

bioRxiv2025

HydraRNA: a hybrid architecture based full-length RNA language model

RNA, an essential component of the central dogma of molecular biology, plays versatile roles in all cellular processes. RNA large language models (LLMs) are emerging as powerful methods in RNA research to decipher its intricate network of function and regulation. However, previous RNA LLMs were based on the transformer model and pre-trained on short segment of non-coding RNAs, which limits their general usability. Here we present the first full-length RNA foundation model, HydraRNA, which is based on a hybrid architecture of bidirectional state space model and multi-head attention mechanism, and is pre-trained on a large amount of both protein-coding and non-coding RNAs. Despite being pre-trained with the fewest parameters and the least GPU resources, HydraRNA learns better RNA representations and outperforms the existing foundation models on a variety of downstream tasks, including RNA classification, prediction of RNA secondary structure, RBP binding sites, mRNA stability and translation efficiency. Furthermore, HydraRNA can accurately predict the effect of mutations and estimate the relative contributions of different mRNA regions to the RNA stability and translation. We anticipate that HydraRNA will enable dissecting the diverse properties of RNA, accelerating the research of RNA regulation and facilitating the optimal design of RNA therapeutics.

bioinformatics↗

bioRxiv2025

Discovery of Expression-Governing Residues in Proteins

Understanding how amino acids influence protein expression is crucial for advancements in biotechnology and synthetic biology. In this study, we introduce Venus-TIGER, a deep learning model designed to accurately identify amino acids critical for expression. By constructing a two-dimensional matrix that links model representations to experimental fitness, Venus-TIGER achieves improved predictive accuracy and enhanced extrapolation capability. We validated our approach on both public deep mutational scanning datasets and low-throughput experimental datasets, demonstrating notable performance compared to traditional methods. Venus-TIGER exhibits robust trans-ferability in zero-shot predicting scenarios and enhanced predictive performance in few-shot learning, even with limited experimental data. This capability is particularly valuable for protein design aimed at enhancing expression, where generating large datasets can be costly and time-consuming. Additionally, we conducted a statistical analysis to identify expression-associated features, such as sequence and structural preferences, distinguishing between those linked to high and low expression. Our investigation also revealed a correlation among stability, activity and expression, providing insight into their interconnected roles and underlying mechanisms.

bioengineering↗

bioRxiv2024

CBAM-ResNet34-based classification and evaluation method for developmental processes of greenhouse strawberries

Strawberries, known for their economic significance and rich nutritional value, are cultivated extensively worldwide. However, a host of workers need to be employed every year to identify and categorize the developmental stages of the strawberries in the greenhouses, which is not only time-consuming, inefficient, increasing the cultivation cost, but also difficult to guarantee the classification accuracy. Meanwhile, affected by the complicated background, occlusions, and color interference, the features of strawberries are proven challenging to be extracted via the traditional neural networks due to serious gradient disappearance. Therefore, an improved CBAM-ResNet34- based classification evaluation method for developmental processes of greenhouse strawberries is investigated. The procedure of this method is as follows: firstly, the developmental stages of greenhouse strawberries are classified by experts into four stages: Stage I (initial stage), Stage II (green and white fruit stage), Stage III (early ripening stage), and Stage IV (fully ripe stage). The 627, 640, 604, and 340 strawberry images for these four stages are captured. Subsequently, the images are divided into training, validation, as well as testing sets and then undergo image pre- processing, expansion, and augmentation. Whereafter, the 7x7 convolution kernel in the first layer of the network is replaced by three consecutive 3x3 convolution cores to eliminate the redundant weights and unnecessary model parameters, and the BasicBlocks configuration is adjusted. Finally, the CBAM attention mechanism is added to each BasicBlock so as to pinpoint the spatial position of the strawberries and extract their major features such as shape, size, and color. Comparison experiments with the conventional deep neural networks LeNet5, AlexNet, VGG16, ResNet18, ResNet34, and every improved part of CBAM-ResNet34 demonstrated that when the learning rate is 0.001, the Dropout rate is 0.3, and the Adams weight decay parameter is 0.001, the accuracies for validation and testing sets can reach to 92.36% and 87.56% with F1 scores of 0.92, 0.87, 0.85 and 0.88.

plant biology↗

bioRxiv2024

How to select the best zero-shot model for the viral proteins?

Predicting the fitness of viral proteins holds notable implications for understanding viral evolution, advancing fundamental biological research, and informing drug discovery. However, the considerable variability and evolution of viral proteins make predicting mutant fitness a major challenge. This study introduces the ProPEC, a Perplexity-based Ensemble Model, aimed at improving the performance of zero-shot predictions for protein fitness across diverse viral datasets. We selected five representative pretrained language models (PLMs) as base models. ProPEC, which integrates perplexity-weighted scores from these PLMs with GEMME, demonstrates superior performance compared to individual models. Through parameter sensitivity analysis, we highlight the robustness of perplexity-based model selection in ProPEC. Additionally, a case study on T7 RNA polymerase activity dataset underscores ProPECs predictive capabilities. These findings suggest that ProPEC offers an effective approach for advancing viral protein fitness prediction, providing valuable insights for virology research and therapeutic development. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/616860v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@30be8forg.highwire.dtl.DTLVardef@2ebf35org.highwire.dtl.DTLVardef@10b517eorg.highwire.dtl.DTLVardef@134681_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

Multisensory gamma stimulation enhances adult neurogenesis and improves cognitive function in a mouse model of Down syndrome

Down syndrome (DS) has been linked with deficits in hippocampal dependent cognitive tasks and adult neurogenesis, yet treatment options are still very limited. We and others previously showed that a non-invasive multisensory gamma stimulation using light and sound at 40 Hz ameliorated Alzheimers disease pathology and symptoms in mouse models. In this study, we tested the effects of 40 Hz multisensory stimulation in the Ts65Dn mice, a mouse model of DS. For three weeks, mice were exposed daily to one hour of stimulation or one hour of ambient light and sound. Mice receiving the stimulation showed improved object recognition and spatial working memory. Using single nuclei RNA-seq and experimental validations in mouse hippocampal samples, we identified underlying expression changes in gene regulatory networks and demonstrated increased adult neurogenesis and reorganization of synapses as potential mechanisms for these improved cognitive phenotypes. Together, our data reveal a novel effect of multisensory gamma stimulation on adult neurogenesis and beneficial effects of 40 Hz treatment on cognitive function in DS model mice. Significance StatementWe present strong evidence, using a well-characterized mouse model, that the cognitive and neurogenesis deficits in Down syndrome can be improved through non-invasive multi-sensory gamma stimulation. Employing a systems biology approach, we provide extensive hippocampal single-cell resolution gene expression signatures and changes in gene regulatory networks in response to sensory gamma stimulation.

neuroscience↗

bioRxiv2024

Development of potent and selective CK1α Molecular Glue Degraders

Molecular glue degraders (MGDs) are small molecules that facilitate proximity between a target protein and an E3 ubiquitin ligase thereby inducing target protein degradation. Glutarimide-containing compounds are MGDs that bind to cereblon (CRBN) and recruit neosubstrates. Through explorative synthesis of a glutarimide-based library, we discovered a series of molecules that induce casein kinase 1 alpha (CK1) degradation. By scaffold hopping and rational modification of the chemical scaffold, we identified an imidazo[1,2-a]pyrimidine compound that induces potent and selective CK1 degradation. A structure-activity relationship study of the lead compound, QXG-6442, identified the structural features that contribute to degradation potency and selectivity compared to other frequently observed neosubstrates. The glutarimide library screening and structure activity relationship medicinal chemistry approach we employed is generally useful for developing new molecular glue degraders towards new targets of interest.

biochemistry↗