Biology subjects

Yu, N.

Publications and source records attributed to Yu, N..

At least 19 recordsLinked to original sources

bioRxiv2026

How body size and microclimate determine when solar absorptivity matters for insect body temperature

1. The net effect of solar absorption on body temperature is difficult to intuit because it depends on nonlinear interactions between the traits of organisms and their immediate environment. 2. We used biophysical modelling to quantify solar absorption effects for Australian jewel beetles across their wide natural range of solar absorptivity (0.67-0.93), mass (more than 2 orders of magnitude) and shape under naturally co-occurring combinations of microclimatic conditions across Australia. 3. Under hot, calm conditions, solar absorptivity could increase steady-state body temperature by 2.3{degrees}C on average. Higher wind reduced this effect by more than half, whereas increasing shortwave radiation amplified it. Transient simulations indicated substantial damping of these effects at a time scale of one minute. 4. Across daylight hours during the spring-early summer period when adult jewel beetles are active, the absorptivity effect averaged 0.36{degrees}C across Australia. Under locally extreme high-radiation, low-wind conditions, the effect reached 5.69{degrees}C in the largest beetles and was more than twice as large at maximum as at minimum body mass. 5. Whether solar absorptivity is of biological significance to heat budgets is therefore highly contextual. Experiments to infer the adaptive significance of insect colour for heat budgets should account for transient responses under realistic environmental combinations.

ecology↗

bioRxiv2026

Upregulation of ATP-purinergic P2x2 receptors in the cochlea over-amplifies hearing sensitivity leading to hyperacusis and attenuation by antagonists

Hearing hypersensitivity (hyperacusis) is a common hearing stress and can cause many psychological diseases, e.g., anxiety, learning disabilities, and attention-deficit/hyperactivity disorder (ADHD). Here, we report an unexpected finding that the upregulation of P2x2 ATP-purinergic receptors in the cochlea links to hyperacusis generation. We found that P2x2 expression in the cochlea but not in auditory centers was upregulated in the hyperacusis generated by Cx26 deficiency. Overexpression of P2x2 in the cochlea also caused hyperacusis. Conversely, downregulation of P2x2 expression or administration of P2x2 antagonists attenuated hyperacusis. We further found that upregulation of P2x2 receptors in the cochlea increased outer hair cell (OHC) electromotility through the post-transcription functional modulation to potentiate active cochlear amplification leading to hearing hypersensitivity. Such enhancements in OHC electromotility and active cochlear amplification were also suppressed by P2x2 receptor antagonists. Overall, these findings demonstrate that P2x2-mediated ATP-purinergic signaling in the cochlea plays a critical role in hyperacusis generation; targeting P2x2 receptors can attenuate hyperacusis stress, which may also offer a therapeutic strategy for other related psychological comorbidities. Significance statementHearing hypersensitivity is a common hearing stress and can cause many other psychological disorders. However, little is known about the underlying genetic and cellular mechanisms. Also, it lacks efficient drugs for their treatments in the clinic. In this study, we found that upregulation of P2x2 ATP-purinergic receptors in the cochlea can potentiate outer hair cell electromotility, which is an active cochlear amplifier in mammals and can increase hearing sensitivity and frequency selectivity, through post-transcription functional modulation to enhance active cochlear amplification leading to hearing hypersensitivity. These enhancements can be inhibited by administrations of P2x2 receptor antagonists both in vitro and in vivo. These findings revealed a new genetic and cellular mechanism underlying hyperacusis generation and opened a new avenue to develop an efficient therapy for this common hearing stress and other associated psychological comorbidities.

physiology↗

bioRxiv2026

Somatic-to-germline transmission of horizontally acquired extrachromosomal circular DNA in Brassica graft chimeras

Whether somatically acquired traits can contribute to heritable variation has remained an open question in plant biology for over a century. Here, using graft-induced periclinal chimeras between Brassica juncea and Brassica oleracea, we identified extrachromosomal circular DNA (eccDNA) as mobile genetic elements capable of crossing histological boundaries and entering the germline. We demonstrated that grafting could promote the horizontal transfer of eccDNA between somatic cell layers, enabling its stable maintenance in recipient tissues and permitting transmission through sexual reproduction across multiple generations. We found that transmitted eccDNA is non-randomly distributed across the genome, preferentially originating from gene-dense regions, and enriched for features associated with molecular persistence, such as inverted repeats, hairpin-forming sequences, and autonomously replicating sequence (ARS) consensus motifs. These genetic regions conferred replication competence, as evidenced by autonomous propagation in an ARS-less heterologous yeast system. Sexual progeny carrying graft-acquired eccDNA exhibited reproducible and lineage-dependent alterations in leaf morphology and drought tolerance, accompanied by coordinated transcriptional reprogramming. Notably, a subset of inherited eccDNA remained transcriptionally active in progeny, producing transcripts absent from self-grafted controls. Our findings establish eccDNA as heritable extrachromosomal elements that link graft-mediated somatic genetic transfer with stable germline transmission, thereby expanding the molecular scope of heritable variation in plants and providing a conceptual framework for graft-based trait transmission.

plant biology↗

bioRxiv2026

A role for tubulin in cellular quality control and proteostasis

Microtubules, stiff rods built up from tubulin dimers, form a cytoskeletal network whose structure, behaviour, and function have been extensively investigated, mainly from a mechanical perspective. Here, we describe a role for tubulin in the cellular stress response. We overexpressed tubulin dimers in a controlled fashion in 293F cells. Despite the engagement of autoregulation, a mechanism that degrades tubulin-encoding mRNAs when tubulin levels are high, a surplus of tubulin and microtubules is detected in overexpressing cells. This leads to altered microtubule behaviour, mitotic problems, deregulation of the cell cycle, and replication stress. Surprisingly, we also observe proteostasis defects in tubulin overexpressing cells, which we attribute to mitochondrial stress-related translation attenuation. Conversely, tubulin and microtubules are downregulated as part of the response to oxygen or glutamine deprivation. Together, our data link tubulin levels, and hence autoregulation, to cellular quality control and proteostasis. We propose that competitive interactions with key partners, including the mitochondrial protein import and general translation machinery, underlie the tubulin-mediated control of cellular homeostasis.

cell biology↗

bioRxiv2026

Quantifying PD1 saturation by PDL1 in tumor tissue using a novel RNA aptamer-based assay

BackgroundTherapeutic agents targeting the PD1-PDL1 interaction are of great clinical value, however accurately predicting which patients are most likely to benefit is challenging. Improved predictive biomarkers for anti-PD1 therapy are clearly needed. Quantifying PD1 saturation by PDL1 in tumor tissue has the potential to serve as such a biomarker. Here we report a novel bioassay called the PD1 Ligand Receptor Complex Aptamer (LIRECAP) assay and demonstrate it can be used to quantify the saturation of PD1 by PDL1 in formalin-fixed paraffin-embedded tumor biospecimens. ResultsThe PD1 LIRECAP assay was developed by identifying a pair of RNA aptamers. One aptamer preferentially binds to unoccupied PD1 (P aptamer) and the other to the PD1-PDL1 complex (C aptamer). P and C aptamers were added together to a formalin-fixed sample, and bound aptamer extracted. A 2-color qRT-PCR assay using a single set of primers was used to determine the ratio of the sample-bound C to P aptamers (C:P ratio) which reflected PD1 saturation by PDL1 in the sample. Quantification of PD1 saturation by PDL1 as determined by the PD1 LIRECAP assay correlated closely with PD1-mediated signaling and PD1-PDL1 proximity. Analysis of sarcoma FFPE biospecimens confirmed the assay is technically reproducible on clinical biospecimens. There were significant differences in PD1 saturation by PDL1 between patients as well as considerable intratumoral heterogeneity. ConclusionsThe PD1 LIRECAP assay is novel assay that can be used to quantify PD1 saturation by PDL1 in clinical biospecimens. The assay is technically feasible, reproducible, and has the potential to serve as a superior predictive biomarker for PD1/PDL1-based therapy. Similar assays based on this platform could be used in other systems and settings to quantify interaction between two molecules.

immunology↗

bioRxiv2026

AI-Guided Structure-Aware Modeling and Thermal Proteomics Reveal Direct Demethylzeylasteral-ACLY Interaction

Target deconvolution of bioactive natural products (NPs) is frequently hampered by the inability of traditional thermal shift assays to distinguish direct ligand binding from indirect proteomic stabilization. Here, we developed an integrated target discovery framework combining MAPS-iTSA thermal proteomics with a structure-aware graph neural network (HoloGNN). This strategy identified ATP-citrate lyase (ACLY) as a high-confidence target of Demethylzeylasteral, a bioactive triterpenoid from Tripterygium wilfordii. Crucially, orthogonal biochemical assays, including surface plasmon resonance and limited proteolysis, validated its direct binding (KD = 9.86 M) and profound enzymatic inhibition (IC50 = 3.84 M). To establish the relevance of this interaction in the context of the source herb, thermal profiling of Tripterygium wilfordii extract together with ACLY-based affinity-ultrafiltration mass spectrometry supported ACLY engagement and identified Demethylzeylasteral as an ACLY-binding constituent. Given the established role of ACLY-mediated lipid metabolic reprogramming in psoriasis, we further evaluated the pharmacological significance of this interaction. Demethylzeylasteral suppressed keratinocyte proliferation, alleviated imiquimod-induced psoriasiform dermatitis, and reduced inflammatory cytokine expression in vivo. Single-cell RNA sequencing further revealed reversal of ACLY-SREBP-associated lipogenic reprogramming in keratinocytes. Collectively, these findings establish ACLY as a functionally relevant target of DEM and provide a robust AI-chemoproteomic paradigm for mechanism-guided NP drug discovery.

biochemistry↗

bioRxiv2026

AlphaGenome -enabled analysis of non-coding regulatory variants underlying RHD Expression

Systematic identification of functional non-coding regulatory variants remains a major challenge in human genetics. Conventional approaches such as large-scale CRISPR screening and genome-wide association studies (GWAS) are powerful but often prohibitively expensive, time-consuming, and experimentally intensive, limiting their scalability for locus-specific mechanistic studies. Recent advances in artificial intelligence offer the potential to partially replace or substantially augment these approaches by prioritizing regulatory variants with high functional likelihood. The RHD antigen, a major contributor to red blood cell alloimmunization, hemolytic transfusion reactions, and hemolytic disease of the fetus and newborn, serves as an excellent model for this paradigm, since coding variants alone do not fully account for differences in RHD expression. Here, we present an integrated artificial intelligence (AI)-guided and experimental framework to identify and validate functional non-coding regulatory variants governing RHD expression. We first applied AlphaGenome (AG), a deep-learning model released in 2025 for non-coding variant impact prediction, to systematically interrogate the RHD locus. By integrating multi-omics datasets, AG prioritized regulatory regions within the promoter, 5' untranslated region (5'UTR), and intragenic regions. In silico deletion- and Single Nucleotide Polymorphism (SNP)-based perturbation analyses consistently predicted that variants within the promoter and its proximal regions, as well as within intragenic regions, exert strong suppressive effects on RHD expression. To experimentally validate these predictions, we performed CRISPR-mediated base editing in K562 cells at AG-prioritized non-coding SNP sites. Editing of a high-score predicted variant (chr1:25272434 G>A) achieved efficient base conversion and was accompanied by additional nearby edits, all predicted by AG to downregulate RHD expression. In contrast, editing of low-score predicted sites (chr1:25272422 C>T) produced much smaller functional effects. Quantitative polymerase chain reaction (qPCR) analysis of full-length RHD transcripts, together with flow cytometry-based analysis of RHD expression, confirmed strong concordance between AI-based predictions and transcriptional as well as phenotypic outcomes. Taken together, our results demonstrate that the combination of AI-guided regulatory variant prioritization and targeted base editing provides a potentially scalable and cost-effective alternative to traditional CRISPR screening for decoding functional non-coding variants in blood group genes, with direct implications for genomics-based RHD typing and transfusion medicine. To our knowledge, this study also represents the first validation of AlphaGenome predictions at the phenotypic level using wet-lab experiments.

genomics↗

bioRxiv2025

Discovery of A Small Molecule non-IMiD Degrader of ZBTB7A for the Treatment of β-hemoglobinopathies

Sickle cell disease and {beta}-thalassemia, two major {beta}-hemoglobinopathies, pose significant clinical challenges globally. Current treatments often face limitations in efficacy and tolerability. The transcription factor ZBTB7A has emerged as a promising therapeutic target for reactivating fetal hemoglobin expression. Here, we report the discovery and characterization of SH6, a small molecule non-IMiD degrader of ZBTB7A. SH6 induces fetal hemoglobin in erythroid cell lines in a CRBN and ZBTB7A-dependent manner, and it is capable of inducing fetal hemoglobin expression in healthy donor, SCD and {beta}-thalassemia patient CD34+ cell derived erythroid cells. The efficacy of SH6 is confirmed in a xenotransplantation humanized mouse model. SH6 outperforms currently available therapeutic agents in vitro, and shows synergy with hypomethylating agents. SH6 exhibits a favorable in vivo toxicity profile. Our findings establish SH6 as a promising therapeutic lead candidate for further optimization towards clinical development for treatment of sickle cell disease and {beta}-thalassemia.

cell biology↗

bioRxiv2025

TabSyM: A Generative Pipeline for Small Multi-Cohort Omics Tabular Data

Machine learning applications in biomedicine such as omics data analysis are frequently hindered by datasets that are small, high-dimensional, and affected by batch effects across different patient cohorts. To address these challenges, we introduce TabSyM, a modular generative pipeline that synthesizes high-quality, task-relevant data to improve predictive modeling. TabSyM integrates three key stages: it extends a diffusion-based model (TabDDPM) to generate new omics data, employs a novel task-aware sampling mechanism guided by Bayesian optimization to select the most informative synthetic samples, and uses a Multi-Domain Adversarial Network (MDAN) to align data distributions for cross-cohort generalization. We validated our pipeline on a challenging, real-world task of predicting 3-year survival in gastric cancer patients from high-dimensional scRNA-seq data across five cohorts. The full TabSyM pipeline achieved a 30.2% AUROC improvement over the best tree-based models and an 11.5% AUROC gain over leading automated machine learning frameworks. Furthermore, the generative and sampling components are model-agnostic and can substantially boost the performance of classical models like XGBoost independently. These results establish that combining generative modeling with task-aware sampling and domain adaptation provides a robust and effective strategy for overcoming critical data limitations in biomedical tabular data analysis.

bioinformatics↗

bioRxiv2024

Genetic basis of an adaptive polymorphism controlling butterfly silver iridescence

Identifying the genes and mutations that drive phenotypic variation and which are subject to selection is crucial for understanding evolutionary processes. Mormon Fritillary butterflies (Speyeria mormonia) exhibit a striking wing color polymorphism throughout their range: typical morphs bear silver spots on their ventral surfaces, and can co-occur with unsilvered morphs displaying a dull coloration1. Through genome-wide association studies in two polymorphic populations, we fine-map this difference in silvering to the 3 region of the transcription factor gene optix. The expression of optix is confined to the unsilvered regions that surround the spots, and these patterns are transformed to a silver identity upon optix RNAi knockdown, implicating optix as a repressor of silver scales in this butterfly. We show that the unsilvered optix haplotype shows signatures of recent selective sweeps, and that this allele is shared with the monomorphic, unsilvered species Speyeria hydaspe, suggesting that introgressions facilitate the exchange of variants of adaptive potential across species. Remarkably, these findings parallel the role of introgressions and cis-regulatory modulation of optix in shaping the aposematic red patterns of Heliconius butterflies2-7, a lineage that separated from Speyeria 45 million years ago8. The genetic basis of adaptive variation can thus be more predictable than often presumed, even for traits that appear divergent across large evolutionary distances. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=199 SRC="FIGDIR/small/628425v1_ufig1.gif" ALT="Figure 1"> View larger version (112K): org.highwire.dtl.DTLVardef@5d2a9borg.highwire.dtl.DTLVardef@982c74org.highwire.dtl.DTLVardef@8e90b3org.highwire.dtl.DTLVardef@1bddf3c_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract C_FIG

evolutionary biology↗

bioRxiv2024

ATP-gated P2x7 receptor is a major channel type at type II auditory nerves and required for hearing sensitivity efferent controlling and noise protection

Hearing sensitivity and noise protection are mediated and determined by negative feedback of the cochlear efferent system. Type II auditory nerves (ANs) innervate outer hair cells (OHCs) in the cochlea and provide an input to this efferent control. However, little is known about underlying channel information. Here, we report that ATP-gated P2x7 receptor had a predominant expression at type II ANs and the synaptic areas under inner hair cells and OHCs with lateral and medial olivocochlear efferent nerves. Knockout (KO) of P2x7 increased hearing sensitivity with enhanced acoustic startle response (ASR), auditory brainstem response (ABR), and cochlear microphonics (CM) by increasing OHC electromotility, an active cochlear amplifier in mammals. P2x7 KO also increased susceptibility to noise. Middle level noise exposure could impair active cochlear mechanics resulting in permanent hearing loss in P2x7 KO mice. These data demonstrate that P2x7 receptors have a critical role in type II AN function and the cochlear efferent system to control hearing sensitivity; deficiency of P2x7 receptors can impair the cochlear efferent suppression leading to hearing oversensitivity and susceptibility to noise.

neuroscience↗

bioRxiv2024

stMMR: accurate and robust spatial domain identification from spatially resolved transcriptomics with multi-modal feature representation

Deciphering spatial domains using spatially resolved transcriptomics (SRT) is of great value for the characterizing and understanding of tissue architecture. However, the inherent heterogeneity and varying spatial resolutions present challenges in the joint analysis of multi-modal SRT data. We introduce a multi-modal geometric deep learning method, named stMMR, to effectively integrate gene expression, spatial location and histological information for accurate identifying spatial domains from SRT data. stMMR uses graph convolutional networks (GCN) and self-attention module for deep embedding of features within unimodal and incorporates similarity contrastive learning for integrating features across modalities. Comprehensive benchmark analysis on various types of spatial data shows superior performance of stMMR in multiple analyses, including spatial domain identification, pseudo-spatiotemporal analysis, and domain-specific gene discovery. In chicken heart development, stMMR reconstruct the spatiotemporal lineage structures indicating accurate developmental sequence. In breast cancer and lung cancer, stMMR clearly delineated the tumor microenvironment and identified marker genes associated with diagnosis and prognosis. Overall, stMMR is capable of effectively utilizing the multi-modal information of various SRT data to explore and characterize tissue architectures of homeostasis, development and tumor.

bioinformatics↗

bioRxiv2024

Brain transcriptomics reveals features of the host response after dengue virus type I infection induced central nervous system injury in AGB6 mice

Dengue viruses are the causative agents of dengue fever, dengue hemorrhagic fever, and dengue shock syndrome, which are mainly transmitted by Aedes aegypti and Aedes albopictus mosquitoes, and cost billions of dollars annually in medical and mosquito control. It is estimated that up to 20% of dengue virus infections affect the brain. The incidence of DENV infection of the nervous system is increasing, suggesting that more people are at risk for neurological complications of the infection. Currently, understanding the pathogenic mechanisms of dengue virus infection of the nervous system is hampered by the lack of suitable small animal models. Here, we analyzed the pathogenicity of dengue virus in type I and type interferon receptor-deficient mice by intraperitoneal inoculation of DENV-I. Infected mice showed such neurological symptoms as opisthotonus, hunching, ataxia, and paralysis of one or both hind limbs. Viremia can be detected 3 days after infection. The virus showed evident brain tropism post-inoculation and viral loads peaked at 14 days post-inoculation in infected mice brain. Significant histopathologic changes were observed in brain tissue (hippocampal region and cerebral cortex). Immunohistochemistry and fluorescence showed clear fluorescent signals. Hematological analysis showed hemorrhage and hemoconcentration in DENV-I infected mice. Subsequently, brain tissue transcriptome sequencing was performed to assess host response characteristics in DENV-I infected AGB6 mice. Functional enrichment analysis and analysis of significantly differentially expressed genes (DEGs) were used to identify the key molecular mechanisms of brain tissue injury. Transcriptional patterns in brain tissue suggest that aberrant expression of pro-inflammatory cytokines induces antiviral responses to dengue virus and tissue damage. Combined with the KEGG results, it was hypothesized that the probable cause of the neurological damage affecting the neurological system and hindlimb paralysis was the marked enrichment of upregulated DEGs in the cytokine-cytokine receptor interaction signaling pathway. Screening of hub genes and their characterization by qPCR and ELISA, it was hypothesized that IL-6 and IFN-{gamma} might be the key factors in dengue virus-induced neurological manifestations.

immunology↗

bioRxiv2024

Target-aware Molecule Generation for Drug Design Using a Chemical Language Model

Generative drug design facilitates the creation of compounds effective against pathogenic target proteins. This opens up the potential to discover novel compounds within the vast chemical space and fosters the development of innovative therapeutic strategies. However, the practicality of generated molecules is often limited, as many designs focus on a narrow set of drug-related properties, failing to improve the success rate of subsequent drug discovery process. To overcome these challenges, we develop TamGen, a method that employs a GPT-like chemical language model and enables target-aware molecule generation and compound refinement. We demonstrate that the compounds generated by TamGen have improved molecular quality and viability. Additionally, we have integrated TamGen into a drug discovery pipeline and identified 7 compounds showing compelling inhibitory activity against the Tuberculosis ClpP protease, with the most effective compound exhibiting a half maximal inhibitory concentration (IC50) of 1.9 M. Our findings underscore the practical potential and real-world applicability of generative drug design approaches, paving the way for future advancements in the field.

biochemistry↗

bioRxiv2023

stGCL: A versatile cross-modality fusion method based on multi-modal graph contrastive learning for spatial transcriptomics

Advances in spatial transcriptomics (ST) technologies have provided unprecedented opportunities to depict transcriptomic and histological landscapes in the spatial context. Multi-modal ST data provide abundant and comprehensive information about cellular status, function, and organization. However, in dealing with the processing and analysis of spatial transcriptomics data, existing algorithms struggle to effectively fuse the multi-modal information contained within ST data. Here, we propose a graph contrastive learning-based cross-modality fusion model named stGCL for accurate and robust integrating gene expression, spatial information as well as histological profiles simultaneously. stGCL adopts a novel histology-based Vision Transformer (H-ViT) method to effectively encode histological features and combines multi-modal graph attention auto-encoder (GATE) with contrastive learning to fuse cross-modality features. In addition, stGCL introduces a pioneering spatial coordinate correcting and registering strategy for tissue slices integration, which can reduce batch effects and identify cross-sectional domains precisely. Compared with state-of-the-art methods on spatial transcriptomics data across platforms and resolutions, stGCL achieves a superior clustering performance and is more robust in unraveling spatial patterns of biological significance. Additionally, stGCL successfully reconstructed three-dimensional (3D) brain tissue structures by integrating vertical and horizontal slices respectively. Application of stGCL in human bronchiolar adenoma (BA) data reveals intratumor spatial heterogeneity and identifies candidate gene biomarkers. In summary, stGCL enables the fusion of various spatial modality data and is a powerful tool for analytical tasks such as spatial domain identification and multi-slice integration.

bioinformatics↗

bioRxiv2023

Quantitative Learning of Cellular Features From Single-cell Transcriptomics Data Facilitates Effective Drug Repurposing

In this study, we have devised a computational framework SuperFeat that allows for the training of a machine learning model and evaluate the canonical cellular states/features in pathological tissues that underlie the progression of disease. This framework also enables the identification of potential drugs that target the presumed detrimental cellular features. This framework was constructed on the basis of an artificial neural network with the gene expression profiles serving as input nodes. The training data comprised single-cell RNA-seq datasets that encompassed the specific cell lineage during the developmental progression of cell features. A few models of the canonical cancer-involved cellular states/features were tested by such framework. Finally, we have illustrated the drug repurposing pipeline, utilizing the training parameters derived from the adverse cellular states/features, which has yielded successful validation results both in vitro and in vivo. SuperFeat is accessible at https://github.com/weilin-genomics/rSuperFeat.

cell biology↗

bioRxiv2023

Well-ST-seq: cost-effective spatial transcriptomics at cellular level and high RNA capture efficiency

Spatial transcriptomic technologies are promising tools to reveal fine anatomical profiles of tissues. As for the methodologies based on barcoded probe arrays, improving the balance among probe barcoding complexity and cost, gene capture sensitivity, and spatial resolution can accelerate the spreading of spatial transcriptomic in basic science and clinical work. Here, based on miniaturized microfluidic and microarray technologies, we constructed a spatially cellular-level RNA-capture probe arrays. Owing to the predetermined and cost-effective probe fixation characteristics of the methodology, the consumable cost and fabrication time of the probe array can be reduced to $1.21/mm2 and approximately 2 hours, and the preparation process does not rely on large precision instruments. Moreover, the efficiency of the transcript captured by the probe array is even comparable to conventional single-cell RNA sequencing. Based on this technology, we achieved the spatial transcriptome expression mapping and gained insight into spatial cell heterogeneity of the mouse hippocampus.

genomics↗

bioRxiv2023

Convergent evolution of noxious heat sensing by TRPA5, a novel class of heat sensor in Rhodnius prolixus

As ectotherms, insects need a multifaceted repertoire of heat-sensitive receptors to monitor environmental temperatures and finely control behavioral thermoregulation. Here, we show that TRPA5 genes, a class of ankyrin transient receptor potential channels lost in genomes of model fruit flies or mosquitoes, are widespread across insect orders, and encode a previously uncharacterized type of heat receptors. We demonstrate that RpTRPA5B, a TRPA5 channel of the triatomine bug Rhodnius prolixus (Insect: Hemiptera), primary vector of Chagas disease, forms a homo-tetrameric channel displaying a uniquely high thermosensitivity. The channel biophysical determinants include a large channel activation enthalpy change (72 kcal/mol), a high temperature coefficient (Q10 = 25), and temperature-induced currents from 53 {degrees}C to 68 {degrees}C (T0.5= 58.6 {degrees}C) in vitro, similar to mammalian noxious TRPV heat receptors. Monomeric and tetrameric predictions of the ion channel architecture show reliable and conserved structural parallels with fruit fly dTRPA1, albeit depicting structural uniqueness from dTRPA, Painless and Pyrexia in the ankyrin repeat domain and the channel selectivity filter, potential modulator regions of functional characteristics of TRPs. The channel activation response, structural features and ubiquitous sensory tissue expression delineate a potential thermosensitive physiological niche close to that of Pyrexia genes, lost during the evolution of true bugs. Overall, the finding of TRPA5 genes as a class of temperature-activated receptor illustrates the dynamic evolution of a large family of insect molecular heat detectors, with TRPs as promising multimodal sensory targets for triatomine vector control.

evolutionary biology↗