Biology subjects

Tao, T.

Publications and source records attributed to Tao, T..

At least 19 recordsLinked to original sources

bioRxiv2026

The contribution of recent and historical demographic histories to genomic diversity and conservation status in plant species

Demographic factors are intrinsically crucial to evaluate species extinction risk. However, measuring them remains difficult and time-consuming and the use of genomic summary statistics has been advocated to assess the conservation status of a species. In the present study, we estimated (i) the census number (Nc), (ii) effective population size (Ne) over three different time periods, recent, historical and ancient, (iii) neutral genetic diversity ({pi}4), and (iv) a measure of the efficacy of purifying selection ({pi}0/{pi}4) for 101 plant species using population genomic sequencing data. Twenty-one species are from the Plant Species with Extremely Small Populations (PSESP) program of SW China. Threatened species exhibited significantly lower Ne, Nc,{pi} 4, and weaker purifying selection, but had a higher Ne/Nc ratio than non-threatened ones. Nc was the main determinant in identifying conservation status, and contemporary neutral genetic diversity was predominantly influenced by historical Ne. In the absence of demographic information, genetic parameters are a good proxy of conservation status, likely because currently threatened species also had a low historical population size. In summary, our findings suggest that direct estimates of Nc are more useful than{pi} 4, although the latter remains a valuable conservation indicator. Hence, efforts such as the PSESP should be extended. Significance StatementExtinction is essentially a demographic process. However, evaluating the risk of extinction of species based only on demographic parameters remains a difficult task, and today only 18% of plant species are included in the International Union for Conservation of Nature (IUCN) Red List of Threatened Species. Here, we use demographic data and whole genomic data from 101 plant species to show that while census number remains the best predictor of extinction risk, genomic based parameters provide good alternatives. More specifically, current neutral nucleotide diversity and historical effective population size are good predictors of the extinction risk of plant species.

evolutionary biology↗

bioRxiv2026

ANCHOR: haplotype-aware allelic and isoform inference from single-cell long-read RNA sequencing with de novo variant calling

Long-read RNA sequencing enables haplotype- and isoform-resolved allelic analysis of transcriptomes, yet extending this capability to single cells and distinct cell types remains computationally challenging due to sparse coverage, sequencing errors, incomplete variant information, and reference-biased transcript assignment. Here we present ANCHOR, a haplotype-aware framework for single-cell long-read RNA sequencing that performs de novo expressed-variant discovery, molecule-level haplotype assignment and isoform-resolved allelic quantification. ANCHOR combines a signed-graph variant caller, pair hidden Markov modelling and beta-binomial UMI aggregation to infer parental allele counts for genes and splice-resolved isoforms, without requiring a pre-existing phased genotype or deep learning. In human single-cell long-read RNA benchmarks, ANCHOR improved variant-calling performance over tested long-read RNA callers at single-cell and low-to-moderate coverage, and its beta-binomial model reduced depth-driven false positives in allele-specific expression testing. Applied to newly generated single-cell long-read RNA-seq data from reciprocal mouse crosses during gastrulation, ANCHOR resolved cell-type- and isoform-specific parent-of-origin imprinting and identified an antagonistic maternally biased Sgce isoform. ANCHOR provides a general framework for allele-and isoform-resolved analysis of diploid single-cell long-read transcriptomes.

bioinformatics↗

bioRxiv2025

3' to 5' Translation of Circular RNAs?

For decades, the 5[->]3 direction of translation has been a central dogma of molecular biology(1). Here we present evidence that eukaryotic circular RNAs (circRNAs) can serve as templates for 3[->]5 backward translation (BT), yielding polypeptides with distinct sequence and structural features not found in canonical proteomes. Mining of more than 6,000 multi-omics datasets identified [~]1 million candidate BT proteins across eukaryotes, including 59,000 high-confidence human BT proteins supported by mass spectrometry. Genetic combinatorial experiments (KO and KI) and cell-free translation of synthetic circRNAs establish BT as a conserved mechanism. Loss-of-function studies of hs.circCAPN15 in human and dm.circROLS in Drosophila underscore the functional importance of BT-derived proteins. Our work challenges the long-standing unidirectional translation paradigm, expands the functional landscape of the genetic code, and reveals a hitherto hidden layer of proteomic complexity with broad implications for biology and therapeutics.

molecular biology↗

bioRxiv2025

Feasibility of a parent-delivered attention and working memory intervention for early school-aged children born preterm

Preterm birth is a common neurodevelopmental condition that can have lasting impacts on cognition, including attention and working memory. Interventions that strengthen these skills in early childhood could support school readiness. Dino Island is a tablet-based intervention that combines process-specific practice of attention and working memory skills with a compensatory component that teaches metacognitive skills to scaffold learning. In this pilot study, we evaluated the feasibility of parent-delivered Dino Island in young children born preterm (N=10), alongside a control group who played educational games (N=12). Both groups were instructed to play 2-3 times per week for approximately 20 minutes over a 12-week period. Attention and working memory on untrained tasks were assessed before and after completing the intervention. Parents provided fidelity data through tracking sheets and participated in exit interviews to offer feedback and identify barriers and facilitators. We found that the Dino Island program was successfully delivered by parents with high fidelity. Attrition was higher in the Dino Island group, likely reflecting the challenges of delivering cognitive remediation in home settings. Comparison of attention and working memory scores on untrained tasks pre- and post-showed practice effects but no specific benefit of Dino Island. However, parent reports suggested behavioral improvements specific to the Dino Island group, noting far-transfer effects where children applied metacognitive strategies in other contexts. Overall, this work shows feasibility and tolerability of Dino Island in young children born preterm. Future research should examine its potential impact on school readiness and longer-term academic outcomes in this population.

neuroscience↗

bioRxiv2025

PRIME: A Multi-Agent Environment for Orchestrating Dynamic Computational Workflows in Protein Engineering

Artificial intelligence (AI) is revolutionizing protein engineering, yet its practical application is often hindered by a fragmented toolchain and the specialized expertise required to orchestrate complex computational workflows. To address these challenges, we have developed PRIME, an autonomous protein engineering multi-agent system. The core innovation of PRIME is dynamic workflow synthesis: it interprets high-level engineering objectives, reasons over a curated library of more than 65 validated protein tools, and autonomously constructs custom computational pathways. Crucially, by grounding every step in verifiable tool execution, PRIME mitigates the risk of model hallucination. It also automates the complete pipeline for developing specialized AI models, from data acquisition through training. In a benchmark of 213 multi-step protein engineering tasks, PRIME exhibited superior performance, successfully completing the majority of tasks where state-of-the-art, general-purpose AI agents fail. We validated its capabilities in demanding real-world applications, including the fully autonomous training of a machine learning classifier and the de novo design of a therapeutic antibody against SARS-CoV-2. By abstracting technical complexity, PRIME empowers scientists to execute sophisticated computational experiments with unprecedented flexibility, establishing a new paradigm for autonomous scientific discovery. The PRIME agent and source code will be made publicly available.

bioinformatics↗

bioRxiv2024

Accurate and General DNA Representations Emerge from Genome Foundation Models at Scale

Language models applied to protein sequences have become a panacea, enabling therapeutics development, materials engineering, and core biology research. Despite the successes of protein language models, genome language models remain nascent. Recent studies suggest the bottleneck is data volume or modeling context size, since long-range interactions are widely acknowledged but sparsely annotated. However, it may be the case that even short DNA sequences are modeled poorly by existing approaches, and current models are unable to represent the wide array of functions encoded by DNA. To study this, we develop AIDO.DNA, a pretrained module for DNA representation in an AI-driven Digital Organism [1]. AIDO.DNA is a seven billion parameter encoder-only transformer trained on 10.6 billion nucleotides from a dataset of 796 species. By scaling model size while maintaining a short context length of 4k nucleotides, AIDO.DNA shows substantial improvements across a breadth of supervised, generative, and zero-shot tasks relevant to functional genomics, synthetic biology, and drug development. Notably, AIDO.DNA outperforms prior encoder-only architectures without new data, suggesting that new scaling laws are needed to achieve computeoptimal DNA language models. Models and code are available through Model-Generator in https://github.com/genbio-ai/AIDO and on Hugging Face at https://huggingface.co/genbio-ai.

bioinformatics↗

bioRxiv2024

Scaling Dense Representations for Single Cell with Transcriptome-Scale Context

Developing a unified model of cellular systems is a canonical challenge in biology. Recently, a wealth of public single-cell RNA sequencing data as well as rapid scaling of self-supervised learning methods have provided new avenues to address this longstanding challenge. However, rapid parameter scaling has been essential to the success of large language models in text and images, while similar scaling has not been attempted with Transformer architectures for cellular modeling. To produce accurate, transferable, and biologically meaningful representations of cellular systems, we develop AIDO.Cell, a pretrained module for representing gene expression and cellular systems in an AI-driven Digital Organism [1]. AIDO.Cell contains a series of 3M, 10M, 100M, and 650M parameter encoder-only dense Transformer models pre-trained on 50 million human cells from diverse tissues using a read-depth-aware masked gene expression pretraining objective. Unlike previous models, AIDO.Cell is capable of handling the entire human transcriptome as input without truncation or sampling tricks, thus learning accurate and general representations of the human cells entire transcriptional context. This pretraining with a longer context was enabled through FlashAttention-2, mixed precision, and large-scale distributed systems training. AIDO.Cell (100M) achieves state-of-the-art results in tasks such as zero-shot clustering, cell-type classification, and perturbation modeling. Our findings reveal interesting loss scaling behaviors as we increase AIDO.Cells parameters from 3M to 650M, providing insights for future directions in single-cell modeling. Models and code are available through ModelGenerator in https://github.com/genbio-ai/AIDO and on Hugging Face.

bioinformatics↗

bioRxiv2024

Mixture of Experts Enable Efficient and Effective Protein Understanding and Design

Proteins play a fundamental role in life. Understanding the language of proteins offers significant potential for gaining mechanistic insights into biological systems and introduces new avenues for treating diseases, enhancing agriculture, and safeguarding the environment. While large protein language models (PLMs) like ESM2-15B and xTrimoPGLM-100B have achieved remarkable performance in diverse protein understanding and design tasks, these models, being dense transformer models, pose challenges due to their computational inefficiency during training and deployment. In this work, we introduce AIDO.Protein, a pretrained module for protein representation in an AI-driven Digital Organism [1]. AIDO.Protein is also the first mixture-of-experts (MoE) model in the protein domain, with model size scales to 16 billion parameters. Leveraging a sparse MoE architecture with 8 experts within each transformer block and selectively activating 2 experts for each input token, our model is significantly more efficient in training and inference. Through pre-training on 1.2 trillion amino acids collected from UniRef90 and ColabfoldDB, our model achieves state-of-the-art results across most tasks in the xTrimoPGLM benchmark. Furthermore, on over 280 ProteinGym Deep Mutational Scanning (DMS) assays, our model achieves nearly 99% of the overall performance of the best MSA-based model and significantly outperforms the previously reported state-of-the-art models that do not utilize MSA. We also adapted this model for structure-conditioned protein sequence generation tasks and achieved new SOTA in this domain. These results indicate that AIDO.Protein can serve as a strong foundation model for protein understanding and design. Models and codes are available through ModelGenerator in https://github.com/genbio-ai/AIDO and on Hugging Face.

bioinformatics↗

bioRxiv2024

A Large-Scale Foundation Model for RNA Function and Structure Prediction

Originally marginalized as an intermediate in the information flow from DNA to protein, RNA has become the star of modern biology, holding the key to precision therapeutics, genetic engineering, evolutionary origins, and our understanding of fundamental cellular processes. Yet RNA is as mysterious as it is prolific, serving as an information store, a messenger, and a catalyst, spanning many underchar-acterized functional and structural classes. Deciphering the language of RNA is important not only for a mechanistic understanding of its biological functions but also for accelerating drug design. Toward this goal, we introduce AIDO.RNA, a pre-trained module for RNA in an AI-driven Digital Organism [1]. AIDO.RNA contains a scale of 1.6 billion parameters, trained on 42 million non-coding RNA (ncRNA) sequences at single-nucleotide resolution, and it achieves state-of-the-art performance on a comprehensive set of tasks, including structure prediction, genetic regulation, molecular function across species, and RNA sequence design. AIDO.RNA after domain adaptation learns to model essential parts of protein translation that protein language models, which have received widespread attention in recent years, do not. More broadly, AIDO.RNA hints at the generality of biological sequence modeling and the ability to leverage the central dogma to improve many biomolecular representations. Models and code are available through ModelGenerator in https://github.com/genbio-ai/AIDO and on Hugging Face.

bioinformatics↗

bioRxiv2024

Astrocyte transcriptomic analysis identifies glypican 5 downregulation as a contributor to synaptic dysfunction in Alzheimer disease models

Synaptic dysfunction is an early feature in Alzheimers disease (AD) and correlates with cognitive decline. Astrocytes are essential regulators of synapses, impacting synapse formation, maturation, elimination and function. To understand if synapse-supportive functions of astrocytes are altered in AD, we used astrocyte BacTRAP mice to generate a comprehensive dataset of hippocampal astrocyte transcriptional alterations in two mouse models of Alzheimers pathology (APPswe/PS1dE9 and Tau P301S), characterizing sex and age-dependent changes. We found that astrocytes from both models downregulate genes important for synapse regulation and function such as the synapse-maturation factor Glypican 5. This transcriptional signature is shared with human post-mortem AD patients. Manipulating a key component of this signature by in vivo overexpression of Glypican 5 in astrocytes is sufficient to prevent early synaptic dysfunction and improve spatial learning in APPswe/PS1dE9 mice. These findings open new avenues to target astrocytic factors to mitigate AD synaptic dysfunction.

neuroscience↗

bioRxiv2024

Linalool's Multifaceted Antimicrobial Potential: Unveiling its Antimicrobial Efficacy and Immunomodulatory Role Against Saprolegnia parasitica

Saprolegnia parasitica (S. parasitica) is one of the most virulent oomycete species in freshwater aquatic environments, causing severe saprolegniasis and leading to significant economic losses in the aquaculture industry. Thus far, the prevention and control of saprolegniasis face a shortage of medications. Linalool, a natural antibiotic alternative found in various essential oils, exhibits promising antimicrobial activity against a wide range of pathogens. In this study, the specific role of linalool in protecting S. parasitica infection at both in vitro and in vivo levels was investigated. Linalool showed multifaceted anti-oomycetes potential by both of antimicrobial efficacy and immunomodulatory efficacy. For in vitro test, linalool exhibited strong anti-oomycetes activity and mode of action included: (1) Linalool disrupted the cell membrane of the mycelium, causing the intracellular components leak out; (2) Linalool prohibited ribosome function, thereby inhibiting protein synthesis and ultimately affecting mycelium growth. Surprisingly, meanwhile we found the potential immune protective mechanism of linalool in the in vivo test: (1) Linalool enhanced the complement and coagulation system which in turn activated host immune defense and lysate S. parasitica cells; (2) Linalool promoted wound healing, tissue repair, and phagocytosis to cope with S. parasitica infection; (3) Linalool positively modulated the immune response by increasing the abundance of beneficial Actinobacteriota; (4) Linalool stimulated the production of inflammatory cytokines and chemokines to lyse S. parasitica cells. In all, our findings showed that linalool possessed multifaceted anti-oomycetes potential which would be a promising natural antibiotic alternative to cope with S. parasitica infection in the aquaculture industry.

microbiology↗

bioRxiv2024

Mechano-regulation of GLP-1 production by Piezo1 in intestinal L cells

Glucagon-like peptide 1 (GLP-1) is a gut-derived hormone secreted by intestinal L cells and vital for postprandial glycemic control. As open-type enteroendocrine cells, whether L cells can sense mechanical stimuli caused by chyme and thus regulate GLP-1 synthesis and secretion is unexplored. Our study showed expression of Piezo1 in intestinal L cells. Its level varied in different energy status and correlates with blood glucose and GLP-1 levels. Mice with L cell-specific loss of Piezo1 (IntL-Piezo1-/-) exhibited impaired glucose tolerance, increased body weight, reduced GLP-1 production and decreased CaMKK{beta}/CaMKIV-mTORC1 signaling pathway under normal chow diet or high fed diet. Activation of the intestinal Piezo1 by its agonist Yoda1 or intestinal bead implantation increased the synthesis and secretion of GLP-1, thus alleviated glucose intolerance in diet-induced-diabetic mice. Overexpression of Piezo1, Yoda1 treatment or stretching stimulated GLP-1 production and CaMKK{beta}/CaMKIV-mTORC1 signaling pathway, which could be abolished by knockdown or blockage of Piezo1 in primary cultured mouse L cells and STC-1 cells. These findings suggest a previously undiscovered mechano-regulation of GLP-1 production in L cells, which may shed new light on the treatments of diabetes.

physiology↗

bioRxiv2023

Genetic Predisposition to Neuroblastoma Results from a Regulatory Polymorphism that Promotes the Adrenergic Cell State

Childhood neuroblastomas exhibit plasticity between an undifferentiated neural crest-like "mesenchymal" cell state and a more differentiated sympathetic "adrenergic" cell state. These cell states are governed by autoregulatory transcriptional loops called core regulatory circuitries (CRCs), which drive the early development of sympathetic neuronal progenitors from migratory neural crest cells during embryogenesis. The adrenergic cell identity of neuroblastoma requires LMO1 as a transcriptional co-factor. Both LMO1 expression levels and the risk of developing neuroblastoma in children are associated with a single nucleotide polymorphism G/T that affects a GATA motif in the first intron of LMO1. Here we show that wild-type zebrafish with the GATA genotype develop adrenergic neuroblastoma, while knock-in of the protective TATA allele at this locus reduces the penetrance of MYCN-driven tumors, which are restricted to the mesenchymal cell state. Whole genome sequencing of childhood neuroblastomas demonstrates that TATA/TATA tumors also exhibit a mesenchymal cell state and are low risk at diagnosis. Thus, conversion of the regulatory GATA to a TATA allele in the first intron of LMO1 reduces the neuroblastoma initiation rate by preventing formation of the adrenergic cell state, a mechanism that is conserved over 400 million years of evolution separating zebrafish and humans.

cancer biology↗

bioRxiv2022

Lactate-derived HDAC6 Lactylation as a new target for neuronal protection in cerebral ischemic reperfusion injury

Lactylation is a newly found lactate-derived post-translational modification, which is firstly reported on histone protein lysine residues to regulate gene expression. Here, we provided evidence that higher serum lactate level was possibly associated with better prognosis in ischemic stroke patients. Meanwhile, inhibition of endogenous lactate increased the infarct volume and decreased protein pan-lactylation in the affected brain tissue in cerebral ischemia mice. We further demonstrated that downregulating lactylation level of Hdac6 (Histone Deacetylase 6) in neurons exacerbated ischemic neuronal injury. The underlying mechanisms involved Hdac6 lactylation disturbing calcium homeostasis through binding BiP (Binding immunoglobulin protein), an endoplasmic reticulum-related protein. Collectively, our findings identified a new effect of lactate for neuroprotection through Hdac6 lactylation during cerebral ischemic reperfusion injury which introduced a potential therapeutic approach against ischemic stroke.

neuroscience↗

bioRxiv2020

Differentiation independent neuroprotective role of Vcan+ oligodendrocyte precursor cells in poststroke cognitive impairment recovery

The cell type-specific molecular pathology of post-stroke cognitive impairment (PSCI) in the hippocampus has not been thoroughly elucidated. We analyzed 27,069 cells by using single-cell RNA sequencing, and four oligodendrocyte precursor cell (OPC) subtypes were identified, Vcan+ OPCs, which were determined to be the primary cluster among them. Additionally, we examined the features of endothelial cells (ECs) and found that Lcn2+ ECs might play neuroprotective roles via Vwf after stroke. These results may facilitate further studies attempting to identify new avenues of research and novel targets for PSCI treatment.

neuroscience↗

bioRxiv2020

Modeling SARS-CoV-2 infection in vitro with a human intestine-on-chip device

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus (SARS-CoV-2) has given rise to a global pandemic. The gastrointestinal symptoms of some COVID-19 patients are underestimated. There is an urgent need to develop physiologically relevant model that can accurately reflect human response to viral infection. Here, we report the creation of a biomimetic human intestine infection model on a chip system that allows to recapitulate the intestinal injury and immune response induced by SARS-CoV-2, for the first time. The microengineered intestine-on-chip device contains human intestinal epithelium (co-cultured human intestinal epithelial Caco-2 cells and mucin secreting HT-29 cells) lined in upper channel and vascular endothelium (human umbilical vein endothelial cells, HUVECs) in a parallel lower channel under fluidic flow condition, sandwiched by a porous PDMS membrane coated with extracellular matrix (ECM). At day 3 post-infection of SARS-CoV-2, the intestine epithelium showed high susceptibility to viral infection and obvious morphological changes with destruction of intestinal villus, dispersed distribution of mucus secreting cells and reduced expression of tight junction (E-cadherin), indicating the destruction of mucous layer and the integrity of intestinal barrier caused by virus. Moreover, the endothelium exhibited abnormal cell morphology with disrupted expression of adherent junction protein (VE-cadherin). Transcriptional analysis revealed the abnormal RNA and protein metabolism, as well as activated immune responses in both epithelial and endothelial cells after viral infection (e.g., up-regulated cytokine genes, TNF signaling and NF-kappa B signaling-related genes). This bioengineered in vitro model system can mirror the human relevant pathophysiology and response to viral infection at the organ level, which is not possible in existing in vitro culture systems. It may provide a promising tool to accelerate our understanding of COVID-19 and devising novel therapies.

bioengineering↗

bioRxiv2020

Retinoic acid rewires the adrenergic core regulatory circuitry of neuroblastoma but can be subverted by enhancer hijacking of MYC or MYCN

Neuroblastoma cell identity depends on a core regulatory circuit (CRC) of transcription factors that collaborate with MYCN to drive the oncogenic gene expression program. For neuroblastomas dependent on the adrenergic CRC, treatment with retinoids can inhibit cell growth and induce differentiation. Here we show that when MYCN-amplified neuroblastomas cells are treated with retinoic acid, histone H3K27 acetylation and methylation become redistributed to decommission super-enhancers driving the expression of PHOX2B and GATA3, together with the activation of new super-enhancers that drive high levels of MEIS1 and SOX4 expression. These findings indicate that treatment with retinoids can reprogram the enhancer landscape, resulting in downregulation of MYCN expression, while establishing a new retino-sympathetic CRC that causes proliferative arrest and sympathetic differentiation. Thus, we provide mechanisms that account for the beneficial effects of retinoids in high-risk neuroblastoma and explain the rapid downregulation of expression of MYCN despite massive levels of amplification of this gene.

cancer biology↗

bioRxiv2020

A human disease model of SARS-CoV-2-induced lung injury and immune responses with a microengineered organ chip

Coronavirus disease 2019 (COVID-19) is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that seriously endangers human health. There is an urgent need to build physiological relevant human models for deep understanding the complex organ-level disease processes and facilitating effective therapeutics for COVID-19. Here, we first report the use of microengineered alveolus chip to create a human disease model of lung injury and immune responses induced by native SARS-CoV-2 at organ-level. This biomimetic system is able to reconstitute the key features of human alveolar-capillary barrier by co-culture of alveolar epithelial and microvascular endothelial cells under microfluidic flow. The epithelial cells on chip showed higher susceptibility to SARS-CoV-2 infection than endothelial cells identified by viral spike protein expression. Transcriptional analysis showed distinct responses of two cell types to SARS-CoV-2 infection, including activated type I interferon (IFN-I) signaling pathway in epithelium and activated JAK-STAT signaling pathway in endothelium. Notably, in the presence of circulating immune cells, a series of alveolar pathological changes were observed, including the detachment of endothelial cells, recruitment of immune cells, and increased production of inflammatory cytokines (IL-6, IL-8, IL-1{beta} and TNF-). These new findings revealed a crucial role of immune cells in mediating lung injury and exacerbated inflammation. Treatment with antiviral compound remdesivir could suppress viral copy and alleviate the disruption of alveolar barrier integrity induced by viral infection. This bioengineered human organ chip system can closely mirror human-relevant lung pathogenesis and immune responses to SARS-CoV-2 infection, not possible by other in vitro models, which provides a promising and alternative platform for COVID-19 research and preclinical trials.

bioengineering↗