Biology subjects

Shuai, Y.

Publications and source records attributed to Shuai, Y..

18 recordsLinked to original sources

bioRxiv2026

Multidimensional telomere diversity and inheritance at individual and population scales

Variation in telomere length, sequence composition and epigenetic state influences genome stability, aging and disease, yet its high-resolution characterization across species remains challenging. Here we present TeloXplorer, a computational framework for long-read data that jointly profiles telomere length, telomere variant repeats (TVRs) and DNA methylation at chromosome-end and haplotype resolution. Across simulated and empirical datasets from humans, Arabidopsis and yeast, TeloXplorer accurately resolved chromosome-end-specific telomere features and highlighted the importance of sample-matched, haplotype-resolved assemblies. Analysis of two human trios revealed concordant relative telomere-length profiles, predominantly Mendelian transmission of TVR haplotypes and family-conserved methylation patterns. Across 232 individuals from the Human Pangenome Reference Consortium, chromosome-end telomere-length rankings were conserved across five continental and 28 population groups. High-accuracy reads from 73 individuals further revealed elevated TVR haplotype diversity among individuals of African ancestry, together with extensive interchromosomal sharing and duplication of TVR architectures. Subtelomeric TAR1 elements were strongly associated with local DNA methylation and telomere motif diversity. Together, these analyses provide a multidimensional atlas of telomere diversity across species, chromosome ends, haplotypes and populations, revealing how telomere architecture varies and is inherited across biological scales.

genomics↗

bioRxiv2026

Genetic background diversifies mutational paths but converges on membrane depolarization for bleomycin resistance in yeast

Drug resistance evolution is shaped by both selective agent and genetic background in which resistant mutations arise, but most experimental evolution studies rely on a single genetic background. Here we performed parallel experimental evolution across six genetically diverged Saccharomyces cerevisiae strains, propagating 48 replicate populations for [~]225 generations under the selection of DNA-damaging chemotherapeutic bleomycin. Over 70% of the evolved populations acquired resistance with the magnitude and repeatability of adaptation varying markedly across genetic backgrounds. Whole-genome sequencing showed that bleomycin elevated single-nucleotide and insertion-deletion mutation rates, and induced background-specific aneuploidies and structural variants, including Ty-element-mediated translocations and tandem duplications. Notably, mutation rates varied up to 10-fold among backgrounds, with the most commonly-used lab strain exhibiting the highest mutagenic susceptibility, underscoring that evolutionary insights derived from single-background models may not universally generalize across a species. Despite this background-dependent genomic heterogeneity, we identified recurrent loss-of-function mutations in PMA1 and PTK2 that arose independently across nearly all genetic backgrounds and conferred strong resistance. Functional and biophysical assays revealed that these mutations impair Pma1p H+-ATPase activity and depolarize the plasma membrane, thereby limiting the cellular uptake of the positively charged drug. Our findings demonstrate that while the mutational routes to drug resistance are heavily shaped by genetic background, a convergent, background-independent mechanism centered on membrane potential regulation can dominate phenotypic adaptation. This work underscores the necessity of integrating genetic diversity into experimental evolution paradigm and nominates plasma membrane potential as an important lever for modulating chemotherapeutic efficacy.

genetics↗

bioRxiv2026

Comparing Harmonization Approaches for Protocol-Related Variability in Multisite Diffusion MRI Data

Diffusion MRI (dMRI) enables assessment of white matter microstructural abnormalities in Alzheimers disease (AD), and multisite datasets enable more robust modeling of non-biological variation that can confound analyses. The Alzheimers Disease Neuroimaging Initiative (ADNI) includes over 10 dMRI protocols, necessitating robust methods to model protocol-related variability when pooling data. Here, we compared three harmonization approaches: (1) mixed-effects models, (2) ComBat-GAM, and (3) eHarmonize, a reference-based lifespan method. We assessed their ability to reduce protocol-related variability in diffusion tensor imaging fractional anisotropy (FA) and mean diffusivity (MD) while preserving associations with cognitive impairment (CI), and amyloid-beta (A{beta}) and tau PET burden in 1,086 ADNI3/4 participants. All approaches yielded more closely aligned FA/MD distributions across protocols. Associations with clinical indicators of CI were highly consistent across approaches, whereas PET associations were less widespread and more variable. Overall, multiple strategies effectively modeled protocol-related variability while preserving AD-related associations.

neuroscience↗

bioRxiv2026

SPECTRA: Spatial Inference for Tractometry Toward Precision Mapping of White Matter Microstructure

Diffusion MRI tractometry characterizes white matter microstructure along fiber bundles, but standard along-tract profiling collapses measurements across the bundle cross-section, obscuring radial heterogeneity and producing spatially inconsistent units of inference. We present SPECTRA (Spatial Inference for Tractometry), a framework designed to address these limitations through a unified design of parameterization and statistical inference. First, we propose a 2D bundle parameterization that extends along-tract profiling to include a radial dimension defined on the atlas bundle. Second, we develop a two-stage hierarchical false discovery rate (hFDR) procedure for multi-bundle inference, which aggregates evidence at a coarser spatial scale before proceeding to finer-grained inference, with spatial scales derived from a Matern kernel. Across extensive simulation conditions, we found that hFDR improves statistical power and reduces the sample size required to detect effects compared to global FDR correction, while maintaining appropriate error control. We further characterized how sensitivity-specificity tradeoffs depend on sample size, the magnitude, spatial extent, and configurations of effects, thereby providing practical guidance for tractometry study design. In an empirical analysis of mild cognitive impairment and dementia in more than 4,000 subjects across 63 bundles, SPECTRA revealed spatially localized patterns that were absent in 1D profiles. Together, these results demonstrate that spatially resolved parameterization and adaptive error control jointly enable precise mapping of white matter microstructure in large-scale tractometry studies. SPECTRA is openly available as a Python package.

neuroscience↗

bioRxiv2026

An Isoform-Centric, Structure-Aware Framework for Protein Function Prediction and Evaluation, Instantiated in 3DisoDeepPF

Understanding and accurately predicting function across protein isoforms has been a long-standing challenge with profound implications for both biological and translational research. However, most functional annotations and benchmarks remain tied to genes or reference protein sequences, leaving limited support for distinguishing protein isoforms from the same gene whose sequence, structure and domain composition may lead to different functions. To address this gap, we developed an isoform-centric framework for protein function and domain annotation that integrates a dense graph of sequence and structure similarity with protein features. We implemented this framework as 3DisoDeepPF, a graph-based multimodal model, and applied it to a breast cancer isoform atlas. Across canonical benchmarks and evaluations at isoform resolution, 3DisoDeepPF showed strong performance in predicting GO terms and Pfam domains and remained robust in tests with homology control. It further captured changes in Pfam domain composition among isoforms from the same gene, including reference-relative domain gain and loss. An evidence tracing module links predicted labels to supporting proteins in the graph, as illustrated by the calcium and integrin binding protein 1 (CIB1). Together, this study provides a framework informed by protein structure for function and domain annotation at isoform resolution, converting human protein isoform diversity into traceable functional evidence for cancer atlas interpretation.

cancer biology↗

bioRxiv2026

Revisiting the genome assembly of Lupinus species reveals differential diploidization after a shared whole-genome duplication

Accurate genome assemblies are essential for comparative genomics, yet Hi-C-guided scaffolding can introduce structural errors that misrepresent chromosome architecture and bias evolutionary inferences. Here, we identified pervasive scaffolding errors - including artificial fusions, internal inversions, and incomplete contig mounting - in two previously published Lupinus genomes (L. cosentinii and L. digitatus) using a segmentation method based on LTR retrotransposon density. We re-assembled both genomes, producing chromosome-level references of 472.7 Mb (16 chromosomes) and 427.2 Mb (21 chromosomes), with BUSCO completeness >98.5%. Synteny validation and re-application of LTR profiling confirmed that all prior errors were resolved. Using these corrected genomes together with four additional Lupinus species and two outgroup legumes, we investigated post-polyploid evolution. Synonymous substitution rate (Ks) analysis revealed a genus-specific whole-genome duplication (WGD) event (Ks = 0.17) shared by all six Lupinus species. The proportion of WGD-derived genes varied markedly, from 60% in L. digitatus to only 36% in L. mutabilis, indicating differential diploidization. While all species retained a core set of WGD duplicates enriched in cytoskeleton organization, ion transport, and defense responses, each exhibited lineage-specific functional trajectories: cell wall modification in L. cosentinii and L. digitatus, nitrogen metabolism in L. albus and L. angustifolius, flower development in L. luteus, and stress/lipid metabolism in L. mutabilis. Our corrected assemblies provide optimal references for Lupinus comparative genomics, and our findings demonstrate that a shared WGD event can lead to both conserved and highly divergent post-polyploid fates, likely underpinning adaptive diversification within the genus.

genomics↗

bioRxiv2025

Assessing the Influence of Tractography Methods on White Matter Microstructure and Tractometry Analysis in Alzheimer's Disease

Tractometry enables detailed mapping of white matter microstructure along individual tracts and is widely used to study disease effects such as those seen in Alzheimers disease (AD). However, how different tractography algorithms influence tractometry outcomes remains unclear. Here, we compared whole-brain deterministic and probabilistic tractography using the BUndle ANalytics (BUAN) framework in the Alzheimers Disease Neuroimaging Initiative (ADNI) dataset, including 118 AD and 728 cognitively normal (CN) participants. Both approaches revealed the expected pattern of lower fractional anisotropy (FA) and higher mean, radial, and axial diffusivity (MD, RD, AxD) in AD, consistent with white matter degeneration. Despite broadly similar global trends, substantial bundle-level differences emerged between the two tractography methods. Probabilistic tracking produced stronger and more spatially extended effects in the fornix, a small and highly curved limbic pathway vulnerable to AD-related degeneration, whereas deterministic tracking showed greater sensitivity in the posterior segments of the right superior longitudinal fasciculus (SLF R). These discrepancies highlight that the choice of tractography algorithm can alter detecting disease effects, emphasizing the need for cross-method validation to ensure the robustness and interpretability of along-tract measures.

neuroscience↗

bioRxiv2025

DTI versus NODDI White Matter Microstructural Biomarkers of Alzheimer's Disease

Diffusion MRI (dMRI) is a powerful tool to assess white matter (WM) microstructural abnormalities in Alzheimers disease (AD). The fourth phase of the Alzheimers Disease Neuroimaging Initiative (ADNI) now includes multiple multishell dMRI protocols, enabling both traditional and advanced dMRI model analyses. There is a need to evaluate whether multishell data offer deeper insights into WM pathology in AD than more widely available single-shell data by overcoming single-shell model limitations. Here, we fit single-shell DTI and multishell NODDI to dMRI data from 533 ADNI3/4 participants to assess their sensitivity to key clinical indicators of AD such as cognitive impairment, amyloid-beta and tau PET burden. Overall, we found that NODDI offered no major advantages in detecting cognitive impairment and tau pathology, but NODDI was marginally more sensitive to amyloid pathology.

neuroscience↗

bioRxiv2025

Streamline Density Normalization: A Robust Approach to Mitigate Bundle Variability in Multi-Site Diffusion MRI

Tractometry enables quantitative analysis of tissue microstructure is sensitive to variability introduced during tractography and bundle segmentation. Differences in processing parameters and bundle geometry can lead to inconsistent streamline reconstructions and sampling, ultimately affecting the reproducibility of tractometry analysis. In this study, we introduce Streamline Density Normalization (SDNorm), a supervised two-step method designed to reduce variability in bundle reconstructions. SDNorm first computes streamline weights using linear regression to match a subjects bundle to a template streamline density map, then iteratively prunes streamlines to achieve a target density using a novel metric called effective Streamline Point Density (eSPD). We evaluate SDNorm across multiple bundles and acquisition protocols in dMRI data from a subset of subjects from Alzheimers Disease Neuroimaging Initiative and demonstrate that it can significantly reduce variability in streamline density, improve consistency in along-tract microstructure profiles, and provide useful metrics for automated bundle quality control. These results suggest that SDNorm can help enhance the reproducibility and robustness of bundle reconstruction across heterogeneous image acquisition protocols and tractography settings, making it well-suited for large-scale and multi-site neuroimaging studies.

neuroscience↗

bioRxiv2025

Super-pangenome analysis of 3562 human and animal papillomavirus isolates illuminates their genome and pathogenicity evolution

Papillomaviruses (PVs) are groups of ubiquitous DNA viruses infecting a wide range of vertebrate hosts, including human. The infection of some human PVs (i.e., HPVs) are strongly associated with cancers, but it remains elusive how such pathogenicity evolved at the genomic level. Here we performed a super-pangenome analysis on a compendium of well-curated 3562 human and animal PV genomes. We found their global genomic diversity being strongly associated with their body sites and tissue origins, but not with their host, geographical, or disease specificity. By reconciling time-resolved phylogenies of PVs and their hosts, we revealed a high-resolution view on how virus-host cospeciation, horizontal host switches, and duplication/loss turnovers collectively shaped the evolution of PVs. We identified key selection-driven mutations that defined the genus-specific divergence of PVs, with those underlying the rise of Alpha-PVs highly enriched in the N-terminal domain of E1 gene. Especially, we identified a group of risk-defining variant sites showing contrasting genotypes between low-risk and high-risk HPVs. With four independent PV genome cohorts, we further validated their high sensitivity and specificity on HPV risk classification even at single site level, underscoring their application values in large-scale HPV screening tests. Finally, with cervical and oropharyngeal cancer patient cohorts assembled by this study, we also evaluated the prognostic value of specific HPV16 variants in predicting patient survival. Taken together, such illumination on the genome and pathogenicity evolution of PVs paves the road for better prevention, control, and treatment of PV-related cancers. Significance statementPapillomaviruses (PVs) are ancient and widespread DNA viruses co-evolved with many vertebrate hosts, including human. Some human PVs (e.g., HPV16 and HPV18) bear strong association with cancers, raising a pressing public health challenge. A better understanding of how such pathogenicity evolved holds the key in addressing this challenge. Here we systematically investigated the genomic and evolutionary diversity of PVs via a pangenome study on over 3500 human and animal PV isolates, which revealed key selection-driven mutations shaping their genome and pathogenicity evolution. With independently acquired HPV-associated cancer cohorts, we further demonstrated how some of these important HPV variants can serve as clinically informative predictive and prognostic biomarkers. Such comprehensive characterization on PVs genome diversity, pathogenicity evolution, and clinical implication paves the road for better prevention, control, and treatment of PV-related cancers.

genomics↗

bioRxiv2025

RubyACRs Enable Red-Shifted Optogenetic Inhibition in Freely Behaving Drosophila

Optogenetic activators with red-shifted excitation spectra, such as Chrimson, have significantly advanced Drosophila neuroscience. However, until recently, available optogenetic inhibitors required shorter activation wavelengths, which dont penetrate tissue as effectively and are stronger visual stimuli to the animal, potentially confounding behavioral results. Here, we assess the efficacy of two newly identified anion-conducting channelrhodopsins with spectral sensitivities similar to Chrimson: A1ACR and HfACR (RubyACRs). Electrophysiology and functional imaging confirmed that RubyACRs effectively hyperpolarize neurons, with stronger and faster effects than the widely used inhibitor GtACR1. Activation of RubyACRs led to circuit-specific behavioral changes in three different neuronal groups. In glutamatergic motor neurons, activating RubyACRs suppressed adult locomotor activity. In PPL1-{gamma}1pedc dopaminergic neurons, pairing odors with RubyACR activation during learning produced odor responses consistent with synaptic silencing. Finally, activation of RubyACRs in the pIP10 neuron suppressed pulse song during courtship. Together, these results demonstrate that RubyACRs are effective and reliable tools for neuronal inhibition in Drosophila, expanding the optogenetic toolkit for circuit dissection in freely behaving animals.

neuroscience↗

bioRxiv2023

CRISPRmap: Sequencing-free optical pooled screens mapping multi-omic phenotypes in cells and tissue

Pooled genetic screens are powerful tools to study gene function in a high-throughput manner. Typically, sequencing-based screens require cell lysis, which limits the examination of critical phenotypes such as cell morphology, protein subcellular localization, and cell-cell/tissue interactions. In contrast, emerging optical pooled screening methods enable the investigation of these spatial phenotypes in response to targeted CRISPR perturbations. In this study, we report a multi-omic optical pooled CRISPR screening method, which we have named CRISPRmap. Our method combines a novel in situ CRISPR guide identifying barcode readout approach with concurrent multiplexed immunofluorescence and in situ RNA detection. CRISPRmap barcodes are detected and read out through combinatorial hybridization of DNA oligos, enhancing barcode detection efficiency, while reducing both dependency on third party proprietary sequencing reagents and assay cost. Notably, we conducted a multi-omic base-editing screen in a breast cancer cell line on core DNA damage repair genes involved in the homologous recombination and Fanconi anemia pathways investigating how nucleotide variants in those genes influence DNA damage signaling and cell cycle regulation following treatment with ionizing radiation or DNA damaging agents commonly used for cancer therapy. Approximately a million cells were profiled with our multi-omic approach, providing a comprehensive phenotypic assessment of the functional consequences of the studied variants. CRISPRmap enabled us to pinpoint likely-pathogenic patient-derived mutations that were previously classified as variants of unknown clinical significance. Furthermore, our approach effectively distinguished barcodes of a pooled library in tumor tissue, and we coupled it with cell-type and molecular phenotyping by cyclic immunofluorescence. Multi-omic spatial analysis of how CRISPR-perturbed cells respond to various environmental cues in the tissue context offers the potential to significantly expand our understanding of tissue biology in both health and disease.

genomics↗

bioRxiv2023

Driver lines for studying associative learning in Drosophila

AbstractThe mushroom body (MB) is the center for associative learning in insects. In Drosophila, intersectional split-GAL4 drivers and electron microscopy (EM) connectomes have laid the foundation for precise interrogation of the MB neural circuits. However, investigation of many cell types upstream and downstream of the MB has been hindered due to lack of specific driver lines. Here we describe a new collection of over 800 split-GAL4 and split-LexA drivers that cover approximately 300 cell types, including sugar sensory neurons, putative nociceptive ascending neurons, olfactory and thermo-/hygro-sensory projection neurons, interneurons connected with the MB-extrinsic neurons, and various other cell types. We characterized activation phenotypes for a subset of these lines and identified a sugar sensory neuron line most suitable for reward substitution. Leveraging the thousands of confocal microscopy images associated with the collection, we analyzed neuronal morphological stereotypy and discovered that one set of mushroom body output neurons, MBON08/MBON09, exhibits striking individuality and asymmetry across animals. In conjunction with the EM connectome maps, the driver lines reported here offer a powerful resource for functional dissection of neural circuits for associative learning in adult Drosophila.

neuroscience↗

bioRxiv2023

A modular chemigenetic calcium indicator enables in vivo functional imaging with near-infrared light

Genetically encoded fluorescent calcium indicators have revolutionized neuroscience and other biological fields by allowing cellular-resolution recording of physiology during behavior. However, we currently lack bright, genetically targetable indicators in the near infrared that can be used in animals. Here, we describe WHaloCaMP, a modular chemigenetic calcium indicator built from bright dye-ligands and protein sensor domains that can be genetically targeted to specific cell populations. Fluorescence change in WHaloCaMP results from reversible quenching of the bound dye via a strategically placed tryptophan. WHaloCaMP is compatible with rhodamine dye-ligands that fluoresce from green to near-infrared, including several dye-ligands that efficiently label the central nervous system in animals. When bound to a near-infrared dye-ligand, WHaloCaMP1a is more than twice as bright as jGCaMP8s, and shows a 7x increase in fluorescence intensity and a 2.1 ns increase in fluorescence lifetime upon calcium binding. We use WHaloCaMP1a with near-infrared fluorescence emission to image Ca2+ responses in flies and mice, to perform three-color multiplexed functional imaging of hundreds of neurons and astrocytes in zebrafish larvae, and to quantitate calcium concentration using fluorescence lifetime imaging microscopy (FLIM).

neuroscience↗

bioRxiv2023

Estimating single cell clonal dynamics in human blood using coalescent theory

While evolutionary approaches to medicine show promise, measuring evolution itself is difficult due to experimental constraints and the dynamic nature of body systems. In cancer evolution, continuous observation of clonal architecture is impossible, and longitudinal samples from multiple timepoints are rare. Increasingly available DNA sequencing datasets at single cell resolution enable the reconstruction of past evolution using mutational history, allowing for a better understanding of dynamics prior to detectable disease. We derive methods based on coalescent theory for estimating the net growth rate of clones from either reconstructed phylogenies or the number of shared mutations. Using single-cell datasets from blood, we apply and validate our analytical methods for estimating the net growth rate of hematopoietic clones, eliminating the need for complex simulations. We show that our estimates may have broad applications to improve mechanistic understanding and prognostic ability. Compared to clones with a single or unknown driver mutation, clones with multiple drivers have significantly increased growth rates (median 0.94 vs. 0.25 per year; p = 1.6 x 10-6). Further, stratifying patients with a myeloproliferative neoplasm (MPN) by the growth rate of their fittest clone shows that higher growth rates are associated with shorter time from clone initiation to MPN diagnosis (median 13.9 vs. 26.4 months; p = 0.0026).

evolutionary biology↗

bioRxiv2022

Neural circuit mechanisms for transforming learned olfactory valences into wind-oriented movement

How memories are used by the brain to guide future action is poorly understood. In olfactory associative learning in Drosophila, multiple compartments of the mushroom body act in parallel to assign valence to a stimulus. Here, we show that appetitive memories stored in different compartments induce different levels of upwind locomotion. Using a photoactivation screen of a new collection of split-GAL4 drivers and EM connectomics, we identified a cluster of neurons postsynaptic to the mushroom body output neurons (MBONs) that can trigger robust upwind steering. These UpWind Neurons (UpWiNs) integrate inhibitory and excitatory synaptic inputs from MBONs of appetitive and aversive memory compartments, respectively. After training, disinhibition from the appetitive-memory MBONs enhances the response of UpWiNs to reward-predicting odors. Blocking UpWiNs impaired appetitive memory and reduced upwind locomotion during retrieval. Photoactivation of UpWiNs also increased the chance of returning to a location where activation was initiated, suggesting an additional role in olfactory navigation. Thus, our results provide insight into how learned abstract valences are gradually transformed into concrete memory-driven actions through divergent and convergent networks, a neuronal architecture that is commonly found in the vertebrate and invertebrate brains.

neuroscience↗

bioRxiv2021

Sensitivity optimization of a rhodopsin-based fluorescent voltage indicator

The ability to optically image cellular transmembrane voltage at millisecond-timescale resolution can offer unprecedented insight into the function of living brains in behaving animals. The chemigenetic voltage indicator Voltron is bright and photostable, making it a favorable choice for long in vivo imaging of neuronal populations at cellular resolution. Improving the voltage sensitivity of Voltron would allow better detection of spiking and subthreshold voltage signals. We performed site saturation mutagenesis at 40 positions in Voltron and screened for increased {Delta}F/F0 in response to action potentials (APs) in neurons. Using a fully automated patch-clamp system, we discovered a Voltron variant (Voltron.A122D) that increased the sensitivity to a single AP by 65% compared to Voltron. This variant (named Voltron2) also exhibited approximately 3-fold higher sensitivity in response to sub-threshold membrane potential changes. Voltron2 retained the sub-millisecond kinetics and photostability of its predecessor, with lower baseline fluorescence. Introducing the same A122D substitution to other Ace2 opsin-based voltage sensors similarly increased their sensitivity. We show that Voltron2 enables improved sensitivity voltage imaging in mice, zebrafish and fruit flies. Overall, we have discovered a generalizable mutation that significantly increases the sensitivity of Ace2 rhodopsin-based sensors, improving their voltage reporting capability.

neuroscience↗

bioRxiv2018

Bright and photostable chemigenetic indicators for extended in vivo voltage imaging

Imaging changes in membrane potential using genetically encoded fluorescent voltage indicators (GEVIs) has great potential for monitoring neuronal activity with high spatial and temporal resolution. Brightness and photostability of fluorescent proteins and rhodopsins have limited the utility of existing GEVIs. We engineered a novel GEVI, Voltron, that utilizes bright and photostable synthetic dyes instead of protein-based fluorophores, extending the combined duration of imaging and number of neurons imaged simultaneously by more than tenfold relative to existing GEVIs. We used Voltron for in vivo voltage imaging in mice, zebrafish, and fruit flies. In mouse cortex, Voltron allowed single-trial recording of spikes and subthreshold voltage signals from dozens of neurons simultaneously, over 15 minutes of continuous imaging. In larval zebrafish, Voltron enabled the precise correlation of spike timing with behavior.

neuroscience↗