Biology subjects

Feng, Y.

Publications and source records attributed to Feng, Y..

At least 145 records · Page 8Linked to original sources

bioRxiv2024

Exploring the diversity of anti-defense systems across prokaryotes, phages, and mobile genetic elements

The co-evolution of prokaryotes, phages, and mobile genetic elements (MGEs) over the past billions of years has driven the emergence and diversification of defense and anti-defense systems alike. Anti-defense proteins have diverse functional domains, sequences, and are typically small, creating a challenge to detect anti-defense homologs across the prokaryotic genomes. To date, no tools comprehensively annotate anti-defense proteins within a desired genome or MGE. Here, we developed "AntiDefenseFinder" - a free open-source tool and web service that detects 156 anti-defense systems (of one or more proteins) in any genomic sequence. Using this dataset, we identified 47,981 anti-defense systems distributed across prokaryotes, phage, and MGEs. We found that some genes co-localize in "anti-defense islands", including E. coli T4 and Lambda phages, although many are standalone. Out of the 112 systems detected in bacteria, 100 systems localize only or preferentially in prophages, plasmids, phage satellites, integrons, and integrative and conjugative elements. However, over 80% of anti-Pycsar protein 1 (Apyc1) resides in non-mobile regions of bacteria. Evolutionary and functional analyses revealed that Apyc1 likely originated in bacteria to regulate cNMP signaling, but was co-opted multiple times by phages to overcome cNMP-utilizing defenses. With the AntiDefenseFinder tool, we hope to facilitate the identification of the full repertoire of anti-defense systems in MGEs, the discovery of new protein functions, and a deeper understanding of host-pathogen arms race.

microbiology↗

bioRxiv2024

BundleAGE: Predicting White Matter Age using Along-Tract Microstructural Profiles from Diffusion MRI

Brain Age Gap Estimation (BrainAGE) is an estimate of the gap between a persons chronological age (CA) and a measure of their brains biological age (BA). This metric is often used as a marker of accelerated aging, albeit with some caveats. Age prediction models trained on brain structural and functional MRI have been employed to derive BrainAGE biomarkers, for predicting the risk of neurodegeneration. While voxel-based and along-tract microstructural maps from diffusion MRI have been used to study brain aging, no studies have evaluated along-tract microstructure for computing BrainAGE. In this study, we train machine learning models to predict a persons age using along-tract microstructural profiles from diffusion tensor imaging. We were able to demonstrate differential aging patterns across different white matter bundles and microstructural measures. The novel Bundle Age Gap Estimation (BundleAGE) biomarker shows potential in quantifying risk factors for neurodegenerative diseases and aging, while incorporating finer scale information throughout white matter bundles.

neuroscience↗

bioRxiv2024

Mycn regulates vascular development through PI3K signaling pathway in zebrafish

Mycn, a MYC gene family member, is implicated in both carcinogenesis through amplification and Feingold syndrome through its deficiency. Previous studies have indicated that increased Mycn expression enhances vascularization in human neuroblastomas, yet its precise role in vascular development remains elusive. In this study, we utilized single-cell RNA-seq and live imaging analyses to confirm that mycn is expressed during zebrafish vasculogenesis. We investigated vascular development in zebrafish using a genetically engineered mycn mutation. Our findings reveal that mycn-deficient zebrafish exhibit reduced intersegmental vessels and malformed subintestinal vessels, primarily due to decreased cell proliferation in vascular cells. Importantly, we discovered that activation of PI3K signaling significantly ameliorates these vascular abnormalities.

developmental biology↗

bioRxiv2024

A Framework of Multi-View Machine Learning for Biological Spectral Unmixing of Fluorophores with Overlapping Excitation and Emission Spectra

The accuracy in assigning fluorophore identity and abundance, termed spectral unmixing, in biological fluorescence microscopy images remains challenging due to the unavoidable and significant overlap in emission spectra among fluorophores. In conventional laser scanning confocal spectral microscopy, fluorophore information is acquired by recording emission spectra with a single combination of discrete excitation wavelengths. As a matter of fact, organic fluorophores have not only unique emission spectral signatures but also have unique and characteristic excitation spectra. In this paper, we propose a generalized multi-view machine learning approach, which makes use of both excitation and emission spectra to greatly improve the accuracy in differentiating multiple highly overlapping fluorophores in a single image. By recording emission spectra of the same field with multiple combinations of excitation wavelengths, we obtain data representing these different views of the underlying fluorophore distribution in the sample. We then propose a framework of multi-view machine learning methods, which allows us to flexibly incorporate noise information and abundance constraints, to extract the spectral signatures of fluorophores from their reference images and to efficiently recover their corresponding abundances in unknown mixed images. Numerical experiments on simulated image data demonstrate the methods efficacy in improving accuracy, allowing for the discrimination of 100 fluorophores with highly overlapping spectra. Furthermore, validation on images of mixtures of fluorescently labeled E. coli demonstrates the power of the proposed multi-view strategy in discriminating fluorophores with spectral overlap in real biological images.

bioinformatics↗

bioRxiv2024

High-Resolution Genome Assembly and Population Genetic Study of the Endangered Maple Acer pentaphyllum (Sapindaceae): Implications for Conservation Strategies

Acer pentaphyllum Diels (Sapindaceae), a highly threatened maple endemic to dry-hot valleys of the Yalong River in western Sichuan, China, requires urgent conservation efforts due to its extremely rarity and restricted distribution. In this study, we present a high-quality chromosome-level reference genome of A. pentaphyllum (626 Mb, 2n = 26), comprising 70.64% repetitive sequences and 38,540 protein-coding genes. Phylogenetic analysis shows that A. pentaphyllum diverged from a clade consisting of A. yangbiense and A. saccharum in the late Eocene ([~]37.83 Ma). In the genome of A. pentaphyllum, genes involved in responding to external environmental change have undergone expansion and positive selection, potentially reflecting its adaptive strategies. While no recent independent whole-genome duplication events were detected, a recent rapid amplification of transposable elements was observed. Population genomic analysis of 227 individuals from 28 populations revealed low genetic diversity (1.04 {+/-} 0.97 x 10-3) compared to other woody species. Phylogeographic patterns suggest an upstream colonization along the Yalong River, with two severe population bottlenecks correlating with major Pleistocene climatic transitions. In addition, most populations exhibit high inbreeding and genetic load, particularly those at range edges (TKX, CDG, TES). Based on these genomic insights, we propose targeted conservation strategies, including genetic rescue measures, to safeguard this unique maple species. These findings not only contribute to the preservation of A. pentaphyllum but also enhance our understanding of plant adaptation to extreme environments and the impacts of climate change on species with restricted distributions.

genetics↗

bioRxiv2024

Amyloid, Tau, and APOE in Alzheimer's Disease: Impact on White Matter Tracts

Alzheimers disease (AD) is characterized by cognitive decline and memory loss due to the abnormal accumulation of amyloid-beta (A{beta}) plaques and tau tangles in the brain; its onset and progression also depend on genetic factors such as the apolipoprotein E (APOE) genotype. Understanding how these factors affect the brains neural pathways is important for early diagnostics and interventions. Tractometry is an advanced technique for 3D quantitative assessment of white matter tracts, localizing microstructural abnormalities in diseased populations in vivo. In this work, we applied BUAN (Bundle Analytics) tractometry to 3D diffusion MRI data from 730 participants in ADNI3 (phase 3 of the Alzheimers Disease Neuroimaging Initiative; age range: 55-95 years, 349M/381F, 214 with mild cognitive impairment, 69 with AD, and 447 cognitively healthy controls). Using along-tract statistical analysis, we assessed the localized impact of amyloid, tau, and APOE genetic variants on the brains neural pathways. BUAN quantifies microstructural properties of white matter tracts, supporting along-tract statistical analyses that identify factors associated with brain microstructure. We visualize the 3D profile of white matter tract associations with tau and amyloid burden in Alzheimers disease; strong associations near the cortex may support models of disease propagation along neural pathways. Relative to the neutral genotype, APOE{varepsilon} 3/{varepsilon}3, carriers of the AD-risk conferring APOE{varepsilon} 4 genotype show microstructural abnormalities, while carriers of the protective{varepsilon} 2 genotype also show subtle differences. Of all the microstructural metrics, mean diffusivity (MD) generally shows the strongest associations with AD pathology, followed by axial diffusivity (AxD) and radial diffusivity (RD), while fractional anisotropy (FA) is typically the least sensitive metric. Along-tract microstructural metrics are sensitive to tau and amyloid accumulation, showing the potential of diffusion MRI to track AD pathology and map its impact on neural pathways.

neuroscience↗

bioRxiv2024

Simultaneous dual-color calcium imaging in freely-behaving mice

Miniaturized fluorescence microscopes (miniscopes) enable imaging of calcium events from a large population of neurons in freely behaving animals. Traditionally, miniscopes have only been able to record from a single fluorescence wavelength. Here, we present a new open-source dual-channel Miniscope that simultaneously records two wavelengths in freely behaving animals. To enable simultaneous acquisition of two fluorescent wavelengths, we incorporated two CMOS sensors into a single Miniscope. To validate our dual-channel Miniscope, we imaged hippocampal CA1 region that co-expressed a dynamic calcium indicator (GCaMP) and a static nuclear signal (tdTomato) while mice ran on a linear track. Our results suggest that, even when neurons were registered across days using tdTomato signals, hippocampal spatial coding changes over time. In conclusion, our novel dual-channel Miniscope enables imaging of two fluorescence wavelengths with minimal crosstalk between the two channels, opening the doors to a multitude of new experimental possibilities. TeaserNovel open-source dual-channel Miniscope that simultaneously records two wavelengths with minimal crosstalk in freely behaving animals.

neuroscience↗

bioRxiv2024

RNADiffFold: Generative RNA Secondary Structure Prediction using Discrete Diffusion Models

RNA molecules are essential macromolecules that perform diverse biological functions in living beings. Precise prediction of RNA secondary structures is instrumental in deciphering their complex three-dimensional architecture and functionality. Traditional methodologies for RNA structure prediction, including energy-based and learning-based approaches, often depict RNA secondary structures from a static perspective and rely on stringent a priori constraints. Inspired by the success of diffusion models, in this work, we introduce RNADiffFold, an innovative generative prediction approach of RNA secondary structures based on multinomial diffusion. We reconceptualize the prediction of contact maps as akin to pixel-wise segmentation and accordingly train a denoising model to refine the contact maps starting from a noise-infused state progressively. We also devise a potent conditioning mechanism that harnesses features extracted from RNA sequences to steer the model toward generating an accurate secondary structure. These features encompass one-hot encoded sequences, probabilistic maps generated from a pre-trained scoring network, and embeddings and attention maps derived from RNA-FM. Experimental results on both within- and cross-family datasets demonstrate RNADiffFolds competitive performance compared with current state-of-the-art methods. Additionally, RNADiffFold has shown a notable proficiency in capturing the dynamic aspects of RNA structures, a claim corroborated by its performance on datasets comprising multiple conformations.

bioinformatics↗

bioRxiv2024

Structures of PSI-FCPI from Thalassiosira pseudonana in high light provide convergent evolution and light-adaptive strategies in diatom FCPIs

Diatoms achieve great survival success in the fluctuating oceanic environment, rely on fucoxanthin chlorophyll a/c-binding proteins (FCPs) to complete light harvesting and quenching, which provide about 20% primary productivity on earth. We report two cryo-electron microscopic structures of photosystem I (PSI) with 13 or 5 FCPIs respectively at 2.78 [A] and 3.20 [A] resolution from Thalassiosira pseudonana under high light conditions. 8 Lhcr FCPIs are found detached from the PSI-13FCPI supercomplex under high light conditions, remaining 5 FCPIs are stably combined with the PSI core including Lhcr3, RedCAP, Lhcq8, Lhcf10, and FCP3 subunits. The specific pigment network in this centric diatom T. pseudonana demonstrates a higher proportion of Chlorophylls a, diadinoxanthins, and diatoxanthins but fewer fucoxanthins compared with the huge PSI-FCPI from another centric diatom Chaetoceros gracilis, thus exhibiting more efficiency in energy transfer and dissipation among FCPI antennas. These results reveal the assembly mechanism of several types of peripheral FCPIs and corresponding light-adaptive strategies in T. pseudonana, as well as the convergent evolution of the diatoms PSI-FCPI structures.

plant biology↗

bioRxiv2024

Adaptive loss of shortwave cone opsins during genomic evolution in cartilaginous fish

There is ample evidence of the loss of the shortwave visual protein gene in cartilaginous fish. However, the underlying logic and mechanisms by which organisms need to adapt to the environment behind the opsin loss are still unclear. Here, we report the assembly structure of the whole genome of Okamejei kenojei and Prionace glauca, identify TcMar difference between skates/rays and sharks/chimaeras, and analyze the distribution characteristics and intra group differentiation of opsin-related genes in cartilaginous fish. By establishing a zebrafish model with short wave opsin gene deletion, we find blue or violet light via short-wave sensitive opsins SWS1 or SWS2 can cause the photoreceptor layer thinning (the biomarker for age-related macular degeneration in human eye) through enactive the cell aging. The loss of sws is helpful for alleviating the short-wave light damage on the eye. Since the tapetum lucidum in the eye is found broad existence in various cartilaginous fish, a logical hypothesis was creatively proposed that the existence of tapetum lucidum on the retina of cartilaginous fish is interdependent with the loss of shortwave visual protein gene, providing a new perspective and explanatory path for further understanding of the evolution of visual genes in cartilaginous fish.

bioinformatics↗

bioRxiv2024

The phylogenetic position of the Yunxian cranium elucidates the origin of Dragon Man and the Denisovans

Diverse Middle Pleistocene forms of Homo coexisted in Africa, Europe, and Asia, and its controversial whether these fossil humans represent different species or clades. The [~]1 Ma old Yunxian 2 fossil from China is crucial for understanding the cladogenesis of Homo and the origin of Homo sapiens. Here, we restored and reconstructed the distorted Yunxian 2 cranium using new technology. The results show that this cranium displays mosaic features of plesiomorphy and apomorphy. Geometric-morphometric, phylogenetic and Bayesian tip-dating analyses including the reconstructed Yunxian 2 suggest that it is an early member of the Asian Homo longi clade, which probably includes the Denisovans, and is the sister group to the Homo sapiens clade. Both the H. sapiens and H. longi clades had deep roots extending beyond the Middle Pleistocene, and the basal position of the Yunxian fossil cranium suggests that it represents a population lying close to the last common ancestor of the two clades. One-Sentence SummaryThe newly-reconstructed Yunxian 2 cranium represents a basal member of the Homo longi and Denisovan clade, and probably lies close to the last common ancestor of that clade and the clade of H. sapiens.

evolutionary biology↗

bioRxiv2024

Structural insights into transcription regulation of the global virulence factor PhoP from Mycobacterium tuberculosis

Mycobacterium tuberculosis (Mtb), remaining as the leading cause of the worldwide threat Tuberculosis, relies heavily on its transcriptional reprogramming of diverse stress genes to swiftly adapt to adverse environments and ensure infections. The global virulence factor PhoP plays a pivotal role in coordinating transcription activation or repression of the essential phosphate-nitrogen metabolic remodeling genes. However, what defines PhoP to deferentially act as an activator or a repressor remains largely unexplored. Here, we determine one cryo-EM structure of Mtb RNAP-promoter open complex, three cryo-EM structures of PhoP-dependent transcription activation complexes (PhoP-TACs) consisting of Mtb RNA polymerase (RNAP), different number of PhoP molecules binding to different types of well-characterized consensus promoters, and one cryo-EM structure of Mtb PhoP-dependent transcription repression complex (PhoP-TRC) comprising of Mtb RNAP, PhoP, the nitrogen metabolism regulator GlnR and their co-regulated promoter. Structural comparisons reveal phosphorylation of PhoP is required for stabilization of PhoP-TACs, PhoP specifically recognizes promoters as novel tandem dimers and recruits RNAP through extensively interacting with its conserved {beta} flap and {sigma}AR4 domains. Strikingly, the distinct promoter spacer length and PhoP-GlnR interactions in PhoP-TRC constrain the upstream DNA into a distinct topology and retain PhoP in a novel dragging repression mode. Collectively, these data highlight the dual regulatory mechanisms of PhoP-dependent transcription regulation in governing stress adaptation. These findings provide structural basis for developing potential anti-tuberculosis drugs and/or interventions.

microbiology↗

bioRxiv2024

Fast adaptive super-resolution lattice light-sheet microscopy for rapid, long-term, near-isotropic subcellular imaging

Lattice light-sheet microscopy (LLSM) provides a crucial observation window into intra- and inter-cellular physiology of living specimens with high speed and low phototoxicity, however, at the diffraction-limited resolution or anisotropic super-resolution with structured illumination. Here we present the meta-learning-empowered reflective lattice light-sheet virtual structured illumination microscopy (Meta-rLLS-VSIM), which instantly upgrades LLSM to a near-isotropic super resolution of [~]120-nm laterally and [~]160-nm axially, more than twofold improvement in each dimension, without any modification of the optical system or sacrifice of other imaging metrics. Moreover, to alleviate the tremendous demands on training data and time necessitated by existing deep-learning (DL) methods, we devised an adaptive online training approach by synergizing the front-end imaging system and back-end meta-learning framework, which reduced the total time for data acquisition and model training down to tens of seconds. With this method, a new model can be well-trained with tenfold less data and three orders of magnitude less time than current standard supervised learning. We demonstrate the versatile functionalities of Meta-rLLS-VSIM by imaging a variety of bioprocesses with ultrahigh spatiotemporal resolution for long duration of hundreds of multi-color volumes, characterizing the dynamic regulation of contractile ring filaments during mitosis and the growth of pollen tubes, and delineating the nanoscale distributions, dispersion, and interaction pattern of multiple organelles in embryos and eukaryotic cells.

biophysics↗

bioRxiv2024

Automated Cytometric Gating with Human-Level Performance Using Bivariate Segmentation

Recent advances in cytometry technology have enabled high-throughput data collection with multiple single-cell protein expression measurements. The significant biological and technical variance between samples in cytometry has long posed a formidable challenge during the gating process, especially for the initial gates which deal with unpredictable events, such as debris and technical artifacts. Even with the same experimental machine and protocol, the target population, as well as the cell population that needs to be excluded, may vary across different measurements. To address this challenge and mitigate the labor-intensive manual gating process, we propose a deep learning framework UNITO to rigorously identify the hierarchical cytometric subpopulations. The UNITO framework transformed a cell-level classification task into an image-based semantic segmentation problem. For reproducibility purposes, the framework was applied to three independent cohorts and successfully detected initial gates that were required to identify single cellular events as well as subsequent cell gates. We validated the UNITO framework by comparing its results with previous automated methods and the consensus of at least four experienced immunologists. UNITO outperformed existing automated methods and differed from human consensus by no more than each individual human. Most critically, UNITO framework functions as a fully automated pipeline after training and does not require human hints or prior knowledge. Unlike existing multi-channel classification or clustering pipelines, UNITO can reproduce a similar contour compared to manual gating for each intermediate gating to achieve better interpretability and provide post hoc visual inspection. Beyond acting as a pioneering framework that uses image segmentation to do auto-gating, UNITO gives a fast and interpretable way to assign the cell subtype membership, and the speed of UNITO will not be impacted by the number of cells from each sample. The pre-gating and gating inference takes approximately 2 minutes for each sample using our pre-defined 9 gates system, and it can also adapt to any sequential prediction with different configurations.

immunology↗

bioRxiv2024

StereoMM: A Graph Fusion Model for Integrating Spatial Transcriptomic Data and Pathological Images

Spatially resolved omics technologies generating multimodal and high-throughput data lead to the urgent need for advanced analysis to allow the biological discoveries by comprehensively utilizing information from multi-omics data. The H&E image and spatial transcriptomic data indicate abundant features which are different and complementary to each other. AI algorithms can perform nonlinear analysis on these aligned or unaligned complex datasets to decode tumoral heterogeneity for detecting functional domain. However,the interpretability of AI-generated outcomes for human experts is a problem hindering application of multi-modal analysis in clinic. We presented a machine learning based toolchain called StereoMM, which is a graph fusion model that can integrate gene expression, histological images, and spatial location. StereoMM firstly performs information interaction on transcriptomic and imaging features through the attention module, guaranteeing explanations for its decision-making processes. The interactive features are input into the graph autoencoder together with the graph of spatial position, so that multimodal features are fused in a self-supervised manner. Here, StereoMM was subjected to mouse brain tissue, demonstrating its capability to discern fine tissue architecture, while highlighting its advantage in computational speed. Utilizing data from Stereo-seq of human lung adenosquamous carcinoma and 10X Visium of human breast cancer, we showed its superior performance in spatial domain recognition over competing software and its ability to reveal tumor heterogeneity. The fusion approach for imaging and gene expression data within StereoMM aids in the more accurate identification of domains, unveils critical molecular features, and elucidates the connections between different domains, thereby laying the groundwork for downstream analysis.

bioinformatics↗

bioRxiv2024

Microstructural Mapping of Neural Pathways in Alzheimer's Disease using Macrostructure-Informed Normative Tractometry

IntroductionDiffusion MRI is sensitive to the microstructural properties of brain tissues, and shows great promise in detecting the effects of degenerative diseases. However, many approaches analyze single measures averaged over regions of interest, without considering the underlying fiber geometry. MethodsHere, we propose a novel Macrostructure-Informed Normative Tractometry (MINT) framework, to investigate how white matter microstructure and macrostructure are jointly altered in mild cognitive impairment (MCI) and dementia. We compare MINT-derived metrics with univariate metrics from diffusion tensor imaging (DTI), to examine how fiber geometry may impact interpretation of microstructure. ResultsIn two multi-site cohorts from North America and India, we find consistent patterns of microstructural and macrostructural anomalies implicated in MCI and dementia; we also rank diffusion metrics sensitivity to dementia. DiscussionWe show that MINT, by jointly modeling tract shape and microstructure, has potential to disentangle and better interpret the effects of degenerative disease on the brains neural pathways.

neuroscience↗

bioRxiv2024

CRISPR screens reveal convergent targeting strategies against evolutionarily distinct chemoresistance in cancer

Resistance to chemotherapy has been a major hurdle that limits therapeutic benefits for many types of cancer. Here we systematically identify genetic drivers underlying chemoresistance by performing 30 genome-scale CRISPR knockout screens for seven chemotherapeutic agents in multiple cancer cells. Chemoresistance genes vary between conditions primarily due to distinct genetic background and mechanism of action of drugs, manifesting heterogeneous and multiplexed routes towards chemoresistance. By focusing on oxaliplatin and irinotecan resistance in colorectal cancer, we unravel that evolutionarily distinct chemoresistance can share consensus vulnerabilities identified by 26 second-round CRISPR screens with druggable gene library. We further pinpoint PLK4 as a therapeutic target to overcome oxaliplatin resistance in various models via genetic ablation or pharmacological inhibition, highlighting a single-agent strategy to antagonize evolutionarily distinct chemoresistance. Our study not only provides resources and insights into the molecular basis of chemoresistance, but also proposes potential biomarkers and therapeutic strategies against such resistance.

cancer biology↗

bioRxiv2024

Knowledge Graph-based Thought: a knowledge graph enhanced LLMs framework for pan-cancer question answering

BackgroundIn recent years, Large Language Models (LLMs) have shown promise in various domains, notably in biomedical sciences. However, their real-world application is often limited by issues like erroneous outputs and hallucinatory responses. ResultsWe developed the Knowledge Graph-based Thought (KGT) framework, an innovative solution that integrates LLMs with Knowledge Graphs (KGs) to improve their initial responses by utilizing verifiable information from KGs, thus significantly reducing factual errors in reasoning. The KGT framework demonstrates strong adaptability and performs well across various open-source LLMs. Notably, KGT can facilitate the discovery of new uses for existing drugs through potential drug-cancer associations, and can assist in predicting resistance by analyzing relevant biomarkers and genetic mechanisms. To evaluate the Knowledge Graph Question Answering (KGQA) task within biomedicine, we utilize a pan-cancer knowledge graph to develop a pan-cancer question answering benchmark, named the Pan-cancer Question Answering (PcQA). ConclusionsThe KGT framework substantially improves the accuracy and utility of LLMs in the biomedical field. This study serves as a proof-of-concept, demonstrating its exceptional performance in biomedical question answering. Key PointsO_LIWe introduce a framework combining LLMs with KGs to improve factual accuracy in LLM reasoning. C_LIO_LIOur system is a flexible architecture that seamlessly integrates various LLMs. C_LIO_LIUtilizing a pan-cancer knowledge graph, we have proposed the first KGQA benchmark in the field of biomedicine. C_LIO_LICase studies reveal our method enhanced LLMs in addressing biomedical challenges such as drug repositioning, resistance research, individualized treatment, and biomarker analysis. C_LIO_LIThe method performs favorably in comparison to existing methods. C_LI

bioinformatics↗