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Fan, C.

Publications and source records attributed to Fan, C..

At least 55 records · Page 3Linked to original sources
bioRxiv2024

Successive pattern in winter wheat main stem structure modeling

1.With the rise of research on functional structural plant models (FSPMs), it is particularly important to realistically describe the structural traits of a plant. In 3D FSPMs, the true structural information of the canopy can make the models estimation of functional traits more accurate. Since canopy is the medium of the crop stands that sense and respond to the external environment, accurate estimation of detailed organ sizes in crop canopy is crucial, and current FSPMs still need to be improved. Here the structure of wheat (Triticum aestivum) was measured in detail at the phytomer level. Dataset was built which covers a set of [~]100 cultivars and 11 site-year under various field managements such as sowing density, inter-row, nitrogen fertilizer and sowing date. The relationships of successive phytomer sizes were quantified and the variance was analyzed for each independent experiment, only site was found as a significant impact factor. A conceptual model based on the successive pattern was proposed and validated for detailed final phytomer sizes. Then we coupled the meteorology data - the ratio of average daily gross radiation and temperature during the emergence and architecture mature with the model. The validation has taken place three times to emphasize models independent of cultivars, sites dependent and the ability of model parameters deriving from environmental factors, respectively. The results revealed the feasibility of the newly built wheat main stem structure model with the R2 higher than 0.85. Compared with ADEL-Wheat and Sirius Quality, the model achieved better performance in blade width and blade area estimation. According to the concise and easily acquired parameters of this model, it has potential for high throughput phenotyping detail wheat canopy structure. Previous work on the relationship between blade width and circumference of apex shoot in maize illustrated that the cell number was the most likely explanation for this pattern, which means the same work holds for similar crops.

developmental biology↗
bioRxiv2024

Intra-genomic genes-to-genes correlation enables genome representaion

The bacterial pan-genome consists of core genes shared by all members of a taxonomy and accessory genes found in only a subset. The correlation among genes within the pan-genome could advance our understanding of evolution and tackle medical challenges. Here, we discovered a strong intra-genomic correlation among bacterial pan-genes within each of Escherichia coli, Listeria monocytogenes, Staphylococcus aureus, and Campylobacter jejuni. With a convolutional neural network assisted core genome knock-out simulation, we found that different combinations of fewer than 20 highly variable core genes could recover the sub-species type classified by complete core genome with accuracy >95%. This observation led us to test the genes-to-genes predictability: with more than 52,000 assemblies from each species, combinations of highly variable core genes could predict the sequence variants of other core genes (average accuracy >94%) within the same genome and could also predict sequence variants (average accuracy >91%) as well as the presence (average AUROC >0.91) of some accessory genes. Furthermore, combinations of highly variable core genes could also predict multiple antibiotic resistances (AUROC >0.80) in large published datasets of E. coli, S. aureus, and Mycobacterium tuberculosis. Collectively, we propose that genes within the same genome can strongly correlate with each other. Therefore, the strain phylogeny and the status of other genes could be uniformly represented by combinations of highly variable core genes, which could further represent certain phenotypes including in vitro resistance.

bioinformatics↗
bioRxiv2024

Cell-type specific auditory responses in the striatum are shaped by feed forward inhibition

Summary/AbstractThe posterior "tail" region of the striatum receives dense innervation from sensory brain regions and has been demonstrated to play a role in behaviors that require sensorimotor integration including discrimination1,2, avoidance3 and defense4 responses. The output neurons of the striatum, the D1 and D2 striatal projection neurons (SPNs) that make up the direct and indirect pathways, respectively, are thought to play differential roles in these behavioral responses, although it remains unclear if or how these neurons display differential responsivity to sensory stimuli. Here, we used whole-cell recordings in vivo and ex vivo to examine the strength of excitatory and inhibitory synaptic inputs onto D1 and D2 SPNs following the stimulation of upstream auditory pathways. While D1 and D2 SPNs both displayed stimulus-evoked depolarizations, D1 SPN responses were stronger and faster for all stimuli tested in vivo as well as in brain slices. This difference did not arise from differences in the strength of excitatory inputs but from differences in the strength of feed forward inhibition. Indeed, fast spiking interneurons, which are readily engaged by auditory afferents exerted stronger inhibition onto D2 SPNs compared to D1 SPNs. Our results support a model in which differences in feed forward inhibition enable the preferential recruitment of the direct pathway in response to auditory stimuli, positioning this pathway to initiate sound-driven actions.

neuroscience↗
bioRxiv2024

Artesunate, EDTA and colistin work synergistically against MCR-negative and -positive colistin-resistant Salmonella

Discovering new strategies to combat the multi-drug resistance bacteria constitutes a major medical challenge of our time. Previously, artesunate (AS) has been reported to exert antibacterial enhancement activity in combination with {beta}-lactam antibiotics, via inhibition of the efflux pump AcrB. However, combination of AS and colistin (COL) revealed weak synergistic effect against a limited number of strains, and few studies have further explored its possible mechanism of synergistic action. In this paper, we found that AS and EDTA could strikingly enhance the antibacterial effects of COL against mcr-1- and mcr-1+ Salmonella strains either in vitro or in vivo, when used in triple combination. The excellent bacteriostatic effect was primarily related to the increased cell membrane damage, accumulation of toxic compounds and inhibition of MCR-1. The potential binding sites of AS to MCR-1 (THR283, SER284, and TYR287) were critical for its inhibition of MCR-1 activity. Additionally, we also demonstrated that the CheA of chemosensory system and virulence-related protein SpvD were critical for the bacteriostatic synergistic effects of the triple combination. Selectively targeting CheA, SpvD or MCR using the natural compound AS could be further investigated as an attractive strategy for treatment of Salmonella infection. Collectively, our work opens new avenues towards the potentiation of COL and revealed an alternative drug combination strategy to overcome COL resistant bacterial infections.

microbiology↗
bioRxiv2024

Vestibular modulation by stimulant derivatives in a pentameric ligand-gated ion channel

Allosteric modulation of pentameric ligand-gated ion channels (pLGICs) is critical to the action of neurotransmitters and many psychoactive drugs. However, details of their modulatory mechanisms remain unclear, especially beyond the orthosteric neurotransmitter-binding sites. The recently reported prokaryotic channel sTeLIC, a pH-gated homolog of eukaryotic receptors in the pLGIC family, is thought to be modulated by aromatic compounds via a relatively uncharacterized modulatory site in the extracellular vestibule. Here, we show that sTeLIC is sensitive to potentiation by psychostimulant derivatives. By determining new cryo-EM and X-ray structures in closed and open states, and testing the impact of targeted mutations on electrophysiological behavior, we show that several amphiphilic compounds preferentially bind a vestibular pocket in the contracted open-state extracellular domain. This work provides a detailed structure-function mechanism for allosteric potentiation via a noncanonical lig- and site, with potential conservation in eukaryotic pentameric ligand-gated ion channels.

biophysics↗
bioRxiv2024

A flexible intracortical brain-computer interface for typing using finger movements

Keyboard typing with finger movements is a versatile digital interface for users with diverse skills, needs, and preferences. Currently, such an interface does not exist for people with paralysis. We developed an intracortical brain-computer interface (BCI) for typing with attempted flexion/extension movements of three finger groups on the right hand, or both hands, and demonstrated its flexibility in two dominant typing paradigms. The first paradigm is "point-and-click" typing, where a BCI user selects one key at a time using continuous real-time control, allowing selection of arbitrary sequences of symbols. During cued character selection with this paradigm, a human research participant with paralysis achieved 30-40 selections per minute with nearly 90% accuracy. The second paradigm is "keystroke" typing, where the BCI user selects each character by a discrete movement without real-time feedback, often giving a faster speed for natural language sentences. With 90 cued characters per minute, decoding attempted finger movements and correcting errors using a language model resulted in more than 90% accuracy. Notably, both paradigms matched the state-of-the-art for BCI performance and enabled further flexibility by the simultaneous selection of multiple characters as well as efficient decoder estimation across paradigms. Overall, the high-performance interface is a step towards the wider accessibility of BCI technology by addressing unmet user needs for flexibility.

neuroscience↗
bioRxiv2024

Generative Adversarial Networks Accurately Reconstruct Pan-Cancer Histology from Pathologic, Genomic, and Radiographic Latent Features

Artificial intelligence models have been increasingly used in the analysis of tumor histology to perform tasks ranging from routine classification to identification of novel molecular features. These approaches distill cancer histologic images into high-level features which are used in predictions, but understanding the biologic meaning of such features remains challenging. We present and validate a custom generative adversarial network - HistoXGAN - capable of reconstructing representative histology using feature vectors produced by common feature extractors. We evaluate HistoXGAN across 29 cancer subtypes and demonstrate that reconstructed images retain information regarding tumor grade, histologic subtype, and gene expression patterns. We leverage HistoXGAN to illustrate the underlying histologic features for deep learning models for actionable mutations, identify model reliance on histologic batch effect in predictions, and demonstrate accurate reconstruction of tumor histology from radiographic imaging for a virtual biopsy.

cancer biology↗
bioRxiv2024

Mosaic sarbecovirus vaccination elicits cross-reactive responses in pre-immunized animals

Immunization with mosaic-8b [60-mer nanoparticles presenting 8 SARS-like betacoronavirus (sarbecovirus) receptor-binding domains (RBDs)] elicits more broadly cross-reactive antibodies than homotypic SARS-CoV-2 RBD-only nanoparticles and protects against sarbecoviruses. To investigate original antigenic sin (OAS) effects on mosaic-8b efficacy, we evaluated effects of prior COVID-19 vaccinations in non-human primates and mice on anti-sarbecovirus responses elicited by mosaic-8b, admix-8b (8 homotypics), or homotypic SARS-CoV-2 immunizations, finding greatest cross-reactivity for mosaic-8b. As demonstrated by molecular fate-mapping in which antibodies from specific cohorts of B cells are differentially detected, B cells primed by WA1 spike mRNA-LNP dominated antibody responses after RBD-nanoparticle boosting. While mosaic-8b- and homotypic-nanoparticles boosted cross-reactive antibodies, de novo antibodies were predominantly induced by mosaic-8b, and these were specific for variant RBDs with increased identity to RBDs on mosaic-8b. These results inform OAS mechanisms and support using mosaic-8b to protect COVID-19 vaccinated/infected humans against as-yet-unknown SARS-CoV-2 variants and animal sarbecoviruses with human spillover potential.

immunology↗
bioRxiv2024

Divergent mechanisms of steroid inhibition in the human {rho}1 GABA(A) receptor

{rho}-type {gamma}-aminobutyric acid-A (GABAA) receptors are widely distributed in the retina and brain, and are potential drug targets for the treatment of visual, sleep and cognitive disorders. Endogenous neuroactive steroids including {beta}-estradiol and pregnenolone sulfate negatively modulate the function of {rho}1 GABAA receptors, but their inhibitory mechanisms are not clear. By combining four new cryo-EM structures with electrophysiology and molecular dynamics simulations, we characterize binding sites and negative modulation mechanisms of {beta}-estradiol and pregnenolone sulfate at the human {rho}1 GABAA receptor. {beta}-estradiol binds in a pocket at the interface between extracellular and transmembrane domains, apparently specific to the {rho} subfamily, and disturbs allosteric conformational transitions linking GABA binding to pore opening. In contrast, pregnenolone sulfate binds inside the pore to block ion permeation, with a preference for activated structures. These results illuminate contrasting mechanisms of {rho}1 inhibition by two different neuroactive steroids, with potential implications for subtype-specific gating and pharmacological design.

biophysics↗
bioRxiv2023

De novo genome assembly of four Andean potato weevil species (Premnotrypes,Rhigopsidius), the primary agricultural pest of the potato in South America

The Andean potato weevil complex are the most widespread and serious insect pests to potato crops in the Andes. More broadly, genomic assemblies of insect pests are currently lacking in agricultural research, especially those from the order Coleoptera. These genome data are essential for identifying potential underlying mechanisms important to biological control strategies and food security in the highlands. Here, we present the de novo genome assemblies for four species of the Andean potato weevil complex: Premnotrypes vorax, P. suturicallus, P. latithorax, and Rhigopsidius piercei. Genome assemblies exceeded the average size of those from the order Coleoptera and were highly repetitive: for P. vorax (1.33 Gb, 71.51% repetitive), P. latithorax (623 Mb, 59.03% repetitive), P. suturicallus (1.23 Gb, 70.19% repetitive), and R. piercei (1.55 Gb, 71.91% repetitive). We examined genomic regions related to metabolic potato plant detoxification and insecticide resistance using the available Colorado potato beetle (Leptinotarsa decemlineata) genome annotations as a guide. Our analysis of these weevil genomes identified chemosensory receptors and odorant binding proteins that could be related to detecting their hosts, the potato plant (Solanum tuberosum), as well as many genomic regions involved in subverting pesticide resistance. We have generated the first whole-genome assemblies of the Andean potato weevil complex that will be foundational for future agricultural pest management and entomological research in South America. Author SummaryWithin the South American Andean mountains the Andean potato weevil insects are the most widespread and serious pests to potatoes, destroying around 89% of potato harvests a year when insecticides are not used. Here, we collected and performed whole-genome sequencing for the first time for four Andean potato weevil species: Premnotrypes vorax, P. suturicallus, P. latithorax, and Rhigopsidius piercei. After analysis of these genome assemblies, we found that they were large and highly repetitive compared to other published beetle genome data in the order Coleoptera. After further examination of these genome assemblies, we found regions related to metabolic potato plant detoxification, insecticide resistance, and chemosensory and odorant binding protein receptors that could be related to detecting potato plants. These genomic identifications provide novel molecular insight into regions associated with insecticide resistance, metabolic abilities, and environmental receptors, and can serve as a future valuable resource in classifying phylogenetic relationships as well as identifying regions of interest for improved pest management for potato farmers.

genomics↗
bioRxiv2023

ProTokens: A Machine-Learned Language for Compact and Informative Encoding of Protein 3D Structures

Understanding functions of proteins and designing proteins committed to specific functions in silico are highly valuable for science, industry and therapeutics. However, there is a long-standing divergence in how to present function-related protein structures to the machine learning models: Although the 1-dimensional (1D) representation of proteins via Anfinsens tokens (i.e., amino acids) is sufficient in principle and more machine-friendly, it is less successful in structure-oriented protein design compared to symmetry-constrained 3D representation (i.e., atom coordinates). Aiming to bridge the gap between 1D and 3D protein representations and harvest the advantages of the two, we develop probabilistic tokenization theory for metastable protein structures. We present an unsupervised learning strategy, which conjugates inverse folding with structure prediction, to encode protein structures into artificial amino-acid tokens (ProTokens) and decode them back to atom coordinates. We show that tokenizing protein structures variationally can lead to compact and informative representations. Compared to amino acids -- the Anfinsens tokens -- ProTokens are easier to detokenize and more descriptive of finer conformational ensembles. Therefore, protein structures can be efficiently compressed, stored, aligned and compared in the form of ProTokens. By unifying the discrete and continuous representations of protein structures, ProTokens also enable all-atom protein structure design via various generative models without the concern of symmetry or modality mismatch, and allows scalable foundation models to perceive, process and explore the microscopic structures of biomolecules effectively.

bioinformatics↗
bioRxiv2023

Cross-Dataset Identification of Human Disease-Specific Cell Subtypes Enabled by the Gene Print-based Algorithm--gPRINT

Despite extensive efforts in developing cell annotation algorithms for single cell RNA sequencing results, most algorithms fail to achieve cross-dataset mapping of cell subtypes due to factors such as batch effects between datasets. This limitation is particularly evident when rapidly annotating disease-specific cell subtypes across multiple datasets. In this study, we present gPRINT, a machine learning tool that utilizes the unique one-dimensional "gene print" expression patterns of individual cells. gPRINT is capable of automatically predicting cell types and annotating disease-specific cell subtypes. The development of gPRINT involved curation and harmonization of public datasets, algorithm validation within and across datasets, and the annotation of disease-specific fibroblast subtypes across various disease subgroups and datasets. Additionally, we created a preliminary single-cell atlas of human tendinopathy fibroblasts and successfully achieved automatic prediction of disease-specific cell subtypes in tendon disease. Furthermore, we conducted an exploration of key targets and related drugs specific to this subtype in tendon disease. The proposed approach offers an automated and unified method for identifying disease-specific cell subtypes across datasets, serving as a valuable reference for annotating fibroblast-specific subtypes in different disease states and facilitating the exploration of therapeutic targets in tendon disease.

bioinformatics↗
bioRxiv2023

A likelihood-based framework for demographic inference from genealogical trees

The demographic history of a population drives the pattern of genetic variation and is encoded in the gene-genealogical trees of the sampled alleles. However, existing methods to infer demographic history from genetic data tend to use relatively low-dimensional summaries of the genealogy, such as allele frequency spectra. As a step toward capturing more of the information encoded in the genome-wide sequence of genealogical trees, here we propose a novel framework called the genealogical likelihood (gLike), which derives the full likelihood of a genealogical tree under any hypothesized demographic history. Employing a graph-based structure, gLike summarizes across independent trees the relationships among all lineages in a tree with all possible trajectories of population memberships through time and efficiently computes the exact marginal probability under a parameterized demographic model. Through extensive simulations and empirical applications on populations that have experienced multiple admixtures, we showed that gLike can accurately estimate dozens of demographic parameters when the true genealogy is known, including ancestral population sizes, admixture timing, and admixture proportions. Moreover, when using genealogical trees inferred from genetic data, we showed that gLike outperformed conventional demographic inference methods that leverage only the allele-frequency spectrum and yielded parameter estimates that align with established historical knowledge of the past demographic histories for populations like Latino Americans and Native Hawaiians. Furthermore, our framework can trace ancestral histories by analyzing a sample from the admixed population without proxies for its source populations, removing the need to sample ancestral populations that may no longer exist. Taken together, our proposed gLike framework harnesses underutilized genealogical information to offer exceptional sensitivity and accuracy in inferring complex demographies for humans and other species, particularly as estimation of genome-wide genealogies improves.

genetics↗
bioRxiv2023

Targeting EIF4A triggers an interferon response to synergize with chemotherapy and suppress triple-negative breast cancer

Protein synthesis is frequently dysregulated in cancer and selective inhibition of mRNA translation represents an attractive cancer therapy. Here, we show that therapeutically targeting the RNA helicase eIF4A by Zotatifin, the first-in-class eIF4A inhibitor, exerts pleiotropic effects on both tumor cells and the tumor immune microenvironment in a diverse cohort of syngeneic triple-negative breast cancer (TNBC) mouse models. Zotatifin not only suppresses tumor cell proliferation but also directly repolarizes macrophages towards an M1-like phenotype and inhibits neutrophil infiltration, which sensitizes tumors to immune checkpoint blockade. Mechanistic studies revealed that Zotatifin reprograms the tumor translational landscape, inhibits the translation of Sox4 and Fgfr1, and induces an interferon response uniformly across models. The induction of an interferon response is partially due to the inhibition of Sox4 translation by Zotatifin. A similar induction of interferon-stimulated genes was observed in breast cancer patient biopsies following Zotatifin treatment. Surprisingly, Zotatifin significantly synergizes with carboplatin to trigger DNA damage and an even heightened interferon response resulting in T cell-dependent tumor suppression. These studies identified a vulnerability of eIF4A in TNBC, potential pharmacodynamic biomarkers for Zotatifin, and provide a rationale for new combination regimens comprising Zotatifin and chemotherapy or immunotherapy as treatments for TNBC. One Sentence SummaryTargeting EIF4A sensitizes TNBC to immune therapy and chemotherapy by suppressing Sox4, inducing an interferon response, and reprograming the tumor immune microenvironment.

cancer biology↗
bioRxiv2023

A Bayesian Regularized and Anotation-Informed Integrative Analysis of Cognition (BRAINIAC)

1Here we present a development of the novel Bayesian Regularized and Anotation-Informed Integrative Analysis of Cognition (BRAINIAC) model. BRAINIAC allows for both estimation of total variance explained by all features for a given cognitive phenotype, as well as a principled assessment of the impact of annotations on relative enrichment of features compared to others in terms of variance explained, without relying on a potentially unrealistic assumption of sparsity of brain-cognition associations. We apply the BRAINIAC model to resting state fMRI data from the Adolescent Brain Cognitive Development Study.

neuroscience↗
bioRxiv2023

Structure and dynamics of differential ligand binding in the human {rho}-type GABA(A) receptor

The neurotransmitter {gamma}-aminobutyric acid (GABA) drives critical inhibitory processes in and beyond the nervous system, partly via ionotropic type-A receptors (GABAARs). Pharmacological properties of {rho}-type GABAARs are particularly distinctive, yet the structural basis for their specialization remains unclear. Here we present cryo-EM structures of a lipid-embedded human {rho}1 GABAAR, including a partial intracellular domain, under apo, inhibited, and desensitized conditions. An apparent resting state, determined first in the absence of modulators, was recapitulated with the specific inhibitor (1,2,5,6-tetrahydropyridin-4-yl)methylphosphinic acid and blocker picrotoxin, and provided a rationale for bicuculline insensitivity. Comparative structures, mutant recordings, and molecular simulations with and without GABA further explained the sensitized but slower activation of {rho}1 relative to canonical subtypes. Combining GABA with picrotoxin also captured an apparent uncoupled intermediate state. This work reveals structural mechanisms of gating and modulation with applications to {rho}-specific pharmaceutical design, and to our biophysical understanding of ligand-gated ion channels.

biophysics↗
bioRxiv2023

IL-15 priming alters IFN-γ regulation in murine NK cells

Natural killer (NK) effector functions can be triggered by inflammatory cytokines and engagement of activating receptors. NK cell production of IFN-{gamma}, an important immunoregulatory cytokine, exhibits activation-specific IFN-{gamma} regulation. Resting murine NK cells exhibit activation-specific metabolic requirements for IFN-{gamma} production, which are reversed for activating receptor-mediated stimulation following IL-15 priming. While both cytokine and activating receptor stimulation leads to similar IFN-{gamma} protein production, only cytokine stimulation upregulates Ifng transcript, suggesting that protein production is translationally regulated after receptor stimulation. Based on these differences in IFN-{gamma} regulation, we hypothesized that ex vivo IL-15 priming of murine NK cells allows a switch to IFN-{gamma} transcription upon activating receptor engagement. Transcriptional analysis of primed NK cells compared to naive cells or cells cultured with low-dose IL-15 demonstrated that primed cells strongly upregulated Ifng transcript following activating receptor stimulation. This was not due to chromatin accessibility changes in the Ifng locus or changes in ITAM signaling, but was associated with a distinct transcriptional signature induced by ITAM stimulation of primed compared to naive NK cells. Transcriptional analyses identified a common signature of c-Myc (Myc) targets associated with Ifng transcription. While Myc marked NK cells capable of Ifng transcription, Myc itself was not required for Ifng transcription using a genetic model of Myc deletion. This work highlights altered regulatory networks in IL-15 primed cells, resulting in distinct gene expression patterns and IFN-{gamma} regulation in response to activating receptor stimulation.

immunology↗