Biology subjects

Deng, Y.

Publications and source records attributed to Deng, Y..

At least 127 records · Page 7Linked to original sources

bioRxiv2024

Exploiting functional regions in the viral RNA genome as druggable entities

RNA-targeting compounds or small interfering RNAs (siRNAs) offer a potent means for controlling viral infections. An essential prerequisite to their design depends on identifying conserved and functional viral RNA structures in cells. Techniques that probe RNA structures in situ have been developed recently including SHAPE-MaP, which has been helpful in analyzing the secondary structures of RNA. In this study, we report on the application of SHAPE-MaP to the Porcine Epidemic Diarrhoea Virus (PEDV) RNA genome to categorize different functional regions including potential quadruplex forming sequence and target sites of siRNA. Our results show that these structures can be exploited to inhibit viral proliferation and that SHAPE-MaP is an effective method to the identification of secondary structures in RNA genomes.

genomics↗

bioRxiv2024

{Phi}-Space: Continuous phenotyping of single-cell multi-omics data

Single-cell multi-omics technologies have empowered increasingly refined characterisation of the heterogeneity of cell populations. Automated cell type annotation methods have been developed to transfer cell type labels from well-annotated reference datasets to emerging query datasets. However, these methods suffer from some common caveats, including the failure to characterise transitional and novel cell states, sensitivity to batch effects and under-utilisation of phenotypic information other than cell types (e.g. sample source and disease conditions). We developed {Phi}-Space, a computational framework for the continuous phenotyping of single-cell multi-omics data. In {Phi}-Space we adopt a highly versatile modelling strategy to continuously characterise query cell identity in a low-dimensional phenotype space, defined by reference phenotypes. The phenotype space embedding enables various downstream analyses, including insightful visualisations, clustering and cell type labelling. We demonstrate through three case studies that {Phi}-Space (i) characterises developing and out-of-reference cell states; (ii) is robust against batch effects in both reference and query; (iii) adapts to annotation tasks involving multiple omics types; (iv) overcomes technical differences between reference and query. The versatility of {Phi}-Space makes it applicable to a wide range analytical tasks beyond cell type transfer, and its ability to model complex phenotypic variation will facilitate biological discoveries from different omics types.

bioinformatics↗

bioRxiv2024

Urine Proteomics in Methamphetamine Addicted Patients

Drug addiction is a serious chronic relapsing brain disease, and methamphetamine dependence has a complex course and is difficult to treat, causing a serious public health burden. In this study, we compared and analyzed the urine proteome of methamphetamine-using patients, methamphetamine-withdrawing patients, healthy individuals. The urine proteome of methamphetamine-using patients and methamphetamine-withdrawing patients was significantly different from that of healthy individuals, and some differential proteins and their enriched biological functions showed that they were associated with addiction or neurotoxicity of methamphetamine. The fact, that the urine proteome of patients who withdrew methamphetamine for more than 3 months were significantly different from that of healthy patients, may reveal the reasons for the high rate of methamphetamine relapse and potential intervention targets. This study proved that urine proteome can reflect the effects of addiction on the body comprehensively, and has the potential to provide clues for addiction.

biochemistry↗

bioRxiv2024

Poxvirus A52 is a host range factor for modified vaccinia virus Ankara (MVA) and promotes viral replication by disturbing the formation of autolysosomes

Many members of the poxvirus family are important zoonotic pathogens that pose a significant threat to human and animal health worldwide. Autophagy is a multi-step degradation pathway within cells, and one of its primary biological functions includes the clearance of invading viruses. Nevertheless, the interplay between poxviruses and host cell autophagy has not been fully elucidated. Here, we demonstrate that vaccinia virus (VACV) and lumpy skin disease virus (LSDV) induce incomplete autophagy and inhibit the fusion of autophagosomes and lysosomes, while modified vaccinia virus Ankara (MVA), an attenuated strain of VACV unable to replicate in almost all human cells, does not. Additionally, we screened and identified the VACV protein A52 as a key factor that obstruct the formation of autolysosomes. Mechanistically, A52 interacts with SNAP29 and inhibits its interaction with STX17 and VAMP8, both of which are binding partners of SNAP29 and are essential for complete autophagy. Moreover, A52 promotes the proteasomal degradation of SNAP29, which facilitates viral replication. We further revealed that SNAP29 functions as a restriction factor for MVA, as the suppression of SNAP29 allowed the replication of MVA in human cells. In summary, our data present a molecular mechanism by which poxviruses manipulate the cellular autophagic machinery and provide additional explanation for the restriction of MVA in human cells.

microbiology↗

bioRxiv2024

The long-chain fatty acid-CoA ligase FadD1 monitors Pseudomonas aeruginosa quorum sensing as a receptor of cis-2-decenoic acid

Diffusible signal factor (DSF)-family quorum sensing (QS) signals are widely utilized by many bacterial pathogens to modulate various biological functions and virulence. Previous studies showed that cis-2-decenoic acid (cis-DA) is involved in modulation of biofilm dispersion in Pseudomonas aeruginosa, but its signaling mechanism remains vague. Here, we report that cis-DA regulates the physiology and virulence of P. aeruginosa through the long-chain fatty acid-CoA ligase FadD1. Cis-DA specifically binds to FadD1 with high affinity and enhances the binding of FadD1 to the promoter DNA region of lasR. Further analysis showed that the FadD1 is a response regulator of cis-DA with a DNA-binding leucine zipper motif to control the transcription of various target genes. Moreover, FadD1 exhibited catalytic activity on cis-2-dodecenoic acid (BDSF) of Burkholderia cenocepacia and enhanced the competitiveness of P. aeruginosa. Together, our work presents a new DSF-type QS signaling system, which is highlighted by its receptor and response regulator evolved from a canonical enzyme of fatty acid metabolism. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=135 SRC="FIGDIR/small/598097v2_ufig1.gif" ALT="Figure 1"> View larger version (15K): org.highwire.dtl.DTLVardef@66b8aaorg.highwire.dtl.DTLVardef@1e5b9b9org.highwire.dtl.DTLVardef@1043525org.highwire.dtl.DTLVardef@13d3ed1_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsThe long-chain fatty acid-CoA ligase FadD1 acts as a global transcriptional regulator in P. aeruginosa. FadD1 is a receptor of QS signal cis-DA and functions at the top of the QS hierarchy in P. aeruginosa. The homolog of FadD1 in P. fluorescens could bind to the target gene promoter by responding to cis-DA. In briefPseudomonas aeruginosa is a human pathogen with antibiotic resistance and a wide range of dynamic defenses makes it an extremely challenging organism to treat in modern-day medicine. It employs cis-2-decenoic acid (cis-DA) quorum sensing to regulate the physiology and pathogenesis. Song et al. demonstrate that the long-chain fatty acid-CoA ligase FadD1 controls the important biological functions and virulence in P. aeruginosa as a novel response regulator of cis-DA signal.

microbiology↗

bioRxiv2024

LONP1 regulation of mitochondrial protein folding provides insight into beta cell failure in type 2 diabetes

Proteotoxicity is a contributor to the development of type 2 diabetes (T2D), but it is unknown whether protein misfolding in T2D is generalized or has special features. Here, we report a robust accumulation of misfolded proteins within the mitochondria of human pancreatic islets in T2D and elucidate its impact on {beta} cell viability. Surprisingly, quantitative proteomics studies of protein aggregates reveal that human islets from donors with T2D have a signature more closely resembling mitochondrial rather than ER protein misfolding. The matrix protease LonP1 and its chaperone partner mtHSP70 were among the proteins enriched in protein aggregates. Deletion of LONP1 in mice yields mitochondrial protein misfolding and reduced respiratory function, ultimately leading to {beta} cell apoptosis and hyperglycemia. Intriguingly, LONP1 gain of function ameliorates mitochondrial protein misfolding and restores human {beta} cell survival following glucolipotoxicity via a protease-independent effect requiring LONP1-mtHSP70 chaperone activity. Thus, LONP1 promotes {beta} cell survival and prevents hyperglycemia by facilitating mitochondrial protein folding. These observations may open novel insights into the nature of impaired proteostasis on {beta} cell loss in the pathogenesis of T2D that could be considered as future therapeutic targets.

cell biology↗

bioRxiv2024

Advancing the genetic engineering toolbox by combining AsCas12a knock-in mice with ultra-compact screening

Cas12a is a gene-editing tool that simplifies multiplexed gene targeting through its RNase activity, enabling maturation of individual crRNAs from a pre-crRNA-encoding RNA. Here, we present a mouse model that constitutively expresses enhanced Acidaminococcus sp. Cas12a (enAsCas12a) linked to an mCherry fluorescent reporter. We demonstrate efficient single and multiplexed gene-editing in cells from enAsCas12aKI mice. To test in vivo activity, we transduced haematopoietic stem cells from E-MycT/+;enAsCas12aKI/+animals with Trp53-targeting pre-crRNAs followed by transplantation into irradiated recipient animals. Tumour development was accelerated and TRP53 protein lost. We generated compact, genome-wide Cas12a knockout libraries targeting each gene with four guide RNAs encoded on two (Menuetto) or one (Scherzo) vector. Introducing these libraries into E-MycT/+;enAsCas12aKI/+lymphoma cells followed by treatment with an MCL-1 inhibitor (S63845) or TRP53-inducer (nutlin-3a) identified known and novel drug resistance genes. Finally, we demonstrate simultaneous gene knockouts (Trp53 or combined Bax/Bak) and activation (Cd19) in primary T cells and mouse dermal fibroblasts from crosses of our enAsCas12a and CRISPR activation models (dCas9a-SAM). Our enAsCas12a mouse model and accompanying libraries enhance genome engineering capabilities and complements current CRISPR technologies.

molecular biology↗

bioRxiv2024

Soybean PHR1-regulated low phosphorus-responsive GmRALF22 increases uptake of phosphate via stimulating GmPTs expression

Phosphorus (P) is one of essential macronutrients for plant growth and development. Rapid Alkalization Factors (RALFs) play crucial roles in plant responses to nutrient stresses, however, the functions of Glycine max RALFs (GmRALFs) under low P (LP) stress remain elusive. In this study, we first identified 27 GmRALFs in soybean, then we revealed that GmRALF10, GmRALF11, and GmRALF22 are induced in both roots and leaves, while only GmRALF5, GmRALF6, and GmRALF25 are up-regulated in leaves in LP conditions. Furthermore, GmRALF22 was found to be the target gene of transcription factor GmPHR1, which binds the P1BS cis-element in the promoter of GmRALF22 via electrophoretic mobility shift assay (EMSA) and DUAL-LUC experiment. Colonization of Bacillus subtilis that deliver GmRALF22 increases the expression of high affinity phosphate (Pi) transporter gene GmPT2, GmPT11, GmPT13 and GmPT14, thus increases the total amount of dry matter and soluble Pi in soybean. RNA-sequencing uncovered that GmRALF22 alleviates LP stress by regulating the expression of JA-, SA- and immune-related genes. Finally, we verified that GmRALF22 is dependent on FERONIA to promote Arabidopsis primary root growth under LP conditions. In summary, GmPHR1-GmRALF22 module positively regulates soybeans tolerance to LP. HighlightsO_LISoybean genome has 27 GmRALFs. GmRALF5, GmRALF6, GmRALF10, GmRALF11, GmRALF22 and GmRALF25 are induced in low phosphorus (LP). C_LIO_LIGmPHR1 directly regulate the transcription of GmRALF22 via binding the promoter P1BS cis-element. C_LIO_LISecretion of GmRALF22 protein by Bacillus subtilis promoted soybean growth under LP conditions by improving soybean P nutrition through increased expression of high affinity phosphate (Pi) transporter gene. C_LIO_LIGmRALF22 regulates soybean P nutrition by JA-, SA- and immune-related genes expression at transcriptome level. That application of GmRALF22 promoted primary root growth in Arabidopsis in LP is dependent on FER receptor. C_LI

molecular biology↗

bioRxiv2024

Structural and Dynamical Analyses of Apo and Cap-binding eIF4E: An in silico Study

Molecular dynamics (MD) simulations were utilized to investigate the dynamic behavior of eukaryotic translation initiation factor 4E (eIF4E) in its apo (ligand-free) and Cap-bound (complexed with mRNA Cap) forms. Specifically, we focused on the rearrangement of critical eIF4E residues, including W102, E103, W56, and key charged residues within the alpha helix, such as lysine and arginine. Three independent 1 {micro}s MD simulations were performed for four eIF4E structures based on an available Capbound eIF4E crystal structure (PDB: 4TPW) to sample protein conformations and explore metastable states. Metastable states were successfully sampled within the 1 {micro}s trajectories for all four structures; however, 2D-root-mean-square deviation (RMSD) and free-energy landscape (FEL) analyses indicate that additional trajectory data may be needed to fully explore the conformational space. Notably, the clearest state transitions were observed in the Cap-A simulations. Our data suggests that previously reported allosteric site eIF4E inhibitor 4EGI-1 stabilized the 7mGTP-eIF4E interaction by binding to a site dorsal from the eIF4G binding site, facilitating stable protein states even after ligand removal and promoting cleaner state transitions in simulations. Root-mean-square fluctuation (RMSF) and distance analyses aligned consistently with experimental results, affirming the validity of our MD simulation system. Observations on apo and Cap eIF4E structures revealed structural changes in one of the alpha helices (i.e., H3), particularly the transition from alpha helix to loop upon Cap binding. This transition was also observed during the cap to apo transition, where residues E105-R109 underwent structural rearrangements from loop to alpha helix. These findings support the strategy of designing agents targeting critical Cap-binding residues (W56, W102, E103) and H3 lysine residues (K106, K108) to disrupt the 7mGTP-eIF4E interaction, potentially informing future drug design efforts targeting this oncogenic protein. Future work will focus on studying the apo to Cap transition using available eIF4E apo structures, such as PDB entry 2GPQ, to deepen our understanding of eIF4Es metastable states during Cap binding.

biochemistry↗

bioRxiv2024

An AI-Driven Framework for Discovery of BACE1 Inhibitors for Alzheimer's Disease

Alzheimers Disease (AD) is a progressive neurodegenerative disorder that affects over 51 million individuals globally. The {beta}-secretase (BACE1) enzyme is responsible for the production of amyloid beta (A{beta}) plaques in the brain. The accumulation of A{beta} plaques leads to neuronal death and the impairment of cognitive abilities, both of which are fundamental symptoms of AD. Thus, BACE1 has emerged as a promising therapeutic target for AD. Previous BACE1 inhibitors have faced various issues related to molecular size and blood-brain barrier permeability, preventing any of them from maturing into FDA-approved AD drugs. In this work, a generative AI framework is developed as the first AI application to the de novo generation of BACE1 inhibitors. Through a simple, robust, and accurate molecular representation, a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP), and a Genetic Algorithm (GA), the framework generates and optimizes over 1,000,000 candidate inhibitors that improve upon the bioactive and pharmacological properties of current BACE1 inhibitors. Then, the molecular docking simulation models the candidate inhibitors and identifies 14 candidate drugs that exhibit stronger binding interactions to the BACE1 active site than previous candidate BACE1 drugs from clinical trials. Overall, the framework successfully discovers BACE1 inhibitors and candidate AD drugs, accelerating the developmental process for a novel AD treatment.

biochemistry↗

bioRxiv2024

Development of the overlapping network modules in the human brain

Developmental connectomic studies have shown that the modular organization of functional networks in the human brain undergoes substantial reorganization with age to support cognitive growth. However, these studies implicitly assume that each brain region belongs to one and only one specific network module, ignoring the potential spatial overlap between functional modules. How the overlapping functional modular architecture develops and whether this development is related to structural signatures remain unknown. Using longitudinal multimodal structural, functional, and diffusion MRI data from 305 children (aged 6-14 years), we investigated the development of the overlapping modular architecture of functional networks, and further explored their structural associations. Specifically, an edge-centric network model was used to identify the overlapping functional modules, and the nodal overlap in module affiliations was quantified using the entropy measure. We showed a remarkable regional inhomogeneity in module overlap in children, with higher entropy in the ventral attention, somatomotor, and subcortical networks and lower entropy in the visual and default-mode networks. Furthermore, the overlapping modules developed in a linear, spatially dissociable manner from childhood to adolescence, with significantly reduced entropy in the prefrontal cortex and putamen and increased entropy in the parietal lobules. Personalized overlapping modular patterns capture individual brain maturity as characterized by brain age. Finally, the overlapping functional modules can be significantly predicted by integrating gray matter morphology and white matter network properties. Our findings highlight the maturation of overlapping network modules and their structural substrates, thereby advancing our understanding of the principles of connectome development.

neuroscience↗

bioRxiv2024

5-HT Neurons Integrate GABA and Dopamine Inputs to Regulate Meal Initiation

Obesity is a growing global health epidemic with limited effective therapeutics. Serotonin (5-HT) is one major neurotransmitter which remains an excellent target for new weight-loss therapies, but there remains a gap in knowledge on the mechanisms involved in 5-HT produced in the dorsal Raphe nucleus (DRN) and its involvement in meal initiation. Using a closed-loop optogenetic feeding paradigm, we showed that the 5-HTDRN{lozenge}arcuate nucleus (ARH) circuit plays an important role in regulating meal initiation. Incorporating electrophysiology and ChannelRhodopsin-2-Assisted Circuit Mapping, we demonstrated that 5-HTDRN neurons receive inhibitory input partially from GABAergic neurons in the DRN, and the 5-HT response to GABAergic inputs can be enhanced by hunger. Additionally, deletion of the GABAA receptor subunit in 5-HT neurons inhibits meal initiation with no effect on the satiation process. Finally, we identified the instrumental role of dopaminergic inputs via dopamine receptor D2 in 5-HTDRN neurons in enhancing the response to GABA-induced feeding. Thus, our results indicate that 5-HTDRN neurons are inhibited by synergistic inhibitory actions of GABA and dopamine, which allows for the initiation of a meal.

neuroscience↗

bioRxiv2024

Marker selection strategies for circulating tumor DNA guided by phylogenetic inference

MotivationBlood-based profiling of tumor DNA ("liquid biopsy") has offered great prospects for non-invasive early cancer diagnosis, treatment monitoring, and clinical guidance, but require further advances in computational methods to become a robust quantitative assay of tumor clonal evolution. We propose new methods to better characterize tumor clonal dynamics from circulating tumor DNA (ctDNA), through application to two specific questions: 1) How to apply longitudinal ctDNA data to refine phylogeny models of clonal evolution, and 2) how to quantify changes in clonal frequencies that may be indicative of treatment response or tumor progression. We pose these questions through a probabilistic framework for optimally identifying maximum likelihood markers and applying them to characterizing clonal evolution. ResultsWe first estimate a distribution over plausible clonal lineage models, using bootstrap samples over pre-treatment tissue-based sequence data. We then refine these lineage models and the clonal frequencies they imply over successive longitudinal samples. We use the resulting framework for modeling and refining tree distributions to pose a set of optimization problems to select ctDNA markers to maximize measures of utility capturing ability to solve the two questions of reducing uncertain in phylogeny models or quantifying clonal frequencies given the models. We tested our methods on synthetic data and showed them to be effective at refining distributions of tree models and clonal frequencies so as to minimize measures of tree distance relative to the ground truth. Application of the tree refinement methods to real tumor data further demonstrated their effectiveness in refining a clonal lineage model and assessing its clonal frequencies. The work shows the power of computational methods to improve marker selection, clonal lineage reconstruction, and clonal dynamics profiling for more precise and quantitative assays of tumor progression. Availabilityhttps://github.com/CMUSchwartzLab/Mase-phi.git. Contactrussells@andrew.cmu.edu

bioinformatics↗

bioRxiv2024

Normative growth modeling of brain morphology reveals neuroanatomical heterogeneity and biological subtypes in children with ADHD

BackgroundNeuroimaging studies suggest substantial individual heterogeneity in brain phenotypes in attention-deficit/hyperactivity disorder (ADHD). However, how these individual-level brain phenotypes contribute to the identification of ADHD biotypes and whether these biotypes have different treatment outcomes and neurobiological underpinnings remain largely unknown. MethodsWe collected multisite, high-quality structural magnetic resonance imaging data from 1,006 children aged 6-14 years, including 351 children with ADHD and 655 typically developing children. Normative growth models of cortical thickness were established for 219 regions in the typically developing children. Individual-level deviations from these normal references were quantified and clustered to identify ADHD biotypes. We validated the replicability and generalizability of the ADHD biotypes using two independent datasets and evaluated the associations of the biotypes with symptomatic, cognitive, and gene expression profiles, as well as follow-up treatment outcomes. FindingsNo more than 10% of children with ADHD had extreme deviations in cortical thickness in a single region, suggesting high heterogeneity among individuals with ADHD. On the basis of the brain deviation maps, we discovered two robust ADHD biotypes, an infra-normal subtype with cortical thinning associated with ADHD symptoms and a supranormal subtype with cortical thickening associated with cognition. Patients with the infra-normal subtype responded better to methylphenidate than to atomoxetine, although both subtypes showed treatment efficacy. Brain deviations in the infra-normal subtype were explained by the expression levels of genes enriched in presynaptic and axonal development and polygenic risk of ADHD. InterpretationWe identified anatomically distinct, clinically valuable, and biologically informed ADHD subtypes, providing insight into the neurobiological basis of clinical heterogeneity and facilitating a personalized medication strategy for ADHD patients. Panel: Research in contextO_ST_ABSEvidence before this studyC_ST_ABSSubstantial individual heterogeneity in brain phenotypes in attention-deficit/hyperactivity disorder (ADHD) motivates the need to discover homogeneous biotypes. We searched PubMed for research articles on ADHD biotypes using brain MRI published before December 1, 2023, using the search terms ((attention deficit hyperactivity disorder [Title/Abstract]) OR (ADHD [Title/Abstract])) AND ((subtypes [Title/Abstract]) OR (subgroups [Title/Abstract]) OR (subtyping [Title/Abstract])) AND ((MRI [Title/Abstract]) OR (neuroimaging [Title/Abstract]) OR (brain [Title/Abstract])) without language restrictions. Of the eight included studies, two identified ADHD biotypes using structural morphology, four used functional activity, and two used multimodal features. However, none of these studies considered the developmental effect of the brain phenotypes, examined treatment response, or investigated the genetic correlates of the biotypes. Added value of this studyThis study is the first to use individualized brain measures extracted from normative models to investigate ADHD biotypes in a large sample of more than 1,000 children. We identified two reproducible ADHD biotypes, characterized by distinct symptomatic, cognitive, and gene expression profiles, as well as differential treatment responses. This study advances our understanding of the neurobiological basis underlying the clinical heterogeneity of ADHD and highlights the critical need to discover ADHD biotypes using an unbiased and individualized approach. Implications of all the available evidenceThis study revealed remarkable neuroanatomical heterogeneity in ADHD patients and identified anatomically distinct, clinically valuable, and biologically informed ADHD biotypes. Our findings have potential value for the investigation of data-driven biotypes to evaluate treatment efficacy and facilitate personalized treatment. We also highlight the need for future studies to move beyond the understanding of ADHD solely based on the "average patient" perspective.

neuroscience↗

bioRxiv2024

Robust and Accurate Bayesian Inference of Genome-Wide Genealogies for Large Samples

The Ancestral Recombination Graph (ARG), which describes the full genealogical history of a sample of genomes, is a vital tool in population genomics and biomedical research. Recent advancements have increased ARG reconstruction scalability to tens or hundreds of thousands of genomes, but these methods rely on heuristics, which can reduce accuracy, particularly in the presence of model misspecification. Moreover, they reconstruct only a single ARG topology and cannot quantify the considerable uncertainty associated with ARG inferences. To address these challenges, we here introduce SINGER, a novel method that accelerates ARG sampling from the posterior distribution by two orders of magnitude, enabling accurate inference and uncertainty quantification for large samples. Through extensive simulations, we demonstrate SINGERs enhanced accuracy and robustness to model misspecification compared to existing methods. We illustrate the utility of SINGER by applying it to African populations within the 1000 Genomes Project, identifying signals of local adaptation and archaic introgression, as well as strong support of trans-species polymorphism and balancing selection in HLA regions.

genetics↗

bioRxiv2024

Analysis and visualization of quantitative proteomics data using FragPipe-Analyst

The FragPipe computational proteomics platform is gaining widespread popularity among the proteomics research community because of its fast processing speed and user-friendly graphical interface. Although FragPipe produces well-formatted output tables that are ready for analysis, there is still a need for an easy-to-use and user-friendly downstream statistical analysis and visualization tool. FragPipe-Analyst addresses this need by providing an R shiny web server to assist FragPipe users in conducting downstream analyses of the resulting quantitative proteomics data. It supports major quantification workflows including label-free quantification, tandem mass tags, and data-independent acquisition. FragPipe-Analyst offers a range of useful functionalities, such as various missing value imputation options, data quality control, unsupervised clustering, differential expression (DE) analysis using Limma, and gene ontology and pathway enrichment analysis using Enrichr. To support advanced analysis and customized visualizations, we also developed FragPipeAnalystR, an R package encompassing all FragPipe-Analyst functionalities that is extended to support site-specific analysis of post-translational modifications (PTMs). FragPipe-Analyst and FragPipeAnalystR are both open-source and freely available.

bioinformatics↗

bioRxiv2024

Active filtering of sequences of neural activity by recurrent circuits of sensory cortex

Recurrent neural networks can generate dynamics, but in sensory cortex it has been unclear if any dynamic processing is supported by the dense recurrent excitatory-excitatory network. Here we show a new role for recurrent connections in mouse visual cortex: they support powerful dynamical computations, but by filtering sequences of input instead of generating sequences. Using two-photon optogenetics, we measure neural responses to natural images and play them back, finding responses are boosted when inputs are played back during the correct movie dynamic context-- when the preceding sequence corresponds to natural vision. This sequence selectivity depends on a network mechanism: earlier input patterns produce responses in other local neurons, which interact with later input patterns. We confirm this mechanism by designing sequences of inputs that are boosted or attenuated by the network. These data suggest recurrent cortical connections perform predictive processing, encoding the statistics of the natural world in input-output transformations.

neuroscience↗

bioRxiv2024

Root-specific secondary metabolism at the single-cell level: a case study of theanine metabolism and regulation in the roots of tea plants (Camellia sinensis)

Root-synthesized secondary metabolites are critical quality-conferring compounds of foods, plant-derived medicines, and beverages. However, information at a single-cell level on root-specific secondary metabolism remains largely unexplored. L-theanine, an important quality component of tea, is primarily synthesized in roots, from which it is then transported to new shoots of tea plant. In this study, we present a single-cell RNA sequencing (scRNA-seq)-derived map for the tea plant root, which enabled cell-type-specific analysis of glutamate and ethylamine (two precursors of theanine biosynthesis) metabolism, and theanine biosynthesis, storage, and transport. Our findings support a model in which the theanine biosynthesis pathway occurs via multicellular compartmentation and does not require high co-expression levels of transcription factors and their target genes within the same cell cluster. This study provides novel insights into theanine metabolism and regulation, at the single-cell level, and offers an example for studying root-specific secondary metabolism in other plant systems.

plant biology↗