Biology subjects

Wagle, S.

Publications and source records attributed to Wagle, S..

9 recordsLinked to original sources

bioRxiv2026

Transient excitability and synaptic consolidation support stable memory despite neural drift

Long-term memories remain behaviorally stable despite turnover in the neuronal populations that encode them. This representational drift differs across brain regions: hippocampal representations can reconfigure within hours, whereas cortical ensembles remain comparatively stable over days to weeks. This asymmetry poses a challenge for systems consolidation, in which hippocampal activity is thought to instruct the formation of persistent cortical memory traces. We developed a two-region recurrent network model of the hippocampus (HPC) and anterior cingulate cortex (ACC) incorporating region-specific synaptic plasticity, excitability-dependent neuronal allocation, hippocampal-to-cortical coupling, and activity-dependent intrinsic plasticity. When the regions evolved independently, faster synaptic turnover in HPC produced pronounced drift, whereas persistent ACC connectivity preserved a more stable cortical ensemble. In the intact circuit, hippocampal input recruited ACC neurons but also propagated hippocampal variability into cortex, destabilizing the emerging cortical engram and impairing memory expression. A transient increase in the intrinsic excitability of recruited ACC neurons counteracted this instability by promoting repeated reactivation during an early consolidation window, stabilizing cortical ensemble membership without preventing hippocampal drift. Simulated erasure of learning-induced potentiation further reproduced the early dependence of memory on hippocampal, but not ACC, plasticity. Together, these results support a sequential mechanism in which hippocampal recruitment, transient cortical intrinsic plasticity, and persistent cortical synaptic plasticity transform a dynamic hippocampal representation into a stable cortical memory trace.

neuroscience↗

bioRxiv2026

Quantifying Asymmetric Coevolutionary Dynamics using Normalized Phylogenetic Costs

AO_SCPLOWBSTRACTC_SCPLOWCoevolutionary studies aim to characterize associations, such as virus-host relationships, by using phylogenetic distances to quantify the topological concordance between the phylogenies of interacting taxa. However, phylogenetic distances cannot capture asymmetrical relationships that arise from differences in sampling, evolutionary rates, or characterizations between datasets. Furthermore, a lack of accurate normalization complicates the interpretation and validation of coevolutionary analyses. To address these limitations, we employed the Asymmetric Cluster Affinity and Cluster Support costs as a general framework to quantify coevolutionary patterns across multiple biological scales. We benchmarked the precision of these costs by reanalyzing a curated dataset documenting interspecies transmission frequencies across nineteen virus-host phylogenies. Our results corroborate prior findings showing that all virus families under study can cross species boundaries; however, the asymmetric costs provide a more granular representation, demonstrating that the frequency of such events varies significantly across families. We then applied the Asymmetric Cluster Support cost to quantify preferential gene segment pairings within the Bluetongue virus genome. This analysis revealed a close phylogenetic association between the outer capsid proteins VP2 and VP5, likely reflecting shared selective pressures due to their critical roles in cell entry and exit. In contrast, gene segments encoding nonstructural proteins exhibited discordant evolutionary histories relative to other segments. Finally, we demonstrated that the Asymmetric Cluster Support cost can detect coevolutionary dynamics in swine influenza A virus, identifying novel gene pairings indicative of major viral reassortment events. Overall, our approach demonstrates that normalized asymmetric phylogenetic costs accurately capture complex biological relationships and provide a robust framework for quantifying fine-scale coevolutionary dynamics in rapidly evolving pathogens.

bioinformatics↗

bioRxiv2025

Enzymatic Glycosylation of Anthranilates for Enhanced Functionality

Anthranilate (ANT) is a precursor for the synthesis of valuable compounds, including its alkyl esters (AEANTs), such as methyl anthranilate (MANT). These derivatives are industrial petrochemical products used as flavouring agents and bird repellents. Due to the mandatory green transition, their biological industrial production must be considered. However, their antimicrobial activity and physicochemical properties inhibit efficient microbial production and challenge their practical use. To overcome this, we explored enzymatic glycosylation using UDP-dependent glycosyltransferases (UGTs). Screening identified three UGTs with activity on a selected AEANT panel, with UGT72B68 from Solanum lycopersicum showing the highest efficiency (840 s-1 M-1) for MANT. Rational engineering produced a mutant (F145M) with improved activity for bulkier AEANTs. We scaled up enzymatic synthesis, producing 9.3 g of MANT-N-glucose (>99% purity, 74% yield). With this in hand, we observed that MANT-N-glucose has a significantly lower impact on the growth of E. coli and P. putida, supporting microbial production. Furthermore, we found that MANT-N-glucose completely inhibited sunflower seed consumption, compared to a 70% reduction observed in a previous study using MANT, when tested on captured red-winged blackbirds. Finally, a preliminary life-cycle assessment demonstrated that microbially produced MANT-N-glucose is a viable alternative to chemically synthesised MANT as a bird repellent.

biochemistry↗

bioRxiv2025

An integrative model of AMPA receptor trafficking reveals the central contribution of local translation in subtype-specific kinetics

AMPA-type glutamate receptors (AMPARs) underlie most of the excitatory synaptic transmission in the brain and are crucial for implementing long-term synaptic plasticity. AMPARs are multi-protein complexes composed of two types of subunits: pore-forming subunits GluA1-4 that assemble in the endoplasmic reticulum and form the glutamate-gated ion channel, and auxiliary subunits that modulate receptor bio-physical properties and mediate their forward trafficking to the plasma membrane. Here, using a combination of theoretical and experimental approaches, we elucidate the kinetics of essential trafficking steps and the protein sources necessary to explain the experimentally observed distribution of AMPARs and the response of different AMPAR subtypes to LTP induction. Our data indicate that the mRNA coding for one of the most abundant AMPAR auxiliary subunits, CNIH-2, is abundant in dendrites. Consistent with this mRNA distribution, CNIH-2 is locally synthesized. In contrast, the pore-forming subunits GluA1 and GluA2 are mostly synthesized in the cell body. CNIH-2 synthesis increases after the (chemical) induction of long-term potentiation. Strikingly, the translation of CNIH-2 is required for the plasma membrane insertion of GluA2-containing receptors and not GluA1-homomeric AMPARs. Using the selective trafficking of GluA2-containing AMPARs by CNIH-2, our computational model can explain the distinct temporal profiles in response to plasticity induction of two major subtypes of AMPARs, the slow-response of the calciumimpermeable (GluA2-containing) and fast kinetics of the calcium-permeable (GluA2-lacking) AMPARs.

neuroscience↗

bioRxiv2024

SpyDen: Automating molecular and structural analysis across spines and dendrites

Investigating the molecular composition across neural compartments such as axons, dendrites, or synapses is critical for our understanding of learning and memory. State-of-the-art microscopy techniques can now resolve individual molecules and pinpoint their position with micrometre or even nanometre resolution across tens or hundreds of micrometres, allowing the labelling of multiple structures of interest simultaneously. Algorithmically, tracking individual molecules across hundreds of micrometres and determining whether they are inside any cellular compartment of interest can be challenging. Historically, microscopy images are annotated manually, often using multiple software packages to detect fluorescence puncta (e.g. labelled mRNAs) and then trace and quantify cellular compartments of interest. Advanced ANN-based automated tools, while powerful, are often able to help only with selected parts of the data analysis pipeline, may be optimised for specific spatial resolutions or cell preparations or may not be fully open source and open access to be sufficiently customisable. To address these challenges, we developed SpyDen. SpyDen is a Python package based upon three principles: i) ease of use for multi-task scenarios, ii) open-source accessibility and data export to a common, open data format, iii) the ability to edit any software-generated annotation and generalise across spatial resolutions. Equipped with a graphical user interface and accompanied by video tutorials, SpyDen provides a collection of powerful algorithms that can be used for neurite and synapse detection as well as fluorescent puncta and intensity analysis. We validated SpyDen using expert annotation across numerous use cases to prove a powerful, integrated platform for efficient and reproducible molecular imaging analysis.

neuroscience↗

bioRxiv2024

Emergence and interstate spread of highly pathogenic avian influenza A(H5N1) in dairy cattle

Highly pathogenic avian influenza (HPAI) viruses cross species barriers and have the potential to cause pandemics. In North America, HPAI A(H5N1) viruses related to the goose/Guangdong 2.3.4.4b hemagglutinin phylogenetic clade have infected wild birds, poultry, and mammals. Our genomic analysis and epidemiological investigation showed that a reassortment event in wild bird populations preceded a single wild bird-to-cattle transmission episode. The movement of asymptomatic cattle has likely played a role in the spread of HPAI within the United States dairy herd. Some molecular markers in virus populations were detected at low frequency that may lead to changes in transmission efficiency and phenotype after evolution in dairy cattle. Continued transmission of H5N1 HPAI within dairy cattle increases the risk for infection and subsequent spread of the virus to human populations.

microbiology↗

bioRxiv2022

PARNAS: Objectively Selecting the Most Representative Taxa on a Phylogeny

The use of next-generation sequencing technology has enabled phylogenetic studies with hundreds of thousands of taxa. Such large-scale phylogenies have become a critical component in genomic epidemiology in pathogens such as SARS-CoV-2 and influenza A virus. However, detailed phenotypic characterization of pathogens or generating a computationally tractable dataset for detailed phylogenetic analyses requires bias free subsampling of taxa. To address this need, we propose parnas, an objective and flexible algorithm to sample and select taxa that best represent observed diversity by solving a generalized k-medoids problem on a phylogenetic tree. parnas solves this problem efficiently and exactly by novel optimizations and adapting algorithms from operations research. For more nuanced selections, taxa can be weighted with metadata or genetic sequence parameters, and the pool of potential representatives can be user-constrained. Motivated by influenza A virus genomic surveillance and vaccine design, parnas can be applied to identify representative taxa that optimally cover the diversity in a phylogeny within a specified distance radius. We demonstrated that parnas is more efficient and flexible than current approaches, and applied it to select representative influenza A virus in swine genes derived from over 5 years of genomic surveillance data. Our objective selection of 4 to 6 strains selected every two years from the 16 distinct genetic clades were sufficient to cover 80% of diversity circulating in US swine. We suggest that this method, through the objective selection of representatives in a phylogeny, provides criteria for rational multivalent vaccine design and for quantifying diversity. PARNAS is available at https://github.com/flu-crew/parnas.

evolutionary biology↗

bioRxiv2021

RF-Net 2: Fast Inference of Virus Reassortment and Hybridization Networks

MotivationA phylogenetic network is a powerful model to represent entangled evolutionary histories with both divergent (speciation) and convergent (e.g., hybridization, reassortment, recombination) evolution. The standard approach to inference of hybridization networks is to (i) reconstruct rooted gene trees and (ii) leverage gene tree discordance for network inference. Recently, we introduced a method called RF-Net for accurate inference of virus reassortment and hybridization networks from input gene trees in the presence of errors commonly found in phylogenetic trees. While RF-Net demonstrated the ability to accurately infer networks with up to four reticulations from erroneous input gene trees, its application was limited by the number of reticulations it could handle in a reasonable amount of time. This limitation is particularly restrictive in the inference of the evolutionary history of segmented RNA viruses such as influenza A virus (IAV), where reassortment is one of the major mechanisms shaping the evolution of these pathogens. ResultsHere we expand the functionality of RF-Net that makes it significantly more applicable in practice. Crucially, we introduce a fast extension to RF-Net, called Fast-RF-Net, that can handle large numbers of reticulations without sacrificing accuracy. Additionally, we develop automatic stopping criteria to select the appropriate number of reticulations heuristically and implement a feature for RF-Net to output error-corrected input gene trees. We then conduct a comprehensive study of the original method and its novel extensions and confirm their efficacy in practice using extensive simulation and empirical influenza A virus evolutionary analyses. AvailabilityRF-Net 2 is available at https://github.com/flu-crew/rf-net-2.

evolutionary biology↗

bioRxiv2020

A smartphone microscope method for simultaneous detection of (oo)cyst of Cryptosporodium and Giardia

Gastrointestinal disorders caused by ingestion of (oo)cysts of Cryptosporodium and Giardia is one of the major health problems in developing countries. We developed a smartphone based microscopic assay method to screen (oo)cysts of Cryptosporodium and Giardia contamination in vegetable and water samples. We used sapphire ball lens as the major imaging element to modify a smartphone as a microscope. Imaging parameters such as field of view and magnification, and image contrast under different staining and illumination conditions were measured. The smartphone microscope method consisting of ball lens of 1 mm diameter, white LED as illumination source and Lugolss iodine staining provided magnification and contrast capable of distinguishing (oo)cysts of Crypstopsporodium and Giardia in the same sample. The analytical performance of the method was tested by spike recovery experiments. The spiking recovery experiments performed on cabbage, carrot, cucumber, radish, tomatoes, and water resulted 26.8{+/-}10.3, 40.1{+/-}8.5, 44.4{+/-}7.3, 47.6{+/-}11.3, 49.2 {+/-}10.9, and 30.2{+/-}7.9% recovery for Cryptosporodium, respectively and 10.2{+/-}4.0, 14.1{+/-}7.3, 24.2{+/-}12.1, 23.2{+/-}13.7, 17.1{+/-}13.9, and 37.6{+/-}2.4 % recovery for Giardia, respectively. These recovery results were found to be similar when compared with the commercial brightfield and fluorescence microscopes. We tested the smartphone microscope system for detecting (oo)cysts on 7 types of vegetable (n=196) and river water (n=18) samples. Forty two percent vegetable and thirty-nine percent water samples were found to be contaminated with Cryptosporodium oocyst. Similarly, thirty one percent vegetable and thirty three percent water samples were contaminated with Giardia cyst. This study showed that the developed method can be a cheaper alternative for simultaneous detection of (oo)cysts in vegetable and water samples.

pathology↗