Biology subjects

Ivanova, M.

Publications and source records attributed to Ivanova, M..

At least 19 recordsLinked to original sources

bioRxiv2026

Cell-type-specific circadian and light-responsive transcriptional dynamics in adult Drosophila neurons

The Drosophila adult central brain contains 240 circadian neurons, of which there are more than 25 different neuron subtypes based on connectomic data. Recent single cell RNA-seq (scRNAseq) characterization of these neurons "around the clock" also indicates a similar number of molecular subtypes of circadian neurons, but other conclusions from these transcriptomic studies warranted verifying and extending with other approaches. To this end: 1) We used a genetic multiplexing strategy to profile the transcriptomes of circadian neurons from multiple time points in a single experiment, reducing confounding technical variation between timepoints; 2) Large numbers of single nuclei were sequenced (snRNA-seq), which was enabled because the new method EL-INTACT purifies nuclei from frozen heads; 3) We assayed 12 time points under both light-dark (LD) and constant darkness (DD) conditions. These approaches showed dramatic transcriptional differences between time points in many circadian neuron types and enhanced time-of-day gene expression analysis. The data indicate that most of this regulation is transcriptional and circadian. There were however a small number of light-dependent transcripts, including a few that correspond to mammalian immediate-early genes. They probably play a role in the light-regulation of gene expression and behavior in specific neurons, perhaps circadian entrainment or phase-shifting. The results taken together provide a more comprehensive picture of gene expression heterogeneity within adult Drosophila circadian neurons including how intrinsic clock mechanisms and light cues are integrated across circadian neuron subtypes.

neuroscience↗

bioRxiv2026

Syndromic cholera diagnosis masks diverse causes of diarrhoeal disease in Burundi revealed by portable metagenomics

BackgroundCholera outbreaks remain a major public-health challenge in sub-Saharan Africa, where diagnostic capacity is limited and clinical case definitions are non-specific and reply heavily on syndromic diagnosis. Rapid identification of Vibrio cholerae is critical, yet cholera-suspected diarrhoea can have multiple infectious causes not captured by targeted diagnostics. MethodsWe evaluated a mobile, culture-independent metagenomic sequencing workflow for on-site detection of gastrointestinal pathogens directly from faecal samples in Burundi. The offline workflow combined long-read ONT sequencing with rapid, laptop-based taxonomic and antimicrobial resistance (AMR) screening and was deployed across a health centre, a district hospital, and a refugee transit camp. The frontline and real-time results were verified using both conventional culturing and in-depth bioinformatic analyses. ResultsV. cholerae signals were only detected in a subset of suspected cholera cases, while many samples were dominated by alternative bacterial taxa, most frequently Escherichia coli. V. cholerae abundance correlated strongly with detection of the cholera toxin phage CTX{varphi}, supporting differentiation between toxigenic signal and background exposure. AMR genes were detected across samples, providing early situational insight into resistance determinants among gastrointestinal bacteria. ConclusionsMobile, offline metagenomic sequencing enables rapid frontline characterization of gastrointestinal disease, especially cholera-suspected, in resource-limited settings and complements existing diagnostics by improving etiological resolution and outbreak response. Author SummaryCholera remains a major cause of severe diarrhoeal disease in many low-resource settings, where diagnosis often relies on symptoms and limited laboratory testing. However, patients suspected of cholera can be infected by a wide range of other pathogens that are not detected by standard diagnostics. In this study, we evaluated a portable, sequencing-based approach that allows direct identification of pathogens from stool samples at the point of care, without the need for laboratory infrastructure, internet access, or culture. Using this approach in multiple settings in Burundi, including a health centre, hospital, and refugee camp, we found a subset of suspected cholera cases were associated with Vibrio cholerae. Other cases were also dominated by other bacteria, particularly Escherichia coli. We also detected antimicrobial resistance genes across samples, providing additional information relevant for treatment and surveillance. Our findings demonstrate that mobile metagenomic sequencing can improve the identification of disease causes directly in outbreak settings and help distinguish true cholera cases from other gastrointestinal infections. This approach has the potential to strengthen outbreak response, improve patient management, and support more accurate disease surveillance in resource-limited environments.

microbiology↗

bioRxiv2025

PULPO: Pipeline of understanding large-scale patterns of oncogenomic signatures

Short Structured AbstractO_ST_ABSSummaryC_ST_ABSPULPO v1.0 is a novel, fully automated pipeline designed for the preprocess and extraction of mutational signatures from raw Optical Genome Mapping (OGM) data. Built using Snakemake and executed within an isolated, Conda-managed environment, PULPO transforms complex cytogenetic alterations, captured at ultra-high resolution, into Catalogue of somatic mutations in Cancer (COSMIC)-based mutational signatures. This innovative approach not only enables researchers to work directly from raw OGM inputs but also streamlines the traditionally complex process of signature extraction, making advanced oncogenomic analyses accessible to users with varying levels of bioinformatics expertise. By facilitating the integration of comprehensive structural variants (SVs) and copy number variants (CNVs) data with established signature catalogs, PULPO paves the way for improved diagnostic accuracy and personalized therapeutic strategies. Availability and ImplementationThe pipeline is open source and freely available under the MIT License at https://github.com/OncologyHNJ/PULPO.

bioinformatics↗

bioRxiv2024

Somatic gonad morphogenesis in C. elegans requires the heterochronic pathway acting through HBL-1

Characteristically, the heterochronic genes control the stage-specific timing of developmental events throughout the developing Caenorhaditis elegans larva while gonad development occurs mostly normally. In a few cases, some timing regulators lead to detectable variations in gonad development with little to no effect on fertility. We found that a double mutant of a lin-28 null allele and hbl-1 hypomorphic allele results in a catastrophic failure of gonad morphogenesis resulting in sterility. This defect includes a high-penetrance disruption of normal gonad arm migration as well as frequent absence of one or both spermathecae. We demonstrate that the abnormal gonad morphology and novel sterility phenotype is ultimately due to loss of hbl-1 activity in larval development. The mechanism of how lin-28 positively influences hbl-1 activity is unresolved. We demonstrate a direct interaction between the RNA-binding protein LIN- 28 and the 5UTR of lin-46, and in turn, a direct interaction between LIN-46 and two zinc fingers of HBL-1. Nevertheless, our genetic analysis indicates that lin-46 accounts for only part of lin-28s regulation of hbl-1 and that some portion of lin-28s effect is independent of lin-46.

molecular biology↗

bioRxiv2024

Towards Robust Evaluation of Protein Generative Models: A Systematic Analysis of Metrics

AO_SCPLOWBSTRACTC_SCPLOWThe rapid advancement of protein generative models necessitates robust and principled methods for their evaluation and comparison. As new models of increasing complexity continue to emerge, it is crucial to ensure that the metrics used for assessment are well-understood and reliable. In this work, we conduct a systematic investigation of commonly used metrics for evaluating protein generative models, focusing on quality, diversity, and distributional similarity. We examine the behavior of these metrics under various conditions, including synthetic perturbations and real-world generative models. Our analysis explores different design choices, parameters, and underlying representation models, revealing how these factors influence metric performance. We identify several challenges in applying these metrics, such as sample size dependencies, sensitivity to data distribution shifts, and computational efficiency trade-offs. By testing metrics on both synthetic datasets with controlled properties and outputs from state-of-the-art protein generators, we provide insights into each metrics strengths, limitations, and practical applicability. Based on our findings, we offer a set of practical recommendations for researchers to consider when evaluating protein generative models, aiming to contribute to the development of more robust and meaningful evaluation practices in the field of protein design.

bioinformatics↗

bioRxiv2024

Rapid, interpretable, data-driven models of neural dynamics using Recurrent Mechanistic Models

Obtaining predictive models of a neural system is notoriously challenging. Detailed models suffer from excess model complexity and are difficult to fit efficiently. Simplified models must negotiate a tradeoff between tractability, predictive power and ease of interpretation. We present a new modelling paradigm for estimating predictive mechanistic-like models of neurons and small circuits that navigates these issues using methods from systems theory. The key insight is that membrane currents can be modelled using two scalable system components optimized for learning: linear state space models, and nonlinear artificial neural networks (ANNs). Combining these components, we construct two types of membrane currents: lumped currents, which are flexible, and data-driven conductance-based currents, which are interpretable. The resulting class of models -- which we call Recurrent Mechanistic Models (RMMs) -- can be trained in a matter of seconds to minutes on intracellular recordings during an electrophysiology experiment, representing a step change in performance over previous approaches. As a proof-of-principle, we use RMMs to learn the dynamics of two groups of neurons, and their synaptic connections, in the Stomatogastric Ganglion (STG), a well-known central pattern generator. We show that RMMs are efficiently trained using teacher forcing and multiple shooting. Due to their reliability, efficiency and interpretability, RMMs enable qualitatively new kinds of experiments using predictive models in closed-loop neurophysiology and online estimation of neural properties in living preparations. Significance StatementOur ability to understand the nervous system has been hindered by the difficulty of constructing good predictive models of neurons and circuits. This difficulty persists despite vast accumulated knowledge of how the basic components of the nervous system work, marking a gap in our ability to explain neural dynamics in terms of underlying mechanisms. This work describes a new data-driven modelling approach for neurons and small circuits that combines predictive power with mechanistic interpretation.

neuroscience↗

bioRxiv2024

Application of barcode sequencing to increase the throughput and complexity of Plasmodium falciparum genetic screening

All the pathology and symptoms associated with malaria are caused by the growth of Plasmodium parasites inside human red blood cells. This process, which in the case of the major human malaria pathogen Plasmodium falciparum takes place over a 48-hour period, involves multiple tightly regulated developmental transitions. Understanding the P. falciparum genes that regulate these key processes could lead to the identification of targets for new drugs. However, while large-scale sequencing efforts have led to a good understanding of the P. falciparum genome and how it evolves over time and space, a disconnect remains between the amount of genome sequence data available and the amount of data describing what exactly the genes contained within it do - the phenotype. We have generated a panel of 66 P. falciparum lines carrying individual gene knockouts tagged with unique DNA barcodes. We then used these lines in a series of assays that combine flow cytometry, cell sorting and DNA barcode quantification using next generation sequencing (Barcode Sequencing or BarSeq) to phenotype key aspects of the parasite life cycle such as growth, replication capacity and cell cycle progression. This approach both yields new data about individual gene function, and outlines a new approach where barcoded P. falciparum lines are used in pooled BarSeq-based assays to generate more precise phenotype data at scale.

microbiology↗

bioRxiv2024

A scalable CRISPR-Cas9 gene editing system facilitates CRISPR screens in the malaria parasite Plasmodium berghei

Many Plasmodium genes remain uncharacterised due to low genetic tractability. Previous large scale knockout screens have only been able to target about half of the genome in the more genetically tractable rodent malaria parasite Plasmodium berghei. To overcome this limitation, we have developed a scalable CRISPR system called PbHiT, which uses a single cloning step to generate targeting vectors with 100 bp homology arms physically linked to a guide RNA (gRNA) that effectively integrate into the target locus. We show that PbHiT coupled with gRNA sequencing robustly recapitulates known knockout mutant phenotypes in pooled transfections. Furthermore, we provide vector designs and sequences to target the entire P. berghei genome and scale-up vector production using a pooled ligation approach. This work presents for the first time a tool for high-throughput CRISPR screens in Plasmodium for studying the parasites biology at scale.

molecular biology↗

bioRxiv2024

Frequency-specific changes in prefrontal activity associated with maladaptive belief updating in volatile environments in euthymic bipolar disorder

Bipolar disorder (BD) involves altered reward processing and decision-making, with inconsistencies across studies. Here, we integrated hierarchical Bayesian modelling with magnetoencephalography (MEG) to characterise maladaptive belief updating in this condition. First, we determined if previously reported increased learning rates in BD stem from a heightened expectation of environmental changes. Additionally, we examined if this increased expectation speeds up belief updating in decision-making, associated with modulation of rhythmic neural activity within the prefrontal, orbitofrontal, and anterior cingulate cortex (PFC, OFC, ACC). Twenty-two 22 euthymic BD and 27 healthy control (HC) participants completed a reward-based motor decision-making task in a volatile setting. Hierarchical Bayesian modelling revealed BD participants anticipated greater environmental volatility, resulting in a more stochastic mapping from beliefs to actions and paralleled by lower win rates and a reduced tendency to repeat rewarded actions than HC. Despite this, BD individuals adjusted their expectations of action-outcome contingencies more slowly, but both groups invigorated their actions similarly. On a neural level, while healthy individuals exhibited an alpha-beta suppression and gamma increase during belief updating, BD participants showed dampened effects, extending across the PFC, OFC, and ACC regions. This was accompanied by an abnormally increased beta-band directed information flow in BD. Overall, the results suggest euthymic BD individuals anticipate environmental change without adequately learning from it, contributing to maladaptive belief updating. Alterations in frequency-domain amplitude and functional connectivity within the PFC, OFC, and ACC during belief updating underlie the computational effects and could serve as potential indicators for predicting relapse in future research.

neuroscience↗

bioRxiv2024

Aberrant subcellular localization of mismatch repair protein MLH1 dysregulates the cell cycle to create new therapeutic opportunities

Estrogen receptor positive (ER+) breast cancer is one of the most common causes of cancer-related death in women. Mortality is largely driven by recurrence of treatment-resistant disease after many years of apparent response, making the molecular events that cause recurrence a critical area of investigation. Loss of expression of MLH1, a tumor suppressor best studied in its role in mismatch repair, induces resistance of ER+ breast cancer cells to standard estrogen-targeting therapies. It does so by delinking cell cycle progression from estrogen regulation, a role distinct from its function in mismatch repair. MLH1 loss, as currently clinically diagnosed by detecting genomic instability or by immunohistochemistry for absence of protein, occurs in 12-15% of all cancers. Here, we demonstrate that sub-clonal, patient-derived mutations in MLH1, which neither impact protein abundance nor contribute sufficiently to genomic instability to be detected diagnostically, seed endocrine treatment resistance by enabling estrogen-independent growth in vitro, ex vivo in patient-derived organoids (p=0.005) and in vivo (p=0.0001). The mechanism underlying this endocrine treatment resistance is aberrant localization of MLH1 to the cytoplasm in vitro and in vivo (p=0.04), which precludes cell cycle arrest in response to endocrine therapy while simultaneously rendering cells acutely dependent on CDK4/6 activity. Consequently, administration of CDK4/6 inhibitors causes extreme regression in cells with cytoplasmic MLH1 compared to control cell populations with nuclear localization of MLH1 in vitro (p=0.00000009), ex vivo (p=0.01) and in vivo (p=0.01). As aberrant cytoplasmic localization occurs in an additional [~]12% of ER+ breast cancer patients, it constitutes a new, major contributor to MLH1 dysregulation. The potential applicability of cytoplasmic MLH1 as a predictor of responsiveness to existing targeted therapies in a hard-to-treat breast cancer subtype posits an update of current clinical diagnostic criteria and therapeutic strategies. This is particularly important in the adjuvant setting where identification of biomarkers predicting responsiveness to CDK4/6 inhibitors remains an urgent, unmet clinical need.

cancer biology↗

bioRxiv2024

A temporal sequence of heterochronic gene activities promotes stage-specific developmental events in C. elegans

The heterochronic genes of the nematode Caenorhabditis elegans control the succession of postembryonic developmental events. The four core heterochronic genes lin-14, lin-28, hbl-1, and lin-41 act in a sequence to specify cell fates specific to each of the four larval stages. It was previously shown that lin-14 has two activities separated in time that promote L1 and L2 developmental events, respectively. Using the auxin-inducible degron system, we find that lin-28 and hbl-1 each have two activities that control L2 and L3 events which are also separated in time. Relative to events they control, both lin-28 and hbl-1 appear to act just prior to or concurrently with events of the L2. Relative to each other, lin-28 and hbl-1 appear to act simultaneously. By contrast, the lin-14 activity controlling L2 events precedes those of lin-28 and hbl-1 controlling the same events, suggesting lin-14s regulation of lin-28 is responsible for the delay. Likewise, the activities of lin-28 and hbl-1 controlling L3 fates act well in advance of those fates, suggesting a similar regulatory gap. lin-41 acts early in the L3 to affect fates of the L4, although it was not possible to determine whether it too has two temporally separated activities. We also uncovered a feedback phenomenon that prevents the reactivation of heterochronic gene activity late in development after it has been down-regulated. This study places the heterochronic gene activities into a timeline of postembryonic development relative to one another and to the developmental events whose timing they control.

genetics↗

bioRxiv2024

ListPred: A predictive ML tool for virulence potential and disinfectant tolerance in Listeria monocytogenes

Despite current surveillance and sanitation strategies, foodborne pathogens continue to threaten the food industry and public health. Whole genome sequencing (WGS) has reached an unprecedented resolution to analyse and compare pathogenic bacterial isolates. The increased resolution significantly enhances the possibility of tracing transmission routes and contamination sources of foodborne pathogens. In addition, machine learning (ML) on WGS data has shown promising applications for predicting important microbial traits such as virulence, growth potential, and resistance to antimicrobials. Many regulatory agencies have already adapted WGS and ML methods. However, the food industry hasnt followed a similarly enthusiastic implementation. Some possible reasons for this might be the lack of computational resources and limited expertise to analyse WGS and ML data and interpret the results. Here, we present ListPred, a ML tool to analyse WGS data of Listeria monocytogenes, a very concerning foodborne pathogen. ListPred is able to predict two important bacterial traits, namely virulence potential and disinfectant tolerance, and only requires limited computational resources and practically no bioinformatic expertise, which is essential for a broad application in the food industry. AUTHOR SUMMARYThe contamination of food products with microbes such as pathogenic bacteria is a big concern for the food industry. The rapid detection, characterisation and eradication of microbial contaminants are of utmost importance to ensure safe food products. Fortunately, strict food safety regulations and stringent cleaning protocols prevent the transmission of harmful bacteria to humans. Individual bacteria of the same species can have varying abilities to resist cleaning agents or infect a host, meaning that some pathogen isolates might be more dangerous than others. Novel techniques such as genome sequencing and machine learning can help to determine such differences in individual bacteria. Unfortunately, these techniques require a lot of computational power and expertise that is limited in the food industry. This is why we developed an easy-to-use software tool called ListPred that can be used with few computational requirements and little expertise. ListPred helps food companies to answer two essential questions: how dangerous are Listeria monocytogenes pathogens, and how to get rid of them most efficiently?

bioinformatics↗

bioRxiv2023

Quantitative prediction of disinfectant tolerance in Listeria monocytogenes using whole genome sequencing and machine learning

Listeria monocytogenes is a potentially severe disease-causing bacteria mainly transmitted through food. This pathogen is of great concern for public health and the food industry in particular. Many countries have implemented thorough regulations, and some have even set zero-tolerance thresholds for particular food products to minimise the risk of L. monocytogenes outbreaks. This emphasises that proper sanitation of food processing plants is of utmost importance. Consequently in recent years, there has been an increased interest in L. monocytogenes tolerance to disinfectants used in the food industry. Even though many studies are focusing on laboratory quantification of L. monocytogenes tolerance, the possibility of predictive models remains poorly studied. Within this study, we explore the prediction of tolerance and minimum inhibitory concentrations (MIC) using whole genome sequencing (WGS) and machine learning (ML). We used WGS data and MIC values to quaternary ammonium compound (QAC) disinfectants from 1649 L. monocytogenes isolates to train different ML predictors. Our study shows promising results for predicting tolerance to QAC disinfectants using WGS and machine learning. We were able to train high-performing ML classifiers to predict tolerance with balanced accuracy scores up to 0.97{+/-}0.02. For the prediction of MIC values, we were able to train ML regressors with mean squared error as low as 0.07{+/-}0.02. We also identified several new genes related to cell wall anchor domains, plasmids, and phages, putatively associated with disinfectant tolerance in L. monocytogenes. The findings of this study are a first step towards prediction of L. monocytogenes tolerance to QAC disinfectants used in the food industry. In the future, predictive models might be used to monitor disinfectant tolerance in food production and might support the conceptualisation of more nuanced sanitation programs. AUTHOR SUMMARYMicrobial contamination challenges food safety by potentially transmitting harmful microbes such as bacteria to consumers. Listeria monocytogenes is an example of such a bacteria, which is primarily transmitted through food and can cause severe diseases in at-risk groups. Fortunately, strict food safety regulations and stringent cleaning protocols are in place to prevent the transmission of Listeria monocytogenes. However, in recent years, there has been an increase in tolerance towards disinfectants used in the food industry, which can reduce their effectiveness. In this study, we used genome sequencing and phenotypic data to train machine learning models that can accurately predict whether individual Listeria monocytogenes isolates are tolerant to selected disinfectants. We were able to train models that are not only able to distinguish sensitive/tolerant isolates but also can predict different degrees of tolerance to disinfectants. Further, we were able to report a set of genes that were important for the machine learning prediction and could give information about possible tolerance mechanisms. In the future, similar predictive models might be used to guide cleaning and disinfection protocols to facilitate maximum effectiveness.

bioinformatics↗

bioRxiv2023

Large-scale phenotypic and genomic characterization of Listeria monocytogenes susceptibility to quaternary ammonium compounds

Listeria monocytogenes is a significant concern for the food industry due to its ability to persist in the food processing environment. Decreased susceptibility to disinfectants is one of the factors that contribute to the persistence of L. monocytogenes. The objective of this study was to explore the diversity of L. monocytogenes susceptibility to quaternary ammonium compounds (QACs) using 1,671 L. monocytogenes isolates. This was used to determine the phenotype-genotype concordance and characterize genomes of the QAC sensitive and tolerant isolates for stress resistance, virulence and plasmid replicon genes. Distribution of QAC tolerance genes among 37,897 publicly available L. monocytogenes genomes were also examined. The minimum inhibitory concentration to QACs was determined by the broth microdilution method and non-sequenced isolates (n=1,244) were whole genome sequenced. Genotype-phenotype concordance was 99% for benzalkonium chloride, DDAC and a commercial QAC based sanitizer. Prevalence of QAC tolerance genes was 23% and 28% in our L. monocytogenes collection and in the global dataset, respectively. qacH was the most prevalent gene in our collection (61%), with 19% prevalence in the global dataset. Notably, bcrABC was most common (72%) globally, while 25% in our collection. Prevalence of emrC and emrE was comparable in both datasets, 7% and 2%, respectively. Replicon genes, indicative of plasmid harborage, were detected in 44% of the isolates and associated with the QAC tolerant phenotype. The presented analysis is based on the biggest L. monocytogenes collection in diversity and quantity for characterization of the L. monocytogenes QAC tolerance at both phenotypic and genomic levels. IMPORTANCEContamination of Listeria monocytogenes within the food processing environment is of concern to the food industry due to challenges in eradicating the pathogen once it becomes persistent in the environment. Genetic markers associated with increased tolerance to disinfectants have been identified, which alongside factors favor the persistence of L. monocytogenes in the production environment. By employing a comprehensive large-scale phenotypic testing and genomic analysis our study significantly enhances the understanding of the prevalence of quaternary ammonium compound (QAC) tolerant L. monocytogenes and the genetic determinants associated with the increased tolerance. Furthermore, we report on the prevalence of QAC tolerance genes among 37,897 publicly available L. monocytogenes sequences and their distribution within clonal complexes, isolation sources and geographical locations. As the propagation of QAC tolerance showed not be evenly distributed globally this highlights that understanding the development of L. monocytogenes disinfectant tolerance can be monitored using publicly available WGS data.

microbiology↗

bioRxiv2023

Proteomic insights into mental health status: plasma markers in young adults

Global emphasis on enhancing prevention and treatment strategies necessitates increased understanding of biological mechanisms of psychopathology. Plasma proteomics is a powerful tool that has been applied in the context of specific mental disorders for biomarker identification. The p-factor, also known as the "general psychopathology factor", is a concept in psychopathology suggesting that there is a common underlying factor that contributes to the development of various forms of mental disorders. It has been proposed that the p-factor can be used to understand the overall mental health status of an individual. Here we aimed to discover plasma proteins associated with the p-factor in 775 young adults in the FinnTwin12 cohort. Using liquid chromatography-tandem mass spectrometry, 14 proteins with a significant connection with the p-factor were identified, 9 of which were linked to epidermal growth factor receptor (EGFR) signalling. This exploratory study provides new insight into biological alterations associated with mental health status in young adults.

neuroscience↗

bioRxiv2023

Orthologs of the C. elegans heterochronic genes have divergent functions in C. briggsae

The heterochronic genes of C. elegans comprise the best-studied pathway controlling the timing of tissue and organ formation in an animal. To begin to understand the evolution of this pathway and the significance of the relationships among its components, we characterized 11 C. briggsae orthologs of C. elegans heterochronic genes. Using CRISPR/Cas9, we made a variety of alleles and found that several mutant phenotypes differ in significant ways from those of C. elegans. Although most mutant orthologs displayed defects in developmental timing, their phenotypes could differ in which stages were affected, the penetrance and expressivity of the phenotypes, or by having additional pleiotropies that were not obviously connected to developmental timing. However, when examining pairwise epistasis and synergistic relationships, we found those paralleled the known relationships between their C. elegans orthologs, suggesting that the arrangements of these genes in functional modules is conserved, but the modules relationships to each other and/or to their targets has drifted since the time of the species last common ancestor. Furthermore, our investigation has revealed a relationship to this pathway to other aspects of the animals growth and development, including gonad development, that is relevant to both species.

developmental biology↗

bioRxiv2022

Changes in oscillations in anterior cingulate and medial prefrontal cortex are associated with altered signatures of Bayesian predictive coding in trait anxiety

Recent advances in the computational understanding of decision-making processes have led to proposals that anxiety biases how individuals form beliefs and estimate uncertainty. The anxiety and decision-making circuitry broadly overlap in regions such as the medial prefrontal cortex (mPFC), anterior cingulate cortex (ACC), and orbitofrontal cortex (OFC). Changes in activity across these brain areas could help explain how misestimation of uncertainty and altered belief updating can lead to impaired learning in anxiety. To test this prediction, this study built on recent progress in rhythm-based formulations of Bayesian predictive coding to identify sources of oscillatory modulations across the ACC, mPFC, and OFC that are associated with altered learning in subclinical trait anxiety. In a magnetoencephalography (MEG) experiment, two groups of human participants pre-screened with high and low trait anxiety (HTA, LTA: 39) performed a volatile probabilistic reward-based learning task. We modelled behaviour using a hierarchical Bayesian learning model. Furthermore, we quantified the parametric effects of trial-wise estimates of unsigned precision-weighted prediction errors (pwPEs) and, separately, precision weights and surprise on source-reconstructed MEG time-frequency responses using convolution modelling. We showed that HTA interferes with overall reward-based learning performance associated with more stochastic decisions and more pronounced lose-shift tendencies. These behavioural effects were explained by an overestimation of volatility and faster belief updating in HTA when compared to LTA. On a neural level, we observed enhanced gamma responses and decreased alpha/beta activity in HTA during the encoding of unsigned pwPEs about about stimulus outcomes relative to LTA. These effects emerged primarily in the ACC and dorsomedial PFC (dmPFC), and they were accompanied by additional ACC alpha/beta modulations representing differential encoding of precision weights in each anxiety group. Our study supports the association between subclinical trait anxiety and faster updating of beliefs in a volatile environment through gamma and alpha/beta activity changes in the ACC and dmPFC.

neuroscience↗

bioRxiv2021

An engineered, orthogonal auxin analog/AtTIR1(F79G) pairing improves both specificity and efficacy of the auxin degradation system in Caenorhabditis elegans

The auxin-inducible degradation system in C. elegans allows for spatial and temporal control of protein degradation via heterologous expression of a single Arabidopsis thaliana F-box protein, transport inhibitor response 1 (AtTIR1). In this system, exogenous auxin (Indole-3-acetic acid; IAA) enhances the ability of AtTIR1 to function as a substrate recognition component that adapts engineered degron-tagged proteins to the endogenous C. elegans E3 ubiquitin ligases complex (SKR-1/2-CUL-1-F-box (SCF)), targeting them for degradation by the proteosome. While this system has been employed to dissect the developmental functions of many C. elegans proteins, we have found that several auxin-inducible degron (AID)-tagged proteins are constitutively degraded by AtTIR1 in the absence of auxin, leading to undesired loss-of-function phenotypes. In this manuscript, we adapt an orthogonal auxin-derivative/mutant AtTIR1 pair (C. elegans AID version 2 (C.e.AIDv2)) that transforms the specificity of allosteric regulation of TIR1 from IAA to one that is dependent on an auxin derivative harboring a bulky aryl group (5-Ph-IAA). We find that a mutant AtTIR1(F79G) allele that alters the ligand binding interface of TIR1 dramatically reduces ligand-independent degradation of multiple AID*-tagged proteins. In addition to solving the ectopic degradation problem for some AID targets, addition of 5-Ph-IAA to culture media of animals expressing AtTIR1(F79G) leads to more penetrant loss-of-function phenotypes for AID*-tagged proteins than those elicited by the AtTIR1-IAA pairing at similar auxin analog concentrations. The improved specificity and efficacy afforded by the mutant AtTIR1(F79G) allele expands the utility of the AID system and broadens the number of proteins that can be effectively targeted with it. ARITCLE SUMMARYImplementation of the auxin induced degradation (AID) system has increased the power if the C. elegans model through its ability to rapidly degrade target proteins in the presence of the plant hormone auxin (IAA). The current C.e.AID system is limited in that a substantial level of target degradation occurs in the absence of ligand and full levels of target protein degradation require high levels of auxin inducer. In this manuscript, we modify the AID system to solve these problems.

molecular biology↗