Biology subjects

Meehan, C.

Publications and source records attributed to Meehan, C..

11 recordsLinked to original sources

bioRxiv2025

Molecular characterization of capulet2 reveals the importance of ANAPHASE PROMOTING COMPLEX 6 maternal expression in endosperm development

Flowering plants are characterized by a double fertilization event, and the fertilized female gametes develop into the endosperm and embryo. Genomic imprinting promotes parental allele-specific gene expression in the endosperm by epigenetic modifications such as DNA methylation. Similarly, gametophyte maternal effects influence gene function in the female gametophyte that affects development of the endosperm and embryo post-fertilization. While most imprinted genes do not display a seed phenotype upon mutation, gametophyte maternal effect mutants are characterized by distorted seed development upon maternal transmission of the mutant allele. Here, we have investigated the gametophyte maternal effect mutant capulet2 (cap2). We have established CAP2 to be encoded by ANAPHASE PROMOTING COMPLEX 6 (APC6), a subunit of the anaphase promoting complex/cyclosome (APC/C). Investigation of further cap2/apc6 alleles revealed female gametophyte maternal effects both in mutant segregation and seed phenotype, and both cap2 and apc6 phenotypes were rescued by an APC6 transgene. Furthermore, we demonstrate that APC6 is a maternally expressed imprinted gene, in line with the observed female gametophyte maternal effect phenotype. To this end, we observed irregular nuclear division of the endosperm coenocyte in cap2/apc6 mutants, suggesting a role for the APC/C in early endosperm development. We further demonstrate that similar endosperm defects are also produced by mutation of APC1, another subunit of the APC/C. Similar to cap2/apc6, APC1 is imprinted and only expressed from the maternal allele, suggesting a maternal bias in the control of APC/C in the developing endosperm.

developmental biology↗

bioRxiv2025

MUDflow: Combining Neural Networks, UMAP and DBM Clustering to Identify Cell Populations Accurately, Quickly and Easily in Mass and Fluorescence Cytometry

Identifying cell populations in flow cytometry data is mostly done via a "manual gating" method that often lacks verifiability and reproducibility, even in the hands of experienced investigators. Recently developed automatic gating methods have been shown to have good performance in cell population identification, but may require fine-tuned setup from experts or struggle to identify small populations. Here, we introduce an easily trainable multilayer perceptron neural network for automatic gating (MLPgater). Compared to the three popular automatic gating methods LDA, FlowSOM and PhenoGraph on three mass and six fluorescence cytometry datasets, MLPgater is most accurate by a substantial margin, tied with LDA as the fastest and uniquely able to replace manual gating except for training purposes. Furthermore, we show that combining MLPgater with UMAPs guided dimensionality reduction feature and DBMs clustering (MUDflow) effectively detects new populations that did not exist or were not identified in the training set.

bioinformatics↗

bioRxiv2024

Automatic Phenotyping Using Exhaustive Projection Pursuit

One of the most common objectives in the analysis of flow cytometry data is the identification and delineation of phenotypes, distinct populations of cells with shared characteristics in the measurement dimensions. We have developed an automated tool to comprehensively identify these cell populations by Exhaustive Projection Pursuit (EPP). The method evaluates all two-dimensional projections among the suitable data dimensions and creates an optimized sequence of statistically significant gating regions that identify all phenotypes supported by the data. We evaluate the results of EPP on four well characterized data sets from the literature. The C++ code for EPP can be called from any computing environment. We illustrate this with a MATLAB utility that integrates EPP with FlowJo. All source code is freely available.

bioinformatics↗

bioRxiv2023

A new method for the reproducible development of aptamers (Neomers)

The development of aptamers has been almost exclusively performed based on the SELEX method since their inception. While this method represents a powerful means of harnessing the in vitro evolution of sequences that bind to a given target, there are significant constraints in the design. The most significant constraint has been the reliance on counter selection on off-targets to drive specificity. Counter selection has not been as effective at driving aptamer specificity as the presence of immune tolerance, the capacity of the immune system to remove antibodies that bind to host targets, is for antibody development. This deficiency has constrained the commercial efficacy of aptamers to date. These limitations have been addressed with our design of a novel platform for aptamer identification. This new design is based on what we refer to as a Neomer library with sixteen random nucleotides interspersed with fixed sequences. The fixed sequences are designed to minimize the potential for hybridization, such that secondary structure is driven by the random nucleotides. The use of sixteen random nucleotides reduces the possible library sequences to 4.29 x 109. This enables the application of the same sequences to either the same target or different targets while maintaining a high level of structural diversity. In effect, this introduces the capacity for reproducibility in aptamer selection and in silico approach to replicating immune tolerance We provide here an overview of the new method and a description of the performance of aptamers selected for interleukin 6 developed using this approach.

biochemistry↗

bioRxiv2023

A reproducible approach for the use of aptamer libraries for the identification of Aptamarkers for brain amyloid deposition based on plasma analysis.

An approach for the agnostic identification and validation of aptamers for the prediction of a medical state from plasma analysis is presented in application to a key risk factor for Alzheimers disease. brain amyloid deposition. This method involved the use of a newly designed aptamer library with sixteen random nucleotides interspersed with fixed sequences called a Neomer library. The Neomer library approach enables the direct application of the same starting library on multiple plasma samples. without the requirement for pre-enrichment associated with the traditional approach. Eight aptamers were identified as a result of the selection process and screened across 390 plasma samples by qPCR assay. Results from both processes were analysed using multiple machine learning algorithms from the Scikit-learn package along with clinical variables including cognitive status. age and sex to create predictive models. An Extra Trees Classifier model provided the highest predictive power. The Neomer approach resulted in a sensitivity of 0.88. specificity of 0.76. and AUC of 0.79. The only clinical variables that were included in the model were age and sex. We conclude that the Neomer approach represents a clear improvement for the agnostic identification of aptamers (Aptamarkers) that bind to unknown biomarkers of a medical state.

bioinformatics↗

bioRxiv2023

Single nucleotide variation catalogue from clinical isolates mapped on tertiary and quaternary structures of ESX-1 related proteins reveals critical regions as putative Mtb therapeutic targets

Proteins encoded by the ESX-1 genes of interests are essential for full virulence in all Mycobacterium tuberculosis complex (MTBc) lineages, the pathogens with the highest mortality worldwide. Identifying critical regions in these ESX-1 related proteins could provide preventive or therapeutic targets for MTB infection, the game changer needed for tuberculosis control. We analysed a compendium of whole genome sequences of clinical MTB isolates from all lineages from >32,000 patients and identified single nucleotide variations (SNV). When mutations corresponding to all nonsynonymous SNPs were mapped on the surface of known and AlphaFold-predicted ternary protein structures, fully conserved regions emerged. Some could be assigned to known quaternary structures, whereas others could be predicted to be involved in yet-to-be-discovered interactions. Some mutants had clonally expanded (found in >1% of the isolates): these were mostly located at the surface of globular domains, remote from known intra- and inter-molecular protein-protein interactions. Fully conserved intrinsically disordered regions (IDRs) of proteins were found, suggesting that these are crucial for the pathogenicity of the MTBc. Altogether, our findings provide an evolutionary structural perspective on MTB virulence and highlight fully conserved regions of proteins as attractive vaccine antigens and drug targets. Extending this approach to other pathogens can provide a novel critical resource for the development of innovative tools for pathogen control.

genomics↗

bioRxiv2022

Predicting MmpR-based bedaquiline resistance using sequence- and structure-based features

Accurate molecular detection of resistance to bedaquiline, a core drug for the treatment of drug resistant tuberculosis, remains challenging. In this study, we investigated whether three sequence- and nine structure-based features describing the impact of Rv0678 variants on the MmpR transcriptional repressor could predict the bedaquiline phenotype of isolates containing Rv0678 variants. Paired genotypic and phenotypic data was used to train a binary random forest classifier. The mean value of the individual features was similar for resistant and susceptible variants (p[≥]0.05). Leave one out cross validation showed predictability of bedaquiline resistance from Rv0678 variants when using a binary classifier with a combination of sequence and structural features (ROC AUC = 0.766; f1 score = 0.746), but the performance was too low for clinically use. Evolutionary conservation of the affected residue was the most important individual feature (mean decrease in impurity = 0.226 {+/-} 0.003) to discriminate bedaquiline resistance from susceptibility. Prediction of bedaquiline resistance was only possible when restricting the data to missense variants, as the selected features could not be applied to Rv0678 insertions and deletions. Additionally, prediction of bedaquiline resistance was only possible when restricting the data to isolates for which the phenotype was determined using Mycobacterial Growth Indicator Tubes, suggesting possible misclassification of the bedaquiline phenotype by other methods. The results of this study suggest that structural features describing Rv0678 missense variants could be used to predict bedaquiline resistance, albeit not yet at the performance level required for clinical practice. Author summaryGuidelines by the World Health Organization indicate that antibiotic resistant tuberculosis should be treated with bedaquiline, a newly discovered antibiotic. However, resistance to bedaquiline has already emerged and is spreading rapidly. More than 500 different variants have been discovered in the bedaquiline resistance genes in Mycobacterium tuberculosis. It is not known which DNA variants specifically drive bedaquiline resistance due to insufficiency of data. Here, we used a machine learning technique to predict whether a variant in the Rv0678 gene confers bedaquiline resistance. To this end, for each variant we defined features that describe the impact of the variant on the MmpR (encoded by the Rv0678 gene) protein structure. These features were then fed into a classification algorithm to predict bedaquiline resistance. We successfully developed a bedaquiline resistance prediction model, although performance was limited. We also found that accurate predictions were only possible when restricting the data to samples that were tested on the MGIT platform, a World Health Organization endorsed method to test for bedaquiline resistance. Our study shows that predicting bedaquiline resistance from protein structural features is feasible. Furthermore, our study provides new insights into phenotypic testing platforms to assess bedaquiline resistance.

microbiology↗

bioRxiv2022

Predictable and stable epimutations induced during clonal propagation with embryonic transcription factors

Although clonal propagation is frequently used in commercial plant breeding and plant biotechnology programs because it minimizes genetic variation, it is not uncommon to observe clonal plants with stable phenotypic changes, a phenomenon known as somaclonal variation. Several studies have shown that epigenetic modifications induced during regeneration are associated with this newly acquired phenotypic variation. However, the factors that determine the extent of somaclonal variation and the molecular changes associated with it remain poorly understood. To address this gap in our knowledge, we compared clonally propagated Arabidopsis thaliana plants derived from somatic embryogenesis using two different embryonic transcription factors-RWP-RK DOMAIN-CONTAINING 4 (RKD4) or LEAFY COTYLEDON2 (LEC2) and from two epigenetically distinct tissues. We found that both the epi(genetic) status of explant and the regeneration protocol employed play critical roles in shaping the molecular and phenotypic state of clonal plants. Phenotypic variation of regenerated plants can be largely explained by the inheritance of tissue-specific DNA methylation imprints, which are associated with specific transcriptional and metabolic changes in sexual progeny of clonal plants. Moreover, regenerants from roots were particularly affected by the inheritance of epigenetic imprints, which resulted in increased accumulation of salicylic acid in leaves and accelerated plant senescence. Collectively, our data reveal pathways for targeted manipulation of phenotypic variation in clonal plants.

plant biology↗

bioRxiv2020

Characterization of genomic variants associated with resistance to bedaquiline and delamanid in naïve Mycobacterium tuberculosis clinical strains

The role of genetic mutations in genes associated to phenotypic resistance to bedaquiline (BDQ) and delamanid (DLM) in Mycobacterium tuberculosis complex (MTBc) strains is poorly understood. However, a clear understanding of the role of each genetic variant is crucial to guide the development of molecular-based drug susceptibility testing (DST). In this work, we analysed all mutations in candidate genomic regions associated with BDQ- and DLM-resistant phenotypes using a whole genome sequencing (WGS) dataset from a collection of 4795 MTBc clinical isolates from six high-burden countries of tuberculosis (TB). From WGS analysis, we identified 61 and 158 unique mutations in genomic regions potentially involved in BDQ- and DLM-resistant phenotypes, respectively. Importantly, all strains were isolated from patients who likely have never been exposed to the medicines. In order to characterize the role of mutations, we performed an energetic in silico analysis to evaluate their effect in the available protein structures Ddn (DLM), Fgd1 (DLM) and Rv0678 (BDQ), and minimum inhibitory concentration (MIC) assays on a subset of MTBc strains carrying mutations to assess their phenotypic effect. The combination of structural protein information and phenotypic data allowed for cataloging the mutations clearly associated with resistance to BDQ (n= 4) and DLM (n= 35), as well as about a hundred genetic variants without any correlation with resistance. Importantly, these results show that both BDQ and DLM resistance-related mutations are diverse and distributed across the entire region of each gene target, which is of critical importance to the development of comprehensive molecular diagnostic tools. ImportancePhenotypic drug susceptibility tests (DST) are too slow to provide an early indication of drug susceptibility status at the time of treatment initiation and very demanding in terms of specimens handling and biosafety. The development of molecular assays to detect resistance to bedaquiline (BDQ) and delamanid (DLM) requires accurate categorization of genetic variants according to their association with phenotypic resistance. We have evaluated a large multi-country set of clinical isolates to identify mutations associated with increased minimum inhibitory concentrations (MICs) and used an in silico protein structure analysis to further unravel the potential role of these mutations in drug resistance mechanisms. The results of this study are an important source of information for the development of molecular diagnostic tests to improve the provision of appropriate treatment and care to TB patients.

microbiology↗

bioRxiv2019

Two Receptor-Like Kinases Required For Arabidopsis Endodermal Root Organisation Shape The Rhizosphere Microbiome

The Casparian Strip (CS) constitutes a physical diffusion barrier to water and nutrients in plant roots, and is formed by the polar deposition of lignin polymer in the endodermis. This precise pattern of lignin deposition is thought to be mediated by the scaffolding activity of membrane-bound Casparian Strip domain proteins (CASPs). However, we show that endodermis-specific receptor-like kinase 1 (ERK1) and ROP Binding Kinase1 (RBK1) are also involved in this intricate process, with the former playing an essential role both in the localization of CASP1 and in lignin deposition. We further characterised ERK1 and determined its subcellular localisation in the cytoplasm and nucleus of the endodermis, as well as provide evidence for its involvement in a signalling pathway together with the circadian clock regulator, Time for Coffee (TIC). We also show that disruption to CS organisation and increased suberisation in the endodermis due to loss of function of either ERK1 or TIC collectively leads to an altered root microbiome composition. Thus, our work reveals additional players in the complex cascade of signalling events operating in the root endodermis to establish both the CS diffusion barrier and the microbial composition of the rhizosphere.

plant biology↗

bioRxiv2017

Comparative genomics of Mycobacterium africanum Lineage 5 and Lineage 6 from Ghana suggests different ecological niches

Mycobacterium africanum (Maf) causes up to half of human tuberculosis in West Africa, but little is known on this pathogen. We compared the genomes of 253 Maf clinical isolates from Ghana, including both L5 and L6. We found that the genomic diversity of L6 was higher than in L5, and the selection pressures differed between both groups. Regulatory proteins appeared to evolve neutrally in L5 but under purifying selection in L6. Conversely, human T cell epitopes were under purifying selection in L5, but under positive selection in L6. Although only 10% of the T cell epitopes were variable, mutations were mostly lineage-specific. Our findings indicate that Maf L5 and L6 are genomically distinct, possibly reflecting different ecological niches.

genomics↗