Biology subjects

Alexander, E.

Publications and source records attributed to Alexander, E..

9 recordsLinked to original sources

bioRxiv2026

Epistasis limits but does not prevent the transfer of mutation-drug resistance mapping across 600 million years of fungal evolution

Whether different pathogens acquire resistance to antimicrobials through the same mutations is a major question in evolution and microbiology. Most antifungal drugs are used to treat infections caused by multiple fungal species, many of which have diverged for millions of years. If the evolution of resistance was to converge onto the same set of mutations across species, knowing the mechanism of resistance in one would allow us to predict and track it in others. The extent of this convergence remains unknown due to the lack of systematic data on resistance mutations. Here, we quantify the conservation of resistance mutations in the cytosine deaminase (CD), a protein responsible for resistance to flucytosine, one of the oldest antifungal drugs. By comparing the crystal structures of this enzyme through 600 My of evolution, we show that the CD structure is highly conserved. We compared the full CD mutational resistance spectrum of resistance from an ascomycete and a basidiomycete. We found that resistance mutations in one ortholog can be used to predict resistance in the other at a high level of accuracy. However, because of epistasis, around 10% of mutations have distinct effects in the two orthologs, which imposes an upper limit to the transferability of the knowledge of resistance mutations from one species to another. Using biochemical assays and by structural characterization of several mutants, we identify distinct mechanisms of epistasis, one important being that the local physiochemical environment of some position has evolved in a way that makes the same substitutions destabilizing or entirely inactivating in an ortholog-specific manner. Our results show that resistance mutations can be conserved in fungi across hundreds of millions of years of evolution but that epistasis eventually limits the accuracy of these predictions.

microbiology↗

bioRxiv2025

Analyzing and Visualizing Distance Measures for Clonal Trees

Cancer is an evolutionary process, in that a tumor results from a series of genomic mutations that are acquired over time. The history of how a tumor evolved is often represented using a clonal tree: a particular type of rooted tree where vertices represent tumor cell clones and edges represent ancestral relationships between them. In recent years, numerous algorithms have been developed that infer clonal trees from sequencing data. Correspondingly, researchers have also developed distance measures to compare clonal trees. While such distance measures have proven valuable, they are limited in a variety of ways. First off, they usually only utilize one or a few types of specific information when being computed - making it challenging to assess how well they capture other common types of differences between clonal trees. Secondly, their final output is just a single numerical value - meaning that information on what parts of the trees contributed to the distance is not being readily conveyed. In this work, we address both of these issues. We define a set of four classes of tree differences that describe common ways that clonal trees might differ from each other and analyze these classes using three different clonal tree distance measures. We then introduce a visualization tool called VECTr (Visual Encodings for Clonal Trees) that provides an enhanced pairwise comparison of clonal trees using the same three distance measures. In particular, VECTr contains three types of visualizations that allow a user to easily identify what parts of a tree contribute to its distance, including information relevant to when the trees differ in a manner that corresponds to one of our four classes of tree differences. We demonstrate the utility of our visualization tool using a variety of different use cases, including application to both simulated and real clonal trees. Code associated with the project is freely available at: https://github.com/quocodile/visualize-clonal-trees CCS ConceptsApplied computing Molecular evolution; Computational genomics; Computational biology.

bioinformatics↗

bioRxiv2024

FungAMR: A comprehensive portrait of antimicrobial resistance mutations in fungi

Antimicrobial resistance (AMR) is a global threat. To optimize the use of our antifungal arsenal, we need rapid detection and monitoring tools that rely on high-quality AMR mutation data. Here, we performed a thorough manual curation of published AMR mutations in fungal pathogens to produce the FungAMR reference dataset. A total of 501 papers were curated, leading to 35,792 mutation entries all classified with the degree of evidence that supports their role in resistance. FungAMR covers 95 species, 246 genes and 208 drugs. We combined variant effect predictors with FungAMR resistance mutations and showed that these tools could be used to help predict the potential impact of mutations on AMR. Additionally, a comparative analysis among species revealed a high level of convergence in the molecular basis of resistance, highlighting some potentially universal resistance mutations. The analysis also showed that a significant number of resistance mutations lead to cross-resistance within antifungals of a class, as well as between classes for certain mutated genes. The acquisition of fungal resistance in the clinic and the field is an urging concern. Finally, we provide a computational tool, ChroQueTas, that leverages FungAMR to screen fungal genomes for AMR mutations. These resources are anticipated to have great utility for researchers in the fight against antifungal resistance.

microbiology↗

bioRxiv2024

Pharmacogenomics Guided Spaceflight: the intersection between space-flown drugs and space genes

Ten years ago, it was predicted that the multi-omics revolution would also revolutionize space pharmacogenomics. Current barriers related to the findable, accessible, interoperable, and reproducible use of space-flown pharmaceutical data have contributed to a lack of progress beyond application of earth-based principles. To directly tackle these challenges, we have produced a novel database of all the drugs flown into space, compiled from publicly available ontological and spaceflight-related datasets, to exemplify analyses for describing significant spaceflight-related targets. By focusing on mechanisms perturbed by spaceflight, we have provided a novel avenue for identifying the most relevant changes within the drug absorption, distribution, metabolism, and excretion pathways. We suggest a set of space genes, by necessity limited to available tissue types, that can be expanded and modified based on future tissue-specific and mechanistic-specific high-throughput assays. In sum, we provide the justification and a definitive starting point for pharmacogenomics guided spaceflight as a foundation of precision medicine, which will enable long-term human habitation of the Moon, Mars, and beyond. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=147 SRC="FIGDIR/small/575951v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@48f739org.highwire.dtl.DTLVardef@5ecdb0org.highwire.dtl.DTLVardef@121c93org.highwire.dtl.DTLVardef@1122b3f_HPS_FORMAT_FIGEXP M_FIG C_FIG

pharmacology and toxicology↗

bioRxiv2023

Dynamic protein assembly and architecture of the large solitary membraneless organelle during germline development in the wasp Nasonia vitripennis

Germ cells in different animals assemble characteristic membraneless organelles referred to as germ granules, which contain RNA and proteins required for germline development. Typically, the germ granules are small spherical or amorphous cytoplasmic granules and often, they assemble around membrane-bound organelles such as nuclei, mitochondria and endoplasmic reticulum. In particular, in egg chambers of the fruit fly Drosophila, nurse cells assemble perinuclear granules, referred to as nuage, along with multiple small germ granules formed at the posterior pole of the oocyte (polar granules). Nuage is assembled in a very similar way in the wasp Nasonia vitripennis, despite the long evolutionary distance from Drosophila. In contrast, Nasonia forms a very different single germ granule, called the oosome, at the posterior, which is about 40 times larger than a homologous Drosophila polar granule. Here, using molecular and super-resolution imaging approaches, we provide insights into protein assembly and architecture of the oosome during germline development. Interestingly, unlike the fly, the wasp utilizes alternatively spliced RNA-helicase Vasa isoforms during germline development and oosome formation. The isoforms differ by an unstructured region, containing repeats of phenylalanine and glycine, that is similar to functional domains characteristic of nucleoporins. In addition, while other conserved components of germ granules, such as Oskar, Aubergine and Tudor proteins are recruited to the oosome, these polypeptides show a distinct and specific localization within the oosome. Of particular note, Tudor protein forms a shell encapsulating the oosome, while small Oskar/Vasa/Aubergine granules occur inside the oosome core. Also, in surprising contrast to Drosophila egg chambers, we found that a subset of the wasp nurse cells located in anterior show dramatic DNA damage and assemble higher levels of nuage than their posterior counterparts. The characteristics of two distinct nurse cell populations suggest a mechanistic link between the higher amounts of nuage assembled in anterior nurse cells and their need to silence transposable elements in the presence of double-strand DNA breaks. Our results point to the high degree of plasticity in the assembly of membraneless organelles, which adapt to specific developmental needs of different organisms, and suggest that novel molecular features of conserved proteins result in the unique architecture of the oosome in the wasp.

cell biology↗

bioRxiv2023

Transformations of sensory information in the brain reflect a changing definition of optimality

Neurons throughout the brain modulate their firing rate lawfully in response to changes in sensory input. Theories of neural computation posit that these modulations reflect the outcome of a constrained optimization: neurons aim to efficiently and robustly represent sensory information under resource limitations. Our understanding of how this optimization varies across the brain, however, is still in its infancy. Here, we show that neural responses transform along the dorsal stream of the visual system in a manner consistent with a transition from optimizing for information preservation to optimizing for perceptual discrimination. Focusing on binocular disparity - the slight differences in how objects project to the two eyes - we re-analyze measurements from neurons characterizing tuning curves in macaque monkey brain regions V1, V2, and MT, and compare these to measurements of the natural visual statistics of binocular disparity. The changes in tuning curve characteristics are computationally consistent with a shift in optimization goals from maximizing the information encoded about naturally occurring binocular disparities to maximizing the ability to support fine disparity discrimination. We find that a change towards tuning curves preferring larger disparities is a key driver of this shift. These results provide new insight into previously-identified differences between disparity-selective regions of cortex and suggest these differences play an important role in supporting visually-guided behavior. Our findings support a key re-framing of optimal coding in regions of the brain that contain sensory information, emphasizing the need to consider not just information preservation and neural resources, but also relevance to behavior. SignificanceA major role of the brain is to transform information from the sensory organs into signals that can be used to guide behavior. Neural activity is noisy and can consume large amount of energy, so sensory neurons must optimize their information processing so as to limit energy consumption while maintaining key behaviorally-relevant information. In this report, we re-examine classically-defined brain areas in the visual processing hierarchy, and ask whether neurons in these areas vary lawfully in how they represent sensory information. Our results suggest that neurons in these brain areas shift from being an optimal conduit of sensory information to optimally supporting perceptual discrimination during natural tasks.

neuroscience↗

bioRxiv2021

Dynamic predictive coding across the left fronto-temporal language hierarchy: Evidence from MEG, EEG and fMRI

We used MEG and ERPs to track the time-course and localization of evoked activity produced by expected, unexpected plausible and implausible words during incremental language comprehension. We interpret the full pattern of evoked responses and functional connectivity within a hierarchical predictive coding framework in which evoked activity reflects residual information (error) that is passed up and down the cortical hierarchy. Between 300-500ms, the three conditions produced progressively larger responses within left temporal cortex (lexico-semantic prediction error), while implausible inputs produced a selectively enhanced response within left inferior frontal cortex (event-level prediction error). Between 600-1000ms, unexpected plausible words activated left inferior frontal and middle temporal cortices (feedback activity that induced top-down shifts of event-level and lexico-semantic representations), while highly implausible inputs activated posterior fusiform and medial temporal cortices, supporting orthographic reanalysis and new learning. Therefore, predictive coding may provide a unifying theory that links language comprehension to other domains of cognition.

neuroscience↗

bioRxiv2019

Neural evidence for the prediction of animacy features during language comprehension: Evidence from MEG and EEG Representational Similarity Analysis

It has been proposed that people can generate probabilistic predictions at multiple levels of representation during language comprehension. We used Magnetoencephalography (MEG) and Electroencephalography (EEG), in combination with Representational Similarity Analysis (RSA), to seek neural evidence for the prediction of animacy features. In two studies, MEG and EEG activity was measured as human participants (both sexes) read three-sentence scenarios. Verbs in the final sentences constrained for either animate or inanimate semantic features of upcoming nouns, and the broader discourse context constrained for either a specific noun or for multiple nouns belonging to the same animacy category. We quantified the similarity between spatial patterns of brain activity following the verbs until just before the presentation of the nouns. The MEG and EEG datasets revealed converging evidence that the similarity between spatial patterns of neural activity following animate constraining verbs was greater than following inanimate constraining verbs. This effect could not be explained by lexical-semantic processing of the verbs themselves. We therefore suggest that it reflected the inherent difference in the semantic similarity structure of the predicted animate and inanimate nouns. Moreover, the effect was present regardless of whether a specific word could be predicted, providing strong evidence for the prediction of coarse-grained semantic features that goes beyond the prediction of individual words. Significance statementLanguage inputs unfold very quickly during real-time communication. By predicting ahead we can give our brains a "head-start", so that language comprehension is faster and more efficient. While most contexts do not constrain strongly for a specific word, they do allow us to predict some upcoming information. For example, following the context, "they cautioned the...", we can predict that the next word will be animate rather than inanimate (we can caution a person, but not an object). Here we used EEG and MEG techniques to show that the brain is able to use these contextual constraints to predict the animacy of upcoming words during sentence comprehension, and that these predictions are associated with specific spatial patterns of neural activity.

neuroscience↗

bioRxiv2019

M. tuberculosis microvariation is common and is associated with transmission: analysis of three years prospective universal sequencing in England

BackgroundThe prevalence, association with disease status, and public health impact of infection with mixtures of M. tuberculosis strains is unclear, in part due to limitations of existing methods for detecting mixed infections.\n\nMethodsWe developed an algorithm to identify mixtures of M. tuberculosis strains using next generation sequencing data, assessing performance using simulated sequences. We identified mixed M. tuberculosis strains when there was at least one mixed nucleotide position, and where both the mixtures components were present in similar isolates from other individuals. We determined risk factors for mixed infection among isolations of M. tuberculosis in England using logistic regression. We used survival analyses to assess the association between mixed infection and putative transmission.\n\nFindings6,560 isolations of TB were successfully sequenced in England 2016-2018. Of 3,691 (56%) specimens for which similar sequences had been isolated from at least two other individuals, 341 (9.2%) were mixed. Infection with lineages other than Lineage 4 were associated with mixed infection. Among the 1,823 individuals with pulmonary infection with Lineage 4 M. tuberculosis, mixed infection was associated with significantly increased risk of subsequent isolation of closely related organisms from a different individual (HR 1.43, 95% CI 1.05,1.94), indicative of transmission.\n\nInterpretationMixtures of transmissible strains occur in at least 5% of tuberculosis infections in England; when present in pulmonary disease, such mixtures are associated with an increased risk of tuberculosis transmission.\n\nFundingPublic Health England; NIHR Health Protection Research Unit Oxford; European Union.\n\nResearch in Context\n\nEvidence Before This StudyWe searched Pubmed using the search terms tuberculosis and mixed or mixture for English Language articles published up to 1 April 2019. Studies, most performed without the benefit of genomic sequencing, report mixed TB infection from a range of medium and high prevalence areas and show it to be associated with delayed treatment response. Modelling suggests detection and treatment of mixed TB infection is an important goal for TB eradication campaigns. Although routine DNA sequencing of M. tuberculosis isolates is becoming widespread, efficient methods for detecting mixed infection from such data are underdeveloped, and the true prevalence of mixed infection and its association with transmission is unclear.\n\nAdded Value of This StudyThis study investigated a large series of TB isolations obtained as part of a routine Mycobacterial sequencing program by two reference laboratories, in a low incidence area, England. We developed an efficient generalisable approach to identify transmitted mixed M. tuberculosis infection; our approach is capable of sensitive and specific detection of a single mixed nucleotide position. We identified mixed infection of similar strains ( microvariation) in about 9.2% of the M. tuberculosis samples which we were able to assess, and found evidence of increased transmission from individuals with mixed infection.\n\nImplications of All the Available EvidenceTB microvariation is a risk factor for TB transmission, even in the low incidence area studied. Although an efficient and highly specific technique identifying microvariation exists, it relies on comparison with similar sequences isolated from other patients. Sharing of sequence data from the many TB sequencing programs being deployed globally will increase the sensitivity of microvariation detection, and may assist targeted public health interventions.

microbiology↗