Phenotype Of Organism Research Articles

RNA language models predict mutations that improve RNA function.

Structured RNA lies at the heart of many central biological processes, from gene expression to catalysis. While advances in deep learning enable the prediction of accurate protein structural models, RNA structure prediction is not possible at present due to a lack of abundant high-quality reference data 1 . Furthermore, available sequence data are generally not associated with organismal phenotypes that could inform RNA function 2-4 . We created GARNET (Gtdb Acquired RNa with Environmental Temperatures), a new database for RNA structural and functional analysis anchored to the Genome Taxonomy Database (GTDB) 5 . GARNET links RNA sequences derived from GTDB genomes to experimental and predicted optimal growth temperatures of GTDB reference organisms. This enables construction of deep and diverse RNA sequence alignments to be used for machine learning. Using GARNET, we define the minimal requirements for a sequence- and structure-aware RNA generative model. We also develop a GPT-like language model for RNA in which overlapping triplet tokenization provides optimal encoding. Leveraging hyperthermophilic RNAs in GARNET and these RNA generative models, we identified mutations in ribosomal RNA that confer increased thermostability to the Escherichia coli ribosome. The GTDB- derived data and deep learning models presented here provide a foundation for understanding the connections between RNA sequence, structure, and function.

CIEVaD: A Lightweight Workflow Collection for the Rapid and On-Demand Deployment of End-to-End Testing for Genomic Variant Detection.

The identification of genomic variants has become a routine task in the age of genome sequencing. In particular, small genomic variants of a single or few nucleotides are routinely investigated for their impact on an organism's phenotype. Hence, the precise and robust detection of the variants' exact genomic locations and changes in nucleotide composition is vital in many biological applications. Although a plethora of methods exist for the many key steps of variant detection, thoroughly testing the detection process and evaluating its results is still a cumbersome procedure. In this work, we present a collection of easy-to-apply and highly modifiable workflows to facilitate the generation of synthetic test data, as well as to evaluate the accordance of a user-provided set of variants with the test data. The workflows are implemented in Nextflow and are open-source and freely available on Github under the GPL-3.0 license.

An autoregulatory poison exon in Smndc1 is conserved across kingdoms and influences organism growth.

Many of the most highly conserved elements in the human genome are "poison exons," alternatively spliced exons that contain premature termination codons and permit post-transcriptional regulation of mRNA abundance through induction of nonsense-mediated mRNA decay (NMD). Poison exons are widely assumed to be highly conserved due to their presumed importance for organismal fitness, but this functional importance has never been tested in the context of a whole organism. Here, we report that a poison exon in Smndc1 is conserved across mammals and plants and plays a molecular autoregulatory function in both kingdoms. We generated mouse and A. thaliana models lacking this poison exon to find its loss leads to deregulation of SMNDC1 protein levels, pervasive alterations in mRNA processing, and organismal size restriction. Together, these models demonstrate the importance of poison exons for both molecular and organismal phenotypes that likely explain their extraordinary conservation.

Visualizing scRNA-Seq Data at Population Scale with GloScope.

Increasingly, scRNA-Seq studies explore cell populations across different samples and the effect of sample heterogeneity on organism's phenotype. However, relatively few bioinformatic methods have been developed which adequately address the variation between samples for such population-level analyses. We propose a framework for representing the entire single-cell profile of a sample, which we call a GloScope representation. We implement GloScope on scRNA-Seq datasets from study designs ranging from 12 to over 300 samples and demonstrate how GloScope allows researchers to perform essential bioinformatic tasks at the sample-level, in particular visualization and quality control assessment.

NMR-based plant metabolomics protocols: a step-by-step guide

Metabolomics is one of the “omics” sciences that can reveal the metabolic phenotype of organisms. This capability makes it a valuable tool for plant investigation, as plants present a vast chemical diversity. From the analytical point of view, two main techniques are frequently used in metabolomics and are often complementary: Mass spectrometry (MS) and Nuclear Magnetic Resonance (NMR) spectroscopy. Here, we describe NMR and its applications in plant metabolomics. We start by contextualizing the research field to then explore study design, sample collection, sample preparation, NMR data acquisition, and data analysis, showing the key features for achieving quality and relevant results. Within these topics, the most common databases used for plant metabolites identification and assignments are listed, as these help to shorten the laborious task of metabolomics investigation of natural products. Concerning NMR parameters, we discuss the key pulse sequences, recommend acquisition parameters, and examine the data each sequence can provide. Similarly, we delve into data analysis, highlighting the most commonly used chemometric methods and how to achieve high-quality results. Therefore, this review aims to provide a comprehensive guide for NMR-based metabolomics analysis of plants.

DNA Methylation Profiling in Genetically Selected Clarias magur (Hamilton, 1822) Provides Insights into the Epigenetic Regulation of Growth and Development.

DNA methylation is an epigenetic alteration that impacts gene expression without changing the DNA sequence affecting an organism's phenotype. This study utilized a reduced representation bisulfite sequencing (RRBS) approach to investigate the patterns of DNA methylation in genetically selected Clarias magur stocks. RRBS generated 249.22 million reads, with an average of 490,120 methylation sites detected in various parts of genes, including exons, introns, and intergenic regions. A total of 896 differentially methylated regions (DMRs) were identified; 356 and 540 were detected as hyper-methylated and hypo-methylated regions, respectively. The DMRs and their association with overlapping genes were explored using whole genome data of magur, which revealed 205 genes in exonic, 210 in intronic, and 480 in intergenic regions. The analysis identified the maximum number of genes enriched in biological processes such as RNA biosynthetic process, response to growth factors, nervous system development, neurogenesis, and anatomical structure morphogenesis. Differentially methylated genes (DMGs) such as myrip, mylk3, mafb, egr3, ndnf, meis2a, foxn3, bmp1a, plxna3, fgf6, sipa1l1, mcu, cnot8, trim55b, and myof were associated with growth and development. The selected DMGs were analyzed using real-time PCR, which showed altered mRNA expression levels. This work offers insights into the epigenetic mechanisms governing growth performance regulation in magur stocks. This work provides a valuable resource of epigenetic data that could be integrated into breeding programs to select high-performing individuals.

The Integrative Life history of Maternal Effects.

Context-dependent allocation of resources drives trade-offs among fitness-related traits and other phenotypes to which those traits are linked. In addition, the amount and type of acquired resources can also affect the phenotypes of other organisms through indirect genetic effects, as exemplified by the maternal provisioning of offspring. Despite a large literature on maternal effects, we lack a comprehensive understanding of the extent to which mothers might affect the phenotypes of their offspring, as well as the various mechanisms by which they do so, particularly with regard to many functional traits that are key determinants of survival and reproduction. Our goals in this paper are to review the various approaches to measuring and understanding maternal effects, and to highlight some promising avenues for integration of maternal effects with some other key areas of evolutionary ecology. We focus especially on nutritional geometry; maternal age; and traits proximate to fitness such as whole-organism performance. Finally, we discuss the logistic and practical limits of quantifying these effects in many animal systems, and emphasize the value of integrative approaches in understanding the mechanisms underlying maternal influence on offspring phenotypes.

Early life thermal conditioning alters heat-shock protein expression in response to an adult thermal stressor.

Developmental environmental stressors can have instructive effects on an organism's phenotype. This developmental plasticity can prepare organisms for potentially stressful future environments, circumventing detrimental effects on fitness. However, the physiological mechanisms underlying such adaptive plasticity are understudied, especially in vertebrates. We hypothesized that captive male zebra finches (Taeniopygia castanotis) exposed to a mild heat conditioning during development would acquire a persisting thermotolerance, and exhibit increased heat-shock protein (HSP) levels associated with a decrease in oxidative damage when exposed to a high-intensity stressor in adulthood. To test this, we exposed male finches to a prolonged mild heat conditioning (38°C) or control (22°C) treatment as juveniles. Then in a 2 × 2 factorial manner, these finches were exposed to a high heat stressor (42°C) or control (22°C) treatment as adults. Following the adult treatment, we collected testes and liver tissue and measured HSP70, HSP90, and HSP60 protein levels. In the testes, finches exhibited lower levels of HSP90 and HSP60 when exposed to the high heat stressor in adulthood if they were exposed to the mild heat conditioning as juveniles. In the liver, finches exposed to the high heat stressor in adulthood had reduced HSP90 and HSP60 levels, regardless of whether they were conditioned as juveniles. In some cases, elevated testes HSP60 levels were associated with increased liver oxidative damage and diminishment of a condition-dependent trait, indicating potential stress-induced tradeoffs. Our results indicate that a mild conditioning during development can have persisting effects on HSP expression and acquired thermotolerance.

Thermal plasticity in protective wing pigmentation is modulated by genotype and food availability in an insect model of seasonal polyphenism

Abstract Phenotypic variation in natural populations results from complex interactions between organisms and their changing environments. The environment shapes both phenotypic frequencies (during adaptation) and organismal phenotypes (through phenotypic plasticity). Developmental plasticity, in particular, refers to the phenomenon whereby an organism's phenotype depends on the environmental conditions during development. It can match phenotype to ecological conditions and help organisms to cope with environmental heterogeneity, including differences between alternating seasons. Experimental studies of developmental plasticity often focus on the impact of individual environmental cues and do not take explicit account of genetic variation. In contrast, natural environments are complex, comprising multiple variables with combined effects that are poorly understood and may vary among genotypes. We investigated the effects of multifactorial environments on the development of the seasonally plastic eyespots of Bicyclus anynana butterflies. Eyespot size depends on developmental temperature and is involved in alternative seasonal strategies for predator avoidance. In nature, both temperature and food availability undergo seasonal fluctuations. However, our understanding of how thermal plasticity in eyespot size varies in response to food availability and across genotypes remains limited. To address this, we investigated the combined effects of temperature (T; two levels: 20°C and 27°C) and food availability (N; two levels: control and limited) during development. We examined their impact on wing and eyespot size in adult males and females from multiple genotypes (G; 28 families). We found evidence of thermal and nutritional plasticity and temperature‐by‐nutrition interactions (significant T × N) on the size of eyespots in both sexes. Food limitation resulted in relatively smaller eyespots and tempered the effects of temperature. Additionally, we found differences among families for thermal plasticity (significant G × T effects), but not for nutritional plasticity (non‐significant G × N effects) nor for the combined effects of temperature and food limitation (non‐significant G × T × N effects). Our results reveal the context dependence of thermal plasticity, with the slope of thermal reaction norms varying across genotypes and across nutritional environments. We discuss these results in the light of the ecological significance of pigmentation and the value of considering thermal plasticity in studies of the biological impact of climate change. Read the free Plain Language Summary for this article on the Journal blog.

Organism-specific Trends in Carbapenem-resistant Enterobacterales Infections in a Cohort of Hospitalized Patients, 2012–2022

Background: Carbapenem-resistant Enterobacterales (CRE) infections are an urgent public health threat. An estimated 12,700 CRE (including E. coli, Klebsiella spp., and Enterobacter spp.) infections occurred in the United States in 2020. While the estimated incidence of CRE infections has been relatively stable between 2012 and 2020, organism-specific trends, including those for organisms not typically included in CRE surveillance definitions, have not been described. We estimated the annual rate of carbapenem-resistant Enterobacterales infections, disaggregated by organism, from 2012 to 2022. Methods: Data on inpatient hospitalizations from a dynamic cohort of short-term acute care hospitals reporting microbiology data between 2012 and 2022 were obtained from the PINC AI Database and the BD Insights Research Database. We included patients with clinical isolates of E. coli, Enterobacter spp., Klebsiella spp., Citrobacter spp., Serratia marcescens, Proteus mirabilis, and Morganella spp. and sufficient susceptibility results to identify carbapenem resistance. We limited our analysis to incident isolates, defined as a patient’s first isolate of a given organism and carbapenem resistance phenotype in a 14-day period. We calculated the annual rate of carbapenem-resistant infections per 10,000 hospitalizations for each organism. Results: There were 3,018,792 incident isolates from 55.8 million hospitalizations included in the analysis. Overall, 31,226 incident carbapenem-resistant isolates were identified. The rate of carbapenem-resistant infections varied by organism and over time (Table 1). The rate of carbapenem-resistant Klebsiella spp. infections appeared to decline from 3.94 in 2012 to 2.44 infections per 10,000 hospitalizations in 2022. The rate of carbapenem-resistant Enterobacter spp. infections appeared to increase from 1.05 in 2012 to 1.44 infections per 10,000 hospitalizations in 2022. The rate of carbapenem-resistant E. coli infections also appeared to increase, from 0.61 in 2012 to 0.85 infections per 10,000 hospitalizations in 2022. Rates of carbapenem-resistant Proteus mirabilis, Morganella spp., Citrobacter spp., or Serratia marcescens infections were similar in 2022 compared to 2012. Conclusions: Disaggregating data by organism revealed heterogeneous trends, with apparent increases in rates of carbapenem-resistant Enterobacter spp. and E. coli infections and apparent decreases in rates of carbapenem-resistant Klebsiella spp. infections. Organism-specific CRE analyses may provide additional insight into CRE epidemiology.

TFTF: An R-Based Integrative Tool for Decoding Human Transcription Factor-Target Interactions.

Transcription factors (TFs) are crucial in modulating gene expression and sculpting cellular and organismal phenotypes. The identification of TF-target gene interactions is pivotal for comprehending molecular pathways and disease etiologies but has been hindered by the demanding nature of traditional experimental approaches. This paper introduces a novel web application and package utilizing the R program, which predicts TF-target gene relationships and vice versa. Our application integrates the predictive power of various bioinformatic tools, leveraging their combined strengths to provide robust predictions. It merges databases for enhanced precision, incorporates gene expression correlation for accuracy, and employs pan-tissue correlation analysis for context-specific insights. The application also enables the integration of user data with established resources to analyze TF-target gene networks. Despite its current limitation to human data, it provides a platform to explore gene regulatory mechanisms comprehensively. This integrated, systematic approach offers researchers an invaluable tool for dissecting the complexities of gene regulation, with the potential for future expansions to include a broader range of species.

Replication stress as a driver of cellular senescence and aging.

Replication stress refers to slowing or stalling of replication fork progression during DNA synthesis that disrupts faithful copying of the genome. While long considered a nexus for DNA damage, the role of replication stress in aging is under-appreciated. The consequential role of replication stress in promotion of organismal aging phenotypes is evidenced by an extensive list of hereditary accelerated aging disorders marked by molecular defects in factors that promote replication fork progression and operate uniquely in the replication stress response. Additionally, recent studies have revealed cellular pathways and phenotypes elicited by replication stress that align with designated hallmarks of aging. Here we review recent advances demonstrating the role of replication stress as an ultimate driver of cellular senescence and aging. We discuss clinical implications of the intriguing links between cellular senescence and aging including application of senotherapeutic approaches in the context of replication stress.

Copy number and sequence variation in rDNA of Daphnia pulex from natural populations: insights from whole-genome sequencing.

Ribosomal DNA (rDNA) has a vital role in ribosome biogenesis as it contains the genes that encode ribosomal RNA (rRNA) separated by intergenic spacers (IGSs). The rRNA genes occur in hundreds to tens of thousands of copies per haploid genome in eukaryotes and are generally highly conserved with low variation within species. Due to the repetitive nature and large size of rDNA arrays, detecting intraindividual variation can be difficult. In this study, we use whole-genome sequences of 169 Daphnia pulex individuals from 10 natural populations to measure the copy number and sequence variation in rDNA. This revealed that variation in rDNA copy number between individuals spans an order of magnitude. We further observed a substantial level of sequence variation within individual genomes. As expected, single-nucleotide polymorphisms occurred in regions of lower functional constraint such as the IGS and expansion segments of the rRNA genes. The presence of strong linkage disequilibrium among variants facilitated identification of haplotypes within each population. Although there was evidence of recombination among haplotypes from different populations, it is insufficient to eliminate linkage disequilibrium within populations. Estimating copy number and haplotype diversity within individuals revealed that the level of intraindividual sequence variation is not strongly correlated with copy number. The observed patterns of variation highlight a complex evolutionary history of rDNA in D. pulex. Future research should explore the functional implications of rDNA copy number and sequence variation on organismal phenotypes.

Glucose-Induced Cellular Morphological Changes Across Mammalian Species

All organisms encounter environmental fluctuations and must contend with these changes to maintain homeostasis. Some mammals tolerate a wide range of intracellular conditions while others do not. Altered glucose levels are an example of a physiological challenge that can be encountered by cells. We studied responses of dermal fibroblasts obtained from skin biopsies to altered glucose treatments in culture using a comparative approach. Specifically, we investigated responses of mammals known to tolerate a range of blood glucose levels (fruit-eating geladas and Egyptian rousettes) as well as a hibernator (ground squirrel), which exhibits a marked winter decrease in glucose metabolism. As a comparison, we investigated species that maintain fairly stable blood glucose (human and rat). Fibroblasts were cultured at baseline (8mM glucose), then subjected to hypoglycemic (2.5mM) and hyperglycemic (30mM) treatment over 24h. We assayed glycolytic rate in human and rat fibroblasts using a Seahorse XFp analyzer, and found no significance between oxygen consumption or glycolysis across glucose treatments (all p>0.05). To investigate underlying cell phenotypes, we used Cell Painting, employing fluorescent stains to mark organelles. Hoechst 34580 stained DNA, to quantify number and size of nuclei, offering insights into proliferation. MitoTracker Deep Red visualized mitochondria, providing information about energy generation. Alexa Fluor 488 Concanavalin A conjugate marked endoplasmic reticulum. F-actin was stained with Alexa Fluor 555 Phalloidin conjugate, detecting cytoskeletal organization and overall cell structure. Images were analyzed with CellProfiler software, which extracts ~1400 morphological cell features, including area, compactness, fluorescence intensity, and cell connectivity to quantify subtle changes in phenotype. Each extracted feature will be compared across treatments and within a comparative context. We predict that fibroblasts exposed to hyperglycemia exhibit mitochondrial fragmentation and redistribution (MitoTracker) and increased production of reactive oxygen species (assessed with MitoSOX), which is linked to the opening of mitochondrial permeability transition pores. Cell Profiler analysis will identify structural changes that could indicate negative outcomes, such as mitophagy. Hyperglycemia may also induce endoplasmic stress, increasing lipid droplets and altering shape and size of the endoplasmic reticulum. In contrast, hypoglycemia will cause mitochondrial degradation. In both hyper- and hypoglycemia, proliferation rates are predicted to decrease, resulting in fewer nuclei and reduced cell connectivity. Phenotypic differences across species, evaluated in the context of their organismal phenotype, will help explain the mechanisms of cell function in mammals under environmental stress. Funding by NSF #2022046. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Ageing as a two-phase process: theoretical framework.

Human ageing, along with the ageing of conventional model organisms, is depicted as a continuous and progressive decline of biological capabilities accompanied by an exponentially increasing mortality risk. However, not all organisms experience ageing identically and our understanding of the phenomenon is coloured by human-centric views. Ageing is multifaceted and influences a diverse range of species in varying ways. Some undergo swift declines post-reproduction, while others exhibit insubstantial changes throughout their existence. This vast array renders defining universally applicable "ageing attributes" a daunting task. It is nonetheless essential to recognize that not all ageing features are organism-specific. These common attributes have paved the way for identifying "hallmarks of ageing," processes that are intertwined with age, amplified during accelerated ageing, and manipulations of which can potentially modulate or even reverse the ageing process. Yet, a glaring observation is that individuals within a single population age at varying rates. To address this, demographers have coined the term 'frailty'. Concurrently, scientific advancements have ushered in the era of molecular clocks. These innovations enable a distinction between an individual's chronological age (time since birth) and biological age (physiological status and mortality risk). In 2011, the "Smurf" phenotype was unveiled in Drosophila, delineating an age-linked escalation in intestinal permeability that presages imminent mortality. It not only acts as a predictor of natural death but identifies individuals exhibiting traits normally described as age-related. Subsequent studies have revealed the phenotype in organisms like nematodes, zebrafish, and mice, invariably acting as a death predictor. Collectively, these findings have steered our conception of ageing towards a framework where ageing is not linear and continuous but marked by two distinct, necessary phases, discernible in vivo, courtesy of the Smurf phenotype. This framework includes a mathematical enunciation of longevity trends based on three experimentally measurable parameters. It facilitates a fresh perspective on the evolution of ageing as a function. In this article, we aim to delineate and explore the foundational principles of this innovative framework, emphasising its potential to reshape our understanding of ageing, challenge its conventional definitions, and recalibrate our comprehension of its evolutionary trajectory.

Pleiotropy, epistasis and the genetic architecture of quantitative traits.

Pleiotropy (whereby one genetic polymorphism affects multiple traits) and epistasis (whereby non-linear interactions between genetic polymorphisms affect the same trait) are fundamental aspects of the genetic architecture of quantitative traits. Recent advances in the ability to characterize the effects of polymorphic variants on molecular and organismal phenotypes in human and model organism populations have revealed the prevalence of pleiotropy and unexpected shared molecular genetic bases among quantitative traits, including diseases. By contrast, epistasis is common between polymorphic loci associated with quantitative traits in model organisms, such that alleles at one locus have different effects in different genetic backgrounds, but is rarely observed for human quantitative traits and common diseases. Here, we review the concepts and recent inferences about pleiotropy and epistasis, and discuss factors that contribute to similarities and differences between the genetic architecture of quantitative traits in model organisms and humans.

Regional random mutagenesis driven by multiple sgRNAs and diverse on-target genome editing events to identify functionally important elements in non-coding regions.

Functional regions that regulate biological phenomena are interspersed throughout eukaryotic genomes. The most definitive approach for identifying such regions is to confirm the phenotype of cells or organisms in which specific regions have been mutated or removed from the genome. This approach is invaluable for the functional analysis of genes with a defined functional element, the protein-coding sequence. By contrast, no functional analysis platforms have been established for the study of cis-elements or microRNA cluster regions consisting of multiple microRNAs with functional overlap. Whole-genome mutagenesis approaches, such as via N-ethyl-N-nitrosourea and gene trapping, have greatly contributed to elucidating the function of coding genes. These methods almost never induce deletions of genomic regions or multiple mutations within a narrow region. In other words, cis-elements and microRNA clusters cannot be effectively targeted in such a manner. Herein, we established a novel region-specific random mutagenesis method named CRISPR- and transposase-based regional mutagenesis (CTRL-mutagenesis). We demonstrate that CTRL-mutagenesis randomly induces diverse mutations within target regions in murine embryonic stem cells. Comparative analysis of mutants harbouring subtly different mutations within the same region would facilitate the further study of cis-element and microRNA clusters.

Differences on the modifying inheritance between the species; Fragaria vesca L. and Fragaria ananassa

By modifying variability, let’s mean that a certain feature of the organism undergoes changes and does not maintain uniformity during its appearance (ie during the transfer of information from the genetic structure to the phenotypic one). In this paper, it is shown how the modifying inheritance is presented in the two types of the strawberry plant, in the one that grows in nature, Fragaria vesca L., and in the one cultivated in greenhouses, Fragaria ananassa. The number of leaf teeth, of these species, has served as the analyzed feature, on which our search for this type of inheritance is based. The paper sheds light on the great role of the environment in elaborating the inheritance of organisms by causing modifying changes in their phenotype in function of the status of the environmental factors in which this organism grows and develops. When these phenotypic modifications manage to become heritable, then it is possible to deal with the action of the epigenetic effect in inheritance. For this reason, studies of modifying inheritance in the phenotype of organisms are necessary. From the comparison of the results obtained, in terms of the modification changes observed between these two species, the null Hypothesis (Ho) is rejected; so, the observed changes have a real basis and are not a random finding. Thus it becomes clear to us that the modifying changes in the inheritance of the plant grown in the natural environment (with significant changes in environmental factors) are greater than those of the species grown in strict conditions – in greenhouses (without large variations in environmental factors). Of course, these changes have an impact on the nutritional and taste values of their fruits, as they affect the nutrient processing laboratory, the leaves

Assessing computational predictions of antimicrobial resistance phenotypes from microbial genomes.

The advent of rapid whole-genome sequencing has created new opportunities for computational prediction of antimicrobial resistance (AMR) phenotypes from genomic data. Both rule-based and machine learning (ML) approaches have been explored for this task, but systematic benchmarking is still needed. Here, we evaluated four state-of-the-art ML methods (Kover, PhenotypeSeeker, Seq2Geno2Pheno and Aytan-Aktug), an ML baseline and the rule-based ResFinder by training and testing each of them across 78 species-antibiotic datasets, using a rigorous benchmarking workflow that integrates three evaluation approaches, each paired with three distinct sample splitting methods. Our analysis revealed considerable variation in the performance across techniques and datasets. Whereas ML methods generally excelled for closely related strains, ResFinder excelled for handling divergent genomes. Overall, Kover most frequently ranked top among the ML approaches, followed by PhenotypeSeeker and Seq2Geno2Pheno. AMR phenotypes for antibiotic classes such as macrolides and sulfonamides were predicted with the highest accuracies. The quality of predictions varied substantially across species-antibiotic combinations, particularly for beta-lactams; across species, resistance phenotyping of the beta-lactams compound, aztreonam, amoxicillin/clavulanic acid, cefoxitin, ceftazidime and piperacillin/tazobactam, alongside tetracyclines demonstrated more variable performance than the other benchmarked antibiotics. By organism, Campylobacter jejuni and Enterococcus faecium phenotypes were more robustly predicted than those of Escherichia coli, Staphylococcus aureus, Salmonella enterica, Neisseria gonorrhoeae, Klebsiella pneumoniae, Pseudomonas aeruginosa, Acinetobacter baumannii, Streptococcus pneumoniae and Mycobacterium tuberculosis. In addition, our study provides software recommendations for each species-antibiotic combination. It furthermore highlights the need for optimization for robust clinical applications, particularly for strains that diverge substantially from those used for training.

Easy-Amanida: An R Shiny application for the meta-analysis of aggregate results in clinical metabolomics using Amanida and Webchem.

Meta-analysis is a useful tool in clinical research, as it combines the results of multiple clinical studies to improve precision when answering a particular scientific question. While there has been a substantial increase in publications using meta-analysis in various clinical research topics, the number of published meta-analyses in metabolomics is significantly lower compared to other omics disciplines. Metabolomics is the study of small chemical compounds in living organisms, which provides important insights into an organism's phenotype. However, the wide variety of compounds and the different experimental methods used in metabolomics make it challenging to perform a thorough meta-analysis. Additionally, there is a lack of consensus on reporting statistical estimates, and the high number of compound naming synonyms further complicates the process. Easy-Amanida is a new tool that combines two R packages, "amanida" and "webchem", to enable meta-analysis of aggregate statistical data, like p-value and fold-change, while ensuring the compounds naming harmonization. The Easy-Amanida app is implemented in Shiny, an R package add-on for interactive web apps, and provides a workflow to optimize the naming combination. This article describes all the steps to perform the meta-analysis using Easy-Amanida, including an illustrative example for interpreting the results. The use of aggregate statistics metrics extends the use of Easy-Amanida beyond the metabolomics field.