The Antagonistic Gene Paralogs Upf3a and Upf3b Govern Nonsense-Mediated RNA Decay.

Gene duplication is a major evolutionary force driving adaptation and speciation, as it allows for the acquisition of new functions and can augment or diversify existing functions. Here, we report a gene duplication event that yielded another outcome--the generation of antagonistic functions. One product of this duplication event--UPF3B--is critical for the nonsense-mediated RNA decay (NMD) pathway, while its autosomal counterpart--UPF3A--encodes an enigmatic protein previously shown to have trace NMD activity. Using loss-of-function approaches in vitro and in vivo, we discovered that UPF3A acts primarily as a potent NMD inhibitor that stabilizes hundreds of transcripts. Evidence suggests that UPF3A acquired repressor activity through simple impairment of a critical domain, a rapid mechanism that may have been widely used in evolution. Mice conditionally lacking UPF3A exhibit "hyper" NMD and display defects in embryogenesis and gametogenesis. Our results support a model in which UPF3A serves as a molecular rheostat that directs developmental events.

Author Correction: A new antibiotic selectively kills Gram-negative pathogens.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Unveiling the microbial realm with VEBA 2.0: a modular bioinformatics suite for end-to-end genome-resolved prokaryotic, (micro)eukaryotic and viral multi-omics from either short- or long-read sequencing.

The microbiome is a complex community of microorganisms, encompassing prokaryotic (bacterial and archaeal), eukaryotic, and viral entities. This microbial ensemble plays a pivotal role in influencing the health and productivity of diverse ecosystems while shaping the web of life. However, many software suites developed to study microbiomes analyze only the prokaryotic community and provide limited to no support for viruses and microeukaryotes. Previously, we introduced the Viral Eukaryotic Bacterial Archaeal (VEBA) open-source software suite to address this critical gap in microbiome research by extending genome-resolved analysis beyond prokaryotes to encompass the understudied realms of eukaryotes and viruses. Here we present VEBA 2.0 with key updates including a comprehensive clustered microeukaryotic protein database, rapid genome/protein-level clustering, bioprospecting, non-coding/organelle gene modeling, genome-resolved taxonomic/pathway profiling, long-read support, and containerization. We demonstrate VEBA's versatile application through the analysis of diverse case studies including marine water, Siberian permafrost, and white-tailed deer lung tissues with the latter showcasing how to identify integrated viruses. VEBA represents a crucial advancement in microbiome research, offering a powerful and accessible software suite that bridges the gap between genomics and biotechnological solutions.

A new antibiotic selectively kills Gram-negative pathogens.

The current need for novel antibiotics is especially acute for drug-resistant Gram-negative pathogens1,2. These microorganisms have a highly restrictive permeability barrier, which limits the penetration of most compounds3,4. As a result, the last class of antibiotics that acted against Gram-negative bacteria was developed in the 1960s2. We reason that useful compounds can be found in bacteria that share similar requirements for antibiotics with humans, and focus on Photorhabdus symbionts of entomopathogenic nematode microbiomes. Here we report a new antibiotic that we name darobactin, which was obtained using a screen of Photorhabdus isolates. Darobactin is coded by a silent operon with little production under laboratory conditions, and is ribosomally synthesized. Darobactin has an unusual structure with two fused rings that form post-translationally. The compound is active against important Gram-negative pathogens both in vitro and in animal models of infection. Mutants that are resistant to darobactin map to BamA, an essential chaperone and translocator that folds outer membrane proteins. Our study suggests that bacterial symbionts of animals contain antibiotics that are particularly suitable for development into therapeutics.

Genus-Wide Transcriptional Landscapes Reveal Correlated Gene Networks Underlying Microevolutionary Divergence in Diatoms.

Marine microbes like diatoms make up the base of marine food webs and drive global nutrient cycles. Despite their key roles in ecology, biogeochemistry, and biotechnology, we have limited empirical data on how forces other than adaptation may drive diatom diversification, especially in the absence of environmental change. One key feature of diatom populations is frequent extreme reductions in population size, which can occur both in situ and ex situ as part of bloom-and-bust growth dynamics. This can drive divergence between closely related lineages, even in the absence of environmental differences. Here, we combine experimental evolution and transcriptome landscapes (t-scapes) to reveal repeated evolutionary divergence within several species of diatoms in a constant environment. We show that most of the transcriptional divergence can be captured on a reduced set of axes, and that repeatable evolution can occur along a single major axis of variation defined by core ortholog expression comprising common metabolic pathways. Previous work has associated specific transcriptional changes in gene networks with environmental factors. Here, we find that these same gene networks diverge in the absence of environmental change, suggesting these pathways may be central in generating phenotypic diversity as a result of both selective and random evolutionary forces. If this is the case, these genes and the functions they encode may represent universal axes of variation. Such axes that capture suites of interacting transcriptional changes during diversification improve our understanding of both global patterns in local adaptation and microdiversity, as well as evolutionary forces shaping algal cultivation.

VEBA: a modular end-to-end suite for in silico recovery, clustering, and analysis of prokaryotic, microeukaryotic, and viral genomes from metagenomes.

BACKGROUND: With the advent of metagenomics, the importance of microorganisms and how their interactions are relevant to ecosystem resilience, sustainability, and human health has become evident. Cataloging and preserving biodiversity is paramount not only for the Earth's natural systems but also for discovering solutions to challenges that we face as a growing civilization. Metagenomics pertains to the in silico study of all microorganisms within an ecological community in situ, however, many software suites recover only prokaryotes and have limited to no support for viruses and eukaryotes. RESULTS: In this study, we introduce the Viral Eukaryotic Bacterial Archaeal (VEBA) open-source software suite developed to recover genomes from all domains. To our knowledge, VEBA is the first end-to-end metagenomics suite that can directly recover, quality assess, and classify prokaryotic, eukaryotic, and viral genomes from metagenomes. VEBA implements a novel iterative binning procedure and hybrid sample-specific/multi-sample framework that yields more genomes than any existing methodology alone. VEBA includes a consensus microeukaryotic database containing proteins from existing databases to optimize microeukaryotic gene modeling and taxonomic classification. VEBA also provides a unique clustering-based dereplication strategy allowing for sample-specific genomes and genes to be directly compared across non-overlapping biological samples. Finally, VEBA is the only pipeline that automates the detection of candidate phyla radiation bacteria and implements the appropriate genome quality assessments. VEBA's capabilities are demonstrated by reanalyzing 3 existing public datasets which recovered a total of 948 MAGs (458 prokaryotic, 8 eukaryotic, and 482 viral) including several uncharacterized organisms and organisms with no public genome representatives. CONCLUSIONS: The VEBA software suite allows for the in silico recovery of microorganisms from all domains of life by integrating cutting edge algorithms in novel ways. VEBA fully integrates both end-to-end and task-specific metagenomic analysis in a modular architecture that minimizes dependencies and maximizes productivity. The contributions of VEBA to the metagenomics community includes seamless end-to-end metagenomics analysis but also provides users with the flexibility to perform specific analytical tasks. VEBA allows for the automation of several metagenomics steps and shows that new information can be recovered from existing datasets.

A synonymous UPF3B variant causing a speech disorder implicates NMD as a regulator of neurodevelopmental disorder gene networks.

Loss-of-function mutations of the X-chromosome gene UPF3B cause male neurodevelopmental disorders (NDDs) via largely unknown mechanisms. We investigated initially by interrogating a novel synonymous UPF3B variant in a male with absent speech. In silico and functional studies using cell lines derived from this individual show altered UPF3B RNA splicing. The resulting mRNA species encodes a frame-shifted protein with a premature termination codon (PTC) predicted to elicit degradation via nonsense-mediated mRNA decay (NMD). UPF3B mRNA was reduced in the cell line, and no UPF3B protein was produced, confirming a loss-of-function allele. UPF3B is itself involved in the NMD mechanism which degrades both PTC-bearing mutant transcripts and also many physiological transcripts. RNAseq analysis showed that ~1.6% of mRNAs exhibited altered expression. These mRNA changes overlapped and correlated with those we identified in additional cell lines obtained from individuals harbouring other UPF3B mutations, permitting us to interrogate pathogenic mechanisms of UPF3B-associated NDDs. We identified 102 genes consistently deregulated across all UPF3B mutant cell lines. Of the 51 upregulated genes, 75% contained an NMD-targeting feature, thus identifying high-confidence direct NMD targets. Intriguingly, 22 of the dysregulated genes encoded known NDD genes, suggesting UPF3B-dependent NMD regulates gene networks critical for cognition and behaviour. Indeed, we show that 78.5% of all NDD genes encode a transcript predicted to be targeted by NMD. These data describe the first synonymous UPF3B mutation in a patient with prominent speech and language disabilities and identify plausible mechanisms of pathology downstream of UPF3B mutations involving the deregulation of NDD-gene networks.

Predicting antimicrobial mechanism-of-action from transcriptomes: A generalizable explainable artificial intelligence approach.

To better combat the expansion of antibiotic resistance in pathogens, new compounds, particularly those with novel mechanisms-of-action [MOA], represent a major research priority in biomedical science. However, rediscovery of known antibiotics demonstrates a need for approaches that accurately identify potential novelty with higher throughput and reduced labor. Here we describe an explainable artificial intelligence classification methodology that emphasizes prediction performance and human interpretability by using a Hierarchical Ensemble of Classifiers model optimized with a novel feature selection algorithm called Clairvoyance; collectively referred to as a CoHEC model. We evaluated our methods using whole transcriptome responses from Escherichia coli challenged with 41 known antibiotics and 9 crude extracts while depositing 122 transcriptomes unique to this study. Our CoHEC model can properly predict the primary MOA of previously unobserved compounds in both purified forms and crude extracts at an accuracy above 99%, while also correctly identifying darobactin, a newly discovered antibiotic, as having a novel MOA. In addition, we deploy our methods on a recent E. coli transcriptomics dataset from a different strain and a Mycobacterium smegmatis metabolomics timeseries dataset showcasing exceptionally high performance; improving upon the performance metrics of the original publications. We not only provide insight into the biological interpretation of our model but also that the concept of MOA is a non-discrete heuristic with diverse effects for different compounds within the same MOA, suggesting substantial antibiotic diversity awaiting discovery within existing MOA.

Mechanism-of-Action Classification of Antibiotics by Global Transcriptome Profiling.

Antimicrobial resistance (AMR) is an ever-growing public health problem worldwide. The low rate of antibiotic discovery coupled with the rapid spread of drug-resistant bacterial pathogens is causing a global health crisis. To facilitate the drug discovery processes, we present a large-scale study of reference antibiotic challenge bacterial transcriptome profiles, which included 37 antibiotics across 6 mechanisms of actions (MOAs) and provide an economical approach to aid in antimicrobial dereplication in the discovery process. We demonstrate that classical MOAs can be sorted based upon the magnitude of gene expression profiles despite some overlap in the secondary effects of antibiotic exposures across MOAs. Additionally, using gene subsets, we were able to subdivide broad MOA classes into subMOAs. Furthermore, we provide a biomarker gene set that can be used to classify most antimicrobial challenges according to their canonical MOA. We also demonstrate the ability of this rapid MOA diagnostic tool to predict and classify the expression profiles of pure compounds and crude extracts to their expression profile-associated MOA class.

Applications of weighted association networks applied to compositional data in biology.

Next-generation sequencing technologies have generated, and continue to produce, an increasingly large corpus of biological data. The data generated are inherently compositional as they convey only relative information dependent upon the capacity of the instrument, experimental design and technical bias. There is considerable information to be gained through network analysis by studying the interactions between components within a system. Network theory methods using compositional data are powerful approaches for quantifying relationships between biological components and their relevance to phenotype, environmental conditions or other external variables. However, many of the statistical assumptions used for network analysis are not designed for compositional data and can bias downstream results. In this mini-review, we illustrate the utility of network theory in biological systems and investigate modern techniques while introducing researchers to frameworks for implementation. We overview (1) compositional data analysis, (2) data transformations and (3) network theory along with insight on a battery of network types including static-, temporal-, sample-specific- and differential-networks. The intention of this mini-review is not to provide a comprehensive overview of network methods, rather to introduce microbiology researchers to (semi)-unsupervised data-driven approaches for inferring latent structures that may give insight into biological phenomena or abstract mechanics of complex systems.

Diatom centromeres suggest a mechanism for nuclear DNA acquisition.

Centromeres are essential for cell division and growth in all eukaryotes, and knowledge of their sequence and structure guides the development of artificial chromosomes for functional cellular biology studies. Centromeric proteins are conserved among eukaryotes; however, centromeric DNA sequences are highly variable. We combined forward and reverse genetic approaches with chromatin immunoprecipitation to identify centromeres of the model diatom Phaeodactylum tricornutum We observed 25 unique centromere sequences typically occurring once per chromosome, a finding that helps to resolve nuclear genome organization and indicates monocentric regional centromeres. Diatom centromere sequences contain low-GC content regions but lack repeats or other conserved sequence features. Native and foreign sequences with similar GC content to P. tricornutum centromeres can maintain episomes and recruit the diatom centromeric histone protein CENH3, suggesting nonnative sequences can also function as diatom centromeres. Thus, simple sequence requirements may enable DNA from foreign sources to persist in the nucleus as extrachromosomal episomes, revealing a potential mechanism for organellar and foreign DNA acquisition.

The Airplane Cabin Microbiome.

Serving over three billion passengers annually, air travel serves as a conduit for infectious disease spread, including emerging infections and pandemics. Over two dozen cases of in-flight transmissions have been documented. To understand these risks, a characterization of the airplane cabin microbiome is necessary. Our study team collected 229 environmental samples on ten transcontinental US flights with subsequent 16S rRNA sequencing. We found that bacterial communities were largely derived from human skin and oral commensals, as well as environmental generalist bacteria. We identified clear signatures for air versus touch surface microbiome, but not for individual types of touch surfaces. We also found large flight-to-flight beta diversity variations with no distinguishing signatures of individual flights, rather a high between-flight diversity for all touch surfaces and particularly for air samples. There was no systematic pattern of microbial community change from pre- to post-flight. Our findings are similar to those of other recent studies of the microbiome of built environments. In summary, the airplane cabin microbiome has immense airplane to airplane variability. The vast majority of airplane-associated microbes are human commensals or non-pathogenic, and the results provide a baseline for non-crisis-level airplane microbiome conditions.

Identification of novel post-transcriptional features in olfactory receptor family mRNAs.

Olfactory receptor (Olfr) genes comprise the largest gene family in mice. Despite their importance in olfaction, how most Olfr mRNAs are regulated remains unexplored. Using RNA-seq analysis coupled with analysis of pre-existing databases, we found that Olfr mRNAs have several atypical features suggesting that post-transcriptional regulation impacts their expression. First, Olfr mRNAs, as a group, have dramatically higher average AU-content and lower predicted secondary structure than do control mRNAs. Second, Olfr mRNAs have a higher density of AU-rich elements (AREs) in their 3'UTR and upstream open reading frames (uORFs) in their 5 UTR than do control mRNAs. Third, Olfr mRNAs have shorter 3' UTR regions and with fewer predicted miRNA-binding sites. All of these novel properties correlated with higher Olfr expression. We also identified striking differences in the post-transcriptional features of the mRNAs from the two major classes of Olfr genes, a finding consistent with their independent evolutionary origin. Together, our results suggest that the Olfr gene family has encountered unusual selective forces in neural cells that have driven them to acquire unique post-transcriptional regulatory features. In support of this possibility, we found that while Olfr mRNAs are degraded by a deadenylation-dependent mechanism, they are largely protected from this decay in neural lineage cells.

Unveiling the Microbial Realm with VEBA 2.0: A modular bioinformatics suite for end-to-end genome-resolved prokaryotic, (micro)eukaryotic, and viral multi-omics from either short- or long-read sequencing.

The microbiome is a complex community of microorganisms, encompassing prokaryotic (bacterial and archaeal), eukaryotic, and viral entities. This microbial ensemble plays a pivotal role in influencing the health and productivity of diverse ecosystems while shaping the web of life. However, many software suites developed to study microbiomes analyze only the prokaryotic community and provide limited to no support for viruses and microeukaryotes. Previously, we introduced the Viral Eukaryotic Bacterial Archaeal (VEBA) open-source software suite to address this critical gap in microbiome research by extending genome-resolved analysis beyond prokaryotes to encompass the understudied realms of eukaryotes and viruses. Here we present VEBA 2.0 with key updates including a comprehensive clustered microeukaryotic protein database, rapid genome/protein-level clustering, bioprospecting, non-coding/organelle gene modeling, genome-resolved taxonomic/pathway profiling, long-read support, and containerization. We demonstrate VEBA's versatile application through the analysis of diverse case studies including marine water, Siberian permafrost, and white-tailed deer lung tissues with the latter showcasing how to identify integrated viruses. VEBA represents a crucial advancement in microbiome research, offering a powerful and accessible platform that bridges the gap between genomics and biotechnological solutions.

Efficient formation of single-copy human artificial chromosomes.

Large DNA assembly methodologies underlie milestone achievements in synthetic prokaryotic and budding yeast chromosomes. While budding yeast control chromosome inheritance through ~125-base pair DNA sequence-defined centromeres, mammals and many other eukaryotes use large, epigenetic centromeres. Harnessing centromere epigenetics permits human artificial chromosome (HAC) formation but is not sufficient to avoid rampant multimerization of the initial DNA molecule upon introduction to cells. We describe an approach that efficiently forms single-copy HACs. It employs a ~750-kilobase construct that is sufficiently large to house the distinct chromatin types present at the inner and outer centromere, obviating the need to multimerize. Delivery to mammalian cells is streamlined by employing yeast spheroplast fusion. These developments permit faithful chromosome engineering in the context of metazoan cells.

Host-microbiome associations in saliva predict COVID-19 severity.

Established evidence indicates that oral microbiota plays a crucial role in modulating host immune responses to viral infection. Following severe acute respiratory syndrome coronavirus 2, there are coordinated microbiome and inflammatory responses within the mucosal and systemic compartments that are unknown. The specific roles the oral microbiota and inflammatory cytokines play in the pathogenesis of coronavirus disease 2019 (COVID-19) are yet to be explored. Here, we evaluated the relationships between the salivary microbiome and host parameters in different groups of COVID-19 severity based on their oxygen requirement. Saliva and blood samples (n = 80) were collected from COVID-19 and from noninfected individuals. We characterized the oral microbiomes using 16S ribosomal RNA gene sequencing and evaluated saliva and serum cytokines and chemokines using multiplex analysis. Alpha diversity of the salivary microbial community was negatively associated with COVID-19 severity, while diversity increased with health. Integrated cytokine evaluations of saliva and serum showed that the oral host response was distinct from the systemic response. The hierarchical classification of COVID-19 status and respiratory severity using multiple modalities separately (i.e. microbiome, salivary cytokines, and systemic cytokines) and simultaneously (i.e. multimodal perturbation analyses) revealed that the microbiome perturbation analysis was the most informative for predicting COVID-19 status and severity, followed by the multimodal. Our findings suggest that oral microbiome and salivary cytokines may be predictive of COVID-19 status and severity, whereas atypical local mucosal immune suppression and systemic hyperinflammation provide new cues to understand the pathogenesis in immunologically compromised populations.

Mixotrophic growth of a ubiquitous marine diatom.

Diatoms are major players in the global carbon cycle, and their metabolism is affected by ocean conditions. Understanding the impact of changing inorganic nutrients in the oceans on diatoms is crucial, given the changes in global carbon dioxide levels. Here, we present a genome-scale metabolic model (iMK1961) for Cylindrotheca closterium, an in silico resource to understand uncharacterized metabolic functions in this ubiquitous diatom. iMK1961 represents the largest diatom metabolic model to date, comprising 1961 open reading frames and 6718 reactions. With iMK1961, we identified the metabolic response signature to cope with drastic changes in growth conditions. Comparing model predictions with Tara Oceans transcriptomics data unraveled C. closterium's metabolism in situ. Unexpectedly, the diatom only grows photoautotrophically in 21% of the sunlit ocean samples, while the majority of the samples indicate a mixotrophic (71%) or, in some cases, even a heterotrophic (8%) lifestyle in the light. Our findings highlight C. closterium's metabolic flexibility and its potential role in global carbon cycling.

Host-Microbiome Associations in Saliva Predict COVID-19 Severity.

Established evidence indicates that oral microbiota plays a crucial role in modulating host immune responses to viral infection. Following Severe Acute Respiratory Syndrome Coronavirus 2 - SARS-CoV-2 - there are coordinated microbiome and inflammatory responses within the mucosal and systemic compartments that are unknown. The specific roles that the oral microbiota and inflammatory cytokines play in the pathogenesis of COVID-19 are yet to be explored. We evaluated the relationships between the salivary microbiome and host parameters in different groups of COVID-19 severity based on their Oxygen requirement. Saliva and blood samples (n = 80) were collected from COVID-19 and from non-infected individuals. We characterized the oral microbiomes using 16S ribosomal RNA gene sequencing and evaluated saliva and serum cytokines using Luminex multiplex analysis. Alpha diversity of the salivary microbial community was negatively associated with COVID-19 severity. Integrated cytokine evaluations of saliva and serum showed that the oral host response was distinct from the systemic response. The hierarchical classification of COVID-19 status and respiratory severity using multiple modalities separately (i.e., microbiome, salivary cytokines, and systemic cytokines) and simultaneously (i.e., multi-modal perturbation analyses) revealed that the microbiome perturbation analysis was the most informative for predicting COVID-19 status and severity, followed by the multi-modal. Our findings suggest that oral microbiome and salivary cytokines may be predictive of COVID-19 status and severity, whereas atypical local mucosal immune suppression and systemic hyperinflammation provide new cues to understand the pathogenesis in immunologically naïve populations.

Genotypic diversity and unrecognized antifungal resistance among populations of Candida glabrata from positive blood cultures.

The longstanding paradigm is that most bloodstream infections (BSIs) are caused by a single organism. We performed whole genome sequencing of five-to-ten strains from blood culture (BC) bottles in each of ten patients with Candida glabrata BSI. We demonstrated that BCs contained mixed populations of clonal but genetically diverse strains. Genetically distinct strains from two patients exhibited phenotypes that were potentially important during BSIs, including differences in susceptibility to antifungal agents and phagocytosis. In both patients, the clinical microbiology lab recovered a fluconazole-susceptible index strain, but we identified mixed fluconazole-susceptible and â€"resistant populations. Diversity in drug susceptibility was likely clinically relevant, as fluconazole-resistant strains were subsequently recovered by the clinical laboratory during persistent or relapsing infections. In one patient, unrecognized respiration-deficient small colony variants were fluconazole-resistant and significantly attenuated for virulence during murine candidiasis. Our data suggest a new population-based paradigm of C. glabrata genotypic and phenotypic diversity during BSIs.

Genotypic diversity and unrecognized antifungal resistance among populations of Candida glabrata from positive blood cultures.

The longstanding model is that most bloodstream infections (BSIs) are caused by a single organism. We perform whole genome sequencing of five-to-ten strains from blood culture (BC) bottles in each of ten patients with Candida glabrata BSI. We demonstrate that BCs contain mixed populations of clonal but genetically diverse strains. Genetically distinct strains from two patients exhibit phenotypes that are potentially important during BSIs, including differences in susceptibility to antifungal agents and phagocytosis. In both patients, the clinical microbiology lab recovered a fluconazole-susceptible index strain, but we identify mixed fluconazole-susceptible and -resistant populations. Diversity in drug susceptibility is likely clinically relevant, as fluconazole-resistant strains were subsequently recovered by the clinical laboratory during persistent or relapsing infections. In one patient, unrecognized respiration-deficient small colony variants are fluconazole-resistant and significantly attenuated for virulence during murine candidiasis. Our data suggest a population-based model of C. glabrata genotypic and phenotypic diversity during BSIs.