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bioRxiv2023

Transglobal spread of an ecologically significant sea urchin parasite

Mass mortality of the dominant coral reef herbivore Diadema antillarum in the Caribbean in the early 1980s led to a persistent phase shift from coral-to algal-dominated reefs. In 2022, a scuticociliate most closely related to Philaster apodigitiformis caused further mass mortality of D. antillarum across the Caribbean, leading to >95% mortality at affected sites. Mortality was also reported in the related species Diadema setosum in the Mediterranean in 2022, where urchins experienced gross signs compatible with scuticociliatosis. However, the causative agent of the Mediterranean outbreak has not yet been determined. In April 2023, mass mortality of D. setosum occurred along the Sultanate of Omans coastline. Urchins displayed signs compatible with scuticociliatosis including abnormal behavior, drooping and loss of spines, followed by tissue necrosis and death. Here we report the detection of an 18S rRNA gene sequence in abnormal urchins from Muscat, Oman that is identical to the Philaster strain responsible for D. antillarum mass mortality in the Caribbean. We also show that scuticociliatosis signs can be elicited in D. setosum by experimental challenge with the cultivated Philaster strain associated with Caribbean scuticociliatosis. These results demonstrate the Philaster sp. associated with D. antillarum mass mortality has rapidly spread to geographically distant coral reefs, compelling global-scale awareness and monitoring for this devastating condition through field surveys, microscopy, and molecular microbiological approaches, and prompting investigation of long-range transmission mechanisms.

ecology↗

bioRxiv2023

ProkBERT Family: Genomic Language Models for Microbiome Applications

Machine learning offers transformative capabilities in microbiology and microbiome analysis, deciphering intricate microbial interactions, predicting functionalities, and unveiling novel patterns in vast datasets. This enriches our comprehension of microbial ecosystems and their influence on health and disease. However, the integration of machine learning in these fields contends with issues like the scarcity of labeled datasets, the immense volume and complexity of microbial data, and the subtle interactions within microbial communities. Addressing these challenges, we introduce the ProkBERT model family. Built on transfer learning and self-supervised methodologies, ProkBERT models capitalize on the abundant available data, demonstrating adaptability across diverse scenarios. The models learned representations align with established biological understanding, shedding light on phylogenetic relationships. With the novel Local Context-Aware (LCA) tokenization, the ProkBERT family overcomes the context size limitations of traditional transformer models without sacrificing performance or the information rich local context. In bioinformatics tasks like promoter prediction and phage identification, ProkBERT models excel. For promoter predictions, the best performing model achieved an MCC of 0.74 for E. coli and 0.62 in mixed-species contexts. In phage identification, they all consistently outperformed tools like VirSorter2 and DeepVirFinder, registering an MCC of 0.85. Compact yet powerful, the ProkBERT models are efficient, generalizable, and swift. They cater to both supervised and unsupervised tasks, providing an accessible tool for the community. The models are available on GitHub and HuggingFace.

bioinformatics↗

bioRxiv2024

Zooplankton impacts on the persistence of the anthropogenic pollution marker intI1 in lake water

Wastewater treatment plants (WWTP) effluents can release microbiological pollutants, including the intI1 gene (integrases of class 1 integrons), which has been proposed as a target for monitoring anthropogenic pollution in surface waters. This gene has also a strong correlation with antibiotic resistance, making of it an important proxy to evaluate the level of genetic contamination in aquatic environments. he ecological factors that influence the abundance and dynamics of intI1 within natural water bodies are largely unknown. To better understand the fate of class 1 integrons in aquatic systems, we resorted to classical limnological monitoring of intI1 over multiple years. We also conducted experiments to elucidate the impact of Daphnia grazing on its abundance. The monitoring of different size fractions of the Lake Maggiore microbial community has shown a particle-bound life-style for intI1-harbouring bacteria. Most of the bacteria hosting intI1, originating from both a wastewater effluent that discharges intro Lake Maggiore and lake water itself, grow on particulate substrates in open waters, making them particularly vulnerable to grazing by large filter feeders such as Daphnia. Daphnia grazing is independent from the origin (lake water or wastewater) of the bacterial genera; it selectively removes bacteria that are present in aggregates or even filamentous forms from both origins. To understand if intI1 is related to viable bacteria or just DNA residues, it is important to study the persistence of class 1 integrons with their gene cassettes, which often contain antibiotic resistance genes in freshwater ecosystems. Significance StatementWhile faecal pollution of freshwaters is commonly monitored, genetic pollution through wastewater treatment plant outflows, such as antibiotic resistance genes, is difficult to monitor due to the diverse nature of genes present. The intI1 gene is proposed as a proxy for anthropogenic pollution; however, there is a major lack of understanding regarding the persistence of this gene in freshwaters. In this study, we demonstrate that intI1 in freshwaters is associated with both the natural microbial community and allochthonous microbes arriving from wastewater. Furthermore, we show that intI1 harbouring bacteria preferentially reside in the aggregated microbial fraction and are easily removed by zooplankton grazing. This study is the first limnological investigation of this gene and highlights a significant gap in our knowledge regarding the ecology of class 1 integrons. Genetic pollution of surface waters is however a global problem and of very broad interest on the one hand, on the other hand, the question of the establishment of an allochthonous gene into a natural microbial community is also an interesting fundamental question in ecology, thus this study has both more applied and more fundamental aspects. Therefore, we consider it perfect for the readership of L&O.

ecology↗

bioRxiv2024

Dissecting the membrane association mechanism of aerolysin pore at femtomolar concentrations using water as a probe

Aerolysin is a bacterial pore-forming toxin able to form transmembrane pores at the host plasma membrane of narrow internal diameter and great stability. These assets make it a highly promising nanopore for the detection of biopolymers such as nucleic acids and peptides. While much is known about aerolysin from a microbiological and structural side, its membrane association and pore-formation mechanism are not yet fully disclosed. Here, we studied the interaction of femtomolar concentrations of aerolysin and its mutants with liposomes in aqueous solution using angle-resolved second harmonic scattering (AR-SHS), in combination with single-channel current measurements. The measurements were so sensitive to detect electrostatic changes on the membrane-bound aerolysin induced by pH variation induced by the changes in the hydration shell of aerolysin. We reported for the first time the membrane binding affinity of aerolysin at different stages of the pore formation mechanism: while wt aerolysin has a binding affinity as high as 20 fM, the quasi-pore state and the prepore state show gradually decreasing membrane affinities, incomplete insertion and pore opening signature. Moreover, we quantitatively characterized the membrane affinity of mutants relevant for applications to nanopore sensing. This approach opens new possibilities to efficiently screen biological pores suitable for conducting molecular sensing and sequencing measurements, as well as to probe pore forming processes.

biophysics↗

bioRxiv2024

Pre-trained Maldi Transformers improve MALDI-TOF MS-based prediction

For the last decade, matrix-assisted laser desportion/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has been the reference method for species identification in clinical microbiology. Hampered by a historical lack of open data, machine learning research towards models specifically adapted to MALDI-TOF MS remains in its infancy. Given the growing complexity of available datasets (such as large-scale antimicrobial resistance prediction), a need for models that (1) are specifically designed for MALDI-TOF MS data, and (2) have high representational capacity, presents itself. Here, we introduce Maldi Transformer, an adaptation of the state-of-the-art transformer architecture to the MALDI-TOF mass spectral domain. We propose the first self-supervised pre-training technique specifically designed for mass spectra. The technique is based on shuffling peaks across spectra, and pre-training the transformer as a peak discriminator. Extensive benchmarks confirm the efficacy of this novel design. The final result is a model exhibiting state-of-the-art (or competitive) performance on downstream prediction tasks. In addition, we show that Maldi Transformers identification of noisy spectra may be leveraged towards higher predictive performance. All code supporting this study is distributed on PyPI and is packaged under: https://github.com/gdewael/maldi-nn

bioinformatics↗

bioRxiv2024

Electrospun Poly(ε-caprolactone) Membranes Modified with Heparin and Essential Fatty Acids for Biomedical Applications

The study aimed to enhance wound healing by modifying poly({varepsilon}-caprolactone) (PCL) membranes with sodium heparin (HS) and Essential Fatty Acids (EFA). Electrospinning was used to prepare the membranes containing the maximum concentration of HS and AGE, which were then sterilized with ozone. Microbiological tests confirmed effective sterilization. The membrane characterization included scanning electron microscopy (SEM) for morphological analysis, wettability studies for contact angle determination, Fourier-transform infrared spectroscopy (FT-IR) analysis, and thermogravimetry (TGA) for thermal analysis. Results indicated successful preparation and sterilization of PCL membranes modified with HS and EFA. Morphological analysis showed well-formed and randomly distributed fibers, although the PCL+HS membrane exhibited beads on its fibers. Adding HS and EFA affected the fiber diameter, with PCL+HS fibers having a smaller diameter than pure PCL and PCL+EFA fibers. Wettability analysis demonstrated modified surface properties with reduced contact angles. FT-IR analysis showed slight contributions of HS and EFA in the modified PCL membranes, while thermal analysis revealed no substantial changes in thermal stability. In conclusion, PCL membranes modified with HS and EFA can potentially accelerate wound healing, presenting an innovative and cost-effective approach to treating skin injuries.

bioengineering↗

bioRxiv2024

Analysis of justification for and gender bias in author order among those contributing equally

The practice of designating two or more authors as equal contributors (EC) on a scientific publication is increasingly common as a form of sharing credit. However, EC authors are often unclearly attributed on CVs or citation engines, and it is unclear how research teams determine author order within an EC listing. In response to studies showing that male authors were more likely to be placed first in an EC listing, the American Society of Microbiology (ASM) required that authors explain the reasons for author order beginning in 2020. In this study we analyze data from over 2500 ASM publications to see how this policy affected gender bias and how research teams are making decisions on author order. Data on publications from 2018-2021 show that gender bias was largely nonsignificant both before and after authors were asked by ASM to provide an EC statement. The most likely reasons for EC order included alphabetical order, seniority, and chance, although there were differences for publications from different geographic regions. However, many research teams used unique methods in order selection, highlighting the importance of EC statements to provide clarity for readers, funding agencies, and tenure committees.

scientific communication and education↗

bioRxiv2024

High-content high-resolution microscopy and deep learning assisted analysis reveals host and bacterial heterogeneity during Shigella infection

Shigella flexneri is a Gram-negative bacterial pathogen and causative agent of bacillary dysentery. S. flexneri is closely related to Escherichia coli but harbors a virulence plasmid that encodes a Type III Secretion System (T3SS) required for host cell invasion. Widely recognized as a paradigm for research in cellular microbiology, S. flexneri has emerged as important to study mechanisms of cell-autonomous immunity, including septin cage entrapment. Here we use high-content high-resolution microscopy to monitor the dynamic and heterogeneous S. flexneri infection process by assessing multiple host and bacterial parameters (DNA replication, protein translation, T3SS activity). In the case of infected host cells, we report a reduction in DNA and protein synthesis together with morphological changes that suggest S. flexneri can induce cell-cycle arrest. We developed an artificial intelligence image analysis approach using Convolutional Neural Networks to reliably quantify, in an automated and unbiased manner, the recruitment of SEPT7 to intracellular bacteria. We discover that heterogeneous SEPT7 assemblies are recruited to bacteria with increased T3SS activation. Our automated microscopy workflow is useful to illuminate diverse host and bacterial interactions at the single-cell and population level, and to fully characterise the intracellular microenvironment controlling the S. flexneri infection process.

cell biology↗

bioRxiv2024

AllTheBacteria - all bacterial genomes assembled, available and searchable

Public microbial genomes encode an immense record of biological diversity, evolution and molecular function, but much of this information remains difficult to reuse because raw sequencing data are not uniformly assembled, quality controlled, annotated or searchable at scale. Here we present AllTheBacteria, an open, community-built resource that transforms public bacterial short-read whole-genome sequencing reads into a uniformly processed discovery platform. The current analysed release contains 2,440,377 high-quality bacterial and archaeal genomes from 11,273 species, together with standardized taxonomic assignments, genome annotations, antimicrobial resistance calls, antiphage-defence annotations, protein structure predictions and AI-ready sequence tables. We show that this infrastructure enables applications that would otherwise be impractical, from global sequence search and outbreak contextualization to pangenome method development, antimicrobial resistance reservoir mapping and antiphage-defence ecology. As a stringent experimental demonstration, we mined 3,919,096 encrypted peptide fragments from AllTheBacteria proteomes using our deep learning model APEX 1.1, identifying 1,867 candidates with predicted antimicrobial activity. We synthesized 24 representative peptides and tested them against 20 clinically relevant bacterial strains, including antibiotic-resistant pathogens. Multiple peptides showed low-micromolar activity, membrane-responsive conformational transitions and selective envelope perturbation. A lead molecule, ATB20, reduced Acinetobacter baumannii burden in a murine skin abscess model with efficacy comparable to polymyxin B and no overt toxicity. Together, these results establish AllTheBacteria as both a foundational community resource for microbiology and a renewable engine for AI-guided antimicrobial discovery.

bioinformatics↗

bioRxiv2024

Evolution of Virulence in Emerging Epidemics: From Theory to Experimental Evolution and Back

The experimental validation of theoretical predictions is a crucial step in demonstrating the predictive power of a model. While quantitative validations are common in infectious diseases epidemiology, experimental microbiology primarily focuses on the evaluation of a qualitative match between model predictions and experiments. In this study, we develop a method to deepen the quantitative validation process with a polymorphic viral population. We analyse the data from an experiment carried out to monitor the evolution of the temperate bacteriophage {lambda} spreading in continuous cultures of Escherichia coli. This experimental work confirmed the influence of the epidemiological dynamics on the evolution of transmission and virulence of the virus. A variant with larger propensity to lyse bacterial cells was favoured in emerging epidemics (when the density of susceptible cells was large), but counter-selected when most cells were infected. Although this approach qualitatively validated an important theoretical prediction, no attempt was made to fit the model to the data nor to further develop the model to improve the goodness of fit. Here, we show how theoretical analysis - including calculations of the selection gradients - and model fitting can be used to estimate key parameters of the phage life cycle and yield new insights on the evolutionary epidemiology of the phage {lambda}. First, we show that modelling explicitly the infected bacterial cells which will eventually be lysed improves the fit of the transient dynamics of the model to the data. Second, we carry out a theoretical analysis that yields useful approximations that capture at the onset and at the end of an epidemic the effects of epidemiological dynamics on selection and differentiation across distinct life stages of the virus. Finally, we estimate key phenotypic traits characterizing the two strains of the virus used in our experiment such as the rates of prophage reactivation or the probabilities of lysogenization. This study illustrates the synergy between experimental, theoretical and statistical approaches; and especially how interpreting the temporal variation in the selection gradient and the differentiation across distinct life stages of a novel variant is a powerful tool to elucidate the evolutionary epidemiology of emerging infectious diseases.

evolutionary biology↗

bioRxiv2024

Rhizosphere Mysteries: Metabolite Reduction Down-regulated Fungal Diversity and Community Function

The rhizosphere serves as the primary defense against pathogens, but rhizosphere metabolites can also act as carbon sources and signaling molecules that attract soil borne pathogenic fungi to the destruction of rhizosphere defenses. We propose that whether reducing rhizosphere metabolites improves complex microbial networks defense. Here, we found that reducing rhizosphere metabolites altered fungal community structure more than bacteria, resulting in a downward trend in fungal diversity, soil-borne pathogenic fungal Fusarium abundance, and soil microbial community functions, e.g., metabolic functions, enzyme activities, and protein expression. However, the trend is more favorable to plant growth, which might be explained by the combined effect of the upward trend in bacterial diversity in the rhizosphere and bulk soil. Furthermore, we identified biomarkers Monographella, Acremonium, Geosmithia, and Funneliformis, which negatively correlated with other differential microbiology, play a competitive role in community member interactions. they optimized the microbial ecology with functions that mobilize soil nutrients, reduce pathogens and soil acidification, and lower phenolic acids. Integrating our findings proposes new avenues for understanding the complex soil rhizosphere mysteries of the critical role of metabolites in "soil environment - microorganisms - metabolites" ecology interactions and provides a design to build synthetic microbial community to enhance defense. IMPORTANCEWhile rhizosphere metabolites are known to regulate microorganisms composition to enhance plant immunity cooperatively. However, they also have a harmful side, which attracts soil-borne pathogenic fungi to form synergistic damage that inhibits beneficial bacteria, produces autotoxicity, destroys the rhizosphere microbial ecology, and negatively affects soil productivity and plant health. Currently, our planet is experiencing unprecedented anthropogenic-induced changes. Moreover, the complex and dynamic ecological network in the rhizosphere-an important microbial hotspot-is among the most fascinating yet elusive topics in microbial ecology. Whether reduced rhizosphere metabolites improves the microbial ecological networks remains unknown. Our findings revealed that reduced rhizosphere metabolites decrease fungal diversity, microbial community function, and pathogen abundance, while increase bacterial diversity, soil nutrients, pH, and similar factors. It is clear that reduced rhizosphere metabolites is undoubtedly beneficial for plant health and the rhizosphere ecology. Ultimately, This study provided a new comprehensive understanding of how fungi and bacteria assemble and alter in the rhizosphere and bulk soil when reduced rhizosphere metabolites. Understanding the critical role of rhizosphere metabolites in restoring micro-ecological balance will allow us to focus on regulating microbial community metabolism and root exudates, facilitate the discovery of new metabolites and interactions with microorganisms, and harness their the beneficial properties that contribute to rhizosphere microbial community assembly.

ecology↗

bioRxiv2024

A Novel Magnetic Field Device: Effects of Magnetic Fields on Planktonic Yeasts and Fungal Mats

Microorganisms evolved within the geomagnetic field and can be affected by magnetic field exposure. However, the mechanisms underlying many magnetic phenomena in microbes remain to be elucidated. We develop a 3D-printed magnetic field exposure device to perform experiments on microbes. This device is designed in AutoCAD, modeled in COMSOL, and validated using a Gaussmeter. Using the magnetic field exposure device, we perform static magnetic field experiments on different strains of the budding yeast Saccharomyces cerevisiae. We find that static magnetic field exposure slows the spatially-structured expansion of yeast mats that expands in two dimensions, but not yeast mats that expand in three dimensions, across the surface of semi-solid media. We also find that magnetic fields do not affect the growth of yeast cells in well-mixed liquid media. This study provides a novel device for magnetic field exposure experiments on microorganisms and advances our understanding of the effects of magnetic fields on fungi. Why it mattersMicroorganisms have evolved to function, survive, and reproduce in Earths magnetic field. However, the mechanisms underlying magnetic phenomena in microorganisms are unknown. This is especially true for fungi, which are important microorganisms for microbiological research, industrial application, and infectious disease. To elucidate mechanisms driving magnetic phenomena, we need devices to perform controlled experiments in a variety of conditions. We develop a 3D-printed magnetic field exposure device using computer-aided design, physics modeling software, and a magnetometer. Using this novel magnetic field device, we discover that magnetic fields can slow the growth of yeast on agar plates, but that magnetic fields do not affect the growth of yeast in liquid media.

biophysics↗

bioRxiv2024

Development and Implementation of a Core Genome Multilocus Sequence Typing (cgMLST) scheme for Haemophilus influenzae

2.Haemophilus influenzae is part of the human nasopharyngeal microbiota and a pathogen causing invasive disease. The extensive genetic diversity observed in H. influenzae necessitates discriminatory analytical approaches to evaluate its population structure. This study developed a core genome MLST (cgMLST) scheme for H. influenzae using pangenome analysis tools and validated the cgMLST scheme using datasets consisting of complete reference genomes (N=14) and high-quality draft H. influenzae genomes (N=2,297). The draft genome dataset was divided into a development (N=921) and a validation dataset (N=1,376). The development dataset was used to identify potential core genes with the validation dataset used to refine the final core gene list to ensure the reliability of the proposed cgMLST scheme. Functional classifications were made for all resulting core genes. Phylogenetic analyses were performed using both allelic profiles and nucleotide sequence alignments of the core genome to test congruence, as assessed by Spearmans correlation and Ordinary Least Square linear regression tests. Preliminary analyses using the development dataset identified 1,067 core genes, which were refined to 1,037 with the validation dataset. More than 70% of core genes were predicted to encode proteins essential for metabolism or genetic information processing. Phylogenetic and statistical analyses indicated that the core genome allelic profile accurately represented phylogenetic relatedness among the isolates (R2 = 0.945). We used this cgMLST scheme to define a high-resolution population structure for H. influenzae, which enhances the genomic analysis of this clinically relevant human pathogen. 3. Impact statementDiscriminating H. influenzae variants and evaluating population structure has been challenging and largely unstandardised. To address this, we have developed a cgMLST scheme for H. influenzae. Since an accurate typing approach relies on precise reflection of the underlying population structure, we explored various methods to define the scheme. The core genes included in this scheme were predicted to encode functions in essential biological pathways, such as metabolism and genetic information processing, and could be reliably assembled from short-read sequence data. Single-linkage clustering, based on core genome allelic profiles, showed high congruence to genealogy reconstructed by Maximum-Likelihood (ML) methods from the core genome nucleotide alignment. The cgMLST scheme v1 enables rapid and accurate depiction of high-resolution H. influenzae population structure, and making this scheme accessible via the PubMLST database, ensures that microbiology reference laboratories and public health authorities worldwide can use it for genomic surveillance. 4. Data summaryThe H. influenzae cgMLST scheme is accessible via https://pubmlst.org/organisms/haemophilus-influenzae. The list of isolate IDs available publicly from pubmlst.org is provided in Supplementary File 1. The pipeline for cgMLST scheme development and validation is published at https://www.protocols.io/private/EF6DB7FE429311EEB8630A58A9FEAC02. All in-house R and Python scripts for data processing and analysis are available from https://gitfront.io/r/user-4399403/ZHt8DArALHcY/cgmlst-hinf/.

genomics↗

bioRxiv2024

Measuring autofluorescence spectral signatures for detecting antibiotic-resistant bacteria using Bigfoot spectral flow cytometer

Application of flow cytometry to microbiology has been limited due to inadequate availability of bacterial-specific stains, expensive antibody-based fluorophores, ineffective stain cell permeability and challenges in differentiating bacterial cells from cell debris due to their similarity and their small size. In addition, staining cells demands multiple washing steps which limits the sensitivity of detection due to the cell volume that is up to two orders smaller than typical eukaryotic cells. Further, most flow cytometers are not equipped to handle pathogenic organisms. Autofluorescence-based detection of cells can be a useful method for bacterial detection as multiple washing steps can be avoided and it also reduces the time and cost of using stains. Multiple studies have shown that the autofluorescence in bacterial cells are mainly linked to specific proteins, enzymes, or enzyme cofactors such as Flavin Adenine Dinucleotide (FAD) and Nicotinamide Adenine Dinucleotide (NAD) which are involved in bacterial metabolism. In this report, we present a novel method for differentiation between clinically isolated antibiotic resistant and non-resistant bacteria by utilizing their autofluorescence spectral signatures. We utilized a spectral cytometer known as Bigfoot which is equipped with an integrated biosafety cabinet allowing easy handling of pathogenic organisms unlike any other flow cytometers. Bigfoot also has 9 lasers and 54 fluorescence detectors which we utilize to capture the bacterial spectral autofluorescence signatures. As a proof of principle, we initially stressed different types of bacteria (E.coli and Salmonella sp.) using gentamicin antibiotics by collecting spectral autofluorescence over different time points. The spectral signatures were compared with the non-stressed bacteria. We observed that the stressed bacteria showed an increase in autofluorescence at distinct excitation (Ultraviolet, Violet, and blue color) and emission wavelengths whereas the non-stressed did not. The same experiments were repeated to compare the autofluorescence signatures between Methicillin-resistant and methicillin susceptible staphylococcus aureus (MRSA and MSSA) which were stressed with oxacillin antibiotics. MSSA showed an increase in autofluorescence between 4 - 6 h after exposure to oxacillin. MRSA on the other hand showed no increase in autofluorescence and the autofluorescence between stressed and non-stressed MRSA had similar signatures. This demonstrated that the antibiotic resistant or susceptible strain can be detected by observing the change in autofluorescence signature at specific wavelengths in a few hours. This label-free, quantitative, and resistant-specific autofluorescence spectral signatures from the Bigfoot spectral flow cytometer could potentially be utilized for rapid detection of antibioticresistant strain.

bioengineering↗

bioRxiv2024

BakRep - A searchable large-scale web repository for bacterial genomes, characterizations and metadata

Bacteria are fascinating research objects in many disciplines for countless reasons, and whole-genome sequencing has become the paramount methodology to advance our microbiological understanding. Meanwhile, access to cost-effective sequencing platforms has accelerated bacterial whole-genome sequencing to unprecedented levels introducing new challenges in terms of data accessibility, computational demands, heterogeneity of analysis workflows, and thus, ultimately its scientific usability. To that end, Blackwell et al. released a uniformly processed set of 661,405 bacterial genome assemblies obtained from the European Nucleotide Archive as of November 2018. Building on these accomplishments, we conducted further genome-based analyses like taxonomic classification, MLST subtyping and annotation of all genomes. Here we present BakRep, a searchable large-scale web repository of these genomes enriched with consistent genome characterizations and original metadata. The platform provides a flexible search engine combining taxonomic, genomic and metadata information, as well as interactive elements to visualize genomic features. Furthermore, all results can be downloaded for offline analyses via an accompanying command line tool. The web repository is accessible via https://bakrep.computational.bio.

bioinformatics↗

bioRxiv2024

A diverse and distinct microbiome inside living trees

Despite significant advances in microbiome research across various environments1, the microbiome of Earths largest biomass reservoir- the wood of living trees2- remains largely unexplored. This oversight neglects a critical aspect of global biodiversity and potentially key players in tree health and forest ecosystem functions. Here we illuminate the microbiome inhabiting and adapted to wood, and further specialized to individual host species. We demonstrate that a single tree can host approximately a trillion microbes in its aboveground internal tissues, with microbial communities partitioned between heartwood and sapwood, each maintaining a distinct microbiome with minimal similarity to other plant tissues or nearby ecosystem components. Notably, the heartwood microbiome emerges as a unique ecological niche, distinguished in part by endemic archaea and anaerobic bacteria that drive consequential biogeochemical processes. Our research supports the emerging idea of a plant as a "holobiont"3,4--a single ecological unit comprising host and associated microorganisms--and parallels human microbiome research in its implications for host health, disease, and functionality5. By mapping the structure, composition, and potential sources and functions of the tree internal microbiome, our findings pave the way for novel insights into tree physiology and forest ecology, and establish a new frontier in environmental microbiology.

ecology↗

bioRxiv2024

Population sequencing for diversity and transmission analyses

Genomic diversity in a pathogen population is the foundation for evolution and adaptations in virulence, drug resistance, pathogenesis, and immune evasion. Characterizing, analyzing, and understanding population-level diversity is also essential for epidemiological and forensic tracking of sources and revealing detailed pathways of transmission and spread. For bacteria, culturing, isolating, and sequencing the large number of individual colonies required to adequately sample diversity can be prohibitively time-consuming and expensive. While sequencing directly from a mixed population will show variants among reads, they cannot be linked to reveal allele combinations associated with particular traits or phylogenetic inheritance patterns. Here, we describe the theory and method of how population sequencing directly from a mixed sample can be used in conjunction with sequencing a very small number of colonies to describe the phylogenetic diversity of a population without haplotype reconstruction. To demonstrate the utility of population sequencing in capturing phylogenetic diversity, we compared isogenic clones to population sequences of Burkholderia pseudomallei from the sputum of a single patient. We also analyzed population sequences of Staphylococcus aureus derived from different people and different body sites. Sequencing results confirm our ability to capture and characterize phylogenetic diversity in our samples. Our analyses of B. pseudomallei populations led to the surprising discovery that the pathogen population is highly structured in sputum, suggesting that for some pathogens, sputum sampling may preserve structuring in the lungs and thus present a non-invasive alternative to understanding colonization, movement, and pathogen/host interactions. Our analyses of S. aureus samples show how comparing phylogenetic diversity across populations can reveal directionality of transmission between hosts and across body sites, demonstrating the power and utility for characterizing the spread of disease and identification of reservoirs at the finest levels. We anticipate that population sequencing and analysis can be broadly applied to accelerate research in a broad range of fields reliant on a foundational understanding of population diversity. Author SummaryThe ability to characterize diversity in a single bacterial population (i.e., a single host or even a single body site) is critical for understanding adaptation and evolution, with far-reaching implications on disease treatment and prevention that include revealing patterns of spread and persistence. While the scientific community has made great strides in sequencing methods to characterize single colonies and entire communities, there is a dearth of studies at the population level. This is because 1) the need to culture and sequence a sufficiently representative number of isogenic colonies is prohibitive, and 2) the theoretical foundation for characterizing a population by sequencing a single sample (as is done for microbiome and metagenomic analyses) has not been developed. Here, we introduce this theoretical foundation and validate its applicability by characterizing a lung infection caused by Burkholderia pseudomallei. We also demonstrate the utility of this method in determining the directionality of spread of Staphylococcus aureus between people and across body sites within the same host (a level of spatial resolution that has not been previously performed). We anticipate that this work will open the door to a host of new studies and discoveries across a diverse set of microbiological fields.

evolutionary biology↗

bioRxiv2024

PathoGFAIR: a collection of FAIR and adaptable (meta)genomics workflows for (foodborne) pathogens detection and tracking

BackgroundFood contamination by pathogens poses a global health threat, affecting an estimated 600 million people annually. During a foodborne outbreak investigation, microbiological analysis of food vehicles detects responsible pathogens and traces contamination sources. Metagenomic approaches offer a comprehensive view of the genomic composition of microbial communities, facilitating the detection of potential pathogens in samples. Combined with sequencing techniques like Oxford Nanopore sequencing, such metagenomic approaches become faster and easier to apply. A key limitation of these approaches is the lack of accessible, easy-to-use, and openly available pipelines for pathogen identification and tracking from (meta)genomic data. FindingsPathoGFAIR is a collection of Galaxy-based FAIR workflows employing state-of-the-art tools to detect and track pathogens from metagenomic Nanopore sequencing. Although initially developed to detect pathogens in food datasets, the workflows can be applied to other metagenomic Nanopore pathogenic data. PathoGFAIR incorporates visualisations and reports for comprehensive results. We tested PathoGFAIR on 130 samples containing different pathogens from multiple hosts under various experimental conditions. For all but one sample, workflows have successfully detected expected pathogens at least at the species rank. Further taxonomic ranks are detected for samples with sufficiently high Colony-forming unit (CFU) and low Cycle Threshold (Ct) values. ConclusionsPathoGFAIR detects the pathogens at species and subspecies taxonomic ranks in all but one tested sample, regardless of whether the pathogen is isolated or the sample is incubated before sequencing. Importantly, PathoGFAIR is easy to use and can be straightforwardly adapted and extended for other types of analysis and sequencing techniques, making it usable in various pathogen detection scenarios. PathoGFAIR homepage: https://usegalaxy-eu.github.io/PathoGFAIR/

genomics↗