BacDive in 2022: the knowledge base for standardized bacterial and archaeal data

BGCFlow: systematic pangenome workflow for the analysis of biosynthetic gene clusters across large genomic datasets

Genome mining is revolutionizing natural products discovery efforts. The rapid increase in available genomes demands comprehensive computational platforms to effectively extract biosynthetic knowledge encoded across bacterial pangenomes. Here, we present BGCFlow, a novel systematic workflow integrating analytics for large-scale genome mining of bacterial pangenomes. BGCFlow incorporates several genome analytics and mining tools grouped into five common stages of analysis such as: (i) data selection, (ii) functional annotation, (iii) phylogenetic analysis, (iv) genome mining, and (v) comparative analysis. Furthermore, BGCFlow provides easy configuration of different projects, parallel distribution, scheduled job monitoring, an interactive database to visualize tables, exploratory Jupyter Notebooks, and customized reports. Here, we demonstrate the application of BGCFlow by investigating the phylogenetic distribution of various biosynthetic gene clusters detected across 42 genomes of the Saccharopolyspora genus, known to produce industrially important secondary/specialized metabolites. The BGCFlow-guided analysis predicted more accurate dereplication of BGCs and guided the targeted comparative analysis of selected RiPPs. The scalable, interoperable, adaptable, re-entrant, and reproducible nature of the BGCFlow will provide an effective novel way to extract the biosynthetic knowledge from the ever-growing genomic datasets of biotechnologically relevant bacterial species.

Warming inhibits HgII methylation but stimulates methylmercury demethylation in paddy soils

Inorganic mercury (HgII) can be transformed into neurotoxic methylmercury (MeHg) by microorganisms in paddy soils, and the subsequent accumulation in rice grains poses an exposure risk for human health. Warming as an important manifestation of climate change, changes the composition and structure of microbial communities, and regulates the biogeochemical cycles of Hg in natural environments. However, the response of specific HgII methylation/demethylation to the changes in microbial communities caused by warming remain unclear. Here, nationwide sampling of rice paddy soils and a temperature-adjusted incubation experiment coupled with isotope labeling technique (202HgII and Me198Hg) were conducted to investigate the effects of temperature on HgII methylation, MeHg demethylation, and microbial mechanisms in paddy soils along Hg gradients. We showed that increasing temperature significantly inhibited HgII methylation but promoted MeHg demethylation. The reduction in the relative abundance of Hg-methylating microorganisms and increase in the relative abundance of MeHg-demethylating microorganisms are the likely reasons. Consequently, the net Hg methylation production potential in rice paddy soils was largely inhibited under the increasing temperature. Collectively, our findings offer insights into the decrease in net MeHg production potential associated with increasing temperature and highlight the need for further evaluation of climate change for its potential effect on Hg transformation in Hg-sensitive ecosystems.

Wallace's line structures seagrass microbiota and is a potential barrier to the dispersal of marine bacteria

BACKGROUND

The processes that shape microbial biogeography are not well understood, and concepts that apply to macroorganisms, like dispersal barriers, may not affect microorganisms in the same predictable ways. To better understand how known macro-scale biogeographic processes can be applied at micro-scales, we examined seagrass associated microbiota on either side of Wallace's line to determine the influence of this cryptic dispersal boundary on the community structure of microorganisms. Communities were examined from twelve locations throughout Indonesia on either side of this theoretical line.

RESULTS

We found significant differences in microbial community structure on either side of this boundary (R2 = 0.09; P = 0.001), and identified seven microbial genera as differentially abundant on either side of the line, six of these were more abundant in the West, with the other more strongly associated with the East. Genera found to be differentially abundant had significantly smaller minimum cell dimensions (GLM: t923 = 59.50, P < 0.001) than the overall community.

CONCLUSION

Despite the assumed excellent dispersal ability of microbes, we were able to detect significant differences in community structure on either side of this cryptic biogeographic boundary. Samples from the two closest islands on opposite sides of the line, Bali and Komodo, were more different from each other than either was to its most distant island on the same side. We suggest that limited dispersal across this barrier coupled with habitat differences are primarily responsible for the patterns observed. The cryptic processes that drive macroorganism community divergence across this region may also play a role in the bigeographic patterns of microbiota.

A Structural Proteome Screen Identifies Protein Mimicry in Host-Microbe Systems

Host-microbe systems are evolutionary niches that produce coevolved biological interactions and are a key component of global health. However, these systems have historically been a difficult field of biological research due to their experimental intractability. Impactful advances in global health will be obtained by leveraging in silico screens to identify genes involved in mediating interspecific interactions. These predictions will progress our understanding of these systems and lay the groundwork for future in vitro and in vivo experiments and bioengineering projects. A driver of host-manipulation and intracellular survival utilized by host-associated microbes is molecular mimicry, a critical mechanism that can occur at any level from DNA to protein structures. We applied protein structure prediction and alignment tools to explore host-associated bacterial structural proteomes for examples of protein structure mimicry. By leveraging the Legionella pneumophila proteome and its many known structural mimics, we developed and validated a screen that can be applied to virtually any host-microbe system to uncover signals of protein mimicry. These mimics represent candidate proteins that mediate host interactions in microbial proteomes. We successfully applied this screen to other microbes with demonstrated effects on global health, Helicobacter pylori and Wolbachia , identifying protein mimic candidates in each proteome. We discuss the roles these candidates may play in important Wolbachia -induced phenotypes and show that Wobachia infection can partially rescue the loss of one of these factors. This work demonstrates how a genome-wide screen for candidates of host-manipulation and intracellular survival offers an opportunity to identify functionally important genes in host-microbe systems.

High-Temperature Tolerance Protein Engineering through Deep Evolution

Protein engineering aimed at increasing temperature tolerance through iterative mutagenesis and high-throughput screening is often labor-intensive. Here, we developed a deep evolution (DeepEvo) strategy to engineer protein high-temperature tolerance by generating and selecting functional sequences using deep learning models. Drawing inspiration from the concept of evolution, we constructed a high-temperature tolerance selector based on a protein language model, acting as selective pressure in the high-dimensional latent spaces of protein sequences to enrich those with high-temperature tolerance. Simultaneously, we developed a variant generator using a generative adversarial network to produce protein sequence variants containing the desired function. Afterward, the iterative process involving the generator and selector was executed to accumulate high-temperature tolerance traits. We experimentally tested this approach on the model protein glyceraldehyde 3-phosphate dehydrogenase, obtaining 8 variants with high-temperature tolerance from just 30 generated sequences, achieving a success rate of over 26%, demonstrating the high efficiency of DeepEvo in engineering protein high-temperature tolerance.

Multi-Attribute Subset Selection enables prediction of representative phenotypes across microbial populations

The interpretation of complex biological datasets requires the identification of representative variables that describe the data without critical information loss. This is particularly important in the analysis of large phenotypic datasets (phenomics). Here we introduce Multi-Attribute Subset Selection (MASS), an algorithm which separates a matrix of phenotypes (e.g., yield across microbial species and environmental conditions) into predictor and response sets of conditions. Using mixed integer linear programming, MASS expresses the response conditions as a linear combination of the predictor conditions, while simultaneously searching for the optimally descriptive set of predictors. We apply the algorithm to three microbial datasets and identify environmental conditions that predict phenotypes under other conditions, providing biologically interpretable axes for strain discrimination. MASS could be used to reduce the number of experiments needed to identify species or to map their metabolic capabilities. The generality of the algorithm allows addressing subset selection problems in areas beyond biology.

Pulsed electric field treatment for preservation of Chlorella suspensions and retention of gelling capacity

Pulsed electric field (PEF) processing has emerged as an alternative to thermal pasteurization for the shelf-life extension of heat-sensitive liquids at industrial scale. It offers the advantage of minimal alteration in physicochemical characteristics and functional properties. In this study, a pilot-scale continuous PEF processing (Toutlet < 55 °C) was applied to microalgae Chlorella vulgaris (Cv) suspensions (pH = 6.5), which was proposed as a functional ingredient for plant-based foods. Cv suspensions were inoculated with three distinct food spoilage microorganisms (Pseudomonas guariconensis, Enterobacter soli and Lactococcus lactis), isolated from the Cv biomass. PEF treatments were applied with varying electric field strength Eel of 16 to 28 kV/cm, pulse repetition rate f of 100 to 140 Hz, with a pulse width τ of 20 μs and an inlet product temperature Tin of 30 °C. The aim was to evaluate the PEF-induced microbial reduction and monitor the microbial outgrowth during a 10-day cold storage period (10 °C). Maximum inactivation of 4.1, 3.7 and 3.6 logs was achieved (28 kV/cm and 120 Hz) for the investigated isolates, respectively. Under these conditions, the critical electric field strengths Ecrit, above which inactivation was observed, ranged from 22.6 to 24.6 kV/cm. Moreover, repeated PEF treatment resulted in similar inactivation efficiency, indicating its potential to enhance shelf-life further.

Microbial Composition and Diversity of High-demand Street-vended Foods in Ecuador

Developing countries such as Ecuador carry a heavy food safety burden but reports on the microbiological quality of their foods are scarce. In this investigation, the microbial diversity of 10 high-risk and mass-consumption street-vended foods including bolones, encebollado, food dressings, ceviche, chopped fruits, fruit juices, fruit salads, cheese, raw chicken, and ground beef in Quito, Guayaquil, and Cuenca, three major population centers in Ecuador, were evaluated using 16S rRNA gene High Throughput Sequencing. In total, 1,840 amplicon sequence variants (ASVs) were classified into 23 phyla, 253 families, 645 genera, and 829 species. In the tested food samples, Proteobacteria and Firmicutes were the most abundant phyla accounting for 97.41% of relative abundance (RA). At genus level, 10 dominant genera were identified: Acinetobacter (12.61% RA), Lactococcus (12.08% RA), Vibrio (8.23% RA), Weissella (7.43% RA), Aeromonas (6.18% RA), Photobacterium (6.32% RA), Pseudomonas (3.92% RA), Leuconostoc (3.51% RA), Klebsiella (3.49% RA), and Cupriavidus (2.86% RA). The highest microbial diversity indices were found in raw chicken, encebollados, fruit salads, and fruit juices from Guayaquil and Cuenca. From sampled foods, 29 species were classified as food spoilage bacteria and 24 as opportunistic pathogenic bacteria. Two groups associated with human diseases were identified, including 11 enteric species and 26 species of fecal bacteria. The occurrence of recognized and opportunistic pathogenic bacteria, as well as enteric and fecal microorganisms, in the street-vended foods indicated extensive risks for the consumers' health. This study demonstrated the application of culture-independent amplicon sequencing in providing a more comprehensive view of microbial safety for street-vended food, which could be a useful tool to facilitate the control of foodborne diseases.

Update on the proposed minimal standards for the use of genome data for the taxonomy of prokaryotes

The field of microbial taxonomy is dynamic, aiming to provide a stable and contemporary classification system for prokaryotes. Traditionally, reliance on phenotypic characteristics limited the comprehensive understanding of microbial diversity and evolution. The introduction of molecular techniques, particularly DNA sequencing and genomics, has transformed our perception of prokaryotic diversity. In the past two decades, advancements in genome sequencing have transitioned from traditional methods to a genome-based taxonomic framework, not only to define species, but also higher taxonomic ranks. As technology and databases rapidly expand, maintaining updated standards is crucial. This work seeks to revise the 2018 guidelines for applying genome sequencing data in microbial taxonomy, adapting minimal standards and recommendations to reflect technological progress during this period.

Routes of dispersion of antibiotic resistance genes from the poultry farm system

Poultry farms are hotspots for the development and spread of antibiotic resistance genes (ARGs), due to high stocking densities and extensive use of antibiotics, posing a threat of spread and contagion to workers and the external environment. Here, we applied shotgun metagenome sequencing to characterize the gut microbiome and resistome of poultry, workers and their households - also including microbiomes from the internal and external farm environment - in three different farms in Italy during a complete rearing cycle. Our results highlighted a relevant overlap among the microbiomes of poultry, workers, and their families (gut and skin), with clinically relevant ARGs and associated mobile elements shared in both poultry and human samples. On a finer scale, the reconstruction of species-level genome bins (SGBs) allowed us to delineate the dynamics of microorganism and ARGs dispersion from farm systems. We found the associations with worker microbiomes representing the main route of ARGs dispersion from poultry to human populations. Collectively, our findings clearly demonstrate the urgent need to implement more effective procedures to counteract ARGs dispersion from poultry food systems and the relevance of metagenomics-based metacommunity approaches to monitor the ARGs dispersion process for the safety of the working environment on farms.

Intestinal Dysbiosis in Subjects with Obesity from Western Mexico and Its Association with a Proinflammatory Profile and Disturbances of Folate (B9) and Carbohydrate Metabolism

Obesity is a public health problem with a growing prevalence worldwide. In Mexico, it is estimated that one out of three adults suffer from obesity. In these patients, the intestinal microbiota (IM) undergoes pathological changes that are associated with a dysbiotic state; however, the microbiota profile of adult subjects with obesity from western Mexico has not been described. To assess this, fecal samples were obtained from 65 participants (Obese = 38; Control = 27). The microbial composition was characterized by 16S rRNA amplicon sequencing. The IM of the group with obesity revealed a clear decrease in richness and diversity (p < 0.001), as well as a significant increase in proinflammatory bacterial groups, mainly genera belonging to the Negativicutes class, Escherichia/Shigella, and Prevotella. Likewise, an increase in short-chain fatty acid-producing bacteria was found, especially the genus Lachnoclostridium. Additionally, PICRUSt2 analysis showed a depletion of vitamin B9 metabolism and an increase in saccharolytic pathways. The IM of patients with obesity possesses a dysbiotic, proinflammatory environment, possibly contributing to lipogenesis and adiposity. Thus, assessing the IM will allow for a better understanding of the pathophysiology of metabolic diseases of high prevalence, such as obesity. These findings are described for the first time in the adult population of western Mexico.

DeepPPThermo: A Deep Learning Framework for Predicting Protein Thermostability Combining Protein-Level and Amino Acid-Level Features

Using wet experimental methods to discover new thermophilic proteins or improve protein thermostability is time-consuming and expensive. Machine learning methods have shown powerful performance in the study of protein thermostability in recent years. However, how to make full use of multiview sequence information to predict thermostability effectively is still a challenge. In this study, we proposed a deep learning-based classifier named DeepPPThermo that fuses features of classical sequence features and deep learning representation features for classifying thermophilic and mesophilic proteins. In this model, deep neural network (DNN) and bi-long short-term memory (Bi-LSTM) are used to mine hidden features. Furthermore, local attention and global attention mechanisms give different importance to multiview features. The fused features are fed to a fully connected network classifier to distinguish thermophilic and mesophilic proteins. Our model is comprehensively compared with advanced machine learning algorithms and deep learning algorithms, proving that our model performs better. We further compare the effects of removing different features on the classification results, demonstrating the importance of each feature and the robustness of the model. Our DeepPPThermo model can be further used to explore protein diversity, identify new thermophilic proteins, and guide directed mutations of mesophilic proteins.

Antimicrobial resistance crisis: could artificial intelligence be the solution?

Antimicrobial resistance is a global public health threat, and the World Health Organization (WHO) has announced a priority list of the most threatening pathogens against which novel antibiotics need to be developed. The discovery and introduction of novel antibiotics are time-consuming and expensive. According to WHO's report of antibacterial agents in clinical development, only 18 novel antibiotics have been approved since 2014. Therefore, novel antibiotics are critically needed. Artificial intelligence (AI) has been rapidly applied to drug development since its recent technical breakthrough and has dramatically improved the efficiency of the discovery of novel antibiotics. Here, we first summarized recently marketed novel antibiotics, and antibiotic candidates in clinical development. In addition, we systematically reviewed the involvement of AI in antibacterial drug development and utilization, including small molecules, antimicrobial peptides, phage therapy, essential oils, as well as resistance mechanism prediction, and antibiotic stewardship.

A unified compendium of prokaryotic and viral genomes from over 300 anaerobic digestion microbiomes

BACKGROUND

The anaerobic digestion process degrades organic matter into simpler compounds and occurs in strictly anaerobic and microaerophilic environments. The process is carried out by a diverse community of microorganisms where each species has a unique role and it has relevant biotechnological applications since it is used for biogas production. Some aspects of the microbiome, including its interaction with phages, remains still unclear: a better comprehension of the community composition and role of each species is crucial for a cured understanding of the carbon cycle in anaerobic systems and improving biogas production.

RESULTS

The primary objective of this study was to expand our understanding on the anaerobic digestion microbiome by jointly analyzing its prokaryotic and viral components. By integrating 192 additional datasets into a previous metagenomic database, the binning process generated 11,831 metagenome-assembled genomes from 314 metagenome samples published between 2014 and 2022, belonging to 4,568 non-redundant species based on ANI calculation and quality verification. CRISPR analysis on these genomes identified 76 archaeal genomes with active phage interactions. Moreover, single-nucleotide variants further pointed to archaea as the most critical members of the community. Among the MAGs, two methanogenic archaea, Methanothrix sp. 43zhSC_152 and Methanoculleus sp. 52maCN_3230, had the highest number of SNVs, with the latter having almost double the density of most other MAGs.

CONCLUSIONS

This study offers a more comprehensive understanding of microbial community structures that thrive at different temperatures. The findings revealed that the fraction of archaeal species characterized at the genome level and reported in public databases is higher than that of bacteria, although still quite limited. The identification of shared spacers between phages and microbes implies a history of phage-bacterial interactions, and specifically lysogenic infections. A significant number of SNVs were identified, primarily comprising synonymous and nonsynonymous variants. Together, the findings indicate that methanogenic archaea are subject to intense selective pressure and suggest that genomic variants play a critical role in the anaerobic digestion process. Overall, this study provides a more balanced and diverse representation of the anaerobic digestion microbiota in terms of geographic location, temperature range and feedstock utilization.

Divergent rhizosphere and non-rhizosphere soil microbial structure and function in long-term warmed steppe due to altered root exudation

While there is an extensive body of research on the influence of climate warming on total soil microbial communities, our understanding of how rhizosphere and non-rhizosphere soil microorganisms respond to warming remains limited. To address this knowledge gap, we investigated the impact of 4 years of soil warming on the diversity and composition of microbial communities in the rhizosphere and non-rhizosphere soil of a temperate steppe, focusing on changes in root exudation rates and exudate compositions. We used open top chambers to simulate warming conditions, resulting in an average soil temperature increase of 1.1°C over a span of 4 years. Our results showed that, in the non-rhizosphere soil, warming had no significant impact on dissolved organic carbon concentrations, compositions, or the abundance of soil microbial functional genes related to carbon and nitrogen cycling. Moreover, soil microbial diversity and community composition remained largely unaffected, although warming resulted in increased complexity of soil bacteria and fungi in the non-rhizosphere soil. In contrast, warming resulted in a substantial decrease in root exudate carbon (by 19%) and nitrogen (by 12%) concentrations and induced changes in root exudate compositions, primarily characterized by a reduction in the abundance in alcohols, coenzymes and vitamins, and phenylpropanoids and polyketides. These changes in root exudation rates and exudate compositions resulted in significant shifts in rhizosphere soil microbial diversity and community composition, ultimately leading to a reduction in the complexity of rhizosphere bacterial and fungal community networks. Altered root exudation and rhizosphere microbial community composition therefore decreased the expression of functional genes related to soil carbon and nitrogen cycling. Interestingly, we found that changes in soil carbon-related genes were primarily driven by the fungal communities and their responses to warming, both in the rhizosphere and non-rhizosphere soil. The study of soil microbial structure and function in rhizosphere and non-rhizosphere soil provides an ideal setting for understanding mechanisms for governing rhizosphere and non-rhizosphere soil carbon and nitrogen cycles. Our results highlight the distinctly varied responses of soil microorganisms in the rhizosphere and non-rhizosphere soil to climate warming. This suggests the need for models to address these processes individually, enabling more accurate predictions of the impacts of climate change on terrestrial carbon cycling.

Constructed wetland mesocosms improve the biodegradation of microcystin-LR and cylindrospermopsin by indigenous bacterial consortia

Cyanobacterial blooms releasing harmful cyanotoxins, such as microcystin (MC) and cylindrospermopsin (CYN), are prominent threats to human and animal health. Constructed wetlands (CW) may be a nature-based solution for bioremediation of lake surface water containing cyanotoxins, due to its low-cost requirement of infrastructure and environmentally friendly operation. There is recent evidence that microcystin-LR (MC-LR) can efficiently be removed in CW microcosms where CYN degradation in CW is unknown. Likewise, the mechanistic background regarding cyanotoxins transformation in CW is not yet elucidated. In the present study, the objective was to compare MC-LR and CYN degradation efficiencies by two similar microbial communities obtained from CW mesocosms, by two different experiments setup: 1) in vitro batch experiment in serum bottles with an introduced CW community, and 2) degradation in CW mesocosms. In experiment 1) MC-LR and CYN were spiked at 100 µg L-1 and in experiment 2) 200 µg L-1 were spiked. Results showed that MC-LR was degraded to ≤1 µg L-1 within seven days in both experiments. However, with a markedly higher degradation rate constant in the CW mesocosms (0.18 day-1 and 0.75 day-1, respectively). No CYN removal was detected in the in vitro incubations, whereas around 50 % of the spiked CYN was removed in the CW mesocosms. The microbial community responded markedly to the cyanotoxin treatment, with the most prominent increase of bacteria affiliated with Methylophilaceae (order: Methylophilales, phylum: Proteobacteria). The results strongly indicate that CWs can develop an active microbial community capable of efficient removal of MC-LR and CYN. However, the CW operational conditions need to be optimized to achieve a full CYN degradation. To the best of our knowledge, this study is the first to report the ability of CW mesocosms to degrade CYN.

Constructions of quorum sensing signaling network for activated sludge microbial community

In wastewater treatment systems, the interactions among various microbes based on chemical signals, namely quorum sensing (QS), play critical roles in influencing microbial structure and function. However, it is challenging to understand the QS-controlled behaviors and the underlying mechanisms in complex microbial communities. In this study, we constructed a QS signaling network, providing insights into the intra- and interspecies interactions of activated sludge microbial communities based on diverse QS signal molecules. Our research underscores the role of diffusible signal factors in both intra- and interspecies communication among activated sludge microorganisms, and signal molecules commonly considered to mediate intraspecies communication may also participate in interspecies communication. QS signaling molecules play an important role as communal resources among the entire microbial group. The communication network within the microbial community is highly redundant, significantly contributing to the stability of natural microbial systems. This work contributes to the establishment of QS signaling network for activated sludge microbial communities, which may complement metabolic exchanges in explaining activated sludge microbial community structure and may help with a variety of future applications, such as making the dynamics and resilience of highly complex ecosystems more predictable.

Acetaminophen-traces bioremediation with novel phenotypically and genotypically characterized 2 Streptomyces strains using chemo-informatics, in vivo, and in vitro experiments for cytotoxicity and biological activity

We isolated two novel bacterial strains, active against the environmental pollutant acetaminophen/Paracetamol®. Streptomyces chrestomyceticus (symbol RS2) and Flavofuscus (symbol M33) collected from El-Natrun Valley, Egypt-water, sediment, and sand samples, taxonomically characterized using a transmission electron microscope (TEM). Genotypic identification, based on 16S rRNA gene sequence analysis followed by BLAST alignment, were deposited on the NCBI as 2 novel strains https://www.ncbi.nlm.nih.gov/nuccore/OM665324  and https://www.ncbi.nlm.nih.gov/nuccore/OM665325 . The phylogenetic tree was constructed. Acetaminophen secondary or intermediate product's chemical structure was identified by GC/LC MS. Some selected acetaminophen secondary-product extracts and derived compounds were examined against a panel of test micro-organisms and fortunately showed a good anti-microbial effect. In silico chemo-informatics Swiss ADMET evaluation was used in the selected bio-degradation extracts for absorption (gastric), distribution (to CNS), metabolism (hepatic), excretion (renal), and finally not toxic, being non-mutagenic/teratogenic or genotoxic, virtually. Moreover, in vitro cytotoxic activity of these selected bio-degradation secondary products was examined against HepG2 and MCF7 cancer cell lines, where M33 and RS2 extract effects on acetaminophen/paracetamol bio-degradation products were safe, with higher IC50 on HepG2 and MCF7 than the acetaminophen/paracetamol IC50 of 108.5 μg/ml. Moreover, an in vivo oral acute single-dose toxicity experiment was conducted, to confirm these in vitro and in silico lower toxicity (better safety) than acetaminophen/paracetamol.

MEnTaT: A machine-learning approach for the identification of mutations to increase protein stability

Enhancing protein thermal stability is important for biomedical and industrial applications as well as in the research laboratory. Here, we describe a simple machine-learning method which identifies amino acid substitutions that contribute to thermal stability based on comparison of the amino acid sequences of homologous proteins derived from bacteria that grow at different temperatures. A key feature of the method is that it compares the sequences based not simply on the amino acid identity, but rather on the structural and physicochemical properties of the side chain. The method accurately identified stabilizing substitutions in three well-studied systems and was validated prospectively by experimentally testing predicted stabilizing substitutions in a polyamine oxidase. In each case, the method outperformed the widely used bioinformatic consensus approach. The method can also provide insight into fundamental aspects of protein structure, for example, by identifying how many sequence positions in a given protein are relevant to temperature adaptation.

Genome characterization and taxonomy of Actinomyces acetigenes sp. nov., and Actinomyces stomatis sp. nov., previously isolated from the human oral cavity

BACKGROUND

Actinomyces strains are commonly found as part of the normal microflora on human tissue surfaces, including the oropharynx, gastrointestinal tract, and female genital tract. Understanding the diversity and characterization of Actinomyces species is crucial for human health, as they play an important role in dental plaque formation and biofilm-related infections. Two Actinomyces strains ATCC 49340 T and ATCC 51655 T have been utilized in various studies, but their accurate species classification and description remain unresolved.

RESULTS

To investigate the genomic properties and taxonomic status of these strains, we employed both 16S rRNA Sanger sequencing and whole-genome sequencing using the Illumina HiSeq X Ten platform with PE151 (paired-end) sequencing. Our analyses revealed that the draft genome of Actinomyces acetigenes ATCC 49340 T was 3.27 Mbp with a 68.0% GC content, and Actinomyces stomatis ATCC 51655 T has a genome size of 3.08 Mbp with a 68.1% GC content. Multi-locus (atpA, rpoB, pgi, metG, gltA, gyrA, and core genome SNPs) sequence analysis supported the phylogenetic placement of strains ATCC 51655 T and ATCC 49340 T as independent lineages. Digital DNA-DNA hybridization (dDDH), average nucleotide identity (ANI), and average amino acid identity (AAI) analyses indicated that both strains represented novel Actinomyces species, with values below the threshold for species demarcation (70% dDDH, 95% ANI and AAI). Pangenome analysis identified 5,731 gene clusters with strains ATCC 49340 T and ATCC 51655 T possessing 1,515 and 1,518 unique gene clusters, respectively. Additionally, genomic islands (GIs) prediction uncovered 24 putative GIs in strain ATCC 49340 T and 16 in strain ATCC 51655 T, contributing to their genetic diversity and potential adaptive capabilities. Pathogenicity analysis highlighted the potential human pathogenicity risk associated with both strains, with several virulence-associated factors identified. CRISPR-Cas analysis exposed the presence of CRISPR and Cas genes in both strains, indicating these strains might evolve a robust defense mechanism against them.

CONCLUSION

This study supports the classification of strains ATCC 49340 T and ATCC 51655 T as novel species within the Actinomyces, in which the name Actinomyces acetigenes sp. nov. (type strain ATCC 49340 T = VPI D163E-3 T = CCUG 34286 T = CCUG 35339 T) and Actinomyces stomatis sp. nov. (type strain ATCC 51655 T = PK606T = CCUG 33930 T) are proposed.

Statistical prediction of microbial metabolic traits from genomes

The metabolic activity of microbial communities is central to their role in biogeochemical cycles, human health, and biotechnology. Despite the abundance of sequencing data characterizing these consortia, it remains a serious challenge to predict microbial metabolic traits from sequencing data alone. Here we culture 96 bacterial isolates individually and assay their ability to grow on 10 distinct compounds as a sole carbon source. Using these data as well as two existing datasets, we show that statistical approaches can accurately predict bacterial carbon utilization traits from genomes. First, we show that classifiers trained on gene content can accurately predict bacterial carbon utilization phenotypes by encoding phylogenetic information. These models substantially outperform predictions made by constraint-based metabolic models automatically constructed from genomes. This result solidifies our current knowledge about the strong connection between phylogeny and metabolic traits. However, phylogeny-based predictions fail to predict traits for taxa that are phylogenetically distant from any strains in the training set. To overcome this we train improved models on gene presence/absence to predict carbon utilization traits from gene content. We show that models that predict carbon utilization traits from gene presence/absence can generalize to taxa that are phylogenetically distant from the training set either by exploiting biochemical information for feature selection or by having sufficiently large datasets. In the latter case, we provide evidence that a statistical approach can identify putatively mechanistic genes involved in metabolic traits. Our study demonstrates the potential power for predicting microbial phenotypes from genotypes using statistical approaches.

Complete genome sequences of butyrate producing Anaerostipes hadrus strains BA1 and GIF7 isolated from the terminal ileum of a healthy lean male

Anaerostipes hadrus strains BA1 and GIF7 were isolated from a healthy man. The complete genomes' sizes are 2,946,270 bp (BA1) and 2,907,308 bp (GIF7), with high average nucleotide identity (ANIb = 100%) and alignments ≥96.86% between strains. Conversely, both strains share 97.47% (ANIb) identity and ≤77.36% alignments to A. hadrus ATCC 29173T.

Mycobacteriaceae Phenome Atlas (MPA): A Standardized Atlas for the Mycobacteriaceae Phenome Based on Heterogeneous Sources

The bacterial family Mycobacteriaceae includes pathogenic and nonpathogenic bacteria, and systematic research on their genome and phenome can give comprehensive perspectives for exploring their disease mechanism. In this study, the phenotypes of Mycobacteriaceae were inferred from available phenomic data, and 82 microbial phenotypic traits were recruited as data elements of the microbial phenome. This Mycobacteriaceae phenome contains five categories and 20 subcategories of polyphasic phenotypes, and three categories and eight subcategories of functional phenotypes, all of which are complementary to the existing data standards of microbial phenotypes. The phenomic data of Mycobacteriaceae strains were compiled by literature mining, third-party database integration, and bioinformatics annotation. The phenotypes were searchable and comparable from the website of the Mycobacteriaceae Phenome Atlas (MPA, https://www.biosino.org/mpa/). A topological data analysis of MPA revealed the co-evolution between Mycobacterium tuberculosis and virulence factors, and uncovered potential pathogenicity-associated phenotypes. Two hundred and sixty potential pathogen-enriched pathways were found by Fisher's exact test. The application of MPA may provide novel insights into the pathogenicity mechanism and antimicrobial targets of Mycobacteriaceae.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-023-00101-5.

The phenotype and genotype of fermentative prokaryotes

Fermentation is a type of metabolism pervasive in oxygen-deprived environments. Despite its importance, we know little about the range and traits of organisms that carry out this metabolism. Our study addresses this gap with a comprehensive analysis of the phenotype and genotype of fermentative prokaryotes. We assembled a dataset with phenotypic records of 8350 organisms plus 4355 genomes and 13.6 million genes. Our analysis reveals fermentation is both widespread (in ~30% of prokaryotes) and complex (forming ~300 combinations of metabolites). Furthermore, it points to previously uncharacterized proteins involved in this metabolism. Previous studies suggest that metabolic pathways for fermentation are well understood, but metabolic models built in our study show gaps in our knowledge. This study demonstrates the complexity of fermentation while showing that there is still much to learn about this metabolism. All resources in our study can be explored by the scientific community with an online, interactive tool.

Thiorhodovibrio frisius and Trv. litoralis spp. nov., Two Novel Members from a Clade of Fastidious Purple Sulfur Bacteria That Exhibit Unique Red-Shifted Light-Harvesting Capabilities

In the pursuit of cultivating anaerobic anoxygenic phototrophs with unusual absorbance spectra, a purple sulfur bacterium was isolated from the shoreline of Baltrum, a North Sea island of Germany. It was designated strain 970, due to a predominant light harvesting complex (LH) absorption maximum at 963-966 nm, which represents the furthest infrared-shift documented for such complexes containing bacteriochlorophyll a. A polyphasic approach to bacterial systematics was performed, comparing genomic, biochemical, and physiological properties. Strain 970 is related to Thiorhodovibrio winogradskyi DSM 6702T by 26.5, 81.9, and 98.0% similarity via dDDH, ANI, and 16S rRNA gene comparisons, respectively. The photosynthetic properties of strain 970 were unlike other Thiorhodovibrio spp., which contained typical LH absorbing characteristics of 800-870 nm, as well as a newly discovered absorption band at 908 nm. Strain 970 also had a different photosynthetic operon composition. Upon genomic comparisons with the original Thiorhodovibrio strains DSM 6702T and strain 06511, the latter was found to be divergent, with 25.3, 79.1, and 97.5% similarity via dDDH, ANI, and 16S rRNA gene homology to Trv. winogradskyi, respectively. Strain 06511 (=DSM 116345T) is thereby described as Thiorhodovibrio litoralis sp. nov., and the unique strain 970 (=DSM 111777T) as Thiorhodovibrio frisius sp. nov.

BugSigDB captures patterns of differential abundance across a broad range of host-associated microbial signatures

The literature of human and other host-associated microbiome studies is expanding rapidly, but systematic comparisons among published results of host-associated microbiome signatures of differential abundance remain difficult. We present BugSigDB, a community-editable database of manually curated microbial signatures from published differential abundance studies accompanied by information on study geography, health outcomes, host body site and experimental, epidemiological and statistical methods using controlled vocabulary. The initial release of the database contains >2,500 manually curated signatures from >600 published studies on three host species, enabling high-throughput analysis of signature similarity, taxon enrichment, co-occurrence and coexclusion and consensus signatures. These data allow assessment of microbiome differential abundance within and across experimental conditions, environments or body sites. Database-wide analysis reveals experimental conditions with the highest level of consistency in signatures reported by independent studies and identifies commonalities among disease-associated signatures, including frequent introgression of oral pathobionts into the gut.

Macromonas nakdongensis sp. nov., Isolated from Freshwater and Characterization of Bacteriophage BK-30P-The First Phage That Infects Genus Macromonas

A Gram-stain-negative, non-motile, non-pigmented, rod-shaped bacterium was isolated from a freshwater sample of Nakdong River in South Korea and designated as strain BK-30T. An analysis of the 16S rRNA gene sequence of strain BK-30T revealed its closest phylogenetic neighbors were members of the genus Macromonas. Specifically, the strain formed a robust clade with Macromonas bipunctata DSM 12705T, sharing 98.4% similarity in their 16S rRNA gene sequences. The average nucleotide identity value between strain BK-30T and M. bipunctata DSM 12705T was 79.8%, and the genome-to-genome distance averaged 21.3%, indicating the representation of a novel genomic species. Strain BK-30T exhibited optimum growth at 30 °C and pH 7.0, in the absence of NaCl. The major respiratory isoprenoid quinone identified was ubiquinone-8 (Q-8). The principal fatty acids detected were C16:1ω7c and/or C16:1ω6c (49.6%), C16:0 (27.5%), and C18:1ω7c and/or C18:1 ω6c (9.2%). The DNA G+C content of the strain was determined to be 67.3 mol%. Based on these data, we propose a novel species within the genus Macromonas, named Macromonas nakdongensis sp. nov., to accommodate the bacterial isolate. Strain BK-30T is designated as the type strain (=KCTC 52161T = JCM 31376T = FBCC-B1). Additionally, we present the isolation and complete genome sequence of a lytic phage infecting strain BK-30T, named BK-30P. This bacteriophage is the first reported to infect Macromonas, leading us to propose the name "Macromonasphage".

Distribution and abundance of tetraether lipid cyclization genes in terrestrial hot springs reflect pH

Many Archaea produce membrane-spanning lipids that enable life in extreme environments. These isoprenoid glycerol dibiphytanyl glycerol tetraethers (GDGTs) may contain up to eight cyclopentyl and one cyclohexyl ring, where higher degrees of cyclization are associated with more acidic, hotter or energy-limited conditions. Recently, the genes encoding GDGT ring synthases, grsAB, were identified in two Sulfolobaceae; however, the distribution and abundance of grs homologs across environments inhabited by these and related organisms remain a mystery. To address this, we examined the distribution of grs homologs in relation to environmental temperature and pH, from thermal springs across Earth, where sequences derive from metagenomes, metatranscriptomes, single-cell and cultivar genomes. The abundance of grs homologs shows a strong negative correlation to pH, but a weak positive correlation to temperature. Archaeal genomes and metagenome-assembled genomes (MAGs) that carry two or more grs copies are more abundant in low pH springs. We also find grs in 12 archaeal classes, with the most representatives in Thermoproteia, followed by MAGs of the uncultured Korarchaeia, Bathyarchaeia and Hadarchaeia, while several Nitrososphaeria encodes >3 copies. Our findings highlight the key role of grs-catalysed lipid cyclization in archaeal diversification across hot and acidic environments.

Evidence that OLE RNA is a component of a major stress-responsive ribonucleoprotein particle in extremophilic bacteria

OLE RNA is a ~600-nucleotide noncoding RNA present in many Gram-positive bacteria that thrive mostly in extreme environments, including elevated temperature, salt, and pH conditions. The precise biochemical functions of this highly conserved RNA remain unknown, but it forms a ribonucleoprotein (RNP) complex that localizes to cell membranes. Genetic disruption of the RNA or its essential protein partners causes reduced cell growth under various stress conditions. These phenotypes include sensitivity to short-chain alcohols, cold intolerance, reduced growth on sub-optimal carbon sources, and intolerance of even modest concentrations of Mg2+ . Thus, many bacterial species appear to employ OLE RNA as a component of an intricate RNP apparatus to monitor fundamental cellular processes and make physiological and metabolic adaptations. Herein we hypothesize that the OLE RNP complex is functionally equivalent to the eukaryotic TOR complexes, which integrate signals from various diverse pathways to coordinate processes central to cell growth, replication, and survival.

Metagenomic Analysis of a Concrete Bridge Reveals a Microbial Community Dominated by Halophilic Bacteria and Archaea

Concrete hosts a small but diverse microbiome that changes over time. Shotgun metagenomic sequencing would enable assessment of both the diversity and function of the microbial community in concrete, but a number of unique challenges make this difficult for concrete samples. The high concentration of divalent cations in concrete interferes with nucleic acid extraction, and the extremely low biomass in concrete means that DNA from laboratory contamination may be a large fraction of the sequence data. Here, we develop an improved method for DNA extraction from concrete, with higher yield and lower laboratory contamination. To show that this method provides DNA of sufficient quality and quantity to do shotgun metagenomic sequencing, DNA was extracted from a sample of concrete obtained from a road bridge and sequenced with an Illumina MiSeq system. This microbial community was dominated by halophilic Bacteria and Archaea, with enriched functional pathways related to osmotic stress responses. Although this was a pilot-scale effort, we demonstrate that metagenomic sequencing can be used to characterize microbial communities in concrete and that older concrete structures may host different microbes than recently poured concrete. IMPORTANCE Prior work on the microbial communities of concrete focused on the surfaces of concrete structures such as sewage pipes or bridge pilings, where thick biofilms were easy to observe and sample. Because the biomass inside concrete is so low, more recent analyses of the microbial communities inside concrete used amplicon sequencing methods to describe those communities. However, to understand the activity and physiology of microbes in concrete, or to develop living infrastructure, we must develop more direct methods of community analysis. The method developed here for DNA extraction and metagenomic sequencing can be used for analysis of microbial communities inside concrete and can likely be adapted for other cementitious materials.

MariClus: Your One-Stop Platform for Information on Marine Natural Products, Their Gene Clusters and Producing Organisms

BACKGROUND

The marine environment hosts the vast majority of living species and marine microbes that produce natural products with great potential in providing lead compounds for drug development. With over 70% of Earth's surface covered in water and the high interaction rate associated with liquid environments, this has resulted in many marine natural product discoveries. Our improved understanding of the biosynthesis of these molecules, encoded by gene clusters, along with increased genomic information will aid us in uncovering even more novel compounds.

RESULTS

We introduce MariClus (https://www.mariclus.com), an online user-friendly platform for mining and visualizing marine gene clusters. The first version contains information on clusters and the predicted molecules for over 500 marine-related prokaryotes. The user-friendly interface allows scientists to easily search by species, cluster type or molecule and visualize the information in table format or graphical representation.

CONCLUSIONS

This new online portal simplifies the exploration and comparison of gene clusters in marine species for scientists and assists in characterizing the bioactive molecules they produce. MariClus integrates data from public sources, like GenBank, MIBiG and PubChem, with genome mining results from antiSMASH. This allows users to access and analyze various aspects of marine natural product biosynthesis and diversity.

Metabolic alterations in patients with Helicobacter pylori-related gastritis: The H. pylori-gut microbiota-metabolism axis in progression of the chronic inflammation in the gastric mucosa

PURPOSE

To characterize the serum metabolism in patients with Helicobacter pylori-positive and H. pylori-negative gastritis.

METHODS

Clinical data and serum gastric function parameters, PGI (pepsinogen I), PGII, PGR (PGI/II), and G-17 (gastrin-17) of 117 patients with chronic gastritis were collected, including 57 H. pylori positive and 60 H. pylori negative subjects. Twenty cases in each group were randomly selected to collect intestinal mucosa specimens and serum samples. The gut microbiota profiles were generated by 16S rRNA gene sequencing, and the serum metabolites were analyzed by a targeted metabolomics approach based on liquid chromatography-mass spectrometry (LC-MS) technology.

RESULTS

Altered expression of 20 metabolites, including isovaleric acid, was detected in patients with HPAG. Some taxa of Bacteroides, Fusobacterium, and Prevotella in the gut microbiota showed significant correlations with differentially expressed metabolites between H. pylori positive and H. pylori negative individuals. As a result, an H. pylori-gut microbiota-metabolism (HGM) axis was proposed.

CONCLUSION

Helicobacter pylori infection may influence the progression of mucosal diseases and the emergence of other complications in the host by altering the gut microbiota, and thus affecting the host serum metabolism.

Functional gene-guided enrichment plus in situ microsphere cultivation enables isolation of new crucial ureolytic bacteria from the rumen of cattle

BACKGROUND

Ruminants can utilize urea as a dietary nitrogen source owing to their ability to recycle urea-N back to the rumen where numerous ureolytic bacteria hydrolyze urea into ammonia, which is used by numerous bacteria as their nitrogen source. Rumen ureolytic bacteria are the key microbes making ruminants the only type of animals independent of pre-formed amino acids for survival, thus having attracted much research interest. Sequencing-based studies have helped gain new insights into ruminal ureolytic bacterial diversity, but only a limited number of ureolytic bacteria have been isolated into pure cultures or studied, hindering the understanding of ureolytic bacteria with respect to their metabolism, physiology, and ecology, all of which are required to effectively improve urea-N utilization efficiency.

RESULTS

We established and used an integrated approach, which include urease gene (ureC) guided enrichment plus in situ agarose microsphere embedding and cultivation under rumen-simulating conditions, to isolate ureolytic bacteria from the rumen microbiome. We optimized the dilutions of the rumen microbiome during the enrichment, single-cell embedding, and then in situ cultivation of microsphere-embedded bacteria using dialysis bags placed in rumen fluid. Metabonomic analysis revealed that the dialysis bags had a fermentation profile very similar to the simulated rumen fermentation. In total, we isolated 404 unique strains of bacteria, of which 52 strains were selected for genomic sequencing. Genomic analyses revealed that 28 strains, which were classified into 12 species, contained urease genes. All these ureolytic bacteria represent new species ever identified in the rumen and represented the most abundant ureolytic species. Compared to all the previously isolated ruminal ureolytic species combined, the newly isolated ureolytic bacteria increased the number of genotypically and phenotypically characterized ureolytic species by 34.38% and 45.83%, respectively. These isolated strains have unique genes compared to the known ureolytic strains of the same species indicating their new metabolic functions, especially in energy and nitrogen metabolism. All the ureolytic species were ubiquitous in the rumen of six different species of ruminants and were correlated to dietary urea metabolism in the rumen and milk protein production. We discovered five different organizations of urease gene clusters among the new isolates, and they had varied approaches to hydrolyze urea. The key amino acid residues of the UreC protein that potentially plays critical regulatory roles in urease activation were also identified.

CONCLUSIONS

We established an integrated methodology for the efficient isolation of ureolytic bacteria, which expanded the biological resource of crucial ureolytic bacteria from the rumen. These isolates play a vital role in the incorporation of dietary nitrogen into bacterial biomass and hence contribute to ruminant growth and productivity. Moreover, this methodology can enable efficient isolation and cultivation of other bacteria of interest in the environment and help bridge the knowledge gap between genotypes and phenotypes of uncultured bacteria. Video abstract.

Clinical implications of gut microbiota and cytokine responses in coronavirus disease prognosis

Objectives

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infects gut luminal cells through the angiotensin-converting enzyme-2 receptor and disrupts the gut microbiome. We investigated whether the gut microbiome in the early stage of SARS-CoV-2 infection was associated with the prognosis of coronavirus disease (COVID-19).

Methods

Thirty COVID-19 patients and 16 healthy controls were prospectively enrolled. Blood and stool samples and clinical details were collected on days 0 (enrollment), 7, 14, and 28. Participants were categorized into four groups by their clinical course.

Results

Gut microbiota composition varied during the clinical course of COVID-19 and was closely associated with cytokine levels (p=0.003). A high abundance of the genus Dialister (linear discriminant analysis [LDA] effect size: 3.97856, p=0.004), species Peptoniphilus lacrimalis (LDA effect size: 4.00551, p=0.020), and Anaerococcus prevotii (LDA effect size: 4.00885, p=0.007) was associated with a good prognosis. Starch, sucrose, and galactose metabolism was highly activated in the gut microbiota of the poor prognosis group. Glucose-lowering diets, including whole grains, were positively correlated with a good prognosis.

Conclusion

Gut microbiota may mediate the prognosis of COVID-19 by regulating cytokine responses and controlling glucose metabolism, which is implicated in the host immune response to SARS-CoV-2.

Causal relationships between gut microbiota and programmed cell death protein 1/programmed cell death-ligand 1: A bidirectional Mendelian randomization study

Background

Multiple clinical studies have indicated that the gut microbiota influences the effects of immune checkpoint blockade (ICB) therapy comprising PD-1/PD-L1 inhibitors, but the causal relationship is unclear. Because of numerous confounders, many microbes related to PD-1/PD-L1 have not been identified. This study aimed to determine the causal relationship between the microbiota and PD-1/PD-L1 and identify possible biomarkers for ICB therapy.

Method

We used bidirectional two-sample Mendelian randomization with two different thresholds to explore the potential causal relationship between the microbiota and PD-1/PD-L1 and species-level microbiota GWAS to verify the result.

Result

In the primary forward analysis, genus_Holdemanella showed a negative correlation with PD-1 [βIVW = -0.25; 95% CI (-0.43 to -0.07); PFDR = 0.028] and genus_Prevotella9 showed a positive correlation with PD-1 [βIVW = 0.2; 95% CI (0.1 to 0.4); PFDR = 0.027]; order_Rhodospirillales [βIVW = 0.2; 95% CI (0.1 to 0.4); PFDR = 0.044], family_Rhodospirillaceae [βIVW = 0.2; 95% CI (0 to 0.4); PFDR = 0.032], genus_Ruminococcaceae_UCG005 [βIVW = 0.29; 95% CI (0.08 to 0.5); PFDR = 0.028], genus_Ruminococcus_gnavus_group [βIVW = 0.22; 95% CI (0.05 to 0.4); PFDR = 0.029], and genus_Coprococcus_2 [βIVW = 0.4; 95% CI (0.1 to 0.6); PFDR = 0.018] were positively correlated with PD-L1; and phylum_Firmicutes [βIVW = -0.3; 95% CI (-0.4 to -0.1); PFDR = 0.031], family_ClostridialesvadinBB60group [βIVW = -0.31; 95% CI (-0.5 to -0.11), PFDR = 0.008], family_Ruminococcaceae [βIVW = -0.33; 95% CI (-0.58 to -0.07); PFDR = 0.049], and genus_Ruminococcaceae_UCG014 [βIVW = -0.35; 95% CI (-0.57 to -0.13); PFDR = 0.006] were negatively correlated with PD-L1. The one significant species in further analysis was species_Parabacteroides_unclassified [βIVW = 0.2; 95% CI (0-0.4); PFDR = 0.029]. Heterogeneity (P &gt; 0.05) and pleiotropy (P &gt; 0.05) analyses confirmed the robustness of the MR results.

ICU infection surveillance can be based on electronic routine data: results of a case study

BACKGROUND

The surveillance of hospital-acquired infections in Germany is usually conducted via manual chart review; this, however, proves resource intensive and is prone to a certain degree of subjectivity. Documentation based on electronic routine data may present an alternative to manual methods. We compared the data derived via manual chart review to that which was derived from electronic routine data.

METHODS

Data used for the analyses was obtained from five of the University of Leipzig Medical Center's (ULMC) ICUs. Clinical data was collected according to the Protection against Infection Act (IfSG); documentation thereof was carried out in hospital information systems (HIS) as well as in the ICU-KISS module provided by the National Reference Center for the Surveillance of Nosocomial Infections (NRZ). Algorithmically derived data was generated via an algorithm developed in the EFFECT study; ward-movement data was linked with microbiological test results, generating a data set that allows for evaluation as to whether or not an infection was ICU-acquired.

RESULTS

Approximately 75% of MDRO cases and 85% of cases of sepsis/primary bacteremia were classified as ICU-acquired by both manual chart review and EFFECT. Most discrepancies between the manual and algorithmic approaches were due to differentiating definitions regarding the patients' time at risk for acquiring MDRO/bacteremia.

CONCLUSIONS

The concordance between manual chart review and algorithmically generated data was considerable. This study shows that hospital infection surveillance based on electronically generated routine data may be a worthwhile and sustainable alternative to manual chart review.

nov., a Rare and Metabolically Versatile Member in the Class Alphaproteobacteria

Mangrove microorganisms are a major part of the coastal ecosystem and are directly associated with nutrient cycling. Despite their ecological significance, the collection of culturable mangrove microbes is limited due to difficulties in isolation and cultivation. Here, we report the isolation and genome sequence of strain FT118^T, the first cultured representative of a previously uncultivated order UBA8317 within Alphaproteobacteria, based on the combined results of 16S rRNA gene similarity, phylogenomic, and average amino acid identity analyses. We propose Futianiales ord. nov. and Futianiaceae fam. nov. with Futiania as the type genus, and FT118^T represents the type species with the name Futiania mangrovii gen. nov, sp. nov. The 16S rRNA gene sequence comparison reveals that this novel order is a rare member but has a ubiquitous distribution across various habitats worldwide, which is corroborated by the experimental confirmation that this isolate can physiologically adapt to a wide range of oxygen levels, temperatures, pH and salinity levels. Biochemical characterization, genomic annotation, and metatranscriptomic analysis of FT118^T demonstrate that it is metabolically versatile and active in situ. Genomic analysis reveals adaptive features of Futianiales to fluctuating mangrove environments, including the presence of high- and low-affinity terminal oxidases, N-type ATPase, and the genomic capability of producing various compatible solutes and polyhydroxybutyrate, which possibly allow for the persistence of this novel order across various habitats. Collectively, these results expand the current culture collection of mangrove microorganisms, providing genomic insights of how this novel taxon adapts to fluctuating environments and the culture reference to unravel possible microbe-environment interactions. IMPORTANCE The rare biosphere constitutes an essential part of the microbial community and may drive nutrient cycling and other geochemical processes. However, the difficulty in microbial isolation and cultivation has hampered our understanding of the physiology and ecology of uncultured rare lineages. In this study, we successfully isolated a novel alphaproteobacterium, designated as FT118^T, and performed a combination of phenotypic, phylogenetic, and phylogenomic analyses, confirming that this isolate represents the first cultured member of a previously uncultivated order UBA8317 within Alphaproteobacteria. It is a rare species with a ubiquitous distribution across different habitats. Genomic and metatranscriptomic analyses demonstrate that it is metabolically versatile and active in situ, suggesting its potential role in nutrient cycling despite being scarce. This work not only expands the current phylogeny of isolated Alphaproteobacteria but also provides genomic and culture reference to unravel microbial adaptation strategies in mangrove sediments and possible microbe-environment interactions.

StrainSelect: A novel microbiome reference database that disambiguates all bacterial strains, genome assemblies and extant cultures worldwide

Motivation: Microbial metagenomic profiling software and databases are advancing rapidly for development of novel disease biomarkers and therapeutics yet three problems impede analyses: 1) the conflation of "genome assembly" and "strain" in reference databases; 2) difficulty connecting DNA biomarkers to a procurable strain for laboratory experimentation; and 3) absence of a comprehensive and unified strain-resolved reference database for integrating both shotgun metagenomics and 16S rRNA gene data. Results: We demarcated 681,087 strains, the largest collection of its kind, by filtering public data into a knowledge graph of vertices representing contiguous DNA sequences, genome assemblies, strain monikers and bio-resource center (BRC) catalog numbers then adding inter-vertex edges only for synonyms or direct derivatives. Surprisingly, for 10,043 important strains, we found replicate RefSeq genome assemblies obstructing interpretation of database searches. We organized each strain into eight taxonomic ranks with bootstrap confidence inversely correlated with genome assembly contamination. The StrainSelect database is suited for applications where a taxonomic, functional or procurement reference is needed for shotgun or amplicon metagenomics since 636,568 strains have at least one 16S rRNA gene, 245,005 have at least one annotated genome assembly, and 36,671 are procurable from at least one BRC. The database overcomes all three aforementioned problems since it disambiguates strains from assemblies, locates strains at BRCs, and unifies a taxonomic reference for both 16S rRNA and shotgun metagenomics. Availability: The StrainSelect database is available in igraph and tabular vertex-edge formats compatible with Neo4J. Dereplicated MinHash and fasta databases are distributed for sourmash and usearch pipelines at http://strainselect.secondgenome.com. Contact:todd.desantis@gmail.com. Supplementary information: Supplementary data are available online.

Anaerobic sulphide removal by haloalkaline sulphide oxidising bacteria

Sulphide is a toxic and corrosive compound and requires removal from waste streams. Recent discoveries show that sulphide oxidising bacteria (SOB) from modern desulphurisation plants are able to spatially separate sulphide removal and oxygen reduction when exposed to intermittent anaerobic and aerobic environments. Here, SOB act as electron shuttles between electron donor and acceptor. The underlying mechanisms for electron shuttling are of yet unknown. To investigate the anaerobic sulphide removal of SOB, batch experiments and mathematical models were applied. The sulphide removal capacity decreased at increasing biomass concentrations. At 0.6 mgN/L SOB could remove up to 8 mgS/mgN in 30 min. It was found that biological activity determines sulphide removal, alongside chemical processes. Anaerobic oxidation of electron carriers was determined to only explain 0.1% of charge storage, where irreversible cleavage of long chain polysulphides could explain full sulphide storage. Different sulphide removal and intracellular storage processes are postulated.

Fragment Ends of Circulating Microbial DNA as Signatures for Pathogen Detection in Sepsis

BACKGROUND

Nuclear-derived cell-free DNA (cfDNA) molecules in blood plasma are nonrandomly fragmented, bearing a wealth of information related to tissues of origin. DNASE1L3 (deoxyribonuclease 1 like 3) is an important player in shaping the fragmentation of nuclear-derived cfDNA molecules, preferentially generating molecules with 5 CC dinucleotide termini (i.e., 5 CC-end motif). However, the fragment end properties of microbial cfDNA and its clinical implication remain to be explored.

METHODS

We performed end motif analysis on microbial cfDNA fragments in plasma samples from patients with sepsis. A sequence context-based normalization method was used to minimize the potential biases for end motif analysis.

RESULTS

The end motif profiles of microbial cfDNA appeared to resemble that of nuclear cfDNA (Spearman correlation coefficient: 0.82, P value 0.001). The CC-end motif was the most preferred end motif in microbial cfDNA, suggesting that DNASE1L3 might also play a role in the fragmentation of microbe-derived cfDNA in plasma. Of note, differential end motifs were present between microbial cfDNA originating from infection-causing pathogens (enriched at the CC-end) and contaminating microbial DNA potentially derived from reagents or the environment (nearly random). The use of fragment end signatures allowed differentiation between confirmed pathogens and contaminating microbes, with an area under the receiver operating characteristic curve of 0.99. The performance appeared to be superior to conventional analysis based on microbial cfDNA abundance alone.

CONCLUSIONS

The use of fragmentomic features could facilitate the differentiation of underlying contaminating microbes from true pathogens in sepsis. This work demonstrates the potential usefulness of microbial cfDNA fragmentomics in metagenomics analysis.

Impact of different environmental pollution processes on bacterial key-indicators in tropical rivers: scoping review

Freshwater ecosystems are an essential resource for human use and natural populations, but they are exposed to different sources of man-made pollution. This study analyses how different environmental pollution processes influence the structure of bacterial communities in tropical rivers. A scoping review was performed to characterize the bacterial communities in freshwater ecosystems in tropical regions that have been reported to be associated with pollution of different kinds. The statistical analyses allowed us to categorize the genera found into three large groups (pollution generalists, middle types, and pollution specialists) according to the types of pollutants with which they were associated. The results show that Escherichia has a greater association with fecal contamination, while Enterococcus is more associated with domestic wastewater and organic and synthetic chemicals. The present study proposes Streptomyces as a potential indicator of waters with microbial contamination, as well as some other genera as possible indicators of waters with heavy metal contamination.

Twenty-five years of Genomes OnLine Database (GOLD): data updates and new features in v.9

The Genomes OnLine Database (GOLD) (https://gold.jgi.doe.gov/) at the Department of Energy Joint Genome Institute (DOE-JGI) continues to maintain its role as one of the flagship genomic metadata repositories of the world. The ever-increasing number of projects and metadata are freely available to the user community world-wide. GOLD's metadata is consumed by scientists and remains an important source for large-scale comparative genomics analysis initiatives. Encouraged by this active user engagement and growth, GOLD has continued to add new components and capabilities. The new features such as a public Application Programming Interface (API) and Ecosystem landing page as well as the growth of different entities in this current GOLD v.9 edition are described in detail in this manuscript.

proGenomes3: approaching one million accurately and consistently annotated high-quality prokaryotic genomes

The interpretation of genomic, transcriptomic and other microbial 'omics data is highly dependent on the availability of well-annotated genomes. As the number of publicly available microbial genomes continues to increase exponentially, the need for quality control and consistent annotation is becoming critical. We present proGenomes3, a database of 907 388 high-quality genomes containing 4 billion genes that passed stringent criteria and have been consistently annotated using multiple functional and taxonomic databases including mobile genetic elements and biosynthetic gene clusters. proGenomes3 encompasses 41 171 species-level clusters, defined based on universal single copy marker genes, for which pan-genomes and contextual habitat annotations are provided. The database is available at http://progenomes.embl.de/.

Expanding the taxonomic and environmental extent of an underexplored carbon metabolism-oxalotrophy

Oxalate serves various functions in the biological processes of plants, fungi, bacteria, and animals. It occurs naturally in the minerals weddellite and whewellite (calcium oxalates) or as oxalic acid. The environmental accumulation of oxalate is disproportionately low compared to the prevalence of highly productive oxalogens, namely plants. It is hypothesized that oxalotrophic microbes limit oxalate accumulation by degrading oxalate minerals to carbonates via an under-explored biogeochemical cycle known as the oxalate-carbonate pathway (OCP). Neither the diversity nor the ecology of oxalotrophic bacteria is fully understood. This research investigated the phylogenetic relationships of the bacterial genes oxc, frc, oxdC, and oxlT, which encode key enzymes for oxalotrophy, using bioinformatic approaches and publicly available omics datasets. Phylogenetic trees of oxc and oxdC genes demonstrated grouping by both source environment and taxonomy. All four trees included genes from metagenome-assembled genomes (MAGs) that contained novel lineages and environments for oxalotrophs. In particular, sequences of each gene were recovered from marine environments. These results were supported with marine transcriptome sequences and description of key amino acid residue conservation. Additionally, we investigated the theoretical energy yield from oxalotrophy across marine-relevant pressure and temperature conditions and found similar standard state Gibbs free energy to "low energy" marine sediment metabolisms, such as anaerobic oxidation of methane coupled to sulfate reduction. These findings suggest further need to understand the role of bacterial oxalotrophy in the OCP, particularly in marine environments, and its contribution to global carbon cycling.

Ontology-driven analysis of marine metagenomics: what more can we learn from our data?

BACKGROUND

The proliferation of metagenomic sequencing technologies has enabled novel insights into the functional genomic potentials and taxonomic structure of microbial communities. However, cyberinfrastructure efforts to manage and enable the reproducible analysis of sequence data have not kept pace. Thus, there is increasing recognition of the need to make metagenomic data discoverable within machine-searchable frameworks compliant with the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles for data stewardship. Although a variety of metagenomic web services exist, none currently leverage the hierarchically structured terminology encoded within common life science ontologies to programmatically discover data.

RESULTS

Here, we integrate large-scale marine metagenomic datasets with community-driven life science ontologies into a novel FAIR web service. This approach enables the retrieval of data discovered by intersecting the knowledge represented within ontologies against the functional genomic potential and taxonomic structure computed from marine sequencing data. Our findings highlight various microbial functional and taxonomic patterns relevant to the ecology of prokaryotes in various aquatic environments.

CONCLUSIONS

In this work, we present and evaluate a novel Semantic Web architecture that can be used to ask novel biological questions of existing marine metagenomic datasets. Finally, the FAIR ontology searchable data products provided by our API can be leveraged by future research efforts.

metaGOflow: a workflow for the analysis of marine Genomic Observatories shotgun metagenomics data

BACKGROUND

Genomic Observatories (GOs) are sites of long-term scientific study that undertake regular assessments of the genomic biodiversity. The European Marine Omics Biodiversity Observation Network (EMO BON) is a network of GOs that conduct regular biological community samplings to generate environmental and metagenomic data of microbial communities from designated marine stations around Europe. The development of an effective workflow is essential for the analysis of the EMO BON metagenomic data in a timely and reproducible manner.

FINDINGS

Based on the established MGnify resource, we developed metaGOflow. metaGOflow supports the fast inference of taxonomic profiles from GO-derived data based on ribosomal RNA genes and their functional annotation using the raw reads. Thanks to the Research Object Crate packaging, relevant metadata about the sample under study, and the details of the bioinformatics analysis it has been subjected to, are inherited to the data product while its modular implementation allows running the workflow partially. The analysis of 2 EMO BON samples and 1 Tara Oceans sample was performed as a use case.

CONCLUSIONS

metaGOflow is an efficient and robust workflow that scales to the needs of projects producing big metagenomic data such as EMO BON. It highlights how containerization technologies along with modern workflow languages and metadata package approaches can support the needs of researchers when dealing with ever-increasing volumes of biological data. Despite being initially oriented to address the needs of EMO BON, metaGOflow is a flexible and easy-to-use workflow that can be broadly used for one-sample-at-a-time analysis of shotgun metagenomics data.

Microbiome and metagenomic analysis of Lake Hillier Australia reveals pigment-rich polyextremophiles and wide-ranging metabolic adaptations

Lake Hillier is a hypersaline lake known for its distinctive bright pink color. The cause of this phenomenon in other hypersaline sites has been attributed to halophiles, Dunaliella, and Salinibacter, however, a systematic analysis of the microbial communities, their functional features, and the prevalence of pigment-producing-metabolisms has not been previously studied. Through metagenomic sequencing and culture-based approaches, our results evidence that Lake Hillier is composed of a diverse set of microorganisms including archaea, bacteria, algae, and viruses. Our data indicate that the microbiome in Lake Hillier is composed of multiple pigment-producer microbes, including Dunaliella, Salinibacter, Halobacillus, Psychroflexus, Halorubrum, many of which are cataloged as polyextremophiles. Additionally, we estimated the diversity of metabolic pathways in the lake and determined that many of these are related to pigment production. We reconstructed complete or partial genomes for 21 discrete bacteria (N = 14) and archaea (N = 7), only 2 of which could be taxonomically annotated to previously observed species. Our findings provide the first metagenomic study to decipher the source of the pink color of Australia's Lake Hillier. The study of this pink hypersaline environment is evidence of a microbial consortium of pigment producers, a repertoire of polyextremophiles, a core microbiome and potentially novel species.

Toward FAIR Representations of Microbial Interactions

Despite an ever-growing number of data sets that catalog and characterize interactions between microbes in different environments and conditions, many of these data are neither easily accessible nor intercompatible. These limitations present a major challenge to microbiome research by hindering the streamlined drawing of inferences across studies. Here, we propose guiding principles to make microbial interaction data more findable, accessible, interoperable, and reusable (FAIR). We outline specific use cases for interaction data that span the diverse space of microbiome research, and discuss the untapped potential for new insights that can be fulfilled through broader integration of microbial interaction data. These include, among others, the design of intercompatible synthetic communities for environmental, industrial, or medical applications, and the inference of novel interactions from disparate studies. Lastly, we envision potential trajectories for the deployment of FAIR microbial interaction data based on existing resources, reporting standards, and current momentum within the community.

Supplemental Oxygen Alters the Airway Microbiome in Cystic Fibrosis

Features of the airway microbiome in persons with cystic fibrosis (pwCF) are correlated with disease progression. Microbes have traditionally been classified for their ability to tolerate oxygen. It is unknown whether supplemental oxygen, a common medical intervention, affects the airway microbiome of pwCF. We hypothesized that hyperoxia significantly impacts the pulmonary microbiome in cystic fibrosis. In this study, we cultured spontaneously expectorated sputum from pwCF in artificial sputum medium under 21%, 50%, and 100% oxygen conditions using a previously validated model system that recapitulates microbial community composition in uncultured sputum. Culture aliquots taken at 24, 48, and 72 h, along with uncultured sputum, underwent shotgun metagenomic sequencing with absolute abundance values obtained with the use of spike-in bacteria. Raw sequencing files were processed using the bioBakery pipeline to determine changes in taxonomy, predicted function, antimicrobial resistance genes, and mobile genetic elements. Hyperoxia reduced absolute microbial load, species richness, and diversity. Hyperoxia reduced absolute abundance of specific microbes, including facultative anaerobes such as Rothia and some Streptococcus species, with minimal impact on canonical CF pathogens such as Pseudomonas aeruginosa and Staphylococcus aureus. The effect size of hyperoxia on predicted functional pathways was stronger than that on taxonomy. Large changes in microbial cooccurrence networks were noted. Hyperoxia exposure perturbs airway microbial communities in a manner well tolerated by key pathogens. Supplemental oxygen use may enable the growth of lung pathogens and should be further studied in the clinical setting. IMPORTANCE The airway microbiome in persons with cystic fibrosis (pwCF) is correlated with lung function and disease severity. Supplemental oxygen use is common in more advanced CF, yet its role in perturbing airway microbial communities is unknown. By culturing sputum samples from pwCF under normal and elevated oxygen conditions, we found that increased oxygen led to reduced total numbers and diversity of microbes, with relative sparing of common CF pathogens such as Pseudomonas aeruginosa and Staphylococcus aureus. Supplemental oxygen use may enable the growth of lung pathogens and should be further studied in the clinical setting.

Selection of Relevant Bacterial Strains for Novel Therapeutic Testing: a Guidance Document for Priority Cystic Fibrosis Lung Pathogens

Purpose of Review

People with cystic fibrosis (CF) suffer chronic lung infections with a range of antimicrobial-resistant bacterial pathogens. There is an urgent need for researchers to develop novel anti-infectives to treat these problematic infections, but how can we select bacterial strains which are relevant for robust testing and comparative research?

Recent Findings

Pseudomonas aeruginosa, Burkholderia cepacia complex and Burkholderia gladioli, Mycobacterium abscessus complex, Staphylococcus aureus, Haemophilus influenza, and several multidrug-resistant Gram-negative species were selected as key CF infections that urgently require new therapeutics. Reference isolates and strain panels were identified, and a summary of the known genotypic diversity of each pathogen was provided.

Summary

Here, we summarise the current strain resources available for priority CF bacterial pathogens and highlight systematic selection criteria that researchers can use to select strains for use in therapeutic testing.