Enzyme-specific profiles for genome annotation: PRIAM

PacBio Hi-Fi genome assembly of Sipha maydis, a model for the study of multipartite mutualism in insects

Dependence on multiple nutritional endosymbionts has evolved repeatedly in insects feeding on unbalanced diets. However, reference genomes for species hosting multi-symbiotic nutritional systems are lacking, even though they are essential for deciphering the processes governing cooperative life between insects and anatomically integrated symbionts. The cereal aphid Sipha maydis is a promising model for addressing these issues, as it has evolved a nutritional dependence on two bacterial endosymbionts that complement each other. In this study, we used PacBio High fidelity (HiFi) long-read sequencing to generate a highly contiguous genome assembly of S. maydis with a length of 410 Mb, 3,570 contigs with a contig N50 length of 187 kb, and BUSCO completeness of 95.5%. We identified 117 Mb of repetitive sequences, accounting for 29% of the genome assembly, and predicted 24,453 protein-coding genes, of which 2,541 were predicted enzymes included in an integrated metabolic network with the two aphid-associated endosymbionts. These resources provide valuable genetic and metabolic information for understanding the evolution and functioning of multi-symbiotic systems in insects.

The microbiome of a Pacific moon jellyfish Aurelia coerulea

The impact of microbiome in animal physiology is well appreciated, but characterization of animal-microbe symbiosis in marine environments remains a growing need. This study characterizes the microbial communities associated with the moon jellyfish Aurelia coerulea, first isolated from the East Pacific Ocean and has since been utilized as an experimental system. We find that the microbiome of this Pacific Aurelia culture is dominated by two taxa, a Mollicutes and Rickettsiales. The microbiome is stable across life stages, although composition varies. Mining the host sequencing data, we assembled the bacterial metagenome-assembled genomes (MAGs). The bacterial MAGs are highly reduced, and predict a high metabolic dependence on the host. Analysis using multiple metrics suggest that both bacteria are likely new species. We therefore propose the names Ca. Mariplasma lunae (Mollicutes) and Ca. Marinirickettsia aquamalans (Rickettsiales). Finally, comparison with studies of Aurelia from other geographical populations suggests the association with Ca. Mariplasma lunae occurs in Aurelia from multiple geographical locations. The low-diversity microbiome of Aurelia provides a relatively simple system to study host-microbe interactions.

Machine Learning and Deep Learning in Synthetic Biology: Key Architectures, Applications, and Challenges

Machine learning (ML), particularly deep learning (DL), has made rapid and substantial progress in synthetic biology in recent years. Biotechnological applications of biosystems, including pathways, enzymes, and whole cells, are being probed frequently with time. The intricacy and interconnectedness of biosystems make it challenging to design them with the desired properties. ML and DL have a synergy with synthetic biology. Synthetic biology can be employed to produce large data sets for training models (for instance, by utilizing DNA synthesis), and ML/DL models can be employed to inform design (for example, by generating new parts or advising unrivaled experiments to perform). This potential has recently been brought to light by research at the intersection of engineering biology and ML/DL through achievements like the design of novel biological components, best experimental design, automated analysis of microscopy data, protein structure prediction, and biomolecular implementations of ANNs (Artificial Neural Networks). I have divided this review into three sections. In the first section, I describe predictive potential and basics of ML along with myriad applications in synthetic biology, especially in engineering cells, activity of proteins, and metabolic pathways. In the second section, I describe fundamental DL architectures and their applications in synthetic biology. Finally, I describe different challenges causing hurdles in the progress of ML/DL and synthetic biology along with their solutions.

Evidential deep learning for trustworthy prediction of enzyme commission number

The rapid growth of uncharacterized enzymes and their functional diversity urge accurate and trustworthy computational functional annotation tools. However, current state-of-the-art models lack trustworthiness on the prediction of the multilabel classification problem with thousands of classes. Here, we demonstrate that a novel evidential deep learning model (named ECPICK) makes trustworthy predictions of enzyme commission (EC) numbers with data-driven domain-relevant evidence, which results in significantly enhanced predictive power and the capability to discover potential new motif sites. ECPICK learns complex sequential patterns of amino acids and their hierarchical structures from 20 million enzyme data. ECPICK identifies significant amino acids that contribute to the prediction without multiple sequence alignment. Our intensive assessment showed not only outstanding enhancement of predictive performance on the largest databases of Uniprot, Protein Data Bank (PDB) and Kyoto Encyclopedia of Genes and Genomes (KEGG), but also a capability to discover new motif sites in microorganisms. ECPICK is a reliable EC number prediction tool to identify protein functions of an increasing number of uncharacterized enzymes.

KIPEs3: Automatic annotation of biosynthesis pathways

Flavonoids and carotenoids are pigments involved in stress mitigation and numerous other processes. Both pigment classes can contribute to flower and fruit coloration. Flavonoid aglycones and carotenoids are produced by a pathway that is largely conserved across land plants. Glycosylations, acylations, and methylations of the flavonoid aglycones can be species-specific and lead to a plethora of biochemically diverse flavonoids. We previously developed KIPEs for the automatic annotation of biosynthesis pathways and presented an application on the flavonoid aglycone biosynthesis. KIPEs3 is an improved version with additional features and the potential to identify not just the core biosynthesis players, but also candidates involved in the decoration steps and in the transport of flavonoids. Functionality of KIPEs3 is demonstrated through the analysis of the flavonoid biosynthesis in Arabidopsis thaliana Nd-1, Capsella grandiflora, and Dioscorea dumetorum. We demonstrate the applicability of KIPEs to other pathways by adding the carotenoid biosynthesis to the repertoire. As a technical proof of concept, the carotenoid biosynthesis was analyzed in the same species and Daucus carota. KIPEs3 is available as an online service to enable access without prior bioinformatics experience. KIPEs3 facilitates the automatic annotation and analysis of biosynthesis pathways with a consistent and high quality in a large number of plant species. Numerous genome sequencing projects are generating a huge amount of data sets that can be analyzed to identify evolutionary patterns and promising candidate genes for biotechnological and breeding applications.

Machine Learning-Guided Protein Engineering

Recent progress in engineering highly promising biocatalysts has increasingly involved machine learning methods. These methods leverage existing experimental and simulation data to aid in the discovery and annotation of promising enzymes, as well as in suggesting beneficial mutations for improving known targets. The field of machine learning for protein engineering is gathering steam, driven by recent success stories and notable progress in other areas. It already encompasses ambitious tasks such as understanding and predicting protein structure and function, catalytic efficiency, enantioselectivity, protein dynamics, stability, solubility, aggregation, and more. Nonetheless, the field is still evolving, with many challenges to overcome and questions to address. In this Perspective, we provide an overview of ongoing trends in this domain, highlight recent case studies, and examine the current limitations of machine learning-based methods. We emphasize the crucial importance of thorough experimental validation of emerging models before their use for rational protein design. We present our opinions on the fundamental problems and outline the potential directions for future research.

MetaPro: a scalable and reproducible data processing and analysis pipeline for metatranscriptomic investigation of microbial communities

BACKGROUND

Whole microbiome RNASeq (metatranscriptomics) has emerged as a powerful technology to functionally interrogate microbial communities. A key challenge is how best to process, analyze, and interpret these complex datasets. In a typical application, a single metatranscriptomic dataset may comprise from tens to hundreds of millions of sequence reads. These reads must first be processed and filtered for low quality and potential contaminants, before being annotated with taxonomic and functional labels and subsequently collated to generate global bacterial gene expression profiles.

RESULTS

Here, we present MetaPro, a flexible, massively scalable metatranscriptomic data analysis pipeline that is cross-platform compatible through its implementation within a Docker framework. MetaPro starts with raw sequence read input (single-end or paired-end reads) and processes them through a tiered series of filtering, assembly, and annotation steps. In addition to yielding a final list of bacterial genes and their relative expression, MetaPro delivers a taxonomic breakdown based on the consensus of complementary prediction algorithms, together with a focused breakdown of enzymes, readily visualized through the Cytoscape network visualization tool. We benchmark the performance of MetaPro against two current state-of-the-art pipelines and demonstrate improved performance and functionality.

CONCLUSIONS

MetaPro represents an effective integrated solution for the processing and analysis of metatranscriptomic datasets. Its modular architecture allows new algorithms to be deployed as they are developed, ensuring its longevity. To aid user uptake of the pipeline, MetaPro, together with an established tutorial that has been developed for educational purposes, is made freely available at https://github.com/ParkinsonLab/MetaPro . The software is freely available under the GNU general public license v3. Video Abstract.

Enzyme Commission Number Prediction and Benchmarking with Hierarchical Dual-core Multitask Learning Framework

Enzyme commission (EC) numbers, which associate a protein sequence with the biochemical reactions it catalyzes, are essential for the accurate understanding of enzyme functions and cellular metabolism. Many ab initio computational approaches were proposed to predict EC numbers for given input protein sequences. However, the prediction performance (accuracy, recall, and precision), usability, and efficiency of existing methods decreased seriously when dealing with recently discovered proteins, thus still having much room to be improved. Here, we report HDMLF, a hierarchical dual-core multitask learning framework for accurately predicting EC numbers based on novel deep learning techniques. HDMLF is composed of an embedding core and a learning core; the embedding core adopts the latest protein language model for protein sequence embedding, and the learning core conducts the EC number prediction. Specifically, HDMLF is designed on the basis of a gated recurrent unit framework to perform EC number prediction in the multi-objective hierarchy, multitasking manner. Additionally, we introduced an attention layer to optimize the EC prediction and employed a greedy strategy to integrate and fine-tune the final model. Comparative analyses against 4 representative methods demonstrate that HDMLF stably delivers the highest performance, which improves accuracy and F1 score by 60% and 40% over the state of the art, respectively. An additional case study of tyrB predicted to compensate for the loss of aspartate aminotransferase aspC, as reported in a previous experimental study, shows that our model can also be used to uncover the enzyme promiscuity. Finally, we established a web platform, namely, ECRECer (https://ecrecer.biodesign.ac.cn), using an entirely could-based serverless architecture and provided an offline bundle to improve usability.

Recent advances in microbial production of medium chain fatty acid from renewable carbon resources: a comprehensive review

Microbial production of medium chain length fatty acids (MCFAs) from renewable resources is becoming increasingly important in establishing a sustainable and clean chemical industry. This review comprehensively summarizes current advances in microbial MCFA production from renewable resources. Detailed information is provided on two major MCFA production pathways using various renewable resources and other auxiliary pathways supporting MCFA production to help understand the fundamentals of bio-based MCFA production. In addition, conventional and well-studied MCFA producers are classified into two categories, natural and synthetic producers, and their characteristics on MCFA production are outlined. Moreover, various engineering strategies employed to achieve the highest MCFAs production up to date are showcased together with key enzymes suggested for MCFA overproduction. Finally, future challenges and perspectives are discussed towards more efficient production of bio-based MCFA production.

Draft Genome Sequence of Escherichia coli DBS1, Isolated from a Patient with Urinary Tract Infections in Morocco

Here, we present the draft genome assembly of Escherichia coli DBS1, which was originally isolated from a urine sample from a male patient with urinary tract infections in Rabat, Morocco.

Discovery of a novel filamentous prophage in the genome of the Mimosa pudica microsymbiont Cupriavidus taiwanensis STM 6018

Integrated virus genomes (prophages) are commonly found in sequenced bacterial genomes but have rarely been described in detail for rhizobial genomes. Cupriavidus taiwanensis STM 6018 is a rhizobial Betaproteobacteria strain that was isolated in 2006 from a root nodule of a Mimosa pudica host in French Guiana, South America. Here we describe features of the genome of STM 6018, focusing on the characterization of two different types of prophages that have been identified in its genome. The draft genome of STM 6018 is 6,553,639 bp, and consists of 80 scaffolds, containing 5,864 protein-coding genes and 61 RNA genes. STM 6018 contains all the nodulation and nitrogen fixation gene clusters common to symbiotic Cupriavidus species; sharing >99.97% bp identity homology to the nod/nif/noeM gene clusters from C. taiwanensis LMG19424^T and "Cupriavidus neocalidonicus" STM 6070. The STM 6018 genome contains the genomes of two prophages: one complete Mu-like capsular phage and one filamentous phage, which integrates into a putative dif site. This is the first characterization of a filamentous phage found within the genome of a rhizobial strain. Further examination of sequenced rhizobial genomes identified filamentous prophage sequences in several Beta-rhizobial strains but not in any Alphaproteobacterial rhizobia.

Synthetic Biology Meets Machine Learning

This chapter outlines the myriad applications of machine learning (ML) in synthetic biology, specifically in engineering cell and protein activity, and metabolic pathways. Though by no means comprehensive, the chapter highlights several prominent computational tools applied in the field and their potential use cases. The examples detailed reinforce how ML algorithms can enhance synthetic biology research by providing data-driven insights into the behavior of living systems, even without detailed knowledge of their underlying mechanisms. By doing so, ML promises to increase the efficiency of research projects by modeling hypotheses in silico that can then be tested through experiments. While challenges related to training dataset generation and computational costs remain, ongoing improvements in ML tools are paving the way for smarter and more streamlined synthetic biology workflows that can be readily employed to address grand challenges across manufacturing, medicine, engineering, agriculture, and beyond.

Application of comparative genomics of Acetobacter species facilitates genome-scale metabolic reconstruction of the Acetobacter ghanensis LMG 23848T and Acetobacter senegalensis 108B cocoa strains

Acetobacter species play an import role during cocoa fermentation. However, Acetobacter ghanensis and Acetobacter senegalensis are outcompeted during fermentation of the cocoa pulp-bean mass, whereas Acetobacter pasteurianus prevails. In this paper, an in silico approach aimed at delivering some insights into the possible metabolic adaptations of A. ghanensis LMG 23848^T and A. senegalensis 108B, two candidate starter culture strains for cocoa fermentation processes, by reconstructing genome-scale metabolic models (GEMs). Therefore, genome sequence data of a selection of strains of Acetobacter species were used to perform a comparative genomic analysis. Combining the predicted orthologous groups of protein-encoding genes from the Acetobacter genomes with gene-reaction rules of GEMs from two reference bacteria, namely a previously manually curated model of A. pasteurianus 386B (iAp386B454) and two manually curated models of Escherichia coli (EcoCyc and iJO1366), allowed to predict the set of reactions present in A. ghanensis LMG 23848^T and A. senegalensis 108B. The predicted metabolic network was manually curated using genome re-annotation data, followed by the reconstruction of species-specific GEMs. This approach additionally revealed possible differences concerning the carbon core metabolism and redox metabolism among Acetobacter species, pointing to a hitherto unexplored metabolic diversity. More specifically, the presence or absence of reactions related to citrate catabolism and the glyoxylate cycle for assimilation of C2 compounds provided not only new insights into cocoa fermentation but also interesting guidelines for future research. In general, the A. ghanensis LMG 23848^T and A. senegalensis 108B GEMs, reconstructed in a semi-automated way, provided a proof-of-concept toward accelerated formation of GEMs of candidate functional starter cultures for food fermentation processes.

The banana genome hub: a community database for genomics in the Musaceae

The Banana Genome Hub provides centralized access for genome assemblies, annotations, and the extensive related omics resources available for bananas and banana relatives. A series of tools and unique interfaces are implemented to harness the potential of genomics in bananas, leveraging the power of comparative analysis, while recognizing the differences between datasets. Besides effective genomic tools like BLAST and the JBrowse genome browser, additional interfaces enable advanced gene search and gene family analyses including multiple alignments and phylogenies. A synteny viewer enables the comparison of genome structures between chromosome-scale assemblies. Interfaces for differential expression analyses, metabolic pathways and GO enrichment were also added. A catalogue of variants spanning the banana diversity is made available for exploration, filtering, and export to a wide variety of software. Furthermore, we implemented new ways to graphically explore gene presence-absence in pangenomes as well as genome ancestry mosaics for cultivated bananas. Besides, to guide the community in future sequencing efforts, we provide recommendations for nomenclature of locus tags and a curated list of public genomic resources (assemblies, resequencing, high density genotyping) and upcoming resources-planned, ongoing or not yet public. The Banana Genome Hub aims at supporting the banana scientific community for basic, translational, and applied research and can be accessed at https://banana-genome-hub.southgreen.fr.

A chromosome-level, haplotype-phased Vanilla planifolia genome highlights the challenge of partial endoreplication for accurate whole-genome assembly

Vanilla planifolia, the species cultivated to produce one of the world's most popular flavors, is highly prone to partial genome endoreplication, which leads to highly unbalanced DNA content in cells. We report here the first molecular evidence of partial endoreplication at the chromosome scale by the assembly and annotation of an accurate haplotype-phased genome of V. planifolia. Cytogenetic data demonstrated that the diploid genome size is 4.09 Gb, with 16 chromosome pairs, although aneuploid cells are frequently observed. Using PacBio HiFi and optical mapping, we assembled and phased a diploid genome of 3.4 Gb with a scaffold N50 of 1.2 Mb and 59 128 predicted protein-coding genes. The atypical k-mer frequencies and the uneven sequencing depth observed agreed with our expectation of unbalanced genome representation. Sixty-seven percent of the genes were scattered over only 30% of the genome, putatively linking gene-rich regions and the endoreplication phenomenon. By contrast, low-coverage regions (non-endoreplicated) were rich in repeated elements but also contained 33% of the annotated genes. Furthermore, this assembly showed distinct haplotype-specific sequencing depth variation patterns, suggesting complex molecular regulation of endoreplication along the chromosomes. This high-quality, anchored assembly represents 83% of the estimated V. planifolia genome. It provides a significant step toward the elucidation of this complex genome. To support post-genomics efforts, we developed the Vanilla Genome Hub, a user-friendly integrated web portal that enables centralized access to high-throughput genomic and other omics data and interoperable use of bioinformatics tools.

Architect: A tool for aiding the reconstruction of high-quality metabolic models through improved enzyme annotation

Constraint-based modeling is a powerful framework for studying cellular metabolism, with applications ranging from predicting growth rates and optimizing production of high value metabolites to identifying enzymes in pathogens that may be targeted for therapeutic interventions. Results from modeling experiments can be affected at least in part by the quality of the metabolic models used. Reconstructing a metabolic network manually can produce a high-quality metabolic model but is a time-consuming task. At the same time, current methods for automating the process typically transfer metabolic function based on sequence similarity, a process known to produce many false positives. We created Architect, a pipeline for automatic metabolic model reconstruction from protein sequences. First, it performs enzyme annotation through an ensemble approach, whereby a likelihood score is computed for an EC prediction based on predictions from existing tools; for this step, our method shows both increased precision and recall compared to individual tools. Next, Architect uses these annotations to construct a high-quality metabolic network which is then gap-filled based on likelihood scores from the ensemble approach. The resulting metabolic model is output in SBML format, suitable for constraints-based analyses. Through comparisons of enzyme annotations and curated metabolic models, we demonstrate improved performance of Architect over other state-of-the-art tools, notably with higher precision and recall on the eukaryote C. elegans and when compared to UniProt annotations in two bacterial species. Code for Architect is available at https://github.com/ParkinsonLab/Architect. For ease-of-use, Architect can be readily set up and utilized using its Docker image, maintained on Docker Hub.

Systematic profiling of the chicken gut microbiome reveals dietary supplementation with antibiotics alters expression of multiple microbial pathways with minimal impact on community structure

BACKGROUND

The emergence of antimicrobial resistance is a major threat to global health and has placed pressure on the livestock industry to eliminate the use of antibiotic growth promotants (AGPs) as feed additives. To mitigate their removal, efficacious alternatives are required. AGPs are thought to operate through modulating the gut microbiome to limit opportunities for colonization by pathogens, increase nutrient utilization, and reduce inflammation. However, little is known concerning the underlying mechanisms. Previous studies investigating the effects of AGPs on the poultry gut microbiome have largely focused on 16S rDNA surveys based on a single gastrointestinal (GI) site, diet, and/or timepoint, resulting in an inconsistent view of their impact on community composition.

METHODS

In this study, we perform a systematic investigation of both the composition and function of the chicken gut microbiome, in response to AGPs. Birds were raised under two different diets and AGP treatments, and 16S rDNA surveys applied to six GI sites sampled at three key timepoints of the poultry life cycle. Functional investigations were performed through metatranscriptomics analyses and metabolomics.

RESULTS

Our study reveals a more nuanced view of the impact of AGPs, dependent on age of bird, diet, and intestinal site sampled. Although AGPs have a limited impact on taxonomic abundances, they do appear to redefine influential taxa that may promote the exclusion of other taxa. Microbiome expression profiles further reveal a complex landscape in both the expression and taxonomic representation of multiple pathways including cell wall biogenesis, antimicrobial resistance, and several involved in energy, amino acid, and nucleotide metabolism. Many AGP-induced changes in metabolic enzyme expression likely serve to redirect metabolic flux with the potential to regulate bacterial growth or produce metabolites that impact the host.

CONCLUSIONS

As alternative feed additives are developed to mimic the action of AGPs, our study highlights the need to ensure such alternatives result in functional changes that are consistent with site-, age-, and diet-associated taxa. The genes and pathways identified in this study are therefore expected to drive future studies, applying tools such as community-based metabolic modeling, focusing on the mechanistic impact of different dietary regimes on the microbiome. Consequently, the data generated in this study will be crucial for the development of next-generation feed additives targeting gut health and poultry production. Video Abstract.

Comparative Genomics and Physiology of Akkermansia muciniphila Isolates from Human Intestine Reveal Specialized Mucosal Adaptation

Akkermansia muciniphila is a champion of mucin degradation in the human gastrointestinal tract. Here, we report the isolation of six novel strains from healthy human donors and their genomic, proteomic and physiological characterization in comparison to the type-strains A. muciniphila MucT and A. glycaniphila PytT. Complete genome sequencing revealed that, despite their large genomic similarity (>97.6%), the novel isolates clustered into two distinct subspecies of A. muciniphila: Amuc1, which includes the type-strain MucT, and AmucU, a cluster of unassigned strains that have not yet been well characterized. CRISPR analysis showed all strains to be unique and confirmed that single healthy subjects can carry more than one A. muciniphila strain. Mucin degradation pathways were strongly conserved amongst all isolates, illustrating the exemplary niche adaptation of A. muciniphila to the mucin interface. This was confirmed by analysis of the predicted glycoside hydrolase profiles and supported by comparing the proteomes of A. muciniphila strain H2, belonging to the AmucU cluster, to MucT and A. glycaniphila PytT (including 610 and 727 proteins, respectively). While some intrinsic resistance was observed among the A. muciniphila straind, none of these seem to pose strain-specific risks in terms of their antibiotic resistance patterns nor a significant risk for the horizontal transfer of antibiotic resistance determinants, opening the way to apply the type-strain MucT or these new A. muciniphila strains as next generation beneficial microbes.

Acetate Degradation at Low pH by the Moderately Acidophilic Sulfate Reducer Acididesulfobacillus acetoxydans gen. nov. sp. nov

In acid drainage environments, biosulfidogenesis by sulfate-reducing bacteria (SRB) attenuates the extreme conditions by enabling the precipitation of metals as their sulfides, and the neutralization of acidity through proton consumption. So far, only a handful of moderately acidophilic SRB species have been described, most of which are merely acidotolerant. Here, a novel species within a novel genus of moderately acidophilic SRB is described, Acididesulfobacillus acetoxydans gen. nov. sp. nov. strain INE, able to grow at pH 3.8. Bioreactor studies with strain INE at optimum (5.0) and low (3.9) pH for growth showed that strain INE alkalinized its environment, and that this was more pronounced at lower pH. These studies also showed the capacity of strain INE to completely oxidize organic acids to CO₂, which is uncommon among acidophilic SRB. Since organic acids are mainly in their protonated form at low pH, which increases their toxicity, their complete oxidation may be an acid stress resistance mechanism. Comparative proteogenomic and membrane lipid analysis further indicated that the presence of saturated ether-bound lipids in the membrane, and their relative increase at lower pH, was a protection mechanism against acid stress. Interestingly, other canonical acid stress resistance mechanisms, such as a Donnan potential and increased active charge transport, did not appear to be active.

Candidatus Kaistella beijingensis sp. nov., Isolated from a Municipal Wastewater Treatment Plant, Is Involved in Sludge Foaming

Biological foaming (or biofoaming) is a frequently occurring problem in wastewater treatment plants (WWTPs) and is attributed to the overwhelming growth of filamentous bulking and foaming bacteria (BFB). Biological foaming has been intensively investigated, with BFB like Microthrix and Skermania having been identified from WWTPs and implicated in foaming. Nevertheless, studies are still needed to improve our understanding of the microbial diversity of WWTP biofoams and how microbial activities contribute to foaming. In this study, sludge foaming at the Qinghe WWTP of China was monitored, and sludge foams were investigated using culture-dependent and culture-independent microbiological methods. The foam microbiomes exhibited high abundances of Skermania, Mycobacterium, Flavobacteriales, and Kaistella. A previously unknown bacterium, Candidatus Kaistella beijingensis, was cultivated from foams, its genome was sequenced, and it was phenotypically characterized. Ca. K. beijingensis exhibits hydrophobic cell surfaces, produces extracellular polymeric substances (EPS), and metabolizes lipids. Ca. K. beijingensis abundances were proportional to EPS levels in foams. Several proteins encoded by the Ca. K. beijingensis genome were identified from EPS that was extracted from sludge foams. Ca. K. beijingensis populations accounted for 4 to 6% of the total bacterial populations in sludge foam samples within the Qinghe WWTP, although their abundances were higher in spring than in other seasons. Cooccurrence analysis indicated that Ca. K. beijingensis was not a core node among the WWTP community network, but its abundances were negatively correlated with those of the well-studied BFB Skermania piniformis among cross-season Qinghe WWTP communities. IMPORTANCE Biological foaming, also known as scumming, is a sludge separation problem that has become the subject of major concern for long-term stable activated sludge operation in decades. Biological foaming was considered induced by foaming bacteria. However, the occurrence and deterioration of foaming in many WWTPs are still not completely understood. Cultivation and characterization of the enriched bacteria in foaming are critical to understand their genetic, physiological, phylogenetic, and ecological traits, as well as to improve the understanding of their relationships with foaming and performance of WWTPs.

Plant Metabolic Network 15: A resource of genome-wide metabolism databases for 126 plants and algae

To understand and engineer plant metabolism, we need a comprehensive and accurate annotation of all metabolic information across plant species. As a step towards this goal, we generated genome-scale metabolic pathway databases of 126 algal and plant genomes, ranging from model organisms to crops to medicinal plants (https://plantcyc.org). Of these, 104 have not been reported before. We systematically evaluated the quality of the databases, which revealed that our semi-automated validation pipeline dramatically improves the quality. We then compared the metabolic content across the 126 organisms using multiple correspondence analysis and found that Brassicaceae, Poaceae, and Chlorophyta appeared as metabolically distinct groups. To demonstrate the utility of this resource, we used recently published sorghum transcriptomics data to discover previously unreported trends of metabolism underlying drought tolerance. We also used single-cell transcriptomics data from the Arabidopsis root to infer cell type-specific metabolic pathways. This work shows the quality and quantity of our resource and demonstrates its wide-ranging utility in integrating metabolism with other areas of plant biology.

Genomic convergence between Akkermansia muciniphila in different mammalian hosts

Background

Akkermansia muciniphila is a member of the human gut microbiota where it resides in the mucus layer and uses mucin as the sole carbon, nitrogen and energy source. A. muciniphila is the only representative of the Verrucomicrobia phylum in the human gut. However, A. muciniphila 16S rRNA gene sequences have also been found in the intestines of many vertebrates.

Results

We detected A. muciniphila-like bacteria in the intestines of animals belonging to 15 out of 16 mammalian orders. In addition, other species belonging to the Verrucomicrobia phylum were detected in fecal samples. We isolated 10 new A. muciniphila strains from the feces of chimpanzee, siamang, mouse, pig, reindeer, horse and elephant. The physiology and genome of these strains were highly similar in comparison to the type strain A. muciniphila Muc^T. Overall, the genomes of the new strains showed high average nucleotide identity (93.9 to 99.7%). In these genomes, we detected considerable conservation of at least 75 of the 78 mucin degradation genes that were previously detected in the genome of the type strain Muc^T.

Conclusions

The low genomic divergence observed in the new strains may indicate that A. muciniphila favors mucosal colonization independent of the differences in hosts. In addition, the conserved mucus degradation capability points towards a similar beneficial role of the new strains in regulating host metabolic health.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-021-02360-6.

Decreased Abundance of Akkermansia muciniphila Leads to the Impairment of Insulin Secretion and Glucose Homeostasis in Lean Type 2 Diabetes

Although obesity occurs in most of the patients with type 2 diabetes (T2D), a fraction of patients with T2D are underweight or have normal weight. Several studies have linked the gut microbiome to obesity and T2D, but the role of gut microbiota in lean individuals with T2D having unique clinical characteristics remains unclear. A metagenomic and targeted metabolomic analysis is conducted in 182 lean and abdominally obese individuals with and without newly diagnosed T2D. The abundance of Akkermansia muciniphila (A. muciniphila) significantly decreases in lean individuals with T2D than without T2D, but not in the comparison of obese individuals with and without T2D. Its abundance correlates inversely with serum 3β-chenodeoxycholic acid (βCDCA) levels and positively with insulin secretion and fibroblast growth factor 15/19 (FGF15/19) concentrations. The supplementation with A. muciniphila is sufficient to protect mice against high sucrose-induced impairment of glucose intolerance by decreasing βCDCA and increasing insulin secretion and FGF15/19. Furthermore, βCDCA inhibits insulin secretion and FGF15/19 expression. These findings suggest that decreased abundance of A. muciniphila is linked to the impairment of insulin secretion and glucose homeostasis in lean T2D, paving the way for new therapeutic options for the prevention or treatment of diabetes.

Sawfly Genomes Reveal Evolutionary Acquisitions That Fostered the Mega-Radiation of Parasitoid and Eusocial Hymenoptera

The tremendous diversity of Hymenoptera is commonly attributed to the evolution of parasitoidism in the last common ancestor of parasitoid sawflies (Orussidae) and wasp-waisted Hymenoptera (Apocrita). However, Apocrita and Orussidae differ dramatically in their species richness, indicating that the diversification of Apocrita was promoted by additional traits. These traits have remained elusive due to a paucity of sawfly genome sequences, in particular those of parasitoid sawflies. Here, we present comparative analyses of draft genomes of the primarily phytophagous sawfly Athalia rosae and the parasitoid sawfly Orussus abietinus. Our analyses revealed that the ancestral hymenopteran genome exhibited traits that were previously considered unique to eusocial Apocrita (e.g., low transposable element content and activity) and a wider gene repertoire than previously thought (e.g., genes for CO₂ detection). Moreover, we discovered that Apocrita evolved a significantly larger array of odorant receptors than sawflies, which could be relevant to the remarkable diversification of Apocrita by enabling efficient detection and reliable identification of hosts.

Transcriptor: a comprehensive platform for annotation of the enzymatic functions of transcripts

MOTIVATION

Rapid advances in sequencing technology have resulted huge increases in the accessibility of sequencing data. Moreover, researchers are focusing more on organisms that lack a reference genome. However, few easy-to-use web servers focusing on annotations of enzymatic functions are available. Accordingly, in this study, we describe Transcriptor, a novel platform for annotating transcripts encoding enzymes.

RESULTS

The transcripts were evaluated using more than 300 000 in-house enzymatic reactions through bridges of Enzyme Commission numbers. Transcriptor also enabled ontology term identification and along with associated enzymes, visualization and prediction of domains and annotation of regulatory structure, such as long noncoding RNAs, which could facilitate the discovery of new functions in model or nonmodel species. Transcriptor may have applications in elucidation of the roles of organs transcriptomes and secondary metabolite biosynthesis in organisms lacking a reference genome.

AVAILABILITY AND IMPLEMENTATION

Transcriptor is available at http://design.rxnfinder.org/transcriptor/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A multi-omic characterization of temperature stress in a halotolerant Scenedesmus strain for algal biotechnology

Microalgae efficiently convert sunlight into lipids and carbohydrates, offering bio-based alternatives for energy and chemical production. Improving algal productivity and robustness against abiotic stress requires a systems level characterization enabled by functional genomics. Here, we characterize a halotolerant microalga Scenedesmus sp. NREL 46B-D3 demonstrating peak growth near 25 °C that reaches 30 g/m2/day and the highest biomass accumulation capacity post cell division reported to date for a halotolerant strain. Functional genomics analysis revealed that genes involved in lipid production, ion channels and antiporters are expanded and expressed. Exposure to temperature stress shifts fatty acid metabolism and increases amino acids synthesis. Co-expression analysis shows that many fatty acid biosynthesis genes are overexpressed with specific transcription factors under cold stress. These and other genes involved in the metabolic and regulatory response to temperature stress can be further explored for strain improvement.

Genomic insights into the host specific adaptation of the Pneumocystis genus

Pneumocystis jirovecii, the fungal agent of human Pneumocystis pneumonia, is closely related to macaque Pneumocystis. Little is known about other Pneumocystis species in distantly related mammals, none of which are capable of establishing infection in humans. The molecular basis of host specificity in Pneumocystis remains unknown as experiments are limited due to an inability to culture any species in vitro. To explore Pneumocystis evolutionary adaptations, we have sequenced the genomes of species infecting macaques, rabbits, dogs and rats and compared them to available genomes of species infecting humans, mice and rats. Complete whole genome sequence data enables analysis and robust phylogeny, identification of important genetic features of the host adaptation, and estimation of speciation timing relative to the rise of their mammalian hosts. Our data reveals insights into the evolution of P. jirovecii, the sole member of the genus able to infect humans.

Cissé, Ma et al. utilize genomic data from Pneumocystis species infecting macaques, rabbit, dogs and rats to investigate the molecular basis of host specificity in Pneumocystis. Their analyses provide insight to the specific adaptations enabling the infection of humans by P. jirovecii.

Machine learning for metabolic engineering: A review

Machine learning provides researchers a unique opportunity to make metabolic engineering more predictable. In this review, we offer an introduction to this discipline in terms that are relatable to metabolic engineers, as well as providing in-depth illustrative examples leveraging omics data and improving production. We also include practical advice for the practitioner in terms of data management, algorithm libraries, computational resources, and important non-technical issues. A variety of applications ranging from pathway construction and optimization, to genetic editing optimization, cell factory testing, and production scale-up are discussed. Moreover, the promising relationship between machine learning and mechanistic models is thoroughly reviewed. Finally, the future perspectives and most promising directions for this combination of disciplines are examined.

Predicting metabolic pathways of plant enzymes without using sequence similarity: Models from machine learning

Most of the bioinformatics tools for enzyme annotation focus on enzymatic function assignments. Sequence similarity to well-characterized enzymes is often used for functional annotation and to assign metabolic pathways. However, these approaches are not feasible for all sequences leading to inaccurate annotations or lack of metabolic pathway information. Here we present the mApLe (metabolic pathway predictor of plant enzymes), a high-performance machine learning-based tool with models to label the metabolic pathway of enzymes rather than specifying enzymes' reactions. The mApLe uses molecular descriptors of the enzyme sequences to perform predictions without considering sequence similarities with reference sequences. Hence, mApLe can classify a diversity of enzymes, even the ones without any homolog or with incomplete EC numbers. This tool can be used to improve the quality of genomic annotation of plants or to narrow down the number of candidate genes for metabolic engineering researches. The mApLe tool is available online, and the GUI can be locally installed.

Implementation of homology based and non-homology based computational methods for the identification and annotation of orphan enzymes: using Mycobacterium tuberculosis H37Rv as a case study

Background

Homology based methods are one of the most important and widely used approaches for functional annotation of high-throughput microbial genome data. A major limitation of these methods is the absence of well-characterized sequences for certain functions. The non-homology methods based on the context and the interactions of a protein are very useful for identifying missing metabolic activities and functional annotation in the absence of significant sequence similarity. In the current work, we employ both homology and context-based methods, incrementally, to identify local holes and chokepoints, whose presence in the Mycobacterium tuberculosis genome is indicated based on its interaction with known proteins in a metabolic network context, but have not been annotated. We have developed two computational procedures using network theory to identify orphan enzymes (‘Hole finding protocol’) coupled with the identification of candidate proteins for the predicted orphan enzyme (‘Hole filling protocol’). We propose an integrated interaction score based on scores from the STRING database to identify candidate protein sequences for the orphan enzymes from M. tuberculosis, as a case study, which are most likely to perform the missing function.

Results

The application of an automated homology-based enzyme identification protocol, ModEnzA, on M. tuberculosis genome yielded 56 novel enzyme predictions. We further predicted 74 putative local holes, 6 choke points, and 3 high confidence local holes in the genome using ‘Hole finding protocol’. The ‘Hole-filling protocol’ was validated on the E. coli genome using artificial in-silico enzyme knockouts where our method showed 25% increased accuracy, compared to other methods, in assigning the correct sequence for the knocked-out enzyme amongst the top 10 ranks. The method was further validated on 8 additional genomes.

Conclusions

We have developed methods that can be generalized to augment homology-based annotation to identify missing enzyme coding genes and to predict a candidate protein for them. For pathogens such as M. tuberculosis, this work holds significance in terms of increasing the protein repertoire and thereby, the potential for identifying novel drug targets.

The reductive glycine pathway allows autotrophic growth of Desulfovibrio desulfuricans

Six CO2 fixation pathways are known to operate in photoautotrophic and chemoautotrophic microorganisms. Here, we describe chemolithoautotrophic growth of the sulphate-reducing bacterium Desulfovibrio desulfuricans (strain G11) with hydrogen and sulphate as energy substrates. Genomic, transcriptomic, proteomic and metabolomic analyses reveal that D. desulfuricans assimilates CO2 via the reductive glycine pathway, a seventh CO2 fixation pathway. In this pathway, CO2 is first reduced to formate, which is reduced and condensed with a second CO2 to generate glycine. Glycine is further reduced in D. desulfuricans by glycine reductase to acetyl-P, and then to acetyl-CoA, which is condensed with another CO2 to form pyruvate. Ammonia is involved in the operation of the pathway, which is reflected in the dependence of the autotrophic growth rate on the ammonia concentration. Our study demonstrates microbial autotrophic growth fully supported by this highly ATP-efficient CO2 fixation pathway.

eCAMI: simultaneous classification and motif identification for enzyme annotation

MOTIVATION

Carbohydrate-active enzymes (CAZymes) are extremely important to bioenergy, human gut microbiome, and plant pathogen researches and industries. Here we developed a new amino acid k-mer-based CAZyme classification, motif identification and genome annotation tool using a bipartite network algorithm. Using this tool, we classified 390 CAZyme families into thousands of subfamilies each with distinguishing k-mer peptides. These k-mers represented the characteristic motifs (in the form of a collection of conserved short peptides) of each subfamily, and thus were further used to annotate new genomes for CAZymes. This idea was also generalized to extract characteristic k-mer peptides for all the Swiss-Prot enzymes classified by the EC (enzyme commission) numbers and applied to enzyme EC prediction.

RESULTS

This new tool was implemented as a Python package named eCAMI. Benchmark analysis of eCAMI against the state-of-the-art tools on CAZyme and enzyme EC datasets found that: (i) eCAMI has the best performance in terms of accuracy and memory use for CAZyme and enzyme EC classification and annotation; (ii) the k-mer-based tools (including PPR-Hotpep, CUPP and eCAMI) perform better than homology-based tools and deep-learning tools in enzyme EC prediction. Lastly, we confirmed that the k-mer-based tools have the unique ability to identify the characteristic k-mer peptides in the predicted enzymes.

AVAILABILITY AND IMPLEMENTATION

https://github.com/yinlabniu/eCAMI and https://github.com/zhanglabNKU/eCAMI.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Identification, Characterization, and Genomic Analysis of Novel Serratia Temperate Phages from a Gold Mine

Bacteria of the genus Serratia inhabit a variety of ecological niches like water, soil, and the bodies of animals, and have a wide range of lifestyles. Currently, the complete genome sequences of 25 Serratia phages are available in the NCBI database. All of them were isolated from nutrient-rich environments like sewage, with the use of clinical Serratia strains as hosts. In this study, we identified a novel Serratia myovirus named vB_SspM_BZS1. Both the phage and its host Serratia sp. OS31 were isolated from the same oligotrophic environment, namely, an abandoned gold mine (Zloty Stok, Poland). The BZS1 phage was thoroughly characterized here in terms of its genomics, morphology, and infection kinetics. We also demonstrated that Serratia sp. OS31 was lysogenized by mitomycin-inducible siphovirus vB_SspS_OS31. Comparative analyses revealed that vB_SspM_BZS1 and vB_SspS_OS31 were remote from the known Serratia phages. Moreover, vB_SspM_BZS1 was only distantly related to other viruses. However, we discovered similar prophage sequences in genomes of various bacteria here. Additionally, a protein-based similarity network showed a high diversity of Serratia phages in general, as they were scattered across nineteen different clusters. In summary, this work broadened our knowledge on the diverse relationships of Serratia phages.

Modeling the metabolic interplay between a parasitic worm and its bacterial endosymbiont allows the identification of novel drug targets

The filarial nematode Brugia malayi represents a leading cause of disability in the developing world, causing lymphatic filariasis in nearly 40 million people. Currently available drugs are not well-suited to mass drug administration efforts, so new treatments are urgently required. One potential vulnerability is the endosymbiotic bacteria Wolbachia-present in many filariae-which is vital to the worm. Genome scale metabolic networks have been used to study prokaryotes and protists and have proven valuable in identifying therapeutic targets, but have only been applied to multicellular eukaryotic organisms more recently. Here, we present iDC625, the first compartmentalized metabolic model of a parasitic worm. We used this model to show how metabolic pathway usage allows the worm to adapt to different environments, and predict a set of 102 reactions essential to the survival of B. malayi. We validated three of those reactions with drug tests and demonstrated novel antifilarial properties for all three compounds.

Characterization of a SPM-1 metallo-beta-lactamase-producing Pseudomonas aeruginosa by comparative genomics and phenotypic analysis

Pseudomonas aeruginosa is one of the most common pathogens related to healthcare-associated infections. The Brazilian isolate, named CCBH4851, is a multidrug-resistant clone belonging to the sequence type 277. The antimicrobial resistance mechanisms of the CCBH4851 strain are associated with the presence of the bla\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_\text {SPM-1}$$\end{document}SPM-1 gene, encoding a metallo-beta-lactamase, in combination with other exogenously acquired genes. Whole-genome sequencing studies focusing on emerging pathogens are essential to identify key features of their physiology that may lead to the identification of new targets for therapy. Using both Illumina and PacBio sequencing data, we obtained a single contig representing the CCBH4851 genome with annotated features that were consistent with data reported for the species. However, comparative analysis with other Pseudomonas aeruginosa strains revealed genomic differences regarding virulence factors and regulatory proteins. In addition, we performed phenotypic assays that revealed CCBH4851 is impaired in bacterial motilities and biofilm formation. On the other hand, CCBH4851 genome contained acquired genomic islands that carry transcriptional factors, virulence and antimicrobial resistance-related genes. Presence of single nucleotide polymorphisms in the core genome, mainly those located in resistance-associated genes, suggests that these mutations may also influence the multidrug-resistant behavior of CCBH4851. Overall, characterization of Pseudomonas aeruginosa CCBH4851 complete genome revealed the presence of features that strongly relates to the virulence and antibiotic resistance profile of this important infectious agent.

Metagenomic approaches in microbial ecology: an update on whole-genome and marker gene sequencing analyses

Metagenomics and marker gene approaches, coupled with high-throughput sequencing technologies, have revolutionized the field of microbial ecology. Metagenomics is a culture-independent method that allows the identification and characterization of organisms from all kinds of samples. Whole-genome shotgun sequencing analyses the total DNA of a chosen sample to determine the presence of micro-organisms from all domains of life and their genomic content. Importantly, the whole-genome shotgun sequencing approach reveals the genomic diversity present, but can also give insights into the functional potential of the micro-organisms identified. The marker gene approach is based on the sequencing of a specific gene region. It allows one to describe the microbial composition based on the taxonomic groups present in the sample. It is frequently used to analyse the biodiversity of microbial ecosystems. Despite its importance, the analysis of metagenomic sequencing and marker gene data is quite a challenge. Here we review the primary workflows and software used for both approaches and discuss the current challenges in the field.

The genome sequence of the grape phylloxera provides insights into the evolution, adaptation, and invasion routes of an iconic pest

BACKGROUND

Although native to North America, the invasion of the aphid-like grape phylloxera Daktulosphaira vitifoliae across the globe altered the course of grape cultivation. For the past 150 years, viticulture relied on grafting-resistant North American Vitis species as rootstocks, thereby limiting genetic stocks tolerant to other stressors such as pathogens and climate change. Limited understanding of the insect genetics resulted in successive outbreaks across the globe when rootstocks failed. Here we report the 294-Mb genome of D. vitifoliae as a basic tool to understand host plant manipulation, nutritional endosymbiosis, and enhance global viticulture.

RESULTS

Using a combination of genome, RNA, and population resequencing, we found grape phylloxera showed high duplication rates since its common ancestor with aphids, but similarity in most metabolic genes, despite lacking obligate nutritional symbioses and feeding from parenchyma. Similarly, no enrichment occurred in development genes in relation to viviparity. However, phylloxera evolved > 2700 unique genes that resemble putative effectors and are active during feeding. Population sequencing revealed the global invasion began from the upper Mississippi River in North America, spread to Europe and from there to the rest of the world.

CONCLUSIONS

The grape phylloxera genome reveals genetic architecture relative to the evolution of nutritional endosymbiosis, viviparity, and herbivory. The extraordinary expansion in effector genes also suggests novel adaptations to plant feeding and how insects induce complex plant phenotypes, for instance galls. Finally, our understanding of the origin of this invasive species and its genome provide genetics resources to alleviate rootstock bottlenecks restricting the advancement of viticulture.

An in silico survey of Clostridioides difficile extrachromosomal elements

The Gram-positive enteropathogen Clostridioides difficile (Clostridium difficile) is the major cause of healthcare-associated diarrhoea and is also an important cause of community-acquired infectious diarrhoea. Considering the burden of the disease, many studies have employed whole-genome sequencing of bacterial isolates to identify factors that contribute to virulence and pathogenesis. Though extrachromosomal elements (ECEs) such as plasmids are important for these processes in other bacteria, the few characterized plasmids of C. difficile have no relevant functions assigned and no systematic identification of plasmids has been carried out to date. Here, we perform an in silico analysis of publicly available sequence data to show that ~13 % of all C. difficile strains contain ECEs, with 1–6 elements per strain. Our approach identifies known plasmids (e.g. pCD6, pCD630 and cloning plasmids) and six novel putative plasmid families. Our study shows that plasmids are abundant and may encode functions that are relevant for C. difficile physiology. The newly identified plasmids may also form the basis for the construction of novel cloning plasmids for C. difficile that are compatible with existing tools.

Resources and tools for the high-throughput, multi-omic study of intestinal microbiota

The human gut microbiome impacts several aspects of human health and disease, including digestion, drug metabolism and the propensity to develop various inflammatory, autoimmune and metabolic diseases. Many of the molecular processes that play a role in the activity and dynamics of the microbiota go beyond species and genic composition and thus, their understanding requires advanced bioinformatics support. This article aims to provide an up-to-date view of the resources and software tools that are being developed and used in human gut microbiome research, in particular data integration and systems-level analysis efforts. These efforts demonstrate the power of standardized and reproducible computational workflows for integrating and analysing varied omics data and gaining deeper insights into microbe community structure and function as well as host-microbe interactions.

Drosophila-associated bacteria differentially shape the nutritional requirements of their host during juvenile growth

The interplay between nutrition and the microbial communities colonizing the gastrointestinal tract (i.e., gut microbiota) determines juvenile growth trajectory. Nutritional deficiencies trigger developmental delays, and an immature gut microbiota is a hallmark of pathologies related to childhood undernutrition. However, how host-associated bacteria modulate the impact of nutrition on juvenile growth remains elusive. Here, using gnotobiotic Drosophila melanogaster larvae independently associated with Acetobacter pomorum^WJL (Ap^WJL) and Lactobacillus plantarum^NC8 (Lp^NC8), 2 model Drosophila-associated bacteria, we performed a large-scale, systematic nutritional screen based on larval growth in 40 different and precisely controlled nutritional environments. We combined these results with genome-based metabolic network reconstruction to define the biosynthetic capacities of Drosophila germ-free (GF) larvae and its 2 bacterial partners. We first established that Ap^WJL and Lp^NC8 differentially fulfill the nutritional requirements of the ex-GF larvae and parsed such difference down to individual amino acids, vitamins, other micronutrients, and trace metals. We found that Drosophila-associated bacteria not only fortify the host’s diet with essential nutrients but, in specific instances, functionally compensate for host auxotrophies by either providing a metabolic intermediate or nutrient derivative to the host or by uptaking, concentrating, and delivering contaminant traces of micronutrients. Our systematic work reveals that beyond the molecular dialogue engaged between the host and its bacterial partners, Drosophila and its associated bacteria establish an integrated nutritional network relying on nutrient provision and utilization.

A study of gnotobiotic fruit flies shows that the animal is involved in an integrated nutritional network with its facultative commensal bacteria, centered around the utilization and sharing of nutrients.

Identification and Characterization of the First Virulent Phages, Including a Novel Jumbo Virus, Infecting Ochrobactrum spp

The Ochrobactrum genus consists of an extensive repertoire of biotechnologically valuable bacterial strains but also opportunistic pathogens. In our previous study, a novel strain, Ochrobactrum sp. POC9, which enhances biogas production in wastewater treatment plants (WWTPs) was identified and thoroughly characterized. Despite an insightful analysis of that bacterium, its susceptibility to bacteriophages present in WWTPs has not been evaluated. Using raw sewage sample from WWTP and applying the enrichment method, two virulent phages, vB_OspM_OC and vB_OspP_OH, which infect the POC9 strain, were isolated. These are the first virulent phages infecting Ochrobactrum spp. identified so far. Both phages were subjected to thorough functional and genomic analyses, which allowed classification of the vB_OspM_OC virus as a novel jumbo phage, with a genome size of over 227 kb. This phage encodes DNA methyltransferase, which mimics the specificity of cell cycle regulated CcrM methylase, a component of the epigenetic regulatory circuits in Alphaproteobacteria. In this study, an analysis of the overall diversity of Ochrobactrum-specific (pro)phages retrieved from databases and extracted in silico from bacterial genomes was also performed. Complex genome mining allowed us to build similarity networks to compare 281 Ochrobactrum-specific viruses. Analyses of the obtained networks revealed a high diversity of Ochrobactrum phages and their dissimilarity to the viruses infecting other bacteria.

Comparative genomics applied to Mucor species with different lifestyles

BACKGROUND

Despite a growing number of investigations on early diverging fungi, the corresponding lineages have not been as extensively characterized as Ascomycota or Basidiomycota ones. The Mucor genus, pertaining to one of these lineages is not an exception. To this date, a restricted number of Mucor annotated genomes is publicly available and mainly correspond to the reference species, Mucor circinelloides, and to medically relevant species. However, the Mucor genus is composed of a large number of ubiquitous species as well as few species that have been reported to specifically occur in certain habitats. The present study aimed to expand the range of Mucor genomes available and identify potential genomic imprints of adaptation to different environments and lifestyles in the Mucor genus.

RESULTS

In this study, we report four newly sequenced genomes of Mucor isolates collected from non-clinical environments pertaining to species with contrasted lifestyles, namely Mucor fuscus and Mucor lanceolatus, two species used in cheese production (during ripening), Mucor racemosus, a recurrent cheese spoiler sometimes described as an opportunistic animal and human pathogen, and Mucor endophyticus, a plant endophyte. Comparison of these new genomes with those previously available for six Mucor and two Rhizopus (formerly identified as M. racemosus) isolates allowed global structural and functional description such as their TE content, core and species-specific genes and specialized genes. We proposed gene candidates involved in iron metabolism; some of these genes being known to be involved in pathogenicity; and described patterns such as a reduced number of CAZymes in the species used for cheese ripening as well as in the endophytic isolate that might be related to adaptation to different environments and lifestyles within the Mucor genus.

CONCLUSIONS

This study extended the descriptive data set for Mucor genomes, pointed out the complexity of obtaining a robust phylogeny even with multiple genes families and allowed identifying contrasting potentially lifestyle-associated gene repertoires. The obtained data will allow investigating further the link between genetic and its biological data, especially in terms of adaptation to a given habitat.

Casimicrobium huifangae gen. nov., sp. nov., a Ubiquitous "Most-Wanted" Core Bacterial Taxon from Municipal Wastewater Treatment Plants

Microorganisms in wastewater treatment plants (WWTPs) play a key role in the removal of pollutants from municipal and industrial wastewaters. A recent study estimated that activated sludge from global municipal WWTPs harbors 1 × 10⁹ to 2 × 10⁹ microbial species, the majority of which have not yet been cultivated, and 28 core taxa were identified as "most-wanted" ones (L. Wu, D. Ning, B. Zhang, Y. Li, et al., Nat Microbiol 4:1183-1195, 2019, https://doi.org/10.1038/s41564-019-0426-5). Cultivation and characterization of the "most-wanted" core bacteria are critical to understand their genetic, physiological, phylogenetic, and ecological traits, as well as to improve the performance of WWTPs. In this study, we isolated a bacterial strain, designated SJ-1, that represents a novel cluster within Betaproteobacteria and corresponds to OTU_16 within the 28 core taxa in the "most-wanted" list. Strain SJ-1 was identified and nominated as Casimicrobium huifangae gen. nov., sp. nov., of a novel family, Casimicrobiaceae. C. huifangae is ubiquitously distributed and is metabolically versatile. In addition to mineralizing various carbon sources (including carbohydrates, aromatic compounds, and short-chain fatty acids), C. huifangae is capable of nitrate reduction and phosphorus accumulation. The population of C. huifangae accounted for more than 1% of the bacterial population of the activated sludge microbiome from the Qinghe WWTP, which showed seasonal dynamic changes. Cooccurrence analysis suggested that C. huifangae was an important module hub in the bacterial network of Qinghe WWTP.IMPORTANCE The activated sludge process is the most widely applied biotechnology and is one of the best ecosystems to address microbial ecological principles. Yet, the cultivation of core bacteria and the exploration of their physiology and ecology are limited. In this study, the core and novel bacterial taxon C. huifangae was cultivated and characterized. This study revealed that C. huifangae functioned as an important module hub in the activated sludge microbiome, and it potentially plays an important role in municipal wastewater treatment plants.

Genome-Scale Metabolic Reconstruction of Acetobacter pasteurianus 386B, a Candidate Functional Starter Culture for Cocoa Bean Fermentation

Acetobacter pasteurianus 386B is a candidate functional starter culture for the cocoa bean fermentation process. To allow in silico simulations of its related metabolism in response to different environmental conditions, a genome-scale metabolic model for A. pasteurianus 386B was reconstructed. This is the first genome-scale metabolic model reconstruction for a member of the genus Acetobacter. The metabolic network reconstruction process was based on extensive genome re-annotation and comparative genomics analyses. The information content related to the functional annotation of metabolic enzymes and transporters was placed in a metabolic context by exploring and curating a Pathway/Genome Database of A. pasteurianus 386B using the Pathway Tools software. Metabolic reactions and curated gene-protein-reaction associations were bundled into a genome-scale metabolic model of A. pasteurianus 386B, named iAp386B454, containing 454 genes, 322 reactions, and 296 metabolites embedded in two cellular compartments. The reconstructed model was validated by performing growth experiments in a defined medium, which revealed that lactic acid as the sole carbon source could sustain growth of this strain. Further, the reconstruction of the A. pasteurianus 386B genome-scale metabolic model revealed knowledge gaps concerning the metabolism of this strain, especially related to the biosynthesis of its cell envelope and the presence or absence of metabolite transporters.

Rewiring of Metabolic Network in Mycobacterium tuberculosis During Adaptation to Different Stresses

Metabolic adaptation of Mycobacterium tuberculosis (M. tuberculosis) to microbicidal intracellular environment of host macrophages is fundamental to its pathogenicity. However, an in-depth understanding of metabolic adjustments through key reaction pathways and networks is limited. To understand how such changes occur, we measured the cellular metabolome of M. tuberculosis subjected to four microbicidal stresses using liquid chromatography-mass spectrometric multiple reactions monitoring (LC-MRM/MS). Overall, 87 metabolites were identified. The metabolites best describing the separation between stresses were identified through multivariate analysis. The coupling of the metabolite measurements with existing genome-scale metabolic model, and using constraint-based simulation led to several new concepts and unreported observations in M. tuberculosis; such as (i) the high levels of released ammonia as an adaptive response to acidic stress was due to increased flux through L-asparaginase rather than urease activity; (ii) nutrient starvation-induced anaplerotic pathway for generation of TCA intermediates from phosphoenolpyruvate using phosphoenolpyruvate kinase; (iii) quenching of protons through GABA shunt pathway or sugar alcohols as possible mechanisms of early adaptation to acidic and oxidative stresses; and (iv) usage of alternate cofactors by the same enzyme as a possible mechanism of rewiring metabolic pathways to overcome stresses. Besides providing new leads and important nodes that can be used for designing intervention strategies, the study advocates the strength of applying flux balance analyses coupled with metabolomics to get a global picture of complex metabolic adjustments.

The MAR databases: development and implementation of databases specific for marine metagenomics

We introduce the marine databases; MarRef, MarDB and MarCat (https://mmp.sfb.uit.no/databases/), which are publicly available resources that promote marine research and innovation. These data resources, which have been implemented in the Marine Metagenomics Portal (MMP) (https://mmp.sfb.uit.no/), are collections of richly annotated and manually curated contextual (metadata) and sequence databases representing three tiers of accuracy. While MarRef is a database for completely sequenced marine prokaryotic genomes, which represent a marine prokaryote reference genome database, MarDB includes all incomplete sequenced prokaryotic genomes regardless level of completeness. The last database, MarCat, represents a gene (protein) catalog of uncultivable (and cultivable) marine genes and proteins derived from marine metagenomics samples. The first versions of MarRef and MarDB contain 612 and 3726 records, respectively. Each record is built up of 106 metadata fields including attributes for sampling, sequencing, assembly and annotation in addition to the organism and taxonomic information. Currently, MarCat contains 1227 records with 55 metadata fields. Ontologies and controlled vocabularies are used in the contextual databases to enhance consistency. The user-friendly web interface lets the visitors browse, filter and search in the contextual databases and perform BLAST searches against the corresponding sequence databases. All contextual and sequence databases are freely accessible and downloadable from https://s1.sfb.uit.no/public/mar/.

Identification of small molecule enzyme inhibitors as broad-spectrum anthelmintics

Targeting chokepoint enzymes in metabolic pathways has led to new drugs for cancers, autoimmune disorders and infectious diseases. This is also a cornerstone approach for discovery and development of anthelmintics against nematode and flatworm parasites. Here, we performed omics-driven knowledge-based identification of chokepoint enzymes as anthelmintic targets. We prioritized 10 of 186 phylogenetically conserved chokepoint enzymes and undertook a target class repurposing approach to test and identify new small molecules with broad spectrum anthelmintic activity. First, we identified and tested 94 commercially available compounds using an in vitro phenotypic assay, and discovered 11 hits that inhibited nematode motility. Based on these findings, we performed chemogenomic screening and tested 32 additional compounds, identifying 6 more active hits. Overall, 6 intestinal (single-species), 5 potential pan-intestinal (whipworm and hookworm) and 6 pan-Phylum Nematoda (intestinal and filarial species) small molecule inhibitors were identified, including multiple azoles, Tadalafil and Torin-1. The active hit compounds targeted three different target classes in humans, which are involved in various pathways, including carbohydrate, amino acid and nucleotide metabolism. Last, using representative inhibitors from each target class, we demonstrated in vivo efficacy characterized by negative effects on parasite fecundity in hamsters infected with hookworms.

Complete Assembly of the Genome of an Acidovorax citrulli Strain Reveals a Naturally Occurring Plasmid in This Species

Acidovorax citrulli is the causal agent of bacterial fruit blotch (BFB), a serious threat to cucurbit crop production worldwide. Based on genetic and phenotypic properties, A. citrulli strains are divided into two major groups: group I strains have been generally isolated from melon and other non-watermelon cucurbits, while group II strains are closely associated with watermelon. In a previous study, we reported the genome of the group I model strain, M6. At that time, the M6 genome was sequenced by MiSeq Illumina technology, with reads assembled into 139 contigs. Here, we report the assembly of the M6 genome following sequencing with PacBio technology. This approach not only allowed full assembly of the M6 genome, but it also revealed the occurrence of a ∼53 kb plasmid. The M6 plasmid, named pACM6, was further confirmed by plasmid extraction, Southern-blot analysis of restricted fragments and obtention of M6-derivative cured strains. pACM6 occurs at low copy numbers (average of ∼4.1 ± 1.3 chromosome equivalents) in A. citrulli M6 and contains 63 open reading frames (ORFs), most of which (55.6%) encoding hypothetical proteins. The plasmid contains several genes encoding type IV secretion components, and typical plasmid-borne genes involved in plasmid maintenance, replication and transfer. The plasmid also carries an operon encoding homologs of a Fic-VbhA toxin-antitoxin (TA) module. Transcriptome data from A. citrulli M6 revealed that, under the tested conditions, the genes encoding the components of this TA system are among the highest expressed genes in pACM6. Whether this TA module plays a role in pACM6 maintenance is still to be determined. Leaf infiltration and seed transmission assays revealed that, under tested conditions, the loss of pACM6 did not affect the virulence of A. citrulli M6. We also show that pACM6 or similar plasmids are present in several group I strains, but absent in all tested group II strains of A. citrulli.