DeepCheck: multitask learning aids in assessing microbial genome quality.

Metagenomic analyses facilitate the exploration of the microbial world, advancing our understanding of microbial roles in ecological and biological processes. A pivotal aspect of metagenomic analysis involves assessing the quality of metagenome-assembled genomes (MAGs), crucial for accurate biological insights. Current machine learning-based methods often treat completeness and contamination prediction as separate tasks, overlooking their inherent relationship and limiting models' generalization. In this study, we present DeepCheck, a multitasking deep learning framework for simultaneous prediction of MAG completeness and contamination. DeepCheck consistently outperforms existing tools in accuracy across various experimental settings and demonstrates comparable speed while maintaining high predictive accuracy even for new lineages. Additionally, we employ interpretable machine learning techniques to identify specific genes and pathways that drive the model's predictions, enabling independent investigation and assessment of these biological elements for deeper insights.

MICROPHERRET: MICRObial PHEnotypic tRait ClassifieR using Machine lEarning Techniques.

BACKGROUND: In recent years, there has been a rapid increase in the number of microbial genomes reconstructed through shotgun sequencing, and obtained by newly developed approaches including metagenomic binning and single-cell sequencing. However, our ability to functionally characterize these genomes by experimental assays is orders of magnitude less efficient. Consequently, there is a pressing need for the development of swift and automated strategies for the functional classification of microbial genomes. RESULTS: The present work leverages a suite of supervised machine learning algorithms to establish a range of 86 metabolic and other ecological functions, such as methanotrophy and plastic degradation, starting from widely obtainable microbial genome annotations. Tests performed on independent datasets demonstrated robust performance across complete, fragmented, and incomplete genomes above a 70% completeness level for most of the considered functions. Application of the algorithms to the Biogas Microbiome database yielded predictions broadly consistent with current biological knowledge and correctly detecting functionally-related nuances of archaeal genomes. Finally, a case study focused on acetoclastic methanogenesis demonstrated how the developed machine learning models can be refined or expanded with models describing novel functions of interest. CONCLUSIONS: The resulting tool, MICROPHERRET, incorporates a total of 86 models, one for each tested functional class, and can be applied to high-quality microbial genomes as well as to low-quality genomes derived from metagenomics and single-cell sequencing. MICROPHERRET can thus aid in understanding the functional role of newly generated genomes within their micro-ecological context.

The Comparative Genome Dashboard.

The Comparative Genome Dashboard is a web-based software tool for interactive exploration of the similarities and differences in gene functions between organisms. It provides a high-level graphical survey of cellular functions, and enables the user to drill down to examine subsystems of interest in greater detail. At its highest level the Comparative Dashboard contains panels for cellular systems such as biosynthesis, energy metabolism, transport, and response to stimulus. Each panel contains a set of bar graphs that plot the numbers of compounds or gene products for each organism across a set of subsystems of that panel. Users can interactively drill down to focus on subsystems of interest and see grids of compounds produced or consumed by each organism, specific GO term assignments, pathway diagrams, and links to more detailed comparison pages. For example, the dashboard enables users to compare the cofactors that a set of organisms can synthesize, the metal ions that they are able to transport, their DNA damage repair capabilities, their biofilm-formation genes, and their viral response proteins. The dashboard enables users to quickly perform comprehensive comparisons at varying levels of detail.

Scoary2: rapid association of phenotypic multi-omics data with microbial pan-genomes.

Unraveling bacterial gene function drives progress in various areas, such as food production, pharmacology, and ecology. While omics technologies capture high-dimensional phenotypic data, linking them to genomic data is challenging, leaving 40-60% of bacterial genes undescribed. To address this bottleneck, we introduce Scoary2, an ultra-fast microbial genome-wide association studies (mGWAS) software. With its data exploration app and improved performance, Scoary2 is the first tool to enable the study of large phenotypic datasets using mGWAS. As proof of concept, we explore the metabolome of yogurts, each produced with a different Propionibacterium reichii strain and discover two genes affecting carnitine metabolism.

Draft genome sequence of Streptomyces sp. OS603R isolated from holy basil roots with promising antimicrobial and antitumor potential.

We report the genome sequence of Streptomyces sp. OS603R, isolated from holy basil roots. The strain possesses genes potentially responsible for antimicrobial and antitumor agents. The genome assembly comprises 7,521,075 bps with 72.29% GC content. The genome provides the basis for studies involving genes related to relevant bioactive compounds.

Powerful Microbial Base-Editing Toolbox: From Optimization Strategies to Versatile Applications.

Base editors (BE) based on CRISPR systems are practical gene-editing tools which continue to drive frontier advances of life sciences. BEs are able to efficiently induce point mutations at target sites without double-stranded DNA cleavage. Hence, they are widely employed in the fields of microbial genome engineering. As applications of BEs continue to expand, the demands for base-editing efficiency, fidelity, and versatility are also on the rise. In recent years, a series of optimization strategies for BEs have been developed. By engineering the core components of BEs or adopting different assembly methods, the performance of BEs has been well optimized. Moreover, series of newly established BEs have significantly expanded the base-editing toolsets. In this Review, we will summarize the current efforts for BE optimization, introduce several novel BEs with versatility, and look forward to the broadened applications for industrial microorganisms.

Shine: A novel strategy to extract specific, sensitive and well-conserved biomarkers from massive microbial genomic datasets.

BACKGROUND: Concentrations of the pathogenic microorganisms' DNA in biological samples are typically low. Therefore, DNA diagnostics of common infections are costly, rarely accurate, and challenging. Limited by failing to cover updated epidemic testing samples, computational services are difficult to implement in clinical applications without complex customized settings. Furthermore, the combined biomarkers used to maintain high conservation may not be cost effective and could cause several experimental errors in many clinical settings. Given the limitations of recent developed technology, 16S rRNA is too conserved to distinguish closely related species, and mosaic plasmids are not effective as well because of their uneven distribution across prokaryotic taxa. RESULTS: Here, we provide a computational strategy, Shine, that allows extraction of specific, sensitive and well-conserved biomarkers from massive microbial genomic datasets. Distinguished with simple concatenations with blast-based filtering, our method involves a de novo genome alignment-based pipeline to explore the original and specific repetitive biomarkers in the defined population. It can cover all members to detect newly discovered multicopy conserved species-specific or even subspecies-specific target probes and primer sets. The method has been successfully applied to a number of clinical projects and has the overwhelming advantages of automated detection of all pathogenic microorganisms without the limitations of genome annotation and incompletely assembled motifs. Using on our pipeline, users may select different configuration parameters depending on the purpose of the project for routine clinical detection practices on the website https://bioinfo.liferiver.com.cn with easy registration. CONCLUSIONS: The proposed strategy is suitable for identifying shared phylogenetic markers while featuring low rates of false positive or false negative. This technology is suitable for the automatic design of minimal and efficient PCR primers and other types of detection probes.

Microbial and Viral Genome and Proteome Nitrogen Demand Varies across Multiple Spatial Scales within a Marine Oxygen Minimum Zone.

Nutrient availability can significantly influence microbial genomic and proteomic streamlining, for example, by selecting for lower nitrogen to carbon ratios. Oligotrophic open ocean microbes have streamlined genomic nitrogen requirements relative to those of their counterparts in nutrient-rich coastal waters. However, steep gradients in nutrient availability occur at meter-level, and even micron-level, spatial scales. It is unclear whether such gradients also structure genomic and proteomic stoichiometry. Focusing on the eastern tropical North Pacific oxygen minimum zone (OMZ), we use comparative metagenomics to examine how nitrogen availability shapes microbial and viral genome properties along the vertical gradient across the OMZ and between two size fractions, distinguishing free-living microbes versus particle-associated microbes. We find a substantial increase in the nitrogen content of encoded proteins in particle-associated over free-living bacteria and archaea across nitrogen availability regimes over depth. Within each size fraction, we find that bacterial and viral genomic nitrogen tends to increase with increasing nitrate concentrations with depth. In contrast to cellular genes, the nitrogen content of virus proteins does not differ between size fractions. We identified arginine as a key amino acid in the modulation of the C:N ratios of core genes for bacteria, archaea, and viruses. Functional analysis reveals that particle-associated bacterial metagenomes are enriched for genes that are involved in arginine metabolism and organic nitrogen compound catabolism. Our results are consistent with nitrogen streamlining in both cellular and viral genomes on spatial scales of meters to microns. These effects are similar in magnitude to those previously reported across scales of thousands of kilometers. IMPORTANCE The genomes of marine microbes can be shaped by nutrient cycles, with ocean-scale gradients in nitrogen availability being known to influence microbial amino acid usage. It is unclear, however, how genomic properties are shaped by nutrient changes over much smaller spatial scales, for example, along the vertical transition into oxygen minimum zones (OMZs) or from the exterior to the interior of detrital particles. Here, we measure protein nitrogen usage by marine bacteria, archaea, and viruses by using metagenomes from the nitracline of the eastern tropical North Pacific OMZ, including both particle-associated and nonassociated biomass. Our results show higher genomic and proteomic nitrogen content in particle-associated microbes and at depths with higher nitrogen availability for cellular and viral genomes. This discovery suggests that stoichiometry influences microbial and viral evolution across multiple scales, including the micrometer to millimeter scale associated with particle-associated versus free-living lifestyles.

Reference-based read clustering improves the de novo genome assembly of microbial strains.

Constructing accurate microbial genome assemblies is necessary to understand genetic diversity in microbial genomes and its functional consequences. However, it still remains as a challenging task especially when only short-read sequencing technologies are used. Here, we present a new read-clustering algorithm, called RBRC, for improving de novo microbial genome assembly, by accurately estimating read proximity using multiple reference genomes. The performance of RBRC was confirmed by simulation-based evaluation in terms of assembly contiguity and the number of misassemblies, and was successfully applied to existing fungal and bacterial genomes by improving the quality of the assemblies without using additional sequencing data. RBRC is a very useful read-clustering algorithm that can be used (i) for generating high-quality genome assemblies of microbial strains when genome assemblies of related strains are available, and (ii) for upgrading existing microbial genome assemblies when the generation of additional sequencing data, such as long reads, is difficult.

Prevalence and heterogeneity of antibiotic resistance genes in Orientia tsutsugamushi and other rickettsial genomes.

Despite a million infections every year and an estimated one billion people at risk, scrub typhus is regarded as a neglected tropical disease. The causative bacterium Orientia tsutsugamushi, a member of rickettsiae, seems to be intrinsically resistant to several classes of antibiotics. The emergence of antibiotic-resistant scrub typhus is likely to become a global public health concern. Yet, it is unknown as to how common antibiotic resistance genes are in O. tsutsugamushi, and how variable these loci are among the genomes of rickettsiae. By using the comprehensive antibiotic resistance database, we explored 79 complete genomes from 24 species of rickettsiae for antibiotic resistance loci. There were 244 unique antibiotic resistance genes in rickettsiae. Both the total and unique antibiotic resistance genes in O. tsutsugamushi were significantly less compared to other members of rickettsiae. However, antibiotic resistance genes in O. tsutsugamushi genomes were more unique and highly variable. Many genes such as resistant variants of evgS, and vanS A/G were present in numerous copies. These results will have important implications in the context of antibiotic-resistant scrub typhus.

microTrait: A Toolset for a Trait-Based Representation of Microbial Genomes.

Remote sensing approaches have revolutionized the study of macroorganisms, allowing theories of population and community ecology to be tested across increasingly larger scales without much compromise in resolution of biological complexity. In microbial ecology, our remote window into the ecology of microorganisms is through the lens of genome sequencing. For microbial organisms, recent evidence from genomes recovered from metagenomic samples corroborate a highly complex view of their metabolic diversity and other associated traits which map into high physiological complexity. Regardless, during the first decades of this omics era, microbial ecological research has primarily focused on taxa and functional genes as ecological units, favoring breadth of coverage over resolution of biological complexity manifested as physiological diversity. Recently, the rate at which provisional draft genomes are generated has increased substantially, giving new insights into ecological processes and interactions. From a genotype perspective, the wide availability of genome-centric data requires new data synthesis approaches that place organismal genomes center stage in the study of environmental roles and functional performance. Extraction of ecologically relevant traits from microbial genomes will be essential to the future of microbial ecological research. Here, we present microTrait, a computational pipeline that infers and distills ecologically relevant traits from microbial genome sequences. microTrait maps a genome sequence into a trait space, including discrete and continuous traits, as well as simple and composite. Traits are inferred from genes and pathways representing energetic, resource acquisition, and stress tolerance mechanisms, while genome-wide signatures are used to infer composite, or life history, traits of microorganisms. This approach is extensible to any microbial habitat, although we provide initial examples of this approach with reference to soil microbiomes.

Pediococcus pentosaceus IMI 507025 genome sequencing data.

The genome sequence data for the pickled cucumbers isolate, Pediococcus pentosaceus IMI 507025, is reported. The raw reads and analysed genome reads were deposited at NCBI under Bioproject with the accession number PRJNA814992. The number of contigs before and after trimming were 17 and 12 contigs, respectively. The total size of the genome was 1,795,439 bp containing 1,811 total genes, of which 1,751 were coding sequences. IMI 507025 identity was determined via average nucleotide identity (ANI), obtaining an identity value of 99.5994% between IMI 507025 and the type strain P. pentosaceus ATCC 33316, identifying the strain as P. pentosaceus. Screening for the antimicrobial resistance (AMR) and virulence genes in the genome of IMI 507025 showed no hits, confirming the safety of the tested strain. Presence of plasmids was not found.

KEMET - A python tool for KEGG Module evaluation and microbial genome annotation expansion.

Background: The rapid accumulation of sequencing data from metagenomic studies is enabling the generation of huge collections of microbial genomes, with new challenges for mapping their functional potential. In particular, metagenome-assembled genomes are typically incomplete and harbor partial gene sequences that can limit their annotation from traditional tools. New scalable solutions are thus needed to facilitate the evaluation of functional potential in microbial genomes. Methods: To resolve annotation gaps in microbial genomes, we developed KEMET, an open-source Python library devised for the analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG) functional units. KEMET focuses on the in-depth analysis of metabolic reaction networks to identify missing orthologs through hidden Markov model profiles. Results: We evaluate the potential of KEMET for expanding functional annotations by simulating the effect of assembly issues on real gene sequences and showing that our approach can identify missing KEGG orthologs. Additionally, we show that recovered gene annotations can sensibly increase the quality of draft genome-scale metabolic models obtained from metagenome-assembled genomes, in some cases reaching the accuracy of models generated from complete genomes. Conclusions: KEMET therefore allows expanding genome annotations by targeted searches for orthologous sequences, enabling a better qualitative and quantitative assessment of metabolic capabilities in novel microbial organisms.

Mismatch Intolerance of 5'-Truncated sgRNAs in CRISPR/Cas9 Enables Efficient Microbial Single-Base Genome Editing.

The CRISPR/Cas9 system has recently emerged as a useful gene-specific editing tool. However, this approach occasionally results in the digestion of both the DNA target and similar DNA sequences due to mismatch tolerance, which remains a significant drawback of current genome editing technologies. However, our study determined that even single-base mismatches between the target DNA and 5'-truncated sgRNAs inhibited target recognition. These results suggest that a 5'-truncated sgRNA/Cas9 complex could be used to negatively select single-base-edited targets in microbial genomes. Moreover, we demonstrated that the 5'-truncated sgRNA method can be used for simple and effective single-base editing, as it enables the modification of individual bases in the DNA target, near and far from the 5' end of truncated sgRNAs. Further, 5'-truncated sgRNAs also allowed for efficient single-base editing when using an engineered Cas9 nuclease with an expanded protospacer adjacent motif (PAM; 5'-NG), which may enable whole-genome single-base editing.

Chromosome-Scale Genome Sequence of Alternaria alternata Causing Alternaria Brown Spot of Citrus.

Alternaria brown spot (ABS), caused by Alternaria alternata, is an economically important fungal disease of citrus worldwide. The ABS pathogen A. alternata tangerine pathotype can produce a host-specific ACT toxin, which is regulated by ACT toxin gene cluster located in the conditionally dispensable chromosome (CDC). Previously, we have assembled a draft genome of A. alternata tangerine pathotype strain Z7, which comprises 165 contigs. In this study, we report a chromosome-level genome assembly of A. alternata Z7 through the combination of Oxford Nanopore sequencing and Illumina sequencing technologies. The assembly of A. alternata Z7 had a total size of 34.28 Mb, with a GC content of 51.01% and contig N50 of 3.08 Mb. The genome is encompassed 12,067 protein-coding genes, 34 ribosomal RNAs, and 107 transfer RNAs. Interestingly, A. alternata Z7 is composed of 10 essential chromosomes and 2 CDCs, which is consistent with the experimental evidences of pulsed-field gel electrophoresis. To our best knowledge, this is the first chromosome-level genome assembly of A. alternata. In addition, a database for citrus-related Alternaria genomes has been established to provide public resources for the sequences, annotation and comparative genomics data of Alternaria spp. The improved genome sequence and annotation at the chromosome level is a significant step toward a better understanding of the pathogenicity of A. alternata. The database will be updated regularly whenever the genomes of newly isolated Alternaria spp. are available. The citrus-related Alternaria genomes database is open accessible through the Citrus Fungal Disease Database.[Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

The next frontier of oncotherapy: accomplishing clinical translation of oncolytic bacteria through genetic engineering.

The development of a 'smart' drug capable of distinguishing tumor from host cells has been sought for centuries, but the microenvironment of solid tumors continues to confound therapeutics. Solid tumors present several challenges for current oncotherapeutics, including aberrant vascularization, hypoxia, necrosis, abnormally high pH and local immune suppression. While traditional chemotherapeutics are limited by such an environment, oncolytic microbes are drawn to it - having an innate ability to selectively infect, colonize and eradicate solid tumors. Development of an oncolytic species would represent a shift in the cancer therapeutic paradigm, with ramifications reaching from the medical into the socio-economic. Modern genetic engineering techniques could be implemented to customize 'Frankenstein' bacteria with advantageous characteristics from several species.

Lay abstract Side effects of chemotherapeutics are thought to often be a reflection of our inability to target these toxic substances to only cancer cells; hence, scientists have spent centuries searching for alternative treatments that would confine their actions to tumor cells, sparing healthy tissue. Unfortunately, the dense nature of tumor tissue along with altered blood vessels, that lead to diminished tumor tissue oxygenation, altered tissue pH and cellular metabolic inactivity or even cell death have proven challenging. Importantly, these barriers have contributed to local and even sometimes systemic suppression of the patient's immune system that can allow the tumor to grow and progress unchecked. While most non-cancer cells are inhibited by the local tumor environment, certain microbes, including some bacteria and viruses, are drawn to it, possessing a natural ability to selectively infect, colonize and eradicate solid tumors. These microbes may also restore the patient's immune balance. However, use of these microbes is not without its own problems; nevertheless, modern genetic engineering techniques could be implemented to develop customized, safe, effective bacteria with advantageous characteristics. The development and clinical translation of cancer-fighting bacteria would represent a shift in cancer therapeutics and would have ramifications that reach beyond medical efficacy into the realm of socioeconomics. This review seeks to marry the current field of oncolytic bacteria with the expanding field of modern bacterial genetic engineering techniques in prospect of such a therapeutic.

The discovery of potent immunostimulatory CpG-ODNs widely distributed in bacterial genomes.

Oligodeoxynucleotides containing unmethylated CpG dinucleotides (CpG-ODN) can be specifically recognized by Toll-like receptor 9 (TLR9), provoking innate immune responses. Designed according to this structural feature, many synthetic phosphorothioate CpG-ODNs successfully activate macrophages. However, it is difficult to find potent stimulatory CpG-DNA fragments in microbial genomes. Therefore, whether microbial CpG-DNA substantially contributes to infectious and immune diseases remains controversial. In this study, high-throughput scanning was carried out for thousands of bacterial genomes with bioinformatics tools to comprehensively evaluate the distribution of CpG-DNA fragments. A random sampling test was then performed to verify their immunostimulatory properties by experiments in vitro and in vivo. Natural TLR9-dependent and potent stimulatory CpG-DNA fragments were found in microbial genomes. Interestingly, highly conserved stimulatory CpG-DNA fragments were found in 16S and 23S rDNA sequences with multiple copies, while others were species-specific. Additionally, we found that the reported active motifs were mostly non-stimulatory in natural CpG fragments. This evidence indicates that the previous structural descriptions of functional CpG-ODNs are incomplete. Our study has assessed the distribution of microbial CpG-DNA fragments, and identified natural stimulatory CpG-DNA fragments. These findings provide a deeper understanding of CpG-ODN structures and new evidence for microbial DNA inflammatory function and pathogenicity.

Phylogenomic Analysis Reveals the Evolutionary Route of Resistant Genes in Staphylococcus aureus.

Multidrug-resistant Staphylococcus aureus is a leading concern worldwide. Coagulase-Negative Staphylococci are claimed to be the reservoir and source of important resistant elements in S. aureus. However, the origin and evolutionary route of resistant genes in S. aureus are still remaining unknown. Here, we performed a detailed phylogenomic analysis of 152 completely sequenced S. aureus strains in comparison with 7,529 non-Staphylococcus aureus reference bacterial genomes. Our results reveal that S. aureus has a large open pan-genome where 97 (55%) of its known resistant-related genes belonging to its accessory genome. Among these genes, 47 (27%) were located within the Staphylococcal Cassette Chromosome mec (SCCmec), a transposable element responsible for resistance against major classes of antibiotics including beta-lactams, macrolides, and aminoglycosides. However, the physically linked mec-box genes (MecA-MecR-MecI) that are responsible for the maintenance of SCCmec elements is not unique to S. aureus, instead it is widely distributed within Staphylococcaceae family. The phyletic patterns of SCCmec-encoded resistant genes in Staphylococcus species are significantly different from that of its core genes indicating frequent exchange of these genes between Staphylococcus species. Our in-depth analysis of SCCmec-resistant gene phylogenies reveals that genes such as blaZ, ble, kmA, and tetK that are responsible for beta-lactam, bleomycin, kanamycin, and tetracycline resistance in S. aureus were laterally transferred from non-Staphylococcus sources. In addition, at least 11 non-SCCmec-encoded resistant genes in S. aureus, were laterally acquired from distantly related species. Our study evidently shows that gene transfers played a crucial role in shaping the evolution of antibiotic resistance in S. aureus.

On the evolution and physiology of cable bacteria.

Cable bacteria of the family Desulfobulbaceae form centimeter-long filaments comprising thousands of cells. They occur worldwide in the surface of aquatic sediments, where they connect sulfide oxidation with oxygen or nitrate reduction via long-distance electron transport. In the absence of pure cultures, we used single-filament genomics and metagenomics to retrieve draft genomes of 3 marine Candidatus Electrothrix and 1 freshwater Ca. Electronema species. These genomes contain >50% unknown genes but still share their core genomic makeup with sulfate-reducing and sulfur-disproportionating Desulfobulbaceae, with few core genes lost and 212 unique genes (from 197 gene families) conserved among cable bacteria. Last common ancestor analysis indicates gene divergence and lateral gene transfer as equally important origins of these unique genes. With support from metaproteomics of a Ca. Electronema enrichment, the genomes suggest that cable bacteria oxidize sulfide by reversing the canonical sulfate reduction pathway and fix CO2 using the Wood-Ljungdahl pathway. Cable bacteria show limited organotrophic potential, may assimilate smaller organic acids and alcohols, fix N2, and synthesize polyphosphates and polyglucose as storage compounds; several of these traits were confirmed by cell-level experimental analyses. We propose a model for electron flow from sulfide to oxygen that involves periplasmic cytochromes, yet-unidentified conductive periplasmic fibers, and periplasmic oxygen reduction. This model proposes that an active cable bacterium gains energy in the anodic, sulfide-oxidizing cells, whereas cells in the oxic zone flare off electrons through intense cathodic oxygen respiration without energy conservation; this peculiar form of multicellularity seems unparalleled in the microbial world.