Horizontal Gene Transfer Building Prokaryote Genomes: Genes Related to Exchange Between Cell and Environment are Frequently Transferred

Sulfate-Reducing Bacteria Isolated from an Oil Field in Kazakhstan and a Description of Pseudodesulfovibrio karagichevae sp. nov

Sulfidogenic bacteria cause numerous issues in the oil industry since they produce sulfide, corroding steel equipment, reducing oil quality, and worsening the environmental conditions in oil fields. The purpose of this work was to isolate and taxonomically identify the sulfidogenic bacteria responsible for the corrosion of steel equipment at the Karazhanbas oil field (Kazakhstan). In this study, we characterized five sulfidogenic strains of the genera Pseudodesulfovibrio, Oleidesulfovibrio, and Acetobacterium isolated from the formation water of the Karazhanbas oil field (Kazakhstan). Sulfate-reducing strain 9FUST revealed 98.9% similarity of the 16S rRNA gene sequence with the closely related strain 'Pseudodesulfovibrio methanolicus' 5S69T and was studied in detail to enhance the taxonomic resolution. Strain 9FUST grew optimally at 23-28 °C, pH 6.5, and 0-2% (w/v) NaCl. The strain used lactate, pyruvate, methanol, ethanol, fructose, ribose, and H2/CO2 (in the presence of acetate) as carbon and energy sources for sulfate reduction. Iso-C17:1 ω11, C15:0, iso-C15:0, and C16:0 were the predominant fatty acids. The genome is 4.20 Mbp with a G + C content of 64.0%. The average nucleotide identity and digital DNA-DNA hybridization values with Pseudodesulfovibrio spp. genomes were 72.5-91.6% (<95%) and 18.5-45.0% (<70%), respectively, and supported our conclusion that 9FUST (=VKM B-3654T = KCTC 25498T) belonged to a novel Pseudodesulfovibrio species, for which the name Pseudodesulfovibrio karagichevae sp. nov. is proposed. Pangenome analysis of sixteen Pseudodesulfovibrio species and functional annotation analysis of identified genes revealed complete modules of enzymes of the main metabolic pathways, characteristic of bacteria of this genus, and unique genes highlighting the adaptations of strain 9FUST in carbohydrate metabolism, nutrient uptake, and environmental stress response. Isolation of these strains expands our understanding of the diversity of sulfidogens in oil reservoirs and can be used to test the effectiveness of biocides used in an oil field.

Meta-analysis of wastewater microbiome for antibiotic resistance profiling

The microbial composition and stress molecules are main drivers influencing the development and spread of antibiotic resistance bacteria (ARBs) and genes (ARGs) in the environment. A reliable and rapid method for identifying associations between microbiome composition and resistome remains challenging. In the present study, secondary metagenome data of sewage and hospital wastewaters were assessed for differential taxonomic and ARG profiling. Subsequently, Random Forest (RF)-based ML models were used to predict ARG profiles based on taxonomic composition and model validation on hospital wastewaters. Total ARG abundance was significantly higher in hospital wastewaters (15 ppm) than sewage (5 ppm), while the resistance towards methicillin, carbapenem, and fluoroquinolone were predominant. Although, Pseudomonas constituted major fraction, Streptomyces, Enterobacter, and Klebsiella were characteristic of hospital wastewaters. Prediction modeling showed that the relative abundance of pathogenic genera Escherichia, Vibrio, and Pseudomonas contributed most towards variations in total ARG count. Moreover, the model was able to identify host-specific patterns for contributing taxa and related ARGs with >90% accuracy in predicting the ARG subtype abundance. More than >80% accuracy was obtained for hospital wastewaters, demonstrating that the model can be validly extrapolated to different types of wastewater systems. Findings from the study showed that the ML approach could identify ARG profile based on bacterial composition including 16S rDNA amplicon data, and can serve as a viable alternative to metagenomic binning for identification of potential hosts of ARGs. Overall, this study demonstrates the promising application of ML techniques for predicting the spread of ARGs and provides guidance for early warning of ARBs emergence.

A global survey of prokaryotic genomes reveals the eco-evolutionary pressures driving horizontal gene transfer

Horizontal gene transfer, the exchange of genetic material through means other than reproduction, is a fundamental force in prokaryotic genome evolution. Genomic persistence of horizontally transferred genes has been shown to be influenced by both ecological and evolutionary factors. However, there is limited availability of ecological information about species other than the habitats from which they were isolated, which has prevented a deeper exploration of ecological contributions to horizontal gene transfer. Here we focus on transfers detected through comparison of individual gene trees to the species tree, assessing the distribution of gene-exchanging prokaryotes across over a million environmental sequencing samples. By analysing detected horizontal gene transfer events, we show distinct functional profiles for recent versus old events. Although most genes transferred are part of the accessory genome, genes transferred earlier in evolution tend to be more ubiquitous within present-day species. We find that co-occurring, interacting and high-abundance species tend to exchange more genes. Finally, we show that host-associated specialist species are most likely to exchange genes with other host-associated specialist species, whereas species found across different habitats have similar gene exchange rates irrespective of their preferred habitat. Our study covers an unprecedented scale of integrated horizontal gene transfer and environmental information, highlighting broad eco-evolutionary trends.

Dissecting the HGT network of carbon metabolic genes in soil-borne microbiota

The microbiota inhabiting soil plays a significant role in essential life-supporting element cycles. Here, we investigated the occurrence of horizontal gene transfer (HGT) and established the HGT network of carbon metabolic genes in 764 soil-borne microbiota genomes. Our study sheds light on the crucial role of HGT components in microbiological diversification that could have far-reaching implications in understanding how these microbial communities adapt to changing environments, ultimately impacting agricultural practices. In the overall HGT network of carbon metabolic genes in soil-borne microbiota, a total of 6,770 nodes and 3,812 edges are present. Among these nodes, phyla Proteobacteria, Actinobacteriota, Bacteroidota, and Firmicutes are predominant. Regarding specific classes, Actinobacteria, Gammaproteobacteria, Alphaproteobacteria, Bacteroidia, Actinomycetia, Betaproteobacteria, and Clostridia are dominant. The Kyoto Encyclopedia of Genes and Genomes (KEGG) functional assignments of glycosyltransferase (18.5%), glycolysis/gluconeogenesis (8.8%), carbohydrate-related transporter (7.9%), fatty acid biosynthesis (6.5%), benzoate degradation (3.1%) and butanoate metabolism (3.0%) are primarily identified. Glycosyltransferase involved in cell wall biosynthesis, glycosylation, and primary/secondary metabolism (with 363 HGT entries), ranks first overwhelmingly in the list of most frequently identified carbon metabolic HGT enzymes, followed by pimeloyl-ACP methyl ester carboxylesterase, alcohol dehydrogenase, and 3-oxoacyl-ACP reductase. Such HGT events mainly occur in the peripheral functions of the carbon metabolic pathway instead of the core section. The inter-microbe HGT genetic traits in soil-borne microbiota genetic sequences that we recognized, as well as their involvement in the metabolism and regulation processes of carbon organic, suggest a pervasive and substantial effect of HGT on the evolution of microbes.

Discrepancies between prokaryotes and eukaryotes need to be considered in soil DNA-based studies

Metabarcoding approaches are exponentially increasing our understanding of soil biodiversity, with a major focus on the bacterial part of the microbiome. Part of the soil diversity are also eukaryotes that include fungi, algae, protists and Metazoa. Nowadays, soil eukaryotes are targeted with the same approaches developed for bacteria and archaea (prokaryotes). However, fundamental differences exist between domains. After providing a short historical overview of the developments of metabarcoding applied to environmental microbiology, we compile the most important differences between domains that prevent direct method transfers between prokaryotic and eukaryotic soil metabarcoding approaches, currently dominated by short-read sequencing. These include the existence of divergent diversity concepts and the variations in eukaryotic morphology that affect sampling and DNA extraction. Furthermore, eukaryotes experienced much more variable evolutionary rates than prokaryotes, which prevent capturing the entire eukaryotic diversity in a soil with a single amplification protocol fit for short-read sequencing. In the final part we focus on future potentials for optimization of eukaryotic metabarcoding that include superior possibility of functionally characterizing eukaryotes and to extend the current information obtained, such as by adding a real quantitative component. This review should optimize future metabarcoding approaches targeting soil eukaryotes and kickstart this promising research direction.

Predicting the abundance of metal resistance genes in subtropical estuaries using amplicon sequencing and machine learning

Heavy metals are a group of anthropogenic contaminants in estuary ecosystems. Bacteria in estuaries counteract the highly concentrated metal toxicity through metal resistance genes (MRGs). Presently, metagenomic technology is popularly used to study MRGs. However, an easier and less expensive method of acquiring MRG information is needed to deepen our understanding of the fate of MRGs. Thus, this study explores the feasibility of using a machine learning approach-namely, random forests (RF)-to predict MRG abundance based on the 16S rRNA amplicon sequenced datasets from subtropical estuaries in China. Our results showed that the total MRG abundance could be predicted by RF models using bacterial composition at different taxonomic levels. Among them, the relative abundance of bacterial phyla had the highest predicted accuracy (71.7 %). In addition, the RF models constructed by bacterial phyla predicted the abundance of six MRG types and nine MRG subtypes with substantial accuracy (R² > 0.600). Five bacterial phyla (Firmicutes, Bacteroidetes, Patescibacteria, Armatimonadetes, and Nitrospirae) substantially determined the variations in MRG abundance. Our findings prove that RF models can predict MRG abundance in South China estuaries during the wet season by using the bacterial composition obtained by 16S rRNA amplicon sequencing.

The Spread of Antibiotic Resistance Genes In Vivo Model

Infections caused by antibiotic-resistant bacteria are a major public health threat. The emergence and spread of antibiotic resistance genes (ARGs) in the environment or clinical setting pose a serious threat to human and animal health worldwide. Horizontal gene transfer (HGT) of ARGs is one of the main reasons for the dissemination of antibiotic resistance in vitro and in vivo environments. There is a consensus on the role of mobile genetic elements (MGEs) in the spread of bacterial resistance. Most drug resistance genes are located on plasmids, and the spread of drug resistance genes among microorganisms through plasmid-mediated conjugation transfer is the most common and effective way for the spread of multidrug resistance. Experimental studies of the processes driving the spread of antibiotic resistance have focused on simple in vitro model systems, but the current in vitro protocols might not correctly reflect the HGT of antibiotic resistance genes in realistic conditions. This calls for better models of how resistance genes transfer and disseminate in vivo. The in vivo model can better mimic the situation that occurs in patients, helping study the situation in more detail. This is crucial to develop innovative strategies to curtail the spread of antibiotic resistance genes in the future. This review aims to give an overview of the mechanisms of the spread of antibiotic resistance genes and then demonstrate the spread of antibiotic resistance genes in the in vivo model. Finally, we discuss the challenges in controlling the spread of antibiotic resistance genes and their potential solutions.

Phylogenies of the 16S rRNA gene and its hypervariable regions lack concordance with core genome phylogenies

BACKGROUND

The 16S rRNA gene is used extensively in bacterial phylogenetics, in species delineation, and now widely in microbiome studies. However, the gene suffers from intragenomic heterogeneity, and reports of recombination and an unreliable phylogenetic signal are accumulating. Here, we compare core gene phylogenies to phylogenies constructed using core gene concatenations to estimate the strength of signal for the 16S rRNA gene, its hypervariable regions, and all core genes at the intra- and inter-genus levels. Specifically, we perform four intra-genus analyses (Clostridium, n = 65; Legionella, n = 47; Staphylococcus, n = 36; and Campylobacter, n = 17) and one inter-genus analysis [41 core genera of the human gut microbiome (31 families, 17 orders, and 12 classes), n = 82].

RESULTS

At both taxonomic levels, the 16S rRNA gene was recombinant and subject to horizontal gene transfer. At the intra-genus level, the gene showed one of the lowest levels of concordance with the core genome phylogeny (50.7% average). Concordance for hypervariable regions was lower still, with entropy masking providing little to no benefit. A major factor influencing concordance was SNP count, which showed a positive logarithmic association. Using this relationship, we determined that 690 ± 110 SNPs were required for 80% concordance (average 16S rRNA gene SNP count was 254). We also found a wide range in 16S-23S-5S rRNA operon copy number among genomes (1-27). At the inter-genus level, concordance for the whole 16S rRNA gene was markedly higher (73.8% - 10th out of 49 loci); however, the most concordant hypervariable regions (V4, V3-V4, and V1-V2) ranked in the third quartile (62.5 to 60.0%).

CONCLUSIONS

Ramifications of a poor phylogenetic performance for the 16S rRNA gene are far reaching. For example, in addition to incorrect species/strain delineation and phylogenetic inference, it has the potential to confound community diversity metrics if phylogenetic information is incorporated - for example, with popular approaches such as Faith's phylogenetic diversity and UniFrac. Our results highlight the problematic nature of these approaches and their use (along with entropy masking) is discouraged. Lastly, the wide range in 16S rRNA gene copy number among genomes also has a strong potential to confound diversity metrics. Video Abstract.

Quantitative microbiological risk assessment of complex microbial community in Prawn farm wastewater and applicability of nanoparticles and probiotics for eliminating of antibiotic-resistant bacteria

The current review highlighted the quantitative microbiological risk assessment of Vibrio parahaemolyticus in Prawn farm wastewaters (PFWWs) and the applicability of nanoparticles for eliminating antibiotic-resistant bacteria (ARB). The high availability of the antibiotics in the environment and their transmission into human through the food-chain might cause unknown health effects. The aquaculture environments are considered as a reservoir for the antibiotic resistance genes (ARGs) and contributed effectively in the increasing of ABR. The metagenomic analysis is used to explore ARGs in the non-clinical environment. V. parahaemolyticus is among the pathogenic bacteria which are transmitted through sea food causing human acute gastroenteritis due to available thermostable direct hemolysin (tdh), adhesins, TDH related hemolysin (trh). The inactivation of pathogenic bacteria using nanoparticles act by disturbing the cell membrane, interrupting the transport system, DNA and mitochondria damage, and oxidizing the cellular component by reactive oxygen species (ROS). The chloramphenicol, nitrofurans, and nitroimidazole are among the prohibited drugs in fish and fishery product. The utilization of probiotics is the most effective and safe alternative for antibiotics in Prawn aquaculture. This review will ensure public understanding among the readers on how they can decrease the risk of the antimicrobial resistance distribution in the environment.

Genome data vs MLST for exploring intraspecific evolutionary history in bacteria: Much is not always better

Genome-based phylogeny has been proposed to be more accurate than phylogeny based in a few genes as MLST-based phylogeny. However, much is not always better. Here we analyzed 368 complete genomes corresponding to 9 bacterial species in order to address intraspecific phylogeny. The studied species were: Burkholderia pseudomallei, Campylobacter jejuni, Chlamydia trachomatis, Helicobacter pylori, Klebsiella pneumoniae, Listeria monocytogenes, Salmonella enterica, Staphylococcus aureus and Streptococcus pyogenes. The intra-specific phylogenies were inferred using the complete genome sequences of different strains of these species and their MLST schemes. A supermatrix approach was used to infer maximum likelihood phylogenies in both cases. The phylogenetic incongruence between the supermatrix-based genome or MLST tree and individual trees (constructed from genome fragments or MLST genes, respectively) was analyzed. In supermatrix-based trees for genomes, most branches showed a high branch support; however, a high number of branches also showed high percentage of topologically incongruent individual trees. Interestingly, genome and MLST trees showed similar levels of incongruence in the phylogeny for each bacteria specie. Both genome and MLST approaches showed that C. trachomatis and S. aureus have a tree-like evolutionary history (low levels of internal incongruence). Instead, B. pseudomallei and S. pyogenes show high levels of incongruence (network-like evolutionary story) probably caused by HGT (horizontal gene transfer). Concluding, our analysis showed that: high branch supports obtained in genome phylogenies could be an artifact probably caused by data size; MLST is valid to address intraspecific phylogenetic structure; and, each species has its own evolutionary history, which could be affected by HGT to different extents.

Identical sequences found in distant genomes reveal frequent horizontal transfer across the bacterial domain

Horizontal gene transfer (HGT) is an essential force in microbial evolution. Despite detailed studies on a variety of systems, a global picture of HGT in the microbial world is still missing. Here, we exploit that HGT creates long identical DNA sequences in the genomes of distant species, which can be found efficiently using alignment-free methods. Our pairwise analysis of 93,481 bacterial genomes identified 138,273 HGT events. We developed a model to explain their statistical properties as well as estimate the transfer rate between pairs of taxa. This reveals that long-distance HGT is frequent: our results indicate that HGT between species from different phyla has occurred in at least 8% of the species. Finally, our results confirm that the function of sequences strongly impacts their transfer rate, which varies by more than three orders of magnitude between different functional categories. Overall, we provide a comprehensive view of HGT, illuminating a fundamental process driving bacterial evolution.

The past, present and future of the tree of life

The advent of comparative genomics in the late 1990s led to the discovery of extensive lateral gene transfer in prokaryotes. The resulting debate over whether life as a whole is best represented as a tree or a network has since given way to a general consensus in which trees and networks co-exist rather than stand in opposition. Embracing this consensus allows us to move beyond the question of which is true or false. The future of the tree of life debate lies in asking what trees and networks can, and should, do for science.

First Steps in the Analysis of Prokaryotic Pan-Genomes

Pan-genome is defined as the set of orthologous and unique genes of a specific group of organisms. The pan-genome is composed by the core genome, accessory genome, and species- or strain-specific genes. The pan-genome is considered open or closed based on the alpha value of the Heap law. In an open pan-genome, the number of gene families will continuously increase with the addition of new genomes to the analysis, while in a closed pan-genome, the number of gene families will not increase considerably. The first step of a pan-genome analysis is the homogenization of genome annotation. The same software should be used to annotate genomes, such as GeneMark or RAST. Subsequently, several software are used to calculate the pan-genome such as BPGA, GET_HOMOLOGUES, PGAP, among others. This review presents all these initial steps for those who want to perform a pan-genome analysis, explaining key concepts of the area. Furthermore, we present the pan-genomic analysis of 9 bacterial species. These are the species with the highest number of genomes deposited in GenBank. We also show the influence of the identity and coverage parameters on the prediction of orthologous and paralogous genes. Finally, we cite the perspectives of several research areas where pan-genome analysis can be used to answer important issues.

Horizontally Acquired Homologs of Xenogeneic Silencers: Modulators of Gene Expression Encoded by Plasmids, Phages and Genomic Islands

Acquisition of mobile elements by horizontal gene transfer can play a major role in bacterial adaptation and genome evolution by providing traits that contribute to bacterial fitness. However, gaining foreign DNA can also impose significant fitness costs to the host bacteria and can even produce detrimental effects. The efficiency of horizontal acquisition of DNA is thought to be improved by the activity of xenogeneic silencers. These molecules are a functionally related group of proteins that possess affinity for the acquired DNA. Binding of xenogeneic silencers suppresses the otherwise uncontrolled expression of genes from the newly acquired nucleic acid, facilitating their integration to the bacterial regulatory networks. Even when the genes encoding for xenogeneic silencers are part of the core genome, homologs encoded by horizontally acquired elements have also been identified and studied. In this article, we discuss the current knowledge about horizontally acquired xenogeneic silencer homologs, focusing on those encoded by genomic islands, highlighting their distribution and the major traits that allow these proteins to become part of the host regulatory networks.

Gene Transmission in the One Health Microbiosphere and the Channels of Antimicrobial Resistance

Antibiotic resistance is a field in which the concept of One Health can best be illustrated. One Health is based on the definition of communication spaces among diverse environments. Antibiotic resistance is encoded by genes, however, these genes are propagated in mobile genetic elements (MGEs), circulating among bacterial species and clones that are integrated into the multiple microbiotas of humans, animals, food, sewage, soil, and water environments, the One Health microbiosphere. The dynamics and evolution of antibiotic resistance depend on the communication networks linking all these ecological, biological, and genetic entities. These communications occur by environmental overlapping and merging, a critical issue in countries with poor sanitation, but also favored by the homogenizing power of globalization. The overwhelming increase in the population of highly uniform food animals has contributed to the parallel increase in the absolute size of their microbiotas, consequently enhancing the possibility of microbiome merging between humans and animals. Microbial communities coalescence might lead to shared microbiomes in which the spread of antibiotic resistance (of human, animal, or environmental origin) is facilitated. Intermicrobiome communication is exerted by shuttle bacterial species (or clones within species) belonging to generalist taxa, able to multiply in the microbiomes of various hosts, including humans, animals, and plants. Their integration into local genetic exchange communities fosters antibiotic resistance gene flow, following the channels of accessory genome exchange among bacterial species. These channels delineate a topology of gene circulation, including dense clusters of species with frequent historical and recent exchanges. The ecological compatibility of these species, sharing the same niches and environments, determines the exchange possibilities. In summary, the fertility of the One Health approach to antibiotic resistance depends on the progress of understanding multihierarchical systems, encompassing communications among environments (macro/microaggregates), among microbiotas (communities), among bacterial species (clones), and communications among MGEs.