Bipartite Network Analysis of Gene Sharings in the Microbial World

Unveiling microbial diversity: harnessing long-read sequencing technology

Long-read sequencing has recently transformed metagenomics, enhancing strain-level pathogen characterization, enabling accurate and complete metagenome-assembled genomes, and improving microbiome taxonomic classification and profiling. These advancements are not only due to improvements in sequencing accuracy, but also happening across rapidly changing analysis methods. In this Review, we explore long-read sequencing's profound impact on metagenomics, focusing on computational pipelines for genome assembly, taxonomic characterization and variant detection, to summarize recent advancements in the field and provide an overview of available analytical methods to fully leverage long reads. We provide insights into the advantages and disadvantages of long reads over short reads and their evolution from the early days of long-read sequencing to their recent impact on metagenomics and clinical diagnostics. We further point out remaining challenges for the field such as the integration of methylation signals in sub-strain analysis and the lack of benchmarks.

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.

Ecology, more than antibiotics consumption, is the major predictor for the global distribution of aminoglycoside-modifying enzymes

Antibiotic consumption and its abuses have been historically and repeatedly pointed out as the major driver of antibiotic resistance emergence and propagation. However, several examples show that resistance may persist despite substantial reductions in antibiotic use, and that other factors are at stake. Here, we study the temporal, spatial, and ecological distribution patterns of aminoglycoside resistance, by screening more than 160,000 publicly available genomes for 27 clusters of genes encoding aminoglycoside-modifying enzymes (AME genes). We find that AME genes display a very ubiquitous pattern: about 25% of sequenced bacteria carry AME genes. These bacteria were sequenced from all the continents (except Antarctica) and terrestrial biomes, and belong to a wide number of phyla. By focusing on European countries between 1997 and 2018, we show that aminoglycoside consumption has little impact on the prevalence of AME-gene-carrying bacteria, whereas most variation in prevalence is observed among biomes. We further analyze the resemblance of resistome compositions across biomes: soil, wildlife, and human samples appear to be central to understand the exchanges of AME genes between different ecological contexts. Together, these results support the idea that interventional strategies based on reducing antibiotic use should be complemented by a stronger control of exchanges, especially between ecosystems.

Mining and the sustainable development goals: Prioritizing SDG targets for proper environmental governance

Inability to ensure sustainable mining practice has brought the awareness that mining enterprises must be more pragmatic on achieving the sustainable development goals (SDGs) in their operations. This research propose a new approach to select and prioritize relevant targets to the industry, which will allow companies, communities, and public authorities to establish a proper framework for environmental governance. The methodology includes appraisal of questionnaires, prioritization of targets following a thorough screening and quantitative assessment, and a bipartite network analysis approach. The results indicate that 55 out of the 169 targets were considered relevant for oil sands mining. The targets in SDGs 1, 12, and 16 were identified as having high or very high priority. There was high correlation between proposed conditions and targets based on the bipartite analysis, which signifies that the people's opinion has relevance in the priority ratings. To achieve SDGs, the implication of mining activities on the environment must be addressed. It was concluded that targets with high relevance in the three phases of mining should be given high consideration when establishing governance principles. Furthermore, engagement of relevant stakeholders that will be impacted directly or indirectly by mining operations is critical in the pursuit of achieving SDGs.

Bioinformatic Analysis of a Set of 14 Temperate Bacteriophages Isolated from Staphylococcus aureus Strains Highlights Their Massive Genetic Diversity

Epidemiology and virulence studies of Staphylococcus aureus showed that temperate bacteriophages are one of the most powerful drivers for its evolution not only because of their abundance but also because of the richness of their genetic payload. Here, we report the isolation, genome sequencing, and bioinformatic analysis of 14 bacteriophages induced from lysogenic S. aureus strains from human or veterinary (cattle) origin. The bacteriophages belonged to the Siphoviridae family; were of similar genome size (40 to 45 kbp); and fell into clusters B2, B3, B5, and B7 according to a recent clustering proposal. One of the phages, namely, vB_SauS_308, was the most unusual one, belonging to the sparsely populated subcluster B7 but showing differences in protein family contents compared with the rest of the members. This phage contains a type I endolysin (one catalytic domain and noncanonical cell wall domain [CBD]) and a host recognition module lacking receptor binding protein, cell wall hydrolase, and tail fiber proteins. This phage also lacked virulence genes, which is opposite to what has been reported for subcluster B6 and B7 members. None of six phages, taken as representatives of each of the four subclusters, showed activity on coagulase-negative staphylococci (excepted for two Staphylococcus hominis strains in which propagation and a very slow adsorption rate were observed) nor transducing ability. Immunity tests on S. aureus RN4220 lysogens with each of these phages showed no cross immunity. IMPORTANCE To the best of our knowledge, this set of sequenced bacteriophages is the largest one in South America. Our report describes for the first time the utilization of MultiTwin software to analyze the relationship between phage protein families. Notwithstanding the fact that most of the genetic information obtained correlated with recently published information, due to their geographical origin, the reported analysis adds up to and confirms currently available knowledge of Staphylococcus aureus temperate bacteriophages in terms of phylogeny and role in host evolution.

High arsenic levels increase activity rather than diversity or abundance of arsenic metabolism genes in paddy soils

Arsenic (As) metabolism genes are generally present in soils but their diversity, relative abundance, and transcriptional activity in response to different As concentrations remain unclear, limiting our understanding of the microbial activities that control the fate of an important environmental pollutant. To address this issue, we applied metagenomics and metatranscriptomics to paddy soils showing a gradient of As concentrations to investigate As resistance genes (ars) including arsR, acr3, arsB, arsC, arsM, arsI, arsP, and arsH as well as energy-generating As respiratory oxidation (aioA) and reduction (arrA) genes. Somewhat unexpectedly, the relative DNA abundances and diversity of ars, aioA, and arrA genes were not significantly different between low and high (∼10 vs ∼100 mg kg^-1) As soils. By comparison to available metagenomes from other soils, geographic distance rather than As levels drove the different compositions of microbial communities. Arsenic significantly increased ars genes abundance only when its concentration was higher than 410 mg kg^-1. In contrast, between low and high As soils, metatranscriptomics revealed a significant increase in transcription of ars and aioA genes, which are induced by arsenite, the dominant As species in paddy soils, but not arrA genes, which are induced by arsenate. These patterns appeared to be community-wide as opposed to taxon-specific. Collectively, our findings advance understanding of how microbes respond to high As levels and the diversity of As metabolism genes in paddy soils and indicated that future studies of As metabolism in soil, or other environments, should include the function (transcriptome) level. IMPORTANCE Arsenic (As) is a toxic metalloid pervasively present in the environment. Microorganisms have evolved the capacity to metabolize As, and As metabolism genes are ubiquitously present in the environment even in the absence of high concentrations of As. However, these previous studies were carried out at the DNA level and thus, the activity of the As metabolism genes detected remains essentially speculative. Here, we show that the high As levels in paddy soils increased the transcriptional activity rather than the relative DNA abundance and diversity of As metabolism genes. These findings advance our understanding of how microbes respond to and cope with high As levels and have implications for better monitoring and managing an important toxic metalloid in agricultural soils and possibly other ecosystems.

Entropic measure unveils country competitiveness and product specialization in the World trade web

We show how the Shannon entropy function can be used as a basis to set up complexity measures weighting the economic efficiency of countries and the specialization of products beyond bare diversification. This entropy function guarantees the existence of a fixed point which is rapidly reached by an iterative scheme converging to our self-consistent measures. Our approach naturally allows to decompose into inter-sectorial and intra-sectorial contributions the country competitivity measure if products are partitioned into larger categories. Besides outlining the technical features and advantages of the method, we describe a wide range of results arising from the analysis of the obtained rankings and we benchmark these observations against those established with other economical parameters. These comparisons allow to partition countries and products into various main typologies, with well-revealed characterizing features. Our methods have wide applicability to general problems of ranking in bipartite networks.

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.

Coevolving Plasmids Drive Gene Flow and Genome Plasticity in Host-Associated Intracellular Bacteria

Plasmids are important in microbial evolution and adaptation to new environments. Yet, carrying a plasmid can be costly, and long-term association of plasmids with their hosts is poorly understood. Here, we provide evidence that the Chlamydiae, a phylum of strictly host-associated intracellular bacteria, have coevolved with their plasmids since their last common ancestor. Current chlamydial plasmids are amalgamations of at least one ancestral plasmid and a bacteriophage. We show that the majority of plasmid genes are also found on chromosomes of extant chlamydiae. The most conserved plasmid gene families are predominantly vertically inherited, while accessory plasmid gene families show significantly increased mobility. We reconstructed the evolutionary history of plasmid gene content of an entire bacterial phylum over a period of around one billion years. Frequent horizontal gene transfer and chromosomal integration events illustrate the pronounced impact of coevolution with these extrachromosomal elements on bacterial genome dynamics in host-dependent microbes.

A network approach to elucidate and prioritize microbial dark matter in microbial communities

Microbes compose most of the biomass on the planet, yet the majority of taxa remain uncharacterized. These unknown microbes, often referred to as “microbial dark matter,” represent a major challenge for biology. To understand the ecological contributions of these Unknown taxa, it is essential to first understand the relationship between unknown species, neighboring microbes, and their respective environment. Here, we establish a method to study the ecological significance of “microbial dark matter” by building microbial co-occurrence networks from publicly available 16S rRNA gene sequencing data of four extreme aquatic habitats. For each environment, we constructed networks including and excluding unknown organisms at multiple taxonomic levels and used network centrality measures to quantitatively compare networks. When the Unknown taxa were excluded from the networks, a significant reduction in degree and betweenness was observed for all environments. Strikingly, Unknown taxa occurred as top hubs in all environments, suggesting that “microbial dark matter” play necessary ecological roles within their respective communities. In addition, novel adaptation-related genes were detected after using 16S rRNA gene sequences from top-scoring hub taxa as probes to blast metagenome databases. This work demonstrates the broad applicability of network metrics to identify and prioritize key Unknown taxa and improve understanding of ecosystem structure across diverse habitats.

The Ingenuity of Bacterial Genomes

The genomes of bacteria contain fewer genes and substantially less noncoding DNA than those of eukaryotes, and as a result, they have much less raw material to invent new traits. Yet, bacteria are vastly more taxonomically diverse, numerically abundant, and globally successful in colonizing new habitats compared to eukaryotes. Although bacterial genomes are generally considered to be optimized for efficient growth and rapid adaptation, nonadaptive processes have played a major role in shaping the size, contents, and compact organization of bacterial genomes and have allowed the establishment of deleterious traits that serve as the raw materials for genetic innovation.

Antibiotic resistance gene sharing networks and the effect of dietary nutritional content on the canine and feline gut resistome

BACKGROUND

As one of the most densely populated microbial communities on Earth, the gut microbiota serves as an important reservoir of antibiotic resistance genes (ARGs), referred to as the gut resistome. Here, we investigated the association of dietary nutritional content with gut ARG diversity and composition, using publicly available shotgun metagenomic sequence data generated from canine and feline fecal samples. Also, based on network theory, we explored ARG-sharing patterns between gut bacterial genera by identifying the linkage structure between metagenomic assemblies and their functional genes obtained from the same data.

RESULTS

In both canine and feline gut microbiota, an increase in protein and a reduction in carbohydrate in the diet were associated with increased ARG diversity. ARG diversity of the canine gut microbiota also increased, but less strongly, after a reduction in protein and an increase in carbohydrate in the diet. The association between ARG and taxonomic composition suggests that diet-induced changes in the gut microbiota may be responsible for changes in ARG composition, supporting the links between protein metabolism and antibiotic resistance in gut microbes. In the analysis of the ARG-sharing patterns, 22 ARGs were shared among 46 genera in the canine gut microbiota, and 11 ARGs among 28 genera in the feline gut microbiota. Of these ARGs, the tetracycline resistance gene tet(W) was shared among the largest number of genera, predominantly among Firmicutes genera. Bifidobacterium, a genus extensively used in the fermentation of dairy products and as probiotics, shared tet(W) with a wide variety of other genera. Finally, genera from the same phylum were more likely to share ARGs than with those from different phyla.

CONCLUSIONS

Our findings show that dietary nutritional content, especially protein content, is associated with the gut resistome and suggest future research to explore the impact of dietary intervention on the development of antibiotic resistance in clinically-relevant gut microbes. Our network analysis also reveals that the genetic composition of bacteria acts as an important barrier to the horizontal transfer of ARGs. By capturing the underlying gene-sharing relationships between different bacterial taxa from metagenomes, our network approach improves our understanding of horizontal gene transfer dynamics.

Networks Consolidate the Core Concepts of Evolution by Natural Selection

Microbiology has unraveled rich evidence of ongoing reticulate evolutionary processes and complex interactions both within and between cells. These phenomena feature real biological networks, which can logically be analyzed using network-based tools. It is thus not surprising that network sciences, a field independent from evolutionary biology and microbiology, have recently pervasively infused their methods into both fields. Importantly, network tools bring forward observations enhancing the understanding of three core evolutionary concepts: variation, fitness, and heredity. Consequently, our work shows how network sciences can enhance evolutionary theory by explaining the evolution by natural selection of a broad diversity of units of selection, while updating the popular figure of Darwin's tree of life with a comprehensive sketch of the networks of evolution.

Eukaryote Genes Are More Likely than Prokaryote Genes to Be Composites

The formation of new genes by combining parts of existing genes is an important evolutionary process. Remodelled genes, which we call composites, have been investigated in many species, however, their distribution across all of life is still unknown. We set out to examine the extent to which genomes from cells and mobile genetic elements contain composite genes. We identify composite genes as those that show partial homology to at least two unrelated component genes. In order to identify composite and component genes, we constructed sequence similarity networks (SSNs) of more than one million genes from all three domains of life, as well as viruses and plasmids. We identified non-transitive triplets of nodes in this network and explored the homology relationships in these triplets to see if the middle nodes were indeed composite genes. In total, we identified 221,043 (18.57%) composites genes, which were distributed across all genomic and functional categories. In particular, the presence of composite genes is statistically more likely in eukaryotes than prokaryotes.

Exploring the evolutionary dynamics of Rhizobium plasmids through bipartite network analysis

The genus Rhizobium usually has a multipartite genome architecture with a chromosome and several plasmids, making these bacteria a perfect candidate for plasmid biology studies. As there are no universally shared genes among typical plasmids, network analyses can complement traditional phylogenetics in a broad-scale study of plasmid evolution. Here, we present an exhaustive analysis of 216 plasmids from 49 complete genomes of Rhizobium by constructing a bipartite network that consists of two classes of nodes, the plasmids and homologous protein families that connect them. Dissection of the network using a hierarchical clustering strategy reveals extensive variety, with 34 homologous plasmid clusters. Four large clusters including one cluster of symbiotic plasmids and two clusters of chromids carrying some truly essential genes are widely distributed among Rhizobium. In contrast, the other clusters are quite small and rare. Symbiotic clusters and rare accessory clusters are exogenetic and do not appear to have co-evolved with the common accessory clusters; the latter ones have a large coding potential and functional complementarity for different lifestyles in Rhizobium. The bipartite network also provides preliminary evidence of Rhizobium plasmid variation and formation including genetic exchange, plasmid fusion and fission, exogenetic plasmid transfer, host plant selection, and environmental adaptation.

Viruses as key reservoirs of antibiotic resistance genes in the environment

Antibiotic resistance is a rapidly growing health care problem globally and causes many illnesses and deaths. Bacteria can acquire antibiotic resistance genes (ARGs) by horizontal transfer mediated by mobile genetic elements, where the role of phages in their dissemination in natural environments has not yet been clearly resolved. From metagenomic studies, we showed that the mean proportion of predicted ARGs found in prophages (0-0.0028%) was lower than those present in the free viruses (0.001-0.1%). Beta-lactamase, from viruses in the swine gut, represented 0.10 % of the predicted genes. Overall, in the environment, the ARG distribution associated with viruses was strongly linked to human activity, and the low dN/dS ratio observed advocated for a negative selection of the ARGs harbored by the viruses. Our network approach showed that viruses were linked to putative pathogens (Enterobacterales and vibrionaceae) and were considered key vehicles in ARG transfer, similar to plasmids. Therefore, these ARGs could then be disseminated at larger temporal and spatial scales than those included in the bacterial genomes, allowing for time-delayed genetic exchanges.

MultiTwin: A Software Suite to Analyze Evolution at Multiple Levels of Organization Using Multipartite Graphs

The inclusion of introgressive processes in evolutionary studies induces a less constrained view of evolution. Network-based methods (like large-scale similarity networks) allow to include in comparative genomics all extrachromosomic carriers (like viruses, the most abundant biological entities on the planet) with their cellular hosts. The integration of several levels of biological organization (genes, genomes, communities, environments) enables more comprehensive analyses of gene sharing and improved sequence-based classifications. However, the algorithmic tools for the analysis of such networks are usually restricted to people with high programming skills. We present an integrated suite of software tools named MultiTwin, aimed at the construction, structuring, and analysis of multipartite graphs for evolutionary biology. Typically, this kind of graph is useful for the comparative analysis of the gene content of genomes in microbial communities from the environment and for exploring patterns of gene sharing, for example between distantly related cellular genomes, pangenomes, or between cellular genomes and their mobile genetic elements. We illustrate the use of this tool with an application of the bipartite approach (using gene family-genome graphs) for the analysis of pathogenicity traits in prokaryotes.

Towards a Dynamic Interaction Network of Life to unify and expand the evolutionary theory

The classic Darwinian theory and the Synthetic evolutionary theory and their linear models, while invaluable to study the origins and evolution of species, are not primarily designed to model the evolution of organisations, typically that of ecosystems, nor that of processes. How could evolutionary theory better explain the evolution of biological complexity and diversity? Inclusive network-based analyses of dynamic systems could retrace interactions between (related or unrelated) components. This theoretical shift from a Tree of Life to a Dynamic Interaction Network of Life, which is supported by diverse molecular, cellular, microbiological, organismal, ecological and evolutionary studies, would further unify evolutionary biology.