Genome content predicts the carbon catabolic preferences of heterotrophic bacteria

Simple Genomic Traits Predict Rates of Polysaccharide Biodegradation

Natural and chemically modified polysaccharides are extensively employed across a wide array of industries, leading to their prevalence in the waste streams of industrialized societies. With projected increasing demand, a pressing challenge is to swiftly assess and predict their biodegradability to inform the development of new sustainable materials. In this study, we developed a scalable method to evaluate polysaccharide breakdown by measuring microbial growth and analyzing microbial genomes. Our approach, applied to polysaccharides with various structures, correlates strongly with well-established regulatory methods based on oxygen demand. We show that modifications to the polysaccharide structure decreased degradability and favored the growth of microbes adapted to break down chemically modified sugars. More broadly, we discovered two main types of microbial communities associated with different polysaccharide structures─one dominated by fast-growing microbes and another by specialized degraders. Surprisingly, we were able to predict biodegradation rates based only on two genomic features that define these communities: the abundance of genes related to rRNA (indicating fast growth) and the abundance of glycoside hydrolases (enzymes that break down polysaccharides), which together predict nearly 70% of the variation in polysaccharide breakdown. This suggests a trade-off, whereby microbes are either adapted for fast growth or for degrading complex polysaccharide chains, but not both. Finally, we observe that viral elements (prophages) encoded in the genomes of degrading microbes are induced in easily degradable polysaccharides, leading to complex dynamics in biomass accumulation during degradation. In summary, our work provides a practical approach for efficiently assessing polymer degradability and offers genomic insights into how microbes break down polysaccharides.

Metagenomic clustering links specific metabolic functions to globally relevant ecosystems

Metagenomic sequencing has advanced our understanding of biogeochemical processes by providing an unprecedented view into the microbial composition of different ecosystems. While the amount of metagenomic data has grown rapidly, simple-to-use methods to analyze and compare across studies have lagged behind. Thus, tools expressing the metabolic traits of a community are needed to broaden the utility of existing data. Gene abundance profiles are a relatively low-dimensional embedding of a metagenome's functional potential and are, thus, tractable for comparison across many samples. Here, we compare the abundance of KEGG Ortholog Groups (KOs) from 6,539 metagenomes from the Joint Genome Institute's Integrated Microbial Genomes and Metagenomes (JGI IMG/M) database. We find that samples cluster into terrestrial, aquatic, and anaerobic ecosystems with marker KOs reflecting adaptations to these environments. For instance, functional clusters were differentiated by the metabolism of antibiotics, photosynthesis, methanogenesis, and surprisingly GC content. Using this functional gene approach, we reveal the broad-scale patterns shaping microbial communities and demonstrate the utility of ortholog abundance profiles for representing a rapidly expanding body of metagenomic data.

IMPORTANCE

Metagenomics, or the sequencing of DNA from complex microbiomes, provides a view into the microbial composition of different environments. Metagenome databases were created to compile sequencing data across studies, but it remains challenging to compare and gain insight from these large data sets. Consequently, there is a need to develop accessible approaches to extract knowledge across metagenomes. The abundance of different orthologs (i.e., genes that perform a similar function across species) provides a simplified representation of a metagenome's metabolic potential that can easily be compared with others. In this study, we cluster the ortholog abundance profiles of thousands of metagenomes from diverse environments and uncover the traits that distinguish them. This work provides a simple to use framework for functional comparison and advances our understanding of how the environment shapes microbial communities.

Evolution and stability of complex microbial communities driven by trade-offs

Microbial communities are ubiquitous in nature and play an important role in ecology and human health. Cross-feeding is thought to be core to microbial communities, though it remains unclear precisely why it emerges. Why have multi-species microbial communities evolved in many contexts and what protects microbial consortia from invasion? Here, we review recent insights into the emergence and stability of coexistence in microbial communities. A particular focus is the long-term evolutionary stability of coexistence, as observed for microbial communities that spontaneously evolved in the E. coli long-term evolution experiment (LTEE). We analyze these findings in the context of recent work on trade-offs between competing microbial objectives, which can constitute a mechanistic basis for the emergence of coexistence. Coexisting communities, rather than monocultures of the 'fittest' single strain, can form stable endpoints of evolutionary trajectories. Hence, the emergence of coexistence might be an obligatory outcome in the evolution of microbial communities. This implies that rather than embodying fragile metastable configurations, some microbial communities can constitute formidable ecosystems that are difficult to disrupt.

On the evolution of chromosomal regions with high gene strand bias in bacteria

On circular bacterial chromosomes, the majority of genes are coded on the leading strand. This gene strand bias (GSB) ranges from up to 85% in some Bacillota to a little more than 50% in other phyla. The factors determining the extent of the strand bias remain to be found. Here, we report that species in the phylum Gemmatimonadota share a unique chromosome architecture, distinct from neighboring phyla: in a conserved 600-kb region around the terminus of replication, almost all genes were located on the leading strands, while on the remaining part of the chromosome, the strand preference was more balanced. The high strand bias (HSB) region harbors the rRNA clusters, core, and highly expressed genes. Selective pressure for reduction of collisions with DNA replication to minimize detrimental mutations can explain the conservation of essential genes in this region. Repetitive and mobile elements are underrepresented, suggesting reduced recombination frequency by structural isolation from other parts of the chromosome. We propose that the HSB region forms a distinct chromosomal domain. Gemmatimonadota chromosomes evolved mainly by expansion through horizontal gene transfer and duplications outside of the ancient high strand bias region. In support of our hypothesis, we could further identify two Spiroplasma strains on a similar evolutionary path.IMPORTANCEOn bacterial chromosomes, a preferred location of genes on the leading strand has evolved to reduce conflicts between replication and transcription. Despite a vast body of research, the question why bacteria show large differences in their gene strand bias is still not solved. The discovery of "hybrid" chromosomes in different phyla, including Gemmatimonadota, in which a conserved high strand bias is found exclusively in a region at ter, points toward a role of nucleoid structure, additional to replication, in the evolution of strand preferences. A fine-grained structural analysis of the ever-increasing number of available bacterial genomes could help to better understand the forces that shape the sequential and spatial organization of the cell's information content.

Carbonic anhydrase and bacterial metabolism: a chance for antibacterial drug discovery

INTRODUCTION

Carbonic anhydrases (CAs, EC 4.2.1.1) play a pivotal role in the regulation of carbon dioxide , bicarbonate, and hydrogen ions within bacterial cells, ensuring pH homeostasis and facilitating energy production. We conducted a systematic literature search (PubMed, Web of Science, and Google Scholar) to examine the intricate interplay between CAs and bacterial metabolism, revealing the potential of CA inhibitors (CAIs) as innovative therapeutic agents against pathogenic bacteria.

AREA COVERED

Inhibition of bacterial CAs was explored in various pathogens, emphasizing the CA roles in microbial virulence, survival, and adaptability. Escherichia coli, a valid and convenient model microorganism, was recently used to investigate the effects of acetazolamide (AAZ) on the bacterial life cycle. Furthermore, the effectiveness of CAIs against pathogenic bacteria has been further substantiated for Vancomycin-Resistant Enterococci (VRE) and antibiotic-resistant Neisseria gonorrhoeae strains.

EXPERT OPINION

CAIs target bacterial metabolic pathways, offering alternatives to conventional therapies. They hold promise against drug-resistant microorganisms such as VRE and N. gonorrhoeae strains. CAIs offer promising avenues for addressing antibiotic resistance and underscore their potential as novel antibacterial agents. Recognizing the central role of CAs in bacterial growth and pathogenicity will pave the way for innovative infection control and treatment strategies possibly also for other antibiotic resistant species.

Shaping of microbial phenotypes by trade-offs

Growth rate maximization is an important fitness strategy for microbes. However, the wide distribution of slow-growing oligotrophic microbes in ecosystems suggests that rapid growth is often not favored across ecological environments. In many circumstances, there exist trade-offs between growth and other important traits (e.g., adaptability and survival) due to physiological and proteome constraints. Investments on alternative traits could compromise growth rate and microbes need to adopt bet-hedging strategies to improve fitness in fluctuating environments. Here we review the mechanistic role of trade-offs in controlling bacterial growth and further highlight its ecological implications in driving the emergences of many important ecological phenomena such as co-existence, population heterogeneity and oligotrophic/copiotrophic lifestyles.

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

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

Predictions of rhizosphere microbiome dynamics with a genome-informed and trait-based energy budget model

Soil microbiomes are highly diverse, and to improve their representation in biogeochemical models, microbial genome data can be leveraged to infer key functional traits. By integrating genome-inferred traits into a theory-based hierarchical framework, emergent behaviour arising from interactions of individual traits can be predicted. Here we combine theory-driven predictions of substrate uptake kinetics with a genome-informed trait-based dynamic energy budget model to predict emergent life-history traits and trade-offs in soil bacteria. When applied to a plant microbiome system, the model accurately predicted distinct substrate-acquisition strategies that aligned with observations, uncovering resource-dependent trade-offs between microbial growth rate and efficiency. For instance, inherently slower-growing microorganisms, favoured by organic acid exudation at later plant growth stages, exhibited enhanced carbon use efficiency (yield) without sacrificing growth rate (power). This insight has implications for retaining plant root-derived carbon in soils and highlights the power of data-driven, trait-based approaches for improving microbial representation in biogeochemical models.

Nitrogen removal characteristics of novel bacterium Klebsiella sp. TSH15 by assimilatory/dissimilatory nitrate reduction and ammonia assimilation

A novel strain with heterotrophic nitrification and aerobic denitrification was screened and identified as Klebsiella sp. TSH15 by 16S rRNA. The results demonstrated that the ammonia-N and nitrate-N removal rates were 2.99 mg/L/h and 2.53 mg/L/h under optimal conditions, respectively. The analysis of the whole genome indicated that strain TSH15 contained the key genes involved in assimilatory/dissimilatory nitrate reduction and ammonia assimilation, including nas, nar, nir, nor, glnA, gltB, gdhA, and amt. The relative expression levels of key nitrogen removal genes were further detected by RT-qPCR. The results indicated that the N metabolic pathways of strain TSH15 were the conversion of nitrate or nitrite to ammonia by assimilatory/dissimilatory nitrate reduction (NO3-→NO2-→NH4+) and further conversion of ammonia to glutamate (NH4+-N → Glutamate) by ammonia assimilation. These results indicated that the strain TSH15 had the potential to be applied to practical sewage treatment in the future.

Dynamics of carbon substrate competition among heterotrophic microorganisms

Growing evidence suggests that interactions among heterotrophic microorganisms influence the efficiency and rate of organic matter turnover. These interactions are dynamic and shaped by the composition and availability of resources in their surrounding environment. Heterotrophic microorganisms inhabiting marine environments often encounter fluctuations in the quality and quantity of carbon inputs, ranging from simple sugars to large, complex compounds. Here, we experimentally tested how the chemical complexity of carbon substrates affects competition and growth dynamics between two heterotrophic marine isolates. We tracked cell density using species-specific polymerase chain reaction (PCR) assays and measured rates of microbial CO2 production along with associated isotopic signatures (13C and 14C) to quantify the impact of these interactions on organic matter remineralization. The observed cell densities revealed substrate-driven interactions: one species exhibited a competitive advantage and quickly outgrew the other when incubated with a labile compound whereas both species seemed to coexist harmoniously in the presence of more complex organic matter. Rates of CO2 respiration revealed that coincubation of these isolates enhanced organic matter turnover, sometimes by nearly 2-fold, compared to their incubation as mono-cultures. Isotopic signatures of respired CO2 indicated that coincubation resulted in a greater remineralization of macromolecular organic matter. These results demonstrate that simple substrates promote competition whereas high substrate complexity reduces competitiveness and promotes the partitioning of degradative activities into distinct niches, facilitating coordinated utilization of the carbon pool. Taken together, this study yields new insight into how the quality of organic matter plays a pivotal role in determining microbial interactions within marine environments.

Direct observations of microbial community succession on sinking marine particles

Microbial community dynamics on sinking particles control the amount of carbon that reaches the deep ocean and the length of time that carbon is stored, with potentially profound impacts on Earth's climate. A mechanistic understanding of the controls on sinking particle distributions has been hindered by limited depth- and time-resolved sampling and methods that cannot distinguish individual particles. Here, we analyze microbial communities on nearly 400 individual sinking particles in conjunction with more conventional composite particle samples to determine how particle colonization and community assembly might control carbon sequestration in the deep ocean. We observed community succession with corresponding changes in microbial metabolic potential on the larger sinking particles transporting a significant fraction of carbon to the deep sea. Microbial community richness decreased as particles aged and sank; however, richness increased with particle size and the attenuation of carbon export. This suggests that the theory of island biogeography applies to sinking marine particles. Changes in POC flux attenuation with time and microbial community composition with depth were reproduced in a mechanistic ecosystem model that reflected a range of POC labilities and microbial growth rates. Our results highlight microbial community dynamics and processes on individual sinking particles, the isolation of which is necessary to improve mechanistic models of ocean carbon uptake.