Intraspecies heterogeneity in microbial interactions

Decoding populations in the ocean microbiome

Understanding the characteristics and structure of populations is fundamental to comprehending ecosystem processes and evolutionary adaptations. While the study of animal and plant populations has spanned a few centuries, microbial populations have been under scientific scrutiny for a considerably shorter period. In the ocean, analyzing the genetic composition of microbial populations and their adaptations to multiple niches can yield important insights into ecosystem function and the microbiome's response to global change. However, microbial populations have remained elusive to the scientific community due to the challenges associated with isolating microorganisms in the laboratory. Today, advancements in large-scale metagenomics and metatranscriptomics facilitate the investigation of populations from many uncultured microbial species directly from their habitats. The knowledge acquired thus far reveals substantial genetic diversity among various microbial species, showcasing distinct patterns of population differentiation and adaptations, and highlighting the significant role of selection in structuring populations. In the coming years, population genomics is expected to significantly increase our understanding of the architecture and functioning of the ocean microbiome, providing insights into its vulnerability or resilience in the face of ongoing global change. Video Abstract.

Bioinoculants as a means of increasing crop tolerance to drought and phosphorus deficiency in legume-cereal intercropping systems

Ensuring plant resilience to drought and phosphorus (P) stresses is crucial to support global food security. The phytobiome, shaped by selective pressures, harbors stress-adapted microorganisms that confer host benefits like enhanced growth and stress tolerance. Intercropping systems also offer benefits through facilitative interactions, improving plant growth in water- and P-deficient soils. Application of microbial consortia can boost the benefits of intercropping, although questions remain about the establishment, persistence, and legacy effects within resident soil microbiomes. Understanding microbe- and plant-microbe dynamics in drought-prone soils is key. This review highlights the beneficial effects of rhizobacterial consortia-based inoculants in legume-cereal intercropping systems, discusses challenges, proposes a roadmap for development of P-solubilizing drought-adapted consortia, and identifies research gaps in crop-microbe interactions.

Plant pathogenesis: Toward multidimensional understanding of the microbiome

Single pathogen-targeted disease management measure has shown drawbacks in field efficacy under the scenario of global change. An in-depth understanding of plant pathogenesis will provide a promising solution but faces the challenges of the emerging paradigm involving the plant microbiome. While the beneficial impact of the plant microbiome is well characterized, their potential role in facilitating pathological processes has so far remained largely overlooked. To address these unsolved controversies and emerging challenges, we hereby highlight the pathobiome, the disease-assisting portion hidden in the plant microbiome, in the plant pathogenesis paradigm. We review the detrimental actions mediated by the pathobiome at multiple scales and further discuss how natural and human triggers result in the prevalence of the plant pathobiome, which would probably provide a clue to the mitigation of plant disease epidemics. Collectively, the article would advance the current insight into plant pathogenesis and also pave a new way to cope with the upward trends of plant disease by designing the pathobiome-targeted measure.

RespectM revealed metabolic heterogeneity powers deep learning for reshaping the DBTL cycle

Synthetic biology, relying on Design-Build-Test-Learn (DBTL) cycle, aims to solve medicine, manufacturing, and agriculture problems. However, the DBTL cycle's Learn (L) step lacks predictive power for the behavior of biological systems, resulting from the incompatibility between sparse testing data and chaotic metabolic networks. Herein, we develop a method, "RespectM," based on mass spectrometry imaging, which is able to detect metabolites at a rate of 500 cells per hour with high efficiency. In this study, 4,321 single cell level metabolomics data were acquired, representing metabolic heterogeneity. An optimizable deep neural network was applied to learn from metabolic heterogeneity and a "heterogeneity-powered learning (HPL)" based model was trained as well. By testing the HPL based model, we suggest minimal operations to achieve high triglyceride production for engineering. The HPL strategy could revolutionize rational design and reshape the DBTL cycle.

Nitrite-resistance mechanisms on wastewater treatment in denitrifying phosphorus removal process revealed by machine learning, co-occurrence, and metagenomics analysis

Nitrite is a key intermediate in nitrogen metabolism that determines microbial transformations of N and P, greenhouse gas (N₂O) emissions, and system nutrient removal efficiency. However, nitrite also exerts toxic effects on microorganisms. A lack of understanding of high nitrite-resistance mechanisms at community- and genome-scale resolutions hinders the optimization for robustness of wastewater treatment systems. Here, we established nitrite-dependent denitrifying and phosphorus removal (DPR) systems under a gradient concentration of nitrite (0, 5, 10, 15, 20, and 25 mg N/L), relying on 16S rRNA gene amplicon and metagenomics to explore high nitrite-resistance mechanism. The results demonstrated that specific taxa were adopted to change the metabolic relationship of the community through phenotypic evolution to resist toxic nitrite contributing to the enhancement of denitrification and inhibition of nitrification and phosphorus removal. The key specific species, Thauera enhanced denitrification, whereas Candidatus Nitrotoga decreased in abundance to maintain partial nitrification. The extinction of Candidatus Nitrotoga induced a simpler restructuring-community, forcing high nitrite-stimulating microbiome to establish a more focused denitrification rather than nitrification or P metabolism in response to nitrite toxicity. Our work provides insights for understanding microbiome adaptation to toxic nitrite and giving theoretical support for operation strategy of nitrite-based wastewater treatment technology.

Bacterial-fungal interactions and their impact on microbial pathogenesis

Microbial communities of the human microbiota exhibit diverse effects on human health and disease. Microbial homeostasis is important for normal physiological functions and changes to the microbiota are associated with many human diseases including diabetes, cancer, and colitis. In addition, there are many microorganisms that are either commensal or acquired from environmental reservoirs that can cause diverse pathologies. Importantly, the balance between health and disease is intricately connected to how members of the microbiota interact and affect one another's growth and pathogenicity. However, the mechanisms that govern these interactions are only beginning to be understood. In this review, we outline bacterial-fungal interactions in the human body, including examining the mechanisms by which bacteria govern fungal growth and virulence, as well as how fungi regulate bacterial pathogenesis. We summarize advances in the understanding of chemical, physical, and protein-based interactions, and their role in exacerbating or impeding human disease. We focus on the three fungal species responsible for the majority of systemic fungal infections in humans: Candida albicans, Cryptococcus neoformans, and Aspergillus fumigatus. We conclude by summarizing recent studies that have mined microbes for novel antimicrobials and antivirulence factors, highlighting the potential of the human microbiota as a rich resource for small molecule discovery.

Effect of Operational Parameters on the Cultivation of the Gut Microbiome in Continuous Bioreactors Inoculated with Feces: A Systematic Review

Since the early 1980s, multiple researchers have contributed to the development of in vitro models of the human gastrointestinal system for the mechanistic interrogation of the gut microbiome ecology. Using a bioreactor for simulating all the features and conditions of the gastrointestinal system is a massive challenge. Some conditions, such as temperature and pH, are readily controlled, but a more challenging feature to simulate is that both may vary in different regions of the gastrointestinal tract. Promising solutions have been developed for simulating other functionalities, such as dialysis capabilities, peristaltic movements, and biofilm growth. This research field is under constant development, and further efforts are needed to drive these models closer to in vivo conditions, thereby increasing their usefulness for studying the gut microbiome impact on human health. Therefore, understanding the influence of key operational parameters is fundamental for the refinement of the current bioreactors and for guiding the development of more complex models. In this review, we performed a systematic search for operational parameters in 229 papers that used continuous bioreactors seeded with human feces. Despite the reporting of operational parameters for the various bioreactor models being variable, as a result of a lack of standardization, the impact of specific operational parameters on gut microbial ecology is discussed, highlighting the advantages and limitations of the current bioreactor systems.

Effects of Inoculation With Acinetobacter on Fermentation of Cigar Tobacco Leaves

Metabolic activity of the microbial community greatly affects the quality of cigar tobacco leaves (CTLs). To improve the quality of CTLs, two extrinsic microbes (Acinetobacter sp. 1H8 and Acinetobacter indicus 3B2) were inoculated into CTLs. The quality of CTLs were significantly improved after fermentation. The content of solanone, 6-methyl-5-hepten-2-one, benzeneacetic acid, ethyl ester, cyclohexanone, octanal, acetophenone, and 3,5,5-trimethyl-2-cyclohexen-1-one were significantly increased after inoculated Acinetobacter sp. 1H8. The inoculation of Acinetobacter sp. 1H8 enhanced the normal evolutionary trend of bacterial community. The content of trimethyl-pyrazine, 2,6-dimethyl-pyrazine, and megastigmatrienone were significantly increased after inoculated Acinetobacter indicus 3B2. The inoculation of Acinetobacter indicus 3B2 completely changed the original bacterial community. Network analysis revealed that Acinetobacter was negatively correlated with Aquabacterium, positively correlated with Bacillus, and had significant correlations with many volatile flavor compounds. This work may be helpful for improving fermentation product quality by regulating microbial community, and gain insight into the microbial ecosystem.

Metabolic basis for the evolution of a common pathogenic Pseudomonas aeruginosa variant

Microbes frequently evolve in reproducible ways. Here, we show that differences in specific metabolic regulation rather than inter-strain interactions explain the frequent presence of lasR loss-of-function (LOF) mutations in the bacterial pathogen Pseudomonas aeruginosa. While LasR contributes to virulence through its role in quorum sensing, lasR mutants have been associated with more severe disease. A model based on the intrinsic growth kinetics for a wild type strain and its LasR- derivative, in combination with an experimental evolution based genetic screen and further genetics analyses, indicated that differences in metabolism were sufficient to explain the rise of these common mutant types. The evolution of LasR- lineages in laboratory and clinical isolates depended on activity of the two-component system CbrAB, which modulates substrate prioritization through the catabolite repression control pathway. LasR- lineages frequently arise in cystic fibrosis lung infections and their detection correlates with disease severity. Our analysis of bronchoalveolar lavage fluid metabolomes identified compounds that negatively correlate with lung function, and we show that these compounds support enhanced growth of LasR- cells in a CbrB-controlled manner. We propose that in vivo metabolomes contribute to pathogen evolution, which may influence the progression of disease and its treatment.