Trends in Microbiology最新文献

Increasing evidence suggests that the human microbiome plays an important role in cancer risk and treatment. Untargeted ‘omics’ techniques have accelerated research into microbiome–cancer interactions, supporting the discovery of novel associations and mechanisms. However, these techniques require careful selection and use to avoid biases and other pitfalls. In this essay, we discuss selected challenges involved in the analysis of microbiome data in the context of cancer, including the application of machine learning (ML). We focus on DNA sequencing-based (e.g., metagenomics) methods, but many of the pitfalls and opportunities generalize to other omics technologies as well. We advocate for extended training opportunities, community standards, and best practices for sharing data and code to advance transparency and reproducibility in cancer microbiome research.

Nucleotide signalling molecules - mainly cyclic 3',5'-adenosine phosphate (cAMP), bis-(3',5')-cyclic diguanosine monophosphate (c-di-GMP), and bis-(3',5')-cyclic diadenosine monophosphate (c-di-AMP) - contribute to the regulation of cellular pathways. Numerous recent works have focused on the involvement of these cyclic nucleotide phosphates (cNPs) in bacterial resistance and tolerance to antimicrobial treatment. Indeed, the rise of antimicrobial resistance (AMR) is a rising global threat to human health, while the rise of antimicrobial tolerance underlies the development of AMR and long-term infections, placing an additional burden on this problem. Here, we summarise the current understanding of cNP signalling in bacterial physiology with a focus on our understanding of how cNP signalling affects AMR and antimicrobial tolerance in different bacterial species. We also discuss additional cNP-related drug targets in bacterial pathogens that may have therapeutic potential.

Virtually all multicellular organisms on Earth live in symbiotic associations with complex microbial communities: the microbiome. This ancient relationship is of fundamental importance for both the host and the microbiome. Recently, the analyses of numerous microbiomes have revealed an incredible diversity and complexity of symbionts, with different mechanisms identified as potential drivers of this diversity. However, the interplay of ecological and evolutionary forces generating these complex associations is still poorly understood. Here we explore and summarise the suite of ecological and evolutionary mechanisms identified as relevant to different aspects of microbiome complexity and diversity. We argue that microbiome assembly is a dynamic product of ecology and evolution at various spatio-temporal scales. We propose a theoretical framework to classify mechanisms and build mechanistic host-microbiome models to link them to empirical patterns. We develop a cohesive foundation for the theoretical understanding of the combined effects of ecology and evolution on the assembly of complex symbioses.

Plant growth-promoting rhizobacteria (PGPR) can improve crop yields, nutrient use efficiency, plant tolerance to stressors, and confer benefits to future generations of crops grown in the same soil. Unlocking the potential of microbial communities in the rhizosphere and endosphere is therefore of great interest for sustainable agriculture advancements. Before plant microbiomes can be engineered to confer desirable phenotypic effects on their plant hosts, a deeper understanding of the interacting factors influencing rhizosphere community structure and function is needed. Dealing with this complexity is becoming more feasible using computational approaches. In this review, we discuss recent advances at the intersection of experimental and computational strategies for the investigation of plant-microbiome interactions and the engineering of desirable soil microbiomes.

Shigella is an important human-adapted pathogen which contributes to a large global burden of diarrhoeal disease. Together with the increasing threat of antimicrobial resistance and lack of an effective vaccine, there is great urgency to identify novel therapeutics and preventatives to combat Shigella infection. In this review, we discuss the development of innovative technologies and animal models to study mechanisms underlying Shigella infection of humans. We examine recent literature introducing (i) the organ-on-chip model, and its substantial contribution towards understanding the biomechanics of Shigella infection, (ii) the zebrafish infection model, which has delivered transformative insights into the epidemiological success of clinical isolates and the innate immune response to Shigella, (iii) a pioneering oral mouse model of shigellosis, which has helped to discover new inflammasome biology and protective mechanisms against shigellosis, and (iv) the controlled human infection model, which has been effective in translating basic research into human health impact and assessing suitability of novel vaccine candidates. We consider the recent contributions of each model and discuss where the future of modelling Shigella infection lies.

Marine phytoplankton play crucial roles in the Earth's ecological, chemical, and geological processes. They are responsible for about half of global primary production and drive the ocean biological carbon pump. Understanding how plankton species may adapt to the Earth's rapidly changing environments is evidently an urgent priority. This problem requires evolutionary genetic approaches as evolution occurs at the level of allele frequency change within populations driven by genetic drift and natural selection (microevolution). Plankters such as the coccolithophore Gephyrocapsa huxleyi and the cyanobacterium Prochlorococcus 'marinus' are among Earth's most abundant organisms. In this opinion paper we discuss how evolution in astronomically large populations of superabundant microbes (SAMs) may act fundamentally differently than it does in the populations of more modest size found in well-studied organisms. This offers exciting opportunities to study evolution in the conditions that have yet to be explored and also leads to unique challenges. Exploring these opportunities and challenges is the goal of this article.

The natural process of evolutionary adaptation is often exploited as a powerful tool to obtain microbes with desirable traits. For industrial microbes, evolutionary engineering is often used to generate variants that show increased yields or resistance to stressful industrial environments, thus obtaining superior microbial cell factories. However, even in large populations, the natural supply of beneficial mutations is typically low, which implies that obtaining improved microbes is often time-consuming and inefficient. To overcome this limitation, different techniques have been developed that boost mutation rates. While some of these methods simply increase the overall mutation rate across a genome, others use recent developments in DNA synthesis, synthetic biology, and CRISPR-Cas techniques to control the type and location of mutations. This review summarizes the most important recent developments and methods in the field of evolutionary engineering in model microorganisms. It discusses how both in vitro and in vivo approaches can increase the genetic diversity of the host, with a special emphasis on in vivo techniques for the optimization of metabolic pathways for precision fermentation.