Slicing through the challenge of maintaining Pneumocystis in the laboratory.

Pneumocystis jirovecii is a major fungal pathogen of humans that causes life-threatening lung infections in immunocompromised individuals. Despite its huge global impact upon human health, our understanding of the pathobiology of this deadly fungus remains extremely limited, largely because it is not yet possible to cultivate Pneumocystis in vitro, independently of the host. However, a recent paper by Munyonho et al. offers a major step forward (F. T. Munyonho, R. D. Clark, D. Lin, M. S. Khatun, et al., 2023, mBio 15:e01464-23, https://doi.org/10.1128/mbio.01464-23). They show that it is possible to maintain both the trophozoite and cyst forms of the mouse pathogen, Pneumocystis murina, in precision-cut lung slices for several weeks. Furthermore, they demonstrate that this offers the exciting opportunity to examine potential virulence factors such as possible biofilm formation as well as antifungal drug responses in the lung.

Analysis of Pneumocystis Transcription Factor Evolution and Implications for Biology and Lifestyle.

Pneumocystis jirovecii kills hundreds of thousands of immunocompromised patients each year. Yet many aspects of the biology of this obligate pathogen remain obscure because it is not possible to culture the fungus in vitro independently of its host. Consequently, our understanding of Pneumocystis pathobiology is heavily reliant upon bioinformatic inferences. We have exploited a powerful combination of genomic and phylogenetic approaches to examine the evolution of transcription factors in Pneumocystis species. We selected protein families (Pfam families) that correspond to transcriptional regulators and used bioinformatic approaches to compare these families in the seven Pneumocystis species that have been sequenced to date with those from other yeasts, other human and plant pathogens, and other obligate parasites. Some Pfam families of transcription factors have undergone significant reduction during their evolution in the Pneumocystis genus, and other Pfam families have been lost or appear to be in the process of being lost. Meanwhile, other transcription factor families have been retained in Pneumocystis species, and some even appear to have undergone expansion. On this basis, Pneumocystis species seem to have retained transcriptional regulators that control chromosome maintenance, ribosomal gene regulation, RNA processing and modification, and respiration. Meanwhile, regulators that promote the assimilation of alternative carbon sources, amino acid, lipid, and sterol biosynthesis, and iron sensing and homeostasis appear to have been lost. Our analyses of transcription factor retention, loss, and gain provide important insights into the biology and lifestyle of Pneumocystis. IMPORTANCE Pneumocystis jirovecii is a major fungal pathogen of humans that infects healthy individuals, colonizing the lungs of infants. In immunocompromised and transplant patients, this fungus causes life-threatening pneumonia, and these Pneumocystis infections remain among the most common and serious infections in HIV/AIDS patients. Yet we remain remarkably ignorant about the biology and epidemiology of Pneumocystis due to the inability to culture this fungus in vitro. Our analyses of transcription factor retentions, losses, and gains in sequenced Pneumocystis species provide valuable new views of their specialized biology, suggesting the retention of many metabolic and stress regulators and the loss of others that are essential in free-living fungi. Given the lack of in vitro culture methods for Pneumocystis, this powerful bioinformatic approach has advanced our understanding of the lifestyle of P. jirovecii and the nature of its dependence on the host for survival.

Predicting microbial growth dynamics in response to nutrient availability.

Developing mathematical models to accurately predict microbial growth dynamics remains a key challenge in ecology, evolution, biotechnology, and public health. To reproduce and grow, microbes need to take up essential nutrients from the environment, and mathematical models classically assume that the nutrient uptake rate is a saturating function of the nutrient concentration. In nature, microbes experience different levels of nutrient availability at all environmental scales, yet parameters shaping the nutrient uptake function are commonly estimated for a single initial nutrient concentration. This hampers the models from accurately capturing microbial dynamics when the environmental conditions change. To address this problem, we conduct growth experiments for a range of micro-organisms, including human fungal pathogens, baker's yeast, and common coliform bacteria, and uncover the following patterns. We observed that the maximal nutrient uptake rate and biomass yield were both decreasing functions of initial nutrient concentration. While a functional form for the relationship between biomass yield and initial nutrient concentration has been previously derived from first metabolic principles, here we also derive the form of the relationship between maximal nutrient uptake rate and initial nutrient concentration. Incorporating these two functions into a model of microbial growth allows for variable growth parameters and enables us to substantially improve predictions for microbial dynamics in a range of initial nutrient concentrations, compared to keeping growth parameters fixed.

Anticipatory Stress Responses and Immune Evasion in Fungal Pathogens.

In certain niches, microbes encounter environmental challenges that are temporally linked. In such cases, microbial fitness is enhanced by the evolution of anticipatory responses where the initial challenge simultaneously activates pre-emptive protection against the second impending challenge. The accumulation of anticipatory responses in domesticated yeasts, which have been termed 'adaptive prediction', has led to the emergence of 'core stress responses' that provide stress cross-protection. Protective anticipatory responses also seem to be common in fungal pathogens of humans. These responses reflect the selective pressures that these fungi have faced relatively recently in their evolutionary history. Consequently, some pathogens have evolved 'core environmental responses' which exploit host signals to trigger immune evasion strategies that protect them against imminent immune attack.

Predicting community dynamics of antibiotic-sensitive and -resistant species in fluctuating environments.

Microbes occupy almost every niche within and on their human hosts. Whether colonizing the gut, mouth or bloodstream, microorganisms face temporal fluctuations in resources and stressors within their niche but we still know little of how environmental fluctuations mediate certain microbial phenotypes, notably antimicrobial-resistant ones. For instance, do rapid or slow fluctuations in nutrient and antimicrobial concentrations select for, or against, resistance? We tackle this question using an ecological approach by studying the dynamics of a synthetic and pathogenic microbial community containing two species, one sensitive and the other resistant to an antibiotic drug where the community is exposed to different rates of environmental fluctuation. We provide mathematical models, supported by experimental data, to demonstrate that simple community outcomes, such as competitive exclusion, can shift to coexistence and ecosystem bistability as fluctuation rates vary. Theory gives mechanistic insight into how these dynamical regimes are related. Importantly, our approach highlights a fundamental difference between resistance in single-species populations, the context in which it is usually assayed, and that in communities. While fast environmental changes are known to select against resistance in single-species populations, here we show that they can promote the resistant species in mixed-species communities. Our theoretical observations are verified empirically using a two-species Candida community.

Mathematical models of microbial growth and metabolism: a whole-organism perspective.

We review the principles underpinning the development of mathematical models of the metabolic activities of micro-organisms. Such models are important to understand and chart the substantial contributions made by micro-organisms to geochemical cycles, and also to optimise the performance of bioreactors that exploit the biochemical capabilities of these organisms. We advocate an approach based on the principle of dynamic allocation. We survey the biological background that motivates this approach, including nutrient assimilation, the regulation of gene expression, and the principles of microbial growth. In addition, we discuss the classic models of microbial growth as well as contemporary approaches. The dynamic allocation theory generalises these classic models in a natural manner and is readily amenable to the additional information provided by transcriptomics and proteomics approaches. Finally, we touch upon these organising principles in the context of the transition from the free-living unicellular mode of life to multicellularity.

Optimal management of nutrient reserves in microorganisms under time-varying environmental conditions.

Intracellular reserves are a conspicuous feature of many bacteria; such internal stores are often present in the form of inclusions in which polymeric storage compounds are accumulated. Such reserves tend to increase in times of plenty and be used up in times of scarcity. Mathematical models that describe the dynamical nature of reserve build-up and use are known as "cell quota," "dynamic energy/nutrient budget," or "variable-internal-stores" models. Here we present a stoichiometrically consistent macro-chemical model that accounts for variable stores as well as adaptive allocation of building blocks to various types of catalytic machinery. The model posits feedback loops linking expression of assimilatory machinery to reserve density. The precise form of the "regulatory law" at the heart of such a loop expresses how the cell manages internal stores. We demonstrate how this "regulatory law" can be recovered from experimental data using several empirical data sets. We find that stores should be expected to be negligibly small in stable growth-sustaining environments, but prominent in environments characterised by marked fluctuations on time scales commensurate with the inherent dynamic time scale of the organismal system.

Variable-Internal-Stores models of microbial growth and metabolism with dynamic allocation of cellular resources.

Variable-Internal-Stores models of microbial metabolism and growth have proven to be invaluable in accounting for changes in cellular composition as microbial cells adapt to varying conditions of nutrient availability. Here, such a model is extended with explicit allocation of molecular building blocks among various types of catalytic machinery. Such an extension allows a reconstruction of the regulatory rules employed by the cell as it adapts its physiology to changing environmental conditions. Moreover, the extension proposed here creates a link between classic models of microbial growth and analyses based on detailed transcriptomics and proteomics data sets. We ascertain the compatibility between the extended Variable-Internal-Stores model and the classic models, demonstrate its behaviour by means of simulations, and provide a detailed treatment of the uniqueness and the stability of its equilibrium point as a function of the availabilities of the various nutrients.