Niche partitioning of a pathogenic microbiome driven by chemical gradients

Microbiome in the nasopharynx: Insights into the impact of COVID-19 severity

Background

The respiratory tract harbors a variety of microbiota, whose composition and abundance depend on specific site factors, interaction with external factors, and disease. The aim of this study was to investigate the relationship between COVID-19 severity and the nasopharyngeal microbiome.

Methods

We conducted a prospective cohort study in Mexico City, collecting nasopharyngeal swabs from 30 COVID-19 patients and 14 healthy volunteers. Microbiome profiling was performed using 16S rRNA gene analysis. Taxonomic assignment, classification, diversity analysis, core microbiome analysis, and statistical analysis were conducted using R packages.

Results

The microbiome data analysis revealed taxonomic shifts within the nasopharyngeal microbiome in severe COVID-19. Particularly, we observed a significant reduction in the relative abundance of Lawsonella and Cutibacterium genera in critically ill COVID-19 patients (p < 0.001). In contrast, these patients exhibited a marked enrichment of Streptococcus, Actinomyces, Peptostreptococcus, Atopobium, Granulicatella, Mogibacterium, Veillonella, Prevotella_7, Rothia, Gemella, Alloprevotella, and Solobacterium genera (p < 0.01). Analysis of the core microbiome across all samples consistently identified the presence of Staphylococcus, Corynebacterium, and Streptococcus.

Conclusions

Our study suggests that the disruption of physicochemical conditions and barriers resulting from inflammatory processes and the intubation procedure in critically ill COVID-19 patients may facilitate the colonization and invasion of the nasopharynx by oral microorganisms.

Neural network establishes co-occurrence links between transformation products of the contaminant and the soil microbiome

It remains challenging to establish reliable links between transformation products (TPs) of contaminants and corresponding microbes. This challenge arises due to the sophisticated experimental regime required for TP discovery and the compositional nature of 16S rRNA gene amplicon sequencing and mass spectrometry datasets, which can potentially confound statistical inference. In this study, we present a new strategy by combining the use of 2H-labeled Stable Isotope-Assisted Metabolomics (2H-SIAM) with a neural network-based algorithm (i.e., MMvec) to explore links between TPs of pyrene and the soil microbiome. The links established by this novel strategy were further validated using different approaches. Briefly, a metagenomic study provided indirect evidence for the established links, while the identification of pyrene degraders from soils, and a DNA-based stable isotope probing (DNA-SIP) study offered direct evidence. The comparison among different approaches, including Pearson's and Spearman's correlations, further confirmed the superior performance of our strategy. In conclusion, we summarize the unique features of the combined use of 2H-SIAM and MMvec. This study not only addresses the challenges in linking TPs to microbes but also introduces an innovative and effective approach for such investigations. Environmental Implication: Taxonomically diverse bacteria performing successive metabolic steps of the contaminant were firstly depicted in the environmental matrix.

Marine-derived bioactive materials as antibiofilm and antivirulence agents

Microbial infections are major human health issues, and, recently, the mortality rate owing to bacterial and fungal infections has been increasing. In addition to intrinsic and extrinsic antimicrobial resistance mechanisms, biofilm formation is a key adaptive resistance mechanism. Several bioactive compounds from marine organisms have been identified for use in biofilm therapy owing to their structural complexity, biocompatibility, and economic viability. In this review, we discuss recent trends in the application of marine natural compounds, marine-bioinspired nanomaterials, and marine polymer conjugates as possible therapeutic agents for controlling biofilms and virulence factors. We also comprehensively discuss the mechanisms underlying biofilm formation and inhibition of virulence factors by marine-derived materials and propose possible applications of novel and effective antibiofilm and antivirulence agents.

The wound microbiota: microbial mechanisms of impaired wound healing and infection

The skin barrier protects the human body from invasion by exogenous and pathogenic microorganisms. A breach in this barrier exposes the underlying tissue to microbial contamination, which can lead to infection, delayed healing, and further loss of tissue and organ integrity. Delayed wound healing and chronic wounds are associated with comorbidities, including diabetes, advanced age, immunosuppression and autoimmune disease. The wound microbiota can influence each stage of the multi-factorial repair process and influence the likelihood of an infection. Pathogens that commonly infect wounds, such as Staphylococcus aureus and Pseudomonas aeruginosa, express specialized virulence factors that facilitate adherence and invasion. Biofilm formation and other polymicrobial interactions contribute to host immunity evasion and resistance to antimicrobial therapies. Anaerobic organisms, fungal and viral pathogens, and emerging drug-resistant microorganisms present unique challenges for diagnosis and therapy. In this Review, we explore the current understanding of how microorganisms present in wounds impact the process of skin repair and lead to infection through their actions on the host and the other microbial wound inhabitants.

Horizontal gene transfer is predicted to overcome the diversity limit of competing microbial species

Natural microbial ecosystems harbor substantial diversity of competing species. Explaining such diversity is challenging, because in classic theories it is extremely infeasible for a large community of competing species to stably coexist in homogeneous environments. One important aspect mostly overlooked in these theories, however, is that microbes commonly share genetic materials with their neighbors through horizontal gene transfer (HGT), which enables the dynamic change of species growth rates due to the fitness effects of the mobile genetic elements (MGEs). Here, we establish a framework of species competition by accounting for the dynamic gene flow among competing microbes. Combining theoretical derivation and numerical simulations, we show that in many conditions HGT can surprisingly overcome the biodiversity limit predicted by the classic model and allow the coexistence of many competitors, by enabling dynamic neutrality of competing species. In contrast with the static neutrality proposed by previous theories, the diversity maintained by HGT is highly stable against random perturbations of microbial fitness. Our work highlights the importance of considering gene flow when addressing fundamental ecological questions in the world of microbes and has broad implications for the design and engineering of complex microbial consortia.

Repurposing High-Throughput Screening Identifies Unconventional Drugs with Antibacterial and Antibiofilm Activities against Pseudomonas aeruginosa under Experimental Conditions Relevant to Cystic Fibrosis

Pseudomonas aeruginosa is the most common pathogen infecting cystic fibrosis (CF) lungs, causing acute and chronic infections. Intrinsic and acquired antibiotic resistance allow P. aeruginosa to colonize and persist despite antibiotic treatment, making new therapeutic approaches necessary. Combining high-throughput screening and drug repurposing is an effective way to develop new therapeutic uses for drugs. This study screened a drug library of 3,386 drugs, mostly FDA approved, to identify antimicrobials against P. aeruginosa under physicochemical conditions relevant to CF-infected lungs. Based on the antibacterial activity, assessed spectrophotometrically against the prototype RP73 strain and 10 other CF virulent strains, and the toxic potential evaluated toward CF IB3-1 bronchial epithelial cells, five potential hits were selected for further analysis: the anti-inflammatory and antioxidant ebselen, the anticancer drugs tirapazamine, carmofur, and 5-fluorouracil, and the antifungal tavaborole. A time-kill assay showed that ebselen has the potential to cause rapid and dose-dependent bactericidal activity. The antibiofilm activity was evaluated by viable cell count and crystal violet assays, revealing carmofur and 5-fluorouracil as the most active drugs in preventing biofilm formation regardless of the concentration. In contrast, tirapazamine and tavaborole were the only drugs actively dispersing preformed biofilms. Tavaborole was the most active drug against CF pathogens other than P. aeruginosa, especially against Burkholderia cepacia and Acinetobacter baumannii, while carmofur, ebselen, and tirapazamine were particularly active against Staphylococcus aureus and B. cepacia. Electron microscopy and propidium iodide uptake assay revealed that ebselen, carmofur, and tirapazamine significantly damage cell membranes, with leakage and cytoplasm loss, by increasing membrane permeability. IMPORTANCE Antibiotic resistance makes it urgent to design new strategies for treating pulmonary infections in CF patients. The repurposing approach accelerates drug discovery and development, as the drugs' general pharmacological, pharmacokinetic, and toxicological properties are already well known. In the present study, for the first time, a high-throughput compound library screening was performed under experimental conditions relevant to CF-infected lungs. Among 3,386 drugs screened, the clinically used drugs from outside infection treatment ebselen, tirapazamine, carmofur, 5-fluorouracil, and tavaborole showed, although to different extents, anti-P. aeruginosa activity against planktonic and biofilm cells and broad-spectrum activity against other CF pathogens at concentrations not toxic to bronchial epithelial cells. The mode-of-action studies revealed ebselen, carmofur, and tirapazamine targeted the cell membrane, increasing its permeability with subsequent cell lysis. These drugs are strong candidates for repurposing for treating CF lung P. aeruginosa infections.

Predicting metabolomic profiles from microbial composition through neural ordinary differential equations

Characterizing the metabolic profile of a microbial community is crucial for understanding its biological function and its impact on the host or environment. Metabolomics experiments directly measuring these profiles are difficult and expensive, while sequencing methods quantifying the species composition of microbial communities are well-developed and relatively cost-effective. Computational methods that are capable of predicting metabolomic profiles from microbial compositions can save considerable efforts needed for metabolomic profiling experimentally. Yet, despite existing efforts, we still lack a computational method with high prediction power, general applicability, and great interpretability. Here we develop a method - mNODE (Metabolomic profile predictor using Neural Ordinary Differential Equations), based on a state-of-the-art family of deep neural network models. We show compelling evidence that mNODE outperforms existing methods in predicting the metabolomic profiles of human microbiomes and several environmental microbiomes. Moreover, in the case of human gut microbiomes, mNODE can naturally incorporate dietary information to further enhance the prediction of metabolomic profiles. Besides, susceptibility analysis of mNODE enables us to reveal microbe-metabolite interactions, which can be validated using both synthetic and real data. The presented results demonstrate that mNODE is a powerful tool to investigate the microbiome-diet-metabolome relationship, facilitating future research on precision nutrition.

Inflammation and Infection in Cystic Fibrosis: Update for the Clinician

Inflammation and infection play an important role in the pathophysiology of cystic fibrosis, and they are significant causes of morbidity and mortality in CF. The presence of thick mucus in the CF airways predisposes to local hypoxia and promotes infection and inflammation. A vicious cycle of airway obstruction, inflammation, and infection is of critical importance for the progression of the disease, and new data elucidate the different factors that influence it. Recent research has been focused on improving infection and inflammation in addition to correcting the basic gene defect. This review aims to summarize important advances in infection and inflammation as well as the effect of new treatments modulating the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) protein. New approaches to target infection and inflammation are being studied, including gallium, nitric oxide, and phage therapy for infection, along with retinoids and neutrophil elastase inhibitors for inflammation.

Supplemental Oxygen Alters the Airway Microbiome in Cystic Fibrosis

Features of the airway microbiome in persons with cystic fibrosis (pwCF) are correlated with disease progression. Microbes have traditionally been classified for their ability to tolerate oxygen. It is unknown whether supplemental oxygen, a common medical intervention, affects the airway microbiome of pwCF. We hypothesized that hyperoxia significantly impacts the pulmonary microbiome in cystic fibrosis. In this study, we cultured spontaneously expectorated sputum from pwCF in artificial sputum medium under 21%, 50%, and 100% oxygen conditions using a previously validated model system that recapitulates microbial community composition in uncultured sputum. Culture aliquots taken at 24, 48, and 72 h, along with uncultured sputum, underwent shotgun metagenomic sequencing with absolute abundance values obtained with the use of spike-in bacteria. Raw sequencing files were processed using the bioBakery pipeline to determine changes in taxonomy, predicted function, antimicrobial resistance genes, and mobile genetic elements. Hyperoxia reduced absolute microbial load, species richness, and diversity. Hyperoxia reduced absolute abundance of specific microbes, including facultative anaerobes such as Rothia and some Streptococcus species, with minimal impact on canonical CF pathogens such as Pseudomonas aeruginosa and Staphylococcus aureus. The effect size of hyperoxia on predicted functional pathways was stronger than that on taxonomy. Large changes in microbial cooccurrence networks were noted. Hyperoxia exposure perturbs airway microbial communities in a manner well tolerated by key pathogens. Supplemental oxygen use may enable the growth of lung pathogens and should be further studied in the clinical setting. IMPORTANCE The airway microbiome in persons with cystic fibrosis (pwCF) is correlated with lung function and disease severity. Supplemental oxygen use is common in more advanced CF, yet its role in perturbing airway microbial communities is unknown. By culturing sputum samples from pwCF under normal and elevated oxygen conditions, we found that increased oxygen led to reduced total numbers and diversity of microbes, with relative sparing of common CF pathogens such as Pseudomonas aeruginosa and Staphylococcus aureus. Supplemental oxygen use may enable the growth of lung pathogens and should be further studied in the clinical setting.

Complex and unexpected outcomes of antibiotic therapy against a polymicrobial infection

Antibiotics are our primary approach to treating complex infections, yet we have a poor understanding of how these drugs affect microbial communities. To better understand antimicrobial effects on host-associated microbial communities we treated cultured sputum microbiomes from people with cystic fibrosis (pwCF, n = 24) with 11 different antibiotics, supported by theoretical and mathematical modeling-based predictions in a mucus-plugged bronchiole microcosm. Treatment outcomes we identified in vitro that were predicted in silico were: 1) community death, 2) community resistance, 3) pathogen killing, and 4) fermenter killing. However, two outcomes that were not predicted when antibiotics were applied were 5) community profile shifts with little change in total bacterial load (TBL), and 6) increases in TBL. The latter outcome was observed in 17.8% of samples with a TBL increase of greater than 20% and 6.8% of samples with an increase greater than 40%, demonstrating significant increases in community carrying capacity in the presence of an antibiotic. An iteration of the mathematical model showed that TBL increase was due to antibiotic-mediated release of pH-dependent inhibition of pathogens by anaerobe fermentation. These dynamics were verified in vitro when killing of fermenters resulted in a higher community carrying capacity compared to a no antibiotic control. Metagenomic sequencing of sputum samples during antibiotic therapy revealed similar dynamics in clinical samples. This study shows that the complex microbial ecology dictates the outcomes of antibiotic therapy against a polymicrobial infection.

Effect of invasive mechanical ventilation on the diversity of the pulmonary microbiota

Pulmonary microbial diversity may be influenced by biotic or abiotic conditions (e.g., disease, smoking, invasive mechanical ventilation (MV), etc.). Specially, invasive MV may trigger structural and physiological changes in both tissue and microbiota of lung, due to gastric and oral microaspiration, altered body posture, high O2 inhalation-induced O2 toxicity in hypoxemic patients, impaired airway clearance and ventilator-induced lung injury (VILI), which in turn reduce the diversity of the pulmonary microbiota and may ultimately lead to poor prognosis. Furthermore, changes in (local) O2 concentration can reduce the diversity of the pulmonary microbiota by affecting the local immune microenvironment of lung. In conclusion, systematic literature studies have found that invasive MV reduces pulmonary microbiota diversity, and future rational regulation of pulmonary microbiota diversity by existing or novel clinical tools (e.g., lung probiotics, drugs) may improve the prognosis of invasive MV treatment and lead to more effective treatment of lung diseases with precision.

Antibiotics Drive Expansion of Rare Pathogens in a Chronic Infection Microbiome Model

Chronic (long-lasting) infections are globally a major and rising cause of morbidity and mortality. Unlike typical acute infections, chronic infections are ecologically diverse, characterized by the presence of a polymicrobial mix of opportunistic pathogens and human-associated commensals. To address the challenge of chronic infection microbiomes, we focus on a particularly well-characterized disease, cystic fibrosis (CF), where polymicrobial lung infections persist for decades despite frequent exposure to antibiotics. Epidemiological analyses point to conflicting results on the benefits of antibiotic treatment yet are confounded by the dependency of antibiotic exposures on prior pathogen presence, limiting their ability to draw causal inferences on the relationships between antibiotic exposure and pathogen dynamics. To address this limitation, we develop a synthetic infection microbiome model representing CF metacommunity diversity and benchmark on clinical data. We show that in the absence of antibiotics, replicate microbiome structures in a synthetic sputum medium are highly repeatable and dominated by oral commensals. In contrast, challenge with physiologically relevant antibiotic doses leads to substantial community perturbation characterized by multiple alternate pathogen-dominant states and enrichment of drug-resistant species. These results provide evidence that antibiotics can drive the expansion (via competitive release) of previously rare opportunistic pathogens and offer a path toward microbiome-informed conditional treatment strategies.

IMPORTANCE We develop and clinically benchmark an experimental model of the cystic fibrosis (CF) lung infection microbiome to investigate the impacts of antibiotic exposures on chronic, polymicrobial infections. We show that a single experimental model defined by metacommunity data can partially recapitulate the diversity of individual microbiome states observed across a population of people with CF. In the absence of antibiotics, we see highly repeatable community structures, dominated by oral microbes. Under clinically relevant antibiotic exposures, we see diverse and frequently pathogen-dominated communities, and a nonevolutionary enrichment of antimicrobial resistance on the community scale, mediated by competitive release. The results highlight the potential importance of nonevolutionary (community-ecological) processes in driving the growing global crisis of increasing antibiotic resistance.

Association of bacterial community types, functional microbial processes and lung disease in cystic fibrosis airways

Bacterial infection and inflammation of the airways are the leading causes of morbidity and mortality in persons with cystic fibrosis (CF). The ecology of the bacterial communities inhabiting CF airways is poorly understood, especially with respect to how community structure, dynamics, and microbial metabolic activity relate to clinical outcomes. In this study, the bacterial communities in 818 sputum samples from 109 persons with CF were analyzed by sequencing bacterial 16S rRNA gene amplicons. We identified eight alternative community types (pulmotypes) by using a Dirichlet multinomial mixture model and studied their temporal dynamics in the cohort. Across patients, the pulmotypes displayed chronological patterns in the transition among each other. Furthermore, significant correlations between pulmotypes and patient clinical status were detected by using multinomial mixed effects models, principal components regression, and statistical testing. Constructing pulmotype-specific metabolic activity profiles, we found that pulmotype microbiota drive distinct community functions including mucus degradation or increased acid production. These results indicate that pulmotypes are the result of ordered, underlying drivers such as predominant metabolism, ecological competition, and niche construction and can form the basis for quantitative, predictive models supporting clinical treatment decisions.

Predicting microbiome compositions from species assemblages through deep learning

Microbes can form complex communities that perform critical functions in maintaining the integrity of their environment or their hosts' well-being. Rationally managing these microbial communities requires improving our ability to predict how different species assemblages affect the final species composition of the community. However, making such a prediction remains challenging because of our limited knowledge of the diverse physical, biochemical, and ecological processes governing microbial dynamics. To overcome this challenge, we present a deep learning framework that automatically learns the map between species assemblages and community compositions from training data only, without knowing any of the above processes. First, we systematically validate our framework using synthetic data generated by classical population dynamics models. Then, we apply our framework to data from in vitro and in vivo microbial communities, including ocean and soil microbiota, Drosophila melanogaster gut microbiota, and human gut and oral microbiota. We find that our framework learns to perform accurate out-of-sample predictions of complex community compositions from a small number of training samples. Our results demonstrate how deep learning can enable us to understand better and potentially manage complex microbial communities.

Validated In Silico Model for Biofilm Formation in Escherichia coli

Using Escherichia coli as the representative biofilm former, we report here the development of an in silico model built by simulating events that transform a free-living bacterial entity into self-encased multicellular biofilms. Published literature on ∼300 genes associated with pathways involved in biofilm formation was curated, static maps were created, and suitably interconnected with their respective metabolites using ordinary differential equations. Precise interplay of genetic networks that regulate the transitory switching of bacterial growth pattern in response to environmental changes and the resultant multicomponent synthesis of the extracellular matrix were appropriately represented. Subsequently, the in silico model was analyzed by simulating time-dependent changes in the concentration of components by using the R and python environment. The model was validated by simulating and verifying the impact of key gene knockouts (KOs) and systematic knockdowns on biofilm formation, thus ensuring the outcomes were comparable with the reported literature. Similarly, specific gene KOs in laboratory and pathogenic E. coli were constructed and assessed. MiaA, YdeO, and YgiV were found to be crucial in biofilm development. Furthermore, qRT-PCR confirmed the elevation of expression in biofilm-forming clinical isolates. Findings reported in this study offer opportunities for identifying biofilm inhibitors with applications in multiple industries. The application of this model can be extended to the health care sector specifically to develop novel adjunct therapies that prevent biofilms in medical implants and reduce emergence of biofilm-associated resistant polymicrobial-chronic infections. The in silico framework reported here is open source and accessible for further enhancements.

Gradients and consequences of heterogeneity in biofilms

Historically, appreciation for the roles of resource gradients in biology has fluctuated inversely to the popularity of genetic mechanisms. Nevertheless, in microbiology specifically, widespread recognition of the multicellular lifestyle has recently brought new emphasis to the importance of resource gradients. Most microorganisms grow in assemblages such as biofilms or spatially constrained communities with gradients that influence, and are influenced by, metabolism. In this Review, we discuss examples of gradient formation and physiological differentiation in microbial assemblages growing in diverse settings. We highlight consequences of physiological heterogeneity in microbial assemblages, including division of labour and increased resistance to stress. Our impressions of microbial behaviour in various ecosystems are not complete without complementary maps of the chemical and physical geographies that influence cellular activities. A holistic view, incorporating these geographies and the genetically encoded functions that operate within them, will be essential for understanding microbial assemblages in their many roles and potential applications.

Environmental structure impacts microbial composition and secondary metabolism

Determining the drivers of microbial community assembly is a central theme of microbial ecology, and chemical ecologists seek to characterize how secondary metabolites mediate these assembly patterns. Environmental structure affects how communities assemble and what metabolic pathways aid in that assembly. Here, we bridged these two perspectives by addressing the chemical drivers of community assembly within a spatially structured landscape with varying oxygen availability. We hypothesized that structured environments would favor higher microbial diversity and metabolite diversity. We anticipated that the production of a compound would be more advantageous in a structured environment (less mixing) compared to an unstructured environment (more mixing), where the molecule would have a diminished local effect. We observed this to be partially true in our experiments: structured environments had similar microbial diversity compared to unstructured environments but differed significantly in the metabolites produced. We also found that structured environments selected for communities with higher evenness, rather than communities with higher richness. This supports the idea that when characterizing the drivers of community assembly, it matters less about who is there and more about what they are doing. Overall, these data contribute to a growing effort to approach microbial community assembly with interdisciplinary tools and perspectives.

The cystic fibrosis lung microenvironment alters antibiotic activity: causes and effects

Chronic airway colonisation by Pseudomonas aeruginosa, a hallmark of cystic fibrosis (CF) lung disease, is associated with increased morbidity and mortality and despite aggressive antibiotic treatment, P. aeruginosa is able to persist in CF airways. In vitro antibiotic susceptibility assays are poor predictors of antibiotic efficacy to treat respiratory tract infections in the CF patient population and the selection of the antibiotic(s) is often made on an empirical base. In the current review, we discuss the factors that are responsible for the discrepancies between antibiotic activity in vitro and clinical efficacy in vivo We describe how the CF lung microenvironment, shaped by host factors (such as iron, mucus, immune mediators and oxygen availability) and the microbiota, influences antibiotic activity and varies widely between patients. A better understanding of the CF microenvironment and population diversity may thus help improve in vitro antibiotic susceptibility testing and clinical decision making, in turn increasing the success rate of antibiotic treatment.

The Sputum Microbiome in Pulmonary Tuberculosis and Its Association With Disease Manifestations: A Cross-Sectional Study

Each day, approximately 27,000 people become ill with tuberculosis (TB), and 4,000 die from this disease. Pulmonary TB is the main clinical form of TB, and affects the lungs with a considerably heterogeneous manifestation among patients. Immunomodulation by an interplay of host-, environment-, and pathogen-associated factors partially explains such heterogeneity. Microbial communities residing in the host's airways have immunomodulatory effects, but it is unclear if the inter-individual variability of these microbial communities is associated with the heterogeneity of pulmonary TB. Here, we investigated this possibility by characterizing the microbial composition in the sputum of 334 TB patients from Tanzania, and by assessing its association with three aspects of disease manifestations: sputum mycobacterial load, severe clinical findings, and chest x-ray (CXR) findings. Compositional data analysis of taxonomic profiles based on 16S-rRNA gene amplicon sequencing and on whole metagenome shotgun sequencing, and graph-based inference of microbial associations revealed that the airway microbiome of TB patients was shaped by inverse relationships between Streptococcus and two anaerobes: Selenomonas and Fusobacterium. Specifically, the strength of these microbial associations was negatively correlated with Faith's phylogenetic diversity (PD) and with the accumulation of transient genera. Furthermore, low body mass index (BMI) determined the association between abnormal CXRs and community diversity and composition. These associations were mediated by increased abundance of Selenomonas and Fusobacterium, relative to the abundance of Streptococcus, in underweight patients with lung parenchymal infiltrates and in comparison to those with normal chest x-rays. And last, the detection of herpesviruses and anelloviruses in sputum microbial assemblage was linked to co-infection with HIV. Given the anaerobic metabolism of Selenomonas and Fusobacterium, and the hypoxic environment of lung infiltrates, our results suggest that in underweight TB patients, lung tissue remodeling toward anaerobic conditions favors the growth of Selenomonas and Fusobacterium at the expense of Streptococcus. These new insights into the interplay among particular members of the airway microbiome, BMI, and lung parenchymal lesions in TB patients, add a new dimension to the long-known association between low BMI and pulmonary TB. Our results also drive attention to the airways virome in the context of HIV-TB coinfection.

Impact of artificial sputum media formulation on Pseudomonas aeruginosa secondary metabolite production

In vitro culture media are being developed to understand how host site-specific nutrient profiles influence microbial pathogenicity and ecology. To mimic the cystic fibrosis (CF) lung environment, a variety of artificial sputum media (ASM) have been created. However, the composition of these ASM vary in the concentration of key nutrients, including amino acids, lipids, DNA, and mucin. In this work, we used feature-based molecular networking (FBMN) to perform comparative metabolomics of Pseudomonas aeruginosa, the predominant opportunistic pathogen infecting the lungs of people with CF, cultured in nine different ASM. We found that the concentration of aromatic amino acids and iron from mucin added to the media contributes to differences in the production of P. aeruginosa virulence-associated secondary metabolites.

IMPORTANCE Different media formulations aiming to replicate in vivo infection environments contain different nutrients, which affects interpretation of experimental results. Inclusion of undefined components, such as commercial porcine gastric mucin (PGM), in an otherwise chemically defined medium can alter the nutrient content of the medium in unexpected ways and influence experimental outcomes.

Post-ingestion conversion of dietary indoles into anticancer agents

There are health benefits from consuming cruciferous vegetables that release indole-3-carbinol (I3C), but the in vivo transformation of I3C-related indoles remains underinvestigated. Here we detail the post-ingestion conversion of I3C into antitumor agents, 2-(indol-3-ylmethyl)-3,3^′-diindolylmethane (LTr1) and 3,3^′-diindolylmethane (DIM), by conceptualizing and materializing the reaction flux derailing (RFD) approach as a means of unraveling these stepwise transformations to be non-enzymatic but pH-dependent and gut microbe-sensitive. In the upper (or acidic) gastrointestinal tract, LTr1 is generated through Michael addition of 3-methyleneindolium (3MI, derived in situ from I3C) to DIM produced from I3C via the formaldehyde-releasing (major) and CO₂-liberating (minor) pathways. In the large intestine, ‘endogenous’ I3C and DIM can form, respectively, from couplings of formaldehyde with one and two molecules of indole (a tryptophan catabolite). Acid-producing gut bacteria such as Lactobacillus acidophilus facilitate the H⁺-promotable steps. This work updates our understanding of the merits of I3C consumption and identifies LTr1 as a drug candidate.

Feature-based molecular networking for identification of organic micropollutants including metabolites by non-target analysis applied to riverbank filtration

Due to growing concern about organic micropollutants and their transformation products (TP) in surface and drinking water, reliable identification of unknowns is required. Here, we demonstrate how non-target liquid chromatography (LC)-high-resolution tandem mass spectrometry (MS/MS) and the feature-based molecular networking (FBMN) workflow provide insight into water samples from four riverbank filtration sites with different redox conditions. First, FBMN prioritized and connected drinking water relevant and seasonally dependent compounds based on a modification-aware MS/MS cosine similarity. Within the resulting molecular networks, forty-three compounds were annotated. Here, carbamazepine, sartans, and their respective TP were investigated exemplarily. With chromatographic information and spectral similarity, four additional TP (dealkylated valsartan, dealkylated irbesartan, two oxygenated irbesartan isomers) and olmesartan were identified and partly verified with an authentic standard. In this study, sartans and TP were investigated and grouped regarding their removal behavior under different redox conditions and seasons for the first time. Antihypertensives were grouped into compounds being well removed during riverbank filtration, those primarily removed under anoxic conditions, and rather persistent compounds. Observed seasonal variations were mainly limited to varying river water concentrations. FBMN is a powerful tool for identifying previously unknown or unexpected compounds and their TP in water samples by non-target analysis.

MiMeNet: Exploring microbiome-metabolome relationships using neural networks

The advance in microbiome and metabolome studies has generated rich omics data revealing the involvement of the microbial community in host disease pathogenesis through interactions with their host at a metabolic level. However, the computational tools to uncover these relationships are just emerging. Here, we present MiMeNet, a neural network framework for modeling microbe-metabolite relationships. Using ten iterations of 10-fold cross-validation on three paired microbiome-metabolome datasets, we show that MiMeNet more accurately predicts metabolite abundances (mean Spearman correlation coefficients increase from 0.108 to 0.309, 0.276 to 0.457, and -0.272 to 0.264) and identifies more well-predicted metabolites (increase in the number of well-predicted metabolites from 198 to 366, 104 to 143, and 4 to 29) compared to state-of-art linear models for individual metabolite predictions. Additionally, we demonstrate that MiMeNet can group microbes and metabolites with similar interaction patterns and functions to illuminate the underlying structure of the microbe-metabolite interaction network, which could potentially shed light on uncharacterized metabolites through “Guilt by Association”. Our results demonstrated that MiMeNet is a powerful tool to provide insights into the causes of metabolic dysregulation in disease, facilitating future hypothesis generation at the interface of the microbiome and metabolomics.

The microbiome has shown to functionally interact with its host or environment at a metabolic level, however the exact nature of these interactions is not well understood. In addition, metabolic dysregulation caused by the microbiome is believed to contribute to the development of diseases such as inflammatory bowel disease, diabetes mellitus, and obesity. In this manuscript, we introduce a computational framework to integrate microbiome and metabolome data to uncover microbe-metabolite interactions in a data-driven manner. Our model uses neural networks to predict metabolite abundances from microbe abundances. The trained models are then used to derive microbe-metabolite feature scores, which are used for clustering microbes and metabolites into functional modules. These module-based interactions are useful in generating biological insights and facilitating hypothesis generation for the investigation of their roles in various metabolic diseases. The software of our model is made freely available to interested researchers.

Airway Surface Liquid pH Regulation in Airway Epithelium Current Understandings and Gaps in Knowledge

Knowledge on the mechanisms of acid and base secretion in airways has progressed recently. The aim of this review is to summarize the known mechanisms of airway surface liquid (ASL) pH regulation and their implication in lung diseases. Normal ASL is slightly acidic relative to the interstitium, and defects in ASL pH regulation are associated with various respiratory diseases, such as cystic fibrosis. Basolateral bicarbonate (HCO3-) entry occurs via the electrogenic, coupled transport of sodium (Na+) and HCO3-, and, together with carbonic anhydrase enzymatic activity, provides HCO3- for apical secretion. The latter mainly involves CFTR, the apical chloride/bicarbonate exchanger pendrin and paracellular transport. Proton (H+) secretion into ASL is crucial to maintain its relative acidity compared to the blood. This is enabled by H+ apical secretion, mainly involving H+/K+ ATPase and vacuolar H+-ATPase that carry H+ against the electrochemical potential gradient. Paracellular HCO3- transport, the direction of which depends on the ASL pH value, acts as an ASL protective buffering mechanism. How the transepithelial transport of H+ and HCO3- is coordinated to tightly regulate ASL pH remains poorly understood, and should be the focus of new studies.

The yellow perch (Perca flavescens) microbiome revealed resistance to colonisation mostly associated with neutralism driven by rare taxa under cadmium disturbance

Background

Disentangling the dynamics of microbial interactions within communities improves our comprehension of metacommunity assembly of microbiota during host development and under perturbations. To assess the impact of stochastic variation of neutral processes on microbiota structure and composition under disturbance, two types of microbial habitats, free-living (water), and host-associated (skin and gut) were experimentally exposed to either a constant or gradual selection regime exerted by two sublethal cadmium chloride dosages (CdCl₂). Yellow Perch (Perca flavescens) was used as a piscivorous ecotoxicological model. Using 16S rDNA gene based metataxonomics, quantitative diversity metrics of water, skin and gut microbial communities were characterized along with development and across experimental conditions.

Results

After 30 days, constant and gradual selection regimes drove a significant alpha diversity increase for both skin and gut microbiota. In the skin, pervasive negative correlations between taxa in both selection regimes in addition to the taxonomic convergence with the environmental bacterial community, suggest a loss of colonisation resistance resulting in the dysbiosis of yellow perch microbiota. Furthermore, the network connectivity in gut microbiome was exclusively maintained by rare (low abundance) OTUs, while most abundant OTUs were mainly composed of opportunistic invaders such as Mycoplasma and other genera related to fish pathogens such as Flavobacterium. Finally, the mathematical modelling of community assembly using both non-linear least squares models (NLS) based estimates of migration rates and normalized stochasticity ratios (NST) based beta-diversity distances suggested neutral processes drove by taxonomic drift in host and water communities for almost all treatments. The NLS models predicted higher demographic stochasticity in the cadmium-free host and water microbiomes, however, NST models suggested higher ecological stochasticity under perturbations.

Conclusions

Neutral models agree that water and host-microbiota assembly promoted by rare taxa have evolved predominantly under neutral processes with potential involvement of deterministic forces sourced from host filtering and cadmium selection. The early signals of perturbations in the skin microbiome revealed antagonistic interactions by a preponderance of negative correlations in the co-abundance networks. Our findings enhance our understanding of community assembly host-associated and free-living under anthropogenic selective pressure.

Reconsidering ventilator-associated pneumonia from a new dimension of the lung microbiome

Complex microbial communities that reside in the lungs, skin and gut are now appreciated for their role in maintaining organ, tissue and immune homoeostasis. As lungs are currently seen as an ecosystem, the shift in paradigm calls for the consideration of new algorithms related to lung ecology in pulmonology. Evidence of lung microbiota does not solely challenge the traditional physiopathology of ventilator-associated pneumonia (VAP); indeed, it also reinforces the need to include molecular techniques in VAP diagnosis and accelerate the use of immunomodulatory drugs, including corticosteroids, and other supplements such as probiotics for VAP prevention and/or treatment. With that stated, both microbiome and virome, including phageome, can lead to new opportunities in further understanding the relationship between health and dysbiosis in VAP. Previous knowledge may be, however, reconsidered at a microbiome scale.

Reproducible molecular networking of untargeted mass spectrometry data using GNPS

Global Natural Product Social Molecular Networking (GNPS) is an interactive online small molecule-focused tandem mass spectrometry (MS2) data curation and analysis infrastructure. It is intended to provide as much chemical insight as possible into an untargeted MS2 dataset and to connect this chemical insight to the user's underlying biological questions. This can be performed within one liquid chromatography (LC)-MS2 experiment or at the repository scale. GNPS-MassIVE is a public data repository for untargeted MS2 data with sample information (metadata) and annotated MS2 spectra. These publicly accessible data can be annotated and updated with the GNPS infrastructure keeping a continuous record of all changes. This knowledge is disseminated across all public data; it is a living dataset. Molecular networking-one of the main analysis tools used within the GNPS platform-creates a structured data table that reflects the molecular diversity captured in tandem mass spectrometry experiments by computing the relationships of the MS2 spectra as spectral similarity. This protocol provides step-by-step instructions for creating reproducible, high-quality molecular networks. For training purposes, the reader is led through a 90- to 120-min procedure that starts by recalling an example public dataset and its sample information and proceeds to creating and interpreting a molecular network. Each data analysis job can be shared or cloned to disseminate the knowledge gained, thus propagating information that can lead to the discovery of molecules, metabolic pathways, and ecosystem/community interactions.

Activity of Antibiotics against Pseudomonas aeruginosa in an In Vitro Model of Biofilms in the Context of Cystic Fibrosis: Influence of the Culture Medium

Pseudomonas aeruginosa is a major cause of respiratory biofilm-related infections in patients with cystic fibrosis. We developed an in vitro pharmacodynamic model to study the activity of antipseudomonal antibiotics against PAO1 biofilms grown in artificial sputum medium with agar [ASM(+)] versus that against biofilms grown in Trypticase soy broth supplemented with glucose and NaCl (TGN). We measured bacterial counts, metabolic activity (fluorescein diacetate [FDA] hydrolysis), and biomass (crystal violet absorbance). Biofilms grew slower in ASM(+) than in TGN but reached the same CFU counts and metabolic activity in both media and a slightly higher biomass after 48 h in ASM(+) than in TGN. The concentration-response curves of the antibiotics after 24 h of incubation with mature biofilms showed maximal effects ranging from a 3 (ciprofloxacin)- to a 1.5 (ceftazidime, meropenem)-log₁₀-CFU decrease, with tobramycin and colistin showing intermediate values. These maximal reductions in the numbers of CFU were similar in both media for ciprofloxacin and β-lactams but lower in ASM(+) than in TGN for tobramycin and colistin; they were reached at concentrations lower than the human maximum concentration in plasma for ciprofloxacin and β-lactams only. The reductions in metabolic activity and in biomass were low in both media. Small-colony variants were selected by tobramycin in ASM(+) and by ciprofloxacin in both media. The model was then successfully applied to 4 isolates from patients with cystic fibrosis. These biofilms showed CFU counts similar to those of PAO1 biofilms in ASM(+) but a higher biomass than PAO1 biofilms in ASM(+) and moderate differences in their susceptibility to antibiotics from that of PAO1 biofilms grown in this medium. This model proved useful to establish the pharmacodynamic profile of drugs against P. aeruginosa biofilms in the context of cystic fibrosis.

Learning representations of microbe-metabolite interactions

Integrating multiomics datasets is critical for microbiome research; however, inferring interactions across omics datasets has multiple statistical challenges. We solve this problem by using neural networks (https://github.com/biocore/mmvec) to estimate the conditional probability that each molecule is present given the presence of a specific microorganism. We show with known environmental (desert soil biocrust wetting) and clinical (cystic fibrosis lung) examples, our ability to recover microbe-metabolite relationships, and demonstrate how the method can discover relationships between microbially produced metabolites and inflammatory bowel disease.

Interplay between host-microbe and microbe-microbe interactions in cystic fibrosis

The respiratory tract of individuals with cystic fibrosis is host to polymicrobial infections that persist for decades and lead to significant morbidity and mortality. Improving our understanding of CF respiratory infections requires coordinated efforts from researchers in the fields of microbial physiology, genomics, and ecology, as well as epithelial biology and immunology. Here, we have highlighted examples from recent CF microbial pathogenesis literature of how the host nutritional environment, immune response, and microbe-microbe interactions can feedback onto each other, leading to diverse effects on lung disease pathogenesis in CF.

Optimal Microbiome Networks: Macroecology and Criticality

The human microbiome is an extremely complex ecosystem considering the number of bacterial species, their interactions, and its variability over space and time. Here, we untangle the complexity of the human microbiome for the Irritable Bowel Syndrome (IBS) that is the most prevalent functional gastrointestinal disorder in human populations. Based on a novel information theoretic network inference model, we detected potential species interaction networks that are functionally and structurally different for healthy and unhealthy individuals. Healthy networks are characterized by a neutral symmetrical pattern of species interactions and scale-free topology versus random unhealthy networks. We detected an inverse scaling relationship between species total outgoing information flow, meaningful of node interactivity, and relative species abundance (RSA). The top ten interacting species are also the least relatively abundant for the healthy microbiome and the most detrimental. These findings support the idea about the diminishing role of network hubs and how these should be defined considering the total outgoing information flow rather than the node degree. Macroecologically, the healthy microbiome is characterized by the highest Pareto total species diversity growth rate, the lowest species turnover, and the smallest variability of RSA for all species. This result challenges current views that posit a universal association between healthy states and the highest absolute species diversity in ecosystems. Additionally, we show how the transitory microbiome is unstable and microbiome criticality is not necessarily at the phase transition between healthy and unhealthy states. We stress the importance of considering portfolios of interacting pairs versus single node dynamics when characterizing the microbiome and of ranking these pairs in terms of their interactions (i.e., species collective behavior) that shape transition from healthy to unhealthy states. The macroecological characterization of the microbiome is useful for public health and disease diagnosis and etiognosis, while species-specific analyses can detect beneficial species leading to personalized design of pre- and probiotic treatments and microbiome engineering.

Perspective and Guidelines for Metaproteomics in Microbiome Studies

The microbiome is emerging as a prominent factor affecting human health, and its dysbiosis is associated with various diseases. Compositional profiling of microbiome is increasingly being supplemented with functional characterization. Metaproteomics is intrinsically focused on functional changes and therefore will be an important tool in those studies of the human microbiome. In the past decade, development of new experimental and bioinformatic approaches for metaproteomics has enabled large-scale human metaproteomic studies. However, challenges still exist, and there remains a lack of standardizations and guidelines for properly performing metaproteomic studies on human microbiome. Herein, we provide a perspective of recent developments, the challenges faced, and the future directions of metaproteomics and its applications. In addition, we propose a set of guidelines/recommendations for performing and reporting the results from metaproteomic experiments for the study of human microbiomes. We anticipate that these guidelines will be optimized further as more metaproteomic questions are raised and addressed, and metaproteomic applications are published, so that they are eventually recognized and applied in the field.

Cystic Fibrosis-Associated Stenotrophomonas maltophilia Strain-Specific Adaptations and Responses to pH

Understanding bacterial responses to physiological conditions is an important priority for combating opportunistic infections. The majority of CF patients succumb to inflammation and necrosis in the airways, arising from chronic infection due to ineffective mucociliary clearance. Steep pH gradients characterize the CF airways but are not often incorporated in standard microbiology culture conditions. Stenotrophomonas maltophilia is a prevalent CF opportunistic pathogen also found in many disparate environments, yet this bacterium’s contribution to CF lung damage and its response to changing environmental factors remain largely understudied. Here, we show that pH impacts the physiology and antibiotic susceptibility of S. maltophilia, with implications for the development of relevant in vitro models and assessment of antibiotic sensitivity.

The airway fluids of cystic fibrosis (CF) patients contain local pH gradients and are more acidic than those of healthy individuals. pH is a critical factor that is often overlooked in studies seeking to recapitulate the infection microenvironment. We sought to determine the impact of pH on the physiology of a ubiqituous yet understudied microbe, Stenotrophomonas maltophilia. Phylogenomics was first used to reconstruct evolutionary relationships between 74 strains of S. maltophilia (59 from CF patients). Neither the core genome (2,158 genes) nor the accessory genome (11,978 genes) distinguish the CF and non-CF isolates; however, strains from similar isolation sources grouped into the same subclades. We grew two human and six CF S. maltophilia isolates from different subclades at a range of pH values and observed impaired growth and altered antibiotic tolerances at pH 5. Transcriptomes revealed increased expression of both antibiotic resistance and DNA repair genes in acidic conditions. Although the gene expression profiles of S. maltophilia in lab cultures and CF sputum were distinct, we found that the same genes associated with low pH were also expressed during infection, and the higher pH cultures were more similar to sputum metatranscriptomes. Our findings suggest that S. maltophilia is not well adapted to acidity and may cope with low pH by expressing stress response genes and colonizing less acidic microenvironments. As a whole, our study underlines the impact of microenvironments on bacterial colonization and adaptation in CF infections.

IMPORTANCE Understanding bacterial responses to physiological conditions is an important priority for combating opportunistic infections. The majority of CF patients succumb to inflammation and necrosis in the airways, arising from chronic infection due to ineffective mucociliary clearance. Steep pH gradients characterize the CF airways but are not often incorporated in standard microbiology culture conditions. Stenotrophomonas maltophilia is a prevalent CF opportunistic pathogen also found in many disparate environments, yet this bacterium’s contribution to CF lung damage and its response to changing environmental factors remain largely understudied. Here, we show that pH impacts the physiology and antibiotic susceptibility of S. maltophilia, with implications for the development of relevant in vitro models and assessment of antibiotic sensitivity.