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Ambient energy harvesting has great potential to contribute to sustainable development and address growing environmental challenges. Converting waste energy from energy-intensive processes and systems (e.g. combustion engines and furnaces) is crucial to reducing their environmental impact and achieving net-zero emissions. Compact energy harvesters will also be key to powering the exponentially growing smart devices ecosystem that is part of the Internet of Things, thus enabling futuristic applications that can improve our quality of life (e.g. smart homes, smart cities, smart manufacturing, and smart healthcare). To achieve these goals, innovative materials are needed to efficiently convert ambient energy into electricity through various physical mechanisms, such as the photovoltaic effect, thermoelectricity, piezoelectricity, triboelectricity, and radiofrequency wireless power transfer. By bringing together the perspectives of experts in various types of energy harvesting materials, this Roadmap provides extensive insights into recent advances and present challenges in the field. Additionally, the Roadmap analyses the key performance metrics of these technologies in relation to their ultimate energy conversion limits. Building on these insights, the Roadmap outlines promising directions for future research to fully harness the potential of energy harvesting materials for green energy anytime, anywhere.

Ulcerative colitis (UC) is driven by disruptions in host-microbiota homoeostasis, but current treatments exclusively target host inflammatory pathways. To understand how host-microbiota interactions become disrupted in UC, we collected and analysed six faecal- or serum-based omic datasets (metaproteomic, metabolomic, metagenomic, metapeptidomic and amplicon sequencing profiles of faecal samples and proteomic profiles of serum samples) from 40 UC patients at a single inflammatory bowel disease centre, as well as various clinical, endoscopic and histologic measures of disease activity. A validation cohort of 210 samples (73 UC, 117 Crohn's disease, 20 healthy controls) was collected and analysed separately and independently. Data integration across both cohorts showed that a subset of the clinically active UC patients had an overabundance of proteases that originated from the bacterium Bacteroides vulgatus. To test whether B. vulgatus proteases contribute to UC disease activity, we first profiled B. vulgatus proteases found in patients and bacterial cultures. Use of a broad-spectrum protease inhibitor improved B. vulgatus-induced barrier dysfunction in vitro, and prevented colitis in B. vulgatus monocolonized, IL10-deficient mice. Furthermore, transplantation of faeces from UC patients with a high abundance of B. vulgatus proteases into germfree mice induced colitis dependent on protease activity. These results, stemming from a multi-omics approach, improve understanding of functional microbiota alterations that drive UC and provide a resource for identifying other pathways that could be inhibited as a strategy to treat this disease.

We engineered a machine learning approach, MSHub, to enable auto-deconvolution of gas chromatography-mass spectrometry (GC-MS) data. We then designed workflows to enable the community to store, process, share, annotate, compare and perform molecular networking of GC-MS data within the Global Natural Product Social (GNPS) Molecular Networking analysis platform. MSHub/GNPS performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization and quantifies the reproducibility of fragmentation patterns across samples.

Antibiotics are a mainstay of modern medicine, but as they kill their target pathogen(s), they often affect the commensal microbiota. Antibiotic-induced microbiome dysbiosis is a growing research focus and health concern, often assessed via analysis of fecal samples. However, such analysis does not inform how antibiotics influence the microbiome across the whole host or how such changes subsequently alter host chemistry. In this study, we investigated the acute (1 day postadministration) and delayed (6 days postadministration) effects of a single parenteral dose of two common antibiotics, ampicillin or vancomycin, on the global metabolome and microbiome of mice across 77 different body sites from 25 different organs. The broader-spectrum agent ampicillin had the greatest impact on the microbiota in the lower gastrointestinal tract (cecum and colon), where microbial diversity is highest. In the metabolome, the greatest effects were seen 1 day posttreatment, and changes in metabolite abundances were not confined to the gut. The local abundance of ampicillin and its metabolites correlated with increased metabolome effect size and a loss of alpha diversity versus control mice. Additionally, small peptides were elevated in the lower gastrointestinal tract of mice 1 day after antibiotic treatment. While a single parenteral dose of antibiotic did not drastically alter the microbiome, nevertheless, changes in the metabolome were observed both within and outside the gut. This study provides a framework for how whole-organism -omics approaches can be employed to understand the impact of antibiotics on the entire host.IMPORTANCE We are just beginning to understand the unintended effects of antibiotics on our microbiomes and health. In this study, we aimed to define an approach by which one could obtain a comprehensive picture of (i) how antibiotics spatiotemporally impact commensal microbes throughout the gut and (ii) how these changes influence host chemistry throughout the body. We found that just a single dose of antibiotic altered host chemistry in a variety of organs and that microbiome alterations were not uniform throughout the gut. As technological advances increase the feasibility of whole-organism studies, we argue that using these approaches can provide further insight on both the wide-ranging effects of antibiotics on health and how to restore microbial communities to mitigate these effects.

We present ReDU ( https://redu.ucsd.edu/ ), a system for metadata capture of public mass spectrometry-based metabolomics data, with validated controlled vocabularies. Systematic capture of knowledge enables the reanalysis of public data and/or co-analysis of one's own data. ReDU enables multiple types of analyses, including finding chemicals and associated metadata, comparing the shared and different chemicals between groups of samples, and metadata-filtered, repository-scale molecular networking.

Molecular networking has become a key method to visualize and annotate the chemical space in non-targeted mass spectrometry data. We present feature-based molecular networking (FBMN) as an analysis method in the Global Natural Products Social Molecular Networking (GNPS) infrastructure that builds on chromatographic feature detection and alignment tools. FBMN enables quantitative analysis and resolution of isomers, including from ion mobility spectrometry.

Microbial diversity in the cystic fibrosis (CF) lung decreases over decades as pathogenic bacteria such as Pseudomonas aeruginosa take over. The dynamics of the CF microbiome and metabolome over shorter time frames, however, remain poorly studied. Here, we analyze paired microbiome and metabolome data from 594 sputum samples collected over 401 days from six adult CF subjects (subject mean = 179 days) through periods of clinical stability and 11 CF pulmonary exacerbations (CFPE). While microbiome profiles were personalized (permutational multivariate analysis of variance [PERMANOVA] r 2 = 0.79, P

A mosaic of cross-phylum chemical interactions occurs between all metazoans and their microbiomes. A number of molecular families that are known to be produced by the microbiome have a marked effect on the balance between health and disease1-9. Considering the diversity of the human microbiome (which numbers over 40,000 operational taxonomic units10), the effect of the microbiome on the chemistry of an entire animal remains underexplored. Here we use mass spectrometry informatics and data visualization approaches11-13 to provide an assessment of the effects of the microbiome on the chemistry of an entire mammal by comparing metabolomics data from germ-free and specific-pathogen-free mice. We found that the microbiota affects the chemistry of all organs. This included the amino acid conjugations of host bile acids that were used to produce phenylalanocholic acid, tyrosocholic acid and leucocholic acid, which have not previously been characterized despite extensive research on bile-acid chemistry14. These bile-acid conjugates were also found in humans, and were enriched in patients with inflammatory bowel disease or cystic fibrosis. These compounds agonized the farnesoid X receptor in vitro, and mice gavaged with the compounds showed reduced expression of bile-acid synthesis genes in vivo. Further studies are required to confirm whether these compounds have a physiological role in the host, and whether they contribute to gut diseases that are associated with microbiome dysbiosis.

Recent interest in the role of microbiota in health and disease has implicated gut microbiota dysbiosis in psychiatric disorders including major depressive disorder. Several antidepressant drugs that belong to the class of selective serotonin reuptake inhibitors have been found to display antimicrobial activities. In fact, one of the first antidepressants discovered serendipitously in the 1950s, the monoamine-oxidase inhibitor Iproniazid, was a drug used for the treatment of tuberculosis. In the current study we chronically treated DBA/2J mice for 2 weeks with paroxetine, a selective serotonin reuptake inhibitor, and collected fecal pellets as a proxy for the gut microbiota from the animals after 7 and 14 days. Behavioral testing with the forced swim test revealed significant differences between paroxetine- and vehicle-treated mice. Untargeted mass spectrometry and 16S rRNA profiling of fecal pellet extracts showed several primary and secondary bile acid level, and microbiota alpha diversity differences, respectively between paroxetine- and vehicle-treated mice, suggesting that microbiota functions are altered by the drug. In addition to their lipid absorbing activities bile acids have important signaling activities and have been associated with gastrointestinal diseases and colorectal cancer. Antidepressant drugs like paroxetine should therefore be used with caution to prevent undesirable side effects.

As metagenomic studies move to increasing numbers of samples, communities like the human gut may benefit more from the assembly of abundant microbes in many samples, rather than the exhaustive assembly of fewer samples. We term this approach leaderboard metagenome sequencing. To explore protocol optimization for leaderboard metagenomics in real samples, we introduce a benchmark of library prep and sequencing using internal references generated by synthetic long-read technology, allowing us to evaluate high-throughput library preparation methods against gold-standard reference genomes derived from the samples themselves. We introduce a low-cost protocol for high-throughput library preparation and sequencing.

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.

To visualize the personalized distributions of pathogens and chemical environments, including microbial metabolites, pharmaceuticals, and their metabolic products, within and between human lungs afflicted with cystic fibrosis (CF), we generated three-dimensional (3D) microbiome and metabolome maps of six explanted lungs from three cystic fibrosis patients. These 3D spatial maps revealed that the chemical environments differ between patients and within the lungs of each patient. Although the microbial ecosystems of the patients were defined by the dominant pathogen, their chemical diversity was not. Additionally, the chemical diversity between locales in the lungs of the same individual sometimes exceeded interindividual variation. Thus, the chemistry and microbiome of the explanted lungs appear to be not only personalized but also regiospecific. Previously undescribed analogs of microbial quinolones and antibiotic metabolites were also detected. Furthermore, mapping the chemical and microbial distributions allowed visualization of microbial community interactions, such as increased production of quorum sensing quinolones in locations where Pseudomonas was in contact with Staphylococcus and Granulicatella, consistent with in vitro observations of bacteria isolated from these patients. Visualization of microbe-metabolite associations within a host organ in early-stage CF disease in animal models will help elucidate the complex interplay between the presence of a given microbial structure, antibiotics, metabolism of antibiotics, microbial virulence factors, and host responses.IMPORTANCE Microbial infections are now recognized to be polymicrobial and personalized in nature. Comprehensive analysis and understanding of the factors underlying the polymicrobial and personalized nature of infections remain limited, especially in the context of the host. By visualizing microbiomes and metabolomes of diseased human lungs, we reveal how different the chemical environments are between hosts that are dominated by the same pathogen and how community interactions shape the chemical environment or vice versa. We highlight that three-dimensional organ mapping methods represent hypothesis-building tools that allow us to design mechanistic studies aimed at addressing microbial responses to other microbes, the host, and pharmaceutical drugs.

Environmental microbial communities are stratified by chemical gradients that shape the structure and function of these systems. Similar chemical gradients exist in the human body, but how they influence these microbial systems is more poorly understood. Understanding these effects can be particularly important for dysbiotic shifts in microbiome structure that are often associated with disease. We show that pH and oxygen strongly partition the microbial community from a diseased human lung into two mutually exclusive communities of pathogens and anaerobes. Antimicrobial treatment disrupted this chemical partitioning, causing complex death, survival, and resistance outcomes that were highly dependent on the individual microorganism and on community stratification. These effects were mathematically modeled, enabling a predictive understanding of this complex polymicrobial system. Harnessing the power of these chemical gradients could be a drug-free method of shaping microbial communities in the human body from undesirable dysbiotic states.

Corals and humans represent two extremely disparate metazoan lineages and are therefore useful for comparative evolutionary studies. Two lipid-based molecules that are central to human immunity, platelet-activating factor (PAF) and Lyso-PAF were recently identified in scleractinian corals. To identify processes in corals that involve these molecules, PAF and Lyso-PAF biosynthesis was quantified in conditions known to stimulate PAF production in mammals (tissue growth and exposure to elevated levels of ultraviolet light) and in conditions unique to corals (competing with neighbouring colonies over benthic space). Similar to observations in mammals, PAF production was higher in regions of active tissue growth and increased when corals were exposed to elevated levels of ultraviolet light. PAF production also increased when corals were attacked by the stinging cells of a neighbouring colony, though only the attacked coral exhibited an increase in PAF. This reaction was observed in adjacent areas of the colony, indicating that this response is coordinated across multiple polyps including those not directly subject to the stress. PAF and Lyso-PAF are involved in coral stress responses that are both shared with mammals and unique to the ecology of cnidarians.

Corals and humans represent two extremely disparate metazoan lineages and are therefore useful for comparative evolutionary studies. Two lipid-based molecules that are central to human immunity, platelet-activating factor (PAF) and Lyso-PAF were recently identified in scleractinian corals. To identify processes in corals that involve these molecules, PAF and Lyso-PAF biosynthesis was quantified in conditions known to stimulate PAF production in mammals (tissue growth and exposure to elevated levels of ultraviolet light) and in conditions unique to corals (competing with neighbouring colonies over benthic space). Similar to observations in mammals, PAF production was higher in regions of active tissue growth and increased when corals were exposed to elevated levels of ultraviolet light. PAF production also increased when corals were attacked by the stinging cells of a neighbouring colony, though only the attacked coral exhibited an increase in PAF. This reaction was observed in adjacent areas of the colony, indicating that this response is coordinated across multiple polyps including those not directly subject to the stress. PAF and Lyso-PAF are involved in coral stress responses that are both shared with mammals and unique to the ecology of cnidarians.

Advances in sequencing technologies have enabled novel insights into microbial niche differentiation, from analyzing environmental samples to understanding human diseases and informing dietary studies. However, identifying the microbial taxa that differentiate these samples can be challenging. These issues stem from the compositional nature of 16S rRNA gene data (or, more generally, taxon or functional gene data); the changes in the relative abundance of one taxon influence the apparent abundances of the others. Here we acknowledge that inferring properties of individual bacteria is a difficult problem and instead introduce the concept of balances to infer meaningful properties of subcommunities, rather than properties of individual species. We show that balances can yield insights about niche differentiation across multiple microbial environments, including soil environments and lung sputum. These techniques have the potential to reshape how we carry out future ecological analyses aimed at revealing differences in relative taxonomic abundances across different samples. IMPORTANCE By explicitly accounting for the compositional nature of 16S rRNA gene data through the concept of balances, balance trees yield novel biological insights into niche differentiation. The software to perform this analysis is available under an open-source license and can be obtained at https://github.com/biocore/gneiss. Author Video: An author video summary of this article is available.

The cars we drive, the homes we live in, the restaurants we visit, and the laboratories and offices we work in are all a part of the modern human habitat. Remarkably, little is known about the diversity of chemicals present in these environments and to what degree molecules from our bodies influence the built environment that surrounds us and vice versa. We therefore set out to visualize the chemical diversity of five built human habitats together with their occupants, to provide a snapshot of the various molecules to which humans are exposed on a daily basis. The molecular inventory was obtained through untargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis of samples from each human habitat and from the people that occupy those habitats. Mapping MS-derived data onto 3D models of the environments showed that frequently touched surfaces, such as handles (e.g., door, bicycle), resemble the molecular fingerprint of the human skin more closely than other surfaces that are less frequently in direct contact with humans (e.g., wall, bicycle frame). Approximately 50% of the MS/MS spectra detected were shared between people and the environment. Personal care products, plasticizers, cleaning supplies, food, food additives, and even medications that were found to be a part of the human habitat. The annotations indicate that significant transfer of chemicals takes place between us and our built environment. The workflows applied here will lay the foundation for future studies of molecular distributions in medical, forensic, architectural, space exploration, and environmental applications.

The potential of the diverse chemistries present in natural products (NP) for biotechnology and medicine remains untapped because NP databases are not searchable with raw data and the NP community has no way to share data other than in published papers. Although mass spectrometry (MS) techniques are well-suited to high-throughput characterization of NP, there is a pressing need for an infrastructure to enable sharing and curation of data. We present Global Natural Products Social Molecular Networking (GNPS; http://gnps.ucsd.edu), an open-access knowledge base for community-wide organization and sharing of raw, processed or identified tandem mass (MS/MS) spectrometry data. In GNPS, crowdsourced curation of freely available community-wide reference MS libraries will underpin improved annotations. Data-driven social-networking should facilitate identification of spectra and foster collaborations. We also introduce the concept of 'living data' through continuous reanalysis of deposited data.

Cystic fibrosis (CF) lungs are filled with thick mucus that obstructs airways and facilitates chronic infections. Pseudomonas aeruginosa is a significant pathogen of this disease that produces a variety of toxic small molecules. We used molecular networking-based metabolomics to investigate the chemistry of CF sputa and assess how the microbial molecules detected reflect the microbiome and clinical culture history of the patients. Metabolites detected included xenobiotics, P. aeruginosa specialized metabolites and host sphingolipids. The clinical culture and microbiome profiles did not correspond to the detection of P. aeruginosa metabolites in the same samples. The P. aeruginosa molecules that were detected in sputum did not match those from laboratory cultures. The pseudomonas quinolone signal (PQS) was readily detectable from cultured strains, but absent from sputum, even when its precursor molecules were present. The lack of PQS production in vivo is potentially due to the chemical nature of the CF lung environment, indicating that culture-based studies of this pathogen may not explain its behavior in the lung. The most differentially abundant molecules between CF and non-CF sputum were sphingolipids, including sphingomyelins, ceramides and lactosylceramide. As these highly abundant molecules contain the inflammatory mediator ceramide, they may have a significant role in CF hyperinflammation. This study demonstrates that the chemical makeup of CF sputum is a complex milieu of microbial, host and xenobiotic molecules. Detection of a bacterium by clinical culturing and 16S rRNA gene profiling do not necessarily reflect the active production of metabolites from that bacterium in a sputum sample.

Holobionts are assemblages of microbial symbionts and their macrobial host. As extant representatives of some of the oldest macro-organisms, corals and algae are important for understanding how holobionts develop and interact with one another. Using untargeted metabolomics, we show that non-self interactions altered the coral metabolome more than self-interactions (i.e. different or same genus, respectively). Platelet activating factor (PAF) and Lyso-PAF, central inflammatory modulators in mammals, were major lipid components of the coral holobionts. When corals were damaged during competitive interactions with algae, PAF increased along with expression of the gene encoding Lyso-PAF acetyltransferase; the protein responsible for converting Lyso-PAF to PAF. This shows that self and non-self recognition among some of the oldest extant holobionts involve bioactive lipids identical to those in highly derived taxa like humans. This further strengthens the hypothesis that major players of the immune response evolved during the pre-Cambrian.

Multi-omics methods have greatly advanced our understanding of the biological organism and its microbial associates. However, they are not routinely used in clinical or industrial applications, due to the length of time required to generate and analyze omics data. Here, we applied a novel integrated omics pipeline for the analysis of human and environmental samples in under 48 h. Human subjects that ferment their own foods provided swab samples from skin, feces, oral cavity, fermented foods, and household surfaces to assess the impact of home food fermentation on their microbial and chemical ecology. These samples were analyzed with 16S rRNA gene sequencing, inferred gene function profiles, and liquid chromatography-tandem mass spectrometry (LC-MS/MS) metabolomics through the Qiita, PICRUSt, and GNPS pipelines, respectively. The human sample microbiomes clustered with the corresponding sample types in the American Gut Project (http://www.americangut.org), and the fermented food samples produced a separate cluster. The microbial communities of the household surfaces were primarily sourced from the fermented foods, and their consumption was associated with increased gut microbial diversity. Untargeted metabolomics revealed that human skin and fermented food samples had separate chemical ecologies and that stool was more similar to fermented foods than to other sample types. Metabolites from the fermented foods, including plant products such as procyanidin and pheophytin, were present in the skin and stool samples of the individuals consuming the foods. Some food metabolites were modified during digestion, and others were detected in stool intact. This study represents a first-of-its-kind analysis of multi-omics data that achieved time intervals matching those of classic microbiological culturing. IMPORTANCE Polymicrobial infections are difficult to diagnose due to the challenge in comprehensively cultivating the microbes present. Omics methods, such as 16S rRNA sequencing, metagenomics, and metabolomics, can provide a more complete picture of a microbial community and its metabolite production, without the biases and selectivity of microbial culture. However, these advanced methods have not been applied to clinical or industrial microbiology or other areas where complex microbial dysbioses require immediate intervention. The reason for this is the length of time required to generate and analyze omics data. Here, we describe the development and application of a pipeline for multi-omics data analysis in time frames matching those of the culture-based approaches often used for these applications. This study applied multi-omics methods effectively in clinically relevant time frames and sets a precedent toward their implementation in clinical medicine and industrial microbiology.

In the context of a polymicrobial infection, treating a specific pathogen poses challenges because of unknown consequences on other members of the community. The presence of ecological interactions between microbes can change their physiology and response to treatment. For example, in the cystic fibrosis lung polymicrobial infection, antimicrobial susceptibility testing on clinical isolates is often not predictive of antibiotic efficacy. Novel approaches are needed to identify the interrelationships within the microbial community to better predict treatment outcomes. Here we used an ecological networking approach on the cystic fibrosis lung microbiome characterized using 16S rRNA gene sequencing and metagenomics. This analysis showed that the community is separated into three interaction groups: Gram-positive anaerobes, Pseudomonas aeruginosa, and Staphylococcus aureus. The P. aeruginosa and S. aureus groups both anti-correlate with the anaerobic group, indicating a functional antagonism. When patients are clinically stable, these major groupings were also stable, however, during exacerbation, these communities fragment. Co-occurrence networking of functional modules annotated from metagenomics data supports that the underlying taxonomic structure is driven by differences in the core metabolism of the groups. Topological analysis of the functional network identified the non-mevalonate pathway of isoprenoid biosynthesis as a keystone for the microbial community, which can be targeted with the antibiotic fosmidomycin. This study uses ecological theory to identify novel treatment approaches against a polymicrobial disease with more predictable outcomes.

Background. Cystic fibrosis (CF) is a genetic disease that results in chronic infections of the lungs. CF patients experience intermittent pulmonary exacerbations (CFPE) that are associated with poor clinical outcomes. CFPE involves an increase in disease symptoms requiring more aggressive therapy. Methods. Longitudinal sputum samples were collected from 11 patients (n = 44 samples) to assess the effect of exacerbations on the sputum metabolome using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The data was analyzed with MS/MS molecular networking and multivariate statistics. Results. The individual patient source had a larger influence on the metabolome of sputum than the clinical state (exacerbation, treatment, post-treatment, or stable). Of the 4,369 metabolites detected, 12% were unique to CFPE samples; however, the only known metabolites significantly elevated at exacerbation across the dataset were platelet activating factor (PAF) and a related monacylglycerophosphocholine lipid. Due to the personalized nature of the sputum metabolome, a single patient was followed for 4.2 years (capturing four separate exacerbation events) as a case study for the detection of personalized biomarkers with metabolomics. PAF and related lipids were significantly elevated during CFPEs of this patient and ceramide was elevated during CFPE treatment. Correlating the abundance of bacterial 16S rRNA gene amplicons to metabolomics data from the same samples during a CFPE demonstrated that antibiotics were positively correlated to Stenotrophomonas and Pseudomonas, while ceramides and other lipids were correlated with Streptococcus, Rothia, and anaerobes. Conclusions. This study identified PAF and other inflammatory lipids as potential biomarkers of CFPE, but overall, the metabolome of CF sputum was patient specific, supporting a personalized approach to molecular detection of CFPE onset.

There is a poor understanding of how the physiology of polymicrobial communities in cystic fibrosis (CF) lungs contributes to pulmonary exacerbations and lung function decline. In this study, a microbial culture system based on the principles of the Winogradsky column (WinCF system) was developed to study the physiology of CF microbes. The system used glass capillary tubes filled with artificial sputum medium to mimic a clogged airway bronchiole. Chemical indicators were added to observe microbial physiology within the tubes. Characterization of sputum samples from seven patients showed variation in pH, respiration, biofilm formation and gas production, indicating that the physiology of CF microbial communities varied among patients. Incubation of homogenized tissues from an explant CF lung mirrored responses of a Pseudomonas aeruginosa pure culture, supporting evidence that end-stage lungs are dominated by this pathogen. Longitudinal sputum samples taken through two exacerbation events in a single patient showed that a two-unit drop in pH and a 30% increase in gas production occurred in the tubes prior to exacerbation, which was reversed with antibiotic treatment. Microbial community profiles obtained through amplification and sequencing of the 16S rRNA gene showed that fermentative anaerobes became more abundant during exacerbation and were then reduced during treatment where P. aeruginosa became the dominant bacterium. Results from the WinCF experiments support the model where two functionally different CF microbial communities exist, the persistent Climax Community and the acute Attack Community. Fermentative anaerobes are hypothesized to be the core members of the Attack Community and production of acidic and gaseous products from fermentation may drive developing exacerbations. Treatment targeting the Attack Community may better resolve exacerbations and resulting lung damage.

The airways of cystic fibrosis (CF) patients are chronically colonized by patient-specific polymicrobial communities. The conditions and nutrients available in CF lungs affect the physiology and composition of the colonizing microbes. Recent work in bioreactors has shown that the fermentation product 2,3-butanediol mediates cross-feeding between some fermenting bacteria and Pseudomonas aeruginosa, and that this mechanism increases bacterial current production. To examine bacterial fermentation in the respiratory tract, breath gas metabolites were measured and several metagenomes were sequenced from CF and non-CF volunteers. 2,3-butanedione was produced in nearly all respiratory tracts. Elevated levels in one patient decreased during antibiotic treatment, and breath concentrations varied between CF patients at the same time point. Some patients had high enough levels of 2,3-butanedione to irreversibly damage lung tissue. Antibiotic therapy likely dictates the activities of 2,3-butanedione-producing microbes, which suggests a need for further study with larger sample size. Sputum microbiomes were dominated by P. aeruginosa, Streptococcus spp. and Rothia mucilaginosa, and revealed the potential for 2,3-butanedione biosynthesis. Genes encoding 2,3-butanedione biosynthesis were disproportionately abundant in Streptococcus spp, whereas genes for consumption of butanedione pathway products were encoded by P. aeruginosa and R. mucilaginosa. We propose a model where low oxygen conditions in CF lung lead to fermentation and a decrease in pH, triggering 2,3-butanedione fermentation to avoid lethal acidification. We hypothesize that this may also increase phenazine production by P. aeruginosa, increasing reactive oxygen species and providing additional electron acceptors to CF microbes.

A continuously mixed series of microbial communities inhabits various points of the respiratory tract, with community composition determined by distance from colonization sources, colonization rates, and extinction rates. Ecology and evolution theory developed in the context of biogeography is relevant to clinical microbiology and could reframe the interpretation of recent studies comparing communities from lung explant samples, sputum samples, and oropharyngeal swabs. We propose an island biogeography model of the microbial communities inhabiting different niches in human airways. Island biogeography as applied to communities separated by time and space is a useful parallel for exploring microbial colonization of healthy and diseased lungs, with the potential to inform our understanding of microbial community dynamics and the relevance of microbes detected in different sample types. In this perspective, we focus on the intermixed microbial communities inhabiting different regions of the airways of patients with cystic fibrosis.

The airways of cystic fibrosis (CF) patients are chronically colonized by patient-specific polymicrobial communities. The conditions and nutrients available in CF lungs affect the physiology and composition of the colonizing microbes. Recent work in bioreactors has shown that the fermentation product 2,3-butanediol mediates cross-feeding between some fermenting bacteria and Pseudomonas aeruginosa, and that this mechanism increases bacterial current production. To examine bacterial fermentation in the respiratory tract, breath gas metabolites were measured and several metagenomes were sequenced from CF and non-CF volunteers. 2,3-butanedione was produced in nearly all respiratory tracts. Elevated levels in one patient decreased during antibiotic treatment, and breath concentrations varied between CF patients at the same time point. Some patients had high enough levels of 2,3-butanedione to irreversibly damage lung tissue. Antibiotic therapy likely dictates the activities of 2,3-butanedione-producing microbes, which suggests a need for further study with larger sample size. Sputum microbiomes were dominated by P. aeruginosa, Streptococcus spp. and Rothia mucilaginosa, and revealed the potential for 2,3-butanedione biosynthesis. Genes encoding 2,3-butanedione biosynthesis were disproportionately abundant in Streptococcus spp, whereas genes for consumption of butanedione pathway products were encoded by P. aeruginosa and R. mucilaginosa. We propose a model where low oxygen conditions in CF lung lead to fermentation and a decrease in pH, triggering 2,3-butanedione fermentation to avoid lethal acidification. We hypothesize that this may also increase phenazine production by P. aeruginosa, increasing reactive oxygen species and providing additional electron acceptors to CF microbes.

The cystic fibrosis (CF) lung contains thick mucus colonized by opportunistic pathogens which adapt to the CF lung environment over decades. The difficulty associated with sampling airways has impeded a thorough examination of the biochemical microhabitats these pathogens are exposed to. An indirect approach is to study the responses of microbial communities to these microhabitats, facilitated by high-throughput sequencing of microbial DNA and RNA from sputum samples. Microbial metagenomes and metatranscriptomes were sequenced from multiple CF patients, and the reads were assigned taxonomy and function through sequence homology to NCBI and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database hierarchies. For a comparison, saliva microbial metagenomes from the Human Microbiome Project (HMP) were also analyzed. These analyses identified that functions encoded and expressed by CF microbes were significantly enriched for amino acid catabolism, folate biosynthesis, and lipoic acid biosynthesis. The data indicate that the community uses oxidative phosphorylation as a major energy source but that terminal electron acceptors were diverse. Nitrate reduction was the most abundant anaerobic respiratory pathway, and genes for nitrate reductase were largely assigned to Pseudomonas and Rothia. Although many reductive pathways of the nitrogen cycle were present, the cycle was incomplete, because the oxidative pathways were absent. Due to the abundant amino acid catabolism and incomplete nitrogen cycle, the CF microbial community appears to accumulate ammonia. This finding was verified experimentally using a CF bronchiole culture model system. The data also revealed abundant sensing and transport of iron, ammonium, zinc, and other metals along with a low-oxygen environment. This study reveals the core biochemistry and physiology of the CF microbiome. IMPORTANCE The cystic fibrosis (CF) microbial community is complex and adapts to the environmental conditions of the lung over the lifetime of a CF patient. This analysis illustrates the core functions of the CF microbial community in the context of CF lung biochemistry. There are many studies of the metabolism and physiology of individual microbes within the CF lung, but none that collectively analyze data from the whole microbiome. Understanding the core metabolism of microbes that inhabit the CF lung can provide new targets for novel therapies. The fundamental processes that CF pathogens rely on for survival may represent an Achilles heel for this pathogenic community. Novel therapies that are designed to disrupt understudied survival strategies of the CF microbial community may succeed against otherwise untreatable or antibiotic-resistant microbes.

Langerhans cell histiocytosis (LCH) is a clonal proliferation of bone marrow derived antigen-presenting cells that can involve a spectrum of cutaneous findings, with or without internal organ involvement. Neonatal LCH almost always presents with skin findings, usually petechial papules and/or erosions in a seborrheic distribution, with or without extracutaneous involvement. Previously described as varying entities, LCH is now considered a single disease process demonstrating a spectrum of clinical findings. We report a unique case of neonatal LCH presenting with a “blueberry muffin” rash in conjunction with a large soft tissue tumor.

A preposition, as one of the eight parts of speech, indicates a relationship between persons, places or things mentioned in a sentence. Many state curricula introduce prepositions at intermediate grade levels. Other states wait until middle school to do so. Students at such advanced levels of language learning should be able to readily assimilate prepositions into learning. Developing youngsters’ ability to recognize and use spatial language, such as the preposition, is an extremely important goal in the language arts. Fundamental, perhaps, to gaining entrance into the world of prepositions is the ability to visualize spatial relationships. The visual arts provide an ideal venue for discussing spatial concepts in written and spoken language, particularly through the use of prepositions. This article describes a unit of instruction used to engage pre-service generalist educators in an artmaking experience in illustration, printmaking, and bookmaking. The aim of the unit of instruction was to teach these undergraduates how to enhance their future students’ visual literacy in order to familiarize students to prepositions as a part of speech and the functions of prepositional phrases in a sentence. The goal of the unit was to create an alphabet book of illustrations representative of a variety of prepositional phrases. Since a prepositional phrase is comprised of a preposition, its object and any associated adjectives or adverbs, the visual arts provide an excellent way to envision the relationships between the preposition, its object, and any modifying words. In the unit of instruction, the students generated a variety of prepositional phrases derived from a collaboratively selected theme. Using a provided chart, students were assisted in generating outstanding prepositional phrases. Students were encouraged to create a sentence that provided rich visual imagery that could easily be illustrated. Students illustrated the prepositional phrase using a simple linoleum block printmaking process. The class’s finished illustrations were then collected together for a class book.