Author Correction: A new antibiotic selectively kills Gram-negative pathogens.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

A new antibiotic selectively kills Gram-negative pathogens.

The current need for novel antibiotics is especially acute for drug-resistant Gram-negative pathogens1,2. These microorganisms have a highly restrictive permeability barrier, which limits the penetration of most compounds3,4. As a result, the last class of antibiotics that acted against Gram-negative bacteria was developed in the 1960s2. We reason that useful compounds can be found in bacteria that share similar requirements for antibiotics with humans, and focus on Photorhabdus symbionts of entomopathogenic nematode microbiomes. Here we report a new antibiotic that we name darobactin, which was obtained using a screen of Photorhabdus isolates. Darobactin is coded by a silent operon with little production under laboratory conditions, and is ribosomally synthesized. Darobactin has an unusual structure with two fused rings that form post-translationally. The compound is active against important Gram-negative pathogens both in vitro and in animal models of infection. Mutants that are resistant to darobactin map to BamA, an essential chaperone and translocator that folds outer membrane proteins. Our study suggests that bacterial symbionts of animals contain antibiotics that are particularly suitable for development into therapeutics.

Antibiotics at birth and later antibiotic courses: effects on gut microbiota.

BACKGROUND: Intrapartum antibiotic prophylaxis (IAP) is widely used, but the evidence of the long-term effects on the gut microbiota and subsequent health of children is limited. Here, we compared the impacts of perinatal antibiotic exposure and later courses of antibiotic courses on gut microbiota. METHODS: This was a prospective, controlled cohort study among 100 vaginally delivered infants with different perinatal antibiotic exposures: control (27), IAP (27), postnatal antibiotics (24), and IAP and postnatal antibiotics (22). At 1 year of age, we performed next-generation sequencing of the bacterial 16S ribosomal RNA gene of fecal samples. RESULTS: Exposure to the perinatal antibiotics had a clear impact on the gut microbiota. The abundance of the Bacteroidetes phylum was significantly higher in the control group, whereas the relative abundance of Escherichia coli was significantly lower in the control group. The impact of the perinatal antibiotics on the gut microbiota composition was greater than exposure to later courses of antibiotics (28% of participants). CONCLUSIONS: Perinatal antibiotic exposure had a marked impact on the gut microbiota at the age of 1 year. The timing of the antibiotic exposure appears to be the critical factor for the changes observed in the gut microbiota. IMPACT: Infants are commonly exposed to IAP and postnatal antibiotics, and later to courses of antibiotics during the first year of life. Perinatal antibiotics have been associated with an altered gut microbiota during the first months of life, whereas the evidence regarding the long-term impact is more limited. Perinatal antibiotic exposure had a marked impact on the infant's gut microbiota at 1 year of age. Impact of the perinatal antibiotics on the gut microbiota composition was greater than that of the later courses of antibiotics at the age of 1 year.

Predicting antimicrobial mechanism-of-action from transcriptomes: A generalizable explainable artificial intelligence approach.

To better combat the expansion of antibiotic resistance in pathogens, new compounds, particularly those with novel mechanisms-of-action [MOA], represent a major research priority in biomedical science. However, rediscovery of known antibiotics demonstrates a need for approaches that accurately identify potential novelty with higher throughput and reduced labor. Here we describe an explainable artificial intelligence classification methodology that emphasizes prediction performance and human interpretability by using a Hierarchical Ensemble of Classifiers model optimized with a novel feature selection algorithm called Clairvoyance; collectively referred to as a CoHEC model. We evaluated our methods using whole transcriptome responses from Escherichia coli challenged with 41 known antibiotics and 9 crude extracts while depositing 122 transcriptomes unique to this study. Our CoHEC model can properly predict the primary MOA of previously unobserved compounds in both purified forms and crude extracts at an accuracy above 99%, while also correctly identifying darobactin, a newly discovered antibiotic, as having a novel MOA. In addition, we deploy our methods on a recent E. coli transcriptomics dataset from a different strain and a Mycobacterium smegmatis metabolomics timeseries dataset showcasing exceptionally high performance; improving upon the performance metrics of the original publications. We not only provide insight into the biological interpretation of our model but also that the concept of MOA is a non-discrete heuristic with diverse effects for different compounds within the same MOA, suggesting substantial antibiotic diversity awaiting discovery within existing MOA.

Vertical Transmission of Gut Microbiome and Antimicrobial Resistance Genes in Infants Exposed to Antibiotics at Birth.

Vertical transmission of maternal microbes is a major route for establishing the gut microbiome in newborns. The impact of perinatal antibiotics on vertical transmission of microbes and antimicrobial resistance is not well understood. Using a metagenomic approach, we analyzed the fecal samples from mothers and vaginally delivered infants from a control group (10 pairs) and a treatment group (10 pairs) receiving perinatal antibiotics. Antibiotic-usage had a significant impact on the main source of inoculum in the gut microbiome of newborns. The control group had significantly more species transmitted from mothers to infants (P = .03) than the antibiotic-treated group. Approximately 72% of the gut microbial population of infants at 3-7 days after birth in the control group was transmitted from their mothers, versus only 25% in the antibiotic-treated group. In conclusion, perinatal antibiotics markedly disturbed vertical transmission and changed the source of gut colonization towards horizontal transfer from the environment to the infants.

Microbial Species that Initially Colonize the Human Gut at Birth or in Early Childhood Can Stay in Human Body for Lifetime.

In recent years, many studies have described the composition and function of the human microbiome at different body sites and suggested a role for the microbiome in various diseases and health conditions. Some studies, using longitudinal samples, have also suggested how the microbiome changes over time due to disease, diet, development, travel, and other environmental factors. However, to date, no study has demonstrated whether the microorganisms established at birth or in early childhood, either transmitted from parents or obtained from the environment, can stay in the human body until adult or senior age. To directly answer this question is difficult, because microbiome samples at childhood and at later adulthood for the same individual will need to be compared and the field is not old enough to have allowed for that type of sample collection. Here, using a metagenomic approach, we analyzed 1004 gut microbiome samples from senior adults (65 ± 7.8 years) from the TwinsUK cohort. Our data indicate that many species in the human gut acquired in early childhood can stay for a lifetime until senior ages. We identified the rare genomic variants (single nucleotide variation and indels) for 27 prevalent species with enough sequencing coverage for confident genomic variant identification. We found that for some species, twin pairs, including both monozygotic (MZ) and dizygotic (DZ) twins, share significantly more rare variants than unrelated subject pairs. But no significant difference is found between MZ and DZ twin pairs. These observations strongly suggest that these species acquired in early childhood remained in these persons until senior adulthood.

Characterizing Microbial Signatures on Sculptures and Paintings of Similar Provenance.

The preservation of artwork challenges museums, collectors, and art enthusiasts. Currently, reducing moisture, adjusting the type of lighting, and preventing the formation of mold are primary methods to preserving and preventing deterioration. Other methods such as ones based in detailed knowledge of molecular biology such as microbial community characterization using polymerase chain reaction (PCR) and sequencing have yet to be explored. Such molecular biology approaches are essential to explore as some environmental bacteria are capable of oxidizing nonpolar chemical substances rich in hydrocarbons such as oil-based paints. Using 16S rDNA Illumina Sequencing, we demonstrate a novel finding that there are differing bacterial communities for artwork from roughly the same era when comparing paintings on wood, paintings on canvases, and sculptures made of stone and marble. We also demonstrate that there are specific genera such as Aeromonas known for having oxidase positive strains, present on paintings on wood and paintings on canvas that could potentially be responsible for deterioration and fading as such organisms produce water or hydrogen peroxide as a byproduct of cytochrome c oxidase activity. The advantages of these genomics-based approaches to characterizing the microbial population on deteriorating artwork provides immense potential by identifying potentially damaging species that may not be detected using conventional methods in addition to addressing challenges to identification, restoration, and preservation efforts.

Oral Microbial Species and Virulence Factors Associated with Oral Squamous Cell Carcinoma.

The human microbiome has been the focus of numerous research efforts to elucidate the pathogenesis of human diseases including cancer. Oral cancer mortality is high when compared with other cancers, as diagnosis often occurs during late stages. Its prevalence has increased in the USA over the past decade and accounts for over 40,000 new cancer patients each year. Additionally, oral cancer pathogenesis is not fully understood and is likely multifactorial. To unravel the relationships that are associated with the oral microbiome and their virulence factors, we used 16S rDNA and metagenomic sequencing to characterize the microbial composition and functional content in oral squamous cell carcinoma (OSCC) tumor tissue, non-tumor tissue, and saliva from 18 OSCC patients. Results indicate a higher number of bacteria belonging to the Fusobacteria, Bacteroidetes, and Firmicutes phyla associated with tumor tissue when compared with all other sample types. Additionally, saliva metaproteomics revealed a significant increase of Prevotella in five OSCC subjects, while Corynebacterium was mostly associated with ten healthy subjects. Lastly, we determined that there are adhesion and virulence factors associated with Streptococcus gordonii as well as from known oral pathogens belonging to the Fusobacterium genera found mostly in OSCC tissues. From these results, we propose that not only will the methods utilized in this study drastically improve OSCC diagnostics, but the organisms and specific virulence factors from the phyla detected in tumor tissue may be excellent biomarkers for characterizing disease progression.

Mechanism-of-Action Classification of Antibiotics by Global Transcriptome Profiling.

Antimicrobial resistance (AMR) is an ever-growing public health problem worldwide. The low rate of antibiotic discovery coupled with the rapid spread of drug-resistant bacterial pathogens is causing a global health crisis. To facilitate the drug discovery processes, we present a large-scale study of reference antibiotic challenge bacterial transcriptome profiles, which included 37 antibiotics across 6 mechanisms of actions (MOAs) and provide an economical approach to aid in antimicrobial dereplication in the discovery process. We demonstrate that classical MOAs can be sorted based upon the magnitude of gene expression profiles despite some overlap in the secondary effects of antibiotic exposures across MOAs. Additionally, using gene subsets, we were able to subdivide broad MOA classes into subMOAs. Furthermore, we provide a biomarker gene set that can be used to classify most antimicrobial challenges according to their canonical MOA. We also demonstrate the ability of this rapid MOA diagnostic tool to predict and classify the expression profiles of pure compounds and crude extracts to their expression profile-associated MOA class.

Applications of weighted association networks applied to compositional data in biology.

Next-generation sequencing technologies have generated, and continue to produce, an increasingly large corpus of biological data. The data generated are inherently compositional as they convey only relative information dependent upon the capacity of the instrument, experimental design and technical bias. There is considerable information to be gained through network analysis by studying the interactions between components within a system. Network theory methods using compositional data are powerful approaches for quantifying relationships between biological components and their relevance to phenotype, environmental conditions or other external variables. However, many of the statistical assumptions used for network analysis are not designed for compositional data and can bias downstream results. In this mini-review, we illustrate the utility of network theory in biological systems and investigate modern techniques while introducing researchers to frameworks for implementation. We overview (1) compositional data analysis, (2) data transformations and (3) network theory along with insight on a battery of network types including static-, temporal-, sample-specific- and differential-networks. The intention of this mini-review is not to provide a comprehensive overview of network methods, rather to introduce microbiology researchers to (semi)-unsupervised data-driven approaches for inferring latent structures that may give insight into biological phenomena or abstract mechanics of complex systems.

Persistent Gut Microbial Dysbiosis in Children with Acute Lymphoblastic Leukemia (ALL) During Chemotherapy.

Prophylactic or therapeutic antibiotic use along with chemotherapy treatment potentially has a long-standing adverse effect on the resident gut microbiota. We have established a case-control cohort of 32 pediatric and adolescent acute lymphoblastic leukemia (ALL) patients and 25 healthy siblings (sibling controls) to assess the effect of chemotherapy as well as antibiotic prophylaxis on the gut microbiota. We observe that the microbiota diversity and richness of the ALL group is significantly lower than that of the control group at diagnosis and during chemotherapy. The microbiota diversity is even lower in antibiotics-exposed ALL patients. Although the gut microbial diversity tends to stabilize after 1-year post-chemotherapy, their abundances were altered because of chemotherapy and prophylactic antibiotic treatments. Specifically, the abundances of mucolytic gram-positive anaerobic bacteria, including Ruminococcus gnavus and Ruminococcus torques, tended to increase during the chemotherapy regimen and continued to be elevated 1 year beyond the initiation of chemotherapy. This dysbiosis may contribute to the development of gastrointestinal complications in ALL children following chemotherapy. These findings set the stage to further understand the role of the gut microbiome dynamics in ALL patients and their potential role in alleviating some of the adverse side effects of chemotherapy and antibiotics use in immunocompromised children.

OMeta: an ontology-based, data-driven metadata tracking system.

BACKGROUND: The development of high-throughput sequencing and analysis has accelerated multi-omics studies of thousands of microbial species, metagenomes, and infectious disease pathogens. Omics studies are enabling genotype-phenotype association studies which identify genetic determinants of pathogen virulence and drug resistance, as well as phylogenetic studies designed to track the origin and spread of disease outbreaks. These omics studies are complex and often employ multiple assay technologies including genomics, metagenomics, transcriptomics, proteomics, and metabolomics. To maximize the impact of omics studies, it is essential that data be accompanied by detailed contextual metadata (e.g., specimen, spatial-temporal, phenotypic characteristics) in clear, organized, and consistent formats. Over the years, many metadata standards developed by various metadata standards initiatives have arisen; the Genomic Standards Consortium's minimal information standards (MIxS), the GSCID/BRC Project and Sample Application Standard. Some tools exist for tracking metadata, but they do not provide event based capabilities to configure, collect, validate, and distribute metadata. To address this gap in the scientific community, an event based data-driven application, OMeta, was created that allows users to quickly configure, collect, validate, distribute, and integrate metadata. RESULTS: A data-driven web application, OMeta, has been developed for use by researchers consisting of a browser-based interface, a command-line interface (CLI), and server-side components that provide an intuitive platform for configuring, capturing, viewing, and sharing metadata. Project and sample metadata can be set based on existing standards or based on projects goals. Recorded information includes details on the biological samples, procedures, protocols, and experimental technologies, etc. This information can be organized based on events, including sample collection, sample quantification, sequencing assay, and analysis results. OMeta enables configuration in various presentation types: checkbox, file, drop-box, ontology, and fields can be configured to use the National Center for Biomedical Ontology (NCBO), a biomedical ontology server. Furthermore, OMeta maintains a complete audit trail of all changes made by users and allows metadata export in comma separated value (CSV) format for convenient deposition of data into public databases. CONCLUSIONS: We present, OMeta, a web-based software application that is built on data-driven principles for configuring and customizing data standards, capturing, curating, and sharing metadata.

Self-Assembled STrap for Global Proteomics and Salivary Biomarker Discovery.

Clinical biomarkers identified by shotgun proteomics require proteins in body fluids or tissues to be enzymatically digested before being separated and sequenced by liquid chromatography-tandem mass spectrometry. How well peptide signals can be resolved and detected is largely dependent on the quality of sample preparation. Conventional approaches such as in-gel, in-solution, and filter-based digestion, despite their extensive implementation by the community, become less appealing due to their unsatisfying protein/peptide recovery rate, lengthy sample processing, and/or lowcost-effectiveness. Suspension trapping has recently been demonstrated as an ultrafast approach for proteomic analysis. Here, for the first time, we extend its application to human salivary proteome analyses. In particular, we present a simple self-assembled glass fiber filter device which can be packed with minimal difficulty, is extremely cost-effective, and maintains the same performance as commercial filters. As a proof-of-principle, we analyzed the whole saliva from 8 healthy individuals as well as a cohort of 10 subjects of oral squamous cell carcinoma (OSCC) patients and non-OSCC subjects. Label-free quantification revealed surprisingly low interindividual variability and several known markers. Our study provides the first evidence of an easy-to-use and low-cost device for clinical proteomics as well as for general proteomic sample preparation.

Characterization of Early-Phase Neutrophil Extracellular Traps in Urinary Tract Infections.

Neutrophils have an important role in the antimicrobial defense and resolution of urinary tract infections (UTIs). Our research suggests that a mechanism known as neutrophil extracellular trap (NET) formation is a defense strategy to combat pathogens that have invaded the urinary tract. A set of human urine specimens with very high neutrophil counts had microscopic evidence of cellular aggregation and lysis. Deoxyribonuclease I (DNase) treatment resulted in disaggregation of such structures, release of DNA fragments and a proteome enriched in histones and azurophilic granule effectors whose quantitative composition was similar to that of previously described in vitro-formed NETs. The effector proteins were further enriched in DNA-protein complexes isolated in native PAGE gels. Immunofluorescence microscopy revealed a flattened morphology of neutrophils associated with decondensed chromatin, remnants of granules in the cell periphery, and myeloperoxidase co-localized with extracellular DNA, features consistent with early-phase NETs. Nuclear staining revealed that a considerable fraction of bacterial cells in these structures were dead. The proteomes of two pathogens, Staphylococcus aureus and Escherichia coli, were indicative of adaptive responses to early-phase NETs, specifically the release of virulence factors and arrest of ribosomal protein synthesis. Finally, we discovered patterns of proteolysis consistent with widespread cleavage of proteins by neutrophil elastase, proteinase 3 and cathepsin G and evidence of citrullination in many nuclear proteins.

The blood DNA virome in 8,000 humans.

The characterization of the blood virome is important for the safety of blood-derived transfusion products, and for the identification of emerging pathogens. We explored non-human sequence data from whole-genome sequencing of blood from 8,240 individuals, none of whom were ascertained for any infectious disease. Viral sequences were extracted from the pool of sequence reads that did not map to the human reference genome. Analyses sifted through close to 1 Petabyte of sequence data and performed 0.5 trillion similarity searches. With a lower bound for identification of 2 viral genomes/100,000 cells, we mapped sequences to 94 different viruses, including sequences from 19 human DNA viruses, proviruses and RNA viruses (herpesviruses, anelloviruses, papillomaviruses, three polyomaviruses, adenovirus, HIV, HTLV, hepatitis B, hepatitis C, parvovirus B19, and influenza virus) in 42% of the study participants. Of possible relevance to transfusion medicine, we identified Merkel cell polyomavirus in 49 individuals, papillomavirus in blood of 13 individuals, parvovirus B19 in 6 individuals, and the presence of herpesvirus 8 in 3 individuals. The presence of DNA sequences from two RNA viruses was unexpected: Hepatitis C virus is revealing of an integration event, while the influenza virus sequence resulted from immunization with a DNA vaccine. Age, sex and ancestry contributed significantly to the prevalence of infection. The remaining 75 viruses mostly reflect extensive contamination of commercial reagents and from the environment. These technical problems represent a major challenge for the identification of novel human pathogens. Increasing availability of human whole-genome sequences will contribute substantial amounts of data on the composition of the normal and pathogenic human blood virome. Distinguishing contaminants from real human viruses is challenging.

Co-occurrence of Anaerobes in Human Chronic Wounds.

Chronic wounds are wounds that have failed to heal after 3 months of appropriate wound care. Previous reports have identified a diverse collection of bacteria in chronic wounds, and it has been postulated that bacterial profile may contribute to delayed healing. The purpose of this study was to perform a microbiome assessment of the Wound Healing and Etiology (WE-HEAL) Study cohort, including underlying comorbidities less commonly studied in the context of chronic wounds, such as autoimmune diseases, and investigate possible relationships of the wound microbiota with clinical healing trends. We examined chronic wound specimens from 60 patients collected through the WE-HEAL Study using 16S ribosomal RNA gene sequencing. A group of co-occurring obligate anaerobes was identified from taxonomic analysis guided by Dirichlet multinomial mixtures (DMM) modeling. The group includes members of the Gram-positive anaerobic cocci (GPAC) of the Clostridia class (i.e., Anaerococcus, Finegoldia, and Peptoniphilus) and additional strict anaerobes (i.e., Porphyromonas and Prevotella). We showed that the co-occurring group of obligate anaerobes not only co-exists with commonly identified wound species (such as Staphylococcus aureus, Staphylococcus epidermidis, Pseudomonas, Corynebacterium, and Streptococcus), but importantly, they could also predominate the wound microbiota. Furthermore, examination of clinical comorbidities of the WE-HEAL specimens showed that specific obligate and facultative anaerobes were significantly reduced in wounds presented with autoimmune disease. With respect to future healing trends, no association with the wound microbiome community or the abundance of individual wound species could be established. In conclusion, we identified a co-occurring obligate anaerobic community type that predominated some human chronic wounds and underrepresentation of anaerobes in wounds associated with autoimmune diseases. Possible elucidation of host environments or key factors that influence anaerobe colonization warrants further investigation in a larger cohort.

Gut microbiome composition of wild western lowland gorillas is associated with individual age and sex factors.

OBJECTIVES: Environmental and ecological factors, such as geographic range, anthropogenic pressure, group identity, and feeding behavior are known to influence the gastrointestinal microbiomes of great apes. However, the influence of individual host traits such as age and sex, given specific dietary and social constraints, has been less studied. The objective of this investigation was to determine the associations between an individual's age and sex on the diversity and composition of the gut microbiome in wild western lowland gorillas. MATERIALS AND METHODS: Publicly available 16S rRNA data generated from fecal samples of different groups of Gorilla gorilla gorilla in the Central African Republic were downloaded and bioinformatically processed. The groups analyzed included habituated, partially habituated and unhabituated gorillas, sampled during low fruit (dry, n = 28) and high fruit (wet, n = 82) seasons. Microbial community analyses (alpha and beta diversity and analyses of discriminant taxa), in tandem with network-wide approaches, were used to (a) mine for specific age and sex based differences in gut bacterial community composition and to (b) asses for gut community modularity and bacterial taxa with potential functional roles, in the context of seasonal food variation, and social group affiliation. RESULTS: Both age and sex significantly influenced gut microbiome diversity and composition in wild western lowland gorillas. However, the largest differences were observed between infants and adults in habituated groups and between adults and immature gorillas within all groups, and across dry and wet seasons. Specifically, although adults always showed greater bacterial richness than infants and immature gorillas, network-wide analyses showed higher microbial community complexity and modularity in the infant gorilla gut. Sex-based microbiome differences were not evident among adults, being only detected among immature gorillas. CONCLUSIONS: The results presented point to a dynamic gut microbiome in Gorilla spp., associated with ontogeny and individual development. Of note, the gut microbiomes of breastfeeding infants seemed to reflect early exposure to complex, herbaceous vegetation. Whether increased compositional complexity of the infant gorilla gut microbiome is an adaptive response to an energy-limited diet and an underdeveloped gut needs to be further tested. Overall, age and sex based gut microbiome differences, as shown here, maybe mainly attributed to access to specific feeding sources, and social interactions between individuals within groups.

Nasopharyngeal Lactobacillus is associated with a reduced risk of childhood wheezing illnesses following acute respiratory syncytial virus infection in infancy.

BACKGROUND: Early life acute respiratory infection (ARI) with respiratory syncytial virus (RSV) has been strongly associated with the development of childhood wheezing illnesses, but the pathways underlying this association are poorly understood. OBJECTIVE: To examine the role of the nasopharyngeal microbiome in the development of childhood wheezing illnesses following RSV ARI in infancy. METHODS: We conducted a nested cohort study of 118 previously healthy, term infants with confirmed RSV ARI by RT-PCR. We used next-generation sequencing of the V4 region of the 16S ribosomal RNA gene to characterize the nasopharyngeal microbiome during RSV ARI. Our main outcome of interest was 2-year subsequent wheeze. RESULTS: Of the 118 infants, 113 (95.8%) had 2-year outcome data. Of these, 46 (40.7%) had parental report of subsequent wheeze. There was no association between the overall taxonomic composition, diversity, and richness of the nasopharyngeal microbiome during RSV ARI with the development of subsequent wheeze. However, the nasopharyngeal detection and abundance of Lactobacillus was consistently higher in infants who did not develop this outcome. Lactobacillus also ranked first among the different genera in a model distinguishing infants with and without subsequent wheeze. CONCLUSIONS: The nasopharyngeal detection and increased abundance of Lactobacillus during RSV ARI in infancy are associated with a reduced risk of childhood wheezing illnesses at age 2 years.

Impact of stress on the gut microbiome of free-ranging western lowland gorillas.

Exposure to stressors can negatively impact the mammalian gastrointestinal microbiome (GIM). Here, we used 454 pyrosequencing of 16S rRNA bacterial gene amplicons to evaluate the impact of physiological stress, as evidenced by faecal glucocorticoid metabolites (FGCM; ng/g), on the GIM composition of free-ranging western lowland gorillas (Gorilla gorilla gorilla). Although we found no relationship between GIM alpha diversity (H) and FGCM levels, we observed a significant relationship between the relative abundances of particular bacterial taxa and FGCM levels. Specifically, members of the family Anaerolineaceae (ρ=0.4, FDR q=0.01), genus Clostridium cluster XIVb (ρ=0.35, FDR q=0.02) and genus Oscillibacter (ρ=0.35, FDR q=0.02) were positively correlated with FGCM levels. Thus, while exposure to stressors appears to be associated with minor changes in the gorilla GIM, the consequences of these changes are unknown. Our results may have implications for conservation biology as well as for our overall understanding of factors influencing the non-human primate GIM.

Where next for microbiome research?

The development of high-throughput sequencing technologies has transformed our capacity to investigate the composition and dynamics of the microbial communities that populate diverse habitats. Over the past decade, these advances have yielded an avalanche of metagenomic data. The current stage of "van Leeuwenhoek"-like cataloguing, as well as functional analyses, will likely accelerate as DNA and RNA sequencing, plus protein and metabolic profiling capacities and computational tools, continue to improve. However, it is time to consider: what's next for microbiome research? The short pieces included here briefly consider the challenges and opportunities awaiting microbiome research.