Explore the vast range of titles available.

To identify intestinal bacteria that produce phenols (phenol and p-cresol), we screened 153 strains within 152 species in 44 genera by culture-based assay using broth media supplemented with 200 µM each of tyrosine and its predicted microbial metabolic intermediates (4-hydroxyphenylpyruvate, DL-4-hydroxyphenyllactate, 3-(p-hydroxyphenyl)propionate, 4-hydroxyphenylacetate and 4-hydroxybenzoate). Phenol-producing activity was found in 36 strains and p-cresol-producing activity in 55 strains. Fourteen strains had both types of activity. Phylogenetic analysis based on the 16S rRNA gene sequences of strains that produced 100 µM or more of phenols revealed that 16 phenol producers belonged to the Coriobacteriaceae, Enterobacteriaceae, Fusobacteriaceae and Clostridium clusters I and XIVa; four p-cresol-producing bacteria belonged to the Coriobacteriaceae and Clostridium clusters XI and XIVa; and one strain producing both belonged to the Coriobacteriaceae. A genomic search for protein homologs of enzymes involved in the metabolism of tyrosine to phenols in 10 phenol producers and four p-cresol producers, the draft genomes of which were available in public databases, predicted that phenol producers harbored tyrosine phenol-lyase or hydroxyarylic acid decarboxylase, or both, and p-cresol producers harbored p-hydroxyphenylacetate decarboxylase or tyrosine lyase, or both. These results provide important information about the bacterial strains that contribute to production of phenols in the intestine.

A critical challenge in microbiome data analysis is the existence of many non-biological zeros, which distort taxon abundance distributions, complicate data analysis, and jeopardize the reliability of scientific discoveries. To address this issue, we propose the first imputation method for microbiome data—mbImpute—to identify and recover likely non-biological zeros by borrowing information jointly from similar samples, similar taxa, and optional metadata including sample covariates and taxon phylogeny. We demonstrate that mbImpute improves the power of identifying disease-related taxa from microbiome data of type 2 diabetes and colorectal cancer, and mbImpute preserves non-zero distributions of taxa abundances.

Background Although the common, silver, and bighead carps are native and sparsely distributed in Eurasia, these fish have become abundant and invasive in North America. An understanding of the biology of these species may provide insights into sustainable control methods. The animal-associated microbiome plays an important role in host health. Characterization of the carp microbiome and the factors that affect its composition is an important step toward understanding the biology and interrelationships between these species and their environments. Results We compared the fecal microbiomes of common, silver, and bighead carps from wild and laboratory environments using Illumina sequencing of bacterial 16S ribosomal RNA (rRNA). The fecal bacterial communities of fish were diverse, with Shannon indices ranging from 2.3 to 4.5. The phyla Proteobacteria , Firmicutes , and Fusobacteria dominated carp guts, comprising 76.7 % of total reads. Environment played a large role in shaping fecal microbial community composition, and microbiomes among captive fishes were more similar than among wild fishes. Although differences among wild fishes could be attributed to feeding preferences, diet did not strongly affect microbial community structure in laboratory-housed fishes. Comparison of wild- and lab-invasive carps revealed five shared OTUs that comprised approximately 40 % of the core fecal microbiome. Conclusions The environment is a dominant factor shaping the fecal bacterial communities of invasive carps. Captivity alters the microbiome community structure relative to wild fish, while species differences are pronounced within habitats. Despite the absence of a true stomach, invasive carp species exhibited a core microbiota that warrants future study.

Limited information is available describing the development of the neonatal fecal microbiome in dogs. Feces from puppies were collected at 2, 21, 42, and 56 days after birth. Feces were also collected from the puppies’ mothers at a single time point within 24 hours after parturition. DNA was extracted from fecal samples and 454-pyrosequencing was used to profile 16S rRNA genes. Species richness continued to increase significantly from 2 days of age until 42 days of age in puppies. Furthermore, microbial communities clustered separately from each other at 2, 21, and 42 days of age. The microbial communities belonging to dams clustered separately from that of puppies at any given time point. Major phylogenetic changes were noted at all taxonomic levels with the most profound changes being a shift from primarily Firmicutes in puppies at 2 days of age to a co-dominance of Bacteroidetes, Fusobacteria, and Firmicutes by 21 days of age. Further studies are needed to elucidate the relationship between puppy microbiota development, physiological growth, neonatal survival, and morbidity.

Gut microbiota has shown tight and coordinated connection with various functions of its host such as metabolism, immunity, energy utilization, and health maintenance. To gain insight into whether gut microbes affect the metabolism of fish, we employed fast-growing transgenic common carp (Cyprinus carpio L.) to study the connections between its large body feature and gut microbes. Metagenome-based fingerprinting and high-throughput sequencing on bacterial 16S rRNA genes indicated that fish gut was dominated by Proteobacteria, Fusobacteria, Bacteroidetes and Firmicutes, which displayed significant differences between transgenic fish and wild-type controls. Analyses to study the association of gut microbes with the fish metabolism discovered three major phyla having significant relationships with the host metabolic factors. Biochemical and histological analyses indicated transgenic fish had increased carbohydrate but decreased lipid metabolisms. Additionally, transgenic fish has a significantly lower Bacteroidetes:Firmicutes ratio than that of wild-type controls, which is similar to mammals between obese and lean individuals. These findings suggest that gut microbiotas are associated with the growth of fast growing transgenic fish, and the relative abundance of Firmicutes over Bacteroidetes could be one of the factors contributing to its fast growth. Since the large body size of transgenic fish displays a proportional body growth, which is unlike obesity in human, the results together with the findings from others also suggest that the link between obesity and gut microbiota is likely more complex than a simple Bacteroidetes:Firmicutes ratio change.

The oral microbiome, which is closely associated with many diseases, and the resident pathogenic oral bacteria, which can be transferred by close physical contact, are important public health considerations. Although the dog is the most common companion animal, the composition of the canine oral microbiome, which may include human pathogenic bacteria, and its relationship with that of their owners are unclear. In this study, 16S rDNA pyrosequencing was used to compare the oral microbiomes of 10 dogs and their owners and to identify zoonotic pathogens. Pyrosequencing revealed 246 operational taxonomic units in the 10 samples, representing 57 genera from eight bacterial phyla. Firmicutes (57.6%), Proteobacteria (21.6%), Bacteroidetes (9.8%), Actinobacteria (7.1%), and Fusobacteria (3.9%) were the predominant phyla in the human oral samples, whereas Proteobacteria (25.7%), Actinobacteria (21%), Bacteroidetes (19.7%), Firmicutes (19.3%), and Fusobacteria (12.3%) were predominant in the canine oral samples. The predominant genera in the human samples were Streptococcus (43.9%), Neisseria (10.3%), Haemophilus (9.6%), Prevotella (8.4%), and Veillonella (8.1%), whereas the predominant genera in the canine samples were Actinomyces (17.2%), Unknown (16.8), Porphyromonas (14.8), Fusobacterium (11.8), and Neisseria (7.2%). The oral microbiomes of dogs and their owners were appreciably different, and similarity in the microbiomes of canines and their owners was not correlated with residing in the same household. Oral-to-oral transfer of Neisseria shayeganii, Porphyromonas canigingivalis, Tannerella forsythia, and Streptococcus minor from dogs to humans was suspected. The finding of potentially zoonotic and periodontopathic bacteria in the canine oral microbiome may be a public health concern.

The Australian saltwater crocodile ( Crocodylus porosus ) industry began commercially in the 1980s, producing skins for export and crocodile meat as a by-product. Industry research has thus far focused on strategies to improve production efficiency. In the current study, we utilised 16S rRNA sequencing to characterise the intestinal microbiome of Australian saltwater crocodiles. Samples were collected from 13 commercially farmed crocodiles from six sample sites along the length of the intestinal tract. The results indicate a similar microbiome composition to that found in the freshwater alligator, with the dominate phyla represented by Firmicutes , primarily Clostridia , and Fusobacteria , which appears to be distinct from mammalian, fish, and other reptile phyla which are generally dominated by Firmicutes and Bacteroidetes . The high abundance of ‘pathogenic’ bacteria, with no apparent consequence to the host’s health, is of great interest and warrants further additional investigation. This will enable expansion of the current understanding of host immune function and how it is modified by host and intestinal microbiome interactions.

Background Parasitic protozoans, helminths, alter the gut microbiota in mammals, yet little is known about the influence of intestinal cestodes on gut microbiota in fish. In the present study, the composition and diversity of the hindgut microbiota were determined in the intestine of common carp ( Cyprinus carpio ) infected with two tapeworm species, Khawia japonensis and Atractolytocestus tenuicollis . Results The intestine contained a core microbiota composed of Proteobacteria, Fusobacteria and Tenericutes. Infection with the two cestode species had no significant effect on the microbial diversity and richness, but it altered the microbial composition at the genus level. PCoA analysis indicated that microbial communities in the infected and uninfected common carp could not be distinguished from each other. However, a Mantel test indicated that the abundance of K. japonensis was significantly correlated with the microbial composition ( P  = 0.015), while the abundance of A. tenuicollis was not ( P  = 0.954). According to Pearsonʼs correlation analysis, the abundance of K. japonensis exhibited an extremely significant ( P  < 0.001) positive correlation with the following gut microbiota taxa: Epulopiscium , U114 , Bacteroides , Clostridium and Peptostreptococcaceae (0.8<  r  < 0.9); and a significant ( P  < 0.05) correlation with Enterobacteriaceae , Micrococcaceae , Rummeliibacillus , Lysinibacillus boronitolerans , Veillonellaceae , Oxalobacteraceae , Aeromonadaceae (negative), Marinibacillus and Chitinilyticum (0.4<  r  < 0.7). Conclusions These results suggest that the composition of gut microbiota was somewhat affected by the K. japonensis infection. Additionally, increased ratios of pathogenic bacteria ( Lawsonia and Plesiomonas ) were also associated with the K. japonensis infection, which may therefore increase the likelihood of disease.

Host-microbe interactions have been implicated in the pathogenesis of chronic fatigue syndrome (CFS), but whether the oral microbiome is altered in CFS patients is unknown. We explored alterations of the oral microbiome in Chinese Han CFS patients using 16S rRNA gene sequencing and alterations in the functional potential of the oral microbiome using PICRUSt. We found that Shannon and Simpson diversity indices were not different in CFS patients compared to healthy controls, but the overall oral microbiome composition was different (MANOVA, p < 0.01). CFS patients had a higher relative abundance of Fusobacteria compared with healthy controls. Further, the genera Leptotrichia, Prevotella, and Fusobacterium were enriched and Haemophilus, Veillonella, and Porphyromonas were depleted in CFS patients compared to healthy controls. Functional analysis from inferred metagenomes showed that bacterial genera altered in CFS patients were primarily associated with amino acid and energy metabolism. Our findings demonstrate that the oral microbiome in CFS patients is different from healthy controls, and these differences lead to shifts in functional pathways with implications for CFS pathogenesis. These findings increase our understanding of the relationship between the oral microbiota and CFS, which will advance our understanding of CFS pathogenesis and may contribute to future improvements in treatment and diagnosis.

As apex predators, pinnipeds are considered to be useful bioindicators of marine and coastal environments. Endemic to a small archipelago in the South Pacific, the Juan Fernandez fur seal (JFFS) is one of the less‐studied members of the pinniped family Otariidae. This study aimed to characterize the fecal microbiome of the JFFS for the first time, to establish a baseline for future studies of host–microbial–environment interactions and monitoring programs. During two consecutive reproductive seasons, 57 fecal samples were collected from seven different JFFS colonies within the Juan Fernandez Archipelago, Chile. Bacterial composition and abundance were characterized by sequencing the V4 region of the 16S rRNA gene. The overall microbiome composition was dominated by five phyla: Firmicutes (40% ±24), Fusobacteria (30% ±17), Bacteroidetes (22% ±10), Proteobacteria (6% ±4), and Actinobacteria (2% ±3). Alpha diversity was higher in Tierras Blancas. However, location was not found to be a dominant driver of microbial composition. Interestingly, the strongest signal in the data was a negative association between the genera Peptoclostridium and Fusobacterium, which explained 29.7% of the total microbial composition variability between samples. The genus Peptoclostridium has not been reported in other pinniped studies, and its role here is unclear, with interpretation challenging due to a lack of information regarding microbiome functionality in marine mammals. As a first insight into the JFFS fecal microbiome, these results contribute towards our understanding of the natural microbial diversity and composition in free‐ranging pinnipeds. Two distinct microbial patterns were identified in Juan Fernandez fur seal fecal samples. We suspect that sex, age, and especially diet are most likely to be influencing the observed patterns.