Evaluation of the microbiome of decaying alder nodules by next generation sequencing

This work investigated the microbial content of decaying nodules from alders. The 16S rDNA composition of the microbiome of six senescent alder nodules was investigated by 454 sequencing. All nodules still had some Frankia sequences present, but in each case it was only detected at minor levels, with other organisms predominating. Although organisms from three different phyla (Bacteroidetes, Proteobacteria and Actinobacteria) constituted almost all (98% or more) of all sequences, Bacteroidetes were most abundant in four nodules with Proteobacteria being most abundant in the other two. In addition a few families were represented at a level of 10% or more of the total sequences: Sphingobacteriaceae (all 6 nodules); Chitinophagaceae (5 of 6); non-Frankia Actinomycetales (2 of 6); Caulobacteraceae (2 of 6); Flavobacteriaceae (2 of 6); Oxalobacteraceae (1 of 6); and Xanthomoadaceae (1 of 6). Analysis at the genus level showed a diverse range of organisms, with members of the genus Pedobacter being found at an abundant level within most nodules

Molecular phylogeny of Spirodinium equi, Triadinium caudatum and Blepharocorys sp. From the equine hindgut

Single cell morphotypes of the species Triadinium caudatum and Spirodinium equi, together with a representative of the genus Blepharocorys (Blepharocorys sp.) were used for phylogenetic analysis based on their 18S rRNA genes. Spirodinium equi clustered with sequences already described for the entodiniomorphs isolated from horses and the Blepharocorys sp. also grouped within the Entodiniomorphida clade, although both sequences were distinct from those described from rumen ciliates. Triadinium caudatum clustered within the Vestibuliferida, and most closely to that of Paraisotricha, only other member of this order which has been described in the horse. It was concluded that although members of the orders Entodiniomorphida and Vestibuliferida are present in the equine gut, and that they share an ancient linage with their rumen counterparts, they are ancestrally different groups

Addressing global ruminant agricultural challenges through understanding the rumen microbiome::Past, present and future

The rumen is a complex ecosystem composed of anaerobic bacteria, protozoa, fungi, methanogenic archaea and phages. These microbes interact closely to breakdown plant material that cannot be digested by humans, whilst providing metabolic energy to the host and, in the case of archaea, producing methane. Consequently, ruminants produce meat and milk, which are rich in high-quality protein, vitamins and minerals, and therefore contribute to food security. As the world population is predicted to reach approximately 9.7 billion by 2050, an increase in ruminant production to satisfy global protein demand is necessary, despite limited land availability, and whilst ensuring environmental impact is minimized. Although challenging, these goals can be met, but depend on our understanding of the rumen microbiome. Attempts to manipulate the rumen microbiome to benefit global agricultural challenges have been ongoing for decades with limited success, mostly due to the lack of a detailed understanding of this microbiome and our limited ability to culture most of these microbes outside the rumen. The potential to manipulate the rumen microbiome and meet global livestock challenges through animal breeding and introduction of dietary interventions during early life have recently emerged as promising new technologies. Our inability to phenotype ruminants in a high-throughput manner has also hampered progress, although the recent increase in “omic” data may allow further development of mathematical models and rumen microbial gene biomarkers as proxies. Advances in computational tools, high-throughput sequencing technologies and cultivation-independent “omics” approaches continue to revolutionize our understanding of the rumen microbiome. This will ultimately provide the knowledge framework needed to solve current and future ruminant livestock challenges

Molecular phylogeny of Spirodinium equi, Triadinium caudatum and Blepharocorys sp. From the equine hindgut

Single cell morphotypes of the species Triadinium caudatum and Spirodinium equi, together with a representative of the genus Blepharocorys (Blepharocorys sp.) were used for phylogenetic analysis based on their 18S rRNA genes. Spirodinium equi clustered with sequences already described for the entodiniomorphs isolated from horses and the Blepharocorys sp. also grouped within the Entodiniomorphida clade, although both sequences were distinct from those described from rumen ciliates. Triadinium caudatum clustered within the Vestibuliferida, and most closely to that of Paraisotricha, only other member of this order which has been described in the horse. It was concluded that although members of the orders Entodiniomorphida and Vestibuliferida are present in the equine gut, and that they share an ancient linage with their rumen counterparts, they are ancestrally different groups

Urinary tract infections in children:building a causal model-based decision support tool for diagnosis with domain knowledge and prospective data

BACKGROUND: Diagnosing urinary tract infections (UTIs) in children in the emergency department (ED) is challenging due to the variable clinical presentations and difficulties in obtaining a urine sample free from contamination. Clinicians need to weigh a range of observations to make timely diagnostic and management decisions, a difficult task to achieve without support due to the complex interactions among relevant factors. Directed acyclic graphs (DAG) and causal Bayesian networks (BN) offer a way to explicitly outline the underlying disease, contamination and diagnostic processes, and to further make quantitative inference on the event of interest thus serving as a tool for decision support. METHODS: We prospectively collected data on children present to ED with suspected UTIs. Through knowledge elicitation workshops and one-on-one meetings, a DAG was co-developed with clinical domain experts (the Expert DAG) to describe the causal relationships among variables relevant to paediatric UTIs. The Expert DAG was combined with prospective data and further domain knowledge to inform the development of an application-oriented BN (the Applied BN), designed to support the diagnosis of UTI. We assessed the performance of the Applied BN using quantitative and qualitative methods. RESULTS: We summarised patient background, clinical and laboratory characteristics of 431 episodes of suspected UTIs enrolled from May 2019 to November 2020. The Expert DAG was presented with a narrative description, elucidating how infection, specimen contamination and management pathways causally interact to form the complex picture of paediatric UTIs. Parameterised using prospective data and expert-elicited parameters, the Applied BN achieved an excellent and stable performance in predicting Escherichia coli culture results, with a mean area under the receiver operating characteristic curve of 0.86 and a mean log loss of 0.48 based on 10-fold cross-validation. The BN predictions were reviewed via a validation workshop, and we illustrate how they can be presented for decision support using three hypothetical clinical scenarios. CONCLUSION: Causal BNs created from both expert knowledge and data can integrate case-specific information to provide individual decision support during the diagnosis of paediatric UTIs in ED. The model aids the interpretation of culture results and the diagnosis of UTIs, promising the prospect of improved patient care and judicious use of antibiotics. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-022-01695-6

Addressing Global Ruminant Agricultural Challenges Through Understanding the Rumen Microbiome: Past, Present, and Future.

The rumen is a complex ecosystem composed of anaerobic bacteria, protozoa, fungi, methanogenic archaea and phages. These microbes interact closely to breakdown plant material that cannot be digested by humans, whilst providing metabolic energy to the host and, in the case of archaea, producing methane. Consequently, ruminants produce meat and milk, which are rich in high-quality protein, vitamins and minerals, and therefore contribute to food security. As the world population is predicted to reach approximately 9.7 billion by 2050, an increase in ruminant production to satisfy global protein demand is necessary, despite limited land availability, and whilst ensuring environmental impact is minimized. Although challenging, these goals can be met, but depend on our understanding of the rumen microbiome. Attempts to manipulate the rumen microbiome to benefit global agricultural challenges have been ongoing for decades with limited success, mostly due to the lack of a detailed understanding of this microbiome and our limited ability to culture most of these microbes outside the rumen. The potential to manipulate the rumen microbiome and meet global livestock challenges through animal breeding and introduction of dietary interventions during early life have recently emerged as promising new technologies. Our inability to phenotype ruminants in a high-throughput manner has also hampered progress, although the recent increase in "omic" data may allow further development of mathematical models and rumen microbial gene biomarkers as proxies. Advances in computational tools, high-throughput sequencing technologies and cultivation-independent "omics" approaches continue to revolutionize our understanding of the rumen microbiome. This will ultimately provide the knowledge framework needed to solve current and future ruminant livestock challenges