Combining ontologies and workflows to design formal protocols for biological laboratories

Background Laboratory protocols in life sciences tend to be written in natural language, with negative consequences on repeatability, distribution and automation of scientific experiments. Formalization of knowledge is becoming popular in science. In the case of laboratory protocols two levels of formalization are needed: one for the entities and individuals operations involved in protocols and another one for the procedures, which can be manually or automatically executed. This study aims to combine ontologies and workflows for protocol formalization. Results A laboratory domain specific ontology and the COW (Combining Ontologies with Workflows) software tool were developed to formalize workflows built on ontologies. A method was specifically set up to support the design of structured protocols for biological laboratory experiments. The workflows were enhanced with ontological concepts taken from the developed domain specific ontology. The experimental protocols represented as workflows are saved in two linked files using two standard interchange languages (i.e. XPDL for workflows and OWL for ontologies). A distribution package of COW including installation procedure, ontology and workflow examples, is freely available from http://www.bmr-genomics.it/farm/cow webcite. Conclusions Using COW, a laboratory protocol may be directly defined by wet-lab scientists without writing code, which will keep the resulting protocol's specifications clear and easy to read and maintain

Analysis of 22 deletion breakpoints in dystrophin intron 49

Over 60% of Duchenne and Becker muscular dystrophies are caused by deletions spanning tens or hundreds of kilobases in the dystrophin gene. The molecular mechanisms underlying the loss of DNA at this genomic locus are not yet understood. By studying the distribution of deletion breakpoints at the genomic level, we have previously shown that intron 49 exhibits a higher relative density of breakpoints than most dystrophin introns. To determine whether the mechanisms leading to deletions in this intron preferentially involve specific sequence elements, we sublocalized 22 deletion endpoints along its length by a polymerase-chain-reaction-based approach and, in particular, analyzed the nucleotide sequences of five deletion junctions. Deletion breakpoints were homogeneously distributed throughout the intron length, and no extensive homology was observed between the sequences adjacent to each breakpoint. However, a short sequence able to curve the DNA molecule was found at or near three breakpoint junctions

Following food microbiome development during shelf-life in the Nextgen era.

Food microbial populations are complex and dynamic: they develop and change their composition all throughout shelf life, establishing tangled interaction that traditional culture-dependent methods cannot completely point out. Thus, in this field, Next generation sequencing has become more interesting since the study of microbial communities has reached a depth of analysis never seen before: species richness and evenness, the detection of rare bacterial populations, the analysis of the genetical responses to food environments are some information provided by Nextgen techniques. In this work an industrial ricotta cheese microbial community was monitored starting from raw materials to product expiry date: Illumina MiSeq 2x300bp 16S amplicon analysis was exploited with a simple protocol for library production, normalization and pooling, in parallel with culture-dependent analysis. On one hand raw material and the product were processed all throughout shelf-life using classic culture-dependent methods while, at the same time, RNA or DNA was extracted from the samples and sequenced with Nextgen approach. Microbial populations were different between the raw matters (whey and cream) and showed typical environmental contaminants, lactic acid bacteria and aerobic spores (Pseudomonas, Aeromonas, Streptococcus, Lactococcus, Kocuria, Hafnia). Most of the microbial populations in the ricotta cheese samples appeared to be composed of Bacillus and Paenibacillus. Both genera derived from spores germination and increased exponentially within two weeks of 8\ub0C storage, reaching 107 CFU/ml; within 20 days cheese pH decreased from 6,4-6,2 to 5,7-5,5, showing acidification due to active bacterial metabolism. With this approach we precisely understood what kind of bacteria were present in the tested products from raw matters to expiry date: these essential informations can be exploited in order to improve ricotta cheese chain production

Faecal Microbiome Transplantation as a Solution to Chronic Enteropathies in Dogs: A Case Study of Beneficial Microbial Evolution

Chronic enteropathies (CE) are gastrointestinal diseases that afflict about one in five dogs in Europe. Conventional therapeutic approaches include dietary intervention, pharmacological treatment and probiotic supplements. The patient response can be highly variable and the interventions are often not resolutive. Moreover, the therapeutic strategy is usually planned (and gradually corrected) based on the patient’s response to empirical treatment, with few indirect gut health indicators useful to drive clinicians’ decisions. The ever-diminishing cost of high-throughput sequencing (HTS) allows clinicians to directly follow and characterise the evolution of the whole gut microbial community in order to highlight possible weaknesses. In this framework, faecal microbiome transplantation (FMT) is emerging as a feasible solution to CE, based on the implant of a balanced, eubiotic microbial community from a healthy donor to a dysbiotic patient. In this study, we report the promising results of FMT carried out in a 9-year-old dog suffering from CE for the last 3 years. The patient underwent a two-cycle oral treatment of FMT and the microbiota evolution was monitored by 16S rRNA gene sequencing both prior to FMT and after the two administrations. We evaluated the variation of microbial composition by calculating three different alpha diversity indices and compared the patient and donor data to a healthy control population of 94 dogs. After FMT, the patient’s microbiome and clinical parameters gradually shifted to values similar to those observed in healthy dogs. Symptoms disappeared during a follow-up period of six months after the second FMT. We believe that this study opens the door for potential applications of FMT in clinical veterinary practice and highlights the need to improve our knowledge on this relevant topic

Machine Learning and Canine Chronic Enteropathies: A New Approach to Investigate FMT Effects

Fecal microbiota transplantation (FMT) represents a very promising approach to decreasing disease activity in canine chronic enteropathies (CE). However, the relationship between remission mechanisms and microbiome changes has not been elucidated yet. The main objective of this study was to report the clinical effects of oral freeze-dried FMT in CE dogs, comparing the fecal microbiomes of three groups: pre-FMT CE-affected dogs, post-FMT dogs, and healthy dogs. Diversity analysis, differential abundance analysis, and machine learning algorithms were applied to investigate the differences in microbiome composition between healthy and pre-FMT samples, while Canine Chronic Enteropathy Clinical Activity Index (CCECAI) changes and microbial diversity metrics were used to evaluate FMT effects. In the healthy/pre-FMT comparison, significant differences were noted in alpha and beta diversity and a list of differentially abundant taxa was identified, while machine learning algorithms predicted sample categories with 0.97 (random forest) and 0.87 (sPLS-DA) accuracy. Clinical signs of improvement were observed in 74% (20/27) of CE-affected dogs, together with a statistically significant decrease in CCECAI (median value from 5 to 2 median). Alpha and beta diversity variations between pre- and post-FMT were observed for each receiver, with a high heterogeneity in the response. This highlighted the necessity for further research on a larger dataset that could identify different healing patterns of microbiome changes