Administration of galacto-oligosaccharide prebiotics in the Flinders Sensitive Line animal model of depression

INTRODUCTION: Major depressive disorder is the leading source of disability globally and current pharmacological treatments are less than adequate. Animal models such as the Flinders Sensitive Line (FSL) rats are used to mimic aspects of the phenotype in the human disorder and to characterise candidate antidepressant agents. Communication between the gut microbiome and the brain may play an important role in psychiatric disorders such as depression. Interventions targeting the gut microbiota may serve as potential treatments for depression, and this drives increasing research into the effect of probiotics and prebiotics in neuropsychiatric disorders. Prebiotics, galacto-oligosaccharides and fructooligosaccharides that stimulate the activity of gut bacteria have been reported to have a positive impact, reducing anxiety and depressive-like phenotypes and stress-related physiology in mice and rats, as well as in humans. Bimuno, the commercially available beta-galacto-oligosaccharide, has been shown to increase gut microbiota diversity. AIM: Here, we aim to investigate the effect of Bimuno on rat anxiety-like and depressive-like behaviour and gut microbiota composition in the FSL model, a genetic model of depression, in comparison to their control, the Flinders Resistant Line (FRL) rats. METHODS: Sixty-four male rats aged 5–7 weeks, 32 FSL and 32 FRL rats, will be randomised to receive Bimuno or control (4 g/kg) daily for 4 weeks. Animals will be tested by an experimenter unaware of group allocation on the forced swim test to assessed depressive-like behaviour, the elevated plus maze to assess anxiety-like behaviour and the open field test to assess locomotion. Animals will be weighed and food and water intake, per kilogram of bodyweight, will be recorded. Faeces will be collected from each animal prior to the start of the experiment and on the final day to assess the bacterial diversity and relative abundance of bacterial genera in the gut. All outcomes and statistical analysis will be carried out blinded to group allocation, group assignments will be revealed after raw data have been uploaded to Open Science Framework. Two-way analysis of variance will be carried out to investigate the effect of treatment (control or prebiotic) and strain (FSL or FRL) on depressive-like and anxiety-like behaviours

Understanding in vivo modelling of depression

Major Depressive Disorder (MDD) is the leading source of disability globally. Treatment-resistance among patients is common and even effective pharmacological therapies have a delayed effect on symptom relief. Better understanding of the mechanisms underlying depression and the search for potential effective and novel therapeutic targets are high research and healthcare priorities. Animal models are commonly used to mimic aspects of the phenotype of the human disorder to characterise candidate antidepressant agents. Despite these tools, no new pharmacological interventions have been discovered in the last decade and no reliable biomarkers have been identified for clinical use. Systematically reviewing the literature on animal models of depression may provide an overview of our current understanding of the underlying biological mechanisms and why no new therapies have been effectively translated to clinic. This field of research is large, and over 70,000 potentially relevant articles were identified in 2016. Therefore systematically reviewing this literature presents challenges for human resources. To combat these challenges, the following contributions to the field have been made: (1) the novel application of machine learning techniques to identify errors in human systematic review citation screening; and (2), the novel application of regular expression dictionaries to large corpuses of preclinical animal literature to help cluster publications into the disease model investigated and drug intervention tested. These tools have been applied for systematic review and meta-analysis methodology to the field of animal models of depression. All literature on animal models of depression has been systematically identified using searches carried out in PubMed and EMBASE in May 2016. This literature has been screened with the help of machine learning classification algorithms, based on a random set of dual human screened records (5749 records). This achieved a sensitivity of 98.7% and a specificity of 86% as assessed on in an independent validation dataset. Machine learning has been used to identify human screening errors in the set of documents used to train the algorithm. Correction of these errors with further human intervention, sees an improvement in specificity to 88.3%. These algorithms allow irrelevant documents to be automatically removed, reducing the corpus to 18,407 articles that highly likely to be relevant to the research area of animal models of depression. Custom-made regular expression dictionaries of (1) techniques to induce depressive-like phenotypes in animals, and (2) known antidepressants have been curated. The text-mining dictionaries for anti-depressant drugs and commonly used methods of model induction have been applied to categorise and visualise this large corpus of records to allow prioritisation of sub-topics of depression for further in depth systematic review and meta-analyses. These machine-assisted tools for systematic review methodology are available free to use, online. Systematic review and meta-analysis has been conducted on two sub-topics of the literature on animal models of depression. Firstly, the literature on the effects of ketamine as an anti-depressant in animal models of depression has been summarised with systematic review techniques and the effects of ketamine on depressive-like behaviour in the forced swim test, has been pooled using meta-analysis. The timing of administration of ketamine relative to the outcome assessment was significantly associated with decreases in effect size. This meta-analysis revealed no statistically significant heterogeneity between the studies. Secondly, the literature on use of gut microbial altering interventions to induce and treat depressive-like phenotypes in animal models of depression has been summarised and their effects have been pooled across studies using meta-analysis. The systematic review and meta-analysis of microbiota interventions identified a broad range of outcomes investigated in the primary literature and several probiotic treatments to reduce depressive-like behaviour were investigate gaps in the literature. Finally, a primary hypothesis-confirming animal experiment, where measures to reduce the risk of bias have been implemented was carried out to investigate the effects of prebiotics on depressive- and anxiety-like behaviour in a genetic animal model of depression, the Flinders Sensitive Line (FSL) rats. Online tools have been developed to provide an overview of animal models of depression and anti-depressant drugs investigated in the literature, using systematic review methodology and automation tools. This thesis reports meta-analyses on two sub-topics within animal models of depression; the effect of microbiota interventions, and the effects of ketamine; along with a primary animal experiment to test the effects of prebiotics on depressive-like behaviour in a genetic rodent model of depression

Systematic online living evidence summaries:emerging tools to accelerate evidence synthesis

Systematic reviews and meta-analysis are the cornerstones of evidence-based decision making and priority setting. However, traditional systematic reviews are time and labour intensive, limiting their feasibility to comprehensively evaluate the latest evidence in research-intensive areas. Recent developments in automation, machine learning and systematic review technologies have enabled efficiency gains. Building upon these advances, we developed Systematic Online Living Evidence Summaries (SOLES) to accelerate evidence synthesis. In this approach, we integrate automated processes to continuously gather, synthesise and summarise all existing evidence from a research domain, and report the resulting current curated content as interrogatable databases via interactive web applications. SOLES can benefit various stakeholders by (i) providing a systematic overview of current evidence to identify knowledge gaps, (ii) providing an accelerated starting point for a more detailed systematic review, and (iii) facilitating collaboration and coordination in evidence synthesis

Risk of bias reporting in the recent animal focal cerebral ischaemia literature

BACKGROUND: Findings from in vivo research may be less reliable where studies do not report measures to reduce risks of bias. The experimental stroke community has been at the forefront of implementing changes to improve reporting, but it is not known whether these efforts are associated with continuous improvements. Our aims here were firstly to validate an automated tool to assess risks of bias in published works, and secondly to assess the reporting of measures taken to reduce the risk of bias within recent literature for two experimental models of stroke. METHODS: We developed and used text analytic approaches to automatically ascertain reporting of measures to reduce risk of bias from full-text articles describing animal experiments inducing middle cerebral artery occlusion (MCAO) or modelling lacunar stroke. RESULTS: Compared with previous assessments, there were improvements in the reporting of measures taken to reduce risks of bias in the MCAO literature but not in the lacunar stroke literature. Accuracy of automated annotation of risk of bias in the MCAO literature was 86% (randomization), 94% (blinding) and 100% (sample size calculation); and in the lacunar stroke literature accuracy was 67% (randomization), 91% (blinding) and 96% (sample size calculation). DISCUSSION: There remains substantial opportunity for improvement in the reporting of animal research modelling stroke, particularly in the lacunar stroke literature. Further, automated tools perform sufficiently well to identify whether studies report blinded assessment of outcome, but improvements are required in the tools to ascertain whether randomization and a sample size calculation were reported

Development and uptake of an online systematic review platform: the early years of the CAMARADES Systematic Review Facility (SyRF)

Preclinical research is a vital step in the drug discovery pipeline and more generally in helping to better understand human disease aetiology and its management. Systematic reviews (SRs) can be powerful in summarising and appraising this evidence concerning a specific research question, to highlight areas of improvements, areas for further research and areas where evidence may be sufficient to take forward to other research domains, for instance clinical trial. Guidance and tools for preclinical research synthesis remain limited despite their clear utility. We aimed to create an online end-to-end platform primarily for conducting SRs of preclinical studies, that was flexible enough to support a wide variety of experimental designs, was adaptable to different research questions, would allow users to adopt emerging automated tools and support them during their review process using best practice. In this article, we introduce the Systematic Review Facility (https://syrf.org.uk), which was launched in 2016 and designed to support primarily preclinical SRs from small independent projects to large, crowdsourced projects. We discuss the architecture of the app and its features, including the opportunity to collaborate easily, to efficiently manage projects, to screen and annotate studies for important features (metadata), to extract outcome data into a secure database, and tailor these steps to each project. We introduce how we are working to leverage the use of automation tools and allow the integration of these services to accelerate and automate steps in the systematic review workflow

TIER2: enhancing Trust, Integrity and Efficiency in Research through next-level Reproducibility

Lack of reproducibility of research results has become a major theme in recent years. As we emerge from the COVID-19 pandemic, economic pressures and exposed consequences of lack of societal trust in science make addressing reproducibility of urgent importance. TIER2 is a new international project funded by the European Commission under their Horizon Europe programme. Covering three broad research areas (social, life and computer sciences) and two cross-disciplinary stakeholder groups (research publishers and funders) to systematically investigate reproducibility across contexts, TIER2 will significantly boost knowledge on reproducibility, create tools, engage communities, implement interventions and policy across different contexts to increase re-use and overall quality of research results in the European Research Area and global R&I, and consequently increase trust, integrity and efficiency in research