A method for automated pathogenic content estimation with application to rheumatoid arthritis

Pharmacomicrobiomics of Classical Immunosuppressant Drugs: A Systematic Review

The clinical response to classical immunosuppressant drugs (cIMDs) is highly variable among individuals. We performed a systematic review of published evidence supporting the hypothesis that gut microorganisms may contribute to this variability by affecting cIMD pharmacokinetics, efficacy or tolerability. The evidence that these drugs affect the composition of intestinal microbiota was also reviewed. The PubMed and Scopus databases were searched using specific keywords without limits of species (human or animal) or time from publication. One thousand and fifty five published papers were retrieved in the initial database search. After screening, 50 papers were selected to be reviewed. Potential effects on cIMD pharmacokinetics, efficacy or tolerability were observed in 17/20 papers evaluating this issue, in particular with tacrolimus, cyclosporine, mycophenolic acid and corticosteroids, whereas evidence was missing for everolimus and sirolimus. Only one of the papers investigating the effect of cIMDs on the gut microbiota reported negative results while all the others showed significant changes in the relative abundance of specific intestinal bacteria. However, no unique pattern of microbiota modification was observed across the different studies. In conclusion, the available evidence supports the hypothesis that intestinal microbiota could contribute to the variability in the response to some cIMDs, whereas data are still missing for others.

New Studies of Pathogenesis of Rheumatoid Arthritis with Collagen-Induced and Collagen Antibody-Induced Arthritis Models: New Insight Involving Bacteria Flora

Much public research suggests that autoimmune diseases such as rheumatoid arthritis (RA) are induced by aberrant “self” immune responses attacking autologous tissues and organ components. However, recent studies have reported that autoimmune diseases may be triggered by dysbiotic composition changes of the intestinal bacteria and an imbalance between these bacteria and intestinal immune systems. However, there are a few solid concepts or methods to study the putative involvement and relationship of these inner environmental factors in RA pathogenesis. Fortunately, Collagen-Induced Arthritis (CIA) and Collagen Antibody-Induced Arthritis (CAIA) models have been widely used as animal models for studying the pathogenesis of RA. In addition to RA, these models can be extensively used as animal models for studying complicated hypotheses in many diseases. In this review, we introduce some basic information about the CIA and CAIA models as well as how to apply these models effectively to investigate relationships between the pathogenesis of autoimmune diseases, especially RA, and the dysbiosis of intestinal bacterial flora.

Host-Microbiome Synergistic Control on Sphingolipid Metabolism by Mechanotransduction in Model Arthritis

Chronic inflammatory autoimmune disorders are systemic diseases with increasing incidence and still lack a cure. More recently, attention has been placed in understanding gastrointestinal (GI) dysbiosis and, although important progress has been made in this area, it is currently unclear to what extent microbiome manipulation can be used in the treatment of autoimmune disorders. Via the use of appropriate models, rheumatoid arthritis (RA), a well-known exemplar of such pathologies, can be exploited to shed light on the currently overlooked effects of existing therapies on the GI microbiome. In this direction, we here explore the crosstalk between the GI microbiome and the host immunity in model arthritis (collagen induced arthritis, CIA). By exploiting omics from samples of limited invasiveness (blood and stools), we assess the host-microbiome responses to standard therapy (methotrexate, MTX) combined with mechanical subcutaneous stimulation (MS) and to mechanical stimulation alone. When MS is involved, results reveal the sphingolipid metabolism as the trait d’union among known hallmarks of (model) RA, namely: Imbalance in the S1P-S1PR1 axis, expansion of Prevotella sp., and invariant Natural Killer T (iNKT)-penia, thus offering the base of a rationale to mechanically modulate this pathway as a therapeutic target in RA.

'NetShift': a methodology for understanding 'driver microbes' from healthy and disease microbiome datasets

The combined effect of mutual association within the co-inhabiting microbes in human body is known to play a major role in determining health status of individuals. The differential taxonomic abundance between healthy and disease are often used to identify microbial markers. However, in order to make a microbial community based inference, it is important not only to consider microbial abundances, but also to quantify the changes observed among inter microbial associations. In the present study, we introduce a method called 'NetShift' to quantify rewiring and community changes in microbial association networks between healthy and disease. Additionally, we devise a score to identify important microbial taxa which serve as 'drivers' from the healthy to disease. We demonstrate the validity of our score on a number of scenarios and apply our methodology on two real world metagenomic datasets. The 'NetShift' methodology is also implemented as a web-based application available at https://web.rniapps.net/netshift.

Dissection of the module network implementation "LemonTree": enhancements towards applications in metagenomics and translation in autoimmune maladies

Under the current deluge of omics, module networks distinctively emerge as methods capable of not only identifying inherently coherent groups (modules), thus reducing dimensionality, but also hypothesizing cause-effect relationships between modules and their regulators. Module networks were first designed in the transcriptomic era and further exploited in the multi-omic context to assess (for example) miRNA regulation of gene expression. Despite a number of available implementations, expansion of module networks to other omics is constrained by a limited characterization of the solutions' (modules plus regulators) accuracy and stability - an immediate need for the better characterization of molecular biology complexity in silico. We hence carefully assessed for LemonTree - a popular and open source module network implementation - the dependency of the software performances (sensitivity, specificity, false discovery rate, solutions' stability) on the input parameters and on the data quality (sample size, expression noise) based on synthetic and real data. In the process, we uncovered and fixed an issue in the code for the regulator assignment procedure. We concluded this evaluation with a table of recommended parameter settings. Finally, we applied these recommended settings to gut-intestinal metagenomic data from rheumatoid arthritis patients, to characterize the evolution of the gut-intestinal microbiome under different pharmaceutical regimens (methotrexate and prednisone) and we inferred innovative clinical recommendations with therapeutic potential, based on the computed module network.

Statistical analysis of co-occurrence patterns in microbial presence-absence datasets

Drawing on a long history in macroecology, correlation analysis of microbiome datasets is becoming a common practice for identifying relationships or shared ecological niches among bacterial taxa. However, many of the statistical issues that plague such analyses in macroscale communities remain unresolved for microbial communities. Here, we discuss problems in the analysis of microbial species correlations based on presence-absence data. We focus on presence-absence data because this information is more readily obtainable from sequencing studies, especially for whole-genome sequencing, where abundance estimation is still in its infancy. First, we show how Pearson's correlation coefficient (r) and Jaccard's index (J)-two of the most common metrics for correlation analysis of presence-absence data-can contradict each other when applied to a typical microbiome dataset. In our dataset, for example, 14% of species-pairs predicted to be significantly correlated by r were not predicted to be significantly correlated using J, while 37.4% of species-pairs predicted to be significantly correlated by J were not predicted to be significantly correlated using r. Mismatch was particularly common among species-pairs with at least one rare species (<10% prevalence), explaining why r and J might differ more strongly in microbiome datasets, where there are large numbers of rare taxa. Indeed 74% of all species-pairs in our study had at least one rare species. Next, we show how Pearson's correlation coefficient can result in artificial inflation of positive taxon relationships and how this is a particular problem for microbiome studies. We then illustrate how Jaccard's index of similarity (J) can yield improvements over Pearson's correlation coefficient. However, the standard null model for Jaccard's index is flawed, and thus introduces its own set of spurious conclusions. We thus identify a better null model based on a hypergeometric distribution, which appropriately corrects for species prevalence. This model is available from recent statistics literature, and can be used for evaluating the significance of any value of an empirically observed Jaccard's index. The resulting simple, yet effective method for handling correlation analysis of microbial presence-absence datasets provides a robust means of testing and finding relationships and/or shared environmental responses among microbial taxa.

Systemic Wound Healing Associated with local sub-Cutaneous Mechanical Stimulation

Degeneration is a hallmark of autoimmune diseases, whose incidence grows worldwide. Current therapies attempt to control the immune response to limit degeneration, commonly promoting immunodepression. Differently, mechanical stimulation is known to trigger healing (regeneration) and it has recently been proposed locally for its therapeutic potential on severely injured areas. As the early stages of healing consist of altered intra- and inter-cellular fluxes of soluble molecules, we explored the potential of this early signal to spread, over time, beyond the stimulation district and become systemic, to impact on distributed or otherwise unreachable injured areas. We report in a model of arthritis in rats how stimulations delivered in the subcutaneous dorsal tissue result, over time, in the control and healing of the degeneration of the paws' joints, concomitantly with the systemic activation of wound healing phenomena in blood and in correlation with a more eubiotic microbiome in the gut intestinal district.