P009 Disease classifier and microbial dysbiosis index tools cross-predict various pathogenic conditions due to general microbial signal
Abbas Egbariya, H.(1);Braun, T.(2);Hadar , R.(2);Gal-Mor, O.(1);Shental, N.(3);Haberman, Y.(4);Amir, A.(2);
(1)Tel Aviv university - Faculty of Medicine, microbiology, Tel Aviv, Israel;(2)Sheba medical center, microbiome center, Ramat Gan, Israel;(3)The Open University of Israel, Department of Mathematics and Computer Science, Ra’anana, Israel;(4)Sheba medical center, pediatric gastroenterology, Ramat Gan, Israel
Background
Microbial dysbiosis is widely described in inflammatory bowel disease (IBD), and has been shown to predict IBD state. However, many other diseases including neuro-psychiatric, metabolic, and malignancies, most of which do not result in gut inflammation, are also linked with gut microbial alteration. Since most studies focus on a single disease, the extent of similarity between different diseases is usually not examined.
Methods
We reanalyzed raw sequencing data from 12,838 human gut V4 16Sseq samples, spanning 59 case-controls comparisons and 28 unique diseases. Novel statistical approach was applied to reduce the effect of the different cohorts; all samples were processed uniformly, and differentially expressed amplicon sequence variants (ASVs) were identified within each cohort. The resulting behavior (direction of change and effect size) of each ASV were then combined across all studies. We used random forest as our classifier and generated non-specific dysbiosis index (NSDI).
Results
For the disease prediction, each cohort was randomly subsampled to 23 healthy and 23 disease samples. Random forest classifier was trained on one disease/control cohort, and the trained classifier was then used to predict the status of a different disease/control cohort. Disease classifiers performed well in identifying many sick vs. healthy states but failed to differentiate between different diseases. For example, a classifier trained on IBD cohort classified relatively good also disease/control in lupus, schizophrenia, or Parkinson’s from different cohorts. We show this cross-identification is due to a large number of shared disease-associated bacteria and utilize these bacteria to define a novel non-specific dysbiosis index (NSDI). After, we identified 114 non-disease specific ASVs (86 up and 28 down regulated ASVs across diseases in comparison to controls), we calculate the per-sample NSDI by rank-transforming the bacteria within the sample and computing the normalized log ratio of the sum of the ranks of the 86 down and 28 up regulated ASVs. The resulting NSDI is shown to perform better than the previously published CD dysbiosis index (Gevers et al, 2014; PMID: 24629344) indicating that NSDI can successfully differentiate between most cases and controls across a wide variety of diseases.
Conclusion
A robust non-specific general response of the gut microbiome is detected across different diseases, some of which is shared with IBD. Classifiers trained on a single disease may identify the general non-specific signal and therefore care should be taken when interpreting the classifier predictions. Finally, our NSDI can be used to prioritize the per-sample degree of dysbiosis.