title: A method for automated pathogenic content estimation with application to rheumatoid arthritis.
abstract: Sequencing technologies applied to mammals' microbiomes have revolutionized our understanding of health and disease. Hence, to assess diseases' progression as well as therapies longterm effects, the impact of maladies and drugs on the gut-intestinal (GI) microbiome has to be evaluated. Typical metagenomic analyses are run to associate to a condition (disease, therapy, diet) a pool of bacteria, whose eubiotic/dysbiotic potential is assessed either by α-diversity, a measure of the varieties populating the microbiome, or by Firmicutes to Bacteroides ratio, associated to systemic inflammation, and finally by manual and direct inspection of bacteria's biological functions, when known. These approaches lead to results sometimes difficult to interpret in terms of the evolution towards a specific microbial composition, harmed by large areas of unknown.
  
  
  
  We propose to additionally evaluate a microbiome based on its global composition, by automatic annotation of pathogenic genera and statistical assessment of the net varied frequency of harmless versus harmful organisms. This application is intuitive, quantitative and computationally efficient and designed to cope with the currently incomplete species' functional knowledge. Our results, applied to human GI-microbiome data exemplify how this layer of information provides additional insights into treatments' impact on the GI microbiome, allowing to characterize a more physiologic effects of Prednisone versus Methotrexate, two treatments for rheumatoid arthritis (RA) a complex autoimmune systemic disease.
  
  
  
  Our quantitative analysis integrates with previous approaches offering an additional systemic level of interpretation here applied, for its potential to translate into clinically relevant information, to the therapies for RA.
authors.affiliation: Group of Clinical Genomic Networks, Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Shanghai, People's Republic of China.|University of Chinese Academy of Sciences, Beijing, People's Republic of China.
authors.forename: Xiaoyuan
authors.initials: X
authors.lastname: Zhou
authors.affiliation: Group of Clinical Genomic Networks, Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Shanghai, People's Republic of China. christine.nardini.rsrc@gmail.com.|University of Chinese Academy of Sciences, Beijing, People's Republic of China. christine.nardini.rsrc@gmail.com.|Personalgenomics, Verona, Italy. christine.nardini.rsrc@gmail.com.
authors.forename: Christine
authors.initials: C
authors.lastname: Nardini
mesh_terms: D001172:Arthritis, Rheumatoid / Q000382:microbiology*; D001331:Automation; D044822:Biodiversity; D019295:Computational Biology / Q000379:methods*; D000069196:Gastrointestinal Microbiome; D006801:Humans
keywords: Microbiome; Pathogens; Rheumatoid arthritis