On the limits of computational functional genomics for bacterial lifestyle prediction

We review the level of genomic specificity regarding actinobacterial pathogenicity. As they occupy various niches in diverse habitats, one may assume the existence of lifestyle-specific genomic features. We include 240 actinobacteria classified into four pathogenicity classes: human pathogens (HP), broad-spectrum pathogens (BP), opportunistic pathogens (OP), and non-pathogenic (NP). We hypothesize: (H1) Pathogens (HPs and BPs) possess specific pathogenicity signature genes. (H2) The same holds for opportunistic pathogens. (H3) Broad-spectrum and exclusively human pathogens cannot be distinguished from each other due to an observation bias, i.e. many HPs might be yet unclassified BPs. (H4) There is no intrinsic genomic characteristic of opportunistic pathogens compared to pathogens, as small mutations are likely to play a more dominant role in order to survive the immune system. To study these hypotheses, we implemented a bioinformatics pipeline that combines evolutionary sequence analysis with statistical learning methods (Random Forest with feature selection, model tuning and robustness analysis). Essentially, we present orthologous gene sets that computationally distinguish pathogens from non-pathogens (H1). We further show a clear limit in differentiating opportunistic pathogens from both, non-pathogens (H2) and pathogens (H4). Human pathogens may also not be distinguished from bacteria annotated as broad-spectrum pathogens based on a small set of orthologous genes only (H3), as many human pathogens might as well target a broad range of mammals but have not been annotated accordingly. In conclusion, we illustrate that even in the post-genome era and despite next-generation sequencing technology our ability to efficiently deduce real-world conclusions, such as pathogenicity classification, remains quite limited.