Correlations, interactions, and predictability in virus-microbe networks

Abstract

1Microbes are present in high abundances in the environment and in human-associated microbiomes, often exceeding one million per milliliter. Viruses of microbes are present in even higher abundances and are important in shaping microbial populations, communities, and ecosystems. Given the relative specificity of viral infection, it is essential to identify the functional linkages between viruses and their microbial hosts, particularly given dynamic changes in virus and host abundances. Multiple approaches have been proposed to infer infection networks from time-series of in situ communities, among which correlation-based approaches have emerged as the de facto standard. In this work, we evaluate the accuracy of correlation-based inference methods using an in silico approach. In doing so, we compare predicted networks to actual networks to assess the self-consistency of correlation-based inference. At odds with assumptions underlying its widespread use, we find that correlation is a poor predictor of interactions in the context of viral infection and lysis of microbial hosts. The failure to predict interactions holds for methods which leverage product-moment, time-lagged, and relative-abundance based correlations. In closing, we discuss alternative inference methods, particularly model-based methods, as a means to infer interactions in complex microbial communities with viruses.\n\n2 ImportanceInferring interactions from population time-series is an active and ongoing area of research. It is relevant across many biological systems - in particular in virus-microbe communities, but also in gene regulatory networks, neural networks, and ecological communities broadly. Correlation-based inference - using correlations to predict interactions - is widespread. However, it is well known that \"correlation does not imply causation\". Despite this, many studies apply correlation-based inference methods to experimental time-series without first assessing the potential scope for accurate inference. Here, we find that several correlation-based inference methods fail to recover interactions within in silica virus-microbe communities, raising questions on their relevance when applied in situ.