The advent of large-scale microbiome studies affords newfound analytical opportunities to understand how these communities of microbes operate and relate to their environment. However, the analytical methodology needed to model microbiome data and integrate them with other data constructs remains nascent. This emergent analytical toolset frequently ports over techniques developed in other multi-omics investigations, especially the growing array of statistical and computational techniques for integrating and representing data through networks. While network analysis has emerged as a powerful approach to modeling microbiome data, oftentimes by integrating these data with other types of omics data to discern their functional linkages, it is not always evident if the statistical details of the approach being applied are consistent with the assumptions of microbiome data or how they impact data interpretation. In this review, we overview some of the most important network methods for integrative analysis, with an emphasis on methods that have been applied or have great potential to be applied to the analysis of multi-omics integration of microbiome data. We compare advantages and disadvantages of various statistical tools, assess their applicability to microbiome data, and discuss their biological interpretability. We also highlight on-going statistical challenges and opportunities for integrative network analysis of microbiome data.

LASSO is a popular statistical tool often used in conjunction with generalized linear models that can simultaneously select variables and estimate parameters. When there are many variables of interest, as in current biological and biomedical studies, the power of LASSO can be limited. Fortunately, so much biological and biomedical data have been collected and they may contain useful information about the importance of certain variables. This article proposes an extension of LASSO, namely, prior LASSO (pLASSO), to incorporate that prior information into penalized generalized linear models. The goal is achieved by adding in the LASSO criterion function an additional measure of the discrepancy between the prior information and the model. For linear regression, the whole solution path of the pLASSO estimator can be found with a procedure similar to the least angle regression (LARS). Asymptotic theories and simulation results show that pLASSO provides significant improvement over LASSO when the prior information is relatively accurate. When the prior information is less reliable, pLASSO shows great robustness to the misspecification. We illustrate the application of pLASSO using a real dataset from a genome-wide association study. Supplementary materials for this article are available online.

Two diametric paradigms have been proposed to model the molecular co-evolution of microbial mutualists and their eukaryotic hosts. In one, mutualist and host exhibit an antagonistic arms race and each partner evolves rapidly to maximize their own fitness from the interaction at potential expense of the other. In the opposing model, conflicts between mutualist and host are largely resolved and the interaction is characterized by evolutionary stasis. We tested these opposing frameworks in two lineages of mutualistic rhizobia, Sinorhizobium fredii and Bradyrhizobium japonicum. To examine genes demonstrably important for host-interactions we coupled the mining of genome sequences to a comprehensive functional screen for type III effector genes, which are necessary for many Gram-negative pathogens to infect their hosts. We demonstrate that the rhizobial type III effector genes exhibit a surprisingly high degree of conservation in content and sequence that is in contrast to those of a well characterized plant pathogenic species. This type III effector gene conservation is particularly striking in the context of the relatively high genome-wide diversity of rhizobia. The evolution of rhizobial type III effectors is inconsistent with the molecular arms race paradigm. Instead, our results reveal that these loci are relatively static in rhizobial lineages and suggest that fitness conflicts between rhizobia mutualists and their host plants have been largely resolved. Rhizobia are an important group of bacteria that can enter into mutually beneficial symbiotic interactions with legume plants to fix atmospheric nitrogen. However, in order to do so, a complex dialog involving the exchange of chemical and molecular signals must occur between partners. Some species of beneficial rhizobia employ a type III secretion system, a well-characterized virulence mechanism used by pathogens to inject bacterial-encoded type III effector proteins directly into host cells to coerce the host into accommodating the microbe. In this study, we generated draft genome sequences and employed computational as well as experimental methods to identify type III effectors from eight strains representing Sinorhizobium fredii and Bradyrhizobium japonicum. We demonstrate that the type III effector genes of these rhizobial species are highly conserved in content with little diversity between strains. This work is an important step towards understanding the roles for type III secretion systems and their effectors in mutualistic interactions.

Mild, mitochondrial uncoupling increases energy expenditure and can reduce the generation of reactive oxygen species (ROS). Activation of cellular, adaptive stress response pathways can result in an enhanced capacity to reduce oxidative damage. Together, these strategies target energy imbalance and oxidative stress, both underlying factors of obesity and related conditions such as type 2 diabetes. Here we describe a metabolomics-driven effort to uncover the anti-obesity mechanism(s) of xanthohumol (XN), a prenylated flavonoid from hops. Metabolomics analysis of fasting plasma from obese, Zucker rats treated with XN revealed decreases in products of dysfunctional fatty acid oxidation and ROS, prompting us to explore the effects of XN on muscle cell bioenergetics. At low micromolar concentrations, XN acutely increased uncoupled respiration in several different cell types, including myocytes. Tetrahydroxanthohumol also increased respiration, suggesting electrophilicity did not play a role. At higher concentrations, XN inhibited respiration in a ROS-dependent manner. In myocytes, time course metabolomics revealed acute activation of glutathione recycling and long term induction of glutathione synthesis as well as several other changes indicative of short term elevated cellular stress and a concerted adaptive response. Based on these findings, we hypothesize that XN may ameliorate metabolic syndrome, at least in part, through mitochondrial uncoupling and stress response induction. In addition, time course metabolomics appears to be an effective strategy for uncovering metabolic events that occur during a stress response.