Microbiota triggers STING-type I IFN-dependent monocyte reprogramming of the tumor microenvironment.

The tumor microenvironment (TME) influences cancer progression and therapy response. Therefore, understanding what regulates the TME immune compartment is vital. Here we show that microbiota signals program mononuclear phagocytes in the TME toward immunostimulatory monocytes and dendritic cells (DCs). Single-cell RNA sequencing revealed that absence of microbiota skews the TME toward pro-tumorigenic macrophages. Mechanistically, we show that microbiota-derived stimulator of interferon genes (STING) agonists induce type I interferon (IFN-I) production by intratumoral monocytes to regulate macrophage polarization and natural killer (NK) cell-DC crosstalk. Microbiota modulation with a high-fiber diet triggered the intratumoral IFN-I-NK cell-DC axis and improved the efficacy of immune checkpoint blockade (ICB). We validated our findings in individuals with melanoma treated with ICB and showed that the predicted intratumoral IFN-I and immune compositional differences between responder and non-responder individuals can be transferred by fecal microbiota transplantation. Our study uncovers a mechanistic link between the microbiota and the innate TME that can be harnessed to improve cancer therapies.

Reconciling differential gene expression data with molecular interaction networks.

MOTIVATION: Many techniques have been developed to compute the response network of a cell. A recent trend in this area is to compute response networks of small size, with the rationale that only part of a pathway is often changed by disease and that interpreting small subnetworks is easier than interpreting larger ones. However, these methods may not uncover the spectrum of pathways perturbed in a particular experiment or disease. RESULTS: To avoid these difficulties, we propose to use algorithms that reconcile case-control DNA microarray data with a molecular interaction network by modifying per-gene differential expression P-values such that two genes connected by an interaction show similar changes in their gene expression values. We provide a novel evaluation of four methods from this class of algorithms. We enumerate three desirable properties that this class of algorithms should address. These properties seek to maintain that the returned gene rankings are specific to the condition being studied. Moreover, to ease interpretation, highly ranked genes should participate in coherent network structures and should be functionally enriched with relevant biological pathways. We comprehensively evaluate the extent to which each algorithm addresses these properties on a compendium of gene expression data for 54 diverse human diseases. We show that the reconciled gene rankings can identify novel disease-related functions that are missed by analyzing expression data alone. AVAILABILITY: C++ software implementing our algorithms is available in the NetworkReconciliation package as part of the Biorithm software suite under the GNU General Public License: http://bioinformatics.cs.vt.edu/∼murali/software/biorithm-docs.

Transkingdom Network Analysis (TkNA): a systems framework for inferring causal factors underlying host-microbiota and other multi-omic interactions.

We present Transkingdom Network Analysis (TkNA), a unique causal-inference analytical framework that offers a holistic view of biological systems by integrating data from multiple cohorts and diverse omics types. TkNA helps to decipher key players and mechanisms governing host-microbiota (or any multi-omic data) interactions in specific conditions or diseases. TkNA reconstructs a network that represents a statistical model capturing the complex relationships between different omics in the biological system. It identifies robust and reproducible patterns of fold change direction and correlation sign across several cohorts to select differential features and their per-group correlations. The framework then uses causality-sensitive metrics, statistical thresholds and topological criteria to determine the final edges forming the transkingdom network. With the subsequent network's topological features, TkNA identifies nodes controlling a given subnetwork or governing communication between kingdoms and/or subnetworks. The computational time for the millions of correlations necessary for network reconstruction in TkNA typically takes only a few minutes, varying with the study design. Unlike most other multi-omics approaches that find only associations, TkNA focuses on establishing causality while accounting for the complex structure of multi-omic data. It achieves this without requiring huge sample sizes. Moreover, the TkNA protocol is user friendly, requiring minimal installation and basic familiarity with Unix. Researchers can access the TkNA software at https://github.com/CAnBioNet/TkNA/ .

Vitamin D regulates microbiome-dependent cancer immunity.

A role for vitamin D in immune modulation and in cancer has been suggested. In this work, we report that mice with increased availability of vitamin D display greater immune-dependent resistance to transplantable cancers and augmented responses to checkpoint blockade immunotherapies. Similarly, in humans, vitamin D-induced genes correlate with improved responses to immune checkpoint inhibitor treatment as well as with immunity to cancer and increased overall survival. In mice, resistance is attributable to the activity of vitamin D on intestinal epithelial cells, which alters microbiome composition in favor of Bacteroides fragilis, which positively regulates cancer immunity. Our findings indicate a previously unappreciated connection between vitamin D, microbial commensal communities, and immune responses to cancer. Collectively, they highlight vitamin D levels as a potential determinant of cancer immunity and immunotherapy success.

Transkingdom Network Analysis (TkNA): a systems framework for inferring causal factors underlying host-microbiota and other multi-omic interactions.

Technological advances have generated tremendous amounts of high-throughput omics data. Integrating data from multiple cohorts and diverse omics types from new and previously published studies can offer a holistic view of a biological system and aid in deciphering its critical players and key mechanisms. In this protocol, we describe how to use Transkingdom Network Analysis (TkNA), a unique causal-inference analytical framework that can perform meta-analysis of cohorts and detect master regulators among measured parameters that govern pathological or physiological responses of host-microbiota (or any multi-omic data) interactions in a particular condition or disease. TkNA first reconstructs the network that represents a statistical model capturing the complex relationships between the different omics of the biological system. Here, it selects differential features and their per-group correlations by identifying robust and reproducible patterns of fold change direction and sign of correlation across several cohorts. Next, a causality-sensitive metric, statistical thresholds, and a set of topological criteria are used to select the final edges that form the transkingdom network. The second part of the analysis involves interrogating the network. Using the network's local and global topology metrics, it detects nodes that are responsible for control of given subnetwork or control of communication between kingdoms and/or subnetworks. The underlying basis of the TkNA approach involves fundamental principles including laws of causality, graph theory and information theory. Hence, TkNA can be used for causal inference via network analysis of any host and/or microbiota multi-omics data. This quick and easy-to-run protocol requires very basic familiarity with the Unix command-line environment.

Microbiota and adipocyte mitochondrial damage in type 2 diabetes are linked by Mmp12+ macrophages.

Microbiota contribute to the induction of type 2 diabetes by high-fat/high-sugar (HFHS) diet, but which organs/pathways are impacted by microbiota remain unknown. Using multiorgan network and transkingdom analyses, we found that microbiota-dependent impairment of OXPHOS/mitochondria in white adipose tissue (WAT) plays a primary role in regulating systemic glucose metabolism. The follow-up analysis established that Mmp12+ macrophages link microbiota-dependent inflammation and OXPHOS damage in WAT. Moreover, the molecular signature of Mmp12+ macrophages in WAT was associated with insulin resistance in obese patients. Next, we tested the functional effects of MMP12 and found that Mmp12 genetic deficiency or MMP12 inhibition improved glucose metabolism in conventional, but not in germ-free mice. MMP12 treatment induced insulin resistance in adipocytes. TLR2-ligands present in Oscillibacter valericigenes bacteria, which are expanded by HFHS, induce Mmp12 in WAT macrophages in a MYD88-ATF3-dependent manner. Thus, HFHS induces Mmp12+ macrophages and MMP12, representing a microbiota-dependent bridge between inflammation and mitochondrial damage in WAT and causing insulin resistance.

Intestinal microbiota signatures of clinical response and immune-related adverse events in melanoma patients treated with anti-PD-1.

Ample evidence indicates that the gut microbiome is a tumor-extrinsic factor associated with antitumor response to anti-programmed cell death protein-1 (PD-1) therapy, but inconsistencies exist between published microbial signatures associated with clinical outcomes. To resolve this, we evaluated a new melanoma cohort, along with four published datasets. Time-to-event analysis showed that baseline microbiota composition was optimally associated with clinical outcome at approximately 1 year after initiation of treatment. Meta-analysis and other bioinformatic analyses of the combined data show that bacteria associated with favorable response are confined within the Actinobacteria phylum and the Lachnospiraceae/Ruminococcaceae families of Firmicutes. Conversely, Gram-negative bacteria were associated with an inflammatory host intestinal gene signature, increased blood neutrophil-to-lymphocyte ratio, and unfavorable outcome. Two microbial signatures, enriched for Lachnospiraceae spp. and Streptococcaceae spp., were associated with favorable and unfavorable clinical response, respectively, and with distinct immune-related adverse effects. Despite between-cohort heterogeneity, optimized all-minus-one supervised learning algorithms trained on batch-corrected microbiome data consistently predicted outcomes to programmed cell death protein-1 therapy in all cohorts. Gut microbial communities (microbiotypes) with nonuniform geographical distribution were associated with favorable and unfavorable outcomes, contributing to discrepancies between cohorts. Our findings shed new light on the complex interaction between the gut microbiome and response to cancer immunotherapy, providing a roadmap for future studies.

Transkingdom interactions between Lactobacilli and hepatic mitochondria attenuate western diet-induced diabetes.

Western diet (WD) is one of the major culprits of metabolic disease including type 2 diabetes (T2D) with gut microbiota playing an important role in modulating effects of the diet. Herein, we use a data-driven approach (Transkingdom Network analysis) to model host-microbiome interactions under WD to infer which members of microbiota contribute to the altered host metabolism. Interrogation of this network pointed to taxa with potential beneficial or harmful effects on host's metabolism. We then validate the functional role of the predicted bacteria in regulating metabolism and show that they act via different host pathways. Our gene expression and electron microscopy studies show that two species from Lactobacillus genus act upon mitochondria in the liver leading to the improvement of lipid metabolism. Metabolomics analyses revealed that reduced glutathione may mediate these effects. Our study identifies potential probiotic strains for T2D and provides important insights into mechanisms of their action.

Fecal microbiota transplant overcomes resistance to anti-PD-1 therapy in melanoma patients.

Anti-programmed cell death protein 1 (PD-1) therapy provides long-term clinical benefits to patients with advanced melanoma. The composition of the gut microbiota correlates with anti-PD-1 efficacy in preclinical models and cancer patients. To investigate whether resistance to anti-PD-1 can be overcome by changing the gut microbiota, this clinical trial evaluated the safety and efficacy of responder-derived fecal microbiota transplantation (FMT) together with anti-PD-1 in patients with PD-1-refractory melanoma. This combination was well tolerated, provided clinical benefit in 6 of 15 patients, and induced rapid and durable microbiota perturbation. Responders exhibited increased abundance of taxa that were previously shown to be associated with response to anti-PD-1, increased CD8+ T cell activation, and decreased frequency of interleukin-8-expressing myeloid cells. Responders had distinct proteomic and metabolomic signatures, and transkingdom network analyses confirmed that the gut microbiome regulated these changes. Collectively, our findings show that FMT and anti-PD-1 changed the gut microbiome and reprogrammed the tumor microenvironment to overcome resistance to anti-PD-1 in a subset of PD-1 advanced melanoma.

Dietary fiber and probiotics influence the gut microbiome and melanoma immunotherapy response.

Gut bacteria modulate the response to immune checkpoint blockade (ICB) treatment in cancer, but the effect of diet and supplements on this interaction is not well studied. We assessed fecal microbiota profiles, dietary habits, and commercially available probiotic supplement use in melanoma patients and performed parallel preclinical studies. Higher dietary fiber was associated with significantly improved progression-free survival in 128 patients on ICB, with the most pronounced benefit observed in patients with sufficient dietary fiber intake and no probiotic use. Findings were recapitulated in preclinical models, which demonstrated impaired treatment response to anti­programmed cell death 1 (anti­PD-1)­based therapy in mice receiving a low-fiber diet or probiotics, with a lower frequency of interferon-γ­positive cytotoxic T cells in the tumor microenvironment. Together, these data have clinical implications for patients receiving ICB for cancer.

Improvements in Metabolic Syndrome by Xanthohumol Derivatives Are Linked to Altered Gut Microbiota and Bile Acid Metabolism.

SCOPE: Two hydrogenated xanthohumol (XN) derivatives, α,ß-dihydro-XN (DXN) and tetrahydro-XN (TXN), improved parameters of metabolic syndrome (MetS), a critical risk factor of cardiovascular disease (CVD) and type 2 diabetes, in a diet-induced obese murine model. It is hypothesized that improvements in obesity and MetS are linked to changes in composition of the gut microbiota, bile acid metabolism, intestinal barrier function, and inflammation. METHODS AND RESULTS: To test this hypothesis, 16S rRNA genes were sequenced and bile acids were measured in fecal samples from C57BL/6J mice fed a high-fat diet (HFD) or HFD containing XN, DXN or TXN. Expression of genes associated with epithelial barrier function, inflammation, and bile acid metabolism were measured in the colon, white adipose tissue (WAT), and liver, respectively. Administration of XN derivatives decreases intestinal microbiota diversity and abundance-specifically Bacteroidetes and Tenericutes-alters bile acid metabolism, and reduces inflammation. In WAT, TXN supplementation decreases pro-inflammatory gene expression by suppressing macrophage infiltration. Transkingdom network analysis connects changes in the microbiota to improvements in MetS in the host. CONCLUSION: Changes in the gut microbiota and bile acid metabolism may explain, in part, the improvements in obesity and MetS associated with administration of XN and its derivatives.

Gut-resident CX3CR1hi macrophages induce tertiary lymphoid structures and IgA response in situ.

Intestinal mononuclear phagocytes (MPs) are composed of heterogeneous dendritic cell (DC) and macrophage subsets necessary for the initiation of immune response and control of inflammation. Although MPs in the normal intestine have been extensively studied, the heterogeneity and function of inflammatory MPs remain poorly defined. We performed phenotypical, transcriptional, and functional analyses of inflammatory MPs in infectious Salmonella colitis and identified CX3CR1+ MPs as the most prevalent inflammatory cell type. CX3CR1+ MPs were further divided into three distinct populations, namely, Nos2 +CX3CR1lo, Ccr7 +CX3CR1int (lymph migratory), and Cxcl13 +CX3CR1hi (mucosa resident), all of which were transcriptionally aligned with macrophages and derived from monocytes. In follow-up experiments in vivo, intestinal CX3CR1+ macrophages were superior to conventional DC1 (cDC1) and cDC2 in inducing Salmonella-specific mucosal IgA. We next examined spatial organization of the immune response induced by CX3CR1+ macrophage subsets and identified mucosa-resident Cxcl13 +CX3CR1hi macrophages as the antigen-presenting cells responsible for recruitment and activation of CD4+ T and B cells to the sites of Salmonella invasion, followed by tertiary lymphoid structure formation and the local pathogen-specific IgA response. Using mice we developed with a floxed Ccr7 allele, we showed that this local IgA response developed independently of migration of the Ccr7 +CX3CR1int population to the mesenteric lymph nodes and contributed to the total mucosal IgA response to infection. The differential activity of intestinal macrophage subsets in promoting mucosal IgA responses should be considered in the development of vaccines to prevent Salmonella infection and in the design of anti-inflammatory therapies aimed at modulating macrophage function in inflammatory bowel disease.

Unprecedented bacterial community richness in soybean nodules vary with cultivar and water status.

BACKGROUND: Soybean (Glycine max) and other legumes are key crops grown around the world, providing protein and nutrients to a growing population, in a way that is more sustainable than most other cropping systems. Diazotrophs inhabiting root nodules provide soybean with nitrogen required for growth. Despite the knowledge of culturable Bradyrhizobium spp. and how they can differ across cultivars, less is known about the overall bacterial community (bacteriome) diversity within nodules, in situ. This variability could have large functional ramifications for the long-standing scientific dogma related to the plant-bacteriome interaction. Water availability also impacts soybean, in part, as a result of water-deficit sensitive nodule diazotrophs. There is a dearth of information on the effects of cultivar and water status on in situ rhizobia and non-rhizobia populations of nodule microbiomes. Therefore, soybean nodule microbiomes, using 16S rRNA and nifH genes, were sampled from nine cultivars treated with different field water regimes. It was hypothesized that the nodule bacteriome, composition, and function among rhizobia and non-rhizobia would differ in response to cultivar and soil water status. RESULTS: 16S rRNA and nifH showed dominance by Bradyrhizobiaceae, but a large diversity was observed across phylogenetic groups with < 1% and up to 45% relative abundance in cultivars. Other groups primarily included Pseudomonadaceae and Enterobacteriaceae. Thus, nodule bacteriomes were not only dominated by rhizobia, but also described by high variability and partly dependent on cultivar and water status. Consequently, the function of the nodule bacteriomes differed, especially due to cultivar. Amino acid profiling within nodules, for example, described functional changes due to both cultivar and water status. CONCLUSIONS: Overall, these results reveal previously undescribed richness and functional changes in Bradyrhizobiaceae and non-rhizobia within the soybean nodule microbiome. Though the exact role of these atypical bacteria and relative variations in Bradyrhizobium spp. is not clear, there is potential for exploitation of these novel findings of microbiome diversity and function. This diversity needs consideration as part of bacterial-inclusive breeding of soybean to improve traits, such as yield and seed quality, and environmental resilience.

Transkingdom Networks: A Systems Biology Approach to Identify Causal Members of Host-Microbiota Interactions.

Improvements in sequencing technologies and reduced experimental costs have resulted in a vast number of studies generating high-throughput data. Although the number of methods to analyze these "omics" data has also increased, computational complexity and lack of documentation hinder researchers from analyzing their high-throughput data to its true potential. In this chapter we detail our data-driven, transkingdom network (TransNet) analysis protocol to integrate and interrogate multi-omics data. This systems biology approach has allowed us to successfully identify important causal relationships between different taxonomic kingdoms (e.g., mammals and microbes) using diverse types of data.

COREMIC: a web-tool to search for a niche associated CORE MICrobiome.

Microbial diversity on earth is extraordinary, and soils alone harbor thousands of species per gram of soil. Understanding how this diversity is sorted and selected into habitat niches is a major focus of ecology and biotechnology, but remains only vaguely understood. A systems-biology approach was used to mine information from databases to show how it can be used to answer questions related to the core microbiome of habitat-microbe relationships. By making use of the burgeoning growth of information from databases, our tool "COREMIC" meets a great need in the search for understanding niche partitioning and habitat-function relationships. The work is unique, furthermore, because it provides a user-friendly statistically robust web-tool (http://coremic2.appspot.com or http://core-mic.com), developed using Google App Engine, to help in the process of database mining to identify the "core microbiome" associated with a given habitat. A case study is presented using data from 31 switchgrass rhizosphere community habitats across a diverse set of soil and sampling environments. The methodology utilizes an outgroup of 28 non-switchgrass (other grasses and forbs) to identify a core switchgrass microbiome. Even across a diverse set of soils (five environments), and conservative statistical criteria (presence in more than 90% samples and FDR q-val <0.05% for Fisher's exact test) a core set of bacteria associated with switchgrass was observed. These included, among others, closely related taxa from Lysobacter spp., Mesorhizobium spp, and Chitinophagaceae. These bacteria have been shown to have functions related to the production of bacterial and fungal antibiotics and plant growth promotion. COREMIC can be used as a hypothesis generating or confirmatory tool that shows great potential for identifying taxa that may be important to the functioning of a habitat (e.g. host plant). The case study, in conclusion, shows that COREMIC can identify key habitat-specific microbes across diverse samples, using currently available databases and a unique freely available software.

Transcriptomic Analysis of Hepatic Cells in Multicellular Organotypic Liver Models.

Liver homeostasis requires the presence of both parenchymal and non-parenchymal cells (NPCs). However, systems biology studies of the liver have primarily focused on hepatocytes. Using an organotypic three-dimensional (3D) hepatic culture, we report the first transcriptomic study of liver sinusoidal endothelial cells (LSECs) and Kupffer cells (KCs) cultured with hepatocytes. Through computational pathway and interaction network analyses, we demonstrate that hepatocytes, LSECs and KCs have distinct expression profiles and functional characteristics. Our results show that LSECs in the presence of KCs exhibit decreased expression of focal adhesion kinase (FAK) signaling, a pathway linked to LSEC dedifferentiation. We report the novel result that peroxisome proliferator-activated receptor alpha (PPARα) is transcribed in LSECs. The expression of downstream processes corroborates active PPARα signaling in LSECs. We uncover transcriptional evidence in LSECs for a feedback mechanism between PPARα and farnesoid X-activated receptor (FXR) that maintains bile acid homeostasis; previously, this feedback was known occur only in HepG2 cells. We demonstrate that KCs in 3D liver models display expression patterns consistent with an anti-inflammatory phenotype when compared to monocultures. These results highlight the distinct roles of LSECs and KCs in maintaining liver function and emphasize the need for additional mechanistic studies of NPCs in addition to hepatocytes in liver-mimetic microenvironments.

Antibiotic-Induced Alterations in Gut Microbiota Are Associated with Changes in Glucose Metabolism in Healthy Mice.

The gut microbiome plays an important role in health and disease. Antibiotics are known to alter gut microbiota, yet their effects on glucose tolerance in lean, normoglycemic mice have not been widely investigated. In this study, we aimed to explore mechanisms by which treatment of lean mice with antibiotics (ampicillin, metronidazole, neomycin, vancomycin, or their cocktail) influences the microbiome and glucose metabolism. Specifically, we sought to: (i) study the effects on body weight, fasting glucose, glucose tolerance, and fasting insulin, (ii) examine the changes in expression of key genes of the bile acid and glucose metabolic pathways in the liver and ileum, (iii) identify the shifts in the cecal microbiota, and (iv) infer interactions between gene expression, microbiome, and the metabolic parameters. Treatment with individual or a cocktail of antibiotics reduced fasting glucose but did not affect body weight. Glucose tolerance changed upon treatment with cocktail, ampicillin, or vancomycin as indicated by reduced area under the curve of the glucose tolerance test. Antibiotic treatment changed gene expression in the ileum and liver, and shifted the alpha and beta diversities of gut microbiota. Network analyses revealed associations between Akkermansia muciniphila with fasting glucose and liver farsenoid X receptor (Fxr) in the top ranked host-microbial interactions, suggesting possible mechanisms by which this bacterium can mediate systemic changes in glucose metabolism. We observed Bacteroides uniformis to be positively and negatively correlated with hepatic Fxr and Glucose 6-phosphatase, respectively. Overall, our transkingdom network approach is a useful hypothesis generating strategy that offers insights into mechanisms by which antibiotics can regulate glucose tolerance in non-obese healthy animals. Experimental validation of our predicted microbe-phenotype interactions can help identify mechanisms by which antibiotics affect host phenotypes and gut microbiota.

Honey Bee Gut Microbiome Is Altered by In-Hive Pesticide Exposures.

Honey bees (Apis mellifera) are the primary pollinators of major horticultural crops. Over the last few decades, a substantial decline in honey bees and their colonies have been reported. While a plethora of factors could contribute to the putative decline, pathogens, and pesticides are common concerns that draw attention. In addition to potential direct effects on honey bees, indirect pesticide effects could include alteration of essential gut microbial communities and symbionts that are important to honey bee health (e.g., immune system). The primary objective of this study was to determine the microbiome associated with honey bees exposed to commonly used in-hive pesticides: coumaphos, tau-fluvalinate, and chlorothalonil. Treatments were replicated at three independent locations near Blacksburg Virginia, and included a no-pesticide amended control at each location. The microbiome was characterized through pyrosequencing of V2-V3 regions of the bacterial 16S rRNA gene and fungal ITS region. Pesticide exposure significantly affected the structure of bacterial but not fungal communities. The bee bacteriome, similar to other studies, was dominated by sequences derived from Bacilli, Actinobacteria, α-, ß-, γ-proteobacteria. The fungal community sequences were dominated by Ascomycetes and Basidiomycetes. The Multi-response permutation procedures (MRPP) and subsequent Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) analysis indicated that chlorothalonil caused significant change to the structure and functional potential of the honey bee gut bacterial community relative to control. Putative genes for oxidative phosphorylation, for example, increased while sugar metabolism and peptidase potential declined in the microbiome of chlorothalonil exposed bees. The results of this field-based study suggest the potential for pesticide induced changes to the honey bee gut microbiome that warrant further investigation.

Plant Invasions Associated with Change in Root-Zone Microbial Community Structure and Diversity.

The importance of plant-microbe associations for the invasion of plant species have not been often tested under field conditions. The research sought to determine patterns of change in microbial communities associated with the establishment of invasive plants with different taxonomic and phenetic traits. Three independent locations in Virginia, USA were selected. One site was invaded by a grass (Microstegium vimineum), another by a shrub (Rhamnus davurica), and the third by a tree (Ailanthus altissima). The native vegetation from these sites was used as reference. 16S rRNA and ITS regions were sequenced to study root-zone bacterial and fungal communities, respectively, in invaded and non-invaded samples and analyzed using Quantitative Insights Into Microbial Ecology (QIIME). Though root-zone microbial community structure initially differed across locations, plant invasion shifted communities in similar ways. Indicator species analysis revealed that Operational Taxonomic Units (OTUs) closely related to Proteobacteria, Acidobacteria, Actinobacteria, and Ascomycota increased in abundance due to plant invasions. The Hyphomonadaceae family in the Rhodobacterales order and ammonia-oxidizing Nitrospirae phylum showed greater relative abundance in the invaded root-zone soils. Hyphomicrobiaceae, another bacterial family within the phyla Proteobacteria increased as a result of plant invasion, but the effect associated most strongly with root-zones of M. vimineum and R. davurica. Functional analysis using Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) showed bacteria responsible for nitrogen cycling in soil increased in relative abundance in association with plant invasion. In agreement with phylogenetic and functional analyses, greater turnover of ammonium and nitrate was associated with plant invasion. Overall, bacterial and fungal communities changed congruently across plant invaders, and support the hypothesis that nitrogen cycling bacteria and functions are important factors in plant invasions. Whether the changes in microbial communities are driven by direct plant microbial interactions or a result of plant-driven changes in soil properties remains to be determined.

Top-down network analysis to drive bottom-up modeling of physiological processes.

Top-down analyses in systems biology can automatically find correlations among genes and proteins in large-scale datasets. However, it is often difficult to design experiments from these results. In contrast, bottom-up approaches painstakingly craft detailed models that can be simulated computationally to suggest wet lab experiments. However, developing the models is a manual process that can take many years. These approaches have largely been developed independently. We present LINKER, an efficient and automated data-driven method that can analyze molecular interactomes to propose extensions to models that can be simulated. LINKER combines teleporting random walks and k-shortest path computations to discover connections from a source protein to a set of proteins collectively involved in a particular cellular process. We evaluate the efficacy of LINKER by applying it to a well-known dynamic model of the cell division cycle in Saccharomyces cerevisiae. Compared to other state-of-the-art methods, subnetworks computed by LINKER are heavily enriched in Gene Ontology (GO) terms relevant to the cell cycle. Finally, we highlight how networks computed by LINKER elucidate the role of a protein kinase (Cdc5) in the mitotic exit network of a dynamic model of the cell cycle.