Viruses have long been considered potential triggers of autoimmune diseases. Here we defined the intestinal virome from birth to the development of autoimmunity in children at risk for type 1 diabetes (T1D). A total of 220 virus-enriched preparations from serially collected fecal samples from 11 children (cases) who developed serum autoantibodies associated with T1D (of whom five developed clinical T1D) were compared with samples from controls. Intestinal viromes of case subjects were less diverse than those of controls. Among eukaryotic viruses, we identified significant enrichment of Circoviridae-related sequences in samples from controls in comparison with cases. Enterovirus, kobuvirus, parechovirus, parvovirus, and rotavirus sequences were frequently detected but were not associated with autoimmunity. For bacteriophages, we found higher Shannon diversity and richness in controls compared with cases and observed that changes in the intestinal virome over time differed between cases and controls. Using Random Forests analysis, we identified disease-associated viral bacteriophage contigs after subtraction of age-associated contigs. These disease-associated contigs were statistically linked to specific components of the bacterial microbiome. Thus, changes in the intestinal virome preceded autoimmunity in this cohort. Specific components of the virome were both directly and inversely associated with the development of human autoimmune disease. Significance Type 1 diabetes (T1D) is a major autoimmune disease with increasing incidence in recent years. In this study, we found that the intestinal viromes of cases were less diverse than those of controls. We identified eukaryotic viruses and bacteriophage contigs that are associated with the presence or absence of autoimmunity. These viruses provide targets for future mechanistic studies to differentiate causal and incidental associations between the virome and protection against the development of T1D.

Recent studies of cellular networks have revealed modular organizations of genes and proteins. For example, in interactome networks, a module refers to a group of interacting proteins that form molecular complexes and/or biochemical pathways and together mediate a biological process. However, it is still poorly understood how biological information is transmitted between different modules. We have developed information flow analysis, a new computational approach that identifies proteins central to the transmission of biological information throughout the network. In the information flow analysis, we represent an interactome network as an electrical circuit, where interactions are modeled as resistors and proteins as interconnecting junctions. Construing the propagation of biological signals as flow of electrical current, our method calculates an information flow score for every protein. Unlike previous metrics of network centrality such as degree or betweenness that only consider topological features, our approach incorporates confidence scores of protein–protein interactions and automatically considers all possible paths in a network when evaluating the importance of each protein. We apply our method to the interactome networks of Saccharomyces cerevisiae and Caenorhabditis elegans. We find that the likelihood of observing lethality and pleiotropy when a protein is eliminated is positively correlated with the protein's information flow score. Even among proteins of low degree or low betweenness, high information scores serve as a strong predictor of loss-of-function lethality or pleiotropy. The correlation between information flow scores and phenotypes supports our hypothesis that the proteins of high information flow reside in central positions in interactome networks. We also show that the ranks of information flow scores are more consistent than that of betweenness when a large amount of noisy data is added to an interactome. Finally, we combine gene expression data with interaction data in C. elegans and construct an interactome network for muscle-specific genes. We find that genes that rank high in terms of information flow in the muscle interactome network but not in the entire network tend to play important roles in muscle function. This framework for studying tissue-specific networks by the information flow model can be applied to other tissues and other organisms as well. Protein–protein interactions mediate numerous biological processes. In the last decade, there have been efforts to comprehensively map protein–protein interactions occurring in an organism. The interaction data generated from these high-throughput projects can be represented as interconnected networks. It has been found that knockouts of proteins residing in topologically central positions in the networks more likely result in lethality of the organism than knockouts of peripheral proteins. However, it is difficult to accurately define topologically central proteins because high-throughput data is error-prone and some interactions are not as reliable as others. In addition, the architecture of interaction networks varies in different tissues for multi-cellular organisms. To this end, we present a novel computational approach to identify central proteins while considering the confidence of data and gene expression in tissues. Moreover, our approach takes into account multiple alternative paths in interaction networks. We apply our method to yeast and nematode interaction networks. We find that the likelihood of observing lethality and pleiotropy when a given protein is eliminated correlates better with our centrality score for that protein than with its scores based on traditional centrality metrics. Finally, we set up a framework to identify central proteins in tissue-specific interaction networks.

Sensory neurons with cell bodies in dorsal root ganglia (DRG) represent a useful model to study axon regeneration. Whereas regeneration and functional recovery occurs after peripheral nerve injury, spinal cord injury or dorsal root injury is not followed by regenerative outcomes. Regeneration of sensory axons in peripheral nerves is not entirely cell autonomous. Whether the DRG microenvironment influences the different regenerative capacities after injury to peripheral or central axons remains largely unknown. To answer this question, we performed a single-cell transcriptional profiling of mouse DRG in response to peripheral (sciatic nerve crush) and central axon injuries (dorsal root crush and spinal cord injury). Each cell type responded differently to the three types of injuries. All injuries increased the proportion of a cell type that shares features of both immune cells and glial cells. A distinct subset of satellite glial cells (SGC) appeared specifically in response to peripheral nerve injury. Activation of the PPARα signaling pathway in SGC, which promotes axon regeneration after peripheral nerve injury, failed to occur after central axon injuries. Treatment with the FDA-approved PPARα agonist fenofibrate increased axon regeneration after dorsal root injury. This study provides a map of the distinct DRG microenvironment responses to peripheral and central injuries at the single-cell level and highlights that manipulating non-neuronal cells could lead to avenues to promote functional recovery after CNS injuries or disease.