Gut microbiota and the immune system interact to maintain tissue homeostasis, but whether this interaction is involved in the pathogenesis of systemic lupus erythematosus (SLE) is unclear. Here we report that oral antibiotics given during active disease removed harmful bacteria from the gut microbiota and attenuated SLE-like disease in lupus-prone mice. Using MRL/lpr mice, we showed that antibiotics given after disease onset ameliorated systemic autoimmunity and kidney histopathology. They decreased IL-17-producing cells and increased the level of circulating IL-10. In addition, antibiotics removed Lachnospiraceae and increased the relative abundance of Lactobacillus spp., two groups of bacteria previously shown to be associated with deteriorated or improved symptoms in MRL/lpr mice, respectively. Moreover, we showed that the attenuated disease phenotype could be recapitulated with a single antibiotic vancomycin, which reshaped the gut microbiota and changed microbial functional pathways in a time-dependent manner. Furthermore, vancomycin treatment increased the barrier function of the intestinal epithelium, thus preventing the translocation of lipopolysaccharide, a cell wall component of Gram-negative Proteobacteria and known inducer of lupus in mice, into the circulation. These results suggest that mixed antibiotics or a single antibiotic vancomycin ameliorate SLE-like disease in MRL/lpr mice by changing the composition of gut microbiota.

BackgroundNew sequencing technologies have lowered financial barriers to whole genome sequencing, but resulting assemblies are often fragmented and far from ‘finished’. Updating multi-scaffold drafts to chromosome-level status can be achieved through experimental mapping or re-sequencing efforts. Avoiding the costs associated with such approaches, comparative genomic analysis of gene order conservation (synteny) to predict scaffold neighbours (adjacencies) offers a potentially useful complementary method for improving draft assemblies.ResultsWe evaluated and employed 3 gene synteny-based methods applied to 21 Anopheles mosquito assemblies to produce consensus sets of scaffold adjacencies. For subsets of the assemblies, we integrated these with additional supporting data to confirm and complement the synteny-based adjacencies: 6 with physical mapping data that anchor scaffolds to chromosome locations, 13 with paired-end RNA sequencing (RNAseq) data, and 3 with new assemblies based on re-scaffolding or long-read data. Our combined analyses produced 20 new superscaffolded assemblies with improved contiguities: 7 for which assignments of non-anchored scaffolds to chromosome arms span more than 75% of the assemblies, and a further 7 with chromosome anchoring including an 88% anchored Anopheles arabiensis assembly and, respectively, 73% and 84% anchored assemblies with comprehensively updated cytogenetic photomaps for Anopheles funestus and Anopheles stephensi.ConclusionsExperimental data from probe mapping, RNAseq, or long-read technologies, where available, all contribute to successful upgrading of draft assemblies. Our evaluations show that gene synteny-based computational methods represent a valuable alternative or complementary approach. Our improved Anopheles reference assemblies highlight the utility of applying comparative genomics approaches to improve community genomic resources.

BackgroundSpecific genomic loci, termed Piwi-interacting RNA (piRNA) clusters, manufacture piRNAs that serve as guides for the inactivation of complementary transposable elements (TEs). The piRNA pathway has been accurately detailed in Drosophila melanogaster, while it remains poorly examined in other insects. This pathway is increasingly recognized as critical for germline development and reproduction. Understanding of the piRNA functions in mosquitoes could offer an opportunity for disease vector control by the reduction of their reproductive potential.ResultsTo analyze the similarities and differences in this pathway between Drosophila and mosquito, we performed an in-depth analysis of the genomic loci producing piRNAs and their targets in the African malaria vector Anopheles gambiae. We identified 187 piRNA clusters in the An. gambiae genome and 155 piRNA clusters in the D. melanogaster genome. We demonstrate that many more piRNA clusters in the mosquito compared with the fruit fly are uni-directionally transcribed and are located outside pericentromeric heterochromatin. About 11 % of the An. gambiae piRNA population map to gene transcripts. This is a noticeable increase compared with the ~6 % of the piRNA population mapped to genes in D. melanogaster. A subset of the piRNA-enriched genes in An. gambiae has functions related to reproduction and development. At least 24 and 65 % of the mapped piRNAs correspond to genomic TE sequences in An. gambiae and D. melanogaster, respectively. DNA transposons and non-LTR retrotransposons are more abundant in An. gambiae, while LTR retrotransposons are more abundant in D. melanogaster. Yet, piRNAs predominantly target LTR retrotransposons in both species, which may point to a distinct feature of these elements compared to the other classes of TEs concerning their silencing by the piRNA pathway.ConclusionsHere, we demonstrate that piRNA-producing loci have more ubiquitous distribution in the An. gambiae genome than in the genome of D. melanogaster. Also, protein-coding genes have an increased role in production of piRNAs in the germline of this mosquito. Genes involved in germline and embryonic development of An. gambiae generate a substantial portion of piRNAs, suggesting a role of the piRNA pathway in the epigenetic regulation of the reproductive processes in the African malaria vector.Electronic supplementary materialThe online version of this article (doi:10.1186/s13072-015-0041-5) contains supplementary material, which is available to authorized users.

Recent advances in genomic technologies have generated data on large-scale protein–DNA interactions and open chromatin regions for many eukaryotic species. How to identify condition-specific functions of transcription factors using these data has become a major challenge in genomic research. To solve this problem, we have developed a method called ConSReg, which provides a novel approach to integrate regulatory genomic data into predictive machine learning models of key regulatory genes. Using Arabidopsis as a model system, we tested our approach to identify regulatory genes in data sets from single cell gene expression and from abiotic stress treatments. Our results showed that ConSReg accurately predicted transcription factors that regulate differentially expressed genes with an average auROC of 0.84, which is 23.5–25% better than enrichment-based approaches. To further validate the performance of ConSReg, we analyzed an independent data set related to plant nitrogen responses. ConSReg provided better rankings of the correct transcription factors in 61.7% of cases, which is three times better than other plant tools. We applied ConSReg to Arabidopsis single cell RNA-seq data, successfully identifying candidate regulatory genes that control cell wall formation. Our methods provide a new approach to define candidate regulatory genes using integrated genomic data in plants.

We previously showed that all-trans-retinoic acid (tRA), an active metabolite of vitamin A, exacerbated pre-existing autoimmunity in lupus; however, its effects before the development of autoimmunity are unknown. Here, using a pristane-induced model, we show that tRA exerts differential effects when given at the initiation vs. continuation phase of lupus. Unlike tRA treatment during active disease, pre-pristane treatment with tRA aggravated glomerulonephritis through increasing renal expression of pro-fibrotic protein laminin β1, activating bone marrow conventional dendritic cells (cDCs), and upregulating the interaction of ICAM-1 and LFA-1 in the spleen, indicating an active process of leukocyte activation and trafficking. Transcriptomic analysis revealed that prior to lupus induction, tRA significantly upregulated the expression of genes associated with cDC activation and migration. Post-pristane tRA treatment, on the other hand, did not significantly alter the severity of glomerulonephritis; rather, it exerted immunosuppressive functions of decreasing circulatory and renal deposition of autoantibodies as well as suppressing the renal expression of proinflammatory cytokines and chemokines. Together, these findings suggest that tRA differentially modulate lupus-associated kidney inflammation depending on the time of administration. Interestingly, both pre-and post-pristane treatments with tRA reversed pristane-induced leaky gut and modulated the gut microbiota in a similar fashion, suggesting a gut microbiota-independent mechanism by which tRA affects the initiation vs. continuation phase of lupus.

Subclinical doses of LPS (SD-LPS) are known to cause low-grade inflammatory activation of monocytes, which could lead to inflammatory diseases including atherosclerosis and metabolic syndrome. Sodium 4-phenylbutyrate is a potential therapeutic compound which can reduce the inflammation caused by SD-LPS. To understand the gene regulatory networks of these processes, we have generated scRNA-seq data from mouse monocytes treated with these compounds and identified 11 novel cell clusters. We have developed a machine learning method to integrate scRNA-seq, ATAC-seq, and binding motifs to characterize gene regulatory networks underlying these cell clusters. Using guided regularized random forest and feature selection, our method achieved high performance and outperformed a traditional enrichment-based method in selecting candidate regulatory genes. Our method is particularly efficient in selecting a few candidate genes to explain observed expression pattern. In particular, among 531 candidate TFs, our method achieves an auROC of 0.961 with only 10 motifs. Finally, we found two novel subpopulations of monocyte cells in response to SD-LPS and we confirmed our analysis using independent flow cytometry experiments. Our results suggest that our new machine learning method can select candidate regulatory genes as potential targets for developing new therapeutics against low grade inflammation.

BackgroundCurrent Genome-Wide Association Studies (GWAS) are performed in a single trait framework without considering genetic correlations between important disease traits. Hence, the GWAS have limitations in discovering genetic risk factors affecting pleiotropic effects.ResultsThis work reports a novel data mining approach to discover patterns of multiple phenotypic associations over 52 anthropometric and biochemical traits in KARE and a new analytical scheme for GWAS of multivariate phenotypes defined by the discovered patterns. This methodology applied to the GWAS for multivariate phenotype highLDLhighTG derived from the predicted patterns of the phenotypic associations. The patterns of the phenotypic associations were informative to draw relations between plasma lipid levels with bone mineral density and a cluster of common traits (Obesity, hypertension, insulin resistance) related to Metabolic Syndrome (MS). A total of 15 SNPs in six genes (PAK7, C20orf103, NRIP1, BCL2, TRPM3, and NAV1) were identified for significant associations with highLDLhighTG. Noteworthy findings were that the significant associations included a mis-sense mutation (PAK7:R335P), a frame shift mutation (C20orf103) and SNPs in splicing sites (TRPM3).ConclusionsThe six genes corresponded to rat and mouse quantitative trait loci (QTLs) that had shown associations with the common traits such as the well characterized MS and even tumor susceptibility. Our findings suggest that the six genes may play important roles in the pleiotropic effects on lipid metabolism and the MS, which increase the risk of Type 2 Diabetes and cardiovascular disease. The use of the multivariate phenotypes can be advantageous in identifying genetic risk factors, accounting for the pleiotropic effects when the multivariate phenotypes have a common etiological pathway.

Scalable microfluidic devices containing reaction and loading chambers were developed to conduct single-cell transcriptomic studies.