Background: New 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. Results: We 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. Conclusions: Experimental 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.

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 removedLachnospiraceaeand increased the relative abundance ofLactobacillusspp., 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-negativeProteobacteriaand 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.

Increasing evidence indicates that many, if not all, small genes encoding proteins ≤100 aa are missing in annotations of bacterial genomes currently available. To uncover unannotated small genes in the model bacterium Salmonella enterica Typhimurium 14028s, we used the genomic technique ribosome profiling, which provides a snapshot of all mRNAs being translated (translatome) in a given growth condition. For comprehensive identification of unannotated small genes, we obtained Salmonella translatomes from four different growth conditions: LB, MOPS rich defined medium, and two infection-relevant conditions low Mg²⁺ (10 µM) and low pH (5.8). To facilitate the identification of small genes, ribosome profiling data were analyzed in combination with in silico predicted putative open reading frames and transcriptome profiles. As a result, we uncovered 130 unannotated ORFs. Of them, 98% were small ORFs putatively encoding peptides/proteins ≤100 aa, and some of them were only expressed in the infection-relevant low Mg²⁺ and/or low pH condition. We validated the expression of 25 of these ORFs by western blot, including the smallest, which encodes a peptide of 7 aa residues. Our results suggest that many sequenced bacterial genomes are underannotated with regard to small genes and their gene annotations need to be revised.

Specific 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. To 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. Here, 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.

Summary: An essential step in the analysis of single‐cell RNA sequencing data is to classify cells into specific cell types using marker genes. In this study, we have developed a machine learning pipeline called single‐cell predictive marker (SPmarker) to identify novel cell‐type marker genes in the Arabidopsis root. Unlike traditional approaches, our method uses interpretable machine learning models to select marker genes. We have demonstrated that our method can: assign cell types based on cells that were labelled using published methods; project cell types identified by trajectory analysis from one data set to other data sets; and assign cell types based on internal GFP markers. Using SPmarker, we have identified hundreds of new marker genes that were not identified before. As compared to known marker genes, the new marker genes have more orthologous genes identifiable in the corresponding rice single‐cell clusters. The new root hair marker genes also include 172 genes with orthologs expressed in root hair cells in five non‐Arabidopsis species, which expands the number of marker genes for this cell type by 35–154%. Our results represent a new approach to identifying cell‐type marker genes from scRNA‐seq data and pave the way for cross‐species mapping of scRNA‐seq data in plants.

Background: Current 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. Results: This 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). Conclusions: The 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.