Deciphering the principles and mechanisms by which gene activity orchestrates complex cellular arrangements in multicellular organisms has far-reaching implications for research in the life sciences. Recent technological advancements in next-generation sequencing-based and imaging based approaches have established the potential of spatial transcriptomics to measure expression levels of all or most genes systematically throughout tissue space, and have been adopted to generate biological insight in neuroscience, development, plant biology, and a range of diseases including cancer. Similar to datasets made possible by genomic sequencing and population health surveys, the large-scale atlases generated by this technology lend themselves to exploratory data analysis for hypothesis generation. Here, we review spatial transcriptomic technologies and describe the repertoire of operations available for paths of analysis of the resulting data. Spatial transcriptomics can also be deployed for hypothesis testing using experimental designs comparing timepoints or conditions -including genetic or environmental perturbations. Finally, spatial transcriptomic data is naturally amenable to integration with other data modalities providing an expandable framework for insight into tissue organization.Many of the notable discoveries in the life sciences followed from the recognition that cellular organization within tissues is intimately linked to biological function. In developmental biology, central topics such as symmetry-breaking between daughter cells and cell fate decisions are based on spatial relationships between cells 1 . In clinical settings, histopathology is often used as a conclusive diagnostic, precisely because diseases are characterized by abnormal spatial organization within tissues 2 . Infectious and inflammatory processes can drastically change the cellular organization of tissues 3 . These discoveries were supported by methods in molecular biology -including in situ hybridization 4 (ISH) and immunohistochemistry 5 -that provided the ability to visualize biological processes more directly by mapping DNA, RNA and protein within tissues. However, these methods limit analysis to at most a handful of genes or proteins at a time.

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MicroRNAs (miRNAs) are a class of small (∼22 nucleotides) non-coding RNAs that post-transcriptionally regulate gene expression by interacting with target mRNAs. A majority of miRNAs is located within intronic or exonic regions of protein-coding genes (host genes), and increasing evidence suggests a functional relationship between these miRNAs and their host genes. Here, we introduce miRIAD, a web-service to facilitate the analysis of genomic and structural features of intragenic miRNAs and their host genes for five species (human, rhesus monkey, mouse, chicken and opossum). miRIAD contains the genomic classification of all miRNAs (inter- and intragenic), as well as classification of all protein-coding genes into host or non-host genes (depending on whether they contain an intragenic miRNA or not). We collected and processed public data from several sources to provide a clear visualization of relevant knowledge related to intragenic miRNAs, such as host gene function, genomic context, names of and references to intragenic miRNAs, miRNA binding sites, clusters of intragenic miRNAs, miRNA and host gene expression across different tissues and expression correlation for intragenic miRNAs and their host genes. Protein–protein interaction data are also presented for functional network analysis of host genes. In summary, miRIAD was designed to help the research community to explore, in a user-friendly environment, intragenic miRNAs, their host genes and functional annotations with minimal effort, facilitating hypothesis generation and in-silico validations.Database URL: http://www.miriad-database.org

Increasing evidence has shown that recent miRNAs tend to emerge within coding genes. Here we conjecture that human miRNA evolution is tightly influenced by the genomic context, especially by host genes. Our findings show a preferential emergence of intragenic miRNAs within old genes. We found that miRNAs within old host genes are significantly more broadly expressed than those within young ones. Young miRNAs within old genes are more broadly expressed than their intergenic counterparts, suggesting that young miRNAs have an initial advantage by residing in old genes, and benefit from their hosts' expression control and from the exposure to diverse cellular contexts and target genes. Our results demonstrate that host genes may provide stronger expression constraints to intragenic miRNAs in the long run. We also report associated functional implications, highlighting the genomic context and host genes as driving factors for the expression and evolution of human miRNAs.

Recent studies have revealed the involvement of microRNAs (miRNAs) in the control of cardiac hypertrophy and myocardial function. In addition, several reports have demonstrated that high fat (HF) diet induces cardiac hypertrophy and remodeling. In the current study, we investigated the effect of diets containing different percentages of fat on the cardiac miRNA expression signature. To address this question, male C57Bl/6 mice were fed with a low fat (LF) diet or two HF diets, containing 45 kcal% fat (HF45%) and 60 kcal% fat (HF60%) for 10 and 20 weeks. HF60% diet promoted an increase on body weight, fasting glycemia, insulin, leptin, total cholesterol, triglycerides, and induced glucose intolerance. HF feeding promoted cardiac remodeling, as evidenced by increased cardiomyocyte transverse diameter and interstitial fibrosis. RNA sequencing analysis demonstrated that HF feeding induced distinct miRNA expression patterns in the heart. HF45% diet for 10 and 20 weeks changed the abundance of 64 and 26 miRNAs in the heart, respectively. On the other hand, HF60% diet for 10 and 20 weeks altered the abundance of 27 and 88 miRNAs in the heart, respectively. Bioinformatics analysis indicated that insulin signaling pathway was overrepresented in response to HF diet. An inverse correlation was observed between cardiac levels of GLUT4 and miRNA-29c. Similarly, we found an inverse correlation between expression of GSK3β and the expression of miRNA-21a-3p, miRNA-29c-3p, miRNA-144-3p, and miRNA-195a-3p. In addition, miRNA-1 overexpression prevented cardiomyocyte hypertrophy. Taken together, our results revealed differentially expressed miRNA signatures in the heart in response to different HF diets. J. Cell. Physiol. 231: 1771-1783, 2016. © 2015 Wiley Periodicals, Inc.

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Supertypes are groups of human leukocyte antigen (HLA) alleles which bind overlapping sets of peptides due to sharing specific residues at the anchor positions—the B and F pockets—of the peptide-binding region (PBR). HLA alleles within the same supertype are expected to be functionally similar, while those from different supertypes are expected to be functionally distinct, presenting different sets of peptides. In this study, we applied the supertype classification to the HLA-A and HLA-B data of 55 worldwide populations in order to investigate the effect of natural selection on supertype rather than allelic variation at these loci. We compared the nucleotide diversity of the B and F pockets with that of the other PBR regions through a resampling procedure and compared the patterns of within-population heterozygosity (He) and between-population differentiation (GST) observed when using the supertype definition to those estimated when using randomized groups of alleles. At HLA-A, low levels of variation are observed at B and F pockets and randomized He and GST do not differ from the observed data. By contrast, HLA-B concentrates most of the differences between supertypes, the B pocket showing a particularly high level of variation. Moreover, at HLA-B, the reassignment of alleles into random groups does not reproduce the patterns of population differentiation observed with supertypes. We thus conclude that differently from HLA-A, for which supertype and allelic variation show similar patterns of nucleotide diversity within and between populations, HLA-B has likely evolved through specific adaptations of its B pocket to local pathogens.Electronic supplementary materialThe online version of this article (doi:10.1007/s00251-015-0875-9) contains supplementary material, which is available to authorized users.

BackgroundPhysical protein-protein interaction (PPI) is a critical phenomenon for the function of most proteins in living organisms and a significant fraction of PPIs are the result of domain-domain interactions. Exon shuffling, intron-mediated recombination of exons from existing genes, is known to have been a major mechanism of domain shuffling in metazoans. Thus, we hypothesized that exon shuffling could have a significant influence in shaping the topology of PPI networks.ResultsWe tested our hypothesis by compiling exon shuffling and PPI data from six eukaryotic species: Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, Cryptococcus neoformans and Arabidopsis thaliana. For all four metazoan species, genes enriched in exon shuffling events presented on average higher vertex degree (number of interacting partners) in PPI networks. Furthermore, we verified that a set of protein domains that are simultaneously promiscuous (known to interact to multiple types of other domains), self-interacting (able to interact with another copy of themselves) and abundant in the genomes presents a stronger signal for exon shuffling.ConclusionsExon shuffling appears to have been a recurrent mechanism for the emergence of new PPIs along metazoan evolution. In metazoan genomes, exon shuffling also promoted the expansion of some protein domains. We speculate that their promiscuous and self-interacting properties may have been decisive for that expansion.