The analysis of microbiota composition in humans, animals, and built environments is important because of emerging roles and applications in a broad range of disease and ecological phenotypes. Next Generation Sequencing is the current method of choice to characterize microbial community composition. The taxonomic profile of a microbial community can be obtained either by shotgun analysis of random DNA fragments or through 16S ribosomal RNA gene (rDNA) amplicon sequencing. It has been previously shown that the 16S rDNA amplicon sequencing approach yields quantitatively and qualitatively different results compared to shotgun metagenomics when the two techniques are used to assess microbial community composition on the same samples. However, most of such comparisons were either based on the recovery of 16S rDNA sequences in the shotgun metagenomics data or limited to a single microbiome or synthetic samples. Direct comparison of shotgun metagenomics and 16S rDNA amplicon sequencing on the same samples was performed only once in the recent literature, suggesting that the two methods yield comparable results. Here, we set out to compare the outcome of these two alternative approaches to the microbiome characterization in human gut microbiomes from stool samples. To this end, we processed six different samples with both techniques. We report here that shotgun next generation sequencing metagenomics allows much deeper characterization of the microbiome complexity, allowing identification of a larger number of species for each sample, compared to 16S rDNA amplicon sequencing. Further comparative studies in independent samples are called for.

We report on a hitherto poorly characterized class of genes that are expressed in all tissues, except in one. Often, these genes have been classified as housekeeping genes, based on their nearly ubiquitous expression. However, the specific repression in one tissue defines a special class of “disallowed genes.” In this paper, we used the intersection-union test to screen for such genes in a multi-tissue panel of genome-wide mRNA expression data. We propose that disallowed genes need to be repressed in the specific target tissue to ensure correct tissue function. We provide mechanistic data of repression with two metabolic examples, exercise-induced inappropriate insulin release and interference with ketogenesis in liver. Developmentally, this repression is established during tissue maturation in the early postnatal period involving epigenetic changes in histone methylation. In addition, tissue-specific expression of microRNAs can further diminish these repressed mRNAs. Together, we provide a systematic analysis of tissue-specific repression of housekeeping genes, a phenomenon that has not been studied so far on a genome-wide basis and, when perturbed, can lead to human disease.

Metagenomics is not only one of the newest omics system science technologies but also one that has arguably the broadest set of applications and impacts globally. Metagenomics has found vast utility not only in environmental sciences, ecology, and public health but also in clinical medicine and looking into the future, in planetary health. In line with the One Health concept, metagenomics solicits collaboration between molecular biologists, geneticists, microbiologists, clinicians, computational biologists, plant biologists, veterinarians, and other health care professionals. Almost every ecological niche of our planet hosts an extremely diverse community of organisms that are still poorly characterized. Detailed characterization of the features of such communities is instrumental to our comprehension of ecological, biological, and clinical complexity. This expert review article evaluates how metagenomics is improving our knowledge of microbiota composition from environmental to human samples. Furthermore, we offer an analysis of the common technical and methodological challenges and potential pitfalls arising from metagenomics approaches, such as metagenomics study design, data processing, and interpretation. All in all, at this critical juncture of further growth of the metagenomics field, it is time to critically reflect on the lessons learned and the future prospects of next-generation metagenomics science, technology, and conceivable applications, particularly from the standpoint of a metagenomics methodology perspective.