The increasing interest of microbial communities as promising biocatalyst is leading an intense effort into the development of computational frameworks assisting the analysis and rational engineering of such complex ecosystems. Here, we critically review the recent computational and model-guided advances in the system-level engineering of microbiome, including both the rational bottom-up and the evolutionary top-down approaches. Furthermore, we highlight modeling and computational methods supporting both engineering paradigms. Finally, we discuss the advantages of combining both strategies into a hybrid top-down/bottom-up (middle-out) strategy to engineer synthetic microbial communities with improved performance and scope.

T cell factor 1 (Tcf1) is the first T cell–specific protein induced by Notch signaling in the thymus, leading to the activation of two major target genes, Gata3 and Bcl11b. Tcf1 deficiency results in partial arrests in T cell development, high apoptosis, and increased development of B and myeloid cells. Phenotypically, seemingly fully T cell–committed thymocytes with Tcf1 deficiency have promiscuous gene expression and an altered epigenetic profile and can dedifferentiate into more immature thymocytes and non-T cells. Restoring Bcl11b expression in Tcf1-deficient cells rescues T cell development but does not strongly suppress the development of non-T cells; in contrast, expressing Gata3 suppresses their development but does not rescue T cell development. Thus, T cell development is controlled by a minimal transcription factor network involving Notch signaling, Tcf1, and the subsequent division of labor between Bcl11b and Gata3, thereby ensuring a properly regulated T cell gene expression program.

Bacillus subtilis is an effective workhorse for the production of many industrial products. The high interest aroused by B. subtilis has guided a large metabolic modeling effort of this species. Genome-scale metabolic models (GEMs) are powerful tools for predicting the metabolic capabilities of a given organism. However, high-quality GEMs are required in order to provide accurate predictions. In this work, we construct a high-quality, mostly manually curated genome-scale model for B. subtilis (iBB1018). The model was validated by means of growth performance and carbon flux distribution and provided significantly more accurate predictions than previous models. iBB1018 was able to predict carbon source utilization with great accuracy while identifying up to 28 metabolites as potential novel carbon sources. The constructed model was further used as a tool for the construction of the panphenome of B. subtilis as a species, by means of multistrain genome-scale reconstruction. The panphenome space was defined in the context of 183 GEMs representative of 183 B. subtilis strains and the array of carbon sources sustaining growth. Our analysis highlights the large metabolic versatility of the species and the important role of the accessory metabolism as a driver of the panphenome, at a species level.

Summary: Coronavirus‐related disease 2019 (COVID‐19) became a pandemic in February 2020, and worldwide researchers try to tackle the disease with approved drugs of all kinds, or to develop novel compounds inhibiting viral spreading. Flavonoids, already investigated as antivirals in general, also might bear activities specific for the viral agent causing COVID‐19, SARS‐CoV‐2. Microbial biotechnology and especially synthetic biology may help to produce flavonoids, which are exclusive plant secondary metabolites, at a larger scale or indeed to find novel pharmaceutically active flavonoids. Here, we review the state of the art in (i) antiviral activity of flavonoids specific for coronaviruses and (ii) results derived from computational studies, mostly docking studies mainly inhibiting specific coronaviral proteins such as the 3CL (main) protease, the spike protein or the RNA‐dependent RNA polymerase. In the end, we strive towards a synthetic biology pipeline making the fast and tailored production of valuable antiviral flavonoids possible by applying the last concepts of division of labour through co‐cultivation/microbial community approaches to the DBTL (Design, Build, Test, Learn) principle.

Methyltransferase (MTase) enzymes catalyze the addition of a methyl group to a variety of biological substrates. MTase-like (METTL) proteins are Class I MTases whose enzymatic activities contribute to the epigenetic and epitranscriptomic regulation of multiple cellular processes. N⁶-adenosine methylation (m⁶A) is a common chemical modification of eukaryotic and viral RNA whose abundance is jointly regulated by MTases and METTLs, demethylases, and m⁶A binding proteins. m⁶A affects various cellular processes including RNA degradation, post-transcriptional processing, and antiviral immunity. Here, we used Nicotiana benthamiana and plum pox virus (PPV), an RNA virus of the Potyviridae family, to investigated the roles of MTases in plant–virus interaction. RNA sequencing analysis identified MTase transcripts that are differentially expressed during PPV infection; among these, accumulation of a METTL gene was significantly downregulated. Two N. benthamiana METTL transcripts (NbMETTL1 and NbMETTL2) were cloned and further characterized. Sequence and structural analyses of the two encoded proteins identified a conserved S-adenosyl methionine (SAM) binding domain, showing they are SAM-dependent MTases phylogenetically related to human METTL16 and Arabidopsis thaliana FIONA1. Overexpression of NbMETTL1 and NbMETTL2 caused a decrease of PPV accumulation. In sum, our results indicate that METTL homologues participate in plant antiviral responses.

Disruption of cell division cycle associated 7 (CDCA7) has been linked to aberrant DNA hypomethylation, but the impact of DNA methylation loss on transcription has not been investigated. Here, we show that CDCA7 is critical for maintaining global DNA methylation levels across multiple tissues in vivo. A pathogenic Cdca7 missense variant leads to the formation of large, aberrantly hypomethylated domains overlapping with the B genomic compartment but without affecting the deposition of H3K9 trimethylation (H3K9me3). CDCA7-associated aberrant DNA hypomethylation translated to localized, tissue-specific transcriptional dysregulation that affected large gene clusters. In the brain, we identify CDCA7 as a transcriptional repressor and epigenetic regulator of clustered protocadherin isoform choice. Increased protocadherin isoform expression frequency is accompanied by DNA methylation loss, gain of H3K4 trimethylation (H3K4me3), and increased binding of the transcriptional regulator CCCTC-binding factor (CTCF). Overall, our in vivo work identifies a key role for CDCA7 in safeguarding tissue-specific expression of gene clusters via the DNA methylation pathway.