diastatochromogenes 1628 led to onefold increase in TM production (312.9 mg/l vs. 152.1 mg/l) as well as the transcription of all toy genes. The toy gene cluster was engineered in which the native promoter of toyA gene, toyM gene, toyBD operon, and toyEI operon was, respectively, replaced by permE∗ or SPL57. To further improve TM production, the engineered toy gene cluster was, respectively, introduced and overexpressed in S. diastatochromogenes 1628 to generate recombinant strains S. diastatochromogenes 1628-EC and 1628-SC.  https://www.selleckchem.com/products/Sodium-butyrate.html  After 84 h, S. diastatochromogenes 1628-EC and 1628-SC produced 456.5 mg/l and 638.9 mg/l TM, respectively, which is an increase of 2- and 3.2-fold compared with the wild-type strain.Due to their widespread use in industrial applications in recent decades, Polychlorobiphenyls (PCBs) and heavy metals (HMs) are the most common soil contaminants worldwide, posing a risk for both ecosystems and human health. In this study, a poplar-assisted bioremediation strategy has been applied for more than 4 years to a historically contaminated area (PCBs and HMs) in Southern Italy using the Monviso poplar clone. This clone was effective in promoting a decrease in all contaminants and an increase in soil quality in terms of organic carbon and microbial abundance. Moreover, a significant shift in the structure and predicted function of the belowground microbial community was also observed when analyzing both DNA and cDNA sequencing data. In fact, an increase in bacterial genera belonging to Proteobacteria able to degrade PCBs and resist HMs was observed. Moreover, the functional profiling of the microbial community predicted by PICRUSt2 made it possible to identify several genes associated with PCB transformation (e.g., bphAa, bphAb, bphB, bphC), response to HM oxidative stress (e.g., catalase, superoxide reductase, peroxidase) and HM uptake and expulsion (e.g., ABC transporters). This work demonstrated the effectiveness of the poplar clone Monviso in stimulating the natural belowground microbial community to remove contaminants and improve the overall soil quality. It is a practical example of a nature based solution involving synergic interactions between plants and the belowground microbial community.Bacterial biofilms have an impact in medical and industrial environments because they often confer protection to bacteria against harmful agents, and constitute a source from which microorganisms can disperse. Conjugative plasmids can enhance bacterial ability to form biofilms because conjugative pili act as adhesion factors. However, plasmids may interact with each other, either facilitating or inhibiting plasmid transfer. Accordingly, we asked whether effects on plasmid transfer also impacts biofilm formation. We measured biofilm formation of Escherichia coli cells harboring two plasmid types, or when the two plasmids were present in the same population but carried in different cells. Using eleven natural isolated conjugative plasmids, we confirmed that some indeed promote biofilm formation and, importantly, that this ability is correlated with conjugative efficiency. Further we studied the effect of plasmid pairs on biofilm formation. We observed increased biofilm formation in approximately half of the combinations when both plasmids inhabited the same cell or when the plasmids were carried in different cells. Moreover, in approximately half of the combinations, independent of the co-inhabitation conditions, one of the plasmids alone determined the extent of biofilm formation - thus having a dominant effect over the other plasmid. The molecular mechanisms responsible for these interactions were not evaluated here and future research is required to elucidate them.Duck Tembusu virus (DTMUV) infection has caused great economic losses to the poultry industry in China, since its first discovery in 2010. Understanding of host anti-DTMUV responses, especially the innate immunity against DTMUV infection, would be essential for the prevention and control of this viral disease. In this study, transcriptomic analysis of duck embryonic fibroblasts (DEFs) infected with DTMUV reveals that several innate immunity-related pathways, including Toll-like, NOD-like, and retinoic acid-inducible gene I (RIG-I)-like receptor signaling pathways, are activated. Further verification by RT-qPCR confirmed that RIG-I, MAD5, TLR3, TLR7, IFN-α, IFN-β, MX, PKR, MHCI, MHCII, IL-1β, IL-6, (IFN-regulatory factor 1) IRF1, VIPERIN, IFIT5, and CMPK2 were up-regulated in cells infected with DTMUV. Through overexpression and knockdown/out of gene expression, we demonstrated that both VIPERIN and IRF1 played an important role in the regulation of DTMUV replication. Overexpression of either one significantly reduced viral replication as characterized by reduced viral RNA copy numbers and the envelope protein expression. Consistently, down-regulation of either one resulted in the enhanced replication of DTMUV in the knockdown/out cells. We further proved that IRF1 can up-regulate VIPERIN gene expression during DTMUV infection, through induction of type 1 IFNs as well as directly binding to and activation of the VIPERIN promoter. This study provides a genome-wide differential gene expression profile in cells infected with DTMUV and reveals an important function for IRF1 as well as several other interferon-stimulated genes in restricting DTMUV replication.Capturing group-specific sequences between two groups of genomic/metagenomic sequences is critical for the follow-up identifications of singular nucleotide variants (SNVs), gene families, microbial species or other elements associated with each group. A sequence that is present, or rich, in one group, but absent, or scarce, in another group is considered a "group-specific" sequence in our study. We developed a user-friendly tool, KmerGO, to identify group-specific sequences between two groups of genomic/metagenomic long sequences or high-throughput sequencing datasets. Compared with other tools, KmerGO captures group-specific k-mers (k up to 40 bps) with much lower requirements for computing resources in much shorter running time. For a 1.05 TB dataset (.fasta), it takes KmerGO about 21.5 h (including k-mer counting) to return assembled group-specific sequences on a regular stand-alone workstation with no more than 1 GB memory. Furthermore, KmerGO can also be applied to capture trait-associated sequences for continuous-trait.