Polydnaviruses (PDVs) play a critical role in altering host gene expression to induce immunosuppression. However, it remains largely unclear how PDV genes affect host genes. Here, the complete genome sequence of Microplitis bicoloratus bracovirus (MbBV), which is known to be an apoptosis inducer, was determined. The MbBV genome consisted of 17 putative double-stranded DNA circles and 179 fragments with a total size of 336,336 bp and contained 116 open reading frames (ORFs). Based on conserved domains, nine gene families were identified, of which the IκB-like viral ankyrin (vank) family included 28 members and was one of the largest families. Among the 116 ORFs, 13 MbBV genes were expressed in hemocytes undergoing MbBV-induced apoptosis and further analyzed. Three vank genes (vank86, vank92, vank101) were expressed in hemocytes collected from Spodoptera litura larvae parasitized by M. bicoloratus, in which host NF-κB/IκBs, including relish, dorsal, and cactus, were also persistently expressed. When Spli221 cells were infected with MbBV viral particles, mRNA levels of host and viral NF-κB/IκB genes were persistent and also varied in Spli221 cells undergoing virus-induced pre-apoptosis cell from 1 to 5 hours postinfection. Both were then expressed in a time-dependent expression in virus-induced apoptotic cells. These data show that viral IκB-like transcription does not inhibit host NF-κB/IκB expression, suggesting that transcription of these genes might be regulated by different mechanisms.

Thermoanaerobacterium aotearoense SCUT27, isolated from a hot spring in China, is a strictly anaerobic, thermophilic bacterium capable of degrading xylan and converting both pentose and hexose to ethanol with high yields. Here, we report the draft genome sequence of SCUT27, which reveals insights into the mechanisms of carbon source coutilization and xylan degradation in this thermophilic microorganism.

The COVID-19 pandemic caused by the SARS-CoV-2 virus has resulted in millions of deaths worldwide. The disease presents with various manifestations that can vary in severity and long-term outcomes. Previous efforts have contributed to the development of effective strategies for treatment and prevention by uncovering the mechanism of viral infection. We now know all the direct protein–protein interactions that occur during the lifecycle of SARS-CoV-2 infection, but it is critical to move beyond these known interactions to a comprehensive understanding of the “full interactome” of SARS-CoV-2 infection, which incorporates human microRNAs (miRNAs), additional human protein-coding genes, and exogenous microbes. Potentially, this will help in developing new drugs to treat COVID-19, differentiating the nuances of long COVID, and identifying histopathological signatures in SARS-CoV-2-infected organs. To construct the full interactome, we developed a statistical modeling approach called MLCrosstalk (multiple-layer crosstalk) based on latent Dirichlet allocation. MLCrosstalk integrates data from multiple sources, including microbes, human protein-coding genes, miRNAs, and human protein–protein interactions. It constructs "topics" that group SARS-CoV-2 with genes and microbes based on similar patterns of co-occurrence across patient samples. We use these topics to infer linkages between SARS-CoV-2 and protein-coding genes, miRNAs, and microbes. We then refine these initial linkages using network propagation to contextualize them within a larger framework of network and pathway structures. Using MLCrosstalk, we identified genes in the IL1-processing and VEGFA–VEGFR2 pathways that are linked to SARS-CoV-2. We also found that Rothia mucilaginosa and Prevotella melaninogenica are positively and negatively correlated with SARS-CoV-2 abundance, a finding corroborated by analysis of single-cell sequencing data.