Three stop codons in bacteria represent different translation termination signals, and their usage is expected to depend on their differences in translation termination efficiency, mutation bias, and relative abundance of release factors (RF1 decoding UAA and UAG, and RF2 decoding UAA and UGA). In 14 bacterial species (covering Proteobacteria, Firmicutes, Cyanobacteria, Actinobacteria and Spirochetes) with cellular RF1 and RF2 quantified, UAA is consistently over-represented in highly expressed genes (HEGs) relative to lowly expressed genes (LEGs), whereas UGA usage is the opposite even in species where RF2 is far more abundant than RF1. UGA usage relative to UAG increases significantly with PRF2 [=RF2/(RF1 + RF2)] as expected from adaptation between stop codons and their decoders. PRF2 is > 0.5 over a wide range of AT content (measured by PAT3 as the proportion of AT at third codon sites), but decreases rapidly toward zero at the high range of PAT3. This explains why bacterial lineages with high PAT3 often have UGA reassigned because of low RF2. There is no indication that UAG is a minor stop codon in bacteria as claimed in a recent publication. The claim is invalid because of the failure to apply the two key criteria in identifying a minor codon: (1) it is least preferred by HEGs (or most preferred by LEGs) and (2) it corresponds to the least abundant decoder. Our results suggest a more plausible explanation for why UAA usage increases, and UGA usage decreases, with PAT3, but UAG usage remains low over the entire PAT3 range.

Bacterial translation initiation is influenced by base pairing between the Shine-Dalgarno (SD) sequence in the 5′ UTR of mRNA and the anti-SD (aSD) sequence at the free 3′ end of the 16S rRNA (3′ TAIL) due to: 1) the SD/aSD sequence binding location and 2) SD/aSD binding affinity. In order to understand what makes an SD/aSD interaction optimal, we must define: 1) terminus of the 3′ TAIL and 2) extent of the core aSD sequence within the 3′ TAIL. Our approach to characterize these components in Escherichia coli and Bacillus subtilis involves 1) mapping the 3′ boundary of the mature 16S rRNA using high-throughput RNA sequencing (RNA-Seq), and 2) identifying the segment within the 3′ TAIL that is strongly preferred in SD/aSD pairing. Using RNA-Seq data, we resolve previous discrepancies in the reported 3′ TAIL in B. subtilis and recovered the established 3′ TAIL in E. coli. Furthermore, we extend previous studies to suggest that both highly and lowly expressed genes favor SD sequences with intermediate binding affinity, but this trend is exclusive to SD sequences that complement the core aSD sequences defined herein.

The degree to which codon usage can be explained by tRNA abundance in bacterial species is often inadequate, partly because differential tRNA abundance is often approximated by tRNA copy numbers. To better understand the coevolution between tRNA abundance and codon usage, we provide a better estimate of tRNA abundance by profiling tRNA mapped reads (tRNA tpm) using publicly available RNA Sequencing data. To emphasize the feasibility of our approach, we demonstrate that tRNA tpm is consistent with tRNA abundances derived from RNA fingerprinting experiments in Escherichia coli , Bacillus subtilis , and Salmonella enterica . Furthermore, we do not observe an appreciable reduction in tRNA sequencing efficiency due to post-transcriptional methylations in the seven bacteria studied. To determine optimal codons, we calculate codon usage in highly and lowly expressed genes determined by protein per transcript. We found that tRNA tpm is sensitive to identify more translationally optimal codons than gene copy number and early tRNA fingerprinting abundances. Additionally, tRNA tpm improves the predictive power of tRNA adaptation index over codon preference. Our results suggest that dependence of codon usage on tRNA availability is not always associated with species growth-rate. Conversely, tRNA availability is better optimized to codon usage in fast-growing than slow-growing species.

SARS-CoV-2 can transmit efficiently in humans, but it is less clear which other mammals are at risk of being infected. SARS-CoV-2 encodes a Spike (S) protein that binds to human ACE2 receptor to mediate cell entry. A species with a human-like ACE2 receptor could therefore be at risk of being infected by SARS-CoV-2. We compared between 132 mammalian ACE2 genes and between 17 coronavirus S proteins. We showed that while global similarities reflected by whole ACE2 gene alignments are poor predictors of high-risk mammals, local similarities at key S protein-binding sites highlight several high-risk mammals that share good ACE2 homology with human. Bats are likely reservoirs of SARS-CoV-2, but there are other high-risk mammals that share better ACE2 homologies with human. Both SARS-CoV-2 and SARS-CoV are closely related to bat coronavirus. Yet, among host-specific coronaviruses infecting high-risk mammals, key ACE2-binding sites on S proteins share highest similarities between SARS-CoV-2 and Pangolin-CoV and between SARS-CoV and Civet-CoV. These results suggest that direct coronavirus transmission from bat to human is unlikely, and that rapid adaptation of a bat SARS-like coronavirus in different high-risk intermediate hosts could have allowed it to acquire distinct high binding potential between S protein and human-like ACE2 receptors.

The gut microbiota plays a critical role in obesity and lipid metabolism disorder. Chokeberry (Aronia melanocarpa L.) are rich in polyphenols with various physiological and pharmacological activities. We determined serum physiological parameters and fecal microbial components by using related kits, liquid chromatography-mass spectrometry (LC-MS) and 16S rRNA gene sequencing every 10 days. Real-time PCR analysis was used to measure gene expression of bile acids (BAs) and lipid metabolism in liver and adipose tissues. We analyzed the effects of different Chokeberry polyphenol (CBPs) treatment time on obesity and lipid metabolism in high fat diet (HFD)-fed rats. The results indicated that CBPs treatment prevents obesity, liver steatosis and improves dyslipidemia in HFDfed rats. CBPs modulated the composition of the gut microbiota with the extended treatment time, reducing the Firmicutes/Bacteroidetes ratio (F/B ratio) and increasing the relative abundance of Bacteroides, Prevotella, Akkermansia and other bacterial species associated with anti-obesity properties. We found that CBPs treatment gradually decreased the total BAs pool and particularly reduced the relative content of cholic acid (CA), deoxycholic acid (DCA) and enhanced the relative content of chenodeoxycholic acid (CDCA). These changes were positively correlated Bacteroides, Prevotella and negatively correlated with Clostridium, Eubacterium, Ruminococcaceae. In liver and white adipose tissues, the gene expression of lipogenesis, lipolysis and BAs metabolism were regulated after CBPs treatment in HFD-fed rats, which was most likely mediated through FXR and TGR-5 signaling pathway to improve lipid metabolism. In addition, the mRNA expression of PPARγ, UCP1 and PGC-1α were upregulated markedly in interscapular brown adipose tissue (iBAT) after CBPs treatment. We confirmed that CBPs could reduce the body weight of HFD-fed rats by accelerating energy homeostasis and thermogenesis in iBAT. Finally, the fecal microbiota transplantation (FMT) experiment results demonstrated that FMT from CBPs-treated rats failed to reduce the weight of HFD-fed rats. However, FMT from CBPs-treated rats improved dyslipidemia and reshaped gut microbiota in HFD-fed rats. In conclusion, CBPs treatment improved obesity and complications by regulating gut microbiota in HFD-fed rats. The gut microbiota plays an important role in BAs metabolism after CBPs treatment, and BAs have therefore emerged as major effectors in microbe-host signaling events that influence host lipid metabolism, energy metabolism and thermogenesis.