Deep whole-genome resequencing sheds light on the distribution and effect of amphioxus SNPs

Whole-genome resequencing reveals genetic diversity and selection signals in warm temperate and subtropical Sillago sinica populations

BACKGROUND

Genetic diversity and heterogeneous genomic signatures in marine fish populations may result from selection pressures driven by the strong effects of environmental change. Nearshore fishes are often exposed to complex environments and human activities, especially those with small ranges. However, studies on genetic diversity and population selection signals in these species have mostly been based on a relatively small number of genetic markers. As a newly recorded species of Sillaginidae, the population genetics and genomic selection signals of Sillago sinica are fragmented or even absent.

RESULTS

To address this theoretical gap, we performed whole-genome resequencing of 43 S. sinica individuals from Dongying (DY), Qingdao (QD) and Wenzhou (WZ) populations and obtained 4,878,771 high-quality SNPs. Population genetic analysis showed that the genetic diversity of S. sinica populations was low, but the genetic diversity of the WZ population was higher than that of the other two populations. Interestingly, the three populations were not strictly clustered within the group defined by their sampling location but showed an obvious geographic structure signal from the warm temperate to the subtropics. With further analysis, warm-temperate populations exhibited strong selection signals in genomic regions related to nervous system development, sensory function and immune function. However, subtropical populations showed more selective signalling for environmental tolerance and stress signal transduction.

CONCLUSIONS

Genome-wide SNPs provide high-quality data to support genetic studies and localization of selection signals in S. sinica populations. The reduction in genetic diversity may be related to the bottleneck effect. Considering that low genetic diversity leads to reduced environmental adaptability, conservation efforts and genetic diversity monitoring of this species should be increased in the future. Differences in genomic selection signals between warm temperate and subtropical populations may be related to human activities and changes in environmental complexity. This study deepened the understanding of population genetics and genomic selection signatures in nearshore fishes and provided a theoretical basis for exploring the potential mechanisms of genomic variation in marine fishes driven by environmental selection pressures.

Amphioxus adenosine-to-inosine tRNA-editing enzyme that can perform C-to-U and A-to-I deamination of DNA

Adenosine-to-inosine tRNA-editing enzyme has been identified for more than two decades, but the study on its DNA editing activity is rather scarce. We show that amphioxus (Branchiostoma japonicum) ADAT2 (BjADAT2) contains the active site 'HxE-PCxxC' and the key residues for target-base-binding, and amphioxus ADAT3 (BjADAT3) harbors both the N-terminal positively charged region and the C-terminal pseudo-catalytic domain important for recognition of substrates. The sequencing of BjADAT2-transformed Escherichia coli genome suggests that BjADAT2 has the potential to target E. coli DNA and can deaminate at TCG and GAA sites in the E. coli genome. Biochemical analyses further demonstrate that BjADAT2, in complex with BjADAT3, can perform A-to-I editing of tRNA and convert C-to-U and A-to-I deamination of DNA. We also show that BjADAT2 preferentially deaminates adenosines and cytidines in the loop of DNA hairpin structures of substrates, and BjADAT3 also affects the type of DNA substrate targeted by BjADAT2. Finally, we find that C89, N113, C148 and Y156 play critical roles in the DNA editing activity of BjADAT2. Collectively, our study indicates that BjADAT2/3 is the sole naturally occurring deaminase with both tRNA and DNA editing capacity identified so far in Metazoa.

AlphaFold2 and its applications in the fields of biology and medicine

AlphaFold2 (AF2) is an artificial intelligence (AI) system developed by DeepMind that can predict three-dimensional (3D) structures of proteins from amino acid sequences with atomic-level accuracy. Protein structure prediction is one of the most challenging problems in computational biology and chemistry, and has puzzled scientists for 50 years. The advent of AF2 presents an unprecedented progress in protein structure prediction and has attracted much attention. Subsequent release of structures of more than 200 million proteins predicted by AF2 further aroused great enthusiasm in the science community, especially in the fields of biology and medicine. AF2 is thought to have a significant impact on structural biology and research areas that need protein structure information, such as drug discovery, protein design, prediction of protein function, et al. Though the time is not long since AF2 was developed, there are already quite a few application studies of AF2 in the fields of biology and medicine, with many of them having preliminarily proved the potential of AF2. To better understand AF2 and promote its applications, we will in this article summarize the principle and system architecture of AF2 as well as the recipe of its success, and particularly focus on reviewing its applications in the fields of biology and medicine. Limitations of current AF2 prediction will also be discussed.

Utilizing an artificial intelligence system to build the digital structural proteome of reef-building corals

BACKGROUND

Reef-building corals play an important role in the marine ecosystem, and analyzing their proteomes from a structural perspective will exert positive effects on exploring their biology. Here we integrated mass spectrometry with newly published ColabFold to obtain digital structural proteomes of dominant reef-building corals.

RESULTS

Of the 8,382 homologous proteins in Acropora muricata, Montipora foliosa, and Pocillopora verrucosa identified, 8,166 received predicted structures after about 4,060 GPU hours of computation. The resulting dataset covers 83.6% of residues with a confident prediction, while 25.9% have very high confidence.

CONCLUSIONS

Our work provides insight-worthy predictions for coral research, confirms the reliability of ColabFold in practice, and is expected to be a reference case in the impending high-throughput era of structural proteomics.