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BackgroundFor genomic selection in populations with a small reference population, combining populations of the same breed or populations of related breeds is an effective way to increase the size of the reference population. However, genomic predictions based on single nucleotide polymorphism (SNP)-chip genotype data using combined populations with different genetic backgrounds or from different breeds have not shown a clear advantage over using within-population or within-breed predictions. The increasing availability of whole-genome sequencing (WGS) data provides new opportunities for combined population genomic prediction. Our objective was to investigate the accuracy of genomic prediction using imputation-based WGS data from combined populations in pigs. Using 80K SNP panel genotypes, WGS genotypes, or genotypes on WGS variants that were pruned based on linkage disequilibrium (LD), three methods [genomic best linear unbiased prediction (GBLUP), single-step (ss)GBLUP, and genomic feature (GF)BLUP] were implemented with different prior information to identify the best method to improve the accuracy of genomic prediction for combined populations in pigs.ResultsIn total, 2089 and 2043 individuals with production and reproduction phenotypes, respectively, from three Yorkshire populations with different genetic backgrounds were genotyped with the PorcineSNP80 panel. Imputation accuracy from 80K to WGS variants reached 92%. The results showed that use of the WGS data compared to the 80K SNP panel did not increase the accuracy of genomic prediction in a single population, but using WGS data with LD pruning and GFBLUP with prior information did yield higher accuracy than the 80K SNP panel. For the 80K SNP panel genotypes, using the combined population resulted in a slight improvement, no change, or even a slight decrease in accuracy in comparison with the single population for GBLUP and ssGBLUP, while accuracy increased by 1 to 2.4% when using WGS data. Notably, the GFBLUP method did not perform well for both the combined population and the single populations.ConclusionsThe use of WGS data was beneficial for combined population genomic prediction. Simply increasing the number of SNPs to the WGS level did not increase accuracy for a single population, while using pruned WGS data based on LD and GFBLUP with prior information could yield higher accuracy than the 80K SNP panel.

Nuage is an electron-dense cytoplasmic structure in germ cells that contains ribonucleoproteins and participates in piRNA biosynthesis. Despite the observation that clustered mitochondria are associated with a specific type of nuage called intermitochondrial cement (pi-body), the importance of mitochondrial functions in nuage formation and spermatogenesis is yet to be determined. We show that a germ cell-specific protein GASZ contains a functional mitochondrial targeting signal and is largely localized at mitochondria both endogenously in germ cells and in somatic cells when ectopically expressed. In addition, GASZ interacts with itself at the outer membrane of mitochondria and promotes mitofusion in a mitofusin/MFN-dependent manner. In mice, deletion of the mitochondrial targeting signal reveals that mitochondrial localization of GASZ is essential for nuage formation, mitochondrial clustering, transposon repression, and spermatogenesis. MFN1 deficiency also leads to defects in mitochondrial activity and male infertility. Our data thus reveal a requirement for GASZ and MFNmediated mitofusion during spermatogenesis.

Kinases use ATP to phosphorylate substrates; recent findings underscore the additional regulatory roles of ATP. Here, we propose a mechanism for allosteric regulation of Akt1 kinase phosphorylation by ATP. Our 4.7-μs molecular dynamics simulations of Akt1 and its mutants in the ATP/ADP bound/unbound states revealed that ATP occupancy of the ATP-binding site stabilizes the closed conformation, allosterically protecting pT308 by restraining phosphatase access and key interconnected residues on the ATP→pT308 allosteric pathway. Following ATP→ADP hydrolysis, pT308 is exposed and readily dephosphorylated. Site-directed mutagenesis validated these predictions and indicated that the mutations do not impair PDK1 and PP2A phosphatase recruitment. We further probed the function of residues around pT308 at the atomic level, and predicted and experimentally confirmed that Akt1(H194R/R273H) double mutant rescues pathology-related Akt1(R273H). Analysis of classical Akt homologs suggests that this mechanism can provide a general model of allosteric kinase regulation by ATP; as such, it offers a potential avenue for allosteric drug discovery.

Spermatogenesis is a highly coordinated process that requires tightly regulated gene expression programmed by transcription factors and epigenetic modifiers. In this study, we found that nuclear respiratory factor (NRF)-1, a key transcription factor for mitochondrial biogenesis, cooperated with DNA methylation to directly regulate the expression of multiple germ cell-specific genes, including In addition, conditional ablation of NRF1 in gonocytes dramatically down-regulated these germline genes, blocked germ cell proliferation, and subsequently led to male infertility in mice. Our data highlight a precise crosstalk between transcriptional regulation by NRF1 and epigenetic modulation during germ cell development and unequivocally demonstrate a novel role of NRF1 in spermatogenesis.-Wang, J., Tang, C., Wang, Q., Su, J., Ni, T., Yang, W., Wang, Y., Chen, W., Liu, X., Wang, S., Zhang, J., Song, H., Zhu, J., Wang, Y. NRF1 coordinates with DNA methylation to regulate spermatogenesis.

The allosteric regulation triggering the protein's functional activity via conformational changes is an intrinsic function of protein under many physiological and pathological conditions, including cancer. Identification of the biological effects of specific somatic variants on allosteric proteins and the phenotypes that they alter during tumor initiation and progression is a central challenge for cancer genomes in the post-genomic era. Here, we mapped more than 47,000 somatic missense mutations observed in approximately 7,000 tumor-normal matched samples across 33 cancer types into protein allosteric sites to prioritize the mutated allosteric proteins and we tested our prediction in cancer cell lines. We found that the deleterious mutations identified in cancer genomes were more significantly enriched at protein allosteric sites than tolerated mutations, suggesting a critical role for protein allosteric variants in cancer. Next, we developed a statistical approach, namely AlloDriver, and further identified 15 potential mutated allosteric proteins during pan-cancer and individual cancer-type analyses. More importantly, we experimentally confirmed that p.Pro360Ala on PDE10A played a potential oncogenic role in mediating tumorigenesis in non-small cell lung cancer (NSCLC). In summary, these findings shed light on the role of allosteric regulation during tumorigenesis and provide a useful tool for the timely development of targeted cancer therapies.

In genomic prediction, single-step method has been demonstrated to outperform multi-step methods. This study investigated the efficiency of genomic prediction for seven body measurement traits in Yorkshire population and simulated data using single-step method. For Yorkshire population, in total, 592 individuals were genotyped with Illumina PorcineSNP80 marker panel. We compared the prediction accuracy obtained from a traditional pedigree-based method (BLUP), a genomic BLUP (GBLUP) and a single-step genomic BLUP (ssGBLUP) through 20 replicates of 5-fold cross-validation (CV). In addition, we also compared the performance of two-trait ssGBLUP and single-trait ssGBLUP for the traits with different gradients of genetic correlation. Our results indicated the GBLUP method generally provided lower accuracies of prediction than BLUP and ssGBLUP methods, and the average standard deviation of unbiasedness was as large as 0.278. For single-step methods, the accuracies of ssGBLUP for seven body measurement traits ranged from 0.543 to 0.785, and the unbiasedness of ssGBLUP ranged from 0.834 to 1.026, respectively. ssGBLUP generally generated 1% on average higher prediction accuracy than traditional BLUP, the improvement of ssGBLUP and the performance of GBLUP was lower than expected mainly due to the small number of genotyped animals, it was further demonstrated by our simulation study. We simulated two traits with heritabilities 0.1, 0.3, and with high genetic correlation 0.7, our results also showed that the prediction accuracies were low for GBLUP compared with other three methods with different genotyped reference population sizes and the accuracies were improved with increasing the genotyped reference population size. However, the increase was small for ssGBLUP compared with BLUP when the genotyped reference population size was <500. Our results also demonstrated that the accuracies of genomic prediction can be further improved by implementing two-trait ssGBLUP model, the maximum gain on accuracy was 2 and 2.6% for trait of chest width compared to single-trait ssGBLUP and traditional BLUP, while the gain was decreased with the weakness of genetic correlation. Two-trait ssGBLUP even performed worse than single trait analysis in the scenario of low genetic correlation.

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