Genomic analyses reveal natural selection on reproduction related genes between two closely related Populus (Salicaceae) species

Identifying the factors that cause reproductive isolation and their relative importance in species divergence is crucial to our understanding of speciation processes. In most species, natural selection is commonly considered to play a large role in driving speciation. Based on whole genome re-sequencing data from 27 Populus alba and 28 Populus adenopoda individuals, we explored the factors related to reproductive isolation of these two closely related species. The results showed that the two species diverged ~5–10 million years ago (Ma), when the Qinghai–Tibet Plateau reached a certain height and the inland climate of the Asian continent became arid. In highly differentiated genomic regions, the relative divergence (F_ST) and absolute divergence (d_xy) were significantly higher than the genomic background, θπ and shared polymorphisms decreased whereas fixed differences increased, which indicated that natural selection played a key role in the reproductive isolation of the two species. In addition, we found several genes that were related to reproduction that may be involved in explaining the reproductive isolation. Using phylogenetic trees resolved from haplotype data of Populus tomentosa and P. adenopoda, the maternal origin of P. tomentosa from P. adenopoda was likely to be located in Hubei and Chongqing Provinces.     

Genomic analyses reveal distinct genetic architectures and selective pressures in Chinese donkeys

Donkey (Equus asinus) is an important livestock animal in China because of its draft and medicinal value. After a long period of natural and artificial selection, the variety and phenotype of donkeys have become abundant. We clarified the genetic and demographic characteristics of Chinese domestic donkeys and the selection pressures by analyzing 78 whole genomes from 12 breeds. According to population structure, most Chinese domestic donkeys showed a dominant ancestral type. However, the Chinese donkeys still represented a significant geographical distribution trend. In the selective sweep, gene annotation, functional enrichment, and differential expression analyses between large and small donkey groups, we identified selective signals, including NCAPG and LCORL, which are related to rapid growth and large body size. Our findings elucidate the evolutionary history and formation of different donkey breeds and provide theoretical insights into the genetic mechanism underlying breed characteristics and molecular breeding programs of donkey clades.     

Genomic consequences of selection on self-incompatibility genes

Frequency-dependent selection at plant self-incompatibility systems is inherent and well understood theoretically. A self-incompatibility locus leads to a strong peak of diversity in the genome, to a unique distribution of diversity across the species and possibly to increased introgression between closely related species. We review recent empirical studies demonstrating these features and relate the empirical findings to theoretical predictions. We show how these features are being exploited in searches for other genes under multi-allelic balancing selection and for inference on recent breakdown of self-incompatibility.     

Genomic signatures reveal selection of characteristics within and between Meishan pig populations

The Chinese Meishan pig breed is well known for its high prolificacy. Moreover, this breed can be divided into three types based on their body size: big Meishan, middle Meishan (MMS) and small Meishan (SMS) pigs. Few studies have reported on the genetic signatures of Meishan pigs, particularly on a genome‐wide scale. Exploring for genetic signatures could be quite valuable for revealing the genetic architecture of phenotypic variation. Thus, we performed research in two parts based on the genome reducing and sequencing data of 143 Meishan pigs (74 MMS pigs, 69 SMS pigs). First, we detected the selection signatures among all Meishan pigs studied using the relative extended haplotype homozygosity test. Second, we detected the selection signatures between MMS and SMS pigs using the cross‐population extended haplotype homozygosity and F_ST methods. A total of 111 398 SNPs were identified from the sequenced genomes. In the population analysis, the most significant genes were associated with the mental development (RGMA), reproduction (HDAC4, FOXL2) and lipid metabolism (ACACB). From the cross‐population analysis, we detected genes related to body weight (SPDEF, PACSIN1) in both methods. We suggest that rs341373351, located within the PACSIN1 gene, might be the causal variant. This study may have achieved consistency between selection signatures and characteristics within and between Meishan pig populations. These findings can provide insight into investigating the molecular background of high prolificacy and body size in pig.     

Genomic analyses based on pulmonary adenocarcinoma in situ reveal early lung cancer signature

Background

Non-small cell lung cancer (NSCLC) represents more than about 80% of the lung cancer. The early stages of NSCLC can be treated with complete resection with a good prognosis. However, most cases are detected at late stage of the disease. The average survival rate of the patients with invasive lung cancer is only about 4%. Adenocarcinoma in situ (AIS) is an intermediate subtype of lung adenocarcinoma that exhibits early stage growth patterns but can develop into invasion.

Methods

In this study, we used RNA-seq data from normal, AIS, and invasive lung cancer tissues to identify a gene module that represents the distinguishing characteristics of AIS as AIS-specific genes. Two differential expression analysis algorithms were employed to identify the AIS-specific genes. Then, the subset of the best performed AIS-specific genes for the early lung cancer prediction were selected by random forest. Finally, the performances of the early lung cancer prediction were assessed using random forest, support vector machine (SVM) and artificial neural networks (ANNs) on four independent early lung cancer datasets including one tumor-educated blood platelets (TEPs) dataset.

Results

Based on the differential expression analysis, 107 AIS-specific genes that consisted of 93 protein-coding genes and 14 long non-coding RNAs (lncRNAs) were identified. The significant functions associated with these genes include angiogenesis and ECM-receptor interaction, which are highly related to cancer development and contribute to the smoking-free lung cancers. Moreover, 12 of the AIS-specific lncRNAs are involved in lung cancer progression by potentially regulating the ECM-receptor interaction pathway. The feature selection by random forest identified 20 of the AIS-specific genes as early stage lung cancer signatures using the dataset obtained from The Cancer Genome Atlas (TCGA) lung adenocarcinoma samples. Of the 20 signatures, two were lncRNAs, BLACAT1 and CTD-2527I21.15 which have been reported to be associated with bladder cancer, colorectal cancer and breast cancer. In blind classification for three independent tissue sample datasets, these signature genes consistently yielded about 98% accuracy for distinguishing early stage lung cancer from normal cases. However, the prediction accuracy for the blood platelets samples was only 64.35% (sensitivity 78.1%, specificity 50.59%, and AUROC 0.747).

Conclusions

The comparison of AIS with normal and invasive tumor revealed diseases-specific genes and offered new insights into the mechanism underlying AIS progression into an invasive tumor. These genes can also serve as the signatures for early diagnosis of lung cancer with high accuracy. The expression profile of gene signatures identified from tissue cancer samples yielded remarkable early cancer prediction for tissues samples, however, relatively lower accuracy for boold platelets samples.