ALBERT

Description: Potassium (K) is one of the most important mineral nutrients for wheat. In this study, the effects of low K (LK) treatments and the quantitative trait loci (QTLs) for K, calcium (Ca), and magnesium (Mg) use efficiency traits, both at the seedling and maturity stages of wheat, were investigated. The set of “Tainong 18 × Linmai 6” recombinant inbred lines (RILs) were used to identify the QTLs under different K treatments using hydroponic culture and field trials. The majority of K concentrations and content-related traits at seedling and maturity stages decreased with reduced K supply, but the K use efficiency-related traits increased. In contrast, with reduced K supply, the contents of Ca and Mg increased, while the Ca and Mg use efficiency decreased. A total of 217 QTLs for seedling traits and 89 QTLs for adult traits were detected. Four relatively high-frequency QTLs (RHF-QTLs) and 18 QTL clusters (colocation of QTLs for more than two traits) were detected. Eight clusters were detected for K-, Ca-, and Mg-related traits simultaneously. This means that these traits might be controlled by the same QTL. In addition, we highlight that 4B might be an important chromosome regulating the nutrition of K, Ca, and Mg in wheat. The 4B chromosome and four hot QTL clusters, which located 45 QTLs, might be important potential targets for further investigation.

Description: Captive breeding has been used as an effective approach to protecting endangered animals but its effect on the gut microbiome and the conservation status of these species is largely unknown. The giant panda is a flagship species for the conservation of wildlife. With integrated efforts including captive breeding, this species has been recently upgraded from “endangered” to “vulnerable” (IUCN 2016). Since a large proportion (21.8%) of their global population is still captive, it is critical to understand how captivity changes the gut microbiome of these pandas and how such alterations to the microbiome might affect their future fitness and potential impact on the ecosystem after release into the wild. Here, we use 16S rRNA (ribosomal RNA) marker gene sequencing and shotgun metagenomics sequencing to demonstrate that the fecal microbiomes differ substantially between wild and captive giant pandas. Fecal microbiome diversity was significantly lower in captive pandas, as was the diversity of functional genes. Additionally, captive pandas have reduced functional potential for cellulose degradation but enriched metabolic pathways for starch metabolism, indicating that they may not adapt to a wild diet after being released into the wild since a major component of their diet in the wild will be bamboo. Most significantly, we observed a significantly higher level of amylase activity but a lower level of cellulase activity in captive giant panda feces than those of wild giant pandas, shown by an in vitro experimental assay. Furthermore, antibiotic resistance genes and virulence factors, as well as heavy metal tolerance genes were enriched in the microbiomes of captive pandas, which raises a great concern of spreading these genes to other wild animals and ecosystems when they are released into a wild environment. Our results clearly show that captivity has altered the giant panda microbiome, which could have unintended negative consequences on their adaptability and the ecosystem during the reintroduction of giant pandas into the wild.

Description: Accurate prognosis of patients with cancer is important for the stratification of patients, the optimization of treatment strategies, and the design of clinical trials. Both clinical features and molecular data can be used for this purpose, for instance, to predict the survival of patients censored at specific time points. Multi-omics data, including genome-wide gene expression, methylation, protein expression, copy number alteration, and somatic mutation data, are becoming increasingly common in cancer studies. To harness the rich information in multi-omics data, we developed GDP (Group lass regularized Deep learning for cancer Prognosis), a computational tool for survival prediction using both clinical and multi-omics data. GDP integrated a deep learning framework and Cox proportional hazard model (CPH) together, and applied group lasso regularization to incorporate gene-level group prior knowledge into the model training process. We evaluated its performance in both simulated and real data from The Cancer Genome Atlas (TCGA) project. In simulated data, our results supported the importance of group prior information in the regularization of the model. Compared to the standard lasso regularization, we showed that group lasso achieved higher prediction accuracy when the group prior knowledge was provided. We also found that GDP performed better than CPH for complex survival data. Furthermore, analysis on real data demonstrated that GDP performed favorably against other methods in several cancers with large-scale omics data sets, such as glioblastoma multiforme, kidney renal clear cell carcinoma, and bladder urothelial carcinoma. In summary, we demonstrated that GDP is a powerful tool for prognosis of patients with cancer, especially when large-scale molecular features are available.

Description: Sugars are critical for plant growth and development as suppliers of carbon and energy, as signal molecules, or as solute molecules for osmotic homeostasis. Monosaccharide transporter (MST) genes are involved in various processes of plant growth and development as well as in response to abiotic stresses. However, the evolution and their roles of MST genes in growth and development and in coping with abiotic stresses in rice are poorly known. Here, we identified 64 MST genes in rice genome, which are classified into seven subfamilies: STP, PLT, AZT, ERD, pGlcT, INT, and XTPH. MST genes are not evenly distributed between chromosomes (Chrs) with a bias to Chr 3, 4, 7, and 11, which could be a result of duplication of fragments harboring MST genes. In total, 12 duplication events were found in the rice MST family, among which, two pairs were derived from fragmental duplications and ten pairs were from tandem duplications. The synonymous and nonsynonymous substitution rates of duplicate gene pairs demonstrated that the MST family was under a strong negative selection during the evolution process. Furthermore, a comprehensive expression analysis conducted in 11 different tissues, three abiotic stresses, five hormone treatments, and three sugar treatments revealed different expression patterns of MST genes and indicated diversified functions of them. Our results suggest that MST genes play important roles not only in various abiotic stresses but also in hormone and sugar responses. The present results will provide a vital insight into the functional divergence of the MST family in the future study.

Description: Inositol polyphosphate 5-phosphatase (5PTase), a key enzyme that hydrolyzes the 5′ position of the inositol ring, has essential functions in growth, development, and stress responses in plants, yeasts, and animals. However, the evolutionary history and patterns of 5PTases have not been examined systematically. Here, we report a comprehensive molecular evolutionary analysis of the 5PTase gene family and define four groups. These four groups are different from former classifications, which were based on in vitro substrate specificity. Most orthologous groups appear to be conserved as single or low-copy genes in all lineages in Groups II–IV, whereas 5PTase genes in Group I underwent several duplication events in angiosperm, resulting in multiple gene copies. Whole-genome duplication (WGD) was the main mechanism for 5PTase duplications in angiosperm. Plant 5PTases have more members than that of animals, and most plant 5PTase genes appear to have evolved under strong purifying selection. The paralogs have diverged in substrate specificity and expression pattern, showing evidence of selection pressure. Meanwhile, the increase in 5PTases and divergences in sequence, expression, and substrate might have contributed to the divergent functions of 5PTase genes, allowing the angiosperms to successfully adapt to a great number of ecological niches.

Description: Heat stress has a severe effect on animal health and can reduce the productivity and reproductive efficiency; it is therefore necessary to explore the molecular mechanism involved in heat stress response, which is helpful for the cultivation of an animal breed with resistance to heat stress. However, little research about heat stress-responsive molecular analysis has been reported in sheep. Therefore, in this study, RNA sequencing (RNA-Seq) was used to investigate the transcriptome profiling in the liver of Hu sheep with and without heat stress. In total, we detected 520 and 22 differentially expressed mRNAs and lncRNAs, respectively. The differentially expressed mRNAs were mainly associated with metabolic processes, the regulation of biosynthetic processes, and the regulation of glucocorticoid; additionally, they were significantly enriched in the heat stress related pathways, including the carbon metabolism, the PPAR signaling pathway, and vitamin digestion and absorption. The co-located differentially expressed lncRNA Lnc_001782 might positively influence the expression of the corresponding genes APOA4 and APOA5, exerting co-regulative effects on the liver function. Thus, we made the hypothesis that Lnc_001782, APOA4 and APOA5 might function synergistically to regulate the anti-heat stress ability in Hu sheep. This study provides a catalog of Hu sheep liver mRNAs and lncRNAs, and will contribute to a better understanding of the molecular mechanism underlying heat stress responses.

Description: Late embryogenesis-abundant (LEA) proteins are a large and highly diverse family believed to function in normal plant growth and development, and in protecting cells from abiotic stress. This study presents a characterisation of 74 Solanum tuberosum LEA (StLEA) proteins belonging to nine groups. StLEA genes have few introns (≤2) and are distributed on all chromosomes, occurring as gene clusters on chromosomes 1, 2, and 10. All four StASR (StLEA7 group) genes were concentrated on chromosome 4, suggesting their evolutionary conservation on one chromosome. Expression profiles of StLEA genes, in different tissues and in response to hormone and stress treatments, indicated that 71 StLEA genes had differential expression levels, of which 68 StLEA genes were differentially expressed in response to hormones and stress exposure in the potato. Continuous high expression of StASR-2, StLEA3-3, StDHN-3, StLEA2-29, and StLEA2-14 in different tissues indicated their contribution to plant development processes. StLEA2-14, StLEA2-31, StLEA3-3, StASR-1, and StDHN-1 were upregulated by six abiotic stresses, showing their tolerance to a wide spectrum of environmental stresses. Expression analysis of 17 selected StLEA genes in response to drought, salt, heavy metal, heat, and cold treatments by quantitative real-time polymerase chain reaction indicated that StLEA proteins may be involved in distinct signalling pathways. Taken together, StLEA3, StDHN, and StASR subgroup genes may be excellent resources for potato defence against environmental stresses. These results provide valuable information and robust candidate genes for future functional analysis aimed at improving the stress tolerance of the potato.

Description: Genes, Vol. 9, Pages 327: Transcriptome-Wide Identification of an Aurone Glycosyltransferase with Glycosidase Activity from Ornithogalum saundersiae Genes doi: 10.3390/genes9070327 Authors: Shuai Yuan Ming Liu Yan Yang Jiu-Ming He Ya-Nan Wang Jian-Qiang Kong Aurone glycosides display a variety of biological activities. However, reports about glycosyltransferases (GTs) responsible for aurones glycosylation are limited. Here, the transcriptome-wide discovery and identification of an aurone glycosyltransferase with glycosidase activity is reported. Specifically, a complementary DNA (cDNA), designated as OsUGT1, was isolated from the plant Ornithogalum saundersiae based on transcriptome mining. Conserved domain (CD)-search speculated OsUGT1 as a flavonoid GT. Phylogenetically, OsUGT1 is clustered as the same phylogenetic group with a putative 5,6-dihydroxyindoline-2-carboxylic acid (cyclo-DOPA) 5-O-glucosyltransferase, suggesting OsUGT1 may be an aurone glycosyltransferase. The purified OsUGT1 was therefore used as a biocatalyst to incubate with the representative aurone sulfuretin. In vitro enzymatic analyses showed that OsUGT1 was able to catalyze sulfuretin to form corresponding monoglycosides, suggesting OsUGT1 was indeed an aurone glycosyltransferase. OsUGT1 was observed to be a flavonoid GT, specific for flavonoid substrates. Moreover, OsUGT1 was demonstrated to display transglucosylation activity, transferring glucosyl group to sulfuretin via o-Nitrophenyl-β-d-glucopyranoside (oNP-β-Glc)-dependent fashion. In addition, OsUGT1-catalyzed hydrolysis was observed. This multifunctionality of OcUGT1 will broaden the application of OcUGT1 in glycosylation of aurones and other flavonoids.

Description: Tissue-specific gene expression has long been recognized as a crucial key for understanding tissue development and function. Efforts have been made in the past decade to identify tissue-specific expression profiles, such as the Human Proteome Atlas and FANTOM5. However, these studies mainly focused on “qualitatively tissue-specific expressed genes” which are highly enriched in one or a group of tissues but paid less attention to “quantitatively tissue-specific expressed genes”, which are expressed in all or most tissues but with differential expression levels. In this study, we applied machine learning algorithms to build a computational method for identifying “quantitatively tissue-specific expressed genes” capable of distinguishing 25 human tissues from their expression patterns. Our results uncovered the expression of 432 genes as optimal features for tissue classification, which were obtained with a Matthews Correlation Coefficient (MCC) of more than 0.99 yielded by a support vector machine (SVM). This constructed model was superior to the SVM model using tissue enriched genes and yielded MCC of 0.985 on an independent test dataset, indicating its good generalization ability. These 432 genes were proven to be widely expressed in multiple tissues and a literature review of the top 23 genes found that most of them support their discriminating powers. As a complement to previous studies, our discovery of these quantitatively tissue-specific genes provides insights into the detailed understanding of tissue development and function.

Description: Arkansas Regressor (AR) chickens, unlike Arkansas Progressor (AP) chickens, regress tumors induced by the v-src oncogene. To better understand the genetic factors responsible for this tumor regression property, whole genome resequencing was conducted using Illumina Hi-Seq 2 × 100 bp paired-end read method (San Diego, CA, USA) with AR (confirmed tumor regression property) and AP chickens. Sequence reads were aligned to the chicken reference genome (galgal5) and produced coverage of 11× and 14× in AR and AP, respectively. A total of 7.1 and 7.3 million single nucleotide polymorphisms (SNPs) were present in AR and AP genomes, respectively. Through a series of filtration processes, a total of 12,242 SNPs were identified in AR chickens that were associated with non-synonymous, frameshift, nonsense, no-start and no-stop mutations. Further filtering of SNPs based on read depth ≥ 10, SNP% ≥ 0.75, and non-synonymous mutations identified 63 reliable marker SNPs which were chosen for gene network analysis. The network analysis revealed that the candidate genes identified in AR chickens play roles in networks centered to ubiquitin C (UBC), phosphoinositide 3-kinases (PI3K), and nuclear factor kappa B (NF-kB) complexes suggesting that the tumor regression property in AR chickens might be associated with ubiquitylation, PI3K, and NF-kB signaling pathways. This study provides an insight into genetic factors that could be responsible for the tumor regression property.