The mineralization characteristics of organic carbon and particle composition analysis in reconstructed soil with different proportions of soft rock and sand
The organic carbon mineralization process reflects the release intensity of soil CO2. Therefore, the study of organic carbon mineralization and particle composition analysis of soft rock and sand compound soil can provide technical support and a theoretical basis for soil organic reconstruction (soil structure, materials and biological nutrition). Based on previous research, four treatments were selected: CK (soft rock:sand=0:1), C1 (soft rock:sand=1:5), C2 (soft rock:sand=1:2) and C3 (soft rock:sand=1:1), respectively. Specifically, we analyzed the organic carbon mineralization process and soil particle composition by lye absorption, laser granulometer, and scanning electron microscope. The results showed that there was no significant difference in organic carbon content between C1, C2 , and C3 treatments, but they were significantly higher than in the CK treatment (P < 0.05). The organic carbon mineralization rate in each treatment accords with a logarithmic function throughout the incubation period (P < 0.01), which can be divided into a rapid decline phase in days 1 to 11 followed by a steady decline phase in days 11 to 30. The cumulative mineralization on the 11th day reached 54.96%–74.44% of the total mineralization amount. At the end of the incubation, the cumulative mineralization and potential mineralizable organic carbon content of the C1, C2 and C3 treatments were significantly higher than those of the CK treatment. The cumulative mineralization rate was also the lowest in the C1 and C2 treatment. The turnover rate constant of soil organic carbon in each treatment was significantly lower than that of the CK treatment, and the residence time increased. With the increase of volume fraction of soft rock, the content of silt and clay particles increased gradually, the texture of soil changed from sandy soil to sandy loam, loam , and silty loam, respectively. With the increase of small particles, the structure of soil appear ed to collapse when the volume ratio of soft rock was 50%. A comprehensive mineralization index and scanning electron microscopy analysis, when the ratio of soft rock to sand volume was 1:5–1:2, this can effectively increase the accumulation of soil organic carbon. Then, the distribution of soil particles was more uniform, the soil structure was stable (not collapsed), and the mineralization level of unit organic carbon was lower. Our research results have practical significance for the large area popularization of soft rock and sand compound technology.
Guo et al. (2019) The mineralization characteristics of organic carbon and particle composition analysis in reconstructed soil with different proportions of soft rock and sand. PeerJ 7:e7707 https://doi.org/10.7717/peerj.7707
AURKB: a promising biomarker in clear cell renal cell carcinoma
Background Aurora kinase B (AURKB) is an important carcinogenic factor in various tumors, while its role in clear cell renal cell carcinoma (ccRCC) still remains unclear. This study aimed to investigate its prognostic value and mechanism of action in ccRCC. Methods Gene expression profiles and clinical data of ccRCC patients were downloaded from The Cancer Genome Atlas database. R software was utilized to analyze the expression and prognostic role of AURKB in ccRCC. Gene set enrichment analysis (GSEA) was used to analyze AURKB related signaling pathways in ccRCC. Results AURKB was expressed at higher levels in ccRCC tissues than normal kidney tissues. Increased AURKB expression in ccRCC correlated with high histological grade, pathological stage, T stage, N stage and distant metastasis (M stage). Kaplan-Meier survival analysis suggested that high AURKB expression patients had a worse prognosis than patients with low AURKB expression levels. Multivariate Cox analysis showed that AURKB expression is a prognostic factor of ccRCC. GSEA indicated that genes involved in autoimmune thyroid disease, intestinal immune network for IgA production, antigen processing and presentation, cytokine-cytokine receptor interaction, asthma, etc., were differentially enriched in the AURKB high expression phenotype. Conclusions AURKB is a promising biomarker for predicting prognosis of ccRCC patients and a potential therapeutic target. In addition, AURKB might regulate progression of ccRCC through modulating intestinal immune network for IgA production and cytokine-cytokine receptor interaction, etc. signaling pathways. However, more research is necessary to validate the findings.
Wan et al. (2019) AURKB: a promising biomarker in clear cell renal cell carcinoma. PeerJ 7:e7718 https://doi.org/10.7717/peerj.7718
Prediction model for patients with acute respiratory distress syndrome: use of a genetic algorithm to develop a neural network model
Background Acute respiratory distress syndrome (ARDS) is associated with significantly increased risk of death, and early risk stratification may help to choose the appropriate treatment. The study aimed to develop a neural network model by using a genetic algorithm (GA) for the prediction of mortality in patients with ARDS. Methods This was a secondary analysis of two multicenter randomized controlled trials conducted in forty-four hospitals that are members of the National Heart, Lung, and Blood Institute, founded to create an acute respiratory distress syndrome Clinical Trials Network. Model training and validation were performed using the SAILS and OMEGA studies, respectively. A GA was employed to screen variables in order to predict 90-day mortality, and a neural network model was trained for the prediction. This machine learning model was compared to the logistic regression model and APACHE III score in the validation cohort. Results A total number of 1,071 ARDS patients were included for analysis. The GA search identified seven important variables, which were age, AIDS, leukemia, metastatic tumor, hepatic failure, lowest albumin, and FiO2. A representative neural network model was constructed using the forward selection procedure. The area under the curve (AUC) of the neural network model evaluated with the validation cohort was 0.821 (95% CI [0.753–0.888]), which was greater than the APACHE III score (0.665; 95% CI [0.590–0.739]; p = 0.002 by Delong’s test) and logistic regression model, albeit not statistically significant (0.743; 95% CI [0.669–0.817], p = 0.130 by Delong’s test). Conclusions The study developed a neural network model using a GA, which outperformed conventional scoring systems for the prediction of mortality in ARDS patients.
Zhang (2019) Prediction model for patients with acute respiratory distress syndrome: use of a genetic algorithm to develop a neural network model. PeerJ 7:e7719 https://doi.org/10.7717/peerj.7719
CeRNA network analysis and functional enrichment of salt sensitivity of blood pressure by weighted-gene co-expression analysis
Background Salt sensitivity of blood pressure (SSBP) is an independent risk factor for cardiovascular disease. The pathogenic mechanisms of SSBP are still uncertain. This study aimed to construct the co-regulatory network of SSBP and data mining strategy based on the competitive endogenous RNA (ceRNA) theory. Methods LncRNA and mRNA microarray was performed to screen for candidate RNAs. Four criteria were used to select the potential differently expressed RNAs. The weighted correlation network analysis (WGCNA) package of R software and target miRNA and mRNA prediction online databases were used to construct the ceRNA co-regulatory network and discover the pathways related to SSBP. Gene ontology enrichment, gene set enrichment analysis (GSEA) and KEGG pathway analysis were performed to explore the functions of hub genes in networks. Results There were 274 lncRNAs and 36 mRNAs that differently expressed between salt-sensitive and salt-resistant groups (P < 0.05). Using WGCNA analysis, two modules were identified (blue and turquoise). The blue module had a positive relationship with salt-sensitivity (R = 0.7, P < 0.01), high-density lipoprotein (HDL) (R = 0.53, P = 0.02), and total cholesterol (TC) (R = 0.55, P = 0.01). The turquoise module was positively related with triglyceride (TG) (R = 0.8, P < 0.01) and low-density lipoprotein (LDL) (R = 0.54, P = 0.01). Furthermore, 84 ceRNA loops were identified and one loop may be of great importance for involving in pathogenesis of SSBP. KEGG analysis showed that differently expressed mRNAs were mostly enriched in the SSBP-related pathways. However, the enrichment results of GSEA were mainly focused on basic physical metabolic processes. Conclusion The microarray data mining process based on WGCNA co-expression analysis had identified 84 ceRNA loops that closely related with known SSBP pathogenesis. The results of our study provide implications for further understanding of the pathogenesis of SSBP and facilitate the precise diagnosis and therapeutics.
Cao et al. (2019) CeRNA network analysis and functional enrichment of salt sensitivity of blood pressure by weighted-gene co-expression analysis. PeerJ 7:e7534 https://doi.org/10.7717/peerj.7534
Genome skimming is a low-cost and robust strategy to assemble complete mitochondrial genomes from ethanol preserved specimens in biodiversity studies
Global loss of biodiversity is an ongoing process that concerns both local and global authorities. Studies of biodiversity mainly involve traditional methods using morphological characters and molecular protocols. However, conventional methods are a time consuming and resource demanding task. The development of high-throughput sequencing (HTS) techniques has reshaped the way we explore biodiversity and opened a path to new questions and novel empirical approaches. With the emergence of HTS, sequencing the complete mitochondrial genome became more accessible, and the number of genome sequences published has increased exponentially during the last decades. Despite the current state of knowledge about the potential of mitogenomics in phylogenetics, this is still a relatively under-explored area for a multitude of taxonomic groups, especially for those without commercial relevance, non-models organisms and with preserved DNA. Here we take the first step to assemble and annotate the genomes from HTS data using a new protocol of genome skimming which will offer an opportunity to extend the field of mitogenomics to under-studied organisms. We extracted genomic DNA from specimens preserved in ethanol. We used Nextera XT DNA to prepare indexed paired-end libraries since it is a powerful tool for working with diverse samples, requiring a low amount of input DNA. We sequenced the samples in two different Illumina platform (MiSeq or NextSeq 550). We trimmed raw reads, filtered and had their quality tested accordingly. We performed the assembly using a baiting and iterative mapping strategy, and the annotated the putative mitochondrion through a semi-automatic procedure. We applied the contiguity index to access the completeness of each new mitogenome. Our results reveal the efficiency of the proposed method to recover the whole mitogenomes of preserved DNA from non-model organisms even if there are gene rearrangement in the specimens. Our findings suggest the potential of combining the adequate platform and library to the genome skimming as an innovative approach, which opens a new range of possibilities of its use to obtain molecular data from organisms with different levels of preservation.
Trevisan et al. (2019) Genome skimming is a low-cost and robust strategy to assemble complete mitochondrial genomes from ethanol preserved specimens in biodiversity studies. PeerJ 7:e7543 https://doi.org/10.7717/peerj.7543
Biochar application significantly affects the N pool and microbial community structure in purple and paddy soils
Background The increasing demand for food production has resulted in the use of large quantities of chemical fertilizers. This has created major environmental problems, such as increased ammonia volatilization, N2O emission, and nitrogen (N) leaching from agricultural soil. In particular, the utilization rate of N fertilizer is low in subtropical southern parts of China due to high rainfall. This causes not only large financial losses in agriculture, but also serious environmental pollution. Methods In this study, 16S rDNA-based analysis and static-chamber gas chromatography were used to elucidate the effects of continuous straw biochar application on the N pool and bacteria environment in two typical soil types, purple and paddy soils, in southern China. Results Straw biochar application (1) improved the soil N pool in both rhizosphere and non-rhizosphere soils; (2) significantly reduced the emission of N2O, with no difference in emission between 1 and 2 years of application; (3) increased the abundance of N-processing bacteria in the soil and altered the bacterial community structure; and (4) improved the tobacco yield and N use efficiency in paddy soil. These findings suggest that, in southern China, the application of straw biochar can promote N transformation in purple and paddy soils and reduce the emission of the greenhouse gas N2O.
Yan et al. (2019) Biochar application significantly affects the N pool and microbial community structure in purple and paddy soils. PeerJ 7:e7576 https://doi.org/10.7717/peerj.7576
Notes on vocalizations of Brazilian amphibians IV: advertisement calls of 20 Atlantic Forest frog species
Bioacoustics is a powerful tool used for anuran species diagnoses, given that advertisement calls are signals related to specific recognition and mate attraction. Thus, call descriptions can support species taxonomy. In spite of that, call descriptions are lacking for many species, delaying advances in biodiversity research. Here, we describe the advertisement calls of 20 anuran species from the Brazilian Atlantic Forest. We accessed 50 digital recordings deposited in the Fonoteca Neotropical Jacques Vielliard. Acoustic analyses were carried out in the software Raven pro 1.5. We provide a general comparison of call structure among species inside taxonomic groups and genera. The vocalizations described here belong to poorly known species, which are representatives of six families: Brachycephalidae, Bufonidae, Ceratophryidae, Cycloramphidae, Hylidae, and Phyllomedusidae. Despite this, still there are 163 species of anurans from Atlantic Forest with calls not formally described. Our work represents an important step in providing data for a taxonomic perspective and improving the knowledge of the Atlantic Forest anuran diversity.
Forti et al. (2019) Notes on vocalizations of Brazilian amphibians IV: advertisement calls of 20 Atlantic Forest frog species. PeerJ 7:e7612 https://doi.org/10.7717/peerj.7612
Analysis of small RNA changes in different Brassica napus synthetic allopolyploids
Allopolyploidy is an evolutionary and mechanisticaly intriguing process involving the reconciliation of two or more sets of diverged genomes and regulatory interactions, resulting in new phenotypes. In this study, we explored the small RNA changes of eight F2 synthetic B. napus using small RNA sequencing. We found that a part of miRNAs and siRNAs were non-additively expressed in the synthesized B. napus allotetraploid. Differentially expressed miRNAs and siRNAs differed among eight F2 individuals, and the differential expression of miR159 and miR172 was consistent with that of flowering time trait. The GO enrichment analysis of differential expression miRNA target genes found that most of them were concentrated in ATP-related pathways, which might be a potential regulatory process contributing to heterosis. In addition, the number of siRNAs present in the offspring was significantly higher than that of the parent, and the number of high parents was significantly higher than the number of low parents. The results have shown that the differential expression of miRNA lays the foundation for explaining the trait separation phenomenon, and the significant increase of siRNA alleviates the shock of the newly synthesized allopolyploidy. It provides a new perspective between small RNA changes and trait separation in the early stages of allopolyploid polyploid formation.
Wei et al. (2019) Analysis of small RNA changes in different Brassica napus synthetic allopolyploids. PeerJ 7:e7621 https://doi.org/10.7717/peerj.7621
EnContact: predicting enhancer-enhancer contacts using sequence-based deep learning model
Chromatin contacts between regulatory elements are of crucial importance for the interpretation of transcriptional regulation and the understanding of disease mechanisms. However, existing computational methods mainly focus on the prediction of interactions between enhancers and promoters, leaving enhancer-enhancer (E-E) interactions not well explored. In this work, we develop a novel deep learning approach, named Enhancer-enhancer contacts prediction (EnContact), to predict E-E contacts using genomic sequences as input. We statistically demonstrated the predicting ability of EnContact using training sets and testing sets derived from HiChIP data of seven cell lines. We also show that our model significantly outperforms other baseline methods. Besides, our model identifies finer-mapping E-E interactions from region-based chromatin contacts, where each region contains several enhancers. In addition, we identify a class of hub enhancers using the predicted E-E interactions and find that hub enhancers tend to be active across cell lines. We summarize that our EnContact model is capable of predicting E-E interactions using features automatically learned from genomic sequences.
Gan et al. (2019) EnContact: predicting enhancer-enhancer contacts using sequence-based deep learning model. PeerJ 7:e7657 https://doi.org/10.7717/peerj.7657
Screening and identification of SipC-interacting proteins in Salmonella enteritidis using Gal4 yeast two-hybrid system in duck
The zoonotic pathogen Salmonella not only reduces the production performance in ducks, but also poses a serious threat to human health through eggs and pollutes water bodies through feces. SipC, an effector protein of type III secretion systems (T3SS) in Salmonella, mediates translocation of effectors into the eukaryotic host. However, the precise role of SipC effectors remains unknown in ducks. In this study, the SipC from duck granulosa cells (dGCs) was selected as bait, and the SipC-interacting proteins in Salmonella enteritidis (SE) were screened using Gal4 yeast two-hybrid system in duck. Twelve SipC-interacting proteins were identified. Among those, the p53-effector related to PMP-22 (PERP) and TGF-β activated kinase 1-binding protein 2 (TAB2) were selected to further confirm the function by GST pull-down in vitro. Over-expression of PERP resulted in not only increasing SE adhesion and invasion but also triggering the production of IL-1β and IFN-α in SE infected dGCs, while knock-down PERP showed the opposite tendency (P < 0.01). In addition, TAB2 significantly induced the production of IL-6, IL-1β, IFN-α, and INF-γ in SE infected dGCs (P < 0.05), but did not cause obvious changes in SE adhesion and invasion. When the sipC in SE was deleted, the activities of duck PERP and TAB2 were abolished because they could not bind to SipC. Taken together, although the protein of PERP and TAB2 can interact with SipC, their mechanisms were different in duck challenged by SE. Therefore, PERP was involved in SE invasion and inflammatory response of dGC ovaries, and TAB2 only contributed to dGCs inflammatory response, which provided critical insights about the mechanism in host- bacterium protein interactions during Salmonella invasion in duck.
Zhang et al. (2019) Screening and identification of SipC-interacting proteins in Salmonella enteritidis using Gal4 yeast two-hybrid system in duck. PeerJ 7:e7663 https://doi.org/10.7717/peerj.7663