Using encrypted genotypes and phenotypes for collaborative genomic analyses to maintain data confidentiality.

To adhere to and capitalize on the benefits of the FAIR (findable, accessible, interoperable, and reusable) principles in agricultural genome-to-phenome studies, it is crucial to address privacy and intellectual property issues that prevent sharing and reuse of data in research and industry. Direct sharing of genotype and phenotype data is often prohibited due to intellectual property and privacy concerns. Thus, there is a pressing need for encryption methods that obscure confidential aspects of the data, without affecting the outcomes of certain statistical analyses. A homomorphic encryption method for genotypes and phenotypes (HEGP) has been proposed for single-marker regression in genome-wide association studies (GWAS) using linear mixed models with Gaussian errors. This methodology permits frequentist likelihood-based parameter estimation and inference. In this paper, we extend HEGP to broader applications in genome-to-phenome analyses. We show that HEGP is suited to commonly used linear mixed models for genetic analyses of quantitative traits including genomic best linear unbiased prediction (GBLUP) and ridge-regression best linear unbiased prediction (RR-BLUP), as well as Bayesian variable selection methods (e.g. those in Bayesian Alphabet), for genetic parameter estimation, genomic prediction, and GWAS. By advancing the capabilities of HEGP, we offer researchers and industry professionals a secure and efficient approach for collaborative genomic analyses while preserving data confidentiality.

Microbiome-enabled genomic selection improves prediction accuracy for nitrogen-related traits in maize.

Root-associated microbiomes in the rhizosphere (rhizobiomes) are increasingly known to play an important role in nutrient acquisition, stress tolerance, and disease resistance of plants. However, it remains largely unclear to what extent these rhizobiomes contribute to trait variation for different genotypes and if their inclusion in the genomic selection protocol can enhance prediction accuracy. To address these questions, we developed a microbiome-enabled genomic selection method that incorporated host SNPs and amplicon sequence variants from plant rhizobiomes in a maize diversity panel under high and low nitrogen (N) field conditions. Our cross-validation results showed that the microbiome-enabled genomic selection model significantly outperformed the conventional genomic selection model for nearly all time-series traits related to plant growth and N responses, with an average relative improvement of 3.7%. The improvement was more pronounced under low N conditions (8.4-40.2% of relative improvement), consistent with the view that some beneficial microbes can enhance N nutrient uptake, particularly in low N fields. However, our study could not definitively rule out the possibility that the observed improvement is partially due to the amplicon sequence variants being influenced by microenvironments. Using a high-dimensional mediation analysis method, our study has also identified microbial mediators that establish a link between plant genotype and phenotype. Some of the detected mediator microbes were previously reported to promote plant growth. The enhanced prediction accuracy of the microbiome-enabled genomic selection models, demonstrated in a single environment, serves as a proof-of-concept for the potential application of microbiome-enabled plant breeding for sustainable agriculture.

Interpreting single-step genomic evaluation as a neural network of three layers: pedigree, genotypes, and phenotypes.

The single-step approach has become the most widely-used methodology for genomic evaluations when only a subset of phenotyped individuals in the pedigree are genotyped, where the genotypes for non-genotyped individuals are imputed based on gene contents (i.e., genotypes) of genotyped individuals through their pedigree relationships. We proposed a new method named single-step neural network with mixed models (NNMM) to represent single-step genomic evaluations as a neural network of three sequential layers: pedigree, genotypes, and phenotypes. These three sequential layers of information create a unified network instead of two separate steps, allowing the unobserved gene contents of non-genotyped individuals to be sampled based on pedigree, observed genotypes of genotyped individuals, and phenotypes. In addition to imputation of genotypes using all three sources of information, including phenotypes, genotypes, and pedigree, single-step NNMM provides a more flexible framework to allow nonlinear relationships between genotypes and phenotypes, and for individuals to be genotyped with different single-nucleotide polymorphism (SNP) panels. The single-step NNMM has been implemented in the software package "JWAS'.

Learning functional conservation between human and pig to decipher evolutionary mechanisms underlying gene expression and complex traits.

Assessment of genomic conservation between humans and pigs at the functional level can improve the potential of pigs as a human biomedical model. To address this, we developed a deep learning-based approach to learn the genomic conservation at the functional level (DeepGCF) between species by integrating 386 and 374 functional profiles from humans and pigs, respectively. DeepGCF demonstrated better prediction performance compared with the previous method. In addition, the resulting DeepGCF score captures the functional conservation between humans and pigs by examining chromatin states, sequence ontologies, and regulatory variants. We identified a core set of genomic regions as functionally conserved that plays key roles in gene regulation and is enriched for the heritability of complex traits and diseases in humans. Our results highlight the importance of cross-species functional comparison in illustrating the genetic and evolutionary basis of complex phenotypes.

Computed Tomography Features and Tumor Spread Through Air Spaces in Lung Adenocarcinoma: A Meta-analysis.

To compare computed tomography (CT)-based radiologic features in patients, who are diagnosed with lung adenocarcinoma with the pathologically detected spread of tumor cells through air spaces (STAS positive [STAS+]) and those with no STAS. PubMed, Embase, and Scopus databases were systematically searched for observational studies (either retrospective or prospective) of patients with lung adenocarcinoma that had compared CT-based features between STAS+ and STAS-negative cases (STAS-). The pooled effect sizes were reported as odds ratio (OR) and weighted mean difference (WMD). STATA software was used for statistical analysis. The meta-analysis included 10 studies. Compared with STAS-, STAS+ adenocarcinoma was associated with increased odds of solid nodule (OR: 3.30, 95% CI: 2.52, 4.31), spiculation (OR: 2.05, 95% CI: 1.36, 3.08), presence of cavitation (OR: 1.49, 95% CI: 1.00, 2.22), presence of clear boundary (OR: 3.01, 95% CI: 1.70, 5.32), lobulation (OR: 1.65, 95% CI: 1.11, 2.47), and pleural indentation (OR: 1.98, 95% CI: 1.41, 2.77). STAS+ tumors had significant association with the presence of pulmonary vessel convergence (OR: 2.15, 95% CI: 1.61, 2.87), mediastinal lymphadenopathy (OR: 2.06, 95% CI: 1.20, 3.56), and pleural thickening (OR: 2.58, 95% CI: 1.73, 3.84). The mean nodule diameter (mm) (WMD: 6.19, 95% CI: 3.71, 8.66) and the mean solid component (%) (WMD: 24.5, 95% CI: 10.5, 38.6) were higher in STAS+ tumors, compared with STAS- ones. The findings suggest a significant association of certain CT-based features with the presence of STAS in patients with lung adenocarcinoma. These features may be important in influencing the nature of surgical management.

Extend mixed models to multilayer neural networks for genomic prediction including intermediate omics data.

With the growing amount and diversity of intermediate omics data complementary to genomics (e.g. DNA methylation, gene expression, and protein abundance), there is a need to develop methods to incorporate intermediate omics data into conventional genomic evaluation. The omics data help decode the multiple layers of regulation from genotypes to phenotypes, thus forms a connected multilayer network naturally. We developed a new method named NN-MM to model the multiple layers of regulation from genotypes to intermediate omics features, then to phenotypes, by extending conventional linear mixed models ("MM") to multilayer artificial neural networks ("NN"). NN-MM incorporates intermediate omics features by adding middle layers between genotypes and phenotypes. Linear mixed models (e.g. pedigree-based BLUP, GBLUP, Bayesian Alphabet, single-step GBLUP, or single-step Bayesian Alphabet) can be used to sample marker effects or genetic values on intermediate omics features, and activation functions in neural networks are used to capture the nonlinear relationships between intermediate omics features and phenotypes. NN-MM had significantly better prediction performance than the recently proposed single-step approach for genomic prediction with intermediate omics data. Compared to the single-step approach, NN-MM can handle various patterns of missing omics measures and allows nonlinear relationships between intermediate omics features and phenotypes. NN-MM has been implemented in an open-source package called "JWAS".

Interpretable artificial neural networks incorporating Bayesian alphabet models for genome-wide prediction and association studies.

In conventional linear models for whole-genome prediction and genome-wide association studies (GWAS), it is usually assumed that the relationship between genotypes and phenotypes is linear. Bayesian neural networks have been used to account for non-linearity such as complex genetic architectures. Here, we introduce a method named NN-Bayes, where "NN" stands for neural networks, and "Bayes" stands for Bayesian Alphabet models, including a collection of Bayesian regression models such as BayesA, BayesB, BayesC, and Bayesian LASSO. NN-Bayes incorporates Bayesian Alphabet models into non-linear neural networks via hidden layers between single-nucleotide polymorphisms (SNPs) and observed traits. Thus, NN-Bayes attempts to improve the performance of genome-wide prediction and GWAS by accommodating non-linear relationships between the hidden nodes and the observed trait, while maintaining genomic interpretability through the Bayesian regression models that connect the SNPs to the hidden nodes. For genomic interpretability, the posterior distribution of marker effects in NN-Bayes is inferred by Markov chain Monte Carlo approaches and used for inference of association through posterior inclusion probabilities and window posterior probability of association. In simulation studies with dominance and epistatic effects, performance of NN-Bayes was significantly better than conventional linear models for both GWAS and whole-genome prediction, and the differences on prediction accuracy were substantial in magnitude. In real-data analyses, for the soy dataset, NN-Bayes achieved significantly higher prediction accuracies than conventional linear models, and results from other four different species showed that NN-Bayes had similar prediction performance to linear models, which is potentially due to the small sample size. Our NN-Bayes is optimized for high-dimensional genomic data and implemented in an open-source package called "JWAS." NN-Bayes can lead to greater use of Bayesian neural networks to account for non-linear relationships due to its interpretability and computational performance.

Fast parallelized sampling of Bayesian regression models for whole-genome prediction.

BACKGROUND: Bayesian regression models are widely used in genomic prediction, where the effects of all markers are estimated simultaneously by combining the information from the phenotypic data with priors for the marker effects and other parameters such as variance components or membership probabilities. Inferences from most Bayesian regression models are based on Markov chain Monte Carlo methods, where statistics are computed from a Markov chain constructed to have a stationary distribution that is equal to the posterior distribution of the unknown parameters. In practice, chains of tens of thousands steps are typically used in whole-genome Bayesian analyses, which is computationally intensive. METHODS: In this paper, we propose a fast parallelized algorithm for Bayesian regression models called independent intensive Bayesian regression models (BayesXII, "X" stands for Bayesian alphabet methods and "II" stands for "parallel") and show how the sampling of each marker effect can be made independent of samples for other marker effects within each step of the chain. This is done by augmenting the marker covariate matrix by adding p (the number of markers) new rows such that columns of the augmented marker covariate matrix are orthogonal. Ideally, the computations at each step of the MCMC chain can be accelerated by k times, where k is the number of computer processors, up to p times, where p is the number of markers. RESULTS: We demonstrate the BayesXII algorithm using the prior for BayesC[Formula: see text], a Bayesian variable selection regression method, which is applied to simulated data with 50,000 individuals and a medium-density marker panel ([Formula: see text] 50,000 markers). To reach about the same accuracy as the conventional samplers for BayesC[Formula: see text] required less than 30 min using the BayesXII algorithm on 24 nodes (computer used as a server) with 24 cores on each node. In this case, the BayesXII algorithm required one tenth of the computation time of conventional samplers for BayesC[Formula: see text]. Addressing the heavy computational burden associated with Bayesian methods by parallel computing will lead to greater use of these methods.

miR-146b-5p Plays a Critical Role in the Regulation of Autophagy in ∆per Brucella melitensis-Infected RAW264.7 Cells.

Brucella-caused brucellosis is one of the most widespread worldwide zoonoses. Lipopolysaccharide (LPS) of Brucella, which functions as pathogen-associated molecular patterns (PAMPs), is an important virulence factor that elicits protective antibodies. Per of B. melitensis is involved in the biosynthesis of the O-side chain of LPS. Autophagy is a crucial element of the innate immune response against intracellular pathogens including Brucella. In this study, we observed that autophagy was inhibited in RAW264.7 cells infected with Brucella melitensis ∆per. And, a high-throughput array-based screen and qRT-PCR validation were performed to identify the differentially expressed miRNAs in RAW264.7 cells infected with B. melitensis M5-90 ∆per. The results suggested that mmu-miR-146a-5p, mmu-miR-155-5p, mmu-miR-146b-5p, and mmu-miR-3473a were upregulated and mmu-miR-30c-5p was downregulated. During B. melitensis M5-90 ∆per infection, the increased expression of miR-146b-5p inhibited the autophagy activation in RAW264.7 cells. Using a bioinformatics approach, Tbc1d14 was predicted to be a potential target of miR-146b-5p. The results of a luciferase reporter assay indicated that miR-146b-5p directly targeted the 3'-UTR of Tbc1d14, and the interaction between miR-146b-5p and the 3'-UTR of Tbc1d14 was sequence-specific. High-throughput RNA-Seq-based screening was performed to identify differentially expressed genes in Tbc1d14-expressing RAW264.7 cells, and these were validated by qRT-PCR. Among the differentially expressed genes, four autophagy associated genes, IFNγ-inducible p47 GTPase 1 (IIGP1), nuclear receptor binding protein 2 (Nrbp2), transformation related protein 53 inducible nuclear protein 1 (Trp53inp1), and immunity-related GTPase family M member 1 (Irgm1), were obtained. Our findings provide important insights into the functional mechanism of LPS of B. melitensis.

Transcriptome Analysis of HepG2 Cells Expressing ORF3 from Swine Hepatitis E Virus to Determine the Effects of ORF3 on Host Cells.

Hepatitis E virus- (HEV-) mediated hepatitis has become a global public health problem. An important regulatory protein of HEV, ORF3, influences multiple signal pathways in host cells. In this study, to investigate the function of ORF3 from the swine form of HEV (SHEV), high-throughput RNA-Seq-based screening was performed to identify the differentially expressed genes in ORF3-expressing HepG2 cells. The results were validated with quantitative real-time PCR and gene ontology was employed to assign differentially expressed genes to functional categories. The results indicated that, in the established ORF3-expressing HepG2 cells, the mRNA levels of CLDN6, YLPM1, APOC3, NLRP1, SCARA3, FGA, FGG, FGB, and FREM1 were upregulated, whereas the mRNA levels of SLC2A3, DKK1, BPIFB2, and PTGR1 were downregulated. The deregulated expression of CLDN6 and FREM1 might contribute to changes in integral membrane protein and basement membrane protein expression, expression changes for NLRP1 might affect the apoptosis of HepG2 cells, and the altered expression of APOC3, SCARA3, and DKK1 may affect lipid metabolism in HepG2 cells. In conclusion, ORF3 plays a functional role in virus-cell interactions by affecting the expression of integral membrane protein and basement membrane proteins and by altering the process of apoptosis and lipid metabolism in host cells. These findings provide important insight into the pathogenic mechanism of HEV.

Development of pH-sensitive self-nanoemulsifying drug delivery systems for acid-labile lipophilic drugs.

Oral administration is the most convenient way of all the drug delivery routes. Orally administered bioactive compounds must resist the harsh acidic fluids or enzyme digestion in stomach, to reach their absorbed destination in small intestine. This is the case for silibinin, a drug used to protect liver cells against toxins that has also been demonstrated in vitro to possess anti-cancer effects. However, as many other drugs, silibinin can degrade in the stomach due to the action of the gastric fluid. The use of pH-sensitive self-nanoemulsifying drug delivery systems (pH-SNEDDS) could overcome the drawback due to degradation of the drug in the stomach while enhancing its solubility and dissolution rate. In this paper we have investigated pH-sensitive self-nanoemulsifying formulations containing silibinin as model drug. Pseudo-ternary phase diagrams have been constructed in order to identify the self-emulsification regions under different pH. Solubility of silibinin in selected formulations has been assessed and stability of the pure drug and of the silibinin loaded pH-SNEDDS formulations in simulated gastric fluid had been compared. Droplet size of the optimized pH-SNEDDS has been correlated to pH, volume of dilution medium and silibinin loading amount. TEM (transmission electron microscopy) studies have shown that emulsion droplets had spherical shape and narrow size distribution. In vitro drug release studies of the optimal pH-SNEDDS indicated substantial increase of the drug release and release rate in comparison to pure silibinin and to the commercial silibinin tablet. The results indicated that pH-SNEDDS have potential to improve the biopharmaceutics properties of acid-labile lipophilic drugs.

Up-regulation of TDAG51 is a dependent factor of LPS-induced RAW264.7 macrophages proliferation and cell cycle progression.

CONTEXT: As a component of the outer membrane in Gram-negative bacteria, lipopolysaccharide (LPS)-induced proliferation and cell cycle progression of monocytes/macrophages. It has been suggested that the proapoptotic T-cell death-associated gene 51 (TDAG51) might be associated with cell proliferation and cell cycle progression; however, its role in the interaction between LPS and macrophages remains unclear. OBJECTIVE: We attempted to elucidate the role(s) of TDAG51 played in the interaction between LPS and macrophages. MATERIALS AND METHODS: We investigated TDAG51 expression in RAW264.7 cells stimulated with LPS and examined the effects of RNA interference-mediated TDAG51 down-regulation. We used CCK-8 assay and flow cytometry analysis to evaluate the interaction between TDAG51 and LPS-induced proliferation and cell cycle progression in RAW264.7 cells. RESULTS: Our findings indicate that TDAG51 is up-regulated in LPS-stimulated RAW264.7 cells, the TDAG51 siRNA effectively reduced TDAG51 protein up-regulation following LPS stimulation in RAW264.7 cells, the significant changes of the proliferation and cell cycle progression of RAW264.7 cells in TDAG51 Knockdown RAW264.7 cells treated with LPS were observed. CONCLUSION: These findings suggested that TDAG51 up-regulation is a dependent event during LPS-mediated proliferation and cell cycle progression, and which increase our understanding of the interaction mechanism between LPS and macrophages.

Mmu-miR-27a-5p-Dependent Upregulation of MCPIP1 Inhibits the Inflammatory Response in LPS-Induced RAW264.7 Macrophage Cells.

Lipopolysaccharide (LPS) stimulates macrophages to release proinflammatory cytokines. MicroRNAs (miRNAs) are short noncoding RNAs that are involved in inflammatory reaction. Our previously study identified the downregulated expression of mmu-miR-27a-5p in RAW267.4 cells treated with LPS. To dissect the mechanism that mmu-miR-27a-5p regulates target genes and affects proinflammatory cytokine secretion more clearly, based on previous bioinformatics prediction data, one of the potential target genes, MCPIP1 was observed to be upregulated with qRT-PCR and western blot. Luciferase reporter assays were performed to further confirm in silico prediction and determine that MCPIP1 is the target of mmu-miR-27-5p. The results suggested that mmu-miR-27a-5p directly targeted the 3'-UTR of MCPIP1 and the interaction between mmu-miR-27-5p and the 3'-UTR of MCPIP1 is sequence-specific. MCPIP1 overexpression decreased the secretion of IL-6, IL-1ß, and IL-10 in macrophage cells stimulated with LPS. Our findings may provide the important information for the precise roles of mmu-miR-27a-5p in the macrophage inflammatory response to LPS stimulation in the future.

Design and optimization of self-nanoemulsifying formulations for lipophilic drugs.

The purpose of the current study was to develop and optimize novel self-nanoemulsifying drug delivery systems (SNEDDS) with a high proportion of essential oil as carriers for lipophilic drugs. Solubility and droplet size as a function of the composition were investigated, and a ternary phase diagram was constructed in order to identify the self-emulsification regions. The optimized SNEDDS formulation consisted of lemon essential oil (oil), Cremophor RH40 (surfactant) and Transcutol HP (co-surfactant) in the ratio 50:30:20 (v/v). Ibuprofen was chosen as the model drug. The droplet size, ζ-potential and stability of the drug-loaded optimized formulations were determined. The stability of SNEDDS was proved after triple freezing/thawing cycles and storage at 4 °C and 25 °C for 3 months. In vitro drug release studies of optimized SNEDDS revealed a significant increase of the drug release and release rate in comparison to the Ibuprofen suspension (80% versus approximately 40% in 2 h). The results indicated that these SNEDDS formulations could be used to improve the bioavailability of lipophilic drugs.

Complete genome sequence of bovine papillomavirus genotype 13 from local yellow cattle in hainan province, china.

Here, we present the complete genome sequence of bovine papillomavirus genotype 13 isolated from local yellow cattle in Hainan, China. The genome is 7,961 bp and contains six early genes and two late genes. This analysis provides important information for the research of bovine papillomavirus (BPV) in China.

Identification of microRNAs dysregulated in CD14 gene silencing RAW264.7 macrophage cells.

A cluster of differentiation antigen 14 (CD14) is involved in lipopolysaccharide (LPS)-induced proinflammatory cytokine release and LPS-induced septic shock. MicroRNAs (miRNAs) are short non-coding RNAs that are involved in the epigenetic regulation of cellular process and bacterial infection. Our previous study indicated that siRNA against CD14 effectively inhibited LPS-induced tumor necrosis factor alpha, chemokine (C-X-C motif) ligand 2, interleukin-6 release, and NO production. To identify miRNAs which are affected by CD14 gene silencing and dissect the mechanisms of the attenuating of LPS-induced damaging immune activation more clearly, based on the CD14 knockdown RAW264.7 macrophage cell line established in our previous study, miRNAs expression profiling of CD14 knockdown RAW264.7 cells were analyzed with miRNA microarray and validated by qRT-PCR, the potential targets were predicted and subjected to gene ontology (GO) pathway and biological processes analysis. We demonstrated for the first time that CD14 knockdown significantly changed the expression of 199a-3p, miR-199a-5p, and miR-21-5p in RAW264.7 cells, and significantly enriched GO terms in the predicted target genes of these miRNAs were apoptosis process, immune response, inflammatory response, innate immune response, anti-apoptosis, cytokine production, and cytokine-mediated signaling pathway. These findings may improve our understanding about functional mechanism of miRNAs in the attenuating of LPS-induced damaging immune activation more clearly.