Characterization of blueberry exosome-like nanoparticles and miRNAs with potential cross-kingdom human gene targets

Edible plant derived exosome-like nanoparticles(ELNs)have been shown to have multiple nutraceutical functions.However,the diversity of plant materials makes the plant derived ELN study challenging.More efforts are still needed to explore the feasible isolation methods of edible plant derived ELNs and the possible roles of food-derived ELNs in improving human health.In this study,a size exclusion chromatography based method was compared with the traditional ultracentrifugation method to isolate blueberry derived ELNs(B-ELNs),and the miRNA profile of B-ELNs was analyzed by high-throughput sequencing.A total of 36 miRNAs were found to be enriched in B-ELNs compared with berry tissue,and their potential cross-kingdom human gene targets were further predicted.Results showed that size exclusion chromatography was effective for B-ELN isolation.The most abundant miRNAs in B-ELNs mainly belonged to the miR166 family and miR396 family.Target gene prediction indicated that B-ELNs could potentially regulate pathways related to the human digestive system,immune system and infectious diseases.

A Brief Review of Computational Gene Prediction Methods

With the development of genome sequencing for many organisms, more and moreraw sequences need to be annotated. Gene prediction by computational methods for finding thelocation of protein coding regions is one of the essential issues in bioinformatics. Two classes ofmethods are generally adopted: similarity based searches and ab initio prediction. Here, we reviewthe development of gene prediction methods, summarize the measures for evaluating predictor quality,highlight open problems in this area, and discuss future research directions.

The Prediction of Rice Gene by Fgenesh

This study has been carried out to give some scientific reasons for genome annotation, shorten the annotating time, and improve the results of gene prediction. Taking the sequence of the 6th chromosome, which has more length sequences than others, of Oryza sativa L. ssp. japonica cv. Nipponbare as analysis data in this research, the gene prediction of monocots module, rice, has been done by using Fgenesh ver. 2.0, and the predicting results have been explored particularly by bioinformatics methods. Results showed that the number of predicted genes for this chromosome was very close to the number of TIGR annotated genes. The majority of the predicted genes were multi-exon genes which had a percentage of 77.52. Length range was very big in the predicted genes. According to the significant match number, multi-exon genes can be predicted more veracity than single exon genes and the support can be reached up to 100% by TIGR annotation and up to 78% by cDNA. From the angle of predicted exons location of multi-exon genes, the internal exons and last exons had a high support of cDNA. The length of internal exons was relatively short in high （〉95% length, 〉78% similarity） cDNA and/or TIGR annotation support multi-exon genes, but the first exons and last exons were on the reverse. The majority of single exon genes which had more than 95% in length, and 78% in similarity support by cDNA and/or TIGR annotation was relatively short in length. From the angle of exon number, the majority of the multi-exon genes of high （〉 95% length, 〉 78% similarity） cDNA and/or TIGR annotation support had no more than 5 exon number. It was concluded that the rice gene prediction by Fgenesh was very good but needed modification manually to some extent according to cDNA support after aligning the predicting sequence of genes with cDNA database of rice.

Rediscovery and analysis of Phytophthora carbohydrate esterase(CE) genes revealing their evolutionary diversity

A continuous co-evolutionary arms-race between pathogens and their host plants promotes the development of pathogenic factors by microbes, including carbohydrate esterase(CE) genes to overcome the barriers in plant cell walls. Identification of CEs is essential to facilitate their functional and evolutionary investigations; however, current methods may have a limit in detecting some conserved domains, and ignore evolutionary relationships of CEs, as well as do not distinguish CEs from proteases. Here, candidate CEs were annotated using conserved functional domains, and orthologous gene detection and phylogenetic relationships were used to identify new CEs in 16 oomycete genomes, excluding genes with protease domains. In our method, 41 new putative CEs were discovered comparing to current methods, including three CE4, 14 CE5, eight CE12, five CE13, and 11 CE14. We found that significantly more CEs were identified in Phytophthora than in Hyaloperonospora and Pythium, especially CE8, CE12, and CE13 that are putatively involved in pectin degradation. The abundance of these CEs in Phytophthora may be due to a high frequency of multiple-copy genes, supporting by the phylogenetic distribution of CE13 genes, which showed five units of Phytophthora CE13 gene clusters each displaying a species tree like topology, but without any gene from Hyaloperonospora or Pythium species. Additionally, diverse proteins associated with products of CE13 genes were identified in Phytophthora strains. Our analyses provide a highly effective method for CE discovery, complementing current methods, and have the potential to advance our understanding of function and evolution of CEs.

A major and stable QTL for wheat spikelet number per spike validated in different genetic backgrounds

The spikelet number per spike(SNS)contributes greatly to grain yield in wheat.Identifying various genes that control wheat SNS is vital for yield improvement.This study used a recombinant inbred line population genotyped by the Wheat55K single-nucleotide polymorphism array to identify two major and stably expressed quantitative trait loci(QTLs)for SNS.One of them(QSns.sau-2SY-2D.1)was reported previously,while the other(QSns.sau-2SY-7A)was newly detected and further analyzed in this study.QSns.sau-2SY-7A had a high LOD value ranging from 4.46 to 16.00 and explained 10.21-40.78%of the phenotypic variances.QSns.sau-2SY-7A was flanked by the markers AX-110518554 and AX-110094527 in a 4.75-cM interval on chromosome arm 7AL.The contributions and interactions of both major QTLs were further analyzed and discussed.The effect of QSns.sau-2SY-7A was successfully validated by developing a tightly linked kompetitive allele specific PCR marker in an F_(2:3) population and a panel of 101 high-generation breeding wheat lines.Furthermore,several genes including the previously reported WHEAT ORTHOLOG OF APO1(WAPO1),an ortholog of the rice gene ABERRANT PANICLE ORGANIZATION 1(APO1)related to SNS,were predicted in the interval of QSns.sau-2SY-7A.In summary,these results revealed the genetic basis of the multi-spikelet genotype of wheat line 20828 and will facilitate subsequent fine mapping and breeding utilization of the major QTLs.

The involvement of proline-rich protein Mus musculus predicted gene 4736 in ocular surface functions

AIM: To research the two homologous predicted proline -rich protein genes, Mus musculus predicted gene 4736 (MP4) and proline-rich protein BstNI subfamily 1 (Prb1) which were significantly upregulated in cultured corneal organs when encountering fungal pathogen preparations. This study was to confirm the expression and potential functions of these two genes in ocular surface. METHODS: A Pseudomonas aeruginosa keratitis model was established in Balb/c mice. One day post infection, mRNA level of MP4 was measured using real-time polymerase chain reaction (PCR), and MP4 protein detected by immunohistochemistry (IHC) or Western blot using a customized polyclonal anti -MP4 antibody preparation. Lacrimal glands from normal mice were also subjected to IHC staining for MP4. An online bioinformatics program, BioGPS, was utilized to screen public data to determine other potential locations of MP4. RESULTS: One day after keratitis induction, MP4 was upregulated in the corneas at both mRNA level as measured using real -time PCR and protein levels as measured using Western blot and IHC. BioGPS analysis of public data suggested that the MP4 gene was most abundantly expressed in the lacrimal glands, and IHC revealed that normal murine lacrimal glands were positive for MP4 staining. CONCLUSION: MP4 and Prb1 are closely related with the physiology and pathological processes of the ocular surface. Considering the significance of ocular surface abnormalities like dry eye, we propose that MP4 and Prb1 contribute to homeostasis of ocular surface, and deserve more extensive functional and disease correlation studies.

Identification of microRNA-21 as a valuable diagnostic marker of oral squamous cell carcinoma and potential target

Objective The aim of the study was to summarize the diagnostic value of miR-21 as a biomarker in oral squamous cell carcinoma(OSCC)using a review of the literature and data from the cancer genome atlas(TCGA)database.Methods Data from TCGA database was sorted and analyzed by bioinformatics to determine the expression level of miR-21 in OSCC.Further,we searched for relevant articles in Embase,PubMed/Medline,Scopus,and Web of Science published before March 2021,extracted the data,and conducted quality assessment.The bivariate meta-analysis model with Stata 16.0 was used to analyze the diagnostic value of miR-21 for OSCC.Results A total of 304 related articles were identified,and seven were selected for meta-analysis.The diagnostic results after analysis were as follows:sensitivity 0.76[95%confidence interval(CI),0.57-0.88];specificity 0.77(95%CI,0.58-0.89);positive likelihood ratio 3.34(95%CI,1.58-7.08);negative likelihood ratio 0.31(95%CI,0.15-0.63);diagnostic odds ratio 10.75(95%CI,2.85-40.51);and area under the curve 0.83(95%CI,0.80-0.86).The Deeks’funnel chart showed that there was no potential bias(P=0.54).Prediction analysis of the potential target genes of miR-21 was performed via the biological website,and DAVID was used to cross target genes for gene ontology(GO)annotation function analysis.Conclusion The results showed that miR-21-3p and miR-21-5p were significantly more highly expressed in OSCC tissues than in normal tissues(P<0.05),and the results of the meta-analysis indicated that they could be used as potential biomarkers in the diagnosis of OSCC.In addition,58 potential target genes of miR-21 were significantly enriched in 28 GO annotation functional pathways,which provided a biological basis for further clinical diagnostic value research.

SCGPred:A Score-based Method for Gene Structure Prediction by Combining Multiple Sources of Evidence

Predicting protein-coding genes still remains a significant challenge. Although a variety of computational programs that use commonly machine learning methods have emerged, the accuracy of predictions remains a low level when implementing in large genomic sequences. Moreover, computational gene finding in newly se- quenced genomes is especially a difficult task due to the absence of a training set of abundant validated genes. Here we present a new gene-finding program, SCGPred, to improve the accuracy of prediction by combining multiple sources of evidence. SCGPred can perform both supervised method in previously well-studied genomes and unsupervised one in novel genomes. By testing with datasets composed of large DNA sequences from human and a novel genome of Ustilago maydi, SCGPred gains a significant improvement in comparison to the popular ab initio gene predictors. We also demonstrate that SCGPred can significantly improve prediction in novel genomes by combining several foreign gene finders with similarity alignments, which is superior to other unsupervised methods. Therefore, SCGPred can serve as an alternative gene-finding tool for newly sequenced eukaryotic genomes. The program is freely available at http：//bio.scu.edu.cn/SCGPred/.

Novel strategy for disease risk prediction incorporating predicted gene expression and DNA methylation data:a multi-phased study of prostate cancer

Background:DNA methylation and gene expression are known to play important roles in the etiology of human diseases such as prostate cancer(PCa).However,it has not yet been possible to incorporate information of DNA methylation and gene expression into polygenic risk scores(PRSs).Here,we aimed to develop and validate an improved PRS for PCa risk by incorporating genetically predicted gene expression and DNA methylation,and other genomic information using an integrative method.Methods:Using data from the PRACTICAL consortium,we derived multiple sets of genetic scores,including those based on available single-nucleotide polymorphisms through widely used methods of pruning and thresholding,LDpred,LDpred-funt,AnnoPred,and EBPRS,as well as PRS constructed using the genetically predicted gene expression and DNA methylation through a revised pruning and thresholding strategy.In the tuning step,using the UK Biobank data(1458 prevalent cases and 1467 controls),we selected PRSs with the best performance.Using an independent set of data from the UK Biobank,we developed an integrative PRS combining information from individual scores.Furthermore,in the testing step,we tested the performance of the integrative PRS in another independent set of UK Biobank data of incident cases and controls.Results:Our constructed PRS had improved performance(C statistics:76.1%)over PRSs constructed by individual benchmark methods(from 69.6%to 74.7%).Furthermore,our new PRS had much higher risk assessment power than family history.The overall net reclassification improvement was 69.0%by adding PRS to the baseline model compared with 12.5%by adding family history.Conclusions:We developed and validated a new PRS which may improve the utility in predicting the risk of developing PCa.Our innovative method can also be applied to other human diseases to improve risk prediction across multiple outcomes.

A Method for Identification of Selenoprotein Genes in Archaeal Genomes

The genetic codon UGA has a dual function： serving as a terminator and encoding selenocysteine. However, most popular gene annotation programs only take it as a stop signal, resulting in misannotation or completely missing selenoprotein genes. We developed a computational method named Asec-Prediction that is specific for the prediction of archaeal selenoprotein genes. To evaluate its effectiveness, we first applied it to 14 archaeal genomes with previously known selenoprotein genes, and Asec-Prediction identified all reported selenoprotein genes without redundant results. When we applied it to 12 archaeal genomes that had not been researched for selenoprotein genes, Asec-Prediction detected a novel selenoprotein gene in Methanosarcina acetivorans. Further evidence was also collected to support that the predicted gene should Asec-Prediction is effective be a real selenoprotein gene. for the prediction of archaeal The result shows that selenoprotein genes.

Identifcation of Immunity-related Genes in Arabidopsis and Cassava Using Genomic Data

Recent advances in genomic and post-genomic technologies have provided the opportu- nity to generate a previously unimaginable amount of information. However, biological knowledge is still needed to improve the understanding of complex mechanisms such as plant immune responses. Better knowledge of this process could improve crop production and management. Here, we used holistic analysis to combine our own microarray and RNA-seq data with public genomic data from Arabidopsis and cassava in order to acquire biological knowledge about the relationships between proteins encoded by immunity-related genes （IRGs） and other genes. This approach was based on a kernel method adapted for the construction of gene networks. The obtained results allowed us to propose a list of new IRGs. A putative function in the immunity pathway was predicted for the new IRGs. The analysis of networks revealed that our predicted IRGs are either well documented or recognized in previous co-expression studies. In addition to robust relationships between IRGs, there is evidence suggesting that other cellular processes may be also strongly related to immunity.

Gene Expression Versus Sequence for Predicting Function: Glia Maturation Factor Gamma Is Not A Glia Maturation Factor

It is standard practice, whenever a researcher finds a new gene, to search databases for genes that have a similar sequence. It is not standard practice, whenever a researcher finds a new gene, to search for genes that have similar expression (co-expression). Failure to perform co-expression searches has lead to incorrect conclusions about the likely function of new genes, and has lead to wasted laboratory attempts to confirm functions incorrectly predicted. We present here the example of Glia Maturation Factor gamma (GMF-gamma). Despite its name, it has not been shown to participate in glia maturation. It is a gene of unknown function that is similar in sequence to GMF-beta. The sequence homology and chromosomal location led to an unsuccessful search for GMF-gamma mutations in glioma. We examined GMF-gamma expression in 1432 human cDNA libraries. Highest expression occurs in phagocytic, antigen-presenting and other hematopoietic cells. We found GMF-gamma mRNA in almost every tissue examined, with expression in nervous tissue no higher than in any other tissue. Our evidence indicates that GMF-gamma participates in phagocytosis in antigen presenting cells. Searches for genes with similar sequences should be supplemented with searches for genes with similar expression to avoid incorrect predictions.

Recent Applications of Hidden Markov Models in Computational Biology

This paper examines recent developments and applications of Hidden MarkovModels (HMMs) to various problems in computational biology, including multiple sequence alignment,homology detection, protein sequences classification, and genomic annotation.

A Modified Statistically Optimal Null Filter Method for Recognizing Protein-coding Regions

Computer-aided protein-coding gene prediction in uncharacterized genomic DNA sequences is one of the most important issues of bio- logical signal processing. A modified filter method based on a statistically optimal null filter （SONF） theory is proposed for recognizing protein-coding regions. The square deviation gain （SDG） between the input and output of the model is used to identify the coding regions. The effective SDG amplification model with Class I and Class II enhancement is designed to suppress the non-coding regions. Also, an evaluation algorithm has been used to compare the modified model with most gene prediction methods currently available in terms of sensitivity, specificity and precision. The performance for identification of protein-coding regions has been evaluated at the nucleotide level using benchmark datasets and 91.4%, 96%, 93.7% were obtained for sensitivity, specificity and precision, respectively. These results suggest that the proposed model is potentially useful in gene finding field, which can help recognize protein-coding regions with higher precision and speed than present algorithms.