Identification and Validation of Lipid Metabolism Gene FASN-Associated miRNA in Wilms Tumor.

miRNA has been a research hotspot in recent years and its scope of action is very wide, involving the regulation of cell proliferation, differentiation, apoptosis, and other biological behaviors. This study intends to explore the role of miRNA in the lipid metabolism and development of Wilms tumor (WT) by detecting and analyzing the differences in the expression profiles of miRNAs between the tumor and adjacent normal tissue. Gene detection was performed in tumor tissues and adjacent normal tissues of three cases of WT to screen differentially expressed miRNAs (DEMs). According to our previous research, FASN, which participates in the lipid metabolism pathway, may be a target of WT. The starBase database was used to predict FASN-targeted miRNAs. The above two groups of miRNAs were intersected to obtain FASN-targeted DEMs and then GO Ontology (GO) functional enrichment analysis of FASN-targeted DEMs was performed. Finally, the FASN-targeted DEMs were compared and further verified by qRT‒PCR. Through gene sequencing and differential analysis, 287 DEMs were obtained, including 132 upregulated and 155 downregulated miRNAs. The top ten DEMs were all downregulated. Fourteen miRNAs targeted by the lipid metabolism-related gene FASN were predicted by starBase. After intersection with the DEMs, three miRNAs were finally obtained, namely, miR-107, miR-27a-3p, and miR-335-5p. GO enrichment analysis was mainly concentrated in the Parkin-FBXW7-Cul1 ubiquitin ligase complex and response to prostaglandin E. Further experimental verification showed that miR-27a-3p was significantly correlated with WT (P = 0.0018). Imbalanced expression of miRNAs may be involved in the occurrence and development of WT through lipid metabolism. The expression of miR-27a-3p is related to the malignant degree of WT, and it may become the target of diagnosis, prognosis, and treatment of WT in the later stage.

Causal relationship between Alzheimer's disease and cardiovascular disease: a bidirectional Mendelian randomization analysis.

Observational studies suggest that cardiovascular disease (CVD) increases the risk of developing Alzheimer's disease (AD). However, the causal relationship between the two is not clear. This study applied a two-sample bidirectional Mendelian randomization method to explore the causal relationship between CVD and AD. Genome-wide association study (GWAS) data from 46 datasets of European populations (21,982 cases of AD and 41,944 controls) were utilized to obtain genetic instrumental variables for AD. In addition, genetic instrumental variables for atrial fibrillation (AF), heart failure (HF), myocardial infarction (MI), coronary heart disease (CHD), angina pectoris (AP), and ischemic stroke (IS) (including large-artery atherosclerotic stroke [LAS] and cardioembolic stroke [CES]) were selected from GWAS data of European populations (P < 5E-8). The inverse variance weighting method was employed as the major Mendelian randomization analysis method. Genetically predicted AD odds ratios (OR) (1.06) (95% CI: 1.02-1.10, P = 0.003) were linked to higher AP analysis. A higher genetically predicted OR for CES (0.9) (95% CI 0.82-0.99, P = 0.02) was linked to a decreased AD risk. This Mendelian randomized study identified AD as a risk factor for AP. In addition, CES was related to a reduced incidence of AD. Therefore, these modifiable risk factors are crucial targets for preventing and treating AD.

Causality between heart failure and epigenetic age: a bidirectional Mendelian randomization study.

AIMS: Heart failure (HF) is a prevalent age-related cardiovascular disease with poor prognosis in the elderly population. This study aimed to establish the causal relationship between ageing and HF by conducting a bidirectional Mendelian randomization (MR) analysis on epigenetic age (a marker of ageing) and HF. METHODS AND RESULTS: Genome-wide association study data for epigenetic age (GrimAge, HorvathAge, HannumAge, and PhenoAge) and HF were collected and assessed for significant genetic variables. A bidirectional MR analysis was carried out using the random-effects inverse-variance weighted (IVW) method as the primary approach, while other methods (MR-Egger, weighted median, simple mode, and weighted mode) and multiple sensitivity analyses (heterogeneity analysis, leave-one-out sensitivity analysis, and horizontal pleiotropy analysis) were employed to evaluate the impact of epigenetic age on HF and vice versa. Bidirectional MR analysis of two samples revealed that the epigenetic PhenoAge clock increased the risk of HF [IVW odds ratio (OR) 1.015, 95% confidence interval (CI) 1.002-1.028, P = 0.028 and weighted median OR 1.020, 95% CI 1.001-1.038, P = 0.039]. Other results were not statistically significant. CONCLUSIONS: The bidirectional MR analysis demonstrated a causal link between genetically predicted epigenetic age and HF in individuals of European descent. Further research into epigenetic age in other populations and additional genetic information related to HF is warranted.

A bioinformatics approach to identifying the biomarkers and pathogenesis of major depressive disorder combined with acute myocardial infarction.

This study investigated the pathogenesis of major depressive disorder (MDD) and acute myocardial infarction (AMI) using bioinformatics. We analyzed MDD and AMI (MDD-AMI) datasets provided by the Gene Expression Omnibus (GEO) database for genes common to MDD and AMI using GEO2R and weighted gene co-expression network analysis (WGCNA). We also performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, and we used Disease Ontology (DO) analysis to identify a) the pathways through which genes function and b) comorbidities. We also created a protein-protein interaction (PPI) network using the STRING database to identify the hub genes and biomarkers. NetworkAnalyst 3.0 was used to construct a transcription factor (TF) gene regulatory network. We also identified relevant complications and potential drug candidates. The 27 genes common to MDD and AMI were enriched in the pathways regulating TFs and mediating immunity and inflammation. The hub genes in the PPI network included TLR2, HP, ICAM1, LCN2, LTF, VCAN, S100A9 and NFKBIA. Key TFs were KLF9, KLF11, ZNF24, and ZNF580. Cardiovascular, pancreatic, and skeletal diseases were common complications. Hydrocortisone, simvastatin, and estradiol were candidate treatment drugs. Identification of these genes and their pathways may provide new targets for further research on the pathogenesis, biomarkers, and treatment of MDD-AMI. Together our results suggested that TLR2 and VCAN might be the key genes associated with MDD complicated by AMI.

A 4-lineage Statistical Suite to Evaluate the Support of Large-Scale Retrotransposon Insertion Data to Reconstruct Evolutionary Trees.

Retrophylogenomics makes use of genome-wide retrotransposon presence/absence insertion patterns to resolve questions in phylogeny and population genetics. In the genomics era, evaluating high-throughput data requires the associated development of appropriately powerful statistical tools. The currently used KKSC 3-lineage statistical test for estimating the significance of retrophylogenomic data is limited by the number of possible tree topologies it can assess in one step. To improve on this, we have extended the analysis to simultaneously compare four lineages, enabling us to evaluate ten distinct presence/absence insertion patterns for 26 possible tree topologies plus 129 trees with different incidences of hybridization or introgression. The new tool provides statistics for cases involving multiple ancestral hybridizations/introgressions, ancestral incomplete lineage sorting, bifurcation, and polytomy. The test is embedded in a user-friendly web R application (http://retrogenomics.uni-muenster.de:3838/hammlet/) and is available for use by the scientific community. [ancestral hybridization/introgression; ancestral incomplete lineage sorting (ILS); empirical distribution; KKSC-statistics; 4-lineage (4-LIN) insertion polymorphism; polytomy; retrophylogenomics.].

Causal association of epigenetic aging and COVID-19 severity and susceptibility: A bidirectional Mendelian randomization study.

Observational data from China, the United States, France, and Italy suggest that chronological age is an adverse COVID-19 outcome risk factor, with older patients having a higher severity and mortality rate than younger patients. Most studies have gotten the same view. However, the role of aging in COVID-19 adverse effects is unclear. To more accurately assess the effect of aging on adverse COVID-19, we conducted this bidirectional Mendelian randomization (MR) study. Epigenetic clocks and telomere length were used as biological indicators of aging. Data on epigenetic age (PhenoAge, GrimAge, Intrinsic HorvathAge, and HannumAge) were derived from an analysis of biological aging based on genome-wide association studies (GWAS) data. The telomere length data are derived from GWAS and the susceptibility and severity data are derived from the COVID-19 Host Genetics Initiative (HGI). Firstly, epigenetic age and telomere length were used as exposures, and following a screen for appropriate instrumental variables, we used random-effects inverse variance weighting (IVW) for the main analysis, and combined it with other analysis methods (e.g., MR Egger, Weighted median, simple mode, Weighted mode) and multiple sensitivity analysis (heterogeneity analysis, horizontal multiplicity analysis, "leave-one-out" analysis). For reducing false-positive rates, Bonferroni corrected significance thresholds were used. A reverse Mendelian randomization analysis was subsequently performed with COVID-19 susceptibility and severity as the exposure. The results of the MR analysis showed no significant differences in susceptibility to aging and COVID-19. It might suggest that aging is not a risk factor for COVID-19 infection (P-values are in the range of 0.05-0.94). According to the results of our analysis, we found that aging was not a risk factor for the increased severity of COVID-19 (P > 0.05). However, severe COVID-19 can cause telomere lengths to become shorter (beta = -0.01; se = 0.01; P = 0.02779). In addition to this, severe COVID-19 infection can slow the acceleration of the epigenetic clock "GrimAge" (beta = -0.24, se = 0.07, P = 0.00122), which may be related to the closely correlation of rs35081325 and COVID-19 severity. Our study provides partial evidence for the causal effects of aging on the susceptibility and severity of COVID-19.

Machine learning and bioinformatics to identify 8 autophagy-related biomarkers and construct gene regulatory networks in dilated cardiomyopathy.

Dilated cardiomyopathy (DCM) is a condition of impaired ventricular remodeling and systolic diastole that is often complicated by arrhythmias and heart failure with a poor prognosis. This study attempted to identify autophagy-related genes (ARGs) with diagnostic biomarkers of DCM using machine learning and bioinformatics approaches. Differential analysis of whole gene microarray data of DCM from the Gene Expression Omnibus (GEO) database was performed using the NetworkAnalyst 3.0 platform. Differentially expressed genes (DEGs) matching (|log2FoldChange ≥ 0.8, p value < 0.05|) were obtained in the GSE4172 dataset by merging ARGs from the autophagy gene libraries, HADb and HAMdb, to obtain autophagy-related differentially expressed genes (AR-DEGs) in DCM. The correlation analysis of AR-DEGs and their visualization were performed using R language. Gene Ontology (GO) enrichment analysis and combined multi-database pathway analysis were served by the Enrichr online enrichment analysis platform. We used machine learning to screen the diagnostic biomarkers of DCM. The transcription factors gene regulatory network was constructed by the JASPAR database of the NetworkAnalyst 3.0 platform. We also used the drug Signatures database (DSigDB) drug database of the Enrichr platform to screen the gene target drugs for DCM. Finally, we used the DisGeNET database to analyze the comorbidities associated with DCM. In the present study, we identified 23 AR-DEGs of DCM. Eight (PLEKHF1, HSPG2, HSF1, TRIM65, DICER1, VDAC1, BAD, TFEB) molecular markers of DCM were obtained by two machine learning algorithms. Transcription factors gene regulatory network was established. Finally, 10 gene-targeted drugs and complications for DCM were identified.

ExoPLOT: Representation of alternative splicing in human tissues and developmental stages with transposed element (TE) involvement.

Advances in RNA high-throughput sequencing and large-scale functional assays yield new insights into the multifaceted activities of transposed elements (TE) and many other previously undiscovered sequence elements. Currently, no tool for easy access, analysis, quantification, and visualization of alternatively spliced exons across multiple tissues or developmental stages is available. Also, analysis pipelines demand computational skills or hardware requirements, which often are hard to meet by wet-lab scientists. We developed ExoPLOT to enable simplified access to massive RNA high throughput sequencing datasets to facilitate the analysis of alternative splicing across many biological samples. To demonstrate the functonality of ExoPLOT, we analyzed the contributon of exonized TEs to human coding sequences (CDS). mRNA splice variants containing the TE-derived exon were quantified and compared to expression levels of TE-free splice variants. For analysis, we utilized 313 human cerebrum, cerebellum, heart, kidney, liver, ovary, and testis transcriptomes, representing various pre- and postnatal developmental stages. ExoPLOT visualizes the relative expression levels of alternative transcripts, e.g., caused by the insertion of new TE-derived exons, across different developmental stages of and among multiple tissues. This tool also provides a unique link between evolution and function during exonization (gain of a new exon) and exaptation (recruitment/co-optation) of a new exon. As input for analysis, we derived a database of 1151 repeat-masked, exonized TEs, representing all prominent families of transposons in the human genome and the collection of human consensus coding sequences (CCDS). ExoPLOT screened preprocessed RNA high-throughput sequencing datasets from seven human tissues to quantify and visualize the dynamics in RNA splicing for these 1151 TE-derived exons during the entire human organ development. In addition, we successfully mapped and analyzed 993 recently described exonized sequences from the human frontal cortex onto these 313 transcriptome libraries. ExoPLOT's approach to preprocessing RNA deep sequencing datasets facilitates alternative splicing analysis and significantly reduces processing times. In addition, ExoPLOT's design allows studying alternative RNA isoforms other than TE-derived in a customized - coordinate-based manner and is available at http://retrogenomics3.uni-muenster.de:3838/exz-plot-d/.

Identification of critical genes and molecular pathways in COVID-19 myocarditis and constructing gene regulatory networks by bioinformatic analysis.

BACKGROUND: There is growing evidence of a strong relationship between COVID-19 and myocarditis. However, there are few bioinformatics-based analyses of critical genes and the mechanisms related to COVID-19 Myocarditis. This study aimed to identify critical genes related to COVID-19 Myocarditis by bioinformatic methods, explore the biological mechanisms and gene regulatory networks, and probe related drugs. METHODS: The gene expression data of GSE150392 and GSE167028 were obtained from the Gene Expression Omnibus (GEO), including cardiomyocytes derived from human induced pluripotent stem cells infected with SARS-CoV-2 in vitro and GSE150392 from patients with myocarditis infected with SARS-CoV-2 and the GSE167028 gene expression dataset. Differentially expressed genes (DEGs) (adjusted P-Value <0.01 and |Log2 Fold Change| ≥2) in GSE150392 were assessed by NetworkAnalyst 3.0. Meanwhile, significant modular genes in GSE167028 were identified by weighted gene correlation network analysis (WGCNA) and overlapped with DEGs to obtain common genes. Functional enrichment analyses were performed by using the "clusterProfiler" package in the R software, and protein-protein interaction (PPI) networks were constructed on the STRING website (https://cn.string-db.org/). Critical genes were identified by the CytoHubba plugin of Cytoscape by 5 algorithms. Transcription factor-gene (TF-gene) and Transcription factor-microRibonucleic acid (TF-miRNA) coregulatory networks construction were performed by NetworkAnalyst 3.0 and displayed in Cytoscape. Finally, Drug Signatures Database (DSigDB) was used to probe drugs associated with COVID-19 Myocarditis. RESULTS: Totally 850 DEGs (including 449 up-regulated and 401 down-regulated genes) and 159 significant genes in turquoise modules were identified from GSE150392 and GSE167028, respectively. Functional enrichment analysis indicated that common genes were mainly enriched in biological processes such as cell cycle and ubiquitin-protein hydrolysis. 6 genes (CDK1, KIF20A, PBK, KIF2C, CDC20, UBE2C) were identified as critical genes. TF-gene interactions and TF-miRNA coregulatory network were constructed successfully. A total of 10 drugs, (such as Etoposide, Methotrexate, Troglitazone, etc) were considered as target drugs for COVID-19 Myocarditis. CONCLUSIONS: Through bioinformatics method analysis, this study provides a new perspective to explore the pathogenesis, gene regulatory networks and provide drug compounds as a reference for COVID-19 Myocarditis. It is worth highlighting that critical genes (CDK1, KIF20A, PBK, KIF2C, CDC20, UBE2C) may be potential biomarkers and treatment targets of COVID-19 Myocarditis for future study.

The Effects of Sn Doping MnNiFeO4 NTC Ceramic: Preparation, Microstructure and Electrical Properties.

Sn-doped MnNiFeO4 ceramic with negative temperature coefficient (NTC) was prepared through the low-temperature solid-phase reaction route (LTSPR), aiming at improving the sintering behavior and modulating the electrical properties. The experimental results of the ceramic powder precursor indicate that the calcination of the ceramic precursors at above ~300 °C is an exothermic process, which contributes to the transition of the ceramic powder from the amorphous phase into the crystal spinel phase; the spinel phase of ceramic powders can be formed initially at ~450 °C and well-formed at ~750 °C. A high densification of ~98% relative densities and evenly distributed grains within an average size of 2~12 µm for the sintered Sn-doped specimen were obtained. The specific resistance and B-value were notably increased from 12.63 KΩ·cm to ~24.65 KΩ·cm, and from 3438 K to ~3779 K, respectively, with the Sn-doping amount. In contrast, the aging rates of the Sn-doped specimen have not changed markedly larger, waving around ~2.7%. The as-designed Sn-doped MnNiFeO4 can be presented as a candidate for some defined NTC requirements.

Dengue Risk Forecast with Mosquito Vector: A Multicomponent Fusion Approach Based on Spatiotemporal Analysis.

Dengue as an acute infectious disease threatens global public health and has sparked broad research interest. However, existing studies generally ignore the spatial dependencies involved in dengue forecast, and consideration of temporal periodicity is absent. In this work, we propose a spatiotemporal component fusion model (STCFM) to solve the dengue risk forecast issue. Considering that mosquitoes are an important vector of dengue transmission, we introduce feature factors involving mosquito abundance and spatiotemporal lags to model temporal trends and spatial distributions separately on the basis of statistical properties. Specifically, we conduct multiscale modeling of temporal dependencies to enhance the forecast capability of relevant periods by capturing the historical variation patterns of the data across different segments in the temporal dimension. In the spatial dimension, we quantify the multivariate spatial correlation analysis as additional features to strengthen the spatial feature representation and adopt the ConvLSTM model to learn spatial dependencies adequately. The final forecast results are obtained by stacking strategy fusion in ensemble learning. We conduct experiments on real dengue datasets. The results indicate that STCFM improves prediction accuracy through effective spatiotemporal feature representations and outperforms candidate models with a reasonable component construction strategy.

Efficacy evaluation of intravitreal ranibizumab therapy for three types of retinopathy of prematurity.

AIM: To evaluate efficacy of intravitreal ranibizumab (IVR) therapy for aggressive posterior retinopathy of prematurity (ROP), threshold ROP disease and type 1 pre-threshold ROP. METHODS: A retrospective analysis was performed on 40 patients (76 eyes) who had IVR as the primary treatment for ROP from April 2017 to January 2018. According to disease pathogenic features, the 76 eyes were divided into three groups: aggressive posterior ROP (AP-ROP) group (16 eyes), threshold ROP group (28 eyes) and type 1 pre-threshold ROP group (32 eyes). The characteristics of patients and lesions situation before the first intravitreal injection, and posttreatment fundus outcomes determined by wide-angle RetCam fundus imaging were recorded. RESULTS: The birth weight and postmenstrual age of first IVR treatment in AP-ROP, threshold ROP, and type 1 pre-threshold ROP groups were significant difference (1087.50±246.78, 1103.75±168.30, 1257.03±210.82 g, P=0.005; 34.50±1.46, 36.89±2.97, 36.50±2.36wk, P=0.008), while the gestational age was not difference (28.00±2.00, 28.54±1.90, 28.59±1.43wk, P=0.510). The retina hemorrhage ratio (with/without: 14/2, 8/20, 5/27), iris neovascularization or vascular engorgement ratio (with/without: 12/4, 11/17, 6/26), and the zone I (inside/outside: 16/0, 2/26, 5/27) in AP-ROP, threshold ROP, and type 1 pre-threshold ROP group were difference significantly (all P<0.05). The regression rates were 37.5%, 92.86%, and 100%, and the recurrence rates were 62.5%, 7.14%, and 0 in AP-ROP, threshold ROP, and type 1 pre-threshold ROP group, respectively (both P<0.05). The recurrence eyes were cured by secondary IVR or retinal laser photocoagulation. CONCLUSION: IVR is an effective treatment for all types of ROP. The regression of AP-ROP is significantly lower than type 1 pre-threshold and threshold disease. Birth weight, retinal hemorrhage, iris neovascularization or vascular engorgement and lesions located in zone I may be associated with AP-ROP recurrence and retreatment, which should be noted in follow-up.

Sequence coverage required for accurate genotyping by sequencing in polyploid species.

Polyploidy plays an important role in the evolution of eukaryotes, especially for flowering plants. Many of ecologically or agronomically important plant or crop species are polyploids, including sycamore maple (tetraploid), the world second and third largest food crops wheat (hexaploid) and potato (tetraploid) as well as economically important aquaculture animals such as Atlantic salmon and trout. The next generation sequencing data enables to allocate genotype at a sequence variant site, known as genotyping by sequencing (GBS). GBS has stimulated enormous interests in population based genomics studies in almost all diploid and many polyploid organisms. DNA sequence polymorphisms are codominant and thus fully informative about the underlying genotype at the polymorphic site, making GBS a straightforward task in diploids. However, sequence data may usually be uninformative in polyploid species, making GBS a far more challenging task in polyploids. This paper presents novel and rigorous statistical methods for predicting the number of sequence reads needed to ensure accurate GBS at a polymorphic site bared by the reads in polyploids and shows that a dozen of reads can ensure a probability of 95% to recover all constituent alleles of any tetraploid genotype but several hundreds of reads are needed to accurately uncover the genotype with probability confidence of 90%, subverting the proposition of GBS using low coverage sequence data in the literature. The theoretical prediction was tested by use of RAD-seq data from tetraploid potato cultivars. The paper provides polyploid experimentalists with theoretical guides and methods for designing and conducting their sequence-based studies.

Application of machine learning in CT images and X-rays of COVID-19 pneumonia.

ABSTRACT: Coronavirus disease (COVID-19) has spread worldwide. X-ray and computed tomography (CT) are 2 technologies widely used in image acquisition, segmentation, diagnosis, and evaluation. Artificial intelligence can accurately segment infected parts in X-ray and CT images, assist doctors in improving diagnosis efficiency, and facilitate the subsequent assessment of the severity of the patient infection. The medical assistant platform based on machine learning can help radiologists make clinical decisions and helper in screening, diagnosis, and treatment. By providing scientific methods for image recognition, segmentation, and evaluation, we summarized the latest developments in the application of artificial intelligence in COVID-19 lung imaging, and provided guidance and inspiration to researchers and doctors who are fighting the COVID-19 virus.

Sampling Variation of RAD-Seq Data from Diploid and Tetraploid Potato (Solanum tuberosum L.).

The new sequencing technology enables identification of genome-wide sequence-based variants at a population level and a competitively low cost. The sequence variant-based molecular markers have motivated enormous interest in population and quantitative genetic analyses. Generation of the sequence data involves a sophisticated experimental process embedded with rich non-biological variation. Statistically, the sequencing process indeed involves sampling DNA fragments from an individual sequence. Adequate knowledge of sampling variation of the sequence data generation is one of the key statistical properties for any downstream analysis of the data and for implementing statistically appropriate methods. This paper reports a thorough investigation on modeling the sampling variation of the sequence data from the optimized RAD-seq (Restriction sit associated DNA sequencing) experiments with two parents and their offspring of diploid and autotetraploid potato (Solanum tuberosum L.). The analysis shows significant dispersion in sampling variation of the sequence data over that expected under multinomial distribution as widely assumed in the literature and provides statistical methods for modeling the variation and calculating the model parameters, which may be easily implemented in real sequence datasets. The optimized design of RAD-seq experiments enabled effective control of presentation of undesirable chloroplast DNA and RNA genes in the sequence data generated.

A tetrasomic inheritance model and likelihood-based method for mapping quantitative trait loci in autotetraploid species.

Dissecting the genetic architecture of quantitative traits in autotetraploid species is a methodologically challenging task, but a pivotally important goal for breeding globally important food crops, including potato and blueberry, and ornamental species such as rose. Mapping quantitative trait loci (QTLs) is now a routine practice in diploid species but is far less advanced in autotetraploids, largely due to a lack of analytical methods that account for the complexities of tetrasomic inheritance. We present a novel likelihood-based method for QTL mapping in outbred segregating populations of autotetraploid species. The method accounts properly for sophisticated features of gene segregation and recombination in an autotetraploid meiosis. It may model and analyse molecular marker data with or without allele dosage information, such as that from microarray or sequencing experiments. The method developed outperforms existing bivalent-based methods, which may fail to model and analyse the full spectrum of experimental data, in the statistical power of QTL detection, and accuracy of QTL location, as demonstrated by an intensive simulation study and analysis of data sets collected from a segregating population of potato (Solanum tuberosum). The study enables QTL mapping analysis to be conducted in autotetraploid species under a rigorous tetrasomic inheritance model.

Genome Duplication Increases Meiotic Recombination Frequency: A Saccharomyces cerevisiae Model.

Genetic recombination characterized by reciprocal exchange of genes on paired homologous chromosomes is the most prominent event in meiosis of almost all sexually reproductive organisms. It contributes to genome stability by ensuring the balanced segregation of paired homologs in meiosis, and it is also the major driving factor in generating genetic variation for natural and artificial selection. Meiotic recombination is subjected to the control of a highly stringent and complex regulating process and meiotic recombination frequency (MRF) may be affected by biological and abiotic factors such as sex, gene density, nucleotide content, and chemical/temperature treatments, having motivated tremendous researches for artificially manipulating MRF. Whether genome polyploidization would lead to a significant change in MRF has attracted both historical and recent research interests; however, tackling this fundamental question is methodologically challenging due to the lack of appropriate methods for tetrasomic genetic analysis, thus has led to controversial conclusions in the literature. This article presents a comprehensive and rigorous survey of genome duplication-mediated change in MRF using Saccharomyces cerevisiae as a eukaryotic model. It demonstrates that genome duplication can lead to consistently significant increase in MRF and rate of crossovers across all 16 chromosomes of S. cerevisiae, including both cold and hot spots of MRF. This ploidy-driven change in MRF is associated with weakened recombination interference, enhanced double-strand break density, and loosened chromatin histone occupation. The study illuminates a significant evolutionary feature of genome duplication and opens an opportunity to accelerate response to artificial and natural selection through polyploidization.

The multicomparative 2-n-way genome suite.

To effectively analyze the increasing amounts of available genomic data, improved comparative analytical tools that are accessible to and applicable by a broad scientific community are essential. We built the "2-n-way" software suite to provide a fundamental and innovative processing framework for revealing and comparing inserted elements among various genomes. The suite comprises two user-friendly web-based modules. The 2-way module generates pairwise whole-genome alignments of target and query species. The resulting genome coordinates of blocks (matching sequences) and gaps (missing sequences) from multiple 2-ways are then transferred to the n-way module and sorted into projects, in which user-defined coordinates from reference species are projected to the block/gap coordinates of orthologous loci in query species to provide comparative information about presence (blocks) or absence (gaps) patterns of targeted elements over many entire genomes and phylogroups. Thus, the 2-n-way software suite is ideal for performing multidirectional, non-ascertainment-biased screenings to extract all possible presence/absence data of user-relevant elements in orthologous sequences. To highlight its applicability and versatility, we used 2-n-way to expose approximately 100 lost introns in vertebrates, analyzed thousands of potential phylogenetically informative bat and whale retrotransposons, and novel human exons as well as thousands of human polymorphic retrotransposons.

Robotic arm-assisted vs conventional unicompartmental knee arthroplasty: A meta-analysis of the effects on clinical outcomes.

BACKGROUND: Robotic arm-assisted unicompartmental knee arthroplasty (UKA) has been recommended for treatment of unicompartmental knee osteoarthritis. However, its effectiveness and safeness remain controversial compared with conventional UKA. Therefore, the goal of this study was to perform a meta-analysis to re-evaluate the effects of robotic arm-assisted UKA on clinical functional outcomes. METHODS: PubMed, Embase, and Cochrane Library databases were searched to screen the relevant studies. Continuous data (surgical time, knee excursion during weight acceptance, American knee society score [AKSS], Oxford knee score [OKS], forgotten joint score [FJS], visual analog scale [VAS], and range of motion [ROM]) were pooled using a standardized mean difference (SMD) with their corresponding 95% confidence intervals (CIs) to estimate the effect size, while dichotomous data (complication rate, revision rate) were pooled to obtain the relative risk (RR) with a 95% CI by STATA 13.0 software. RESULTS: Eleven studies involving 498 patients undergoing robotic-assisted UKA and 589 patients receiving conventional UKA were included. Our pooled results demonstrated that robotic-assisted could significantly reduce the complication rate (RR: 0.62, 95% CI: 0.45-0.85; P = .0041) and improve the knee excursion during weight acceptance (SMD: 0.62, 95% CI: 0.25-1.00; P = .001), but prolonged the surgical time (SMD: 0.74, 95% CI: 0.40-1.08; P < .001). No significant difference in the revision rate, AKSS, OKS, FJS, VAS, and ROM between robotic-assisted and conventional UKA groups. CONCLUSION: This meta-analysis demonstrates robotic-assisted UKA may be an effective and safe surgical procedure for treatment of unicompartmental knee osteoarthritis.

Orthogonal contrast based models for quantitative genetic analysis in autotetraploid species.

Dissecting the genetic architecture of quantitative traits is a crucial goal for efficient breeding of polyploid plants, including autotetraploid crop species, such as potato and coffee, and ornamentals such as rose. To meet this goal, a quantitative genetic model is needed to link the genetic effects of genes or genotypes at quantitative trait loci (QTL) to the phenotype of quantitative traits. We present a statistically tractable quantitative genetic model for autotetraploids based on orthogonal contrast comparisons in the general linear model. The new methods are suitable for autotetraploid species with any population genetic structure and take full account of the essential features of autotetrasomic inheritance. The statistical properties of the new methods are explored and compared to an alternative method in the literature by simulation studies. We have shown how these methods can be applied for quantitative genetic analysis in autotetraploids by analysing trait phenotype data from an autotetraploid potato segregating population. Using trait segregation analysis, we showed that both highly heritable traits of flowering time and plant height were under the control of major QTL. The orthogonal model directly dissects genetic variance into independent components and gives consistent estimates of genetic effects provided that tetrasomic gene segregation is considered.