Spatial rank-based multifactor dimensionality reduction to detect gene–gene interactions for multivariate phenotypes

Background Identifying interaction effects between genes is one of the main tasks of genome-wide association studies aiming to shed light on the biological mechanisms underlying complex diseases. Multifactor dimensionality reduction (MDR) is a popular approach for detecting gene–gene interactions that has been extended in various forms to handle binary and continuous phenotypes. However, only few multivariate MDR methods are available for multiple related phenotypes. Current approaches use Hotellings T2 statistic to evaluate interaction models, but it is well known that Hotellings T2 statistic is highly sensitive to heavily skewed distributions and outliers. Results We propose a robust approach based on nonparametric statistics such as spatial signs and ranks. The new multivariate rank-based MDR (MR-MDR) is mainly suitable for analyzing multiple continuous phenotypes and is less sensitive to skewed distributions and outliers. MR-MDR utilizes fuzzy k-means clustering and classifies multi-locus genotypes into two groups. Then, MR-MDR calculates a spatial rank-sum statistic as an evaluation measure and selects the best interaction model with the largest statistic. Our novel idea lies in adopting nonparametric statistics as an evaluation measure for robust inference. We adopt tenfold cross-validation to avoid overfitting. Intensive simulation studies were conducted to compare the performance of MR-MDR with current methods. Application of MR-MDR to a real dataset from a Korean genome-wide association study demonstrated that it successfully identified genetic interactions associated with four phenotypes related to kidney function. The R code for conducting MR-MDR is available at https://github.com/statpark/MR-MDR Conclusions Intensive simulation studies comparing MR-MDR with several current methods showed that the performance of MR-MDR was outstanding for skewed distributions. Additionally, for symmetric distributions, MR-MDR showed comparable power. Therefore, we conclude that MR-MDR is a useful multivariate non-parametric approach that can be used regardless of the phenotype distribution, the correlations between phenotypes, and sample size.This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government(MSIT) (2013M3A9C4078158, NRF-2021R1A2C1007788)

A set of stage-specific gene transcripts identified in EK stage X and HH stage 3 chick embryos

<p>Abstract</p> <p>Background</p> <p>The embryonic developmental process in avian species is quite different from that in mammals. The first cleavage begins 4 h after fertilization, but the first differentiation does not occur until laying of the egg (Eyal-Giladi and Kochav (EK) stage X). After 12 to 13 h of incubation (Hamburger and Hamilton (HH) stage 3), the three germ layers form and germ cell segregation in the early chick embryo are completed. Thus, to identify genes associated with early embryonic development, we compared transcript expression patterns between undifferentiated (stage X) and differentiated (HH stage 3) embryos.</p> <p>Results</p> <p>Microarray analysis primarily showed 40 genes indicating the significant changes in expression levels between stage X and HH stage 3, and 80% of the genes (32/40) were differentially expressed with more than a twofold change. Among those, 72% (23/32) were relatively up-regulated at stage X compared to HH stage 3, while 28% (9/32) were relatively up-regulated at HH stage 3 compared to stage X. Verification and gene expression profiling of these GeneChip expression data were performed using quantitative RT-PCR for 32 genes at developmental four points; stage X (0 h), HH stage 3 (12 h), HH stage 6 (24 h), and HH stage 9 (30 h). Additionally, we further analyzed four genes with less than twofold expression increase at HH stage 3. As a result, we identified a set of stage-specific genes during the early chick embryo development; 21 genes were relatively up-regulated in the stage X embryo and 12 genes were relatively up-regulated in the HH stage 3 embryo based on both results of microarray and quantitative RT-PCR.</p> <p>Conclusion</p> <p>We identified a set of genes with stage-specific expression from microarray Genechip and quantitative RT-PCR. Discovering stage-specific genes will aid in uncovering the molecular mechanisms involved the formation of the three germ layers and germ cell segregation in the early chick embryos.</p

Ovarian Cancer Prognostic Prediction Model Using RNA Sequencing Data

Ovarian cancer is one of the leading causes of cancer-related deaths in gynecological malignancies. Over 70% of ovarian cancer cases are high-grade serous ovarian cancers and have high death rates due to their resistance to chemotherapy. Despite advances in surgical and pharmaceutical therapies, overall survival rates are not good, and making an accurate prediction of the prognosis is not easy because of the highly heterogeneous nature of ovarian cancer. To improve the patient’s prognosis through proper treatment, we present a prognostic prediction model by integrating high-dimensional RNA sequencing data with their clinical data through the following steps: gene filtration, pre-screening, gene marker selection, integrated study of selected gene markers and prediction model building. These steps of the prognostic prediction model can be applied to other types of cancer besides ovarian cancer

Microbiome of Saliva and Plaque in Children According to Age and Dental Caries Experience

Dental caries are one of the chronic diseases caused by organic acids made from oral microbes. However, there was a lack of knowledge about the oral microbiome of Korean children. The aim of this study was to analyze the metagenome data of the oral microbiome obtained from Korean children and to discover bacteria highly related to dental caries with machine learning models. Saliva and plaque samples from 120 Korean children aged below 12 years were collected. Bacterial composition was identified using Illumina HiSeq sequencing based on the V3-V4 hypervariable region of the 16S rRNA gene. Ten major genera accounted for approximately 70% of the samples on average, including Streptococcus, Neisseria, Corynebacterium, and Fusobacterium. Differential abundant analyses revealed that Scardovia wiggsiae and Leptotrichia wadei were enriched in the caries samples, while Neisseria oralis was abundant in the non-caries samples of children aged below 6 years. The caries and non-caries samples of children aged 6-12 years were enriched in Streptococcus mutans and Corynebacterium durum, respectively. The machine learning models based on these differentially enriched taxa showed accuracies of up to 83%. These results confirmed significant alterations in the oral microbiome according to dental caries and age, and these differences can be used as diagnostic biomarkers

Identification of Novel Reference Genes Using Multiplatform Expression Data and Their Validation for Quantitative Gene Expression Analysis

Normalization of mRNA levels using endogenous reference genes (ERGs) is critical for an accurate comparison of gene expression between different samples. Despite the popularity of traditional ERGs (tERGs) such as GAPDH and ACTB, their expression variability in different tissues or disease status has been reported. Here, we first selected candidate housekeeping genes (HKGs) using human gene expression data from different platforms including EST, SAGE, and microarray, and 13 novel ERGs (nERGs) (ARL8B, CTBP1, CUL1, DIMT1L, FBXW2, GPBP1, LUC7L2, OAZ1, PAPOLA, SPG21, TRIM27, UBQLN1, ZNF207) were further identified from these HKGs. The mean coefficient variation (CV) values of nERGs were significantly lower than those of tERGs and the expression level of most nERGs was relatively lower than high expressing tERGs in all dataset. The higher expression stability and lower expression levels of most nERGs were validated in 108 human samples including formalin-fixed paraffin-embedded (FFPE) tissues, frozen tissues and cell lines, through quantitative real-time RT-PCR (qRT-PCR). Furthermore, the optimal number of nERGs required for accurate normalization was as few as two, while four genes were required when using tERGs in FFPE tissues. Most nERGs identified in this study should be better reference genes than tERGs, based on their higher expression stability and fewer numbers needed for normalization when multiple ERGs are required

Clinical features of COVID-19 among patients with end-stage renal disease on hemodialysis in the context of high vaccination coverage during the omicron surge period: a retrospective cohort study

Background We determined the clinical presentation and outcomes of the Omicron variant of severe acute respiratory syndrome coronavirus 2 infection in hemodialysis patients and identified the risk factors for severe coronavirus disease (COVID-19) and mortality in the context of high vaccination coverage. Methods This was a retrospective cohort study involving hemodialysis patients who were vaccinated against COVID-19 during March–September 2022, when the Omicron variant was predominant, and the COVID-19 vaccination rate was high. The proportion of people with severe COVID-19 or mortality was evaluated using univariate logistic regression. Results Eighty-three (78.3%) patients had asymptomatic/mild symptoms, 10 (9.4%) had moderate symptoms, and 13 (12.3%) had severe symptoms. Six (5.7%) patients required intensive care admission, two (1.9%) required mechanical ventilation, and one (0.9%) was kept on high-flow nasal cannula. Of the five (4.7%) mortality cases, one was directly attributed to COVID-19 and four to pre-existing comorbidities. Risk factors for both severe COVID-19 and mortality were advanced age; number of comorbidities; cardiovascular diseases; increased levels of aspartate transaminase, lactate dehydrogenase, blood urea nitrogen/creatinine ratio, brain natriuretic peptide, and red cell distribution; and decreased levels of hematocrit and albumin. Moreover, the number of COVID-19 vaccinations wasa protective factor against both severe disease and mortality. Conclusions Clinical features of hemodialysis patients during the Omicron surge with high COVID-19 vaccination coverage were significant for low mortality. The risk features for severe COVID-19 or mortality were similar to those in the pre-Omicron period in the context of low vaccination coverage.This work was supported by a research fund of Chung-Ang Jeil Hospital, Chungbuk, South Korea (CAJ-2022-AS 01). Data analysis was supported by the Bio and Medical Technology Development Program of the National Research Foundation, funded by the Korean government (No. 2021M3E5E3081425)

Snake fang-inspired stamping patch for transdermal delivery of liquid formulations

A flexible microneedle patch that can transdermally deliver liquid-phase therapeutics would enable direct use of existing, approved drugs and vaccines, which are mostly in liquid form, without the need for additional drug solidification, efficacy verification, and subsequent approval. Specialized dissolving or coated microneedle patches that deliver reformulated, solidified therapeutics have made considerable advances; however, microneedles that can deliver liquid drugs and vaccines still remain elusive because of technical limitations. Here, we present a snake fang-inspired microneedle patch that can administer existing liquid formulations to patients in an ultrafast manner (&lt; 15 s). Rear-fanged snakes have an intriguing molar with a groove on the surface, which enables rapid and efficient infusion of venom or saliva into prey. Liquid delivery is based on surface tension and capillary action. The microneedle patch uses multiple open groove architectures that emulate the grooved fangs of rear-fanged snakes: Similar to snake fangs, the microneedles can rapidly and efficiently deliver diverse liquid-phase drugs and vaccines in seconds under capillary action with only gentle thumb pressure, without requiring a complex pumping system. Hydrodynamic simulations show that the snake fang-inspired open groove architectures enable rapid capillary force-driven delivery of liquid formulations with varied surface tensions and viscosities. We demonstrate that administration of ovalbumin and influenza virus with the snake fang-inspired microneedle patch induces robust antibody production and protective immune response in guinea pigs and mice

Analysis of significant protein abundance from multiple reaction-monitoring data

Background Discovering reliable protein biomarkers is one of the most important issues in biomedical research. The ELISA is a traditional technique for accurate quantitation of well-known proteins. Recently, the multiple reaction-monitoring (MRM) mass spectrometry has been proposed for quantifying newly discovered protein and has become a popular alternative to ELISA. For the MRM data analysis, linear mixed modeling (LMM) has been used to analyze MRM data. MSstats is one of the most widely used tools for MRM data analysis that is based on the LMMs. However, LMMs often provide various significance results, depending on model specification. Sometimes it would be difficult to specify a correct LMM method for the analysis of MRM data. Here, we propose a new logistic regression-based method for Significance Analysis of Multiple Reaction Monitoring (LR-SAM). Results Through simulation studies, we demonstrate that LMM methods may not preserve type I error, thus yielding high false- positive errors, depending on how random effects are specified. Our simulation study also shows that the LR-SAM approach performs similarly well as LMM approaches, in most cases. However, LR-SAM performs better than the LMMs, particularly when the effects sizes of peptides from the same protein are heterogeneous. Our proposed method was applied to MRM data for identification of proteins associated with clinical responses of treatment of 115 hepatocellular carcinoma (HCC) patients with the tyrosine kinase inhibitor sorafenib. Of 124 candidate proteins, LMM approaches provided 6 results varying in significance, while LR-SAM, by contrast, yielded 18 significant results that were quite reproducibly consistent. Conclusion As exemplified by an application to HCC data set, LR-SAM more effectively identified proteins associated with clinical responses of treatment than LMM did.This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI16C2037, HI15C2165). Publication of this article was sponsored by HI16C2037 grant