Gene Ranking Research Articles

Identification and validation of reference genes for normalization of gene expression analysis using qRT-PCR in Megalurothrips usitatus (thysanoptera: thripidae).

Introduction: Gene expression analysis by reverse transcription quantitative polymerase chain reaction (qRT-PCR) has been widely used in research including insects. The selection of appropriate reference genes is the key to obtaining accurate and reliable results from qRT-PCR. However, studies on the expression stability of reference genes in Megalurothrips usitatus remain lacking. Methods: In this study, qRT-PCR was used to analyze the expression stability of candidate reference genes in M. usitatus. The expression levels of six candidate reference gene transcription of M. usitatus were analyzed. GeNorm, NormFinder, BestKeeper, and ΔCt were used to analyze the expression stability of M. usitatus treated with biological factors (developmental period treatment) and abiotic factors (light, temperature, insecticide treatment, respectively). Comprehensive stability ranking of candidate reference genes was recommended by RefFinder. Results and Discussion: Results showed that ribosomal protein S (RPS) was the most suitable expression in insecticide treatment. Ribosomal protein L (RPL) was the most suitable expression at developmental stage and light treatment, whereas elongation factor was the most suitable expression in temperature treatment. RefFinder was used to comprehensively analyze the above four treatments, and the results showed that RPL and actin (ACT) showed high stability in each treatment. Therefore, this study identified these two genes as reference genes in the qRT-PCR analysis of different treatment conditions of M. usitatus. Ourfindings will be beneficial for improving the accuracy of qRT-PCR analysis for future functional analysis of the target gene expression in M. usitatus.

RefFinder: a web-based tool for comprehensively analyzing and identifying reference genes.

Although many genes may serve as reference genes, they may cause different expression patterns by selecting different reference genes because no single gene is expressed consistently in all tested tissues of an organism under all environmental and developmental conditions. Thus, it is becoming increasingly important and necessary to identify suitable reference genes before performing gene expression analysis. Currently, there are several computational tools available for evaluating the stability of candidate reference genes. These tools are based on different statistical algorithms and may produce different rankings in stability within the same reference gene study. To date, the RefFinder is the only web-based tool available for comparing and evaluating housekeeping genes as candidates to be reference genes. In this tool, we integrated the four currently available computational programs (geNorm, NormFinder, BestKeeper, and the comparative ΔCt method) into a web-based tool for evaluating the stability and reliability of reference genes. According to the gene stability rankings derived from the four programs, we assigned an appropriate weight to each gene and calculated the geometric mean of weights for the final rankings. Aside from the overall ranking, a single program or combination of the four programs can be selected for evaluating the ranking of candidate reference genes. This tool has been widely used and validated by many research laboratories around the world. You may use this tool at http://www.heartcure.com.au/reffinder/ or https://blooge.cn/RefFinder/ . You can also download this algorithm program from https://github.com/fulxie/RefFinder and setup on your own computer. RefFinder is developed by PHP. Users can deploy it to a Php-based server (Apache + PHP) and run it.

Genome-wide association study for single nucleotide polymorphism associated with mural and cumulus granulosa cells of PCOS (polycystic ovary syndrome) and non-PCOS patients

BackgroundThe genetic make-up of local granulosa cells and their function in the pathophysiology of polycystic ovary syndrome (PCOS) is crucial to a full comprehension of the disorder. The major purpose of this study was to compare the Single Nucleotide Polymorphism (SNP) of cumulus granulosa cells (CGCs) and mural granulosa cells (MGCs) between healthy individuals and women with PCOS using genome-wide association analysis (GWA). A case–control study was conducted in a total of 24 women diagnosed with PCOS and 24 healthy non-PCOS women of reproductive age aggregated into 4 samples of 6 patients each. GWA studies entail several processes, such as cell separation, cellular DNA extraction, library preparation followed by interpretation using bioinformatics databases. SNP locations were identified by reference gene also involves the use of Matrix-assisted laser desorption/ionisation-time of flight (MALDI-TOF) mass spectrometry (MS) (MALDI-TOF-MS) for the first sorting. Hybridization with the gene chip was followed by reading the SNP genotypes according to the publications in the literature. TASSEL (Trait Analysis by aSSociation, Evolution and Linkage) program and methods were used for GWA studies.ResultsAn aggregate of 21,039 SNP calls were obtained from our samples. Genes of autoimmune illnesses, obesity, inflammatory illnesses, nervous system diseases such as retinitis pigmentosa, autism, neural tube defects, and Alzheimer's disease; and various malignancies such as lung cancer, colorectal cancer, breast cancer were also identified in these cells. Gene ranking score reveals that granulosa cells carry key genes of neurological system and reproductive systems especially in brain and testis, respectively.ConclusionsMural and Cumulus Granulosa cells were shown to have the PCOS directly and indirectly related genes MMP9, PRKAA2, COMT and HP. We found that the expression of ARID4B, MUC5AC, NID2, CREBBP, GNB1, KIF2C, COL18A1, and HNRNPC by these cells may contribute to PCOS.Graphical abstract

Optimal Reference Genes for RT-qPCR Experiments in Hippocampus and Cortex of Rats Chronically Exposed to Excessive Fluoride.

Normalization of the quantitative real-time PCR (RT-qPCR) data to the stably expressed reference genes is critically important for obtaining reliable results. However, all previous studies focused on F- toxicity for brain tissues used a single, non-validated reference gene, what might be a cause of contradictory or false results. The present study was designed to analyze the expression of a series of reference genes to select optimal ones for RT-qPCR analysis in cortex and hippocampus of rats chronically exposed to excessive fluoride (F-) amounts. Six-week-old male Wistar rats randomly assigned to four groups consumed regular tap water with 0.4 (control), 5, 20, and 50 ppm F- (NaF) for 12 months. The expression of six genes (Gapdh, Pgk1, Eef1a1, Ppia, Tbp, Helz) was compared by RT-qPCR in brain tissues from control and F--exposed animals. The stability of candidate reference genes was evaluated by coefficient of variation (CV) analysis and RefFinder online program summarizing the results of four well-acknowledged statistical methods (Delta-Ct, BestKeeper, NormFinder, and GeNorm). In spite of some discrepancies in gene ranking between these algorisms, Pgk1, Eef1a1, and Ppia were found to be most valid in cortex, while Ppia, Eef1a1, and Helz showed the greatest expression stability in hippocampus. Tbp and Helz were identified as the least stable genes in cortex, whereas Gapdh and Tbp are unsuitable for hippocampus. These data indicate that reliable mRNA quantification in the cortex and hippocampus of F--poisoned rats is possible using normalization to geometric mean of Pgk1+Eef1a1 or Ppia+Eef1a1 expression, respectively.

MP20-09 GENOME-WIDE CRISPR SCREENS IDENTIFY NOVEL TARGET GENES WHICH ALTER PROSTATE CANCER SENSITIVITY TO RADIATION THERAPY

You have accessJournal of UrologyCME1 Apr 2023MP20-09 GENOME-WIDE CRISPR SCREENS IDENTIFY NOVEL TARGET GENES WHICH ALTER PROSTATE CANCER SENSITIVITY TO RADIATION THERAPY Koji Hatano, Toshiki Oka, Keisuke Nimura, Sassi Nesrine, Yohei Okuda, Akinaru Yamamoto, Toshihiro Uemura, Gaku Yamamichi, Eisuke Tomiyama, Yu Ishizuya, Yoshiyuki Yamamoto, Taigo Kato, Atsunari Kawashima, Kazutoshi Fujita, and Norio Nonomura Koji HatanoKoji Hatano More articles by this author , Toshiki OkaToshiki Oka More articles by this author , Keisuke NimuraKeisuke Nimura More articles by this author , Sassi NesrineSassi Nesrine More articles by this author , Yohei OkudaYohei Okuda More articles by this author , Akinaru YamamotoAkinaru Yamamoto More articles by this author , Toshihiro UemuraToshihiro Uemura More articles by this author , Gaku YamamichiGaku Yamamichi More articles by this author , Eisuke TomiyamaEisuke Tomiyama More articles by this author , Yu IshizuyaYu Ishizuya More articles by this author , Yoshiyuki YamamotoYoshiyuki Yamamoto More articles by this author , Taigo KatoTaigo Kato More articles by this author , Atsunari KawashimaAtsunari Kawashima More articles by this author , Kazutoshi FujitaKazutoshi Fujita More articles by this author , and Norio NonomuraNorio Nonomura More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003245.09AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Radiation therapy (RT), including radioligand therapy, is widely used for the treatment of prostate cancer (PCa). Although the majority of PCa can be effectively treated with RT, approximately 50% of men with high risk PCa will recur. A number of mechanisms, including DNA repair, cell cycle and apoptotic pathways can influence cellular sensitivity to radiation and chemotherapy. Despite recent progress in genomic profiling of PCa, RT-related biomarkers for improving outcome and reducing toxicity remain unknown. Here, we used genome-wide CRISPR screens to systematically map radiosensitivity and gene interactions and to identify novel therapeutic targets in PCa. METHODS: PCa cells lines, DU145, 22Rv1, and LNCaP, were used to establish CRISPR/Cas9 cell libraries. CRISPR library contained 90,709 sgRNAs targeting 18,010 genes. CRISPR/Cas9 cell libraries were irradiated (4 or 6 Gy) or untreated (control), and cultured for 11 days. The number of each sgRNA was evaluated by sequencing and compared to controls (Figure 1). sgRNA dropoff indicates radiosensitizing genes and sgRNA enrich indicates radioprotective genes. Robust Rank Aggregation (RRA) score was utilized as an estimation for robust identification of CRISPR-screen hits. Top ranked radiosensitizing genes were selected by RRA score in CRISPR screen by three PCa cell lines. RESULTS: CRISPR screens showed that radiosensitizing genes abundantly contain DNA repair pathway genes, while radioprotective genes contain cell cycle and apoptosis-related genes. Among PCa-related genes, ATM, NBN and PTEN were identified as radiosensitizing genes, while CHEK2 and TP53 were identified as radioprotective genes. The top ranked radiosensitizing genes were identified by CRISPR screens of three PCa cell lines, and validated by each gene knockout using multiple cell lines. The interaction of androgen receptor signaling and radiosensitizing genes was demonstrated using androgen suppression and testosterone addition in LNCaP cells. CONCLUSIONS: Functional CRISPR screens revealed genome-wide RT responses in PCa, providing new information for the development of personalized RT. Source of Funding: Japan Society for the Promotion of Science KAKENHI, grant number 20K18090 © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e278 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Koji Hatano More articles by this author Toshiki Oka More articles by this author Keisuke Nimura More articles by this author Sassi Nesrine More articles by this author Yohei Okuda More articles by this author Akinaru Yamamoto More articles by this author Toshihiro Uemura More articles by this author Gaku Yamamichi More articles by this author Eisuke Tomiyama More articles by this author Yu Ishizuya More articles by this author Yoshiyuki Yamamoto More articles by this author Taigo Kato More articles by this author Atsunari Kawashima More articles by this author Kazutoshi Fujita More articles by this author Norio Nonomura More articles by this author Expand All Advertisement PDF downloadLoading ...

Analysis of factors affecting testicular spermatogenesis capacity by using the tissue transcriptome data from GTEx.

In human, endo- or exogeneous factors might alter the cellular composition, the endocrine and inflammatory micro-environments and the metabolic balance in testis. These factors will further impair the testicular spermatogenesis capacity and alter the transcriptome of testis. Conversely, it should be possible that the alteration of the transcriptomes in testes be used as an indicator to evaluate the testicular spermatogenesis capacity and to predict the causing factors. In this study, using the transcriptome data of human testes and whole blood which were collected by the genotype-tissue expression project (GTEx), we analyzed the transcriptome differences in human testes and explored those factors that affecting spermatogenesis. As a result, testes were clustered into five clusters according to their transcriptomic features, and each cluster of testes was evaluated as having different spermatogenesis capacity. High rank genes of each cluster and the differentially expressed genes in lower functional testes were analyzed. Transcripts in whole blood which may be associated with testis function were also analyzed by the correlation test. As a result, factors such as immune response, oxygen transport, thyrotropin, prostaglandin and tridecapeptide neurotensin were found associated with spermatogenesis. These results revealed multiple clues about the spermatogenesis regulation in testis and provided potential targets to improve the fertility of men in clinic.

Exploiting machine learning models to identify novel Alzheimer’s disease biomarkers and potential targets

We still do not have an effective treatment for Alzheimer's disease (AD) despite it being the most common cause of dementia and impaired cognitive function. Thus, research endeavors are directed toward identifying AD biomarkers and targets. In this regard, we designed a computational method that exploits multiple hub gene ranking methods and feature selection methods with machine learning and deep learning to identify biomarkers and targets. First, we used three AD gene expression datasets to identify 1/ hub genes based on six ranking algorithms (Degree, Maximum Neighborhood Component (MNC), Maximal Clique Centrality (MCC), Betweenness Centrality (BC), Closeness Centrality, and Stress Centrality), 2/ gene subsets based on two feature selection methods (LASSO and Ridge). Then, we developed machine learning and deep learning models to determine the gene subset that best distinguishes AD samples from the healthy controls. This work shows that feature selection methods achieve better prediction performances than the hub gene sets. Beyond this, the five genes identified by both feature selection methods (LASSO and Ridge algorithms) achieved an AUC = 0.979. We further show that 70% of the upregulated hub genes (among the 28 overlapping hub genes) are AD targets based on a literature review and six miRNA (hsa-mir-16-5p, hsa-mir-34a-5p, hsa-mir-1-3p, hsa-mir-26a-5p, hsa-mir-93-5p, hsa-mir-155-5p) and one transcription factor, JUN, are associated with the upregulated hub genes. Furthermore, since 2020, four of the six microRNA were also shown to be potential AD targets. To our knowledge, this is the first work showing that such a small number of genes can distinguish AD samples from healthy controls with high accuracy and that overlapping upregulated hub genes can narrow the search space for potential novel targets.

Transcriptomic profile comparison of monocytes from rheumatoid arthritis patients in treatment with methotrexate, anti-TNFa, abatacept or tocilizumab.

It is well documented that patients affected by rheumatoid arthritis (RA) have distinct susceptibility to the different biologic DMARDs available on the market, probably because of the many facets of the disease. Monocytes are deeply involved in the pathogenesis of RA and we therefore evaluated and compared the transcriptomic profile of monocytes isolated from patients on treatment with methotrexate alone or in combination with tocilizumab, anti-TNFα or abatacept and from healthy donors. Whole-genome transcriptomics yielded a list of regulated genes by Rank Product statistics and DAVID was then used for functional annotation enrichment analysis. Last, data were validated by qRT-PCR. Abatacept, tocilizumab and anti-TNFa cohorts were separately compared with methotrexate, leading to the identification of 78, 6, and 436 differentially expressed genes, respectively. The upper-most ranked genes were related to inflammatory processes and immune responses. Such an approach draws the genomic profile of monocytes in treated RA patients and lays the basis for finding gene signature for tailored therapeutic choices.

Identification of genes related to immune enhancement caused by heterologous ChAdOx1-BNT162b2 vaccines in lymphocytes at single-cell resolution with machine learning methods.

The widely used ChAdOx1 nCoV-19 (ChAd) vector and BNT162b2 (BNT) mRNA vaccines have been shown to induce robust immune responses. Recent studies demonstrated that the immune responses of people who received one dose of ChAdOx1 and one dose of BNT were better than those of people who received vaccines with two homologous ChAdOx1 or two BNT doses. However, how heterologous vaccines function has not been extensively investigated. In this study, single-cell RNA sequencing data from three classes of samples: volunteers vaccinated with heterologous ChAdOx1-BNT and volunteers vaccinated with homologous ChAd-ChAd and BNT-BNT vaccinations after 7 days were divided into three types of immune cells (3654 B, 8212 CD4+ T, and 5608 CD8+ T cells). To identify differences in gene expression in various cell types induced by vaccines administered through different vaccination strategies, multiple advanced feature selection methods (max-relevance and min-redundancy, Monte Carlo feature selection, least absolute shrinkage and selection operator, light gradient boosting machine, and permutation feature importance) and classification algorithms (decision tree and random forest) were integrated into a computational framework. Feature selection methods were in charge of analyzing the importance of gene features, yielding multiple gene lists. These lists were fed into incremental feature selection, incorporating decision tree and random forest, to extract essential genes, classification rules and build efficient classifiers. Highly ranked genes include PLCG2, whose differential expression is important to the B cell immune pathway and is positively correlated with immune cells, such as CD8+ T cells, and B2M, which is associated with thymic T cell differentiation. This study gave an important contribution to the mechanistic explanation of results showing the stronger immune response of a heterologous ChAdOx1-BNT vaccination schedule than two doses of either BNT or ChAdOx1, offering a theoretical foundation for vaccine modification.

Abstract P2-03-20: Clinical profiling and comprehensive analysis of candidate genes related to breast cancer estrogen receptor intratumour heterogeneity

Abstract 1. Background and Aim Breast cancer has the highest incidence among the world’s population. One third of patients diagnosed with early breast cancer progressed into metastatic outcomes due to the inherent heterogeneity and evolutionary features of tumors. The main clinical manifestation is the discordance of receptor expression in metastases. ESR1 the estrogen receptor (ER) encoding gene, has been proved affected ER expression. Nevertheless, the correlation between ER transformation and the somatic mutation profiles in breast cancer metastatic lesions remains unclear. Availability of advanced high-throughput technologies, and the development of bioinformatics tools has greatly accelerated our cognition of the molecular basis of cancer. In this study, we aimed to evaluate the frequency, clinical characteristics and prognostic value of ER receptor conversion between primary and first metastatic lesions in 115 patients whose primary ER status are positive. We also compared the results of NGS of 42 patients and explored latent genes related to ER discordance in expression. 2. Method Qualified metastatic tumor tissue sequencing information was available for 42 patients. Samples were accessed using the FoundationOne CDx assay (Foundation Medicine, Cambridge, MA, USA) panel including 733 tumor related genes. High frequency mutant genes were defined as the genes count more than 4 times in each group. All data were analyzed using SPSS 25.0 software (Chicago, IL, USA). Pearson’s chi-squared (χ2) test, Fisher’s exact test, were used to test the associations between different variables. The Cox proportional hazard models was used to evaluate the relationship between KMT2C mutation and Disease free survival (DFS) in the patients cohorts. The results with p < 0.05 were considered statistically significant. 3. Results and Conclusions All 115 patients (primary ER status is positive) were divided into two groups, 70(60.87%) patients ER status remained positive while 45(39.14%) patients ER status transformed into negative. The histological type (p=0.008) and DFS (p=0.004) were significantly different in two subgroups. There were no significant correlations between the age at the time of diagnosis, BMI classification, menopausal status, surgery, histological grade, tumor size, lymph node status, metastasis, Ki67, adjuvant radiotherapy, neoadjuvant, metastasis site before second biopsy, first treatment after recurrent and first PFS after rebiopsy. Univariable analysis of DFS proved metastasis to be distinct risk factor for poor survival (hazard ratio [HR] > 1, p = 0.001). By contrast, neoadjuvant proved to be protective factor for better survival (hazard ratio [HR] < 1, p < 0.001). The surgery, histological type, histological grade, tumorsize, metastasis, neoadjuvant, first treatment after recurrent, first PFS after rebiopsy were subsequently analyzed with the multivariable Cox analysis. Metastasis (HR>1, p=0.007) and neoadjuvant (HR<1, p<0.001) remained independent prognostic factors of DFS. We compared the differentially mutated genes in the ER differential expression BC patient groups. Mutations in TP53 (n=24) and PIK3CA (n=19) occurred most frequently in both groups and account for 57.14% and 49.23% separately. Notably, mutations in KMT2C (n=6) was detected in ER+ to – subgroup and accounted for 37.5%. According to the results of NGS, the distribution of KMT2C mutations was different among the two cohorts. We identified the mutation in codon 321 was the hot spot of KMT2C in our patient cohort. Next, we used the Kyoto Encyclopedia of Genes and Genomes (KEGG) database to analyze the enriched pathways of KMT2C. KEGG pathway analysis revealed that patients with KMT2C mutations harbored significantly more mutations in genes involved in the Ubiquitin mediated proteolysis and Lysine degradation signaling pathway. Table 1. Patients’ basic clinicopathological characteristics among two groups Table 2. Univariate and Multivariate Analysis Between Clinicopathological Characteristics and DFS of 115 BC patients Table 3. Mutated Genes Rank and patients number Citation Format: Xi Wang, Yongmei Yin, Ziyi Fu. Clinical profiling and comprehensive analysis of candidate genes related to breast cancer estrogen receptor intratumour heterogeneity [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P2-03-20.

A novel Hoxd13 mutation causes synpolydactyly and promotes osteoclast differentiation by regulating pSmad5/p65/c-Fos/Rank axis

The mutations of HOXD13 gene have been involved in synpolydactyly (SPD), and the polyalanine extension mutation of Hoxd13 gene could lead to SPD in mice. In this study, a novel missense mutation of Hoxd13 (NM_000523: exon2: c.G917T: p.R306L) was identified in a Chinese family with SPD. The mice carrying the corresponding Hoxd13mutation were generated. The results showed that the homozygous mutation of Hoxd13 also caused SPD, but heterozygous mutation did not affect limbs development, which was different from that of SPD patients. With the increasing generation, the mice with homozygous Hoxd13 mutation presented more severe syndactyly. Western blotting showed that this mutation did not affect the protein expression of Hoxd13, suggesting that this mutation did not result in haploinsufficiency. Further analysis demonstrated that this homozygous Hoxd13mutation promoted osteoclast differentiation and bone loss, and enhanced the mRNA and protein expression of osteoclast-related genes Rank, c-Fos, and p65. Meanwhile, this homozygous Hoxd13 mutation elevated the level of phosphorylated Smad5 (pSmad5). Co-immunoprecipitation verified that this mutation attenuated the interaction between pSmad5 and HOXD13, suggesting that this mutation released more pSmad5. Inhibition of pSmad5 reduced the expression of Rank, c-Fos, and p65 despite in the mutation group. In addition, inhibition of pSmad5 repressed the osteoclast differentiation. ChIP assay confirmed that p65 and c-Fos could bind to the promoter of Rank. These results suggested that this novel Hoxd13 mutation promoted osteoclast differentiation by regulating Smad5/p65/c-Fos/Rank axis, which might provide a new insight into SPD development.

Identification of potential immune-related ceRNA Regulatory Network in UVB-irradiated human skin

ABSTRACT For a better understanding the molecular biomarkers in UVB-induced skin damage, and its potential mechanism, we downloaded two microarray data sets on skin UVB damage from the Gene Expression Omnibus (GEO): GSE21429, GSE56754. By using the Limma package to analyze differential gene expression and co-expression network analysis to screen module genes, 16 common genes were identified (16 up-regulated). Gene Ontology analysis to explore the functional roles of these genes indicated that the common genes were associated mainly with melanin biosynthetic process and metabolic process. Gene Set Enrichment Analysis provided evidence that the most gene sets enriched in immune and inflammation-related signaling pathways in the UVB-treated subjects, as compared with the untreated subjects. The PPI network genes were ranked according to the degree of connectivity, the top three ranked genes: ”MLANA”, ”GPR143” and ”SFTPC” were identified as potential biomarkers using the area under the receiver operating characteristic curve. The relative proportion of 22 immune cell types was then calculated by using the CIBERSORT algorithm. A higher follicular helper T cell ratio in UVB-treated samples compared to untreated samples was observed. Moreover, three hub genes have also been shown to be associated with immune cells. Finally, through multiple online miRNA databases, we propose MLANA-miR-573-MALAT1/NEAT1, GPR143-miR-138-5p-MALAT1/ KCNQ1OT1 might be potential RNA regulatory pathways that control disease progression in UVB-induced skin damage. In summary, the present results provide novel insights into the UVB-radiation related biological process changes, and further offer a new clinical application for prognosis and diagnostic prediction of UVB radiation-mediated skin damage.

Improved drug response prediction by drug target data integration via network-based profiling.

Drug response prediction (DRP) is important for precision medicine to predict how a patient would react to a drug before administration. Existing studies take the cell line transcriptome data, and the chemical structure of drugs as input and predict drug response as IC50 or AUC values. Intuitively, use of drug target interaction (DTI) information can be useful for DRP. However, use of DTI is difficult because existing drug response database such as CCLE and GDSC do not have information about transcriptome after drug treatment. Although transcriptome after drug treatment is not available, if we can compute the perturbation effects by the pharmacologic modulation of target gene, we can utilize the DTI information in CCLE and GDSC. In this study, we proposed a framework that can improve existing deep learning-based DRP models by effectively utilizing drug target information. Our framework includes NetGP, a module to compute gene perturbation scores by the network propagation technique on a network. NetGP produces genes in a ranked list in terms of gene perturbation scores and the ranked genes are input to a multi-layer perceptron to generate a fixed dimension vector for the integration with existing DRP models. This integration is done in a model-agnostic way so that any existing DRP tool can be incorporated. As a result, our framework boosts the performance of existing DRP models, in 64 of 72 comparisons. The performance gains are larger especially for test scenarios with samples with unseen drugs by large margins up to 34% in Pearson's correlation coefficient.

‘QUICKDASH’ TO FIND UNIQUE GENES AND BIOLOGICAL PROCESSES ASSOCIATED WITH SHOULDER OSTEOARTHRITIS: A PROSPECTIVE CASE-CONTROL STUDY

Osteoarthritis (OA) is a disease of the synovial joint with synovial inflammation, capsular contracture, articular cartilage degradation, subchondral sclerosis and osteophyte formation contributing to pain and disability. Transcriptomic datasets have identified genetic loci in hip and knee OA demonstrating joint specificity. A limited number of studies have directly investigated transcriptional changes in shoulder OA. Further, gene expression patterns of periarticular tissues in OA have not been thoroughly investigated. This prospective case control series details transcriptomic expression of shoulder OA by analysing periarticular tissues in patients undergoing shoulder replacement for OA as correlated with a validated patient reported outcome measure of shoulder function, an increasing (clinically worsening) QuickDASH score. We then compared transcriptomic expression profiles in capsular tissue biopsies from the OA group (N=6) as compared to patients undergoing shoulder stabilisation for recurrent instability (the control group, N=26). Results indicated that top ranked genes associated with increasing QuickDASH score across all tissues involved inflammation and response to stress, namely interleukins, chemokines, complement components, nuclear response factors and immediate early response genes. Some of these genes were upregulated, and some downregulated, suggestive of a state of flux between inflammatory and anti-inflammatory signalling pathways. We have also described gene expression pathways in shoulder OA not previously identified in hip and knee OA, as well as novel genes involved in shoulder OA.

Bioinformatics and Connectivity Map Analysis Suggest Viral Infection as a Critical Causative Factor of Hashimoto's Thyroiditis.

Hashimoto's thyroiditis (HT) is a common autoimmune disease, and its prevalence is rapidly increasing. Both genetic and environmental risk factors contribute to the development of HT. Recently, viral infection has been suggested to act as a trigger of HT by eliciting the host immune response and subsequent autoreactivity. We analyzed the features of HT through bioinformatics analysis so as to identify the markers of HT development. We accessed public microarray data of HT patients from the Gene Expression Omnibus (GEO) and obtained differentially expressed genes (DEGs) under HT. Gene Ontology (GO) and KEGG-pathway-enrichment analyses were performed for functional clustering of our protein-protein interaction (PPI) network. Utilizing ranked gene lists, we performed a Gene Set Enrichment Analysis (GSEA) by using the clusterprofiler R package. By comparing the expression signatures of the huge perturbation database with the queried rank-ordered gene list, a connectivity map (CMap) analysis was performed to screen potential therapeutic targets and agents. The gene expression profile of the HT group was in line with the general characteristics of HT. Biological processes related to the immune response and viral infection pathways were obtained for the upregulated DEGs. The GSEA results revealed activation of autoimmune-disease-related pathways and several viral-infection pathways. Autoimmune-disease and viral-infection pathways were highly interconnected by common genes, while the HLA genes, which are shared by both, were significantly upregulated. The CMap analysis suggested that perturbagens, including SRRM1, NLK, and CCDC92, have the potential to reverse the HT expression profile. Several lines of evidence suggested that viral infection and the host immune response are activated during HT. Viral infection is suspected to act as a key trigger of HT by causing autoimmunity. SRRM1, an alternative splicing factor which responds to viral activity, might serve as potential marker of HT.

EasyCellType: marker-based cell-type annotation by automatically querying multiple databases.

Cell label annotation is a challenging step in the analysis of single-cell RNA sequencing (scRNA-seq) data, especially for tissue types that are less commonly studied. The accumulation of scRNA-seq studies and biological knowledge leads to several well-maintained cell marker databases. Manually examining the cell marker lists against these databases can be difficult due to the large amount of available information. Additionally, simply overlapping the two lists without considering gene ranking might lead to unreliable results. Thus, an automated method with careful statistical testing is needed to facilitate the usage of these databases. We develop a user-friendly computational tool, EasyCellType, which automatically checks an input marker list obtained by differential expression analysis against the databases and provides annotation recommendations in graphical outcomes. The package provides two statistical tests, gene set enrichment analysis and a modified version of Fisher's exact test, as well as customized database and tissue type choices. We also provide an interactive shiny application to annotate cells in a user-friendly graphical user interface. The simulation study and real-data applications demonstrate favorable results by the proposed method. https://biostatistics.mdanderson.org/shinyapps/EasyCellType/; https://bioconductor.org/packages/devel/bioc/html/EasyCellType.html. Supplementary data are available at Bioinformatics Advances online.

Unsupervised Co-Optimization of A Graph Neural Network and A Knowledge Graph Embedding Model to Prioritize Causal Genes for Alzheimer’s Disease

Data obtained from clinical trials for a given disease often capture reliable empirical features of the highest quality which are limited to few studies/experiments. In contrast, knowledge data extracted from biomedical literature captures a wide range of clinical information relevant to a given disease that may not be as reliable as the experimental data. Therefore, we propose a novel method of training that co-optimizes two AI algorithms on experimental data and knowledge-based information from literature respectively to supplement the learning of one algorithm with that of the other and apply this method to prioritize/rank causal genes for Alzheimer’s Disease (AD). One algorithm generates unsupervised embeddings for gene nodes in a protein-protein interaction network associated with experimental data. The other algorithm generates embeddings for the nodes/entities in a knowledge graph constructed from biomedical literature. Both these algorithms are co-optimized to leverage information from each other’s domain. Therefore; a downstream inferencing task to rank causal genes for AD ensures the consideration of experimental and literature data available to implicate any given gene in the geneset. Rank-based evaluation metrics computed to validate the gene rankings prioritized by our algorithm showed that the top ranked positions were highly enriched with genes from a ground truth set that were experimentally verified to be causal for the progression of AD.

In situ sequence-specific visualization of single methylated cytosine on tissue sections using ICON probe and rolling-circle amplification

Since epigenetic modifications differ from cell to cell, detecting the DNA methylation status of individual cells is requisite. Therefore, it is important to conduct “morphology-based epigenetics research”, in which the sequence-specific DNA methylation status is observed while maintaining tissue architecture. Here we demonstrate a novel histochemical technique that efficiently shows the presence of a single methylated cytosine in a sequence-dependent manner by applying ICON (interstrand complexation with osmium for nucleic acids) probes. By optimizing the concentration and duration of potassium osmate treatment, ICON probes selectively hybridize to methylated cytosine on tissue sections. Since the elongation process by rolling-circle amplification through the padlock probe and synchronous amplification by the hyperbranching reaction at a constant temperature efficiently amplifies the reaction, it is possible to specifically detect the presence of a single methylated cytosine. Since the ICON probe is cross-linked to the nuclear or mitochondrial DNA of the target cell, subsequent elongation and multiplication reactions proceed like a tree growing in soil with its roots firmly planted, thus facilitating the demonstration of methylated cytosine in situ. Using this novel ICON-mediated histochemical method, detection of the methylation of DNA in the regulatory region of the RANK gene in cultured cells and of mitochondrial DNA in paraffin sections of mouse cerebellar tissue was achievable. This combined ICON and rolling-circle amplification method is the first that shows evidence of the presence of a single methylated cytosine in a sequence-specific manner in paraffin sections, and is foreseen as applicable to a wide range of epigenetic studies.

GAMB-GNN: Graph Neural Networks learning from gene structure relations and Markov Blanket ranking for cancer classification in microarray data

Microarray data plays a significant role in the classification and prediction of cancer, which has become an essential area of research in computational biology and bioinformatics. Several machine learning methods have been applied to help classify and predict cancer using microarray data. However, previous methods have mainly leveraged the expression level of a subset of genes as a sample feature. These methods lack the utilization of the structure relation information among genes. At the same time, there is a lack of analysis of redundancy among features in the existing gene selection algorithms. This paper proposes a Graph Neural Networks model with the Markov Blanket ranking method for cancer classification (GAMB-GNN) in microarray data to combat these challenges. The problem of cancer classification in the microarray data is considered by gene attributes and multi-type relation networks among genes in our model. The proposed GAMB-GNN obtains the gene scores and ranking according to the relevance of genes and cancer by the gene ranking algorithm based on Markov Blanket and then selects a top fraction of the ranking list to construct the multi-type gene relations graph. Further, GAMB-GNN employs a graph classifier with a relation attention mechanism and node aggregation pooling to learn the multiple relations structure and weight to obtain a better feature representation of the sample. The proposed model is validated through experiments in six public microarray datasets. The experiment results show that the accuracy and f1-score of our method on the cancer classification task on all tested microarray datasets improved by 3.98%–24.36% and 4.22%–31.93% on average over the baseline and state-of-the-art methods.

A Multi-Cohort Gene Expression Classifier (ALLCatchR) Identifies B-Precursor ALL Subtypes and Their Developmental Trajectories across Age Groups

The molecular landscape of B cell precursor acute lymphoblastic leukemia (BCP-ALL) describes >20 disease subtypes defined by initiating genomic alterations and corresponding gene expression signatures. Some subtypes represent phenocopies with shared gene expression signatures but distinct or absent genomic drivers. The majority of BCP-ALL subtypes have been acknowledged as diagnostic entities by the currently revised classifications of hematologic malignancies (WHO-HAEM5 / International Consensus Classification). Thus, unified diagnostic approaches are needed to systematically identify subtypes in individual BCP-ALL cases in clinical routine. Gene expression signatures represent the shared functional equivalent of heterogeneous genomic driver alterations. Therefore, we established a gene expression-based classifier to identify BCP-ALL subtypes and related clinical and biological phenotypes. We analyzed a total of n=3477 BCP-ALL gene expression profiles obtained from transcriptome sequencing of adult (n=1253) and pediatric patients (n=2224) including publicly available (n=2074; Gu Z et al. Nat. Genet. 2019; Schmidt B. et al. Blood Adv. 2022) and own data sets (n=1403) from adult and pediatric European study groups (GMALL, AIEOP-BFM, CELL). Raw read counts from 6 cohorts representing heterogeneous library preps (i.e., stranded/unstranded; mRNA/total RNA), sequencing platforms/depths, and gene count quantifications were included. All samples had been previously allocated to molecular BCP-ALL subtypes based on integration of independent genomic profiling, gene fusion calling and gene expression. For classification, data with defined subgroup allocation were randomly split into training (n=1831; 70%) and testing sets (n=785; 30%) and two hold-out studies (n=305). To define gene expression signatures of 21 BCP-ALL subtypes, we used training set samples and extracted specific gene sets employing different feature selection algorithms as well as ranking of genes in individual subtypes. Based on these definitions we established a BCP-ALL subtype classifier (ALLCatchR), which integrates machine learning models (support vector machines) and nearest-neighbors associations of single sample gene set enrichment analyses (Fig. A). ALLCatchR performed molecular subtype allocation for the testing set at high sensitivity (0.934±0.165) and specificity (0.997±0.005) on average across subtypes. A similar performance was achieved in the two hold-out cohorts despite completely independent data structures which have not been shown to the classifier (sensitivity: 0.946±0.097 / specificity: 0.996±0.007). Overall accuracies of 0.952 and 0.92 for testing and hold-out-data sets indicate that ALLCatchR reliably identifies molecular BCP-ALL subtypes in mixed adult / pediatric cohorts based on gene expression profiles alone (Fig. A, below). Among n=44/1090 (4%) samples of the combined hold-out/testing set with ambiguous subtype allocation (deemed 'unclassified'), ALLCatchR provides candidate subtype allocations (sensitivity: 0.671±0.393 / specificity: 0.978±0.036) for confirmation based on genomic driver alterations. In addition, we modified our classifier to predict baseline variables such as patient's sex (accuracy: 0.991), immunophenotype (accuracy: 0.935) or blast count (R=0.633). To define the corresponding B cell differentiation stage of origin, we compared BCP-ALL subtypes to differentiation stage-specific gene sets extracted from 7 FACS-sorted lymphopoietic subsets from healthy bone marrow donors (n=4). We observed defined BCP-ALL subtype-specific enrichment patterns which can be grouped along 4 major differentiation stages (Pro-B to Pre-B-II-Large, Fig. 1B) with a high degree of similarity in independent adult and pediatric cohorts. Thus, B cell developmental trajectories underlying BCP-ALL subtypes are dissected at high resolution by gene expression profiling and differentiation stages may favor the selection of specific leukemogenic drivers. ALLCatchR is a gene expression classifier for 21 established BCP-ALL molecular subtypes, which performs at independently validated cohorts with an accuracy of >90% and allows imputation of clinical baseline variables as well as underlying B cell developmental trajectories. It provides a reliable basis for systematic diagnostic approaches in pediatric and adult BCP-ALL patients. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal