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1
Ortiz, Randy, Priyanka Gera, Christopher Rivera, and Juan C. Santos. "Pincho: A Modular Approach to High Quality De Novo Transcriptomics." Genes 12, no. 7 (June 22, 2021): 953. http://dx.doi.org/10.3390/genes12070953.
Abstract:
Transcriptomic reconstructions without reference (i.e., de novo) are common for data samples derived from non-model biological systems. These assemblies involve massive parallel short read sequence reconstructions from experiments, but they usually employ ad-hoc bioinformatic workflows that exhibit limited standardization and customization. The increasing number of transcriptome assembly software continues to provide little room for standardization which is exacerbated by the lack of studies on modularity that compare the effects of assembler synergy. We developed a customizable management workflow for de novo transcriptomics that includes modular units for short read cleaning, assembly, validation, annotation, and expression analysis by connecting twenty-five individual bioinformatic tools. With our software tool, we were able to compare the assessment scores based on 129 distinct single-, bi- and tri-assembler combinations with diverse k-mer size selections. Our results demonstrate a drastic increase in the quality of transcriptome assemblies with bi- and tri- assembler combinations. We aim for our software to improve de novo transcriptome reconstructions for the ever-growing landscape of RNA-seq data derived from non-model systems. We offer guidance to ensure the most complete transcriptomic reconstructions via the inclusion of modular multi-assembly software controlled from a single master console.
2
Hynst, Jakub, Karla Plevova, Lenka Radova, Vojtech Bystry, Karol Pal, and Sarka Pospisilova. "Bioinformatic pipelines for whole transcriptome sequencing data exploitation in leukemia patients with complex structural variants." PeerJ 7 (June 12, 2019): e7071. http://dx.doi.org/10.7717/peerj.7071.
Abstract:
Background Extensive genome rearrangements, known as chromothripsis, have been recently identified in several cancer types. Chromothripsis leads to complex structural variants (cSVs) causing aberrant gene expression and the formation of de novo fusion genes, which can trigger cancer development, or worsen its clinical course. The functional impact of cSVs can be studied at the RNA level using whole transcriptome sequencing (total RNA-Seq). It represents a powerful tool for discovering, profiling, and quantifying changes of gene expression in the overall genomic context. However, bioinformatic analysis of transcriptomic data, especially in cases with cSVs, is a complex and challenging task, and the development of proper bioinformatic tools for transcriptome studies is necessary. Methods We designed a bioinformatic workflow for the analysis of total RNA-Seq data consisting of two separate parts (pipelines): The first pipeline incorporates a statistical solution for differential gene expression analysis in a biologically heterogeneous sample set. We utilized results from transcriptomic arrays which were carried out in parallel to increase the precision of the analysis. The second pipeline is used for the identification of de novo fusion genes. Special attention was given to the filtering of false positives (FPs), which was achieved through consensus fusion calling with several fusion gene callers. We applied the workflow to the data obtained from ten patients with chronic lymphocytic leukemia (CLL) to describe the consequences of their cSVs in detail. The fusion genes identified by our pipeline were correlated with genomic break-points detected by genomic arrays. Results We set up a novel solution for differential gene expression analysis of individual samples and de novo fusion gene detection from total RNA-Seq data. The results of the differential gene expression analysis were concordant with results obtained by transcriptomic arrays, which demonstrates the analytical capabilities of our method. We also showed that the consensus fusion gene detection approach was able to identify true positives (TPs) efficiently. Detected coordinates of fusion gene junctions were in concordance with genomic breakpoints assessed using genomic arrays. Discussion Byapplying our methods to real clinical samples, we proved that our approach for total RNA-Seq data analysis generates results consistent with other genomic analytical techniques. The data obtained by our analyses provided clues for the study of the biological consequences of cSVs with far-reaching implications for clinical outcome and management of cancer patients. The bioinformatic workflow is also widely applicable for addressing other research questions in different contexts, for which transcriptomic data are generated.
3
Krishnan, Vidya S., and Sulev Kõks. "Transcriptional Basis of Psoriasis from Large Scale Gene Expression Studies: The Importance of Moving towards a Precision Medicine Approach." International Journal of Molecular Sciences 23, no. 11 (May 30, 2022): 6130. http://dx.doi.org/10.3390/ijms23116130.
Abstract:
Transcriptome profiling techniques, such as microarrays and RNA sequencing (RNA-seq), are valuable tools for deciphering the regulatory network underlying psoriasis and have revealed large number of differentially expressed genes in lesional and non-lesional skin. Such approaches provide a more precise measurement of transcript levels and their isoforms than any other methods. Large cohort transcriptomic analyses have greatly improved our understanding of the physiological and molecular mechanisms underlying disease pathogenesis and progression. Here, we mostly review the findings of some important large scale psoriatic transcriptomic studies, and the benefits of such studies in elucidating potential therapeutic targets and biomarkers for psoriasis treatment. We also emphasised the importance of looking into the alternatively spliced RNA isoforms/transcripts in psoriasis, rather than focussing only on the gene-level annotation. The neutrophil and blood transcriptome signature in psoriasis is also briefly reviewed, as it provides the immune status information of patients and is a less invasive platform. The application of precision medicine in current management of psoriasis, by combining transcriptomic data, improves the clinical response outcome in individual patients. Drugs tailored to individual patient’s genetic profile will greatly improve patient outcome and cost savings for the healthcare system.
4
Sauta, Elisabetta, Matteo Zampini, Daniele Dall'Olio, Claudia Sala, Gabriele Todisco, Erica Travaglino, Luca Lanino, et al. "Combining Gene Mutation with Transcriptomic Data Improves Outcome Prediction in Myelodysplastic Syndromes." Blood 142, Supplement 1 (November 28, 2023): 1863. http://dx.doi.org/10.1182/blood-2023-186222.
Abstract:
Background and Aim. Myelodysplastic syndromes (MDS) are myeloid neoplasms characterized by peripheral blood cytopenias and risk of progression to acute myeloid leukemia (AML). Disease management is challenged by heterogeneity in clinical courses and survival probability. Recently, the genomic screening integration (by Molecular International Prognostic Scoring System, IPSS-M) into patient's assessment has resulted into a significant improvement in predicting clinical outcomes compared to the conventional prognostic score (Revised IPSS, IPSS-R). Many of the consequences of genetic and cytogenetic alterations will affect gene expression by means of transcriptional and epigenetic instability and altered microenviromental signaling. The aim of this project conducted by GenoMed4All and Synthema EU consortia is to link genomic information with transcriptomic data for possibly improving the prediction of clinical outcomes in MDS patients. Patients and Methods.Clinical, cytogenetic, genomic (somatic mutations screening of 31 target genes) and transcriptomic (bulk RNA-seq of CD34 + bone marrow cells) data were collected at diagnosis in 389 MDS patients. Transcriptomic and genomic profiles were processed and the former were normalized before Principal Component Analysis (PCA) dimensionality reduction to mine the interdependency of expression-wide perturbation and recurrent genomic alterations. The prognostic impacts of genetic, cytogenetic, transcriptomic, clinical and demographic features were assessed with a penalized Cox's proportional hazards model [Gerstung M et al, Nat Commun. 2015. 6, 5901] considering the Overall Survival (OS) as primary end point. A 5-fold cross-validating (CV) scheme was exploited to control bias in risk estimation. Model accuracy was assessed using Harrell's concordance index (C-index). An independent validation of the results on 202 patients was planned. Results.We first processed each data layer assessing data robustness, removed not informative variables and scaled quantitative ones. We considered recurrent genomic and cytogenetic lesions (present in ≥5 patients), platelets, hemoglobin and bone marrow blasts (%), age and sex as covariates. To explore the main patterns of expression changes, PCA was performed to reduce multidimensional correlated expression features (20 PCs was selected, explaining 42% of the total transcriptomic variability). To evaluate the prognostic power of each data layer we grouped all available features into five groups: gene mutations (n=15), cytogenetic alterations (n=7), expression data (n=20), blood counts (n=3) and demographic variables (n=2). Within a 5-fold CV we combined these variables in our integrative model to calculate MDS patients risk. The obtained predictive accuracy (C-index) for OS was 0.83, underlying that transcriptomic data significantly improved the current standard prognostic scoring systems. Accordingly, in our patient population, the C-index of the conventional IPSS-R score and the new IPSS-M were 0.68 and 0.76, respectively. A similar improvement by adding transcriptomic data was observed in prediction of the risk of AML evolution. Moreover, by analyzing the contribution of each feature category to the OS probability ( Figure 1), in term of explained variance, the relative impact of transcriptomic is 40%, with the remaining prognostic information distributed among genomic features (somatic gene mutations and cytogenetics lesions, 24%), demographics (20%) and clinical features (15%). An independent validation of these results on 202 patients is currently ongoing. Figure 2 shows an example of personalized survival prediction using patients from the study population. In two subjects with same clinical phenotype and mutations leading to a similar IPSS-M prognosis, the integrative model captures additional prognostic information and efficiently predicts clinical outcome. Given the complexity of our model, specific technological support is needed to combine data at individual patient level and to translate it into a personalized outcome prediction. To this aim, we created a prototype web portal based on our dataset for user-defined genomic/transcriptomic and clinical features. Conclusion. In predicting survival of MDS patients, genomic, transcriptomic and diagnostic clinical variables all have utility, with a significant contribution from the transcriptome.
5
Dunn, Jemma, Vasileios P. Lenis, David A. Hilton, Rolf Warta, Christel Herold-Mende, C. Oliver Hanemann, and Matthias E. Futschik. "Integration and Comparison of Transcriptomic and Proteomic Data for Meningioma." Cancers 12, no. 11 (November 5, 2020): 3270. http://dx.doi.org/10.3390/cancers12113270.
Abstract:
Meningioma are the most frequent primary intracranial tumour. Management of aggressive meningioma is complex, and development of effective biomarkers or pharmacological interventions is hampered by an incomplete knowledge of molecular landscape. Here, we present an integrated analysis of two complementary omics studies to investigate alterations in the “transcriptome–proteome” profile of high-grade (III) compared to low-grade (I) meningiomas. We identified 3598 common transcripts/proteins and revealed concordant up- and downregulation in grade III vs. grade I meningiomas. Concordantly upregulated genes included FABP7, a fatty acid binding protein and the monoamine oxidase MAOB, the latter of which we validated at the protein level and established an association with Food and Drug Administration (FDA)-approved drugs. Notably, we derived a plasma signature of 21 discordantly expressed genes showing positive changes in protein but negative in transcript levels of high-grade meningiomas, including the validated genes CST3, LAMP2, PACS1 and HTRA1, suggesting the acquisition of these proteins by tumour from plasma. Aggressive meningiomas were enriched in processes such as oxidative phosphorylation and RNA metabolism, whilst concordantly downregulated genes were related to reduced cellular adhesion. Overall, our study provides the first transcriptome–proteome characterisation of meningioma, identifying several novel and previously described transcripts/proteins with potential grade III biomarker and therapeutic significance.
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Huang, Kexin, Yun Zhang, Haoran Gong, Zhengzheng Qiao, Tiangang Wang, Weiling Zhao, Liyu Huang, and Xiaobo Zhou. "Inferring evolutionary trajectories from cross-sectional transcriptomic data to mirror lung adenocarcinoma progression." PLOS Computational Biology 19, no. 5 (May 25, 2023): e1011122. http://dx.doi.org/10.1371/journal.pcbi.1011122.
Abstract:
Lung adenocarcinoma (LUAD) is a deadly tumor with dynamic evolutionary process. Although much endeavors have been made in identifying the temporal patterns of cancer progression, it remains challenging to infer and interpret the molecular alterations associated with cancer development and progression. To this end, we developed a computational approach to infer the progression trajectory based on cross-sectional transcriptomic data. Analysis of the LUAD data using our approach revealed a linear trajectory with three different branches for malignant progression, and the results showed consistency in three independent cohorts. We used the progression model to elucidate the potential molecular events in LUAD progression. Further analysis showed that overexpression of BUB1B, BUB1 and BUB3 promoted tumor cell proliferation and metastases by disturbing the spindle assembly checkpoint (SAC) in the mitosis. Aberrant mitotic spindle checkpoint signaling appeared to be one of the key factors promoting LUAD progression. We found the inferred cancer trajectory allows to identify LUAD susceptibility genetic variations using genome-wide association analysis. This result shows the opportunity for combining analysis of candidate genetic factors with disease progression. Furthermore, the trajectory showed clear evident mutation accumulation and clonal expansion along with the LUAD progression. Understanding how tumors evolve and identifying mutated genes will help guide cancer management. We investigated the clonal architectures and identified distinct clones and subclones in different LUAD branches. Validation of the model in multiple independent data sets and correlation analysis with clinical results demonstrate that our method is effective and unbiased.
7
Chen, Huapu, Zhiyuan Li, Yaorong Wang, Hai Huang, Xuewei Yang, Shuangfei Li, Wei Yang, and Guangli Li. "Comparison of Gonadal Transcriptomes Uncovers Reproduction-Related Genes with Sexually Dimorphic Expression Patterns in Diodon hystrix." Animals 11, no. 4 (April 7, 2021): 1042. http://dx.doi.org/10.3390/ani11041042.
Abstract:
Diodon hystrix is a new and emerging aquaculture species in south China. However, due to the lack of understanding of reproductive regulation, the management of breeding and reproduction under captivity remains a barrier for the commercial aquaculture of D. hystrix. More genetic information is needed to identify genes critical for gonadal development. Here, the first gonadal transcriptomes of D. hystrix were analyzed and 151.89 million clean reads were generated. All reads were assembled into 57,077 unigenes, and 24,574 could be annotated. By comparing the gonad transcriptomes, 11,487 differentially expressed genes were obtained, of which 4599 were upregulated and 6888 were downregulated in the ovaries. Using enrichment analyses, many functional pathways were found to be associated with reproduction regulation. A set of sex-biased genes putatively involved in gonad development and gametogenesis were identified and their sexually dimorphic expression patterns were characterized. The detailed transcriptomic data provide a useful resource for further research on D. hystrix reproductive manipulation.
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Lindholm-Perry, Amanda. "90 Leveraging the Potential of Molecular and Genetic Markers to Improve Feed Efficiency in Beef Cattle." Journal of Animal Science 101, Supplement_3 (November 6, 2023): 94–95. http://dx.doi.org/10.1093/jas/skad281.115.
Abstract:
Abstract Genetic and other molecular markers are powerful tools with the potential to improve livestock production traits, like feed efficiency. Feed efficiency is a complex biological trait mediated by many genes. Molecular based research, including transcriptome studies to evaluate gene expression profiles to determine the genes and pathways that contribute to feed efficiency in beef cattle have been widely performed over the last decade. Despite its widespread use, challenges remain in transcriptomic data analysis, in particular non-reproducibility of results between studies. These discrepancies exist for a number of reasons including small numbers of replicates, technical differences (e.g. sample preparation, sequencing platform), and biological differences (e.g., environmental, management, and genetic effects). Recent developments in technology and analytic tools provide an opportunity to integrate and synthesize the results of existing and future data. The potential to utilize these tools to have greater impact on feed efficiency will also be discussed. USDA is an equal opportunity provider and employer.
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Bao, Riyue, Lei Huang, Jorge Andrade, Wei Tan, Warren A. Kibbe, Hongmei Jiang, and Gang Feng. "Review of Current Methods, Applications, and Data Management for the Bioinformatics Analysis of Whole Exome Sequencing." Cancer Informatics 13s2 (January 2014): CIN.S13779. http://dx.doi.org/10.4137/cin.s13779.
Abstract:
The advent of next-generation sequencing technologies has greatly promoted advances in the study of human diseases at the genomic, transcriptomic, and epigenetic levels. Exome sequencing, where the coding region of the genome is captured and sequenced at a deep level, has proven to be a cost-effective method to detect disease-causing variants and discover gene targets. In this review, we outline the general framework of whole exome sequence data analysis. We focus on established bioinformatics tools and applications that support five analytical steps: raw data quality assessment, preprocessing, alignment, post-processing, and variant analysis (detection, annotation, and prioritization). We evaluate the performance of open-source alignment programs and variant calling tools using simulated and benchmark datasets, and highlight the challenges posed by the lack of concordance among variant detection tools. Based on these results, we recommend adopting multiple tools and resources to reduce false positives and increase the sensitivity of variant calling. In addition, we briefly discuss the current status and solutions for big data management, analysis, and summarization in the field of bioinformatics.
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Franses, Joseph W., Michael J. Raabe, Amaya Pankaj, Bidish Patel, Avril Coley, Irun Bhan, Martin Aryee, and David T. Ting. "Abstract PO016: Spatial transcriptomic profiling to characterize the tumor-vascular interactome of hepatocellular carcinoma." Clinical Cancer Research 28, no. 17_Supplement (September 1, 2022): PO016. http://dx.doi.org/10.1158/1557-3265.liverca22-po016.
Abstract:
Abstract BACKGROUND: The hepatocellular carcinoma (HCC) tumor microenvironment (TME) is composed of a complex ecosystem dominated by cancer cells and the endothelial cells that line tumor blood vessels. Although many genomic drivers have been identified and at least three transcriptional subsets have been proposed, these efforts have not yet led to novel therapies or otherwise significantly impacted management options. Single cell transcriptional profiling has generated deep insights into the multiple heterogeneous cell types within tissues, but the spatial context of these data is lost during single cell processing. Spatial transcriptomic approaches aim to bridge the gap between dissociative single cell technologies and in situ histopathological characterization.METHODS: To gain insight into potential in situ cancer-endothelial crosstalk interactions, we utilized the Nanostring GeoMx spatial transcriptomics platform with the Cancer Transcriptome Atlas ~1800 gene oligonucleotide probe panel to generate tumor (Arginase+) and blood vessel (CD31+) areas of interest (AOI) gene expression profiles from formalin-fixed, paraffin-embedded archival tissue specimens obtained from HCC resection specimens. Oligonucleotides released from each microscopic AOI were then captured, processed by DNA sequencing, and analyzed using custom computational pipelines.RESULTS: Using the 119 ROI containing data from both tumor and vessels that passed quality control filters, we performed unbiased hierarchical clustering of both the tumor and vessel areas of interest (AOI) within each ROI using the most highly variable genes for each AOI set and identified at least 3 clusters within each AOI type (tumor and vessel). Based on gene ontology analysis of the tumor AOIs, the two subsets were distinguished by unique immune and inflammatory-related genes. Analogous ontology-based characterization of the vessel AOIs demonstrated two groups: 1) an interferon-activated, inflamed progenitor, and immune checkpoint-associated cluster; and 2) a TGF-beta and oxidative stress-associated cluster. Notably, both vessel clusters also contained significant numbers of leukocyte genes, concordant with the intimate relationship of the vasculature and immune system. Canonical correlation analysis (CCA) utilizing both the most variable genes within each AOI set showed significant correlated gene sets within tumor AOIs and vessel AOIs, implying biologically significant interactions in multiple signaling pathways.CONCLUSIONS: Spatial transcriptomic profiling enables an understanding of cell-cell interactions in situ that can uncover biologically distinct tumor and blood vessel niches within the HCC microenvironment. Subsequent efforts will be focused on functionally assessing the spatially linked cancer and endothelial cell phenotypes with the goals of developing improved prognostic and predictive biomarkers and generating novel drug targets. Citation Format: Joseph W Franses, Michael J Raabe, Amaya Pankaj, Bidish Patel, Avril Coley, Irun Bhan, Martin Aryee, David T Ting. Spatial transcriptomic profiling to characterize the tumor-vascular interactome of hepatocellular carcinoma [abstract]. In: Proceedings of the AACR Special Conference: Advances in the Pathogenesis and Molecular Therapies of Liver Cancer; 2022 May 5-8; Boston, MA. Philadelphia (PA): AACR; Clin Cancer Res 2022;28(17_Suppl):Abstract nr PO016.
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Roumani, Marwa, Jacques Le Bot, Michel Boisbrun, Florent Magot, Arthur Péré, Christophe Robin, Frédérique Hilliou, and Romain Larbat. "Transcriptomics and Metabolomics Analyses Reveal High Induction of the Phenolamide Pathway in Tomato Plants Attacked by the Leafminer Tuta absoluta." Metabolites 12, no. 6 (May 26, 2022): 484. http://dx.doi.org/10.3390/metabo12060484.
Abstract:
Tomato plants are attacked by a variety of herbivore pests and among them, the leafminer Tuta absoluta, which is currently a major threat to global tomato production. Although the commercial tomato is susceptible to T. absoluta attacks, a better understanding of the defensive plant responses to this pest will help in defining plant resistance traits and broaden the range of agronomic levers that can be used for an effective integrated pest management strategy over the crop cycle. In this study, we developed an integrative approach combining untargeted metabolomic and transcriptomic analyses to characterize the local and systemic metabolic responses of young tomato plants to T. absoluta larvae herbivory. From metabolomic analyses, the tomato response appeared to be both local and systemic, with a local response in infested leaves being much more intense than in other parts of the plant. The main response was a massive accumulation of phenolamides with great structural diversity, including rare derivatives composed of spermine and dihydrocinnamic acids. The accumulation of this family of specialized metabolites was supported by transcriptomic data, which showed induction of both phenylpropanoid and polyamine precursor pathways. Moreover, our transcriptomic data identified two genes strongly induced by T. absoluta herbivory, that we functionally characterized as putrescine hydroxycinnamoyl transferases. They catalyze the biosynthesis of several phenolamides, among which is caffeoylputrescine. Overall, this study provided new mechanistic clues of the tomato/T. absoluta interaction.
12
Manem, Venkata S. K., Olga Sazonova, Andréanne Gagné, Michèle Orain, Babak Khoshkrood-Mansoori, Nathalie Gaudreault, Yohan Bossé, and Philippe Joubert. "Unravelling actionable biology using transcriptomic data to integrate mitotic index and Ki-67 in the management of lung neuroendocrine tumors." Oncotarget 12, no. 3 (February 2, 2021): 209–20. http://dx.doi.org/10.18632/oncotarget.27874.
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Guo, Eddie, Pouria Torabi, Daiva E. Nielsen, and Matthew Pietrosanu. "Deep learning transcriptomic model for prediction of pan-drug chemotherapeutic sensitivity." STEM Fellowship Journal 7, no. 1 (December 1, 2021): 40–53. http://dx.doi.org/10.17975/sfj-2021-013.
Abstract:
The emergence of precision oncology approaches has begun to inform clinical decision-making in diagnostic, prognostic, and treatment contexts. High-throughput technology has enabled machine learning algorithms to use the molecular characteristics of tumors to generate personalized therapies. However, precision oncology studies have yet to develop a predictive biomarker incorporating pan-cancer gene expression profiles to stratify tumors into similar drug sensitivity profiles. Here we show that a neural network with ten hidden layers accurately classifies pancancer cell lines into two distinct chemotherapeutic response groups based on a pan-drug dataset with 89.0% accuracy (AUC = 0.904). Using unsupervised clustering algorithms, we found a cohort of cell line gene expression data from the Genomics of Drug Sensitivity in Cancer could be clustered into two response groups with significant differences in pan-drug chemotherapeutic sensitivity. After applying the Boruta feature selection algorithm to this dataset, a deep learning model was developed to predict chemotherapeutic response groups. The model’s high classification efficacy validates our hypothesis that cell lines with similar gene expression profiles present similar pan-drug chemotherapeutic sensitivity. This finding provides evidence for the potential use of similar combinatorial biomarkers to select potent candidate drugs that maximize therapeutic response and minimize the cytotoxic burden. Future investigations should aim to recursively subcluster cell lines within the response clusters defined in this study to provide a higher resolution of potential patient response to chemotherapeutics.
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Carreras, Joaquim. "Artificial Intelligence Analysis of Ulcerative Colitis Using an Autoimmune Discovery Transcriptomic Panel." Healthcare 10, no. 8 (August 5, 2022): 1476. http://dx.doi.org/10.3390/healthcare10081476.
Abstract:
Ulcerative colitis is a bowel disease of unknown cause. This research is a proof-of-concept exercise focused on determining whether it is possible to identify the genes associated with ulcerative colitis using artificial intelligence. Several machine learning and artificial neural networks analyze using an autoimmune discovery transcriptomic panel of 755 genes to predict and model ulcerative colitis versus healthy donors. The dataset GSE38713 of 43 cases from the Hospital Clinic of Barcelona was selected, and 16 models were used, including C5, logistic regression, Bayesian network, discriminant analysis, KNN algorithm, LSVM, random trees, SVM, Tree-AS, XGBoost linear, XGBoost tree, CHAID, Quest, C&R tree, random forest, and neural network. Conventional analysis, including volcano plot and gene set enrichment analysis (GSEA), were also performed. As a result, ulcerative colitis was successfully predicted with several machine learning techniques and artificial neural networks (multilayer perceptron), with an overall accuracy of 95–100%, and relevant pathogenic genes were highlighted. One of them, programmed cell death 1 ligand 1 (PD-L1, CD274, PDCD1LG1, B7-H1) was validated in a series from the Tokai University Hospital by immunohistochemistry. In conclusion, artificial intelligence analysis of transcriptomic data of ulcerative colitis is a feasible analytical strategy.
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Tana, Michele May-Sien, Arielle Klepper, Amy Lyden, Angela Oliveira Pisco, Maira Phelps, Breann McGee, Kelsey Green, et al. "Transcriptomic profiling of blood from autoimmune hepatitis patients reveals potential mechanisms with implications for management." PLOS ONE 17, no. 3 (March 21, 2022): e0264307. http://dx.doi.org/10.1371/journal.pone.0264307.
Abstract:
Autoimmune hepatitis (AIH) is a poorly understood, chronic disease, for which corticosteroids are still the mainstay of therapy and most patients undergo liver biopsy to obtain a diagnosis. We aimed to determine if there was a transcriptomic signature of AIH in the peripheral blood and investigate underlying biologic pathways revealed by gene expression analysis. Whole blood RNA from 75 AIH patients and 25 healthy volunteers was extracted and sequenced. Differential gene expression analysis revealed 249 genes that were significantly differentially expressed in AIH patients compared to controls. Using a random forest algorithm, we determined that less than 10 genes were sufficient to differentiate the two groups in our cohort. Interferon signaling was more active in AIH samples compared to controls, regardless of treatment status. Pegivirus sequences were detected in five AIH samples and 1 healthy sample. The gene expression data and clinical metadata were used to determine 12 genes that were significantly associated with advanced fibrosis in AIH. AIH patients with a partial response to therapy demonstrated decreased evidence of a CD8+ T cell gene expression signal. These findings represent progress in understanding a disease in need of better tests, therapies, and biomarkers.
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Guo, Qi. "#277 : Transcriptome Analysis Revealed a Delayed and Attenuated Estrogen Response in IUA Patients." Fertility & Reproduction 05, no. 04 (December 2023): 724. http://dx.doi.org/10.1142/s2661318223744302.
Abstract:
Background and Aims: Intrauterine adhesion (IUA) is characterized by the formation of scar tissue in the endometrium following a variety of insults such as endometrial damage, miscarriage, and infection. Despite current management with intrauterine adhesion separation and adjuvant therapies such as estrogen supplementation, a high recurrence rate and inadequate therapeutic response persist in some patients. More importantly, compromised endometrial proliferation in these patients lead to subsequent infertility. However, effective therapies to improve the impaired response to estrogen have yet to be identified. This study seeks to elucidate the molecular basis for the adverse estrogen response in IUA patients and to identify novel therapeutic targets. Method: Whole-Transcriptome sequencing: proliferative endometrium of normal and IUA patients. Results: By performing differential and enrichment analysis of transcriptomic data, we observed a considerable downregulation of estrogen-relatedgenesets in IUA patients. The expression of enhancer RNAs (eRNAs) serves as a hallmark of transcriptional activation. By identifying eRNAs in transcriptomic data and co-analyzing with ERa ChIPseq and H3K27ac ChIPseq datasets, we identified a series of downregulated eRNAs, which is mediated by estrogen. We further confirmed the downregulation of eRNAs and target genes. Interestingly, the expression of ESR1 is similar in both groups, we performed immunofluorescence analysis and demonstrated a considerable reduction of ERa-positive cells with a loss of nuclear localization in IUA. This finding provides further evidence of the insensitivity of IUA patients to estrogen and suggests a loss of estrogen genomic effects. Furthermore, we found that prolonging estrogen exposure time and increasing estrogen concentration partially enhanced estrogen effects in IUA patients, yet remained compromised, indicating the need for alternative therapeutic strategies. Conclusion: The compromised response to estrogen in IUA patients is attributed to the attenuation of estrogen receptor alpha (ERa)-mediated genomic effect.
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Ortigosa, Francisco, Concepción Ávila, Lourdes Rubio, Lucía Álvarez-Garrido, José A. Carreira, Rafael A. Cañas, and Francisco M. Cánovas. "Transcriptome Analysis and Intraspecific Variation in Spanish Fir (Abies pinsapo Boiss.)." International Journal of Molecular Sciences 23, no. 16 (August 19, 2022): 9351. http://dx.doi.org/10.3390/ijms23169351.
Abstract:
Spanish fir (Abies pinsapo Boiss.) is an endemic, endangered tree that has been scarcely investigated at the molecular level. In this work, the transcriptome of Spanish fir was assembled, providing a large catalog of expressed genes (22,769), within which a high proportion were full-length transcripts (12,545). This resource is valuable for functional genomics studies and genome annotation in this relict conifer species. Two intraspecific variations of A. pinsapo can be found within its largest population at the Sierra de las Nieves National Park: one with standard green needles and another with bluish-green needles. To elucidate the causes of both phenotypes, we studied different physiological and molecular markers and transcriptome profiles in the needles. “Green” trees showed higher electron transport efficiency and enhanced levels of chlorophyll, protein, and total nitrogen in the needles. In contrast, needles from “bluish” trees exhibited higher contents of carotenoids and cellulose. These results agreed with the differential transcriptomic profiles, suggesting an imbalance in the nitrogen status of “bluish” trees. Additionally, gene expression analyses suggested that these differences could be associated with different epigenomic profiles. Taken together, the reported data provide new transcriptome resources and a better understanding of the natural variation in this tree species, which can help improve guidelines for its conservation and the implementation of adaptive management strategies under climatic change.
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Rahmat, Nur Lina, Anis Nadyra Zifruddin, Cik Mohd Rizuan Zainal Abidin, Nor-Azlan Nor Muhammad, and Maizom Hassan. "The Developmental Transcriptome of Bagworm, Metisa plana (Lepidoptera: Psychidae) and Insights into Chitin Biosynthesis Genes." Genes 12, no. 1 (December 23, 2020): 7. http://dx.doi.org/10.3390/genes12010007.
Abstract:
Bagworm, Metisa plana (Lepidoptera: Psychidae) is a ubiquitous insect pest in the oil palm plantations. M. plana infestation could reduce the oil palm productivity by 40% if it remains untreated over two consecutive years. Despite the urgency to tackle this issue, the genome and transcriptome of M. plana have not yet been fully elucidated. Here, we report a comprehensive transcriptome dataset from four different developmental stages of M. plana, comprising of egg, third instar larva, pupa and female adult. The de novo transcriptome assembly of the raw data had produced a total of 193,686 transcripts, which were then annotated against UniProt, NCBI non-redundant (NR) database, Gene Ontology, Cluster of Orthologous Group, and Kyoto Encyclopedia of Genes and Genomes databases. From this, 46,534 transcripts were annotated and mapped to 146 known metabolic or signalling KEGG pathways. The paper further identified 41 differentially expressed transcripts encoding seven genes in the chitin biosynthesis pathways, and their expressions across each developmental stage were further analysed. The genetic diversity of M. plana was profiled whereby there were 21,516 microsatellite sequences and 379,895 SNPs loci found in the transcriptome of M. plana. These datasets add valuable transcriptomic resources for further study of developmental gene expression, transcriptional regulations and functional gene activities involved in the development of M. plana. Identification of regulatory genes in the chitin biosynthesis pathway may also help in developing an RNAi-mediated pest control management by targeting certain pathways, and functional studies of the genes in M. plana.
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Manem, Venkata S. K., Olga Sazonova, Andréanne Gagné, Michèle Orain, Babak Khoshkrood-Mansoori, Nathalie Gaudreault, Yohan Bossé, and Philippe Joubert. "Addendum: Unravelling actionable biology using transcriptomic data to integrate mitotic index and Ki-67 in the management of lung neuroendocrine tumors." Oncotarget 13, no. 1 (January 1, 2022): 1302. http://dx.doi.org/10.18632/oncotarget.28321.
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Nguyen, HD, A. Allaire, P. Diamandis, M. Bisaillon, MS Scott, and M. Richer. "A Machine Learning Analysis of TCGA Expression Data to Finding Signatures for “Normal-Like” IDH-WT Diffuse Gliomas with a Longer Survival." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 48, s1 (May 2021): S2. http://dx.doi.org/10.1017/cjn.2021.88.
Abstract:
Classification of primary CNS tumours is currently achieved by complementing histologic analysis with molecular information, in accordance with the WHO guidelines, and aims at providing accurate prognosis and optimal patient management. cIMPACT-NOW update 3 now recommends grading diffuse IDH-wild type astrocytomas as grade IV glioblastomas if they bear one or more of the following molecular alterations: EGFR amplification, TERT promoter mutation, and whole-chromosome 7 gain combined with chromosome 10 loss. In this reanalysis of the Cancer Genome Atlas (TCGA) glioma expression datasets, we identified 14 IDH-wt infiltrating astrocytic gliomas displaying a “normal-like (NL)” transcriptomic profile associated with a longer survival rate. Some of these tumours would be considered as GBM-equivalents with the current diagnostic algorithm. A k-nearest neighbors model was used to identify 3-gene signatures able to identify NL IDH-WT gliomas. Genes such as C5AR1 (complement receptor) SLC32A1 (vesicular gamma-aminobutyric acid transporter), and SMIM10L2A (long non-coding RNA) were overrepresented in these signatures which were validated further using the Chinese Glioma Genome and Ivy Glioblastoma Atlases. They showed high discriminative power and correlation with survival. This finding could lead to the validation of an immunohistochemical or PCR test which would facilitate classification of IDH-WT astrocytomas with unclear histological grading. Furthermore, associated signaling pathways might represent novel treatment targets for aggressive tumours.LEARNING OBJECTIVESThis presentation will enable the learner to: 1.Reconsider recent updates in the WHO classification of infiltrating gliomas.2.Discuss advanced bioinformatics profiling of the brain cancer transcriptome.
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Berrios, Daniel C., Jonathan Galazka, Kirill Grigorev, Samrawit Gebre, and Sylvain V. Costes. "NASA GeneLab: interfaces for the exploration of space omics data." Nucleic Acids Research 49, no. D1 (October 20, 2020): D1515—D1522. http://dx.doi.org/10.1093/nar/gkaa887.
Abstract:
Abstract The mission of NASA’s GeneLab database (https://genelab.nasa.gov/) is to collect, curate, and provide access to the genomic, transcriptomic, proteomic and metabolomic (so-called ‘omics’) data from biospecimens flown in space or exposed to simulated space stressors, maximizing their utilization. This large collection of data enables the exploration of molecular network responses to space environments using a systems biology approach. We review here the various components of the GeneLab platform, including the new data repository web interface, and the GeneLab Online Data Entry (GEODE) web portal, which will support the expansion of the database in the future to include companion non-omics assay data. We discuss our design for GEODE, particularly how it promotes investigators providing more accurate metadata, reducing the curation effort required of GeneLab staff. We also introduce here a new GeneLab Application Programming Interface (API) specifically designed to support tools for the visualization of processed omics data. We review the outreach efforts by GeneLab to utilize the spaceflight data in the repository to generate novel discoveries and develop new hypotheses, including spearheading data analysis working groups, and a high school student training program. All these efforts are aimed ultimately at supporting precision risk management for human space exploration.
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Li, Shuxin, Jiarui Wang, Jiale Li, Meihong Yue, Chuncheng Liu, Libing Ma, and Ying Liu. "Integrative analysis of transcriptome complexity in pig granulosa cells by long-read isoform sequencing." PeerJ 10 (May 25, 2022): e13446. http://dx.doi.org/10.7717/peerj.13446.
Abstract:
Background In intensive and large-scale farms, abnormal estradiol levels in sows can cause reproductive disorders. The high incidence rate of reproductive disturbance will induce the elimination of productive sows in large quantities, and the poor management will bring great losses to the pig farms. The change in estradiol level has an important effect on follicular development and estrus of sows. To solve this practical problem and improve the productive capacity of sows, it is significant to further clarify the regulatory mechanism of estradiol synthesis in porcine granulosa cells (GCs). The most important function of granulosa cells is to synthesize estradiol. Thus, the studies about the complex transcriptome in porcine GCs are significant. As for precursor-messenger RNAs (pre-mRNAs), their post-transcriptional modification, such as alternative polyadenylation (APA) and alternative splicing (AS), together with long non-coding RNAs (lncRNAs), may regulate the functions of granulosa cells. However, the above modification events and their function are unclear within pig granulosa cells. Methods Combined PacBio long-read isoform sequencing (Iso-Seq) was conducted in this work for generating porcine granulosa cells’ transcriptomic data. We discovered new transcripts and possible gene loci via comparison against reference genome. Later, combined Iso-Seq data were adopted to uncover those post-transcriptional modifications such as APA or AS, together with lncRNA within porcine granulosa cells. For confirming that the Iso-Seq data were reliable, we chose four AS genes and analyzed them through RT-PCR. Results The present article illustrated that pig GCs had a complex transcriptome, which gave rise to 8,793 APA, 3,465 AS events, 703 candidate new gene loci, as well as 92 lncRNAs. The results of this study revealed the complex transcriptome in pig GCs. It provided a basis for the interpretation of the molecular mechanism in GCs.
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Park, Jihye, Kyuho Kang, Yeonghoon Son, Kwang Seok Kim, Keunsoo Kang, and Hae-June Lee. "Low-Dose Radiation-Induced Transcriptomic Changes in Diabetic Aortic Endothelial Cells." Data 8, no. 5 (May 18, 2023): 92. http://dx.doi.org/10.3390/data8050092.
Abstract:
Low-dose radiation refers to exposure to ionizing radiation at levels that are generally considered safe and not expected to cause immediate health effects. However, the effects of low-dose radiation are still not fully understood, and research in this area is ongoing. In this study, we investigated the alterations in gene expression profiles of human aortic endothelial cells (HAECs) and diabetic human aortic endothelial cells (T2D-HAECs) derived from patients with type 2 diabetes. To this end, we used RNA-seq to profile the transcriptomes of cells exposed to varying doses of low-dose radiation (0.1 Gy, 0.5 Gy, and 2.0 Gy) and compared them to a control group with no radiation exposure. Differentially expressed genes and enriched pathways were identified using the DESeq2 and gene set enrichment analysis (GSEA) methods, respectively. The data generated in this study are publicly available through the gene expression omnibus (GEO) database with the accession number GSE228572. This study provides a valuable resource for examining the effects of low-dose radiation on HAECs and T2D-HAECs, thereby contributing to a better understanding of the potential human health risks associated with low-dose radiation exposure.
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Gyorffy, Balazs, and Libero Santarpia. "Abstract PO2-03-08: Uncovering Novel Potential Prognostic Biomarkers in Basal-Like Breast Cancer using Transcriptomic Data of 1,899 Patients." Cancer Research 84, no. 9_Supplement (May 2, 2024): PO2–03–08—PO2–03–08. http://dx.doi.org/10.1158/1538-7445.sabcs23-po2-03-08.
Abstract:
Abstract Introduction: Patients with basal-like breast cancer (BLBC) predominantly represented by triple-negative breast cancer have shown a high recurrence rate and are characterized by poor prognosis. There is an urgent need to undercover reliable prognostic biomarkers that can help in the clinical management of such patients and identify additional therapeutic targets. The objective of this study was to create a comprehensive transcriptomic database on a large scale and leverage it to identify and prioritize cancer-related genes associated with BLBC patients’ outcomes. Methods: We identified breast cancer cohorts from public repositories that contained gene expression data at the transcriptome level, along with clinical follow-up information. BLBC were identified using the PAM50 signature. All samples were standardized using a standard array normalization coupled with scaling to have a mean expression across all genes of 1000 in each sample and incorporated into a unified database. Redundant samples were removed. For each gene, Cox univariate survival analysis was conducted, to account for multiple hypothesis testing, the false discovery rate was computed, and a significant cutoff of 1% was employed to determine the highest statistical significance. Association with RFS and OS was performed. Multivariate analysis was performed for selected genes involving clinical and pathological variables. To uncover higher-level functions related to altered RFS, gene ontology analysis was performed using the enrichGO function in the TNM plotter (http://www.tnmplot.com). Results: The complete integrated database comprises 1,899 samples from 52 breast cancer datasets. Altogether, 2,342 genes were correlated with relapse-free survival (RFS), and 1,149 genes were correlated with overall survival (OS). 619 genes were statistically significant for both RFS and OS. The most significant genes were ANGPTL4 (p=4.25E-08, HR=2.02), NHP2 (p=5.98E-10, HR=1.93), STK3 (p=4.86E-10, HR=1.93), GBE1 (p=2.77E-09, HR=1.86), and PMVK (p=3.65E-09, HR=1.85) for RFS and PINK1 (p=1.64E-05 , HR=3.31), CAMK2N1 (p=1.06E-07 , HR=2.93), CACFD1 (p=4.79E-04 , HR=2.61), SCAP (p=3.29E-04 , HR=2.6), SDC1 (p=2.81E-04 , HR=2.57), for OS. The most significant gene ontology biological processes upregulated in tumors with a worse prognosis include GO:0000184, nuclear-transcribed mRNA catabolic process, nonsense-mediated decay (p=6.64E-18); GO:0045047 , protein targeting to ER (p=6.64E-18); GO:0006614, SRP-dependent cotranslational protein targeting to membrane (p=9.08E-18); GO:0072599, establishment of protein localization to endoplasmic reticulum (p=1.30E-17); and GO:0006613, cotranslational protein targeting to membrane (p=1.90E-17). Conclusions: Our results help to prioritize genes and to neglect those which are most likely to fail in studies aiming to establish new clinically useful biomarkers and therapeutic targets in BLBC. Citation Format: Balazs Gyorffy, Libero Santarpia. Uncovering Novel Potential Prognostic Biomarkers in Basal-Like Breast Cancer using Transcriptomic Data of 1,899 Patients [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-03-08.
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Lindskrog, Sia V., Sofie S. Schmøkel, Iver Nordentoft, Philippe Lamy, Michael Knudsen, Jørgen B. Jensen, and Lars Dyrskjøt. "Abstract 3483: Single-nucleus RNA-sequencing of human bladder tumors delineates intra-tumor cellular and subtype heterogeneity." Cancer Research 82, no. 12_Supplement (June 15, 2022): 3483. http://dx.doi.org/10.1158/1538-7445.am2022-3483.
Abstract:
Abstract Introduction: Single cell technologies now make it possible to study tumor ecosystems at single cell resolution and may improve our biological understanding of disease aggressiveness and tumor heterogeneity. Whereas single-cell RNA-sequencing of clinical tumor biopsies requires immediate processing after tissue acquisition, single-nucleus RNA-sequencing (snRNA-seq) allows profiling of single nuclei isolated from frozen tumor tissue from patients with long-term follow-up and known clinical outcomes. Methods: We performed snRNA-seq of frozen tumors from 48 bladder cancer (BC) patients (10 Ta, 13 T1, 25 T2-4) using an optimized DroNc-seq protocol. Nuclei were isolated from frozen biopsies using IgePal lysis buffer and droplets were created using the Dolomite Bio platform followed by library generation and sequencing on an Illumina NovaSeq 6000. All epithelial nuclei were classified according to the UROMOL classes of non-muscle-invasive BC or the consensus classes of muscle-invasive BC. Bulk total RNA-sequencing (RNA-seq) was available for 44 of the tumors for comparison. Three tumors were additionally analyzed using 10x Chromium for validation and four tumors were analyzed using 10x Visium Spatial Transcriptomics. Results: After pre-processing the raw sequencing data, we obtained data from 117,653 nuclei in total and 59,201 nuclei remained after quality control filtering (1,233 nuclei per tumor and 529 expressed genes per nuclei on average). We focused our analysis on the epithelial compartment, as it constituted the bulk of the tumors (99% of all nuclei). UMAP visualization and clustering of all tumors were mainly driven by patient origin indicating a high level of inter-tumor heterogeneity. To explore intra-tumor heterogeneity and the association to disease aggressiveness, we characterized the tumors individually using hallmark BC gene signatures. Finally, we explored the composition of transcriptomic classes for each tumor and found that 52% of tumors displayed profound intra-tumor class heterogeneity with less than 70% of all nuclei belonging to a single class. The dominating transcriptomic class of single nuclei was only consistent in 44% of the tumors when compared to the overall transcriptomic class from bulk RNA-seq. This may be explained by several levels of heterogeneity and method differences, including the technical challenges of applying a bulk classifier to single nuclei data. We are currently investigating whether specific epithelial subpopulations are associated to outcome and whether signatures derived from snRNA-seq data can be recovered in bulk RNA-seq data and used as prognostic predictors. Conclusion: Our results highlight the biological complexity of bladder tumors and underline the importance of considering the extent of intra-tumor heterogeneity in the clinical management of BC patients. Citation Format: Sia V. Lindskrog, Sofie S. Schmøkel, Iver Nordentoft, Philippe Lamy, Michael Knudsen, Jørgen B. Jensen, Lars Dyrskjøt. Single-nucleus RNA-sequencing of human bladder tumors delineates intra-tumor cellular and subtype heterogeneity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3483.
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Arce-Leal, Ángela Paulina, Rocío Bautista, Edgar Antonio Rodríguez-Negrete, Miguel Ángel Manzanilla-Ramírez, José Joaquín Velázquez-Monreal, María Elena Santos-Cervantes, Jesús Méndez-Lozano, et al. "Gene Expression Profile of Mexican Lime (Citrus aurantifolia) Trees in Response to Huanglongbing Disease caused by Candidatus Liberibacter asiaticus." Microorganisms 8, no. 4 (April 7, 2020): 528. http://dx.doi.org/10.3390/microorganisms8040528.
Abstract:
Nowadays, Huanglongbing (HLB) disease, associated with Candidatus Liberibacter asiaticus (CLas), seriously affects citriculture worldwide, and no cure is currently available. Transcriptomic analysis of host–pathogen interaction is the first step to understand the molecular landscape of a disease. Previous works have reported the transcriptome profiling in response to HLB in different susceptible citrus species; however, similar studies in tolerant citrus species, including Mexican lime, are limited. In this work, we have obtained an RNA-seq-based differential expression profile of Mexican lime plants challenged against CLas infection, at both asymptomatic and symptomatic stages. Typical HLB-responsive differentially expressed genes (DEGs) are involved in photosynthesis, secondary metabolism, and phytohormone homeostasis. Enrichment of DEGs associated with biotic response showed that genes related to cell wall, secondary metabolism, transcription factors, signaling, and redox reactions could play a role in the tolerance of Mexican lime against CLas infection. Interestingly, despite some concordance observed between transcriptional responses of different tolerant citrus species, a subset of DEGs appeared to be species-specific. Our data highlights the importance of studying the host response during HLB disease using as model tolerant citrus species, in order to design new and opportune diagnostic and management methods.
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Duan, Xin-le, Bi-an Zhao, Ying Liu, Man-qiong Xiong, Nan He, Shao-kang Huang, Wei-fone Huang, and Jiang-hong Li. "Development and characterization of six novel microsatellite markers for honey bee parasitic mite Varroa destructor (Mesostigmata: Varroidae) ." Systematic and Applied Acarology 25, no. 10 (October 2, 2020): 1733–44. http://dx.doi.org/10.11158/saa.25.10.2.
Abstract:
The Varroa destructor is an ectoparasitic mite and the most serious biotic threat to honey bee and apiculture worldwide. Genetic types of V. destructor determine its ability of transmission and pathogenicity. Population genetics tools could supply useful information for comprehensive management of the mite. In this study, transcriptome information of V. destructor was analyzed for mining more polymorphic microsatellite markers for the population genetics investigation with further experimental verified. A total of 83,711 unigenes were assembled with the N50 length of 1,826 bp and the GC content of 40.03%. A total of 27,775 potential microsatellite loci were identified in 18,563 unigenes. The di-nucleotide and mono-nucleotide were most abundant repeat motifs and the most dominant di-nucleotides and mono-nucleotide repeat motifs were A/T and AT/TA. Forty-two of sixty randomly selected microsatellite loci were successfully amplified. Six of them were confirmed to be polymorphic in five V. destructor geographical populations. Our result showed transcriptomic data provide valuable resources for molecular marker development, and these novel microsatellite loci would be valuable in facilitating population genetic and evolutionary of V. destructor and related species.
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Chen, Han, Honghua Su, Shuai Zhang, Tianxing Jing, Zhe Liu, and Yizhong Yang. "Transcriptomic and Metabolomic Responses in Cotton Plant to Apolygus lucorum Infestation." Insects 13, no. 4 (April 15, 2022): 391. http://dx.doi.org/10.3390/insects13040391.
Abstract:
With the wide-scale adoption of transgenic Bacillus thuringiensis (Bt) cotton, Apolygus lucorum (Meyer-Dür) has become the most serious pest and has caused extensive yield loss in cotton production. However, little is known about the defense responses of cotton at the seedling stage to A. lucorum feeding. In this study, to elucidate the cotton defense mechanism, cotton leaves were damaged by A. lucorum for 0, 4, 12 and 24 h. The transcriptomic results showed that A. lucorum feeding elicits a rapid and strong defense response in gene expression during the whole infestation process in cotton plants. Further analysis revealed that at each assessment time, more differentially expressed genes were up-regulated than down-regulated. The integrated analysis of transcriptomic and metabolic data showed that most of the genes involved in jasmonic acid (JA) biosynthesis were initially up-regulated, and this trend continued during an infestation. Meanwhile, the content levels of JA and its intermediate products were also significantly increased throughout the whole infestation process. The similar trend was displayed in condensed tannins biosynthesis. This research proved that, after plants are damaged by A. lucorum, the JA pathway mediates the defense mechanisms in cotton plants by promoting the accumulation of condensed tannins as a defense mechanism against A. lucorum. These results will help us to discover unknown defensive genes and improve the integrated pest management of A. lucorum.
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Guo, Yaning, Siyu Zhang, Jing Ai, Panpan Zhang, Han Yao, Yunfei Liu, and Xiong Zhang. "Transcriptomic and biochemical analyses of drought response mechanism in mung bean (Vignaradiata (L.) Wilczek) leaves." PLOS ONE 18, no. 5 (May 10, 2023): e0285400. http://dx.doi.org/10.1371/journal.pone.0285400.
Abstract:
Drought is a major factor that limiting mung bean development. To clarify the molecular mechanism of mung bean in response to drought stress, 2 mung bean groups were established, the experimental group (drought-treated) and the control group (normal water management). With prominent difference of 2 groups in stomatal conductance, relative water content and phenotype, leaf samples were collected at 4 stages, and the physiological index of MDA, POD, chlorophyll, and soluble proteins were estimated. RNA-seq was used to obtain high quality data of samples, and differentially expressed genes were identified by DESeq2. With GO and KEGG analysis, DEGs were enriched into different classifications and pathways. WGCNA was used to detect the relationship between physiological traits and genes, and qPCR was performed to confirm the accuracy of the data. We obtained 169.49 Gb of clean data from 24 samples, and the Q30 of each date all exceeded 94%. In total, 8963 DEGs were identified at 4 stages between the control and treated samples, and the DEGs were involved in most biological processes. 1270 TFs screened from DEGs were clustered into 158 TF families, such as AP2, RLK-Pelle-DLSVA, and NAC TF families. Genes related to physiological traits were closely related to plant hormone signaling, carotenoid biosynthesis, chlorophyll metabolism, and protein processing. This paper provides a large amount of data for drought research in mung bean.
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Xiang, Xiao, Stéphanie Langlois, Marie-Eve St-Pierre, Anna Blinder, Philippe Charron, Tyson E. Graber, Stephanie L. Fowler, et al. "Identification of pannexin 1-regulated genes, interactome, and pathways in rhabdomyosarcoma and its tumor inhibitory interaction with AHNAK." Oncogene 40, no. 10 (February 9, 2021): 1868–83. http://dx.doi.org/10.1038/s41388-020-01623-2.
Abstract:
AbstractRhabdomyosarcoma (RMS), the most common soft tissue sarcoma in children, is an aggressive cancer with a poor prognosis. Despite current management, the 5-year survival rate for patients with metastatic RMS is ∼30%; underscoring the need to develop better treatment strategies. We have recently reported that pannexin 1 (PANX1) levels are downregulated in RMS and that restoring its expression inhibits RMS progression. Here, we have surveyed and characterized the molecular changes induced by PANX1 re-expression in RMS. We cataloged transcriptomic changes in this context by RNA sequencing. At the protein level, we unveiled PANX1 interactors using BioID, complemented by co-immunoprecipitation coupled to high-performance liquid chromatography/electrospray ionization tandem mass spectrometry performed in PANX1-enriched fractions. Using these data, we generated searchable public databases for the PANX1 interactome and changes to the RMS transcriptome occurring when PANX1 expression is restored. STRING network analyses revealed a PANX1 interactome involving plasma membrane and cytoskeleton-associated proteins including the previously undescribed interactor AHNAK. Indeed, AHNAK knockdown abrogated the PANX1-mediated reduction in RMS cell viability and migration. Using these unbiased approaches, we bring insight to the mechanisms by which PANX1 inhibits RMS progression, identifying the cell migration protein AHNAK as a key modifier of PANX1-mediated changes in RMS malignant properties.
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Shang, Yi, Yanbo Wang, Jianyu Deng, Xunyue Liu, Yihao Fang, Qiong Rao, and Huiming Wu. "Comparative Transcriptome Analysis Reveals the Mechanism Related to Fluazinam Stress of Panonychus citri (Acarina: Tetranychidae)." Insects 11, no. 11 (October 26, 2020): 730. http://dx.doi.org/10.3390/insects11110730.
Abstract:
The use of a large number of chemical acaricides to control these pest mites has led to an increasing problem of pesticide resistance, which has always been the difficulty in integrated pest management (IPM). Fluazinam has a good control effect on Panonychus citri, the serious pest on citrus; however, we only know the mechanism of action of fluazinam as a fungicide and its mechanism of action on mites remains unclear. Through analysis using Illumina high-throughput transcriptomic sequencing and differential expression genes in P. citri treated with fluazinam, 59 cytochrome P450 genes, 23 glutathione s-transferase genes, five carboxylate esterase genes, 11 superoxide dismutase genes and 15 catalase genes were identified. The Gene Ontology enrichment and the enrichment of KEGG results showed that the treatment were enrichment for redox enzyme pathways. Evaluating the efficacy of fluazinam, and analyzing the transcriptome data of P. citri under fluazinam stress, potentially provide a new agent for prevention and control of P. citri, and also preliminary research results for exploring the mechanism of action of fluazinam on P. citri. Given the up-regulated expression levels of genes for Mn-superoxide dismutase and catalase, we speculate that they play an important role in fluazinam-stress action on P. citri.
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Heyckendorf, Jan, Sebastian Marwitz, Maja Reimann, Korkut Avsar, Andrew R. DiNardo, Gunar Günther, Michael Hoelscher, et al. "Prediction of anti-tuberculosis treatment duration based on a 22-gene transcriptomic model." European Respiratory Journal 58, no. 3 (February 11, 2021): 2003492. http://dx.doi.org/10.1183/13993003.03492-2020.
Abstract:
BackgroundThe World Health Organization recommends standardised treatment durations for patients with tuberculosis (TB). We identified and validated a host-RNA signature as a biomarker for individualised therapy durations for patients with drug-susceptible (DS)- and multidrug-resistant (MDR)-TB.MethodsAdult patients with pulmonary TB were prospectively enrolled into five independent cohorts in Germany and Romania. Clinical and microbiological data and whole blood for RNA transcriptomic analysis were collected at pre-defined time points throughout therapy. Treatment outcomes were ascertained by TBnet criteria (6-month culture status/1-year follow-up). A whole-blood RNA therapy-end model was developed in a multistep process involving a machine-learning algorithm to identify hypothetical individual end-of-treatment time points.Results50 patients with DS-TB and 30 patients with MDR-TB were recruited in the German identification cohorts (DS-GIC and MDR-GIC, respectively); 28 patients with DS-TB and 32 patients with MDR-TB in the German validation cohorts (DS-GVC and MDR-GVC, respectively); and 52 patients with MDR-TB in the Romanian validation cohort (MDR-RVC). A 22-gene RNA model (TB22) that defined cure-associated end-of-therapy time points was derived from the DS- and MDR-GIC data. The TB22 model was superior to other published signatures to accurately predict clinical outcomes for patients in the DS-GVC (area under the curve 0.94, 95% CI 0.9–0.98) and suggests that cure may be achieved with shorter treatment durations for TB patients in the MDR-GIC (mean reduction 218.0 days, 34.2%; p<0.001), the MDR-GVC (mean reduction 211.0 days, 32.9%; p<0.001) and the MDR-RVC (mean reduction of 161.0 days, 23.4%; p=0.001).ConclusionBiomarker-guided management may substantially shorten the duration of therapy for many patients with MDR-TB.
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McGrew, Brooklyn, Aman Shrivastava, Philip Fernandes, Lubaina Ehsan, Yash Sharma, Dawson Payne, Lillian Dillard, et al. "IDENTIFYING RELEVANT PATHWAYS AND BIOMARKERS IN CROHN’S DISEASE USING CONTEXTUALIZED METABOLIC NETWORK MODEL." Inflammatory Bowel Diseases 27, Supplement_1 (January 1, 2021): S9—S10. http://dx.doi.org/10.1093/ibd/izaa347.022.
Abstract:
Abstract Background Candidate markers for Crohn’s Disease (CD) may be identified via gene expression-based construction of metabolic networks (MN). These can computationally describe gene-protein-reaction associations for entire tissues and also predict the flux of reactions (rate of turnover of specific molecules via a metabolic pathway). Recon3D is the most comprehensive human MN to date. We used publicly available CD transcriptomic data along with Recon3D to identify metabolites as potential diagnostic and prognostic biomarkers. Methods Terminal ileal gene expression profiles (36,372 genes; 218 CD. 42 controls) from the RISK cohort (Risk Stratification and Identification of Immunogenetic and Microbial Markers of Rapid Disease Progression in Children with Crohn’s Disease) and their transcriptomic abundances were used. Recon3D was pruned to only include RISK dataset transcripts which determined metabolic reaction linkage with transcriptionally active genes. Flux balance analysis (FBA) was then run using RiPTiDe with context specific transcriptomic data to further constrain genes (Figure 1). RiPTiDe was independently run on transcriptomic data from both CD and controls. From the pruned and constricted MN obtained, reactions were extracted for further analysis. Results After applying the necessary constraints to modify Recon3D, 527 CD and 537 control reactions were obtained. Reaction comparison with a publicly available list of healthy small intestinal epithelial reactions (n=1282) showed an overlap of 80 CD and 84 control reactions. These were then further grouped based on their metabolic pathways. RiPTiDe identified context specific metabolic pathway activity without supervision and the percentage of forward, backward, and balanced reactions for each metabolic pathway (Figure 2). The metabolite concentrations in the small intestine was altered among CD patients. Notably, the citric acid cycle and malate-aspartate shuttle were affected, highlighting changes in mitochondrial metabolic pathways. This is illustrated by changes in the number of reactions at equilibrium between CD and control. Conclusions The results are relevant as cytosolic acetyl-CoA is needed for fatty acid synthesis and is obtained by removing citrate from the citric acid cycle. An intermediate removal from the cycle has significant cataplerotic effects. The malate-aspartate shuttle also allows electrons to move across the impermeable membrane in the mitochondria (fatty acid synthesis location). These findings are reported by previously published studies where gene expression for fatty acid synthesis is altered in CD patients along with mitochondrial metabolic pathway changes, resulting in altered cell homeostasis. In-depth analysis is currently underway with our work supporting the utility of potential metabolic biomarkers for CD diagnosis, management and improved care.
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Robinson, Leslie C., Sandro Santagata, and Todd C. Hankinson. "Potential evolution of neurosurgical treatment paradigms for craniopharyngioma based on genomic and transcriptomic characteristics." Neurosurgical Focus 41, no. 6 (December 2016): E3. http://dx.doi.org/10.3171/2016.9.focus16308.
Abstract:
The recent genomic and transcriptomic characterization of human craniopharyngiomas has provided important insights into the pathogenesis of these tumors and supports that these tumor types are distinct entities. Critically, the insights provided by these data offer the potential for the introduction of novel therapies and surgical treatment paradigms for these tumors, which are associated with high morbidity rates and morbid conditions. Mutations in the CTNNB1 gene are primary drivers of adamantinomatous craniopharyngioma (ACP) and lead to the accumulation of β-catenin protein in a subset of the nuclei within the neoplastic epithelium of these tumors. Dysregulation of epidermal growth factor receptor (EGFR) and of sonic hedgehog (SHH) signaling in ACP suggest that paracrine oncogenic mechanisms may underlie ACP growth and implicate these signaling pathways as potential targets for therapeutic intervention using directed therapies. Recent work shows that ACP cells have primary cilia, further supporting the potential importance of SHH signaling in the pathogenesis of these tumors. While further preclinical data are needed, directed therapies could defer, or replace, the need for radiation therapy and/or allow for less aggressive surgical interventions. Furthermore, the prospect for reliable control of cystic disease without the need for surgery now exists. Studies of papillary craniopharyngioma (PCP) are more clinically advanced than those for ACP. The vast majority of PCPs harbor the BRAFv600e mutation. There are now 2 reports of patients with PCP that had dramatic therapeutic responses to targeted agents. Ongoing clinical and research studies promise to not only advance our understanding of these challenging tumors but to offer new approaches for patient management.
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Papadopoulou, Gethsimani, Eleni Manoloudi, Nikolena Repousi, Lemonia Skoura, Tara Hurst, and Timokratis Karamitros. "Molecular and Clinical Prognostic Biomarkers of COVID-19 Severity and Persistence." Pathogens 11, no. 3 (March 2, 2022): 311. http://dx.doi.org/10.3390/pathogens11030311.
Abstract:
The coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), poses several challenges to clinicians, due to its unpredictable clinical course. The identification of laboratory biomarkers, specific cellular, and molecular mediators of immune response could contribute to the prognosis and management of COVID-19 patients. Of utmost importance is also the detection of differentially expressed genes, which can serve as transcriptomic signatures, providing information valuable to stratify patients into groups, based on the severity of the disease. The role of biomarkers such as IL-6, procalcitonin, neutrophil–lymphocyte ratio, white blood cell counts, etc. has already been highlighted in recently published studies; however, there is a notable amount of new evidence that has not been summarized yet, especially regarding transcriptomic signatures. Hence, in this review, we assess the latest cellular and molecular data and determine the significance of abnormalities in potential biomarkers for COVID-19 severity and persistence. Furthermore, we applied Gene Ontology (GO) enrichment analysis using the genes reported as differentially expressed in the literature in order to investigate which biological pathways are significantly enriched. The analysis revealed a number of processes, such as inflammatory response, and monocyte and neutrophil chemotaxis, which occur as part of the complex immune response to SARS-CoV-2.
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Yang, Hongyan, Jingyi Lu, Kui Wang, Chaoyan Wu, Bin Yang, and Jiaying Zhu. "Transcriptome Analysis Reveals the Venom Genes of the Ectoparasitoid Habrobracon hebetor (Hymenoptera: Braconidae)." Insects 15, no. 6 (June 5, 2024): 426. http://dx.doi.org/10.3390/insects15060426.
Abstract:
The ectoparasitoid Habrobracon hebetor (Hymenoptera: Braconidae) exhibits a broad parasitic capability towards various lepidopteran pests, with venom serving as a crucial virulent factor ensuring successful parasitization and subsequent host mortality. Analyzing the constituents of its venom is essential for elucidating the mechanisms underlying efficient host killing by this parasitoid and for exploring potentially functional venom proteins. Through a transcriptomic analysis, a total of 34 venom proteins were identified within the venom of H. hebetor, encompassing known components such as serine protease, metalloproteinase, esterase, and serine protease inhibitors commonly present in parasitoid venoms. Unique components like paralytic protein and ion transport peptide-like were identified, possibly specific to certain parasitoids, along with novel proteins with uncharacterized functions. Spatial gene expression profiling of the identified venom proteins using transcriptomic data, corroborated by quantitative PCR validation for 13 randomly selected proteins, revealed abundant expression levels in the venom apparatus, affirming them as genuine venom components. Notably, the paralytic protein exhibited prominent expression, with the highest FPKM (fragments per kilobase of transcript per million fragments mapped) value of 24,704.87 in the venom apparatus, indicative of its significant role in successful parasitism by H. hebetor. The identification of these venom proteins establishes a foundation for the further exploration of bioactive agents for pest management strategies.
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Segaert, Pieter, Marta B. Lopes, Sandra Casimiro, Susana Vinga, and Peter J. Rousseeuw. "Robust identification of target genes and outliers in triple-negative breast cancer data." Statistical Methods in Medical Research 28, no. 10-11 (August 27, 2018): 3042–56. http://dx.doi.org/10.1177/0962280218794722.
Abstract:
Correct classification of breast cancer subtypes is of high importance as it directly affects the therapeutic options. We focus on triple-negative breast cancer which has the worst prognosis among breast cancer types. Using cutting edge methods from the field of robust statistics, we analyze Breast Invasive Carcinoma transcriptomic data publicly available from The Cancer Genome Atlas data portal. Our analysis identifies statistical outliers that may correspond to misdiagnosed patients. Furthermore, it is illustrated that classical statistical methods may fail to identify outliers due to their heavy influence, prompting the need for robust statistics. Using robust sparse logistic regression we obtain 36 relevant genes, of which ca. 60% have been previously reported as biologically relevant to triple-negative breast cancer, reinforcing the validity of the method. The remaining 14 genes identified are new potential biomarkers for triple-negative breast cancer. Out of these, JAM3, SFT2D2, and PAPSS1 were previously associated to breast tumors or other types of cancer. The relevance of these genes is confirmed by the new DetectDeviatingCells outlier detection technique. A comparison of gene networks on the selected genes showed significant differences between triple-negative breast cancer and non-triple-negative breast cancer data. The individual role of FOXA1 in triple-negative breast cancer and non-triple-negative breast cancer, and the strong FOXA1-AGR2 connection in triple-negative breast cancer stand out. The goal of our paper is to contribute to the breast cancer/triple-negative breast cancer understanding and management. At the same time it demonstrates that robust regression and outlier detection constitute key strategies to cope with high-dimensional clinical data such as omics data.
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Nadorp, Bettina, Audrey Lasry, Sanam Loghavi, Ravi Patel, Ben Kelly, Christopher J. Walker, Stephanie LaHaye, et al. "The Genomic and Transcriptomic Landscape of Myeloid Sarcoma and Associated Acute Myeloid Leukemia." Blood 142, Supplement 1 (November 28, 2023): 292. http://dx.doi.org/10.1182/blood-2023-182967.
Abstract:
Background: Myeloid sarcoma (MS) is a distinct form of acute myeloid leukemia (AML) found in ~10% of patients, in which mass-forming myeloid blasts proliferate in extramedullary sites. MS may present as an isolated lesion as the primary manifestation of disease, co-occur with AML, or, more commonly, arise in the setting of AML relapse. It is associated with poor outcomes, and treatment options are sparse. While increased knowledge of AML and its biology have improved the care and survival of AML patients, those with MS continue to have limited successful treatment options, as MS has typically not been included in AML genetic and genomic profiling efforts, and patients are excluded from clinical trials. Leukemia cutis (LC) is a form of extramedullary AML characterized by cutaneous involvement, often with an infiltrative and non-mass forming pattern of growth. Still, historically the literature has not made a clear distinction between MS and LC. Although recent small pilot studies have suggested differences in AML-associated mutations between medullary AML and MS, knowledge of the complete molecular and cellular composition of MS and comprehensive comparison with AML is lacking. While molecular characterization of the extramedullary disease site is encouraged in the updated clinical recommendations, no definitive guidance exists for changes in the management and care of AML patients with MS, given the substantial gap in our current knowledge and understanding of extramedullary AML. Methods: Here, we performed RNA-Seq (n=96), single-cell (sc) RNA-Seq (n=33), and mutation profiling using either whole exome sequencing (WES, n=6) or a 409-gene targeted sequencing panel (n=48) of MS or LC patient samples and bone marrow (BM) aspirates from AML patients with and without extramedullary disease to present the first comprehensive characterization of MS and LC. Results: Our WES data showed thatMS and LC evolve independently in the extramedullary site from concurrent medullary disease with distinct dominant clones, characterized by higher median tumor mutation burden (1.66 vs. 064 mutations per megabase, respectively; p=0.0059) and increased copy number aberrations, which both validated in an MS Tet2 HR mouse model. Notably, we observed location-specific molecular evolution in patients with multiple concurrent MS sites (Figure 1A) and patients with recurring MS in WES and targeted panel data. While targetable mutations (i.e., IDH1/2 mutations and FLT3-ITD/TKD) occur, the inter-site variability of molecular aberrations may explain the short-lived responses of AML patients with MS to IDH/FLT3 inhibitors. Transcriptional profile differences between MS and associated medullary disease included up-regulation of extracellular matrix organization and cell-substrate adhesion and down-regulation of inflammatory response pathways in both bulk and scRNA-seq data (Figure 1B). Further, involved BM malignant cells of MS patients compared to patients without MS up-regulated specific pathways, including interferon-gamma response observed in both bulk and scRNA-seq, as well as chromatin organization and splicing. We further revealed that MS patients without overt BM involvement show remodeling of the BM immune microenvironment, with increased fractions of hematopoietic stem and progenitor cells that down-regulate HLA class II genes. Comparing LC and MS in the skin revealed distinct differences in their expression profiles, including high expression of genes involved in cell migration and immune response in LC, suggesting LC and MS represent distinct disease entities. Lastly, we demonstrated high expression of BCL2 in extramedullary disease (p=0.0074) and presented initial evidence that treatment with BH3-mimetics may be beneficial for the treatment of isolated MS. Conclusions: Myeloid sarcoma shows distinct molecular evolution and microenvironmental composition compared to medullary disease, including location-specific evolution necessitating personalized management consideration. Leukemia cutis is distinct from MS tumorous lesions and should be considered a separate entity. High BCL2 expression in extramedullary disease might represent a promising targetable vulnerability in patients with isolated MS.
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Alonso Paz, Sandra, Ignacio Duran, Enrique Grande, and Alvaro Pinto. "Evaluation of deep learning techniques (DL) in RNA sequencing data for the prediction of response to immune checkpoint inhibitors in patients with metastatic renal cell cancer m(RCC)." Journal of Clinical Oncology 41, no. 6_suppl (February 20, 2023): 641. http://dx.doi.org/10.1200/jco.2023.41.6_suppl.641.
Abstract:
641 Background: Immune checkpoint inhibitors have become a cornerstone in the management of mRCC. However, to identify the most suitable patients for this treatment is an unmet medical need. We aimed to explore the utility of DL integrating clinical and molecular data to predict response to immunotherapy. Methods: We conducted a retrospective analysis using publicly available data from patients treated with nivolumab in the clinical trials Checkmate 009, 010 and 025. The primary objective was to assess the performance of different DL models (autoencoder and convolutional neural network (CNN)) predicting the PFS of these patients. With that scope, we followed several research lines including the creation of combined datasets with clinic and RNA sequencing and comparing the results of the DL models against the performance of traditional machine learning (ML) models. Finally, we came up with an interpretability analysis of those black-box models using LIME and SHAP values. Results: Clinical and transcriptomic data were available from 181 nivolumab-treated patients. Outcomes achieved confirmed that we can model the response for NIVOLUMAB using RNA sequencing data. (Table) However, DL models have not demonstrated to be significantly better than traditional ML methods when predicting response (p= 0.068). Deep autoencoder provided 68.9% accuracy, but the most accurate model was logistic regression classifier which achieved 86.4% of hit rate. Interpretability results revealed that most relevant genes for the decision making were related with the immune response and the regulation of kinases. Regarding interpretability, best results were achieved integrating both transcriptomic and clinical data (7 out of 10 DL and ML tested models achieved higher hit rates with combined data set. Logistic regression classifier improved its accuracy in 11%). Conclusions: The integration of clinical and molecular data could lead to more accurate predictions of outcome than any dataset by its own. However, further research is intended in the field of the DL analysis, as data codification and data structure could bias the results. The ongoing study ART (Artificial Intelligence in Renal Tumors) will address this issue prospectively. [Table: see text]
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Subramani, Jothimani, G. Sathish Kumar, and Thippa Reddy Gadekallu. "Gene-Based Predictive Modelling for Enhanced Detection of Systemic Lupus Erythematosus Using CNN-Based DL Algorithm." Diagnostics 14, no. 13 (June 24, 2024): 1339. http://dx.doi.org/10.3390/diagnostics14131339.
Abstract:
Systemic Lupus Erythematosus (SLE) is a multifaceted autoimmune disease that presents with a diverse array of clinical signs and unpredictable disease progression. Conventional diagnostic methods frequently fall short in terms of sensitivity and specificity, which can result in delayed diagnosis and less-than-optimal management. In this study, we introduce a novel approach for improving the identification of SLE through the use of gene-based predictive modelling and Stacked deep learning classifiers. The study proposes a new method for diagnosing SLE using Stacked Deep Learning Classifiers (SDLC) trained on Gene Expression Omnibus (GEO) database data. By combining transcriptomic data from GEO with clinical features and laboratory results, the SDLC model achieves a remarkable accuracy value of 0.996, outperforming traditional methods. Individual models within the SDLC, such as SBi-LSTM and ACNN, achieved accuracies of 92% and 95%, respectively. The SDLC’s ensemble learning approach allows for identifying complex patterns in multi-modal data, enhancing accuracy in diagnosing SLE. This study emphasises the potential of deep learning methods, in conjunction with open repositories like GEO, to advance the diagnosis and management of SLE. Overall, this research shows strong performance and potential for improving precision medicine in managing SLE.
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Zaccagnini, Germana, Biagina Maimone, Paola Fuschi, Marialucia Longo, Daniel Da Silva, Matteo Carrara, Christine Voellenkle, et al. "Hypoxia-Induced miR-210 Is Necessary for Vascular Regeneration upon Acute Limb Ischemia." International Journal of Molecular Sciences 21, no. 1 (December 24, 2019): 129. http://dx.doi.org/10.3390/ijms21010129.
Abstract:
Critical limb ischemia is the most serious form of peripheral artery disease, characterized by severe functional consequences, difficult clinical management and reduced life expectancy. The goal of this study was to investigate the miR-210 role in the neo-angiogenic response after acute limb ischemia. Complementary approaches were used in a mouse model of hindlimb ischemia: miR-210 loss-of-function was obtained by administration of LNA-oligonucleotides anti-miR-210; for miR-210 gain-of-function, a doxycycline-inducible miR-210 transgenic mouse was used. We tested miR-210 ability to stimulate vascular regeneration following ischemia. We found that miR-210 was necessary and sufficient to stimulate blood perfusion recovery, as well as arteriolar and capillary density increase, in the ischemic muscle. To clarify the molecular events underpinning miR-210 pro-angiogenic action, the transcriptomic changes in ischemic muscles upon miR-210 blocking were analyzed. We found that miR-210 impacted the transcriptome significantly, regulating pathways and functions linked to vascular regeneration. In agreement with a pro-angiogenic role, miR-210 also improved cardiac function and left ventricular remodeling after myocardial infarction. Moreover, miR-210 blocking decreased capillary density in a Matrigel plug assay, indicating that miR-210 is necessary for angiogenesis independently of ischemia. Collectively, these data indicate that miR-210 plays a pivotal role in promoting vascular regeneration.
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Kim, Hyung-Yong, Hee-Joo Choi, Jeong-Yeon Lee, and Gu Kong. "Cancer Target Gene Screening: a web application for breast cancer target gene screening using multi-omics data analysis." Briefings in Bioinformatics 21, no. 2 (January 29, 2019): 663–75. http://dx.doi.org/10.1093/bib/bbz003.
Abstract:
Abstract Breast cancer comprises several molecular subtypes with distinct clinical features and treatment responses, and a substantial portion of each subtype remains incurable. A comprehensive analysis of multi-omics data and clinical profiles is required in order to better understand the biological complexity of this cancer type and to identify new prognostic and therapeutic markers. Thus, there arises a need for useful analytical tools to assist in the investigation and clinical management of the disease. We developed Cancer Target Gene Screening (CTGS), a web application that provides rapid and user-friendly analysis of multi-omics data sets from a large number of primary breast tumors. It allows the investigation of genomic and epigenomic aberrations, evaluation of transcriptomic profiles and performance of survival analyses and of bivariate correlations between layers of omics data. Notably, the genome-wide screening function of CTGS prioritizes candidate genes of clinical and biological significance among genes with copy number alteration, DNA methylation and dysregulated expression by the integrative analysis of different types of omics data in customized subgroups of breast cancer patients. These features may help in the identification of druggable cancer driver genes in a specific subtype or the clinical condition of human breast cancer. CTGS is available at http://ctgs.biohackers.net.
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Tang, Howard H. F., Peter D. Sly, Patrick G. Holt, Kathryn E. Holt, and Michael Inouye. "Systems biology and big data in asthma and allergy: recent discoveries and emerging challenges." European Respiratory Journal 55, no. 1 (October 16, 2019): 1900844. http://dx.doi.org/10.1183/13993003.00844-2019.
Abstract:
Asthma is a common condition caused by immune and respiratory dysfunction, and it is often linked to allergy. A systems perspective may prove helpful in unravelling the complexity of asthma and allergy. Our aim is to give an overview of systems biology approaches used in allergy and asthma research. Specifically, we describe recent “omic”-level findings, and examine how these findings have been systematically integrated to generate further insight.Current research suggests that allergy is driven by genetic and epigenetic factors, in concert with environmental factors such as microbiome and diet, leading to early-life disturbance in immunological development and disruption of balance within key immuno-inflammatory pathways. Variation in inherited susceptibility and exposures causes heterogeneity in manifestations of asthma and other allergic diseases. Machine learning approaches are being used to explore this heterogeneity, and to probe the pathophysiological patterns or “endotypes” that correlate with subphenotypes of asthma and allergy. Mathematical models are being built based on genomic, transcriptomic and proteomic data to predict or discriminate disease phenotypes, and to describe the biomolecular networks behind asthma.The use of systems biology in allergy and asthma research is rapidly growing, and has so far yielded fruitful results. However, the scale and multidisciplinary nature of this research means that it is accompanied by new challenges. Ultimately, it is hoped that systems medicine, with its integration of omics data into clinical practice, can pave the way to more precise, personalised and effective management of asthma.
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Médigue, Claudine, Alexandra Calteau, Stéphane Cruveiller, Mathieu Gachet, Guillaume Gautreau, Adrien Josso, Aurélie Lajus, et al. "MicroScope—an integrated resource for community expertise of gene functions and comparative analysis of microbial genomic and metabolic data." Briefings in Bioinformatics 20, no. 4 (September 12, 2017): 1071–84. http://dx.doi.org/10.1093/bib/bbx113.
Abstract:
Abstract The overwhelming list of new bacterial genomes becoming available on a daily basis makes accurate genome annotation an essential step that ultimately determines the relevance of thousands of genomes stored in public databanks. The MicroScope platform (http://www.genoscope.cns.fr/agc/microscope) is an integrative resource that supports systematic and efficient revision of microbial genome annotation, data management and comparative analysis. Starting from the results of our syntactic, functional and relational annotation pipelines, MicroScope provides an integrated environment for the expert annotation and comparative analysis of prokaryotic genomes. It combines tools and graphical interfaces to analyze genomes and to perform the manual curation of gene function in a comparative genomics and metabolic context. In this article, we describe the free-of-charge MicroScope services for the annotation and analysis of microbial (meta)genomes, transcriptomic and re-sequencing data. Then, the functionalities of the platform are presented in a way providing practical guidance and help to the nonspecialists in bioinformatics. Newly integrated analysis tools (i.e. prediction of virulence and resistance genes in bacterial genomes) and original method recently developed (the pan-genome graph representation) are also described. Integrated environments such as MicroScope clearly contribute, through the user community, to help maintaining accurate resources.
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Luo, Jie, Marien Havé, Gilles Clément, Frédérique Tellier, Thierry Balliau, Alexandra Launay-Avon, Florence Guérard, Michel Zivy, and Céline Masclaux-Daubresse. "Integrating multiple omics to identify common and specific molecular changes occurring in Arabidopsis under chronic nitrate and sulfate limitations." Journal of Experimental Botany 71, no. 20 (July 21, 2020): 6471–90. http://dx.doi.org/10.1093/jxb/eraa337.
Abstract:
Abstract Plants have fundamental dependences on nitrogen and sulfur and frequently have to cope with chronic limitations when their supply is sub-optimal. This study aimed at characterizing the metabolomic, proteomic, and transcriptomic changes occurring in Arabidopsis leaves under chronic nitrate (Low-N) and chronic sulfate (Low-S) limitations in order to compare their effects, determine interconnections, and examine strategies of adaptation. Metabolite profiling globally revealed opposite effects of Low-S and Low-N on carbohydrate and amino acid accumulations, whilst proteomic data showed that both treatments resulted in increases in catabolic processes, stimulation of mitochondrial and cytosolic metabolism, and decreases in chloroplast metabolism. Lower abundances of ribosomal proteins and translation factors under Low-N and Low-S corresponded with growth limitation. At the transcript level, the major and specific effect of Low-N was the enhancement of expression of defence and immunity genes. The main effect of chronic Low-S was a decrease in transcripts of genes involved in cell division, DNA replication, and cytoskeleton, and an increase in the expression of autophagy genes. This was consistent with a role of target-of-rapamycin kinase in the control of plant metabolism and cell growth and division under chronic Low-S. In addition, Low-S decreased the expression of several NLP transcription factors, which are master actors in nitrate sensing. Finally, both the transcriptome and proteome data indicated that Low-S repressed glucosinolate synthesis, and that Low-N exacerbated glucosinolate degradation. This showed the importance of glucosinolate as buffering molecules for N and S management.
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Lazar, Alexandra. "Recent Data about the Use of Corticosteroids in Sepsis—Review of Recent Literature." Biomedicines 12, no. 5 (April 30, 2024): 984. http://dx.doi.org/10.3390/biomedicines12050984.
Abstract:
Sepsis, characterized by life-threatening organ dysfunction due to a maladaptive host response to infection, and its more severe form, septic shock, pose significant global health challenges. The incidence of these conditions is increasing, highlighting the need for effective treatment strategies. This review explores the complex pathophysiology of sepsis, emphasizing the role of the endothelium and the therapeutic potential of corticosteroids. The endothelial glycocalyx, critical in maintaining vascular integrity, is compromised in sepsis, leading to increased vascular permeability and organ dysfunction. Corticosteroids have been used for over fifty years to treat severe infections, despite ongoing debate about their efficacy. Their immunosuppressive effects and the risk of exacerbating infections are significant concerns. The rationale for corticosteroid use in sepsis is based on their ability to modulate the immune response, promote cardiovascular stability, and potentially facilitate organ restoration. However, the evidence is mixed, with some studies suggesting benefits in terms of microcirculation and shock reversal, while others report no significant impact on mortality or organ dysfunction. The Surviving Sepsis Campaign provides cautious recommendations for their use. Emerging research highlights the importance of genomic and transcriptomic analyses in identifying patient subgroups that may benefit from corticosteroid therapy, suggesting a move toward personalized medicine in sepsis management. Despite potential benefits, the use of corticosteroids in sepsis requires careful consideration of individual patient risk profiles, and further research is needed to optimize their use and integrate genomic insights into clinical practice. This review underscores the complexity of sepsis treatment and the ongoing need for evidence-based approaches to improve patient outcomes.
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Senevirathna, Jayan Duminda Mahesh, Ryo Yonezawa, Taiki Saka, Yoji Igarashi, Noriko Funasaka, Kazutoshi Yoshitake, Shigeharu Kinoshita, and Shuichi Asakawa. "Transcriptomic Insight into the Melon Morphology of Toothed Whales for Aquatic Molecular Developments." Sustainability 13, no. 24 (December 18, 2021): 13997. http://dx.doi.org/10.3390/su132413997.
Abstract:
Aquatic habitats are home to large animals such as marine mammals. Toothed whales have special fat deposits in the forehead region (called the melon) of their heads that are associated with echolocation underwater. This fat is also important industrially for human use. Due to the lack of gene expression information on the melon fat of toothed whales, we investigated the melon morphology via the transcriptomic approach. Four parts of the melons of three individual Risso’s dolphins were used for total RNA extraction, cDNA library preparation, and sequencing via next-generation sequencing (NGS) technologies. After the downstream analysis of raw sequence data, we determined that the outer layer of the melon’s ML4 region played multifunctional roles. The 36 differentially expressed genes of outer melon included ASB5, MYH13, MYOM2, and MYOM3. These genes are associated with muscle function and energy metabolism. Gene clustering and functional enrichment analyses also represented enrichments, such as the pentose phosphate pathway and morphogenesis related to lipid metabolism and muscle functions. This study will be crucial for muscle and fat functional-related molecular studies on aquatic mammals. Additionally, the study presents potential pathways, such as melon fat biosynthesis, for sustainable future developments.
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Yim, Jaewoo, Sung Won Cho, Beomhee Kim, Sungwoo Park, Yong Hee Han, and Sang Woo Seo. "Transcriptional Profiling of the Probiotic Escherichia coli Nissle 1917 Strain under Simulated Microgravity." International Journal of Molecular Sciences 21, no. 8 (April 11, 2020): 2666. http://dx.doi.org/10.3390/ijms21082666.
Abstract:
Long-term space missions affect the gut microbiome of astronauts, especially the viability of some pathogens. Probiotics may be an effective solution for the management of gut microbiomes, but there is a lack of studies regarding the physiology of probiotics in microgravity. Here, we investigated the effects of microgravity on the probiotic Escherichia coli Nissle 1917 (EcN) by comparing transcriptomic data during exponential and stationary growth phases under simulated microgravity and normal gravity. Microgravity conditions affected several physiological features of EcN, including its growth profile, biofilm formation, stress responses, metal ion transport/utilization, and response to carbon starvation. We found that some changes, such as decreased adhesion ability and acid resistance, may be disadvantageous to EcN relative to gut pathogens under microgravity, indicating the need to develop probiotics optimized for space flight.
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Sung, Kidon, Dan Li, Jungwhan Chon, Ohgew Kweon, Minjae Kim, Joshua Xu, Miseon Park, and Saeed A. Khan. "Transcriptomic Response of Human Nosocomial Pathogen Pseudomonas aeruginosa Biofilms Following Continuous Exposure to Antibiotic-Impregnated Catheters." Data 7, no. 3 (March 17, 2022): 35. http://dx.doi.org/10.3390/data7030035.
Abstract:
Biofilms are complex surface-attached bacterial communities that serve as a protective survival strategy to adapt to an environment. Bacterial contamination and biofilm formation on implantable medical devices pose a serious threat to human health, and these biofilms have become the most important source of nosocomial infections. Although antimicrobial-impregnated catheters have been employed to prevent bacterial infection, there have been concerns about the potential emergence of antibiotic resistance. To investigate the risk of developing resistance, we performed RNA-sequencing gene expression profiling of P. aeruginosa biofilms in response to chronic exposure to clindamycin and rifampicin eluted from antibiotic-coated catheters in a CDC biofilm bioreactor. There were 877 and 178 differentially expressed genes identified in planktonic and biofilm cells after growth for 144 h with control (without antibiotic-impregnation) and clindamycin/rifampicin-impregnated catheters, respectively. The differentially expressed genes were further analyzed by Clusters of Orthologous Groups (COGs) functional classification and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The data are publicly available through the GEO database with accession number GSE153546.
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Feinstein, Yael, Jennifer Claire Walker, Mark J. Peters, Simon Nadel, Nazima Pathan, Naomi Edmonds, Jethro Herberg, et al. "Cohort profile of the Biomarkers of Acute Serious Illness in Children (BASIC) study: a prospective multicentre cohort study in critically ill children." BMJ Open 8, no. 11 (November 2018): e024729. http://dx.doi.org/10.1136/bmjopen-2018-024729.
Abstract:
PurposeDespite significant progress, challenges remain in the management of critically ill children, including early identification of infection and organ failure and robust early risk stratification to predict poor outcome. The Biomarkers of Acute Serious Illness in Children study aims to identify genetic and biological pathways underlying the development of critical illness in infections and organ failure and those leading to poor outcome (death or severe disability) in children requiring emergency intensive care.ParticipantsWe recruited a prospective cohort of critically ill children undergoing emergency transport to four paediatric intensive care units (PICUs) in Southeast England between April 2014 and December 2016.Findings to dateDuring the study period, 1017 patients were recruited by the regional PICU transport team, and blood and urine samples were obtained at/around first contact with the patient by the transport team. Consent for participation in the study was deferred until after PICU admission and 674 parents/carers were consented. Further samples (blood, urine, stool and throat swabs) were collected after consent. Samples were processed and stored for genomic, transcriptomic, proteomic and metabolomic analyses. Demographic, clinical and laboratory data at first contact, during PICU stay and at discharge, were collected, as were detailed data regarding infectious or non-infectious aetiology. In addition, 115 families have completed 12-month validated follow-up questionnaires to assess quality of life and child behaviour.The first phase of sample analyses (transcriptomic profiling) is currently in progress.Future plansStored samples will be analysed using genomic, proteomic and metabolic profiling. Advanced bioinformatics techniques will be used to identify biomarkers for early diagnosis of infection, identification of organ failure and risk stratification to predict poor outcome (death/severe disability).Trial registration numberNCT03238040.