High-throughput Gene Expression Profiling Research Articles

MousiPLIER: A Mouse Pathway-Level Information Extractor Model.

High throughput gene expression profiling measures individual gene expression across conditions. However, genes are regulated in complex networks, not as individual entities, limiting the interpretability of gene expression data. Machine learning models that incorporate prior biological knowledge are a powerful tool to extract meaningful biology from gene expression data. Pathway-level information extractor (PLIER) is an unsupervised machine learning method that defines biological pathways by leveraging the vast amount of published transcriptomic data. PLIER converts gene expression data into known pathway gene sets, termed latent variables (LVs), to substantially reduce data dimensionality and improve interpretability. In the current study, we trained the first mouse PLIER model on 190,111 mouse brain RNA-sequencing samples, the greatest amount of training data ever used by PLIER. We then validated the mousiPLIER approach in a study of microglia and astrocyte gene expression across mouse brain aging. mousiPLIER identified biological pathways that are significantly associated with aging, including one latent variable (LV41) corresponding to striatal signal. To gain further insight into the genes contained in LV41, we performed k-means clustering on the training data to identify studies that respond strongly to LV41. We found that the variable was relevant to striatum and aging across the scientific literature. Finally, we built a web server (http://mousiplier.greenelab.com/) for users to easily explore the learned latent variables. Taken together this study defines mousiPLIER as a method to uncover meaningful biological processes in mouse brain transcriptomic studies.Significance statement RNA-sequencing studies define differential expression of individual genes across conditions. However, genes are regulated in complex networks, not as individual entities. Machine learning models that incorporate biological pathway information are a powerful tool to analyze human gene expression. However, such models are lacking for mouse, despite the vast number of mouse RNA-seq datasets. We trained a mouse pathway-level information extractor model (mousiPLIER) to reduce data dimensionality from over 10,000 genes to 196 'latent variables' that map to known biological pathways. To validate this approach, we applied mousiPLIER to differential expression across mouse brain aging. We identified 26 functional pathways (latent variables) that varied across aging. Finally, we developed a web server to facilitate use of mousiPLIER by the scientific community.

Spatial analysis toolkits for RNA in situ sequencing.

Spatial transcriptomics (ST) is featured by high-throughput gene expression profiling within their native cell and tissue context, offering a means to investigate gene regulatory networks in tissue microenvironment. In situ sequencing (ISS) is an imaging-based ST technology that simultaneously detects hundreds to thousands of genes at subcellular resolution. As a highly reproducible and robust technique, ISS has been widely adapted and undergone a series of technical iterations. As the interest in ISS-based spatial transcriptomic analysis grows, scalable and integrated data analysis workflows are needed to facilitate the applications of ISS in different research fields. This review presents the state-of-the-art bioinformatic toolkits for ISS data analysis, which covers the upstream and downstream analysis workflows, including image analysis, cell segmentation, clustering, functional enrichment, detection of spatially variable genes and cell clusters, spatial cell-cell interactions, and trajectory inference. To assist the community in choosing the right tools for their research, the application of each tool and its compatibility with ISS data are reviewed in detailed. Finally, future perspectives and challenges concerning how to integrate heterogeneous tools into a user-friendly analysis pipeline are discussed. This article is categorized under: RNA Methods > RNA Analyses In Vitro and In Silico.

Reaping the benefits of liquid handlers for high-throughput gene expression profiling in a marine model invertebrate

BackgroundModern high-throughput technologies enable the processing of a large number of samples simultaneously, while also providing rapid and accurate procedures. In recent years, automated liquid handling workstations have emerged as an established technology for reproducible sample preparation. They offer flexibility, making them suitable for an expanding range of applications. Commonly, such approaches are well-developed for experimental procedures primarily designed for cell-line processing and xenobiotics testing. Conversely, little attention is focused on the application of automated liquid handlers in the analysis of whole organisms, which often involves time-consuming laboratory procedures.ResultsHere, we present a fully automated workflow for all steps, from RNA extraction to real-time PCR processing, for gene expression quantification in the ascidian marine model Ciona robusta. For procedure validation, we compared the results obtained with the liquid handler with those of the classical manual procedure. The outcome revealed comparable results, demonstrating a remarkable time saving particularly in the initial steps of sample processing.ConclusionsThis work expands the possible application fields of this technology to whole-body organisms, mitigating issues that can arise from manual procedures. By minimizing errors, avoiding cross-contamination, decreasing hands-on time and streamlining the procedure, it could be employed for large-scale screening investigations.

Automated exploitation of deep learning for cancer patient stratification across multiple types.

Recent frameworks based on deep learning have been developed to identify cancer subtypes from high-throughput gene expression profiles. Unfortunately, the performance of deep learning is highly dependent on its neural network architectures which are often hand-crafted with expertise in deep neural networks, meanwhile, the optimization and adjustment of the network are usually costly and time consuming. To address such limitations, we proposed a fully automated deep neural architecture search model for diagnosing consensus molecular subtypes from gene expression data (DNAS). The proposed model uses ant colony algorithm, one of the heuristic swarm intelligence algorithms, to search and optimize neural network architecture, and it can automatically find the optimal deep learning model architecture for cancer diagnosis in its search space. We validated DNAS on eight colorectal cancer datasets, achieving the average accuracy of 95.48%, the average specificity of 98.07%, and the average sensitivity of 96.24%, respectively. Without the loss of generality, we investigated the general applicability of DNAS further on other cancer types from different platforms including lung cancer and breast cancer, and DNAS achieved an area under the curve of 95% and 96%, respectively. In addition, we conducted gene ontology enrichment and pathological analysis to reveal interesting insights into cancer subtype identification and characterization across multiple cancer types. The source code and data can be downloaded from https://github.com/userd113/DNAS-main. And the web server of DNAS is publicly accessible at 119.45.145.120:5001.

Pleiotropic hubs drive bacterial surface competition through parallel changes in colony composition and expansion.

Bacteria commonly adhere to surfaces where they compete for both space and resources. Despite the importance of surface growth, it remains largely elusive how bacteria evolve on surfaces. We previously performed an evolution experiment where we evolved distinct Bacilli populations under a selective regime that favored colony spreading. In just a few weeks, colonies of Bacillus subtilis showed strongly advanced expansion rates, increasing their radius 2.5-fold relative to that of the ancestor. Here, we investigate what drives their rapid evolution by performing a uniquely detailed analysis of the evolutionary changes in colony development. We find mutations in diverse global regulators, RicT, RNAse Y, and LexA, with strikingly similar pleiotropic effects: They lower the rate of sporulation and simultaneously facilitate colony expansion by either reducing extracellular polysaccharide production or by promoting filamentous growth. Combining both high-throughput flow cytometry and gene expression profiling, we show that regulatory mutations lead to highly reproducible and parallel changes in global gene expression, affecting approximately 45% of all genes. This parallelism results from the coordinated manner by which regulators change activity both during colony development-in the transition from vegetative growth to dormancy-and over evolutionary time. This coordinated activity can however also break down, leading to evolutionary divergence. Altogether, we show how global regulators function as major pleiotropic hubs that drive rapid surface adaptation by mediating parallel changes in both colony composition and expansion, thereby massively reshaping gene expression.

Effects of short-chain per- and polyfluoroalkyl substances (PFAS) on toxicologically relevant gene expression profiles in a liver-on-a-chip model

Short-chain per- and polyfluoroalkyl substances (PFAS) are highly stable and widely used environmental contaminants that pose potential health risks to humans. Aggregating reliable mechanistic information for safety assessments necessitates physiologically relevant high-throughput screening approaches. Here, we demonstrated the utility of a liver-on-a-chip model to investigate the effects of five short-chain PFAS at low (1 nM) and high (1 μM) concentrations on toxicologically-relevant gene expression profiles using the QuantiGene® Plex Assay. We found that the short-chain PFAS tested in this study modulated the expression of ABCG2, a gene encoding for the breast cancer resistance protein (BCRP), with marked and significant upregulation (up to 4-fold) observed for all but one of the short-chain PFAS tested. PFBS and HFPO-DA repressed SLCO1B3 expression, a gene that encodes for an essential liver-specific organic anion transporter. High concentrations of PFBS, PFHxA, and PFHxS upregulated the expression of genes encCYP1A1,CYP2B6 and CYP2C19 with the same treatments resulting in the repression of the expression of the gene encoding CYP1A2. This dysregulation could have consequences for the clearance of endogenous compounds and xenobiotics. However, we acknowledge that increased expression of genes encoding for transporters and biotransformation enzymes may or may not indicate changes to their protein expression or activity. Overall, our study provides important insights into the effects of short-chain PFAS on liver function and their potential implications for human health. The use of the liver-on-a-chip model in combination with the QuantiGene® Plex Assay may be a valuable tool for future high-throughput screening and gene expression profiling in toxicology studies.

Evaluating imputation methods for single-cell RNA-seq data

BackgroundSingle-cell RNA sequencing (scRNA-seq) enables the high-throughput profiling of gene expression at the single-cell level. However, overwhelming dropouts within data may obscure meaningful biological signals. Various imputation methods have recently been developed to address this problem. Therefore, it is important to perform a systematic evaluation of different imputation algorithms.ResultsIn this study, we evaluated 11 of the most recent imputation methods on 12 real biological datasets from immunological studies and 4 simulated datasets. The performance of these methods was compared, based on numerical recovery, cell clustering and marker gene analysis. Most of the methods brought some benefits on numerical recovery. To some extent, the performance of imputation methods varied among protocols. In the cell clustering analysis, no method performed consistently well across all datasets. Some methods performed poorly on real datasets but excellent on simulated datasets. Surprisingly and importantly, some methods had a negative effect on cell clustering. In marker gene analysis, some methods identified potentially novel cell subsets. However, not all of the marker genes were successfully imputed in gene expression, suggesting that imputation challenges remain.ConclusionsIn summary, different imputation methods showed different effects on different datasets, suggesting that imputation may have dataset specificity. Our study reveals the benefits and limitations of various imputation methods and provides a data-driven guidance for scRNA-seq data analysis.

Oversized liposomes boost macrophage-targeted RNA delivery to regulate macrophage polarity

Oversized liposomes boost macrophage-targeted RNA delivery to regulate macrophage polarity

Revealing the molecular mechanisms of zinc accumulation and zinc deficiency responses in quinoa, analyzed by high-throughput gene expression profiling under zinc depletion and resupply

Zinc (Zn) is an essential micronutrient for plant growth and development, but the soil available Zn is low in arable land worldwide. Therefore, crop Zn deficiency is a serious issue that threatens the sustainable development of agriculture. Quinoa (Chenopodium quinoa Willd.) is a high-efficiency Zn utilization crop. However, the molecular regulatory mechanisms underlying Zn uptake and Zn deficiency response in quinoa remain largely unclear. Here, using the method of Zn depletion and resupply, we investigated Zn deficiency responses in the hydroponic-grown quinoa seedlings by integrating physiol-biochemical and transcriptomic analyses. Zn depletion leads to H2O2 accumulation and lipid peroxidation in plants. Accordingly, the increased enzyme activities of superoxide dismutase and peroxidase alleviate Zn deficiency-induced oxidative damage in quinoa roots. Subsequently, a genome-wide transcriptome analysis revealed that the expression of genes encoding Zn sensors bZIP19/23 and metal transporters was upregulated in Zn-depleted plants but downregulated in Zn-resupplied plants. Interestingly, the high expression of several iron (Fe) deficiency-responsive transcription factors/transcription regulator genes may not be induced by Fe status in Zn-depleted plants, suggesting that the expression of these genes may be directly induced by Zn deficiency and subsequently regulate micronutrient homeostasis in plants. Zn depletion decreased the contents of indoacetic acid, trans-zeatin-riboside, abscisic acid and salicylic acid 2-O-β-d-glucose but increased the jasmonic acid content in quinoa roots. These results collectively indicated that Zn deficiency affects plant growth and development by modulating phytohormone pathways. Our results provide comprehensive insight into the mechanism of Zn uptake and Zn deficiency response in quinoa and provide a theoretical basis for breeding Zn-efficient crops.

Abstract 2050: Molecular subclassification of NCI PDMR breast cancer models using PAM50 gene expression signature

Abstract Background: Breast cancer is the second most common cancer in women. In 2022, it accounted for 15% of total new cancer cases and is the number four cause of death among all cancer types. To benefit from precision medicine, distinguishing molecular subtypes for prognosis and treatment in a clinical setting is essential. While intrinsic subtype classification from NGS results of patients is well established, the approach has not been comprehensively described for patient-derived xenograft (PDX) models, which have been shown to be powerful in translational research. The National Cancer Institute's Patient-Derived Models Repository (NCI PDMR; https://pdmr.cancer.gov) provides rich information in developing the method. Materials and Methods: Normalized gene expression data of breast cancer PDX and patient specimens (originators) were extracted using tximport and DESeq2 based on RNA-seq analysis. The immunohistochemistry (IHC) was used to determine the status of ER, PR and HER2 receptor expression in these tumor specimens. The PAM50 classification was performed by the R package Genefu. For further analysis, the PAM50 centroids for all 5 subtypes were also obtained from Genefu. Results: Using the RNA-seq data from 43 PDX models (180 PDX samples, 4~6 samples/model), we were able to predict subtypes at the model level based on the PAM50 method: There are 1 Luminal A subtypes, 5 Luminal B; 6 Her2; 30 Basal and 1 Normal, which encompasses the whole spectrum of PAM50. Thirty originators were also included and there are 8 Luminal A, 9 Luminal B, 2 Her2 and 11 Basal. With the matched 11 originators and the PDX models, 91% of their predicted subtypes are identical; 0.80 Cohen’s kappa was obtained, indicating high inter-rater agreement. We also described subsequent analysis with IHC data-based subtypes. For the 10 originators having IHC-based subtypes, 90% agreement was observed; for 24 PDX models with IHC data, 88% was observed. Of all the 180 PDX samples, 33 of the 43 PDX models (77%) have consistent predicted PAM50 molecular subtypes across different passages and lineages. Within the discordant samples, we observed cases such as a mixture of luminal B and Basal, which can be reasonably interpreted by AR positive signal from IHC. The discrepancy encourages further PDX subclassification from the Basal subtype. Conclusions: Using our high-throughput gene expression profiles from many patients and samples from patient derived models, we have demonstrated the feasibility of applying classic PAM50 classification algorithm, which was originally developed with microarray data, to be able to recognize the expression signals from our RNA-seq data. Overall, this study should set a primer for the identification of PDX-based subtypes, starting from breast cancer. Citation Format: Peter I. Wu, Lindsay Dutko, Shahanawaz Jiwani, Li Chen, Biswajit Das, Ting-Chia Chang, Yvonne A. Evrard, Chris A. Karlovich, Alyssa Chapman, Brandie Fullmer, Ashley Hayes, Ruth Thornton, Nikitha Nair, Kelly Benauer, Gloryvee Rivera, Thomas Forbes, John Carter, Suzanne Borgel, Tiffanie Miner, Chelsea McGlynn, Justine Mills, Shannon Uzelac, Tia Shearer, Lauren Hicks, Michelle Norris, Carley Border, Sergio Alcoser, Thomas Walsh, Michael Mullendore, Michelle Eugeni, Dianne Newton, Melinda G. Hollingshead, P. M. Williams, James H. Doroshow. Molecular subclassification of NCI PDMR breast cancer models using PAM50 gene expression signature [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2050.

Dual-Stream Subspace Clustering Network for revealing gene targets in Alzheimer's disease

The rapid development of scRNA-seq technology in recent years has enabled us to capture high-throughput gene expression profiles at single-cell resolution, reveal the heterogeneity of complex cell populations, and greatly advance our understanding of the underlying mechanisms in human diseases. Traditional methods for gene co-expression clustering are limited to discovering effective gene groups in scRNA-seq data. In this paper, we propose a novel gene clustering method based on convolutional neural networks called Dual-Stream Subspace Clustering Network (DS-SCNet). DS-SCNet can accurately identify important gene clusters from large scales of single-cell RNA-seq data and provide useful information for downstream analysis. Based on the simulated datasets, DS-SCNet successfully clusters genes into different groups and outperforms mainstream gene clustering methods, such as DBSCAN and DESC, across different evaluation metrics. To explore the biological insights of our proposed method, we applied it to real scRNA-seq data of patients with Alzheimer's disease (AD). DS-SCNet analyzed the single-cell RNA-seq data with 10,850 genes, and accurately identified 8 optimal clusters from 6673 cells. Enrichment analysis of these gene clusters revealed functional signaling pathways including the ILS signaling, the Rho GTPase signaling, and hemostasis pathways. Further analysis of gene regulatory networks identified new hub genes such as ELF4 as important regulators of AD, which indicates that DS-SCNet contributes to the discovery and understanding of the pathogenesis in Alzheimer's disease.

Routine application of the Lymph2Cx assay for the subclassification of aggressive B-cell lymphoma: report of a prospective real-world series.

The subclassification of diffuse large B-cell lymphoma (DLBCL) into germinal center B-cell-like (GCB) and activated B-cell-like (ABC) subtypes has become mandatory in the 2017 update of the WHO classification of lymphoid neoplasms and will continue to be used in the WHO 5th edition. The RNA-based Lymph2Cx assay has been validated as a reliable surrogate of high-throughput gene expression profiling assays for distinguishing between GCB and ABC DLBCL and provides reliable results from formalin-fixed, paraffin-embedded (FFPE) material. This test has been previously used in clinical trials, but experience from real-world routine application is rare. We routinely applied the Lymph2Cx assay to day-to-day diagnostics on a series of 147 aggressive B-cell lymphoma cases and correlated our results with the immunohistochemical subclassification using the Hans algorithm and fluorescence in situ hybridization findings using break-apart probes for MYC, BCL2, and BCL6. The routine use of the Lymph2Cx assay had a high technical success rate (94.6%) with a low rate of failure due to poor material and/or RNA quality. The Lymph2Cx assay was discordant with the Hans algorithm in 18% (23 of 128 cases). Discordant cases were mainly classified as GCB by the Hans algorithm and as ABC by Lymph2Cx (n = 11, 8.6%). Only 5 cases (3.9%) were classified as non-GCB by the Hans algorithm and as GCB by Lymph2Cx. Additionally, 5.5% of cases (n = 7) were left unclassified by Lymph2Cx, whereas they were defined as GCB (n = 4) or non-GCB (n = 3) by the Hans algorithm. Our data support the routine applicability of the Lymph2Cx assay.

A balancing act: interactions within NuA4/TIP60 regulate picNuA4 function in Saccharomyces cerevisiae and humans.

The NuA4 lysine acetyltransferase complex acetylates histone and nonhistone proteins and functions in transcription regulation, cell cycle progression, and DNA repair. NuA4 harbors an interesting duality in that its catalytic module can function independently and distinctly as picNuA4. At the molecular level, picNuA4 anchors to its bigger brother via physical interactions between the C-terminus of Epl1 and the HSA domain of Eaf1, the NuA4 central scaffolding subunit. This is reflected at the regulatory level, as picNuA4 can be liberated genetically from NuA4 by disrupting the Epl1-Eaf1 interaction. As such, removal of either Eaf1 or the Epl1 C-terminus offers a unique opportunity to elucidate the contributions of Eaf1 and Epl1 to NuA4 biology and in turn their roles in balancing picNuA4 and NuA4 activities. Using high-throughput genetic and gene expression profiling, and targeted functional assays to compare eaf1Δ and epl1-CΔ mutants, we found that EAF1 and EPL1 had both overlapping and distinct roles. Strikingly, loss of EAF1 or its HSA domain led to a significant decrease in the amount of picNuA4, while loss of the Epl1 C-terminus increased picNuA4 levels, suggesting starkly opposing effects on picNuA4 regulation. The eaf1Δ epl1-CΔ double mutants resembled the epl1-CΔ single mutants, indicating that Eaf1's role in picNuA4 regulation depended on the Epl1 C-terminus. Key aspects of this regulation were evolutionarily conserved, as truncating an Epl1 homolog in human cells increased the levels of other picNuA4 subunits. Our findings suggested a model in which distinct aspects of the Epl1-Eaf1 interaction regulated picNuA4 amount and activity.

Abstract IA01: Molecular drivers in hepatocellular carcinoma: The story of what we know and what are we missing

Abstract Hepatocellular carcinoma (HCC) is a highly heterogeneous cancer, both at the molecular and histological level. High-throughput sequencing and gene expression profiling have identified distinct transcriptomic subclasses and recurrently altered cancer driver genes. Several HCC subtypes characterized by histological features have also been identified. HCC phenotype appears to be closely related to particular gene mutation and driven by various oncogenic pathway alterations. Identification of the origin of HCC development and their trajectory of progression is important to understand tumor evolution and to identify the mechanisms of tumor response or resistance to therapies. We will review the early and late molecular alterations driving HCC occurrence and progression of tumors in the context of patient lifespan and exposure to environmental factors. Several questions remain to be answered, in particular on the identification of the cells at the origin of the malignant transformation into HCC and the mechanism of escape to the immunological monitoring. Citation Format: Jessica Zucman-Rossi. Molecular drivers in hepatocellular carcinoma: The story of what we know and what are we missing [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 IA01.

Global Transcriptomic Profiling Identifies Differential Gene Expression Signatures Between Inflammatory and Noninflammatory Aortic Aneurysms.

To identify hallmark genes and biomolecular processes in aortitis using high-throughput gene expression profiling, and to provide a range of potentially new drug targets (genes) and therapeutics from a pharmacogenomic network analysis. Bulk RNA sequencing was performed on surgically resected ascending aortic tissues from inflammatory aneurysms (giant cell arteritis [GCA] with or without polymyalgia rheumatica, n=8; clinically isolated aortitis [CIA], n=17) and noninflammatory aneurysms (n=25) undergoing surgical aortic repair. Differentially expressed genes (DEGs) between the 2 patient groups were identified while controlling for clinical covariates. A protein-protein interaction model, drug-gene target information, and the DEGs were used to construct a pharmacogenomic network for identifying promising drug targets and potentially new treatment strategies in aortitis. Overall, tissue gene expression patterns were the most associated with disease state than with any other clinical characteristic. We identified 159 and 93 genes that were significantly up-regulated and down-regulated, respectively, in inflammatory aortic aneurysms compared to noninflammatory aortic aneurysms. We found that the up-regulated genes were enriched in immune-related functions, whereas the down-regulated genes were enriched in neuronal processes. Notably, gene expression profiles of inflammatory aortic aneurysms from patients with GCA were no different than those from patients with CIA. Finally, our pharmacogenomic network analysis identified genes that could potentially be targeted by immunosuppressive drugs currently approved for other inflammatory diseases. We performed the first global transcriptomics analysis in inflammatory aortic aneurysms from surgically resected aortic tissues. We identified signature genes and biomolecular processes, while finding that CIA may be a limited presentation of GCA. Moreover, our computational network analysis revealed potential novel strategies for pharmacologic interventions and suggests future biomarker discovery directions for the precise diagnosis and treatment of aortitis.

Identification of host transcriptome-guided repurposable drugs for SARS-CoV-1 infections and their validation with SARS-CoV-2 infections by using the integrated bioinformatics approaches.

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is one of the most severe global pandemic due to its high pathogenicity and death rate starting from the end of 2019. Though there are some vaccines available against SAER-CoV-2 infections, we are worried about their effectiveness, due to its unstable sequence patterns. Therefore, beside vaccines, globally effective supporting drugs are also required for the treatment against SARS-CoV-2 infection. To explore commonly effective repurposable drugs for the treatment against different variants of coronavirus infections, in this article, an attempt was made to explore host genomic biomarkers guided repurposable drugs for SARS-CoV-1 infections and their validation with SARS-CoV-2 infections by using the integrated bioinformatics approaches. At first, we identified 138 differentially expressed genes (DEGs) between SARS-CoV-1 infected and control samples by analyzing high throughput gene-expression profiles to select drug target key receptors. Then we identified top-ranked 11 key DEGs (SMAD4, GSK3B, SIRT1, ATM, RIPK1, PRKACB, MED17, CCT2, BIRC3, ETS1 and TXN) as hub genes (HubGs) by protein-protein interaction (PPI) network analysis of DEGs highlighting their functions, pathways, regulators and linkage with other disease risks that may influence SARS-CoV-1 infections. The DEGs-set enrichment analysis significantly detected some crucial biological processes (immune response, regulation of angiogenesis, apoptotic process, cytokine production and programmed cell death, response to hypoxia and oxidative stress), molecular functions (transcription factor binding and oxidoreductase activity) and pathways (transcriptional mis-regulation in cancer, pathways in cancer, chemokine signaling pathway) that are associated with SARS-CoV-1 infections as well as SARS-CoV-2 infections by involving HubGs. The gene regulatory network (GRN) analysis detected some transcription factors (FOXC1, GATA2, YY1, FOXL1, TP53 and SRF) and micro-RNAs (hsa-mir-92a-3p, hsa-mir-155-5p, hsa-mir-106b-5p, hsa-mir-34a-5p and hsa-mir-19b-3p) as the key transcriptional and post- transcriptional regulators of HubGs, respectively. We also detected some chemicals (Valproic Acid, Cyclosporine, Copper Sulfate and arsenic trioxide) that may regulates HubGs. The disease-HubGs interaction analysis showed that our predicted HubGs are also associated with several other diseases including different types of lung diseases. Then we considered 11 HubGs mediated proteins and their regulatory 6 key TFs proteins as the drug target proteins (receptors) and performed their docking analysis with the SARS-CoV-2 3CL protease-guided top listed 90 anti-viral drugs out of 3410. We found Rapamycin, Tacrolimus, Torin-2, Radotinib, Danoprevir, Ivermectin and Daclatasvir as the top-ranked 7 candidate-drugs with respect to our proposed target proteins for the treatment against SARS-CoV-1 infections. Then, we validated these 7 candidate-drugs against the already published top-ranked 11 target proteins associated with SARS-CoV-2 infections by molecular docking simulation and found their significant binding affinity scores with our proposed candidate-drugs. Finally, we validated all of our findings by the literature review. Therefore, the proposed candidate-drugs might play a vital role for the treatment against different variants of SARS-CoV-2 infections with comorbidities, since the proposed HubGs are also associated with several comorbidities.

TGF\u03b1-EGFR pathway in breast carcinogenesis, association with WWOX expression and estrogen activation

WWOX is a tumor-suppressive steroid dehydrogenase, which relationship with hormone receptors was shown both in animal models and breast cancer patients. Herein, through nAnT-iCAGE high-throughput gene expression profiling, we studied the interplay of estrogen receptors and the WWOX in breast cancer cell lines (MCF7, T47D, MDA-MB-231, BT20) under estrogen stimulation and either introduction of the WWOX gene by retroviral transfection (MDA-MB-231, T47D) or silenced with shRNA (MCF7, BT20). Additionally, we evaluated the consequent biological characteristics by proliferation, apoptosis, invasion, and adhesion assays. TGFα-EGFR signaling was found to be significantly affected in all examined breast cancer cell lines in response to estrogen and strongly associated with the level of WWOX expression, especially in ER-positive MCF7 cells. Under the influence of 17β-estradiol presence, biological characteristics of the cell lines were also delineated. The study revealed modulation of adhesion, invasion, and apoptosis. The obtained results point at a complex role of the WWOX gene in the carcinogenesis of the breast tissue, which seems to be closely related to the presence of estrogen α and/or β receptors.

Molecular mechanisms of zinc toxicity in the potworm Enchytraeus crypticus, analysed by high-throughput gene expression profiling

Zinc (Zn) is known to be relatively toxic to some soil-living invertebrates including the ecologically important enchytraeid worms. To reveal the molecular mechanisms of zinc toxicity we assessed the gene expression profile of Enchytraeus crypticus (Enchytraeidae), exposed to the reproduction effect concentrations EC10 and EC50, over 4 consecutive days, using a high-throughput microarray (species customized). Three main mechanisms of toxicity to Zn were observed: 1) Zn trafficking (upregulation of zinc transporters, a defence response to regulate the cellular zinc level), 2) oxidative stress (variety of defence mechanisms, triggered by Reactive Oxygen Species (ROS)), and 3) effects on the nervous system (possibly the primary lesion explaining the avoidance behaviour and also why enchytraeids are relatively susceptible to Zn). The adverse outcome at the organism level (reproduction EC50) could be predicted based on gene expression (male gonad development, oocyte maturation), with Zn at the EC50 affecting processes related to higher stress levels. The gene expression response was time-dependent and reflected the cascade of events taking place over-time. The 1 to 4 days of exposure design was a good strategy as it captured the time for sequence of events towards zinc adverse outcomes in E. crypticus.

The prognostic and clinical significance of IFI44L aberrant downregulation in patients with oral squamous cell carcinoma

BackgroundIt is a basic task in high-throughput gene expression profiling studies to identify differentially expressed genes (DEGs) between two phenotypes. RankComp, an algorithm, could analyze the highly stable within-sample relative expression orderings (REOs) of gene pairs in a particular type of human normal tissue that are widely reversed in the cancer condition, thereby detecting DEGs for individual disease samples measured by a particular platform.MethodsIn the present study, Gene Expression Omnibus (GEO) Series (GSE) GSE75540, GSE138206 were downloaded from GEO, by analyzing DEGs in oral squamous cell carcinoma based on online datasets using the RankComp algorithm, using the Kaplan-Meier survival analysis and Cox regression analysis to survival analysis, Gene Set Enrichment Analysis (GSEA) to explore the potential molecular mechanisms underlying.ResultsWe identified 6 reverse gene pairs with stable REOs. All the 12 genes in these 6 reverse gene pairs have been reported to be associated with cancers. Notably, lower Interferon Induced Protein 44 Like (IFI44L) expression was associated with poorer overall survival (OS) and Disease-free survival (DFS) in oral squamous cell carcinoma patients, and IFI44L expression showed satisfactory predictive efficiency by receiver operating characteristic (ROC) curve. Moreover, low IFI44L expression was identified as risk factors for oral squamous cell carcinoma patients’ OS. IFI44L downregulation would lead to the activation of the FRS-mediated FGFR1, FGFR3, and downstream signaling pathways, and might play a role in the PI3K-FGFR cascades.ConclusionsCollectively, we identified 6 reverse gene pairs with stable REOs in oral squamous cell carcinoma, which might serve as gene signatures playing a role in the diagnosis in oral squamous cell carcinoma. Moreover, high expression of IFI44L, one of the DEGs in the 6 reverse gene pairs, might be associated with favorable prognosis in oral squamous cell carcinoma patients and serve as a tumor suppressor by acting on the FRS-mediated FGFR signaling.

175-OR: The Mitochondrial Life Cycle Determines ß-Cell Maturity through Activation of the Integrated Stress Response

Mitochondrial function is pivotal to β-cell competence. The mitochondrial life cycle balances mitochondrial biogenesis and turnover (mitophagy) to ensure optimal metabolic function. Human type 2 diabetic (T2D) β-cells are known to develop mitochondrial structural/functional defects, suggestive of a defective mitochondrial life cycle. However, it is unclear if these defects are a cause or consequence of T2D. Here, we observed that human T2D β-cells had reduced mitophagic flux, mtDNA content, and expression of mitochondrially encoded genes. To test the importance of the mitochondrial life cycle to drive β-cell failure in T2D, we developed 2 distinct β-cell specific mouse models: βTfamKO (to deplete mtDNA) and βClec16aΚΟ (to impair mitophagy). We observed age dependent loss of glucose tolerance, glucose stimulated insulin secretion and β-cell mass in both models, which was exacerbated by obesity in βClec16aΚΟ mice. Loss of β-cell mass was largely independent of changes in proliferation or apoptosis but rather, due to an induction of β-cell immaturity. Using lineage tracing approaches, we confirmed that βClec16aKO and βTfamKO mice induce formation of both insulin-negative immature β-cells and β-to-α cell transdifferentiation. Further, high-throughput gene expression profiling, biochemical, and metabolic assays highlighted that either Clec16a or Tfam deficiency induces an aberrant retrograde signaling program, manifested by reductions in cellular ATP and activation of the integrated stress response (ISR). Importantly, inhibition of the ISR in vivo ameliorated glucose intolerance, defective insulin secretion, β-cell immaturity, and loss of β-cell mass, suggesting that aberrant retrograde signaling may directly lead to loss of β-cell identity. Taken together, our studies illustrate that a unified mitochondrial lifecycle is necessary to maintain β-cell mass and identity and may be targeted to prevent β-cell failure in T2D. Disclosure G. Pearson: None. N. Lawlor: None. J. Zhu: None. E. M. Walker: None. E. C. Reck: None. M. L. Stitzel: None. S. Soleimanpour: None. Funding American Diabetes Association (1-19-PDF-063 to G.P.)