Differential Network Analysis Research Articles

Fluid flow shear stress and tissue remodeling-an orthodontic perspective: evidence synthesis and differential gene expression network analysis.

Introduction: This study aimed to identify and analyze in vitro studies investigating the biological effect of fluid-flow shear stress (FSS) on cells found in the periodontal ligament and bone tissue. Method: We followed the PRISMA guideline for systematic reviews. A PubMed search strategy was developed, studies were selected according to predefined eligibility criteria, and the risk of bias was assessed. Relevant data related to cell source, applied FSS, and locus-specific expression were extracted. Based on this evidence synthesis and, as an original part of this work, analysis of differential gene expression using over-representation and network-analysis was performed. Five relevant publicly available gene expression datasets were analyzed using gene set enrichment analysis (GSEA). Result: A total of 6,974 articles were identified. Titles and abstracts were screened, and 218 articles were selected for full-text assessment. Finally, 120 articles were included in this study. Sample size determination and statistical analysis related to methodological quality and the ethical statement item in reporting quality were most frequently identified as high risk of bias. The analyzed studies mostly used custom-made fluid-flow apparatuses (61.7%). FSS was most frequently applied for 0.5h, 1h, or 2h, whereas FSS magnitudes ranged from 6 to 20dyn/cm2 depending on cell type and flow profile. Fluid-flow frequencies of 1Hz in human cells and 1 and 5Hz in mouse cells were mostly applied. FSS upregulated genes/metabolites responsible for tissue formation (AKT1, alkaline phosphatase, BGLAP, BMP2, Ca2+, COL1A1, CTNNB1, GJA1, MAPK1/MAPK3, PDPN, RUNX2, SPP1, TNFRSF11B, VEGFA, WNT3A) and inflammation (nitric oxide, PGE-2, PGI-2, PTGS1, PTGS2). Protein-protein interaction networks were constructed and analyzed using over-representation analysis and GSEA to identify shared signaling pathways. Conclusion: To our knowledge, this is the first review giving a comprehensive overview and discussion of methodological technical details regarding fluid flow application in 2D cell culture in vitro experimental conditions. Therefore, it is not only providing valuable information about cellular molecular events and their quantitative and qualitative analysis, but also confirming the reproducibility of previously published results.

Transcriptome-Wide Analysis of Neutrophil-Related Circ_22232 in Neuroinflammation from Ischemic Stroke Mice.

Ischemic stroke (IS) often leads to high rates of disability and mortality worldwide with secondary damage due to neuroinflammation. Identification of potential therapeutic targets via the novel circular RNAs (circRNAs) would advance the field and provide a better treatment option for neuroinflammation after IS. Gene Ontology Term Enrichment (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were applied to identify differentially expressed genes/miRNAs/circRNAs in the genome-wide RNA-seq profiles of ischemic mice. Meanwhile, relevant circRNAs were screened by differential expression analysis and coexpression RNA regulation network analysis. To explore the function of circ_22232 (Specc1l), we generated circ_22232 knockdown mice and applied middle cerebral artery occlusion (MCAO) to study IS. Cytokine levels were detected by enzyme-linked immunosorbent assay. Morphological changes were observed with immunohistochemical staining and hematoxylin-eosin staining. The circ_22232/miR-847-3p/Bmp1 axis was found to be highly correlated with neutrophil-associated neuroinflammation in cerebral tissue of mice. Immunohistochemical showed a progressive increase in the proportion of neutrophils after IS. In in vivo experiments, the circ_22232 knockdown alleviated cerebral injury by reducing the activation of neutrophils and inflammatory cytokine production. This suggests that circ_22232 is associated with inflammation, which may serve as a potential therapeutic target for IS.

Banff Human Organ Transplant Consensus Gene Panel for the Detection of Antibody Mediated Rejection in Heart Allograft Biopsies.

The molecular refinement of the diagnosis of heart allograft rejection based on whole-transcriptome analyses faces several hurdles that greatly limit its widespread clinical application. The targeted Banff Human Organ Transplant gene panel (B-HOT, including 770 genes of interest) has been developed to facilitate reproducible and cost-effective gene expression analysis of solid organ allografts. We aimed to determine in silico the ability of this targeted panel to capture the antibody-mediated rejection (AMR) molecular profile using whole-transcriptome data from 137 heart allograft biopsies (71 biopsies reflecting the entire landscape of histologic AMR, 66 non-AMR control biopsies including cellular rejection and non-rejection cases). Differential gene expression, pathway and network analyses demonstrated that the B-HOT panel captured biologically and clinically relevant genes (IFNG-inducible, NK-cells, injury, monocytes-macrophage, B-cell-related genes), pathways (interleukin and interferon signaling, neutrophil degranulation, immunoregulatory interactions, endothelial activation) and networks reflecting the pathophysiological mechanisms underlying the AMR process previously identified in whole-transcriptome analysis. Our findings support the potential clinical use of the B-HOT-gene panel as a reliable proxy to whole-transcriptome analysis for the gene expression profiling of cardiac allograft rejection.

Testing the differential network between two gaussian graphical models with false discovery rate control

This paper focuses on the differential network analysis between two Gaussian graphical models (GGMs). We introduce a new framework for inferring the structural differences between two GGMs by adopting the elegant symmetrized data aggregation (SDA) which proceeds by sample splitting, data screening, and information aggregation to achieve the false discovery rate (FDR) control. The theoretical guarantee for the FDR control is established to verify the validity of this procedure. Simulation studies show that the proposed method delivers a reasonable FDR control with remarkable power. The method is applied to a TCGA breast cancer data set to identify the gene network rewiring between the luminal A and basal-like subtypes.

Gut ecological networks reveal associations between bacteria, exercise, and clinical profile in non-alcoholic fatty liver disease patients.

Gut microbial dysbiosis has been observed in non-alcoholic fatty liver disease (NAFLD). The beneficial impact of exercise, the recommended lifestyle change for NAFLD patients, might be mediated by the gut microbiome (GM). However, the exact taxonomic and functional signatures associated with the host's clinical and biochemical improvement during exercise in NAFLD patients have not been elucidated yet. To investigate the impact of exercise on GM and reveal GM structures associated with NAFLD improvement during exercise. Stool samples from a 12-week randomized controlled exercise study on NAFLD subjects (N = 39) were retrieved, and shotgun metagenomics was performed at baseline and endpoint. Differential correlation network and enrichment analysis were applied to characterize the GM taxonomic and functional changes during the exercise intervention and to associate GM changes with biomarkers of liver status and metabolic dysregulation. Network analysis demonstrated that exercise induced significant changes in the bacterial interactome, which were associated with waist circumference; resting metabolic rate; plasma fasting concentrations of triglyceride, glucose, insulin, and glycated hemoglobin A1c; and homeostasis model assessment for insulin resistance. Grouping the patients in the exercise group (N = 20) as responders (N = 13) and non-responders (N = 7) using their intrahepatic lipid content (IHL) change allowed us to identify bacteria consortia contributing to the levels of alanine fermentation, methanol-, creatinine-, and protocatechuate degradation and as a result to the plasma concentrations of liver injury markers alanine transaminase, gamma-glutamyl transaminase, and aspartate transaminase. We showed that even though exercise has not a significant impact on the alpha- and beta-diversity of NAFLD patients, it leads to a significant re-structuring of the gut bacteria interactome and that specific structural changes can be linked with improvements in IHL. IMPORTANCE Our study is applying a community-based approach to examine the influence of exercise on gut microbiota (GM) and discover GM structures linked with NAFLD improvements during exercise. The majority of microbiome research has focused on finding specific species that may contribute to the development of human diseases. However, we believe that complex diseases, such as NAFLD, would be more efficiently treated using consortia of species, given that bacterial functionality is based not only on its own genetic information but also on the interaction with other microorganisms. Our results revealed that exercise significantly changes the GM interaction and that structural alterations can be linked with improvements in intrahepatic lipid content and metabolic functions. We believe that the identification of these characteristics in the GM enhances the development of exercise treatment for NAFLD and will attract general interest in this field.

Longitudinal MicroRNA Signature of Conversion to Psychosis.

The emergence of psychosis in ultra-high-risk subjects (UHR) is influenced by gene-environment interactions that rely on epigenetic mechanisms such as microRNAs. However, whether they can be relevant pathophysiological biomarkers of psychosis' onset remains unknown. We present a longitudinal study of microRNA expression, measured in plasma by high-throughput sequencing at baseline and follow-up, in a prospective cohort of 81 UHR, 35 of whom developed psychosis at follow-up (converters). We combined supervised machine learning and differential graph analysis to assess the relative weighted contribution of each microRNA variation to the difference in outcome and identify outcome-specific networks. We then applied univariate models to the resulting microRNA variations common to both strategies, to interpret them as a function of demographic and clinical covariates. We identified 207 microRNA variations that significantly contributed to the classification. The differential network analysis found 276 network-specific correlations of microRNA variations. The combination of both strategies identified 25 microRNAs, whose gene targets were overrepresented in cognition and schizophrenia genome-wide association studies findings. Interpretable univariate models further supported the relevance of miR-150-5p and miR-3191-5p variations in psychosis onset, independent of age, sex, cannabis use, and medication. In this first longitudinal study of microRNA variation during conversion to psychosis, we combined 2 methodologically independent data-driven strategies to identify a dynamic epigenetic signature of the emergence of psychosis that is pathophysiologically relevant.

Identification of shared biological features in four different lung cell lines infected with SARS-CoV-2 virus through RNA-seq analysis.

The COVID-19 pandemic caused by SARS-CoV-2 has resulted in millions of confirmed cases and deaths worldwide. Understanding the biological mechanisms of SARS-CoV-2 infection is crucial for the development of effective therapies. This study conducts differential expression (DE) analysis, pathway analysis, and differential network (DN) analysis on RNA-seq data of four lung cell lines, NHBE, A549, A549.ACE2, and Calu3, to identify their common and unique biological features in response to SARS-CoV-2 infection. DE analysis shows that cell line A549.ACE2 has the highest number of DE genes, while cell line NHBE has the lowest. Among the DE genes identified for the four cell lines, 12 genes are overlapped, associated with various health conditions. The most significant signaling pathways varied among the four cell lines. Only one pathway, "cytokine-cytokine receptor interaction", is found to be significant among all four cell lines and is related to inflammation and immune response. The DN analysis reveals considerable variation in the differential connectivity of the most significant pathway shared among the four lung cell lines. These findings help to elucidate the mechanisms of SARS-CoV-2 infection and potential therapeutic targets.

The cluster D-trace loss for differential network analysis

ABSTRACT A growing literature suggests that gene expression can be greatly altered in disease conditions, and identifying those changes will improve the understanding of complex diseases such as cancers or diabetes. A prevailing direction in the analysis of gene expression studies the changes in gene pathways which include sets of related genes. Therefore, introducing structured exploration to differential analysis of gene expression networks may lead to meaningful discoveries. The topic of this paper is differential network analysis, which focuses on capturing the differences between two or more precision matrices. We discuss the connection between the thresholding method and the D-trace loss method on differential network analysis in the case that the precision matrices share the common connected components. Based on this connection, we further propose the cluster D-trace loss method which directly estimates the differential network and achieves model selection consistency. Simulation studies demonstrate its improved performance and computational efficiency. Finally, the usefulness of our proposed estimator is demonstrated by a real-data analysis on non-small cell lung cancer.

MiRNA-103-3p-Hlf regulates apoptosis and autophagy by targeting hepatic leukaemia factor in heart failure.

Cardiomyocyte apoptosis is an important factor leading to the occurrence and development of heart failure (HF), which is associated with high mortality of patients with cardiovascular diseases. This study aims to investigate the underlying mechanisms of HF in terms of expression and regulation patterns using bioinformatics and experimental validation. Two HF datasets were collected: a dataset GSE112056 downloaded from the GEO database (including mRNA and miRNA sequencing data) and another is the laboratory-owned mRNA dataset. Differential mRNAs and miRNAs in the two datasets were screened using the raw Bayesian approach method. Gene Ontology was used to perform functional enrichment analysis of the differential mRNAs and co-expression network analysis of the differential mRNAs, combined with nuclear transcription factors in the differential miRNAs and mRNAs for target gene prediction. A HF cell model was constructed using mouse cardiomyocytes (HL-1), and the role and mechanism of miRNA-103-3p-Hlf (hepatic leukaemia factor) in the process of HF was verified by cell transfection, luciferase reporter gene, WB, and qPCR. We found that Hlf gene expression was decreased in the HF model group and strongly correlated with FYCO1 (FYVE and coiled-coil domain-containing protein 1) gene, a phenomenon enriched in apoptotic autophagy-related pathways. MiR-103-3p expression was up-regulated in the HF model group, and its targeting correlation with Hlf was confirmed by luciferase activity assay. In the HL-1 cell model, miR-103-3p significantly promoted apoptosis and inhibited autophagy in HL-1 cells (all P<0.05), and overexpression of the Hlf gene reversed this phenomenon, inhibiting apoptosis and promoting autophagy in HL-1 cells (all P<0.05). MiR-103-3p affects myocardial cells apoptosis and autophagy by targeting Hlf, playing as a potential therapeutic biomarker for HF progression.

Differential network analysis between sex of the genes related to comorbidities of type 2 mellitus diabetes

BackgroundSome phenotypical changes may be related to changes in the associations among genes. The set of such associations is referred to as gene interaction (or association) networks. An association network represents the set of associations among genes in a given condition. Given two experimental conditions, Differential network analysis (DNA) algorithms analyse these differences by deriving a novel network representing the differences. Such algorithms receive as input experimental gene-expression data of two different conditions (e.g. healthy vs. diseased), then they derive experimental networks of associations among genes and, finally, they analyse differences among networks using statistical approaches. We explore the possibility to study possible rewiring due to sex factors, differently from classical approaches.MethodsWe apply DNA methods to evidence possible sex based differences on genes responsible for comorbidities of type 2 diabetes mellitus.ResultsOur analysis evidences the presence of differential networks in tissues that may explain the difference in the insurgence of comorbidities between males and females.ConclusionMain contributions of this work are (1) the definition of a novel framework of analysis able to shed light on the differences between males and females; (2) the identification of differential networks related to diabetes comorbidities.

Domestication over Speciation in Allopolyploid Cotton Species: A Stronger Transcriptomic Pull.

Cotton has been domesticated independently four times for its fiber, but the genomic targets of selection during each domestication event are mostly unknown. Comparative analysis of the transcriptome during cotton fiber development in wild and cultivated materials holds promise for revealing how independent domestications led to the superficially similar modern cotton fiber phenotype in upland (G. hirsutum) and Pima (G. barbadense) cotton cultivars. Here we examined the fiber transcriptomes of both wild and domesticated G. hirsutum and G. barbadense to compare the effects of speciation versus domestication, performing differential gene expression analysis and coexpression network analysis at four developmental timepoints (5, 10, 15, or 20 days after flowering) spanning primary and secondary wall synthesis. These analyses revealed extensive differential expression between species, timepoints, domestication states, and particularly the intersection of domestication and species. Differential expression was higher when comparing domesticated accessions of the two species than between the wild, indicating that domestication had a greater impact on the transcriptome than speciation. Network analysis showed significant interspecific differences in coexpression network topology, module membership, and connectivity. Despite these differences, some modules or module functions were subject to parallel domestication in both species. Taken together, these results indicate that independent domestication led G. hirsutum and G. barbadense down unique pathways but that it also leveraged similar modules of coexpression to arrive at similar domesticated phenotypes.

423-P: Urinary Extracellular Vesicles in African Americans with Chronic Kidney Disease With and Without Type 2 Diabetes Mellitus

Introduction: Type 2 diabetes (T2D) is the leading cause for chronic kidney disease (CKD). Novel biomarkers are critical in early detection of CKD. Methods: Healthy Eatonville Project (HEP) was a prospective study (n=300, females (F)=71%, T2D 32%, CKD 25%) in historically black community of Eatonville, FL. We characterized EVtrap®-isolated urinary extracellular vesicles (uEV) proteins from a balanced HEP cohort of 48 African Americans (mean age 62y, F=73%, BMI 32.2 kg/m2, T2D 46%) with CKD risk ranging from low, intermediate, to high-very high (H-VH), with and without T2D. Differential expression and coexpression network analyses were implemented. Results: We detected 5265 uEV proteins, 46 of which were significantly (fold change&amp;gt;1.5, P&amp;lt;0.05, FDR&amp;lt;0.1) differentially expressed (DE) in CKD risk groups. Of those, 24 were significantly upregulated in H-VH risk CKD (Figure Module Pr.1). A set of 15 DE proteins appeared to represent a protective pathway against CKD (Figure Module Pr.2). Key biomarkers of acute kidney injury IGFBP7, TIMP2, and IL18 had significant negative correlations with CKD risk (-0.59 ≤ r ≤ -0.44, P&amp;lt;0.002). Supporting our findings, several of these biomarkers had been previously reported in kidney disease studies. Conclusion: The uEV proteomic landscape is representative of kidney pathophysiology and suggests potential biomarkers of CKD in Africans Americans. Disclosure T.K.Thethi: Other Relationship; Bayer Inc., Novo Nordisk. A.Bilal: None. M.S.Zaino: None. A.Iliuk: None. R.E.Pratley: Other Relationship; Bayer Inc., Corcept Therapeutics, Dexcom, Inc., Gasherbrum Bio, Inc., Hanmi Pharm. Co., Ltd., Hengrui (USA) Ltd., Merck Sharp &amp; Dohme Corp., Novo Nordisk, Pfizer Inc., Rivus Pharmaceuticals Inc., Sanofi, Scohia Pharma Inc., Sun Pharmaceutical Industries Ltd. Y.O.Nunez lopez: None.

1509-P: Global Metabolomic Profiling and the Pathobiology of Prediabetes and Type 2 Diabetes

Introduction: Metabolomics has provided valuable insights into metabolic alterations associated with diabetes. However, prior studies profiled only a small subset of metabolites that was possible with the contemporaneous technology. We aimed to reassess and extend the current knowledge by implementing comprehensive LC-MS/MS metabolomic analyses. Methods: Untargeted metabolomic analysis was performed using serum samples from people with normal glucose tolerance, prediabetes, and type 2 diabetes (n=30, balanced ORIGINS cohort subset, NCT02226640), at baseline and 2 hours (2h) after an oral glucose tolerance test (OGTT). Differential expression (MetaboAnalyst) and coexpression network (WGCNA) analyses were implemented. Random Forest classification was used to further assess variable importance. Results: A total of 697 known metabolites were detected in more than 80% of the samples. This represents a significant increase in the number of detected circulating metabolites compared to previous studies in diabetes. WGCNA identified modules of correlated metabolites that associated with glucose tolerance at baseline and 2h. The top module for each timepoint was enriched in metabolites that significantly changed in response to the glucose challenge and highlighted an underappreciated role of glyoxylate and dicarboxylate metabolism in early stages of diabetes evolution. Elevation of hexoses and bile acids, and downregulation of amino acids, lipids, and free fatty acids were demonstrated after the glucose challenge. Random forest classification further unveiled the biomarker potential of baseline levels of aminoadipic and methyluric acids. Conclusions: Comprehensive profiling of serum metabolites in humans underscored the role of carbohydrate metabolism, the citric acid and urea cycles, suppression of lipolysis, ketogenesis, and proteolysis in response to a glucose challenge. Potential biomarkers of early diabetes development and progression were identified. Disclosure D.Dubey: None. T.Dutta: None. Y.O.Nunez lopez: None. S.J.Gardell: None. R.E.Pratley: Other Relationship; Bayer Inc., Corcept Therapeutics, Dexcom, Inc., Gasherbrum Bio, Inc., Hanmi Pharm. Co., Ltd., Hengrui (USA) Ltd., Merck Sharp &amp; Dohme Corp., Novo Nordisk, Pfizer Inc., Rivus Pharmaceuticals Inc., Sanofi, Scohia Pharma Inc., Sun Pharmaceutical Industries Ltd. Funding AdventHealth Translational Research Institute

Integrating differential expression, co-expression and gene network analysis for the identification of common genes associated with tumor angiogenesis deregulation

Angiogenesis is essential for tumor growth and cancer metastasis. Identifying the molecular pathways involved in this process is the first step in the rational design of new therapeutic strategies to improve cancer treatment. In recent years, RNA-seq data analysis has helped to determine the genetic and molecular factors associated with different types of cancer. In this work we performed integrative analysis using RNA-seq data from human umbilical vein endothelial cells (HUVEC) and patients with angiogenesis-dependent diseases to find genes that serve as potential candidates to improve the prognosis of tumor angiogenesis deregulation and understand how this process is orchestrated at the genetic and molecular level.We downloaded four RNA-seq datasets (including cellular models of tumor angiogenesis and ischaemic heart disease) from the Sequence Read Archive. Our integrative analysis includes a first step to determine differentially and co-expressed genes. For this, we used the ExpHunter Suite, an R package that performs differential expression, co-expression and functional analysis of RNA-seq data. We used both differentially and co-expressed genes to explore the human gene interaction network and determine which genes were found in the different datasets that may be key for the angiogenesis deregulation. Finally, we performed drug repositioning analysis to find potential targets related to angiogenesis inhibition.We found that that among the transcriptional alterations identified, SEMA3D and IL33 genes are deregulated in all datasets. Microenvironment remodeling, cell cycle, lipid metabolism and vesicular transport are the main molecular pathways affected. In addition to this, interacting genes are involved in intracellular signaling pathways, especially in immune system and semaphorins, respiratory electron transport and fatty acid metabolism. The methodology presented here can be used for finding common transcriptional alterations in other genetically-based diseases.

Transcriptomics reveal the genetic coordination of early defense to Armillaria root rot (ARR) in Prunus spp.

Armillaria root rot (ARR) poses a significant threat to the long-term productivity of stone-fruit and nut crops in the predominant production area of the United States. To mitigate this issue, the development of ARR-resistant and horticulturally-acceptable rootstocks is a crucial step towards the maintenance of production sustainability. To date, genetic resistance to ARR has been found in exotic plum germplasm and a peach/plum hybrid rootstock, 'MP-29'. However, the widely-used peach rootstock Guardian® is susceptible to the pathogen. To understand the molecular defense mechanisms involved in ARR resistance in Prunus rootstocks, transcriptomic analyses of one susceptible and two resistant Prunus spp. were performed using two causal agents of ARR, including Armillaria mellea and Desarmillaria tabescens. The results of in vitro co-culture experiments revealed that the two resistant genotypes showed different temporal response dynamics and fungus-specific responses, as seen in the genetic response. Gene expression analysis over time indicated an enrichment of defense-related ontologies, including glucosyltransferase activity, monooxygenase activity, glutathione transferase activity, and peroxidase activity. Differential gene expression and co-expression network analysis highlighted key hub genes involved in the sensing and enzymatic degradation of chitin, GSTs, oxidoreductases, transcription factors, and biochemical pathways likely involved in Armillaria resistance. These data provide valuable resources for the improvement of ARR resistance in Prunus rootstocks through breeding.

Effect of personalized immunization with GNOS-PV02 of patients with hepatocellular carcinoma on TCR clonal expansion, tumor infiltration, and vaccine-specific reactivity.

2629 Background: Tumor-specific neoantigens can be identified from cancer biopsies and used to develop personalized therapeutic cancer vaccines (PTCV) to prime neoantigen-specific T cell responses. Here, we characterized the antitumor neoantigen-specific reactivity of tumor-infiltrating, high-frequency TCR clones in a patient treated with personalized therapeutic DNA cancer vaccine GNOS-PV02 in the ongoing GT-30 advanced hepatocellular carcinoma single-arm open-label multi-center phase Ib/IIa trial (NCT04251117). Methods: Paired blood and tumor biopsy samples from patient #8 enrolled in the GT-30 study were collected before and after treatment with GNOS-PV02 (1mg) + plasmid-encoded IL-12 (0.3mg) + Pembrolizumab (200mg). GNOS-PV02 and IL-12 were administered at Q3W for the first 4 doses, then, at Q9W. Pembrolizumab was administered Q3W. Neoantigen positivity was evaluated by IFNγ-ELISpot. TCRβ sequencing was performed on all 4 samples and single-cell TCR and transcriptome sequencing was performed from T cells isolated from the post-treatment blood sample. After vaccination, three newly identified TCRs in blood and tumor were inserted into a pMXs-IRES-GFP retroviral plasmid vector and used to generate engineered TCR T cells. Engineered T cells were tested against the neoantigens included in PTCV by flow cytometry. Results: The treatment resulted in a partial response with a decrease in tumor size of 44% by RECIST1.1. Five vaccine-encoded responding neoantigens were identified. Differential abundance frame network analysis revealed that 27 of 42 (64.28%) significantly expanded peripheral TCR clones were also found enriched in the tumors post-treatment. Importantly, we observed an increase in cumulative frequency (from 0.4 to 7.7%), and absolute numbers (from 3 to 14) of significantly expanded vaccine-specific TCR clones in the tumor. Increased TCR clonality confirmed a focused tumor repertoire response. Single-cell sequencing data analysis revealed that the 6 most expanded clones in blood were activated CD8+CD69+ T cells (81.82%). Three full TCR sequences from T cell clones newly present in the tumor post-vaccination were selected, synthesized and cloned. TCR-engineered patient-specific T cells showed a dose-dependent CD8+ and CD4+ T cell activation (CD69+) upon stimulation with a pool of epitopes covering all the neoantigens in the patient’s PTCV. Conclusions: PTCV treatment resulted in neoantigen-specific T cell responses, clonal expansion in the periphery and primary lesion, and tumor infiltration of T cells with an activated phenotype. TCR-engineered high-frequency T cells found in the tumor are reactive to PTCV-encoded neoantigens post-treatment. These results may account for the observed objective decrease in the primary tumor size.

Tissues and mechanisms associated with Verticillium wilt resistance in tomato using bi-grafted near-isogenic lines.

Host resistance is the primary means to control Verticillium dahliae, a soil-borne pathogen causing major losses on a broad range of plants, including tomato. The tissues and mechanisms responsible for resistance remain obscure. In the field, resistant tomato used as rootstocks does not confer resistance. Here, we created bi-grafted plants with near-isogenic lines (NILs) exhibiting (Ve1) or lacking (ve1) resistance to V. dahliae race 1. Ten days after inoculation, scion and rootstock tissues were subjected to differential gene expression and co-expression network analyses. Symptoms only developed in susceptible scions regardless of the rootstock. Infection caused more dramatic alteration of tomato gene expression in susceptible compared to resistant tissues, including pathogen receptor, signaling pathway, PR protein, and cell wall modification genes. Differences were observed between scions and rootstocks, primarily related to physiological processes in these tissues. Gene expression in scions was influenced by the rootstock genotype. A few genes were associated with the Ve1 genotype, which was independent of infection or tissue type. Several were physically clustered, some near the Ve1 locus on chromosome 9. Transcripts mapped to V. dahliae were dominated by secreted candidate effector proteins. These findings advance knowledge of molecular mechanisms underlying the tomato-V. dahliae interaction.

The fusiform gyrus exhibits differential gene-gene co-expression in Alzheimer's disease.

Alzheimer's Disease (AD) is an irreversible neurodegenerative disease clinically characterized by the presence of β-amyloid plaques and tau deposits in various regions of the brain. However, the underlying factors that contribute to the development of AD remain unclear. Recently, the fusiform gyrus has been identified as a critical brain region associated with mild cognitive impairment, which may increase the risk of AD development. In our study, we performed gene co-expression and differential co-expression network analyses, as well as gene-expression-based prediction, using RNA-seq transcriptome data from post-mortem fusiform gyrus tissue samples collected from both cognitively healthy individuals and those with AD. We accessed differential co-expression networks in large cohorts such as ROSMAP, MSBB, and Mayo, and conducted over-representation analyses of gene pathways and gene ontology. Our results comprise four exclusive gene hubs in co-expression modules of Alzheimer's Disease, including FNDC3A, MED23, NRIP1, and PKN2. Further, we identified three genes with differential co-expressed links, namely FAM153B, CYP2C8, and CKMT1B. The differential co-expressed network showed moderate predictive performance for AD, with an area under the curve ranging from 0.71 to 0.76 (+/- 0.07). The over-representation analysis identified enrichment for Toll-Like Receptors Cascades and signaling pathways, such as G protein events, PIP2 hydrolysis and EPH-Epherin mechanism, in the fusiform gyrus. In conclusion, our findings shed new light on the molecular pathophysiology of AD by identifying new genes and biological pathways involved, emphasizing the crucial role of gene regulatory networks in the fusiform gyrus.

Abstract 6668: Immune infiltrate co-occurrence and neoantigen similarity are prognostic factors in early stage NSCLC

Abstract The prevalence of early-stage non-small cell lung cancer (NSCLC) with curative treatment options is expected to increase with recent implementation of annual screening programs. Predictors and molecular drivers of disease relapse, especially the role of intra-tumoral immune dysfunction, remains unclear but critical for the refinement of therapeutic decisions. By leveraging a comprehensive individual portrait of each patient's immune system potential novel mechanisms associated with tumor relapse in early-stage NSCLC may be identified. We profiled 11 non-relapsed (at least 2 year FU) lung adenocarcinoma patients and 11 covariate-matched (gender, age, stage) relapsed patients, who underwent curative treatment in stage IA-IIIB disease. We used NeXT SummitTM for variant and CNA calling, gene expression quantification, neoantigen prediction, HLA profiling (typing, mutation, and loss of heterozygosity), T-cell receptor and tumor microenvironment (TME) profiling. Neoantigen peptide sequences were subjected to further filtering and clustering based on between-patient similarity scores, with the goal of identifying shared clusters of relapse-associated neoantigens in each possible pair of patients. Differential network analyses were applied to the TME composition estimates to investigate relapse-associated patterns of cellular co-occurrence and interaction. When considering neoantigens selected on the basis of similarity, we found that those belonging to non-relapsed patients had significantly lower HLA binding rank (17.8 points) compared to that of relapsed patients (P=0.02), indicating weaker binding for relapsed cases. Clustering of both the most similar and frequently shared neoantigens correlated with relapse (P &amp;lt; 0.002). In the TME, we observed differential immune cell co-occurrence associated with relapse status, such as Tregs are positively correlated with B and CD4 T cells only in relapsed patients (Pearson’s R=0.7 and 0.74, both P&amp;lt;0.02 vs. R=0.18 and 0.35, both P&amp;gt;0.2 in non-relapsed patients), indicating suppressive anti-tumor immunity. Relapsed patients did not share significant enrichment of mutations in any biological pathway. Surprisingly, mutation purity (less mutations than expected by chance) was observed in relapsed patients, suggesting selective killing and escape. In this pilot cohort, we used an integrated platform to broadly characterize both the tumor and immune system, enabling identification of relapse-associated neoantigens that may share universal features which enhance HLA binding. Relapses in early-stage LUAD patients were associated with neoantigens with lower immunogenicity and an immunosuppressive TME. These findings demonstrate that deeper profiling of shared neoantigen features has the potential to become an early biomarker of relapse, informing patient therapy selection and surveillance. Citation Format: Martina M. Sykora, Jason Pugh, Bailiang Li, Finn O. Mildner, Hubert Hackl, Arno Amann, Fabienne I. Nocera, Rachel M. Pyke, Lee McDaniel, Charles W. Abbott, Sean M. Boyle, Richard O. Chen, Dominik Wolf, Sieghart Sopper, Gabriele Gamerith. Immune infiltrate co-occurrence and neoantigen similarity are prognostic factors in early stage NSCLC [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 6668.

Cyclooxygenase-2 as a therapeutic target against human breast cancer: A comprehensive review.

Cyclooxygenase-2 (COX-2) is a key aspect of the physiology and pathogenesis of various cancer types. Overexpression of this enzyme is responsible for the elevated prostaglandin production and characteristic feature of breast cancer. Inhibition of COX-2 derived prostanoids facilitates anti-inflammatory, analgesic, and antipyretic effects of non-steroid anti-inflammation drugs. The overexpression of COX-2 is associated with inflammation, pain, and fever. The present study provides the updated relevant literature describing the role of well-characterized isoforms of cyclooxygenase with particular emphasis on COX-2, mechanism of action, the effect of the drug, combinatorial drugs, and microarray-based differential expression analysis and network analysis. We have discussed the currently used combinatorial treatments and their challenges in breast cancer. This article is categorized under: Cancer > Computational Models Cancer > Molecular and Cellular Physiology.