Differential Network Analysis Research Articles

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.

The Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks

Inference and analysis of gene regulatory networks (GRNs) require software that integrates multi-omic data from various sources. The Network Zoo (netZoo; netzoo.github.io) is a collection of open-source methods to infer GRNs, conduct differential network analyses, estimate community structure, and explore the transitions between biological states. The netZoo builds on our ongoing development of network methods, harmonizing the implementations in various computing languages and between methods to allow better integration of these tools into analytical pipelines. We demonstrate the utility using multi-omic data from the Cancer Cell Line Encyclopedia. We will continue to expand the netZoo to incorporate additional methods.

Host immune responses to enzootic and invasive pathogen lineages vary in magnitude, timing, and efficacy.

Infectious diseases of wildlife continue to pose a threat to biodiversity worldwide, yet pathogens are far from uniform in virulence or host disease outcome. Within the same pathogen species, virulence can vary considerably depending on strain or lineage, in turn eliciting variable host responses. One pathogen that has caused extensive biodiversity loss is the amphibian-killing fungus, Batrachochytrium dendrobatidis (Bd), which is comprised of a globally widespread hypervirulent lineage (Bd-GPL), and multiple geographically restricted, enzootic lineages. Whereas host immunogenomic responses to Bd-GPL have been characterized in a number of amphibian species, immunogenomic responses to geographically restricted, enzootic Bd lineages are less clear. To examine lineage-specific host immune responses to Bd, we exposed a species of pumpkin toadlet, Brachycephalus pitanga, which is endemic to Brazil's Southern Atlantic Forest, to either the Bd-GPL or the enzootic Bd-Asia-2/Brazil (hereafter Bd-Brazil) lineage. Using temporal samples from early, mid, and late infection stages, we quantified functional immunogenomic responses over the course of infection using differential gene expression tests and coexpression network analyses. Host immune responses varied significantly with Bd lineage. Relative to controls, toadlet responses to Bd-Brazil were weak at early infection (25 genes significantly differentially expressed), peaked by mid-stage infection (414 genes), and were nearly fully resolved by late-stage infection (nine genes). In contrast, responses to Bd-GPL were magnified and delayed; toadlets significantly differentially expressed 111 genes early, 87 genes at mid-stage infection, and 726 genes by late-stage infection relative to controls. Given that infection intensity did not vary between mid- and late-stage disease in either Bd-Brazil or Bd-GPL treatments, this suggests that pumpkin toadlets may be at least partially tolerant to the enzootic Bd-Brazil lineage. In contrast, late-stage immune activation against Bd-GPL was consistent with immune dysregulation previously observed in other species. Our results demonstrate that both the timing of immune response and the particular immune pathways activated are specific to Bd lineage. Within regions where multiple Bd lineages co-occur, and given continued global Bd movement, these differential host responses may influence not only individual disease outcome, but transmission dynamics at the population and community levels.

Abstract P3-11-08: Inhibition of Sirtuin 3 Sensitizes Breast Cancer Cells to Fulvestrant

Abstract Endocrine therapy can lead to nutrient deprivation by reducing glucose and glutamine uptake and total cellular ATP production. The ability of cells to bypass this metabolic stress is fundamental to how they regulate BC growth and acquire endocrine resistance. Sirtuins (SIRTs) are NAD+-dependent deacylases and regulate a widerange of intracellular processes including metabolism. SIRT3, activated by calorie restriction, modulates mitochondrial adaptation to low energy input. It has a key role in mitochondrial integrity and function, regulating cell survival, death and metabolic pathways, regulating the shift to amino-acid and fatty-acid catabolism. SIRT3 can maintain ROS levels at the appropriate levels for sustaining a proliferative phenotype, preventing apoptosis and promoting carcinogenesis. A role of SIRT3 in epigenetic regulation has also been reported. We used Differential Dependency Network analysis to compare the wiring of the SIRTs and key metabolism-related genes in matched antiestrogen- sensitive vs. resistant breast cancer (BC) cells, which identified several novel signaling hubs including CDKN2A (p16), linking SIRT3 to cell proliferation. The ability of SIRTs to sense and respond to changes in energy, coupled with their deacetylase/deacylase functions, could provide a mechanism for the cell to rewire signaling and maintain a newly acquired drug-resistant phenotype. Using endocrine sensitive (LCC1) and resistant (LCC9) BC cells, we confirmed the higher expression of SIRT3 in LCC9, compared to LCC1 cells. Upregulation of SIRT3 expression within 24 hrs of Fulvestrant (ICI 1 µM) treatment (p=0.0176) in LCC1 was shown, but no effect was observed in LCC9 cells. Conversely, treatment with 17β-estradiol (E2- 10 nM) did not significantly affect SIRT3 expression in either LCC1 or LCC9 cells. Inhibition of SIRT3 acitivity, using the LC0296 inhibitor (15 µM), reduced growth rate in LCC1 and SIRT3-silenced LCC9 cells under treatment with ICI (1 nM and 1 µM, respectively) (p&amp;lt; 0.0001, p&amp;lt; 0.001 respectively). Treatment with SIRTUIN 3 inhibitor and ICI induced apoptosis in LCC1 and SIRT3-silenced LCC9 cells compared to the untreated control and ICI only treated cells (p&amp;lt; 0.001). The combination SIRT3 inhibitor (15 µM) plus ICI (0.5 µM) increased ROS production in LCC9-SIRT3 silenced cells in 24 hours of treatment compared to the untreated control and ICI only treated cells (p&amp;lt; 0.001). SIRT3 inhibitor (15 µM) decreased LCC1 cell spheroid formation (p&amp;lt; 0.05), however it didn’t show additive effect to the effect of ICI (0.5 µM) in the same conditions. The combination of SIRT3 inhibitor (15 µM) and ICI (1 µM) reduced the growth of LCC9 SIRT3-silenced cells in detached condition compared to untreated control and ICI only treated cells (p&amp;lt; 0.001). The results show that SIRT3 may have a role in Fulvestrant response in BC cells; the mechanism is under investigation in our laboratory. Citation Format: Karla Andrade de Oliveira, Surojeet Sengupta, Lu Jin, Fabia de Oliveira Andrade, Melike Ozgu-Onal, Anil Yadav, Robert Clarke. Inhibition of Sirtuin 3 Sensitizes Breast Cancer Cells to Fulvestrant [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P3-11-08.