Differential Gene Expression Data Research Articles

Global trends and risk factors in gastric cancer: a comprehensive analysis of the Global Burden of Disease Study 2021 and multi-omics data.

Background: Gastric cancer (GC) remains a significant global health challenge. This study aimed to comprehensively analyze GC epidemiology and risk factors to inform prevention and intervention strategies. Methods: We analyzed the Global Burden of Disease Study 2021 data, conducted 16 different machine learning (ML) models of NHANES data, performed Mendelian randomization (MR) studies on disease phenotypes, dietary preferences, microbiome, blood-based markers, and integrated differential gene expression and expression quantitative trait loci (eQTL) data from multiple cohorts to identify factors associated with GC risk. Results: Global age-standardized disability-adjusted life year rates (ASDR) for GC declined from 886.24 to 358.42 per 100,000 population between 1990 and 2030, with significant regional disparities. Despite this decline, total disability-adjusted life years show a concerning upward trend from 2015, rising from approximately 22.9 million to a projected 24.3 million by 2030. The slope index of inequality shifted from 87 in 1990 to -184 in 2021, indicating a reversal in GC burden distribution, with higher ASDR now associated with lower socio-demographic index countries. The ML models analysis identified higher levels of clinical characteristics such as phosphorus, calcium, eosinophils percent, and triglycerides, as well as lower levels of iron and monocyte percent, may be associated with an increased risk of GC. MR analyses revealed causal associations between GC risk and disease phenotypes such as Helicobacter pylori infection, chronic gastritis, obesity, depression, and dietary preferences such as dairy and processed meats. Gut microbiome analysis showed associations with microbiome such as Phascolarctobacterium and Ruminococcaceae species. Blood-based markers analysis identified protective and risk effects for cortisol, glutamate, nicotinamide, Natural Killer %lymphocyte, CD4-CD8- T cell Absolute Count, Phosphatidylcholine (16:0_18:1), and Interleukin-1-alpha. Integrated genomic analysis identified 10 genes significantly associated with GC risk, with strong evidence for colocalization in genes such as CCR6 and PILRB. Conclusions: This systematic analysis reveals complex global trends in GC burden and identifies novel clinical, disease phenotypes, dietary preferences, microbial, blood-based, and genetic risk factors. These findings provide potential targets for improved risk stratification, prevention, and intervention strategies to reduce the global burden of GC.

A New Differential Gene Expression Based Simulated Annealing for Solving Gene Selection Problem: A Case Study on Eosinophilic Esophagitis and Few Other Gastro-intestinal Diseases.

Identifying the set of genes collectively responsible for causing a disease from differential gene expression data is called gene selection problem. Though many complex methodologies have been applied to solve gene selection, formulated as an optimization problem, this study introduces a new simple, efficient, and biologically plausible solution procedure where the collective power of the targeted gene set to discriminate between diseased and normal gene expression profiles was focused. It uses Simulated Annealing to solve the underlying optimization problem and termed here as Differential Gene Expression Based Simulated Annealing (DGESA). The Ranked Variance (RV) method has been applied to prioritize genes to form reference set to compare with the outcome of DGESA. In a case study on Eosinophilic Esophagitis (EoE) and other gastrointestinal diseases, RV identified the top 40 high-variance genes, overlapping with disease-causing genes from DGESA. DGESA identified 40 gene pathways each for EoE, Crohn's Disease (CD), and Ulcerative Colitis (UC), with 10 genes for EoE, 8 for CD, and 7 for UC confirmed in literature. For EoE, confirmed genes include KRT79, CRISP2, IL36G, SPRR2B, SPRR2D, and SPRR2E. For CD, validated genes are NPDC1, SLC2A4RG, LGALS8, CDKN1A, XAF1, and CYBA. For UC, confirmed genes include TRAF3, BAG6, CCDC80, CDC42SE2, and HSPA9. RV and DGESA effectively elucidate molecular signatures in gastrointestinal diseases. Validating genes like SPRR2B, SPRR2D, SPRR2E, and STAT6 for EoE demonstrates DGESA's efficacy, highlighting potential targets for future research.

Anticancer Effects of New Disulfiram Analogs.

Disulfiram (DSF), an irreversible aldehyde dehydrogenase 2 (ALDH2) inhibitor, is an U.S. Food and Drug Administration (FDA)-approved drug for the treatment of chronic alcoholism. Recent studies have reported an interesting antitumor activity of DSF against a wide range of human malignancies, while a growing number of ongoing and completed clinical trials have provided evidence supporting the repurposing of DSF as an anticancer treatment. Nevertheless, despite its current clinical indications and potential future therapeutic applications for treating various diseases, DSF is associated with serious side effects attributed to the inhibition of ALDH2. We have recently synthesized DSF analogs (2a-v) with limited inhibition of ALDH2. Here, we report the anticancer activity of these molecules by highlighting their effects on cell signaling in Jurkat cells. DSF and two DSF analogs, 2g and 2r, all stimulated apoptotic signaling pathways, although the 2g analog activated more apoptosis-related genes and induced higher levels of apoptosis in vitro. Differential gene expression data suggested that compounds 2g and 2r specifically reprogram target cells to downregulate pathways related to cell growth and division, while upregulating pathways related to apoptosis or differentiation. Interestingly, both compounds 2g and 2r had more differentially expressed genes related to DNA damage pathways (including those related to apoptosis) when compared to DSF, which may offer insights into their mechanisms of action.

Multi-omics characterization of lymphedema-induced adipose tissue resulting from breast cancer-related surgery.

Secondary lymphedema (LE) following breast cancer-related surgery is a life-long complication, which currently has no cure. LE induces significant regional adipose tissue deposition, requiring liposuction as a treatment. Here, we aimed to elucidate the transcriptional, metabolomic, and lipidomic signature of the adipose tissue developed due to the surgery-induced LE in short- and long-term LE patients and compared the transcriptomic landscape of LE adipose tissue to the obesity-induced adipose tissue. Adipose tissue biopsies were obtained from breast cancer-operated females with LE from the affected and non-affected arms (n = 20 patients). To decipher the molecular properties of the LE adipose tissue, we performed RNA sequencing, metabolomics, and lipidomics combined with bioinformatics analyses. Differential gene expression data from a cohort of lean and obese patients without LE was used for comparisons. Integrative analysis of functional genomics revealed that inflammatory response, cell chemotaxis, and angiogenesis were upregulated biological processes in the LE arm, indicating a sustained inflammation in the edematous adipose tissue; whereas, epidermal differentiation, cell-cell junction organization, water homeostasis, and neurogenesis were downregulated in the LE arm. Surprisingly, only a few genes were found to be the same in the LE-induced and the obesity-induced adipose tissue expansion, indicating a different type of adipose tissue development in these two conditions. In metabolomics analysis, we found reduced levels of a branched-chain amino acid valine in the LE arm and downregulation of the mRNA levels of its transporter SLC6A15. Lipidomics analyses did not show any significant differences between the LE and non-LE arms, suggesting that other factors affect the lipid composition of the adipose tissue more than the LE in these patients. Our results provide a detailed molecular characterization of adipose tissue in secondary LE after breast cancer-related surgery. We also show distinct differences in transcriptomic signatures between LE-induced adipose tissue and obesity-induced adipose tissue, but only minor differences in metabolome and lipidome between the LE and the non-LE arm.

26 Applying single cell RNA sequencing of sarcomatoid renal cell carcinoma for the development of a novel transcriptomic biomarker to predict immunotherapy response

Abstract Background Renal cell carcinoma with sarcomatoid features (sRCC) is a unique kidney cancer subtype associated with aggressive biology and poor clinical outcomes. Intriguingly, sRCC has recently exhibited preferential responsiveness to immune checkpoint blockade (ICB) therapies. Within the RCC population, however, there currently is a paucity of biomarkers predictive for ICB response, representing a critical unmet clinical need for optimal therapy selection. Therefore, we sought to derive a transcriptomic signature from sRCC samples encapsulating this paradoxical hyper-aggression and ICB-responsiveness to identify RCC patients most likely to benefit from ICB irrespective of sarcomatoid feature presence. Methods Nephrectomy specimens from patients with RCC were processed for single cell RNA sequencing (scRNAseq). Clustering was performed and annotated tumor cells were computationally isolated. Tumor cell counts were aggregated and differential expression between sRCC and non-sRCC cases was performed. Genes significantly upregulated in sRCC were next filtered against differential gene expression data of sRCC vs non-sRCC from three clinical RCC datasets (TCGA, CheckMate, & Javelin101) to identify genes enriched in sRCC across scRNAseq and bulk RNA sequencing. The prognostic and predictive features of this gene signature were then assessed in cohorts of patients with RCC receiving ICB. Results In total, 73,123 unique cells from 18 RCC patients (10 sRCC; 8 clear cell RCC) were analyzed by scRNAseq, including 5,386 tumor cells. Differential gene expression of tumor clones revealed 20 genes significantly enriched in sRCC relative to ccRCC. Filtering against public differential gene expression data resulted in 10 genes included within the final Sarcomatoid Signature (SS). SS expression scores for clinical specimens were calculated by single-sample gene set enrichment analysis. SS scores were significantly enriched in sRCC patient tumors across TCGA KIRC, CheckMate, and IMmotion151 datasets (p<0.001 for all cohorts). Within TCGA KIRC, SS scores were significantly increased in patients with nuclear grade 4 (p<0.001) and metastatic (p<0.001) disease, while stratification by median SS score revealed worsened overall (HR=2.19, p<0.001) and disease-free (HR=2.08, p<0.001) survival among SS-high patients. Amongst the CheckMate cohort (including patient samples from the CheckMate-010 & CheckMate-025 trials), SS-high patients experienced improved progression free survival (PFS) when treated with nivolumab relative to everolimus (HR=0.70, p=0.055), whereas no difference was seen between therapies in the SS-low patient population (HR=0.98, p=0.9). In the IMmotion151 trial, which was not utilized for gene filtering and signature refinement and thus represents an independent validation cohort, SS-high patients experienced reduced PFS duration (HR=1.39, p<0.001) irrespective of treatment arm. However, SS-high patients experienced improved objective response rates with the ICB-containing atezolizumab/bevacizumab regimen compared to sunitinib (p<0.001) and relative to SS-low patients receiving atezolizumab/bevacizumab (p=0.028) (Figure 1). Within the SS-high population, patients experienced prolonged PFS with atezolizumab/bevacizumab (HR=0.70, p=0.003) relative to sunitinib whereas no PFS difference between arms was seen in the SS-low population (HR=1.01, p>0.99). Conclusions This novel SS derived from scRNAseq demonstrates negative prognostic yet positive predictive value for ICB response. In a domain currently void of efficacious biomarkers, the SS, upon prospective validation, may assist in optimal therapy selection for patients with RCC.

Functional Copy-Number Alterations as Diagnostic and Prognostic Biomarkers in Neuroendocrine Tumors.

Functional copy-number alterations (fCNAs) are DNA copy-number changes with concordant differential gene expression. These are less likely to be bystander genetic lesions and could serve as robust and reproducible tumor biomarkers. To identify candidate fCNAs in neuroendocrine tumors (NETs), we integrated chromosomal microarray (CMA) and RNA-seq differential gene-expression data from 31 pancreatic (pNETs) and 33 small-bowel neuroendocrine tumors (sbNETs). Tumors were resected from 47 early-disease-progression (<24 months) and 17 late-disease-progression (>24 months) patients. Candidate fCNAs that accurately differentiated these groups in this discovery cohort were then replicated using fluorescence in situ hybridization (FISH) on formalin-fixed, paraffin-embedded (FFPE) tissues in a larger validation cohort of 60 pNETs and 82 sbNETs (52 early- and 65 late-disease-progression samples). Logistic regression analysis revealed the predictive ability of these biomarkers, as well as the assay-performance metrics of sensitivity, specificity, and area under the curve. Our results indicate that copy-number changes at chromosomal loci 4p16.3, 7q31.2, 9p21.3, 17q12, 18q21.2, and 19q12 may be used as diagnostic and prognostic NET biomarkers. This involves a rapid, cost-effective approach to determine the primary tumor site for patients with metastatic liver NETs and to guide risk-stratified therapeutic decisions.

Potential Mechanism by which Eriodictyol Protects against Doxorubicininduced Cardiotoxicity based on Network Pharmacology, Molecular Docking, and Molecular Dynamics Simulation

Background: The clinical use of doxorubicin (DOX), an anthracycline antibiotic with broad-spectrum applications against various malignant tumors, is limited by doxorubicininduced cardiotoxicity (DIC). Eriodictyol (ERD) has shown cardioprotective effects, but the mechanism of its protective effect on DIC remains unknown. Aims: This study aimed to explore the potential mechanisms by which ERD confers protection against DIC. Methods: ERD and DIC targets were identified from the TCMSP, PharmMaper, SwissTargetPrediction, TargetNet, BATMAN, GeneCards, and PharmGKB databases. Differential gene expression data between DIC and normal tissues were extracted from the GEO database. A protein‒ protein interaction (PPI) network of the intersecting ERD-DIC targets was constructed using the STRING platform and visualized with Cytoscape 3.10.0 software. Gene Ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis for ERD-DIC cross-targets were conducted. Validation included molecular docking with AutoDock Tools software and molecular dynamics simulations with Gromacs 2019.6 software. Results: Network pharmacology analysis revealed 43 intersecting ERD-DIC targets, including 6 key targets. GO functional enrichment analysis indicated that the intersecting targets were enriched in 550 biological processes, 45 cell components, and 41 molecular functions. KEGG pathway enrichment analysis identified 114 enriched signaling pathways. Molecular docking revealed a strong binding affinity between ERD and 6 key targets, as well as multiple targets within the ROS pathway. Molecular dynamics simulations indicated that ERD has favorable binding with 3 crucial targets. Conclusion: The systematic network pharmacology analysis suggests that ERD may mitigate DIC through multiple targets and pathways, with the ROS pathway potentially playing a crucial role. These findings provide a reference for foundational research and clinical applications of ERD in treating DIC. conclusion: Systematic network pharmacology analysis suggests that ERD may mitigate DIC through multiple targets and pathways, with the ROS pathway potentially playing a crucial role. These findings provide references for foundational research and clinical applications of ERD in treating DIC.

Expression and Prognosis of Differential Gene Troponin T1 Between Right and Left Colon Cancers.

Colorectal cancer (CRC) is one of the most common digestive tract tumors in humans. At present, many scholars believe that the primary site of the tumor has a direct and profound impact on its curative effect. There are significant differences in the expression of many genes, tumor microenvironment, and prognosis between the left and right colon. However, there is a lack of detailed studies on whether the differentially expressed genes in the left and right colon significantly impact the prognosis of patients with CRC. Troponin T1 ( TNNT1 ) is an important gene that affects the prognosis difference between left and right colon cancer screening from "The Cancer Genome Atlas" database. By analyzing the differential gene expression data and clinical data of the left and right hemicolons in the database, the online prognostic database was used to screen the key molecules that significantly affect the tumor immune microenvironment and patient prognosis and to predict their functions and pathways. Quantitative reverse transcription-polymerase chain reaction was used to verify the expression difference of TNNT1 in CRC cell lines SW480 and HCT116, and normal human colorectal epithelial cell line FHC. The relationship between TNNT1 expression in 88 pairs of CRC samples and clinical information and pathologic parameters of patients with CRC was analyzed to judge the impact of TNNT1 expression on patient survival. Database analysis showed that TNNT1 was significantly overexpressed in CRC, and TNNT1 was one of the main differential genes between left colon cancer (LCC) and right colon cancer (RCC). The expression of TNNT1 was significantly increased in RCC, which could lead to poor prognosis of patients. Quantitative reverse transcription-polymerase chain reaction indicated that the expression of TNNT1 was significantly up-regulated in CRC cell lines SW480 and HCT116. Eighty-eight immunohistochemistry (IHC) of CRC tissues and adjacent tissues suggested that the expression of TNNT1 in CRC was significantly higher than that in normal adjacent tissues. By analyzing the clinical information and pathologic indicators matched with these clinical samples, we found that high TNNT1 expression in the primary tumor location (right colon) and high N stage (N2, N3) were unfavorable factors affecting the prognosis of patients with CRC. Multivariate Cox regression analysis suggested that high expression of TNNT1 may be an independent risk factor for the prognosis of patients with CRC. As one of the main differential genes between LCC and RCC, TNNT1 is representative to some extent. Its high expression may be one of the reasons why the prognosis of patients with RCC is worse than that of patients with LCC.

Transcriptomic Analyses of B Cells Infected In Vitro with Transmitted/Founder HIV-1 of R5 Phenotype (HIV-R5) Reveal Downregulation of IGLL1 Gene in Persistent Infection.

Abstract HIV-neutralizing antibodies against T cell-dependent antigens of HIVenv permit persistent infection. Conceivably, B cell repertoire has a clone capable of eliciting antibodies to eliminate any HIV. Receptors for T cell-independent conformational antigens (TiCA) of the trimeric envelope glycoprotein subunits (TEGS) of HIV permit persistent infection of that clone. To test this concept, a human B cell line (CA46, ATCC) was chosen for in vitro infection with HIV-X4 or HIV-R5. Both infected cell lines continued to produce infectious virions. Transcriptomic analyses of uninfected and HIV-infected cells was done, and data analyzed by the Edge R and Surpi software. Differential gene expression data for paired analyses of B cells infected with HIV-R5 versus uninfected cells showed that 108 genes were upregulated, while 148 genes were downregulated. Notably, the IGLL1 gene that determines surface expression of Ig-receptors on B cells, was down-regulated in cells infected with HIV-R5 (logFC= 3.30). Such a difference was not seen in B cells infected with HIV-X4. Heatmaps showing RNA-Seq data for pathway analysis revealed distinct patterns of gene expression in B cells infected with HIV-R5 versus HIV-X4. These data are in accord with HIV-R5 initiating all persistent HIV infections worldwide. We conclude 1) HIV-R5 infects B cells to establish persistent infection, 2) Studies with CD19+ normal B cells would enable synthesis of antibodies against TiCA and TEGSs for immune prevention of HIV.

Effects of a second iron-dextran injection administered to piglets during lactation on differential gene expression in liver and duodenum at weaning.

Six female littermate piglets were used in an experiment to evaluate the mRNA expression in tissues from piglets given one or two 1mL injections of iron dextran (200mg Fe/mL). All piglets in the litter were administered the first 1mL injection < 24h after birth. On day 7, piglets were paired by weight (mean body weight = 1.72 ± 0.13kg) and one piglet from each pair was randomly selected as control (CON) and the other received a second injection (+Fe). At weaning on day 22, each piglet was anesthetized, and samples of liver and duodenum were taken from the anesthetized piglets and preserved until mRNA extraction. differential gene expression data were analyzed with a fold change cutoff (FC) of |1.2| P < 0.05. Pathway analysis was conducted with Z-score cutoff of P < 0.05. In the duodenum 435 genes were significantly changed with a FC ≥ |1.2| P < 0.05. In the duodenum, Claudin 1 and Claudin 2 were inversely affected by + Fe. Claudin 1 (CLDN1) plays a key role in cell-to-cell adhesion in the epithelial cell sheets and was upregulated (FC = 4.48, P = 0.0423). Claudin 2 (CLDN2) is expressed in cation leaky epithelia, especially during disease or inflammation and was downregulated (FC = -1.41, P = 0.0097). In the liver, 362 genes were expressed with a FC ≥ |1.2| P < 0.05. The gene most affected by a second dose of 200mg Fe was hepcidin antimicrobial peptide (HAMP) with a FC of 40.8. HAMP is a liver-produced hormone that is the main circulating regulator of Fe absorption and distribution across tissues. It also controls the major flows of Fe into plasma by promoting endocytosis and degradation of ferroportin (SLC4A1). This leads to the retention of Fe in Fe-exporting cells and decreased flow of Fe into plasma. Gene expression related to metabolic pathway changes in the duodenum and liver provides evidence for the improved feed conversion and growth rates in piglets given two iron injections preweaning with contemporary pigs in a companion study. In the duodenum, there is a downregulation of gene clusters associated with gluconeogenesis (P < 0.05). Concurrently, there was a decrease in the mRNA expression of genes for enzymes required for urea production in the liver (P < 0.05). These observations suggest that there may be less need for gluconeogenesis, and possibly less urea production from deaminated amino acids. The genomic and pathway analyses provided empirical evidence linking gene expression with phenotypic observations of piglet health and growth improvements.

Functional Connectome Hierarchy in Schizotypy and Its Associations With Expression of Schizophrenia-Related Genes.

Schizotypy has been conceptualized as a continuum of symptoms with marked genetic, neurobiological, and sensory-cognitive overlaps to schizophrenia. Hierarchical organization represents a general organizing principle for both the cortical connectome supporting sensation-to-cognition continuum and gene expression variability across the cortex. However, a mapping of connectome hierarchy to schizotypy remains to be established. Importantly, the underlying changes of the cortical connectome hierarchy that mechanistically link gene expressions to schizotypy are unclear. The present study applied novel connectome gradient on resting-state fMRI data from 1013 healthy young adults to investigate schizotypy-associated sensorimotor-to-transmodal connectome hierarchy and assessed its similarity with the connectome hierarchy of schizophrenia. Furthermore, normative and differential postmortem gene expression data were utilized to examine transcriptional profiles linked to schizotypy-associated connectome hierarchy. We found that schizotypy was associated with a compressed functional connectome hierarchy. Moreover, the pattern of schizotypy-related hierarchy exhibited a positive correlation with the connectome hierarchy observed in schizophrenia. This pattern was closely colocated with the expression of schizophrenia-related genes, with the correlated genes being enriched in transsynaptic, receptor signaling and calcium ion binding. The compressed connectome hierarchy suggests diminished functional system differentiation, providing a novel and holistic system-level basis for various sensory-cognition deficits in schizotypy. Importantly, its linkage with schizophrenia-altered hierarchy and schizophrenia-related gene expression yields new insights into the neurobiological continuum of psychosis. It also provides mechanistic insight into how gene variation may drive alterations in functional hierarchy, mediating biological vulnerability of schizotypy to schizophrenia.

Exploring the pharmacological and molecular mechanisms of Salvia chinensis Benth in colorectal cancer: A network pharmacology and molecular docking study.

While Salvia chinensis Benth (commonly known as "Shijianchuan" in Chinese, and abbreviated as SJC) is commonly used in adjuvant therapy for colorectal cancer (CRC) in traditional Chinese medicine, its mechanism of action remains unclear. In this study, Initially, we examined the impact of SJC on CRC cells in an in vitro setting. Next, we initially retrieved the primary active components and targets of SJC from databases such as TCMSP and existing literature. Subsequently, we integrated differential gene expression data from the GEO database and collected CRC-related targets from resources like DisGeNET. The matching of these datasets enabled the identification of SJC-CRC targets. We constructed a protein-protein interaction network and identified core targets through topological analysis. GO and KEGG enrichment analyses were performed using clusterProfiler. We established networks linking traditional Chinese medicine components to targets and core targets to signaling pathways. Additionally, we performed molecular docking to validate interactions between the main compounds and targets, and employed Western blot analysis to explore how the major components of SJC affect crucial signaling pathways. In this study, SJC inhibited the viability of HCT-116 and HT-29 cells. We identified a total of 11 active components in SJC along with 317 target genes. Among these, there were 8612 target genes associated with CRC, and we successfully matched 276 SJC-CRC target genes. Through topological analysis of the protein-protein interaction network, we pinpointed 20 core targets. It was revealed that SJC effects are linked to genes governing processes like cell apoptosis, proliferation, hypoxia, oxidative stress, and signaling pathways such as PI3K-Akt through GO and KEGG pathway enrichment analyses. Additionally, we applied molecular docking techniques and observed that the majority of active compounds displayed robust binding affinity with the selected targets. In vitro experiments suggested that SJC and its key component, Ursolic acid, may exert its anti-CRC effects by modulating the core PI3K/AKT signaling pathway through inhibiting the phosphorylation of the target Akt1. This discovery is consistent with the predictions derived from network pharmacology methods. This study marks the inaugural utilization of bioinformatics methods in conjunction with in vitro experiments to comprehensively investigate the pharmacological and molecular mechanisms responsible for SJC anti-CRC effects.

198 Differential Gene Expression and Metabolic Pathway Comparisons of Liver and Duodenum from Suckling Piglets Given One or Two Iron Dextran Injections

Abstract Six female littermate pairs were used in an experiment to evaluate the mRNA expression in tissues from piglets given one or two 1 mL injections of iron dextran (200 mg Fe/mL, Uniferon, Pharmacosmos Inc. Watchung, NJ). All pigs in the litter were administered the first 1 mL injection &amp;lt; 24 hours after birth and on d 7, pigs were paired by body weight (BW; 1.72 ± 0.13 kg) and one pig from each pair was randomly selected as control (CON) and the other received a second injection (+Fe). At weaning on d 22, each piglet was anesthetized, and samples of liver and duodenum were taken from the anesthetized piglets and placed in DNA/RNA Shield (Zymo, Irvine, CA) for preservation until mRNA extraction. RNA-Seq libraries were sequenced to a depth of at least 30 million read pairs (150 bp paired-end sequencing) per sample. Differential Gene Expression data were analyzed with GeneSpring (Agilent, Santa Clara, CA) with a fold-change (FC) of |1.2| P &amp;lt; 0.05 (Table 1). Pathway analysis was conducted with Ingenuity Pathway Analysis (IPA QIAGEN, Redwood City, CA) software with Z-score cutoff of P &amp;lt; 0.05. In the duodenum 435 genes were significantly changed with a FC ≥|1.2| P &amp;lt; 0.05. Most notably, Claudin 1 and Claudin 2 were inversely affected by the second dose of iron. Claudin 1 (CLDN1, FC = 4.48, P = 0.0423) is responsible for cell-to-cell adhesion in the epithelial cell sheets and serves as a barrier to prevent water and solutes from passing through the paracellular space. Claudin 2 (CLDN2, FC = -1.41, P = 0.0097) was down regulated because it is expressed in cation leaky epithelia. In the liver 362 genes were expressed with a FC ≥|1.2| P &amp;lt; 0.05. The gene most affected by a second dose of 200 mg Fe was HAMP (Hepcidin Antimicrobial Peptide) with an FC of 40.8. HAMP is a liver-produced hormone that is the main circulating regulator of Fe absorption and distribution across tissues. It also controls the major flows of Fe into plasma by promoting endocytosis and degradation of ferroportin (SLC4A1). This leads to the retention of Fe in Fe-exporting cells and decreased flow of Fe into plasma. Metabolic pathway changes in the duodenum and liver provide evidence for the improved feed conversion and growth rates in pigs given two iron injections pre-weaning. In the duodenum, there is a down regulation of gluconeogenesis (P &amp;lt; 0.05). Concurrently, there is a decrease in the production of urea in the liver (P &amp;lt; 0.05). These observations show that there is less need for gluconeogenesis, thus less urea production from deaminated amino acids. The genomic and pathway analyses provided empirical evidence linking gene expression with phenotypic observations of piglet health and growth improvements.

Differential Gene Expression Data Analysis of ASD Using Random Forest.

Autism spectrum disorder (ASD) is a developmental disability caused by differences in the brain regions. Analysis of differential expression (DE) of transcriptomic data allows for genome-wide analysis of gene expression changes related to ASD. De-novo mutations may play a vital role in ASD, but the list of genes involved is still far from complete. Differentially expressed genes (DEGs) are treated as candidate biomarkers and a small set of DEGs might be identified as biomarkers using either biological knowledge or data-driven approaches like machine learning and statistical analysis. In this study, we employed a machine learning-based approach to identify the differential gene expression between ASD and Typical Development (TD). The gene expression data of 15 ASD and 15 TD were obtained from the NCBI GEO database. Initially, we extracted the data and used a standard pipeline to pre-process the data. Further, Random Forest (RF) was used to discriminate genes between ASD and TD. We identified the top 10 prominent differential genes and compared them with the statistical test results. Our results show that the proposed RF model yields 5-fold cross-validation accuracy, sensitivity and specificity of 96.67%. Further, we obtained precision and F-measure scores of 97.5% and 96.57%, respectively. Moreover, we found 34 unique DEG chromosomal locations having influential contributions in identifying ASD from TD. We have also identified chr3:113322718-113322659 as the most significant contributing chromosomal location in discriminating ASD and TD. Our machine learning-based method of refining DE analysis is promising for finding biomarkers from gene expression profiles and prioritizing DEGs. Moreover, our study reported top 10 gene signatures for ASD may facilitate the development of reliable diagnosis and prognosis biomarkers for screening ASD.

Abstract 4708: Application of spatially resolved transcriptomics to screen multiple tumor biospecimens using tissue microarrays

Abstract The tumor microenvironment is composed of highly heterogeneous structures and cell types that dynamically influence and communicate with each other. Although examination of singular biospecimens is sufficient for diagnostic purposes, it is inadequate and cost prohibitive when scaling for complex and overarching studies. Thus, high density multi-tumor tissue microarrays (TMAs) have been a practical and effective solution for high-throughput molecular analysis of tissues. Introduced more than a decade ago, TMAs have been instrumental in the recent study of tumor biology, the development of diagnostic tests, the establishment of quality control, and the investigation and identification of oncological biomarkers. Here, we demonstrate the pairing of the 10x Genomics Visium Cytassist Spatial Gene Expression Solution and Xenium In-Situ Platform on multi-tumor TMAs to screen for common biomarkers among a cohort of samples. Spatial transcriptomics technology has proven valuable in mapping the whole transcriptome with spatial context (Visium), whereas In Situ (Xenium) enables high-throughput cellular characterization at single-cell resolution. With the addition of our CytAssist platform, we expand on the pre-existing standard Visium solution by facilitating the retrieval of RNA transcriptomic information from tissues placed on standard or archival slides. On the other hand, the novel Xenium platform compliments whole transcriptome Visium data by unlocking the potential to assign transcripts to a particular cell with spatial context and subcellular resolution. The combination of spatial transcriptomics and targeted in situ data with FFPE TMAs promotes a high-throughput method to accelerate the uncovering of molecular signatures suitable to understanding the tumor microenvironment. We showcase the ability to spatially and comprehensively resolve individual oncogene and tumor suppressor genes associated with multiple tumors from a cohort of cancer patients and from multiple different tumor samples. In addition, these markers are mapped back to distinct morphological features within each tissue core, and use differential gene expression data to identify distinct cell types throughout the different patient tissues. By combining the throughput of TMA samples and depth of the Visium and Xenium platforms, the strategy enables greater insights into cell-type specifics while also expanding the spectrum of biospecimen types that can be analyzed. Citation Format: Syrus Mohabbat, Hardeep Singh, Stephen R. Williams, Lauren M M. Gutgesell, David J. Sukovich, Govinda M. Kamath, Hanyoup Kim, Amanda Janesick, Robert Shelansky, Ghezal Beliakoff, Augusto M. Tentori, Albert Kim, Cedric R. Uytingco, Sarah Taylor. Application of spatially resolved transcriptomics to screen multiple tumor biospecimens using tissue microarrays. [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 4708.

Molecular Landscape of Pelvic Organ Prolapse Provides Insights into Disease Etiology.

Pelvic organ prolapse (POP) represents a major health care burden in women, but its underlying pathophysiological mechanisms have not been elucidated. We first used a case-control design to perform an exome chip study in 526 women with POP and 960 control women to identify single nucleotide variants (SNVs) associated with the disease. We then integrated the functional interactions between the POP candidate proteins derived from the exome chip study and other POP candidate molecules into a molecular landscape. We found significant associations between POP and SNVs in 54 genes. The proteins encoded by 26 of these genes fit into the molecular landscape, together with 43 other POP candidate molecules. The POP landscape is located in and around epithelial cells and fibroblasts of the urogenital tract and harbors four interacting biological processes-epithelial-mesenchymal transition, immune response, modulation of the extracellular matrix, and fibroblast function-that are regulated by sex hormones and TGFB1. Our findings were corroborated by enrichment analyses of differential gene expression data from an independent POP cohort. Lastly, based on the landscape and using vaginal fibroblasts from women with POP, we predicted and showed that metformin alters gene expression in these fibroblasts in a beneficial direction. In conclusion, our integrated molecular landscape of POP provides insights into the biological processes underlying the disease and clues towards novel treatments.

Differential Expression Feature Extraction (DEFE): A Case Study in Wheat FHB RNA-Seq Data Analysis.

In differential gene expression data analysis, one objective is to identify groups of co-expressed genes from a large dataset in order to detect the association between such a group of genes and an experimental condition. This is often done through a clustering approach, such as k-means or bipartition hierarchical clustering, based on particular similarity measures in the grouping process. In such a dataset, the gene differential expression itself is an innate attribute that can be used in the feature extraction process. For example, in a dataset consisting of multiple treatments versus their controls, the expression of a gene in each treatment would have three possible behaviors, upregulated, downregulated, or unchanged. We present in this chapter, a differential expression feature extraction (DEFE) method by using a string consisting of three numerical values at each character to denote such behavior, i.e., 1=up, 2=down, and 0=unchanged, which results in up to 3B differential expression patterns across all B comparisons. This approach has been successfully applied in many research projects, and among these, we demonstrate the strength of DEFE in a case study on RNA-sequencing (RNA-seq) data analysis of wheat challenged with the phytopathogenic fungus, Fusarium graminearum. Combinations of multiple schemes of DEFE patterns revealed groups of genes putatively associated with resistance or susceptibility to FHB.

Pathogenesis of Human Immunodeficiency Virus and Mycobacterium tuberculosis Infection as Revealed by Transcriptome and Interactome Data.

Tuberculosis (TB) among patients with human immunodeficiency virus (HIV) is a major global health burden and contributes to a high mortality rate due to HIV-mediated immunosuppression and subsequent susceptibility to TB. It is imperative to understand the pathogenesis of the association between HIV and TB for therapeutic innovation and preventive medicine. In the present study, we employed transcriptomic and bioinformatic analyses of differential gene expression data obtained from Gene Expression Omnibus (GEO) of the National Center for Biotechnology Information. The expression data of Mycobacterium tuberculosis-infected macrophages and blood samples from TB patients (GSE54992, GSE52819, and GSE19435) and blood samples from HIV patients (GSE30310) were accessed for identification of differentially expressed genes (DEGs). Data from 20 healthy subjects and 19 patients with TB and 16 healthy subjects and 16 patients with HIV were analyzed. We report here the DEGs shared by HIV and TB infection. Moreover, HIV and TB host-pathogen interaction data were collected from BIOGRID, v 4.4.210, for identifying significantly modulated genes' targets and their interactions with the host. Host targets, including PLSCR1 (phospholipid scramblase 1), STAT1 (signal transducer and activator of transcription-1 alpha/beta), FBXO6 (F-box only protein 6), ITGAL (integrin alpha-L), and APP (amyloid beta precursor protein), are commonly modulated in both diseases. The function of these targets was screened from and reconciled with the literature to understand their role in the pathogenesis of HIV and TB. Overall, the study results suggest that these targets may potentially be important contributors to the pathogenesis of this comorbidity. Further experimental work is needed for evaluating these new observations, with a view to future therapeutic innovation for patients with HIV and TB.

DriverMP enables improved identification of cancer driver genes.

Cancer is widely regarded as a complex disease primarily driven by genetic mutations. A critical concern and significant obstacle lies in discerning driver genes amid an extensive array of passenger genes. We present a new method termed DriverMP for effectively prioritizing altered genes on a cancer-type level by considering mutated gene pairs. It is designed to first apply nonsilent somatic mutation data, protein‒protein interaction network data, and differential gene expression data to prioritize mutated gene pairs, and then individual mutated genes are prioritized based on prioritized mutated gene pairs. Application of this method in 10 cancer datasets from The Cancer Genome Atlas demonstrated its great improvements over all the compared state-of-the-art methods in identifying known driver genes. Then, a comprehensive analysis demonstrated the reliability of the novel driver genes that are strongly supported by clinical experiments, disease enrichment, or biological pathway analysis. The new method, DriverMP, which is able to identify driver genes by effectively integrating the advantages of multiple kinds of cancer data, is available at https://github.com/LiuYangyangSDU/DriverMP. In addition, we have developed a novel driver gene database for 10 cancer types and an online service that can be freely accessed without registration for users. The DriverMP method, the database of novel drivers, and the user-friendly online server are expected to contribute to new diagnostic and therapeutic opportunities for cancers.

Admixture mapping identifies novel Alzheimer's disease risk regions in African Americans.

This study used admixture mapping to prioritize the genetic regions associated with Alzheimer's disease (AD) in African American (AA) individuals, followed by ancestry-aware regression analysis to fine-map the prioritized regions. We analyzed 10,271 individuals from 17 different AA datasets. We performed admixture mapping and meta-analyzed the results. We then used regression analysis, adjusting for local ancestry main effects and interactions with genotype, to refine the regions identified from admixture mapping. Finally, we leveraged in silico annotation and differential gene expression data to prioritize AD-related variants and genes. Admixture mapping identified two genome-wide significant loci on chromosomes 17p13.2 (p=2.2×10-5 ) and 18q21.33 (p=1.2×10-5 ). Our fine mapping of the chromosome 17p13.2 and 18q21.33 regions revealed several interesting genes such as the MINK1, KIF1C, and BCL2. Our ancestry-aware regression approach showed that AA individuals have a lower risk of AD if they inherited African ancestry admixture block at the 17p13.2 locus. We identified two genome-wide significant admixture mapping signals: on chromosomes 17p13.2 and 18q21.33, which are novel in African American (AA) populations. Our ancestry-aware regression approach showed that AA individuals have a lower risk of Alzheimer's disease (AD) if they inherited African ancestry admixture block at the 17p13.2 locus. We found that the overall proportion of African ancestry does not differ between the cases and controls that suggest African genetic ancestry alone is not likely to explain the AD prevalence difference between AA and non-Hispanic White populations.