List of fold change proteomic data sets of dFCM- 39 vs. 12Day and105 vs. 12Day, cFCM- 40 vs. 12Day and115 vs. 12Day , mouse heart 90 vs. 1 day after selecting mitochondrial proteins from the scaffold software for Ingenuity Pathway Analysis (IPA Qiagen)

Sheet A in S3 Table (S6C Fig): Gene list (with RNA-Seq values) used for pathway analysis in S6C Fig. Cellular genes upregulated in both glucose/IFN-γ/mock infected and glucose/IFN-γ/vaccinia virus-infected relative to matching conditions but in galactose media. Comparison made using DEGs from 1) glucose/galactose A549 cultured cells treated with IFN-γ and mock infected with 2) glucose/galactose A549 cultured cells treated with IFN-γ and infected with vaccinia virus (MOI = 0.01). RNA-Seq data were curated with the cut-off values of log2 fold-change ≥ 1 or log2 fold-change ≤ -1 with an adjusted p-value ≤ 0.01 under mock infection condition. Data were analyzed with QIAGEN Ingenuity Pathway Analysis (QIAGEN IPA). Sheet B in S3 Table (S6C Fig): Gene list (with RNA-Seq values) used for pathway analysis in S6C Fig. Cellular genes upregulated in both galactose/IFN-γ/mock infected and galactose/IFN-γ/vaccinia virus infected relative to matching conditions but in glucose media. Comparison made using DEGs from 1) glucose/galactose A549 cultured cells treated with IFN-γ and mock infected with 2) glucose/galactose A549 cultured cells treated with IFN-γ and infected with vaccinia virus (M.O.I. = 0.01). RNA-Seq data were curated with the cut-off values of log2 fold-change ≥ 1 or log2 fold-change ≤ -1 with an adjusted p-value ≤ 0.01 under mock infection condition. Data were analyzed with QIAGEN Ingenuity Pathway Analysis (QIAGEN IPA). (XLSX)

Proteomics data sets, including all Qiagen IPA and Reactome Pathway Analysis, for Manuscript 'Exercise training induces depot-specific remodelling of protein secretion in skeletal muscle and adipose tissue of obese male mice'.

Six female littermate piglets 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). All piglets in the litter were administered the first 1 mL injection < 24 hours after birth. On d 7, piglets were paired by weight (mean BW = 1.72 ± 0.13 kg) and one piglet 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 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 200 mg Fe was HAMP (Hepcidin Antimicrobial Peptide) 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. Metabolic pathway changes in the duodenum and liver provide evidence for the improved feed conversion and growth rates in piglets given two iron injections pre-weaning with contemporary pigs in a companion study. In the duodenum, there is a down regulation 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. Methods RNA Sequencing Samples were submitted to Zymo Research (Irvine, CA, USA) for total mRNA extraction, cDNA library preparation and RNA sequencing. Total RNA-Seq libraries were constructed from 250 ng of total RNA. To remove rRNA, a method described by Bogdanova et al., 2011 was followed. Libraries were prepared using the Zymo-Seq RiboFree Total RNA Library Prep Kit (Cat # R3000) according to the manufacturer’s instructions (Zymo-Research, 2022). The RNA- Seq libraries were sequenced on an Illumina NovaSeq to a sequencing depth of at least 30 million read pairs (150 bp paired-end sequencing) per sample. Detection of DGE and Bioinformatic Data Handling Differentially expressed genes were detected using GeneSpring software (Agilent, Santa Clara, CA) using selection criteria that accepted a DGE threshold of greater than a |1.2|-FC in expression level and statistical probability levels of P < 0.05. The filtered genes were then subjected to Ingenuity Pathway Analysis (IPA; QIAGEN Inc., Redwood City, CA; https://digitalinsights.qiagen.com/products/) to gain insights into canonical pathways, networks, and biological functions. Qiagen IPA uses algorithms, tools, and visualizations to combine the directional information from gene expression patterns (up- or down-regulation) with the expected causal effects of the genes, as reported in the published literature. A prediction for effects of a treatment on a particular biological pathway function or disease can then be made based upon the direction of change in gene expression and calculated Z-scores. Briefly, a Z-score is used to compare data that have different means and standard deviations. The Z-score is the distance of a point, such as a complete pathway, from the mean of the distribution in terms of the standard deviation (Corchete et al., 2020; Shao et al., 2020; Wieder, Lai and Ebbels, 2022).

Summary:The datasets described here were gathered while investigating the molecular processes by which some human ductal carcinoma in situ (DCIS) lesions advance to the more aggressive form while others remain indolent.Data access:All RNA sequencing data have been deposited in the Gene Expression Omnibus with accession https://identifiers.org/geo:GSE143790 All Chip-Exo data have been deposited in the Gene Expression Omnibus with accession https://identifiers.org/geo:GSE143313 RPPA data is included together with this data record, in the file Supplementary Figure 3-RPPA.xlsx. The Raw RPPA and ANOVA tabs are the results, while the other tabs are IPA analysis performed by the authors. Permission to use figures and data generated using QIAGEN Ingenuity Pathway Analysis (IPA) is given in the file QIAGEN Ingenuity Product Support Permission letter for Dr. Behbod.pdf.The specific data underlying each figure and supplementary figure in the manuscript are provided as part of this data record, and are as follows:Figure 1-BCL9-STAT3 interaction.xlsxFigure 2-ChIP Exo.xlsxFigure 3-ChIP.xlsxFigure 4-MMP16 and avb3 MIND xenografts.xlsxFigure 5-MMP16 avb3 TMA analysis.xlsxFigure 6-Carnosic data.xlsxSupplementary Figure 3-RPPA.xlsxSupplementary Figure 4-STAT3 Reporter.xlsxSupplementary Figure 5-ChIP Exo Motifs.xlsxSupplementary Figure 6-integrin data.xlsxSupplementary Figure 7-MMP data.xlsxStudy approval and patient consent: Patients gave written informed consent for participation in the University of Kansas Medical Center Institutional Review Board–approved study allowing collection of additional biopsies and or surgical tissue for research. Animal experiments were conducted following protocols approved by the University of Kansas School of Medicine Animal Care and Use and Human Subjects Committee. Study aims and methodology: The aim of the related study was to determine the molecular processes underlying progression to invasion in DCIS using PDX DCIS MIND animal models. Using a novel intraductal model they identify downregulation of specific STAT3 targets to promote progression and use a purified component from rosemary extract to show that treatment in vivo decreases DCIS progression in patient derived DCIS and cell line models.

A significant area of rare diseases research is the investigation of druggable protein encoding genes that contribute to pathogenesis. Niemann Pick Type C (NPC) disease can be considered as a challenging one, among all rare diseases, because it has been associated with a poor prognosis and an unclear molecular pathogenesis. In recent years, Proteomics analysis has become a functional and useful technology for profiling protein expression and find possible drugs targets. In the present study, hepatocytes derived from wild type and Npc1 deficient mice were analyzed by mass spectrometry-based proteomics, followed by pathway analysis and statistical interpretation, performed with the QIAGEN Ingenuity Pathway Analysis (IPA) software. Applications for protein function was built using IPA and gene ontology analysis. We identified and reliably quantified a total of 3833 proteins, among them 416 presented a significant p-value (<0.05) being classified 149 as upregulated (log2 fold change >1) and 6 as downregulated (Log2 fold change <-1). Our analysis revealed that the most significant changed proteins are related to liver damage, lipid metabolism and inflammatory response. In addition, in the group of up/downregulated proteins, 47 proteins were identified as lysosomal proteins and 22 as mitochondrial proteins. Importantly, we found that of those proteins CTSB, LIPA, DPP7, GLMP and DECR1 are related to liver fibrosis, liver damage and steatosis.

The project contains raw and result files from a proteomic profiling of a male breast cancer (MBC) case. Label-free quantification-mass spectrometry (LFQ-MS) and bioinformatics analysis were employed to investigate the differentially expressed proteins (DEPs) among distinct tissue samples: the primary breast tumor, axillary metastatic lymph nodes and the adjacent non-tumor breast tissue. An additional proteomic comparative analysis was performed with a primary breast tumor of a female patient. A number of Ingenuity® Pathway Analysis (IPA) (QIAGEN Inc.) and functional annotation tools were used to further analyze the DEPs. Altogether, our findings revealed deregulated proteins into signaling pathways involved in the cancer development and provided a landscape of proteomic data for the MBC research.

Macrophages play a key role in ozone-induced lung injury by regulating both the initiation and resolution of inflammation. These distinct activities are mediated by pro-inflammatory and anti-inflammatory/pro-resolution macrophages which sequentially accumulate in injured tissues. Macrophage activation is dependent, in part, on intracellular metabolism. Herein, we used RNA-sequencing (seq) to identify signaling pathways regulating macrophage immunometabolic activity following exposure of mice to ozone (0.8 ppm, 3 hr) or air control. Analysis of lung macrophages using an Agilent Seahorse showed that inhalation of ozone increased macrophage glycolytic activity and oxidative phosphorylation at 24 and 72 hr post exposure. An increase in the percentage of macrophages in the S phase of the cell cycle was observed 24 hr post ozone. RNA-seq revealed significant enrichment of pathways involved in innate immune signaling and cytokine production among differentially expressed genes at both 24 and 72 hr after ozone, while pathways involved in cell cycle regulation were upregulated at 24 hr and intracellular metabolism at 72 hr. An interaction network analysis identified tumor suppressor 53 (TP53), E2F family of transcription factors (E2Fs), Cyclin Dependent Kinase Inhibitor 1A (CDKN1a/p21), and Cyclin D1 (CCND1) as upstream regulators of cell cycle pathways at 24 hr and TP53, nuclear receptor subfamily 4 group a member 1 (NR4A1/Nur77), and estrogen receptor alpha (ESR1/ERα) as central upstream regulators of mitochondrial respiration pathways at 72 hr. These results highlight the complex interaction between cell cycle, intracellular metabolism, and macrophage activation which may be important in the initiation and resolution of inflammation following ozone exposure. Methods Total RNA was extracted as described above from 3 mice/treatment group. In a pilot study, we found that 3 mice were sufficient to identify a significant difference in Ptgs2 gene expression by qPCR at α = 0.05 and power = 80%. RNA integrity numbers (RINs) were confirmed to be ≥ 8.8 using a 2100 Bioanalyzer Instrument (Agilent, Santa Clara, CA). cDNA libraries were prepared using mouse TruSeq® Stranded Total RNA Library Prep kit (illumina, San Diego, CA) and quantified using a KAPA Library Quantification kit (Roche, Pleasanton, CA). cDNA libraries were sequenced (75 bp single-ended, ~35-44M reads per sample) on an Illumina NextSeq instrument. Raw reads in FastQ files were trimmed using Trimmomatic-0.39 (Bolger et al. 2014) and quality control of trimmed files performed using FastQC. Salmon was used to align reads in mapping-based mode with selective alignment against a decoy-aware transcriptome generated from mouse transcriptome GENCODE Release M23 (GRCm38.p6). Estimated counts per transcript were generated using the gcBias flag and normalized to transcript length to correct for potential changes in gene length across samples from differential isoform usage (Love et al. 2016; Patro et al. 2017). Transcript level quantitation data were aggregated to the gene-level using tximport (Soneson et al. 2015). Differential gene expression analysis was performed with air exposed mice as controls using DESeq2 with corrections for differences in library size (Love et al. 2014) in R version 4.0.3. Significantly enriched canonical pathways and upstream regulators were identified with Ingenuity IPA Version 65367011 (QIAGEN Inc, https://www.qiagenbioinformatics.com/products/ingenuity-pathway-analysis/) using a right-tailed Fisher’s Exact Test (Krämer et al. 2014). A less stringent criteria (fold change > 1.3 and experimental false discovery rate [padj] < 0.05) was used to augment the number of genes included in the pathway analysis (Bennett et al. 2024). Data were deposited NCBI’s Gene Expression Omnibus (Edgar et al. 2002) and are accessible through GEO Series accession number GSE237594 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE237594).

Human adult and neonatal cardiovascular progenitor cells were cultured aboard the International Space Station for 30-days before returning to Earth live for RNA collection. The transcriptome of cells cultured in space was compared to the transcriptome of clonally identical cells cultured on Earth. Using Qiagen IPA, DAVID, StemChecker, and DIANA mirPath, we demonstrate that in response to microgravity, cells present with elevated transcripts related to stemness and cell proliferation. Additionally, transcripts related to cell cycle re-entry, cardiovascular development, and oxidative stress were induced as well as relevant proliferation signaling pathways.

Renal cell cancer is among the most common forms of cancer in humans, with around 35,000 deaths attributed to kidney carcinoma in the European Union (EU) in 2012 alone. Clear cell renal cell carcinoma (ccRCC) represents the most common form of kidney cancer and the most lethal of all genitourinary cancers. Here we apply omics technologies to archival core biopsies to investigate the biology underlying ccRCC. Knowledge of these underlying processes should be useful for the discovery and/or confirmation of novel therapeutic approaches and ccRCC biomarker development. From partial or full nephrectomies of 11 patients, paired core biopsies of ccRCC affected tissue and adjacent non-tumorous tissue were both sampled and subjected to proteomics analyses. We combined proteomics results with our published mRNA-seq from the same patients and with published miRNA-seq data from an overlapping patient cohort from our institution. Statistical analysis and pathway analysis were performed with JMP Genomics (SAS) and Ingenuity Pathway Analysis (IPA, Qiagen), respectively. Proteomics analysis confirmed the involvement of metabolism and oxidative stress-related pathways in ccRCC, while the most affected pathways in the mRNA-seq data were related to the immune system. Unlike proteomics or mRNA-seq alone, a combinatorial cross-omics pathway analysis approach captured a broad spectrum of biological processes underlying ccRCC, such as mitochondrial damage, repression of apoptosis, and immune system pathways. Sirtuins, immunoproteasome genes and CD74 are proposed as potential targets for the treatment of ccRCC.