We’re always on the lookout for new and interesting ways in which researchers are using our solutions. Here is a quick recap of a few recent papers that included gene expression data analysis from our Ingenuity® Pathway Analysis™ (IPA) customers.
Network Topology Analysis of Post-Mortem Brain Microarrays Identifies More Alzheimer’s Related Genes and MicroRNAs and Points to Novel Routes for Fighting with the Disease
First author: Sreedevi Chandrasekaran
PLoS One recently published new findings about the battle against Alzheimer’s disease from a research team based at Virginia Commonwealth University. This study is part of a larger effort to understand neurodegenerative disorders, including Parkinson’s and Huntington’s, with the aim of identifying a unified underlying molecular mechanism for all three diseases. Using what may be the first-ever network-based approach to study Alzheimer’s, the team attempted to single out new drug targets by using IPA core analysis in one stage of gene expression to delve into the underlying cellular mechanisms and molecular factors of the disease. They looked at deregulated genes, biological processes, and the interactions between them to decipher the complexity of the condition, enabling them to identify patterns and heterogeneous datasets.
Blood Genome-Wide Transcriptional Profiles of HER2 Negative Breast Cancer Patients
First author: Ovidiu Balacescu
A Romanian research team recently reported using IPA to identify which biological processes and pathways were affected by gene expression changes in triple-negative breast cancer, which is also known as TNBC/ER−PR−HER2−. Triple-negative breast cancer is currently only treated with chemotherapy. Unlike ER+ and HER2+ tumors, it does not have a validated target therapy, which means that it typically has a poor clinical outcome as well as greater risk of recurrence and distant metastasis. The study showed that targeted immunotherapy could feasibly be used in conjunction with chemotherapy to treat triple-negative breast cancer and improve clinical outcomes.
Transcriptome Profiling of Musculus Longissimus Dorsi in Two Cattle Breeds with Different Intramuscular Fat Deposition
First author: Elke Albrecht
Agricultural genomics is a significant topic for today’s science community, and this paper in Genomics Data sheds new light on potential paths to improve meat quality. The authors discuss how gene expression provides details about the different intramuscular fat depositions — also known as marbling, which dictates quality and flavor — in two cattle breeds. The team compared transcriptomes of muscle cells in both breeds, identifying 569 differentially expressed genes in Japanese Black cattle. They then used IPA to locate a gene network that links parameters of cell morphology and maintenance with lipid metabolism.
Transcriptomic Sequencing Reveals a Set of Unique Genes Activated by Butyrate-Induced Histone Modification
First author: Cong-Jun Li
In this paper published by the NIH’s Gene Regulation and Systems Biology journal, a research team studied the role of butyrate, a mammalian nutritional element produced by bacterial fermentation of dietary fibers. Using normal bovine cells, the team used IPA to analyze genetic networks of differentially expressed genes as well as molecular processes and functions, ultimately discovering butyrate-induced differential expression of genes and unique genes, which are related to major cellular functions. This is a step toward understanding epigenomic regulation at the molecular level.
Post-weaning Blood Transcriptomic Differences between Yorkshire Pigs Divergently Selected for Residual Feed Intake
First author: Haibo Liu
In this BMC Genomics study, researchers from Iowa State University used IPA to help them understand variations of global gene expression in the blood transcriptome of two separate lines of recently-weaned pigs to potentially inform the development of predictive biomarkers for residual feed intake, or RFI — a standard measure for feed efficiency. The team found a difference between the low and high RFI classifications of pigs, potentially leading to improved feed efficiency through genetic selection.
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