Some human studies may be unfeasible or unethical, making cross-species research critical for drug discovery and biomarker validation. Cross-species research is a crucial method to collect data to examine potential toxicity for a candidate drug, determine the efficacious doses that may be suitable for humans, identify potential biomarkers for a disease of interest or a therapeutic response and understand the mechanisms of disease or treatment. While animal models and humans have similar anatomy and physiology, the subtle differences among organisms in the animal kingdom need to be considered and data collected must be interpreted using a meaningful method.
Using QIAGEN IPA, you can perform comparative analyses across various animal models, even combining different time points, treatments, tissues and cell types with data generated from a wide variety of ‘omics technologies (RNA-seq, scRNA-seq, proteomics, metabolomics, etc.). In this training, you will learn how to:
1. Generate activity heatmaps and expression charts comparing different pathways and regulatory networks across different species
2. Use Activity Plot, Pattern Search and Analysis Match to compare your own data against thousands of public data pre-curated and pre-analyzed representing an array of disease states, conditions and other biological conditions
3. Create expression and correlation plots using pre-curated and pre-analyzed public data to validate and confirm findings derived from a comparison analysis
Please consider reviewing the below tutorials before this meeting.
https://qiagen.showpad.com/share/SQjinvdxIs1iGhL8H3eOd
Today's discovery scientists are challenged with identifying new biomarkers and therapeutic targets for disease, and the competition is fierce. With thousands of 'omics studies on human diseases and animal models published each year, it's becoming even more difficult to wade through the sea of information to achieve insights and make new discoveries. Scientists need data that have been processed and curated to make it discoverable, comparable and explorable. They must interpret an immense amount of data on what's been done before and understand how this relates to their own data. Among the most challenging aspects is the data and information 'noise' that can confound and convolute true knowledge and insights.
The secret to successful and impactful biomarker and target discoveries is in the scope and integration of the data analyzed on the journey to discovery. Effective, reproducible and objective tools for combining data-driven and knowledge-based approaches are key to identifying potential biomarkers and targets. Such tools enable scientists to quickly identify data that looks most promising and then validate it with their own experiments.
In today's world of advanced bioinformatics approaches, it's no longer enough to look at the association of a set of genes within the context of mere static pathway diagrams. Analyses that simultaneously harness the biomedical literature, clinical trials and curated databases like OMIM, JAX, ClinVar and COSMIC are fundamental to predicting disease and phenotypic outcomes and to understanding their upstream molecular drivers. It's also imperative to contextualize initial interpretations against the tens of thousands of other existing analyses to identify patterns that may lead to novel insights.
QIAGEN Digital Insights makes this possible. Our solutions enable scientists to rapidly identify the best biomarker and target candidates based on biological characteristics most relevant to their discovery study. Our powerful and high-quality tools are driven by over 20 million scientific findings and over 580,000 curated samples. They help researchers save valuable time and resources while empowering biomarker research and driving new discoveries.
We offer an array of tools for advanced pathway analysis that let scientists go beyond outdated, basic pathway analysis approaches and gain deep insights into their data by combining multiple sources of knowledge and insights into easy-to-use tools. The QIAGEN Knowledge Base, upon which our bioinformatics software is built, is updated weekly and provides the integrated knowledge graph that powers these analyses. It accompanies our highly-curated collections of disease-relevant and cancer-focused 'omics datasets that enable scientists to discover and validate new biomarkers and drug targets by exploring evidence from all types of 'omics experiments to compare against their own experiments. Our broad range of biomarker and target discovery solutions let scientists dig deep into their 'omics data to identify and prioritize molecular candidates, uncover potential mechanisms of action and explore molecules and pathways associated with disease.
Learn more about the applications supported by our biomarker and target discovery solutions. Browse various use cases, articles, webinars and other resources to understand how our solutions are applicable to a variety of research questions. Watch this short video featuring one of our field application scientists who describes how she helps discovery scientists reach their goals of finding the right therapeutic target, discovering the next cancer biomarker or understanding a mechanism of action. Request a consultation with one of our experts to find the right QIAGEN Digital Insights toolset for your research goals.
Progranulin (PGRN) is a growth factor and immune regulatory protein involved in the regulation of host-defense signaling pathways during infection and inflammation. It is critical in innate immunity against bacteria and targets TLR4 which recognizes LPS (1–3). Progranulin deficiency in animal models leads to increased vulnerability to LPS-induced septic shock and high mortality (1). Increased progranulin plasma levels have been described in in patients with sepsis (4).
Exciting research on progranulin as a novel biomarker was recently presented at the Sepsis Update 2019 conference which took place on September 11–13 in Weimar, Germany. QIAGEN’s Senior Principal Scientist for Bioinformatics, Dr. Jean-Noel Billaud, collaborated on this research with Dr. Gustav Schelling’s team from Klinikum der Universität München, who presented their progranulin research findings at the conference. The aim of their research was to study the performance characteristics of progranulin as a potential biomarker for sepsis, compared to established markers such as procalcitonin (PCT), and to delineate molecular networks involved in upregulating progranulin in sepsis.
To achieve this, the team used QIAGEN bioinformatics software OmicSoft ArrayStudio to obtain the differentiation profile after DESEq2 analysis, and performed biological interpretation using Ingenuity Pathway Analysis (IPA). NGS data from sepsis patient samples were used to identify the canonical gene network (targeted miRNA-mRNA network) involved in the early antimicrobial response of progranulin, followed by RT-qPCR confirmation.
NGS revealed significantly upregulated mRNA transcripts of GRN from human blood cell samples (the progranulin gene) (log2FC = 2.23, padj=3.46E-8) and SORT1 (sortilin, an important regulator of progranulin) (log2FC = 5.56, padj=1.38E-8), whereas comprehensive NGS did not detect any transcripts of CALC-1 (PCT) in blood cells. Filtering and pairing of NGS miRNA/mRNA data using IPA revealed a network (Figure 1) including TP53 and TLR4 as well as progranulin and sortilin, shown to be regulated by miR-16, miR-150 and others. The miRNAs and mRNAs from the network, including progranulin and sortilin, were confirmed by RT-qPCR.
Figure 1. Upregulation of progranulin (GRN gene transcript) in a molecular network activated during early antimicrobial response in septic shock. The network was constructed using high-throughput sequencing (NGS) followed by RT-qPCR confirmation. Red indicates upregulation of the respective molecules and green indicates downregulation.
This research performed using QIAGEN bioinformatics solutions indicates how progranulin is part of a key blood-cell derived network involved in early antimicrobial response in sepsis, and performs just as well as other more established biomarkers for the differentiation between systemic inflammatory response syndrome (SIRS) and sepsis. Based on this research progranulin represents a novel and sensitive biomarker for sepsis.
References.
1. Jian J, Konopka J and Liu C. (2013) Insights into the role of progranulin in immunity, infection, and inflammation. J Leukoc Biol 93: 199–208.
2. McIsaac SM, Stadnyk AW and Lin TJ. (2012) Toll-like receptors in the host defense against Pseudomonas aeruginosa respiratory infection and cystic fibrosis. J Leukoc Biol 92: 977–985.
3. Abella V, et al (2016). The novel adipokine progranulin counteracts IL-1 and TLR4-driven inflammatory response in human and murine chondrocytes via TNFR1. Sci Rep 6: 20356.
4. Yan W, et al. (2016) Progranulin Controls Sepsis via C/EBPalpha-Regulated Il10 Transcription and Ubiquitin Ligase/Proteasome-Mediated Protein Degradation. J Immunol 197: 3393–3405.
OncoLand is a sophisticated oncology-focused database designed to accelerate cancer research. Integrating published research and large consortium cancer datasets, robust data visualization and discovery tools, OncoLand saves you valuable time and resources in your pursuit for actionable discoveries.
Start your free trial of OncoLand today!
Check out these recent articles citing Biomedical Genomics Workbench, a comprehensive, highly accurate NGS data analysis platform, providing researchers with a user-friendly, customizable human hereditary disease and cancer analysis solution for biomarker discovery and validation. Below are a few examples of how researchers from Pennsylvania to Japan are using Biomedical Genomics Workbench to accelerate their research.
Relaxin Reverses Inflammatory and Immune Signals in Aged Hearts
First author: Brian Martin
A team based out of the University of Pennsylvania studied the cardiovascular benefits of relaxin—a pregnancy hormone—on both young and old rats to determine its effects on the heart’s aging process. They extracted RNA and analyzed genomic changes, importing raw transcript data into Biomedical Genomics Workbench and mapping reads to the rat reference genome. The study, which ran in PLOS ONE, concluded that relaxin both alters gene transcription and suppresses inflammatory pathways and genes associated with heart failure and aging. This has therapeutic potential for cardiovascular and inflammation-related diseases, such as heart failure, diabetes and atrial fibrillation.
Comparison of Genetic Profiling of Primary Central Nervous System (CNS) Lymphoma Before and After Extra-CNS Relapse
First author: Kosuke Toyoda
In 2017, a team of Japanese scientists studied the mechanism of chemotherapy resistance in lymphomas of the CNS (central nervous system), which were previously identified as promising targets for immune checkpoint blockade therapy. They performed comprehensive genomic analysis in the hope of better understanding tumor oncogenic evolution and overcoming the immune privilege. The team compared the impact of extra-CNS relapse, using Biomedical Genomics Workbench to call variants. Their report, which ran in Blood Journal, suggested that the evolution of mutations enabled systemic disease progression with a breakthrough of immune privilege, characterized by immunological overpowering and the dysregulation of B-cell proliferation signaling.
Assessing the GeneRead SNP for Analysis of Low-Template and PCR-Inhibitory Samples
First author: Maja Sidstedt
When forensic DNA laboratories use massive parallel sequencing for human identification purposes, chances are good that the DNA samples are heterogeneous and of varying quality. SNP assays must therefore be able to handle impurities and low amounts of DNA. Using Biomedical Genomics Workbench to analyze sequencing data, a Swedish team evaluated the GeneRead Individual Identity SNP panel, which handled multiple extraction methods and withstood inhibitor solutions and was concluded to be satisfactory for casework-like samples. Read about the study, which ran in PLOS ONE in January this year.
To request your no-obligation trial of Biomedical Genomics Workbench, just click here.
