When it comes to data-driven science, your results are only as good as your data and your software. Whether free or premium, your choice of software will greatly impact your research and reputation, and you shouldn’t make it lightly. It’s not enough for a tool to be cheap – it also needs to do its job.
You might think that all a pathway analysis program needs to do is just that: analyze genes and proteins in the context of pathways. But you’re analyzing more than a simple collection of genes. You need the causal relationships that connect them, including activation, inhibition, downstream outcomes and upstream factors.
While the free price tag might be enticing, barebones pathway analysis tools can't provide any depth beyond simple, non-directional relationships.
Is the basic package good enough? Let’s check out what premium gets you.
Your pathway analysis program should empower you to extract the insights you need for publishing. While premium analysis programs might seem like just a shiny interface, they are much more powerful. They’re backed by an extensive human-curated knowledge base, allowing you to analyze and compare vast amounts of data from the comfort of a single platform.
QIAGEN Ingenuity Pathway Analysis (IPA) pays for itself by giving you the tools you need, instead of forcing you to find them on the internet (or build them from scratch). Plus, if you're ever unsure of anything, IPA is supported by a team of PhD-level scientists who are ready to help out. IPA can perform a comprehensive set of over 20 unique analyses, which will help you:
When it comes to pathway analysis tools, the price reflects the value. Sometimes, you just need more.
Here's the scenario - You're a passionate scientist in a leading pharmaceutical company hoping to uncover a transformative drug candidate. Naturally, you use artificial intelligence (AI) to help you target the most promising leads. After weeks of dedicated work, you start to realize that something seems a little off with your results. Maybe you recognize that the algorithm-proposed drug candidate has a history of poor tolerance in human clinical trials. Or perhaps the drug candidate fails to reproduce even the most basic PK/PD modeling results in vitro. Just like you, many drug discovery researchers have found themselves misled by the results proposed by AI.
Even with state-of-the-art algorithms, outcomes of AI for drug discovery heavily depend on the data and context backing them up. Many researchers, just like you, are seeking ways to navigate the intricacies and challenges of this rapidly evolving field. The path to successful AI-driven drug discovery may appear complex, but with the right guidance, AI can significantly enhance both the efficiency and effectiveness of your drug discovery journey.
Here are 3 of our best secrets to help ensure your success when using AI for drug discovery:
1. Start with quality data
The foundation of any successful AI model lies in the quality of its training data. Inconsistent or noisy biomedical data can introduce biases, potentially making the AI model veer off course. Imagine trying to master a language using an inaccurate dictionary; the outcome would be a garbled mess.
Similarly, training an AI model on low-quality biomedical data can lead to misguided conclusions. Data quality, integrity and relevance are paramount. Using expert-curated databases ensures the model begins with accurate and comprehensive knowledge.
That's where our QIAGEN Biomedical Knowledge Base (BKB) database comes in. Curated by experts and continuously updated, QIAGEN BKB ensures you equip your AI models with the best possible start. It offers a strong foundation for building knowledge graphs and data models. Just as a building's strength depends on its foundation, your AI model's efficacy depends on starting with quality data.
2. Root AI inferences in real biological contexts
The power of AI lies in its ability to process vast amounts of information quickly. But it's worth remembering that an AI model, regardless of its sophistication, doesn't inherently understand the complexities of human biology. It sees numbers, patterns and correlations but not causations.
An AI model might draw associations that, at a glance, seem significant. However, without the biological context, these associations can be misleading. To avoid chasing after false positives, it's crucial to ensure the AI's conclusions are rooted within the biological realities.
Here's the good news: QIAGEN BKB and QIAGEN Ingenuity Pathway Analysis (IPA) have built-in causality. With IPA you can quickly check the conclusions your AI generates. IPA's intuitive GUI interface provides visual pathways, disease networks, upstream regulators/downstream effects and isoform-level differential expression analysis, all with the ability to bring in primary datasets for custom-tailored analyses.
3. Validate findings with peer-reviewed research
Science, at its core, thrives on collaboration, verification and iteration. A discovery today can be the stepping stone for a revolutionary breakthrough tomorrow. AI can be a potent tool in accelerating these discoveries, but its suggestions need validation.
While using AI for drug discovery can uncover potential candidates, it's essential to validate these findings using published, peer-reviewed studies. Not only does this process lend credibility to your findings, but it also provides invaluable insights. For instance, understanding which cell lines have been used in previous studies can guide your preclinical testing, ensuring you're on the right track.
For this crucial step, QIAGEN OmicSoft's curated omics data collection is your ally, especially for enterprises in need of high-quality multi-omics datasets. You can tap into a comprehensive landscape of sources, offering validation from published studies beyond just a single public repository. Such validation lends credibility to your discoveries and provides invaluable insights. QIAGEN OmicSoft's curated omics data collection facilitates this crucial step, bridging the gap between AI predictions and experimental data to construct disease models and digital twins of cells/organs/organisms.
Validating your cell line selection is also a critical factor for successful preclinical research. Using ATCC Cell Line Land, you can access authenticated cell line ‘omics data to make informed decisions before purchasing cell lines, helping to streamline your workflows, save time and resources, and enhance the predictability and reproducibility of your studies.
You can be confident in steering your research in the right direction with AI, provided you eliminate guesswork and maximize efficiency by using quality biomedical data, ensure biological soundness of AI results and validate your findings. By applying these three powerful tweaks to your AI, you'll surely revolutionize your drug discovery by spotting promising leads much quicker.
We design our QIAGEN Digital Insights knowledge and software with your success in mind.
After all, the future of new therapies is waiting, and we want to ensure you're well-equipped to lead the way. Want to uncover more secrets to drive drug discovery success from our experts?
Continue reading to see how QIAGEN can power your research.
Looking to collaborate further? Fast track your analysis with QIAGEN Discovery Bioinformatics Services.
Do you ever struggle to formulate hypotheses based on your experimental expression data? You may be comparing results from healthy versus tumor cell lines or treated versus untreated samples. What do the differences between expression patterns in the data mean? Many of us struggle to make biological sense of our RNA-seq or microarray data. The massive amount of expression data generated from experiments leaves us with thousands of data points but often no understanding of their biological meaning.
Advanced pathway analysis is an excellent way to gain a deeper understanding of expression data and experimental results. Here we offer three easy ways to go from expression data to pathway analysis so you can give your experimental data biological context to start gathering meaningful insights.
QIAGEN Ingenuity Pathway Analysis (IPA) is a popular tool for analyzing, comparing and contextualizing differential gene expression results from experiments in human, mouse or rat, among other organisms. QIAGEN CLC Genomics Workbench has convenient tools for processing raw data from RNA-seq or microarray experiments and performing differential gene expression analysis. In addition, with an IPA license and the Ingenuity Pathway Analysis Pathway plugin installed, you can upload results to IPA directly. By combining QIAGEN CLC Genomics Workbench with QIAGEN IPA, we offer a versatile platform for linking various instrument readout formats to biological insights.
These are the three most common use cases we see among our customers:
Use case 1: FASTQ data to IPA
Typical experiments you may be running involve sending RNA (mRNA, miRNA, lncRNA, etc.) from treatment and control samples to an NGS sequencing facility. After sequencing, you perform bioinformatics analysis using QIAGEN CLC Genomics Workbench on the FASTQ file(s) returned by the facility. First, you can do QC and trimming using the Prepare Raw Data workflow. Samples that meet QC criteria are then associated with metadata describing the experimental setup. You can identify differentially expressed genes (DEGs) using the RNA-Seq and Differential Gene Expression Analysis workflow. Differential gene expression analysis is based on the fit of a Generalized Linear Model with a negative binomial distribution, like the approaches taken by the popular tools EdgeR (Robinson et al., 2010) and DESeq2 (Love et al., 2014). Paired designs are supported, and it is possible to control for batch effects. You can then upload DEGs directly to IPA for additional analysis, comparison and contextualization. Analyze Expression Data and Upload Comparisons to IPA provides a convenient Sample to Insight workflow.
See also our manual on RNA-seq and small RNA analysis.
Use case 2: RNA-seq expression data to IPA
In this use case, the sequencing facility processes the raw FASTQ files and returns an expression matrix file, which takes up much less space than FASTQ files do. You can import expression matrices using the Import Expression Matrix tool in QIAGEN CLC Genomics Workbench. Then you can apply QC criteria, associate metadata and compare the experimental groups as described for use case 1. This use case is also supported by a Sample to Insight workflow: Analyze Count Matrix and Upload Comparisons to IPA.
Use case 3: Microarray expression data to IPA
In this third scenario, the samples have been processed on microarrays, not using NGS. Various generic and vendor-specific formats are supported in QIAGEN CLC Genomics Workbench. Steps include setting up a microarray experiment to group the samples, followed by transforming and normalizing the expression data and running a statistical test to identify differential expression. Several tests are available, including proportion-based tests, t-tests and ANOVA. Filtered DEGs can be uploaded to IPA for pathway analysis as described for use case 1.
Ready to give it a try?
QIAGEN Digital Insights bioinformatics tools for transcriptomics support microarray and RNA-seq data analysis with a variety of specialized tools. They enable you quickly go from raw instrument output to biological insights, as well as compare to over 100,000 curated public datasets. Learn more and request a consultation about our portfolio of tools for biomarker and target discovery that support expression data analysis. Ready to try these applications? Request a trial of QIGEN CLC Genomics Workbench and QIAGEN IPA to see how these tools can work together to streamline your insights from expression data.
References:
Resources:
Plugin:
Blogs:
Best practices for RNA-seq data analysis
Compare biological expression results with QIAGEN IPA Analysis Match
Webinars:
Training on RNA-seq and pathway analysis
Example analysis performed using QIAGEN CLC Genomics Workbench:
Tutorials:
Expression analysis using RNA-Seq data
Advanced RNA-seq analysis with upload to IPA
Microarray-based expression analysis
Building workflows in QIAGEN CLC Genomics Workbench video tutorials
How to include external applications to QIAGEN CLC Genomics Server video tutorials
Scientists working in drug discovery are all too aware that toxicity testing is critical for drug development. Yet many lack the ideal software to study drug toxicity and waste time juggling multiple tools. New interactive and automated analytical software tools could revolutionize drug toxicity testing.
A common method to study and document drug toxicity is the use of adverse outcome pathways (AOPs), but until now there has been no available software to easily create, share and manage them. Toxicity researchers resort to underpowered and hard-to-use open-source or homebrew software tools, or even curate them by hand as drawings and tables in PowerPoint and Excel. This creates unnecessary work and hampers our ability to produce AOPs that adequately support drug development.
What makes AOPs powerful for studying toxicity?
AOPs demonstrate sequential, causally linked molecular and cellular events that produce a toxic effect when an organism is exposed to a substance (1). AOPs help us create models of biological interactions and toxicity mechanisms that describe how exposure to a substance might cause illness or injury. They suggest cell- or biochemical-based tests for pathway elements that could be used to develop testing strategies for targeted toxicity. We can use AOPs to identify and characterize the mechanisms of toxicity (2), helping to save time and costs in downstream drug testing and validation.
What are the requirements for robust AOP software?
The ideal AOP software lets us easily explore molecular interactions based on causal mechanistic details powered by underlying content. It should include interactive diagrams to visualize these interactions and enable secure sharing, as well as controlled vocabularies.
QIAGEN Ingenuity Pathway Analysis (IPA) offers these optimal AOP capabilities and is a game changer for drug development. Using the new IPA AOP feature, we can build AOPs, connect important events and concepts, and then label them systematically with the correct AOP nomenclature, as shown below.
Figure 1. Example of AOPs in QIAGEN IPA.
Simply start with a molecular initiating event (MIE) and use IPA to pinpoint the event in the diagram, then add other events and outcomes using IPA’s controlled vocabulary. If we want to delve deeper, we can automatically score gene expression and other analyses against the AOPs. What's more, we can easily and securely share and maintain these AOPs within our organization.
Toxicity testing programs shouldn’t be a bottleneck in drug screening. Using IPA, we can now easily ramp up a strategy for drug toxicity testing with AOPs. Explore more about QIAGEN IPA and request a consultation to ask about adding AOP capabilities to your IPA license.
References:
Ever wonder what goes on behind the scenes in R&D at QIAGEN Digital Insights? Our team of expert scientists is busy collaborating with researchers worldwide. They conduct Sample to Insight studies using QIAGEN’s sample preparation kits and bioinformatics software to elaborate proof of concept studies and contribute to active research efforts. This helps us bring extra value to our customers by helping them apply our solutions to answer their research questions. Here we share two recent research studies comparing molecular signaling in sepsis and COVID-19 to discover new biomarkers.
Progranulin signaling in sepsis, community‑acquired bacterial pneumonia and COVID‑19: A comparative, observational study
Researchers from multiple institutions in Germany collaborated with QIAGEN Digital Insights scientists Dr. Jean-Noël Billaud, Dr. Joseph Pearson and Dr. Nirav Amin in this recent observational study by Brandes et al. The team studied the functional role of the pleiotropic growth factor progranulin in cohorts of sepsis patient cases and compared progranulin plasma levels among sepsis, systemic inflammatory response syndrome (SIRS), severe localized infections, community-acquired bacterial pneumonia and COVID-19.
They used QIAGEN Ingenuity Pathway Analysis (IPA) to analyze differential expression data from blood taken from septic-shock patient cases and healthy controls and constructed molecular response networks for progranulin. The team used QIAGEN OmicSoft Suite to process and analyze the mRNA sequencing data from the case vs. control groups and sent the results directly to QIAGEN IPA for further biological analysis. Using IPA, the team identified miRNA and mRNA regulation and networks resulting from their high-throughput miRNA/mRNA expression data.
The team found statistically significant molecular differences in the plasma among these disorders. They discovered important relationships between disease severity and progranulin concentrations, identifying the important role of progranulin signaling in the early antimicrobial response in sepsis. This study provides evidence for potentially using progranulin as a biomarker for sepsis and pneumonia, which could be developed to differentiate between these disorders.
This study demonstrates a fantastic example of Sample to Insight workflow implementation using QIAGEN solutions. Prior to molecular analysis of NGS data from mRNA sequencing, QIAGEN’s PAXgene blood miRNA Kit was used for extraction of cellular RNA from whole blood. QIAGEN’s QuantiTect Reverse Transcription Kit was then used for reverse transcription of the isolated mRNA and QIAGEN’s miRCURY LNA SYBR Green PCR Kit was used to set up a real-time PCR reaction prior to amplification using QIAGEN’s Rotor-Gene Q thermal cycler.
Well done to the QIAGEN Digital Insights team and their collaborators on their impactful publication!
Differences in molecular signaling networks underly the clinical distinction between COVID-19 ARDS and the sepsis-induced ARDS phenotypes
Dr. Florian Brandes of the University Hospital, Ludwig-Maximilians-University in Munich, received a prestigious research prize from a major German conference on Intensive Care Medicine for his abstract on molecular signaling networks in different acute respiratory distress syndrome (ARDS) phenotypes. Dr. Jean-Noël Billaud, Senior Principal Scientist for QIAGEN Digital Insights, collaborated on the study. The team researched differentially and significantly regulated miRNA and target mRNA from COVID-19-induced ARDS patient cases, bacterial-induced sepsis-ARDS patient cases and 20 healthy controls. They used QIAGEN IPA to construct signaling networks, comparing the three groups. Their analyses conclude that COVID-ARDS is a unique clinical entity with specific molecular signaling cascades that are unique from sepsis-induced ARDS. Their study suggests that novel biomarkers and different therapeutic approaches should be used from those used in sepsis-ARDS.
Congratulations to our collaborator, Dr. Brandes, on receiving this prestigious award.
Learn more about how QIAGEN Digital Insights helped discover progranulin as a potential biomarker for sepsis, and read the published study here.
Learn more here about SARS-CoV-2 analysis solutions from QIAGEN Digital Insights.
Discover more about QIAGEN’s Sample to Insight research solutions for COVID-19.
Basic pathway analysis has several limitations that can slow down research progress and hinder insights from biological data. One of those limitations includes the inability to compare and contrast your experimental data to public data. Such a capability helps you better understand pathogenesis, disease and biological processes, find key targets or biomarkers and help reaffirm your results. Analyzing how your dataset compares to other similar datasets provides reassurance and evidence that your hypotheses and conclusions are on the right track and lets you make unexpected connections to other research areas.
Analysis Match from QIAGEN Ingenuity Pathway Analysis (IPA) was built to help you compare and contrast your biological data to re-curated public data from GEO, ArrayExpress, SRA, TCGA and many more sources, so you can more quickly and accurately strengthen hypotheses and discover new biological insights. Analysis Match automatically identifies curated datasets with significant similarities and differences to your results, enabling you to compare results, validate interpretation and better understand causal connections between diseases, genes, and networks of upstream regulators.
One hundred thousand analyses are now available in Analysis Match!
Analysis Match now lets you instantly scan your analyses against over 100,000 curated publicly available datasets and any of your own previous experiments you wish to include. Strengthen hypotheses and discover new biological insights by combining an enormous compendium of knowledge from the literature with a massive collection of gene expression measurements. Analysis Match debuted with just over 6000 datasets four years ago and has rapidly expanded to more than 100,000 today, covering vastly more research areas than ever before, including a large representation of single-cell clusters from human and mouse.
You might be tempted to curate datasets yourself, but this dataset collection represents over 460,000 samples analyzed with RNA-seq and microarrays and required over 30 person-years and more than 100 CPU-years to create. An extensive and unbiased data collection for comparison enables serendipitous discovery of closely matching or even “anti-matching” datasets that inform your research.
What's more, IPA provides other ways of looking at this massive data collection. For example, it enables you to answer questions like “under what conditions is the Coronavirus Pathogenesis Pathway inhibited?”, enabling in silico research that leverages the enormous amount of initial work done by other scientists to generate the data.
Don’t miss a research breakthrough. Discover why advanced pathway analysis tools such as Analysis Match are something your research can’t do without.
Sound like something you’d be interested in? Learn more about QIAGEN IPA Analysis Match and request a trial today.
'Omics research has made it easy to gather large volumes of data on differentially expressed genes in various conditions. Changes in gene expression levels give an unprecedented birds-eye view on what functional changes are going on in a disease or biological process. However, this advantage comes with its own set of challenges. These extensive sets of important up- and down-regulated genes come without biological context and are impossible to interpret manually. This is where causal pathway analysis is an invaluable approach to identify and group interconnected genes in a network or pathway, and annotate functional changes brought about by the differences in gene expression. The combination of human-curated biological knowledge from databases, statistical analysis and computational modeling allow us to make these connections easily, with the right software tools.
The most essential factor in determining the suitability of the tool is the quality of the knowledge it holds on molecular connections and the specific kind of interactions that form relationships among biological molecules. This is where most freely-available interaction/knowledge databases fail. They are not updated on a regular basis and therefore lack the most recently discovered molecular interactions. More importantly, they also lack causality – whether a gene or chemical has an activating or inhibitory effect on another gene or pathway or disease.
This is where QIAGEN Ingenuity Pathway Analysis (IPA) reigns supreme. The database which supports IPA, the QIAGEN Knowledge Base, currently contains over 7 million findings and is continually updated, with an average of over 1000 new findings added daily. Our scientists read the biomedical literature and capture the granular context and causality in each curated paper which they enter into the knowledge base.
Use these top five tips on performing pathway analysis in QIAGEN IPA, and you can master pathway analysis in no time:
1. Pathway Activation Prediction: QIAGEN IPA goes beyond basic pathway analysis. Other applications, such as DAVID, simply tell you which signaling pathways are enriched from a limited collection of such pathways, yet don't tell you whether the overlapping genes are up- or down-regulated in your dataset – information which is crucial to achieving deeper insights into your data. IPA analyzes your data in the context of the most extensively curated set of signaling and metabolic pathways. Not only will IPA identify the most significant pathways, but it also tells you which pathways are predicted to be activated or inhibited based on your data. This is a key feature that helps you understand the biological mechanisms underlying your data.
2. Regulatory Network Analysis: After identifying significant pathways and systems based on your data, one of the next steps is to identify key regulators that are likely responsible for the changes observed in your data. IPA's Regulator Effects analysis enables you to understand regulatory networks by identifying key regulators upstream and how they drive the predicted downstream effects on biological and disease processes from your data. This provides additional insights by integrating Upstream Regulator results with Downstream Effects results. By connecting cause and effect, you can develop actionable hypotheses that explain what is occurring upstream – resulting in particular downstream phenotypic or functional outcomes.
3. Molecule Activity Predictor (MAP): Identifying networks or pathways that contain a gene or molecule of interest can be challenging, and digging deeper by identifying which of those networks are activated or inhibited is nearly impossible using many applications. QIAGEN IPA's MAP function allows you to interrogate sub-networks and Canonical Pathways and generate hypotheses by selecting a molecule of interest and indicate up- or down-regulation, simulating directional consequences in downstream molecules and the inferred activity upstream in the network or pathway.
4. IPA Analysis Match: Once you have a working hypothesis, one of the most time consuming and resource-intensive next steps is to validate your hypothesis experimentally. Fortunately, IPA's Analysis Match allows you to automatically identify datasets with significant similarities and differences from a library of over 65,000 curated analyses. This enables you to locate and compare results and better understand causal connections among diseases, genes and networks of upstream regulators in other biological settings. This unique feature allows you to strengthen and verify your existing hypothesis and discover new biological insights by comparing your data to published, peer-reviewed results.
Download our brochure to learn more about QIAGEN IPA with Analysis Match
5. IPA with Land Explorer: Searching for information about a gene of interest in the public domain can take hours, and requires filtering and evaluating a large amount of data. Now with the click of a button, you can explore expertly-curated 'omics data on over 500,000 biological samples across tens of thousands of genes, with robust visualizations. Jump from a gene of interest in QIAGEN IPA to discover its expression in various tissues and cell types with Land Explorer. Explore the diseases and treatment contexts in which it is up-or-down-regulated. Visualize how mutations correlate with changes in expression, the effect of mutations on clinical outcomes and survival and much more. All this information with customizable visualizations is instantly at your fingertips, from a single software interface.
Download our brochure to learn more about QIAGEN IPA with Land Explorer
With these unique features, you can perform your pathway analysis like a pro and be part of the QIAGEN IPA expert community. With more than 7 million findings, 40 integrated databases, over 4500 publications citing IPA each year and regular product feature updates, QIAGEN IPA is the best-suited pathway analysis tool to quickly and easily get you deeper insights into your biological data.
Don’t have QIAGEN IPA, or perhaps you are missing the latest features? Request a 14-day trial now.
Researchers across the world are using QIAGEN Digital Insights solutions to accelerate their work in a variety of applications
Powerful insights help innovate, integrate and translate scientific results into impactful discoveries. Many noteworthy papers cite QIAGEN Digital Insights solutions and demonstrate how our tools help drive research insights and discoveries. These papers use QIAGEN Ingenuity Pathway Analysis (IPA), QIAGEN CLC and/or QIAGEN OmicSoft to help drive success. The QIAGEN Digital Insights portfolio encompasses a comprehensive, easy-to-use toolbox that ensures continuity in NGS workflow. Here, we have curated a selection of just a few recent papers to offer a sense of the diversity of the research for which QIAGEN Digital Insights solutions makes a difference.
A novel panel of differentially-expressed microRNAs in breast cancer brain metastasis may predict patient survival
First Author: Athina Giannoudis
In honor of World Cancer Day, discover how Dr. Giannoudis and colleagues at the University of Liverpool investigate differentially expressed miRNAs in breast cancer that has metastasized to the brain. See how the team uses QIAGEN IPA to identify miRNA biomarkers that may be predictive of survival. Read the paper here.
CD4+ T cell help creates memory CD8+ T cells with innate and help-independent recall capacities
First Author: Tomasz Ahrends
Read about the exciting research by T. Ahrends and colleagues at Netherlands Cancer Institute who perform a whole genome analysis to study how CD4+ T cells help generate CD8+ cytotoxic T cells. In order to identify the function and subcellular localization of the genes, the team uses QIAGEN IPA to determine which genes are differentially expressed when support from CD4+ T cells is available. Read the entire paper here.
Mediator MED23 regulates inflammatory responses and liver fibrosis
First Author: Zhichao Wang
Dive into the details of new and noteworthy research by Z. Wang and colleagues at Fudan University who study the role of MED23 in the development of liver fibrosis. Read about how the team uses QIAGEN IPA to tease out the involvement of MED23 by predicting upstream regulators of upregulated genes in a MED23 knockout mouse model, and potential targets for therapeutic intervention in liver fibrosis. Delve into the team’s research here.
Changes in DNA methylation from pre- to post-adolescence are associated with pubertal exposures
First Author: Luhang Han
To recognize Reproductive Health Day, read fascinating research by L. Han and colleagues at the University of Memphis, who perform a longitudinal study to identify epigenetic changes from pre- to post-adolescence. See how the team uses QIAGEN IPA to investigate pathways affected by the DNA methylation changes associated with puberty and environmental factors. Explore the paper here.
Genetics of glucocorticoid-associated osteonecrosis in children with acute lymphoblastic leukemia
First author: Seth E. Karol
In honor of International Childhood Cancer Day (February 14, 2020), we are highlighting this previous paper from researchers at St. Jude. Seth Karol and colleagues performed a genome-wide association study with over 2000 children and use QIAGEN IPA to identify the genetic causes of osteonecrosis that occur during glucocorticoid therapy given to children with acute lymphoblastic leukemia. Read the full paper here.
A novel mouse model of enteric Vibrio parahaemolyticus infection reveals that the type III secretion system 2 effector VOPC plays a key role in tissue invasion and gastroenteritis
First author: Hyungiun Yang
Dig into this interesting study by H. Yang and colleagues from the University of British Columbia, which reveals how V. parahaemolyticus, a bacteria commonly found in contaminated seafood, causes gastroenteritis in humans. Read how the team uses QIAGEN IPA to study the T3SS2 secretion system to show that its effectors are necessary to cause gut infection. Access the full article here.
Loss of amyloid precursor protein exacerbates early inflammation in Niemann-Pick disease type C
First author: Samuel D. Shin
For Rare Disease Day, February 28, 2020, we are highlighting research by Samuel Shin and colleagues from Loma Linda University who are studying Niemann-Pick disease type C, a lethal neurodegenerative condition, affecting one in 100,000 thousand children. Find out how the team uses QIAGEN IPA to reveal how the loss of amyloid precursor protein contributes to the neuroinflammation observed in this disease. Access the paper here.
Expression of microRNA in follicular fluid in women with and without PCOS
First author: Alexandra E. Butler
Dr. A. Butler and colleagues from Hamad Bin Khalifa University (HBKU) look at the differences in miRNA expression in follicular fluid of women with polycystic ovary syndrome (PCOS). In this recent paper, the team used QIAGEN IPA to extensively look at the differential expression of these small non-coding RNAs and identified 12 miRNAs that are involved in reproductive pathways, 12 related to inflammatory disease and 6 implicated in benign pelvic disease.
Multi-omics approach for studying tears in treatment-naïve glaucoma patients
First author: Claudia Rossi
Researchers use QIAGEN IPA to analyze the tears of glaucoma patients in this multi-'omics study to understand primary open-angle glaucoma (PAOG). In the research paper, the team performed metabolomics and proteomics analyses to identify key differences that may result in new screening options for this disease, which is the leading cause of irreversible blindness.
Longitudinal multi-omics of host-microbe dynamics in prediabetes
First author: Wenyu Zhou
Zhou and colleagues from Stanford University perform a multi-'omics study looking into the connection between host-microbiome interaction and the predisposition to type-2 diabetes. In this Nature article, read how the team uses QIAGEN IPA to search for enriched pathways to understand how changes during respiratory viral infections can lead to the development of diabetes.
Trans-ethnic association study of blood pressure determinants in over 750,000 individuals
First author: Ayush Giri
Significant research by Dr. A. Giri and colleagues from Vanderbilt University who are involved in the investigation of over 750,000 individuals for genetic variants that affect blood pressure. Discover how the team uses QIAGEN IPA to identify enriched pathways involving the 840 genes predicted to be associated with blood pressure regulation.
Fibrogenic activity of MECP2 is regulated by phosphorylation in hepatic stellate cells
First author: Eva Moran-Salvador
Discover the research by Dr. E. Moran-Salvador and colleagues from Newcastle University who study the role of MECP2 expressed by hepatic stellate cells (HSCs) in liver fibrosis. See how the team uses QIAGEN IPA to identify enriched pathways where MECP2 is involved, and how the deletion of MECP2 leads to reduced fibrosis in mice.
Immunological observations and transcriptomic analysis of trimester‐specific full‐term placentas from three Zika virus-infected women
First author: Fok-Moon Lum
Dr. F Lum and colleagues from Agency for Science, Technology and Research, Singapore look at placental development during pregnancy after a Zika virus infection. In this paper, see how the team uses QIAGEN IPA to identify eIF2 as the major canonical pathway involved in the differential gene expression pattern when compared to healthy controls.
Proteomic profiling of extracellular vesicles isolated from cerebrospinal fluid of former national football league players at risk for chronic traumatic encephalopathy
First author: Satoshi Muraoka
Dr. S. Muraoka and colleagues look at the proteomic profile of cerebrospinal fluid samples from former NFL players to understand the biology of chronic traumatic encephalopathy, a condition that affects individuals with a history of repetitive mild traumatic brain injury. In this research paper, the team use QIAGEN IPA to look at upstream regulators, pathways and functional networks of the differentially expressed proteins to identify a plausible biomarker.
Pre- and peri-implantation Zika virus infection impairs fetal development by targeting trophectoderm cells
First author: Lei Tan
Crucial research by Dr. L. Tan and colleagues from Weill Cornell Medical College strives to reveal the outcomes of a Zika virus infection during the pre- and peri-implantation stage of pregnancy. Learn how the team used QIAGEN IPA to identify two key gene networks that are strongly affected by the virus.
Multi-omics analysis identifies mitochondrial pathways associated with anxiety-related behavior
First author: Zuzanna Misiewicz
Check out this interesting paper by Dr. Z. Misiewicz and colleagues from University of Helsinki who use a multi-'omics approach to understand the molecular mechanisms behind anxiety and stress disorders. Discover how the team use QIAGEN IPA to identify certain mitochondrial genes in blood cells related to these debilitating disorders.
Microbe-host interplay in atopic dermatitis and psoriasis
First author: Nanna Fyhrquist
Interesting research by Dr. N. Fyhrquist and colleagues from the Karolinska Institute who study the interplay between the skin microbiome and skin diseases such as dermatitis and psoriasis. The team use QIAGEN IPA to identify key regulators and pathway activation in host cells to identify transcriptomic signatures for skin barrier function, tryptophan metabolism and immune activation as a basis for plausible biomarkers and targeted therapies.
Zinc chelation specifically inhibits early stages of Dengue virus replication by activation of NF-κB and induction of antiviral response in epithelial cells
First author: Meenakashi Kar
Dr. M. Kar and colleagues from Translational Health Science and Technology Institute in Faridabad, India (THSTI) perform cutting-edge immunology research using QIAGEN IPAs to understand how zinc chelation can inhibit early stages of Dengue virus by activating NFkB to induce an antiviral response in epithelial cells.
Combined transcriptome and metabolome analysis identifies defense responses in spider mite-infested pepper (Capsicum annuum)
First Author: Yuanyuan Zhang
Researchers at Wageningen University use QIAGEN CLC Genomics Workbench to study the leaf transcriptomes and metabolomes of Capsicum peppers to identify how they fight spider mite infections. Read the full story here.
Hippocampal clock regulates memory retrieval via Dopamine and PKA-induced GluA1 phosphorylation
First author: Shunsuke Hasegawa
Intriguing research by S. Hasegawa and colleagues at Tokyo University who investigate the role of the circadian clock in memory retrieval. See how the team uses both QIAGEN CLC Genomics Workbench and QIAGEN IPA to understand how circadian-dependent transcription factor BMAL1 contributes to loss of memory retrieval in the late afternoon. Read the details here.
Rousette bat dendritic cells overcome Marburg virus-mediated antiviral responses by upregulation of interferon-related genes while downregulating proinflammatory disease mediators
First author: Joseph Prescott
Fascinating research by J. Prescott and colleagues from the US Centers for Disease Control and Prevention (CDC) which focuses on how the immune system of the rousette bat coexists with the Marburg virus. See how the team uses QIAGEN's CLC Genomics Workbench and QIAGEN IPA to understand how bat dendritic cells downregulate immune maturation while upregulating pathogen-sensing pathways during a viral infection. Explore the topic further here.
Coronary arterial development is regulated by a Dll4-Jag1-EphrinB2 signaling cascade
First author: Stanislao Igor Travisano
In honor of American Heart Month, we are highlighting research by S. Travisano and team from Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Spain, who investigate the role of the Notch signaling pathway in coronary arterial development. Learn how they use both QIAGEN CLC Genomics Workbench and QIAGEN IPA to show the importance of the Dll4-Jag1-EphrinB2 signaling cascade in coronary angiogenesis. Get the details by accessing the full paper here.
Screening identifies small molecules that enhance the maturation of human pluripotent stem cell-derived myotubes
First author: Sridhar Selvaraj
Exciting research from the University of Minnesota where Selvaraj et al. examine a combination of small molecules can help with the development of pluripotent stem cells into mature myotubules. Read how the team uses QIAGEN CLC Genomics Workbench and QIAGEN IPA to understand how these small molecules help with stem cell maturation.
PD-L1 blockade by atezolizumab downregulates signaling pathways associated with tumor growth, metastasis and hypoxia in human triple-negative breast cancer
First author: Reem Saleh
In this paper, read how researchers at HBKU use QIAGEN Genomics Workbench and QIAGEN IPA to understand how atezolizumab targets PD-L1 and helps in the treatment of triple-negative breast cancer, the most aggressive type of breast cancer.
Experimental evolution reveals a general role for the methyltransferase Hmt1 in noise buffering
First author: Shu-Ting You
Delve into this interesting research paper by You and colleagues from Academia Sinica in Taiwan where they study the role of methyltransferase Hmt1 in regulating noise buffering. See how the team uses QIAGEN CLC Genomics Workbench to identify Hmt-1 as a master regulator that adjusts protein noise levels in response to stressful environments.
Micro RNA transcriptome profile in canine oral melanoma
First author: Md. Mahfuzur Rahman
In this recent paper, researchers use QIAGEN CLC Genomics Workbench to understand the miRNA transcriptome profile in canine oral melanoma and how it plays a role in cancer pathogenesis. The team links their observations to three oncogenic miRNAs targets (miR-450b, 301a and 223) from a human study that were also down-regulated in canine oral melanoma and had a significant negative correlation with their respective miRNAs.
An optimised CRISPR/Cas9 protocol to create targeted mutations in homoeologous genes and an efficient genotyping protocol to identify edited events in wheat
First author: Xiucheng Cui
Fascinating research out of the Ottawa Research and Development Centre where a team has developed a method to delete large segments of the genome using the CRISPR/Cas9 technique. See how they apply QIAGEN CLC Genomics Workbench to help them use an optimized Cas-9 plant codon to edit hexaploidic wheat genomes.
Genome sequence of a novel Enterococcus faecalis sequence type 922 strain isolated from a door handle in the intensive care unit of a district hospital in Durban, South Africa
First author: Christiana Shobo
December 1–7 is National Handwashing Awareness Week. Washing your hands is one of the easiest ways to prevent overuse of antibiotics and fight antimicrobial resistance. This work by Dr. C. Shobo and colleagues from the University of KwaZulu-Natal demonstrate this by using QIAGEN CLC Genomics Workbench to investigate the resistome of a novel Enterococcus faecalis found on the door handle of an intensive care unit (ICU) in South Africa. Check it out!
Human perivascular stem cell-derived extracellular vesicles mediate bone repair
First author: Jiajia Xu
Interesting research by Dr. J Xu and colleagues from Johns Hopkins University show how extracellular vesicles (EVs) derived from human perivascular stem cells (PSCs) are able to repair bone by stimulating osteoblasts just like PSCs. Discover how the team use QIAGEN CLC Genomics Server and Workbench to understand the transcriptomics of the EVs.
Comparative modulation of lncRNAs in wild-type and rag1-heterozygous mutant zebrafish exposed to immune challenge with spring viraemia of carp virus (SVCV)
First author: Valentina Valenzuela-Muñoz
Research by V. Valenzuela-Muñoz and colleagues from University of Concepción use QIAGEN CLC Genomics Workbench to discover the role of long non-coding RNAs (lncRNAs) in the infection of zebrafish with spring viraemia of carp virus.
Small extracellular vesicles convey the stress-induced adaptive responses of melanoma cells
First author: Maria Harmati
M. Harmati and colleagues from the University of Szeged use both QIAGEN CLC Genomics Workbench and QIAGEN Ingenuity Pathway Analysis to investigate how extracellular vesicles from melanoma cells convey adaptive stress responses. In their paper, they leverage these insights to illustrate how to predict different stress responses which could influence efficacy of treatments based on therapy-induced host responses.
Obesity and disease severity magnify disturbed microbiome-immune interactions in asthma patients
First Author: David Michalovich
Discover this interesting and relevant research by a team at GSK who leverage QIAGEN OmicSoft Array Studio to investigate the connection between obesity and asthma severity. They find the gut microbiome plays a significant role in these conditions. Check out the full paper here.
Reduced TRPM8 expression underpins reduced migraine risk and attenuated cold pain sensation in humans
First author: Narender R. Gavva
Researchers from Amgen use QIAGEN OmicSoft Array Studio in this study to understand how a specific allele in TRPM8 can act as a cold sensor to reduce migraine risk in humans located in colder climates.
Inhibition of mir-378a-3p by inflammation enhances IL-33 levels: A novel mechanism of alarmin modulation in ulcerative colitis
First author: Karen Dubois-Camacho
In this recent paper, researchers at the Universidad de Chile use QIAGEN OmicSoft Array Studio to study the role of miRNAs in regulating pro-inflammatory mediators such as IL-33 in ulcerative colitis, a form of inflammatory bowel disease.
PD-1hiCXCR5– T peripheral helper cells promote B cell responses in lupus via MAF and IL-21
First author: Alexandra Bocharnikov
Researchers from Harvard, Merck, Johns Hopkins and several other prominent institutions collaborate in this research paper and use QIAGEN OmicSoft Array Studio and QIAGEN IPA to understand how T peripheral helper cells contribute to B cell dysfunction in lupus.
The CSF-1-receptor inhibitor, JNJ-40346527 (PRV-6527), reduced inflammatory macrophage recruitment to the intestinal mucosa and suppressed murine T cell-mediated colitis
First author: Carl L Manthey
In this recent paper, read how researchers from Janssen Research and Development use QIAGEN OmicSoft Array Studio and QIAGEN IPA to demonstrate the involvement of macrophages in Crohn's disease and how inhibition of the CSF-1 pathway helped in attenuating the disease in mice.
Identification of predictive genetic signatures of Cytarabine responsiveness using a 3D acute myeloid leukaemia model
First author: Haiyan Xu
Dr. H. Xu and colleagues from Merck study the ability of bone marrow cells from acute myeloid leukaemia (AML) patients to resist cancer treatment in a 3D cell culture system. Read how the team use QIAGEN OmicSoft Studio to identify unique gene expression signatures and novel genetic mutations associated with sensitivity to Ara‐C treatment in proliferating AML cells. These unique signatures could potentially be used as predictive biomarkers to determine optimal treatment regimens.
Cell-autonomous and non-cell autonomous effects of neuronal BIN1 loss in vivo
First author: Kathleen M. McAvoy
Interesting research from Dr. K. McAvoy and colleagues from Biogen and Harvard Medical School who study the genetic contribution of neuronal-specific BIN1 isoforms in late onset Alzheimer's disease. Learn how the team use QIAGEN OmicSoft Array Studio and QIAGEN Ingenuity Pathway Analysis to look at gene enrichment and activated pathways in BIN1-knockout mice to better understand this disease.
To request information on the QIAGEN Digital Insight Solutions, contact bioinformaticssales@qiagen.com.
Neurofibromatosis (NF) is a genetic disorder that causes tumors to form on nerve tissue. These tumors can develop anywhere in the nervous system, including the brain, spinal cord and nerves. NF is usually diagnosed in childhood or early adulthood.
Every year, scientists and clinicians who focus on advancing basic, translational and clinical research in NF gather at the Children's Tumor Foundation's NF conference to foster important discussion and collaboration within the NF community. As a prelude to the NF conference, this year the 2nd NF Hackathon took place from September 13–15, and experts from QIAGEN participated, helping to broaden NF awareness and introduce new perspectives to NF research.
During the three-day event, participants explored and analyzed data from the NF Data Portal, the leading open source collection of genomic and clinical data dedicated to this genetic disorder. This year, the NF Hackathon focused on analyzing diverse datasets including genomic, drug screening, drug-target association, imaging and other data for all the three types of NF (NF1, NF2, Schwannomatosis).
Team QIAGEN was among the winners of the hackathon, with their submission ‘Inferring regulators and pathways involved in NF1 and NF2 tumors originating from Schwann cells using gene expression data’. They used several QIAGEN bioinformatics tools in their analyses, including OmicSoft Array Studio, Ingenuity Pathway Analysis and the QIAGEN Knowledge Base. The team concluded that NF1 and NF2 are clearly differentiated, and that their approach is able to discriminate between tumor types by identifying drivers and signaling cascades. The team also identified potential therapeutic targets, including SMARCA4. As a result of their findings, Team QIAGEN was selected to present their work at the Children's Tumor Foundation's NF conference, held recently from Sept. 21–24 in San Francisco.
Congratulations to all the winners!
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.
