lunes, 31 de julio de 2017

Stem cell-based method selectively targets cancerous tissue while preventing toxic side effects

Stem cell-based method selectively targets cancerous tissue while preventing toxic side effects

News-Medical

Stem cell-based method selectively targets cancerous tissue while preventing toxic side effects

A stem cell-based method created by University of California, Irvine scientists can selectively target and kill cancerous tissue while preventing some of the toxic side effects of chemotherapy by treating the disease in a more localized way.
Weian Zhao, associate professor of pharmaceutical sciences, and colleagues have programmed human bone marrow stem cells to identify the unique physical properties of cancerous tissue. They added a piece of "code" to their engineered cells so that they can detect distinctively stiff cancerous tissue, lock into it and activate therapeutics.
In a study appearing in Science Translational Medicine, the researchers report they have effectively and safely employed this stem cell-targeting system in mice to treat metastatic breast cancer that had spread to the lung. They first transplanted the engineered stem cells to let them find and settle into the tumor site where they secreted enzymes called cytosine deaminase. The mice were then administered an inactive chemotherapy called prodrug 5-flurocytosine, which was triggered into action by the tumor site enzymes.
Zhao said his team specifically focused on metastatic cancer, which comes when the disease spreads to other parts of the body. Metastatic tumors are particularly deadly and the cause of 90 percent of cancer deaths.
"This is a new paradigm for cancer therapy," Zhao said. "We are going in a direction that few have explored before, and we hope to offer an alternative and potentially more effective cancer treatment."
Zhao added that this stem cell-targeting approach can provide an alternative to many forms of chemotherapy, which has a number of bad side effects. While this widely used method is powerful enough to kill rapidly growing cancer cells, it also can harm healthy ones.
"Our new type of treatment only targets metastatic tissue, which enables us to avoid some of conventional chemotherapy's unwanted side effects," said Zhao, who is a member of the Chao Family Comprehensive Cancer Center and the Sue & Bill Gross Stem Cell Research Center at UCI.
"This published work is focused on breast cancer metastases in the lungs," he added. "However, the technology will be applicable to other metastases as well, because many solid tumors have the hallmark of being stiffer than normal tissue. This is why our system is innovative and powerful, as we don't have to spend the time to identify and develop a new genetic or protein marker for every kind of cancer."
So far, the Zhao team has done preclinical animal studies to demonstrate that the treatment works and is safe, and they hope to transition to human studies in the near future. They are currently expanding to include other type of cells, including cancer tissue-sensing, engineered immune-system T cells (called CAR-T) to treat metastasizing breast and colon cancers. They also plan to transform the technology for other diseases such as fibrosis and diabetes, which result in stiffening of otherwise healthy tissue.

UC scientists take different approach to investigating how influenza spreads through the lungs

UC scientists take different approach to investigating how influenza spreads through the lungs

News-Medical

UC scientists take different approach to investigating how influenza spreads through the lungs

Influenza is a recurring global health threat that, according to the World Health Organization, is responsible for as many as 500,000 deaths every year, most due to influenza pneumonia, or viral pneumonia. Infection with influenza most typically results in lung manifestations limited to dry cough and fever, and understanding how the transition to pneumonia occurs could shed light on interventions that reduce mortality. Research led by University of Cincinnati (UC) scientists takes a different approach to investigating how influenza spreads through the lungs by focusing on how resistant or susceptible cells lining the airway are to viral infection.
The work published today in the Proceedings of the National Academy of Sciences (PNAS)shows how stimuli that induce cell division in the lung promote spread of influenza from the airway to the gas exchanging units of the lung, known as the alveoli. The UC study also demonstrates that interventions that prevent alveolar cells from dividing reduce influenza mortality in animal models, suggesting a potential prophylactic and/or therapeutic strategy for influenza pneumonia.
"Almost all research into susceptibility or resistance to influenza focuses on host immune responses," says Nikolaos Nikolaidis, PhD, research scientist in the Division of Pulmonary, Critical Care and Sleep Medicine in the Department of Internal Medicine at the UC College of Medicine and lead author on the paper. "Our approach was to examine factors that influence the vulnerability of alveolar cells to influenza infection, separate from how the immune system is dealing with the virus."
"Less than 1 percent of alveolar cells are actively dividing at any given time in the healthy lung, rendering it naturally resistant to influenza infection," says Frank McCormack, MD, Gordon and Helen Hughes Taylor Professor of Internal Medicine and director of the Division of Pulmonary, Critical Care and Sleep Medicine and senior author on the paper. "Recovery from lung injury due to supplemental oxygen therapy, cigarette smoke or scarring lung diseases is associated with expression of growth factors that result in multiplication of lung cells. Our work demonstrated that these mitogenically stimulated cells are rich targets for influenza infection while they are dividing."
The researchers found that when sirolimus, which is FDA-approved for use as an anti-growth agent for the rare lung disease, lymphangioleiomyomatosis (LAM), was given to influenza-infected animal models, it prevented alveolar cells from dividing, and as a result, protected the mice from viral pneumonia and death.
"Although sirolimus also has off target immunosuppressive properties that could potentially pose added risks of side effects in virus-infected patients, trials of inhaled sirolimus could lead to approaches that do not entail systemic exposure," says McCormack.
The McCormack lab expressed optimism that this observation has the potential to ultimately inform understanding of other unexplained risk factors for influenza, including very young age and pregnancy, and perhaps even to change medical management, such as more judicious use of supplemental oxygen in patients admitted with suspected viral pneumonia. Further, the team has hopes that the research could lead to a paradigm shift in the approach to therapy.
Nikolaidis says the next step in this research is to further explore why the multiplying alveolar epithelial cell is a better target for influenza. "Is it because the virus gets into the dividing cell more easily, because multiplying stimuli expand the pool of cellular machinery used by the virus to replicate, or because proliferation is associated with a reduction in innate cellular defenses? We are anxious to explore these and other potential mechanisms of viral susceptibility," he adds.

ASU-TGen researchers find source of altered ANK1 gene expression linked to Alzheimer's disease

ASU-TGen researchers find source of altered ANK1 gene expression linked to Alzheimer's disease



News-Medical

ASU-TGen researchers find source of altered ANK1 gene expression linked to Alzheimer's disease

Researchers led by Arizona State University (ASU) and the Translational Genomics Research Institute (TGen) have identified altered expression of a gene called ANK1, which only recently has been associated with memory robbing Alzheimer's disease, in specific cells in the brain.
Using an extremely precise method of isolating cells called "laser capture microdissection," researchers looked at three specific cell types -- microglia, astrocytes and neurons -- in the brain tissue of individuals with a pathological diagnosis of Alzheimer's disease, and compared them to brain samples from healthy individuals and those with Parkinson's disease.
Following sequencing of each of these cell types, the ASU-TGen led team found that altered ANK1 expression originates in microglia, a type of immune cell found in the brain and central nervous system, according to the study published today in the scientific journal PLOS ONE.
"Although previous genetic and epigenetic-wide association studies had shown a significant association between ANK1 and AD, they were unable to identify the class of cells that may be responsible for such association because of the use of brain homogenates. Here, we provide evidence that microglia are the source of the previously observed differential expression patterns in the ANK1 gene in Alzheimer's disease," said Dr. Diego Mastroeni, an Assistant Research Professor at Biodesign's ASU-Banner Neurodegenerative Disease Research Center, and the study's lead author.
All three of the cell types in this study were derived from the hippocampus, a small looping structure shaped like a seahorse (its name derives from the Greek words for horse and sea monster). The hippocampus resides deep inside the human brain and plays important roles in the consolidation of both short-term and long-term memory, and in the spatial memory that enables the body to navigate.
In Alzheimer's disease -- and other forms of dementia -- the hippocampus is one of the first regions of the brain to suffer damage, resulting in short-term memory loss and disorientation. Individuals with extensive damage to the hippocampus are unable to form and retain new memories.
"Using our unique data set, we show that in the hippocampus, ANK1 is significantly increased four-fold in Alzheimer's disease microglia, but not in neurons or astrocytes from the same individuals," said Dr. Winnie Liang, an Assistant Professor, Director of TGen Scientific Operations and Director of TGen's Collaborative Sequencing Center. "These findings emphasize that expression analysis of defined classes of cells is required to understand what genes and pathways are dysregulated in Alzheimer's."
Alzheimer's features many signs of chronic inflammation, and microglia are key regulators of the inflammatory cascade, proposed as an early event in the development of Alzheimer's, the study said.
Because the study found that ANK1 also was increased two-fold in Parkinson's disease, "these data suggest that alterations in ANK1, at lease in microglia, may not be disease specific, but rather a response, or phenotype associated with neurodegeneration ... more specifically, neuroinflammation."
More than 5 million Americans have Alzheimer's, an irreversible and progressive brain disorder that slowly destroys memory, thinking skills and eventually the ability to conduct even the simplest of tasks. For most patients, symptoms first appear in the mid-60s. For older Americans, it is the third leading cause of death, following heart disease and cancer, according to the National Institutes of Health.
"The success of this, and many other studies, owes a great deal to the support and collaborative nature of the people of the Arizona Alzheimer's Consortium. The results obtained in this work emphasize the importance of methods that enable us to characterize the molecular profile of defined cells, either as a group or as single cells, that have been defined by any of several means," said Dr. Paul Coleman, Research Professor at Biodesign's ASU-Banner Neurodegenerative Disease Research Center, and the study's senior author.
Dr. Eric Reiman, Director of the Arizona Alzheimer's Consortium and Clinical Director of Neurogenomics at TGen, said: "This study demonstrates the value of bringing together talented researchers from different disciplines and organizations to advance the scientific fight against Alzheimer's disease."

Normalization: An Essential Part of Bioluminescent Reporter Assay

Normalization: An Essential Part of Bioluminescent Reporter Assay



News-Medical

Normalization: An Essential Part of Bioluminescent Reporter Assay

Why include normalization in your experiment?

There are many types of experimental methods that often use normalization to fix the differences induced by factors other than what is immediately being analyzed. In particular, normalization can be very useful in luminescent genetic reporter experiments, because transient transfection techniques that are frequently used can cause variability from several sources, like those provided in Table 1.
When a normalization step is included, variability is reduced, data comparisons are made easier, and statistical importance and confidence in the data are improved. While several techniques are available to account for the normalization process, this article mainly focuses on data analysis and experimental optimization when utilizing co-transfected control reporters.
Table 1. Potential sources of variability in transient transfection methods
Source of Variability
Potential Causes
Starting cell number
Pipetting variation, problems with clumping/dispersion
Transfection efficiency
Difficult to transfect cells, suboptimal transfection, variable cell density.
Ending cell number
Cytotoxic effect of treatment, detached cells lost in media transfers or washing steps.
Position in multiwell plate
Edge effects brought about by differences in heat distribution and humidity across a plate.

Available options for normalization

Normalization to cell health (viability), normalization to total protein content, or normalization to a co-transfected internal control reporter constitute the standard methods used for normalization of genetic reporter data.
For instance, normalization to total protein content using a Bradford assay not only controls the variation in total cell number but may also be utilized when working with steadily transfected cells where variability sources are much more restricted. Protein assays, however, are the least facile option (for example, these assays cannot be multiplexed with reporter assays) and hence, are not discussed in this article.

Multiplexing with markers of cell health

Multiplexing a reporter assay with a compatible cell viability — although not essentially used for normalization — provides a way to gain a better insight into reporter gene expression with regard to cell health.
A nonlytic, fluorescent viability assay, for example, can be carried out upstream of a luminescent reporter assay which will allow sequential analysis of reporter expression as well as viable cells from the same well.
In a single-reporter assay or dual-reporter assay using two experimental reporters, a cell health assay will make it easy to understand the data and at the same time, will account for reduced reporter activity induced by the toxicity of compound treatment, as shown in Figure 1.
Figure 1. Measuring antioxidant response element (ARE) and heat shock response element (HSE) responses followed by cell viability from the same sample.
pGL4.41[luc2P/HSE] and pNL[NlucP/ARE/Hygro] were used to transfect HepG2 cells, which were subsequently treated with tBHQ. After incubating the HepG2 cells overnight, cell viability was measured using the CellTiter-Fluor™ Cell Viability Assay, and ARE and HSE responses were measured using the NanoDLR™ assay.

Working with internal control reporters

The best way to regulate the variables introduced in transfection-based experiments is normalization to an internal control reporter. In this approach, a constitutively expressed control reporter vector is used which is then co-transfected with the empirical vector.
A dual-reporter assay (Table 2) is used to sequentially measure the luminescence of both vectors, and the “Experimental Reporter Activity/Control Reporter Activity” is calculated to obtain a normalized ratio for each well.
This technique accounts for variability from one well to another caused by variations in transfection efficiency, number of viable cells, cell number, or edge effects induced by the plate position.
Shown in Figure 2 is the difference in coefficients of variation (CV) for data obtained from one single-reporter assay without normalization against the data acquired from a dual-reporter assay such as Dual-Glo® or NanoDLR™, with normalization. Data normalization for each well not only fixes the variability but also cuts down the overall CVs acquired to a large extent, thereby enhancing data quality.

Table 2. Comparison of Dual-Luciferase Reporter Assays

Dual-Luciferase®Assay
Dual-Glo®Assay
Nano-Glo® Dual-Luciferase® Assay
Format
Non-Homogeneous
Homogeneous
Homogeneous (Also compatible with prelysis)
Sample Process
Bench-Scale
Bench to Batch
Bench to Batch
Number of Steps
5
2
2
Sensitivity
Higher
Lower
Higher
Firefly Signal Half-Life
~ 9 minutes
~ 2 hours
~ 2 hours
Renilla Signal Half-Life
~ 2 minutes
~ 2 hours
NA
NanoLuc® Signal Half-Life
NA
NA
2 hours
Precision
High
High
High
Cell Lysis Time
~ 15 minutes
~ 10 minutes
~ 3 minutes
Recommended Experimental reporter
Firefly
Firefly
Firefly or NanoLuc®
Non-Homogeneous: Lysate created before reagent addition.
Homogeneous: Reagent added directly to cells in culture.
NA: Not applicable.
Figure 2. Comparison of coefficients of variation (CVs) obtained from a single reporter read vs. normalized data using either the NanoDLR™ or Dual‐Glo® assays.
Constitutively expressed firefly luciferase and constitutively expressed Renilla luciferase (Dual-Glo® Assay) or NanoLuc® (NanoDLR™) were used to transiently transfect the inner 60 wells of a 96-well plate of HEK293 cells for each reagent.
After overnight incubation, the media was removed, the plate was washed with PBS, and the new media was added. Firefly luciferase levels were first determined and then Renillaor NanoLuc® levels were measured using Dual-Glo® or NanoDLR™, respectively. For individual reads, CVs were determined across each plate and again determined after normalizing the data.

Optimizing your control-reporter experiments

It is important to choose a control reporter and promoter. It is also imperative to make sure that the reporter employed as the internal control is entirely different from the one employed as the experimental reporter.
This is because the activity of both reporters will be determined in the same cell lysates or cells, as indicated in Table 2. There should be enough signal from the control reporter to enable accurate and easy determination but at the same time should be low enough, so that there is no interference with the experimental reporter expression, which is discussed below in detail, and that any issues reacted to detection assay (saturation of the detection instrument or substrate depletion) is also prevented.
Providentially, this leaves a very broad range. The overall signal obtained is further contributed by the luminescent reporter as well as the promoter driving its expression. From each reporter/promoter combination, the relative luminescence can differ based on the specific type of cells being used, as illustrated in Figure 3. As a result, it is vital to consider and optimize co-transfection conditions for specific experimental conditions.
Figure 3. Relative luminescence for firefly and NanoLuc® luciferases expressed from constitutive promoters in multiple cell types.
Using different quantities of DNA containing NanoLuc® or firefly luciferase expressed from TK, PGK, or CMV constitutive promoters, cells were transiently transfected. The NanoDLR™ Assay was used to measure luminescence following 24 hours expression and the estimated relative luminescence as opposed to TK-NanoLuc® was measured for those amounts of DNA that give usable signals.
The values obtained can inform the option of control reporter construct as well as the appropriate dilution or amount of DNA that needs to be transfected to obtain the required signal.

Optimizing co-transfection conditions

In addition, the amount of the control vector contained in the co-transfection determines the expression level of the control reporter construct. It is also important to determine the optimal experimental vector:control vector ratio.
Normally, the minimum amount required will be co-transfected to provide considerable reporter activity over the background. Transcriptional squelching, and other similar obstruction with the experimental promoter, that is, trans effects between the promoters may occur due to transfection of high amounts of control plasmid.
The significance of reducing co-reporter levels is shown in Figure 4. When the Renilla co-reporter was co-transfected at a ratio of 10:1 (firefly:Renilla), the firefly luciferase signal was reduced almost by half, i.e., 50%, and at a ratio of 1:1 the same firefly luciferase signal was reduced by approximately 70%.
An interesting fact was that co-transfection of the NanoLuc® luciferase did not had had a major effect on the firefly luciferase signal and causes just 20% inhibition at 1:1 and 10:1 transfection ratios.

Figure 4. The effect of co-reporter expression levels on firefly signal with NanoDLR™ or DLR™ Assay.
Co-reporter (Renilla or NanoLuc®) vector mass considerably differed from 5 to 500µg, while firefly luciferase vector mass was sustained at 500ng. N = 8 for each condition. Transfection carrier DNA was added to maintain the total transfection amount at 1μg.
It is advised to first optimize the conditions for the experimental reporter to improve the amount of co-reporter required for assay conditions. Next, the amount of co-reporter required can be empirically determined by co-transfecting a differing amount of control vector to establish the least amount that gives adequate response to be confidently determined.
Shown in Figure 5 is at typical example of this kind of optimization experiment, wherein firefly levels are maintained constant as the experimental reporter, while transfecting an increasing amount of a TK-driven co-reporter, NanoLuc® or Renilla luciferase.
The above data shows that both the detection reagent and the co-reporter will determine the optimal amount of co-reporter. When Renilla luciferase is used as a co-reporter, higher co-transfection amounts would need to be used to achieve an acceptable level of signal, more so if the Dual-Glo® detection reagent is used, and extreme care should also be taken to make sure that the effect on the experimental reporter is not seen.
When Renilla luciferase is used as control reporter, adequate expression levels may be obtained by using a stronger promoter. On the contrary, a strong response is given by NanoLuc® luciferase even at the lowest co-transfection level, allowing the NanoLuc®control reporter to be applied at levels that are 10,000 times lower than that of the firefly reporter (10,000:1).
To achieve proper expression levels, it is advised to express NanoLuc® luciferase from a constitutive promoter that is not as active as a CMV promoter, such as TK or PGK. Also, it is not necessary for the signal given by the control reporter to be lower or higher than the signal given by the experimental reporter.
Figure 5. Raw RLUs for firefly and co-reporter (NanoLuc® or Renilla) luciferase when co-reporter DNA amount was titrated against a constant level of firefly luciferase (Fluc).
Co-reporter (Renilla or NanoLuc®) vector differed from 500pg to 50ng, while firefly luciferase vector was held constant at 500ng. N = 8 for each condition. Transfection carrier DNA was added to maintain the total transfection amount at 1μg.

Analyzing your co-reporter data

Once the normalized ratio is measured for each well, the data obtained can be directly used, or based on the experimental design, can be further examined in several ways. As an easy method of analysis, the normalized data can be averaged for each treatment and the fold change in activity between the two test groups can be determined by using the equation given below:
By using this method, the relative difference in activity between multiple treatments within one experiment can be obtained. From each experiment, the normalized fold change in activity can be easily averaged and statistical analysis can be carried out.

Calculating a relative response ratio

Calculating a relative response ratio (RRR) is another method. The RRR makes it possible to compare various treatments from different experiments as it gives a basis to compare treatment effects. For RRR calculation, two sets of controls on each plate have to be included — a negative control that gives basal or minimal expression and a positive control that gives the highest or maximal expression from the experimental reporter.
If both of these samples are added on each plate, then the effect of each new compound can be quantitatively assessed by its effect on the experimental reporter within the context of the negative and positive control.
For instance, when the ratio of experimental reporter luminescence against control reporter luminescence is 1.3 for the negative control, 53 for the positive control, and 22 for the experimental treatment, then it becomes possible to scale these values so that a value of 1 is assigned to the positive control and a value of 0 is assigned to the negative control. The following formula can be used to measure the RRR for each experimental treatment:
Therefore, the RRR for the above experimental treatment example would be:
The experimental compound was found to be 40% as effective as the positive control at growing expression of the experimental reporter at this specific concentration.
It must however be noted that if the signals’ raw luminescence signal values are close to the background luminescence signal values (from media-only control wells or non-transfected cells), the luminescence values would have to be background subtracted before calculating the ratios.

Conclusion

The most effective method for reducing inconsistency in temporary transfection-based genetic reporter experiments is to include an internal control reporter. When an experiment is being designed to include a control reporter, several parameters have to be taken into account to achieve the best results.
Particular experimental conditions, i.e., the number of cells, the size of the wells, the detection reagent used, and the cells’ ability to be transfected mostly govern the determination of appropriate promoter, co-reporter, and DNA dilution for co-transfection.
While NanoLuc® luciferase is generally used a sensitive experimental reporter, it still serves as a good alternative to Renilla as an appropriate co-reporter for firefly luciferase.
The bright signal of NanoLuc® luciferase, approximately 100 times brighter than Renilla, enables it to be used at extremely low dilutions, thus reducing the effect on the firefly luciferase reporter and minimizing the total amount of DNA needed for the transfection.

Acknowledgements

Produced from materials originally authored by Chris Eggers, Brad Hook, Samantha Lewis, Carl Strayer and Amy Landreman from the Promega Corporation.

About Promega

With a portfolio of more than 3,000 products covering the fields of genomics, protein analysis and expression, cellular analysis, drug discovery and genetic identity, Promega is a global leader in providing innovative solutions and technical support to life scientists in academic, industrial and government settings.
Promega products are used by life scientists who are asking fundamental questions about biological processes as well as by scientists who are applying scientific knowledge to diagnose and treat diseases, discover new therapeutics, and use genetics and DNA testing for human identification.

Sponsored Content Policy: News-Medical.net publishes articles and related content that may be derived from sources where we have existing commercial relationships, provided such content adds value to the core editorial ethos of News-Medical.Net which is to educate and iform site visitors interested in medical research, science, medical devices and treatments.
Last updated: Jul 24, 2017 at 5:15 AM

Interpreting Proteomics Data

Interpreting Proteomics Data

News-Medical

Interpreting Proteomics Data

Proteomes refer to the complete set of proteins expressed by an organism or biological system. Proteomics is, therefore, the large-scale study of proteomes, exploring a range of protein activities including expression, movement and interaction. Proteomics takes a quantitative approach to studies of functional genomics and biological systems through the use of extensive datasets formed by lists of proteins.
The advent of shotgun proteomics, identifying proteins in complex mixtures through high-throughput technologies, has meant that additional methods are required to interpret the resulting large lists of identified proteins. Biostatistics and bioinformatics tools have been applied to the interpretation of proteomics data.
Research in the field of proteomics. New technologies for the study of biological macromolecules. Image Credit: Sergei Drozd / Shutterstock
Research in the field of proteomics. New technologies for the study of biological macromolecules. Image Credit: Sergei Drozd / Shutterstock

Interpreting Proteomics Data with Gene Ontology Annotation

The biological relevance of the vast amount of identified proteins obtained has to be extracted through the use of functional annotation. The functional annotation of proteomics data allows for the mining of biological information databases to predict the function of a protein. The classification of genes and proteins according to their roles in biological systems is also the foundation for the analysis of relationships and interactions between them.
Gene Ontology (GO) is a bioinformatics initiative to develop a controlled vocabulary for all eukaryotes that can classify the gene or protein into a category.
This annotation means the description is within one of three domains:
  1. A biological process.
  2. A molecular function.
  3. A cellular component.
GO annotations are hierarchical, with more general annotated terms at the higher end of the hierarchy and more specific annotated terms at the lower end. This allows for the tracing of relationships between the lower ‘child’ term and potentially multiple higher ‘parent’ terms. Genes and proteins are therefore annotated downwards within the hierarchy and can be traced to the three original domains.
The GO database is constantly revised with new annotation files to reflect better knowledge of a relationship and to remove obsolete terms.

Enrichment Analysis of Proteomics Data

Enrichment analysis can be used to identify overrepresentation of biological information in long protein lists and allow for the visualization of biological processes. Enrichment analysis takes GO terms and uses them to summarize the biological pathways that are most likely related to the proteomic data. Statistical methodologies are used to compare the abundance of GO terms in the dataset with the natural abundance in a reference dataset.
Terms are extracted that are overrepresented in the proteomics dataset by the calculation of a p-value. There are over 60 software tools developed to calculate enrichment analysis through enrichment algorithms.
Different algorithms are used depending on whether one annotation term is being tested at a time via singular enrichment analysis (SEA) or if the whole genome is being taken into account via gene set enrichment algorithms (GSEA).

Biological Network Analysis of Proteomics Data

A biological pathway is the series of cellular chemical reactions that together causes a biological effect. As proteins are involved in the chemical reactions, they can be combined in pathway databases to allow us to interpret the type of biological process within the proteomics dataset. The simplest methods analyze the protein lists for abundances that represent a particular pathway.
Several biological network models have been developed that aid in the interpretation of proteomics data by simulating biological systems. They allow for experimental verification of the processes involved and the simulation of complex cellular interactions. This means that the consequences of each biological pathway can be projected.


Software has also been developed to aid in the visualization of biological processes. Computational tools can process large-scale proteome datasets by integrating the results of the functional enrichment analysis, so that the overrepresented annotations can be displayed as a network.
Easier visualization of proteomics data interpretation can be made through this computational approach. The resulting network display includes nodes which are associated with a molecular component such as proteins, whilst the edges are associated with the different types of interaction between nodes. By processing proteomics data in this way, interpreting long lists of proteins is made easier and the resulting biological information can be applied to a variety of questions within the field of proteomics.
Reviewed by Afsaneh Khetrapal BSc (Hons)

Sources:

  1. www.ebi.ac.uk/.../what-proteomics
  2. Carnielli, C.M. et al. 2015. ‘Functional annotation and biological interpretation of proteomics data’, Biochimica et Biophysica Acta, 1, pp. 46-54. http://www.sciencedirect.com/science/article/pii/S1570963914002799
  3. http://geneontology.org/page/go-enrichment-analysis
  4. Schmidt, A. et al. 2014. ‘Bioinformatics analysis of proteomics data’, BMC Systems Biology, 8, S3. bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-8-S2-S3
  5. Oveland, E. 2015.‘Viewing the proteome: how to visualize proteomics data?’, Proteomics, 15, pp. 1341-1355.

Further Reading

Last Updated: Jul 18, 2017