Tuesday, August 28, 2018

Cracking the Sugar Code

Cracking the sugar code: Why the 'glycome' is the next big thing in health and medicine

File 20180724 194149 1a8cg12.jpg?ixlib=rb 1.1
By molekuul_be/shutterstock.com
Emanual Maverakis, University of California, Davis; Carlito Lebrilla, University of California, Davis, and Jenny Wang, Yeshiva University

When you think of sugar, you probably think of the sweet, white, crystalline table sugar that you use to make cookies or sweeten your coffee. But did you know that within our body, simple sugar molecules can be connected together to create powerful structures that have recently been found to be linked to health problems, including cancer, aging and autoimmune diseases.

These long sugar chains that cover each of our cells are called glycans, and according to the National Academy of Sciences, creating a map of their location and structure will usher us into a new era of modern medicine. This is because the human glycome – the entire collection of sugars within our body – houses yet-to-be-discovered glycans with the potential to aid physicians in diagnosing and treating their patients.

Thanks to the worldwide attention garnered by the 2003 completion of the Human Genome Project, most people have heard about DNA, genomics and even proteomics – the study of proteins. But the study of glycans, also known as glycomics, is about 20 years behind that of other fields. One reason for this lag is that scientists have not developed the tools to rapidly identify glycan structures and their attachment sites on people’s cells. The “sugar coat” has been somewhat of a mystery.

Until now, that is.

While most laboratories focus on cellular or molecular research, our lab is dedicated to developing technology to rapidly characterize glycan structures and their attachment sites. Our ultimate goal is to catalog the hundreds of thousands of sugars and their locations on various cell types, and then to use this information to tailor medical therapies to each individual.

Why do we care about glycans?

In the future, it is likely that analysis of an individual’s glycans will be used to predict our risk for developing diseases like rheumatoid arthritis, cancer or even food allergies. This is because glycome alterations can be specifically tied to particular disease states. Also, biological processes like aging are linked to inflammation in our glycome. It remains to be tested if reversing these changes can help prevent disease, or even slow aging – an intriguing possibility.

Along with DNA, proteins, and fats, glycans are one of the four major macromolecules essential for life. Of these four, glycans are the final arbiters of how our cells behave.

DNA orchestrates what we look like, our capacity to think and behave, and even determines the diseases to which we are most susceptible. Within our DNA are short segments, genes, which often contain instructions for how to synthesize proteins. Proteins in turn are the “workhorses” of the cell, carrying out many of the functions necessary for life.

However, how a protein behaves often depends on what glycans are attached to it. In other words, these sugar molecules can greatly influence how our proteins do their work, and even how our cells will respond to stimuli. For example, if you change a few glycans on the outside of a cell, it might trigger that cell to migrate to a different location in our body.

The main job of glycans is to modify the proteins and fats that sit on the surface of our cells. Together, they create a thick sugar coat around the cell. If we consider the surface of the cell to be soil, then glycans would be the wonderfully diverse plant-life and foliage that sprout up and bring color and identity to the cell. In fact, if you were able to see a cell with your naked eye, it would look very fuzzy. Picture a peach with 10 times more fuzz.

Every single cell in the human body is covered with a collection of glycans which are assembled using various simple sugars like glucose, mannose, galactose, sialic acid, glucosamine and frucose as building blocks. By sensing the type of sugar coat present, our immune cells can identify other cells as friend or foe. This is because bacteria have sugars on their surfaces that are never seen on human cells – the pathogen’s sugars are sensed by the immune system and that identifies the bacteria as ‘foreign.’ Emanual Maverakis, CC BY-SA

Glycans label our own cells and identify them as ‘self’

The fuzz around a cell is its glycan coat. Being on the outside of our cells, glycans are the first point of contact for most cellular interactions and thus influence how our cells communicate with one another. You can also think of the glycans as a unique cellular “barcode.” Thus, a kidney cell’s fuzz will look different from an immune cell’s fuzz. But there are also similarities. In fact, the immune cells that survey our body searching for pathogens know not to attack our own “self” cells because of common features in the glycan “barcode” which are shared by all cells of our body.

In contrast, bacteria and parasites like malaria have different “sugar coats” that are not seen on human cells. When bacterial sugars are tagged as “foreign,” a person’s immune system targets the bacterium for destruction. However, some harmful bacterial pathogens like group B streptococcus, which commonly cause severe infections in babies, can avoid immune detection by impersonating human cells by carrying similar glycans as a disguise – like the wolf dressed in sheepskin.

Unfortunately some pathogens are also able to use our glycans to help them cause disease. Deadly viruses like HIV and Ebola have evolved to grab hold of specific glycans which they then “lock” onto as they infect our human cells. Therapies that either block these viruses from interacting with our glycans, or that attack virus-specific glycans may be a new avenue to treating these infections.

The sugars on our cells and on bacterial cells label them as friend or foe. Emanual Maverakis, CC BY-SA

New research has also shown that glycans play a huge role in the development of autoimmune diseases like rheumatoid arthritis and autoimmune pancreatitis. This is not surprising since glycans directly influence the function of immune cells.

Normally, our immune cells act as our body’s “defense system,” and identify and destroy foreign invaders like harmful bacteria or viruses. But when the body mistakenly labels our own cells as the enemy and launches an internal attack on itself, autoimmunity is born. Interestingly, in such instances, it is the glycans present on the misbehaving self-attacking antibodies that will dictate the strength of the attack on the body. This abnormal immune response can even be directed against glycans. For example, the immune system can mistake “self” glycans as if they were “foreign” molecules. Our research team recently published an article that introduced the glycan theory of autoimmunity, which explains some of these relationships.

Glycans in our food can trigger immune responses

There have been many studies linking consumption of red meat with diseases like atherosclerosis and diabetes, but they have not been able to show why or how this occurs until recently. One intriguing study suggests that the culprit was a sugar with the unwieldy name, nonhuman sialic N-glycolylneuraminic acid, or Neu5Gc for short. Neu5Gc is found in all mammals except humans, because the early humans that could make Neu5Gc died from an ancient malarial parasite.

However, although we now lack the ability to produce Neu5Gc, our bodies still have the ability to incorporate it into the glycans on our cells if we obtain it by eating red meat. Once it becomes part of our cells’ glycan coat, our cells then have a “foreign” substance – Neu5Gc – surrounding them. This can trigger inflammation throughout the body because our immune system recognize Neu5Gc as “foreign” and attacks it. The chronic inflammation caused by these internal attacks can lead to heart attack, stroke and even cancer.

Our bodies synthesize tens of thousands of unique glycans, often with branching structures formed from simple sugar building blocks. Proteins or fats can also be modified by dozens of unique glycans. These countless combinations make mapping glycans a difficult task because we need a practical and efficient way to analyze hundreds of thousands of glycan patterns.

Our research team has now developed methods to rapidly and robustly monitor the human glycome. By capitalizing on engineering advancements and improvements in sample processing, our technique can monitor thousands of glycans at once, which allows us to characterize the glycans in cells from healthy controls and patients with a variety of different diseases. Our goal is to use this data to develop predictive models to help clinicians diagnose and treat all human diseases. We believe that a new wave of medical advancements will arrive as we unlock the “sugar code.”

Jenny Wang was the co-lead author of this article.The Conversation

Emanual Maverakis, Associate Professor- Departments of Medical Microbiology & Immunology and Dermatology | Member- Foods For Health Institute | Member- Comprehensive Cancer Center | Director- Autoimmunity | Director- Immune Monitoring Core, University of California, Davis; Carlito Lebrilla, Distinguished Professor of Chemistry, University of California, Davis, and Jenny Wang, Clinical Research Fellow, University of California, Davis | Medical Student, Albert Einstein College of Medicine, Yeshiva University

This article was originally published on The Conversation. Read the original article.

Monday, July 23, 2018

Uncovering insights hidden within the psoriasis transcriptome.

Herein we explain how our novel data mining strategies for RNA-Seq datasets lead to new insights into the pathophysiology of psoraisis, an immune-mediated disease involving the skin. This story can also be found in our recently published manuscript in JCI Insight. You can click here to read the entire publication (subscription NOT required).


The skin transcriptome is the sum of all messenger RNA molecules expressed in the skin. About a decade ago, next generation DNA sequencing technologies allowed researchers to investigate the skin transcriptome in the setting of psoriasis. This led to the discovery of thousands of genes that were differentially expressed in psoriasis skin; But are all of these genes involved in psoriasis pathophysiology? As is the case with other diseases, knowing the sum of all expressed genes in psoriasis is only a small piece of the puzzle. How to decipher the psoriasis transcriptome has been a challenge that our research team and others are still working on. One strategy to narrow down the thousands of differentially genes to a more reasonable number is to set a threshold as to what represents a meaningful up or down regulation in gene expression. For example, is it meaningful if a gene's expression is increased by only 1.2 fold in the setting of psoriasis?
Meaningfulness is different from significance, which simply means that the observed change is not likely to be due chance. When analyzing DNA sequencing data for significance, p values need to be adjusted for multiple testing, as transcriptome datasets have thousands of genes. Programs used for such tasks take many factors into consideration (number of uniquely mapping reads, total number of genes monitored, variation in read counts etc). However, even when thresholds for significance and meaningfulness are met, investigators can easily be led astray - as data crunching by these classical methods often over or under emphasize the importance of a gene.

The error of meaningfulness thresholds

It is common to set a 2-fold increase or decrease in gene expression as a threshold for meaningfulness. However, is this the best way to identify differentially expressed genes of interest? It depends on the gene, its expression level, and the situation. Lets take for example TNF. During the 1990's, an area of immunology when virtually all cytokines were categorized as belonging to either Th1 or Th2 immune responses, TNF and IFN-g were viewed as the classical Th1 cytokines. This led to the testing of anti-IFN-g and anti-TNF-based therapies in animal models of autoimmunity. In such experiments, anti-TNF therapies showed superior efficacy. In contrast, anti-IFN-g treatments sometimes made autoimmunity paradoxically more severe. Eventually, anti-TNF therapies also proved useful in treating a variety of human autoimmune diseases. With regards to psoriasis, anti-TNF biologic therapies are indeed effective treatments. Although newer psoriasis biologic treatments target different molecules, the prototypic anti-TNF agent, infliximab, is still arguable one of the fastest and most effective medications for psoriasis (in terms of skin clearing at 10 weeks). However, the effectiveness of anti-TNF-based therapies would not have been predicted from TNF gene expression in psoriasis plaques. In comparison to healthy control skin or uninvolved skin, TNF expression within a psoriatic plaque is only marginally increased. In reality, TNF is made by numerous cell types not simply Th1 cells and relatively small changes in its expression have dramatic effects on tissue homeostasis. Thus, TNF blockade has dramatic effects on pathogenic immune responses. Setting a 2-fold change as a threshold for meaningfulness would absolutely miss important genes such as TNF.
For the next example lets consider a hypothetical gene expressed by T cells. If in the setting of psoriasis the expression of this hypothetical gene drops by 30% in T cells, its expression in psoriatic skin will paradoxical not be decreased at all. In fact, its tissue expression will rise more than 2-fold, which may lead some investigators to erroneously conclude that the "up-regulated" gene is involved in the pathophysiology of psoriasis. In this example, the hypothetical gene is decreased in T cells but increased in psoriasis plaques. The increased expression is a result of the simple fact that psoriatic plaques have more T cells not that the expression of the gene is otherwise increased. It is likely that the vast majority of the upregulated genes in psoriasis are a mere consequence of the infiltrating immune cells or proliferation of existing cells within the skin. These are just some examples of why deciphering transcriptomic data is difficult. One method we have chosen to overcome some of these issues is to look at relative gene expression, that is the expression of one gene relative to reference immune genes, especially the genes that encode immune receptors such as the T cell receptor genes. Another strategy is to do single cell sequencing. Both strategies have advantages and disadvantages. For example, the cell preparation techniques used for single cell sequencing will alter the cellular transcriptome.

T cell receptors and T cell repertoire analysis

As just stated, we often use TCR genes as reference genes when characterizing an immune response. However, this strategy has a variety of barriers. TCRs are encoded by productive rearrangements of the alpha beta or gamma delta TCR genes. For the beta and delta chains, the genetic recombination events involve variable (V), diversity (D), joining (J), and constant (C) region exons. This allows for the immune system to express an almost infinite number of immune receptors to project of from invading pathogens and cancer.
Traditionally, TCR genes are sequenced directly from the products of TCR-specific PCR reactions. TCR mining of RNA-Seq datasets is an alternative approach to characterize T cell repertoires, but requires the use of an appropriate bioinformatics alignment pipeline. In our recent JCI Insight manuscript we developed and validated a pipeline to estimate the overall and relative expression of TCR V/J/C segments. Our method has the advantage over traditional TCR repertoire analysis in that it allows investigators to conduct correlative studies between TCR genes and other genes of interest; this is because it detects all TCR-mapping reads in an RNA-Seq dataset. In contrast, bioinformatics tools focused solely on analysis of the TCR CDR3 region cannot accurately estimate TCR gene segment usage and CDR3-mapping only yields a small handful of reads per RNA-Seq dataset, making correlative studies difficult. Also, traditional RNA-Seq analysis pipelines are not aware of TCR rearrangements and may not take into account gene rearrangements, considering them low quality mapping reads.
Our approach allowed us to not only identify novel psoriasis-associated TCR gene segments, but also correlate the transcription of individual alpha beta and gamma delta TCR gene segments with genes of known importance to the pathophysiology of psoriasis. Four independently acquired RNA-Seq sample sets allowed for rigorous statistical validation of our results.
Our manuscript highlights a many exciting findings. For example, by focusing on the expression of individual TCR gene segments relative to all TCR gene segments of the same family, we discovered that TRAJ23 is proportionally over expressed and TRAJ39 is proportionally under expressed in psoriasis (Bonferroni p = 6.19 x 10e-27 and 1.43 x 10e-248, respectively). (TRAJ23 and TRAJ39 are TCR alpha gene segments). We also found that a TCR gamma gene segment is minimally over expressed in psoriasis, TRGV5 (Bonferroni p = 6.03 x 10e-13)). A finding that we were able to validate across 4 different RNA-Seq datasets. The figure below summarizes these results. Please see the full manuscript for more details. This same strategy can be applied to other autoimmune and cancer datasets, and thus carries wide applications. Importantly, our results differ from the alpha beta gene segments previously reported to be disproportionately overrepresented in psoriasis (1-3). These highly conflicting results may be due to the exceedingly small sample sizes used in the prior studies, which for the most part lacked summary statistics to evaluate the significance of the psoriasis-associated TCR genes.

T cells, IL-17A, and psoriasis

Since the demonstrated efficacy of cyclosporine in psoriasis (4), alpha beta T cells have been thought to be central to psoriasis pathophysiology, and recent clinical evidence points to the Th17 subtype as the driver of disease (5), as is evident by the success of IL-17/IL-23-targeted therapies (6-9). However, many groups are pursuing other sources of IL-17 as important mediators of the psoriatic phenotype. For example, some investigators have posited that in psoriasis, gamma delta T cells are expanded disproportionately more than alpha beta T cells, and that the former is the dominant source of IL-17 (10-12).
To address the debate of alpha beta versus gamma delta T cells head on we compared the relative frequencies of the corresponding alpha, beta, gamma, delta gene segments in psoriatic and normal skin (surprisingly nobody has done this before). By looking at relative gene expression, we found no evidence for a differential expansion of gamma delta T cells over alpha beta T cells. In fact, gamma delta TCR gene segments were only marginally over expressed in the setting of psoriasis. We next sought to address the source of IL-17 by plotting the expression of individual TCR gene segments against IL17A expression. Again, we found no evidence that gamma delta TCR gene segments correlated with IL17A expression. Interestingly, TRGV5 expression did correlate with IL36. In contrast, TRAJ23 expression correlated well with IL17A. These results were then validated in four independently collected RNA-Seq datasets. From these results we speculate that TRAJ23-expressing cells (abundant in psoriasis) are a source of IL-17 and TRGV5-expressing T cells possibly expand in response to IL-36, a psoriasis-associated cytokine. These results are summarized in the figure below. For more details of this study and to see our other exciting discoveries please see the published manuscript by clicking here.

1.  Chang YT, Liu HN, Shiao YM, Lin MW, Lee DD, Liu MT, et al. A study of PSORS1C1 gene polymorphisms in Chinese patients with psoriasis. The British journal of dermatology. 2005;153(1):90-6.
2.  Menssen A, Trommler P, Vollmer S, Schendel D, Albert E, Gurtler L, et al. Evidence for an antigen-specific cellular immune response in skin lesions of patients with psoriasis vulgaris. Journal of immunology. 1995;155(8):4078-83.
3.  Vollmer S, Menssen A, and Prinz JC. Dominant lesional T cell receptor rearrangements persist in relapsing psoriasis but are absent from nonlesional skin: evidence for a stable antigen-specific pathogenic T cell response in psoriasis vulgaris. The Journal of investigative dermatology. 2001;117(5):1296-301.
4.    Ellis CN, Fradin MS, Messana JM, Brown MD, Siegel MT, Hartley AH, et al. Cyclosporine for plaque-type psoriasis. Results of a multidose, double-blind trial. N Engl J Med. 1991;324(5):277-84.
5.    Thaci D, Blauvelt A, Reich K, Tsai TF, Vanaclocha F, Kingo K, et al. Secukinumab is superior to ustekinumab in clearing skin of subjects with moderate to severe plaque psoriasis: CLEAR, a randomized controlled trial. J Am Acad Dermatol. 2015;73(3):400-9.
6.    Langley RG, Elewski BE, Lebwohl M, Reich K, Griffiths CE, Papp K, et al. Secukinumab in plaque psoriasis--results of two phase 3 trials. N Engl J Med. 2014;371(4):326-38.
7.    McInnes IB, Mease PJ, Kirkham B, Kavanaugh A, Ritchlin CT, Rahman P, et al. Secukinumab, a human anti-interleukin-17A monoclonal antibody, in patients with psoriatic arthritis (FUTURE 2): a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet. 2015;386(9999):1137-46.
8.    Papp KA, Leonardi C, Menter A, Ortonne JP, Krueger JG, Kricorian G, et al. Brodalumab, an anti-interleukin-17-receptor antibody for psoriasis. N Engl J Med. 2012;366(13):1181-9.
9.    Gordon KB, Blauvelt A, Papp KA, Langley RG, Luger T, Ohtsuki M, et al. Phase 3 Trials of Ixekizumab in Moderate-to-Severe Plaque Psoriasis. N Engl J Med. 2016;375(4):345-56.
10.  Cai Y, Shen X, Ding C, Qi C, Li K, Li X, et al. Pivotal role of dermal IL-17-producing gammadelta T cells in skin inflammation. Immunity. 2011;35(4):596-610.
11.   Yoshiki R, Kabashima K, Honda T, Nakamizo S, Sawada Y, Sugita K, et al. IL-23 from Langerhans cells is required for the development of imiquimod-induced psoriasis-like dermatitis by induction of IL-17A-producing gammadelta T cells. J Invest Dermatol. 2014;134(7):1912-21.
12.  Hartwig T, Pantelyushin S, Croxford AL, Kulig P, and Becher B. Dermal IL-17-producing gammadelta T cells establish long-lived memory in the skin. Eur J Immunol. 2015;45(11):3022-33.

Saturday, March 17, 2018

Cancer, Autoimmunity, and Immunology

Please tune in this March 22nd and 23rd to watch the National Institutes of Health's conference on Cancer, Autoimmunity and Immunology. This conference is hosted by the NCI, NIAID, and NIAMS. Presenters will be from all of the top institutes-> Yale University, Dana-Farber Cancer Institute, Johns Hopkins University, Parker Institute for Cancer Immunotherapy, Broad Institute, MD Anderson Cancer Center, Icahn School of Medicine at Mount Sinai, Massachusetts General Hospital , etc...  The conference can be viewed live at https://videocast.nih.gov/ For a complete speaker list see  https://ncifrederick.cancer.gov/events/CicAutoimmune2018/agenda.asp MY PRESENTATION WILL START AT 1:35pm EST on March 23rd.


Wednesday, February 21, 2018

Pyoderma Gangrenosum- Diagnosis

  • Pyoderma gangrenosum (PG), which can be associated with rheumatoid arthritis and inflammatory bowel disease, is a difficult disease to diagnose and treat.  Traditionally PG has been considered a "diagnosis of exclusion" but the disease has several characteristic features.  These include pathergy-> PG ulcers often appear at a sites of minor trauma.  Another characteristic of the disease is the rapid evolution of the lesions-> they first appear as papules or pustules and then they rapidly ulcerate. On clinical exam cribriform or "wrinkled paper" scars at sites of healed ulcers can also aid in the diagnosis of PG.  The ulcers themselves have a zone of peripheral erythema, undermined borders and are very tender.  Unfortunately PG is often misdiagnoses or over diagnosed leading to mismanagement of one of the most devastating dermatologic diseases.  A group of international experts recently came together to develop diagnostic criteria for this disease.  It is the hope that these criteria will help aid physicians in diagnosing PG and clinical researchers conduct well-designed clinical trials. This research was recently published in JAMA Dermatology.  Please see the following link.
  • Diagnostic Criteria of Ulcerative Pyoderma Gangrenosum: A Delphi Consensus of International ExpertsJAMA Dermatol. Published online February 14, 2018. doi:10.1001/jamadermatol.2017.5980

Thursday, February 8, 2018

PG is a T cell mediated disease directed against follicular adnexal structures.

PYODERMA GANGRENOSUM (PG) is a debilitating ulcerative skin disease commonly associated with inflammatory bowel disease (IBD), rheumatoid arthritis (RA) and malignancy. It is one of the most painful and debilitating diseases cared for by dermatology. The cause of PG is still unknown. In our recently published manuscript on PG we put forth the hypothesis that PG is caused by an autoimmune attack directed against follicular adenexal structures.  Please see the following link to read the article.

PG is a T cell mediated disease directed against follicular adnexal structures.

Gamma Delta T cells protect against Staph aureus

Gamma Delta T cells protect against Staph aureus

Need to characterize an immune response? My research team specializes in developing novel bioinformatic methods to analyze RNA-Seq datasets for immune genes and in performing dedicated T cell receptor gene sequencing. Take a look at our recent article in the Journal of Clinical Investigation, which demonstrates how gamma delta T cells provide long-term protection against Staph aureus. This project was based out of Lloyd Miller's laboratory at Johns Hopkins University.  Dr. Miller is an expert in Staph aureus and wanted to the study anti-Staph immune responses.  For Dr. Miller our research team used our in-house developed bioinformatics pipeline (TCRminer) to correlate the expression of T cell receptor genes with other immune genes of interest.  We also performed dedicated TCR sequencing to answer fundamental questions about how the immune system responds to Staph.  Please see the link to the article and please contact us if this type of analysis can be of use in your research. We are especially interested in immune monitoring in the setting of immunotherapy and autoimmunity. Collaborative studies are most welcome.