Tag Archives: Transcriptomics

Novel computational platform can expand the pool of cancer immunotherapy targets

Researchers at Children’s Hospital of Philadelphia (CHOP) and the University of California, Los Angeles (UCLA) have developed a computational platform capable of discovering tumor antigens derived from alternative RNA splicing, expanding the pool of cancer immunotherapy targets. The tool, called “Isoform peptides from RNA splicing for Immunotherapy target Screening” (IRIS), was described in a paper published today in the Proceedings of the National Academy of Sciences.

Immunotherapy has revolutionized cancer treatment, but for many cancers including pediatric cancers, the repertoire of antigens is incomplete, underscoring a need to expand the inventory of actionable immunotherapy targets. We know that aberrant alternative RNA splicing is widespread in cancer and generates a range of potential immunotherapy targets. In our study, we were able to show that our computational platform was able to identify immunotherapy targets that arise from alternative splicing, introducing a broadly applicable framework for discovering novel cancer immunotherapy targets that arise from this process.”

Yi Xing, PhD, co-senior author, director of the Center for Computational and Genomic Medicine at CHOP

Cancer immunotherapy has ushered in a sea change in the treatment of many hematologic cancers, harnessing the power of a patient’s own immune system to fight the disease. Chimeric antigen receptor T-cell (CAR-T) and T cell receptor-engineered T cell (TCR-T) therapies modify a patient’s own T cells to attack known antigens on the surface of cancer cells and have often led to durable responses for cancers that were once considered incurable. However, the field has encountered challenges in the solid tumor space, in large part due to a lack of known and suitable targets for these cancers, highlighting the need for novel approaches to expand the pool of immunotherapy targets.

Alternative splicing is an essential process that allows for one gene to code for many gene products, based on where the RNA is cut and joined, or spliced, before being translated into proteins. However, the splicing process is dysregulated in cancer cells, which often take advantage of this process to produce proteins that promote growth and survival, allowing them to replicate uncontrollably and metastasize. This happens in many adult and pediatric cancers. Scientists have suggested splicing dysregulation could be a source of novel tumor antigens for immunotherapy, but identifying such antigens has been a challenge.

To address this difficulty, the researchers created IRIS to leverage large-scale tumor and normal RNA sequencing data and incorporate multiple screening approaches to discover tumor antigens that arise due to alternative splicing. Integrating RNA sequencing-based transcriptomics data and mass spectrometry-based proteomics data, the researchers showed that hundreds of IRIS-predicted TCR targets are presented by human leukocyte antigen (HLA) molecules, the part of the human immune system that presents antigens to T cells.

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The researchers then applied IRIS to RNA sequencing data from neuroendocrine prostate cancer (NEPC), a metastatic and highly lethal disease known to involve shifts in RNA splicing, as discovered in a prior study by CHOP and UCLA researchers. From 2,939 alternative splicing events enriched in NEPC, IRIS predicted 1,651 peptides as potential TCR targets. The researchers then applied a more stringent screening test, which prioritized 48 potential targets. Interestingly, the researchers found that these targets were highly enriched for peptides encoded by short sequences of less than 30 nucleotides in length – also known as “microexons” – which may arise from a unique program of splicing dysregulation in this type of cancer.

To validate the immunogenicity of these targets, the researchers isolated T cells reactive to IRIS-predicted targets, and then used single-cell sequencing to identify the TCR sequences. The researchers modified human peripheral blood mononuclear cells with seven TCRs and found they were highly reactive against targets predicted by IRIS to be good immunotherapy candidates. One TCR was particularly efficient at killing tumor cells expressing the target peptide of interest.

“Immunotherapy is a powerful tool that has had a significant impact on the treatment of some cancers, but the benefits have not been fully realized in many lethal cancers that could benefit from this approach,” said Owen N. Witte, MD, University Professor of Microbiology, Immunology, and Molecular Genetics and member of the Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research at UCLA. “The discovery of new antigenic targets that may be shared among different patients – and even different tumor types – could be instrumental in expanding the value of cell-based therapies. Analyzing massive amounts of data on tumor and normal tissues, which requires sophisticated computational tools like those developed by the Xing Lab, provides actionable insights on targets that one day could be tested in the clinic.”

“This proof-of-concept study demonstrates that alternatively spliced RNA transcripts are viable targets for cancer immunotherapy and provides a big data and multiomics-powered computational platform for finding these targets,” Dr. Xing added. “We are applying IRIS for target discovery across a wide range of pediatric and adult cancers. We are also developing a next-generation IRIS platform that harnesses newer transcriptomics technologies, such as long read and single cell analysis.”

This research was supported in part by the Immuno-Oncology Translational Network (IOTN) of the National Cancer Institute’s Cancer Moonshot Initiative, other National Institutes of Health funding, the Parker Institute for Cancer Immunotherapy, the Cancer Research Institute, and the Ressler Family Fund.

Source:
Journal reference:

Pan, Y., et al. (2023) IRIS: Discovery of cancer immunotherapy targets arising from pre-mRNA alternative splicing. PNAS. doi.org/10.1073/pnas.2221116120.

Rheumatoid arthritis (RA)  is a complex, chronic inflammatory disease that is thought to affect about one percent of …

Rheumatoid arthritis (RA)  is a complex, chronic inflammatory disease that is thought to affect about one percent of the world’s population. RA happens when a person’s own antibodies attack joint tissue, causing painful swelling, stiffness, and redness. Some research has suggested that there is a link between RA and gum disease.

Image credit: Pixabay

Gum disease is estimated to affect up to 47 percent of adults, and in the disorder, oral microbes can move to the blood after the gums start to bleed. An increase in disease activity has been observed in RA patients who also have gum disease. Gum disease has been shown to be more common in RA patients who carry a certain type of antibodies, called anti-citrullinated protein antibodies (ACPAs), though ACPAs are often found in the blood of individuals with RA. The presence of ACPAs can often predate the diagnosis of RA by a few years.

A new study investigated the connections between these observations. In this work, the researchers collected blood samples from a small group of ten people with RA, five with and five without gum disease. These samples were collected every week for one year, and the investigators assessed the expression of both human and bacterial genes in those samples.

Certain types of inflammatory immune cells carried gene expression signatures that were associated with the autoimmune flares of arthritis patients who also had periodontal disease, as well as the presence of certain oral bacteria in the blood.

Many of these oral bacteria were chemically altered by deimination; they were citrullinated. Citrullination can change the structure and function of proteins. Although citrullination can be a part of the normal function of tissues, high levels of citrullination have been linked to inflammation.

Citrullination can also create targets for ACPAs; when the normal, unconverted forms of the oral bacteria were incubated with ACPAs, the antibodies did not react, but when the citrullinated oral bacteria were exposed to ACPAs, there was a reaction. ACPAs appear to be bound to oral microbes in RA patients.

The findings have been reported in Science Translational Medicine.

The study noted that the immune response to oral microbes could be influencing RA flares, that oral microbes can trigger a specific antibody reaction in patients with both RA and gum disease, and that RA flares cause varying immune signatures, which could reflect different flare triggers.

It could be that gum disease repeatedly causes the immune system to respond, and as the immune system keeps reacting and repeatedly increasing inflammation, RA may eventually begin to emerge. More work will be needed, however, to fully understand whether gum disease is playing a causative role in the development of RA.

Source: Science Translational Medicine


Carmen Leitch

HIV-1 (human immunodeficiency virus-1, one of the two strains of HIV) is a tenacious pathogen. It forms reservoirs …

HIV-1 (human immunodeficiency virus-1, one of the two strains of HIV) is a tenacious pathogen. It forms reservoirs that establish a life-long presence in the body; these are cells that are infected with HIV-1 but do not actively generate new antiviral particles. While antiretroviral therapy (ART) can dramatically reduce the levels of HIV in circulation, a small number of those infected reservoir cells stick around, making the disease nearly impossible to eliminate completely. The cells that can elude the effects of antiretrovirals and restart active infections when a drug regimen stops are very rare. But now, scientists have characterized HIV reservoir cells obtained from HIV-1 patients. These cells have surface markers that could explain why they persist and resist, and could help scientists develop new ways to destroy them. The findings have been reported in Nature.

ransmission electron micrograph of HIV-1 virus particles (pink) replicating from the plasma membrane of an infected H9 T cell (purple). Image captured at the NIAID Integrated Research Facility (IRF) in Fort Detrick, Maryland. Credit: NIAID

In this work, peripheral blood cells were collected from five HIV patient volunteers, four of whom had been on ART for about a decade. One study participant had undetectable levels of HIV even though they were no longer using ART. Blood samples had also been obtained many years earlier, when participants were only on ART for a year or two. Lymph node cells from HIV patients who had been using ART for 10 to 15 years were also harvested, and CD4+ T cells, which are infected by HIV, were isolated.

A single-cell sequencing technique was created for this study, in which surface biomarkers on virally infected cells were analyzed. This method was called phenotypic and proviral sequencing (PheP-Seq). The tool was used to assess over 530,000 peripheral blood cells and 396,000 lymph node cells individually, and it identified unique biomarkers on reservoir cells.

Retroviruses can incorporate their DNA into host cell genomes. Blood cells that carried complete viral genomes in their DNA – intact proviruses, were often found to carry surface markers that have been linked to resistance against immune cells that protect against viral infection – cytotoxic T and natural killer cells. Reservoir cells were also found to express high levels of immune checkpoints that limit the transcription of viral genes. There seem to be unique features on HIV-1 reservoir cells that shield them from the immune system, which can explain their persistence, and distinguish them from other cells.

Cells collected early on in ART treatment had already begun to increase the activity of these features, though they were far more pronounced at later stages.

The study suggested that only some HIV-infected cells are able to persist through HIV treatment. Unfortunately, the biomarkers of these cells are probably not universal among all HIV patients on ART because of differences in individual immunity. However, the identification of biomarkers that can differentiate reservoir cells from other cells is significant and could open up new treatment options.

“Over more than four decades, HIV has slowly but progressively revealed its secrets, and this is yet another critical secret revealed. The ability to visualize individual reservoir cells was a pipe dream, and now has become a reality. Now we must build on this information to eradicate these cells,” said study co-author Bruce Walker, MD, Director of the Ragon Institute of MGH, MIT and Harvard.

Sources: Brigham and Women’s Hospital, Nature


Carmen Leitch

The vaginal microbiome through the lens of systems biology

The human organism is a complex ecosystem of coexisting microbiomes, including those in the gut, the skin, and the vagina in females. These play a crucial role in health and disease. However, a great deal remains to be learned about them.

A new paper recently published online in Trends in Microbiology journal reviews the systems biology approach to explore the vaginal microbiome (VMB), helping to understand its composition and function and the mechanisms by which it interacts with the host.

Review: New perspectives into the vaginal microbiome with systems biology. Image Credit: Design_Cells / ShutterstockReview: New perspectives into the vaginal microbiome with systems biology. Image Credit: Design_Cells / Shutterstock

Introduction

The VMB is vital in female fertility, and disruptions can be associated with pregnancy disorders, gynecologic diseases such as pelvic inflammatory disease (PID), and an array of infections involving the female genitourinary and reproductive tract. In addition, the VMB may be instrumental in affecting drug efficacy in women.  

However, the VMB is little understood beyond a vague idea that a preponderance of Lactobacillus is associated with a ‘good’ state with a homogeneous community structure. Conversely, an undesirable state of the VMB exists when more diverse species are identified in greater abundance.

This latter suboptimal state is often linked to bacterial vaginosis (BV), found in one in three women during their reproductive period, which can have severe consequences on their fertility. As such, research in this area is required to understand the directionality and magnitude of such associations.

The problem

While many studies have been performed in this area, it is difficult to understand what an optimal VMB looks like because of the complex interactions between microbes and other host factors. This means that the healthy VMB can differ considerably from woman to woman and at different points in the same individual’s life cycle.

Such changes occur within days, which contrasts with the much slower shift seen with the gut, skin, and oral microbiomes, which may change over months or even years. Unfortunately, this makes cross-sectional data quite non-representative when it comes to studying the association of VMB composition, function, and disease – and thus makes most of this data less useful than it could be.

Again, the human VMB differs significantly from that of animals, as well as from culture-based models. In the former, even non-human primates fail to show the characteristic conditions of the human vagina, including the acidic pH and Lactobacillus dominance.

In the latter, some microbes are incredibly resistant to culture in vitro, while various culture conditions are used in different laboratories, depending on the media. This could make the growth environment quite different from that of the human cervix and vagina, invalidating the results of such experiments.

As such, clinical samples from which vaginal microflora are cultured, identified, and quantified form the primary source of information about the human VMB. This information is colored by experimental and host variables, which require sophisticated statistical adaptations to achieve a valid conclusion.

While relevant to all microbiome sites, [this] is particularly applicable to the VMB because of its lack of experimental models that allow for interrogation of vaginal microbiota under controlled conditions.”

The solution

Such an impasse can be solved with a systems biology approach, where quantitative analyses are used to extract the important factors affecting the behavior and function of a microbial community. As such, “Leveraging systems biology techniques applied to other microbiomes, as well as developing novel techniques and applying these methods to the VMB, will have a significant impact on improving women’s health.”

The use of systems biology can overcome the challenges of such complex and multiple external and internal interactive networks. Furthermore, multiple approaches can be used, depending on the type of information available and the aim of the study.

Thus, statistical or data-driven methods are ideal when high-throughput data are abundant in a relatively new field of study. This can help suggest what microbial profiles are linked to disease or health. Since little is known so far about the VMB, data-driven models have predominated so far.

Conversely, based on hypotheses, mechanistic methods are better when much is already known about a system, or at least the fundamental data is available, and the need is to understand the mechanisms of cause-effect associations underlying biological function. In addition, they help to set the ranges within which microbial composition and interactions can occur in normal and abnormal situations.

Some mechanistic methods include mass-action kinetic or population dynamics models (based on differential equations), genome-scale metabolic models (GEMs), and agent-based models (ABMs).

What has been achieved?

The systems biology approach has already helped to identify and categorize community state types (CSTs) associated with health, disease, or transitions between the two. First defined by microbial abundance, they incorporated patient demographic and health data to form hierarchical clustering groups. In addition, other methods like nearest centroid classification have been developed to overcome the inherent variation in the dataset with the former approach.

CST groupings help simplify VMB composition and thus suggest associations with community composition and function. But this is at the cost of overlooking community-specific factors specific to different taxa.

Multi-omics approaches could be integrated with systems biology strategies to identify associations with different types of community and specific metabolomics, transcriptomics, and metagenomics profiles, for instance. In addition, random forest models and other advanced machine learning models are being pressed into service to help distinguish VMBs with a predominance of different microbes, such as L. crispatus vs. L. iners or Bifidobacteriaceae.

Interestingly, neural network models have shown the superiority of metabolomics in describing the cervicovaginal environment accurately compared to either VMB composition or immunoproteomics. The integrated use of these strategies could help pick out the important drivers of VMB states in health and disease.

Especially important could be the insights obtained regarding sexually transmitted infection (STI) risk with an increased abundance of ‘bad’ microbes. For instance, an increase in L. iners seems to be associated with a higher risk for STIs, while L. gasseri is associated with health. Conversely, Gardnerella vaginalis and Prevotella species are linked to Chlamydia infection.

Mechanistic models include the technique called MIMOSA (Model-based Integration of Metabolite Observations and Species Abundances) that uses metabolic network modeling to understand community function via its gene content. This helped identify Prevotella species and Atopobium vaginae as key modulators of the VMB, using a calculated community-based metabolite potential (CMP) score. The CMP shows the turnover of each metabolite by any given community.

Similarly, genome-scale network reconstructions (GENREs) could help understand the role of fastidious microbes in the VMB. Ordinary differential equation (ODE)-based models are being used to examine how drugs can affect the VMB and the ecology of this system, showing how the composition fluctuates following exposure to different factors.

What lies in the future?

A multitude of studies has focused on the gut microbiome, with almost $150 million being poured into developing and standardizing new tools for its exploration. VMB researchers may be able to use these to serve their aims. This includes BURRITO, a web tool that helps visualize a microbiome community by relative abundance. This could be extended to examine VMB metagenomics, showing how patient symptoms relate to the CSTs.

Supervised machine learning approaches to understand the VMB better include Data Integration Analysis for Biomarker Discovery using Latent cOmponents (DIABLO), where omics datasets are integrated by correlation, and Sparse regularized generalized canonical correlation analysis (SRGCCA), used in Crohn’s disease.

To overcome the limitations imposed by the lack of knowledge about the functional classification of the VMB, unsupervised learning strategies may be useful, such as multi-omic factor analysis (MOFA).

Many ODE models can also be used based on the Generalized Lotka–Volterra (gLV) models. These include web-gLV, Microbial dynamical systems inference engine for microbiome time-series analysis (MDSINE), and the learning interactions from microbial time series (LIMITS) method, as well as newer adaptations like the compositional Lotka–Volterra (cLV) and the ‘Biomass Estimation and Model Inference with an Expectation Maximization’ algorithm (BEEM), that are not dependent on the culturability of the community or on the availability of extensive longitudinal datasets.

Newer methods include algorithms like Constant yield expectation framework (conYE) and MMinte, that simulate conditions for community metabolism and growth based on dense interactions between the species. Such ingenious adaptations and approaches could help understand the factors that shape the dynamic VMB in health and disease in different populations.

Journal reference: