Thursday, January 18, 2018

Receptor-ligand specific labeling of immune cell interactions

This week journal Nature published new study in immunology that could be best described as a method paper. I personally don't understand the value of this paper been in Nature. Only positive characteristic I see in it is that experiments reported are done in a classical, cellular immunology "fashion" and very easy to follow and understand. Lets examine.

The new method, called LIPSTIC, which this study reported is about labeling receptor-ligand pair with naturally occurring enzyme, the Staphylococcus aureus transpeptidase sortase A (SrtA). SrtA appears to covalently transfers a substrate containing motif ‘LPXTG’ to a nearby oligoglycine (G5). Basically, receptor is genetically fused with SrtA and ligand is fused with G5 and when they interact, SrtA catalyses the transfer of the substrate onto the G5-tagged receptor. This transfer can be visualized by attaching to the substrate small labels such as biotin or a fluorophore.  

First the authors showed in vitro and ex vivo that the substrate transfer was specific to fused receptor-ligand pair and did not occur when enzyme was inactive or fused to unrelated receptor. Most of the reported experiments were done using CD40L/CD40 pair (CD40 signaling is relevant for DCs and CD8 T cell activation by CD40L expressing CD4 T cells).

Receptor-ligand specificity was maintained in vivo as well. Because CD40L upregulation on CD4 T cells depends on antigen-specific interactions, substrate transfer were restricted to those CD40+ DCs that were pulsed with cognate peptide (OVA).

However, this antigen-specific CD40L/CD40 interaction was maintained only for initial 10h-24h period. When OVA-specific T cells were left with DCs for longer period (48h) then even DCs pulsed with irrelevant peptide (LCMV peptide) got labeled with substrate. 

It is not clear what are the biological consequences of such non-specific T/DCs interactions. Are these two different DCs activated antigen or non-antigen specific manner somehow different with the regard of activation of CD8 T cells? We don't know. The problem with such model is that if activated CD4 T cells expressing CD40L can interact with CD40+ DCs and activate it (license it, to use polly matzinger's words) then we should expect that body should harbor only activated DCs because body constantly contains some number of CD40L+ activated CD4 T cells specific for all kind of antigens. It is possible high number of T cells transferred in these experiments created an artificial outcome.

In summary, this study showed new method how to label receptor-ligand pair in vivo. However, overall relevance of this method is not clear at present.

posted by David Usharauli

Saturday, January 6, 2018

Microbiota-wide association studies and PD-1 immunotherapy

This week Science published 3 back-to-back studies with the findings that responses to PD-1 immunotherapy in cancer patients could be stratified based on presence of certain microbiota species (at least two of these studies were published couple of weeks back as first release papers). 

The trouble is that all three papers found different set of microbiota who they thought mediated responsiveness to PD-1 immunotherapy (dominated by Bifdobacterium, Akkermansiaor Faecalibacterium). 

In one study, it was actually a difference between set of beneficial microbiota versus nonbeneficial ones (not simply a single species), above certain ratio (>1.5), that determined responsiveness to PD-1 therapy.

Moreover, study of germ-free mice transplanted with opposite sets of microbiota (derived from 3 responders/nonresponders) were inconclusive because 1 set of microbiota from each group showed reverse effect with PD-1 immunotherapy. 

In summary, we have no clear understanding of these results. There is no way to predict if the same microbiota set would provide any benefit to a given patient. In general, presently microbiota research lacks direction and rules necessary to untangle its complexity

As far as I know, the model we have developed, SPIRAL, is the only one that provides a rational how to identify specific microbiota species. Right now it is just a guideline but when fully developed it will look similar to period table-like map that will make it easy to accurately pinpoint microbiota species relevant for antigen-specific immune response in any given individual.

posted by David Usharauli


Wednesday, December 27, 2017

Effort to identify tumor-specific antigens: The University Industrial Complex study results

New study in journal Cell is prime example why utilization of sophisticated high-throughput methods and computer technologies does not guarantee generation of clinically useful results. I imagine the only reason this study was even accepted in Cell was the fact that list of authors included many well-known scientists with links to both academia and silicon valley (Stanford University School of Medicine, Chan Zuckerberg Biohub, Parker Institute for Cancer Immunotherapy).   

Idea of this study was to develop techniques to quickly identify tumor-specific antigens (most likely mutated antigens) that could be used in immunotherapy (though there is no evidence that any cancer vaccines based on mutant protein sequences actually work in humans using available practices). 

For this task, the authors took advantage of yeast-display library expressing random peptide covalently linked to the HLA-A*02:01 molecule, an allele which is present in up to 50% of a number of populations. The authors estimated that "approximately 400 million unique peptides ranging from 8 to 11 amino acids are represented in the combined [yeast-display] libraries."

To validate this approach, they used three recombinant 'blinded' positive control TCRs derived from a melanoma patient (their antigen specificity had been identified independently by exome sequencing, tetramer staining and binding prediction algorithms). However, antigen-specificity of only 1 TCR (NKI 2) could be validated using their yeast-display library. As the authors said "targets of NKI 1 and NKI 3 could not be unambiguously identified through this blinded validation."

Of note, in these validation experiments with NKI 2 (specific for ALDPHSGHFV, a peptide neoantigen derived from CDK4 and other DMF5 TCR specific for EAAGIGILTV derived from the MART-1 melanoma antigen, successful validation [specific enrichment + TCR staining] occurred when HA tagged 10-mer epitope library were used. 

The authors anyway went ahead with this "less than perfect" approach to try to identify tumor antigen specificity of T cells derived from 2 patients with colorectal adenocarcinoma and homozygous for the HLA-A*02 allele. The authors focused on 20 TCR most enriched in tumor tissues (based on frequency of occurrence of the same TCR genes). 

Out of these 20, only 4 TCRs could enrich peptide from the library (only with c-Myc tagged 9-mer epitope library) and only 3 TCR could stain yeast samples.  

Next, the authors try to identify epitopes from potential landscape of sequences for each TCR. Several algorithms were deployed (at least 3 or more such as a modified variant of the previous statistical method using a position weight matrix and a method utilizing a two-layer convolutional neural network). They found 1 peptide sequence EYGVSYEW, which closely matches the peptide motif for TCR 1A, however, neither this exome peptide or the anchor-modified exome peptide (EMGVSYEM), nor the human peptide predictions stimulated the cell line modified to express the TCR 1A. TCR 4B was stimulated with several peptides and as the authors write "true in vivo specificity cannot be unambiguously identified without additional tumor information". Regarding TCR 2A and 3B, only 1 peptide stimulated cell line expressing these TCRs. This peptide was MMDFFNAQM, which is derived from U2AF2, a protein involved in an RNA splicing complex. However, in both patients, no mutations were found in U2AF2.

In summary, the authors wrote "although we cannot definitively determine an immune response targeting the peptide derived from U2AF2, the evidence from the yeast-display screen, prediction algorithm, and in vitro stimulation identify this peptide as the likely target". However, when reading this study it is clear that none of the components worked: yeast-display screen performed suboptimally, prediction algorithms provide little clue and in vitro stimulation made it even more confusing. So, what have we learned from all of these? I would say maybe don't do what they did.

posted by David Usharauli