PACCMIT/PACCMIT-CDS: identifying microRNA targets in 3′ UTRs and coding sequences

The purpose of the proposed web server, publicly available at http://paccmit.epfl.ch, is to provide a user-friendly interface to two algorithms for predicting messenger RNA (mRNA) molecules regulated by microRNAs: (i) PACCMIT (Prediction of ACcessible and/or Conserved MIcroRNA Targets), which identifies primarily mRNA transcripts targeted in their 3′ untranslated regions (3′ UTRs), and (ii) PACCMIT-CDS, designed to find mRNAs targeted within their coding sequences (CDSs). While PACCMIT belongs among the accurate algorithms for predicting conserved microRNA targets in the 3′ UTRs, the main contribution of the web server is 2-fold: PACCMIT provides an accurate tool for predicting targets also of weakly conserved or non-conserved microRNAs, whereas PACCMIT-CDS addresses the lack of similar portals adapted specifically for targets in CDS. The web server asks the user for microRNAs and mRNAs to be analyzed, accesses the precomputed P-values for all microRNA–mRNA pairs from a database for all mRNAs and microRNAs in a given species, ranks the predicted microRNA–mRNA pairs, evaluates their significance according to the false discovery rate and finally displays the predictions in a tabular form. The results are also available for download in several standard formats

Annotation of pseudogenic gene segments by massively parallel sequencing of rearranged lymphocyte receptor loci

BACKGROUND: The adaptive immune system generates a remarkable range of antigen-specific T-cell receptors (TCRs), allowing the recognition of a diverse set of antigens. Most of this diversity is encoded in the complementarity determining region 3 (CDR3) of the β chain of the αβ TCR, which is generated by somatic recombination of noncontiguous variable (V), diversity (D), and joining (J) gene segments. Deletion and non-templated insertion of nucleotides at the D-J and V-DJ junctions further increases diversity. Many of these gene segments are annotated as non-functional owing to defects in their primary sequence, the absence of motifs necessary for rearrangement, or chromosomal locations outside the TCR locus. METHODS: We sought to utilize a novel method, based on high-throughput sequencing of rearranged TCR genes in a large cohort of individuals, to evaluate the use of functional and non-functional alleles. We amplified and sequenced genomic DNA from the peripheral blood of 587 healthy volunteers using a multiplexed polymerase chain reaction assay that targets the variable region of the rearranged TCRβ locus, and we determined the presence and the proportion of productive rearrangements for each TCRβ V gene segment in each individual. We then used this information to annotate the functional status of TCRβ V gene segments in this cohort. RESULTS: For most TCRβ V gene segments, our method agrees with previously reported functional annotations. However, we identified novel non-functional alleles for several gene segments, some of which were used exclusively in our cohort to the detriment of reported functional alleles. We also saw that some gene segments reported to have both functional and non-functional alleles consistently behaved in our cohort as either functional or non-functional, suggesting that some reported alleles were not present in the population studied. CONCLUSIONS: In this proof-of-principle study, we used high-throughput sequencing of the TCRβ locus of a large cohort of healthy volunteers to evaluate the use of functional and non-functional alleles of individual TCRβ V gene segments. With some modifications, our method has the potential to be extended to gene segments in the α, γ, and δ TCR loci, as well as the genes encoding for B-cell receptor chains. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13073-015-0238-z) contains supplementary material, which is available to authorized users

Lymphocytic infiltration in stage II microsatellite stable colorectal tumors: A retrospective prognosis biomarker analysis

Background: Identifying stage II patients with colorectal cancer (CRC) at higher risk of progression is a clinical priority in order to optimize the advantages of adjuvant chemotherapy while avoiding unnecessary toxicity. Recently, the intensity and the quality of the host immune response in the tumor microenvironment have been reported to have an important role in tumorigenesis and an inverse association with tumor progression. This association is well established in microsatellite instable CRC. In this work, we aim to assess the usefulness of measures of T-cell infiltration as prognostic biomarkers in 640 stage II, CRC tumors, 582 of them confirmed microsatellite stable. Methods and findings: We measured both the quantity and clonality index of T cells by means of T-cell receptor (TCR) immunosequencing in a discovery dataset (95 patients with colon cancer diagnosed at stage II and microsatellite stable, median age 67, 30% women) and replicated the results in 3 additional series of stage II patients from 2 countries. Series 1 and 2 were recruited in Barcelona, Spain and included 112 fresh frozen (FF, median age 69, 44% women) and 163 formalin-fixed paraffin-embedded (FFPE, median age 67, 39% women) samples, respectively. Series 3 included 270 FFPE samples from patients recruited in Haifa, Northern Israel, as part of a large case-control study of CRC (median age 73, 46% women). Median follow-up time was 81.1 months. Cox regression models were fitted to evaluate the prognostic value of T-cell abundance and Simpson clonality of TCR variants adjusting by sex, age, tumor location, and stage (IIA and IIB). In the discovery dataset, higher TCR abundance was associated with better prognosis (hazard ratio [HR] for ≥Q1 = 0.25, 95% CI 0.10-0.63, P = 0.003). A functional analysis of gene expression on these tumors revealed enrichment in pathways related to immune response. Higher values of clonality index (lower diversity) were not associated with worse disease-free survival, though the HR for ≥Q3 was 2.32 (95% CI 0.90-5.97, P = 0.08). These results were replicated in an independent FF dataset (TCR abundance: HR = 0.30, 95% CI 0.12-0.72, P = 0.007; clonality: HR = 3.32, 95% CI 1.38-7.94, P = 0.007). Also, the association with prognosis was tested in 2 independent FFPE datasets. The same association was observed with TCR abundance (HR = 0.41, 95% CI 0.18-0.93, P = 0.03 and HR = 0.56, 95% CI 0.31-1, P = 0.042, respectively, for each FFPE dataset). However, the clonality index was associated with prognosis only in the FFPE dataset from Israel (HR = 2.45, 95% CI 1.39-4.32, P = 0.002). Finally, a combined analysis combining all microsatellite stable (MSS) samples demonstrated a clear prognosis value both for TCR abundance (HR = 0.39, 95% CI 0.26-0.57, P = 1.3e-06) and the clonality index (HR = 2.13, 95% CI 1.44-3.15, P = 0.0002). These associations were also observed when variables were considered continuous in the models (HR per log2 of TCR abundance = 0.85, 95% CI 0.78-0.93, P = 0.0002; HR per log2 or clonality index = 1.16, 95% CI 1.03-1.31, P = 0.016). Limitations: This is a retrospective study, and samples had been preserved with different methods. Validation series lack complete information about microsatellite instability (MSI) status and pathology assessment. The Molecular Epidemiology of Colorectal Cancer (MECC) study had information about overall survival instead of progression-free survival. Conclusion: Results from this study demonstrate that tumor lymphocytes, assessed by TCR repertoire quantification based on a sequencing method, are an independent prognostic factor in microsatellite stable stage II CRC

Immunogenicity of Ad26.COV2.S vaccine against SARS-CoV-2 variants in humans

The Ad26.COV2.S vaccine1–3 has demonstrated clinical efficacy against symptomatic COVID-19, including against the B.1.351 variant that is partially resistant to neutralizing antibodies1. However, the immunogenicity of this vaccine in humans against SARS-CoV-2 variants of concern remains unclear. Here we report humoral and cellular immune responses from 20 Ad26.COV2.S vaccinated individuals from the COV1001 phase 1/2 clinical trial2 against the original SARS-CoV-2 strain WA1/2020 as well as against the B.1.1.7, CAL.20C, P.1., and B.1.351 variants of concern. Ad26.COV2.S induced median pseudovirus neutralizing antibody titers that were 5.0- and 3.3-fold lower against the B.1.351 and P.1 variants, respectively, as compared with WA1/2020 on day 71 following vaccination. Median binding antibody titers were 2.9- and 2.7-fold lower against the B.1.351 and P.1 variants, respectively, as compared with WA1/2020. Antibody-dependent cellular phagocytosis, complement deposition, and NK cell activation responses were largely preserved against the B.1.351 variant. CD8 and CD4 T cell responses, including central and effector memory responses, were comparable among the WA1/2020, B.1.1.7, B.1.351, P.1, and CAL.20C variants. These data show that neutralizing antibody responses induced by Ad26.COV2.S were reduced against the B.1.351 and P.1 variants, but functional non-neutralizing antibody responses and T cell responses were largely preserved against SARS-CoV-2 variants. These findings have implications for vaccine protection against SARS-CoV-2 variants of concern

Magnitude and dynamics of the T-cell response to SARS-CoV-2 infection at both individual and population levels

IntroductionT cells are involved in the early identification and clearance of viral infections and also support the development of antibodies by B cells. This central role for T cells makes them a desirable target for assessing the immune response to SARS-CoV-2 infection.MethodsHere, we combined two high-throughput immune profiling methods to create a quantitative picture of the T-cell response to SARS-CoV-2. First, at the individual level, we deeply characterized 3 acutely infected and 58 recovered COVID-19 subjects by experimentally mapping their CD8 T-cell response through antigen stimulation to 545 Human Leukocyte Antigen (HLA) class I presented viral peptides. Then, at the population level, we performed T-cell repertoire sequencing on 1,815 samples (from 1,521 COVID-19 subjects) as well as 3,500 controls to identify shared “public” T-cell receptors (TCRs) associated with SARS-CoV-2 infection from both CD8 and CD4 T cells.ResultsCollectively, our data reveal that CD8 T-cell responses are often driven by a few immunodominant, HLA-restricted epitopes. As expected, the T-cell response to SARS-CoV-2 peaks about one to two weeks after infection and is detectable for at least several months after recovery. As an application of these data, we trained a classifier to diagnose SARS-CoV-2 infection based solely on TCR sequencing from blood samples, and observed, at 99.8% specificity, high early sensitivity soon after diagnosis (Day 3–7 = 85.1% [95% CI = 79.9–89.7]; Day 8–14 = 94.8% [90.7–98.4]) as well as lasting sensitivity after recovery (Day 29+/convalescent = 95.4% [92.1–98.3]).DiscussionThe approaches described in this work provide detailed insights into the adaptive immune response to SARS-CoV-2 infection, and they have potential applications in clinical diagnostics, vaccine development, and monitoring

Conjecture: Some auto-immune diseases are caused by the tissue, not a problem with the immune system

Applying highly sensitive modern immune profiling techniques to the blood or inflamed tissue of auto-immune patients with one of the common T-cell mediated diseases fails to detect large clonal expansions or other signs of a dysregulated immune system. Additionally, these new methods have shown that self-reactive T-cells are found in the periphery (and are not completely negatively selected in the thymus as previously believed). Combining these new data with well-established studies in auto-immunity leads to the conjecture that certain auto-immune diseases are likely to be caused by defects in tissue, not by dysregulation of the adaptive immune system. In particular, one hypothesis is that specific tissues up-regulate HLA class 2 genes, presenting epitopes that are bound by CD4+ helper T cells, which facilitates an immune response against the tissue specific cells.</jats:p

Reduction Of Immune Repertoire Diversity Through DNA Sequence Constraints At VDJ Junctions

Abstract B and T lymphocytes are effector cells of the adaptive immune system. These cells express surface receptors that bind a huge variety of antigens, and together they comprise a person’s immune repertoire. A diverse repertoire is essential for mounting robust immune responses against a wide range of pathogens, and repertoire diversity affects the probability that DNA sequencing can uniquely tag a clonally expanded population of cells for the detection of minimum residual disease (MRD) during cancer treatment. Immune repertoire diversity arises partly through the combinatorial splicing of gene segments from the variable (V), diversity (D), and joining (J) regions of a B or T cell receptor locus. Much additional diversity is created through the stochastic insertion and deletion of nucleotides at the splice junctions, and by somatic hypermutation (SHM) in maturing lymphocytes. The generation of junctional diversity is an important part of this process, but it may be constrained by the underlying biological mechanisms. To explore the landscape of junctional diversity among immune receptor loci, we developed a likelihood model that can annotate VDJ junctions in the presence of SHM and compute the probability that a given receptor sequence was generated only once in a person’s repertoire, which is essential for tracking MRD. Using high-throughput sequencing data from several individuals and a range of receptor loci, we identify mechanistic constraints that limit B and T cell receptor diversity. For example, we show that the usual variability in CDR3 length is reduced at the immunoglobulin kappa (IgK) locus, and we connect this finding to sequence motifs that constrain nucleotide deletion at the ends of IgK gene segments. Our findings will inform future genetic studies of the adaptive immune system, and they provide quantitative guidance for deciding which cancer clones can be tracked for reliable MRD detection. Disclosures: Howie: Adaptive Biotechnologies: Employment, Equity Ownership. Robins:Adaptive Biotechnologies: Consultancy, Equity Ownership, Patents &amp; Royalties. Carlson:Adaptive Biotechnologies: Consultancy, Equity Ownership, Patents &amp; Royalties. </jats:sec

Average copy number with and without P nucleotide.

<p>Correlation between average copy number and the P nucleotide at 5′D_β and 5′J_β gene segment in naïve compartments of the CD8⁺ and CD4⁺ T cells. Heights represent the mean of the sum of the normalized average copy number over D_β gene subsets and similarly for J_β gene segment subsets. Copy numbers were normalized by respective number of total reads in each donor. The number in each subplot represents the p-value at the significance level of 0.05. Memory compartments of the CD8⁺ and CD4⁺ T cells are shown in the (<b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052250#pone.0052250.s001" target="_blank">Figure S1</a></b>). The error bar indicates one standard deviation of number of donors.</p

Distributions of P nucleotides.

<p>(<b>A</b>) Histogram of the average palindrome length frequencies at four coding ends of the V(D)J rearrangement in the naïve and memory CD8⁺ T cells of seven healthy donors and the naïve and memory CD4⁺ T cell of six donors. Each entry is normalized by its total number of unique sequences. (<b>B</b>) Mean expected fraction of P nucleotides at the four coding ends. P nucleotides probabilities are calculated using a linear probability model. The expected number of P nucleotides is calculated using these probablities in each donor and then normalized by its total number of unique sequences. The mean height is shown in the figure. P nucleotides mostly occur at the 5′ end of D_β followed by the 5′ end of J_β and 3′ end of V_β. Different colors represent expected fraction of one, two and three base P nucleotides. One base P-nucleotides are more common at the 3′ and the 5′ of V_β and J_β, while two base P nucleotides are most common at the 5′ of D_β. The error bars indicate one standard deviation of the sum of the total P nucleotides. (<b>C</b>) Show the comparison of P nucleotide distribution at the 5′J_β with J_β gene segment usage in TCRβ CDR3 sequences observed in CD8⁺ naïve T cells. The error bars indicate the standard deviation of seven donors.</p

Palindromic Nucleotide Analysis in Human T Cell Receptor Rearrangements

<div><p>Diversity of T cell receptor (TCR) genes is primarily generated by nucleotide insertions upon rearrangement from their germ line-encoded V, D and J segments. Nucleotide insertions at V-D and D-J junctions are random, but some small subsets of these insertions are exceptional, in that one to three base pairs inversely repeat the sequence of the germline DNA. These short complementary palindromic sequences are called P nucleotides. We apply the ImmunoSeq deep-sequencing assay to the third complementarity determining region (CDR3) of the β chain of T cell receptors, and use the resulting data to study P nucleotides in the repertoire of naïve and memory CD8⁺ and CD4⁺ T cells. We estimate P nucleotide distributions in a cross section of healthy adults and different T cell subtypes. We show that P nucleotide frequency in all T cell subtypes ranges from 1% to 2%, and that the distribution is highly biased with respect to the coding end of the gene segment. Classification of observed palindromic sequences into P nucleotides using a maximum conditional probability model shows that single base P nucleotides are very rare in VDJ recombination; P nucleotides are primarily two bases long. To explore the role of P nucleotides in thymic selection, we compare P nucleotides in productive and non-productive sequences of CD8⁺ naïve T cells. The naïve CD8⁺ T cell clones with P nucleotides are more highly expanded.</p> </div