Comprehensive B-Cell Immune Repertoire Analysis of Anti-NMDAR Encephalitis and Anti-LGI1 Encephalitis

Anti-N-methyl-D-aspartate receptor encephalitis (anti-NMDARE) and anti-leucine-rich glioma-inactivated 1 encephalitis (anti-LGI1E) are the two most common types of antibody-mediated autoimmune encephalitis. We performed a comprehensive analysis of the B-cell immune repertoire in patients with anti-NMDARE (n = 7) and anti-LGI1E (n = 10) and healthy controls (n = 4). The results revealed the presence of many common clones between patients with these two types of autoimmune encephalitis, which were mostly class-switched. Additionally, many differences were found among the anti-NMDARE, anti-LGI1E, and healthy control groups, including the diversity of the B-cell immune repertoire and gene usage preference. These findings suggest that the same adaptive immune responses occur in patients with anti-NMDARE and anti-LGI1E, which deserves further exploration.

Biochemical patterns of antibody polyreactivity revealed through a bioinformatics-based analysis of CDR loops

Antibodies are critical components of adaptive immunity, binding with high affinity to pathogenic epitopes. Antibodies undergo rigorous selection to achieve this high affinity, yet some maintain an additional basal level of low affinity, broad reactivity to diverse epitopes, a phenomenon termed ‘polyreactivity’. While polyreactivity has been observed in antibodies isolated from various immunological niches, the biophysical properties that allow for promiscuity in a protein selected for high-affinity binding to a single target remain unclear. Using a database of over 1000 polyreactive and non-polyreactive antibody sequences, we created a bioinformatic pipeline to isolate key determinants of polyreactivity. These determinants, which include an increase in inter-loop crosstalk and a propensity for a neutral binding surface, are sufficient to generate a classifier able to identify polyreactive antibodies with over 75% accuracy. The framework from which this classifier was built is generalizable, and represents a powerful, automated pipeline for future immune repertoire analysis.

Longitudinal Analysis of T and B Cell Receptor Repertoire Transcripts Reveal Dynamic Immune Response in COVID-19 Patients

Severe COVID-19 is associated with profound lymphopenia and an elevated neutrophil to lymphocyte ratio. We applied a novel dimer avoidance multiplexed polymerase chain reaction next-generation sequencing assay to analyze T (TCR) and B cell receptor (BCR) repertoires. Surprisingly, TCR repertoires were markedly diminished during the early onset of severe disease but recovered during the convalescent stage. Monitoring TCR repertoires could serve as an indicative biomarker to predict disease progression and recovery. Panoramic concurrent assessment of BCR repertoires demonstrated isotype switching and a transient but dramatic early IgA expansion. Dominant B cell clonal expansion with decreased diversity occurred following recovery from infection. Profound changes in T cell homeostasis raise critical questions about the early events in COVID-19 infection and demonstrate that immune repertoire analysis is a promising method for evaluating emergent host immunity to SARS-CoV-2 viral infection, with great implications for assessing vaccination and other immunological therapies.

Biochemical Patterns of Antibody Polyreactivity Revealed Through a Bioinformatics-Based Analysis of CDR Loops

Antibodies are critical components of adaptive immunity, binding with high affinity to pathogenic epitopes. Antibodies undergo rigorous selection to achieve this high affinity, yet some maintain an additional basal level of low affinity, broad reactivity to diverse epitopes, a phenomenon termed “polyreactivity”. While polyreactivity has been observed in antibodies isolated from various immunological niches, the biophysical properties that allow for promiscuity in a protein selected for high affinity binding to a single target remain unclear. Using a database of nearly 1,500 polyreactive and non-polyreactive antibody sequences, we created a bioinformatic pipeline to isolate key determinants of polyreactivity. These determinants, which include an increase in inter-loop crosstalk and a propensity for an “inoffensive” binding surface, are sufficient to generate a classifier able to identify polyreactive antibodies with over 75% accuracy. The framework from which this classifier was built is generalizable, and represents a powerful, automated pipeline for future immune repertoire analysis.

The pipeline repertoire of Ig-Seq analysis

With the advent of high-throughput sequencing of immunoglobulins (Ig-Seq), the understanding of antibody repertoires and its dynamics among individuals and populations has become and exiting area of research. There are an increasing number of computational tools that aid in every step of the immune repertoire characterization. However, since not all tools function identically, every pipeline has its unique rationale and capabilities, creating a rich blend of useful features that may appear intimidating for newcomer laboratories with the desire to plunge into immune repertoire analysis to expand and improve their research; hence, all pipeline strengths and differences may not seem evident. In this review we provide an organized list of the current set of computational tools, focusing on their most attractive features and differences in order to carry out the characterization of antibody repertoires so that the reader better decides a strategic approach for the experimental design, and computational analyses of immune repertoires.

The pipeline repertoire of Ig-Seq analysis

With the advent of high-throughput sequencing of immunoglobulins (Ig-Seq), the understanding of antibody repertoires and its dynamics among individuals and populations has become and exiting area of research. There are an increasing number of computational tools that aid in every step of the immune repertoire characterization. However, since not all tools function identically, every pipeline has its unique rationale and capabilities, creating a rich blend of useful features that may appear intimidating for newcomer laboratories with the desire to plunge into immune repertoire analysis to expand and improve their research; hence, all pipeline strengths and differences may not seem evident. In this review we provide an organized list of the current set of computational tools, focusing on their most attractive features and differences in order to carry out the characterization of antibody repertoires so that the reader better decides a strategic approach for the experimental design, and computational analyses of immune repertoires.

IGoR: a tool for high-throughput immune repertoire analysis

High throughput immune repertoire sequencing is promising to lead to new statistical diagnostic tools for medicine and biology. Successful implementations of these methods require a correct characterization, analysis and interpretation of these datasets. We present IGoR - a new comprehensive tool that takes B or T-cell receptors sequence reads and quantitatively characterizes the statistics of receptor generation from both cDNA and gDNA. It probabilistically annotates sequences and its modular structure can investigate models of increasing biological complexity for different organisms. For B-cells IGoR returns the hypermutation statistics, which we use to reveal co-localization of hypermutations along the sequence. We demonstrate that IGoR outperforms existing tools in accuracy and estimate the sample sizes needed for reliable repertoire characterization.