V(D)J Recombination and Affinity Maturation through the GenAIRR Python Package

Simulating Immune DNA Sequences: An Introduction to Ig Sequence Simulation with Python

Introduction

The immune system’s ability to recognize an immense variety of antigens relies on the remarkable diversity of B-cell receptors (BCRs) and T-cell receptors (TCRs). This diversity is primarily generated through a process called V(D)J recombination, followed by somatic hypermutation (SHM) and affinity maturation. In this blog post, we will delve into these biological processes and explore how the GenAIRR Python package simulates them, providing researchers with powerful tools for studying adaptive immune receptor repertoires (AIRRs).

V(D)J Recombination

V(D)J recombination is a mechanism of genetic recombination that occurs in the early development of B and T cells. It involves the random joining of Variable (V), Diversity (D), and Joining (J) gene segments to create unique receptors that can potentially recognize a vast array of antigens.

Source: Wikipedia

Gene Segments and Recombination:

• Variable (V) Segments: Encode the majority of the variable region of the receptor.
• Diversity (D) Segments: Present only in heavy chains of BCRs and beta chains of TCRs, adding further diversity.
• Joining (J) Segments: Encode the remaining part of the variable region.

2. Recombination Process:

• The recombination process is mediated by the RAG1 and RAG2 enzymes, which introduce double-strand breaks at specific recombination signal sequences (RSS).
• The broken DNA ends are then joined together through a process involving non-homologous end joining (NHEJ), resulting in the formation of a complete V(D)J exon that encodes the variable region of the receptor.

Affinity Maturation and Somatic Hypermutation (SHM)

Once a B cell receptor is formed and the B cell encounters its specific antigen, the receptor’s affinity for the antigen can be improved through a process called affinity maturation. This process involves:

Soruce: Akiko Iwasaki — BioRender

1. Somatic Hypermutation (SHM):

• SHM introduces point mutations at a high rate in the variable region of the BCR gene.
• These mutations occur primarily in the germinal centers of lymph nodes, where B cells proliferate and mutate.

2. Selection:

• B cells expressing receptors with higher affinity for the antigen are preferentially selected for survival and proliferation.
• This selection process results in a population of B cells with increasingly higher affinity receptors.

Simulating V(D)J Recombination and Affinity Maturation with GenAIRR

GenAIRR is a Python library designed to simulate realistic AIRR sequences, incorporating the complexities of V(D)J recombination and affinity maturation. It provides extensive customization options to mimic the natural diversity of immune repertoires.

Key Features of GenAIRR

• Realistic Sequence Simulation: Generates heavy and light chain sequences with configurable parameters to reflect the diversity observed in natural immune repertoires.
• Advanced Mutation and Augmentation: Introduces mutations and sequencing artifacts to mimic natural processes like SHM and NHEJ.
• Precision in Allele-Specific Corrections: Handles allele-specific trimming and ambiguities with sophisticated correction maps.
• Indel Simulation Capability: Simulates insertions and deletions within sequences to reflect sequencing data intricacies.

Using GenAIRR: A Quick Start Guide

1. Installation and Setup:

# Install GenAIRR using pip
!pip install GenAIRR

# Import necessary classes
from GenAIRR.simulation import HeavyChainSequenceAugmentor, LightChainSequenceAugmentor, SequenceAugmentorArguments
from GenAIRR.utilities import DataConfig
from GenAIRR.data import builtin_heavy_chain_data_config

# Initialize DataConfig
data_config_builtin = builtin_heavy_chain_data_config()

2. Simulating Heavy Chain Sequences:

# Initialize the HeavyChainSequenceAugmentor
heavy_augmentor = HeavyChainSequenceAugmentor(data_config_builtin, SequenceAugmentorArguments())

# Simulate a heavy chain sequence
heavy_sequence = heavy_augmentor.simulate_augmented_sequence()

print("Simulated Heavy Chain Sequence:", heavy_sequence)

Output:

Simulated Heavy Chain Sequence: {'sequence': 'CAGGTGCAGCTGGAGAAATCTGGGTCTGAGTTGAAGAAGCCTGGGGCCTCAATGAAGTTTTCCTGCAAGACCTCTGGATACGCCTNCACTAGCTATGTTGTGAATTGGCTGCGACAGGCCCCTGGACGCGGACTTGAGTGGNTGGGATGGATGAAAATCAAACACTGGGAGCCCAACTTATGTCCAGGGCTTCACAGGACGGTTTNTCTTCTCCTTGGACGCCTCTGTCAGACNGCCATATCTCCAGAGCAGAAGCCTGAAGTCTCNGGACACTGCCGTGTATTAATGTGCGAACTGGGCGACCGGATAAACCAGTCGTACTGNGGCCAGGGAAGCCGGGTCCACCGTNTCCTCAGGAAACTGTTTGGCAGCAAGACCCAGTCCA', 'v_sequence_start': 0, 'v_sequence_end': 294, 'd_sequence_start': 300, 'd_sequence_end': 307, 'j_sequence_start': 318, 'j_sequence_end': 356, 'v_germline_start': 0, 'v_germline_end': 293, 'd_germline_start': 5, 'd_germline_end': 12, 'j_germline_start': 11, 'j_germline_end': 48, 'junction_sequence_start': 286, 'junction_sequence_end': 324, 'v_call': 'IGHVF5-G16*02,IGHVF5-G16*03', 'd_call': 'IGHD1-14*01,IGHD1-14*01', 'j_call': 'IGHJ4*02', 'c_call': 'IGHG1*14', 'mutation_rate': 0.11428571428571428, 'v_trim_5': 0, 'v_trim_3': 3, 'd_trim_5': 5, 'd_trim_3': 5, 'j_trim_5': 11, 'j_trim_3': 0, 'c_trim_3': 8, 'corruption_event': 'no-corruption', 'corruption_add_amount': 0, 'corruption_remove_amount': 0, 'indels': {341: 'I < C', 161: 'I < A'}, 'productive': False, 'stop_codon': False, 'vj_in_frame': False, 'note': 'Junction length not divisible by 3.'}

3. Customizing Simulations:

# Customize augmentation arguments
custom_args = SequenceAugmentorArguments(min_mutation_rate=0.01, max_mutation_rate=0.05, simulate_indels=True)

# Use custom arguments to simulate a heavy chain sequence
custom_heavy_augmentor = HeavyChainSequenceAugmentor(data_config_builtin, custom_args)
custom_heavy_sequence = custom_heavy_augmentor.simulate_augmented_sequence()

print("Customized Simulated Heavy Chain Sequence:", custom_heavy_sequence)

Output:

Customized Simulated Heavy Chain Sequence: {'sequence': 'CAGGTCACTTNGAAGGAGTCTGGTCCTGCGCTGGTGAAACNCACACAGACCCTCACACTGACTTGCACCTTCTCTGGGTTCTCACTCAGCACTAGTGGAATGCGTGNGAGCTGGAACCGTCAGCCCCCAGGGAAGGCCCTGGAGTGGCTTGCACGCATTGATTGGGATGATGATAAATTCCACAGCACATCTCTGAAGACCAGGCTCNCCATCTCCAAGGACACCTCCAAAAACCAGGTGGTCCTTCCAATGACCAACATGGACCCTGTGGACACGGCCACGTATTACTGTGNACGGCGCTCAAANACGGTGGTAACGGGGACGTTATACTTCCAGCACTGGGGCCAGGGCACCNTGGTCACCGTCTCCTCGGCACATGTTT', 'v_sequence_start': 0, 'v_sequence_end': 297, 'd_sequence_start': 303, 'd_sequence_end': 317, 'j_sequence_start': 326, 'j_sequence_end': 372, 'v_germline_start': 0, 'v_germline_end': 297, 'd_germline_start': 2, 'd_germline_end': 16, 'j_germline_start': 5, 'j_germline_end': 52, 'junction_sequence_start': 288, 'junction_sequence_end': 342, 'v_call': 'IGHVF1-G3*05,IGHVF1-G3*06', 'd_call': 'IGHD4-23*01,IGHD4-23*01', 'j_call': 'IGHJ1*01', 'c_call': 'IGHG4*04', 'mutation_rate': 0.03664921465968586, 'v_trim_5': 0, 'v_trim_3': 4, 'd_trim_5': 2, 'd_trim_3': 3, 'j_trim_5': 5, 'j_trim_3': 0, 'c_trim_3': 27, 'corruption_event': 'no-corruption', 'corruption_add_amount': 0, 'corruption_remove_amount': 0, 'indels': {302: 'I < A', 370: 'D > A'}, 'productive': True, 'stop_codon': False, 'vj_in_frame': True, 'note': ''}

4. Generating Naïve and Mutated Sequences:

from GenAIRR.sequence import HeavyChainSequence
from GenAIRR.mutation import S5F

# Create a naive heavy chain sequence
naive_heavy_sequence = HeavyChainSequence.create_random(data_config_builtin)

# Initialize the S5F mutation model
s5f_model = S5F(min_mutation_rate=0.01, max_mutation_rate=0.05)

# Apply mutations
s5f_mutated_sequence, mutations, mutation_rate = s5f_model.apply_mutation(naive_heavy_sequence)

print("Naïve Heavy Chain Sequence:", naive_heavy_sequence)
print("S5F Mutated Heavy Chain Sequence:", s5f_mutated_sequence)

Output:

Naïve Heavy Chain Sequence: 0|---------------------------------------------------------------------------------V(IGHVF4-G14*04)|296310|------------J(IGHJ5*02)|356|356|--C(IGHA2*01)|364
S5F Mutated Heavy Chain Sequence: GAGGTGCAGCTGGTGCAGTCTGGAGCAGAGGTGAAAAAGCCGGGGGAGTCTCTGAAGATCTCCTATAAGGGTTCTGGATACAGCTTTACCAGCTACTGGATCGGCTGGGTGCGCCAGATGCCCGGGAAAGGCCTGGAGTGGATGGGGATCATCTATCCTGGTGAGTCTGATACCAGATACAGCCCGTCCTTCCAAGGCCAGGTCACCATCTCAGCCGACAAGTCCCTCAGCACCGCCTACCTGCAGTGGAGCAGCCTGAAGGCCTCGGACACCGCCATGTATTACTCTGCGCGACACGTGGCGCGTAATATGGTTCGACCCCTGGGGCCAGGGAAACCTGGTCACCGTCTCCTCAGGCCCATGT

5. Controlling Sequence Generation Variables:
GenAIRR allows users to control various parameters during the sequence augmentation process, enabling precise simulation of different immunogenetic scenarios. Here are some of the key configuration arguments you can set:

custom_args = SequenceAugmentorArguments(
    min_mutation_rate=0.003,  # Minimum mutation rate
    max_mutation_rate=0.25,   # Maximum mutation rate
    simulate_indels=True,     # Simulate indels
    max_indels=5,             # Maximum number of indels
    deletion_proba=0.5,       # Probability of simulating a deletion event
    insertion_proba=0.5,      # Probability of simulating an insertion event
    n_ratio=0.02,             # Ratio of 'N' bases to introduce as noise
    n_proba=0.02,             # Probability of simulating 'N'
    max_sequence_length=512,  # Maximum length of sequences
    mutation_model='S5F',     # Mutation model to use
    corrupt_proba=0.7,        # Probability of corrupting the sequence from the start
    save_mutations_record=True,  # Save record of mutations
    save_ns_record=True,         # Save record of 'N' bases
    productive=True              # Generate productive sequences
)

custom_augmentor = HeavyChainSequenceAugmentor(data_config_builtin, custom_args)
custom_heavy_sequence = custom_augmentor.simulate_augmented_sequence()

Conclusion

Understanding V(D)J recombination and affinity maturation is crucial for appreciating the complexity and adaptability of the immune system. The GenAIRR Python package provides a powerful toolkit for simulating these processes, offering researchers a means to generate realistic AIRR sequences for various analytical purposes. By utilizing GenAIRR, researchers can gain deeper insights into the mechanisms of immune receptor diversity and improve their methodologies for immunogenetics research.

References

1. GenAIRR GitHub Page
2. GenAIRR Paper