Antibody Repertoire Analysis of Hepatitis C Virus Infections Identifies Immune Signatures Associated With Spontaneous...

Area of Science:

• Immunology
• Virology
• Computational Biology

Background:

• Hepatitis C virus (HCV) infects over 70 million globally, risking severe liver disease.
• No vaccine exists, and immune responses to HCV, particularly spontaneous clearance (SC) versus chronic infection (CI), are poorly understood.
• Understanding immune differences between SC and CI individuals can reveal mechanisms of viral control.

Purpose of the Study:

• To analyze adaptive immune receptor repertoires in individuals with current or past HCV infection.
• To compare immune responses between spontaneous clearers (SC) and chronically infected (CI) individuals.
• To identify mechanisms governing viral infection outcomes and develop novel immunotherapies.

Main Methods:

• In-depth analysis of adaptive immune receptor repertoires.
• Machine learning framework utilizing antibody characteristics to predict infection outcome.
• Combinatorial antibody phage display library technology to identify HCV-specific antibody sequences.

Main Results:

• SC individuals exhibit distinct antibody clusters compared to CI patients.
• Antibody characteristics accurately predicted HCV infection outcomes using a machine learning model.
• Two novel antibodies with high neutralization breadth, associated with viral clearance, were constructed.

Conclusions:

• Distinct adaptive immune responses, particularly antibody profiles, differentiate spontaneous HCV clearance from chronic infection.
• Machine learning and antibody engineering approaches can predict infection outcomes and generate effective neutralizing antibodies.
• Findings offer insights into effective immune responses against HCV, potentially guiding prognosis, immunotherapy, and vaccine design.