ArticleThe Journal of infectious diseases2025
Plasma Proteomics Reveals Distinct Signatures in Occult and Microfilaremic Loa loa Infections.
Article in The Journal of infectious diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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Who cites it
1 citing paper in PubMed.
- Cross-platform clinical proteomics using the Charité open standard for plasma proteomics (OSPP).Nature communications · 2025Article
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Authors and funding
20 authors.
Funding
Abstract
backgroundLoiasis, caused by the filarial nematode Loa loa, imposes a significant disease burden in endemic regions in West and Central Africa. Manifestations include adult worms in soft tissue (e.g., the conjunctiva of the eye) and microfilaria in peripheral blood, with clinical presentations ranging from asymptomatic infections to life-threatening organ involvement. Diagnosis remains challenging due to variable microfilaria counts, frequent amicrofilaremic occult infections, and unreliable serological tests. The untargeted plasma proteome reflects broad (patho-)physiological responses, providing valuable insights into the host and disease.
methodsApplying high-throughput plasma proteomics, we investigated the host responses of 274 patients with different L. loa disease states, including occult loiasis (n = 148), microfilaremia (n = 42), or both (n = 84), compared to 136 L. loa-negative controls. Differentially abundant proteins between L. loa-infected individuals and negative controls were validated using targeted proteomics.
resultsFive proteins (IGHG3, IGHG4, ACTBL2, LCP1, and IGLV9-49) were elevated in infected individuals compared to healthy controls. IGHG3, IGHG4, ACTB, and LCP1 increased from L. loa negative over individuals with history of eye worm migration to microfilaremic patients, indicating a comparatively pronounced proteomic host response to microfilaria in the blood. Sixty-three proteins differed depending on self-reported symptoms. The proteomic signatures enabled accurate classification of individuals with occult loiasis (area under the receiver operating characteristic curve [AUROC] = 0.73) and microfilaremia (AUROC = 0.84) by a random forest machine learning model.
conclusionsOverall, infection with Loa loa alters the host plasma proteome, exhibiting distinct host responses in different infection states and allowing for molecular disease classification of this highly neglected parasitic disease.
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