Antibody Cross-Reactivity in Serodiagnosis of Lyme Disease

ProPickML: Advancing Clinical Diagnostics with Automated Peak Picking in Label-Free Targeted Proteomics

In targeted proteomics utilizing Selected Reaction Monitoring (SRM), the precise detection of specific peptides within complex mixtures remains a significant challenge, particularly due to noise and interference in chromatograms. Existing methodologies, such as isotopic labeling and scoring algorithms, offer partial solutions but are constrained by high run times and elevated false discovery rates. To address these limitations, we have developed ProPickML a machine learning-based tool designed to accurately identify peptide peaks across diverse data sets, independent of the assumed presence of the peptide. This model was trained on a manually labeled data set and subsequently validated to assess its predictive accuracy. The results demonstrate that the model reliably identifies peptide peaks in the presence of noise, achieving a Matthews correlation coefficient (MCC) of 0.81 on an independent test data set, surpassing mProphet's MCC of 0.71. Implemented in R as ProPickML, this tool offers a competitive, cost-effective alternative to existing techniques, significantly reducing reliance on isotopic labeling and enhancing the accuracy of peptide identification in SRM workflows.

New Epitopes for the Serodiagnosis of Human Borreliosis

Lyme disease, a zoonotic infection caused by the bacterium Borrelia burgdorferi, is transmitted to humans through the bites of infected ticks. Its diagnosis primarily relies on serological methods; however, the existing borreliosis techniques have shown a variable sensitivity and specificity. Our study aimed to map IgG epitopes from five outer membrane proteins (Omp) from B. burgdorferi [Filament flagellar 41kD (PI1089), flagellar hook-associated protein (Q44767), Flagellar hook k2 protein (O51173), Putative Omp BURGA03 (Q44849), and 31 kDa OspA (P0CL66)] lipoprotein to find specific epitopes for the development of accurate diagnosis methods. Using the spot synthesis technique, a library of 380 peptides was constructed to identify linear B cell epitopes recognized by human IgG in response to specific B. burgdorferi-associated proteins. The reactivity of this epitope when chemically synthesized was then evaluated using ELISA with a panel of the patient's sera. Cross-reactivity was assessed through data bank access and in vitro analysis. Among the 19 epitopes identified, four were selected for further investigation based on their signal intensity, secondary structure, and peptide matching. Validation was performed using ELISA, and ROC curve analysis demonstrated a sensitivity of ≥85.71%, specificity of ≥92.31, accuracy of ≥90.7, and AUC value of ≥0.91 for all peptides. Our cross-reactivity analysis demonstrated that the Burg/02/huG, Burg/03/huG, and Burg/12/huG peptides were not reactive to antibodies from patients with Leptospirosis and syphilis compared to those from the B. burgdorferi group. These peptides indicated an excellent performance in distinguishing between B. burgdorferi-infected and non-infected individuals and exhibited a neglected reactivity to antibodies in sera from patients with Leptospirosis and syphilis. These peptides are promising targets for recombinant development, potentially leading to more accurate serological tests and vaccines.

Aetiologies of bacterial tick-borne febrile illnesses in humans in Africa: diagnostic limitations and the need for improvement

Tick-borne febrile illnesses caused by pathogens like Anaplasma spp., Bartonella spp., Borrelia spp., Ehrlichia spp., Coxiella burnetii, Francisella tularensis, and Rickettsia spp., are significant health concerns in Africa. The epidemiological occurrence of these pathogens is closely linked to the habitats of their vectors, prevalent in rural and semi-urban areas where humans and livestock coexist. The overlapping clinical presentations, non-specific symptoms, and limited access to commercially available in vitro diagnostics in resource-limited settings exacerbate the complexity of accurate diagnoses. This review aimed to systematically extract and analyze existing literature on tick-borne febrile illnesses in Africa, highlighting the diagnostic challenges and presenting an up-to-date overview of the most relevant pathogens affecting human populations. A comprehensive literature search from January 1990 to June 2024 using databases like PubMed, Cochrane Library, Science Direct, EMBASE, and Google Scholar yielded 13,420 articles, of which 70 met the inclusion criteria. Anaplasma spp. were reported in Morocco, Egypt, and South Africa; Francisella spp. in Kenya and Ethiopia; Ehrlichia spp. in Cameroon; Bartonella spp. in Senegal, Namibia, South Africa, and Ethiopia; Borrelia spp. in Senegal, Gabon, Tanzania, and Ethiopia; Coxiella burnetii in 10 countries including Senegal, Mali, and South Africa; and Rickettsia spp. in 14 countries including Senegal, Algeria, and Uganda. Data were analyzed using a fixed-effect model in R version 4.0.1 and visualized on an African map using Tableau version 2022.2. This review highlights the urgent need for improved diagnostics to better manage and control tick-borne febrile illnesses in Africa.

Diagnostic Challenges of Lyme Co-infections: Lessons From a Lyme and Herpes Simplex Virus-1 (HSV-1) Cocktail

Lyme borreliosis (LB) is a complex tick-borne illness with diverse presentations. We report a case of LB meningitis with herpes simplex virus-1 (HSV-1) co-infection in a 55-year-old woman initially presenting with isolated facial nerve palsy. This case illustrates the multifaceted diagnostic challenges associated with Lyme co-infections. It emphasizes the need for thorough testing to identify all potential pathogens and the importance of differentiating between true co-infection and incidental HSV-1 reactivation. Understanding these complexities is crucial for guiding appropriate treatment decisions.

Cross-reaction mediated by distinct key amino acid combinations in the complementary-determining region (CDR) of a monoclonal antibody

In immunology, cross-reaction between antigens and antibodies are commonly observed. Prior research has shown that various monoclonal antibodies (mAbs) can recognize a broad spectrum of epitopes related to influenza viruses. However, existing theories on cross-reactions fall short in explaining the phenomena observed. This study explored the interaction characteristics of H1-74 mAb with three peptides: two natural peptides, LVLWGIHHP and LPFQNI, derived from the hemagglutinin (HA) antigen of the H1N1 influenza virus, and one synthetic peptide, WPFQNY. Our findings indicate that the complementarity-determining region (CDR) of H1-74 mAb comprised five antigen-binding sites, containing eight key amino acid residues from the light chain variable region and 16 from the heavy chain variable region. These critical residues formed distinct hydrophobic or hydrophilic clusters and functional groups within the binding sites, facilitating interaction with antigen epitopes through hydrogen bonding, salt bridge formation, and π-π stacking. The study revealed that the formation of the antibody molecule led to the creation of binding groups and small units in the CDR, allowing the antibody to attach to a variety of antigen epitopes through diverse combinations of these small units and functional groups. This unique ability of the antibody to bind with antigen epitopes provides a new molecular basis for explaining the phenomenon of antibody cross-reaction.