Nasinghe, E. | bioRxiv Science

bioRxiv2026

Novel human monoclonal antibodies with enhanced sensitivity for lipoarabinomannan antigens present in urines of TB patients

Lipoarabinomannan (LAM) is a useful biomarker for detection of M. tuberculosis infection and disease. Related antigens can be detected in urine samples of TB patients by combinations of monoclonal antibodies (mAbs) directed against specific epitopes expressed in LAM. While sensitive for samples from patients with active TB disease who have HIV-1 co-infections, these assays are less effective for other populations, and there is therefore a need for more sensitive antibodies that can improve the sensitivity of these assays. Here we characterize the antigen and epitope specificities, sequence diversity and isotype dependencies of eight LAM-specific human mAbs that target five distinct arabinose- and mannose-dependent epitopes present in LAM and lipoarabinomannan (LM). Whereas all of the mAbs recognized ManLAM, only a few, including A194-01, consistently detected antigens in TB+ urine samples. Converting A194-01 from the IgG1 to the IgM isotype resulted in broader recognition of poly-Ara glycan epitopes, and increased sensitivity for clinical antigens when combined with several capture reagents, including RU95-C1, a novel antibody targeting the mannan domain of LAM. These results define novel epitopes that are differentially expressed in bacterial and urinary forms of LAM, and identify novel antibody combinations which possess enhanced diagnostic utility for clinical forms of LAM.

immunology↗

bioRxiv2024

Gut microbial profiling of COVID-19 patients in Uganda

BackgroundWhile COVID-19 spread globally, the role of the gut microbiota in patient outcomes has remained an area of exploration especially in resource limited settings. This study aimed to comprehensively profile the gut microbiome among Ugandan COVID-19 patients and infer potential implications. MethodsNasopharyngeal swabs, stool, clinical and demographic data were collected from COVID-19 confirmed cases at the COVID-19 isolation and treatment centers in Kampala and Entebbe, Uganda, during the first and second waves of the pandemic in Uganda (i.e., 2020 and 2021, respectively). SARS-CoV-2 presence in the swab samples was confirmed by quantitative real-time RT-PCR assays. 16S rRNA metagenomic next-generation sequencing was performed on the DNA extracted from the stool samples, followed by bioinformatics analysis. Machine learning was used to determine microbes that were associated with disease severity. ResultsWe observed varied gut microbial composition between COVID-19 patients and healthy controls. Potentially pathogenic bacteria such as Klebsiella oxytoca, Salmonella enterica and Serratia marcescens had an increased presence in COVID-19 disease states, especially severe cases. Enrichment of opportunistic pathogens, such as Enterococcus species, and depletion of beneficial microbes, like Alphaproteobacteria, was observed between mild and severe cases. Machine learning identified age and microbes such as Ruminococcaceae, Bacilli, Enterobacteriales, porphyromonadaceae, and Prevotella copri as predictive of severity. ConclusionThese findings suggest that the microbiome plays a role in the dynamics of SARS-CoV-2 infection in African patients. The shift in abundance of specific microbes can moderately predict severity of COVID-19 in this population. Their direct or indirect roles in determining severity should be investigated further for potential therapeutic interventions.

bioinformatics↗