Deep-learning image analysis for high-throughput screening of opsono-phagocytosis-promoting monoclonal antibodies against Neisseria gonorrhoeae.

Antimicrobial resistance (AMR) is nowadays a global health concern as bacterial pathogens are increasingly developing resistance to antibiotics. Monoclonal antibodies (mAbs) represent a powerful tool for addressing AMR thanks to their high specificity for pathogenic bacteria which allows sparing the microbiota, kill bacteria through complement deposition, enhance phagocytosis or inhibit bacterial adhesion to epithelial cells. Here we describe a visual opsono-phagocytosis assay which relies on confocal microscopy to measure the impact of mAbs on phagocytosis of the bacterium Neisseria gonorrhoeae by macrophages. With respect to traditional CFU-based assays, generated images can be automatically analysed by convolutional neural networks. Our results demonstrate that confocal microscopy and deep learning-based analysis allow screening for phagocytosis-promoting mAbs against N. gonorrhoeae, even when mAbs are not purified and are expressed at low concentration. Ultimately, the flexibility of the staining protocol and of the deep-learning approach make the assay suitable for other bacterial species and cell lines where mAb activity needs to be investigated.

Monoclonal Antibodies for Bacterial Pathogens: Mechanisms of Action and Engineering Approaches for Enhanced Effector Functions.

Monoclonal antibody (mAb) therapy has opened a new era in the pharmaceutical field, finding application in various areas of research, from cancer to infectious diseases. The IgG isoform is the most used therapeutic, given its long half-life, high serum abundance, and most importantly, the presence of the Fc domain, which can be easily engineered. In the infectious diseases field, there has been a rising interest in mAbs research to counteract the emerging crisis of antibiotic resistance in bacteria. Various pathogens are acquiring resistance mechanisms, inhibiting any chance of success of antibiotics, and thus may become critically untreatable in the near future. Therefore, mAbs represent a new treatment option which may complement or even replace antibiotics. However, very few antibacterial mAbs have succeeded clinical trials, and until now, only three mAbs have been approved by the FDA. These failures highlight the need of improving the efficacy of mAb therapeutic activity, which can also be achieved with Fc engineering. In the first part of this review, we will describe the mechanisms of action of mAbs against bacteria, while in the second part, we will discuss the recent advances in antibody engineering to increase efficacy of pre-existing anti-bacterial mAbs.

Extremely potent human monoclonal antibodies from COVID-19 convalescent patients.

Human monoclonal antibodies are safe, preventive, and therapeutic tools that can be rapidly developed to help restore the massive health and economic disruption caused by the coronavirus disease 2019 (COVID-19) pandemic. By single-cell sorting 4,277 SARS-CoV-2 spike protein-specific memory B cells from 14 COVID-19 survivors, 453 neutralizing antibodies were identified. The most potent neutralizing antibodies recognized the spike protein receptor-binding domain, followed in potency by antibodies that recognize the S1 domain, the spike protein trimer, and the S2 subunit. Only 1.4% of them neutralized the authentic virus with a potency of 1-10 ng/mL. The most potent monoclonal antibody, engineered to reduce the risk of antibody-dependent enhancement and prolong half-life, neutralized the authentic wild-type virus and emerging variants containing D614G, E484K, and N501Y substitutions. Prophylactic and therapeutic efficacy in the hamster model was observed at 0.25 and 4 mg/kg respectively in absence of Fc functions.