B-Cell-Based Immunotherapy: A Promising New Alternative

The field of immunotherapy has undergone radical conceptual changes over the last decade. There are various examples of immunotherapy, including the use of monoclonal antibodies, cancer vaccines, tumor-infecting viruses, cytokines, adjuvants, and autologous T cells carrying chimeric antigen receptors (CARs) that can bind cancer-specific antigens known as adoptive immunotherapy. While a lot has been achieved in the field of T-cell immunotherapy, only a fraction of patients (20%) see lasting benefits from this mode of treatment, which is why there is a critical need to turn our attention to other immune cells. B cells have been shown to play both anti- and pro-tumorigenic roles in tumor tissue. In this review, we shed light on the dual nature of B cells in the tumor microenvironment. Furthermore, we discussed the different factors affecting the biology and function of B cells in tumors. In the third section, we described B-cell-based immunotherapies and their clinical applications and challenges. These current studies provide a springboard for carrying out future mechanistic studies to help us unleash the full potential of B cells in immunotherapy

Role of Immunoglobulin A in COVID-19 and Influenza Infections

Immunoglobulin A (IgA) is critical in the immune response against respiratory infections like COVID-19 and influenza [...

Supervised Machine Learning Enables Geospatial Microbial Provenance

The recent increase in publicly available metagenomic datasets with geospatial metadata has made it possible to determine location-specific, microbial fingerprints from around the world. Such fingerprints can be useful for comparing microbial niches for environmental research, as well as for applications within forensic science and public health. To determine the regional specificity for environmental metagenomes, we examined 4305 shotgun-sequenced samples from the MetaSUB Consortium dataset—the most extensive public collection of urban microbiomes, spanning 60 different cities, 30 countries, and 6 continents. We were able to identify city-specific microbial fingerprints using supervised machine learning (SML) on the taxonomic classifications, and we also compared the performance of ten SML classifiers. We then further evaluated the five algorithms with the highest accuracy, with the city and continental accuracy ranging from 85–89% to 90–94%, respectively. Thereafter, we used these results to develop Cassandra, a random-forest-based classifier that identifies bioindicator species to aid in fingerprinting and can infer higher-order microbial interactions at each site. We further tested the Cassandra algorithm on the Tara Oceans dataset, the largest collection of marine-based microbial genomes, where it classified the oceanic sample locations with 83% accuracy. These results and code show the utility of SML methods and Cassandra to identify bioindicator species across both oceanic and urban environments, which can help guide ongoing efforts in biotracing, environmental monitoring, and microbial forensics (MF)