Identification of risk factors for contracting and developing serious illness following infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is of paramount interest. Here, we performed a retrospective cohort analysis of all Danish individuals tested for SARS-CoV-2 between 27 February 2020 and 30 July 2020, with a known ABO and RhD blood group, to determine the influence of common blood groups on virus susceptibility. Distribution of blood groups was compared with data from nontested individuals. Participants (29% of whom were male) included 473 654 individuals tested for SARS-CoV-2 using real-time polymerase chain reaction (7422 positive and 466 232 negative) and 2 204 742 nontested individuals, accounting for ∼38% of the total Danish population. Hospitalization and death from COVID-19, age, cardiovascular comorbidities, and job status were also collected for confirmed infected cases. ABO blood groups varied significantly between patients and the reference group, with only 38.41% (95% confidence interval [CI], 37.30-39.50) of the patients belonging to blood group O compared with 41.70% (95% CI, 41.60-41.80) in the controls, corresponding to a relative risk of 0.87 (95% CI, 0.83-0.91) for acquiring COVID-19. This study identifies ABO blood group as a risk factor for SARS-CoV-2 infection but not for hospitalization or death from COVID-19.

Chimeric antigen receptor (CAR) T cells have emerged as a promising treatment for patients with advanced B-cell cancers. However, widespread application of the therapy is currently limited by potentially life-threatening toxicities due to a lack of control of the highly potent transfused cells. Researchers have therefore developed several regulatory mechanisms in order to control CAR T cells in vivo. Clinical adoption of these control systems will depend on several factors, including the need for temporal and spatial control, the immunogenicity of the requisite components as well as whether the system allows reversible control or induces permanent elimination. Here we describe currently available and emerging control methods and review their function, advantages, and limitations.

Precise, analogue regulation of gene expression is critical for cellular function in mammals. In contrast, widely employed experimental and therapeutic approaches such as knock-in/out strategies are more suitable for binary control of gene activity. Here we report on a method for precise control of gene expression levels in mammalian cells using engineered microRNA response elements (MREs). First, we measure the efficacy of thousands of synthetic MRE variants under the control of an endogenous microRNA by high-throughput sequencing. Guided by this data, we establish a library of microRNA silencing-mediated fine-tuners (miSFITs) of varying strength that can be employed to precisely control the expression of user-specified genes. We apply this technology to tune the T-cell co-inhibitory receptor PD-1 and to explore how antigen expression influences T-cell activation and tumour growth. Finally, we employ CRISPR/Cas9 mediated homology directed repair to introduce miSFITs into the BRCA1 3′UTR, demonstrating that this versatile tool can be used to tune endogenous genes.

Background: Scientific data and research results are being published at an unprecedented rate. Many database curators and researchers utilize data and information from the primary literature to populate databases, form hypotheses, or as the basis for analyses or validation of results. These efforts largely rely on manual literature surveys for collection of these data, and while querying the vast amounts of literature using keywords is enabled by repositories such as PubMed, filtering relevant articles from such query results can be a non-trivial and highly time consuming task. Results: We here present a tool that enables users to perform classification of scientific literature by text mining-based classification of article abstracts. BioReader (Biomedical Research Article Distiller) is trained by uploading article corpora for two training categories-e.g. one positive and one negative for content of interest-as well as one corpus of abstracts to be classified and/or a search string to query PubMed for articles. The corpora are submitted as lists of PubMed IDs and the abstracts are automatically downloaded from PubMed, preprocessed, and the unclassified corpus is classified using the best performing classification algorithm out of ten implemented algorithms. Conclusion: BioReader supports data and information collection by implementing text mining-based classification of primary biomedical literature in a web interface, thus enabling curators and researchers to take advantage of the vast amounts of data and information in the published literature. BioReader outperforms existing tools with similar functionalities and expands the features used for mining literature in database curation efforts.

A major obstacle to efficacious T cell-based cancer immunotherapy is the tolerizing tumor microenvironment that rapidly inactivates tumor-infiltrating lymphocytes. In an autochthonous model of prostate cancer, we have previously shown that intratumoral injection of antigen loaded dendritic cells (DCs) delays T cell tolerance induction as well as refunctionalizes already tolerized T cells in the tumor tissue. In this study, we have defined molecular interactions that mediate DCs’ effects. We show that pretreating antigen-loaded DCs with anti-CD70 antibody abolishes DCs’ ability to delay tumor-mediated T cell tolerance induction, whereas interfering with 4-1BBL, CD80, CD86 or both CD80 and CD86 had no significant effect. In contrast, CD80−/− or CD80−/−CD86−/− DCs failed to reactivate already-tolerized T cells in the tumor tissue, whereas interfering with CD70 and 4-1BBL had no effect. Furthermore, despite a high level of PD-1 expression by tumor infiltrating T cells and PD-L1 expression in the prostate, disrupting PD-1/PD-L1 interaction did not enhance T cell function in this model. These findings reveal dynamic requirements for costimulatory signals to overcome tumor induced tolerance and have significant implications for developing more effective cancer immunotherapies.

Combining single-cell cytometry datasets increases the analytical flexibility and the statistical power of data analyses. However, in many cases the full potential of co-analyses is not reached due to technical variance between data from different experimental batches. Here, we present cyCombine, a method to robustly integrate cytometry data from different batches, experiments, or even different experimental techniques, such as CITE-seq, flow cytometry, and mass cytometry. We demonstrate that cyCombine maintains the biological variance and the structure of the data, while minimizing the technical variance between datasets. cyCombine does not require technical replicates across datasets, and computation time scales linearly with the number of cells, allowing for integration of massive datasets. Robust, accurate, and scalable integration of cytometry data enables integration of multiple datasets for primary data analyses and the validation of results using public datasets.

Highlights d Arginine starvation induces ASS1 expression in some cancers but not in T cells d ATF4 binds an internal ASS1 enhancer to drive expression in cancer but not T cells d T cell activation is disrupted by arginine starvation, with a loss of reprogramming d Arginine starvation compacts chromatin in T cells, disrupting ATF4 binding

The mechanisms of immune response to cancer have been studied extensively and great effort has been invested into harnessing the therapeutic potential of the immune system. Immunotherapies have seen significant advances in the past 20 years, but the full potential of protective and therapeutic cancer immunotherapies has yet to be fulfilled. The insufficient efficacy of existing treatments can be attributed to a number of biological and technical issues. In this review, we detail the current limitations of immunotherapy target selection and design, and review computational methods to streamline therapy target discovery in a bioinformatics analysis pipeline. We describe specialized bioinformatics tools and databases for three main bottlenecks in immunotherapy target discovery: the cataloging of potentially antigenic proteins, the identification of potential HLA binders, and the selection epitopes and co-targets for single-epitope and multi-epitope strategies. We provide examples of application to the well-known tumor antigen HER2 and suggest bioinformatics methods to ameliorate therapy resistance and ensure efficient and lasting control of tumors.