Short-chain fatty acids (SCFAs) butyrate and propionate are metabolites from dietary fiber's fermentation by gut microbiota that can affect differentiation or functions of T cells, macrophages and dendritic cells. We show here that at low doses these SCFAs directly impact B cell intrinsic functions to moderately enhance class-switch DNA recombination (CSR), while decreasing at higher doses over a broad physiological range, AID and Blimp1 expression, CSR, somatic hypermutation and plasma cell differentiation. In human and mouse B cells, butyrate and propionate decrease B cell Aicda and Prdm1 by upregulating select miRNAs that target Aicda and Prdm1 mRNA-3′UTRs through inhibition of histone deacetylation (HDAC) of those miRNA host genes. By acting as HDAC inhibitors, not as energy substrates or through GPR-engagement signaling in these B cell-intrinsic processes, these SCFAs impair intestinal and systemic T-dependent and T-independent antibody responses. Their epigenetic impact on B cells extends to inhibition of autoantibody production and autoimmunity in mouse lupus models.

The COVID-19 pandemic has highlighted the need to quickly and reliably prioritize clinically approved compounds for their potential effectiveness for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Here, we deployed algorithms relying on artificial intelligence, network diffusion, and network proximity, tasking each of them to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. To test the predictions, we used as ground truth 918 drugs experimentally screened in VeroE6 cells, as well as the list of drugs in clinical trials that capture the medical community’s assessment of drugs with potential COVID-19 efficacy. We find that no single predictive algorithm offers consistently reliable outcomes across all datasets and metrics. This outcome prompted us to develop a multimodal technology that fuses the predictions of all algorithms, finding that a consensus among the different predictive methods consistently exceeds the performance of the best individual pipelines. We screened in human cells the top-ranked drugs, obtaining a 62% success rate, in contrast to the 0.8% hit rate of nonguided screenings. Of the six drugs that reduced viral infection, four could be directly repurposed to treat COVID-19, proposing novel treatments for COVID-19. We also found that 76 of the 77 drugs that successfully reduced viral infection do not bind the proteins targeted by SARS-CoV-2, indicating that these network drugs rely on network-based mechanisms that cannot be identified using docking-based strategies. These advances offer a methodological pathway to identify repurposable drugs for future pathogens and neglected diseases underserved by the costs and extended timeline of de novo drug development.

Activation-induced cytidine deaminase (AID) mediates immunoglobulin class switch DNA recombination (CSR) and somatic hypermutation (SHM), critical processes for maturation of the antibody response. Epigenetic factors, such as histone deacetylases (HDACs), would underpin B cell differentiation stage–specific AID expression. Here, we showed that NAD+-dependent class III HDAC sirtuin 1 (Sirt1) is highly expressed in resting B cells and down-regulated by stimuli inducing AID. B cell Sirt1 down-regulation, deprivation of NAD+ cofactor, or genetic Sirt1 deletion reduced deacetylation of Aicda promoter histones, Dnmt1, and nuclear factor–κB (NF-κB) p65 and increased AID expression. This promoted class-switched and hypermutated T-dependent and T-independent antibody responses or led to generation of autoantibodies. Genetic Sirt1 overexpression, Sirt1 boost by NAD+, or allosteric Sirt1 enhancement by SRT1720 repressed AID expression and CSR/SHM. By deacetylating histone and nonhistone proteins (Dnmt1 and NF-κB p65), Sirt1 transduces metabolic cues into epigenetic changes to play an important B cell–intrinsic role in modulating antibody and autoantibody responses.