White adipose tissue (WAT) is an essential regulator of energy storage and systemic metabolic homeostasis. Regulatory networks consisting of immune and structural cells are necessary to maintain WAT metabolism, which can become impaired during obesity in mammals. Using single-cell transcriptomics and flow cytometry, we unveil a large-scale comprehensive cellular census of the stromal vascular fraction (SVF) of healthy lean and obese human WAT. We report novel subsets and developmental trajectories of adipose-resident innate lymphoid cells (ILCs), dendritic cells (DCs) and monocyte-derived macrophage populations that accumulate in obese WAT. Analysis of cell-cell ligand receptor interactions and obesity-enriched signaling pathways revealed a switch from immunoregulatory mechanisms in lean WAT to inflammatory networks in obese WAT. These results provide a detailed and unbiased cellular landscape of homeostatic and inflammatory circuits in healthy human WAT.

Innate lymphoid cells (ILCs) are tissue-resident sentinels that are essential for early host protection from pathogens at initial sites of infection. However, whether pathogen-derived antigens directly modulate the responses of tissue-resident ILCs has remained unclear. Here, we found that liver-resident type 1 innate lymphoid cells (ILC1s) expanded locally and persisted after the resolution of infection with mouse cytomegalovirus (MCMV). ILC1s acquired stable transcriptional, epigenetic and phenotypic changes a month after the resolution of MCMV infection, and showed an enhanced protective effector response to secondary challenge with MCMV consistent with a memory lymphocyte response. Memory ILC1 responses were dependent on the MCMV-encoded glycoprotein m12, and were independent of bystander activation by proinflammatory cytokines after heterologous infection. Thus, liver ILC1s acquire adaptive features in an MCMV-specific manner.

Although type 1 innate lymphoid cells (ILC1s) have been originally found as liver-resident ILCs, their pathophysiological role in the liver remains poorly investigated. Here, we demonstrated that carbon tetrachloride (CCl 4 ) injection into mice activated ILC1s, but not natural killer (NK) cells, in the liver. Activated ILC1s produced interferon-g (IFN-g) and protected mice from CCl 4 -induced acute liver injury. IFN-g released from activated ILC1s promoted the survival of hepatocytes through upregulation of Bcl-xL. An activating NK receptor, DNAM-1, was required for the optimal activation and IFN-g production of liver ILC1s. Extracellular adenosine triphosphate accelerated interleukin-12-driven IFN-g production by liver ILC1s. These findings suggest that ILC1s are critical for tissue protection during acute liver injury.

Highlights d Optimized electroporation of cRNP complexes in primary mature innate immune cells d High knockout efficiency of signaling adaptors and transcription factors d cRNP-mediated knockout of Stat4 in primary NK cells during MCMV infection d cRNP knockout reveals MyD88 is required for cDC1dependent control of MCMV infection

Bacteriocins, the ribosomally produced antimicrobial peptides of bacteria, represent an untapped source of promising antibiotic alternatives. However, bacteriocins display diverse mechanisms of action, a narrow spectrum of activity, and inherent challenges in natural product isolation making in vitro verification of putative bacteriocins difficult. A subset of bacteriocins exert their antimicrobial effects through favorable biophysical interactions with the bacterial membrane mediated by the charge, hydrophobicity, and conformation of the peptide. We have developed a pipeline for bacteriocin‐derived compound design and testing that combines sequence‐free prediction of bacteriocins using machine learning and a simple biophysical trait filter to generate 20 amino acid peptides that can be synthesized and evaluated for activity. We generated 28,895 total 20‐mer candidate peptides and scored them for charge, α‐helicity, and hydrophobic moment. Of those, we selected 16 sequences for synthesis and evaluated their antimicrobial, cytotoxicity, and hemolytic activities. Peptides with the overall highest scores for our biophysical parameters exhibited significant antimicrobial activity against Escherichia coli and Pseudomonas aeruginosa. Our combined method incorporates machine learning and biophysical‐based minimal region determination to create an original approach to swiftly discover bacteriocin candidates amenable to rapid synthesis and evaluation for therapeutic use.