sRNA-controlled iron sparing response in Staphylococci.

Staphylococcus aureus, a human opportunist pathogen, adjusts its metabolism to cope with iron deprivation within the host. We investigated the potential role of small non-coding RNAs (sRNAs) in dictating this process. A single sRNA, named here IsrR, emerged from a competition assay with tagged-mutant libraries as being required during iron starvation. IsrR is iron-repressed and predicted to target mRNAs expressing iron-containing enzymes. Among them, we demonstrated that IsrR down-regulates the translation of mRNAs of enzymes that catalyze anaerobic nitrate respiration. The IsrR sequence reveals three single-stranded C-rich regions (CRRs). Mutational and structural analysis indicated a differential contribution of these CRRs according to targets. We also report that IsrR is required for full lethality of S. aureus in a mouse septicemia model, underscoring its role as a major contributor to the iron-sparing response for bacterial survival during infection. IsrR is conserved among staphylococci, but it is not ortholog to the proteobacterial sRNA RyhB, nor to other characterized sRNAs down-regulating mRNAs of iron-containing enzymes. Remarkably, these distinct sRNAs regulate common targets, illustrating that RNA-based regulation provides optimal evolutionary solutions to improve bacterial fitness when iron is scarce.

Deletion of CD36 exhibits limited impact on normal hematopoiesis and the leukemia microenvironment.

BACKGROUND: CD36 has been identified as a potential therapeutic target both in leukemic cells and in the tumor immune microenvironment. In acute myeloid leukemia (AML), we found that APOC2 acts with CD36 to promote leukemia growth by activating the LYN-ERK signaling. CD36 also plays a role in lipid metabolism of cancer associated T-cells leading to impaired cytotoxic CD8+ T-cell and enhanced Treg cell function. To establish CD36 as a viable therapeutic target in AML, we investigated whether targeting CD36 has any detrimental impact on normal hematopoietic cells. METHODS: Differential expression data of CD36 during human and mouse normal hematopoiesis were examined and compared. Cd36 knockout (Cd36-KO) mice were evaluated for blood analysis, hematopoietic stem cells and progenitors (HSPCs) function and phenotype analyses, and T cells in vitro expansion and phenotypes in comparison with wild type (WT) mice. In addition, MLL-PTD/FLT3-ITD leukemic cells were engrafted into Cd36-KO and WT mice, and leukemia burden was compared between groups. RESULTS: RNA-Seq data showed that Cd36 expression was low in HSPCs and increased as cells matured. Phenotypic analysis revealed limited changes in blood count except for a slight yet significantly lower red blood cell count and hemoglobin and hematocrit levels in Cd36-KO mice compared with WT mice (P < 0.05). In vitro cell proliferation assays of splenocytes and HSPCs from Cd36-KO mice showed a similar pattern of expansion to that of cells from WT mice. Characterization of HSPCs showed similar percentages of the different progenitor cell populations between Cd36-KO with WT mice. However, Cd36-KO mice exhibited ~ 40% reduction of the number of colonies developed from HSPCs cells compared with WT mice (P < 0.001). Cd36-KO and WT mice presented comparably healthy BM transplant in non-competitive models and developed similar leukemia burden. CONCLUSIONS: Although the loss of Cd36 affects the hematopoietic stem cell and erythropoiesis, limited detrimental overall impact was observed on normal Hematopoietic and leukemic microenvironments. Altogether, considering the limited impact on normal hematopoiesis, therapeutic approaches to target CD36 in cancer are unlikely to result in toxicity to normal blood cells.

Bisphosphonate inhibitors of squalene synthase protect cells against cholesterol-dependent cytolysins.

Certain species of pathogenic bacteria damage tissues by secreting cholesterol-dependent cytolysins, which form pores in the plasma membranes of animal cells. However, reducing cholesterol protects cells against these cytolysins. As the first committed step of cholesterol biosynthesis is catalyzed by squalene synthase, we explored whether inhibiting this enzyme protected cells against cholesterol-dependent cytolysins. We first synthesized 22 different nitrogen-containing bisphosphonate molecules that were designed to inhibit squalene synthase. Squalene synthase inhibition was quantified using a cell-free enzyme assay, and validated by computer modeling of bisphosphonate molecules binding to squalene synthase. The bisphosphonates were then screened for their ability to protect HeLa cells against the damage caused by the cholesterol-dependent cytolysin, pyolysin. The most effective bisphosphonate reduced pyolysin-induced leakage of lactate dehydrogenase into cell supernatants by >80%, and reduced pyolysin-induced cytolysis from >75% to <25%. In addition, this bisphosphonate reduced pyolysin-induced leakage of potassium from cells, limited changes in the cytoskeleton, prevented mitogen-activated protein kinases cell stress responses, and reduced cellular cholesterol. The bisphosphonate also protected cells against another cholesterol-dependent cytolysin, streptolysin O, and protected lung epithelial cells and primary dermal fibroblasts against cytolysis. Our findings imply that treatment with bisphosphonates that inhibit squalene synthase might help protect tissues against pathogenic bacteria that secrete cholesterol-dependent cytolysins.

Symposium review: Mechanisms linking metabolic stress with innate immunity in the endometrium.

Bacterial infections of the uterus after parturition are ubiquitous in dairy cattle and often cause uterine disease, such as metritis or endometritis. However, the metabolic stress associated with milk production increases the risk of developing disease. Resolution of bacterial infections requires rapid and robust innate immune responses, which depend on host cell receptors recognizing pathogen-associated molecular patterns, such as lipopolysaccharide (LPS) from gram-negative bacteria. Here, we argue that metabolic stress impairs the inflammatory response to pathogens. Glucose and glutamine are the major energy sources for cells, but their abundance is reduced in postpartum dairy cows. Furthermore, inflammatory responses exacerbate metabolic stress, with animals and tissues consuming more glucose when challenged with LPS. However, depriving endometrial tissue of glucose or glutamine impairs the secretion of IL-1ß, IL-6, and IL-8 in response to pathogen-associated molecular patterns. Glycolysis and the intracellular sensor of energy, AMP-activated protein kinase, are important for the response to LPS because perturbing glycolysis or AMP-activated protein kinase activity reduces the secretion of IL-1ß, IL-6, and IL-8 in the endometrium. The mevalonate pathway for cellular cholesterol synthesis may also be linked to immunity, as inhibition of the terminal enzyme in the pathway, squalene synthase, reduces inflammatory responses to pathogenic bacteria and LPS. In contrast, only modest effects on inflammation are found when modulating the sensor of cellular nutrient satiety, mammalian target of rapamycin, or the endocrine regulator of metabolism, insulin-like growth factor-1. We suggest that stressing cellular metabolism increases the risk of uterine disease by impairing endometrial defenses.

The DNA methylation landscape across the TCR loci in patients with acute myeloid leukemia.

The capacity of T cells to initiate anti-leukemia immune responses is determined by the ability of their receptors (TCRs) to recognize leukemia neoantigens. Epigenetic mechanisms including DNA methylation contribute to shaping the TCR repertoire composition and diversity. The DNA hypomethylating agents (HMAs) have been widely used in the treatment of acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). Whether DNA HMAs directly influence TCR gene loci methylation patterns remains unknown. By analyzing public datasets, we compared methylation patterns across TCR loci in AML patients and healthy controls. We also explored how HMAs influence TCR loci DNA methylation in patients with AML. While methylation patterns are largely conserved across the TCR loci, certain V genes exhibit high interindividual variability. Although overall methylation levels within the TCR loci did not show significant differences, specific sites, including 32 TRAV and 12 TRBV sites exhibited distinct methylation patterns when comparing T cells from healthy donors to those from patients with AML. In leukemic cells, decitabine treatment demethylates sites across the TRAV and TRBV genes. While not as significant, a similar pattern of demethylation is observed in T cells. Pretreatment AML samples exhibit higher methylation beta values in differentially methylated positions (DMPs) compared with non-DMPs. Methylation levels of certain TRAV and TRBV genes in leukemic cells are associated with patients' risk status. The presence of disease specific TCR loci methylated signatures that are associated with clinical outcome presents an opportunity for therapeutic intervention. HMAs can modulate the TCR loci methylation patterns, yet whether they could reprogram the TCR repertoire composition remains to be explored.

Features of the TCR repertoire associate with patients' clinical and molecular characteristics in acute myeloid leukemia.

Background: Allogeneic hematopoietic stem cell transplant remains the most effective strategy for patients with high-risk acute myeloid leukemia (AML). Leukemia-specific neoantigens presented by the major histocompatibility complexes (MHCs) are recognized by the T cell receptors (TCR) triggering the graft-versus-leukemia effect. A unique TCR signature is generated by a complex V(D)J rearrangement process to form TCR capable of binding to the peptide-MHC. The generated TCR repertoire undergoes dynamic changes with disease progression and treatment. Method: Here we applied two different computational tools (TRUST4 and MIXCR) to extract the TCR sequences from RNA-seq data from The Cancer Genome Atlas (TCGA) and examine the association between features of the TCR repertoire in adult patients with AML and their clinical and molecular characteristics. Results: We found that only ~30% of identified TCR CDR3s were shared by the two computational tools. Yet, patterns of TCR associations with patients' clinical and molecular characteristics based on data obtained from either tool were similar. The numbers of unique TCR clones were highly correlated with patients' white blood cell counts, bone marrow blast percentage, and peripheral blood blast percentage. Multivariable regressions of TCRA and TCRB median normalized number of unique clones with mutational status of AML patients using TRUST4 showed significant association of TCRA or TCRB with WT1 mutations, WBC count, %BM blast, and sex (adjusted in TCRB model). We observed a correlation between TCRA/B number of unique clones and the expression of T cells inhibitory signal genes (TIGIT, LAG3, CTLA-4) and foxp3, but not IL2RA, CD69 and TNFRSF9 suggestive of exhausted T cell phenotypes in AML. Conclusion: Benchmarking of computational tools is needed to increase the accuracy of the identified clones. The utilization of RNA-seq data enables identification of highly abundant TCRs and correlating these clones with patients' clinical and molecular characteristics. This study further supports the value of high-resolution TCR-Seq analyses to characterize the TCR repertoire in patients.

Prediction of Peptide and TCR CDR3 Loops in Formation of Class I MHC-Peptide-TCR Complexes Using Molecular Models with Solvation.

Formation of major histocompatibility (MHC)-peptide-T cell receptor (TCR) complexes is central to initiation of an adaptive immune response. These complexes form through initial stabilization of the MHC fold via binding of a short peptide, and subsequent interaction of the TCR to form a ternary complex, with contacts made predominantly through the complementarity-determining region (CDR) loops of the TCR. Stimulation of an immune response is central to cancer immunotherapy. This approach depends on identification of the appropriate combinations of MHC molecules, peptides, and TCRs to elicit an antitumor immune response. This prediction is a current challenge in computational biochemistry. In this chapter, we introduce a predictive method that involves generation of multiple peptides and TCR CDR 3 loop conformations, solvation of these conformers in the context of the MHC-peptide-TCR ternary complex, extraction of parameters from the generated complexes, and use of an AI model to evaluate the potential for the assembled ternary complex to support an immune response.