The immune system is the central regulator of tissue homeostasis, ensuring tissue regeneration and protection against both pathogens and the neoformation of cancer cells. Its proper functioning requires homeostatic properties, which are maintained by an adequate balance of myeloid and lymphoid responses. Aging progressively undermines this ability and compromises the correct activation of immune responses, as well as the resolution of the inflammatory response. A subclinical syndrome of “homeostatic frailty” appears as a distinctive trait of the elderly, which predisposes to immune debilitation and chronic low-grade inflammation (inflammaging), causing the uncontrolled development of chronic and degenerative diseases. The innate immune compartment, in particular, undergoes to a sequela of age-dependent functional alterations, encompassing steps of myeloid progenitor differentiation and altered responses to endogenous and exogenous threats. Here, we will review the age-dependent evolution of myeloid populations, as well as their impact on frailty and diseases of the elderly.

Cancer progression generates a chronic inflammatory state that dramatically influences hematopoiesis, originating different subsets of immune cells that can exert pro- or anti-tumor roles. Commitment towards one of these opposing phenotypes is driven by inflammatory and metabolic stimuli derived from the tumor-microenvironment (TME). Current immunotherapy protocols are based on the reprogramming of both specific and innate immune responses, in order to boost the intrinsic anti-tumoral activity of both compartments. Growing pre-clinical and clinical evidence highlights the key role of metabolism as a major influence on both immune and clinical responses of cancer patients. Indeed, nutrient competition (i.e., amino acids, glucose, fatty acids) between proliferating cancer cells and immune cells, together with inflammatory mediators, drastically affect the functionality of innate and adaptive immune cells, as well as their functional cross-talk. This review discusses new advances on the complex interplay between cancer-related inflammation, myeloid cell differentiation and lipid metabolism, highlighting the therapeutic potential of metabolic interventions as modulators of anticancer immune responses and catalysts of anticancer immunotherapy.

Macrophages (Mϕs) play a central role in mucosal immunity by pathogen sensing and instruction of adaptive immune responses. Prior challenge to endotoxin can render Mφs refractory to secondary exposure, suppressing the inflammatory response. Previous studies demonstrated a differential subset-specific sensitivity to endotoxin tolerance (ET), mediated by LPS from the oral pathogen, Porphyromonas gingivalis (PG). The aim of this study was to investigate ET mechanisms associated with Mφ subsets responding to entropathogenic E . coli K12-LPS. M1- and M2-like Mφs were generated in vitro from the THP-1 cell line by differentiation with PMA and Vitamin D 3 , respectively. This study investigated ET mechanisms induced in M1 and M2 Mφ subsets, by measuring modulation of expression by RT-PCR, secretion of cytokines by sandwich ELISA, LPS receptor, TLR4, as well as endogenous TLR inhibitors, IRAK-M and Tollip by Western blotting. In contrast to PG-LPS tolerisation, E . coli K12-LPS induced ET failed to exhibit a subset-specific response with respect to the pro-inflammatory cytokine, TNFα, whereas exhibited a differential response for IL-10 and IL-6. TNFα expression and secretion was significantly suppressed in both M1- and M2-like Mφs. IL-10 and IL-6, on the other hand, were suppressed in M1s and refractory to suppression in M2s. ET suppressed TLR4 mRNA, but not TLR4 protein, yet induced differential augmentation of the negative regulatory molecules, Tollip in M1 and IRAK-M in M2 Mφs. In conclusion, E . coli K12-LPS differentially tolerises Mφ subsets at the level of anti-inflammatory cytokines, associated with a subset-specific divergence in negative regulators and independent of TLR4 down-regulation.

Background and Aims The SARS-CoV-2 pandemic has overwhelmed the treatment capacity of the health care systems during the highest viral diffusion rate. Patients reaching the emergency department had to be either hospitalized (inpatients) or discharged (outpatients). Still, the decision was taken based on the individual assessment of the actual clinical condition, without specific biomarkers to predict future improvement or deterioration, and discharged patients often returned to the hospital for aggravation of their condition. Here, we have developed a new combined approach of omics to identify factors that could distinguish coronavirus disease 19 (COVID-19) inpatients from outpatients. Methods Saliva and blood samples were collected over the course of two observational cohort studies. By using machine learning approaches, we compared salivary metabolome of 50 COVID-19 patients with that of 270 healthy individuals having previously been exposed or not to SARS-CoV-2. We then correlated the salivary metabolites that allowed separating COVID-19 inpatients from outpatients with serum biomarkers and salivary microbiota taxa differentially represented in the two groups of patients. Results We identified nine salivary metabolites that allowed assessing the need of hospitalization. When combined with serum biomarkers, just two salivary metabolites (myo-inositol and 2-pyrrolidineacetic acid) and one serum protein, chitinase 3-like-1 (CHI3L1), were sufficient to separate inpatients from outpatients completely and correlated with modulated microbiota taxa. In particular, we found Corynebacterium 1 to be overrepresented in inpatients, whereas Actinomycetaceae F0332 , Candidatus Saccharimonas , and Haemophilus were all underrepresented in the hospitalized population. Conclusion This is a proof of concept that a combined omic analysis can be used to stratify patients independently from COVID-19.

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