Hugo Barcenilla

Abstract Infectious, inflammatory and autoimmune conditions present differently in males and females. SARS-CoV-2 infection in naive males is associated with increased risk of death, whereas females are at increased risk of long COVID 1 , similar to observations in other infections 2 . Females respond more strongly to vaccines, and adverse reactions are more frequent 3 , like most autoimmune diseases 4 . Immunological sex differences stem from genetic, hormonal and behavioural factors 5 but their relative importance is only partially understood 6–8 . In individuals assigned female sex at birth and undergoing gender-affirming testosterone therapy (trans men), hormone concentrations change markedly but the immunological consequences are poorly understood. Here we performed longitudinal systems-level analyses in 23 trans men and found that testosterone modulates a cross-regulated axis between type-I interferon and tumour necrosis factor. This is mediated by functional attenuation of type-I interferon responses in both plasmacytoid dendritic cells and monocytes. Conversely, testosterone potentiates monocyte responses leading to increased tumour necrosis factor, interleukin-6 and interleukin-15 production and downstream activation of nuclear factor kappa B-regulated genes and potentiation of interferon-γ responses, primarily in natural killer cells. These findings in trans men are corroborated by sex-divergent responses in public datasets and illustrate the dynamic regulation of human immunity by sex hormones, with implications for the health of individuals undergoing hormone therapy and our understanding of sex-divergent immune responses in cisgender individuals.

INTRODUCTION: Given the pivotal role of T lymphocytes in the immune system, patients with septic shock may show T cell abnormalities. We have characterised the T cell compartment in septic shock and assess its clinical implications. METHODS: T lymphocytes from the peripheral blood of 52 patients with septic shock and 36 healthy control subjects were analysed on admission to the intensive care unit, baseline, and 3, 7, 14 and 28 days later. T cell phenotypes (CD3+CD4+/CD3+CD8+, CD45RA+/CD45RO+, CD62L+/CD28+) were assessed by quantitative flow cytometry. RESULTS: CD3+, CD3+CD4+ and CD3+CD8+ lymphocyte counts were significantly lower in patients with septic shock than control subjects. In surviving patients, CD3+CD4+ lymphocytes had normalised after 14 days, yet CD3+CD8+ numbers were still low. Non effector CD45RA+CD45RO- subsets of CD3+CD4+ and CD3+CD8+ were persistently low during patient follow up. CD3+CD8+CD28+ and CD3+CD8+CD62L+ were reduced in patients versus controls and survivors versus nonsurvivors in the first three days. A prediction receptor operative curve revealed that for the CD3+CD8+CD28+ subset, a cutoff of 136 cells/ml showed 70% sensitivity and 100% specificity for predicting death and the area under the curve was 0.84 at admission. Corresponding values for CD3+CD8+CD62L+ were 141 cells/ml, 60% sensitivity, 100% specificity and an area under the curve of 0.75. CONCLUSIONS: A severe redistribution of T lymphocyte subsets is found in septic shock patients. A different kinetic pattern of T cell subset involvement is observed in surviving and nonsurviving patients, with lower numbers of circulating CD3+CD8+CD28+ and CD3+CD8+CD62L+ associated with a better disease outcome.

Type 1 diabetes (T1D) is characterized by autoimmune destruction of insulin producing β-cells. The time from onset of islet autoimmunity to manifest clinical disease can vary widely in length, and it is fairly uncharacterized both clinically and immunologically. In the current study, peripheral blood mononuclear cells from autoantibody-positive children with high risk for T1D, and from age-matched healthy individuals, were analyzed by mass cytometry using a panel of 33 antibodies. Surface markers were chosen to identify multiple cell types including T, B, NK, monocytes, and DC, and antibodies specific for identification of differentiation, activation and functional markers were also included in the panel. By applying dimensional reduction and computational unsupervised clustering approaches, we delineated in an unbiased fashion 132 phenotypically distinct subsets within the major immune cell populations. We were able to identify an effector memory Treg subset expressing HLA-DR, CCR4, CCR6, CXCR3 and GATA3 that was increased in the high-risk group. In addition, two subsets of NK cells defined by CD16+ CD8+ CXCR3+ and CD16+ CD8+ CXCR3+ CD11c+ were also higher in the same subjects. High-risk individuals did not show impaired glucose tolerance at the time of sampling, suggesting that the changes observed were not the result of metabolic imbalance, and might be potential biomarkers predictive of T1D.

BACKGROUND: Late apoptotic cells divide into apoptotic bodies and are missed by current detection methods. This results in an artificially low apoptotic index (AI). METHODS: This study proposes a flow cytometry-based ratiometric method that uses an internal reference standard of microbeads combined with fluorescein-annexin V binding and 7-aminoactinomycin D to enumerate viable, necrotic, and early and late apoptotic cells within specific subsets of a heterogeneous culture. RESULTS: In the absence of cell growth, the number of apoptotic cells that undergo fragmentation into apoptotic bodies in culture can also be determined accurately by this method. This information can then be used to obtain the apoptotic rate (AR), a new indicator of apoptosis that calculates the proportion of cells that have undergone apoptosis with respect to the total number of seeded cells. The main limitation of the method is that the AR is only suitable for the study of apoptosis in noncycling cells. CONCLUSIONS: This study reveals the superiority of the proposed method over the widely used Nicoletti method and current annexin-V binding methods. The AI did not reflect the true incidence of lymphocyte apoptosis, neither in response to lectins or phorbol esters, nor to serum deprivation. AR was more sensitive than AI, detecting apoptosis at lower concentrations of cell death inducers in all the subsets studied.