KIR+CD8+ T cells suppress pathogenic T cells and are active in autoimmune diseases and COVID-19.

In this work, we find that CD8+ T cells expressing inhibitory killer cell immunoglobulin-like receptors (KIRs) are the human equivalent of Ly49+CD8+ regulatory T cells in mice and are increased in the blood and inflamed tissues of patients with a variety of autoimmune diseases. Moreover, these CD8+ T cells efficiently eliminated pathogenic gliadin-specific CD4+ T cells from the leukocytes of celiac disease patients in vitro. We also find elevated levels of KIR+CD8+ T cells, but not CD4+ regulatory T cells, in COVID-19 patients, correlating with disease severity and vasculitis. Selective ablation of Ly49+CD8+ T cells in virus-infected mice led to autoimmunity after infection. Our results indicate that in both species, these regulatory CD8+ T cells act specifically to suppress pathogenic T cells in autoimmune and infectious diseases.

Human KIR + CD8 + T cells target pathogenic T cells in Celiac disease and are active in autoimmune diseases and COVID-19.

Previous reports show that Ly49 + CD8 + T cells can suppress autoimmunity in mouse models of autoimmune diseases. Here we find a markedly increased frequency of CD8 + T cells expressing inhibitory Killer cell Immunoglobulin like Receptors (KIR), the human equivalent of the Ly49 family, in the blood and inflamed tissues of various autoimmune diseases. Moreover, KIR + CD8 + T cells can efficiently eliminate pathogenic gliadin-specific CD4 + T cells from Celiac disease (CeD) patients' leukocytes in vitro . Furthermore, we observe elevated levels of KIR + CD8 + T cells, but not CD4 + regulatory T cells, in COVID-19 and influenza-infected patients, and this correlates with disease severity and vasculitis in COVID-19. Expanded KIR + CD8 + T cells from these different diseases display shared phenotypes and similar T cell receptor sequences. These results characterize a regulatory CD8 + T cell subset in humans, broadly active in both autoimmune and infectious diseases, which we hypothesize functions to control self-reactive or otherwise pathogenic T cells. ONE-SENTENCE SUMMARY: Here we identified KIR + CD8 + T cells as a regulatory CD8 + T cell subset in humans that suppresses self-reactive or otherwise pathogenic CD4 + T cells.

Altered chromatin landscape in circulating T follicular helper and regulatory cells following grass pollen subcutaneous and sublingual immunotherapy.

BACKGROUND: Allergen-specific immunotherapy is a disease-modifying treatment that induces long-term T-cell tolerance. OBJECTIVE: We sought to evaluate the role of circulating CXCR5+PD-1+ T follicular helper (cTFH) and T follicular regulatory (TFR) cells following grass pollen subcutaneous immunotherapy (SCIT) and sublingual immunotherapy (SLIT) and the accompanying changes in their chromatin landscape. METHODS: Phenotype and function of cTFH cells were initially evaluated in the grass pollen-allergic (GPA) group (n = 28) and nonatopic healthy controls (NAC, n = 13) by mathematical algorithms developed to manage high-dimensional data and cell culture, respectively. cTFH and TFR cells were further enumerated in NAC (n = 12), GPA (n = 14), SCIT- (n = 10), and SLIT- (n = 8) treated groups. Chromatin accessibility in cTFH and TFR cells was assessed by assay for transposase-accessible chromatin sequencing (ATAC-seq) to investigate epigenetic mechanisms underlying the differences between NAC, GPA, SCIT, and SLIT groups. RESULTS: cTFH cells were shown to be distinct from TH2- and TH2A-cell subsets, capable of secreting IL-4 and IL-21. Both cytokines synergistically promoted B-cell class switching to IgE and plasma cell differentiation. Grass pollen allergen induced cTFH-cell proliferation in the GPA group but not in the NAC group (P < .05). cTFH cells were higher in the GPA group compared with the NAC group and were lower in the SCIT and SLIT groups (P < .01). Time-dependent induction of IL-4, IL-21, and IL-6 was observed in nasal mucosa following intranasal allergen challenge in the GPA group but not in SCIT and SLIT groups. TFR and IL-10+ cTFH cells were induced in SCIT and SLIT groups (all, P < .01). ATAC-seq analyses revealed differentially accessible chromatin regions in all groups. CONCLUSIONS: For the first time, we showed dysregulation of cTFH cells in the GPA group compared to NAC, SCIT, and SLIT groups and induction of TFR and IL-10+ cTFH cells following SCIT and SLIT. Changes in the chromatin landscape were observed following allergen-specific immunotherapy in cTFH and TFR cells.

Global metabolic profiling to model biological processes of aging in twins.

Aging is intimately linked to system-wide metabolic changes that can be captured in blood. Understanding biological processes of aging in humans could help maintain a healthy aging trajectory and promote longevity. We performed untargeted plasma metabolomics quantifying 770 metabolites on a cross-sectional cohort of 268 healthy individuals including 125 twin pairs covering human lifespan (from 6 months to 82 years). Unsupervised clustering of metabolic profiles revealed 6 main aging trajectories throughout life that were associated with key metabolic pathways such as progestin steroids, xanthine metabolism, and long-chain fatty acids. A random forest (RF) model was successful to predict age in adult subjects (≥16 years) using 52 metabolites (R2  = .97). Another RF model selected 54 metabolites to classify pediatric and adult participants (out-of-bag error = 8.58%). These RF models in combination with correlation network analysis were used to explore biological processes of healthy aging. The models highlighted established metabolites, like steroids, amino acids, and free fatty acids as well as novel metabolites and pathways. Finally, we show that metabolic profiles of twins become more dissimilar with age which provides insights into nongenetic age-related variability in metabolic profiles in response to environmental exposure.

Artificial neural network inference (ANNI): a study on gene-gene interaction for biomarkers in childhood sarcomas.

OBJECTIVE: To model the potential interaction between previously identified biomarkers in children sarcomas using artificial neural network inference (ANNI). METHOD: To concisely demonstrate the biological interactions between correlated genes in an interaction network map, only 2 types of sarcomas in the children small round blue cell tumors (SRBCTs) dataset are discussed in this paper. A backpropagation neural network was used to model the potential interaction between genes. The prediction weights and signal directions were used to model the strengths of the interaction signals and the direction of the interaction link between genes. The ANN model was validated using Monte Carlo cross-validation to minimize the risk of over-fitting and to optimize generalization ability of the model. RESULTS: Strong connection links on certain genes (TNNT1 and FNDC5 in rhabdomyosarcoma (RMS); FCGRT and OLFM1 in Ewing's sarcoma (EWS)) suggested their potency as central hubs in the interconnection of genes with different functionalities. The results showed that the RMS patients in this dataset are likely to be congenital and at low risk of cardiomyopathy development. The EWS patients are likely to be complicated by EWS-FLI fusion and deficiency in various signaling pathways, including Wnt, Fas/Rho and intracellular oxygen. CONCLUSIONS: The ANN network inference approach and the examination of identified genes in the published literature within the context of the disease highlights the substantial influence of certain genes in sarcomas.

DACH1: its role as a classifier of long term good prognosis in luminal breast cancer.

BACKGROUND: Oestrogen receptor (ER) positive (luminal) tumours account for the largest proportion of females with breast cancer. Theirs is a heterogeneous disease presenting clinical challenges in managing their treatment. Three main biological luminal groups have been identified but clinically these can be distilled into two prognostic groups in which Luminal A are accorded good prognosis and Luminal B correlate with poor prognosis. Further biomarkers are needed to attain classification consensus. Machine learning approaches like Artificial Neural Networks (ANNs) have been used for classification and identification of biomarkers in breast cancer using high throughput data. In this study, we have used an artificial neural network (ANN) approach to identify DACH1 as a candidate luminal marker and its role in predicting clinical outcome in breast cancer is assessed. MATERIALS AND METHODS: A reiterative ANN approach incorporating a network inferencing algorithm was used to identify ER-associated biomarkers in a publically available cDNA microarray dataset. DACH1 was identified in having a strong influence on ER associated markers and a positive association with ER. Its clinical relevance in predicting breast cancer specific survival was investigated by statistically assessing protein expression levels after immunohistochemistry in a series of unselected breast cancers, formatted as a tissue microarray. RESULTS: Strong nuclear DACH1 staining is more prevalent in tubular and lobular breast cancer. Its expression correlated with ER-alpha positive tumours expressing PgR, epithelial cytokeratins (CK)18/19 and 'luminal-like' markers of good prognosis including FOXA1 and RERG (p<0.05). DACH1 is increased in patients showing longer cancer specific survival and disease free interval and reduced metastasis formation (p<0.001). Nuclear DACH1 showed a negative association with markers of aggressive growth and poor prognosis. CONCLUSION: Nuclear DACH1 expression appears to be a Luminal A biomarker predictive of good prognosis, but is not independent of clinical stage, tumour size, NPI status or systemic therapy.

Biomarker identification in breast cancer: Beta-adrenergic receptor signaling and pathways to therapeutic response.

Recent preclinical studies have associated beta-adrenergic receptor (ß-AR) signaling with breast cancer pathways such as progression and metastasis. These findings have been supported by clinical and epidemiological studies which examined the effect of beta-blocker therapy on breast cancer metastasis, recurrence and mortality. Results from these studies have provided initial evidence for the inhibition of cell migration in breast cancer by beta-blockers and have introduced the beta-adrenergic receptor pathways as a target for therapy. This paper analyzes gene expression profiles in breast cancer patients, utilising Artificial Neural Networks (ANNs) to identify molecular signatures corresponding to possible disease management pathways and biomarker treatment strategies associated with beta-2-adrenergic receptor (ADRB2) cell signaling. The adrenergic receptor relationship to cancer is investigated in order to validate the results of recent studies that suggest the use of beta-blockers for breast cancer therapy. A panel of genes is identified which has previously been reported to play an important role in cancer and also to be involved in the beta-adrenergic receptor signaling.