Asthma phenotypes, associated comorbidities, and long-term symptoms in COVID-19.

BACKGROUND: It is unclear whether asthma and its allergic phenotype are risk factors for hospitalization or severe disease from SARS-CoV-2. METHODS: All patients over 28 days old testing positive for SARS-CoV-2 between March 1 and September 30, 2020, were retrospectively identified and characterized through electronic analysis at Stanford. A sub-cohort was followed prospectively to evaluate long-term COVID-19 symptoms. RESULTS: 168,190 patients underwent SARS-CoV-2 testing, and 6,976 (4.15%) tested positive. In a multivariate analysis, asthma was not an independent risk factor for hospitalization (OR 1.12 [95% CI 0.86, 1.45], p = .40). Among SARS-CoV-2-positive asthmatics, allergic asthma lowered the risk of hospitalization and had a protective effect compared with non-allergic asthma (OR 0.52 [0.28, 0.91], p = .026); there was no association between baseline medication use as characterized by GINA and hospitalization risk. Patients with severe COVID-19 disease had lower eosinophil levels during hospitalization compared with patients with mild or asymptomatic disease, independent of asthma status (p = .0014). In a patient sub-cohort followed longitudinally, asthmatics and non-asthmatics had similar time to resolution of COVID-19 symptoms, particularly lower respiratory symptoms. CONCLUSIONS: Asthma is not a risk factor for more severe COVID-19 disease. Allergic asthmatics were half as likely to be hospitalized with COVID-19 compared with non-allergic asthmatics. Lower levels of eosinophil counts (allergic biomarkers) were associated with a more severe COVID-19 disease trajectory. Recovery was similar among asthmatics and non-asthmatics with over 50% of patients reporting ongoing lower respiratory symptoms 3 months post-infection.

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.

Transcriptional changes in peanut-specific CD4+ T cells over the course of oral immunotherapy.

Oral immunotherapy (OIT) can successfully desensitize allergic individuals to offending foods such as peanut. Our recent clinical trial (NCT02103270) of peanut OIT allowed us to monitor peanut-specific CD4+ T cells, using MHC-peptide Dextramers, over the course of OIT. We used a single-cell targeted RNAseq assay to analyze these cells at 0, 12, 24, 52, and 104 weeks of OIT. We found a transient increase in TGFß-producing cells at 52 weeks in those with successful desensitization, which lasted until 117 weeks. We also performed clustering and identified 5 major clusters of Dextramer+ cells, which we tracked over time. One of these clusters appeared to be anergic, while another was consistent with recently described TFH13 cells. The other 3 clusters appeared to be Th2 cells by their coordinated production of IL-4 and IL-13, but they varied in their expression of STAT signaling proteins and other markers. A cluster with high expression of STAT family members also showed a possible transient increase at week 24 in those with successful desensitization. Single cell TCRαß repertoire sequences were too diverse to track clones over time. Together with increased TGFß production, these changes may be mechanistic predictors of successful OIT that should be further investigated.

Food allergy and omics.

Food allergy (FA) prevalence has been increasing over the last few decades and is now a global health concern. Current diagnostic methods for FA result in a high number of false-positive results, and the standard of care is either allergen avoidance or use of epinephrine on accidental exposure, although currently with no other approved treatments. The increasing prevalence of FA, lack of robust biomarkers, and inadequate treatments warrants further research into the mechanism underlying food allergies. Recent technological advances have made it possible to move beyond traditional biological techniques to more sophisticated high-throughput approaches. These technologies have created the burgeoning field of omics sciences, which permit a more systematic investigation of biological problems. Omics sciences, such as genomics, epigenomics, transcriptomics, proteomics, metabolomics, microbiomics, and exposomics, have enabled the construction of regulatory networks and biological pathway models. Parallel advances in bioinformatics and computational techniques have enabled the integration, analysis, and interpretation of these exponentially growing data sets and opens the possibility of personalized or precision medicine for FA.

Integrated plasma proteomic and single-cell immune signaling network signatures demarcate mild, moderate, and severe COVID-19.

The biological determinants underlying the range of coronavirus 2019 (COVID-19) clinical manifestations are not fully understood. Here, over 1,400 plasma proteins and 2,600 single-cell immune features comprising cell phenotype, endogenous signaling activity, and signaling responses to inflammatory ligands are cross-sectionally assessed in peripheral blood from 97 patients with mild, moderate, and severe COVID-19 and 40 uninfected patients. Using an integrated computational approach to analyze the combined plasma and single-cell proteomic data, we identify and independently validate a multi-variate model classifying COVID-19 severity (multi-class area under the curve [AUC]training = 0.799, p = 4.2e-6; multi-class AUCvalidation = 0.773, p = 7.7e-6). Examination of informative model features reveals biological signatures of COVID-19 severity, including the dysregulation of JAK/STAT, MAPK/mTOR, and nuclear factor κB (NF-κB) immune signaling networks in addition to recapitulating known hallmarks of COVID-19. These results provide a set of early determinants of COVID-19 severity that may point to therapeutic targets for prevention and/or treatment of COVID-19 progression.

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.

Her-2/neu overexpression due to polysomy 17 in breast cancer: molecular testing to guide therapeutic options.

BACKGROUND: Fluorescent in situ hybridization (FISH) equivocal results for Her-2/neu still pose a diagnostic dilemma in oncology practice. In this study, we evaluate if Her-2/neu mRNA expression is an alternative to FISH for detecting Her-2/neu positivity. PATIENTS AND METHODS: Archival paraffin blocks of 54 breast cancer patients were analyzed for Her-2/neu status using immunohistochemistry (IHC), FISH, and Her-2/neu gene expression using mRNA. RESULTS: There was a 100% positive agreement and 64.7% negative agreement of Her-2/neu mRNA expression with respect to the reference standard (FISH), with the kappa value for agreement being 0.36. mRNA levels correlated positively and strongly with FISH ratio and IHC positivity. For Her-2/neu mRNA expression, Her-2/neu copy number was a significant predictor indicating that mRNA expression is independent of polysomy status. CONCLUSIONS: Her-2/neu mRNA expression may help tide over ambiguity posed by polysomy and FISH equivocal samples.

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.

Integrated plasma proteomic and single-cell immune signaling network signatures demarcate mild, moderate, and severe COVID-19.

The biological determinants of the wide spectrum of COVID-19 clinical manifestations are not fully understood. Here, over 1400 plasma proteins and 2600 single-cell immune features comprising cell phenotype, basal signaling activity, and signaling responses to inflammatory ligands were assessed in peripheral blood from patients with mild, moderate, and severe COVID-19, at the time of diagnosis. Using an integrated computational approach to analyze the combined plasma and single-cell proteomic data, we identified and independently validated a multivariate model classifying COVID-19 severity (multi-class AUCtraining = 0.799, p-value = 4.2e-6; multi-class AUCvalidation = 0.773, p-value = 7.7e-6). Features of this high-dimensional model recapitulated recent COVID-19 related observations of immune perturbations, and revealed novel biological signatures of severity, including the mobilization of elements of the renin-angiotensin system and primary hemostasis, as well as dysregulation of JAK/STAT, MAPK/mTOR, and NF-κB immune signaling networks. These results provide a set of early determinants of COVID-19 severity that may point to therapeutic targets for the prevention of COVID-19 progression.

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.

HER2/neu Testing In 432 Consecutive Breast Cancer Cases using FISH and IHC - A Comparative Study.

INTRODUCTION: The development of trastuzumab, a HER-2/neu targeted monoclonal antibody resulted in significant improvements in clinical response and survival in HER-2/neu gene amplified group of patients. Thus, accurate assessment of HER-2/neu status becomes critical. Fluorescence In Situ Hybridization (FISH) and Immunohistochemistry (IHC) are the most commonly used methods for this purpose and specific recommendations exist with regard to the concordance to be observed between the two tests. AIM: Here, we report and evaluate the concordance rate between FISH and IHC for HER-2/neu status in breast cancer specimens. MATERIALS AND METHODS: Archival paraffin blocks of tumour tissue from 450 patients of breast cancer were analyzed for Her-2/neu status using FISH and IHC. RESULTS: There was a highly significant concordance between the results of FISH and IHC assays in HER-2/neu status assessment in invasive breast cancer cases. There were inverse associations between the expression of Oestrogen Receptors/Progesterone Receptors (ER/PR) and HER-2/neu amplification. CONCLUSION: Although, IHC gave significant concordant results with FISH in determining HER-2/neu status, its subjective grading system precludes its use as a gold standard. FISH should always be used to determine true gene amplification when the IHC results are equivocal.

Relationship between preparation of cells for therapy and cell quality using artificial neural network analysis.

OBJECTIVE: The successful preparation of cells for therapy depends on the characterization of causal factors affecting cell quality. Ultra scale-down methods are used to characterize cells in terms of their response to process engineering causal factors of hydrodynamic shear stress and time. This response is in turn characterized in terms of causal factors relating to variations as may naturally occur during cell preparation, i.e., passage number, generation number, time of the final passage stage and hold time in formulation medium. METHODS: To investigate the influence of all of these causal factors we have adopted a non-linear, multivariate predictive artificial neural network (ANN) based modeling approach to help create clearer insights into their effect on cell membrane integrity and surface marker content. A prostate cancer cell line candidate for cancer therapy (P4E6) was used and cell surface markers CD9, CD147 and HLA A-C were investigated. RESULTS: All causal factors studied were found to be significant in establishing an ANN model for the prediction of cell quality parameters with the extent of exposure to shear stress being the most significant and then passage number (range 57-66) and generation number (range 10-19) determining most strongly the cells' resistance to shear stress. Both the operation of the final cell passage and the hold time of the cells in a formulation buffer also determine the cells' resistance to shear stress. The processing parameters related to cell handling after preparation, i.e., shear stress and time of exposure were found to be the most influential affecting cell quality. CONCLUSION: CD9 surface marker loss was the most sensitive indicator of the effects of shear stress followed by loss of membrane integrity and then HLA A-C, while CD147 remained unaffected by shear stress or even prone to increase. Also greater stability of cell surface marker presence was noted for cells generated at greater passage numbers or generation numbers or for reduction in hold time in formulation buffer.

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.