All Publications


  • Gastrointestinal γδ T cells reveal differentially expressed transcripts and enriched pathways during peanut oral immunotherapy. Allergy Zhang, W., Krishna Dhondalay, G., Liu, T. A., Kaushik, A., Hoh, R., Kwok, S., Kambham, N., Fernandez-Becker, N. Q., Andorf, S., Desai, M., Galli, S. J., Boyd, S. D., Nadeau, K. C., Manohar, M., DeKruyff, R. H., Chinthrajah, R. S. 2022

    View details for DOI 10.1111/all.15250

    View details for PubMedID 35143054

  • CyAnno: A semi-automated approach for cell type annotation of mass cytometry datasets. Bioinformatics (Oxford, England) Kaushik, A., Dunham, D., He, Z., Manohar, M., Desai, M., Nadeau, K. C., Andorf, S. 2021

    Abstract

    MOTIVATION: For immune system monitoring in large-scale studies at the single-cell resolution using CyTOF, (semi-)automated computational methods are applied for annotating live cells of mixed cell types. Here, we show that the live cell pool can be highly enriched with undefined heterogeneous cells, i.e., 'ungated' cells, and that current semi-automated approaches ignore their modeling resulting in misclassified annotations.RESULT: We introduce 'CyAnno', a novel semi-automated approach for deconvoluting the unlabeled cytometry dataset based on a machine learning framework utilizing manually gated training data that allows the integrative modeling of 'gated' cell types and the 'ungated' cells. By applying this framework on several CyTOF datasets, we demonstrated that including the 'ungated' cells can lead to a significant increase in the precision of the 'gated' cell types prediction. CyAnno can be used to identify even a single cell type, including rare cells, with higher efficacy than current state-of-the-art semi-automated approaches.AVAILABILITY: The CyAnno is available as a python script with a user-manual and sample dataset at https://github.com/abbioinfo/CyAnno.SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

    View details for DOI 10.1093/bioinformatics/btab409

    View details for PubMedID 34037686

  • Homologies between SARS-CoV-2 and allergen proteins may direct T cell-mediated heterologous immune responses. Scientific reports Balz, K., Kaushik, A., Chen, M., Cemic, F., Heger, V., Renz, H., Nadeau, K., Skevaki, C. 2021; 11 (1): 4792

    Abstract

    The outbreak of the new severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a public health emergency. Asthma does not represent a risk factor for COVID-19 in several published cohorts. We hypothesized that the SARS-CoV-2 proteome contains T cell epitopes, which are potentially cross-reactive to allergen epitopes. We aimed at identifying homologous peptide sequences by means of two distinct complementary bioinformatics approaches. Pipeline 1 included prediction of MHC Class I and Class II epitopes contained in the SARS-CoV-2 proteome and allergens along with alignment and elaborate ranking approaches. Pipeline 2 involved alignment of SARS-CoV-2 overlapping peptides with known allergen-derived T cell epitopes. Our results indicate a large number of MHC Class I epitope pairs including known as well as de novo predicted allergen T cell epitopes with high probability for cross-reactivity. Allergen sources, such as Aspergillus fumigatus, Phleum pratense and Dermatophagoides species are of particular interest due to their association with multiple cross-reactive candidate peptides, independently of the applied bioinformatic approach. In contrast, peptides derived from food allergens, as well as MHC class II epitopes did not achieve high in silico ranking and were therefore not further investigated. Our findings warrant further experimental confirmation along with examination of the functional importance of such cross-reactive responses.

    View details for DOI 10.1038/s41598-021-84320-8

    View details for PubMedID 33637823

  • Global survey-based assessment of lifestyle changes during the COVID-19 pandemic. PloS one Agarwal, P., Kaushik, A., Sarkar, S., Rao, D., Mukherjee, N., Bharat, V., Das, S., Saha, A. K. 2021; 16 (8): e0255399

    Abstract

    Along with the major impact on public health, the COVID-19 outbreak has caused unprecedented concerns ranging from sudden loss of employment to mental stress and anxiety. We implemented a survey-based data collection platform to characterize how the COVID-19 pandemic has affected the socio-economic, physical and mental health conditions of individuals. We focused on three broad areas, namely, changes in social interaction during home confinement, economic impact and their health status. We identified a substantial increase in virtual interaction among individuals, which might be a way to alleviate the sudden unprecedented mental health burden, exacerbated by general awareness about viral infections or other manifestations associated with them. The majority of participants (85%) lived with one or more companions and unemployment issues did not affect 91% of the total survey takers, which was one of the crucial consequences of the pandemic. Nevertheless, measures such as an increased frequency of technology-aided distant social interaction, focus on physical fitness and leisure activities were adopted as coping mechanisms during this period of home isolation. Collectively, these metrics provide a succinct and informative summary of the socio-economic and health impact of the COVID-19 pandemic on the individuals. Findings from our study reflect that continuous surveillance of the psychological consequences for outbreaks should become routine as part of preparedness efforts worldwide. Given the limitations of analyzing the large number of variables, we have made the raw data publicly available on the OMF ME/CFS Data Center server to facilitate further analyses (https://igenomed.stanford.edu/dataset/survey-study-on-lifestyle-changes-during-covid-19-pandemic).

    View details for DOI 10.1371/journal.pone.0255399

    View details for PubMedID 34388151

  • Homologies between SARS-CoV-2 and allergen proteins may direct T cell-mediated heterologous immune responses. Research square Balz, K. n., Chen, M. n., Kaushik, A. n., Cemic, F. n., Heger, V. n., Renz, H. n., Nadeau, K. n., Skevaki, C. n. 2020

    Abstract

    The outbreak of the new Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is a public health emergency. Asthma does not represent a risk factor for COVID-19 in several published cohorts. We hypothesized that the SARS-CoV-2 proteome contains T cell epitopes, which are potentially cross-reactive to allergen epitopes. We aimed at identifying homologous peptide sequences by means of two distinct complementary bioinformatics approaches. Pipeline 1 included prediction of MHC Class I and Class II epitopes contained in the SARS-CoV-2 proteome and allergens along with alignment and elaborate ranking approaches. Pipeline 2 involved alignment of SARS-CoV-2 overlapping peptides with known allergen-derived T cell epitopes. Our results indicate a large number of MHC Class I epitope pairs including known as well as de novo predicted allergen T cell epitopes with high probability for cross-reactivity. Allergen sources, such as Aspergillus fumigatus , Phleum pratense and Dermatophagoides species are of particular interest due to their association with multiple cross-reactive candidate peptides, independently of the applied bioinformatic approach. In contrast, peptides derived from food allergens, as well as MHC class II epitopes did not achieve high in silico ranking and were therefore not further investigated. Our findings warrant further experimental confirmation along with examination of the functional importance of such cross-reactive responses.

    View details for DOI 10.21203/rs.3.rs-86873/v1

    View details for PubMedID 33052330

    View details for PubMedCentralID PMC7553154