Biology subjects

Ghaemi, M. S.

Publications and source records attributed to Ghaemi, M. S..

3 recordsLinked to original sources

bioRxiv2025

Machine Learning Reveals Distinct Immunogenic Signatures of Th1 Imprinting in ART-Treated Individuals with HIV Following Repeated SARS-CoV-2 Vaccination

The human immune system is intrinsically variable and remarkably diverse across a population. The immune response to antigens is driven by a complex interplay of time-dependent interdependencies across components of the immune system. After repeated vaccination, the humoral and cellular arms of the immune response display highly heterogeneous dynamics, further complicating the attribution of a phenotypic outcome to specific immune system components. We employ a random forest (RF) approach to classify informative differences in immunogenicity between older people living with HIV (PLWH) on ART and an age-matched control group who received up to five SARS-CoV-2 vaccinations over 104 weeks. RFs identify immunological variables of importance, interpreted as evidence for Th1 imprinting, and suggest novel distinguishing immune features, such as saliva-based antibody screening, as promising diagnostic features towards classifying responses (whereas serum IgG is not). Additionally, we implement supervised and unsupervised Machine Learning methods to produce physiologically accurate synthetic datasets that conform to the statistical distribution of the original immunological data, thus enabling further data-driven hypothesis testing and model validation. Our results highlight the effectiveness of RFs in utilizing informative immune feature interdependencies for classification tasks and suggests broad impacts of ML applications for personalized vaccination strategies among high-risk populations.

immunology↗

bioRxiv2022

Longitudinal Assessment of SARS-CoV-2 Specific T Cell Cytokine-Producing Responses for 1 Year Reveals Persistence of Multi-Cytokine Proliferative Responses, with Greater Immunity Associated with Disease Severity

Cellular-mediated immunity is critical for long-term protection against most viral infections, including coronaviruses. We studied 23 SARS-CoV-2-infected survivors over a one year post symptom onset (PSO) interval by ex vivo cytokine ELISpot assay. All subjects demonstrated SARS-CoV-2-specific IFN-{gamma}, IL-2, and Granzyme B (GzmB) T cell responses at presentation, with greater frequencies in severe disease. Cytokines, mainly produced by CD4+ T cells, targeted all structural proteins (Nucleocapsid, Membrane, Spike) except Envelope, with GzmB > IL-2 > IFN-{gamma}. Mathematical modeling predicted that: 1) cytokine responses peaked at 6 days for IFN-{gamma}, 36 days for IL-2, and 7 days for GzmB, 2) severe illness was associated with reduced IFN-{gamma} and GzmB, but increased IL-2 production rates, 3) males displayed greater production of IFN-{gamma}, whereas females produced more GzmB. Ex vivo responses declined over time with persistence of IL-2 in 86% and of IFN-{gamma} and GzmB in 70% of subjects at a median of 336 days PSO. The average half-life of SARS-CoV-2-specific cytokine-producing cells was modelled to be 139 days ([~]4.6 months). Potent T cell proliferative responses persisted throughout observation, were CD4 dominant, and were capable of producing all 3 cytokines. Several immunodominant CD4 and CD8 epitopes identified in this study were shared by seasonal coronaviruses or SARS-CoV-1 in the Nucleocapsid and Membrane regions. Both SARS-CoV-2-specific CD4+ and CD8+ T cell clones were able to kill target cells, though CD8 tended to be more potent. ImportanceOur findings highlight the relative importance of SARS-CoV-2-specific GzmB-producing T cell responses in SARS-CoV-2 control, shared CD4 and CD8 immunodominant epitopes in seasonal coronaviruses or SARS-CoV-1, and indicate robust persistence of T cell memory at least one year after infection. Our findings should inform future strategies to induce T cell vaccines against SARS-CoV-2 and other coronaviruses.

immunology↗

bioRxiv2020

Integration of Mechanistic Immunological Knowledge into a Machine Learning Pipeline Increases Predictive Power

The dense network of interconnected cellular signaling responses quantifiable in peripheral immune cells provide a wealth of actionable immunological insights. While high-throughput single-cell profiling techniques, including polychromatic flow and mass cytometry, have matured to a point that enables detailed immune profiling of patients in numerous clinical settings, limited cohort size together with the high dimensionality of data increases the possibility of false positive discoveries and model overfitting. We introduce a machine learning platform, the immunological Elastic-Net (iEN), which incorporates immunological knowledge directly into the predictive models. Importantly, the algorithm maintains the exploratory nature of the high-dimensional dataset, allowing for the inclusion of immune features with strong predictive power even if not consistent with prior knowledge. In three independent studies our method demonstrates improved predictive power for clinically-relevant outcomes from mass cytometry data generated from whole blood, as well as a large simulated dataset.

bioinformatics↗