Biology subjects

Tummala, S.

Publications and source records attributed to Tummala, S..

3 recordsLinked to original sources

bioRxiv2024

Temporal dynamics of inflammatory and transcriptome changes in the myocardium in a murine model of cardiac arrest

BackgroundIt is increasingly recognized that inflammatory changes play a significant role in myocardial injury following cardiac arrest. However, the pathophysiological mechanisms responsible for functional changes in the myocardium following the event remain incompletely understood. Here, we characterize the transcriptomic and immunological changes in a mouse model of cardiac arrest to explore the roles of epinephrine and ischemia/reperfusion on myocardial inflammation. Methods and ResultsMale and female mice (C57BL/6J) were divided into three groups: (1) Naive (anesthesia only), (2) Sham (anesthesia + saline injection + epinephrine), and (3) Arrest (8-minute cardiac arrest followed by resuscitation (anesthesia + potassium chloride + epinephrine). We monitored the animals over 0.5, 1, 3, and 7 days with serial echocardiography. Hearts were harvested for transcriptomic analysis and inflammatory changes as assessed by flow cytometry and immunohistochemistry. Plasma samples were collected for cytokine signaling analysis by multiplex ELISA. Our data indicate that the cardiac arrest group have increased infiltrating neutrophils in the myocardium and elevated plasma levels of pro-inflammatory cytokines, peaking at 0.5 days after the initial insult and subsequently resolving. In contrast, Sham mice displayed less pronounced inflammatory changes, which peaked at 1 day after the procedure. Notably, these inflammatory alterations coincided with cardiac stunning, as demonstrated by echocardiography. ConclusionsIn our murine model of cardiac arrest, epinephrine exposure and ischemia/reperfusion induce significant inflammatory changes in the myocardium with increased infiltrating neutrophils and elevated circulating cytokines which subsequently resolve in a time-dependent manner. These changes correlate with improving cardiac function by echocardiography.

immunology↗

bioRxiv2023

Molecular pathways and cellular subsets associated with adverse clinical outcomes in overlapping immune-related myocarditis and myositis

Immune checkpoint therapies (ICTs) can induce life-threatening immune-related adverse events, including myocarditis and myositis, which are rare but often concurrent. The molecular pathways and immune subsets underlying these toxicities remain poorly understood. To address this need, we obtained heart and skeletal muscle biopsies for single-cell RNA sequencing in living patients with cancers treated with ICTs admitted to the hospital with myocarditis and /or myositis (overlapping myocarditis plus myositis, n=10; myocarditis-only, n=1) compared to ICT-exposed patients ruled out for toxicity utilized as controls (n=9) within 96 hours of clinical presentation. Analyses of 58,523 cells revealed clonally expanded CD8+ T cells with a cytotoxic phenotype expressing activation/exhaustion markers in both myocarditis and myositis. Furthermore, the analyses identified a population of tissue-resident myeloid cells expressed Fc{gamma}RIIIa, which is known to bind IgG and regulate complement activation. Immunohistochemistry of affected cardiac and skeletal muscle tissues revealed protein expression of pan-IgG and complement product C4d that were associated with the presence of high-titer serum autoantibodies against muscle antigens in a subset of patients. We further identified a population of inflammatory IL-1B+TNF+ myeloid cells specifically enriched in myocarditis and associated with greater toxicity severity and poorer clinical outcomes. These results are the first to recognize these myeloid subsets in human immune-related myocarditis and myositis tissues and nominate new targets for investigation into rational treatments to overcome these high-mortality toxicities. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=168 SRC="FIGDIR/small/556590v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@b5895forg.highwire.dtl.DTLVardef@4dec8corg.highwire.dtl.DTLVardef@1e50cb9org.highwire.dtl.DTLVardef@a653fd_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2020

Machine Learning Framework For Fully Automatic Quality Checking Of Rigid And Affine Registrations In Big Data Brain MRI

Rigid and affine registrations to a common template are the essential steps during pre-processing of brain structural magnetic resonance imaging (MRI) data. Manual quality check (QC) of these registrations is quite tedious if the data contains several thousands of images. Therefore, we propose a machine learning (ML) framework for fully automatic QC of these registrations via local computation of the similarity functions such as normalized cross-correlation, normalized mutual-information, and correlation ratio, and using these as features for training of different ML classifiers. To facilitate supervised learning, misaligned images are generated. A structural MRI dataset consisting of 215 subjects from autism brain imaging data exchange is used for 5-fold cross-validation and testing. Few classifiers such as kNN, AdaBoost, and random forest reached testing F1-scores of 0.98 for QC of both rigid and affine registrations. These tested ML models could be deployed for practical use.

neuroscience↗