LMP student seminars: 26 February

Agile education

Each week during term time, MSc and PhD candidates in the Department of Laboratory Medicine and Pathobiology present their research.

Anyone is welcome. No need to register.

Location: Medical Sciences Building, rooms 4171 or 4279, see below.

As part of the core research curriculum, students taking LMP1001/2/3: Graduate Seminars in Laboratory Medicine and Pathobiology will present their projects. Please see abstracts below.

5. Infectious Diseases, Inflammation and Immunology (ID)

Location: MSB 4279

Maya Aisa Allen

Title: Defining Cellular Infiltrates in DSA+ and DSA- Antibody Mediated Rejection Using Spatial and Unbiased Proteomics
Supervisor: Dr. Ana Konvalinka

Mackenzie Scott

Title: From Result to Response: Investigating the Link between Laboratory Testing and COVID-19 Patient Outcomes
Supervisor: Dr. Jennifer Taher

4. Cardiovascular, Physiology and Metabolism

Location: MSB 4171

Nathaniel James Heayn

Title: TBA
Supervisor: TBA

Abstracts

Maya Aisa Allen: Defining Cellular Infiltrates in DSA+ and DSA- Antibody Mediated Rejection Using Spatial and Unbiased Proteomics

Kidney transplantation is the optimal treatment for end-stage kidney disease, but many kidney allografts fail prematurely, due to antibody mediated rejection (AMR). AMR is caused by donor-specific antibodies (DSAs) against human leukocyte antigens (HLA) on the graft endothelium. DSAs interact with Fcγ receptors (FcγRs) stimulating complement activation, and triggering antibody-dependent cell cytotoxicity in myeloid and NK cells. Intriguingly, 30-60% of DSA-positive kidney allograft recipients never develop rejection, and 30-50% of patients that do develop AMR do not have any detectable DSAs, suggesting the presence of unidentified contributors to the pathogenicity of AMR.

My goal is to define the molecular mechanisms of AMR by evaluating the protein-based differences in clinical biopsies with DSA+ AMR, DSA- AMR, no rejection (NR) despite DSA and T-cell-mediated rejection (TCMR). Using unbiased proteomics, I compared the proteome of biopsies from DSA+ AMR, DSA- AMR, DSA+ NR, and TCMR patients (n=56). Of 1406 proteins quantified, 115 were significantly differentially expressed (ANOVA, p<0.05). The expression of complement factors (C3, C4A, C9) was significantly higher in DSA- AMR compared to DSA+ AMR and DSA+ NR. Conversely, mitochondrial metabolism/oxidative stress proteins were highest in the DSA+ AMR group. I have optimized immunostaining for FcγRs, and imaging mass cytometry. Preliminary data indicate these receptors are expressed in patients with both DSA+ and DSA- AMR, and localized to myeloid cells. In conclusion, myeloid cells expressing FcγRs may be observed in both DSA+ and DSA- AMR patients, whereas other mechanisms (i.e. complement mediated injury) may play a larger role in DSA- AMR.

Mackenzie Scott: From Result to Response: Investigating the Link between Laboratory Testing and COVID-19 Patient Outcomes

Background: Patient characteristics relating to COVID-19 severity are well-documented; however, methods to accurately predict patients’ acute reaction to SARS-CoV-2 infections remain lacking. Evaluating associations between frequently ordered laboratory markers and patient outcomes can help create laboratory-based scoring models to estimate patients’ risk of adverse events.

Methods: 325 inpatients from three hospitals were recruited into the GENCOV project between January 2020 and February 2022. Participants were ≥18 years of age, provided consent, and were hospitalized within 1 month of having a PCR confirmed COVID-19 infection. Extensive clinical data including demographics, laboratory results for 32 biomarkers, and treatment outcomes were extracted from electronic medical records. Logistic regression determined significant associations between laboratory results and patient outcomes. Significant markers were assigned risk values and total risk scores were calculated. Scoring methods were validated on a 20% subset of inpatients. Receiver operating characteristic (ROC) curves evaluated model performance.

Results: Six laboratory markers were associated with COVID-19 patient mortality, controlling for age and sex. Regression modelling showed that elevated creatinine, lactate, white blood cells, and/or low base excess, bicarbonate, and pH results upon admission reflect increased odds of mortality, in comparison to having normal levels for the same markers. Comparison of ROC curves revealed that scoring methods performed similarly in validation (0.800 vs 0.802) and development cohorts (0.821 vs 0.829) with both approaches reflecting higher sensitivity (90% vs 85%) than specificity (58% vs 67%) within validation.

Conclusions: The developed risk scores for COVID-19 patients are promising, although further evaluation in other populations is warranted.

Nathaniel James Heayn: TBA

TBA

Contact

No need to register.

Contact lmp.grad@utoronto.ca with any questions