Biology subjects

Hawn, T. R.

Publications and source records attributed to Hawn, T. R..

3 recordsLinked to original sources

bioRxiv2022

kimma: flexible linear mixed effects modeling with kinship for RNA-seq data

We introduce kimma (Kinship In Mixed Model Analysis), an open-source R package for flexible linear mixed effects modeling of RNA-seq including covariates, weights, random effects, covariance matrices, and fit metrics. In simulated datasets, kimma detects differentially expressed genes (DEGs) with similar specificity, sensitivity, and computational time as limma unpaired and dream paired models. Unlike other software, kimma supports covariance matrices as well as fit metrics like AIC. Utilizing genetic kinship covariance, kimma revealed that kinship impacts model fit and DEG detection in a related cohort. Thus, kimma equals or outcompetes current DEG pipelines in sensitivity, computational time, and model complexity.

bioinformatics↗

bioRxiv2022

Peripheral blood T-cell deficiency and hyperinflammatory monocyte responses associate with MAC lung disease

RationaleAlthough nontuberculous mycobacterial (NTM) disease is a growing problem, available treatments are suboptimal and diagnostic tools are inadequate. Immunological mechanisms of susceptibility to NTM disease are poorly understood. ObjectiveTo understand NTM pathogenesis, we evaluated innate and antigen-specific adaptive immune responses to Mycobacterium avium complex (MAC) in individuals with MAC lung disease (MACDZ). MethodsWe synthesized 15mer MAC-, NTM-, or MAC/Mtb-specific peptides and stimulated peripheral blood mononuclear cells (PBMC) with pools of these peptides. We measured T-cell responses by cytokine production, expression of surface markers, and analysis of global gene expression in 27 MACDZ individuals and 32 healthy controls. We also analyzed global gene expression in Mav-infected and uninfected peripheral blood monocytes from 17 MACDZ and 17 healthy controls. Measurements and Main ResultsWe were unable to detect T-cell responses against the peptide libraries or Mav lysate that has increased reactivity in MACDZ subjects compared to controls. T-cell responses to non-mycobacteria derived antigens were preserved. MACDZ individuals had a lower frequency of Th1 and Th1* T-cell populations. By contrast, global gene expression analysis demonstrated upregulation of proinflammatory pathways in uninfected and Mav-infected monocytes derived from MACDZ subjects compared to controls. ConclusionsPeripheral blood T-cell responses to Mycobacterial antigens and the frequency of Th1 and Th1* cell populations are diminished in individuals with MAC disease. In contrast, MACDZ subjects had hyperinflammatory monocyte responses. Together, these data suggest a novel immunologic defect which underlies MAC pathogenesis and includes concurrent innate and adaptive dysregulation.

immunology↗

bioRxiv2021

Tracking SARS-CoV-2 Spike Protein Mutations in the United States (2020/01 - 2021/03) Using a Statistical Learning Strategy

The emergence and establishment of SARS-CoV-2 variants of interest (VOI) and variants of concern (VOC) highlight the importance of genomic surveillance. We propose a statistical learning strategy (SLS) for identifying and spatiotemporally tracking potentially relevant Spike protein mutations. We analyzed 167,893 Spike protein sequences from US COVID-19 cases (excluding 21,391 sequences from VOI/VOC strains) deposited at GISAID from January 19, 2020 to March 15, 2021. Alignment against the reference Spike protein sequence led to the identification of viral residue variants (VRVs), i.e., residues harboring a substitution compared to the reference strain. Next, generalized additive models were applied to model VRV temporal dynamics, to identify VRVs with significant and substantial dynamics (false discovery rate q-value <0.01; maximum VRV proportion > 10% on at least one day). Unsupervised learning was then applied to hierarchically organize VRVs by spatiotemporal patterns and identify VRV-haplotypes. Finally, homology modelling was performed to gain insight into potential impact of VRVs on Spike protein structure. We identified 90 VRVs, 71 of which have not previously been observed in a VOI/VOC, and 35 of which have emerged recently and are durably present. Our analysis identifies 17 VRVs [~]91 days earlier than their first corresponding VOI/VOC publication. Unsupervised learning revealed eight VRV-haplotypes of 4 VRVs or more, suggesting two emerging strains (B1.1.222 and B.1.234). Structural modeling supported potential functional impact of the D1118H and L452R mutations. The SLS approach equally monitors all Spike residues over time, independently of existing phylogenic classifications, and is complementary to existing genomic surveillance methods.

genomics↗