Ke Xu Successfully Defends His Thesis
Congratulations to Ke Xu on the successful defense of their thesis, "Airway Gene Expression Alterations in Association with Radiographic Abnormalities of the Lung" Great job Ke!
To learn more about Ke's thesis, read their abstract below:
High-resolution computed tomography (HRCT) of the chest is commonly used in the diagnosis of a variety of lung diseases. Structural changes associated with clinical characteristics of disease may also define specific disease-associated physiologic states that may provide insights into disease pathophysiology. Gene expression profiling is potentially a useful adjunct to HRCT to identify molecular correlates of the observed structural changes. However, it is difficult to directly access diseased distal airway or lung parenchyma routinely for profiling studies.
Previously, we have profiled bronchial airway in normal-appearing epithelial cells at the mainstem bronchus, detecting distinct gene expression alterations related to the clinical diagnosis of chronic obstructive pulmonary disease (COPD) and lung cancer. These gene expression alterations offer insights into the molecular events related to diseased tissue at more distal airways and in the parenchyma, which we hypothesize are due to a field-of-injury effect. Here, we expand this prior work by correlating airway gene expression to COPD and bronchiectasis phenotypes defined by HRCT to better understand the pathophysiology of these diseases. Additionally, we classified pulmonary nodules as malignant or benign by combining HRCT nodule imaging characteristics with gene expression profiling of the nasal airway.
First, we collected brushing samples from the main-stem bronchus and assessed gene expression alterations associated with COPD phenotypes defined by K-means clustering of HRCT-based imaging features. We found three imaging clusters, which correlated with incremental severity of COPD: normal, interstitial predominant, and emphysema predominant. 41 genes were differentially expressed between the normal and the emphysema predominant clusters. Functional analysis of the differentially expressed genes suggests a possible induction of inflammatory processes and repression of T-cell related biologic pathways, in the emphysema predominant cluster.
We then discovered gene expression alterations associated with radiographic evidence of bronchiectasis (BE), an underdiagnosed obstructive pulmonary disease with unclear pathophysiology. We found 655 genes were differentially expressed in bronchial epithelium from individuals with radiographic evidence of BE despite none of the study participants having a clinical BE diagnosis. In addition to biological pathways that had been previously associated with BE, novel pathways that may play important roles in BE initiation were also discovered. Furthermore, we leveraged an independent single-cell RNA-sequencing dataset of the bronchial epithelium to explore whether the observed gene expression alterations might be cell-type dependent. We computationally detected an increased presence of ciliated and deuterosomal cells, as well as a decreased presence of basal cells in subjects with widespread radiographic BE, which may reflect a shift in the cellular landscape of the airway during BE initiation.
Finally, we identified gene expression alterations within the nasal epithelium associated with the presence of malignant pulmonary nodules. A computational model was constructed for determining whether a nodule is malignant or benign that combines gene expression and imaging features extracted from HRCT. Leveraging data from single-cell RNA sequencing, we found genes increased in patients with lung cancer are expressed at higher levels within a novel cluster of nasal epithelial cells, termed keratinizing epithelial cells.
In summary, we leveraged gene expression profiling of the proximal airway and discovered novel biological pathways that potentially drive the structural changes representative of physiologic states defined by chest HRCT in COPD and BE. This approach may also be combined with chest HRCT to detect weak signals related to malignant pulmonary nodules.
Major Professor: Marc Lenburg