A specialized population of monocyte-derived tracheal macrophages promote airway epithelial regeneration through a CCR2-dependent mechanism.

Macrophages are critical for maintenance and repair of mucosal tissues. While functionally distinct subtypes of macrophage are known to have important roles in injury response and repair in the lungs, little is known about macrophages in the proximal conducting airways. Single-cell RNA sequencing and flow cytometry demonstrated murine tracheal macrophages are largely monocyte-derived and are phenotypically distinct from lung macrophages at homeostasis. Following sterile airway injury, monocyte-derived macrophages are recruited to the trachea and activate a pro-regenerative phenotype associated with wound healing. Animals lacking the chemokine receptor CCR2 have reduced numbers of circulating monocytes and tracheal macrophages, deficient pro-regenerative macrophage activation and defective epithelial repair. Together, these studies indicate that recruitment and activation of monocyte-derived tracheal macrophages is CCR2-dependent and is required for normal airway epithelial regeneration.

Graph attention-based fusion of pathology images and gene expression for prediction of cancer survival.

Multimodal machine learning models are being developed to analyze pathology images and other modalities, such as gene expression, to gain clinical and biological insights. However, most frameworks for multimodal data fusion do not fully account for the interactions between different modalities. Here, we present an attention-based fusion architecture that integrates a graph representation of pathology images with gene expression data and concomitantly learns from the fused information to predict patient-specific survival. In our approach, pathology images are represented as undirected graphs, and their embeddings are combined with embeddings of gene expression signatures using an attention mechanism to stratify tumors by patient survival. We show that our framework improves the survival prediction of human non-small cell lung cancers, outperforming existing state-of-the-art approaches that leverage multimodal data. Our framework can facilitate spatial molecular profiling to identify tumor heterogeneity using pathology images and gene expression data, complementing results obtained from more expensive spatial transcriptomic and proteomic technologies.

Investigating the sequence landscape in the Drosophila initiator core promoter element using an enhanced MARZ algorithm.

The core promoter elements are important DNA sequences for the regulation of RNA polymerase II transcription in eukaryotic cells. Despite the broad evolutionary conservation of these elements, there is extensive variation in the nucleotide composition of the actual sequences. In this study, we aim to improve our understanding of the complexity of this sequence variation in the TATA box and initiator core promoter elements in Drosophila melanogaster. Using computational approaches, including an enhanced version of our previously developed MARZ algorithm that utilizes gapped nucleotide matrices, several sequence landscape features are uncovered, including an interdependency between the nucleotides in position 2 and 5 in the initiator. Incorporating this information in an expanded MARZ algorithm improves predictive performance for the identification of the initiator element. Overall our results demonstrate the need to carefully consider detailed sequence composition features in core promoter elements in order to make more robust and accurate bioinformatic predictions.