Differential effects of microRNAs miR-21, miR-99 and miR-145 on lung regeneration and inflammation during recovery from influenza pneumonia.

In a mouse model of influenza pneumonia, we previously documented that proliferating alveolar type II (AT2) cells are the major stem cells involved in early lung recovery. Profiling of microRNAs revealed significant dysregulation of specific ones, including miR-21 and miR-99a. Moreover, miR-145 is known to exhibit antagonism to miR-21. This follow-up study investigated the roles of microRNAs miR-21, miR-99a, and miR-145 in the murine pulmonary regenerative process and inflammation during influenza pneumonia. Inhibition of miR-21 resulted in severe morbidity, and in significantly decreased proliferating AT2 cells due to impaired transition from innate to adaptive immune responses. Knockdown of miR-99a culminated in moderate morbidity, with a significant increase in proliferating AT2 cells that may be linked to PTEN downregulation. In contrast, miR-145 antagonism did not impact morbidity nor the proliferating AT2 cell population, and was associated with downregulation of TNF-alpha, IL1-beta, YM1, and LY6G. Hence, a complex interplay exists between expression of specific miRNAs, lung regeneration, and inflammation during recovery from influenza pneumonia. Inhibition of miR-21 and miR-99a (but not miR-145) can lead to deleterious cellular and molecular effects on pulmonary repair and inflammatory processes during influenza pneumonia.

Context-aware transcript quantification from long-read RNA-seq data with Bambu.

Most approaches to transcript quantification rely on fixed reference annotations; however, the transcriptome is dynamic and depending on the context, such static annotations contain inactive isoforms for some genes, whereas they are incomplete for others. Here we present Bambu, a method that performs machine-learning-based transcript discovery to enable quantification specific to the context of interest using long-read RNA-sequencing. To identify novel transcripts, Bambu estimates the novel discovery rate, which replaces arbitrary per-sample thresholds with a single, interpretable, precision-calibrated parameter. Bambu retains the full-length and unique read counts, enabling accurate quantification in presence of inactive isoforms. Compared to existing methods for transcript discovery, Bambu achieves greater precision without sacrificing sensitivity. We show that context-aware annotations improve quantification for both novel and known transcripts. We apply Bambu to quantify isoforms from repetitive HERVH-LTR7 retrotransposons in human embryonic stem cells, demonstrating the ability for context-specific transcript expression analysis.