Residual endotoxin induces primary graft dysfunction through ischemia/reperfusion-primed alveolar macrophages.

Despite the widespread use of antibiotics, bacterial pneumonias in donors strongly predispose to the fatal syndrome of primary graft dysfunction (PGD) following lung transplantation. We report that bacterial endotoxin persists in human donor lungs after pathogen is cleared with antibiotics and is associated with neutrophil infiltration and PGD. In mouse models, depletion of tissue-resident alveolar macrophages (TRAMs) attenuated neutrophil recruitment in response to endotoxin as shown by compartmental staining and intravital imaging. Bone marrow chimeric mice revealed that neutrophils were recruited by TRAM through activation of TLR4 in a MyD88-dependent manner. Intriguingly, low levels of endotoxin, insufficient to cause donor lung injury, promoted TRAM-dependent production of CXCL2, increased neutrophil recruitment, and led to PGD, which was independent of donor NCMs. Reactive oxygen species (ROS) increased in human donor lungs starting from the warm-ischemia phase and were associated with increased transcription and translocation to the plasma membrane of TLR4 in donor TRAMs. Consistently, scavenging ROS or inhibiting their production to prevent TLR4 transcription/translocation or blockade of TLR4 or coreceptor CD14 on donor TRAMs prevented neutrophil recruitment in response to endotoxin and ameliorated PGD. Our studies demonstrate that residual endotoxin after successful treatment of donor bacterial pneumonia promotes PGD through ischemia/reperfusion-primed donor TRAMs.

A Beginner's Guide to Analysis of RNA Sequencing Data.

Since the first publications coining the term RNA-seq (RNA sequencing) appeared in 2008, the number of publications containing RNA-seq data has grown exponentially, hitting an all-time high of 2,808 publications in 2016 (PubMed). With this wealth of RNA-seq data being generated, it is a challenge to extract maximal meaning from these datasets, and without the appropriate skills and background, there is risk of misinterpretation of these data. However, a general understanding of the principles underlying each step of RNA-seq data analysis allows investigators without a background in programming and bioinformatics to critically analyze their own datasets as well as published data. Our goals in the present review are to break down the steps of a typical RNA-seq analysis and to highlight the pitfalls and checkpoints along the way that are vital for bench scientists and biomedical researchers performing experiments that use RNA-seq.