Search

Case Presentation: A 34-year-old previously healthy man of Korean descent (height, 174 cm; weight, 47.4 kg) demonstrated dyspnea with cough and chest tightness. The patient had no relevant occupational exposures and no history of illicit drug or tobacco use. His medical history was notable for chronic sinus tachycardia of undetermined cause, hypertension, gout, glaucoma of the right eye, and a remote history of an intracranial malignancy 24 years prior treated with unspecified chemotherapy, craniotomy, and ventriculoperitoneal shunt placement. His active medications included diltiazem, candesartan, and colchicine as needed., (Copyright © 2021 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.)

Background: Improved understanding of lung transplant disease states is essential because failure rates are high, often due to chronic lung allograft dysfunction. However, histologic assessment of lung transplant transbronchial biopsies (TBBs) is difficult and often uninterpretable even with 10 pieces., Methods: We prospectively studied whether microarray assessment of single TBB pieces could identify disease states and reduce the amount of tissue required for diagnosis. By following strategies successful for heart transplants, we used expression of rejection-associated transcripts (annotated in kidney transplant biopsies) in unsupervised machine learning to identify disease states., Results: All 242 single-piece TBBs produced reliable transcript measurements. Paired TBB pieces available from 12 patients showed significant similarity but also showed some sampling variance. Alveolar content, as estimated by surfactant transcript expression, was a source of sampling variance. To offset sampling variation, for analysis, we selected 152 single-piece TBBs with high surfactant transcripts. Unsupervised archetypal analysis identified 4 idealized phenotypes (archetypes) and scored biopsies for their similarity to each: normal; T-cell‒mediated rejection (TCMR; T-cell transcripts); antibody-mediated rejection (ABMR)-like (endothelial transcripts); and injury (macrophage transcripts). Molecular TCMR correlated with histologic TCMR. The relationship of molecular scores to histologic ABMR could not be assessed because of the paucity of ABMR in this population., Conclusions: Molecular assessment of single-piece TBBs can be used to classify lung transplant biopsies and correlated with rejection histology. Two or 3 pieces for each TBB will probably be needed to offset sampling variance., (Copyright © 2019 International Society for Heart and Lung Transplantation. Published by Elsevier Inc. All rights reserved.)

Background: Somatic cells in vitro have a finite life expectancy before entering a state of senescence, but it is unclear whether this state occurs in vivo in kidney development, growth, and aging. We previously showed that human kidney cortex displays telomere shortening with age. In this study, we compared the structural and functional changes in rat kidney with age to phenomena associated with cellular senescence in vitro., Methods: We assessed the changes in Fischer 344 rat kidneys from age 1 to 9 months to define growth and development and from age 9 to 24 months to define aging., Results: Rat kidney telomeres were approximately 35 to 40 kb long and did not shorten significantly. Expression of mRNA for p16INK4a, a characteristic senescence gene in vitro, was undetectable in most young rats but rose 27 fold during growth and a further 72-fold during aging. p16INK4a protein was localized to the nucleus and increased with age. p16INK4a mRNA also increased in other tissues. Lipofuscin and senescence-associated beta-galactosidase increased in epithelium with growth and aging and their occurrence was significantly associated with each other. Lipofuscin was particularly found in atrophic nephrons., Conclusion: We conclude that cell senescence occurs in both growth and aging in rat kidney and may contribute to the age-related pathology. These changes are not due to telomere shortening, but may reflect cumulative environmental stress.