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Abstract Impaired organelle-specific protein import triggers a variety of cellular stress responses, including adaptive pathways to balance protein homeostasis. Most of the previous studies focus on the cellular stress response triggered by misfolded proteins or defective protein import in the endoplasmic reticulum or mitochondria. However, little is known about the cellular stress response to impaired protein import in the peroxisome, an understudied organelle that has recently emerged as a key signaling hub for cellular and metabolic homeostasis. To uncover evolutionarily conserved cellular responses upon defective peroxisomal import, we carried out a comparative transcriptomic analysis on fruit flies with tissue-specific peroxin knockdown and human HEK293 cells expressing dominant-negative PEX5C11A. Our RNA-seq results reveal that defective peroxisomal import upregulates integrated stress response (ISR) and downregulates ribosome biogenesis in both flies and human cells. Functional analyses confirm that impaired peroxisomal import induces eIF2α phosphorylation and ATF4 expression. Loss of ATF4 exaggerates cellular damage upon peroxisomal import defects, suggesting that ATF4 activation serves as a cellular cytoprotective mechanism upon peroxisomal import stress. Intriguingly, we show that peroxisomal import stress decreases the expression of rRNA processing genes and inhibits early pre-rRNA processing, which leads to the accumulation of 47S precursor rRNA and reduction of downstream rRNA intermediates. Taken together, we identify ISR activation and ribosome biogenesis inhibition as conserved adaptive stress responses to defective peroxisomal import and uncover a novel link between peroxisomal dysfunction and rRNA processing. 

Pressure swing adsorption (PSA) is a widely used technology to separate a gas product from impurities in a variety of fields. Due to the complexity of PSA operations, process and instrument faults can occur at different parts and/or steps of the process. Thus, effective process monitoring is critical for ensuring efficient and safe operations of PSA systems. However, multi-bed PSA processes present several major challenges to process monitoring. First, a PSA process is operated in a periodic or cyclic fashion and never reaches a steady state; Second, the duration of different operation cycles is dynamically controlled in response to various disturbances, which results in a wide range of normal operation trajectories. Third, there is limited data for process monitoring, and bed pressure is usually the only measured variable for process monitoring. These key characteristics of the PSA operation make process monitoring, especially early fault detection, significantly more challenging than that for a continuous process operated at a steady state. To address these challenges, we propose a feature-based statistical process monitoring (SPM) framework for PSA processes, namely feature space monitoring (FSM). Through feature engineering and feature selection, we show that FSM can naturally handle the key challenges in PSA process monitoring and achieve early detection of subtle faults from a wide range of normal operating conditions. The performance of FSM is compared to the conventional SPM methods using both simulated and real faults from an industrial PSA process. The results demonstrate FSM’s superior performance in fault detection and fault diagnosis compared to the traditional SPM methods. In particular, the robust monitoring performance from FSM is achieved without any data preprocessing, trajectory alignment or synchronization required by the conventional SPM methods.