Multicenter evaluation of parametric response mapping as an indicator of bronchiolitis obliterans syndrome after hematopoietic stem cell transplantation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/156134/2/ajt15814_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156134/1/ajt15814.pd

Translational Control of Cytochrome c by RNA-Binding Proteins TIA-1 and HuR

Stresses affecting the endoplasmic reticulum (ER) globally modulate gene expression patterns by altering posttranscriptional processes such as translation. Here, we use tunicamycin (Tn) to investigate ER stress-triggered changes in the translation of cytochrome c, a pivotal regulator of apoptosis. We identified two RNA-binding proteins that associate with its ∼900-bp-long, adenine- and uridine-rich 3′ untranslated region (UTR): HuR, which displayed affinity for several regions of the cytochrome c 3′UTR, and T-cell-restricted intracellular antigen 1 (TIA-1), which preferentially bound the segment proximal to the coding region. HuR did not appear to influence the cytochrome c mRNA levels but instead promoted cytochrome c translation, as HuR silencing greatly diminished the levels of nascent cytochrome c protein. By contrast, TIA-1 functioned as a translational repressor of cytochrome c, with interventions to silence TIA-1 dramatically increasing cytochrome c translation. Following treatment with Tn, HuR binding to cytochrome c mRNA decreased, and both the presence of cytochrome c mRNA within actively translating polysomes and the rate of cytochrome c translation declined. Taken together, our data suggest that the translation rate of cytochrome c is determined by the opposing influences of HuR and TIA-1 upon the cytochrome c mRNA. Under unstressed conditions, cytochrome c mRNA is actively translated, but in response to ER stress agents, both HuR and TIA-1 contribute to lowering its biosynthesis rate. We propose that HuR and TIA-1 function coordinately to maintain precise levels of cytochrome c production under unstimulated conditions and to modify cytochrome c translation when damaged cells are faced with molecular decisions to follow a prosurvival or a prodeath path

Negative Feedback Regulation of MKK6 mRNA Stability by p38α Mitogen-Activated Protein Kinase

p38 mitogen-activated protein (MAP) kinases play an important role in the regulation of cellular responses to all kinds of stresses. The most abundant and broadly expressed p38 MAP kinase is p38α, which can also control the proliferation, differentiation, and survival of several cell types. Here we show that the absence of p38α correlates with the up-regulation of one of its upstream activators, the MAP kinase kinase MKK6, in p38α(−/−) knockout mice and in cultured cells derived from them. In contrast, the expression levels of the p38 activators MKK3 and MKK4 are not affected in p38α-deficient cells. The increase in MKK6 protein concentration correlates with increased amounts of MKK6 mRNA in the p38α(−/−) cells. Pharmacological inhibition of p38α also up-regulates MKK6 mRNA levels in HEK293 cells. Conversely, reintroduction of p38α into p38α(−/−) cells reduces the levels of MKK6 protein and mRNA to the normal levels found in wild-type cells. Moreover, we show that the MKK6 mRNA is more stable in p38α(−/−) cells and that the 3′untranslated region of this mRNA can differentially regulate the stability of the lacZ reporter gene in a p38α-dependent manner. Our data indicate that p38α can negatively regulate the stability of the MKK6 mRNA and thus control the steady-state concentration of one of its upstream activators

Lung cancer lesion detection in histopathology images using graph‐based sparse PCA network

Early detection of lung cancer is critical for improvement of patient survival. To address the clinical need for efficacious treatments, genetically engineered mouse models (GEMM) have become integral in identifying and evaluating the molecular underpinnings of this complex disease that may be exploited as therapeutic targets. Assessment of GEMM tumor burden on histopathological sections performed by manual inspection is both time consuming and prone to subjective bias. Therefore, an interplay of needs and challenges exists for computer-aided diagnostic tools, for accurate and efficient analysis of these histopathology images. In this paper, we propose a simple machine learning approach called the graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E). Our method comprises four steps: 1) cascaded graph-based sparse PCA, 2) PCA binary hashing, 3) block-wise histograms, and 4) support vector machine (SVM) classification. In our proposed architecture, graph-based sparse PCA is employed to learn the filter banks of the multiple stages of a convolutional network. This is followed by PCA hashing and block histograms for indexing and pooling. The meaningful features extracted from this GS-PCA are then fed to an SVM classifier. We evaluate the performance of the proposed algorithm on H&E slides obtained from an inducible K-rasG12D lung cancer mouse model using precision/recall rates, Fβ-score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC) and show that our algorithm is efficient and provides improved detection accuracy compared to existing algorithms

Quantitative CT correlates with local inflammation in lung of patients with subtypes of chronic lung allograft dysfunction

Chronic rejection of lung allografts has two major subtypes, bronchiolitis obliterans syndrome (BOS) and restrictive allograft syndrome (RAS), which present radiologically either as air trapping with small airways disease or with persistent pleuroparenchymal opacities. Parametric response mapping (PRM), a computed tomography (CT) methodology, has been demonstrated as an objective readout of BOS and RAS and bears prognostic importance, but has yet to be correlated to biological measures. Using a topological technique, we evaluate the distribution and arrangement of PRM-derived classifications of pulmonary abnormalities from lung transplant recipients undergoing redo-transplantation for end-stage BOS (N = 6) or RAS (N = 6). Topological metrics were determined from each PRM classification and compared to structural and biological markers determined from microCT and histopathology of lung core samples. Whole-lung measurements of PRM-defined functional small airways disease (fSAD), which serves as a readout of BOS, were significantly elevated in BOS versus RAS patients (p = 0.01). At the core-level, PRM-defined parenchymal disease, a potential readout of RAS, was found to correlate to neutrophil and collagen I levels (p < 0.05). We demonstrate the relationship of structural and biological markers to the CT-based distribution and arrangement of PRM-derived readouts of BOS and RAS

Phosphorylation of HuR by Chk2 Regulates SIRT1 Expression

The RNA binding protein HuR regulates the stability of many target mRNAs. Here, we report that HuR associated with the 3' untranslated region of the mRNA encoding the longevity and stress-response protein SIRT1, stabilized the SIRT1 mRNA, and increased SIRT1 expression levels. Unexpectedly, oxidative stress triggered the dissociation of the [HuR-SIRT1 mRNA] complex, in turn promoting SIRT1 mRNA decay, reducing SIRT1 abundance, and lowering cell survival. The cell cycle checkpoint kinase Chk2 was activated by H2O2, interacted with HuR, and was predicted to phosphorylate HuR at residues S88, S100, and T118. Mutation of these residues revealed a complex pattern of HuR binding, with S100 appearing to be important for [HuR-SIRT1 mRNA] dissociation after H2O2. Our findings demonstrate that HuR regulates SIRT1 expression, underscore functional links between the two stress-response proteins, and implicate Chk2 in these processes