Muscle wasting and the temporal gene expression pattern in a novel rat intensive care unit model

<p>Abstract</p> <p>Background</p> <p>Acute quadriplegic myopathy (AQM) or critical illness myopathy (CIM) is frequently observed in intensive care unit (ICU) patients. To elucidate duration-dependent effects of the ICU intervention on molecular and functional networks that control the muscle wasting and weakness associated with AQM, a gene expression profile was analyzed at time points varying from 6 hours to 14 days in a unique experimental rat model mimicking ICU conditions, i.e., post-synaptically paralyzed, mechanically ventilated and extensively monitored animals.</p> <p>Results</p> <p>During the observation period, 1583 genes were significantly up- or down-regulated by factors of two or greater. A significant temporal gene expression pattern was constructed at short (6 h-4 days), intermediate (5-8 days) and long (9-14 days) durations. A striking early and maintained up-regulation (6 h-14d) of muscle atrogenes (muscle ring-finger 1/tripartite motif-containing 63 and F-box protein 32/atrogin-1) was observed, followed by an up-regulation of the proteolytic systems at intermediate and long durations (5-14d). Oxidative stress response genes and genes that take part in amino acid catabolism, cell cycle arrest, apoptosis, muscle development, and protein synthesis together with myogenic factors were significantly up-regulated from 5 to 14 days. At 9-14 d, genes involved in immune response and the caspase cascade were up-regulated. At 5-14d, genes related to contractile (myosin heavy chain and myosin binding protein C), regulatory (troponin, tropomyosin), developmental, caveolin-3, extracellular matrix, glycolysis/gluconeogenesis, cytoskeleton/sarcomere regulation and mitochondrial proteins were down-regulated. An activation of genes related to muscle growth and new muscle fiber formation (increase of myogenic factors and JunB and down-regulation of myostatin) and up-regulation of genes that code protein synthesis and translation factors were found from 5 to 14 days.</p> <p>Conclusions</p> <p>Novel temporal patterns of gene expression have been uncovered, suggesting a unique, coordinated and highly complex mechanism underlying the muscle wasting associated with AQM in ICU patients and providing new target genes and avenues for intervention studies.</p

CD99 is a novel prognostic stromal marker in non-small cell lung cancer

The complex interaction between cancer cells and the microenvironment plays an essential role in all stages of tumourigenesis. Despite the significance of this interplay, alterations in protein composition underlying tumourstroma interactions are largely unknown. The aim of this study was to identify stromal proteins with clinical relevance in non-small cell lung cancer (NSCLC). A list encompassing 203 stromal candidate genes was compiled based on gene expression array data and available literature. The protein expression of these genes in human NSCLC was screened using the Human Protein Atlas. Twelve proteins were selected that showed a differential stromal staining pattern (BGN, CD99, DCN, EMILIN1, FBN1, PDGFRB, PDLIM5, POSTN, SPARC, TAGLN, TNC and VCAN). The corresponding antibodies were applied on tissue microarrays, including 190 NSCLC samples, and stromal staining was correlated with clinical parameters. Higher stromal expression of CD99 was associated with better prognosis in the univariate (p = 0.037) and multivariate (p = 0.039) analysis. The association was independent from the proportion of tumour stroma, the fraction of inflammatory cells and clinical and pathological parameters like stage, performance status and tumour histology. The prognostic impact of stromal CD99 protein expression was confirmed in an independent cohort of 240 NSCLC patients (p = 0.008). Furthermore, double-staining confocal fluorescence microscopy showed that CD99 was expressed in stromal lymphocytes as well as in cancer-associated fibroblasts. Based on a comprehensive screening strategy the membrane protein CD99 was identified as a novel stromal factor with clinical relevance. The results support the concept that stromal properties have an important impact on tumour progression

Genome-Wide DNA Methylation Profiling of Chronic Lymphocytic Leukemia Subsets Carrying Stereotyped B Cell Receptors

In recent years, subsets of chronic lymphocytic leukemia (CLL) patients carrying quasi-identical or stereotyped B cell receptors (BcRs) have been identified that share clinicobiological features and disease outcome. While these stereotyped subsets show distinct gene expression and genomic profiles, the DNA methylation landscape remains largely unexplored. By applying high-resolution 450K methylation arrays, we investigated 176 CLL subset cases belonging to: (i) the clinically aggressive, IGHV-unmutated (U-CLL) subsets $$1 (clan I genes/IGKV(D)1-39, n=37) and$$8 (IGHV4-39/IGKV1(D)-39, n=21); (ii) the IGHV1-69-expressing U-CLL subsets $$3 (n=12),$$5 (n=9), $$6 (n=22), and$$7 (n=12); and, (iii) the indolent, IGHV-mutated (M-CLL) subset $$4 (IGHV4-34/IGKV2-30, n=28). In addition, we included subset$$2 cases (IGHV3-21/IGLV3-21, mixed mutation status, n=35) that have a poor outcome independent of IGHV mutation status. For comparative purposes, we included a cohort of CLL cases that do not express stereotyped BcRs ('non-subset', n=325). These patients were subgrouped according to the recently proposed epigenetic classification of CLL, i.e. poor-prognostic, naive-like CLL (n-CLL, n=102), favorable-prognostic, memory-like CLL (m-CLL; n=176), broadly corresponding to U-CLL and M-CLL, respectively, and a third intermediate CLL subgroup (i-CLL; n=47), which express borderline mutated IGHV genes and have an intermediate prognosis. Finally, a series of sorted normal subpopulations spanning different stages of B-cell differentiation [precursors (n=22), naive B cells (n=19) and germinal center/memory B-cells (n=33)] were also included in the analysis. Overall, unsupervised analysis of subset vs. non-subset CLL revealed that all U-CLL subsets clustered with n-CLL, subset $$4 clustered with m-CLL, while subset$$2 clustered separately with i-CLL (Figure 1). Supervised analysis revealed a limited number of CpG sites that were differentially methylated when comparing each U-CLL or M-CLL subset with non-subset cases. In contrast, almost all subset $$2 cases clustered separately from i-CLL in supervised analysis, indicating that this subset might represent a distinct subgroup of i-CLL. We recently demonstrated that the number of epigenetic changes that a tumor acquires, compared to its cellular origin (i.e. 'epigenetic burden'), may be a powerful predictor of clinical aggressiveness (Queiros et al, Cancer Cell 2016). When adopting this approach in CLL, comparison of specific subsets vs. their non-subset cases matched by epigenetic subgroup, revealed significant differences in the epigenetic burden amongst the various groupings; for instance, in subset$$1 vs. n-CLL (72K vs. 67K, plt;0.05) and in subset $$2 vs. i-CLL (76K vs. 68K, p=0.001), while no difference was observed between subset$$4 vs. m-CLL (83K vs. 82K, p=not significant). Subset $$2 cases frequently carry del(11q) and harbor SF3B1 mutations, however, neither the IGHV mutation status nor the presence of del(11q) or SF3B1 mutations had any impact on the epigenetic burden within subset$$2. In conclusion, U-CLL and M-CLL subsets generally clustered with n-CLL and m-CLL categories, respectively, implying common cellular origins. In contrast, subset $$2 emerged as the first defined member of the i-CLL group, which in turn alludes to a distinct cellular origin and/or pathogenetic process for subset$$2 and i-CLL patients.Disclosures Papakonstantinou: Janssen Pharmaceuticals: Research Funding; Gilead: Research Funding. Smedby: Janssen: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees. Gaidano: Roche: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria; Gilead: Consultancy, Honoraria; Janssen: Consultancy, Honoraria. Ghia: AbbVie: Consultancy; Adaptive: Consultancy; Gilead: Consultancy, Research Funding, Speakers Bureau; Janssen: Consultancy, Research Funding; Pharmacyclics LLC, an AbbVie Company: Consultancy; Roche: Consultancy; Novartis: Research Funding. Stamatopoulos: Novartis SA: Research Funding; Gilead: Consultancy, Honoraria, Research Funding; Janssen Pharmaceuticals: Honoraria, Research Funding; Abbvie: Honoraria, Research Funding.↵* Asterisk with author names denotes non-ASH members