Transcriptome Profiling of Pediatric Core Binding Factor AML.

 0  5  18  2017-02-01 13:49:34 Report infringing document
RESEARCH ARTICLE Transcriptome Profiling of Pediatric Core Binding Factor AML Chih-Hao Hsu1, Cu Nguyen1, Chunhua Yan1, Rhonda E. Ries2, Qing-Rong Chen1, Ying Hu1, Fabiana Ostronoff2, Derek L. Stirewalt2, George Komatsoulis1, Shawn Levy3, Daoud Meerzaman1☯, Soheil Meshinchi2☯* 1 Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD, 20850, United States of America, 2 Fred Hutchinson Cancer Research Center, Seattle, WA, United States of America, 3 Hudson Alpha Institute for Biotechnology, Huntsville, AL, United States of America ☯ These authors contributed equally to this work. * Abstract OPEN ACCESS Citation: Hsu C-H, Nguyen C, Yan C, Ries RE, Chen Q-R, Hu Y, et al. (2015) Transcriptome Profiling of Pediatric Core Binding Factor AML. PLoS ONE 10(9): e0138782. doi:10.1371/journal.pone.0138782 Editor: Ken Mills, Queen's University Belfast, UNITED KINGDOM Received: June 15, 2015 Accepted: September 3, 2015 Published: September 23, 2015 Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication. Data Availability Statement: All BAM files have been deposited at The database of Genotypes and Phenotypes (dbGaP, under substudy, phs000465.v10.p3, TARGET: Acute Myeloid Leukemia (AML). Funding: This work was funded by a grant from the National Cancer Institute, NCI 5 R01 CA114563-08 ( and a Scientific Leadership NIH National Clinical Trials Network (NCTN) Grant (U10CA180886). Competing Interests: The authors have declared that no competing interests exist. The t(8;21) and Inv(16) translocations disrupt the normal function of core binding factors alpha (CBFA) and beta (CBFB), respectively. These translocations represent two of the most common genomic abnormalities in acute myeloid leukemia (AML) patients, occurring in approximately 25% pediatric and 15% of adult with this malignancy. Both translocations are associated with favorable clinical outcomes after intensive chemotherapy, and given the perceived mechanistic similarities, patients with these translocations are frequently referred to as having CBF-AML. It remains uncertain as to whether, collectively, these translocations are mechanistically the same or impact different pathways in subtle ways that have both biological and clinical significance. Therefore, we used transcriptome sequencing (RNA-seq) to investigate the similarities and differences in genes and pathways between these subtypes of pediatric AMLs. Diagnostic RNA from patients with t(8;21) (N = 17), Inv(16) (N = 14), and normal karyotype (NK, N = 33) were subjected to RNA-seq. Analyses compared the transcriptomes across these three cytogenetic subtypes, using the NK cohort as the control. A total of 1291 genes in t(8;21) and 474 genes in Inv(16) were differentially expressed relative to the NK controls, with 198 genes differentially expressed in both subtypes. The majority of these genes (175/198; binomial test p-value < 10−30) are consistent in expression changes among the two subtypes suggesting the expression profiles are more similar between the CBF cohorts than in the NK cohort. Our analysis also revealed alternative splicing events (ASEs) differentially expressed across subtypes, with 337 t(8;21)-specific and 407 Inv(16)-specific ASEs detected, the majority of which were acetylated proteins (p = 1.5x10-51 and p = 1.8x10-54 for the two subsets). In addition to known fusions, we identified and verified 16 de novo fusions in 43 patients, including three fusions involving NUP98 in six patients. Clustering of differentially expressed genes indicated that the homeobox (HOX) gene family, including two transcription factors (MEIS1 and NKX2-3) were down-regulated in CBF compared to NK samples. This finding supports existing data that the dysregulation of HOX genes play a central role in biology CBF-AML hematopoiesis. These data provide comprehensive transcriptome profiling of CBF-AML and delineate genes and pathways that are differentially expressed, providing insights into the shared biology as well as differences in the two CBF subsets. PLOS ONE | DOI:10.1371/journal.pone.0138782 September 23, 2015 1 / 18 Transcriptome Profiling of Pediatric CBF AML Introduction Acute myeloid leukemia (AML) is a hematopoietic malignancy defined by genetic (and epigenetic) alterations in hematopoietic stem or progenitor cells that lead to dysregulation of critical signal transduction pathways resulting in clonal expansion without complete differentiation. The genomic landscape of AML is under investigation. Distinct profiles have been discovered for different karyotypes and single-nucleotide polymorphisms (SNPs), revealing the heterogeneity and complexity of AML[1]. This genomic complexity leads to variability in responses to chemotherapy and disparate outcomes. Moreover, we and others have found age-dependent shifts in the genomic abnormalities of AML, some of which [2, 3] may contribute to differential outcomes observed in adult vs. pediatric AML[4]. Although these previous studies have helped us to better understand the correlation between genotypes and phenotypes in AML, a more detailed examination of defined molecular subgroups may yield another level of understanding, which is not readily attainable by examining more molecular diverse AML populations. Cytogenetic alterations have been shown to play a critical role in the diagnosis of AML[1]. Fusions involving RUNX1-RUNX1T1 and CBFB-MYH11, collectively referred to as core binding factor (CBF) AML, are one of the most frequent and most-studied genomic events in AML [5, 6]. Despite extensive studies into the biologic implications of these fusion transcripts and their use for risk stratification,[7, 8] knowledge of the presence of these fusions has not led to new targeted interventions. Further, despite the fact that t(8;21) and Inv(16) implicate CBFA and CBFB, respectively, and lead to similar clinical outcomes, potential mechanistic similarities and differences remain to be well defined. RNA-seq for whole-transcriptome sequencing has become a powerful approach for studying mRNA transcripts[9, 10]. In contrast to traditional microarray methods, RNA-seq can identify de novo transcripts that are not represented in the reference genome (i.e., fusion genes) [11] while quantifying previously described reference transcripts[12] and identifying splicing alterations[13]. Recently, several adult AML studies using NGS technologies have been reported. The Cancer Genome Atlas (TCGA) Research Network[14] revealed the genomic and epigenetic landscapes of 200 adult de novo AML patients using whole-genome, whole-exome, RNA, and microRNA sequencing, along with DNA methylation studies. In addition, MacRae et al.[15] used RNA-seq to analyze 55 adult leukemia samples, identifying 119 genes whose expression is more consistent than the commonly used control genes across those leukemia samples. Lilljebjorn et al. [16] also used RNA-seq to identify fusion genes in adult leukemia patients. In contrast, the study of the pathogenesis of pediatric AML using NGS technologies is still in its earliest stages, and large studies have not extensively evaluated CBF-AML patients using this technology. In this report, we use whole-transcriptome sequencing to interrogate the transcript profiles for pediatric CBF-AML, comparing these to transcripts from cases with normal karyotype. The results reveal that t(8;21) and Inv(16) translocations aberrantly impact a set of common genes and molecular pathways and there are unique gene-expression signatures, splicing differences, and fusions observed in the CBF subtype. Results Patient characteristics This cohort includes specimens from 64 patients with de novo AML with either t(8;21), N = 17; Inv(16), N = 14; or normal karyotype (NK), N = 33 treated on Children’s Oncology Group (COG) pediatric AML clinical trials. Patients with NK were selected for those with and without PLOS ONE | DOI:10.1371/journal.pone.0138782 September 23, 2015 2 / 18 Transcriptome Profiling of Pediatric CBF AML Fig 1. Distribution of aligned reads in the human genome (hg19). doi:10.1371/journal.pone.0138782.g001 FLT3/ITD Mutation (N = 14 and 19, respectively). Baseline characteristics of the patients are shown in S1 Table. RNA sequencing in pediatric AML samples RNA sequencing was performed using the Illumina platform for all 64 samples, with an average of 47 million (27,576,734–91,175,150) reads per sample. Ninety-six percent of these reads were mapped to the human reference sequence (hg19/NCBI Build 37) using the next-generation sequencing (NGS) aligner Novoalign (; ~26,000 RefSeq genes were covered by at least one read and ~16,500 RefSeq genes had RPKM (Reads Per Kilobase per Million mapped reads)  1 (S2 Table). Ninety percent of these mapped reads were located within gene regions, including coding, UTR, and intronic regions, and the distribution was very similar among different cytogenetic abnormalities (Fig 1). Identification of differentially expressed genes by RNA sequencing In order to determine differential gene expression patterns specific to different cytogenetic categories, we performed principal component analysis (PCA) (Fig 2A). The PCA using all genes successfully separated out expression profiles for samples with Inv(16), t(8;21), or NK into three distinct clusters, suggesting that cytogenetic abnormalities profoundly affected geneexpression patterns. Two patients with NK had expression profiles that clustered with those with Inv(16). Closer examination of the two cases demonstrated the presence of CBFB-MYH11 through fluorescence in situ hybridization (FISH) in 22% of the studied metaphases in one case. However, the second case did not show CBFB-MYH11 fusions through FISH or real-time polymerase chain reaction (RT-PCR). The only fusion event shared by these two cases was the intergenic fusion NDRG1-ST3GAL1, which was also found in one t(8;21) sample and in one Inv(16) sample, but not in other NK samples. PLOS ONE | DOI:10.1371/journal.pone.0138782 September 23, 2015 3 / 18 Transcriptome Profiling of Pediatric CBF AML Fig 2. Differentially expressed genes characterize different cytogenetic abnormalities. (A) Principal component analysis for samples with different cytogenetic abnormalities. (B-D) Circular plots were drawn with the in-house software application OmicCircos[18] to represent the t(8;21)-specific, Inv(16)specific, and normal-specific differentially expressed genes. The track from outside to inside are the symbols of differentially expressed genes with high significance (p-value < 1.0E-08); genome positions by chromosomes (black lines are cytobands); average expression level for the samples with specific cytogenetic abnormalities (yellow); average expression level for the remaining samples (pink); fold change (red: up-regulated; blue: down-regulated); and the p-values associated with the expression patterns between one subtype and the remaining samples. doi:10.1371/journal.pone.0138782.g002 PLOS ONE | DOI:10.1371/journal.pone.0138782 September 23, 2015 4 / 18 Transcriptome Profiling of Pediatric CBF AML Table 1. Most significantly up- and down-regulated genes in the three cytogenetic categories. t(8; 21) Inv(16) Up-regulation Down-regulation Normal Up-regulation Down-regulation p-value Up-regulation Gene p-value Gene p-value Gene RUNX1T1 2.21E-31 RFX8 7.18E-20 MMP14 CTC-497E21.4 7.55E-22 TSPAN32 5.14E-19 AK5 SIPA1L2 7.91E-19 MEIS1 7.02E-17 XPNPEP2 TRH 3.55E-18 RP11-556E13.1 5.44E-16 EMILIN1 WIPF3 4.34E-14 HOXB2 8.00E-14 CHI3L1 9.33E-11 DBN1 8.50E-06 BAHCC1 POU4F1 9.74E-13 C11orf21 2.28E-13 CD9 2.44E-10 TANC2 2.08E-05 RP1-163G9.1 PALM 1.06E-12 ARHGEF11 1.19E-11 SPARC 2.44E-10 DNAJC12 3.50E-05 TSPAN32 AC141928.1 2.37E-12 HOXB-AS1 2.77E-11 TGFBI 2.44E-10 CD59 5.15E-05 PSD3 1.86E-11 AC004540.5 2.99E-11 LPAR1 4.57E-10 CYP7B1 IRX6 1.07E-10 LAT2 3.96E-11 ME1 1.18E-09 GAPDHP14 1.24E-10 HOXB4 1.70E-10 GPR12 2.14E-09 IGSF1 1.37E-10 CPVL 2.56E-10 COBLL1 SGPP1 1.55E-10 HOXA-AS4 3.69E-10 CLIP3 DOCK6 6.10E-10 HOXA10 5.79E-10 EVC2 1.24E-09 C1orf127 SLCO5A1 1.32E-09 HOXA9 PLCG1 1.67E-09 DOCK1 2.79E-09 CYP2E1 GYLTL1B 2.35E-09 HOXB3 9.41E-09 NT5E Down-regulation Gene p-value Gene p-value Gene p-value 7.06E-22 COL23A1 1.02E-09 NKX2-3 6.60E-17 ADARB2-AS1 7.85E-11 5.32E-21 RP1-249H1.4 5.98E-09 RP11-129J12.2 1.73E-10 MMP28 5.70E-10 2.79E-16 RP11-1055B8.6 1.47E-08 RP11-1055B8.6 7.16E-10 RP11-1134I14.2 1.51E-09 3.26E-16 RANBP17 3.05E-07 PRDM16 1.51E-09 KIRREL 2.42E-09 2.42E-09 LAMB2 2.42E-09 2.91E-09 DLGAP3 4.84E-09 1.25E-07 CD52 6.48E-09 PLXNC1 2.86E-07 RUNX1T1 8.99E-09 5.39E-05 CTD-3179P9.1 5.31E-07 PEAR1 1.44E-08 SPATA6 6.71E-05 SEL1L3 1.21E-06 AADAT 1.63E-08 NR6A1 7.13E-05 AIG1 2.17E-06 PSD3 2.58E-08 2.67E-09 CTC-455F18.3 1.52E-04 CTSG 3.26E-06 CERS4 1.42E-07 5.54E-09 RP11-480D4.3 1.72E-04 MIR4740 5.05E-06 RP11-567J24.4 1.11E-06 LSAMP 7.66E-09 PCNXL2 1.83E-04 RP11-1055B8.4 1.86E-05 ST18 1.62E-06 8.84E-10 CD1B 8.22E-09 LRRC37A16P 1.83E-04 TRGC1 2.27E-05 TPO 1.71E-06 1.80E-09 EMP1 2.93E-08 BAHCC1 1.94E-04 OCLN 2.63E-05 SGK110 3.89E-06 3.19E-08 NKX2-3 2.47E-04 BEND6 2.63E-05 ALS2CL 3.89E-06 6.47E-08 RP5-862P8.2 2.47E-04 HOXA10 2.85E-05 CCDC50 5.05E-06 doi:10.1371/journal.pone.0138782.t001 To identify differentially expressed genes specific to each of the cytogenetic cohorts, we performed differential expression analysis using DESeq package[17], which uses a model based on the negative binomial distribution with variance and mean linked by local regression. Comparing t(8;21) samples with the remaining samples, a total of 827 t(8;21)-specific genes were found to be differentially expressed with an adjusted p-value (multiple testing using the BenjaminiHochberg method) of less than 0.05 (Fig 2B). Among these, 365 genes were up-regulated, with the RUNX1T1 gene most significantly up-regulated (p = 2.21x10-31; Table 1). RNA-seq reads were uniquely mapped into the entire coding regions of RUNX1T1 for the 17 samples with t (8;21), with very few reads mapping to this gene in patients with Inv(16) or NK (S1 Fig). Additionally, 462 genes were down-regulated in samples with t(8;21), with the RFX8 gene being the most under-expressed (p = 7.18 x 10−20). Eight of the top 20 under-expressed genes belonged to the HOX family (Table 1). Similarly, AML samples with Inv(16) displayed 279 genes that were differentially expressed as compared to the remaining samples, with 181 of these genes up-regulated and 98 down-regulated at the adjusted p-value of 0.05 (Fig 2C). Matrix metallopeptidase 14 (membraneinserted) (MMP14) mRNA was most significantly up-regulated (p = 7x10-22). Conversely, collagen type XXIII, alpha 1 (COL23A1) mRNA was the most significantly down-regulated. Three hundred and eighty normal-specific genes were also found (Fig 2D), indicating a widespread presence of differentially expressed genes among different cytogenetic abnormalities. RUNX1 binding sites were enriched in differentially expressed genes RUNX1 has shown to play a crucial role in haematopoiesis during embryonic development [18] and the two subunits of the core binding factors (CBFs), i.e., CBFA and CBFB, have been suggested to modify the transcriptional regulator functions of AML by either altering the normal RUNX1 transcription program, interfering with the RUNX1 assembly, or recruiting histone deacetylases and inhibiting the RUNX1 activity [19–21]. To study the expression of the RUNX1 targeted genes in the three cytogenetic categories, RUNX1 ChIP-Seq data in the ME-1 PLOS ONE | DOI:10.1371/journal.pone.0138782 September 23, 2015 5 / 18 Transcriptome Profiling of Pediatric CBF AML Fig 3. Commonly expressed genes in CBF AML. (A) Differentially expressed genes (red dots in the MA plot) in t(8;21) and Inv(16) vs. those with NK. 198 genes are shared in the two subtypes. (B) Gene Set Enrichment Analysis (GSEA) shows enriched functions for shared down-regulated genes between them. doi:10.1371/journal.pone.0138782.g003 cell line were analyzed [19](GEO accession number GSE46044). 34,654 peaks were identified using HOMER ChIP-Seq analysis package ( and 11,844 out of 20,805 (56.9%) ensemble coding genes (GRCh37) were targeted by these ChIP-Seq peaks. Compared with the differentially expressed genes in the three cytogenetic categories, 72.8% of differentially expressed genes in the t(8;21) samples; 73.8% of differentially expressed genes in the Inv (16) samples and 69.0% of differentially expressed genes in the normal samples were targeted by these ChIP-Seq peaks. There is significant enrichment for the RUNX1 binding sites in the differentially expressed genes in these three cytogenetic categories (Binomial test p-value is 2.1E-17, 7.1E-08 and 2.1E-05 respectively; S3 Table). Genes commonly expressed in CBF AML Because those cases referred to collectively as CBF AML share a common biology, clinical presentation, and outcome, we inquired whether the two cohorts also shared an expression profile. To this purpose, we detected differentially expressed genes in t(8;21) and Inv(16) using NK cohort as the control. Of the total of 1567 genes that are differentially expressed in all CBF AML cases [1291 in t(8;21) and 474 in Inv(16)], compared to samples with normal karyotype (NK), 198 differentially expressed genes are shared by the two subtypes in CBF AML (Fig 3A): 87 of these genes are up-regulated, 88 are down-regulated in both subtypes (S4 Table), while active. HIV-infected patients displayed a redistribution of body fat as the percentage of fat on the limb were lower and the percentage of fat in the trunk was higher compared to control subjects. The patients also had disturbances in their lipid metabolism as fasting triglycerides and total-cholesterol levels were higher, and HDL-cholesterol level was lower. In addition, compared to controls, the HIV-infected patients were characterized by peripheral insulin resistance as whole-body insulin-stimulated glucose uptake (Rd) and incremental glucose uptake (Rd basal – Rd clamp) were decreased. Furthermore, the HIV subjects had higher basal endogenous glucose production, lower insulin-mediated suppression of endogenous glucose production (Ra), but no difference in the incremental suppression of endogenous glucose production during the clamp (Ra basal – Ra clamp) as compared to control subjects. Plasma FGF-21 and FGF-21mRNA in muscle Plasma FGF-21 was elevated in the HIV-group compared to healthy controls (70.4656.8 pg/ml vs 109.1671.8 pg/ml, respectively) (P = 0.04) (Figure 1A). FGF-21 mRNA expression in skeletal muscle was increased 8fold in patients with HIV relative to healthy individuals (P,0.0001, parametric statistics, and p = 0.0002 for non-para- metric statistics) (Figure 1B). The association between plasma FGF-21 and muscle FGF-21 did not reach statistical significance (r = 0.32; p = 0.056). Relationships between FGF-21 mRNA, plasma FGF-21 and insulin resistance Muscle FGF-21 mRNA correlated positively to all markers of insulin resistance: fasting insulin (r = 0.57, p = 0.0008), homeostasis model assessment (HOMA) score (r = 0.55, p = 0.001), and insulinAUC (r = 0.38, p = 0.02) (Fig. 1 C–F). To test whether the association between insulin and FGF-21 mRNA in muscle reflect an association with peripheral or hepatic insulin resistance, we performed a euglycemic-hyperinsulinemic clamp with stable isotopes. We found that muscle FGF-21 mRNA was negatively associated with the insulin-mediated glucose-uptake (Figure 1G), but not with hepatic insulin resistance (data not shown). We did not find any association between plasma FGF-21 and parameters of insulin resistance (fasting insulin, plasma glucose, HOMA-IR, glucose AUC, Insulin AUC, Ra or Rd). However, when investigating by group, plasma FGF-21 correlated positive with insulin stimulated glucose-uptake in healthy subjects but not in the HIV patients (r = 0.51, p = 0.049) (data not shown). Previous studies have demonstrated that insulin resistance in patients with HIV-LD are associated with decreased insulinstimulated glycogen synthase (GS) activity [24]. Therefore, we measured GS activity. The GS fractional velocity of % total GS activity was lower in HIV patients compared with healthy controls (2861.3; 3561.6, respectively) in the basal stage. GS fractional velocity is known to correlate positively with the glucose Rd [25]. As high FGF-21 mRNA in muscle is associated with low rate of disappearance of glucose, this could be linked to low GS fractional velocity in muscle. In accordance with this hypothesis, we found that high levels of FGF-21 mRNA in muscle were associated with decreased GS fractional velocity in muscle (Fig. 1H). Relationships between muscle FGF-21 mRNA, and fat distribution and lipids We found a strong negative association between muscle FGF-21 mRNA and the amount of subcutaneous fat (limb fat mass) (r = 20.46; p = 0.0038) and positive association with trunk-limbfat-ratio (r = 0.51; p = 0.001) and triglycerides (r = 0.56; p = 0.0003). FGF-21 mRNA was not associated with total fat mass or total trunk fat (data not shown). Discussion The novelty and the major findings of our study is that we demonstrate for the first time that FGF-21 mRNA expression is increased in skeletal muscle in patients with HIV-LD compared to healthy age-matched men and that muscle FGF-21 mRNA correlates negatively with the rate of insulin-stimulated glucose disappearance (primarily reflecting muscle). Furthermore, increased FGF-21 mRNA expression in muscle is associated with decreased limb fat mass, increased waist-to-hip ratio and increased triglycerides. Only three studies are published on expression of FGF-21 in muscle in humans [10,12,26] and very little is known about the function of muscle FGF-21. Our result is in agreement with a previous study, in which FGF-21 mRNA was found to be increased in muscle from subjects with type 2 diabetes and the expression was increased by hyperinsulinemia [10]. However, this study did not distinguish between hepatic and peripheral insulin sensitivity as we do in the present study. Vienberg et al. [12] also PLOS ONE | 3 March 2013 | Volume 8 | Issue 3 | e55632 Muscle FGF-21,Insulin Resistance and Lipodystrophy Table 1. Baseline characteristics of patients and healthy controls. Age (years) Duration of HIV infection (years) Duration of antiretroviral therapy (years) CD4+ cell (cells/ml) LogHIV-RNA (copies/ml) Antiretroviral use NNRTI-based HAART, PI-based HAART, NNRTI-,PI-based HAART regime, No. Current Thymidine-NRTI use, No. (%) Current PI use, No. (%) Current NNRTI use, No. (%) Physical activity parameters VO2max (LO2/min) Body composition Body-mass index (kg/m2) Weight (kg) Waist (cm) Waist-to.hip ratio Fat mass (kg) Trunk fat mass (kg) Trunk fat percentage (%) Limb fat mass (kg) Limb fat percentage (%) Trunk-to-limb fat ratio Lean mass (kg) Metabolic parameters Total-cholesterol (mmol/L) HDL-C (mmol/L) LDL-C (mmol/L) Triglycerides (mmol/L) Glucose (mmol/L) Insulin (pmol/L) HOMA-IR Insulin sensitivity Rate of appearance (mmol glucose/kg/min) Basal Clamp Delta{ Rate of disappearance (mmol glucose/kg/min) Basal Clamp Delta{ Glucose tolerance Glucose area under the curve (mmol/Lmin) Insulin area under the curve (pmol/Lmin) HIV patients (n = 23) 47.9 (9.5) 15.6 (9.6) 10.3 (4.3) 558 (208) 1.33 (0.12) 12/14/2 11 (47.8) 13 (56.7) 11 (47.8) 2.3 (0.5) 23.7 (2.9) 73.6 (11.2) 93.6 (6.4) 1.01 (0.04) 13.8 (5.3) 9.8 (3.9) 71.2 (6.2) **** 3.5 (1.6)**** 25.1 (6.1) **** 3.09 (1.17)* 57.0 (6.8) 5.5 (0.9)** 1.23 (0.52)* 3.7 (0.9) 2.55(1.43)**** 5.4 (0.6) 52 (25)**** 2.2 (1.4)**** 14.2 (0.49)*** 6.4 (1.8)** 7.8 (2.1) 14.2 (0.49)*** 40.2 (9.9)** 26.02 (10.1)** 826 (200)* 52360 (31017)** Healthy controls (n = 15) 47.5 (6.1) 2.5 (0.6) 23.7 (1.9) 76.9 (7.4) 90 (5.7) 0.94 (0.03) 15.7 (4.4) 8.9 (3.0) 56.1(5.2) 6.2 (1.5) 40.2 (4.9) 1.4 (0.29) 58.2 (5.2) 4.63 (0.64) 1.51 (0.32) 3.3 (0.6) 0.76 (0.24) 5.2 (0.3) 25 (8.9) 0.99 (0.37) 11.8 (2.0) 4.0 (2.5) 7.8 (1.9) 11.8 (2.0) 48.6 (8.4) 36.81 (7.14) 670 (126) 23505 (10598) Data are presented as mean (SD). {Delta, differences between clamp and basal values. HAART, highly active antiretroviral therapy; PI, protease inhibitor; NRTI, nucleoside reverse transcriptase inhibitor; NNRTI, non- nucleoside reverse transcriptase inhibitor. HOMA-IR, homeostatic model assessment for insulin resistance, Rate of appearance and disappearance, Rate of appearance and disappearance of glucose during a euglycemic-hyperinsulinemic clamp performed in both HIV patients and healthy controls. *P,0.05; **P,0.01; ***P,0.001, ****P,0.0001 by t-test. doi:10.1371/journal.pone.0055632.t001 PLOS ONE | 4 March 2013 | Volume 8 | Issue 3 | e55632 Muscle FGF-21,Insulin Resistance and Lipodystrophy Figure 1. FGF-21 mRNA are increased in muscle from subjects with HIV-lipodystrophy and correlates to several measurement of insulin resistance. (A) Fasting plasma levels of fibroblast growth factor (FGF) 21 are increased 2-fold in HIV subjects with lipodystrophy compared to healthy men; (B) mRNA expression of FGF-21 are increased 8-fold in muscle biopsies from HIV subjects with lipodystrophy compared to healthy men; (C–F) Plots of FGF-21 mRNA in muscle versus several measurements of insulin resistance: FGF-21 mRNA in muscle are positively correlated to fasting insulin (C), HOMA-IR (D), Area under the curve for insulin during an oral glucose tolerance test (E), Area under the curve for C-peptide during Nan oral glucose tolerance test (F), and negatively correlated to the incremental rate of disappearance of glucose (G), and fractionel velocity of glycogen synthesis (H) in healthy (e) and HIV subjects with lipodystrophy ( ). In the dot plots data for each subjects are given and the line represent means. * P,0.05 and ***P,0.001 for healthy vs HIV-lipodystrophy patients. For plots, linear regression lines, correlations coefficient, and significance levels are given for all subjects. doi:10.1371/journal.pone.0055632.g001 PLOS ONE | 5 March 2013 | Volume 8 | Issue 3 | e55632 Muscle FGF-21,Insulin Resistance and Lipodystrophy find that FGF-21 mRNA was expressed in muscle, but they find no activation of muscle FGF-21 mRNA by a short term high fat overfeeding. In the study by Mashili et al. [26], FGF-21 mRNA was expressed in skeletal muscle, but in the aquatic environment for long-term survival and for transmission to arthropod and mammalian hosts. References 1. Ray K, Marteyn B, Sansonetti PJ, Tang CM. Life on the inside: the intracellular lifestyle of cytosolic bacteria. Nature reviews. 2009; 7(5):333–40. doi: 10.1038/nrmicro2112 PMID: 19369949 2. Kingry LC, Petersen JM. Comparative review of Francisella tularensis and Francisella novicida. Frontiers in cellular and infection microbiology. 2014; 4:35. doi: 10.3389/fcimb.2014.00035 PMID: 24660164 3. Sjodin A, Svensson K, Ohrman C, Ahlinder J, Lindgren P, Duodu S, et al. Genome characterisation of the genus Francisella reveals insight into similar evolutionary paths in pathogens of mammals and fish. BMC genomics. 2012; 13:268. doi: 10.1186/1471-2164-13-268 PMID: 22727144 4. Ellis J, Oyston PC, Green M, Titball RW. Tularemia. Clinical microbiology reviews. 2002; 15(4):631–46. PMID: 12364373 5. Gurcan S. Epidemiology of tularemia. Balkan medical journal. 2014; 31(1):3–10. doi: 10.5152/ balkanmedj.2014.13117 PMID: 25207161 6. Petersen JM, Mead PS, Schriefer ME. Francisella tularensis: an arthropod-borne pathogen. Veterinary research. 2009; 40(2):7. doi: 10.1051/vetres:2008045 PMID: 18950590 7. Lundstrom JO, Andersson AC, Backman S, Schafer ML, Forsman M, Thelaus J. Transstadial transmission of Francisella tularensis holarctica in mosquitoes, Sweden. Emerging infectious diseases. 2011; 17(5):794–9. doi: 10.3201/eid1705.100426 PMID: 21529386 8. van Hoek ML. Biofilms: an advancement in our understanding of Francisella species. Virulence. 2013; 4(8):833–46. doi: 10.4161/viru.27023 PMID: 24225421 9. Ryden P, Sjostedt A, Johansson A. Effects of climate change on tularaemia disease activity in Sweden. Global health action. 2009; 2. PLOS Pathogens | DOI:10.1371/journal.ppat.1005208 December 3, 2015 6/8 10. Barker JR, Chong A, Wehrly TD, Yu JJ, Rodriguez SA, Liu J, et al. The Francisella tularensis pathogenicity island encodes a secretion system that is required for phagosome escape and virulence. Molecular microbiology. 2009; 74(6):1459–70. PMID: 20054881 11. de Bruin OM, Duplantis BN, Ludu JS, Hare RF, Nix EB, Schmerk CL, et al. The biochemical properties of the Francisella pathogenicity island (FPI)-encoded proteins IglA, IglB, IglC, PdpB and DotU suggest roles in type VI secretion. Microbiology (Reading, England). 2011; 157(Pt 12):3483–91. 12. Filloux A, Hachani A, Bleves S. The bacterial type VI secretion machine: yet another player for protein transport across membranes. Microbiology (Reading, England). 2008; 154(Pt 6):1570–83. 13. Nguyen JQ, Gilley RP, Zogaj X, Rodriguez SA, Klose KE. Lipidation of the FPI protein IglE contributes to Francisella tularensis ssp. novicida intramacrophage replication and virulence. Pathogens and disease. 2014; 72(1):10–8. doi: 10.1111/2049-632X.12167 PMID: 24616435 14. Hood RD, Singh P, Hsu F, Guvener T, Carl MA, Trinidad RR, et al. A type VI secretion system of Pseudomonas aeruginosa targets a toxin to bacteria. Cell host & microbe. 2010; 7(1):25–37. 15. Mougous JD, Cuff ME, Raunser S, Shen A, Zhou M, Gifford CA, et al. A virulence locus of Pseudomonas aeruginosa encodes a protein secretion apparatus. Science (New York, NY. 2006; 312 (5779):1526–30. 16. Pukatzki S, Ma AT, Revel AT, Sturtevant D, Mekalanos JJ. Type VI secretion system translocates a phage tail spike-like protein into target cells where it cross-links actin. Proceedings of the National Academy of Sciences of the United States of America. 2007; 104(39):15508–13. PMID: 17873062 17. Santic M, Molmeret M, Klose KE, Jones S, Kwaik YA. The Francisella tularensis pathogenicity island protein IglC and its regulator MglA are essential for modulating phagosome biogenesis and subsequent bacterial escape into the cytoplasm. Cellular microbiology. 2005; 7(7):969–79. PMID: 15953029 18. de Bruin OM, Ludu JS, Nano FE. The Francisella pathogenicity island protein IglA localizes to the bacterial cytoplasm and is needed for intracellular growth. BMC microbiology. 2007; 7:1. PMID: 17233889 19. Llewellyn AC, Jones CL, Napier BA, Bina JE, Weiss DS. Macrophage replication screen identifies a novel Francisella hydroperoxide resistance protein involved in virulence. PloS one. 2011; 6(9):e24201. doi: 10.1371/journal.pone.0024201 PMID: 21915295 20. Clemens DL, Lee BY, Horwitz MA. Virulent and avirulent strains of Francisella tularensis prevent acidification and maturation of their phagosomes and escape into the cytoplasm in human macrophages. Infection and immunity. 2004; 72(6):3204–17. PMID: 15155622 21. Moreau GB, Mann BJ. Adherence and uptake of Francisella into host cells. Virulence. 2013; 4(8):826– 32. doi: 10.4161/viru.25629 PMID: 23921460 22. Tamilselvam B, Daefler S. Francisella targets cholesterol-rich host cell membrane domains for entry into macrophages. J Immunol. 2008; 180(12):8262–71. PMID: 18523292 23. Chong A, Wehrly TD, Nair V, Fischer ER, Barker JR, Klose KE, et al. The early phagosomal stage of Francisella tularensis determines optimal phagosomal escape and Francisella pathogenicity island protein expression. Infection and immunity. 2008; 76(12):5488–99. doi: 10.1128/IAI.00682-08 PMID: 18852245 24. Celli J, Zahrt TC. Mechanisms of Francisella tularensis intracellular pathogenesis. Cold Spring Harbor perspectives in medicine. 2013; 3(4):a010314. doi: 10.1101/cshperspect.a010314 PMID: 23545572 25. Santic M, Asare R, Skrobonja I, Jones S, Abu Kwaik Y. Acquisition of the vacuolar ATPase proton pump and phagosome acidification are essential for escape of Francisella tularensis into the macrophage cytosol. Infection and immunity. 2008; 76(6):2671–7. doi: 10.1128/IAI.00185-08 PMID: 18390995 26. Jones CL, Napier BA, Sampson TR, Llewellyn AC, Schroeder MR, Weiss DS. Subversion of host recognition and defense systems by Francisella spp. Microbiol Mol Biol Rev. 2012; 76(2):383–404. doi: 10. 1128/MMBR.05027-11 PMID: 22688817 27. Pierini R, Juruj C, Perret M, Jones CL, Mangeot P, Weiss DS, et al. AIM2/ASC triggers caspase-8dependent apoptosis in Francisella-infected caspase-1-deficient macrophages. Cell death and differentiation. 2012; 19(10):1709–21. doi: 10.1038/cdd.2012.51 PMID: 22555457 28. Asare R, Kwaik YA. Exploitation of host cell biology and evasion of immunity by francisella tularensis. Frontiers in microbiology. 2011; 1:145. doi: 10.3389/fmicb.2010.00145 PMID: 21687747 29. Keim P, Johansson A, Wagner DM. Molecular epidemiology, evolution, and ecology of Francisella. Annals of the New York Academy of Sciences. 2007; 1105:30–66. PMID: 17435120 30. Asare R, Akimana C, Jones S, Abu Kwaik Y. Molecular bases of proliferation of Francisella tularensis in arthropod vectors. Environmental microbiology. 2010; 12(9):2587–612. doi: 10.1111/j.1462-2920. 2010.02230.x PMID: 20482589 PLOS Pathogens | DOI:10.1371/journal.ppat.1005208 December 3, 2015 7/8 31. Vonkavaara M, Telepnev MV, Ryden P, Sjostedt A, Stoven S. Drosophila melanogaster as a model for elucidating the pathogenicity of Francisella tularensis. Cellular microbiology. 2008; 10(6):1327–38. doi: 10.1111/j.1462-5822.2008.01129.x PMID: 18248629 32. Santic M, Akimana C, Asare R, Kouokam JC, Atay S, Kwaik YA. Intracellular fate of Francisella tularensis within arthropod-derived cells. Environmental microbiology. 2009; 11(6):1473–81. doi: 10.1111/j. 1462-2920.2009.01875.x PMID: 19220402 33. Read A, Vogl SJ, Hueffer K, Gallagher LA, Happ GM. Francisella genes required for replication in mosquito cells. Journal of medical entomology. 2008; 45(6):1108–16. PMID: 19058636 34. Ahlund MK, Ryden P, Sjostedt A, Stoven S. Directed screen of Francisella novicida virulence determinants using Drosophila melanogaster. Infection and immunity. 2010; 78(7):3118–28. doi: 10.1128/IAI. 00146-10 PMID: 20479082 35. Abd H, Johansson T, Golovliov I, Sandstrom G, Forsman M. Survival and growth of Francisella tularensis in Acanthamoeba castellanii. Applied and environmental microbiology. 2003; 69(1):600–6. PMID: 12514047 36. Santic M, Ozanic M, Semic V, Pavokovic G, Mrvcic V, Kwaik YA. Intra-Vacuolar Proliferation of F. Novicida within H. Vermiformis. Frontiers in microbiology. 2011; 2:78. doi: 10.3389/fmicb.2011.00078 PMID: 21747796 37. El-Etr SH, Margolis JJ, Monack D, Robison RA, Cohen M, Moore E, et al. Francisella tularensis type A strains cause the rapid encystment of Acanthamoeba castellanii and survive in amoebal cysts for three weeks postinfection. Applied and environmental microbiology. 2009; 75(23):7488–500. doi: 10.1128/ AEM.01829-09 PMID: 19820161 38. Lauriano CM, Barker JR, Yoon SS, Nano FE, Arulanandam BP, Hassett DJ, et al. MglA regulates transcription of virulence factors necessary for Francisella tularensis intraamoebae and intramacrophage survival. Proceedings of the National Academy of Sciences of the United States of America. 2004; 101 (12):4246–9. PMID: 15010524 PLOS Pathogens | DOI:10.1371/journal.ppat.1005208 December 3, 2015 8/8
123dok avatar

Ingressou : 2016-12-29

Documento similar

Transcriptome Profiling of Pediatric Core Bin..