The study investigated the capability of clinical findings, temperature, C-reactive protein (CRP), and white blood cell (WBC) count to discern patients with acute colonic diverticulitis from all other patients admitted with acute abdominal pain.The probability of acute diverticulitis was assessed by the examining doctor, using a scale from 0 (zero probability) to 10 (100 % probability). Receiver operating characteristic (ROC) curves were used to assess the clinical diagnostic accuracy of acute colonic diverticulitis in patients admitted with acute abdominal pain.Of 833 patients admitted with acute abdominal pain, 95 had acute colonic diverticulitis. ROC curve analysis gave an area under the ROC curve (AUC) of 0.95 (CI 0.92 to 0.97) for ages <65 years, AUC = 0.86 (CI 0.78 to 0.93) in older patients. Separate analysis showed an AUC = 0.83 (CI 0.80 to 0.86) of CRP alone. White blood cell count and temperature were almost useless to discriminate acute colonic diverticulitis from other types of acute abdominal pain, AUC = 0.59 (CI 0.53 to 0.65) for white blood cell count and AUC = 0.57 (0.50 to 0.63) for temperature, respectively.This prospective study demonstrates that standard clinical evaluation by non-specialist doctors based on history, physical examination, and initial blood tests on admission provides a high degree of diagnostic precision in patients with acute colonic diverticulitis. As the incidence and admission rates of acute colonic diverticulitis are rising in the Western world, the burden on healthcare systems increases –4. The rDuring recent decades, a significant change in both diagnostics and treatment of acute colonic diverticulitis has evolved. As a supplement to clinical evaluation, computed tomography (CT) scans verify and determine stage of acute colonic diverticulitis, improving the decision-making in non-operative and operative management , 9. CT sThe rate of complicated acute colonic diverticulitis is highest during the primary admission, and the majority of readmitted patients have uncomplicated disease , 15. A rThe main aim of the present prospective study was to assess the accuracy of clinical diagnosis in acute colonic diverticulitis, based on history, physical examination, and initial blood tests on admission to hospital in patients with acute abdominal pain, using a receiver operating characteristic curve (ROC) analysis. The secondary aim was to examine the diagnostic accuracy of temperature, CRP, and white blood cell (WBC) count alone in the diagnosis of acute colonic diverticulitis in patients admitted with acute abdominal pain.Between November 2011 and March 2014, all patients older than 18 years, who were admitted to the Department of Surgery at Levanger Hospital with acute abdominal pain with a duration of less than 1 week, were invited to participate in the study. The hospital, located in Mid-Norway, is a first-line hospital serving the population of ten municipalities in North-Trondelag County, which had 94,174 inhabitants in 2012.After admission, 833 patients gave written consent to participate and were included in the study; 477 (57 %) were women, and 537 (64 %) were below 65 years of age.Based on anamnestic history, primary clinical examination, temperature and the results of initial blood tests (CRP and WBC), and before the results of any radiological examinations were available; the non-specialist doctors  in the emergency department scored the probability of acute colonic diverticulitis for the particular patient. A specific form, based on a categorized score from 0 (zero probability) to 10 (100 % probability) was used. In all, 107 different non-specialist doctors were involved in the scoring, 20 of these examined 50 % of the included patients.The final diagnosis in all patients included in the study was based on the diagnosis at discharge and supplementary radiologic or endoscopic examinations as part of an ambulatory follow-up. Acute non-specific abdominal pain (NSAP) was defined as acute abdominal or pelvic pain without any obvious pathology if routine investigations, which included imaging and blood tests, did not reveal pathology and if the patient responded to non-specific treatment .Acute colonic diverticulitis was confirmed by CT scan before discharge in 83 of 95 patients. Five patients with recurrent acute colonic diverticulitis had a recent CT verifying acute colonic diverticulitis, and five had their diagnosis confirmed by an ambulant CT scan or colonoscopy after discharge. Discharge diagnosis based on clinical examination and laboratory tests only occurred twice; the patients were aged 77 and 81 years.In the study period, an unknown number of patients admitted with acute abdominal pain refused inclusion. Review of data from the hospital’s patient administrative system and patient records revealed a total of 168 patients admitted with acute colonic diverticulitis as discharge diagnosis in the relevant time period; of these, 95 (57 %) were included in the study. There were no statistical differences in gender, age, subtype of acute colonic diverticulitis, or length of hospital stay between patients included and those not included in the study, as shown in Table Proportions were compared using the unconditional z-pooled exact test. The medians of two samples were compared using the Wilcoxon test. The diagnostic accuracy of the clinical score was tested using ROC curves, including the area under the curve (AUC). An AUC value of 0.5 shows no predictive ability for the test in question, whereas a value of 1.0 indicates perfect discrimination . StrengtTwo basic measures (sensitivity and specificity) of diagnostic accuracy were used  calculatp values <0.05 were considered significant. Ninety-five percent confidence intervals (CI) were reported where relevant. Medians are reported with range (minimum to maximum) where relevant.Two-sided The analyses were performed using SPSS 22 , STATA 13 , and StatXact 9 .The Regional Committee for Medical and Health Research Ethics (REC) gave permission for the study (2011/1782/REK midt). Only patients who gave written consent were included in the study.The study included 833 patients admitted with acute abdominal pain. The final diagnosis in relation to age is shown in Table Among included patients, 95 patients had a final diagnosis of acute colonic diverticulitis, including two with abscess and three with perforation and generalized peritonitis. The tentative diagnosis from the primary care physicians had been acute colonic diverticulitis in 49 patients, giving a sensitivity for this diagnosis of 52 %. The rest of the patients with a final diagnosis of acute colonic diverticulitis had been admitted from primary care physicians with the following diagnoses: NSAP (24 patients), acute appendicitis (6 patients), acute intestinal obstruction (6 patients), and other specified abdominal conditions (10 patients). In 19 patients admitted with a misdiagnosis of acute colonic diverticulitis, the final diagnosis was NSAP (7 patients), peptic ulcer (3 patients), acute appendicitis (2 patients), pancreatic cancer with metastases (1 patient), and other diagnoses in 6 patients. The proportion of patients correctly diagnosed by the primary care physicians with another diagnosis than diverticulitis was 719 out of total 738 patients, analog to a specificity of 97 %.Table Table p = 0.036). When we chose a cutoff value of 6 on the scale from 0 to 10 in patients younger than 65 years, ROC curve analysis resulted in a sensitivity of 65 % and a specificity of 96 %. In patients 65 years and older, a cutoff value of 6 resulted in a sensitivity of 68 % and specificity of 92 %.Figure The diagnostic performance of temperature, CRP, and WBC on admission is illustrated in Fig. This prospective series demonstrates that standard clinical evaluation, by non-specialist doctors, provides a high degree of diagnostic precision in patients with acute colonic diverticulitis. The diagnostic precision was slightly higher among patients younger than 65 years compared to those older. Furthermore, the analyses proved CRP to be the most valuable initial laboratory test in the differentiation of acute diverticulitis from other acute abdominal conditions.Few reports have used ROC curve analysis to assess the clinical diagnostic performance in acute colonic diverticulitis , 32; a cThe clinical diagnostic sensitivity of acute colonic diverticulitis, based on examinations before admission to hospital by the primary care physicians, was 52 %. This was inferior to the results achieved by the non-specialist doctors in the hospital, but comparable to a previous study on diagnostic sensitivity of acute colonic diverticulitis in primary health care . VariousIn the present study, the accuracy of the clinical diagnosis was slightly better in younger patients. The presentation of acute abdominal pain among elderly may be different from that seen in younger age groups. Elderly patients tend to have more vague and non-specific symptoms, broader alternatives of differential diagnosis, altered clinical signs that do not correlate with disease severity, higher incidence of comorbidity and multi-pharmacy, and communication difficulties because of hearing and cognitive impairment , 27. ThiROC curve analysis to assess CRP in the diagnostics of acute colonic diverticulitis has previously been reported with an AUC ranging from 0.72 to 0.94 , 30–34, Most previous studies have focused on these parameters in the differentiation between uncomplicated and complicated acute colonic diverticulitis , 32, 33;This study confirms that a clinical diagnosis of acute colonic diverticulitis is achievable by non-specialist doctors at the emergency department and applies specifically to patients younger than 65 years with localized tenderness in the left lower quadrant and an elevated CRP.Previous studies report other criteria as significant in the selection of patients suitable for out-patient diagnosis and treatment such as a CRP cutoff value between 150 and 200 mg/L, absence of vomiting, significant fever and signs of generalized peritonitis, absence of compromised immune status, and significant comorbidities. Of additional importance is a close follow-up and the possibility of a secondary evaluation if symptoms worsen , 31–37. Out-patient treatment would include oral analgesics, with or without oral antibiotics. Follow-up, including clinical examination and CRP with the possibility of ambulant CT scan on day 4 and referral to follow-up endoscopy or CT colonography after 6 weeks of improvement, has been suggested .This evidence should form the basis for a clinical care pathway. A structural approach, involving both the primary and secondary healthcare system, would increase the quality of treatment, define an appropriate level of treatment for the individual patient, and reduce the increasing rates of admission to hospital among patients with acute colonic diverticulitis.Not all patients admitted to hospital because of acute abdominal pain in the study period were included in this study. There were no statistical differences in gender, age, subtype of acute colonic diverticulitis, or hospital stay when comparing patients included or not, as shown in Table Another limitation is the probable selection bias based on the hospital doctors’ awareness of the study. However, the percentage of patients with acute colonic diverticulitis in relation to other types of acute abdomen conditions generally matches other studies .The present prospective study is the first to use a fine graded categorized clinical score and subsequent ROC curve analysis in the evaluation of clinical diagnostic accuracy in patients with acute colonic diverticulitis. The advantage of ROC curve analysis in the present study was the possibility to consider the complete spectrum of the observed results, not only the mean or dichotomous variable denoting “yes or no.”The study highlights that non-specialist doctors, usually in their first year of a clinical career, were able to clinically diagnose acute colonic diverticulitis in patients with acute abdominal pain with a high degree of accuracy. This reflects the possibility of a similar standardized approach applied in the out-patient setting, reducing the need for further referral to hospitals, especially in cases of suspected uncomplicated acute colonic diverticulitis.This prospective study demonstrates that standard clinical evaluation by non-specialist doctors based on history, physical examination, and initial blood tests on admission provides a high degree of diagnostic precision in patients with acute colonic diverticulitis.Furthermore, the analyses proved CRP to be a valuable initial laboratory test in the differentiation of acute colonic diverticulitis from other acute abdominal conditions.


Mouse model induced by azoxymethane (AOM) and dextran sodium sulfate (DSS) is generally accepted as an ideal object to study on the carcinogenesis mechanisms of human colorectal cancer (CRC). The genomic responses to the AOM/DSS treatment in mouse that possibly lead to elucidation of CRC pathological mechanism are still poorly understood. For the first time, we investigated the cancer genome landscape of AOM/DSS mouse model by exome sequencing, to testify its molecular faithfulness to human CRC. Of 14 neoplastic samples, 7575 somatic variants were identified, which resulted in 2507 mutant genes and exhibited a large diversity in both colorectal aberrant crypt foci (ACF) and tumors even those tissues that were gained from the similar morphology or same treatment period. Cross-species comparison of the somatic variants demonstrated the totally different patterns of variable sites, mutant genes and perturbed pathways between mouse and human CRC. We therefore come to a conclusion that the tumorigenesis at genomic level in AOM/DSS model may not be properly comparable with that in human CRC, and the molecular mechanism elicited from this animal model should be carefully evaluated. Animal model, by contrast, is believed to be an ideal alternative for its convenience in controlling experimental conditions, monitoring pathological development, achieving enough materials and avoiding ethical risks. An appropriate model is of great significance in cancer mechanism research, diagnosis and therapeutic evaluation. First reported in 19962, AOM/DSS mouse model was proven as an outstanding model in faithfully mimicking of the pathological changes of “normal-ACF-adenoma-carcinoma” development3 as observed in human CRC4, and hence was widely used in studies of colorectal carcinogenesis and chemopreventive intervention.Human cancer studies are limited by many ethical and practical considerations5. Several investigations focusing on the molecular features of AOM/DSS model were implemented, which reported some common features shared by human and mouse CRC, such as mutation or deregulation of KRAS6 and CTNNB18. Besides, high-throughput technologies were also adopted in exploration of molecular features in this model at both transcriptional and translational level. Using DNA microarray technique, Suzuki et al. discovered hundreds of differentially expressed genes between 5 and 10 weeks of AOM/DSS treatment9. With a similar strategy, Li et al. claimed that abundances of both mRNA and miRNA were impacted by AOM/DSS10. At protein level, Yasui et al. identified 21 differential proteins between tumorous and their adjacent tissues in the animal model using two-dimensional gel electrophoresis and mass spectrometry11. However, in spite of these works, molecular mechanism of AOM/DSS mouse model is still very limited. Firstly, all the high-throughput data was acquired from the tumor/normal mixed tissues or the pooled samples from individuals. Generally, the strategy of which the tumor/normal pairs are individually analyzed enables production of more convincing data. Secondly, the studies based on traditional techniques like PCR and IHC only offered partially molecular responses to AOM/DSS, which those observations often conflicted lab to lab. For instance, the distinct mutation frequencies of Kras and different mutation sites of β-catenin were documented from several labs13. A global and systematical estimation towards the genomic responses to AOM/DSS in mouse could establish an overall feature that benefits a reasonable comparison of pathological genomics between mouse and human CRC.As regards an appropriate cancer model, in addition to similar pathological phenotypes it is highly expected that the model also carries comparable molecular features and follows same carcinogenesis progress with its cognate human cancer type14. Cancer genomics based on next-generation sequencing (NGS) provides an informative resource for characterization of cancer-related genes and identification of novel genetic alterations, and contributes a critical step towards understanding of cancer initiation, progression and metastasis15. Several studies have suggested that sequencing tumor genomes of animal models is also an effective approach for discovering the gene mutations in mouse that are mimic human malignancies. In the example of Trp53-mutated breast cancer mouse model, some key features associated with the cognate human tumors were found, such as the homozygous in-frame deletion of Lrp1b even though with huge diversity of somatic rearrangements16. By sequencing tumor-normal paired samples from a mouse model mimicking the acute promyelocytic leukemia (APL), Wartman et al. revealed a somatic Jak1V657F mutation in the same residue of JAK1 as previously found in human APL18. However, overall evaluation towards the variants at genomic level has been not reported yet in AOM/DSS mouse model.Cancer is believed a disease with genomic defectIn this study, we employed whole exome sequencing to explore the genome landscapes of neoplastic samples with different morphologies or from different phases of tumorigenesis in AOM/DSS mouse model. For the first time, we revealed the cancer genome landscape of both ACF and tumors in this model, and systematically compared of the variable sites, mutant genes and perturbed pathways of CRC between mouse and human. On the basis of the overall evaluation for the faithfulness of this model to human CRC, we concluded that the patterns of genomic variants in AOM/DSS were somehow different from that chronically developed in human CRC.The AOM/DSS mouse model were assessed in five aspects during its establishment. 1) Tumor formation. Under light microscope tubercles were rarely observed in control mice, whereas a number of tubercles were found in all treated mice, mainly in distal part of colorectums  to 1457 (28.6 per Mb), with 393 (7.7 per Mb) and 541 (10.6 per Mb) as the median and mean respectively Fig. . In addiAPC, TP53, KRAS, NRAS, BRAF, PIK3CA, SMAD4 and FBXW7, and mapped them to the mouse exome sequencing data. Surprisingly, none of them was detectable in AOM/DSS mice  and divided the data into three groups, Total that contained all the cases (224), MSS (microsatellite stability) with 159 cases, and MSI (microsatellite instability) with 64 cases. And the cross-species analysis on the genomic variants of mouse and human CRC was implemented. At the site level, three features of genomic variants were comprehensively compared. 1) The average mutation rate of AOM/DSS mouse model (10.6 per Mb) was larger than that of MSS cases (6.7 per Mb), but smaller than that of MSI cases (16.9 per Mb). The range of mutation rates was a critical parameter to assess genomic stability. As shown in Fig. ice Fig. . CollectMLH1, MLH3, MSH2, MSH3, MSH4, MSH5, MSH6, PMS2 and POLE, were reported widely mutated in hypermutated cases of human CRC. These genes were carefully examined in this mouse model, and a few of mutated MMR genes with low frequencies were found in these samples. The huge genome instability caused by AOM/DSS treatments appeared independent from mutations of MMR genes.Totally, 2507 mutant genes  Q-value < 0.1  were evaluated by DrGap, which is an efficient tool to define the SMGs in many cancer types without species limitationsins Fig. , suggestR2) between human groups were ranged from 0.46 to 0.84, whereas 0.07 to 0.08 between mouse and human groups. Huge differences of mutant gene frequencies between mouse and three human groups were indeed perceived. And secondly, we sought the overlap rate of the human CRC SMGs between mouse and human groups. As shown in Fig. 27 were no overlap with the SMGs in AOM/DSS mouse model. If the overlap comparison is extended to the human CRC SMGs and all the somatic mutant genes in AOM/DSS mice, only 6 human SMGs were overlapped with the mouse model at low frequency, approximately 7%. Clearly, the key mutated genes related with human CRC were totally different from the somatic mutant genes in AOM/DSS mice. Besides, some highly mutated genes in human CRC, such as APC, TP53, KRAS and PIK3CA, were rarely identified in this model. Taken all the mutant gene comparisons, we came to a conclusion that the somatic mutant genes induced by acute treatment of AOM/DSS in mouse might be different from that caused by chronically pathological changes in human colorectal mucosa.An evitable issue for mouse model studies is the evaluation of faithfulness of AOM/DSS mouse model in mimicking the pattern of somatic mutant genes in human CRC. We firstly compared the profiling of somatic mutant genes between mouse and human . The analysis resulted in an extensive disturbance of pathways ignited by AOM/DSS: 264 out of 290 (91%) KEGG pathways (Supplementary Table R2) between mouse and human  were much smaller than that between different groups of human CRC .We further pursued whether the profiling of disturbed pathways in AOM/DSS mouse model was similar to that in human CRC. To avoid the bias in the definitions of KEGG pathways in mouse and human, we converted 1523 damaging genes in the mouse and gained a new dataset containing 1359 human homologous genes. With the same strategy as those used in gene level, we first globally evaluate the similarity of disturbed pathway frequencies between AOM/DSS mouse model and each of three human groups. As shown in Supplementary Fig. 28. As mentioned above, two adhesion-related pathways were highly enriched in AOM/DSS. We then focused on comparison of these pathways between mouse and human, for their perturbed frequencies and patterns of damaging genes. Of the seven pathways for comparison, only two pathways, Wnt and p53 signaling, exhibited the obviously different perturbed frequencies between mouse and human . As shown in Fig. TP53 with 44.6% in Total, 49.1% in MSS, and 32.8% in MSI, KRAS with 41.5% in Total, 45.3% in MSS and 32.8% in MSI and PIK3CA with 12.5% in Total, 11.9% in MSS and 14.1% in MSI, however, none of them was detected in AOM/DSS mice. The correlation analysis towards this pathway between the two species in Fig. There were five core pathways related with human CRC found by TCGAman Fig. , while tAs a cornerstone of modern biomedical research, especially in the field of cancer study, mouse is a dominant resource in animal modeling. In this study, we proposed that comparison analysis of genomics between normal and neoplastic tissues in AOM/DSS mouse model could offer a novel view to explore the tumorigenesis mechanism. At genomic view, a set of mice from the same strain is believed to share quite similar genetic background so that these mice are expected to have comparable genomic responses to a same stimulus. Moreover, during construction of AOM/DSS model, the mouse gender, age and drug inducement could be well controlled. The biological heterogeneity among mice is reasoned to be significantly narrow down. Therefore, we did not introduce a large scale animal experiment but placed five mice in each experimental group to evaluate the genomic responses to AOM/DSS. The exome sequencing results described above gave us a shock. Although the genomic alternations in the mice without drug treatment or the colorectal tissues adjacent to tumor remained little, the genomic responses to AOM/DSS in the ACF and CRC tissues were found in a huge diversity even though these tissues shared with the similar morphology or treated with the same drug and time. Further cross-species comparison revealed a distinct pattern of the somatic variants between mouse and human CRC. How do we explain the phenomenon?29, transformation requirements30 and centromere distribution31, are very distinct from that in human. These differences are likely to make the specifically genomic features in the colorectal tumors of mouse. For instance, Wartman et al. generated a mouse model of APL and surprisingly found significant genetic variation from the tumor sample compared with that of the mouse reference genome32. Of over thirty thousand potentially somatic variants, only 3 somatic and nonsynonymous mutations were finally confirmed to be associated with human APL. Varela and his colleagues established genetically engineered mouse models of breast cancer and employed paired-end sequencing to identify the somatic structural variants involved in the carcinogenesis in the model16. A huge diversity of somatic variants was perceived in that model due to heterogeneity of somatic rearrangement and no recurrent fusion gene was found. To evaluate the faithfulness of such models in mimicking human inflammatory diseases, Seok et al. made three murine injury models, burn, trauma and endotoxemia, and profiled the gene expression in such models33. Correlations of the inflammatory-dependent genes by Pearson correlations (R2) revealed low correlation rates at R2 = 0.00–0.13 between mouse and human. Another intriguing phenomenon was that even APC, P53 and KRAS with highly somatic mutations in human CRC have been widely observed, these genes did not show any mutation in this study. When the rat CRC was generated by AOM/DSS, Apc and Kras mutations were identified at the frequency of 8%34 and 30–60%37, respectively, whereas no mutation of p53 was reported35. When the mouse CRC was constructed by AOM/DSS in other labs, there was no mutation found in Apc and p5338, while very low mutation frequency (0–10%) of Kras was detected39. Our results were in a good agreement with these observations, of which the gene mutations provoked by AOM/DSS are species dependent. These studies upon genomics analysis towards to disease mouse model has demonstrated the genomic stability in mouse quite different from that in human. Our study provided another evidence to support the hypothesis that a mouse model may follow a unique pathogenesis, at least at genomic level.The genomic structures of mouse, such as telomere crisis40. As a matter of fact, the IBD-related CRC just accounts for 1–2% of all human CRC41. As the somatic mutations generated from the human CRC genomics analysis were not only focused on the IBD-related CRC, the mutation patterns responsive to AOM/DSS was reasonable not very comparable with the generally genomic mutations in human CRC. Secondarily, AOM is a carcinogen that causes base mispairings by inducing O6-methylguanine adducts in guanine40. Considering the ubiquitous distribution of guanine, the distribution of genomic variants caused by AOM is theoretically random, while our sequencing data -related CRCata Fig.  was in aIn summary, for the first time the rationality of AOM/DSS mouse model is challenged by the exome sequencing evidence obtained from the colorectal tumor tissues. Although at morphological level, the CRC development in the mice after AOM/DSS inducement is basically mimic to human one, the somatic mutations appeared a completely different pattern between mouse and human CRC. Moreover, the diversity of somatic mutations among the pathological tissues of mouse implied that the treatment of AOM/DSS triggered a wide instability in mouse genome. The experimental evidence thus leads to two clues in further study, whether genomic analysis is a valuable means to evaluate a disease animal model and the gene expression at large scale in AOM/DSS model is necessary to be done in near future.42 to establish the AOM/DSS mouse model using two drugs: azoxymethane  and dextran sodium sulfate . As shown in Fig. To well control the animal source, we set the strict criteria for mouse selection, in which all the mice for AOM/DSS model construction should come from C57BL/6N inbred strain within the same brood, male, age with 7 weeks and body weight ranged 18–20 g . Total of 45 mice were selected, 10 as controls without any treatments and 35 as experimentals treated with AOM/DSS. We followed a well-accepted protocolMice were sacrificed in week 7 and 10 for sample preparation. A methylene blue based method without fixation was developed and evaluated  following the standard protocols. The products of exome capture should pass these criteria: 1) the length of fragments: 300 ± 30 bp; and 2) total amount: >600 ng. After exome capturing, the index-tagged samples were pooled and sequenced on Illumina HiSeq 2000 with 90 or 100 bp length.25 samples from five groups were selected for exome sequencing  p-value to call a somatic site is <0.01; 5) supported by both plus and minus strands, and the ratio of supporting reads in one strand should be no more than 95%.Burrows-Wheeler Alignment (v-0.7.4) was employed to align reads to the reference genome (mm9/NCBIM37) with default parameters. Duplicated reads were removed and the BAM files of technical replicates were merged by SAMTools (v-0.1.19). After local realignment and recalibration by the Genome Analysis Toolkit (v-1.6-13-g91f02df), the coverage on both SureSelect target regions and the CCDS were calculated. Sample with <90% coverage in these two regions at 20x were filtered out. Somatic variants were call by VarScan2 (v-2.3.6) in tumor-normal pairs and filtered with self-built Perl scripts. Criteria for somatic variants included: 1) read depth is at least 8 in tumor and normal; 2) minimal mean of base quality is 15; 3) minimal allele frequencies are 0.12 for SNVs and 0.2 for InDels; 4) https://www.mysequenom.com/Tools). Native DNAs were used as templates in most samples, and for those with insufficient native DNA, DNA products from whole genome amplification of both replicates were used. Signals (Peaks) were counted as positive only if they were detected in both replicates. Only those variants detected in tumor samples and undetected in adjacent normal samples were regarded to be successfully validated. Variants failed in validation were filtered out before next analysis.Somatic variants detected in AOM/DSS mouse model were validated by Sequenom Mass ARRAY genotyping assays. Somatic variants were randomly selected from 14 diseased samples followed by primer design using Assay Design Suite v2.0, a free online tool (43 were introduced in annotation of somatic variants based on refGene database (updated in Nov. 2014)45. Self-built Perl scripts were used to annotate the existence of somatic variants in dbSNP (v128) and filter for those somatic variants affecting protein coding sequence. We further evaluated the effect of somatic variants on protein functions by SIFT annotation with PROVEAN46. The Kyoto Encyclopedia of Genes and Genomes 48 was used to define the altered pathways in each sample. LiftOver49 was used to transfer the coordinates of somatic variants from mm9 to mm10.ANNOVAR (Revision:514)19 with default settings. The gene mutation table of mm9, which is necessary in this analysis but not supplied by the author, was calculated and summarized by Perl scripts based on the information supplied by the refGene database which was used in the annotation of somatic variants. This table was available in Supplemental Table Significantly mutated genes in AOM/DSS mouse model were predicted by a computational tool named DrGap (Driver Genes and Pathways)A hypergeometric distribution analysis (Fisher’s Exact Test) followed by a correction for multiple testing (Bejamini-Hochberg FDR correction) was used to determine the pathways overrepresented in AOM/DSS mouse model. In order to remove passengers as much as possible, only “damaging” genes  were used in this analysis. Pathways with less than 10 members or more than 500 members were excluded. All of the 24062 genes collected in refGene database were used as a background gene list. Perl code-based scripts were used in this analysis for data format conversion, summarization and reporting.R2) and data visualization.Exome sequencing data of 224 cases by TCGA, including 169 MSS cases and 64 MSI cases, were collected for the cross-species analysis as the representative of human CRC dataset. Combining the dataset of AOM/DSS mouse model, four groups were derived for cross-species analysis: 1) Human  which including all of the 224 human CRC cases; 2) Human (MSS) which including 169 MSS cases; 3) Human (MSI) which including 64 MSI cases and 4) AOM/DSS Mice which including the 14 cases we sequenced. The cross-species analysis was performed on three levels: variable sites, mutated genes and altered pathways. To avoid the bias in the definitions of KEGG pathway between mouse and human, the damaging genes of AOM/DSS mouse model were converted to human homologs with NCBI homology database. Self-built Perl scripts were used to calculate the frequencies of variable sites, mutated genes and altered pathways in each of the four groups for further comparison, analyze the overlap among different groups and prepare formatted data for visualization. R script were used in the calculation of Pearson Correlation (http://www.ncbi.nlm.nih.gov/sra/). Accession numbers are listed in Supplementary Table The sequence data from this study have been submitted to the NCBI Sequence Read Archive (SRA; Supplementary PDF FileSupplementary Table S3Supplementary Table S4Supplementary Table S5Supplementary Table S6


Birth weight, length and circumferences of the head, chest and arm are key measures of newborn size and health in developing countries. We assessed maternal socio-demographic factors associated with multiple measures of newborn size in a large rural population in Bangladesh using partial least squares (PLS) regression method. PLS regression, combining features from principal component analysis and multiple linear regression, is a multivariate technique with an ability to handle multicollinearity while simultaneously handling multiple dependent variables. We analyzed maternal and infant data from singletons  born during a double-masked, cluster-randomized, placebo-controlled maternal vitamin A or β-carotene supplementation trial in rural northwest Bangladesh. PLS regression results identified numerous maternal factors  significantly (p<0.001) associated with newborn size. Among them, preterm delivery had the largest negative influence on newborn size . Scatter plots of the scores of first two PLS components also revealed an interaction between newborn sex and preterm delivery on birth size. PLS regression was found to be more parsimonious than both ordinary least squares regression and principal component regression. It also provided more stable estimates than the ordinary least squares regression and provided the effect measure of the covariates with greater accuracy as it accounts for the correlation among the covariates and outcomes. Therefore, PLS regression is recommended when either there are multiple outcome measurements in the same study, or the covariates are correlated, or both situations exist in a dataset. In developing countries, small size at birth is common, reflecting combined effects of inadequate intrauterine growth and preterm birth. Both birth conditions are associated with increased risks of poor postnatal growth  and infaIn exploring associations between health outcomes and multiple risk factors, observational epidemiologic studies often deal with data that include both a set of exposure variables and a set of outcome variables. Frequently, the variables are interrelated to some extent and consequently, multicollinearity often exists. Routine statistical approaches such as multiple linear regression or principal component regression (PCR) are usually challenged with multiple testing. Specifically, using separate statistical significance tests for each regression equation when there are multiple outcomes substantially increases the risk of Type 1 error . AdditioThe overall Jivita study protocol was reviewed and approved by both the Bangladesh Medical Research Council (BMRC) and the Institutional Review Board (IRB) of Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA. Documented consent was given by all participating women.In this study, PLS regression was used to assess the effect of maternal and infant factors on newborn size at birth. The data reported in this analysis were collected during a field based study assessing the efficacy of weekly antenatal vitamin A or β-carotene versus placebo supplementation on maternal and infant mortality through 6 months of age from July 2001 to January 2007. Details of this study conducted in a contiguous ~435 sq km area in rural northwestern Gaibandha and Rangpur Districts of Bangladesh with a population of ~650,000 are available elsewhere .st trimester about household socioeconomic condition, education, demographic characteristics, previous pregnancy history, recent morbidity history and 7-day food intake and mid-upper arm circumference. A Living Standard Index (LSI) was constructed using principal component analysis from household socio-economic variables and was used to describe socio-economic status [nd half of the maternal trial [On enrollment into the intervention trial, consenting mothers were interviewed in the 1c status . At 3 moal trial . Shortlyal trial . Small ial trial . SingletThe maternal characteristics included in this study were age at enrollment, parity, mid upper arm circumference (cm), education (yrs), living standard index, number of antenatal care visits, and maternal supplementation group. Additional infant characteristics included preterm (<37 week of gestation) delivery and infant sex .The classical regression methods usually meet four main challenges with: (i) a large number of variables, (ii) correlated predictors, (iii) smaller sample size with a large number of variables and (iv) having more than one response variables simultaneously . To overAlthough PLS regression is comparatively new, its use in research is gradually increasing. The great strength of PLS regression is parsimony . InitialSimilar to principal component regression (PCR), PLS regression analysis is a data-dimension reduction method that extracts a set of orthogonal factors called latent variables which are used as predictors in the regression model . The majThe PLS regression identifies the latent variables stored in matrix T and they model X and predict Y simultaneously. Then the following expression can be written as,PT+ is the Moore-Penrose pseudo-inverse of PT. The matrix BPLS has J rows and K columns and is equivalent to the regression weights of multiple regression.The latent variables are computed iteratively using Singular Value Decomposition (SVD). In each iteration, SVD constructs orthogonal latent variables for X and Y and corresponding regression weights . The alg0 and Y0Step 1: Transform X and Y into Z-scores and store in matrices X0 and Y0, R1 = X0TY0Step 2: Compute the correlation matrix between X1 and produce two sets of orthogonal singular vectors w1 and c1 corresponding to the largest singular value, λ1.Step 3: Perform singular value decomposition (SVD) on R1 = X0Tw1.Step 4: The first latent variable for X is given by T1 such that T1TT1 = 1Step 5: Normalize T0 on T1 is computed asStep 6: The loadings of X1 = Y0c1andStep 7: Compute Ub1 is the slope of the regression of Ŷ on T1. 0. Matrices 1 are then subtracted from the original X0 and Y0 respectively to give deflated X1 and Y1.The scalar Step 8: Compute the input matrices for the next iteration,2 = X1T Y1 we get w2, c2, T2 and b2 and the new deflated matrices X2 and Y2.Step 9: The first set of latent variables has now been extracted. Now perform SVD on Rl).Step 10: The iterative process continues until X is completely decomposed in to L components (where L is the rank of X). When this is done, the weights  for x are stored in the J by L matrix W  for different number of components. The component at which the cross validation RMSEP has a meaningful change is used in the final model. To choose the optimum number of components for both PLS and principal component regression, root mean squared error of prediction (RMSEP) were calculated using different number of components. We performed approximate t-tests of regression coefficients based on jackknife variance estimates .1 vs t2, which helped us to assess if there is any natural grouping or interactions among variables.We constructed a correlation plot of the variables to observe how variables are correlated with each other and also between the birth size variables and maternal variables. The closer a variable appears to the perimeter of the circle, the better it is represented, and if two variables are highly correlated they appear near each other. If two variables are negatively correlated they will tend to appear in opposite extremes. If two variables are uncorrelated, they will be orthogonal to each other. We plotted the scores of first two components, tTo examine the advantage of PLS regression over principal component regression, we calculated Pearson’s correlation coefficients between the predicated values  and the observed values of infant’s size variables. This correlation coefficient indicate the predicative power of the model: if the model has perfect predictive ability then the correlation coefficient will be 1. So, the more the correlation coefficient, the higher the predictive power of a given model is. For this analysis, we used the R packages: “plsdepot”, “pls” and “mixOmics”.More than half of the infants were small at birth. More than 50% mother had parity of at least 1, mean (SD) maternal age was 21.96 (5.88) yrs and maternal early pregnancy MUAC was 22.99 (1.97) cm. About 75% mothers reported not having had any ANC visits throughout the pregnancy. Preterm delivery was 27% and 51% infants were male .The RMSEP was plotted against number of components used in the PLS regression model and principal component regression models in Figs Some of the coefficients estimated using the ordinary linear regression (LR) were different compared to the coefficients estimated using PLS regression . Coefficth quadrant. Among them, age, MUAC and parity were close to each other and they were also closer to the birth size variables in the 1st quadrant. Education, ANC visit, and LSI were very close to each other but were further from to the birth size variables compared to the prior cluster of maternal variables. On the other hand, preterm delivery alone fell in the 3rd quadrant just opposite to the birth size variables and infant’s sex alone fell in the 1st quadrant with the birth size variables. Maternal Vitamin A and β-carotene supplementation fell very close to the center indicating no effect on birth size. All the 5 birth size variables in the 1st quadrant were clustered close to each other. Therefore, this figure demonstrated that preterm delivery was the most important predictor in the opposite direction, followed by infant’s gender, parity, age and MUAC.The correlation plot of the variables for the first two components  depictedst and 2nd PLS components of the predictors displayed four distinct groupings among the study participants due to the interaction effect of preterm delivery and infant’s sex  (The score plot of the 1preterm) .We observed that the magnitude of correlation between the predicted values using PCR and observed values of birth size was substantially increasing with increasing the number of components, however, the correlation was very poor with the first few components . On the We conducted this study to assess the effect of maternal socio-demographic factors on newborn size at birth using PLS regression. Our study revealed that all the maternal variables examined, except vitamin A and β- carotene supplement receipt during pregnancy, were significantly associated with birth size. Preterm delivery had the greatest effect on birth size followed by infant sex, maternal parity, education, and age. In addition, PLS regression facilitated the finding of an interaction effect of preterm delivery and newborn sex on birth size, as revealed by a scatter plot of the first two components . PLS regIn addition to birth weight, the combination of all birth size measurements captures more information than a single measurement in isolation; however, they are highly correlated. Head circumference, for instance, indicates the brain volume  and it mThe PLS regression analysis suggested that maternal and infant factors age, parity, early pregnancy MUAC, LSI, maternal education, number of ANC visits and infant sex were significantly and positively associated with infant’s size at birth; however, preterm delivery had the greatest negative effect on birth size as anticipated. We did not find any effect of maternal vitamin A and β-carotene supplementation on infant’s size at birth and which is consistent with the result found by Christian et al.  and Kabi2 increases with an increasing number of predictors in the model, the variance of regression coefficients also increases. Thus, it is statistically advantageous to have a reduced number of explanatory variables in a given model. Compared to PCR, PLS regression requires a smaller number of components resulting in more stable estimates of regression coefficients. In this analysis, PLS regression required only two components but PCR required five. Despite the fact that PLS components explained a much lower variance (~24%) of the maternal and infant factors, it had more predictive power over the PCR where components explained ~73% variance. This is because, in PLS regression, a pair of components is chosen from the dependent and independent variables so that they are closest to each other; however, in PCR, a component is chosen from the independent variables which capture most of the variability without accounting for how close it is to the dependent variables. Criteria that give penalties on the number of variables, like Akaike Information Criterion (AIC), or those where model performance is evaluated, like the Mallows Cp criteria, also encourage the need to include more variables than the PLS method [PLS regression has the ability of handling multicollinearity and multiple dependent variables simultaneously. It has a proven ability to perform better than other regression methods, which are usually used to handle highly collinear data, such as stepwise multiple regression, PCR or model fitting techniques that apply Maximum Likelihood or Bayesian theory and in addition it equally performs in ideal situations ,33,40. TS method . This asPLS regression was a better choice to analyze this data. It needed only two components, while PCR, the most commonly used method, required 5 components to have similar predictive power. The coefficients obtained from PLS regression were more stable and accurate than that of the general linear model. Using PLS regression, the maternal factors identified as the significant predictors of newborn size at birth were commensurate with other studies –49. So, S1 Dataset(XLSX)Click here for additional data file.S1 File(DOCX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.


Exosomes are small extracellular vesicles that carry heterogeneous cargo, including RNA, between cells. Increasing evidence suggests that exosomes are important mediators of intercellular communication and biomarkers of disease. Despite this, the variability of exosomal RNA between individuals has not been well quantified. To assess this variability, we sequenced the small RNA of cells and exosomes from a 17-member family. Across individuals, we show that selective export of miRNAs occurs not only at the level of specific transcripts, but that a cluster of 74 mature miRNAs on chromosome 14q32 is massively exported in exosomes while mostly absent from cells. We also observe more interindividual variability between exosomal samples than between cellular ones and identify four miRNA expression quantitative trait loci shared between cells and exosomes. Our findings indicate that genomically colocated miRNAs can be exported together and highlight the variability in exosomal miRNA levels between individuals as relevant for exosome use as diagnostics. The samples are from a complete three-generation pedigree that includes four grandparents, two parents, and 11 children. Variant calls from whole genome sequencing data for the 17 individuals were obtained from Complete Genomics .2. Cell cultures were initiated at densities of 500–750 × 105 cells/ml and allowed to grow to 150 × 106 cells, achieving a maximum density of 2 × 106 cells/ml at collection. Fetal calf serum was depleted of bovine exosomes before use by overnight centrifugation at 120,000 × g. The exosome isolation procedure is depicted in g for 10 min and saved for cell miRNA isolation. The remaining growth medium was centrifuged at 16,500 × g for 25 min and then filtered through a 200 nm Acrodisc . The filtered supernatant was centrifuged at 120,000 × g for 70 min to pellet exosomes (Ti45 rotor). Pelleted exosomes were washed 1× in cold PBS and centrifuged again at 120,000 × g for 70 min (TLA 100.3 rotor). All centrifugations were performed at 4°.LCLs were grown in RPMI 1640 supplemented with 10% fetal calf serum  and 1× penicillin/streptomycin (Life Technologies) in humidified 5% COExosomes isolated from LCLs were characterized by Western blot, electron microscopy, and NanoSight nanoparticle tracking analysis . For Western blotting, ∼50 µg total cell and exosome proteins were denatured with 1× sample buffer, reduced with β-mercaptoethanol, and boiled for 5 min. Proteins were resolved on 10% PAGE gels, blotted to nitrocellulose, and probed with antibodies recognizing HSP70 and calnexin. The Stanford University Cell Sciences Imaging Facility performed transmission electron microscopy of isolated intact exosomes. Nanoparticle tracking analysis software (update 2.3) was used to determine the average size of isolated exosomes using a NanoSight LM10-HS at the Stanford PULSE Institute.Small RNA from TRIzol-lysed LCLs and exosomes was isolated from 1 µg total RNA and analyzed for integrity using a Bioanalyzer 2100 Total RNA 6000 Nano Kit. cDNA libraries were produced using the TruSeq Small RNA Sample Prep Kit  per manufacturer’s protocol, with the following modifications: (1) final PCR amplifications were 16 cycles for exosome cDNA and 14 cycles for cell cDNA, (2) instead of gel purification using PAGE, PCR-amplified small RNA libraries were gel-purified with 3% NuSieve GTG low-melt agarose . Target bands of 147–157 nt, containing an adapter sequence of 125 nt, were excised and column purified . All cDNA libraries were indexed with Illumina adapters.Small RNA was sequenced on two lanes (one with cell samples and the other with exosome samples) of a single Illumina HiSeq 2000 flow cell as 36 bp single-end reads. This sequencing setup was chosen to maximize our power for eQTL discovery but meant that lane effects were confounded with the effect of interest for differential expression. Therefore, we resequenced the libraries to lower depth on two runs of an Illumina MiSeq, where each run had both the cells and exosome samples for half the individuals.Figure S1 and described here. Sequencing adapters were trimmed with Trimmomatic (v0.27) and were also provided to Novoalign , which handled incomplete adapter trimming while mapping. The reads were mapped in two steps where they were first mapped to the mature sequences in miRBase (release 20)   and piRNease 20) , then unAn miRNA cluster is defined as a set of miRNAs where every cluster member is within 10 kb of at least one other member. We identified all such clusters in the human genome using miRBase annotations. For each cluster, we summed the read counts of all mature miRNAs mapping to that cluster, excluding mature miRNAs that could have originated from multiple clusters. We used the resulting matrix for cluster-level differential expression. Notably, since many miRNAs do not cluster with any others, their mature and cluster counts are identical. As such, mature and cluster MA plots are visually similar, though not identical.t-test and corrected for multiple testing using the Bonferroni method. When comparing the quantities of different small RNA types, we considered miRNAs that were annotated in either Gencode or miRBase, but for all other analyses we only used the higher confidence set of miRNAs annotated in miRBase, which accounts for >99.5% of the reads mapped to miRNAs. Transcript- and cluster-level differential expression between cells and exosomes was performed separately for miRNA, miRNA clusters, and piRNA using DESeq2 (v1.6.3)  both with and without the sequencing run explicitly included in the DESeq2 model. Because the resequencing data had lower sequencing depth and therefore lower power to detect differential expression, we also compared with downsampling the initial data to 10%, which matched the depth of the resequencing data.For each sample, we computed the proportion of reads mapping to each class of RNA. We compared the small RNA composition of cells and exosomes by averaging the proportions of the 17 individuals for each compartment. We tested for a difference between the compartments with a paired two-sided p-values  reportedWe processed the raw reads counts from an exosome sequencing study in HeLa cells . To identify cell- or exosome-enriched miRNA motifs, we applied the MEME and DREME algorithms from the MEME Suite toolset (v4.11.1) . Then, for the separate exosome-enriched and cell-enriched sets, we defined all other annotated miRNAs as background  for each miRNA hairpin , extracted counts for each 1- or 2- bp NTA, and computed the percentage of all miRBase-aligned reads that possessed each NTA. To further analyze a given NTA or set of NTAs, for each sample we computed the percentage of all reads with that NTA originating from each miRNA hairpin.To profile nontemplated nucleotide additions (NTAs) from sequencing data, we remapped reads to miRBase (release 20) hairpins using Bowtie2 (v2.2.4)  in localFrom the variants identified in the Complete Genomics whole genome sequencing data, we removed any variants with Mendelian inconsistencies and used the haplotype blocks we had previously inferred for the 11 children  of the child at the locus containing the transcript being tested. The coefficients pβ and mβ represent the effect sizes of the parental haplotypes. Each association was tested separately in cells and exosomes and the model fit (2R) was used to obtain p-values for the associations. Only transcripts with a median normalized expression of at least 10 in the given compartment were tested. Transcripts mapping to multiple loci, as well as ones containing variants (which could bias mapping given the short read length), were also excluded.To map miRNA and piRNA The Pearson correlation of the effect sizes in cells and exosomes was used to assess the overall sharing of genetic effects between cells and exosomes. To identify specific instances of shared miRNA eQTLs, we intersected the cell and exosome miRNA eQTLs discovered at a 20% FDR.p-value ≤ 0.05, Wilcoxon rank sum test). Finally, we checked whether the haplotypes of shared miRNA eQTLs were associated with many miRNAs genome-wide in both cells and exosomes. An excess of associations may indicate the haplotype is correlated with a technical factor influencing the expression of many miRNAs. We ranked haplotypes by their number of significantly associated transcripts  to assess which haplotypes are most likely to correlate with a technical factor. In our data, we noticed that the haplotype containing the hsa-miR-151a products is ranked 13th among 697 haplotypes in terms of the number of miRNAs genome-wide that it is associated with in cells . It is possible that this haplotype’s segregation pattern among the children is correlated with a technical factor influencing the expression levels of many miRNAs, but we think this is unlikely since the eQTL replicates both in exosomes and in a different cohort.Several factors were assessed to rule out likely nongenetic explanations for the shared miRNA eQTLs to increase our confidence that we were detecting true genetic effects. We confirmed that the haplotypes of the candidate-shared miRNA eQTLs were not confounded with sex and were not explained by mapping differences. Spurious eQTLs driven by different mapping rates for the two haplotypes would be detectable by differential quantities of k-mers in the unmapped reads. Therefore, for each sample, 10-mers in the unmapped reads were counted with Jellyfish (v2.1.4)  and normFile S1 includes the legends for Table S1, Table S2, and Table S3, which include read mapping statistics, differential expression results, and eQTL data and results, respectively.RNA sequencing fastqs and raw read counts are available at GEO with the accession number GSE74759. We isolated exosomes following the procedure outlined in Table S1). By comparing cells with exosomes, we noticed broad differences in the distribution of RNA biotypes. Cells expressed proportionally more miRNA and small nucleolar RNA, while exosomes contained a greater proportion of Piwi-interacting RNA (piRNA) and ribosomal RNA . Of the 3422 piRNAs expressed, we found 374 (11%) were differentially expressed, with 135 being more highly expressed in cells and 239 more frequent in exosomes compared with cells (Table S2B). Since the cells and exosomes were sequenced on different lanes, confounding the contrast of interest with potential lane effects, we resequenced the libraries this time batching cells and exosomes from the same individual together. Our resequencing data confirmed the differential expression results from our initial sequencing run, establishing that any lane effects were minimal . Our results highlight clear and widespread differences in the levels of particular miRNAs and piRNAs between cells and exosomes.We tested whether individual miRNAs and piRNAs were selectively exported in exosomes or retained in cells . Of the Figure S3). Most of the 42-hairpin 14q32 miRNA cluster, which yields 74 mature miRNAs, was massively exported into exosomes while being nearly undetectable in cells , including polycistronic miRNAs, are transcribed then cleaved into individual hairpins before nuclear export . TherefoFigure S4). Taken together, these data suggest that neither known sequence motifs nor 3′-NTAs explain 14q32-encoded miRNA export or our differential expression results more generally.We searched for exosome- or cell-specific motifs using the MEME algorithm, but did not identify previously characterized motifs . Notably, there was significantly more variability between exosomes samples than between cells samples for both miRNA and piRNA, though the difference was small for miRNA, as well as more variability overall for piRNA compared with miRNA .We clustered samples hierarchically, according to the Spearman correlation of their miRNA and piRNA expression, and found that samples from the same compartment cluster together based on both their miRNA and piRNA expression profiles (Table S3). The excess of small p-values for miRNA eQTL tests in cells suggests that genetic differences may explain a portion of the variability observed between individuals . We observed the same but weaker signal in exosomes, but uncovered little evidence of piRNA eQTLs in either cells or exosomes .Genetic variants associated with the expression of particular genes, known as eQTLs, have been studied in multiple cell types and tissues as a common source of gene expression variability between individuals . HoweverFigure S7). To explore this further, we intersected the list of significant miRNA eQTLs from cells and exosomes at an FDR of 20%  with uncharacterized binding affinities. This hypothesis, similar to what has been previously proposed (In addition to assessing global differences between cells and exosomes, we sought to quantify the variability seen between individuals, as this has not previously been measured in a sample of this size. We observed that miRNA and piRNA levels varied more between exosome samples than between cell samples. This implies that RNA export in exosomes is less tightly controlled than RNA regulation within cells, which has important implications for the use of exosomes in diagnostics. To investigate one possible source of variability in miRNA and piRNA expression between individuals, we performed the first eQTL analysis of exosomes and their parent cells. We identified the products of hsa-miR-151a and hsa-miR-335 as miRNA eQTLs that appear to be shared between cells and exosomes. It is unsurprising that we only identified a few miRNA eQTLs, since previous studies have suggested that they are rare relative to mRNA eQTLs (As exosomes are increasingly explored as diagnostic tools and tied to different aspects of human health, it becomes important for us to understand the different levels of regulation they undergo and how their contents may vary from person to person. Our findings improve our understanding of the factors influencing miRNA export and underscore the importance of considering individual differences when studying exosomes.www.g3journal.org/lookup/suppl/doi:10.1534/g3.116.036137/-/DC1.Supplemental material is available online at Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.


In the last decade, although studies on exosomal microRNAs (miRNAs) derived from serum and other body fluids have increased dramatically; the contents and biological significance of serum exosomes under normal conditions remain unclear. In the present study, we profiled the small RNA content of mouse serum exosomes (mSEs) using small RNAseq and found that fragments of transfer RNAs (tRNAs) and miRNAs were the two predominant exosomal RNA species, accounting for approximately 60% and 10% of mapped reads, respectively. Moreover, 466 known and 5 novel miRNAs were identified from two independent experiments, among which the five most abundant miRNAs  accounted for approximately 60% of all the aligned miRNA sequences. As inferred from the identities of the well known cell- or tissue-specific miRNAs, mSEs were primarily released by RBCs, liver and intestinal cells. Bioinformatics analysis revealed over half of the top 20 miRNAs by abundance were involved in inflammatory responses and further in vitro experiments demonstrated that mSEs potently primed macrophages towards the M2 phenotype. To the best of our knowledge, this is the first study to profile small RNAs from mSEs. In addition to providing a reference for future biomarker studies and extrapolating their origins, our data also suggest the roles of mSEs in maintaining internal homeostasis under normal conditions. Exosomes are 30–150 nm membrane-bound vesicles that are formed in endosomal multivesicular compartments and secreted into the external milieu when these compartments fuse with the plasma membrane . Almost In the last decade, numerous studies have been attempt to identify biomarkers from circulating nucleic acids, especially miRNAs . ExosomeOther than their miRNA content, an even more active area of exosomes research is their roles in intercellular communication. Since the finding that exosomes carry genetic materials, mainly mRNAs and miRNAs, and mediate genetic exchange between cells , accumulin vitro experiments. These data will not only contribute to biomarker studies on serum exosomal miRNAs, but also will deepen our understanding of their biological significance.Next-generation sequencing (NGS) is the best way to comprehensively and quantitatively analysis exosomal RNA content and has been successfully applied to profile exosomal RNA from multiple cultured cells  and huma8 particles/ml.Considering the limited volume of mouse serum and low yield of repeated ultracentrifugation, ExoQuick, a method based on precipitation, was selected to isolate mSEs. Consistent with the fundamental characteristics of exosomes , TEM anaWestern blot was performed to determine the expression of two exosomal markers TSG101 and CD63. As shown in Figure The size distribution and concentration of RNA extracted from the isolated particles were determined using the Bioanalyzer. Total RNA chip analysis showed that the lengths of the major mSEs RNAs were less than 200 nt, with a small portion of RNAs greater than 500 nt in length Figure . Small RSmall RNA sequencing was performed to profile the small RNA content of the mSEs. To evaluate the biological replication, two small RNA sequencing libraries were prepared individually from two batches of total exosomal RNA (7–9 mice per batch). The data produced during library construction are shown in From 2 libraries, 13,239,168 and 12,332,524 raw single-end reads were generated, respectively . This threshold excluded those miRNAs with only one read in both samples. Hence, 427 and 448 known miRNAs were identified, among which 409 were common between samples A and B, and 18 and 39 were unique to samples A and B, respectively Figure . In totaTo determine the reproducibility of biological replicates, linear regression analyses were performed on the log2-transformed read counts derived from two independent RNAseq experiments. The Pearson correlation coefficient between samples A and B was 0.9682 Figure . Three mRecent studies have analyzed the expression patterns of miRNAs in blood cells , 17–19 aIn the over 25 million reads obtained from small RNAseq, only five novel miRNAs were identified . CompareTo analyze the potential biological functions of mSEs, the targets of the top 20 miRNAs, which accounted for 86% of total mapped reads, were predicted using the microRNA target filter in the IPA software. With the selection of ingenuity expert findings and experimentally observed, 118 targets of the top 20 miRNAs were identified . Core anMacrophages are essential components of innate and acquired immunity and play central roles in inflammation and host defense , 32. PlaWhole serum, exosomes-depleted serum, or serum exosomes suspended in an equal volume of media were then added to RAW264.7 cells. After 12 hours, a panel of cytokines, including 4 M1 markers and 3 M2 markers, was detected by qPCR. Whole serum treatment significantly increased the expression of all the three M2 markers, Arg1, IL-10 and TGF-β1, whereas expression of the M1 markers, iNOS, IL 1β and TNF-α, but not IL-6, were significantly suppressed. Notably, levels of Arg1 and iNOS, two representative markers of the M1 and M2 phenotypes , were 2,in vitro experiments demonstrated that mSEs could prime macrophages towards a M2 phenotype. To the best of our knowledge, this is the first systemic and functional study of mSEs. In addition to providing a reference for future functional and biomarker studies on serum exosomes, our data also suggested their potential roles in maintaining homeostasis of the internal environment under normal physiological conditions.In the present study, we found that tRNA fragments and miRNAs were the two predominant small RNA species of mSEs and accounted for approximately 60% and 10% of mapped reads, respectively. Moreover, based on existing knowledge of cell- and tissue-specific miRNAs, our data suggested that the mSEs were mainly derived from RBCs, liver and intestines. Bioinformatics analysis revealed that more than half of the top 20 mouse serum exosomal miRNAs were involved in inflammatory responses, and further The existing RNAseq studies on the small RNA contents of serum exosomes are currently confined to humans , 15. As Several explanations for this discrepancy could exist, such as different methods for exosomes preparation and RNA extraction, different small RNAseq library building methods, and different sequencing platforms and data processing approaches, but we found that the different length distributions of the exosomal RNA could be the main reason. The lengths of the mouse serum exosomal RNAs ranged from 15 to 200 nt, and the small RNAs (15–40 nt) were approximately 30% of the total exosomal RNA Figure . HoweverAnother point worth noting is these detected tRNAs were tRNA fragments. The length that can be detected by the small RNAseq is less than 40 nt, whereas the lengths of mature tRNAs are between 70 and 90 nt. Many experiments have shown that the exosomal RNA component is very stable ; therefoTissue- and/or cell-specific expression of miRNAs is fundamental to biomarker studies, but to date, the contributions of different tissues or cell types to serum exosomes remains unclear. The only such efforts to understand these questions were implemented by Hunter et al., who used flow cytometry and cell-specific surface markers. These authors found that the majority of the peripheral blood microvesicles (MVs) are platelet-derived . Our datFurthermore, for the first time, our data demonstrated that exosomes are released into the body fluid by almost all types of tissues and blood cells, such as liver, muscle, intestine, heart and myeloid cells Table . ElevatiThe most interesting finding of this study was that serum exosomes from normal mice primed the macrophages towards the M2 phenotype Figure . The conMiRNAs are considered to be the main functional components of exosomes , and accAn unresolved puzzle for exosomes studies is whether the miRNA content or a specific miRNA in the exosomes is sufficient to exert effective biological functions, even though the accumulating studies have reported that the biological functions of exosomes could be mediated by even a single type of miRNA , 48. As Taken together, this study established a reference for biomarker studies of mouse serum exosomes, proposed the likelihood of their origin, and provided the first evidence that serum exosomes could prime the macrophage towards an M2 phenotype under normal conditions. Therefore, besides hormones and cytokines, serum exosomes can contribute to the homeostasis via their roles in the intercellular communications. To comprehensively understand the molecular mechanism underlying their multiple biological functions, further studies are needed to profile the other contents in the serum exosomes, especially other RNA species and proteins.The experimental design and procedures involving animals and their care were conducted according to the National Institutes of Health Guide for Care and Use of Laboratory Animals and were approved by the Animal Experimental committee of Jinan University.Male C57BL/6 mice (10 weeks old) were purchased from Medical Experimental Animal Center of Guangdong province. Anesthesia was induced with 1% chloral hydrate. After collecting by left ventricular puncture, blood was placed at room temperature (RT) or 37°C for 1 h to allow it to clot. Afterwards, samples were centrifuged at 500 g at 4°C for 10 min, the clear serum was amber and those with obvious hemolysis (pink or faint red) were discarded at this stage. Serum from 5 to 8 mice was then pooled and centrifuged at 500 g for 10 min and followed by another 10,000 g for 20 min at 4°C, the supernatant was immediately aliquoted and stored at −80°C.RAW264.7 cells, a murine macrophage cell line, were obtained from the American Type Culture Collection  and cultured in high glucose DMEM medium containing 10% heat-inactivated fetal bovine serum and antibiotics (100 U/mL penicillin and 100 U/mL streptomycin) . Cells were incubated at 37°C in a humidified atmosphere of 5% CO2 in air and the medium was changed every 2–3 days. The cultures were passaged when they reached 80% confluence.An ExoQuick kit  was used to isolate mSEs per the manufacturer's instructions with minor modifications. Briefly, frozen mouse serum was thawed on ice and centrifuged at 21,000 g for 15 minutes at 4°C to remove debris. The supernatant was then transferred to a new tube, and 1/4 volume EXOQ5A-1 solution was added and mixed gently. After incubation at 4°C for 2 h, the mixture was centrifugation at 1,500 g for 30 minutes, and the supernatant was removed by aspiration. After another 5 min centrifugation to remove the residual supernatant, pellet-containing exosomes was re-suspended in nuclease-free water or corresponding buffer for RNA or protein extraction.Size distribution and concentration of the isolated mSEs were measured by NTA as described previously . For calFor morphology observation, electron microscopy samples of mSEs were prepared as described by Thery et al. . ExosomeExosomal protein was extracted with RIPA lysis buffer . The protein concentration was determined by BCA assay . Forty-microgram protein was separated by 12% sodium dodecyl sulfate-polyacrylamide gel electrophoresis and electroblotted onto a PVDF membrane . The membrane was blocked with 5% bovine serum albumin for 1 h at RT, and then incubated overnight with antibodies against CD63  and TSG101 . After 3 times wash and incubation with HRP-conjugated secondary antibodies, the proteins were detected by enhanced chemiluminescence .Total RNA was extracted from the serum exosomes using SeraMir kit  according to the manufacturer's protocol. Purity and quantity of the isolated exosomal RNA were determined using Nano Drop . Integrity and size distribution of exosomal RNA were assessed by using Agilent Bioanalyzer 2100 system combined with RNA 6000 pico kit and small RNA kit .® Small RNA Library Prep Set for Illumina®  was used to generate exosomal small RNA libraries for next-generation sequencing on the Illumina platform. Per manufacturer's recommendations, total exosomal RNA was subjected to sequential 3′ and 5′ adapter ligations followed by reverse-transcription into a cDNA library. After PCR enrichment, the miRNA library was size (140 bp) selected from an 8% PAGE-gel and the library quality was assessed on the Agilent Bioanalyzer 2100 system . The qualified cDNA libraries were used for cluster generation and sequenced on the Illumina HiSeq 2500 platform  to obtain 125 bp single-end reads.The NEB NextA flow chart describing the sequencing data analysis is shown in Figure ® Premix Ex Taq™ II, Perfect Real Time kit  on a CFX96 PCR system . The relative expression of the detected miRNAs was expressed as ΔCT = 35- mean CTmiRNA.The exosomal miRNAs were detected by qRT-PCR as described previously . MiDETEC−ΔΔCt.Total RNA was extracted from RAW264.7 cells using Trizol . RNA quantity and quality were determined by a NanoDrop 1000 spectrophotometer . One microgram of total RNA was used to synthesize the cDNA via the Superscript First-Strand Synthesis System for RT-PCR . Primer sequences used for subsequent QPCR analysis were provided in The prediction of targets for exosomal miRNAs and following molecular and cellular functional analyses of these predicted targets were carried out by using the Ingenuity Pathway Analysis (IPA) software .Isolated mSEs were labeled with 10 μM of nucleic acids specific dye SYTO  for 20 min at 37°C, and washed three times with 100 kDa MWCO UltraCentrifugal Filters  to remove the unincorporated dye. The labeled exosomes were suspended in 200 μl PBS and added immediately to RAW264.7 cells; meanwhile, the flow through from the last wash was added as a control. After 2 h of incubation at 37°C, the cellular nuclei were stained with Hoechst 33258 (8 μg/ml) for 5 min, and exosomes uptake was visualized by fluorescence microscopy .t test or one-way analysis of variance using SPSS10.0 software. P values of less than 0.05 or 0.01 were regarded as statistically significant. Unless otherwise specified, all assays were performed in triplicate.The data are presented as the mean ± SEM. Comparisons between means were assessed by unpaired Student's


The geographic distribution and burden of dengue is increasing globally. This study aims to evaluate dengue outbreaks and to substantiate the need for strengthened surveillance, reporting and control in Eritrea.Data from two cross-sectional dengue epidemic investigations in 2005 and 2010 were analyzed. Samples were tested for dengue virus-specific IgM and IgG antibodies using capture enzyme-linked immunosorbent assays. Dengue vectors’ breeding attributes were characterized and epidemic risk indices determined. National routine surveillance weekly reports from 2005 to the second quarter of 2015 were analyzed for spatiotemporal trends.Aedes aegypti were 40 and 39.6 % respectively, with containers having A. aeqypti varying significantly (P < 0.04). Serum from 33.3 % (n = 15) and 88 % (n = 26) of clinical dengue cases in Aroget sub-Zoba (district) of Gash Barka Zoba (region) contained anti-DENV IgM antibody in 2005 and 2006, respectively. The national surveillance data from 2005 to 2015 indicate an overall spatiotemporal increase of dengue fever.Dengue outbreaks increased in Eritrea from 2005 to 2015 with clinical presentation varying markedly among patients. The house and container indices for The increase in dengue outbreaks has been confirmed in Eritrea and necessitates strengthening of surveillance and health worker and laboratory capacity, as well as targeted vector control interventions. Flavivirus, [Aedes aegypti mosquito is the main vector for dengue fever, with Aedes albopictus being a secondary vector [Dengue fever (DF) is a tropical vector-borne disease caused by four sero-types of a single positive-stranded RNA virus  of the genus Globally, 2.5 billion people live in dengue endemic areas, with an estimated 390 million infections occurring annually, and 100 million apparent infections –6. CurreIn Africa, where surveillance for dengue remains poor, epidemics have increased dramatically . Major oEthical clearance for this study was sought from the Eritrean Ministry of Health’s Research Division’s Ethical Committee. Informed consent was sought from study participants, and assent was given by guardians for persons under the consenting age.The case definition used in this study follows that issued by the Eritrean Ministry of Health, based on the World Health Organization from 1997 has been described in detail elsewhere . At diagThis study utilized two main approaches for data collection: analysis of two cross-sectional investigations of dengue outbreaks in 2005 and 2010 involving epidemiological and entomological approaches; the retrospective review of health management information system (HMIS) records from 2005 to 2015; and of integrated disease surveillance and response (IDSR) weekly reports from 2010 thru the second quarter of 2015.The first outbreak of dengue fever was identified in Agordet sub-Zoba (district) of Gash Barka Zoba (region) in 2005 as a result of the investigation of a suspected malaria outbreak. The second outbreak was reported from Massawa sub-Zoba and surrounding areas in Semenawi Keith Bahri (SKB) Zoba in 2010 , 17. DenPatients from the in-patient department (IPD) and outpatient department (OPD) at the time of the investigation were examined by a clinician and the symptoms were recorded in registers at Agordat hospital and the private clinics. The hospital registers were reviewed retrospectively and analyzed for spatial and temporal trends. Case-based outbreak investigation forms were completed for dengue cases at the OPD and those at IPD in the Zonal Referral Hospital of Massawa and Ghedem hospital. Only suspected dengue fever cases that satisfied the inclusion criteria outlined above were interviewed, examined and had blood drawn for serological laboratory analysis using ELISA.Blood samples were collected from suspected dengue cases and processed for serological screening after ruling out malaria infection through the blood film test or rapid diagnostic test (15 samples from Agordet and 26 samples from Massawa). Acute and convalescent sera were transported on cold chain to the National Health Laboratory in Asmara and couriered to collaborating institutions in Kenya for further analysis. Panbio Dengue IgM antibody capture enzyme-linked immunosorbent assays (MAC-ELISA) were used per manufacturer instructions to detect anti-DENV IgM antibodies in convalescent samples . The IgGAe. aegypti in the highly affected area of Agordet. All potential mosquito breeding containers in domestic and peri-domestic areas of dwellings were searched for larvae and pupae. Breeding sites characteristics, household and container indices, proportion of containers positive for Ae. aegypti, and the epidemic risk indices were determined. Potential indoor and outdoor resting places were also search for adult mosquitoes. Adult and larval morphological identifications were done in Asmara using appropriate taxonomic keys [Standard entomological techniques were harnessed for surveillance of immature and adult stages of mic keys .th percentile of the previous year’s weekly cases for each Zoba and National level. National weekly trend graphs are plotted for the entire time series along with the epidemic threshold , arthralgia and back pain (41 %), bleeding in the form of epistaxis (2 %) and myalgia, lymphadenopathy and retro-orbital pain manifested in 20, 18 and 15 % of the dengue patients, respectively. At Massawa, out of the suspected 26 dengue fever cases, 11 (42 %) were from Ghedem Hospital, while the remaining 15 (58 %) were from Massawa Hospital. All dengue patients presented typical signs and symptoms of dengue fever: high fever, headache, fatigue, muscular or joint pain and anorexia; none had any signs of bleeding or other hemorrhagic tendencies. 88 % of all dengue patients were permanent residents of Massawa sub-Zoba (23/26), while one was from Debub Zoba and two were from Maakel Zoba. The distribution of the dengue patients by occupation varied from soldiers 6/26 (23 %), to students 4/26 (15 %) and construction workers 3/26 (12 %).In Agordet, the third quartile threshold for malaria epidemic reporting was crossed in week 8 of the year and triggered the investigation and confirmation of the dengue outbreak in week 13 of 2005 . At MassRecords of 1132 dengue symptomatic cases were traced during the period of the outbreak at Agordat hospital and the private clinics. The outbreak started during week 3 of the year and continued up to week 27 . All ageAe. aegypti, was identified in Agordet with high epidemic risk indices according to both the Breteau Index and house index. The house and container indices were 40 and 39.6 % respectively. Container positivity for Ae. aegypti varied significantly from cement tanks (85.3 %) and clay pots (33.3 %) to plastic tanks (10 %) and iron barrels (4 %) (P < 0.035). At Massawa, breeding sites included seeping water from public tap water, marshy areas, and broken pipes with uncountable larval densities.n = 15) at Agordet sub-Zoba of Gash Barka Zoba. In 2010, sero-positivity for DENV-1 was 88 % (n = 26) at Massawa sub-Zoba and surrounding areas in SKB Zoba.Seropositivity to DENV has been demonstrated in two geographically segregated places in Eritrea. In 2005, seropositivity to IgM antibodies to dengue viruses was 33.3 %  and clay pots (33.3 %) which need to be targeted for an effective control of immature stages of the vector. Furthermore, house and container indices indicated that Ae. aegypti is widespread among cement tanks and clay pots, indicating that targeting peri-domestic and domestic breeding sites with larvicide and other environmental management interventions may be effective in reducing the burden of dengue in Eritrea [Dengue fever is an epidemic prone disease that is rapidly expanding globally  and in E Eritrea , 24. Int Eritrea , Vietnam Eritrea ), Costa  Eritrea , may alsDespite the existence of potentially effective interventions, technical capacity for health workers to control dengue outbreaks remains a notable challenge in Eritrea. The 2005 dengue outbreak was reported to the central IDSR unit 10 weeks after its onset, only after failing to confirm a malaria outbreak . This laAe. aegypti in Eritrea remain sparse and there has not been a surveillance system that would map the areas at risk of dengue in Eritrea. While the numbers of containers with Ae. aegypti larvae and eggs during this entomological survey for dengue were low, the house and container indices were higher than the recommended thresholds of 3 and 4 %, respectively, indicating a potential epidemic risk [Data on the spatial and temporal distribution, abundance and ecology of mic risk . Househomic risk , 31. Stumic risk –36. ThisTo further explore the association between environmental factors and dengue cases and identify the high risk areas for dengue transmission in Eritrea, IDSR data and entomological surveillance data including information from cross sectional surveys should be correlated with environmental data at Zoba, sub-Zoba and community levels. This will optimize the use of the limited resources available for dengue fever prevention and control, facilitate identification of high-risk areas, and increase the effectiveness of interventions. Eritrea should conduct a seroprevalence survey in a random sample of the population to determine which serotypes are circulating and to identify relevant correlates of infection.Aedes aegypti should be strengthened to achieve the most cost-effective outcomes [This study demonstrates spatiotemporal spread of dengue in Eritrea during 2005–2015 indicating that preventative strategies for dengue fever and other potential disease vectored by outcomes , 21. TheExpanding preventative measures should be coupled with extensive entomological surveillance to have a sustained impact on dengue burden.There is a notable increase in dengue outbreaks in Eritrea, which necessitates strengthening of surveillance, health worker and laboratory capacity as well as targeted interventions including vector control. This would allow for faster, more efficient outbreak responses, and improved health outcomes for dengue patients. Increased capacity for vector control would allow for the development, implementation, and then monitoring and evaluation of integrated vector control strategies and community education campaigns to reduce the vector population.


Although acute exposure to hypoxia can disrupt metabolism, longer-term exposure may normalize glucose homeostasis or even improve glucose disposal in the presence of obesity. We examined the effects of two-week exposure to room air (Air), continuous 10% oxygen (C10%), and 12 hr nocturnal periods of 10% oxygen (N10%) on glucose disposal, insulin responsiveness, and mitochondrial function in lean and obese C57BL/6J mice. Both C10% and N10% improved glucose disposal relative to Air in lean and obese mice without evidence of an increase in insulin responsiveness; however, only the metabolic improvements with N10% exposure occurred in the absence of confounding effects of weight loss. In lean mice, N10% exposure caused a decreased respiratory control ratio (RCR) and increased reactive oxygen species (ROS) production in the mitochondria of the muscle and liver compared to Air-exposed mice. In the absence of hypoxia, obese mice exhibited a decreased RCR in the muscle and increased ROS production in the liver compared to lean mice; however, any additional effects of hypoxia in the presence of obesity were minimal. Our data suggest that the development of mitochondrial inefficiency may contribute to metabolic adaptions to hypoxia, independent of weight, and metabolic adaptations to adiposity, independent of hypoxia. Hypoxia is a common consequence of pathology or disease, but also induces adaptive physiologic responses with altitude exposure , 2. HypoAlthough data suggest that acute exposure to hypoxia can induce insulin resistance , longer-Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. Animal protocols were approved by the Institutional Animal Care and Use Committee at the University of Pittsburgh (protocol number 12050422).This study was carried out in strict accordance with the recommendations in the t = 0), mice were injected intraperitoneally with a 2 g/kg glucose bolus and blood glucose was measured  by tail bleed at set time points . On day 1, an ITT was performed after a two-hour fast by administering an intraperitoneal 0.75 U/kg bolus of insulin and measuring blood glucose by tail bleed at t = 15, 30, 45, 60, 90, and 120 min. After completion of the ITT, a random block design was used to determine if mice were exposed to room air, N10%, or C10%. On day 14 of exposure, the GTT was repeated and on day 15 the ITT was repeated. Animals were maintained under exposure conditions until they were killed and tissue was harvested on day 16.Experiments were conducted in lean 10-week-old male C57BL/6J mice on a regular chow diet (14% fat) and 22-week-old obese C57BL/6J mice fed a high-fat diet for 16 weeks . Groups of lean and obese mice were exposed to room air (control), nocturnal 10% inspired oxygen for 12 hrs/day during the light cycle (N10%), or continuous 10% inspired oxygen for 24 hrs/day. Animals were acclimated to the experimental cages over a one-week period prior to beginning the hypoxic interventions and handled daily in preparation for conducting the glucose tolerance test (GTT) and insulin tolerance test (ITT). On day 0, prior to hypoxic intervention and experimental group assignment, a GTT was performed after an overnight fast. After collecting a basal plasma sample (−1) entered the cages from ports evenly spaced near the bottom on all four sides at the level of the bedding material. The cage lid was filled with foam and sealed at the edges, and gas entering through the input ports at the bottom of the cage was exhausted through a vacuum connection in the cage lid. The standard metal grill inside the cage was used to hold food and water supplies.Mice were housed in regular cages that were customized to deliver hypoxic gas or room air as control . Gas .A gas control delivery system regulated the flow of room air and N2, and 5 mM KH2PO4, pH 7.4); in the sealed respirometer, state 4 respiration was measured after the addition of succinate (5 mm). State 3 respiration rate was measured after the addition of ADP (30 nM). Both state 3 and state 4 respiratory rates were completely inhibited in the presence of rotenone (20 μM). Data were normalized to total tissue protein content, and the respiratory control ratio (RCR) was calculated as the product of state 3/state 4.Mitochondrial oxygen consumption was measured in fresh homogenates of the gastrocnemius muscle and liver tissue homogenate using a Clark-type oxygen electrode as previously described , 29. BriMitochondrial hydrogen peroxide production was measured by spectrophotometrically monitoring the rotenone-sensitive oxidation of Amplex Red (at 585 nm) by tissue homogenates in the presence of succinate and ADP.β-actin housekeeping protein.GLUT expression in tissue (lysed and sonicated) was measured by Western blot. Antibodies used included GLUT-1 (ab115730), GLUT-2 (ab104622), GLUT-3 (ab41525), and GLUT-4 (ab654) from Abcam . Western blots quantified as relative intensity ratio of GLUT1–4 to PFK activity was measured in permeabilized tissue using a colorimetric assay kit . Briefly, PFK activity was coupled to the conversion of ADP to AMP and NADH, which reduced a colorless probe to a colored product that was detected at 450 nm.in vitro enzymatic colorimetric method according to the manufacturer's instructions . Plasma lactate levels were measured using an oxygen electrode system  and reaction with lactate oxidase, where the rate of oxygen consumption is directly proportional to lactate concentration in the sample.Plasma fatty acid levels were measured using an p < 0.05), differences between the means were determined by a post hoc Tukey test. The use of paired or unpaired t-test is reported in figure or table legends where appropriate. Data are reported as mean ± s.e.m.Differences between means in lean and obese mice in each of the three experimental groups  were determined by either one-way or two-way ANOVA. Where the ANOVA indicated statistical significance , but the area above the curve was unaffected by both N10% and C10% exposures  and worse glucose tolerance (p < 0.001) compared to lean mice  and 100 ± 7 mg/dl reduction at time 0 of the ITT (p < 0.001). Control obese mice under room air conditions showed a reproducible ITT response  and liver (p < 0.05). Although qualitatively obese mice displayed a similar pattern of response to N10% and C10% exposure as seen in lean mice, only the PFK activity in the muscle was increased with N10% compared to room air control and decreased with C10% compared to N10%.Obese room air control mice had higher PFK activity than lean mice in both muscle (p < 0.05). In contrast, plasma FFA levels were comparable between the lean and obese mice and did not change with hypoxia.For plasma lactate, there was a significant interaction between hypoxia and obesity, with hypoxia increasing plasma lactate in lean mice relative to obese mice  and ITT (2 hr fast) was significantly lower in C10%-exposed mice suggesting reduced hepatic glucose production and increased muscle glucose disposal, respectively. Mitochondrial efficiency with C10% exposure was reduced comparably to N10% exposure, but associated with higher ROS production. Interestingly, the muscle and liver PFK activity after C10% were reduced compared to N10%, with absolute activity levels at or below that seen in lean room air control mice. It is possible that the high lactate levels after C10% could have contributed to an inhibition of PFK activity . Thus, aIn the absence of hypoxia, obese mice exhibited a significant decrease in mitochondrial efficiency in the muscle and a trend to decrease mitochondrial efficiency in the liver compared to lean mice. Similar to the scenario outlined above for responses to hypoxia in lean mice, the decrease in basal RCR in obese mice may represent an adaptive response to improve glucose disposal by uncoupling oxidative phosphorylation and ATP production , 35. Of Hypoxic exposure in obese mice produced marked improvements in glucose tolerance, and similar to lean mice, the response to N10% occurred in the absence of weight loss. However, unlike lean mice, we did not see a decrease in the RCR in obese mice exposed to hypoxia, suggesting that the decrease in basal RCR, discussed above as a potential adaptive response to obesity, may result in a floor effect with hypoxia unable to further reduce mitochondrial efficiency. The pattern of PFK activity in response to hypoxia in obese mice was qualitatively similar to lean mice, although only changes in the muscle PFK activity reached statistical significance. Thus, hypoxia produced similar improvements in glucose disposal in obese and lean mice, but any contribution of reduced mitochondrial efficiency as an adaptive hypoxic response is likely limited to lean mice because the RCR is already reduced in obese mice.Several points regarding our experimental approach require consideration. The weight loss associated with two weeks of C10% exposure resulted in the mice on days 14 and 15 receiving less glucose and insulin on a weight-dependent basis for the GTT and ITT, respectively. The reduced dosing could have contributed to the decreased area under the curve for the GTT and a decreased area above the curve for the ITT with C10% exposure. However, the improved glucose disposal for N10% exposure was independent of weight change and, therefore, the bolus of glucose and insulin administered. We did not age-match the lean mice to the obese mice; if the mice on a regular chow diet had been age-matched to the high-fat-fed mice, they would have modeled an “overweight” phenotype, given the obesity propensity of the C57BL/6J strain . For theAlternatively, short-term exposure to altitude hypoxia can induce acute mountain sickness , whereasOur findings have relevance to high-altitude physiology where human studies have shown that the effects of hypoxic exposure on glucose homeostasis are, in part, dependent on exposure duration. Rapid ascent to altitude can cause acute hyperglycemia within the first 48 hrs, but blood glucose normalizes after seven days of acclimation . MoreoveIn summary, we show that nocturnal exposure to 10% hypoxia improves glucose tolerance, independent of weight loss or any improvement in insulin-mediated glucose responsiveness, in both lean and obese mice. Nocturnal 10% hypoxia also increases the liver and muscle PFK activity in lean and obese mice. Reductions in mitochondrial efficiency may contribute to adaptive responses to hypoxia in lean mice and adaptive responses to adiposity in response to a high-fat diet. One potential implication of our study is that a targeted and appropriately titrated hypoxic therapy could potentially improve glucose homeostasis in obese insulin-resistant patients that are free of comorbid hypoxic disease.