timsTOF HT Improves Protein Identification and Quantitative Reproducibility for Deep Unbiased Plasma Protein Biomarker Discovery.

Mass spectrometry (MS) is a valuable tool for plasma proteome profiling and disease biomarker discovery. However, wide-ranging plasma protein concentrations, along with technical and biological variabilities, present significant challenges for deep and reproducible protein quantitation. Here, we evaluated the qualitative and quantitative performance of timsTOF HT and timsTOF Pro 2 mass spectrometers for analysis of neat plasma samples (unfractionated) and plasma samples processed using the Proteograph Product Suite (Proteograph) that enables robust deep proteomics sampling prior to mass spectrometry. Samples were evaluated across a wide range of peptide loading masses and liquid chromatography (LC) gradients. We observed up to a 76% increase in total plasma peptide precursors identified and a >2-fold boost in quantifiable plasma peptide precursors (CV < 20%) with timsTOF HT compared to Pro 2. Additionally, approximately 4.5 fold more plasma peptide precursors were detected by both timsTOF HT and timsTOF Pro 2 in the Proteograph analyzed plasma vs neat plasma. In an exploratory analysis of 20 late-stage lung cancer and 20 control plasma samples with the Proteograph, which were expected to exhibit distinct proteomes, an approximate 50% increase in total and statistically significant plasma peptide precursors (q < 0.05) was observed with timsTOF HT compared to Pro 2. Our data demonstrate the superior performance of timsTOF HT for identifying and quantifying differences between biologically diverse samples, allowing for improved disease biomarker discovery in large cohort studies. Moreover, researchers can leverage data sets from this study to optimize their liquid chromatography-mass spectrometry (LC-MS) workflows for plasma protein profiling and biomarker discovery. (ProteomeXchange identifier: PXD047854 and PXD047839).

Quantifying neurologic disease using biosensor measurements in-clinic and in free-living settings in multiple sclerosis.

Technological advances in passive digital phenotyping present the opportunity to quantify neurological diseases using new approaches that may complement clinical assessments. Here, we studied multiple sclerosis (MS) as a model neurological disease for investigating physiometric and environmental signals. The objective of this study was to assess the feasibility and correlation of wearable biosensors with traditional clinical measures of disability both in clinic and in free-living in MS patients. This is a single site observational cohort study conducted at an academic neurological center specializing in MS. A cohort of 25 MS patients with varying disability scores were recruited. Patients were monitored in clinic while wearing biosensors at nine body locations at three separate visits. Biosensor-derived features including aspects of gait (stance time, turn angle, mean turn velocity) and balance were collected, along with standardized disability scores assessed by a neurologist. Participants also wore up to three sensors on the wrist, ankle, and sternum for 8 weeks as they went about their daily lives. The primary outcomes were feasibility, adherence, as well as correlation of biosensor-derived metrics with traditional neurologist-assessed clinical measures of disability. We used machine-learning algorithms to extract multiple features of motion and dexterity and correlated these measures with more traditional measures of neurological disability, including the expanded disability status scale (EDSS) and the MS functional composite-4 (MSFC-4). In free-living, sleep measures were additionally collected. Twenty-three subjects completed the first two of three in-clinic study visits and the 8-week free-living biosensor period. Several biosensor-derived features significantly correlated with EDSS and MSFC-4 scores derived at visit two, including mobility stance time with MSFC-4 z-score (Spearman correlation -0.546; p = 0.0070), several aspects of turning including turn angle (0.437; p = 0.0372), and maximum angular velocity (0.653; p = 0.0007). Similar correlations were observed at subsequent clinic visits, and in the free-living setting. We also found other passively collected signals, including measures of sleep, that correlated with disease severity. These findings demonstrate the feasibility of applying passive biosensor measurement techniques to monitor disability in MS patients both in clinic and in the free-living setting.

Deep learning predicts hip fracture using confounding patient and healthcare variables.

Hip fractures are a leading cause of death and disability among older adults. Hip fractures are also the most commonly missed diagnosis on pelvic radiographs, and delayed diagnosis leads to higher cost and worse outcomes. Computer-aided diagnosis (CAD) algorithms have shown promise for helping radiologists detect fractures, but the image features underpinning their predictions are notoriously difficult to understand. In this study, we trained deep-learning models on 17,587 radiographs to classify fracture, 5 patient traits, and 14 hospital process variables. All 20 variables could be individually predicted from a radiograph, with the best performances on scanner model (AUC = 1.00), scanner brand (AUC = 0.98), and whether the order was marked "priority" (AUC = 0.79). Fracture was predicted moderately well from the image (AUC = 0.78) and better when combining image features with patient data (AUC = 0.86, DeLong paired AUC comparison, p = 2e-9) or patient data plus hospital process features (AUC = 0.91, p = 1e-21). Fracture prediction on a test set that balanced fracture risk across patient variables was significantly lower than a random test set (AUC = 0.67, DeLong unpaired AUC comparison, p = 0.003); and on a test set with fracture risk balanced across patient and hospital process variables, the model performed randomly (AUC = 0.52, 95% CI 0.46-0.58), indicating that these variables were the main source of the model's fracture predictions. A single model that directly combines image features, patient, and hospital process data outperforms a Naive Bayes ensemble of an image-only model prediction, patient, and hospital process data. If CAD algorithms are inexplicably leveraging patient and process variables in their predictions, it is unclear how radiologists should interpret their predictions in the context of other known patient data. Further research is needed to illuminate deep-learning decision processes so that computers and clinicians can effectively cooperate.

Next-generation characterization of the Cancer Cell Line Encyclopedia.

Large panels of comprehensively characterized human cancer models, including the Cancer Cell Line Encyclopedia (CCLE), have provided a rigorous framework with which to study genetic variants, candidate targets, and small-molecule and biological therapeutics and to identify new marker-driven cancer dependencies. To improve our understanding of the molecular features that contribute to cancer phenotypes, including drug responses, here we have expanded the characterizations of cancer cell lines to include genetic, RNA splicing, DNA methylation, histone H3 modification, microRNA expression and reverse-phase protein array data for 1,072 cell lines from individuals of various lineages and ethnicities. Integration of these data with functional characterizations such as drug-sensitivity, short hairpin RNA knockdown and CRISPR-Cas9 knockout data reveals potential targets for cancer drugs and associated biomarkers. Together, this dataset and an accompanying public data portal provide a resource for the acceleration of cancer research using model cancer cell lines.

CANDI: an R package and Shiny app for annotating radiographs and evaluating computer-aided diagnosis.

MOTIVATION: Radiologists have used algorithms for Computer-Aided Diagnosis (CAD) for decades. These algorithms use machine learning with engineered features, and there have been mixed findings on whether they improve radiologists' interpretations. Deep learning offers superior performance but requires more training data and has not been evaluated in joint algorithm-radiologist decision systems. RESULTS: We developed the Computer-Aided Note and Diagnosis Interface (CANDI) for collaboratively annotating radiographs and evaluating how algorithms alter human interpretation. The annotation app collects classification, segmentation, and image captioning training data, and the evaluation app randomizes the availability of CAD tools to facilitate clinical trials on radiologist enhancement. AVAILABILITY AND IMPLEMENTATION: Demonstrations and source code are hosted at (https://candi.nextgenhealthcare.org), and (https://github.com/mbadge/candi), respectively, under GPL-3 license. SUPPLEMENTARY INFORMATION: Supplementary material is available at Bioinformatics online.

Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study.

BACKGROUND: There is interest in using convolutional neural networks (CNNs) to analyze medical imaging to provide computer-aided diagnosis (CAD). Recent work has suggested that image classification CNNs may not generalize to new data as well as previously believed. We assessed how well CNNs generalized across three hospital systems for a simulated pneumonia screening task. METHODS AND FINDINGS: A cross-sectional design with multiple model training cohorts was used to evaluate model generalizability to external sites using split-sample validation. A total of 158,323 chest radiographs were drawn from three institutions: National Institutes of Health Clinical Center (NIH; 112,120 from 30,805 patients), Mount Sinai Hospital (MSH; 42,396 from 12,904 patients), and Indiana University Network for Patient Care (IU; 3,807 from 3,683 patients). These patient populations had an age mean (SD) of 46.9 years (16.6), 63.2 years (16.5), and 49.6 years (17) with a female percentage of 43.5%, 44.8%, and 57.3%, respectively. We assessed individual models using the area under the receiver operating characteristic curve (AUC) for radiographic findings consistent with pneumonia and compared performance on different test sets with DeLong's test. The prevalence of pneumonia was high enough at MSH (34.2%) relative to NIH and IU (1.2% and 1.0%) that merely sorting by hospital system achieved an AUC of 0.861 (95% CI 0.855-0.866) on the joint MSH-NIH dataset. Models trained on data from either NIH or MSH had equivalent performance on IU (P values 0.580 and 0.273, respectively) and inferior performance on data from each other relative to an internal test set (i.e., new data from within the hospital system used for training data; P values both <0.001). The highest internal performance was achieved by combining training and test data from MSH and NIH (AUC 0.931, 95% CI 0.927-0.936), but this model demonstrated significantly lower external performance at IU (AUC 0.815, 95% CI 0.745-0.885, P = 0.001). To test the effect of pooling data from sites with disparate pneumonia prevalence, we used stratified subsampling to generate MSH-NIH cohorts that only differed in disease prevalence between training data sites. When both training data sites had the same pneumonia prevalence, the model performed consistently on external IU data (P = 0.88). When a 10-fold difference in pneumonia rate was introduced between sites, internal test performance improved compared to the balanced model (10× MSH risk P < 0.001; 10× NIH P = 0.002), but this outperformance failed to generalize to IU (MSH 10× P < 0.001; NIH 10× P = 0.027). CNNs were able to directly detect hospital system of a radiograph for 99.95% NIH (22,050/22,062) and 99.98% MSH (8,386/8,388) radiographs. The primary limitation of our approach and the available public data is that we cannot fully assess what other factors might be contributing to hospital system-specific biases. CONCLUSION: Pneumonia-screening CNNs achieved better internal than external performance in 3 out of 5 natural comparisons. When models were trained on pooled data from sites with different pneumonia prevalence, they performed better on new pooled data from these sites but not on external data. CNNs robustly identified hospital system and department within a hospital, which can have large differences in disease burden and may confound predictions.

Defects in muscle branched-chain amino acid oxidation contribute to impaired lipid metabolism.

OBJECTIVE: Plasma levels of branched-chain amino acids (BCAA) are consistently elevated in obesity and type 2 diabetes (T2D) and can also prospectively predict T2D. However, the role of BCAA in the pathogenesis of insulin resistance and T2D remains unclear. METHODS: To identify pathways related to insulin resistance, we performed comprehensive gene expression and metabolomics analyses in skeletal muscle from 41 humans with normal glucose tolerance and 11 with T2D across a range of insulin sensitivity (SI, 0.49 to 14.28). We studied both cultured cells and mice heterozygous for the BCAA enzyme methylmalonyl-CoA mutase (Mut) and assessed the effects of altered BCAA flux on lipid and glucose homeostasis. RESULTS: Our data demonstrate perturbed BCAA metabolism and fatty acid oxidation in muscle from insulin resistant humans. Experimental alterations in BCAA flux in cultured cells similarly modulate fatty acid oxidation. Mut heterozygosity in mice alters muscle lipid metabolism in vivo, resulting in increased muscle triglyceride accumulation, increased plasma glucose, hyperinsulinemia, and increased body weight after high-fat feeding. CONCLUSIONS: Our data indicate that impaired muscle BCAA catabolism may contribute to the development of insulin resistance by perturbing both amino acid and fatty acid metabolism and suggest that targeting BCAA metabolism may hold promise for prevention or treatment of T2D.

AXL mediates resistance to PI3Kα inhibition by activating the EGFR/PKC/mTOR axis in head and neck and esophageal squamous cell carcinomas.

Phosphoinositide-3-kinase (PI3K)-α inhibitors have shown clinical activity in squamous cell carcinomas (SCCs) of head and neck (H&N) bearing PIK3CA mutations or amplification. Studying models of therapeutic resistance, we have observed that SCC cells that become refractory to PI3Kα inhibition maintain PI3K-independent activation of the mammalian target of rapamycin (mTOR). This persistent mTOR activation is mediated by the tyrosine kinase receptor AXL. AXL is overexpressed in resistant tumors from both laboratory models and patients treated with the PI3Kα inhibitor BYL719. AXL dimerizes with and phosphorylates epidermal growth factor receptor (EGFR), resulting in activation of phospholipase Cγ (PLCγ)-protein kinase C (PKC), which, in turn, activates mTOR. Combined treatment with PI3Kα and either EGFR, AXL, or PKC inhibitors reverts this resistance.

Loss of Tuberous Sclerosis Complex 2 (TSC2) Is Frequent in Hepatocellular Carcinoma and Predicts Response to mTORC1 Inhibitor Everolimus.

Hepatocellular carcinoma (HCC) is the third leading cause of cancer deaths worldwide and hyperactivation of mTOR signaling plays a pivotal role in HCC tumorigenesis. Tuberous sclerosis complex (TSC), a heterodimer of TSC1 and TSC2, functions as a negative regulator of mTOR signaling. In the current study, we discovered that TSC2 loss-of-function is common in HCC. TSC2 loss was found in 4 of 8 HCC cell lines and 8 of 28 (28.6%) patient-derived HCC xenografts. TSC2 mutations and deletions are likely to be the underlying cause of TSC2 loss in HCC cell lines, xenografts, and primary tumors for most cases. We further demonstrated that TSC2-null HCC cell lines and xenografts had elevated mTOR signaling and, more importantly, were significantly more sensitive to RAD001/everolimus, an mTORC1 inhibitor. These preclinical findings led to the analysis of TSC2 status in HCC samples collected in the EVOLVE-1 clinical trial of everolimus using an optimized immunohistochemistry assay and identified 15 of 139 (10.8%) samples with low to undetectable levels of TSC2. Although the sample size is too small for formal statistical analysis, TSC2-null/low tumor patients who received everolimus tended to have longer overall survival than those who received placebo. Finally, we performed an epidemiology survey of more than 239 Asian HCC tumors and found the frequency of TSC2 loss to be approximately 20% in Asian HBV(+) HCC. Taken together, our data strongly argue that TSC2 loss is a predictive biomarker for the response to everolimus in HCC patients.

CDK 4/6 inhibitors sensitize PIK3CA mutant breast cancer to PI3K inhibitors.

Activation of the phosphoinositide 3-kinase (PI3K) pathway occurs frequently in breast cancer. However, clinical results of single-agent PI3K inhibitors have been modest to date. A combinatorial drug screen on multiple PIK3CA mutant cancers with decreased sensitivity to PI3K inhibitors revealed that combined CDK 4/6-PI3K inhibition synergistically reduces cell viability. Laboratory studies revealed that sensitive cancers suppress RB phosphorylation upon treatment with single-agent PI3K inhibitors but cancers with reduced sensitivity fail to do so. Similarly, patients' tumors that responded to the PI3K inhibitor BYL719 demonstrated suppression of pRB, while nonresponding tumors showed sustained or increased levels of pRB. Importantly, the combination of PI3K and CDK 4/6 inhibitors overcomes intrinsic and adaptive resistance leading to tumor regressions in PIK3CA mutant xenografts.

Characterization of the novel and specific PI3Kα inhibitor NVP-BYL719 and development of the patient stratification strategy for clinical trials.

Somatic PIK3CA mutations are frequently found in solid tumors, raising the hypothesis that selective inhibition of PI3Kα may have robust efficacy in PIK3CA-mutant cancers while sparing patients the side-effects associated with broader inhibition of the class I phosphoinositide 3-kinase (PI3K) family. Here, we report the biologic properties of the 2-aminothiazole derivative NVP-BYL719, a selective inhibitor of PI3Kα and its most common oncogenic mutant forms. The compound selectivity combined with excellent drug-like properties translates to dose- and time-dependent inhibition of PI3Kα signaling in vivo, resulting in robust therapeutic efficacy and tolerability in PIK3CA-dependent tumors. Novel targeted therapeutics such as NVP-BYL719, designed to modulate aberrant functions elicited by cancer-specific genetic alterations upon which the disease depends, require well-defined patient stratification strategies in order to maximize their therapeutic impact and benefit for the patients. Here, we also describe the application of the Cancer Cell Line Encyclopedia as a preclinical platform to refine the patient stratification strategy for NVP-BYL719 and found that PIK3CA mutation was the foremost positive predictor of sensitivity while revealing additional positive and negative associations such as PIK3CA amplification and PTEN mutation, respectively. These patient selection determinants are being assayed in the ongoing NVP-BYL719 clinical trials.

Carboxyl-ester lipase maturity-onset diabetes of the young is associated with development of pancreatic cysts and upregulated MAPK signaling in secretin-stimulated duodenal fluid.

Carboxyl-ester lipase (CEL) maturity-onset diabetes of the young (MODY) is a monogenic form of diabetes and pancreatic exocrine dysfunction due to mutations in the CEL gene encoding CEL. The pathogenic mechanism for diabetes development is unknown. Since CEL is expressed mainly in pancreatic acinar cells, we asked whether we could find structural pancreatic changes in CEL-MODY subjects during the course of diabetes development. Furthermore, we hypothesized that the diseased pancreas releases proteins that are detectable in pancreatic fluid and potentially reflect activation or inactivation of disease-specific pathways. We therefore investigated nondiabetic and diabetic CEL-mutation carriers by pancreatic imaging studies and secretin-stimulated duodenal juice sampling. The secretin-stimulated duodenal juice was studied using cytokine assays, mass spectrometry (MS) proteomics, and multiplexed MS-based measurement of kinase activities. We identified multiple pancreatic cysts in all eight diabetic mutation carriers but not in any of the four nondiabetic mutation carriers or the six healthy controls. Furthermore, we identified upregulated mitogen-activated protein kinase (MAPK) target proteins and MAPK-driven cytokines and increased MAPK activity in the secretin-stimulated duodenal juice. These findings show that subjects with CEL-MODY develop multiple pancreatic cysts by the time they develop diabetes and that upregulated MAPK signaling in the pancreatic secretome may reflect the pathophysiological development of pancreatic cysts and diabetes.

mTORC1 inhibition is required for sensitivity to PI3K p110α inhibitors in PIK3CA-mutant breast cancer.

Activating mutations of the PIK3CA gene occur frequently in breast cancer, and inhibitors that are specific for phosphatidylinositol 3-kinase (PI3K) p110α, such as BYL719, are being investigated in clinical trials. In a search for correlates of sensitivity to p110α inhibition among PIK3CA-mutant breast cancer cell lines, we observed that sensitivity to BYL719 (as assessed by cell proliferation) was associated with full inhibition of signaling through the TORC1 pathway. Conversely, cancer cells that were resistant to BYL719 had persistently active mTORC1 signaling, although Akt phosphorylation was inhibited. Similarly, in patients, pS6 (residues 240/4) expression (a marker of mTORC1 signaling) was associated with tumor response to BYL719, and mTORC1 was found to be reactivated in tumors from patients whose disease progressed after treatment. In PIK3CA-mutant cancer cell lines with persistent mTORC1 signaling despite PI3K p110α blockade (that is, resistance), the addition of the allosteric mTORC1 inhibitor RAD001 to the cells along with BYL719 resulted in reversal of resistance in vitro and in vivo. Finally, we found that growth factors such as insulin-like growth factor 1 and neuregulin 1 can activate mammalian target of rapamycin (mTOR) and mediate resistance to BYL719. Our findings suggest that simultaneous administration of mTORC1 inhibitors may enhance the clinical activity of p110α-targeted drugs and delay the appearance of resistance.

The HSP90 inhibitor NVP-AUY922 potently inhibits non-small cell lung cancer growth.

Heat shock protein 90 (HSP90) is involved in protein folding and functions as a chaperone for numerous client proteins, many of which are important in non-small cell lung cancer (NSCLC) pathogenesis. We sought to define preclinical effects of the HSP90 inhibitor NVP-AUY922 and identify predictors of response. We assessed in vitro effects of NVP-AUY922 on proliferation and protein expression in NSCLC cell lines. We evaluated gene expression changes induced by NVP-AUY922 exposure. Xenograft models were evaluated for tumor control and biological effects. NVP-AUY922 potently inhibited in vitro growth in all 41 NSCLC cell lines evaluated with IC50 < 100 nmol/L. IC100 (complete inhibition of proliferation) < 40 nmol/L was seen in 36 of 41 lines. Consistent gene expression changes after NVP-AUY922 exposure involved a wide range of cellular functions, including consistently decreased dihydrofolate reductase after exposure. NVP-AUY922 slowed growth of A549 (KRAS-mutant) xenografts and achieved tumor stability and decreased EGF receptor (EGFR) protein expression in H1975 xenografts, a model harboring a sensitizing and a resistance mutation for EGFR-tyrosine kinase inhibitors in the EGFR gene. These data will help inform the evaluation of correlative data from a recently completed phase II NSCLC trial and a planned phase IB trial of NVP-AUY922 in combination with pemetrexed in NSCLCs.

The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity.

The systematic translation of cancer genomic data into knowledge of tumour biology and therapeutic possibilities remains challenging. Such efforts should be greatly aided by robust preclinical model systems that reflect the genomic diversity of human cancers and for which detailed genetic and pharmacological annotation is available. Here we describe the Cancer Cell Line Encyclopedia (CCLE): a compilation of gene expression, chromosomal copy number and massively parallel sequencing data from 947 human cancer cell lines. When coupled with pharmacological profiles for 24 anticancer drugs across 479 of the cell lines, this collection allowed identification of genetic, lineage, and gene-expression-based predictors of drug sensitivity. In addition to known predictors, we found that plasma cell lineage correlated with sensitivity to IGF1 receptor inhibitors; AHR expression was associated with MEK inhibitor efficacy in NRAS-mutant lines; and SLFN11 expression predicted sensitivity to topoisomerase inhibitors. Together, our results indicate that large, annotated cell-line collections may help to enable preclinical stratification schemata for anticancer agents. The generation of genetic predictions of drug response in the preclinical setting and their incorporation into cancer clinical trial design could speed the emergence of 'personalized' therapeutic regimens.

A systems biology approach identifies inflammatory abnormalities between mouse strains prior to development of metabolic disease.

OBJECTIVE: Type 2 diabetes and obesity are increasingly affecting human populations around the world. Our goal was to identify early molecular signatures predicting genetic risk to these metabolic diseases using two strains of mice that differ greatly in disease susceptibility. RESEARCH DESIGN AND METHODS: We integrated metabolic characterization, gene expression, protein-protein interaction networks, RT-PCR, and flow cytometry analyses of adipose, skeletal muscle, and liver tissue of diabetes-prone C57BL/6NTac (B6) mice and diabetes-resistant 129S6/SvEvTac (129) mice at 6 weeks and 6 months of age. RESULTS: At 6 weeks of age, B6 mice were metabolically indistinguishable from 129 mice, however, adipose tissue showed a consistent gene expression signature that differentiated between the strains. In particular, immune system gene networks and inflammatory biomarkers were upregulated in adipose tissue of B6 mice, despite a low normal fat mass. This was accompanied by increased T-cell and macrophage infiltration. The expression of the same networks and biomarkers, particularly those related to T-cells, further increased in adipose tissue of B6 mice, but only minimally in 129 mice, in response to weight gain promoted by age or high-fat diet, further exacerbating the differences between strains. CONCLUSIONS: Insulin resistance in mice with differential susceptibility to diabetes and metabolic syndrome is preceded by differences in the inflammatory response of adipose tissue. This phenomenon may serve as an early indicator of disease and contribute to disease susceptibility and progression.

Accelerated postnatal growth increases lipogenic gene expression and adipocyte size in low-birth weight mice.

OBJECTIVE: To characterize the hormonal milieu and adipose gene expression in response to catch-up growth (CUG), a growth pattern associated with obesity and diabetes risk, in a mouse model of low birth weight (LBW). RESEARCH DESIGN AND METHODS: ICR mice were food restricted by 50% from gestational days 12.5-18.5, reducing offspring birth weight by 25%. During the suckling period, dams were either fed ad libitum, permitting CUG in offspring, or food restricted, preventing CUG. Offspring were killed at age 3 weeks, and gonadal fat was removed for RNA extraction, array analysis, RT-PCR, and evaluation of cell size and number. Serum insulin, thyroxine (T4), corticosterone, and adipokines were measured. RESULTS: At age 3 weeks, LBW mice with CUG (designated U-C) had body weight comparable with controls (designated C-C); weight was reduced by 49% in LBW mice without CUG (designated U-U). Adiposity was altered by postnatal nutrition, with gonadal fat increased by 50% in U-C and decreased by 58% in U-U mice (P < 0.05 vs. C-C mice). Adipose expression of the lipogenic genes Fasn, AccI, Lpin1, and Srebf1 was significantly increased in U-C compared with both C-C and U-U mice (P < 0.05). Mitochondrial DNA copy number was reduced by >50% in U-C versus U-U mice (P = 0.014). Although cell numbers did not differ, mean adipocyte diameter was increased in U-C and reduced in U-U mice (P < 0.01). CONCLUSIONS: CUG results in increased adipose tissue lipogenic gene expression and adipocyte diameter but not increased cellularity, suggesting that catch-up fat is primarily associated with lipogenesis rather than adipogenesis in this murine model.

Network-based analysis of affected biological processes in type 2 diabetes models.

Type 2 diabetes mellitus is a complex disorder associated with multiple genetic, epigenetic, developmental, and environmental factors. Animal models of type 2 diabetes differ based on diet, drug treatment, and gene knockouts, and yet all display the clinical hallmarks of hyperglycemia and insulin resistance in peripheral tissue. The recent advances in gene-expression microarray technologies present an unprecedented opportunity to study type 2 diabetes mellitus at a genome-wide scale and across different models. To date, a key challenge has been to identify the biological processes or signaling pathways that play significant roles in the disorder. Here, using a network-based analysis methodology, we identified two sets of genes, associated with insulin signaling and a network of nuclear receptors, which are recurrent in a statistically significant number of diabetes and insulin resistance models and transcriptionally altered across diverse tissue types. We additionally identified a network of protein-protein interactions between members from the two gene sets that may facilitate signaling between them. Taken together, the results illustrate the benefits of integrating high-throughput microarray studies, together with protein-protein interaction networks, in elucidating the underlying biological processes associated with a complex disorder.