Identification of key regulators of pancreatic cancer progression through multidimensional systems-level analysis

Background: Pancreatic cancer is an aggressive cancer with dismal prognosis, urgently necessitating better biomarkers to improve therapeutic options and early diagnosis. Traditional approaches of biomarker detection that consider only one aspect of the biological continuum like gene expression alone are limited in their scope and lack robustness in identifying the key regulators of the disease. We have adopted a multidimensional approach involving the cross-talk between the omics spaces to identify key regulators of disease progression. Methods: Multidimensional domain-specific disease signatures were obtained using rank-based meta-analysis of individual omics profiles (mRNA, miRNA, DNA methylation) related to pancreatic ductal adenocarcinoma (PDAC). These domain-specific PDAC signatures were integrated to identify genes that were affected across multiple dimensions of omics space in PDAC (genes under multiple regulatory controls, GMCs). To further pin down the regulators of PDAC pathophysiology, a systems-level network was generated from knowledge-based interaction information applied to the above identified GMCs. Key regulators were identified from the GMC network based on network statistics and their functional importance was validated using gene set enrichment analysis and survival analysis. Results: Rank-based meta-analysis identified 5391 genes, 109 miRNAs and 2081 methylation-sites significantly differentially expressed in PDAC (false discovery rate ≤ 0.05). Bimodal integration of meta-analysis signatures revealed 1150 and 715 genes regulated by miRNAs and methylation, respectively. Further analysis identified 189 altered genes that are commonly regulated by miRNA and methylation, hence considered GMCs. Systems-level analysis of the scale-free GMCs network identified eight potential key regulator hubs, namely E2F3, HMGA2, RASA1, IRS1, NUAK1, ACTN1, SKI and DLL1, associated with important pathways driving cancer progression. Survival analysis on individual key regulators revealed that higher expression of IRS1 and DLL1 and lower expression of HMGA2, ACTN1 and SKI were associated with better survival probabilities. Conclusions: It is evident from the results that our hierarchical systems-level multidimensional analysis approach has been successful in isolating the converging regulatory modules and associated key regulatory molecules that are potential biomarkers for pancreatic cancer progression. Electronic supplementary material The online version of this article (doi:10.1186/s13073-016-0282-3) contains supplementary material, which is available to authorized users

Bench-to-bedside review: Future novel diagnostics for sepsis - a systems biology approach

The early, accurate diagnosis and risk stratification of sepsis remains an important challenge in the critically ill. Since traditional biomarker strategies have not yielded a gold standard marker for sepsis, focus is shifting towards novel strategies that improve assessment capabilities. The combination of technological advancements and information generated through the human genome project positions systems biology at the forefront of biomarker discovery. While previously available, developments in the technologies focusing on DNA, gene expression, gene regulatory mechanisms, protein and metabolite discovery have made these tools more feasible to implement and less costly, and they have taken on an enhanced capacity such that they are ripe for utilization as tools to advance our knowledge and clinical research. Medicine is in a genome-level era that can leverage the assessment of thousands of molecular signals beyond simply measuring selected circulating proteins. Genomics is the study of the entire complement of genetic material of an individual. Epigenetics is the regulation of gene activity by reversible modifications of the DNA. Transcriptomics is the quantification of the relative levels of messenger RNA for a large number of genes in specific cells or tissues to measure differences in the expression levels of different genes, and the utilization of patterns of differential gene expression to characterize different biological states of a tissue. Proteomics is the large-scale study of proteins. Metabolomics is the study of the small molecule profiles that are the terminal downstream products of the genome and consists of the total complement of all low-molecular-weight molecules that cellular processes leave behind. Taken together, these individual fields of study may be linked during a systems biology approach. There remains a valuable opportunity to deploy these technologies further in human research. The techniques described in this paper not only have the potential to increase the spectrum of diagnostic and prognostic biomarkers in sepsis, but they may also enable the discovery of new disease pathways. This may in turn lead us to improved therapeutic targets. The objective of this paper is to provide an overview and basic framework for clinicians and clinical researchers to better understand the 'omics technologies' to enhance further use of these valuable tools

BhairPred: prediction of β-hairpins in a protein from multiple alignment information using ANN and SVM techniques

This paper describes a method for predicting a supersecondary structural motif, β-hairpins, in a protein sequence. The method was trained and tested on a set of 5102 hairpins and 5131 non-hairpins, obtained from a non-redundant dataset of 2880 proteins using the DSSP and PROMOTIF programs. Two machine-learning techniques, an artificial neural network (ANN) and a support vector machine (SVM), were used to predict β-hairpins. An accuracy of 65.5% was achieved using ANN when an amino acid sequence was used as the input. The accuracy improved from 65.5 to 69.1% when evolutionary information (PSI-BLAST profile), observed secondary structure and surface accessibility were used as the inputs. The accuracy of the method further improved from 69.1 to 79.2% when the SVM was used for classification instead of the ANN. The performances of the methods developed were assessed in a test case, where predicted secondary structure and surface accessibility were used instead of the observed structure. The highest accuracy achieved by the SVM based method in the test case was 77.9%. A maximum accuracy of 71.1% with Matthew's correlation coefficient of 0.41 in the test case was obtained on a dataset previously used by X. Cruz, E. G. Hutchinson, A. Shephard and J. M. Thornton (2002) Proc. Natl Acad. Sci. USA, 99, 11157–11162. The performance of the method was also evaluated on proteins used in the ‘6th community-wide experiment on the critical assessment of techniques for protein structure prediction (CASP6)’. Based on the algorithm described, a web server, BhairPred (), has been developed, which can be used to predict β-hairpins in a protein using the SVM approach

The Simple prEservatioN of Single cElls method for cryopreservation enables the generation of single-cell immune profiles from whole blood

IntroductionCurrent multistep methods utilized for preparing and cryopreserving single-cell suspensions from blood samples for single-cell RNA sequencing (scRNA-seq) are time-consuming, requiring trained personnel and special equipment, so limiting their clinical adoption. We developed a method, Simple prEservatioN of Single cElls (SENSE), for single-step cryopreservation of whole blood (WB) along with granulocyte depletion during single-cell assay, to generate high quality single-cell profiles (SCP).MethodsWB was cryopreserved using the SENSE method and peripheral blood mononuclear cells (PBMCs) were isolated and cryopreserved using the traditional density-gradient method (PBMC method) from the same blood sample (n=6). The SCPs obtained from both methods were processed using a similar pipeline and quality control parameters. Further, entropy calculation, differential gene expression, and cellular communication analysis were performed to compare cell types and subtypes from both methods.ResultsHighly viable (86.3 ± 1.51%) single-cell suspensions (22,353 cells) were obtained from the six WB samples cryopreserved using the SENSE method. In-depth characterization of the scRNA-seq datasets from the samples processed with the SENSE method yielded high-quality profiles of lymphoid and myeloid cell types which were in concordance with the profiles obtained with classical multistep PBMC method processed samples. Additionally, the SENSE method cryopreserved samples exhibited significantly higher T-cell enrichment, enabling deeper characterization of T-cell subtypes. Overall, the SENSE and PBMC methods processed samples exhibited transcriptional, and cellular communication network level similarities across cell types with no batch effect except in myeloid lineage cells.DiscussionComparative analysis of scRNA-seq datasets obtained with the two cryopreservation methods i.e., SENSE and PBMC methods, yielded similar cellular and molecular profiles, confirming the suitability of the former method’s incorporation in clinics/labs for cryopreserving and obtaining high-quality single-cells for conducting critical translational research

Temporal retinal transcriptome and systems biology analysis identifies key pathways and hub genes in Staphylococcus aureus endophthalmitis

Bacterial endophthalmitis remains a devastating inflammatory condition associated with permanent vision loss. Hence, assessing the host response in this disease may provide new targets for intervention. Using a mouse model of Staphylococcus aureus (SA) endophthalmitis and performing retinal transcriptome analysis, we discovered progressive changes in the expression of 1,234 genes. Gene ontology (GO) and pathway analyses revealed the major pathways impacted in endophthalmitis includes: metabolism, inflammatory/immune, antimicrobial, cell trafficking, and lipid biosynthesis. Among the immune/inflammation pathways, JAK/Stat and IL-17A signaling were the most significantly affected. Interactive network-based analyses identified 13 focus hub genes (IL-6, IL-1β, CXCL2, STAT3, NUPR1, Jun, CSF1, CYR61, CEBPB, IGF-1, EGFR1, SPP1, and TGM2) within these important pathways. The expression of hub genes confirmed by qRT-PCR, ELISA (IL-6, IL-1β, and CXCL2), and Western blot or immunostaining (CEBP, STAT3, NUPR1, and IGF1) showed strong correlation with transcriptome data. Since TLR2 plays an important role in SA endophthalmitis, counter regulation analysis of TLR2 ligand pretreated retina or the use of retinas from TLR2 knockout mice showed the down-regulation of inflammatory regulatory genes. Collectively, our study provides, for the first time, a comprehensive analysis of the transcriptomic response and identifies key pathways regulating retinal innate responses in staphylococcal endophthalmitis

Preliminary Biomarkers for Identification of Human Ascending Thoracic Aortic Aneurysm

Background: Human ascending thoracic aortic aneurysms (ATAAs) are life threatening and constitute a leading cause of mortality in the United States. Previously, we demonstrated that collagens α2(V) and α1(XI) mRNA and protein expression levels are significantly increased in ATAAs. Methods and Results: In this report, the authors extended these preliminary studies using high‐throughput proteomic analysis to identify additional biomarkers for use in whole blood real‐time RT‐PCR analysis to allow for the identification of ATAAs before dissection or rupture. Human ATAA samples were obtained from male and female patients aged 65±14 years. Both bicuspid and tricuspid aortic valve patients were included and compared with nonaneurysmal aortas (mean diameter 2.3 cm). Five biomarkers were identified as being suitable for detection and identification of ATAAs using qRT‐PCR analysis of whole blood. Analysis of 41 samples (19 small, 13 medium‐sized, and 9 large ATAAs) demonstrated the overexpression of 3 of these transcript biomarkers correctly identified 79.4% of patients with ATAA of ≥4.0 cm (P<0.001, sensitivity 0.79, CI=0.62 to 0.91; specificity 1.00, 95% CI=0.42 to 1.00). Conclusion: A preliminary transcript biomarker panel for the identification of ATAAs using whole blood qRT‐PCR analysis in men and women is presented

Pentraxin-3 is a PI3K signaling target that promotes stem cell–like traits in basal-like breast cancers

Basal-like breast cancers (BLBCs) exhibit hyperactivation of the phosphoinositide 3-kinase (PI3K) signaling pathway because of the frequent mutational activation of the PIK3CA catalytic subunit and the genetic loss of its negative regulators PTEN (phosphatase and tensin homolog) and INPP4B (inositol polyphosphate-4-phosphatase type II). However, PI3K inhibitors have had limited clinical efficacy in BLBC management because of compensatory amplification of PI3K downstream signaling loops. Therefore, identification of critical PI3K mediators is paramount to the development of effective BLBC therapeutics. Using transcriptomic analysis of activated PIK3CA-expressing BLBC cells, we identified the gene encoding the humoral pattern recognition molecule pentraxin-3 (PTX3) as a critical target of oncogenic PI3K signaling. We found that PTX3 abundance is stimulated, in part, through AKT- and nuclear factor κB (NF-κB)-dependent pathways and that presence of PTX3 is necessary for PI3K-induced stem cell-like traits. We further showed that PTX3 expression is greater in tumor samples from patients with BLBC and that it is prognostic of poor patient survival. Our results thus reveal PTX3 as a newly identified PI3K-regulated biomarker and a potential therapeutic target in BLBC

Meta-analysis of transcriptome data identifies a novel 5-gene pancreatic adenocarcinoma classifier

Purpose: Pancreatic ductal adenocarcinoma (PDAC) is largely incurable due to late diagnosis. Superior early detection biomarkers are critical to improving PDAC survival and risk stratification. Experimental Design: Optimized meta-analysis of PDAC transcriptome datasets identified and validated key PDAC biomarkers. PDAC-specific expression of a 5-gene biomarker panel was measured by qRT-PCR in microdissected patient-derived FFPE tissues. Cell-based assays assessed impact of two of these biomarkers, TMPRSS4 and ECT2, on PDAC cells. Results: A 5-gene PDAC classifier (TMPRSS4, AHNAK2, POSTN, ECT2, SERPINB5) achieved on average 95% sensitivity and 89% specificity in discriminating PDAC from non-tumor samples in four training sets and similar performance (sensitivity = 94%, specificity = 89.6%) in five independent validation datasets. This classifier accurately discriminated PDAC from chronic pancreatitis (AUC = 0.83), other cancers (AUC = 0.89), and non-tumor from PDAC precursors (AUC = 0.92) in three independent datasets. Importantly, the classifier distinguished PanIN from healthy pancreas in the PDX1-Cre;LSL-KrasG12D PDAC mouse model. Discriminatory expression of the PDAC classifier genes was confirmed in microdissected FFPE samples of PDAC and matched surrounding non-tumor pancreas or pancreatitis. Notably, knock-down of TMPRSS4 and ECT2 reduced PDAC soft agar growth and cell viability and TMPRSS4 knockdown also blocked PDAC migration and invasion. Conclusions: This study identified and validated a highly accurate 5-gene PDAC classifier for discriminating PDAC and early precursor lesions from non-malignant tissue that may facilitate early diagnosis and risk stratification upon validation in prospective clinical trials. Cell-based experiments of two overexpressed proteins encoded by the panel, TMPRSS4 and ECT2, suggest a causal link to PDAC development and progression, confirming them as potential therapeutic targets

MSC-Regulated MicroRNAs Converge on the Transcription Factor FOXP2 and Promote Breast Cancer Metastasis

SummaryMesenchymal stem/stromal cells (MSCs) are progenitor cells shown to participate in breast tumor stroma formation and to promote metastasis. Despite expanding knowledge of their contributions to breast malignancy, the underlying molecular responses of breast cancer cells (BCCs) to MSC influences remain incompletely understood. Here, we show that MSCs cause aberrant expression of microRNAs, which, led by microRNA-199a, provide BCCs with enhanced cancer stem cell (CSC) properties. We demonstrate that such MSC-deregulated microRNAs constitute a network that converges on and represses the expression of FOXP2, a forkhead transcription factor tightly associated with speech and language development. FOXP2 knockdown in BCCs was sufficient in promoting CSC propagation, tumor initiation, and metastasis. Importantly, elevated microRNA-199a and depressed FOXP2 expression levels are prominent features of malignant clinical breast cancer and are associated significantly with poor survival. Our results identify molecular determinants of cancer progression of potential utility in the prognosis and therapy of breast cancer

PGC1α-dependent NAD biosynthesis links oxidative metabolism to renal protection

The energetic burden of continuously concentrating solutes against gradients along the tubule may render the kidney especially vulnerable to ischemia. Indeed, acute kidney injury (AKI) affects 3% of all hospitalized patients.1,2 Here we show that the mitochondrial biogenesis regulator, PGC1α,3,4 is a pivotal determinant of renal recovery from injury by regulating NAD biosynthesis. Following renal ischemia, PGC1α−/− mice developed local deficiency of the NAD precursor niacinamide (Nam), marked fat accumulation, and failure to re-establish normal function. Remarkably, exogenous Nam improved local NAD levels, fat accumulation, and renal function in post-ischemic PGC1α−/− mice. Inducible tubular transgenic mice (iNephPGC1α) recapitulated the effects of Nam supplementation, including more local NAD and less fat accumulation with better renal function after ischemia. PGC1α coordinately upregulated the enzymes that synthesize NAD de novo from amino acids whereas PGC1α deficiency or AKI attenuated the de novo pathway. Nam enhanced NAD via the enzyme NAMPT and augmented production of the fat breakdown product beta-hydroxybutyrate (β-OHB), leading to increased prostaglandin PGE2, a secreted autocoid that maintains renal function.5 Nam treatment reversed established ischemic AKI and also prevented AKI in an unrelated toxic model. Inhibition of β-OHB signaling or prostaglandins similarly abolished PGC1α-dependent renoprotection. Given the importance of mitochondrial health in aging and the function of metabolically active organs, the results implicate Nam and NAD as key effectors for achieving PGC1α-dependent stress resistance