ArrayMining: a modular web-application for microarray analysis combining ensemble and consensus methods with cross-study normalization

A comparative transcriptomics analysis reveals ethylene glycol derivatives of squalene ameliorate excessive lipogenesis and inflammatory response in 3T3-L1 preadipocytes

Squalene (SQ) is a natural compound with anti-inflammatory, anti-cancer, and anti-oxidant effects, but due to its low solubility, its biological properties have been greatly underestimated. This study aims to explore the differences in gene expression patterns of four newly synthesized amphipathic ethylene glycol (EG) derivatives of SQ by whole-genome transcriptomics analysis using DNA microarray to examine the mRNA expression profile of adipocytes differentiated from 3T3-L1 cells treated with SQ and its EG derivatives. Enrichment analyses of the transcriptional data showed that compared with SQ, its EG derivatives exerted different, in most cases desirable, biological responses. EG derivatives showed increased enrichment of mitochondrial functions, lipid and glucose metabolism, and inflammatory response. Mono-, di-, and tetra-SQ showed higher enrichment of the cellular component-ribosome. Histological staining showed EG derivatives prevented excessive lipid accumulation. Additionally, mitochondrial transcription factors showed upregulation in tetra-SQ-treated cells. Notably, EG derivatives showed better anti-inflammatory effects. Further, gene-disease association analysis predicted substantial improvement in the bioactivities of SQ derivatives in metabolic diseases. Cluster analyses revealed di- and tetra-SQ had more functional similarities than others, reflected in their scanning electron microscopy images; both di- and tetra-SQ self-organized into similar sizes and shapes of vesicles, subsequently improving their cation binding activities. Protein-protein interaction networks further revealed that cation binding activity might explain a major part, if not all, of the differences observed in functional analyses. Altogether, the addition of EG derivatives may improve the biological responses of SQ and thus may enhance its health-promoting potential.

Unsupervised Algorithms for Microarray Sample Stratification

The amount of data made available by microarrays gives researchers the opportunity to delve into the complexity of biological systems. However, the noisy and extremely high-dimensional nature of this kind of data poses significant challenges. Microarrays allow for the parallel measurement of thousands of molecular objects spanning different layers of interactions. In order to be able to discover hidden patterns, the most disparate analytical techniques have been proposed. Here, we describe the basic methodologies to approach the analysis of microarray datasets that focus on the task of (sub)group discovery.

APT: An Automated Probe Tracker From Gene Expression Data

Out of currently available semi-automatic tools for detecting diagnostic probes relevant to a pathophysiological condition, ArrayMining and GEO2R of NCBI are most popular. The shortcomings of ArrayMining and GEO2R are that both tools list the probes ordering them on the basis of their individual statistical level of significances with only difference of statistical methods used by them. While the latest tool GEO2R outputs either top 250 or all genes following its own ranking mechanism, ArrayMining requires number of probes to be inputted by the user. This study provided a way for automatic selection of probe-set that can be obtained from the voting of outputs resulted from statistical methods, t-Test, Mann-Whitney Test and Empirical Bayes Moderated t-test. It was also intriguing to find that the parameters of these statistical methods can be represented as a mathematical function of group fisher's discriminant ratio of a disease-control expression data-pair. Result of this fully automatic method, APT shows 88.97 percent success in comparison to 80.40 and 87.60 percent successes of ArrayMining and GEO2R respectively to include reported probes. Furthermore, out of 10 fold cross validation and 5 new test cases, APT shows a better performance than both ArrayMining and GEO2R in regards to sensitivity and specificity.

LC3A-mediated autophagy regulates lung cancer cell plasticity

ABBREVIATIONS

ATG14: autophagy related 14; CDH2: cadherin 2; ChIP-qPCR: chromatin immunoprecipitation quantitative polymerase chain reaction; CQ: chloroquine; ECAR: extracellular acidification rate; EMT: epithelial-mesenchymal transition; EPCAM: epithelial cell adhesion molecule; MAP1LC3A/LC3A: microtubule associated protein 1 light chain 3 alpha; MAP1LC3B/LC3B: microtubule associated protein 1 light chain 3 beta; MAP1LC3C/LC3C: microtubule associated protein 1 light chain 3 gamma; NDUFV2: NADH:ubiquinone oxidoreductase core subunit V2; OCR: oxygen consumption rate; ROS: reactive oxygen species; RT-qPCR: reverse-transcriptase quantitative polymerase chain reaction; SC: scrambled control; shRNA: short hairpin RNA; SNAI2: snail family transcriptional repressor 2; SOX2: SRY-box transcription factor 2; SQSTM1/p62: sequestosome 1; TGFB/TGF-β: transforming growth factor beta; TOMM20: translocase of outer mitochondrial membrane 20; ZEB1: zinc finger E-box binding homeobox 1.

Nebulized curcumin protects neonatal lungs from antenatal insult in rats

Intrauterine growth restriction (IUGR) increases the risk of bronchopulmonary dysplasia (BPD), one of the major complications of prematurity. Antenatal low-protein diet (LPD) exposure in rats induces IUGR and mimics BPD-related alveolarization disorders. Peroxisome proliferator-activated receptor-γ (PPARγ) plays a key role in normal lung development and was found deregulated following LPD exposure. The objective of this article was to investigate the effects of nebulized curcumin, a natural PPARγ agonist, to prevent IUGR-related abnormal lung development. We studied rat pups antenatally exposed to an LPD or control diet (CTL) and treated with nebulized curcumin (50 mg/kg) or vehicle from postnatal (P) days 1 to 5. The primary readouts were lung morphometric analyses at P21. Immunohistochemistry (P21) and microarrays (P6 and P11) were compared within animals exposed to LPD versus controls, with and without curcumin treatment. Quantitative morphometric analyses revealed that LPD induced abnormal alveolarization as evidenced by a significant increase in mean linear intercept (MLI) observed in P21 LPD-exposed animals. Early curcumin treatment prevented this effect, and two-way ANOVA analysis demonstrated significant interaction between diet and curcumin both for MLI [F_(1,39) = 12.67, P = 0.001] and radial alveolar count at P21 [F_(1,40) = 6.065, P = 0.0182]. Immunohistochemistry for fatty acid binding protein 4 (FABP4), a major regulator of PPARγ pathway, showed a decreased FABP4⁺ alveolar cell density in LPD-exposed animals treated by curcumin. Transcriptomic analysis showed that early curcumin significantly prevented the activation of profibrotic pathways observed at P11 in LPD-exposed animals. Nebulized curcumin appears to be a promising strategy to prevent alveolarization disorders in IUGR rat pups, targeting pathways involved in lung development.

COMSUC: A web server for the identification of consensus molecular subtypes of cancer based on multiple methods and multi-omics data

Extensive amounts of multi-omics data and multiple cancer subtyping methods have been developed rapidly, and generate discrepant clustering results, which poses challenges for cancer molecular subtype research. Thus, the development of methods for the identification of cancer consensus molecular subtypes is essential. The lack of intuitive and easy-to-use analytical tools has posed a barrier. Here, we report on the development of the COnsensus Molecular SUbtype of Cancer (COMSUC) web server. With COMSUC, users can explore consensus molecular subtypes of more than 30 cancers based on eight clustering methods, five types of omics data from public reference datasets or users' private data, and three consensus clustering methods. The web server provides interactive and modifiable visualization, and publishable output of analysis results. Researchers can also exchange consensus subtype results with collaborators via project IDs. COMSUC is now publicly and freely available with no login requirement at http://comsuc.bioinforai.tech/ (IP address: http://59.110.25.27/). For a video summary of this web server, see S1 Video and S1 File.

Ehlers-Danlos syndrome-related genes and serum strontium, zinc, and lithium levels in generalized joint hypermobility: a case-control study

Aim of the study: Generalized joint hypermobility (GJH) is a common feature of almost all Ehlers-Danlos syndrome (EDS) types; however, its genetic basis remains unclear. Therefore, it is crucial to distinguish the genetic basis of GJH from other connective tissue disorders, including the different subtypes of EDS. The aim of this study was to determine the blood EDS-related gene expressions and serum element levels in GJH and reveal their predictive characteristics and correlations with the Beighton score. Materials and Methods: A total of 39 women aged 18-23 years with GJH and 38 age- and sex-matched controls were included in the study. Inductively coupled plasma mass spectrometry was used to analyze the serum levels of zinc (Zn), strontium (Sr), and lithium (Li). The relative expression levels of the EDS-related genes were determined using quantitative real-time polymerase chain reaction (PCR). Results: Our results showed that women with GJH possessed significantly lower Li and higher Zn and Sr levels than the controls. In addition, the gene expressions of TNXB and SLC39A13 were significantly higher, whereas those of COL1A1, COL1A2, COL5A1, FKBP14, and DSE were lower in the GJH group. Pearson correlation analyses revealed a strong negative correlation between the Beighton score and B4GALT7, FKBP14, COL1A1, and Li. However, a significant positive correlation was noted between the Beighton score and SLC39A13, TNXB, Zn, Sr, and B3GALT6. Conclusion: Our findings provide valuable basal levels for conducting gene function analysis of joint hypermobility-related connective tissue disorders.

HDG-select: A novel GUI based application for gene selection and classification in high dimensional datasets

The selection and classification of genes is essential for the identification of related genes to a specific disease. Developing a user-friendly application with combined statistical rigor and machine learning functionality to help the biomedical researchers and end users is of great importance. In this work, a novel stand-alone application, which is based on graphical user interface (GUI), is developed to perform the full functionality of gene selection and classification in high dimensional datasets. The so-called HDG-select application is validated on eleven high dimensional datasets of the format CSV and GEO soft. The proposed tool uses the efficient algorithm of combined filter-GBPSO-SVM and it was made freely available to users. It was found that the proposed HDG-select outperformed other tools reported in literature and presented a competitive performance, accessibility, and functionality.

Melatonin regulates oxidative stress and apoptosis in fetal hearts of pinealectomised RUPP rats

OBJECTIVE

This study aimed to investigate the effects of melatonin on cardiac oxidative stress and apoptosis in the fetal heart in RUPP rats.

METHODS

The fetal heart samples were obtained from melatonin administrated RUPP rats.

RESULTS

Our results indicate that preeclampsia exacerbated by melatonin deficiency triggers hypoxic conditions, both mis/un-folded protein response, oxidative stress-induced DNA damage and apoptosis. Melatonin treatment provided significant therapeutic effects on fetal hearts via regulating all these stress response at cellular and molecular levels.

CONCLUSION

Melatonin may be considered as a potential molecule for development of preventive strategies to reduce the PE induced risk of cardiovascular diseases in offspring.

'Multi-omic' data analysis using O-miner

Innovations in -omics technologies have driven advances in biomedical research. However, integrating and analysing the large volumes of data generated from different high-throughput -omics technologies remain a significant challenge to basic and clinical scientists without bioinformatics skills or access to bioinformatics support. To address this demand, we have significantly updated our previous O-miner analytical suite, to incorporate several new features and data types to provide an efficient and easy-to-use Web tool for the automated analysis of data from '-omics' technologies. Created from a biologist's perspective, this tool allows for the automated analysis of large and complex transcriptomic, genomic and methylomic data sets, together with biological/clinical information, to identify significantly altered pathways and prioritize novel biomarkers/targets for biological validation. Our resource can be used to analyse both in-house data and the huge amount of publicly available information from array and sequencing platforms. Multiple data sets can be easily combined, allowing for meta-analyses. Here, we describe the analytical pipelines currently available in O-miner and present examples of use to demonstrate its utility and relevance in maximizing research output. O-miner Web server is free to use and is available at http://www.o-miner.org.

The Role of Melatonin in Oxidative Stress, DNA Damage, Apoptosis and Angiogenesis in Fetal Eye under Preeclampsia and Melatonin Deficiency Stress

Aim: The aim of this study was to investigate the possible mechanisms of ocular damage induced by pinealectomy (PNX) and preeclampsia (PE), and to determine the cellular and molecular effects of melatonin treatment on oxidative stress, DNA damage, molecular chaperone responses, induction of apoptosis and angiogenesis in the fetal eye of both PNX and PNX+PE animals. Material and Methods: We analysed therapeutic potential of melatonin on fetal eye damage in PNX and PNX+PE animals using Malondialdehyde (MDA), Random Amplified Polymorphic DNA (RAPD), qRT-PCR and Western blot assays. Results: Our study presents three preliminary findings: (a) in fetal eye tissues, PNX and PNX+PE significantly induce oxidative damage to both DNA and protein contents, leading to a dramatic increase in caspase-dependent apoptotic signalling in both mitochondrial and death receptor pathways; (b) the same conditions trigger hypoxia biomarkers in addition to significant overexpression of HIF1-α, HIF1-β, MMP9 and VEGF genes in the fetal eye; (c) finally, melatonin regulates not only the expression of genes encoding antioxidant enzymes and increase in DNA damage as well as lipid peroxidation but also limits programmed cell death processes in the fetal eye of PNX and PNX+PE animals . Furthermore, melatonin can relatively modulate genes in the HIF1 family, TNF-α and VEGF, thus acting as a direct anti-angiogenic molecule. In conclusion, both PNX and PNX+PE induce ocular damage at both cellular and molecular levels in fetal eye tissue of rats. Conclusion: Our results clearly indicate the potential of melatonin as a preventative therapeutic intervention for fetal ocular damage triggered by both PNX and PNX+PE.

Low-dose aspirin protective effects are correlated with deregulation of HNF factor expression in the preeclamptic placentas from mice and humans

Aspirin (acetyl-salicylic acid) is one of the most ancient drugs of the human pharmacopeia. Nonetheless, its action at low doses is not well understood at the molecular level. One of the applications of low-dose aspirin treatment is the prevention of preeclampsia (PE) in patients at risk. Foeto-placental overexpression of the STOX1A transcription factor in mice triggers PE symptoms. Transcriptomic analysis of the placentas, showed that aspirin massively down-regulates genes of the coagulation and complement cascade, as well as genes involved in lipid transport. The genes modified by aspirin treatment are not the ones that are modified by STOX1 overexpression, suggesting that aspirin could act downstream, symptomatically on the preeclamptic disease. Bioinformatics analysis of the promoters of the deregulated genes showed that they are strongly enriched in HNF transcription factors-binding sites, in accordance with existing literature showing their roles as regulators of coagulation. Two of these transcription factors, Hnf1β and Hnf4α are found down-regulated by aspirin treatment. In parallel, we show that in human patient placentas, aspirin-induced deregulations of genes of the coagulation cascade are also observed. Finally, the expression of Hnf1β target sequences (Kif12, F2, Hnf4α promoters and a synthetic concatemer of the Hnf1β-binding site) were investigated by transfection in trophoblast cell models, with or without aspirin treatment and with or without STOX1A overexpression. In this model we observed that STOX1A and aspirin tended to synergize in the down-regulation of Hnf1β target genes in trophoblasts.

Melatonin attenuates caspase-dependent apoptosis in the thoracic aorta by regulating element balance and oxidative stress in pinealectomised rats

The aim of this study was to explain the possible mechanisms by which melatonin deficiency results in cardiovascular injury and to investigate the effects of melatonin administration on important signalling pathways and element equilibrium in the thoracic aorta (TA). For this purpose, we analysed the cellular and molecular effects of melatonin deficiency or administration on oxidative stress, DNA damage, molecular chaperone response, and apoptosis induction in TA tissues of pinealectomised rats using ELISA, RAPD, qRT-PCR, and Western blot assays. The results showed that melatonin deficiency led to an imbalance in essential element levels, unfolded or misfolded proteins, increased lipid peroxidation, and selectively induced caspase-dependent apoptosis in TA tissues without significantly affecting the Bcl-2/BAX ratio (2.28 in pinealectomised rats, 2.73 in pinealectomised rats treated with melatonin). In pinealectomised rats, the genomic template stability (80.22%) was disrupted by the significantly increased oxidative stress, and heat shock protein 70 (20.96-fold), TNF-α (1.73-fold), caspase-8 (2.03-fold), and caspase-3 (2.87-fold) were markedly overexpressed compared with the sham group. Melatonin treatment was protective against apoptosis and inhibited oxidative damage. In addition, melatonin increased the survivin level and improved the regulation of element equilibrium in TA tissues. The results of the study indicate that melatonin deficiency induces TNF-α-related extrinsic apoptosis signals and that the administration of pharmacological doses of melatonin attenuates cardiovascular toxicity by regulating the increase in the rate of apoptosis caused by melatonin deficiency in TA tissue of Sprague–Dawley rats.

Efficacy of Integrating a Novel 16-Gene Biomarker Panel and Intelligence Classifiers for Differential Diagnosis of Rheumatoid Arthritis and Osteoarthritis

Introducing novel biomarkers for accurately detecting and differentiating rheumatoid arthritis (RA) and osteoarthritis (OA) using clinical samples is essential. In the current study, we searched for a novel data-driven gene signature of synovial tissues to differentiate RA from OA patients. Fifty-three RA, 41 OA, and 25 normal microarray-based transcriptome samples were utilized. The area under the curve random forests (RF) variable importance measurement was applied to seek the most influential differential genes between RA and OA. Five algorithms including RF, k-nearest neighbors (kNN), support vector machines (SVM), naïve-Bayes, and a tree-based method were employed for the classification. We found a 16-gene signature that could effectively differentiate RA from OA, including TMOD1, POP7, SGCA, KLRD1, ALOX5, RAB22A, ANK3, PTPN3, GZMK, CLU, GZMB, FBXL7, TNFRSF4, IL32, MXRA7, and CD8A. The externally validated accuracy of the RF model was 0.96 (sensitivity = 1.00, specificity = 0.90). Likewise, the accuracy of kNN, SVM, naïve-Bayes, and decision tree was 0.96, 0.96, 0.96, and 0.91, respectively. Functional meta-analysis exhibited the differential pathological processes of RA and OA; suggested promising targets for further mechanistic and therapeutic studies. In conclusion, the proposed genetic signature combined with sophisticated classification methods may improve the diagnosis and management of RA patients.

Nonoccupational Exposure of Agricultural Area Residents to Pesticides: Pesticide Accumulation and Evaluation of Genotoxicity

Although many studies related the toxic effects of pesticides on agricultural workers, little research has been done about agricultural area residents. The purpose of this work was to monitor the presence of pesticides, as well as their genotoxic and cytotoxic potential, in humans with blood samples collected from control and intensive agricultural areas in the Thrace region. Pesticide accumulations were determined by LC-MS/MS. Cytotoxicity and genotoxicity were analyzed by comet assay, and the effect of pesticide accumulation on oxidative stress, DNA repair, and molecular chaperone response were analyzed by qRT-PCR assays in the human blood samples. The agricultural area residents had a significantly higher concentration of pesticides than those in the control area at all three sampling times, and the total pesticide amounts were 4.3 and 10 times significantly higher in blood sampled in the pesticide use period (August 2015 and 2016, respectively) than in the nonuse period (November 2015). The results showed that the pesticide level in blood during the use period led to oxidative stress, DNA damage (mean comet length and % tail DNA), and unfolded/misfolded protein response. Particularly, in pesticide use season, difference between these parameters was found statistically significant with comparison to control. Our results indicate that individuals residing around a monoculture rice farming area comprise an at-risk group as a result of increased genotoxicity evidenced in human blood. We suggest that biological monitoring efforts should be used to control nonoccupational exposures to pesticides and thus safeguard the health of agricultural area residents.

Computational systems biology approaches for Parkinson's disease

Parkinson's disease (PD) is a prime example of a complex and heterogeneous disorder, characterized by multifaceted and varied motor- and non-motor symptoms and different possible interplays of genetic and environmental risk factors. While investigations of individual PD-causing mutations and risk factors in isolation are providing important insights to improve our understanding of the molecular mechanisms behind PD, there is a growing consensus that a more complete understanding of these mechanisms will require an integrative modeling of multifactorial disease-associated perturbations in molecular networks. Identifying and interpreting the combinatorial effects of multiple PD-associated molecular changes may pave the way towards an earlier and reliable diagnosis and more effective therapeutic interventions. This review provides an overview of computational systems biology approaches developed in recent years to study multifactorial molecular alterations in complex disorders, with a focus on PD research applications. Strengths and weaknesses of different cellular pathway and network analyses, and multivariate machine learning techniques for investigating PD-related omics data are discussed, and strategies proposed to exploit the synergies of multiple biological knowledge and data sources. A final outlook provides an overview of specific challenges and possible next steps for translating systems biology findings in PD to new omics-based diagnostic tools and targeted, drug-based therapeutic approaches.

Cancer reversion with oocyte extracts is mediated by cell cycle arrest and induction of tumour dormancy

Inducing stable control of tumour growth by tumour reversion is an alternative approach to cancer treatment when eradication of the disease cannot be achieved. The process requires re-establishment of normal control mechanisms that are lost in cancer cells so that abnormal proliferation can be halted. Embryonic environments can reset cellular programmes and we previously showed that axolotl oocyte extracts can reprogram breast cancer cells and reverse their tumorigenicity. In this study, we analysed the gene expression profiles of oocyte extract-treated tumour xenografts to show that tumour reprogramming involves cell cycle arrest and acquisition of a quiescent state. Tumour dormancy is associated with increased P27 expression, restoration of RB function and downregulation of mitogen-activated signalling pathways. We also show that the quiescent state is associated with increased levels of H4K20me3 and decreased H4K20me1, an epigenetic profile leading to chromatin compaction. The epigenetic reprogramming induced by oocyte extracts is required for RB hypophosphorylation and induction of P27 expression, both occurring during exposure to the extracts and stably maintained in reprogrammed tumour xenografts. Therefore, this study demonstrates the value of oocyte molecules for inducing tumour reversion and for the development of new chemoquiescence-based therapies.

KIAA0101 is associated with human renal cell carcinoma proliferation and migration induced by erythropoietin

Erythropoietin (EPO) is a frequently prescribed anti-anemic drug for patients with advanced renal carcinoma. However, recent evidence from clinical studies suggested that EPO accelerated tumor progression and jeopardized the 5-year survival. Herein, we show, starting from the in silico microarray bioinformatics analysis, that activation of Erythropoietin signaling pathway enhanced renal clear carcinoma (RCC) progression. EPO accelerated the proliferative and migratory ability in 786-O and Caki-2 cells. Moreover, comparative proteomics expression profiling suggested that exogenous EPO stimulated RCC progression via up-regulation of KIAA0101 expression. Loss of KIAA0101 impeded the undesirable propensity of EPO in RCC. Finally, low expression of KIAA0101 was associated with the excellent prognosis and prognosticated a higher 5-year survival in human patients with renal carcinoma. Overall, KIAA0101 appears to be a key promoter of RCC malignancy induced by EPO, which provide mechanistic insights into KIAA0101 functions, and pave the road to develop new therapeutics for treatment of cancer-related and chemotherapy-induced anemia in patients with RCC.

Antibody Profiling of Kawasaki Disease Using Escherichia coli Proteome Microarrays

Kawasaki disease (KD) is a form of systemic vasculitis that generally occurs in children under 5 years old. Currently, KD is still diagnosed according to its clinical symptoms, including prolonged fever, skin rash, conjunctivitis, neck lymphadenopathy, palm erythema, and oral mucosa changes. Because KD is a type of inflammation without specific marker for diagnosis, we plan to profile the plasma antibodies by using E. coli proteome microarray and analyze the differences between KD and healthy subjects. Plasmas were collected from KD patient before intravenous immunoglobulin treatment (KD1), at least 3 weeks after treatment (KD3), nonfever control (NC), and fever control (FC) children. The initial screening, which consisted of 20 KD1, 20 KD3, 20 NC, and 20 FC, were explored using E. coli proteome microarrays (∼4200 unique proteins). About ∼70 proteins were shown to have high accuracy, e.g. 0.78∼0.92, with regard to separating KD1, KD3, NC, and FC. Those proteins were then purified to fabricate KD focus arrays for training (n = 20 each) and blind-testing (n = 20 each). It only took 125 pl of plasma, less than a drop of blood, in the focus array assays. The AUC scores for blind tests of KD1 versus NC (17 protein markers), KD1 versus FC (20 protein markers), KD3 versus NC (9 protein markers), and KD1 versus KD3 (6 protein markers) were 0.84, 0.75, 0.99 and 0.98, respectively. This study is the first to profile plasma antibodies in KD and demonstrate that an E. coli proteome microarray can screen differences among patients with KD, nonfever controls, and fever controls.

Systematic assessment of cervical cancer initiation and progression uncovers genetic panels for deep learning-based early diagnosis and proposes novel diagnostic and prognostic biomarkers

Although many outstanding achievements in the management of cervical cancer (CxCa) have obtained, it still imposes a major burden which has prompted scientists to discover and validate new CxCa biomarkers to improve the diagnostic and prognostic assessment of CxCa. In this study, eight different gene expression data sets containing 202 cancer, 115 cervical intraepithelial neoplasia (CIN), and 105 normal samples were utilized for an integrative systems biology assessment in a multi-stage carcinogenesis manner. Deep learning-based diagnostic models were established based on the genetic panels of intrinsic genes of cervical carcinogenesis as well as on the unbiased variable selection approach. Survival analysis was also conducted to explore the potential biomarker candidates for prognostic assessment. Our results showed that cell cycle, RNA transport, mRNA surveillance, and one carbon pool by folate were the key regulatory mechanisms involved in the initiation, progression, and metastasis of CxCa. Various genetic panels combined with machine learning algorithms successfully differentiated CxCa from CIN and normalcy in cross-study normalized data sets. In particular, the 168-gene deep learning model for the differentiation of cancer from normalcy achieved an externally validated accuracy of 97.96% (99.01% sensitivity and 95.65% specificity). Survival analysis revealed that ZNF281 and EPHB6 were the two most promising prognostic genetic markers for CxCa among others. Our findings open new opportunities to enhance current understanding of the characteristics of CxCa pathobiology. In addition, the combination of transcriptomics-based signatures and deep learning classification may become an important approach to improve CxCa diagnosis and management in clinical practice.

Transcriptional profiling reveals functional dichotomy between human slan+ non-classical monocytes and myeloid dendritic cells

Human 6-sulfo LacNac-positive (slan⁺) cells have been subject to a paradigm debate. They have previously been classified as a distinct dendritic cell (DC) subset. However, evidence has emerged that they may be more related to monocytes than to DCs. To gain deeper insight into the functional specialization of slan⁺ cells, we have compared them with both conventional myeloid DC subsets (CD1c⁺ and CD141⁺) in human peripheral blood (PB). With the use of genome-wide transcriptional profiling, as well as functional tests, we clearly show that slan⁺ cells form a distinct, non-DC-like population. They cluster away from both DC subsets, and their gene-expression profile evidently suggests involvement in distinct inflammatory processes. An extensive transcriptional meta-analysis confirmed the relationship of slan⁺ cells with the monocytic compartment rather than with DCs. From a functional perspective, their ability to prime CD4⁺ and CD8⁺ T cells is relatively low. Combined with the finding that "antigen presentation by MHC class II" is at the top of under-represented pathways in slan⁺ cells, this points to a minimal role in directing adaptive T cell immunity. Rather, the higher expression levels of complement receptors on their cell surface, together with their high secretion of IL-1β and IL-6, imply a specific role in innate inflammatory processes, which is consistent with their recent identification as non-classical monocytes. This study extends our knowledge on DC/monocyte subset biology under steady-state conditions and contributes to our understanding of their role in immune-mediated diseases and their potential use in immunotherapeutic strategies.

Id2 Collaborates with Id3 To Suppress Invariant NKT and Innate-like Tumors

Inhibitor of DNA binding (Id) proteins, including Id1-4, are transcriptional regulators involved in promoting cell proliferation and survival in various cell types. Although upregulation of Id proteins is associated with a broad spectrum of tumors, recent studies have identified that Id3 plays a tumor-suppressor role in the development of Burkitt's lymphoma in humans and hepatosplenic T cell lymphomas in mice. In this article, we report rapid lymphoma development in Id2/Id3 double-knockout mice that is caused by unchecked expansion of invariant NKT (iNKT) cells or a unique subset of innate-like CD1d-independent T cells. These populations began to expand in neonatal mice and, upon malignant transformation, resulted in mortality between 3 and 11 mo of age. The malignant cells also gave rise to lymphomas upon transfer to Rag-deficient and wild-type hosts, reaffirming their inherent tumorigenic potential. Microarray analysis revealed a significantly modified program in these neonatal iNKT cells that ultimately led to their malignant transformation. The lymphoma cells demonstrated chromosome instability along with upregulation of several signaling pathways, including the cytokine-cytokine receptor interaction pathway, which can promote their expansion and migration. Dysregulation of genes with reported driver mutations and the NF-κB pathway were found to be shared between Id2/Id3 double-knockout lymphomas and human NKT tumors. Our work identifies a distinct premalignant state and multiple tumorigenic pathways caused by loss of function of Id2 and Id3. Thus, conditional deletion of Id2 and Id3 in developing T cells establishes a unique animal model for iNKT and relevant innate-like lymphomas.

Discriminative identification of transcriptional responses of promoters and enhancers after stimulus

Promoters and enhancers regulate the initiation of gene expression and maintenance of expression levels in spatial and temporal manner. Recent findings stemming from the Cap Analysis of Gene Expression (CAGE) demonstrate that promoters and enhancers, based on their expression profiles after stimulus, belong to different transcription response subclasses. One of the most promising biological features that might explain the difference in transcriptional response between subclasses is the local chromatin environment. We introduce a novel computational framework, PEDAL, for distinguishing effectively transcriptional profiles of promoters and enhancers using solely histone modification marks, chromatin accessibility and binding sites of transcription factors and co-activators. A case study on data from MCF-7 cell-line reveals that PEDAL can identify successfully the transcription response subclasses of promoters and enhancers from two different stimulations. Moreover, we report subsets of input markers that discriminate with minimized classification error MCF-7 promoter and enhancer transcription response subclasses. Our work provides a general computational approach for identifying effectively cell-specific and stimulation-specific promoter and enhancer transcriptional profiles, and thus, contributes to improve our understanding of transcriptional activation in human.

A feature selection method based on multiple kernel learning with expression profiles of different types

Background

With the development of high-throughput technology, the researchers can acquire large number of expression data with different types from several public databases. Because most of these data have small number of samples and hundreds or thousands features, how to extract informative features from expression data effectively and robustly using feature selection technique is challenging and crucial. So far, a mass of many feature selection approaches have been proposed and applied to analyse expression data of different types. However, most of these methods only are limited to measure the performances on one single type of expression data by accuracy or error rate of classification.

Results

In this article, we propose a hybrid feature selection method based on Multiple Kernel Learning (MKL) and evaluate the performance on expression datasets of different types. Firstly, the relevance between features and classifying samples is measured by using the optimizing function of MKL. In this step, an iterative gradient descent process is used to perform the optimization both on the parameters of Support Vector Machine (SVM) and kernel confidence. Then, a set of relevant features is selected by sorting the optimizing function of each feature. Furthermore, we apply an embedded scheme of forward selection to detect the compact feature subsets from the relevant feature set.

Conclusions

We not only compare the classification accuracy with other methods, but also compare the stability, similarity and consistency of different algorithms. The proposed method has a satisfactory capability of feature selection for analysing expression datasets of different types using different performance measurements.

Electronic supplementary material

The online version of this article (doi:10.1186/s13040-017-0124-x) contains supplementary material, which is available to authorized users.

A global view of regulatory networks in lung cancer: An approach to understand homogeneity and heterogeneity

A number of new biotechnologies are used to identify potential biomarkers for the early detection of lung cancer, enabling a personalized therapy to be developed in response. The combinatorial cross-regulation of hundreds of biological function-specific transcription factors (TFs) is defined as the understanding of regulatory networks of molecules within the cell. Here we integrated global databases with 537 patients with lung adenocarcinoma (ADC), 140 with lung squamous carcinoma (SCC), 9 with lung large-cell carcinoma (LCC), 56 with small-cell lung cancer (SCLC), and 590 without cancer with the understanding of TF functions. The present review aims at the homogeneity or heterogeneity of gene expression profiles among subtypes of lung cancer. About 5, 136, 52, or 16 up-regulated or 19, 24, 122, or 97down-regulated type-special TF genes were identified in ADC, SCC, LCC or SCLC, respectively. DNA-binding and transcription regulator activity associated genes play a dominant role in the differentiation of subtypes in lung cancer. Subtype-specific TF gene regulatory networks with elements should be an alternative for diagnostic and therapeutic targets for early identification of lung cancer and can provide insightful clues to etiology and pathogenesis.

Construction and evaluation of yeast expression networks by database-guided predictions

DNA-Microarrays are powerful tools to obtain expression data on the genome-wide scale. We performed microarray experiments to elucidate the transcriptional networks, which are up- or down-regulated in response to the expression of toxic polyglutamine proteins in yeast. Such experiments initially generate hit lists containing differentially expressed genes. To look into transcriptional responses, we constructed networks from these genes. We therefore developed an algorithm, which is capable of dealing with very small numbers of microarrays by clustering the hits based on co-regulatory relationships obtained from the SPELL database. Here, we evaluate this algorithm according to several criteria and further develop its statistical capabilities. Initially, we define how the number of SPELL-derived co-regulated genes and the number of input hits influences the quality of the networks. We then show the ability of our networks to accurately predict further differentially expressed genes. Including these predicted genes into the networks improves the network quality and allows quantifying the predictive strength of the networks based on a newly implemented scoring method. We find that this approach is useful for our own experimental data sets and also for many other data sets which we tested from the SPELL microarray database. Furthermore, the clusters obtained by the described algorithm greatly improve the assignment to biological processes and transcription factors for the individual clusters. Thus, the described clustering approach, which will be available through the ClusterEx web interface, and the evaluation parameters derived from it represent valuable tools for the fast and informative analysis of yeast microarray data.

Integrated pathway-based transcription regulation network mining and visualization based on gene expression profiles

Conventionally, workflows examining transcription regulation networks from gene expression data involve distinct analytical steps. There is a need for pipelines that unify data mining and inference deduction into a singular framework to enhance interpretation and hypotheses generation. We propose a workflow that merges network construction with gene expression data mining focusing on regulation processes in the context of transcription factor driven gene regulation. The pipeline implements pathway-based modularization of expression profiles into functional units to improve biological interpretation. The integrated workflow was implemented as a web application software (TransReguloNet) with functions that enable pathway visualization and comparison of transcription factor activity between sample conditions defined in the experimental design. The pipeline merges differential expression, network construction, pathway-based abstraction, clustering and visualization. The framework was applied in analysis of actual expression datasets related to lung, breast and prostrate cancer.

Gene expression signatures, pathways and networks in carotid atherosclerosis

BACKGROUND

Embolism from unstable atheromas in the carotid bifurcation is a major cause of stroke. Here, we analysed gene expression in endarterectomies from patients with symptomatic (S) and asymptomatic (AS) carotid stenosis to identify pathways linked to plaque instability.

METHODS

Microarrays were prepared from plaques (n = 127) and peripheral blood samples (n = 96) of S and AS patients. Gene set enrichment, pathway mapping and network analyses of differentially expressed genes were performed.

RESULTS

These studies revealed upregulation of haemoglobin metabolism (P = 2.20E-05) and bone resorption (P = 9.63E-04) in S patients. Analysis of subgroups of patients indicated enrichment of calcification and osteoblast differentiation in S patients on statins, as well as inflammation and apoptosis in plaques removed >1 month compared to <2 weeks after symptom. By prediction profiling, a panel of 30 genes, mostly transcription factors, discriminated between plaques from S versus AS patients with 78% accuracy. By meta-analysis, common gene networks associated with atherosclerosis mapped to hypoxia, chemokines, calcification, actin cytoskeleton and extracellular matrix. A set of dysregulated genes (LMOD1, SYNPO2, PLIN2 and PPBP) previously not described in atherosclerosis were identified from microarrays and validated by quantitative PCR and immunohistochemistry.

CONCLUSIONS

Our findings confirmed a central role for inflammation and proteases in plaque instability, and highlighted haemoglobin metabolism and bone resorption as important pathways. Subgroup analysis suggested prolonged inflammation following the symptoms of plaque instability and calcification as a possible stabilizing mechanism by statins. In addition, transcriptional regulation may play an important role in the determination of plaque phenotype. The results from this study will serve as a basis for further exploration of molecular signatures in carotid atherosclerosis.

Multi-Scale Genomic, Transcriptomic and Proteomic Analysis of Colorectal Cancer Cell Lines to Identify Novel Biomarkers

Selecting colorectal cancer (CRC) patients likely to respond to therapy remains a clinical challenge. The objectives of this study were to establish which genes were differentially expressed with respect to treatment sensitivity and relate this to copy number in a panel of 15 CRC cell lines. Copy number variations of the identified genes were assessed in a cohort of CRCs. IC50's were measured for 5-fluorouracil, oxaliplatin, and BEZ-235, a PI3K/mTOR inhibitor. Cell lines were profiled using array comparative genomic hybridisation, Illumina gene expression analysis, reverse phase protein arrays, and targeted sequencing of KRAS hotspot mutations. Frequent gains were observed at 2p, 3q, 5p, 7p, 7q, 8q, 12p, 13q, 14q, and 17q and losses at 2q, 3p, 5q, 8p, 9p, 9q, 14q, 18q, and 20p. Frequently gained regions contained EGFR, PIK3CA, MYC, SMO, TRIB1, FZD1, and BRCA2, while frequently lost regions contained FHIT and MACROD2. TRIB1 was selected for further study. Gene enrichment analysis showed that differentially expressed genes with respect to treatment response were involved in Wnt signalling, EGF receptor signalling, apoptosis, cell cycle, and angiogenesis. Stepwise integration of copy number and gene expression data yielded 47 candidate genes that were significantly correlated. PDCD6 was differentially expressed in all three treatment responses. Tissue microarrays were constructed for a cohort of 118 CRC patients and TRIB1 and MYC amplifications were measured using fluorescence in situ hybridisation. TRIB1 and MYC were amplified in 14.5% and 7.4% of the cohort, respectively, and these amplifications were significantly correlated (p≤0.0001). TRIB1 protein expression in the patient cohort was significantly correlated with pERK, Akt, and Caspase 3 expression. In conclusion, a set of candidate predictive biomarkers for 5-fluorouracil, oxaliplatin, and BEZ235 are described that warrant further study. Amplification of the putative oncogene TRIB1 has been described for the first time in a cohort of CRC patients.

Transcriptome-wide functional characterization reveals novel relationships among differentially expressed transcripts in developing soybean embryos

BACKGROUND

Transcriptomics reveals the existence of transcripts of different coding potential and strand orientation. Alternative splicing (AS) can yield proteins with altered number and types of functional domains, suggesting the global occurrence of transcriptional and post-transcriptional events. Many biological processes, including seed maturation and desiccation, are regulated post-transcriptionally (e.g., by AS), leading to the production of more than one coding or noncoding sense transcript from a single locus.

RESULTS

We present an integrated computational framework to predict isoform-specific functions of plant transcripts. This framework includes a novel plant-specific weighted support vector machine classifier called CodeWise, which predicts the coding potential of transcripts with over 96 % accuracy, and several other tools enabling global sequence similarity, functional domain, and co-expression network analyses. First, this framework was applied to all detected transcripts (103,106), out of which 13 % was predicted by CodeWise to be noncoding RNAs in developing soybean embryos. Second, to investigate the role of AS during soybean embryo development, a population of 2,938 alternatively spliced and differentially expressed splice variants was analyzed and mined with respect to timing of expression. Conserved domain analyses revealed that AS resulted in global changes in the number, types, and extent of truncation of functional domains in protein variants. Isoform-specific co-expression network analysis using ArrayMining and clustering analyses revealed specific sub-networks and potential interactions among the components of selected signaling pathways related to seed maturation and the acquisition of desiccation tolerance. These signaling pathways involved abscisic acid- and FUSCA3-related transcripts, several of which were classified as noncoding and/or antisense transcripts and were co-expressed with corresponding coding transcripts. Noncoding and antisense transcripts likely play important regulatory roles in seed maturation- and desiccation-related signaling in soybean.

CONCLUSIONS

This work demonstrates how our integrated framework can be implemented to make experimentally testable predictions regarding the coding potential, co-expression, co-regulation, and function of transcripts and proteins related to a biological process of interest.

Inference of Longevity-Related Genes from a Robust Coexpression Network of Seed Maturation Identifies Regulators Linking Seed Storability to Biotic Defense-Related Pathways

Seed longevity, the maintenance of viability during storage, is a crucial factor for preservation of genetic resources and ensuring proper seedling establishment and high crop yield. We used a systems biology approach to identify key genes regulating the acquisition of longevity during seed maturation of Medicago truncatula. Using 104 transcriptomes from seed developmental time courses obtained in five growth environments, we generated a robust, stable coexpression network (MatNet), thereby capturing the conserved backbone of maturation. Using a trait-based gene significance measure, a coexpression module related to the acquisition of longevity was inferred from MatNet. Comparative analysis of the maturation processes in M. truncatula and Arabidopsis thaliana seeds and mining Arabidopsis interaction databases revealed conserved connectivity for 87% of longevity module nodes between both species. Arabidopsis mutant screening for longevity and maturation phenotypes demonstrated high predictive power of the longevity cross-species network. Overrepresentation analysis of the network nodes indicated biological functions related to defense, light, and auxin. Characterization of defense-related wrky3 and nf-x1-like1 (nfxl1) transcription factor mutants demonstrated that these genes regulate some of the network nodes and exhibit impaired acquisition of longevity during maturation. These data suggest that seed longevity evolved by co-opting existing genetic pathways regulating the activation of defense against pathogens.

The long non-coding RNA lnc-ZNF180-2 is a prognostic biomarker in patients with clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is among the most common human malignancies. Long non-coding RNAs (lncRNA) regulate various cellular functions and have been implicated in ccRCC pathogenesis. In order to decipher the molecular biology of this tumor and to identify potential prognostic biomarkers and therapeutic targets, we re-evaluated published lncRNA expression profiling data. An expression profile of 49 lncRNAs allowed discrimination of localized and advanced ccRCC. The expression profile of six lncRNAs transcripts (lnc-ACO1625, lnc-CYP4A22-2/3, lnc-PEAK1.1-1, lnc-PCYOX1L, lnc-VCAN-1, lnc-ZNF180-2) with potential prognostic interest were validated in a cohort of 50 normal renal, 57 localized ccRCC and 45 advanced ccRCC tissues. lnc-ZNF180-2 levels were similar in localized ccRCC and normal renal tissue, but we observed a significant increase of lnc-ZNF180-2 expression in advanced ccRCC tissue. Furthermore, lnc-ZNF180-2 expression levels were an independent predictor of progression-free survival, cancer-specific survival and overall survival in ccRCC patients. We also observed that lnc-CYP4A22-2/3 expression levels allowed discrimination of ccRCC and normal renal tissue. In conclusion, lncRNAs are involved in renal carcinogenesis, and quantification of lnc-ZNF180-2 may be useful for the prediction of ccRCC patients outcome following nephrectomy.

Identification of a molecular dialogue between developing seeds of Medicago truncatula and seedborne xanthomonads

Plant pathogenic bacteria disseminate and survive mainly in association with seeds. This study addresses whether seeds are passive carriers or engage a molecular dialogue with pathogens during their development. We developed two pathosystems using Medicago truncatula with Xanthomonas alfalfae subsp. alfalfae (Xaa), the natural Medicago sp. pathogen and Xanthomonas campestris pv. campestris (Xcc), a Brassicaceae pathogen. Three days after flower inoculation, the transcriptome of Xcc-infected pods showed activation of an innate immune response that was strongly limited in Xcc mutated in the type three secretion system, demonstrating an incompatible interaction of Xcc with the reproductive structures. In contrast, the presence of Xaa did not result in an activation of defence genes. Transcriptome profiling during development of infected seeds exhibited time-dependent and differential responses to Xcc and Xaa. Gene network analysis revealed that the transcriptome of Xcc-infected seeds was mainly affected during seed filling whereas that of Xaa-infected seeds responded during late maturation. The Xcc-infected seed transcriptome exhibited an activation of defence response and a repression of targeted seed maturation pathways. Fifty-one percent of putative ABSCISIC ACID INSENSITIVE3 targets were deregulated by Xcc, including oleosin, cupin, legumin and chlorophyll degradation genes. At maturity, these seeds displayed decreased weight and increased chlorophyll content. In contrast, these traits were not affected by Xaa infection. These findings demonstrate the existence of a complex molecular dialogue between xanthomonads and developing seeds and provides insights into a previously unexplored trade-off between seed development and pathogen defence.

DNA microarray integromics analysis platform

BACKGROUND

The study of interactions between molecules belonging to different biochemical families (such as lipids and nucleic acids) requires specialized data analysis methods. This article describes the DNA Microarray Integromics Analysis Platform, a unique web application that focuses on computational integration and analysis of "multi-omics" data. Our tool supports a range of complex analyses, including - among others - low- and high-level analyses of DNA microarray data, integrated analysis of transcriptomics and lipidomics data and the ability to infer miRNA-mRNA interactions.

RESULTS

We demonstrate the characteristics and benefits of the DNA Microarray Integromics Analysis Platform using two different test cases. The first test case involves the analysis of the nutrimouse dataset, which contains measurements of the expression of genes involved in nutritional problems and the concentrations of hepatic fatty acids. The second test case involves the analysis of miRNA-mRNA interactions in polysaccharide-stimulated human dermal fibroblasts infected with porcine endogenous retroviruses.

CONCLUSIONS

The DNA Microarray Integromics Analysis Platform is a web-based graphical user interface for "multi-omics" data management and analysis. Its intuitive nature and wide range of available workflows make it an effective tool for molecular biology research. The platform is hosted at https://lifescience.plgrid.pl/.

Identification of novel differentially expressed lncRNA and mRNA transcripts in clear cell renal cell carcinoma by expression profiling

Clear cell renal cell carcinoma (ccRCC) is a common human malignancy. Despite numerous efforts, there is still no reliable biomarker or combination of biomarkers available for daily practice. Our study was designed to explore the expression profile of messenger RNA (mRNA) and long non-coding RNA (lncRNA) transcripts in ccRCC in order to identify potential diagnostic biomarkers for patients with ccRCC. Total RNA from corresponding normal and malignant tissue of 15 patients with ccRCC was isolated. Expression profiling was performed using a custom Agilent gene expression microarray which allowed the analysis of 34,144 mRNA and 32,183 lncRNA transcripts. We observed that a subset of mRNA (n = 1064; 3.1%) and lncRNA (n = 1308; 4.1%) transcripts are dysregulated (fold change > 2) in ccRCC tissue. The relative higher number of differentially expressed lncRNAs indicates that lncRNA profiling may be better suited for diagnostic purposes; a number of so far unknown RNAs with potential diagnostic interest in ccRCC are identified by our gene expression profiling study. The data are deposited in the Gene Expression Omnibus (GSE61763).

Transcriptomic analysis of human placenta in intrauterine growth restriction

BACKGROUND

Intrauterine growth restriction (IUGR) is a frequent complication of pregnancy defined as a restriction of fetal growth. The objective of this work was to improve the knowledge on the pathophysiology of IUGR using a genome-wide method of expression analysis.

METHODS

We analyzed differentially expressed genes in pooled placental tissues from vascular IUGR (four pools of three placentas) and normal pregnancies (four pools of three placentas) using a long nucleotide microarray platform (Nimblegen). We first did a global bioinformatics analysis based only on P value without any a priori. We secondly focused on "target" genes among the most modified ones. Finally, reverse transcription quantitative polymerase chain reaction (RT-qPCR) was performed on an extended panel of tissue samples (n = 62) on selected "target".

RESULTS

We identified 636 modified genes among which 206 were upregulated (1.5 and higher; P < 0.05). Groups of patients were classified unambiguously. Genes involved in mitochondrial function and oxidative phosphorylation were decreased affecting three out of five complexes of the respiratory chain of the mitochondria, and thus energy production and metabolism. Among the most induced genes, we identified LEP, IGFBP1, and RBP4.

CONCLUSION

Complementary studies on the role and function of LEP, IGFBP1, and RBP4 in IUGR pathophysiology and also in fetal programming remain necessary.

Differential Genes Expression between Fertile and Infertile Spermatozoa Revealed by Transcriptome Analysis

Background

It was believed earlier that spermatozoa have no traces of RNA because of loss of most of the cytoplasm. Recent studies have revealed the presence of about 3000 different kinds of mRNAs in ejaculated spermatozoa. However, the correlation of transcriptome profile with infertility remains obscure.

Methods

Total RNA from sperm (after exclusion of somatic cells) of 60 men consisting of individuals with known fertility (n=20), idiopathic infertility (normozoospermic patients, n=20), and asthenozoospermia (n=20) was isolated. After RNA quality check on Bioanalyzer, AffymetrixGeneChip Human Gene 1.0 ST Array was used for expression profiling, which consisted of >30,000 coding transcripts and >11,000 long intergenic non-coding transcripts.

Results

Comparison between all three groups revealed that two thousand and eighty one transcripts were differentially expressed. Analysis of these transcripts showed that some transcripts [ribosomal proteins (RPS25, RPS11, RPS13, RPL30, RPL34, RPL27, RPS5), HINT1, HSP90AB1, SRSF9, EIF4G2, ILF2] were up-regulated in the normozoospermic group, but down-regulated in the asthenozoospermic group in comparison to the control group. Some transcripts were specific to the normozoospermic group (up-regulated: CAPNS1, FAM153C, ARF1, CFL1, RPL19, USP22; down-regulated: ZNF90, SMNDC1, c14orf126, HNRNPK), while some were specific to the asthenozoospermic group (up-regulated: RPL24, HNRNPM, RPL4, PRPF8, HTN3, RPL11, RPL28, RPS16, SLC25A3, C2orf24, RHOA, GDI2, NONO, PARK7; down-regulated: HNRNPC, SMARCAD1, RPS24, RPS24, RPS27A, KIFAP3). A number of differentially expressed transcripts in spermatozoa were related to reproduction (n = 58) and development (n= 210). Some of these transcripts were related to heat shock proteins (DNAJB4, DNAJB14), testis specific genes (TCP11, TESK1, TSPYL1, ADAD1), and Y-chromosome genes (DAZ1, TSPYL1).

Conclusion

A complex RNA population in spermatozoa consisted of coding and non-coding RNAs. A number of transcripts that participate in a host of cellular processes, including reproduction and development were differentially expressed between fertile and infertile individuals. Differences between comparison groups suggest that sperm RNA has strong potential of acting as markers for fertility evaluation.

DWFS: a wrapper feature selection tool based on a parallel genetic algorithm

Many scientific problems can be formulated as classification tasks. Data that harbor relevant information are usually described by a large number of features. Frequently, many of these features are irrelevant for the class prediction. The efficient implementation of classification models requires identification of suitable combinations of features. The smaller number of features reduces the problem’s dimensionality and may result in higher classification performance. We developed DWFS, a web-based tool that allows for efficient selection of features for a variety of problems. DWFS follows the wrapper paradigm and applies a search strategy based on Genetic Algorithms (GAs). A parallel GA implementation examines and evaluates simultaneously large number of candidate collections of features. DWFS also integrates various filtering methods that may be applied as a pre-processing step in the feature selection process. Furthermore, weights and parameters in the fitness function of GA can be adjusted according to the application requirements. Experiments using heterogeneous datasets from different biomedical applications demonstrate that DWFS is fast and leads to a significant reduction of the number of features without sacrificing performance as compared to several widely used existing methods. DWFS can be accessed online at www.cbrc.kaust.edu.sa/dwfs.

Bioinformatic description of immunotherapy targets for pediatric T-cell leukemia and the impact of normal gene sets used for comparison

Pediatric lymphoid leukemia has the highest cure rate of all pediatric malignancies, yet due to its prevalence, still accounts for the majority of childhood cancer deaths and requires long-term highly toxic therapy. The ability to target B-cell ALL with immunoglobulin-like binders, whether anti-CD22 antibody or anti-CD19 CAR-Ts, has impacted treatment options for some patients. The development of new ways to target B-cell antigens continues at rapid pace. T-cell ALL accounts for up to 20% of childhood leukemia but has yet to see a set of high-value immunotherapeutic targets identified. To find new targets for T-ALL immunotherapy, we employed a bioinformatic comparison to broad normal tissue arrays, hematopoietic stem cells (HSC), and mature lymphocytes, then filtered the results for transcripts encoding plasma membrane proteins. T-ALL bears a core T-cell signature and transcripts encoding TCR/CD3 components and canonical markers of T-cell development predominate, especially when comparison was made to normal tissue or HSC. However, when comparison to mature lymphocytes was also undertaken, we identified two antigens that may drive, or be associated with leukemogenesis; TALLA-1 and hedgehog interacting protein. In addition, TCR subfamilies, CD1, activation and adhesion markers, membrane-organizing molecules, and receptors linked to metabolism and inflammation were also identified. Of these, only CD52, CD37, and CD98 are currently being targeted clinically. This work provides a set of targets to be considered for future development of immunotherapies for T-ALL.

Differential microRNA expression profiles in peripheral arterial disease

BACKGROUND

Peripheral arterial disease (PAD) is a clinical condition caused by an atherosclerotic process affecting the arteries of the limbs. Despite major improvements in surgical endovascular techniques, PAD is still associated with high mortality and morbidity. Recently, microRNAs (miRNAs), a class of short noncoding RNA controlling gene expression, have emerged as major regulators of multiple biological processes.

METHODS AND RESULTS

A whole-miRNA transcriptome profiling was performed in peripheral blood from an initial sample set of patients and controls. A 12-miRNA PAD-specific signature, which includes let 7e, miR-15b, -16, -20b, -25, -26b, -27b, -28-5p, -126, -195, -335, and -363, was further investigated and validated in 2 additional sample sets. Each of these 12 miRNAs exhibited good diagnostic value as evidenced by receiver operating characteristic curve analyses. Pathway enrichment analysis using predicted and validated targets identified several signaling pathways relevant to vascular disorders. Several of these pathways, including cell adhesion molecules, were confirmed by quantifying the expression level of several candidate genes regulating the initial stages of the inflammatory atherosclerotic process. The expression level of 7 of these candidate genes exhibits striking inverse correlation with that of several, if not all, of the miRNAs of the PAD-specific miRNA signature.

CONCLUSIONS

These results demonstrate the potential of miRNAs for the diagnosis of PAD and provide further insight into the molecular mechanisms leading to the development of PAD, with the potential for future therapeutic targets.

Multiplex quantitative measurement of mRNAs from fixed tissue microarray sections

The development of prognostic and diagnostic biomarkers, such as those from gene expression studies, requires independent validation in clinical specimens. Immunohistochemical analysis on tissue microarrays (TMAs) of formalin-fixed paraffin-embedded tissue is often used to increase the statistical power, and it is used more often than in situ hybridization, which can be technically limiting. Herein, we introduce a method for performing quantitative gene expression analysis across a TMA using an adaptation of 2D-RT-qPCR, a recently developed technology for measuring transcript levels in a histologic section while maintaining 2-dimensional positional information of the tissue sample. As a demonstration of utility, a TMA with tumor and normal human prostate samples was used to validate expression profiles from previous array-based gene discovery studies of prostate cancer. The results show that 2D-RT-qPCR expands the utility of TMAs to include sensitive and accurate gene expression measurements.

BioMet Toolbox 2.0: genome-wide analysis of metabolism and omics data

Analysis of large data sets using computational and mathematical tools have become a central part of biological sciences. Large amounts of data are being generated each year from different biological research fields leading to a constant development of software and algorithms aimed to deal with the increasing creation of information. The BioMet Toolbox 2.0 integrates a number of functionalities in a user-friendly environment enabling the user to work with biological data in a web interface. The unique and distinguishing feature of the BioMet Toolbox 2.0 is to provide a web user interface to tools for metabolic pathways and omics analysis developed under different platform-dependent environments enabling easy access to these computational tools.

A regulatory network-based approach dissects late maturation processes related to the acquisition of desiccation tolerance and longevity of Medicago truncatula seeds

In seeds, desiccation tolerance (DT) and the ability to survive the dry state for prolonged periods of time (longevity) are two essential traits for seed quality that are consecutively acquired during maturation. Using transcriptomic and metabolomic profiling together with a conditional-dependent network of global transcription interactions, we dissected the maturation events from the end of seed filling to final maturation drying during the last 3 weeks of seed development in Medicago truncatula. The network revealed distinct coexpression modules related to the acquisition of DT, longevity, and pod abscission. The acquisition of DT and dormancy module was associated with abiotic stress response genes, including late embryogenesis abundant (LEA) genes. The longevity module was enriched in genes involved in RNA processing and translation. Concomitantly, LEA polypeptides accumulated, displaying an 18-d delayed accumulation compared with transcripts. During maturation, gulose and stachyose levels increased and correlated with longevity. A seed-specific network identified known and putative transcriptional regulators of DT, including ABSCISIC ACID-INSENSITIVE3 (MtABI3), MtABI4, MtABI5, and APETALA2/ ETHYLENE RESPONSE ELEMENT BINDING PROTEIN (AtAP2/EREBP) transcription factor as major hubs. These transcriptional activators were highly connected to LEA genes. Longevity genes were highly connected to two MtAP2/EREBP and two basic leucine zipper transcription factors. A heat shock factor was found at the transition of DT and longevity modules, connecting to both gene sets. Gain- and loss-of-function approaches of MtABI3 confirmed 80% of its predicted targets, thereby experimentally validating the network. This study captures the coordinated regulation of seed maturation and identifies distinct regulatory networks underlying the preparation for the dry and quiescent states.

Layered signaling regulatory networks analysis of gene expression involved in malignant tumorigenesis of non-resolving ulcerative colitis via integration of cross-study microarray profiles

Background

Ulcerative colitis (UC) was the most frequently diagnosed inflammatory bowel disease (IBD) and closely linked to colorectal carcinogenesis. By far, the underlying mechanisms associated with the disease are still unclear. With the increasing accumulation of microarray gene expression profiles, it is profitable to gain a systematic perspective based on gene regulatory networks to better elucidate the roles of genes associated with disorders. However, a major challenge for microarray data analysis is the integration of multiple-studies generated by different groups.

Methodology/Principal Findings

In this study, firstly, we modeled a signaling regulatory network associated with colorectal cancer (CRC) initiation via integration of cross-study microarray expression data sets using Empirical Bayes (EB) algorithm. Secondly, a manually curated human cancer signaling map was established via comprehensive retrieval of the publicly available repositories. Finally, the co-differently-expressed genes were manually curated to portray the layered signaling regulatory networks.

Results

Overall, the remodeled signaling regulatory networks were separated into four major layers including extracellular, membrane, cytoplasm and nucleus, which led to the identification of five core biological processes and four signaling pathways associated with colorectal carcinogenesis. As a result, our biological interpretation highlighted the importance of EGF/EGFR signaling pathway, EPO signaling pathway, T cell signal transduction and members of the BCR signaling pathway, which were responsible for the malignant transition of CRC from the benign UC to the aggressive one.

Conclusions

The present study illustrated a standardized normalization approach for cross-study microarray expression data sets. Our model for signaling networks construction was based on the experimentally-supported interaction and microarray co-expression modeling. Pathway-based signaling regulatory networks analysis sketched a directive insight into colorectal carcinogenesis, which was of significant importance to monitor disease progression and improve therapeutic interventions.

Identification of prognosis-relevant subgroups in patients with chemoresistant triple-negative breast cancer

PURPOSE

Patients with triple-negative breast cancer (TNBC) and residual disease after neoadjuvant chemotherapy generally have worse outcome; however, some patients with residual tumor after neoadjuvant chemotherapy do not relapse. We hypothesize that there are subgroups of patients with chemoresistant TNBC with different prognosis.

EXPERIMENTAL DESIGN

Forty-nine chemoresistant cases from 111 patients with TNBC treated with neoadjuvant chemotherapy (M.D. Anderson Cancer Center, Houston, TX) constituted the discovery cohort, and 25 chemoresistant samples from 47 neoadjuvant chemotherapy-treated TNBC (The Methodist Hospital, Houston, TX) were chosen for validation. Extended validation was carried out in 269 operable TNBC predicted to be chemoresistant by expression pattern from published datasets.

RESULTS

We established a seven-gene prognostic signature using dChip and gene set enrichment analyses. In the independent validation cohort, the classifier predicted correctly with positive predictive value of 75.0% and negative predictive value (i.e., relapse-free survival; RFS) of 76.9% at 3 years. Those predicted to relapse had a HR of 4.67 [95% confidence interval (CI): 1.27-17.15] for relapse in 3 years. In extended validation, patients predicted not to relapse exhibited 3-year RFS of 78.9%, whereas the 3-year RFS was 48.5% for patients predicted to relapse, with HR of 2.61 (95% CI: 1.52-4.49). The TNBC subgroup that predicted to have relatively favorable prognosis was characterized by high expression of "luminal-like" genes [androgen-receptor (AR) and GATA3], whereas the subgroup with worse prognosis was characterized by expression of cancer stem-cell markers.

CONCLUSION

We developed a clinically relevant signature for patients with chemoresistant TNBC. For these women, new therapeutic strategies like targeting AR activation or cancer stem cells may need to be developed.

Cancer Gene Expression Data Analysis Using Rough Based Symmetrical Clustering

Identification of cancer subtypes is the central goal in the cancer gene expression data analysis. Modified symmetry-based clustering is an unsupervised learning technique for detecting symmetrical convex or non-convex shaped clusters. To enable fast automatic clustering of cancer tissues (samples), in this chapter, the authors propose a rough set based hybrid approach for modified symmetry-based clustering algorithm. A natural basis for analyzing gene expression data using the symmetry-based algorithm is to group together genes with similar symmetrical patterns of microarray expressions. Rough-set theory helps in faster convergence and initial automatic optimal classification, thereby solving the problem of unknown knowledge of number of clusters in gene expression measurement data. For rough-set-theoretic decision rule generation, each cluster is classified using heuristically searched optimal reducts to overcome overlapping cluster problem. The rough modified symmetry-based clustering algorithm is compared with another newly implemented rough-improved symmetry-based clustering algorithm and existing K-Means algorithm over five benchmark cancer gene expression data sets, to demonstrate its superiority in terms of validity. The statistical analyses are also performed to establish the significance of this rough modified symmetry-based clustering approach.

virtualArray: a R/bioconductor package to merge raw data from different microarray platforms

Background

Microarrays have become a routine tool to address diverse biological questions. Therefore, different types and generations of microarrays have been produced by several manufacturers over time. Likewise, the diversity of raw data deposited in public databases such as NCBI GEO or EBI ArrayExpress has grown enormously.

This has resulted in databases currently containing several hundred thousand microarray samples clustered by different species, manufacturers and chip generations. While one of the original goals of these databases was to make the data available to other researchers for independent analysis and, where appropriate, integration with their own data, current software implementations could not provide that feature.

Only those data sets generated on the same chip platform can be readily combined and even here there are batch effects to be taken care of. A straightforward approach to deal with multiple chip types and batch effects has been missing.

The software presented here was designed to solve both of these problems in a convenient and user friendly way.

Results

The virtualArray software package can combine raw data sets using almost any chip types based on current annotations from NCBI GEO or Bioconductor. After establishing congruent annotations for the raw data, virtualArray can then directly employ one of seven implemented methods to adjust for batch effects in the data resulting from differences between the chip types used. Both steps can be tuned to the preferences of the user. When the run is finished, the whole dataset is presented as a conventional Bioconductor “ExpressionSet” object, which can be used as input to other Bioconductor packages.

Conclusions

Using this software package, researchers can easily integrate their own microarray data with data from public repositories or other sources that are based on different microarray chip types. Using the default approach a robust and up-to-date batch effect correction technique is applied to the data.