Serial analysis of gene expression

Soluble Guanylate Cyclase β1 Subunit Represses Human Glioblastoma Growth

Malignant glioma is the most common and deadly brain tumor. A marked reduction in the levels of sGC (soluble guanylyl cyclase) transcript in the human glioma specimens has been revealed in our previous studies. In the present study, restoring the expression of sGCβ1 alone repressed the aggressive course of glioma. The antitumor effect of sGCβ1 was not associated with enzymatic activity of sGC since overexpression of sGCβ1 alone did not influence the level of cyclic GMP. Additionally, sGCβ1-induced inhibition of the growth of glioma cells was not influenced by treatment with sGC stimulators or inhibitors. The present study is the first to reveal that sGCβ1 migrated into the nucleus and interacted with the promoter of the TP53 gene. Transcriptional responses induced by sGCβ1 caused the G0 cell cycle arrest of glioblastoma cells and inhibition of tumor aggressiveness. sGCβ1 overexpression impacted signaling in glioblastoma multiforme, including the promotion of nuclear accumulation of p53, a marked reduction in CDK6, and a significant decrease in integrin α6. These anticancer targets of sGCβ1 may represent clinically important regulatory pathways that contribute to the development of a therapeutic strategy for cancer treatment.

Non-coding antisense transcripts: fine regulation of gene expression in cancer

Natural antisense transcripts (NATs) are coding or non-coding RNA sequences transcribed on the opposite direction from the same genomic locus. NATs are widely distributed throughout the human genome and seem to play crucial roles in physiological and pathological processes, through newly described and targeted mechanisms. NATs represent the intricate complexity of the genome organization and constitute another layer of potential targets in disease. Here, we focus on the interesting and unique role of non-coding NATs in cancer, paying particular attention to those acting as miRNA sponges.

Ocean Aerobiology

Ocean aerobiology is defined here as the study of biological particles of marine origin, including living organisms, present in the atmosphere and their role in ecological, biogeochemical, and climate processes. Hundreds of trillions of microorganisms are exchanged between ocean and atmosphere daily. Within a few days, tropospheric transport potentially disperses microorganisms over continents and between oceans. There is a need to better identify and quantify marine aerobiota, characterize the time spans and distances of marine microorganisms’ atmospheric transport, and determine whether microorganisms acclimate to atmospheric conditions and remain viable, or even grow. Exploring the atmosphere as a microbial habitat is fundamental for understanding the consequences of dispersal and will expand our knowledge of biodiversity, biogeography, and ecosystem connectivity across different marine environments. Marine organic matter is chemically transformed in the atmosphere, including remineralization back to CO₂. The magnitude of these transformations is insignificant in the context of the annual marine carbon cycle, but may be a significant sink for marine recalcitrant organic matter over long (∼10⁴ years) timescales. In addition, organic matter in sea spray aerosol plays a significant role in the Earth’s radiative budget by scattering solar radiation, and indirectly by affecting cloud properties. Marine organic matter is generally a poor source of cloud condensation nuclei (CCN), but a significant source of ice nucleating particles (INPs), affecting the formation of mixed-phase and ice clouds. This review will show that marine biogenic aerosol plays an impactful, but poorly constrained, role in marine ecosystems, biogeochemical processes, and the Earth’s climate system. Further work is needed to characterize the connectivity and feedbacks between the atmosphere and ocean ecosystems in order to integrate this complexity into Earth System models, facilitating future climate and biogeochemical predictions.

Reference genes for proximal femoral epiphysiolysis expression studies in broilers cartilage

The use of reference genes is required for relative quantification in gene expression analysis and the stability of these genes can be variable depending on the experimental design. Therefore, it is indispensable to test the reliability of endogenous genes previously to their use. This study evaluated nine candidate reference genes to select the most stable genes to be used as reference in gene expression studies with the femoral cartilage of normal and epiphysiolysis-affected broilers. The femur articular cartilage of 29 male broilers with 35 days of age was collected, frozen and further submitted to RNA extraction and quantitative PCR (qPCR) analysis. The candidate reference genes evaluated were GAPDH, HMBS, HPRT1, MRPS27, MRPS30, RPL30, RPL4, RPL5, and RPLP1. For the gene stability evaluation, three software were used: GeNorm, BestKeeper and NormFinder, and a global ranking was generated using the function RankAggreg. In this study, the RPLP1 and RPL5 were the most reliable endogenous genes being recommended for expression studies with femur cartilage in broilers with epiphysiolysis and possible other femur anomalies.

A Brief Overview of lncRNAs in Endothelial Dysfunction-Associated Diseases: From Discovery to Characterization

Long non-coding RNAs (lncRNAs) are a novel class of regulatory RNA molecules and they are involved in many biological processes and disease developments. Several unique features of lncRNAs have been identified, such as tissue-and/or cell-specific expression pattern, which suggest that they could be potential candidates for therapeutic and diagnostic applications. More recently, the scope of lncRNA studies has been extended to endothelial biology research. Many of lncRNAs were found to be critically involved in the regulation of endothelial function and its associated disease progression. An improved understanding of endothelial biology can thus facilitate the discovery of novel biomarkers and therapeutic targets for endothelial dysfunction-associated diseases, such as abnormal angiogenesis, hypertension, diabetes, and atherosclerosis. Nevertheless, the underlying mechanism of lncRNA remains undefined in previous published studies. Therefore, in this review, we aimed to discuss the current methodologies for discovering and investigating the functions of lncRNAs and, in particular, to address the functions of selected lncRNAs in endothelial dysfunction-associated diseases.

Single-cell RNA sequencing reveals gene expression signatures of breast cancer-associated endothelial cells

Tumor endothelial cells (TEC) play an indispensible role in tumor growth and metastasis although much of the detailed mechanism still remains elusive. In this study we characterized and compared the global gene expression profiles of TECs and control ECs isolated from human breast cancerous tissues and reduction mammoplasty tissues respectively by single cell RNA sequencing (scRNA-seq). Based on the qualified scRNA-seq libraries that we made, we found that 1302 genes were differentially expressed between these two EC phenotypes. Both principal component analysis (PCA) and heat map-based hierarchical clustering separated the cancerous versus control ECs as two distinctive clusters, and MetaCore disease biomarker analysis indicated that these differentially expressed genes are highly correlated with breast neoplasm diseases. Gene Set Enrichment Analysis software (GSEA) enriched these genes to extracellular matrix (ECM) signal pathways and highlighted 127 ECM-associated genes. External validation verified some of these ECM-associated genes are not only generally overexpressed in various cancer tissues but also specifically overexpressed in colorectal cancer ECs and lymphoma ECs. In conclusion, our data demonstrated that ECM-associated genes play pivotal roles in breast cancer EC biology and some of them could serve as potential TEC biomarkers for various cancers.

The most common technologies and tools for functional genome analysis

Since the sequence of the human genome is complete, the main issue is how to understand the information written in the DNA sequence. Despite numerous genome-wide studies that have already been performed, the challenge to determine the function of genes, gene products, and also their interaction is still open. As changes in the human genome are highly likely to cause pathological conditions, functional analysis is vitally important for human health.

For many years there have been a variety of technologies and tools used in functional genome analysis. However, only in the past decade there has been rapid revolutionizing progress and improvement in high-throughput methods, which are ranging from traditional real-time polymerase chain reaction to more complex systems, such as next-generation sequencing or mass spectrometry. Furthermore, not only laboratory investigation, but also accurate bioinformatic analysis is required for reliable scientific results. These methods give an opportunity for accurate and comprehensive functional analysis that involves various fields of studies: genomics, epigenomics, proteomics, and interactomics. This is essential for filling the gaps in the knowledge about dynamic biological processes at both cellular and organismal level. However, each method has both advantages and limitations that should be taken into account before choosing the right method for particular research in order to ensure successful study. For this reason, the present review paper aims to describe the most frequent and widely-used methods for the comprehensive functional analysis.

Jellyfish Bioactive Compounds: Methods for Wet-Lab Work

The study of bioactive compounds from marine animals has provided, over time, an endless source of interesting molecules. Jellyfish are commonly targets of study due to their toxic proteins. However, there is a gap in reviewing successful wet-lab methods employed in these animals, which compromises the fast progress in the detection of related biomolecules. Here, we provide a compilation of the most effective wet-lab methodologies for jellyfish venom extraction prior to proteomic analysis-separation, identification and toxicity assays. This includes SDS-PAGE, 2DE, gel chromatography, HPLC, DEAE, LC-MS, MALDI, Western blot, hemolytic assay, antimicrobial assay and protease activity assay. For a more comprehensive approach, jellyfish toxicity studies should further consider transcriptome sequencing. We reviewed such methodologies and other genomic techniques used prior to the deep sequencing of transcripts, including RNA extraction, construction of cDNA libraries and RACE. Overall, we provide an overview of the most promising methods and their successful implementation for optimizing time and effort when studying jellyfish.

Large-scale imaginal disc sorting: A protocol for "omics"-approaches

Imaginal discs, especially the wing imaginal disc, are powerful model systems to study organ development. The traditional methods to analyze wing imaginal discs depend on the laborious and time-consuming dissection of larvae. "Omics"-based approaches, such as RNA-seq, ChIP-seq, proteomics and lipidomics, offer new opportunities for the systems-level investigation of organ development. However, it is impractical to manually isolate the required starting material. This is even more problematic when experiments strive for enhanced temporal and spatial resolution. The mass isolation workflow discussed in this review, solves this problem. The semi-automated sorting of 1000 wing imaginal discs in less than 3h forms the basis of a workflow that can be connected to biochemical analyses of organ patterning and growth. In addition to the mass isolation workflow we briefly describe key "omics" technologies and their applications. The combination of mass isolation and "omics"-approaches ensures that the wing imaginal disc will continue to be a key model organ for studying developmental processes, both on the genetic, but increasingly also on the biochemical level.

High throughput transcriptome analysis of lipid metabolism in Syrian hamster liver in absence of an annotated genome

Background

Whole transcriptome analyses are an essential tool for understanding disease mechanisms. Approaches based on next-generation sequencing provide fast and affordable data but rely on the availability of annotated genomes. However, there are many areas in biomedical research that require non-standard animal models for which genome information is not available. This includes the Syrian hamster Mesocricetus auratus as an important model for dyslipidaemia because it mirrors many aspects of human disease and pharmacological responses. We show that complementary use of two independent next generation sequencing technologies combined with mapping to multiple genome databases allows unambiguous transcript annotation and quantitative transcript imaging. We refer to this approach as “triple match sequencing” (TMS).

Results

Contigs assembled from a normalized Roche 454 hamster liver library comprising 1.2 million long reads were used to identify 10’800 unique transcripts based on homology to RefSeq database entries from human, mouse, and rat. For mRNA quantification we mapped 82 million SAGE tags (SOLiD) from the same RNA source to the annotated hamster liver transcriptome contigs. We compared the liver transcriptome of hamster with equivalent data from human, rat, minipig, and cynomolgus monkeys to highlight differential gene expression with focus on lipid metabolism. We identify a cluster of five genes functionally related to HDL metabolism that is expressed in human, cynomolgus, minipig, and hamster but lacking in rat as a non-responder species for lipid lowering drugs.

Conclusions

The TMS approach is suited for fast and inexpensive transcript profiling in cells or tissues of species where a fully annotated genome is not available. The continuously growing number of well annotated reference genomes will further empower reliable transcript identification and thereby raise the utility of the method for any species of interest.

Next-generation sequencing applications for wheat crop improvement

Bread wheat (Triticum aestivum; Poaceae) is a crop plant of great importance. It provides nearly 20% of the world's daily food supply measured by calorie intake, similar to that provided by rice. The yield of wheat has doubled over the last 40 years due to a combination of advanced agronomic practice and improved germplasm through selective breeding. More recently, yield growth has been less dramatic, and a significant improvement in wheat production will be required if demand from the growing human population is to be met. Next-generation sequencing (NGS) technologies are revolutionizing biology and can be applied to address critical issues in plant biology. Technologies can produce draft sequences of genomes with a significant reduction to the cost and timeframe of traditional technologies. In addition, NGS technologies can be used to assess gene structure and expression, and importantly, to identify heritable genome variation underlying important agronomic traits. This review provides an overview of the wheat genome and NGS technologies, details some of the problems in applying NGS technology to wheat, and describes how NGS technologies are starting to impact wheat crop improvement.

Differential gene expression in the siphonophore Nanomia bijuga (Cnidaria) assessed with multiple next-generation sequencing workflows

We investigated differential gene expression between functionally specialized feeding polyps and swimming medusae in the siphonophore Nanomia bijuga (Cnidaria) with a hybrid long-read/short-read sequencing strategy. We assembled a set of partial gene reference sequences from long-read data (Roche 454), and generated short-read sequences from replicated tissue samples that were mapped to the references to quantify expression. We collected and compared expression data with three short-read expression workflows that differ in sample preparation, sequencing technology, and mapping tools. These workflows were Illumina mRNA-Seq, which generates sequence reads from random locations along each transcript, and two tag-based approaches, SOLiD SAGE and Helicos DGE, which generate reads from particular tag sites. Differences in expression results across workflows were mostly due to the differential impact of missing data in the partial reference sequences. When all 454-derived gene reference sequences were considered, Illumina mRNA-Seq detected more than twice as many differentially expressed (DE) reference sequences as the tag-based workflows. This discrepancy was largely due to missing tag sites in the partial reference that led to false negatives in the tag-based workflows. When only the subset of reference sequences that unambiguously have tag sites was considered, we found broad congruence across workflows, and they all identified a similar set of DE sequences. Our results are promising in several regards for gene expression studies in non-model organisms. First, we demonstrate that a hybrid long-read/short-read sequencing strategy is an effective way to collect gene expression data when an annotated genome sequence is not available. Second, our replicated sampling indicates that expression profiles are highly consistent across field-collected animals in this case. Third, the impacts of partial reference sequences on the ability to detect DE can be mitigated through workflow choice and deeper reference sequencing.

Gene network reconstruction from transcriptional dynamics under kinetic model uncertainty: a case for the second derivative

Motivation: Measurements of gene expression over time enable the reconstruction of transcriptional networks. However, Bayesian networks and many other current reconstruction methods rely on assumptions that conflict with the differential equations that describe transcriptional kinetics. Practical approximations of kinetic models would enable inferring causal relationships between genes from expression data of microarray, tag-based and conventional platforms, but conclusions are sensitive to the assumptions made.

Results: The representation of a sufficiently large portion of genome enables computation of an upper bound on how much confidence one may place in influences between genes on the basis of expression data. Information about which genes encode transcription factors is not necessary but may be incorporated if available. The methodology is generalized to cover cases in which expression measurements are missing for many of the genes that might control the transcription of the genes of interest. The assumption that the gene expression level is roughly proportional to the rate of translation led to better empirical performance than did either the assumption that the gene expression level is roughly proportional to the protein level or the Bayesian model average of both assumptions.

Availability:http://www.oisb.ca points to R code implementing the methods (R Development Core Team 2004).

Contact:dbickel@uottawa.ca

Supplementary information:http://www.davidbickel.com

Serial analysis of gene expression identifies connective tissue growth factor expression as a prognostic biomarker in gallbladder cancer

BACKGROUND

Gallbladder cancer (GBC) is an uncommon neoplasm in the United States, but one with high mortality rates. This malignancy remains largely understudied at the molecular level such that few targeted therapies or predictive biomarkers exist.

EXPERIMENTAL DESIGN

We built the first series of serial analysis of gene expression (SAGE) libraries from GBC and nonneoplastic gallbladder mucosa, composed of 21-bp long-SAGE tags. SAGE libraries were generated from three stage-matched GBC patients (representing Hispanic/Latino, Native American, and Caucasian ethnicities, respectively) and one histologically alithiasic gallbladder. Real-time quantitative PCR was done on microdissected epithelium from five matched GBC and corresponding nonneoplastic gallbladder mucosa. Immunohistochemical analysis was done on a panel of 182 archival GBC in high-throughput tissue microarray format.

RESULTS

SAGE tags corresponding to connective tissue growth factor (CTGF) transcripts were identified as differentially overexpressed in all pairwise comparisons of GBC (P < 0.001). Real-time quantitative PCR confirmed significant overexpression of CTGF transcripts in microdissected primary GBC (P < 0.05), but not in metastatic GBC, compared with nonneoplastic gallbladder epithelium. By immunohistochemistry, 66 of 182 (36%) GBC had high CTGF antigen labeling, which was significantly associated with better survival on univariate analysis (P = 0.0069, log-rank test).

CONCLUSIONS

An unbiased analysis of the GBC transcriptome by SAGE has identified CTGF expression as a predictive biomarker of favorable prognosis in this malignancy. The SAGE libraries from GBC and nonneoplastic gallbladder mucosa are publicly available at the Cancer Genome Anatomy Project web site and should facilitate much needed research into this lethal neoplasm.

Exploring and exploiting carotenoid accumulation in Dunaliella salina for cell-factory applications

The unicellular alga Dunaliella salina is the most interesting cell factory for the commercial production of beta-carotene because this species accumulates carotenoids to high concentrations. Nevertheless, little is known about the underlying mechanisms of carotenoid accumulation. Here, we review the regulatory mechanisms involved in beta-carotene overproduction in D. salina. The potential roles of reactive oxygen species and the plastoquinone redox state in signal sensing are discussed, together with available evidence on transcriptional and (post)translational regulation. Moreover, future directions that might further our knowledge in this area are given. Ultimately, a better understanding of the regulatory mechanisms involved in beta-carotene overproduction will facilitate innovative production of specific carotenoids and other products in D. salina and in related organisms.

Challenges of functional genomics applied to farm animal gametes and pre-hatching embryos

The genomes of many commercially important farm animals have already been or are in the process of being decrypted. The genomic era is generating an important wave of downstream developments and derived disciplines are also progressing at a very fast pace. The post-genomic era is already ongoing as exemplified by the introduction of new concepts such as phenomics and functional genomics. These new fields are complementary but do not necessarily target similar applications even though they are often used to refer to one another. In an attempt to categorize the fields according to their respective potential applications, a brief comparative description of phenomics and functional genomics has been put together. However, the focus of this paper is mainly directed toward the introduction of functional genomics specifically applied to the study of the molecular mechanisms underlying gamete and early mammalian developments. Many aspects of the peculiar nature of these cells are introducing numerous methodological challenges to the applicability of functional genomics to unravel their molecular physiology. This is particularly true for transcriptomic studies and it is currently of high relevance for the field of reproductive biology to take into consideration these technical hurdles before tackling the implementation of this technology on a large scale. Nonetheless, functional genomics should prove to be up to the expectations in providing sound information to better understand the fascinating window spanning gamete development that leads to the first weeks of life.