Assessment of methods for serum extracellular vesicle small RNA sequencing to support biomarker development.

Extracellular vesicles (EVs) have great potential as a source for clinically relevant biomarkers since they can be readily isolated from biofluids and carry microRNA (miRNA), mRNA, and proteins that can reflect disease status. However, the biological and technical variability of EV content is unknown making comparisons between healthy subjects and patients difficult to interpret. In this study, we sought to establish a laboratory and bioinformatics analysis pipeline to analyse the small RNA content within EVs from patient serum that could serve as biomarkers and to assess the biological and technical variability of EV RNA content in healthy individuals. We sequenced EV small RNA from multiple individuals (biological replicates) and sequenced multiple replicates per individual (technical replicates) using the Illumina Truseq protocol. We observed that the replicates of samples clustered by subject indicating that the biological variability (~95%) was greater than the technical variability (~0.50%). We observed that ~30% of the sequencing reads were miRNAs. We evaluated the technical parameters of sequencing by spiking the EV RNA preparation with a mix of synthetic small RNA and demonstrated a disconnect between input concentration of the spike-in RNA and sequencing read frequencies indicating that bias was introduced during library preparation. To determine whether there are differences between library preparation platforms, we compared the Truseq with the Nextflex protocol that had been designed to reduce bias in library preparation. While both methods were technically robust, the Nextflex protocol reduced the bias and exhibited a linear range across input concentrations of the synthetic spike-ins. Altogether, our results indicate that technical variability is much smaller than biological variability supporting the use of EV small RNAs as potential biomarkers. Our findings also indicate that the choice of library preparation method leads to artificial differences in the datasets generated invalidating the comparability of sequencing data across library preparation platforms.

The inadequacy of the reductionist approach in discovering new therapeutic agents against complex diseases.

For more than 20 years, drug discovery has relied on two assumptions, i.e. (i) a therapeutic response can be triggered by modulating the activity of a single gene product, and (ii) a compound uncovered by its activity on a recombinant protein in vitro can perform its activity in vivo. Drug discovery operates accordingly by using the concepts of targets and pipelines. The target, such as a gene product, is the intended point of therapeutic intervention, and compounds that modulate its activity in vitro follow a series of downstream developments. This reductionist approach has developed due to advances in combinatorial chemistry, robotics, molecular biology, and genomics. The expectation of this approach is that the frequency of drug discovery will dramatically increase, while its associated cost would decrease. However, the frequency of new drug discovery has decreased, while the associated costs have surged. We performed a retrospective study that examined how successful development programs have led to marketed drugs for all indications except anti-infective and anti-neoplastic agents. We concluded that the target and pipeline paradigms are limited and are actually causing the drug development industry to collectively fail to meet the critical medical needs. Impact statement The initial scope of this investigation was to build the set of human genes that are presumed to be the therapeutic intervention points of US FDA-approved drugs, in all therapeutics areas but oncology. The prerequisite for this study was the establishment of the non-redundant set of all active pharmaceutical ingredients for these disease areas. Pertaining to complex diseases, the main observation was that there is not a single instance in the history of drug discovery, where a compound, initially selected by means of a biochemical assay, achieved a significant therapeutic response. The whole field of Drug R&D faces an unacceptable lack of new treatments to address unmet medical needs. The conclusion is that complex biological assays have to be designed for the primary selection of candidate therapeutics.

Inter-Laboratory Variability in Array-Based RNA Quantification Methods.

Ribonucleic acids (RNA) are hypothesized to have preceded their derivatives, deoxyribonucleic acids (DNA), as the molecular media of genetic information when life emerged on earth. Molecular biologists are accustomed to the dramatic effects a subtle variation in the ribose moiety composition between RNA and DNA can have on the stability of these molecules. While DNA is very stable after extraction from biological samples and subsequent treatment, RNA is notoriously labile. The short half-life property, inherent to RNA, benefits cells that do not need to express their entire repertoire of proteins. The cellular machinery turns off the production of a given protein by shutting down the transcription of its cognate coding gene and by either actively degrading the remaining mRNA or allowing it to decay on its own. The steady-state level of each mRNA in a given cell varies continuously and is specified by changing kinetics of synthesis and degradation. Because it is technically possible to simultaneously measure thousands of nucleic acid molecules, these quantities have been studied by the life sciences community to investigate a range of biological problems. Since the RNA abundance can change according to a wide range of perturbations, this makes it the molecule of choice for exploring biological systems; its instability, on the other hand, could be an underestimated source of technical variability. We found that a large fraction of the RNA abundance originally present in the biological system prior to extraction was masked by the RNA labeling and measurement procedure. The method used to extract RNA molecules from cells and to label them prior to hybridization operations on DNA arrays affects the original distribution of RNA. Only if RNA measurements are performed according to the same procedure can biological information be inferred from the assay read out.

Seed germination and vigor.

Germination vigor is driven by the ability of the plant embryo, embedded within the seed, to resume its metabolic activity in a coordinated and sequential manner. Studies using "-omics" approaches support the finding that a main contributor of seed germination success is the quality of the messenger RNAs stored during embryo maturation on the mother plant. In addition, proteostasis and DNA integrity play a major role in the germination phenotype. Because of its pivotal role in cell metabolism and its close relationships with hormone signaling pathways regulating seed germination, the sulfur amino acid metabolism pathway represents a key biochemical determinant of the commitment of the seed to initiate its development toward germination. This review highlights that germination vigor depends on multiple biochemical and molecular variables. Their characterization is expected to deliver new markers of seed quality that can be used in breeding programs and/or in biotechnological approaches to improve crop yields.

Opening the gate for genomics data into clinical research: a use case in managing patients' DNA samples from the bench to drug development.

The use of human genetic polymorphism data in drug development is not a recent event. Typically, the detection of patients' genetic variations in drug-metabolizing enzymes has become common practice in clinical laboratories. What is new is the scale and diversity of genomics data that has entered into the drug research and development decision-making process. At least three concurrent events contribute to this paradigm shift: first the growing body of evidence that establishes that interindividual variation in both therapeutic response and adverse events are attributable to a genetic component; second the technological progress that enables the consistent and reproducible detection of human genomic quantities; third the expectation that the productivity of new drug development will be increased by identifying which patients would benefit from candidate therapies early in the clinical process. This influx of human genomics data into clinical laboratories requires some logistical adjustment in terms of data management. The major specifications of an information solution system intended for a clinical genomic laboratory are its compliance with regulatory procedures, regarding the handling of human genetic data and its subsequent integration into an existing clinical data management system from the hosting institution. The purpose of this article is to inform the community of the challenges in setting up a center for genomics data that ensures accurate, traceable and integrated data for laboratory management. This is by no means the only way to accomplish the same goal, and is simply presented as one way that Pfizer chose to solve these issues.

Current and future applications of toxicogenomics: Results summary of a survey from the HESI Genomics State of Science Subcommittee.

BACKGROUND: In spite of the application of toxicogenomic (TGx) data to the field of toxicology for the past 10 years, the broad implementation and full impact of TGx for chemical and drug evaluation to improve decision making within organizations and by policy makers has not been achieved. OBJECTIVES: The goal of the Health and Environmental Sciences Institute (HESI) Committee on the Application of Genomics to Mechanism-based Risk Assessment was to construct and summarize a multisector survey, addressing key issues and perspectives on the current and future practical uses and challenges of implementing TGx data to facilitate discussions for decision making within organizations and by policy makers. METHODS: An online survey to probe the current status and future challenges facing the field of TGx for drug and chemical evaluation in experimental and nonclinical models was taken by scientists and scientific decision/policy makers actively engaged in the field of TGx within industrial, academic, and regulatory sectors of the United States, Europe, and Japan. For this survey, TGx refers specifically to the analysis of gene expression responses to evaluate xenobiotic exposure in experimental and preclinical models. RESULTS: The survey results are summarized from questions covering broad areas including technology used, organizational capacity and resource allocation, experimental approaches, data storage and exchange, perceptions of benefits and hurdles, and future expectations. CONCLUSIONS: The survey findings provide valuable information on the current state of the science of TGx applications and identify key areas in which TGx will have an impact as well as the key hurdles in applying TGx data to address issues. The findings serve as a public resource to facilitate discussions on the focus of future TGx efforts to ensure that a maximal benefit can be obtained from toxicogenomic studies.

Monoclonal antibody proteomics: discovery and prevalidation of chronic obstructive pulmonary disease biomarkers in a single step.

We define mAb proteomics as the global generation of disease specific antibodies that permit mass screening of biomarkers. An integrated, high-throughput, disease-specific mAb-based biomarker discovery platform has been developed. The approach readily provided new biomarker leads with the focus on large-scale discovery and production of mAb-based, disease-specific clinical assay candidates. The outcome of the biomarker discovery process was a highly specific and sensitive assay, applicable for testing of clinical validation paradigms, like response to treatment or correlation with other clinical parameters. In contrast to MS-based or systems biology-based strategies, our process produced prevalidated clinical assays as the outcome of the discovery process. By re-engineering the biomarker discovery paradigm, the encouraging results presented in this paper clearly demonstrate the efficiency of the mAb proteomics approach, and set the grounds for the next steps of studies, namely, the hunt for candidate biomarkers that respond to drug treatment.

Gene expression databases and data mining.

The DNA microarray technology has arguably caught the attention of the worldwide life science community and is now systematically supporting major discoveries in many fields of study. The majority of the initial technical challenges of conducting experiments are being resolved, only to be replaced with new informatics hurdles, including statistical analysis, data visualization, interpretation, and storage. Two systems of databases, one containing expression data and one containing annotation data are quickly becoming essential knowledge repositories of the research community. This present paper surveys several databases, which are considered "pillars" of research and important nodes in the network. This paper focuses on a generalized workflow scheme typical for microarray experiments using two examples related to cancer research. The workflow is used to reference appropriate databases and tools for each step in the process of array experimentation. Additionally, benefits and drawbacks of current array databases are addressed, and suggestions are made for their improvement.

Molecular characterization of AtNAM: a member of the Arabidopsis NAC domain superfamily.

The petunia NAM and ArabidopsisATAF1 and CUC2 genes define the conserved NAC domain. In petunia, loss-of-function nam mutants result in embryos that fail to elaborate shoot apical meristems (SAM), and nam seedlings do not develop shoots and leaves. We have isolated a NAC domain gene, AtNAM, from an Arabidopsis developing seed cDNA library. Expression of AtNAM mRNA is restricted primarily to the region of the embryo including the SAM. The AtNAM gene contains three exons and is located on Chromosome 1. In vivo assays in yeast demonstrate that AtNAM encodes a transcription factor and that the NAC domain includes a specific DNA binding domain (DBD). The AtNAM DBD is contained within a 60 amino acid region which potentially folds into a helix-turn-helix motif that specifically binds to the CaMV 35S promoter. The putative transcriptional activation domain is located in the C-terminal region of the protein, a highly divergent region among NAC domain-containing genes. The Arabidopsis genome contains 90 predicted NAC domain genes; we refer to these collectively as the AtNAC superfamily. The first two exons of all members of this superfamily encode the NAC domain. Most AtNAC genes contain three exons with the last exon encoding an activation domain. A subfamily of AtNAC genes contains additional terminal exons coding for protein domains whose functions are unknown.