The brave new world of RNA

RNAi-based drug design: considerations and future directions

More than 25 years after its discovery, the post-transcriptional gene regulation mechanism termed RNAi is now transforming pharmaceutical development, proved by the recent FDA approval of multiple small interfering RNA (siRNA) drugs that target the liver. Synthetic siRNAs that trigger RNAi have the potential to specifically silence virtually any therapeutic target with unprecedented potency and durability. Bringing this innovative class of medicines to patients, however, has been riddled with substantial challenges, with delivery issues at the forefront. Several classes of siRNA drug are under clinical evaluation, but their utility in treating extrahepatic diseases remains limited, demanding continued innovation. In this Review, we discuss principal considerations and future directions in the design of therapeutic siRNAs, with a particular emphasis on chemistry, the application of informatics, delivery strategies and the importance of careful target selection, which together influence therapeutic success.

MicroRNAs and Cardiovascular Disease Risk

PURPOSE OF REVIEW

MicroRNAs (miRNAs)-short, non-coding RNAs-play important roles in almost all aspects of cardiovascular biology, and changes in intracellular miRNA expression are indicative of cardiovascular disease development and progression. Extracellular miRNAs, which are easily measured in blood and can be reflective of changes in intracellular miRNA levels, have emerged as potential non-invasive biomarkers for disease. This review summarizes current knowledge regarding miRNAs as biomarkers for assessing cardiovascular disease risk and prognosis.

RECENT FINDINGS

Numerous studies over the last 10-15 years have identified associations between extracellular miRNA profiles and cardiovascular disease, supporting the potential use of extracellular miRNAs as biomarkers for risk stratification. However, clinical application of extracellular miRNA profiles has been hampered by poor reproducibility and inter-study variability that is due largely to methodological differences between studies. While recent studies indicate that circulating extracellular miRNAs are promising biomarkers for cardiovascular disease, evidence for clinical implementation is lacking. This highlights the need for larger, well-designed studies that use standardized methods for sample preparation, miRNA isolation, quantification, and normalization.

The role of miRNAs carried by extracellular vesicles in type 2 diabetes and its complications

Diabetes poses severe global public health problems and places heavy burdens on the medical and economic systems of society. Type 2 diabetes (T2D) accounts for 90% of these cases. Diabetes also often accompanies serious complications that threaten multiple organs such as the brain, eyes, kidneys, and the cardiovascular system. MicroRNAs (miRNAs) carried by extracellular vesicles (EV-miRNAs) are considered to mediate cross-organ and cross-cellular communication and have a vital role in the pathophysiology of T2D. They also offer promising sources of diabetes-related biomarkers and serve as effective therapeutic targets. Here, we briefly reviewed studies of EV-miRNAs in T2D and related complications. Specially, we innovatively explore the targeting nature of miRNA action due to the target specificity of vesicle binding, aiding mechanism understanding as well as the detection and treatment of diseases.

Pseudoknots in RNA Structure Prediction

RNA molecules play active roles in the cell and are important for numerous applications in biotechnology and medicine. The function of an RNA molecule stems from its structure. RNA structure determination is time consuming, challenging, and expensive using experimental methods. Thus, much research has been directed at RNA structure prediction through computational means. Many of these methods focus primarily on the secondary structure of the molecule, ignoring the possibility of pseudoknotted structures. However, pseudoknots are known to play functional roles in many RNA molecules or in their method of interaction with other molecules. Improving the accuracy and efficiency of computational methods that predict pseudoknots is an ongoing challenge for single RNA molecules, RNA-RNA interactions, and RNA-protein interactions. To improve the accuracy of prediction, many methods focus on specific applications while restricting the length and the class of the pseudoknotted structures they can identify. In recent years, computational methods for structure prediction have begun to catch up with the impressive developments seen in biotechnology. Here, we provide a non-comprehensive overview of available pseudoknot prediction methods and their best-use cases. © 2023 Wiley Periodicals LLC.

MicroRNA Networks in Breast Cancer Cells

A variety of molecular techniques can be used in order to unravel the molecular composition of cells. In particular, the microarray technology has been used to identify novel biomarkers that may be useful in the diagnosis, prognosis, or treatment of cancer. The microarray technology is ideal for biomarker discovery as it allows for the screening of a large number of molecules at once. In this review, we focus on microRNAs (miRNAs) which are key molecules in cells and regulate gene expression post-transcriptionally. miRNAs are small, single-stranded RNA molecules that bind to complementary mRNAs. Binding of miRNAs to mRNAs leads either to degradation, or translational inhibition of the target mRNA. Roughly one third of all the mRNAs are postulated to be regulated by miRNAs. miRNAs are known to be deregulated in different types of cancer, including breast cancer, and it has been demonstrated that deregulation of several miRNAs can be used as biological markers in cancer. miRNA expression can for example discriminate between normal, benign and malignant breast tissue, and between different breast cancer subtypes.In the post-genomic era, an important task of molecular biology is to understand gene regulation in the context of biological networks. Because miRNAs have such a pronounced role in cells, it is pivotal to understand the mechanisms that underlie their control, and to identify how miRNAs influence cancer development and progression.

Unraveling long non-coding RNAs through analysis of high-throughput RNA-sequencing data

Extensive genome-wide transcriptome study mediated by high throughput sequencing technique has revolutionized the study of genetics and epigenetic at unprecedented resolution. The research has revealed that besides protein-coding RNAs, large proportions of mammalian transcriptome includes a heap of regulatory non protein-coding RNAs, the number encoded within human genome is enigmatic. Many taboos developed in the past categorized these non-coding RNAs as ''dark matter" and "junks". Breaking the myth, RNA-seq-- a recently developed experimental technique is widely being used for studying non-coding RNAs which has acquired the limelight due to their physiological and pathological significance. The longest member of the ncRNA family-- long non-coding RNAs, acts as stable and functional part of a genome, guiding towards the important clues about the varied biological events like cellular-, structural- processes governing the complexity of an organism. Here, we review the most recent and influential computational approach developed to identify and quantify the long non-coding RNAs serving as an assistant for the users to choose appropriate tools for their specific research.

MicroRNA Detection Using a Double Molecular Beacon Approach: Distinguishing Between miRNA and Pre-miRNA

MicroRNAs (miRNAs) are small, noncoding RNAs that post-transcriptionally regulate gene expression and are recognized for their roles both as modulators of disease progression and as biomarkers of disease activity, including neurological diseases, cancer, and cardiovascular disease (CVD). Commonly, miRNA abundance is assessed using quantitative real-time PCR (qRT-PCR), however, qRT-PCR for miRNA can be labor intensive, time consuming, and may lack specificity for detection of mature versus precursor forms of miRNA. Here, we describe a novel double molecular beacon approach to miRNA assessment that can distinguish and quantify mature versus precursor forms of miRNA in a single assay, an essential feature for use of miRNAs as biomarkers for disease. Using this approach, we found that molecular beacons with DNA or combined locked nucleic acid (LNA)-DNA backbones can detect mature and precursor miRNAs (pre-miRNAs) of low (< 1 nM) abundance in vitro. The double molecular beacon assay was accurate in assessing miRNA abundance in a sample containing a mixed population of mature and precursor miRNAs. In contrast, qRT-PCR and the single molecular beacon assay overestimated miRNA abundance. Additionally, the double molecular beacon assay was less labor intensive than traditional qRT-PCR and had 10-25% increased specificity. Our data suggest that the double molecular beacon-based approach is more precise and specific than previous methods, and has the promise of being the standard for assessing miRNA levels in biological samples.

Sparse RNA folding revisited: space-efficient minimum free energy structure prediction

Background

RNA secondary structure prediction by energy minimization is the central computational tool for the analysis of structural non-coding RNAs and their interactions. Sparsification has been successfully applied to improve the time efficiency of various structure prediction algorithms while guaranteeing the same result; however, for many such folding problems, space efficiency is of even greater concern, particularly for long RNA sequences. So far, space-efficient sparsified RNA folding with fold reconstruction was solved only for simple base-pair-based pseudo-energy models.

Results

Here, we revisit the problem of space-efficient free energy minimization. Whereas the space-efficient minimization of the free energy has been sketched before, the reconstruction of the optimum structure has not even been discussed. We show that this reconstruction is not possible in trivial extension of the method for simple energy models. Then, we present the time- and space-efficient sparsified free energy minimization algorithm SparseMFEFold that guarantees MFE structure prediction. In particular, this novel algorithm provides efficient fold reconstruction based on dynamically garbage-collected trace arrows. The complexity of our algorithm depends on two parameters, the number of candidates Z and the number of trace arrows T; both are bounded by \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n^2$$\end{document}n2, but are typically much smaller. The time complexity of RNA folding is reduced from \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$O(n^3)$$\end{document}O(n3) to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$O(n^2+nZ)$$\end{document}O(n2+nZ); the space complexity, from \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$O(n^2)$$\end{document}O(n2) to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$O(n + T + Z)$$\end{document}O(n+T+Z). Our empirical results show more than 80 % space savings over RNAfold [Vienna RNA package] on the long RNAs from the RNA STRAND database (≥2500 bases).

Conclusions

The presented technique is intentionally generalizable to complex prediction algorithms; due to their high space demands, algorithms like pseudoknot prediction and RNA–RNA-interaction prediction are expected to profit even stronger than “standard” MFE folding. SparseMFEFold is free software, available at http://www.bioinf.uni-leipzig.de/~will/Software/SparseMFEFold.

MicroRNAs: Non-coding fine tuners of receptor tyrosine kinase signalling in cancer

Emerging evidence point to a crucial role for non-coding RNAs in modulating homeostatic signaling under physiological and pathological conditions. MicroRNAs, the best-characterized non-coding RNAs to date, can exquisitely integrate spatial and temporal signals in complex networks, thereby confer specificity and sensitivity to tissue response to changes in the microenvironment. MicroRNAs appear as preferential partners for Receptor Tyrosine Kinases (RTKs) in mediating signaling under stress conditions. Stress signaling can be especially relevant to disease. Here we focus on the ability of microRNAs to mediate RTK signaling in cancer, by acting as both tumor suppressors and oncogenes. We will provide a few general examples of microRNAs modulating specific tumorigenic functions downstream of RTK signaling and integrate oncogenic signals from multiple RTKs. A special focus will be devoted to epidermal growth factor receptor (EGFR) signaling, a system offering relatively rich information. We will explore the role of selected microRNAs as bidirectional modulators of EGFR functions in cancer cells. In addition, we will present the emerging evidence for microRNAs being specifically modulated by oncogenic EGFR mutants and we will discuss how this impinges on EGFRmut driven chemoresistance, which fits into the tumor heterogeneity-driven cancer progression. Finally, we discuss how other non-coding RNA species are emerging as important modulators of cancer progression and why the scenario depicted herein is destined to become increasingly complex in the future.

Computational Approaches to Nucleic Acid Origami

Recent advances in experimental DNA origami have dramatically expanded the horizon of DNA nanotechnology. Complex 3D suprastructures have been designed and developed using DNA origami with applications in biomaterial science, nanomedicine, nanorobotics, and molecular computation. Ribonucleic acid (RNA) origami has recently been realized as a new approach. Similar to DNA, RNA molecules can be designed to form complex 3D structures through complementary base pairings. RNA origami structures are, however, more compact and more thermodynamically stable due to RNA's non-canonical base pairing and tertiary interactions. With all these advantages, the development of RNA origami lags behind DNA origami by a large gap. Furthermore, although computational methods have proven to be effective in designing DNA and RNA origami structures and in their evaluation, advances in computational nucleic acid origami is even more limited. In this paper, we review major milestones in experimental and computational DNA and RNA origami and present current challenges in these fields. We believe collaboration between experimental nanotechnologists and computer scientists are critical for advancing these new research paradigms.

MicroRNAs: Emerging Regulators of Immune-Mediated Diseases

MicroRNAs (miRNAs) represent the most abundant class of regulators of gene expression in humans: they regulate one-third of human protein-coding genes. These small noncoding ∼22-nucleotides (nt)-long RNAs originate by multistep process from miRNA genes localized in the genomic DNA. To date, more than 1420 miRNAs have been identified in humans (miRBase v17). The main mechanism of miRNA action is the posttranscriptional regulation via RNA interference with their target mRNAs. The majority of target mRNAs (more than 80%) undergo degradation after recognition by complementary miRNA; the translational inhibition with little or no influence on mRNA levels has been also reported. Each miRNA may suppress multiple mRNA targets (average ∼200), and at the same time, one mRNA can be targeted by many miRNAs enabling to control a spectrum wide range of cellular processes. Recently, the role of miRNAs in the development of immune cells and the maintenance of immune system homeostasis gained attention, and the involvement of miRNAs in the pathogenesis of several immune system diseases has emerged. This review focuses on the role of miRNAs in autoimmune disorders (systemic lupus erythematosus, rheumatoid arthritis, multiple sclerosis, inflammatory bowel disease and psoriasis), inflammatory pathologies of distinct organ (atherosclerosis, osteoarthritis and atopic eczema) and/or systemic locations such as allergy. The role of miRNAs, their predicted and known mRNA targets and description of their actions in physiological immune reactions and in the pathological processes ongoing in immune-mediated human disorders will be discussed. Finally, miRNA-based diagnostics and therapeutic potentials will be highlighted.

“Reduction” responsive thymine-conjugated biodynamers: synthesis and solution properties

Nucleobase-conjugated biodynamers are generated by RAFT polymerization and the transthioesterification reaction. The biodynamers containing thioester linkages demonstrate GSH-responsive feature, and can interact with melamine and ATP in water.

TNF-α alters the release and transfer of microparticle-encapsulated miRNAs from endothelial cells

MicroRNAs (miRNAs) encapsulated within microparticles (MPs) are likely to have a role in cell-to-cell signaling in a variety of diseases, including atherosclerosis. However, little is known about the mechanisms by which different cell types release and transfer miRNAs. Here, we examined TNF-α-induced release of MP-encapsulated miR-126, miR-21, and miR-155 from human aortic endothelial cells (ECs) and their transfer to recipient cells. ECs were treated with TNF-α (100 ng/ml) in the presence or absence of inhibitors that target different MP production pathways. MPs released in response to TNF-α were characterized by: 1) 70-80% decrease in miRNA/MP levels for miR-126 and -21 but a significant increase in pre-miR-155 and miR-155 (P < 0.05), 2) 50% reduction in uptake by recipient cells (P < 0.05), and 3) diminished ability to transfer miRNA to recipient cells. Cotreatment of donor ECs with TNF-α and caspase inhibitor (Q-VD-OPH, 10 μM) produced MPs that had: 1) 1.5- to 2-fold increase in miRNA/MP loading, 2) enhanced uptake by recipient cells (2-fold), and 3) increased ability to transfer miR-155. Cotreatment of ECs with TNF-α and Rho-associated kinase (ROCK) inhibitor (10 μM) produced MPs with features similar to those produced by TNF-α treatment alone. Our data indicate that TNF-α induced the production of distinct MP populations: ROCK-dependent, miRNA-rich MPs that effectively transferred their cargo and were antiapoptotic, and caspase-dependent, miRNA-poor MPs that were proapoptotic. These data provide insight into the relationship between MP production and extracellular release of miRNA, as well as the potential of encapsulated miRNA for cell-to-cell communication.

A fast and robust iterative algorithm for prediction of RNA pseudoknotted secondary structures

Background

Improving accuracy and efficiency of computational methods that predict pseudoknotted RNA secondary structures is an ongoing challenge. Existing methods based on free energy minimization tend to be very slow and are limited in the types of pseudoknots that they can predict. Incorporating known structural information can improve prediction accuracy; however, there are not many methods for prediction of pseudoknotted structures that can incorporate structural information as input. There is even less understanding of the relative robustness of these methods with respect to partial information.

Results

We present a new method, Iterative HFold, for pseudoknotted RNA secondary structure prediction. Iterative HFold takes as input a pseudoknot-free structure, and produces a possibly pseudoknotted structure whose energy is at least as low as that of any (density-2) pseudoknotted structure containing the input structure. Iterative HFold leverages strengths of earlier methods, namely the fast running time of HFold, a method that is based on the hierarchical folding hypothesis, and the energy parameters of HotKnots V2.0.

Our experimental evaluation on a large data set shows that Iterative HFold is robust with respect to partial information, with average accuracy on pseudoknotted structures steadily increasing from roughly 54% to 79% as the user provides up to 40% of the input structure.

Iterative HFold is much faster than HotKnots V2.0, while having comparable accuracy. Iterative HFold also has significantly better accuracy than IPknot on our HK-PK and IP-pk168 data sets.

Conclusions

Iterative HFold is a robust method for prediction of pseudoknotted RNA secondary structures, whose accuracy with more than 5% information about true pseudoknot-free structures is better than that of IPknot, and with about 35% information about true pseudoknot-free structures compares well with that of HotKnots V2.0 while being significantly faster. Iterative HFold and all data used in this work are freely available at http://www.cs.ubc.ca/~hjabbari/software.php.

Coronary heart disease alters intercellular communication by modifying microparticle-mediated microRNA transport

Coronary heart disease (CHD) is characterized by abnormal intercellular communication and circulating microRNAs (miRNAs) are likely involved in this process. Here, we show that CHD was associated with changes in the transport of circulating miRNA, particularly decreased miRNA enrichment in microparticles (MPs). Additionally, MPs from CHD patients were less efficient at transferring miRNA to cultured HUVECs, which correlated with their diminished capacity to bind developmental endothelial locus-1 (Del-1). In summary, CHD was associated with distinct changes in circulating miRNA transport and these changes may contribute to the abnormal intercellular communication that underlies CHD initiation and progression.

MicroRNAs may solve the mystery of chronic hepatitis B virus infection

Hepatitis B virus (HBV) infection is a global public health problem that causes persistent liver diseases such as chronic hepatitis, cirrhosis, and hepatocellular carcinoma. A large amount of people die annually from HBV infection. However, the pathogenesises of the HBV-related diseases are ill defined and the therapeutic strategies for the diseases are less than optimum. The recently discovered microRNAs (miRNAs) are tiny noncoding RNAs that regulate gene expression primarily at the post-transcriptional level by binding to mRNAs. miRNAs contribute to a variety of physiological and pathological processes. A number of miRNAs have been found to play a pivotal role in the host-virus interaction including host-HBV interaction. Numerous studies have indicated that HBV infection could change the cellular miRNA expression patterns and different stages of HBV associated disease have displayed distinctive miRNA profiles. Furthermore, the differential expressed miRNAs have been found involved in the progression of HBV-related diseases, for instance some miRNAs are involved in liver tumorigenesis and tumor metastasis. Studies have also shown that the circulating miRNA in serum or plasma might be a very useful biomarker for the diagnosis and prognosis of HBV-related diseases. In addition, miRNA-based therapy strategies have attracted increasing attention, indicating a promising future in the treatment of HBV-related diseases.

Generation of cDNA libraries from RNP-derived regulatory noncoding RNAs

Next-generation sequencing of noncoding RNA (ncRNA) libraries has become an essential tool for the profiling of ncRNAs and the identification of novel ncRNA species. Here, we describe the generation of a ncRNA-derived complementary DNA (cDNA) library by 3'-tailing of ncRNAs by CTP and poly(A) polymerase, followed by 5'-adapter ligation by T4 RNA ligase and reverse transcription of ncRNAs with an oligo-d(G) anchor primer. Preliminary selection of ncRNAs from ribonucleoprotein particles (RNPs) enables a strong enrichment of the generated libraries with functional regulatory ncRNAs compared to classical approaches.

Analysis of energy-based algorithms for RNA secondary structure prediction

Background

RNA molecules play critical roles in the cells of organisms, including roles in gene regulation, catalysis, and synthesis of proteins. Since RNA function depends in large part on its folded structures, much effort has been invested in developing accurate methods for prediction of RNA secondary structure from the base sequence. Minimum free energy (MFE) predictions are widely used, based on nearest neighbor thermodynamic parameters of Mathews, Turner et al. or those of Andronescu et al. Some recently proposed alternatives that leverage partition function calculations find the structure with maximum expected accuracy (MEA) or pseudo-expected accuracy (pseudo-MEA) methods. Advances in prediction methods are typically benchmarked using sensitivity, positive predictive value and their harmonic mean, namely F-measure, on datasets of known reference structures. Since such benchmarks document progress in improving accuracy of computational prediction methods, it is important to understand how measures of accuracy vary as a function of the reference datasets and whether advances in algorithms or thermodynamic parameters yield statistically significant improvements. Our work advances such understanding for the MFE and (pseudo-)MEA-based methods, with respect to the latest datasets and energy parameters.

Results

We present three main findings. First, using the bootstrap percentile method, we show that the average F-measure accuracy of the MFE and (pseudo-)MEA-based algorithms, as measured on our largest datasets with over 2000 RNAs from diverse families, is a reliable estimate (within a 2% range with high confidence) of the accuracy of a population of RNA molecules represented by this set. However, average accuracy on smaller classes of RNAs such as a class of 89 Group I introns used previously in benchmarking algorithm accuracy is not reliable enough to draw meaningful conclusions about the relative merits of the MFE and MEA-based algorithms. Second, on our large datasets, the algorithm with best overall accuracy is a pseudo MEA-based algorithm of Hamada et al. that uses a generalized centroid estimator of base pairs. However, between MFE and other MEA-based methods, there is no clear winner in the sense that the relative accuracy of the MFE versus MEA-based algorithms changes depending on the underlying energy parameters. Third, of the four parameter sets we considered, the best accuracy for the MFE-, MEA-based, and pseudo-MEA-based methods is 0.686, 0.680, and 0.711, respectively (on a scale from 0 to 1 with 1 meaning perfect structure predictions) and is obtained with a thermodynamic parameter set obtained by Andronescu et al. called BL* (named after the Boltzmann likelihood method by which the parameters were derived).

Conclusions

Large datasets should be used to obtain reliable measures of the accuracy of RNA structure prediction algorithms, and average accuracies on specific classes (such as Group I introns and Transfer RNAs) should be interpreted with caution, considering the relatively small size of currently available datasets for such classes. The accuracy of the MEA-based methods is significantly higher when using the BL* parameter set of Andronescu et al. than when using the parameters of Mathews and Turner, and there is no significant difference between the accuracy of MEA-based methods and MFE when using the BL* parameters. The pseudo-MEA-based method of Hamada et al. with the BL* parameter set significantly outperforms all other MFE and MEA-based algorithms on our large data sets.

Plant small RNAs: biogenesis, mode of action and their roles in abiotic stresses

Small RNAs (sRNAs) are 18-30 nt non-coding regulatory elements found in diverse organisms, which were initially identified as small double-stranded RNAs in Caenorhabditis elegans. With the development of new and improved technologies, sRNAs have also been identified and characterized in plant systems. Among them, micro RNAs (miRNAs) and small interfering RNAs (siRNAs) are found to be very important riboregulators in plants. Various types of sRNAs differ in their mode of biogenesis and in their function of gene regulation. sRNAs are involved in gene regulation at both transcriptional and post-transcriptional levels. They are known to regulate growth and development of plants. Furthermore, sRNAs especially plant miRNAs have been found to be involved in various stress responses, such as oxidative, mineral nutrient deficiency, dehydration, and even mechanical stimulus. Therefore, in the present review, we focus on the current understanding of biogenesis and regulatory mechanisms of plant sRNAs and their responses to various abiotic stresses.

Risks and Precautions of Genetically Modified Organisms

Commercial potential of biotechnology is immense since the scope of its activity covers the entire spectrum of human life. The most potent biotechnological approach is the transfer of specifically constructed gene assemblies through various techniques. However, this deliberate modification and the resulting entities thereof have become the bone of contention all over the world. Benefits aside, genetically modified organisms (GMOs) have always been considered a threat to environment and human health. In view of this, it has been considered necessary by biosafety regulations of individual countries to test the feasibility of GMOs in contained and controlled environments for any potential risks they may pose. This paper describes the various aspects of risk, its assessment, and management which are imperative in decision making regarding the safe use of GMOs. Efficient efforts are necessary for implementation of regulations. Importance of the risk assessment, management, and precautionary approach in environmental agreements and activism is also discussed.

MicroRNAs in skin and wound healing

MicroRNAs (miRNAs) are small endogenous RNA molecules ∼22 nt in length. miRNAs are capable of posttranscriptional gene regulation by binding to their target messenger RNAs (mRNAs), leading to mRNA degradation or suppression of translation. miRNAs have recently been shown to play pivotal roles in skin development and are linked to various skin pathologies, cancer, and wound healing. This review focuses on the role of miRNAs in cutaneous biology, the various methods of miRNA modulation, and the therapeutic opportunities in treatment of skin diseases and wound healing.

MicroRNAs in colorectal cancer: translation of molecular biology into clinical application

MicroRNAs (miRNAs) are small non-coding RNAs 18-25 nucleotides in length that downregulate gene expression during various crucial cell processes such as apoptosis, differentiation and development. Changes in the expression profiles of miRNAs have been observed in a variety of human tumors, including colorectal cancer (CRC). Functional studies indicate that miRNAs act as tumor suppressors and oncogenes. These findings significantly extend Vogelstein's model of CRC pathogenesis and have shown great potential for miRNAs as a novel class of therapeutic targets. Several investigations have also described the ability of miRNA expression profiles to predict prognosis and response to selected treatments in CRC patients, and support diagnosis of CRC among cancer of unknown primary site. miRNAs' occurrence has been repeatedly observed also in serum and plasma, and miRNAs as novel minimally invasive biomarkers have indicated reasonable sensitivity for CRC detection and compare favorably with the fecal occult blood test. In this review, we summarize the knowledge regarding miRNAs' functioning in CRC while emphasizing their significance in pathogenetic signaling pathways and their potential to serve as disease biomarkers and novel therapeutic targets.

SARA: a server for function annotation of RNA structures

Recent interest in non-coding RNA transcripts has resulted in a rapid increase of deposited RNA structures in the Protein Data Bank. However, a characterization and functional classification of the RNA structure and function space have only been partially addressed. Here, we introduce the SARA program for pair-wise alignment of RNA structures as a web server for structure-based RNA function assignment. The SARA server relies on the SARA program, which aligns two RNA structures based on a unit-vector root-mean-square approach. The likely accuracy of the SARA alignments is assessed by three different P-values estimating the statistical significance of the sequence, secondary structure and tertiary structure identity scores, respectively. Our benchmarks, which relied on a set of 419 RNA structures with known SCOR structural class, indicate that at a negative logarithm of mean P-value higher or equal than 2.5, SARA can assign the correct or a similar SCOR class to 81.4% and 95.3% of the benchmark set, respectively. The SARA server is freely accessible via the World Wide Web at http://sgu.bioinfo.cipf.es/services/SARA/.

Neuronal plasticity after ischemic preconditioning and TIA-like preconditioning ischemic periods

Transient ischemic attacks (TIAs) have recently become the center of attention since they are thought to share some characteristics with experimental ischemic preconditioning (IPC). This phenomenon describes the situation that a brief, per se harmless, cerebral ischemic period renders the brain resistant to a subsequent severe and normally damaging ischemia. Preconditioning (PC) is not restricted to the brain but also occurs in other organs. Furthermore, apart from a short ischemia, the PC event may comprise nearly any noxious stimulus which, however, must not exceed the threshold to tissue damage. In the last two decades, our knowledge concerning the underlying molecular basis of PC has substantially grown and there is hope to potentially imitate the induction of an endogenous neuroprotective state in patients with a high risk of cerebral ischemia. While, at present, there is virtually no neuropathological data on changes after TIAs or TIA-like PC ischemic periods in human brains, the following review will briefly summarize the current knowledge of plastic neuronal changes after PC in animal models, still awaiting their detection in the human brain.

Diversity and evolution of MicroRNA gene clusters

microRNA (miRNA) gene clusters are a group of miRNA genes clustered within a proximal distance on a chromosome. Although a large number of miRNA clusters have been uncovered in animal and plant genomes, the functional consequences of this arrangement are still poorly understood. Located in a polycistron, the coexpressed miRNA clusters are pivotal in coordinately regulating multiple processes, including embryonic development, cell cycles and cell differentiation. In this review, based on recent progress, we discuss the genomic diversity of miRNA gene clusters, the coordination of expression and function of the clustered miRNAs, and the evolutionarily adaptive processes with gain and loss of the clustering miRNA genes mediated by duplication and transposition events.

Noncoding RNA in development

Non-protein-coding sequences increasingly dominate the genomes of multicellular organisms as their complexity increases, in contrast to protein-coding genes, which remain relatively static. Most of the mammalian genome and indeed that of all eukaryotes is expressed in a cell- and tissue-specific manner, and there is mounting evidence that much of this transcription is involved in the regulation of differentiation and development. Different classes of small and large noncoding RNAs (ncRNAs) have been shown to regulate almost every level of gene expression, including the activation and repression of homeotic genes and the targeting of chromatin-remodeling complexes. ncRNAs are involved in developmental processes in both simple and complex eukaryotes, and we illustrate this in the latter by focusing on the animal germline, brain, and eye. While most have yet to be systematically studied, the emerging evidence suggests that there is a vast hidden layer of regulatory ncRNAs that constitutes the majority of the genomic programming of multicellular organisms and plays a major role in controlling the epigenetic trajectories that underlie their ontogeny.

Novel and efficient RNA secondary structure prediction using hierarchical folding

Algorithms for prediction of RNA secondary structure-the set of base pairs that form when an RNA molecule folds-are valuable to biologists who aim to understand RNA structure and function. Improving the accuracy and efficiency of prediction methods is an ongoing challenge, particularly for pseudoknotted secondary structures, in which base pairs overlap. This challenge is biologically important, since pseudoknotted structures play essential roles in functions of many RNA molecules, such as splicing and ribosomal frameshifting. State-of-the-art methods, which are based on free energy minimization, have high run-time complexity (typically Theta(n(5)) or worse), and can handle (minimize over) only limited types of pseudoknotted structures. We propose a new approach for prediction of pseudoknotted structures, motivated by the hypothesis that RNA structures fold hierarchically, with pseudoknot-free (non-overlapping) base pairs forming first, and pseudoknots forming later so as to minimize energy relative to the folded pseudoknot-free structure. Our HFold algorithm uses two-phase energy minimization to predict hierarchically formed secondary structures in O(n(3)) time, matching the complexity of the best algorithms for pseudoknot-free secondary structure prediction via energy minimization. Our algorithm can handle a wide range of biological structures, including kissing hairpins and nested kissing hairpins, which have previously required Theta(n(6)) time.

The current status of cDNA cloning

The cloning of cDNAs, copies of cellular RNA, is one of the classical technologies in molecular biology. Over the past 30 years cDNA cloning technologies have been improved to enable the cloning of large cDNA collections, which are fundamental to today's understanding of the utilization of genetic information. With the discovery of noncoding RNAs, additional new approaches to the cloning of short RNAs have been developed. However, with the realization that much larger portions of genomes are transcribed than anticipated from genome annotations, cDNA cloning faces new challenges to uncover rare transcripts and to make the corresponding cDNAs available for functional studies. This review provides an overview on the current status of cDNA cloning and possibilities for the discovery and characterization of new RNA families.

An annotation infrastructure for the analysis and interpretation of Affymetrix exon array data

An annotation database (X:MAP) and BioConductor/R package (exonmap) have been developed to support fine-grained analysis of exon array data.

Affymetrix exon arrays contain probesets intended to target every known and predicted exon in the entire genome, posing significant challenges for high-throughput genome-wide data analysis. X:MAP , an annotation database, and exonmap , a BioConductor/R package, are designed to support fine-grained analysis of exon array data. The system supports the application of standard statistical techniques, prior to the use of genome scale annotation to provide gene-, transcript- and exon-level summaries and visualization tools.

Parsing nucleic acid pseudoknotted secondary structure: algorithm and applications

Accurate prediction of pseudoknotted nucleic acid secondary structure is an important computational challenge. Prediction algorithms based on dynamic programming aim to find a structure with minimum free energy according to some thermodynamic ("sum of loop energies") model that is implicit in the recurrences of the algorithm. However, a clear definition of what exactly are the loops in pseudoknotted structures, and their associated energies, has been lacking. In this work, we present a complete classification of loops in pseudoknotted nucleic secondary structures, and describe the Rivas and Eddy and other energy models as sum-of-loops energy models. We give a linear time algorithm for parsing a pseudoknotted secondary structure into its component loops. We give two applications of our parsing algorithm. The first is a linear time algorithm to calculate the free energy of a pseudoknotted secondary structure. This is useful for heuristic prediction algorithms, which are widely used since (pseudoknotted) RNA secondary structure prediction is NP-hard. The second application is a linear time algorithm to test the generality of the dynamic programming algorithm of Akutsu for secondary structure prediction. Together with previous work, we use this algorithm to compare the generality of state-of-the-art algorithms on real biological structures.

MicroRNA expression and function in cancer

MicroRNAs are small non-coding RNAs of 19-24 nucleotides in length that downregulate gene expression during various crucial cell processes such as apoptosis, differentiation and development. Recent work supports a role for miRNAs in the initiation and progression of human malignancies. Large high-throughput studies in patients revealed that miRNA profiling have the potential to classify tumors with high accuracy and predict outcome. Functional studies, some of which involve animal models, indicate that miRNAs act as tumor suppressors and oncogenes. Here, we summarize miRNA-profiling studies in human malignancies and examine the role of miRNAs in the pathogenesis of cancer. We also discuss the implications of these findings for the diagnosis and treatment of cancer.

An RNA-centered view of eukaryotic cells

Emerging evidence suggests that the introns and intergenic sequences of the genomes of higher eukaryotes (the "junk" DNA) codes for a vast, RNA-based, genetic regulatory network. It is believed that this network is responsible for the variety and complexity of terrestrial life. We conjecture that this regulatory network is more properly viewed as an RNA "community", composed of a rich and largely unexplored biochemical web of RNA interactions. Viewed as an RNA-community, we hypothesize that the RNA regulatory network of higher eukaryotes can re-wire itself, and employ various and evolvable mutational strategies in response to external pressures. Thus, we argue that much evolutionary change is due to intracellular, RNA-mediated learning processes. Successful strategies and pathways are then recorded (hard-wired) into the DNA genome via reverse transcriptase. We present evidence, which is consistent with this viewpoint, and make specific theorems, which could be used to test the utility of our framework. If essentially correct, the RNA-community view of eukaryotic cells could reconcile measured point mutation and gene duplication rates with actual rates of evolutionary change. Futhermore, the RNA-community view of eukaryotic cells suggests that agent-based modeling techniques, used in mathematical economics, game theory, and neuroscience, will likely be as useful in understanding the functioning of eukaryotic cells as the pathway-based approaches of systems biology. We conclude this paper by arguing that a sufficient amount of biological knowledge has been accumulated to initiate a systematic program of experimental and computational studies of the origins and macroevolution of terrestrial life.

A Novel Method to Detect Functional MicroRNA Targets

MicroRNA (miRNA) molecules are non-coding RNAs, 19 to 24 nt in length that have been identified recently as important regulators of gene expression. Several computational methods have been developed to describe the target recognition mechanism by miRNA. We propose here a novel method to detect miRNA-mRNA complexes in eukaryotic cells. As a first step, we synthesize cDNA on an mRNA template using miRNAs as the endogenous cytoplasmic primer. This step extends miRNA and overcomes the problem of low complementary binding of miRNAs to their targets. Purified hybrid 3'-cDNA-miRNA-5' molecules are used in a second round of reverse transcription to anneal to target mRNA in a highly gene-specific manner. The 5'-end analysis of these cDNA molecules demonstrated that primers for cDNAs were "signatures" of miRNA molecules, and over-expression of their full-length mature miRNAs resulted in functional inhibition of target protein expression.

Clusters of internally primed transcripts reveal novel long noncoding RNAs

Non-protein-coding RNAs (ncRNAs) are increasingly being recognized as having important regulatory roles. Although much recent attention has focused on tiny 22- to 25-nucleotide microRNAs, several functional ncRNAs are orders of magnitude larger in size. Examples of such macro ncRNAs include Xist and Air, which in mouse are 18 and 108 kilobases (Kb), respectively. We surveyed the 102,801 FANTOM3 mouse cDNA clones and found that Air and Xist were present not as single, full-length transcripts but as a cluster of multiple, shorter cDNAs, which were unspliced, had little coding potential, and were most likely primed from internal adenine-rich regions within longer parental transcripts. We therefore conducted a genome-wide search for regional clusters of such cDNAs to find novel macro ncRNA candidates. Sixty-six regions were identified, each of which mapped outside known protein-coding loci and which had a mean length of 92 Kb. We detected several known long ncRNAs within these regions, supporting the basic rationale of our approach. In silico analysis showed that many regions had evidence of imprinting and/or antisense transcription. These regions were significantly associated with microRNAs and transcripts from the central nervous system. We selected eight novel regions for experimental validation by northern blot and RT-PCR and found that the majority represent previously unrecognized noncoding transcripts that are at least 10 Kb in size and predominantly localized in the nucleus. Taken together, the data not only identify multiple new ncRNAs but also suggest the existence of many more macro ncRNAs like Xist and Air.

The human genome has been sequenced, and, intriguingly, less than 2% specifies the information for the basic protein building blocks of our bodies. So, what does the other 98% do? It now appears that the mammalian genome also specifies the instructions for many previously undiscovered “non protein-coding RNA” (ncRNA) genes. However, what these ncRNAs do is largely unknown. In recent years, strategies have been designed that have successfully identified hundreds of short ncRNAs—termed microRNAs—many of which have since been shown to act as genetic regulators. Also known to be functionally important are a handful of ncRNAs orders of magnitude larger in size than microRNAs. The availability of complete genome and comprehensive transcript sequences allows for the systematic discovery of more large ncRNAs. The authors developed a computational strategy to screen the mouse genome and identify large ncRNAs. They detected existing large ncRNAs, thus validating their approach, but, more importantly, discovered more than 60 other candidates, some of which were subsequently confirmed experimentally. This work opens the door to a virtually unexplored world of large ncRNAs and beckons future experimental work to define the cellular functions of these molecules.

Non-coding RNA

The term non-coding RNA (ncRNA) is commonly employed for RNA that does not encode a protein, but this does not mean that such RNAs do not contain information nor have function. Although it has been generally assumed that most genetic information is transacted by proteins, recent evidence suggests that the majority of the genomes of mammals and other complex organisms is in fact transcribed into ncRNAs, many of which are alternatively spliced and/or processed into smaller products. These ncRNAs include microRNAs and snoRNAs (many if not most of which remain to be identified), as well as likely other classes of yet-to-be-discovered small regulatory RNAs, and tens of thousands of longer transcripts (including complex patterns of interlacing and overlapping sense and antisense transcripts), most of whose functions are unknown. These RNAs (including those derived from introns) appear to comprise a hidden layer of internal signals that control various levels of gene expression in physiology and development, including chromatin architecture/epigenetic memory, transcription, RNA splicing, editing, translation and turnover. RNA regulatory networks may determine most of our complex characteristics, play a significant role in disease and constitute an unexplored world of genetic variation both within and between species.

Reverse transcription in genome evolution

As reverse transcription is predominantly performed by retrotransposable elements, the process is often entirely associated with the propagation of these elements. However, as a unique tool for transmitting information from the dynamic RNA to the more inert DNA, reverse transcription has been instrumental in shaping extant genomes. This review aims at presenting the diversity by which reverse transcription has influenced modern genomic structures.

Camels and zebrafish, viruses and cancer: a microRNA update

MicroRNAs (miRNAs) form an extensive class of RNA molecules that regulate gene expression at post-transcriptional level. In recent years, much progress has been made in dissection of biogenesis and functions of miRNAs. There are at least several hundred miRNA genes in the human genome, and the emerging evidence suggests that miRNAs are broadly implicated in gene regulation. Here, we review some recent advances, and particularly we discuss how comparative genomics helps to identify novel miRNA genes, how studies in zebrafish reveal roles of miRNAs in morphogenesis, how changes in miRNA expression patterns are connected with cancer and how host-virus coevolution exploits miRNA regulatory pathways.

Small regulatory RNAs in mammals

Mammalian cells harbor numerous small non-protein-coding RNAs, including small nucleolar RNAs (snoRNAs), microRNAs (miRNAs), short interfering RNAs (siRNAs) and small double-stranded RNAs, which regulate gene expression at many levels including chromatin architecture, RNA editing, RNA stability, translation, and quite possibly transcription and splicing. These RNAs are processed by multistep pathways from the introns and exons of longer primary transcripts, including protein-coding transcripts. Most show distinctive temporal- and tissue-specific expression patterns in different tissues, including embryonal stem cells and the brain, and some are imprinted. Small RNAs control a wide range of developmental and physiological pathways in animals, including hematopoietic differentiation, adipocyte differentiation and insulin secretion in mammals, and have been shown to be perturbed in cancer and other diseases. The extent of transcription of non-coding sequences and the abundance of small RNAs suggests the existence of an extensive regulatory network on the basis of RNA signaling which may underpin the development and much of the phenotypic variation in mammals and other complex organisms and which may have different genetic signatures from sequences encoding proteins.

The cell-type-specificity of inherited predispositions to tumours: review and hypothesis

Most hereditary predispositions to tumours affect only one particular cell type of the body but the genes bearing the relevant germ-line mutation are not cell-type-specific. Some predisposition syndromes include increased risks of lesions (developmental or tumourous) of unrelated cell types, in any individual predisposed to the main lesion (e.g. osteosarcoma in patients predisposed to retinoblastoma). Other predispositions to additional lesions occur only in members of some families with the predisposition to the basic lesion (e.g. Gardner's syndrome in some families suffering familial adenomatous polyposis). In yet other predisposition syndromes, different mutations of the same gene are associated with markedly differing family-specific clinical syndromes. In particular, identical germline mutations (e.g. in APC, RET and PTEN genes), have been found associated with differing clinical syndromes in different families. This paper reviews previously suggested mechanisms of the cell-type specificity of inherited predispositions to tumour. Models of tumour formation in predisposition syndromes are discussed, especially those involving a germline mutation (the first 'hit') of a tumour suppressor gene (TSG) and a second (somatic) hit on the second allele of the same TSG. A modified model is suggested, such that the second hit is a co-mutation of the second allele of the TSG and a regulator which is specific for growth and/or differentiation of the cell type which is susceptible to the tumour predisposition. In some cases of tumour, the second hit may be large enough to be associated with a cytogenetically-demonstrable abnormality of the part of the chromosome carrying the TSG, but in other cases, the co-mutation may be of 'sub-cytogenetic' size (i.e. 10(2)-10(5) bases). For the latter, mutational mechanisms of frameshift and impaired fidelity of replication of DNA by DNA polyerases may sometimes be involved. Candidate cell-type-specific regulators may include microRNAs and perhaps transcription factors. It is suggested that searching the introns within 10(5)-10(6) bases either side of known of exonic mutations of TSGs associated with inherited tumour predisposition might reveal microRNA cell-type-specific regulators. Additional investigations may involve fluorescent in situ hybridisations on interphase tumour nuclei.

Computational physiology and the Physiome Project

Bioengineering analyses of physiological systems use the computational solution of physical conservation laws on anatomically detailed geometric models to understand the physiological function of intact organs in terms of the properties and behaviour of the cells and tissues within the organ. By linking behaviour in a quantitative, mathematically defined sense across multiple scales of biological organization--from proteins to cells, tissues, organs and organ systems--these methods have the potential to link patient-specific knowledge at the two ends of these spatial scales. A genetic profile linked to cardiac ion channel mutations, for example, can be interpreted in relation to body surface ECG measurements via a mathematical model of the heart and torso, which includes the spatial distribution of cardiac ion channels throughout the myocardium and the individual kinetics for each of the approximately 50 types of ion channel, exchanger or pump known to be present in the heart. Similarly, linking molecular defects such as mutations of chloride ion channels in lung epithelial cells to the integrated function of the intact lung requires models that include the detailed anatomy of the lungs, the physics of air flow, blood flow and gas exchange, together with the large deformation mechanics of breathing. Organizing this large body of knowledge into a coherent framework for modelling requires the development of ontologies, markup languages for encoding models, and web-accessible distributed databases. In this article we review the state of the field at all the relevant levels, and the tools that are being developed to tackle such complexity. Integrative physiology is central to the interpretation of genomic and proteomic data, and is becoming a highly quantitative, computer-intensive discipline.

Design, implementation and evaluation of a practical pseudoknot folding algorithm based on thermodynamics

BACKGROUND

The general problem of RNA secondary structure prediction under the widely used thermodynamic model is known to be NP-complete when the structures considered include arbitrary pseudoknots. For restricted classes of pseudoknots, several polynomial time algorithms have been designed, where the O(n6)time and O(n4) space algorithm by Rivas and Eddy is currently the best available program.

RESULTS

We introduce the class of canonical simple recursive pseudoknots and present an algorithm that requires O(n4) time and O(n2) space to predict the energetically optimal structure of an RNA sequence, possible containing such pseudoknots. Evaluation against a large collection of known pseudoknotted structures shows the adequacy of the canonization approach and our algorithm.

CONCLUSIONS

RNA pseudoknots of medium size can now be predicted reliably as well as efficiently by the new algorithm.

International Conference on Translational Research ICTR 2003 Conference Summary: marshalling resources in a complex time

The knowledge, tools, and environment for the practice of radiation oncology are changing rapidly. The National Cancer Institute has articulated the need for a balanced portfolio, including the interrelated components of discovery, development, and delivery. Underpinning practice is the emerging knowledge from molecular, cellular, and tumor biology that is the engine of discovery. The use of high-throughput technologies to analyze biochemical and molecular profiles will ultimately enable the individualization of cancer treatment requiring the appropriate integration of radiation with a range of systemic therapies, including chemotherapy, biologic therapy, and immunotherapy. Technological advances in treatment delivery using photons, brachytherapy, particle therapy, radioisotopes, and other forms of energy require an improved ability to localize the tumor and critical subregions and to ensure necessary tissue immobilization and/or real-time target adjustment. Functional imaging is helping to define tumor characteristics and response to treatment. The development of appropriate radiation oncology treatment requires a wide range of expertise, a multimodality approach, and multi-institutional collaboration to provide improved and cost-effective outcome. The delivery of appropriate cancer care to those who need it requires biology and technology but also reaching the underserved populations worldwide. ICTR 2003 demonstrated substantial progress in translational radiation oncology. Faced with financial constraints for research and patient care, the broad field of radiation oncology must continually examine and balance its research and development portfolio and invest in its future leaders to enable it be an important contributor to the future of cancer care.

Challenging the dogma: the hidden layer of non-protein-coding RNAs in complex organisms

The central dogma of biology holds that genetic information normally flows from DNA to RNA to protein. As a consequence it has been generally assumed that genes generally code for proteins, and that proteins fulfil not only most structural and catalytic but also most regulatory functions, in all cells, from microbes to mammals. However, the latter may not be the case in complex organisms. A number of startling observations about the extent of non-protein-coding RNA (ncRNA) transcription in the higher eukaryotes and the range of genetic and epigenetic phenomena that are RNA-directed suggests that the traditional view of the structure of genetic regulatory systems in animals and plants may be incorrect. ncRNA dominates the genomic output of the higher organisms and has been shown to control chromosome architecture, mRNA turnover and the developmental timing of protein expression, and may also regulate transcription and alternative splicing. This paper re-examines the available evidence and suggests a new framework for considering and understanding the genomic programming of biological complexity, autopoietic development and phenotypic variation.

Nylon/DNA: Single-stranded DNA with a covalently stitched nylon lining

The synthesis of DNA/nylon ladder oligomers is described. Three stages of the development are addressed: the synthesis of 2'-beta-substituted phosphoramidites, the deprotection/purification protocols of ODNs modified with both amino and carboxyl groups, and amide bond-forming reactions on the ODNs. The established technology and the novel DNA-based ladder oligomer structure opens a pathway to the synthesis of topological molecular objects and networks templated by DNA through versatile DNA nanotechnology. The DNA-based ladder oligomers may find application in the antisense area.

Trace element biology: the knowledge base and its application for the nutrition of individuals and populations

Impressive strides are being made in the understanding of trace element metabolism and function. This is underscored by the many contributions in these proceedings. However, not so impressive are: i) the precise recognition of mild trace element deficiencies and how to establish their functional consequences, possibly confounded by concurrent trace element inadequacies, are difficult to assess, ii) approaches to the quantitative determination of requirements for trace elements remain unsatisfactory and archaic, in so many ways, iii) our understanding of the extent of the biological basis for the variation in requirements among apparently similar individuals is poor, and iv) much needs to be learned about the quantitative extent to which genetic, epigenetic and dietary factors interact to determine the nutritional phenotype. Some ideas are presented as to how we might embrace, in the context of a reconstructive approach, the exciting new knowledge and related techniques emerging during the postgenome era and develop new paradigms for assessing trace element needs and status, and for establishing effective nutrient intake under different conditions of complex genotype-environment interactions. Metabolites are functional cellular entities and I also urge a vigorous application of metabolomics and of metabolic profiling that is closely linked with genomics, proteomics, trace element kinetics and system analysis, as components of the new integrative paradigm. We need to understand the system and its strategy, not only the molecular details of its component parts and its individual controls. An interdisciplinary research and teaching enterprise will be necessary to best achieve this aim. All of this is related to our common goal to promote, through expanded biological knowledge and its effective application, the enhanced role of trace elements for human well-being.