Introduction to microarray technology

PMF-GRN: a variational inference approach to single-cell gene regulatory network inference using probabilistic matrix factorization

Inferring gene regulatory networks (GRNs) from single-cell data is challenging due to heuristic limitations. Existing methods also lack estimates of uncertainty. Here we present Probabilistic Matrix Factorization for Gene Regulatory Network Inference (PMF-GRN). Using single-cell expression data, PMF-GRN infers latent factors capturing transcription factor activity and regulatory relationships. Using variational inference allows hyperparameter search for principled model selection and direct comparison to other generative models. We extensively test and benchmark our method using real single-cell datasets and synthetic data. We show that PMF-GRN infers GRNs more accurately than current state-of-the-art single-cell GRN inference methods, offering well-calibrated uncertainty estimates.

At the dawn of the transcriptomic medicine

Progress in genomic analytical technologies has improved our possibilities to obtain information regarding DNA, RNA, and their dynamic changes that occur over time or in response to specific challenges. This information describes the blueprint for cells, tissues, and organisms and has fundamental importance for all living organisms. This review focuses on the technological challenges to analyze the transcriptome and what is the impact of transcriptomics on precision medicine. The transcriptome is a term that covers all RNA present in cells and a substantial part of it will never be translated into protein but is nevertheless functional in determining cell phenotype. Recent developments in transcriptomics have challenged the fundamentals of the central dogma of biology by providing evidence of pervasive transcription of the genome. Such massive transcriptional activity is challenging the definition of a gene and especially the term "pseudogene" that has now been demonstrated in many examples to be both transcribed and translated. We also review the common sources of biomaterials for transcriptomics and justify the suitability of whole blood RNA as the current optimal analyte for clinical transcriptomics. At the end of the review, a brief overview of the clinical implications of transcriptomics in clinical trial design and clinical diagnosis is given. Finally, we introduce the transcriptome as a target for modern drug development as a tool for extending our capacity for precision medicine in multiple diseases.

Microarray Technologies in Fungal Diagnostics

Microarray technologies have been a major research tool in the last decades. In addition they have been introduced into several fields of diagnostics including diagnostics of infectious diseases. Microarrays are highly parallelized assay systems that initially were developed for multiparametric nucleic acid detection. From there on they rapidly developed towards a tool for the detection of all kind of biological compounds (DNA, RNA, proteins, cells, nucleic acids, carbohydrates, etc.) or their modifications (methylation, phosphorylation, etc.). The combination of closed-tube systems and lab on chip devices with microarrays further enabled a higher automation degree with a reduced contamination risk. Microarray-based diagnostic applications currently complement and may in the future replace classical methods in clinical microbiology like blood cultures, resistance determination, microscopic and metabolic analyses as well as biochemical or immunohistochemical assays. In addition, novel diagnostic markers appear, like noncoding RNAs and miRNAs providing additional room for novel nucleic acid based biomarkers. Here I focus an microarray technologies in diagnostics and as research tools, based on nucleic acid-based arrays.

Toxicogenomic responses of human alveolar epithelial cells to tungsten boride nanoparticles

During the recent years, microarray analysis of gene expression has become an inevitable tool for exploring toxicity of drugs and other chemicals on biological systems. Therefore, toxicogenomics is considered as a fruitful area for searching cellular pathways and mechanisms including cancer, immunological diseases, environmental responses, gene-gene interactions and chemical toxicity. In this work, we examined toxic effects of Tungsten Borides NPs on gene expression profiling of the human lung alveolar epithelial cells (HPAEpiC). In line with this purpose, a single crystal of tungsten boride (mixture of WB and W₂B) nanoparticles was synthesized by means of zone melting method, and characterized via using X-ray crystallography (XRD), transmission electron microscope (TEM), scanning electron microscope (SEM) and energy-dispersive X-ray spectroscopy (EDX) techniques. Cell viability and cytotoxicity were determined by 3-(4,5-dimethyl-thiazol-2-yl) 2,5-diphenyltetrazolium bromide (MTT), neutral red (NR) and lactate dehydrogenase (LDH) release tests. The whole genome microarray expression analysis was performed to find out the effects of WB and W₂B NPs mixture on gene expression of the HPAEpiC cell culture. 123 of 40,000 gene probes were assigned to characterize expression profile for WB/W2B NPs exposure. According to results; 70 genes were up-regulated and 53 genes were down-regulated (≥2 fold change). For further investigations, these genes were functionally classified by using DAVID (The Database for Annotation, Visualization and Integrated Discovery) with gene ontology (GO) analysis. In the light of the data gained from this study, it could be concluded that the mixture of WB/W2B NPs can affect cytokine/chemokine metabolism, angiogenesis and prevent migration/invasion by activating various genes.

The Potentials and Pitfalls of Microarrays in Neglected Tropical Diseases: A Focus on Human Filarial Infections

Data obtained from expression microarrays enables deeper understanding of the molecular signatures of infectious diseases. It provides rapid and accurate information on how infections affect the clustering of gene expression profiles, pathways and networks that are transcriptionally active during various infection states compared to conventional diagnostic methods, which primarily focus on single genes or proteins. Thus, microarray technologies offer advantages in understanding host-parasite interactions associated with filarial infections. More importantly, the use of these technologies can aid diagnostics and helps translate current genomic research into effective treatment and interventions for filarial infections. Studying immune responses via microarray following infection can yield insight into genetic pathways and networks that can have a profound influence on the development of anti-parasitic vaccines.

Microarrays as Research Tools and Diagnostic Devices

Molecular diagnostics comprises a main analytical division in clinical laboratory diagnostics. The analysis of RNA or DNA helps to diagnose infectious diseases and identify genetic determined disorders or even cancer. Starting from mono-parametric tests within the last years, technologies have evolved that allow for the detection of many parameters in parallel, e.g., by using multiplex nucleic acid amplification techniques, microarrays, or next-generation sequencing technologies. The introduction of closed-tube systems as well as lab-on-a-chip devices further resulted in a higher automation degree with a reduced contamination risk. These applications complement or even stepwise replace classical methods in clinical microbiology like virus cultures, resistance determination, microscopic and metabolic analyses, as well as biochemical or immunohistochemical assays. In addition, novel diagnostic markers appear, like noncoding RNAs and miRNAs providing additional room for novel biomarkers.

This article provides an overview of microarrays as diagnostics devices and research tools. Introduced in 1995 for transcription analysis, microarrays are used today to detect several different biomolecules like DNA, RNA, miRNA, and proteins among others. Mainly used in research, some microarrays also found their way to clinical diagnostics. Further, closed lab-on-a-chip devices that use DNA microarrays as detection tools are discussed, and additionally, an outlook toward applications of next-generation sequencing tools in diagnostics will be given.

Modeling the Drosophila gene cluster regulation network for muscle development

The development of accurate and reliable dynamical modeling procedures that describe the time evolution of gene expression levels is a prerequisite to understanding and controlling the transcription process. We focused on data from DNA microarray time series for 20 Drosophila genes involved in muscle development during the embryonic stage. Genes with similar expression profiles were clustered on the basis of a translation-invariant and scale-invariant distance measure. The time evolution of these clusters was modeled using coupled differential equations. Three model structures involving a transcription term and a degradation term were tested. The parameters were identified in successive steps: network construction, parameter optimization, and parameter reduction. The solutions were evaluated on the basis of the data reproduction and the number of parameters, as well as on two biology-based requirements: the robustness with respect to parameter variations and the values of the expression levels not being unrealistically large upon extrapolation in time. Various solutions were obtained that satisfied all our evaluation criteria. The regulatory networks inferred from these solutions were compared with experimental data. The best solution has half of the experimental connections, which compares favorably with previous approaches. Biasing the network toward the experimental connections led to the identification of a model that is only slightly less good on the basis of the evaluation criteria. The non-uniqueness of the solutions and the variable agreement with experimental connections were discussed in the context of the different hypotheses underlying this type of approach.

Posttranscriptional regulatory networks: from expression profiling to integrative analysis of mRNA and microRNA data

Protein coding RNAs are posttranscriptionally regulated by microRNAs, a class of small noncoding RNAs. Insights in messenger RNA (mRNA) and microRNA (miRNA) regulatory interactions facilitate the understanding of fine-tuning of gene expression and might allow better estimation of protein synthesis. However, in silico predictions of mRNA-microRNA interactions do not take into account the specific transcriptomic status of the biological system and are biased by false positives. One possible solution to predict rather reliable mRNA-miRNA relations in the specific biological context is to integrate real mRNA and miRNA transcriptomic data as well as in silico target predictions. This chapter addresses the workflow and methods one can apply for expression profiling and the integrative analysis of mRNA and miRNA data, as well as how to analyze and interpret results, and how to build up models of posttranscriptional regulatory networks.

Modelling gene expression profiles related to prostate tumor progression using binary states

BACKGROUND

Cancer is a complex disease commonly characterized by the disrupted activity of several cancer-related genes such as oncogenes and tumor-suppressor genes. Previous studies suggest that the process of tumor progression to malignancy is dynamic and can be traced by changes in gene expression. Despite the enormous efforts made for differential expression detection and biomarker discovery, few methods have been designed to model the gene expression level to tumor stage during malignancy progression. Such models could help us understand the dynamics and simplify or reveal the complexity of tumor progression.

METHODS

We have modeled an on-off state of gene activation per sample then per stage to select gene expression profiles associated to tumor progression. The selection is guided by statistical significance of profiles based on random permutated datasets.

RESULTS

We show that our method identifies expected profiles corresponding to oncogenes and tumor suppressor genes in a prostate tumor progression dataset. Comparisons with other methods support our findings and indicate that a considerable proportion of significant profiles is not found by other statistical tests commonly used to detect differential expression between tumor stages nor found by other tailored methods. Ontology and pathway analysis concurred with these findings.

CONCLUSIONS

Results suggest that our methodology may be a valuable tool to study tumor malignancy progression, which might reveal novel cancer therapies.

Applications of peptide nucleic acids (PNAs) and locked nucleic acids (LNAs) in biosensor development

Nucleic acid biosensors have a growing number of applications in genetics and biomedicine. This contribution is a critical review of the current state of the art concerning the use of nucleic acid analogues, in particular peptide nucleic acids (PNA) and locked nucleic acids (LNA), for the development of high-performance affinity biosensors. Both PNA and LNA have outstanding affinity for natural nucleic acids, and the destabilizing effect of base mismatches in PNA- or LNA-containing heterodimers is much higher than in double-stranded DNA or RNA. Therefore, PNA- and LNA-based biosensors have unprecedented sensitivity and specificity, with special applicability in DNA genotyping. Herein, the most relevant PNA- and LNA-based biosensors are presented, and their advantages and their current limitations are discussed. Some of the reviewed technology, while promising, still needs to bridge the gap between experimental status and the harder reality of biotechnological or biomedical applications.

Robust non-linear differential equation models of gene expression evolution across Drosophila development

Background

This paper lies in the context of modeling the evolution of gene expression away from stationary states, for example in systems subject to external perturbations or during the development of an organism. We base our analysis on experimental data and proceed in a top-down approach, where we start from data on a system's transcriptome, and deduce rules and models from it without a priori knowledge. We focus here on a publicly available DNA microarray time series, representing the transcriptome of Drosophila across evolution from the embryonic to the adult stage.

Results

In the first step, genes were clustered on the basis of similarity of their expression profiles, measured by a translation-invariant and scale-invariant distance that proved appropriate for detecting transitions between development stages. Average profiles representing each cluster were computed and their time evolution was analyzed using coupled differential equations. A linear and several non-linear model structures involving a transcription and a degradation term were tested. The parameters were identified in three steps: determination of the strongest connections between genes, optimization of the parameters defining these connections, and elimination of the unnecessary parameters using various reduction schemes. Different solutions were compared on the basis of their abilities to reproduce the data, to keep realistic gene expression levels when extrapolated in time, to show the biologically expected robustness with respect to parameter variations, and to contain as few parameters as possible.

Conclusions

We showed that the linear model did very well in reproducing the data with few parameters, but was not sufficiently robust and yielded unrealistic values upon extrapolation in time. In contrast, the non-linear models all reached the latter two objectives, but some were unable to reproduce the data. A family of non-linear models, constructed from the exponential of linear combinations of expression levels, reached all the objectives. It defined networks with a mean number of connections equal to two, when restricted to the embryonic time series, and equal to five for the full time series. These networks were compared with experimental data about gene-transcription factor and protein-protein interactions. The non-uniqueness of the solutions was discussed in the context of plasticity and cluster versus single-gene networks.

Multi-scale genetic dynamic modelling II: application to synthetic biology: an algorithmic Markov chain based approach

We model in detail a simple synthetic genetic clock that was engineered in Atkinson et al. (Cell 113(5):597-607, 2003) using Escherichia coli as a host organism. Based on this engineered clock its theoretical description uses the modelling framework presented in Kirkilionis et al. (Theory Biosci. doi: 10.1007/s12064-011-0125-0 , 2011, this volume). The main goal of this accompanying article was to illustrate that parts of the modelling process can be algorithmically automatised once the model framework we called 'average dynamics' is accepted (Sbano and Kirkilionis, WMI Preprint 7/2007, 2008c; Kirkilionis and Sbano, Adv Complex Syst 13(3):293-326, 2010). The advantage of the 'average dynamics' framework is that system components (especially in genetics) can be easier represented in the model. In particular, if once discovered and characterised, specific molecular players together with their function can be incorporated. This means that, for example, the 'gene' concept becomes more clear, for example, in the way the genetic component would react under different regulatory conditions. Using the framework it has become a realistic aim to link mathematical modelling to novel tools of bioinformatics in the future, at least if the number of regulatory units can be estimated. This should hold in any case in synthetic environments due to the fact that the different synthetic genetic components are simply known (Elowitz and Leibler, Nature 403(6767):335-338, 2000; Gardner et al., Nature 403(6767):339-342, 2000; Hasty et al., Nature 420(6912):224-230, 2002). The paper illustrates therefore as a necessary first step how a detailed modelling of molecular interactions with known molecular components leads to a dynamic mathematical model that can be compared to experimental results on various levels or scales. The different genetic modules or components are represented in different detail by model variants. We explain how the framework can be used for investigating other more complex genetic systems in terms of regulation and feedback.

Keeping Track of Viruses

This chapter reviews methods of isolating, identifying, and tracking viruses with potential applications to microbial forensic investigations. Viruses are the most abundant biological entities on earth. These obligate parasites infect every form of life, from archaea and eubacteria to fungi, plants, and animals. Viruses play key roles in global ecology—they form a vast reservoir of genetic diversity, influence the composition and evolution of host populations, and affect the cycling of chemical compounds through the environment. Research has focused on the tiny fraction that causes disease in humans, domestic animals, and crops; sequencing surveys have suggested that the majority of viruses are completely unknown. The ability of viruses to jump species barriers, move between habitats, and circle the globe rapidly underscores the importance of continued vigilance for naturally emerging or deliberately engineered outbreaks. Viruses are extremely simple “life” forms without metabolic capacity, organelles, translational machinery, or autonomous replicative potential. Virus particles constitute a minimal set of components, primarily those required to deliver the genome to the target cell and initiate replication. Consequently, virus particles (or virions) are extremely small, most in the range of 20 to 200 nm in diameter. Virions are diverse not only in size but also in composition, morphology, and genome characteristics. Virus particles may be irregular in shape or possess a distinct symmetry, such as helical or icosahedral. Particles may be surrounded by a host-derived membrane, termed “enveloped,” or a tight protein shell, termed “nonenveloped.”

Nanoparticles in molecular diagnostics

The aim of this chapter is to provide an overview of the available and emerging molecular diagnostic methods that take advantage of the unique nanoscale properties of nanoparticles (NPs) to increase the sensitivity, detection capabilities, ease of operation, and portability of the biodetection assemblies. The focus will be on noble metal NPs, especially gold NPs, fluorescent NPs, especially quantum dots, and magnetic NPs, the three main players in the development of probes for biological sensing. The chapter is divided into four sections: a first section covering the unique physicochemical properties of NPs of relevance for their utilization in molecular diagnostics; the second section dedicated to applications of NPs in molecular diagnostics by nucleic acid detection; and the third section with major applications of NPs in the area of immunoassays. Finally, a concluding section highlights the most promising advances in the area and presents future perspectives.

Design of custom oligonucleotide microarrays for single species or interspecies hybrids using Array Oligo Selector

New technologies for DNA sequencing have made it feasible to determine the genome sequence of any organism of interest. This sequence is the resource required to create tools for downstream studies, including DNA microarrays. A number of vendors can produce DNA microarrays containing customer-specified sequences, allowing investigators to design and order arrays customized for any species of interest. Freely available, user-friendly computer programs are available for designing microarray probes. These design programs can be used to create probes that distinguish between two related genomes, allowing investigation of gene expression or gene representation in intra- or interspecies hybrids or in samples containing DNA from multiple species.

Bacterial artificial chromosomes as analytical basis for gene transcriptional machineries

Bacterial Artificial Chromosomes (BACs) had been minimal components of various genome-sequencing projects, constituting perfect analytical basis for functional genomics. Here we describe an enhancer screening strategy in which BAC clones that cover any genomic segments of interest are modified to harbor a reporter cassette by transposon tagging, then processed to carry selected combinations of gene regulatory modules by homologous recombination mediated systematic deletions. Such engineered BAC-reporter constructs in bacterial cells are ready for efficient transgenesis in mice to evaluate activities of gene regulatory modules intact or absent in the constructs. By utilizing the strategy, we could speedily identify a critical genomic fragment for spatio-temporally regulated expression of a mouse cadherin gene whose structure is extraordinarily huge and intricate. This BAC-based methodology would hence provide a novel screening platform for gene transcriptional machineries that dynamically fluctuate during development, pathogenesis and/or evolution.

Technical advances in rhinologic basic science research

Chronic rhinosinusitis (CRS) is the single most common self-reported chronic health condition in the United States and is estimated to affect 16% of the adult population annually. Despite the prevalence of this disease, there still exists an incomplete understanding of CRS pathophysiology. In this review, the authors highlight technological advances in rhinology: real-time polymerase chain reaction, epithelial cell culture, flow cytometry, genomics/single-nucleotide polymorphism detection, microarrays, and genetic/nongenetic animal models of sinusitis. The purpose of this review is to describe these methodologies and their contributions toward achieving a better understanding of CRS.

Nucleation capacity and presence of centrioles define a distinct category of centrosome abnormalities that induces multipolar mitoses in cancer cells

Analysis of centrosome number and structure has become one means of assessing the potential for aberrant chromosome segregation and aneuploidy in tumor cells. Centrosome amplification directly causes multipolar catastrophic mitoses in mouse embryonic fibroblasts (MEFs) deficient for the tumor suppressor genes Brca1 or Trp53. We observed supernumerary centrosomes in cell lines established from aneuploid, but not from diploid, colorectal carcinomas; however, multipolar mitoses were never observed. This discrepancy prompted us to thoroughly characterize the centrosome abnormalities in these and other cancer cell lines with respect to both structure and function. The most striking result was that supernumerary centrosomes in aneuploid colorectal cancer cell lines were unable to nucleate microtubules, despite the presence of gamma-tubulin, pericentrin, PLK1, and AURKA. Analysis by scanning electron microscopy revealed that these supernumerary structures are devoid of centrioles, a result significantly different from observations in aneuploid pancreatic cancer cell lines and in Trp53 or Brca1 deficient MEFs. Thus, multipolar mitoses are dependent upon the ability of extra gamma-tubulin containing structures to nucleate microtubules, and this correlated with the presence of centrioles. The assessment of centrosome function with respect to chromosome segregation must therefore take into consideration the presence of centrioles and the capacity to nucleate microtubules. The patterns and mechanisms of chromosomal aberrations in hematologic malignancies and solid tumors are fundamentally different. The former is characterized by specific chromosome translocations, whose consequence is the activation of oncogenes. Most carcinomas, however, reveal variations in the nuclear DNA content. The observed genomic imbalances and gross variations in chromosome number can result from unequal chromosome segregation during mitotic cell division. It is therefore fundamental to elucidate mechanisms involved in distribution of the genome to daughter cells. Prior to cell division, the centrosome organizes microtubules and the mitotic spindle. Deciphering the consequences of alterations in centrosome number, structure, and function is an important step towards understanding how a diploid genome is maintained. Although extra centrosomes have now been observed in carcinomas and were correlated with aneuploidy, a careful functional investigation of these structures and their role in generating chromosome imbalances may lead to the identification of distinct mechanistic pathways of genomic instability. Understanding these pathways will also be important in determining whether they are potential molecular targets of therapeutic intervention.

High-throughput crystallography for structural genomics

Protein X-ray crystallography recently celebrated its 50th anniversary. The structures of myoglobin and hemoglobin determined by Kendrew and Perutz provided the first glimpses into the complex protein architecture and chemistry. Since then, the field of structural molecular biology has experienced extraordinary progress and now more than 55000 protein structures have been deposited into the Protein Data Bank. In the past decade many advances in macromolecular crystallography have been driven by world-wide structural genomics efforts. This was made possible because of third-generation synchrotron sources, structure phasing approaches using anomalous signal, and cryo-crystallography. Complementary progress in molecular biology, proteomics, hardware and software for crystallographic data collection, structure determination and refinement, computer science, databases, robotics and automation improved and accelerated many processes. These advancements provide the robust foundation for structural molecular biology and assure strong contribution to science in the future. In this report we focus mainly on reviewing structural genomics high-throughput X-ray crystallography technologies and their impact.

Mechanisms and evolution of control logic in prokaryotic transcriptional regulation

A major part of organismal complexity and versatility of prokaryotes resides in their ability to fine-tune gene expression to adequately respond to internal and external stimuli. Evolution has been very innovative in creating intricate mechanisms by which different regulatory signals operate and interact at promoters to drive gene expression. The regulation of target gene expression by transcription factors (TFs) is governed by control logic brought about by the interaction of regulators with TF binding sites (TFBSs) in cis-regulatory regions. A factor that in large part determines the strength of the response of a target to a given TF is motif stringency, the extent to which the TFBS fits the optimal TFBS sequence for a given TF. Advances in high-throughput technologies and computational genomics allow reconstruction of transcriptional regulatory networks in silico. To optimize the prediction of transcriptional regulatory networks, i.e., to separate direct regulation from indirect regulation, a thorough understanding of the control logic underlying the regulation of gene expression is required. This review summarizes the state of the art of the elements that determine the functionality of TFBSs by focusing on the molecular biological mechanisms and evolutionary origins of cis-regulatory regions.

Dramatic increase in signal by integration of polymerase chain reaction and hybridization on surface of DNA microarray

The cumbersome process required for diagnosis by DNA microarray can be simplified by simple extraction of nucleic acid from cells and by integration of liquid-phase polymerase chain reaction (PCR) and hybridization on the surface of a microarray slide. An unexpected benefit was the large (five- to sixfold) increase in detection signal that also is translated into an increase in sensitivity and the confidence level of diagnosis. The large increase in the detection signal appears to be due to participation of PCR primers as well as to extension of the immobilized capture probes during the hybridization process. The reason for the large increase in signal is not clear in view of only one round of DNA synthesis during the hybridization step. The integrated process correctly identified various genotypes of human papillomavirus (HPV) in the infected clinical human cervical specimens with specificity and efficiency. The process described in this article saves labor, time, and cost and should be applicable for automation of diagnosis by DNA microarray.