Genomics, gene expression and DNA arrays

Detection of Alpha-Fetoprotein in Hepatocellular Carcinoma Patient Plasma with Graphene Field-Effect Transistor

The detection of alpha-fetoprotein (AFP) in plasma is important in the diagnosis of hepatocellular carcinoma (HCC) in humans. We developed a biosensor to detect AFP in HCC patient plasma and in a phosphate buffer saline (PBS) solution using a graphene field-effect transistor (G-FET). The G-FET was functionalized with 1-pyrenebutyric acid N-hydroxysuccinimide ester (PBASE) for immobilization of an anti-AFP antibody. AFP was detected by assessing the shift in the voltage of the Dirac point (ΔV_Dirac) after binding of AFP to the anti-AFP-immobilized G-FET channel surface. This anti-AFP-immobilized G-FET biosensor was able to detect AFP at a concentration of 0.1 ng mL⁻¹ in PBS, and the detection sensitivity was 16.91 mV. In HCC patient plasma, the biosensor was able to detect AFP at a concentration of 12.9 ng mL⁻¹, with a detection sensitivity of 5.68 mV. The sensitivity (ΔV_Dirac) depended on the concentration of AFP in either PBS or HCC patient plasma. These data suggest that G-FET biosensors could have practical applications in diagnostics.

Transcriptome profile analysis of leg muscle tissues between slow- and fast-growing chickens

Chicken is widely favored by consumers because of some unique features. The leg muscles occupy an important position in the market. However, the specific mechanism for regulating muscle growth speed is not clear. In this experiment, we used Jinghai yellow chickens with different body weights at 300 days as research subjects. The chickens were divided into fast- and slow-growing groups, and we collected leg muscles after slaughtering for use in RNA-seq. After comparing the two groups, 87 differentially expressed genes (DEGs) were identified (fold change ≥ 2 and FDR < 0.05). The fast-growing group had 42 up-regulated genes and 45 down-regulated genes among these DEGs compared to the slow-growing group. Six items were significantly enriched in the biological process: embryo development ending in birth or egg hatching, chordate embryonic development, embryonic skeletal system development, and embryo development as well as responses to ketones and the sulfur compound biosynthetic process. Two significantly enriched pathways were found in the KEGG pathway analysis (P-value < 0.05): the insulin signaling pathway and the adipocytokine signaling pathway. This study provides a theoretical basis for the molecular mechanism of chicken growth and for improving the production of Jinghai yellow chicken.

Parallel Algorithms for Inferring Gene Regulatory Networks: A Review

System biology problems such as whole-genome network construction from large-scale gene expression data are sophisticated and time-consuming. Therefore, using sequential algorithms are not feasible to obtain a solution in an acceptable amount of time. Today, by using massively parallel computing, it is possible to infer large-scale gene regulatory networks. Recently, establishing gene regulatory networks from large-scale datasets have drawn the noticeable attention of researchers in the field of parallel computing and system biology. In this paper, we attempt to provide a more detailed overview of the recent parallel algorithms for constructing gene regulatory networks. Firstly, fundamentals of gene regulatory networks inference and large-scale datasets challenges are given. Secondly, a detailed description of the four parallel frameworks and libraries including CUDA, OpenMP, MPI, and Hadoop is discussed. Thirdly, parallel algorithms are reviewed. Finally, some conclusions and guidelines for parallel reverse engineering are described.

Randomly distributed embedding making short-term high-dimensional data predictable

Future state prediction for nonlinear dynamical systems is a challenging task, particularly when only a few time series samples for high-dimensional variables are available from real-world systems. In this work, we propose a model-free framework, named randomly distributed embedding (RDE), to achieve accurate future state prediction based on short-term high-dimensional data. Specifically, from the observed data of high-dimensional variables, the RDE framework randomly generates a sufficient number of low-dimensional "nondelay embeddings" and maps each of them to a "delay embedding," which is constructed from the data of a to be predicted target variable. Any of these mappings can perform as a low-dimensional weak predictor for future state prediction, and all of such mappings generate a distribution of predicted future states. This distribution actually patches all pieces of association information from various embeddings unbiasedly or biasedly into the whole dynamics of the target variable, which after operated by appropriate estimation strategies, creates a stronger predictor for achieving prediction in a more reliable and robust form. Through applying the RDE framework to data from both representative models and real-world systems, we reveal that a high-dimension feature is no longer an obstacle but a source of information crucial to accurate prediction for short-term data, even under noise deterioration.

Gene Expression Is Not Random: Scaling, Long-Range Cross-Dependence, and Fractal Characteristics of Gene Regulatory Networks

Gene expression is a vital process through which cells react to the environment and express functional behavior. Understanding the dynamics of gene expression could prove crucial in unraveling the physical complexities involved in this process. Specifically, understanding the coherent complex structure of transcriptional dynamics is the goal of numerous computational studies aiming to study and finally control cellular processes. Here, we report the scaling properties of gene expression time series in Escherichia coli and Saccharomyces cerevisiae. Unlike previous studies, which report the fractal and long-range dependency of DNA structure, we investigate the individual gene expression dynamics as well as the cross-dependency between them in the context of gene regulatory network. Our results demonstrate that the gene expression time series display fractal and long-range dependence characteristics. In addition, the dynamics between genes and linked transcription factors in gene regulatory networks are also fractal and long-range cross-correlated. The cross-correlation exponents in gene regulatory networks are not unique. The distribution of the cross-correlation exponents of gene regulatory networks for several types of cells can be interpreted as a measure of the complexity of their functional behavior.

Simultaneous Imaging of Ribonucleic Acid and Hydrogen Sulfide in Living Systems with Distinct Fluorescence Signals Using a Single Fluorescent Probe

Ribonucleic acid (RNA) and hydrogen sulfide (H2S) are important genes and gaseous signal molecules in physiological environment. However, simultaneous investigation of distribution and interrelation of RNA and H2S in living systems is restricted by lack of functional molecular tools. To address this critical challenge, the development of TP-MIVC is described as the first paradigm of the probes that can concurrently report ribonucleic acid and hydrogen sulfide with distinct fluorescence signals in the cancer cells, zebrafish, and living animals. The advantageous features of the probe include high stability, low background fluorescence, high sensitivity, and two-photon imaging property. Significantly, regardless of normal mice or tumor mice, tumor tissues exhibit stronger fluorescence intensity than other organs. More interestingly, it is found that TP-MIVC is capable of distinguishing normal mice and tumor mice by in vivo imaging. This study may open a new pathway for distinguishing malignant and benign tumor by fluorescence imaging of RNA.

Adsorption Kinetics of Single-Stranded DNA on Functional Silica Surfaces and Its Influence Factors: An Evanescent-Wave Biosensor Study

Thorough understandings on the real-time kinetics involved in DNA adsorption on a solid surface is essential in various fields, such as in DNA hybridization studies, DNA extraction and purification, DNA-based biosensing, and gene-based medicine discovery. Herein, the real-time properties of single-stranded DNA (ssDNA) adsorption onto functional silica surfaces under various conditions were investigated using an evanescent wave optical biosensing platform. Results demonstrated that the driving force and adsorption mechanism of DNA were closely related to the kind of functional groups on the silica surfaces. The main driving forces of DNA adsorption onto hydroxyl- and protein-modified solid surfaces were the hydrophobic interaction, hydrogen bonding, and the interaction between DNA phosphate and functional groups on the silica surface, which strengthened with increased ionic strength. However, the electrostatic attraction between the negative charge of DNA and positive charge of the amino silica surface was likely the most important factor influencing DNA adsorption onto the amino surface. This influence can be reduced by increasing the ionic strength. Although low-ionic-strength Mg²⁺ provided a greater adsorption efficiency than high-ionic-strength Na⁺, the balance of ssDNA adsorption onto hydroxyl- and ovalbumin (OVA)-modified silica surfaces was achieved faster in the presence of Na⁺ than in the presence of Mg²⁺. DNA adsorption was also influenced significantly by pH, and the hydroxyl- and OVA-modified surfaces exhibited the strongest adsorption at pH 3.0, whereas DNA adsorption onto the amino surface increased with increased pH. DNA adsorption onto various functional surfaces could be perfectly fitted by second-order Langmuir models, indicating that the process was a single-molecular-layer adsorption.

Transcriptome based individualized therapy of refractory pediatric sarcomas: feasibility, tolerability and efficacy

Survival rates of pediatric sarcoma patients stagnated during the last two decades, especially in adolescents and young adults (AYAs). Targeted therapies offer new options in refractory cases. Gene expression profiling provides a robust method to characterize the transcriptome of each patient’s tumor and guide the choice of therapy.

Twenty patients with refractory pediatric sarcomas (age 8-35 years) were assessed with array profiling: ten had Ewing sarcoma, five osteosarcoma, and five soft tissue sarcoma. Overexpressed genes and deregulated pathways were identified as actionable targets and an individualized combination of targeted therapies was recommended. Disease status, survival, adverse events (AEs), and quality of life (QOL) were assessed in patients receiving targeted therapy (TT) and compared to patients without targeted therapy (non TT).

Actionable targets were identified in all analyzed biopsies. Targeted therapy was administered in nine patients, while eleven received no targeted therapy. No significant difference in risk factors between these two groups was detected. Overall survival (OS) and progression free survival (PFS) were significantly higher in the TT group (OS: P=0.0014, PFS: P=0.0011). Median OS was 8.83 versus 4.93 months and median PFS was 6.17 versus 1.6 months in TT versus non TT group, respectively. QOL did not differ at baseline as well as at four week intervals between the two groups. TT patients had less grade 1 AEs (P=0.009). The frequency of grade 2-4 AEs did not differ.

Overall, expression based targeted therapy is a feasible and likely beneficial approach in patients with refractory pediatric sarcomas that warrants further study.

Facile immobilization of DNA using an enzymatic his-tag mimic

Methods for immobilization of DNA on solid supports are in high demand. Herein, we present a generally applicable enzymatic method for the immobilization of DNA without any prior chemical derivatization. This strategy relies on the homopolymerization of the modified triphosphate dImTP by the TdT. The resulting enzymatic his-tag mimic ensures binding of DNA on Ni-NTA agarose. The usefulness of this method is highlighted by the immobilization of functional nucleic acids without impairing their specific activities.

A new regulator of cellulase and xylanase in the thermophilic fungus Myceliophthora thermophila strain ATCC 42464

Myceliophthora thermophila (ATCC 42464) is a thermophilic fungus that produces cellulolytic enzymes with high thermal stability. Unlike its mesophile counterparts, study on gene expression regulation of cellulolytic enzymes in M. thermophila is inadequate. This work identified the function of MHR1, a putative transcription regulator of cellulolytic enzymes in M. thermophila that was found through RNA-Seq based gene expression profile analysis. RNA interference was used to study the role of MHR1. A recombinant plasmid, pUC19-Ppdc-mhr1-Tpdc, which contained the RNAi sequence for mhr1 was constructed and transformed into M. thermophila. One of the transformants, MtR5, in which the RNA interference efficiency was the highest, was used for the following studies. In the mhr1-silenced strain MtR5, the filter paper hydrolyzing activity was 1.33-fold; β-1, 4-endoglucanase activity was 1.65-fold; and xylanase activity was 1.48-fold higher than those of the parental strain after induction, respectively, by wheat straw powder. qRT-PCR showed that gene expression of cbh1, cbh2, egl3 and xyr1 were 9.56-, 37.36-, 56.14- and 28.30-fold higher in MtR5 than in wild type, respectively. Our findings suggest that the transcription factor MHR1 of M. thermophila can repress cellulase and xylanase activities. Silenced mhr1 results in increased expression not only of the main cellulase genes, but also of the positive regulatory gene xyr1. This work is relevant to the development of M. thermophila as an industrial production host for cellulolytic and hemicellulolytic enzymes, which could be used to degrade a wide range of different biomass, converting lignocellulosic feedstock into sugar precursors for biofuels.

Single Platform for Gene and Protein Expression Analyses Using Luminescent Gold Nanoclusters

A single platform for gene and protein expression studies is proposed to pursue rapid diagnostics. A common method to synthesize gold (Au) nanoclusters on both DNA and protein template was developed using a benchtop device. The method of synthesis is rapid and versatile and can be applied to different classes of DNA/protein. Employing luminescent Au nanoclusters as the signal-generating agents, the device enables carrying out reverse transcriptase polymerase chain reaction and array-based analyses of multiple genes/proteins simultaneously using switchable holders and custom-designed software. The device and methods were applied to evaluate gene profiling related to apoptosis in HeLa cancer cells and further to analyze the protein expressions of glutathione-S-transferase (GST) and GST-tagged human granulocyte macrophage colony-stimulating factor (GST-hGMCSF) recombinant proteins purified from bacterial strains of BL21(DE3) Escherichia coli (E. coli). The device with user-friendly methods for diagnosis using the luminescence of Au nanoclusters offers potential use in disease diagnostics with a vision to extend health care facilities especially to remote geographical locations.

Microarray Missing Value Imputation: A Regularized Local Learning Method

Microarray experiments on gene expression inevitably generate missing values, which impedes further downstream biological analysis. Therefore, it is key to estimate the missing values accurately. Most of the existing imputation methods tend to suffer from the over-fitting problem. In this study, we propose two regularized local learning methods for microarray missing value imputation. Motivated by the grouping effect of L2 regularization, after selecting the target gene, we train an L2 Regularized Local Least Squares imputation model (RLLSimpute_L2) on the target gene and its neighbors to estimate the missing values of the target gene. Furthermore, RLLSimpute_L2 imputes the missing values in an ascending order based on the associated missing rate with each target gene. This contributes to fully utilizing the previously estimated values. Besides L2, we further explore L1 regularization and propose an L1 Regularized Local Least Squares imputation model (RLLSimpute_L1). To evaluate their effectiveness, we conducted extensive experimental studies on six benchmark datasets covering both time series and non-time series cases. Nine state-of-the-art imputation methods are compared with RLLSimpute_L2 and RLLSimpute_L1 in terms of three performance metrics. The comparative experimental results indicate that RLLSimpute_L2 outperforms its competitors by achieving smaller imputation errors and better structure preservation of differentially expressed genes.

Identification and Experimental Characterization of an Extremophilic Brine Pool Alcohol Dehydrogenase from Single Amplified Genomes

Because only 0.01% of prokaryotic genospecies can be cultured and in situ observations are often impracticable, culture-independent methods are required to understand microbial life and harness potential applications of microbes. Here, we report a methodology for the production of proteins with desired functions based on single amplified genomes (SAGs) from unculturable species. We use this method to resurrect an alcohol dehydrogenase (ADH/D1) from an uncharacterized halo-thermophilic archaeon collected from a brine pool at the bottom of the Red Sea. Our crystal structure of 5,6-dihydroxy NADPH-bound ADH/D1 combined with biochemical analyses reveal the molecular features of its halo-thermophily, its unique habitat adaptations, and its possible reaction mechanism for atypical oxygen activation. Our strategy offers a general guide for using SAGs as a source for scientific and industrial investigations of "microbial dark matter."

Homogeneous enzyme-free and entropy-driven isothermal fluorescent assay for nucleic acids based on a dual-signal output amplification strategy

The proposed fluorescent biosensor improves the reaction rate, has excellent analytical performance (LOD 15.6 fM) and shows outstanding recognition toward mismatched DNA strands. This approach provides a potential universal platform for the determination of other nucleic acids.

RNA-Seq and Expression Arrays: Selection Guidelines for Genome-Wide Expression Profiling

The development of genome-wide gene expression profiling technologies over the past two decades has produced great opportunity for researchers to explore the transcriptome and to better understand biological systems and their perturbation. In this chapter we provide an overview of microarray and massively parallel sequencing technologies and their application to gene expression analysis. We discuss factors that impact expression data generation and analysis that which should be considered in the application of these technology platforms. We further present the results of a simple illustration study to highlight performance similarities and differences in expression profiling of protein-coding mRNAs with each platform. Based on technical and analytical differences between the two platforms, reports in the literature comparing arrays and RNA-Seq for gene expression, and our own example study and experience, we provide recommendations for platform selection for gene expression studies.

Palladium-Catalyzed Synthesis of (E)-5-(3-Aminoallyl)-Uridine-5'-O-Triphosphates

This unit describes a simple, reliable, and efficient chemical method for the synthesis of 5-(3-aminoallyl)-2'-deoxyuridine-5'-O-triphosphate (AA-dUTP) and 5-(3-aminoallyl)-uridine-5'-O-triphosphate (AA-UTP), starting from the corresponding nucleoside triphosphate. The presented strategy involves regioselective iodination of nucleoside triphosphate using N-iodosuccinimide followed by the palladium-catalyzed Heck coupling with allylamine to provide the corresponding (E)-5-aminoallyl-uridine-5'-O-triphosphate in good yields. It is noteworthy that the protocol not only provides a high-purity product but also eliminates the use of toxic mercuric reagents. © 2017 by John Wiley & Sons, Inc.

Identification of Disease Critical Genes Using Collective Meta-heuristic Approaches: An Application to Preeclampsia

Identifying a small subset of disease critical genes out of a large size of microarray gene expression data is a challenge in computational life sciences. This paper has applied four meta-heuristic algorithms, namely, honey bee mating optimization (HBMO), harmony search (HS), differential evolution (DE) and genetic algorithm (basic version GA) to find disease critical genes of preeclampsia which affects women during gestation. Two hybrid algorithms, namely, HBMO-kNN and HS-kNN have been newly proposed here where kNN (k nearest neighbor classifier) is used for sample classification. Performances of these new approaches have been compared with other two hybrid algorithms, namely, DE-kNN and SGA-kNN. Three datasets of different sizes have been used. In a dataset, the set of genes found common in the output of each algorithm is considered here as disease critical genes. In different datasets, the percentage of classification or classification accuracy of meta-heuristic algorithms varied between 92.46 and 100%. HBMO-kNN has the best performance (99.64-100%) in almost all data sets. DE-kNN secures the second position (99.42-100%). Disease critical genes obtained here match with clinically revealed preeclampsia genes to a large extent.

Using airbrushes to pattern reagents for microarrays and paper-fluidic devices

We report using an airbrush to pattern a number of reagents, including small molecules, proteins, DNA, and conductive microparticles, onto a variety of mechanical substrates such as paper and glass. Airbrushing is more economical and easier to perform than many other patterning methods available (for example, inkjet printing). In this work, we investigated the controllable parameters that affect patterned line width and studied their mechanisms of action, and we provide examples of possible patterns. This airbrushing approach allowed us to pattern lines and dot arrays from hundreds of μm to tens of mm with length scales comparable to those of other patterning methods. Two applications, enzymatic assays and DNA hybridization, were chosen to demonstrate the compatibility of the method with biomolecules. This airbrushing method holds promise in making paper-based platforms less expensive and more accessible.

Synthesis of Gold Nanoparticle-Embedded Silver Cubic Mesh Nanostructures Using AgCl Nanocubes for Plasmonic Photocatalysis

A novel room-temperature aqueous synthesis for gold nanoparticle-embedded silver cubic mesh nanostructures using AgCl templates via a template-assisted coreduction method is developed. The cubic AgCl templates are coreduced in the presence of AuCl₄^- and Ag⁺ , resulting in the reduction of AuCl₄^- into gold nanoparticles on the outer region of AgCl templates, followed by the reduction of AgCl and Ag⁺ into silver cubic mesh nanostructures. Removal of the template clearly demonstrates the delicately designed silver mesh nanostructures embedded with gold nanoparticles. The synthetic mechanism, structural properties, and surface functionalization are spectroscopically investigated. The plasmonic photocatalysis of the cubic mesh nanostructures for the degradation of organic pollutants and removal of highly toxic metal ions is investigated; the photocatalytic activity of the cubic mesh nanostructures is superior to those of conventional TiO₂ catalysts and they are catalytically functional even in natural water, owing to their high surface area and excellent chemical stability. The synthetic development presented in this study can be exploited for the highly elaborate, yet, facile design of nanomaterials with outstanding properties.

Transcriptome Profiling in Human Diseases: New Advances and Perspectives

In the last decades, transcriptome profiling has been one of the most utilized approaches to investigate human diseases at the molecular level. Through expression studies, many molecular biomarkers and therapeutic targets have been found for several human pathologies. This number is continuously increasing thanks to total RNA sequencing. Indeed, this new technology has completely revolutionized transcriptome analysis allowing the quantification of gene expression levels and allele-specific expression in a single experiment, as well as to identify novel genes, splice isoforms, fusion transcripts, and to investigate the world of non-coding RNA at an unprecedented level. RNA sequencing has also been employed in important projects, like ENCODE (Encyclopedia of the regulatory elements) and TCGA (The Cancer Genome Atlas), to provide a snapshot of the transcriptome of dozens of cell lines and thousands of primary tumor specimens. Moreover, these studies have also paved the way to the development of data integration approaches in order to facilitate management and analysis of data and to identify novel disease markers and molecular targets to use in the clinics. In this scenario, several ongoing clinical trials utilize transcriptome profiling through RNA sequencing strategies as an important instrument in the diagnosis of numerous human pathologies.

Rinsing paired-agent model (RPAM) to quantify cell-surface receptor concentrations in topical staining applications of thick tissues

Conventional molecular assessment of tissue through histology, if adapted to fresh thicker samples, has the potential to enhance cancer detection in surgical margins and monitoring of 3D cell culture molecular environments. However, in thicker samples, substantial background staining is common despite repeated rinsing, which can significantly reduce image contrast. Recently, 'paired-agent' methods-which employ co-administration of a control (untargeted) imaging agent-have been applied to thick-sample staining applications to account for background staining. To date, these methods have included (1) a simple ratiometric method that is relatively insensitive to noise in the data but has accuracy that is dependent on the staining protocol and the characteristics of the sample; and (2) a complex paired-agent kinetic modeling method that is more accurate but is more noise-sensitive and requires a precise serial rinsing protocol. Here, a new simplified mathematical model-the rinsing paired-agent model (RPAM)-is derived and tested that offers a good balance between the previous models, is adaptable to arbitrary rinsing-imaging protocols, and does not require calibration of the imaging system. RPAM is evaluated against previous models and is validated by comparison to estimated concentrations of targeted biomarkers on the surface of 3D cell culture and tumor xenograft models. This work supports the use of RPAM as a preferable model to quantitatively analyze targeted biomarker concentrations in topically stained thick tissues, as it was found to match the accuracy of the complex paired-agent kinetic model while retaining the low noise-sensitivity characteristics of the ratiometric method.

Mass spectrometry as a tool for biomarkers searching in gynecological oncology

Tumors of the female reproductive tract are an important target for the development of diagnostic, prognostic and therapeutic strategies. Recent research has turned to proteomics based on mass spectrometry techniques, to achieve more effective diagnostic results. Mass spectrometry (MS) enables identification and quantification of multiple molecules simultaneously in a single experiment according to mass to charge ratio (m/z). Several proteomic strategies may be applied to establish the function of a particular protein/peptide or to identify a novel disease and specific biomarkers related to it. Therefore, MS could facilitate treatment in patients with tumors by helping researchers discover new biomarkers and narrowly targeted drugs. This review presents a comprehensive discussion of mass spectrometry as a tool for biomarkers searching that may lead to the discovery of easily available diagnostic tests in gynecological oncology with emphasis on clinical proteomics over the past decade. The article provides an insight into different MS based proteomic approaches.

Structured Sparse Subspace Clustering: A Joint Affinity Learning and Subspace Clustering Framework

Subspace clustering refers to the problem of segmenting data drawn from a union of subspaces. State-of-the-art approaches for solving this problem follow a two-stage approach. In the first step, an affinity matrix is learned from the data using sparse or low-rank minimization techniques. In the second step, the segmentation is found by applying spectral clustering to this affinity. While this approach has led to the state-of-the-art results in many applications, it is suboptimal, because it does not exploit the fact that the affinity and the segmentation depend on each other. In this paper, we propose a joint optimization framework - Structured Sparse Subspace Clustering (S³C) - for learning both the affinity and the segmentation. The proposed S³C framework is based on expressing each data point as a structured sparse linear combination of all other data points, where the structure is induced by a norm that depends on the unknown segmentation. Moreover, we extend the proposed S³C framework into Constrained S³C (CS³C) in which available partial side-information is incorporated into the stage of learning the affinity. We show that both the structured sparse representation and the segmentation can be found via a combination of an alternating direction method of multipliers with spectral clustering. Experiments on a synthetic data set, the Extended Yale B face data set, the Hopkins 155 motion segmentation database, and three cancer data sets demonstrate the effectiveness of our approach.

Digital triplex DNA assay based on plasmonic nanocrystals

A new analytical method has been developed to detect three kinds of DNA simultaneously based on magnetic beads and color-encoded plasmonic nanocrystals. Magnetic beads modified with capture DNA are employed to collect the specific target DNA, and color-encoded plasmonic nanocrystals are applied to signal the target through DNA hybridization. As a proof of concept, three types of representative metal nanocrystals of gold nanoparticle (AuNP), gold nanorod (AuNR), and gold/silver nanoparticle (Au/AgNP) were employed to signal three dissimilar virus-related protective antigen genes, Ebola virus (EV), Variola virus (VV), and Bacillus anthracis (BA), respectively. Detection limits of 0.5-3 fM were obtained showing the high sensitivity for DNA detection. The microscopic discrimination of the encoded nanoparticles allows simple, rapid, accurate, and cost-effective detection of multiple DNA molecules, indicative of the potential in practical applications. Graphical abstract Development of a novel digital triplex DNA assay based on single-countable color-encoded plasmonic nanocrystals.

A concept for holistic whole body MRI data analysis, Imiomics

Purpose

To present and evaluate a whole-body image analysis concept, Imiomics (imaging–omics) and an image registration method that enables Imiomics analyses by deforming all image data to a common coordinate system, so that the information in each voxel can be compared between persons or within a person over time and integrated with non-imaging data.

Methods

The presented image registration method utilizes relative elasticity constraints of different tissue obtained from whole-body water-fat MRI.

The registration method is evaluated by inverse consistency and Dice coefficients and the Imiomics concept is evaluated by example analyses of importance for metabolic research using non-imaging parameters where we know what to expect.

The example analyses include whole body imaging atlas creation, anomaly detection, and cross-sectional and longitudinal analysis.

Results

The image registration method evaluation on 128 subjects shows low inverse consistency errors and high Dice coefficients. Also, the statistical atlas with fat content intensity values shows low standard deviation values, indicating successful deformations to the common coordinate system.

The example analyses show expected associations and correlations which agree with explicit measurements, and thereby illustrate the usefulness of the proposed Imiomics concept.

Conclusions

The registration method is well-suited for Imiomics analyses, which enable analyses of relationships to non-imaging data, e.g. clinical data, in new types of holistic targeted and untargeted big-data analysis.

An Iterative Locally Auto-Weighted Least Squares Method for Microarray Missing Value Estimation

Microarray data often contain missing values which significantly affect subsequent analysis. Existing LLSimpute-based imputation methods for dealing with missing data have been shown to be generally efficient. However, all of the LLSimpute-based methods do not consider the different importance of different neighbors of the target gene in the missing value estimation process and treat all the neighbors equally. In this paper, a locally auto-weighted least squares imputation (LAW-LSimpute) method is proposed for missing value estimation, which can automatically weight the neighboring genes based on the importance of the genes. Then, an accelerating strategy is added to the LAW-LSimpute method in order to improve the convergence. Furthermore, an iterative missing value estimation framework of LAW-LSimpute (ILAW-LSimpute) is designed. Experimental results show that the ILAW-LSimpute method is able to reduce the estimation error.

Quantum material accompanied nonenzymatic cascade amplification for ultrasensitive photoelectrochemical DNA sensing

A novel cascade photoelectrochemical (PEC) signal amplification sensing strategy was designed and applied in target DNA detection by introducing quantum dots (QD) as the accompanying tag.

Genome Annotation

The dynamic structure and functions of genomes are being revealed simultaneously with the progress of technologies and researches in genomics. Evidence indicating genome regional characteristics (genome annotations in a broad sense) provide the basis for further analyses. Target listing and screening can be effectively performed in silico using such data. Here, we describe steps to obtain publicly available genome annotations or to construct new annotations based on your own analyses, as well as an overview of the types of available genome annotations and corresponding resources.

Real-time, label-free characterization of oligosaccharide-binding proteins using carbohydrate microarrays and an ellipsometry-based biosensor

Carbohydrates present on cell surfaces mediate cell behavior through interactions with other biomolecules. Due to their structural complexity, diversity, and heterogeneity, it is difficult to fully characterize a variety of carbohydrates and their binding partners. As a result, novel technologies for glycomics applications have been developed, including carbohydrate microarrays and label-free detection methods. In this paper, we report using the combination of oligosaccharide microarrays and the label-free oblique-incidence reflectivity difference (OI-RD) microscopy for real-time characterization of oligosaccharide binding proteins. Aminated human milk oligosaccharides were immobilized on epoxy-coated glass substrates as microarrays for reactions with Family 1 of solute binding proteins from Bifidobacterium longum subsp. infantis (B. infantis). Binding affinities of these protein-oligosaccharide interactions showed preferences of Family 1 of solute binding proteins to host glycans, which helps in characterizing the complex process of human milk oligosaccharides foraging by B. infantis.

Genomic profiling of gynecologic cancers and implications for clinical practice

PURPOSE OF REVIEW

This article summarizes advances in the application of next-generation sequencing (NGS) to the personalized treatment of gynecologic malignancies.

RECENT FINDINGS

Many recurrent genomic alterations (GA) in gynecologic malignancies have been identified by studies applying NGS to tumor tissue, which can provide insights into tumor biology, diagnostic or prognostic information, and potential targeted therapy options. NGS can be used to assay single genes, portions of multiple genes ("hot-spot" panels), or the complete coding sequence of a broad range of cancer-associated genes [i.e. comprehensive genomic profiling (CGP)]. CGP of a patient's tumor reveals to practitioners clinically relevant GA (CRGA) and associated biomarker-matched treatments, with a goal of improving therapeutic response while limiting cumulative chemotherapeutic toxicities. Although the use of precision medicine for gynecologic cancers holds much promise, the data detailing impact on survival and quality of life is still accumulating, lagging behind other areas of oncology. Enrolling gynecologic oncology patients in genotype-matched trials remains challenging and highlights the need for more molecular-based basket trials for reproductive tract malignancies.

SUMMARY

Identification of molecular subsets with distinct clinical attributes, prognostic significance, and targeted therapy directed options is now feasible in clinical gynecologic oncology practice.

Stimuli-Directed Helical Chirality Inversion and Bio-Applications

Helical structure is a sophisticated ubiquitous motif found in nature, in artificial polymers, and in supramolecular assemblies from microscopic to macroscopic points of view. Significant progress has been made in the synthesis and structural elucidation of helical polymers, nevertheless, a new direction for helical polymeric materials, is how to design smart systems with controllable helical chirality, and further use them to develop chiral functional materials and promote their applications in biology, biochemistry, medicine, and nanotechnology fields. This review summarizes the recent progress in the development of high-performance systems with tunable helical chirality on receiving external stimuli and discusses advances in their applications as drug delivery vesicles, sensors, molecular switches, and liquid crystals. Challenges and opportunities in this emerging area are also presented in the conclusion.

Animal breeding in the (post-) genomic era

One of the benefits of the genomics revolution for animal production will be knowledge of genes that can be used to select more profitable livestock. Although it is possible to use genetic markers linked to genes of economic importance, tests for the genes themselves will be much more successful. Consequently finding genes of economic importance to livestock will be a major research aim for the future. Most traits of economic importance are quantitative traits affected by many genes. Mutations at many genes (e.g. 500) and at many positions within a gene (e.g. 1000 coding and non-coding bases) can affect a typical quantitative trait. The effect of these mutations on phenotype is usually small (e.g. 0·1 standard deviation) but occasionally large. Many mutations are lost from the population through genetic drift and selection, so that polymorphisms exist at only a subset of the relevant genes (e.g. 100 genes). Finding these genes, that have relatively small effects, is more difficult than finding genes for a classical Mendellian trait but, as the genomic tools become more powerful, it is becoming feasible and some successes have already occurred. The standard approach is to map a quantitative trait loci (QTL) to a chromosome region using linkage and linkage disequilibrium. Then test polymorphisms in positional candidate genes for an effect on the trait. Tools such as genomic sequence, EST collections and comparative maps make this approach feasible. Candidate genes can be selected based on functional data such as gene expression obtained from microarrays. At present the gain in rate of genetic improvement from use of DNA-based tests for QTL is small, because selection without them is already quite accurate, not enough QTL have been identified and genotyping is too expensive. However, in the future, with many QTL identified and inexpensive genotyping combined with decreased generation intervals, large gains are possible.

A global learning with local preservation method for microarray data imputation

Microarray data suffer from missing values for various reasons, including insufficient resolution, image noise, and experimental errors. Because missing values can hinder downstream analysis steps that require complete data as input, it is crucial to be able to estimate the missing values. In this study, we propose a Global Learning with Local Preservation method (GL2P) for imputation of missing values in microarray data. GL2P consists of two components: a local similarity measurement module and a global weighted imputation module. The former uses a local structure preservation scheme to exploit as much information as possible from the observable data, and the latter is responsible for estimating the missing values of a target gene by considering all of its neighbors rather than a subset of them. Furthermore, GL2P imputes the missing values in ascending order according to the rate of missing data for each target gene to fully utilize previously estimated values. To validate the proposed method, we conducted extensive experiments on six benchmarked microarray datasets. We compared GL2P with eight state-of-the-art imputation methods in terms of four performance metrics. The experimental results indicate that GL2P outperforms its competitors in terms of imputation accuracy and better preserves the structure of differentially expressed genes. In addition, GL2P is less sensitive to the number of neighbors than other local learning-based imputation methods.

Edwin

Characterization of gene expression is a central tenet of the synthetic biology design cycle. Sometimes it requires high-throughput approaches that allow quantification of the gene expression of different elements in diverse conditions. Recently, several large-scale studies have highlighted the importance of posttranscriptional regulation mechanisms and their impact on correlations between mRNA and protein abundance. Here, we introduce Edwin, a robotic workstation that enables the automated propagation of microbial cells and the dynamic characterization of gene expression. We developed an automated procedure that integrates customized RNA extraction and analysis into the typical high-throughput characterization of reporter gene expression. To test the system, we engineered Escherichia coli strains carrying different promoter/ gfp fusions. We validated Edwin's abilities: (1) preparation of custom cultures of microbial cells and (2) dynamic quantification of fluorescence signal and bacterial growth and simultaneous RNA extraction and analysis at different time points. We confirmed that RNA obtained during this automated process was suitable for use in qPCR analysis. Our results established that Edwin is a powerful platform for the automated analysis of microbial gene expression at the protein and RNA level. This platform could be used in a high-throughput manner to characterize not only natural regulatory elements but also synthetic ones.

Oligonucleotide-based label-free detection with optical microresonators: strategies and challenges

This review targets diversified oligonucleotide-based biodetection techniques, focusing on the use of microresonators of whispering gallery mode (WGM) type as optical biosensors mostly integrated with lab-on-a-chip systems. On-chip and microfluidics combined devices along with optical microresonators provide rapid, robust, reproducible and multiplexed biodetection abilities in considerably small volumes. We present a detailed overview of the studies conducted so far, including biodetection of various oligonucleotide biomarkers as well as deoxyribonucleic acids (DNAs), ribonucleic acids (RNAs) and proteins. We particularly advert to chemical surface modifications for specific and selective biosensing.

Renal epithelial gene expression profile and bismuth-induced resistance against cisplatin nephrotoxicity

Nephrotoxicity is the most important dose-limiting factor in cisplatin based anti-neoplastic treatment. Pretreatment with bismuth salts, used as pharmaceuticals to treat gastric disorders, has been demonstrated to reduce cisplatin-induced renal cell death in clinical settings and during in vivo and in vitro animal experiments. To investigate the genomic basis of this renoprotective effect, we exposed NRK-52E cells, a cell line of rat proximal tubular epithelial origin, to 33 mM Bi3for 12 hours, which made them resistant to cisplatin-induced apoptosis. Differentially expressed genes in treated and untreated NRK-52E cells were detected by subtraction PCR and microarray techniques. Genes found to be down regulated (0.17/0.31-times) were cytochrome c oxidase subunit I, BAR (an apoptosis regulator), heat-shock protein 70-like protein, and three proteins belonging to the translation machinery (ribosomal proteins S7 and L17, and S1, a member of the elongation factor 1-alpha family). The only up-regulated gene was glutathione Stransferase subunit 3A (1.89-times). Guided by the expression levels of these genes, it may be possible to improve renoprotective treatments during anti-neoplastic therapies.

Expressed miRNAs target feather related mRNAs involved in cell signaling, cell adhesion and structure during chicken epidermal development

MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression at the post-transcriptional level. Previous studies have shown that miRNA regulation contributes to a diverse set of processes including cellular differentiation and morphogenesis which leads to the creation of different cell types in multicellular organisms and is thus key to animal development. Feathers are one of the most distinctive features of extant birds and are important for multiple functions including flight, thermal regulation, and sexual selection. However, the role of miRNAs in feather development has been woefully understudied despite the identification of cell signaling pathways, cell adhesion molecules and structural genes involved in feather development. In this study, we performed a microarray experiment comparing the expression of miRNAs and mRNAs among three embryonic stages of development and two tissues (scutate scale and feather) of the chicken. We combined this expression data with miRNA target prediction tools and a curated list of feather related genes to produce a set of 19 miRNA-mRNA duplexes. These targeted mRNAs have been previously identified as important cell signaling and cell adhesion genes as well as structural genes involved in feather and scale morphogenesis. Interestingly, the miRNA target site of the cell signaling pathway gene, Aldehyde Dehydrogenase 1 Family, Member A3 (ALDH1A3), is unique to birds indicating a novel role in Aves. The identified miRNA target site of the cell adhesion gene, Tenascin C (TNC), is only found in specific chicken TNC splice variants that are differentially expressed in developing scutate scale and feather tissue indicating an important role of miRNA regulation in epidermal differentiation. Additionally, we found that β-keratins, a major structural component of avian and reptilian epidermal appendages, are targeted by multiple miRNA genes. In conclusion, our work provides quantitative expression data on miRNAs and mRNAs during feather and scale development and has produced a highly diverse, but manageable list of miRNA-mRNA duplexes for future validation experiments.