Critical evaluation of the reliability of DNA methylation probes on the Illumina MethylationEPIC v1.0 BeadChip microarrays

DNA methylation (DNAm) plays a crucial role in a number of complex diseases. However, the reliability of DNAm levels measured using Illumina arrays varies across different probes. Previous research primarily assessed probe reliability by comparing duplicate samples between the 450k-450k or 450k-EPIC platforms, with limited investigations on Illumina EPIC v1.0 arrays. We conducted a comprehensive assessment of the EPIC v1.0 array probe reliability using 69 blood DNA samples, each measured twice, generated by the Alzheimer's Disease Neuroimaging Initiative study. We observed higher reliability in probes with average methylation beta values of 0.2 to 0.8, and lower reliability in type I probes or those within the promoter and CpG island regions. Importantly, we found that probe reliability has significant implications in the analyses of Epigenome-wide Association Studies (EWAS). Higher reliability is associated with more consistent effect sizes in different studies, the identification of differentially methylated regions (DMRs) and methylation quantitative trait locus (mQTLs), and significant correlations with downstream gene expression. Moreover, blood DNAm measurements obtained from probes with higher reliability are more likely to show concordance with brain DNAm measurements. Our findings, which provide crucial reliability information for probes on the EPIC v1.0 array, will serve as a valuable resource for future DNAm studies.

Ultrasimple size encoded microfluidic chip for rapid simultaneous multiplex detection of DNA sequences

Simultaneous multiplexed analysis can provide comprehensive information for disease diagnosis. However, the current multiplex methods rely on sophisticated barcode technology, which hinders its wider application. In this study, an ultrasimple size encoding method is proposed for multiplex detection using a wedge-shaped microfluidic chip. Driving by negative pressure, microparticles are naturally arranged in distinct stripes based on their sizes within the chip. This size encoding method demonstrates a high level of precision, allowing for accuracy in distinguishing 3-5 sizes of microparticles with a remarkable accuracy rate of up to 99%, even the microparticles with a size difference as small as 0.5 μm. The entire size encoding process is completed in less than 5 min, making it ultrasimple, reliable, and easy to operate. To evaluate the function of this size encoding microfluidic chip, three commonly co-infectious viruses' nucleic acid sequences (including complementary DNA sequences of HIV and HCV, and DNA sequence of HBV) are employed for multiplex detection. Results indicate that all three DNA sequences can be sensitively detected without any cross-interference. This size-encoding microfluidic chip-based multiplex detection method is simple, rapid, and high-resolution, its successful application in serum samples renders it highly promising for potential clinical promotion.

Low-input and single-cell methods for Infinium DNA methylation BeadChips

The Infinium BeadChip is the most widely used DNA methylome assay technology for population-scale epigenome profiling. However, the standard workflow requires over 200 ng of input DNA, hindering its application to small cell-number samples, such as primordial germ cells. We developed experimental and analysis workflows to extend this technology to suboptimal input DNA conditions, including ultra-low input down to single cells. DNA preamplification significantly enhanced detection rates to over 50% in five-cell samples and ∼25% in single cells. Enzymatic conversion also substantially improved data quality. Computationally, we developed a method to model the background signal's influence on the DNA methylation level readings. The modified detection P-value calculation achieved higher sensitivities for low-input datasets and was validated in over 100 000 public diverse methylome profiles. We employed the optimized workflow to query the demethylation dynamics in mouse primordial germ cells available at low cell numbers. Our data revealed nuanced chromatin states, sex disparities, and the role of DNA methylation in transposable element regulation during germ cell development. Collectively, we present comprehensive experimental and computational solutions to extend this widely used methylation assay technology to applications with limited DNA.

Effect of genotyping errors on linkage map construction based on repeated chip analysis of two recombinant inbred line populations in wheat (Triticum aestivum L.)

Linkage maps are essential for genetic mapping of phenotypic traits, gene map-based cloning, and marker-assisted selection in breeding applications. Construction of a high-quality saturated map requires high-quality genotypic data on a large number of molecular markers. Errors in genotyping cannot be completely avoided, no matter what platform is used. When genotyping error reaches a threshold level, it will seriously affect the accuracy of the constructed map and the reliability of consequent genetic studies. In this study, repeated genotyping of two recombinant inbred line (RIL) populations derived from crosses Yangxiaomai × Zhongyou 9507 and Jingshuang 16 × Bainong 64 was used to investigate the effect of genotyping errors on linkage map construction. Inconsistent data points between the two replications were regarded as genotyping errors, which were classified into three types. Genotyping errors were treated as missing values, and therefore the non-erroneous data set was generated. Firstly, linkage maps were constructed using the two replicates as well as the non-erroneous data set. Secondly, error correction methods implemented in software packages QTL IciMapping (EC) and Genotype-Corrector (GC) were applied to the two replicates. Linkage maps were therefore constructed based on the corrected genotypes and then compared with those from the non-erroneous data set. Simulation study was performed by considering different levels of genotyping errors to investigate the impact of errors and the accuracy of error correction methods. Results indicated that map length and marker order differed among the two replicates and the non-erroneous data sets in both RIL populations. For both actual and simulated populations, map length was expanded as the increase in error rate, and the correlation coefficient between linkage and physical maps became lower. Map quality can be improved by repeated genotyping and error correction algorithm. When it is impossible to genotype the whole mapping population repeatedly, 30% would be recommended in repeated genotyping. The EC method had a much lower false positive rate than did the GC method under different error rates. This study systematically expounded the impact of genotyping errors on linkage analysis, providing potential guidelines for improving the accuracy of linkage maps in the presence of genotyping errors.

MESBC: A novel mutually exclusive spectral biclustering method for cancer subtyping

Many soft biclustering algorithms have been developed and applied to various biological and biomedical data analyses. However, few mutually exclusive (hard) biclustering algorithms have been proposed, which could better identify disease or molecular subtypes with survival significance based on genomic or transcriptomic data. In this study, we developed a novel mutually exclusive spectral biclustering (MESBC) algorithm based on spectral method to detect mutually exclusive biclusters. MESBC simultaneously detects relevant features (genes) and corresponding conditions (patients) subgroups and, therefore, automatically uses the signature features for each subtype to perform the clustering. Extensive simulations revealed that MESBC provided superior accuracy in detecting pre-specified biclusters compared with the non-negative matrix factorization (NMF) and Dhillon's algorithm, particularly in very noisy data. Further analysis of the algorithm on real datasets obtained from the TCGA database showed that MESBC provided more accurate (i.e., smaller p-value) overall survival prediction in patients with lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) cancers when compared to the existing, gold-standard subtypes for lung cancers (integrative clustering). Furthermore, MESBC detected several genes with significant prognostic value in both LUAD and LUSC patients. External validation on an independent, unseen GEO dataset of LUAD showed that MESBC-derived clusters based on TCGA data still exhibited clear biclustering patterns and consistent, outstanding prognostic predictability, demonstrating robust generalizability of MESBC. Therefore, MESBC could potentially be used as a risk stratification tool to optimize the treatment for the patient, improve the selection of patients for clinical trials, and contribute to the development of novel therapeutic agents.

A rapid and low-cost platform for detection of bacterial based on microchamber PCR microfluidic chip

Polymerase chain reaction (PCR) has been considered as the gold standard for detecting nucleic acids. The simple PCR system is of great significance for medical applications in remote areas, especially for the developing countries. Herein, we proposed a low-cost self-assembled platform for microchamber PCR. The working principle is rotating the chamber PCR microfluidic chip between two heaters with fixed temperature to solve the problem of low temperature variation rate. The system consists of two temperature controllers, a screw slide rail, a chamber array microfluidic chip and a self-built software. Such a system can be constructed at a cost of about US$60. The micro chamber PCR can be finished by rotating the microfluidic chip between two heaters with fixed temperature. Results demonstrated that the sensitivity of the temperature controller is 0.1℃. The relative error of the duration for the microfluidic chip was 0.02 s. Finally, we successfully finished amplification of the target gene of Porphyromonas gingivalis in the chamber PCR microfluidic chip within 35 min and on-site detection of its PCR products by fluorescence. The chip consisted of 3200 cylindrical chambers. The volume of reagent in each volume is as low as 0.628 nL. This work provides an effective method to reduce the amplification time required for micro chamber PCR.

Population characteristic exploitation-based multi-orientation multi-objective gene selection for microarray data classification

Gene selection is a process of selecting discriminative genes from microarray data that helps to diagnose and classify cancer samples effectively. Swarm intelligence evolution-based gene selection algorithms can never circumvent the problem that the population is prone to local optima in the process of gene selection. To tackle this challenge, previous research has focused primarily on two aspects: mitigating premature convergence to local optima and escaping from local optima. In contrast to these strategies, this paper introduces a novel perspective by adopting reverse thinking, where the issue of local optima is seen as an opportunity rather than an obstacle. Building on this foundation, we propose MOMOGS-PCE, a novel gene selection approach that effectively exploits the advantageous characteristics of populations trapped in local optima to uncover global optimal solutions. Specifically, MOMOGS-PCE employs a novel population initialization strategy, which involves the initialization of multiple populations that explore diverse orientations to foster distinct population characteristics. The subsequent step involved the utilization of an enhanced NSGA-II algorithm to amplify the advantageous characteristics exhibited by the population. Finally, a novel exchange strategy is proposed to facilitate the transfer of characteristics between populations that have reached near maturity in evolution, thereby promoting further population evolution and enhancing the search for more optimal gene subsets. The experimental results demonstrated that MOMOGS-PCE exhibited significant advantages in comprehensive indicators compared with six competitive multi-objective gene selection algorithms. It is confirmed that the "reverse-thinking" approach not only avoids local optima but also leverages it to uncover superior gene subsets for cancer diagnosis.

Biporous silica nanostructure-induced nanovortex in microfluidics for nucleic acid enrichment, isolation, and PCR-free detection

Efficient pathogen enrichment and nucleic acid isolation are critical for accurate and sensitive diagnosis of infectious diseases, especially those with low pathogen levels. Our study introduces a biporous silica nanofilms-embedded sample preparation chip for pathogen and nucleic acid enrichment/isolation. This chip features unique biporous nanostructures comprising large and small pore layers. Computational simulations confirm that these nanostructures enhance the surface area and promote the formation of nanovortex, resulting in improved capture efficiency. Notably, the chip demonstrates a 100-fold lower limit of detection compared to conventional methods used for nucleic acid detection. Clinical validations using patient samples corroborate the superior sensitivity of the chip when combined with the luminescence resonance energy transfer assay. The enhanced sample preparation efficiency of the chip, along with the facile and straightforward synthesis of the biporous nanostructures, offers a promising solution for polymer chain reaction-free detection of nucleic acids.

A Gene Selection Method Considering Measurement Errors

The analysis of gene expression data has made significant contributions to understanding disease mechanisms and developing new drugs and therapies. In such analysis, gene selection is often required for identifying informative and relevant genes and removing redundant and irrelevant ones. However, this is not an easy task as gene expression data have inherent challenges such as ultra-high dimensionality, biological noise, and measurement errors. This study focuses on the measurement errors in gene selection problems. Typically, high-throughput experiments have their own intrinsic measurement errors, which can result in an increase of falsely discovered genes. To alleviate this problem, this study proposes a gene selection method that takes into account measurement errors using generalized liner measurement error models. The method consists of iterative filtering and selection steps until convergence, leading to fewer false positives and providing stable results under measurement errors. The performance of the proposed method is demonstrated through simulation studies and applied to a lung cancer data set.

Identification of Culprit Genes for Different Diseases by Analyzing Microarray Data

The identification of disease-causing genes is the first and most important step toward understanding the biological mechanisms underlying a disease. Microarray analysis is one such powerful method that is widely used to identify genes that are expressed differently in two or more conditions (disease vs. normal). Because of its large library of statistical R packages and user-friendly interface, the R programming language provides a platform for microarray analysis. In this chapter, we will go over how to identify disease-causing culprit genes from the raw microarray data, using various packages of R programming. The pipeline overviews the steps in microarray analysis, such as data pre-processing, normalization, and statistical analysis using visualization techniques such as heatmaps, box plots, and so on. To better understand the function of the altered genes, gene ontology and pathway analysis are performed.

A Cre-dependent massively parallel reporter assay allows for cell-type specific assessment of the functional effects of non-coding elements in vivo

The function of regulatory elements is highly dependent on the cellular context, and thus for understanding the function of elements associated with psychiatric diseases these would ideally be studied in neurons in a living brain. Massively Parallel Reporter Assays (MPRAs) are molecular genetic tools that enable functional screening of hundreds of predefined sequences in a single experiment. These assays have not yet been adapted to query specific cell types in vivo in a complex tissue like the mouse brain. Here, using a test-case 3'UTR MPRA library with genomic elements containing variants from autism patients, we developed a method to achieve reproducible measurements of element effects in vivo in a cell type-specific manner, using excitatory cortical neurons and striatal medium spiny neurons as test cases. This targeted technique should enable robust, functional annotation of genetic elements in the cellular contexts most relevant to psychiatric disease.

Roastgsa: a comparison of rotation-based scores for gene set enrichment analysis

BACKGROUND

Gene-wise differential expression is usually the first major step in the statistical analysis of high-throughput data obtained from techniques such as microarrays or RNA-sequencing. The analysis at gene level is often complemented by interrogating the data in a broader biological context that considers as unit of measure groups of genes that may have a common function or biological trait. Among the vast number of publications about gene set analysis (GSA), the rotation test for gene set analysis, also referred to as roast, is a general sample randomization approach that maintains the integrity of the intra-gene set correlation structure in defining the null distribution of the test.

RESULTS

We present roastgsa, an R package that contains several enrichment score functions that feed the roast algorithm for hypothesis testing. These implemented methods are evaluated using both simulated and benchmarking data in microarray and RNA-seq datasets. We find that computationally intensive measures based on Kolmogorov-Smirnov (KS) statistics fail to improve the rates of simpler measures of GSA like mean and maxmean scores. We also show the importance of accounting for the gene linear dependence structure of the testing set, which is linked to the loss of effective signature size. Complete graphical representation of the results, including an approximation for the effective signature size, can be obtained as part of the roastgsa output.

CONCLUSIONS

We encourage the usage of the absmean (non-directional), mean (directional) and maxmean (directional) scores for roast GSA analysis as these are simple measures of enrichment that have presented dominant results in all provided analyses in comparison to the more complex KS measures.

Dipodal Silanes Greatly Stabilize Glass Surface Functionalization for DNA Microarray Synthesis and High-Throughput Biological Assays

Glass is by far the most common substrate for biomolecular arrays, including high-throughput sequencing flow cells and microarrays. The native glass hydroxyl surface is modified by using silane chemistry to provide appropriate functional groups and reactivities for either in situ synthesis or surface immobilization of biologically or chemically synthesized biomolecules. These arrays, typically of oligonucleotides or peptides, are then subjected to long incubation times in warm aqueous buffers prior to fluorescence readout. Under these conditions, the siloxy bonds to the glass are susceptible to hydrolysis, resulting in significant loss of biomolecules and concomitant loss of signal from the assay. Here, we demonstrate that functionalization of glass surfaces with dipodal silanes results in greatly improved stability compared to equivalent functionalization with standard monopodal silanes. Using photolithographic in situ synthesis of DNA, we show that dipodal silanes are compatible with phosphoramidite chemistry and that hybridization performed on the resulting arrays provides greatly improved signal and signal-to-noise ratios compared with surfaces functionalized with monopodal silanes.

A New Binary Biclustering Algorithm Based on Weight Adjacency Difference Matrix for Analyzing Gene Expression Data

Biclustering algorithms are essential for processing gene expression data. However, to process the dataset, most biclustering algorithms require preprocessing the data matrix into a binary matrix. Regrettably, this type of preprocessing may introduce noise or cause information loss in the binary matrix, which would reduce the biclustering algorithm's ability to effectively obtain the optimal biclusters. In this paper, we propose a new preprocessing method named Mean-Standard Deviation (MSD) to resolve the problem. Additionally, we introduce a new biclustering algorithm called Weight Adjacency Difference Matrix Binary Biclustering (W-AMBB) to effectively process datasets containing overlapping biclusters. The basic idea is to create a weighted adjacency difference matrix by applying weights to a binary matrix that is derived from the data matrix. This allows us to identify genes with significant associations in sample data by efficiently identifying similar genes that respond to specific conditions. Furthermore, the performance of the W-AMBB algorithm was tested on both synthetic and real datasets and compared with other classical biclustering methods. The experiment results demonstrate that the W-AMBB algorithm is significantly more robust than the compared biclustering methods on the synthetic dataset. Additionally, the results of the GO enrichment analysis show that the W-AMBB method possesses biological significance on real datasets.

Identification of monotonically expressed long non-coding RNA signatures for breast cancer using variational autoencoders

As breast cancer is a multistage progression disease resulting from a genetic sequence of mutations, understanding the genes whose expression values increase or decrease monotonically across pathologic stages can provide insightful clues about how breast cancer initiates and advances. Utilizing variational autoencoder (VAE) networks in conjunction with traditional statistical testing, we successfully ascertain long non-coding RNAs (lncRNAs) that exhibit monotonically differential expression values in breast cancer. Subsequently, we validate that the identified lncRNAs really present monotonically changed patterns. The proposed procedure identified 248 monotonically decreasing expressed and 115 increasing expressed lncRNAs. They correspond to a total of 65 and 33 genes respectively, which possess unique known gene symbols. Some of them are associated with breast cancer, as suggested by previous studies. Furthermore, enriched pathways by the target mRNAs of these identified lncRNAs include the Wnt signaling pathway, human papillomavirus (HPV) infection, and Rap 1 signaling pathway, which have been shown to play crucial roles in the initiation and development of breast cancer. Additionally, we trained a VAE model using the entire dataset. To assess the effectiveness of the identified lncRNAs, a microarray dataset was employed as the test set. The results obtained from this evaluation were deemed satisfactory. In conclusion, further experimental validation of these lncRNAs with a large-sized study is warranted, and the proposed procedure is highly recommended.

A systematic evaluation of normalization methods and probe replicability using infinium EPIC methylation data

BACKGROUND

The Infinium EPIC array measures the methylation status of > 850,000 CpG sites. The EPIC BeadChip uses a two-array design: Infinium Type I and Type II probes. These probe types exhibit different technical characteristics which may confound analyses. Numerous normalization and pre-processing methods have been developed to reduce probe type bias as well as other issues such as background and dye bias.

METHODS

This study evaluates the performance of various normalization methods using 16 replicated samples and three metrics: absolute beta-value difference, overlap of non-replicated CpGs between replicate pairs, and effect on beta-value distributions. Additionally, we carried out Pearson's correlation and intraclass correlation coefficient (ICC) analyses using both raw and SeSAMe 2 normalized data.

RESULTS

The method we define as SeSAMe 2, which consists of the application of the regular SeSAMe pipeline with an additional round of QC, pOOBAH masking, was found to be the best performing normalization method, while quantile-based methods were found to be the worst performing methods. Whole-array Pearson's correlations were found to be high. However, in agreement with previous studies, a substantial proportion of the probes on the EPIC array showed poor reproducibility (ICC < 0.50). The majority of poor performing probes have beta values close to either 0 or 1, and relatively low standard deviations. These results suggest that probe reliability is largely the result of limited biological variation rather than technical measurement variation. Importantly, normalizing the data with SeSAMe 2 dramatically improved ICC estimates, with the proportion of probes with ICC values > 0.50 increasing from 45.18% (raw data) to 61.35% (SeSAMe 2).

[Interferon-related gene array in predicting the efficacy of interferon therapy in chronic hepatitis B]

This study aims to clarify host factors of IFN treatment in the treatment of chronic hepatitis B (CHB) patients by screening the differentially expressed genes of IFN pathway CHB patients with different response to interferon (IFN) therapy. Three cases were randomly selected in IFN-responding CHB patients (Rs), non-responding CHB patients (NRs) and healthy participants, respectively. The human type I IFN response RT ² profiler PCR array was used to detect the expression levels of IFN-related genes in peripheral blood monocytes (PBMCs) from healthy participants and CHB patients before and after Peg-IFN-α 2a treatment. The results showed that more differentially expressed genes appeared in Rs group than NRs group after IFN treatment. Comparing with healthy participants, IFNG, IL7R, IRF1, and IRF8 were downregulated in both Rs and NRs group before IFN treatment; CXCL10, IFIT1, and IFITM1 were upregulated in the Rs; IL13RA1 and IFI35 were upregulated in the NRs, while IFRD2, IL11RA, IL4R, IRF3, IRF4, PYHIN1, and ADAR were downregulated. The expression of IL15, IFI35 and IFI44 was downregulated by 4.09 ( t = 10.58, P < 0.001), 5.59 ( t = 3.37, P = 0.028) and 10.83 ( t = 2.8, P = 0.049) fold in the Rs group compared with the NRs group, respectively. In conclusion, IFN-response-related gene array is able to evaluate IFN treatment response by detecting IFN-related genes levels in PBMC. High expression of CXCL10, IFIT1 and IFITM1 before treatment may suggest satisfied IFN efficacy, while high expression of IL13RA1, IL15, IFI35 and IFI44 molecules and low expression of IFRD2, IL11RA, IL4R, IRF3, IRF4, PYHIN1 and ADAR molecules may be associated with poor IFN efficacy.

An Efficient Feature Selection Algorithm for Gene Families Using NMF and ReliefF

Gene families, which are parts of a genome's information storage hierarchy, play a significant role in the development and diversity of multicellular organisms. Several studies have focused on the characteristics of gene families, such as function, homology, or phenotype. However, statistical and correlation analyses on the distribution of gene family members in the genome have yet to be conducted. Here, a novel framework incorporating gene family analysis and genome selection based on NMF-ReliefF is reported. Specifically, the proposed method starts by obtaining gene families from the TreeFam database and determining the number of gene families within the feature matrix. Then, NMF-ReliefF is used to select features from the gene feature matrix, which is a new feature selection algorithm that overcomes the inefficiencies of traditional methods. Finally, a support vector machine is utilized to classify the acquired features. The results show that the framework achieved an accuracy of 89.1% and an AUC of 0.919 on the insect genome test set. We also employed four microarray gene data sets to evaluate the performance of the NMF-ReliefF algorithm. The outcomes show that the proposed method may strike a delicate balance between robustness and discrimination. Additionally, the proposed method's categorization is superior to state-of-the-art feature selection approaches.

Enzymatic Synthesis of High-Density RNA Microarrays

Oligonucleotide microarrays are used to investigate the interactome of nucleic acids. DNA microarrays are commercially available, whereas equivalent RNA microarrays are not. This protocol describes a method to convert DNA microarrays of any density and complexity into RNA microarrays using only readily available materials and reagents. This simple conversion protocol will facilitate the accessibility of RNA microarrays to a wide range of researchers. In addition to general considerations for the design of a template DNA microarray, this procedure describes the experimental steps of hybridization of an RNA primer to the immobilized DNA, followed by its covalent attachment via psoralen-mediated photocrosslinking. The subsequent enzymatic processing steps comprise the extension of the primer with T7 RNA polymerase to generate complementary RNA, and finally the removal of the DNA template with TURBO DNase. Beyond the conversion process, we also describe approaches to detect the RNA product either by internal labeling with fluorescently labeled NTPs or via hybridization to the product strand, a step that can then be complemented by an RNase H assay to confirm the nature of the product. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Conversion of a DNA microarray to an RNA microarray Alternate Protocol: Detection of RNA via incorporation of Cy3-UTP Support Protocol 1: Detection of RNA via hybridization Support Protocol 2: RNase H assay.

An Automated cDNA Microarray Image Analysis for the Determination of Gene Expression Ratios

This paper proposes a fully automated technique for cDNA microarray image analysis. Initially, an effective preprocessing stage combined with gridding is built to get the individual spot regions of images. Current work begins with the proposal of a new rule to get the foreground (spot) and background regions in the spot blocks, which uses TV-L1 image denoising, spot block binarization, and finds the most accurate spot label by measuring the centroid differences of labelled regions in the block with that of the spot block centroid. The credibility of the segmentation rule on real images is evaluated by metrics: mean absolute error (MAE) and coefficient of variation (CV) and on synthetic images by metrics: probability of error (PE) and discrepancy distance (DD). The performance values on real and synthetic datasets reveal better results than the competitive methods. After the segmentation, prior to the spot intensity extraction, background intensity correction and flagging of noisy spots are executed. Using the lowess method, intensities are normalized, and gene expression ratios are determined. To comprehend the linearities of red and green intensities and to discern up and down-regulated genes (abnormal), fold-change factor, scatter and box plots are also used to represent the gene expression levels.

Peptide Microarray-Based Protein Interaction Studies Across Affinity Ranges: Enzyme Stalling, Cross-Linking, Depletion, and Neutralization

While an ever-increasing number of protein-protein interactions were studied by peptide microarrays with great success, array-based investigations of transiently binding proteins, such as HDACs, and precise binding quantification, remained challenging. Here, we present an updated protocol for the preparation and use of peptide microarrays including the necessary adjustments for simple semi-quantitative and precise measurements across affinity ranges. This procedure describes the mass spectrometric controlled preparation of peptide microarrays in μSPOT format, and their application in binding profiling of recombinant, as well as endogenous, native proteins. We further highlight how cross-linking, blocking, and enzyme stalling can be leveraged to enhance sensitivity and describe how in situ on-chip binding neutralization can enhance the predictive value and robustness of the binding readout. Finally, we included examples for the integration of precise biophysical binding readouts that complement the traditional array-based binding assays.

Detection of miRNAs

MicroRNAs (miRNAs) are small, noncoding RNAs that regulate gene expression. They play an important role in many biological processes including human diseases. However, miRNAs are challenging to detect due to their short sequence length and low copy number. A number of conventional (e.g., Northern blot, microarray, and RT-qPCR) and emerging (e.g., nanostructured materials and electrochemical methods) techniques have been developed to detect miRNA, each with their own strengths and weaknesses. Some of these techniques have been combined to detect miRNAs as disease biomarkers in point-of-care (POC) settings. Nonetheless, there is still potential for further innovation to facilitate the detection of miRNAs.

Robust integrative biclustering for multi-view data

In many biomedical research, multiple views of data (e.g. genomics, proteomics) are available, and a particular interest might be the detection of sample subgroups characterized by specific groups of variables. Biclustering methods are well-suited for this problem as they assume that specific groups of variables might be relevant only to specific groups of samples. Many biclustering methods exist for detecting row-column clusters in a view but few methods exist for data from multiple views. The few existing algorithms are heavily dependent on regularization parameters for getting row-column clusters, and they impose unnecessary burden on users thus limiting their use in practice. We extend an existing biclustering method based on sparse singular value decomposition for single-view data to data from multiple views. Our method, integrative sparse singular value decomposition (iSSVD), incorporates stability selection to control Type I error rates, estimates the probability of samples and variables to belong to a bicluster, finds stable biclusters, and results in interpretable row-column associations. Simulations and real data analyses show that integrative sparse singular value decomposition outperforms several other single- and multi-view biclustering methods and is able to detect meaningful biclusters. iSSVD is a user-friendly, computationally efficient algorithm that will be useful in many disease subtyping applications.

[A Biochip for Genotyping Polymorphisms Associated with Eye, Hair, Skin Color, AB0 Blood Group, Sex, Y Chromosome Core Haplogroup, and Its Application to Study the Slavic Population]

This paper presents a method for genotyping a panel of 60 single nucleotide polymorphisms (SNPs) using single-stage PCR followed by hybridization on a hydrogel biochip. The pool of analyzed polymorphisms consists of 41 SNPs included in the HIrisPlex-S panel, 4 SNPs of the AB0 gene (261G>Del, 297A>G, 657C>T, 681G>A), markers of the AMELX and AMELY genes, and 14 SNP markers of the Y chromosome haplogroups: B (M60), C (M130), D (CTS3946), E (M5388), G (P257), H (M2920), I (U179), J (M304), L (M185), N (M231), O (M175), Q (M1105), R (P224) and T (M272). These genetic data allow one to predict the phenotype of the desired person according to the characteristics of eye, hair, skin color, AB0 blood group, sex, and genogeographic origin in the male line. The setting protocol is simplified as much as possible to facilitate the introduction of the method into practice. The distribution of allele frequencies of the studied polymorphisms, as well as AB0 blood groups among the Slavs (N = 482), originating mainly from central Russia, was established.

Comparison of High-Throughput Technologies in the Classification of Adult-Onset Still's Disease Patients

A meta-analysis study was conducted to compare high-throughput technologies in the classification of Adult-Onset Still's Disease patients, using differentially expressed genes from independent profiling experiments. We exploited two publicly available datasets from the Gene Expression Omnibus and performed a separate differential expression analysis on each dataset to extract statistically important genes. We then mapped the genes of the two datasets and subsequently we employed well-established machine learning algorithms to evaluate the denoted genes as candidate biomarkers. Using next-generation sequencing data, we managed to achieve the maximum (100%) classification accuracy, sensitivity and specificity with the Gradient Boosting and the Random Forest classifiers, compared to the 83% of the DNA microarray data. Clinical Relevance- When biomarkers derived from one study are applied to the data of another, in many cases the results may diverge significantly. Here we establish that in cross-profiling meta-analysis approaches based on differential expression analysis, next-generation sequencing data provide more accurate results than microarray experiments in the classification of Adult-Onset Still's Disease patients.

Iterative bicluster-based Bayesian principal component analysis and least squares for missing-value imputation in microarray and RNA-sequencing data

Microarray and RNA-sequencing (RNA-seq) techniques each produce gene expression data that can be expressed as a matrix that often contains missing values. Thus, a process of missing-value imputation that uses coherence information of the dataset is necessary. Existing imputation methods, such as iterative bicluster-based least squares (bi-iLS), use biclustering to estimate the missing values because genes are only similar under correlative experimental conditions. Also, they use the row average to obtain a temporary complete matrix, but the use of the row average is considered to be a flaw. The row average cannot reflect the real structure of the dataset because the row average only uses the information of an individual row. Therefore, we propose the use of Bayesian principal component analysis (BPCA) to obtain the temporary complete matrix instead of using the row average in bi-iLS. This alteration produces new missing values imputation method called iterative bicluster-based Bayesian principal component analysis and least squares (bi-BPCA-iLS). Several experiments have been conducted on two-dimension independent gene expression datasets, which are microarray (e.g., cell-cycle expression dataset of yeast saccharomyces cerevisiae) and RNA-seq (gene expression data from schizosaccharomyces pombe) datasets. In the case of the microarray dataset, our proposed bi-BPCA-iLS method showed a significant overall improvement in the normalized root mean square error (NRMSE) values of 10.6% from the local least squares (LLS) and 0.6% from the bi-iLS. In the case of the RNA-seq dataset, our proposed bi-BPCA-iLS method showed an overall improvement in the NRMSE values of 8.2% from the LLS and 3.1% from the bi-iLS. The additional computational time of bi-BPCA-iLS is not significant compared to bi-iLS.

Triclustering method for finding biomarkers in human immunodeficiency virus-1 gene expression data

HIV-1 is a virus that destroys CD4 + cells in the body's immune system, causing a drastic decline in immune system performance. Analysis of HIV-1 gene expression data is urgently needed. Microarray technology is used to analyze gene expression data by measuring the expression of thousands of genes in various conditions. The gene expression series data, which are formed in three dimensions, are analyzed using triclustering. Triclustering is an analysis technique for 3D data that aims to group data simultaneously into rows and columns across different times/conditions. The result of this technique is called a tricluster. A tricluster is a subspace in the form of a subset of rows, columns, and time/conditions. In this study, we used the δ-Trimax, THD Tricluster, and MOEA methods by applying different measures, namely, transposed virtual error, the New Residue Score, and the Multi Slope Measure. The gene expression data consisted of 22,283 probe gene IDs, 40 observations, and four conditions: normal, acute, chronic, and non-progressor. Tricluster evaluation was carried out based on intertemporal homogeneity. An analysis of the probe ID gene that affects AIDS was carried out through this triclustering process. Based on this analysis, a gene symbol which is biomarkers associated with AIDS due to HIV-1, HLA-C, was found in every condition for normal, acute, chronic, and non-progressive HIV-1 patients.

Gene coexpression analysis in Arabidopsis thaliana based on public microarray data

Coexpressed genes tend to participate in related biological processes. Gene coexpression analysis allows the discovery of functional gene partners or the assignment of biological roles to genes of unknown function. In this protocol, we describe the steps necessary to create a gene coexpression tree for Arabidopsis thaliana, using publicly available Affymetrix CEL microarray data. Because the computational analysis described here is highly dependent on sample quality, we detail an automatic quality control approach.

For complete details on the use and execution of this protocol, please refer to Zogopoulos et al. (2021).

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Download and quality control of raw microarray data from multiple public repositories

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Normalization of microarray samples using SCAN algorithm and the latest BrainArray CDF

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Creation of a gene coexpression tree using UPGMA hierarchical clustering

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Biological term enrichment analysis in gene coexpression tree subclades

Row and Column Structure-Based Biclustering for Gene Expression Data

Due to the development of high-throughput technologies for gene analysis, the biclustering method has attracted much attention. However, existing methods have problems with high time and space complexity. This paper proposes a biclustering method, called Row and Column Structure-based Biclustering (RCSBC), with low time and space complexity to find checkerboard patterns within microarray data. First, the paper describes the structure of bicluster by using the structure of rows and columns. Second, the paper chooses the representative rows and columns with two algorithms. Finally, the gene expression data are biclustered on the space spanned by representative rows and columns. To the best of our knowledge, this paper is the first to exploit the relationship between the row/column structure of a gene expression matrix and the structure of biclusters. Both the synthetic datasets and the real-life gene expression datasets are used to validate the effectiveness of our method. It can be seen from the experiment results that the RCSBC outperforms the state-of-the-art algorithms both on clustering accuracy and time/space complexity. This study offers new insights into biclustering the large-scale gene expression data without loading the whole data into memory.

An Algorithm for the Selection of Probes for Specific Detection of Human Disease Pathogens Using the DNA Microarray Technology

The aim of the study was to develop an algorithm for the selection of discriminating probes to identify a wide range of causative agents of human infectious diseases.

Healthcare Biclustering-Based Prediction on Gene Expression Dataset

In this paper, we develop a healthcare biclustering model in the field of healthcare to reduce the inconveniences linked to the data clustering on gene expression. The present study uses two separate healthcare biclustering approaches to identify specific gene activity in certain environments and remove the duplication of broad gene information components. Moreover, because of its adequacy in the problem where populations of potential solutions allow exploration of a greater portion of the research area, machine learning or heuristic algorithm has become extensively used for healthcare biclustering in the field of healthcare. The study is evaluated in terms of average match score for nonoverlapping modules, overlapping modules through the influence of noise for constant bicluster and additive bicluster, and the run time. The results show that proposed FCM blustering method has higher average match score, and reduced run time proposed FCM than the existing PSO-SA and fuzzy logic healthcare biclustering methods.

Genetic diversity of local and introduced cassava germplasm in Burundi using DArTseq molecular analyses

In Burundi most small-scale farmers still grow traditional cassava landraces that are adapted to local conditions and have been selected for consumer preferred attributes. They tend to be susceptible, in varying degrees, to devastating cassava viral diseases such as Cassava Brown Streak Disease (CBSD) and Cassava Mosaic Disease (CMD) with annual production losses of US$1 billion. For long term resistance to the disease, several breeding strategies have been proposed. A sound basis for a breeding program is to understand the genetic diversity of both landraces and elite introduced breeding cultivars. This will also assist in efforts to conserve landraces ahead of the broad distribution of improved varieties which have the possibility of replacing landraces. Our study aimed at determining the genetic diversity and relationships within and between local landraces and introduced elite germplasm using morphological and single nucleotide polymorphism (SNP) markers. A total of 118 cultivars were characterized for morphological trait variation based on leaf, stem and root traits, and genetic variation using SNP markers. Results of morphological characterization based on Ward’s Method revealed three main clusters and five accessions sharing similar characteristics. Molecular characterization identified over 18,000 SNPs and six main clusters and three pairs of duplicates which should be pooled together as one cultivar to avoid redundancy. Results of population genetic analysis showed low genetic distance between populations and between local landraces and elite germplasm. Accessions that shared similar morphological traits were divergent at the molecular level indicating that clustering using morphological traits was inconsistent. Despite the variabilities found within the collection, it was observed that cassava germplasm in Burundi have a narrow genetic base.

Ensemble feature selection for stable biomarker identification and cancer classification from microarray expression data

Microarray technology facilitates the simultaneous measurement of expression of tens of thousands of genes and enables us to study cancers and tumors at the molecular level. Because microarray data are typically characterized by small sample size and high dimensionality, accurate and stable feature selection is thus of fundamental importance to the diagnostic accuracy and deep understanding of disease mechanism. Hence, we in this study present an ensemble feature selection framework to improve the discrimination and stability of finally selected features. Specifically, we utilize sampling techniques to obtain multiple sampled datasets, from each of which we use a base feature selector to select a subset of features. Afterwards, we develop two aggregation strategies to combine multiple feature subsets into one set. Finally, comparative experiments are conducted on four publicly available microarray datasets covering both binary and multi-class cases in terms of classification accuracy and three stability metrics. Results show that the proposed method obtains better stability scores and achieves comparable to and even better classification performance than its competitors.

Whole-Genome Genotyping Using DNA Microarrays for Population Genetics

The field of population genetics has exploded in the last two decades following the sequencing of the human genome in 2001 (Green et al. Nature 526:29-31, 2015). Tools to measure genetic variation have matured significantly throughout this advancement in knowledge (Lenoir and Giannella. J Biomed Discov Collab 1:11, 2006; Marzancola et al. Methods Mol Biol 1368:161-178, 2016). In this chapter, the focus is on the laboratory methods developed to perform genome-wide genotyping utilizing DNA microarrays, which is one of the most commonly used molecular techniques to assess global genetic variation (Heller MJ, Annu Rev Biomed Eng 4:129-153, 2002). DNA microarrays allow for the interrogation of hundreds of thousands of SNPs (single nucleotide polymorphisms) at once utilizing array-based technology in conjunction with fluorescent molecular labels in a process referred to as genotyping (Marzancola et al. Methods Mol Biol 1368:161-178, 2016). Genotype data can be utilized to associate certain phenotypes in relation with specific genetic variants within a population in a process known as genome-wide association studies or GWAS (Charlesworth and Charlesworth. Heredity (Edinb) 118(1):2-9, 2017; Casillas and Barbadilla. Genetics 205(3):1003-1035, 2017). This experimental technique is a multiple-day process involving the combination of DNA extraction, amplification, fragmentation, binding, and staining (Illumina Infinium HTS Assay Protocol Guide, 2013). Many vendors supply platforms and products to assess global genetic variation using DNA microarrays (Illumina Infinium HTS Assay Protocol Guide, 2013). In this chapter, the focus is on the methods utilized to generate high-quality genotype data with the Illumina^® Infinium Global Screening Array. Although data analysis and quality control are not the focus for this chapter, they are also briefly addressed.

Fabrication and use of silicon hollow-needle arrays to achieve tissue nanotransfection in mouse tissue in vivo

Tissue nanotransfection (TNT) is an electromotive gene transfer technology that was developed to achieve tissue reprogramming in vivo. This protocol describes how to fabricate the required hardware, commonly referred to as a TNT chip, and use it for in vivo TNT. Silicon hollow-needle arrays for TNT applications are fabricated in a standardized and reproducible way. In <1 s, these silicon hollow-needle arrays can be used to deliver plasmids to a predetermined specific depth in murine skin in response to pulsed nanoporation. Tissue nanotransfection eliminates the need to use viral vectors, minimizing the risk of genomic integration or cell transformation. The TNT chip fabrication process typically takes 5-6 d, and in vivo TNT takes 30 min. This protocol does not require specific expertise beyond a clean room equipped for basic nanofabrication processes.

Development of high-resolution multiple-SNP arrays for genetic analyses and molecular breeding through genotyping by target sequencing and liquid chip

Genotyping platforms, as critical supports for genomics, genetics, and molecular breeding, have been well implemented at national institutions/universities in developed countries and multinational seed companies that possess high-throughput, automatic, large-scale, and shared facilities. In this study, we integrated an improved genotyping by target sequencing (GBTS) system with capture-in-solution (liquid chip) technology to develop a multiple single-nucleotide polymorphism (mSNP) approach in which mSNPs can be captured from a single amplicon. From one 40K maize mSNP panel, we developed three types of markers (40K mSNPs, 251K SNPs, and 690K haplotypes), and generated multiple panels with various marker densities (1K-40K mSNPs) by sequencing at different depths. Comparative genetic diversity analysis was performed with genic versus intergenic markers and di-allelic SNPs versus non-typical SNPs. Compared with the one-amplicon-one-SNP system, mSNPs and within-mSNP haplotypes are more powerful for genetic diversity detection, linkage disequilibrium decay analysis, and genome-wide association studies. The technologies, protocols, and application scenarios developed for maize in this study will serve as a model for the development of mSNP arrays and highly efficient GBTS systems in animals, plants, and microorganisms.

Cross-platform transcriptomic profiling of the response to recombinant human erythropoietin

RNA-seq has matured and become an important tool for studying RNA biology. Here we compared two RNA-seq (MGI DNBSEQ and Illumina NextSeq 500) and two microarray platforms (GeneChip Human Transcriptome Array 2.0 and Illumina Expression BeadChip) in healthy individuals administered recombinant human erythropoietin for transcriptome-wide quantification of differential gene expression. The results show that total RNA DNB-seq generated a multitude of target genes compared to other platforms. Pathway enrichment analyses revealed genes correlate to not only erythropoiesis and oxygen transport but also a wide range of other functions, such as tissue protection and immune regulation. This study provides a knowledge base of genes relevant to EPO biology through cross-platform comparisons and validation.

High-density DArT-based SilicoDArT and SNP markers for genetic diversity and population structure studies in cassava (Manihot esculenta Crantz)

Cassava (Manihot esculenta Crantz) is an important industrial and staple crop due to its high starch content, low input requirement, and resilience which makes it an ideal crop for sustainable agricultural systems and marginal lands in the tropics. However, the lack of genomic information on local genetic resources has impeded efficient conservation and improvement of the crop and the exploration of its full agronomic and breeding potential. This work was carried out to obtain information on population structure and extent of genetic variability among some local landraces conserved at the Plant Genetic Resources Research Institute, Ghana and exotic cassava accessions with Diversity Array Technology based SilicoDArT and SNP markers to infer how the relatedness in the genetic materials can be used to enhance germplasm curation and future breeding efforts. A total of 10521 SilicoDArT and 10808 SNP markers were used with varying polymorphic information content (PIC) values. The average PIC was 0.36 and 0.28 for the SilicoDArT and SNPs respectively. Population structure and average linkage hierarchical clustering based on SNPs revealed two distinct subpopulations and a large number of admixtures. Both DArT platforms identified 22 landraces as potential duplicates based on Gower's genetic dissimilarity. The expected heterozygosity which defines the genetic variation within each subpopulation was 0.008 for subpop1 which were mainly landraces and 0.391 for subpop2 indicating the homogeneous and admixture nature of the two subpopulations. Further analysis upon removal of the duplicates increased the expected heterozygosity of subpop1 from 0.008 to 0.357. A mantel test indicated strong interdependence (r = 0.970; P < 0.001) between SilicoDArT and DArTSeq SNP genotypic data suggesting both marker platforms as a robust system for genomic studies in cassava. These findings provide important information for efficient ex-situ conservation of cassava, future heterosis breeding, and marker-assisted selection (MAS) to enhance cassava improvement.

Quick detection of causative bacteria in cases of acute cholangitis and cholecystitis using a multichannel gene autoanalyzer

PURPOSES

Acute cholangitis and cholecystitis can become severe conditions as a result of inappropriate therapeutic administration and thereafter become increasingly resistant to antimicrobial treatment. The simultaneous detection of the bacterial nucleic acid and antimicrobial resistance gene is covered by the national health insurance program in Japan for sepsis. In this study, we evaluate the use of a multichannel gene autoanalyzer (Verigene system) for the quick detection of causative bacteria in cases of acute cholangitis and cholecystitis.

METHODS

This study included 108 patients diagnosed with acute cholangitis or cholecystitis between June 2015 and November 2018. A bacterial culture test and Verigene assay were used to evaluate the bile samples.

RESULTS

The most commonly isolated bacteria were Escherichia coli, which includes six extended-spectrum beta-lactamase (ESBL)-producing E. coli. Among the patients with positive bile cultures, bacteria were detected in 35.7% of cases via the Verigene system. The detection rates of the Verigene system significantly increased when the number of bacterial colonies was ≥ 10⁶ colony-forming unit (CFU)/mL (58.1%). Cases with a maximum colony quantity of ≥ 10⁶ CFU/mL exhibited higher inflammation, suggesting the presence of a bacterial infection.

CONCLUSIONS

The Verigene system might be a new method for the quick detection of causative bacteria in patients with infectious acute cholangitis and cholecystitis.

Bioinformatics Pipeline to Analyze lncRNA Arrays

Despite the fact that next-generation sequencing approaches, in particular RNA sequencing, provide deep genome-wide expression data that allow both careful annotations/mapping of long noncoding RNA (lncRNA) molecules and de-novo sequencing, lncRNA expression studies by microarray is a still cost-effective procedure that could allow to have a landscape of the most characterized lncRNA species. However, microarray design does not always correctly address the overlap between coding and noncoding samples to discriminate between the original transcript source. In order to overcome this issue, in this chapter we present a bioinformatics pipeline that enables accurate annotation of GeneChip^® microarrays, to date the most commonly adopted among the commercial solutions. Overall, this approach holds two main advantages, a gain in specificity of transcript detection and the adaptability to the whole panel of GeneChip^® arrays.

Micro-Raman Characterization of Structural Features of High-k Stack Layer of SOI Nanowire Chip, Designed to Detect Circular RNA Associated with the Development of Glioma

The application of micro-Raman spectroscopy was used for characterization of structural features of the high-k stack (h-k) layer of "silicon-on-insulator" (SOI) nanowire (NW) chip (h-k-SOI-NW chip), including Al₂O₃ and HfO₂ in various combinations after heat treatment from 425 to 1000 °C. After that, the NW structures h-k-SOI-NW chip was created using gas plasma etching optical lithography. The stability of the signals from the monocrine phase of HfO₂ was shown. Significant differences were found in the elastic stresses of the silicon layers for very thick (>200 nm) Al₂O₃ layers. In the UV spectra of SOI layers of a silicon substrate with HfO₂, shoulders in the Raman spectrum were observed at 480-490 cm^-1 of single-phonon scattering. The h-k-SOI-NW chip created in this way has been used for the detection of DNA-oligonucleotide sequences (oDNA), that became a synthetic analog of circular RNA-circ-SHKBP1 associated with the development of glioma at a concentration of 1.1 × 10^-16 M. The possibility of using such h-k-SOI NW chips for the detection of circ-SHKBP1 in blood plasma of patients diagnosed with neoplasm of uncertain nature of the brain and central nervous system was shown.

Revisiting the Characteristics of Testicular Germ Cell Lines GC-1(spg) and GC-2(spd)ts

Spermatogenesis is a multifaceted and meticulously orchestrated process involving meiosis, chromatin build up, transcriptional and translational hushing, and spermiogenesis. Male germ cell lines GC-1spg (GC-1) and GC-2(spd)ts (GC-2) provide a useful resource to comprehend the molecular events occurring during such a tightly regulated process. Using cDNA microarray, expression profiling of GC-1 and GC-2 cell lines was done to precisely understand their characteristics and uniqueness. Our observations indicate that whilst both the cell lines are indeed of testicular origin, GC-2 is not haploid as was originally thought. Data analysis of the 23,351 transcripts detected in GC-1 and 20,992 in GC-2 cell lines demonstrates an 80% transcript overlap between GC-1 and GC-2 cells and ~ 40% similarity of both with the primary spermatocyte transcriptome. 3152 and 793 transcripts exclusive to GC-1 and GC-2, respectively, were identified. The presence of transcripts for 36 genes was validated in these cell lines including those showing testis-specific expression, as well as genes not reported previously. Overall, this study provides the transcriptome database of GC-1 and GC-2 cells. Analysis of the data demonstrates the transcriptomic transitions between GC-1 and GC-2 thus providing a glimpse to the process of germ cell differentiation from type B spermatogonium into preleptotene spermatocyte.

FRL: An Integrative Feature Selection Algorithm Based on the Fisher Score, Recursive Feature Elimination, and Logistic Regression to Identify Potential Genomic Biomarkers

Accurate screening on cancer biomarkers contributes to health assessment, drug screening, and targeted therapy for precision medicine. The rapid development of high-throughput sequencing technology has identified abundant genomic biomarkers, but most of them are limited to single-cancer analysis. Based on the combination of Fisher score, Recursive feature elimination, and Logistic regression (FRL), this paper proposes an integrative feature selection algorithm named FRL to explore potential cancer genomic biomarkers on cancer subsets. Fisher score is initially used to calculate the weights of genes to rapidly reduce the dimension. Recursive feature elimination and Logistic regression are then jointly employed to extract the optimal subset. Compared to the current differential expression analysis tool GEO2R based on the Limma algorithm, FRL has greater classification precision than Limma. Compared with five traditional feature selection algorithms, FRL exhibits excellent performance on accuracy (ACC) and F1-score and greatly improves computational efficiency. On high-noise datasets such as esophageal cancer, the ACC of FRL is 30% superior to the average ACC achieved with other traditional algorithms. As biomarkers found in multiple studies are more reliable and reproducible, and reveal stronger association on potential clinical value than single analysis, through literature review and spatial analyses of gene functional enrichment and functional pathways, we conduct cluster analysis on 10 diverse cancers with high mortality and form a potential biomarker module comprising 19 genes. All genes in this module can serve as potential biomarkers to provide more information on the overall oncogenesis mechanism for the detection of diverse early cancers and assist in targeted anticancer therapies for further developments in precision medicine.

Assessing Various Control Samples for Microarray Gene Expression Profiling of Laryngeal Squamous Cell Carcinoma

Selection of optimal control samples is crucial in expression profiling tumor samples. To address this issue, we performed microarray expression profiling of control samples routinely used in head and neck squamous cell carcinoma studies: human bronchial and tracheal epithelial cells, squamous cells obtained by laser uvulopalatoplasty and tumor surgical margins. We compared the results using multidimensional scaling and hierarchical clustering versus tumor samples and laryngeal squamous cell carcinoma cell lines. A general observation from our study is that the analyzed cohorts separated according to two dominant factors: “malignancy”, which separated controls from malignant samples and “cell culture-microenvironment” which reflected the differences between cultured and non-cultured samples. In conclusion, we advocate the use of cultured epithelial cells as controls for gene expression profiling of cancer cell lines. In contrast, comparisons of gene expression profiles of cancer cell lines versus surgical margin controls should be treated with caution, whereas fresh frozen surgical margins seem to be appropriate for gene expression profiling of tumor samples.

Ultrasensitive hybridization capture: Reliable detection of <1 copy/mL short cell-free DNA from large-volume urine samples

Urine cell-free DNA (cfDNA) is a valuable non-invasive biomarker with broad potential clinical applications, but there is no consensus on its optimal pre-analytical methodology, including the DNA extraction step. Due to its short length (majority of fragments <100 bp) and low concentration (ng/mL), urine cfDNA is not efficiently recovered by conventional silica-based extraction methods. To maximize sensitivity of urine cfDNA assays, we developed an ultrasensitive hybridization method that uses sequence-specific oligonucleotide capture probes immobilized on magnetic beads to improve extraction of short cfDNA from large-volume urine samples. Our hybridization method recovers near 100% (95% CI: 82.6-117.6%) of target-specific DNA from 10 mL urine, independent of fragment length (25-150 bp), and has a limit of detection of ≤5 copies of double-stranded DNA (0.5 copies/mL). Pairing hybridization with an ultrashort qPCR design, we can efficiently capture and amplify fragments as short as 25 bp. Our method enables amplification of cfDNA from 10 mL urine in a single qPCR well, tolerates variation in sample composition, and effectively removes non-target DNA. Our hybridization protocol improves upon both existing silica-based urine cfDNA extraction methods and previous hybridization-based sample preparation protocols. Two key innovations contribute to the strong performance of our method: a two-probe system enabling recovery of both strands of double-stranded DNA and dual biotinylated capture probes, which ensure consistent, high recovery by facilitating optimal probe density on the bead surface, improving thermostability of the probe-bead linkage, and eliminating interference by endogenous biotin. We originally designed the hybridization method for tuberculosis diagnosis from urine cfDNA, but expect that it will be versatile across urine cfDNA targets, and may be useful for other cfDNA sample types and applications beyond cfDNA. To make our hybridization method accessible to new users, we present a detailed protocol and straightforward guidelines for designing new capture probes.

Independent component analysis recovers consistent regulatory signals from disparate datasets

The availability of bacterial transcriptomes has dramatically increased in recent years. This data deluge could result in detailed inference of underlying regulatory networks, but the diversity of experimental platforms and protocols introduces critical biases that could hinder scalable analysis of existing data. Here, we show that the underlying structure of the E. coli transcriptome, as determined by Independent Component Analysis (ICA), is conserved across multiple independent datasets, including both RNA-seq and microarray datasets. We subsequently combined five transcriptomics datasets into a large compendium containing over 800 expression profiles and discovered that its underlying ICA-based structure was still comparable to that of the individual datasets. With this understanding, we expanded our analysis to over 3,000 E. coli expression profiles and predicted three high-impact regulons that respond to oxidative stress, anaerobiosis, and antibiotic treatment. ICA thus enables deep analysis of disparate data to uncover new insights that were not visible in the individual datasets.

Cells adapt to diverse environments by regulating gene expression. Genome-wide measurements of gene expression levels have exponentially increased in recent years, but successful integration and analysis of these datasets are limited. Recently, we showed that independent component analysis (ICA), a signal deconvolution algorithm, can separate a large bacterial gene expression dataset into groups of co-regulated genes. This previous study focused on data generated by a standardized pipeline and did not address whether ICA extracts the same quantitative co-expression signals across expression profiling platforms. In this study, we show that ICA finds similar co-regulation patterns underlying multiple gene expression datasets and can be used as a tool to integrate and interpret diverse datasets. Using a dataset containing over 3,000 expression profiles, we predicted three new regulons and characterized their activities. Since large, standardized expression datasets only exist for a few bacterial strains, these results broaden the possible applications of this tool to better understand transcriptional regulation across a wide range of microbes.

Insight into molecular profile changes after skeletal muscle contusion using microarray and bioinformatics analyses

Muscle trauma frequently occurs in daily life. However, the molecular mechanisms of muscle healing, which partly depend on the extent of the damage, are not well understood. The present study aimed to investigate gene expression profiles following mild and severe muscle contusion, and to provide more information about the molecular mechanisms underlying the repair process. A total of 33 rats were divided randomly into control (n=3), mild contusion (n=15), and severe contusion (n=15) groups; the contusion groups were further divided into five subgroups (1, 3, 24, 48, and 168 h post-injury; n=3 per subgroup). A total of 2844 and 2298 differentially expressed genes (DEGs) were identified using microarray analyses in the mild and severe contusions, respectively. From the analysis of the 1620 coexpressed genes in mildly and severely contused muscle, we discovered that the gene profiles in functional modules and temporal clusters were similar between the mild and severe contusion groups; moreover, the genes showed time-dependent patterns of expression, which allowed us to identify useful markers of wound age. The functional analyses of genes in the functional modules and temporal clusters were performed, and the hub genes in each module-cluster pair were identified. Interestingly, we found that genes down-regulated at 24-48 h were largely associated with metabolic processes, especially of the oxidative phosphorylation (OXPHOS), which has been rarely reported. These results improve our understanding of the molecular mechanisms underlying muscle repair, and provide a basis for further studies of wound age estimation.

Thyroseq v3, Afirma GSC, and microRNA Panels Versus Previous Molecular Tests in the Preoperative Diagnosis of Indeterminate Thyroid Nodules: A Systematic Review and Meta-Analysis

BACKGROUND

Molecular tests are being used increasingly as an auxiliary diagnostic tool so as to avoid a diagnostic surgery approach for cytologically indeterminate thyroid nodules (ITNs). Previous test versions, Thyroseq v2 and Afirma Gene Expression Classifier (GEC), have proven shortcomings in malignancy detection performance.

OBJECTIVE

This study aimed to evaluate the diagnostic performance of the established Thyroseq v3, Afirma Gene Sequencing Classifier (GSC), and microRNA-based assays versus prior iterations in ITNs, in light of "rule-in" and "rule-out" concepts. It further analyzed the impact of noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) reclassification and Bethesda cytological subtypes on the performance of molecular tests.

METHODS

Pubmed, Scopus, and Web of Science were the databases used for the present research, a process that lasted until September 2020. A random-effects bivariate model was used to estimate the summary sensitivity, specificity, positive (PLR) and negative likelihood ratios (NLR), and area under the curve (AUC) for each panel. The conducted sensitivity analyses addressed different Bethesda categories and NIFTP thresholds.

RESULTS

A total of 40 eligible studies were included with 7,831 ITNs from 7,565 patients. Thyroseq v3 showed the best overall performance (AUC 0.95; 95% confidence interval: 0.93-0.97), followed by Afirma GSC (AUC 0.90; 0.87-0.92) and Thyroseq v2 (AUC 0.88; 0.85-0.90). In terms of "rule-out" abilities Thyroseq v3 (NLR 0.02; 95%CI: 0.0-2.69) surpassed Afirma GEC (NLR 0.18; 95%CI: 0.10-0.33). Thyroseq v2 (PLR 3.5; 95%CI: 2.2-5.5) and Thyroseq v3 (PLR 2.8; 95%CI: 1.2-6.3) achieved superior "rule-in" properties compared to Afirma GSC (PLR 1.9; 95%CI: 1.3-2.8). Evidence for Thyroseq v3 seems to have higher quality, notwithstanding the paucity of studies. Both Afirma GEC and Thyroseq v2 performance have been affected by NIFTP reclassification. ThyGenNEXT/ThyraMIR and RosettaGX show prominent preliminary results.

CONCLUSION

The newly emerged tests, Thyroseq v3 and Afirma GSC, designed for a "rule-in" purpose, have been proved to outperform in abilities to rule out malignancy, thus surpassing previous tests no longer available, Thyroseq 2 and Afirma GEC. However, Thyroseq v2 still ranks as the best rule-in molecular test.

SYSTEMATIC REVIEW REGISTRATION

http://www.crd.york.ac.uk/PROSPERO, identifier CRD42020212531.

A Method to Investigate the Helicobacter pylori-Associated DNA Methylome

The protocol described here for methylome profiling consists of two parts. One is the experimental part for a genome-wide analysis of methylation level, and the other is the bioinformatics analysis of the methylome data. DNA methylation measurement is conducted using the commercially available array-based "Infinium Human Methylation 450K BeadChip" kit (or its updated version, Infinium MethylationEPICBeadChip). This BeadChip allows the high-throughput DNA methylation analysis suitable for genome-wide studies with large sample size. The results give intensities of the beads providing information on the unmethylated and methylated CpG sites. Bioinformatics data analysis involves reading the intensities as methylation values using R packages. Here, we provide a detailed analysis tool for each of the data analysis steps.

Modeling Intestinal Epithelial Response to Interferon-γ in Induced Pluripotent Stem Cell-Derived Human Intestinal Organoids

Human intestinal organoids (HIOs) are increasingly being used to model intestinal responses to various stimuli, yet few studies have confirmed the fidelity of this modeling system. Given that the interferon-gamma (IFN-γ) response has been well characterized in various other cell types, our goal was to characterize the response to IFN-γ in HIOs derived from induced pluripotent stem cells (iPSCs). To achieve this, iPSCs were directed to form HIOs and subsequently treated with IFN-γ. Our results demonstrate that IFN-γ phosphorylates STAT1 but has little effect on the expression or localization of tight and adherens junction proteins in HIOs. However, transcriptomic profiling by microarray revealed numerous upregulated genes such as IDO1, GBP1, CXCL9, CXCL10 and CXCL11, which have previously been shown to be upregulated in other cell types in response to IFN-γ. Notably, "Response to Interferon Gamma" was determined to be one of the most significantly upregulated gene sets in IFN-γ-treated HIOs using gene set enrichment analysis. Interestingly, similar genes and pathways were upregulated in publicly available datasets contrasting the gene expression of in vivo biopsy tissue from patients with IBD against healthy controls. These data confirm that the iPSC-derived HIO modeling system represents an appropriate platform to evaluate the effects of various stimuli and specific environmental factors responsible for the alterations in the intestinal epithelium seen in various gastrointestinal conditions such as inflammatory bowel disease.