Affymetrix GeneChip microarray preprocessing for multivariate analyses

Comparing preprocessing strategies for 3D-Gene microarray data of extracellular vesicle-derived miRNAs

BACKGROUND

Extracellular vesicle-derived (EV)-miRNAs have potential to serve as biomarkers for the diagnosis of various diseases. miRNA microarrays are widely used to quantify circulating EV-miRNA levels, and the preprocessing of miRNA microarray data is critical for analytical accuracy and reliability. Thus, although microarray data have been used in various studies, the effects of preprocessing have not been studied for Toray's 3D-Gene chip, a widely used measurement method. We aimed to evaluate batch effect, missing value imputation accuracy, and the influence of preprocessing on measured values in 18 different preprocessing pipelines for EV-miRNA microarray data from two cohorts with amyotrophic lateral sclerosis using 3D-Gene technology.

RESULTS

Eighteen different pipelines with different types and orders of missing value completion and normalization were used to preprocess the 3D-Gene microarray EV-miRNA data. Notable results were suppressed in the batch effects in all pipelines using the batch effect correction method ComBat. Furthermore, pipelines utilizing missForest for missing value imputation showed high agreement with measured values. In contrast, imputation using constant values for missing data exhibited low agreement.

CONCLUSIONS

This study highlights the importance of selecting the appropriate preprocessing strategy for EV-miRNA microarray data when using 3D-Gene technology. These findings emphasize the importance of validating preprocessing approaches, particularly in the context of batch effect correction and missing value imputation, for reliably analyzing data in biomarker discovery and disease research.

Robust normalization and transformation techniques for constructing gene coexpression networks from RNA-seq data

BACKGROUND

Constructing gene coexpression networks is a powerful approach for analyzing high-throughput gene expression data towards module identification, gene function prediction, and disease-gene prioritization. While optimal workflows for constructing coexpression networks, including good choices for data pre-processing, normalization, and network transformation, have been developed for microarray-based expression data, such well-tested choices do not exist for RNA-seq data. Almost all studies that compare data processing and normalization methods for RNA-seq focus on the end goal of determining differential gene expression.

RESULTS

Here, we present a comprehensive benchmarking and analysis of 36 different workflows, each with a unique set of normalization and network transformation methods, for constructing coexpression networks from RNA-seq datasets. We test these workflows on both large, homogenous datasets and small, heterogeneous datasets from various labs. We analyze the workflows in terms of aggregate performance, individual method choices, and the impact of multiple dataset experimental factors. Our results demonstrate that between-sample normalization has the biggest impact, with counts adjusted by size factors producing networks that most accurately recapitulate known tissue-naive and tissue-aware gene functional relationships.

CONCLUSIONS

Based on this work, we provide concrete recommendations on robust procedures for building an accurate coexpression network from an RNA-seq dataset. In addition, researchers can examine all the results in great detail at https://krishnanlab.github.io/RNAseq_coexpression to make appropriate choices for coexpression analysis based on the experimental factors of their RNA-seq dataset.

SLC25A24 gene methylation and gray matter volume in females with and without conduct disorder: an exploratory epigenetic neuroimaging study

Conduct disorder (CD), a psychiatric disorder characterized by a repetitive pattern of antisocial behaviors, results from a complex interplay between genetic and environmental factors. The clinical presentation of CD varies both according to the individual's sex and level of callous-unemotional (CU) traits, but it remains unclear how genetic and environmental factors interact at the molecular level to produce these differences. Emerging evidence in males implicates methylation of genes associated with socio-affective processes. Here, we combined an epigenome-wide association study with structural neuroimaging in 51 females with CD and 59 typically developing (TD) females to examine DNA methylation in relation to CD, CU traits, and gray matter volume (GMV). We demonstrate an inverse pattern of correlation between CU traits and methylation of a chromosome 1 region in CD females (positive) as compared to TD females (negative). The identified region spans exon 1 of the SLC25A24 gene, central to energy metabolism due to its role in mitochondrial function. Increased SLC25A24 methylation was also related to lower GMV in multiple brain regions in the overall cohort. These included the superior frontal gyrus, prefrontal cortex, and supramarginal gyrus, secondary visual cortex and ventral posterior cingulate cortex, which are regions that have previously been implicated in CD and CU traits. While our findings are preliminary and need to be replicated in larger samples, they provide novel evidence that CU traits in females are associated with methylation levels in a fundamentally different way in CD and TD, which in turn may relate to observable variations in GMV across the brain.

A new LSTM-based gene expression prediction model: L-GEPM

Molecular biology combined with in silico machine learning and deep learning has facilitated the broad application of gene expression profiles for gene function prediction, optimal crop breeding, disease-related gene discovery, and drug screening. Although the acquisition cost of genome-wide expression profiles has been steadily declining, the requirement generates a compendium of expression profiles using thousands of samples remains high. The Library of Integrated Network-Based Cellular Signatures (LINCS) program used approximately 1000 landmark genes to predict the expression of the remaining target genes by linear regression; however, this approach ignored the nonlinear features influencing gene expression relationships, limiting the accuracy of the experimental results. We herein propose a gene expression prediction model, L-GEPM, based on long short-term memory (LSTM) neural networks, which captures the nonlinear features affecting gene expression and uses learned features to predict the target genes. By comparing and analyzing experimental errors and fitting the effects of different prediction models, the LSTM neural network-based model, L-GEPM, can achieve low error and a superior fitting effect.

Genome-Wide Association Study in African Americans with Acute Respiratory Distress Syndrome Identifies the Selectin P Ligand Gene as a Risk Factor

RATIONALE

Genetic factors are involved in acute respiratory distress syndrome (ARDS) susceptibility. Identification of novel candidate genes associated with increased risk and severity will improve our understanding of ARDS pathophysiology and enhance efforts to develop novel preventive and therapeutic approaches.

OBJECTIVES

To identify genetic susceptibility targets for ARDS.

METHODS

A genome-wide association study was performed on 232 African American patients with ARDS and 162 at-risk control subjects. The Identify Candidate Causal SNPs and Pathways platform was used to infer the association of known gene sets with the top prioritized intragenic SNPs. Preclinical validation of SELPLG (selectin P ligand gene) was performed using mouse models of LPS- and ventilator-induced lung injury. Exonic variation within SELPLG distinguishing patients with ARDS from sepsis control subjects was confirmed in an independent cohort.

MEASUREMENTS AND MAIN RESULTS

Pathway prioritization analysis identified a nonsynonymous coding SNP (rs2228315) within SELPLG, encoding P-selectin glycoprotein ligand 1, to be associated with increased susceptibility. In an independent cohort, two exonic SELPLG SNPs were significantly associated with ARDS susceptibility. Additional support for SELPLG as an ARDS candidate gene was derived from preclinical ARDS models where SELPLG gene expression in lung tissues was significantly increased in both ventilator-induced (twofold increase) and LPS-induced (5.7-fold increase) murine lung injury models compared with controls. Furthermore, Selplg-/- mice exhibited significantly reduced LPS-induced inflammatory lung injury compared with wild-type C57/B6 mice. Finally, an antibody that neutralizes P-selectin glycoprotein ligand 1 significantly attenuated LPS-induced lung inflammation.

CONCLUSIONS

These findings identify SELPLG as a novel ARDS susceptibility gene among individuals of European and African descent.

Standardising RNA profiling based biomarker application in cancer-The need for robust control of technical variables

Histopathology-based staging of colorectal cancer (CRC) has utility in assessing the prognosis of patient subtypes, but as yet cannot accurately predict individual patient's treatment response. Transcriptomics approaches, using array based or next generation sequencing (NGS) platforms, of formalin fixed paraffin embedded tissue can be harnessed to develop multi-gene biomarkers for predicting both prognosis and treatment response, leading to stratification of treatment. While transcriptomics can shape future biomarker development, currently <1% of published biomarkers become clinically validated tests, often due to poor study design or lack of independent validation. In this review of a large number of CRC transcriptional studies, we identify recurrent sources of technical variability that encompass collection, preservation and storage of malignant tissue, nucleic acid extraction, methods to quantitate RNA transcripts and data analysis pipelines. We propose a series of defined steps for removal of these confounding issues, to ultimately aid in the development of more robust clinical biomarkers.

Microarray Analysis to Monitor Bacterial Cell Wall Homeostasis

Transcriptomics, the genome-wide analysis of gene transcription, has become an important tool for characterizing and understanding the signal transduction networks operating in bacteria. Here we describe a protocol for quantifying and interpreting changes in the transcriptome of Streptomyces coelicolor that take place in response to treatment with three antibiotics active against different stages of peptidoglycan biosynthesis. The results defined the transcriptional responses associated with cell envelope homeostasis including a generalized response to all three antibiotics involving activation of transcription of the cell envelope stress sigma factor σ(E), together with elements of the stringent response, and of the heat, osmotic, and oxidative stress regulons. Many antibiotic-specific transcriptional changes were identified, representing cellular processes potentially important for tolerance to each antibiotic. The principles behind the protocol are transferable to the study of cell envelope homeostatic mechanisms probed using alternative chemical/environmental insults or in other bacterial strains.

Integration of flow studies for robust selection of mechanoresponsive genes

Blood flow is an essential contributor to plaque growth, composition and initiation. It is sensed by endothelial cells, which react to blood flow by expressing > 1000 genes. The sheer number of genes implies that one needs genomic techniques to unravel their response in disease. Individual genomic studies have been performed but lack sufficient power to identify subtle changes in gene expression. In this study, we investigated whether a systematic meta-analysis of available microarray studies can improve their consistency. We identified 17 studies using microarrays, of which six were performed in vivo and 11 in vitro. The in vivo studies were disregarded due to the lack of the shear profile. Of the in vitro studies, a cross-platform integration of human studies (HUVECs in flow cells) showed high concordance (> 90 %). The human data set identified > 1600 genes to be shear responsive, more than any other study and in this gene set all known mechanosensitive genes and pathways were present. A detailed network analysis indicated a power distribution (e. g. the presence of hubs), without a hierarchical organisation. The average cluster coefficient was high and further analysis indicated an aggregation of 3 and 4 element motifs, indicating a high prevalence of feedback and feed forward loops, similar to prokaryotic cells. In conclusion, this initial study presented a novel method to integrate human-based mechanosensitive studies to increase its power. The robust network was large, contained all known mechanosensitive pathways and its structure revealed hubs, and a large aggregate of feedback and feed forward loops.

Sensitivity of housekeeping genes in the suprachiasmatic nucleus of the mouse brain to diet and the daily light-dark cycle

The endogenous timing system within the suprachiasmatic nuclei (SCN) of the hypothalamus drives the cyclic expression of the clock molecules across the 24h day-night cycle controlling downstream molecular pathways and physiological processes. The developing fetal clock system is sensitive to the environment and physiology of the pregnant mother and as such disruption of this system could lead to altered physiology in the offspring. Characterizing the gene profiles of the endogenous molecular clock system by quantitative reverse transcription polymerase chain reaction is dependent on normalization by appropriate housekeeping genes (HKGs). However, many HKGs commonly used as internal controls, although stably expressed under control conditions, can vary significantly in their expression under certain experimental conditions. Here we analyzed the expression of 10 classic HKG across the 24h light-dark cycle in the SCN of mouse offspring exposed to normal chow or a high fat diet during early development and in postnatal life. We found that the HKGs glyceraldehyde-3-phosphate dehydrogenase, beta actin and adenosine triphosphate synthase subunit to be the most stably expressed genes in the SCN regardless of diet or time within the 24h light-dark cycle, and are therefore suitable to be used as internal controls. However SCN samples collected during the light and dark periods did show differences in expression and as such the timing of collection should be considered when carrying out gene expression studies.

A replication study for genome-wide gene expression levels in two layer lines elucidates differentially expressed genes of pathways involved in bone remodeling and immune responsiveness

The current replication study confirmed significant differences in gene expression profiles of the cerebrum among the two commercial layer lines Lohmann Selected Leghorn (LSL) and Lohmann Brown (LB). Microarray analyses were performed for 30 LSL and another 30 LB laying hens kept in the small group housing system Eurovent German. A total of 14,103 microarray probe sets using customized Affymetrix ChiGene-1_0-st Arrays with 20,399 probe sets were differentially expressed among the two layer lines LSL and LB (FDR adjusted P-value <0.05). An at least 2-fold change in expression levels could be observed for 388 of these probe sets. In LSL, 214 of the 388 probe sets were down- and 174 were up-regulated and vice versa for the LB layer line. Among the 174 up-regulated probe sets in LSL, we identified 51 significantly enriched Gene ontology (GO) terms of the biological process category. A total of 63 enriched GO-terms could be identified for the 214 down-regulated probe sets of the layer line LSL. We identified nine genes significantly differentially expressed between the two layer lines in both microarray experiments. These genes play a crucial role in protection of neuronal cells from oxidative stress, bone mineral density and immune response among the two layer lines LSL and LB. Thus, the different regulation of these genes may significantly contribute to phenotypic trait differences among these layer lines. In conclusion, these novel findings provide a basis for further research to improve animal welfare in laying hens and these layer lines may be of general interest as an animal model.

Use of Attribute Driven Incremental Discretization and Logic Learning Machine to build a prognostic classifier for neuroblastoma patients

BACKGROUND

Cancer patient's outcome is written, in part, in the gene expression profile of the tumor. We previously identified a 62-probe sets signature (NB-hypo) to identify tissue hypoxia in neuroblastoma tumors and showed that NB-hypo stratified neuroblastoma patients in good and poor outcome 1. It was important to develop a prognostic classifier to cluster patients into risk groups benefiting of defined therapeutic approaches. Novel classification and data discretization approaches can be instrumental for the generation of accurate predictors and robust tools for clinical decision support. We explored the application to gene expression data of Rulex, a novel software suite including the Attribute Driven Incremental Discretization technique for transforming continuous variables into simplified discrete ones and the Logic Learning Machine model for intelligible rule generation.

RESULTS

We applied Rulex components to the problem of predicting the outcome of neuroblastoma patients on the bases of 62 probe sets NB-hypo gene expression signature. The resulting classifier consisted in 9 rules utilizing mainly two conditions of the relative expression of 11 probe sets. These rules were very effective predictors, as shown in an independent validation set, demonstrating the validity of the LLM algorithm applied to microarray data and patients' classification. The LLM performed as efficiently as Prediction Analysis of Microarray and Support Vector Machine, and outperformed other learning algorithms such as C4.5. Rulex carried out a feature selection by selecting a new signature (NB-hypo-II) of 11 probe sets that turned out to be the most relevant in predicting outcome among the 62 of the NB-hypo signature. Rules are easily interpretable as they involve only few conditions.

CONCLUSIONS

Our findings provided evidence that the application of Rulex to the expression values of NB-hypo signature created a set of accurate, high quality, consistent and interpretable rules for the prediction of neuroblastoma patients' outcome. We identified the Rulex weighted classification as a flexible tool that can support clinical decisions. For these reasons, we consider Rulex to be a useful tool for cancer classification from microarray gene expression data.

CD4+ T-cell responses among adults and young children in response to Streptococcus pneumoniae and Haemophilus influenzae vaccine candidate protein antigens

We characterized cytokine profiles of CD4(+) T-helper (h) cells in adults and young children to ascertain if responses occur to next-generation candidate vaccine antigens PspA, PcpA, PhtD, PhtE, Ply, LytB of Streptococcus pneumonia (Spn) and protein D and OMP26 of non-typeable Haemophilus influenzae (NTHi). Adults had vaccine antigen-specific Th1 and Th2 cells responsive to all antigens evaluated whereas young children had significant numbers of vaccine antigen-specific CD4(+) T cells producing IL-2, (p=0.004). Vaccine antigen-specific CD4(+) T-cell populations in adults were largely of effector (TEM) and/or central memory (TCM) phenotypes as defined by CD45RA(-)CCR7(+) or CD45RA(-)CCR7(-) respectively; however among young children antigen-specific IL-2 producing CD4(+) T cells demonstrated CD45RA(+) expression (non-memory cells). We conclude that adults have circulating memory CD4(+) T cells (CD45RA(-)) that can be stimulated by all the tested Spn and NTHi protein vaccine candidate antigens, whereas young children have a more limited response.