The affymetrix GeneChip platform: an overview

Recent progress in DNA data storage based on high-throughput DNA synthesis

DNA data storage has emerged as a solution for storing massive volumes of data by utilizing nucleic acids as a digital information medium. DNA offers exceptionally high storage density, long durability, and low maintenance costs compared to conventional storage media such as flash memory and hard disk drives. DNA data storage consists of the following steps: encoding, DNA synthesis (i.e., writing), preservation, retrieval, DNA sequencing (i.e., reading), and decoding. Out of these steps, DNA synthesis presents a bottleneck due to imperfect coupling efficiency, low throughput, and excessive use of organic solvents. Overcoming these challenges is essential to establish DNA as a viable data storage medium. In this review, we provide the overall process of DNA data storage, presenting the recent progress of each step. Next, we examine a detailed overview of DNA synthesis methods with an emphasis on their limitations. Lastly, we discuss the efforts to overcome the constraints of each method and their prospects.

Meta-analysis towards FSHD reveals misregulation of neuromuscular junction, nuclear envelope, and spliceosome

Facioscapulohumeral muscular dystrophy (FSHD) is one of the most common autosomal dominant muscle disorders, yet no cure or amelioration exists. The clinical presentation is diverse, making it difficult to identify the actual driving pathomechanism among many downstream events. To unravel this complexity, we performed a meta-analysis of 13 original omics datasets (in total 171 FSHD and 129 control samples). Our approach confirmed previous findings about the disease pathology and specified them further. We confirmed increased expression of former proposed DUX4 biomarkers, and furthermore impairment of the respiratory chain. Notably, the meta-analysis provides insights about so far not reported pathways, including misregulation of neuromuscular junction protein encoding genes, downregulation of the spliceosome, and extensive alterations of nuclear envelope protein expression. Finally, we developed a publicly available shiny app to provide a platform for researchers who want to search our analysis for genes of interest in the future.

High-throughput DNA synthesis for data storage

With the explosion of digital world, the dramatically increasing data volume is expected to reach 175 ZB (1 ZB = 1012 GB) in 2025. Storing such huge global data would consume tons of resources. Fortunately, it has been found that the deoxyribonucleic acid (DNA) molecule is the most compact and durable information storage medium in the world so far. Its high coding density and long-term preservation properties make itself one of the best data storage carriers for the future. High-throughput DNA synthesis is a key technology for "DNA data storage", which encodes binary data stream (0/1) into quaternary long DNA sequences consisting of four bases (A/G/C/T). In this review, the workflow of DNA data storage and the basic methods of artificial DNA synthesis technology are outlined first. Then, the technical characteristics of different synthesis methods and the state-of-the-art of representative commercial companies, with a primary focus on silicon chip microarray-based synthesis and novel enzymatic DNA synthesis are presented. Finally, the recent status of DNA storage and new opportunities for future development in the field of high-throughput, large-scale DNA synthesis technology are summarized.

HIHISIV: a database of gene expression in HIV and SIV host immune response

In the battle of the host against lentiviral pathogenesis, the immune response is crucial. However, several questions remain unanswered about the interaction with different viruses and their influence on disease progression. The simian immunodeficiency virus (SIV) infecting nonhuman primates (NHP) is widely used as a model for the study of the human immunodeficiency virus (HIV) both because they are evolutionarily linked and because they share physiological and anatomical similarities that are largely explored to understand the disease progression. The HIHISIV database was developed to support researchers to integrate and evaluate the large number of transcriptional data associated with the presence/absence of the pathogen (SIV or HIV) and the host response (NHP and human). The datasets are composed of microarray and RNA-Seq gene expression data that were selected, curated, analyzed, enriched, and stored in a relational database. Six query templates comprise the main data analysis functions and the resulting information can be downloaded. The HIHISIV database, available at  https://hihisiv.github.io , provides accurate resources for browsing and visualizing results and for more robust analyses of pre-existing data in transcriptome repositories.

Boldine promotes stemness of human urine-derived stem cells by activating the Wnt/β-catenin signaling pathway

Human urine-derived stem cells (hUSCs) process self-renewal and multilineage differentiation ability. Due to their non-invasive and easily available clinical source, hUSCs represent a promising alternative source of mesenchymal stem cells (MSCs) for application potential in cytotherapy. However, technical limitations, such as stemness property maintenance, have hindered hUSCs' clinical application. Certain some small molecules have been recognized with advantage in maintaining the stemness of stem cells. In this study, we identified stemness-regulated key targets of hUSCs based on the StemCellNet database, CMAP database and literature mining. Furthermore, we identified a small molecule compound, boldine, which may have the potential to promote the stemness of hUSCs. It promotes cell proliferation, multilineage differentiation and maintains stemness of hUSCs by cell viability assay, single-cell clone formation, osteogenic differentiation and stemness marker expression (OCT-4 and C-MYC). We identified that boldine may be a potential GSK-3β inhibitor by molecular docking and confirmed that it can upregulate the level of β-catenin and promote translocation of β-catenin into nucleus of hUSCs using Western blotting and immunofluorescence analysis. Our study indicates boldine activates the Wnt/β-catenin signaling pathway in hUSCs and provides an effective strategy for MSCs research and application of small molecules in maintaining the stemness of hUSCs.

Microarray fabrication techniques for multiplexed bioassay applications

Microarrays are powerful tools for high-throughput bioassays that can extract information from tens of thousands of micro-spots consisting of biomolecules. This information is invaluable to many applications, such as drug discovery and disease diagnostics. Different applications of these microarrays need spots of different shapes, sizes, and chemistries to achieve their goals. Micro/nano-fabrication techniques are used to make microarrays with different feature structures and array densities for required assay procedures. Understanding these fabrication methods is essential to creating an effective microarray. The purpose of this article is to critically review fabrication methods used in recent microarray-based bioassay studies. We summarized commonly used microarray fabrication techniques and filled the gap in recent literature on relevant topics. We discussed recent examples of how microarrays were fabricated and used in a variety of bioassays. Specifically, we examined microarray printing, various microlithography techniques, and microfluidics-based microarray fabrication. We evaluated how their application shaped the fabrication methods and compared their performance based on different applications. In the end, we discussed current challenges and outlined potential future directions. This review addressed the gap in literature and provided important insights for choosing appropriate fabrication techniques towards different applications.

The Progression in Developing Genomic Resources for Crop Improvement

Sequencing technologies have rapidly evolved over the past two decades, and new technologies are being continually developed and commercialized. The emerging sequencing technologies target generating more data with fewer inputs and at lower costs. This has also translated to an increase in the number and type of corresponding applications in genomics besides enhanced computational capacities (both hardware and software). Alongside the evolving DNA sequencing landscape, bioinformatics research teams have also evolved to accommodate the increasingly demanding techniques used to combine and interpret data, leading to many researchers moving from the lab to the computer. The rich history of DNA sequencing has paved the way for new insights and the development of new analysis methods. Understanding and learning from past technologies can help with the progress of future applications. This review focuses on the evolution of sequencing technologies, their significant enabling role in generating plant genome assemblies and downstream applications, and the parallel development of bioinformatics tools and skills, filling the gap in data analysis techniques.

RNA Sequencing of Pooled Samples Effectively Identifies Differentially Expressed Genes

Analysis of gene expression changes across the genome provides a powerful, unbiased tool for gaining insight into molecular mechanisms. We have effectively used RNA sequencing to identify differentially expressed genes in long-lived genetic mutants in C. elegans to advance our understanding of the genetic pathways that control longevity. Although RNA sequencing costs have come down, cost remains a barrier to examining multiple strains and time points with a sufficient number of biological replicates. To circumvent this, we have examined the efficacy of identifying differentially expressed genes by sequencing a pooled RNA sample from long-lived isp-1 mitochondrial mutant worms. We found that sequencing a pooled RNA sample could effectively identify genes that were found to be significantly upregulated in the two individually sequenced RNA-seq experiments. Finally, we compared the genes significantly upregulated in the two individually sequenced RNA-seq experiments to two previous microarray experiments to come up with a high-confidence list of modulated genes in long-lived isp-1 mutant worms. Overall, this work demonstrates that RNA sequencing of pooled RNA samples can be used to identify differentially expressed genes.

A transient receptor potential channel-related model based on machine learning for evaluating tumor microenvironment and immunotherapeutic strategies in acute myeloid leukemia

Background

Acute myeloid leukemia (AML) is an aggressive hematopoietic malignancy. Transient receptor potential (TRP) channels in AML still need to be further explored. A TRP channel-related model based on machine learning was established in this study.

Methods

The data were downloaded from TCGA-LAML and Genome-Tissue Expression (GTEx). TRP-related genes (TRGs) were extracted from previous literature. With the use of Single-Sample Gene Set Enrichment Analysis (ssGSEA), TRP enrichment scores (TESs) were calculated. The limma package was used to identify differentially expressed genes (DEGs), and univariate Cox regression analysis was performed to identify prognostic DEGs. The above prognostic DEGs were analyzed by Random Survival Forest and least absolute shrinkage and selection operator (Lasso) analysis to create the TRP signature. The Kaplan-Meier and receiver operating characteristic (ROC) curves were plotted to investigate the efficiency and accuracy of prognostic prediction. Moreover, genomic mutation analysis was based on GISTIC analysis. Based on ESTIMATE, TIMER, MCPcounter, and ssGSEA, the tumor microenvironment and immunological characteristics were expressly evaluated to explore immunotherapeutic strategies. Enrichment analysis for TRP signature was based on the Kyoto Encyclopedia of Genes Genomes (KEGG), Gene Ontology (GO), over-representation analysis (ORA), and Gene Set Enrichment Analysis (GSEA). Genomics of Drug Sensitivity in Cancer (GDSC) and pRRophetic were used to carry out drug sensitivity analysis. Conclusively, SCHIP1 was randomly selected to perform in vitro cyto-functional experiments.

Results

The worse clinical outcomes of patients with higher TESs were observed. There were 107 differentially expressed TRGs identified. Our data revealed 57 prognostic TRGs. Eight TRGs were obtained to establish the prognostic TRP signature, and the worse clinical outcomes of patients with higher TRP scores were found. The efficiency and accuracy of TRP signature in predicting prognosis were confirmed by ROC curves and five external validation datasets. Our data revealed that the mutation rates of DNMT3A, IDH2, MUC16, and TTN were relatively high. The level of infiltrating immune cell populations, stromal, immune, and ESTIMATE scores increased as the TRP scores increased. Nevertheless, AML patients with lower TRP scores exhibited more tumor purity. The TRP scores were found to be correlated with immunomodulators and immune checkpoints, thus revealing immune characteristics and immunotherapeutic strategies. The IC50 values of six chemotherapeutics were lower in the high TRP score (HTS) group. Finally, it was found that SCHIP1 may be the oncogenic gene.

Conclusion

The results of this study will help in understanding the role of TRP and SCHIP1 in the prognosis and development of AML.

Myocardial toxicity induced by silica nanoparticles in a transcriptome profile

The deleterious effects of silica nanoparticles (SiNPs) on human health and the ecological system have gradually gained attention owing to their heavy annual output and extensive global flux. The updated epidemiological or experimental investigations have demonstrated the potential myocardial toxicity triggered by SiNPs, but the underlying mechanisms and long-lasting cardiac effects are still poorly understood. Here, a rat model of sub-chronic respiratory exposure to SiNPs was conducted, and the histopathological analysis and ultrastructural investigation of heart tissues were carried out. More importantly, a comprehensive analysis of whole-genome transcription was utilized in rat heart to uncover key biological and cellular mechanisms triggered by SiNPs. The widening of myocardial space and partial fiber rupture were clearly manifested in rat heart after prolonged SiNPs exposure, particularly accompanied by mitochondrial swelling and cristae rupture. With the aid of Affymetrix GeneChips, 3153 differentially expressed genes (DEGs) were identified after SiNPs exposure, including 1916 down- and 1237 up-regulated genes. GO and KEGG analysis illustrated many important biological processes and pathways perturbed by SiNPs, mainly specializing in cellular stress, energy metabolism, actin filament dynamics and immune response. Signal-net analysis revealed that Prkaca (PKA) plays a core role in the cardiac toxification process of prolonged exposure of SiNPs to rats. Furthermore, qRT-PCR verified that PKA-mediated calcium signaling is probably responsible for SiNPs-induced cardiac injury. Conclusively, our study revealed that SiNPs caused myocardial injury, and particularly, provided transcriptomic insight into the role of PKA-calcium signaling triggered by SiNPs, which would facilitate SiNPs-based nanosafety assessment and biomedicine development.

Tracking ebolavirus genomic drift with a resequencing microarray

Filoviruses are emerging pathogens that cause acute fever with high fatality rate and present a global public health threat. During the 2013–2016 Ebola virus outbreak, genome sequencing allowed the study of virus evolution, mutations affecting pathogenicity and infectivity, and tracing the viral spread. In 2018, early sequence identification of the Ebolavirus as EBOV in the Democratic Republic of the Congo supported the use of an Ebola virus vaccine. However, field-deployable sequencing methods are needed to enable a rapid public health response. Resequencing microarrays (RMA) are a targeted method to obtain genomic sequence on clinical specimens rapidly, and sensitively, overcoming the need for extensive bioinformatic analysis. This study presents the design and initial evaluation of an ebolavirus resequencing microarray (Ebolavirus-RMA) system for sequencing the major genomic regions of four Ebolaviruses that cause disease in humans. The design of the Ebolavirus-RMA system is described and evaluated by sequencing repository samples of three Ebolaviruses and two EBOV variants. The ability of the system to identify genetic drift in a replicating virus was achieved by sequencing the ebolavirus glycoprotein gene in a recombinant virus cultured under pressure from a neutralizing antibody. Comparison of the Ebolavirus-RMA results to the Genbank database sequence file with the accession number given for the source RNA and Ebolavirus-RMA results compared to Next Generation Sequence results of the same RNA samples showed up to 99% agreement.

Biomolecule and Bioentity Interaction Databases in Systems Biology: A Comprehensive Review

Technological advances in high-throughput techniques have resulted in tremendous growth of complex biological datasets providing evidence regarding various biomolecular interactions. To cope with this data flood, computational approaches, web services, and databases have been implemented to deal with issues such as data integration, visualization, exploration, organization, scalability, and complexity. Nevertheless, as the number of such sets increases, it is becoming more and more difficult for an end user to know what the scope and focus of each repository is and how redundant the information between them is. Several repositories have a more general scope, while others focus on specialized aspects, such as specific organisms or biological systems. Unfortunately, many of these databases are self-contained or poorly documented and maintained. For a clearer view, in this article we provide a comprehensive categorization, comparison and evaluation of such repositories for different bioentity interaction types. We discuss most of the publicly available services based on their content, sources of information, data representation methods, user-friendliness, scope and interconnectivity, and we comment on their strengths and weaknesses. We aim for this review to reach a broad readership varying from biomedical beginners to experts and serve as a reference article in the field of Network Biology.

Lithographic Processes for the Scalable Fabrication of Micro- and Nanostructures for Biochips and Biosensors

Since the early 2000s, extensive research has been performed to address numerous challenges in biochip and biosensor fabrication in order to use them for various biomedical applications. These biochips and biosensor devices either integrate biological elements (e.g., DNA, proteins or cells) in the fabrication processes or experience post fabrication of biofunctionalization for different downstream applications, including sensing, diagnostics, drug screening, and therapy. Scalable lithographic techniques that are well established in the semiconductor industry are now being harnessed for large-scale production of such devices, with additional development to meet the demand of precise deposition of various biological elements on device substrates with retained biological activities and precisely specified topography. In this review, the lithographic methods that are capable of large-scale and mass fabrication of biochips and biosensors will be discussed. In particular, those allowing patterning of large areas from 10 cm² to m², maintaining cost effectiveness, high throughput (>100 cm² h^-1), high resolution (from micrometer down to nanometer scale), accuracy, and reproducibility. This review will compare various fabrication technologies and comment on their resolution limit and throughput, and how they can be related to the device performance, including sensitivity, detection limit, reproducibility, and robustness.

Cleaning the Medicago Microarray Database to Improve Gene Function Analysis

Transcriptomics studies have been facilitated by the development of microarray and RNA-Seq technologies, with thousands of expression datasets available for many species. However, the quality of data can be highly variable, making the combined analysis of different datasets difficult and unreliable. Most of the microarray data for Medicago truncatula, the barrel medic, have been stored and made publicly accessible on the web database Medicago truncatula Gene Expression atlas (MtGEA). The aim of this work is to ameliorate the quality of the MtGEA database through a general method based on logical and statistical relationships among parameters and conditions. The initial 716 columns available in the dataset were reduced to 607 by evaluating the quality of data through the sum of the expression levels over the entire transcriptome probes and Pearson correlation among hybridizations. The reduced dataset shows great improvements in the consistency of the data, with a reduction in both false positives and false negatives resulting from Pearson correlation and GO enrichment analysis among genes. The approach we used is of general validity and our intent is to extend the analysis to other plant microarray databases.

MiR-375 reduces the stemness of gastric cancer cells through triggering ferroptosis

BACKGROUND

Gastric cancer stem cells (CSCs) are the main causes of metastasis and drug resistance. We previously indicated that miR-375 can inhibit Helicobacter pylori-induced gastric carcinogenesis; here, we aim to explore the effects and mechanisms of miR-375 on gastric cancer (GC) cell stemness.

METHODS

Lentivirus infection was used to construct GC cells with ectopic expression of miR-375. In vitro and in vivo experiments, including analysis of tumor spheroid formation, CD44+ sub-population with stemness, stemness marker expression, and tumor-initiating ability, were performed to evaluate the effects of miR-375 on the stemness of GC cells. Furthermore, microarray and bioinformatics analysis were performed to search the potential targets of miR-375 in GC cells. Luciferase reporter, RNA immunoprecipitation, and RNA-FISH assays were carried out to verify the targeting of miR-375. Subsequently, combined with tissue microarray analysis, erastin-resistant GC cells, transmission electron microscopy, a series of agonists and oxidative stress markers, the underlying mechanisms contributing to miR-375-mediated effects were explored.

RESULTS

MiR-375 reduced the stemness of GC cells in vitro and in vivo. Mechanistically, SLC7A11 was identified as a direct target of miR-375 and miR-375 attenuated the stemness of GC cells mainly through triggering SLC7A11-dependent ferroptosis.

CONCLUSION

MiR-375 can trigger the ferroptosis through targeting SLC7A11, which is essential for miR-375-mediated inhibition on GC cell stemness. These results suggest that the miR-375/SLC7A11 regulatory axis could serve as a potential target to provoke the ferroptosis and thus attenuate the stemness of GC cells.

Uncertainties in synthetic DNA-based data storage

Deoxyribonucleic acid (DNA) has evolved to be a naturally selected, robust biomacromolecule for gene information storage, and biological evolution and various diseases can find their origin in uncertainties in DNA-related processes (e.g. replication and expression). Recently, synthetic DNA has emerged as a compelling molecular media for digital data storage, and it is superior to the conventional electronic memory devices in theoretical retention time, power consumption, storage density, and so forth. However, uncertainties in the in vitro DNA synthesis and sequencing, along with its conjugation chemistry and preservation conditions can lead to severe errors and data loss, which limit its practical application. To maintain data integrity, complicated error correction algorithms and substantial data redundancy are usually required, which can significantly limit the efficiency and scale-up of the technology. Herein, we summarize the general procedures of the state-of-the-art DNA-based digital data storage methods (e.g. write, read, and preservation), highlighting the uncertainties involved in each step as well as potential approaches to correct them. We also discuss challenges yet to overcome and research trends in the promising field of DNA-based data storage.

Using proteomic and transcriptomic data to assess activation of intracellular molecular pathways

Analysis of molecular pathway activation is the recent instrument that helps to quantize activities of various intracellular signaling, structural, DNA synthesis and repair, and biochemical processes. This may have a deep impact in fundamental research, bioindustry, and medicine. Unlike gene ontology analyses and numerous qualitative methods that can establish whether a pathway is affected in principle, the quantitative approach has the advantage of exactly measuring the extent of a pathway up/downregulation. This results in emergence of a new generation of molecular biomarkers-pathway activation levels, which reflect concentration changes of all measurable pathway components. The input data can be the high-throughput proteomic or transcriptomic profiles, and the output numbers take both positive and negative values and positively reflect overall pathway activation. Due to their nature, the pathway activation levels are more robust biomarkers compared to the individual gene products/protein levels. Here, we review the current knowledge of the quantitative gene expression interrogation methods and their applications for the molecular pathway quantization. We consider enclosed bioinformatic algorithms and their applications for solving real-world problems. Besides a plethora of applications in basic life sciences, the quantitative pathway analysis can improve molecular design and clinical investigations in pharmaceutical industry, can help finding new active biotechnological components and can significantly contribute to the progressive evolution of personalized medicine. In addition to the theoretical principles and concepts, we also propose publicly available software for the use of large-scale protein/RNA expression data to assess the human pathway activation levels.

signatureSearch: environment for gene expression signature searching and functional interpretation

signatureSearch is an R/Bioconductor package that integrates a suite of existing and novel algorithms into an analysis environment for gene expression signature (GES) searching combined with functional enrichment analysis (FEA) and visualization methods to facilitate the interpretation of the search results. In a typical GES search (GESS), a query GES is searched against a database of GESs obtained from large numbers of measurements, such as different genetic backgrounds, disease states and drug perturbations. Database matches sharing correlated signatures with the query indicate related cellular responses frequently governed by connected mechanisms, such as drugs mimicking the expression responses of a disease. To identify which processes are predominantly modulated in the GESS results, we developed specialized FEA methods combined with drug-target network visualization tools. The provided analysis tools are useful for studying the effects of genetic, chemical and environmental perturbations on biological systems, as well as searching single cell GES databases to identify novel network connections or cell types. The signatureSearch software is unique in that it provides access to an integrated environment for GESS/FEA routines that includes several novel search and enrichment methods, efficient data structures, and access to pre-built GES databases, and allowing users to work with custom databases.

Deviation from Mendelian transmission of autosomal SNPs can be used to estimate germline mutations in humans exposed to ionizing radiation

We aimed to estimate the rate of germline mutations in the offspring of individuals accidentally exposed to Cesium-137 ionizing radiation. The study included two distinct groups: one of cases, consisting of males and females accidentally exposed to low doses of ionizing radiation of Cs137, and a control group of non-exposed participants. The cases included 37 people representing 11 families and 15 children conceived after the accident. Exposed families incurred radiation absorbed doses in the range of 0.2 to 0.5 Gray. The control group included 15 families and 15 children also conceived after 1987 in Goiânia with no history of radiation exposure. DNA samples from peripheral blood were analyzed with the Affymetrix GeneChip® CytoScanHD™ to estimate point mutations in autosomal SNPs. A set of scripts previously developed was used to detect de novo mutations by comparing parent and offspring genotypes at the level of each SNP marker. Overall numbers of observed Mendelian deviations were statistically significant between the exposed and control groups. Our retrospective transgenerational DNA analysis showed a 44.0% increase in the burden of SNP mutations in the offspring of cases when compared to controls, based on the average of MFMD for the two groups. Parent-of-origin and type of nucleotide substitution were also inferred. This proved useful in a retrospective estimation of the rate of de novo germline mutations in a human population accidentally exposed to low doses of radiation from Cesium-137. Our results suggested that observed burden of germline mutations identified in offspring was a potentially useful biomarker of effect to estimate parental exposure to low doses of IR and could become an important marker suitable for biomonitoring human population exposed to environmental mutagens.

DPPN-SVM: Computational Identification of Mis-Localized Proteins in Cancers by Integrating Differential Gene Expressions With Dynamic Protein-Protein Interaction Networks

Eukaryotic cells contain numerous components, which are known as subcellular compartments or subcellular organelles. Proteins must be sorted to proper subcellular compartments to carry out their molecular functions. Mis-localized proteins are related to various cancers. Identifying mis-localized proteins is important in understanding the pathology of cancers and in developing therapies. However, experimental methods, which are used to determine protein subcellular locations, are always costly and time-consuming. We tried to identify cancer-related mis-localized proteins in three different cancers using computational approaches. By integrating gene expression profiles and dynamic protein-protein interaction networks, we established DPPN-SVM (Dynamic Protein-Protein Network with Support Vector Machine), a predictive model using the SVM classifier with diffusion kernels. With this predictive model, we identified a number of mis-localized proteins. Since we introduced the dynamic protein-protein network, which has never been considered in existing works, our model is capable of identifying more mis-localized proteins than existing studies. As far as we know, this is the first study to incorporate dynamic protein-protein interaction network in identifying mis-localized proteins in cancers.

MatchMixeR: a cross-platform normalization method for gene expression data integration

MOTIVATION

Combining gene expression (GE) profiles generated from different platforms enables previously infeasible studies due to sample size limitations. Several cross-platform normalization methods have been developed to remove the systematic differences between platforms, but they may also remove meaningful biological differences among datasets. In this work, we propose a novel approach that removes the platform, not the biological differences. Dubbed as 'MatchMixeR', we model platform differences by a linear mixed effects regression (LMER) model, and estimate them from matched GE profiles of the same cell line or tissue measured on different platforms. The resulting model can then be used to remove platform differences in other datasets. By using LMER, we achieve better bias-variance trade-off in parameter estimation. We also design a computationally efficient algorithm based on the moment method, which is ideal for ultra-high-dimensional LMER analysis.

RESULTS

Compared with several prominent competing methods, MatchMixeR achieved the highest after-normalization concordance. Subsequent differential expression analyses based on datasets integrated from different platforms showed that using MatchMixeR achieved the best trade-off between true and false discoveries, and this advantage is more apparent in datasets with limited samples or unbalanced group proportions.

AVAILABILITY AND IMPLEMENTATION

Our method is implemented in a R-package, 'MatchMixeR', freely available at: https://github.com/dy16b/Cross-Platform-Normalization.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

DNA-based microarray studies in visceral leishmaniasis: identification of biomarkers for diagnostic, prognostic and drug target for treatment

Visceral leishmaniasis (VL) is one of the major infectious diseases affecting the poorest regions of the world. Current therapy is not very much satisfactory. The alarming rise of drug resistance and the unavailability of an effective vaccine against VL urges research towards identifying new targets or biomarkers for its effective treatment. New technology developments offer some fresh hope in its diagnosis, treatment, and control. DNA microarray approach is now broadly used in parasitology research to facilitate the thoughtful of mechanisms of disease and identification of drug targets and biomarkers for diagnostic and therapeutic development. An electronic search on "VL" and "Microarray" was conducted in Medline and Scopus and papers published in the English mentioning use of DNA microarray on VL were selected and read to write this paper review. Functional analysis and interpretation of microarray results remain very challenging due to the inherent nature of experimental workflows, access, cost, and complexity of data obtained. We have explained and emphasized the use of curate knowledge of microarray in the case of VL for the identification of therapeutic target and biomarker and their selection/implementation in clinical use.

Microarray analysis of the expression profile of immune-related gene in rapid recurrence early-stage lung adenocarcinoma

Background

Although much progress has been made in the diagnosis of early-stage lung adenocarcinoma (ES-LUAD), the prognosis for ES-LUAD patients with rapid recurrence is still poor. Importantly, there is currently no effective and precise method to screen patients who may develop rapid recurrence. Therefore, it is necessary to identify potential differentially expressed genes (DEGs) in ES-LUAD patients with rapid recurrence and non-rapid recurrence.

Methods

Affymetrix GeneChip Human Transcriptome Array was used to identify DEGs between ES-LUAD patients with rapid recurrence and non-rapid recurrence. Rapid recurrence was defined as recurrence-free survival (RFS) ≦ 1 year and non-rapid recurrence was defined as RFS ≧ 3 years. The biological functions of the DEGs were analyzed by GO and KEGG pathway enrichment analyses. The protein–protein interaction (PPI) network of identified DEGs was conducted by STRING and Cytoscape software. The expression level of crucial hub genes and tumor-infiltrating lymphocytes (TILs) was verified by immunohistochemistry (IHC).

Results

A total of 416 DEGs were identified between ES-LUAD patients with and without rapid recurrence. The results of GO analysis revealed that 2 of the top 10 categories in the domain of cellular component, 2 of the top 10 in the domain of molecular function, and 9 of the top 10 in the domain of biological process were functionally related to immunity. The results of KEGG analysis showed that 6 of the top 8 pathways were functionally involved in immune regulation and inflammatory response. The PPI network analysis identified ten crucial nodal protein, including EGFR, MMP9, IL-1β, PTGS2, MMP1, and 5 histone proteins, which constituted 25 key interactions. IL-1β and PTGS2 expression were closely related to immunity and IHC analysis further revealed that low expression of IL-1β and PTGS2 is associated with rapid recurrence. Kaplan–Meier analysis further revealed that LUAD patients with lower IL-1β or PTGS2 expression had a worse RFS. When the TIL density of CD3⁺, CD4⁺, CD8⁺ and CD20⁺ subsets was less than 20%, ES-LUAD patients have a higher probability of rapid recurrence.

Conclusion

There were significant differences in the expression of immune-related genes between patients with rapid recurrence and patient with non-rapid recurrence. Immune-related genes such as IL-1β and PTGS2 and TIL density (20%) play important roles in rapid recurrence of ES-LUAD. This study provided a theoretical basis for distinguishing the two types of patients from an immunological perspective.

Electronic supplementary material

The online version of this article (10.1007/s00432-020-03287-7) contains supplementary material, which is available to authorized users.

MMP9, CXCR1, TLR6, and MPO participant in the progression of coronary artery disease

Coronary artery disease (CAD) is the most frequent cardiovascular disease, which is induced by the decreased myocardial blood supply. The present study is conducted to understand the mechanisms of CAD. The GSE98583, GSE69587, and GSE71226 datasets from the Gene Expression Omnibus database were obtained. The differentially expressed genes (DEGs) were analyzed by the limma package, then the DEGs appeared in two or three datasets were selected as the coregulated genes using the VENNY tool, followed by enrichment analysis using DAVID tool. Protein-protein interaction (PPI) network, microRNA-transcription factor-target regulatory network, and drug-gene network were visualized. Finally, quantitative PCR and dual-luciferase reporter assay were conducted to validate the expression of key genes and the target relationship. There were 221 coregulated genes in GSE98583, GSE69587, and GSE71226. Besides, four pathways and 23 functional terms for co-upregulated genes, and 11 functional terms for co-downregulated genes were enriched. The degrees of PPI network nodes matrix metallopeptidase 9 (MMP9), C-X-C motif chemokine receptor 1 (CXCR1), toll-like receptor 6 (TLR6), and myeloperoxidase (MPO) were relatively higher. Moreover, MPO could interact with MMP9, CXCR1, and TLR6 in the PPI network. In the regulatory network, TLR6 and MMP9 separately were targeted by miR-3960 and v-rel avian reticuloendotheliosis viral oncogene homolog A (RELA). Additionally, MMP9, CXCR1, and MPO were involved in the drug-gene network. The expression of MMP9, CXCR1, TLR6, and MPO were significantly upregulated in CAD samples than control, and miR-3960 could bind to TLR6 to inhibit its expression. CXCR1 and MPO might be involved in the progression of CAD. Besides, miR-3960 might function in the pathogenesis of CAD through targeting TLR6, and RELA might exert its role in CAD via targeting MMP9.

The cooperative interaction of triplex forming oligonucleotides on DNA-triplex formation at electrode surface: Molecular dynamics studies and experimental evidences

An extensive study on cooperative interaction of Triplex Forming Oligonucleotides (TFOs) with a double strand DNA, to form a triplex-DNA structure at electrode surface, is here reported. The cooperative effect on triplex structure formation was assumed by the higher binding enthalpy value, calculated for the interaction between the duplex DNA structure and the TFO1 and TFO2 probes (-67.3 KJ/mol), respect the sum of the single duplex-TFO1 and duplex-TFO2 interactions (-47.0 kJ/mol). The formation of triplex-DNA structure was proven by kinetic modelling study performed using the Luzar and Chandler model. The results indicate that after 500 ns from interaction, H-bonds between the base pairs in the double strand DNA are weaken while new H-bonds between the TFOs and duplex DNA are formed. Molecular dynamic (MD) simulations indicate that the TFOs sequence distance (138 bps) and the amount of TA*T triplet units are the keystones for the effectiveness of the cooperative effect, reaching for the selected target a minimum of energy value of -19452.6 kJ/mol. The MD data were experimentally corroborated by electrochemical measurements, detecting a HBV-clone genome at TFOs modified electrode surface. The interaction was electrochemical transduced by an intercalative Osmium based compound. The Langmuir isotherm model reports for the forming triplex DNA an association constant value of about 2.9 × 10¹⁶M^-1, this high value could be attributed to the synergic contribution of the TFOs cooperative effect and to the rigid circular duplex structure. Finally, AFM and SEM investigations suggest the formation of a triplex-DNA structure at electrode surface, consisting in circles of about 1.5 um in diameter with asymmetric stranded thickness. This finding data paving the way to future development of advanced miniaturized DNA computing and biosensors.

Strigolactone Signaling Genes Showing Differential Expression Patterns in Arabidopsis max Mutants

Strigolactone (SL) is a recently discovered class of phytohormone that inhibits shoot branching. The molecular mechanism underlying SL biosynthesis, perception, and signal transduction is vital to the plant branching phenotype. Some aspects of their biosynthesis, perception, and signaling include the role of four MORE AXILLARY GROWTH genes, MAX3, MAX4, MAX1, and MAX2. It is important to identify downstream genes that are involved in SL signaling. To achieve this, we studied the genomic aspects of the strigolactone biosynthesis pathway using microarray analysis of four max mutants. We identified SL signaling candidate genes that showed differential expression patterns in max mutants. More specifically, 1-AMINOCYCLOPROPANE-1-CARBOXYLATE SYNTHASE 4 (ACC4) and PROTEIN KINASE 3 (PKS3) displayed contrasting expression patterns, indicating a regulatory mechanism in SL signaling pathway to control different phenotypes apart from branching phenotype.

Prognostic significance of mRNA expression of CASPs in gastric cancer

Current studies suggest that the cysteinyl aspartate specific proteinase (caspase/CASP) family may be closely associated with apoptosis. Scientists have suggested that caspases may be a key to the development of more effective anti-cancer therapies. However, the prognostic value of CASP expression in gastric cancer (GC) remains unclear. Using a Kaplan-Meier plotter online database, the predictive prognostic significance of the expression of 12 CASPs genes (CASP1, CASP2, CASP3, CASP4, CASP5, CASP6, CASP7, CASP8, CASP9, CASP10, CASP12 and CASP14) to overall survival (OS) in different clinicopathological features, including Lauren classification, pathological stages, therapies employed and differentiation in gastric cancer patients was explored. The present study revealed that higher CASP1, 2, 3, 4, 5, 6, 7 and 8 mRNA expression was associated with better OS, whereas higher expression of CASP9, 10, 12 and 14 showed an unfavorable OS in all GC patients. Moreover, CASP1 to 8 were all associated with favorable OS in intestinal type and diffuse type classified by Lauren classification. Therefore, the results of the present study suggested that the CASP family may function as new prognostic indicators in GC and may be helpful in making treatment decisions.

RNA sequencing for research and diagnostics in clinical oncology

Molecular diagnostics is becoming one of the major drivers of personalized oncology. With hundreds of different approved anticancer drugs and regimens of their administration, selecting the proper treatment for a patient is at least nontrivial task. This is especially sound for the cases of recurrent and metastatic cancers where the standard lines of therapy failed. Recent trials demonstrated that mutation assays have a strong limitation in personalized selection of therapeutics, consequently, most of the drugs cannot be ranked and only a small percentage of patients can benefit from the screening. Other approaches are, therefore, needed to address a problem of finding proper targeted therapies. The analysis of RNA expression (transcriptomic) profiles presents a reasonable solution because transcriptomics stands a few steps closer to tumor phenotype than the genome analysis. Several recent studies pioneered using transcriptomics for practical oncology and showed truly encouraging clinical results. The possibility of directly measuring of expression levels of molecular drugs' targets and profiling activation of the relevant molecular pathways enables personalized prioritizing for all types of molecular-targeted therapies. RNA sequencing is the most robust tool for the high throughput quantitative transcriptomics. Its use, potentials, and limitations for the clinical oncology will be reviewed here along with the technical aspects such as optimal types of biosamples, RNA sequencing profile normalization, quality controls and several levels of data analysis.

Identification of potential agents for thymoma by integrated analyses of differentially expressed tumour-associated genes and molecular docking experiments

Thymoma, derived from the epithelial cells of the thymus, is a rare malignant tumour type. Following diagnosis with thymoma, patients generally undergo surgical treatment. However, patients with advanced-stage disease are only candidates for chemotherapy and have poor survival. Therefore, it is urgently required to explore effective chemotherapeutic agents for the treatment of thymoma. In the present study, a Bioinformatics analysis was performed to identify novel drugs for thymoma. Differentially expressed genes (DEGs) in thymoma were obtained by Gene Expression Profiling Interactive Analysis. Subsequently, these genes were processed by Connectivity Map analysis to identify suitable compounds. In addition, Metascape software was used to verify drug and target binding. Molecular docking technology was used to verify drug and target binding. Finally, absorption, distribution, metabolism and excretion parameters in the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform database were used for drug screening and for evaluation of the potential clinical value. In total, 2,447 DEGs, including 2,204 upregulated and 243 downregulated genes, were identified from 118 thymoma patients and 339 normal samples. The top 10 drugs displaying the most significant negative correlations were fulvestrant, hesperetin, zidovudine, hydrocortisone, rolitetracycline, ellipticine, sirolimus, quinisocaine, oestradiol (estradiol) and harmine. The predicted targets of these drugs were then confirmed. The score for the association between estrogen receptor 1 (ESR1) and fulvestrant was 0.99. According to the molecular docking analysis, the total scores for the interaction between ESR1 were 10.26, and those for the interaction between tamoxifen and ESR1 were 6.60. The oral bioavailability (%), drug-likeness and drug half-life for hesperetin were 70.31, 0.27 and 15.78, respectively; those for oestradiol were 53.56, 0.32 and 3.50, respectively; and those for harmine were 56.80, 0.13 and 5.04, respectively. In conclusion, several potential therapeutic drugs for thymoma were identified in the present study. The results suggested that the compounds, including fulvestrant, estradiol, hesperetin and ellipticine, represent the most likely drugs for the treatment of thymoma. Future studies should focus on testing these novel compounds in vitro and in vivo.

Text Mining for Drug Discovery

Recent advances in technology have led to the exponential growth of scientific literature in biomedical sciences. This rapid increase in information has surpassed the threshold for manual curation efforts, necessitating the use of text mining approaches in the field of life sciences. One such application of text mining is in fostering in silico drug discovery such as drug target screening, pharmacogenomics, adverse drug event detection, etc. This chapter serves as an introduction to the applications of various text mining approaches in drug discovery. It is divided into two parts with the first half as an overview of text mining in the biosciences. The second half of the chapter reviews strategies and methods for four unique applications of text mining in drug discovery.

Nano-plasmonics and electronics co-integration in CMOS enabling a pill-sized multiplexed fluorescence microarray system

The ultra-miniaturization of massively multiplexed fluorescence-based bio-molecular sensing systems for proteins and nucleic acids into a chip-scale form, small enough to fit inside a pill (∼ 0.1cm3), can revolutionize sensing modalities in-vitro and in-vivo. Prior miniaturization techniques have been limited to focusing on traditional optical components (multiple filter sets, lenses, photo-detectors, etc.) arranged in new packaging systems. Here, we report a method that eliminates all external optics and miniaturizes an entire multiplexed fluorescence system into a 2 × 1 mm2 chip through co-integration for the first time of massively scalable nano-plasmonic multi-functional optical elements and electronic processing circuitry realized in an industry standard complementary-metal-oxide semiconductor (CMOS) foundry process with absolutely 'no change' in fabrication or processing. The implemented nano-waveguide based filters operating in the visible and near-IR realized with the embedded sub-wavelength multi-layer copper-based electronic interconnects inside the chip show for the first time a sub-wavelength surface plasmon polariton mode inside CMOS. This is the principle behind the angle-insensitive nature of the filtering that operates in the presence of uncollimated and scattering environments, enabling the first optics-free 96-sensor CMOS fluorescence sensing system. The chip demonstrates the surface sensitivity of zeptomoles of quantum dot-based labels, and volume sensitivities of ∼ 100 fM for nucleic acids and ∼ 5 pM for proteins that are comparable to, if not better, than commercial fluorescence readers. The ability to integrate multi-functional nano-optical structures in a commercial CMOS process, along with all the complex electronics, can have a transformative impact and enable a new class of miniaturized and scalable chip-sized optical sensors.

Gene expression profiles in neurological tissues during West Nile virus infection: a critical meta-analysis

Background

Infections with the West Nile virus (WNV) can attack neurological tissues in the host and alter gene expression levels therein. Several individual studies have analyzed these changes in the transcriptome based on measurements with DNA microarrays. Individual microarray studies produce a high-dimensional data structure with the number of studied genes exceeding the available sample size by far. Therefore, the level of scientific evidence of these studies is rather low and results can remain uncertain. Furthermore, the individual studies concentrate on different types of tissues or different time points after infection. A general statement regarding the transcriptional changes through WNV infection in neurological tissues is therefore hard to make. We screened public databases for transcriptome expression studies related to WNV infections and used different analysis pipelines to perform meta-analyses of these data with the goal of obtaining more stable results and increasing the level of evidence.

Results

We generated new lists of genes differentially expressed between WNV infected neurological tissues and control samples. A comparison with these genes to findings of a meta-analysis of immunological tissues is performed to figure out tissue-specific differences. While 5.879 genes were identified exclusively in the neurological tissues, 15 genes were found exclusively in the immunological tissues, and 44 genes were commonly detected in both tissues. Most findings of the original studies could be confirmed by the meta-analysis with a higher statistical power, but some genes and GO terms related to WNV were newly detected, too. In addition, we identified gene ontology terms related to certain infection processes, which are significantly enriched among the differentially expressed genes. In the neurological tissues, 17 gene ontology terms were found significantly different, and 2 terms in the immunological tissues.

Conclusions

A critical discussion of our findings shows benefits but also limitations of the meta-analytic approach. In summary, the produced gene lists, identified gene ontology terms and network reconstructions appear to be more reliable than the results from the individual studies. Our meta-analysis provides a basis for further research on the transcriptional mechanisms by WNV infections in neurological tissues.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4914-4) contains supplementary material, which is available to authorized users.

All-Electrical Graphene DNA Sensor Array

Electrical sensing of biomolecules has been an important pursuit due to the label-free operation and chip-scale construct such sensing modality can enable. In particular, electrical biomolecular sensors based on nanomaterials such as semiconductor nanowires, carbon nanotubes, and graphene have demonstrated high sensitivity, which in the case of nanowires and carbon nanotubes can surpass typical optical detection sensitivity. Among these nanomaterials, graphene is well suited for a practical candidate for implementing a large-scale array of biomolecular sensors, as its two-dimensional morphology is readily compatible with industry standard top-down fabrication techniques. In our recent work published in 2014 Nature Communications, we demonstrated these benefits by creating DNA sensor arrays from chemical vapor deposition (CVD) graphene. The present chapter, which is a review of this recent work, outlines procedures demonstrating the use of individual graphene sites of the array in dual roles--electrophoretic electrodes for site specific probe DNA immobilization and field effect transistor (FET) sensors for detection of target DNA hybridization. The 100 fM detection sensitivity achieved in 7 out of 8 graphene FET sensors in the array combined with the alternative use of the graphene channels as electrophoretic electrodes for probe deposition represent steps toward creating an all-electrical multiplexed DNA array.

Intramuscular triglyceride synthesis: importance in muscle lipid partitioning in humans

Intramuscular triglyceride (IMTG) concentration is elevated in insulin-resistant individuals and was once thought to promote insulin resistance. However, endurance-trained athletes have equivalent concentration of IMTG compared with individuals with type 2 diabetes, and have very low risk of diabetes, termed the "athlete's paradox." We now know that IMTG synthesis is positively related to insulin sensitivity, but the exact mechanisms for this are unclear. To understand the relationship between IMTG synthesis and insulin sensitivity, we measured IMTG synthesis in obese control subjects, endurance-trained athletes, and individuals with type 2 diabetes during rest, exercise, and recovery. IMTG synthesis rates were positively related to insulin sensitivity, cytosolic accumulation of DAG, and decreased accumulation of C18:0 ceramide and glucosylceramide. Greater rates of IMTG synthesis in athletes were not explained by alterations in FFA concentration, DGAT1 mRNA expression, or protein content. IMTG synthesis during exercise in Ob and T2D indicate utilization as a fuel despite unchanged content, whereas IMTG concentration decreased during exercise in athletes. mRNA expression for genes involved in lipid desaturation and IMTG synthesis were increased after exercise and recovery. Further, in a subset of individuals, exercise decreased cytosolic and membrane di-saturated DAG content, which may help explain insulin sensitization after acute exercise. These data suggest IMTG synthesis rates may influence insulin sensitivity by altering intracellular lipid localization, and decreasing specific ceramide species that promote insulin resistance.

A Fully Integrated CMOS Fluorescence Biochip for DNA and RNA Testing

Design and successful implementation of a fully-integrated CMOS fluorescence biochip for DNA/RNA testing in molecular diagnostics (MDx) is presented. The biochip includes a 32×32 array of continuous wave fluorescence detection biosensing elements. Each biosensing element is capable of having unique DNA probe sequences, wavelength-selective multi-dielectric emission filter (OD of 3.6), resistive heater for thermal cycling, and a high performance and programmable photodetector. The dimension of each biosensor is 100µm×100µm with a 50µm×50µm N_well-P_sub photodiode acting as the optical transducer, and a ΣΔ modulator based photocurrent sensor. The measured photodetector performance shows ~116 dB detection dynamic range (10fA - 10nA) over the 25°C - 100°C temperature range, while being ~1 dB away from the fundamental shot-noise limit. To empirically demonstrate the compatibility of this biochip with MDx applications, we have successfully utilized the array and its thermal cycling capability to adopt a 7-plex panel for detection of 6 human upper respiratory viruses.

Recommendation on Design, Execution, and Reporting of Animal Atherosclerosis Studies: A Scientific Statement From the American Heart Association

Animal studies are a foundation for defining mechanisms of atherosclerosis and potential targets of drugs to prevent lesion development or reverse the disease. In the current literature, it is common to see contradictions of outcomes in animal studies from different research groups, leading to the paucity of extrapolations of experimental findings into understanding the human disease. The purpose of this statement is to provide guidelines for development and execution of experimental design and interpretation in animal studies. Recommendations include the following: (1) animal model selection, with commentary on the fidelity of mimicking facets of the human disease; (2) experimental design and its impact on the interpretation of data; and (3) standard methods to enhance accuracy of measurements and characterization of atherosclerotic lesions.

Recommendation on Design, Execution, and Reporting of Animal Atherosclerosis Studies: A Scientific Statement From the American Heart Association

Animal studies are a foundation for defining mechanisms of atherosclerosis and potential targets of drugs to prevent lesion development or reverse the disease. In the current literature, it is common to see contradictions of outcomes in animal studies from different research groups, leading to the paucity of extrapolations of experimental findings into understanding the human disease. The purpose of this statement is to provide guidelines for development and execution of experimental design and interpretation in animal studies. Recommendations include the following: (1) animal model selection, with commentary on the fidelity of mimicking facets of the human disease; (2) experimental design and its impact on the interpretation of data; and (3) standard methods to enhance accuracy of measurements and characterization of atherosclerotic lesions.

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.

Current Technologies and Recent Developments for Screening of HPV-Associated Cervical and Oropharyngeal Cancers

Mucosal infection by the human papillomavirus (HPV) is responsible for a growing number of malignancies, predominantly represented by cervical cancer and oropharyngeal squamous cell carcinoma. Because of the prevalence of the virus, persistence of infection, and long latency period, novel and low-cost methods are needed for effective population level screening and monitoring. We review established methods for screening of cervical and oral cancer as well as commercially-available techniques for detection of HPV DNA. We then describe the ongoing development of microfluidic nucleic acid-based biosensors to evaluate circulating host microRNAs that are produced in response to an oncogenic HPV infection. The goal is to develop an ideal screening platform that is low-cost, portable, and easy to use, with appropriate signal stability, sensitivity and specificity. Advances in technologies for sample lysis, pre-treatment and concentration, and multiplexed nucleic acid detection are provided. Continued development of these devices provides opportunities for cancer screening in low resource settings, for point-of-care diagnostics and self-screening, and for monitoring response to vaccination or surgical treatment.

Muscle sphingolipids during rest and exercise: a C18:0 signature for insulin resistance in humans

AIMS/HYPOTHESES

Ceramides and other sphingolipids comprise a family of lipid molecules that accumulate in skeletal muscle and promote insulin resistance. Chronic endurance exercise training decreases muscle ceramides and other sphingolipids, but less is known about the effects of a single bout of exercise.

METHODS

We measured basal relationships and the effect of acute exercise (1.5 h at 50% [Formula: see text]) and recovery on muscle sphingolipid content in obese volunteers, endurance trained athletes and individuals with type 2 diabetes.

RESULTS

Muscle C18:0 ceramide (p = 0.029), dihydroceramide (p = 0.06) and glucosylceramide (p = 0.03) species were inversely related to insulin sensitivity without differences in total ceramide, dihydroceramide, and glucosylceramide concentration. Muscle C18:0 dihydroceramide correlated with markers of muscle inflammation (p = 0.04). Transcription of genes encoding sphingolipid synthesis enzymes was higher in athletes, suggesting an increased capacity for sphingolipid synthesis. The total concentration of muscle ceramides and sphingolipids increased during exercise and then decreased after recovery, during which time ceramide levels reduced to significantly below basal levels.

CONCLUSIONS/INTERPRETATION

These data suggest ceramide and other sphingolipids containing stearate (18:0) are uniquely related to insulin resistance in skeletal muscle. Recovery from an exercise bout decreased muscle ceramide concentration; this may represent a mechanism promoting the insulin-sensitising effects of acute exercise.

Skeletal muscle phosphatidylcholine and phosphatidylethanolamine are related to insulin sensitivity and respond to acute exercise in humans

Several recent reports indicate that the balance of skeletal muscle phosphatidylcholine (PC) and phosphatidylethanolamine (PE) is a key determinant of muscle contractile function and metabolism. The purpose of this study was to determine relationships between skeletal muscle PC, PE and insulin sensitivity, and whether PC and PE are dynamically regulated in response to acute exercise in humans. Insulin sensitivity was measured via intravenous glucose tolerance in sedentary obese adults (OB; n = 14), individuals with type 2 diabetes (T2D; n = 15), and endurance-trained athletes (ATH; n = 15). Vastus lateralis muscle biopsies were obtained at rest, immediately after 90 min of cycle ergometry at 50% maximal oxygen consumption (V̇o2 max), and 2-h postexercise (recovery). Skeletal muscle PC and PE were measured via infusion-based mass spectrometry/mass spectrometry analysis. ATH had greater levels of muscle PC and PE compared with OB and T2D (P < 0.05), with total PC and PE positively relating to insulin sensitivity (both P < 0.05). Skeletal muscle PC:PE ratio was elevated in T2D compared with OB and ATH (P < 0.05), tended to be elevated in OB vs. ATH (P = 0.07), and was inversely related to insulin sensitivity among the entire cohort (r = -0.43, P = 0.01). Muscle PC and PE were altered by exercise, particularly after 2 h of recovery, in a highly group-specific manner. However, muscle PC:PE ratio remained unchanged in all groups. In summary, total muscle PC and PE are positively related to insulin sensitivity while PC:PE ratio is inversely related to insulin sensitivity in humans. A single session of exercise significantly alters skeletal muscle PC and PE levels, but not PC:PE ratio.

Single-Molecule Fluorescence Imaging of Interfacial DNA Hybridization Kinetics at Selective Capture Surfaces

Accurate knowledge of the kinetics of complementary oligonucleotide hybridization is integral to the design and understanding of DNA-based biosensors. In this work, single-molecule fluorescence imaging is applied to measuring rates of hybridization between fluorescently labeled target ssDNA and unlabeled probe ssDNA immobilized on glass surfaces. In the absence of probe site labeling, the capture surface must be highly selective to avoid the influence of nonspecific adsorption on the interpretation of single-molecule imaging results. This is accomplished by increasing the probe molecule site densities by a factor of ∼100 compared to optically resolvable sites so that nonspecific interactions compete with a much greater number of capture sites and by immobilizing sulfonate groups to passivate the surface between probe strands. The resulting substrates exhibit very low nonspecific adsorption, and the selectivity for binding a complementary target sequence exceeds that of a scrambled sequence by nearly 3 orders of magnitude. The population of immobilized DNA probe sites is quantified by counting individual DNA duplexes at low target concentrations, and those results are used to calibrate fluorescence intensities on the same sample at much higher target concentrations to measure a full binding isotherm. Dissociation rates are determined from interfacial residence times of individual DNA duplexes. Equilibrium and rate constants of hybridization, K(a) = 38 (±1) μM(-1), k(on) = 1.64 (±0.06) × 10(6) M(-1) s(-1), and k(off) = 4.3 (±0.1) × 10(-2) s(-1), were found not to change with surface density of immobilized probe DNA, indicating that hybridization events at neighboring probe sites are independent. To test the influence of probe-strand immobilization on hybridization, the kinetics of the probe target reaction at the surface were compared with the same reaction in free solution, and the equilibrium constants and dissociation and association rates were found to be nearly equivalent. The selectivity of these capture surfaces should facilitate sensitive investigations of DNA hybridization at the limit of counting molecules. Because the immobilized probe DNA on these surfaces is unlabeled, photobleaching of a probe label is not an issue, allowing capture substrates to be used for long periods of time or even reused in multiple experiments.