DNA microarray applications in functional genomics

Genomics and Epigenomics of Gestational Diabetes Mellitus: Understanding the Molecular Pathways of the Disease Pathogenesis

One of the most common complications during pregnancy is gestational diabetes mellitus (GDM), hyperglycemia that occurs for the first time during pregnancy. The condition is multifactorial, caused by an interaction between genetic, epigenetic, and environmental factors. However, the underlying mechanisms responsible for its pathogenesis remain elusive. Moreover, in contrast to several common metabolic disorders, molecular research in GDM is lagging. It is important to recognize that GDM is still commonly diagnosed during the second trimester of pregnancy using the oral glucose tolerance test (OGGT), at a time when both a fetal and maternal pathophysiology is already present, demonstrating the increased blood glucose levels associated with exacerbated insulin resistance. Therefore, early detection of metabolic changes and associated epigenetic and genetic factors that can lead to an improved prediction of adverse pregnancy outcomes and future cardio-metabolic pathologies in GDM women and their children is imperative. Several genomic and epigenetic approaches have been used to identify the genes, genetic variants, metabolic pathways, and epigenetic modifications involved in GDM to determine its etiology. In this article, we explore these factors as well as how their functional effects may contribute to immediate and future pathologies in women with GDM and their offspring from birth to adulthood. We also discuss how these approaches contribute to the changes in different molecular pathways that contribute to the GDM pathogenesis, with a special focus on the development of insulin resistance.

Strain-based and sex-biased differences in adrenal and pancreatic gene expression between KK/HlJ and C57BL/6 J mice

Background

The ever-increasing prevalence of diabetes and associated comorbidities serves to highlight the necessity of biologically relevant small-animal models to investigate its etiology, pathology and treatment. Although the C57BL/6 J model is amongst the most widely used mouse model due to its susceptibility to diet-induced obesity (DIO), there are a number of limitations namely [1] that unambiguous fasting hyperglycemia can only be achieved via dietary manipulation and/or chemical ablation of the pancreatic beta cells. [2] Heterogeneity in the obesogenic effects of hypercaloric feeding has been noted, together with sex-dependent differences, with males being more responsive. The KK mouse strain has been used to study aspects of the metabolic syndrome and prediabetes. We recently conducted a study which characterized the differences in male and female glucocentric parameters between the KK/HlJ and C57BL/6 J strains as well as diabetes-related behavioral differences (Inglis et al. 2019). In the present study, we further characterize these models by examining strain- and sex-dependent differences in pancreatic and adrenal gene expression using Affymetrix microarray together with endocrine-associated serum analysis.

Results

In addition to strain-associated differences in insulin tolerance, we found significant elevations in KK/HlJ mouse serum leptin, insulin and aldosterone. Additionally, glucagon and corticosterone were elevated in female mice of both strains. Using 2-factor ANOVA and a significance level set at 0.05, we identified 10,269 pancreatic and 10,338 adrenal genes with an intensity cut-off of ≥2.0 for all 4 experimental groups. In the pancreas, gene expression upregulated in the KK/HlJ strain related to increased insulin secretory granule biofunction and pancreatic hyperplasia, whereas ontology of upregulated adrenal differentially expressed genes (DEGs) related to cell signaling and neurotransmission. We established a network of functionally related DEGs commonly upregulated in both endocrine tissues of KK/HlJ mice which included the genes coding for endocrine secretory vesicle biogenesis and regulation: PCSK2, PCSK1N, SCG5, PTPRN, CHGB and APLP1. We also identified genes with sex-biased expression common to both strains and tissues including the paternally expressed imprint gene neuronatin.

Conclusion

Our novel results have further characterized the commonalities and diversities of pancreatic and adrenal gene expression between the KK/HlJ and C57BL/6 J strains as well as differences in serum markers of endocrine physiology.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07495-4.

Computational Systems Biology in Cancer: Modeling Methods and Applications

In recent years it has become clear that carcinogenesis is a complex process, both at the molecular and cellular levels. Understanding the origins, growth and spread of cancer, therefore requires an integrated or system-wide approach. Computational systems biology is an emerging sub-discipline in systems biology that utilizes the wealth of data from genomic, proteomic and metabolomic studies to build computer simulations of intra and intercellular processes. Several useful descriptive and predictive models of the origin, growth and spread of cancers have been developed in an effort to better understand the disease and potential therapeutic approaches. In this review we describe and assess the practical and theoretical underpinnings of commonly-used modeling approaches, including ordinary and partial differential equations, petri nets, cellular automata, agent based models and hybrid systems. A number of computer-based formalisms have been implemented to improve the accessibility of the various approaches to researchers whose primary interest lies outside of model development. We discuss several of these and describe how they have led to novel insights into tumor genesis, growth, apoptosis, vascularization and therapy.

ArrayIDer: automated structural re-annotation pipeline for DNA microarrays

Background

Systems biology modeling from microarray data requires the most contemporary structural and functional array annotation. However, microarray annotations, especially for non-commercial, non-traditional biomedical model organisms, are often dated. In addition, most microarray analysis tools do not readily accept EST clone names, which are abundantly represented on arrays. Manual re-annotation of microarrays is impracticable and so we developed a computational re-annotation tool (ArrayIDer) to retrieve the most recent accession mapping files from public databases based on EST clone names or accessions and rapidly generate database accessions for entire microarrays.

Results

We utilized the Fred Hutchinson Cancer Research Centre 13K chicken cDNA array – a widely-used non-commercial chicken microarray – to demonstrate the principle that ArrayIDer could markedly improve annotation. We structurally re-annotated 55% of the entire array. Moreover, we decreased non-chicken functional annotations by 2 fold. One beneficial consequence of our re-annotation was to identify 290 pseudogenes, of which 66 were previously incorrectly annotated.

Conclusion

ArrayIDer allows rapid automated structural re-annotation of entire arrays and provides multiple accession types for use in subsequent functional analysis. This information is especially valuable for systems biology modeling in the non-traditional biomedical model organisms.

Advances and perspectives in aptamer arrays

Aptamers are oligonucleotides (typically 10-60 bases in length) capable of binding target ligands with affinities similar to antibodies. The generation of high density multiplexed aptamer arrays for molecular diagnostics was first proposed nearly ten years ago for the quantification of the thousands of proteins within biological samples, including blood and urine. The tagless aptameric detection of small molecular compounds extends the application of such arrays to bioanalyses at the metabolite level. We present here a minireview on some existing technologies and highlight recent innovations that are being applied to this field, which may facilitate the vision of highly multi-parallelized arrays for the quantitative analysis of biological systems.

OligoArrayDb: pangenomic oligonucleotide microarray probe sets database

OligoArrayDb is a comprehensive database containing pangenomic oligonucleotide microarray probe sets designed for most of the sequenced genomes that are not covered by commercial catalog arrays. The availability of probe sequences, associated with custom microarray fabrication services offered by many companies and cores presents the unequalled possibility to perform microarray experiments on most of the sequenced organisms. OligoArrayDb contains more than 2.8 probes per gene in average for more than 600 organisms, mostly archaea and bacteria strains available from public database. On average, 98% of the annotated genes have at least one probe which is predicted to be specific to its intended target in >94% of the cases. OligoArrayDb is weekly updated as new sequenced genomes become available. Probe sequences, in addition to a comprehensive set of annotations can be downloaded from this database. OligoArrayDb is publicly accessible online at http://berry.engin.umich.edu/oligoarraydb.

Primer: genomic and proteomic tools for the molecular dissection of disease

Completion of the Human Genome Project has been rapidly followed by the emergence of high-throughput technologies that combine automation, miniaturization, and many other strategies and tools to enable systematic surveys of genome composition and gene expression. Of particular relevance to the prevention and management of disease are technologies such as high-throughput DNA genotyping, microarray-based gene-expression profiling, and mass spectrometry-facilitated protein profiling--platforms that collectively support the comprehensive analysis of DNA sequence variants across the genome and the global gene and protein expression changes that distinguish health from disease. Now used extensively in all facets of biomedical investigation, genomic and proteomic tools are already beginning to pinpoint molecular variants that influence risk and outcome in common diseases, and to thereby inform and direct development of novel molecular biomarkers and drug targets. As evidenced by recent advances in DNA sequencing methods, continued improvements in the scope, power, and cost efficiency of genomic and proteomic technologies should ensure their capacity to provide the scale and depth of knowledge required for translating genome sequence information into major medical impact.

Analysis of single mammalian cells on-chip

A goal of modern biology is to understand the molecular mechanisms underlying cellular function. The ability to manipulate and analyze single cells is crucial for this task. The advent of microengineering is providing biologists with unprecedented opportunities for cell handling and investigation on a cell-by-cell basis. For this reason, lab-on-a-chip (LOC) technologies are emerging as the next revolution in tools for biological discovery. In the current discussion, we seek to summarize the state of the art for conventional technologies in use by biologists for the analysis of single, mammalian cells, and then compare LOC devices engineered for these same single-cell studies. While a review of the technical progress is included, a major goal is to present the view point of the practicing biologist and the advances that might increase adoption by these individuals. The LOC field is expanding rapidly, and we have focused on areas of broad interest to the biology community where the technology is sufficiently far advanced to contemplate near-term application in biological experimentation. Focus areas to be covered include flow cytometry, electrophoretic analysis of cell contents, fluorescent-indicator-based analyses, cells as small volume reactors, control of the cellular microenvironment, and single-cell PCR.