Expression and alternative splicing of folate pathway genes in HapMap lymphoblastoid cell lines

DDX52 gene expression in LUAD tissues indicates potential as a prognostic biomarker and therapeutic target

Lung adenocarcinoma (LUAD) remains a leading cause of cancer-related morbidity and mortality globally. While DDX52, an ATP-dependent RNA helicase, plays a role in several biological processes, its specific involvement in LUAD is yet to be elucidated. We utilized ROC curves to determine DDX52's predictive potential for LUAD. Kaplan-Meier survival curves, along with univariate and multivariate Cox analyses, assessed the prognostic implications of DDX52 in LUAD. We constructed nomogram models to further delineate DDX52's influence on prognosis, employed GSEA for functional analysis, and used qRT-PCR to examine DDX52 expression in LUAD tissues. DDX52 expression was notably higher in LUAD tissues, suggesting its potential as a negative prognostic marker. We observed a direct relationship between DDX52 expression and advanced T and N stages, as well as higher grading and staging in LUAD patients. Cox analyses further underscored DDX52's role as an independent prognostic determinant for LUAD. GSEA insights indicated DDX52's influence on LUAD progression via multiple signaling pathways. Our nomogram, founded on DDX52 expression, effectively projected LUAD patient survival, as validated by calibration curves. Elevated DDX52 expression in LUAD tissues signals its potential as a poor prognostic marker. Our findings emphasize DDX52's role not only as an independent prognostic factor for LUAD but also as a significant influencer in its progression through diverse signaling pathways. The constructed nomogram also underscores the feasibility of predicting LUAD patient survival based on DDX52 expression.

Exon Array Biomarkers for the Differential Diagnosis of Schizophrenia and Bipolar Disorder

This study developed potential blood-based biomarker tests for diagnosing and differentiating schizophrenia (SZ), bipolar disorder type I (BD), and normal control (NC) subjects using mRNA gene expression signatures. A total of 90 subjects (n = 30 each for the three groups of subjects) provided blood samples at two visits. The Affymetrix exon microarray was used to profile the expression of over 1.4 million probesets. We selected potential biomarker panels using the temporal stability of the probesets and also back-tested them at two different visits for each subject. The 18-gene biomarker panels, using logistic regression modeling, correctly differentiated the three groups of subjects with high accuracy across the two different clinical visits (83-88% accuracy). The results are also consistent with the actual data and the "leave-one-out" analyses, indicating that the models should be predictive when applied to independent data cohorts. Many of the SZ and BD subjects were taking antipsychotic and mood stabilizer medications at the time of blood draw, raising the possibility that these drugs could have affected some of the differential transcription signatures. Using an independent Illumina data set of gene expression data from antipsychotic medication-free SZ subjects, the 18-gene biomarker panels produced a receiver operating characteristic curve accuracy greater than 0.866 in patients that were less than 30 years of age and medication free. We confirmed select transcripts by quantitative PCR and the nCounter® System. The episodic nature of psychiatric disorders might lead to highly variable results depending on when blood is collected in relation to the severity of the disease/symptoms. We have found stable trait gene panel markers for lifelong psychiatric disorders that may have diagnostic utility in younger undiagnosed subjects where there is a critical unmet need. The study requires replication in subjects for ultimate proof of the utility of the differential diagnosis.

An Unexpectedly Complex Architecture for Skin Pigmentation in Africans

Approximately 15 genes have been directly associated with skin pigmentation variation in humans, leading to its characterization as a relatively simple trait. However, by assembling a global survey of quantitative skin pigmentation phenotypes, we demonstrate that pigmentation is more complex than previously assumed, with genetic architecture varying by latitude. We investigate polygenicity in the KhoeSan populations indigenous to southern Africa who have considerably lighter skin than equatorial Africans. We demonstrate that skin pigmentation is highly heritable, but known pigmentation loci explain only a small fraction of the variance. Rather, baseline skin pigmentation is a complex, polygenic trait in the KhoeSan. Despite this, we identify canonical and non-canonical skin pigmentation loci, including near SLC24A5, TYRP1, SMARCA2/VLDLR, and SNX13, using a genome-wide association approach complemented by targeted resequencing. By considering diverse, under-studied African populations, we show how the architecture of skin pigmentation can vary across humans subject to different local evolutionary pressures.

Human iPSC-derived neurons and lymphoblastoid cells for personalized medicine research in neuropsychiatric disorders

The development and clinical implementation of personalized medicine crucially depends on the availability of high-quality human biosamples; animal models, although capable of modeling complex human diseases, cannot reflect the large variation in the human genome, epigenome, transcriptome, proteome, and metabolome. Although the biosamples available from public biobanks that store human tissues and cells may represent the large human diversity for most diseases, these samples are not always sufficient for developing biomarkers for patient-tailored therapies for neuropsychiatric disorders. Postmortem human tissues are available from many biobanks; nevertheless, collections of neuronal human cells from large patient cohorts representing the human diversity remain scarce. Two tools are gaining popularity for personalized medicine research on neuropsychiatric disorders: human induced pluripotent stem cell-derived neurons and human lymphoblastoid cell lines. This review examines and contrasts the advantages and limitations of each tool for personalized medicine research.

Genomics of alternative splicing: evolution, development and pathophysiology

Alternative splicing is a major cellular mechanism in metazoans for generating proteomic diversity. A large proportion of protein-coding genes in multicellular organisms undergo alternative splicing, and in humans, it has been estimated that nearly 90 % of protein-coding genes-much larger than expected-are subject to alternative splicing. Genomic analyses of alternative splicing have illuminated its universal role in shaping the evolution of genomes, in the control of developmental processes, and in the dynamic regulation of the transcriptome to influence phenotype. Disruption of the splicing machinery has been found to drive pathophysiology, and indeed reprogramming of aberrant splicing can provide novel approaches to the development of molecular therapy. This review focuses on the recent progress in our understanding of alternative splicing brought about by the unprecedented explosive growth of genomic data and highlights the relevance of human splicing variation on disease and therapy.

Multivariate methods and software for association mapping in dose-response genome-wide association studies

BACKGROUND

The large sample sizes, freedom of ethical restrictions and ease of repeated measurements make cytotoxicity assays of immortalized lymphoblastoid cell lines a powerful new in vitro method in pharmacogenomics research. However, previous studies may have over-simplified the complex differences in dose-response profiles between genotypes, resulting in a loss of power.

METHODS

The current study investigates four previously studied methods, plus one new method based on a multivariate analysis of variance (MANOVA) design. A simulation study was performed using differences in cancer drug response between genotypes for biologically meaningful loci. These loci also showed significance in separate genome-wide association studies. This manuscript builds upon a previous study, where differences in dose-response curves between genotypes were constructed using the hill slope equation.

CONCLUSION

Overall, MANOVA was found to be the most powerful method for detecting real signals, and was also the most robust method for detection using alternatives generated with the previous simulation study. This method is also attractive because test statistics follow their expected distributions under the null hypothesis for both simulated and real data. The success of this method inspired the creation of the software program MAGWAS. MAGWAS is a computationally efficient, user-friendly, open source software tool that works on most platforms and performs GWASs for individuals having multivariate responses using standard file formats.

The success of pharmacogenomics in moving genetic association studies from bench to bedside: study design and implementation of precision medicine in the post-GWAS era

Pharmacogenomics is emerging as a popular type of study for human genetics in recent years. This is primarily due to the many success stories and high potential for translation to clinical practice. In this review, the strengths and limitations of pharmacogenomics are discussed as well as the primary epidemiologic, clinical trial, and in vitro study designs implemented. A brief discussion of molecular and analytic approaches will be reviewed. Finally, several examples of bench-to-bedside clinical implementations of pharmacogenetic traits will be described. Pharmacogenomics continues to grow in popularity because of the important genetic associations identified that drive the possibility of precision medicine.

Cellular genomics for complex traits

Recent developments in the collection and analysis of cellular multilayered data in large cohorts with extensive organismal phenotyping promise to reveal links between genetic variation and biological processes. The use of these cellular resources as models for human biology - known as 'cellular phenotyping' - is likely to transform our understanding of the genetic and long-term environmental influences on complex traits. I discuss the advantages and caveats of a deeper analysis of cellular phenotypes in large cohorts and assess the methodological advances, resource needs and prospects of this new approach.

A comparison of association methods for cytotoxicity mapping in pharmacogenomics

Cytotoxicity assays of immortalized lymphoblastoid cell lines (LCLs) represent a promising new in vitro approach in pharmacogenomics research. However, previous studies employing LCLs in gene mapping have used simple association methods, which may not adequately capture the true differences in non-linear response profiles between genotypes. Two common approaches summarize each dose-response curve with either the IC50 or the slope parameter estimates from a hill slope fit and treat these estimates as the response in a linear model. The current study investigates these two methods, as well as four novel methods, and compares their power to detect differences between the response profiles of genotypes under a variety of different alternatives. The four novel methods include two methods that summarize each dose-response by its area under the curve, one method based off of an analysis of variance (ANOVA) design, and one method that compares hill slope fits for all individuals of each genotype. The power of each method was found to depend not only on the choice of alternative, but also on the choice for the set of dosages used in cytotoxicity measurements. The ANOVA-based method was found to be the most robust across alternatives and dosage sets for power in detecting differences between genotypes.

An integrative functional genomics approach for discovering biomarkers in schizophrenia

Schizophrenia (SZ) is a complex disorder resulting from both genetic and environmental causes with a lifetime prevalence world-wide of 1%; however, there are no specific, sensitive and validated biomarkers for SZ. A general unifying hypothesis has been put forward that disease-associated single nucleotide polymorphisms (SNPs) from genome-wide association study (GWAS) are more likely to be associated with gene expression quantitative trait loci (eQTL). We will describe this hypothesis and review primary methodology with refinements for testing this paradigmatic approach in SZ. We will describe biomarker studies of SZ and testing enrichment of SNPs that are associated both with eQTLs and existing GWAS of SZ. SZ-associated SNPs that overlap with eQTLs can be placed into gene-gene expression, protein-protein and protein-DNA interaction networks. Further, those networks can be tested by reducing/silencing the gene expression levels of critical nodes. We present pilot data to support these methods of investigation such as the use of eQTLs to annotate GWASs of SZ, which could be applied to the field of biomarker discovery. Those networks that have association with SNP markers, especially cis-regulated expression, might lead to a more clear understanding of important candidate genes that predispose to disease and alter expression. This method has general application to many complex disorders.

Genome-wide expression profiling of human lymphoblastoid cell lines identifies CHL1 as a putative SSRI antidepressant response biomarker

AIMS

Selective serotonin reuptake inhibitors (SSRIs) are the most commonly used class of antidepressants for treating major depression. However, approximately 30% of patients do not respond sufficiently to first-line antidepressant drug treatment and require alternative therapeutics. Genome-wide studies searching for SSRI response DNA biomarkers or studies of candidate serotonin-related genes so far have given inconclusive or contradictory results. Here, we present an alternative transcriptome-based genome-wide approach for searching antidepressant drug-response biomarkers by using drug-effect phenotypes in human lymphoblastoid cell lines (LCLs).

MATERIALS & METHODS

We screened 80 LCLs from healthy adult female individuals for growth inhibition by paroxetine. A total of 14 LCLs with reproducible high and low sensitivities to paroxetine (seven from each phenotypic group) were chosen for genome-wide expression profiling with commercial microarrays.

RESULTS

The most notable genome-wide transcriptome difference between LCLs displaying high versus low paroxetine sensitivities was a 6.3-fold lower (p = 0.0000256) basal expression of CHL1, a gene coding for a neuronal cell adhesion protein implicated in correct thalamocortical circuitry, schizophrenia and autism. The microarray findings were confirmed by real-time PCR (36-fold lower CHL1 expression levels in the high paroxetine sensitivity group). Several additional genes implicated in synaptogenesis or in psychiatric disorders, including ARRB1, CCL5, DDX60, DDX60L, ENDOD1, ENPP2, FLT1, GABRA4, GAP43, MCTP2 and SPRY2, also differed by more than 1.5-fold and a p-value of less than 0.005 between the two paroxetine sensitivity groups, as confirmed by real-time PCR experiments.

CONCLUSION

Genome-wide transcriptional profiling of in vitro phenotyped LCLs identified CHL1 and additional genes implicated in synaptogenesis and brain circuitry as putative SSRI response biomarkers. This method might be used as a preliminary tool for searching for potential depression treatment biomarkers.

Human lymphoblastoid cell line panels: novel tools for assessing shared drug pathways

Aims: While powerful in silico tools are emerging for predicting drug targets and pathways, general in vitro tools for assessing such predictions are lacking. We present a novel in vitro method for distinguishing shared versus distinct drug pathways based on comparative cell growth inhibition profiles across a small panel of human lymphoblastoid cell lines (LCLs) from individual donors. Materials & methods: LCLs from unrelated healthy donors were examined in parallel for growth inhibition profiles of various drugs, including antidepressants (paroxetine, fluoxetine, fluvoxamine, citalopram, amitriptyline and imipramine); anticancer drugs (5-fluorouracil, 6-mercaptopurine, azathioprine, methotrexate and resveratrol); steroid drugs (dexamethasone, beclomethasone and prednisolone); and antipsychotic drugs (haloperidol and clozapine). Cell growth was assessed by the colorimetric 2,3-bis(2-methoxy-4-nitro-5-sulfophenly)-5-[(phenylamino) carbonyl]-2H-tetrazolium hydroxide method following 72 h of drug exposure. Results: LCLs from unrelated individuals exhibited a wide range of sensitivities to growth inhibition by a given drug, which were independent of basal cell replication rates. Yet, each individual cell line demonstrated a consistent sensitivity to multiple drugs from the same family. High goodness-of-fit values (R2 > 0.6) were consistently observed for plots comparing the growth-inhibition profiles for paired drugs sharing a similar pathway, for example antidepressants, steroid drugs, antipsychotics, or 6-mercaptopurine compared with azathioprine, but not for drugs with different pathways. The method’s utility is demonstrated by the observation that chlorpheniramine, an antihistamine drug long suspected to also possess antidepressant-like properties, exhibits a growth-inhibition profile very similar to antidepressants. Conclusion: Comparing the growth-inhibition profiles of drugs (or compounds) of interest with the profiles of drugs with known pathways may assist in drug pathway classification. The method is useful for in vitro assessment of in silico-generated drug pathway predictions and for distinguishing shared versus distinct pathways for compounds of interest. Comparative transcriptomics analysis of human lymphoblastoid cell lines exhibiting ‘edge’ sensitivities can subsequently be utilized in the search for drug response biomarkers for personalized pharmacotherapy. The limitations and advantages of the method are discussed.