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1.  Reviewer acknowledgement 2014 
Genome Medicine  2015;7(1):14.
Contributing reviewers
The editors of Genome Medicine are extremely grateful for the time, hard work and support of all our reviewers, and would like to thank everyone who contributed to the journal in Volume 6 (2014).
PMCID: PMC4334913
2.  The three-body problem of therapy with induced pluripotent stem cells 
Genome Medicine  2015;7(1):15.
Regenerative medicine has a three-body problem: alignment of the dynamics of the genome, stem cell and patient. Focusing on the rare inherited fragile skin disorder epidermolysis bullosa, three recent innovative studies have used induced pluripotent stem cells and gene correction, revertant mosaicism or genome editing to advance the prospects of better cell-based therapeutics to restore skin structure and function for epidermolysis bullosa and potentially other inherited diseases.
PMCID: PMC4335559
3.  Copy number variation and brain structure: lessons learned from chromosome 16p11.2 
Genome Medicine  2015;7(1):13.
Recent work has linked specific genetic variation found in human populations to risk for developing neuropsychiatric diseases. How that risk is mediated through molecular-, cellular- and systems-level mechanisms now becomes the central question in this field. Two recent papers studying high-penetrance copy number variation at chromosome 16p11.2 find large changes in brain structure, refining hypotheses about the regions of the brain that are affected and implicating specific neurodevelopmental processes in these changes.
PMCID: PMC4329653
4.  Associations between self-referral and health behavior responses to genetic risk information 
Genome Medicine  2015;7(1):10.
Studies examining whether genetic risk information about common, complex diseases can motivate individuals to improve health behaviors and advance planning have shown mixed results. Examining the influence of different study recruitment strategies may help reconcile inconsistencies.
Secondary analyses were conducted on data from the REVEAL study, a series of randomized clinical trials examining the impact of genetic susceptibility testing for Alzheimer’s disease (AD). We tested whether self-referred participants (SRPs) were more likely than actively recruited participants (ARPs) to report health behavior and advance planning changes after AD risk and APOE genotype disclosure.
Of 795 participants with known recruitment status, 546 (69%) were self-referred and 249 (31%) had been actively recruited. SRPs were younger, less likely to identify as African American, had higher household incomes, and were more attentive to AD than ARPs (all P < 0.01). They also dropped out of the study before genetic risk disclosure less frequently (26% versus 41%, P < 0.001). Cohorts did not differ in their likelihood of reporting a change to at least one health behavior 6 weeks and 12 months after genetic risk disclosure, nor in intentions to change at least one behavior in the future. However, interaction effects were observed where ε4-positive SRPs were more likely than ε4-negative SRPs to report changes specifically to mental activities (38% vs 19%, p < 0.001) and diets (21% vs 12%, p = 0.016) six weeks post-disclosure, whereas differences between ε4-positive and ε4-negative ARPs were not evident for mental activities (15% vs 21%, p = 0.413) or diets (8% versus 16%, P = 0.190). Similarly, ε4-positive participants were more likely than ε4-negative participants to report intentions to change long-term care insurance among SRPs (20% vs 5%, p < 0.001), but not ARPs (5% versus 9%, P = 0.365).
Individuals who proactively seek AD genetic risk assessment are more likely to undergo testing and use results to inform behavior changes than those who respond to genetic testing offers. These results demonstrate how the behavioral impact of genetic risk information may vary according to the models by which services are provided, and suggest that how participants are recruited into translational genomics research can influence findings.
Trial registration NCT00089882 and NCT00462917
Electronic supplementary material
The online version of this article (doi:10.1186/s13073-014-0124-0) contains supplementary material, which is available to authorized users.
PMCID: PMC4311425  PMID: 25642295
5.  Data sharing policy design for consortia: challenges for sustainability 
Genome Medicine  2014;6(1):4.
The field of human genomics has led advances in the sharing of data with a view to facilitating translation of research into innovations for human health. This change in scientific practice has been implemented through new policy developed by many principal investigators, project managers and funders, which has ultimately led to new forms of practice and innovative governance models for data sharing. Here, we examine the development of the governance of data sharing in genomics, and explore some of the key challenges associated with the design and implementation of these policies. We examine how the incremental nature of policy design, the perennial problem of consent, the gridlock caused by multiple and overlapping access systems, the administrative burden and the problems with incentives and acknowledgment all have an impact on the potential for data sharing to be maximized. We conclude by proposing ways in which the scientific community can address these problems, to improve the sustainability of data sharing into the future.
PMCID: PMC3978924  PMID: 24475754
6.  TET proteins and the control of cytosine demethylation in cancer 
Genome Medicine  2015;7(1):9.
The discovery that ten-eleven translocation (TET) proteins are α-ketoglutarate-dependent dioxygenases involved in the conversion of 5-methylcytosines (5-mC) to 5-hydroxymethylcytosine (5-hmC), 5-formylcytosine and 5-carboxycytosine has revealed new pathways in the cytosine methylation and demethylation process. The description of inactivating mutations in TET2 suggests that cellular transformation is in part caused by the deregulation of this 5-mC conversion. The direct and indirect deregulation of methylation control through mutations in DNA methyltransferase and isocitrate dehydrogenase (IDH) genes, respectively, along with the importance of cytosine methylation in the control of normal and malignant cellular differentiation have provided a conceptual framework for understanding the early steps in cancer development. Here, we review recent advances in our understanding of the cytosine methylation cycle and its implication in cellular transformation, with an emphasis on TET enzymes and 5-hmC. Ongoing clinical trials targeting the activity of mutated IDH enzymes provide a proof of principle that DNA methylation is targetable, and will trigger further therapeutic applications aimed at controlling both early and late stages of cancer development.
PMCID: PMC4308928  PMID: 25632305
7.  Genomics for clinical utility: the future is near 
Genome Medicine  2014;6(1):3.
A report on the Precision Medicine: Personal Genomes and Pharmacogenomics meeting, Cold Spring Harbor Laboratory, USA, November 13–16, 2013.
PMCID: PMC3978760  PMID: 24468134
8.  Using inactivating mutations to provide insight into drug action 
Genome Medicine  2015;7(1):7.
The role of ezetimibe in lowering plasma cholesterol has been established; however, controversy remains about its clinical benefit. A recent study utilizes naturally occurring genetic variation within the NPC1-like 1 gene (NPC1L1) to demonstrate the potential for pharmacologic inhibition of the protein to reduce the risk of coronary heart disease. This research demonstrates the application of the concept of genocopy to a population-based validation of NPC1L1 as a therapeutic target.
PMCID: PMC4307143  PMID: 25628760
9.  Higher gene expression variability in the more aggressive subtype of chronic lymphocytic leukemia 
Genome Medicine  2015;7(1):8.
Chronic lymphocytic leukemia (CLL) presents two subtypes which have drastically different clinical outcomes, IgVH mutated (M-CLL) and IgVH unmutated (U-CLL). So far, these two subtypes are not associated to clear differences in gene expression profiles. Interestingly, recent results have highlighted important roles for heterogeneity, both at the genetic and at the epigenetic level in CLL progression.
We analyzed gene expression data of two large cohorts of CLL patients and quantified expression variability across individuals to investigate differences between the two subtypes using different measures and statistical tests. Functional significance was explored by pathway enrichment and network analyses. Furthermore, we implemented a random forest approach based on expression variability to classify patients into disease subtypes.
We found that U-CLL, the more aggressive type of the disease, shows significantly increased variability of gene expression across patients and that, overall, genes that show higher variability in the aggressive subtype are related to cell cycle, development and inter-cellular communication. These functions indicate a potential relation between gene expression variability and the faster progression of this CLL subtype. Finally, a classifier based on gene expression variability was able to correctly predict the disease subtype of CLL patients.
There are strong relations between gene expression variability and disease subtype linking significantly increased expression variability to phenotypes such as aggressiveness and resistance to therapy in CLL.
Electronic supplementary material
The online version of this article (doi:10.1186/s13073-014-0125-z) contains supplementary material, which is available to authorized users.
PMCID: PMC4308895  PMID: 25632304
10.  Bayesian models for syndrome- and gene-specific probabilities of novel variant pathogenicity 
Genome Medicine  2015;7(1):5.
With the advent of affordable and comprehensive sequencing technologies, access to molecular genetics for clinical diagnostics and research applications is increasing. However, variant interpretation remains challenging, and tools that close the gap between data generation and data interpretation are urgently required. Here we present a transferable approach to help address the limitations in variant annotation.
We develop a network of Bayesian logistic regression models that integrate multiple lines of evidence to evaluate the probability that a rare variant is the cause of an individual’s disease. We present models for genes causing inherited cardiac conditions, though the framework is transferable to other genes and syndromes.
Our models report a probability of pathogenicity, rather than a categorisation into pathogenic or benign, which captures the inherent uncertainty of the prediction. We find that gene- and syndrome-specific models outperform genome-wide approaches, and that the integration of multiple lines of evidence performs better than individual predictors. The models are adaptable to incorporate new lines of evidence, and results can be combined with familial segregation data in a transparent and quantitative manner to further enhance predictions.
Though the probability scale is continuous, and innately interpretable, performance summaries based on thresholds are useful for comparisons. Using a threshold probability of pathogenicity of 0.9, we obtain a positive predictive value of 0.999 and sensitivity of 0.76 for the classification of variants known to cause long QT syndrome over the three most important genes, which represents sufficient accuracy to inform clinical decision-making. A web tool APPRAISE [] provides access to these models and predictions.
Our Bayesian framework provides a transparent, flexible and robust framework for the analysis and interpretation of rare genetic variants. Models tailored to specific genes outperform genome-wide approaches, and can be sufficiently accurate to inform clinical decision-making.
Electronic supplementary material
The online version of this article (doi:10.1186/s13073-014-0120-4) contains supplementary material, which is available to authorized users.
PMCID: PMC4308924  PMID: 25649125
11.  Variant interpretation through Bayesian fusion of frequency and genomic knowledge 
Genome Medicine  2015;7(1):4.
Variant interpretation is a central challenge in genomic medicine. A recent study demonstrates the power of Bayesian statistical approaches to improve interpretation of variants in the context of specific genes and syndromes. Such Bayesian approaches combine frequency (in the form of observed genetic variation in cases and controls) with biological annotations to determine a probability of pathogenicity. These Bayesian approaches complement other efforts to catalog human variation.
See related Research; 10.1186/s13073-014-0120-4
PMCID: PMC4308929  PMID: 25632303
13.  How behavioral economics can help to avoid ‘The last mile problem’ in whole genome sequencing 
Genome Medicine  2015;7(1):3.
Editorial summary
Failure to consider lessons from behavioral economics in the case of whole genome sequencing may cause us to run into the ‘last mile problem’ - the failure to integrate newly developed technology, on which billions of dollars have been invested, into society in a way that improves human behavior and decision-making.
PMCID: PMC4302430  PMID: 25614766
14.  Comparison of DNA methylation profiles in human fetal and adult red blood cell progenitors 
Genome Medicine  2015;7(1):1.
DNA methylation is an epigenetic modification that plays an important role during mammalian development. Around birth in humans, the main site of red blood cell production moves from the fetal liver to the bone marrow. DNA methylation changes at the β-globin locus and a switch from fetal to adult hemoglobin production characterize this transition. Understanding this globin switch may improve the treatment of patients with sickle cell disease and β-thalassemia, two of the most common Mendelian diseases in the world. The goal of our study was to describe and compare the genome-wide patterns of DNA methylation in fetal and adult human erythroblasts.
We used the Illumina HumanMethylation 450 k BeadChip to measure DNA methylation at 402,819 CpGs in ex vivo-differentiated erythroblasts from 12 fetal liver and 12 bone marrow CD34+ donors.
We identified 5,937 differentially methylated CpGs that overlap with erythroid enhancers and binding sites for erythropoiesis-related transcription factors. Combining this information with genome-wide association study results, we show that erythroid enhancers define particularly promising genomic regions to identify new genetic variants associated with fetal hemoglobin (HbF) levels in humans. Many differentially methylated CpGs are located near genes with unanticipated roles in red blood cell differentiation and proliferation. For some of these new candidate genes, we confirm the correlation between DNA methylation and gene expression levels in red blood cell progenitors. We also provide evidence that DNA methylation and genetic variation at the β-globin locus independently control globin gene expression in adult erythroblasts.
Our DNA methylome maps confirm the widespread dynamic changes in DNA methylation that occur during human erythropoiesis. These changes tend to happen near erythroid enhancers, further highlighting their importance in erythroid regulation and HbF production. Finally, DNA methylation may act independently of the transcription factor BCL11A to repress fetal hemoglobin production. This provides cues on strategies to more efficiently re-activate HbF production in sickle cell disease and β-thalassemia patients.
Electronic supplementary material
The online version of this article (doi:10.1186/s13073-014-0122-2) contains supplementary material, which is available to authorized users.
PMCID: PMC4298057  PMID: 25606059
15.  VERSE: a novel approach to detect virus integration in host genomes through reference genome customization 
Genome Medicine  2015;7(1):2.
Fueled by widespread applications of high-throughput next generation sequencing (NGS) technologies and urgent need to counter threats of pathogenic viruses, large-scale studies were conducted recently to investigate virus integration in host genomes (for example, human tumor genomes) that may cause carcinogenesis or other diseases. A limiting factor in these studies, however, is rapid virus evolution and resulting polymorphisms, which prevent reads from aligning readily to commonly used virus reference genomes, and, accordingly, make virus integration sites difficult to detect. Another confounding factor is host genomic instability as a result of virus insertions. To tackle these challenges and improve our capability to identify cryptic virus-host fusions, we present a new approach that detects Virus intEgration sites through iterative Reference SEquence customization (VERSE). To the best of our knowledge, VERSE is the first approach to improve detection through customizing reference genomes. Using 19 human tumors and cancer cell lines as test data, we demonstrated that VERSE substantially enhanced the sensitivity of virus integration site detection. VERSE is implemented in the open source package VirusFinder 2 that is available at
Electronic supplementary material
The online version of this article (doi:10.1186/s13073-015-0126-6) contains supplementary material, which is available to authorized users.
PMCID: PMC4333248
16.  Does family always matter? Public genomes and their effect on relatives 
Genome Medicine  2013;5(12):107.
PMCID: PMC3978598  PMID: 24342550
17.  Transcriptome-wide signatures of tumor stage in kidney renal clear cell carcinoma: connecting copy number variation, methylation and transcription factor activity 
Genome Medicine  2014;6(12):117.
Comparative analysis of expression profiles between early and late stage cancers can help to understand cancer progression and metastasis mechanisms and to predict the clinical aggressiveness of cancer. The observed stage-dependent expression changes can be explained by genetic and epigenetic alterations as well as transcription dysregulation. Unlike genetic and epigenetic alterations, however, activity changes of transcription factors, generally occurring at the post-transcriptional or post-translational level, are hard to detect and quantify.
Here we developed a statistical framework to infer the activity changes of transcription factors by simultaneously taking into account the contributions of genetic and epigenetic alterations to mRNA expression variations.
Applied to kidney renal clear cell carcinoma (KIRC), the model underscored the role of methylation as a significant contributor to stage-dependent expression alterations and identified key transcription factors as potential drivers of cancer progression.
Integrating copy number, methylation, and transcription factor activity signatures to explain stage-dependent expression alterations presented a precise and comprehensive view on the underlying mechanisms during KIRC progression.
Electronic supplementary material
The online version of this article (doi:10.1186/s13073-014-0117-z) contains supplementary material, which is available to authorized users.
PMCID: PMC4293006  PMID: 25648588
18.  Design, methods, and participant characteristics of the Impact of Personal Genomics (PGen) Study, a prospective cohort study of direct-to-consumer personal genomic testing customers 
Genome Medicine  2014;6(12):96.
Designed in collaboration with 23andMe and Pathway Genomics, the Impact of Personal Genomics (PGen) Study serves as a model for academic-industry partnership and provides a longitudinal dataset for studying psychosocial, behavioral, and health outcomes related to direct-to-consumer personal genomic testing (PGT). Web-based surveys administered at three time points, and linked to individual-level PGT results, provide data on 1,464 PGT customers, of which 71% completed each follow-up survey and 64% completed all three surveys. The cohort includes 15.7% individuals of non-white ethnicity, and encompasses a range of income, education, and health levels. Over 90% of participants agreed to re-contact for future research.
Electronic supplementary material
The online version of this article (doi:10.1186/s13073-014-0096-0) contains supplementary material, which is available to authorized users.
PMCID: PMC4256737  PMID: 25484922
19.  Linking hypothetical knowledge patterns to disease molecular signatures for biomarker discovery in Alzheimer’s disease 
Genome Medicine  2014;6(11):97.
A number of compelling candidate Alzheimer’s biomarkers remain buried within the literature. Indeed, there should be a systematic effort towards gathering this information through approaches that mine publicly available data and substantiate supporting evidence through disease modeling methods. In the presented work, we demonstrate that an integrative gray zone mining approach can be used as a way to tackle this challenge successfully.
The methodology presented in this work combines semantic information retrieval and experimental data through context-specific modeling of molecular interactions underlying stages in Alzheimer’s disease (AD). Information about putative, highly speculative AD biomarkers was harvested from the literature using a semantic framework and was put into a functional context through disease- and stage-specific models. Staging models of AD were further validated for their functional relevance and novel biomarker candidates were predicted at the mechanistic level.
Three interaction models were built representing three stages of AD, namely mild, moderate, and severe stages. Integrated analysis of these models using various arrays of evidence gathered from experimental data and published knowledge resources led to identification of four candidate biomarkers in the mild stage. Mode of action of these candidates was further reasoned in the mechanistic context of models by chains of arguments. Accordingly, we propose that some of these ‘emerging’ potential biomarker candidates have a reasonable mechanistic explanation and deserve to be investigated in more detail.
Systematic exploration of derived hypothetical knowledge leads to generation of a coherent overview on emerging knowledge niches. Integrative analysis of this knowledge in the context of disease mechanism is a promising approach towards identification of candidate biomarkers taking into consideration the complex etiology of disease. The added value of this strategy becomes apparent particularly in the area of biomarker discovery for neurodegenerative diseases where predictive biomarkers are desperately needed.
Electronic supplementary material
The online version of this article (doi:10.1186/s13073-014-0097-z) contains supplementary material, which is available to authorized users.
PMCID: PMC4256903  PMID: 25484918
20.  Deciphering miRNA transcription factor feed-forward loops to identify drug repurposing candidates for cystic fibrosis 
Genome Medicine  2014;6(12):94.
Cystic fibrosis (CF) is a fatal genetic disorder caused by mutations in the CF transmembrane conductance regulator (CFTR) gene that primarily affects the lungs and the digestive system, and the current drug treatment is mainly able to alleviate symptoms. To improve disease management for CF, we considered the repurposing of approved drugs and hypothesized that specific microRNA (miRNA) transcription factors (TF) gene networks can be used to generate feed-forward loops (FFLs), thus providing treatment opportunities on the basis of disease specific FFLs.
Comprehensive database searches revealed significantly enriched TFs and miRNAs in CF and CFTR gene networks. The target genes were validated using ChIPBase and by employing a consensus approach of diverse algorithms to predict miRNA gene targets. STRING analysis confirmed protein-protein interactions (PPIs) among network partners and motif searches defined composite FFLs. Using information extracted from SM2miR and Pharmaco-miR, an in silico drug repurposing pipeline was established based on the regulation of miRNA/TFs in CF/CFTR networks.
In human airway epithelium, a total of 15 composite FFLs were constructed based on CFTR specific miRNA/TF gene networks. Importantly, nine of them were confirmed in patient samples and CF epithelial cells lines, and STRING PPI analysis provided evidence that the targets interacted with each other. Functional analysis revealed that ubiquitin-mediated proteolysis and protein processing in the endoplasmic reticulum dominate the composite FFLs, whose major functions are folding, sorting, and degradation. Given that the mutated CFTR gene disrupts the function of the chloride channel, the constructed FFLs address mechanistic aspects of the disease and, among 48 repurposing drug candidates, 26 were confirmed with literature reports and/or existing clinical trials relevant to the treatment of CF patients.
The construction of FFLs identified promising drug repurposing candidates for CF and the developed strategy may be applied to other diseases as well.
Electronic supplementary material
The online version of this article (doi:10.1186/s13073-014-0094-2) contains supplementary material, which is available to authorized users.
PMCID: PMC4256829  PMID: 25484921
21.  Systematic evaluation of connectivity map for disease indications 
Genome Medicine  2014;6(12):540.
Connectivity map data and associated methodologies have become a valuable tool in understanding drug mechanism of action (MOA) and discovering new indications for drugs. One of the key ideas of connectivity map (CMAP) is to measure the connectivity between disease gene expression signatures and compound-induced gene expression profiles. Despite multiple impressive anecdotal validations, only a few systematic evaluations have assessed the accuracy of this aspect of CMAP, and most of these utilize drug-to-drug matching to transfer indications across the two drugs.
To assess CMAP methodologies in a more direct setting, namely the power of classifying known drug-disease relationships, we evaluated three CMAP-based methods on their prediction performance against a curated dataset of 890 true drug-indication pairs. The disease signatures were generated using Gene Logic BioExpress™ system and the compound profiles were derived from the Connectivity Map database (CMAP, build 02,
The similarity scoring algorithm called eXtreme Sum (XSum) performs better than the standard Kolmogorov-Smirnov (KS) statistic in terms of the area under curve and can achieve a four-fold enrichment at 0.01 false positive rate level, with AUC = 2.2E-4, P value = 0.0035.
Connectivity map can significantly enrich true positive drug-indication pairs given an effective matching algorithm.
Electronic supplementary material
The online version of this article (doi:10.1186/s13073-014-0095-1) contains supplementary material, which is available to authorized users.
PMCID: PMC4278345  PMID: 25606058
22.  A HIF-1 network reveals characteristics of epithelial-mesenchymal transition in acute promyelocytic leukemia 
Genome Medicine  2014;6(12):84.
Acute promyelocytic leukemia (APL) is a sub-type of acute myeloid leukemia (AML) characterized by a block of myeloid differentiation at the promyelocytic stage and the predominant t(15:17) chromosomal translocation. We have previously determined that cells from APL patients show increased expression of genes regulated by hypoxia-inducible transcription factors (HIFs) compared to normal promyelocytes. HIFs regulate crucial aspects of solid tumor progression and are currently being implicated in leukemogenesis.
To investigate the contribution of hypoxia-related signaling in APL compared to other AML sub-types, we reverse engineered a transcriptional network from gene expression profiles of AML patients’ samples, starting from a list of direct target genes of HIF-1. A HIF-1-dependent subnetwork of genes specifically dysregulated in APL was derived from the comparison between APL and other AMLs.
Interestingly, this subnetwork shows a unique involvement of genes related to extracellular matrix interaction and cell migration, with decreased expression of genes involved in cell adhesion and increased expression of genes implicated in motility and invasion, thus unveiling the presence of characteristics of epithelial-mesenchymal transition (EMT). We observed that the genes of this subnetwork, whose dysregulation shows a peculiar pattern across different AML sub-types, distinguish malignant from normal promyelocytes, thus ruling out dependence on a myeloid developmental stage. Also, expression of these genes is reversed upon treatment of APL-derived NB4 cells with all-trans retinoic acid and cell differentiation.
Our data suggest that pathways related to EMT-like processes can be implicated also in hematological malignancies besides solid tumors, and can identify specific AML sub-types.
Electronic supplementary material
The online version of this article (doi:10.1186/s13073-014-0084-4) contains supplementary material, which is available to authorized users.
PMCID: PMC4249615  PMID: 25452766
23.  From small studies to precision medicine: prioritizing candidate biomarkers 
Genome Medicine  2013;5(11):104.
There are still many open questions in data-analytic research pertaining to biomarker development in the era of personalized/precision medicine and big data. Among them is the question of what constitutes best practice for the extraction of prioritized lists of candidate biomarkers from smaller studies that are ‘hypothesis generating’ in nature. A recent comparison of methods to detect patient-specific aberrant expression events in small- to medium-sized (10 to 50 samples) studies provides results that favor the use of outlying degree methods.
See related Research,
PMCID: PMC3978446  PMID: 24286480
24.  Identifying population differences in genes that affect body mass index 
Genome Medicine  2013;5(11):102.
Genomic regions of interest can be narrowed by studying populations that have patterns of low linkage disequilibrium. A recent study of body mass index in African Americans demonstrated this point and, through cross-population analyses, revealed additional genomic associations. This comparative analysis showed how rare alleles that associate with traits in specific populations can be detected in cohorts where the same alleles are not rare, and highlights how population diversity can aid genetic analyses.
PMCID: PMC3978763  PMID: 24286457
25.  Plasmodium falciparum gene expression measured directly from tissue during human infection 
Genome Medicine  2014;6(11):110.
During the latter half of the natural 48-h intraerythrocytic life cycle of human Plasmodium falciparum infection, parasites sequester deep in endothelium of tissues, away from the spleen and inaccessible to peripheral blood. These late-stage parasites may cause tissue damage and likely contribute to clinical disease, and a more complete understanding of their biology is needed. Because these life cycle stages are not easily sampled due to deep tissue sequestration, measuring in vivo gene expression of parasites in the trophozoite and schizont stages has been a challenge.
We developed a custom nCounter® gene expression platform and used this platform to measure malaria parasite gene expression profiles in vitro and in vivo. We also used imputation to generate global transcriptional profiles and assessed differential gene expression between parasites growing in vitro and those recovered from malaria-infected patient tissues collected at autopsy.
We demonstrate, for the first time, global transcriptional expression profiles from in vivo malaria parasites sequestered in human tissues. We found that parasite physiology can be correlated with in vitro data from an existing life cycle data set, and that parasites in sequestered tissues show an expected schizont-like transcriptional profile, which is conserved across tissues from the same patient. Imputation based on 60 landmark genes generated global transcriptional profiles that were highly correlated with genome-wide expression patterns from the same samples measured by microarray. Finally, differential expression revealed a limited set of in vivo upregulated transcripts, which may indicate unique parasite genes involved in human clinical infections.
Our study highlights the utility of a custom nCounter® P. falciparum probe set, validation of imputation within Plasmodium species, and documentation of in vivo schizont-stage expression patterns from human tissues.
Electronic supplementary material
The online version of this article (doi:10.1186/s13073-014-0110-6) contains supplementary material, which is available to authorized users.
PMCID: PMC4269068  PMID: 25520756

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