Population-specific Gene Expression Research Articles

Population-Specific gene expression profiles in prostate cancer: insights from Weighted Gene Co-expression Network Analysis (WGCNA)

This study investigates the genetic factors contributing to the disparity in prostate cancer incidence and progression among African American men (AAM) compared to European American men (EAM). The research focuses on employing Weighted Gene Co-expression Network Analysis (WGCNA) on public microarray data obtained from prostate cancer patients. The study employed WGCNA to identify clusters of genes with correlated expression patterns, which were then analyzed for their connection to population backgrounds. Additionally, pathway enrichment analysis was conducted to understand the significance of the identified gene modules in prostate cancer pathways. The Least Absolute Shrinkage and Selection Operator (LASSO) and Correlation-based Feature Selection (CFS) methods were utilized for selection of biomarker genes. The results revealed 353 differentially expressed genes (DEGs) between AAM and EAM. Six significant gene expression modules were identified through WGCNA, showing varying degrees of correlation with prostate cancer. LASSO and CFS methods pinpointed critical genes, as well as six common genes between both approaches, which are indicative of their vital role in the disease. The XGBoost classifier validated these findings, achieving satisfactory prediction accuracy. Genes such as APRT, CCL2, BEX2, MGC26963, and PLAU were identified as key genes significantly associated with cancer progression. In conclusion, the research underlines the importance of incorporating AAM and EAM population diversity in genomic studies, particularly in cancer research. In addition, the study highlights the effectiveness of integrating machine learning techniques with gene expression analysis as a robust methodology for identifying critical genes in cancer research.

Abstract 4830: Predicting drug treatment response in African American lung cancer patients who smoke

Abstract Background: Lung cancer is the third most commonly diagnosed cancer in the US and the leading cause of cancer-related death. African Americans (AAs) have higher lung cancer incidence and mortality rates, and lower survival rates, when compared to their European American (EA) counterparts. It is widely known that cigarette smoking is the strongest risk factor for lung cancer development, but it also plays a significant role in clinical outcome. Cigarette smoking after lung cancer diagnosis (up to 30% patients) comes with several risks, including increased secondary tumors and mortality rates (by 20%), as well as poor treatment outcomes (higher radiation and chemotherapy failure and higher drug clearance). There are limited transcriptomic studies that describe molecular characterizations of AA smokers with lung cancer to predict treatment outcomes. Hypothesis: Cigarette smoking leads to greater drug resistance-related gene expression in AA lung cancer patients. Methods: mRNA expression changes in an AA (NCI-H2172) and EA (NCI-H1944) lung cancer cell line pair exposed to liquid cigarette smoke condensate (CSC) were profiled. The cell lines were matched based on age (62 years), sex (female), histology (lung adenocarcinoma, LUAD, the most common subtype), and culture properties (adherent). The CSC was derived from Murty Pharmaceuticals and prepared by smoking University of Kentucky's 3R4F Standard Research Cigarette on an FTC Smoke Machine. A total of 500,000 cells per well were treated for one hour under standard incubation conditions (37°C, pH 7.0, 5% CO2). Cells were pelleted, RNAs were isolated, treated with DNAse-I to remove contaminating genomic DNA, quantified, and sent for bulk mRNA-sequencing (Novogene Co). Three biological replicates and technical triplicates were performed. Partek Flow and Partek Genomics Suite workflows were used to identify population-specific differentially expressed gene (DEG) signatures and pathway enrichment after CSC exposure. DEG were profiled in LUAD tissues from current smokers in an AA and EA TCGA cohort (n = 18 AAs, 86 EAs). Results: Population-specific gene expression changes were discovered (n = 52 AA DEGs, n = 31 EA DEGs; P <0.001). All of the DEGs were mutually exclusive with no overlap. Some pathways were shared between both populations and others were unique. PI3K/Akt/mTOR signaling pathway enrichment was unique in AA LUAD cells and ECM receptor interactions were distinct in EA LUAD cells. AKT2 (which activates mTOR) had significantly higher expression in LUAD tumor tissues from current AA smokers compared to current EA smokers. Conclusion: Population-specific PI3K/Akt/mTOR pathway alterations may help explain drug resistance in LUAD tumors from AA patients. PI3K signaling is considered a significant cause of resistance to chemotherapy, targeted therapy, and immunotherapy. Future Directions: Expose AA and EA LUAD cell lines to CSC and PI3K/Akt/mTOR pathway inhibitors. Citation Format: Mia Ray, Isaiah Osei-Gyening, Kwabena Acheampong, Marisa Riebesell, Khadijah A. Mitchell. Predicting drug treatment response in African American lung cancer patients who smoke [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4830.

Predicting cell population-specific gene expression from genomic sequence.

Most regulatory elements, especially enhancer sequences, are cell population-specific. One could even argue that a distinct set of regulatory elements is what defines a cell population. However, discovering which non-coding regions of the DNA are essential in which context, and as a result, which genes are expressed, is a difficult task. Some computational models tackle this problem by predicting gene expression directly from the genomic sequence. These models are currently limited to predicting bulk measurements and mainly make tissue-specific predictions. Here, we present a model that leverages single-cell RNA-sequencing data to predict gene expression. We show that cell population-specific models outperform tissue-specific models, especially when the expression profile of a cell population and the corresponding tissue are dissimilar. Further, we show that our model can prioritize GWAS variants and learn motifs of transcription factor binding sites. We envision that our model can be useful for delineating cell population-specific regulatory elements.

Global comparative transcriptomes uncover novel and population-specific gene expression in esophageal squamous cell carcinoma

BackgroundEsophageal squamous cell carcinoma (ESCC) has a poor prognosis and is one of the deadliest gastrointestinal malignancies. Despite numerous transcriptomics studies to understand its molecular basis, the impact of population-specific differences on this disease remains unexplored.AimsThis study aimed to investigate the population-specific differences in gene expression patterns among ESCC samples obtained from six distinct global populations, identify differentially expressed genes (DEGs) and their associated pathways, and identify potential biomarkers for ESCC diagnosis and prognosis. In addition, this study deciphers population specific microbial and chemical risk factors in ESCC.MethodsWe compared the gene expression patterns of ESCC samples from six different global populations by analyzing microarray datasets. To identify DEGs, we conducted stringent quality control and employed linear modeling. We cross-compared the resulting DEG lists of each populations along with ESCC ATLAS to identify known and novel DEGs. We performed a survival analysis using The Cancer Genome Atlas Program (TCGA) data to identify potential biomarkers for ESCC diagnosis and prognosis among the novel DEGs. Finally, we performed comparative functional enrichment and toxicogenomic analysis.ResultsHere we report 19 genes with distinct expression patterns among populations, indicating population-specific variations in ESCC. Additionally, we discovered 166 novel DEGs, such as ENDOU, SLCO1B3, KCNS3, IFI35, among others. The survival analysis identified three novel genes (CHRM3, CREG2, H2AC6) critical for ESCC survival. Notably, our findings showed that ECM-related gene ontology terms and pathways were significantly enriched among the DEGs in ESCC. We also found population-specific variations in immune response and microbial infection-related pathways which included genes enriched for HPV, Ameobiosis, Leishmaniosis, and Human Cytomegaloviruses. Our toxicogenomic analysis identified tobacco smoking as the primary risk factor and cisplatin as the main drug chemical interacting with the maximum number of DEGs across populations.ConclusionThis study provides new insights into population-specific differences in gene expression patterns and their associated pathways in ESCC. Our findings suggest that changes in extracellular matrix (ECM) organization may be crucial to the development and progression of this cancer, and that environmental and genetic factors play important roles in the disease. The novel DEGs identified may serve as potential biomarkers for diagnosis, prognosis and treatment.

Predicting cell population-specific gene expression from genomic sequence

Predicting cell population-specific gene expression from genomic sequence

Species and population specific gene expression in blood transcriptomes of marine turtles

BackgroundTranscriptomic data has demonstrated utility to advance the study of physiological diversity and organisms’ responses to environmental stressors. However, a lack of genomic resources and challenges associated with collecting high-quality RNA can limit its application for many wild populations. Minimally invasive blood sampling combined with de novo transcriptomic approaches has great potential to alleviate these barriers. Here, we advance these goals for marine turtles by generating high quality de novo blood transcriptome assemblies to characterize functional diversity and compare global transcriptional profiles between tissues, species, and foraging aggregations.ResultsWe generated high quality blood transcriptome assemblies for hawksbill (Eretmochelys imbricata), loggerhead (Caretta caretta), green (Chelonia mydas), and leatherback (Dermochelys coriacea) turtles. The functional diversity in assembled blood transcriptomes was comparable to those from more traditionally sampled tissues. A total of 31.3% of orthogroups identified were present in all four species, representing a core set of conserved genes expressed in blood and shared across marine turtle species. We observed strong species-specific expression of these genes, as well as distinct transcriptomic profiles between green turtle foraging aggregations that inhabit areas of greater or lesser anthropogenic disturbance.ConclusionsObtaining global gene expression data through non-lethal, minimally invasive sampling can greatly expand the applications of RNA-sequencing in protected long-lived species such as marine turtles. The distinct differences in gene expression signatures between species and foraging aggregations provide insight into the functional genomics underlying the diversity in this ancient vertebrate lineage. The transcriptomic resources generated here can be used in further studies examining the evolutionary ecology and anthropogenic impacts on marine turtles.

Magnitude and Predictability of pH Fluctuations Shape Plastic Responses to Ocean Acidification.

AbstractPhenotypic plasticity is expected to facilitate the persistence of natural populations as global change progresses. The attributes of fluctuating environments that favor the evolution of plasticity have received extensive theoretical investigation, yet empirical validation of these findings is still in its infancy. Here, we combine high-resolution environmental data with a laboratory-based experiment to explore the influence of habitat pH fluctuation dynamics on the plasticity of gene expression in two populations of the Mediterranean mussel, Mytilus galloprovincialis. We linked differences in the magnitude and predictability of pH fluctuations in two habitats to population-specific gene expression profiles in ambient and stressful pH treatments. Our results demonstrate population-based differentiation in gene expression plasticity, whereby mussels native to a habitat exhibiting a large magnitude of pH fluctuations with low predictability display reduced phenotypic plasticity between experimentally imposed pH treatments. This work validates recent theoretical findings on evolution in fluctuating environments, suggesting that the predictability of fluctuating selection pressures may play a predominant role in shaping the phenotypic variation observed across natural populations.

Transcriptome-wide analysis of introgression-resistant regions reveals genetic divergence genes under positive selection in Populus trichocarpa.

Comparing gene expression patterns and genetic polymorphisms between populations is of central importance for understanding the origin and maintenance of biodiversity. Based on population-specific gene expression levels and allele frequency differences, we sought to identify population divergence (PD) genes across the introgression-resistant genomic regions of Populus trichocarpa. Genes containing highly diverged loci [i.e., genetic divergence (GD)] or showing expression divergence (ED) between populations were widely distributed in the genome and substantially enriched in functional categories related to stress responses, disease resistance, timing of flowering, cell cycle regulation, plant growth, and development. Nine genomic regions showing evidence of strong positive selection were overlapped with GD genes, which had significant differences between Oregon (a southernmost peripheral deme) and the other demes. However, we did not find evidence that genes under positive selection show an enrichment for ED. PD genes and genes under selection pertained to the same gene classes, such as SERINE/CYSTEINE PROTEASE, ABC TRANSPORTER, GLYCOSYLTRANSFERASE and other transferases. Our analysis also revealed that GD genes were polymorphic within the species (41.9 ± 3.66 biallelic variants per gene), as previously reported in herbaceous plants. By contrast, ED genes contained less genetic variants (10.73 ± 1.14) and were likely highly expressed. In addition, we found that trans- rather than cis-acting variants considerably contribute to the evolution of >90% PD genes. Overall, this study elucidates that cohorts of PD genes agree with the general attributes of known speciation genes and GD genes will provide substrates for positive selection to operate on.

Abstract IA29: Racial differences in the lung cancer transcriptome: Insights from coding, noncoding, and alternative polyadenylated genes

Abstract Deciphering the post-transcriptional mechanisms (PTM) regulating gene expression is an important step in understanding the molecular mechanisms underlying cancer transcriptomics and heterogeneity. Such studies are a core component of precision medicine and systems-level approaches to understanding the complexity of cancer. Historically, African Americans have been under-represented in clinical cancer research, a field that is increasingly focused on classifying patients according to molecular profiles. Thus, diversity helps to ensure equal access to new cancer therapies and better treatment for everyone. In the first analysis to study transcriptomic differences in lung cancer from European Americans and African Americans, we provided a detailed molecular analysis from paired non-small cell lung cancer tissues that identified differential coding and noncoding RNA expression in African Americans and European Americans. African American-enriched differential gene expression was characterized by stem cell and invasion pathways, while differential gene expression in lung tumors from European Americans was primarily characterized by cell proliferation pathways. Integration of noncoding RNA profiles suggested that population-specific gene expression was at least partly driven by population-specific miRNA expression profiles. Similar to other tumor types, we determined that race-enriched gene and miRNA expression signatures suggest a more aggressive disease in African Americans. Moreover, on the basis of predicted drug resistance to adjuvant chemotherapies, African Americans may not equally benefit from the same range of clinical drugs as European Americans. For example, drug susceptibility predictions using the CMAP resource revealed a strong inverse correlation between African Americans' resistance and European Americans' sensitivity to the same panel of drugs. While these are predicted findings, they demonstrate a proof of principle that population differences in gene expression could be important to consider in the context of drug development. Transcriptome profiling studies in cancer health disparities are not restricted to coding or noncoding gene expression. Alternative polyadenylation (APA) is a key PTM mechanism, whose comprehensive analysis remains an important open challenge. In our recent studies, we used a novel APA pipeline that sequences 3′ end-enriched RNA and maps polyA sites directly, an approach that is different to traditional 5′-3′ sequencing methods. We then comprehensively mapped the APA landscape in lung cancer for the first time, analyzing matched tissues derived from European American and African American patients. Consistent with previous observations and a finding that shortened 3′ UTRs are enriched in proliferating cells, we identified widespread shortening of the 3′ UTR in lung cancer. Many of these events were also associated with clinical outcome. Interestingly, we also observed racial differences in APA. Consistent with our previous findings showing that lung tumors from European Americans are enriched in cell proliferation pathways, tumors from EA were 2 times more likely to have shorter 3′ UTR transcripts. Further, we identified several genes where the 3′ UTR was lengthened in African Americans and shortened in European Americans, including PTEN, a known tumor suppressor previously linked with disparities in other cancer types. These data point towards APA as a mechanism of tumor suppressor inactivation that is independent of somatic copy number or mutational changes. Collectively, our data show that many aspects of the lung cancer transcriptome are shared between European Americans and African Americans, but that distinct patterns of expression and regulation are found in each population. Moreover, our work shows that the inclusion of patients of differing ethnicities also has the potential to inform our understanding of the biologic mechanisms of health disparities. Citation Format: Brid Ryan. Racial differences in the lung cancer transcriptome: Insights from coding, noncoding, and alternative polyadenylated genes [abstract]. In: Proceedings of the Eleventh AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2018 Nov 2-5; New Orleans, LA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl):Abstract nr IA29.

Microenvironment of ruptured cerebral aneurysms discovered using data driven analysis of gene expression.

BackgroundIt is well known that ruptured intracranial aneurysms are associated with substantial morbidity and mortality, yet our understanding of the genetic mechanisms of rupture remains poor. We hypothesize that applying novel techniques to the genetic analysis of aneurysmal tissue will yield key rupture-associated mechanisms and novel drug candidates for the prevention of rupture.MethodsWe applied weighted gene co-expression networks (WGCNA) and population-specific gene expression analysis (PSEA) to transcriptomic data from 33 ruptured and unruptured aneurysm domes. Mechanisms were annotated using Gene Ontology, and gene network/population-specific expression levels correlated with rupture state. We then used computational drug repurposing to identify plausible drug candidates for the prevention of aneurysm rupture.ResultsNetwork analysis of bulk tissue identified multiple immune mechanisms to be associated with aneurysm rupture. Targeting these processes with computational drug repurposing revealed multiple candidates for preventing rupture including Btk inhibitors and modulators of hypoxia inducible factor. In the macrophage-specific analysis, we identify rupture-associated mechanisms MHCII antigen processing, cholesterol efflux, and keratan sulfate catabolism. These processes map well onto several of highly ranked drug candidates, providing further validation.ConclusionsOur results are the first to demonstrate population-specific expression levels and intracranial aneurysm rupture, and propose novel drug candidates based on network-based transcriptomics.

Gene Expression Contributes to the Recent Evolution of Host Resistance in a Model Host Parasite System.

Heritable population differences in immune gene expression following infection can reveal mechanisms of host immune evolution. We compared gene expression in infected and uninfected threespine stickleback (Gasterosteus aculeatus) from two natural populations that differ in resistance to a native cestode parasite, Schistocephalus solidus. Genes in both the innate and adaptive immune system were differentially expressed as a function of host population, infection status, and their interaction. These genes were enriched for loci controlling immune functions known to differ between host populations or in response to infection. Coexpression network analysis identified two distinct processes contributing to resistance: parasite survival and suppression of growth. Comparing networks between populations showed resistant fish have a dynamic expression profile while susceptible fish are static. In summary, recent evolutionary divergence between two vertebrate populations has generated population-specific gene expression responses to parasite infection, affecting parasite establishment and growth.

Human population-specific gene expression and transcriptional network modification with polymorphic transposable elements.

Transposable element (TE) derived sequences are known to contribute to the regulation of the human genome. The majority of known TE-derived regulatory sequences correspond to relatively ancient insertions, which are fixed across human populations. The extent to which human genetic variation caused by recent TE activity leads to regulatory polymorphisms among populations has yet to be thoroughly explored. In this study, we searched for associations between polymorphic TE (polyTE) loci and human gene expression levels using an expression quantitative trait loci (eQTL) approach. We compared locus-specific polyTE insertion genotypes to B cell gene expression levels among 445 individuals from 5 human populations. Numerous human polyTE loci correspond to both cis and trans eQTL, and their regulatory effects are directly related to cell type-specific function in the immune system. PolyTE loci are associated with differences in expression between European and African population groups, and a single polyTE loci is indirectly associated with the expression of numerous genes via the regulation of the B cell-specific transcription factor PAX5. The polyTE-gene expression associations we found indicate that human TE genetic variation can have important phenotypic consequences. Our results reveal that TE-eQTL are involved in population-specific gene regulation as well as transcriptional network modification.

A screen for short-range paracrine interactions.

Conventional methods for studying paracrine signaling in vitro may not be sensitive to short-range effects resulting from signal dilution or decay. We employ a microfabricated culture substrate to maintain two cell populations in microscale proximity. Individual populations can be quickly retrieved for cell-specific readouts by standard high-throughput assays. We show that this platform is sensitive to short-range interactions that are not detectable by common methods such as conditioned media transfer or porous cell culture inserts, as revealed by gene expression changes in a tumor-stromal crosstalk model. In addition, we are able to detect population-specific gene expression changes that would have been masked in mixed co-cultures. We thus demonstrate a tool for investigating an important class of intercellular communication that may be overlooked in conventional biological studies.

Expression differences by continent of origin point to the immortalization process

Analysis of recently available microarray expression data sets obtained from immortalized cell lines of the individuals represented in the HapMap project have led to inconclusive comparisons across cohorts with different ancestral continent of origin (ACOO). To address this apparent inconsistency, we applied a novel approach to accentuate population-specific gene expression signatures for the CEU [homogeneous US residents with northern and western European ancestry (HapMap samples)] and YRI [homogenous Yoruba people of Ibadan, Nigeria (HapMap samples)] trios. In this report, we describe how four independent data sets point to the differential expression across ACOO of gene networks implicated in transforming the normal lymphoblast into immortalized lymphoblastoid cells. In particular, Werner syndrome helicase and related genes are differentially expressed between the YRI and CEU cohorts. We further demonstrate that these differences correlate with viral titer and that both the titer and expression differences are associated with ACOO. We use the 14 genes most differentially expressed to construct an ACOO-specific ‘immortalization network’ comprised of 40 genes, one of which show significant correlation with genomic variation (eQTL). The extent to which these measured group differences are due to differences in the immortalization procedures used for each group or reflect ACOO-specific biological differences remains to be determined. That the ACOO group differences in gene expression patterns may depend strongly on the process of transforming cells to establish immortalized lines should be considered in such comparisons.