Public Transcriptomic Datasets Research Articles

APOL1 promotes endothelial cell activation beyond the glomerulus

Apolipoprotein L1 (APOL1) high-risk genotypes are associated with increased risk of chronic kidney disease (CKD) in people of West African ancestry. Given the importance of endothelial cells (ECs) in CKD, we hypothesized that APOL1 high-risk genotypes may contribute to disease via EC-intrinsic activation and dysfunction. Single cell RNA sequencing (scRNA-seq) analysis of the Kidney Precision Medicine Project dataset revealed APOL1 expression in ECs from various renal vascular compartments. Utilizing two public transcriptomic datasets of kidney tissue from African Americans with CKD and a dataset of APOL1-expressing transgenic mice, we identified an EC activation signature; specifically, increased intercellular adhesion molecule 1 (ICAM-1) expression and enrichment in leukocyte migration pathways. Invitro, APOL1 expression in ECs derived from genetically modified human induced pluripotent stem cells and glomerular ECs triggered changes in ICAM-1 and platelet endothelial cell adhesion molecule 1 (PECAM-1) leading to an increase in monocyte attachment. Overall, our data suggest the involvement of APOL1 as an inducer of EC activation in multiple renal vascular beds with potential effects beyond the glomerular vasculature.

Diagnosis of T-cell-mediated kidney rejection by biopsy-based proteomic biomarkers and machine learning.

Biopsy-based diagnosis is essential for maintaining kidney allograft longevity by ensuring prompt treatment for graft complications. Although histologic assessment remains the gold standard, it carries significant limitations such as subjective interpretation, suboptimal reproducibility, and imprecise quantitation of disease burden. It is hoped that molecular diagnostics could enhance the efficiency, accuracy, and reproducibility of traditional histologicmethods. Quantitative label-free mass spectrometry analysis was performed on a set of formalin-fixed, paraffin-embedded (FFPE) biopsies from kidney transplant patients, including five samples each with diagnosis of T-cell-mediated rejection (TCMR), polyomavirus BK nephropathy (BKPyVN), and stable (STA) kidney function control tissue. Using the differential protein expression result as a classifier, three different machine learning algorithms were tested to build a molecular diagnostic model for TCMR. The label-free proteomics method yielded 800-1350 proteins that could be quantified with high confidence per sample by single-shot measurements. Among these candidate proteins, 329 and 467 proteins were defined as differentially expressed proteins (DEPs) for TCMR in comparison with STA and BKPyVN, respectively. Comparing the FFPE quantitative proteomics data set obtained in this study using label-free method with a data set we previously reported using isobaric labeling technology, a classifier pool comprised of features from DEPs commonly quantified in both data sets, was generated for TCMR prediction. Leave-one-out cross-validation result demonstrated that the random forest (RF)-based model achieved the best predictive power. In a follow-up blind test using an independent sample set, the RF-based model yields 80% accuracy for TCMR and 100% for STA. When applying the established RF-based model to two public transcriptome datasets, 78.1%-82.9% sensitivity and 58.7%-64.4% specificity was achieved respectively. This proof-of-principle study demonstrates the clinical feasibility of proteomics profiling for FFPE biopsies using an accurate, efficient, and cost-effective platform integrated of quantitative label-free mass spectrometry analysis with a machine learning-based diagnostic model. It costs less than 10 dollars per test.

The integration of large-scale public data and network analysis uncovers molecular characteristics of psoriasis

In recent years, a growing interest in the characterization of the molecular basis of psoriasis has been observed. However, despite the availability of a large amount of molecular data, many pathogenic mechanisms of psoriasis are still poorly understood. In this study, we performed an integrated analysis of 23 public transcriptomic datasets encompassing both lesional and uninvolved skin samples from psoriasis patients. We defined comprehensive gene co-expression network models of psoriatic lesions and uninvolved skin. Moreover, we curated and exploited a wide range of functional information from multiple public sources in order to systematically annotate the inferred networks. The integrated analysis of transcriptomics data and co-expression networks highlighted genes that are frequently dysregulated and show aberrant patterns of connectivity in the psoriatic lesion compared with the unaffected skin. Our approach allowed us to also identify plausible, previously unknown, actors in the expression of the psoriasis phenotype. Finally, we characterized communities of co-expressed genes associated with relevant molecular functions and expression signatures of specific immune cell types associated with the psoriasis lesion. Overall, integrating experimental driven results with curated functional information from public repositories represents an efficient approach to empower knowledge generation about psoriasis and may be applicable to other complex diseases.

Identification and characterization of drought-induced long noncoding RNAs (DRILs) in rice

Long noncoding RNAs (lncRNAs) act as transcriptional regulators in plants and animals. To date, they have been reported to regulate various biological processes, such as phosphate homeostasis, grain yield, and fertility in rice (Oryza sativa L.). However, the lncRNAs involved in abiotic stress responses remain poorly identified in rice. In this study, we analyzed the expression profiles of lncRNAs using public rice transcriptome datasets derived from abiotic stress-treated samples. We found that the expression of thousands of rice lncRNAs was significantly altered in the shoot and root tissues under different abiotic stresses (drought, high salinity, low temperature, and abscisic acid). We selected six novel drought-induced lncRNAs (DRILs, specifically DRIL1 to DRIL6) for further study. Real-time polymerase chain reaction analysis revealed the differential expression patterns of these DRILs under various stress conditions. The expression of abiotic stress-responsive genes was upregulated in the protoplasts by transiently overexpressed DRIL1 and DRIL4. Therefore, DRILs may be involved in the transcriptional regulation of abiotic stress-responsive genes in rice.

Assessing the potential relevance of CEACAM6 as a blood transcriptional biomarker

Background Changes in blood transcript abundance levels have been associated with pathogenesis in a wide range of diseases. While next generation sequencing technology can measure transcript abundance on a genome-wide scale, downstream clinical applications often require small sets of genes to be selected for inclusion in targeted panels. Here we set out to gather information from the literature and transcriptome datasets that would help researchers determine whether to include the gene CEACAM6 in such panels. Methods We employed a workflow to systematically retrieve, structure, and aggregate information derived from both the literature and public transcriptome datasets. It consisted of profiling the CEACAM6 literature to identify major diseases associated with this candidate gene and establish its relevance as a biomarker. Accessing blood transcriptome datasets identified additional instances where CEACAM6 transcript levels differ in cases vs controls. Finally, the information retrieved throughout this process was captured in a structured format and aggregated in interactive circle packing plots. Results Although it is not routinely used clinically, the relevance of CEACAM6 as a biomarker has already been well-established in the cancer field, where it has invariably been found to be associated with poor prognosis. Focusing on the blood transcriptome literature, we found studies reporting elevated levels of CEACAM6 abundance across a wide range of pathologies, especially diseases where inflammation plays a dominant role, such as asthma, psoriasis, or Parkinson’s disease. The screening of public blood transcriptome datasets completed this picture, showing higher abundance levels in patients with infectious diseases caused by viral and bacterial pathogens. Conclusions Targeted assays measuring CEACAM6 transcript abundance in blood may be of potential utility for the management of patients with diseases presenting with systemic inflammation and for the management of patients with cancer, where the assay could potentially be run both on blood and tumor tissues.

Platelet RNA enables accurate detection of ovarian cancer: an intercontinental, biomarker identification study.

Platelets are reprogrammed by cancer via a process called education, which favors cancer development. The transcriptional profile of tumor-educated platelets (TEPs) is skewed and therefore practicable for cancer detection. This intercontinental, hospital-based, diagnostic study included 761 treatment-naïve inpatients with histologically confirmed adnexal masses and 167 healthy controls from nine medical centers (China, n = 3; Netherlands, n = 5; Poland, n = 1) between September 2016 and May 2019. The main outcomes were the performance of TEPs and their combination with CA125 in two Chinese (VC1 and VC2) and the European (VC3) validation cohorts collectively and independently. Exploratory outcome was the value of TEPs in public pan-cancer platelet transcriptome datasets. The AUCs for TEPs in the combined validation cohort, VC1, VC2, and VC3 were 0.918 (95% CI 0.889-0.948), 0.923 (0.855-0.990), 0.918 (0.872-0.963), and 0.887 (0.813-0.960), respectively. Combination of TEPs and CA125 demonstrated an AUC of 0.922 (0.889-0.955) in the combined validation cohort; 0.955 (0.912-0.997) in VC1; 0.939 (0.901-0.977) in VC2; 0.917 (0.824-1.000) in VC3. For subgroup analysis, TEPs exhibited an AUC of 0.858, 0.859, and 0.920 to detect early-stage, borderline, non-epithelial diseases and 0.899 to discriminate ovarian cancer from endometriosis. TEPs had robustness, compatibility, and universality for preoperative diagnosis of ovarian cancer since it withstood validations in populations of different ethnicities, heterogeneous histological subtypes, and early-stage ovarian cancer. However, these observations warrant prospective validations in a larger population before clinical utilities.

Overexpression of Dehydrogenase/Reductase 9 Predicts Poor Response to Concurrent Chemoradiotherapy and Poor Prognosis in Rectal Cancer Patients.

Objective: To reduce the risk of locoregional recurrence, the addition of neoadjuvant concurrent chemoradiotherapy (CCRT) is recommended before surgical management for rectal cancer patients. However, despite identical tumor histology, individual patient response to neoadjuvant CCRT varies greatly. Accordingly, a comprehensive molecular characterization that is used to predict CCRT efficacy is instantly needed. Methods: Pearson’s chi-squared test was utilized to correlate dehydrogenase/reductase 9 (DHRS9) expression with clinicopathological features. Survival curves were created applying the Kaplan-Meier method, and the log-rank test was conducted to compare prognostic utility between high and low DHRS9 expression groups. Multivariate Cox proportional hazards regression analysis was applied to identify independent prognostic biomarkers based on variables with prognostic utility at the univariate level. Results: Utilizing a public transcriptome dataset, we identified that the DHRS9 gene is the most considerably upregulated gene related to epithelial cell differentiation (GO: 0030855) among rectal cancer patients with CCRT resistance. Employing immunohistochemical staining, we also demonstrated that high DHRS9 immunoexpression is considerably associated with an aggressive clinical course and CCRT resistance in our rectal cancer cohort. Among all variables with prognostic utility at the univariate level, only high DHRS9 immunoexpression was independently unfavorably prognostic of all three endpoints (all p ≤ 0.048) in the multivariate analysis. In addition, applying bioinformatic analysis, we also linked DHRS9 with unrevealed functions, such as keratan sulfate and mucin synthesis which may be implicated in CCRT resistance. Conclusion: Altogether, DHRS9 expression may serve as a helpful predictive and prognostic biomarker and assist decision-making for rectal cancer patients who underwent neoadjuvant CCRT.

Aberrant Expression of Thymosin Beta-4 Correlates With Advanced Disease and BRAF V600E Mutation in Thyroid Cancer.

Thymosin beta-4 (TMSB4X) was recently identified as a differentially expressed gene between malignant and non-malignant thyroid cells via single-cell RNA sequencing. In the present study, we aimed to study the immunostaining pattern of TMSB4X in benign and malignant thyroid neoplasms. Immunohistochemical analysis revealed that normal thyroid tissue or benign thyroid disorders exhibited undetectable immunoreactivity against TMSB4X except for positive staining of inflammatory infiltrates and stromal cells associated with autoimmune thyroid disease. By contrast, overexpression of TMSB4X was observed in a variety of thyroid malignancies, including papillary, follicular, poorly differentiated, and undifferentiated thyroid cancer. Among 141 patients with differentiated thyroid cancer, higher TMSB4X expression was associated with papillary tumor type, extrathyroidal extension, lymph node metastasis, and BRAF V600E mutation. The results were consistent with those from the public transcriptomic datasets. In summary, TMSB4X expression was aberrantly increased in various types of thyroid cancer, and higher TMSB4X expression was correlated with advanced disease characteristics. Thymosin beta-4 may be a novel downstream effector of the BRAF V600E mutation.

A training curriculum for retrieving, structuring, and aggregating information derived from the biomedical literature and large-scale data repositories.

Background: Biomedical research over the past two decades has become data and information rich. This trend has been in large part driven by the development of systems-scale molecular profiling capabilities and by the increasingly large volume of publications contributed by the biomedical research community. It has therefore become important for early career researchers to learn to leverage this wealth of information in their own research. Methods: Here we describe in detail a training curriculum focusing on the development of foundational skills necessary to retrieve, structure, and aggregate information available from vast stores of publicly available information. It is provided along with supporting material and an illustrative use case. The stepwise workflow encompasses; 1) Selecting a candidate gene; 2) Retrieving background information about the gene; 3) Profiling its literature; 4) Identifying in the literature instances where its transcript abundance changes in blood of patients; 5) Retrieving transcriptional profiling data from public blood transcriptome and reference datasets; and 6) Drafting a manuscript, submitting it for peer-review, and publication. Results: This resource may be leveraged by instructors who wish to organize hands-on workshops. It can also be used by independent trainees as a self-study toolkit. The workflow presented as proof-of-concept was designed to establish a resource for assessing a candidate gene’s potential utility as a blood transcriptional biomarker. Trainees will learn to retrieve literature and public transcriptional profiling data associated with a specific gene of interest. They will also learn to extract, structure, and aggregate this information to support downstream interpretation efforts as well as the preparation of a manuscript. Conclusions: This resource should support early career researchers in their efforts to acquire skills that will permit them to leverage the vast amounts of publicly available large-scale profiling data.

Meta-analysis of fungal plant pathogen Fusarium oxysporum infection-related gene profiles using transcriptome datasets.

Fusarium oxysporum is a serious soil-borne fungal pathogen that affects the production of many economically important crops worldwide. Recent reports suggest that this fungus is becoming the dominant species in soil and could become the main infectious fungus in the future. However, the infection mechanisms employed by F. oxysporum are poorly understood. In the present study, using a network meta-analysis technique and public transcriptome datasets for different F. oxysporum and plant interactions, we aimed to explore the common molecular infection strategy used by this fungus and to identify vital genes involved in this process. Principle component analysis showed that all the fungal culture samples from different datasets were clustered together, and were clearly separated from the infection samples, suggesting the feasibility of an integrated analysis of heterogeneous datasets. A total of 335 common differentially expressed genes (DEGs) were identified among these samples, of which 262 were upregulated and 73 were downregulated significantly across the datasets. The most enriched functional categories of the common DEGs were carbohydrate metabolism, amino acid metabolism, and lipid metabolism. Nine co-expression modules were identified, and two modules, the turquoise module and the blue module, correlated positively and negatively with all the infection processes, respectively. Co-expression networks were constructed for these two modules and hub genes were identified and validated. Our results comprise a cross fungal-host interaction resource, highlighting the use of a network biology approach to gain molecular insights.

Novel insights into host responses to Japanese Encephalitis Virus infection: Reanalysis of public transcriptome and microRNAome datasets

PurposeJapanese encephalitis (JE), caused by the Japanese encephalitis virus (JEV), is the principal cause of viral encephalitis in South-East Asian and Western Pacific countries; accounting for 68,000 cases, and up to 20,400 fatalities, annually across the world. Despite being a high-risk condition, there is no specific treatment for JE. Given rapid additions in genomics databases and the power of data reanalysis in addressing critical medical questions, the present study was designed to identify novel host factors that might have potential roles in JEV infection. MethodsWe extracted microarray and RNA-Seq data sets from NCBI-GEO and compared mock and JEV-infected samples. Raw data from all the studies were re-analyzed to identify host factors associated with JEV replication. ResultsWe identified several coding and non-coding host factors that had no prior known role in viral infections. Of these, the coding transcripts: Myosin Heavy Chain 10 (MYH10), Progestin and AdipoQ Receptor Family Member 8 (PAQR8), and the microRNAs: hsa-miR-193b-5p, hsa-miR-3714 and hsa-miR-513a-5p were found to be novel host factors deregulated during JEV infection. MYH10 encodes a conventional non-muscle myosin, and mutations in MYH10 have been shown to cause neurological defects. PAQR8 has been associated with epilepsy, which exhibits symptoms similar to JEV infection. JE is a neuro-degenerative disease, and the known involvement of MYH10 and PAQR8 in neurological disorders strongly indicates potential roles of these host factors in JEV infection. Additionally, we observed that MYH10 and PAQR8 had a significant negative correlation with Activating transcription factor 3 (ATF3), which is a previously validated modulator of JEV infection. ATF3 is a transcription factor that binds to the promotors of genes encoding other transcription factors or interferon-stimulated genes and negatively regulates host antiviral responses during JE. ConclusionOur findings demonstrate the significance of data reanalysis in the identification of novel host factors that may become targets for diagnosis/ therapy against viral diseases of major concern, such as, JE. The deregulated coding and non-coding transcripts identified in this study need further experimental analysis for validation.

Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with SpaTalk

Spatially resolved transcriptomics provides genetic information in space toward elucidation of the spatial architecture in intact organs and the spatially resolved cell-cell communications mediating tissue homeostasis, development, and disease. To facilitate inference of spatially resolved cell-cell communications, we here present SpaTalk, which relies on a graph network and knowledge graph to model and score the ligand-receptor-target signaling network between spatially proximal cells by dissecting cell-type composition through a non-negative linear model and spatial mapping between single-cell transcriptomic and spatially resolved transcriptomic data. The benchmarked performance of SpaTalk on public single-cell spatial transcriptomic datasets is superior to that of existing inference methods. Then we apply SpaTalk to STARmap, Slide-seq, and 10X Visium data, revealing the in-depth communicative mechanisms underlying normal and disease tissues with spatial structure. SpaTalk can uncover spatially resolved cell-cell communications for single-cell and spot-based spatially resolved transcriptomic data universally, providing valuable insights into spatial inter-cellular tissue dynamics.

Identification of key enzyme genes involved in biosynthesis pathways of lignan and lignin in Eucommia ulmoides based on transcriptome assembly

Lignan is the main medicinal component of Eucommia ulmoides, and lignin is involved in the defense of plants against diseases and insect pests.They are synthesized from coniferyl alcohol with the help of dirigent(DIR) and peroxidase(POD), respectively.In this study, transcriptome assembly of stems and leaves of E.ulmoides was performed, yielding 112 578 unigenes.Among them, 70 459 were annotated in seven databases.A total of 59 unigenes encodes 11 key enzymes in the biosynthesis pathways of lignin and lignin, of which 11 encode POD and 8 encode DIR.A total of 13 unigenes encoding transcription factors are involved in phenylpropanoid metabolism. Compared with leaves of E.ulmoides, 7 575 unigenes were more highly expressed in stems, of which 462 were involved in phenylpropanoid biosynthesis.Our results extend the public transcriptome dataset of E.ulmoides, which provide valuable information for the analysis of biosynthesis pathways of lignan and lignin in E.ulmoides and lay a foundation for further study on the functions and regulation mechanism of key enzymes in lignan and lignin biosynthesis pathways.

Abstract LB168: Platelet RNA signature enables early and accurate detection of ovarian cancer: An intercontinental, biomarker identification study

Abstract Background: Morpho-physiological alternations of platelets provided a rationale to harness RNA sequencing of tumor-educated platelets (TEPs) for preoperative diagnosis of cancer. Timely, accurate, and non-invasive detection of ovarian cancer in women with adnexal masses presents a significant clinical challenge. Patients and Methods: This intercontinental, hospital-based, diagnostic study included 761 treatment-naïve inpatients with histologically confirmed adnexal masses and 167 healthy controls from nine medical centers (China, n=3; Netherlands, n=5; Poland, n=1) between September 2016 and May 2019. The main outcomes were the performance of TEPs and their combination with CA125 in two Chinese (VC1 and VC2) and the European (VC3) validation cohorts collectively and independently. Exploratory outcome was the value of TEPs in public pan-cancer platelet transcriptome datasets. Results: The AUCs for TEPs in the combined validation cohort, VC1, VC2, and VC3 were 0.918 (95% CI 0.889-0.948), 0.923 (0.855-0.990), 0.918 (0.872-0.963), and 0.887 (0.813-0.960), respectively. Combination of TEPs and CA125 demonstrated an AUC of 0.922 (0.889-0.955) in the combined validation cohort; 0.955 (0.912-0.997) in VC1; 0.939 (0.901-0.977) in VC2; 0.917 (0.824-1.000) in VC3. For subgroup analysis, TEPs exhibited an AUC of 0.858, 0.859, and 0.920 to detect early-stage, borderline, non-epithelial diseases and 0.899 to discriminate ovarian cancer from endometriosis. Analysis of public datasets suggested that TEPs had potential to detect multiple malignancies (Table 1). Conclusions: TEPs had robustness, compatibility, and universality for preoperative diagnosis of ovarian cancer since it withstood validations in populations of different ethnicities, heterogeneous histological subtypes, early-stage ovarian cancer as well as other malignancies. However, these observations warrant prospective validations in a larger population before clinical utilities. Table 1. Performance for TEPs in public pan-cancer datasets. Disease n Healthy Control AUC, area under the curve (95% CI) Women NSCLC (non-small-cell lung cancer) 126 77 0.758 (0.691-0.825) Breast cancer 38 77 0.817 (0.726-0.909) Colorectal cancer 18 77 0.973 (0.945-1.000) Pancreatic cancer 16 77 0.993 (0.981-1.000) Glioblastoma 10 77 0.923 (0.831-1.000) Men NSCLC 119 82 0.746 (0.677-0.815) Colorectal cancer 25 82 0.933 (0.884-0.982) Pancreatic cancer 22 82 0.993 (0.984-1.000) Glioblastoma 19 82 0.981 (0.959-1.000) All NSCLC 245 159 0.774 (0.728-0.820) Colorectal cancer 40 159 0.978 (0.961-0.996) Breast cancer 38 159 0.821 (0.736-0.906) Pancreatic cancer 35 159 0.987 (0.974-0.999) Glioblastoma 35 159 0.931 (0.890-0.972) Hepatobiliary carcinomas 14 159 0.991 (0.978-1.000) Citation Format: Yue Gao, Chun-Jie Liu, Hua-Yi Li, Xiao-Ming Xiong, Sjors G.j.g. In ‘t Veld, Gui-Ling Li, Jia-Hao Liu, Guang-Yao Cai, Gui-Yan Xie, Shao-Qing Zeng, Yuan Wu, Jian-Hua Chi, Qiong Zhang, Xiao-Fei Jiao, Lin-Li Shi, Wan-Rong Lu, Wei-Guo Lv, Xing-Sheng Yang, Jurgen M.j. Piek, Cornelis D de Kroon, C.a.r. Lok, Anna Supernat, Sylwia Łapińska-Szumczyk, Anna Łojkowska, Anna J. Żaczek, Jacek Jassem, Bakhos A. Tannous, Nik Sol, Edward Post, Myron G. Best, Bei-Hua Kong, Xing Xie, Ding Ma, Thomas Wurdinger, An-Yuan Guo, Qing-Lei Gao. Platelet RNA signature enables early and accurate detection of ovarian cancer: An intercontinental, biomarker identification study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB168.

Abstract 3751: Altered prostatic HOX code is associated with decreased YAP1 expression during neuroendocrine prostate cancer development

Abstract Background: Neuroendocrine (NE) phenotype contributes to 25 to 30% androgen deprivation resistant prostate cancer cases. Until now, this aggressive phenotype is untreatable. Therefore, it is urgent to study the molecular mechanism of NE development to reveal therapeutic strategies. Methods: Data were downloaded from CCLE, cBioPortal, and GEO and analyzed by R. RT-qPCR, western-blot, Immunohistochemistry, and Immunofluorescence were used to assess gene expression. Results: Neuroendocrine prostate cancer (NEPCa) develops from prostate adenocarcinoma (AdPCa). We screened and identified the consensus differentially expressed genes across all public NEPCa transcriptome datasets. We found several HOX genes are included. HOXB13 is deceased but HOXB3, HOXD8 are increased in NEPCa. Based on the HOX code (total 39 HOX genes expression) theory, different cell lineages have distinct HOX codes and HOX codes can represent tissue identities, we established 42 HOX codes for 42 cells lineages including prostate through bioinformatics analysis of 1019 cancer cell lines transcriptome. We found NEPCa samples have changed prostatic HOX code. We also found YAP1, a previously characterized oncogene, is decreased in mRNA level in NEPCa than AdPCa. We confirmed the YAP1 protein is not detected in NEPCa human/mouse samples, PDX samples, and cell line samples (NCI-H660). We used YAP1 as a model. We hypothesized that the signaling involved in silencing YAP1 expression can also contribute to NE development. We first confirmed the loss of YAP1 expression in NEPCa cells is under epigenetic control by inducing YAP1 mRNA expression in primary mouse NEPCa cells through epigenetic inhibitors treatments (HDACs/DNA methyltransferase inhibitors). We characterized the YAP1 promoter through data mining chromatin ChIP-seq data and DNA methylation-seq data. We identified and cloned a 3kb DNA fragment across the YAP1 transcription start site as YAP1 regulatory element. We then built a YAP1-promoter-luciferase reporter using this fragment. We also generated a list of transcription factors that can bind the YAP1 regulatory element. We will then confirm the roles of these transcriptional factors in both regulating YAP1 and NE development. We also found YAP1 expression is co-expressed with many HOX genes in human samples. This suggested the potential regulatory function of HOX code to YAP1 expression. Conclusion: We proposed 2 critical events during NE development, changed prostatic HOX code and loss of YAP expression. We identified HOX code change as one of the potential regulatory mechanisms of YAP1. We then proposed to use YAP1 regulatory element as a model system to study the NE development mechanism. Funding: The LSUHS Office of Research SEED awards, NIH R01 CA226285, and Feist-Weiller Cancer Center pre-doc fellowship to SC. Acknowledgment: We acknowledge Feist-Weiller Cancer Center for travel support to SC. Citation Format: Siyuan Cheng, Shu Yang, Yingli Shi, Xiuping Yu. Altered prostatic HOX code is associated with decreased YAP1 expression during neuroendocrine prostate cancer development [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3751.

Open chromatin analysis in Trypanosoma cruzi life forms highlights critical differences in genomic compartments and developmental regulation at tDNA loci

BackgroundGenomic organization and gene expression regulation in trypanosomes are remarkable because protein-coding genes are organized into codirectional gene clusters with unrelated functions. Moreover, there is no dedicated promoter for each gene, resulting in polycistronic gene transcription, with posttranscriptional control playing a major role. Nonetheless, these parasites harbor epigenetic modifications at critical regulatory genome features that dynamically change among parasite stages, which are not fully understood.ResultsHere, we investigated the impact of chromatin changes in a scenario commanded by posttranscriptional control exploring the parasite Trypanosoma cruzi and its differentiation program using FAIRE-seq approach supported by transmission electron microscopy. We identified differences in T. cruzi genome compartments, putative transcriptional start regions, and virulence factors. In addition, we also detected a developmental chromatin regulation at tRNA loci (tDNA), which could be linked to the intense chromatin remodeling and/or the translation regulatory mechanism required for parasite differentiation. We further integrated the open chromatin profile with public transcriptomic and MNase-seq datasets. Strikingly, a positive correlation was observed between active chromatin and steady-state transcription levels.ConclusionTaken together, our results indicate that chromatin changes reflect the unusual gene expression regulation of trypanosomes and the differences among parasite developmental stages, even in the context of a lack of canonical transcriptional control of protein-coding genes.

Molecular Basis and Role of Siglec-7 Ligand Expression on Chronic Lymphocytic Leukemia B Cells.

Siglec-7 (sialic acid–binding immunoglobulin-like lectin 7) is an immune checkpoint-like glycan recognition protein on natural killer (NK) cells. Cancer cells often upregulate Siglec ligands to subvert immunosurveillance, but the molecular basis of Siglec ligands has been elusive. In this study, we investigated Siglec-7 ligands on chronic lymphocytic leukemia (CLL) B cells. CLL B cells express higher levels of Siglec-7 ligands compared with healthy donor B cells, and enzymatic removal of sialic acids or sialomucins makes them more sensitive to NK cell cytotoxicity. Gene knockout experiments have revealed that the sialyltransferase ST6GalNAc-IV is responsible for the biosynthesis of disialyl-T (Neu5Acα2–3Galβ1–3[Neu5Acα2–6]GalNAcα1–), which is the glycotope recognized by Siglec-7, and that CD162 and CD45 are the major carriers of this glycotope on CLL B cells. Analysis of public transcriptomic datasets indicated that the low expression of GCNT1 (encoding core 2 GlcNAc transferase, an enzyme that competes against ST6GalNAc-IV) and high expression of ST6GALNAC4 (encoding ST6GalNAc-IV) in CLL B cells, together enhancing the expression of the disialyl-T glycotope, are associated with poor patient prognosis. Taken together, our results determined the molecular basis of Siglec-7 ligand overexpression that protects CLL B cells from NK cell cytotoxicity and identified disialyl-T as a potential prognostic marker of CLL.

Single-cell transcriptomic analysis reveals circadian rhythm disruption associated with poor prognosis and drug-resistance in lung adenocarcinoma.

Circadian rhythm disruption (CRD) represents a major contributor to tumor proliferation. Nonetheless, the role of CRD in the clinical prediction of cancer outcomes has not been well studied. In this study, we developed a computational algorithm, which was implemented in an open-source R package CRDscore, to define the intratumoral status of circadian disruption in three representative single-cell RNA-seq data sets of lung adenocarcinoma. We found that the malignant cells with high CRDscore were characterized by activation of glycolysis and epithelial-mesenchymal transition pathways. Furthermore, cell communication analysis indicated that CRD played a pivotal role in T cell exhaustion, which may be responsible for the poor prognosis of the malignancy. We then validated the findings with public bulk transcriptome datasets involving 22 cancer types. Cox regression analysis revealed that the CRDscore was a valuable prognostic biomarker. A model containing 23 circadian-related genes performed well in predicting immunotherapeutic outcomes in 14 independent cohorts. Importantly, decreased CRDscore was detect by RNA sequencing on H1299 cells with melatonin treatment. Meanwhile, the cells downregulated the expression level of SNAIL and TWIST, which contributed to an invasive phenotype. In conclusion, this study provides a novel computational framework for characterizing CRD status using single-cell transcriptomic data and further confirmed the molecular mechanisms underlying metabolic reprogramming and T cell exhaustion under CRD. The better understanding of the mechanisms may provide new possibilities for incorporating "anticancer approaches based on circadian clocks" into the treatment protocols of precision medicine.

The Tsallis generalized entropy enhances the interpretation of transcriptomics datasets.

Identifying differentially expressed genes between experimental conditions is still the gold-standard approach to interpret transcriptomic profiles. Alternative approaches based on diversity measures have been proposed to complement the interpretation of such datasets but are only used marginally. Here, we reinvestigated diversity measures, which are commonly used in ecology, to characterize mice pregnancy microenvironments based on a public transcriptome dataset. Mainly, we evaluated the Tsallis entropy function to explore the potential of a collection of diversity measures for capturing relevant molecular event information. We demonstrate that the Tsallis entropy function provides additional information compared to the traditional diversity indices, such as the Shannon and Simpson indices. Depending on the relative importance given to the most abundant transcripts based on the Tsallis entropy function parameter, our approach allows appreciating the impact of biological stimulus on the inter-individual variability of groups of samples. Moreover, we propose a strategy for reducing the complexity of transcriptome datasets using a maximation of the beta diversity. We highlight that a diversity-based analysis is suitable for capturing complex molecular events occurring during physiological events. Therefore, we recommend their use through the Tsallis entropy function to analyze transcriptomics data in addition to differential expression analyses.

Meta-analysis of transcriptional regulatory networks for lipid metabolism in neural cells from schizophrenia patients based on an open-source intelligence approach

There have been a number of reports about the transcriptional regulatory networks in schizophrenia. However, most of these studies were based on a specific transcription factor or a single dataset, an approach that is inadequate to understand the diverse etiology and underlying common characteristics of schizophrenia. Here we reconstructed and compared the transcriptional regulatory network for lipid metabolism enzymes using 15 public transcriptome datasets of neural cells from schizophrenia patients. Since many of the well-known schizophrenia-related SNPs are in enhancers, we reconstructed a network including enhancer-dependent regulation and found that 53.3 % of the total number of edges (7,577 pairs) involved regulation via enhancers. By examining multiple datasets, we found common and unique transcriptional modes of regulation. Furthermore, enrichment analysis of SNPs that were connected with genes in the transcriptional regulatory networks by eQTL suggested an association with hematological cell counts and some other traits/diseases, whose relationship to schizophrenia was either not or insufficiently reported in previous studies. Based on these results, we suggest that in future studies on schizophrenia, information on genotype, comorbidities and hematological cell counts should be included, along with the transcriptome, for a more detailed genetic stratification and mechanistic exploration of schizophrenia.