The remission status of AML patients after allo-HCT is associated with a distinct single-cell bone marrow T-cell signature.

ABSTRACT: Acute myeloid leukemia (AML) is a hematologic malignancy for which allogeneic hematopoietic cell transplantation (allo-HCT) often remains the only curative therapeutic approach. However, incapability of T cells to recognize and eliminate residual leukemia stem cells might lead to an insufficient graft-versus-leukemia (GVL) effect and relapse. Here, we performed single-cell RNA-sequencing (scRNA-seq) on bone marrow (BM) T lymphocytes and CD34+ cells of 6 patients with AML 100 days after allo-HCT to identify T-cell signatures associated with either imminent relapse (REL) or durable complete remission (CR). We observed a higher frequency of cytotoxic CD8+ effector and gamma delta (γδ) T cells in CR vs REL samples. Pseudotime and gene regulatory network analyses revealed that CR CD8+ T cells were more advanced in maturation and had a stronger cytotoxicity signature, whereas REL samples were characterized by inflammatory tumor necrosis factor/NF-κB signaling and an immunosuppressive milieu. We identified ADGRG1/GPR56 as a surface marker enriched in CR CD8+ T cells and confirmed in a CD33-directed chimeric antigen receptor T cell/AML coculture model that GPR56 becomes upregulated on T cells upon antigen encounter and elimination of AML cells. We show that GPR56 continuously increases at the protein level on CD8+ T cells after allo-HCT and confirm faster interferon gamma (IFN-γ) secretion upon re-exposure to matched, but not unmatched, recipient AML cells in the GPR56+ vs GPR56- CD8+ T-cell fraction. Together, our data provide a single-cell reference map of BM-derived T cells after allo-HCT and propose GPR56 expression dynamics as a surrogate for antigen encounter after allo-HCT.

GRaNIE and GRaNPA: inference and evaluation of enhancer-mediated gene regulatory networks.

Enhancers play a vital role in gene regulation and are critical in mediating the impact of noncoding genetic variants associated with complex traits. Enhancer activity is a cell-type-specific process regulated by transcription factors (TFs), epigenetic mechanisms and genetic variants. Despite the strong mechanistic link between TFs and enhancers, we currently lack a framework for jointly analysing them in cell-type-specific gene regulatory networks (GRN). Equally important, we lack an unbiased way of assessing the biological significance of inferred GRNs since no complete ground truth exists. To address these gaps, we present GRaNIE (Gene Regulatory Network Inference including Enhancers) and GRaNPA (Gene Regulatory Network Performance Analysis). GRaNIE (https://git.embl.de/grp-zaugg/GRaNIE) builds enhancer-mediated GRNs based on covariation of chromatin accessibility and RNA-seq across samples (e.g. individuals), while GRaNPA (https://git.embl.de/grp-zaugg/GRaNPA) assesses the performance of GRNs for predicting cell-type-specific differential expression. We demonstrate their power by investigating gene regulatory mechanisms underlying the response of macrophages to infection, cancer and common genetic traits including autoimmune diseases. Finally, our methods identify the TF PURA as a putative regulator of pro-inflammatory macrophage polarisation.

Transcriptional profiling unveils molecular subgroups of adaptive and maladaptive right ventricular remodeling in pulmonary hypertension.

Right ventricular (RV) function is critical to prognosis in all forms of pulmonary hypertension. Here we perform molecular phenotyping of RV remodeling by transcriptome analysis of RV tissue obtained from 40 individuals, and two animal models of RV dysfunction of both sexes. Our unsupervised clustering analysis identified 'early' and 'late' subgroups within compensated and decompensated states, characterized by the expression of distinct signaling pathways, while fatty acid metabolism and estrogen response appeared to underlie sex-specific differences in RV adaptation. The circulating levels of several extracellular matrix proteins deregulated in decompensated RV subgroups were assessed in two independent cohorts of individuals with pulmonary arterial hypertension, revealing that NID1, C1QTNF1 and CRTAC1 predicted the development of a maladaptive RV state, as defined by magnetic resonance imaging parameters, and were associated with worse clinical outcomes. Our study provides a resource for subphenotyping RV states, identifying state-specific biomarkers, and potential therapeutic targets for RV dysfunction.

Pan-cancer analysis of microRNA expression profiles highlights microRNAs enriched in normal body cells as effective suppressors of multiple tumor types: A study based on TCGA database.

BACKGROUND: MicroRNAs (miRNAs) are frequently deregulated in various types of cancer. While antisense oligonucleotides are used to block oncomiRs, delivery of tumour-suppressive miRNAs holds great potential as a potent anti-cancer strategy. Here, we aim to determine, and functionally analyse, miRNAs that are lowly expressed in various types of tumour but abundantly expressed in multiple normal tissues. METHODS: The miRNA sequencing data of 14 cancer types were downloaded from the TCGA dataset. Significant differences in miRNA expression between tumor and normal samples were calculated using limma package (R programming). An adjusted p value < 0.05 was used to compare normal versus tumor miRNA expression profiles. The predicted gene targets were obtained using TargetScan, miRanda, and miRDB and then subjected to gene ontology analysis using Enrichr. Only GO terms with an adjusted p < 0.05 were considered statistically significant. All data from wet-lab experiments (cell viability assays and flow cytometry) were expressed as means ± SEM, and their differences were analyzed using GraphPad Prism software (Student's t test, p < 0.05). RESULTS: By compiling all publicly available miRNA profiling data from The Cancer Genome Atlas (TCGA) Pan-Cancer Project, we reveal a small set of tumour-suppressing miRNAs (which we designate as 'normomiRs') that are highly expressed in 14 types of normal tissues but poorly expressed in corresponding tumour tissues. Interestingly, muscle-enriched miRNAs (e.g. miR-133a/b and miR-206) and miRNAs from DLK1-DIO3 locus (e.g. miR-381 and miR-411) constitute a large fraction of the normomiRs. Moreover, we define that the CCCGU motif is absent in the oncomiRs' seed sequences but present in a fraction of tumour-suppressive miRNAs. Finally, the gain of function of candidate normomiRs across several cancer cell types indicates that miR-206 and miR-381 exert the most potent inhibition on multiple cancer types in vitro. CONCLUSION: Our results reveal a pan-cancer set of tumour-suppressing miRNAs and highlight the potential of miRNA-replacement therapies for targeting multiple types of tumour.

A novel panel of blood-based microRNAs capable of discrimination between benign breast disease and breast cancer at early stages.

Breast cancer (BC) as a leading cause of cancer death among women, exhibits a wide range of genetic heterogeneity in affected individuals. Satisfactory management of BC depends on early diagnosis and proper monitoring of patients' response to therapy. In this study, we aimed to assess the relation between the expression patterns of blood-based microRNAs (miRNAs) with demographic characteristics of the patients with BC in an attempt to find novel diagnostic markers for BC with acceptable precision in clinical applications. To this end, we performed comprehensive statistical analysis of the data of the Cancer Genome Atlas (TCGA) database and the blood miRNome dataset (GSE31309). As a result, 21 miRNAs were selected for experimental verification by quantitative RT-PCR on blood samples of 70 BC patients and 60 normal individuals (without any lesions or benign breast diseases). Statistical one-way ANOVA revealed no significant difference in the blood levels of the selected miRNAs in BC patients compared to any lesions or benign breast diseases. However, the multi-marker panel consisting of hsa-miR-106b-5p, -126-3p, -140-3p, -193a-5p, and -10b-5p could detect early-stages of BC with 0.79 sensitivity, 0.86 specificity and 0.82 accuracy. Furthermore, this multi-marker panel showed the potential of detecting benign breast diseases from BC patients with 0.67 sensitivity, 0.80 specificity, and 0.74 accuracy. In conclusion, these data indicate that the present panel might be considered an asset in detecting benign breast disease and BC.

MicroRNA profiling reveals important functions of miR-125b and let-7a during human retinal pigment epithelial cell differentiation.

Retinal pigment epithelial (RPE) cells are indispensable for eye organogenesis and vision. To realize the therapeutic potential of in vitro-generated RPE cells for cell-replacement therapy of RPE-related retinopathies, molecular mechanisms of RPE specification and maturation need to be investigated. So far, many attempts have been made to decipher the regulatory networks involved in the differentiation of human pluripotent stem cells into RPE cells. Here, we exploited a highly-efficient RPE differentiation protocol to determine global expression patterns of microRNAs (miRNAs) during human embryonic stem cell (hESC) differentiation into RPE using small RNA sequencing. Our results revealed a significant downregulation of pluripotency-associated miRNAs along with a significant upregulation of RPE-associated miRNAs in differentiating cells. Our functional analyses indicated that two RPE-enriched miRNAs (i.e. miR-125b and let-7a) could promote RPE fate at the expense of neural fate during RPE differentiation. Taken together, these mechanistic interrogations might shed light on a better understanding of RPE cell development and provide insights for the future application of these cells in regenerative medicine.

Cell Identity Codes: Understanding Cell Identity from Gene Expression Profiles using Deep Neural Networks.

Understanding cell identity is an important task in many biomedical areas. Expression patterns of specific marker genes have been used to characterize some limited cell types, but exclusive markers are not available for many cell types. A second approach is to use machine learning to discriminate cell types based on the whole gene expression profiles (GEPs). The accuracies of simple classification algorithms such as linear discriminators or support vector machines are limited due to the complexity of biological systems. We used deep neural networks to analyze 1040 GEPs from 16 different human tissues and cell types. After comparing different architectures, we identified a specific structure of deep autoencoders that can encode a GEP into a vector of 30 numeric values, which we call the cell identity code (CIC). The original GEP can be reproduced from the CIC with an accuracy comparable to technical replicates of the same experiment. Although we use an unsupervised approach to train the autoencoder, we show different values of the CIC are connected to different biological aspects of the cell, such as different pathways or biological processes. This network can use CIC to reproduce the GEP of the cell types it has never seen during the training. It also can resist some noise in the measurement of the GEP. Furthermore, we introduce classifier autoencoder, an architecture that can accurately identify cell type based on the GEP or the CIC.