DNABERT-S: LEARNING SPECIES-AWARE DNA EMBEDDING WITH GENOME FOUNDATION MODELS.

Effective DNA embedding remains crucial in genomic analysis, particularly in scenarios lacking labeled data for model fine-tuning, despite the significant advancements in genome foundation models. A prime example is metagenomics binning, a critical process in microbiome research that aims to group DNA sequences by their species from a complex mixture of DNA sequences derived from potentially thousands of distinct, often uncharacterized species. To fill the lack of effective DNA embedding models, we introduce DNABERT-S, a genome foundation model that specializes in creating species-aware DNA embeddings. To encourage effective embeddings to error-prone long-read DNA sequences, we introduce Manifold Instance Mixup (MI-Mix), a contrastive objective that mixes the hidden representations of DNA sequences at randomly selected layers and trains the model to recognize and differentiate these mixed proportions at the output layer. We further enhance it with the proposed Curriculum Contrastive Learning (C2LR) strategy. Empirical results on 18 diverse datasets showed DNABERT-S's remarkable performance. It outperforms the top baseline's performance in 10-shot species classification with just a 2-shot training while doubling the Adjusted Rand Index (ARI) in species clustering and substantially increasing the number of correctly identified species in metagenomics binning. The code, data, and pre-trained model are publicly available at https://github.com/Zhihan1996/DNABERT_S.

Epithelial-mesenchymal transition: an organizing principle of mammalian regeneration.

Introduction: The MRL mouse strain is one of the few examples of a mammal capable of healing appendage wounds by regeneration, a process that begins with the formation of a blastema, a structure containing de-differentiating mesenchymal cells. HIF-1α expression in the nascent MRL wound site blastema is one of the earliest identified events and is sufficient to initiate the complete regenerative program. However, HIF-1α regulates many cellular processes modulating the expression of hundreds of genes. A later signal event is the absence of a functional G1 checkpoint, leading to G2 cell cycle arrest with increased cellular DNA but little cell division observed in the blastema. This lack of mitosis in MRL blastema cells is also a hallmark of regeneration in classical invertebrate and vertebrate regenerators such as planaria, hydra, and newt. Results and discussion: Here, we explore the cellular events occurring between HIF-1α upregulation and its regulation of the genes involved in G2 arrest (EVI-5, γH3, Wnt5a, and ROR2), and identify epithelial-mesenchymal transition (EMT) (Twist and Slug) and chromatin remodeling (EZH-2 and H3K27me3) as key intermediary processes. The locus of these cellular events is highly regionalized within the blastema, occurring in the same cells as determined by double staining by immunohistochemistry and FACS analysis, and appears as EMT and chromatin remodeling, followed by G2 arrest determined by kinetic expression studies.

Deep multi-omics integration by learning correlation-maximizing representation identifies prognostically stratified cancer subtypes.

Motivation: Molecular subtyping by integrative modeling of multi-omics and clinical data can help the identification of robust and clinically actionable disease subgroups; an essential step in developing precision medicine approaches. Results: We developed a novel outcome-guided molecular subgrouping framework, called Deep Multi-Omics Integrative Subtyping by Maximizing Correlation (DeepMOIS-MC), for integrative learning from multi-omics data by maximizing correlation between all input -omics views. DeepMOIS-MC consists of two parts: clustering and classification. In the clustering part, the preprocessed high-dimensional multi-omics views are input into two-layer fully connected neural networks. The outputs of individual networks are subjected to Generalized Canonical Correlation Analysis loss to learn the shared representation. Next, the learned representation is filtered by a regression model to select features that are related to a covariate clinical variable, for example, a survival/outcome. The filtered features are used for clustering to determine the optimal cluster assignments. In the classification stage, the original feature matrix of one of the -omics view is scaled and discretized based on equal frequency binning, and then subjected to feature selection using RandomForest. Using these selected features, classification models (for example, XGBoost model) are built to predict the molecular subgroups that were identified at clustering stage. We applied DeepMOIS-MC on lung and liver cancers, using TCGA datasets. In comparative analysis, we found that DeepMOIS-MC outperformed traditional approaches in patient stratification. Finally, we validated the robustness and generalizability of the classification models on independent datasets. We anticipate that the DeepMOIS-MC can be adopted to many multi-omics integrative analyses tasks. Availability and implementation: Source codes for PyTorch implementation of DGCCA and other DeepMOIS-MC modules are available at GitHub (https://github.com/duttaprat/DeepMOIS-MC). Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Integrated Multi-Omic Analysis Reveals Immunosuppressive Phenotype Associated with Poor Outcomes in High-Grade Serous Ovarian Cancer.

High-grade serous ovarian cancer (HGSOC) is characterized by a complex genomic landscape, with both genetic and epigenetic diversity contributing to its pathogenesis, disease course, and response to treatment. To better understand the association between genomic features and response to treatment among 370 patients with newly diagnosed HGSOC, we utilized multi-omic data and semi-biased clustering of HGSOC specimens profiled by TCGA. A Cox regression model was deployed to select model input features based on the influence on disease recurrence. Among the features most significantly correlated with recurrence were the promotor-associated probes for the NFRKB and DPT genes and the TREML1 gene. Using 1467 transcriptomic and methylomic features as input to consensus clustering, we identified four distinct tumor clusters-three of which had noteworthy differences in treatment response and time to disease recurrence. Each cluster had unique divergence in differential analyses and distinctly enriched pathways therein. Differences in predicted stromal and immune cell-type composition were also observed, with an immune-suppressive phenotype specific to one cluster, which associated with short time to disease recurrence. Our model features were additionally used as a neural network input layer to validate the previously defined clusters with high prediction accuracy (91.3%). Overall, our approach highlights an integrated data utilization workflow from tumor-derived samples, which can be used to uncover novel drivers of clinical outcomes.

Rapid, High-Throughput Single-Cell Multiplex In Situ Tagging (MIST) Analysis of Immunological Disease with Machine Learning.

The cascade of immune responses involves activation of diverse immune cells and release of a large amount of cytokines, which leads to either normal, balanced inflammation or hyperinflammatory responses and even organ damage by sepsis. Conventional diagnosis of immunological disorders based on multiple cytokines in the blood serum has varied accuracy, and it is difficult to distinguish normal inflammation from sepsis. Herein, we present an approach to detect immunological disorders through rapid, ultrahigh-multiplex analysis of T cells using single-cell multiplex in situ tagging (scMIST) technology. scMIST permits simultaneous detection of 46 markers and cytokines from single cells without the assistance of special instruments. A cecal ligation and puncture sepsis model was built to supply T cells from two groups of mice that survived the surgery or died after 1 day. The scMIST assays have captured the T cell features and the dynamics over the course of recovery. Compared with cytokines in the peripheral blood, T cell markers show different dynamics and cytokine levels. We have applied a random forest machine learning model to single T cells from two groups of mice. Through training, the model has been able to predict the group of mice through T cell classification and majority rule with 94% accuracy. Our approach pioneers the direction of single-cell omics and could be widely applicable to human diseases.

Epigenetic priming enhances antitumor immunity in platinum-resistant ovarian cancer.

BackgroundImmune checkpoint inhibitors (ICIs) have modest activity in ovarian cancer (OC). To augment their activity, we used priming with the hypomethylating agent guadecitabine in a phase II study.MethodsEligible patients had platinum-resistant OC, normal organ function, measurable disease, and received up to 5 prior regimens. The treatment included guadecitabine (30 mg/m2) on days 1-4, and pembrolizumab (200 mg i.v.) on day 5, every 21 days. The primary endpoint was the response rate. Tumor biopsies, plasma, and PBMCs were obtained at baseline and after treatment.ResultsAmong 35 evaluable patients, 3 patients had partial responses (8.6%), and 8 (22.9%) patients had stable disease, resulting in a clinical benefit rate of 31.4% (95% CI: 16.9%-49.3%). The median duration of clinical benefit was 6.8 months. Long-interspersed element 1 (LINE1) was hypomethylated in post-treatment PBMCs, and methylomic and transcriptomic analyses showed activation of antitumor immunity in post-treatment biopsies. High-dimensional immune profiling of PBMCs showed a higher frequency of naive and/or central memory CD4+ T cells and of classical monocytes in patients with a durable clinical benefit or response (CBR). A higher baseline density of CD8+ T cells and CD20+ B cells and the presence of tertiary lymphoid structures in tumors were associated with a durable CBR.ConclusionEpigenetic priming using a hypomethylating agent with an ICI was feasible and resulted in a durable clinical benefit associated with immune responses in selected patients with recurrent OC.Trial registrationClinicalTrials.gov NCT02901899.FundingUS Army Medical Research and Material Command/Congressionally Directed Medical Research Programs (USAMRMC/CDMRP) grant W81XWH-17-0141; the Diana Princess of Wales Endowed Professorship and LCCTRAC funds from the Robert H. Lurie Comprehensive Cancer Center; Walter S. and Lucienne Driskill Immunotherapy Research funds; Astex Pharmaceuticals; Merck & Co.; National Cancer Institute (NCI), NIH grants CCSG P30 CA060553, CCSG P30 CA060553, and CA060553.

ExTraMapper: exon- and transcript-level mappings for orthologous gene pairs.

MOTIVATION: Access to large-scale genomics and transcriptomics data from various tissues and cell lines allowed the discovery of wide-spread alternative splicing events and alternative promoter usage in mammalians. Between human and mouse, gene-level orthology is currently present for nearly 16k protein-coding genes spanning a diverse repertoire of over 200k total transcript isoforms. RESULTS: Here, we describe a novel method, ExTraMapper, which leverages sequence conservation between exons of a pair of organisms and identifies a fine-scale orthology mapping at the exon and then transcript level. ExTraMapper identifies more than 350k exon mappings, as well as 30k transcript mappings between human and mouse using only sequence and gene annotation information. We demonstrate that ExTraMapper identifies a larger number of exon and transcript mappings compared to previous methods. Further, it identifies exon fusions, splits and losses due to splice site mutations, and finds mappings between microexons that are previously missed. By reanalysis of RNA-seq data from 13 matched human and mouse tissues, we show that ExTraMapper improves the correlation of transcript-specific expression levels suggesting a more accurate mapping of human and mouse transcripts. We also applied the method to detect conserved exon and transcript pairs between human and rhesus macaque genomes to highlight the point that ExTraMapper is applicable to any pair of organisms that have orthologous gene pairs. AVAILABILITY AND IMPLEMENTATION: The source code and the results are available at https://github.com/ay-lab/ExTraMapper and http://ay-lab-tools.lji.org/extramapper. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

RP58 Represses Transcriptional Programs Linked to Nonneuronal Cell Identity and Glioblastoma Subtypes in Developing Neurons.

How mammalian neuronal identity is progressively acquired and reinforced during development is not understood. We have previously shown that loss of RP58 (ZNF238 or ZBTB18), a BTB/POZ-zinc finger-containing transcription factor, in the mouse brain leads to microcephaly, corpus callosum agenesis, and cerebellum hypoplasia and that it is required for normal neuronal differentiation. The transcriptional programs regulated by RP58 during this process are not known. Here, we report for the first time that in embryonic mouse neocortical neurons a complex set of genes normally expressed in other cell types, such as those from mesoderm derivatives, must be actively repressed in vivo and that RP58 is a critical regulator of these repressed transcriptional programs. Importantly, gene set enrichment analysis (GSEA) analyses of these transcriptional programs indicate that repressed genes include distinct sets of genes significantly associated with glioma progression and/or pluripotency. We also demonstrate that reintroducing RP58 in glioma stem cells leads not only to aspects of neuronal differentiation but also to loss of stem cell characteristics, including loss of stem cell markers and decrease in stem cell self-renewal capacities. Thus, RP58 acts as an in vivo master guardian of the neuronal identity transcriptome, and its function may be required to prevent brain disease development, including glioma progression.

A first-in-human phase 0 clinical study of RNA interference-based spherical nucleic acids in patients with recurrent glioblastoma.

Glioblastoma (GBM) is one of the most difficult cancers to effectively treat, in part because of the lack of precision therapies and limited therapeutic access to intracranial tumor sites due to the presence of the blood-brain and blood-tumor barriers. We have developed a precision medicine approach for GBM treatment that involves the use of brain-penetrant RNA interference-based spherical nucleic acids (SNAs), which consist of gold nanoparticle cores covalently conjugated with radially oriented and densely packed small interfering RNA (siRNA) oligonucleotides. On the basis of previous preclinical evaluation, we conducted toxicology and toxicokinetic studies in nonhuman primates and a single-arm, open-label phase 0 first-in-human trial (NCT03020017) to determine safety, pharmacokinetics, intratumoral accumulation and gene-suppressive activity of systemically administered SNAs carrying siRNA specific for the GBM oncogene Bcl2Like12 (Bcl2L12). Patients with recurrent GBM were treated with intravenous administration of siBcl2L12-SNAs (drug moniker: NU-0129), at a dose corresponding to 1/50th of the no-observed-adverse-event level, followed by tumor resection. Safety assessment revealed no grade 4 or 5 treatment-related toxicities. Inductively coupled plasma mass spectrometry, x-ray fluorescence microscopy, and silver staining of resected GBM tissue demonstrated that intravenously administered SNAs reached patient tumors, with gold enrichment observed in the tumor-associated endothelium, macrophages, and tumor cells. NU-0129 uptake into glioma cells correlated with a reduction in tumor-associated Bcl2L12 protein expression, as indicated by comparison of matched primary tumor and NU-0129-treated recurrent tumor. Our results establish SNA nanoconjugates as a potential brain-penetrant precision medicine approach for the systemic treatment of GBM.

DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome.

MOTIVATION: Deciphering the language of non-coding DNA is one of the fundamental problems in genome research. Gene regulatory code is highly complex due to the existence of polysemy and distant semantic relationship, which previous informatics methods often fail to capture especially in data-scarce scenarios. RESULTS: To address this challenge, we developed a novel pre-trained bidirectional encoder representation, named DNABERT, to capture global and transferrable understanding of genomic DNA sequences based on up and downstream nucleotide contexts. We compared DNABERT to the most widely used programs for genome-wide regulatory elements prediction and demonstrate its ease of use, accuracy and efficiency. We show that the single pre-trained transformers model can simultaneously achieve state-of-the-art performance on prediction of promoters, splice sites and transcription factor binding sites, after easy fine-tuning using small task-specific labeled data. Further, DNABERT enables direct visualization of nucleotide-level importance and semantic relationship within input sequences for better interpretability and accurate identification of conserved sequence motifs and functional genetic variant candidates. Finally, we demonstrate that pre-trained DNABERT with human genome can even be readily applied to other organisms with exceptional performance. We anticipate that the pre-trained DNABERT model can be fined tuned to many other sequence analyses tasks. AVAILABILITY AND IMPLEMENTATION: The source code, pretrained and finetuned model for DNABERT are available at GitHub (https://github.com/jerryji1993/DNABERT). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Distinct mechanisms control genome recognition by p53 at its target genes linked to different cell fates.

The tumor suppressor p53 integrates stress response pathways by selectively engaging one of several potential transcriptomes, thereby triggering cell fate decisions (e.g., cell cycle arrest, apoptosis). Foundational to this process is the binding of tetrameric p53 to 20-bp response elements (REs) in the genome (RRRCWWGYYYN0-13RRRCWWGYYY). In general, REs at cell cycle arrest targets (e.g. p21) are of higher affinity than those at apoptosis targets (e.g., BAX). However, the RE sequence code underlying selectivity remains undeciphered. Here, we identify molecular mechanisms mediating p53 binding to high- and low-affinity REs by showing that key determinants of the code are embedded in the DNA shape. We further demonstrate that differences in minor/major groove widths, encoded by G/C or A/T bp content at positions 3, 8, 13, and 18 in the RE, determine distinct p53 DNA-binding modes by inducing different Arg248 and Lys120 conformations and interactions. The predictive capacity of this code was confirmed in vivo using genome editing at the BAX RE to interconvert the DNA-binding modes, transcription pattern, and cell fate outcome.

Personizing the prediction of future susceptibility to a specific disease.

A traceable biomarker is a member of a disease's molecular pathway. A disease may be associated with several molecular pathways. Each different combination of these molecular pathways, to which detected traceable biomarkers belong, may serve as an indicative of the elicitation of the disease at a different time frame in the future. Based on this notion, we introduce a novel methodology for personalizing an individual's degree of future susceptibility to a specific disease. We implemented the methodology in a working system called Susceptibility Degree to a Disease Predictor (SDDP). For a specific disease d, let S be the set of molecular pathways, to which traceable biomarkers detected from most patients of d belong. For the same disease d, let S' be the set of molecular pathways, to which traceable biomarkers detected from a certain individual belong. SDDP is able to infer the subset S'' ⊆{S-S'} of undetected molecular pathways for the individual. Thus, SDDP can infer undetected molecular pathways of a disease for an individual based on few molecular pathways detected from the individual. SDDP can also help in inferring the combination of molecular pathways in the set {S'+S''}, whose traceable biomarkers collectively is an indicative of the disease. SDDP is composed of the following four components: information extractor, interrelationship between molecular pathways modeler, logic inferencer, and risk indicator. The information extractor takes advantage of the exponential increase of biomedical literature to automatically extract the common traceable biomarkers for a specific disease. The interrelationship between molecular pathways modeler models the hierarchical interrelationships between the molecular pathways of the traceable biomarkers. The logic inferencer transforms the hierarchical interrelationships between the molecular pathways into rule-based specifications. It employs the specification rules and the inference rules for predicate logic to infer as many as possible undetected molecular pathways of a disease for an individual. The risk indicator outputs a risk indicator value that reflects the individual's degree of future susceptibility to the disease. We evaluated SDDP by comparing it experimentally with other methods. Results revealed marked improvement.

Frizzled-7 Identifies Platinum-Tolerant Ovarian Cancer Cells Susceptible to Ferroptosis.

Defining traits of platinum-tolerant cancer cells could expose new treatment vulnerabilities. Here, new markers associated with platinum-tolerant cells and tumors were identified using in vitro and in vivo ovarian cancer models treated repetitively with carboplatin and validated in human specimens. Platinum-tolerant cells and tumors were enriched in ALDH+ cells, formed more spheroids, and expressed increased levels of stemness-related transcription factors compared with parental cells. Additionally, platinum-tolerant cells and tumors exhibited expression of the Wnt receptor Frizzled-7 (FZD7). Knockdown of FZD7 improved sensitivity to platinum, decreased spheroid formation, and delayed tumor initiation. The molecular signature distinguishing FZD7+ from FZD7- cells included epithelial-to-mesenchymal (EMT), stemness, and oxidative phosphorylation-enriched gene sets. Overexpression of FZD7 activated the oncogenic factor Tp63, driving upregulation of glutathione metabolism pathways, including glutathione peroxidase 4 (GPX4), which protected cells from chemotherapy-induced oxidative stress. FZD7+ platinum-tolerant ovarian cancer cells were more sensitive and underwent ferroptosis after treatment with GPX4 inhibitors. FZD7, Tp63, and glutathione metabolism gene sets were strongly correlated in the ovarian cancer Tumor Cancer Genome Atlas (TCGA) database and in residual human ovarian cancer specimens after chemotherapy. These results support the existence of a platinum-tolerant cell population with partial cancer stem cell features, characterized by FZD7 expression and dependent on the FZD7-ß-catenin-Tp63-GPX4 pathway for survival. The findings reveal a novel therapeutic vulnerability of platinum-tolerant cancer cells and provide new insight into a potential "persister cancer cell" phenotype. SIGNIFICANCE: Frizzled-7 marks platinum-tolerant cancer cells harboring stemness features and altered glutathione metabolism that depend on GPX4 for survival and are highly susceptible to ferroptosis.

Identification of Geographic Specific SARS-Cov-2 Mutations by Random Forest Classification and Variable Selection Methods.

RNA viral genomes have very high mutations rates. As infection spreads in the host populations, different viral lineages emerge acquiring independent mutations that can lead to varied infection and death rates in different parts of the world. By application of Random Forest classification and feature selection methods, we developed an analysis pipeline for identification of geographic specific mutations and classification of different viral lineages, focusing on the missense-variants that alter the function of the encoded proteins. We applied the pipeline on publicly available SARS-CoV-2 datasets and demonstrated that the analysis pipeline accurately identified country or region-specific viral lineages and specific mutations that discriminate different lineages. The results presented here can help designing country-specific diagnostic strategies and prioritizing the mutations for functional interpretation and experimental validations.

FTO-Dependent N 6-Methyladenosine Modifications Inhibit Ovarian Cancer Stem Cell Self-Renewal by Blocking cAMP Signaling.

N 6-Methyladenosine (m6A) is the most abundant modification of mammalian mRNAs. RNA methylation fine tunes RNA stability and translation, altering cell fate. The fat mass- and obesity-associated protein (FTO) is an m6A demethylase with oncogenic properties in leukemia. Here, we show that FTO expression is suppressed in ovarian tumors and cancer stem cells (CSC). FTO inhibited the self-renewal of ovarian CSC and suppressed tumorigenesis in vivo, both of which required FTO demethylase activity. Integrative RNA sequencing and m6A mapping analysis revealed significant transcriptomic changes associated with FTO overexpression and m6A loss involving stem cell signaling, RNA transcription, and mRNA splicing pathways. By reducing m6A levels at the 3'UTR and the mRNA stability of two phosphodiesterase genes (PDE1C and PDE4B), FTO augmented second messenger 3', 5'-cyclic adenosine monophosphate (cAMP) signaling and suppressed stemness features of ovarian cancer cells. Our results reveal a previously unappreciated tumor suppressor function of FTO in ovarian CSC mediated through inhibition of cAMP signaling. SIGNIFICANCE: A new tumor suppressor function of the RNA demethylase FTO implicates m6A RNA modifications in the regulation of cyclic AMP signaling involved in stemness and tumor initiation.

In silico analysis of alternative splicing on drug-target gene interactions.

Identifying and evaluating the right target are the most important factors in early drug discovery phase. Most studies focus on one protein ignoring the multiple splice-variant or protein-isoforms, which might contribute to unexpected therapeutic activity or adverse side effects. Here, we present computational analysis of cancer drug-target interactions affected by alternative splicing. By integrating information from publicly available databases, we curated 883 FDA approved or investigational stage small molecule cancer drugs that target 1,434 different genes, with an average of 5.22 protein isoforms per gene. Of these, 618 genes have ≥5 annotated protein-isoforms. By analyzing the interactions with binding pocket information, we found that 76% of drugs either miss a potential target isoform or target other isoforms with varied expression in multiple normal tissues. We present sequence and structure level alignments at isoform-level and make this information publicly available for all the curated drugs. Structure-level analysis showed ligand binding pocket architectures differences in size, shape and electrostatic parameters between isoforms. Our results emphasize how potentially important isoform-level interactions could be missed by solely focusing on the canonical isoform, and suggest that on- and off-target effects at isoform-level should be investigated to enhance the productivity of drug-discovery research.

Immune landscapes associated with different glioblastoma molecular subtypes.

Recent work has highlighted the tumor microenvironment as a central player in cancer. In particular, interactions between tumor and immune cells may help drive the development of brain tumors such as glioblastoma multiforme (GBM). Despite significant research into the molecular classification of glioblastoma, few studies have characterized in a comprehensive manner the immune infiltrate in situ and within different GBM subtypes.In this study, we use an unbiased, automated immunohistochemistry-based approach to determine the immune phenotype of the four GBM subtypes (classical, mesenchymal, neural and proneural) in a cohort of 98 patients. Tissue Micro Arrays (TMA) were stained for CD20 (B lymphocytes), CD5, CD3, CD4, CD8 (T lymphocytes), CD68 (microglia), and CD163 (bone marrow derived macrophages) antibodies. Using automated image analysis, the percentage of each immune population was calculated with respect to the total tumor cells. Mesenchymal GBMs displayed the highest percentage of microglia, macrophage, and lymphocyte infiltration. CD68+ and CD163+ cells were the most abundant cell populations in all four GBM subtypes, and a higher percentage of CD163+ cells was associated with a worse prognosis. We also compared our results to the relative composition of immune cell type infiltration (using RNA-seq data) across TCGA GBM tumors and validated our results obtained with immunohistochemistry with an external cohort and a different method. The results of this study offer a comprehensive analysis of the distribution and the infiltration of the immune components across the four commonly described GBM subgroups, setting the basis for a more detailed patient classification and new insights that may be used to better apply or design immunotherapies for GBM.

Transcriptional consequences of impaired immune cell responses induced by cystic fibrosis plasma characterized via dual RNA sequencing.

BACKGROUND: In cystic fibrosis (CF), impaired immune cell responses, driven by the dysfunctional CF transmembrane conductance regulator (CFTR) gene, may determine the disease severity but clinical heterogeneity remains a major therapeutic challenge. The characterization of molecular mechanisms underlying impaired immune responses in CF may reveal novel targets with therapeutic potential. Therefore, we utilized simultaneous RNA sequencing targeted at identifying differentially expressed genes, transcripts, and miRNAs that characterize impaired immune responses triggered by CF and its phenotypes. METHODS: Peripheral blood mononuclear cells (PBMCs) extracted from a healthy donor were stimulated with plasma from CF patients (n = 9) and healthy controls (n = 3). The PBMCs were cultured (1 × 105 cells/well) for 9 h at 37 ° C in 5% CO2. After culture, total RNA was extracted from each sample and used for simultaneous total RNA and miRNA sequencing. RESULTS: Analysis of expression signatures from peripheral blood mononuclear cells induced by plasma of CF patients and healthy controls identified 151 genes, 154 individual transcripts, and 41 miRNAs differentially expressed in CF compared to HC while the expression signatures of 285 genes, 241 individual transcripts, and seven miRNAs differed due to CF phenotypes. Top immune pathways influenced by CF included agranulocyte adhesion, diapedesis signaling, and IL17 signaling, while those influenced by CF phenotypes included natural killer cell signaling and PI3K signaling in B lymphocytes. Upstream regulator analysis indicated dysregulation of CCL5, NF-κB and IL1A due to CF while dysregulation of TREM1 and TP53 regulators were associated with CF phenotype. Five miRNAs showed inverse expression patterns with three target genes relevant in CF-associated impaired immune pathways while two miRNAs showed inverse expression patterns with two target genes relevant to a dysregulated immune pathway associated with CF phenotypes. CONCLUSIONS: Our results indicate that miRNAs and individual transcript variants are relevant molecular targets contributing to impaired immune cell responses in CF.

Platform-Independent Classification System to Predict Molecular Subtypes of High-Grade Serous Ovarian Carcinoma.

PURPOSE: Molecular cancer subtyping is an important tool in predicting prognosis and developing novel precision medicine approaches. We developed a novel platform-independent gene expression-based classification system for molecular subtyping of patients with high-grade serous ovarian carcinoma (HGSOC). METHODS: Unprocessed exon array (569 tumor and nine normal) and RNA sequencing (RNA-seq; 376 tumor) HGSOC data sets, with clinical annotations, were downloaded from the Genomic Data Commons portal. Sample clustering was performed by non-negative matrix factorization by using isoform-level expression estimates. The association between the subtypes and overall survival was evaluated by Cox proportional hazards regression model after adjusting for the covariates. A novel classification system was developed for HGSOC molecular subtyping. Robustness and generalizability of the gene signatures were validated using independent microarray and RNA-seq data sets. RESULTS: Sample clustering recaptured the four known The Cancer Genome Atlas molecular subtypes but switched the subtype for 22% of the cases, which resulted in significant (P = .006) survival differences among the refined subgroups. After adjusting for covariate effects, the mesenchymal subgroup was found to be at an increased hazard for death compared with the immunoreactive subgroup. Both gene- and isoform-level signatures achieved more than 92% prediction accuracy when tested on independent samples profiled on the exon array platform. When the classifier was applied to RNA-seq data, the subtyping calls agreed with the predictions made from exon array data for 95% of the 279 samples profiled by both platforms. CONCLUSION: Isoform-level expression analysis successfully stratifies patients with HGSOC into groups with differing prognosis and has led to the development of robust, platform-independent gene signatures for HGSOC molecular subtyping. The association of the refined The Cancer Genome Atlas HGSOC subtypes with overall survival, independent of covariates, enhances the clinical annotation of the HGSOC cohort.

IDH3α regulates one-carbon metabolism in glioblastoma.

Mutation or transcriptional up-regulation of isocitrate dehydrogenases 1 and 2 (IDH1 and IDH2) promotes cancer progression through metabolic reprogramming and epigenetic deregulation of gene expression. Here, we demonstrate that IDH3α, a subunit of the IDH3 heterotetramer, is elevated in glioblastoma (GBM) patient samples compared to normal brain tissue and promotes GBM progression in orthotopic glioma mouse models. IDH3α loss of function reduces tricarboxylic acid (TCA) cycle turnover and inhibits oxidative phosphorylation. In addition to its impact on mitochondrial energy metabolism, IDH3α binds to cytosolic serine hydroxymethyltransferase (cSHMT). This interaction enhances nucleotide availability during DNA replication, while the absence of IDH3α promotes methionine cycle activity, S-adenosyl methionine generation, and DNA methylation. Thus, the regulation of one-carbon metabolism via an IDH3α-cSHMT signaling axis represents a novel mechanism of metabolic adaptation in GBM.