Expression Of Individual Genes Research Articles

Target and Gene-Based Therapeutic Strategies against Pancreatic Cancer: Current and Future Prospects.

Despite tremendous advancements in knowledge, diagnosis, and availability of both traditional and innovative treatments, pancreatic cancer remains a dangerous disease with a high death rate and dismal prognosis. The traditional strategy in adjuvant and palliative settings is still cytotoxic chemotherapy predicated on the purine derivative gemcitabine; nevertheless, there is an increasing need for new medicines that target the primary molecular pathways and pathophysiological abnormalities implicated. There is now just a tiny amount of evidence of therapeutic benefit when the targeted drug erlotinib is added to the conventional gemcitabine treatment. In preclinical and clinical trials, novel medications targeting mTOR, NF-κB, and proteasome, including the enzyme histone deacetylase, are currently being studied alongside the well-established monoclonal antibody treatments and small-molecule protein tyrosine kinase inhibitors. These novel medications may change the negative natural progression of this illness in conjunction with gene therapy and immunotherapy, both of which are undergoing clinical study. In this regard, leveraging miRNA manipulation to combat cancer is appealing due to its promise to deliver personalized treatment tailored to an individual's distinct gene or miRNA expression profile. Preclinical studies involving animals have showcased the effectiveness of miRNA-based therapies, with several of these treatments now progressing into human clinical trials for various malignancies and other medical conditions. This review describes the important developments of targeted therapeutics that are associated with pancreatic cancer and the discoveries which can help in dealing with this fatal malignancy in a more significant manner.

Cortexa: a comprehensive resource for studying gene expression and alternative splicing in the murine brain

BackgroundGene expression and alternative splicing are strictly regulated processes that shape brain development and determine the cellular identity of differentiated neural cell populations. Despite the availability of multiple valuable datasets, many functional implications, especially those related to alternative splicing, remain poorly understood. Moreover, neuroscientists working primarily experimentally often lack the bioinformatics expertise required to process alternative splicing data and produce meaningful and interpretable results. Notably, re-analyzing publicly available datasets and integrating them with in-house data can provide substantial novel insights. However, such analyses necessitate developing harmonized data handling and processing pipelines which in turn require considerable computational resources and in-depth bioinformatics expertise.ResultsHere, we present Cortexa—a comprehensive web portal that incorporates RNA-sequencing datasets from the mouse cerebral cortex (longitudinal or cell-specific) and the hippocampus. Cortexa facilitates understandable visualization of the expression and alternative splicing patterns of individual genes. Our platform provides SplicePCA—a tool that allows users to integrate their alternative splicing dataset and compare it to cell-specific or developmental neocortical splicing patterns. All standardized gene expression and alternative splicing datasets can be downloaded for further in-depth downstream analysis without the need for extensive preprocessing.ConclusionsCortexa provides a robust and readily available resource for unraveling the complexity of gene expression and alternative splicing regulatory processes in the mouse brain. The data portal is available at https://cortexa-rna.com/

Insights into the influence of high-intensity interval training on skeletal muscle clock gene expression in insulin-resistant individuals.

Insights into the influence of high-intensity interval training on skeletal muscle clock gene expression in insulin-resistant individuals.

CellCover Captures Neural Stem Cell Progression in Mammalian Neocortical Development.

Definition of cell classes across the tissues of living organisms is central in the analysis of growing atlases of single-cell RNA sequencing (scRNA-seq) data across biomedicine. Marker genes for cell classes are most often defined by differential expression (DE) methods that serially assess individual genes across landscapes of diverse cells. This serial approach has been extremely useful, but is limited because it ignores possible redundancy or complementarity across genes that can only be captured by analyzing multiple genes simultaneously. We aim to identify discriminating panels of genes. To efficiently explore the vast space of possible marker panels, leverage the large number of cells often sequenced, and overcome zero-inflation in scRNA-seq data, we propose viewing gene panel selection as a variation of the "minimal set-covering problem" in combinatorial optimization. We show that this new method, CellCover, captures cell-class-specific signals in the developing mouse neocortex that are distinct from those defined by DE methods. Transfer learning experiments across mouse, primate, and human data demonstrate that CellCover identifies markers of conserved cell classes in neurogenesis, as well as temporal progression in both progenitors and neurons. Exploring markers of human outer radial glia (oRG, or basal RG) across mammals, we show that transcriptomic elements of this key cell type in the expansion of the human cortex appeared in gliogenic precursors of the rodent before the full program emerged in the primate lineage. We have assembled the public datasets we use in this report at NeMO analytics where the expression of individual genes {NeMO Individual Genes} and marker gene panels can be freely explored {NeMO: Telley 3 Sets Covering Panels}, {NeMO: Telley 12 Sets Covering Panels}, and {NeMO: Sorted Brain Cell Covering Panels}. CellCover is available in {CellCover R} and {CellCover Python}.

MOSBY enables multi-omic inference and spatial biomarker discovery from whole slide images

The utility of deep neural nets has been demonstrated for mapping hematoxylin-and-eosin (H&E) stained image features to expression of individual genes. However, these models have not been employed to discover clinically relevant spatial biomarkers. Here we develop MOSBY (Multi-Omic translation of whole slide images for Spatial Biomarker discoverY) that leverages contrastive self-supervised pretraining to extract improved H&E whole slide images features, learns a mapping between image and bulk omic profiles (RNA, DNA, and protein), and utilizes tile-level information to discover spatial biomarkers. We validate MOSBY gene and gene set predictions with spatial transcriptomic and serially-sectioned CD8 IHC image data. We demonstrate that MOSBY-inferred colocalization features have survival-predictive power orthogonal to gene expression, and enable concordance indices highly competitive with survival-trained multimodal networks. We identify and validate (1) an ER stress-associated colocalization feature as a chemotherapy-specific risk factor in lung adenocarcinoma, and (2) the colocalization of T effector cell vs cysteine signatures as a negative prognostic factor in multiple cancer indications. The discovery of clinically relevant biologically interpretable spatial biomarkers showcases the utility of the model in unraveling novel insights in cancer biology as well as informing clinical decision-making.

The Effect of Maternal High-Fat or High-Carbohydrate Diet during Pregnancy and Lactation on Cytochrome P450 2D (CYP2D) in the Liver and Brain of Rat Offspring.

Cytochrome P450 2D (CYP2D) is important in psychopharmacology as it is engaged in the metabolism of drugs, neurosteroids and neurotransmitters. An unbalanced maternal diet during pregnancy and lactation can cause neurodevelopmental abnormalities and increases the offspring's predisposition to neuropsychiatric diseases. The aim of the present study was to evaluate the effect of maternal modified types of diet: a high-fat diet (HFD) and high-carbohydrate diet (HCD) during pregnancy and lactation on CYP2D in the liver and brain of male offspring at 28 (adolescent) or 63 postnatal days (young adult). The CYP2D activity and protein level were measured in the liver microsomes and the levels of mRNAs of CYP2D1, 2D2 and 2D4 were investigated both in the liver and brain. In the liver, both HFD and HCD increased the mRNA levels of all the three investigated CYP2D genes in adolescents, but an opposite effect was observed in young adults. The CYP2D protein level increased in adolescents but not in young adults. In contrast, young adults showed significantly decreased CYP2D activity. Similar effect of HFD on the CYP2D mRNAs was observed in the prefrontal cortex, while the effect of HCD was largely different than in the liver (the CYP2D2 expression was not affected, the CYP2D4 expression was decreased in young adults). In conclusion, modified maternal diets influence the expression of individual CYP2D1, CYP2D2 and CYP2D4 genes in the liver and brain of male offspring, which may affect the metabolism of CYP2D endogenous substrates and drugs and alter susceptibility to brain diseases and pharmacotherapy outcome.

Gap-App: A sex-distinct AI-based predictor for pancreatic ductal adenocarcinoma survival as a web application open to patients and physicians.

In this study, using RNA-Seq gene expression data and advanced machine learning techniques, we identified distinct gene expression profiles between male and female pancreatic ductal adenocarcinoma (PDAC) patients. Building upon this insight, we developed sex-specific 3-year survival predictive models along with a single comprehensive model. These sex-specific models outperformed the single general model despite the smaller sample sizes. We further refined our models by using the most important features extracted from these initial models. The refined sex-specific predictive models achieved improved accuracies of 92.62% for males and 91.96% for females, respectively, versus an accuracy of 87.84% from the refined comprehensive model, further highlighting the value of sex-specific analysis. Based on these findings, we created Gap-App, a web application that enables the use of individual gene expression profiles combined with sex information for personalized survival predictions. Gap-App, the first online tool aiming to bridge the gap between complex genomic data and clinical application and facilitating more precise and individualized cancer care, marks a significant advancement in personalized prognosis. The study not only underscores the importance of acknowledging sex differences in personalized prognosis, but also sets the stage for the shift from traditional one-size-fits-all to more personalized and targeted medicine. The GAP-App service is freely available at www.gap-app.org.

Itaconate is a metabolic regulator of bone formation in homeostasis and arthritis

ObjectivesBone remodelling is a highly dynamic process dependent on the precise coordination of osteoblasts and haematopoietic-cell derived osteoclasts. Changes in core metabolic pathways during osteoclastogenesis, however, are largely unexplored and...

Sensitive detection of synthetic response to cancer immunotherapy driven by gene paralog pairs.

Emerging immunotherapies such as immune checkpoint blockade (ICB) and chimeric antigen receptor T-cell (CAR-T) therapy have revolutionized cancer treatment and have improved the survival of patients with multiple cancer types. Despite this success many patients are unresponsive to these treatments or relapse following treatment. CRISPR activation and knockout (KO) screens have been used to identify novel single gene targets that can enhance effector T cell function and promote immune cell targeting and eradication of tumors. However, cancer cells often employ multiple genes to promote an immunosuppressive pathway and thus modulating individual genes often has a limited effect. Paralogs are genes that originate from common ancestors and retain similar functions. They often have complex effects on a particular phenotype depending on factors like gene family similarity, each individual gene's expression and the physiological or pathological context. Some paralogs exhibit synthetic lethal interactions in cancer cell survival; however, a thorough investigation of paralog pairs that could enhance the efficacy of cancer immunotherapy is lacking. Here we introduce a sensitive computational approach that uses sgRNA sets enrichment analysis to identify cancer-intrinsic paralog pairs which have the potential to synergistically enhance T cell-mediated tumor destruction. We have further developed an ensemble learning model that uses an XGBoost classifier and incorporates features such as gene characteristics, sequence and structural similarities, protein-protein interaction (PPI) networks, and gene coevolution data to predict paralog pairs that are likely to enhance immunotherapy efficacy. We experimentally validated the functional significance of these predicted paralog pairs using double knockout (DKO) of identified paralog gene pairs as compared to single gene knockouts (SKOs). These data and analyses collectively provide a sensitive approach to identify previously undetected paralog pairs that can enhance cancer immunotherapy even when individual genes within the pair has a limited effect.

Changes in the Expression of Inflammatory Genes Induced by Chronic Exercise in the Adipose Tissue: Differences by Sex.

The impact of obesity on adipose tissue function is well acknowledged, but the role of physical exercise in regulating inflammatory markers and gene expression in obese individuals remains uncertain. This study aims to investigate the effects of chronic exercise on inflammatory gene expression in adipose tissue and to explore sex differences in response to exercise. The study involved 29 obese participants (13 men, 16 women) aged 38 to 54 years with a mean BMI of 36.05 ± 4.99 kg/m2. Participants underwent an 8-week concurrent training program comprising three weekly sessions of ~60 min each. The sessions included joint mobility exercises, cardiovascular activation, and cardiorespiratory resistance exercises at medium to low intensity. A fine-needle aspiration biopsy of abdominal subcutaneous adipose tissue was performed for gene expression analysis using quantitative polymerase chain reaction (qPCR). The study demonstrated that chronic exercise modulates the expression of pro-inflammatory genes in subcutaneous adipose tissue, particularly ADIPOR2 (p = 0.028), leptin (p = 0.041), and IFNg (p = 0.040) (downregulated). Interestingly, regardless of sex, the exercise programs had an independent effect on pro-inflammatory genes. Overall, this study provides insight into the role of chronic exercise in modulating adipose tissue gene expression in obese individuals. Further research involving both sexes is recommended to tailor exercise interventions for better outcomes.

Molecular Subtypes of High-Grade Serous Ovarian Cancer across Racial Groups and Gene Expression Platforms.

High-grade serous carcinoma (HGSC) gene expression subtypes are associated with differential survival. We characterized HGSC gene expression in Black individuals and considered whether gene expression differences by self-identified race may contribute to poorer HGSC survival among Black versus White individuals. We included newly generated RNA sequencing data from Black and White individuals and array-based genotyping data from four existing studies of White and Japanese individuals. We used K-means clustering, a method with no predefined number of clusters or dataset-specific features, to assign subtypes. Cluster- and dataset-specific gene expression patterns were summarized by moderated t-scores. We compared cluster-specific gene expression patterns across datasets by calculating the correlation between the summarized vectors of moderated t-scores. After mapping to The Cancer Genome Atlas-derived HGSC subtypes, we used Cox proportional hazards models to estimate subtype-specific survival by dataset. Cluster-specific gene expression was similar across gene expression platforms and racial groups. Comparing the Black population with the White and Japanese populations, the immunoreactive subtype was more common (39% vs. 23%-28%) and the differentiated subtype was less common (7% vs. 22%-31%). Patterns of subtype-specific survival were similar between the Black and White populations with RNA sequencing data; compared with mesenchymal cases, the risk of death was similar for proliferative and differentiated cases and suggestively lower for immunoreactive cases [Black population HR = 0.79 (0.55, 1.13); White population HR = 0.86 (0.62, 1.19)]. Although the prevalence of HGSC subtypes varied by race, subtype-specific survival was similar. HGSC subtypes can be consistently assigned across platforms and self-identified racial groups.

Association between DRD4 gene and perception of architectural spaces by using EEG

The psychological effects of the environment on humans have been well established. However, the genetic mechanisms of individuals in environmental perception and preferences have not been studied yet. In this research the correlation between the expression of the DRD4 gene and the preference for architectural spaces was investigated. Firstly, architectural spaces simulated in VR animation. The brain responses of the participants (N = 42) to architectural stimuli were recorded. Secondly, using the qPCR method, the DRD4 gene expression of individuals was compared based on the frequency of their brain waves. A significant difference was observed in the DRD4 gene expression of the participant groups. Based on the results, the increase in the expression of the DRD4 gene was associated with a decrease in the intensity of the brain's response to stimuli. Also, the highest correlation was seen between DRD4 gene expression and the stimuli of Low-privacy (91%), complexity (87.8%), and color (84.6%).

Features of HPV+ OPSCC predictive of recurrence identified by transcriptional profiling of a case-control cohort.

6055 Background: De-escalating therapy for HPV+ oropharyngeal cancers (OPSCCs) is hampered by poor ability to predict recurrence, and limited insight into the biologic traits predisposing to recurrence impedes personalizing therapy. We aimed to (1) define biologic features underlying therapy resistance by transcriptional profiling and (2) evaluate the features for prognostic utility. Methods: A single institution cohort of 851 HPV+ OPSCC patients undergoing transoral robotic surgery during 2007-2020 was used to identify 50 cases that recurred locoregionally in the adjuvant RT field (n=14) and/or at distant sites (n=43). As controls, we used 49 recurrence-free patients with long-term follow up and similar stage, smoking history, and adjuvant therapy. RNAseq of pretreatment tumors was used to compare individual gene expression and Hallmark/Kegg pathway activity between cases and controls using unpaired t-test (p<.05) and Gene Set Enrichment Analysis (p-adj<.05), respectively. Activity of the significant pathways was quantified in individual tumors using Gene Set Variation Analysis (GSVA), and a regression-based composite of GSVA scores was developed to distinguish cases from controls by ROC analysis. The composite score was used to test for stratification of recurrence free survival (RFS) in external cohorts using Youden Index and logrank test. Results: The21 Hallmark/Kegg pathways downregulated in cases indicated suppression of anti-tumor immunity, and the 20 upregulated ones revealed increased biosynthetic function and tumor cell proliferation. The 1472 upregulated mRNAs contained several components of the ATR-chk1 DNA repair pathway and trans-lesion synthesis polymerases, suggesting that mitigation of DNA replication stress enhanced tumor growth. The 958 downregulated mRNAs suggested reductions in the cytoplasmic dsDNA sensing and downstream NF-kB signals that lead to replication stress-driven anti-tumor immunity. Two GSVA scores were created to quantify the key tumor-intrinsic and immune suppressive features separately in each tumor. The scores correlated with each other (R=0.6, p<.001), and a regression-based combination of them distinguished cases from controls (AUC=.76, p<.001). This composite score also stratified RFS in three external cohorts: TCGA (n=52, p<0.001) and two single institution cohorts containing both surgical and nonsurgical cases (n=46, p=.002; n=81, p<0.001). Conclusions: HPV+ OPSCCs failing primary surgical therapy show evidence of reduced replication stress mediating tumor progression and low anti-tumor immunity. These features appear generalizable to heterogeneously treated external cohorts, where they predicted recurrence. Our results provide a new basis for creating transcriptomic predictors of treatment response and suggest targetable molecular mechanisms to overcome therapy resistance.

MousiPLIER: A Mouse Pathway-Level Information Extractor Model.

High-throughput gene expression profiling measures individual gene expression across conditions. However, genes are regulated in complex networks, not as individual entities, limiting the interpretability of gene expression data. Machine learning models that incorporate prior biological knowledge are a powerful tool to extract meaningful biology from gene expression data. Pathway-level information extractor (PLIER) is an unsupervised machine learning method that defines biological pathways by leveraging the vast amount of published transcriptomic data. PLIER converts gene expression data into known pathway gene sets, termed latent variables (LVs), to substantially reduce data dimensionality and improve interpretability. In the current study, we trained the first mouse PLIER model on 190,111 mouse brain RNA-sequencing samples, the greatest amount of training data ever used by PLIER. We then validated the mousiPLIER approach in a study of microglia and astrocyte gene expression across mouse brain aging. mousiPLIER identified biological pathways that are significantly associated with aging, including one latent variable (LV41) corresponding to striatal signal. To gain further insight into the genes contained in LV41, we performed k-means clustering on the training data to identify studies that respond strongly to LV41. We found that the variable was relevant to striatum and aging across the scientific literature. Finally, we built a Web server (http://mousiplier.greenelab.com/) for users to easily explore the learned latent variables. Taken together, this study defines mousiPLIER as a method to uncover meaningful biological processes in mouse brain transcriptomic studies.

#1290 Adverse proinflammatory responses in the kidney after FGF23 administration in three murine kidney disease models

Abstract Background and Aims The hormone FGF23 is a biomarker associated with morbidity and mortality in individuals with and without kidney disease, but the role of FGF23 excess as a pathogenic factor is incompletely understood. Here, we investigated renal FGF23 signaling in kidney disease models. Method This study used three independent disease models: i) Male C57BL/6 mice (n = 16) were treated with Freud's adjuvant and nephrotoxic serum (NTS) to induce anti-glomerular basement membrane (anti-GBM) disease. ii) Female BALB/c mice (n = 8) were treated with adriamycin (doxorubicin) 10.5 mg/kg to induce focal and segmental glomerulosclerosis (FSGS). iii) Male DBA/2J mice (n = 20) were fed a diet containing adenine 0.2%. Control mice of the respective same strain and sex received vehicle injections. Mice of the anti-GBM and FSGS models and controls received intravenous injections of recombinant FGF23 1 µg or vehicle for six consecutive days (anti-GBM) or once (FSGS), with dissection 24 h after the last injection. Mice of the adenine model were dissected after 15 weeks. Following dissection, ex vivo precision-cut kidney slices (PCKS) were obtained from n = 4 mice and maintained for 24 at 37°C, 95% O2 and 5% CO2, followed by a 1 h treatment with recombinant FGF23 200 ng/ml or vehicle. Glomerular filtration rate (GFR) or renal retention parameters in serum and albuminuria were assessed. Kidneys underwent histological assessment using hematoxylin & eosin or Masson's trichrome. Renal RNA was isolated for bulk RNAseq. Differential gene expression was assessed to determine the effect of FGF23 treatment in disease-free controls, in each disease model, and the interaction between FGF23 X disease effect for the anti-GBM and FSGS groups. Gene set enrichment analysis was performed with a focus on the immune system. Results Anti-GBM mice showed a 45 ± 27% GFR decline (mean ± SD) compared to baseline, whereas controls showed an GFR increase of 11 ± 36% (p < 0.01). Kidney histology revealed an interstitial infiltrate. The FSGS model showed elevated serum creatinine and albuminuria, and hypercellularity in some glomeruli. Mice of the adenine model showed progressively increased blood urea nitrogen concentrations starting after 2 weeks, up to a 3-fold increase at week 14. Histology revealed interstitial fibrosis and tubular atrophy. Bulk RNAseq revealed proinflammatory transcriptional signatures after FGF23 treatment in all disease models, regardless of which timepoint or model: In the anti-GBM model, Hallmark gene sets of inflammatory response, interferon-α response, interferon-β response and allograft rejection were significantly increased after 6 days of FGF23 treatment, whereas disease-free controls showed no significantly changed immune gene sets after 6 days of FGF23. In FSGS mice, none of the Hallmark gene sets were changed after 1 FGF23 injection, but top individual gene expression affected by 1 FGF23 injection were an increased proinflammatory transcription factor Cebpb and an increased DNA damage transcript Dclre1b. Finally, the PCKS of adenine model revealed no significant Hallmark gene sets, but the two genes with differential expression were a proinflammatory macrophage/fibroblast chemokine Ccl4 (increased) and an immunoglobulin heavy variable Ighv2-2 (decreased). Conclusion FGF23-driven patterns of proinflammatory gene expression were observed in three experimental models of kidney disease, both in vivo and ex vivo assessment of FGF23 signaling, thus excluding effects of FGF23 on other organs. As these findings were limited to transcriptional analyses, the present data warrant further confirmation and exploration of FGF23 as a pathogenic agent in kidney disease.

RNA degradation in human mitochondria: the journey is not finished.

Mitochondria are vital organelles present in almost all eukaryotic cells. Although most of the mitochondrial proteins are nuclear-encoded, mitochondria contain their own genome, whose proper expression is necessary for mitochondrial function. Transcription of the human mitochondrial genome results in the synthesis of long polycistronic transcripts that are subsequently processed by endonucleases to release individual RNA molecules, including precursors of sense protein-encoding mRNA (mt-mRNA) and a vast amount of antisense noncoding RNAs. Because of mitochondrial DNA (mtDNA) organization, the regulation of individual gene expression at the transcriptional level is limited. Although transcription of most protein-coding mitochondrial genes occurs with the same frequency, steady-state levels of mature transcripts are different. Therefore, post-transcriptional processes are important for regulating mt-mRNA levels. The mitochondrial degradosome is a complex composed of the RNA helicase SUV3 (also known as SUPV3L1) and polynucleotide phosphorylase (PNPase, PNPT1). It is the best-characterized RNA-degrading machinery in human mitochondria, which is primarily responsible for the decay of mitochondrial antisense RNA. The mechanism of mitochondrial sense RNA decay is less understood. This review aims to provide a general picture of mitochondrial genome expression, with a particular focus on mitochondrial RNA (mtRNA) degradation.

Conserved role of hnRNPL in alternative splicing of epigenetic modifiers enables B cell activation

The multifunctional RNA-binding protein hnRNPL is implicated in antibody class switching but its broader function in B cells is unknown. Here, we show that hnRNPL is essential for B cell activation, germinal center formation, and antibody responses. Upon activation, hnRNPL-deficient B cells show proliferation defects and increased apoptosis. Comparative analysis of RNA-seq data from activated B cells and another eight hnRNPL-depleted cell types reveals common effects on MYC and E2F transcriptional programs required for proliferation. Notably, while individual gene expression changes are cell type specific, several alternative splicing events affecting histone modifiers like KDM6A and SIRT1, are conserved across cell types. Moreover, hnRNPL-deficient B cells show global changes in H3K27me3 and H3K9ac. Epigenetic dysregulation after hnRNPL loss could underlie differential gene expression and upregulation of lncRNAs, and explain common and cell type-specific phenotypes, such as dysfunctional mitochondria and ROS overproduction in mouse B cells. Thus, hnRNPL is essential for the resting-to-activated B cell transition by regulating transcriptional programs and metabolism, at least in part through the alternative splicing of several histone modifiers.

A Spatial Transcriptomics Browser for Discovering Gene Expression Landscapes across Microscopic Tissue Sections.

A crucial feature of life is its spatial organization and compartmentalization on the molecular, cellular, and tissue levels. Spatial transcriptomics (ST) technology has opened a new chapter of the sequencing revolution, emerging rapidly with transformative effects across biology. This technique produces extensive and complex sequencing data, raising the need for computational methods for their comprehensive analysis and interpretation. We developed the ST browser web tool for the interactive discovery of ST images, focusing on different functional aspects such as single gene expression, the expression of functional gene sets, as well as the inspection of the spatial patterns of cell-cell interactions. As a unique feature, our tool applies self-organizing map (SOM) machine learning to the ST data. Our SOM data portrayal method generates individual gene expression landscapes for each spot in the ST image, enabling its downstream analysis with high resolution. The performance of the spatial browser is demonstrated by disentangling the intra-tumoral heterogeneity of melanoma and the microarchitecture of the mouse brain. The integration of machine-learning-based SOM portrayal into an interactive ST analysis environment opens novel perspectives for the comprehensive knowledge mining of the organization and interactions of cellular ecosystems.

High-density genetic map construction and QTL mapping of a zigzag-shaped stem trait in tea plant (Camellia sinensis)

The highly unique zigzag-shaped stem phenotype in tea plants boasts significant ornamental value and is exceptionally rare. To investigate the genetic mechanism behind this trait, we developed BC1 artificial hybrid populations. Our genetic analysis revealed the zigzag-shaped trait as a qualitative trait. Utilizing whole-genome resequencing, we constructed a high-density genetic map from the BC1 population, incorporating 5,250 SNP markers across 15 linkage groups, covering 3,328.51 cM with an average marker interval distance of 0.68 cM. A quantitative trait locus (QTL) for the zigzag-shaped trait was identified on chromosome 4, within a 61.2 to 97.2 Mb range, accounting for a phenotypic variation explained (PVE) value of 13.62%. Within this QTL, six candidate genes were pinpointed. To better understand their roles, we analyzed gene expression in various tissues and individuals with erect and zigzag-shaped stems. The results implicated CsXTH (CSS0035625) and CsCIPK14 (CSS0044366) as potential key contributors to the zigzag-shaped stem formation. These discoveries lay a robust foundation for future functional genetic mapping and tea plant genetic enhancement.

Abstract PS14-07: Exploring Circulating Leukocyte RNA Expression: Implications for Treatment Outcomes and Immune-Related Adverse Events in Patients with Triple Negative Breast Cancer Enrolled in the GeparNuevo Trial

Abstract Abstract Exploring Circulating Leukocyte RNA Expression: Implications for Treatment Outcomes and Immune-Related Adverse Events in Patients with Triple Negative Breast Cancer Enrolled in the GeparNuevo Trial Background: Significant research has been conducted on the influence of immune checkpoint inhibitor therapy on tumor microenvironment, particularly with regard to tumor-infiltrating immune cells. Nevertheless, our understanding of the circulating immune repertoire and its association with treatment outcomes remains limited. Consequently, our subproject of the GeparNuevo trial aimed to explore the RNA phenotype of circulating leukocytes and its impact on overall survival (OS), and adverse events of patients enrolled in the GeparNuevo trial (Loibl S et al. Annals Oncol 2022). Methods: The GeparNuevo phase II trial focused on the effects of neoadjuvant nab-paclitaxel followed by epirubicin/cyclophosphamide (nabP-EC) chemotherapy combined with the anti-PD-L1 immune checkpoint inhibitor durvalumab versus placebo in patients with non-metastatic triple-negative breast cancer. RNA-stabilizing PAXgene tubes were used to collect blood samples prior to treatment initiation. RNA was extracted from circulating leukocytes of 117 patients and analyzed using a custom NanoString nCounter CodeSet, including 290 immune-related target genes. The associations between 16 immune cell scores, 26 immune signaling scores, 31 individual gene expression patterns, OS, and immune-related adverse events (irAEs) were analyzed. Results: Univariate Cox regression analysis using continuous scores revealed a significant correlation between PIP3 activates AKT signaling, T cells, CDK2, and TIMP1 expression with OS in the placebo arm. Higher expression of PIP3 activates AKT signaling, T cells, and CDK2, as well as lower expression of TIMP1, were associated with prolonged survival. Notably, T cell scores and CDK2 expression exhibited a significant interaction with the treatment arm (p=0.0489 and 0.0210, respectively). Multivariate Cox regression analysis demonstrated a significant association of DPP4, ICOS and MYC expression with OS. Additionally, CDK2, CDKN2A, F5 and HLA-DRA expression were linked to the presence of irAEs. In the durvalumab arm, TNFR2 non canonical NFkB pathway signaling, CDK2 and CDKN2A expression showed an inverse association with the occurrence of irAEs. Conclusions: Our study provides preliminary evidence that RNA derived from circulating leukocytes may serve as a potential biomarker for predicting treatment outcomes and identifying patients prone to develop side effects during standard-of-care chemotherapy or immune checkpoint therapy. These findings highlight the potential utility of peripheral immune cell RNA profiling in improving treatment strategies and patient management. Further research and validation are necessary to fully comprehend the clinical significance and broader implications of these findings. Citation Format: Hanna Hübner, Matthias Ruebner, Andreas Schneeweiss, Carsten Denkert, Hans-Peter Sinn, Michael Braun, Thomas Karn, Bruno V. Sinn, Dirk-Michael Zahm, Jörg Thomalla, Jens Huober, Claus Hanusch, Michael Untch, Christine Solbach, Theresa Link, Natalie Filmann, Julia Rey, Johannes Holtschmidt, Sibylle Loibl, Peter A. Fasching. Exploring Circulating Leukocyte RNA Expression: Implications for Treatment Outcomes and Immune-Related Adverse Events in Patients with Triple Negative Breast Cancer Enrolled in the GeparNuevo Trial [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS14-07.