Characterization Of Gene Expression Research Articles

FLT4 causes developmental disorders of the cardiovascular and lymphovascular systems via pleiotropic molecular mechanisms.

Rare, deleterious genetic variants in FLT4 are associated with Tetralogy of Fallot (TOF), the most common cyanotic congenital heart disease (CHD). Distinct genetic variants in FLT4 are also an established cause of Milroy disease, the most prevalent form of primary hereditary lymphoedema. Phenotypic features of these two conditions are non-overlapping, implying pleiotropic cellular mechanisms during development. Here, we show that FLT4 variants identified in TOF patients, when expressed in primary human endothelial cells, cause aggregation of FLT4 protein in the perinuclear endoplasmic reticulum, activating proteostatic and metabolic signalling, whereas lymphoedema-associated FLT4 variants and wildtype FLT4 do not. FLT4 TOF variants display characteristic gene expression profiles in key developmental signalling pathways, revealing a role for FLT4 in cardiogenesis distinct from its role in lymphatic development. Inhibition of proteostatic signalling abrogates these effects, identifying potential avenues for therapeutic intervention. Depletion of flt4 in zebrafish caused cardiac phenotypes of reduced heart size and altered heart looping. These phenotypes were rescued with coinjection of wildtype human FLT4 mRNA, but incompletely or not at all by mRNA harbouring FLT4 TOF variants. Taken together, we identify a pathogenic mechanism for FLT4 variants predisposing to TOF that is distinct from the known dominant negative mechanism of Milroy-causative variants. FLT4 variants give rise to conditions of the two circulatory subdivisions of the vascular system via distinct developmental pleiotropic molecular mechanisms. Proteostatic dysfunction, if confirmed as a mechanism of CHD pathogenesis for other predisposing genes, may identify pathways to therapeutic interventions. Distinguishing mechanistically how variants in FLT4 give rise to CHD may have potential to individualise genetic counselling in affected families.

Beaconet: A Reference-Free Method for Integrating Multiple Batches of Single-Cell Transcriptomic Data in Original Molecular Space.

Integrating multiple single-cell datasets is essential for the comprehensive understanding of cell heterogeneity. Batch effect is the undesired systematic variations among technologies or experimental laboratories that distort biological signals and hinder the integration of single-cell datasets. However, existing methods typically rely on a selected dataset as a reference, leading to inconsistent integration performance using different references, or embed cells into uninterpretable low-dimensional feature space. To overcome these limitations, a reference-free method, Beaconet, for integrating multiple single-cell transcriptomic datasets in original molecular space by aligning the global distribution of each batch using an adversarial correction network is presented. Through extensive comparisons with 13 state-of-the-art methods, it is demonstrated that Beaconet can effectively remove batch effect while preserving biological variations and is superior to existing unsupervised methods using all possible references in overall performance. Furthermore, Beaconet performs integration in the original molecular feature space, enabling the characterization of cell types and downstream differential expression analysis directly using integrated data with gene-expression features. Additionally, when applying to large-scale atlas data integration, Beaconet shows notable advantages in both time- and space-efficiencies. In summary, Beaconet serves as an effective and efficient batch effect removal tool that can facilitate the integration of single-cell datasets in a reference-free and molecular feature-preserved mode.

Prioritizing genomic variants through neuro-symbolic, knowledge-enhanced learning.

Whole-exome and genome sequencing have become common tools in diagnosing patients with rare diseases. Despite their success, this approach leaves many patients undiagnosed. A common argument is that more disease variants still await discovery, or the novelty of disease phenotypes results from a combination of variants in multiple disease-related genes. Interpreting the phenotypic consequences of genomic variants relies on information about gene functions, gene expression, physiology, and other genomic features. Phenotype-based methods to identify variants involved in genetic diseases combine molecular features with prior knowledge about the phenotypic consequences of altering gene functions. While phenotype-based methods have been successfully applied to prioritizing variants, such methods are based on known gene-disease or gene-phenotype associations as training data and are applicable to genes that have phenotypes associated, thereby limiting their scope. In addition, phenotypes are not assigned uniformly by different clinicians, and phenotype-based methods need to account for this variability. We developed an Embedding-based Phenotype Variant Predictor (EmbedPVP), a computational method to prioritize variants involved in genetic diseases by combining genomic information and clinical phenotypes. EmbedPVP leverages a large amount of background knowledge from human and model organisms about molecular mechanisms through which abnormal phenotypes may arise. Specifically, EmbedPVP incorporates phenotypes linked to genes, functions of gene products, and the anatomical site of gene expression, and systematically relates them to their phenotypic effects through neuro-symbolic, knowledge-enhanced machine learning. We demonstrate EmbedPVP's efficacy on a large set of synthetic genomes and genomes matched with clinical information. EmbedPVP and all evaluation experiments are freely available at https://github.com/bio-ontology-research-group/EmbedPVP.

Abstract PO1-15-10: Lower pre-treatment B-cell gene expression signatures correspond with improved overall survival with palbociclib + endocrine therapy in HR+/HER2- metastatic breast cancer: a biomarker analysis from the GEICAM/2013-02 PEARL trial

Abstract Introduction: CDK4/6 inhibitors (CDK4/6i) with endocrine therapy (ET) is standard-of-care for HR+/HER2- metastatic breast cancer (MBC), but little is known about which gene expression and microenvironment features are associated with benefit from CDK4/6i or differentiate response compared to chemotherapy. We examined the relationship of gene expression signatures (GES) to progression-free (PFS) and overall survival (OS) in the GEICAM/2013-02 PEARL phase III randomized study of palbociclib + ET vs. capecitabine in aromatase inhibitor-resistant HR+/HER2- MBC (NCT02028507). Methods: RNA sequencing (RNAseq) was performed on baseline tumor samples from 328 patients. Intrinsic subtype was derived by the PAM50 predictor after HER2/estrogen receptor subgroup-specific normalization. Correlations to intrinsic subtype signatures, PAM50 proliferation signature, CCNE1 expression, and 10 GES were selected for analysis. After excluding 15 Normal-like samples, Cox models were fitted for PFS and OS endpoints on each selected variable for the combined study population as well as for each treatment arm, adjusted for disease site, prior ET sensitivity, prior chemotherapy for MBC, number of involved sites, treatment arm, and intrinsic subtype. P-values were adjusted based on the Bonferroni method, with an adjusted p-value < 0.05 defined as significant. Results: PAM50 intrinsic subtype (54.95% Luminal A, 38.98% Luminal B and 6.07% non-luminal (4.79% Her2-enriched and 1.28% Basal-like)) was associated with worse PFS with palbociclib + ET for non-luminal vs. Luminal A tumors (HR 3.19, 95% CI 1.65-6.16) and for Luminal B vs. Luminal A tumors (HR 1.90, 95% CI 1.28-2.82). PAM50 luminal status (Luminal A vs. Luminal B vs. non-luminal) was significantly associated with PFS with palbociclib + ET (p=0.0003) but not capecitabine (p=0.89). In the palbociclib + ET arm, lower expression of two B-cell-associated signatures (BCAS) correlated with improved OS - the Immune1 TCGA BRCA GES (PMID: 32573490) (HR 1.52, 95% CI 1.20-1.92, p=0.009) and the B-cell/T-cell cooperativity GES (PMID: 31730857) (HR 1.42, 95% CI 1.14-1.79, p=0.04). Interaction analysis for OS showed that when stratified by low and high expression using the median, the Immune1 TCGA BRCA GES had a significant interaction with treatment arm (p=0.014). On comparison of outcomes between treatment arms, low Immune1 TCGA BRCA GES expression was significantly associated with improved OS with palbociclib + ET compared to capecitabine (HR 0.44, 95% CI 0.26-0.74, p=0.03), whereas high expression was not associated with a significant OS difference (HR 1.73, 95% CI 1.07-2.79, p=0.40). Further exploratory analysis of 164 immune GES found that, after adjustment for confounders, the top eight GES associated with OS with palbociclib + ET were BCAS (unadjusted p-values < 0.005), including our pre-selected Immune1 TCGA BRCA and B-cell/T-cell cooperativity GES, and all eight negatively correlated with OS. In the capecitabine arm, this exploratory analysis failed to demonstrate a similar association between the immune GES and OS. Conclusion: PAM50 intrinsic subtype was significantly associated with PFS with second-line palbociclib + ET. Lower expression of BCAS was associated with improved OS outcomes with palbociclib + ET independent of intrinsic subtype, and for the Immune1 TCGA BRCA GES, lower expression corresponded with improved OS with palbociclib + ET compared to capecitabine. This informs recent findings that in HR+/HER2- breast cancer, tumor immune activity may be a negative predictor of CDK4/6i benefit. Citation Format: Yash Agrawal, Aranzazu Fernandez-Martinez, Miguel Gil-Gil, Christoph Zielinski, Manuel Ruíz- Borrego, Eva Ciruelos, Montserrat Muñoz, Mireia Margelí, Begoña Bermejo, Antonio Antón, Zsuzsanna Kahan, Tibor Csöszi, José L Alonso-Romero, Jose Angel García-Sáenz, Pedro Sánchez-Rovira, Elena Álvarez, J. Ignacio Chacón, Santiago González-Santiago, César A Rodríguez, Sonia Servitja, Adam D. Pfefferle, Jesús Herranz, Yuan Liu, Lisa A. Carey, Isabel Romero - Camarero, Rosalía Caballero, Charles M. Perou, Miguel Martín. Lower pre-treatment B-cell gene expression signatures correspond with improved overall survival with palbociclib + endocrine therapy in HR+/HER2- metastatic breast cancer: a biomarker analysis from the GEICAM/2013-02 PEARL 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 PO1-15-10.

Abstract 2084: Endothelial Cells With Silenced Flrt2 Expression Exhibit Enhanced Retention On Vascular Graft Materials Under Fluid Shear Stress In Vitro

Myocardial infraction is a leading cause of death, and surgical intervention is often required to treat severe coronary artery disease. Autologous vessel grafts are the only option for bypass graft procedures. Synthetic vascular grafts less than 6 mm diameter fail due to unacceptable patency rates (~60% at 1 year). The loss of patency is accredited to platelet activation, thrombosis, and neointimal hyperplasia. A promising strategy to improve patency is to endothelialize synthetic vascular grafts ex-vivo, but previous attempts have met with modest success due to detachment of cells upon exposure to fluid shear stress. We previously used RNA-seq to characterized gene expression of endothelial cells (ECs) which remained adherent to synthetic grafts post shear stress. We found that fibronectin leucine-rich transmembrane protein 2 (FLRT2) was significantly downregulated in adherent subpopulations. We therefore hypothesize that suppressing FLRT2 expression in ECs will result in better retention on synthetic vascular grafts upon exposure to shear stress. We used silencing RNA approach using lipofectamine and confirmed successful knockdown of FLRT2 expression (Si-FLRT2). We then seeded Si-FLRT2 cells onto a commercially available Goretex graft material and subjected them to fluid shear stress of for 20 min. We then stained the samples with DAPI. We observed increased retention of Si-FLRT2 ECs on Goretex graft material in comparison to non-specifically targeted scramble siRNA containing ECs. In addition, we compared both vascular cell adhesion molecule 1 (VCAM-1) and intercellular adhesion molecule 1 (ICAM-1) levels to assess endothelial cell activation and found that silencing of FLRT2 lead to decreased levels of both VCAM-1 (by 49.2%±3.5%) and ICAM-1 (by 57.1%±3.4%) suggestive of decreased endothelial cell activation. In conclusion, we show that FLRT2 plays an important role in adhesion of ECs onto a commercially available synthetic graft material and silencing its expression ex-vivo may have beneficial clinical outcomes. In addition, we found that FLRT2 silencing may lead to decreased endothelial cell activation and we would like to further investigate the molecular mechanisms underlying the role of FLRT2 in both EC adhesion and activation.

Functional Characterization of the MeSSIII-1 Gene and Its Promoter from Cassava.

Soluble starch synthases (SSs) play important roles in the synthesis of cassava starch. However, the expression characteristics of the cassava SSs genes have not been elucidated. In this study, the MeSSIII-1 gene and its promoter, from SC8 cassava cultivars, were respectively isolated by PCR amplification. MeSSIII-1 protein was localized to the chloroplasts. qRT-PCR analysis revealed that the MeSSIII-1 gene was expressed in almost all tissues tested, and the expression in mature leaves was 18.9 times more than that in tuber roots. MeSSIII-1 expression was induced by methyljasmonate (MeJA), abscisic acid (ABA), and ethylene (ET) hormones in cassava. MeSSIII-1 expression patterns were further confirmed in proMeSSIII-1 transgenic cassava. The promoter deletion analysis showed that the -264 bp to -1 bp MeSSIII-1 promoter has basal activity. The range from -1228 bp to -987 bp and -488 bp to -264 bp significantly enhance promoter activity. The regions from -987 bp to -747 bp and -747 bp to -488 bp have repressive activity. These findings will provide an important reference for research on the potential function and transcriptional regulation mechanisms of the MeSSIII-1 gene and for further in-depth exploration of the regulatory network of its internal functional elements.

Skin in the game: a review of single-cell and spatial transcriptomics in dermatological research.

Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) are two emerging research technologies that uniquely characterize gene expression microenvironments on a cellular or subcellular level. The skin, a clinically accessible tissue composed of diverse, essential cell populations, serves as an ideal target for these high-resolution investigative approaches. Using these tools, researchers are assembling a compendium of data and discoveries in healthy skin as well as a range of dermatologic pathophysiologies, including atopic dermatitis, psoriasis, and cutaneous malignancies. The ongoing advancement of single-cell approaches, coupled with anticipated decreases in cost with increased adoption, will reshape dermatologic research, profoundly influencing disease characterization, prognosis, and ultimately clinical practice.

Microglia differential genes and their functions in paraquat-induced Parkinson's disease-like in mice's brains based on single-cell RNA sequencing

Objective: To analyze the differential genes and related signaling pathways of microglia subpopulations in Parkinson's disease (PD) -like mouse brains induced by paraquat (PQ) based on single-cell RNA sequencing, and provide clues to elucidate the mechanism of PQ-induced PD-like changes in the brain of animals. Methods: In September 2021, six male 6-week-old C57BL/6 mice were randomly divided into control group and experimental group (three mice in each group) . The mice were injected with saline, 10.0 mg/kg PQ intraperitoneally, once every three days, and 10 consecutive injections were used for modeling. After infection, the brains of mice were taken and 10×Genomics single-cell RNA sequencing was performed. Microglia subpopulations were screened based on gene expression characteristics, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed. The differential genes of microglia subpopulations between the experimental group and control group were further screened, and functional enrichment analysis was performed using bioinformatics tools. Mouse microglia (BV2 cells) were treated with 0, 60, 90 μmol/L PQ solution, respectively. And real-time fluorescence quantitative PCR experiments were conducted to validate the expressions of differential genes hexokinase 2 (Hk2) , ATPase H+ Transporting V0 Subunit B (Atp6v0b) and Neuregulin 1 (Nrg1) . Results: Cluster 7 and Cluster 20 were identified as microglia subpopulations based on the signature genes inositol polyphosphate-5-phosphatase d, Inpp5d (Inpp5d) and transforming growth factor beta receptor 1 (Tgfbr1) , and they reflected the microglia-activated M2 phenotype. The bioinformatics analysis showed that the characteristic genes of identified microglia subpopulations were enriched in endocytosis. In terms of molecular function, it mainly enriched in transmembrane receptor protein kinase activity and cytokine binding. The up-regulated genes of Cluster 7 were mainly enriched in lysosomal pathway, endocytosis pathway, and down-regulated genes were mainly enriched in neurodegenerative disease and other signaling pathways. The up-regulated genes of Cluster 20 were mainly enriched in signaling pathways related to PD, and down-regulated genes were mainly enriched in cyclic adenosine 3', 5'-monophosphate (cAMP) signaling pathways, neurological development, synaptic function and other signaling pathways. The results of real-time fluorescence quantitative PCR showed that the expressions of Hk2 mRNA and Atp6v0b mRNA increased and the expression of Nrg1 mRNA decreased in the 90 μmol/L PQ-treated BV2 cells compared with the 0 μmol/L, and the differences were statistically significant (P<0.05) . Conclusion: Microglia are activated in the PQ-induced PD-like mouse model and polarized toward the M2 phenotype. And their functions are associated with lysosomal (endocytosis) , synaptic functions and the regulation of PD-related pathways.

ELAPOR1 induces the classical/progenitor subtype and contributes to reduced disease aggressiveness through metabolic reprogramming in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is a heterogeneous disease with distinct molecular subtypes described as classical/progenitor and basal-like/squamous PDAC. We hypothesized that integrative transcriptome and metabolome approaches can identify candidate genes whose inactivation contributes to the development of the aggressive basal-like/squamous subtype. Using our integrated approach, we identified endosome-lysosome associated apoptosis and autophagy regulator 1 (ELAPOR1/KIAA1324) as a candidate tumor suppressor in both our NCI-UMD-German cohort and additional validation cohorts. Diminished ELAPOR1 expression was linked to high histological grade, advanced disease stage, the basal-like/squamous subtype, and reduced patient survival in PDAC. In vitro experiments demonstrated that ELAPOR1 transgene expression not only inhibited the migration and invasion of PDAC cells but also induced gene expression characteristics associated with the classical/progenitor subtype. Metabolome analysis of patient tumors and PDAC cells revealed a metabolic program associated with both upregulated ELAPOR1 and the classical/progenitor subtype, encompassing upregulated lipogenesis and downregulated amino acid metabolism. 1-Methylnicotinamide, a known oncometabolite derived from S-adenosylmethionine, was inversely associated with ELAPOR1 expression and promoted migration and invasion of PDAC cells in vitro. Taken together, our data suggest that enhanced ELAPOR1 expression promotes transcriptome and metabolome characteristics that are indicative of the classical/progenitor subtype, whereas its reduction associates with basal-like/squamous tumors with increased disease aggressiveness in PDAC patients. These findings position ELAPOR1 as a promising candidate for diagnostic and therapeutic targeting in PDAC.

Analysis of the sodium pump subunit ATP1A3 in glioma patients: Potential value in prognostic prediction and immunotherapy

The ATP1A3 gene is associated with the development and progression of neurological diseases. However, the pathological function and therapeutic value of ATP1A3 in glioblastoma (GBM) remains unknown. In this study, we tried to explore the correlation between the ATP1A3 gene expression and immune features in GBM samples. We found that ATP1A3 gene expression levels showed significant negative correlation with immune checkpoints such as PD-L1, CTLA-4 and IDO1. Next, ATP1A3 gene expression levels showed significant negative correlation with the anti-cancer immune cell process, the immune score and stromal score. By grouping ATP1A3 expression levels, we found that that immunomodulator-related genes and tumor-associated immune cell effector gene expression levels were associated with lower ATP1A3 expression. In addition, immunotherapy prediction pathway activity and a majority of the anti-cancer immune cell process activity levels were also showed to be correlated with lower ATP1A3 gene expression. Further, nine prognostic factors were identified by prognostic analysis, and a GBM prognostic model (risk score) was established. We applied the model to the TCGA GBM training set sample and the GSE4412 validation set sample and found that patients in the high risk score subgroup had significantly shorter survival time, demonstrating the prognostic value and prognostic efficacy of the risk score. Furthermore, ATP1A3 overexpression has also been found to sensitize cancer cells to anti-PD-1 therapy. In conclusion, we showed that ATP1A3 is a highly promising treatment target in GBM and the risk score is an independent prognostic factor for cancer and can be used to help guide the prediction of survival time in patients with GBM.

Deciphering age-specific molecular features in cervical cancer and constructing an angio-immune prognostic model.

Cancer incidence is increasingly seen in younger individuals. Molecular distinctions between young and elderly patients at onset are understudied. This study used public databases to explore genomic, transcriptomic, and immune-related features across age groups in cervical cancer. Additionally, it aims to create a prognostic model applicable across diverse age cohorts, enabling precise patient stratification, and personalized therapies. Gene mutations, expression data, and clinicopathological information were obtained from 317 cervical cancer patients. These patients were divided into a young group and an old group based on the median age of onset. The characteristics of differential gene mutation, gene expression, and immune cells analysis were analyzed by R software. Finally, the prognostic model was constructed by univariate Cox, least absolute shrinkage and selection operator, and multivariate Cox regression analyses of angiogenic and immune gene sets. Its validity was further confirmed using an additional 300 cervical squamous cell carcinoma and endocervical adenocarcinoma tissues. Cervical cancer patients at elderly onset age exhibit a significantly higher frequency of NOTCH1 and TP53 driver mutations compared to young patients, along with a notably higher tumor mutational burden. However, there were no significant differences between the 2 groups in terms of genomic instability and age-related mutational signatures. Differential gene expression analysis revealed that the young group significantly upregulated interferon-alpha and gamma responses and exhibited significantly higher activity in multiple metabolic pathways. Immune microenvironment analysis indicated enrichment of dendritic cells and natural killer cells in the young group, while transforming growth factor-β signature was enriched in the elderly group, indicating a higher degree of immune exclusion. A multigene prognostic model based on angiogenesis and T cell immune gene sets showed excellent prognostic performance independent of clinical factors such as age. High-risk groups identified by the model exhibit significant activation of tumor-promoting processes, such as metastasis and angiogenesis. Our study reveals distinct patterns in cancer-driving mechanisms, biological processes, and immune system status between young and elderly patients at onset with cervical cancer. These findings shed light on the age-specific underlying mechanisms of carcinogenesis. Furthermore, an independent molecular prognostic model is constructed to provide valuable references for patient stratification and the development of potential drug targets.

Strategies for improving the performance of prediction models for response to immune checkpoint blockade therapy in cancer

Immune checkpoint blockade (ICB) therapy holds promise for bringing long-lasting clinical gains for the treatment of cancer. However, studies show that only a fraction of patients respond to the treatment. In this regard, it is valuable to develop gene expression signatures based on RNA sequencing (RNAseq) data and machine learning methods to predict a patient’s response to the ICB therapy, which contributes to more personalized treatment strategy and better management of cancer patients. However, due to the limited sample size of ICB trials with RNAseq data available and the vast number of candidate gene expression features, it is challenging to develop well-performed gene expression signatures. In this study, we used several published melanoma datasets and investigated approaches that can improve the construction of gene expression-based prediction models. We found that merging datasets from multiple studies and incorporating prior biological knowledge yielded prediction models with higher predictive accuracies. Our finding suggests that these two strategies are of high value to identify ICB response biomarkers in future studies.

Single-cell profiling reveals transcriptome dynamics during bovine oocyte growth

BackgroundMammalian follicle development is characterized by extensive changes in morphology, endocrine responsiveness, and function, providing the optimum environment for oocyte growth, development, and resumption of meiosis. In cattle, the first signs of transcription activation in the oocyte are observed in the secondary follicle, later than during mouse and human oogenesis. While many studies have generated extensive datasets characterizing gene expression in bovine oocytes, they are mostly limited to the analysis of fully grown and matured oocytes. The aim of the present study was to apply single-cell RNA sequencing to interrogate the transcriptome of the growing bovine oocyte from the secondary follicle stage through to the mid-antral follicle stage.ResultsSingle-cell RNA-seq libraries were generated from oocytes of known diameters (< 60 to > 120 μm), and datasets were binned into non-overlapping size groups for downstream analysis. Combining the results of weighted gene co-expression network and Trendy analyses, and differently expressed genes (DEGs) between size groups, we identified a decrease in oxidative phosphorylation and an increase in maternal -genes and transcription regulators across the bovine oocyte growth phase. In addition, around 5,000 genes did not change in expression, revealing a cohort of stable genes. An interesting switch in gene expression profile was noted in oocytes greater than 100 μm in diameter, when the expression of genes related to cytoplasmic activities was replaced by genes related to nuclear activities (e.g., chromosome segregation). The highest number of DEGs were detected in the comparison of oocytes 100–109 versus 110–119 μm in diameter, revealing a profound change in the molecular profile of oocytes at the end of their growth phase.ConclusionsThe current study provides a unique dataset of the key genes and pathways characteristic of each stage of oocyte development, contributing an important resource for a greater understanding of bovine oogenesis.

Transcriptomic analysis of Anopheles gambiae from Benin reveals overexpression of salivary and cuticular proteins associated with cross-resistance to pyrethroids and organophosphates

BackgroundInsecticide resistance (IR) is one of the major threats to malaria vector control programs in endemic countries. However, the mechanisms underlying IR are poorly understood. Thus, investigating gene expression patterns related to IR can offer important insights into the molecular basis of IR in mosquitoes. In this study, RNA-Seq was used to characterize gene expression in Anopheles gambiae surviving exposure to pyrethroids (deltamethrin, alphacypermethrin) and an organophosphate (pirimiphos-methyl).ResultsLarvae of An. gambiae s.s. collected from Bassila and Djougou in Benin were reared to adulthood and phenotyped for IR using a modified CDC intensity bottle bioassay. The results showed that mosquitoes from Djougou were more resistant to pyrethroids (5X deltamethrin: 51.7% mortality; 2X alphacypermethrin: 47.4%) than Bassila (1X deltamethrin: 70.7%; 1X alphacypermethrin: 77.7%), while the latter were more resistant to pirimiphos-methyl (1.5X: 48.3% in Bassila and 1X: 21.5% in Djougou). RNA-seq was then conducted on resistant mosquitoes, non-exposed mosquitoes from the same locations and the laboratory-susceptible An. gambiae s.s. Kisumu strain. The results showed overexpression of detoxification genes, including cytochrome P450s (CYP12F2, CYP12F3, CYP4H15, CYP4H17, CYP6Z3, CYP9K1, CYP4G16, and CYP4D17), carboxylesterase genes (COEJHE5E, COE22933) and glutathione S-transferases (GSTE2 and GSTMS3) in all three resistant mosquito groups analyzed. Genes encoding cuticular proteins (CPR130, CPR10, CPR15, CPR16, CPR127, CPAP3-C, CPAP3-B, and CPR76) were also overexpressed in all the resistant groups, indicating their potential role in cross resistance in An. gambiae. Salivary gland protein genes related to ‘salivary cysteine-rich peptide’ and ‘salivary secreted mucin 3’ were also over-expressed and shared across all resistant groups.ConclusionOur results suggest that in addition to metabolic enzymes, cuticular and salivary gland proteins could play an important role in cross-resistance to multiple classes of insecticides in Benin. These genes warrant further investigation to validate their functional role in An. gambiae resistance to insecticides.

Abstract NG08: Dissecting and quantifying pancreatic cancer plasticity using single-cell multiomics, lineage tracing and functional genomics reveals novel mediators of therapy resistance

Abstract NG08: Dissecting and quantifying pancreatic cancer plasticity using single-cell multiomics, lineage tracing and functional genomics reveals novel mediators of therapy resistance

Histological morphology and gene expression in the digestive system of Procambarus clarkii

To unravel the intricate morphological structure and functional aspects of the digestive system in Procambarus clarkii, this study employed histology and transcriptome sequencing techniques for a comprehensive analysis of its digestive system. The investigation revealed that the digestive system of P. clarkii comprises the esophagus, stomach (including cardiac and pyloric regions), caeca, midgut, hindgut, and hepatopancreas. The esophageal lumen displayed an "X" shape, with clearly visible radial muscle bundles within the connecting tissues. The internal architecture of the stomach and intestines mirrored that of the esophagus. Notably, the pyloric region of the stomach exhibited a comb-like structure, which facilitated food selection and filtration. The caeca showcased a larger lumen within the intestines, while the hindgut displayed smaller folds. The hepatopancreas, representing the digestive glands, demonstrated predominant bilaterally symmetrical features, encompassing the midgut on both sides. It consisted of multi-stage branching hepatic ducts, crucial for digestion and absorption. Transcriptomic analysis of the digestive system revealed significant gene expression in the esophagus, stomach, caeca, midgut, hindgut, and hepatopancreas, with 23,006, 22,208, 23,485, 23,196, 22,781, and 21,375 genes expressed, respectively. Moreover, tissue-specific expression was observed in 161, 459, 374, 547, 337, and 1080 genes. Additionally, a subset of 447, 453, 553, 506, 433, and 711 genes exhibited high expression levels. Further analysis led to the identification of 36 digestive enzyme genes across six distinct digestive tissues, categorized into three groups: carbohydrate metabolism, lipid breakdown, and protein metabolism. In conclusion, this study provides a comprehensive understanding of the morphological structure, gene expression characteristics, and classification of digestive enzyme genes in the digestive system of P. clarkii. These findings lay a solid foundation for future investigations on the digestive physiology and food digestion in P. clarkii.

Epigenetic programming of stochastic olfactory receptor choice.

The mammalian sense of smell relies upon a vast array of receptor proteins to detect odorant compounds present in the environment. The proper deployment of these receptor proteins in olfactory sensory neurons is orchestrated by a suite of epigenetic processes that remodel the olfactory genes in differentiating neuronal progenitors. The goal of this review is to elucidate the central role of gene regulatory processes acting in neuronal progenitors of olfactory sensory neurons that lead to a singular expression of an odorant receptor in mature olfactory sensory neurons. We begin by describing the principal features of odorant receptor gene expression in mature olfactory sensory neurons. Next, we delineate our current understanding of how these features emerge from multiple gene regulatory mechanisms acting in neuronal progenitors. Finally, we close by discussing the key gaps in our understanding of how these regulatory mechanisms work and how they interact with each other over the course of differentiation.

Bayesian shrinkage models for integration and analysis of multiplatform high‐dimensional genomics data

AbstractWith the increasing availability of biomedical data from multiple platforms of the same patients in clinical research, such as epigenomics, gene expression, and clinical features, there is a growing need for statistical methods that can jointly analyze data from different platforms to provide complementary information for clinical studies. In this paper, we propose a two‐stage hierarchical Bayesian model that integrates high‐dimensional biomedical data from diverse platforms to select biomarkers associated with clinical outcomes of interest. In the first stage, we use Expectation Maximization‐based approach to learn the regulating mechanism between epigenomics (e.g., gene methylation) and gene expression while considering functional gene annotations. In the second stage, we group genes based on the regulating mechanism learned in the first stage. Then, we apply a group‐wise penalty to select genes significantly associated with clinical outcomes while incorporating clinical features. Simulation studies suggest that our model‐based data integration method shows lower false positives in selecting predictive variables compared with existing method. Moreover, real data analysis based on a glioblastoma (GBM) dataset reveals our method's potential to detect genes associated with GBM survival with higher accuracy than the existing method. Moreover, most of the selected biomarkers are crucial in GBM prognosis as confirmed by existing literature.

HyGAnno: hybrid graph neural network-based cell type annotation for single-cell ATAC sequencing data.

Reliable cell type annotations are crucial for investigating cellular heterogeneity in single-cell omics data. Although various computational approaches have been proposed for single-cell RNA sequencing (scRNA-seq) annotation, high-quality cell labels are still lacking in single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) data, because of extreme sparsity and inconsistent chromatin accessibility between datasets. Here, we present a novel automated cell annotation method that transfers cell type information from a well-labeled scRNA-seq reference to an unlabeled scATAC-seq target, via a parallel graph neural network, in a semi-supervised manner. Unlike existing methods that utilize only gene expression or gene activity features, HyGAnno leverages genome-wide accessibility peak features to facilitate the training process. In addition, HyGAnno reconstructs a reference-target cell graph to detect cells with low prediction reliability, according to their specific graph connectivity patterns. HyGAnno was assessed across various datasets, showcasing its strengths in precise cell annotation, generating interpretable cell embeddings, robustness to noisy reference data and adaptability to tumor tissues.

DeepFGRN: inference of gene regulatory network with regulation type based on directed graph embedding.

The inference of gene regulatory networks (GRNs) from gene expression profiles has been a key issue in systems biology, prompting many researchers to develop diverse computational methods. However, most of these methods do not reconstruct directed GRNs with regulatory types because of the lack of benchmark datasets or defects in the computational methods. Here, we collect benchmark datasets and propose a deep learning-based model, DeepFGRN, for reconstructing fine gene regulatory networks (FGRNs) with both regulation types and directions. In addition, the GRNs of real species are always large graphs with direction and high sparsity, which impede the advancement of GRN inference. Therefore, DeepFGRN builds a node bidirectional representation module to capture the directed graph embedding representation of the GRN. Specifically, the source and target generators are designed to learn the low-dimensional dense embedding of the source and target neighbors of a gene, respectively. An adversarial learning strategy is applied to iteratively learn the real neighbors of each gene. In addition, because the expression profiles of genes with regulatory associations are correlative, a correlation analysis module is designed. Specifically, this module not only fully extracts gene expression features, but also captures the correlation between regulators and target genes. Experimental results show that DeepFGRN has a competitive capability for both GRN and FGRN inference. Potential biomarkers and therapeutic drugs for breast cancer, liver cancer, lung cancer and coronavirus disease 2019 are identified based on the candidate FGRNs, providing a possible opportunity to advance our knowledge of disease treatments.