Type 2 diabetes (T2D) is a common metabolic disease due to insufficient insulin secretion by pancreatic beta cells in the context of insulin resistance. Islet molecular pathology reveals a role for protein misfolding in beta cell dysfunction and loss with islet amyloid derived from islet amyloid polypeptide (IAPP), a protein co-expressed and co-secreted with insulin. The most toxic form of misfolded IAPP is intracellular membrane disruptive toxic oligomers present in beta cells in T2D and in beta cells of mice transgenic for human IAPP (hIAPP). Prior work revealed a high degree of overlap of transcriptional changes in islets from T2D and pre-diabetic 9-10-week-old mice transgenic for hIAPP with most changes being pro-survival adaptations and therefore of limited therapeutic guidance. Here we investigated islets from hIAPP transgenic mice at an earlier age (6 weeks) to screen for potential mediators of hIAPP toxicity that precede predominance of pro-survival signaling. We identified early suppression of cholesterol synthesis and trafficking along with aberrant intra-beta cell cholesterol and lipid deposits, and impaired cholesterol trafficking to cell membranes. These findings align with comparable lipid deposits present in beta cells in T2D and increased vulnerability to develop T2D in individuals taking medications that suppress cholesterol synthesis.

Introduction: The Euchromatic Histone Methyl Transferase Protein 2 (EHMT2), also known as G9a, deposits transcriptionally repressive chromatin marks that play pivotal roles in the maturation and homeostasis of multiple organs. Recently, we have shown that Ehmt2 inactivation in the mouse pancreas alters growth and immune gene expression networks, antagonizing Kras-mediated pancreatic cancer initiation and promotion. Here, we elucidate the essential role of Ehmt2 in maintaining a transcriptional landscape that protects organs from inflammation. Methods: Comparative RNA-seq studies between normal postnatal and young adult pancreatic tissue from Ehmt2 conditional knockout animals (Ehmt2 fl/fl ) targeted to the exocrine pancreatic epithelial cells (Pdx1-Cre and P48 Cre/+ ), reveal alterations in gene expression networks in the whole organ related to injury-inflammation-repair, suggesting an increased predisposition to damage. Thus, we induced an inflammation repair response in the Ehmt2 fl/fl pancreas and used a data science-based approach to integrate RNA-seq-derived pathways and networks, deconvolution digital cytology, and spatial transcriptomics. We also analyzed the tissue response to damage at the morphological, biochemical, and molecular pathology levels. Results and discussion: The Ehmt2 fl/fl pancreas displays an enhanced injury-inflammation-repair response, offering insights into fundamental molecular and cellular mechanisms involved in this process. More importantly, these data show that conditional Ehmt2 inactivation in exocrine cells reprograms the local environment to recruit mesenchymal and immunological cells needed to mount an increased inflammatory response. Mechanistically, this response is an enhanced injury-inflammation-repair reaction with a small contribution of specific Ehmt2-regulated transcripts. Thus, this new knowledge extends the mechanisms underlying the role of the Ehmt2-mediated pathway in suppressing pancreatic cancer initiation and modulating inflammatory pancreatic diseases.

UNASSIGNED: Circular RNAs (circRNAs) have been shown to play critical roles in the initiation and progression of chronic glomerulonephritis (CGN), while their role from mesangial cells in contributing to the pathogenesis of CGN is rarely understood. Our study aims to explore the potential functions of mesangial cell-derived circRNAs using RNA sequencing (RNA-seq) and bioinformatics analysis.
UNASSIGNED: Mouse mesangial cells (MMCs) were stimulated by lipopolysaccharide (LPS) to establish an in vitro model of CGN. Pro-inflammatory cytokines and cell cycle stages were detected by Enzyme-linked immunosorbent assay (ELISA) and Flow Cytometry experiment, respectively. Subsequently, differentially expressed circRNAs (DE-circRNAs) were identified by RNA-seq. GEO microarrays were used to identify differentially expressed mRNAs (DE-mRNAs) between CGN and healthy populations. Weighted co-expression network analysis (WGCNA) was utilized to explore clinically significant modules of CGN. CircRNA-associated CeRNA networks were constructed by bioinformatics analysis. The hub mRNAs from CeRNA network were identified using LASSO algorithms. Furthermore, utilizing protein-protein interaction (PPI), gene ontology (GO), pathway enrichment (KEGG), and GSEA analyses to explore the potential biological function of target genes from CeRNA network. In addition, we investigated the relationships between immune cells and hub mRNAs from CeRNA network using CIBERSORT.
UNASSIGNED: The expression of pro-inflammatory cytokines IL-1β, IL-6, and TNF-α was drastically increased in LPS-induced MMCs. The number of cells decreased significantly in the G1 phase but increased significantly in the S/G2 phase. A total of 6 DE-mRNAs were determined by RNA-seq, including 4 up-regulated circRNAs and 2 down-regulated circRNAs. WGCNA analysis identified 1747 DE-mRNAs of the turquoise module from CGN people in the GEO database. Then, the CeRNA networks, including 6 circRNAs, 38 miRNAs, and 80 mRNAs, were successfully constructed. The results of GO and KEGG analyses revealed that the target mRNAs were mainly enriched in immune, infection, and inflammation-related pathways. Furthermore, three hub mRNAs (BOC, MLST8, and HMGCS2) from the CeRNA network were screened using LASSO algorithms. GSEA analysis revealed that hub mRNAs were implicated in a great deal of immune system responses and inflammatory pathways, including IL-5 production, MAPK signaling pathway, and JAK-STAT signaling pathway. Moreover, according to an evaluation of immune infiltration, hub mRNAs have statistical correlations with neutrophils, plasma cells, monocytes, and follicular helper T cells.
UNASSIGNED: Our findings provide fundamental and novel insights for further investigations into the role of mesangial cell-derived circRNAs in CGN pathogenesis.

BACKGROUND: Circulating tumor DNA (ctDNA) has emerged as a promising tool for early cancer detection and minimal residual disease monitoring. However, the biology underlying ctDNA release and its variation across cancer types and histologies remains poorly understood. This study investigated the biology behind ctDNA shedding in colorectal cancer.
METHODS: The study included a local cohort of 747 stage I-III colorectal cancer patients. All patients had ctDNA measurement prior to treatment and extensive clinical data. Primary tumor RNA sequencing and whole exome sequencing was performed in 95 and 652 patients respectively. Additionally, the study evaluated 89 non-small cell lung cancer patients from the TRACERx cohort, comprising primary tumor RNA sequencing and ctDNA measurement.
RESULTS: We found tumor size and proliferative capacity to be key factors associated with ctDNA shedding in colorectal cancer. Furthermore, we found that the secretory and CMS3 colorectal cancer subtypes exhibited lower ctDNA shedding, while microsatellite instability (MSI) tumors had higher levels of ctDNA. Mutational analysis did not reveal any genes or pathways associated with ctDNA shedding in colorectal cancer. A comparison of transcriptomic profiles across multiple cancer types demonstrated that colorectal cancer and lung squamous cell carcinoma tumors shared a high-proliferative ctDNA shedding phenotype, while lung adenocarcinoma tumors displayed a distinct low-proliferative subgroup. Additionally, proliferation levels correlated with ctDNA detection sensitivity across multiple cancer types.
CONCLUSIONS: These findings suggest that tumor size and proliferative capacity are drivers of ctDNA release in colorectal cancer and provide insights into the biology of ctDNA shedding on a pan-cancer level.

Placental hypoxia is hazardous to maternal health as well as fetal growth and development. Preeclampsia and intrauterine growth restriction are common pregnancy problems, and one of the causes is placental hypoxia. Placental hypoxia is linked to a number of pregnancy illnessesv. To investigate their potential function in anoxic circumstances, we mimicked the anoxic environment of HTR-8/Svneo cells and performed lncRNA and circRNA studies on anoxic HTR-8/Svneo cells using high-throughput RNA sequencing. The miRNA target genes were predicted by integrating the aberrant expression of miRNAs in the placenta of preeclampsia and intrauterine growth restriction, and a ceRNA network map was developed to conduct a complete transcriptomic and bioinformatics investigation of circRNAs and lncRNAs. The signaling pathways in which the genes were primarily engaged were predicted using GO and KEGG analyses. To propose a novel explanation for trophoblastic organism failure caused by lncRNAs and circRNAs in an anoxic environment.

BACKGROUND: High-throughput RNA sequencing has become indispensable for decoding gene activities, yet the challenge of reconstructing full-length transcripts persists. Traditional single-sample assemblers frequently produce fragmented transcripts, especially in single-cell RNA-seq data. While algorithms designed for assembling multiple samples exist, they encounter various limitations.
RESULTS: We present Aletsch, a new assembler for multiple bulk or single-cell RNA-seq samples. Aletsch incorporates several algorithmic innovations, including a \"bridging\" system that can effectively integrate multiple samples to restore missed junctions in individual samples, and a new graph-decomposition algorithm that leverages \"supporting\" information across multiple samples to guide the decomposition of complex vertices. A standout feature of Aletsch is its application of a random forest model with 50 well-designed features for scoring transcripts. We demonstrate its robust adaptability across different chromosomes, datasets, and species. Our experiments, conducted on RNA-seq data from several protocols, firmly demonstrate Aletsch\'s significant outperformance over existing meta-assemblers. As an example, when measured with the partial area under the precision-recall curve (pAUC, constrained by precision), Aletsch surpasses the leading assemblers TransMeta by 22.9%-62.1% and PsiCLASS by 23.0%-175.5% on human datasets.
METHODS: Aletsch is freely available at https://github.com/Shao-Group/aletsch. Scripts that reproduce the experimental results of this manuscript is available at https://github.com/Shao-Group/aletsch-test.

BACKGROUND: Short-read single-cell RNA-sequencing (scRNA-seq) has been used to study cellular heterogeneity, cellular fate, and transcriptional dynamics. Modeling splicing dynamics in scRNA-seq data is challenging, with inherent difficulty in even the seemingly straightforward task of elucidating the splicing status of the molecules from which sequenced fragments are drawn. This difficulty arises, in part, from the limited read length and positional biases, which substantially reduce the specificity of the sequenced fragments. As a result, the splicing status of many reads in scRNA-seq is ambiguous because of a lack of definitive evidence. We are therefore in need of methods that can recover the splicing status of ambiguous reads which, in turn, can lead to more accuracy and confidence in downstream analyses.
RESULTS: We develop Forseti, a predictive model to probabilistically assign a splicing status to scRNA-seq reads. Our model has two key components. First, we train a binding affinity model to assign a probability that a given transcriptomic site is used in fragment generation. Second, we fit a robust fragment length distribution model that generalizes well across datasets deriving from different species and tissue types. Forseti combines these two trained models to predict the splicing status of the molecule of origin of reads by scoring putative fragments that associate each alignment of sequenced reads with proximate potential priming sites. Using both simulated and experimental data, we show that our model can precisely predict the splicing status of many reads and identify the true gene origin of multi-gene mapped reads.
METHODS: Forseti and the code used for producing the results are available at https://github.com/COMBINE-lab/forseti under a BSD 3-clause license.

UNASSIGNED: Sodium pheophorbide a (SPA) is a natural plant-derived photosensitizer, with high photoactivated antifungal activity against some phytopathogenic fungi. However, its fungicidal effect on Diaporthe mahothocarpus, a novel pathogen that causes Camellia oleifera leaf spot blight, is unclear.
UNASSIGNED: In the present study, we explored its inhibitory effects on spore germination and mycelial growth of D. mahothocarpus. Then we determined its effects on the cell membrane, mycelial morphology, redox homeostasis, and cell death through bioassay. Finally, RNA-seq was used further to elucidate its mode of action at the transcriptional level.
UNASSIGNED: We found that SPA effectively inhibited the growth of D. mahothocarpus, with half-maximal effective concentrations to inhibit mycelial growth and spore germination of 1.059 and 2.287 mg/mL, respectively. After 1.0 mg/mL SPA treatment, the conductivity and malondialdehyde content of D. mahothocarpus were significantly increased. Scanning electron microscopy and transmission electron microscopy indicated that SPA significantly affected the morphology and ultrastructure of D. mahothocarpus hyphae, revealing that SPA can destroy the mycelial morphology and cell structure, especially the cell membrane of D. mahothocarpus. Furthermore, transcriptome analysis revealed that SPA significantly suppressed the expression of genes involved in morphology, cell membrane permeability, and oxidative stress. Then, we also found that SPA significantly promoted the accumulation of reactive oxygen species (ROS) in of D. mahothocarpus, while it decreased the content of reduced glutathione, inhibited the enzyme activities of superoxide dismutase and catalase, and exacerbated DNA damage. Annexin V-FITC/PI staining also confirmed that 1.0 mg/mL SPA could significantly induce apoptosis and necrosis.
UNASSIGNED: Generally, SPA can induce ROS-mediated oxidative stress and cell death, thus destroying the cell membrane and hyphal morphology, and ultimately inhibiting mycelial growth, which indicates that SPA has multiple modes of action, providing a scientific basis for the use of SPA as an alternative plant-derived photoactivated fungicide against C. oleifera leaf spot blight.

BACKGROUND: Tuberculosis is a serious life-threatening disease among the top global health challenges and rapid and effective diagnostic biomarkers are vital for early diagnosis especially given the increasing prevalence of multidrug resistance.
METHODS: Two human whole blood microarray datasets, GSE42826 and GSE42830 were retrieved from publicly available gene expression omnibus (GEO) database. Deregulated genes (DEGs) were identified using GEO2R online tool and Gene Ontology (GO), protein-protein interaction (PPI) network analysis was performed using Metascape and STRING databases. Significant genes (n = 8) were identified using T-test/ANOVA and Molecular Complex Detection (MCODE) score ≥10, which was validated in GSE34608 dataset. The diagnostic potential of three biomarkers was assessed using Area Under Curve (AUC) of Receiver Operating Characteristic (ROC) plot. The transcriptional levels of these genes were also examined in a separate dataset GSE31348, to monitor the patterns of variation during tuberculosis treatment.
RESULTS: A total of 62 common DEGs (57 upregulated, 7 downregulated genes) were identified in two discovery datasets. GO functions and pathway enrichment analysis shed light on the functional roles of these DEGs in immune response and type-II interferon signaling. The genes in Module-1 (n = 18) were linked to innate immune response, interferon-gamma signaling. The common genes (n = 8) were validated in GSE34608 dataset, that corroborates the results obtained from discovery sets. The gene expression levels demonstrated responsiveness to Mtb infection during anti-TB therapy in GSE31348 dataset. In GSE34608 dataset, the expression levels of three specific genes, GBP5, IFITM3, and EPSTI1, emerged as potential diagnostic makers. In combination, these genes scored remarkable diagnostic performance with 100% sensitivity and 89% specificity, resulting in an impressive Area Under Curve (AUC) of 0.958. However, GBP5 alone showed the highest AUC of 0.986 with 100% sensitivity and 89% specificity.
CONCLUSIONS: The study presents valuable insights into the critical gene network perturbed during tuberculosis. These genes are determinants for assessing the effectiveness of an anti-TB response and distinguishing between active TB and healthy individuals. GBP5, IFITM3 and EPSTI1 emerged as candidate core genes in TB and holds potential as novel molecular targets for the development of interventions in the treatment of TB.

BACKGROUND: In the past decade, single-cell RNA sequencing (scRNA-seq) has emerged as a pivotal method for transcriptomic profiling in biomedical research. Precise cell-type identification is crucial for subsequent analysis of single-cell data. And the integration and refinement of annotated data are essential for building comprehensive databases. However, prevailing annotation techniques often overlook the hierarchical organization of cell types, resulting in inconsistent annotations. Meanwhile, most existing integration approaches fail to integrate datasets with different annotation depths and none of them can enhance the labels of outdated data with lower annotation resolutions using more intricately annotated datasets or novel biological findings.
RESULTS: Here, we introduce scPLAN, a hierarchical computational framework designed for scRNA-seq data analysis. scPLAN excels in annotating unlabeled scRNA-seq data using a reference dataset structured along a hierarchical cell-type tree. It identifies potential novel cell types in a systematic, layer-by-layer manner. Additionally, scPLAN effectively integrates annotated scRNA-seq datasets with varying levels of annotation depth, ensuring consistent refinement of cell-type labels across datasets with lower resolutions. Through extensive annotation and novel cell detection experiments, scPLAN has demonstrated its efficacy. Two case studies have been conducted to showcase how scPLAN integrates datasets with diverse cell-type label resolutions and refine their cell-type labels.
BACKGROUND: https://github.com/michaelGuo1204/scPLAN.