Transcriptomic Datasets Research Articles

Ultraconserved elements from transcriptome and genome dataprovide insight into the phylogenomics of Sternorrhyncha (Insecta: Hemiptera).

Sternorrhyncha, one of the four major suborders of Hemiptera, is a phytophagous taxon inclusive of nearly 18 000 described species. The phylogenetic relationships within the taxon and the earliest-branching lineage of its infraorders remain incompletely understood. This study attempted to illuminate the phylogenetic relationships within Sternorrhyncha through the use of maximum likelihood, Bayesian inference and maximum parsimony analyses, employing ultraconserved element (UCE) data from 39 genomic and 62 transcriptomic datasets and thereby representing most families within the taxon. The probe set Hemiptera 2.7Kv1 was used to recover a total of 2731 UCE loci: from 547 to 1699 (with an average of 1084) across all genomic datasets and from 108 to 849 (with an average of 329) across all transcriptomic datasets. All three types of phylogenetic analyses employed in this study produced robust statistical support for Sternorrhyncha being a monophyletic group. The different methods of phylogenetic analysis produced inconsistent descriptions of topological structure at the infraorder level: while maximum likelihood and Bayesian inference analyses produced strong statistical evidence (100%) indicating the clade Psylloidea + Aleyrodoidea to be a sister of the clade Aphidoidea (Aphidomorpha) + Coccoidea (Coccomorpha), the maximum parsimony analysis failed to recover a similar result. Our results also provide detail on the phylogenetic relationships within each infraorder. This study presents the first use of UCE data to investigate the phylogeny of Sternorrhyncha. It also shows the viability of amalgamating genomic and transcriptomic data in studies of phylogenetic relationships, potentially highlighting a resource-efficient approach for future inquiries into diverse taxa through the integration of varied data sources.

Harnessing the Transcriptomic Resources of Millets to Decipher Climate Resilience and Nutrient Enrichment Traits

Rich nutrients, climate resilience, and the economic importance of millets are believed to ensure food security for future generations. Millets have the habit of growing against abiotic stresses (particularly drought stress). Millets show much better climate resilience and nutrient supplementation properties compared to other major cereals. Understanding the molecular mechanisms of genes that respond to stresses and nutrient transport will help understand the tolerance mechanism and improve against both stresses. Genome sequences are currently available for two major (sorghum and pearl millet) and eight minor millets (foxtail millet, finger millet, kodo millet, barnyard millet, proso millet, job’s tear, fonio millet, and tef). Five minor millets (little millet, kodo millet, brown top millet, guinea millet, and raishan) do not have genome sequences to date. Transcriptome studies help to identify differentially expressed genes (DEGs), mine genes induced in a particular stress and develop several molecular markers for all plants, including millets. Some millets have reports on transcriptome datasets for plants exposed to various biotic and abiotic stresses and for nutritional traits. Unfortunately, the transcriptome datasets of millets have not been adequately leveraged to explore genes associated with traits such as climate resilience, nutrient enrichment, and crop improvement. This underutilization stems from a lack of high-resolution studies and limited exploration within the field. As a result, the potential insights and genetic understanding offered by these datasets remain largely untapped. Through this review, we plan to elucidate the current status of transcriptome resources on millets and draw future insights on the utilization of these resources. This review will motivate researchers to utilize the available transcriptome resources for millet improvement.

Pan-cancer analysis combined with experiments predicts NNMT as a therapeutic target for human cancers

The identification of effective therapeutic targets plays a pivotal role in advancing cancer treatment outcomes. We employed a comprehensive pan-cancer analysis, complemented by experimental validation, to explore the potential of Nicotinamide N-methyltransferase (NNMT) as a promising therapeutic strategy for human cancers. By analyzing large-scale transcriptomic datasets across various cancer types, we consistently observed upregulated expression of NNMT. Furthermore, elevated NNMT expression correlated with inferior overall survival in multiple cancer cohorts, underscoring its significance as a prognostic biomarker. Additionally, we investigated the relationship between NNMT expression and the tumor immune microenvironment, which plays a crucial role in regulating anti-tumor immune responses. To confirm the malignant functions of NNMT in tumor cells, we conducted a series of cell-based experiments, revealing that NNMT promotes cancer cell proliferation and invasion, indicative of its oncogenic properties. The integration of computational analysis and experimental validation in our study firmly establishes NNMT as a potential therapeutic target for human cancers. Specifically, targeting NNMT holds promise for the development of innovative and effective cancer treatments. Further investigations into NNMT's role in cancer pathogenesis could potentially pave the way for groundbreaking advancements in cancer treatment.

Molecular Signatures for Microbe-Associated Colorectal Cancers.

Genetic factors and microbial imbalances play crucial roles in colorectal cancers (CRCs), yet the impact of infections on cancer initiation remains poorly understood. While bioinformatic approaches offer valuable insights, the rising incidence of CRCs creates a pressing need to precisely identify early CRC events. We constructed a network model to identify continuum states during CRC initiation spanning normal colonic tissue to pre-cancer lesions (adenomatous polyps) and examined the influence of microbes and host genetics. A Boolean network was built using a publicly available transcriptomic dataset from healthy and adenoma affected patients to identify an invariant Microbe-Associated Colorectal Cancer Signature (MACS). We focused on Fusobacterium nucleatum ( Fn ), a CRC-associated microbe, as a model bacterium. MACS-associated genes and proteins were validated by RT-qPCR, RNA seq, ELISA, IF and IHCs in tissues and colon-derived organoids from genetically predisposed mice ( CPC-APC Min+/- ) and patients (FAP, Lynch Syndrome, PJS, and JPS). The MACS that is upregulated in adenomas consists of four core genes/proteins: CLDN2/Claudin-2 (leakiness), LGR5/leucine-rich repeat-containing receptor (stemness), CEMIP/cell migration-inducing and hyaluronan-binding protein (epithelial-mesenchymal transition) and IL8/Interleukin-8 (inflammation). MACS was induced upon Fn infection, but not in response to infection with other enteric bacteria or probiotics. MACS induction upon Fn infection was higher in CPC-APC Min+/- organoids compared to WT controls. The degree of MACS expression in the patient-derived organoids (PDOs) generally corresponded with the known lifetime risk of CRCs. Computational prediction followed by validation in the organoid-based disease model identified the early events in CRC initiation. MACS reveals that the CRC-associated microbes induce a greater risk in the genetically predisposed hosts, suggesting its potential use for risk prediction and targeted cancer prevention.

Identification of key modules and hub genes involved in regulating the feather follicle development of Wannan chickens using WGCNA

Carcass appearance is important economic trait, which affects customers in making purchase decisions. Both density and diameter of feather follicles are two important indicators of carcass appearance. However, the regulatory network and key genes be involved in feather follicle development remain poorly understood. To identify key genes and modules that involved in feather follicle development in chickens, 16 transcriptome datasets of Wannan chickens skin tissue (3 birds at the E9, E11, and E14, respectively, and 7 birds at the 12W) were used for weighted gene co-expression network analysis (WGCNA) analysis, and 12 skin tissue samples (3 birds for each stage) were selected for DEGs analysis. A total of 5,025, 2,337, and 10,623 DEGs were identified in 3 comparison groups, including the E9 vs. E11, the E11 vs. E14, and the E14 vs. 12W. Additionally, 31 co-expression gene modules were identified by WGCNA and the dark-orange, cyan, and blue module were found to be significantly associated with feather follicle development (p < 0.01). In total, 92,898 and 8,448 hub genes were obtained in the dark-orange, cyan, and blue modules, respectively. We focused on the cyan and blue modules, as 6 and 336 hub genes of these modules were identified to overlap with the DEGs of the three comparison groups, respectively. The 6 overlapped genes such as LAMC2, COL6A3, and COL6A2 etc., were over-represented in 12 categories such as focal adhesion and ECM-receptor interaction signaling pathway. Among the 336 genes that overlapped between the blue module and different DEGs comparison groups several genes including WNT7A and WNT9B were enriched in Wnt and ECM-receptor interaction signaling pathway. These results suggested that the LAMC2, COL6A3, COL6A2, WNT7A, and WNT9B genes may play a crucial role in the regulation of feather follicle development in Wannan chickens. Our results provided a reference for the molecular regulatory network and key genes in the development of feather follicles and contribute to molecular breeding for carcass appearance traits in chickens.

CYP4G subfamily genes mediate larval integument development in Spodoptera frugiperda.

Cytochrome P450 (CYP) 4G subfamily is closely related to the synthesis of cuticular hydrocarbons, leading to the enhanced desiccation and insecticide resistance of pests. However, functions of CYP4Gs in larval integument development remain unknown in Spodoptera frugiperda (J. E. Smith) (Lepidoptera: Noctuidae), which is a major transboundary migratory pest and become a common pest in China. On the basis of the genome and transcriptome datasets of S. frugiperda, CYP4G74, CYP4G75, CYP4G108, and CYP4G109 were identified, which contained the conserved domains of P450s and CYP4Gs. The spatial and temporal expression analysis showed that CYP4G74 and CYP4G75 were significantly highly expressed in adults and larval integuments, while CYP4G108 and CYP4G109 had low expressions in larval integuments. After silencing CYP4G74 and CYP4G75 by RNA interference, abnormal integument development occurred in larvae, some of which became smaller and dead, indicating important roles of CYP4G74 and CYP4G75 in the synthesis and development of integuments. The results clarify the functions of CYP4Gs in S. frugiperda and provide potential targets for the control of this pest.

#2485 CellMatchR—RNA-seq based matching of kidney cell lines to kidney reference cell types

Abstract Background and Aims Cultured kidney cell lines are broadly used to model the physiology and pathophysiology of the kidney. Due to immortalization, passaging and culture conditions, a cell line's gene expression profile might differ greatly from the primary cells it was originally derived from. This might be further enhanced by experimental treatments with compounds or genetic modifications. It is therefore a relevant question if a given cell culture system is still the appropriate model for a certain disease or scientific investigations. Reasoning that RNA sequencing (RNA-seq) based transcriptomes are generated as part of many experimental setups and hence may be used to address this question, we developed and tested an RNA-seq based approach, CellMatchR. This approach matches kidney cell lines, primary cells and even tissue specimen to known kidney reference cell types to determine which primary kidney cell type is the most similar and to what degree global gene expression or the expression of selected marker genes differs. Method CellMatchR uses published murine and human kidney single cell- and tubule-level transcriptomic datasets from healthy mice and human donors as references. Single cell datasets were further processed to pseudobulk references for each of the contained cell types. Then RNA-seq data from cell lines or tissues of interest (test data) was compared to the reference (pseudo)bulk data with Spearman correlations of gene counts-per-million values (CPM) or Euclidean distance using log-transformed CPM values. Both approaches were systematically tested for various combinations of test and reference datasets across different species and using global gene expression (i.e. all genes measured in reference and test datasets) and a set of 315 manually curated, kidney cell type marker genes. Results We sequentially used different kidney tissue types, primary cells and cell lines as positive controls and matched them to our reference datasets. Spearman correlations of gene expression rankings showed the highest correlation coefficients with biologically correct reference cell types (examples in Fig. 1A-C). We found Spearman correlations to be superior to Euclidean distances as method of comparison. Analyses based on global gene expression compared to our curated set of 315 kidney cell type marker genes yielded similar results. Notably, correlation coefficients were higher and showed less variation between reference cell types when using the global gene set. Matchings across species, i.e. using murine test and human reference data or vice versa still yielded mostly correct results. However, correlation coefficients were generally lower (i.e. rho = 0.9 vs. 0.6) and varied more across reference datasets if comparisons were performed across species. Using published RNA-seq data for different cell lines of the proximal tubule (e.g. human HK-2 and opossum OKH cells) we observed low similarities with proximal tubule cells (Fig. 1D), whereas two tested cell lines of the collecting duct (mIMCD-3 and mpkCCD cells) plausibly showed the highest similarity to medullary cell types like collecting duct and Loop of Henle cells. The most similar reference cell types did not change in mIMCD-3 cells when kept in 2-dimensional vs. 3-dimensional culture conditions, and in mpkCCD cells when the osmolality of the culture medium was changed from 300 to 600 mosmol/kg. Also, a Pkd1 knockout in mIMCD-3 cells did not change the most similar reference cell type in our analyses (Fig. 1E and F). Conclusion Our CellMatchR approach uses publicly available kidney single cell and bulk RNA-seq datasets and combines these with simple, computationally fast yet effective statistical methods to determine similarities of kidney cell lines and tissue samples to reference cell types of interest. It can easily be implemented by trained users or integrated into online resources for usage with a visual interface. It relies on RNA-seq data from the cell lines of interest and hence presents a feasible complementary method to check the general similarity of cell culture models to kidney cell types and assess their stability across experimental conditions.

Meningioma transcriptomic landscape demonstrates novel subtypes with regional associated biology and patient outcome

Meningiomas, although mostly benign, can be recurrent and fatal. World Health Organization (WHO) grading of the tumor does not always identify high-risk meningioma, and better characterizations of their aggressive biology are needed. To approach this problem, we combined 13 bulk RNA sequencing (RNA-seq) datasets to create a dimension-reduced reference landscape of 1,298 meningiomas. The clinical and genomic metadata effectively correlated with landscape regions, which led to the identification of meningioma subtypes with specific biological signatures. The time to recurrence also correlated with the map location. Further, we developed an algorithm that maps new patients onto this landscape, where the nearest neighbors predict outcome. This study highlights the utility of combining bulk transcriptomic datasets to visualize the complexity of tumor populations. Further, we provide an interactive tool for understanding the disease and predicting patient outcomes. This resource is accessible via the online tool Oncoscape, where the scientific community can explore the meningioma landscape.

Transcriptomic Analysis of Lipid Metabolism Genes in Alzheimer's Disease: Highlighting Pathological Outcomes and Compartmentalized Immune Status.

The dysregulation of lipid metabolism has been strongly associated with Alzheimer's disease (AD) and has intricate connections with various aspects of disease progression, such as amyloidogenesis, bioenergetic deficit, oxidative stress, neuroinflammation, and myelin degeneration. Here, a comprehensive bioinformatic assessment was conducted on lipid metabolism genes in the brains and peripheral blood of AD-derived transcriptome datasets, characterizing the correlation betweendifferentially expressed genes (DEGs) of lipid metabolism and disease pathologies, as well as immune cell preferences. Through the application of weighted geneco-expression network analysis (WGCNA),modules eigengenes related to lipid metabolism were pinpointed, and the examination oftheir molecular functions within biological processes, molecular pathways, andtheir associations with pathological phenotypes and molecular networkshas been characterized. Analysis of biological networks indicates notable discrepancies in the expression patterns of the DEGs between neuronal and immune cells, as well as variations in cell type enrichments within both brain tissue and peripheral blood. Additionally, drugs targeting the DEGs from central and peripheral and a diagnostic model for hub genes from the blood were retrieved and assessed, some of which were shown to be useful for therapeutic and diagnostic.These results revealed the distinctive pattern of transcriptionally abnormal lipid metabolism in central, peripheral, and immune cell activation, providing valuable insight into lipid metabolism for diagnosing and guiding more effectivetreatment forAD.

Deep Batch Integration and Denoise of Single-Cell RNA-Seq Data.

Numerous single-cell transcriptomic datasets from identical tissues or cell lines are generated from different laboratories or single-cell RNA sequencing (scRNA-seq) protocols. The denoising of these datasets to eliminate batch effects is crucial for data integration, ensuring accurate interpretation and comprehensive analysis of biological questions. Although many scRNA-seq data integration methods exist, most are inefficient and/or not conducive to downstream analysis. Here, DeepBID, a novel deep learning-based method for batch effect correction, non-linear dimensionality reduction, embedding, and cell clustering concurrently, is introduced. DeepBID utilizes a negative binomial-based autoencoder with dual Kullback-Leibler divergence loss functions, aligning cell points from different batches within a consistent low-dimensional latent space and progressively mitigating batch effects through iterative clustering. Extensive validation on multiple-batch scRNA-seq datasets demonstrates that DeepBID surpasses existing tools in removing batch effects and achieving superior clustering accuracy. When integrating multiple scRNA-seq datasets from patients with Alzheimer's disease, DeepBID significantly improves cell clustering, effectively annotating unidentified cells, and detecting cell-specific differentially expressed genes.

Gene interactions analysis of brain spatial transcriptome for Alzheimer's disease

Recent studies have explored the spatial transcriptomics patterns of Alzheimer's disease (AD) brain by spatial sequencing in mouse models, enabling the identification of unique genome-wide transcriptomic features associated with different spatial regions and pathological status. However, the dynamics of gene interactions that occur during amyloid-β accumulation remain largely unknown. In this study, we performed analyses on ligand-receptor communication, transcription factor regulatory network, and spot-specific network to reveal the dependence and the dynamics of gene associations/interactions on spatial regions and pathological status with mouse and human brains. We first used a spatial transcriptomics dataset of the AppNL-G-F knock-in AD and wild-type mouse model. We revealed 17 ligand-receptor pairs with opposite tendencies throughout the amyloid-β accumulation process and showed the specific ligand-receptor interactions across the hippocampus layers at different extents of pathological changes. We then identified nerve function related transcription factors in the hippocampus and entorhinal cortex, as well as genes with different transcriptomic association degrees in AD versus wild-type mice. Finally, another independent spatial transcriptomics dataset from different AD mouse models and human single-nuclei RNA-seq data/AlzData database were used for validation. This is the first study to identify various gene associations throughout amyloid-β accumulation based on spatial transcriptomics, establishing the foundations to reveal advanced and in-depth AD etiology from a novel perspective based on the comprehensive analyses of gene interactions that are spatio-temporal dependent.

Anoikis-related gene signatures in colorectal cancer: implications for cell differentiation, immune infiltration, and prognostic prediction

Colorectal cancer (CRC) is a malignant tumor originating from epithelial cells of the colon or rectum, and its invasion and metastasis could be regulated by anoikis. However, the key genes and pathways regulating anoikis in CRC are still unclear and require further research. The single cell transcriptome dataset GSE221575 of GEO database was downloaded and applied to cell subpopulation type identification, intercellular communication, pseudo time cell trajectory analysis, and receptor ligand expression analysis of CRC. Meanwhile, the RNA transcriptome dataset of TCGA, the GSE39582, GSE17536, and GSE17537 datasets of GEO were downloaded and merged into one bulk transcriptome dataset. The differentially expressed genes (DEGs) related to anoikis were extracted from these data sets, and key marker genes were obtained after feature selection. A clinical prognosis prediction model was constructed based on the marker genes and the predictive effect was analyzed. Subsequently, gene pathway analysis, immune infiltration analysis, immunosuppressive point analysis, drug sensitivity analysis, and immunotherapy efficacy based on the key marker genes were conducted for the model. In this study, we used single cell datasets to determine the anoikis activity of cells and analyzed the DEGs of cells based on the score to identify the genes involved in anoikis and extracted DEGs related to the disease from the transcriptome dataset. After dimensionality reduction selection, 7 marker genes were obtained, including TIMP1, VEGFA, MYC, MSLN, EPHA2, ABHD2, and CD24. The prognostic risk model scoring system built by these 7 genes, along with patient clinical data (age, tumor stage, grade), were incorporated to create a nomogram, which predicted the 1-, 3-, and 5-years survival of CRC with accuracy of 0.818, 0.821, and 0.824. By using the scoring system, the CRC samples were divided into high/low anoikis-related prognosis risk groups, there are significant differences in immune infiltration, distribution of immune checkpoints, sensitivity to chemotherapy drugs, and efficacy of immunotherapy between these two risk groups. Anoikis genes participate in the differentiation of colorectal cancer tumor cells, promote tumor development, and could predict the prognosis of colorectal cancer.

Exploring Alashan Ground Squirrel (Spermophilus alashanicus) Diversity: Metagenomic and Transcriptomic Datasets from the Helan Mountains

This study investigates the adaptive strategies of the Alashan Ground Squirrel (Spermophilus alashanicus) in response to habitat changes, as rodents are sensitive indicators of ecosystem changes. Despite its ecological importance, the genome and microbiome of this species have not been thoroughly studied. This research fills this gap by presenting the first comprehensive metagenomic and transcriptomic datasets of the species. Transcriptomic data was collected from five tissue types, including heart, liver, cecum, muscle, and blood, resulting in the assembly of 72,156 unigenes. Metagenomic sequencing identified predominant bacterial groups such as Firmicutes, Bacteroidetes, Verrucomicrobia, Urovircota, and Proteobacteria. Our workflow involved RNA and DNA extraction, library preparation, assembly, and annotation, yielding valuable insights into gene discovery, microbial composition, and further genome and microbial function studies. In conclusion, our findings have significant implications for understanding the adaptive mechanisms of this species in response to environmental changes.

Gas Chromatography-Mass Spectrometry Metabolites and Transcriptome Profiling Reveal Molecular Mechanisms and Differences in Terpene Biosynthesis in Two Torrya grandis Cultivars during Postharvest Ripening.

Terpene aroma compounds are key quality attributes of postharvest Torreya grandis nuts, contributing to their commercial value. However, terpene biosynthesis and regulatory networks in different T. grandis cvs. are still poorly understood. Here, chief cvs. 'Xi Fei' and 'Xiangya Fei' were investigated for their differences in terpene biosynthesis and gene expression levels during postharvest ripening using headspace solid-phase microextraction (HS-SPME) coupled with gas chromatography-mass spectrometry (GC-MS) and transcriptomic datasets. A total of 28 and 22 aroma compounds were identified in 'Xi Fei' and 'Xiangya Fei', respectively. Interestingly, differences in aroma composition between the two cvs. were mostly attributed to D-limonene and α-pinene levels as key determinants in Torreya nuts' flavor. Further, transcriptome profiling, correlation analysis, and RT-qPCR annotated two novel genes, TgTPS1 in 'Xi Fei' and TgTPS2 in 'Xiangya Fei', involved in terpene biosynthesis. In addition, six transcription factors (TFs) with comparable expression patterns to TgTPS1 and four TFs to TgTPS2 were identified via correlation analysis of a volatile and transcriptome dataset to be involved in terpene biosynthesis. Our study provides novel insight into terpene biosynthesis and its regulation at the molecular level in T. grandis nut and presents a valuable reference for metabolic engineering and aroma improvement in this less explored nut.

Integrated re-analysis of transcriptomic and proteomic datasets reveals potential mechanisms for Zika viral-based oncolytic therapy in neuroblastoma.

Paediatric neuroblastoma and brain tumours account for a third of all childhood cancer-related mortality. High-risk neuroblastoma is highly aggressive and survival is poor despite intensive multi-modal therapies with significant toxicity. Novel therapies are desperately needed. The Zika virus (ZIKV) can access the nervous system and there is growing interest in employing ZIKV as a potential therapy against paediatric nervous system tumours, including neuroblastoma. Here, we perform extensive data mining, integration and re-analysis of ZIKV infection datasets to highlight molecular mechanisms that may govern the oncolytic response in neuroblastoma cells. We collate infection data of multiple neuroblastoma cell lines by different ZIKV strains from a body of published literature to inform the susceptibility of neuroblastoma to the ZIKV oncolytic response. Integrating published transcriptomics, interaction proteomics, dependency factor and compound datasets we propose the involvement of multiple host systems during ZIKV infection. Through data mining of published literature, we observed most paediatric neuroblastoma cell lines to be highly susceptible to ZIKV infection and propose the PRVABC59 ZIKV strain to be the most promising candidate for neuroblastoma oncolytic virotherapy. ZIKV induces TNF signalling, lipid metabolism, the Unfolded Protein Response (UPR), and downregulates cell cycle and DNA replication processes. ZIKV infection is dependent on sterol regulatory element binding protein (SREBP)-regulated lipid metabolism and three protein complexes; V-ATPase, ER Membrane Protein Complex (EMC) and mammalian translocon. We propose ZIKV non-structural protein 4B (NS4B) as a likely mediator of ZIKVs interaction with IRE1-mediated UPR, lipid metabolism and mammalian translocon. Our work provides a significant understanding of ZIKV infection in neuroblastoma cells, which will facilitate the progression of ZIKV-based oncolytic virotherapy through pre-clinical research and clinical trials.

A C-type lectin with dual carbohydrate recognition domains functions in innate immune response in Asian corn borer, Ostrinia furnacalis.

C-type lectins (CTLs) act as pattern recognition receptors (PRRs) to initiate the innate immune response in insects. A CTL with dual carbohydrate recognition domains (CRDs) (named immulectin-4 [IML-4]) was selected from the Ostrinia furnacalis transcriptome dataset for functional studies. We cloned the full-length complementary DNA of O. furnacalis IML-4 (OfIML-4). It encodes a 328-residue protein with a Glu-Pro-Asn (EPN) and Gln-Pro-Asp (QPD) motifs in 2 CRDs, respectively. OfIML-4 messenger RNA levels increased significantly upon the bacterial and fungal infection. Recombinant OfIML-4 (rIML-4) and its individual CRDs (rCRD1 and rCRD2) exhibited the binding ability to various microorganisms including Escherichia coli, Micrococcus luteus, Pichia pastoris, and Beauveria bassiana, and the cell wall components including lipopolysaccharide from E. coli, peptidoglycan from M. luteus or Bacillus subtilis, and curdlan from Alcaligenes faecalis. The binding further induced the agglutination of E. coli, M. luteus, and B. bassiana in the presence of calcium, the phagocytosis of Staphylococcus aureus by the hemocytes, in vitro encapsulation and melanization of nickel-nitrilotriacetic acid beads, and a significant increase in phenoloxidase activity of plasma. In addition, rIML-4 significantly enhanced the phagocytosis, nodulation, and resistance of O. furnacalis to B. bassiana. Taken together, our results suggest that OfIML-4 potentially works as a PRR to recognize the invading microorganisms, and functions in the innate immune response in O. furnacalis.

CoCo-ST: Comparing and Contrasting Spatial Transcriptomics data sets using graph contrastive learning.

Traditional feature dimension reduction methods have been widely used to uncover biological patterns or structures within individual spatial transcriptomics data. However, these methods are designed to yield feature representations that emphasize patterns or structures with dominant high variance, such as the normal tissue spatial pattern in a precancer setting. Consequently, they may inadvertently overlook patterns of interest that are potentially masked by these high-variance structures. Herein we present our graph contrastive feature representation method called CoCo-ST (Comparing and Contrasting Spatial Transcriptomics) to overcome this limitation. By incorporating a background data set representing normal tissue, this approach enhances the identification of interesting patterns in a target data set representing precancerous tissue. Simultaneously, it mitigates the influence of dominant common patterns shared by the background and target data sets. This enables discerning biologically relevant features crucial for capturing tissue-specific patterns, a capability we showcased through the analysis of serial mouse precancerous lung tissue samples.

USP22 as a key regulator of glycolysis pathway in osteosarcoma: insights from bioinformatics and experimental approaches.

Osteosarcoma is the most common primary malignant bone tumor, but its pathogenesis remains unclear. Ubiquitin-specific processing peptidase 22 (USP22) is reported to be highly expressed and associated with tumor malignancy and prognosis in cancers. However, the role and mechanism of USP22 in osteosarcoma is not fully understood. This study aims to investigate the function and potential mechanism of USP22 in osteosarcoma using bioinformatics analysis combined with experimental validation. We first integrated transcriptomic datasets and clinical information of osteosarcoma from GEO and TCGA databases to assess the expression and prognostic value of USP22 in osteosarcoma. Then, differential expression analysis and weighted gene co-expression network analysis (WGCNA) were conducted to identify USP22-related co-expressed genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to explore the biological functions and signaling pathways of USP22 co-expressed genes. To validate the accuracy of bioinformatics analyses, we downregulated USP22 expression in osteosarcoma cell line Sao-2 using siRNA and assessed its effect on cell proliferation, migration, invasion, apoptosis, and regulation of key signaling pathways. We found that USP22 was highly expressed in osteosarcoma tissues and correlated with poor prognosis in osteosarcoma patients. USP22 also showed potential as a diagnostic marker for osteosarcoma. In addition, 344 USP22-related co-expressed genes were identified, mainly involved in signaling pathways such as glycolysis, oxidative phosphorylation, spliceosome, thermogenesis, and cell cycle. The in vitro experiments confirmed the accuracy and reliability of bioinformatics analyses. We found that downregulation of USP22 could inhibit Sao-2 cell proliferation, migration, invasion, and induce apoptosis. Furthermore, downregulation of USP22 significantly reduced aerobic glycolysis levels in Sao-2 cells and inhibited the expression of key enzymes and transporters in aerobic glycolysis pathways such as HK2, PKM2, and GLUT1. USP22 plays a critical role in the occurrence, development, and prognosis of osteosarcoma. USP22 could influence Sao-2 cell proliferation, apoptosis, migration, and invasion by regulating the glycolysis pathway, thereby promoting osteosarcoma progression. Therefore, USP22 may be a potential therapeutic target for the treatment of osteosarcoma.

Peptides with Antimicrobial Activity in the Saliva of the Malaria Vector Anopheles coluzzii.

Mosquito saliva plays a crucial physiological role in both sugar and blood feeding by helping sugar digestion and exerting antihemostatic functions. During meal acquisition, mosquitoes are exposed to the internalization of external microbes. Since mosquitoes reingest significant amounts of saliva during feeding, we hypothesized that salivary antimicrobial components may participate in the protection of mouthparts, the crop, and the gut by inhibiting bacterial growth. To identify novel potential antimicrobials from mosquito saliva, we selected 11 candidates from Anopheles coluzzii salivary transcriptomic datasets and obtained them either using a cell-free transcription/translation expression system or, when feasible, via chemical synthesis. Hyp6.2 and hyp13, which were predicted to be produced as propeptides and cleaved in shorter mature forms, showed the most interesting results in bacterial growth inhibition assays. Hyp6.2 (putative mature form, 35 amino acid residues) significantly inhibited the growth of Gram-positive (Staphylococcus aureus) and Gram-negative (Escherichia coli and Serratia marcescens) bacteria. Hyp13 (short form, 19 amino acid residues) dose-dependently inhibited E. coli and S. marcescens growth, inducing membrane disruption in both Gram-positive and Gram-negative bacteria as indicated with scanning electron microscopy. In conclusion, we identified two A. coluzzii salivary peptides inhibiting Gram-positive and Gram-negative bacteria growth and possibly contributing to the protection of mosquito mouthparts and digestive tracts from microbial infection during and/or after feeding.

Deciphering smooth muscle cell heterogeneity in atherosclerotic plaques and constructing model: a multi-omics approach with focus on KLF15/IGFBP4 axis

BackgroundRuptured atherosclerotic plaques often precipitate severe ischemic events, such as stroke and myocardial infarction. Unraveling the intricate molecular mechanisms governing vascular smooth muscle cell (VSMC) behavior in plaque stabilization remains a formidable challenge.MethodsIn this study, we leveraged single-cell and transcriptomic datasets from atherosclerotic plaques retrieved from the gene expression omnibus (GEO) database. Employing a combination of single-cell population differential analysis, weighted gene co-expression network analysis (WGCNA), and transcriptome differential analysis techniques, we identified specific genes steering the transformation of VSMCs in atherosclerotic plaques. Diagnostic models were developed and validated through gene intersection, utilizing the least absolute shrinkage and selection operator (LASSO) and random forest (RF) methods. Nomograms for plaque assessment were constructed. Tissue localization and expression validation were performed on specimens from animal models, utilizing immunofluorescence co-localization, western blot, and reverse-transcription quantitative-polymerase chain reaction (RT-qPCR). Various online databases were harnessed to predict transcription factors (TFs) and their interacting compounds, with determination of the cell-specific localization of TF expression using single-cell data.ResultsFollowing rigorous quality control procedures, we obtained a total of 40,953 cells, with 6,261 representing VSMCs. The VSMC population was subsequently clustered into 5 distinct subpopulations. Analyzing inter-subpopulation cellular communication, we focused on the SMC2 and SMC5 subpopulations. Single-cell subpopulation and WGCNA analyses revealed significant module enrichments, notably in collagen-containing extracellular matrix and cell-substrate junctions. Insulin-like growth factor binding protein 4 (IGFBP4), apolipoprotein E (APOE), and cathepsin C (CTSC) were identified as potential diagnostic markers for early and advanced plaques. Notably, gene expression pattern analysis suggested that IGFBP4 might serve as a protective gene, a hypothesis validated through tissue localization and expression analysis. Finally, we predicted TFs capable of binding to IGFBP4, with Krüppel-like family 15 (KLF15) emerging as a prominent candidate showing relative specificity within smooth muscle cells. Predictions about compounds associated with affecting KLF15 expression were also made.ConclusionOur study established a plaque diagnostic and assessment model and analyzed the molecular interaction mechanisms of smooth muscle cells within plaques. Further analysis revealed that the transcription factor KLF15 may regulate the biological behaviors of smooth muscle cells through the KLF15/IGFBP4 axis, thereby influencing the stability of advanced plaques via modulation of the PI3K-AKT signaling pathway. This could potentially serve as a target for plaque stability assessment and therapy, thus driving advancements in the management and treatment of atherosclerotic plaques.