Analysis Of Transcriptomic Data Research Articles

A study on construction of a prognosis model for liver cancer based on analgesic targets and screening therapeutic drugs.

Liver cancer is one of the most malignant liver diseases in the world, and the 5-year survival rate of such patients is low. Analgesics are often used to cure pain prevalent in liver cancer. The expression changes and clinical significance of the analgesic targets (ATs) in liver cancer have not been deeply understood. The purpose of this study is to clarify the expression pattern of ATs gene in liver cancer and its clinical significance. Through the comprehensive analysis of transcriptome data and clinical parameters, the prognosis model related to ATs gene is established, and the drug information sensitive to ATs is mined. The study primarily utilized transcriptomic data and clinical information from liver cancer patients sourced from The Cancer Genome Atlas (TCGA) database. These data were employed to analyze the expression of ATs, conduct survival analysis, gene set variation analysis (GSVA), immune cell infiltration analysis, establish a prognostic model, and perform other bioinformatic analyses. Additionally, data from liver cancer patients in the International Cancer Genome Consortium (ICGC) were utilized to validate the accuracy of the model. Furthermore, the impact of analgesics on key genes in the prognostic model was assessed using data from the Comparative Toxicogenomics Database (CTD). The study investigated the differential expression of 58 ATs genes in liver cancer compared to normal tissues. Patients were stratified based on ATs expression, revealing varied survival outcomes. Functional enrichment analysis highlighted distinctions in spindle organization, centrosome, and spindle microtubule functions. Prognostic modeling identified low TP53 expression as protective, while elevated CCNA2, NEU1, and HTR2C levels posed risks. Commonly used analgesics, including acetaminophen and others, were found to influence the expression of these genes. These findings provide insights into potential therapeutic strategies for liver cancer and shed light on the molecular mechanisms underlying its progression. The collective analysis of gene signatures associated with ATs suggests their potential as prognostic predictors in hepatocellular carcinoma patients. These findings not only offer insights into cancer therapy but also provide novel avenues for the development of indications for analgesics.

The prognostic genes model of breast cancer drug resistance based on single-cell sequencing analysis and transcriptome analysis

Breast cancer (BC) represents a multifaceted malignancy, with escalating incidence and mortality rates annually. Chemotherapy stands as an indispensable approach for treating breast cancer, yet drug resistance poses a formidable challenge. Through transcriptome data analysis, we have identified two sets of genes exhibiting differential expression in this context. Furthermore, we have confirmed the overlap between these genes and those associated with exosomes, which were subsequently validated in cell lines. The investigation screened the identified genes to determine prognostic markers for BC and utilized them to formulate a prognostic model. The disparities in prognosis and immunity between the high- and low-risk groups were validated using the test dataset. We have discerned different BC subtypes based on the expression levels of prognostic genes in BC samples. Variations in prognosis, immunity, and drug sensitivity among distinct subtypes were examined. Leveraging data from single-cell sequencing and prognostic gene expression, the AUCell algorithm was employed to score individual cell clusters and analyze the pathways implicated in high-scoring groups. Prognostic genes (CCT4, CXCL13, MTDH, PSMD2, and RAB27A) were subsewoquently validated using RT-qPCR. Consequently, we have established a model for predicting prognosis in breast cancer that hinges on drug resistance and ERGs. Furthermore, we have evaluated the prognostic value of this model. The genes identified as prognostic markers can now serve as a reference for precise treatment of this condition.

Molecular Insights into Female Hybrid Sterility in Interspecific Crosses between Drosophila melanogaster and Drosophila simulans

Species of the genus Drosophila have served as favorite models in speciation studies; however, genetic factors of interspecific reproductive incompatibility are under-investigated. Here, we performed an analysis of hybrid female sterility by crossing Drosophila melanogaster females and Drosophila simulans males. Using transcriptomic data analysis and molecular, cellular, and genetic approaches, we analyzed differential gene expression, transposable element (TE) activity, piRNA biogenesis, and functional defects of oogenesis in hybrids. Premature germline stem cell loss was the most prominent defect of oogenesis in hybrid ovaries. Because of the differential expression of genes encoding piRNA pathway components, rhino and deadlock, the functional RDCmel complex in hybrid ovaries was not assembled. However, the activity of the RDCsim complex was maintained in hybrids independent of the genomic origin of piRNA clusters. Despite the identification of a cohort of overexpressed TEs in hybrid ovaries, we found no evidence that their activity can be considered the main cause of hybrid sterility. We revealed a complicated pattern of Vasa protein expression in the hybrid germline, including partial AT-chX piRNA targeting of the vasasim allele and a significant zygotic delay in vasamel expression. We arrived at the conclusion that the hybrid sterility phenotype was caused by intricate multi-locus differences between the species.

The First Identification of Homomorphic XY Sex Chromosomes by Integrating Cytogenetic and Transcriptomic Approaches in Plestiodon elegans (Scincidae)

The sex chromosomes of skinks are usually poorly differentiated and hardly distinguished by cytogenetic methods. Therefore, identifying sex chromosomes in species lacking easily recognizable heteromorphic sex chromosomes is necessary to fully understand sex chromosome diversity. In this paper, we employed cytogenetics, sex quantification of genes, and transcriptomic approaches to characterize the sex chromosomes in Plestiodon elegans. Cytogenetic examination of metaphases revealed a diploid number of 2n = 26, consisting of 12 macrochromosomes and 14 microchromosomes, with no significant heteromorphic chromosome pairs, speculating that the sex chromosomes may be homomorphic or poorly differentiated. The results of the sex quantification of genes showed that Calumenin (calu), COPI coat complex subunit γ 2 (copg2), and Smoothened (smo) were at half the dose in males as in females, suggesting that they are on the X chromosome. Transcriptomic data analysis from the gonads yielded the excess expression male-specific genes (n = 16), in which five PCR molecular markers were developed. Restricting the observed heterozygosity to males suggests the presence of homomorphic sex chromosomes in P. elegans, XX/XY. This is the first breakthrough in the study of the sex chromosomes of Plestiodon.

MNMST: topology of cell networks leverages identification of spatial domains from spatial transcriptomics data

Advances in spatial transcriptomics provide an unprecedented opportunity to reveal the structure and function of biology systems. However, current algorithms fail to address the heterogeneity and interpretability of spatial transcriptomics data. Here, we present a multi-layer network model for identifying spatial domains in spatial transcriptomics data with joint learning. We demonstrate that spatial domains can be precisely characterized and discriminated by the topological structure of cell networks, facilitating identification and interpretability of spatial domains, which outperforms state-of-the-art baselines. Furthermore, we prove that network model offers an effective and efficient strategy for integrative analysis of spatial transcriptomics data from various platforms.

Comparative Metabolome and Transcriptome Analyses Reveal Differential Enrichment of Metabolites with Age in Panax notoginseng Roots

Panax notoginseng is a perennial plant well known for its versatile medicinal properties, including hepatoprotective, antioxidant, anti-inflammatory, anti-tumor, estrogen-like, and antidepressant characteristics. It has been reported that plant age affects the quality of P. notoginseng. This study aimed to explore the differential metabolome and transcriptome of 2-year (PN2) and 3-year-old (PN3) P. notoginseng plant root samples. Principal component analysis of metabolome and transcriptome data revealed major differences between the two groups (PN2 vs. PN3). A total of 1813 metabolites and 28,587 genes were detected in this study, of which 255 metabolites and 3141 genes were found to be differential (p < 0.05) between PN2 vs. PN3, respectively. Among differential metabolites and genes, 155 metabolites and 1217 genes were up-regulated, while 100 metabolites and 1924 genes were down-regulated. The KEGG pathway analysis revealed differentially enriched metabolites belonging to class lipids (“13S-hydroperoxy-9Z, 11E-octadecadionic acid”, “9S-hydroxy-10E, 12Z-octadecadionic acid”, “9S-oxo-10E, 12Z-octadecadionic acid”, and “9,10,13-trihydroxy-11-octadecadionic acid”), nucleotides and derivatives (guanine and cytidine), and phenolic acids (chlorogenic acid) were found to be enriched (p < 0.05) in PN3 compared to PN2. Further, these differentially enriched metabolites were found to be significantly (p < 0.05) regulated via linoleic acid metabolism, nucleotide metabolism, plant hormone signal transduction, and arachidonic acid metabolism pathways. Furthermore, the transcriptome analysis showed the up-regulation of key genes MAT, DMAS, SDH, gallate 1-beta-glucosyltransferase, and beta-D-glucosidase in various plants’ secondary metabolic pathways and SAUR, GID1, PP2C, ETR, CTR1, EBF1/2, and ERF1/2 genes observed in phytohormone signal transduction pathway that is involved in plant growth and development, and protection against the various stressors. This study concluded that the roots of a 3-year-old P. notoginseng plant have better metabolome and transcriptome profiles compared to a 2-year-old plant with importantly enriched metabolites and genes in pathways related to metabolism, plant hormone signal transduction, and various biological processes. These findings provide insights into the plant’s dynamic biochemical and molecular changes during its growth that have several implications regarding its therapeutic use.

Protein N-glycans in Healthy and Sclerotic Glomeruli in Diabetic Kidney Disease.

Diabetes is expected to directly impact renal glycosylation, yet to date, there has not been a comprehensive evaluation of alterations in N-glycan composition in the glomeruli of patients with diabetic kidney disease (DKD). We used untargeted mass spectrometry imaging to identify N-glycan structures in healthy and sclerotic glomeruli in FFPE sections from needle biopsies of five patients with DKD and three healthy kidney samples. Regional proteomics was performed on glomeruli from additional biopsies from the same patients to compare the abundances of enzymes involved in glycosylation. Secondary analysis of single nuclei transcriptomics (snRNAseq) data was used to inform on transcript levels of glycosylation machinery in different cell types and states. We detected 120 N-glycans, and among them identified twelve of these protein post-translated modifications that were significantly increased in glomeruli. All glomeruli-specific N-glycans contained an N-acetyllactosamine (LacNAc) epitope. Five N-glycan structures were highly discriminant between sclerotic and healthy glomeruli. Sclerotic glomeruli had an additional set of glycans lacking fucose linked to their core, and they did not show tetra-antennary structures that are common in healthy glomeruli. Orthogonal omics analyses revealed lower protein abundance and lower gene expression involved in synthesizing fucosylated and branched N-glycans in sclerotic podocytes. In snRNAseq and regional proteomics analyses, we observed that genes and/or proteins involved in sialylation and LacNAc synthesis were also downregulated in DKD glomeruli, but this alteration remained undetectable by our spatial N-glycomics assay. Integrative spatial glycomics, proteomics, and transcriptomics revealed protein N-glycosylation characteristic of sclerotic glomeruli in DKD.

Protein expression patterns and metal metabolites in a protogynous hermaphrodite fish, the ricefield eel (Monopterus albus)

BackgroundThe ricefield eel Monopterus albus undergoes a natural sex change from female to male during its life cycle, and previous studies have shown the potential mechanisms of this transition at the transcriptional and protein levels. However, the changes in protein levels have not been fully explored, especially in the intersexual stage.ResultsIn the present study, the protein expression patterns in the gonadal tissues from five different periods, the ovary (OV), early intersexual stage gonad (IE), middle intersexual stage gonad (IM), late intersexual stage gonad (IL), and testis (TE), were determined by untargeted proteomics sequencing. A total of 5125 proteins and 394 differentially expressed proteins (DEPs) were detected in the gonadal tissues. Of the 394 DEPs, there were 136 between the OV and IE groups, 20 between the IM and IE groups, 179 between the IL and IM groups, and 59 between the TE and IL groups. Three candidate proteins, insulin-like growth factor 2 mRNA-binding protein 3 isoform X1 (Igf2bp3), triosephosphate isomerase (Tpi), and Cu-Zn superoxide dismutase isoform X1 [(Cu-Zn) Sod1], were validated by western blotting to verify the reliability of the data. Furthermore, metal metabolite-related proteins were enriched in the IL vs. IM groups and TE vs. IL groups, which had close relationships with sex change, including Cu2+-, Ca2+-, Zn2+- and Fe2+/Fe3+-related proteins. Analysis of the combined transcriptome data revealed consistent protein/mRNA expression trends for two metal metabolite-related proteins/genes [LOC109953912 and calcium Binding Protein 39 Like (cab39l)]. Notably, we detected significantly higher levels of Cu2+ during the sex change process, suggesting that Cu2+ is a male-related metal metabolite that may have an important function in male reproductive development.ConclusionsIn summary, we analyzed the protein profiles of ricefield eel gonadal tissues in five sexual stages (OV, IE, IM, IL, and TE) and verified the plausibility of the data. After preforming the functional enrichment of metal metabolite-related DEPs, we detected the contents of the metal metabolites Zn2+, Cu2+, Ca2+, and Fe2+/Fe3+ at these five stages and screened for (Cu-Zn) Sod1 and Mmp-9 as possible key proteins in the sex reversal process.

Prediction of response to promising first-line chemotherapy in ovarian cancer patients with residual peritoneal tumors: practical biomarkers and robust multiplex models.

Platinum/taxane (TC) chemotherapy with debulking surgery stays the mainstay of the treatment in ovarian cancer patients with peritoneal metastasis, and recently its novel modality, intraperitoneal carboplatin with dose-dense paclitaxel (ddTCip), was shown to have greater therapeutic impact. Nevertheless, the response varies among patients and consequent recurrence, or relapse often occurs. Discovery of therapeutic response predictor to ddTCip and/or TC therapy is eagerly awaited to improve the treatment outcome. Using datasets in 76 participants in our ddTCip study and published databases on patients received TC therapy, we first validated a total of 75 previously suggested markers, sought out more active biomarkers through the association analyses of genome-wide transcriptome and genotyping data with progression-free survival (PFS) and adverse events, and then developed multiplex statistical prediction models for PFS and toxicity by mainly using multiple regression analysis and the classification and regression tree (CART) algorithm. The association analyses revealed that SPINK1 could be a possible biomarker of ddTCip efficacy, while ABCB1 rs1045642 and ERCC1 rs11615 would be a predictor of hematologic toxicity and peripheral neuropathy, respectively. Multiple regression analyses and CART algorithm finally provided a potent efficacy prediction model using 5 gene expression data and robust multiplex toxicity prediction models-CART models using a total of 4 genotype combinations and multiple regression models using 15 polymorphisms on 12 genes. Biomarkers and multiplex models composed here could work well in the response prediction of ddTCip and/or TC therapy, which might contribute to realize optimal selection of the key therapy.

Inverse and Proportional Trans Modulation of Gene Expression in Human Aneuploidies.

Genomic imbalance in aneuploidy is often detrimental to organisms. To gain insight into the molecular basis of aneuploidies in humans, we analyzed transcriptome data from several autosomal and sex chromosome aneuploidies. The results showed that in human aneuploid cells, genes located on unvaried chromosomes are inversely or proportionally trans-modulated, while a subset of genes on the varied chromosomes are compensated. Less genome-wide modulation is found for sex chromosome aneuploidy compared with autosomal aneuploidy due to X inactivation and the retention of dosage sensitive regulators on both sex chromosomes to limit the effective dosage change. We also found that lncRNA and mRNA can have different responses to aneuploidy. Furthermore, we analyzed the relationship between dosage-sensitive transcription factors and their targets, which illustrated the modulations and indicates genomic imbalance is related to stoichiometric changes in components of gene regulatory complexes.In summary, this study demonstrates the existence of trans-acting effects and compensation mechanisms in human aneuploidies and contributes to our understanding of gene expression regulation in unbalanced genomes and disease states.

Nitrogen starvation causes lipid remodeling in Rhodotorula toruloides

BackgroundThe oleaginous yeast Rhodotorula toruloides is a promising chassis organism for the biomanufacturing of value-added bioproducts. It can accumulate lipids at a high fraction of biomass. However, metabolic engineering efforts in this organism have progressed at a slower pace than those in more extensively studied yeasts. Few studies have investigated the lipid accumulation phenotype exhibited by R. toruloides under nitrogen limitation conditions. Consequently, there have been only a few studies exploiting the lipid metabolism for higher product titers.ResultsWe performed a multi-omic investigation of the lipid accumulation phenotype under nitrogen limitation. Specifically, we performed comparative transcriptomic and lipidomic analysis of the oleaginous yeast under nitrogen-sufficient and nitrogen deficient conditions. Clustering analysis of transcriptomic data was used to identify the growth phase where nitrogen-deficient cultures diverged from the baseline conditions. Independently, lipidomic data was used to identify that lipid fractions shifted from mostly phospholipids to mostly storage lipids under the nitrogen-deficient phenotype. Through an integrative lens of transcriptomic and lipidomic analysis, we discovered that R. toruloides undergoes lipid remodeling during nitrogen limitation, wherein the pool of phospholipids gets remodeled to mostly storage lipids. We identify specific mRNAs and pathways that are strongly correlated with an increase in lipid levels, thus identifying putative targets for engineering greater lipid accumulation in R. toruloides. One surprising pathway identified was related to inositol phosphate metabolism, suggesting further inquiry into its role in lipid accumulation.ConclusionsIntegrative analysis identified the specific biosynthetic pathways that are differentially regulated during lipid remodeling. This insight into the mechanisms of lipid accumulation can lead to the success of future metabolic engineering strategies for overproduction of oleochemicals.

O22 Unravelling the pathophysiology of KLICK syndrome to identify therapeutically targetable pathways

Abstract Introduction and aims Keratosis linearis with ichthyosis congenita and sclerosing keratoderma (KLICK) syndrome is an ultrarare genodermatosis associated with a homozygous pathogenic variant, c.-95delC in POMP, which encodes for proteasome maturation protein. Clinical manifestations include linear hyperkeratotic papules in the flexures, palmoplantar keratoderma and pseudoainhum. The pathophysiology of KLICK syndrome is poorly understood, resulting in nonspecific, limited treatment options. The aim of this work is to characterize KLICK syndrome at the cellular and molecular level, with the utilization of transcriptomic techniques for potential drug repurposing. Methods A lesional skin biopsy was obtained, following informed consent, from a 32-year-old female patient with KLICK syndrome. Histology and immunostaining were used to characterize the disrupted skin architecture. RNA sequencing was used to identify key dysregulated pathways and the L1000FWD reverse transcriptomics tool enabled the generation of a shortlist of potential therapeutics for KLICK syndrome. CRISPR editing techniques and induced pluripotent stem cell (iPSC) reprogramming Methods were used to generate cellular based models carrying the pathogenic POMP variant in order to support evaluation of the shortlisted therapeutic options. Results KLICK syndrome skin histology featured hyperkeratosis, hypergranulosis and delayed cellular flattening. Consistent with pathological features, pathway analysis of transcriptomic data revealed keratinocyte differentiation and epidermis development as key upregulated pathways in KLICK syndrome. Interestingly, the Panther pathway analysis tool suggested dysregulation in epidermal growth factor receptor (EGFR) signalling, and reverse transcriptomics identified erlotinib, an EGFR inhibitor, as a potential therapeutic treatment for KLICK syndrome. HaCaT keratinocytes were successfully edited using CRISPR-Cas9 to carry the KLICK patient POMP variant and a KLICK patient iPSC line was generated to support in vitro KLICK syndrome modelling. Conclusions KLICK syndrome is a hyperproliferative inflammatory skin disorder associated with a pathogenic variant in POMP. Reverse transcriptomics is a useful tool for shortlisting drugs with biologically relevant mechanisms of action with potential for treatment of rare genetic diseases such as KLICK syndrome.

Exploring the molecular landscape of cancer of unknown primary: A comparative analysis with other metastatic cancers.

Cancer of unknown primary (CUP) tumors are biologically very heterogeneous, which complicates stratification of patients for treatment. Consequently, these patients face limited treatment options and a poor prognosis. With this study, we aim to expand on the current knowledge of CUP biology by analyzing two cohorts: a well-characterized cohort of 44 CUP patients, and 213 metastatic patients with known primary. These cohorts were treated at the same institution and characterized by identical molecular assessments. Through comparative analysis of genomic and transcriptomic data, we found that CUP tumors were characterized by high expression of immune-related genes and pathways compared to other metastatic tumors. Moreover, CUP tumors uniformly demonstrated high levels of tumor-infiltrating leukocytes and circulating Tcells, indicating a strong immune response. Finally, the genetic landscape of CUP tumors resembled that of other metastatic cancers and demonstrated mutations in established cancer genes. In conclusion, CUP tumors possess a distinct immunophenotype that distinguishes them from other metastatic cancers. These results may suggest an immune response in CUP that facilitates metastatic tumor growth while limiting growth of the primary tumor.

Magnaporthe oryzae infection triggers rice resistance to brown planthopper through the influence of jasmonic acid on the flavonoid biosynthesis pathway.

In agroecosystems, plants are constantly exposed to attack from diverse herbivorous insects and microbes, and infestation with one species may change the plant defense response to other species. In our investigation of the relationships among rice plants, the brown planthopper Nilaparvata lugens (Stål) and the rice blast fungus Magnaporthe oryzae, we observed a significant increase in the resistance of rice treated with rice blast to N.lugens, as evidenced by improved plant survival rates in a small population resistance study. Subsequent transcriptome data analysis revealed that the rice blast fungus can induce the expression of genes in the jasmonic acid (JA) and flavonoid pathways. Similar to the flavonoid pathway, the JA pathway also contains 2 types of genes that exhibit similar and opposite trends in response to N.lugens and rice blast. Among these genes, the osjaz1 mutant and the osmyc2 mutant were phenotypically confirmed to positively and negatively regulate rice resistance to N.lugens and rice blast, respectively. Subsequent mass spectrometry and quantification experiments showed that the exogenous application of methyl jasmonate (MeJA) can induce the accumulation of eriodictyol, naringenin and quercetin, as well as the expression of OsF3H, Os4CL5 and OsCHI in the flavonoid pathway. This suggests a close connection between the JA pathway and the flavonoid pathway. However, OsF3'H, which negatively regulates rice resistance to N.lugens and rice blast, did not show increased expression. Phenotypic and molecular experiments confirmed that OsMYC2 can bind to and inhibit the expression of OsF3'H, thus revealing the mechanism of rice resistance to N.lugens after treatment with rice blast. These findings will deepen our understanding of the interactions among rice, N.lugens and rice blast.

Machine Learning-Based Integrated Multiomics Characterization of Colorectal Cancer Reveals Distinctive Metabolic Signatures.

The metabolic signature identification of colorectal cancer is critical for its early diagnosis and therapeutic approaches that will significantly block cancer progression and improve patient survival. Here, we combined an untargeted metabolic analysis strategy based on internal extractive electrospray ionization mass spectrometry and the machine learning approach to analyze metabolites in 173 pairs of cancer samples and matched normal tissue samples to build robust metabolic signature models for diagnostic purposes. Screening and independent validation of metabolic signatures from colorectal cancers via machine learning methods (Logistic Regression_L1 for feature selection and eXtreme Gradient Boosting for classification) was performed to generate a panel of seven signatures with good diagnostic performance (the accuracy of 87.74%, sensitivity of 85.82%, and specificity of 89.66%). Moreover, seven signatures were evaluated according to their ability to distinguish between cancer and normal tissues, with the metabolic molecule PC (30:0) showing good diagnostic performance. In addition, genes associated with PC (30:0) were identified by multiomics analysis (combining metabolic data with transcriptomic data analysis) and our results showed that PC (30:0) could promote the proliferation of colorectal cancer cell SW480, revealing the correlation between genetic changes and metabolic dysregulation in cancer. Overall, our results reveal potential determinants affecting metabolite dysregulation, paving the way for a mechanistic understanding of altered tissue metabolites in colorectal cancer and design interventions for manipulating the levels of circulating metabolites.

Machine-learning developed an iron, copper, and sulfur-metabolism associated signature predicts lung adenocarcinoma prognosis and therapy response

BackgroundPrevious studies have largely neglected the role of sulfur metabolism in LUAD, and no study has combine iron, copper, and sulfur-metabolism associated genes together to create prognostic signatures.MethodsThis study encompasses 1564 LUAD patients, 1249 NSCLC patients, and over 10,000 patients with various cancer types from diverse cohorts. We employed the R package ConsensusClusterPlus to separate patients into different ICSM (Iron, Copper, and Sulfur-Metabolism) subtypes. Various machine-learning methods were utilized to develop the ICSMI. Enrichment analyses were conducted using ClusterProfiler and GSVA, while IOBR quantified immune cell infiltration. GISTIC2.0 and maftools were utilized for CNV and SNV data analysis. The Oncopredict package predicted drug information based on GDSC1. TIDE algorithm and cohorts GSE91061 and IMvigor210 evaluated patient response to immunotherapy. Single-cell data was processed using the Seurat package, AUCell package calculated cells geneset activity scores, and the Scissor algorithm identified ICSMI-associated cells. In vitro experiments was conducted to explore the role of ICSMRGs in LUAD.ResultsUnsupervised clustering identified two distinct ICSM subtypes of LUAD, each with unique clinical characteristics. The ICSMI, comprising 10 genes, was constructed using integrated machine-learning methods. Its prognostic power was validated in 10 independent datasets, revealing that LUAD patients with higher ICSMI levels had poorer prognoses. Furthermore, ICSMI demonstrated superior predictive abilities compared to 102 previously published signatures. A nomogram incorporating ICSMI and clinical features exhibited high predictive performance. ICSMI positively correlated with patients gene mutations, and integrated analysis of bulk and single-cell transcriptome data revealed its association with TME modulators. Cells representing the high-ICSMI phenotype exhibited more malignant features. LUAD patients with high ICSMI levels exhibited sensitivity to chemotherapy and targeted therapy but displayed resistance to immunotherapy. In a comprehensive analysis across various cancers, ICSMI retained significant prognostic value and emerged as a risk factor for the majority of cancer patients.ConclusionsICSMI provides critical prognostic insights for LUAD patients, offering valuable insights into the tumor microenvironment and predicting treatment responsiveness.

Genomic identification and expression analysis of acid invertase (AINV) gene family in Dendrobium officinale Kimura et Migo

BackgroundDendrobium officinale Kimura et Migo, a renowned traditional Chinese orchid herb esteemed for its significant horticultural and medicinal value, thrives in adverse habitats and contends with various abiotic or biotic stresses. Acid invertases (AINV) are widely considered enzymes involved in regulating sucrose metabolism and have been revealed to participate in plant responses to environmental stress. Although members of AINV gene family have been identified and characterized in multiple plant genomes, detailed information regarding this gene family and its expression patterns remains unknown in D. officinale, despite their significance in polysaccharide biosynthesis.ResultsThis study systematically analyzed the D. officinale genome and identified four DoAINV genes, which were classified into two subfamilies based on subcellular prediction and phylogenetic analysis. Comparison of gene structures and conserved motifs in DoAINV genes indicated a high-level conservation during their evolution history. The conserved amino acids and domains of DoAINV proteins were identified as pivotal for their functional roles. Additionally, cis-elements associated with responses to abiotic and biotic stress were found to be the most prevalent motif in all DoAINV genes, indicating their responsiveness to stress. Furthermore, bioinformatics analysis of transcriptome data, validated by quantitative real-time reverse transcription PCR (qRT-PCR), revealed distinct organ-specific expression patterns of DoAINV genes across various tissues and in response to abiotic stress. Examination of soluble sugar content and interaction networks provided insights into stress release and sucrose metabolism.ConclusionsDoAINV genes are implicated in various activities including growth and development, stress response, and polysaccharide biosynthesis. These findings provide valuable insights into the AINV gene amily of D. officinale and will aid in further elucidating the functions of DoAINV genes.

Combined transcriptomics and proteomics unveil the impact of vitamin C in modulating specific protein abundance in the mouse liver

BackgroundVitamin C (ascorbate) is a water-soluble antioxidant and an important cofactor for various biosynthetic and regulatory enzymes. Mice can synthesize vitamin C thanks to the key enzyme gulonolactone oxidase (Gulo) unlike humans. In the current investigation, we used Gulo−/− mice, which cannot synthesize their own ascorbate to determine the impact of this vitamin on both the transcriptomics and proteomics profiles in the whole liver. The study included Gulo−/− mouse groups treated with either sub-optimal or optimal ascorbate concentrations in drinking water. Liver tissues of females and males were collected at the age of four months and divided for transcriptomics and proteomics analysis. Immunoblotting, quantitative RT-PCR, and polysome profiling experiments were also conducted to complement our combined omics studies.ResultsPrincipal component analyses revealed distinctive differences in the mRNA and protein profiles as a function of sex between all the mouse cohorts. Despite such sexual dimorphism, Spearman analyses of transcriptomics data from females and males revealed correlations of hepatic ascorbate levels with transcripts encoding a wide array of biological processes involved in glucose and lipid metabolisms as well as in the acute-phase immune response. Moreover, integration of the proteomics data showed that ascorbate modulates the abundance of various enzymes involved in lipid, xenobiotic, organic acid, acetyl-CoA, and steroid metabolism mainly at the transcriptional level, especially in females. However, several proteins of the mitochondrial complex III significantly correlated with ascorbate concentrations in both males and females unlike their corresponding transcripts. Finally, poly(ribo)some profiling did not reveal significant enrichment difference for these mitochondrial complex III mRNAs between Gulo−/− mice treated with sub-optimal and optimal ascorbate levels.ConclusionsThus, the abundance of several subunits of the mitochondrial complex III are regulated by ascorbate at the post-transcriptional levels. Our extensive omics analyses provide a novel resource of altered gene expression patterns at the transcriptional and post-transcriptional levels under ascorbate deficiency.

Proteogenomics in Nephrology: A New Frontier in Nephrological Research.

Proteogenomics represents a transformative intersection in nephrology, uniting genomics, transcriptomics, and proteomics to unravel the molecular intricacies of kidney diseases. This review encapsulates the methodological essence of proteogenomics and its profound implications in chronic kidney disease (CKD) research. We explore the proteogenomic pipeline, highlighting the integrated analysis of genomic, transcriptomic, and proteomic data and its pivotal role in enhancing our understanding of kidney pathologies. Through case studies, we showcase the application of proteogenomics in clear cell renal cell carcinoma (ccRCC) and Autosomal Recessive Polycystic Kidney Disease (ARPKD), emphasizing its potential in personalized treatment strategies and biomarker discovery. The review also addresses the challenges in proteogenomic analysis, including data integration complexities and bioinformatics limitations, and proposes solutions for advancing the field. Ultimately, this review underscores the prospective future of proteogenomics in nephrology, particularly in advancing personalized medicine and providing novel therapeutic insights.

Physiological and transcriptomic responses of silkworms to graphene oxide exposure

The growing use of nanomaterials has sparked significant interest in assessing the insect toxicities of nanoparticles. The silkworm, as an economically important insect, serves as a promising model for studying how insects respond to harmful substances. Here, we conducted a comprehensive investigation on the impact of graphene oxide (GO) on silkworms using a combination of physiological and transcriptome analyses. GO can enter the midguts and posterior silk glands of silkworms. High GO concentrations (> 25 mg/L) significantly (P < 0.01) inhibited larval growth. Additionally, GO (> 5 mg/L) significantly reduced the cocooning rate, and GO (> 15 mg/L) hindered oviduct development and egg laying in silkworms. GO increased the reactive oxygen species content and regulated catalase activity, suggesting that it may affect insect growth by regulating reactive oxygen detoxification. The transcriptome data analysis showed that 35 metabolism-related genes and 20 ribosome biogenesis-related genes were differentially expressed in response to GO, and their expression levels were highly correlated. Finally, we propose that a Ribosome biogenesis–Metabolic signaling network is involved in responses to GO. The research provides a new perspective on the molecular responses of insects to GO.