High-throughput Transcriptome Research Articles

High-throughput mechanical phenotyping and transcriptomics of single cells

The molecular system regulating cellular mechanical properties remains unexplored at single-cell resolution mainly due to a limited ability to combine mechanophenotyping with unbiased transcriptional screening. Here, we describe an electroporation-based lipid-bilayer assay for cell surface tension and transcriptomics (ELASTomics), a method in which oligonucleotide-labelled macromolecules are imported into cells via nanopore electroporation to assess the mechanical state of the cell surface and are enumerated by sequencing. ELASTomics can be readily integrated with existing single-cell sequencing approaches and enables the joint study of cell surface mechanics and underlying transcriptional regulation at an unprecedented resolution. We validate ELASTomics via analysis of cancer cell lines from various malignancies and show that the method can accurately identify cell types and assess cell surface tension. ELASTomics enables exploration of the relationships between cell surface tension, surface proteins, and transcripts along cell lineages differentiating from the haematopoietic progenitor cells of mice. We study the surface mechanics of cellular senescence and demonstrate that RRAD regulates cell surface tension in senescent TIG-1 cells. ELASTomics provides a unique opportunity to profile the mechanical and molecular phenotypes of single cells and can dissect the interplay among these in a range of biological contexts.

Neuromodulatory co-expression in cardiac vagal motor neurons of the Dorsal Motor Nucleus of the Vagus

Dorsal Motor Nucleus of the Vagus (DMV) activity is required for cardioprotection from physiological interventions such as remote ischemic pre-conditioning (RIPC). Herein, we discover the transcriptomic phenotypes of cardioprotective DMV neurons, by combining high throughput transcriptomics, 3D anatomical mapping, and neural tracing in 12 week old male and female Sprague Dawley rats. We find that DMV neurons appear to participate in neuromodulatory co-expression, including those that project to the heart. To distinguish potential mechanisms of cardioprotection, we highlight the functional significance of several transcripts in cardiac-projecting DMV neurons and we begin to validate these findings at the protein level. In our sc-RNAseq data we find that Snca (α-synuclein), the cardioprotective neuromodulator Adcyap1 (PACAP), and the neuropeptide Cartpt (CART) are enriched in cardiac-projecting DMV neurons. To contrast our molecular findings in cardioprotective DMV neurons, we have also collected single neuron HT-RTqPCR data from the other source of cardiac vagal motoneurons, the Nucleus Ambiguus (NA). Catecholaminergic transcripts, including Slc18a2 (VMAT2), Ddc, and Th, are enriched in cardiac-projecting DMV neurons relative to those of the NA. These findings implicate catecholaminergic processes, synuclein, and these secreted neuropeptides in cardioprotection. At the protein level, we found PACAP, CART, SNCA and TH enrichment, as well as co-localization of PACAP, TH, and ChAT, in cardiac-projecting DMV neurons. There is consistency between our findings from three spatial transcriptomics techniques and across sexes. Based on these results, we propose a 3D anatomical and molecular map of cardiac vagal motoneurons. This work fills a major gap in the understanding of the cellular mechanisms by which parasympathetic drive provides cardioprotection and provides a future translational path to emulating the positive vagal neuromodulatory effects on heart health. Funding was provided by the R01 HL161696 grant through National Heart, Lung, and Blood institute and the National Institutes of Health Common Fund SPARC Program Grant OT2OD030534. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Spectral neural approximations for models of transcriptional dynamics

The advent of high-throughput transcriptomics provides an opportunity to advance mechanistic understanding of transcriptional processes and their connections to cellular function at an unprecedented, genome-wide scale. These transcriptional systems, which involve discrete stochastic events, are naturally modeled using chemical master equations (CMEs), which can be solved for probability distributions to fit biophysical rates that govern system dynamics. While CME models have been used as standards in fluorescence transcriptomics for decades to analyze single-species RNA distributions, there are often no closed-form solutions to CMEs that model multiple species, such as nascent and mature RNA transcript counts. This has prevented the application of standard likelihood-based statistical methods for analyzing high-throughput, multi-species transcriptomic datasets using biophysical models. Inspired by recent work in machine learning to learn solutions to complex dynamical systems, we leverage neural networks and statistical understanding of system distributions to produce accurate approximations to a steady-state bivariate distribution for a model of the RNA life cycle that includes nascent and mature molecules. The steady-state distribution to this simple model has no closed-form solution and requires intensive numerical solving techniques: our approach reduces likelihood evaluation time by several orders of magnitude. We demonstrate two approaches, whereby solutions are approximated by 1) learning the weights of kernel distributions with constrained parameters or 2) learning both weights and scaling factors for parameters of kernel distributions. We show that our strategies, denoted by kernel weight regression and parameter-scaled kernel weight regression, respectively, enable broad exploration of parameter space and can be used in existing likelihood frameworks to infer transcriptional burst sizes, RNA splicing rates, and mRNA degradation rates from experimental transcriptomic data.

Characterization and evaluation of cytotoxic and antimicrobial activities of cyclotides from Viola japonica

Cyclotides are a type of defense peptide most commonly found in the Violaceae family of plants, exhibiting various biological activities. In this study, we focused on the Viola japonica as our research subject and conducted transcriptome sequencing and analysis using high-throughput transcriptomics techniques. During this process, we identified 61 cyclotides, among which 25 were previously documented, while the remaining 36 were designated as vija 1 to vija 36. Mass spectrometry detection showed that 21 putative cyclotides were found in the extract of V. japonica. Through isolation, purification and tandem mass spectrometry, we characterized and investigated the activities of five cyclotides. Our results demonstrated inhibitory effects of these cyclotides on the growth of Acinetobacter baumannii and Bacillus subtilis, with minimum inhibitory concentrations (MICs) of 4.2 μM and 2.1 μM, respectively. Furthermore, time killing kinetic assays revealed that cyclotides at concentration of 4 MICs achieved completely bactericidal effects within 2 h. Additionally, fluorescence staining experiments confirmed that cyclotides disrupt microbial membranes. Moreover, cytotoxicity studies showed that cyclotides possess cytotoxic effects, with IC50 values ranging from 0.1 to 3.5 μM. In summary, the discovery of new cyclotide sequences enhances our understanding of peptide diversity and the exploration of their activity lays the foundation for a deeper investigation into the mechanisms of action of cyclotides.

The Evolutionary Process of Acid Adaptation and Deep Transcriptional Analysis in Bacillus subtilis 168

This study is focused on investigating the variations in gene expression of Bacillus subtilis 168 following acid adaptation evolution. Through simulating prolonged cultivation in a natural acidic setting, we have observed the occurrence of adaptive evolution within the strain and conducted an extensive analysis of gene expression in the evolved strain using high-throughput transcriptomics technology. The findings from this research shed light on the fundamental molecular mechanisms employed by Bacillus subtilis to adapt to acidic environments, thereby providing crucial scientific evidence for comprehending its survival strategies and potential applications in acidic settings.

Cytochrome P450 CYP736A12 is crucial for Trichoderma asperellum-induced alleviation of phoxim phytotoxicity and reduction of pesticide residue in tomato roots

Trichoderma can enhance the metabolism of organophosphate pesticides in plants, but the mechanism is unclear. Here, we performed high-throughput transcriptome sequencing of roots upon Trichoderma asperellum (TM) inoculation and phoxim (P) application in tomato (Solanum lycopersicum L.). A total of 4059 differentially expressed genes (DEGs) were obtained, including 2110 up-regulated and 1949 down-regulated DEGs in P vs TM+P. COG and KOG analysis indicated that DEGs were mainly enriched in signal transduction mechanisms. We then focused on the pesticide detoxification pathway and screened out cytochrome P450 CYP736A12 as a putative gene for functional analysis. We suppressed the expression of CYP736A12 in tomato plants by virus-induced gene silencing and analyzed tissue-specific phoxim residues, oxidative stress markers, glutathione pool, GST activity and related gene expression. Silencing CYP736A12 significantly increased phoxim residue and induced oxidative stress in tomato plants, by attenuating the TM-induced increased activity of antioxidant and detoxification enzymes, redox homeostasis and transcripts of detoxification genes including CYP724B2, GSH1, GSH2, GR, GPX, GST1, GST2, GST3, and ABC. The study revealed a critical mechanism by which TM promotes the metabolism of phoxim in tomato roots, which can be useful for further understanding the Trichoderma-induced xenobiotic detoxification and improving food safety.

Transcriptomic analysis of the response of Avena sativa to Bacillus amyloliquefaciens DGL1.

Bacillus amyloliquefaciens DGL1, isolated from the arid sandy areas in Dagler, Qinghai Province, China, promotes the growth of Avena sativa variety "Qing Yan 1". To elucidate the transcriptomic changes in the oat root system following interaction with DGL1 and to reveal the molecular mechanism by which DGL1 promotes oat growth, treatment and control groups of oat roots at 2, 4, 8, and 12 h after inoculation with a suspension of strain DGL1 were analyzed using Illumina high-throughput transcriptome sequencing technology. The differentially expressed genes were determined through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, and the metabolic pathways and key genes were analyzed. The results showed that 7874, 13,392, 13,169, and 19,026 differentially expressed genes were significantly enriched in the glycolysis/gluconeogenesis pathway, amino acid metabolism, nitrogen metabolism, plant hormone signal transduction, and other related metabolic pathways in the oat roots at 2, 4, 8, and 12 h after inoculation with a DGL1 suspension. The GO and KEGG enrichment analyses revealed that the genes encoding plasma membrane ATPase, phosphoglycerate kinase gene PGK, ammonium transporter protein gene AMT, cellulose synthase gene CSLF6, and growth hormone response family gene IAA18 were significantly upregulated. It is hypothesized that the pro-growth mechanism of strain DGL1 in oats is the result of the coordination of multiple pathways through the promotion of oat energy metabolism, phytohormone signaling, secondary metabolite synthesis, and amino acid metabolism.

A 50-gene biomarker identifies estrogen receptor-modulating chemicals in a microarray compendium

High throughput transcriptomics (HTTr) profiling has the potential to rapidly and comprehensively identify molecular targets of environmental chemicals that can be linked to adverse outcomes. We describe here the construction and characterization of a 50-gene expression biomarker designed to identify estrogen receptor (ER) active chemicals in HTTr datasets. Using microarray comparisons, the genes in the biomarker were identified as those that exhibited consistent directional changes when ER was activated (4 ER agonists; 4 ESR1 gene constitutively active mutants) and opposite directional changes when ER was suppressed (4 antagonist treatments; 4 ESR1 knockdown experiments). The biomarker was evaluated as a predictive tool using the Running Fisher algorithm by comparison to annotated gene expression microarray datasets including those evaluating the transcriptional effects of hormones and chemicals in MCF-7 cells. Depending on the reference dataset used, the biomarker had a predictive accuracy for activation of up to 96%. To demonstrate applicability for HTTr data analysis, the biomarker was used to identify ER activators in a set of 15 chemicals that are considered potential bisphenol A (BPA) alternatives examined at up to 10 concentrations in MCF-7 cells and analyzed by full-genome TempO-Seq. Using benchmark dose (BMD) modeling, the biomarker genes stratified the ER potency of BPA alternatives consistent with previous studies. These results demonstrate that the ER biomarker can be used to accurately identify ER activators in transcript profile data derived from MCF-7 cells.

Prognostic Value of IGF2BP3 Gene Expression Levels in Patients with Acute Myeloid Leukemia

To investigate the relationship between IGF2BP3 gene expression and prognosis in patients with acute myeloid leukemia (AML). High throughput transcriptome sequencing was performed on bone marrow primary leukemia cells from 27 patients with AML in our center, the relationship between IGF2BP3 expression levels and clinical characteristics were analyzed and verify the samples from patients with newly treated AML and refractory AML. The expression level of IGF2BP3 gene were analyzed in 20 healthy subjects and 26 patients with AML. The expression of IGF2BP3 in two anthracycline-resistant cell lines (HL60/ADR, K562/ADR) was detected by RT-qPCR and Western blot, and the expression difference of IGF2BP3 was compared with that in sensitive cells (HL60, K562). The relationship between the expression level of IGF2BP3 in patients with AML and prognostic were analyzed through data analysis of 746 patients with AML, and the prognostic value of IGF2BP3 in AML was analyzed by multivariate Cox regression analysis. In the bone marrow primary leukemia cells of 27 AML patients in our center, the expression level of IGF2BP3 in patients with refractory AML was significantly higher than that in chemotherapy sensitive patients (P =0.0343). The expression of IGF2BP3 in leukemia patients with extramedullary infiltration (EMI) was significantly higher than that in AML patients without extramedullary infiltration (P =0.0049). Compared with healthy subjects (n=20), IGF2BP3 expression in AML patients (n=26) was higher (P =0.0009). The expression of IGF2BP3 mRNA in the anthracycline resistant cell lines (HL60/ADR, K562/ADR) was significantly higher than that in the sensitive cell lines (K562/ADR vs K562,P =0.0430; HL60/ADR vs HL60, P =0.7369). Western blot results showed that the expression of IGF2BP3 protein in mycin resistant cells was significantly higher than that in sensitive cells (P < 0.001). qPCR results showed that the expression level of IGF2BP3 mRNA in refractory AML patients was significantly higher than that in patients with chemotherapy sensitive (P =0.002). High expression of IGF2BP3 was associated with poor prognosis in AML (P < 0.05) in 3 large sample cohorts of AML patients. Univariate and multivariate prognostic analyses demonstrated that high expression of IGF2BP3 was significantly associated with shorter event-free survival (EFS, HR=1.887, P =0.024) and overall survival (OS, HR=1.619, P =0.016). The high expression of IGF2BP3 gene may be an important factor in the poor prognosis of AML, suggesting that IGF2BP3 gene may be a new molecular marker for the clinical prognosis evaluation and treatment strategy of AML.

Multifaced roles of the long non-coding RNA DRAIC in cancer progression.

Long non-coding RNAs (lncRNA) are functional RNAs, with over 200 nucleotides in length and lacking protein-coding potential. Studies have indicated that lncRNAs are important gene regulators under physiological conditions. Aberrant lncRNA expression is associated with the initiation and progression of various diseases, including cancers. High-throughput transcriptome analyses have revealed thousands of lncRNAs as putative tumor suppressors or promoters in various cancers, but the detailed molecular mechanisms of each lncRNA remain unclear. Downregulated RNA In Cancer, inhibitor of cell invasion and migration (DRAIC) (also known as LOC145837 and RP11-279F6.1) is a lncRNA that inhibits or promotes cancer progression with several modes of action. DRAIC was originally identified as a tumor-suppressive lncRNA in prostate adenocarcinoma. Subsequent studies also revealed that it has an anti-tumor role in glioblastoma, triple-negative breast cancer, and stomach adenocarcinoma. However, DRAIC exhibits oncogenic functions in other malignancies, such as lung adenocarcinoma and esophageal carcinoma, indicating its highly context-dependent effects on cancer progression and clinical outcomes. DRAIC and its associated pathways regulate various biological processes, including proliferation, invasion, metastasis, autophagy, and neuroendocrine function. This review introduces the multifaceted roles of DRAIC, particularly in cancer progression, and discusses its biological significance and clinical implications.

High-Throughput Transcriptomics of Nontumorigenic Breast Cells Exposed to Environmentally Relevant Chemicals.

There is a suite of chemicals, including metals, pesticides, and personal care product compounds, which are commonly detected at high levels in US Center for Disease Control's National Health and Nutrition Examination Survey (NHANES) chemical biomarker screens. Whether these chemicals influence development of breast cancer is not well understood. The objectives were to perform an unbiased concentration-dependent assessment of these chemicals, to quantify differences in cancer-specific genes and pathways, to describe if these differences occur at human population-relevant concentrations, and to specifically test for differences in markers of stemness and cellular plasticity. We treated nontumorigenic mammary epithelial cells, MCF10A, with 21 chemicals at four concentrations (, , , and ) for 48 h. We conducted RNA-sequencing for these 408 samples, adapting the plexWell plate-based RNA-sequencing method to analyze differences in gene expression. We calculated gene and biological pathway-specific benchmark concentrations (BMCs) using BMDExpress3, identifying differentially expressed genes and generating the best fit benchmark concentration models for each chemical across all genes. We identified enriched biological processes and pathways for each chemical and tested whether chemical exposures change predicted cell type distributions. We contextualized benchmark concentrations relative to human population biomarker concentrations in NHANES. We detected chemical concentration-dependent differences in gene expression for thousands of genes. Enrichment and cell type distribution analyses showed benchmark concentration responses correlated with differences in breast cancer-related pathways, including induction of basal-like characteristics for some chemicals, including arsenic, lead, copper, and methyl paraben. Comparison of benchmark data to NHANES chemical biomarker (urine or blood) concentrations indicated an overlap between exposure levels and levels sufficient to cause a gene expression response. These analyses revealed that many of these 21 chemicals resulted in differences in genes and pathways involved in breast cancer in vitro at human exposure-relevant concentrations. https://doi.org/10.1289/EHP12886.

Curare and GenExVis: a versatile toolkit for analyzing and visualizing RNA-Seq data

Even though high-throughput transcriptome sequencing is routinely performed in many laboratories, computational analysis of such data remains a cumbersome process often executed manually, hence error-prone and lacking reproducibility. For corresponding data processing, we introduce Curare, an easy-to-use yet versatile workflow builder for analyzing high-throughput RNA-Seq data focusing on differential gene expression experiments. Data analysis with Curare is customizable and subdivided into preprocessing, quality control, mapping, and downstream analysis stages, providing multiple options for each step while ensuring the reproducibility of the workflow. For a fast and straightforward exploration and visualization of differential gene expression results, we provide the gene expression visualizer software GenExVis. GenExVis can create various charts and tables from simple gene expression tables and DESeq2 results without the requirement to upload data or install software packages. In combination, Curare and GenExVis provide a comprehensive software environment that supports the entire data analysis process, from the initial handling of raw RNA-Seq data to the final DGE analyses and result visualizations, thereby significantly easing data processing and subsequent interpretation.

Empirical Characterization of False Discovery Rates of Differentially Expressed Genes and Transcriptomic Benchmark Concentrations in Zebrafish Embryos.

High-throughput transcriptomics (HTTr) is increasingly applied to zebrafish embryos to survey the toxicological effects of environmental chemicals. Before the adoption of this approach in regulatory testing, it is essential to characterize background noise in order to guide experimental designs. We thus empirically quantified the HTTr false discovery rate (FDR) across different embryo pool sizes, sample sizes, and concentration groups for toxicology studies. We exposed zebrafish embryos to 0.1% dimethyl sulfoxide (DMSO) for 5 days. Pools of 1, 5, 10, and 20 embryos were created (n = 24 samples for each pool size). Samples were sequenced on the TempO-Seq platform and then randomly assigned to mock treatment groups before differentially expressed gene (DEG), pathway, and benchmark concentration (BMC) analyses. Given that all samples were treated with DMSO, any significant DEGs, pathways, or BMCs are false positives. As expected, we found decreasing FDRs for DEG and pathway analyses with increasing pool and sample sizes. Similarly, FDRs for BMC analyses decreased with increasing pool size and concentration groups, with more stringent BMC premodel filtering reducing BMC FDRs. Our study provides foundational data for determining appropriate experiment designs for regulatory toxicity testing with HTTr in zebrafish embryos.

Alterations in HCN1 expression and distribution during epileptogenesis in rats

BackgroundThe hyperpolarization-activated cyclic nucleotide-gated cation channel (HCN1) is predominantly located in key regions associated with epilepsy, such as the neocortex and hippocampus. Under normal physiological conditions, HCN1 plays a crucial role in the excitatory and inhibitory regulation of neuronal networks. In temporal lobe epilepsy, the expression of HCN1 is decreased in the hippocampi of both animal models and patients. However, whether HCN1 expression changes during epileptogenesis preceding spontaneous seizures remains unclear. ObjectiveThe aim of this study was to determine whether the expression of HCN1 is altered during the epileptic prodromal phase, thereby providing evidence for its role in epileptogenesis. MethodsWe utilized a cobalt wire-induced rat epilepsy model to observe changes in HCN1 during epileptogenesis and epilepsy. Additionally, we also compared HCN1 alterations in epileptogenic tissues between cobalt wire- and pilocarpine-induced epilepsy rat models. Long-term video EEG recordings were used to confirm seizures development. Transcriptional changes, translation, and distribution of HCN1 were assessed using high-throughput transcriptome sequencing, total protein extraction, membrane and cytoplasmic protein fractionation, western blotting, immunohistochemistry, and immunofluorescence techniques. ResultsIn the cobalt wire-induced rat epilepsy model during the epileptogenesis phase, total HCN1 mRNA and protein levels were downregulated. Specifically, the membrane expression of HCN1 was decreased, whereas cytoplasmic HCN1 expression showed no significant change. The distribution of HCN1 in the distal dendrites of neurons decreased. During the epilepsy period, similar HCN1 alterations were observed in the neocortex of rats with cobalt wire-induced epilepsy and hippocampus of rats with lithium pilocarpine-induced epilepsy, including downregulation of mRNA levels, decreased total protein expression, decreased membrane expression, and decreased distal dendrite expression. ConclusionsAlterations in HCN1 expression and distribution are involved in epileptogenesis beyond their association with seizure occurrence. Similarities in HCN1 alterations observed in epileptogenesis-related tissues from different models suggest a shared pathophysiological pathway in epileptogenesis involving HCN1 dysregulation. Therefore, the upregulation of HCN1 expression in neurons, maintenance of the HCN1 membrane, and distal dendrite distribution in neurons may represent promising disease-modifying strategies in epilepsy.

Transcriptome profiling and analysis of patients with esophageal squamous cell carcinoma from Kazakhstan.

Esophageal squamous cell carcinoma (ESCC) is the predominant subtype of esophageal cancer in Central Asia, often diagnosed at advanced stages. Understanding population-specific patterns of ESCC is crucial for tailored treatments. This study aimed to unravel ESCC's genetic basis in Kazakhstani patients and identify potential biomarkers for early diagnosis and targeted therapies. ESCC patients from Kazakhstan were studied. We analyzed histological subtypes and conducted in-depth transcriptome sequencing. Differential gene expression analysis was performed, and significantly dysregulated pathways were identified using KEGG pathway analysis (p-value < 0.05). Protein-protein interaction networks were constructed to elucidate key modules and their functions. Among Kazakhstani patients, ESCC with moderate dysplasia was the most prevalent subtype. We identified 42 significantly upregulated and two significantly downregulated KEGG pathways, highlighting molecular mechanisms driving ESCC pathogenesis. Immune-related pathways, such as viral protein interaction with cytokines, rheumatoid arthritis, and oxidative phosphorylation, were elevated, suggesting immune system involvement. Conversely, downregulated pathways were associated with extracellular matrix degradation, crucial in cancer invasion and metastasis. Protein-protein interaction network analysis revealed four distinct modules with specific functions, implicating pathways in esophageal cancer development. High-throughput transcriptome sequencing elucidated critical molecular pathways underlying esophageal carcinogenesis in Kazakhstani patients. Insights into dysregulated pathways offer potential for early diagnosis and precision treatment strategies for ESCC. Understanding population-specific patterns is essential for personalized approaches to ESCC management.

Exploring the allelopathic autotoxicity mechanism of ginsenosides accumulation under ginseng decomposition based on integrated analysis of transcriptomics and metabolomics.

Continuous cropping obstacles seriously constrained the sustainable development of the ginseng industry. The allelopathic autotoxicity of ginsenosides is the key "trigger" of continuous cropping obstacles in ginseng. During harvest, the ginseng plants could be broken and remain in the soil. The decomposition of ginseng residue in soil is one of the important release ways of ginsenosides. Therefore, the allelopathic mechanism of ginsenosides through the decomposed release pathway needs an in-depth study. To investigate this allelopathic regulation mechanism, the integrated analysis of transcriptomics and metabolomics was applied. The prototype ginsenosides in ginseng were detected converse to rare ginsenosides during decomposition. The rare ginsenosides caused more serious damage to ginseng hairy root cells and inhibited the growth of ginseng hairy roots more significantly. By high-throughput RNA sequencing gene transcriptomics study, the significantly differential expressed genes (DEGs) were obtained under prototype and rare ginsenoside interventions. These DEGs were mainly enriched in the biosynthesis of secondary metabolites and metabolic pathways, phytohormone signal transduction, and protein processing in endoplasmic reticulum pathways. Based on the functional enrichment of DEGs, the targeted metabolomics analysis based on UPLC-MS/MS determination was applied to screen endogenous differential metabolized phytohormones (DMPs). The influence of prototype and rare ginsenosides on the accumulation of endogenous phytohormones was studied. These were mainly involved in the biosynthesis of diterpenoid, zeatin, and secondary metabolites, phytohormone signal transduction, and metabolic pathways. After integrating the transcriptomics and metabolomics analysis, ginsenosides could regulate the genes in phytohormone signaling pathways to influence the accumulation of JA, ABA, and SA. The conclusion was that the prototype ginsenosides were converted into rare ginsenosides by ginseng decomposition and released into the soil, which aggravated its allelopathic autotoxicity. The allelopathic mechanism was to intervene in the response regulation of genes related to the metabolic accumulation of endogenous phytohormones in ginseng. This result provides a reference for the in-depth study of continuous cropping obstacles of ginseng.

Transcriptomics-Based Points of Departure for Daphnia magna Exposed to 18 Per- and Polyfluoroalkyl Substances.

Per- and polyfluoroalkyl substances (PFAS) represent a large group of contaminants of concern based on their widespread use, environmental persistence, and potential toxicity. Many traditional models for estimating toxicity, bioaccumulation, and other toxicological properties are not well suited for PFAS. Consequently, there is a need to generate hazard information for PFAS in an efficient and cost-effective manner. In the present study, Daphnia magna were exposed to multiple concentrations of 22 different PFAS for 24 h in a 96-well plate format. Following exposure, whole-body RNA was extracted and extracts, each representing five exposed individuals, were subjected to RNA sequencing. Following analytical measurements to verify PFAS exposure concentrations and quality control on processed cDNA libraries for sequencing, concentration-response modeling was applied to the data sets for 18 of the tested compounds, and the concentration at which a concerted molecular response occurred (transcriptomic point of departure; tPOD) was calculated. The tPODs, based on measured concentrations of PFAS, generally ranged from 0.03 to 0.58 µM (9.9-350 µg/L; interquartile range). In most cases, these concentrations were two orders of magnitude lower than similarly calculated tPODs for human cell lines exposed to PFAS. They were also lower than apical effect concentrations reported for seven PFAS for which some crustacean or invertebrate toxicity data were available, although there were a few exceptions. Despite being lower than most other available hazard benchmarks, D. magna tPODs were, on average, four orders of magnitude greater than the maximum aqueous concentrations of PFAS measured in Great Lakes tributaries. Overall, this high-throughput transcriptomics assay with D. magna holds promise as a component of a tiered hazard evaluation strategy employing new approach methodologies. Environ Toxicol Chem 2024;00:1-16. © 2024 SETAC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Population-level comparisons of gene regulatory networks modeled on high-throughput single-cell transcriptomics data.

Single-cell technologies enable high-resolution studies of phenotype-defining molecular mechanisms. However, data sparsity and cellular heterogeneity make modeling biological variability across single-cell samples difficult. Here we present SCORPION, a tool that uses a message-passing algorithm to reconstruct comparable gene regulatory networks from single-cell/nuclei RNA-sequencing data that are suitable for population-level comparisons by leveraging the same baseline priors. Using synthetic data, we found that SCORPION outperformed 12 existing gene regulatory network reconstruction techniques. Using supervised experiments, we show that SCORPION can accurately identify differences in regulatory networks between wild-type and transcription factor-perturbed cells. We demonstrate SCORPION's scalability to population-level analyses using a single-cell RNA-sequencing atlas containing 200,436 cells from colorectal cancer and adjacent healthy tissues. The differences between tumor regions detected by SCORPION are consistent across multiple cohorts as well as with our understanding of disease progression, and elucidate phenotypic regulators that may impact patient survival.

Pan-histone deacetylase inhibitor vorinostat suppresses osteoclastic bone resorption through modulation of RANKL-evoked signaling and ameliorates ovariectomy-induced bone loss

BackgroundEstrogen deficiency-mediated hyperactive osteoclast represents the leading role during the onset of postmenopausal osteoporosis. The activation of a series of signaling cascades triggered by RANKL-RANK interaction is crucial mechanism underlying osteoclastogenesis. Vorinostat (SAHA) is a broad-spectrum pan-histone deacetylase inhibitor (HDACi) and its effect on osteoporosis remains elusive.MethodsThe effects of SAHA on osteoclast maturation and bone resorptive activity were evaluated using in vitro osteoclastogenesis assay. To investigate the effect of SAHA on the osteoclast gene networks during osteoclast differentiation, we performed high-throughput transcriptome sequencing. Molecular docking and the assessment of RANKL-induced signaling cascades were conducted to confirm the underlying regulatory mechanism of SAHA on the action of RANKL-activated osteoclasts. Finally, we took advantage of a mouse model of estrogen-deficient osteoporosis to explore the clinical potential of SAHA.ResultsWe showed here that SAHA suppressed RANKL-induced osteoclast differentiation concentration-dependently and disrupted osteoclastic bone resorption in vitro. Mechanistically, SAHA specifically bound to the predicted binding site of RANKL and blunt the interaction between RANKL and RANK. Then, by interfering with downstream NF-κB and MAPK signaling pathway activation, SAHA negatively regulated the activity of NFATc1, thus resulting in a significant reduction of osteoclast-specific gene transcripts and functional osteoclast-related protein expression. Moreover, we found a significant anti-osteoporotic role of SAHA in ovariectomized mice, which was probably realized through the inhibition of osteoclast formation and hyperactivation.ConclusionThese data reveal a high affinity between SAHA and RANKL, which results in blockade of RANKL-RANK interaction and thereby interferes with RANKL-induced signaling cascades and osteoclastic bone resorption, supporting a novel strategy for SAHA application as a promising therapeutic agent for osteoporosis.

ScRNA-seq Reveals Novel Genetic Pathways and Sex Chromosome Regulation in Tribolium Spermatogenesis.

Spermatogenesis is critical to sexual reproduction yet evolves rapidly in many organisms. High-throughput single-cell transcriptomics promises unparalleled insight into this important process but understanding can be impeded in nonmodel systems by a lack of known genes that can reliably demarcate biologically meaningful cell populations. Tribolium castaneum, the red flour beetle, lacks known markers for spermatogenesis found in insect species like Drosophila melanogaster. Using single-cell sequencing data collected from adult beetle testes, we implement a strategy for elucidating biologically meaningful cell populations by using transient expression stage identification markers, weighted principal component clustering, and SNP-based haploid/diploid phasing. We identify populations that correspond to observable points in sperm differentiation and find species specific markers for each stage. Our results indicate that molecular pathways underlying spermatogenesis in Coleoptera are substantially diverged from those in Diptera. We also show that most genes on the X chromosome experience meiotic sex chromosome inactivation. Temporal expression of Drosophila MSL complex homologs coupled with spatial analysis of potential chromatin entry sites further suggests that the dosage compensation machinery may mediate escape from meiotic sex chromosome inactivation and postmeiotic reactivation of the X chromosome.