Prediction of cell-cell communication patterns of dorsal root ganglion cells: single-cell RNA sequencing data analysis

JOURNAL/nrgr/04.03/01300535-202406000-00042/inline-graphic1/v/2023-10-30T152229Z/r/image-tiff

Dorsal root ganglion neurons transmit peripheral somatic information to the central nervous system, and dorsal root ganglion neuron excitability affects pain perception. Dorsal root ganglion stimulation is a new approach for managing pain sensation. Knowledge of the cell-cell communication among dorsal root ganglion cells may help in the development of new pain and itch management strategies. Here, we used the single-cell RNA-sequencing (scRNA-seq) database to investigate intercellular communication networks among dorsal root ganglion cells. We collected scRNA-seq data from six samples from three studies, yielding data on a total of 17,766 cells. Based on genetic profiles, we identified satellite glial cells, Schwann cells, neurons, vascular endothelial cells, immune cells, fibroblasts, and vascular smooth muscle cells. Further analysis revealed that eight types of dorsal root ganglion neurons mediated proprioceptive, itch, touch, mechanical, heat, and cold sensations. Moreover, we predicted several distinct forms of intercellular communication among dorsal root ganglion cells, including cell-cell contact, secreted signals, extracellular matrix, and neurotransmitter-mediated signals. The data mining predicted that Mrgpra3-positive neurons robustly express the genes encoding the adenosine Adora2b (A2B) receptor and glial cell line-derived neurotrophic factor family receptor alpha 1 (GFRα-1). Our immunohistochemistry results confirmed the coexpression of the A2B receptor and GFRα-1. Intrathecal injection of the A2B receptor antagonist PSB-603 effectively prevented histamine-induced scratching behaviour in a dose-dependent manner. Our results demonstrate the involvement of the A2B receptor in the modulation of itch sensation. Furthermore, our findings provide insight into dorsal root ganglion cell-cell communication patterns and mechanisms. Our results should contribute to the development of new strategies for the regulation of dorsal root ganglion excitability.

Correlation between the gut microbiome and neurodegenerative diseases: a review of metagenomics evidence

A growing body of evidence suggests that the gut microbiota contributes to the development of neurodegenerative diseases via the microbiota-gut-brain axis. As a contributing factor, microbiota dysbiosis always occurs in pathological changes of neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis. High-throughput sequencing technology has helped to reveal that the bidirectional communication between the central nervous system and the enteric nervous system is facilitated by the microbiota's diverse microorganisms, and for both neuroimmune and neuroendocrine systems. Here, we summarize the bioinformatics analysis and wet-biology validation for the gut metagenomics in neurodegenerative diseases, with an emphasis on multi-omics studies and the gut virome. The pathogen-associated signaling biomarkers for identifying brain disorders and potential therapeutic targets are also elucidated. Finally, we discuss the role of diet, prebiotics, probiotics, postbiotics and exercise interventions in remodeling the microbiome and reducing the symptoms of neurodegenerative diseases.

Identification and comparison of milk fat globule membrane and whey proteins from Selle Français, Welsh pony, and Tieling Draft horse mare's milk

Horse's milk, with a high nutritional value and few allergenic proteins, could substitute cow's milk for infant consumption. Herein, a label-free, proteomic method was used to identify and compare milk fat globule membrane (MFGM) and whey proteins from three different horse breeds: Selle Français (SF), Welsh pony (WP), and Tieling Draft Horse (TDH). In MFGMs, 16 (SF), 66 (WP), and 45 (TDH) unique proteins were identified, which are involved in the endocytosis, ribosome, and staphylococcus aureus infection pathways, respectively. In whey, 31 (SF), 75 (WP), and 23 (TDH) unique proteins were identified, which are involved in the autophagy-animal, phenylalanine metabolism, and Vasopressin-regulated water reabsorption pathways, respectively. SF contained the lowest concentration of β-lactoglobulin, which can cause allergic reactions in humans. Our findings describe the nutritional differences and functional diversities of MFGM and whey proteins in different horse breeds, which could support developing formula more suitable for human infants.

The mechanisms and roles of mitochondrial dynamics in C. elegans

If mitochondria are the powerhouses of the cell, then mitochondrial dynamics are the power grid that regulates how that energy output is directed and maintained in response to unique physiological demands. Fission and fusion dynamics are highly regulated processes that fine-tune the mitochondrial networks of cells to enable appropriate responses to intrinsic and extrinsic stimuli, thereby maintaining cellular and organismal homeostasis. These dynamics shape many aspects of an organism's healthspan including development, longevity, stress resistance, immunity, and response to disease. In this review, we discuss the latest findings regarding the mechanisms and roles of mitochondrial dynamics by focussing on the nematode Caenorhabditis elegans. Whole live-animal studies in C. elegans have enabled a true organismal-level understanding of the impact that mitochondrial dynamics play in homeostasis over a lifetime.

Quantitative analysis of key components in Qingke beer brewing process by multispectral analysis combined with chemometrics

In order to monitor the Qingke beer brewing process in real time, this paper presents an analytical method for predicting the content of key components in the wort during the mashing and boiling stages using multi-spectroscopy combined with chemometrics. The results showed that the Neural Networks (NN) model based on Raman spectroscopy (RPD = 3.9727) and the NN model based on NIR spectroscopy (RPD = 5.1952) had the best prediction performance for the reducing sugar content in the mashing and boiling stages; The partial least Squares (PLS) model based on Raman spectroscopy (RPD = 2.7301) and the NN model based on Raman spectroscopy (RPD = 4.3892) predicted the content of free amino nitrogen best; The PLS model based on UV-Vis spectroscopy (RPD = 4.0412) and the NN model based on Raman spectroscopy (RPD = 4.0540) are most suitable for the quantitative analysis of total phenols. The results can be used as a guide for real-time control of wort quality in industrial production.

Toward a predictive understanding of epithelial cell death

Epithelial cell death is highly prevalent during development and tissue homeostasis. While we have a rather good understanding of the molecular regulators of programmed cell death, especially for apoptosis, we still fail to predict when, where, how many and which specific cells will die in a tissue. This likely relies on the much more complex picture of apoptosis regulation in a tissular and epithelial context, which entails cell autonomous but also non-cell autonomous factors, diverse feedback and multiple layers of regulation of the commitment to apoptosis. In this review, we illustrate this complexity of epithelial apoptosis regulation by describing these different layers of control, all demonstrating that local cell death probability is a complex emerging feature. We first focus on non-cell autonomous factors that can locally modulate the rate of cell death, including cell competition, mechanical input and geometry as well as systemic effects. We then describe the multiple feedback mechanisms generated by cell death itself. We also outline the multiple layers of regulation of epithelial cell death, including the coordination of extrusion and regulation occurring downstream of effector caspases. Eventually, we propose a roadmap to reach a more predictive understanding of cell death regulation in an epithelial context.

Proteotoxic stress and the ubiquitin proteasome system

The ubiquitin proteasome system maintains protein homeostasis by regulating the breakdown of misfolded proteins, thereby preventing misfolded protein aggregates. The efficient elimination is vital for preventing damage to the cell by misfolded proteins, known as proteotoxic stress. Proteotoxic stress can lead to the collapse of protein homeostasis and can alter the function of the ubiquitin proteasome system. Conversely, impairment of the ubiquitin proteasome system can also cause proteotoxic stress and disrupt protein homeostasis. This review examines two impacts of proteotoxic stress, 1) disruptions to ubiquitin homeostasis (ubiquitin stress) and 2) disruptions to proteasome homeostasis (proteasome stress). Here, we provide a mechanistic description of the relationship between proteotoxic stress and the ubiquitin proteasome system. This relationship is illustrated by findings from several protein misfolding diseases, mainly neurodegenerative diseases, as well as from basic biology discoveries from yeast to mammals. In addition, we explore the importance of the ubiquitin proteasome system in endoplasmic reticulum quality control, and how proteotoxic stress at this organelle is alleviated. Finally, we highlight how cells utilize the ubiquitin proteasome system to adapt to proteotoxic stress and how the ubiquitin proteasome system can be genetically and pharmacologically manipulated to maintain protein homeostasis.

Identification of Potential Genomic Alterations Using Pan-Cancer Cell-Free DNA Next-Generation Sequencing in Patients With Gastric Cancer

Background

Molecular cancer profiling may lead to appropriate trials for molecularly targeted therapies. Cell-free DNA (cfDNA) is a promising diagnostic and/or prognostic biomarker in gastric cancer (GC). We characterized somatic genomic alterations in cfDNA of patients with GC.

Methods

Medical records and cfDNA data of 81 patients diagnosed as having GC were reviewed. Forty-nine and 32 patients were tested using the Oncomine Pan-Cancer Cell-Free Assay on the Ion Torrent platform and AlphaLiquid 100 kit on the Illumina platform, respectively.

Results

Tier I or II alterations were detected in 64.2% (52/81) of patients. Biomarkers for potential targeted therapy were detected in 55.6% of patients (45/81), and clinical trials are underway. ERBB2 amplification is actionable and was detected in 4.9% of patients (4/81). Among biomarkers showing potential for possible targeted therapy, TP53 mutation (38.3%, 35 variants in 31 patients, 31/81) and FGFR2 amplification (6.2%, 5/81) were detected the most.

Conclusions

Next-generation sequencing of cfDNA is a promising technique for the molecular profiling of GC. Evidence suggests that cfDNA analysis can provide accurate and reliable information on somatic genomic alterations in patients with GC, potentially replacing tissue biopsy as a diagnostic and prognostic tool. Through cfDNA analysis for molecular profiling, it may be possible to translate the molecular classification into therapeutic targets and predictive biomarkers, leading to personalized treatment options for patients with GC in the future.

Effect of tillage state of paddy soils with heavy metal pollution on the nosZ gene of N2O reductase

Paddy soils are an important source of atmospheric nitrous oxide (N2O). However, numerous studies have focused on N2O production during the soil tillage period, neglecting the N2O production during the dry fallow period. In this study, we conducted an incubation experiment using the acetylene inhibition technique to investigate N2O emission and reduction rates of paddy soil profiles (0-1 m) from Guangdong Province and Jinlin Province in China, with different heavy-metal pollution levels. The abundance and community structures of denitrifying bacteria were determined via quantitative-PCR and Illumina MiSeq sequencing of nosZ, nirK, and nirS genes. Our results showed that the potential N2O emission rate, N2O production rate, and denitrification rate have decreased with increasing soil vertical depth and heavy-metal pollution. More importantly, we found that the functional gene type of N2O reductase switched with the tillage state of paddy soils, which clade Ⅱ nosZ genes were the dominant gene during the tillage period, while clade Ⅰ nosZ genes were the dominant gene during the dry fallow period. The heavy-metal pollution has less effect on the niche differentiation of the nosZ gene. The N2O emission rate was significantly regulated by the genus Bradyhizobium, which contains both N2O reductase and nitrite reductase genes. Our findings suggests that the nosZ gene of N2O reductase can significantly impact the N2O emission from paddy soils.

ExplORRNet: An interactive web tool to explore stage-wise miRNA expression profiles and their interactions with mRNA and lncRNA in human breast and gynecological cancers

Background

MicroRNAs (miRNAs) are key regulators of gene expression that have been implicated in gynecological and breast cancers. Understanding the cancer stage-wise expression patterns of miRNAs and their interactions with other RNA molecules in cancer is crucial to improve cancer diagnosis and treatment planning. Comprehensive web tools that integrate data on the transcriptome, circulating miRNAs, and their validated targets to derive beneficial conclusions in cancer research are lacking.

Methods

Using the Shiny R package, we developed a web tool called ExplORRNet that integrates transcriptomic profiles from The Cancer Genome Atlas and miRNA expression data derived from various sources, including tissues, cell lines, exosomes, serum, and plasma, available in the Gene Expression Omnibus database. Differential expression analyses between normal and tumor tissue samples as well as different stages of cancer, accompanied by gene enrichment and survival analyses, can be performed using specialized R packages. Additionally, a miRNA-messenger RNA (mRNA)-long non-coding RNA (lncRNA) networks are constructed to identify regulatory modules.

Results

Our tool identifies cancer stage-wise differentially regulated miRNAs, mRNAs, and lncRNAs in gynecological and breast cancers. Survival analysis identifies miRNAs associated with patient survival, and functional enrichment analysis provides insights into dysregulated miRNA-related biological processes and pathways. The miRNA-mRNA-lncRNA networks highlight interconnected regulatory molecular modules driving cancer progression. Case studies demonstrate the utility of the ExplORRNet for studying gynecological and breast cancers.

Conclusion

ExplORRNet is an intuitive and user-friendly web tool that provides a deeper understanding of dysregulated miRNAs and their functional implications in gynecological and breast cancers. We hope our ExplORRNet tool has potential utility among the clinical and basic researchers and will be beneficial to the entire cancer genomics community to encourage and facilitate mining the rapidly growing public databases to progress the field of precision oncology. The ExplORRNet is available at https://mirna.cs.ut.ee.

circRNAs deregulation in exosomes derived from BEAS-2B cells is associated with vascular stiffness induced by PM2.5

As an environmental pollutant, ambient fine particulate matter (PM2.5) was linked to cardiovascular diseases. The molecular mechanisms underlying PM2.5-induced extrapulmonary disease has not been elucidated clearly. In this study the ambient PM2.5 exposure mice model we established was to explore adverse effects of vessel and potential mechanisms. Long-term PM2.5 exposure caused reduced lung function and vascular stiffness in mice. And chronic PM2.5 induced migration and epithelial-mesenchymal transition (EMT) phenotype in BEAS-2B cells. After PM2.5 treatment, the circRNAs and mRNAs levels of exosomes released by BEAS-2B cells were detected by competing endogenous RNA (ceRNA) array, which contained 1664 differentially expressed circRNAs (DE-circRNAs) and 308 differentially expressed mRNAs (DE-mRNAs). By bioinformatics analysis on host genes of DE-circRNAs, vascular diseases and some pathways related to vascular diseases including focal adhesion, tight junction and adherens junction were enriched. Then, ceRNA network was constructed, and DE-mRNAs in ceRNA network were conducted functional enrichment analysis by Ingenuity Pathway Analysis, which indicated that hsa_circ_0012627, hsa_circ_0053261 and hsa_circ_0052810 were related to vascular endothelial dysfunction. Furthermore, it was verified experimentally that ExoPM2.5 could induce endothelial dysfunction by increased endothelial permeability and decreased relaxation in vitro. In present study, we investigated in-depth knowledge into the molecule events related to PM2.5 toxicity and pathogenesis of vascular diseases.

Incorporating adaptive genomic variation into predictive models for invasion risk assessment

Global climate change is expected to accelerate biological invasions, necessitating accurate risk forecasting and management strategies. However, current invasion risk assessments often overlook adaptive genomic variation, which plays a significant role in the persistence and expansion of invasive populations. Here we used Molgula manhattensis, a highly invasive ascidian, as a model to assess its invasion risks along Chinese coasts under climate change. Through population genomics analyses, we identified two genetic clusters, the north and south clusters, based on geographic distributions. To predict invasion risks, we employed the gradient forest and species distribution models to calculate genomic offset and species habitat suitability, respectively. These approaches yielded distinct predictions: the gradient forest model suggested a greater genomic offset to future climatic conditions for the north cluster (i.e., lower invasion risks), while the species distribution model indicated higher future habitat suitability for the same cluster (i.e, higher invasion risks). By integrating these models, we found that the south cluster exhibited minor genome-niche disruptions in the future, indicating higher invasion risks. Our study highlights the complementary roles of genomic offset and habitat suitability in assessing invasion risks under climate change. Moreover, incorporating adaptive genomic variation into predictive models can significantly enhance future invasion risk predictions and enable effective management strategies for biological invasions in the future.

Automated Recognition of Visual Acuity Measurements in Ophthalmology Clinical Notes Using Deep Learning

Purpose

Visual acuity (VA) is a critical component of the eye examination but is often only documented in electronic health records (EHRs) as unstructured free-text notes, making it challenging to use in research. This study aimed to improve on existing rule-based algorithms by developing and evaluating deep learning models to perform named entity recognition of different types of VA measurements and their lateralities from free-text ophthalmology notes: VA for each of the right and left eyes, with and without glasses correction, and with and without pinhole.

Design

Cross-sectional study.

Subjects

A total of 319 756 clinical notes with documented VA measurements from approximately 90 000 patients were included.

Methods

The notes were split into train, validation, and test sets. Bidirectional Encoder Representations from Transformers (BERT) models were fine-tuned to identify VA measurements from the progress notes and included BERT models pretrained on biomedical literature (BioBERT), critical care EHR notes (ClinicalBERT), both (BlueBERT), and a lighter version of BERT with 40% fewer parameters (DistilBERT). A baseline rule-based algorithm was created to recognize the same VA entities to compare against BERT models.

Main Outcome Measures

Model performance was evaluated on a held-out test set using microaveraged precision, recall, and F1 score for all entities.

Results

On the human-annotated subset, BlueBERT achieved the best microaveraged F1 score (F1 = 0.92), followed by ClinicalBERT (F1 = 0.91), DistilBERT (F1 = 0.90), BioBERT (F1 = 0.84), and the baseline model (F1 = 0.83). Common errors included labeling VA in sections outside of the examination portion of the note, difficulties labeling current VA alongside a series of past VAs, and missing nonnumeric VAs.

Conclusions

This study demonstrates that deep learning models are capable of identifying VA measurements from free-text ophthalmology notes with high precision and recall, achieving significant performance improvements over a rule-based algorithm. The ability to recognize VA from free-text notes would enable a more detailed characterization of ophthalmology patient cohorts and enhance the development of models to predict ophthalmology outcomes.

Financial Disclosures

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Sludge composition and characteristics shaped microbial community and further determined process performance: A study on full-scale thermal hydrolysis-anaerobic digestion processes

Anaerobic digestion (AD) with thermal hydrolysis (TH) pretreatment is a promising process for excess sludge treatment, while there lacks of the knowledge from full-scale process about the impact of sludge composition and characteristics on microbial community and performance. The sludge physiochemical indices, microbial community and performance data of four full-scale TH-AD plants were characterized, and their relationships was elucidated. The four plants were operated under almost similar total organic loading rate (OLR) but their methanogenesis performance differentiate into two groups, namely superior group (SupG) and the inferior group (InfG). In both groups, TH effectively solubilized particulate organic compounds, meanwhile raised the ammonia nitrogen (NH4+-N) and volatile fatty acid (VFA) concentration. Compared with the SupG, thermal hydrolyzed sludge of InfG had higher level of VFAs, NH4+-N and total chemical oxygen demand (tCOD), which showed higher inhibition effect on microbes, leading to a community with lower diversity, lower abundance of carbohydrate degrading functional guild, higher protein degrading one, and methanogens that adapted to limited substrates, and further declined the methane production rate. Thus, it was recommended that OLR alone was not sufficient for controlling the system in design and operation, the concentration of VFAs, NH4+-N and tCOD should be equally considered. Their higher concentration, together with the higher abundance of Defluviitoga and Proteiniphilum were recommended as indicators for inferior running condition. Our results proposed that microbial communities played a role of bridge between environmental factors and performance, provided implications for engineering ecology and operational regulation for full-scale sludge TH-AD process.