Injuries to the spinal cord nervous system often result in permanent loss of sensory, motor, and autonomic functions. Accurately identifying the cellular state of spinal cord nerves is extremely important and could facilitate the development of new therapeutic and rehabilitative strategies. Existing experimental techniques for identifying the development of spinal cord nerves are both labor-intensive and costly. In this study, we developed a machine learning predictor, ScnML, for predicting subpopulations of spinal cord nerve cells as well as identifying marker genes. The prediction performance of ScnML was evaluated on the training dataset with an accuracy of 94.33%. Based on XGBoost, ScnML on the test dataset achieved 94.08% 94.24%, 94.26%, and 94.24% accuracies with precision, recall, and F1-measure scores, respectively. Importantly, ScnML identified new significant genes through model interpretation and biological landscape analysis. ScnML can be a powerful tool for predicting the status of spinal cord neuronal cells, revealing potential specific biomarkers quickly and efficiently, and providing crucial insights for precision medicine and rehabilitation recovery.

A common result of infection is an abnormal immune response, which may be detrimental to the host. To control the infection, the immune system might undergo regulation, therefore producing an excess of either pro-inflammatory or anti-inflammatory pathways that can lead to widespread inflammation, tissue damage, and organ failure. A dysregulated immune response can manifest as changes in differentiated immune cell populations and concentrations of circulating biomarkers. To propose an early diagnostic system that enables differentiation and identifies the severity of immune-dysregulated syndromes, we built an artificial intelligence tool that uses input data from single-cell RNA sequencing. In our results, single-cell transcriptomics successfully distinguished between mild and severe sepsis and COVID-19 infections. Moreover, by interpreting the decision patterns of our classification system, we identified that different immune cells upregulating or downregulating the expression of the genes CD3, CD14, CD16, FOSB, S100A12, and TCRɣδ can accurately differentiate between different degrees of infection. Our research has identified genes of significance that effectively distinguish between infections, offering promising prospects as diagnostic markers and providing potential targets for therapeutic intervention.

The brain regulates multiple physiological processes in fish. Despite this, knowledge about the basic structure and function of distinct brain regions in non-model fish species remains limited due to their diversity and the scarcity of common biomarkers. In the present study, four major brain parts, the telencephalon, diencephalon, mesencephalon and rhombencephalon, were isolated in largemouth bass, Micropterus salmoides. Within these parts, nine brain regions and 74 nuclei were further identified through morphological and cytoarchitectonic analysis. Transcriptome analysis revealed a total of 7153 region-highly expressed genes and 176 region-specifically expressed genes. Genes related to growth, reproduction, emotion, learning, and memory were significantly overexpressed in the olfactory bulb and telencephalon (OBT). Feeding and stress-related genes were in the hypothalamus (Hy). Visual system-related genes were predominantly enriched in the optic tectum (OT), while vision and hearing-related genes were widely expressed in the cerebellum (Ce) region. Sensory input and motor output-related genes were in the medulla oblongata (Mo). Osmoregulation, stress response, sleep/wake cycles, and reproduction-related genes were highly expressed in the remaining brain (RB). Three candidate marker genes were further identified for each brain regions, such as neuropeptide FF (npff) for OBT, pro-melanin-concentrating hormone (pmch) for Hy, vesicular inhibitory amino acid transporter (viaat) for OT, excitatory amino acid transporter 1 (eaat1) for Ce, peripherin (prph) for Mo, and isotocin neurophysin (itnp) for RB. Additionally, the distribution of seven neurotransmitter-type neurons and five types of non-neuronal cells across different brain regions were analyzed by examining the expression of their marker genes. Notably, marker genes for glutamatergic and GABAergic neurons showed the highest expression levels across all brain regions. Similarly, the marker gene for radial astrocytes exhibited high expression compared to other markers, while those for microglia were the least expressed. Overall, our results provide a comprehensive overview of the structural and functional characteristics of distinct brain regions in the largemouth bass, which offers a valuable resource for understanding the role of central nervous system in regulating physiological processes in teleost.

Cyclic nucleotide-gated ion channels (CNGCs), as non-selective cation channels, play essential roles in plant growth and stress responses. However, they have not been identified in Qingke (Hordeum vulgare L.). Here, we performed a comprehensive genome-wide identification and function analysis of the HvCNGC gene family to determine its role in drought tolerance. Phylogenetic analysis showed that 27 HvCNGC genes were divided into four groups and unevenly located on seven chromosomes. Transcription analysis revealed that two closely related members of HvCNGC3 and HvCNGC16 were highly induced and the expression of both genes were distinctly different in two extremely drought-tolerant materials. Transient expression revealed that the HvCNGC3 and HvCNGC16 proteins both localized to the plasma membrane and karyotheca. Overexpression of HvCNGC3 and HvCNGC16 in Arabidopsis thaliana led to impaired seed germination and seedling drought tolerance, which was accompanied by higher hydrogen peroxide (H2O2), malondialdehyde (MDA), proline accumulation and increased cell damage. In addition, HvCNGC3 and HvCNGC16-overexpression lines reduced ABA sensitivity, as well as lower expression levels of some ABA biosynthesis and stress-related gene in transgenic lines. Furthermore, Yeast two hybrid (Y2H) and bimolecular fluorescence complementation (BiFC) assays revealed that HvCNGC3 and HvCNGC16 interacted with calmodulin/calmodulin-like proteins (CaM/CML), which, as calcium sensors, participate in the perception and decoding of intracellular calcium signaling. Thus, this study provides information on the CNGC gene family and provides insight into the function and potential regulatory mechanism of HvCNGC3 and HvCNGC16 in drought tolerance in Qingke.

UNASSIGNED: Spinal cord injury (SCI) is a profoundly disabling and devastating neurological condition, significantly impacting patients\' quality of life. It imposes unbearable psychological and economic pressure on both patients and their families, as well as placing a heavy burden on society.
UNASSIGNED: In this study, we integrated datasets GSE5296 and GSE47681 as training groups, analyzed gene variances between sham group and SCI group mice, and conducted Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis based on the differentially expressed genes. Subsequently, we performed Weighted Gene Correlation Network Analysis (WGCNA) and Lasso regression analyses.
UNASSIGNED: We identified four characteristic disease genes: Icam1, Ch25h, Plaur and Tm4sf1. We examined the relationship between SCI and immune cells, and validated the expression of the identified disease-related genes in SCI rats using PCR and immunohistochemistry experiments.
UNASSIGNED: In conclusion, we have identified and verified four genes related to SCI: Icam1, Ch25h, Plaur and Tm4sf1, which could offer insights for SCI treatment.

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) measurements of gene expression show great promise for studying the cellular heterogeneity of rice roots. How precisely annotating cell identity is a major unresolved problem in plant scRNA-seq analysis due to the inherent high dimensionality and sparsity.
RESULTS: To address this challenge, we present NRTPredictor, an ensemble-learning system, to predict rice root cell stage and mine biomarkers through complete model interpretability. The performance of NRTPredictor was evaluated using a test dataset, with 98.01% accuracy and 95.45% recall. With the power of interpretability provided by NRTPredictor, our model recognizes 110 marker genes partially involved in phenylpropanoid biosynthesis. Expression patterns of rice root could be mapped by the above-mentioned candidate genes, showing the superiority of NRTPredictor. Integrated analysis of scRNA and bulk RNA-seq data revealed aberrant expression of Epidermis cell subpopulations in flooding, Pi, and salt stresses.
CONCLUSIONS: Taken together, our results demonstrate that NRTPredictor is a useful tool for automated prediction of rice root cell stage and provides a valuable resource for deciphering the rice root cellular heterogeneity and the molecular mechanisms of flooding, Pi, and salt stresses. Based on the proposed model, a free webserver has been established, which is available at https://www.cgris.net/nrtp .

Diabetic kidney disease (DKD) is characterized by macrophage infiltration, which requires further investigation. This study aims to identify immune-related genes (IRGs) in macrophage and explore their potential as therapeutic targets. This study analyzed isolated glomerular cells from three diabetic mice and three control mice. A total of 59 glomeruli from normal kidney samples and 66 from DKD samples were acquired from four kidney transcriptomic profiling datasets. Bioinformatics analysis was conducted using both single-cell RNA (scRNA) and bulk RNA sequencing data to investigate inflammatory responses in DKD. Additionally, the \"AUCell\" function was used to investigate statistically different gene sets. The significance of each interaction pair was determined by assigning a probability using \"CellChat.\" The study also analyzed the biological diagnostic importance of immune hub genes for DKD and validated the expression of these immune genes in mice models. The top 2000 highly variable genes (HVGs) were identified after data normalization. Subsequently, a total of eight clusters were identified. It is worth mentioning that macrophages showed the highest percentage increase among all cell types in the DKD group. Furthermore, the present study observed significant differences in gene sets related to inflammatory responses and complement pathways. The study also identified several receptor-ligand pairs and co-stimulatory interactions between endothelial cells and macrophages. Notably, SYK, ITGB2, FCER1G, and VAV1 were identified as immunological markers of DKD with promising predictive ability. This study identified distinct cell clusters and four marker genes. SYK, ITGB2, FCER1G, and VAV1 may be important roles. Consequently, the present study extends our understanding regarding IRGs in DKD and provides a foundation for future investigations into the underlying mechanisms.

BACKGROUND: The Pacific oyster, Crassostrea gigas, is an economically important shellfish around the world. Great efforts have been made to improve its growth rate through genetic breeding. However, the candidate marker genes, pathways, and potential lncRNAs involved in oyster growth regulation remain largely unknown. To identify genes, lncRNAs, and pathways involved in growth regulation, C. gigas spat was cultured at a low temperature (15 ℃) to yield a growth-inhibited model, which was used to conduct comparative transcriptome analysis with spat cultured at normal temperature (25 ℃).
RESULTS: In total, 8627 differentially expressed genes (DEGs) and 1072 differentially expressed lncRNAs (DELs) were identified between the normal-growth oysters (cultured at 25 ℃, hereinafter referred to as NG) and slow-growth oysters (cultured at 15 ℃, hereinafter referred to as SG). Functional enrichment analysis showed that these DEGs were mostly enriched in the AMPK signaling pathway, MAPK signaling pathway, insulin signaling pathway, autophagy, apoptosis, calcium signaling pathway, and endocytosis process. LncRNAs analysis identified 265 cis-acting pairs and 618 trans-acting pairs that might participate in oyster growth regulation. The expression levels of LNC_001270, LNC_003322, LNC_011563, LNC_006260, and LNC_012905 were inducible to the culture temperature and food abundance. These lncRNAs were located at the antisense, upstream, or downstream of the SREBP1/p62, CDC42, CaM, FAS, and PIK3CA genes, respectively. Furthermore, the expression of the trans-acting lncRNAs, including XR_9000022.2, LNC_008019, LNC_015817, LNC_000838, LNC_00839, LNC_011859, LNC_007294, LNC_006429, XR_002198885.1, and XR_902224.2 was also significantly associated with the expression of genes enriched in AMPK signaling pathway, insulin signaling pathway, autophagy, apoptosis, calcium signaling pathway, and endocytosis process.
CONCLUSIONS: In this study, we identified the critical growth-related genes and lncRNAs that could be utilized as candidate markers to illustrate the molecular mechanisms underlying the growth regulation of Pacific oysters.

It has been well documented that an infestation of the piercing-sucking herbivore, brown planthopper (BPH), Nilaparvata lugens, activates strong local defenses in rice. However, whether a BPH infestation elicits systemic responses in rice remains largely unknown. In this study, we investigated BPH-induced systemic defenses by detecting the change in expression levels of 12 JA- and/or SA-signaling-responsive marker genes in different rice tissues upon a BPH attack. We found that an infestation of gravid BPH females on rice leaf sheaths significantly increased the local transcript level of all 12 marker genes tested except OsVSP, whose expression was induced only weakly at a later stage of the BPH infestation. Moreover, an infestation of gravid BPH females also systemically up-regulated the transcription levels of three JA-signaling-responsive genes (OsJAZ8, OsJAMyb, and OsPR3), one SA-signaling-responsive gene (OsWRKY62), and two JA- and SA- signaling-responsive genes (OsPR1a and OsPR10a). Our results demonstrate that an infestation of gravid BPH females systemically activates JA- and SA-dependent defenses in rice, which may in turn influence the composition and structure of the community in the rice ecosystem.

BACKGROUND: The placenta, as a unique exchange organ between mother and fetus, is essential for successful human pregnancy and fetal health. Preeclampsia (PE) caused by placental dysfunction contributes to both maternal and infant morbidity and mortality. Accurate identification of PE patients plays a vital role in the formulation of treatment plans. However, the traditional clinical methods of PE have a high misdiagnosis rate.
RESULTS: Here, we first designed a computational biology method that used single-cell transcriptome (scRNA-seq) of healthy pregnancy (38 wk) and early-onset PE (28-32 wk) to identify pathological cell subpopulations and predict PE risk. Based on machine learning methods and feature selection techniques, we observed that the Tuning ReliefF (TURF) score hybrid with XGBoost (TURF_XGB) achieved optimal performance, with 92.61% accuracy and 92.46% recall for classifying nine cell subpopulations of healthy placentas. Biological landscapes of placenta heterogeneity could be mapped by the 110 marker genes screened by TURF_XGB, which revealed the superiority of the TURF feature mining. Moreover, we processed the PE dataset with LASSO to obtain 497 biomarkers. Integration analysis of the above two gene sets revealed that dendritic cells were closely associated with early-onset PE, and C1QB and C1QC might drive preeclampsia by mediating inflammation. In addition, an ensemble model-based risk stratification card was developed to classify preeclampsia patients, and its area under the receiver operating characteristic curve (AUC) could reach 0.99. For broader accessibility, we designed an accessible online web server ( http://bioinfor.imu.edu.cn/placenta ).
CONCLUSIONS: Single-cell transcriptome-based preeclampsia risk assessment using an ensemble machine learning framework is a valuable asset for clinical decision-making. C1QB and C1QC may be involved in the development and progression of early-onset PE by affecting the complement and coagulation cascades pathway that mediate inflammation, which has important implications for better understanding the pathogenesis of PE.