OBJECTIVE: Bariatric surgery is highly effective for the treatment of obesity in individuals without (OB1) and in those with type 2 diabetes (T2D2). However, whether bariatric surgery triggers similar or distinct molecular changes in OB and T2D remains unknown. Given that individuals with type 2 diabetes often exhibit more severe metabolic deterioration, we hypothesized that bariatric surgery induces distinct molecular adaptations in skeletal muscle, the major site of glucose uptake, of OB and T2D after surgery-induced weight loss.
METHODS: All participants (OB, n = 13; T2D, n = 13) underwent detailed anthropometry before and one year after the surgery. Skeletal muscle biopsies were isolated at both time points and subjected to transcriptome and methylome analyses using a comprehensive bioinformatic pipeline.
RESULTS: Before surgery, T2D had higher fasting glucose and insulin levels but lower whole-body insulin sensitivity, only glycemia remained higher in T2D than in OB after surgery. Surgery-mediated weight loss affected different subsets of genes with 2,013 differentially expressed in OB and 959 in T2D. In OB differentially expressed genes were involved in insulin, PPAR signaling and oxidative phosphorylation pathways, whereas ribosome and splicesome in T2D. LASSO regression analysis revealed distinct candidate genes correlated with improvement of phenotypic traits in OB and T2D. Compared to OB, DNA methylation was less affected in T2D in response to bariatric surgery. This may be due to increased global hydroxymethylation accompanied by decreased expression of one of the type 2 diabetes risk gene, TET2, encoding a demethylation enzyme in T2D.
CONCLUSIONS: OB and T2D exhibit differential skeletal muscle transcriptome responses to bariatric surgery, presumably resulting from perturbed epigenetic flexibility.

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 development of a rapid and accurate model for determining the genotoxicity and carcinogenicity of chemicals is crucial for effective cancer risk assessment. This study aims to develop a 1-day, single-dose model for identifying genotoxic hepatocarcinogens (GHCs) in rats. Microarray gene expression data from the livers of rats administered a single dose of 58 compounds, including 5 GHCs, was obtained from the Open TG-GATEs database and used for the identification of marker genes and the construction of a predictive classifier to identify GHCs in rats. We identified 10 gene markers commonly responsive to all 5 GHCs and used them to construct a support vector machine-based predictive classifier. In the silico validation using the expression data of the Open TG-GATEs database indicates that this classifier distinguishes GHCs from other compounds with high accuracy. To further assess the model\'s effectiveness and reliability, we conducted multi-institutional 1-day single oral administration studies on rats. These studies examined 64 compounds, including 23 GHCs, with gene expression data of the marker genes obtained via quantitative PCR 24 h after a single oral administration. Our results demonstrate that qPCR analysis is an effective alternative to microarray analysis. The GHC predictive model showed high accuracy and reliability, achieving a sensitivity of 91% (21/23) and a specificity of 93% (38/41) across multiple validation studies in three institutions. In conclusion, the present 1-day single oral administration model proves to be a reliable and highly sensitive tool for identifying GHCs and is anticipated to be a valuable tool in identifying and screening potential GHCs.

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.

BACKGROUND: With increasing significance of developmental programming effects associated with placental dysfunction, more investigations are devoted to improving the characterization and understanding of placental signatures in health and disease. The placenta is a transitory but dynamic organ adapting to the shifting demands of fetal development and available resources of the maternal supply throughout pregnancy. Trophoblasts (cytotrophoblasts, syncytiotrophoblasts, and extravillous trophoblasts) are placental-specific cell types responsible for the main placental exchanges and adaptations. Transcriptomic studies with single-cell resolution have led to advances in understanding the placenta\'s role in health and disease. These studies, however, often show discrepancies in characterization of the different placental cell types.
OBJECTIVE: We aim to review the knowledge regarding placental structure and function gained from the use of single-cell RNA sequencing (scRNAseq), followed by comparing cell-type-specific genes, highlighting their similarities and differences. Moreover, we intend to identify consensus marker genes for the various trophoblast cell types across studies. Finally, we will discuss the contributions and potential applications of scRNAseq in studying pregnancy-related diseases.
METHODS: We conducted a comprehensive systematic literature review to identify different cell types and their functions at the human maternal-fetal interface, focusing on all original scRNAseq studies on placentas published before March 2023 and published reviews (total of 28 studies identified) using PubMed search. Our approach involved curating cell types and subtypes that had previously been defined using scRNAseq and comparing the genes used as markers or identified as potential new markers. Next, we reanalyzed expression matrices from the six available scRNAseq raw datasets with cell annotations (four from first trimester and two at term), using Wilcoxon rank-sum tests to compare gene expression among studies and annotate trophoblast cell markers in both first trimester and term placentas. Furthermore, we integrated scRNAseq raw data available from 18 healthy first trimester and nine term placentas, and performed clustering and differential gene expression analysis. We further compared markers obtained with the analysis of annotated and raw datasets with the literature to obtain a common signature gene list for major placental cell types.
RESULTS: Variations in the sampling site, gestational age, fetal sex, and subsequent sequencing and analysis methods were observed between the studies. Although their proportions varied, the three trophoblast types were consistently identified across all scRNAseq studies, unlike other non-trophoblast cell types. Notably, no marker genes were shared by all studies for any of the investigated cell types. Moreover, most of the newly defined markers in one study were not observed in other studies. These discrepancies were confirmed by our analysis on trophoblast cell types, where hundreds of potential marker genes were identified in each study but with little overlap across studies. From 35 461 and 23 378 cells of high quality in the first trimester and term placentas, respectively, we obtained major placental cell types, including perivascular cells that previously had not been identified in the first trimester. Importantly, our meta-analysis provides marker genes for major placental cell types based on our extensive curation.
CONCLUSIONS: This review and meta-analysis emphasizes the need for establishing a consensus for annotating placental cell types from scRNAseq data. The marker genes identified here can be deployed for defining human placental cell types, thereby facilitating and improving the reproducibility of trophoblast cell annotation.

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.

Microcystis-dominated cyanobacterial harmful algal blooms (cyanoHABs) have a global impact on freshwater environments, affecting both wildlife and human health. Microcystis diversity and function in field samples and laboratory cultures can be determined by sequencing whole genomes of cultured isolates or natural populations, but these methods remain computationally and financially expensive. Amplicon sequencing of marker genes is a lower cost and higher throughput alternative to characterize strain composition and diversity in mixed samples. However, the selection of appropriate marker gene region(s) and primers requires prior understanding of the relationship between single gene genotype, whole genome content, and phenotype. To identify phylogenetic markers of Microcystis strain diversity, we compared phylogenetic trees built from each of 2,351 individual core genes to an established phylogeny and assessed the ability of these core genes to predict whole genome content and bioactive compound genotypes. We identified single-copy core genes better able to resolve Microcystis phylogenies than previously identified marker genes. We developed primers suitable for current Illumina-based amplicon sequencing with near-complete coverage of available Microcystis genomes and demonstrate that they outperform existing options for assessing Microcystis strain composition. Results showed that genetic markers can be used to infer Microcystis gene content and phenotypes such as potential production of bioactive compounds , although marker performance varies by bioactive compound gene and sequence similarity. Finally, we demonstrate that these markers can be used to characterize the Microcystis strain composition of laboratory or field samples like those collected for surveillance and modeling of Microcystis-dominated cyanobacterial harmful algal blooms.

Single-cell transcriptomics is the current gold standard for global gene expression profiling, not only in mammals and model species, but also in non-model fish species. This is a rapidly expanding field, creating a deeper understanding of tissue heterogeneity and the distinct functions of individual cells, making it possible to explore the complexities of immunology and gene expression on a highly resolved level. In this study, we compared two single cell transcriptomic approaches to investigate cellular heterogeneity within the head kidney of healthy farmed Atlantic salmon (Salmo salar). We compared 14,149 cell transcriptomes assayed by single cell RNA-seq (scRNA-seq) with 18,067 nuclei transcriptomes captured by single nucleus RNA-Seq (snRNA-seq). Both approaches detected eight major cell populations in common: granulocytes, heamatopoietic stem cells, erythrocytes, mononuclear phagocytes, thrombocytes, B cells, NK-like cells, and T cells. Four additional cell types, endothelial, epithelial, interrenal, and mesenchymal cells, were detected in the snRNA-seq dataset, but appeared to be lost during preparation of the single cell suspension submitted for scRNA-seq library generation. We identified additional heterogeneity and subpopulations within the B cells, T cells, and endothelial cells, and revealed developmental trajectories of heamatopoietic stem cells into differentiated granulocyte and mononuclear phagocyte populations. Gene expression profiles of B cell subtypes revealed distinct IgM and IgT-skewed resting B cell lineages and provided insights into the regulation of B cell lymphopoiesis. The analysis revealed eleven T cell sub-populations, displaying a level of T cell heterogeneity in salmon head kidney comparable to that observed in mammals, including distinct subsets of cd4/cd8-negative T cells, such as tcrγ positive, progenitor-like, and cytotoxic cells. Although snRNA-seq and scRNA-seq were both useful to resolve cell type-specific expression in the Atlantic salmon head kidney, the snRNA-seq pipeline was overall more robust in identifying several cell types and subpopulations. While scRNA-seq displayed higher levels of ribosomal and mitochondrial genes, snRNA-seq captured more transcription factor genes. However, only scRNA-seq-generated data was useful for cell trajectory inference within the myeloid lineage. In conclusion, this study systematically outlines the relative merits of scRNA-seq and snRNA-seq in Atlantic salmon, enhances understanding of teleost immune cell lineages, and provides a comprehensive list of markers for identifying major cell populations in the head kidney with significant immune relevance.