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The recent emergence of the genotype five Japanese encephalitis virus (G5 JEV) has once again drawn public attention to public health concerns. Plaque assay analysis of G5 JEV parental strain XZ0934 revealed two distinct plaque morphologies. To investigate this phenotypic heterogeneity, we performed single‐plaque purification and isolated two strains, designated as XZ0934‐L (large plaque) and XZ0934‐S (small plaque). Subsequently, deep mutational scanning revealed an amino acid mutation at position 138 (E138K) in the E protein of the XZ0934‐S strain. Viral titer determination and plaque morphology analysis showed that the titers of XZ0934‐L and XZ0934‐S in BHK‐21 cells were 10 7.06 PFU/mL and 10 7.35 PFU/mL, respectively, with plaque diameters of 0.87 ± 0.12 mm and 0.38 ± 0.08 mm. However, while both strains induced cytopathic effects across six cell lines used in this study, XZ0934‐S produced markedly weaker CPE than XZ0934‐L in N2a cells. Spatial modeling predicted that the E138K substitution did not significantly alter the overall conformation of the E protein. In contrast, transcriptomic analysis demonstrated that infections with different JEV genotypes induced significantly distinct host gene expression profiles in N2a cells. The XZ0934‐S strain caused the mildest transcriptional perturbations, and the perturbation of regulatory pathways was markedly weaker than those of the XZ0934‐L strain. Previous studies have suggested that the E138K mutation can attenuate the neurovirulence of JEV. This study provides the first comprehensive in vitro characterization of an E138K mutant in G5 JEV. The XZ0934‐S mutant strain, given its small plaque phenotype and reduced transcriptional impact, represents a promising candidate for further vaccine development. Future studies should rigorously evaluate its safety profile, genetic stability, and immunogenicity in animal models.

During the 2023 surveillance of mosquito-borne viruses in Ningxia Hui Autonomous Region, a strain of Umatilla virus (UMAV) was isolated from a pool of Culex pipiens pallens (NX23166) collected in Xiji County and cultured in C6/36 cells. Electron microscopy revealed that NX23166-infected mosquito cells showed approximately 70-nm virus particles, typical of the genus Orbivirus. Through next-generation sequencing, 10 double-stranded RNA (dsRNA) segments of the virus were obtained. Phylogenetic and homology analyses based on these sequences revealed that this strain was most closely related to the first Chinese isolate from Yunnan in 2013 (DH13M98) and an Australian isolate from 2015 (M4941_15). However, the VP3 protein of this strain showed the closest evolutionary relationship to a German isolate from 2019 (ED-I-205-19), with an amino acid sequence identity of 94.00%. In contrast, the identity of the VP3 protein to that of other strains ranged only from 47.38% to 51.49%, suggesting that these two strains may belong to the same serotype. Nevertheless, this hypothesis needs to be further verified by a serum neutralization test. Furthermore, transcriptome sequencing analysis showed that infection with the Ningxia isolate of UMAV induced significant temporal transcriptomic reprogramming in C6/36 cells. This reprogramming was characterized by early activation of innate immune responses such as the Toll signaling pathway and autophagy, followed by significant suppression of metabolic pathways, including oxidative phosphorylation in the mid to late stages of infection, demonstrating a molecular phenotype of coordinated immune activation and metabolic suppression. These results provide new insights into the genetic diversity and geographic distribution of the species UMAV.

Background Large-scale initiatives like The Cancer Genome Atlas (TCGA) performed genomics studies on predominantly Caucasian kidney cancer. In this study, we aimed to investigate genomics of Chinese clear cell renal cell carcinoma (ccRCC). Methods We performed whole-transcriptomic sequencing on 55 tumor tissues and 11 matched normal tissues from Chinese ccRCC patients. We systematically analyzed the data from our cohort and comprehensively compared with the TCGA ccRCC cohort. Results It found that PBRM1 mutates with a frequency of 11% in our cohort, much lower than that in TCGA Caucasians (33%). Besides, 31 gene fusions including 5 recurrent ones, that associated with apoptosis, tumor suppression and metastasis were identified. We classified our cohort into three classes by gene expression. Class 1 shows significantly elevated gene expression in the VEGF pathway, while Class 3 has comparably suppressed expression of this pathway. Class 2 is characterized by increased expression of extracellular matrix organization genes and is associated with high-grade tumors. Applying the classification to TCGA ccRCC patients revealed better distinction of tumor prognosis than reported classifications. Class 2 shows worst survival and Class 3 is a rare subtype ccRCC in the TCGA cohort. Furthermore, computational analysis on the immune microenvironment of ccRCC identified immune-active and tolerant tumors with significant increased macrophages and depleted CD4 positive T-cells, thus some patients may benefit from immunotherapies. Conclusion In summary, results presented in this study shed light into distinct genomic expression profiles in Chinese population, modified the stratification patterns by new molecular classification, and gave practical guidelines on clinical treatment of ccRCC patients.

Single-cell methodologies and technologies have started a revolution in biology which until recently has primarily been limited to deep sequencing and imaging modalities. With the advent and subsequent torrid development of single-cell proteomics over the last 5 years, despite the fact that proteins cannot be amplified like transcripts, it has now become abundantly clear that it is a worthy complement to single-cell transcriptomics. In this review, we engage in an assessment of the current state of the art of single-cell proteomics including workflow, sample preparation techniques, instrumentation, and biological applications. We investigate the challenges associated with working with very small sample volumes and the acute need for robust statistical methods for data interpretation. We delve into what we believe is a promising future for biological research at single-cell resolution and highlight some of the exciting discoveries that already have been made using single-cell proteomics, including the identification of rare cell types, characterization of cellular heterogeneity, and investigation of signaling pathways and disease mechanisms. Finally, we acknowledge that there are a number of outstanding and pressing problems that the scientific community vested in advancing this technology needs to resolve. Of prime importance is the need to set standards so that this technology becomes widely accessible allowing novel discoveries to be easily verifiable. We conclude with a plea to solve these problems rapidly so that single-cell proteomics can be part of a robust, high-throughput, and scalable single-cell multi-omics platform that can be ubiquitously applied to elucidating deep biological insights into the diagnosis and treatment of all diseases that afflict us.

Bone marrow (BM) aspirates are the gold standard for patient prognostication and genetic characterization in multiple myeloma (MM). However, they represent an important limitation for periodic disease monitoring because they entail an aggressive procedure. Moreover, recent findings show that a single BM aspirate is unable to reflect the complex MM heterogeneity. Recent advances in flow cytometry, microfluidics, and “omics” technologies have opened Pandora’s box of MM: The detection and isolation of circulating tumor cells (CTCs) offer a promising and minimally invasive alternative for tumor assessment and metastasis study. CTCs are detectable in premalignant and active MM states, and their enumeration has strong prognostic value, to the extent that it is challenging current stratification systems. In addition, CTCs reflect with high precision both intra- and extra-medullary disease at the phenotypic, genomic, and transcriptomic levels. Despite this high resemblance between tumor clones in distinct locations, some subtle (not random) differences might shed some light on the metastatic process. Thus, it has been suggested that a hypoxic and pro-inflammatory microenvironment could induce an arrest in proliferation forcing tumor cells to recirculate. Herein, we summarize data on the characterization of MM CTCs as well as their clinical and research potential.

The production of biopharmaceuticals relies on robust cell systems that can produce recombinant proteins at high levels and grow and survive in the stressful bioprocess environment. Chinese hamster ovary cells (CHO) as the main production hosts offer a variety of advantages including robust growth and survival in a bioprocess environment. Cell surface proteins are of special interest for the understanding of how CHO cells react to their environment while maintaining growth and survival phenotypes, since they enable cellular reactions to external stimuli and potentially initiate signaling pathways. To provide deeper insight into functions of this special cell surface sub-proteome, pathway enrichment analysis of the determined CHO surfaceome was conducted. Enrichment of growth/ survival-pathways such as the phosphoinositide-3-kinase (PI3K)–protein kinase B (AKT), mitogen-activated protein kinase (MAPK), Janus kinase/signal transducers and activators of transcription (JAK-STAT), and RAP1 pathways were observed, offering novel insights into how cell surface receptors and ligand-mediated signaling enable the cells to grow and survive in a bioprocess environment. When supplementing surfaceome data with RNA expression data, several growth/survival receptors were shown to be co-expressed with their respective ligands and thus suggesting self-induction mechanisms, while other receptors or ligands were not detectable. As data about the presence of surface receptors and their associated expressed ligands may serve as base for future studies, further pathway characterization will enable the implementation of optimization strategies to further enhance cellular growth and survival behavior. Key points • PI3K/AKT, MAPK, JAK-STAT, and RAP1 pathway receptors are enriched on the CHO cell surface and downstream pathways present on mRNA level. • Detected pathways indicate strong CHO survival and growth phenotypes. • Potential self-induction of surface receptors and respective ligands. Graphical abstract

Qing-Kai-Ling (QKL) oral liquid has been increasingly used in metabolic dysfunction-associated steatotic liver disease (MASLD). However, the specific metabolic differentials and metabolic pathway mechanisms that affect the MASLD regulated by QKL remained unclear. In this study, serum biochemical analyses and hematoxylin–eosin staining of the liver revealed QKL reduced liver injury and enhanced lipid metabolism ability, respectively. To clarify the therapeutic mechanism of the QKL in the treatment of MASLD, UHPLC-QTOF-MS non-target metabolomics and RNA-Seq high-throughput sequencing analysis were used to explore the mechanism of the QKL in the treatment of MASLD from the perspective of metabolic-gene interactions. UHPLC-QTOF-MS-based untargeted metabolomics further revealed that there were 196 common differentially expressed metabolites identified among 3 groups; QKL significantly up-regulated 44 metabolites, while 11 metabolites (including N-phenylacetylglutamic acid and glycocholic acid) were downregulated significantly. Moreover, the main metabolic pathways regulated by QKL included amino acids, peptides, bile acids, carbohydrates, linoleic acids, etc. Additionally, the result of the RNA sequencing-based transcriptomics showed that a total of 984 differential genes (DEGs) were identified and 9 important DEGs were obtained. The result of the Kyoto Encyclopedia of Genes and Genomes (KEGG) demonstrated that the 984 DEGs were linked to bile acid metabolism, glycerophospholipid metabolism, insulin resistance, AMPK signaling pathway, etc. Overall, this work was the first to show that QKL regulated metabolites and genes to alleviate MASLD by the UHPLC-QTOF-MS-based untargeted metabolomics joint high-throughput RNA sequencing–based transcriptomics analysis, providing the basis and research method for the treatment of metabolic diseases by QKL and other drugs. Graphical Abstract

Immortalized cell lines play a crucial role in biomedical research by enabling reproducible experiments and enhancing our understanding of complex diseases. HuH-7, originally derived from a human hepatocellular carcinoma, is particularly valuable for studying liver cancer dynamics, viral hepatitis, and drug metabolism. However, concerns about cell line misidentification and genetic drift in cell lines highlight the importance of rigorous authentication to maintain the reliability of research outcomes, despite their widespread use. In this study, we present a detailed (cyto)genetic and molecular analysis of HuH-7 cells, focusing on their hepatocellular characteristics and potential applications in translational research. Through thorough genomic profiling and next-generation mRNA expression analyses, we aimed to confirm the authenticity of the cell line and identify key genetic signatures associated with tumorigenic pathways. Our results emphasize the importance of regular identity verification, such as short tandem repeat (STR) profiling, and demonstrate how subtle genetic variations can affect phenotypic traits relevant to modeling liver disease. By providing insights into the genetic and transcriptomic features of HuH-7 cells, this study establishes a robust basis for future research and therapeutic investigations using this widely accepted liver cell model. It also emphasizes the importance for maintaining high-quality standards and robust authentication practices to ensure that cell-based studies produce reliable and reproducible results.

Background Native bone marrow (BM) mesenchymal stem/stromal cells (BM-MSCs) participate in generating and shaping the skeleton and BM throughout the lifespan. Moreover, BM-MSCs regulate hematopoiesis by contributing to the hematopoietic stem cell niche in providing critical cytokines, chemokines and extracellular matrix components. However, BM-MSCs contain a heterogeneous cell population that remains ill-defined. Although studies on the taxonomy of native BM-MSCs in mice have just started to emerge, the taxonomy of native human BM-MSCs remains unelucidated. Methods By using single-cell RNA sequencing (scRNA-seq), we aimed to define a proper taxonomy for native human BM non-hematopoietic subsets including endothelial cells (ECs) and mural cells (MCs) but with a focal point on MSCs. To this end, transcriptomic scRNA-seq data were generated from 5 distinct BM donors and were analyzed together with other transcriptomic data and with computational biology analyses at different levels to identify, characterize and classify distinct native cell subsets with relevant biomarkers. Results We could ascribe novel specific biomarkers to ECs, MCs and MSCs. Unlike ECs and MCs, MSCs exhibited an adipogenic transcriptomic pattern while co-expressing genes related to hematopoiesis support and multilineage commitment potential. Furthermore, by a comparative analysis of scRNA-seq of BM cells from humans and mice, we identified core genes conserved in both species. Notably, we identified MARCKS , CXCL12 , PDGFRA , and LEPR together with adipogenic factors as archetypal biomarkers of native MSCs within BM. In addition, our data suggest some complex gene nodes regulating critical biological functions of native BM-MSCs together with a preferential commitment toward an adipocyte lineage. Conclusions Overall, our taxonomy for native BM non-hematopoietic compartment provides an explicit depiction of gene expression in human ECs, MCs and MSCs at single-cell resolution. This analysis helps enhance our understanding of the phenotype and the complexity of biological functions of native human BM-MSCs.

Omics mainly includes genomics, epigenomics, transcriptomics, proteomics and metabolomics. The rapid development of omics technology has opened up new ways to study disease diagnosis and prognosis and to define prospective information of complex diseases. Since omics data are usually large and complex, the method used to analyze the data and to define important information is crucial in omics study. In this review, we focus on advances in biomarker discovery methods based on omics data in the last decade, and categorize them as individual feature analysis, combinatorial feature analysis and network analysis. We also discuss the challenges and perspectives in this field.