Explore the vast range of titles available.

The vertebrate kidneys play two evolutionary conserved roles in waste excretion and osmoregulation. Besides, the kidney of fish is considered as a functional ortholog of mammalian bone marrow that serves as a hematopoietic hub for generating blood cell lineages and immunological responses. However, knowledge about the properties of kidney hematopoietic cells, and the functionality of the kidney in fish immune systems remains to be elucidated. To this end, our present study generated a comprehensive atlas with 59 hematopoietic stem/progenitor cell (HSPC) and immune-cells types from zebrafish kidneys via single-cell transcriptome profiling analysis. These populations included almost all known cells associated with innate and adaptive immunity, and displayed differential responses to viral infection, indicating their diverse functional roles in antiviral immunity. Remarkably, HSPCs were found to have extensive reactivities to viral infection, and the trained immunity can be effectively induced in certain HSPCs. In addition, the antigen-stimulated adaptive immunity can be fully generated in the kidney, suggesting the kidney acts as a secondary lymphoid organ. These results indicated that the fish kidney is a dual-functional entity with functionalities of both primary and secondary lymphoid organs. Our findings illustrated the unique features of fish immune systems, and highlighted the multifaced biology of kidneys in ancient vertebrates.

Epilepsy is a progression of development and advancement over time. However, the molecular features of epilepsy were poorly studied from a dynamic developmental perspective. We intend to investigate the key mechanisms in the process of epilepsy by exploring the roles of stage-specifically expressed genes. By using time-course transcriptomic data of epileptic samples, we first analyzed the molecular features of epilepsy in different stages and divided it into progression and remission stages based on their transcriptomic features. 34 stage-specifically expressed genes were then identified by the Tau index and verified in other epileptic datasets. These genes were then enriched for immune-related biological functions. Furthermore, we found that the level of immune infiltration and mechanisms at different stages were different, which may result from different types of immune cells playing leading roles in distinct stages. Our findings indicated an essential role of immune regulation as the potential mechanism of epilepsy development.

Tumor-infiltrating immune cells shape the tumor microenvironment and are closely related to clinical outcomes. Several transcription factors (TFs) have also been reported to regulate the antitumor activity and immune cell infiltration. This study aimed to quantify the populations of different immune cells infiltrated in tumor samples based on the bulk RNA sequencing data obtained from 50 cancer patients using the CIBERSORT and the EPIC algorithm. Weighted gene coexpression network analysis (WGCNA) identified eigengene modules strongly associated with tumorigenesis and the activation of CD4+ memory T cells, dendritic cells, and macrophages. TF genes FOXM1 , MYBL2 , TAL1 , and ERG are central in the subnetworks of the eigengene modules associated with immune-related genes. The analysis of The Cancer Genome Atlas (TCGA) cancer data confirmed these findings and further showed that the expression of these potential TF genes regulating immune infiltration, and the immune-related genes that they regulated, was associated with the survival of patients within multiple cancers. Exome-seq was performed on 24 paired samples that also had RNA-seq data. The expression quantitative trait loci (eQTL) analysis showed that mutations were significantly more frequent in the regions flanking the TF genes compared with those of non-TF genes, suggesting a driver role of these TF genes regulating immune infiltration. Taken together, this study presented a practical method for identifying genes that regulate immune infiltration. These genes could be potential biomarkers for cancer prognosis and possible therapeutic targets.

In the pathology of pelvic organ prolapse (POP), little is known about the contributing role of pelvic microenvironment. Also, the age-related differences in pelvic microenvironment of POP patients is always ignored. In the present study, we investigated the age-related differences in pelvic microenvironment between Young POP patients and Old POP patients, and the novel cell types and critical regulators which contributes to the age-related differences. Single-cell transcriptomic analyses were used to detect the changes in cell composition and gene expression from the pelvic microenvironment of control group (<60 years), Young POP group (<60 years) and Old POP group (>60 years). Then, immunohistochemistry and immunofluorescence were used to verify the novel cell types and critical regulators in the pelvic microenvironment. Furthermore, histopathological alteration and mechanical property alteration in POP with different ages were revealed by vaginal tissue histology and biomechanical testing. The up-regulated biological process in Old women with POP is mainly related to chronic inflammation, while the up-regulated biological process in Young women with POP is mainly related to extracellular matrix metabolism. Meantime, CSF3+ endothelial cells and FOLR2+ macrophages were found to play a central role in inducing pelvic chronic inflammation. Furthermore, the collagen fiber and mechanical property of POP patients decreased with aging. Taken together, this work provides a valuable resource for deciphering the aging-related immune cell types and the critical regulators in pelvic microenvironment. With better understanding of normal and abnormal events in this pelvic microenvironment, we provided rationales of personalized medicine for POP patients with different ages.

Alzheimer’s disease (AD), a prevalent neurodegenerative disorder, is characterized by mitochondrial dysfunction and immune dysregulation. This study is aimed at developing a risk prediction model for AD by integrating multi-omics data and exploring the interplay between mitochondrial energy metabolism-related genes (MEMRGs) and immune cell dynamics. We integrated four GEO datasets (GSE132903, GSE29378, GSE33000, GSE5281) for differential gene expression analysis, functional enrichment, and weighted gene co-expression network analysis (WGCNA). We identified two key gene modules (turquoise and magenta) significantly correlated with AD. Subsequently, we constructed a risk prediction model incorporating five MEMRGs (MRPL15, RBP4, ABCA1, MPV17, and MRPL37) and clinical factors using LASSO regression. The model demonstrated robust predictive performance (AUC > 0.815) in both internal and external validation (GSE44770) cohorts. Downregulation of MRPL15, RBP4, MPV17, and MRPL37 in AD brain regions (validated using AlzData and qRT-PCR) suggests impaired mitochondrial function. Conversely, ABCA1 upregulation may represent a compensatory response. Furthermore, significant differences in immune cell proportions, particularly gamma delta T cells ( p  = 0.002) and activated CD4 memory T cells ( p  = 0.027), were found between AD and non-demented samples. We observed significant correlations between MEMRG expression and specific immune cell fractions, indicating a potential link between mitochondrial dysfunction and immune dysregulation in AD. Our study provides a reliable risk prediction model for AD and highlights the crucial roles of MEMRGs and immune responses in disease pathogenesis, offering potential targets for therapeutic interventions.

Cancer immunotolerance may be reversed by checkpoint inhibitor immunotherapy; however, only a subset of patients responds to immunotherapy. The prediction of clinical response in the individual patient remains a challenge. CD4+ T cells play a role in activating adaptive immune responses against cancer, while the conversion to immunosuppression is mainly caused by CD4+ regulatory T cell (Treg) cells. Signal transduction pathways (STPs) control the main functions of immune cells. A novel previously described assay technology enables the quantitative measurement of activity of multiple STPs in individual cell and tissue samples. The activities of the TGFβ, NFκB, PI3K-FOXO, JAK-STAT1/2, JAK-STAT3, and Notch STPs were measured in CD4+ T cell subsets and used to investigate cellular mechanisms underlying breast cancer-induced immunotolerance. Methods: STP activity scores were measured on Affymetrix expression microarray data of the following: (1) resting and immune-activated CD4+ T cells; (2) CD4+ T-helper 1 (Th1) and T-helper 2 (Th2) cells; (3) CD4+ Treg cells; (4) immune-activated CD4+ T cells incubated with breast cancer tissue supernatants; and (5) CD4+ T cells from blood, lymph nodes, and cancer tissue of 10 primary breast cancer patients. Results: CD4+ T cell activation induced PI3K, NFκB, JAK-STAT1/2, and JAK-STAT3 STP activities. Th1, Th2, and Treg cells each showed a typical pathway activity profile. The incubation of activated CD4+ T cells with cancer supernatants reduced the PI3K, NFκB, and JAK-STAT3 pathway activities and increased the TGFβ pathway activity, characteristic of an immunotolerant state. Immunosuppressive Treg cells were characterized by high NFκB, JAK-STAT3, TGFβ, and Notch pathway activity scores. An immunotolerant pathway activity profile was identified in CD4+ T cells from tumor infiltrate and blood of a subset of primary breast cancer patients, which was most similar to the pathway activity profile in immunosuppressive Treg cells. Conclusion: Signaling pathway assays can be used to quantitatively measure the functional immune response state of lymphocyte subsets in vitro and in vivo. Clinical results suggest that, in primary breast cancer, the adaptive immune response of CD4+ T cells may be frequently replaced by immunosuppressive Treg cells, potentially causing resistance to checkpoint inhibition. In vitro study results suggest that this is mediated by soluble factors from cancer tissue. Signaling pathway activity analysis on TIL and/or blood samples may improve response prediction and monitoring response to checkpoint inhibitors and may provide new therapeutic targets (e.g., the Notch pathway) to reduce resistance to immunotherapy.

Deep learning has been applied in precision oncology to address a variety of gene expression-based phenotype predictions. However, gene expression data’s unique characteristics challenge the computer vision-inspired design of popular Deep Learning (DL) models such as Convolutional Neural Network (CNN) and ask for the need to develop interpretable DL models tailored for transcriptomics study. To address the current challenges in developing an interpretable DL model for modeling gene expression data, we propose a novel interpretable deep learning architecture called T-GEM, or Transformer for Gene Expression Modeling. We provided the detailed T-GEM model for modeling gene–gene interactions and demonstrated its utility for gene expression-based predictions of cancer-related phenotypes, including cancer type prediction and immune cell type classification. We carefully analyzed the learning mechanism of T-GEM and showed that the first layer has broader attention while higher layers focus more on phenotype-related genes. We also showed that T-GEM’s self-attention could capture important biological functions associated with the predicted phenotypes. We further devised a method to extract the regulatory network that T-GEM learns by exploiting the attributions of self-attention weights for classifications and showed that the network hub genes were likely markers for the predicted phenotypes.

Mothers undergo physiological and molecular changes over the course of gestation. These modifications “get under the skin” and may be reflected in the maternal epigenome through processes such as DNA methylation. Such an epigenetic mark may offer insights into maternal responses to prenatal influences and biological cues from the developing fetus, thereby functioning as an indirect indicator of the conditions the fetus experiences in utero. We measured whole blood DNA methylation using the MethylationEPIC BeadChip Infinium microarray v1.0 in 22 pregnant women from Pakistan, a low- and middle-income country (LMIC), at two timepoints during their term pregnancies (early: 10–19 weeks and late-mid: 22–29 weeks). We used DNA methylation profiles to predict immune cell proportions and tested differences in these proportions and DNA methylation patterns between the two timepoints. Further, we evaluated DNA methylation associations with gestational age at each timepoint and examined the contribution of genetic, psychosocial, and biological factors. Our analyses documented changes in immune cell proportions and DNA methylation profiles over the course of gestation, albeit in a small percentage of the measured DNA methylome. We also observed timepoint-specific DNA methylation associations with gestational age, predominantly at early pregnancy, with predicted interleukin-6 level and socioeconomic status contributing to a few of these associations. On comparing to three external cohorts from different sociocultural contexts, we also noted these signatures to be unique to LMIC settings. Overall, these changes measured in term pregnancies may be used to assess both fluctuations in pregnancy and birth outcomes, particular in women from LMIC settings.

Gestational diabetes mellitus (GDM), one of the prevalent pregnancy-related metabolic disorders, have shown immediate or long-term adverse health outcomes for maternal and fetal health. Therefore, it is crucial to understand the ongoing cellular and molecular changes in GDM patients for characterizing novel biomarkers for diagnosis and therapeutic purposes. In the current study, we analyzed 3 transcriptomic datasets, characterized 449 unique upregulated and 785 downregulated DEGs, and performed several analyses. Gene ontology shows enrichment of migration, development, and immune-related processes in GDM patients. KEGG pathway shows enrichment of pathways like “type 1 diabetes mellitus” and “graft versus host disease”. Disease ontology shows enrichment of “female reproductive system disease,” “anemia,” etc. Integration of methylation and transcriptomic data identified 11 genes ( RASSF2 , WSCD1 , TNFAIP3 , TPST1 , UBASH3B , ZFP36 , CRISPLD2 , IGFBP7 , TNS3 , TPM2 , and VTRNA1-2 ), as potential novel diagnostic biomarkers and therapeutic targets. Furthermore, immune cell-type infiltration analysis shows higher memory B-cells and lower M1 macrophages and CD8 T-cells. Protein-protein interaction analysis followed by ROC analysis in an independent dataset identified 7 hub genes ( POLR2G , VWF , COL5A1 , COL6A1 , CD44 , COL3A1 , and COL1A1 ) with high diagnostic potential. Overall, we obtained 18 genes that could serve as novel diagnostic biomarkers and therapeutic targets in GDM patients.

Single-cell multi-omics studies, such as multidimensional transcriptomics (whole transcriptomic analysis, WTA), and surface marker analysis (antibody sequencing, AbSeq), have turned out to be valuable techniques that offer inaccessible possibilities for single-cell profiling of mRNA, lncRNA, and proteins. We used this technique to understand the dynamics of mRNA and protein-level differences in healthy, COVID-19-infected and recovered individuals using peripheral blood mononuclear cells (PBMCs). Our results demonstrate that compared to mRNA expression, protein abundance is a better indicator of the disease state. We demonstrate that compared to mRNA expression, protein abundance is a better indicator of the disease state. We observed high levels of cell identity and regulatory markers, , , , , , , and in healthy individuals, whereas markers related to cell activation, , , , , , and elevated in the SARS-CoV-2 infected patients at both WTA and AbSeq levels. Curiously, in recovered individuals, there was a high expression of cytokine and chemokine receptors ( , and ). We also observed variations in the expression of markers within cell populations under different states. Furthermore, our study emphasizes the significance of employing an oligo-based method (AbSeq) that can help in diagnosis, prognosis, and protection from disease/s by identifying cell surface markers that are unique to different cell types or states. It also allows simultaneous study of a vast array of markers, surpassing the constraints of techniques like FACS to query the vast repertoire of proteins.