SGAE: single-cell gene association entropy for revealing critical states of cell transitions during embryonic development.

The critical point or pivotal threshold of cell transition occurs in early embryonic development when cell differentiation culminates in its transition to specific cell fates, at which the cell population undergoes an abrupt and qualitative shift. Revealing such critical points of cell transitions can track cellular heterogeneity and shed light on the molecular mechanisms of cell differentiation. However, precise detection of critical state transitions proves challenging when relying on single-cell RNA sequencing data due to their inherent sparsity, noise, and heterogeneity. In this study, diverging from conventional methods like differential gene analysis or static techniques that emphasize classification of cell types, an innovative computational approach, single-cell gene association entropy (SGAE), is designed for the analysis of single-cell RNA-seq data and utilizes gene association information to reveal critical states of cell transitions. More specifically, through the translation of gene expression data into local SGAE scores, the proposed SGAE can serve as an index to quantitatively assess the resilience and critical properties of genetic regulatory networks, consequently detecting the signal of cell transitions. Analyses of five single-cell datasets for embryonic development demonstrate that the SGAE method achieves better performance in facilitating the characterization of a critical phase transition compared with other existing methods. Moreover, the SGAE value can effectively discriminate cellular heterogeneity over time and performs well in the temporal clustering of cells. Besides, biological functional analysis also indicates the effectiveness of the proposed approach.

Low expression of the dynamic network markers FOS/JUN in pre-deteriorated epithelial cells is associated with the progression of colorectal adenoma to carcinoma.

BACKGROUND: Deterioration of normal intestinal epithelial cells is crucial for colorectal tumorigenesis. However, the process of epithelial cell deterioration and molecular networks that contribute to this process remain unclear. METHODS: Single-cell data and clinical information were downloaded from the Gene Expression Omnibus (GEO) database. We used the recently proposed dynamic network biomarker (DNB) method to identify the critical stage of epithelial cell deterioration. Data analysis and visualization were performed using R and Cytoscape software. In addition, Single-Cell rEgulatory Network Inference and Clustering (SCENIC) analysis was used to identify potential transcription factors, and CellChat analysis was conducted to evaluate possible interactions among cell populations. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set variation analysis (GSVA) analyses were also performed. RESULTS: The trajectory of epithelial cell deterioration in adenoma to carcinoma progression was delineated, and the subpopulation of pre-deteriorated epithelial cells during colorectal cancer (CRC) initialization was identified at the single-cell level. Additionally, FOS/JUN were identified as biomarkers for pre-deteriorated epithelial cell subpopulations in CRC. Notably, FOS/JUN triggered low expression of P53-regulated downstream pro-apoptotic genes and high expression of anti-apoptotic genes through suppression of P53 expression, which in turn inhibited P53-induced apoptosis. Furthermore, malignant epithelial cells contributed to the progression of pre-deteriorated epithelial cells through the GDF signaling pathway. CONCLUSIONS: We demonstrated the trajectory of epithelial cell deterioration and used DNB to characterize pre-deteriorated epithelial cells at the single-cell level. The expression of DNB-neighboring genes and cellular communication were triggered by DNB genes, which may be involved in epithelial cell deterioration. The DNB genes FOS/JUN provide new insights into early intervention in CRC.

Infiltration of Apoptotic M2 Macrophage Subpopulation Is Negatively Correlated with the Immunotherapy Response in Colorectal Cancer.

The polarization of tumor-associated macrophages (TAMs) plays a key role in tumor development and immunotherapy in colorectal cancer (CRC) patients. However, the impact of apoptosis on TAM polarization and immunotherapy efficacy in patients with different mismatch repair statuses (MMR) remains unclear. Here, we constructed an atlas of macrophage and found a higher rate of infiltration of M2-like TAM subpopulation in pMMR CRC tumor tissues compared with that in dMMR CRC tumor tissues. Importantly, a lower infiltration rate of M2c-like TAMs was associated with immunotherapy response. The M2 polarization trajectory revealed the apoptosis of M2c-like TAMs in dMMR while the differentiation of M2c-like TAMs in pMMR, implying a higher polarization level of M2 in pMMR. Furthermore, we found that a high expression of S100A6 induces the apoptosis of M2c-like TAMs in dMMR. In conclusion, we identified apoptotic TAM subpopulations in the M2 polarization trajectory and found that apoptosis caused by the high expression of S100A6 reduces their infiltration in tumors as well as the level of M2 polarization and contributes to a favorable immunotherapy response. These findings provide new insights into the potential role of apoptosis in suppressing tumors and enhancing immunotherapeutic efficacy.

Identifying the critical state of complex biological systems by the directed-network rank score method.

MOTIVATION: Catastrophic transitions are ubiquitous in the dynamic progression of complex biological systems; that is, a critical transition at which complex systems suddenly shift from one stable state to another occurs. Identifying such a critical point or tipping point is essential for revealing the underlying mechanism of complex biological systems. However, it is difficult to identify the tipping point since few significant differences in the critical state are detected in terms of traditional static measurements. RESULTS: In this study, by exploring the dynamic changes in gene cooperative effects between the before-transition and critical states, we presented a model-free approach, the directed-network rank score (DNRS), to detect the early-warning signal of critical transition in complex biological systems. The proposed method is applicable to both bulk and single-cell RNA-sequencing (scRNA-seq) data. This computational method was validated by the successful identification of the critical or pre-transition state for both simulated and six real datasets, including three scRNA-seq datasets of embryonic development and three tumor datasets. In addition, the functional and pathway enrichment analyses suggested that the corresponding DNRS signaling biomarkers were involved in key biological processes. AVAILABILITY AND IMPLEMENTATION: The source code is freely available at https://github.com/zhongjiayuan/DNRS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Development of a dynamic network biomarkers method and its application for detecting the tipping point of prior disease development.

The dynamic network biomarker (DNB) method has advanced since it was first proposed. This review discusses advances in the DNB method that can identify the dynamic change in the expression signature related to the critical time point of disease progression by utilizing different kinds of transcriptome data. The DNB method is good at identifying potential biomarkers for cancer and other disease development processes that are represented by a limited molecular profile change between the normal and critical stages. We highlight that the cancer tipping point or premalignant state has been widely discovered for different types of cancer by using the DNB method that utilizes bulk or single-cell RNA sequencing data. This method could also be applied to other dynamic research studies and help identify early warning signals, such as the prediction of a pre-outbreak of COVID-19. We also discuss how the identification of reliable biomarkers of cancer and the development of new methods can be utilized for early detection and intervention and provide insights into emerging paths of the widespread biomarker candidate pool for further validation and disease/health management.

scGET: Predicting Cell Fate Transition During Early Embryonic Development by Single-cell Graph Entropy.

During early embryonic development, cell fate commitment represents a critical transition or "tipping point" of embryonic differentiation, at which there is a drastic and qualitative shift of the cell populations. In this study, we presented a computational approach, scGET, to explore the gene-gene associations based on single-cell RNA sequencing (scRNA-seq) data for critical transition prediction. Specifically, by transforming the gene expression data to the local network entropy, the single-cell graph entropy (SGE) value quantitatively characterizes the stability and criticality of gene regulatory networks among cell populations and thus can be employed to detect the critical signal of cell fate or lineage commitment at the single-cell level. Being applied to five scRNA-seq datasets of embryonic differentiation, scGET accurately predicts all the impending cell fate transitions. After identifying the "dark genes" that are non-differentially expressed genes but sensitive to the SGE value, the underlying signaling mechanisms were revealed, suggesting that the synergy of dark genes and their downstream targets may play a key role in various cell development processes.The application in all five datasets demonstrates the effectiveness of scGET in analyzing scRNA-seq data from a network perspective and its potential to track the dynamics of cell differentiation. The source code of scGET is accessible at https://github.com/zhongjiayuna/scGET_Project.

Significant Difference of Immune Cell Fractions and Their Correlations With Differential Expression Genes in Parkinson's Disease.

Parkinson's disease (PD) is the second most neurodegenerative disease in the world. T cell infiltration in the central nervous system (CNS) has provided insights that the peripheral immune cells participate in the pathogenesis of PD. However, the association between the peripheral immune system and CNS remains to be elucidated. In this study, we analyzed incorporative substantia nigra (SN) expression data and blood expression data using the CIBERSORT to obtain the 22 immune cell fractions and then explored the molecular function to identify the potential key immune cell types and genes of PD. We observed that the proportions of naïve CD4 T cells, gamma delta T cells, resting natural killer (NK) cells, neutrophils in the blood, and regulatory T cells (Tregs) in the SN were significantly different between patients with PD and healthy controls (HCs). We identified p53-induced death domain protein 1 (PIDD1) as the hub gene of a PD-related module. The enrichment score of the neuron-specific gene set was significantly different between PD and HC, and genes in the neuron-related module were enriched in the biological process about mitochondria and synapses. These results suggested that the fractions of naïve CD4 T cells, gamma delta T cells, resting NK cells, and neutrophils may be used as a combined diagnostic marker in the blood, and Tregs in SN may be a potential therapeutic design target for PD.

Single-cell transcriptomics reveal DHX9 in mature B cell as a dynamic network biomarker before lymph node metastasis in CRC.

Increasing evidence indicates that mature B cells in the adjacent tumor tissue, both as an intermediate state, are vital in advanced colorectal cancer (CRC), which is associated with a low survival rate. Developing predictive biomarkers that detect the tipping point of mature B cells before lymph node metastasis in CRC is critical to prevent irreversible deterioration. We analyzed B cells in the adjacent tissues of CRC samples from different stages using the dynamic network biomarker (DNB) method. Single-cell profiling of 725 CRC-derived B cells revealed the emergence of a mature B cell subtype. Using the DNB method, we identified stage II as a critical period before lymph node metastasis and that reversed difference genes triggered by DNBs were enriched in the Janus kinase (JAK)-signal transducer and activator of transcription (STAT) pathway involving B cell immune capability. DHX9 (DEAH-box helicase 9) was a specific para-cancerous tissue DNB key gene. The dynamic expression levels of DHX9 and its proximate network genes involved in B cell-related pathways were reversed at the network level from stage I to III. In summary, DHX9 in mature B cells of CRC-adjacent tissues may serve as a predictable biomarker and a potential immune target in CRC progression.

Dynamic Network Biomarker of Pre-Exhausted CD8+ T Cells Contributed to T Cell Exhaustion in Colorectal Cancer.

Immunotherapy has achieved positive clinical responses in various cancers. However, in advanced colorectal cancer (CRC), immunotherapy is challenging because of the deterioration of T-cell exhaustion, the mechanism of which is still unclear. In this study, we depicted CD8+ T-cell developmental trajectories and characterized the pre-exhausted T cells isolated from CRC patients in the scRNA-seq data set using a dynamic network biomarker (DNB). Moreover, CCT6A identified by DNB was a biomarker for pre-exhausted T-cell subpopulation in CRC. Besides, TUBA1B expression was triggered by CCT6A as DNB core genes contributing to CD8+ T cell exhaustion, indicating that core genes serve as biomarkers in pre-exhausted T cells. Remarkably, both TUBA1B and CCT6A expressions were significantly associated with the overall survival of COAD patients in the TCGA database (p = 0.0082 and p = 0.026, respectively). We also observed that cellular communication between terminally differentiated exhausted T cells and pre-exhausted T cells contributes to exhaustion. These findings provide new insights into the mechanism of T-cell exhaustion and provide clue for targeted immunotherapy in CRC.

Multiregion single-cell sequencing reveals the transcriptional landscape of the immune microenvironment of colorectal cancer.

The tumor microenvironment is a complex ecosystem formed by distinct and interacting cell populations, and its composition is related to cancer prognosis and response to clinical treatment. In this study, we have taken the advantage of two single-cell RNA sequencing technologies (Smart-seq2 and DNBelab C4) to generate an atlas of 15,115 immune and nonimmune cells from primary tumors and hepatic metastases of 18 colorectal cancer (CRC) patients. We observed extensive changes in the proportions and functional states of T cells and B cells in tumor tissues, compared to those of paired non-tumor tissues. Importantly, we found that B cells from early CRC tumor were identified to be pre-B like expressing tumor suppressors, whereas B cells from advanced CRC tumors tended to be developed into plasma cells. We also identified the association of IgA+ IGLC2+ plasma cells with poor CRC prognosis, and demonstrated a significant interaction between B-cell and myeloid-cell signaling, and found CCL8+ cycling B cells/CCR5+ T-cell interactions as a potential antitumoral mechanism in advanced CRC tumors. Our results provide deeper insights into the immune infiltration within CRC, and a new perspective for the future research in immunotherapies for CRC.

Single-Sample Node Entropy for Molecular Transition in Pre-deterioration Stage of Cancer.

A complex disease, especially cancer, always has pre-deterioration stage during its progression, which is difficult to identify but crucial to drug research and clinical intervention. However, using a few samples to find mechanisms that propel cancer crossing the pre-deterioration stage is still a complex problem. In this study, we successfully developed a novel single-sample model based on node entropy with a priori established protein interaction network. Using this model, critical stages were successfully detected in simulation data and four TCGA datasets, indicating its sensitivity and robustness. Besides, compared with the results of the differential analysis, our results showed that most of dynamic network biomarkers identified by node entropy, such as NKD2 or DAAM1, located in upstream in many important cancer-related signaling pathways regulated intergenic signaling within pathways. We also identified some novel prognostic biomarkers such as PER2, TNFSF4, MMP13 and ENO4 using node entropy rather than expression level. More importantly, we found the switch of non-specific pathways related to DNA damage repairing was the main driven force for cancer progression. In conclusion, we have successfully developed a dynamic node entropy model based on single case data to find out tipping point and possible mechanism for cancer progression. These findings may provide new target genes in therapeutic intervention tactics.

Characterization of a pathogenic variant in GBA for Parkinson's disease with mild cognitive impairment patients.

Parkinson's disease (PD) is the second most common neurodegenerative disease, and mild cognitive impairment (MCI) is a well-established risk factor for the development of dementia in PD. A growing body of evidence suggests that low expression of glucocerebrosidase (GBA) promotes the transmission of α-synuclein (α-Syn) interpolymers and the progression of PD. However, how GBA mutations affect the pathogenesis of PD via abnormal aggregation of α-Syn is unclear, and no clinically valid PD-MCI genetic markers have been identified. Here, we first located a GBA eQTL, rs12411216, by analysing DHS, eQTL SNP, and transcription factor binding site data using the UCSC database. Subsequently, we found that rs12411216 was significantly associated with PD-MCI (P < 0.05) in 306 PD patients by genotyping. In exploring the relationship between rs12411216 and GBA expression, the SNP was found to be associated with GBA expression in 50 PD patients through qPCR verification. In a further CRISPR/Cas9-mediated genome editing module, the SNP was identified to cause a decrease in GBA expression, weaken enzymatic activity and enhance the abnormal aggregation of α-Syn in SH-SY5Y cells. Additionally, using an electrophoretic mobility shift assay, we confirmed that the binding efficiency of transcription factor E2F4 was affected by the rs12411216 SNP. In conclusion, our results showed that rs12411216 regulated GBA expression, supporting its potential role as a PD-MCI genetic biomarker and highlighting novel mechanisms underlying Parkinson's disease.

Transcriptome Analysis of the Acid Stress Response of Desulfovibrio vulgaris ATCC 7757.

The application of sulfate-reducing bacteria (SRB) shows great potential in the anaerobic biological treatment of acid mine wastewater; therefore, it has attracted much attention. The low pH in acidic wastewater affects the growth and reducing power of SRB. To uncover the mechanism underlying the reduction efficiency of SRB under acidic conditions, in this study, transcriptomic analysis was performed with Desulfovibrio vulgaris ATCC 7757 under three different pH conditions (pH 4.0, 5.5 and 7.0) and in the initial inoculation, logarithmic growth and plateau phases. Our results showed that ATCC 7757 still had biological activity at pH 4.0 and exhibited gene expression patterns at pH 4.0 that were different from those at pH 5.5 and pH 7. Importantly, the gene expression pattern was similar between pH 5.5 and pH 7. Transcriptomic analysis identified differentially expressed genes that affected the growth of ATCC 7757 under pH 7.0 at 22 h compared to 15 h; 196 of these genes were upregulated and 575 were downregulated. These differentially expressed genes were mainly enriched in genetic information processing and metabolism. Additionally, we identified 57 candidate genes associated with low-pH tolerance. Adaptation to low pH was reflected by an increase in the expression of genes involved in cell membrane structure and proton transport. The expression of genes involved in the reduction process decreased, including the genes DVU0499 and sat, which encode proteins that affect the sulfate reduction process. Both gene activities were validated by qPCR. Our results will contribute to further promoting the reducing power of SRB in acid mine wastewater and the development of successful bioremediation strategies.

Evolution and Comprehensive Analysis of DNaseI Hypersensitive Sites in Regulatory Regions of Primate Brain-Related Genes.

How the human brain differs from those of non-human primates is largely unknown and the complex drivers underlying such differences at the genomic level remain unclear. In this study, we selected 243 brain-related genes, based on Gene Ontology, and identified 184,113 DNaseI hypersensitive sites (DHSs) within their regulatory regions. To performed comprehensive evolutionary analyses, we set strict filtering criteria for alignment quality and filtered 39,132 DHSs for inclusion in the investigation and found that 2,397 (~6%) exhibited evidence of accelerated evolution (aceDHSs), which was a much higher proportion that DHSs genome-wide. Target genes predicted to be regulated by brain-aceDHSs were functionally enriched for brain development and exhibited differential expression between human and chimpanzee. Alignments indicated 61 potential human-specific transcription factor binding sites in brain-aceDHSs, including for CTCF, FOXH1, and FOXQ1. Furthermore, based on GWAS, Hi-C, and eQTL data, 16 GWAS SNPs, and 82 eQTL SNPs were in brain-aceDHSs that regulate genes related to brain development or disease. Among these brain-aceDHSs, we confirmed that one enhanced the expression of GPR133, using CRISPR-Cas9 and western blotting. The GPR133 gene is associated with glioblastoma, indicating that SNPs within DHSs could be related to brain disorders. These findings suggest that brain-related gene regulatory regions are under adaptive evolution and contribute to the differential expression profiles among primates, providing new insights into the genetic basis of brain phenotypes or disorders between humans and other primates.