Incorporating network diffusion and peak location information for better single-cell ATAC-seq data analysis.

Single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) data provided new insights into the understanding of epigenetic heterogeneity and transcriptional regulation. With the increasing abundance of dataset resources, there is an urgent need to extract more useful information through high-quality data analysis methods specifically designed for scATAC-seq. However, analyzing scATAC-seq data poses challenges due to its near binarization, high sparsity and ultra-high dimensionality properties. Here, we proposed a novel network diffusion-based computational method to comprehensively analyze scATAC-seq data, named Single-Cell ATAC-seq Analysis via Network Refinement with Peaks Location Information (SCARP). SCARP formulates the Network Refinement diffusion method under the graph theory framework to aggregate information from different network orders, effectively compensating for missing signals in the scATAC-seq data. By incorporating distance information between adjacent peaks on the genome, SCARP also contributes to depicting the co-accessibility of peaks. These two innovations empower SCARP to obtain lower-dimensional representations for both cells and peaks more effectively. We have demonstrated through sufficient experiments that SCARP facilitated superior analyses of scATAC-seq data. Specifically, SCARP exhibited outstanding cell clustering performance, enabling better elucidation of cell heterogeneity and the discovery of new biologically significant cell subpopulations. Additionally, SCARP was also instrumental in portraying co-accessibility relationships of accessible regions and providing new insight into transcriptional regulation. Consequently, SCARP identified genes that were involved in key Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to diseases and predicted reliable cis-regulatory interactions. To sum up, our studies suggested that SCARP is a promising tool to comprehensively analyze the scATAC-seq data.

ROBO1 deficiency impairs HSPC homeostasis and erythropoiesis via CDC42 and predicts poor prognosis in MDS.

Myelodysplastic syndrome (MDS) is a group of clonal hematopoietic neoplasms originating from hematopoietic stem progenitor cells (HSPCs). We previously identified frequent roundabout guidance receptor 1 (ROBO1) mutations in patients with MDS, while the exact role of ROBO1 in hematopoiesis remains poorly delineated. Here, we report that ROBO1 deficiency confers MDS-like disease with anemia and multilineage dysplasia in mice and predicts poor prognosis in patients with MDS. More specifically, Robo1 deficiency impairs HSPC homeostasis and disrupts HSPC pool, especially the reduction of megakaryocyte erythroid progenitors, which causes a blockage in the early stages of erythropoiesis in mice. Mechanistically, transcriptional profiling indicates that Cdc42, a member of the Rho-guanosine triphosphatase family, acts as a downstream target gene for Robo1 in HSPCs. Overexpression of Cdc42 partially restores the self-renewal and erythropoiesis of HSPCs in Robo1-deficient mice. Collectively, our result implicates the essential role of ROBO1 in maintaining HSPC homeostasis and erythropoiesis via CDC42.

PathExpSurv: pathway expansion for explainable survival analysis and disease gene discovery.

BACKGROUND: In the field of biology and medicine, the interpretability and accuracy are both important when designing predictive models. The interpretability of many machine learning models such as neural networks is still a challenge. Recently, many researchers utilized prior information such as biological pathways to develop neural networks-based methods, so as to provide some insights and interpretability for the models. However, the prior biological knowledge may be incomplete and there still exists some unknown information to be explored. RESULTS: We proposed a novel method, named PathExpSurv, to gain an insight into the black-box model of neural network for cancer survival analysis. We demonstrated that PathExpSurv could not only incorporate the known prior information into the model, but also explore the unknown possible expansion to the existing pathways. We performed downstream analyses based on the expanded pathways and successfully identified some key genes associated with the diseases and original pathways. CONCLUSIONS: Our proposed PathExpSurv is a novel, effective and interpretable method for survival analysis. It has great utility and value in medical diagnosis and offers a promising framework for biological research.

The bcl6 corepressor mutation regulates the progression and transformation of myelodysplastic syndromes by repressing the autophagy flux.

The occurrence of autophagy dysregulation is vital in the development of myelodysplastic syndrome and its transformation to acute myeloid leukemia. However, the mechanisms are largely unknown. Here, we have investigated the mechanism of the bcl6 corepressor mutation in myelodysplastic syndrome development and its transformation to acute myeloid leukemia. We identified a novel pathway involving histone deacetylase 6 and forkhead box protein O1, which leads to autophagy defects following the bcl6 corepressor mutation. And this further causes apoptosis and cell cycle arrest. The bcl6 corepressor-mutation-repressed autophagy resulted in the accumulation of damaged mitochondria, DNA, and reactive oxygen species in myelodysplastic syndrome cells, which could then lead to genomic instability and spontaneous mutation. Our results suggest that the bcl6 corepressor inactivating mutations exert pro-carcinogenic effects through survival strike, which is only an intermediate process. These findings provide mechanistic insights into the role of the bcl6 corepressor gene in myelodysplastic syndrome.

P38-DAPK1 axis regulated LC3-associated phagocytosis (LAP) of microglia in an in vitro subarachnoid hemorrhage model.

BACKGROUND: The phagocytosis and homeostasis of microglia play an important role in promoting blood clearance and improving prognosis after subarachnoid hemorrhage (SAH). LC3-assocaited phagocytosis (LAP) contributes to the microglial phagocytosis and homeostasis via autophagy-related components. With RNA-seq sequencing, we found potential signal pathways and genes which were important for the LAP of microglia. METHODS: We used an in vitro model of oxyhemoglobin exposure as SAH model in the study. RNA-seq sequencing was performed to seek critical signal pathways and genes in regulating LAP. Bioparticles were used to access the phagocytic ability of microglia. Western blot (WB), immunoprecipitation, quantitative polymerase chain reaction (qPCR) and immunofluorescence were performed to detect the expression change of LAP-related components and investigate the potential mechanisms. RESULTS: In vitro SAH model, there were increased inflammation and decreased phagocytosis in microglia. At the same time, we found that the LAP of microglia was inhibited in all stages. RNA-seq sequencing revealed the importance of P38 MAPK signal pathway and DAPK1 in regulating microglial LAP. P38 was found to regulate the expression of DAPK1, and P38-DAPK1 axis was identified to regulate the LAP and homeostasis of microglia after SAH. Finally, we found that P38-DAPK1 axis regulated expression of BECN1, which indicated the potential mechanism of P38-DAPK1 axis regulating microglial LAP. CONCLUSION: P38-DAPK1 axis regulated the LAP of microglia via BECN1, affecting the phagocytosis and homeostasis of microglia in vitro SAH model. Video Abstract.

The non-cell-autonomous function of ID1 promotes AML progression via ANGPTL7 from the microenvironment.

The bone marrow microenvironment (BMM) can regulate leukemia stem cells (LSCs) via secreted factors. Increasing evidence suggests that dissecting the mechanisms by which the BMM maintains LSCs may lead to the development of effective therapies for the eradication of leukemia. Inhibitor of DNA binding 1 (ID1), a key transcriptional regulator in LSCs, previously identified by us, controls cytokine production in the BMM, but the role of ID1 in acute myeloid leukemia (AML) BMM remains obscure. Here, we report that ID1 is highly expressed in the BMM of patients with AML, especially in BM mesenchymal stem cells, and that the high expression of ID1 in the AML BMM is induced by BMP6, secreted from AML cells. Knocking out ID1 in mesenchymal cells significantly suppresses the proliferation of cocultured AML cells. Loss of Id1 in the BMM results in impaired AML progression in AML mouse models. Mechanistically, we found that Id1 deficiency significantly reduces SP1 protein levels in mesenchymal cells cocultured with AML cells. Using ID1-interactome analysis, we found that ID1 interacts with RNF4, an E3 ubiquitin ligase, and causes a decrease in SP1 ubiquitination. Disrupting the ID1-RNF4 interaction via truncation in mesenchymal cells significantly reduces SP1 protein levels and delays AML cell proliferation. We identify that the target of Sp1, Angptl7, is the primary differentially expression protein factor in Id1-deficient BM supernatant fluid to regulate AML progression in mice. Our study highlights the critical role of ID1 in the AML BMM and aids the development of therapeutic strategies for AML.

[Response of Soil Organic Carbon Accumulation in Paddy Fields in Fujian Province to Future Temperature Increases].

Understanding the effect of the soil carbon "source-sink" in cropland in China under future warming scenarios is the basis for making reasonable carbon neutralization policies. This study focused on the paddy soil in Fujian Province, a typical subtropical region in China including 84 counties (cities and districts). We employed the 1:50000 soil database and biogeochemical process model (DNDC) to simulate the dynamic changes in paddy soil organic carbon under different warming scenarios for the period of 2017-2053. The results indicated that in the context of normal temperature (control run) and 2, 4, and 6℃ of warming, the total amounts of carbon sequestration of paddy soil in Fujian Province were 11.56,9.44, 7.08, and 4.91 Tg, respectively; accordingly, the average annual carbon sequestration rates (expressed by C) were 173, 141, 106, and 74 kg·(hm2·a)-1, indicating that the rate of carbon sequestration was decreasing with the increase in future temperature. However, overall, the paddy field soil in the province was still a "carbon sink" under the warming of 6 (C. We also found that the gleyed paddy soil was mostly affected by the increase in temperature, and the decrease in carbon sequestration rate ranged from 20% to 69% using different treatments. In contrast, the salinized paddy soil was slightly affected, with a 14%-43% decrease in carbon sequestration rates. As for the different administrative regions, Sanming City was the most affected by temperature increase, with the rate of carbon sequestration decreasing by 27%-83% using different treatments. However, it was reduced by only 10%-41% and 14%-42% in Quanzhou and Putian (coastal areas), respectively. Overall, due to different soil properties, fertilization management, and climatic environment, there was a strong variability in the carbon sequestration rates of paddy soil for different soil subtypes and administrative regions in Fujian in response to future climatic warming.

Supervised learning of high-confidence phenotypic subpopulations from single-cell data.

Accurately identifying phenotype-relevant cell subsets from heterogeneous cell populations is crucial for delineating the underlying mechanisms driving biological or clinical phenotypes. Here, by deploying a learning with rejection strategy, we developed a novel supervised learning framework called PENCIL to identify subpopulations associated with categorical or continuous phenotypes from single-cell data. By embedding a feature selection function into this flexible framework, for the first time, we were able to select informative features and identify cell subpopulations simultaneously, which enables the accurate identification of phenotypic subpopulations otherwise missed by methods incapable of concurrent gene selection. Furthermore, the regression mode of PENCIL presents a novel ability for supervised phenotypic trajectory learning of subpopulations from single-cell data. We conducted comprehensive simulations to evaluate PENCILs versatility in simultaneous gene selection, subpopulation identification and phenotypic trajectory prediction. PENCIL is fast and scalable to analyze 1 million cells within 1 hour. Using the classification mode, PENCIL detected T-cell subpopulations associated with melanoma immunotherapy outcomes. Moreover, when applied to scRNA-seq of a mantle cell lymphoma patient with drug treatment across multiple time points, the regression mode of PENCIL revealed a transcriptional treatment response trajectory. Collectively, our work introduces a scalable and flexible infrastructure to accurately identify phenotype-associated subpopulations from single-cell data.

Long-Term Elevated Siglec-10 in Cerebral Spinal Fluid Heralds Better Prognosis for Patients with Aneurysmal Subarachnoid Hemorrhage.

The presence of aneurysmal subarachnoid hemorrhage (aSAH) is usually accompanied by excessive inflammatory response leading to damage of the central nervous system, and the sialic acid-binding Ig-like lectin 10 (Siglec-10) is a recognized factor being able to modify the inflammatory reaction. To investigate the potential role of Siglec-10 in aSAH, we collected the cerebrospinal fluid (CSF) of control (n = 11) and aSAH (n = 14) patients at separate times and measured the Siglec-10 concentration utilizing the enzyme-linked immunosorbent assay (ELISA) and evaluated the alterations of GOS and GCS during the disease process. In accordance with the STROBE statement, results showed that Siglec-10 in CSF rose quickly in response aSAH attack and then fell back to a slightly higher range above baseline, while it remained at relative high concentration and last longer in several severely injured patients. In general, higher Siglec-10 expression over a longer period usually indicated a better clinical prognosis. This prospective cohort study suggested that Siglec-10 could possibly be used as a biomarker for predicting prognosis of aSAH due to its ability to balance aSAH-induced nonsterile inflammation. Additionally, these findings might provide novel therapeutic perspectives for aSAH and other inflammation-related diseases.

Targeting UHRF1-SAP30-MXD4 axis for leukemia initiating cell eradication in myeloid leukemia.

Aberrant self-renewal of leukemia initiation cells (LICs) drives aggressive acute myeloid leukemia (AML). Here, we report that UHRF1, an epigenetic regulator that recruits DNMT1 to methylate DNA, is highly expressed in AML and predicts poor prognosis. UHRF1 is required for myeloid leukemogenesis by maintaining self-renewal of LICs. Mechanistically, UHRF1 directly interacts with Sin3A-associated protein 30 (SAP30) through two critical amino acids, G572 and F573 in its SRA domain, to repress gene expression. Depletion of UHRF1 or SAP30 derepresses an important target gene, MXD4, which encodes a MYC antagonist, and leads to suppression of leukemogenesis. Further knockdown of MXD4 can rescue the leukemogenesis by activating the MYC pathway. Lastly, we identified a UHRF1 inhibitor, UF146, and demonstrated its significant therapeutic efficacy in the myeloid leukemia PDX model. Taken together, our study reveals the mechanisms for altered epigenetic programs in AML and provides a promising targeted therapeutic strategy against AML.

Interaction-based transcriptome analysis via differential network inference.

Gene-based transcriptome analysis, such as differential expression analysis, can identify the key factors causing disease production, cell differentiation and other biological processes. However, this is not enough because basic life activities are mainly driven by the interactions between genes. Although there have been already many differential network inference methods for identifying the differential gene interactions, currently, most studies still only use the information of nodes in the network for downstream analyses. To investigate the insight into differential gene interactions, we should perform interaction-based transcriptome analysis (IBTA) instead of gene-based analysis after obtaining the differential networks. In this paper, we illustrated a workflow of IBTA by developing a Co-hub Differential Network inference (CDN) algorithm, and a novel interaction-based metric, pivot APC2. We confirmed the superior performance of CDN through simulation experiments compared with other popular differential network inference algorithms. Furthermore, three case studies are given using colorectal cancer, COVID-19 and triple-negative breast cancer datasets to demonstrate the ability of our interaction-based analytical process to uncover causative mechanisms.

High Expression of PDK4 Could Play a Potentially Protective Role by Attenuating Oxidative Stress after Subarachnoid Hemorrhage.

Pyruvate dehydrogenase (PDH), a key enzyme on the mitochondrial outer membrane, has been found to decrease activity notably in early brain injury (EBI) after subarachnoid hemorrhage (SAH). It has been demonstrated that PDH is associated with the production of reactive oxygen species (ROS) and apoptosis. Hence, in this study, we aimed to determine the cause of the decreased PDH activity and explore the potential role of PDH in EBI. We investigated the expression changes of PDH and pyruvate dehydrogenase kinase (PDK) in vivo and in vitro. Then, we explored the possible effects of PDH and ROS after SAH. The results showed that early overexpression of PDK4 promoted the phosphorylation of PDH, inhibited PDH activity, and may play a protective role after SAH in vivo and in vitro. Finally, we investigated the levels of PDK4 and pyruvate, which accumulated due to decreased PDH activity, in the cerebrospinal fluid (CSF) of 34 patients with SAH. Statistical analysis revealed that PDK4 and pyruvate expression was elevated in the CSF of SAH patients compared with that of controls, and this high expression correlated with the degree of neurological impairment and long-term outcome. Taken together, the results show that PDK4 has the potential to serve as a new therapeutic target and biomarker for assisting in the diagnosis of SAH severity and prediction of recovery.

Effect of prior malignancy on the prognosis of gastric cancer and somatic mutation.

BACKGROUND: Cancer survivors have a higher risk of developing secondary cancer, with previous studies showing heterogeneous effects of prior cancer on cancer survivors. AIM: To describe the features and clinical significance of a prior malignancy in patients with gastric cancer (GC). METHODS: We identified eligible patients from the Surveillance, Epidemiology, and End Results (SEER) database, and compared the clinical features of GC patients with/without prior cancer. Kaplan-Meier curves and Cox analyses were used to assess the prognostic impact of prior cancer on overall survival (OS) and cancer-specific survival (CSS) outcomes. We also validated our results in The Cancer Genome Atlas (TCGA) cohort and compared mutation patterns. RESULTS: In the SEER dataset, of the 35492 patients newly diagnosed with GC between 2004 and 2011, 4,001 (11.3%) had at least one prior cancer, including 576 (1.62%) patients with multiple cancers. Patients with a prior cancer history tended to be elderly, with a more localized stage and less positive lymph nodes. The prostate (32%) was the most common initial cancer site. The median interval from initial cancer diagnosis to secondary GC was 68 mo. By using multivariable Cox analyses, we found that a prior cancer history was not significantly associated with OS (hazard ratio [HR]: 1.01, 95% confidence interval [CI]: 0.97-1.05). However, a prior cancer history was significantly associated with better GC-specific survival (HR: 0.82, 95% CI: 0.78-0.85). In TCGA cohort, no significant difference in OS was observed for GC patients with or without prior cancer. Also, no significant differences in somatic mutations were observed between groups. CONCLUSION: The prognosis of GC patients with previous diagnosis of cancer was not inferior to that of primary GC patients.

Importance-Penalized Joint Graphical Lasso (IPJGL): differential network inference via GGMs.

MOTIVATION: Differential network inference is a fundamental and challenging problem to reveal gene interactions and regulation relationships under different conditions. Many algorithms have been developed for this problem; however, they do not consider the differences between the importance of genes, which may not fit the real-world situation. Different genes have different mutation probabilities, and the vital genes associated with basic life activities have less fault tolerance to mutation. Equally treating all genes may bias the results of differential network inference. Thus, it is necessary to consider the importance of genes in the models of differential network inference. RESULTS: Based on the Gaussian graphical model with adaptive gene importance regularization, we develop a novel Importance-Penalized Joint Graphical Lasso method (IPJGL) for differential network inference. The presented method is validated by the simulation experiments as well as the real datasets. Furthermore, to precisely evaluate the results of differential network inference, we propose a new metric named APC2 for the differential levels of gene pairs. We apply IPJGL to analyze the TCGA colorectal and breast cancer datasets and find some candidate cancer genes with significant survival analysis results, including SOST for colorectal cancer and RBBP8 for breast cancer. We also conduct further analysis based on the interactions in the Reactome database and confirm the utility of our method. AVAILABILITY AND IMPLEMENTATION: R source code of Importance-Penalized Joint Graphical Lasso is freely available at https://github.com/Wu-Lab/IPJGL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identifying phenotype-associated subpopulations by integrating bulk and single-cell sequencing data.

Single-cell RNA sequencing (scRNA-seq) distinguishes cell types, states and lineages within the context of heterogeneous tissues. However, current single-cell data cannot directly link cell clusters with specific phenotypes. Here we present Scissor, a method that identifies cell subpopulations from single-cell data that are associated with a given phenotype. Scissor integrates phenotype-associated bulk expression data and single-cell data by first quantifying the similarity between each single cell and each bulk sample. It then optimizes a regression model on the correlation matrix with the sample phenotype to identify relevant subpopulations. Applied to a lung cancer scRNA-seq dataset, Scissor identified subsets of cells associated with worse survival and with TP53 mutations. In melanoma, Scissor discerned a T cell subpopulation with low PDCD1/CTLA4 and high TCF7 expression associated with an immunotherapy response. Beyond cancer, Scissor was effective in interpreting facioscapulohumeral muscular dystrophy and Alzheimer's disease datasets. Scissor identifies biologically and clinically relevant cell subpopulations from single-cell assays by leveraging phenotype and bulk-omics datasets.

PDK4 Decrease Neuronal Apoptosis via Inhibiting ROS-ASK1/P38 Pathway in Early Brain Injury After Subarachnoid Hemorrhage.

Aims: Metabolic disorders may play key roles in oxidative stress and neuronal apoptosis in response to early brain injury (EBI) after subarachnoid hemorrhage (SAH). Pyruvate dehydrogenase (PDH) is related to oxidative stress in EBI, and its activity obviously decreases after SAH. We discovered that only pyruvate dehydrogenase kinase 4 (PDK4) expression was obviously increased among the four PDK isozymes after SAH in preliminary experiments. Therefore, we attempted to investigate the effects and corresponding mechanisms of PDK4 on oxidative stress after SAH. Results: First, we confirmed that PDK4 overexpression promoted PDH phosphorylation, inhibited PDH activity, and changed cell metabolism after SAH. A small interfering RNA (siRNA) targeting PDK4, a lentiviral PDK4 overexpression vector, and dichloroacetic acid (DCA) were used to regulate the expression and activity of PDK4. The siRNA decreased PDH phosphorylation, promoted reactive oxygen species (ROS) production, activated the apoptosis signal-regulating kinase 1 (ASK1)/P38 pathway, and induced neuronal apoptosis. The lentivirus further attenuated PDH activity, oxidative stress, and neuronal apoptosis. DCA inhibited the activity of PDK4, but increased the expression of PDK4 due to a feedback mechanism. Inactivated PDK4 did not effectively suppress PDH activity, which increased ROS production, activated the ASK1/P38 pathway, and led to neuronal apoptosis. Innovation: This study provides new insights into the potential antioxidant and antiapoptotic effects of the PDK4-PDH axis on EBI after SAH. Conclusions: The early overexpression of PDK4 after SAH may attenuate neuronal apoptosis by reducing oxidative stress via the ROS/ASK1/P38 pathway. PDK4 may be a new potential therapeutic target to ameliorate EBI after SAH. Antioxid. Redox Signal. 36, 505-524.

SIRT1 Promotes M2 Microglia Polarization via Reducing ROS-Mediated NLRP3 Inflammasome Signaling After Subarachnoid Hemorrhage.

Mounting evidence has suggested that modulating microglia polarization from pro-inflammatory M1 phenotype to anti-inflammatory M2 state might be a potential therapeutic approach in the treatment of subarachnoid hemorrhage (SAH) injury. Our previous study has indicated that sirtuin 1 (SIRT1) could ameliorate early brain injury (EBI) in SAH by reducing oxidative damage and neuroinflammation. However, the effects of SIRT1 on microglial polarization and the underlying molecular mechanisms after SAH have not been fully illustrated. In the present study, we first observed that EX527, a potent selective SIRT1 inhibitor, enhanced microglial M1 polarization and nod-like receptor pyrin domain-containing 3 (NLRP3) inflammasome activation in microglia after SAH. Administration of SRT1720, an agonist of SIRT1, significantly enhanced SIRT1 expression, improved functional recovery, and ameliorated brain edema and neuronal death after SAH. Moreover, SRT1720 modulated the microglia polarization shift from the M1 phenotype and skewed toward the M2 phenotype. Additionally, SRT1720 significantly decreased acetylation of forkhead box protein O1, inhibited the overproduction of reactive oxygen species (ROS) and suppressed NLRP3 inflammasome signaling. In contrast, EX527 abated the upregulation of SIRT1 and reversed the inhibitory effects of SRT1720 on ROS-NLRP3 inflammasome activation and EBI. Similarly, in vitro, SRT1720 suppressed inflammatory response, oxidative damage, and neuronal degeneration, and improved cell viability in neurons and microglia co-culture system. These effects were associated with the suppression of ROS-NLRP3 inflammasome and stimulation of SIRT1 signaling, which could be abated by EX527. Altogether, these findings indicate that SRT1720, an SIRT1 agonist, can ameliorate EBI after SAH by shifting the microglial phenotype toward M2 via modulation of ROS-mediated NLRP3 inflammasome signaling.