Energy sensor AMPK gamma regulates translation via phosphatase PPP6C independent of AMPK alpha.

Maintenance of energy level to drive movements and material exchange with the environment is a basic principle of life. AMP-activated protein kinase (AMPK) senses energy level and is a major regulator of cellular energy responses. The gamma subunit of AMPK senses elevated ratio of AMP to ATP and allosterically activates the alpha catalytic subunit to phosphorylate downstream effectors. Here, we report that knockout of AMPKγ, but not AMPKα, suppressed phosphorylation of eukaryotic translation elongation factor 2 (eEF2) induced by energy starvation. We identified PPP6C as an AMPKγ-regulated phosphatase of eEF2. AMP-bound AMPKγ sequesters PPP6C, thereby blocking dephosphorylation of eEF2 and thus inhibiting translation elongation to preserve energy and to promote cell survival. Further phosphoproteomic analysis identified additional targets of PPP6C regulated by energy stress in an AMPKγ-dependent manner. Thus, AMPKγ senses cellular energy availability to regulate not only AMPKα kinase, but also PPP6C phosphatase and possibly other effectors.

Mitochondrial integrated stress response controls lung epithelial cell fate.

Alveolar epithelial type 1 (AT1) cells are necessary to transfer oxygen and carbon dioxide between the blood and air. Alveolar epithelial type 2 (AT2) cells serve as a partially committed stem cell population, producing AT1 cells during postnatal alveolar development and repair after influenza A and SARS-CoV-2 pneumonia1-6. Little is known about the metabolic regulation of the fate of lung epithelial cells. Here we report that deleting the mitochondrial electron transport chain complex I subunit Ndufs2 in lung epithelial cells during mouse gestation led to death during postnatal alveolar development. Affected mice displayed hypertrophic cells with AT2 and AT1 cell features, known as transitional cells. Mammalian mitochondrial complex I, comprising 45 subunits, regenerates NAD+ and pumps protons. Conditional expression of yeast NADH dehydrogenase (NDI1) protein that regenerates NAD+ without proton pumping7,8 was sufficient to correct abnormal alveolar development and avert lethality. Single-cell RNA sequencing revealed enrichment of integrated stress response (ISR) genes in transitional cells. Administering an ISR inhibitor9,10 or NAD+ precursor reduced ISR gene signatures in epithelial cells and partially rescued lethality in the absence of mitochondrial complex I function. Notably, lung epithelial-specific loss of mitochondrial electron transport chain complex II subunit Sdhd, which maintains NAD+ regeneration, did not trigger high ISR activation or lethality. These findings highlight an unanticipated requirement for mitochondrial complex I-dependent NAD+ regeneration in directing cell fate during postnatal alveolar development by preventing pathological ISR induction.

Unsupervised and supervised discovery of tissue cellular neighborhoods from cell phenotypes.

It is poorly understood how different cells in a tissue organize themselves to support tissue functions. We describe the CytoCommunity algorithm for the identification of tissue cellular neighborhoods (TCNs) based on cell phenotypes and their spatial distributions. CytoCommunity learns a mapping directly from the cell phenotype space to the TCN space using a graph neural network model without intermediate clustering of cell embeddings. By leveraging graph pooling, CytoCommunity enables de novo identification of condition-specific and predictive TCNs under the supervision of sample labels. Using several types of spatial omics data, we demonstrate that CytoCommunity can identify TCNs of variable sizes with substantial improvement over existing methods. By analyzing risk-stratified colorectal and breast cancer data, CytoCommunity revealed new granulocyte-enriched and cancer-associated fibroblast-enriched TCNs specific to high-risk tumors and altered interactions between neoplastic and immune or stromal cells within and between TCNs. CytoCommunity can perform unsupervised and supervised analyses of spatial omics maps and enable the discovery of condition-specific cell-cell communication patterns across spatial scales.

Disruption of mitochondrial complex I induces progressive parkinsonism.

Loss of functional mitochondrial complex I (MCI) in the dopaminergic neurons of the substantia nigra is a hallmark of Parkinson's disease1. Yet, whether this change contributes to Parkinson's disease pathogenesis is unclear2. Here we used intersectional genetics to disrupt the function of MCI in mouse dopaminergic neurons. Disruption of MCI induced a Warburg-like shift in metabolism that enabled neuronal survival, but triggered a progressive loss of the dopaminergic phenotype that was first evident in nigrostriatal axons. This axonal deficit was accompanied by motor learning and fine motor deficits, but not by clear levodopa-responsive parkinsonism-which emerged only after the later loss of dopamine release in the substantia nigra. Thus, MCI dysfunction alone is sufficient to cause progressive, human-like parkinsonism in which the loss of nigral dopamine release makes a critical contribution to motor dysfunction, contrary to the current Parkinson's disease paradigm3,4.

Type VII secretion system extracellular protein B targets STING to evade host anti-Staphylococcus aureus immunity.

Staphylococcus aureus (S. aureus) can evade antibiotics and host immune defenses by persisting within infected cells. Here, we demonstrate that in infected host cells, S. aureus type VII secretion system (T7SS) extracellular protein B (EsxB) interacts with the stimulator of interferon genes (STING) protein and suppresses the inflammatory defense mechanism of macrophages during early infection. The binding of EsxB with STING disrupts the K48-linked ubiquitination of EsxB at lysine 33, thereby preventing EsxB degradation. Furthermore, EsxB-STING binding appears to interrupt the interaction of 2 vital regulatory proteins with STING: aspartate-histidine-histidine-cysteine domain-containing protein 3 (DHHC3) and TNF receptor-associated factor 6. This persistent dual suppression of STING interactions deregulates intracellular proinflammatory pathways in macrophages, inhibiting STING's palmitoylation at cysteine 91 and its K63-linked ubiquitination at lysine 83. These findings uncover an immune-evasion mechanism by S. aureus T7SS during intracellular macrophage infection, which has implications for developing effective immunomodulators to combat S. aureus infections.

Statistical modeling and significance estimation of multi-way chromatin contacts with HyperloopFinder.

Recent advances in chromatin conformation capture technologies, such as SPRITE and Pore-C, have enabled the detection of simultaneous contacts among multiple chromatin loci. This has made it possible to investigate the cooperative transcriptional regulation involving multiple genes and regulatory elements at the resolution of a single molecule. However, these technologies are unavoidably subject to the random polymer looping effect and technical biases, making it challenging to distinguish genuine regulatory relationships directly from random polymer interactions. Here, we present HyperloopFinder, a method for identifying regulatory multi-way chromatin contacts (hyperloops) by jointly modeling the random polymer looping effect and technical biases to estimate the statistical significance of multi-way contacts. The results show that our model can accurately estimate the expected interaction frequency of multi-way contacts based on the distance distribution of pairwise contacts, revealing that most multi-way contacts can be formed by randomly linking the pairwise contacts adjacent to each other. Moreover, we observed the spatial colocalization of the interaction sites of hyperloops from image-based data. Our results also revealed that hyperloops can function as scaffolds for the cooperation among multiple genes and regulatory elements. In summary, our work contributes novel insights into higher-order chromatin structures and functions and has the potential to enhance our understanding of transcriptional regulation and other cellular processes.

Adjustment of scRNA-seq data to improve cell-type decomposition of spatial transcriptomics.

Most sequencing-based spatial transcriptomics (ST) technologies do not achieve single-cell resolution where each captured location (spot) may contain a mixture of cells from heterogeneous cell types, and several cell-type decomposition methods have been proposed to estimate cell type proportions of each spot by integrating with single-cell RNA sequencing (scRNA-seq) data. However, these existing methods did not fully consider the effect of distribution difference between scRNA-seq and ST data for decomposition, leading to biased cell-type-specific genes derived from scRNA-seq for ST data. To address this issue, we develop an instance-based transfer learning framework to adjust scRNA-seq data by ST data to correctly match cell-type-specific gene expression. We evaluate the effect of raw and adjusted scRNA-seq data on cell-type decomposition by eight leading decomposition methods using both simulated and real datasets. Experimental results show that data adjustment can effectively reduce distribution difference and improve decomposition, thus enabling for a more precise depiction on spatial organization of cell types. We highlight the importance of data adjustment in integrative analysis of scRNA-seq with ST data and provide guidance for improved cell-type decomposition.

DeepGRNCS: deep learning-based framework for jointly inferring gene regulatory networks across cell subpopulations.

Inferring gene regulatory networks (GRNs) allows us to obtain a deeper understanding of cellular function and disease pathogenesis. Recent advances in single-cell RNA sequencing (scRNA-seq) technology have improved the accuracy of GRN inference. However, many methods for inferring individual GRNs from scRNA-seq data are limited because they overlook intercellular heterogeneity and similarities between different cell subpopulations, which are often present in the data. Here, we propose a deep learning-based framework, DeepGRNCS, for jointly inferring GRNs across cell subpopulations. We follow the commonly accepted hypothesis that the expression of a target gene can be predicted based on the expression of transcription factors (TFs) due to underlying regulatory relationships. We initially processed scRNA-seq data by discretizing data scattering using the equal-width method. Then, we trained deep learning models to predict target gene expression from TFs. By individually removing each TF from the expression matrix, we used pre-trained deep model predictions to infer regulatory relationships between TFs and genes, thereby constructing the GRN. Our method outperforms existing GRN inference methods for various simulated and real scRNA-seq datasets. Finally, we applied DeepGRNCS to non-small cell lung cancer scRNA-seq data to identify key genes in each cell subpopulation and analyzed their biological relevance. In conclusion, DeepGRNCS effectively predicts cell subpopulation-specific GRNs. The source code is available at https://github.com/Nastume777/DeepGRNCS.

Brain macrophage senescence in glioma.

Gliomas are a diverse group of primary central nervous system neoplasms with no curative therapies available. Brain macrophages comprise microglia in the brain parenchyma, border-associated macrophages in the meningeal-choroid plexus-perivascular space and monocyte-derived macrophages infiltrating the brain. With the great improvement of our recognition of brain macrophages, diverse macrophage populations have been found in the context of glioma, which exhibit functional and phenotypic heterogeneity. We have long thought that brain macrophage senescence is detrimental, manifested by specialized forms of persistent cell cycle arrest and chronic low-grade inflammation. Persistent senescence of macrophages may result in immune dysfunction, potentially contributing to glioma initiation and development. Given the crucial roles played by brain macrophages in glioma, we unravel how brain macrophages undergo reprogramming and their contribution to glioma. We outline general molecular alterations and specific biomarkers in senescent brain macrophages, as well as functional changes (such as metabolism, autophagy, phagocytosis, antigen presentation, and infiltration and recruitment). In addition, recent advances in genetic regulation and mechanisms linked to senescent brain macrophages are discussed. In particular, this review emphasizes the contribution of senescent brain macrophages to glioma, which may drive translational efforts to utilize brain macrophages as a prognostic marker or/and treatment target in glioma. An in-depth comprehending of how brain macrophage senescence functionally influences the tumor microenvironment will be key to our development of innovative therapeutics for glioma.

Contrastive pre-training and 3D convolution neural network for RNA and small molecule binding affinity prediction.

MOTIVATION: The diverse structures and functions inherent in RNAs present a wealth of potential drug targets. Some small molecules are anticipated to serve as leading compounds, providing guidance for the development of novel RNA-targeted therapeutics. Consequently, the determination of RNA-small molecule binding affinity is a critical undertaking in the landscape of RNA-targeted drug discovery and development. Nevertheless, to date, only one computational method for RNA-small molecule binding affinity prediction has been proposed. The prediction of RNA-small molecule binding affinity remains a significant challenge. The development of a computational model is deemed essential to effectively extract relevant features and predict RNA-small molecule binding affinity accurately. RESULTS: In this study, we introduced RLaffinity, a novel deep learning model designed for the prediction of RNA-small molecule binding affinity based on 3D structures. RLaffinity integrated information from RNA pockets and small molecules, utilizing a 3D convolutional neural network (3D-CNN) coupled with a contrastive learning-based self-supervised pre-training model. To the best of our knowledge, RLaffinity was the first deep learning based method for the prediction of RNA-small molecule binding affinity. Our experimental results exhibited RLaffinity's superior performance compared to baseline methods, revealed by all metrics. The efficacy of RLaffinity underscores the capability of 3D-CNN to accurately extract both global pocket information and local neighbor nucleotide information within RNAs. Notably, the integration of a self-supervised pre-training model significantly enhanced predictive performance. Ultimately, RLaffinity was also proved as a potential tool for RNA-targeted drugs virtual screening. AVAILABILITY AND IMPLEMENTATION: https://github.com/SaisaiSun/RLaffinity.

DiSMVC: a multi-view graph collaborative learning framework for measuring disease similarity.

MOTIVATION: Exploring potential associations between diseases can help in understanding pathological mechanisms of diseases and facilitating the discovery of candidate biomarkers and drug targets, thereby promoting disease diagnosis and treatment. Some computational methods have been proposed for measuring disease similarity. However, these methods describe diseases without considering their latent multi-molecule regulation and valuable supervision signal, resulting in limited biological interpretability and efficiency to capture association patterns. RESULTS: In this study, we propose a new computational method named DiSMVC. Different from existing predictors, DiSMVC designs a supervised graph collaborative framework to measure disease similarity. Multiple bio-entity associations related to genes and miRNAs are integrated via cross-view graph contrastive learning to extract informative disease representation, and then association pattern joint learning is implemented to compute disease similarity by incorporating phenotype-annotated disease associations. The experimental results show that DiSMVC can draw discriminative characteristics for disease pairs, and outperform other state-of-the-art methods. As a result, DiSMVC is a promising method for predicting disease associations with molecular interpretability. AVAILABILITY AND IMPLEMENTATION: Datasets and source codes are available at https://github.com/Biohang/DiSMVC.

Inhibition of RhoGEF/RhoA alleviates regorafenib resistance and cancer stemness via Hippo signaling pathway in hepatocellular carcinoma.

Patients with hepatocellular carcinoma (HCC) are vulnerable to drug resistance. Although drug resistance has been taken much attention to HCC therapy, little is known of regorafenib and regorafenib resistance (RR). This study aimed to determine the drug resistance pattern and the role of RhoA in RR. Two regorafenib-resistant cell lines were constructed based on Huh7 and Hep3B cell lines. In vitro and in vivo assays were conducted to study RhoA expression, the activity of Hippo signaling pathway and cancer stem cell (CSC) traits. The data showed that RhoA was highly expressed, Hippo signaling was hypoactivated and CSC traits were more prominent in RR cells. Inhibiting RhoA could reverse RR, and the alliance of RhoA inhibition and regorafenib synergistically attenuated CSC phenotype. Furthermore, inhibiting LARG/RhoA increased Kibra/NF2 complex formation, prevented YAP from shuttling into the nucleus and repressed CD44 mRNA expression. Clinically, the high expression of RhoA correlated with poor prognosis. LARG, RhoA, YAP1 and CD44 show positive correlation with each other. Thus, inhibition of RhoGEF/RhoA has the potential to reverse RR and repress CSC phenotype in HCC.

Overexpression of long noncoding RNA DUXAP8 inhibits ER-phagy through activating AKT/mTOR signaling and contributes to preeclampsia.

Preeclampsia (PE) is a life-threatening pregnancy-specific complication with controversial mechanisms and no effective treatment except delivery is available. Currently, increasing researchers suggested that PE shares pathophysiologic features with protein misfolding/aggregation disorders, such as Alzheimer disease (AD). Evidences have proposed defective autophagy as a potential source of protein aggregation in PE. Endoplasmic reticulum-selective autophagy (ER-phagy) plays a critical role in clearing misfolded proteins and maintaining ER homeostasis. However, its roles in the molecular pathology of PE remain unclear. We found that lncRNA DUXAP8 was upregulated in preeclamptic placentae and significantly correlated with clinical indicators. DUXAP8 specifically binds to PCBP2 and inhibits its ubiquitination-mediated degradation, and decreased levels of PCBP2 reversed the activation effect of DUXAP8 overexpression on AKT/mTOR signaling pathway. Function experiments showed that DUXAP8 overexpression inhibited trophoblastic proliferation, migration, and invasion of HTR-8/SVneo and JAR cells. Moreover, pathological accumulation of swollen and lytic ER (endoplasmic reticulum) was observed in DUXAP8-overexpressed HTR8/SVneo cells and PE placental villus trophoblast cells, which suggesting that ER clearance ability is impaired. Further studies found that DUXAP8 overexpression impaired ER-phagy and caused protein aggregation medicated by reduced FAM134B and LC3II expression (key proteins involved in ER-phagy) via activating AKT/mTOR signaling pathway. The increased level of FAM134B significantly reversed the inhibitory effect of DUXAP8 overexpression on the proliferation, migration, and invasion of trophoblasts. In vivo, DUXAP8 overexpression through tail vein injection of adenovirus induced PE-like phenotypes in pregnant rats accompanied with activated AKT/mTOR signaling, decreased expression of FAM134B and LC3-II proteins and increased protein aggregation in placental tissues. Our study reveals the important role of lncRNA DUXAP8 in regulating trophoblast biological behaviors through FAM134B-mediated ER-phagy, providing a new theoretical basis for understanding the pathogenesis of PE.

Oxygen regulation of breathing is abolished in mitochondrial complex III-deficient arterial chemoreceptors.

Acute oxygen (O2) sensing is essential for adaptation of organisms to hypoxic environments or medical conditions with restricted exchange of gases in the lung. The main acute O2-sensing organ is the carotid body (CB), which contains neurosecretory chemoreceptor (glomus) cells innervated by sensory fibers whose activation by hypoxia elicits hyperventilation and increased cardiac output. Glomus cells have mitochondria with specialized metabolic and electron transport chain (ETC) properties. Reduced mitochondrial complex (MC) IV activity by hypoxia leads to production of signaling molecules (NADH and reactive O2 species) in MCI and MCIII that modulate membrane ion channel activity. We studied mice with conditional genetic ablation of MCIII that disrupts the ETC in the CB and other catecholaminergic tissues. Glomus cells survived MCIII dysfunction but showed selective abolition of responsiveness to hypoxia (increased [Ca2+] and transmitter release) with normal responses to other stimuli. Mitochondrial hypoxic NADH and reactive O2 species signals were also suppressed. MCIII-deficient mice exhibited strong inhibition of the hypoxic ventilatory response and altered acclimatization to sustained hypoxia. These data indicate that a functional ETC, with coupling between MCI and MCIV, is required for acute O2 sensing. O2 regulation of breathing results from the integrated action of mitochondrial ETC complexes in arterial chemoreceptors.

Minimal residual disease profiling predicts pathological complete response in esophageal squamous cell carcinoma.

Accurate presurgical prediction of pathological complete response (pCR) can guide treatment decisions, potentially avoiding unnecessary surgeries and improving the quality of life for cancer patients. We developed a minimal residual disease (MRD) profiling approach with enhanced sensitivity and specificity for detecting minimal tumor DNA from cell-free DNA (cfDNA). The approach was validated in two independent esophageal squamous cell carcinoma (ESCC) cohorts. In a cohort undergoing neoadjuvant, surgical, and adjuvant therapy (NAT cohort), presurgical MRD status precisely predicted pCR. All MRD-negative cases (10/10) were confirmed as pCR by pathological evaluation on the resected tissues. In contrast, MRD-positive cases included all the 27 non-pCR cases and only one pCR case (10/10 vs 1/28, P < 0.0001, Fisher's exact test). In a definitive radiotherapy cohort (dRT cohort), post-dRT MRD status was closely correlated with patient prognosis. All MRD-negative patients (25/25) remained progression-free during the follow-up period, while 23 of the 26 MRD-positive patients experienced disease progression (25/25 vs 3/26, P < 0.0001, Fisher's exact test; progression-free survival, P < 0.0001, log-rank test). The MRD profiling approach effectively predicted the ESCC patients who would achieve pCR with surgery and those likely to remain progression-free without surgery. This suggests that the cancer cells in these MRD-negative patients have been effectively eliminated and they could be suitable candidates for a watch-and-wait strategy, potentially avoiding unnecessary surgery.

Intercalation of Metal into Transition Metal Dichalcogenides in Molten Salts.

Van der Waals (vdW)-layered materials have drawn tremendous interests due to their unique properties. Atom intercalation in the vdW gap of layered materials can tune their electronic structure and generate unexpected properties. Here a chemical-scissor-mediated method that enables metal intercalation into transition metal dichalcogenides (TMDCs) in molten salts is reported. By using this approach, various guest metal atoms (Mn, Fe, Co, Ni, Cu, and Ag) are intercalated into various TMDC hosts (such as TiS2 , NbS2 , TaS2 , TiSe2 , NbSe2 , TaSe2 , and Ti0.5 V0.5 S2 ). The structure of the intercalated compound and intercalation mechanism are investigated. The results indicate that the vdW gap and valence state of TMDCs can be modified through metal intercalation, and the intercalation behavior is dictated by the electron work function. The adjustable charge transfer and intercalation endow a channel for rapid mass transfer to enhance the electrochemical performances. Such a chemical-scissor-mediated intercalation provides an approach to tune the physical and chemical properties of TMDCs, which may open an avenue in functional application ranging from energy conversion to electronics.

MiRNA-disease association prediction based on meta-paths.

Since miRNAs can participate in the posttranscriptional regulation of gene expression, they may provide ideas for the development of new drugs or become new biomarkers for drug targets or disease diagnosis. In this work, we propose an miRNA-disease association prediction method based on meta-paths (MDPBMP). First, an miRNA-disease-gene heterogeneous information network was constructed, and seven symmetrical meta-paths were defined according to different semantics. After constructing the initial feature vector for the node, the vector information carried by all nodes on the meta-path instance is extracted and aggregated to update the feature vector of the starting node. Then, the vector information obtained by the nodes on different meta-paths is aggregated. Finally, miRNA and disease embedding feature vectors are used to calculate their associated scores. Compared with the other methods, MDPBMP obtained the highest AUC value of 0.9214. Among the top 50 predicted miRNAs for lung neoplasms, esophageal neoplasms, colon neoplasms and breast neoplasms, 49, 48, 49 and 50 have been verified. Furthermore, for breast neoplasms, we deleted all the known associations between breast neoplasms and miRNAs from the training set. These results also show that for new diseases without known related miRNA information, our model can predict their potential miRNAs. Code and data are available at https://github.com/LiangYu-Xidian/MDPBMP.

Reconstruction of human protein-coding gene functional association network based on machine learning.

Networks consisting of molecular interactions are intrinsically dynamical systems of an organism. These interactions curated in molecular interaction databases are still not complete and contain false positives introduced by high-throughput screening experiments. In this study, we propose a framework to integrate interactions of functional associated protein-coding genes from 31 data sources to reconstruct a network with high coverage and quality. For each interaction, 369 features were constructed including properties of both the interaction and the involved genes. The training and validation sets were built on the pathway interactions as positives and the potential negative instances resulting from our proposed semi-supervised strategy. Random forest classification method was then applied to train and predict multiple times to give a score for each interaction. After setting a threshold estimated by a Binomial distribution, a Human protein-coding Gene Functional Association Network (HuGFAN) was reconstructed with 20 383 genes and 1185 429 high confidence interactions. Then, HuGFAN was compared with other networks from data sources with respect to network properties, suggesting that HuGFAN is more function and pathway related. Finally, HuGFAN was applied to identify cancer driver through two famous network-based methods (DriverNet and HotNet2) to show its outstanding performance compared with other networks. HuGFAN and other supplementary files are freely available at https://github.com/xthuang226/HuGFAN.

Research progress of miRNA-disease association prediction and comparison of related algorithms.

With an in-depth understanding of noncoding ribonucleic acid (RNA), many studies have shown that microRNA (miRNA) plays an important role in human diseases. Because traditional biological experiments are time-consuming and laborious, new calculation methods have recently been developed to predict associations between miRNA and diseases. In this review, we collected various miRNA-disease association prediction models proposed in recent years and used two common data sets to evaluate the performance of the prediction models. First, we systematically summarized the commonly used databases and similarity data for predicting miRNA-disease associations, and then divided the various calculation models into four categories for summary and detailed introduction. In this study, two independent datasets (D5430 and D6088) were compiled to systematically evaluate 11 publicly available prediction tools for miRNA-disease associations. The experimental results indicate that the methods based on information dissemination and the method based on scoring function require shorter running time. The method based on matrix transformation often requires a longer running time, but the overall prediction result is better than the previous two methods. We hope that the summary of work related to miRNA and disease will provide comprehensive knowledge for predicting the relationship between miRNA and disease and contribute to advanced computation tools in the future.