Search

Abstract Autophagy is a conserved, catabolic process essential for maintaining cellular homeostasis. Malfunctional autophagy contributes to neurodevelopmental and neurodegenerative diseases. However, the exact role and targets of autophagy in human neurons remain elusive. Here we report a systematic investigation of neuronal autophagy targets through integrated proteomics. Deep proteomic profiling of multiple autophagy-deficient lines of human induced neurons, mouse brains, and brain LC3-interactome reveals roles of neuronal autophagy in targeting proteins of multiple cellular organelles/pathways, including endoplasmic reticulum (ER), mitochondria, endosome, Golgi apparatus, synaptic vesicle (SV) for degradation. By combining phosphoproteomics and functional analysis in human and mouse neurons, we uncovered a function of neuronal autophagy in controlling cAMP-PKA and c-FOS-mediated neuronal activity through selective degradation of the protein kinase A - cAMP-binding regulatory (R)-subunit I (PKA-RI) complex. Lack of AKAP11 causes accumulation of the PKA-RI complex in the soma and neurites, demonstrating a constant clearance of PKA-RI complex through AKAP11-mediated degradation in neurons. Our study thus reveals the landscape of autophagy degradation in human neurons and identifies a physiological function of autophagy in controlling homeostasis of PKA-RI complex and specific PKA activity in neurons.

Abstract Secondary lung injury after SCI is a major cause of patient mortality, with apoptosis playing a key role. This study aimed to explore the impact of treadmill training and miR145-5p on the MAPK/Erk signaling pathway and apoptosis in rats with complete SCI. SD rats were used to establish T10 segmental complete SCI models and underwent treadmill training 3, 7, or 14 days postinjury. Various techniques including arterial blood gas analysis, lung wet/dry weight ratio, HE staining, immunofluorescence staining, immunohistochemical staining, qRT-PCR, and Western blotting were employed to assess alterations in lung function and the expression levels of crucial apoptosis-related factors. In order to elucidate the specific mechanism, the impact of miR145-5p on the MAPK/Erk pathway and its role in apoptosis in lung cells were confirmed through miR145-5p overexpression and knockdown experiments. Following spinal cord injury (SCI), an increase in apoptosis, activation of the MAPK/Erk pathway, and impairment of lung function were observed in SCI rats. Conversely, treadmill training resulted in a reduction in alveolar cell apoptosis, suppression of the MAPK/Erk pathway, and enhancement of lung function. The gene MAP3K3 was identified as a target of miR145-5p. The influence of miR145-5p on the MAPK/Erk pathway and its impact on apoptosis in alveolar cells were confirmed through the manipulation of miR145-5p expression levels. The upregulation of miR145-5p in spinal cord injury (SCI) rats led to a reduction in MAP3K3 protein expression within lung tissues, thereby inhibiting the MAPK/Erk signaling pathway and decreasing apoptosis. Contrarily, rats with miR145-5p knockdown undergoing treadmill training exhibited an increase in miR145-5p expression levels, resulting in the inhibition of MAP3K3 protein expression in lung tissues, suppression of the MAPK/Erk pathway, and mitigation of lung cell apoptosis. Ultimately, the findings suggest that treadmill training may attenuate apoptosis in lung cells post-spinal cord injury by modulating the MAP3K3 protein through miR145-5p to regulate the MAPK/Erk signaling pathway.

Abstract Ubiquitination is a post-translational modification initiated by the E1 enzyme UBA1, which transfers ubiquitin to ~35 E2 ubiquitin-conjugating enzymes. While UBA1 loss is cell lethal, it remains unknown how partial reduction in UBA1 activity is endured. Here, we utilize deep-coverage mass spectrometry to define the E1-E2 interactome and to determine the proteins that are modulated by knockdown of UBA1 and of each E2 in human cells. These analyses define the UBA1/E2-sensitive proteome and the E2 specificity in protein modulation. Interestingly, profound adaptations in peroxisomes and other organelles are triggered by decreased ubiquitination. While the cargo receptor PEX5 depends on its mono-ubiquitination for binding to peroxisomal proteins and importing them into peroxisomes, we find that UBA1/E2 knockdown induces the compensatory upregulation of other PEX proteins necessary for PEX5 docking to the peroxisomal membrane. Altogether, this study defines a homeostatic mechanism that sustains peroxisomal protein import in cells with decreased ubiquitination capacity.

Circulating tumor DNA (ctDNA), a tumor-derived fraction of cell-free DNA (cfDNA), has emerged as a promising marker in targeted therapy, immunotherapy, and minimal residual disease (MRD) monitoring in postsurgical patients. However, ctDNA level in early-stage cancers and postsurgical patients is very low, which posed many technical challenges to improve the detection rate and sensitivity, especially in the clinical practice of MRD detection. These challenges usually include insufficient DNA input amount, limit of detection (LOD), and high experimental costs. To resolve these challenges, we developed an ultrasensitive ctDNA MRD detection system in this study, namely PErsonalized Analysis of Cancer (PEAC), to simultaneously detect up to 37 mutations, which account for 70–80% non-small cell lung cancer (NSCLC) driver mutations from low plasma sample volume and enables LOD of 0.01% at a single-site level. We demonstrated the high performance achieved by PEAC on both cfDNA reference standards and clinical plasma samples from three NSCLC patient cohorts. For cfDNA reference standards, PEAC achieved a specificity of 99% and a sensitivity of 87% for the mutations at 0.01% allele fraction. In the second cohort, PEAC showed 100% concordance rate between ddPCR and Next-generation sequencing (NGS) among 29 samples. In the third cohort, 22 of 59 patients received EGFR TKI treatment. Among them, three in four patients identified low level actionable gene mutations only by PEAC had partial responses after targeted therapy, demonstrating high ctDNA detection ability of PEAC. Overall, the developed PEAC system can detect the majority of NSCLC driver mutations using 8–10 ml plasma samples, and has the advantages of high detection sensitivity and lower costs compared with the existing technologies such as ddPCR and NGS. These advantages make the PEAC system quite appropriate for ctDNA and MRD detection in early-stage NSCLC and postsurgical recurrence monitoring.

Resource utilization of SO2 from flue gas is a key challenge for the green development of the smelting industry. Recovery of sulfur resources in the form of elemental sulfur (S0), which has a high market demand and is a current focus in the field of SO2 treatment, can reduce the solid waste from desulfurization. In this study, it was found that Pb as a cathode material had good catalytic activity for the electrochemical reduction of SO2 and good selectivity towards elemental sulfur. However, the cathode suffers from sulfur poisoning. The problem was resolved by introducing surfactant, specifically sodium dodecyl benzene sulfonate (SDBS), which significantly lowered the sulfur content ratio on the surface of the electrode from 31.82% to 2.17%. Through the optimization of electrocatalytic parameters, this method enables efficient conversion of SO2 to S0, achieving a selectivity of 83% at pH = 0.25 and E = −0.8 V (vs. SCE).

The molecular differences in genetic and epigenetic profiling between early-stage (ES) and late-stage (LS) lung adenocarcinoma (LUAD), which might help to understand cancer progression and biomarker guided precision treatment, need further be investigated. In this study, we performed comprehensive analysis using multi-omics next-generation sequencing (NGS) on tissue samples from 7 ES (stage I) and 10 LS (stage III/IV) LUAD patients to study molecular characteristics between the two groups. Characterization of the genomic and transcriptomic profiles showed stage-specific somatic mutations, copy number variations (CNVs) and differentially expressed genes (DEGs). LS samples tend to have more TP53, ERBB2 and CHD4 mutations. Gene copy number loss occurs in immune-related gene pathways in the late stage of LUAD. ATAC-seq analysis showed that LS samples harbored more open chromatin peaks around promoter regions and transcription start sites (TSS) than ES samples. We then identified the known transcription factor (TF) binding motifs for the differentially abundant ATAC-seq peaks between the ES and LS samples and found distinct regulatory mechanisms related to each stage. Furthermore, integrative analysis of ATAC-seq with WGS and RNA-seq data showed that the degree of chromatin accessibility is related to copy number changes, and the open chromatin regions could directly regulate the expression of some DEGs. In conclusion, we performed a comprehensive multi-omics analysis of the early and late stages of LUAD and highlighted some important molecular differences in regulatory mechanisms during cancer progression. Those findings help to further understand mechanism and biomarker related targeted therapy.

Long non-coding RNA (lncRNA)-mediated competitive endogenous RNA (ceRNA) networks act as essential mechanisms in tumor initiation and progression, but their diagnostic and prognostic significance in prostate cancer (PCa) remains poorly understood. Presently, using the RNA expression data derived from multiple independent PCa-related studies, we constructed a high confidence and PCa-specific core ceRNA network by employing three lncRNA-gene inference approaches and key node filter strategies and then established a logistic model and risk score formula to evaluate its diagnostic and prognostic values, respectively. The core ceRNA network consists of 10 nodes, all of which are significantly associated with clinical outcomes. Combination of expression of the 10 ceRNAs with a logistic model achieved AUC of ROC and PR curve up to ∼96 and 99% in excluding normal prostate samples, respectively. Additionally, a risk score formula constructed with the ceRNAs exhibited significant association with disease-free survival. More importantly, utilizing the expression of RNAs in the core ceRNA network as a molecular signature, the TCGA-PRAD cohort was divided into four novel clinically relevant subgroups with distinct expression patterns, highlighting a feasible way for improving patient stratification in the future. Overall, we constructed a PCa-specific core ceRNA network, which provides diagnostic and prognostic value.

Coronary heart disease (CHD) is often accompanied with diabetes mellitus, which leads the limited effect of monotherapy. Drug combinations are deemed to a promising approach for treating CHD, and traditional Chinese medicine (TCM) formulas are important source of drug combinations discovery. In this study, to dissect the synergic bioactive compounds from TCM formula for CHD, Gualou Xiebai Decoction (GXD), is exemplified by employing a systems pharmacology strategy. 16 potential bioactive compounds are screened out by ADME evaluation. The Probability Ensemble Approach is used to predict potential drug combination pairs. Then, we exploit network analysis to dissect the synergistic mechanisms and find compound combinations act on multiple pathways. In vitro experiments confirm that the three compound combinations alleviate high-glucose induced myocardial injury. In summary, the integrated systems pharmacology method supplies accurate illustration of the molecular mechanisms of GXD to treat CHD and provides a new way for developing combination therapeutics.

Psychiatric disorders have become the biggest cause of non-fatal health burden and are the major contributor to suicide worldwide. The currently limitations of standard psychotherapy lead to an increasing attention in complementary and alternative therapies, in particular the traditional herbal medicine. Lonicerae Japonicae Flos (LJF) is one of the most consumed herbal tea beverages for medical purposes. LJF contains flavonoids and phenolic acid as the major constituents, hence the anti-inflammatory and anti-oxidative properties reported earlier. Nevertheless, the neuro-pharmacological properties of LJF are largely unknown. In this study, combined systems pharmacology strategy and RNA-Seq technology, we uncovered psychotherapeutic effects of LJF. In brief, LJF and its active ingredient luteolin can reversed depressive/anxiety-like behavior in rats exposed to chronic mild stress (CMS), restored abnormal gene expression changes and neurotransmission in the striatum of CMS rats, these effects were mediated through triggering the activation of the HTR2A/PLCγ/ERK/CREB pathway.

The herbs have proven to hold great potential to improve people's health and wellness during clinical practice over the past millennia. However, herbal medicine for the personalized treatment of disease is still under investigation owing to the complex multi-component interactions in herbs. To reveal the valuable insights for herbal synergistic therapy, we have chosen Traditional Chinese Medicine (TCM) as a case to illustrate the art and science behind the complicated multi-molecular, multi-genes interaction systems, and how the good practices of herbal combination therapy are applicable to personalized treatment. Here, we design system-wide interaction map strategy to provide a generic solution to establish the links between diseases and herbs based on comprehensive testing of molecular signatures in herb-disease pairs. Firstly, we integrated gene expression profiles from 189 diseases to characterize the disease-pathological feature. Then, we generated the perturbation signatures from the huge chemical informatics data and pharmacological data for each herb, which were represented the targets affected by the ingredients in the herb. So that we could assess the effects of herbs on the individual. Finally, we integrated the data of 189 diseases and 502 herbs, yielding the optimal herbal combinations for the diseases based on the strategy, and verifying the reliability of the strategy through the permutation testing and literature verification. Furthermore, we propose a novel formula as a candidate therapeutic drugs of rheumatoid arthritis and demonstrate its therapeutic mechanism through the systematic analysis of the influencing targets and biological processes. Overall, this computational method provides a systematic approach, which blended herbal medicine and omics data sets, allowing for the development of novel drug combinations for complex human diseases.

Identified as a treasure of natural herbal products, traditional Chinese medicine (TCM) has attracted extensive attention for their moderate treatment effect and lower side effect. Cardio-cerebrovascular diseases (CCVD) are a leading cause of death. TCM is used in China to prevent and treat CCVD. However, the complexity of TCM poses challenges in understanding the mechanisms of herbs at a systems-level, thus hampering the modernization and globalization of TCM. A novel model, termed traditional Chinese medicine systems pharmacology (TCMSP) analysis platform, which relies on the theory of systems pharmacology and integrates absorption, distribution, metabolism, excretion and toxicity (ADME/T) evaluation, target prediction and network/pathway analysis, was proposed to address these problems. Here, we review the development of systems pharmacology, the TCMSP approach and its applications in the investigations of CCVD and compare it with other methods. TCMSP assists in uncovering the mechanisms of action of herbal formulas used in treating CCVD. It can also be applied in ascertaining the different syndrome patterns of coronary artery disease, decoding the multi-scale mechanisms of herbs, and in understanding the mechanisms of herbal synergism.

Designing maximally selective ligands that act on individual drug targets with high binding affinity has been the central dogma of drug discovery and development for the past two decades. However, many low-affinity drugs that aim for several targets at the same time are found more effective than the high-affinity binders when faced with complex disease conditions, such as cancers, Alzheimer's disease and cardiovascular diseases. The aim of this study was to appreciate the importance and reveal the features of weak-binding drugs and propose an integrated strategy for discovering them. Weak-binding drugs can be characterized by their high dissociation rates and transient interactions with their targets. In addition, network topologies and dynamics parameters involved in the targets of weak-binding drugs also influence the effects of the drugs. Here, we first performed a dynamics analysis for 33 elementary subgraphs to determine the desirable topology and dynamics parameters among targets. Then, by applying the elementary subgraphs to the mitogen-activated protein kinase (MAPK) pathway, several optimal target combinations were obtained. Combining drug-target interaction prediction with molecular dynamics simulation, we got two potential weak-binding drug candidates, luteolin and tanshinone IIA, acting on these targets. Further, the binding affinity of these two compounds to their targets and the anti-inflammatory effects of them were validated through in vitro experiments. In conclusion, weak-binding drugs have real opportunities for maximum efficiency and may show reduced adverse reactions, which can offer a bright and promising future for new drug discovery. [ABSTRACT FROM AUTHOR]

Motivation: Drug combinations are a promising strategy for combating complex diseases by improving the efficacy and reducing corresponding side effects. Currently, a widely studied problem in pharmacology is to predict effective drug combinations, either through empirically screening in clinic or pure experimental trials. However, the large-scale prediction of drug combination by a systems method is rarely considered. Results: We report a systems pharmacology framework to predict drug combinations (PreDCs) on a computational model, termed probability ensemble approach (PEA), for analysis of both the efficacy and adverse effects of drug combinations. First, a Bayesian network integrating with a similarity algorithm is developed to model the combinations from drug molecular and pharmacological phenotypes, and the predictions are then assessed with both clinical efficacy and adverse effects. It is illustrated that PEA can predict the combination efficacy of drugs spanning different therapeutic classes with high specificity and sensitivity (AUC=0.90), which was further validated by independent data or new experimental assays. PEA also evaluates the adverse effects (AUC=0.95) quantitatively and detects the therapeutic indications for drug combinations. Finally, the PreDC database includes 1571 known and 3269 predicted optimal combinations as well as their potential side effects and therapeutic indications. [ABSTRACT FROM AUTHOR]

Background Given the complex nature of cardiovascular disease (CVD), information derived from a systems-level will allow us to fully interrogate features of CVD to better understand disease pathogenesis and to identify new drug targets. Results Here, we describe a systematic assessment of the multi-layer interactions underlying cardiovascular drugs, targets, genes and disorders to reveal comprehensive insights into cardiovascular systems biology and pharmacology. We have identified 206 effect-mediating drug targets, which are modulated by 254 unique drugs, of which, 43% display activities across different protein families (sequence similarity < 30%), highlighting the fact that multitarget therapy is suitable for CVD. Although there is little overlap between cardiovascular protein targets and disease genes,the two groups have similar pleiotropy and intimate relationships in the human disease gene-gene and cellular networks, supporting their similar characteristics in disease development and response to therapy. We also characterize the relationships between different cardiovascular disorders, which reveal that they share more etiological commonalities with each other rooted in the global disease-disease networks. Furthermore, the disease modular analysis demonstrates apparent molecular connection between 227 cardiovascular disease pairs. Conclusions All these provide important consensus as to the cause, prevention, and treatment of various CVD disorders from systems-level perspective. [ABSTRACT FROM AUTHOR]

Background: Addition and subtraction theory (AST), a basic theory of herb combination in traditional Chinese medicine (TCM), is often used to add or subtract the "fundamental formulae" to generate more targeted prescriptions. This theory plays a core role in individualized medicine and compound compatibility of TCM. However, the mechanisms underlying AST have largely remained elusive. Methods: An integrated platform of systems pharmacology was proposed for revealing how the oral administration, drug half-life, and target interactions affect the pharmacological functions of herbal medicines. This platform was further applied on two classical prescriptions, i.e., Xiao Chaihu decoction (XCHD) and Da Chaihu decoction (DCHD) to dissect the addition and subtraction theory (AST). Results: We uncovered the candidate compounds, key molecular targets and interaction network involved in XCHD and DCHD, and summarized its pharmacological characters and therapeutic indications. The results show that the "fundamental formula" is responsible for the major therapeutic effects, whereas the "additive herbs" synergistically enhance the treatment outcomes by targeting the same or complementary proteins between the foundational and additive herbs. Conclusion: This work has established a novel method to comprehensively understand the mechanism of AST, which would be beneficial for the TCM recipe optimization as well as the production of new herbal formula with desirable therapeutic effects. [ABSTRACT FROM AUTHOR]