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Proteome profiling is a powerful tool in biological and biomedical studies, starting with samples at bulk, single cell, or single cell type levels. Reliable methods for extracting specific cell type proteome are in need, especially for the cells (e.g., neurons) that cannot be readily isolated. Here, we present an innovative proximity labeling (PL) strategy for single cell type proteomics of mouse brain, in which TurboID (an engineered biotin ligase) is used to label almost all proteins in a specific cell type. This strategy bypasses the requirement of cell isolation and includes five major steps: (i) constructing recombinant adeno-associated viruses (AAV) to express TurboID driven by cell type-specific promoters, (ii) delivering the AAV to mouse brains by direct intravenous injection, (iii) enhancing PL labeling by biotin administration, (iv) purifying biotinylated proteins followed by on-bead protein digestion, and (v) quantitative tandem-mass-tag (TMT). We first confirmed that TurboID can label a wide range of cellular proteins in human HEK293 cells and optimized the single cell type proteomics pipeline. To analyze specific brain cell types, we generated recombinant AAVs to co-express TurboID and mCherry proteins, driven by neuron- or astrocyte-specific promoters, and validated the expected cell expression by co-immunostaining of mCherry and cellular markers. Subsequent biotin purification and TMT analysis identified ~10,000 unique proteins from a few micrograms of protein samples with excellent reproducibility. Comparative and statistical analysis indicated these PL proteomes contain cell type specific cellular pathways. Although proximity labeling was originally developed for studying protein-protein interactions and subcellular proteomes, we extend it to efficiently tag the entire proteomes of specific cell types in the mouse brain using TurboID biotin ligase. This simple, effective in vivo approach should be broadly applicable to single cell type proteomics.

An overview of the strategies for boosting C2+ selectivity by manipulating CO*(CO).

Autophagy is a catabolic and self-degradative process crucial for maintaining cellular homeostasis. Malfunctional autophagy is implicated in neurodevelopmental and neurodegenerative diseases. However, the exact role and targets of autophagy in human neurons remain elusive. Here we reported 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 LC3-interactome uncovers a role of neuronal autophagy in targeting primarily endoplasmic reticulum (ER), mitochondria, endosome, Golgi apparatus, synaptic vesicle (SV) proteins, and cAMP-PKA pathway, for degradation. Tubular ER and specific SV proteins are significant autophagy cargos in the axons. Functional validation identified calumenin as an ER resident autophagy receptor and illuminated a role of autophagy in regulating PKA and neuronal activity through AKAP11-mediated degradation. Our study thus reveals the landscape of autophagy degradation in human neurons and offers molecular insight into mechanisms of neurological disorders linked to autophagy deficiency.HighlightIntegrated proteomics reveals the landscape of autophagy degradation in human neuronsAutophagy clears tubular ER and selective ER and synaptic vesicle proteins in neuronsCalumenin is an ER resident autophagy receptorAutophagy controls PKA pathway and neuronal activity through autophagy receptor AKAP11

Rapid development of high-throughput technologies has permitted the identification of an increasing number of disease-associated genes (DAGs), which are important for understanding disease initiation and developing precision therapeutics. However, DAGs often contain large amounts of redundant or false positive information, leading to difficulties in quantifying and prioritizing potential relationships between these DAGs and human diseases. In this study, a network-oriented gene entropy approach (NOGEA) is proposed for accurately inferring master genes that contribute to specific diseases by quantitatively calculating their perturbation abilities on directed disease-specific gene networks. In addition, we confirmed that the master genes identified by NOGEA have a high reliability for predicting disease-specific initiation events and progression risk. Master genes may also be used to extract the underlying information of different diseases, thus revealing mechanisms of disease comorbidity. More importantly, approved therapeutic targets are topologically localized in a small neighborhood of master genes in the interactome network, which provides a new way for predicting drug-disease associations. Through this method, 11 old drugs were newly identified and predicted to be effective for treating pancreatic cancer and then validated by in vitro experiments. Collectively, the NOGEA was useful for identifying master genes that control disease initiation and co-occurrence, thus providing a valuable strategy for drug efficacy screening and repositioning. NOGEA codes are publicly available at https://github.com/guozihuaa/NOGEA.

Recent proteome and transcriptome profiling of Alzheimer's disease (AD) brains reveals RNA splicing dysfunction and U1 small nuclear ribonucleoprotein (snRNP) pathology containing U1-70K and its N-terminal 40-KDa fragment (N40K). Here we present a causative role of U1 snRNP dysfunction to neurodegeneration in primary neurons and transgenic mice (N40K-Tg), in which N40K expression exerts a dominant-negative effect to downregulate full-length U1-70K. N40K-Tg recapitulates N40K insolubility, erroneous splicing events, neuronal degeneration and cognitive impairment. Specifically, N40K-Tg shows the reduction of GABAergic synapse components (e.g., the GABA receptor subunit of GABRA2), and concomitant postsynaptic hyperexcitability that is rescued by a GABA receptor agonist. Crossing of N40K-Tg and the 5xFAD amyloidosis model indicates that the RNA splicing defect synergizes with the amyloid cascade to remodel the brain transcriptome and proteome, deregulate synaptic proteins, and accelerate cognitive decline. Thus, our results support the contribution of U1 snRNP-mediated splicing dysfunction to AD pathogenesis.

Chemoproteomics is a key platform for characterizing the mode of action for compounds, especially for targeted protein degraders such as proteolysis targeting chimeras (PROTACs) and molecular glues. With deep proteome coverage, multiplexed tandem mass tag-mass spectrometry (TMT-MS) can tackle up to 18 samples in a single experiment. Here, we present a pooling strategy for further enhancing the throughput and apply the strategy to an FDA-approved drug library (95 best-in-class compounds). The TMT-MS-based pooling strategy was evaluated in the following steps. First, we demonstrated the capability of TMT-MS by analyzing more than 15 000 unique proteins (12 000 gene products) in HEK293 cells treated with five PROTACs (two BRD/BET degraders and three degraders for FAK, ALK, and BTK kinases). We then introduced a rationalized pooling strategy to separate structurally similar compounds in different pools and identified the proteomic response to 14 pools from the drug library. Finally, we validated the proteomic response from one pool by reprofiling the cells via treatment with individual drugs with sufficient replicates. Interestingly, numerous proteins were found to change upon drug treatment, including AMD1, ODC1, PRKX, PRKY, EXO1, AEN, and LRRC58 with 7-hydroxystaurosporine; C6orf64, HMGCR, and RRM2 with Sorafenib; SYS1 and ALAS1 with Venetoclax; and ATF3, CLK1, and CLK4 with Palbocilib. Thus, pooling chemoproteomics screening provides an efficient method for dissecting the molecular targets of compound libraries.

With recent advances in mass spectrometry-based proteomics technologies, deep profiling of hundreds of proteomes has become increasingly feasible. However, deriving biological insights from such valuable datasets is challenging. Here we introduce a systems biology-based software JUMPn, and its associated protocol to organize the proteome into protein co-expression clusters across samples and protein-protein interaction (PPI) networks connected by modules (e.g., protein complexes). Using the R/Shiny platform, the JUMPn software streamlines the analysis of co-expression clustering, pathway enrichment, and PPI module detection, with integrated data visualization and a user-friendly interface. The main steps of the protocol include installation of the JUMPn software, the definition of differentially expressed proteins or the (dys)regulated proteome, determination of meaningful co-expression clusters and PPI modules, and result visualization. While the protocol is demonstrated using an isobaric labeling-based proteome profile, JUMPn is generally applicable to a wide range of quantitative datasets (e.g., label-free proteomics). The JUMPn software and protocol thus provide a powerful tool to facilitate biological interpretation in quantitative proteomics.

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.

Background/aims The theory of inflammation is one of the important theories in the pathogenesis of diabetic nephropathy (DN). We herein aimed to explore whether loganin affected macrophage infiltration and activation upon diabetic nephropathy (DN) by a spontaneous DN mice and a co-culture system of glomerular mesangial cells (GMCs) and macrophage cells (RAW264.7) which was induced by advanced glycation end products (AGEs). Methods and key findings Loganin showed remarkable capacity on protecting renal from damage by mitigating diabetic symptoms, improving the histomorphology of the kidney, decreasing the expression of extracellular matrix such as FN, COL-IV and TGF-β, reversing the production of IL-12 and IL-10 and decreasing the number of infiltrating macrophages in the kidney. Moreover, loganin showed markedly effects by suppressing iNOS and CD16/32 expressions (M1 markers), increasing Arg-1 and CD206 expressions (M2 markers), which were the phenotypic transformation of macrophage. These effects may be attributed to the inhibition of the receptor for AGEs (RAGE) /monocyte chemotactic protein-1 (MCP-1)/CC chemokine receptor 2 (CCR2) signaling pathway, with significantly down-regulated expressions of RAGE, MCP-1 and CCR2 by loganin. Loganin further decreased MCP-1 secretion when RAGE was silenced, which means other target was involved in regulating the MCP-1 expression. While loganin combinated with the inhibitor of CCR2 exerted stronger anti-inhibition effects of iNOS expression, suggesting that CCR2 was the target of loganin in regulating the activation of macrophages. Significance Loganin could ameliorate DN kidney damage by inhibiting macrophage infiltration and activation via the MCP-1/CCR2 signaling pathway in DN.

Rapid development of high-throughput technologies has permitted the identification of an increasing number of disease-associated genes (DAGs), which are important for understanding disease initiation and developing precision therapeutics. However, DAGs often contain large amounts of redundant or false positive information, leading to difficulties in quantifying and prioritizing potential relationships between these DAGs and human diseases. In this study, a network-oriented gene entropy approach (NOGEA) is proposed for accurately inferring master genes that contribute to specific diseases by quantitatively calculating their perturbation abilities on directed disease-specific gene networks. In addition, we confirmed that the master genes identified by NOGEA have a high reliability for predicting disease-specific initiation events and progression risk. Master genes may also be used to extract the underlying information of different diseases, thus revealing mechanisms of disease comorbidity. More importantly, approved therapeutic targets are topologically localized in a small neighborhood of master genes on the interactome network, which provides a new way for predicting new drug-disease associations. Through this method, 11 old drugs were newly identified and predicted to be effective for treating pancreatic cancer and then validated by in vitro experiments. Collectively, the NOGEA was useful for identifying master genes that control disease initiation and co-occurrence, thus providing a valuable strategy for drug efficacy screening and repositioning. NOGEA codes are publicly available at https://github.com/guozihuaa/NOGEA.

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.

Background Dengue virus (DENV) is an increasing global health threat and associated with induction of both a long-lived protective immune response and immune-suppression. So far, the potency of treatment of DENV via antiviral drugs is still under investigation. Recently, increasing evidences suggest the potential role of microRNAs (miRNAs) in regulating DENV. The present study focused on the function of miRNAs in innate insusceptible reactions and organization of various types of immune cells and inflammatory responses for DENV. Three drugs were tested including antiviral herbal medicine ReDuNing (RDN), Loratadine (LRD) and Acetaminophen. Results By the microarray expression of miRNAs in 165 Patients. Results showed that 89 active miRNAs interacted with 499 potential target genes, during antiviral treatment throughout the critical stage of DENV. Interestingly, reduction of the illness threats using RDN combined with LRD treatment showed better results than Acetaminophen alone. The inhibitions of DENV was confirmed by decrease concentrations of cytokines and interleukin parameters; like TNF-α, IFN-γ, TGF-β1, IL-4, IL-6, IL-12, and IL-17; after treatment and some coagulants factors increased. Conclusions This study showed a preliminary support to suggest that the herbal medicine RDN combined with LRD can reduce both susceptibility and the severity of DENV.

The aim of this study was to examine the comprehensive neuroprotective mechanism of ligustrazine, which is extracted from Ligusticum Chuanxiong Hort., against vascular dementia (VD) in rats and apoptosis in oxygen and glucose deprivation (OGD) PC12 cells. Rats were subjected to bilateral common carotid artery occlusion (BCCAO) surgery and administered ligustrazine intragastrically for 6 weeks. At the end of the experiments, the hippocampal biomarkers brain-derived neurotrophic factor (BDNF), monocyte chemotactic protein 1 (MCP-1), and homocysteine (Hcy) were examined. In experiments in vitro, OGD PC12 cells were treated with ligustrazine for 0.5, 1, 3, 6, 12, or 24 h. The cell-released biomarkers BDNF, MCP-1, and Hcy were examined. Microscopy, acridine orange-ethidium bromide (AO/EB) staining, and flow cytometry assays were performed to investigate apoptosis. Cleaved caspase-3, Bcl-2 associated X protein (Bax), and B cell lymphoma 2 (Bcl-2) expression was examined using Western blot assays. The results showed that biomarkers, including MCP-1 and Hcy, were significantly increased in both the in vivo and in vitro models, while the BDNF level was significantly decreased compared with the sham or vehicle models. Microscopy, AO/EB staining, and flow cytometry analysis showed that severe cell damage occurred in OGD PC12 cells, and apoptosis played a major role in this environment. Further Western blot studies showed that the apoptosis-related Bax/Bcl-2 protein ratio and cleaved caspase-3 were significantly increased in the experiment. However, ligustrazine profoundly suppressed the imbalance of these biomarkers, reduced cell damage, decreased the Bax/Bcl-2, and downregulated cleaved caspase-3. Pro- and anti-apoptotic biomarkers of multiple pathways including BDNF, MCP-1, and Hcy played a joint role in triggering the activation of the mitochondria-related Bax/Bcl-2 and caspase-3 apoptosis pathway in VD. Ligustrazine attenuated VD by comprehensively regulating BDNF, MCP-1, and Hcy and inactivating the Bax/Bcl-2 and caspase-3 apoptosis pathway. Our data provide novel insight into ligustrazine, which is a promising neuroprotective agent for VD disease treatment strategies. © IUBMB Life, 70(1):60-70, 2018.

Background/aims Rehmanniae Radix (RR) and Cornus officinalis (CO) are a typical herbal pair used to treat diabetic nephropathy (DN) in clinical practice. DN can be effectively treated by catalpol (Cat) and loganin (Log), the main active components of RR and CO respectively, through combating apoptosis, oxidative stress and inflammation. Herein, a spontaneous DN and podocyte injury model induced by advanced glycation end products (AGEs), i.e. KK-Ay mice, was used to explore the cooperative effects of Log and Cat on DN and the mechanism targeting the AGEs-RAGE (receptor for AGE) pathway. Methods and key findings Log and Cat alone or in combination mitigated diabetic symptoms, decreased the level of fasting blood glucose, and increased that of serum insulin. The two drugs alone or in combination protected renal function from damage, prevented extracellular matrix hyperplasia and glycogen deposition, as well as alleviated the loss of podocytes detected by histological assay and immunohistochemistry. Flow cytometry revealed that Log and Cat alone or in combination relieved the apoptosis of AGEs-induced podocytes in vitro. Silencing RAGE by RNA interference played a protective role in podocyte apoptosis, whereas overexpression of it worked oppositely. Western blot exhibited that Log and Cat alone or in combination inhibited the activation of RAGE/p38 MAPK/p65 NF-κB and RAGE/Nox4/p65 NF-κB pathways in podocytes. The inhibitory effects of drug combination were more evident than those of individual treatments. Significance Log and Cat cooperatively resisted the apoptosis of podocytes upon DN by targeting AGEs-RAGE and its downstream pathways p38 MAPK and Nox4.

Diabetes mellitus (DM) damages male reproduction at multiple levels, such as endocrine secretion, spermatogenesis and penile erection. We herein investigated the protective effects and mechanism of loganin targeting the advanced glycation end products (AGEs)/receptor for AGEs (RAGE)/p38 mitogen‐activated protein kinase (p38MAPK)/NF‐κB signalling pathway. Loganin relieved the general DM symptoms and decreased the blood glucose level of KK‐Ay DM mice. Haematoxylin‐eosin staining demonstrated that loganin ameliorated testicular histology and function and enhanced the activities of testis‐specific markers lactate dehydrogenase (LDH), acid phosphatase (ACP) and gamma‐glutamyl transferase (γ‐GT). Loganin also showed evident anti‐oxidative stress, anti‐apoptotic and anti‐inflammatory effects on DM‐induced reproductive damage by restoring glutathione (GSH) level and superoxide dismutase (SOD) activity, as well as reducing reactive oxygen species (ROS) level and Bax/Bcl‐2 ratio in vivo and in vitro. Western blotting exhibited that loganin significantly inhibited the AGEs/RAGE/p38MAPK/NF‐κB signalling pathway. Acridine orange and ethidium bromide staining (AOEB) and Western blotting showed that loganin in combination with inhibitors of RAGE, p38MAPK and NF‐κB exerted stronger anti‐apoptotic effects on AGE‐induced GC‐2 cell damage compared with loganin alone. In conclusion, loganin can protect against DM‐induced reproductive damage, probably by suppressing the AGEs/RAGE/p38MAPK/NF‐κB pathway.

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.

Temporal lobe epilepsy (TLE) is the most prevalent and often devastating form of epilepsy. The molecular mechanism underlying the development of TLE remains largely unknown, which hinders the discovery of effective anti-epileptogenic drugs. In this study, we built a systems-level analytic framework which integrates gene meta-signatures, gene coexpression network and cellular regulatory network to unveil the evolution landscape of epileptogenic process and to identify key regulators that govern the transition between different epileptogenesis stages. The time-specific hippocampal transcriptomic profiles from five independent rodent TLE models were grouped into acute, latent and chronic stages of epileptogenesis, and were utilized for generating stage-specific gene expression signatures. 13 cell-type specific functional modules were identified from the epilepsy-context coexpression network, and five of them were significantly associated with the entire epileptogenic process. By inferring the differential protein activity of gene regulators in each stage, 265 key regulators underlying epileptogenesis were obtained. Among them, 122 regulators were demonstrated being associated with high seizure frequency and/or hippocampal sclerosis in human TLE patients. Importantly, we discovered four new gene regulators (ANXA5, FAM107A, SEPT2 and SPARC) whose upregulation may drive the process of epileptogenesis and further lead to chronic recurrent seizures or hippocampal sclerosis. Our findings provide a landscape of the gene network dynamics underlying epileptogenesis and uncovered candidate regulators that may serve as potential targets for future anti-epileptogenic therapy development.

The endogenous metabolite methylglyoxal (MGO) has recently emerged as a potential mediator of psychiatric disorders, such as anxiety and depression, but its precise mechanism of action remains poorly understood. Here, we find that MGO concentrations are decreased in the prefrontal cortex and hippocampus in rats subjected to chronic stress, and low-dose MGO treatment remarkedly enhances resilience to stress and alleviates depression-like symptoms. This effect is achieved by MGO’s promotion on the synaptic plasticity in prefrontal cortex and hippocampus. Bothin vitroandin vivoexperiments show that MGO provokes the dimerization and autophosphorylation of TrkB and the subsequent activation of downstream Akt/CREB signaling, which leads to a rapid and sustained expression of brain-derived neurotrophic factor (BDNF). We further demonstrate that MGO directly binds to the extracellular domain of TrkB, but not its intracellular domain. In addition, we also identify a natural product luteolin and its derivative lutD as potent inhibitors of Glyoxalase 1 and validate their antidepressant effects in chronic stress rat models. The antidepressant role of endogenous MGO provides a new basis for the understanding and therapeutic intervention design for stress-associated mental disorders.

With the rapid development of high-throughput genomic technologies and the accumulation of genome-wide datasets for human disease, it has been shown that using only reductionistic principles has been difficult to capture the complex biological networks and design rational medication. However, the emerging paradigm of "network based methodology" proposes to harness the power of networks to uncover relationships between various data types of interest for drug discovery. Recent advances include networks that encompass relationships between drugs, disease-related genes, therapeutic targets and diseases. It is shown how network techniques can help in the investigation of the mechanism of action of existing drugs, new molecules, or to identify novel disease genes and targets. We review how these different types of network analysis approaches facilitate drug discovery and their associated challenges. Some representative examples are reviewed to show that network analysis is a powerful, integrated, computational and experimental approach to improve the drug discovery process.