Exploring the mechanism of physcion-1-O-β-D-monoglucoside against acute lymphoblastic leukaemia based on network pharmacology and experimental validation

Objective

To explore the mechanism of PG against acute lymphoblastic leukaemia (ALL) by network pharmacology and experimental verification in vitro.

Methods

First, the biological activity of PG against B-ALL was determined by CCK-8 and flow cytometry. Then, the potential targets of PG were obtained from the PharmMapper database. ALL-related genes were collected from the GeneCards, OMIM and PharmGkb databases. The two datasets were intersected to obtain the target genes of PG in ALL. Then, protein interaction networks were constructed using the STRING database. The key targets were obtained by topological analysis of the network with Cytoscape 3.8.0 software. In addition, the mechanism of PG in ALL was confirmed by protein‒protein interaction, gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. Furthermore, molecular docking was carried out by AutoDock Vina. Finally, Western blotting was performed to confirm the effect of PG on NALM6 cells.

Results

PG inhibited the proliferation of NALM6 cells. A total of 174 antileukaemic targets of PG were obtained by network pharmacology. The key targets included AKT1, MAPK14, EGFR, ESR1, LCK, PTPN11, RHOA, IGF1, MDM2, HSP90AA1, HRAS, SRC and JAK2. Enrichment analysis found that PG had antileukaemic effects by regulating key targets such as MAPK signalling, and PG had good binding activity with MAPK14 protein (-8.9 kcal/mol). PG could upregulate the expression of the target protein p-P38, induce cell cycle arrest, and promote the apoptosis of leukaemia cells.

Conclusion

MAPK14 was confirmed to be one of the key targets and pathways of PG by network pharmacology and molecular experiments.

"Slow walk" mimetic tensile loading maintains human meniscus tissue resident progenitor cells homeostasis in photocrosslinked gelatin hydrogel

Meniscus, the cushion in knee joint, is a load-bearing tissue that transfers mechanical forces to extracellular matrix (ECM) and tissue resident cells. The mechanoresponse of human tissue resident stem/progenitor cells in meniscus (hMeSPCs) is significant to tissue homeostasis and regeneration but is not well understood. This study reports that a mild cyclic tensile loading regimen of ∼1800 loads/day on hMeSPCs seeded in 3-dimensional (3D) photocrosslinked gelatin methacryloyl (GelMA) hydrogel is critical in maintaining cellular homeostasis. Experimentally, a "slow walk" biomimetic cyclic loading regimen (10% tensile strain, 0.5 Hz, 1 h/day, up to 15 days) is applied to hMeSPCs encapsulated in GelMA hydrogel with a magnetic force-controlled loading actuator. The loading significantly increases cell differentiation and fibrocartilage-like ECM deposition without affecting cell viability. Transcriptomic analysis reveals 332 mechanoresponsive genes, clustered into cell senescence, mechanical sensitivity, and ECM dynamics, associated with interleukins, integrins, and collagens/matrix metalloproteinase pathways. The cell-GelMA constructs show active ECM remodeling, traced using a green fluorescence tagged (GFT)-GelMA hydrogel. Loading enhances nascent pericellular matrix production by the encapsulated hMeSPCs, which gradually compensates for the hydrogel loss in the cultures. These findings demonstrate the strong tissue-forming ability of hMeSPCs, and the importance of mechanical factors in maintaining meniscus homeostasis.

Selective disruption of NRF2-KEAP1 interaction leads to NASH resolution and reduction of liver fibrosis in mice

Background & Aims

Oxidative stress is recognized as a major driver of non-alcoholic steatohepatitis (NASH) progression. The transcription factor NRF2 and its negative regulator KEAP1 are master regulators of redox, metabolic and protein homeostasis, as well as detoxification, and thus appear to be attractive targets for the treatment of NASH.

Methods

Molecular modeling and X-ray crystallography were used to design S217879 - a small molecule that could disrupt the KEAP1-NRF2 interaction. S217879 was highly characterized using various molecular and cellular assays. It was then evaluated in two different NASH-relevant preclinical models, namely the methionine and choline-deficient diet (MCDD) and diet-induced obesity NASH (DIO NASH) models.

Results

Molecular and cell-based assays confirmed that S217879 is a highly potent and selective NRF2 activator with marked anti-inflammatory properties, as shown in primary human peripheral blood mononuclear cells. In MCDD mice, S217879 treatment for 2 weeks led to a dose-dependent reduction in NAFLD activity score while significantly increasing liver Nqo1 mRNA levels, a specific NRF2 target engagement biomarker. In DIO NASH mice, S217879 treatment resulted in a significant improvement of established liver injury, with a clear reduction in both NAS and liver fibrosis. αSMA and Col1A1 staining, as well as quantification of liver hydroxyproline levels, confirmed the reduction in liver fibrosis in response to S217879. RNA-sequencing analyses revealed major alterations in the liver transcriptome in response to S217879, with activation of NRF2-dependent gene transcription and marked inhibition of key signaling pathways that drive disease progression.

Conclusions

These results highlight the potential of selective disruption of the NRF2-KEAP1 interaction for the treatment of NASH and liver fibrosis.

Impact and implications

We report the discovery of S217879 - a potent and selective NRF2 activator with good pharmacokinetic properties. By disrupting the KEAP1-NRF2 interaction, S217879 triggers the upregulation of the antioxidant response and the coordinated regulation of a wide spectrum of genes involved in NASH disease progression, leading ultimately to the reduction of both NASH and liver fibrosis progression in mice.

HPIPred: Host-pathogen interactome prediction with phenotypic scoring

Protein-protein interactions (PPIs) are involved in most cellular processes. Unfortunately, current knowledge of host-pathogen interactomes is still very limited. Experimental methods used to detect PPIs have several limitations, including increasing complexity and economic cost in large-scale screenings. Hence, computational methods are commonly used to support experimental data, although they generally suffer from high false-positive rates. To address this issue, we have created HPIPred, a host-pathogen PPI prediction tool based on numerical encoding of physicochemical properties. Unlike other available methods, HPIPred integrates phenotypic data to prioritize biologically meaningful results. We used HPIPred to screen the entire Homo sapiens and Pseudomonas aeruginosa PAO1 proteomes to generate a host-pathogen interactome with 763 interactions displaying a highly connected network topology. Our predictive model can be used to prioritize protein-protein interactions as potential targets for antibacterial drug development. Available at: https://github.com/SysBioUAB/hpi_predictor.

Biological interacting units identified in human protein networks reveal tissue-functional diversification and its impact on disease

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Biological processes are exerted by groups of physically interacting proteins.

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Proteins display variable biological roles depending on tissue-interactomic context.

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Tissue-specific protein-protein interaction networks reveal functional diversification.

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Most disease associated genes/proteins display tissue-specific phenotypes.

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Protein interaction network analysis is a valuable resource to identify disease genes.

Protein-protein interactions (PPI) play an essential role in the biological processes that occur in the cell. Therefore, the dissection of PPI networks becomes decisive to model functional coordination and predict pathological de-regulation. Cellular networks are dynamic and proteins display varying roles depending on the tissue-interactomic context. Thus, the use of centrality measures in individual proteins fall short to dissect the functional properties of the cell. For this reason, there is a need for more comprehensive, relational, and context-specific ways to analyze the multiple actions of proteins in different cells and identify specific functional assemblies within global biomolecular networks. Under this framework, we define Biological Interacting units (BioInt-U) as groups of proteins that interact physically and are enriched in a common Gene Ontology. A search strategy was applied on 33 tissue-specific (TS) PPI networks to generate BioInt libraries associated with each particular human tissue. The cross-tissue comparison showed that housekeeping assemblies incorporate different proteins and exhibit distinct network properties depending on the tissue. Furthermore, disease genes (DGs) of tissue-associated pathologies preferentially accumulate in units in the expected tissues, which in turn were more central in the TS networks. Overall, the study reveals a tissue-specific functional diversification based on the identification of specific protein units and suggests vulnerabilities specific of each tissue network, which can be applied to refine protein-disease association methods.

Crystallographic mining of ASK1 regulators to unravel the intricate PPI interfaces for the discovery of small molecule

Protein seldom performs biological activities in isolation. Understanding the protein–protein interactions’ physical rewiring in response to pathological conditions or pathogen infection can help advance our comprehension of disease etiology, progression, and pathogenesis, which allow us to explore the alternate route to control the regulation of key target interactions, timely and effectively. Nonalcoholic steatohepatitis (NASH) is now a global public health problem exacerbated due to the lack of appropriate treatments. The most advanced anti-NASH lead compound (selonsertib) is withdrawn, though it is able to inhibit its target Apoptosis signal-regulating kinase 1 (ASK1) completely, indicating the necessity to explore alternate routes rather than complete inhibition. Understanding the interaction fingerprints of endogenous regulators at the molecular level that underpin disease formation and progression may spur the rationale of designing therapeutic strategies. Based on our analysis and thorough literature survey of the various key regulators and PTMs, the current review emphasizes PPI-based drug discovery’s relevance for NASH conditions. The lack of structural detail (interface sites) of ASK1 and its regulators makes it challenging to characterize the PPI interfaces. This review summarizes key regulators interaction fingerprinting of ASK1, which can be explored further to restore the homeostasis from its hyperactive states for therapeutics intervention against NASH.

Fragment-based exploration of the 14-3-3/Amot-p130 interface

The modulation of protein-protein interactions (PPIs) has developed into a well-established field of drug discovery. Despite the advances achieved in the field, many PPIs are still deemed as ‘undruggable’ targets and the design of PPIs stabilizers remains a significant challenge. The application of fragment-based methods for the identification of drug leads and to evaluate the ‘tractability’ of the desired protein target has seen a remarkable development in recent years. In this study, we explore the molecular characteristics of the 14-3-3/Amot-p130 PPI and the conceptual possibility of targeting this interface using X-ray crystallography fragment-based screening. We report the first structural elucidation of the 14-3-3 binding motif of Amot-p130 and the characterization of the binding mode and affinities involved. We made use of fragments to probe the ‘ligandability’ of the 14-3-3/Amot-p130 composite binding pocket. Here we disclose initial hits with promising stabilizing activity and an early-stage selectivity toward the Amot-p130 motifs over other representatives 14-3-3 partners. Our findings highlight the potential of using fragments to characterize and explore proteins' surfaces and might provide a starting point toward the development of small molecules capable of acting as molecular glues.

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Phosphorylation of Ser 175 mediates binding of Amot-p130 to 14-3-3.

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The crystal structure of the 14-3-3σΔC/Amot-p130 peptide complex describes the interface.

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A fragment-based exploration of the interface assesses ‘ligandability’.

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Fragments binding at the 14-3-3/Amot-p130 interface display an initial stabilizing activity.

Penta-peptide ATN-161 based neutralization mechanism of SARS-CoV-2 spike protein

SARS-CoV-2 has become a big challenge for the scientific community worldwide. SARS-CoV-2 enters into the host cell by the spike protein binding with an ACE2 receptor present on the host cell. Developing safe and effective inhibitor appears an urgent need to interrupt the binding of SARS-CoV-2 spike protein with ACE2 receptor in order to reduce the SARS-CoV-2 infection. We have examined the penta-peptide ATN-161 as potential inhibitor of ACE2 and SARS-CoV-2 spike protein binding, where ATN-161 has been commercially approved for the safety and possess high affinity and specificity towards the receptor binding domain (RBD) of S1 subunit in SARS-CoV-2 spike protein. We carried out experiments and confirmed these phenomena that the virus bindings were indeed minimized. ATN-161 peptide can be used as an inhibitor of protein-protein interaction (PPI) stands as a crucial interaction in biological systems. The molecular docking finding suggests that the binding energy of the ACE2-spike protein complex is reduced in the presence of ATN-161. Protein-protein docking binding energy (-40.50 kcal/mol) of the spike glycoprotein toward the human ACE2 and binding of ATN-161 at their binding interface reduced the biding energy (-26.25 kcal/mol). The finding of this study suggests that ATN-161 peptide can mask the RBD of the spike protein and be considered as a neutralizing candidate by binding with the ACE2 receptor. Peptide-based masking of spike S1 protein (RBD) and its neutralization is a highly promising strategy to prevent virus penetration into the host cell. Thus masking of the RBD leads to the loss of receptor recognition property which can reduce the chance of infection host cells.

Identification of potential therapeutic target of naringenin in breast cancer stem cells inhibition by bioinformatics and in vitro studies

Cancer therapy is a strategic measure in inhibiting breast cancer stem cell (BCSC) pathways. Naringenin, a citrus flavonoid, was found to increase breast cancer cells' sensitivity to chemotherapeutic agents. Bioinformatics study and 3D tumorsphere in vitro modeling in breast cancer (mammosphere) were used in this study, which aims to explore the potential therapeutic targets of naringenin (PTTNs) in inhibiting BCSCs. Bioinformatic analyses identified direct target proteins (DTPs), indirect target proteins (ITPs), naringenin-mediated proteins (NMPs), BCSC regulatory genes, and PTTNs. The PTTNs were further analyzed for gene ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, protein-protein interaction (PPI) networks, and hub protein selection. Mammospheres were cultured in serum-free media. The effects of naringenin were measured by MTT-based cytotoxicity, mammosphere forming potential (MFP), colony formation, scratch wound-healing assay, and flow cytometry-based cell cycle analyses and apoptosis assays. Gene expression analysis was performed using real-time quantitative polymerase chain reaction (q-RT PCR). Bioinformatics analysis revealed p53 and estrogen receptor alpha (ERα) as PTTNs, and KEGG pathway enrichment analysis revealed that TGF-ß and Wnt/ß-catenin pathways are regulated by PTTNs. Naringenin demonstrated cytotoxicity and inhibited mammosphere and colony formation, migration, and epithelial to mesenchymal transition in the mammosphere. The mRNA of tumor suppressors P53 and ERα were downregulated in the mammosphere, but were significantly upregulated upon naringenin treatment. By modulating the P53 and ERα mRNA, naringenin has the potential of inhibiting BCSCs. Further studies on the molecular mechanism and formulation of naringenin in BCSCs would be beneficial for its development as a BCSC-targeting drug.

Chinese herbal compounds against SARS-CoV-2: Puerarin and quercetin impair the binding of viral S-protein to ACE2 receptor

The outbreak of COVID-19 raises an urgent need for the therapeutics to contain the emerging pandemic. However, no effective treatment has been found for SARS-CoV-2 infection to date. Here, we identified puerarin (PubChem CID: 5281807), quercetin (PubChem CID: 5280343) and kaempferol (PubChem CID: 5280863) as potential compounds with binding activity to ACE2 by using Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Molecular docking analysis showed that puerarin and quercetin exhibit good binding affinity to ACE2, which was validated by surface plasmon resonance (SPR) assay. Furthermore, SPR-based competition assay revealed that puerarin and quercetin could significantly affect the binding of viral S-protein to ACE2 receptor. Notably, quercetin could also bind to the RBD domain of S-protein, suggesting not only a receptor blocking, but also a virus neutralizing effect of quercetin on SARS-CoV-2. The results from network pharmacology and bioinformatics analysis support a view that quercetin is involved in host immunomodulation, which further renders it a promising candidate against COVID-19. Moreover, given that puerarin is already an existing drug, results from this study not only provide insight into its action mechanism, but also propose a prompt application of it on COVID-19 patients for assessing its clinical feasibility.

An Evaluation of Machine Learning Approaches for the Prediction of Essential Genes in Eukaryotes Using Protein Sequence-Derived Features

The availability of whole-genome sequences and associated multi-omics data sets, combined with advances in gene knockout and knockdown methods, has enabled large-scale annotation and exploration of gene and protein functions in eukaryotes. Knowing which genes are essential for the survival of eukaryotic organisms is paramount for an understanding of the basic mechanisms of life, and could assist in identifying intervention targets in eukaryotic pathogens and cancer. Here, we studied essential gene orthologs among selected species of eukaryotes, and then employed a systematic machine-learning approach, using protein sequence-derived features and selection procedures, to investigate essential gene predictions within and among species. We showed that the numbers of essential gene orthologs comprise small fractions when compared with the total number of orthologs among the eukaryotic species studied. In addition, we demonstrated that machine-learning models trained with subsets of essentiality-related data performed better than random guessing of gene essentiality for a particular species. Consistent with our gene ortholog analysis, the predictions of essential genes among multiple (including distantly-related) species is possible, yet challenging, suggesting that most essential genes are unique to a species. The present work provides a foundation for the expansion of genome-wide essentiality investigations in eukaryotes using machine learning approaches.

Interfaces Between Alpha-helical Integral Membrane Proteins: Characterization, Prediction, and Docking

Protein-protein interaction (PPI) is an essential mechanism by which proteins perform their biological functions. For globular proteins, the molecular characteristics of such interactions have been well analyzed, and many computational tools are available for predicting PPI sites and constructing structural models of the complex. In contrast, little is known about the molecular features of the interaction between integral membrane proteins (IMPs) and few methods exist for constructing structural models of their complexes. Here, we analyze the interfaces from a non-redundant set of complexes of α-helical IMPs whose structures have been determined to a high resolution. We find that the interface is not significantly different from the rest of the surface in terms of average hydrophobicity. However, the interface is significantly better conserved and, on average, inter-subunit contacting residue pairs correlate more strongly than non-contacting pairs, especially in obligate complexes. We also develop a neural network-based method, with an area under the receiver operating characteristic curve of 0.75 and a Pearson correlation coefficient of 0.70, for predicting interface residues and their weighted contact numbers (WCNs). We further show that predicted interface residues and their WCNs can be used as restraints to reconstruct the structure α-helical IMP dimers through docking for fourteen out of a benchmark set of sixteen complexes. The RMSD100 values of the best-docked ligand subunit to its native structure are <2.5 Å for these fourteen cases. The structural analysis conducted in this work provides molecular details about the interface between α-helical IMPs and the WCN restraints represent an efficient means to score α-helical IMP docking candidates.

A functional biological network centered on XRCC3: a new possible marker of chemoradiotherapy resistance in rectal cancer patients

Preoperative chemoradiotherapy is widely used to improve local control of disease, sphincter preservation and to improve survival in patients with locally advanced rectal cancer. Patients enrolled in the present study underwent preoperative chemoradiotherapy, followed by surgical excision. Response to chemoradiotherapy was evaluated according to Mandard's Tumor Regression Grade (TRG). TRG 3, 4 and 5 were considered as partial or no response while TRG 1 and 2 as complete response. From pretherapeutic biopsies of 84 locally advanced rectal carcinomas available for the analysis, only 42 of them showed 70% cancer cellularity at least. By determining gene expression profiles, responders and non-responders showed significantly different expression levels for 19 genes (P < 0.001). We fitted a logistic model selected with a stepwise procedure optimizing the Akaike Information Criterion (AIC) and then validated by means of leave one out cross validation (LOOCV, accuracy = 95%). Four genes were retained in the achieved model: ZNF160, XRCC3, HFM1 and ASXL2. Real time PCR confirmed that XRCC3 is overexpressed in responders group and HFM1 and ASXL2 showed a positive trend. In vitro test on colon cancer resistant/susceptible to chemoradioterapy cells, finally prove that XRCC3 deregulation is extensively involved in the chemoresistance mechanisms. Protein-protein interactions (PPI) analysis involving the predictive classifier revealed a network of 45 interacting nodes (proteins) with TRAF6 gene playing a keystone role in the network. The present study confirmed the possibility that gene expression profiling combined with integrative computational biology is useful to predict complete responses to preoperative chemoradiotherapy in patients with advanced rectal cancer.