Transcriptomic analysis of World Trade Center particulate Matter-induced pulmonary inflammation and drug treatments.

Over 400,000 people are estimated to have been exposed to World Trade Center particulate matter (WTCPM) since the attack on the Twin Towers in Lower Manhattan on September 11, 2001. Epidemiological studies have found that exposure to dust may cause respiratory ailments and cardiovascular diseases. However, limited studies have performed a systematic analysis of transcriptomic data to elucidate the biological responses to WTCPM exposure and the therapeutic options. Here, we developed an in vivo mouse exposure model of WTCPM and administered two drugs (i.e., rosoxacin and dexamethasone) to generate transcriptomic data from lung samples. WTCPM exposure increased the inflammation index, and this index was significantly reduced by both drugs. We analyzed the transcriptomics derived omics data using a hierarchical systems biology model (HiSBiM) with four levels, including system, subsystem, pathway, and gene analyses. Based on the selected differentially expressed genes (DEGs) from each group, WTCPM and the two drugs commonly affected the inflammatory responses, consistent with the inflammation index. Among these DEGs, the expression of 31 genes was affected by WTCPM exposure and consistently reversed by the two drugs, and these genes included Psme2, Cldn18, and Prkcd, which are involved in immune- and endocrine-related subsystems and pathways such as thyroid hormone synthesis, antigen processing and presentation, and leukocyte transendothelial migration. Furthermore, the two drugs reduced the inflammatory effects of WTCPM through distinct pathways, e.g., vascular-associated signaling by rosoxacin, whereas mTOR-dependent inflammatory signaling was found to be regulated by dexamethasone. To the best of our knowledge, this study constitutes the first investigation of transcriptomics data of WTCPM and an exploration of potential therapies. We believe that these findings provide strategies for the development of promising optional interventions and therapies for airborne particle exposure.

Inhibition of IRAK1 Is an Effective Therapy for Autoimmune Hypophysitis in Mice.

Autoimmune hypophysitis (AH) is an autoimmune disease of the pituitary for which the pathogenesis is incompletely known. AH is often treated with corticosteroids; however, steroids may lead to considerable side effects. Using a mouse model of AH (experimental autoimmune hypophysitis, EAH), we show that interleukin-1 receptor-associated kinase 1 (IRAK1) is upregulated in the pituitaries of mice that developed EAH. We identified rosoxacin as a specific inhibitor for IRAK1 and found it could treat EAH. Rosoxacin treatment at an early stage (day 0-13) slightly reduced disease severity, whereas treatment at a later stage (day 14-27) significantly suppressed EAH. Further investigation indicated rosoxacin reduced production of autoantigen-specific antibodies. Rosoxacin downregulated production of cytokines and chemokines that may dampen T cell differentiation or recruitment to the pituitary. Finally, rosoxacin downregulated class II major histocompatibility complex expression on antigen-presenting cells that may lead to impaired activation of autoantigen-specific T cells. These data suggest that IRAK1 may play a pathogenic role in AH and that rosoxacin may be an effective drug for AH and other inflammatory diseases involving IRAK1 dysregulation.

Exploring kinase family inhibitors and their moiety preferences using deep SHapley additive exPlanations.

BACKGROUND: While it has been known that human protein kinases mediate most signal transductions in cells and their dysfunction can result in inflammatory diseases and cancers, it remains a challenge to find effective kinase inhibitor as drugs for these diseases. One major challenge is the compensatory upregulation of related kinases following some critical kinase inhibition. To circumvent the compensatory effect, it is desirable to have inhibitors that inhibit all the kinases belonging to the same family, instead of targeting only a few kinases. However, finding inhibitors that target a whole kinase family is laborious and time consuming in wet lab. RESULTS: In this paper, we present a computational approach taking advantage of interpretable deep learning models to address this challenge. Specifically, we firstly collected 9,037 inhibitor bioassay results (with 3991 active and 5046 inactive pairs) for eight kinase families (including EGFR, Jak, GSK, CLK, PIM, PKD, Akt and PKG) from the ChEMBL25 Database and the Metz Kinase Profiling Data. We generated 238 binary moiety features for each inhibitor, and used the features as input to train eight deep neural networks (DNN) models to predict whether an inhibitor is active for each kinase family. We then employed the SHapley Additive exPlanations (SHAP) to analyze the importance of each moiety feature in each classification model, identifying moieties that are in the common kinase hinge sites across the eight kinase families, as well as moieties that are specific to some kinase families. We finally validated these identified moieties using experimental crystal structures to reveal their functional importance in kinase inhibition. CONCLUSION: With the SHAP methodology, we identified two common moieties for eight kinase families, 9 EGFR-specific moieties, and 6 Akt-specific moieties, that bear functional importance in kinase inhibition. Our result suggests that SHAP has the potential to help finding effective pan-kinase family inhibitors.

Identification of pan-kinase-family inhibitors using graph convolutional networks to reveal family-sensitive pre-moieties.

BACKGROUND: Human protein kinases, the key players in phosphoryl signal transduction, have been actively investigated as drug targets for complex diseases such as cancer, immune disorders, and Alzheimer's disease, with more than 60 successful drugs developed in the past 30 years. However, many of these single-kinase inhibitors show low efficacy and drug resistance has become an issue. Owing to the occurrence of highly conserved catalytic sites and shared signaling pathways within a kinase family, multi-target kinase inhibitors have attracted attention. RESULTS: To design and identify such pan-kinase family inhibitors (PKFIs), we proposed PKFI sets for eight families using 200,000 experimental bioactivity data points and applied a graph convolutional network (GCN) to build classification models. Furthermore, we identified and extracted family-sensitive (only present in a family) pre-moieties (parts of complete moieties) by utilizing a visualized explanation (i.e., where the model focuses on each input) method for deep learning, gradient-weighted class activation mapping (Grad-CAM). CONCLUSIONS: This study is the first to propose the PKFI sets, and our results point out and validate the power of GCN models in understanding the pre-moieties of PKFIs within and across different kinase families. Moreover, we highlight the discoverability of family-sensitive pre-moieties in PKFI identification and drug design.

Discovery of moiety preference by Shapley value in protein kinase family using random forest models.

BACKGROUND: Human protein kinases play important roles in cancers, are highly co-regulated by kinase families rather than a single kinase, and complementarily regulate signaling pathways. Even though there are > 100,000 protein kinase inhibitors, only 67 kinase drugs are currently approved by the Food and Drug Administration (FDA). RESULTS: In this study, we used "merged moiety-based interpretable features (MMIFs)," which merged four moiety-based compound features, including Checkmol fingerprint, PubChem fingerprint, rings in drugs, and in-house moieties as the input features for building random forest (RF) models. By using > 200,000 bioactivity test data, we classified inhibitors as kinase family inhibitors or non-inhibitors in the machine learning. The results showed that our RF models achieved good accuracy (> 0.8) for the 10 kinase families. In addition, we found kinase common and specific moieties across families using the Shapley Additive exPlanations (SHAP) approach. We also verified our results using protein kinase complex structures containing important interactions of the hinges, DFGs, or P-loops in the ATP pocket of active sites. CONCLUSIONS: In summary, we not only constructed highly accurate prediction models for predicting inhibitors of kinase families but also discovered common and specific inhibitor moieties between different kinase families, providing new opportunities for designing protein kinase inhibitors.

A novel graph convolutional neural network for predicting interaction sites on protein kinase inhibitors in phosphorylation.

Protein kinase-inhibitor interactions are key to the phosphorylation of proteins involved in cell proliferation, differentiation, and apoptosis, which shows the importance of binding mechanism research and kinase inhibitor design. In this study, a novel machine learning module (i.e., the WL Box) was designed and assembled to the Prediction of Interaction Sites of Protein Kinase Inhibitors (PISPKI) model, which is a graph convolutional neural network (GCN) to predict the interaction sites of protein kinase inhibitors. The WL Box is a novel module based on the well-known Weisfeiler-Lehman algorithm, which assembles multiple switch weights to effectively compute graph features. The PISPKI model was evaluated by testing with shuffled datasets and ablation analysis using 11 kinase classes. The accuracy of the PISPKI model with the shuffled datasets varied from 83 to 86%, demonstrating superior performance compared to two baseline models. The effectiveness of the model was confirmed by testing with shuffled datasets. Furthermore, the performance of each component of the model was analyzed via the ablation study, which demonstrated that the WL Box module was critical. The code is available at https://github.com/feiqiwang/PISPKI .

The inhibitory effects of PGG and EGCG against the SARS-CoV-2 3C-like protease.

The coronavirus disease (COVID-19) pandemic, resulting from human-to-human transmission of a novel severe acute respiratory syndrome coronavirus (SARS-CoV-2), has led to a global health crisis. Given that the 3 chymotrypsin-like protease (3CLpro) of SARS-CoV-2 plays an indispensable role in viral polyprotein processing, its successful inhibition halts viral replication and thus constrains virus spread. Therefore, developing an effective SARS-CoV-2 3CLpro inhibitor to treat COVID-19 is imperative. A fluorescence resonance energy transfer (FRET)-based method was used to assess the proteolytic activity of SARS-CoV-2 3CLpro using intramolecularly quenched fluorogenic peptide substrates corresponding to the cleavage sequence of SARS-CoV-2 3CLpro. Molecular modeling with GEMDOCK was used to simulate the molecular interactions between drugs and the binding pocket of SARS-CoV-2 3CLpro. This study revealed that the Vmax of SARS-CoV-2 3CLpro was about 2-fold higher than that of SARS-CoV 3CLpro. Interestingly, the proteolytic activity of SARS-CoV-2 3CLpro is slightly more efficient than that of SARS-CoV 3CLpro. Meanwhile, natural compounds PGG and EGCG showed remarkable inhibitory activity against SARS-CoV-2 3CLpro than against SARS-CoV 3CLpro. In molecular docking, PGG and EGCG strongly interacted with the substrate binding pocket of SARS-CoV-2 3CLpro, forming hydrogen bonds with multiple residues, including the catalytic residues C145 and H41. The activities of PGG and EGCG against SARS-CoV-2 3CLpro demonstrate their inhibition of viral protease activity and highlight their therapeutic potentials for treating SARS-CoV-2 infection.

Methotrexate inhibition of SARS-CoV-2 entry, infection and inflammation revealed by bioinformatics approach and a hamster model.

Background: Drug repurposing is a fast and effective way to develop drugs for an emerging disease such as COVID-19. The main challenges of effective drug repurposing are the discoveries of the right therapeutic targets and the right drugs for combating the disease. Methods: Here, we present a systematic repurposing approach, combining Homopharma and hierarchal systems biology networks (HiSBiN), to predict 327 therapeutic targets and 21,233 drug-target interactions of 1,592 FDA drugs for COVID-19. Among these multi-target drugs, eight candidates (along with pimozide and valsartan) were tested and methotrexate was identified to affect 14 therapeutic targets suppressing SARS-CoV-2 entry, viral replication, and COVID-19 pathologies. Through the use of in vitro (EC50 = 0.4 µM) and in vivo models, we show that methotrexate is able to inhibit COVID-19 via multiple mechanisms. Results: Our in vitro studies illustrate that methotrexate can suppress SARS-CoV-2 entry and replication by targeting furin and DHFR of the host, respectively. Additionally, methotrexate inhibits all four SARS-CoV-2 variants of concern. In a Syrian hamster model for COVID-19, methotrexate reduced virus replication, inflammation in the infected lungs. By analysis of transcriptomic analysis of collected samples from hamster lung, we uncovered that neutrophil infiltration and the pathways of innate immune response, adaptive immune response and thrombosis are modulated in the treated animals. Conclusions: We demonstrate that this systematic repurposing approach is potentially useful to identify pharmaceutical targets, multi-target drugs and regulated pathways for a complex disease. Our findings indicate that methotrexate is established as a promising drug against SARS-CoV-2 variants and can be used to treat lung damage and inflammation in COVID-19, warranting future evaluation in clinical trials.

Pharmacophore anchor models of ATAT1 to discover potential inhibitors and lead optimization.

Post-translation modification of microtubules is associated with many diseases like cancer. Alpha Tubulin Acetyltransferase 1 (ATAT1) is a major enzyme that acetylates 'Lys-40' in alpha-tubulin on the luminal side of microtubules and is a drug target that lacks inhibitors. Here, we developed pharmacophore anchor models of ATAT1 which were constructed statistically using thousands of docked compounds, for drug design and investigating binding mechanisms. Our models infer the compound moiety preferences with the physico-chemical properties for the ATAT1 binding site. The results from the pharmacophore anchor models show the three main sub-pockets, including S1 acetyl site, S2 adenine site, and S3 diphosphate site with anchors, where conserved moieties interact with respective sub-pocket residues in each site and help in guiding inhibitor discovery. We validated these key anchors by analyzing 162 homologous protein sequences (>99 species) and over 10 structures with various bound ligands and mutations. Our results were consistent with previous works also providing new interesting insights. Our models applied in virtual screening predicted several ATAT1 potential inhibitors. We believe that our model is useful for future inhibitor discovery and for guiding lead optimization.

Repurposing existing drugs: identification of SARS-CoV-2 3C-like protease inhibitors.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for coronavirus disease 2019 (COVID-19). Since its emergence, the COVID-19 pandemic has not only distressed medical services but also caused economic upheavals, marking urgent the need for effective therapeutics. The experience of combating SARS-CoV and MERS-CoV has shown that inhibiting the 3-chymotrypsin-like protease (3CLpro) blocks the replication of the virus. Given the well-studied properties of FDA-approved drugs, identification of SARS-CoV-2 3CLpro inhibitors in an FDA-approved drug library would be of great therapeutic value. Here, we screened a library consisting of 774 FDA-approved drugs for potent SARS-CoV-2 3CLpro inhibitors, using an intramolecularly quenched fluorescence (IQF) peptide substrate. Ethacrynic acid, naproxen, allopurinol, butenafine hydrochloride, raloxifene hydrochloride, tranylcypromine hydrochloride, and saquinavir mesylate have been found to block the proteolytic activity of SARS-CoV-2 3CLpro. The inhibitory activity of these repurposing drugs against SARS-CoV-2 3CLpro highlights their therapeutic potential for treating COVID-19 and other Betacoronavirus infections.

Uncovering Flexible Active Site Conformations of SARS-CoV-2 3CL Proteases through Protease Pharmacophore Clusters and COVID-19 Drug Repurposing.

The infectious SARS-CoV-2 causes COVID-19, which is now a global pandemic. Aiming for effective treatments, we focused on the key drug target, the viral 3C-like (3CL) protease. We modeled a big dataset with 42 SARS-CoV-2 3CL protease-ligand complex structures from ∼98.7% similar SARS-CoV 3CL protease with abundant complex structures. The diverse flexible active site conformations identified in the dataset were clustered into six protease pharmacophore clusters (PPCs). For the PPCs with distinct flexible protease active sites and diverse interaction environments, we identified pharmacophore anchor hotspots. A total of 11 "PPC consensus anchors" (a distinct set observed in each PPC) were observed, of which three "PPC core anchors" EHV2, HV1, and V3 are strongly conserved across PPCs. The six PPC cavities were then applied in virtual screening of 2122 FDA drugs for repurposing, using core anchor-derived "PPC scoring S" to yield seven drug candidates. Experimental testing by SARS-CoV-2 3CL protease inhibition assay and antiviral cytopathic effect assays discovered active hits, Boceprevir and Telaprevir (HCV drugs) and Nelfinavir (HIV drug). Specifically, Boceprevir showed strong protease inhibition with micromolar IC50 of 1.42 µM and an antiviral activity with EC50 of 49.89 µM, whereas Telaprevir showed moderate protease inhibition only with an IC50 of 11.47 µM. Nelfinavir solely showed antiviral activity with a micromolar EC50 value of 3.28 µM. Analysis of binding mechanisms of protease inhibitors revealed the role of PPC core anchors. Our PPCs revealed the flexible protease active site conformations, which successfully enabled drug repurposing.

Alternative splicing in human cancer cells is modulated by the amiloride derivative 3,5-diamino-6-chloro-N-(N-(2,6-dichlorobenzoyl)carbamimidoyl)pyrazine-2-carboxide.

Alternative splicing (AS) is a process that enables the generation of multiple protein isoforms with different biological properties from a single mRNA. Cancer cells often use the maneuverability conferred by AS to produce proteins that contribute to growth and survival. In our previous studies, we identified that amiloride modulates AS in cancer cells. However, the effective concentration of amiloride required to modulate AS is too high for use in cancer treatment. In this study, we used computational algorithms to screen potential amiloride derivatives for their ability to regulate AS in cancer cells. We found that 3,5-diamino-6-chloro-N-(N-(2,6-dichlorobenzoyl)carbamimidoyl)pyrazine-2-carboxamide (BS008) can regulate AS of apoptotic gene transcripts, including HIPK3, SMAC, and BCL-X, at a lower concentration than amiloride. This splicing regulation involved various splicing factors, and it was accompanied by a change in the phosphorylation state of serine/arginine-rich proteins (SR proteins). RNA sequencing was performed to reveal that AS of many other apoptotic gene transcripts, such as AATF, ATM, AIFM1, NFKB1, and API5, was also modulated by BS008. In vivo experiments further indicated that treatment of tumor-bearing mice with BS008 resulted in a marked decrease in tumor size. BS008 also had inhibitory effects in vitro, either alone or in a synergistic combination with the cytotoxic chemotherapeutic agents sorafenib and nilotinib. BS008 enabled sorafenib dose reduction without compromising antitumor activity. These findings suggest that BS008 may possess therapeutic potential for cancer treatment.

Spectroscopy, cytotoxicity and DNA-binding of the lanthanum(III) complex of an L-valine derivative of 1,10-phenanthroline.

The interaction of the lanthanum(III) La(III)-L (L=N,N'-bis-(1-carboxy-2-methylpropyl)-1,10-phenanthroline-2,9-dimethanamine) complex with calf thymus DNA was studied by electronic spectra, fluorescence spectra and circular dichroic spectra. The La(III)-L complex was assayed for antitumor activity in vitro against the HL-60 (the human leucocytoma) cells, HCT-8 (the human coloadenocarcinoma) cells, BGC-823 (the human carcinoma of stomach) cells, Bel-7402 (the human liver carcinoma) cells and KB (the human nasopharyngeal carcinoma) cells. The results show that the La(III)-L complex has activity against HL-60 cells, Bel-7402 cells and KB cells. Moreover, it is slightly more effective against Bel-7402 cell line than cisplatin. Using ethidium bromide as a fluorescence probe, the binding mode of the La(III)-L complex to calf-thymus DNA was studied spectroscopically. For comparison, the same measurements were carried out with La(III)-Phen [La(III)-1,10-phenanthroline complex] and La(III)-Val [La(III)-L-valine complex]. The results indicate that the La(III)-L and La(III)-Phen complexes possibly interact with calf-thymus DNA by both intercalative and coordination binding, whereas the La(III)-Val complex interacts with calf-thymus DNA by coordination binding. Kinetics of binding of the three complexes to DNA is for the first time studied using ethidium bromide as a fluorescence probe with stopped-flow spectrophotometer under pseudo-first-order condition. The strong two-step mechanisms in the process of the La(III)-L and La(III)-Phen complexes and one step in the process of the complex La(III)-Val interacting with DNA are observed, and the k(obs) (observed pseudo-first-order rate constant) and E(a) (observed energy of activation) values of binding to DNA are obtained.