bioRxiv [Preprint]. 2024 Feb 29:2024.02.27.582333. doi: 10.1101/2024.02.27.582333.

ABSTRACT

Microbiome studies have revealed gut microbiota's potential impact on complex diseases. However, many studies often focus on one disease per cohort. We developed a meta-analysis workflow for gut microbiome profiles and analyzed shotgun metagenomic data covering 11 diseases. Using interpretable machine learning and differential abundance analysis, our findings reinforce the generalization of binary classifiers for Crohn's disease (CD) and colorectal cancer (CRC) to hold-out cohorts and highlight the key microbes driving these classifications. We identified high microbial similarity in disease pairs like CD vs ulcerative colitis (UC), CD vs CRC, Parkinson's disease vs type 2 diabetes (T2D), and schizophrenia vs T2D. We also found strong inverse correlations in Alzheimer's disease vs CD and UC. These findings detected by our pipeline provide valuable insights into these diseases.

IMPORTANCE: Assessing disease similarity is an essential initial step preceding disease-based approach for drug repositioning. Our study provides a modest first step in underscoring the potential of integrating microbiome insights into the disease similarity assessment. Recent microbiome research has predominantly focused on analyzing individual disease to understand its unique characteristics, which by design excludes comorbidities individuals. We analyzed shotgun metagenomic data from existing studies and identified previously unknown similarities between diseases. Our research represents a pioneering effort that utilize both interpretable machine learning and differential abundance analysis to assess microbial similarity between diseases.

PMID:38464323 | PMC:PMC10925178 | DOI:10.1101/2024.02.27.582333

bioRxiv [Preprint]. 2024 Feb 29:2024.02.28.582164. doi: 10.1101/2024.02.28.582164.

ABSTRACT

Computational modeling of perturbation biology identifies relationships between molecular elements and cellular response, and an accurate understanding of these systems will support the full realization of precision medicine. Traditional deep learning, while often accurate in predicting response, is unlikely to capture the true sequence of involved molecular interactions. Our work is motivated by two assumptions: 1) Methods that encourage mechanistic prediction logic are likely to be more trustworthy, and 2) problem-specific algorithms are likely to outperform generic algorithms. We present an alternative to Graph Neural Networks (GNNs) termed Graph Structured Neural Networks (GSNN), which uses cell signaling knowledge, encoded as a graph data structure, to add inductive biases to deep learning. We apply our method to perturbation biology using the LINCS L1000 dataset and literature-curated molecular interactions. We demonstrate that GSNNs outperform baseline algorithms in several prediction tasks, including 1) perturbed expression, 2) cell viability of drug combinations, and 3) disease-specific drug prioritization. We also present a method called GSNNExplainer to explain GSNN predictions in a biologically interpretable form. This work has broad application in basic biological research and pre-clincal drug repurposing. Further refinement of these methods may produce trustworthy models of drug response suitable for use as clinical decision aids.

PMID:38464019 | PMC:PMC10925270 | DOI:10.1101/2024.02.28.582164

EMBO Mol Med. 2024 Mar 10. doi: 10.1038/s44321-024-00048-8. Online ahead of print.

ABSTRACT

Recessive dystrophic epidermolysis bullosa (RDEB) is a rare inherited skin disease characterized by defects in type VII collagen leading to a range of fibrotic pathologies resulting from skin fragility, aberrant wound healing, and altered dermal fibroblast physiology. Using a novel in vitro model of fibrosis based on endogenously produced extracellular matrix, we screened an FDA-approved compound library and identified antivirals as a class of drug not previously associated with anti-fibrotic action. Preclinical validation of our lead hit, daclatasvir, in a mouse model of RDEB demonstrated significant improvement in fibrosis as well as overall quality of life with increased survival, weight gain and activity, and a decrease in pruritus-induced hair loss. Immunohistochemical assessment of daclatasvir-treated RDEB mouse skin showed a reduction in fibrotic markers, which was supported by in vitro data demonstrating TGFβ pathway targeting and a reduction of total collagen retained in the extracellular matrix. Our data support the clinical development of antivirals for the treatment of patients with RDEB and potentially other fibrotic diseases.

PMID:38462666 | DOI:10.1038/s44321-024-00048-8

Zhonghua Yan Ke Za Zhi. 2024 Mar 11;60(3):272-274. doi: 10.3760/cma.j.cn112142-20231025-00181.

ABSTRACT

A 61-year-old male patient presented with blurred vision in the right eye for 1 day. The patient had previously undergone phacoemulsification with intraocular lens implantation (10 years ago) and intravitreal implantation of dexamethasone (due to uveitis) in the eye. There was edema in the inferior cornea, along with Descemet membrane folds. The rod-shaped dexamethasone implant was visible in the inferior anterior chamber. Without pupil dilation, the patient was asked to keep a supine position and avoid head tilting for 1 day. The implant spontaneously relocated into the vitreous cavity, resulting in a reduction of corneal edema. This suggests that the dislocation of the intravitreal implant into the anterior chamber may be caused by a local zonular abnormality, and the dislocated implant has the potential to reposition itself spontaneously.

PMID:38462376 | DOI:10.3760/cma.j.cn112142-20231025-00181

Biomed Pharmacother. 2024 Mar 9;173:116394. doi: 10.1016/j.biopha.2024.116394. Online ahead of print.

ABSTRACT

Recently, anthelmintics have showcased versatile therapeutic potential in addressing various diseases, positioning them as promising candidates for drug repurposing. However, challenges such as low bioavailability and a lack of a solid pharmacokinetic basis impede successful repurposing. To overcome these flaws, we aimed to investigate the key pharmacokinetic factors of anthelmintics mainly focusing on the absorption, distribution, and metabolism profiles by employing niclosamide (NIC) as a model drug. The intestinal permeability of NIC is significantly influenced by solubility and doesn't function as a substrate for efflux transporters. It showed high plasma protein binding. Also, the metabolism study indicated that NIC would have low metabolic stability by extensively undergoing the intestinal glucuronidation. Additionally, we investigated the CYP-mediated drug-drug interaction potential of NIC in both direct and time-dependent ways. NIC showed strong inhibitory effects on CYP1A2 and CYP2C8 and is not likely to become a time-dependent inhibitor. Our findings could contribute to the identification of essential factors in the pharmacokinetics of anthelmintics, potentially facilitating their repositioning.

PMID:38461686 | DOI:10.1016/j.biopha.2024.116394

BMC Cancer. 2024 Mar 8;24(1):323. doi: 10.1186/s12885-024-12064-5.

ABSTRACT

BACKGROUND: Increased mitochondrial activities contributing to cancer cell proliferation, invasion, and metastasis have been reported in different cancers; however, studies on the therapeutic targeting of mitochondria in regulating cell proliferation and invasiveness are limited. Because mitochondria are believed to have evolved through bacterial invasion in mammalian cells, antibiotics could provide an alternative approach to target mitochondria, especially in cancers with increased mitochondrial activities. In this study, we investigated the therapeutic potential of bacteriostatic antibiotics in regulating the growth potential of colorectal cancer (CRC) cells, which differ in their metastatic potential and mitochondrial functions.

METHODS: A combination of viability, cell migration, and spheroid formation assays was used to measure the effect on metastatic potential. The effect on mitochondrial mechanisms was investigated by measuring mitochondrial DNA copy number by qPCR, biogenesis (by qPCR and immunoblotting), and functions by measuring reactive oxygen species, membrane potential, and ATP using standard methods. In addition, the effect on assembly and activities of respiratory chain (RC) complexes was determined using blue native gel electrophoresis and in-gel assays, respectively). Changes in metastatic and cell death signaling were measured by immunoblotting with specific marker proteins and compared between CRC cells.

RESULTS: Both tigecycline and tetracycline effectively reduced the viability, migration, and spheroid-forming capacity of highly metastatic CRC cells. This increased sensitivity was attributed to reduced mtDNA content, mitochondrial biogenesis, ATP content, membrane potential, and increased oxidative stress. Specifically, complex I assembly and activity were significantly inhibited by these antibiotics in high-metastatic cells. Significant down-regulation in the expression of mitochondrial-mediated survival pathways, such as phospho-AKT, cMYC, phospho-SRC, and phospho-FAK, and upregulation in cell death (apoptosis and autophagy) were observed, which contributed to the enhanced sensitivity of highly metastatic CRC cells toward these antibiotics. In addition, the combined treatment of the CRC chemotherapeutic agent oxaliplatin with tigecycline/tetracycline at physiological concentrations effectively sensitized these cells at early time points.

CONCLUSION: Altogether, our study reports that bacterial antibiotics, such as tigecycline and tetracycline, target mitochondrial functions specifically mitochondrial complex I architecture and activity and would be useful in combination with cancer chemotherapeutics for high metastatic conditions.

PMID:38459456 | DOI:10.1186/s12885-024-12064-5

IEEE J Biomed Health Inform. 2024 Mar 8;PP. doi: 10.1109/JBHI.2024.3375025. Online ahead of print.

ABSTRACT

The identification of drug-target interactions (DTIs) is an essential step in drug discovery. In vitro experimental methods are expensive, laborious, and time-consuming. Deep learning has witnessed promising progress in DTI prediction. However, how to precisely represent drug and protein features is a major challenge for DTI prediction. Here, we developed an end-to-end DTI identification framework called BINDTI based on bi-directional Intention network. First, drug features are encoded with graph convolutional networks based on its 2D molecular graph obtained by its SMILES string. Next, protein features are encoded based on its amino acid sequence through a mixed model called ACmix, which integrates self-attention mechanism and convolution. Third, drug and target features are fused through bi-directional Intention network, which combines Intention and multi-head attention. Finally, unknown drug-target (DT) pairs are classified through multilayer perceptron based on the fused DT features. The results demonstrate that BINDTI greatly outperformed four baseline methods (i.e., CPI-GNN, TransfomerCPI, MolTrans, and IIFDTI) on the BindingDB, BioSNAP, DrugBank, and Human datasets. More importantly, it was more appropriate to predict new DTIs than the four baseline methods on imbalanced datasets. Ablation experimental results elucidated that both bi-directional Intention and ACmix could greatly advance DTI prediction. The fused feature visualization and case studies manifested that the predicted results by BINDTI were basically consistent with the true ones. We anticipate that the proposed BINDTI framework can find new low-cost drug candidates, improve drugs' virtual screening, and further facilitate drug repositioning as well as drug discovery. BINDTI is publicly available at https://github.com/plhhnu/BINDTI.

PMID:38457318 | DOI:10.1109/JBHI.2024.3375025

Oncol Lett. 2024 Feb 22;27(4):170. doi: 10.3892/ol.2024.14303. eCollection 2024 Apr.

ABSTRACT

Lung cancer is the leading cause of cancer-related death worldwide, and ~85% of lung cancers are non-small cell lung cancer (NSCLC), which has a low 5-year overall survival rate and high mortality. Several therapeutic strategies have been developed, such as targeted therapy, immuno-oncotherapy and combination therapy. However, the low survival rate indicates the urgent need for new NSCLC treatments. Vasculogenic mimicry (VM) is an endothelial cell-free tumor blood supply system of aggressive and metastatic tumor cells present during tumor neovascularization. VM is clinically responsible for tumor metastasis and resistance, and is correlated with poor prognosis in NSCLC, making it a potential therapeutic target. In the present study, A549 cells formed glycoprotein-rich lined tubular structures, and transcript levels of VM-related genes were markedly upregulated in VM-forming cells. Based on a drug repurposing strategy, it was demonstrated that doxazosin (an antihypertensive drug) displayed inhibitory activity on VM formation at non-cytotoxic concentrations. Doxazosin significantly reduced the levels of vascular endothelial growth factor A (VEGF-A) and matrix metalloproteinase-2 (MMP-2) in the cell media during VM formation. Further experiments revealed that the protein expression levels of VEGF-A and vascular endothelial-cadherin (VE-cadherin), which contribute to tumor aggressiveness and VM formation, were downregulated following doxazosin treatment. Moreover, the downstream signaling Ephrin type-A receptor 2 (EphA2)/AKT/mTOR/MMP/Laminin-5γ2 network was inhibited in response to doxazosin treatment. In conclusion, the present study demonstrated that doxazosin displayed anti-VM activity in an NSCLC cell model through the downregulation of VEGF-A and VE-cadherin levels, and the suppression of signaling pathways related to the receptor tyrosine kinase, EphA2, protein kinases, AKT and mTOR, and proteases, MMP-2 and MMP-9. These results support the add-on anti-VM effect of doxazosin as a potential agent against NSCLC.

PMID:38455663 | PMC:PMC10918514 | DOI:10.3892/ol.2024.14303

Brain Pathol. 2024 Mar 7:e13252. doi: 10.1111/bpa.13252. Online ahead of print.

ABSTRACT

Alzheimer's disease (AD) is the most common form of dementia, characterized by an early olfactory dysfunction, progressive memory loss, and behavioral deterioration. Albeit substantial progress has been made in characterizing AD-associated molecular and cellular events, there is an unmet clinical need for new therapies. In this study, olfactory tract proteotyping performed in controls and AD subjects (n = 17/group) showed a Braak stage-dependent proteostatic impairment accompanied by the progressive modulation of amyloid precursor protein and tau functional interactomes. To implement a computational repurposing of drug candidates with the capacity to reverse early AD-related olfactory omics signatures (OMSs), we generated a consensual OMSs database compiling differential omics datasets obtained by mass-spectrometry or RNA-sequencing derived from initial AD across the olfactory axis. Using the Connectivity Map-based drug repurposing approach, PKC, EGFR, Aurora kinase, Glycogen synthase kinase, and CDK inhibitors were the top pharmacologic classes capable to restore multiple OMSs, whereas compounds with targeted activity to inhibit PI3K, Insulin-like growth factor 1 (IGF-1), microtubules, and Polo-like kinase (PLK) represented a family of drugs with detrimental potential to induce olfactory AD-associated gene expression changes. To validate the potential therapeutic effects of the proposed drugs, in vitro assays were performed. These validation experiments revealed that pretreatment of human neuron-like SH-SY5Y cells with the EGFR inhibitor AG-1478 showed a neuroprotective effect against hydrogen peroxide-induced damage while the pretreatment with the Aurora kinase inhibitor Reversine reduced amyloid-beta (Aβ)-induced neurotoxicity. Taken together, our data pointed out that OMSs may be useful as substrates for drug repurposing to propose novel neuroprotective treatments against AD.

PMID:38454090 | DOI:10.1111/bpa.13252

Comput Biol Med. 2024 Feb 28;172:108233. doi: 10.1016/j.compbiomed.2024.108233. Online ahead of print.

ABSTRACT

BACKGROUND: Cancer cachexia is a severe metabolic syndrome marked by skeletal muscle atrophy. A successful clinical intervention for cancer cachexia is currently lacking. The study of cachexia mechanisms is largely based on preclinical animal models and the availability of high-throughput transcriptomic datasets of cachectic mouse muscles is increasing through the extensive use of next generation sequencing technologies.

METHODS: Cachectic mouse muscle transcriptomic datasets of ten different studies were combined and mined by seven attribute weighting models, which analysed both categorical variables and numerical variables. The transcriptomic signature of cancer cachexia was identified by attribute weighting algorithms and was used to evaluate the performance of eleven pattern discovery models. The signature was employed to find the best combination of drugs (drug repurposing) for developing cancer cachexia treatment strategies, as well as to evaluate currently used cachexia drugs by literature mining.

RESULTS: Attribute weighting algorithms ranked 26 genes as the transcriptomic signature of muscle from mice with cancer cachexia. Deep Learning and Random Forest models performed better in differentiating cancer cachexia cases based on muscle transcriptomic data. Literature mining revealed that a combination of melatonin and infliximab has negative interactions with 2 key genes (Rorc and Fbxo32) upregulated in the transcriptomic signature of cancer cachexia in muscle.

CONCLUSIONS: The integration of machine learning, meta-analysis and literature mining was found to be an efficient approach to identifying a robust transcriptomic signature for cancer cachexia, with implications for improving clinical diagnosis and management of this condition.

PMID:38452471 | DOI:10.1016/j.compbiomed.2024.108233

J Biomol Struct Dyn. 2024 Mar 7:1-20. doi: 10.1080/07391102.2024.2323701. Online ahead of print.

ABSTRACT

The significant mortality rate associated with Marburg virus infection made it the greatest hazard among infectious diseases. Drug repurposing using in silico methods has been crucial in identifying potential compounds that could prevent viral replication by targeting the virus's primary proteins. This study aimed at repurposing the drugs of SARS-CoV-2 for identifying potential candidates against the matrix protein VP40 of the Marburg virus. Virtual screening was performed where the control compound, Nilotinib, showed a binding score of -9.99 kcal/mol. Based on binding scores, hit compounds 9549298, 11960895, 44545852, 51039094, and 89670174 were selected that had a lower binding score than the control. Subsequent molecular dynamics (MD) simulation revealed that compound 9549298 consistently formed a hydrogen bond with the residue Gln290. This was observed both in molecular docking and MD simulation poses, indicating a strong and significant interaction with the protein. 11960895 had the most stable and consistent RMSD pattern exhibited in 100 ns simulation, while 9549298 had the most identical RMSD plot compared to the control molecule. MM/PBSA analysis showed that the binding free energy (ΔG) of 9549298 and 11960895 was lower than the control, with -30.84 and -38.86 kcal/mol, respectively. It was observed by the PCA (principal component analysis) and FEL (free energy landscape) analysis that compounds 9549298 and 11960895 had lesser conformational variation. Overall, this study proposed 9549298 and 11960895 as potential binders of VP40 MARV that can cause its inhibition, however it inherently lacks experimental validation. Furthermore, the study proposes in-vitro experiments as the next step to validate these computational findings, offering a practical approach to further explore these compounds' potential as antiviral agents.Communicated by Ramaswamy H. Sarma.

PMID:38450706 | DOI:10.1080/07391102.2024.2323701

Front Pharmacol. 2024 Feb 21;15:1339862. doi: 10.3389/fphar.2024.1339862. eCollection 2024.

ABSTRACT

Introduction: Drug repurposing is fast growing and becoming an attractive approach for identifying novel targets, such as exosomes for cancer and antiviral therapy. Exosomes are a specialized class of extracellular vesicles that serve as functional mediators in intercellular communication and signaling that are important in normal physiological functions. A continuously growing body of evidence has established a correlation between the abnormal release of exosomes with various viral disease pathologies including cancer. Cells that are virus-infected release exosomes known to influence the process via the loading and transfer of viral components, such as miRNA, small (s) RNA, DNA, and proteins. Inhibition of exosome release may abate the spread and severity of viral infection, thus making exosomes an attractive target for antiviral therapies. We previously demonstrated the pharmacological inhibition of exosomes. Methods: Herein, we used a cell-based assay to determine the effect of Human adenovirus type 3 (HAdV3) on the exosome inhibition process by azole and Heparin derivatives. HAdV3-infected cells were treated with two concentrations of each inhibitor at different time points. Results: HAdV3 activities led to increased total sRNA, DNA, and exosome particle concentrations via particle tracking in the presence of Climbazole and Heparin relative to uninfected exosomes. In addition, there was an increased expression of classical markers such as ALG-2 interacting protein X (ALIX), and tetraspanin (CD63), (p < 0.05) and upregulated transcription factor interferon regulatory factor (IRF) 8 in the presence of HAdV3 after 24 hours (h) of treatment. Whereas higher concentrations of Climbazole and Heparin sodium salt were found to inhibit total exosome protein (p < 0.001) and exo-RNA (p < 0.01) content even in the presence of HAdV3 relative to infected exosomes only. Activities of HAdV3 in the presence of selected inhibitors resulted in the positive regulation of exosome related DNA damage/repair signaling proteins. Blocking exosome secretion partially obstructed viral entry. Immunological studies revealed that HAdV3 fiber protein levels in A549 cells were reduced at all concentrations of Climbazole and Heparin and both multiplicities of infections (p < 0.001). Discussion: Our findings suggest that while HAdV may bolster inhibited exosome content and release when modulating certain activities of the endosomal pathway mediators, HAdV entry might be constrained by the activities of these pharmacological agents.

PMID:38449802 | PMC:PMC10915030 | DOI:10.3389/fphar.2024.1339862

Braz J Psychiatry. 2024 Mar 6. doi: 10.47626/1516-4446-2023-3441. Online ahead of print.

ABSTRACT

OBJECTIVE: The present study combined transcriptomic data and computational techniques based on gene expression signatures to identify novel bioactive compounds or FDA-approved drugs for the management of Bipolar Disorder (BD).

METHODS: Five transcriptomic datasets, comprising a total of 165 blood samples from BD case-control, were selected from the Gene Expression Omnibus repository (GEO). The number of subjects varied from 6 to 60, with a mean age ranging from 35 to 48, with a gender variation between them. Most of the patients were on pharmacological treatment. Master Regulator Analysis (MRA) and Gene Set Enrichment Analysis (GSEA) were performed to identify statistically significant genes between BD and HC and their association with the mood states of BD. Additionally, existing molecules with the potential to reverse the transcriptomic profiles of disease-altered regulons in BD were identified using the LINCS and cMap databases.

RESULTS: MRA identified 59 potential MRs candidates modulating the regulatory units enriched with genes altered in BD, while the GSEA identified 134 enriched genes, and a total of 982 regulons had their activation state determined. Both analyses showed genes exclusively associated with mania, depression, or euthymia, and some genes were common between the three mood states. We identified bioactive compounds and licensed drug candidates, including antihypertensives and antineoplastics, as promising candidates for treating BD. Nevertheless, experimental validation is essential to authenticate these findings in subsequent studies.

CONCLUSION: Although preliminary, our data provides some insights regarding the biological patterns of BD into distinct mood states and potential therapeutic targets. The combined transcriptomic and bioinformatics strategy offers a route to advance drug discovery and personalized medicine by tapping into gene expression information.

PMID:38446713 | DOI:10.47626/1516-4446-2023-3441

BMC Med. 2024 Mar 5;22(1):96. doi: 10.1186/s12916-024-03314-1.

ABSTRACT

BACKGROUND: There is a lack of effective therapeutic strategies for amyotrophic lateral sclerosis (ALS); therefore, drug repurposing might provide a rapid approach to meet the urgent need for treatment.

METHODS: To identify therapeutic targets associated with ALS, we conducted Mendelian randomization (MR) analysis and colocalization analysis using cis-eQTL of druggable gene and ALS GWAS data collections to determine annotated druggable gene targets that exhibited significant associations with ALS. By subsequent repurposing drug discovery coupled with inclusion criteria selection, we identified several drug candidates corresponding to their druggable gene targets that have been genetically validated. The pharmacological assays were then conducted to further assess the efficacy of genetics-supported repurposed drugs for potential ALS therapy in various cellular models.

RESULTS: Through MR analysis, we identified potential ALS druggable genes in the blood, including TBK1 [OR 1.30, 95%CI (1.19, 1.42)], TNFSF12 [OR 1.36, 95%CI (1.19, 1.56)], GPX3 [OR 1.28, 95%CI (1.15, 1.43)], TNFSF13 [OR 0.45, 95%CI (0.32, 0.64)], and CD68 [OR 0.38, 95%CI (0.24, 0.58)]. Additionally, we identified potential ALS druggable genes in the brain, including RESP18 [OR 1.11, 95%CI (1.07, 1.16)], GPX3 [OR 0.57, 95%CI (0.48, 0.68)], GDF9 [OR 0.77, 95%CI (0.67, 0.88)], and PTPRN [OR 0.17, 95%CI (0.08, 0.34)]. Among them, TBK1, TNFSF12, RESP18, and GPX3 were confirmed in further colocalization analysis. We identified five drugs with repurposing opportunities targeting TBK1, TNFSF12, and GPX3, namely fostamatinib (R788), amlexanox (AMX), BIIB-023, RG-7212, and glutathione as potential repurposing drugs. R788 and AMX were prioritized due to their genetic supports, safety profiles, and cost-effectiveness evaluation. Further pharmacological analysis revealed that R788 and AMX mitigated neuroinflammation in ALS cell models characterized by overly active cGAS/STING signaling that was induced by MSA-2 or ALS-related toxic proteins (TDP-43 and SOD1), through the inhibition of TBK1 phosphorylation.

CONCLUSIONS: Our MR analyses provided genetic evidence supporting TBK1, TNFSF12, RESP18, and GPX3 as druggable genes for ALS treatment. Among the drug candidates targeting the above genes with repurposing opportunities, FDA-approved drug-R788 and AMX served as effective TBK1 inhibitors. The subsequent pharmacological studies validated the potential of R788 and AMX for treating specific ALS subtypes through the inhibition of TBK1 phosphorylation.

PMID:38443977 | DOI:10.1186/s12916-024-03314-1

BMC Neurosci. 2024 Mar 4;25(1):11. doi: 10.1186/s12868-024-00857-0.

ABSTRACT

BACKGROUND: Parkinson disease (PD) is the fastest growing neurodegenerative disease. The molecular pathology of PD in the prodromal phase is poorly understood; as such, there are no specific prognostic or diagnostic tests. A validated Pink1 genetic knockout rat was used to model early-onset and progressive PD. Male Pink1-/- rats exhibit progressive declines in ultrasonic vocalizations as well as hindlimb and forelimb motor deficits by mid-to-late adulthood. Previous RNA-sequencing work identified upregulation of genes involved in disease pathways and inflammation within the brainstem and vocal fold muscle. The purpose of this study was to identify gene pathways within the whole blood of young Pink1-/- rats (3 months of age) and to link gene expression to early acoustical changes. To accomplish this, limb motor testing (open field and cylinder tests) and ultrasonic vocalization data were collected, immediately followed by the collection of whole blood and RNA extraction. Illumina® Total RNA-Seq TruSeq platform was used to profile differential expression of genes. Statistically significant genes were identified and Weighted Gene Co-expression Network Analysis was used to construct co-expression networks and modules from the whole blood gene expression dataset as well as the open field, cylinder, and USV acoustical dataset. ENRICHR was used to identify the top up-regulated biological pathways.

RESULTS: The data suggest that inflammation and interferon signaling upregulation in the whole blood is present during early PD. We also identified genes involved in the dysregulation of ribosomal protein and RNA processing gene expression as well as prion protein gene expression.

CONCLUSIONS: These data identified several potential blood biomarkers and pathways that may be linked to anxiety and vocalization acoustic parameters and are key candidates for future drug-repurposing work and comparison to human datasets.

PMID:38438964 | DOI:10.1186/s12868-024-00857-0

Assay Drug Dev Technol. 2024 Mar 5. doi: 10.1089/adt.2024.011. Online ahead of print.

NO ABSTRACT

PMID:38437578 | DOI:10.1089/adt.2024.011

J Phys Chem B. 2024 Mar 4. doi: 10.1021/acs.jpcb.3c05564. Online ahead of print.

ABSTRACT

A novel in silico drug design procedure is described targeting the Main protease (Mpro) of the SARS-CoV-2 virus. The procedure combines molecular docking, molecular dynamics (MD), and fragment molecular orbital (FMO) calculations. The binding structure and properties of Mpro were predicted for Nelfinavir (NFV), which had been identified as a candidate compound through drug repositioning, targeting Mpro. Several poses of the Mpro and NFV complexes were generated by docking, from which four docking poses were selected by scoring with FMO energy. Then, each pose was subjected to MD simulation, 100 snapshot structures were sampled from each of the generated MD trajectories, and the structures were evaluated by FMO calculations to rank the pose based on binding energy. Several residues were found to be important in ligand recognition, including Glu47, Asp48, Glu166, Asp187, and Gln189, all of which interacted strongly with NFV. Asn142 is presumably regarded to form hydrogen bonds or CH/π interaction with NFV; however, in the present calculation, their interactions were transient. Moreover, the tert-butyl group of NFV had no interaction with Mpro. Identifying such strong and weak interactions provides candidates for maintaining and substituting ligand functional groups and important suggestions for drug discovery using drug repositioning. Besides the interaction between NFV and the amino acid residues of Mpro, the desolvation effect of the binding pocket also affected the ranking order. A similar procedure of drug design was applied to Lopinavir, and the calculated interaction energy and experimental inhibitory activity value trends were consistent. Our approach provides a new guideline for structure-based drug design starting from a candidate compound whose complex crystal structure has not been obtained.

PMID:38437183 | DOI:10.1021/acs.jpcb.3c05564

IEEE J Biomed Health Inform. 2024 Mar 4;PP. doi: 10.1109/JBHI.2024.3372527. Online ahead of print.

ABSTRACT

Drug repositioning greatly reduces drug development costs and time by discovering new indications for existing drugs. With the development of technology and large-scale biological databases, computational drug repositioning has increasingly attracted remarkable attention, which can narrow down repositioning candidates. Recently, graph neural networks (GNNs) have been widely used and achieved promising results in drug repositioning. However, the existing GNNs based methods usually focus on modeling the complex drug-disease association graph, but ignore the semantic information on the graph, which may lead to a lack of consistency of global topology information and local semantic information for the learned features. To alleviate the above challenge, we propose a novel drug repositioning model based on graph contrastive learning, termed DRGCL. First, we treat the known drug-disease associations as the topology graph. Second, we select the top- K similar neighbor from drug/disease similarity information to construct the semantic graph rather than use the traditional data augmentation strategy, thereby maximally retaining rich semantic information. Finally, we pull closer to embedding consistency of the different embedding spaces by graph contrastive learning to enhance the topology and semantic feature on the graph. We have evaluated DRGCL on four benchmark datasets and the experiment results show that the proposed DRGCL is superior to the state-of-the-art methods. Especially, the average result of DRGCL is 11.92% higher than that of the second-best method in terms of AUPRC. The case studies further demonstrate the reliability of DRGCL. Experimental datasets and experimental codes can be found in https://github.com/Jiaxiao123/DRGCL.

PMID:38437145 | DOI:10.1109/JBHI.2024.3372527

ACS Infect Dis. 2024 Mar 4. doi: 10.1021/acsinfecdis.3c00686. Online ahead of print.

ABSTRACT

The growing threat of bacterial infections coupled with the dwindling arsenal of effective antibiotics has heightened the urgency for innovative strategies to combat bacterial pathogens, particularly Gram-negative strains, which pose a significant challenge due to their outer membrane permeability barrier. In this study, we repurpose clinically approved anticancer agents as targeted antibacterials. We report two new siderophore-platinum(IV) conjugates, both of which consist of an oxaliplatin-based Pt(IV) prodrug (oxPt(IV)) conjugated to enterobactin (Ent), a triscatecholate siderophore employed by Enterobacteriaceae for iron acquisition. We demonstrate that l/d-Ent-oxPt(IV) (l/d-EOP) are selectively delivered into the Escherichia coli cytoplasm, achieving targeted antibacterial activity, causing filamentous morphology, and leading to enhanced Pt uptake by bacterial cells but reduced Pt uptake by human cells. d-EOP exhibits enhanced potency compared to oxaliplatin and l-EOP, primarily attributed to the intrinsic antibacterial activity of its non-native siderophore moiety. To further elucidate the antibacterial activity of Ent-Pt(IV) conjugates, we probed DNA damage caused by l/d-EOP and the previously reported cisplatin-based conjugates l/d-Ent-Pt(IV) (l/d-EP). A comparative analysis of these four conjugates reveals a correlation between antibacterial activity and the ability to induce DNA damage. This work expands the scope of Pt cargos targeted to the cytoplasm of Gram-negative bacteria via Ent conjugation, provides insight into the cellular consequences of Ent-Pt(IV) conjugates in E. coli, and furthers our understanding of the potential of Pt-based therapeutics for antibacterial applications.

PMID:38436588 | DOI:10.1021/acsinfecdis.3c00686

J Cancer Sci Clin Ther. 2023;7(4):249-252. doi: 10.26502/jcsct.5079217. Epub 2023 Dec 8.

ABSTRACT

High-throughput drug screens are a powerful tool for cancer drug development. However, the results of such screens are often made available only as raw data, which is intractable for researchers without informatics skills, or as highly processed summary statistics, which can lack essential information for translating screening results into clinically meaningful discoveries. To improve the usability of these datasets, we developed Simplicity, a robust and user-friendly web interface for visualizing, exploring, and summarizing raw and processed data from high- throughput drug screens. Importantly, Simplicity allows for easy recalculation of summary statistics at user-defined drug concentrations. This allows Simplicity's outputs to be used with methods that rely on statistics being calculated at clinically relevant doses. Simplicity can be freely accessed at https://oncotherapyinformatics.org/simplicity/.

PMID:38435702 | PMC:PMC10906814 | DOI:10.26502/jcsct.5079217