Candidate Small Molecules Research Articles

A two-step strategy to expand primary human hepatocytes in vitro with efficient metabolic and regenerative capacities

BackgroundPrimary human hepatocytes (PHHs) are highly valuable for drug-metabolism evaluation, liver disease modeling and hepatocyte transplantation. However, their availability is significantly restricted due to limited donor sources, alongside their constrained proliferation capabilities and reduced functionality when cultured in vitro. To address this challenge, we aimed to develop a novel method to efficiently expand PHHs in vitro without a loss of function.MethodsBy mimicking the in vivo liver regeneration route, we developed a two-step strategy involving the de-differentiation/expansion and subsequent maturation of PHHs to generate abundant functional hepatocytes in vitro. Initially, we applied SiPer, a prediction algorithm, to identify candidate small molecules capable of activating liver regenerative transcription factors, thereby formulating a novel hepatic expansion medium to de-differentiate PHHs into proliferative human hepatic progenitor-like cells (ProHPLCs). These ProHPLCs were then re-differentiated into functionally mature hepatocytes using a new hepatocyte maturation condition. Additionally, we investigated the underlying mechanism of PHHs expansion under our new conditions.ResultsThe novel hepatic expansion medium containing hydrocortisone facilitated the de-differentiation of PHHs into ProHPLCs, which exhibited key hepatic progenitor characteristics and demonstrated a marked increase in proliferation capacity compared to cells cultivated in previously established expansion conditions. Remarkably, these subsequent matured hepatocytes rivaled PHHs in terms of transcriptome profiles, drug metabolizing activities and in vivo engraftment capabilities. Importantly, our findings suggest that the enhanced expansion of PHHs by hydrocortisone may be mediated through the PPARα signaling pathway and regenerative transcription factors.ConclusionsThis study presents a two-step strategy that initially induces PHHs into a proliferative state (ProHPLCs) to ensure sufficient cell quantity, followed by the maturation of ProHPLCs into fully functional hepatocytes to guarantee optimal cell quality. This approach offers a promising means of producing large numbers of seeding cells for hepatocyte-based applications.

Do Molecular Fingerprints Identify Diverse Active Drugs in Large-Scale Virtual Screening? (No).

Computational approaches for small-molecule drug discovery now regularly scale to the consideration of libraries containing billions of candidate small molecules. One promising approach to increased the speed of evaluating billion-molecule libraries is to develop succinct representations of each molecule that enable the rapid identification of molecules with similar properties. Molecular fingerprints are thought to provide a mechanism for producing such representations. Here, we explore the utility of commonly used fingerprints in the context of predicting similar molecular activity. We show that fingerprint similarity provides little discriminative power between active and inactive molecules for a target protein based on a known active-while they may sometimes provide some enrichment for active molecules in a drug screen, a screened data set will still be dominated by inactive molecules. We also demonstrate that high-similarity actives appear to share a scaffold with the query active, meaning that they could more easily be identified by structural enumeration. Furthermore, even when limited to only active molecules, fingerprint similarity values do not correlate with compound potency. In sum, these results highlight the need for a new wave of molecular representations that will improve the capacity to detect biologically active molecules based on their similarity to other such molecules.

In Silico Analysis of Alzheimer's Disease Mechanism Through DNA Methylation and Gene Expression Data

Alzheimer's Disease (AD) is a debilitating disease impairing memory and thought process with currently no cure. In the current study, GEO2R, an integrated tool in the GEO database, was used to analyze DNA methylation and gene expression datasets associated with AD. Data from the BioGRID Database were used to create a PPI network of differentially methylation and expressed AD genes (DEMEGs). Cytoscape was used to image and analyze the PPI network topologically. The DAVID bioinformatics program was utilized to do enrichment analysis in order to uncover disease associations and signaling pathways. Furthermore, small molecules were predicted using Connectivity Map 2 (Cmap 2) as potential therapeutic agents that might be exploited as pharmacological targets for the study's DEMEGs. As a result, 502 mutual DEMEGs and several hubs that may be researched further as new biomarker candidates for AD such as SMURF1 and UBE2D2 were identified. The link between AD and the MAPK signaling pathway, as well as addiction and brain diseases such as ADHD and epilepsy has been established. Additionally, candidate small molecules that can be used as therapeutics such as flucloxacillin, butamben, and acetohexamide were proposed. This study integrated DNA methylation and gene expression data to further our knowledge of the AD disease mechanism.

Decoding cardiovascular risks: analyzing type 2 diabetes mellitus and ASCVD gene expression.

ASCVD is the primary cause of mortality in individuals with T2DM. A potential link between ASCVD and T2DM has been suggested, prompting further investigation. We utilized linear and multivariate logistic regression, Wilcoxon test, and Spearman's correlation toanalyzethe interrelation between ASCVD and T2DM in NHANES data from 2001-2018.The Gene Expression Omnibus (GEO) database and Weighted Gene Co-expression Network Analysis (WGCNA) wereconducted to identify co-expression networks between ASCVD and T2DM. Hub genes were identified using LASSO regression analysis and further validated in two additional cohorts. Bioinformatics methods were employed for gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, along with the prediction of candidate small molecules. Our analysis of the NHANES dataset indicated a significant impact of blood glucose on lipid levels within diabetic cohort, suggesting that abnormal lipid metabolism is a critical factor in ASCVD development. Cross-phenotyping analysis revealed two pivotal genes, ABCC5 and WDR7, associated with both T2DM and ASCVD. Enrichment analyses demonstrated the intertwining of lipid metabolism in both conditions, encompassing adipocytokine signaling pathway, fatty acid degradation and metabolism, and the regulation of adipocyte lipolysis. Immune infiltration analysis underscored the involvement of immune processes in both diseases. Notably, RITA, ON-01910, doxercalciferol, and topiramate emerged as potential therapeutic agents for both T2DM and ASCVD, indicating their possible clinical significance. Our findings pinpoint ABCC5 and WDR7 as new target genes between T2DM and ASCVD, with RITA, ON-01910, doxercalciferol, and topiramate highlighted as promising therapeutic agents.

A Proximity Complementation Assay to Identify Small Molecules That Enhance the Traffic of ABCA4 Misfolding Variants.

ABCA4-related retinopathy is the most common inherited Mendelian eye disorder worldwide, caused by biallelic variants in the ATP-binding cassette transporter ABCA4. To date, over 2200 ABCA4 variants have been identified, including missense, nonsense, indels, splice site and deep intronic defects. Notably, more than 60% are missense variants that can lead to protein misfolding, mistrafficking and degradation. Currently no approved therapies target ABCA4. In this study, we demonstrate that ABCA4 misfolding variants are temperature-sensitive and reduced temperature growth (30 °C) improves their traffic to the plasma membrane, suggesting the folding of these variants could be rescuable. Consequently, an in vitro platform was developed for the rapid and robust detection of ABCA4 traffic to the plasma membrane in transiently transfected cells. The system was used to assess selected candidate small molecules that were reported to improve the folding or traffic of other ABC transporters. Two candidates, 4-PBA and AICAR, were identified and validated for their ability to enhance both wild-type ABCA4 and variant trafficking to the cell surface in cell culture. We envision that this platform could serve as a primary screen for more sophisticated in vitro testing, enabling the discovery of breakthrough agents to rescue ABCA4 protein defects and mitigate ABCA4-related retinopathy.

Drug Repurposing Analysis for Colorectal Cancer through Network Medicine Framework: Novel Candidate Drugs and Small Molecules

This study aimed to reveal the drug-repurposing candidates for colorectal cancer (CRC) via drug-repurposing methods and network biology approaches. A novel, differentially co-expressed, highly interconnected, and co-regulated prognostic gene module was identified for CRC. Based on the gene module, polyethylene glycol (PEG), gallic acid, pyrazole, cordycepin, phenothiazine, pantoprazole, cysteamine, indisulam, valinomycin, trametinib, BRD-K81473043, AZD8055, dovitinib, BRD-A17065207, and tyrphostin AG1478 presented as drugs and small molecule candidates previously studied in the CRC. Lornoxicam, suxamethonium, oprelvekin, sirukumab, levetiracetam, sulpiride, NVP-TAE684, AS605240, 480743.cdx, HDAC6 inhibitor ISOX, BRD-K03829970, and L-6307 are proposed as novel drugs and small molecule candidates for CRC.

Exploring molecular markers and drug candidates for colorectal cancer through comprehensive bioinformatics analysis.

Colorectal cancer (CRC) often has a poor prognosis and identifying useful and novel agents for treating CRC is urgently required. This study aimed to examine molecular markers associated with CRC prognosis and to identify potential drug candidates. The differentially expressed genes (DEGs) of CRC in TCGA were identified. The genes associated with CRC, summarized from NCBI-gene, OMIM, and the DEGs, were used to construct a co-expression network by WGCNA. Moreover, the co-expression genes from modules of interest were used to carry out functional enrichment. A total of 2742 DEGs, including 1674 upregulated and 1068 downregulated genes, were identified. Thirteen co-expression modules were constructed with WGCNA. Brown and blue co-expression modules with significant differences in disease phenotype were found. Functional enrichment analysis showed that genes in the brown module were mainly related to cell cycle, cell proliferation, DNA replication, and RNA transport. The genes in the blue module were mainly associated with fatty acid degradation, sulfur metabolism, PPAR signaling pathway and bile secretion. In addition, both the genes in brown and blue were associated with tumor staging. Some prognostic markers and candidate small molecules drugs for CRC treatment were identified. In conclusion, we revealed molecular biomarker profiles in CRC by systematic bioinformatics analysis, constructed regulatory networks of mRNA, ncRNA and transcriptional regulators (TFs), and identified potential drugs targeting hub proteins and TFs.

Searching for Novel Candidate Small Molecules for Ameliorating Idiopathic Pulmonary Fibrosis: a Narrative Review.

Idiopathic pulmonary fibrosis (IPF) can be defined as a progressive chronic pulmonary disease showing scarring in the lung parenchyma, thereby resulting in increase in mortality and decrease in the quality of life. The pathophysiologic mechanism of fibrosis in IPF is still unclear. Repetitive microinjuries to alveolar epithelium with genetical predisposition and an abnormal restorative reaction accompanied by excessive deposition of collagens are involved in the pathogenesis. Although the two FDA-approved drugs, pirfenidone and nintedanib, are under use for retarding the decline in lung function of patients suffered from IPF, they are not able to improve the survival rate or quality of life. Therefore, a novel therapeutic agent acting on the major steps of the pathogenesis of disease and/or, at least, managing the clinical symptoms of IPF should be developed for the effective regulation of this incurable disease. In the present review, we tried to find a potential of managing the clinical symptoms of IPF by natural products derived from medicinal plants used for controlling the pulmonary inflammatory diseases in traditional Asian medicine. A multitude of natural products have been reported to exert an antifibrotic effect in vitro and in vivo through acting on the epithelial-mesenchymal transition pathway, transforming growth factor (TGF)-β-induced intracellular signaling, and the deposition of extracellular matrix. However, clinical antifibrotic efficacy of these natural products on IPF have not been elucidated yet. Thus, those effects should be proven by further examinations including the randomized clinical trials, in order to develop the ideal and optimal candidate for the therapeutics of IPF.

Predicting Key Genes and Therapeutic Molecular Modelling to Explain the Association between Porphyromonas gingivalis (P. gingivalis) and Alzheimer’s Disease (AD)

The association between Porphyromonas gingivalis (P. gingivalis) and Alzheimer's disease (AD) remains unclear. The major aim of this study was to elucidate the role of genes and molecular targets in P. gingivalis-associated AD. Two Gene Expression Omnibus (GEO) datasets, GSE5281 for AD (n = 84 Alzheimer's, n = 74 control) and GSE9723 (n = 4 P. gingivalis, n = 4 control), were downloaded from the GEO database. Differentially expressed genes (DEGs) were obtained, and genes common to both diseases were drawn. Additionally, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis was performed from the top 100 genes (50 upregulated and 50 downregulated genes). We then proceeded with CMap analysis to screen for possible small drug molecules targeting these genes. Subsequently, we performed molecular dynamics simulations. A total of 10 common genes (CALD1, HES1, ID3, PLK2, PPP2R2D, RASGRF1, SUN1, VPS33B, WTH3DI/RAB6A, and ZFP36L1) were identified with a p-value < 0.05. The PPI network of the top 100 genes showed UCHL1, SST, CHGB, CALY, and INA to be common in the MCC, DMNC, and MNC domains. Out of the 10 common genes identified, only 1 was mapped in CMap. We found three candidate small drug molecules to be a fit for PLK2, namely PubChem ID: 24971422, 11364421, and 49792852. We then performed molecular docking of PLK2 with PubChem ID: 24971422, 11364421, and 49792852. The best target, 11364421, was used to conduct the molecular dynamics simulations. The results of this study unravel novel genes to P. gingivalis-associated AD that warrant further validation.

A knowledge-based protein-protein interaction inhibition (KPI) pipeline: an insight from drug repositioning for COVID-19 inhibition

The inhibition of protein-protein interactions (PPIs) by small molecules is an exciting drug discovery strategy. Here, we aimed to develop a pipeline to identify candidate small molecules to inhibit PPIs. Therefore, KPI, a Knowledge-based Protein-Protein Interaction Inhibition pipeline, was introduced to improve the discovery of PPI inhibitors. Then, phytochemicals from a collection of known Middle Eastern antiviral herbs were screened to identify potential inhibitors of key PPIs involved in COVID-19. Here, the following investigations were sequenced: 1) Finding the binding partner and the interface of the proteins in PPIs, 2) Performing the blind ligand-protein inhibition (LPI) simulations, 3) Performing the local LPI simulations, 4) Simulating the interactions of the proteins and their binding partner in the presence and absence of the ligands, and 5) Performing the molecular dynamics simulations. The pharmacophore groups involved in the LPI were also characterized. Aloin, Genistein, Neoglucobrassicin, and Rutin are our new pipeline candidates for inhibiting PPIs involved in COVID-19. We also propose KPI for drug repositioning studies. Communicated by Ramaswamy H. Sarma

Using In Silico Bioinformatics Algorithms for the Accurate Prediction of the Impact of Spike Protein Mutations on the Pathogenicity, Stability, and Functionality of the SARS-CoV-2 Virus and Analysis of Potential Therapeutic Targets.

Coronavirus disease 2019 is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We have used bioinformatics to investigate seventeen mutations in the spike protein of SARS-CoV-2, as this mediates infection of human cells and is the target of most vaccine strategies and antibody-based therapies. Two mutations, H146Y and S221W, were identified as being most pathogenic. Mutations at positions D614G, A829T, and P1263L might also have deleterious effects on protein function. We hypothesized that candidate small molecules may be repurposed to combat viral infection. We investigated changes in binding energies of the ligands and the mutant proteins by assessing molecular docking. For an understanding of cellular function and organization, protein–protein interactions are also critical. Protein–protein docking for naïve and mutated structures of SARS-CoV-2 S protein was evaluated for their binding energy with the angiotensin-converting enzyme 2 (ACE2). These interactions might limit the binding of the SARS-CoV-2 spike protein to the ACE2 receptor or may have a deleterious effect on protein function that may limit infection. These results may have important implications for the transmission of SARS-CoV-2, its pathogenesis, and the potential for drug repurposing and immune therapies.

CASP9 As a Prognostic Biomarker and Promising Drug Target Plays a Pivotal Role in Inflammatory Breast Cancer.

Background Inflammatory breast cancer (IBC) is one of the most rare and aggressive subtypes of primary breast cancer (BC). Our study aimed to explore hub genes related to the pathogenesis of IBC, which could be considered as novel molecular biomarkers for IBC diagnosis and prognosis. Material and Methods. Two datasets from gene expression omnibus database (GEO) were selected. Enrichment analysis and protein-protein interaction (PPI) network for the DEGs were performed. We analyzed the prognostic values of hub genes in the Kaplan-Meier Plotter. Connectivity Map (CMap) and Comparative Toxicogenomics Database (CTD) was used to find candidate small molecules capable to reverse the gene status of IBC. Results 157 DEGs were selected in total. We constructed the PPI network with 154 nodes interconnected by 128 interactions. The KEGG pathway analysis indicated that the DEGs were enriched in apoptosis, pathways in cancer and insulin signaling pathway. PTEN, PSMF1, PSMC6, AURKB, FZR1, CASP9, CASP6, CASP8, BAD, AKR7A2, ZNF24, SSX2IP, SIGLEC1, MS4A4A, and VSIG4 were selected as hub genes based on the high degree of connectivity. Six hub genes (PSMC6, AURKB, CASP9, BAD, ZNF24, and SSX2IP) that were significantly associated with the prognosis of breast cancer. The expression of CASP9 protein was associated with prognosis and immune cells infiltration of breast cancer. CASP9- naringenin (NGE) is expected to be the most promising candidate gene-compound interaction for the treatment of IBC. Conclusion Taken together, CASP9 can be used as a prognostic biomarker and a novel therapeutic target in IBC.

Virtual screening for small-molecule pathway regulators by image-profile matching.

Identifying the chemical regulators of biological pathways is a time-consuming bottleneck in developing therapeutics and research compounds. Typically, thousands to millions of candidate small molecules are tested in target-based biochemical screens or phenotypic cell-based screens, both expensive experiments customized to each disease. Here, our uncustomized, virtual, profile-based screening approach instead identifies compounds that match to pathways based on the phenotypic information in public cell image data, created using the Cell Painting assay. Our straightforward correlation-based computational strategy retrospectively uncovered the expected, known small-molecule regulators for 32% of positive-control gene queries. In prospective, discovery mode, we efficiently identified new compounds related to three query genes and validated them in subsequent gene-relevant assays, including compounds that phenocopy or pheno-oppose YAP1 overexpression and kill a Yap1-dependent sarcoma cell line. This image-profile-based approach could replace many customized labor- and resource-intensive screens and accelerate the discovery of biologically and therapeutically useful compounds.

Identification of novel key genes and potential candidate small molecule drugs in diabetic kidney disease using comprehensive bioinformatics analysis.

Objective: The currently established diagnostic and prognostic tools for diabetic kidney disease (DKD) have limitations, which demands the necessity to find new genes and pathways associated with diagnosis and treatment. Our study aims to reveal the gene expression alteration and discover critical genes involved in the development of DKD, thus providing novel diagnostic molecular markers and therapeutic targets. Materials and methods: The differences of infiltrating immune cells within kidney were compared between healthy living donors and DKD patients. Besides, differentially expressed genes (DEGs) within kidney from healthy living donor, early stage DKD and advanced stage DKD samples were detected. Furthermore, the weighted co-expressed network (WGCNA) and protein-protein interaction (PPI) network were constructed, followed by recognition of core hub genes and module analysis. Receiver operating characteristic (ROC) curve analysis was implemented to determine the diagnostic value of hub genes, correlation analysis was employed to explore the association between hub genes and infiltrating immune cells, and certain hub genes was validated by quantitative real-time PCR and immunohistochemistry staining in cultured tubule cells and diabetic mice kidney. Finally, the candidate small molecules as potential drugs to treat DKD were anticipated through utilizing virtual screening and molecular docking investigation. Results: Our study revealed significantly higher proportion of infiltrating immune cells within kidney from DKD patients via probing the immune landscape by single-cell transcriptomics. Besides, 126 commonly shared DEGs identified among three group samples were enriched in immune biological process. In addition, the ROC curve analysis demonstrated the strong diagnostic accuracy of recognized hub genes (NFKB1, DYRK2, ATAD2, YAP1, and CHD3) from PPI network. Correlation analysis further confirmed the positive association between these hub genes with infiltrating natural killer cells. More importantly, the mRNA transcripts and protein abundance of YAP1 were significantly higher in high glucose-treated renal tubule cells and diabetic mice kidney, and the small molecules exhibiting the best binding affinities with YAP1 were predicted and acquired. Conclusion: Our findings for the first time indicate that NFKB1, DYRK2, ATAD2, YAP1, and CHD3 might be potential novel biomarkers and therapeutic targets for DKD, providing insights into the molecular mechanisms underlying the pathogenesis of DKD.

Ricolinostat promotes the generation of megakaryocyte progenitors from human hematopoietic stem and progenitor cells

BackgroundEx vivo production of induced megakaryocytes (MKs) and platelets from stem cells is an alternative approach for supplying transfusible platelets. However, it is difficult to generate large numbers of MKs and platelets from hematopoietic stem cells and progenitor cells (HSPCs).MethodsTo optimize the differentiation efficiency of megakaryocytic cells from HSPCs, we first employed a platelet factor 4 (PF4)-promoter reporter and high-throughput screening strategy to screen for small molecules. We also investigated the effects and possible mechanisms of candidate small molecules on megakaryocytic differentiation of human HSPCs.ResultsThe small molecule Ricolinostat remarkably promoted the expression of PF4-promoter reporter in the megakaryocytic cell line. Notably, Ricolinostat significantly enhanced the cell fate commitment of MK progenitors (MkPs) from cord blood HSPCs and promoted the proliferation of MkPs based on cell surface marker detection, colony-forming unit-MK assay, and quantitative real-time PCR analyses. MkPs generated from Ricolinostat-induced HSPCs differentiated into mature MKs and platelets. Mechanistically, we found that Ricolinostat enhanced MkP fate mainly by inhibiting the secretion of IL-8 and decreasing the expression of the IL-8 receptor CXCR2.ConclusionThe addition of Ricolinostat to the culture medium promoted MkP differentiation from HSPCs and enhanced the proliferation of MkPs mainly by suppressing the IL-8/CXCR2 pathway. Our results can help the development of manufacturing protocols for the efficient generation of MKs and platelets from stem cells in vitro.

Small Molecule Screening Discovers Compounds that Reduce FMRpolyG Protein Aggregates and Splicing Defect Toxicity in Fragile X-Associated Tremor/Ataxia Syndrome.

Expansion of CGG trinucleotide repeats in 5' untranslated region of the FMR1 gene is the causative mutation of neurological diseases such as fragile X syndrome (FXS), fragile X-associated tremor/ataxia syndrome (FXTAS), and ovarian disorder such as fragile X-associated primary ovarian insufficiency (FXPOI). CGG repeats containing FMR1 transcripts form the toxic ribonuclear aggregates, abrupt pre-mRNA splicing, and cause repeat-associated non-AUG translation, leading to the disease symptoms. Here, we utilized a small molecule library of ~ 250,000 members obtained from the National Cancer Institute (NCI) and implemented a shape-based screening approach to identify the candidate small molecules that mitigate toxic CGG RNA-mediated pathogenesis. The compounds obtained from screening were further assessed for their affinity and selectivity towards toxic CGG repeat RNA by employing fluorescence-binding experiment and isothermal calorimetry titration assay. Three candidate molecules B1, B4, and B11 showed high affinity and selectivity for expanded CGG repeats RNA. Further, NMR spectroscopy, gel mobility shift assay, CD spectroscopy, UV-thermal denaturation assay, and molecular docking affirmed their high affinity and selectivity for toxic CGG RNAs. Next, these lead compounds selectively improved the pre-mRNA alternative splicing defects with no perturbation in global splicing efficacy and simultaneouslyreduced the FMR1polyG protein aggregate formation without affecting the downstream expression of the gene. Taken together these findings, we addressed compound B1, B4, and B11 as potential lead molecules for developing promising therapeutics against FXTAS. Herein, this study, we have utilized shape similarity approach to screen the NCI library and found out the potential candidate which improves the pre-mRNA splicing defects and reduces FMR1polyG aggregations.

Promising novel biomarkers and candidate small-molecule drugs for lung adenocarcinoma: Evidence from bioinformatics analysis of high-throughput data.

Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer associated with an unstable prognosis. Thus, there is an urgent demand for the identification of novel diagnostic and prognostic biomarkers as well as targeted drugs for LUAD. The present study aimed to identify potential new biomarkers associated with the pathogenesis and prognosis of LUAD. Three microarray datasets (GSE10072, GSE31210, and GSE40791) from the Gene Expression Omnibus database were integrated to identify the differentially expressed genes (DEGs) in normal and LUAD samples using the limma package. Bioinformatics tools were used to perform functional and signaling pathway enrichment analyses for the DEGs. The expression and prognostic values of the hub genes were further evaluated by Gene Expression Profiling Interactive Analysis and real-time quantitative polymerase chain reaction. Furthermore, we mined the “Connectivity Map” (CMap) to explore candidate small molecules that can reverse the tumoral of LUAD based on the DEGs. A total of 505 DEGs were identified, which included 337 downregulated and 168 upregulated genes. The PPI network was established with 1,860 interactions and 373 nodes. The most significant pathway and functional enrichment associated with the genes were cell adhesion and extracellular matrix-receptor interaction, respectively. Seven DEGs with high connectivity degrees (ZWINT, RRM2, NDC80, KIF4A, CEP55, CENPU, and CENPF) that were significantly associated with worse survival were chosen as hub genes. Lastly, top 20 most important small molecules which reverses the LUAD gene expressions were identified. The findings contribute to revealing the molecular mechanisms of the initiation and progression of LUAD and provide new insights for integrating multiple biomarkers in clinical practice.

Barcoded Asaia bacteria enable mosquito in vivo screens and identify novel systemic insecticides and inhibitors of malaria transmission.

This work addresses the need for new chemical matter in product development for control of pest insects and vector-borne diseases. We present a barcoding strategy that enables phenotypic screens of blood-feeding insects against small molecules in microtiter plate-based arrays and apply this to discovery of novel systemic insecticides and compounds that block malaria parasite development in the mosquito vector. Encoding of the blood meals was achieved through recombinant DNA-tagged Asaia bacteria that successfully colonised Aedes and Anopheles mosquitoes. An arrayed screen of a collection of pesticides showed that chemical classes of avermectins, phenylpyrazoles, and neonicotinoids were enriched for compounds with systemic adulticide activity against Anopheles. Using a luminescent Plasmodium falciparum reporter strain, barcoded screens identified 48 drug-like transmission-blocking compounds from a 400-compound antimicrobial library. The approach significantly increases the throughput in phenotypic screening campaigns using adult insects and identifies novel candidate small molecules for disease control.

Identification of HCC Subtypes With Different Prognosis and Metabolic Patterns Based on Mitophagy.

Background: Mitophagy is correlated with tumor initiation and development of malignancy. However, HCC heterogeneity with reference to mitophagy has yet not been systematically explored. Materials and Methods: Mitophagy-related, glycolysis-related, and cholesterol biosynthesis-related gene sets were obtained from the Reactome database. Mitophagy-related and metabolism-related subtypes were identified using the ConsensusClusterPlus algorithm. Univariate Cox regression was analysis was performed to identify prognosis-related mitophagy regulators. Principal component analysis (PCA) was used to create composite measures of the prognosis-related mitophagy regulators (mitophagyscore). Individuals with a mitophagyscore higher or lower than the median value were classified in high- or low-risk groups. Kaplan-Meier survival and ROC curve analyses were utilized to evaluate the prognostic value of the mitophagyscore. The nomogram and calibration curves were plotted using the“rms” R package. The package “limma” was used for differential gene expression analysis. Differentially expressed genes (DEGs) between high- and low-risk groups were used as queries in the CMap database. R package “pRRophetic” and Genomics of Drug Sensitivity in Cancer (GDSC) database were used to determine the sensitivity of 21 previously reported anti-HCC drugs. Results: Three distinct HCC subtypes with different mitophagic accumulation (low, high, and intermediate mitophagy subtypes) were identified. High mitophagy subtype had the worst outcome and highest glycolysis level. The lowest degree of hypoxia and highest cholesterol biosynthesis was observed in the low mitophagy subtype; oncogenic dedifferentiation level in the intermediate mitophagy subtype was the lowest. Mitophagyscore could serve as a novel prognostic indicator for HCC.High-risk patients had a poorer prognosis (log-rank test, p < 0.001). The area under the ROC curve for mitophagyscore in 1-year survival was 0.77 in the TCGA cohort and 0.75 in the ICGC cohort. Nine candidate small molecules which were potential drugs for HCC treatment were identified from the CMap database. A decline in the sensitivity towards 21 anti-HCC drugs was observed in low-risk patients by GDSC database. We also identified a novel key gene, SPP1, which was highly associated with different mitophagic subtypes. Conclusion: Based on bioinformatic analyses, we systematically examined the HCC heterogeneity with reference to mitophagy and observed three distinct HCC subtypes having different prognoses and metabolic patterns.

ACE2 : S1 RBD Interaction-Targeted Peptides and Small Molecules as Potential COVID-19 Therapeutics.

The COVID-19 pandemic that began in late 2019 continues with new challenges arising due to antigenic drift as well as individuals who cannot or choose not to take the vaccine. There is therefore an urgent need for additional therapies that complement vaccines and approved therapies such as antibodies in the fight to end or slow down the pandemic. SARS-CoV-2 initiates invasion of the human target cell through direct contact between the receptor-binding domain of its Spike protein and its cellular receptor, angiotensin-converting enzyme-2 (ACE2). The ACE2 and S1 RBD interaction, therefore, represents an attractive therapeutic intervention to prevent viral entry and spread. In this study, we developed a proximity-based AlphaScreen™ assay that can be utilized to quickly and efficiently screen for inhibitors that perturb the ACE2 : S1 RBD interaction. We then designed several peptides candidates from motifs in ACE2 and S1 RBD that play critical roles in the interaction, with and without modifications to the native sequences. We also assessed the possibility of reprofiling of candidate small molecules that previously have been shown to interfere with the viral entry of SARS-CoV. Using our optimized AlphaScreen™ assay, we evaluated the activity and specificity of these peptides and small molecules in inhibiting the binding of ACE2 : S1 RBD. This screen identified cepharanthine as a promising candidate for development as a SARS-CoV-2 entry inhibitor.