Identifying off-target effects and hidden phenotypes of drugs in human cells

Structural analysis of genomic and proteomic signatures reveal dynamic expression of intrinsically disordered regions in breast cancer

Structural features of proteins capture underlying information about protein evolution and function, which enhances the analysis of proteomic and transcriptomic data. Here, we develop Structural Analysis of Gene and protein Expression Signatures (SAGES), a method that describes expression data using features calculated from sequence-based prediction methods and 3D structural models. We used SAGES, along with machine learning, to characterize tissues from healthy individuals and those with breast cancer. We analyzed gene expression data from 23 breast cancer patients and genetic mutation data from the Catalog of Somatic Mutations In Cancer database as well as 17 breast tumor protein expression profiles. We identified prominent expression of intrinsically disordered regions in breast cancer proteins as well as relationships between drug perturbation signatures and breast cancer disease signatures. Our results suggest that SAGES is generally applicable to describe diverse biological phenomena including disease states and drug effects.

Transient infection of Euprymna scolopes with an engineered D-alanine auxotroph of Vibrio fischeri

The symbiosis between Vibrio fischeri and the Hawaiian bobtail squid, Euprymna scolopes, is a tractable and well-studied model of bacteria-animal mutualism. Here, we developed a method to transiently colonize E. scolopes using D-alanine (D-ala) auxotrophy of the symbiont, controlling the persistence of viable infection by supplying or withholding D-ala. We generated alanine racemase (alr) mutants of V. fischeri that lack avenues for mutational suppression of auxotrophy or reversion to prototrophy. Surprisingly, an ∆alr mutant did not require D-ala to grow in a minimal medium, a phenomenon requiring metC, which encodes cystathionine β-lyase. Likewise, overexpression of metC suppressed D-ala auxotrophy in a rich medium. To block potential mechanisms of suppression, we combined the ∆alr mutation with deletions of metC and/or bsrF, which encodes a broad-spectrum racemase and investigated the suppression rates of four D-ala auxotrophic strains. We then focused on ∆alr ∆bsrF mutant MC13, which has a suppression rate of <10-9. When D-ala was removed from a growing culture of MC13, cells rounded and lysed within 40 minutes. Transient colonization of E. scolopes was achieved by inoculating squid in seawater containing MC13 and D-ala, and then transferring the squid into water lacking D-ala, which resulted in loss of viable symbionts within hours. Interestingly, the symbionts within crypt 3 persisted longer than those of crypt 1, suggesting a difference in bacterial growth rate in distinct crypt environments. Our study highlights a new approach for inducing transient colonization and provides insight into the biogeography of the E. scolopes light organ.IMPORTANCEThe importance of this study is multi-faceted, providing a valuable methodological tool and insight into the biology of the symbiosis between Vibrio fischeri and Euprymna scolopes. First, the study sheds light on the critical role of D-ala for bacterial growth, and the underpinnings of D-ala synthesis. Our observations that metC obviates the need for D-ala supplementation of an alr mutant in minimal medium and that MetC-dependent growth correlates with D-ala in peptidoglycan, corroborate and extend previous findings in Escherichia coli regarding a role of MetC in D-ala production. Second, our isolation of robust D-ala auxotrophs led us to a novel method for studying the squid-Vibrio symbiosis, allowing for transient colonization without the use of antibiotics, and revealed intriguing differences in symbiont growth parameters in distinct light organ crypts. This work and the methodology developed will contribute to our understanding of the persistence and dynamics of V. fischeri within its host.

HIV-1 Gag Compact form Stabilized by Intramolecular Interactions is Crucial for Infectious Particle Production

HIV-1 Gag polyprotein plays a pivotal role in assembly and budding of new particles, by specifically packaging two copies of viral gRNA in the host cell cytoplasm and selecting the cell plasma membrane for budding. Both gRNA and membrane selections are thought to be mediated by the compact form of Gag. This compact form binds to gRNA through both its matrix (MA) and nucleocapsid (NC) domains in the cytoplasm. At the plasma membrane, the membrane competes with gRNA for Gag binding, resulting in a transition to the extended form of Gag found in immature particles with MA bound to membrane lipids and NC to gRNA. The Gag compact form was previously evidenced in vitro. Here, we demonstrated the compact form of Gag in cells by confocal microscopy, using a bimolecular fluorescence complementation approach with a split-GFP bipartite system. Using wild-type Gag and Gag mutants, we showed that the compact form is highly dependent on the binding of MA and NC domains to RNA, as well as on interactions between MA and CA domains. In contrast, Gag multimerization appears to be less critical for the accumulation of the compact form. Finally, mutations altering the formation of Gag compact form led to a strong reduction in viral particle production and infectivity, revealing its key role in the production of infectious viral particles.

Chemoresistance Mechanisms in Non-Small Cell Lung Cancer-Opportunities for Drug Repurposing

Globally, lung cancer contributes significantly to the public health burden-associated mortality. As this form of cancer is insidious in nature, there is an inevitable diagnostic delay leading to chronic tumor development. Non-small cell lung cancer (NSCLC) constitutes 80-85% of all lung cancer cases, making this neoplasia form a prevalent subset of lung carcinoma. One of the most vital aspects for proper diagnosis, prognosis, and adequate therapy is the precise classification of non-small cell lung cancer based on biomarker expression profiling. This form of biomarker profiling has provided opportunities for improvements in patient stratification, mechanistic insights, and probable druggable targets. However, numerous patients have exhibited numerous toxic side effects, tumor relapse, and development of therapy-based chemoresistance. As a result of these exacting situations, there is a dire need for efficient and effective new cancer therapeutics. De novo drug development approach is a costly and tedious endeavor, with an increased attrition rate, attributed, in part, to toxicity-related issues. Drug repurposing, on the other hand, when combined with computer-assisted systems biology approach, provides alternatives to the discovery of new, efficacious, and safe drugs. Therefore, in this review, we focus on a comparison of the conventional therapy-based chemoresistance mechanisms with the repurposed anti-cancer drugs from three different classes-anti-parasitic, anti-depressants, and anti-psychotics for cancer treatment with a primary focus on NSCLC therapeutics. Certainly, amalgamating these novel therapeutic approaches with that of the conventional drug regimen in NSCLC-affected patients will possibly complement/synergize the existing therapeutic modalities. This approach has tremendous translational significance, since it can combat drug resistance and cytotoxicity-based side effects and provides a relatively new strategy for possible application in therapy of individuals with NSCLC.

The alpha-adrenergic antagonist prazosin promotes cytosolic siRNA delivery from lysosomal compartments

The widespread use of small interfering RNA (siRNA) is limited by the multiple extra- and intracellular barriers upon in vivo administration. Hence, suitable delivery systems, based on siRNA encapsulation in nanoparticles or its conjugation to targeting ligands, have been developed. Nevertheless, at the intracellular level, these state-of-the-art delivery systems still suffer from a low endosomal escape efficiency. Consequently, the bulk of the endocytosed siRNA drug rapidly accumulates in the lysosomal compartment. We recently reported that a wide variety of cationic amphiphilic drugs (CADs) can promote small nucleic acid delivery from the endolysosomal compartment into the cytosol via transient induction of lysosomal membrane permeabilization. Here, we describe the identification of alternate siRNA delivery enhancers from the NIH Clinical Compound Collection that do not have the typical physicochemical properties of CADs. Additionally, we demonstrate improved endolysosomal escape of siRNA via a cholesterol conjugate and polymeric carriers with the α1-adrenergic antagonist prazosin, which was identified as the best performing delivery enhancer from the compound screen. A more detailed assessment of the mode-of-action of prazosin suggests that a different cellular phenotype compared to typical CAD adjuvants drives cytosolic siRNA delivery. As it has been described in the literature that prazosin also induces cancer cell apoptosis and promotes antigen cross-presentation in dendritic cells, the proof-of-concept data in this work provides opportunities for the repurposing of prazosin in an anti-cancer combination strategy with siRNA.

A Thia-Analogous Indirubin N-Glycoside Disrupts Mitochondrial Function and Causes the Death of Human Melanoma and Cutaneous Squamous Cell Carcinoma Cells

Skin cancer is the most common malignant disease worldwide and, therefore, also poses a challenge from a pharmacotherapeutic perspective. Derivatives of indirubin are an interesting option in this context. In the present study, the effects of 3-[3'-oxo-benzo[b]thiophen-2'-(Z)-ylidene]-1-(β-d-glucopyranosyl)-oxindole (KD87), a thia-analogous indirubin N-glycoside, on the viability and mitochondrial properties of melanoma (A375) and squamous cell carcinoma cells (A431) of the skin were investigated. In both cell lines, KD87 caused decreased viability, the activation of caspases-3 and -7, and the inhibition of colony formation. At the mitochondrial level, a concentration-dependent decrease in both the basal and ATP-linked oxygen consumption rate and in the reserve capacity of oxidative respiration were registered in the presence of KD87. These changes were accompanied by morphological alterations in the mitochondria, a release of mitochondrial cytochrome c into the cytosol and significant reductions in succinate dehydrogenase complex subunit B (SDHB, subunit of complex II) in A375 and A431 cells and NADH:ubiquinone oxidoreductase subunit B8 (NDUFB8, subunit of complex I) in A375 cells. The effect of KD87 was accompanied by a significant upregulation of the enzyme heme oxygenase-1, whose inhibition led to a partial but significant reduction in the metabolic-activity-reducing effect of KD87. In summary, our data show a mitochondria-targeting effect of KD87 as part of the cytotoxic effect of this compound on skin cancer cells, which should be considered in future studies with this class of compounds.

In vitro and in silico binding studies of phytochemical isochroman with calf thymus DNA using multi-spectroscopic and computational modelling techniques

A wide range of therapeutic molecules uses deoxyribonucleic acid (DNA) as an intracellular target. The interaction of small molecules to DNA is a key feature in pharmacology and plays a vital role in the development of novel and more efficient drugs with increased selective activity and enhanced therapeutic effectiveness. Isochroman (IC) is a constituent of Olea europea plant, which has been shown to exhibit several beneficial pharmacological activities. At present, its interaction studies using calf thymus DNA (ct-DNA) have not been explained. A set of multi-spectroscopic techniques has been performed to determine the interaction mechanism of isochroman with ct-DNA. Absorption spectra and quenching in fluorescence studies show that isochroman and ct-DNA form a complex. The static mode of quenching was determined by the Stern-Volmer plot. The value of binding constant, Kb = 4.0 × 103 M-1 revealed moderate type of binding. Effects of single-stranded DNA (ssDNA) and double-stranded DNA (dsDNA) and ionic strength were studied to examine the isochroman binding to ct-DNA. Potassium iodide (KI) quenching effects and competitive binding studies clearly showed that isochroman binds in the minor groove of ct-DNA. Circular dichroic and DNA melting experiments also confirmed these results. The experimental outputs were further corroborated via in silico computational modelling studies. Lipinski's rule of 5 and SwissADME showed drug-likeness and oral bioavailability scores. Protox ІІ online software predicts oral and organ toxicity.Communicated by Ramaswamy H. Sarma.

Menin Maintains Cholesterol Content in Colorectal Cancer via Repression of LXR-Mediated Transcription

BACKGROUND AND AIMS

Menin is a nuclear scaffold protein that regulates gene transcription in an oftentimes tissue-specific manner. Our previous work showed that menin is over-expressed in colorectal cancer (CRC); however, the full spectrum of menin function in colonic neoplasia remains unclear. Herein, we aimed to uncover novel menin-regulated pathways important for colorectal carcinogenesis.

METHODS

RNA-Seq analysis identified that menin regulates LXR-target gene expressions in CRC cell lines. Isolated colonic epithelium from Men1f/f;Vil1-Cre and Men1f/f mice was used to validate the results in vivo. Cholesterol content was quantified via an enzymatic assay.

RESULTS

RNA-Seq analysis in the HT-29 CRC cell line identified that menin inhibition upregulated LXR-target genes, specifically ABCG1 and ABCA1, with protein products that promote cellular cholesterol efflux. Similar results were noted across other CRC cell lines and with different methods of menin inhibition. Consistent with ABCG1 and ABCA1 upregulation, and similarly to LXR agonists, menin inhibition reduced the total cellular cholesterol in both HT-29 and HCT-15 cells. To confirm the effects of menin inhibition in vivo, we assessed Men1f/f;Vil1-Cre mice lacking menin expression in the colonic epithelium. Men1f/f;Vil1-Cre mice were found to have no distinct baseline phenotype compared to control Men1f/f mice. However, similarly to CRC cell lines, Men1f/f;Vil1-Cre mice showed an upregulation of Abcg1 and a reduction in total cellular cholesterol. Promoting cholesterol efflux, either via menin inhibition or LXR activation, was found to synergistically suppress CRC cell growth under cholesterol-depleted conditions and when administered concomitantly with small molecule EGFR inhibitors.

CONCLUSIONS

Menin represses the transcription of LXR-target genes, including ABCA1 and ABCG1 in the colonic epithelium and CRC. Menin inhibition conversely upregulates LXR-target genes and reduces total cellular cholesterol, demonstrating that menin inhibition may be an important mechanism for targeting cholesterol-dependent pathways in colorectal carcinogenesis.

HSP90β promotes osteoclastogenesis by dual-activation of cholesterol synthesis and NF-κB signaling

Heat shock protein 90β (Hsp90β, encoded by Hsp90ab1 gene) is the most abundant proteins in the cells and contributes to variety of biological processes including metabolism, cell growth and neural functions. However, genetic evidences showing Hsp90β in vivo functions using tissue specific knockout mice are still lacking. Here, we showed that Hsp90β exerted paralogue-specific role in osteoclastogenesis. Using myeloid-specific Hsp90ab1 knockout mice, we provided the first genetic evidence showing the in vivo function of Hsp90β. Hsp90β binds to Ikkβ and reduces its ubiquitylation and proteasomal degradation, thus leading to activated NF-κB signaling. Meanwhile, Hsp90β increases cholesterol biosynthesis by activating Srebp2. Both pathways promote osteoclastogenic genes expression. Genetic deletion of Hsp90ab1 in osteoclast or pharmacological inhibition of Hsp90β alleviates bone loss in ovariectomy-induced mice. Therefore, Hsp90β is a promising druggable target for the treatment of osteoporosis.

Fortified anti-proliferative activity of niclosamide for breast cancer treatment: In-vitro and in-vivo assessment

Breast cancer represents one of the top lethal cancer types among the females worldwide. Several factors manipulate the clinical outcome of the treatment as the stage of the cancer upon detection, genetic and hormonal factors, drug resistance and metastasis. Accordingly, drug's repositioning, enhancing the bioavailability and encapsulation into nanoparticles (NPs) are among the predilected pathways for enhanced therapeutic outcome. Niclosamide (NIC) is an anthelmintic drug and has been repositioned as anticancer agent after revealing its anti-neoplastic activity. Piperine (PIP) was used as food spice until its anticancer activity was discovered. However, their hydrophobicity constrains their therapeutic efficiency. The cytotoxicity of both drugs in the free form was tested on MCF-7 cells, and the results indicated a NIC cytotoxicity enhancement by PIP. Then, NIC and PIP were encapsulated successfully into F127-NPs with entrapment efficiency of 97 % and 82 %, respectively. Particle size, zeta potential, TEM and FTIR confirmed the micellization process and drug encapsulation. The developed NIC-NPs and PIP-NPs exerted potent anticancer effect as compared to the free forms. Accordingly, the mixture; NIC-NPs/PIP-NPs was tested and its cytotoxicity exceeded the individually encapsulated drugs. Flowcytometry assessment was performed and demonstrated an induced cell death through the apoptotic stage. Additionally, in-vivo therapeutic efficiency of NIC-NPs/PIP-NPs was assessed through Ehrlich ascites tumor and the nanocombination therapy exerted superior additive anticancer effect when compared to NIC-NPs which is attributed to the PIP-NPs induced bioavailability. The study can be considered the first one investigating the PIP role in bioenhancing the anti-proliferative activity of NIC to combat breast cancer.

Structural Analysis of Genomic and Proteomic Signatures Reveal Dynamic Expression of Intrinsically Disordered Regions in Breast Cancer and Tissue

Structural features of proteins capture underlying information about protein evolution and function, which enhances the analysis of proteomic and transcriptomic data. Here we develop Structural Analysis of Gene and protein Expression Signatures (SAGES), a method that describes expression data using features calculated from sequence-based prediction methods and 3D structural models. We used SAGES, along with machine learning, to characterize tissues from healthy individuals and those with breast cancer. We analyzed gene expression data from 23 breast cancer patients and genetic mutation data from the COSMIC database as well as 17 breast tumor protein expression profiles. We identified prominent expression of intrinsically disordered regions in breast cancer proteins as well as relationships between drug perturbation signatures and breast cancer disease signatures. Our results suggest that SAGES is generally applicable to describe diverse biological phenomena including disease states and drug effects.

Androgen receptor-dependent regulation of metabolism in high grade bladder cancer cells

The observed sex disparity in bladder cancer (BlCa) argues that androgen receptor (AR) signaling has a role in these malignancies. BlCas express full-length AR (FL-AR), constitutively active AR splice variants, including AR-v19, or both, and their depletion limits BlCa viability. However, the mechanistic basis of AR-dependence is unknown. Here, we depleted FL-AR, AR-v19, or all AR forms (T-AR), and performed RNA-seq studies to uncover that different AR forms govern distinct but partially overlapping transcriptional programs. Overlapping alterations include a decrease in mTOR and an increase of hypoxia regulated transcripts accompanied by a decline in oxygen consumption rate (OCR). Queries of BlCa databases revealed a significant negative correlation between AR expression and multiple hypoxia-associated transcripts arguing that this regulatory mechanism is a feature of high-grade malignancies. Our analysis of a 1600-compound library identified niclosamide as a strong ATPase inhibitor that reduces OCR in BlCa cells, decreased cell viability and induced apoptosis in a dose and time dependent manner. These results suggest that BlCa cells hijack AR signaling to enhance metabolic activity, promoting cell proliferation and survival; hence targeting this AR downstream vulnerability presents an attractive strategy to limit BlCa.

Glycyrrhetinic acid restricts mitochondrial energy metabolism by targeting SHMT2

Glycyrrhetinic acid (GA) is a natural product of licorice with mitochondria targeting properties and shows broad anticancer activities, but its targets and underlying mechanisms remain elusive. Here, we identified the mitochondrial enzyme serine hydroxymethyltransferase 2 (SHMT2) as a target of GA by using chemical proteomics. Binding to and inhibiting the activity of SHMT2 by GA were validated in vitro and in vivo. Knockout of SHMT2 or inhibiting SHMT2 with GA restricts mitochondrial energy supplies by downregulating mitochondrial oxidative phosphorylation (OXPHOS) and fatty acid β-oxidation, and consequently suppresses cancer cell proliferation and tumor growth. Crystal structures of GA derivatives indicate that GA occupies SHMT2 folate-binding pocket and regulates SHMT2 activity. Modifications at GA carboxylic group with diamines significantly improved its anticancer potency, demonstrating GA as a decent structural template for SHMT2 inhibitor development.

Investigation of binding characteristics of ritonavir with calf thymus DNA with the help of spectroscopic techniques and molecular simulation

The binding behavior of ritonavir (RTV), a HIV/AIDS protease inhibitor, with ct-DNA was characterized through multiple testing technologies and theoretical calculation. The findings revealed that the RTV-DNA complex was formed through the noncovalent interaction mainly including conventional hydrogen bonds and carbon hydrogen bonds as well as hydrophobic interactions (pi-alkyl interactions). The stoichiometry and binding constant of the RTV-DNA complex were 1:1 and 1.87 × 10³ M^-1 at 298 K, respectively, indicating that RTV has moderate affinity with ct-DNA. The findings confirmed that RTV binds to the minor groove of DNA. The outcomes of CD experiments showed that the binding with RTV changed the conformation of DNA slightly. However, the conformation of RTV had obvious changes after binding to DNA, meaning that the flexibility of RTV molecule played an important role in stabilizing the RTV-DNA complex. Meanwhile, the results of DFT calculation revealed that the RTV and DNA interaction caused the changes in the frontier molecular orbitals, dipole moment and atomic charge distribution of RTV, altering the chemical properties of RTV when it bound to DNA. Communicated by Ramaswamy H. Sarma.

Collateral responses to classical cytotoxic chemotherapies are heterogeneous and sensitivities are sparse

Chemotherapy resistance is a major obstacle to curing cancer patients. Combination drug regimens have shown promise as a method to overcome resistance; however, to date only some cancers have been cured with this method. Collateral sensitivity-the phenomenon whereby resistance to one drug is co-occurrent with sensitivity to a second drug-has been gaining traction as a promising new concept to guide rational design of combination regimens. Here we evolved over 100 subclones of the Eµ-Myc; p19^ARF-/- cell line to be resistant to one of four classical chemotherapy agents: doxorubicin, vincristine, paclitaxel, and cisplatin. We then surveyed collateral responses to acquisition of resistance to these agents. Although numerous collateral sensitivities have been documented for antibiotics and targeted cancer therapies, we observed only one collateral sensitivity: half of cell lines that acquired resistance to paclitaxel also acquired a collateral sensitivity to verapamil. However, we found that the mechanism of this collateral sensitivity was unrelated to the mechanism of paclitaxel resistance. Interestingly, we observed heterogeneity in the phenotypic response to acquisition of resistance to most of the drugs we tested, most notably for paclitaxel, suggesting the existence of multiple different states of resistance. Surprisingly, this phenotypic heterogeneity in paclitaxel resistant cell lines was unrelated to transcriptomic heterogeneity among those cell lines. These features of phenotypic and transcriptomic heterogeneity must be taken into account in future studies of treated tumor subclones and in design of chemotherapy combinations.

Probing the interaction of anti-HIV drug Darunavir with dsDNA and HSA using electrochemical and spectroscopic measurements

Investigation of electrochemical and spectroscopic characteristics of anti-human immunodeficiency virus (HIV) drug provides important information related to the efficacy of the drug in relation with its interaction with several important biomolecules. In the present investigation we have developed an electrochemical and spectroscopic method for the detection of anti-HIV drug Darunavir (DRV) using the carbon paste as the working electrode. The analytical method has generated the detection limit of 1.86 µM (S/N = 3). The electrochemical investigations have also been carried out for the exploration of the interaction of DRV with double stranded deoxyribose nucleic acid DNA (dsDNA) and human serum albumin (HSA). Electrochemical investigations were supported from the spectroscopic measurements in evaluating the interaction. The results obtained from voltammetric and spectroscopic experiments shows strong interaction between the drug and the macromolecules. It has been observed that DRV forms strong complexes with HSA and dsDNA with the formation constants of 2.7 × 10⁴ and 4.2 × 10⁴ M^-1 respectively. The formation constants are varied with the pH of the solution, which leads to the assertion of the mechanism of the interaction between DRV and dsDNA.

OUP accepted manuscript

Barcode fusion genetics (BFG) utilizes deep sequencing to improve the throughput of protein–protein interaction (PPI) screening in pools. BFG has been implemented in Yeast two-hybrid (Y2H) screens (BFG-Y2H). While Y2H requires test protein pairs to localize in the nucleus for reporter reconstruction, dihydrofolate reductase protein-fragment complementation assay (DHFR-PCA) allows proteins to localize in broader subcellular contexts and proves to be largely orthogonal to Y2H. Here, we implemented BFG to DHFR-PCA (BFG-PCA). This plasmid-based system can leverage ORF collections across model organisms to perform comparative analysis, unlike the original DHFR-PCA that requires yeast genomic integration. The scalability and quality of BFG-PCA were demonstrated by screening human and yeast interactions for >11 000 bait-prey pairs. BFG-PCA showed high-sensitivity and high-specificity for capturing known interactions for both species. BFG-Y2H and BFG-PCA capture distinct sets of PPIs, which can partially be explained based on the domain orientation of the reporter tags. BFG-PCA is a high-throughput protein interaction technology to interrogate binary PPIs that exploits clone collections from any species of interest, expanding the scope of PPI assays.

Connecting chemistry and biology through molecular descriptors

Through the representation of small molecule structures as numerical descriptors and the exploitation of the similarity principle, chemoinformatics has made paramount contributions to drug discovery, from unveiling mechanisms of action and repurposing approved drugs to de novo crafting of molecules with desired properties and tailored targets. Yet, the inherent complexity of biological systems has fostered the implementation of large-scale experimental screenings seeking a deeper understanding of the targeted proteins, the disrupted biological processes and the systemic responses of cells to chemical perturbations. After this wealth of data, a new generation of data-driven descriptors has arisen providing a rich portrait of small molecule characteristics that goes beyond chemical properties. Here, we give an overview of biologically relevant descriptors, covering chemical compounds, proteins and other biological entities, such as diseases and cell lines, while aligning them to the major contributions in the field from disciplines, such as natural language processing or computer vision. We now envision a new scenario for chemical and biological entities where they both are translated into a common numerical format. In this computational framework, complex connections between entities can be unveiled by means of simple arithmetic operations, such as distance measures, additions, and subtractions.

Mitochondria-affecting small molecules ameliorate proteostasis defects associated with neurodegenerative diseases

Macroautophagic recycling of dysfunctional mitochondria, known as mitophagy, is essential for mitochondrial homeostasis and cell viability. Accumulation of defective mitochondria and impaired mitophagy have been widely implicated in many neurodegenerative diseases, and loss-of-function mutations of PINK1 and Parkin, two key regulators of mitophagy, are amongst the most common causes of heritable parkinsonism. This has led to the hypothesis that pharmacological stimulation of mitophagy may be a feasible approach to combat neurodegeneration. Toward this end, we screened ~ 45,000 small molecules using a high-throughput, whole-organism, phenotypic screen that monitored accumulation of PINK-1 protein, a key event in mitophagic activation, in a Caenorhabditis elegans strain carrying a Ppink-1::PINK-1::GFP reporter. We obtained eight hits that increased mitochondrial fragmentation and autophagosome formation. Several of the compounds also reduced ATP production, oxygen consumption, mitochondrial mass, and/or mitochondrial membrane potential. Importantly, we found that treatment with two compounds, which we named PS83 and PS106 (more commonly known as sertraline) reduced neurodegenerative disease phenotypes, including delaying paralysis in a C. elegans β-amyloid aggregation model in a PINK-1-dependent manner. This report presents a promising step toward the identification of compounds that will stimulate mitochondrial turnover.

Revealing Adverse Outcome Pathways from Public High-Throughput Screening Data to Evaluate New Toxicants by a Knowledge-Based Deep Neural Network Approach

Traditional experimental testing to identify endocrine disruptors that enhance estrogenic signaling relies on expensive and labor-intensive experiments. We sought to design a knowledge-based deep neural network (k-DNN) approach to reveal and organize public high-throughput screening data for compounds with nuclear estrogen receptor α and β (ERα and ERβ) binding potentials. The target activity was rodent uterotrophic bioactivity driven by ERα/ERβ activations. After training, the resultant network successfully inferred critical relationships among ERα/ERβ target bioassays, shown as weights of 6521 edges between 1071 neurons. The resultant network uses an adverse outcome pathway (AOP) framework to mimic the signaling pathway initiated by ERα and identify compounds that mimic endogenous estrogens (i.e., estrogen mimetics). The k-DNN can predict estrogen mimetics by activating neurons representing several events in the ERα/ERβ signaling pathway. Therefore, this virtual pathway model, starting from a compound's chemistry initiating ERα activation and ending with rodent uterotrophic bioactivity, can efficiently and accurately prioritize new estrogen mimetics (AUC = 0.864-0.927). This k-DNN method is a potential universal computational toxicology strategy to utilize public high-throughput screening data to characterize hazards and prioritize potentially toxic compounds.

A Convolutional Neural Network System to Discriminate Drug-Target Interactions

Biological targets are most commonly proteins such as enzymes, ion channels, and receptors. They are anything within a living organism to bind with some other entities (like an endogenous ligand or a drug), resulting in change in their behaviors or functions. Exploring potential drug-target interactions (DTIs) are crucial for drug discovery and effective drug development. Computational methods were widely applied in drug-target interactions, since experimental methods are extremely time-consuming and resource-intensive. In this paper, we proposed a novel deep learning-based prediction system, with a new negative instance generation, to identify DTIs. As a result, our method achieved an accuracy of 0.9800 on our created dataset. Another dataset derived from DrugBank was used to further assess the generalization of the model, which yielded a good performance with accuracy of 0.8814 and AUC value of 0.9527 on the dataset. The outcome of our experimental results indicated that the proposed method, involving the credible negative generation, can be employed to discriminate the interactions between drugs and targets. Website: http://www.dlearningapp.com/web/DrugCNN.htm.

Protein-fragment complementation assays for large-scale analysis of protein-protein interactions

Protein-protein interactions (PPIs) orchestrate nearly all biological processes. They are also considered attractive drug targets for treating many human diseases, including cancers and neurodegenerative disorders. Protein-fragment complementation assays (PCAs) provide a direct and straightforward way to study PPIs in living cells or multicellular organisms. Importantly, PCAs can be used to detect the interaction of proteins expressed at endogenous levels in their native cellular environment. In this review, we present the principle of PCAs and discuss some of their advantages and limitations. We describe their application in large-scale experiments to investigate PPI networks and to screen or profile PPI targeting compounds.

Anthelmintics for drug repurposing: Opportunities and challenges

Drug repositioning is defined as a process to identify a new application for drugs. This approach is critical as it takes advantage of well-known pharmacokinetics, pharmacodynamics, and toxicity profiles of the drugs; thus, the chance of their future failure decreases, and the cost of their development and the required time for their approval are reduced. Anthelmintics, which are antiparasitic drugs, have recently demonstrated promising anticancer effects in vitro and in vivo. This literature review focuses on the potential of anthelmintics for repositioning in the treatment of cancers. It also discusses their pharmacokinetics and pharmacodynamics as antiparasitic drugs, proposed anticancer mechanisms, present development conditions, challenges in cancer therapy, and strategies to overcome these challenges.

A Perspective on Synthetic Biology in Drug Discovery and Development-Current Impact and Future Opportunities

The global impact of synthetic biology has been accelerating, because of the plummeting cost of DNA synthesis, advances in genetic engineering, growing understanding of genome organization, and explosion in data science. However, much of the discipline's application in the pharmaceutical industry remains enigmatic. In this review, we highlight recent examples of the impact of synthetic biology on target validation, assay development, hit finding, lead optimization, and chemical synthesis, through to the development of cellular therapeutics. We also highlight the availability of tools and technologies driving the discipline. Synthetic biology is certainly impacting all stages of drug discovery and development, and the recognition of the discipline's contribution can further enhance the opportunities for the drug discovery and development value chain.

Predictive modeling of estrogen receptor agonism, antagonism, and binding activities using machine- and deep-learning approaches

As defined by the World Health Organization, an endocrine disruptor is an exogenous substance or mixture that alters function(s) of the endocrine system and consequently causes adverse health effects in an intact organism, its progeny, or (sub)populations. Traditional experimental testing regimens to identify toxicants that induce endocrine disruption can be expensive and time-consuming. Computational modeling has emerged as a promising and cost-effective alternative method for screening and prioritizing potentially endocrine-active compounds. The efficient identification of suitable chemical descriptors and machine-learning algorithms, including deep learning, is a considerable challenge for computational toxicology studies. Here, we sought to apply classic machine-learning algorithms and deep-learning approaches to a panel of over 7500 compounds tested against 18 Toxicity Forecaster assays related to nuclear estrogen receptor (ERα and ERβ) activity. Three binary fingerprints (Extended Connectivity FingerPrints, Functional Connectivity FingerPrints, and Molecular ACCess System) were used as chemical descriptors in this study. Each descriptor was combined with four machine-learning and two deep- learning (normal and multitask neural networks) approaches to construct models for all 18 ER assays. The resulting model performance was evaluated using the area under the receiver- operating curve (AUC) values obtained from a fivefold cross-validation procedure. The results showed that individual models have AUC values that range from 0.56 to 0.86. External validation was conducted using two additional sets of compounds (n = 592 and n = 966) with established interactions with nuclear ER demonstrated through experimentation. An agonist, antagonist, or binding score was determined for each compound by averaging its predicted probabilities in relevant assay models as an external validation, yielding AUC values ranging from 0.63 to 0.91. The results suggest that multitask neural networks offer advantages when modeling mechanistically related endpoints. Consensus predictions based on the average values of individual models remain the best modeling strategy for computational toxicity evaluations.

Image-based profiling for drug discovery: due for a machine-learning upgrade?

Image-based profiling is a maturing strategy by which the rich information present in biological images is reduced to a multidimensional profile, a collection of extracted image-based features. These profiles can be mined for relevant patterns, revealing unexpected biological activity that is useful for many steps in the drug discovery process. Such applications include identifying disease-associated screenable phenotypes, understanding disease mechanisms and predicting a drug's activity, toxicity or mechanism of action. Several of these applications have been recently validated and have moved into production mode within academia and the pharmaceutical industry. Some of these have yielded disappointing results in practice but are now of renewed interest due to improved machine-learning strategies that better leverage image-based information. Although challenges remain, novel computational technologies such as deep learning and single-cell methods that better capture the biological information in images hold promise for accelerating drug discovery.

High-Throughput Screening to Identify Chemical Cardiotoxic Potential

Cardiovascular (CV) disease is one of the most prevalent public health concerns, and mounting evidence supports the contribution of environmental chemicals to CV disease burden. In this study, we performed cardiotoxicity profiling for the Tox21 chemical library by focusing on high-throughput screening (HTS) assays whose targets are associated with adverse events related to CV failure modes. Our objective was to develop new hypotheses around environmental chemicals of potential interest for adverse CV outcomes using Tox21/ToxCast HTS data. Molecular and cellular events linked to six failure modes of CV toxicity were cross-referenced with 1399 Tox21/ToxCast assays to identify cardio-relevant bioactivity signatures. The resulting 40 targets, measured in 314 assays, were integrated via a ToxPi visualization tool and ranking system to prioritize 1138 chemicals based upon formal integration across multiple domains of information. Filtering was performed based on cytotoxicity and generalized cell stress endpoints to try and isolate chemicals with effects specific to CV biology, and bioactivity- and structure-based clustering identified subgroups of chemicals preferentially affecting targets such as ion channels and vascular tissue biology. Our approach identified drugs with known cardiotoxic effects, such as estrogenic modulators like clomiphene and raloxifene, anti-arrhythmic drugs like amiodarone and haloperidol, and antipsychotic drugs like chlorpromazine. Several classes of environmental chemicals such as organotins, bisphenol-like chemicals, pesticides, and quaternary ammonium compounds demonstrated strong bioactivity against CV targets; these were compared to existing data in the literature (e.g., from cardiomyocytes, animal data, or human epidemiological studies) and prioritized for further testing.

Small Molecule Enhancers of Endosome-to-Cytosol Import Augment Anti-tumor Immunity

Cross-presentation of antigens by dendritic cells (DCs) is critical for initiation of anti-tumor immune responses. Yet, key steps involved in trafficking of antigens taken up by DCs remain incompletely understood. Here, we screen 700 US Food and Drug Administration (FDA)-approved drugs and identify 37 enhancers of antigen import from endolysosomes into the cytosol. To reveal their mechanism of action, we generate proteomic organellar maps of control and drug-treated DCs (focusing on two compounds, prazosin and tamoxifen). By combining organellar mapping, quantitative proteomics, and microscopy, we conclude that import enhancers undergo lysosomal trapping leading to membrane permeation and antigen release. Enhancing antigen import facilitates cross-presentation of soluble and cell-associated antigens. Systemic administration of prazosin leads to reduced growth of MC38 tumors and to a synergistic effect with checkpoint immunotherapy in a melanoma model. Thus, inefficient antigen import into the cytosol limits antigen cross-presentation, restraining the potency of anti-tumor immune responses and efficacy of checkpoint blockers.

In silico drug repositioning of FDA-approved drugs to predict new inhibitors for alpha-synuclein aggregation

One of the hallmarks of Parkinson's disease (PD), a long-term neurodegenerative syndrome, is the accumulation of alpha-synuclein (α-syn) fibrils. Despite numerous studies and efforts, inhibition of α-syn protein aggregation is still a challenge. To overcome this issue, we propose an in silico pharmacophore-based repositioning strategy, to find a pharmaceutical drug that, in addition to their defined role, can be used to prevent aggregation of the α-syn protein. Ligand-based pharmacophore modeling was developed and the best model was selected with validation parameters including 72 % sensitivity, 98 % specificity and goodness score about 0.7. The optimal model has three groups of hydrogen bond donor (HBD), three groups of hydrogen bond acceptor (HBA), and two aromatic rings (AR). The FDA-Approved reports in the ZINC15 database were screened with the pharmacophore model taken from inhibitor compounds. The model identified 22 hits, as promising candidate drugs for Parkinson's therapy. It is noteworthy that among these, 10 drugs have been reported to inhibition of α-syn aggregation or treat/reduce Parkinson's pathogenesis. This model was used to virtual screen ZINC, NCI databases, and natural products from the pomegranate. The results of this screen were filtered for their inability to cross the blood-brain barrier, poor oral bioavailability, etc. Finally, the selected compounds of two ZINC and NCI databases were combined and structurally clustered. Remained compounds were clustered in 28 different clusters, and the 17 compounds were introduced as final candidates.

Loss of MicroRNA-137 Impairs the Homeostasis of Potassium in Neurons via KCC2

Neuropsychiatric disorders are the leading cause of mental and intellectual disabilities worldwide. Current therapies against neuropsychiatric disorders are very limited, and very little is known about the onset and development of these diseases, and their most effective treatments. MIR137 has been previously identified as a risk gene for the etiology of schizophrenia, bipolar disorder, and autism spectrum disorder. Here we generated a forebrain-specific MIR137 knockout mouse model, and provided evidence that loss of miR-137 resulted in impaired homeostasis of potassium in mouse hippocampal neurons. KCC2, a potassium-chloride co-transporter, was a direct downstream target of miR-137. The KCC2 specific antagonist VU0240551 could balance the current of potassium in miR-137 knockout neurons, and knockdown of KCC2 could ameliorate anxiety-like behavior in MIR137 cKO mice. These data suggest that KCC2 antagonists or knockdown might be beneficial to neuropsychiatric disorders due to the deficiency of miR-137.

Investigating the mechanism of binding of nalidixic acid with deoxyribonucleic acid and serum albumin: a biophysical and molecular docking approaches

Nalidixic acid is a bacterial DNA gyrase inhibitor and the first member of the synthetic quinolone antibiotics. It is used in the treatment of various infectious diseases like urinary tract infections, respiratory infections, sexually transmitted diseases, acute bronchitis, and sinusitis. Interactions studies are of great significance as it will be beneficial for designing new therapeutic molecules with preferable plasma solubility and its efficacy. In this paper, we have aim to ascertain the binding mode of nalidixic acid with calf thymus DNA (ct-DNA) and bovine serum albumin (BSA) through various biophysical and in silico method. UV-visible absorption and fluorescence spectroscopic experiments confirmed the formation of a complex between nalidixic acid with ct-DNA. The binding constant is in the range of 10³ M^-1, indicating the groove binding mode between ct-DNA and nalidixic acid. Groove binding mode was also validated by competitive displacement assay, potassium iodide quenching experiment, circular dichroism, DNA melting studies. In the case of BSA, UV-visible absorption and fluorescence spectroscopic experiments confirmed the formation of a complex between nalidixic acid with BSA. The value of a binding constant in the case of BSA was found to be 1.517 × 10⁵ M^-1. The site marker displacement experiment revealed the binding location of nalidixic acid to a site I in BSA. Secondary structural and microenvironmental changes also studied through circular dichroism and three-dimensional fluorescence. Furthermore, the synchronous fluorescence spectra of BSA with nalidixic acid showed that there were changes in the microenvironment around tryptophan residues. In silico molecular docking further confirmed the binding of nalidixic acid to site I in BSA and the minor groove of DNA.Communicated by Ramaswamy H. Sarma.

Genetics' Piece of the PI: Inferring the Origin of Complex Traits and Diseases from Proteome-Wide Protein-Protein Interaction Dynamics

How do common and rare genetic polymorphisms contribute to quantitative traits or disease risk and progression? Multiple human traits have been extensively characterized at the genomic level, revealing their complex genetic architecture. However, it is difficult to resolve the mechanisms by which specific variants contribute to a phenotype. Recently, analyses of variant effects on molecular traits have uncovered intermediate mechanisms that link sequence variation to phenotypic changes. Yet, these methods only capture a fraction of genetic contributions to phenotype. Here, in reviewing the field, it is proposed that complex traits can be understood by characterizing the dynamics of biochemical networks within living cells, and that the effects of genetic variation can be captured on these networks by using protein-protein interaction (PPI) methodologies. This synergy between PPI methodologies and the genetics of complex traits opens new avenues to investigate the molecular etiology of human diseases and to facilitate their prevention or treatment.

A Multi-Label Learning Framework for Drug Repurposing

Drug repurposing plays an important role in screening old drugs for new therapeutic efficacy. The existing methods commonly treat prediction of drug-target interaction as a problem of binary classification, in which a large number of randomly sampled drug-target pairs accounting for over 50% of the entire training dataset are necessarily required. Such a large number of negative examples that do not come from experimental observations inevitably decrease the credibility of predictions. In this study, we propose a multi-label learning framework to find new uses for old drugs and discover new drugs for known target genes. In the framework, each drug is treated as a class label and its target genes are treated as the class-specific training data to train a supervised learning model of l₂-regularized logistic regression. As such, the inter-drug associations are explicitly modelled into the framework and all the class-specific training data come from experimental observations. In addition, the data constraint is less demanding, for instance, the chemical substructures of a drug are no longer needed and the novel target genes are inferred only from the underlying patterns of the known genes targeted by the drug. Stratified multi-label cross-validation shows that 84.9% of known target genes have at least one drug correctly recognized, and the proposed framework correctly recognizes 86.73% of the independent test drug-target interactions (DTIs) from DrugBank. These results show that the proposed framework could generalize well in the large drug/class space without the information of drug chemical structures and target protein structures. Furthermore, we use the trained model to predict new drugs for the known target genes, identify new genes for the old drugs, and infer new associations between old drugs and new disease phenotypes via the OMIM database. Gene ontology (GO) enrichment analyses and the disease associations reported in recent literature provide supporting evidences to the computational results, which potentially shed light on new clinical therapies for new and/or old disease phenotypes.

R-propranolol is a small molecule inhibitor of the SOX18 transcription factor in a rare vascular syndrome and hemangioma

Propranolol is an approved non-selective β-adrenergic blocker that is first line therapy for infantile hemangioma. Despite the clinical benefit of propranolol therapy in hemangioma, the mechanistic understanding of what drives this outcome is limited. Here, we report successful treatment of pericardial edema with propranolol in a patient with Hypotrichosis-Lymphedema-Telangiectasia and Renal (HLTRS) syndrome, caused by a mutation in SOX18. Using a mouse pre-clinical model of HLTRS, we show that propranolol treatment rescues its corneal neo-vascularisation phenotype. Dissection of the molecular mechanism identified the R(+)-propranolol enantiomer as a small molecule inhibitor of the SOX18 transcription factor, independent of any anti-adrenergic effect. Lastly, in a patient-derived in vitro model of infantile hemangioma and pre-clinical model of HLTRS we demonstrate the therapeutic potential of the R(+) enantiomer. Our work emphasizes the importance of SOX18 etiological role in vascular neoplasms, and suggests R(+)-propranolol repurposing to numerous indications ranging from vascular diseases to metastatic cancer.

Development and Applications of Superfolder and Split Fluorescent Protein Detection Systems in Biology

Molecular engineering of the green fluorescent protein (GFP) into a robust and stable variant named Superfolder GFP (sfGFP) has revolutionized the field of biosensor development and the use of fluorescent markers in diverse area of biology. sfGFP-based self-associating bipartite split-FP systems have been widely exploited to monitor soluble expression in vitro, localization, and trafficking of proteins in cellulo. A more recent class of split-FP variants, named « tripartite » split-FP, that rely on the self-assembly of three GFP fragments, is particularly well suited for the detection of protein–protein interactions. In this review, we describe the different steps and evolutions that have led to the diversification of superfolder and split-FP reporter systems, and we report an update of their applications in various areas of biology, from structural biology to cell biology.

Chloramphenicol inhibits eukaryotic Ser/Thr phosphatase and infection-specific cell differentiation in the rice blast fungus

Chloramphenicol (Cm) is a broad-spectrum classic antibiotic active against prokaryotic organisms. However, Cm has severe side effects in eukaryotes of which the cause remains unknown. The plant pathogenic fungus Magnaporthe oryzae, which causes rice blast, forms an appressorium to infect the host cell via single-cell differentiation. Chloramphenicol specifically inhibits appressorium formation, which indicates that Cm has a novel molecular target (or targets) in the rice blast fungus. Application of the T7 phage display method inferred that MoDullard, a Ser/Thr-protein phosphatase, may be a target of Cm. In animals Dullard functions in cell differentiation and protein synthesis, but in fungi its role is poorly understood. In vivo and in vitro analyses showed that MoDullard is required for appressorium formation, and that Cm can bind to and inhibit MoDullard function. Given that human phosphatase CTDSP1 complemented the MoDullard function during appressorium formation by M. oryzae, CTDSP1 may be a novel molecular target of Cm in eukaryotes.

Photolysis of Staphyloxanthin in Methicillin-Resistant Staphylococcus aureus Potentiates Killing by Reactive Oxygen Species

Confronted with the severe situation that the pace of resistance acquisition is faster than the clinical introduction of new antibiotics, health organizations are calling for effective approaches to combat methicillin-resistant Staphylococcus aureus (MRSA) infections. Here, an approach to treat MRSA through photolysis of staphyloxanthin, an antioxidant residing in the microdomain of S. aureus membrane, is reported. This photochemistry process is uncovered through transient absorption imaging and quantitated by absorption spectroscopy, Raman spectroscopy, and mass spectrometry. Photolysis of staphyloxanthin transiently elevates the membrane permeability and renders MRSA highly susceptible to hydrogen peroxide attack. Consequently, staphyloxanthin photolysis by low-level 460 nm light eradicates MRSA synergistically with hydrogen peroxide and other reactive oxygen species. The effectiveness of this synergistic therapy is well validated in MRSA planktonic culture, MRSA-infected macrophage cells, stationary-phase MRSA, persisters, S. aureus biofilms, and two mice wound infection models. Collectively, the work demonstrates that staphyloxanthin photolysis is a new therapeutic platform to treat MRSA infections.

Niclosamide repositioning for treating cancer: Challenges and nano-based drug delivery opportunities

Drug repositioning may be defined as a process when new biological effects for known drugs are identified, leading to recommendations for new therapeutic applications. Niclosamide, present in the Model List of Essential Medicines, from the World Health Organization, has been used since the 1960s for tapeworm infection. Several preclinical studies have been shown its impressive anticancer effects, which led to clinical trials for colon and prostate cancer. Despite high expectations, proof of efficacy and safety are still required, which are associated with diverse biopharmaceutical challenges, such as the physicochemical properties of the drug and its oral absorption, and their relationship with clinical outcomes. Nanostructured systems are innovative drug delivery strategies, which may provide interesting pharmaceutical advantages for this candidate. The aim of this review is to discuss challenges involving niclosamide repositioning for cancer diseases, and the opportunities of therapeutic benefits from nanosctrutured system formulations containing this compound.

Hedgehog pathway and smoothened inhibitors in cancer therapies

The hedgehog (Hh) pathway plays an important role in cancer development and maintenance, as ~25% of all cancers have aberrant Hh pathway activation. Targeted therapy for inhibition of the Hh pathway was thought to be promising for achieving clinical response in the Hh-dependent cancers. However, the results of new clinical trials with smoothened (SMO) antagonists do not show much success in cancers other than basal cell carcinoma. The studies suggest that the Hh pathway involves multiple mechanisms of activation or inhibition in primary cilia and interactions between several related pathways in different types of cells, which makes this pathway extremely complex. The SMO-specific antagonists may not stop all relevant pathways that may lead to escape or development of resistance. Therefore, in the Hh-dependent cancers, the inhibition of two or more oncogenic pathways (including the Hh pathway) with use of a single agent of a suitable multitarget profile or a combination of drugs seems promising for achieving clinical response in patients and decrease in resistance development with prolonged use of the specific SMO antagonists. Furthermore, for studying the effect of new treatments, the inclusion criteria should be more specific for selection of patients with aberrant Hh pathway activity confirmed by tests. These considerations will be very helpful for choosing the right patients and the right drugs for the best therapeutic outcome.

Changes of Cell Biochemical States Are Revealed in Protein Homomeric Complex Dynamics

We report here a simple and global strategy to map out gene functions and target pathways of drugs, toxins, or other small molecules based on "homomer dynamics" protein-fragment complementation assays (hdPCA). hdPCA measures changes in self-association (homomerization) of over 3,500 yeast proteins in yeast grown under different conditions. hdPCA complements genetic interaction measurements while eliminating the confounding effects of gene ablation. We demonstrate that hdPCA accurately predicts the effects of two longevity and health span-affecting drugs, the immunosuppressant rapamycin and the type 2 diabetes drug metformin, on cellular pathways. We also discovered an unsuspected global cellular response to metformin that resembles iron deficiency and includes a change in protein-bound iron levels. This discovery opens a new avenue to investigate molecular mechanisms for the prevention or treatment of diabetes, cancers, and other chronic diseases of aging.

N-substituted phenylbenzamides of the niclosamide chemotype attenuate obesity related changes in high fat diet fed mice

Obesity and insulin resistance are primary risk factors for Non-Alcoholic Fatty Liver Disease (NAFLD). NAFLD is generally exhibited by non-progressive simple steatosis. However, a significant subset of patient’s progress to nonalcoholic steatohepatitis (NASH) that is defined by the presence of steatosis, inflammation and hepatocyte injury with fibrosis. Unfortunately, there are no approved therapies for NAFLD or NASH and therefore therapeutic approaches are urgently needed. Niclosamide is an U.S. Food and Drug Administration (FDA)-approved anthelmintic drug that mediates its effect by uncoupling oxidative phosphorylation. Niclosamide and its salt forms, Niclosamide Ethanolamine (NEN), and Niclosamide Piperazine (NPP) have shown efficacy in murine models of diet induced obesity characterized by attenuation of the prominent fatty liver disease phenotype and improved glucose metabolism. While the exact mechanism(s) underlying these changes remains unclear, the ability to uncouple oxidative phosphorylation leading to increased energy expenditure and lipid metabolism or attenuation of PKA mediated glucagon signaling in the liver have been proposed. Unfortunately, niclosamide has very poor water solubility, leading to low oral bioavailability. This, in addition to mitochondrial uncoupling activity and potential genotoxicity have reduced enthusiasm for its clinical use. More recently, salt forms of niclosamide, NEN and NPP, have demonstrated improved oral bioavailability while retaining activity. This suggests that development of safer more effective niclosamide derivatives for the treatment of NAFLD and Type 2 Diabetes may be possible. Herein we explored the ability of a series of N-substituted phenylbenzamide derivatives of the niclosamide salicylanilide chemotype to attenuate hepatic steatosis using a novel phenotypic in vitro model of fatty liver and the high fat diet-fed mouse model of diet induced obesity. These studies identified novel compounds with improved pre-clinical properties that attenuate hepatic steatosis in vitro and in vivo. These compounds with improved drug properties may be useful in alleviating symptoms and protection against disease progression in patients with metabolic syndrome and NAFLD.

Computational Approaches Towards Kinases as Attractive Targets for Anticancer Drug Discovery and Development

BACKGROUND

One of the major goals of computational chemists is to determine and develop the pathways for anticancer drug discovery and development. In recent past, high performance computing systems elicited the desired results with little or no side effects. The aim of the current review is to evaluate the role of computational chemistry in ascertaining kinases as attractive targets for anticancer drug discovery and development.

METHODS

Research related to computational studies in the field of anticancer drug development is reviewed. Extensive literature on achievements of theorists in this regard has been compiled and presented with special emphasis on kinases being the attractive anticancer drug targets.

RESULTS

Different approaches to facilitate anticancer drug discovery include determination of actual targets, multi-targeted drug discovery, ligand-protein inverse docking, virtual screening of drug like compounds, formation of di-nuclear analogs of drugs, drug specific nano-carrier design, kinetic and trapping studies in drug design, multi-target QSAR (Quantitative Structure Activity Relationship) model, targeted co-delivery of anticancer drug and siRNA, formation of stable inclusion complex, determination of mechanism of drug resistance, and designing drug like libraries for the prediction of drug-like compounds. Protein kinases have gained enough popularity as attractive targets for anticancer drugs. These kinases are responsible for uncontrolled and deregulated differentiation, proliferation, and cell signaling of the malignant cells which result in cancer.

CONCLUSION

Interest in developing drugs through computational methods is a growing trend, which saves equally the cost and time. Kinases are the most popular targets among the other for anticancer drugs which demand attention. 3D-QSAR modelling, molecular docking, and other computational approaches have not only identified the target-inhibitor binding interactions for better anticancer drug discovery but are also designing and predicting new inhibitors, which serve as lead for the synthetic preparation of drugs. In light of computational studies made so far in this field, the current review highlights the importance of kinases as attractive targets for anticancer drug discovery and development.

Uncertainty quantification in ToxCast high throughput screening

High throughput screening (HTS) projects like the U.S. Environmental Protection Agency's ToxCast program are required to address the large and rapidly increasing number of chemicals for which we have little to no toxicity measurements. Concentration-response parameters such as potency and efficacy are extracted from HTS data using nonlinear regression, and models and analyses built from these parameters are used to predict in vivo and in vitro toxicity of thousands of chemicals. How these predictions are impacted by uncertainties that stem from parameter estimation and propagated through the models and analyses has not been well explored. While data size and complexity makes uncertainty quantification computationally expensive for HTS datasets, continued advancements in computational resources have allowed these computational challenges to be met. This study uses nonparametric bootstrap resampling to calculate uncertainties in concentration-response parameters from a variety of HTS assays. Using the ToxCast estrogen receptor model for bioactivity as a case study, we highlight how these uncertainties can be propagated through models to quantify the uncertainty in model outputs. Uncertainty quantification in model outputs is used to identify potential false positives and false negatives and to determine the distribution of model values around semi-arbitrary activity cutoffs, increasing confidence in model predictions. At the individual chemical-assay level, curves with high variability are flagged for manual inspection or retesting, focusing subject-matter-expert time on results that need further input. This work improves the confidence of predictions made using HTS data, increasing the ability to use this data in risk assessment.

Small near-infrared photochromic protein for photoacoustic multi-contrast imaging and detection of protein interactions in vivo

Photoacoustic (PA) computed tomography (PACT) benefits from genetically encoded probes with photochromic behavior, which dramatically increase detection sensitivity and specificity through photoswitching and differential imaging. Starting with a DrBphP bacterial phytochrome, we have engineered a near-infrared photochromic probe, DrBphP-PCM, which is superior to the full-length RpBphP1 phytochrome previously used in differential PACT. DrBphP-PCM has a smaller size, better folding, and higher photoswitching contrast. We have imaged both DrBphP-PCM and RpBphP1 simultaneously on the basis of their unique signal decay characteristics, using a reversibly switchable single-impulse panoramic PACT (RS-SIP-PACT) with a single wavelength excitation. The simple structural organization of DrBphP-PCM allows engineering a bimolecular PA complementation reporter, a split version of DrBphP-PCM, termed DrSplit. DrSplit enables PA detection of protein-protein interactions in deep-seated mouse tumors and livers, achieving 125-µm spatial resolution and 530-cell sensitivity in vivo. The combination of RS-SIP-PACT with DrBphP-PCM and DrSplit holds great potential for noninvasive multi-contrast deep-tissue functional imaging.

ESCRT machinery components are required for Orthobunyavirus particle production in Golgi compartments

Peribunyaviridae is a large family of RNA viruses with several members that cause mild to severe diseases in humans and livestock. Despite their importance in public heath very little is known about the host cell factors hijacked by these viruses to support assembly and cell egress. Here we show that assembly of Oropouche virus, a member of the genus Orthobunyavirus that causes a frequent arboviral infection in South America countries, involves budding of virus particles toward the lumen of Golgi cisternae. As viral replication progresses, these Golgi subcompartments become enlarged and physically separated from Golgi stacks, forming Oropouche viral factory (Vfs) units. At the ultrastructural level, these virally modified Golgi cisternae acquire an MVB appearance, and while they lack typical early and late endosome markers, they become enriched in endosomal complex required for transport (ESCRT) proteins that are involved in MVB biogenesis. Further microscopy and viral replication analysis showed that functional ESCRT machinery is required for efficient Vf morphogenesis and production of infectious OROV particles. Taken together, our results indicate that OROV attracts ESCRT machinery components to Golgi cisternae to mediate membrane remodeling events required for viral assembly and budding at these compartments. This represents an unprecedented mechanism of how viruses hijack host cell components for coordinated morphogenesis.

Investigation into the Mechanism of Homo- and Heterodimerization of Angiotensin-Converting Enzyme

Angiotensin-converting enzyme (ACE) plays a central role in the renin-angiotensin system (RAS), which is primarily responsible for blood pressure homeostasis. Studies have shown that ACE inhibitors yield cardiovascular benefits that cannot be entirely attributed to the inhibition of ACE catalytic activity. It is possible that these benefits are due to interactions between ACE and RAS receptors that mediate the protective arm of the RAS, such as angiotensin II receptor type 2 (AT2R) and the receptor MAS. Therefore, in this study, we investigated the molecular interactions of ACE, including ACE homodimerization and heterodimerization with AT2R and MAS, respectively. Molecular interactions were assessed by fluorescence resonance energy transfer and bimolecular fluorescence complementation in human embryonic kidney 293 cells and Chinese hamster ovary-K1 cells transfected with vectors encoding fluorophore-tagged proteins. The specificity of dimerization was verified by competition experiments using untagged proteins. These techniques were used to study several potential requirements for the germinal isoform of angiotensin-converting enzyme expressed in the testes (tACE) dimerization as well as the effect of ACE inhibitors on both somatic isoforms of angiotensin-converting enzyme expressed in the testes (sACE) and tACE dimerization. We demonstrated constitutive homodimerization of sACE and of both of its domains separately, as well as heterodimerization of both sACE and tACE with AT2R, but not MAS. In addition, we investigated both soluble sACE and the sACE N domain using size-exclusion chromatography-coupled small-angle X-ray scattering and we observed dimers in solution for both forms of the enzyme. Our results suggest that ACE homo- and heterodimerization does occur under physiologic conditions.

MultiBacMam Bimolecular Fluorescence Complementation (BiFC) tool-kit identifies new small-molecule inhibitors of the CDK5-p25 protein-protein interaction (PPI)

Protein-protein interactions (PPIs) are at the core of virtually all biological processes in cells. Consequently, targeting PPIs is emerging at the forefront of drug discovery. Cellular assays which closely recapitulate native conditions in vivo are instrumental to understand how small molecule drugs can modulate such interactions. We have integrated MultiBacMam, a baculovirus-based mammalian gene delivery tool we developed, with bimolecular fluorescence complementation (BiFC), giving rise to a highly efficient system for assay development, identification and characterization of PPI modulators. We used our system to analyze compounds impacting on CDK5-p25 PPI, which is implicated in numerous diseases including Alzheimer’s. We evaluated our tool-kit with the known inhibitor p5T, and we established a mini-screen to identify compounds that modulate this PPI in dose-response experiments. Finally, we discovered several compounds disrupting CDK5-p25 PPI, which had not been identified by other screening or structure-based methods before.