Improve BBB Penetration and Cytotoxicity of Palbociclib in U87-MG Glioblastoma Cells Delivered by Dual Peptide Functionalized Nanoparticles.

Palbociclib (PBC) is an FDA-approved CDK4/6 inhibitor used for breast cancer treatment. PBC has been demonstrated its ability to suppress the proliferation of glioma cells by inducing cell cycle arrest. However, the efflux transporters on the blood-brain barrier (BBB) restricts the delivery of PBC to the brain. The nano-delivery strategy with BBB-penetrating and glioma-targeting abilities was designed. Poly(lactide-co-glycolide)-poly(ethylene glycol) (PLGA-PEG) was functionalized with the potential peptide, T7 targeting peptide and/or R9 penetrating peptide, to prepare PBC-loaded nanoparticles (PBC@NPs). The size of PBC@NPs was in the range of 168.4 ± 4.3-185.8 ± 4.4 nm (PDI < 0.2), and the zeta potential ranged from -17.8 ± 1.4 mV to -14.3 ± 1.0 mV dependent of conjugated peptide. The transport of PBC@NPs across the bEnd.3 cell model was in the order of dual-peptide modified NPs > T7-peptide modified NPs > peptide-free NPs > free PBC, indicating facilitated delivery of PBC by NPs, particularly the T7/R9 dual-peptide modified NPs. Moreover, PBC@NPs significantly enhanced U87-MG glioma cell apoptosis by 2.3-6.5 folds relative to PBC, where the dual-peptide modified NPs was the most effective one. In conclusion, the PBC loaded dual-peptide functionalized NPs improved cellular uptake in bEnd.3 cells followed by targeting to U87-MG glioma cells, leading to effective cytotoxicity and promoting cell death.

tidytof: a user-friendly framework for scalable and reproducible high-dimensional cytometry data analysis.

Summary: While many algorithms for analyzing high-dimensional cytometry data have now been developed, the software implementations of these algorithms remain highly customized-this means that exploring a dataset requires users to learn unique, often poorly interoperable package syntaxes for each step of data processing. To solve this problem, we developed {tidytof}, an open-source R package for analyzing high-dimensional cytometry data using the increasingly popular 'tidy data' interface. Availability and implementation: {tidytof} is available at https://github.com/keyes-timothy/tidytof and is released under the MIT license. It is supported on Linux, MS Windows and MacOS. Additional documentation is available at the package website (https://keyes-timothy.github.io/tidytof/). Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Single-cell technologies uncover intra-tumor heterogeneity in childhood cancers.

Childhood cancer is the second leading cause of death in children aged 1 to 14. Although survival rates have vastly improved over the past 40 years, cancer resistance and relapse remain a significant challenge. Advances in single-cell technologies enable dissection of tumors to unprecedented resolution. This facilitates unraveling the heterogeneity of childhood cancers to identify cell subtypes that are prone to treatment resistance. The rapid accumulation of single-cell data from different modalities necessitates the development of novel computational approaches for processing, visualizing, and analyzing single-cell data. Here, we review single-cell approaches utilized or under development in the context of childhood cancers. We review computational methods for analyzing single-cell data and discuss best practices for their application. Finally, we review the impact of several studies of childhood tumors analyzed with these approaches and future directions to implement single-cell studies into translational cancer research in pediatric oncology.

CytofIn enables integrated analysis of public mass cytometry datasets using generalized anchors.

The increasing use of mass cytometry for analyzing clinical samples offers the possibility to perform comparative analyses across public datasets. However, challenges in batch normalization and data integration limit the comparison of datasets not intended to be analyzed together. Here, we present a data integration strategy, CytofIn, using generalized anchors to integrate mass cytometry datasets from the public domain. We show that low-variance controls, such as healthy samples and stable channels, are inherently homogeneous, robust against stimulation, and can serve as generalized anchors for batch correction. Single-cell quantification comparing mass cytometry data from 989 leukemia files pre- and post normalization with CytofIn demonstrates effective batch correction while recapitulating the gold-standard bead normalization. CytofIn integration of public cancer datasets enabled the comparison of immune features across histologies and treatments. We demonstrate the ability to integrate public datasets without necessitating identical control samples or bead standards for fast and robust analysis using CytofIn.

Benefit of a Short Chain Peptide as a Targeting Ligand of Nanocarriers for a Brain-Driven Purpose.

Treatment of glioma remains a critical challenge worldwide, since the therapeutic effect is greatly hindered by poor transportation across the blood brain barrier (BBB) and low penetration into tumor cells. In this study, a peptide-conjugated nano-delivery system was explored for the purpose of glioma therapy. A peptide-decorated copolymer was used to prepare nanoparticles (NPs) by a solvent evaporation method. The particle size was in the range of 160.9 ± 3.3-173.5 ± 3.6 nm with monodistribution, and the zeta potentials ranged from -18.6 ± 1.2 to +7.9 ± 0.6 mV showing an increasing trend with R9-peptide. An in vitro cocultured BBB model illustrated the internalization of peptide-conjugated NPs in bEnd.3 cells followed by uptake by U87-MG cells indicating both BBB-crossing and glioma-penetrating abilities. IVIS (In Vivo Imaging System) images revealed that T7-conjugated NPs specifically accumulated in the brain more than peptide-free NPs and had less biodistribution in nontarget tissues than T7/R9 dual-peptide conjugated NPs. The benefit of T7-peptide as a targeting ligand for NPs across the BBB with accumulation in the brain was elucidated.

Regulation of Iron Homeostasis through Parkin-Mediated Lactoferrin Ubiquitylation.

Somatic mutations that perturb Parkin ubiquitin ligase activity and the misregulation of iron homeostasis have both been linked to Parkinson's disease. Lactotransferrin (LTF) is a member of the family of transferrin iron binding proteins that regulate iron homeostasis, and increased levels of LTF and its receptor have been observed in neurodegenerative disorders like Parkinson's disease. Here, we report that Parkin binds to LTF and ubiquitylates LTF to influence iron homeostasis. Parkin-dependent ubiquitylation of LTF occurred most often on lysines (K) 182 and 649. Substitution of K182 or K649 with alanine (K182A or K649A, respectively) led to a decrease in the level of LTF ubiquitylation, and substitution at both sites led to a major decrease in the level of LTF ubiquitylation. Importantly, Parkin-mediated ubiquitylation of LTF was critical for regulating intracellular iron levels as overexpression of LTF ubiquitylation site point mutants (K649A or K182A/K649A) led to an increase in intracellular iron levels measured by ICP-MS/MS. Consistently, RNAi-mediated depletion of Parkin led to an increase in intracellular iron levels in contrast to overexpression of Parkin that led to a decrease in intracellular iron levels. Together, these results indicate that Parkin binds to and ubiquitylates LTF to regulate intracellular iron levels. These results expand our understanding of the cellular processes that are perturbed when Parkin activity is disrupted and more broadly the mechanisms that contribute to Parkinson's disease.

A Cancer Biologist's Primer on Machine Learning Applications in High-Dimensional Cytometry.

The application of machine learning and artificial intelligence to high-dimensional cytometry data sets has increasingly become a staple of bioinformatic data analysis over the past decade. This is especially true in the field of cancer biology, where protocols for collecting multiparameter single-cell data in a high-throughput fashion are rapidly developed. As the use of machine learning methodology in cytometry becomes increasingly common, there is a need for cancer biologists to understand the basic theory and applications of a variety of algorithmic tools for analyzing and interpreting cytometry data. We introduce the reader to several keystone machine learning-based analytic approaches with an emphasis on defining key terms and introducing a conceptual framework for making translational or clinically relevant discoveries. The target audience consists of cancer cell biologists and physician-scientists interested in applying these tools to their own data, but who may have limited training in bioinformatics. © 2020 International Society for Advancement of Cytometry.

Homology modeling of TMPRSS2 yields candidate drugs that may inhibit entry of SARS-CoV-2 into human cells.

The most rapid path to discovering treatment options for the novel coronavirus SARS-CoV-2 is to find existing medications that are active against the virus. We have focused on identifying repurposing candidates for the transmembrane serine protease family member II (TMPRSS2), which is critical for entry of coronaviruses into cells. Using known 3D structures of close homologs, we created seven homology models. We also identified a set of serine protease inhibitor drugs, generated several conformations of each, and docked them into our models. We used three known chemical (non-drug) inhibitors and one validated inhibitor of TMPRSS2 in MERS as benchmark compounds and found six compounds with predicted high binding affinity in the range of the known inhibitors. We also showed that a previously published weak inhibitor, Camostat, had a significantly lower binding score than our six compounds. All six compounds are anticoagulants with significant and potentially dangerous clinical effects and side effects. Nonetheless, if these compounds significantly inhibit SARS-CoV-2 infection, they could represent a potentially useful clinical tool.

Leukemia Cell Cycle Chemical Profiling Identifies the G2-Phase Leukemia Specific Inhibitor Leusin-1.

Targeting the leukemia proliferation cycle has been a successful approach to developing antileukemic therapies. However, drug screening efforts to identify novel antileukemic agents have been hampered by the lack of a suitable high-throughput screening platform for suspension cells that does not rely on flow-cytometry analyses. We report the development of a novel leukemia cell-based high-throughput chemical screening platform for the discovery of cell cycle phase specific inhibitors that utilizes chemical cell cycle profiling. We have used this approach to analyze the cell cycle response of acute lymphoblastic leukemia CCRF-CEM cells to each of 181420 druglike compounds. This approach yielded cell cycle phase specific inhibitors of leukemia cell proliferation. Further analyses of the top G2-phase and M-phase inhibitors identified the leukemia specific inhibitor 1 (Leusin-1). Leusin-1 arrests cells in G2 phase and triggers an apoptotic cell death. Most importantly, Leusin-1 was more active in acute lymphoblastic leukemia cells than other types of leukemias, non-blood cancers, or normal cells and represents a lead molecule for developing antileukemic drugs.

Pocket similarity identifies selective estrogen receptor modulators as microtubule modulators at the taxane site.

Taxanes are a family of natural products with a broad spectrum of anticancer activity. This activity is mediated by interaction with the taxane site of beta-tubulin, leading to microtubule stabilization and cell death. Although widely used in the treatment of breast cancer and other malignancies, existing taxane-based therapies including paclitaxel and the second-generation docetaxel are currently limited by severe adverse effects and dose-limiting toxicity. To discover taxane site modulators, we employ a computational binding site similarity screen of > 14,000 drug-like pockets from PDB, revealing an unexpected similarity between the estrogen receptor and the beta-tubulin taxane binding pocket. Evaluation of nine selective estrogen receptor modulators (SERMs) via cellular and biochemical assays confirms taxane site interaction, microtubule stabilization, and cell proliferation inhibition. Our study demonstrates that SERMs can modulate microtubule assembly and raises the possibility of an estrogen receptor-independent mechanism for inhibiting cell proliferation.

Computational analysis of kinase inhibitor selectivity using structural knowledge.

Motivation: Kinases play a significant role in diverse disease signaling pathways and understanding kinase inhibitor selectivity, the tendency of drugs to bind to off-targets, remains a top priority for kinase inhibitor design and clinical safety assessment. Traditional approaches for kinase selectivity analysis using biochemical activity and binding assays are useful but can be costly and are often limited by the kinases that are available. On the other hand, current computational kinase selectivity prediction methods are computational intensive and can rarely achieve sufficient accuracy for large-scale kinome wide inhibitor selectivity profiling. Results: Here, we present a KinomeFEATURE database for kinase binding site similarity search by comparing protein microenvironments characterized using diverse physiochemical descriptors. Initial selectivity prediction of 15 known kinase inhibitors achieved an >90% accuracy and demonstrated improved performance in comparison to commonly used kinase inhibitor selectivity prediction methods. Additional kinase ATP binding site similarity assessment (120 binding sites) identified 55 kinases with significant promiscuity and revealed unexpected inhibitor cross-activities between PKR and FGFR2 kinases. Kinome-wide selectivity profiling of 11 kinase drug candidates predicted novel as well as experimentally validated off-targets and suggested structural mechanisms of kinase cross-activities. Our study demonstrated potential utilities of our approach for large-scale kinase inhibitor selectivity profiling that could contribute to kinase drug development and safety assessment. Availability and implementation: The KinomeFEATURE database and the associated scripts for performing kinase pocket similarity search can be downloaded from the Stanford SimTK website (https://simtk.org/projects/kdb). Supplementary information: Supplementary data are available at Bioinformatics online.

Machine learning in chemoinformatics and drug discovery.

Chemoinformatics is an established discipline focusing on extracting, processing and extrapolating meaningful data from chemical structures. With the rapid explosion of chemical 'big' data from HTS and combinatorial synthesis, machine learning has become an indispensable tool for drug designers to mine chemical information from large compound databases to design drugs with important biological properties. To process the chemical data, we first reviewed multiple processing layers in the chemoinformatics pipeline followed by the introduction of commonly used machine learning models in drug discovery and QSAR analysis. Here, we present basic principles and recent case studies to demonstrate the utility of machine learning techniques in chemoinformatics analyses; and we discuss limitations and future directions to guide further development in this evolving field.

Microtubins: a novel class of small synthetic microtubule targeting drugs that inhibit cancer cell proliferation.

Microtubule targeting drugs like taxanes, vinca alkaloids, and epothilones are widely-used and effective chemotherapeutic agents that target the dynamic instability of microtubules and inhibit spindle functioning. However, these drugs have limitations associated with their production, solubility, efficacy and unwanted toxicities, thus driving the need to identify novel antimitotic drugs that can be used as anticancer agents. We have discovered and characterized the Microtubins (Microtubule inhibitors), a novel class of small synthetic compounds, which target tubulin to inhibit microtubule polymerization, arrest cancer cells predominantly in mitosis, activate the spindle assembly checkpoint and trigger an apoptotic cell death. Importantly, the Microtubins do not compete for the known vinca or colchicine binding sites. Additionally, through chemical synthesis and structure-activity relationship studies, we have determined that specific modifications to the Microtubin phenyl ring can activate or inhibit its bioactivity. Combined, these data define the Microtubins as a novel class of compounds that inhibit cancer cell proliferation by perturbing microtubule polymerization and they could be used to develop novel cancer therapeutics.

Computational Cell Cycle Profiling of Cancer Cells for Prioritizing FDA-Approved Drugs with Repurposing Potential.

Discovery of first-in-class medicines for treating cancer is limited by concerns with their toxicity and safety profiles, while repurposing known drugs for new anticancer indications has become a viable alternative. Here, we have developed a new approach that utilizes cell cycle arresting patterns as unique molecular signatures for prioritizing FDA-approved drugs with repurposing potential. As proof-of-principle, we conducted large-scale cell cycle profiling of 884 FDA-approved drugs. Using cell cycle indexes that measure changes in cell cycle profile patterns upon chemical perturbation, we identified 36 compounds that inhibited cancer cell viability including 6 compounds that were previously undescribed. Further cell cycle fingerprint analysis and 3D chemical structural similarity clustering identified unexpected FDA-approved drugs that induced DNA damage, including clinically relevant microtubule destabilizers, which was confirmed experimentally via cell-based assays. Our study shows that computational cell cycle profiling can be used as an approach for prioritizing FDA-approved drugs with repurposing potential, which could aid the development of cancer therapeutics.

Fatostatin Inhibits Cancer Cell Proliferation by Affecting Mitotic Microtubule Spindle Assembly and Cell Division.

The sterol regulatory element-binding protein (SREBP) transcription factors have become attractive targets for pharmacological inhibition in the treatment of metabolic diseases and cancer. SREBPs are critical for the production and metabolism of lipids and cholesterol, which are essential for cellular homeostasis and cell proliferation. Fatostatin was recently discovered as a specific inhibitor of SREBP cleavage-activating protein (SCAP), which is required for SREBP activation. Fatostatin possesses antitumor properties including the inhibition of cancer cell proliferation, invasion, and migration, and it arrests cancer cells in G2/M phase. Although Fatostatin has been viewed as an antitumor agent due to its inhibition of SREBP and its effect on lipid metabolism, we show that Fatostatin's anticancer properties can also be attributed to its inhibition of cell division. We analyzed the effect of SREBP activity inhibitors including Fatostatin, PF-429242, and Betulin on the cell cycle and determined that only Fatostatin possessed antimitotic properties. Fatostatin inhibited tubulin polymerization, arrested cells in mitosis, activated the spindle assembly checkpoint, and triggered mitotic catastrophe and reduced cell viability. Thus Fatostatin's ability to inhibit SREBP activity and cell division could prove beneficial in treating aggressive types of cancers such as glioblastomas that have elevated lipid metabolism and fast proliferation rates and often develop resistance to current anticancer therapies.

3D Chemical Similarity Networks for Structure-Based Target Prediction and Scaffold Hopping.

Target identification remains a major challenge for modern drug discovery programs aimed at understanding the molecular mechanisms of drugs. Computational target prediction approaches like 2D chemical similarity searches have been widely used but are limited to structures sharing high chemical similarity. Here, we present a new computational approach called chemical similarity network analysis pull-down 3D (CSNAP3D) that combines 3D chemical similarity metrics and network algorithms for structure-based drug target profiling, ligand deorphanization, and automated identification of scaffold hopping compounds. In conjunction with 2D chemical similarity fingerprints, CSNAP3D achieved a >95% success rate in correctly predicting the drug targets of 206 known drugs. Significant improvement in target prediction was observed for HIV reverse transcriptase (HIVRT) compounds, which consist of diverse scaffold hopping compounds targeting the nucleotidyltransferase binding site. CSNAP3D was further applied to a set of antimitotic compounds identified in a cell-based chemical screen and identified novel small molecules that share a pharmacophore with Taxol and display a Taxol-like mechanism of action, which were validated experimentally using in vitro microtubule polymerization assays and cell-based assays.

The X-Linked-Intellectual-Disability-Associated Ubiquitin Ligase Mid2 Interacts with Astrin and Regulates Astrin Levels to Promote Cell Division.

Mid1 and Mid2 are ubiquitin ligases that regulate microtubule dynamics and whose mutation is associated with X-linked developmental disorders. We show that astrin, a microtubule-organizing protein, co-purifies with Mid1 and Mid2, has an overlapping localization with Mid1 and Mid2 at intercellular bridge microtubules, is ubiquitinated by Mid2 on lysine 409, and is degraded during cytokinesis. Mid2 depletion led to astrin stabilization during cytokinesis, cytokinetic defects, multinucleated cells, and cell death. Similarly, expression of a K409A mutant astrin in astrin-depleted cells led to the accumulation of K409A on intercellular bridge microtubules and an increase in cytokinetic defects, multinucleated cells, and cell death. These results indicate that Mid2 regulates cell division through the ubiquitination of astrin on K409, which is critical for its degradation and proper cytokinesis. These results could help explain how mutation of MID2 leads to misregulation of microtubule organization and the downstream disease pathology associated with X-linked intellectual disabilities.

Large-scale chemical similarity networks for target profiling of compounds identified in cell-based chemical screens.

Target identification is one of the most critical steps following cell-based phenotypic chemical screens aimed at identifying compounds with potential uses in cell biology and for developing novel disease therapies. Current in silico target identification methods, including chemical similarity database searches, are limited to single or sequential ligand analysis that have limited capabilities for accurate deconvolution of a large number of compounds with diverse chemical structures. Here, we present CSNAP (Chemical Similarity Network Analysis Pulldown), a new computational target identification method that utilizes chemical similarity networks for large-scale chemotype (consensus chemical pattern) recognition and drug target profiling. Our benchmark study showed that CSNAP can achieve an overall higher accuracy (>80%) of target prediction with respect to representative chemotypes in large (>200) compound sets, in comparison to the SEA approach (60-70%). Additionally, CSNAP is capable of integrating with biological knowledge-based databases (Uniprot, GO) and high-throughput biology platforms (proteomic, genetic, etc) for system-wise drug target validation. To demonstrate the utility of the CSNAP approach, we combined CSNAP's target prediction with experimental ligand evaluation to identify the major mitotic targets of hit compounds from a cell-based chemical screen and we highlight novel compounds targeting microtubules, an important cancer therapeutic target. The CSNAP method is freely available and can be accessed from the CSNAP web server (http://services.mbi.ucla.edu/CSNAP/).

Tctex1d2 associates with short-rib polydactyly syndrome proteins and is required for ciliogenesis.

Short-rib polydactyly syndromes (SRPS) arise from mutations in genes involved in retrograde intraflagellar transport (IFT) and basal body homeostasis, which are critical for cilia assembly and function. Recently, mutations in WDR34 or WDR60 (candidate dynein intermediate chains) were identified in SRPS. We have identified and characterized Tctex1d2, which associates with Wdr34, Wdr60 and other dynein complex 1 and 2 subunits. Tctex1d2 and Wdr60 localize to the base of the cilium and their depletion causes defects in ciliogenesis. We propose that Tctex1d2 is a novel dynein light chain important for trafficking to the cilium and potentially retrograde IFT and is a new molecular link to understanding SRPS pathology.

A unique insertion in STARD9's motor domain regulates its stability.

STARD9 is a largely uncharacterized mitotic kinesin and putative cancer target that is critical for regulating pericentriolar material cohesion during bipolar spindle assembly. To begin to understand the mechanisms regulating STARD9 function and their importance to cell division, we took a multidisciplinary approach to define the cis and trans factors that regulate the stability of the STARD9 motor domain. We show that, unlike the other ∼50 mammalian kinesins, STARD9 contains an insertion in loop 12 of its motor domain (MD). Working with the STARD9-MD, we show that it is phosphorylated in mitosis by mitotic kinases that include Plk1. These phosphorylation events are important for targeting a pool of STARD9-MD for ubiquitination by the SCFß-TrCP ubiquitin ligase and proteasome-dependent degradation. Of interest, overexpression of nonphosphorylatable/nondegradable STARD9-MD mutants leads to spindle assembly defects. Our results with STARD9-MD imply that in vivo the protein levels of full-length STARD9 could be regulated by Plk1 and SCFß-TrCP to promote proper mitotic spindle assembly.