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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.

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.

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.

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.

Aim: To evaluate real‐world abatacept retention and clinical outcomes in patients with rheumatoid arthritis in Taiwan. Methods: This prospective, observational study enrolled patients with rheumatoid arthritis aged ≥20 years who received abatacept in real‐world practice. The primary endpoint was the abatacept retention rate at 24 months. Patients were categorized into subgroups based on abatacept treatment status and previous biological disease‐modifying antirheumatic drug (bDMARD) therapy. Risk factors affecting abatacept retention were determined by regression analysis. Results: A total of 212 patients were enrolled. The overall abatacept retention rate at 24 months among all patients was 59.9% (95% confidence interval 53.0%–66.6%). Patients who were ongoing users of abatacept and bDMARD‐naïve had the highest retention rate (76.3%); of these, 31.6% achieved low disease activity or remission after 2 years. Previous treatment with bDMARDs was associated with an increased risk of abatacept discontinuation (hazard ratio 1.99; p =.002). The most common reasons for abatacept discontinuation were drug switch (11.3%) and loss to follow‐up (6.1%). Abatacept was well‐tolerated with no new safety signals. Conclusion: The 24‐month retention rate of abatacept was 59.9%; abatacept was associated with improved clinical outcomes and was well‐tolerated in the real‐world setting in Taiwan. [ABSTRACT FROM AUTHOR]

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 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.

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.

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.

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/).

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 SCFβ-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 SCFβ-TrCP to promote proper mitotic spindle assembly.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Hand-held ultrasound devices have been widely used in the field of healthcare and power-efficient, real-time imaging is essential. This work presents the world's first ultrasound imaging processor supporting advanced modes, including vector flow imaging and elastography imaging. Plane-wave beamforming is utilized to ensure that the pulse repetition frequency (PRF) is sufficiently high for the advanced mode. The storage size and power consumption are minimized through algorithm-architecture co-optimization. The proposed plane-wave beamforming reduces the storage size of the required delay values by 43.7%. By exchanging the processing order, the storage size is reduced by 78.1% for elastography imaging. Parallel beamforming and interleaved firing are employed to achieve real-time imaging for all the supported modes. Fabricated in 40-nm CMOS technology, the proposed processor integrates 4.7M logic gates in core area of 3.24mm 2 . This work achieves a 20.3× higher beamforming rate with 5.3-to-29.1× lower power consumption than the state-of- the-art design. It also has 60% lower hardware complexity (in terms of gate count), in addition to the capability for supporting the advanced mode.