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Nanotechnology for Modern Medicine: Next Steps Towards Clinical Translation.

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Nanotechnology for Modern Medicine: Next Steps Towards Clinical Translation.

J Intern Med. 2021 Jan 22;:

Authors: Sindhwani S, Chan WCW

Abstract
The field of nanotechnology has been a significant research focus in the last thirty years. This emphasis is due to the unique optical, electrical, magnetic, chemical and biological properties of materials approximately ten thousand times smaller than the diameter of a hair strand. Researchers have developed methods to synthesize and characterize large libraries of nanomaterials and have demonstrated their preclinical utility. We have entered a new phase of nanomedicine development, where the focus is to translate these technologies to benefit patients. This review article provides an overview of nanomedicine's unique properties, the current state of the field, and discusses the challenge of clinical translation. Finally, we discuss the need to build and strengthen partnerships between engineers and clinicians to create a feedback loop between the bench and bedside. This partnership will guide fundamental studies on the nanoparticle-biological interactions, address clinical challenges, and change the development and evaluation of new drug delivery systems, sensors, imaging agents, and therapeutic systems.

PMID: 33480120 [PubMed - as supplied by publisher]



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The promise of machine learning to inform the management of juvenile idiopathic arthritis.

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The promise of machine learning to inform the management of juvenile idiopathic arthritis.

Expert Rev Clin Immunol. 2021 Jan 21;:

Authors: Eng SWM, Yeung RSM, Morris Q

PMID: 33475006 [PubMed - as supplied by publisher]



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Reconstructing tumor evolutionary histories and clone trees in polynomial-time with SubMARine.

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Reconstructing tumor evolutionary histories and clone trees in polynomial-time with SubMARine.

PLoS Comput Biol. 2021 Jan 19;17(1):e1008400

Authors: Sundermann LK, Wintersinger J, Rätsch G, Stoye J, Morris Q

Abstract
Tumors contain multiple subpopulations of genetically distinct cancer cells. Reconstructing their evolutionary history can improve our understanding of how cancers develop and respond to treatment. Subclonal reconstruction methods cluster mutations into groups that co-occur within the same subpopulations, estimate the frequency of cells belonging to each subpopulation, and infer the ancestral relationships among the subpopulations by constructing a clone tree. However, often multiple clone trees are consistent with the data and current methods do not efficiently capture this uncertainty; nor can these methods scale to clone trees with a large number of subclonal populations. Here, we formalize the notion of a partially-defined clone tree (partial clone tree for short) that defines a subset of the pairwise ancestral relationships in a clone tree, thereby implicitly representing the set of all clone trees that have these defined pairwise relationships. Also, we introduce a special partial clone tree, the Maximally-Constrained Ancestral Reconstruction (MAR), which summarizes all clone trees fitting the input data equally well. Finally, we extend commonly used clone tree validity conditions to apply to partial clone trees and describe SubMARine, a polynomial-time algorithm producing the subMAR, which approximates the MAR and guarantees that its defined relationships are a subset of those present in the MAR. We also extend SubMARine to work with subclonal copy number aberrations and define equivalence constraints for this purpose. Further, we extend SubMARine to permit noise in the estimates of the subclonal frequencies while retaining its validity conditions and guarantees. In contrast to other clone tree reconstruction methods, SubMARine runs in time and space that scale polynomially in the number of subclones. We show through extensive noise-free simulation, a large lung cancer dataset and a prostate cancer dataset that the subMAR equals the MAR in all cases where only a single clone tree exists and that it is a perfect match to the MAR in most of the other cases. Notably, SubMARine runs in less than 70 seconds on a single thread with less than one Gb of memory on all datasets presented in this paper, including ones with 50 nodes in a clone tree. On the real-world data, SubMARine almost perfectly recovers the previously reported trees and identifies minor errors made in the expert-driven reconstructions of those trees. The freely-available open-source code implementing SubMARine can be downloaded at https://github.com/morrislab/submarine.

PMID: 33465079 [PubMed - as supplied by publisher]



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τ-SGA: synthetic genetic array analysis for systematically screening and quantifying trigenic interactions in yeast.

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τ-SGA: synthetic genetic array analysis for systematically screening and quantifying trigenic interactions in yeast.

Nat Protoc. 2021 Jan 18;:

Authors: Kuzmin E, Rahman M, VanderSluis B, Costanzo M, Myers CL, Andrews BJ, Boone C

Abstract
Systematic complex genetic interaction studies have provided insight into high-order functional redundancies and genetic network wiring of the cell. Here, we describe a method for screening and quantifying trigenic interactions from ordered arrays of yeast strains grown on agar plates as individual colonies. The protocol instructs users on the trigenic synthetic genetic array analysis technique, τ-SGA, for high-throughput screens. The steps describe construction of the double-mutant query strains and the corresponding single-mutant control query strains, which are screened in parallel in two replicates. The screening experimental set-up consists of sequential replica-pinning steps that enable automated mating, meiotic recombination and successive haploid selection steps for the generation of triple mutants, which are scored for colony size as a proxy for fitness, which enables the calculation of trigenic interactions. The procedure described here was used to conduct 422 trigenic interaction screens, which generated ~460,000 yeast triple mutants for trigenic interaction analysis. Users should be familiar with robotic equipment required for high-throughput genetic interaction screens and be proficient at the command line to execute the scoring pipeline. Large-scale screen computational analysis is achieved by using MATLAB pipelines that score raw colony size data to produce τ-SGA interaction scores. Additional recommendations are included for optimizing experimental design and analysis of smaller-scale trigenic interaction screens by using a web-based analysis system, SGAtools. This protocol provides a resource for those who would like to gain a deeper, more practical understanding of trigenic interaction screening and quantification methodology.

PMID: 33462440 [PubMed - as supplied by publisher]



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Global and local tension measurements in biomimetic skeletal muscle tissues reveals early mechanical homeostasis.

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Global and local tension measurements in biomimetic skeletal muscle tissues reveals early mechanical homeostasis.

Elife. 2021 Jan 18;10:

Authors: Hofemeier AD, Limón T, Muenker TM, Wallmeyer B, Jurado A, Afshar ME, Ebrahimi M, Tsukanov R, Oleksiievets N, Enderlein J, Gilbert PM, Betz T

Abstract
Tension and mechanical properties of muscle tissue are tightly related to proper skeletal muscle function, which makes experimental access to the biomechanics of muscle tissue formation a key requirement to advance our understanding of muscle function and development. Recently developed elastic in vitro culture chambers allow for raising 3D muscle tissue under controlled conditions and to measure global tissue force generation. However, these chambers are inherently incompatible with high resolution microscopy limiting their usability to global force measurements, and preventing the exploitation of modern fluorescence based investigation methods for live and dynamic measurements. Here we present a new chamber design pairing global force measurements, quantified from post deflection, with local tension measurements obtained from elastic hydrogel beads embedded in muscle tissue. High resolution 3D video microscopy of engineered muscle formation, enabled by the new chamber, shows an early mechanical tissue homeostasis that remains stable in spite of continued myotube maturation.

PMID: 33459593 [PubMed - as supplied by publisher]



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Sphingosine-1-phosphate receptor 3 potentiates inflammatory programs in normal and leukemia stem cells to promote differentiation.

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Sphingosine-1-phosphate receptor 3 potentiates inflammatory programs in normal and leukemia stem cells to promote differentiation.

Blood Cancer Discov. 2021 Jan 01;2(1):32-53

Authors: Xie SZ, Kaufmann KB, Wang W, Chan-Seng-Yue M, Gan OI, Laurenti E, Garcia-Prat L, Takayanagi SI, Ng SWK, Xu C, Zeng AGX, Jin L, McLeod J, Wagenblast E, Mitchell A, Kennedy JA, Liu Q, Boutzen H, Kleinau M, Jargstorf J, Holmes G, Zhang Y, Voisin V, Bader GD, Wang JCY, Hannun YA, Luberto C, Schroeder T, Minden MD, Dick JE

Abstract
Acute myeloid leukemia (AML) is a caricature of normal hematopoiesis, driven from leukemia stem cells (LSC) that share some hematopoietic stem cell (HSC) programs including responsiveness to inflammatory signaling. Although inflammation dysregulates mature myeloid cells and influences stemness programs and lineage determination in HSC by activating stress myelopoiesis, such roles in LSC are poorly understood. Here, we show that S1PR3, a receptor for the bioactive lipid sphingosine-1-phosphate, is a central regulator which drives myeloid differentiation and activates inflammatory programs in both HSC and LSC. S1PR3-mediated inflammatory signatures varied in a continuum from primitive to mature myeloid states across AML patient cohorts, each with distinct phenotypic and clinical properties. S1PR3 was high in LSC and blasts of mature myeloid samples with linkages to chemosensitivity, while S1PR3 activation in primitive samples promoted LSC differentiation leading to eradication. Our studies open new avenues for therapeutic target identification specific for each AML subset.

PMID: 33458693 [PubMed]



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Interrogation of kinase genetic interactions provides a global view of PAK1-mediated signal transduction pathways.

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Interrogation of kinase genetic interactions provides a global view of PAK1-mediated signal transduction pathways.

J Biol Chem. 2020 Dec 11;295(50):16906-16919

Authors: Kim JH, Seo Y, Jo M, Jeon H, Kim YS, Kim EJ, Seo D, Lee WH, Kim SR, Yachie N, Zhong Q, Vidal M, Roth FP, Suk K

Abstract
Kinases are critical components of intracellular signaling pathways and have been extensively investigated with regard to their roles in cancer. p21-activated kinase-1 (PAK1) is a serine/threonine kinase that has been previously implicated in numerous biological processes, such as cell migration, cell cycle progression, cell motility, invasion, and angiogenesis, in glioma and other cancers. However, the signaling network linked to PAK1 is not fully defined. We previously reported a large-scale yeast genetic interaction screen using toxicity as a readout to identify candidate PAK1 genetic interactions. En masse transformation of the PAK1 gene into 4,653 homozygous diploid Saccharomyces cerevisiae yeast deletion mutants identified ∼400 candidates that suppressed yeast toxicity. Here we selected 19 candidate PAK1 genetic interactions that had human orthologs and were expressed in glioma for further examination in mammalian cells, brain slice cultures, and orthotopic glioma models. RNAi and pharmacological inhibition of potential PAK1 interactors confirmed that DPP4, KIF11, mTOR, PKM2, SGPP1, TTK, and YWHAE regulate PAK1-induced cell migration and revealed the importance of genes related to the mitotic spindle, proteolysis, autophagy, and metabolism in PAK1-mediated glioma cell migration, drug resistance, and proliferation. AKT1 was further identified as a downstream mediator of the PAK1-TTK genetic interaction. Taken together, these data provide a global view of PAK1-mediated signal transduction pathways and point to potential new drug targets for glioma therapy.

PMID: 33453946 [PubMed - in process]



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ELASPIC2 (EL2): Combining contextualized language models and graph neural networks to predict effects of mutations.

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ELASPIC2 (EL2): Combining contextualized language models and graph neural networks to predict effects of mutations.

J Mol Biol. 2021 Jan 12;:166810

Authors: Strokach A, Yu Lu T, Kim PM

Abstract
The ELASPIC web server allows users to evaluate the effect of mutations on protein folding and protein-protein interaction on a proteome-wide scale. It uses homology models of proteins and protein-protein interactions, which have been precalculated for several proteomes, and machine learning models, which integrate structural information with sequence conservation scores, in order to make its predictions. Since the original publication of the ELASPIC web server, several advances have motivated a revisiting of the problem of mutation effect prediction. First, progress in neural network architectures and self-supervised pre-trained has resulted in models which provide more informative embeddings of protein sequence and structure than those used by the original version of ELASPIC. Second, the amount of training data has increased several-fold, largely driven by advances in deep mutation scanning and other multiplexed assays of variant effect. Here, we describe two machine learning models which leverage the recent advances in order to achieve superior accuracy in predicting the effect of mutation on protein folding and protein-protein interaction. The models incorporate features generated using pre-trained transformer- and graph convolution-based neural networks, and are trained to optimize a ranking objective function, which permits the use of heterogeneous training data. The outputs from the new models have been incorporated into the ELASPIC web server, available at http://elaspic.kimlab.org.

PMID: 33450251 [PubMed - as supplied by publisher]



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Poly-Methacrylic Acid Cross-Linked with Collagen Accelerates Diabetic Wound Closure.

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Poly-Methacrylic Acid Cross-Linked with Collagen Accelerates Diabetic Wound Closure.

ACS Biomater Sci Eng. 2020 Nov 09;6(11):6368-6377

Authors: Coindre VF, Hu Y, Sefton MV

Abstract
Impaired blood vessel formation limits the healing of diabetic ulcers and leaves patients at high risk for amputation. Nonbiologic vascular regenerative materials made of methacrylic acid (MAA) copolymer, such as MAA-co-methyl methacrylate beads, have shown to enhance wound healing in a diabetic animal model, but their lack of biodegradability precludes their clinical implementation. Here, a new MAA-based gel was created by cross-linking polyMAA with collagen using carbodiimide chemistry. Using this gel on full-thickness wounds in diabetic db/db mice augmented vascularization of the wound bed, resulting in a faster closure compared to untreated or collagen-only treated wounds. After 21 days, almost all the wounds were closed and re-epithelialized in the polyMAA-collagen group compared to that in the other groups in which most wounds remained open. Histological and fluorescent gel tracking data suggested that the gel resorbed during the phase of tissue remodeling, likely because of the action of macrophages that colonized the gel. We expect the addition of the polyMAA to commercially available collagen-based dressing to be a good candidate to treat diabetic ulcers.

PMID: 33449665 [PubMed - as supplied by publisher]



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Large-scale survey and database of high affinity ligands for peptide recognition modules.

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Large-scale survey and database of high affinity ligands for peptide recognition modules.

Mol Syst Biol. 2020 Dec;16(12):e9310

Authors: Teyra J, Kelil A, Jain S, Helmy M, Jajodia R, Hooda Y, Gu J, D'Cruz AA, Nicholson SE, Min J, Sudol M, Kim PM, Bader GD, Sidhu SS

Abstract
Many proteins involved in signal transduction contain peptide recognition modules (PRMs) that recognize short linear motifs (SLiMs) within their interaction partners. Here, we used large-scale peptide-phage display methods to derive optimal ligands for 163 unique PRMs representing 79 distinct structural families. We combined the new data with previous data that we collected for the large SH3, PDZ, and WW domain families to assemble a database containing 7,984 unique peptide ligands for 500 PRMs representing 82 structural families. For 74 PRMs, we acquired enough new data to map the specificity profiles in detail and derived position weight matrices and binding specificity logos based on multiple peptide ligands. These analyses showed that optimal peptide ligands resembled peptides observed in existing structures of PRM-ligand complexes, indicating that a large majority of the phage-derived peptides are likely to target natural peptide-binding sites and could thus act as inhibitors of natural protein-protein interactions. The complete dataset has been assembled in an online database (http://www.prm-db.org) that will enable many structural, functional, and biological studies of PRMs and SLiMs.

PMID: 33438817 [PubMed - in process]



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