A Bispecific Chimeric Aptamer Design Platform Based on c-MET Aptamer with a Replaceable Redundant Region.

Molecular engineering enables the creation of aptamers with novel functions, but the prerequisite is a deep understanding of their structure and recognition mechanism. The cellular-mesenchymal epithelial transition factor (c-MET) is garnering significant attention due to the critical role of the c-MET/HGF signaling pathway in tumor development and invasion. This study reports a strategy for constructing novel chimeric aptamers that bind to both c-MET and other specific proteins. c-MET was identified to be the molecular target of a DNA aptamer, HF3-58, selected through cell-SELEX. The binding structure and mechanism of HF3-58 with c-MET were systematically studied, revealing the scaffold, recognition, and redundancy regions. Through molecular engineering design, the redundancy region was replaced with other aptamers possessing stem-loop structures, yielding novel chimeric aptamers with bispecificity for binding to c-MET and specific proteins. A chimeric bispecific aptamer HF-3b showed the ability to mediate the adhesion of T-cells to tumor cells, suggesting the prospective utility in tumor immunotherapy. These findings suggest that aptamer HF3-58 can serve as a molecular engineering platform for the development of diverse multifunctional ligands targeting c-MET. Moreover, comprehensive understanding of the binding mechanisms of aptamers will provide guidance for the design of functional aptamers, significantly expanding their potential applications.

Integration of human organoids single-cell transcriptomic profiles and human genetics repurposes critical cell type-specific drug targets for severe COVID-19.

Human organoids recapitulate the cell type diversity and function of their primary organs holding tremendous potentials for basic and translational research. Advances in single-cell RNA sequencing (scRNA-seq) technology and genome-wide association study (GWAS) have accelerated the biological and therapeutic interpretation of trait-relevant cell types or states. Here, we constructed a computational framework to integrate atlas-level organoid scRNA-seq data, GWAS summary statistics, expression quantitative trait loci, and gene-drug interaction data for distinguishing critical cell populations and drug targets relevant to coronavirus disease 2019 (COVID-19) severity. We found that 39 cell types across eight kinds of organoids were significantly associated with COVID-19 outcomes. Notably, subset of lung mesenchymal stem cells increased proximity with fibroblasts predisposed to repair COVID-19-damaged lung tissue. Brain endothelial cell subset exhibited significant associations with severe COVID-19, and this cell subset showed a notable increase in cell-to-cell interactions with other brain cell types, including microglia. We repurposed 33 druggable genes, including IFNAR2, TYK2, and VIPR2, and their interacting drugs for COVID-19 in a cell-type-specific manner. Overall, our results showcase that host genetic determinants have cellular-specific contribution to COVID-19 severity, and identification of cell type-specific drug targets may facilitate to develop effective therapeutics for treating severe COVID-19 and its complications.

DeepGraFT: A novel semantic segmentation auxiliary ROI-based deep learning framework for effective fundus tessellation classification.

Fundus tessellation (FT) is a prevalent clinical feature associated with myopia and has implications in the development of myopic maculopathy, which causes irreversible visual impairment. Accurate classification of FT in color fundus photo can help predict the disease progression and prognosis. However, the lack of precise detection and classification tools has created an unmet medical need, underscoring the importance of exploring the clinical utility of FT. Thus, to address this gap, we introduce an automatic FT grading system (called DeepGraFT) using classification-and-segmentation co-decision models by deep learning. ConvNeXt, utilizing transfer learning from pretrained ImageNet weights, was employed for the classification algorithm, aligning with a region of interest based on the ETDRS grading system to boost performance. A segmentation model was developed to detect FT exits, complementing the classification for improved grading accuracy. The training set of DeepGraFT was from our in-house cohort (MAGIC), and the validation sets consisted of the rest part of in-house cohort and an independent public cohort (UK Biobank). DeepGraFT demonstrated a high performance in the training stage and achieved an impressive accuracy in validation phase (in-house cohort: 86.85 %; public cohort: 81.50 %). Furthermore, our findings demonstrated that DeepGraFT surpasses machine learning-based classification models in FT classification, achieving a 5.57 % increase in accuracy. Ablation analysis revealed that the introduced modules significantly enhanced classification effectiveness and elevated accuracy from 79.85 % to 86.85 %. Further analysis using the results provided by DeepGraFT unveiled a significant negative association between FT and spherical equivalent (SE) in the UK Biobank cohort. In conclusion, DeepGraFT accentuates potential benefits of the deep learning model in automating the grading of FT and allows for potential utility as a clinical-decision support tool for predicting progression of pathological myopia.

Association of mitochondrial DNA variation with high myopia in a Han Chinese population.

High myopia (HM), which is characterized by oxidative stress, is one of the leading causes of visual impairment and blindness across the world. Family and population genetic studies have uncovered nuclear-genome variants in proteins functioned in the mitochondria. However, whether mitochondrial DNA mutations are involved in HM remains unexplored. Here, we performed the first large-scale whole-mitochondrial genome study in 9613 HM cases and 9606 control subjects of Han Chinese ancestry for identifying HM-associated mitochondrial variants. The single-variant association analysis identified nine novel genetic variants associated with HM reaching the entire mitochondrial wide significance level, including rs370378529 in ND2 with an odds ratio (OR) of 5.25. Interestingly, eight out of nine variants were predominantly located in related sub-haplogroups, i.e. m.5261G > A in B4b1c, m.12280A > G in G2a4, m.7912G > A in D4a3b, m.94G > A in D4e1, m.14857 T > C in D4e3, m.14280A > G in D5a2, m.16272A > G in G2a4, m.8718A > G in M71 and F1a3, indicating that the sub-haplogroup background can increase the susceptible risk for high myopia. The polygenic risk score analysis of the target and validation cohorts indicated a high accuracy for predicting HM with mtDNA variants (AUC = 0.641). Cumulatively, our findings highlight the critical roles of mitochondrial variants in untangling the genetic etiology of HM.

Dynamic order Markov model for categorical sequence clustering.

Markov models are extensively used for categorical sequence clustering and classification due to their inherent ability to capture complex chronological dependencies hidden in sequential data. Existing Markov models are based on an implicit assumption that the probability of the next state depends on the preceding context/pattern which is consist of consecutive states. This restriction hampers the models since some patterns, disrupted by noise, may be not frequent enough in a consecutive form, but frequent in a sparse form, which can not make use of the information hidden in the sequential data. A sparse pattern corresponds to a pattern in which one or some of the state(s) between the first and last one in the pattern is/are replaced by wildcard(s) that can be matched by a subset of values in the state set. In this paper, we propose a new model that generalizes the conventional Markov approach making it capable of dealing with the sparse pattern and handling the length of the sparse patterns adaptively, i.e. allowing variable length pattern with variable wildcards. The model, named Dynamic order Markov model (DOMM), allows deriving a new similarity measure between a sequence and a set of sequences/cluster. DOMM builds a sparse pattern from sub-frequent patterns that contain significant statistical information veiled by the noise. To implement DOMM, we propose a sparse pattern detector (SPD) based on the probability suffix tree (PST) capable of discovering both sparse and consecutive patterns, and then we develop a divisive clustering algorithm, named DMSC, for Dynamic order Markov model for categorical sequence clustering. Experimental results on real-world datasets demonstrate the promising performance of the proposed model.

Facile synthesis and photophysical properties of sphere-square shape amphiphiles based on porphyrin-[60]fullerene conjugates.

Molecules constructed from a combination of zero-dimensional ([60]fullerene (C60)) and two-dimensional (porphyrin (Por)) nanobuilding blocks represent an intriguing category of sphere-square "shape amphiphiles". These sphere-square shape amphiphiles possess interesting optoelectronic properties. To efficiently synthesize a large variety of C60-Por shape amphiphiles, a facile route based on Steglich esterification was developed. The synthetic strategy enables the preparation of hydroxy-functionalized Por precursors (9-12) with high purity in a one-pot procedure. All of the C60-Por shape amphiphiles (1-5) can be readily synthesized in good yields through subsequent Steglich esterification with a highly soluble carboxylic acid derivative of methanofullerene (13). Photophysical studies indicated weak electronic coupling between the C60 and Por moieties and suggest an edge-to-face alignment for the moieties. The fluorescence of electronically excited Por portions of each amphiphile was efficiently quenched, which was indicative of electron transfer from (1)Por to the C60 group(s). Increasing the number of C60 groups on the shape amphiphiles led to more pronounced quenching of the Por fluorescence, which indicated the potential for more effective generation of charge-separated species, C60(-.)Por(+.), from the photoexcited C60-Por shape amphiphiles.

Metabolism and disposition of [14C]BMS-690514, an ErbB/vascular endothelial growth factor receptor inhibitor, after oral administration to humans.

(3R,4R)-4-Amino-1-((4-((3-methoxyphenyl)amino)pyrrolo[2,1-f][1,2,4]triazin-5-yl)methyl)-3-piperidinol (BMS-690514), an oral selective inhibitor of human epidermal growth factor receptors 1 (or epidermal growth factor receptor), 2, and 4, and vascular endothelial growth factor receptors 1, 2, and 3, is being developed as a treatment for patients with non-small-cell lung cancer and metastatic breast cancer. The disposition of [(14)C]BMS-690514 was investigated in nine healthy male subjects (group 1, n = 6; group 2, n = 3) after oral administration of a 200-mg dose. Urine, feces, and plasma were collected from all subjects for up to 12 days postdose. In group 2 subjects, bile was collected from 3 to 8 h postdose. Across groups, approximately 50 and 34% of administered radioactivity was recovered in the feces and urine, respectively. An additional 16% was recovered in the bile of group 2 subjects. Less than 28% of the dose was recovered as parent drug in the combined excreta, suggesting that BMS-690514 was highly metabolized. BMS-690514 was rapidly absorbed (median time of maximum observed concentration 0.5 h) with the absorbed fraction estimated to be approximately 50 to 68%. BMS-690514 represented ≤7.9% of the area under the concentration-time curve from time 0 extrapolated to infinite time of plasma radioactivity, indicating that the majority of the circulating radioactivity was from metabolites. BMS-690514 was metabolized via multiple oxidation reactions and direct glucuronidation. Circulating metabolites included a hydroxylated rearrangement product (M1), a direct ether glucuronide (M6), and multiple secondary glucuronide conjugates. None of these metabolites is expected to contribute to the pharmacology of BMS-690514. In summary, BMS-690514 was well absorbed and extensively metabolized via multiple metabolic pathways in humans, with excretion of drug-related radioactivity in both bile and urine.

Metabolism and disposition of [14C]BMS-690514 after oral administration to rats, rabbits, and dogs.

(3R,4R)-4-Amino-1-((4-((3-methoxyphenyl)amino)pyrrolo[2,1-f] [1,2,4]triazin-5-yl)methyl)-3-piperidinol (BMS-690514) is a potent inhibitor of human epidermal growth factor receptors 1, 2, and 4 and vascular endothelial growth factor receptors 1 through 3. BMS-690514 is an oral oncologic agent currently being developed for the treatment of patients with advanced non-small cell lung cancer and breast cancer. In this investigation, a series of studies was conducted to determine the biotransformation of [(14)C]BMS-690514 after oral administration to rats, rabbits, and dogs. After administration of a single oral dose of [(14)C]BMS-690514 to rats and dogs, the majority of the radioactive dose (61-71%) was recovered in the feces, whereas 18 to 20% was eliminated in urine. In bile duct-cannulated rats, 83 and 17% of the administered radioactivity was recovered in the bile and urine, respectively, suggesting that biliary secretion was a major route for the elimination of BMS-690514-derived radioactivity in rats. The parent compound underwent extensive metabolism in both species, with <12% of the administered radioactivity recovered as BMS-690514 in the excreta samples. Metabolite profiles in plasma were qualitatively similar in rats, rabbits, and dogs. Unchanged BMS-690514 was a prominent drug-related component in the plasma profiles from all the species. However, multiple metabolites contributed significantly to the circulating radioactivity, particularly for rabbit and dog, in which metabolites comprised 73 to 93% of the area under the time curve (0-8 h). Circulating metabolites included M6, a direct O-glucuronide conjugate; M1, a hydroxylated metabolite; and glucuronide conjugates of hydroxylated and O-demethylated metabolites. Overall, the results from these studies suggested that BMS-690514 was well absorbed and highly metabolized through multiple pathways in these preclinical species.