FAT1 inhibits AML autophagy and proliferation via downregulating ATG4B expression.

BACKGROUND: Emerging studies have shown that FAT atypical cadherin 1 (FAT1) and autophagy separately inhibits and promotes acute myeloid leukemia (AML) proliferation. However, it is unknown whether FAT1 were associated with autophagy in regulating AML proliferation. METHODS: AML cell lines, 6-week-old male nude mice and AML patient samples were used in this study. qPCR/Western blot and cell viability/3H-TdR incorporation assays were separately used to detect mRNA/protein levels and cell activity/proliferation. Luciferase reporter assay was used to examine gene promoter activity. Co-IP analysis was used to detect the binding of proteins. RESULTS: In this study, we for the first time demonstrated that FAT1 inhibited AML proliferation by decreasing AML autophagy level. Moreover, FAT1 weakened AML autophagy level via decreasing autophagy related 4B (ATG4B) expression. Mechanistically, we found that FAT1 reduced the phosphorylated and intranuclear SMAD family member 2/3 (smad2/3) protein levels, thus decreasing the activity of ATG4B gene promoter. Furthermore, we found that FAT1 competitively bound to TGF-ßR II which decreased the binding of TGF-ßR II to TGF-ßR I and the subsequent phosphorylation of TGF-ßR I, thus reducing the phosphorylation and intranuclear smad2/3. The experiments in nude mice showed that knockdown of FAT1 promoted AML autophagy and proliferation in vivo. CONCLUSIONS: Collectively, these results revealed that FAT1 downregulates ATG4B expression via inhibiting TGFß-smad2/3 signaling activity, thus decreasing the autophagy level and proliferation activity of AML cells. GENERAL SIGNIFICANCE: Our study suggested that the "FAT1-TGFß-smad2/3-ATG4B-autophagy" pathway may be a novel target for developing new targeted drugs to AML treatment.

[Spatio-Temporal Characteristics and Potential Source Areas of Seasonal Atmospheric Pollution in Shijiazhuang].

In order to systematically study the transmission characteristics of seasonal and typical pollutants in Shijiazhuang, hourly data of ground-level pollutants(PM2.5, PM10, O3, NO2, SO2, and CO) from 46 state-and provincial-controlled stations, and meteorological(temperature, humidity, and wind speed) data from 17 counties in Shijiazhuang City from December 2018 to November 2019 was analyzed. The interpolation(IDW) and correlation analysis were applied to seasonal and temporal spatial patterns of pollutant concentration. The backward trajectories analysis was performed to explore the seasonal transmission pattern and potential source areas of pollution in Shijiazhuang by combining with the global data assimilation system(GDAS). The results indicate that the different seasons have characteristic pollutants, as follows:spring(PM10, 48.91%), summer(O3, 81.97%), autumn(PM10 and PM2.5, 47.54% and 32.79%), and winter(PM2.5, 74.44%), which are related to the variation of meteorological conditions. Furthermore, the PM10(spring) concentration correlated negatively with the wind speed, presenting a high distribution in the northwest and low in the southeast, with a southerly transmission direction(53.32%). Central and southern Hebei, central and northern Henan, and central Shanxi are the potential sources of pollution(WPCWTij ≥ 160 µg·m-3), impacting western Shandong and northwest Shanxi(WPSCFij ≥ 0.3) with PM10. Moreover, the O3(summer) concentration correlated positively with temperature, and negatively with humidity. The southeast-south(54.24%) is the source direction of the transmission, and the potential source of O3 pollution is an arc area with Shijiazhuang in the center and Cangzhou and Heze as the double wings. Lastly, the PM2.5(autumn and winter) concentration correlated positively with humidity, and the winter concentration shows an increasing gradient from west to east. The trajectories of PM2.5 clustered the source directions:autumn(northeast-southeast, 74.75%), winter(northwest, 55.47%); central and southern Hebei, central and western Shanxi and northern Henan are the concentrated sources of potential pollution(WPCWTij ≥ 180 µg·m-3).

An efficient computer-aided structural elucidation strategy for mixtures using an iterative dynamic programming algorithm.

The identification of chemical structures in natural product mixtures is an important task in drug discovery but is still a challenging problem, as structural elucidation is a time-consuming process and is limited by the available mass spectra of known natural products. Computer-aided structure elucidation (CASE) strategies seek to automatically propose a list of possible chemical structures in mixtures by utilizing chromatographic and spectroscopic methods. However, current CASE tools still cannot automatically solve structures for experienced natural product chemists. Here, we formulated the structural elucidation of natural products in a mixture as a computational problem by extending a list of scaffolds using a weighted side chain list after analyzing a collection of 243,130 natural products and designed an efficient algorithm to precisely identify the chemical structures. The complexity of such a problem is NP-complete. A dynamic programming (DP) algorithm can solve this NP-complete problem in pseudo-polynomial time after converting floating point molecular weights into integers. However, the running time of the DP algorithm degrades exponentially as the precision of the mass spectrometry experiment grows. To ideally solve in polynomial time, we proposed a novel iterative DP algorithm that can quickly recognize the chemical structures of natural products. By utilizing this algorithm to elucidate the structures of four natural products that were experimentally and structurally determined, the algorithm can search the exact solutions, and the time performance was shown to be in polynomial time for average cases. The proposed method improved the speed of the structural elucidation of natural products and helped broaden the spectrum of available compounds that could be applied as new drug candidates. A web service built for structural elucidation studies is freely accessible via the following link ( http://csccp.cmdm.tw/ ).

Rule-based classification models of molecular autofluorescence.

Fluorescence-based detection has been commonly used in high-throughput screening (HTS) assays. Autofluorescent compounds, which can emit light in the absence of artificial fluorescent markers, often interfere with the detection of fluorophores and result in false positive signals in these assays. This interference presents a major issue in fluorescence-based screening techniques. In an effort to reduce the time and cost that will be spent on prescreening of autofluorescent compounds, in silico autofluorescence prediction models were developed for selected fluorescence-based assays in this study. Five prediction models were developed based on the respective fluorophores used in these HTS assays, which absorb and emit light at specific wavelengths (excitation/emission): Alexa Fluor 350 (A350) (340 nm/450 nm), 7-amino-4-trifluoromethyl-coumarin (AFC) (405 nm/520 nm), Alexa Fluor 488 (A488) (480 nm/540 nm), Rhodamine (547 nm/598 nm), and Texas Red (547 nm/618 nm). The C5.0 rule-based classification algorithm and PubChem 2D chemical structure fingerprints were used to develop prediction models. To optimize the accuracies of these prediction models despite the highly imbalanced ratio of fluorescent versus nonfluorescent compounds presented in the collected data sets, oversampling and undersampling strategies were applied. The average final accuracy achieved for the training set was 97%, and that for the testing set was 92%. In addition, five external data sets were used to further validate the models. Ultimately, 14 representative structural features (or rules) were determined to efficiently predict autofluorescence in data sets containing both fluorescent and nonfluorescent compounds. Several cases were illustrated in this study to demonstrate the applicability of these rules.

A comprehensive support vector machine binary hERG classification model based on extensive but biased end point hERG data sets.

The human ether-a-go-go related gene (hERG) potassium ion channel plays a key role in cardiotoxicity and is therefore a key target as part of preclinical drug discovery toxicity screening. The PubChem hERG Bioassay data set, composed of 1668 compounds, was used to construct an in silico screening model. The corresponding trial models were constructed from a descriptor pool composed of 4D fingerprints (4D-FP) and traditional 2D and 3D VolSurf-like molecular descriptors. A final binary classification model was constructed via a support vector machine (SVM). The resultant model was then validated using the PubChem hERG Bioassay data set (AID 376) and an external hERG data set by evaluating the model's ability to determine hERG blockers from nonblockers. The external data set (the test set) consisted of 356 compounds collected from available literature data and consisting of 287 actives and 69 inactives. Four different sampling protocols and a 10-fold cross-correlation analysis--used in the validation process to evaluate classification models--explored the impact of the active--inactive data imbalance distribution of the PubChem high-throughput data set. Four different data sets were explored, and the one employing Lipinski's rule-of-five coupled with measures of relative molecular lipophilicity performed the best in the 10-fold cross-correlation validation of the training data set as well as overall prediction accuracy of the external test sets. The linear SVM binary classification model building strategy was applied to different combinations of MOE (traditional 2D, "21/2D", and 3D VolSurf-like) and 4D-FP molecular descriptors to further explore and refine previously proposed key descriptors, identify new significant features that contribute to the prediction of hERG toxicity, and construct the optimal SVM binary classification model from a shrunken descriptor pool. The accuracy, sensitivity, and specificity of the best model determined from 10-fold cross-validation are 95, 90, and 96%, respectively; the overall accuracy is near 87% for the external set. The models constructed in this study demonstrate the following: (i) robustness based upon performance in accuracy across the structural diversity of the training set, (ii) ability to predict a compound's "predisposition" to block hERG ion channels, and (iii) define and illustrate structural features that can be overlaid onto the chemical structures to aid in the 3D structure-activity interpretation of the hERG blocking effect.

In silico binary classification QSAR models based on 4D-fingerprints and MOE descriptors for prediction of hERG blockage.

Blockage of the human ether-a-go-go related gene (hERG) potassium ion channel is a major factor related to cardiotoxicity. Hence, drugs binding to this channel have become an important biological end point in side effects screening. A set of 250 structurally diverse compounds screened for hERG activity from the literature was assembled using a set of reliability filters. This data set was used to construct a set of two-state hERG QSAR models. The descriptor pool used to construct the models consisted of 4D-fingerprints generated from the thermodynamic distribution of conformer states available to a molecule, 204 traditional 2D descriptors and 76 3D VolSurf-like descriptors computed using the Molecular Operating Environment (MOE) software. One model is a continuous partial least-squares (PLS) QSAR hERG binding model. Another related model is an optimized binary classification QSAR model that classifies compounds as active or inactive. This binary model achieves 91% accuracy over a large range of molecular diversity spanning the training set. Two external test sets were constructed. One test set is the condensed PubChem bioassay database containing 876 compounds, and the other test set consists of 106 additional compounds found in the literature. Both of the test sets were used to validate the binary QSAR model. The binary QSAR model permits a structural interpretation of possible sources for hERG activity. In particular, the presence of a polar negative group at a distance of 6-8 A from a hydrogen bond donor in a compound is predicted to be a quite structure-specific pharmacophore that increases hERG blockage. Since a data set of high chemical diversity was used to construct the binary model, it is applicable for performing general virtual hERG screening.