De Novo Elastin Assembly Alleviates Development of Supravalvular Aortic Stenosis.

BACKGROUND: A series of incurable cardiovascular disorders arise due to improper formation of elastin during development. Supravalvular aortic stenosis (SVAS), resulting from a haploinsufficiency of ELN, is caused by improper stress sensing by medial vascular smooth muscle cells, leading to progressive luminal occlusion and heart failure. SVAS remains incurable, as current therapies do not address the root issue of defective elastin. METHODS: We use SVAS here as a model of vascular proliferative disease using both human induced pluripotent stem cell-derived vascular smooth muscle cells and developmental Eln± mouse models to establish de novo elastin assembly as a new therapeutic intervention. RESULTS: We demonstrate mitigation of vascular proliferative abnormalities following de novo extracellular elastin assembly through the addition of the polyphenol epigallocatechin gallate to SVAS human induced pluripotent stem cell-derived vascular smooth muscle cells and in utero to Eln± mice. CONCLUSIONS: We demonstrate de novo elastin deposition normalizes SVAS human induced pluripotent stem cell-derived vascular smooth muscle cell hyperproliferation and rescues hypertension and aortic mechanics in Eln± mice, providing critical preclinical findings for the future application of epigallocatechin gallate treatment in humans.

miR-133a and miR-135a Regulate All-Trans Retinoic Acid-Mediated Differentiation in Pediatric Acute Myeloid Leukemia by Inhibiting CDX2 Translation and Serve as Prognostic Biomarkers.

Background: Acute myeloid leukemia (AML) is a type of blood cancer characterized by excessive growth of immature myeloid cells. Unfortunately, the prognosis of pediatric AML remains unfavorable. It is imperative to further our understanding of the mechanisms underlying leukemogenesis and explore innovative therapeutic approaches to enhance overall disease outcomes for patients with this condition. Methods: Quantitative reverse-transcription PCR was used to quantify the expression levels of microRNA (miR)-133a and miR-135a in 68 samples from 59 pediatric patients with AML. Dual-luciferase reporter transfection assay, Cell Counting Kit-8 assay, and western blot analysis were used to investigate the functions of miR-133a and miR-135a. Results: Our study found that all-trans-retinoic acid (ATRA) promoted the expression of miR-133a and miR-135a in AML cells, inhibited caudal type homeobox 2 (CDX2) expression, and subsequently inhibited the proliferation of AML cells. Additionally, miR-133a and miR-135a were highly expressed in patients with complete remission and those with better survival. Conclusions: miR-133a and miR-135a may play an antioncogenic role in pediatric AML through the ATRA-miRNA133a/135a-CDX2 pathway. They hold promise as potentially favorable prognostic indicators and novel therapeutic targets for pediatric AML.

Activating SRC/MAPK signaling via 5-HT1A receptor contributes to the effect of vilazodone on improving thrombocytopenia.

Thrombocytopenia caused by long-term radiotherapy and chemotherapy exists in cancer treatment. Previous research demonstrates that 5-Hydroxtrayptamine (5-HT) and its receptors induce the formation of megakaryocytes (MKs) and platelets. However, the relationships between 5-HT1A receptor (5-HTR1A) and MKs is unclear so far. We screened and investigated the mechanism of vilazodone as a 5-HTR1A partial agonist in promoting MK differentiation and evaluated its therapeutic effect in thrombocytopenia. We employed a drug screening model based on machine learning (ML) to screen the megakaryocytopoiesis activity of Vilazodone (VLZ). The effects of VLZ on megakaryocytopoiesis were verified in HEL and Meg-01 cells. Tg (itga2b: eGFP) zebrafish was performed to analyze the alterations in thrombopoiesis. Moreover, we established a thrombocytopenia mice model to investigate how VLZ administration accelerates platelet recovery and function. We carried out network pharmacology, Western blot, and immunofluorescence to demonstrate the potential targets and pathway of VLZ. VLZ has been predicted to have a potential biological action. Meanwhile, VLZ administration promotes MK differentiation and thrombopoiesis in cells and zebrafish models. Progressive experiments showed that VLZ has a potential therapeutic effect on radiation-induced thrombocytopenia in vivo. The network pharmacology and associated mechanism study indicated that SRC and MAPK signaling are both involved in the processes of megakaryopoiesis facilitated by VLZ. Furthermore, the expression of 5-HTR1A during megakaryocyte differentiation is closely related to the activation of SRC and MAPK. Our findings demonstrated that the expression of 5-HTR1A on MK, VLZ could bind to the 5-HTR1A receptor and further regulate the SRC/MAPK signaling pathway to facilitate megakaryocyte differentiation and platelet production, which provides new insights into the alternative therapeutic options for thrombocytopenia.

Identifying diagnostic indicators for type 2 diabetes mellitus from physical examination using interpretable machine learning approach.

Background: Identification of patients at risk for type 2 diabetes mellitus (T2DM) can not only prevent complications and reduce suffering but also ease the health care burden. While routine physical examination can provide useful information for diagnosis, manual exploration of routine physical examination records is not feasible due to the high prevalence of T2DM. Objectives: We aim to build interpretable machine learning models for T2DM diagnosis and uncover important diagnostic indicators from physical examination, including age- and sex-related indicators. Methods: In this study, we present three weighted diversity density (WDD)-based algorithms for T2DM screening that use physical examination indicators, the algorithms are highly transparent and interpretable, two of which are missing value tolerant algorithms. Patients: Regarding the dataset, we collected 43 physical examination indicator data from 11,071 cases of T2DM patients and 126,622 healthy controls at the Affiliated Hospital of Southwest Medical University. After data processing, we used a data matrix containing 16004 EHRs and 43 clinical indicators for modelling. Results: The indicators were ranked according to their model weights, and the top 25% of indicators were found to be directly or indirectly related to T2DM. We further investigated the clinical characteristics of different age and sex groups, and found that the algorithms can detect relevant indicators specific to these groups. The algorithms performed well in T2DM screening, with the highest area under the receiver operating characteristic curve (AUC) reaching 0.9185. Conclusion: This work utilized the interpretable WDD-based algorithms to construct T2DM diagnostic models based on physical examination indicators. By modeling data grouped by age and sex, we identified several predictive markers related to age and sex, uncovering characteristic differences among various groups of T2DM patients.

Model fusion for predicting unconventional proteins secreted by exosomes using deep learning.

Unconventional secretory proteins (USPs) are vital for cell-to-cell communication and are necessary for proper physiological processes. Unlike classical proteins that follow the conventional secretory pathway via the Golgi apparatus, these proteins are released using unconventional pathways. The primary modes of secretion for USPs are exosomes and ectosomes, which originate from the endoplasmic reticulum. Accurate and rapid identification of exosome-mediated secretory proteins is crucial for gaining valuable insights into the regulation of non-classical protein secretion and intercellular communication, as well as for the advancement of novel therapeutic approaches. Although computational methods based on amino acid sequence prediction exist for predicting unconventional proteins secreted by exosomes (UPSEs), they suffer from significant limitations in terms of algorithmic accuracy. In this study, we propose a novel approach to predict UPSEs by combining multiple deep learning models that incorporate both protein sequences and evolutionary information. Our approach utilizes a convolutional neural network (CNN) to extract protein sequence information, while various densely connected neural networks (DNNs) are employed to capture evolutionary conservation patterns.By combining six distinct deep learning models, we have created a superior framework that surpasses previous approaches, achieving an ACC score of 77.46% and an MCC score of 0.5406 on an independent test dataset.

Alnustone promotes megakaryocyte differentiation and platelet production via the interleukin-17A/interleukin-17A receptor/Src/RAC1/MEK/ERK signaling pathway.

OBJECTIVES: Thrombocytopenia is a disease in which the number of platelets in the peripheral blood decreases. It can be caused by multiple genetic factors, and numerous challenges are associated with its treatment. In this study, the effects of alnustone on megakaryocytes and platelets were investigated, with the aim of developing a new therapeutic approach for thrombocytopenia. METHODS: Random forest algorithm was used to establish a drug screening model, and alnustone was identified as a natural active compound that could promote megakaryocyte differentiation. The effect of alnustone on megakaryocyte activity was determined using cell counting kit-8. The effect of alnustone on megakaryocyte differentiation was determined using flow cytometry, Giemsa staining, and phalloidin staining. A mouse model of thrombocytopenia was established by exposing mice to X-rays at 4 Gy and was used to test the bioactivity of alnustone in vivo. The effect of alnustone on platelet production was determined using zebrafish. Network pharmacology was used to predict targets and signaling pathways. Western blotting and immunofluorescence staining determined the expression levels of proteins. RESULTS: Alnustone promoted the differentiation and maturation of megakaryocytes in vitro and restored platelet production in thrombocytopenic mice and zebrafish. Network pharmacology and western blotting showed that alnustone promoted the expression of interleukin-17A and enhanced its interaction with its receptor, and thereby regulated downstream MEK/ERK signaling and promoted megakaryocyte differentiation. CONCLUSIONS: Alnustone can promote megakaryocyte differentiation and platelet production via the interleukin-17A/interleukin-17A receptor/Src/RAC1/MEK/ERK signaling pathway and thus provides a new therapeutic strategy for the treatment of thrombocytopenia.

Genistin: A Novel Estrogen Analogue Targeting ERß to Alleviate Thrombocytopenia.

Thrombocytopenia, a prevalent hematologic challenge, correlates directly with the mortality of numerous ailments. Current therapeutic avenues for thrombocytopenia are not without limitations. Here, we identify genistin, an estrogen analogue, as a promising candidate for thrombocytopenia intervention, discovered through AI-driven compound library screening. While estrogen's involvement in diverse biological processes is recognized, its role in thrombopoiesis remains underexplored. Our findings elucidate genistin's ability to enhance megakaryocyte differentiation, thereby augmenting platelet formation and production. In vivo assessments further underscore genistin's remedial potential against radiation-induced thrombocytopenia. Mechanistically, genistin's efficacy is attributed to its direct interaction with estrogen receptor ß (ERß), with subsequent activation of both ERK1/2 and the Akt signaling pathways membrane ERß. Collectively, our study positions genistin as a prospective therapeutic strategy for thrombocytopenia, shedding light on novel interplays between platelet production and ERß.

An Innovative Inducer of Platelet Production, Isochlorogenic Acid A, Is Uncovered through the Application of Deep Neural Networks.

(1) Background: Radiation-induced thrombocytopenia (RIT) often occurs in cancer patients undergoing radiation therapy, which can result in morbidity and even death. However, a notable deficiency exists in the availability of specific drugs designed for the treatment of RIT. (2) Methods: In our pursuit of new drugs for RIT treatment, we employed three deep learning (DL) algorithms: convolutional neural network (CNN), deep neural network (DNN), and a hybrid neural network that combines the computational characteristics of the two. These algorithms construct computational models that can screen compounds for drug activity by utilizing the distinct physicochemical properties of the molecules. The best model underwent testing using a set of 10 drugs endorsed by the US Food and Drug Administration (FDA) specifically for the treatment of thrombocytopenia. (3) Results: The Hybrid CNN+DNN (HCD) model demonstrated the most effective predictive performance on the test dataset, achieving an accuracy of 98.3% and a precision of 97.0%. Both metrics surpassed the performance of the other models, and the model predicted that seven FDA drugs would exhibit activity. Isochlorogenic acid A, identified through screening the Chinese Pharmacopoeia Natural Product Library, was subsequently subjected to experimental verification. The results indicated a substantial enhancement in the differentiation and maturation of megakaryocytes (MKs), along with a notable increase in platelet production. (4) Conclusions: This underscores the potential therapeutic efficacy of isochlorogenic acid A in addressing RIT.

Interleukins in Platelet Biology: Unraveling the Complex Regulatory Network.

Interleukins, a diverse family of cytokines produced by various cells, play crucial roles in immune responses, immunoregulation, and a wide range of physiological and pathological processes. In the context of megakaryopoiesis, thrombopoiesis, and platelet function, interleukins have emerged as key regulators, exerting significant influence on the development, maturation, and activity of megakaryocytes (MKs) and platelets. While the therapeutic potential of interleukins in platelet-related diseases has been recognized for decades, their clinical application has been hindered by limitations in basic research and challenges in drug development. Recent advancements in understanding the molecular mechanisms of interleukins and their interactions with MKs and platelets, coupled with breakthroughs in cytokine engineering, have revitalized the field of interleukin-based therapeutics. These breakthroughs have paved the way for the development of more effective and specific interleukin-based therapies for the treatment of platelet disorders. This review provides a comprehensive overview of the effects of interleukins on megakaryopoiesis, thrombopoiesis, and platelet function. It highlights the potential clinical applications of interleukins in regulating megakaryopoiesis and platelet function and discusses the latest bioengineering technologies that could improve the pharmacokinetic properties of interleukins. By synthesizing the current knowledge in this field, this review aims to provide valuable insights for future research into the clinical application of interleukins in platelet-related diseases.

The Potential Transcriptomic and Metabolomic Mechanisms of ATO and ATRA in Treatment of FLT3-ITD Acute Myeloid Leukemia.

BACKGROUND: Acute myeloid leukemia (AML) with Fms-like tyrosine kinase 3 gene internal tandem duplication (FLT3-ITD) mutations has a poor prognosis. The combination of arsenic trioxide (ATO) and all-trans retinoic acid (ATRA) has a synergistic killing effect on leukemia cells with FLT3-ITD mutation. However, the mechanism, especially the changes of gene expression and metabolic activity remain unclear. Here we explore the transcriptome and metabolomics changes of FLT3-ITD AML cells treated with ATO/ATRA. METHODS: RNA-seq was used to identify differential expressed genes (DEGs), and ultra-high performance liquid chromatography-quadrupole electrostatic field orbital trap mass spectrometry (UHPLC-QE-MS) nontargeted metabolomics method was used to screen out the differential metabolites in FLT3-ITD mutant cell lines treated with ATRA and ATO. KEGG pathway database was utilized for pathway exploration and Seahorse XF24 was used to detect extracellular acidification rate (ECAR). Metabolic polymerase chain reaction (PCR) array and real-time quantitative PCR (RT-qPCR) were used to detect mRNA levels of key metabolic genes of glycolysis and fatty acid after drug treatment. RESULTS: A total of 3873 DEGs were identified and enriched in 281 Gene Ontology (GO) terms, among which 210 were related to biological processes, 43 were related to cellular components, and 28 were related to molecular functions. Besides, 1794 and 927 differential metabolites were screened in positive and negative ion mode separately, and 59 different metabolic pathways were involved, including alanine-aspartate-glutamate metabolic pathway, arginine, and proline metabolic pathway, glycerophospholipid metabolic pathways, etc. According to KEGG Pathway analysis of transcriptome combined with metabolome, glycolysis/gluconeogenesis pathway and fatty acid metabolism pathway were significantly founded enriched. ATRA + ATO may inhibit the glycolysis of FLT3-ITD AML cells by inhibiting FLT3 and its downstream AKT/HK2-VDAC1 signaling pathway. CONCLUSIONS: The gene transcription profile and metabolites of FLT3-ITD mutant cells changes significantly after treatment, which might be related to the anti-FLT3-ITD AML effect. The screened DEGs, differential metabolites pathway are helpful in studying the mechanism of anti-leukemia effects and drug targets.

Gynura divaricata (L.) DC. promotes diabetic wound healing by activating Nrf2 signaling in diabetic rats.

THE ETHNOPHARMACOLOGICAL SIGNIFICANCE: Diabetic chronic foot ulcers pose a significant therapeutic challenge as a result of the oxidative stress caused by hyperglycemia. Which impairs angiogenesis and delays wound healing, potentially leading to amputation. Gynura divaricata (L.) DC. (GD), a traditional Chinese herbal medicine with hypoglycemic effects, has been proposed as a potential therapeutic agent for diabetic wound healing. However, the underlying mechanisms of its effects remain unclear. AIM OF THE STUDY: In this study, we aimed to reveal the effect and potential mechanisms of GD on accelerating diabetic wound healing in vitro and in vivo. MATERIALS AND METHODS: The effects of GD on cell proliferation, apoptosis, reactive oxygen species (ROS) production, migration, mitochondrial membrane potential (MMP), and potential molecular mechanisms were investigated in high glucose (HG) stimulated human umbilical vein endothelial cells (HUVECs) using CCK-8, flow cytometry assay, wound healing assay, immunofluorescence, DCFH-DA staining, JC-1 staining, and Western blot. Full-thickness skin defects were created in STZ-induced diabetic rats, and wound healing rate was tracked by photographing them every day. HE staining, immunohistochemistry, and Western blot were employed to investigate the effect and molecular mechanism of GD on wound healing in diabetic rats. RESULTS: GD significantly improved HUVEC survival, decreased apoptosis, lowered ROS production, restored MMP, improved migration ability, and raised VEGF expression. The use of Nrf2-siRNA completely abrogated these effects. Topical application of GD promoted angiogenesis and granulation tissue growth, resulting in faster healing of diabetic wounds. The expression of VEGF, CD31, and VEGFR was elevated in the skin tissue of diabetic rats after GD treatment, which upregulated HO-1, NQO-1, and Bcl-2 expression while downregulating Bax expression via activation of the Nrf2 signaling pathway. CONCLUSION: The findings of this study indicate that GD has the potential to serve as a viable alternative treatment for diabetic wounds. This potential arises from its ability to mitigate the negative effects of oxidative stress on angiogenesis, which is regulated by the Nrf2 signaling pathway. The results of our study offer valuable insights into the therapeutic efficacy of GD in the treatment of diabetic wounds, emphasizing the significance of directing interventions towards the Nrf2 signaling pathway to mitigate oxidative stress and facilitate the process of angiogenesis.

Long-term outcome of children with acute promyelocytic leukemia: a randomized study of oral versus intravenous arsenic by SCCLG-APL group.

Realgar-Indigo naturalis formula (RIF), an oral traditional Chinese medicine mainly containing Realgar (As4S4), is highly effective in treating adult acute promyelocytic leukemia (APL). However, the treatment efficacy and safety of RIF have not been verified in pediatric patients. SCCLG-APL group conducted a multicenter randomized non-inferiority trial to determine whether intravenous arsenic trioxide (ATO) can be substituted by oral RIF in treating pediatric APL. Of 176 eligible patients enrolled, 91 and 85 were randomized to ATO and RIF groups, respectively. Patients were treated with the risk-adapted protocol. Induction, consolidation, and 96-week maintenance treatment contained all-trans-retinoic acid and low-intensity chemotherapy, and either ATO or RIF. The primary endpoint was 5-year event-free survival (EFS). The secondary endpoints were adverse events and hospital days. After a median 6-year follow-up, the 5-year EFS was 97.6% in both groups. However, the RIF group had significantly shorter hospital stays and lower incidence of infection and tended to have less cardiac toxicity. All 4 relapses occurred within 1.5 years after completion of maintenance therapy. No long-term arsenic retentions were observed in either group. Substituting oral RIF for ATO maintains treatment efficacy while reducing hospitalization and adverse events in treating pediatric APL patients, which may be a future treatment strategy for APL.

EnsembleDL-ATG: Identifying autophagy proteins by integrating their sequence and evolutionary information using an ensemble deep learning framework.

Autophagy is a primary mechanism for maintaining cellular homeostasis. The synergistic actions of autophagy-related (ATG) proteins strictly regulate the whole autophagic process. Therefore, accurate identification of ATGs is a first and critical step to reveal the molecular mechanism underlying the regulation of autophagy. Current computational methods can predict ATGs from primary protein sequences, but owing to the limitations of algorithms, significant room for improvement still exists. In this research, we propose EnsembleDL-ATG, an ensemble deep learning framework that aggregates multiple deep learning models to predict ATGs from protein sequence and evolutionary information. We first evaluated the performance of individual networks for various feature descriptors to identify the most promising models. Then, we explored all possible combinations of independent models to select the most effective ensemble architecture. The final framework was built and maintained by an organization of four different deep learning models. Experimental results show that our proposed method achieves a prediction accuracy of 94.5 % and MCC of 0.890, which are nearly 4 % and 0.08 higher than ATGPred-FL, respectively. Overall, EnsembleDL-ATG is the first ATG machine learning predictor based on ensemble deep learning. The benchmark data and code utilized in this study can be accessed for free at https://github.com/jingry/autoBioSeqpy/tree/2.0/examples/EnsembleDL-ATG.

Evaluation and development of deep neural networks for RNA 5-Methyluridine classifications using autoBioSeqpy.

Post-transcriptionally RNA modifications, also known as the epitranscriptome, play crucial roles in the regulation of gene expression during development. Recently, deep learning (DL) has been employed for RNA modification site prediction and has shown promising results. However, due to the lack of relevant studies, it is unclear which DL architecture is best suited for some pyrimidine modifications, such as 5-methyluridine (m5U). To fill this knowledge gap, we first performed a comparative evaluation of various commonly used DL models for epigenetic studies with the help of autoBioSeqpy. We identified optimal architectural variations for m5U site classification, optimizing the layer depth and neuron width. Second, we used this knowledge to develop Deepm5U, an improved convolutional-recurrent neural network that accurately predicts m5U sites from RNA sequences. We successfully applied Deepm5U to transcriptomewide m5U profiling data across different sequencing technologies and cell types. Third, we showed that the techniques for interpreting deep neural networks, including LayerUMAP and DeepSHAP, can provide important insights into the internal operation and behavior of models. Overall, we offered practical guidance for the development, benchmark, and analysis of deep learning models when designing new algorithms for RNA modifications.

Fast and Efficient Design of Deep Neural Networks for Predicting N7-Methylguanosine Sites Using autoBioSeqpy.

N7-Methylguanosine (m7G) is a crucial post-transcriptional RNA modification that plays a pivotal role in regulating gene expression. Accurately identifying m7G sites is a fundamental step in understanding the biological functions and regulatory mechanisms associated with this modification. While whole-genome sequencing is the gold standard for RNA modification site detection, it is a time-consuming, expensive, and intricate process. Recently, computational approaches, especially deep learning (DL) techniques, have gained popularity in achieving this objective. Convolutional neural networks and recurrent neural networks are examples of DL algorithms that have emerged as versatile tools for modeling biological sequence data. However, developing an efficient network architecture with superior performance remains a challenging task, requiring significant expertise, time, and effort. To address this, we previously introduced a tool called autoBioSeqpy, which streamlines the design and implementation of DL networks for biological sequence classification. In this study, we utilized autoBioSeqpy to develop, train, evaluate, and fine-tune sequence-level DL models for predicting m7G sites. We provided detailed descriptions of these models, along with a step-by-step guide on their execution. The same methodology can be applied to other systems dealing with similar biological questions. The benchmark data and code utilized in this study can be accessed for free at http://github.com/jingry/autoBioSeeqpy/tree/2.0/examples/m7G.

Differentiation syndrome and coagulation disorder - comparison between treatment with oral and intravenous arsenics in pediatric acute promyelocytic leukemia.

Realgar-Indigo naturalis formula (RIF), with A4S4 as a major ingredient, is an oral arsenic used in China to treat pediatric acute promyelocytic leukemia (APL). The efficacy of RIF is similar to that of arsenic trioxide (ATO). However, the effects of these two arsenicals on differentiation syndrome (DS) and coagulation disorders, the two main life-threatening events in children with APL, remain unclear. We retrospectively analyzed 68 consecutive children with APL from South China Children Leukemia Group-APL (SCCLG-APL) study. Patients received all-trans retinoic acid (ATRA) on day 1 of induction therapy. ATO 0.16 mg/kg day or RIF 135 mg/kg·day was administrated on day 5, while mitoxantrone was administered on day 3 (non-high-risk) or days 2-4 (high-risk). The incidences of DS were 3.0% and 5.7% in ATO (n = 33) and RIF (n = 35) arms (p = 0.590), and 10.3% and 0% in patients with and without differentiation-related hyperleukocytosis (p = 0.04), respectively. Moreover, in patients with differentiation-related hyperleukocytosis, the incidence of DS was not significantly different between ATO and RIF arms. The dynamic changes of leukocyte count between arms were not statistically different. However, patients with leukocyte count > 2.61 × 109/L or percentage of promyelocytes in peripheral blood > 26.5% tended to develop hyperleukocytosis. The improvement of coagulation indexes in ATO and RIF arms was similar, with fibrinogen and prothrombin time having the quickest recovery rate. This study showed that the incidence of DS and recovery of coagulopathy are similar when treating pediatric APL with RIF or ATO.

Identification of thrombopoiesis inducer based on a hybrid deep neural network model.

Thrombocytopenia is a common haematological problem worldwide. Currently, there are no relatively safe and effective agents for the treatment of thrombocytopenia. To address this challenge, we propose a computational method that enables the discovery of novel drug candidates with haematopoietic activities. Based on different types of molecular representations, three deep learning (DL) algorithms, namely recurrent neural networks (RNNs), deep neural networks (DNNs), and hybrid neural networks (RNNs+DNNs), were used to develop classification models to distinguish between active and inactive compounds. The evaluation results illustrated that the hybrid DL model exhibited the best prediction performance, with an accuracy of 97.8 % and Matthews correlation coefficient of 0.958 on the test dataset. Subsequently, we performed drug discovery screening based on the hybrid DL model and identified a compound from the FDA-approved drug library that was structurally divergent from conventional drugs and showed a potential therapeutic action against thrombocytopenia. The novel drug candidate wedelolactone significantly promoted megakaryocyte differentiation in vitro and increased platelet levels and megakaryocyte differentiation in irradiated mice with no systemic toxicity. Overall, our work demonstrates how artificial intelligence can be used to discover novel drugs against thrombocytopenia.

A novel method for predicting DNA N4-methylcytosine sites based on deep forest algorithm.

N4-methyladenosine (4mC) methylation is an essential epigenetic modification of deoxyribonucleic acid (DNA) that plays a key role in many biological processes such as gene expression, gene replication and transcriptional regulation. Genome-wide identification and analysis of the 4mC sites can better reveal the epigenetic mechanisms that regulate various biological processes. Although some high-throughput genomic experimental methods can effectively facilitate the identification in a genome-wide scale, they are still too expensive and laborious for routine use. Computational methods can compensate for these disadvantages, but they still leave much room for performance improvement. In this study, we develop a non-NN-style deep learning-based approach for accurately predicting 4mC sites from genomic DNA sequence. We generate various informative features represented sequence fragments around 4mC sites, and subsequently implement them into a deep forest (DF) model. After training the deep model using 10-fold cross-validation, the overall accuracies of 85.0%, 90.0%, and 87.8% were achieved for three representative model organisms, A. thaliana, C. elegans, and D. melanogaster, respectively. In addition, extensive experiment results show that our proposed approach outperforms other existing state-of-the-art predictors in the 4mC identification. Our approach stands for the first DF-based algorithm for the prediction of 4mC sites, providing a novel idea in this field.

Inhibitors of the Oncogenic PA2G4-MYCN Protein-Protein Interface.

MYCN is a major oncogenic driver for neuroblastoma tumorigenesis, yet there are no direct MYCN inhibitors. We have previously identified PA2G4 as a direct protein-binding partner of MYCN and drive neuroblastoma tumorigenesis. A small molecule known to bind PA2G4, WS6, significantly decreased tumorigenicity in TH-MYCN neuroblastoma mice, along with the inhibition of PA2G4 and MYCN interactions. Here, we identified a number of novel WS6 analogues, with 80% structural similarity, and used surface plasmon resonance assays to determine their binding affinity. Analogues #5333 and #5338 showed direct binding towards human recombinant PA2G4. Importantly, #5333 and #5338 demonstrated a 70-fold lower toxicity for normal human myofibroblasts compared to WS6. Structure-activity relationship analysis showed that a 2,3 dimethylphenol was the most suitable substituent at the R1 position. Replacing the trifluoromethyl group on the phenyl ring at the R2 position, with a bromine or hydrogen atom, increased the difference between efficacy against neuroblastoma cells and normal myofibroblast toxicity. The WS6 analogues inhibited neuroblastoma cell phenotype in vitro, in part through effects on apoptosis, while their anti-cancer effects required both PA2G4 and MYCN expression. Collectively, chemical inhibition of PA2G4-MYCN binding by WS6 analogues represents a first-in-class drug discovery which may have implications for other MYCN-driven cancers.