Identification of Active Molecules against Thrombocytopenia through Machine Learning.

Thrombocytopenia, which is associated with thrombopoietin (TPO) deficiency, presents very limited treatment options and can lead to life-threatening complications. Discovering new therapeutic agents against thrombocytopenia has proven to be a challenging task using traditional screening approaches. Fortunately, machine learning (ML) techniques offer a rapid avenue for exploring chemical space, thereby increasing the likelihood of uncovering new drug candidates. In this study, we focused on computational modeling for drug-induced megakaryocyte differentiation and platelet production using ML methods, aiming to gain insights into the structural characteristics of hematopoietic activity. We developed 112 different classifiers by combining eight ML algorithms with 14 molecule features. The top-performing model achieved good results on both 5-fold cross-validation (with an accuracy of 81.6% and MCC value of 0.589) and external validation (with an accuracy of 83.1% and MCC value of 0.642). Additionally, by leveraging the Shapley additive explanations method, the best model provided quantitative assessments of molecular properties and structures that significantly contributed to the predictions. Furthermore, we employed an ensemble strategy to integrate predictions from multiple models and performed in silico predictions for new molecules with potential activity against thrombocytopenia, sourced from traditional Chinese medicine and the Drug Repurposing Hub. The findings of this study could offer valuable insights into the structural characteristics and computational prediction of thrombopoiesis inducers.

The role of platelet desialylation as a biomarker in primary immune thrombocytopenia: mechanisms and therapeutic perspectives.

Primary immune thrombocytopenia (ITP) is an acquired autoimmune disorder characterized by the destruction of platelets. Although it was long believed that the critical role of autoantibodies in platelet destruction, primarily through the Fc-dependent platelet clearance pathway, recent findings indicate that the significance of the Fc-independent platelet clearance pathway mediated by hepatocytes, thus shedding light on a previously obscure aspect of ITP pathogenesis. Within this context, the desialylation of platelets has emerged as a pivotal biochemical marker. Consequently, targeting platelet desialylation emerges as a novel therapeutic strategy in the pathogenesis of ITP. Notably, prevailing research has largely focused on antiplatelet antibodies and the glycosylation-associated mechanisms of platelet clearance, while comprehensive analysis of platelet desialylation remains scant. In response, we retrospectively discuss the historical progression, inducing factors, generation process, and molecular regulatory mechanisms underlying platelet desialylation in ITP pathogenesis. By systematically evaluating the most recent research findings, we contribute to a comprehensive understanding of the intricate processes involved. Moreover, our manuscript delves into the potential application of desialylation regulatory strategies in ITP therapy, heralding novel therapeutic avenues. In conclusion, this manuscript not only fills a critical void in existing literature but also paves the way for future research by establishing a systematic theoretical framework. By inspiring new research ideas and offering insights into the development of new therapeutic strategies and targeted drugs, our study is poised to significantly advance the clinical management of ITP.

Correction: Yi et al. An Innovative Inducer of Platelet Production, Isochlorogenic Acid A, Is Uncovered through the Application of Deep Neural Networks. Biomolecules 2024, 14, 267.

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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.

De Novo Elastin Assembly Alleviates Development of Supravalvular Aortic Stenosis-Brief Report.

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.

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.

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.

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.

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.

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.

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.