Skilful nowcasting of extreme precipitation with NowcastNet.

Extreme precipitation is a considerable contributor to meteorological disasters and there is a great need to mitigate its socioeconomic effects through skilful nowcasting that has high resolution, long lead times and local details1-3. Current methods are subject to blur, dissipation, intensity or location errors, with physics-based numerical methods struggling to capture pivotal chaotic dynamics such as convective initiation4 and data-driven learning methods failing to obey intrinsic physical laws such as advective conservation5. We present NowcastNet, a nonlinear nowcasting model for extreme precipitation that unifies physical-evolution schemes and conditional-learning methods into a neural-network framework with end-to-end forecast error optimization. On the basis of radar observations from the USA and China, our model produces physically plausible precipitation nowcasts with sharp multiscale patterns over regions of 2,048 km × 2,048 km and with lead times of up to 3 h. In a systematic evaluation by 62 professional meteorologists from across China, our model ranks first in 71% of cases against the leading methods. NowcastNet provides skilful forecasts at light-to-heavy rain rates, particularly for extreme-precipitation events accompanied by advective or convective processes that were previously considered intractable.

CPSF3 inhibition blocks pancreatic cancer cell proliferation through disruption of core histone mRNA processing.

Pancreatic ductal adenocarcinoma (PDAC) is a lethal disease with limited effective treatment options, potentiating the importance of uncovering novel drug targets. Here, we target cleavage and polyadenylation specificity factor 3 (CPSF3), the 3' endonuclease that catalyzes mRNA cleavage during polyadenylation and histone mRNA processing. We find that CPSF3 is highly expressed in PDAC and is associated with poor prognosis. CPSF3 knockdown blocks PDAC cell proliferation and colony formation in vitro and tumor growth in vivo. Chemical inhibition of CPSF3 by the small molecule JTE-607 also attenuates PDAC cell proliferation and colony formation, while it has no effect on cell proliferation of nontransformed immortalized control pancreatic cells. Mechanistically, JTE-607 induces transcriptional readthrough in replication-dependent histones, reduces core histone expression, destabilizes chromatin structure, and arrests cells in the S-phase of the cell cycle. Therefore, CPSF3 represents a potential therapeutic target for the treatment of PDAC.

Dissection of a Down syndrome-associated trisomy to separate the gene dosage-dependent and -independent effects of an extra chromosome.

As an aneuploidy, trisomy is associated with mammalian embryonic and postnatal abnormalities. Understanding the underlying mechanisms involved in mutant phenotypes is broadly important and may lead to new strategies to treat clinical manifestations in individuals with trisomies, such as trisomy 21 [Down syndrome (DS)]. Although increased gene dosage effects because of a trisomy may account for the mutant phenotypes, there is also the possibility that phenotypic consequences of a trisomy can arise because of the presence of a freely segregating extra chromosome with its own centromere, i.e. a 'free trisomy' independent of gene dosage effects. Presently, there are no reports of attempts to functionally separate these two types of effects in mammals. To fill this gap, here we describe a strategy that employed two new mouse models of DS, Ts65Dn;Df(17)2Yey/+ and Dp(16)1Yey/Df(16)8Yey. Both models carry triplications of the same 103 human chromosome 21 gene orthologs; however, only Ts65Dn;Df(17)2Yey/+ mice carry a free trisomy. Comparison of these models revealed the gene dosage-independent impacts of an extra chromosome at the phenotypic and molecular levels for the first time. They are reflected by impairments of Ts65Dn;Df(17)2Yey/+ males in T-maze tests when compared with Dp(16)1Yey/Df(16)8Yey males. Results from the transcriptomic analysis suggest the extra chromosome plays a major role in trisomy-associated expression alterations of disomic genes beyond gene dosage effects. This model system can now be used to deepen our mechanistic understanding of this common human aneuploidy and obtain new insights into the effects of free trisomies in other human diseases such as cancers.

Comparison of cell response to chromatin and DNA damage.

DNA-targeting drugs are widely used for anti-cancer treatment. Many of these drugs cause different types of DNA damage, i.e. alterations in the chemical structure of DNA molecule. However, molecules binding to DNA may also interfere with DNA packing into chromatin. Interestingly, some molecules do not cause any changes in DNA chemical structure but interfere with DNA binding to histones and nucleosome wrapping. This results in histone loss from chromatin and destabilization of nucleosomes, a phenomenon that we call chromatin damage. Although the cellular response to DNA damage is well-studied, the consequences of chromatin damage are not. Moreover, many drugs used to study DNA damage also cause chromatin damage, therefore there is no clarity on which effects are caused by DNA or chromatin damage. In this study, we aimed to clarify this issue. We treated normal and tumor cells with bleomycin, nuclease mimicking drug which cut predominantly nucleosome-free DNA and therefore causes DNA damage in the form of DNA breaks, and CBL0137, which causes chromatin damage without direct DNA damage. We describe similarities and differences between the consequences of DNA and chromatin damage. Both agents were more toxic for tumor than normal cells, but while DNA damage causes senescence in both normal and tumor cells, chromatin damage does not. Both agents activated p53, but chromatin damage leads to the accumulation of higher levels of unmodified p53, which transcriptional activity was similar to or lower than that of p53 activated by DNA damage. Most importantly, we found that while transcriptional changes caused by DNA damage are limited by p53-dependent activation of a small number of p53 targets, chromatin damage activated many folds more genes in p53 independent manner.

Droplet digital PCR: An effective method for monitoring and prognostic evaluation of minimal residual disease in JMML.

Juvenile myelomonocytic leukaemia (JMML) is a rare myeloproliferative neoplasm requiring haematopoietic stem cell transplantation (HSCT) for potential cure. Relapse poses a significant obstacle to JMML HSCT treatment, as the lack of effective minimal residual disease (MRD)-monitoring methods leads to delayed interventions. This retrospective study utilized the droplet digital PCR (ddPCR) technique, a highly sensitive nucleic acid detection and quantification technique, to monitor MRD in 32 JMML patients. The results demonstrated that ddPCR detected relapse manifestations earlier than traditional methods and uncovered molecular insights into JMML MRD dynamics. The findings emphasized a critical 1- to 3-month window post-HSCT for detecting molecular relapse, with 66.7% (8/12) of relapses occurring within this period. Slow MRD clearance post-HSCT was observed, as 65% (13/20) of non-relapse patients took over 6 months to achieve ddPCR-MRD negativity. Furthermore, bone marrow ddPCR-MRD levels at 1-month post-HSCT proved to be prognostically significant. Relapsed patients exhibited significantly elevated ddPCR-MRD levels at this time point (p = 0.026), with a cut-off of 0.465% effectively stratifying overall survival (p = 0.007), event-free survival (p = 0.035) and cumulative incidence of relapse (p = 0.035). In conclusion, this study underscored ddPCR's superiority in JMML MRD monitoring post-HSCT. It provided valuable insights into JMML MRD dynamics, offering guidance for the effective management of JMML.

Molormer: a lightweight self-attention-based method focused on spatial structure of molecular graph for drug-drug interactions prediction.

Multi-drug combinations for the treatment of complex diseases are gradually becoming an important treatment, and this type of treatment can take advantage of the synergistic effects among drugs. However, drug-drug interactions (DDIs) are not just all beneficial. Accurate and rapid identifications of the DDIs are essential to enhance the effectiveness of combination therapy and avoid unintended side effects. Traditional DDIs prediction methods use only drug sequence information or drug graph information, which ignores information about the position of atoms and edges in the spatial structure. In this paper, we propose Molormer, a method based on a lightweight attention mechanism for DDIs prediction. Molormer takes the two-dimension (2D) structures of drugs as input and encodes the molecular graph with spatial information. Besides, Molormer uses lightweight-based attention mechanism and self-attention distilling to process spatially the encoded molecular graph, which not only retains the multi-headed attention mechanism but also reduces the computational and storage costs. Finally, we use the Siamese network architecture to serve as the architecture of Molormer, which can make full use of the limited data to train the model for better performance and also limit the differences to some extent between networks dealing with drug features. Experiments show that our proposed method outperforms state-of-the-art methods in Accuracy, Precision, Recall and F1 on multi-label DDIs dataset. In the case study section, we used Molormer to make predictions of new interactions for the drugs Aliskiren, Selexipag and Vorapaxar and validated parts of the predictions. Code and models are available at https://github.com/IsXudongZhang/Molormer.

De novo molecular design with deep molecular generative models for PPI inhibitors.

We construct a protein-protein interaction (PPI) targeted drug-likeness dataset and propose a deep molecular generative framework to generate novel drug-likeness molecules from the features of the seed compounds. This framework gains inspiration from published molecular generative models, uses the key features associated with PPI inhibitors as input and develops deep molecular generative models for de novo molecular design of PPI inhibitors. For the first time, quantitative estimation index for compounds targeting PPI was applied to the evaluation of the molecular generation model for de novo design of PPI-targeted compounds. Our results estimated that the generated molecules had better PPI-targeted drug-likeness and drug-likeness. Additionally, our model also exhibits comparable performance to other several state-of-the-art molecule generation models. The generated molecules share chemical space with iPPI-DB inhibitors as demonstrated by chemical space analysis. The peptide characterization-oriented design of PPI inhibitors and the ligand-based design of PPI inhibitors are explored. Finally, we recommend that this framework will be an important step forward for the de novo design of PPI-targeted therapeutics.

Wnt/BMP Mediated Metabolic Reprogramming Preserves Multipotency of Neural Crest-Like Stem Cells.

Neural crest-like stem cells resembling embryonic neural crest cells (NCs) can be derived from adult human tissues such as the epidermis. However, these cells lose their multipotency rapidly in culture limiting their expansion for clinical use. Here, we show that the multipotency of keratinocyte-derived NCs (KC-NCs) can be preserved by activating the Wnt and BMP signaling axis, promoting expression of key NC-specifier genes and ultimately enhancing their differentiation potential. We also show that transcriptional changes leading to multipotency are linked to metabolic reprogramming of KC-NCs to a highly glycolytic state. Specifically, KC-NCs treated with CHIR and BMP2 rely almost exclusively on glycolysis for their energy needs, as seen by increased lactate production, glucose uptake, and glycolytic enzyme activities. This was accompanied by mitochondrial depolarization and decreased mitochondrial ATP production. Interestingly, the glycolytic end-product lactate stabilized ß-catenin and further augmented NC-gene expression. Taken together, our study shows that activation of the Wnt/BMP signaling coordinates the metabolic demands of neural crest-like stem cells governing decisions regarding multipotency and differentiation, with possible implications for regenerative medicine.

Spatiotemporal Single-Photon Airy Bullets.

Uninhibited control of the complex spatiotemporal quantum wave function of a single photon has so far remained elusive even though it can dramatically increase the encoding flexibility and thus the information capacity of a photonic quantum link. By fusing temporal waveform generation in an atomic ensemble and spatial single-photon shaping, we hereby demonstrate for the first time complete spatiotemporal control of a propagation invariant (2+1)D Airy single-photon optical bullet. These correlated photons are not only self-accelerating and impervious to spreading as their classical counterparts, but can be concealed and revealed in the presence of strong classical stray light.

FACT maintains nucleosomes during transcription and stem cell viability in adult mice.

Preservation of nucleosomes during replication has been extensively studied, while the maintenance of nucleosomes during transcription has gotten less attention. The histone chaperone FACT has a role in transcription elongation, although whether it disassembles or assembles nucleosomes during this process is unclear. To elucidate the function of FACT in mammals, we deleted the Ssrp1 subunit of FACT in adult mice. FACT loss is lethal, possibly due to the loss of the earliest progenitors in bone marrow and intestine, while more differentiated cells are not affected. Using cells isolated from several tissues, we show that FACT loss reduces the viability of stem cells but not of cells differentiated in vitro. FACT depletion increases chromatin accessibility in a transcription-dependent manner in adipose mesenchymal stem cells, indicating that nucleosomes are lost in these cells during transcription in the absence of FACT. We also observe activation of interferon (IFN) signaling and the accumulation of immunocytes in organs sensitive to FACT loss. Our data indicate that FACT maintains chromatin integrity during transcription in mammalian adult stem cells, suggesting that chromatin transcription in stem cells and differentiated cells is different.

Genetically proxied PCSK9 inhibition is associated with reduced psoriatic arthritis risk.

BACKGROUND: Lipid pathways play a crucial role in psoriatic arthritis development, and some lipid-lowering drugs are believed to have therapeutic benefits due to their anti-inflammatory properties. Traditional observational studies face issues with confounding factors, complicating the interpretation of causality. This study seeks to determine the genetic link between these medications and the risk of psoriatic arthritis. METHODS: This drug target study utilized the Mendelian randomization strategy. We harnessed high-quality data from population-level genome-wide association studies sourced from the UK Biobank and FinnGen databases. The inverse variance-weighted method, complemented by robust pleiotropy methods, was employed. We examined the causal relationships between three lipid-lowering agents and psoriatic arthritis to unveil the underlying mechanisms. RESULTS: A significant association was observed between genetically represented proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibition and a decreased risk of psoriatic arthritis (odds ratio [OR]: 0.51; 95% CI 0.14-0.88; P < 0.01). This association was further corroborated in an independent dataset (OR 0.60; 95% CI 0.25-0.94; P = 0.03). Sensitivity analyses affirmed the absence of statistical evidence for pleiotropic or genetic confounding biases. However, no substantial associations were identified for either 3-hydroxy-3-methylglutaryl-CoA reductase inhibitors or Niemann-Pick C1-like 1 inhibitors. CONCLUSIONS: This Mendelian randomization analysis underscores the pivotal role of PCSK9 in the etiology of psoriatic arthritis. Inhibition of PCSK9 is associated with reduced psoriatic arthritis risk, highlighting the potential therapeutic benefits of existing PCSK9 inhibitors.

In vitro and in vivo activity of ceftazidime/avibactam and aztreonam alone or in combination against mcr-9, serine- and metallo-ß-lactamases-co-producing carbapenem-resistant Enterobacter cloacae complex.

PURPOSE: Enterobacteriaceae carrying mcr-9, in particularly those also co-containing metallo-ß-lactamase (MBL) and TEM type ß-lactamase, present potential transmission risks and lack adequate clinical response methods, thereby posing a major threat to global public health. The aim of this study was to assess the antimicrobial efficacy of a combined ceftazidime/avibactam (CZA) and aztreonam (ATM) regimen against carbapenem-resistant Enterobacter cloacae complex (CRECC) co-producing mcr-9, MBL and TEM. METHODS: The in vitro antibacterial activity of CZA plus ATM was evaluated using a time-kill curve assay. Furthermore, the in vivo interaction between CZA plus ATM was confirmed using a Galleria mellonella (G. mellonella) infection model. RESULTS: All eight clinical strains of CRECC, co-carrying mcr-9, MBL and TEM, exhibited high resistance to CZA and ATM. In vitro time-kill curve analysis demonstrated that the combination therapy of CZA + ATM exerted significant bactericidal activity against mcr-9, MBL and TEM-co-producing Enterobacter cloacae complex (ECC) isolates with a 100% synergy rate observed in our study. Furthermore, in vivo survival assay using Galleria mellonella larvae infected with CRECC strains co-harboring mcr-9, MBL and TEM revealed that the CZA + ATM combination significantly improved the survival rate compared to the drug-treatment alone and untreated control groups. CONCLUSION: To our knowledge, this study represents the first report on the in vitro and in vivo antibacterial activity of CZA plus ATM against CRECC isolates co-harboring mcr-9, MBL and TEM. Our findings suggest that the combination regimen of CZA + ATM provides a valuable reference for clinicians to address the increasingly complex antibiotic resistance situation observed in clinical microorganisms.

FraHMT: A Fragment-Oriented Heterogeneous Graph Molecular Generation Model for Target Proteins.

The molecular generation task stands as a pivotal step in the domains of computational chemistry and drug discovery, aiming to computationally generate molecular structures for specific properties. In contrast to previous models that focused primarily on SMILES strings or molecular graphs, our model placed a special emphasis on the substructure information on molecules, enabling the model to learn richer chemical rules and structure features from fragments and chemical reaction information on molecules. To accomplish this, we fragmented the molecules to construct heterogeneous graph representations based on atom and fragment information. Then our model mapped the heterogeneous graph data into a latent vector space by using an encoder and employed a self-regressive generative model as a decoder for molecular generation. Additionally, we performed transfer learning on the model using a small set of ligand molecules known to be active against the target protein to generate molecules that bind better to the target protein. Experimental results demonstrate that our model is highly competitive with state-of-the-art models. It can generate valid and diverse molecules with favorable physicochemical properties and drug-likeness. Importantly, they produce novel molecules with high docking scores against the target proteins.

Transformer-Based Molecular Generative Model for Antiviral Drug Design.

Since the Simplified Molecular Input Line Entry System (SMILES) is oriented to the atomic-level representation of molecules and is not friendly in terms of human readability and editable, however, IUPAC is the closest to natural language and is very friendly in terms of human-oriented readability and performing molecular editing, we can manipulate IUPAC to generate corresponding new molecules and produce programming-friendly molecular forms of SMILES. In addition, antiviral drug design, especially analogue-based drug design, is also more appropriate to edit and design directly from the functional group level of IUPAC than from the atomic level of SMILES, since designing analogues involves altering the R group only, which is closer to the knowledge-based molecular design of a chemist. Herein, we present a novel data-driven self-supervised pretraining generative model called "TransAntivirus" to make select-and-replace edits and convert organic molecules into the desired properties for design of antiviral candidate analogues. The results indicated that TransAntivirus is significantly superior to the control models in terms of novelty, validity, uniqueness, and diversity. TransAntivirus showed excellent performance in the design and optimization of nucleoside and non-nucleoside analogues by chemical space analysis and property prediction analysis. Furthermore, to validate the applicability of TransAntivirus in the design of antiviral drugs, we conducted two case studies on the design of nucleoside analogues and non-nucleoside analogues and screened four candidate lead compounds against anticoronavirus disease (COVID-19). Finally, we recommend this framework for accelerating antiviral drug discovery.

Local Scaffold Diversity-Contributed Generator for Discovering Potential NLRP3 Inhibitors.

Deep generative models have become crucial tools in de novo drug design. In current models for multiobjective optimization in molecular generation, the scaffold diversity is limited when multiple constraints are introduced. To enhance scaffold diversity, we herein propose a local scaffold diversity-contributed generator (LSDC), which can be utilized to generate diverse lead compounds capable of satisfying multiple constraints. Compared to the state-of-the-art methods, molecules generated by LSDC exhibit greater diversity when applied to the generation of inhibitors targeting the NOD-like receptor (NLR) family, pyrin domain-containing protein 3 (NLRP3). We present 12 molecules, some of which feature previously unreported scaffolds, and demonstrate their reasonable docking binding modes. Consequently, the modification of selected scaffolds and subsequent bioactivity evaluation lead to the discovery of two potent NLRP3 inhibitors, A22 and A14, with IC50 values of 38.1 nM and 44.43 nM, respectively. And the oral bioavailability of compound A14 is very high (F is 83.09% in mice). This work contributes to the discovery of novel NLRP3 inhibitors and provides a reference for integrating AI-based generation with wet experiments.

Explore drug-like space with deep generative models.

The process of design/discovery of drugs involves the identification and design of novel molecules that have the desired properties and bind well to a given disease-relevant target. One of the main challenges to effectively identify potential drug candidates is to explore the vast drug-like chemical space to find novel chemical structures with desired physicochemical properties and biological characteristics. Moreover, the chemical space of currently available molecular libraries is only a small fraction of the total possible drug-like chemical space. Deep molecular generative models have received much attention and provide an alternative approach to the design and discovery of molecules. To efficiently explore the drug-like space, we first constructed the drug-like dataset and then performed the generative design of drug-like molecules using a Conditional Randomized Transformer approach with the molecular access system (MACCS) fingerprint as a condition and compared it with previously published molecular generative models. The results show that the deep molecular generative model explores the wider drug-like chemical space. The generated drug-like molecules share the chemical space with known drugs, and the drug-like space captured by the combination of quantitative estimation of drug-likeness (QED) and quantitative estimate of protein-protein interaction targeting drug-likeness (QEPPI) can cover a larger drug-like space. Finally, we show the potential application of the model in design of inhibitors of MDM2-p53 protein-protein interaction. Our results demonstrate the potential application of deep molecular generative models for guided exploration in drug-like chemical space and molecular design.

2,2- dimethylbenzopyran derivatives containing pyridone structural fragments as selective dual-targeting inhibitors of HIF-1α and EZH2 for the treatment of lung cancer.

We formerly reported that EZH2 inhibitors sensitized HIF-1 inhibitor-resistant cells and inhibited HIF-1α to promote SUZ12 transcription, leading to enhanced EZH2 enzyme activity and elevated H3K27me3 levels, and conversely, inhibition of EZH2 promoted HIF-1α transcription. HIF-1α and EZH2 interacted to form a negative feedback loop that reinforced each other's activity. In this paper, a series of 2,2- dimethylbenzopyran derivatives containing pyridone structural fragments were designed and synthesized with DYB-03, a HIF-1α inhibitor previously reported by our group, and Tazemetostat, an EZH2 inhibitor approved by FDA, as lead compounds. Among these compounds, D-01 had significant inhibitory activities on HIF-1α and EZH2. In vitro experiments showed that D-01 significantly inhibited the migration of A549 cells, clone, invasion and angiogenesis. Moreover, D-01 had good pharmacokinetic profiles. All the results about compound D-01 could lay a foundation for the research and development of HIF-1α and EZH2 dual-targeting compounds.

TFH cells depend on Tcf1-intrinsic HDAC activity to suppress CTLA4 and guard B-cell help function.

Precise regulation of coinhibitory receptors is essential for maintaining immune tolerance without interfering with protective immunity, yet the mechanism underlying such a balanced act remains poorly understood. In response to protein immunization, T follicular helper (TFH) cells lacking Tcf1 and Lef1 transcription factors were phenotypically normal but failed to promote germinal center formation and antibody production. Transcriptomic profiling revealed that Tcf1/Lef1-deficient TFH cells aberrantly up-regulated CTLA4 and LAG3 expression, and treatment with anti-CTLA4 alone or combined with anti-LAG3 substantially rectified B-cell help defects by Tcf1/Lef1-deficient TFH cells. Mechanistically, Tcf1 and Lef1 restrain chromatin accessibility at the Ctla4 and Lag3 loci. Groucho/Tle corepressors, which are known to cooperate with Tcf/Lef factors, were essential for TFH cell expansion but dispensable for repressing coinhibitory receptors. In contrast, mutating key amino acids in histone deacetylase (HDAC) domain in Tcf1 resulted in CTLA4 derepression in TFH cells. These findings demonstrate that Tcf1-instrinsic HDAC activity is necessary for preventing excessive CTLA4 induction in protein immunization-elicited TFH cells and hence guarding their B-cell help function.

Leveraging the Fragment Molecular Orbital and MM-GBSA Methods in Virtual Screening for the Discovery of Novel Non-Covalent Inhibitors Targeting the TEAD Lipid Binding Pocket.

The Hippo pathway controls organ size and homeostasis and is linked to numerous diseases, including cancer. The transcriptional enhanced associate domain (TEAD) family of transcription factors acts as a receptor for downstream effectors, namely yes-associated protein (YAP) and transcriptional co-activator with PDZ-binding motif (TAZ), which binds to various transcription factors and is essential for stimulated gene transcription. YAP/TAZ-TEAD facilitates the upregulation of multiple genes involved in evolutionary cell proliferation and survival. TEAD1-4 overexpression has been observed in different cancers in various tissues, making TEAD an attractive target for drug development. The central drug-accessible pocket of TEAD is crucial because it undergoes a post-translational modification called auto-palmitoylation. Crystal structures of the C-terminal TEAD complex with small molecules are available in the Protein Data Bank, aiding structure-based drug design. In this study, we utilized the fragment molecular orbital (FMO) method, molecular dynamics (MD) simulations, shape-based screening, and molecular mechanics-generalized Born surface area (MM-GBSA) calculations for virtual screening, and we identified a novel non-covalent inhibitor-BC-001-with IC50 = 3.7 µM in a reporter assay. Subsequently, we optimized several analogs of BC-001 and found that the optimized compound BC-011 exhibited an IC50 of 72.43 nM. These findings can be used to design effective TEAD modulators with anticancer therapeutic implications.

MEF2D Functions as a Tumor Suppressor in Breast Cancer.

The myocyte enhancer factor 2 (MEF2) gene family play fundamental roles in the genetic programs that control cell differentiation, morphogenesis, proliferation, and survival in a wide range of cell types. More recently, these genes have also been implicated as drivers of carcinogenesis, by acting as oncogenes or tumor suppressors depending on the biological context. Nonetheless, the molecular programs they regulate and their roles in tumor development and progression remain incompletely understood. The present study evaluated whether the MEF2D transcription factor functions as a tumor suppressor in breast cancer. The knockout of the MEF2D gene in mouse mammary epithelial cells resulted in phenotypic changes characteristic of neoplastic transformation. These changes included enhanced cell proliferation, a loss of contact inhibition, and anchorage-independent growth in soft agar, as well as the capacity for tumor development in mice. Mechanistically, the knockout of MEF2D induced the epithelial-to-mesenchymal transition (EMT) and activated several oncogenic signaling pathways, including AKT, ERK, and Hippo-YAP. Correspondingly, a reduced expression of MEF2D was observed in human triple-negative breast cancer cell lines, and a low MEF2D expression in tissue samples was found to be correlated with a worse overall survival and relapse-free survival in breast cancer patients. MEF2D may, thus, be a putative tumor suppressor, acting through selective gene regulatory programs that have clinical and therapeutic significance.