Chemical Probes that Competitively and Selectively Inhibit Stat3 Activation

Signal transducer and activator of transcription (Stat) 3 is an oncogene constitutively activated in many cancer systems where it contributes to carcinogenesis. To develop chemical probes that selectively target Stat3, we virtually screened 920,000 small drug-like compounds by docking each into the peptide-binding pocket of the Stat3 SH2 domain, which consists of three sites—the pY-residue binding site, the +3 residue-binding site and a hydrophobic binding site, which served as a selectivity filter. Three compounds satisfied criteria of interaction analysis, competitively inhibited recombinant Stat3 binding to its immobilized pY-peptide ligand and inhibited IL-6-mediated tyrosine phosphorylation of Stat3. These compounds were used in a similarity screen of 2.47 million compounds, which identified 3 more compounds with similar activities. Examination of the 6 active compounds for the ability to inhibit IFN-γ-mediated Stat1 phosphorylation revealed that 5 of 6 were selective for Stat3. Molecular modeling of the SH2 domains of Stat3 and Stat1 bound to compound revealed that compound interaction with the hydrophobic binding site was the basis for selectivity. All 5 selective compounds inhibited nuclear-to-cytoplasmic translocation of Stat3, while 3 of 5 compounds induced apoptosis preferentially of breast cancer cell lines with constitutive Stat3 activation. Thus, virtual ligand screening of compound libraries that targeted the Stat3 pY-peptide binding pocket identified for the first time 3 lead compounds that competitively inhibited Stat3 binding to its pY-peptide ligand; these compounds were selective for Stat3 vs. Stat1 and induced apoptosis preferentially of breast cancer cells lines with constitutively activated Stat3

Enhancing Audio Classification Through MFCC Feature Extraction and Data Augmentation with CNN and RNN Models

Sound classification is a multifaceted task that necessitates the gathering and processing of vast quantities of data, as well as the construction of machine learning models that can accurately distinguish between various sounds. In our project, we implemented a novel methodology for classifying both musical instruments and environmental sounds, utilizing convolutional and recurrent neural networks. We used the Mel Frequency Cepstral Coefficient (MFCC) method to extract features from audio, which emulates the human auditory system and produces highly distinct features. Knowing how important data processing is, we implemented distinctive approaches, including a range of data augmentation and cleaning techniques, to achieve an optimized solution. The outcomes were noteworthy, as both the convolutional and recurrent neural network models achieved a commendable level of accuracy. As machine learning and deep learning continue to revolutionize image classification, it is high time to explore the development of adaptable models for audio classification. Despite the challenges associated with a small dataset, we successfully crafted our models using convolutional and recurrent neural networks. Overall, our strategy for sound classification bears significant implications for diverse domains, encompassing speech recognition, music production, and healthcare. We hold the belief that with further research and progress, our work can pave the way for breakthroughs in audio data classification and analysis

From waste to food : optimising the breakdown of oil palm waste to provide substrate for insects farmed as animal feed

Waste biomass from the palm oil industry is currently burned as a means of disposal and solutions are required to reduce the environmental impact. Whilst some waste biomass can be recycled to provide green energy such as biogas, this investigation aimed to optimise experimental conditions for recycling palm waste into substrate for insects, farmed as a sustainable high-protein animal feed. NMR spectroscopy and LC-HRMS were used to analyse the composition of palm empty fruit bunches (EFB) under experimental conditions optimised to produce nutritious substrate rather than biogas. Statistical pattern recognition techniques were used to investigate differences in composition for various combinations of pre-processing and anaerobic digestion (AD) methods. Pre-processing methods included steaming, pressure cooking, composting, microwaving, and breaking down the EFB using ionic liquids. AD conditions which were modified in combination with pre-processing methods were ratios of EFB:digestate and pH. Results show that the selection of pre-processing method affects the breakdown of the palm waste and subsequently the substrate composition and biogas production. Although large-scale insect feeding trials will be required to determine nutritional content, we found that conditions can be optimised to recycle palm waste for the production of substrate for insect rearing. Pre-processing EFB using ionic liquid before AD at pH6 with a 2:1 digestate:EFB ratio were found to be the best combination of experimental conditions

Therapeutic potential of cladribine in combination with STAT3 inhibitor against multiple myeloma

<p>Abstract</p> <p>Background</p> <p>Cladribine or 2-chlorodeoxyadenosine (2-CDA) is a well-known purine nucleoside analog with particular activity against lymphoproliferative disorders, such as hairy cell leukemia (HCL). Its benefits in multiple myeloma (MM) remain unclear. Here we report the inhibitory effects of cladribine on MM cell lines (U266, RPMI8226, MM1.S), and its therapeutic potential in combination with a specific inhibitor of the signal transducer and activator of transcription 3 (STAT3).</p> <p>Methods</p> <p>MTS-based proliferation assays were used to determine cell viability in response to cladribine. Cell cycle progression was examined by flow cytometry analysis. Cells undergoing apoptosis were evaluated with Annexin V staining and a specific ELISA to quantitatively measure cytoplasmic histone-associated DNA fragments. Western blot analyses were performed to determine the protein expression levels and activation.</p> <p>Results</p> <p>Cladribine inhibited cell proliferation of MM cells in a dose-dependent manner, although the three MM cell lines exhibited a remarkably different responsiveness to cladribine. The IC50 of cladribine for U266, RPMI8226, or MM1.S cells was approximately 2.43, 0.75, or 0.18 μmol/L, respectively. Treatment with cladribine resulted in a significant G1 arrest in U266 and RPMI8226 cells, but only a minor increase in the G1 phase for MM1.S cells. Apoptosis assays with Annexin V-FITC/PI double staining indicated that cladribine induced apoptosis of U266 cells in a dose-dependent manner. Similar results were obtained with an apoptotic-ELISA showing that cladribine dramatically promoted MM1.S and RPMA8226 cells undergoing apoptosis. On the molecular level, cladribine induced PARP cleavage and activation of caspase-8 and caspase-3. Meanwhile, treatment with cladribine led to a remarkable reduction of the phosphorylated STAT3 (P-STAT3), but had little effect on STAT3 protein levels. The combinations of cladribine and a specific STAT3 inhibitor as compared to either agent alone significantly induced apoptosis in all three MM cell lines.</p> <p>Conclusions</p> <p>Cladribine exhibited inhibitory effects on MM cells <it>in vitro</it>. MM1.S is the only cell line showing significant response to the clinically achievable concentrations of cladribine-induced apoptosis and inactivation of STAT3. Our data suggest that MM patients with the features of MM1.S cells may particularly benefit from cladribine monotherapy, whereas cladribine in combination with STAT3 inhibitor exerts a broader therapeutic potential against MM.</p

The Carbon Assimilation Network in Escherichia coli Is Densely Connected and Largely Sign-Determined by Directions of Metabolic Fluxes

Gene regulatory networks consist of direct interactions but also include indirect interactions mediated by metabolites and signaling molecules. We describe how these indirect interactions can be derived from a model of the underlying biochemical reaction network, using weak time-scale assumptions in combination with sensitivity criteria from metabolic control analysis. We apply this approach to a model of the carbon assimilation network in Escherichia coli. Our results show that the derived gene regulatory network is densely connected, contrary to what is usually assumed. Moreover, the network is largely sign-determined, meaning that the signs of the indirect interactions are fixed by the flux directions of biochemical reactions, independently of specific parameter values and rate laws. An inversion of the fluxes following a change in growth conditions may affect the signs of the indirect interactions though. This leads to a feedback structure that is at the same time robust to changes in the kinetic properties of enzymes and that has the flexibility to accommodate radical changes in the environment

LLL-3 inhibits STAT3 activity, suppresses glioblastoma cell growth and prolongs survival in a mouse glioblastoma model

Persistent activation of the signal transducer and activator of transcription 3 (STAT3) signalling has been linked to oncogenesis and the development of chemotherapy resistance in glioblastoma and other cancers. Inhibition of the STAT3 pathway thus represents an attractive therapeutic approach for cancer. In this study, we investigated the inhibitory effects of a small molecule compound known as LLL-3, which is a structural analogue of the earlier reported STAT3 inhibitor, STA-21, on the cell viability of human glioblastoma cells, U87, U373, and U251 expressing constitutively activated STAT3. We also investigated the inhibitory effects of LLL-3 on U87 glioblastoma cell growth in a mouse tumour model as well as the impact it had on the survival time of the treated mice. We observed that LLL-3 inhibited STAT3-dependent transcriptional and DNA binding activities. LLL-3 also inhibited viability of U87, U373, and U251 glioblastoma cells as well as induced apoptosis of these glioblastoma cell lines as evidenced by increased poly (ADP-ribose) polymerase (PARP) and caspase-3 cleavages. Furthermore, the U87 glioblastoma tumour-bearing mice treated with LLL-3 exhibited prolonged survival relative to vehicle-treated mice (28.5 vs 16 days) and had smaller intracranial tumours and no evidence of contralateral invasion. These results suggest that LLL-3 may be a potential therapeutic agent in the treatment of glioblastoma with constitutive STAT3 activation. Originally published in British Journal of Cancer 2009 Vol. 110, No.

Highly pathogenic avian influenza virus infection in chickens but not ducks is associated with elevated host immune and pro-inflammatory responses

Highly pathogenic avian influenza (HPAI) H5N1 viruses cause severe infection in chickens at near complete mortality, but corresponding infection in ducks is typically mild or asymptomatic. To understand the underlying molecular differences in host response, primary chicken and duck lung cells, infected with two HPAI H5N1 viruses and a low pathogenicity avian influenza (LPAI) H2N3 virus, were subjected to RNA expression profiling. Chicken cells but not duck cells showed highly elevated immune and pro-inflammatory responses following HPAI virus infection. HPAI H5N1 virus challenge studies in chickens and ducks corroborated the in vitro findings. To try to determine the underlying mechanisms, we investigated the role of signal transducer and activator of transcription-3 (STAT-3) in mediating pro-inflammatory response to HPAIV infection in chicken and duck cells. We found that STAT-3 expression was down-regulated in chickens but was up-regulated or unaffected in ducks in vitro and in vivo following H5N1 virus infection. Low basal STAT-3 expression in chicken cells was completely inhibited by H5N1 virus infection. By contrast, constitutively active STAT-3 detected in duck cells was unaffected by H5N1 virus infection. Transient constitutively-active STAT-3 transfection in chicken cells significantly reduced pro-inflammatory response to H5N1 virus infection; on the other hand, chemical inhibition of STAT-3 activation in duck cells increased pro-inflammatory gene expression following H5N1 virus infection. Collectively, we propose that elevated pro-inflammatory response in chickens is a major pathogenicity factor of HPAI H5N1 virus infection, mediated in part by the inhibition of STAT-3

Fetal brain tissue annotation and segmentation challenge results.

In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, gray matter, white matter, ventricles, cerebellum, brainstem, deep gray matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero