The dynamic crosslinking between gut microbiota and inflammation during aging: reviewing the nutritional and hormetic approaches against dysbiosis and inflammaging.

The early-life gut microbiota (GM) is increasingly recognized for its contributions to human health and disease over time. Microbiota composition, influenced by factors like race, geography, lifestyle, and individual differences, is subject to change. The GM serves dual roles, defending against pathogens and shaping the host immune system. Disruptions in microbial composition can lead to immune dysregulation, impacting defense mechanisms. Additionally, GM aids digestion, releasing nutrients and influencing physiological systems like the liver, brain, and endocrine system through microbial metabolites. Dysbiosis disrupts intestinal homeostasis, contributing to age-related diseases. Recent studies are elucidating the bacterial species that characterize a healthy microbiota, defining what constitutes a 'healthy' colonic microbiota. The present review article focuses on the importance of microbiome composition for the development of homeostasis and the roles of GM during aging and the age-related diseases caused by the alteration in gut microbial communities. This article might also help the readers to find treatments targeting GM for the prevention of various diseases linked to it effectively.

Aging, oxidative stress and degenerative diseases: mechanisms, complications and emerging therapeutic strategies.

Aging accompanied by several age-related complications, is a multifaceted inevitable biological progression involving various genetic, environmental, and lifestyle factors. The major factor in this process is oxidative stress, caused by an abundance of reactive oxygen species (ROS) generated in the mitochondria and endoplasmic reticulum (ER). ROS and RNS pose a threat by disrupting signaling mechanisms and causing oxidative damage to cellular components. This oxidative stress affects both the ER and mitochondria, causing proteopathies (abnormal protein aggregation), initiation of unfolded protein response, mitochondrial dysfunction, abnormal cellular senescence, ultimately leading to inflammaging (chronic inflammation associated with aging) and, in rare cases, metastasis. RONS during oxidative stress dysregulate multiple metabolic pathways like NF-κB, MAPK, Nrf-2/Keap-1/ARE and PI3K/Akt which may lead to inappropriate cell death through apoptosis and necrosis. Inflammaging contributes to the development of inflammatory and degenerative diseases such as neurodegenerative diseases, diabetes, cardiovascular disease, chronic kidney disease, and retinopathy. The body's antioxidant systems, sirtuins, autophagy, apoptosis, and biogenesis play a role in maintaining homeostasis, but they have limitations and cannot achieve an ideal state of balance. Certain interventions, such as calorie restriction, intermittent fasting, dietary habits, and regular exercise, have shown beneficial effects in counteracting the aging process. In addition, interventions like senotherapy (targeting senescent cells) and sirtuin-activating compounds (STACs) enhance autophagy and apoptosis for efficient removal of damaged oxidative products and organelles. Further, STACs enhance biogenesis for the regeneration of required organelles to maintain homeostasis. This review article explores the various aspects of oxidative damage, the associated complications, and potential strategies to mitigate these effects.

Deployment Optimization Algorithms in Wireless Sensor Networks for Smart Cities: A Systematic Mapping Study.

In recent years, different types of monitoring systems have been designed for various applications, in order to turn the urban environments into smart cities. Most of these systems consist of wireless sensor networks (WSN)s, and the designing of these systems has faced many problems. The first and most important problem is sensor node deployment. The main function of WSNs is to gather the required information, process it, and send it to remote places. A large number of sensor nodes were deployed in the monitored area, so finding the best deployment algorithm that achieves maximum coverage and connectivity with the minimum number of sensor nodes is the significant point of the research. This paper provides a systematic mapping study that includes the latest recent studies, which are focused on solving the deployment problem using optimization algorithms, especially heuristic and meta-heuristic algorithms in the period (2015-2022). It was found that 35% of these studies updated the swarm optimization algorithms to solve the deployment problem. This paper will be helpful for the practitioners and researchers, in order to work out new algorithms and seek objectives for the sensor deployment. A comparison table is provided, and the basic concepts of a smart city and WSNs are presented. Finally, an overview of the challenges and open issues are illustrated.

Deep Learning-Based Computer-Aided Diagnosis (CAD): Applications for Medical Image Datasets.

Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the best optimal features while reducing the amount of data. Lastly, diagnosis prediction (classification) is achieved using learnable classifiers. The novel framework for the extraction and selection of features is based on deep learning, auto-encoder, and ACO. The performance of the proposed approach is evaluated using two medical image datasets: chest X-ray (CXR) and magnetic resonance imaging (MRI) for the prediction of the existence of COVID-19 and brain tumors. Accuracy is used as the main measure to compare the performance of the proposed approach with existing state-of-the-art methods. The proposed system achieves an average accuracy of 99.61% and 99.18%, outperforming all other methods in diagnosing the presence of COVID-19 and brain tumors, respectively. Based on the achieved results, it can be claimed that physicians or radiologists can confidently utilize the proposed approach for diagnosing COVID-19 patients and patients with specific brain tumors.

Detecting Malware by Analyzing App Permissions on Android Platform: A Systematic Literature Review.

Smartphone adaptation in society has been progressing at a very high speed. Having the ability to run on a vast variety of devices, much of the user base possesses an Android phone. Its popularity and flexibility have played a major role in making it a target of different attacks via malware, causing loss to users, both financially and from a privacy perspective. Different malware and their variants are emerging every day, making it a huge challenge to come up with detection and preventive methodologies and tools. Research has spawned in various directions to yield effective malware detection mechanisms. Since malware can adopt different ways to attack and hide, accurate analysis is the key to detecting them. Like any usual mobile app, malware requires permission to take action and use device resources. There are 235 total permissions that the Android app can request on a device. Malware takes advantage of this to request unnecessary permissions, which would enable those to take malicious actions. Since permissions are critical, it is important and challenging to identify if an app is exploiting permissions and causing damage. The focus of this article is to analyze the identified studies that have been conducted with a focus on permission analysis for malware detection. With this perspective, a systematic literature review (SLR) has been produced. Several papers have been retrieved and selected for detailed analysis. Current challenges and different analyses were presented using the identified articles.

Deep Learning-Based Defect Prediction for Mobile Applications.

Smartphones have enabled the widespread use of mobile applications. However, there are unrecognized defects of mobile applications that can affect businesses due to a negative user experience. To avoid this, the defects of applications should be detected and removed before release. This study aims to develop a defect prediction model for mobile applications. We performed cross-project and within-project experiments and also used deep learning algorithms, such as convolutional neural networks (CNN) and long short term memory (LSTM) to develop a defect prediction model for Android-based applications. Based on our within-project experimental results, the CNN-based model provides the best performance for mobile application defect prediction with a 0.933 average area under ROC curve (AUC) value. For cross-project mobile application defect prediction, there is still room for improvement when deep learning algorithms are preferred.

Machine Learning-Based Software Defect Prediction for Mobile Applications: A Systematic Literature Review.

Software defect prediction studies aim to predict defect-prone components before the testing stage of the software development process. The main benefit of these prediction models is that more testing resources can be allocated to fault-prone modules effectively. While a few software defect prediction models have been developed for mobile applications, a systematic overview of these studies is still missing. Therefore, we carried out a Systematic Literature Review (SLR) study to evaluate how machine learning has been applied to predict faults in mobile applications. This study defined nine research questions, and 47 relevant studies were selected from scientific databases to respond to these research questions. Results show that most studies focused on Android applications (i.e., 48%), supervised machine learning has been applied in most studies (i.e., 92%), and object-oriented metrics were mainly preferred. The top five most preferred machine learning algorithms are Naïve Bayes, Support Vector Machines, Logistic Regression, Artificial Neural Networks, and Decision Trees. Researchers mostly preferred Object-Oriented metrics. Only a few studies applied deep learning algorithms including Long Short-Term Memory (LSTM), Deep Belief Networks (DBN), and Deep Neural Networks (DNN). This is the first study that systematically reviews software defect prediction research focused on mobile applications. It will pave the way for further research in mobile software fault prediction and help both researchers and practitioners in this field.

Hybrid Blockchain Platforms for the Internet of Things (IoT): A Systematic Literature Review.

In recent years, research into blockchain technology and the Internet of Things (IoT) has grown rapidly due to an increase in media coverage. Many different blockchain applications and platforms have been developed for different purposes, such as food safety monitoring, cryptocurrency exchange, and secure medical data sharing. However, blockchain platforms cannot store all the generated data. Therefore, they are supported with data warehouses, which in turn is called a hybrid blockchain platform. While several systems have been developed based on this idea, a current state-of-the-art systematic overview on the use of hybrid blockchain platforms is lacking. Therefore, a systematic literature review (SLR) study has been carried out by us to investigate the motivations for adopting them, the domains at which they were used, the adopted technologies that made this integration effective, and, finally, the challenges and possible solutions. This study shows that security, transparency, and efficiency are the top three motivations for adopting these platforms. The energy, agriculture, health, construction, manufacturing, and supply chain domains are the top domains. The most adopted technologies are cloud computing, fog computing, telecommunications, and edge computing. While there are several benefits of using hybrid blockchains, there are also several challenges reported in this study.

Cybersecurity Enterprises Policies: A Comparative Study.

Cybersecurity is a critical issue that must be prioritized not just by enterprises of all kinds, but also by national security. To safeguard an organization's cyberenvironments, information, and communication technologies, many enterprises are investing substantially in cybersecurity these days. One part of the cyberdefense mechanism is building an enterprises' security policies library, for consistent implementation of security controls. Significant and common cybersecurity policies of various enterprises are compared and explored in this study to provide robust and comprehensive cybersecurity knowledge that can be used in various enterprises. Several significant common security policies were identified and discussed in this comprehensive study. This study identified 10 common cybersecurity policy aspects in five enterprises: healthcare, finance, education, aviation, and e-commerce. We aimed to build a strong infrastructure in each business, and investigate the security laws and policies that apply to all businesses in each sector. Furthermore, the findings of this study reveal that the importance of cybersecurity requirements differ across multiple organizations. The choice and applicability of cybersecurity policies are determined by the type of information under control and the security requirements of organizations in relation to these policies.

Longitudinal measurement of subcutaneous and intratibial human prostate cancer xenograft growth and response to ionizing radiation by plasma Alu and LINE-1 ctDNA: A comparison to standard methods.

BACKGROUND: Current preclinical models of metastatic prostate cancer (PCa) require sophisticated technologies and/or genetically engineered cells for the noninvasive monitoring of tumors in remote sites, such as bone. Recent developments in circulating tumor DNA (ctDNA) analysis provide an alternative method for noninvasive tumor monitoring at a low cost. Here, we sought to evaluate human Alu and LINE-1 ctDNA for the longitudinal measurement of subcutaneous and intratibial human PCa xenograft growth and response to ionizing radiation (IR) through comparison with standard slide caliper and bioluminescence measurements. MATERIAL AND METHODS: Intratibial and subcutaneous xenografts were established in male athymic nude mice using LNCaP cells that stably express firefly luciferase. A subset of tumors was treated with a single dose of IR (CT-guided focal IR, 6 Gy). Tumor measurements were simultaneously taken by slide caliper (subcutaneous only), in vivo bioluminescence imaging, and quantitative real-time PCR (qPCR) of human-specific Alu and LINE-1 ctDNA for several weeks. RESULTS: Levels of ctDNA and bioluminescence increased concordantly with subcutaneous and intratibial tumor growth. A statistically significant correlation (Spearman) was observed between ctDNA and subcutaneous tumor volume (LINE-1, r = .94 and Alu, r = .95, p < .0001), ctDNA and bioluminescence (LINE-1, r = .66 and Alu, r = .60, p < .002), and bioluminescence and tumor volume (r = .66, p = .0003). Bioluminescence and ctDNA were also significantly correlated in intratibial tumors (LINE-1, r = .82 and Alu, r = .81, p < .0001). Following external beam IR, the tumor responses varied briefly by method of measurement, but followed a similar trend. Statistically significant correlations were maintained between ctDNA and slide caliper measurement in irradiated subcutaneous tumors (LINE-1, r = .64 and Alu, r = .44, p < .02), and ctDNA and bioluminescence in intratibial tumors (LINE-1, r = .55, p = .018). CONCLUSIONS: Real-time qPCR of circulating human Alu and LINE-1 DNA provides an accurate measurement of subcutaneous and intratibial xenograft burden that is comparable with conventional bioluminescence imaging and slide caliper measurement. Transient differences in measurements were observed following tumor-targeted IR, but overall all measurements mirrored tumor growth and response.

IoT Platform for Seafood Farmers and Consumers.

There has been a strong growth in aquatic products supported by the global seafood industry. Consumers demand information transparency to support informed decisions and to verify nutrition, food safety, and sustainable operations. Supporting these needs rests on the existence of interoperable Internet of Things (IoT) platforms for traceability that goes beyond the minimum "one up, one down" scheme required by regulators. Seafood farmers, being the source of both food and food-information, are critical to achieving the needed transparency. Traditionally, seafood farmers carry the costs of providing information, while downstream actors reap the benefits, causing limited provision of information. Now, global standards for labelling, data from IoT devices, and the reciprocity of utility from collecting data while sharing them represent great potential for farmers to generate value from traceability systems. To enable this, farmers need an IoT platform integrated with other IoT platforms in the value network. This paper presents a case study of an enterprise-level IoT platform for seafood farmers that satisfies consumers' end-to-end traceability needs while extracting data from requests for information from downstream actors.

A Novel Framework Using Deep Auto-Encoders Based Linear Model for Data Classification.

This paper proposes a novel data classification framework, combining sparse auto-encoders (SAEs) and a post-processing system consisting of a linear system model relying on Particle Swarm Optimization (PSO) algorithm. All the sensitive and high-level features are extracted by using the first auto-encoder which is wired to the second auto-encoder, followed by a Softmax function layer to classify the extracted features obtained from the second layer. The two auto-encoders and the Softmax classifier are stacked in order to be trained in a supervised approach using the well-known backpropagation algorithm to enhance the performance of the neural network. Afterwards, the linear model transforms the calculated output of the deep stacked sparse auto-encoder to a value close to the anticipated output. This simple transformation increases the overall data classification performance of the stacked sparse auto-encoder architecture. The PSO algorithm allows the estimation of the parameters of the linear model in a metaheuristic policy. The proposed framework is validated by using three public datasets, which present promising results when compared with the current literature. Furthermore, the framework can be applied to any data classification problem by considering minor updates such as altering some parameters including input features, hidden neurons and output classes.

Structural and functional characterization of the transcriptional regulator Rv3488 of Mycobacterium tuberculosis H37Rv.

Rv3488 of Mycobacterium tuberculosis H37Rv has been assigned to the phenolic acid decarboxylase repressor (PadR) family of transcriptional regulators that play key roles in multidrug resistance and virulence of prokaryotes. The binding of cadmium, zinc, and several other metals to Rv3488 was discovered and characterized by isothermal titration calorimetery to be an exothermic process. Crystal structures of apo-Rv3488 and Rv3488 in complex with cadmium or zinc ions were determined by X-ray crystallography. The structure of Rv3488 revealed a dimeric protein with N-terminal winged-helix-turn-helix DNA-binding domains composed of helices α1, α2, α3, and strands ß1 and ß2, with the dimerization interface being formed of helices α4 and α1. The overall fold of Rv3488 was similar to PadR-s2 and metal sensor transcriptional regulators. In the crystal structure of Rv3488-Cd complex, two octahedrally coordinated Cd2+ ions were present, one for each subunit. The same sites were occupied by zinc ions in the structure of Rv3488-Zn, with two additional zinc ions complexed in one monomer. EMSA studies showed specific binding of Rv3488 with its own 30-bp promoter DNA. The functional role of Rv3488 was characterized by expressing the rv3488 gene under the control of hsp60 promoter in Mycobacterium smegmatis Expression of Rv3488 increased the intracellular survival of recombinant M. smegmatis in murine macrophage cell line J774A.1 and also augmented its tolerance to Cd2+ ions. Overall, the studies show that Rv3488 may have transcription regulation and metal-detoxifying functions and its expression in M. smegmatis increases intracellular survival, perhaps by counteracting toxic metal stress.

Dual phosphorylation in response regulator protein PrrA is crucial for intracellular survival of mycobacteria consequent upon transcriptional activation.

The remarkable ability of Mycobacterium tuberculosis (Mtb) to survive inside human macrophages is attributed to the presence of a complex sensory and regulatory network. PrrA is a DNA-binding regulatory protein, belonging to an essential two-component system (TCS), PrrA/B, which is required for early phase intracellular replication of Mtb. Despite its importance, the mechanism of PrrA/B-mediated signaling is not well understood. In the present study, we demonstrate that the binding of PrrA on the promoter DNA and its consequent activation is cumulatively controlled via dual phosphorylation of the protein. We have further characterized the role of terminal phospho-acceptor domain in the physical interaction of PrrA with its cognate kinase PrrB. The genetic deletion of prrA/B in Mycobacterium smegmatis was possible only in the presence of ectopic copies of the genes, suggesting the essentiality of this TCS in fast-growing mycobacterial strains as well. The overexpression of phospho-mimetic mutant (T6D) altered the growth of M. smegmatis in an in vitro culture and affected the replication of Mycobacterium bovis BCG in mouse peritoneal macrophages. Interestingly, the Thr6 site was found to be conserved in Mtb complex, whereas it was altered in some fast-growing mycobacterial strains, indicating that this unique phosphorylation might be predominant in employing the regulatory circuit in M. bovis BCG and presumably also in Mtb complex.

Peroxiredoxin-1 of macrophage is critical for mycobacterial infection and is controlled by early secretory antigenic target protein through the activation of p38 MAPK.

Early secretory antigenic target protein (ESAT-6) is an important virulent factor which plays a crucial role in Mycobacterium tuberculosis (MTB) pathogenesis. Here, we demonstrate the role of ESAT-6 in phagocytosis and intracellular survival of mycobacteria through a mechanism mediated by regulation of a host protein; Peroxiredoxin-1 (Prdx-1). Prdx-1 is an anti-apoptotic and stress response protein which protects cells from damage by ROS and H2O2. The J774 A.1 cells infected with MTB or over-expressing ESAT-6 through eukaryotic promoter vector showed elevated expression of Prdx-1. Further investigation revealed that the up-regulation of Prdx-1 is mediated through the activation of one of the MAP kinases, p38. The NRF-2, a transcriptional activator of Prdx-1 is translocated to the nucleus upon phosphorylation by p38 and subsequently, regulates expression of Prdx-1. Inhibition of the p38 MAPK by a specific inhibitor, SB203580, abrogates the ESAT-6 mediated induction of Prdx-1 expression as well as the phosphorylation of NRF-2 in a time-dependent manner. The inhibition of Prdx-1 expression by specific siRNA in J774 A.1 cells resulted in the reduced bacterial uptake and intracellular survival of the mycobacteria. This is the first report proclaiming that the ESAT-6 regulates Prdx-1 which is involved in the increase of mycobacterial uptake and survival. The intermediate mechanisms involve the increased Prdx-1 production in macrophages through the activation of p38 and NRF-2 dependent signaling.

EGFR and C/EBP-ß oncogenic signaling is bidirectional in human glioma and varies with the C/EBP-ß isoform.

We investigated the intersection of epidermal growth factor receptor (EGFR) and CCAAT enhancer binding protein (C/EBP)-ß signaling in glioblastoma (GBM), given that both gene products strongly influence neoplastic behavior. C/EBP-ß is known to drive the mesenchymal transcriptional signature in GBM, likely through strong microenvironmental influences, whereas the genetic contributions to its up-regulation in this disease are not well described. We demonstrated that stable overexpression and activation of WT EGFR (U87MG-WT) led to elevated C/EBP-ß expression, as well as enhanced nuclear translocation and DNA-binding activity, leading to up-regulation of C/EBP-ß transcription and translation. Deeper investigation identified bidirectional regulation, with C/EBP-ß also causing up-regulation of EGFR that was at least partially dependent on the STAT3. Based on ChIP-based studies, we also found that that the translational isoforms of C/EBP-ß [liver-enriched transcription-activating protein (LAP)-1/2 and liver inhibitory protein (LIP)] have differential occupancy on STAT3 promoter and opposing roles in transcriptional regulation of STAT3 and EGFR. We further demonstrated that the shorter C/EBP-ß isoform, LIP, promoted proliferation and migration of U87MG glioma cells, potentially via induction of cytokine IL-6. Our molecular dissection of EGFR and C/EBP-ß pathway interactions uncovered a complex signaling network in which increased activity of either EGFR or C/EBP-ß leads to the up-regulation of the other, enhancing oncogenic signaling. Disrupting the EGFR-C/EBP-ß signaling axis could attenuate malignant behavior of glioblastoma.-Selagea, L., Mishra, A., Anand, M., Ross, J., Tucker-Burden, C., Kong, J., Brat, D. J. EGFR and C/EBP-ß oncogenic signaling is bidirectional in human glioma and varies with the C/EBP-ß isoform.

Alternative splicing of the cell fate determinant Numb in hepatocellular carcinoma.

UNLABELLED: The cell fate determinant Numb is aberrantly expressed in cancer. Numb is alternatively spliced, with one isoform containing a long proline-rich region (PRR(L) ) compared to the other with a short PRR (PRR(S) ). Recently, PRR(L) was reported to enhance proliferation of breast and lung cancer cells. However, the importance of Numb alternative splicing in hepatocellular carcinoma (HCC) remains unexplored. We report here that Numb PRR(L) expression is increased in HCC and associated with early recurrence and reduced overall survival after surgery. In a panel of HCC cell lines, PRR(L) generally promotes and PRR(S) suppresses proliferation, migration, invasion, and colony formation. Knockdown of PRR(S) leads to increased Akt phosphorylation and c-Myc expression, and Akt inhibition or c-Myc silencing dampens the proliferative impact of Numb PRR(S) knockdown. In the cell models explored in this study, alternative splicing of Numb PRR isoforms is coordinately regulated by the splicing factor RNA-binding Fox domain containing 2 (RbFox2) and the kinase serine/arginine protein-specific kinase 2 (SRPK2). Knockdown of the former causes accumulation of PRR(L) , while SRPK2 knockdown causes accumulation of PRR(S) . The subcellular location of SRPK2 is regulated by the molecular chaperone heat shock protein 90, and heat shock protein 90 inhibition or knockdown phenocopies SRPK2 knockdown in promoting accumulation of Numb PRR(S) . Finally, HCC cell lines that predominantly express PRR(L) are differentially sensitive to heat shock protein 90 inhibition. CONCLUSION: Alternative splicing of Numb may provide a useful prognostic biomarker in HCC and is pharmacologically tractable.

A Novel Hybrid Machine Learning-Based System Using Deep Learning Techniques and Meta-Heuristic Algorithms for Various Medical Datatypes Classification.

Medicine is one of the fields where the advancement of computer science is making significant progress. Some diseases require an immediate diagnosis in order to improve patient outcomes. The usage of computers in medicine improves precision and accelerates data processing and diagnosis. In order to categorize biological images, hybrid machine learning, a combination of various deep learning approaches, was utilized, and a meta-heuristic algorithm was provided in this research. In addition, two different medical datasets were introduced, one covering the magnetic resonance imaging (MRI) of brain tumors and the other dealing with chest X-rays (CXRs) of COVID-19. These datasets were introduced to the combination network that contained deep learning techniques, which were based on a convolutional neural network (CNN) or autoencoder, to extract features and combine them with the next step of the meta-heuristic algorithm in order to select optimal features using the particle swarm optimization (PSO) algorithm. This combination sought to reduce the dimensionality of the datasets while maintaining the original performance of the data. This is considered an innovative method and ensures highly accurate classification results across various medical datasets. Several classifiers were employed to predict the diseases. The COVID-19 dataset found that the highest accuracy was 99.76% using the combination of CNN-PSO-SVM. In comparison, the brain tumor dataset obtained 99.51% accuracy, the highest accuracy derived using the combination method of autoencoder-PSO-KNN.

Comparative metabolomics of MCF-7 and MCF-7/TAMR identifies potential metabolic pathways in tamoxifen resistant breast cancer cells.

OBJECTIVES: Breast cancer is the most common cancer and the leading cause of cancer-related death among women. An Estrogen Receptor (ER) antagonist called tamoxifen is used as an adjuvant therapy for ER-positive breast cancers. Approximately 40% of patients develop tamoxifen resistance (TAMR) while receiving treatment. Cancer cells can rewire their metabolism to develop resistant phenotypes, and their metabolic state determines how receptive they are to chemotherapy. METHODS: Metabolite extraction from human MCF-7 and MCF-7/TAMR cells was done using the methanol-methanol-water extraction method. After treating the dried samples with methoxamine hydrochloride in pyridine, the samples were derivatized with 2,2,2-Trifluoro-N-methyl-N-(trimethylsilyl)-acetamide, and Chlorotrimethylsilane (MSTFA + 1% TMCS). The Gas chromatography/mass spectrometry (GC-MS) raw data were processed using MSdial and Metaboanalyst for analysis. RESULTS: Univariate analysis revealed that 35 metabolites were elevated in TAMR cells whereas 25 metabolites were downregulated. N-acetyl-D-glucosamine, lysine, uracil, tyrosine, alanine, and o-phosphoserine were upregulated in TAMR cells, while hydroxyproline, glutamine, N-acetyl-L-aspartic acid, threonic acid, pyroglutamic acid, glutamine, o-phosphoethanolamine, oxoglutaric acid, and myoinositol were found to be downregulated. Multivariate analysis revealed a distinct separation between the two cell lines, as evidenced by their metabolite levels. The enriched pathways of deregulated metabolites included valine, leucine, and isoleucine degradation, Citric Acid Cycle, Warburg effect, Malate-Aspartate shuttle, glucose-alanine cycle, propanoate metabolism, and Phospholipid biosynthesis. CONCLUSION: This study revealed dysregulation of various metabolic processes in TAMR cells, which may be crucial in elucidating the molecular basis of the mechanisms underlying acquired tamoxifen resistance.

Protocol for monitoring phagocytosis of cancer cells by TAM-like macrophages using imaging cytometry.

Here, we present a protocol for monitoring phagocytosis by M2-type macrophages using automated counting of phagocytic events with an imaging cytometer. We describe steps for isolating and differentiating peripheral blood mononuclear cell (PBMC)-derived monocytes into M2-like macrophages, preparing cancer cells expressing a green fluorescence marker, labeling with a pH-sensitive dye, and co-culturing with macrophages. We then outline procedures for enumerating phagocytic events using an imaging cytometer. For complete details on the use and execution of this protocol, please refer to Mishra et al.1.