Borazatruxenes as precursors for hybrid C-BN 2D molecular networks

Synthesizing atomically thin, crystalline two-dimensional (2D) molecular materials which combine carbon with other elements is an emerging field requiring both custom-designed molecular precursors and their ability to organize into networks...

Interface between obesity with dysfunctional metabolism and inflammation, and the triple-negative breast cancer in African American women

Obesity has dramatically increased over the past fifty years. In the last decade, it has been noted that augmented body mass, metabolic abnormalities, and the relevant “obese” tumor microenvironment (TME) are connected with signaling molecular networks, which in turn, may contribute to aggressive tumor biology in some patients with breast malignancies. This article presents the associations between obesity, metabolic derangements, inflammatory processes in the adipose tissue or TME, and aggressive behavior of triple-negative breast cancer (TNBC) in African American (AA) women. It also describes some abnormal molecular signaling patterns in the “obese” TME with relevance to TNBC biology. Ethnic disparities in TNBC can be due to a variety of biological features (e.g., genetic mutations and tumor heterogeneity), comorbidities (e.g., cardio-metabolic diseases, including diabetes mellitus), and reproductive factors (e.g., multiparty or short breastfeeding period). Such a constellation of biological variables potentially leads to the association between obesity, metabolic derangements, inflammatory processes in the adipose tissue or TME, and aggressive behavior of TNBC in AA women. Since the TNBC and its TME can display very aggressive behavior, it is crucial that the afflicted AA women make efforts to maintain healthy body weight, “flexible” metabolism, and a well-functioning immune system. Further studies are merited to explore the multi-disciplinary factors that can affect TNBC prevention, management, and outcomes to optimize treatment strategies and survival among AA women.

Smell Detection Agent Optimisation Framework and Systems Biology Approach to Detect Dys-Regulated Subnetwork in Cancer Data

Network biology has become a key tool in unravelling the mechanisms of complex diseases. Detecting dys-regulated subnetworks from molecular networks is a task that needs efficient computational methods. In this work, we constructed an integrated network using gene interaction data as well as protein–protein interaction data of differentially expressed genes derived from the microarray gene expression data. We considered the level of differential expression as well as the topological weight of proteins in interaction network to quantify dys-regulation. Then, a nature-inspired Smell Detection Agent (SDA) optimisation algorithm is designed with multiple agents traversing through various paths in the network. Finally, the algorithm provides a maximum weighted module as the optimum dys-regulated subnetwork. The analysis is performed for samples of triple-negative breast cancer as well as colorectal cancer. Biological significance analysis of module genes is also done to validate the results. The breast cancer subnetwork is found to contain i) valid biomarkers including PIK3CA, PTEN, BRCA1, AR and EGFR; ii) validated drug targets TOP2A, CDK4, HDAC1, IL6, BRCA1, HSP90AA1 and AR; iii) synergistic drug targets EGFR and BIRC5. Moreover, based on the weight values assigned to nodes in the subnetwork, PLK1, CTNNB1, IGF1, AURKA, PCNA, HSPA4 and GAPDH are proposed as drug targets for further studies. For colorectal cancer module, the analysis revealed the occurrence of approved drug targets TYMS, TOP1, BRAF and EGFR. Considering the higher weight values, HSP90AA1, CCNB1, AKT1 and CXCL8 are proposed as drug targets for experimentation. The derived subnetworks possess cancer-related pathways as well. The SDA-derived breast cancer subnetwork is compared with that of tools such as MCODE and Minimum Spanning Tree, and observed a higher enrichment (75%) of significant elements. Thus, the proposed nature-inspired algorithm is a novel approach to derive the optimum dys-regulated subnetwork from huge molecular network.

Feature-Based Molecular Networks Identification of Bioactive Metabolites from Three Plants of the Polynesian Cosmetopoeia Targeting the Dermal Papilla Cells of the Hair Cycle

The term cosmetopoeia refers to the use of plants in folks’ cosmetics. The aerial parts of Bidens pilosa L., the leaves of Calophyllum inophyllum L. and the fruits of Fagraea berteroana A.Gray ex Benth are traditionally used in French Polynesia for hair and skin care. During the hair cycle, dermal papilla cells and their interaction with epithelial cells are essential to promote hair follicle elongation. The aim of our investigations was the identification of metabolites from these three plants and chemical families responsible for their hair growth activity. A bioactivity-based molecular network was produced by mapping the correlation between features obtained from LC-MS/MS data and dermal papilla cell proliferation, using the Pearson correlation coefficient. The analyses pointed out glycosylated flavonols and phenolic acids from B. pilosa and C. inophyllum, along with C-flavonoids, iridoids and secoiridoids from F. berteroana, as potential bioactive molecules involved in the proliferation of hair follicle dermal papilla cells. Our results highlight the metabolites of the plant species potentially involved in the induction of hair follicle growth and support the traditional uses of these plants in hair care.

From mass spectral features to molecules in molecular networks: a novel workflow for untargeted metabolomics.

In the context of untargeted metabolomics, molecular networking is a popular and efficient tool which organizes and simplifies mass spectrometry fragmentation data (LC-MS/MS), by clustering ions based on a cosine similarity score. However, the nature of the ion species is rarely taken into account, causing redundancy as a single compound may be present in different forms throughout the network. Taking advantage of the presence of such redundant ions, we developed a new method named MolNotator. Using the different ion species produced by a molecule during ionization (adducts, dimers, trimers, in-source fragments), a predicted molecule node (or neutral node) is created by triangulation, and ultimately computing the associated molecule calculated mass. These neutral nodes provide researchers with several advantages. Firstly, each molecule is then represented in its ionization context, connected to all produced ions and indirectly to some coeluted compounds, thereby also highlighting unexpected widely present adduct species. Secondly, the predicted neutrals serve as anchors to merge the complementary positive and negative ionization modes into a single network. Lastly, the dereplication is improved by the use of all available ions connected to the neutral nodes, and the computed molecular masses can be used for exact mass dereplication. MolNotator is available as a Python library and was validated using the lichen database spectra acquired on an Orbitrap, computing neutral molecules for >90% of the 156 molecules in the dataset. By focusing on actual molecules instead of ions, MolNotator greatly facilitates the selection of molecules of interest.

Differentially Expressed Genes in Clear Cell Renal Cell Carcinoma as a Potential Marker for Prognostic and Immune Signatures

Clear cell renal cell carcinoma (ccRCC) is characterized by the inactivation of the von Hippel–Lindau (VHL) gene. Of note, no other gene is mutated as frequently as VHL in ccRCC, turning out that patients with inactivated VHL constitute the majority of ccRCC-related character. Thus, differentially expressed genes (DEGs) and their molecular networks caused by VHL mutation were considered as important factors for influencing the prognosis of ccRCC. Here, we first screened out six DEGs (GSTA1, GSTA2, NAT8, FABP7, SLC17A3, and SLC17A4) which downregulated in ccRCC patients with VHL non-mutation than with the mutation. Generally, most DEGs with high expression were associated with a favorable prognosis and low-risk score. Meanwhile, we spotted transcription factors and their kinases as hubs of DEGs. Finally, we clustered ccRCC patients into three subgroups according to the expression of hub proteins, and analyzed these subgroups with clinical profile, outcome, immune infiltration, and potential Immune checkpoint blockade (ICB) response. Herein, DEGs might be a promising biomarker panel for immunotherapy and prognosis in ccRCC. Moreover, the ccRCC subtype associated with high expression of hubs fit better for ICB therapy.

Lower and Upper Bounds of Fractional Metric Dimension of Connected Networks

The distance centric parameter in the theory of networks called by metric dimension plays a vital role in encountering the distance-related problems for the monitoring of the large-scale networks in the various fields of chemistry and computer science such as navigation, image processing, pattern recognition, integer programming, optimal transportation models and drugs discovery. In particular, it is used to find the locations of robots with respect to shortest distance among the destinations, minimum consumption of time, lesser number of the utilized nodes, and to characterize the chemical compounds, having unique presentations in molecular networks. After the arrival of its weighted version, known as fractional metric dimension, the rectification of distance-related problems in the aforementioned fields has revived to a great extent. In this article, we compute fractional as well as local fractional metric dimensions of web-related networks called by subdivided QCL, 2-faced web, 3-faced web, and antiprism web networks. Moreover, we analyse their final results using 2D and 3D plots.

Boundedness of Convex Polytopes Networks via Local Fractional Metric Dimension

Metric dimension is one of the distance-based parameter which is frequently used to study the structural and chemical properties of the different networks in the various fields of computer science and chemistry such as image processing, pattern recognition, navigation, integer programming, optimal transportation models, and drugs discovery. In particular, it is used to find the locations of robots with respect to shortest distance among the destinations, minimum consumption of time, and lesser number of the utilized nodes and to characterize the chemical compounds having unique presentation in molecular networks. The fractional metric dimension being a latest developed weighted version of the metric dimension is used in the distance-related problems of the aforementioned fields to find their nonintegral optimal solutions. In this paper, we have formulated the local resolving neighborhoods with their cardinalities for all the edges of the convex polytopes networks to compute their local fractional metric dimensions in the form of exact values and sharp bounds. Moreover, the boundedness of all the obtained results is also proved.

Noncoding RNA Roles in Pharmacogenomic Responses to Aspirin: New Molecular Mechanisms for an Old Drug

Aspirin, as one of the most frequently prescribed drugs, can have therapeutic effects on different conditions such as cardiovascular and metabolic disorders and malignancies. The effects of this common cardiovascular drug are exerted through different molecular and cellular pathways. Altered noncoding RNA (ncRNA) expression profiles during aspirin treatments indicate a close relationship between these regulatory molecules and aspirin effects through regulating gene expressions. A better understanding of the molecular networks contributing to aspirin efficacy would help optimize efficient therapies for this very popular drug. This review is aimed at discussing and highlighting the identified interactions between aspirin and ncRNAs and their targeting pathways and better understanding pharmacogenetic responses to aspirin.