Deregulation of signalling pathways that regulate cell growth, survival, metabolism, and migration can frequently lead to the progression of cancer. Brain tumours are a large group of malignancies characterised by inter- and intratumoral heterogeneity, with glioblastoma (GBM) being the most aggressive and fatal. The present study aimed to characterise the expression of cancer pathway-related genes (n = 84) in glial tumour cell lines (A172, SW1088, and T98G). The transcriptomic data obtained by the qRT-PCR method were compared to different control groups, and the most appropriate control for subsequent interpretation of the obtained results was chosen. We analysed three widely used control groups (non-glioma cells) in glioblastoma research: Human Dermal Fibroblasts (HDFa), Normal Human Astrocytes (NHA), and commercially available mRNAs extracted from healthy human brain tissues (hRNA). The gene expression profiles of individual glioblastoma cell lines may vary due to the selection of a different control group to correlate with. Moreover, we present the original multicriterial decision making (MCDM) for the possible characterization of gene expression profiles. We observed deregulation of 75 genes out of 78 tested in the A172 cell line, while T98G and SW1088 cells exhibited changes in 72 genes. By comparing the delta cycle threshold value of the tumour groups to the mean value of the three controls, only changes in the expression of 26 genes belonging to the following pathways were identified: angiogenesis FGF2; apoptosis APAF1, CFLAR, XIAP; cellular senescence BM1, ETS2, IGFBP5, IGFBP7, SOD1, TBX2; DNA damage and repair ERCC5, PPP1R15A; epithelial to mesenchymal transition SNAI3, SOX10; hypoxia ADM, ARNT, LDHA; metabolism ATP5A1, COX5A, CPT2, PFKL, UQCRFS1; telomeres and telomerase PINX1, TINF2, TNKS, and TNKS2. We identified a human astrocyte cell line and normal human brain tissue as the appropriate control group for an in vitro model, despite the small sample size. A different method of assessing gene expression levels produced the same disparities, highlighting the need for caution when interpreting the accuracy of tumorigenesis markers.

Transcriptomics studies are available to evaluate the potential toxicity of nanomaterials in plants, and many highlight their effect on stress-responsive genes. However, a comparative analysis of overall expression changes suggests a low impact on the transcriptome. Environmental challenges like pathogens, saline, or drought stress induce stronger transcriptional responses than nanoparticles. Clearly, plants did not have the chance to evolve specific gene regulation in response to novel nanomaterials; but they use common regulatory circuits with other stress responses. A shared effect with abiotic stress is the inhibition of genes for root development and pathogen response. Other works are reviewed here, which also converge on these results.

Artificial proteins can be constructed from stable substructures, whose stability is encoded in their protein sequence. Identifying stable protein substructures experimentally is the only available option at the moment because no suitable method exists to extract this information from a protein sequence. In previous research, we examined the mechanics of E. coli Hsp70 and found four mechanically stable (S class) and three unstable substructures (U class). Of the total 603 residues in the folded domains of Hsp70, 234 residues belong to one of four mechanically stable substructures, and 369 residues belong to one of three unstable substructures. Here our goal is to develop a machine learning model to categorize Hsp70 residues using sequence information. We applied three supervised methods: logistic regression (LR), random forest, and support vector machine. The LR method showed the highest accuracy, 0.925, to predict the correct class of a particular residue only when context-dependent physico-chemical features were included. The cross-validation of the LR model yielded a prediction accuracy of 0.879 and revealed that most of the misclassified residues lie at the borders between substructures. We foresee machine learning models being used to identify stable substructures as candidates for building blocks to engineer new proteins.

Heat shock proteins 70 (Hsp70) are chaperones consisting of a nucleotide-binding domain (NBD) and a substrate-binding domain (SBD), the latter of which binds protein clients. After ATP binds to the NBD, the SBD α/β subdomains’ shared interface opens, and the open SBD docks to the NBD. Such allosteric effects are stabilized by the newly formed NBD-SBD interdomain contacts. In this paper, we examined how such an opening and formation of subdomain interfaces is affected during the evolution of Hsp70. In particular, insertion and deletion events (indels) can be highly disruptive for the mechanical events since such changes introduce a collective shift in the pairing interactions at communicating interfaces. Based on a multiple sequence alignment analysis of data collected from Swiss-Prot/UniProt database, we find several indel-free regions (IFR) in Hsp70. The two largest IFRs are located in interdomain regions that participate in allosteric structural changes. We speculate that the reason why the indels have a lower likelihood of occurrence in these regions is that indel events in these regions cause dysfunction in the protein due to perturbations of the mechanical balance. Thus, the development of functional allosteric machines requires including in the rational design a concept of the balance between structural elements.

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