Unveiling the Dynamics behind Glioblastoma Multiforme Single-Cell Data Heterogeneity.

Glioblastoma Multiforme is a brain tumor distinguished by its aggressiveness. We suggested that this aggressiveness leads single-cell RNA-sequence data (scRNA-seq) to span a representative portion of the cancer attractors domain. This conjecture allowed us to interpret the scRNA-seq heterogeneity as reflecting a representative trajectory within the attractor's domain. We considered factors such as genomic instability to characterize the cancer dynamics through stochastic fixed points. The fixed points were derived from centroids obtained through various clustering methods to verify our method sensitivity. This methodological foundation is based upon sample and time average equivalence, assigning an interpretative value to the data cluster centroids and supporting parameters estimation. We used stochastic simulations to reproduce the dynamics, and our results showed an alignment between experimental and simulated dataset centroids. We also computed the Waddington landscape, which provided a visual framework for validating the centroids and standard deviations as characterizations of cancer attractors. Additionally, we examined the stability and transitions between attractors and revealed a potential interplay between subtypes. These transitions might be related to cancer recurrence and progression, connecting the molecular mechanisms of cancer heterogeneity with statistical properties of gene expression dynamics. Our work advances the modeling of gene expression dynamics and paves the way for personalized therapeutic interventions.

Optimizing therapeutic targets for breast cancer using boolean network models.

Studying gene regulatory networks associated with cancer provides valuable insights for therapeutic purposes, given that cancer is fundamentally a genetic disease. However, as the number of genes in the system increases, the complexity arising from the interconnections between network components grows exponentially. In this study, using Boolean logic to adjust the existing relationships between network components has facilitated simplifying the modeling process, enabling the generation of attractors that represent cell phenotypes based on breast cancer RNA-seq data. A key therapeutic objective is to guide cells, through targeted interventions, to transition from the current cancer attractor to a physiologically distinct attractor unrelated to cancer. To achieve this, we developed a computational method that identifies network nodes whose inhibition can facilitate the desired transition from one tumor attractor to another associated with apoptosis, leveraging transcriptomic data from cell lines. To validate the model, we utilized previously published in vitro experiments where the downregulation of specific proteins resulted in cell growth arrest and death of a breast cancer cell line. The method proposed in this manuscript combines diverse data sources, conducts structural network analysis, and incorporates relevant biological knowledge on apoptosis in cancer cells. This comprehensive approach aims to identify potential targets of significance for personalized medicine.

A Strategy Utilizing Protein-Protein Interaction Hubs for the Treatment of Cancer Diseases.

We describe a strategy for the development of a rational approach of neoplastic disease therapy based on the demonstration that scale-free networks are susceptible to specific attacks directed against its connective hubs. This strategy involves the (i) selection of up-regulated hubs of connectivity in the tumors interactome, (ii) drug repurposing of these hubs, (iii) RNA silencing of non-druggable hubs, (iv) in vitro hub validation, (v) tumor-on-a-chip, (vi) in vivo validation, and (vii) clinical trial. Hubs are protein targets that are assessed as targets for rational therapy of cancer in the context of personalized oncology. We confirmed the existence of a negative correlation between malignant cell aggressivity and the target number needed for specific drugs or RNA interference (RNAi) to maximize the benefit to the patient's overall survival. Interestingly, we found that some additional proteins not generally targeted by drug treatments might justify the addition of inhibitors designed against them in order to improve therapeutic outcomes. However, many proteins are not druggable, or the available pharmacopeia for these targets is limited, which justifies a therapy based on encapsulated RNAi.

A Data Science Approach for the Identification of Molecular Signatures of Aggressive Cancers.

The main hallmarks of cancer include sustaining proliferative signaling and resisting cell death. We analyzed the genes of the WNT pathway and seven cross-linked pathways that may explain the differences in aggressiveness among cancer types. We divided six cancer types (liver, lung, stomach, kidney, prostate, and thyroid) into classes of high (H) and low (L) aggressiveness considering the TCGA data, and their correlations between Shannon entropy and 5-year overall survival (OS). Then, we used principal component analysis (PCA), a random forest classifier (RFC), and protein-protein interactions (PPI) to find the genes that correlated with aggressiveness. Using PCA, we found GRB2, CTNNB1, SKP1, CSNK2A1, PRKDC, HDAC1, YWHAZ, YWHAB, and PSMD2. Except for PSMD2, the RFC analysis showed a different list, which was CAD, PSMD14, APH1A, PSMD2, SHC1, TMEFF2, PSMD11, H2AFZ, PSMB5, and NOTCH1. Both methods use different algorithmic approaches and have different purposes, which explains the discrepancy between the two gene lists. The key genes of aggressiveness found by PCA were those that maximized the separation of H and L classes according to its third component, which represented 19% of the total variance. By contrast, RFC classified whether the RNA-seq of a tumor sample was of the H or L type. Interestingly, PPIs showed that the genes of PCA and RFC lists were connected neighbors in the PPI signaling network of WNT and cross-linked pathways.

Data-Driven Modeling of Breast Cancer Tumors Using Boolean Networks.

Cancer is a genomic disease involving various intertwined pathways with complex cross-communication links. Conceptually, this complex interconnected system forms a network, which allows one to model the dynamic behavior of the elements that characterize it to describe the entire system's development in its various evolutionary stages of carcinogenesis. Knowing the activation or inhibition status of the genes that make up the network during its temporal evolution is necessary for the rational intervention on the critical factors for controlling the system's dynamic evolution. In this report, we proposed a methodology for building data-driven boolean networks that model breast cancer tumors. We defined the network components and topology based on gene expression data from RNA-seq of breast cancer cell lines. We used a Boolean logic formalism to describe the network dynamics. The combination of single-cell RNA-seq and interactome data enabled us to study the dynamics of malignant subnetworks of up-regulated genes. First, we used the same Boolean function construction scheme for each network node, based on canalyzing functions. Using single-cell breast cancer datasets from The Cancer Genome Atlas, we applied a binarization algorithm. The binarized version of scRNA-seq data allowed identifying attractors specific to patients and critical genes related to each breast cancer subtype. The model proposed in this report may serve as a basis for a methodology to detect critical genes involved in malignant attractor stability, whose inhibition could have potential applications in cancer theranostics.

Trypanosoma brucei RRP44 is involved in an early stage of large ribosomal subunit RNA maturation.

Ribosomal RNA precursors undergo a series of structural and chemical modifications to generate matured RNA molecules that will comprise ribosomes. This maturation process involves a large set of accessory proteins as well as ribonucleases, responsible for removal of the external and internal transcribed spacers from the pre-rRNA. Early-diverging eukaryotes belonging to the Kinetoplastida class display several unique characteristics, in particular in terms of RNA synthesis and maturation. These peculiarities include the rRNA biogenesis and the extensive fragmentation of the large ribosomal subunit (LSU) rRNA. The role of specific endo- and exonucleases in the maturation of the unusual rRNA precursor of trypanosomatids remains largely unknown. One of the nucleases involved in rRNA processing is Rrp44, an exosome associated ribonuclease in yeast, which is involved in several metabolic RNA pathways. Here, we investigated the function of Trypanosoma brucei RRP44 orthologue (TbRRP44) in rRNA processing. Our results revealed that TbRRP44 depletion causes unusual polysome profile and accumulation of the complete LSU rRNA precursor, in addition to 5.8S maturation impairment. We also determined the crystal structure of TbRRP44 endonucleolytic domain. Structural comparison with Saccharomyces cerevisiae Rrp44 revealed differences in the catalytic site and substitutions of surface residues, which could provide molecular bases for the lack of interaction of RRP44 with the exosome complex in T. brucei.

Low resolution structure of the human alpha4 protein (IgBP1) and studies on the stability of alpha4 and of its yeast ortholog Tap42.

The yeast Tap42 and mammalian alpha4 proteins belong to a highly conserved family of regulators of the type 2A phosphatases, which participate in the rapamycin-sensitive signaling pathway, connecting nutrient availability to cell growth. The mechanism of regulation involves binding of Tap42 to Sit4 and PPH21/22 in yeast and binding of alpha4 to the catalytic subunits of type 2A-related phosphatases PP2A, PP4 and PP6 in mammals. Both recombinant proteins undergo partial proteolysis, generating stable N-terminal fragments. The full-length proteins and alpha4 C-terminal deletion mutants at amino acids 222 (alpha4Delta222), 236 (alpha4Delta236) and 254 (alpha4Delta254) were expressed in E. coli. alpha4Delta254 undergoes proteolysis, producing a fragment similar to the one generated by full-length alpha4, whereas alpha4Delta222 and alpha4Delta236 are highly stable proteins. alpha4 and Tap42 show alpha-helical circular dichroism spectra, as do their respective N-terminal proteolysis resistant products. The cloned truncated proteins alpha4Delta222 and alpha4Delta236, however, possess a higher content of alpha-helix, indicating that the C-terminal region is less structured, which is consistent with its higher sensitivity to proteolysis. In spite of their higher secondary structure content, alpha4Delta222 and alpha4Delta236 showed thermal unfolding kinetics similar to the full-length alpha4. Based on small angle X-ray scattering (SAXS), the calculated radius of gyration for alpha4 and Tap42 were 41.2 +/- 0.8 A and 42.8 +/- 0.7 A and their maximum dimension approximately 142 A and approximately 147 A, respectively. The radii of gyration for alpha4Delta222 and alpha4Delta236 were 21.6 +/- 0.3 A and 25.7 +/- 0.2 A, respectively. Kratky plots show that all studied proteins show variable degree of compactness. Calculation of model structures based on SAXS data showed that alpha4Delta222 and alpha4Delta236 proteins have globular conformation, whereas alpha4 and Tap42 exhibit elongated shapes.

Concurrence of fragile X and Klinefelter syndromes: report of a new case of paternal nondisjunction.

The concurrence of fragile X and Klinefelter syndromes would be expected occasionally. Therefore, the analysis of the literature showed that the concurrence of both conditions was found at least 16 times. Among them, only seven cases were analyzed for the parental origin of the extra chromosome X, suggesting that the maternal nondisjunction was preferentially inherited. We present the third patient with the concurrence of fragile X and Klinefelter syndromes, in which the parental origin of the supernumerary chromosome X was paternal. This finding reinforces that the parent-of-origin predisposition of the concurrence of the fragile X and Klinefelter syndromes is a pure coincidence.