Gene expression ranking change based single sample pre-disease state detection

Introduction

To prevent disease, it is of great importance to detect the critical point (pre-disease state) when the biological system abruptly transforms from normal to disease state. However, rapid and accurate pre-disease state detection is still a challenge when there is only a single sample available. The state transition of the biological system is driven by the variation in regulations between genes.

Methods

In this study, we propose a rapid single-sample pre-disease state-identifying method based on the change in gene expression ranking, which can reflect the coordinated shifts between genes, that is, S-PCR. The R codes of S-PCR can be accessed at https://github.com/ZhenshenBao/S-PCR.

Results

This model-free method is validated by the successful identification of pre-disease state for both simulated and five real datasets. The functional analyses of the pre-disease state-related genes identified by S-PCR also demonstrate the effectiveness of this computational approach. Furthermore, the time efficiency of S-PCR is much better than that of its peers.

Discussion

Hence, the proposed S-PCR approach holds immense potential for clinical applications in personalized disease diagnosis.

Proteomic study of medicinal mushroom extracts reveals antitumor mechanisms in an advanced colon cancer animal model via ribosomal biogenesis, translation, and metabolic pathways

Introduction

Colorectal cancer ranks as the third most common cancer in both men and women, with approximately 35% of cases being stage IV metastatic at diagnosis. Even with treatment advancements, the survival rates for these patients remain suboptimal. There is a significant focus on developing multi-targeted therapies due to the common issue of drug resistance in standard and targeted cancer treatments. Medicinal mushrooms, both as single compounds and as complex extracts, have undergone extensive research. Numerous types of mushrooms have been shown to be safe, effective inhibitors of cancer pathways and strong enhancers of the immune system.

Methods

In this study, we performed both qualitative and quantitative proteomic analyses using tandem mass tags (TMT) on CT26 wild type (CT26. WT) colon cancer tissues from Balb/c mice, which were treated with a special blend of medicinal mushroom extracts, either alone or in combination with the chemotherapy drug 5-fluorouracil.

Results

The results showed a notable increase in survival rates and indicated that medicinal mushroom preparation Agarikon Plus, both alone and combined with 5-fluorouracil or another medicinal mushroom preparation Agarikon.1, impedes multiple key processes in colorectal cancer progression. The analysis of differentially expressed proteins in treated groups was done by use of bioinformatics tools and a decrease in ribosomal biogenesis (e.g., RPS3) and translation processes (e.g., RPL14) as well as an increase in unfolded protein response (e.g., DNAJC3), lipid metabolism (e.g., ACOT7), and the tricarboxylic acid cycle (e.g., FH) were observed.

Conclusion

The treatment induced various alterations of known biomarkers and protein clusters critical to the progression and prognosis of colorectal cancer, laying a promising foundation for further translational research on this treatment modality.

Cell Cycle Complexity: Exploring the Structure of Persistent Subsystems in 414 Models

Background: The regulation of cellular proliferation and genomic integrity is controlled by complex surveillance mechanisms known as cell cycle checkpoints. Disruptions in these checkpoints can lead to developmental defects and tumorigenesis. Methods: To better understand these mechanisms, computational modeling has been employed, resulting in a dataset of 414 mathematical models in the BioModels database. These models vary significantly in detail and simulated processes, necessitating a robust analytical approach. Results: In this study, we apply the chemical organization theory (COT) to these models to gain insights into their dynamic behaviors. COT, which handles both ordinary and partial differential equations (ODEs and PDEs), is utilized to analyze the compartmentalized structures of these models. COT's framework allows for the examination of persistent subsystems within these models, even when detailed kinetic parameters are unavailable. By computing and analyzing the lattice of organizations, we can compare and rank models based on their structural features and dynamic behavior. Conclusions: Our application of the COT reveals that models with compartmentalized organizations exhibit distinctive structural features that facilitate the understanding of phenomena such as periodicity in the cell cycle. This approach provides valuable insights into the dynamics of cell cycle control mechanisms, refining existing models and potentially guiding future research in this area.

Novel insights into the role of bisphenol A (BPA) in genomic instability

Bisphenol A (BPA) is a phenolic chemical that has been used for over 50 years in the manufacturing of polycarbonate and polyvinyl chloride plastics, and it is one of the highest volume chemicals produced worldwide. Because BPA can bind to and activate estrogen receptors, studies have mainly focused on the effect of BPA in disrupting the human endocrine and reproductive systems. However, BPA also plays a role in promoting genomic instability and has been associated with initiating carcinogenesis. For example, it has been recently shown that exposure to BPA promotes the formation of single stranded DNA gaps, which may be associated with increased genomic instability. In this review, we outline the mechanisms by which BPA works to promote genomic instability including chromosomal instability, DNA adduct formation, ROS production, and estrogen receptor (ER) activation. Moreover, we define the ways in which BPA promotes both carcinogenesis and resistance to chemotherapy, and we provide critical insights into future directions and outstanding questions in the field.

Chromosome biology: Too big to fail

Spindles are microtubule-based machines that segregate chromosomes during cell division. Spindle morphology and dynamics are malleable based on forces within the spindle, and a new study reveals the extreme plasticity of the Saccharomyces cerevisiae spindle to adapt and segregate engineered mega-chromosomes.

CAMK2D serves as a molecular scaffold for RNF8-MAD2 complex to induce mitotic checkpoint in glioma

MAD2 is a spindle assembly checkpoint protein that participates in the formation of mitotic checkpoint complex, which blocks mitotic progression. RNF8, an established DNA damage response protein, has been implicated in mitotic checkpoint regulation but its exact role remains poorly understood. Here, RNF8 proximity proteomics uncovered a role of RNF8-MAD2 in generating the mitotic checkpoint signal. Specifically, RNF8 competes with a small pool of p31comet for binding to the closed conformer of MAD2 via its RING domain, while CAMK2D serves as a molecular scaffold to concentrate the RNF8-MAD2 complex via transient/weak interactions between its p-Thr287 and RNF8's FHA domain. Accordingly, RNF8 overexpression impairs glioma stem cell (GSC) mitotic progression in a FHA- and RING-dependent manner. Importantly, low RNF8 expression correlates with inferior glioma outcome and RNF8 overexpression impedes GSC tumorigenicity. Last, we identify PLK1 inhibitor that mimics RNF8 overexpression using a chemical biology approach, and demonstrate a PLK1/HSP90 inhibitor combination that synergistically reduces GSC proliferation and stemness. Thus, our study has unveiled a previously unrecognized CAMK2D-RNF8-MAD2 complex in regulating mitotic checkpoint with relevance to gliomas, which is therapeutically targetable.

Improved delivery of Mcl-1 and survivin siRNA combination in breast cancer cells with additive siRNA complexes

This study aimed at investigating the influence of commercial transfection reagents (Prime-Fect, Leu-Fect A, and Leu-Fect C) complexed with different siRNAs (CDC20, HSP90, Mcl-1 and Survivin) in MDA-MB-436 breast cancer cells and the impact of incorporating an anionic additive, Trans-Booster, into siRNA formulations for improving in vitro gene silencing and delivery efficiency. Gene silencing was quantitatively analyzed by real-time RT-PCR while cell proliferation and siRNA uptake were evaluated by the MTT assay and flow cytometry, respectively. Amongst the investigated siRNAs and transfection reagents, Mcl-1/Prime-Fect complexes showed the highest inhibition of cell viability and the most effective siRNA delivery. The effect of various formulations on transfection efficiency showed that the additive with 1:1 ratio with siRNA was optimal achieving the lowest cell viability compared to untreated cells and negative control siRNA treatment (p < 0.05). Furthermore, the combination of Mcl-1 and survivin siRNA suppressed the growth of MDA-MB-436 cells more effectively than treatment with the single siRNAs and resulted in cell viability as low as ~ 20% (vs. non-treated cells). This aligned well with the induction of apoptosis as analyzed by flow cytometry, which revealed higher apoptotic cells with the combination treatment group. We conclude that commercial transfection reagents formulated with Mcl-1/Survivin siRNA combination could serve as a potent anti-proliferation agent in the treatment of breast cancers.

The optimal strategy balancing risk and speed predicts DNA damage checkpoint override times

Checkpoints arrest biological processes allowing time for error correction. The phenomenon of checkpoint override (also known as checkpoint adaptation, slippage, or leakage), during cellular self-replication is biologically critical but currently lacks a quantitative, functional, or system-level understanding. To uncover fundamental laws governing error-correction systems, we derived a general theory of optimal checkpoint strategies, balancing the trade-off between risk and self-replication speed. Mathematically, the problem maps onto the optimization of an absorbing boundary for a random walk. We applied the theory to the DNA damage checkpoint (DDC) in budding yeast, an intensively researched model checkpoint. Using novel reporters for double-strand DNA breaks (DSBs), we first quantified the probability distribution of DSB repair in time including rare events and, secondly, the survival probability after override. With these inputs, the optimal theory predicted remarkably accurately override times as a function of DSB numbers, which we measured precisely for the first time. Thus, a first-principles calculation revealed undiscovered patterns underlying highly noisy override processes. Our multi-DSB measurements revise well-known past results and show that override is more general than previously thought.

A supervised machine learning approach identifies gene-regulating factor-mediated competing endogenous RNA networks in hormone-dependent cancers

Competing endogenous RNAs (ceRNAs) have become an emerging topic in cancer research due to their role in gene regulatory networks. To date, traditional ceRNA bioinformatic studies have investigated microRNAs as the only factor regulating gene expression. Growing evidence suggests that genomic (e.g., copy number alteration [CNA]), transcriptomic (e.g., transcription factors [TFs]), and epigenomic (e.g., DNA methylation [DM]) factors can influence ceRNA regulatory networks. Herein, we used the Least absolute shrinkage and selection operator regression, a machine learning approach, to integrate DM, CNA, and TFs data with RNA expression to infer ceRNA networks in cancer risk. The gene‐regulating factors‐mediated ceRNA networks were identified in four hormone‐dependent (HD) cancer types: prostate, breast, colorectal, and endometrial. The shared ceRNAs across HD cancer types were further investigated using survival analysis, functional enrichment analysis, and protein–protein interaction network analysis. We found two (BUB1 and EXO1) and one (RRM2) survival‐significant ceRNA(s) shared across breast‐colorectal‐endometrial and prostate–colorectal–endometrial combinations, respectively. Both BUB1 and BUB1B genes were identified as shared ceRNAs across more than two HD cancers of interest. These genes play a critical role in cell division, spindle‐assembly checkpoint signalling, and correct chromosome alignment. Furthermore, shared ceRNAs across multiple HD cancers have been involved in essential cancer pathways such as cell cycle, p53 signalling, and chromosome segregation. Identifying ceRNAs' roles across multiple related cancers will improve our understanding of their shared disease biology. Moreover, it contributes to the knowledge of RNA‐mediated cancer pathogenesis.

On the Regulation of Mitosis by the Kinetochore, a Macromolecular Complex and Organising Hub of Eukaryotic Organisms

The kinetochore is the multiprotein complex of eukaryotic organisms that is assembled on mitotic or meiotic centromeres to connect centromeric DNA with microtubules. Its function involves the coordinated action of more than 100 different proteins. The kinetochore acts as an organiser hub that establishes physical connections with microtubules and centromere-associated proteins and recruits central protein components of the spindle assembly checkpoint (SAC), an evolutionarily conserved surveillance mechanism of eukaryotic organisms that detects unattached kinetochores and destabilises incorrect kinetochore-microtubule attachments. The molecular communication between the kinetochore and the SAC is highly dynamic and tightly regulated to ensure that cells can progress towards anaphase until each chromosome is properly bi-oriented on the mitotic spindle. This is achieved through an interplay of highly cooperative interactions and concerted phosphorylation/dephosphorylation events that are organised in time and space.This contribution discusses our current understanding of the function, structure and regulation of the kinetochore, in particular, how its communication with the SAC results in the amplification of specific signals to exquisitely control the eukaryotic cell cycle. This contribution also addresses recent advances in machine learning approaches, cell imaging and proteomics techniques that have enhanced our understanding of the molecular mechanisms that ensure the high fidelity and timely segregation of the genetic material every time a cell divides as well as the current challenges in the study of this fascinating molecular machine.