Biomedical subjects

Fei Gu

Publications and source records attributed to Fei Gu.

4 recordsLinked to original sources

PubMed2026

ARL6IP1 Inhibits Breast Cancer Tumor Progression by Targeting OLFM4 to Regulate Glycolysis.

INTRODUCTION: ARL6IP1 has been linked to cancer progression, but its precise role in BC, particularly in metabolism and its interaction with an OLFM4, remains unclear. AIMS: This study aimed to investigate the role of ADP-ribosylation factor-like 6 interacting protein 1 (ARL6IP1) in breast cancer (BC) cell behavior and metabolism and explore its interaction with an olfactomedin-4 (OLFM4) as a potential therapeutic target. OBJECTIVE: The objective of this study was to determine the effects of ARL6IP1 knockdown on BC cell proliferation, invasion, migration, apoptosis, oxidative stress, and glycolysis. Additionally, this study also explored the interaction between ARL6IP1 and OLFM4 and their combined role in BC progression and metabolism. METHODS: Key gene modules in the GSE73540 dataset were identified through weighted gene co-expression network analysis (WGCNA). Three BC-related datasets (GSE73540, GSE22820, and GSE36295) and The Cancer Genome Atlas (TCGA) were applied for additional examination of differentially expressed genes (DEGs). Intersection analysis selected ARL6IP1 as a hub gene for prognostic analysis. In vitro experiments investigated how ARL6IP1 knockdown influences BC cell proliferation, invasion, migration, apoptosis, epithelial-mesenchymal transition (EMT), oxidative stress, and glycolysis. The connection between ARL6IP1 and an OLFM4 was confirmed using Co-immunoprecipitation (Co-IP), and their roles in BC tumor progression and glycolysis were evaluated. RESULTS: ARL6IP1 was elevated in BC datasets and linked with poor BC prognosis. Experiments demonstrated that knockdown of ARL6IP1 significantly reduced BC cell growth while promoting apoptosis and oxidative stress. Besides, ARL6IP1 knockdown reduced glycolysis, as manifested by decreased extracellular acidification rate (ECAR), glucose consumption, adenosine triphosphate (ATP) levels, and lactate production while increasing mitochondrial respiration (OCR). Co-IP validated the connection between ARL6IP1 and OLFM4, and OLFM4 overexpression partially counteracted the suppression of glycolysis and cell behavior resulting from ARL6IP1 knockdown. CONCLUSION: ARL6IP1 is a critical regulator of BC progression, influencing glycolysis, mitochondrial function, and key cellular behaviors. Targeting the ARL6IP1-OLFM4 axis offers a promising therapeutic strategy for managing BC.

Humans↗

PubMed2006

Improved Chou-Fasman method for protein secondary structure prediction.

BACKGROUND: Protein secondary structure prediction is a fundamental and important component in the analytical study of protein structure and functions. The prediction technique has been developed for several decades. The Chou-Fasman algorithm, one of the earliest methods, has been successfully applied to the prediction. However, this method has its limitations due to low accuracy, unreliable parameters, and over prediction. Thanks to the recent development in protein folding type-specific structure propensities and wavelet transformation, the shortcomings in Chou-Fasman method are able to be overcome. RESULTS: We improved Chou-Fasman method in three aspects. (a) Replace the nucleation regions with extreme values of coefficients calculated by the continuous wavelet transform. (b) Substitute the original secondary structure conformational parameters with folding type-specific secondary structure propensities. (c) Modify Chou-Fasman rules. The CB396 data set was tested by using improved Chou-Fasman method and three indices: Q3, Qpre, SOV were used to measure this method. We compared the indices with those obtained from the original Chou-Fasman method and other four popular methods. The results showed that our improved Chou-Fasman method performs better than the original one in all indices, about 10-18% improvement. It is also comparable to other currently popular methods considering all the indices. CONCLUSION: Our method has greatly improved Chou-Fasman method. It is able to predict protein secondary structure as good as current popular methods. By locating nucleation regions with refined wavelet transform technology and by calculating propensity factors with larger size data set, it is likely to get a better result.

Algorithms↗

PubMed2002

Cloning and characterization of a mouse liver-specific gene mfrep-1, up-regulated in liver regeneration.

Human fibrinogen-related protein-1/liver fibrinogen-related protein-1 (HFREP-1/LFIRE-1), a liver-specific protein, is a member of fibrinogen superfamily that exerts various biological activities. However, the function of HFREP-1/LFIRE-1 in liver remains unknown. Here we isolated its mouse ortholog gene-mouse fibrinogen-related protein-1 (mfrep-1), which encoded 314 amino acids, exhibiting 80.4% similarity to HFREP-1/LFIRE-1. Northern blot analysis revealed that 1.2-kb mfrep-1 mRNA was detected selectively in mouse liver. To explore the function of MFREP-1, we examined the levels of mfrep-1 mRNA during regeneration after 70% partial hepatectomy (PHx) in mice. mfrep-1 mRNA increased in the regenerating liver and reached the first shoulder peak at 2-4 h after PHx. Cycloheximide pretreatment could suppress the induction of mfrep-1, indicating the up-regulation of this gene need de novo protein synthesis. Its mRNA continued to elevate at 6 h thereafter and reached the second peak at 24 h. The enhanced expression of mfrep-1 maintained high until 72 h and then declined slowly to the basal level. Immunohistochemistry assessment confirmed the up-regulated expression of MFREP-1 protein in parenchymal cells during liver regeneration. These data suggested that MFREP-1 might play an important role in liver regeneration and be involved in the regulation of cell growth.

Animals↗

PubMedDate not supplied

Design of diverse and focused combinatorial libraries using an alternating algorithm.

There is considerable research in chemistry to develop reaction conditions so that any of a very large number of reactants will successfully form new compounds, e.g. for two components, A(i) + B(j) --> A-B(ij). The numbers of A's and B's usually make it impossible to make all the possible products; with multicomponent reactions, there could easily be millions to billions of possible products. There is a need to identify subsets of reagents so that the resulting products have desirable predicted properties. Our idea is to select reactants sequentially and iteratively to optimize the evolving candidate library. The new Alternating Algorithm, AA, can be used for diversity, a space-filling design, or for a focused design, using either a near neighborhood or structure-activity relationship, SAR. A diversity design seeks to select compounds different from one another; a focused design seeks to find compounds similar to an active compound or compounds that follow a structure activity relationship. The benefit of the method is rapid computation of diversity or focused combinatorial chemical libraries.

Journal Article↗