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Khajeh, K.

Publications and source records attributed to Khajeh, K..

2 recordsLinked to original sources

Deciphering Novel Transcriptional Wiring in Colorectal Cancer: An Integrative Bioinformatic and Experimental Study

Colorectal cancer remains among the most lethal malignancies worldwide, and the proliferative programme that sustains it has proved to be a challenging target, particularly with acceptable selectivity. Herein, we combined stage-resolved transcriptomic analysis with experimental testing in colorectal cancer cells to inquire whether small molecules, in particular melatonin, act on that programme. The comparison of stage II, III and IV colorectal tumours with normal tissue identified 410 genes upregulated at every stage as a core set, dominated by cell-cycle, spindle-assembly and chromosome-segregation functions. Twenty hub genes were extracted from the corresponding protein interaction network, thirteen of which were required for viability across 59 colorectal cancer cell lines in genome-wide CRISPR screening data. Target-set enrichment nominated E2F4, FOXM1, SIN3A and both DNA-binding subunits of NF-Y as upstream regulators. NF-YA and NF-YB were distinctive in one respect: their annotated targets include BUB1 and CCNA2 but exclude NCAPG, yielding a testable prediction. Our experimental results showed melatonin reduces SW480 viability with an IC of 2.63 mM and lowers BUB1 and CCNA2 expression in different manners of concentration-dependency, while NCAPG remains unchanged. Melatonin treatment arrests cells in G1 phase, causes a drastic fall in the cycling S-phase fraction, impairs the migration and proliferation phenotype, and rises apoptosis moderately. We also found {beta}2-microglobulin to be an unsuitable normalization reference gene for CRC research due to changes upon treatment. Selective repression of two NF-Y targets with sparing of a non-target is consistent with reduced NF-Y-dependent transcription, though occupancy and subunit-level evidence are to be established.

cell biology↗

Computational analysis of transition temperatures (Tts) of proteins fused to elastin-like polypeptide (ELP): deep fake evaluation of proteins, linkers, and trailers features

The phase transition temperature (Tt) prediction of the Elastin-like polypeptides (ELPs) is not trivial because it is related to complex sets of variables such as composition, sequence length, hydrophobic characterization, hydrophilic characterization, the sequence order in the fused proteins, linkers and trailer constructs. In this paper, two unique quantitative models are presented for the prediction of the Tt of a family of ELPs that could be fused to different proteins, linkers, and trailers. The lack of need to use multiple software, peptide information, such as PDB file, as well as knowing the second and third structures of proteins are the advantages of this model besides its high accuracy and speed. One of our models could predict the Tt values of the fused ELPs by entering the protein, linker, and trailer features with R2=99%. Also, another model is able to predict the Tt value by entering the fused protein feature with R2=96%. For more reliability, our method is enriched by Artificial Intelligence (AI) to generate similar proteins. In this regard, Generative Adversarial Network (GAN) is our AI method to create fake proteins and similar values. The experimental results show that our strategy for prediction of Tt is reliable in large data. Author SummaryThe application of Elastin-like polypeptides (ELPs) as a protein tag is now developed in a variety of biotechnology aspects especially in proteins purification and drug delivery. ELPs application as a protein tag is owed to retain the phase transition behavior when ELPs fused to other proteins, linkers and tags. ELPs undergo the phase transition behavior by changing from soluble phase to insoluble phase above its inverse transition temperature (Tt) within a short time span. This biophysical behavior is usually reversible at the temperature below the Tt. There are few reports for evaluation of the Tt of ELPs types by using the dissimilar equations and algorithms. Our current predictions are the most accurate calculations presented so far by using the protein, linker and trailer effects and the results were evaluated in accordance with the available experimental data. Furthermore, our results also show that our strategy for prediction of Tt is reliable in large data.

bioinformatics↗