Biology subjects

Dabirmanesh, B.

Publications and source records attributed to Dabirmanesh, B..

3 recordsLinked to original sources

bioRxiv2026

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↗

bioRxiv2026

Absence of 8-HDF and MTHF Antenna Chromophore Binding in ErCRY4a Suggests a Possible Flavin-Only Cofactor State: Insights from Biochemical and Computational Analyses

Cryptochromes and photolyases are blue-light-sensitive flavoproteins that generally bind flavin adenine dinucleotide (FAD) and have distinct functions. Cryptochrome 4a (CRY4a) is a protein expressed in the double-cone photoreceptors of the retina in migratory songbirds like European robin (Erithacus rubecula) and is hypothesized as the primary sensor for avian magnetoreception. In addition to FAD, most photolyases and some cryptochromes bind antenna chromophores such as 8-hydroxy-5-deazaflavin (8-HDF) or 5,10-methenyltetrahydrofolate (MTHF) to enhance light absorption. Here, we investigated whether Erithacus rubecula Cryptochrome 4a (ErCRY4a) also binds 8-HDF and/or MTHF. 8-HDF binding was studied by co-expressing ErCRY4a with the fbIC gene that encodes for 8-HDF synthase and thus for production of 8-HDF in E. coli. As a positive control for 8-HDF binding, we expressed Xenopus laevis 6-4 photolyase (Xl6-4PL) which is known to bind both FAD and 8-HDF. This experiment resulted in successful binding of 8-HDF to Xl6-4PL, but not to ErCRY4a. We studied the binding of MTHF using in vitro reconstitution followed by UV-Vis spectroscopy and isothermal titration calorimetry (ITC) assays. No interaction was observed between MTHF and ErCRY4a. To theoretically understand the binding of potential antenna chromophores to ErCRY4a, we performed computational analyses. We found no similarity at the relevant binding sites between the sequences of ErCRY4a with proteins shown to bind MTHF or 8-HDF. This suggests that the binding pocket is not conserved. Our study proposes that ErCRY4a only harbor one light-sensitive cofactor, which in turn suggests a functional specialization different from most photolyases.

biochemistry↗

bioRxiv2021

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↗