Predicting the Specificity- Determining Positions of Receptor Tyrosine Kinase Axl

Analysis of the role of glucose metabolism-related genes in dilated cardiomyopathy based on bioinformatics

BACKGROUND

Dilated cardiomyopathy (DCM) is a prevalent condition with diverse etiologies, including viral infection, autoimmune response, and genetic factors. Despite the crucial role of energy metabolism in cardiac function, therapeutic targets for key genes in DCM's energy metabolism remain scarce.

METHODS

Our study employed the GSE79962 and GSE42955 datasets from the Gene Expression Omnibus (GEO) database for myocardial tissue sample collection and target gene identification via differential gene expression screening. Using various R packages, GSEA software, and the STRING database, we conducted data analysis, gene set enrichment, and protein-protein interaction predictions. The least absolute shrinkage and selection operator (LASSO) and Support Vector Machine (SVM) algorithms aided in feature gene selection, while the predictive model's efficiency was evaluated via the receiver operating characteristic (ROC) curve analysis. We used the non-negative matrix factorization (NMF) method for molecular typing and the cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm for predicting immune cell infiltration.

RESULTS

The DLAT and LDHA genes may regulate the immune microenvironment of DCM by influencing activated dendritic cells, activated mast cells, and M0 macrophages, respectively. The BPGM, DLAT, PGM2, ADH1A, ADH1C, LDHA, and PFKM genes may regulate m6A methylation in DCM by affecting the ZC3H13, ALKBH5, RBMX, HNRNPC, METTL3, and YTHDC1 genes. Further regulatory mechanism analysis suggested that PFKM, DLAT, PKLR, PGM2, LDHA, BPGM, ADH1A, and ADH1C could be involved in the development of cardiomyopathy by regulating the Toll-like receptor signaling pathway.

CONCLUSIONS

PFKM, DLAT, PKLR, PGM2, LDHA, BPGM, ADH1A, and ADH1C may serve as potential targets for guiding the diagnosis, treatment, and follow-up of DCM.

Characterization of Phosphorylation-Dependent Antibody Binding to Cancer-Mutated Linkers of C2H2 Zinc Finger Proteins by Intact Transition Epitope Mapping-Thermodynamic Weak-Force Order Analysis

With Intact Transition Epitope Mapping-Thermodynamic Weak-force Order (ITEM-TWO) analysis in combination with molecular modeling, the phosphorylation-dependent molecular recognition motif of the anti-HpTGEKP antibody has been investigated with binary and ternary component mixtures consisting of antibody and (phospho-) peptides. Amino acid sequences have been selected to match either the antibody's recognition motif or the cancer-related zinc finger protein mutations and phosphorylations of the respective amino acid residues. Upon electrospraying of all the components of the mixtures, that is, hexapeptides, antibody, and intact immune complexes, the produced ions were subjected to mass spectrometric mass filtering. The antibody ions as well as the immune complex ions traversed into the mass spectrometer's collision chamber, whereas paths of unbound peptide ions were blocked prior to entering the collision cell. After dissociation of the multiply charged immune complexes in the gas phase, the complex-released peptide ions were recorded after having traversed the second mass filter. Complex-released peptides were unambiguously identified by their masses using mass analysis with isotope resolution. From the results of our studies with seven (phospho-) peptides with distinct amino acid sequences, which resembled either the antibody's binding motif or mutations, we conclude the following: (i) A negatively charged phospho group, located near the peptide's N-terminus is mandatory for antibody binding when placed on the peptide surface at a precise distance to the C-terminally located positively charged ε-amino group of a lysinyl residue. (ii) A bulky amino acid residue, such as the tyrosinyl residue at the N-terminal position of the (phospho-) threoninyl residue, abolishes antibody binding. (iii) Two closely spaced phospho groups negatively interfere with the surface polarity pattern and abolish antibody binding as well. (iv) Non-phosphorylated peptides are not binding partners of the anti-HpTGEKP antibody.

Modeling the Dynamics of Protein–Protein Interfaces, How and Why?

Protein–protein assemblies act as a key component in numerous cellular processes. Their accurate modeling at the atomic level remains a challenge for structural biology. To address this challenge, several docking and a handful of deep learning methodologies focus on modeling protein–protein interfaces. Although the outcome of these methods has been assessed using static reference structures, more and more data point to the fact that the interaction stability and specificity is encoded in the dynamics of these interfaces. Therefore, this dynamics information must be taken into account when modeling and assessing protein interactions at the atomistic scale. Expanding on this, our review initially focuses on the recent computational strategies aiming at investigating protein–protein interfaces in a dynamic fashion using enhanced sampling, multi-scale modeling, and experimental data integration. Then, we discuss how interface dynamics report on the function of protein assemblies in globular complexes, in fuzzy complexes containing intrinsically disordered proteins, as well as in active complexes, where chemical reactions take place across the protein–protein interface.

A C-term truncated EIF2Bγ protein encoded by an intronically polyadenylated isoform introduces unfavorable EIF2Bγ-EIF2γ interactions

Eukaryotic translation initiates upon recruitment of the EIF2-GTP·Met-tRNA_i ternary complex (TC) to the ribosomes. EIF2 (α, β, γ subunits) is a GTPase. The GDP to GTP exchange within EIF2 is facilitated by the guanine nucleotide exchange factor EIF2B (α-ε subunits). During stress-induced conditions, phosphorylation of the α-subunit of EIF2 turns EIF2 into an inhibitor of EIF2B. In turn, inhibition of EIF2B decreases TC formation and triggers the internal stress response (ISR), which determines the cell fate. Deregulated ISR has been linked to neurodegenerative disorders and cancer, positioning EIF2B as a promising therapeutic target. Hence, a better understanding of the mechanisms/factors that regulate EIF2B activity is required. Here, combining transcript and protein level analyses, we describe an intronically polyadenylated (IPA) transcript of EIF2B's γ-subunit. We show that the IPA mRNA isoform is translated into a C-terminus truncated protein. Using structural modeling, we predict that the truncated EIF2Bγ protein has unfavorable interactions with EIF2γ, leading to a potential decrease in the stability of the nonproductive EIF2:EIF2B complex. While we discovered and confirmed the IPA mRNA isoform in breast cancer cells, the expression of this isoform is not cancer-specific and is widely present in normal tissues. Overall, our data show that a truncated EIF2Bγ protein co-exists with the canonical protein and is an additional player to regulate the equilibrium between productive and nonproductive states of the EIF2:EIF2B complex. These results may have implications in stress-induced translation control in normal and disease states. Our combinatorial approach demonstrates the need to study noncanonical mRNA and protein isoforms to understand protein interactions and intricate molecular mechanisms.