Win, Lose, or Tie: Mathematical Modeling of Ligand Competition at the Cell-Extracellular Matrix Interface

Ehrlichia SLiM ligand mimetic activates Hedgehog signaling to engage a BCL-2 anti-apoptotic cellular program

Ehrlichia chaffeensis (E. chaffeensis) has evolved eukaryotic ligand mimicry to repurpose multiple cellular signaling pathways for immune evasion. In this investigation, we demonstrate that TRP120 has a novel repetitive short linear motif (SLiM) that activates the evolutionarily conserved Hedgehog (Hh) signaling pathway to inhibit apoptosis. In silico analysis revealed that TRP120 has sequence and functional similarity with Hh ligands and a candidate Hh ligand SLiM was identified. siRNA knockdown of Hh signaling and transcriptional components significantly reduced infection. Co-immunoprecipitation and surface plasmon resonance demonstrated that rTRP120-TR interacted directly with Hh receptor Patched-2 (PTCH2). E. chaffeensis infection resulted in early upregulation of Hh transcription factor GLI-1 and regulation of Hh target genes. Moreover, soluble recombinant TRP120 (rTRP120) activated Hh and induced gene expression consistent with the eukaryotic Hh ligand. The TRP120-Hh-SLiM (NPEVLIKD) induced nuclear translocation of GLI-1 in THP-1 cells and primary human monocytes and induced a rapid and expansive activation of Hh pathway target genes. Furthermore, Hh activation was blocked by an α-TRP120-Hh-SLiM antibody. TRP120-Hh-SLiM significantly increased levels of Hh target, anti-apoptotic protein B-cell lymphoma 2 (BCL-2), and siRNA knockdown of BCL-2 dramatically inhibited infection. Blocking Hh signaling with the inhibitor Vismodegib, induced a pro-apoptotic cellular program defined by decreased mitochondria membrane potential, significant reductions in BCL-2, activation of caspase 3 and 9, and increased apoptotic cells. This study reveals a novel E. chaffeensis SLiM ligand mimetic that activates Hh signaling to maintain E. chaffeensis infection by engaging a BCL-2 anti-apoptotic cellular program.

Thyroid Hormone Disruption by Organophosphate Esters Is Mediated by Nuclear/Membrane Thyroid Hormone Receptors: In Vitro, In Vivo, and In Silico Studies

Earlier mechanistic studies of many prohibited flame retardants (FRs) highlighted their thyroid hormone-disrupting activity through nuclear thyroid hormone receptors (nTRs), whereas some alternative FRs such as organophosphate esters (OPEs) exerted weak nTR-disrupting effects. However, an increasing number of studies have revealed that OPEs also exert thyroid hormone-disrupting effects, and the underlying mechanism is unclear. Herein, the thyroid hormone-disrupting effects and mechanisms of 8 typical OPEs were investigated using integrated in vitro, in vivo, and in silico assays. All tested chemicals competitively bound to the membrane thyroid hormone receptor (mTR) [the 20% relative inhibitory concentration (RIC20): (3.5 ± 0.2) × 10¹ to (4.9 ± 1.0) × 10⁷ nM], and Cl-OPEs and alkyl-OPEs had lower RIC20 values. In contrast, only 4 OPEs showed nTR antagonistic activities at higher concentrations [≥ (4.8 ± 0.8) × 10³ nM]. Cl-OPEs and alkyl-OPEs preferentially interacted with mTR. Molecular docking illustrated that OPEs docked into mTRs, consistent with the competitive binding assay. In vivo analyses of zebrafish embryonic development confirmed that tris(1,3-dichloro-2-propyl) phosphate induced inappropriate expression of proteins, and these protein interactions might be associated with mTR according to the quantitative proteomic analysis. Based on the results, mTR might play a critical role in mediating the thyroid hormone-disrupting effects of OPEs.

ECM-LSE: Prediction of Extracellular Matrix Proteins Using Deep Latent Space Encoding of k-Spaced Amino Acid Pairs

Extracelluar matrix (ECM) proteins create complex networks of macromolecules which fill-in the extracellular spaces of living tissues. They provide structural support and play an important role in maintaining cellular functions. Identification of ECM proteins can play a vital role in studying various types of diseases. Conventional wet lab-based methods are reliable; however, they are expensive and time consuming and are, therefore, not scalable. In this research, we propose a sequence-based novel machine learning approach for the prediction of ECM proteins. In the proposed method, composition of k-spaced amino acid pair (CKSAAP) features are encoded into a classifiable latent space (LS) with the help of deep latent space encoding (LSE). A comprehensive ablation analysis is conducted for performance evaluation of the proposed method. Results are compared with other state-of-the-art methods on the benchmark dataset, and the proposed ECM-LSE approach has shown to comprehensively outperform the contemporary methods.