Bayesian network models identify cooperative GPCR:G protein interactions that contribute to G protein coupling.

Cooperative interactions in protein-protein interfaces demonstrate the interdependency or the linked network-like behavior and their effect on the coupling of proteins. Cooperative interactions also could cause ripple or allosteric effects at a distance in protein-protein interfaces. Although they are critically important in protein-protein interfaces, it is challenging to determine which amino acid pair interactions are cooperative. In this work, we have used Bayesian network modeling, an interpretable machine learning method, combined with molecular dynamics trajectories to identify the residue pairs that show high cooperativity and their allosteric effect in the interface of G protein-coupled receptor (GPCR) complexes with Gα subunits. Our results reveal six GPCR:Gα contacts that are common to the different Gα subtypes and show strong cooperativity in the formation of interface. Both the C terminus helix5 and the core of the G protein are codependent entities and play an important role in GPCR coupling. We show that a promiscuous GPCR coupling to different Gα subtypes, makes all the GPCR:Gα contacts that are specific to each Gα subtype (Gαs, Gαi, and Gαq). This work underscores the potential of data-driven Bayesian network modeling in elucidating the intricate dependencies and selectivity determinants in GPCR:G protein complexes, offering valuable insights into the dynamic nature of these essential cellular signaling components.

Differential CpG methylation at Nnat in the early establishment of beta cell heterogeneity.

AIMS/HYPOTHESIS: Beta cells within the pancreatic islet represent a heterogenous population wherein individual sub-groups of cells make distinct contributions to the overall control of insulin secretion. These include a subpopulation of highly connected 'hub' cells, important for the propagation of intercellular Ca2+ waves. Functional subpopulations have also been demonstrated in human beta cells, with an altered subtype distribution apparent in type 2 diabetes. At present, the molecular mechanisms through which beta cell hierarchy is established are poorly understood. Changes at the level of the epigenome provide one such possibility, which we explore here by focusing on the imprinted gene Nnat (encoding neuronatin [NNAT]), which is required for normal insulin synthesis and secretion. METHODS: Single-cell RNA-seq datasets were examined using Seurat 4.0 and ClusterProfiler running under R. Transgenic mice expressing enhanced GFP under the control of the Nnat enhancer/promoter regions were generated for FACS of beta cells and downstream analysis of CpG methylation by bisulphite sequencing and RNA-seq, respectively. Animals deleted for the de novo methyltransferase DNA methyltransferase 3 alpha (DNMT3A) from the pancreatic progenitor stage were used to explore control of promoter methylation. Proteomics was performed using affinity purification mass spectrometry and Ca2+ dynamics explored by rapid confocal imaging of Cal-520 AM and Cal-590 AM. Insulin secretion was measured using homogeneous time-resolved fluorescence imaging. RESULTS: Nnat mRNA was differentially expressed in a discrete beta cell population in a developmental stage- and DNA methylation (DNMT3A)-dependent manner. Thus, pseudo-time analysis of embryonic datasets demonstrated the early establishment of Nnat-positive and -negative subpopulations during embryogenesis. NNAT expression is also restricted to a subset of beta cells across the human islet that is maintained throughout adult life. NNAT+ beta cells also displayed a discrete transcriptome at adult stages, representing a subpopulation specialised for insulin production, and were diminished in db/db mice. 'Hub' cells were less abundant in the NNAT+ population, consistent with epigenetic control of this functional specialisation. CONCLUSIONS/INTERPRETATION: These findings demonstrate that differential DNA methylation at Nnat represents a novel means through which beta cell heterogeneity is established during development. We therefore hypothesise that changes in methylation at this locus may contribute to a loss of beta cell hierarchy and connectivity, potentially contributing to defective insulin secretion in some forms of diabetes. DATA AVAILABILITY: The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD048465.

Methylglyoxal-Derived Nucleoside Adducts Drive Vascular Dysfunction in a RAGE-Dependent Manner.

Diabetic kidney disease (DKD) is a leading cause of death in patients with diabetes. An early precursor to DKD is endothelial cell dysfunction (ECD), which often precedes and exacerbates vascular disease progression. We previously discovered that covalent adducts formed on DNA, RNA, and proteins by the reactive metabolic by-product methylglyoxal (MG) predict DKD risk in patients with type 1 diabetes up to 16 years pre-diagnosis. However, the mechanisms by which MG adducts contribute to vascular disease onset and progression remain unclear. Here, we report that the most predominant MG-induced nucleoside adducts, N2-(1-carboxyethyl)-deoxyguanosine (CEdG) and N2-(1-carboxyethyl)-guanosine (CEG), drive endothelial dysfunction. Following CEdG or CEG exposure, primary human umbilical vein endothelial cells (HUVECs) undergo endothelial dysfunction, resulting in enhanced monocyte adhesion, increased reactive oxygen species production, endothelial permeability, impaired endothelial homeostasis, and exhibit a dysfunctional transcriptomic signature. These effects were discovered to be mediated through the receptor for advanced glycation end products (RAGE), as an inhibitor for intracellular RAGE signaling diminished these dysfunctional phenotypes. Therefore, we found that not only are MG adducts biomarkers for DKD, but that they may also have a role as potential drivers of vascular disease onset and progression and a new therapeutic modality.

Oligo-PROTAC strategy for cell-selective and targeted degradation of activated STAT3.

Decoy oligodeoxynucleotides (ODNs) allow targeting undruggable transcription factors, such as STAT3, but their limited potency and lack of delivery methods hampered translation. To overcome these challenges, we conjugated a STAT3-specific decoy to thalidomide, a ligand to cereblon in E3 ubiquitin ligase complex, to generate a proteolysis-targeting chimera (STAT3DPROTAC). STAT3DPROTAC downregulated STAT3 in target cells, but not STAT1 or STAT5. Computational modeling of the STAT3DPROTAC ternary complex predicted two surface lysines, K601 and K626, in STAT3 as potential ubiquitination sites. Accordingly, K601/K626 point mutations in STAT3, as well as proteasome inhibition or cereblon deletion, alleviated STAT3DPROTAC effect. Next, we conjugated STAT3DPROTAC to a CpG oligonucleotide targeting Toll-like receptor 9 (TLR9) to generate myeloid/B cell-selective C-STAT3DPROTAC. Naked C-STAT3DPROTAC was spontaneously internalized by TLR9+ myeloid cells, B cells, and human and mouse lymphoma cells but not by T cells. C-STAT3DPROTAC effectively decreased STAT3 protein levels and also STAT3-regulated target genes critical for lymphoma cell proliferation and/or survival (BCL2L1, CCND2, and MYC). Finally, local C-STAT3DPROTAC administration to human Ly3 lymphoma-bearing mice triggered tumor regression, while control C-STAT3D and C-SCR treatments had limited effects. Our results underscore the feasibility of using a PROTAC strategy for cell-selective, decoy oligonucleotide-based STAT3 targeting of and potentially other tumorigenic transcription factors for cancer therapy.

LGR4 is essential for maintaining ß-cell homeostasis through suppression of RANK.

Pancreatic ß-cell stress contributes to diabetes progression. This study demonstrates that Leucine-rich repeat-containing G-protein-coupled-receptor-4 (LGR4) is critical for maintaining ß-cell health and is modulated by stressors. In vitro , Lgr4 knockdown decreases proliferation and survival in rodent ß-cells, while overexpression protects against cytokine-induced cell death in rodent and human ß-cells. Mechanistically, LGR4 suppresses Receptor Activator of Nuclear Factor Kappa B (NFκB) (RANK) and its subsequent activation of NFκB to protect ß-cells. ß-cell-specific Lgr4 -conditional knockout (cko) mice exhibit normal glucose homeostasis but increased ß-cell death in both sexes and decreased proliferation only in females. Male Lgr4 cko mice under stress display reduced ß-cell proliferation and a further increase in ß-cell death. Upon aging, both male and female Lgr4 cko mice display impaired ß-cell homeostasis, however, only female mice are glucose intolerant with decreased plasma insulin. We show that LGR4 is required for maintaining ß-cell health under basal and stress-induced conditions, through suppression of RANK. Teaser: LGR4 receptor is critical for maintaining ß-cell health under basal and stressed conditions, through suppression of RANK.

Leveraging Cancer Phenotypic Plasticity for Novel Treatment Strategies.

Cancer cells, like all other organisms, are adept at switching their phenotype to adjust to the changes in their environment. Thus, phenotypic plasticity is a quantitative trait that confers a fitness advantage to the cancer cell by altering its phenotype to suit environmental circumstances. Until recently, new traits, especially in cancer, were thought to arise due to genetic factors; however, it is now amply evident that such traits could also emerge non-genetically due to phenotypic plasticity. Furthermore, phenotypic plasticity of cancer cells contributes to phenotypic heterogeneity in the population, which is a major impediment in treating the disease. Finally, plasticity also impacts the group behavior of cancer cells, since competition and cooperation among multiple clonal groups within the population and the interactions they have with the tumor microenvironment also contribute to the evolution of drug resistance. Thus, understanding the mechanisms that cancer cells exploit to tailor their phenotypes at a systems level can aid the development of novel cancer therapeutics and treatment strategies. Here, we present our perspective on a team medicine-based approach to gain a deeper understanding of the phenomenon to develop new therapeutic strategies.