TrhA, a bacterial progestin and adiponectin receptor homolog, couples membrane energetics homeostasis and unsaturated fatty acid metabolism.

Members of the widely conserved progestin and adipoQ receptor (PAQR) family function to maintain membrane homeostasis: membrane fluidity and fatty acid composition in eukaryotes and membrane energetics and fatty acid composition in bacteria. All PAQRs consist of a core seven transmembrane domain structure and five conserved amino acids (three histidines, one serine, and one aspartic acid) predicted to form a hydrolase-like catalytic site. PAQR homologs in Bacteria (called TrhA, for transmembrane homeostasis protein A) maintain homeostasis of membrane charge gradients, like the membrane potential and proton gradient that comprise the proton motive force, but their molecular mechanisms are not yet understood. Here, we show that TrhA in Escherichia coli has a periplasmic C-terminus, which places the five conserved residues shared by all PAQRs at the cytoplasmic interface of the membrane. Here, we characterize several conserved residues predicted to form an active site by site-directed mutagenesis. We also identify a specific role for TrhA in modulating unsaturated fatty acid biosynthesis with conserved residues required to either promote or reduce the abundance of unsaturated fatty acids. We also identify distinct roles for the conserved residues in supporting TrhA's role in maintaining membrane energetics homeostasis that suggest that both functions are intertwined and probably partly dependent on one another. An analysis of domain architecture of TrhA-like domains in Bacteria further supports a function of TrhA linking membrane energetics homeostasis with biosynthesis of unsaturated fatty acid in the membrane. IMPORTANCE Progestin and adipoQ receptor (PAQR) family proteins are evolutionary conserved regulators of membrane homeostasis and have been best characterized in eukaryotes. Bacterial PAQR homologs, named TrhA (transmembrane homeostasis protein A), regulate membrane energetics homeostasis through an unknown mechanism. Here, we present evidence linking TrhA to both membrane energetics homeostasis and unsaturated fatty acid biosynthesis. Analysis of domain architecture together with experimental evidence suggests a model where TrhA activity on unsaturated fatty acid biosynthesis is regulated by changes in membrane energetics to dynamically adjust membrane homeostasis.

Bacterial Homologs of Progestin and AdipoQ Receptors (PAQRs) Affect Membrane Energetics Homeostasis but Not Fluidity.

Membrane potential homeostasis is essential for cell survival. Defects in membrane potential lead to pleiotropic phenotypes, consistent with the central role of membrane energetics in cell physiology. Homologs of the progestin and AdipoQ receptors (PAQRs) are conserved in multiple phyla of Bacteria and Eukarya. In eukaryotes, PAQRs are proposed to modulate membrane fluidity and fatty acid (FA) metabolism. The role of bacterial homologs has not been elucidated. Here, we use Escherichia coli and Bacillus subtilis to show that bacterial PAQR homologs, which we name "TrhA," have a role in membrane energetics homeostasis. Using transcriptional fusions, we show that E. coli TrhA (encoded by yqfA) is part of the unsaturated fatty acid biosynthesis regulon. Fatty acid analyses and physiological assays show that a lack of TrhA in both E. coli and B. subtilis (encoded by yplQ) provokes subtle but consistent changes in membrane fatty acid profiles that do not translate to control of membrane fluidity. Instead, membrane proteomics in E. coli suggested a disrupted energy metabolism and dysregulated membrane energetics in the mutant, though it grew similarly to its parent. These changes translated into a disturbed membrane potential in the mutant relative to its parent under various growth conditions. Similar dysregulation of membrane energetics was observed in a different E. coli strain and in the distantly related B. subtilis. Together, our findings are consistent with a role for TrhA in membrane energetics homeostasis, through a mechanism that remains to be elucidated. IMPORTANCE Eukaryotic homologs of the progestin and AdipoQ receptor family (PAQR) have been shown to regulate membrane fluidity by affecting, through unknown mechanisms, unsaturated fatty acid (FA) metabolism. The bacterial homologs studied here mediate small and consistent changes in unsaturated FA metabolism that do not seem to impact membrane fluidity but, rather, alter membrane energetics homeostasis. Together, the findings here suggest that bacterial and eukaryotic PAQRs share functions in maintaining membrane homeostasis (fluidity in eukaryotes and energetics for bacteria with TrhA homologs).

CCM signaling complex (CSC) couples both classic and non-classic Progesterone receptor signaling.

BACKGROUND: Breast cancer, the most diagnosed cancer, remains the second leading cause of cancer death in the United States, and excessive Progesterone (PRG) or Mifepristone (MIF) exposure may be at an increased risk for developing breast cancer. PRG exerts its cellular responses through signaling cascades involving classic, non-classic, or combined responses by binding to either classic nuclear PRG receptors (nPRs) or non-classic membrane PRG receptors (mPRs). Currently, the intricate balance and switch mechanisms between these two signaling cascades remain elusive. Three genes, CCM1-3, form the CCM signaling complex (CSC) which mediates multiple signaling cascades. METHODS: Utilizing molecular, cellular, Omics, and systems biology approaches, we analyzed the relationship among the CSC, PRG, and nPRs/mPRs during breast cancer tumorigenesis. RESULTS: We discovered that the CSC plays an essential role in coupling both classic and non-classic PRG signaling pathways by mediating crosstalk between them, forming the CmPn (CSC-mPRs-PRG-nPRs) signaling network. We found that mPR-specific PRG actions (PRG + MIF) play an essential role in this CmPn network during breast cancer tumorigenesis. Additionally, we have identified 4 categories of candidate biomarkers (9 intrinsic, 2 PRG-inducible, 1 PRG-repressive, 1 mPR-specific PRG-repressive, and 2 mPR-responsive) for Luminal-A breast cancers during tumorigenesis and have confirmed the prognostic application of RPL13 and RPL38 as intrinsic biomarkers using a dual validation method. CONCLUSIONS: We have discovered that the CSC plays an essential role in the CmPn signaling network for Luminal-A breast cancers with identification of two intrinsic biomarkers. Video Abstract.

Membrane Progesterone Receptors (mPRs/PAQRs) Are Going beyond Its Initial Definitions.

Progesterone (PRG) is a key cyclical reproductive hormone that has a significant impact on female organs in vertebrates. It is mainly produced by the corpus luteum of the ovaries, but can also be generated from other sources such as the adrenal cortex, Leydig cells of the testes and neuronal and glial cells. PRG has wide-ranging physiological effects, including impacts on metabolic systems, central nervous systems and reproductive systems in both genders. It was first purified as an ovarian steroid with hormonal function for pregnancy, and is known to play a role in pro-gestational proliferation during pregnancy. The main function of PRG is exerted through its binding to progesterone receptors (nPRs, mPRs/PAQRs) to evoke cellular responses through genomic or non-genomic signaling cascades. Most of the existing research on PRG focuses on classic PRG-nPR-paired actions such as nuclear transcriptional factors, but new evidence suggests that PRG also exerts a wide range of PRG actions through non-classic membrane PRG receptors, which can be divided into two sub-classes: mPRs/PAQRs and PGRMCs. The review will concentrate on recently found non-classical membrane progesterone receptors (mainly mPRs/PAQRs) and speculate their connections, utilizing the present comprehension of progesterone receptors.

Zinc's Association with the CmPn/CmP Signaling Network in Breast Cancer Tumorigenesis.

It is well-known that serum and cellular concentrations of zinc are altered in breast cancer patients. Specifically, there are notable zinc hyper-aggregates in breast tumor cells when compared to normal mammary epithelial cells. However, the mechanisms responsible for zinc accumulation and the consequences of zinc dysregulation are poorly understood. In this review, we detailed cellular zinc regulation/dysregulation under the influence of varying levels of sex steroids and breast cancer tumorigenesis to try to better understand the intricate relationship between these factors based on our current understanding of the CmPn/CmP signaling network. We also made some efforts to propose a relationship between zinc signaling and the CmPn/CmP signaling network.

A new method to determine the QRS axis-QRS axis determination.

BACKGROUND: The development of a perfect method for determining the mean QRS axis (ÂQRS) is still lacking. HYPOTHESIS: We proposed a new simple method, whether this method is accurate is unknown. METHODS: The axis perpendicular to the mean QRS axis (P-ÂQRS) divides six limb leads into two groups. All the leads that are in the range of 180° along the ÂQRS are positive, while all the leads in another 180° are negative, one lead is isodiphasic if it is on the P-ÂQRS. If no lead is isodiphasic, then the P-ÂQRS is located in the middle of two adjacent leads, which can help us determine the P-ÂQRS. The six limb leads that fall in the range of -30° to 120° are as follows: aVL (-30°), I (0°), -aVR (30°), II (60°), aVF (90°), and III (120°). We can check an external lead (aVL or III) first. For example, if lead III is isodiphasic and lead aVF is positive, the P-ÂQRS is 120°; if lead III is negative and lead aVF is positive, then the P-ÂQRS is 105°. If more than one lead is negative, all such leads can be checked individually until a positive or isodiphasic lead is found. The ÂQRS can be easily decided once we know the P-ÂQRS. In total, 200 recorded ECGs were investigated. We obtained the ÂQRS from our new method, computer interpretations, and a standard bipolar method. The Pearson correlation coefficient and Bland-Altman analysis were performed. RESULTS: The mean and SDs were remarkably similar, the correlation coefficient between the P-ÂQRS method and the bipolar method was 0.976 (P < .001). Mean bias (Bland-Altman limits of agreement) between the two methods was 0.885 (-12.37 to 14.14). CONCLUSION: The new method is simple and is able to assess the mean QRS axis accurately.