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Protein localization within cells can be achieved by the targeting and localized translation of mRNA. Yet, our understanding of the dynamics of mRNA targeting and protein localization, and of how general this phenomenon is, is not clear. Plasmid-based expression systems have been used to visualize exogenously expressed mRNAs and proteins; however, these methods typically produce them at levels greater than endogenous and can result in mislocalization. Hence, a method that allows for the simultaneous visualization of endogenous mRNAs and their translation products in living cells is needed. We previously developed a method (m-TAG) to localize endogenously expressed mRNAs in yeast by chromosomal insertion of the MS2 aptamer sequence between the open-reading frame (ORF) and 3' UTR of any gene. Upon coexpression with the MS2 RNA-binding coat protein (MS2-CP) fused with GFP, the aptamer-tagged mRNAs bearing their 3' UTRs are localized using fluorescence microscopy. Here we describe an advanced method (mp-TAG) that allows for the simultaneous visualization of both endogenously expressed mRNAs and their translation products in living yeast for the first time. Homologous recombination is used to insert the mCherry gene and MS2-CP binding sites downstream from any ORF, in order to localize protein and mRNA, respectively. As proof of the concept, we tagged ATP2 as a representative gene and demonstrated that endogenous ATP2 mRNA and protein localize to mitochondria, as shown previously. In addition, we demonstrate that tagged proteins like Hhf2, Vph1, and Yef3 localize to their expected subcellular location, while the localization of their mRNAs is revealed for the first time.

Targeted mRNA localization is a likely determinant of localized protein synthesis. To investigate whether mRNAs encoding mitochondrial proteins (mMPs) localize to mitochondria and, thus, might confer localized protein synthesis and import, we visualized endogenously expressed mMPs in vivo for the first time. We determined the localization of 24 yeast mMPs encoding proteins of the mitochondrial matrix, outer and inner membrane, and intermembrane space and found that many mMPs colocalize with mitochondria in vivo. This supports earlier cell fractionation and microarray-based studies that proposed mMP association with the mitochondrial fraction. Interestingly, a number of mMPs showed a dependency on the mitochondrial Puf3 RNA-binding protein, as well as nonessential proteins of the translocase of the outer membrane (TOM) complex import machinery, for normal colocalization with mitochondria. We examined the specific determinants of ATP2 and OXA1 mRNA localization and found a mutual dependency on the 3' UTR, Puf3, Tom7, and Tom70, but not Tom20, for localization. Tom6 may facilitate the localization of specific mRNAs as OXA1, but not ATP2, mRNA was mislocalized in tom6Δ cells. Interestingly, a substantial fraction of OXA1 and ATP2 RNA granules colocalized with the endoplasmic reticulum (ER) and a deletion in MDM10, which mediates mitochondria–ER tethering, resulted in a significant loss of OXA1 mRNA localization with ER. Finally, neither ATP2 nor OXA1 mRNA targeting was affected by a block in translation initiation, indicating that translation may not be essential for mRNA anchoring. Thus, endogenously expressed mRNAs are targeted to the mitochondria in vivo, and multiple factors contribute to mMP localization.

MDA-MB-231 human breast cancer cells have a survival signal generated by phospholipase D (PLD) that involves the activation of mTOR and MAP kinase. TGF-β signals that block cell cycle progression in G1are suppressed in MDA-MB-231 cells. We report here that the elevated PLD activity in MDA-MB-231 cells suppresses TGF-β signaling. Suppression of PLD activity or PLD expression resulted in increased phosphorylation of Smad2 and Smad3 on Ser 465/467 – sites on Smads that get phosphorylated by the TGF-β receptor and positively regulate TGF-β signaling. The effect of PLD suppression on Smad2/3 phosphorylation was dependent on the presence of TGF-β. Suppression of PLD also suppressed phosphorylation of Smad2 on Ser 245/250/255 – sites that are phosphorylated by MAP kinase and negatively regulate TGF-β signaling. Suppression of PLD also led to increased expression of the cyclin-dependent kinase (CDK) inhibitors p21Cip1 and p27Kip1, the expression of which is stimulated in response to TGF-β. Consistent with the elevated expression of CDK inhibitors, suppression of PLD also suppressed phosphorylation of the CDK substrate pRb. Similar effects were also seen in PANC-1 human pancreatic cancer cells. The data presented here indicate that the suppressed TGF-β signaling in MDA-MB-231 and perhaps many other human cancer cells is due to elevated PLD activity and mediated by mTOR and MAP kinase. These results indicate that the survival signals generated by PLD involve the suppression TGF-β signals that promote G1arrest.

Background:Gaucher disease (GD) is a rare, autosomal recessive condition, characterized by deficiency of the lysosomal enzyme β-glucocerebrosidase. The main disease features are anemia, thrombocytopenia, hepato-splenomegaly and bone infarction, osteonecrosis, and pathological fractures. However, diagnosis of GD can be challenging, especially for non-specialists, owing to wide variability in age at presentation, non-specific features, severity and type of clinical manifestations, and lack of awareness of the early signs and symptoms of the disease. Delayed and misdiagnosis of GD may lead to irreversible bone disease, severe growth retardation, and high risk of bleeding; in rare cases, misdiagnosis may be life-threatening. Developing a system for early and accurate diagnosis of GD is thus an essential unmet need. The development of an algorithm for early diagnosis of patients with rare diseases such as GD may help reduce delays in diagnosis and enable prompt, appropriate initiation of therapy, earlier decision-making, prevent potentially irreversible morbidities and unnecessary tests (some invasive), reduce anxiety, and facilitate genetic counseling. This study aims to develop a predictive model for the accurate diagnosis of GD using machine learning based on real-world clinical data.