Increased mTOR activity and metabolic efficiency in mouse and human cells containing the African-centric tumor-predisposing p53 variant Pro47Ser

The Pro47Ser variant of p53 (S47) exists in African-descent populations and is associated with increased cancer risk in humans and mice. Due to impaired repression of the cystine importe

An African-Specific Variant of TP53 Reveals PADI4 as a Regulator of p53-Mediated Tumor Suppression

TP53 is the most frequently mutated gene in cancer, yet key target genes for p53-mediated tumor suppression remain unidentified. Here, we characterize a rare, African-specific germline variant of TP53 in the DNA-binding domain Tyr107His (Y107H). Nuclear magnetic resonance and crystal structures reveal that Y107H is structurally similar to wild-type p53. Consistent with this, we find that Y107H can suppress tumor colony formation and is impaired for the transactivation of only a small subset of p53 target genes; this includes the epigenetic modifier PADI4, which deiminates arginine to the nonnatural amino acid citrulline. Surprisingly, we show that Y107H mice develop spontaneous cancers and metastases and that Y107H shows impaired tumor suppression in two other models. We show that PADI4 is itself tumor suppressive and that it requires an intact immune system for tumor suppression. We identify a p53–PADI4 gene signature that is predictive of survival and the efficacy of immune-checkpoint inhibitors. Significance: We analyze the African-centric Y107H hypomorphic variant and show that it confers increased cancer risk; we use Y107H in order to identify PADI4 as a key tumor-suppressive p53 target gene that contributes to an immune modulation signature and that is predictive of cancer survival and the success of immunotherapy

Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition)

In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. For example, a key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process versus those that measure fl ux through the autophagy pathway (i.e., the complete process including the amount and rate of cargo sequestered and degraded). In particular, a block in macroautophagy that results in autophagosome accumulation must be differentiated from stimuli that increase autophagic activity, defi ned as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (inmost higher eukaryotes and some protists such as Dictyostelium ) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the fi eld understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. It is worth emphasizing here that lysosomal digestion is a stage of autophagy and evaluating its competence is a crucial part of the evaluation of autophagic flux, or complete autophagy. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. Along these lines, because of the potential for pleiotropic effects due to blocking autophagy through genetic manipulation it is imperative to delete or knock down more than one autophagy-related gene. In addition, some individual Atg proteins, or groups of proteins, are involved in other cellular pathways so not all Atg proteins can be used as a specific marker for an autophagic process. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field

Interleukin -6 -induced STAT3 and AP-1 amplify hepatocyte nuclear factor -1 -mediated transactivation of hepatic genes, an adaptive response to liver injury

The insulin-like growth factor binding protein-1 (IGFBP-1) gene is highly expressed in fetal and regenerating liver. Upregulation is transcriptionally mediated in regenerating liver and occurs in the first few minutes to hours after partial hepatectomy. In transgenic mice a 970 bp region from −776 to +151 of the IGFBP-1 promoter was sufficient for tissue specific and induced expression of the gene in fetal and hepatectomized livers. However weak and/or poorly regulated expression in some transgenic lines suggested the existence of other regulatory regions. Novel tissue-specific sites that interacted with C/EBP and HNF3 transcription factors were identified in the −3100 region. A hepatectomy induced DNA binding complex containing the transcription factor USF1 was identified within the −100 to −300 region of the promoter. However, cytokine stimulation of hepatic IGFBP-1 production, in particular the stimulatory effect of interleukin-6 (IL-6), is a proposed mechanism to account for the disruption of the acute inverse relationship between insulin and IGFBP-1 levels reported in a few clinical conditions. Evidence for a biologic role of IL-6 in IGFBP-1 upregulation was demonstrated by increased expression of hepatic IGFBP-1 in IL-6 transgenic and following injection of IL-6 into non-fasting animals, and reduced expression in IL-6−/− livers posthepatectomy. In both hepatic and nonhepatic cells, IL-6 mediated IGFBP-1 promoter activation was via an intact hepatocyte nuclear factor (HNF)-1 site and was dependent on the presence of HNF-1 and induced factors STAT3 and AP-1 (c-Fos/c-Jun). HNF-1/c-Fos and HNF-1/STAT3 protein complexes were detected in mouse livers and in hepatic and nonhepatic cell lines overexpressing STAT3/c-Fos/HNF-1, and further confirmed in vitro, with recombinant proteins, and in vivo, during transient transfection. Direct physical interactions between HNF-1α/STAT3, HNF-1α/c-Fos, and STAT3/c-Fos were verified using bacterially expressed STAT3, c-Fos, and GST-HNF-1α. HNF-1/IL-6/STAT3/AP-1 mediated transactivation of hepatic gene expression is a general phenomenon after liver injury as verified using glucose-6 phosphatase and α-fibrinogen promoters. These results demonstrate that the two classes of transcription factors, growth induced (STAT3 and AP-1) and tissue specific (HNF-1), can interact as an adaptive response to liver injury to amplify expression of hepatic genes important for the homeostatic response during organ repair

P53 regulates cellular redox state, ferroptosis and metabolism

The tumor protein P53 (TP53, or p53) has complex and at times seemingly contradictory roles in the regulation of metabolism and ferroptosis sensitivity. We find that the actions of p53 influence the redox state, which can trigger changes in redox-sensitive proteins, thereby modifying metabolic processes and response to ferroptosis

Comparison of the substrate-bound HSP70 SBD to other DnaK and HSC70 SBD structures.

<p>A. Comparison of the NRLLLTG-bound HSP70 SBD to the NRLLLTG-bound DnaK SBD (PDB code 1DKZ). HSP70 and DnaK are shown in magenta and blue cartoons, respectively. The corresponding peptide substrates are shown as stick models. B. A close-up view of the hydrophobic interactions within the helical bundle region of the α subdomain. Hydrophobic residues are shown as stick models. C. Comparison of the SBDα subdomain of the human HSP70-NRLLLTG structure with the isolated three-helix bundle region of the same protein in X-ray structure (PDB code 3LOF, blue) and NMR solution structure (PDB code 2LMG, green). D, Structural alignment of the SBDα subdomain of the HSP70-NRLLLTG structure with the three-helix bundle region of <i>C. elegans</i> Hsp70 (PDB code 2P32, cyan) and DnaK (PDB code 1DKZ, blue). E. Superposition of the lid subdomains of human HSP70 and rat HSC70. HSP70 (MolA) and HSC70 are colored in magenta and cyan, respectively. F. A close-up view of the interaction between the SBDα and SBDβ subdomains of HSP70. Interacting residues are highlighted as sticks, and hydrogen bonds are indicated with dotted lines.</p

NRLLLTG peptide substrate binding by HSP70.

<p>A. Omit electron density of the peptide substrate bound to the β subdomain of molecule A is contoured at 3.0 sigma. The NRLLLTG peptide is shown as a stick model in CPK coloring. B. Peptide binding site highlighting the flanking L_1,2 and L_3,4, and supporting L_5,6 and L_4,5 loops. The NRLLLTG peptide and arch residues are shown as stick models. C. Detailed view of the NRLLLTG-binding pocket in HSP70-NRLLLTG highlighting a network of van der Waals contacts with Leu5_p of the NRLLLTG peptide. D. A close-up view of the interactions between Leu4_p-Leu5_p-Thr6_p of the NRLLLTG peptide and the surrounding residues in HSP70-NRLLLTG. All interacting residues are shown as sticks and hydrogen bonds are shown as dotted lines.</p

Overall structure of the human peptide-bound HSP70 SBD.

<p>A. Cartoon representation of NRLLLTG-bound HSP70 SBD Molecule A. The SBD and NRLLLTG peptide are shown in magenta and blue, respectively, and all domains and secondary structural elements are labeled. B. Superposition of molecules A and B in the asymmetric unit cell. Molecules A and B are color-coded magenta and green, respectively. C and D. Hinge region between the α and β subdomains. Molecules A and B are shown in panels C and D, respectively. Interacting (MolA) or potential interacting (MolB) residues are indicated in stick and CPK coloring (oxygen in red and nitrogen in blue). The electron density maps corresponding to these residues are contoured at 1.5 (MolA) and 1.0 (MolB) sigma. Hydrogen bonds are indicated with dotted lines.</p

Sequence alignment of eukaryotic and bacterial HSP70 proteins.

<p>Human HSP70 (HSPA1A or HSP72), HSPA2, HSPA5 (GRP78 or BiP), HSC70 (HSPA8 or HSP73), HSPA9 (GRP75, Mortalin-2 or MTHSP70) and <i>E.coli</i> DnaK (ecDnaK), <i>E. coli</i> HscA, <i>Geobacillus kaustophilus HTA426</i> DnaK (gkDnaK) sequences are used for the alignment. Secondary structure elements and residue numbering for HSP70 is indicated above the protein sequence. The hinge region (also known as loop L_α,β) is highlighted in a red rectangular box, the arch residues are indicated with red asterisks, residues involved in NRLLLTG Leu5_p substrate binding are indicated with green asterisks, and the juncture region residue Asn540 is indicated with a blue asterisk. The conserved hydrophobic residues in the helical bundle region are indicated with red triangles.</p