Monitoring lactoferrin iron levels by fluorescence resonance energy transfer: A combined chemical and computational study

Three forms of lactoferrin (Lf) that differed in their levels of iron loading (Lf, LfFe, and LfFe2) were simultaneously labeled with the fluorophores AF350 and AF430. All three resulting fluorescent lactoferrins exhibited fluorescence resonance energy transfer (FRET), but they all presented different FRET patterns. Whereas only partial FRET was observed for Lf and LfFe, practically complete FRET was seen for the holo form (LfFe2). For each form of metal-loaded lactoferrin, the AF350–AF430 distance varied depending on the protein conformation, which in turn depended on the level of iron loading. Thus, the FRET patterns of these lactoferrins were found to correlate with their iron loading levels. In order to gain greater insight into the number of fluorophores and the different FRET patterns observed (i.e., their iron levels), a computational analysis was performed. The results highlighted a number of lysines that have the greatest influence on the FRET profile. Moreover, despite the lack of an X-ray structure for any LfFe species, our study also showed that this species presents modified subdomain organization of the N-lobe, which narrows its iron-binding site. Complete domain rearrangement occurs during the LfFe to LfFe2 transition. Finally, as an example of the possible applications of the results of this study, we made use of the FRET fingerprints of these fluorescent lactoferrins to monitor the interaction of lactoferrin with a healthy bacterium, namely Bifidobacterium breve. This latter study demonstrated that lactoferrin supplies iron to this bacterium, and suggested that this process occurs with no protein internalization.This work was supported by MINECO and FEDER (projects CTQ2012-32236, CTQ2011-23336, and BIO2012-39682-C02-02) and BIOSEARCH SA. F.C. and V.M.R. are grateful to the Spanish MINECO for FPI fellowships

mTORC1-mediated translational elongation limits intestinal tumour initiation and growth.

Inactivation of APC is a strongly predisposing event in the development of colorectal cancer, prompting the search for vulnerabilities specific to cells that have lost APC function. Signalling through the mTOR pathway is known to be required for epithelial cell proliferation and tumour growth, and the current paradigm suggests that a critical function of mTOR activity is to upregulate translational initiation through phosphorylation of 4EBP1 (refs 6, 7). This model predicts that the mTOR inhibitor rapamycin, which does not efficiently inhibit 4EBP1 (ref. 8), would be ineffective in limiting cancer progression in APC-deficient lesions. Here we show in mice that mTOR complex 1 (mTORC1) activity is absolutely required for the proliferation of Apc-deficient (but not wild-type) enterocytes, revealing an unexpected opportunity for therapeutic intervention. Although APC-deficient cells show the expected increases in protein synthesis, our study reveals that it is translation elongation, and not initiation, which is the rate-limiting component. Mechanistically, mTORC1-mediated inhibition of eEF2 kinase is required for the proliferation of APC-deficient cells. Importantly, treatment of established APC-deficient adenomas with rapamycin (which can target eEF2 through the mTORC1-S6K-eEF2K axis) causes tumour cells to undergo growth arrest and differentiation. Taken together, our data suggest that inhibition of translation elongation using existing, clinically approved drugs, such as the rapalogs, would provide clear therapeutic benefit for patients at high risk of developing colorectal cancer

A Macroecological Analysis of SERA Derived Forest Heights and Implications for Forest Volume Remote Sensing

Individual trees have been shown to exhibit strong relationships between DBH, height and volume. Often such studies are cited as justification for forest volume or standing biomass estimation through remote sensing. With resolution of common satellite remote sensing systems generally too low to resolve individuals, and a need for larger coverage, these systems rely on descriptive heights, which account for tree collections in forests. For remote sensing and allometric applications, this height is not entirely understood in terms of its location. Here, a forest growth model (SERA) analyzes forest canopy height relationships with forest wood volume. Maximum height, mean, H100, and Lorey's height are examined for variability under plant number density, resource and species. Our findings, shown to be allometrically consistent with empirical measurements for forested communities world-wide, are analyzed for implications to forest remote sensing techniques such as LiDAR and RADAR. Traditional forestry measures of maximum height, and to a lesser extent H100 and Lorey's, exhibit little consistent correlation with forest volume across modeled conditions. The implication is that using forest height to infer volume or biomass from remote sensing requires species and community behavioral information to infer accurate estimates using height alone. SERA predicts mean height to provide the most consistent relationship with volume of the height classifications studied and overall across forest variations. This prediction agrees with empirical data collected from conifer and angiosperm forests with plant densities ranging between 102–106 plants/hectare and heights 6–49 m. Height classifications investigated are potentially linked to radar scattering centers with implications for allometry. These findings may be used to advance forest biomass estimation accuracy through remote sensing. Furthermore, Lorey's height with its specific relationship to remote sensing physics is recommended as a more universal indicator of volume when using remote sensing than achieved using either maximum height or H100

Iron Behaving Badly: Inappropriate Iron Chelation as a Major Contributor to the Aetiology of Vascular and Other Progressive Inflammatory and Degenerative Diseases

The production of peroxide and superoxide is an inevitable consequence of aerobic metabolism, and while these particular "reactive oxygen species" (ROSs) can exhibit a number of biological effects, they are not of themselves excessively reactive and thus they are not especially damaging at physiological concentrations. However, their reactions with poorly liganded iron species can lead to the catalytic production of the very reactive and dangerous hydroxyl radical, which is exceptionally damaging, and a major cause of chronic inflammation. We review the considerable and wide-ranging evidence for the involvement of this combination of (su)peroxide and poorly liganded iron in a large number of physiological and indeed pathological processes and inflammatory disorders, especially those involving the progressive degradation of cellular and organismal performance. These diseases share a great many similarities and thus might be considered to have a common cause (i.e. iron-catalysed free radical and especially hydroxyl radical generation). The studies reviewed include those focused on a series of cardiovascular, metabolic and neurological diseases, where iron can be found at the sites of plaques and lesions, as well as studies showing the significance of iron to aging and longevity. The effective chelation of iron by natural or synthetic ligands is thus of major physiological (and potentially therapeutic) importance. As systems properties, we need to recognise that physiological observables have multiple molecular causes, and studying them in isolation leads to inconsistent patterns of apparent causality when it is the simultaneous combination of multiple factors that is responsible. This explains, for instance, the decidedly mixed effects of antioxidants that have been observed, etc...Comment: 159 pages, including 9 Figs and 2184 reference

Using machine learning to identify important predictors of COVID-19 infection prevention behaviors during the early phase of the pandemic

Before vaccines for coronavirus disease 2019 (COVID-19) became available, a set of infection-prevention behaviors constituted the primary means to mitigate the virus spread. Our study aimed to identify important predictors of this set of behaviors. Whereas social and health psychological theories suggest a limited set of predictors, machine-learning analyses can identify correlates from a larger pool of candidate predictors. We used random forests to rank 115 candidate correlates of infection-prevention behavior in 56,072 participants across 28 countries, administered in March to May 2020. The machine-learning model predicted 52% of the variance in infection-prevention behavior in a separate test sample—exceeding the performance of psychological models of health behavior. Results indicated the two most important predictors related to individual-level injunctive norms. Illustrating how data-driven methods can complement theory, some of the most important predictors were not derived from theories of health behavior—and some theoretically derived predictors were relatively unimportant

MNK inhibition sensitizes KRAS-mutant colorectal cancer to mTORC1 inhibition by reducing eIF4E phosphorylation and c-MYC expression.

KRAS-mutant colorectal cancers (CRC) are resistant to therapeutics, presenting a significant problem for ~40% of cases. Rapalogs, which inhibit mTORC1 and thus protein synthesis, are significantly less potent in KRAS-mutant CRC. Using Kras-mutant mouse models and mouse- and patient-derived organoids we demonstrate that KRAS with G12D mutation fundamentally rewires translation to increase both bulk and mRNA-specific translation initiation. This occurs via the MNK/eIF4E pathway culminating in sustained expression of c-MYC. By genetic and small molecule targeting of this pathway, we acutely sensitize KRASG12D models to rapamycin via suppression of c-MYC. We show that 45% of CRCs have high signaling through mTORC1 and the MNKs, with this signature correlating with a 3.5-year shorter cancer-specific survival in a subset of patients. This work provides a c-MYC-dependent co-targeting strategy with remarkable potency in multiple Kras-mutant mouse models and metastatic human organoids and identifies a patient population who may benefit from its clinical application