Measuring transverse relaxation in highly paramagnetic systems

The enhancement of nuclear relaxation rates due to the interaction with a paramagnetic center (known as Paramagnetic Relaxation Enhancement) is a powerful source of structural and dynamics information, widely used in structural biology. However, many signals affected by the hyperfine interaction relax faster than the evolution periods of common NMR experiments and therefore they are broadened beyond detection. This gives rise to a so-called blind sphere around the paramagnetic center, which is a major limitation in the use of PREs. Reducing the blind sphere is extremely important in paramagnetic metalloproteins. The identification, characterization, and proper structural restraining of the first coordination sphere of the metal ion(s) and its immediate neighboring regions is key to understand their biological function. The novel HSQC scheme we propose here, that we termed R2-weighted, HSQC-AP, achieves this aim by detecting signals that escaped detection in a conventional HSQC experiment and provides fully reliable R2 values in the range of 1H R2 rates ca. 50–400 s−1. Independently on the type of paramagnetic center and on the size of the molecule, this experiment decreases the radius of the blind sphere and increases the number of detectable PREs. Here, we report the validation of this approach for the case of PioC, a small protein containing a high potential 4Fe-4S cluster in the reduced [Fe4S4]2+ form. The blind sphere was contracted to a minimal extent, enabling the measurement of R2 rates for the cluster coordinating residues.publishersversionpublishe

A non-systematic approach

Funding Information: This work benefited from access to CERM/CIRMMP, the Instruct-ERIC Italy centre. Financial support was provided by European EC Horizon 2020 TIMB3 (Project 810856) Instruct-ERIC (PID 4509). This article is based upon work from COST Action CA15133, supported by COST (European Cooperation in Science and Technology) . Fondazione Ente Cassa di Risparmio di Firenze ( CRF 2016 0985 ) is acknowledged for providing fellowship to MI. This work was funded by national funds through FCT– Fundação para a Ciência e a Tecnologia , I.P., Project MOSTMICRO-ITQB with refs UIDB/04612/2020 and UIDP/04612/2020, and Fundação para a Ciência e a Tecnologia (FCT) Portugal is acknowledged for Grant PD/BD/135187/2017 to IBT. Funding Information: This work benefited from access to CERM/CIRMMP, the Instruct-ERIC Italy centre. Financial support was provided by European EC Horizon 2020 TIMB3 (Project 810856) Instruct-ERIC (PID 4509). This article is based upon work from COST Action CA15133, supported by COST (European Cooperation in Science and Technology). Fondazione Ente Cassa di Risparmio di Firenze (CRF 2016 0985) is acknowledged for providing fellowship to MI. This work was funded by national funds through FCT? Funda??o para a Ci?ncia e a Tecnologia, I.P. Project MOSTMICRO-ITQB with refs UIDB/04612/2020 and UIDP/04612/2020, and Funda??o para a Ci?ncia e a Tecnologia (FCT) Portugal is acknowledged for Grant PD/BD/135187/2017 to IBT. Publisher Copyright: © 2020 The Author(s) Copyright: Copyright 2020 Elsevier B.V., All rights reserved.The complete assignment of 1H, 13C and 15N protein signals, which is a straightforward task for diamagnetic proteins provided they are folded, soluble and with a molecular mass below 30,000 Da, often becomes an intractable problem in the presence of a paramagnetic center. Indeed, the hyperfine interaction quenches signal intensity; this prevents the detection of scalar and dipolar connectivities and the sequential assignment of protein regions close to the metal ion(s). However, many experiments can be optimized and novel experiments can be designed to circumvent the problem and to revive coherences invisible in standard experiments. The small HiPIP protein PioC provides an interesting case to address this issue: the prosthetic group is a [Fe4S4]2+ cluster that is bound to the 54 amino acids protein via four cysteine residues. The four cluster-bound cysteine residues adopt different binding conformations and therefore each cysteine is affected by paramagnetic relaxation to different extent. A network of tailored experiments succeeded to obtain the complete resonance assignment of cluster bound residues.publishersversionpublishe

1H, 13C and 15N assignment of the paramagnetic high potential iron–sulfur protein (HiPIP) PioC from Rhodopseudomonas palustris TIE-1

High potential iron–sulfur proteins (HiPIPs) are a class of small proteins (50–100 aa residues), containing a 4Fe–4S iron–sulfur cluster. The 4Fe–4S cluster shuttles between the oxidation states [Fe4S4]3+/2+, with a positive redox potential in the range (500–50 mV) throughout the different known HiPIPs. Both oxidation states are paramagnetic at room temperature. HiPIPs are electron transfer proteins, isolated from photosynthetic bacteria and usually provide electrons to the photosynthetic reaction-center. PioC, the HIPIP isolated from Rhodopseudomonas palustris TIE-1, is the smallest among all known HiPIPs. Despite their small dimensions, an extensive NMR assignment is only available for two of them, because paramagnetism prevents the straightforward assignment of all resonances. We report here the complete NMR assignment of 1H, 13C and 15N signals for the reduced [Fe4S4]2+ state of the protein. A set of double and triple resonance experiments performed with standardized parameters/datasets provided the assignment of about 72% of the residues. The almost complete resonance assignment (99.5% of backbone and ca. 90% of side chain resonances) was achieved by combining the above information with those obtained using a second set of NMR experiments, in which acquisition and processing parameters, as well as pulse sequences design, were optimized to account for the peculiar features of this paramagnetic protein.publishersversionpublishe

Optimizing Electroactive Organisms: The Effect of Orthologous Proteins

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Active and poised promoter states drive folding of the extended HoxB locus in mouse embryonic stem cells

Gene expression states influence the three-dimensional conformation of the genome through poorly understood mechanisms. Here, we investigate the conformation of the murine HoxB locus, a gene-dense genomic region containing closely spaced genes with distinct activation states in mouse embryonic stem (ES) cells. To predict possible folding scenarios, we performed computer simulations of polymer models informed with different chromatin occupancy features, which define promoter activation states or CTCF binding sites. Single cell imaging of the locus folding was performed to test model predictions. While CTCF occupancy alone fails to predict the in vivo folding at genomic length scale of 10 kb, we found that homotypic interactions between active and Polycomb-repressed promoters co-occurring in the same DNA fibre fully explain the HoxB folding patterns imaged in single cells. We identify state-dependent promoter interactions as major drivers of chromatin folding in gene-dense regions

Characterisation of Innate Fungal Recognition in the Lung

The innate recognition of fungi by leukocytes is mediated by pattern recognition receptors (PRR), such as Dectin-1, and is thought to occur at the cell surface triggering intracellular signalling cascades which lead to the induction of protective host responses. In the lung, this recognition is aided by surfactant which also serves to maintain the balance between inflammation and pulmonary function, although the underlying mechanisms are unknown. Here we have explored pulmonary innate recognition of a variety of fungal particles, including zymosan, Candida albicans and Aspergillus fumigatus, and demonstrate that opsonisation with surfactant components can limit inflammation by reducing host-cell fungal interactions. However, we found that this opsonisation does not contribute directly to innate fungal recognition and that this process is mediated through non-opsonic PRRs, including Dectin-1. Moreover, we found that pulmonary inflammatory responses to resting Aspergillus conidia were initiated by these PRRs in acidified phagolysosomes, following the uptake of fungal particles by leukocytes. Our data therefore provides crucial new insights into the mechanisms by which surfactant can maintain pulmonary function in the face of microbial challenge, and defines the phagolysosome as a novel intracellular compartment involved in the innate sensing of extracellular pathogens in the lung

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

8 Extracellular Redox Chemistry

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