N-terminal methionine excision from newly synthesized proteins, catalyzed cotranslationally by methionine aminopeptidases (METAPs), is an essential and universally conserved process that plays a key ...role in cell homeostasis and protein biogenesis. However, how METAPs interact with ribosomes and how their cleavage specificity is ensured is unknown. We discovered that in eukaryotes the nascent polypeptide-associated complex (NAC) controls ribosome binding of METAP1. NAC recruits METAP1 using a long, flexible tail and provides a platform for the formation of an active methionine excision complex at the ribosomal tunnel exit. This mode of interaction ensures the efficient excision of methionine from cytosolic proteins, whereas proteins targeted to the endoplasmic reticulum are spared. Our results suggest a broader mechanism for how access of protein biogenesis factors to translating ribosomes is controlled.
The objective of the current study was to optimize the postprocessing pipeline of 7 T chemical exchange saturation transfer (CEST) imaging for reproducibility and to prove this optimization for the ...detection of age differences and differences between patients with Parkinson's disease versus normal subjects. The following 7 T CEST MRI experiments were analyzed: repeated measurements of a healthy subject, subjects of two age cohorts (14 older, seven younger subjects), and measurements of 12 patients with Parkinson's disease. A slab‐selective,
B1+‐homogeneous parallel transmit protocol was used. The postprocessing, consisting of motion correction, smoothing,
B0‐correction, normalization, denoising,
B1+‐correction and Lorentzian fitting, was optimized regarding the intrasubject and intersubject coefficient of variation (CoV) of the amplitudes of the amide pool and the aliphatic relayed nuclear Overhauser effect (rNOE) pool within the brain. Seven “tricks” for postprocessing accomplished an improvement of the mean voxel CoV of the amide pool and the aliphatic rNOE pool amplitudes of less than 5% and 3%, respectively. These postprocessing steps are: motion correction with interpolation of the motion of low‐signal offsets (1) using the amide pool frequency offset image as reference (2), normalization of the Z‐spectrum using the outermost saturated measurements (3),
B0 correction of the Z‐spectrum with moderate spline smoothing (4), denoising using principal component analysis preserving the 11 highest intensity components (5),
B1+ correction using a linear fit (6) and Lorentzian fitting using the five‐pool fit model (7). With the optimized postprocessing pipeline, a significant age effect in the amide pool can be detected. Additionally, for the first time, an aliphatic rNOE contrast between subjects with Parkinson's disease and age‐matched healthy controls in the substantia nigra is detected. We propose an optimized postprocessing pipeline for CEST multipool evaluation. It is shown that by the use of these seven “tricks”, the reproducibility and, thus, the statistical power of a CEST measurement, can be greatly improved and subtle changes can be detected.
The presented postprocessing pipeline provides increased homogeneity and reproducibility. The mean voxel CoV is decreased to less than 5% for amide and to less than 3% for aliphatic rNOE contrast. Healthy aging‐related changes and altered aliphatic rNOE pools in the substantia nigra of patients in the early stages of Parkinson's disease are detected.
Isolated evaluation of multiparametric in vivo chemical exchange saturation transfer (CEST) MRI often requires complex computational processing for both correction of B0 and B1 inhomogeneity and ...contrast generation. For that, sufficiently densely sampled Z‐spectra need to be acquired. The list of acquired frequency offsets largely determines the total CEST acquisition time, while potentially representing redundant information. In this work, a linear projection‐based multiparametric CEST evaluation method is introduced that offers fast B0 and B1 inhomogeneity correction, contrast generation and feature selection for CEST data, enabling reduction of the overall measurement time. To that end, CEST data acquired at 7 T in six healthy subjects and in one brain tumor patient were conventionally evaluated by interpolation‐based inhomogeneity correction and Lorentzian curve fitting. Linear regression was used to obtain coefficient vectors that directly map uncorrected data to corrected Lorentzian target parameters. L1‐regularization was applied to find subsets of the originally acquired CEST measurements that still allow for such a linear projection mapping. The linear projection method allows fast and interpretable mapping from acquired raw data to contrast parameters of interest, generalizing from healthy subject training data to unseen healthy test data and to the tumor patient dataset. The L1‐regularization method shows that a fraction of the acquired CEST measurements is sufficient to preserve tissue contrasts, offering up to a 2.8‐fold reduction of scan time. Similar observations as for the 7‐T data can be made for data from a clinical 3‐T scanner. Being a fast and interpretable computation step, the proposed method is complementary to neural networks that have recently been employed for similar purposes. The scan time acceleration offered by the L1‐regularization (“CEST‐LASSO”) constitutes a step towards better applicability of multiparametric CEST protocols in a clinical context.
A data‐driven evaluation approach for multiparametric in vivo CEST MRI is proposed that allows mapping of uncorrected Z‐spectra to target contrasts (APT, NOE, MT, amine) by a simple, fast, and interpretable linear projection. Applying L1‐regularization–based feature selection (CEST‐LASSO) shows that a fraction of the originally acquired CEST measurements is sufficient to preserve tissue contrasts, offering up to a 2.8‐fold reduction of scan time. This represents a step towards better applicability of multiparametric CEST protocols in a clinical context.
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