Biology subjects

Tietze, A.

Publications and source records attributed to Tietze, A..

4 recordsLinked to original sources

bioRxiv2023

Macromolecule modelling for improved metabolite quantification using short echo time brain 1H-MRS at 3 T and 7 T: The PRaMM Model

PurposeTo improve reliability of metabolite quantification at both, 3 T and 7 T, we propose a novel parametrized macromolecules quantification model (PRaMM) for brain 1H MRS, in which the ratios of macromolecule peak intensities are used as soft constraints. MethodsFull- and metabolite-nulled spectra were acquired in three different brain regions with different ratios of grey and white matter from six healthy volunteers, at both 3 T and 7 T. Metabolite-nulled spectra were used to identify highly correlated macromolecular signal contributions and estimate the ratios of their intensities. These ratios were then used as soft constraints in the proposed PRaMM model for quantification of full spectra. The PRaMM model was validated by comparison with a single component macromolecule model and a macromolecule subtraction technique. Moreover, the influence of the PRaMM model on the repeatability and reproducibility compared to those other methods was investigated. ResultsThe developed PRaMM model performed better than the two other approaches in all three investigated brain regions. Several estimates of metabolite concentration and their Cramer-Rao lower bounds were affected by the PRaMM model reproducibility, and repeatability of the achieved concentrations were tested by evaluating the method on a second repeated acquisitions dataset. While the observed effects on both metrics were not significant, the fit quality metrics were improved for the PRaMM method (p[≤]0.0001). Minimally detectable changes are in the range 0.5 - 1.9 mM and percent coefficients of variations are lower than 10% for almost all the clinically relevant metabolites. Furthermore, potential overparameterization was ruled out. ConclusionHere, the PRaMM model, a method for an improved quantification of metabolites was developed, and a method to investigate the role of the MM background and its individual components from a clinical perspective is proposed.

neuroscience↗

bioRxiv2021

The Functional Nanopore (FuN) Screen: A Versatile Genetic Assay to Study and Engineer Protein Nanopores in Escherichia coli

Nanopores comprise a versatile class of membrane proteins that carry out a range of key physiological functions and are increasingly developed for different biotechnological applications. Yet, a capacity to study and engineer protein nanopores by combinatorial means has so far been hampered by a lack of suitable assays that combine sufficient experimental resolution with throughput. Addressing this technological gap, the Functional Nanopore (FuN) screen now provides a quantitative and dynamic read-out of nanopore assembly and function in E. coli. The assay is based on genetically-encoded fluorescent protein sensors that resolve the nanopore-dependent influx of Ca2+ across the inner membrane of E. coli. Illustrating its versatile capacity, the FuN screen is first applied to dissect the molecular features that underlie the assembly and stability of nanopores formed by the S2168 holin. In a subsequent step, nanopores are engineered by recombining the transmembrane module of S2168 with different ring-shaped oligomeric protein structures that feature defined hexa-, hepta- and octameric geometries. Library screening highlights substantial plasticity in the ability of the S2168 transmembrane module to oligomerize in alternative geometries while the functional properties of the resultant nanopores can be fine-tuned through the identity of the connecting linkers. Overall, the FuN screen is anticipated to facilitate both fundamental studies and complex nanopore engineering endeavors with many potential applications in biomedicine, biotechnology and synthetic biology.

synthetic biology↗

bioRxiv2018

Modification and de novo design of non-ribosomal peptide synthetases (NRPS) using specific assembly points within condensation domains

Many important natural products are produced by non-ribosomal peptide synthetases (NRPSs) 1.These giant enzyme machines activate amino acids in an assembly line fashion in which a set of catalytically active domains is responsible for the section, activation, covalent binding and connection of a specific amino acid to the growing peptide chain 1,2. Since NRPS are not restricted to the incorporation of the 20 proteinogenic amino acids, their efficient manipulation would give access to a diverse range of peptides available biotechnologically. Here we describe a new fusion point inside condensation (C) domains of NRPSs that enables the efficient production of peptides, even containing non-natural amino acids, in yields higher than 280 mg/L. The technology called eXchange Unit 2.0 (XU2.0) also allows the generation of targeted peptide libraries and therefore might be suitable for the future identification of bioactive peptide derivatives for pharmaceutical and other applications.

synthetic biology↗

bioRxiv2018

Lead-DBS v2: Toward a comprehensive pipeline for deep brain stimulation imaging

Deep brain stimulation (DBS) is a highly efficacious treatment option for movement disorders and a growing number of other indications are investigated in clinical trials. To ensure optimal treatment outcome, exact electrode placement is required. Moreover, to analyze the relationship between electrode location and clinical results, a precise reconstruction of electrode placement is required, posing specific challenges to the field of neuroimaging. Since 2014 the open source toolbox Lead-DBS is available, which aims at facilitating this process. The tool has since become a popular platform for DBS imaging. With support of a broad community of researchers worldwide, methods have been continuously updated and complemented by new tools for tasks such as multispectral nonlinear registration, structural / functional connectivity analyses, brain shift correction, reconstruction of microelectrode recordings and orientation detection of segmented DBS leads. The rapid development and emergence of these methods in DBS data analysis require us to revisit and revise the pipelines introduced in the original methods publication. Here we demonstrate the updated DBS and connectome pipelines of Lead-DBS using a single patient example with state-of-the-art high-field imaging as well as a retrospective cohort of patients scanned in a typical clinical setting at 1.5T. Imaging data of the 3T example patient is co-registered using five algorithms and nonlinearly warped into template space using ten approaches for comparative purposes. After reconstruction of DBS electrodes (which is possible using three methods and a specific refinement tool), the volume of tissue activated is calculated for two DBS settings using four distinct models and various parameters. Finally, four whole-brain tractography algorithms are applied to the patients preoperative diffusion MRI data and structural as well as functional connectivity between the stimulation volume and other brain areas are estimated using a total of eight approaches and datasets. In addition, we demonstrate impact of selected preprocessing strategies on the retrospective sample of 51 PD patients. We compare the amount of variance in clinical improvement that can be explained by the computer model depending on the method of choice.\n\nThis work represents a multi-institutional collaborative effort to develop a comprehensive, open source pipeline for DBS imaging and connectomics, which has already empowered several studies, and may facilitate a variety of future studies in the field.

neuroscience↗