Machine perfusion preservation highlights from the Congress of the European Society of Organ Transplantation 2023.

The 21st Congress of the European Society of Organ Transplantation (ESOT), held on September 17-20th, 2023, in Athens, Greece, was a pivotal event in transplantation, focusing on the theme "Disruptive Innovation, Trusted Care." The congress attracted a global audience of 2 826 participants from 82 countries, emphasizing its international significance. Machine perfusion, as a groundbreaking technology in organ transplantation, was one of the central focuses of the conference. This year's meeting had a remarkable increase in accepted abstracts on machine perfusion, evidencing its growing prominence in the field. The collective findings from these abstracts highlighted the efficacy of machine perfusion in improving organ viability and transplant outcomes. Studies demonstrated improvements in graft survival and reduction in complications, as well as novel uses and techniques. Furthermore, the integration of machine perfusion with regenerative medicine and its application across multiple organ types were significant discussion points. The congress also highlighted the challenges and solutions in implementing machine perfusion in clinical settings, emphasizing the importance of practical training and international collaboration for advancing this technology. ESOT 2023 served as a crucial platform for disseminating scientific advancements, fostering practical learning, and facilitating international collaborations in organ transplantation. The congress underscored the evolution and importance of machine perfusion technology, marking a significant step forward in enhancing patient outcomes in the field of organ transplantation.

Structural basis for functional interactions in dimers of SLC26 transporters.

The SLC26 family of transporters maintains anion equilibria in all kingdoms of life. The family shares a 7 + 7 transmembrane segments inverted repeat architecture with the SLC4 and SLC23 families, but holds a regulatory STAS domain in addition. While the only experimental SLC26 structure is monomeric, SLC26 proteins form structural and functional dimers in the lipid membrane. Here we resolve the structure of an SLC26 dimer embedded in a lipid membrane and characterize its functional relevance by combining PELDOR/DEER distance measurements and biochemical studies with MD simulations and spin-label ensemble refinement. Our structural model reveals a unique interface different from the SLC4 and SLC23 families. The functionally relevant STAS domain is no prerequisite for dimerization. Characterization of heterodimers indicates that protomers in the dimer functionally interact. The combined structural and functional data define the framework for a mechanistic understanding of functional cooperativity in SLC26 dimers.

Efficient Ensemble Refinement by Reweighting.

Ensemble refinement produces structural ensembles of flexible and dynamic biomolecules by integrating experimental data and molecular simulations. Here we present two efficient numerical methods to solve the computationally challenging maximum-entropy problem arising from a Bayesian formulation of ensemble refinement. Recasting the resulting constrained weight optimization problem into an unconstrained form enables the use of gradient-based algorithms. In two complementary formulations that differ in their dimensionality, we optimize either the log-weights directly or the generalized forces appearing in the explicit analytical form of the solution. We first demonstrate the robustness, accuracy, and efficiency of the two methods using synthetic data. We then use NMR J-couplings to reweight an all-atom molecular dynamics simulation ensemble of the disordered peptide Ala-5 simulated with the AMBER99SB*-ildn-q force field. After reweighting, we find a consistent increase in the population of the polyproline-II conformations and a decrease of α-helical-like conformations. Ensemble refinement makes it possible to infer detailed structural models for biomolecules exhibiting significant dynamics, such as intrinsically disordered proteins, by combining input from experiment and simulation in a balanced manner.

Structural and functional insights into the interaction and targeting hub TMD0 of the polypeptide transporter TAPL.

The ATP-binding cassette transporter TAPL translocates polypeptides from the cytosol into the lysosomal lumen. TAPL can be divided into two functional units: coreTAPL, active in ATP-dependent peptide translocation, and the N-terminal membrane spanning domain, TMD0, responsible for cellular localization and interaction with the lysosomal associated membrane proteins LAMP-1 and LAMP-2. Although the structure and function of ABC transporters were intensively analyzed in the past, the knowledge about accessory membrane embedded domains is limited. Therefore, we expressed the TMD0 of TAPL via a cell-free expression system and confirmed its correct folding by NMR and interaction studies. In cell as well as cell-free expressed TMD0 forms oligomers, which were assigned as dimers by PELDOR spectroscopy and static light scattering. By NMR spectroscopy of uniformly and selectively isotope labeled TMD0 we performed a complete backbone and partial side chain assignment. Accordingly, TMD0 has a four transmembrane helix topology with a short helical segment in a lysosomal loop. The topology of TMD0 was confirmed by paramagnetic relaxation enhancement with paramagnetic stearic acid as well as by nuclear Overhauser effects with c6-DHPC and cross-peaks with water.

Precision DEER Distances from Spin-Label Ensemble Refinement.

Double electron-electron resonance (DEER) experiments probe nanometer-scale distances in spin-labeled proteins and nucleic acids. Rotamer libraries of the covalently attached spin-labels help reduce position uncertainties. Here we show that rotamer reweighting is essential for precision distance measurements, making it possible to resolve Ångstrom-scale domain motions. We analyze extensive DEER measurements on the three N-terminal polypeptide transport-associated (POTRA) domains of the outer membrane protein Omp85. Using the "Bayesian inference of ensembles" maximum-entropy method, we extract rotamer weights from the DEER measurements. Small weight changes suffice to eliminate otherwise significant discrepancies between experiments and model and unmask 1-3 Å domain motions relative to the crystal structure. Rotamer-weight refinement is a simple yet powerful tool for precision distance measurements that should be broadly applicable to label-based measurements including DEER, paramagnetic relaxation enhancement, and fluorescence resonance energy transfer (FRET).

Systematic evaluation of CS-Rosetta for membrane protein structure prediction with sparse NOE restraints.

We critically test and validate the CS-Rosetta methodology for de novo structure prediction of α-helical membrane proteins (MPs) from NMR data, such as chemical shifts and NOE distance restraints. By systematically reducing the number and types of NOE restraints, we focus on determining the regime in which MP structures can be reliably predicted and pinpoint the boundaries of the approach. Five MPs of known structure were used as test systems, phototaxis sensory rhodopsin II (pSRII), a subdomain of pSRII, disulfide binding protein B (DsbB), microsomal prostaglandin E2 synthase-1 (mPGES-1), and translocator protein (TSPO). For pSRII and DsbB, where NMR and X-ray structures are available, resolution-adapted structural recombination (RASREC) CS-Rosetta yields structures that are as close to the X-ray structure as the published NMR structures if all available NMR data are used to guide structure prediction. For mPGES-1 and Bacillus cereus TSPO, where only X-ray crystal structures are available, highly accurate structures are obtained using simulated NMR data. One main advantage of RASREC CS-Rosetta is its robustness with respect to even a drastic reduction of the number of NOEs. Close-to-native structures were obtained with one randomly picked long-range NOEs for every 14, 31, 38, and 8 residues for full-length pSRII, the pSRII subdomain, TSPO, and DsbB, respectively, in addition to using chemical shifts. For mPGES-1, atomically accurate structures could be predicted even from chemical shifts alone. Our results show that atomic level accuracy for helical membrane proteins is achievable with CS-Rosetta using very sparse NOE restraint sets to guide structure prediction. Proteins 2017; 85:812-826. © 2016 Wiley Periodicals, Inc.

Assembly of the MHC I peptide-loading complex determined by a conserved ionic lock-switch.

Salt bridges in lipid bilayers play a decisive role in the dynamic assembly and downstream signaling of the natural killer and T-cell receptors. Here, we describe the identification of an inter-subunit salt bridge in the membrane within yet another key component of the immune system, the peptide-loading complex (PLC). The PLC regulates cell surface presentation of self-antigens and antigenic peptides via molecules of the major histocompatibility complex class I. We demonstrate that a single salt bridge in the membrane between the transporter associated with antigen processing TAP and the MHC I-specific chaperone tapasin is essential for the assembly of the PLC and for efficient MHC I antigen presentation. Molecular modeling and all-atom molecular dynamics simulations suggest an ionic lock-switch mechanism for the binding of TAP to tapasin, in which an unfavorable uncompensated charge in the ER-membrane is prevented through complex formation. Our findings not only deepen the understanding of the interaction network within the PLC, but also provide evidence for a general interaction principle of dynamic multiprotein membrane complexes in immunity.

The use of reinforcement learning algorithms to meet the challenges of an artificial pancreas.

Blood glucose control, for example, in diabetes mellitus or severe illness, requires strict adherence to a protocol of food, insulin administration and exercise personalized to each patient. An artificial pancreas for automated treatment could boost quality of glucose control and patients' independence. The components required for an artificial pancreas are: i) continuous glucose monitoring (CGM), ii) smart controllers and iii) insulin pumps delivering the optimal amount of insulin. In recent years, medical devices for CGM and insulin administration have undergone rapid progression and are now commercially available. Yet, clinically available devices still require regular patients' or caregivers' attention as they operate in open-loop control with frequent user intervention. Dosage-calculating algorithms are currently being studied in intensive care patients [1] , for short overnight control to supplement conventional insulin delivery [2] , and for short periods where patients rest and follow a prescribed food regime [3] . Fully automated algorithms that can respond to the varying activity levels seen in outpatients, with unpredictable and unreported food intake, and which provide the necessary personalized control for individuals is currently beyond the state-of-the-art. Here, we review and discuss reinforcement learning algorithms, controlling insulin in a closed-loop to provide individual insulin dosing regimens that are reactive to the immediate needs of the patient.