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Stress response pathways detect and alleviate adverse conditions to safeguard cell and tissue homeostasis, yet their prolonged activation induces apoptosis and disrupts organismal health 1 – 3 . How stress responses are turned off at the right time and place remains poorly understood. Here we report a ubiquitin-dependent mechanism that silences the cellular response to mitochondrial protein import stress. Crucial to this process is the silencing factor of the integrated stress response (SIFI), a large E3 ligase complex mutated in ataxia and in early-onset dementia that degrades both unimported mitochondrial precursors and stress response components. By recognizing bifunctional substrate motifs that equally encode protein localization and stability, the SIFI complex turns off a general stress response after a specific stress event has been resolved. Pharmacological stress response silencing sustains cell survival even if stress resolution failed, which underscores the importance of signal termination and provides a roadmap for treating neurodegenerative diseases caused by mitochondrial import defects. The E3 ligase SIFI is identified as a dedicated silencing factor of the integrated stress response, a finding that has implications for the development of therapeutics for neurodegenerative diseases caused by mitochondrial protein import stress.

Thalidomide and its derivatives lenalidomide and pomalidomide, which are used to treat multiple myeloma and del(5q) myelodysplastic syndrome, have antitumor and teratogenic effects through a mechanism involving destabilization of the CD147 and MCT1 proteins. Immunomodulatory drugs (IMiDs), such as thalidomide and its derivatives lenalidomide and pomalidomide, are key treatment modalities for hematologic malignancies, particularly multiple myeloma (MM) and del(5q) myelodysplastic syndrome (MDS). Cereblon (CRBN), a substrate receptor of the CRL4 ubiquitin ligase complex, is the primary target by which IMiDs mediate anticancer and teratogenic effects. Here we identify a ubiquitin-independent physiological chaperone-like function of CRBN that promotes maturation of the basigin (BSG; also known as CD147) and solute carrier family 16 member 1 (SLC16A1; also known as MCT1) proteins. This process allows for the formation and activation of the CD147–MCT1 transmembrane complex, which promotes various biological functions, including angiogenesis, proliferation, invasion and lactate export. We found that IMiDs outcompete CRBN for binding to CD147 and MCT1, leading to destabilization of the CD147–MCT1 complex. Accordingly, IMiD-sensitive MM cells lose CD147 and MCT1 expression after being exposed to IMiDs, whereas IMiD-resistant cells retain their expression. Furthermore, del(5q) MDS cells have elevated CD147 expression, which is attenuated after IMiD treatment. Finally, we show that BSG ( CD147 ) knockdown phenocopies the teratogenic effects of thalidomide exposure in zebrafish. These findings provide a common mechanistic framework to explain both the teratogenic and pleiotropic antitumor effects of IMiDs.

RationaleMultiple myeloma (MM) cells synthesize large amounts of paraproteins, making radiolabeled amino acids promising candidates for PET imaging of MM patients.MethodsWe compare tumor uptake of the two amino acid analogs [18F]-fluoroethyltyrosine and [18F]-FACBC in a MM xenograft model and show the feasibility of PET imaging with [18F]-FACBC in a MM patient.ResultsPreclinically [18F]-FACBC showed superior performance, mainly due to the uptake via the ASC-system. In a subsequent proof-of-concept investigation [18F]-FACBC PET was performed in a MM patient. It allowed identification of both lesions with and without CT correlate (SUVmean 8.0 or 7.9) based on higher uptake compared to normal bone marrow (SUVmean 5.7). Bone signal was elevated compared to non-MM patients, and, thus [18F]-FACBC potentially allows the assessment of bone marrow infiltration.ConclusionThe FDA/EMA approved PET agent [18F]-FACBC is promising for imaging MM and should be further evaluated in prospective clinical studies.

Patients with hematologic malignancies still face a significant risk of severe coronavirus disease 2019 (COVID-19). The severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2)-neutralizing monoclonal antibody combination tixagevimab/cilgavimab (TIX/CGB) could be administered to immunocompromised patients for pre-exposure prophylaxis (PrEP) before the emergence of TIX/CGB-resistant COVID-19 Omicron variants. TIX/CGB application could be carried out regardless of the host’s immune response to previous active SARS-CoV-2 vaccinations or infections. Because the efficacy of COVID-19 PrEP remains unclear, especially in SARS-CoV-2-seropositive patients, German national guidelines recommended TIX/CGB PrEP only for SARS-CoV-2-seronegative patients in addition to an intensified active vaccination schedule. Having followed these guidelines, we now report the characteristics and outcomes of 54 recipients of TIX/CGB PrEP in SARS-CoV-2-seronegative patients with hematological disease from a German tertiary medical center and compare them to 125 seropositive patients who did not receive any PrEP. While the number of patients with B-cell lymphomas was significantly higher in the seronegative cohort (33 (61%) vs. 18 (14%) cases, p < 0.01), patients with myeloid diseases were significantly more frequent in the seropositive cohort (51 (41%) vs. 5 (9%) cases, p < 0.01). Strikingly, patients who had undergone allogeneic hematopoietic stem cell transplantation were significantly more likely (forty-nine (39%) vs. six (11%) cases, p < 0.01) to be SARS-CoV-2 seropositive. We observed that prophylactic application of TIX/CGB PrEP to a highly vulnerable group of SARS-CoV-2-seronegative patients resulted in a similar number of COVID-19 breakthrough infections compared to the untreated seropositive control group (16 (32%) vs. 39 (36%), p = 0.62) and comparable COVID-19-related outcomes like hospitalization and oxygen requirement throughout an extended follow-up period of 12 months. In conclusion, our results support the tailored approach of administering TIX/CGB PrEP only to SARS-CoV-2-seronegative patients during the COVID-19 pandemic and might provide a rationale for similar strategies during future outbreaks/diseases, especially in times of initial limited availability and/or financial constraints.

Metaheuristics provide the means to approximately solve complex optimisation problems when exact optimisers cannot be utilised. This led to an explosion in the number of novel metaheuristics, most of them metaphor-based, using nature as a source of inspiration. Thus, keeping track of their capabilities and innovative components is an increasingly difficult task. This can be resolved by an exhaustive classification system. Trying to classify metaheuristics is common in research, but no consensus on a classification system and the necessary criteria has been established so far. Furthermore, a proposed classification system can not be deemed complete if inherently different metaheuristics are assigned to the same class by the system. In this paper we provide the basis for a new comprehensive classification system for metaheuristics. We first summarise and discuss previous classification attempts and the utilised criteria. Then we present a multi-level architecture and suitable criteria for the task of classifying metaheuristics. A classification system of this kind can solve three main problems when applied to metaheuristics: organise the huge set of existing metaheuristics, clarify the innovation in novel metaheuristics and identify metaheuristics suitable to solve specific optimisation tasks.

A large proportion of the energy consumed by private households is used for space heating and domestic hot water. In the context of the energy transition, the predominant aim is to reduce this consumption. In addition to implementing better energy standards in new buildings and refurbishing old buildings, intelligent energy management concepts can also contribute by operating heat generators according to demand based on an expected heat requirement. This requires forecasting models for heat demand to be as accurate and reliable as possible. In this paper, we present a case study of a newly built medium-sized living quarter in central Europe made up of 66 residential units from which we gathered consumption data for almost two years. Based on this data, we investigate the possibility of forecasting heat demand using a variety of time series models and offline and online machine learning (ML) techniques in a standard data science approach. We chose to analyze different modeling techniques as they can be used in different settings, where time series models require no additional data, offline ML needs a lot of data gathered up front, and online ML could be deployed from day one. A special focus lies on peak demand and outlier forecasting, as well as investigations into seasonal expert models. We also highlight the computational expense and explainability characteristics of the used models. We compare the used methods with naive models as well as each other, finding that time series models, as well as online ML, do not yield promising results. Accordingly, we will deploy one of the offline ML models in our real-world energy management system in the near future.

Chronic stress response activation impairs cell survival and causes devastating degenerative diseases 1 , 2 – 3 . Organisms accordingly deploy silencing factors, such as the E3 ubiquitin ligase silencing factor of the integrated stress response (SIFI), to terminate stress response signalling and ensure cellular homeostasis 4 . How a silencing factor can sense stress across cellular scales to elicit timely stress response inactivation is poorly understood. Here we combine cryo-electron microscopy analysis of endogenous SIFI with AlphaFold modelling and biochemical studies to report the structural and mechanistic basis of the silencing of the integrated stress response. SIFI detects both stress indicators and stress response components through flexible domains within an easily accessible scaffold, before building linkage-specific ubiquitin chains at separate, sterically restricted elongation modules. Ubiquitin handover by a ubiquitin-like domain couples versatile substrate modification to linkage-specific ubiquitin polymer formation. Stress response silencing therefore exploits a catalytic mechanism that is geared towards processing many diverse proteins and therefore allows a single enzyme to monitor and, if needed, modulate a complex cellular state. To efficiently silence the integrated stress response, SIFI detects many diverse substrates that include stress indicators and stress response components through flexible domains within an easily accessible scaffold, before building linkage-specific ubiquitin chains at separate, sterically restricted elongation modules.

A large proportion of the energy consumed by private households is used for space heating and domestic hot water. In the context of the energy transition, the predominant aim is to reduce this consumption. In addition to implementing better energy standards in new buildings and refurbishing old buildings, intelligent energy management concepts can also contribute by operating heat generators according to demand based on an expected heat requirement. This requires forecasting models for heat demand to be as accurate and reliable as possible. In this paper, we present a case study of a newly built medium-sized living quarter in central Europe made up of 66 residential units from which we gathered consumption data for almost two years. Based on this data, we investigate the possibility of forecasting heat demand using a variety of time series models and offline and online machine learning (ML) techniques in a standard data science approach. We chose to analyze different modeling techniques as they can be used in different settings, where time series models require no additional data, offline ML needs a lot of data gathered up front, and online ML could be deployed from day one. A special focus lies on peak demand and outlier forecasting, as well as investigations into seasonal expert models. We also highlight the computational expense and explainability characteristics of the used models. We compare the used methods with naive models as well as each other, finding that time series models, as well as online ML, do not yield promising results. Accordingly, we will deploy one of the offline ML models in our real-world energy management system in the near future.

Automated prediction systems based on machine learning (ML) are employed in practical applications with increasing frequency and stakeholders demand explanations of their decisions. ML algorithms that learn accurate sets of rules, such as learning classifier systems (LCSs), produce transparent and human-readable models by design. However, whether such models can be effectively used, both for predictions and analyses, strongly relies on the optimal placement and selection of rules (in ML this task is known as model selection). In this article, we broaden a previous analysis on a variety of techniques to efficiently place good rules within the search space based on their local prediction errors as well as their generality. This investigation is done within a specific pre-existing LCS, named SupRB, where the placement of rules and the selection of good subsets of rules are strictly separated—in contrast to other LCSs where these tasks sometimes blend. We compare two baselines, random search and ( 1 , λ ) -evolution strategy (ES), with six novelty search variants: three novelty-/fitness weighing variants and for each of those two differing approaches on the usage of the archiving mechanism. We find that random search is not sufficient and sensible criteria, i.e., error and generality, are indeed needed. However, we cannot confirm that the more complicated-to-explain novelty search variants would provide better results than ( 1 , λ ) -ES which allows a good balance between low error and low complexity in the resulting models.