A TBK1 variant causes autophagolysosomal and motoneuron pathology without neuroinflammation in mice.

Heterozygous mutations in the TBK1 gene can cause amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). The majority of TBK1-ALS/FTD patients carry deleterious loss-of-expression mutations, and it is still unclear which TBK1 function leads to neurodegeneration. We investigated the impact of the pathogenic TBK1 missense variant p.E696K, which does not abolish protein expression, but leads to a selective loss of TBK1 binding to the autophagy adaptor protein and TBK1 substrate optineurin. Using organelle-specific proteomics, we found that in a knock-in mouse model and human iPSC-derived motor neurons, the p.E696K mutation causes presymptomatic onset of autophagolysosomal dysfunction in neurons precipitating the accumulation of damaged lysosomes. This is followed by a progressive, age-dependent motor neuron disease. Contrary to the phenotype of mice with full Tbk1 knock-out, RIPK/TNF-α-dependent hepatic, neuronal necroptosis, and overt autoinflammation were not detected. Our in vivo results indicate autophagolysosomal dysfunction as a trigger for neurodegeneration and a promising therapeutic target in TBK1-ALS/FTD.

AI-guided pipeline for protein-protein interaction drug discovery identifies a SARS-CoV-2 inhibitor.

Protein-protein interactions (PPIs) offer great opportunities to expand the druggable proteome and therapeutically tackle various diseases, but remain challenging targets for drug discovery. Here, we provide a comprehensive pipeline that combines experimental and computational tools to identify and validate PPI targets and perform early-stage drug discovery. We have developed a machine learning approach that prioritizes interactions by analyzing quantitative data from binary PPI assays or AlphaFold-Multimer predictions. Using the quantitative assay LuTHy together with our machine learning algorithm, we identified high-confidence interactions among SARS-CoV-2 proteins for which we predicted three-dimensional structures using AlphaFold-Multimer. We employed VirtualFlow to target the contact interface of the NSP10-NSP16 SARS-CoV-2 methyltransferase complex by ultra-large virtual drug screening. Thereby, we identified a compound that binds to NSP10 and inhibits its interaction with NSP16, while also disrupting the methyltransferase activity of the complex, and SARS-CoV-2 replication. Overall, this pipeline will help to prioritize PPI targets to accelerate the discovery of early-stage drug candidates targeting protein complexes and pathways.

Novel multi-objective affinity approach allows to identify pH-specific µ-opioid receptor agonists.

Opioids are essential pharmaceuticals due to their analgesic properties, however, lethal side effects, addiction, and opioid tolerance are extremely challenging. The development of novel molecules targeting the [Formula: see text]-opioid receptor (MOR) in inflamed, but not in healthy tissue, could significantly reduce these unwanted effects. Finding such novel molecules can be achieved by maximizing the binding affinity to the MOR at acidic pH while minimizing it at neutral pH, thus combining two conflicting objectives. Here, this multi-objective optimal affinity approach is presented, together with a virtual drug discovery pipeline for its practical implementation. When applied to finding pH-specific drug candidates, it combines protonation state-dependent structure and ligand preparation with high-throughput virtual screening. We employ this pipeline to characterize a set of MOR agonists identifying a morphine-like opioid derivative with higher predicted binding affinities to the MOR at low pH compared to neutral pH. Our results also confirm existing experimental evidence that NFEPP, a previously described fentanyl derivative with reduced side effects, and recently reported [Formula: see text]-fluorofentanyls and -morphines show an increased specificity for the MOR at acidic pH when compared to fentanyl and morphine. We further applied our approach to screen a >50K ligand library identifying novel molecules with pH-specific predicted binding affinities to the MOR. The presented differential docking pipeline can be applied to perform multi-objective affinity optimization to identify safer and more specific drug candidates at large scale.

AI-guided pipeline for protein-protein interaction drug discovery identifies a SARS-CoV-2 inhibitor.

Protein-protein interactions (PPIs) offer great opportunities to expand the druggable proteome and therapeutically tackle various diseases, but remain challenging targets for drug discovery. Here, we provide a comprehensive pipeline that combines experimental and computational tools to identify and validate PPI targets and perform early-stage drug discovery. We have developed a machine learning approach that prioritizes interactions by analyzing quantitative data from binary PPI assays and AlphaFold-Multimer predictions. Using the quantitative assay LuTHy together with our machine learning algorithm, we identified high-confidence interactions among SARS-CoV-2 proteins for which we predicted three-dimensional structures using AlphaFold Multimer. We employed VirtualFlow to target the contact interface of the NSP10-NSP16 SARS-CoV-2 methyltransferase complex by ultra-large virtual drug screening. Thereby, we identified a compound that binds to NSP10 and inhibits its interaction with NSP16, while also disrupting the methyltransferase activity of the complex, and SARS-CoV-2 replication. Overall, this pipeline will help to prioritize PPI targets to accelerate the discovery of early-stage drug candidates targeting protein complexes and pathways.

Delineation of functional subdomains of Huntingtin protein and their interaction with HAP40.

The huntingtin (HTT) protein plays critical roles in numerous cellular pathways by functioning as a scaffold for its many interaction partners and HTT knock out is embryonic lethal. Interrogation of HTT function is complicated by the large size of this protein so we studied a suite of structure-rationalized subdomains to investigate the structure-function relationships within the HTT-HAP40 complex. Protein samples derived from the subdomain constructs were validated using biophysical methods and cryo-electron microscopy, revealing they are natively folded and can complex with validated binding partner, HAP40. Derivatized versions of these constructs enable protein-protein interaction assays in vitro, with biotin tags, and in cells, with luciferase two-hybrid assay-based tags, which we use in proof-of-principle analyses to further interrogate the HTT-HAP40 interaction. These open-source biochemical tools enable studies of fundamental HTT biochemistry and biology, will aid the discovery of macromolecular or small-molecule binding partners and help map interaction sites across this large protein.

The polyphenol EGCG directly targets intracellular amyloid-ß aggregates and promotes their lysosomal degradation.

The accumulation of amyloidogenic protein aggregates in neurons is a pathogenic hallmark of a large number of neurodegenerative diseases including Alzheimer's disease (AD). Small molecules targeting such structures and promoting their degradation may have therapeutic potential for the treatment of AD. Here, we searched for natural chemical compounds that decrease the abundance of stable, proteotoxic ß-sheet-rich amyloid-ß (Aß) aggregates in cells. We found that the polyphenol (-)-epigallocatechin gallate (EGCG) functions as a potent chemical aggregate degrader in SH-EP cells. We further demonstrate that a novel, fluorescently labeled EGCG derivative (EGC-dihydroxybenzoate (DHB)-Rhodamine) also shows cellular activity. It directly targets intracellular Aß42 aggregates and competes with EGCG for Aß42 aggregate binding in vitro. Mechanistic investigations indicated a lysosomal accumulation of Aß42 aggregates in SH-EP cells and showed that lysosomal cathepsin activity is critical for efficient EGCG-mediated aggregate clearance. In fact, EGCG treatment leads to an increased abundance of active cathepsin B isoforms and increased enzymatic activity in our SH-EP cell model. Our findings suggest that intracellular Aß42 aggregates are cleared through the endo-lysosomal system. We show that EGCG directly targets intracellular Aß42 aggregates and facilitates their lysosomal degradation. Small molecules, which bind to protein aggregates and increase their lysosomal degradation could have therapeutic potential for the treatment of amyloid diseases.

Dynamics of huntingtin protein interactions in the striatum identifies candidate modifiers of Huntington disease.

Huntington disease (HD) is a monogenic neurodegenerative disorder with one causative gene, huntingtin (HTT). Yet, HD pathobiology is multifactorial, suggesting that cellular factors influence disease progression. Here, we define HTT protein-protein interactions (PPIs) perturbed by the mutant protein with expanded polyglutamine in the mouse striatum, a brain region with selective HD vulnerability. Using metabolically labeled tissues and immunoaffinity purification-mass spectrometry, we establish that polyglutamine-dependent modulation of HTT PPI abundances and relative stability starts at an early stage of pathogenesis in a Q140 HD mouse model. We identify direct and indirect PPIs that are also genetic disease modifiers using in-cell two-hybrid and behavioral assays in HD human cell and Drosophila models, respectively. Validated, disease-relevant mHTT-dependent interactions encompass mediators of synaptic neurotransmission (SNAREs and glutamate receptors) and lysosomal acidification (V-ATPase). Our study provides a resource for understanding mHTT-dependent dysfunction in cortico-striatal cellular networks, partly through impaired synaptic communication and endosomal-lysosomal system. A record of this paper's Transparent Peer Review process is included in the supplemental information.

CellFIE: CRISPR- and Cell Fusion-based Two-hybrid Interaction Mapping of Endogenous Proteins.

Numerous genetic methods facilitate the detection of binary protein-protein interactions (PPIs) by exogenous overexpression, which can lead to false results. Here, we describe CellFIE, a CRISPR- and cell fusion-based PPI detection method, which enables the mapping of interactions between endogenously tagged two-hybrid proteins. We demonstrate the specificity and reproducibility of CellFIE in a matrix mapping approach, validating the interactions of VCP with ASPL and UBXD1, and the self-interaction of TDP-43 under endogenous conditions. Furthermore, we show that CellFIE can be used to quantify changes of endogenous PPIs upon stress induction or drug treatment. For the first time, CellFIE facilitates systematic mapping of interactions between endogenously tagged proteins and represents a novel tool to characterize PPIs in live cells under dynamic conditions.

Defective metabolic programming impairs early neuronal morphogenesis in neural cultures and an organoid model of Leigh syndrome.

Leigh syndrome (LS) is a severe manifestation of mitochondrial disease in children and is currently incurable. The lack of effective models hampers our understanding of the mechanisms underlying the neuronal pathology of LS. Using patient-derived induced pluripotent stem cells and CRISPR/Cas9 engineering, we developed a human model of LS caused by mutations in the complex IV assembly gene SURF1. Single-cell RNA-sequencing and multi-omics analysis revealed compromised neuronal morphogenesis in mutant neural cultures and brain organoids. The defects emerged at the level of neural progenitor cells (NPCs), which retained a glycolytic proliferative state that failed to instruct neuronal morphogenesis. LS NPCs carrying mutations in the complex I gene NDUFS4 recapitulated morphogenesis defects. SURF1 gene augmentation and PGC1A induction via bezafibrate treatment supported the metabolic programming of LS NPCs, leading to restored neuronal morphogenesis. Our findings provide mechanistic insights and suggest potential interventional strategies for a rare mitochondrial disease.

N-methyl-D-aspartate receptor dysfunction by unmutated human antibodies against the NR1 subunit.

Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is the most common autoimmune encephalitis related to autoantibody-mediated synaptic dysfunction. Cerebrospinal fluid-derived human monoclonal NR1 autoantibodies showed low numbers of somatic hypermutations or were unmutated. These unexpected germline-configured antibodies showed weaker binding to the NMDAR than matured antibodies from the same patient. In primary hippocampal neurons, germline NR1 autoantibodies strongly and specifically reduced total and synaptic NMDAR currents in a dose- and time-dependent manner. The findings suggest that functional NMDAR antibodies are part of the human naïve B cell repertoire. Given their effects on synaptic function, they might contribute to a broad spectrum of neuropsychiatric symptoms. Ann Neurol 2019;85:771-776.

LuTHy: a double-readout bioluminescence-based two-hybrid technology for quantitative mapping of protein-protein interactions in mammalian cells.

Information on protein-protein interactions (PPIs) is of critical importance for studying complex biological systems and developing therapeutic strategies. Here, we present a double-readout bioluminescence-based two-hybrid technology, termed LuTHy, which provides two quantitative scores in one experimental procedure when testing binary interactions. PPIs are first monitored in cells by quantification of bioluminescence resonance energy transfer (BRET) and, following cell lysis, are again quantitatively assessed by luminescence-based co-precipitation (LuC). The double-readout procedure detects interactions with higher sensitivity than traditional single-readout methods and is broadly applicable, for example, for detecting the effects of small molecules or disease-causing mutations on PPIs. Applying LuTHy in a focused screen, we identified 42 interactions for the presynaptic chaperone CSPα, causative to adult-onset neuronal ceroid lipofuscinosis (ANCL), a progressive neurodegenerative disease. Nearly 50% of PPIs were found to be affected when studying the effect of the disease-causing missense mutations L115R and ∆L116 in CSPα with LuTHy. Our study presents a robust, sensitive research tool with high utility for investigating the molecular mechanisms by which disease-associated mutations impair protein activity in biological systems.

DULIP: A Dual Luminescence-Based Co-Immunoprecipitation Assay for Interactome Mapping in Mammalian Cells.

Mapping of protein-protein interactions (PPIs) is critical for understanding protein function and complex biological processes. Here, we present DULIP, a dual luminescence-based co-immunoprecipitation assay, for systematic PPI mapping in mammalian cells. DULIP is a second-generation luminescence-based PPI screening method for the systematic and quantitative analysis of co-immunoprecipitations using two different luciferase tags. Benchmarking studies with positive and negative PPI reference sets revealed that DULIP allows the detection of interactions with high sensitivity and specificity. Furthermore, the analysis of a PPI reference set with known binding affinities demonstrated that both low- and high-affinity interactions can be detected with DULIP assays. Finally, using the well-characterized interaction between Syntaxin-1 and Munc18, we found that DULIP is capable of detecting the effects of point mutations on interaction strength. Taken together, our studies demonstrate that DULIP is a sensitive and reliable method of great utility for systematic interactome research. It can be applied for interaction screening and validation of PPIs in mammalian cells. Moreover, DULIP permits the specific analysis of mutation-dependent binding patterns.