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Nanobodies are highly valuable tools for numerous bioanalytical and biotechnical applications. Here, we report the characterization of a nanobody that binds a short peptide epitope with extraordinary affinity. Structural analysis reveals an unusual binding mode where the extended peptide becomes part of a β-sheet structure in the nanobody. This interaction relies on sequence-independent backbone interactions augmented by a small number of specificity-determining side chain contacts. Once bound, the peptide is fastened by two nanobody side chains that clamp it in a headlock fashion. Exploiting this unusual binding mode, we generated a novel nanobody-derived capture and detection system. Matrix-coupled nanobody enables the fast and efficient isolation of epitope-tagged proteins from prokaryotic and eukaryotic expression systems. Additionally, the fluorescently labeled nanobody visualizes subcellular structures in different cellular compartments. The high-affinity-binding and modifiable peptide tag of this system renders it a versatile and robust tool to combine biochemical analysis with microscopic studies.

Mechanistic target of rapamycin (mTOR) kinase functions in two multiprotein complexes: lysosomal mTOR complex 1 (mTORC1) and mTORC2 at the plasma membrane. mTORC1 modulates the cell response to growth factors and nutrients by increasing protein synthesis and cell growth, and repressing the autophagy–lysosomal pathway 1 – 4 ; however, dysfunction in mTORC1 is implicated in various diseases 3 , 5 , 6 . mTORC1 activity is regulated by phosphoinositide lipids 7 – 10 . Class I phosphatidylinositol-3-kinase (PI3K)-mediated production of phosphatidylinositol-3,4,5-trisphosphate 6 , 11 at the plasma membrane stimulates mTORC1 signalling, while local synthesis of phosphatidylinositol-3,4-bisphosphate by starvation-induced recruitment of class II PI3K-β (PI3KC2-β) to lysosomes represses mTORC1 activity 12 . How the localization and activity of PI3KC2-β are regulated by mitogens is unknown. We demonstrate that protein kinase N (PKN) facilitates mTORC1 signalling by repressing PI3KC2-β-mediated phosphatidylinositol-3,4-bisphosphate synthesis downstream of mTORC2. Active PKN2 phosphorylates PI3KC2-β to trigger PI3KC2-β complex formation with inhibitory 14-3-3 proteins. Conversely, loss of PKN2 or inactivation of its target phosphorylation site in PI3KC2-β represses nutrient signalling via mTORC1. These results uncover a mechanism that couples mTORC2-dependent activation of PKN2 to the regulation of mTORC1-mediated nutrient signalling by local lipid signals. Wallroth et al. uncover a mechanism by which protein kinase N activates mTORC1 nutrient signalling at the lysosome by inhibiting PI3KC2-β-mediated PtdIns(3,4)P 2 synthesis downstream of mTORC2.

X-linked centronuclear myopathy (XLCNM) is a severe human disease without existing therapies caused by mutations in the phosphoinositide 3-phosphatase MTM1. Loss of MTM1 function is associated with muscle fiber defects characterized by impaired localization of β-integrins and other components of focal adhesions. Here we show that defective focal adhesions and reduced active β-integrin surface levels in a cellular model of XLCNM are rescued by loss of phosphatidylinositiol 3-kinase C2β (PI3KC2β) function. Inactivation of the Mtm1 gene impaired myoblast differentiation into myotubes and resulted in reduced surface levels of active β1-integrins as well as corresponding defects in focal adhesions. These phenotypes were rescued by concomitant genetic loss of Pik3c2b or pharmacological inhibition of PI3KC2β activity. We further demonstrate that a hitherto unknown role of PI3KC2β in the endocytic trafficking of active β1-integrins rather than rescue of phosphatidylinositol 3-phosphate levels underlies the ability of Pik3c2b to act as a genetic modifier of cellular XLCNM phenotypes. Our findings reveal a crucial antagonistic function of MTM1 and PI3KC2β in the control of active β-integrin surface levels, thereby providing a molecular mechanism for the adhesion and myofiber defects observed in XLCNM. They further suggest specific pharmacological inhibition of PI3KC2β catalysis as a viable treatment option for XLCNM patients.

Mechanistic target of rapamycin (mTOR) kinase functions in two multiprotein complexes: lysosomal mTOR complex 1 (mTORC1) and mTORC2 at the plasma membrane. mTORC1 modulates the cell response to growth factors and nutrients by increasing protein synthesis and cell growth, and repressing the autophagy-lysosomal pathway.sup.1-4; however, dysfunction in mTORC1 is implicated in various diseases.sup.3,5,6. mTORC1 activity is regulated by phosphoinositide lipids.sup.7-10. Class I phosphatidylinositol-3-kinase (PI3K)-mediated production of phosphatidylinositol-3,4,5-trisphosphate.sup.6,11 at the plasma membrane stimulates mTORC1 signalling, while local synthesis of phosphatidylinositol-3,4-bisphosphate by starvation-induced recruitment of class II PI3K-[beta] (PI3KC2-[beta]) to lysosomes represses mTORC1 activity.sup.12. How the localization and activity of PI3KC2-[beta] are regulated by mitogens is unknown. We demonstrate that protein kinase N (PKN) facilitates mTORC1 signalling by repressing PI3KC2-[beta]-mediated phosphatidylinositol-3,4-bisphosphate synthesis downstream of mTORC2. Active PKN2 phosphorylates PI3KC2-[beta] to trigger PI3KC2-[beta] complex formation with inhibitory 14-3-3 proteins. Conversely, loss of PKN2 or inactivation of its target phosphorylation site in PI3KC2-[beta] represses nutrient signalling via mTORC1. These results uncover a mechanism that couples mTORC2-dependent activation of PKN2 to the regulation of mTORC1-mediated nutrient signalling by local lipid signals.

‘Angiodiversity’ refers to the structural and functional heterogeneity of endothelial cells (EC) along the segments of the vascular tree and especially within the microvascular beds of different organs. Organotypically differentiated EC ranging from continuous, barrier-forming endothelium to discontinuous, fenestrated endothelium perform organ-specific functions such as the maintenance of the tightly sealed blood–brain barrier or the clearance of macromolecular waste products from the peripheral blood by liver EC-expressed scavenger receptors. The microvascular bed of the liver, composed of discontinuous, fenestrated liver sinusoidal endothelial cells (LSEC), is a prime example of organ-specific angiodiversity. Anatomy and development of LSEC have been extensively studied by electron microscopy as well as linage-tracing experiments. Recent advances in cell isolation and bulk transcriptomics or single-cell RNA sequencing techniques allowed the identification of distinct LSEC molecular programs and have led to the identification of LSEC subpopulations. LSEC execute homeostatic functions such as fine tuning the vascular tone, clearing noxious substances from the circulation, and modulating immunoregulatory mechanisms. In recent years, the identification and functional analysis of LSEC-derived angiocrine signals, which control liver homeostasis and disease pathogenesis in an instructive manner, marks a major change of paradigm in the understanding of liver function in health and disease. This review summarizes recent advances in the understanding of liver vascular angiodiversity and the functional consequences resulting thereof.

The concept of inhibition plays a major role in cognitive psychology. In the present article, we review the evidence for the inhibition of task sets. In the first part, we critically discuss empirical findings of task inhibition from studies that applied variants of the task-switching methodology and argue that most of these findings— such as switch cost asymmetries—are ambiguous. In the second part, we focus on n -22 task-repetition costs, which currently constitute the most convincing evidence for inhibition of task sets. n -22 repetition costs refer to the performance impairment in sequences of the ABA type relative to CBA, which can be interpreted in terms of persisting inhibition of previously abandoned tasks. The available evidence suggests that inhibition is primarily triggered by conflict at selection of stimulus attributes and at the response level. Author Note

Structural disconnectome analyses have provided valuable insights into how a stroke lesion results in widespread network disturbances and how these relate to deficits, recovery patterns, and outcomes. Previous analyses have primarily focused on patients with relatively mild to moderate deficits. However, outcomes vary among survivors of severe strokes, and the mechanisms of recovery remain poorly understood. This study assesses the association between lesion‐induced network disconnection and outcome after severe stroke. Thirty‐eight ischaemic stroke patients underwent MRI brain imaging early after stroke and longitudinal clinical follow‐up. Lesion information was integrated with normative connectome data to infer individual disconnectome profiles on a localized regional and region‐to‐region pathway level. Ordinal logistic regressions were computed to link disconnectome information to the modified Rankin Scale after 3–6 months. Disconnections of ipsilesional frontal, parietal, and temporal cortical brain areas were significantly associated with a worse motor outcome after a severe stroke, adjusted for the initial deficit, lesion volume, and age. The analysis of the underlying pathways mediating this association revealed location‐specific results: For frontal, prefrontal, and temporal brain areas, the association was primarily driven by relatively sparse intrahemispheric disconnections. In contrast, the ipsilesional primary motor cortex, the dorsal premotor cortex, and various parietal brain regions showed a remarkable involvement of either frontoparietal intrahemispheric or additionally interhemispheric disconnections. These results indicate that localized disconnection of multiple regions embedded in the structural frontoparietal network correlates with worse outcomes after severe stroke. Specifically, primary motor and parietal cortices might gain particular importance as they structurally link frontoparietal networks of both hemispheres. These data shed novel light on the significance of distinct brain networks for recovery after a severe stroke. Network disconnection was analyzed in 38 severely impaired acute stroke patients using lesion masks and normative connectome data. Logistic regression models were computed to associate region‐ and pathway‐related disconnections with the outcome. Localized structural frontoparietal network disconnections were significantly linked to a worse outcome after stroke.

Neurodegenerative diseases are a growing burden, and there is an urgent need for better biomarkers for diagnosis, prognosis, and treatment efficacy. Structural and functional brain alterations are reflected in the protein composition of cerebrospinal fluid (CSF). Alzheimer's disease (AD) patients have higher CSF levels of tau, but we lack knowledge of systems‐wide changes of CSF protein levels that accompany AD. Here, we present a highly reproducible mass spectrometry (MS)‐based proteomics workflow for the in‐depth analysis of CSF from minimal sample amounts. From three independent studies (197 individuals), we characterize differences in proteins by AD status (> 1,000 proteins, CV < 20%). Proteins with previous links to neurodegeneration such as tau, SOD1, and PARK7 differed most strongly by AD status, providing strong positive controls for our approach. CSF proteome changes in Alzheimer's disease prove to be widespread and often correlated with tau concentrations. Our unbiased screen also reveals a consistent glycolytic signature across our cohorts and a recent study. Machine learning suggests clinical utility of this proteomic signature. Synopsis A robust proteomic workflow quantifies more than 1,000 proteins in cerebrospinal fluid and reveals an Alzheimer's Disease‐associated signature of more than 20 proteins across three independent cohorts. These include tau, superoxide dismutase 1, PARK7, YKL‐40 and novel biomarker candidates. Proteomics workflow for quantification of more than 1,000 proteins from microliters of cerebrospinal fluid. More than 20 proteins consistently associated with Alzheimer's Disease across three cohorts comprising about 200 individuals in total. Alzheimer's Disease CSF signature of Tau, SOD1, PARK7, YKL‐40, and glycolysis‐related proteins. Graphical Abstract A robust proteomic workflow quantifies more than 1,000 proteins in cerebrospinal fluid and reveals an Alzheimer's Disease‐associated signature of more than 20 proteins across three independent cohorts. These include tau, superoxide dismutase 1, PARK7, YKL‐40 and novel biomarker candidates.

Gilliamella apicola and Snodgrassella alvi are dominant members of the honey bee (Apis spp.) and bumble bee (Bombus spp.) gut microbiota. We generated complete genomes of the type strains G. apicola wkB1 ᵀ and S. alvi wkB2 ᵀ (isolated from Apis), as well as draft genomes for four other strains from Bombus . G. apicola and S. alvi were found to occupy very different metabolic niches: The former is a saccharolytic fermenter, whereas the latter is an oxidizer of carboxylic acids. Together, they may form a syntrophic network for partitioning of metabolic resources. Both species possessed numerous genes [type 6 secretion systems, repeats in toxin (RTX) toxins, RHS proteins, adhesins, and type IV pili] that likely mediate cell–cell interactions and gut colonization. Variation in these genes could account for the host fidelity of strains observed in previous phylogenetic studies. Here, we also show the first experimental evidence, to our knowledge, for this specificity in vivo: Strains of S. alvi were able to colonize their native bee host but not bees of another genus. Consistent with specific, long-term host association, comparative genomic analysis revealed a deep divergence and little or no gene flow between Apis and Bombus gut symbionts. However, within a host type (Apis or Bombus), we detected signs of horizontal gene transfer between G. apicola and S. alvi , demonstrating the importance of the broader gut community in shaping the evolution of any one member. Our results show that host specificity is likely driven by multiple factors, including direct host–microbe interactions, microbe–microbe interactions, and social transmission.

The diagnosis of sinonasal tumors is challenging due to a heterogeneous spectrum of various differential diagnoses as well as poorly defined, disputed entities such as sinonasal undifferentiated carcinomas (SNUCs). In this study, we apply a machine learning algorithm based on DNA methylation patterns to classify sinonasal tumors with clinical-grade reliability. We further show that sinonasal tumors with SNUC morphology are not as undifferentiated as their current terminology suggests but rather reassigned to four distinct molecular classes defined by epigenetic, mutational and proteomic profiles. This includes two classes with neuroendocrine differentiation, characterized by IDH2 or SMARCA4/ARID1A mutations with an overall favorable clinical course, one class composed of highly aggressive SMARCB1-deficient carcinomas and another class with tumors that represent potentially previously misclassified adenoid cystic carcinomas. Our findings can aid in improving the diagnostic classification of sinonasal tumors and could help to change the current perception of SNUCs. Sinonasal tumour diagnosis can be complicated by the heterogeneity of disease and classification systems. Here, the authors use machine learning to classify sinonasal undifferentiated carcinomas into 4 molecular classe with differences in differentiation state and clinical outcome.