An Adaptive Role for DNA Double-Strand Breaks in Hippocampus-Dependent Learning and Memory

DNA double-strand breaks (DSBs), classified as the most harmful type of DNA damage based on the complexity of repair, lead to apoptosis or tumorigenesis. In aging, DNA damage increases and DNA repair decreases. This is exacerbated in disease, as post-mortem tissue from patients diagnosed with mild cognitive impairment (MCI) or Alzheimer’s disease (AD) show increased DSBs. A novel role for DSBs in immediate early gene (IEG) expression, learning, and memory has been suggested. Inducing neuronal activity leads to increases in DSBs and upregulation of IEGs, while increasing DSBs and inhibiting DSB repair impairs long-term memory and alters IEG expression. Consistent with this pattern, mice carrying dominant AD mutations have increased baseline DSBs, and impaired DSB repair is observed. These data suggest an adaptive role for DSBs in the central nervous system and dysregulation of DSBs and/or repair might drive age-related cognitive decline (ACD), MCI, and AD. In this review, we discuss the adaptive role of DSBs in hippocampus-dependent learning, memory, and IEG expression. We summarize IEGs, the history of DSBs, and DSBs in synaptic plasticity, aging, and AD. DSBs likely have adaptive functions in the brain, and even subtle alterations in their formation and repair could alter IEGs, learning, and memory.     

Protein–Protein Interaction Prediction for Targeted Protein Degradation

Protein–protein interactions (PPIs) play a fundamental role in various biological functions; thus, detecting PPI sites is essential for understanding diseases and developing new drugs. PPI prediction is of particular relevance for the development of drugs employing targeted protein degradation, as their efficacy relies on the formation of a stable ternary complex involving two proteins. However, experimental methods to detect PPI sites are both costly and time-intensive. In recent years, machine learning-based methods have been developed as screening tools. While they are computationally more efficient than traditional docking methods and thus allow rapid execution, these tools have so far primarily been based on sequence information, and they are therefore limited in their ability to address spatial requirements. In addition, they have to date not been applied to targeted protein degradation. Here, we present a new deep learning architecture based on the concept of graph representation learning that can predict interaction sites and interactions of proteins based on their surface representations. We demonstrate that our model reaches state-of-the-art performance using AUROC scores on the established MaSIF dataset. We furthermore introduce a new dataset with more diverse protein interactions and show that our model generalizes well to this new data. These generalization capabilities allow our model to predict the PPIs relevant for targeted protein degradation, which we show by demonstrating the high accuracy of our model for PPI prediction on the available ternary complex data. Our results suggest that PPI prediction models can be a valuable tool for screening protein pairs while developing new drugs for targeted protein degradation.     

On the application of the Neuron MOS transistor principle formodern VLSI design

In this paper, the speed performance, power consumption, and layout area of Neuron MOS transistor circuits are monitored considering the requirements of modern VLSI design. The Neuron MOS transistor is a recently discovered device principle which has a number of input gates that couple capacitively to a floating gate. The floating gate potential controls the current of a transistor channel. This device can be used in logic circuits. A threshold current through the Neuron MOS transistor can be defined that causes a switching of the output of the logic circuits as soon as the channel current surmounts or falls below the specified value. We designed two different multiplier cells, one based on a Neuron MOS inverter, and the other on a Neuron MOS n-MOSFET which is used as one input device of a comparator circuit. Functionality of both cells is proven for data rates up to 50 MHz which represents the first high-speed measurement of a circuit based on this new design principle. A perspective for the upper speed limit found at more than 500 MHz is given by simulation. The new design principle has a layout area reduced by more than a factor of two compared to usual multiplier cells. Moreover, it is shown, that depending on the design chosen, high speed operation leads to considerable power savings. In view of those advantages it is concluded that the principle of threshold logic qualifies for a major breakthrough for packing density improvement of CMOS-based applications     

Mitochondrial Dysfunction in Spinocerebellar Ataxia Type 3 Is Linked to VDAC1 Deubiquitination

Dysfunctional mitochondria are linked to several neurodegenerative diseases. Metabolic defects, a symptom which can result from dysfunctional mitochondria, are also present in spinocerebellar ataxia type 3 (SCA3), also known as Machado–Joseph disease, the most frequent, dominantly inherited neurodegenerative ataxia worldwide. Mitochondrial dysfunction has been reported for several neurodegenerative disorders and ataxin-3 is known to deubiquitinylate parkin, a key protein required for canonical mitophagy. In this study, we analyzed mitochondrial function and mitophagy in a patient-derived SCA3 cell model. Human fibroblast lines isolated from SCA3 patients were immortalized and characterized. SCA3 patient fibroblasts revealed circular, ring-shaped mitochondria and featured reduced OXPHOS complexes, ATP production and cell viability. We show that wildtype ataxin-3 deubiquitinates VDAC1 (voltage-dependent anion channel 1), a member of the mitochondrial permeability transition pore and a parkin substrate. In SCA3 patients, VDAC1 deubiquitination and parkin recruitment to the depolarized mitochondria is inhibited. Increased p62-linked mitophagy, autophagosome formation and autophagy is observed under disease conditions, which is in line with mitochondrial fission. SCA3 fibroblast lines demonstrated a mitochondrial phenotype and dysregulation of parkin-VDAC1-mediated mitophagy, thereby promoting mitochondrial quality control via alternative pathways.     

Tumor Infiltration with CD20+CD73+ B Cells Correlates with Better Outcome in Colorectal Cancer

Immunotherapy has become increasingly important in the treatment of colorectal cancer (CRC). Currently, CD73, also known as ecto-5′-nucleotidase (NT5E), has gained considerable interest as a potential therapeutic target. CD73 is one of the key enzymes catalyzing the conversion of extracellular ATP into adenosine, which in turn exerts potent immune suppressive effects. However, the role of CD73 expression on various cell types within the CRC tumor microenvironment remains unresolved. The expression of CD73 on various cell types has been described recently, but the role of CD73 on B-cells in CRC remains unclear. Therefore, we analyzed CD73 on B-cells, especially on tumor-infiltrating B-cells, in paired tumor and adjacent normal tissue samples from 62 eligible CRC patients. The highest expression of CD73 on tumor-infiltrating B-cells was identified on class-switched memory B-cells, followed by naive B-cells, whereas no CD73 expression was observed on plasmablasts. Clinicopathological correlation analysis revealed that higher CD73⁺ B-cells infiltration in the CRC tumors was associated with better overall survival. Moreover, metastasized patients showed a significantly decreased number of tumor-infiltrating CD73⁺ B-cells. Finally, neoadjuvant therapy correlated with reduced CD73⁺ B-cell numbers and CD73 expression on B-cells in the CRC tumors. As promising new immune therapies are being developed, the role of CD73⁺ B-cells and their subsets in the development of colorectal cancer should be further explored to find new therapeutic options.     

Chemical warfare and survival strategies in bacterial range expansions

Dispersal of species is a fundamental ecological process in the evolution and maintenance of biodiversity. Limited control over ecological parameters has hindered progress in understanding of what enables species to colonize new areas, as well as the importance of interspecies interactions. Such control is necessary to construct reliable mathematical models of ecosystems. In our work, we studied dispersal in the context of bacterial range expansions and identified the major determinants of species coexistence for a bacterial model system of three Escherichia coli strains (toxin-producing, sensitive and resistant). Genetic engineering allowed us to tune strain growth rates and to design different ecological scenarios (cyclic and hierarchical). We found that coexistence of all strains depended on three strongly interdependent factors: composition of inoculum, relative strain growth rates and effective toxin range. Robust agreement between our experiments and a thoroughly calibrated computational model enabled us to extrapolate these intricate interdependencies in terms of phenomenological biodiversity laws. Our mathematical analysis also suggested that cyclic dominance between strains is not a prerequisite for coexistence in competitive range expansions. Instead, robust three-strain coexistence required a balance between growth rates and either a reduced initial ratio of the toxin-producing strain, or a sufficiently short toxin range.