Distinct Processes Research Articles

A climate change signal in the tropical Pacific emerges from decadal variability.

The eastern tropical Pacific has defied the global warming trend. There has been a debate about whether this observed trend is forced or natural (i.e., the Interdecadal Pacific Oscillation; IPO) and this study shows that there are two patterns, one that oscillates along with the IPO, and one that is emerging since the mid-1950s, herein called the Pacific Climate Change (PCC) pattern. Here we show these have distinctive and distinguishable atmosphere-ocean signatures. While the IPO features a meridionally broad wedge-shaped SST pattern, the PCC pattern is marked by a narrow equatorial cooling band. These different SST patterns are related to distinct wind-driven ocean dynamical processes. We further show that the recent trends during the satellite era are a combination of IPO and PCC. Our findings set a path to distinguish climate change signals from internal variability through the underlying dynamics of each.

Action expectancy modulates activity in the mirror neuron system and mentalizing system

Action understanding involves two distinct processing levels that engage separate neural mechanisms: perception of concrete kinematic information and recognition of abstract action intentions. The mirror neuron system and the mentalizing system have both been linked to concrete action and abstract information processing, but their specific roles remain debatable. Here, we conducted a functional magnetic resonance imaging study with 26 participants who passively observed expected and unexpected actions. We performed whole-brain activation, region of interest, and effective connectivity analyses to investigate the neural correlates of these actions. Whole-brain activation analyses revealed that expected actions were associated with increased activation in the left medial superior frontal gyrus, while unexpected actions were linked to heightened activity in the left supramarginal gyrus, left superior parietal lobule, right inferior temporal gyrus, and left middle frontal gyrus. Region of interest analyses demonstrated that the left ventral premotor cortex exhibited greater activation during the observation of expected actions compared to unexpected actions, while the left inferior frontal gyrus, left superior parietal lobule, and left precuneus showed stronger activation during the observation of unexpected actions. Effective connectivity was observed between the left ventral premotor cortex and the left angular gyrus, left intraparietal sulcus, left dorsal premotor cortex, and left ventromedial prefrontal cortex with the middle frontal gyrus when observing unexpected, but not expected, actions. These findings suggest that expected actions are primarily processed by the mirror neuron system, whereas unexpected actions engage both the mirror neuron system and the mentalizing system, with these systems playing complementary roles in the understanding of unexpected actions.

Advanced alginate-based nanofiber aerogels: A synthetic matrix for high-efficiency lysozyme adsorption and controlled release

The development of materials with high lysozyme adsorption is critical for drug delivery and skin wound applications, as it enhances antibacterial properties, stability, and controlled release of therapeutic agents, thereby improving treatment efficacy and safety. Alginate-based nanofiber scaffolds, featuring high surface area and multiple adsorption sites, can efficiently absorb lysozyme and regulate its release through tunable pore channels, offering a promising approach to chronic wound management. In this study, we fabricated poly (vinyl alcohol-co-ethylene) (EVOH) nanofiber-based sodium alginate (ENSA) aerogels using a simple two-step crosslinking procedure. The resulting aerogels, with controllable porosity formed via high-pressure spraying techniques (aerogel film) and molding (aerogel sponge), were evaluated for their high-loading capacity and controllable release of lysozyme. The aerogel film exhibited a remarkable lysozyme adsorption capacity of 1965 ± 36 mg/g, while the aerogel sponge sustained lysozyme release over 14 days. Analysis of the drug-release mechanism through four kinetic models revealed two distinct processes: cation exchange and matrix diffusion. The aerogel's pore structure influenced the diffusion processes, enabling tailored drug release profiles. Additionally, the ENSA aerogels demonstrated good mechanical properties, non-cytotoxicity, and potent antibacterial activity, positioning them as promising materials for skin wound dressings.

Slit3 Fragments Orchestrate Neurovascular Expansion and Thermogenesis in Brown Adipose Tissue.

Brown adipose tissue (BAT) represents an evolutionary innovation enabling placental mammals to regulate body temperature through adaptive thermogenesis. Brown adipocytes are surrounded by a dense network of blood vessels and sympathetic nerves that support their development and thermogenic function. Cold exposure stimulates BAT thermogenesis through the coordinated induction of brown adipogenesis, angiogenesis, and sympathetic innervation. However, how these distinct processes are coordinated remains unclear. Here, we identify Slit guidance ligand 3 (Slit3) as a new niche factor that mediates the crosstalk among adipocyte progenitors, endothelial cells, and sympathetic nerves. We show that adipocyte progenitors secrete Slit3 which regulates both angiogenesis and sympathetic innervation in BAT and is essential for BAT thermogenesis in vivo. Proteolytic cleavage of Slit3 generates secreted Slit3-N and Slit3-C fragments, which activate distinct receptors to stimulate angiogenesis and sympathetic innervation, respectively. Moreover, we introduce bone morphogenetic protein-1 (Bmp1) as the first Slit protease identified in vertebrates. In summary, this study underscores the essential role of Slit3-mediated neurovascular network expansion in enabling cold-induced BAT adaptation. The co-regulation of neurovascular expansion by Slit3 fragments provides a bifurcated yet harmonized approach to ensure a synchronized response of BAT to environmental challenges. This study presents the first evidence that adipocyte progenitors regulate tissue innervation, revealing a previously unrecognized dimension of cellular interaction within adipose tissue.

A precise machine learning model: Detecting cervical cancer using feature selection and explainable AI

Cervical cancer is a cancer that remains a significant global health challenge all over the world. Due to improper screening in the early stages, and healthcare disparities, a large number of women are suffering from this disease, and the mortality rate increases day by day. Hence, in these studies, we presented a precise approach utilizing six different machine learning models (decision tree, logistic regression, naïve bayes, random forest, k nearest neighbors, support vector machine), which can predict the early stage of cervical cancer by analysing 36 risk factor attributes of 858 individuals. In addition, two data balancing techniques—Synthetic Minority Oversampling Technique and Adaptive Synthetic Sampling—were used to mitigate the data imbalance issues. Furthermore, Chi-square and Least Absolute Shrinkage and Selection Operator are two distinct feature selection processes that have been applied to evaluate the feature rank, which are mostly correlated to identify the particular disease, and also integrate an explainable artificial intelligence technique, namely Shapley Additive Explanations, for clarifying the model outcome. The applied machine learning model outcome is evaluated by performance evaluation matrices, namely accuracy, sensitivity, specificity, precision, f1-score, false-positive rate and false-negative rate, and area under the Receiver operating characteristic curve score. The decision tree outperformed in Chi-square feature selection with outstanding accuracy with 97.60%, 98.73% sensitivity, 80% specificity, and 98.73% precision, respectively. During the data imbalance, DT performed 97% accuracy, 99.35% sensitivity, 69.23% specificity, and 97.45% precision. This research is focused on developing diagnostic frameworks with automated tools to improve the detection and management of cervical cancer, as well as on helping healthcare professionals deliver more efficient and personalized care to their patients.

Disability and infidelity with a cross-cultural couple: an emotionally focused therapy case study

ABSTRACT This clinical case study delves into the therapeutic process of a cross-cultural couple, facing the intersection of disability and infidelity concerns. This intersection illuminates the intricate dynamics that arise when couples navigate both the complexities of disability and the emotional turmoil associated with infidelity. Utilizing Emotionally Focused Therapy (EFT), case intervention unfolded through three distinct stages, revealing intricate emotional dynamics, attachment styles, and communication patterns. The study explores the couples’ narrative, negative interaction cycles, and distinct emotional processing styles, considering complicating factors such as immigration challenges, cultural norms, and online infidelity. To strengthen bonds, the therapeutic approach promotes cultural sensitivity, intersectionality, and emotional connection. Despite time constraints and visa issues, the study highlights the efficacy of EFT in navigating the complexities of this cross-cultural couple. This case study contributes to the existing literature by examining the interplay of disability, infidelity, and cultural diversity within the framework of couples’ therapy.

Emission Impacts of Direct Reduced Iron‐Ore Processing Systems: A Systems Thinking Approach in Case Studies of Canada and Peru

This study presents a novel methodology for quantifying CO2 emissions in direct reduced iron (DRI)‐grade iron‐ore processing plants using system dynamics modeling (SDM) with an emphasis on associated uncertainties. Two plants in Canada and Peru are analyzed, where similar ore grades (34.89% and 39.10%) but different ore types (hematite and magnetite) lead to distinct mineral processing systems. By incorporating the breakdown of power grid generation sources, the annual CO2 emissions are quantified, finding significantly higher emissions in Peru (22,241.63 tCO2e ton−1) compared to Canada (2,252.85 tCO2e ton−1). These results highlight the critical impact of local energy grids on emissions, underscoring the need to consider both ore characteristics and regional energy profiles in developing decarbonization strategies. This study offers a structured approach to assessing the CO2 impact of raw materials in low‐carbon steel production. It emphasizes the effect of the spatial distribution of raw materials in the steel‐making process. The analysis reveals that emissions from the plant in Peru are nearly 10 times higher than those in Canada, highlighting the significant influence of the local energy grid. These results underscore the influence of ore characteristics and regional energy profiles in developing effective decarbonization strategies.

Probabilistic inference of epigenetic age acceleration from cellular dynamics.

The emergence of epigenetic predictors was a pivotal moment in geroscience, propelling the measurement and concept of biological aging into a quantitative era; however, while current epigenetic clocks show strong predictive power, they are data-driven in nature and are not based on the underlying biological mechanisms driving methylation dynamics. We show that predictions of these clocks are susceptible to several confounding non-age-related phenomena that make interpretation of these estimates and associations difficult. To address these limitations, we developed a probabilistic model describing methylation transitions at the cellular level. Our approach reveals two measurable components, acceleration and bias, which directly reflect perturbations of the underlying cellular dynamics. Acceleration is the proportional increase in the speed of methylation transitions across CpG sites, whereas bias corresponds to global changes in methylation levels. Using data from 15,900 participants from the Generation Scotland study, we develop a robust inference framework and show that these are two distinct processes confounding current epigenetic predictors. Our results show improved associations of acceleration and bias with physiological traits known to impact healthy aging, such as smoking and alcohol consumption, respectively. Furthermore, a genome-wide association study of epigenetic age acceleration identified seven genomic loci.

Comparing alpha-synuclein-interactomes between multiple systems atrophy and Parkinson's disease reveals unique and shared pathological features.

Primary synucleinopathies, such as Parkinson's disease (PD), Dementia with Lewy bodies (DLB), and multiple system atrophy (MSA), are neurodegenerative disorders with some shared clinical and pathological features. Aggregates of alpha-synuclein (αsyn) phosphorylated at serine 129 (PSER129) are the hallmark of synucleinopathies, which for PD/DLB are found predominantly in neurons (Neuronal cytoplasmic inclusions "NCIs"), but for MSA, aggregates are primarily found in oligodendroglia (Glial cytoplasmic inclusions "GCIs"). It remains unclear if the distinct pathological presentation of PD/DLB and MSA are manifestations of distinct or shared pathological processes. We hypothesize that the distinct synucleinopathies MSA and PD/DLB share common molecular features. Using the in-situ proximity labeling technique biotinylation by antibody recognition (BAR), we compare aggregated αsyn-interactomes (BAR-PSER129) and total αsyn-interactomes (BAR-MJFR1) between MSA (n=5) and PD/DLB (n=10) in forebrain and midbrain structures. For BAR-PSER129 and BAR-MJFR1 captures, αsyn was the most significantly enriched protein in PD/DLB and MSA. In PD/DLB, BAR-PSER129 identified 194 αsyn-aggregate-interacting proteins, while BAR-MJFR1 identified 245 αsyn interacting proteins. In contrast, in the MSA brain, only 38 and 175 proteins were identified for each capture, respectively. When comparing MSA and PD/DLB, a high overlap (59.5%) was observed between BAR-MJFR1 captured proteins, whereas less overlap (14.4%) was observed for BAR-PSER129. Direct comparison between MSA and PD/DLB revealed 79 PD/DLB-associated proteins and only three MSA-associated proteins (CBR1, CRYAB, and GFAP). Pathway enrichment analysis revealed PD/DLB interactions were dominated by vesicle/SNARE-associated pathways, in contrast to MSA, which strongly enriched for metabolic/catabolic, iron, and cellular oxidant detoxification pathways. A subnetwork of cytosolic antioxidant enzymes called peroxiredoxins drove cellular detoxification pathways in MSA. A common network of 26 proteins, including neuronal-specific proteins (e.g., SNYGR3) with HSPA8 at the core, was shared between MSA and DLB/PD. Extracellular exosome pathways were universally enriched regardless of disease or BAR target protein. Synucleinopathies have divergent and convergent αsyn-aggregate interactions, indicating unique and shared pathogenic mechanisms. MSA uniquely involves oxidant detoxification processes in glial cells, while vesicular processes in neurons dominate PD/DLB. Shared interactions, specifically SNYGR3 (i.e., a neuronal protein), between MSA and PD/DLB suggest neuronal axons origin for both diseases. In conclusion, we provide αsyn aggregates protein interaction maps for two distinct synucleinopathies.

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease.

Disease mechanisms are usually complex and governed by the interaction of several distinct molecular processes. Complex, multidimensional datasets are a valuable resource to generate more insights into those processes, but the analysis of such datasets can be challenging due to the high dimensionality resulting, for example, from different disease conditions, timepoints, and omics capturing the process at different resolutions. Here, we showcase an approach to analyze and explore such a complex multiomics dataset in an unsupervised way by applying multi-omics factor analysis (MOFA) to a dataset generated from blood samples that capture the immune response in acute and chronic coronary syndromes. The dataset consists of several assays at differing resolutions, including sample-level cytokine data, plasma-proteomics and neutrophil prime-seq, and single-cell RNA-seq (scRNA-seq)data. Further complexity is added by having several different time points measured per patient and several patient subgroups. The analysis workflow outlines how to integrate and analyze the data in several steps: (1) Data pre-processing and harmonization, (2) Estimation of the MOFA model, (3) Downstream analysis. Step 1 outlines how to process the features of the different data types, filter out low-quality features, and normalize them to harmonize their distributions for further analysis. Step 2 shows how to apply the MOFA model and explore the major sources of variance within the dataset across all omics and features. Step 3 presents several strategies for the downstream analysis of the captured patterns, linking them to the disease conditions and potential molecular processes governing those conditions. Overall, we present a workflow for unsupervised data exploration of complex multi-omics datasets to enable the identification of major axes of variation composed of differing molecular features that can also be applied to other contexts and multi-omics datasets (including other assays as presented in the exemplary use case).

Effects of personality traits and mood induction on metamemory judgments and metacognitive beliefs

Although the effects of mood and personality traits on memory performance have previously been studied, their relationship to the metamemory and metacognitive processes is still unknown. In this study, we investigated the effects of mood induction (positive and negative) and personality traits (extroverted and neurotics) on metacognitive beliefs, memory confidence, the judgment of learning (JOL) and feeling of knowing (FOK) judgments during face-name recognition tasks. One hundred twenty-seven participants who met the criteria based on their extraverted and neurotic personality scores on the Big Five Personality Inventory were randomly assigned to positive and negative mood induction conditions. We found that neurotics showed lower JOL judgments and accuracy than extroverts. The interaction effect between mood and personality significantly affected JOL and FOK accuracy, indicating that while extraverts were more accurate during positive induction, neurotics were more accurate during negative induction. In addition, neurotics were underconfident in their memory and reported more negative metacognitive beliefs than extroverts. We concluded that memory and metamemory processes are distinguishable in their relationships with mood states and personality traits. Our data also showed that JOL and FOK are distinct processes that support domain-specific metacognitive judgments.

Operando electron spin probes for the study of battery processes

Operando electron spin probes, namely magnetometry and electron paramagnetic resonance (EPR), provide real-time insights into the electrochemical processes occurring in battery materials and devices. In this work, we describe the design criteria and outline the development of operando magnetometry and EPR electrochemical cells. Notably, we show that a clamping mechanism, or springs, are needed to achieve sufficient compression of the battery stack and an electrochemical performance on par with that of a standard Swagelok-type cell. The tandem use of operando EPR and magnetometry allows us to identify five distinct and reversible redox processes taking place on charge and discharge of the intercalation-type LiNi0.5Mn0.5O2 Li-ion cathode. While redox processes in conversion-type electrodes are notoriously difficult to investigate using standard characterization methods (e.g. X-ray based) and/or post mortem analysis, due to the formation of poorly crystalline and metastable reaction intermediates and products during cycling, we show that operando magnetometry provides unique insight into the kinetics and reversibility of Fe nanoparticle formation in the Na3FeF6 electrode for Na-based batteries. Step increases in the cell magnetization upon extended cycling indicate the build-up of Fe nanoparticles in the system, hinting at only partially reversible charge–discharge processes. The broad applicability of the tools developed herein to a range of electrode chemistries and structures, from intercalation to conversion electrodes, and from crystalline to amorphous systems, makes them particularly promising for the development of electrochemical energy storage technologies and beyond.

Integrating UAV imagery and machine learning via Geographic Object Based Image Analysis (GEOBIA) for enhanced monitoring of Yucca gloriosa in Mediterranean coastal dunes

Effective monitoring and early detection of invasive alien plant species (IAPs) are crucial for mitigating their spread and safeguarding native habitats. Unmanned Aerial Vehicles (UAVs) offer a cost-efficient solution, providing high resolution images. In this study, we aimed to develop a semi-automated methodology using a machine learning algorithm, spatial metrics, and clustering techniques on UAV images to monitor, map, and suggest management measures to counteract Yucca gloriosa, an invasive plant colonizing coastal fixed dunes in central Italy.UAV flights were conducted using two drones: one for the visible spectrum and the other for multispectral bands (Blue, Green, Red, Red Edge, and Near Infrared) along with a Digital Surface Model (DSM). Derived vegetation indices were also utilized. For mapping Y. gloriosa distribution, a Geographic Object Based Image Analysis (GEOBIA) approach was applied to the orthophoto segmentation, followed by a Random Forest algorithm in a training phase, considering three variable combinations (DSM + vegetation indices, DSM + spectral bands, DSM + mixed variables). The most accurate Y. gloriosa map was used to suggest management measures combining the spatial pattern of invaded patches (size, height, isolation level, and aggregation degree) and a mixed clustering approach (hierarchical and partitioning).The results highlighted that the most accurate prediction map was based on the DSM + mixed variables dataset, showing the important role of using a combination of spectral bands and vegetation indices. In all three cases, the DSM emerged as the pivotal variable for discriminating Y. gloriosa from the surrounding environment. Additionally, our results demonstrate the advantages of incorporating vegetation indices in discerning the target invasive alien plant (IAP) from the broader environment, particularly considering its distinctive photosynthesis process and biomass production. From a managerial standpoint, our pilot study indicates that the UAV-based mapping methodology represents an optimal balance between field efforts and costs. This approach allows for the precise identification of containment and removal areas of Y. gloriosa, without compromising the accuracy of the method. The generated prediction maps also hold potential significance for the conservation of coastal dune ecosystems, providing a promising tool for the effective management of invasive species and biodiversity conservation by suggesting management measures for Y. gloriosa.

Solar Light CO2 Photoreduction Enhancement by Mononuclear Rhenium(I) Complexes: Characterization and Mechanistic Insights.

The catalytic efficacy of a novel mononuclear rhenium(I) complex in CO2 reduction is remarkable, with a turnover number (TONCO) of 1517 in 3 h, significantly outperforming previous Re(I) catalysts. This complex, synthesized via a substitution reaction on an aromatic ring to form a bromo-bipyridine derivative, L1 = 2-bromo-6-(1H-pyrazol-1-yl)pyridine, and further reacting with [Re(CO)5Cl], results in the facial-tricarbonyl complex [ReL1(CO)3Cl] (1). The light green solid was obtained with an 80% yield and thoroughly characterized using cyclic voltammetry, nuclear magnetic resonance (NMR), Fourier transform infrared (FTIR) spectroscopy, and ultraviolet-visible (UV-vis) spectroscopy. Cyclic voltammetry under CO2 atmosphere revealed three distinct redox processes, suggesting the formation of new electroactive compounds. The studies on photoreduction highlighted the ability of the catalyst to reduce CO2, while NMR, FTIR, and electrospray ionization (ESI) mass spectrometry provided insights into the mechanism, revealing the formation of solvent-coordinated complexes and new species under varying conditions. Additionally, computational studies (DFT) were undertaken to better understand the electronic structure and reactivity patterns of 1, focusing on the role of the ligand, the spectroscopic features, and the redox behavior. This comprehensive approach provides insights into the intricate dynamics of CO2 photoreduction, showcasing the potential of Re(I) complexes in catalysis.

Multi‑omics identification of a signature based on malignant cell-associated ligand–receptor genes for lung adenocarcinoma

PurposeLung adenocarcinoma (LUAD) significantly contributes to cancer-related mortality worldwide. The heterogeneity of the tumor immune microenvironment in LUAD results in varied prognoses and responses to immunotherapy among patients. Consequently, a clinical stratification algorithm is necessary and inevitable to effectively differentiate molecular features and tumor microenvironments, facilitating personalized treatment approaches.MethodsWe constructed a comprehensive single-cell transcriptional atlas using single-cell RNA sequencing data to reveal the cellular diversity of malignant epithelial cells of LUAD and identified a novel signature through a computational framework coupled with 10 machine learning algorithms. Our study further investigates the immunological characteristics and therapeutic responses associated with this prognostic signature and validates the predictive efficacy of the model across multiple independent cohorts.ResultsWe developed a six-gene prognostic model (MYO1E, FEN1, NMI, ZNF506, ALDOA, and MLLT6) using the TCGA-LUAD dataset, categorizing patients into high- and low-risk groups. This model demonstrates robust performance in predicting survival across various LUAD cohorts. We observed distinct molecular patterns and biological processes in different risk groups. Additionally, analysis of two immunotherapy cohorts (N = 317) showed that patients with a high-risk signature responded more favorably to immunotherapy compared to those in the low-risk group. Experimental validation further confirmed that MYO1E enhances the proliferation and migration of LUAD cells.ConclusionWe have identified malignant cell-associated ligand–receptor subtypes in LUAD cells and developed a robust prognostic signature by thoroughly analyzing genomic, transcriptomic, and immunologic data. This study presents a novel method to assess the prognosis of patients with LUAD and provides insights into developing more effective immunotherapies.

Mental effort during mindless reading? Pupil fluctuations indicate internal processing during levels of inattention.

Mind wandering, an experience characterized by a reduced external focus of attention and an increased internal focus, has seen significant theoretical advancement in understanding its underlying cognitive processes. The levels-of-inattention hypothesis posits that in mind wandering, external attention is reduced in a graded fashion, reflecting different levels of weak versus deep attentional decoupling. However, it has remained unclear whether internal processing during mind wandering, and mindless reading in particular, requires effort and, if so, whether it is graded or distinct. To address this, we analyzed pupil size as a measure of cognitive load in the sustained-attention-to-stimulus task during text reading. We examined whether decoupled external attention is linked to an overall reduction in workload and whether internal focus of attention is graded or represents a distinct cognitive process. Overall, overlooking errors in the text was associated with a small pupil size, indicating reduced effortful processing. However, this effect varied with error type: overlooking high- or medium-level errors (weak decoupling) resulted in reduced pupil size, while overlooking low-level errors (deep decoupling) had no effect on pupil size. Moreover, detecting an error (at any processing level) elicited a task-evoked pupillary response, which was absent when it was overlooked. These findings suggest that weak decoupling reduces internal resource-demanding processing and are in line with the hypothesis that large pupils during deep decoupling may be associated with distinct states of effortful internal processing. They further support both the levels-of-inattention hypothesis and the notion that internal focus is a distinct mode of deeply decoupled processing. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Memory Reconsolidation Updating in Substance Addiction: Applications, Mechanisms, and Future Prospects for Clinical Therapeutics.

Persistent and maladaptive drug-related memories represent a key component in drug addiction. Converging evidence from both preclinical and clinical studies has demonstrated the potential efficacy of the memory reconsolidation updating procedure (MRUP), a non-pharmacological strategy intertwining two distinct memory processes: reconsolidation and extinction-alternatively termed "the memory retrieval-extinction procedure". This procedure presents a promising approach to attenuate, if not erase, entrenched drug memories and prevent relapse. The present review delineates the applications, molecular underpinnings, and operational boundaries of MRUP in the context of various forms of substance dependence. Furthermore, we critically examine the methodological limitations of MRUP, postulating potential refinement to optimize its therapeutic efficacy. In addition, we also look at the potential integration of MRUP and neurostimulation treatments in the domain of substance addiction. Overall, existing studies underscore the significant potential of MRUP, suggesting that interventions predicated on it could herald a promising avenue to enhance clinical outcomes in substance addiction therapy.

TEX264 drives selective autophagy of DNA lesions to promote DNA repair and cell survival

DNA repair and autophagy are distinct biological processes vital for cell survival. Although autophagy helps maintain genome stability, there is no evidence of its direct role in the repair of DNA lesions. We discovered that lysosomes process topoisomerase 1 cleavage complexes (TOP1cc) DNA lesions in vertebrates. Selective degradation of TOP1cc by autophagy directs DNA damage repair and cell survival at clinically relevant doses of topoisomerase 1 inhibitors. TOP1cc are exported from the nucleus to lysosomes through a transient alteration of the nuclear envelope and independent of the proteasome. Mechanistically, the autophagy receptor TEX264 acts as a TOP1cc sensor at DNA replication forks, triggering TOP1cc processing by the p97 ATPase and mediating the delivery of TOP1cc to lysosomes in an MRE11-nuclease- and ATR-kinase-dependent manner. We found an evolutionarily conserved role for selective autophagy in DNA repair that enables cell survival, protects genome stability, and is clinically relevant for colorectal cancer patients.

Implementing Linkage Quality Assessments in Large-Scale Privacy Preserving Infrastructure: Did we kill them a second time and why are there zombies?

The National Clinical Cohort Collaborative (N3C) uses privacy-preserving record linkage (PPRL) to formulate longitudinal patient histories and for de-duplication across disparate data including electronic health records, administrative claims, and mortality data. The N3C employs a linkage honest broker implementation model which separates the entity holding de-identified PPRL tokens, from the entity holding the substantive data payloads. As a result of this data governance structure, linkage quality assessments are necessarily distributed into 2 distinct processes: 1) Linkage Honest Broker receipt and processing of linkages, 2) Data Aggregation entity assembling data based on received linkage crosswalks. Data is released on a weekly basis. This presentation will focus on Step 2 for the Mortality data linkages where the downstream data linkage pipelines must assess linkage quality across several dimensions of quality, focused on plausibility. There are 4 types of mortality data spanning the electronic health record, government Social Security Administration death master files, third-party obituary data, and administrative claims. Each of these sources has different levels of authoritativeness and data latency, compounding the challenges of reconciling fact of death including accurate death reporting dates. To address these challenges, we implemented an automated linkage quality pipeline which generates reports that assess linkage plausibility across the various data types, classifying concordance and discordance between data sources including age, dates of death, surfacing plausible and non-plausible activities post-death. Sharing our real-world implementation will aid conference attendees in understanding how to plan for such activities when considering specific implementation models for privacy-preserving linkages.

GPR37 processing in neurodegeneration: a potential marker for Parkinson’s Disease progression rate

The orphan G protein-coupled receptor 37 (GPR37), widely associated with Parkinson’s disease (PD), undergoes proteolytic processing under physiological conditions. The N-terminus domain is proteolyzed by a disintegrin and metalloproteinase 10 (ADAM-10), which generates various membrane receptor forms and ectodomain shedding (ecto-GPR37) in the extracellular environment. We investigated the processing and density of GPR37 in several neurodegenerative conditions, including Lewy body disease (LBD), multiple system atrophy (MSA), corticobasal degeneration (CBD), progressive supranuclear palsy (PSP), and Alzheimer’s disease (AD). The presence of ecto-GPR37 peptides in the cerebrospinal fluid (CSF) of PD, MSA, CBD and PSP patients was assessed through an in-house nanoluciferase-based immunoassay. This study identified increased receptor processing in early-stage LBD within the PFC and striatum, key brain areas in neurodegeneration. In MSA only the 52 kDa form of GPR37 appeared in the striatum. This form was also significantly elevated in the striatum of AD necropsies. On the contrary, GPR37 processing remained unchanged in the brains of CBD and PSP patients. Furthermore, while CSF ecto-GPR37 increased in PD patients, its levels remained unchanged in MSA, CBD, and PSP subjects. Importantly, patients with PD with rapid progression of the disease did not have elevated ecto-GPR37 in the CSF, while those with slow progression showed a significant increase, suggesting a possible prognostic use of ecto-GPR37 in PD. This research underscores the distinctive processing and density patterns of GPR37 in neurodegenerative diseases, providing crucial insights into its potential role as an indicator of PD progression rates.