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Protein aggregation is a normal response to age-related exposures. According to the thermodynamic hypothesis of protein folding, soluble proteins precipitate into amyloids (pathology) under supersaturated conditions through a process similar to crystallization. This soluble-to-insoluble phase transition occurs via nucleation and may be catalyzed by ectopic surfaces such as lipid nanoparticles, microbes, or chemical pollutants. The increasing prevalence of these exposures with age correlates with the rising incidence of pathology over the lifespan. However, the formation of amyloid fibrils does not inherently cause neurodegeneration. Neurodegeneration emerges when the levels of functional monomeric proteins, from which amyloids form, fall below a critical threshold. The preservation of monomeric proteins may explain neurological resilience, regardless of the extent of amyloid deposition. This biophysical framework challenges the traditional clinicopathological view that considers amyloids intrinsically toxic, despite the absence of a known mechanism of toxicity. Instead, it suggests that chronic exposures driving persistent nucleation consume monomeric proteins as they aggregate. In normal aging, replacement matches loss; in accelerated aging, it does not. A biophysical approach to neurodegenerative diseases has important therapeutic implications, refocusing treatment strategies from removing pathology to restoring monomeric protein homeostasis above the threshold needed to sustain normal brain function.

The resurgence of interest in phenotypic plasticity has resulted in a wellspring of knowledge surrounding how the environment can influence evolutionary trajectory. However, the conversation surrounding it is often driven by similar narratives without accounting for other ways plasticity may shape evolutionary processes. Here, we attempt to broaden the discussion surrounding plasticity and evolution to better understand and interpret the evolution of phenotypic traits. We do this by examining four model systems that illustrate how studying plasticity through different lenses can shift evolutionary interpretations. Overall, we conclude that the multidimensional nature of phenotypic plasticity makes it a daunting task for evolutionary biologists to properly study. Luckily, ecologists have long been interested in understanding how complex environments shape organismal life history, and we argue that future research should take advantage of large ecological data sets when designing experiments meant to examine the evolution of plastic phenotypes.

Can the banal and transient forgetfulness that we all experience at some point in our lives give us clues about the neural mechanisms underlying the onset of severe dementia, such as Alzheimer´s disease (AD)? The hypothesis we propose suggests an affirmative answer. If access to the memory system (MS) depends on matching key input patterns to appropriate contexts, we postulate that if the matching does not occur, the MS is blocked by a neural gate. From empirical observations, we shift to neural models of memories and their modulation by contexts. Our approach provides a possible explanation for transient memory failures but also suggests that the memory gate (MG) can be a crucial neural module that triggers a cascade of events leading to conditions where AD becomes irreversible and catastrophic. This hypothesis suggests ways to slow down the progression of this disease and may be explored with currently available techniques.

This commentary highlights the potential of wastewater-based epidemiology (WBE) as a complementary tool for antimicrobial resistance (AMR) surveillance. WBE can support the early detection of resistance trends at the population level, including in underserved communities. However, several challenges remain, including technical variability, complexities in data interpretation, and regulatory gaps. An additional limitation is the uncertainty surrounding the origin of resistant bacteria and their genes in wastewater, which may derive not only from human sources but also from industrial, agricultural, or infrastructural contributors. Therefore, effective integration of WBE into public health systems will require standardized methods, sustained investment, and cross-sector collaboration. This could be achieved through joint monitoring initiatives that combine hospital wastewater data with agricultural and municipal surveillance to inform antibiotic stewardship policies. Overcoming these barriers could position WBE as an innovative tool for AMR monitoring, enhancing early warning systems and supporting more responsive, equitable, and preventive public health strategies.

MYC proteins are potent oncoproteins that drive tumorigenesis in a wide range of cancers, making it critical to understand their oncogenic functions and underlying mechanisms. Although MYC overexpression induces transcriptional and replication-associated stress, recent studies have paradoxically identified MYC as a key resilience factor that protects cancer cells from these stressors. In this review, we explore the dual role of MYC in both driving and mitigating cellular stress to achieve its oncogenic function. We also examine how MYC-induced transcriptional and replicative stress generates potentially immunogenic nucleic acid species while simultaneously helping cancer cells evade host immune recognition. We propose a model in which MYC plays a critical role in managing the stress it induces, thereby maintaining a balance that promotes tumor growth. Based on this model, we discuss potential therapeutic strategies targeting MYC-dependent stress responses, offering new avenues for cancer treatment and highlighting the complexity of MYC-driven oncogenesis.

Genome instability (GIN) is a cell pathology linked to cancer promotion and tumor evolution. Transcription is an essential cellular process but also a potential source of DNA damage and GIN. Transcription-replication conflicts (TRCs) are a predominant source of GIN, and defective TRC resolution may seriously compromise genome integrity. Importantly, chromatin dynamics helps orchestrate the response to TRCs to preserve genome integrity. Multiple epigenetic deficiencies have been shown to cause transcription-induced replication stress, resulting in DNA breaks and mutations. Consistently, chromatin alterations are frequent in cancer and correlate with increased mutation burden at TRC sites in tumors. Here, we review our current knowledge of TRC processing, the consequences of its dysfunction, and its relevance in cancer. We focus on the interplay between the DNA damage response (DDR) and chromatin dynamics and discuss the clinical potential of targeting TRCs as anticancer strategies and drugging the associated epigenetic signatures.

Ubiquitin C-terminal hydrolase L1 (UCHL1) is a component of the ubiquitin-proteasome system (UPS) linked to neurodegeneration. Despite its exceptionally high abundance in neurons, UCHL1's precise role remains unclear. This review critically examines the proposed functions of UCHL1 and the challenges to understanding its role in neuronal cells. While UCHL1 hydrolyzes small adducts from the C-terminus of ubiquitin, its occluded active site limits the range of possible substrates and restricts its activity as an efficient deubiquitinase (DUB). These constraints, alongside the paucity of identified substrates, challenge the centrality of this proposed role. We also explore the potential of UCHL1 acting as a ubiquitin ligase and its nonenzymatic role in stabilizing mono-ubiquitin by preventing its lysosomal degradation. By highlighting the unresolved complexities surrounding UCHL1, this perspective proposes several approaches to elucidate UCHL1's significance in the brain.

Organismal evolution is a process of discovering better-fitting phenotypes through trial and error across generations. This iterative process resembles learning processes, an analogy recognized since the 1950s. Recognizing this parallel suggests that evolutionary biology and machine learning can mutually benefit from each other; however, ample opportunities for research into their corresponding concepts remain. In this review, we aim to enhance predictive capabilities and theoretical developments in both fields by exploring their conceptual parallels through specific examples that have emerged from recent advances. We focus on the importance of moving beyond predictions by machine learning approaches for specific cases, but instead advocate for interpretable machine learning approaches for discovering common laws for predicting evolutionary outcomes. This approach seeks to establish a theoretical framework that can transform evolutionary science into a field enriched with predictive theory while also inspiring new modeling and algorithmic strategies in machine learning.

Calcium (Ca²⁺) signaling as the primary intracellular second messenger orchestrates a myriad of physiological processes. Maintaining Ca²⁺ homeostasis relies on Ca²⁺ channels, pumps, exchangers, and buffers. Sarco/endoplasmic reticulum Ca²⁺-ATPases (SERCAs or ATP2A) encoded by ATP2A1, ATP2A2, or ATP2A3 are primary Ca²⁺ pumps localized on the endoplasmic reticulum (ER)/sarcoplasmic reticulum (SR) that actively sequester Ca²⁺ from the cytoplasm back to the ER/SR, thereby preventing the detrimental overload of cytoplasmic Ca²⁺ concentration. Recent studies have highlighted the significant roles and the underlying mechanisms of SERCAs in non-excitatory cells such as those within epithelial, adipose, immune, and reproductive systems or tissues. This article aims to provide a comprehensive summary of the functional characteristics and regulatory mechanisms of the SERCA family, with a particular focus on the latest research concerning their roles and mechanisms in various non-excitatory cells and related cancers. This work will provide insight into understanding the Ca²⁺ signaling regulatory networks mediated by SERCAs and their implications for the diagnosis and treatment of Ca²⁺ dyshomeostasis-related diseases and propose potentially constructive suggestions for the direction of research around Ca²⁺ signaling transductions, as well as guiding strategies for disease diagnosis, treatment, and drug development by targeting SERCAs.