Mol Biol Rep. 2024 Jun 21;51(1):772. doi: 10.1007/s11033-024-09635-x.

NO ABSTRACT

PMID:38904724 | DOI:10.1007/s11033-024-09635-x

Elife. 2024 Jun 21;13:RP94547. doi: 10.7554/eLife.94547.

ABSTRACT

A functional nervous system is built upon the proper morphogenesis of neurons to establish the intricate connection between them. The microtubule cytoskeleton is known to play various essential roles in this morphogenetic process. While many microtubule-associated proteins (MAPs) have been demonstrated to participate in neuronal morphogenesis, the function of many more remains to be determined. This study focuses on a MAP called HMMR in mice, which was originally identified as a hyaluronan binding protein and later found to possess microtubule and centrosome binding capacity. HMMR exhibits high abundance on neuronal microtubules and altering the level of HMMR significantly affects the morphology of neurons. Instead of confining to the centrosome(s) like cells in mitosis, HMMR localizes to microtubules along axons and dendrites. Furthermore, transiently expressing HMMR enhances the stability of neuronal microtubules and increases the formation frequency of growing microtubules along the neurites. HMMR regulates the microtubule localization of a non-centrosomal microtubule nucleator TPX2 along the neurite, offering an explanation for how HMMR contributes to the promotion of growing microtubules. This study sheds light on how cells utilize proteins involved in mitosis for non-mitotic functions.

PMID:38904660 | DOI:10.7554/eLife.94547

Front Neurosci. 2024 Jun 6;18:1426177. doi: 10.3389/fnins.2024.1426177. eCollection 2024.

ABSTRACT

Recently a broad range of phenotypic abnormalities related to the neurodevelopmental and neurodegenerative disorder NEDAMSS (Neurodevelopmental Disorder with Regression, Abnormal Movements, Loss of Speech, and Seizures) have been associated with rare single-nucleotide polymorphisms (SNPs) or insertion and deletion variants (Indel) in the intron-less gene IRF2BPL. Up to now, 34 patients have been identified through whole exome sequencing carrying different heterozygous pathogenic variants spanning the intron-less gene from the first polyglutamine tract at the N-terminus to the C3HC4 RING domain of the C-terminus of the protein. As a result, the phenotypic spectrum of the patients is highly heterogeneous and ranges from abnormal neurocognitive development to severe neurodegenerative courses with developmental and seizure-related encephalopathies. While the treatment of IRF2BPL-related disorders has focused on alleviating the patient's symptoms by symptomatic multidisciplinary management, there has been no prospect of entirely relieving the symptoms of the individual patients. Yet, the recent advancement of CRISPR-Cas9-derived gene editing tools, leading to the generation of base editors (BEs) and prime editors (PEs), provide an encouraging new therapeutic avenue for treating NEDAMSS and other neurodevelopmental and neurodegenerative diseases, which contain SNPs or smaller Indels in post-mitotic cell populations of the central nervous system, due to its ability to generate site-specific DNA sequence modifications without creating double-stranded breaks, and recruiting the non-homologous DNA end joining repair mechanism.

PMID:38903604 | PMC:PMC11187338 | DOI:10.3389/fnins.2024.1426177

Bioinformatics. 2024 Jun 20:btae412. doi: 10.1093/bioinformatics/btae412. Online ahead of print.

ABSTRACT

MOTIVATION: Spatial omics data demand computational analysis but many analysis tools have computational resource requirements that increase with the number of cells analyzed. This presents scalability challenges as researchers use spatial omics technologies to profile millions of cells.

RESULTS: To enhance the scalability of spatial omics data analysis, we developed a rasterization preprocessing framework called SEraster that aggregates cellular information into spatial pixels. We apply SEraster to both real and simulated spatial omics data prior to spatial variable gene expression analysis to demonstrate that such preprocessing can reduce computational resource requirements while maintaining high performance, including as compared to other down-sampling approaches. We further integrate SEraster with existing analysis tools to characterize cell-type spatial co-enrichment across length scales. Finally, we apply SEraster to enable analysis of a mouse pup spatial omics dataset with over a million cells to identify tissue-level and cell-type-specific spatially variable genes as well as spatially co-enriched cell-types that recapitulate expected organ structures.

AVAILABILITY: SEraster is implemented as an R package on GitHub (https://github.com/JEFworks-Lab/SEraster) with additional tutorials at https://JEF.works/SEraster.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:38902953 | DOI:10.1093/bioinformatics/btae412

Toxicol Sci. 2024 Jun 20:kfae078. doi: 10.1093/toxsci/kfae078. Online ahead of print.

ABSTRACT

Drug-induced liver injury (DILI) is the most common trigger for acute liver failure and the leading cause of attrition in drug development. In this study, we developed an in-silico framework to screen drug-induced hepatocellular toxicity (INSIGHT) by integrating the post-treatment transcriptomic data from both rodent models and primary human hepatocytes. We first built an early prediction model using logistic regression with elastic net regularization for 123 compounds and established the INSIGHT framework that can screen for drug-induced hepatotoxicity. The 235 signature genes identified by INSIGHT were involved in metabolism, bile acid synthesis, and stress response pathways. Applying the INSIGHT to an independent transcriptomic dataset treated by 185 compounds predicted that 27 compounds show a high DILI risk, including zoxazolamine and emetine. Further integration with cell image data revealed that predicted DILI compounds can induce abnormal morphological changes in the endoplasmic reticulum (ER) and mitochondrion. Clustering analysis of the treatment-induced transcriptomic changes delineated distinct DILI mechanisms induced by these compounds. Our study presents a computational framework for a mechanistic understanding of long-term liver injury and the prospective prediction of DILI.

PMID:38902949 | DOI:10.1093/toxsci/kfae078

Biochim Biophys Acta Mol Basis Dis. 2024 Jun 18:167312. doi: 10.1016/j.bbadis.2024.167312. Online ahead of print.

ABSTRACT

Epithelial ovarian cancer (EOC) is highly lethal due to its unique metastatic characteristics. EOC spheroids enter a non-proliferative state, with hypoxic cores and reduced oncogenic signaling, all of which contribute to tumour dormancy during metastasis. We investigated the metabolomic states of EOC cells progressing through the three steps to metastasis. Metabolomes of adherent, spheroid, and re-adherent cells were validated by isotopic metabolic flux analysis and mitochondrial functional assays to identify metabolic pathways that were previously unknown to promote EOC metastasis. Although spheroids were thought to exist in a dormant state, metabolomic analysis revealed an unexpected upregulation of energy production pathways in spheroids, accompanied by increased abundance of tricarboxylic acid (TCA) cycle and electron transport chain proteins. Tracing of 13C-labelled glucose and glutamine showed increased pyruvate carboxylation and decreased glutamine anaplerosis in spheroids. Increased reductive carboxylation suggests spheroids adjust redox homeostasis by shuttling cytosolic NADPH into mitochondria via isocitrate dehydrogenase. Indeed, we observed spheroids have increased respiratory capacity and mitochondrial ATP production. Relative to adherent cells, spheroids reduced serine consumption and metabolism, processes which were reversed upon spheroid re-adherence. The data reveal a distinct metabolism in EOC spheroids that enhances energy production by the mitochondria while maintaining a dormant state with respect to growth and proliferation. The findings advance our understanding of EOC metastasis and identify the TCA cycle and mitochondrional activity as novel targets to disrupt EOC metastasis, providing new approaches to treat advanced disease.

PMID:38901649 | DOI:10.1016/j.bbadis.2024.167312

Metab Eng. 2024 Jun 18:S1096-7176(24)00077-6. doi: 10.1016/j.ymben.2024.06.004. Online ahead of print.

ABSTRACT

Microbial instability is a common problem during bio-production based on microbial hosts. Halomonas bluephagenesis has been developed as a chassis for next generation industrial biotechnology (NGIB) under open and unsterile conditions. However, the hidden genomic information and peculiar metabolism have significantly hampered its deep exploitation for cell-factory engineering. Based on the freshly completed genome sequence of H. bluephagenesis TD01, which reveals 1889 biological process-associated genes grouped into 84 GO-slim terms. An enzyme constrained genome-scale metabolic model Halo-ecGEM was constructed, which showed strong ability to simulate fed-batch fermentations. A visible salt-stress responsive landscape was achieved by combining GO-slim term enrichment and CVT-based omics profiling, demonstrating that cells deploy most of the protein resources by force to support the essential activity of translation and protein metabolism when exposed to salt stress. Under the guidance of Halo-ecGEM, eight transposases were deleted, leading to a significantly enhanced stability for its growth and bioproduction of various polyhydroxyalkanoates (PHA) including 3-hydroxybutyrate (3HB) homopolymer PHB, 3HB and 3-hydroxyvalerate (3HV) copolymer PHBV, as well as 3HB and 4-hydroxyvalerate (4HB) copolymer P34HB. This study sheds new light on the metabolic characteristics and stress-response landscape of H. bluephagenesis, achieving for the first time to construct a long-term growth stable chassis for industrial applications. For the first time, it was demonstrated that genome encoded transposons are the reason for microbial instability during growth in flasks and fermentors.

PMID:38901556 | DOI:10.1016/j.ymben.2024.06.004

Curr Opin Plant Biol. 2024 Jun 19;81:102575. doi: 10.1016/j.pbi.2024.102575. Online ahead of print.

ABSTRACT

Although plant metabolic engineering enables the sustainable production of valuable metabolites with many applications, we still lack a good understanding of many multi-layered regulatory networks that govern metabolic pathways at the metabolite, protein, transcriptional and cellular level. As transcriptional regulation is better understood and often reviewed, here we highlight recent advances in the cell type-specific and post-translational regulation of plant specialized metabolism. With the advent of single-cell technologies, we are now able to characterize metabolites and their transcriptional regulators at the cellular level, which can refine our searches for missing biosynthetic enzymes and cell type-specific regulators. Post-translational regulation through enzyme inhibition, protein phosphorylation and ubiquitination are clearly evident in specialized metabolism regulation, but not frequently studied or considered in metabolic engineering efforts. Finally, we contemplate how advances in cell type-specific and post-translational regulation can be applied in metabolic engineering efforts in planta, leading to optimization of plants as metabolite production vehicles.

PMID:38901289 | DOI:10.1016/j.pbi.2024.102575

Biomark Res. 2024 Jun 20;12(1):63. doi: 10.1186/s40364-024-00608-7.

ABSTRACT

Ischemic stroke (IS), a devastating cerebrovascular accident, presents with high mortality and morbidity. Following IS onset, a cascade of pathological changes, including excitotoxicity, inflammatory damage, and blood-brain barrier disruption, significantly impacts prognosis. However, current clinical practices struggle with early diagnosis and identifying these alterations. Metabolomics, a powerful tool in systems biology, offers a promising avenue for uncovering early diagnostic biomarkers for IS. By analyzing dynamic metabolic profiles, metabolomics can not only aid in identifying early IS biomarkers but also evaluate Traditional Chinese Medicine (TCM) efficacy and explore its mechanisms of action in IS treatment. Animal studies demonstrate that TCM interventions modulate specific metabolite levels, potentially reflecting their therapeutic effects. Identifying relevant metabolites in cerebral ischemia patients holds immense potential for early diagnosis and improved outcomes. This review focuses on recent metabolomic discoveries of potential early diagnostic biomarkers for IS. We explore variations in metabolites observed across different ages, genders, disease severity, and stages. Additionally, the review examines how specific TCM extracts influence IS development through metabolic changes, potentially revealing their mechanisms of action. Finally, we emphasize the importance of integrating metabolomics with other omics approaches for a comprehensive understanding of IS pathophysiology and TCM efficacy, paving the way for precision medicine in IS management.

PMID:38902829 | DOI:10.1186/s40364-024-00608-7

Biotechnol Biofuels Bioprod. 2024 Jun 20;17(1):84. doi: 10.1186/s13068-024-02525-1.

ABSTRACT

BACKGROUND: The holistic characterization of different microbiomes in anaerobic digestion (AD) systems can contribute to a better understanding of these systems and provide starting points for bioengineering. The present study investigates the microbiome of 80 European full-scale AD systems. Operational, chemical and taxonomic data were thoroughly collected, analysed and correlated to identify the main drivers of AD processes.

RESULTS: The present study describes chemical and operational parameters for a broad spectrum of different AD systems. With this data, Spearman correlation and differential abundance analyses were applied to narrow down the role of the individual microorganisms detected. The authors succeeded in further limiting the number of microorganisms in the core microbiome for a broad range of AD systems. Based on 16S rRNA gene amplicon sequencing, MBA03, Proteiniphilum, a member of the family Dethiobacteraceae, the genus Caldicoprobacter and the methanogen Methanosarcina were the most prevalent and abundant organisms identified in all digesters analysed. High ratios for Methanoculleus are often described for agricultural co-digesters. Therefore, it is remarkable that Methanosarcina was surprisingly high in several digesters reaching ratios up to 47.2%. The various statistical analyses revealed that the microorganisms grouped according to different patterns. A purely taxonomic correlation enabled a distinction between an acetoclastic cluster and a hydrogenotrophic one. However, in the multivariate analysis with chemical parameters, the main clusters corresponded to hydrolytic and acidogenic microorganisms, with SAOB bacteria being particularly important in the second group. Including operational parameters resulted in digester-type specific grouping of microbes. Those with separate acidification stood out among the many reactor types due to their unexpected behaviour. Despite maximizing the organic loading rate in the hydrolytic pretreatments, these stages turned into extremely robust methane production units.

CONCLUSIONS: From 80 different AD systems, one of the most holistic data sets is provided. A very distinct formation of microbial clusters was discovered, depending on whether taxonomic, chemical or operational parameters were combined. The microorganisms in the individual clusters were strongly dependent on the respective reference parameters.

PMID:38902807 | DOI:10.1186/s13068-024-02525-1

BMC Med Inform Decis Mak. 2024 Jun 20;24(Suppl 4):175. doi: 10.1186/s12911-024-02578-0.

ABSTRACT

BACKGROUND: Machine Learning (ML) plays a crucial role in biomedical research. Nevertheless, it still has limitations in data integration and irreproducibility. To address these challenges, robust methods are needed. Pancreatic ductal adenocarcinoma (PDAC), a highly aggressive cancer with low early detection rates and survival rates, is used as a case study. PDAC lacks reliable diagnostic biomarkers, especially metastatic biomarkers, which remains an unmet need. In this study, we propose an ML-based approach for discovering disease biomarkers, apply it to the identification of a PDAC metastatic composite biomarker candidate, and demonstrate the advantages of harnessing data resources.

METHODS: We utilised primary tumour RNAseq data from five public repositories, pooling samples to maximise statistical power and integrating data by correcting for technical variance. Data were split into train and validation sets. The train dataset underwent variable selection via a 10-fold cross-validation process that combined three algorithms in 100 models per fold. Genes found in at least 80% of models and five folds were considered robust to build a consensus multivariate model. A random forest model was constructed using selected genes from the train dataset and tested in the validation set. We also assessed the goodness of prediction by recalibrating a model using only the validation data. The biological context and relevance of signals was explored through enrichment and pathway analyses using QIAGEN Ingenuity Pathway Analysis and GeneMANIA.

RESULTS: We developed a pipeline that can detect robust signatures to build composite biomarkers. We tested the pipeline in PDAC, exploiting transcriptomics data from different sources, proposing a composite biomarker candidate comprised of fifteen genes consistently selected that showed very promising predictive capability. Biological contextualisation revealed links with cancer progression and metastasis, underscoring their potential relevance. All code is available in GitHub.

CONCLUSION: This study establishes a robust framework for identifying composite biomarkers across various disease contexts. We demonstrate its potential by proposing a plausible composite biomarker candidate for PDAC metastasis. By reusing data from public repositories, we highlight the sustainability of our research and the wider applications of our pipeline. The preliminary findings shed light on a promising validation and application path.

PMID:38902676 | DOI:10.1186/s12911-024-02578-0

Nat Cell Biol. 2024 Jun 20. doi: 10.1038/s41556-024-01442-7. Online ahead of print.

ABSTRACT

Women are born with all of their oocytes. The oocyte proteome must be maintained with minimal damage throughout the woman's reproductive life, and hence for decades. Here we report that oocyte and ovarian proteostasis involves extreme protein longevity. Mouse ovaries had more extremely long-lived proteins than other tissues, including brain. These long-lived proteins had diverse functions, including in mitochondria, the cytoskeleton, chromatin and proteostasis. The stable proteins resided not only in oocytes but also in long-lived ovarian somatic cells. Our data suggest that mammals increase protein longevity and enhance proteostasis by chaperones and cellular antioxidants to maintain the female germline for long periods. Indeed, protein aggregation in oocytes did not increase with age and proteasome activity did not decay. However, increasing protein longevity cannot fully block female germline senescence. Large-scale proteome profiling of ~8,890 proteins revealed a decline in many long-lived proteins of the proteostasis network in the aging ovary, accompanied by massive proteome remodeling, which eventually leads to female fertility decline.

PMID:38902423 | DOI:10.1038/s41556-024-01442-7

Sci Rep. 2024 Jun 20;14(1):14200. doi: 10.1038/s41598-024-64305-z.

ABSTRACT

The study of consistent between-individual behavioural variation in single (animal personality) and across two or more behavioural traits (behavioural syndrome) is a central topic of behavioural ecology. Besides behavioural type (individual mean behaviour), behavioural predictability (environment-independent within-individual behavioural variation) is now also seen as an important component of individual behavioural strategy. Research focus is still on the 'Big Five' traits (activity, exploration, risk-taking, sociability and aggression), but another prime candidate to integrate to the personality framework is behavioural thermoregulation in small-bodied poikilotherms. Here, we found animal personality in thermoregulatory strategy (selected body temperature, voluntary thermal maximum, setpoint range) and 'classic' behavioural traits (activity, sheltering, risk-taking) in common lizards (Zootoca vivipara). Individual state did not explain the between-individual variation. There was a positive behavioural type-behavioural predictability correlation in selected body temperature. Besides an activity-risk-taking syndrome, we also found a risk-taking-selected body temperature syndrome. Our results suggest that animal personality and behavioural syndrome are present in common lizards, both including thermoregulatory and 'classic' behavioural traits, and selecting high body temperature with high predictability is part of the risk-prone behavioural strategy. We propose that thermoregulatory behaviour should be considered with equal weight to the 'classic' traits in animal personality studies of poikilotherms employing active behavioural thermoregulation.

PMID:38902323 | DOI:10.1038/s41598-024-64305-z

Nat Commun. 2024 Jun 20;15(1):5141. doi: 10.1038/s41467-024-49119-x.

ABSTRACT

A major challenge in protein design is to augment existing functional proteins with multiple property enhancements. Altering several properties likely necessitates numerous primary sequence changes, and novel methods are needed to accurately predict combinations of mutations that maintain or enhance function. Models of sequence co-variation (e.g., EVcouplings), which leverage extensive information about various protein properties and activities from homologous protein sequences, have proven effective for many applications including structure determination and mutation effect prediction. We apply EVcouplings to computationally design variants of the model protein TEM-1 β-lactamase. Nearly all the 14 experimentally characterized designs were functional, including one with 84 mutations from the nearest natural homolog. The designs also had large increases in thermostability, increased activity on multiple substrates, and nearly identical structure to the wild type enzyme. This study highlights the efficacy of evolutionary models in guiding large sequence alterations to generate functional diversity for protein design applications.

PMID:38902262 | DOI:10.1038/s41467-024-49119-x

Nat Commun. 2024 Jun 20;15(1):5267. doi: 10.1038/s41467-024-48641-2.

ABSTRACT

During the early stages of the SARS-CoV-2 pandemic, before vaccines were available, nonpharmaceutical interventions (NPIs) such as reducing contacts or antigenic testing were used to control viral spread. Quantifying their success is therefore key for future pandemic preparedness. Using 1.8 million SARS-CoV-2 genomes from systematic surveillance, we study viral lineage importations into Germany for the third pandemic wave from late 2020 to early 2021, using large-scale Bayesian phylogenetic and phylogeographic analysis with a longitudinal assessment of lineage importation dynamics over multiple sampling strategies. All major nationwide NPIs were followed by fewer importations, with the strongest decreases seen for free rapid tests, the strengthening of regulations on mask-wearing in public transport and stores, as well as on internal movements and gatherings. Most SARS-CoV-2 lineages first appeared in the three most populous states with most cases, and spread from there within the country. Importations rose before and peaked shortly after the Christmas holidays. The substantial effects of free rapid tests and obligatory medical/surgical mask-wearing suggests these as key for pandemic preparedness, given their relatively few negative socioeconomic effects. The approach relates environmental factors at the host population level to viral lineage dissemination, facilitating similar analyses of rapidly evolving pathogens in the future.

PMID:38902246 | DOI:10.1038/s41467-024-48641-2

Nat Commun. 2024 Jun 20;15(1):5266. doi: 10.1038/s41467-024-49450-3.

ABSTRACT

Functionally characterizing the genetic alterations that drive pancreatic cancer is a prerequisite for precision medicine. Here, we perform somatic CRISPR/Cas9 mutagenesis screens to assess the transforming potential of 125 recurrently mutated pancreatic cancer genes, which revealed USP15 and SCAF1 as pancreatic tumor suppressors. Mechanistically, we find that USP15 functions in a haploinsufficient manner and that loss of USP15 or SCAF1 leads to reduced inflammatory TNFα, TGF-β and IL6 responses and increased sensitivity to PARP inhibition and Gemcitabine. Furthermore, we find that loss of SCAF1 leads to the formation of a truncated, inactive USP15 isoform at the expense of full-length USP15, functionally coupling SCAF1 and USP15. Notably, USP15 and SCAF1 alterations are observed in 31% of pancreatic cancer patients. Our results highlight the utility of in vivo CRISPR screens to integrate human cancer genomics and mouse modeling for the discovery of cancer driver genes with potential prognostic and therapeutic implications.

PMID:38902237 | DOI:10.1038/s41467-024-49450-3

PLoS Pathog. 2024 Jun 20;20(6):e1012288. doi: 10.1371/journal.ppat.1012288. Online ahead of print.

ABSTRACT

Socio-economic disparities were associated with disproportionate viral incidence between neighborhoods of New York City (NYC) during the first wave of SARS-CoV-2. We investigated how these disparities affected the co-circulation of SARS-CoV-2 variants during the second wave in NYC. We tested for correlation between the prevalence, in late 2020/early 2021, of Alpha, Iota, Iota with E484K mutation (Iota-E484K), and B.1-like genomes and pre-existing immunity (seropositivity) in NYC neighborhoods. In the context of varying seroprevalence we described socio-economic profiles of neighborhoods and performed migration and lineage persistence analyses using a Bayesian phylogeographical framework. Seropositivity was greater in areas with high poverty and a larger proportion of Black and Hispanic or Latino residents. Seropositivity was positively correlated with the proportion of Iota-E484K and Iota genomes, and negatively correlated with the proportion of Alpha and B.1-like genomes. The proportion of persisting Alpha lineages declined over time in locations with high seroprevalence, whereas the proportion of persisting Iota-E484K lineages remained the same in high seroprevalence areas. During the second wave, the geographic variation of standing immunity, due to disproportionate disease burden during the first wave of SARS-CoV-2 in NYC, allowed for the immune evasive Iota-E484K variant, but not the more transmissible Alpha variant, to circulate in locations with high pre-existing immunity.

PMID:38900824 | DOI:10.1371/journal.ppat.1012288

Cell Rep. 2024 Jun 18;43(7):114370. doi: 10.1016/j.celrep.2024.114370. Online ahead of print.

ABSTRACT

Protective immunity to dengue virus (DENV) requires antibody response to all four serotypes. Systems vaccinology identifies a multi-OMICs pre-vaccination signature and mechanisms predictive of broad antibody responses after immunization with a tetravalent live attenuated DENV vaccine candidate (Butantan-DV/TV003). Anti-inflammatory pathways, including TGF-β signaling expressed by CD68low monocytes, and the metabolites phosphatidylcholine (PC) and phosphatidylethanolamine (PE) positively correlate with broadly neutralizing antibody responses against DENV. In contrast, expression of pro-inflammatory pathways and cytokines (IFN and IL-1) in CD68hi monocytes and primary and secondary bile acids negatively correlates with broad DENV-specific antibody responses. Induction of TGF-β and IFNs is done respectively by PC/PE and bile acids in CD68low and CD68hi monocytes. The inhibition of viral sensing by PC/PE-induced TGF-β is confirmed in vitro. Our studies show that the balance between metabolites and the pro- or anti-inflammatory state of innate immune cells drives broad and protective B cell response to a live attenuated dengue vaccine.

PMID:38900640 | DOI:10.1016/j.celrep.2024.114370

Elife. 2024 Jun 20;13:e99765. doi: 10.7554/eLife.99765.

ABSTRACT

A study of two enzymes in the brain reveals new insights into how redox reactions regulate the activity of protein kinases.

PMID:38900561 | DOI:10.7554/eLife.99765

Geroscience. 2024 Jun 20. doi: 10.1007/s11357-024-01244-7. Online ahead of print.

ABSTRACT

Studies of longevity rely on baseline life expectancy of reference genotypes measured in standardized conditions. Variation among labs, protocols, and genotypes makes longevity intervention studies difficult to compare. Furthermore, extending lifespan under suboptimal conditions or that of a short-lived genotype may be of a lesser theoretical and translational value than extending the maximal possible lifespan. Daphnia is becoming a model organism of choice for longevity research complementing data obtained on traditional models. In this study, we report longevity of several genotypes of a long-lived species D. magna under a variety of protocols, aiming to document the highest lifespan, factors reducing it, and parameters that change with age and correlate with longevity. Combining longevity data from 25 experiments across two labs, we report a strong intraspecific variation, moderate effects of group size and medium composition, and strong genotype-by-environment interactions with respect to food level. Specifically, short-lived genotypes show no caloric restriction (CR) effect, while long-lived ones expand their lifespan even further under CR. We find that the CR non-responsive clones show little correlation between longevity and two measures of lipid peroxidation. In contrast, the long-lived, CR-responsive clones show a positive correlation between longevity and lipid hydroperoxide abundance, and a negative correlation with MDA concentration. This indicates differences among genotypes in age-related accumulation and detoxification of LPO products and their effects on longevity. Our observations support the hypothesis that a long lifespan can be affected by CR and levels of oxidative damage, while genetically determined short lifespan remains short regardless.

PMID:38900345 | DOI:10.1007/s11357-024-01244-7