Neurobiol Dis. 2024 Jun 22:106575. doi: 10.1016/j.nbd.2024.106575. Online ahead of print.

ABSTRACT

CT1812 is a novel, brain penetrant small molecule modulator of the sigma-2 receptor (S2R) that is currently in clinical development for the treatment of Alzheimer's disease (AD). Preclinical and early clinical data show that, through S2R, CT1812 selectively prevents and displaces binding of amyloid beta (Aβ) oligomers from neuronal synapses and improves cognitive function in animal models of AD. SHINE is an ongoing Phase 2 randomized, double-blind, placebo-controlled clinical trial (COG0201) in participants with mild to moderate AD, designed to assess the safety and efficacy of 6 months of CT1812 treatment. To elucidate the mechanism of action in AD patients and pharmacodynamic biomarkers of CT1812, the present study reports exploratory cerebrospinal fluid (CSF) biomarker data from 18 participants in an interim analysis of the first set of patients in SHINE (part A). Untargeted mass spectrometry-based discovery proteomics detects >2000 proteins in patient CSF and has documented utility in accelerating the identification of novel AD biomarkers reflective of diverse pathophysiologies beyond amyloid and tau, and enabling identification of pharmacodynamic biomarkers in longitudinal interventional trials. We leveraged this technique to analyze CSF samples taken at baseline and after 6 months of CT1812 treatment. Proteome-wide protein levels were detected using tandem mass tag-mass spectrometry (TMT-MS), change from baseline was calculated for each participant, and differential abundance analysis by treatment group was performed. This analysis revealed a set of proteins significantly impacted by CT1812, including pathway engagement biomarkers (i.e., biomarkers tied to S2R biology) and disease modification biomarkers (i.e., biomarkers with altered levels in AD vs. healthy control CSF but normalized by CT1812, and biomarkers correlated with favorable trends in ADAS-Cog11 scores). Brain network mapping, Gene Ontology, and pathway analyses revealed an impact of CT1812 on synapses, lipoprotein and amyloid beta biology, and neuroinflammation. Collectively, the findings highlight the utility of this method in pharmacodynamic biomarker identification and providing mechanistic insights for CT1812, which may facilitate the clinical development of CT1812 and enable appropriate pre-specification of biomarkers in upcoming clinical trials of CT1812.

PMID:38914170 | DOI:10.1016/j.nbd.2024.106575

Nat Microbiol. 2024 Jun 24. doi: 10.1038/s41564-024-01728-4. Online ahead of print.

ABSTRACT

Microbially derived short-chain fatty acids (SCFAs) in the human gut are tightly coupled to host metabolism, immune regulation and integrity of the intestinal epithelium. However, the production of SCFAs can vary widely between individuals consuming the same diet, with lower levels often associated with disease. A systems-scale mechanistic understanding of this heterogeneity is lacking. Here we use a microbial community-scale metabolic modelling (MCMM) approach to predict individual-specific SCFA production profiles to assess the impact of different dietary, prebiotic and probiotic inputs. We evaluate the quantitative accuracy of our MCMMs using in vitro and ex vivo data, plus published human cohort data. We find that MCMM SCFA predictions are significantly associated with blood-derived clinical chemistries, including cardiometabolic and immunological health markers, across a large human cohort. Finally, we demonstrate how MCMMs can be leveraged to design personalized dietary, prebiotic and probiotic interventions aimed at optimizing SCFA production in the gut. Our model represents an approach to direct gut microbiome engineering for precision health and nutrition.

PMID:38914826 | DOI:10.1038/s41564-024-01728-4

J Math Biol. 2024 Jun 24;89(2):18. doi: 10.1007/s00285-024-02115-7.

ABSTRACT

We address several questions in reduced versus extended networks via the elimination or addition of intermediate complexes in the framework of chemical reaction networks with mass-action kinetics. We clarify and extend advances in the literature concerning multistationarity in this context, mainly from Feliu and Wiuf (J R Soc Interface 10:20130484, 2013), Sadeghimanesh and Feliu (Bull Math Biol 81:2428-2462, 2019), Pérez Millán and Dickenstein (SIAM J Appl Dyn Syst 17(2):1650-1682, 2018), Dickenstein et al. (Bull Math Biol 81:1527-1581, 2019). We establish general results about MESSI systems, which we use to compute the circuits of multistationarity for significant biochemical networks.

PMID:38914780 | DOI:10.1007/s00285-024-02115-7

Sci Rep. 2024 Jun 24;14(1):14558. doi: 10.1038/s41598-024-65524-0.

ABSTRACT

Plants offer a cost-effective and scalable pharmaceutical platform devoid of host-derived contamination risks. However, their medical application is complicated by the potential for acute allergic reactions to external proteins. Developing plant-based protein therapeutics for localized diseases with non-invasive treatment modalities may capitalize on the benefits of plant proteins while avoiding their inherent risks. Dupilumab, which is effective against a variety of allergic and autoimmune diseases but has systemic responses and injection-related side effects, may be more beneficial if delivered locally using a small biological form. In this study, we engineered a single-chain variable fragment (scFv) of dupilumab, termed Dup-scFv produced by Nicotiana benthamiana, and evaluated its tissue permeability and anti-inflammatory efficacy in air-liquid interface cultured human nasal epithelial cells (HNECs). Despite showing 3.67- and 17-fold lower binding affinity for IL-4Ra in surface plasmon resonance assays and cell binding assays, respectively, Dup-scFv retained most of the affinity of dupilumab, which was originally high, with a dissociation constant (KD) of 4.76 pM. In HNECs cultured at the air-liquid interface, Dup-scFv administered on the air side inhibited the inflammatory marker CCL26 in hard-to-reach basal cells more effectively than dupilumab. In addition, Dup-scFv had an overall permeability of 0.8% across cell layers compared to undetectable levels of dupilumab. These findings suggest that plant-produced Dup-scFv can be delivered non-invasively to cultured HNESc to alleviate inflammatory signaling, providing a practical approach to utilize plant-based proteins for topical therapeutic applications.

PMID:38914666 | DOI:10.1038/s41598-024-65524-0

Sci Rep. 2024 Jun 24;14(1):14449. doi: 10.1038/s41598-024-64938-0.

ABSTRACT

As genomic databases expand and artificial intelligence tools advance, there is a growing demand for efficient characterization of large numbers of proteins. To this end, here we describe a generalizable pipeline for high-throughput protein purification using small-scale expression in E. coli and an affordable liquid-handling robot. This low-cost platform enables the purification of 96 proteins in parallel with minimal waste and is scalable for processing hundreds of proteins weekly per user. We demonstrate the performance of this method with the expression and purification of the leading poly(ethylene terephthalate) hydrolases reported in the literature. Replicate experiments demonstrated reproducibility and enzyme purity and yields (up to 400 µg) sufficient for comprehensive analyses of both thermostability and activity, generating a standardized benchmark dataset for comparing these plastic-degrading enzymes. The cost-effectiveness and ease of implementation of this platform render it broadly applicable to diverse protein characterization challenges in the biological sciences.

PMID:38914665 | DOI:10.1038/s41598-024-64938-0

Sheng Wu Gong Cheng Xue Bao. 2024 Jun 25;40(6):1620-1643. doi: 10.13345/j.cjb.230640.

ABSTRACT

Compatible solutes are highly water-soluble organic osmolytes produced by microorganisms to adapt to extreme environments, such as high salinity and osmotic pressure. Among these, ectoine plays a crucial role in repairing and protecting nucleic acids, protein, biofilms, and cells. As a result, it has found widespread applications in cosmetics, biological agents, the enzyme industry, medicine, and other fields. Currently, the market value of ectoine is around US$ 1 000/kg, with a global demand reaching 15 000 tons per year. Although halophilic bacteria serve as the natural source of ectoine synthesis, its production in high-salinity media presents challenges such as equipment corrosion and high cost for industrial production. Advancements in functional genomics, systems biology, and synthetic biology have paved the way for the development of high-yielding cell factories through metabolic engineering, leading to significant progress. For example, engineered Escherichia coli achieved a maximum ectoine titer of 131.8 g/L, with a productivity of 1.37 g/(L·h). This review aims to explore the biosynthetic pathway, biochemical characteristics of key enzymes, and the biosynthesis of ectoine, shedding light on current research status and offering insights for industrial-scale ectoine production.

PMID:38914483 | DOI:10.13345/j.cjb.230640

Plant Signal Behav. 2024 Dec 31;19(1):2370719. doi: 10.1080/15592324.2024.2370719. Epub 2024 Jun 24.

ABSTRACT

Cyanobacterium Nostoc commune is a filamentous terrestrial prokaryotic organism widely distributed, which suggest its high adaptive potential to environmental or abiotic stress. Physiological parameters and proteomic analysis were performed in two accession of N. commune with the aim to elucidate the differences of physiological trails between distant geotypes, namely Antarctic (AN) and central European (CE). The result obtained clearly showed that the AN geotype demonstrates elevated levels of total phenols, flavonoids, carotenoids, and phycobiliproteins, indicative of its adaptation to environmental stress as referred by comparison to CE sample. Additionally, we employed LC-MS analysis to investigate the proteomes of N. commune from AN and CE geotypes. In total, 1147 proteins were identified, among which 646 proteins expressed significant (up-regulation) changes in both accessions. In the AN geotype, 83 exclusive proteins were identified compared to 25 in the CE geotype. Functional classification of the significant proteins showed a large fraction involved in photosynthesis, amino acid metabolism, carbohydrate metabolism and protein biosynthesis. Further analysis revealed some defense-related proteins such as, superoxide dismutase (SOD) and glutathione reductase, which are rather explicitly expressed in the AN N. commune. The last two proteins suggest a more stressful condition in AN N. commune. In summary, our findings highlight biochemical processes that safeguard the AN geotype of N. commune from extreme environmental challenges, not recorded in CE accession, probably due to less stressful environment in Europe. This study brings the first ever proteomic analysis of N. commune, emphasizing the need for additional investigations into the climate adaptation of this species with rather plastic genome.

PMID:38913942 | DOI:10.1080/15592324.2024.2370719

Proc Natl Acad Sci U S A. 2024 Jul 2;121(27):e2311807121. doi: 10.1073/pnas.2311807121. Epub 2024 Jun 24.

ABSTRACT

Machine learning has been proposed as an alternative to theoretical modeling when dealing with complex problems in biological physics. However, in this perspective, we argue that a more successful approach is a proper combination of these two methodologies. We discuss how ideas coming from physical modeling neuronal processing led to early formulations of computational neural networks, e.g., Hopfield networks. We then show how modern learning approaches like Potts models, Boltzmann machines, and the transformer architecture are related to each other, specifically, through a shared energy representation. We summarize recent efforts to establish these connections and provide examples on how each of these formulations integrating physical modeling and machine learning have been successful in tackling recent problems in biomolecular structure, dynamics, function, evolution, and design. Instances include protein structure prediction; improvement in computational complexity and accuracy of molecular dynamics simulations; better inference of the effects of mutations in proteins leading to improved evolutionary modeling and finally how machine learning is revolutionizing protein engineering and design. Going beyond naturally existing protein sequences, a connection to protein design is discussed where synthetic sequences are able to fold to naturally occurring motifs driven by a model rooted in physical principles. We show that this model is "learnable" and propose its future use in the generation of unique sequences that can fold into a target structure.

PMID:38913893 | DOI:10.1073/pnas.2311807121

PLoS Comput Biol. 2024 Jun 24;20(6):e1012222. doi: 10.1371/journal.pcbi.1012222. Online ahead of print.

ABSTRACT

Biological structures are defined by rigid elements, such as bones, and elastic elements, like muscles and membranes. Computer vision advances have enabled automatic tracking of moving animal skeletal poses. Such developments provide insights into complex time-varying dynamics of biological motion. Conversely, the elastic soft-tissues of organisms, like the nose of elephant seals, or the buccal sac of frogs, are poorly studied and no computer vision methods have been proposed. This leaves major gaps in different areas of biology. In primatology, most critically, the function of air sacs is widely debated; many open questions on the role of air sacs in the evolution of animal communication, including human speech, remain unanswered. To support the dynamic study of soft-tissue structures, we present a toolkit for the automated tracking of semi-circular elastic structures in biological video data. The toolkit contains unsupervised computer vision tools (using Hough transform) and supervised deep learning (by adapting DeepLabCut) methodology to track inflation of laryngeal air sacs or other biological spherical objects (e.g., gular cavities). Confirming the value of elastic kinematic analysis, we show that air sac inflation correlates with acoustic markers that likely inform about body size. Finally, we present a pre-processed audiovisual-kinematic dataset of 7+ hours of closeup audiovisual recordings of siamang (Symphalangus syndactylus) singing. This toolkit (https://github.com/WimPouw/AirSacTracker) aims to revitalize the study of non-skeletal morphological structures across multiple species.

PMID:38913743 | DOI:10.1371/journal.pcbi.1012222

Elife. 2024 Jun 24;13:e90943. doi: 10.7554/eLife.90943.

ABSTRACT

BACKGROUND: Preterm birth is the leading cause of neonatal morbidity and mortality worldwide. Most cases of preterm birth occur spontaneously and result from preterm labor with intact (spontaneous preterm labor [sPTL]) or ruptured (preterm prelabor rupture of membranes [PPROM]) membranes. The prediction of spontaneous preterm birth (sPTB) remains underpowered due to its syndromic nature and the dearth of independent analyses of the vaginal host immune response. Thus, we conducted the largest longitudinal investigation targeting vaginal immune mediators, referred to herein as the immunoproteome, in a population at high risk for sPTB.

METHODS: Vaginal swabs were collected across gestation from pregnant women who ultimately underwent term birth, sPTL, or PPROM. Cytokines, chemokines, growth factors, and antimicrobial peptides in the samples were quantified via specific and sensitive immunoassays. Predictive models were constructed from immune mediator concentrations.

RESULTS: Throughout uncomplicated gestation, the vaginal immunoproteome harbors a cytokine network with a homeostatic profile. Yet, the vaginal immunoproteome is skewed toward a pro-inflammatory state in pregnant women who ultimately experience sPTL and PPROM. Such an inflammatory profile includes increased monocyte chemoattractants, cytokines indicative of macrophage and T-cell activation, and reduced antimicrobial proteins/peptides. The vaginal immunoproteome has improved predictive value over maternal characteristics alone for identifying women at risk for early (<34 weeks) sPTB.

CONCLUSIONS: The vaginal immunoproteome undergoes homeostatic changes throughout gestation and deviations from this shift are associated with sPTB. Furthermore, the vaginal immunoproteome can be leveraged as a potential biomarker for early sPTB, a subset of sPTB associated with extremely adverse neonatal outcomes.

FUNDING: This research was conducted by the Perinatology Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, U.S. Department of Health and Human Services (NICHD/NIH/DHHS) under contract HHSN275201300006C. ALT, KRT, and NGL were supported by the Wayne State University Perinatal Initiative in Maternal, Perinatal and Child Health.

PMID:38913421 | DOI:10.7554/eLife.90943

J Vis Exp. 2024 Jun 7;(208). doi: 10.3791/66255.

ABSTRACT

High throughput image-based phenotyping is a powerful tool to non-invasively determine the development and performance of plants under specific conditions over time. By using multiple imaging sensors, many traits of interest can be assessed, including plant biomass, photosynthetic efficiency, canopy temperature, and leaf reflectance indices. Plants are frequently exposed to multiple stresses under field conditions where severe heat waves, flooding, and drought events seriously threaten crop productivity. When stresses coincide, resulting effects on plants can be distinct due to synergistic or antagonistic interactions. To elucidate how potato plants respond to single and combined stresses that resemble naturally occurring stress scenarios, five different treatments were imposed on a selected potato cultivar (Solanum tuberosum L., cv. Lady Rosetta) at the onset of tuberization, i.e. control, drought, heat, waterlogging, and combinations of heat, drought, and waterlogging stresses. Our analysis shows that waterlogging stress had the most detrimental effect on plant performance, leading to fast and drastic physiological responses related to stomatal closure, including a reduction in the quantum yield and efficiency of photosystem II and an increase in canopy temperature and water index. Under heat and combined stress treatments, the relative growth rate was reduced in the early phase of stress. Under drought and combined stresses, plant volume and photosynthetic performance dropped with an increased temperature and stomata closure in the late phase of stress. The combination of optimized stress treatment under defined environmental conditions together with selected phenotyping protocols allowed to reveal the dynamics of morphological and physiological responses to single and combined stresses. Here, a useful tool is presented for plant researchers looking to identify plant traits indicative of resilience to several climate change-related stresses.

PMID:38912820 | DOI:10.3791/66255

Microbiol Spectr. 2024 Jun 24:e0422523. doi: 10.1128/spectrum.04225-23. Online ahead of print.

ABSTRACT

SARS-CoV-2 molecular testing coupled with whole-genome sequencing is instrumental for real-time genomic surveillance. Genomic surveillance is critical for monitoring the spread of variants of concern (VOCs) as well as discovery of novel variants. Since the beginning of the pandemic, millions of SARS-CoV-2 genomes have been deposited into public sequence databases. This is the result of efforts of both national and regional diagnostic laboratories. In this study, we describe the results of SARS-CoV-2 genomic surveillance from February 2021 to June 2022 at a metropolitan hospital in the United States. We demonstrate that consistent daily sampling is sufficient to track the regional prevalence and emergence of VOCs and recapitulate national trends. Similar sampling efforts should be considered a viable option for local SARS-CoV-2 genomic surveillance at other regional laboratories.

IMPORTANCE: In our manuscript, we describe the results of SARS-CoV-2 genomic surveillance from February 2021 to June 2022 at a metropolitan hospital in the United States. We demonstrate that consistent daily sampling is sufficient to track the regional prevalence and emergence of variants of concern (VOCs). Similar sampling efforts should be considered a viable option for local SARS-CoV-2 genomic surveillance at other regional laboratories. While the SARS-CoV-2 pandemic has evolved into a more endemic form, we still believe that additional real-world information about sampling, procedures, and data interpretation is valuable for ongoing as well as future genomic surveillance efforts. Our study should be of substantial interest to clinical virologists.

PMID:38912809 | DOI:10.1128/spectrum.04225-23

JAMIA Open. 2024 Jun 21;7(2):ooae037. doi: 10.1093/jamiaopen/ooae037. eCollection 2024 Jul.

ABSTRACT

OBJECTIVES: Anaphylaxis is a severe life-threatening allergic reaction, and its accurate identification in healthcare databases can harness the potential of "Big Data" for healthcare or public health purposes.

MATERIALS AND METHODS: This study used claims data obtained between October 1, 2015 and February 28, 2019 from the CMS database to examine the utility of machine learning in identifying incident anaphylaxis cases. We created a feature selection pipeline to identify critical features between different datasets. Then a variety of unsupervised and supervised methods were used (eg, Sammon mapping and eXtreme Gradient Boosting) to train models on datasets of differing data quality, which reflects the varying availability and potential rarity of ground truth data in medical databases.

RESULTS: Resulting machine learning model accuracies ranged from 47.7% to 94.4% when tested on ground truth data. Finally, we found new features to help experts enhance existing case-finding algorithms.

DISCUSSION: Developing precise algorithms to detect medical outcomes in claims can be a laborious and expensive process, particularly for conditions presented and coded diversely. We found it beneficial to filter out highly potent codes used for data curation to identify underlying patterns and features. To improve rule-based algorithms where necessary, researchers could use model explainers to determine noteworthy features, which could then be shared with experts and included in the algorithm.

CONCLUSION: Our work suggests machine learning models can perform at similar levels as a previously published expert case-finding algorithm, while also having the potential to improve performance or streamline algorithm construction processes by identifying new relevant features for algorithm construction.

PMID:38911332 | PMC:PMC11190610 | DOI:10.1093/jamiaopen/ooae037

Regen Ther. 2024 Jun 5;26:161-169. doi: 10.1016/j.reth.2024.05.015. eCollection 2024 Jun.

ABSTRACT

BACKGROUND: Nosocomial infections caused by multidrug-resistant Pseudomonas aeruginosa are a considerable public health threat, requiring innovative therapeutic approaches.

OBJECTIVES: This study explored preconditioning mesenchymal stem cells (MSCs) with the antimicrobial peptide Nisin to enhance their antibacterial properties while maintaining regenerative capacity.

METHODS: Human MSCs were preconditioned with varying concentrations of Nisin (0.1-1000 IU/mL) to determine an optimal dose. MSCs preconditioned with Nisin were characterized using microscopy, flow cytometry, gene expression analysis, and functional assays. The effects of preconditioning on the viability, phenotype, differentiation capacity, antimicrobial peptide expression, and antibacterial activity of MSCs against Pseudomonas aeruginosa were tested in vitro. The therapeutic efficacy was evaluated by topically applying conditioned media from Nisin-preconditioned versus control MSCs to infected wounds in a rat model, assessing bacterial burden, healing, host response, and survival.

RESULTS: An optimal Nisin dose of 500 IU/mL was identified, which increased MSC antibacterial gene expression and secretome activity without compromising viability or stemness. Nisin-preconditioned MSCs showed upregulated expression of LL37 and hepcidin. Conditioned media from Nisin-preconditioned MSCs exhibited about 4-fold more inhibition of P. aeruginosa growth compared to non-preconditioned MSCs. In the wound infection model, the secretome of Nisin-preconditioned MSCs suppressed bacterial load, accelerated wound closure, modulated inflammation, and improved survival compared to standard MSC treatments.

CONCLUSION: This study explores the effect of preconditioning MSCs with the antimicrobial peptide Nisin on enhancing their antibacterial properties while maintaining regenerative capacity. Secreted factors from Nisin-preconditioned MSCs have the potential to attenuate infections and promote healing in vivo. The approach holds translational promise for managing antibiotic-resistant infections and warrants further development. Preconditioned MSCs with Nisin may offer innovative, multifaceted therapies for combating nosocomial pathogens and promoting tissue regeneration.

PMID:38911027 | PMC:PMC11192785 | DOI:10.1016/j.reth.2024.05.015

Acta Biochim Biophys Sin (Shanghai). 2024 Jun 24. doi: 10.3724/abbs.2024101. Online ahead of print.

ABSTRACT

N-glycans play important roles in a variety of biological processes. In recent years, analytical technologies with high resolution and sensitivity have advanced exponentially, enabling analysts to investigate N-glycomic changes in different states. Specific glycan and glycosylation signatures have been identified in multiple diseases, including cancer, autoimmune diseases, nervous system disorders, and metabolic and cardiovascular diseases. These glycans demonstrate comparable or superior indicating capability in disease diagnosis and prognosis over routine biomarkers. Moreover, synchronous glycan alterations concurrent with disease initiation and progression provide novel insights into pathogenetic mechanisms and potential treatment targets. This review elucidates the biological significance of N-glycans, compares the existing glycomic technologies, and delineates the clinical performance of N-glycans across a range of diseases.

PMID:38910518 | DOI:10.3724/abbs.2024101

Curr Opin Microbiol. 2024 Jun 21;80:102497. doi: 10.1016/j.mib.2024.102497. Online ahead of print.

ABSTRACT

Bacterial microcompartments (BMCs) are polyhedral structures that segregate enzymatic cargo from the cytosol via encapsulation within a protein shell. Unlike other biological polyhedra, such as viral capsids and encapsulins, BMC shells can exhibit a highly advantageous structural and functional plasticity, conforming to a variety of anabolic (CO2 fixation in carboxysomes) and catabolic (nutrient assimilation in metabolosomes) roles. Consequently, understanding the subunit properties and associated protein-protein interaction processes that guide shell assembly and function is a necessary step to fully harness BMCs as modular, biotechnological nanomachines. Here, we describe the recent insights into the dynamics of structural features of the key BMC domain (Pfam00936)-containing proteins, which serve as a structural template for BMC-H and BMC-T shell building blocks.

PMID:38909546 | DOI:10.1016/j.mib.2024.102497

Nat Commun. 2024 Jun 22;15(1):5318. doi: 10.1038/s41467-024-49657-4.

ABSTRACT

During primary varicella zoster virus (VZV) infection, infected lymphocytes drive primary viremia, causing systemic dissemination throughout the host, including the skin. This results in cytokine expression, including interferons (IFNs), which partly limit infection. VZV also spreads from skin keratinocytes to lymphocytes prior to secondary viremia. It is not clear how VZV achieves this while evading the cytokine response. Here, we show that VZV glycoprotein C (gC) binds IFN-γ and modifies its activity, increasing the expression of a subset of IFN-stimulated genes (ISGs), including intercellular adhesion molecule 1 (ICAM1), chemokines and immunomodulatory genes. The higher ICAM1 protein level at the plasma membrane of keratinocytes facilitates lymphocyte function-associated antigen 1-dependent T cell adhesion and expression of gC during infection increases VZV spread to peripheral blood mononuclear cells. This constitutes the discovery of a strategy to modulate IFN-γ activity, upregulating a subset of ISGs, promoting enhanced lymphocyte adhesion and virus spread.

PMID:38909022 | DOI:10.1038/s41467-024-49657-4

Mol Metab. 2024 Jun 20:101969. doi: 10.1016/j.molmet.2024.101969. Online ahead of print.

ABSTRACT

OBJECTIVES: Cachexia is a metabolic disorder and comorbidity with cancer and heart failure. The syndrome impacts more than thirty million people worldwide, accounting for 20% of all cancer deaths. In acute myeloid leukemia, somatic mutations of the metabolic enzyme isocitrate dehydrogenase 1 and 2 cause the production of the oncometabolite D2-hydroxyglutarate (D2-HG). Increased production of D2-HG is associated with heart and skeletal muscle atrophy, but the mechanistic links between metabolic and proteomic remodeling remain poorly understood. Therefore, we assessed how oncometabolic stress by D2-HG activates autophagy and drives skeletal muscle loss.

METHODS: We quantified genomic, metabolomic, and proteomic changes in cultured skeletal muscle cells and mouse models of IDH-mutant leukemia using RNA sequencing, mass spectrometry, and computational modeling.

RESULTS: D2-HG impairs NADH redox homeostasis in myotubes. Increased NAD+ levels drive activation of nuclear deacetylase Sirt1, which causes deacetylation and activation of LC3, a key regulator of autophagy. Using LC3 mutants, we confirm that deacetylation of LC3 by Sirt1 shifts its distribution from the nucleus into the cytosol, where it can undergo lipidation at pre-autophagic membranes. Sirt1 silencing or p300 overexpression attenuated autophagy activation in myotubes. In vivo, we identified increased muscle atrophy and reduced grip strength in response to D2-HG in male vs. female mice. In male mice, glycolytic intermediates accumulated, and protein expression of oxidative phosphorylation machinery was reduced. In contrast, female animals upregulated the same proteins, attenuating the phenotype in vivo. Network modeling and machine learning algorithms allowed us to identify candidate proteins essential for regulating oncometabolic adaptation in mouse skeletal muscle.

CONCLUSIONS: Our multi-omics approach exposes new metabolic vulnerabilities in response to D2-HG in skeletal muscle and provides a conceptual framework for identifying therapeutic targets in cachexia.

PMID:38908793 | DOI:10.1016/j.molmet.2024.101969

Dev Biol. 2024 Jun 20:S0012-1606(24)00161-1. doi: 10.1016/j.ydbio.2024.06.011. Online ahead of print.

ABSTRACT

The ability to label proteins by fusion with genetically encoded fluorescent proteins is a powerful tool for understanding dynamic biological processes. However, current approaches for expressing fluorescent protein fusions possess drawbacks, especially at the whole organism level. Expression by transgenesis risks potential overexpression artifacts while fluorescent protein insertion at endogenous loci is technically difficult and, more importantly, does not allow for tissue-specific study of broadly expressed proteins. To overcome these limitations, we have adopted the split fluorescent protein system mNeonGreen21-10/11 (split-mNG2) to achieve tissue-specific and endogenous protein labeling in zebrafish. In our approach, mNG21-10 is expressed under a tissue-specific promoter using standard transgenesis while mNG211 is inserted into protein-coding genes of interest using CRISPR/Cas-directed gene editing. Each mNG2 fragment on its own is not fluorescent, but when co-expressed the fragments self-assemble into a fluorescent complex. Here, we report successful use of split-mNG2 to achieve differential labeling of the cytoskeleton genes tubb4b and krt8 in various tissues. We also demonstrate that by anchoring the mNG21-10 component to specific cellular compartments, the split-mNG2 system can be used to manipulate protein localization. Our approach should be broadly useful for a wide range of applications.

PMID:38908500 | DOI:10.1016/j.ydbio.2024.06.011

Am J Hum Genet. 2024 Jun 20:S0002-9297(24)00209-X. doi: 10.1016/j.ajhg.2024.06.003. Online ahead of print.

ABSTRACT

Methods of estimating polygenic scores (PGSs) from genome-wide association studies are increasingly utilized. However, independent method evaluation is lacking, and method comparisons are often limited. Here, we evaluate polygenic scores derived via seven methods in five biobank studies (totaling about 1.2 million participants) across 16 diseases and quantitative traits, building on a reference-standardized framework. We conducted meta-analyses to quantify the effects of method choice, hyperparameter tuning, method ensembling, and the target biobank on PGS performance. We found that no single method consistently outperformed all others. PGS effect sizes were more variable between biobanks than between methods within biobanks when methods were well tuned. Differences between methods were largest for the two investigated autoimmune diseases, seropositive rheumatoid arthritis and type 1 diabetes. For most methods, cross-validation was more reliable for tuning hyperparameters than automatic tuning (without the use of target data). For a given target phenotype, elastic net models combining PGS across methods (ensemble PGS) tuned in the UK Biobank provided consistent, high, and cross-biobank transferable performance, increasing PGS effect sizes (β coefficients) by a median of 5.0% relative to LDpred2 and MegaPRS (the two best-performing single methods when tuned with cross-validation). Our interactively browsable online-results and open-source workflow prspipe provide a rich resource and reference for the analysis of polygenic scoring methods across biobanks.

PMID:38908374 | DOI:10.1016/j.ajhg.2024.06.003