Cell Genom. 2025 Jan 28:100765. doi: 10.1016/j.xgen.2025.100765. Online ahead of print.

ABSTRACT

Annotation of cis-regulatory elements that drive transcriptional dysregulation in cancer cells is critical to understanding tumor biology. Herein, we present matched chromatin accessibility (single-cell assay for transposase-accessible chromatin by sequencing [scATAC-seq]) and transcriptome (single-cell RNA sequencing [scRNA-seq]) profiles at single-cell resolution from human breast tumors and healthy mammary tissues processed immediately following surgical resection. We identify the most likely cell of origin for subtype-specific breast tumors and implement linear mixed-effects modeling to quantify associations between regulatory elements and gene expression in malignant versus normal cells. These data unveil cancer-specific regulatory elements and putative silencer-to-enhancer switching events in cells that lead to the upregulation of clinically relevant oncogenes. In addition, we generate matched scATAC-seq and scRNA-seq profiles for breast cancer cell lines, revealing a conserved oncogenic gene expression program between in vitro and in vivo cells. This work highlights the importance of non-coding regulatory mechanisms that underlie oncogenic processes and the ability of single-cell multi-omics to define the regulatory logic of cancer cells.

PMID:39914387 | DOI:10.1016/j.xgen.2025.100765

Science. 2025 Feb 6:eadq2634. doi: 10.1126/science.adq2634. Online ahead of print.

ABSTRACT

Cells have evolved mechanisms to distribute ~10 billion protein molecules to subcellular compartments where diverse proteins involved in shared functions must assemble. Here, we demonstrate that proteins with shared functions share amino acid sequence codes that guide them to compartment destinations. A protein language model, ProtGPS, was developed that predicts with high performance the compartment localization of human proteins excluded from the training set. ProtGPS successfully guided generation of novel protein sequences that selectively assemble in the nucleolus. ProtGPS identified pathological mutations that change this code and lead to altered subcellular localization of proteins. Our results indicate that protein sequences contain not only a folding code, but also a previously unrecognized code governing their distribution to diverse subcellular compartments.

PMID:39913643 | DOI:10.1126/science.adq2634

PLoS One. 2025 Feb 6;20(2):e0314982. doi: 10.1371/journal.pone.0314982. eCollection 2025.

ABSTRACT

Structural variants (SVs) have been associated with changes in gene expression, which may contribute to alterations in phenotypes and disease development. However, the precise identification and characterization of SVs remain challenging. While long-read sequencing offers superior accuracy for SV detection, short-read sequencing remains essential due to practical and cost considerations, as well as the need to analyze existing short-read datasets. Numerous algorithms for short-read SV detection exist, but none are universally optimal, each having limitations for specific SV sizes and types. In this study, we evaluated the efficacy of six advanced SV detection algorithms, including the commercial software DRAGEN, using the GIAB v0.6 Tier 1 benchmark and HGSVC2 cell lines. We employed both individual and combination strategies, with systematic assessments of recall, precision, and F1 scores. Our results demonstrate that the union combination approach enhanced detection capabilities, surpassing single algorithms in identifying deletions and insertions, and delivered comparable recall and F1 scores to the commercial software DRAGEN. Interestingly, expanding the number of algorithms from three to five in the combination did not enhance performance, highlighting the efficiency of a well-chosen ensemble over a larger algorithmic pool.

PMID:39913463 | DOI:10.1371/journal.pone.0314982

STAR Protoc. 2025 Feb 4;6(1):103625. doi: 10.1016/j.xpro.2025.103625. Online ahead of print.

ABSTRACT

Spatially resolved transcriptomics (SRT) data contain intricate noise due to the diffusion of transcripts caused by tissue fixation, permeabilization, and cell lysis during the experiment. Here, we present a protocol for denoising SRT data using SpotGF, an optimal transport-based gene filtering algorithm, without modifying the raw gene expression. We describe steps for data preparation, SpotGF score calculation, filtering threshold determination, denoised data generation, and visualization. Our protocol enhances SRT quality and improves the performance of downstream analyses. For complete details on the use and execution of this protocol, please refer to Du et al.1.

PMID:39913289 | DOI:10.1016/j.xpro.2025.103625

Bull Math Biol. 2025 Feb 6;87(3):39. doi: 10.1007/s11538-025-01415-3.

ABSTRACT

The concept of identifiability describes the possibility of inferring the parameters of a dynamic model by observing its output. It is common and useful to distinguish between structural and practical identifiability. The former property is fully determined by the model equations, while the latter is also influenced by the characteristics of the available experimental data. Structural identifiability can be determined by means of symbolic computations, which may be performed before collecting experimental data, and are hence sometimes called a priori analyses. Practical identifiability is typically assessed numerically, with methods that require simulations-and often also optimization-and are applied a posteriori. An approach to study structural local identifiability is to consider it as a particular case of observability, which is the possibility of inferring the internal state of a system from its output. Thus, both properties can be analysed jointly, by building a generalized observability matrix and computing its rank. The aim of this paper is to investigate to which extent such observability-based methods can also inform about practical aspects related with the experimental setup, which are usually not approached in this way. To this end, we explore a number of possible extensions of the rank tests, and discuss the purposes for which they can be informative as well as others for which they cannot.

PMID:39913007 | DOI:10.1007/s11538-025-01415-3

Mar Biotechnol (NY). 2025 Feb 6;27(1):43. doi: 10.1007/s10126-025-10415-2.

ABSTRACT

Aurantiochytrium not only excels in producing long-chain polyunsaturated fatty acids such as docosahexaenoic acid for humans, but it is also a source of essential fatty acids with minimal impacts on wild fisheries and is vital in the transfer of atmospheric carbon to oceanic carbon sinks and cycles. This study aims to unveil the systems biology of lipid production in the Australian Aurantiochytrium sp. TC20 by comparing the transcriptomic profiles under optimal growth conditions with increased fatty acid production from the early (Day 1) to late exponential growth phase (Day 3). Particular attention was paid to 227 manually annotated genes involved in lipid metabolism, such as FAS (fatty acid synthetase) and subunits of polyunsaturated fatty acids (PUFA) synthase. PCA analysis showed that differentially expressed genes, related to lipid metabolism, efficiently discriminated Day 3 samples from Day 1, highlighting the key robustness of the developed lipid-biosynthesis signature. Highly significant (pFDR < 0.01) upregulation of polyunsaturated fatty acid synthase subunit B (PFAB) involved in fatty acid synthesis, lipid droplet protein (TLDP) involved in TAG-synthesis, and phosphoglycerate mutase (PGAM-2) involved in glycolysis and gluconeogenesis were observed. KEGG enrichment analysis highlighted significant enrichment of the biosynthesis of unsaturated fatty acids (pFDR < 0.01) and carbon metabolism pathways (pFDR < 0.01). This study provides a comprehensive overview of the transcriptional landscape of Australian Aurantiochytrium sp. TC20 in the process of fatty acid production.

PMID:39912956 | DOI:10.1007/s10126-025-10415-2

ACS Infect Dis. 2025 Feb 6. doi: 10.1021/acsinfecdis.4c00912. Online ahead of print.

ABSTRACT

Tetracyclines (TCs) are an important class of antibiotics threatened by enzymatic inactivation. These tetracycline-inactivating enzymes, also known as tetracycline destructases (TDases), are a subfamily of class A flavin monooxygenases (FMOs) that catalyze hydroxyl group transfer and oxygen insertion (Baeyer-Villiger type) reactions on TC substrate scaffolds. Semisynthetic modification of TCs (e.g., tigecycline, omadacycline, eravacycline, and sarecycline) has proven effective in evading certain resistance mechanisms, such as ribosomal protection and efflux, but does not protect against TDase-mediated resistance. Here, we report the design, synthesis, and evaluation of a new series of 22 semisynthetic TDase inhibitors that explore D-ring substitution of anhydrotetracycline (aTC) including 14 C10-benzoate ester and eight C9-benzamides. Overall, the C10-benzoate esters displayed enhanced bioactivity and water solubility compared to the corresponding C9-benzamides featuring the same heterocyclic aryl side chains. The C10-benzoate ester derivatives of aTC were prepared in a high-yield one-step synthesis without the need for protecting groups. The C10-esters are water-soluble, stable toward hydrolysis, and display dose-dependent rescue of tetracycline antibiotic activity in E. coli expressing two types of tetracycline destructases, represented by TetX7 (Type 1) and Tet50 (Type 2). The best inhibitors recovered tetracycline antibiotic activity at concentrations as low as 2 μM, producing synergistic scores <0.5 in the fractional inhibitory concentration index (FICI) against TDase-expressing strains of E. coli and clinical P. aeruginosa. The C10-benzoate ester derivatives of aTC reported here are promising new leads for the development of tetracycline drug combination therapies to overcome TDase-mediated antibiotic resistance.

PMID:39912785 | DOI:10.1021/acsinfecdis.4c00912

Stat Med. 2025 Feb 28;44(5):e10334. doi: 10.1002/sim.10334.

ABSTRACT

High-throughput single-cell RNA-seq (scRNA-seq) data contains an excess of zero values, which can be contributed by unexpressed genes and detection signal dropouts. Existing imputation methods fail to distinguish between these two types of zeros. In this study, we introduce a statistical framework that effectively differentiates true zeros (lack of expression) from false zeros (dropouts). By focusing only on imputing the dropout zeros, we developed a new imputation tool, scRecover. Our approach utilizes a zero-inflated negative binomial framework to model the gene expression of each gene in each cell, enabling the estimation of zero-dropout probability. Additionally, we employ a modified version of the Good and Toulmin model to identify true zeros for each gene. To achieve imputation, scRecover is combined with other imputation methods such as scImpute, SAVER and MAGIC. Down-sampling experiments show that it recovers dropout zeros with higher accuracy and avoids over-imputing true zero values. Experiments conducted on real world data highlight the ability of scRecover to enhance downstream analysis and visualization.

PMID:39912305 | DOI:10.1002/sim.10334

Chem Commun (Camb). 2025 Feb 6. doi: 10.1039/d4cc03807j. Online ahead of print.

ABSTRACT

Bright near-infrared fluorophores are in demand for microscopy. We showcase a deuterated oxazine being 23% brighter vs. ATTO700. With a longer lifetime of 1.85 nanoseconds, we find the best-in-class SulfoOxazine700-d10 to stain mitochondria for confocal microscopy, and demonstrate unaffected diffusion properties in single molecule fluorescence correlation spectroscopy.

PMID:39912228 | DOI:10.1039/d4cc03807j

Biotechnol Notes. 2025 Jan 13;6:67-78. doi: 10.1016/j.biotno.2025.01.001. eCollection 2025.

ABSTRACT

Microbiome engineering has emerged as a transformative approach to enhancing food safety and quality by strategically modulating microbial communities. This review critically examines state-of-the-art techniques, including synthetic biology, artificial intelligence (AI), and systems biology, that are revolutionizing our ability to improve nutritional profiles, extend shelf life, and optimize food production processes. The review further explores complex social, ethical, and regulatory considerations, emphasizing the importance of robust public engagement and the establishment of standardized frameworks to ensure safe and effective implementation. While microbiome engineering holds significant promise for revolutionizing food safety and quality control, further research is needed to address critical challenges, including understanding microbial dynamics in complex food systems and developing harmonized regulatory frameworks. By bridging interdisciplinary gaps, this paper underscores the necessity of collaborative efforts to unlock the full potential of microbiome-driven innovations for a more resilient and sustainable food industry.

PMID:39912062 | PMC:PMC11795101 | DOI:10.1016/j.biotno.2025.01.001

Front Pharmacol. 2025 Jan 22;15:1470931. doi: 10.3389/fphar.2024.1470931. eCollection 2024.

ABSTRACT

INTRODUCTION: Most drugs fail during development and there is a clear and unmet need for approaches to better understand mechanistically how drugs exert both their intended and adverse effects. Gaining traction in this field is the use of disease data linking genes with pathological phenotypes and combining this with drugtarget interaction data.

METHODS: We introduce methodology to associate drugs with effects, both intended and adverse, using a tripartite network approach that combines drug-target and target-phenotype data, in which targets can be represented as proteins and protein domains.

RESULTS: We were able to detect associations for over 140,000 ChEMBL drugs and 3,800 phenotypes, represented as Human Phenotype Ontology (HPO) terms. The overlap of these results with the SIDER databases of known drug side effects was up to 10 times higher than random, depending on the target type, disease database and score threshold used. In terms of overlap with drug-phenotype pairs extracted from the literature, the performance of our methodology was up to 17.47 times greater than random. The top results include phenotype-drug associations that represent intended effects, particularly for cancers such as chronic myelogenous leukemia, which was linked with nilotinib. They also include adverse side effects, such as blurred vision being linked with tetracaine.

DISCUSSION: This work represents an important advance in our understanding of how drugs cause intended and adverse side effects through their action on disease causing genes and has potential applications for drug development and repositioning.

PMID:39911831 | PMC:PMC11794328 | DOI:10.3389/fphar.2024.1470931

Curr Genomics. 2025;26(1):48-80. doi: 10.2174/0113892029308243240709073945. Epub 2024 Jul 18.

ABSTRACT

BACKGROUND: The damage in the liver and hepatocytes is where the primary liver cancer begins, and this is referred to as Hepatocellular Carcinoma (HCC). One of the best methods for detecting changes in gene expression of hepatocellular carcinoma is through bioinformatics approaches.

OBJECTIVE: This study aimed to identify potential drug target(s) hubs mediating HCC progression using computational approaches through gene expression and protein-protein interaction datasets.

METHODOLOGY: Four datasets related to HCC were acquired from the GEO database, and Differentially Expressed Genes (DEGs) were identified. Using Evenn, the common genes were chosen. Using the Fun Rich tool, functional associations among the genes were identified. Further, protein-protein interaction networks were predicted using STRING, and hub genes were identified using Cytoscape. The selected hub genes were subjected to GEPIA and Shiny GO analysis for survival analysis and functional enrichment studies for the identified hub genes. The up-regulating genes were further studied for immunohistopathological studies using HPA to identify gene/protein expression in normal vs HCC conditions. Drug Bank and Drug Gene Interaction Database were employed to find the reported drug status and targets. Finally, STITCH was performed to identify the functional association between the drugs and the identified hub genes.

RESULTS: The GEO2R analysis for the considered datasets identified 735 upregulating and 284 downregulating DEGs. Functional gene associations were identified through the Fun Rich tool. Further, PPIN network analysis was performed using STRING. A comparative study was carried out between the experimental evidence and the other seven data evidence in STRING, revealing that most proteins in the network were involved in protein-protein interactions. Further, through Cytoscape plugins, the ranking of the genes was analyzed, and densely connected regions were identified, resulting in the selection of the top 20 hub genes involved in HCC pathogenesis. The identified hub genes were: KIF2C, CDK1, TPX2, CEP55, MELK, TTK, BUB1, NCAPG, ASPM, KIF11, CCNA2, HMMR, BUB1B, TOP2A, CENPF, KIF20A, NUSAP1, DLGAP5, PBK, and CCNB2. Further, GEPIA and Shiny GO analyses provided insights into survival ratios and functional enrichment studied for the hub genes. The HPA database studies further found that upregulating genes were involved in changes in protein expression in Normal vs HCC tissues. These findings indicated that hub genes were certainly involved in the progression of HCC. STITCH database studies uncovered that existing drug molecules, including sorafenib, regorafenib, cabozantinib, and lenvatinib, could be used as leads to identify novel drugs, and identified hub genes could also be considered as potential and promising drug targets as they are involved in the gene-chemical interaction networks.

CONCLUSION: The present study involved various integrated bioinformatics approaches, analyzing gene expression and protein-protein interaction datasets, resulting in the identification of 20 top-ranked hubs involved in the progression of HCC. They are KIF2C, CDK1, TPX2, CEP55, MELK, TTK, BUB1, NCAPG, ASPM, KIF11, CCNA2, HMMR, BUB1B, TOP2A, CENPF, KIF20A, NUSAP1, DLGAP5, PBK, and CCNB2. Gene-chemical interaction network studies uncovered that existing drug molecules, including sorafenib, regorafenib, cabozantinib, and lenvatinib, can be used as leads to identify novel drugs, and the identified hub genes can be promising drug targets. The current study underscores the significance of targeting these hub genes and utilizing existing molecules to generate new molecules to combat liver cancer effectively and can be further explored in terms of drug discovery research to develop treatments for HCC.

PMID:39911278 | PMC:PMC11793067 | DOI:10.2174/0113892029308243240709073945

Pathog Immun. 2025 Jan 29;10(1):120-139. doi: 10.20411/pai.v10i1.770. eCollection 2024.

ABSTRACT

RATIONALE: Treatment monitoring of tuberculosis patients is complicated by a slow growth rate of Mycobacterium tuberculosis. Recently, host RNA signatures have been used to monitor the response to tuberculosis treatment.

OBJECTIVE: Identifying and validating a whole blood-based RNA signature model to predict microbiological treatment responses in patients on tuberculosis therapy.

METHODS: Using a multi-step machine learning algorithm to identify an RNA-based algorithm to predict the remaining time to culture conversion at flexible time points during anti-tuberculosis therapy.

RESULTS: The identification cohort included 149 patients split into a training and a test cohort, to develop a multistep algorithm consisting of 27 genes (TB27) for predicting the remaining time to culture conversion (TCC) at any given time. In the test dataset, predicted TCC and observed TCC achieved a correlation coefficient of r=0.98. An external validation cohort of 34 patients shows a correlation between predicted and observed days to TCC also of r=0.98.

CONCLUSION: We identified and validated a whole blood-based RNA signature (TB27) that demonstrates an excellent agreement between predicted and observed times to M. tuberculosis culture conversion during tuberculosis therapy. TB27 is a potential useful biomarker for anti-tuberculosis drug development and for prediction of treatment responses in clinical practice.

PMID:39911144 | PMC:PMC11792529 | DOI:10.20411/pai.v10i1.770

BMC Genomics. 2025 Feb 5;26(1):110. doi: 10.1186/s12864-025-11301-w.

ABSTRACT

Small proteins with fewer than 100, particularly fewer than 50, amino acids are still largely unexplored. Nonetheless, they represent an essential part of bacteria's often neglected genetic repertoire. In recent years, the development of ribosome profiling protocols has led to the detection of an increasing number of previously unknown small proteins. Despite this, they are overlooked in many cases by automated genome annotation pipelines, and often, no functional descriptions can be assigned due to a lack of known homologs. To understand and overcome these limitations, the current abundance of small proteins in existing databases was evaluated, and a new dedicated database for small proteins and their potential functions, called 'sORFdb', was created. To this end, small proteins were extracted from annotated bacterial genomes in the GenBank database. Subsequently, they were quality-filtered, compared, and complemented with proteins from Swiss-Prot, UniProt, and SmProt to ensure reliable identification and characterization of small proteins. Families of similar small proteins were created using bidirectional best BLAST hits followed by Markov clustering. Analysis of small proteins in public databases revealed that their number is still limited due to historical and technical constraints. Additionally, functional descriptions were often missing despite the presence of potential homologs. As expected, a taxonomic bias was evident in over-represented clinically relevant bacteria. This new and comprehensive database is accessible via a feature-rich website providing specialized search features for sORFs and small proteins of high quality. Additionally, small protein families with Hidden Markov Models and information on taxonomic distribution and other physicochemical properties are available. In conclusion, the novel small protein database sORFdb is a specialized, taxonomy-independent database that improves the findability and classification of sORFs, small proteins, and their functions in bacteria, thereby supporting their future detection and consistent annotation. All sORFdb data is freely accessible via https://sorfdb.computational.bio .

PMID:39910485 | DOI:10.1186/s12864-025-11301-w

Int J Food Sci Nutr. 2025 Feb 5:1-11. doi: 10.1080/09637486.2025.2461144. Online ahead of print.

ABSTRACT

Our study examined the relationship between diet quality and the prevalence of metabolic syndrome (MetS) among 1779 U.S. cancer survivors using data from the National Health and Nutrition Examination Survey (NHANES, 2005-2016). Diet quality was assessed using the Healthy Eating Index 2015 (HEI-2015). Higher HEI-2015 scores were linked to significantly lower MetS prevalence (OR: 0.51, 95% CI: 0.32-0.80). Specifically, a higher intake of seafood and plant proteins, and fatty acids, coupled with a reduced intake of added sugars, was associated with decreased odds of MetS prevalence (OR: 0.93; 95% CI, 0.86-0.99) in cancer survivors. Additionally, a better diet quality was linked to lower prevalence of high waist circumference, elevated triglycerides, reduced high-density lipoprotein (HDL) cholesterol and high fasting glucose levels (OR, 0.44; 95% CI, 0.27-0.72). These results suggest that adopting healthy dietary habits may prevent MetS in cancer survivors.

PMID:39910439 | DOI:10.1080/09637486.2025.2461144

Nat Biomed Eng. 2025 Feb 5. doi: 10.1038/s41551-025-01359-y. Online ahead of print.

NO ABSTRACT

PMID:39910377 | DOI:10.1038/s41551-025-01359-y

Nature. 2025 Feb 5. doi: 10.1038/s41586-024-08501-x. Online ahead of print.

ABSTRACT

The genetic code is conserved across all domains of life, yet exceptions have revealed variations in codon assignments and associated translation factors1-3. Inspired by this natural malleability, synthetic approaches have demonstrated whole-genome replacement of synonymous codons to construct genomically recoded organisms (GROs)4,5 with alternative genetic codes. However, no efforts have fully leveraged translation factor plasticity and codon degeneracy to compress translation function to a single codon and assess the possibility of a non-degenerate code. Here we describe construction and characterization of Ochre, a GRO that fully compresses a translational function into a single codon. We replaced 1,195 TGA stop codons with the synonymous TAA in ∆TAG Escherichia coli C321.∆A4. We then engineered release factor 2 (RF2) and tRNATrp to mitigate native UGA recognition, translationally isolating four codons for non-degenerate functions. Ochre thus utilizes UAA as the sole stop codon, with UGG encoding tryptophan and UAG and UGA reassigned for multi-site incorporation of two distinct non-standard amino acids into single proteins with more than 99% accuracy. Ochre fully compresses degenerate stop codons into a single codon and represents an important step toward a 64-codon non-degenerate code that will enable precise production of multi-functional synthetic proteins with unnatural encoded chemistries and broad utility in biotechnology and biotherapeutics.

PMID:39910296 | DOI:10.1038/s41586-024-08501-x

Sci Rep. 2025 Feb 5;15(1):4306. doi: 10.1038/s41598-025-88465-8.

ABSTRACT

The genetic contributions to early onset heart failure (HF) are incompletely understood. Genetic testing in advanced HF patients undergoing heart transplantation (HTx) may yield clinical benefits, but data is limited. We performed deep-coverage whole genome sequencing (WGS) in 102 Swedish HTx recipients. Gene lists were compiled through a systematic literature review. Variants were prioritized for pathogenicity and classified manually. We also compared polygenic HF risk scores to a population-based cohort. We found a pathogenic (LP/P) variant in 34 individuals (34%). Testing yield was highest in hypertrophic (63% LP/P carriers), dilated (40%) and arrhythmogenic right ventricular (33%) cardiomyopathy and lower in ischemic cardiomyopathy (10%). A family history was more common in LP/P variant carriers than in non-carriers but was present in less than half of carriers (44% vs 13%, P < 0.001), whereas age was similar. Polygenic risk scores were similar in HTx recipients and the population cohort. In conclusion, we observed a high prevalence of pathogenic cardiomyopathy gene variants in individuals with early-onset advanced HF, which could not accurately be ruled out by family history and age. In contrast, we did not observe higher polygenic risk scores in early onset advanced HF cases than in the general population.

PMID:39910139 | DOI:10.1038/s41598-025-88465-8

NPJ Syst Biol Appl. 2025 Feb 4;11(1):15. doi: 10.1038/s41540-025-00496-z.

ABSTRACT

Predictive mathematical modeling is an essential part of systems biology and is interconnected with information management. Systems biology information is often stored in specialized formats to facilitate data storage and analysis. These formats are not designed for easy human readability and thus require specialized software to visualize and interpret results. Therefore, comprehending modeling and underlying networks and pathways is contingent on mastering systems biology tools, which is particularly challenging for users with no or little background in data science or system biology. To address this challenge, we investigated the usage of public Artificial Intelligence (AI) tools in exploring systems biology resources in mathematical modeling. We tested public AI's understanding of mathematics in models, related systems biology data, and the complexity of model structures. Our approach can enhance the accessibility of systems biology for non-system biologists and help them understand systems biology without a deep learning curve.

PMID:39910106 | DOI:10.1038/s41540-025-00496-z

Gut Microbes. 2025 Dec;17(1):2455506. doi: 10.1080/19490976.2025.2455506. Epub 2025 Feb 5.

ABSTRACT

The relationship between bacteria, cognitive function and obesity is well established, yet the role of archaeal species remains underexplored. We used shotgun metagenomics and neuropsychological tests to identify microbial species associated with cognition in a discovery cohort (IRONMET, n = 125). Interestingly, methanogen archaeas exhibited the strongest positive associations with cognition, particularly Methanobrevibacter smithii (M. smithii). Stratifying individuals by median-centered log ratios (CLR) of M. smithii (low and high M. smithii groups: LMs and HMs) revealed that HMs exhibited better cognition and distinct gut bacterial profiles (PERMANOVA p = 0.001), characterized by increased levels of Verrucomicrobia, Synergistetes and Lentisphaerae species and reduced levels of Bacteroidetes and Proteobacteria. Several of these species were linked to the cognitive test scores. These findings were replicated in a large-scale validation cohort (Aging Imageomics, n = 942). Functional analyses revealed an enrichment of energy, butyrate, and bile acid metabolism in HMs in both cohorts. Global plasma metabolomics by CIL LC-MS in IRONMET identified an enrichment of methylhistidine, phenylacetate, alpha-linolenic and linoleic acid, and secondary bile acid metabolism associated with increased levels of 3-methylhistidine, phenylacetylgluamine, adrenic acid, and isolithocholic acid in the HMs group. Phenylacetate and linoleic acid metabolism also emerged in the Aging Imageomics cohort performing untargeted HPLC-ESI-MS/MS metabolic profiling, while a targeted bile acid profiling identified again isolithocholic acid as one of the most significant bile acid increased in the HMs. 3-Methylhistidine levels were also associated with intense physical activity in a second validation cohort (IRONMET-CGM, n = 116). Finally, FMT from HMs donors improved cognitive flexibility, reduced weight, and altered SCFAs, histidine-, linoleic acid- and phenylalanine-related metabolites in the dorsal striatum of recipient mice. M. smithii seems to interact with the bacterial ecosystem affecting butyrate, histidine, phenylalanine, and linoleic acid metabolism with a positive impact on cognition, constituting a promising therapeutic target to enhance cognitive performance, especially in subjects with obesity.

PMID:39910065 | DOI:10.1080/19490976.2025.2455506