The mechanisms underlying the impact of probiotic supplementation on health remain largely elusive. While previous studies primarily focus on the discovery of novel bioactive bacteria and alterations in the microbiome environment to explain potential probiotic effects, our research delves into the role of living Lactiplantibacillus (formerly known as Lactobacillus) and their conditioned media, highlighting that only the former, not dead bacteria, enhance the healthspan of Caenorhabditis elegans (C. elegans). To elucidate the underlying mechanisms, we conduct transcriptomic profiling through RNA-seq analysis in C. elegans exposed to GTB1, a strain of Lactiplantibacillus plantarum or 3-phenyllactic acid (PLA), mimicking the presence of key candidate metabolites of GTB1 and evaluating healthspan. Our findings reveal that PLA treatment significantly extends the healthspan of C. elegans by promoting energy metabolism and stress resilience in a SKN-1/ATFS-1-dependent manner. Moreover, PLA-mediated longevity is associated with a novel age-related parameter, the Healthy Aging Index (HAI), introduced in this study, which comprises healthspan-related factors such as motility, oxygen consumption rate (OCR), and ATP levels. Extending the relevance of our work to humans, we observe an inverse correlation between blood PLA levels and physical performance in patients with sarcopenia, when compared to age-matched non-sarcopenic controls. Our investigation thus sheds light on the pivotal role of the metabolite PLA in probiotics-mediated enhancement of organismal healthspan, and also hints at its potential involvement in age-associated sarcopenia. These findings warrant further investigation to delineate PLA’s role in mitigating age-related declines in healthspan and resilience to external stressors.

We performed targeted metabolomics with machine learning (ML)-based interpretation to identify metabolites that distinguish the progression of nonalcoholic fatty liver disease (NAFLD) in a cohort. Plasma metabolomics analysis was conducted in healthy control subjects (n = 25) and patients with NAFL (n = 42) and nonalcoholic steatohepatitis (NASH, n = 19) by gas chromatography-tandem mass spectrometry (MS/MS) and liquid chromatography-MS/MS as well as RNA sequencing (RNA-seq) analyses on liver tissues from patients with varying stages of NAFLD (n = 12). The resulting metabolomic data were subjected to routine statistical and ML-based analyses and multi-omics interpretation with RNA-seq data. We found 6 metabolites that were significantly altered in NAFLD among 79 detected metabolites. Random-forest and multinomial logistic regression analyses showed that eight metabolites (glutamic acid, cis-aconitic acid, aspartic acid, isocitric acid, α-ketoglutaric acid, oxaloacetic acid, myristoleic acid, and tyrosine) could distinguish the three groups. Then, the recursive partitioning and regression tree algorithm selected three metabolites (glutamic acid, isocitric acid, and aspartic acid) from these eight metabolites. With these three metabolites, we formulated an equation, the MetaNASH score that distinguished NASH with excellent performance. In addition, metabolic map construction and correlation assays integrating metabolomics data into the transcriptome datasets of the liver showed correlations between the concentration of plasma metabolites and the expression of enzymes governing metabolism and specific alterations of these correlations in NASH. Therefore, these findings will be useful for evaluation of altered metabolism in NASH and understanding of pathophysiologic implications from metabolite profiles in relation to NAFLD progression.

Receptor-mediated signaling could be modulated by ubiquitination of pathway intermediates, but the role of such modification in the pathogenesis of inflammation and inflammation-related cancer is lesser known. The ubiquitin ligase Pellino1 has been shown to modulate immune signals by enabling various immune cells to respond to their receptor signals effectively. Here, we show that Pellino1 levels are elevated in patients with colitis, patients with colitis-associated colon cancer (CAC), and murine models of these conditions. In a monocyte-specific Pellino1 knock-out mouse model, we find reduced macrophage migration and activation, leading to attenuated development of colitis and CAC in male mice. Mechanistically, Pellino1 targets STAT3 for lysine 63-mediated ubiquitination, resulting in pathogenic activation of STAT3 signaling. Taken together, our findings reveal a macrophage-specific ubiquitination signaling axis in colitis and CAC development and suggest that Pellino1 is a potential candidate for treating chronic inflammation and inflammation-related cancer.

Background: Sarcopenia is defined as muscle wasting, characterized by a progressive loss of muscle mass and function due to ageing. Diagnosis of sarcopenia typically involves both muscle imaging and the physical performance of people exhibiting signs of muscle weakness. Despite its worldwide prevalence, a molecular method for accurately diagnosing sarcopenia has not been established.Methods: We develop an artificial intelligence (AI) diagnosis model of sarcopenia using a published transcriptome dataset comprising patients from multiple ethnicities. For the AI model for sarcopenia diagnosis, we use a transcriptome database comprising 17 339 genes from 118 subjects. Among the 17 339 genes, we select 27 features as the model inputs. For feature selection, we use a random forest, extreme gradient boosting and adaptive boosting. Using the top 27 features, we propose a four‐layer deep neural network, named DSnet‐v1, for sarcopenia diagnosis.Results: Among isolated testing datasets, DSnet‐v1 provides high sensitivity (100%), specificity (94.12%), accuracy (95.83%), balanced accuracy (97.06%) and area under receiver operating characteristics (0.99). To extend the number of patient data, we develop a web application (http://sarcopeniaAI.ml/), where the model can be accessed unrestrictedly to diagnose sarcopenia if the transcriptome is available. A focused analysis of the top 27 genes for their differential or co‐expression with other genes implied the potential existence of race‐specific factors for sarcopenia, suggesting the possibility of identifying causal factors of sarcopenia when a more extended dataset is provided.Conclusions: Our new AI model, DSnet‐v1, accurately diagnoses sarcopenia and is currently available publicly to assist healthcare providers in diagnosing and treating sarcopenia.

Sarcopenia is a progressive, age-related condition characterized by the loss of skeletal muscle mass, strength, and function, which increases the risk of falls, frailty, and loss of independence. Despite growing recognition and its incorporation into geriatric assessments, there is still no approved pharmacological treatment. This review provides an updated overview of sarcopenia, encompassing diagnostic criteria, biological mechanisms, and emerging therapeutic strategies. Key molecular features include mitochondrial dysfunction, nicotinamide adenine dinucleotide (NAD⁺) decline, fiber-type alterations, and dysregulation of myokines. Recent singlecell and multi-omics studies have revealed the heterogeneity of muscle tissue and distinct cell-type-specific aging patterns. Therapeutic efforts are evolving beyond lifestyle interventions toward targeted approaches, including myostatin inhibitors, NAD⁺ boosters, senolytics, and microbiome modulators. However, clinical translation remains constrained by heterogeneity in trial design and the absence of standardized outcome measures. Future sarcopenia care will likely involve precision medicine guided by biomarkers and supported by digital monitoring tools. Progressing from molecular discovery to clinical application will be essential for preserving muscle health and function in aging populations.

Seung Min Jeong, Eun-Ju Jin, Shibo Wei, Ju-Hyeon Bae, Yosep Ji, Yunju Jo, Jee-Heon Jeong, Se Jin Im & Dongryeol Ryu. BMB Reports 2023;56:404-9. https://doi.org/10.5483/BMBRep.2023-0068