Phenomics (Cham, Switzerland)最新文献

Most genome-wide association studies (GWAS) of Venous Thromboembolism (VTE) have used data from individuals of European descent, however, genetic factors for VTE have not been fully identified in Chinese populations, which causes the limited use of existing polygenic risk scores (PRS) to identify subpopulations at high risk of VTE for prevention. We, therefore, aimed to curate all the potential VTE-related single-nucleotide polymorphisms (SNPs) for the construction of a new improved PRS model based on the self-adapting method, and then evaluate its utility and effectiveness in the stratification of VTE risk in Chinese populations. We comprehensively analyzed the mutation spectrum of VTE-associated SNPs in the Chinese cohort, and ranked their individual risk effects independently using risk ratio, logistic regression coefficient, and penalty regression coefficient as evaluation criteria. By integrating various algorithms and evaluating their performance, we trained the optimal prediction model of VTE risk in the Chinese population with the least SNP features, established an adaptive PRS model with progressive SNP overlay, and tested it on an independent Chinese population cohort. Self-adaptive polygenic risk score model based on all 318 SNPs or on the 44 most strongly associated SNPs performed similarly (areas under receiver-operating characteristic curves (AUCs) of 0.739 and 0.709, respectively) on the testing dataset of the Chinese VTE cohort, and that achieve the overall best level of the AUC from a conventional PRS model based on known genetic risk factors (0.620-0.718). In addition, we observed the self-adaptive PRS model was an independent effective risk stratification indicator beyond other clinical characteristics including age and smoking status. Our data revealed that only 44 SNPs-derived PRS model can be effectively used in discriminating subpopulations at high risk of VTE. To become clinically useful, our model could benefit from a practically feasible VTE screening program for precision prevention in Chinese populations.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-024-00192-8.

Metagenomics and metabolomics technologies have been widely used to investigate the microbe-metabolite interactions in vivo. However, the computational methods that accurately infer the microbe-metabolite interactions are lacking. We present a context-aware framework for graph representation learning, NetNiche, which predicts microbe-metabolite and microbe-microbe interactions in an accurate manner, by integrating their abundance data with prior knowledge. We applied NetNiche to datasets on gut and soil microbiome, and demonstrated that NetNiche can outperform the state-of-the-art methods, such as SParse InversE Covariance Estimation for Ecological Association Inference (SPIEC-EASI), Sparse Correlations for Compositional data (SparCC) and microbe-metabolite vectors (mmvec). NetNiche is an effective tool with wide applicability for the multi-omics study of human microbiome.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-024-00168-8.

Osteoporosis, a prevalent metabolic bone disorder characterized by reduced bone mass and compromised bone strength, poses a substantial socioeconomic burden worldwide due to its staggering global incidence. Individuals afflicted with osteoporosis face an elevated risk of debilitating fractures, leading to compromised quality of life, disability, and increased mortality. The gut-bone axis, the intricate bidirectional communication between the gut microbiome and skeletal system, has recently emerged as a focal area in bone health research. Accumulating evidence suggests that gut homeostasis, the delicate balance of gut microbial communities, modulates bone metabolism through diverse mechanisms, potentially driving the pathogenesis of osteoporosis. Conversely, gut dysbiosis, the disruption of this equilibrium, may directly or indirectly perturb bone homeostasis, contributing to the onset and progression of osteoporosis. Elucidating the regulatory interplay between gut homeostasis and bone metabolism in the context of osteoporosis holds promising potential for developing novel preventive and therapeutic strategies, bearing profound theoretical and clinical implications.

Although the compositional alterations of gut bacteria in ketogenic diet (KD) have been intensively investigated, the causal relationship between this extreme diet and the microbiota changes is not fully understood. Here, we studied the growth dynamics of intestinal bacteria in KD. We used the CoPTR method to calculate the peak-to-trough ratio (PTR) based on metagenomic sequencing data, serving as an indicator of bacterial growth rates. Notably, Akkermansia muciniphila, a bacterium strongly linked to the therapeutic benefits of KD, exhibited one of the highest growth rates, aligning with its markedly elevated abundance. Our findings also revealed discrepancies in the change patterns of CoPTR values and relative abundances for various bacteria across different diet groups, some of which might be attributed to the exceptionally high or low growth rates of specific species. For some of the species demonstrating obvious differences in growth rates between KD and standard diet, we conducted in vitro culture experiments, supplementing them with diverse nutritional sources to elucidate the underlying mechanisms. The integrative analysis of bacterial abundance and growth dynamics can help deepen our understanding of the gut microbiota changes caused by KD and the therapeutic effects of this special diet.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-025-00228-7.

Pathogens pose significant threats to biosecurity and environmental health due to their potential for widespread outbreaks. Effective pathogen detection requires methods that are rapid, sensitive, specific, and informative. Here, we proposed a multiplex visual detection system that integrated ultrafast polymerase chain reaction (PCR) and molecular beacons, allowing the simultaneous detection of three pathogens in a one-pot reaction. The ultrafast PCR protocol employed cycles of just 7 s each, allowing the entire process-from sampling to result-to be completed within only 10 min. Molecular beacons hybridized with target sequences during ultrafast PCR, generating fluorescence signals that are visually detectable without specialized equipment. Additionally, we developed a compact, portable cartridge integrated with online software for fluorescence visualization and direct result output, eliminating the need for bulky instruments and specialized personnel, thereby facilitating point-of-care testing (POCT). The method demonstrated high specificity and sensitivity, with a limit of detection (LOD) as low as 23 copies per reaction. It achieved a 100% positive detection rate in practical applications, performing comparably to standard PCR. Furthermore, the method effectively identified low concentrations of pathogens in animal infection samples. This ultrafast, highly sensitive, specific, and informative method shows significant potential for POCT applications, including food safety monitoring and clinical diagnostics.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-024-00216-3.

Emerging lipid-modifying agents show potential but lack evidence for the management of uric acid and gout. We aimed to explore the causal effects of lipid traits, lipid-modifying drugs on uric acid levels and risk of gout. Two-sample MR analyses were performed to investigate the associations of genetically predicted lipid traits (LDL-C, HDL-C and TG) and lipid-modifying drug targets (PCSK9, HMGCR, NPC1L1, CETP, ABCG5/G8, APOB, LDLR, LPL, ANGPTL3, and APOC3) with uric acid levels and gout risk. Validation analyses were performed using the independent cohort of the UK Biobank. Summary-data-based MR was further conducted to estimate the associations of the expression of drug target genes with the outcomes. Genetically predicted lower HDL-C and higher TG were significantly associated with elevated uric acid levels (β (95% CI): -0.11 [-0.18, -0.04], p = 0.001 for HDL-C; 0.18 [0.09, 0.27], p < 0.001 for TG) and increased risk of gout (OR (95% CI): 0.83 [0.71, 0.97], p = 0.017 for HDL-C; 1.54 [1.25, 1.91], p < 0.001 for TG). Notably, LPL activation among lipid-modifying drug targets demonstrated significant associations with both reduced uric acid levels (β [95% CI]: -0.13 [-0.16, -0.10], p < 0.001) and decreased risk of gout (OR 95% CI: 0.84 [0.76, 0.93], p = 0.001). These findings were corroborated in the UK Biobank dataset. Furthermore, the expression of LPL was significantly associated with lower uric acid levels (β [95% CI]: -0.03 [-0.04, -0.01], p = 0.002). Our results suggest that LPL activation, which reduces TG levels, holds promise as a candidate drug for the treatment and prevention of hyperuricemia and gout.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-024-00212-7.

A ratiometric dual nanosensor for simultaneously measuring intracellular ATP and oxygen concentration was developed. The nanosensor had a unique core/shell nanostructure, and the hydrophobic oxygen sensitive probe and reference dye are physically encapsulated inside polystyrene nanoparticles. The outer surface of polystyrene nanoparticles was coated with a thin layer of hydrophilic silica oxide, then the ATP-sensitive probe was covalently labelled on the silica surface. The synthesized nanoparticles have uniform size, excellent control in both size and morphology. They are monodispersed and had good stability in aqueous solution. Their net positive surface charge fascinates their uptake by living cells, and the nanosensor showed good biocompatibility with rather low cytotoxicity. The high photostability and fully reversible response of the dual nanosensors made them suitable to measure the oxygen consumption and ATP production in real time. Therefore, we can directly measure the oxygen consumption rate and ATP production, and calculate the P/O ratio in real-time and directly in living cells.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-025-00234-9.

Pancreatic cancer is a highly invasive and heterogeneous malignancy, with its incidence witnessed a global escalation in recent years. This tumor type is typically asymptomatic in its early stages, and the anatomical location of the pancreas renders it obscured, frequently leading to late detection. Its highly invasive growth pattern can readily invade surrounding tissues and organs, and pancreatic cancer exhibits poor sensitivity to radiotherapy and chemotherapy. Surgical resection remains the primary treatment modality, but due to delayed diagnosis, most patients lose the opportunity for curative resection. Pancreatic cancer is one of the most challenging malignancies, with a grave prognosis, and its incidence and mortality rates continue to rise annually. In recent years, the interplay between intestinal homeostasis and pancreatic cancer development has emerged as a burgeoning area of research. Intestinal homeostasis is a dynamic equilibrium state formed by the intricate interplay between the intestinal barrier and the intestinal environment (including intestinal microbiota). Further elucidating intestinal homeostasis and its association with pancreatic cancer pathogenesis will aid in exploring novel therapeutic strategies to combat this devastating disease.

Gut microbiota with co-abundant behaviors is considered belonging to the same guild in micro-ecosystem. In this study, we established co-abundance networks of operational taxonomic units (OTUs) among 2944 Chinese adults from the Shanghai Men's and Women's Health Studies and observed a positive connection-dominated scale-free network using Sparse Correlations for Compositional data (SparCC). The closeness centrality was negatively correlated with other degree-based topological metrics in the network, indicating the isolated modularization of the bacteria. A total of 130 guilds were constructed, with a high modularity of 0.68, and retaining more diversity of OTUs than genus classification. The scores of guild structure similarity for comparisons between all, the healthy and the unhealthy subjects were higher than those derived from randomized permutations, suggesting a robust guild structure. We further used the constructed 130 guilds as the aggregation units to identify gut microbiota that may be associated with type 2 diabetes, and found that the OTUs in 21 significant guilds relevant to diabetes belonged to 19 of 41 (46.3%) previously reported genera (derived from Disbiome database), while only 10 (24.4%) showed different abundances between diabetes patients and healthy subjects in genus-based analysis. Our study reveals modularization of gut microbiota as guilds in Chinese populations, and demonstrates advantages of guild-based analysis in identifying diabetes-related gut bacteria. The analytical method based on microbial networks should be widely used to deepen our understanding of the role of gut microbiota in human health.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-024-00211-8.