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Background: Cigarette smoking is posited as a potential factor in disrupting the balance of the human gut microbiota. However, existing studies with limited sample size have yielded inconclusive results. Methods: Here, we assessed the association between cigarette smoking and gut microbial profile among Chinese males from four independent studies (N total = 3308). Both 16S rRNA and shotgun metagenomic sequencing methods were employed, covering 206 genera and 237 species. Microbial diversity and abundance were compared among non-smokers, current smokers, and former smokers. Results: Actinomyces[g], Atopobium[g], Haemophilus[g], Turicibacter[g], and Lachnospira[g] were found to be associated with smoking status (current smokers vs. non-smokers). Metagenomic data provided a higher resolution at the species level, particularly for the Actinomyces[g] branch. Additionally, serum γ-glutamylcysteine (γ-Glu-Cys) was found to have a potential role in connecting smoking and Actinomyces[g]. Furthermore, we revealed putative mediation roles of the gut microbiome in the associations between smoking and common diseases including cholecystitis and type 2 diabetes. Conclusions: We characterized the gut microbiota profile in male smokers and further revealed their potential involvement in mediating the impact of smoking on health outcomes. These findings advance our understanding of the intricate association between cigarette smoking and the gut microbiome. [ABSTRACT FROM AUTHOR] more...

This study investigates the spatiotemporal variation characteristics and influencing factors of an ecosystem's carbon sequestration rate (CSR) in the Ningxia region from 2001 to 2023, providing scientific evidence for assessing the regional carbon sequestration capacity and formulating carbon neutrality policies. Based on ground observation data and multimodal datasets, the optimal machine learning model (EXT) was used to invert a 30 m high-resolution vegetation and soil carbon density dataset for Ningxia from 2000 to 2023. Annual variation analysis and geographical detector methods were employed to assess the spatiotemporal distribution characteristics of the CSR from 2001 to 2023 and identify the primary influencing factors. The results show that from 2001 to 2023, the CSR of the Ningxia ecosystem exhibits a spatial distribution pattern characterized by higher values in the south and lower values in the north, with a mean value of 21.95 gC·m−2, and an overall fluctuating increasing trend, with an annual growth rate of 0.53 gC·m−2 a−1. Significant differences in the CSR exist across different ecological regions. In terms of land use types, the ranking of carbon sequestration capacity is forest > farmland > grassland > barren, while the ranking of the carbon sequestration enhancement capacity is farmland > forest > grassland > barren. Among land use change types, the carbon sequestration enhancement capacity significantly increased when grassland was converted to forest or shrubland, farmland to forest–grassland, and bare land to forest–grassland, with increases of 42.9%, 9.2%, and 34.6%, respectively. The NDVI is the primary driver of CSR spatiotemporal variation, while the interaction between the Enhanced Vegetation Index (EVI) and soil bulk density has a more significant explanatory power for CSR spatial differentiation. This study shows that ecological restoration projects, such as the conversion of cropland to forest (or grassland) and protective farmland measures, play a significant role in enhancing the carbon sequestration capacity in Ningxia. [ABSTRACT FROM AUTHOR] more...

Background and Aims: Molecular classification is a promising tool for prognosis prediction and optimizing precision therapy for HCC. Here, we aimed to develop a molecular classification of HCC based on the fatty acid degradation (FAD) pathway, fully characterize it, and evaluate its ability in guiding personalized therapy., Approach and Results: We performed RNA sequencing (RNA-seq), PCR-array, lipidomics, metabolomics, and proteomics analysis of 41 patients with HCC, in which 17 patients received anti-programmed cell death-1 (PD-1) therapy. Single-cell RNA sequencing (scRNA-seq) was performed to explore the tumor microenvironment. Nearly, 60 publicly available multiomics data sets were analyzed. The associations between FAD subtypes and response to sorafenib, transarterial chemoembolization (TACE), immune checkpoint inhibitor (ICI) were assessed in patient cohorts, patient-derived xenograft (PDX), and spontaneous mouse model ls. A novel molecular classification named F subtype (F1, F2, and F3) was identified based on the FAD pathway, distinguished by clinical, mutational, epigenetic, metabolic, and immunological characteristics. F1 subtypes exhibited high infiltration with immunosuppressive microenvironment. Subtype-specific therapeutic strategies were identified, in which F1 subtypes with the lowest FAD activities represent responders to compounds YM-155 and Alisertib, sorafenib, anti-PD1, anti-PD-L1, and atezolizumab plus bevacizumab (T + A) treatment, while F3 subtypes with the highest FAD activities are responders to TACE. F2 subtypes, the intermediate status between F1 and F3, are potential responders to T + A combinations. We provide preliminary evidence that the FAD subtypes can be diagnosed based on liquid biopsies., Conclusions: We identified 3 FAD subtypes with unique clinical and biological characteristics, which could optimize individual cancer patient therapy and help clinical decision-making., (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc.) more...

Drug repositioning presents a streamlined and cost-efficient way to expand the range of therapeutic possibilities. Furthermore, drugs with genetic evidence are more likely to progress successfully through clinical trials towards FDA approval. Exploiting these developments, single gene-based drug repositioning methods have been implemented, but approaches leveraging the entire spectrum of molecular signatures are critically underexplored. Most multi-gene-based approaches rely on differential gene expression (DGE) analysis, which is prone to identify the molecular consequence of disease and renders causal inference challenging. We propose a framework TReD (Transcriptome-informed Reversal Distance) that integrates population-level disease signatures robust to reverse causality and cell-based drug-induced transcriptome response profiles. TReD embeds the disease signature and drug profile in a high-dimensional normed space, quantifying the reversal potential of candidate drugs in a disease-related cell screen assay. The robustness is ensured by evaluation in additional cell screens. For an application, we implement the framework to identify potential drugs against COVID-19. Taking transcriptome-wide association study (TWAS) results from four relevant tissues and three DGE results as disease features, we identify 37 drugs showing potential reversal roles in at least four of the seven disease signatures. Notably, over 70% (27/37) of the drugs have been linked to COVID-19 from other studies, and among them, eight drugs are supported by ongoing/completed clinical trials. For example, TReD identifies the well-studied JAK1/JAK2 inhibitor baricitinib, the first FDA-approved immunomodulatory treatment for COVID-19. Novel potential candidates, including enzastaurin, a selective inhibitor of PKC-beta which can be activated by SARS-CoV-2, are also identified. In summary, we propose a comprehensive genetics-anchored framework integrating population-level signatures and cell-based screens that can accelerate the search for new therapeutic strategies. more...