Communications Biology最新文献

Interleukin-6 (IL-6) drives metabolic and inflammatory processes central to disease. Current knowledge implicates epigenetic mechanisms in the regulation of these pathways, including through the methylation of CpG sites. This blood-based meta-analysis of three cohorts (n = 4,361) identifies 401 IL-6-associated CpGs enriched in regulatory regions and linked to key immunometabolic genes, including AIM2, MTOR, and IL6R. Three complementary causal inference approaches support most sites as responding to IL-6, with SOCS3 (Suppressor of Cytokine Signalling 3) methylation statistically mediating inflammatory bowel disease risk. Notably, one CpG connected to NFATC2IP (Nuclear Factor of Activated T-cells 2 Interacting Protein) plausibly influences both IL-6 production and multiple immunometabolic conditions, including body mass index and type 2 diabetes. Collectively, our results map the DNA methylation landscape surrounding circulating IL-6 levels and unveil directional effects and distinct functional relationships between epigenetics and inflammation.

The enteric nervous system (ENS) is involved in many gastrointestinal (GI) disorders and our understanding of how gut morphology is disrupted remains limited due to a lack of tools to investigate tissues at the organ scale. Here we present enGLOW (enteric network Gastrointestinal Lightsheet Optical Workflow), a workflow customized for high spatial-resolution investigation of the ENS in gastrointestinal samples. We demonstrate how enGLOW can extract quantitative data in cubic centimeters of intact tissue. In a single dataset, we quantify intestinal wall metrics in autofluorescence and labeled ENS-associated cells in centimeter-long segments of tissue using three-dimensional (3D) segmentation. With virtual tissue flattening, we separate neuronal plexuses in mouse and human samples, and observe variations in mucosal morphology and labeled signal distribution in genetically modified animals. As an optimized ready-to-use workflow, enGLOW expands the gut research toolbox to enable understanding of enteric network morphology at scales that encompass functional networks.

Single-cell RNA sequencing (scRNA-seq) in multi-condition experiments enables the systematic assessment of treatment effects. Analyzing scRNA-seq data relies on linear dimensionality reduction (DR) methods like principal component analysis (PCA). These methods decompose high-dimensional gene expression profiles into interpretable factor representations and prototypical expression patterns (components). However, integrating study covariates within linear DR frameworks remains a challenging task. We present scPCA, a flexible DR framework that jointly models cellular heterogeneity and conditioning variables, allowing it to recover an integrated factor representation and reveal transcriptional changes across conditions and components of the decomposition. We show that scPCA extracts an interpretable latent representation by analyzing unstimulated and IFNß-treated PBMCs and show its utility in mitigating batch effects. We examine age-related changes in rodent lung cell populations, uncovering a previously unreported surge in Ccl5 expression in T cells. We illustrate how scPCA may be employed to identify coordinated transcriptional changes across multiple time-points in depolarized visual cortex neurons. Finally, we show that scPCA elucidates transcriptional shifts in CRISPR-Cas9 chordin knockout zebrafish single-cell data despite large difference cell abundance across conditions. scPCA is a general method applicable beyond scRNA-seq to other high-dimensional datasets.

Aquatic ectotherms are hypothesized to be vulnerable to warming and deoxygenation associated with environmental change because temperature and oxygen (O₂) supply can restrict aerobic scope (AS) in captivity. However, evidence of a direct association between AS and fitness in the wild is lacking, inspiring debate about the circumstances under which AS is the primary driver of population fluctuations. Using respirometry data, telemetry studies, long-term population monitoring, and in situ predator-prey experiments, we related AS to two Chinook salmon (Oncorhynchus tshawytscha) population bottlenecks in the wild, juvenile rearing and migration. We found that AS, which we quantified using the metabolic index (ɸ), was associated with success probability for these bottlenecks only under a relatively narrow window of viable environmental conditions, depending on intraspecific metabolic trait diversity and hydrologic conditions. Opportunities for potentially high-impact temperature- and O₂-specific conservation and management actions using existing hydraulic engineering infrastructure could therefore exist when AS is between critical (ɸ_crit) and stable (ɸ_stable) values. Outside of this ecological threshold, changes in AS did not yield appreciable fitness benefits because successful rearing and migration were either exceptionally improbable (i.e., AS<ɸ_crit), or seemingly independent of AS (i.e., AS>ɸ_stable). In addition, AS impairments likely increased susceptibility to predation, and this may have been involved in the putative association between AS and fitness in the wild.

Postoperative ileus (POI) is characterized by dysregulated inflammation within the intestinal muscular layer, which significantly disrupts gastrointestinal motility and presents a major challenge to postoperative recovery. Although macrophages are known to contribute to inflammation through glycolytic bursts that support rapid energy production, the role of the stimulator of interferon genes (STING) in orchestrating macrophage glycolysis and modulating phenotypic polarization remains poorly defined. To address this gap, we examined the regulatory relationship between STING and macrophage metabolism. Here, we demonstrate that lipopolysaccharide (LPS)-stimulated RAW 264.7 cells display a pronounced enhancement of glycolysis, an effect that was markedly attenuated in STING knockout (STING KO) cells. Further analysis revealed that STING deletion reduces histone lactylation, consequently restricting chromatin accessibility at the hexokinase 2 (HK2) gene loci. Through CUT&Tag sequencing, we identified IRF3 as a transcription factor that directly binds to the promoter regions of HK2 and enhances its expression. Our results delineate a STING-regulated glycolytic feedback loop in macrophages: STING stabilizes hypoxia-inducible factor 1-alpha (HIF1α), thereby amplifying glycolysis and promoting histone lactylation at HK2 loci. This epigenetic modification facilitates IRF3 binding to the HK2 promoter, further boosting HK2 expression and sustaining glycolytic flux. Together, these findings elucidate a molecular mechanism through which STING modulates macrophage polarization via metabolic reprogramming, highlighting the therapeutic potential of targeting STING to regulate macrophage metabolism, alleviate inflammation, and improve outcomes in POI.

The gating of HCN channels is regulated by both voltage and the binding of cyclic nucleotides to their intracellular domain. However, the molecular determinants underlying this regulation by cyclic nucleotide binding remain unclear and controversial. Here, we combine theoretical and experimental approaches to investigate the binding process in the HCN2 channel. First, molecular dynamics simulations show that the binding of cAMP and cGMP to one HCN2 subunit affects not only the stability of that subunit but also that of neighbouring ones in the absence of any large changes in backbone structure and in a way that is consistent with negative cooperativity. Next, network analysis reveals an inter-subunit communication path that connects cAMP and cGMP binding to the C-linker, which is attached to the pore domain. Finally, experimental analyses confirm that this path is essential for cyclic nucleotide-induced interactions between subunits and high affinity and negatively cooperative binding of ligand that is driven by favourable entropy. Together, these findings provide new insights into the regulatory mechanism of HCN2 gating mediated by cyclic nucleotides and clarify the role of residue E488, which lies on this path and whose mutations are known to cause idiopathic generalized epilepsy.

Focal deficits in ischaemic stroke arise primarily from impaired perfusion downstream of a critical vascular occlusion. Though the consequent parenchymal lesion is traditionally used to predict clinical deficits, the underlying pattern of disrupted perfusion provides information upstream of the lesion, potentially yielding earlier predictive and localising signals. We previously developed a technique to compute perfusion maps from routine CT and CT angiography (CTA), an imaging modality widely deployed in clinical practice and available at large data scales. Analysing computed perfusion maps (derived from CT and CTA) from 1393 CTA-imaged patients with confirmed acute ischaemic stroke, here we use deep generative perfusion-deficit inference to localise the neural substrates of NIHSS sub-scores, explicitly disentangling the distinct topologies of disrupted perfusion and neural dependence. We show that our approach replicates known lesion-deficit relations without knowledge of the lesion itself and reveals novel neural dependents. The high achieved anatomical fidelity suggests acute CTA-derived computed perfusion maps may be of substantial clinical and scientific value in rich phenotyping of acute stroke. By relying only on an imaging modality well-established in the hyperacute setting, deep generative perfusion-deficit inference could power highly expressive models of functional anatomical relations in ischaemic stroke within the critical pre-interventional window.

The Deep Maniots, an isolated population at the southernmost tip of mainland Greece, have drawn scholarly interest for their unique dialect, culture, and patrilineal clan structure. Geographically shielded by the Mani Peninsula, they are thought to have been minimally affected by 6^th-century CE migrations that transformed Balkan demography. To investigate their genetic origins, we analysed Y-DNA and mtDNA from 102 Deep Maniots using next-generation sequencing. Paternally, Deep Maniots exhibit an exceptional prevalence (~80%) of West Asian haplogroup J-M172 (J2a), with subclade J-L930 accounting for ~50% of lineages. We identify Bronze Age Greek ancestry in Y-haplogroups nearly absent elsewhere, highlighting their longstanding genetic isolation. The absence of northeast European-related paternal lineages, common in other mainland Greeks, suggests preservation of southern Greece's pre-Medieval genetic landscape. Y-haplogroup phylogeny reveals strong founder effects dated to ~380-670 CE, while the emergence of clan-based social structure is estimated around 1350 CE, centuries earlier than previously thought. In contrast, maternal lineages display greater heterogeneity, primarily originating from ancient Balkan, Levantine, and West Eurasian sources. These results align with historical and anthropological accounts, showcasing Deep Maniots as a genetic snapshot of pre-Medieval southern Greece, offering new perspectives on population continuity and mobility in the Late Antique eastern Mediterranean.

Predicting how residue variations affect protein stability is crucial for rational protein design and for assessing the impact of disease-related mutations. Recent advances in protein language models have revolutionized computational protein analysis, enabling more accurate predictions of mutational effects. However, balancing predictive accuracy with the fundamental laws of thermodynamics remains a challenge for sequence-based models. Here we show JanusDDG, a physics-informed neural network that leverages embeddings from protein language models and a bidirectional cross-attention transformer architecture to predict stability changes for both single and multiple residue mutations. By adopting a physics-informed paradigm, the model is explicitly constrained to satisfy fundamental thermodynamic principles, such as antisymmetry and transitivity, while maintaining high predictive performance. Instead of conventional self-attention, JanusDDG employs a cross-interleaved attention mechanism that computes the relationship between wild-type and mutant embeddings to capture mutation-induced perturbations while preserving essential contextual information. Our results demonstrate that JanusDDG achieves state-of-the-art performance in predicting stability changes from sequence alone, matching or exceeding the accuracy of structure-based methods for both single and multiple mutations.

Liver fibrosis is a major global health burden with limited treatment options. Transforming growth factor-beta-induced protein (TGFBI) is crucial in fibrotic diseases and tumors, however, its precise mechanism in liver fibrosis remains unclear. Here we show that TGFBI promotes liver fibrosis in male C57BL/6 mice. TGFBI is upregulated in fibrotic livers and derived from non-parenchymal cells. Genetic TGFBI deficiency alleviates liver fibrosis in both CCl₄ (carbon tetrachloride) injection and bile duct ligation (BDL) models. Mechanistically, PDGFRβ is identified via RNA sequencing as a key downstream molecule upregulated by TGFBI in hepatic stellate cells (HSCs) via the integrin αvβ3-FAK-STAT3 pathway, promoting HSC proliferation and activation. Meanwhile, TGFBI increases PDGF-B expression in macrophages through the integrin αvβ3-AKT-ERK pathway, driving their proliferation, migration and differentiation into the profibrotic TREM2⁺CD9⁺ subpopulation. Elevated PDGF-B reversely stimulates TGFBI production in macrophages, which creates a positive feedback loop. This TGFBI-mediated interaction between HSCs and macrophages remodels the profibrotic microenvironment to promote liver fibrosis, identifying a potential therapeutic target.