Gut symbionts alleviate MASH through a secondary bile acid biosynthetic pathway.

The gut microbiota has been found to play an important role in the progression of metabolic dysfunction-associated steatohepatitis (MASH), but the mechanisms have not been established. Here, by developing a click-chemistry-based enrichment strategy, we identified several microbial-derived bile acids, including the previously uncharacterized 3-succinylated cholic acid (3-sucCA), which is negatively correlated with liver damage in patients with liver-tissue-biopsy-proven metabolic dysfunction-associated fatty liver disease (MAFLD). By screening human bacterial isolates, we identified Bacteroides uniformis strains as effective producers of 3-sucCA both in vitro and in vivo. By activity-based protein purification and identification, we identified an enzyme annotated as ß-lactamase in B. uniformis responsible for 3-sucCA biosynthesis. Furthermore, we found that 3-sucCA is a lumen-restricted metabolite and alleviates MASH by promoting the growth of Akkermansia muciniphila. Together, our data offer new insights into the gut microbiota-liver axis that may be leveraged to augment the management of MASH.

Innervation of nociceptor neurons in the spleen promotes germinal center responses and humoral immunity.

Peripheral sensory neurons widely innervate various tissues to continuously monitor and respond to environmental stimuli. Whether peripheral sensory neurons innervate the spleen and modulate splenic immune response remains poorly defined. Here, we demonstrate that nociceptive sensory nerve fibers extensively innervate the spleen along blood vessels and reach B cell zones. The spleen-innervating nociceptors predominantly originate from left T8-T13 dorsal root ganglia (DRGs), promoting the splenic germinal center (GC) response and humoral immunity. Nociceptors can be activated by antigen-induced accumulation of splenic prostaglandin E2 (PGE2) and then release calcitonin gene-related peptide (CGRP), which further promotes the splenic GC response at the early stage. Mechanistically, CGRP directly acts on B cells through its receptor CALCRL-RAMP1 via the cyclic AMP (cAMP) signaling pathway. Activating nociceptors by ingesting capsaicin enhances the splenic GC response and anti-influenza immunity. Collectively, our study establishes a specific DRG-spleen sensory neural connection that promotes humoral immunity, suggesting a promising approach for improving host defense by targeting the nociceptive nervous system.

Multiple early factors anticipate post-acute COVID-19 sequelae.

Post-acute sequelae of COVID-19 (PASC) represent an emerging global crisis. However, quantifiable risk factors for PASC and their biological associations are poorly resolved. We executed a deep multi-omic, longitudinal investigation of 309 COVID-19 patients from initial diagnosis to convalescence (2-3 months later), integrated with clinical data and patient-reported symptoms. We resolved four PASC-anticipating risk factors at the time of initial COVID-19 diagnosis: type 2 diabetes, SARS-CoV-2 RNAemia, Epstein-Barr virus viremia, and specific auto-antibodies. In patients with gastrointestinal PASC, SARS-CoV-2-specific and CMV-specific CD8+ T cells exhibited unique dynamics during recovery from COVID-19. Analysis of symptom-associated immunological signatures revealed coordinated immunity polarization into four endotypes, exhibiting divergent acute severity and PASC. We find that immunological associations between PASC factors diminish over time, leading to distinct convalescent immune states. Detectability of most PASC factors at COVID-19 diagnosis emphasizes the importance of early disease measurements for understanding emergent chronic conditions and suggests PASC treatment strategies.

Multi-Omics Resolves a Sharp Disease-State Shift between Mild and Moderate COVID-19.

We present an integrated analysis of the clinical measurements, immune cells, and plasma multi-omics of 139 COVID-19 patients representing all levels of disease severity, from serial blood draws collected during the first week of infection following diagnosis. We identify a major shift between mild and moderate disease, at which point elevated inflammatory signaling is accompanied by the loss of specific classes of metabolites and metabolic processes. Within this stressed plasma environment at moderate disease, multiple unusual immune cell phenotypes emerge and amplify with increasing disease severity. We condensed over 120,000 immune features into a single axis to capture how different immune cell classes coordinate in response to SARS-CoV-2. This immune-response axis independently aligns with the major plasma composition changes, with clinical metrics of blood clotting, and with the sharp transition between mild and moderate disease. This study suggests that moderate disease may provide the most effective setting for therapeutic intervention.

Spermine enhances antiviral and anticancer responses by stabilizing DNA binding with the DNA sensor cGAS.

Self-nonself discrimination is vital for the immune system to mount responses against pathogens while maintaining tolerance toward the host and innocuous commensals during homeostasis. Here, we investigated how indiscriminate DNA sensors, such as cyclic GMP-AMP synthase (cGAS), make this self-nonself distinction. Screening of a small-molecule library revealed that spermine, a well-known DNA condenser associated with viral DNA, markedly elevates cGAS activation. Mechanistically, spermine condenses DNA to enhance and stabilize cGAS-DNA binding, optimizing cGAS and downstream antiviral signaling. Spermine promotes condensation of viral, but not host nucleosome, DNA. Deletion of viral DNA-associated spermine, by propagating virus in spermine-deficient cells, reduced cGAS activation. Spermine depletion subsequently attenuated cGAS-mediated antiviral and anticancer immunity. Collectively, our results reveal a pathogenic DNA-associated molecular pattern that facilitates nonself recognition, linking metabolism and pathogen recognition.

TREM2 receptor protects against complement-mediated synaptic loss by binding to complement C1q during neurodegeneration.

Triggering receptor expressed on myeloid cells 2 (TREM2) is strongly linked to Alzheimer's disease (AD) risk, but its functions are not fully understood. Here, we found that TREM2 specifically attenuated the activation of classical complement cascade via high-affinity binding to its initiator C1q. In the human AD brains, the formation of TREM2-C1q complexes was detected, and the increased density of the complexes was associated with lower deposition of C3 but higher amounts of synaptic proteins. In mice expressing mutant human tau, Trem2 haploinsufficiency increased complement-mediated microglial engulfment of synapses and accelerated synaptic loss. Administration of a 41-amino-acid TREM2 peptide, which we identified to be responsible for TREM2 binding to C1q, rescued synaptic impairments in AD mouse models. We thus demonstrate a critical role for microglial TREM2 in restricting complement-mediated synaptic elimination during neurodegeneration, providing mechanistic insights into the protective roles of TREM2 against AD pathogenesis.

Black holes regulate cool gas accretion in massive galaxies.

The nucleus of almost all massive galaxies contains a supermassive black hole (BH)1. The feedback from the accretion of these BHs is often considered to have crucial roles in establishing the quiescence of massive galaxies2-14, although some recent studies show that even galaxies hosting the most active BHs do not exhibit a reduction in their molecular gas reservoirs or star formation rates15-17. Therefore, the influence of BHs on galaxy star formation remains highly debated and lacks direct evidence. Here, based on a large sample of nearby galaxies with measurements of masses of both BHs and atomic hydrogen (HI), the main component of the interstellar medium18, we show that the HI gas mass to stellar masses ratio (µHI = MHI/M⋆) is more strongly correlated with BH masses (MBH) than with any other galaxy parameters, including stellar mass, stellar mass surface density and bulge masses. Moreover, once the µHI-MBH correlation is considered, µHI loses dependence on other galactic parameters, demonstrating that MBH serves as the primary driver of µHI. These findings provide important evidence for how the accumulated energy from BH accretion regulates the cool gas content in galaxies, by ejecting interstellar medium gas and/or suppressing gas cooling from the circumgalactic medium.

Conserved class B GPCR activation by a biased intracellular agonist.

Class B G-protein-coupled receptors (GPCRs), including glucagon-like peptide 1 receptor (GLP1R) and parathyroid hormone 1 receptor (PTH1R), are important drug targets1-5. Injectable peptide drugs targeting these receptors have been developed, but orally available small-molecule drugs remain under development6,7. Here we report the high-resolution structure of human PTH1R in complex with the stimulatory G protein (Gs) and a small-molecule agonist, PCO371, which reveals an unexpected binding mode of PCO371 at the cytoplasmic interface of PTH1R with Gs. The PCO371-binding site is totally different from all binding sites previously reported for small molecules or peptide ligands in GPCRs. The residues that make up the PCO371-binding pocket are conserved in class B GPCRs, and a single alteration in PTH2R and two residue alterations in GLP1R convert these receptors to respond to PCO371. Functional assays reveal that PCO371 is a G-protein-biased agonist that is defective in promoting PTH1R-mediated arrestin signalling. Together, these results uncover a distinct binding site for designing small-molecule agonists for PTH1R and possibly other members of the class B GPCRs and define a receptor conformation that is specific only for G-protein activation but not arrestin signalling. These insights should facilitate the design of distinct types of class B GPCR small-molecule agonist for various therapeutic indications.

Au-Pd separation enhances bimetallic catalysis of alcohol oxidation.

In oxidation reactions catalysed by supported metal nanoparticles with oxygen as the terminal oxidant, the rate of the oxygen reduction can be a limiting factor. This is exemplified by the oxidative dehydrogenation of alcohols, an important class of reactions with modern commercial applications1-3. Supported gold nanoparticles are highly active for the dehydrogenation of the alcohol to an aldehyde4 but are less effective for oxygen reduction5,6. By contrast, supported palladium nanoparticles offer high efficacy for oxygen reduction5,6. This imbalance can be overcome by alloying gold with palladium, which gives enhanced activity to both reactions7,8,9; however, the electrochemical potential of the alloy is a compromise between that of the two metals, meaning that although the oxygen reduction can be improved in the alloy, the dehydrogenation activity is often limited. Here we show that by separating the gold and palladium components in bimetallic carbon-supported catalysts, we can almost double the reaction rate compared with that achieved with the corresponding alloy catalyst. We demonstrate this using physical mixtures of carbon-supported monometallic gold and palladium catalysts and a bimetallic catalyst comprising separated gold and palladium regions. Furthermore, we demonstrate electrochemically that this enhancement is attributable to the coupling of separate redox processes occurring at isolated gold and palladium sites. The discovery of this catalytic effect-a cooperative redox enhancement-offers an approach to the design of multicomponent heterogeneous catalysts.

Gut bacteria alleviate smoking-related NASH by degrading gut nicotine.

Tobacco smoking is positively correlated with non-alcoholic fatty liver disease (NAFLD)1-5, but the underlying mechanism for this association is unclear. Here we report that nicotine accumulates in the intestine during tobacco smoking and activates intestinal AMPKα. We identify the gut bacterium Bacteroides xylanisolvens as an effective nicotine degrader. Colonization of B. xylanisolvens reduces intestinal nicotine concentrations in nicotine-exposed mice, and it improves nicotine-exacerbated NAFLD progression. Mechanistically, AMPKα promotes the phosphorylation of sphingomyelin phosphodiesterase 3 (SMPD3), stabilizing the latter and therefore increasing intestinal ceramide formation, which contributes to NAFLD progression to non-alcoholic steatohepatitis (NASH). Our results establish a role for intestinal nicotine accumulation in NAFLD progression and reveal an endogenous bacterium in the human intestine with the ability to metabolize nicotine. These findings suggest a possible route to reduce tobacco smoking-exacerbated NAFLD progression.

Assessing the utility of large language models for phenotype-driven gene prioritization in the diagnosis of rare genetic disease.

Phenotype-driven gene prioritization is fundamental to diagnosing rare genetic disorders. While traditional approaches rely on curated knowledge graphs with phenotype-gene relations, recent advancements in large language models (LLMs) promise a streamlined text-to-gene solution. In this study, we evaluated five LLMs, including two generative pre-trained transformers (GPT) series and three Llama2 series, assessing their performance across task completeness, gene prediction accuracy, and adherence to required output structures. We conducted experiments, exploring various combinations of models, prompts, phenotypic input types, and task difficulty levels. Our findings revealed that the best-performed LLM, GPT-4, achieved an average accuracy of 17.0% in identifying diagnosed genes within the top 50 predictions, which still falls behind traditional tools. However, accuracy increased with the model size. Consistent results were observed over time, as shown in the dataset curated after 2023. Advanced techniques such as retrieval-augmented generation (RAG) and few-shot learning did not improve the accuracy. Sophisticated prompts were more likely to enhance task completeness, especially in smaller models. Conversely, complicated prompts tended to decrease output structure compliance rate. LLMs also achieved better-than-random prediction accuracy with free-text input, though performance was slightly lower than with standardized concept input. Bias analysis showed that highly cited genes, such as BRCA1, TP53, and PTEN, are more likely to be predicted. Our study provides valuable insights into integrating LLMs with genomic analysis, contributing to the ongoing discussion on their utilization in clinical workflows.

GNA14 and GNAQ somatic mutations cause spinal and intracranial extra-axial cavernous hemangiomas.

Extra-axial cavernous hemangiomas (ECHs) are complex vascular lesions mainly found in the spine and cavernous sinus. Their removal poses significant risk due to their vascularity and diffuse nature, and their genetic underpinnings remain incompletely understood. Our approach involved genetic analyses on 31 tissue samples of ECHs employing whole-exome sequencing and targeted deep sequencing. We explored downstream signaling pathways, gene expression changes, and resultant phenotypic shifts induced by these mutations, both in vitro and in vivo. In our cohort, 77.4% of samples had somatic missense variants in GNA14, GNAQ, or GJA4. Transcriptomic analysis highlighted significant pathway upregulation, with the GNAQ c.626A>G (p.Gln209Arg) mutation elevating PI3K-AKT-mTOR and angiogenesis-related pathways, while GNA14 c.614A>T (p.Gln205Leu) mutation led to MAPK and angiogenesis-related pathway upregulation. Using a mouse xenograft model, we observed enlarged vessels from these mutations. Additionally, we initiated rapamycin treatment in a 14-year-old individual harboring the GNAQ c.626A>G (p.Gln209Arg) variant, resulting in gradual regression of cutaneous cavernous hemangiomas and improved motor strength, with minimal side effects. Understanding these mutations and their pathways provides a foundation for developing therapies for ECHs resistant to current therapies. Indeed, the administration of rapamycin in an individual within this study highlights the promise of targeted treatments in treating these complex lesions.

Topological complex-energy braiding of non-Hermitian bands.

Effects connected with the mathematical theory of knots1 emerge in many areas of science, from physics2,3 to biology4. Recent theoretical work discovered that the braid group characterizes the topology of non-Hermitian periodic systems5, where the complex band energies can braid in momentum space. However, such braids of complex-energy bands have not been realized or controlled experimentally. Here, we introduce a tight-binding lattice model that can achieve arbitrary elements in the braid group of two strands 𝔹2. We experimentally demonstrate such topological complex-energy braiding of non-Hermitian bands in a synthetic dimension6,7. Our experiments utilize frequency modes in two coupled ring resonators, one of which undergoes simultaneous phase and amplitude modulation. We observe a wide variety of two-band braiding structures that constitute representative instances of links and knots, including the unlink, the unknot, the Hopf link and the trefoil. We also show that the handedness of braids can be changed. Our results provide a direct demonstration of the braid-group characterization of non-Hermitian topology and open a pathway for designing and realizing topologically robust phases in open classical and quantum systems.

The impact of age and number of mutations on the size of clonal hematopoiesis.

Clonal hematopoiesis (CH) represents the clonal expansion of hematopoietic stem cells and their progeny driven by somatic mutations. Accurate risk assessment of CH is critical for disease prevention and clinical decision-making. The size of CH has been showed to associate with higher disease risk, yet, factors influencing the size of CH are unknown. In addition, the characteristics of CH in long-lived individuals are not well documented. Here, we report an in-depth analysis of CH in longevous (≥90 y old) and common (60~89 y old) elderly groups. Utilizing targeted deep sequencing, we found that the development of CH is closely related to age and the expression of aging biomarkers. The longevous elderly group exhibited a significantly higher incidence of CH and significantly higher frequency of TET2 and ASXL1 mutations, suggesting that certain CH could be beneficial to prolong life. Intriguingly, the size of CH neither correlates significantly to age, in the range of 60 to 110 y old, nor to the expression of aging biomarkers. Instead, we identified a strong correlation between large CH size and the number of mutations per individual. These findings provide a risk assessment biomarker for CH and also suggest that the evolution of the CH is influenced by factor(s) in addition to age.

Aging and comprehensive molecular profiling in acute myeloid leukemia.

Acute myeloid leukemia (AML) is an aging-related and heterogeneous hematopoietic malignancy. In this study, a total of 1,474 newly diagnosed AML patients with RNA sequencing data were enrolled, and targeted or whole exome sequencing data were obtained in 94% cases. The correlation of aging-related factors including age and clonal hematopoiesis (CH), gender, and genomic/transcriptomic profiles (gene fusions, genetic mutations, and gene expression networks or pathways) was systematically analyzed. Overall, AML patients aged 60 y and older showed an apparently dismal prognosis. Alongside age, the frequency of gene fusions defined in the World Health Organization classification decreased, while the positive rate of gene mutations, especially CH-related ones, increased. Additionally, the number of genetic mutations was higher in gene fusion-negative (GF-) patients than those with GF. Based on the status of CH- and myelodysplastic syndromes (MDS)-related mutations, three mutant subgroups were identified among the GF- AML cohort, namely, CH-AML, CH-MDS-AML, and other GF- AML. Notably, CH-MDS-AML demonstrated a predominance of elderly and male cases, cytopenia, and significantly adverse clinical outcomes. Besides, gene expression networks including HOXA/B, platelet factors, and inflammatory responses were most striking features associated with aging and poor prognosis in AML. Our work has thus unraveled the intricate regulatory circuitry of interactions among different age, gender, and molecular groups of AML.

Hidden hotspots of amphibian biodiversity in China.

Identifying and protecting hotspots of endemism and species richness is crucial for mitigating the global biodiversity crisis. However, our understanding of spatial diversity patterns is far from complete, which severely limits our ability to conserve biodiversity hotspots. Here, we report a comprehensive analysis of amphibian species diversity in China, one of the most species-rich countries on Earth. Our study combines 20 y of field surveys with new molecular analyses of 521 described species and also identifies 100 potential cryptic species. We identify 10 hotspots of amphibian diversity in China, each with exceptional species richness and endemism and with exceptional phylogenetic diversity and phylogenetic endemism (based on a new time-calibrated, species-level phylogeny for Chinese amphibians). These 10 hotspots encompass 59.6% of China's described amphibian species, 49.0% of cryptic species, and 55.6% of species endemic to China. Only four of these 10 hotspots correspond to previously recognized biodiversity hotspots. The six new hotspots include the Nanling Mountains and other mountain ranges in South China. Among the 186 species in the six new hotspots, only 9.7% are well covered by protected areas and most (88.2%) are exposed to high human impacts. Five of the six new hotspots are under very high human pressure and are in urgent need of protection. We also find that patterns of richness in cryptic species are significantly related to those in described species but are not identical.

A survey of algorithms for the detection of genomic structural variants from long-read sequencing data.

As long-read sequencing technologies are becoming increasingly popular, a number of methods have been developed for the discovery and analysis of structural variants (SVs) from long reads. Long reads enable detection of SVs that could not be previously detected from short-read sequencing, but computational methods must adapt to the unique challenges and opportunities presented by long-read sequencing. Here, we summarize over 50 long-read-based methods for SV detection, genotyping and visualization, and discuss how new telomere-to-telomere genome assemblies and pangenome efforts can improve the accuracy and drive the development of SV callers in the future.

Understanding YTHDF2-mediated mRNA degradation by m6A-BERT-Deg.

N6-methyladenosine (m6A) is the most abundant mRNA modification within mammalian cells, holding pivotal significance in the regulation of mRNA stability, translation and splicing. Furthermore, it plays a critical role in the regulation of RNA degradation by primarily recruiting the YTHDF2 reader protein. However, the selective regulation of mRNA decay of the m6A-methylated mRNA through YTHDF2 binding is poorly understood. To improve our understanding, we developed m6A-BERT-Deg, a BERT model adapted for predicting YTHDF2-mediated degradation of m6A-methylated mRNAs. We meticulously assembled a high-quality training dataset by integrating multiple data sources for the HeLa cell line. To overcome the limitation of small training samples, we employed a pre-training-fine-tuning strategy by first performing a self-supervised pre-training of the model on 427 760 unlabeled m6A site sequences. The test results demonstrated the importance of this pre-training strategy in enabling m6A-BERT-Deg to outperform other benchmark models. We further conducted a comprehensive model interpretation and revealed a surprising finding that the presence of co-factors in proximity to m6A sites may disrupt YTHDF2-mediated mRNA degradation, subsequently enhancing mRNA stability. We also extended our analyses to the HEK293 cell line, shedding light on the context-dependent YTHDF2-mediated mRNA degradation.

BERT-TFBS: a novel BERT-based model for predicting transcription factor binding sites by transfer learning.

Transcription factors (TFs) are proteins essential for regulating genetic transcriptions by binding to transcription factor binding sites (TFBSs) in DNA sequences. Accurate predictions of TFBSs can contribute to the design and construction of metabolic regulatory systems based on TFs. Although various deep-learning algorithms have been developed for predicting TFBSs, the prediction performance needs to be improved. This paper proposes a bidirectional encoder representations from transformers (BERT)-based model, called BERT-TFBS, to predict TFBSs solely based on DNA sequences. The model consists of a pre-trained BERT module (DNABERT-2), a convolutional neural network (CNN) module, a convolutional block attention module (CBAM) and an output module. The BERT-TFBS model utilizes the pre-trained DNABERT-2 module to acquire the complex long-term dependencies in DNA sequences through a transfer learning approach, and applies the CNN module and the CBAM to extract high-order local features. The proposed model is trained and tested based on 165 ENCODE ChIP-seq datasets. We conducted experiments with model variants, cross-cell-line validations and comparisons with other models. The experimental results demonstrate the effectiveness and generalization capability of BERT-TFBS in predicting TFBSs, and they show that the proposed model outperforms other deep-learning models. The source code for BERT-TFBS is available at https://github.com/ZX1998-12/BERT-TFBS.

Machine learning-assisted substrate binding pocket engineering based on structural information.

Engineering enzyme-substrate binding pockets is the most efficient approach for modifying catalytic activity, but is limited if the substrate binding sites are indistinct. Here, we developed a 3D convolutional neural network for predicting protein-ligand binding sites. The network was integrated by DenseNet, UNet, and self-attention for extracting features and recovering sample size. We attempted to enlarge the dataset by data augmentation, and the model achieved success rates of 48.4%, 35.5%, and 43.6% at a precision of ≥50% and 52%, 47.6%, and 58.1%. The distance of predicted and real center is ≤4 Å, which is based on SC6K, COACH420, and BU48 validation datasets. The substrate binding sites of Klebsiella variicola acid phosphatase (KvAP) and Bacillus anthracis proline 4-hydroxylase (BaP4H) were predicted using DUnet, showing high competitive performance of 53.8% and 56% of the predicted binding sites that critically affected the catalysis of KvAP and BaP4H. Virtual saturation mutagenesis was applied based on the predicted binding sites of KvAP, and the top-ranked 10 single mutations contributed to stronger enzyme-substrate binding varied while the predicted sites were different. The advantage of DUnet for predicting key residues responsible for enzyme activity further promoted the success rate of virtual mutagenesis. This study highlighted the significance of correctly predicting key binding sites for enzyme engineering.