Biology subjects

Feng, X.

Publications and source records attributed to Feng, X..

At least 19 recordsLinked to original sources

bioRxiv2026

Ancient genomes chronicle the history of adaptive evolution in East Asia

The transition from hunting and gathering to farming brought about novel selection pressures related to metabolism and immunity, but for East Asian populations the evolutionary genetic dynamics of these adaptations and their relationships to agriculture remain unclear. Using one of the largest datasets of ancient East Asian genomes (N=2236), we performed a genome-wide scan to identify selected variants and reconstructed their allele frequency changes over time and space. We tested for genetic association with farming using an environmental variable to represent historical farming intensity based on a large database of archaeobotanical sites (N=1220). Our analyses indicate selection began prior to the Neolithic for several well-established adaptive loci such as FADS1, FUT2, and OCA2. For rs1229984 at ADH1B, the strongest selection signal in our genome-wide scan, we observed variation in timing and strength of selection in East Eurasia and found evidence for a southern origin. Furthermore, ADH1B*48His was positively associated with wheat farming. Given these results, we propose a novel hypothesis about the fitness advantage of this variant. Our work underscores the heterogeneity in the strength and timing of selection across East Eurasia and provides a quantitative framework for testing agriculture's role as a selection pressure.

evolutionary biology↗

bioRxiv2026

Chimeric archaeal BHB-human pre-tRNA scaffold HARBOR system enables versatile production of functional circular RNA modalities

Circular RNAs (circRNAs) exhibit superior intrinsic stability and lower immunogenicity compared with linear RNAs, rendering them promising modalities for biomedical and biotechnological applications. Here, we establish the HARBOR (Human-Archaeal RNA Backbone for Overexpression of circular RNA) system, an engineered endogenous circRNA expression platform integrating a human precursor transfer RNA (pre-tRNA) scaffold with an archaeal Bulge-Helix-Bulge (BHB) motif. HARBOR repurposes the endogenous tRNA splicing machinery to generate chimeric pre-tRNA intermediates, which are rapidly degraded following processing, while producing circRNAs ranging from hundreds of nucleotides to 8 knt in cells and in vivo. Notably, it enables robust production of large-sized circRNAs with yields nearly 80-fold than the intronic complementary sequence-mediated back-splicing (3D5) system and can drive protein expression from translatable circRNAs at an efficiency close to 20-fold than the Tornado system. Such prominent properties support diverse applications of HARBOR, including producing therapeutic aptamers targeting PKR-associated neuroinflammation and pathological TDP-43 condensation, as well as protein-coding circRNAs encoding adenine base editors for genome editing and MYOD1 for fibroblast-to-myocyte trans-differentiation. Collectively, HARBOR represents a versatile, scalable, and user-friendly platform that unifies circRNA-based aptamer and protein expression applications, advancing circRNA-enabled engineering and biomedical applications.

molecular biology↗

bioRxiv2026

Maleic acid adjuvates BCG to induce CD4+CD8+ T cells and sterilize tuberculosis

Anti-tuberculosis (TB) immunity typically emphasizes CD4+ T and CD8+ T cells that only provide limited protection against Mycobacterium tuberculosis (Mtb) infection. The anti-TB function of CD4+CD8+ T cells remains unexplored, mostly due to the dogma that ThPOK and Runx3 antagonize each other to block their development and their scarcity. Here, we show that immunization of BCG (Bacillus Calmette-Gu&eacuterin) with Complete Freund's Adjuvant (CFA) markedly induced CD4+CD8+ T cells through the gut microbiota Clostridium butyricum or its metabolite maleic acid. Mtb-specific CD4+CD8+ T cells exhibited a broad T-cell receptor repertoire and sterilized Mtb infection by inducing SLAMF1 and neutrophil responses. Mechanistically, maleic acid interacted with PAK2 to promote STAT5 phosphorylation at Ser127, thus upregulating Ets1 expression. Direct binding of Ets1 to the Runx3 and ThPOK promoters orchestrated their expression to redirect the CD8+ T cell differentiation towards the CD4+CD8+ lineage. Moreover, immunization of BCG plus Clostridium butyricum or maleic acid sterilized Mtb infection in mouse models. These findings establish CD4+CD8+ T cells as superior immune effectors against Mtb, a concept that could fundamentally redirect the current paradigm for anti-TB vaccine development.

microbiology↗

bioRxiv2026

MorphCell learns reconstructable shape representations with explicit scale control for 3D cell morphology

Three-dimensional (3D) cell morphology provides a measurable phenotype of cellular state and function, but representing it across biological systems and imaging modalities remains difficult. Existing approaches do not jointly provide transferable representations, complete surface reconstruction, geometric interpretation and explicit control of physical scale. Here we introduce MorphCell, a self-supervised framework that uses cell-surface point clouds to learn shape-driven representations independently of physical scale. MorphCell combines cross-view reconstruction pretraining with spherical self-reconstruction. The former captures geometric relationships between surface regions, whereas the latter recovers complete 3D morphology from individual representations. Pretrained on non-biological object surfaces, MorphCell transfers to cellular datasets without biological task-specific training and outperforms existing point-cloud representations in morphological classification. The learned representations capture both global contour and local surface variation, enabling geometric interpretation through reconstruction, biophysical descriptors and saliency analysis. By retaining physical scale as a separate variable for controlled fusion, MorphCell further reveals that shape and scale contribute differently across biological distinctions. This framework provides a general approach for representing, reconstructing and interpreting 3D cellular morphology across imaging modalities and biological contexts.

cell biology↗

bioRxiv2026

EMAP-SSN: An Embedding- and Multiple-Alignment-Integrated Sequence Similarity Network Platform for Interactive Exploration of Protein Sequence Space

Sequence similarity networks (SSNs) are graphical representations of sequence relationship frequently used for exploring protein sequence space. Conventional SSN workflows typically use BLAST to calculate sequence similarities and rely on external visualization tools to gener-ate the final networks. Consequently, raw sequence data is often detached from the calculat-ed similarities during visualization, complicating SSN analyses that require residue-level in-formation. To bridge this gap, we present EMAP-SSN, an open-source, cross-platform soft-ware suite that integrates SSN computation, visualization, and analyses in streamlined work-flows. The program provides BLAST- and embedding-based alignment pipelines for se-quence-similarity calculation and directly links network nodes to their original sequences and multiple alignments for analyses. Modular architectures for embedding generation, com-mand integration, and browser-based utilities allow additions of research-specific functionalities and facilitate future development. Using a set of fold-type IV pyridoxal 5'-phosphate-dependent enzymes, we demonstrate how EMAP-SSN connects network topology with residue-level variation to identify sequence clusters, map functional motifs, and detect subgroup-specific conservation patterns. These capabilities provide a practical route from large protein sequence sets to experimentally verifiable hypotheses on enzyme function and targets for protein engineering. The EMAP-SSN program can be accessed from https://github.com/Xuebin-Feng/EMAP-SSN.

bioinformatics↗

bioRxiv2026

Macronutrient Distribution and Protein Secondary Structure of Cool-Season Oats Revealed by Synchrotron-Based Mid-IR Spectroscopy and FTIR Chemical Imaging: Effects of Variety and Steam-Pressure Toasting Duration

This study aims to investigate changes in protein secondary structures (-helix, -sheet, random coils, and -turn) and macronutrient distribution in different cool-season oat varieties and steam-pressure toasting durations using synchrotron-based mid-infrared (Mid-IR) spectroscopy and Fourier Transform Infrared spectroscopy (FTIR) imaging. All oat samples, provided by the Crop Development Center at the University of Saskatchewan, were harvested over three consecutive years (2018, 2019, and 2020). The first experiment compared four oat varieties (CDC Arborg, CDC Nasser, CDC Haymaker, and Summit), while the second examined CDC Nasser oats subjected to steam-pressure toasting (SPT) at 121 for 0, 30, 60, 90, and 120 minutes. FTIR chemical imaging revealed that carbohydrates, proteins and lipids in the four oat varieties were mainly concentrated in the endosperm, aleurone layer and embryo, crease region, and remained unchanged after SPT. Peak-fitting deconvolution of the Amide I band (1700-1600 cm-1) and subsequent quantitative analysis revealed that the four oat varieties exhibited broadly similar protein secondary structure profiles, with statistically significant but subtle variety effects detected for -helix (P = 0.026), -turn (P = 0.047), and the -helix to -sheet ratio (P = 0.048), however, -sheet and random coil proportions did not differ significantly among varieties. In contrast, SPT induced pronounced structural rearrangements, with significant increase in -sheet proportion (P = 0.003) and significant decreases in random coil content (P = 0.026). Notably, 30 minutes of toasting was sufficient to significantly increase -sheet and decrease the random coil contents. These changes are consistent with heat-induced protein denaturation and intermolecular -sheet aggregation, where thermal energy breaks the hydrogen bonds that stabilize the disordered random coil conformation, causing the unfolded polypeptide chains to reassemble into highly ordered -sheet aggregates. After SPT, the peak centers of Amide I and II bands shifted to lower wavenumbers and both bands broadened while their intensities were maintained, reflecting the reorganization of the remaining protein into -sheet aggregates rather than any loss of amide-active protein. These findings suggest that, although genotype has a relatively minor effect on the protein secondary structure of oats, hydrothermal treatments fundamentally reorganize the protein matrix from a disordered to an ordered conformation, which may have implications for protein digestibility, solubility, and nutritional function.

biochemistry↗

bioRxiv2026

Primary Cilia Loss in Striatal Neurons Disrupts Synaptic Connectivity and Excitatory Transmission and Drives Metabolomic Remodeling

Disruption of striatal circuits is a central feature of many neurological and psychiatric disorders, yet the mechanisms that maintain afferent connectivity and synaptic function in striatal neurons remain incompletely defined. Primary cilia are signaling organelles present on almost all striatal medium spiny neurons that are enriched in neuromodulatory receptors, suggesting a role in coordinating striatal neuronal communication and biochemical state. Here, we show that conditional ablation of primary cilia from striatal neurons by AAV-Cre-mediated deletion of Ift88 disrupts the afferent connectivity, synaptic function, and chemical signature of the striatum. Monosynaptic rabies tracing revealed an approximately threefold reduction in brain-wide input convergence onto striatal neurons. Whole-cell recordings showed reduced miniature excitatory postsynaptic current amplitude and frequency together with a reduced NMDA:AMPA ratio, consistent with weakened glutamatergic synaptic transmission. Untargeted metabolomics revealed broad remodeling of the striatal chemical profile, predominantly toward decreased measured levels, with lipid-associated pathways most affected alongside reductions in polyamines, glutamate-related metabolites, and neuromodulatory, particularly excitatory, signaling molecules. By contrast, the cortex, which was not targeted by the AAV injection, showed fewer and directionally opposite molecular changes. These findings identify cilia as essential regulators of the structural, synaptic, and molecular integrity required for normal striatal circuit function.

neuroscience↗

bioRxiv2026

Learning to read a second language establishes a parallel L2 representation alongside the native one in the VWFA

Learning to read a second language requires the brain to incorporate a new writing system into an already established native-language reading network, yet how this process reshapes the Visual Word Form Area (VWFA) remains poorly understood. Using fMRI with a passive viewing paradigm, we systematically investigated how VWFA responses to Chinese (L1) and English (L2) words evolved across three groups of native Mandarin-speaking children at distinct stages of L2 literacy acquisition: L2 pre-readers, L2 beginning readers, and L2 advanced readers. We combined univariate activation analyses, representational similarity analysis, and supervised machine learning classification to investigate two theoretical accounts of VWFA reorganization: the Overlay Model, which predicts stable L1 responses as L2 responses emerge, and the Competing Model, which predicts competitive reallocation of neural resources from L1 to L2. We found that although the VWFA already exhibited robust selective responses to L1 words, L2-word selectivity was absent in L2 pre-readers but emerged robustly in beginning readers, with L1-word selectivity remaining stable throughout. Representational similarity analysis further revealed that robust L2 word representations within the VWFA emerged only after children began learning to read L2, while L1 word representations remained stable across all three groups. Finally, supervised machine learning analyses successfully discriminated among the three L2 literacy groups on the basis of L2 but not L1 activation patterns, indicating that VWFA responses to L2 alone were sufficient to capture childrens stages of L2 literacy acquisition, whereas responses to L1 were insensitive to L2 learning experience, providing no support for the Competing Models prediction of competitive neural reallocation away from L1. Together, these findings support the Overlay Model, demonstrating that the VWFA incorporates a new writing system within its existing cortical resources without compromising L1 print processing.

neuroscience↗

bioRxiv2026

Extracellular injection system combined with peptides for intracellular Staphylococcus aureus treatment

The inaccessibility of intracellular bacteria has long rendered the treatment of Staphylococcus aureus infections an challenge. Studies have demonstrated that the extracellular injection system PVC can accurately deliver proteins into cells, which would not need small molecules, and enables effective intracellular delivery of antimicrobial peptides for treatment. Accordingly, we selected antimicrobial peptides including Cecropin, LL37 and Indolicidin that possess potent bactericidal activity, and established the Directed Antimicrobial Assault platform (DAAT) by leveraging the intracellular delivery capacity of PVC. DAAT Cecropin, DAAT LL37 and DAAT Indolicidin inhibited intracellular bacteria in a dose-dependent manner, with DAAT LL37 reaching 86.76% inhibition; after 72 h of treatment, viable-cell numbers reduse to 66--82-fold those of the control. Tail-fibre retargeting enabled direct extracellular S. aureus killing, while combined DAAT therapy promoted wound healing in mice. These findings expand the utility of PVC-derived nanosyringes and establish DAAT as a modular platform for intracellular antimicrobial peptide therapy.

synthetic biology↗

bioRxiv2026

Morpho-FM: spatial molecular reconstruction from routine H&E histology using transcriptomic foundation-model priors

Routine haematoxylin and eosin (H&E) histology captures tissue architecture at clinical scale, but lacks a direct molecular readout of the transcriptional programmes that organise tumour epithelium, stroma, vasculature and immune compartments. Spatial transcriptomics provides this context, yet cost, workflow complexity and sparse sampling limit routine use. Most existing histology-to-expression models are trained de novo on small paired cohorts and therefore remain weakly constrained when extrapolating from sparse measurements to dense, tissue-wide molecular maps. Here we introduce Morpho-FM, a weakly supervised framework that predicts spatial gene expression from routine H&E whole-slide images by conditioning a pretrained single-cell transcriptomic foundation-model prior on local histological neighbourhoods. A lightweight morphology-to-transcriptome adapter maps cached whole-slide histology features into a transcriptomic decoder, enabling prediction at measured locations, dense full-section reconstruction, and re-aggregation to the original measurement support. Across harmonized prostate cancer benchmarks, Morpho-FM achieved the strongest overall performance among five representative methods, reaching mean per-gene Pearson correlations of 0.286 in rotating single-slide evaluation and 0.298 in multi-slide held-out validation. The framework reproduced this advantage across kidney cancer sections, achieved a mean correlation of 0.210 across 56 directed single-slide evaluations and retained measurable predictive signal after external transfer to clear-cell renal cell carcinoma sections. Controlled ablation analyses identified pretrained transcriptomic initialization as a reproducible source of performance gain exceeding that attributable to changes in the histology feature backbone. Beyond predictive accuracy benchmarks, Morpho-FM recovered ERBB2-enriched tumour compartments, boundary-associated molecular gradients, and annotation-aligned tissue domains across Xenium and HER2ST breast cancer datasets. Together, these results support transcriptomic foundation-model priors as an effective constraint for morphology-conditioned molecular decoding and demonstrate the potential of Morpho-FM to extend spatial transcriptomic insight across routine pathology sections.

bioinformatics↗

bioRxiv2026

Robust integration of weakly anchored spatial multi-omics

Spatial multi-omics holds great promise for dissecting complex biological processes, though inherent technical constraints continue to limit its widespread adoption. Currently, most studies therefore measure distinct omics features on separate tissue sections, necessitating spatial diagonal integration. An emerging practical solution is to leverage hematoxylin and eosin (H&E) images as an integration anchor, given their ubiquity, low cost, and compatibility across tissue preparations. However, this anchor is frequently compromised in real-world settings by variations in H&E staining style, absence of reliable histological landmarks, and mismatches in spatial resolutions across omics modalities. To address this, we introduce SpaWeaver, a computational framework that couples a pathology foundation model with a graph Transformer and a latent feature aligner module, providing a highly robust solution for weakly anchored spatial omics data diagonal integration. Extensive experiments demonstrate that SpaWeaver exhibits superior robustness against isolated or synergistic weak-anchoring factors. The spatial multi-omics profiles generated by SpaWeaver link molecular features originally separated on two sections, unlocking diverse downstream analyses once exclusive to co-assayed spatial multi-omics data, including niche-aware cell-cell communication inference and multi-omics resolved cell state. In this study, it unveils tumor-distance-dependent fibroblast-CD4{square}T-cell signaling in human colon adenocarcinoma and identifies a hypoxic glycolytic tumor state with pyknotic nuclei in human ovarian cancer. Overall, our approach bridges readily accessible single-omics measurements across weakly anchored tissue sections, enabling unified spatial multi-omics characterization and system-level tissue analysis.

bioinformatics↗

bioRxiv2026

Self-supervised Internal Learning Enhances Isotropic Resolution for Three-dimensional Fluorescence Microscopy

Three-dimensional fluorescence microscopy often exhibits anisotropic resolution because axial information is poorly sampled and more blurred than lateral information, which complicates quantitative interpretation of fine 3D structures. Although optical remedies and computational restoration have been explored, many approaches require demanding system calibration or rely on accurate PSF models and assumptions that are difficult to satisfy across all samples and modalities. Here we present DeepIso, a self-supervised isotropy restoration framework that couples supervised pretraining with an internal-learning inference stage to estimate degradation directly from the measured volume. Without explicit PSF specification or enforced lateral-axial structural equivalence, DeepIso recovers axial frequency content and improves the continuity of elongated structures while retaining fine features, with superior performance over existing computational approaches in terms of both visual inspection and quantitative metrics. The method is validated on synthetic benchmarks and experimental datasets, demonstrating isotropy enhancement across confocal, light-sheet, and 3D structured illumination microscopy, thereby supporting downstream volumetric analysis including segmentation and tracking.

bioengineering↗

bioRxiv2026

Dual histone methylation reader ZCWPW2 links histone methylation to initiation of meiotic recombination

Meiotic homologous recombination initiates with the formation of programmed DNA double-strand breaks (DSBs) by complexes comprising SPO11 and accessory proteins at discrete sites called recombination hotspots. In mammals, PRDM9-dependent H3K4me3 and H3K36me3 define recombination hotspots, but how these epigenetic characteristics determine the physiological DSB formation remains unknown. Here we show that dual histone methylation reader ZCWPW2 can recognize H3K4me3 and H3K36me3 marks in testis. The binding activity of ZCWPW2 to histone methylation is dependent on PRDM9 function. Moreover, we find the epigenetic writer-reader axis PRDM9-ZCWPW2 is essential for DSB formation at meiotic recombination hotspots, which may be partly explained by the finding that ZCWPW2 can physically interact with HORMAD1, IHO1, MEI4, and REC114. Finally, the absence of ZCWPW2 leads to disrupted chromosomal synapsis and recombination, thereby obstructing meiotic progression. Taken together, our findings provide new insights into how histone modifications and their associated regulatory proteins collectively regulate meiotic homologous recombination initiation.

cell biology↗

bioRxiv2026

Genos-m: a foundation model for human-associated microbial genomes

Human-associated microbial genomes encode extensive strain-level diversity and niche-specific gene repertoires that are critical to host health. However, these complex sequence features remain difficult to capture using general-purpose DNA foundation models, highlighting the need for dedicated representation learning tailored to the human microbiome. Here, we introduce Genos-m, an open-source foundation model for human-associated microbial genome representation. Genos-m was pretrained on approximately 1.2 trillion nucleotide tokens from a curated microbial genome corpus, including human-associated prokaryotic isolates, high-quality metagenome-assembled genomes (MAGs) and bacteriophages, supplemented with GTDB species-level representative genomes to broaden prokaryotic taxonomic breadth. The model uses a sparsely activated Mixture-of-Experts (MoE) Transformer architecture, with 4.7 billion total parameters, approximately 330 million activated parameters per forward pass and a maximum context length of one million base pairs. We evaluated frozen Genos-m representations across short-sequence and gene-level tasks, biosynthetic gene cluster (BGC)-based regional sequence tasks, whole-genome strain phenotype prediction, and zero-shot transfer on prokaryote-related RNAfitness assays. Across these benchmarks, Genos-m consistently ranked among the leading comparison models, with the best performance in five of eight gene-fitness regression tasks and in BGC type classification. Using sparse autoencoders, we identified sparse features in Genos-m hidden activations that aligned with annotated ORFs, intergenic regions, and tRNA and rRNA loci. In downstream applications, Genos-m-derived genome-informed species representations in-corporated into a human microbiome self-supervised learning model improved colorectal cancer (CRC)-control classification over conventional species-abundance random forest models. Genos-m also generated stable sample-level embeddings from as few as 10,000 metagenomic reads, retaining gut microbial community structure that distinguished geographic origin and aligned with enterotypes defined from full-depth taxonomic profiles. Together, these results support Genos-m as a reusable representation model for microbial genomes and metagenomes, with conclusions bounded by the reported datasets, task definitions and evaluation protocols. Genos-m model weights, inference code, and usage documentation are publicly available on GitHub (https://github.com/BGI-HangzhouAI/Genos-m) and Hugging-Face (https://huggingface.co/BGI-HangzhouAI/Genos-m).

bioinformatics↗

bioRxiv2026

Niche constraints drive differences between mycorrhizal fungal guilds in future range shifts

Mycorrhizal fungi are a diverse and ubiquitous group of plant symbionts whose distribution strongly influences ecosystem function across the globe. Yet, until now, we do not have quantitative data on the range sizes of different mycorrhizal fungal taxa, limiting our capacity to forecast future shifts in community composition and function. Here, we use 115,924 DNA sequence-derived observations to map the distribution of 651 common mycorrhizal fungal taxa and forecast future changes to their range sizes. We demonstrate that climate, plant cover, and soil factors, particularly mean annual temperature, net primary productivity, and soil organic carbon, exert major control over mycorrhizal fungal distributions. Based on these drivers, the ecological niches of mycorrhizal fungi consistently differ between arbuscular and ectomycorrhizal functional guilds. Arbuscular mycorrhizal fungal taxa generally occupy a wider niche breadth than ectomycorrhizal fungi, occurring across larger ranges of climate, soil, plant cover, topography, and disturbance conditions. Our models also predict widespread decreases in the suitable range size of mycorrhizal fungal taxa under projected future global climates, with average ranges decreasing by 13.8% or ~2.2 million km2 under high emissions scenarios (ssp5-8.5). This decrease in projected range size will be most pronounced for ectomycorrhizal fungi, strongly linked to constraints from their smaller overall niches. By generating a global atlas of common mycorrhizal fungi and their associated environmental niche, we establish a critical baseline for widely suspected declines in global fungal biodiversity.

ecology↗

bioRxiv2026

EpiReasoner: An Integrated Artificial Intelligence Framework for Phenotype-to-Genotype Reasoning in Plant Epidermal Development

Achieving high-throughput and precise phenotypic quantification and imaging modalities of stomatal and epidermal cells across diverse species remains a primary bottleneck in elucidating the mechanisms of stomatal dynamics, epidermal patterning, and environmental adaptation of plants. Here, we developed EpiReasoner, an artificial intelligence framework comprising a vision module, EpiVision, and a knowledge-based reasoning module, EpiBrain, for the quantitative phenotypic analysis and domain-specific knowledge reasoning of stomatal complexes and pavement cells in plants. Operating across bright-field, scanning electron microscopy, and differential interference contrast modalities, EpiVision achieves precise instance segmentation in various monocotyledonous, dicotyledonous, and fern species. Its performance significantly surpasses current state-of-the-art models. Moreover, we defined 23 quantitative indices describing stomatal cell morphology and spatial distribution. For domain-specific tasks such as phenotype prediction, genotype deduction, and molecular mechanism reasoning, EpiBrain demonstrates a human preference rate significantly higher than that of general-purpose large language models, including GPT-5 and Claude Sonnet 4. The application of EpiReasoner to phenotypic data of stomatal density derived from a tomato natural population of 170 accessions successfully identified a major quantitative trait locus on chromosome 8. The candidate gene, SKP1-interaction partner 19L (SKIP19L), encoding an F-box family protein, exhibited severe allele frequency drift during tomato domestication, which is highly consistent with the adaptive trend of reduced stomatal density under artificial selection. EpiReasoner provides a novel paradigm that unifies visual phenomics and knowledge-driven reasoning for the biology of stomata and pavement cells, thereby significantly accelerating scientific discovery in plant science.

plant biology↗

bioRxiv2026

Lipid Hydrogen Stable Isotope Probing Reveals Decadal-Scale Generation Times for Archaea in Hot Spring Sediments

Quantifying the lipid biosynthesis rate of archaea in hot spring sediments is necessary to estimate their generation times and environmental significance, with implications for modern biogeochemical cycling and astrobiology. Here, we performed lipid hydrogen stable isotope probing (LH-SIP) experiments on sediments collected from two high-temperature, suboxic, circumneutral hot springs in Yellowstone National Park (USA) and El Tatio Geyserfield (Chile). We determined the incorporation rate of 2H2O into intact polar lipids (IPLs) during short-duration, ex situ incubations, which provides a taxon- and metabolism-agnostic quantification of biosynthesis under near-natural conditions. We targeted isoprenoid glycerol dialkyl glycerol tetraether lipids (iGDGTs) and recovered structures with 0 to 7 cyclopentyl moieties from both springs. We observed minor 2H-uptake into archaeal IPL-iGDGT-derived biphytanes in spring sediments in Yellowstone, corresponding to decadal-scale apparent generation times (16 +/- 3 years), and no detectable uptake in El Tatio sediments, where lipid turnover was slower than could be resolved under the experimental conditions (maximum resolvable apparent generation times ~ 42 +/- 21 years). We conclude that these slow apparent generation times likely reflect substantial dilution by a large standing IPL pool and/or recycling or re-functionalization of relict lipid components, rather than equivalently slow rates of archaeal cell division. These are the first estimates of archaeal lipid production rates in terrestrial hydrothermal systems using LH-SIP incubations and provide critical constraints for interpreting archaeal lipids in modern systems and ancient hot spring deposits.

microbiology↗

bioRxiv2026

Harnessing Human Immune Organoid model for Systemic and Comparative Analysis of Clinically Vaccine Adjuvants

Effective vaccine adjuvants are crucial for eliciting strong, specific, and long-lasting adaptive immune responses. However, traditional vaccine and adjuvant development is often hindered by prolonged animal testing, reliance on singular evaluation metrics, and batch variability, limiting the comparative analysis of immune responses induced by different adjuvants. To our knowledge, systematic adjuvant-focused analysis in human immune organoids remains limited, particularly across distinct antigen-conditioned immune baselines. Here, we established a matrix supported human tonsil immune organoid (M-hTIO) platform, derived from tonsil tissue for systemic adjuvant evaluation. Within 2 days, the platform replicated key germinal center (GC) features, with the potential for antigen-specific antibody production and plasmablast differentiation. Dynamic GC formation and T-B cell interactions were tracked using light-field imaging. Notably, IL-21 significantly accelerates GC-like remodeling and enhanced immune responses. Employing bulk cell transcriptomics we systematically analyzed early immune signatures and prolonged adaptive responses induced by five clinical-grade adjuvants (Alum, MnJ{beta}, AddaSO3, R848, ODN 1018) paired with influenza HA and SARS-CoV-2 RBD antigens across only adjuvant, weak and strong memory backgrounds. By integrating early and prolonged response phases, M-hTIO captured complementary dimensions of adjuvant activity, distinguishing rapid immune activation from sustained adaptive remodeling. Importantly, early activation signatures and prolonged adaptive remodeling provided complementary information, highlighting the value of temporally resolved adjuvant evaluation. AddaSO3 showed a consistent activity pattern across early and prolonged response phases, supporting both rapid immune activation and sustained adaptive remodeling. Moreover, our findings reveal shared/exclusive signaling pathways and gene networks. These results represent systematic and comparative analysis of clinically tested vaccine adjuvants in a human model, paving the way for high-throughput systems vaccinology research.

immunology↗