Biology subjects

Liu, Y.

Publications and source records attributed to Liu, Y..

At least 793 records · Page 44Linked to original sources

bioRxiv2025

Fertility recovery after combined adriamycin, bleomycin, vinblastine and dacarbazine chemotherapy in mice

BackgroundWith combined adriamycin, bleomycin, vinblastine and dacarbazine (ABVD) chemotherapy and radiotherapy, most of Hodgkin lymphoma (HL) patients were cured. The effect of ABVD treatment on ovarian reserve and fertility has not been fully characterized, and whether fertility preservation is required is controversial and not empirically known. MethodsEight-week-old female mice were injected intravenously with saline solution or ABVD for 4 weeks, respectively. Estrous cycles, level of serum anti-Mullerian hormone (AMH), body weight and ovary weight were recorded. Ovarian tissue structure and the number of follicles at each stage were analysed. IVF assay and natural mating trials were performed. Finally, transcriptome sequencing of ovarian tissue was conducted. ResultsMost mice completely lost their estrous cycle soon after ABVD injection. After 4 cycles of treatments, the body weight gain and gross ovarian weight were significantly lower than those of the control. However, estrous cycles were returned to normal at 4 weeks after ABVD discontinuation. Serum AMH and the numbers of primordial follicles were significantly increased compared to controls. ABVD withdrawal did not affect the rate of cleavage or blastocyst formation in IVF assays. Furthermore, the mice were fertile and there was no significant difference in offspring weight, but the number of offspring in the ABVD discontinuation group was smaller compared with the controls. Transcriptome analyses revealed that the JAK/STAT pathway, which is involved in the inhibition of primordial follicles activation and apoptosis, was upregulated in ABVD-treated ovaries, whereas few gene expressions were altered 4 weeks after ABVD withdrawal. ConclusionsThe ovarian reserve and reproductive function can be recovered after ABVD treatment, and combined ABVD treatment for a short time has a beneficial effect on the ovarian reserve, which can be used to provide a basis for guiding fertility preservation decision-making for HL patients. Statements and DeclarationsThe authors declare no competing interests.

developmental biology↗

bioRxiv2025

Macrophage Migration Inhibitory Factor Suppresses Natural Killer Cell Response and Promotes Hypoimmunogenic Stem Cell Engraftment Following Spinal Cord Injury

Human induced pluripotent stem cells (iPSCs) offer immense potential as a source for cell therapy in spinal cord injury (SCI) and other diseases. The development of hypoimmunogenic, universal cells that could be transplanted to any recipient without requiring a matching donor, could significantly enhance their therapeutic potential and accelerate clinical translation. To create off-the-shelf hypoimmunogenic cells, we used CRISPR-Cas9 to delete B2M (HLA class I) and CIITA (master regulator of HLA class II). Double-knockout (DKO) iPSC-derived neural progenitor cells (NPCs) evaded T cell-mediated immune rejection in vitro and after grafting into the injured spinal cord of athymic rats and humanized mice. However, loss of HLA class I heightened susceptibility to host natural killer (NK) cell attack, limiting graft survival. To counter this negative effect, we engineered DKO NPCs to overexpress macrophage migration inhibitory factor (MIF), an NK cell checkpoint ligand. MIF expression markedly reduced NK cell-mediated cytotoxicity and improved long-term engraftment and integration of NPCs in the animal models for spinal cord injury. These findings demonstrate that MIF overexpression, combined with concurrent B2M and CIITA deletion, generates hiPSC neural derivatives that escape both T- and NK-cell surveillance. This strategy provides a scalable route to universal donor cells for regenerative therapies in SCI and potentially other disorders.

cell biology↗

bioRxiv2025

Transcriptome and metabolome analyses reveal novel genetic targets for L-tryptophan overproduction in Corynebacterium glutamicum

Corynebacterium glutamicum is a promising microbial chassis for the industrial production of L-tryptophan, which has exhibited increasing demand due to its diverse applications and high market value. In previous work, we developed an L-tryptophan-overproducing C. glutamicum strain TR26 through multiple rounds of rational metabolic engineering. Here, comparative transcriptome and metabolome analyses were conducted between TR26 and its progenitor strain MB001 to reveal the underlying mechanisms and potential bottlenecks for L-tryptophan production in TR26. Furthermore, by systematically down- and up-regulating differentially expressed genes of interest, two novel genetic targets, glnK and sugR, were identified as being associated with L-tryptophan synthesis. Specifically, the repression of glnK and overexpression of sugR in strain TR26 increased the titer of L-tryptophan by 6.7% and 20.9%, respectively. Gene transcription profiling and intracellular metabolite analysis further suggested that the observed improvements in L-tryptophan synthesis could be attributed to optimized nitrogen transport and metabolism, efficient reallocation of cellular resources and enhanced supply of phosphoenolpyruvate (PEP). This study advances our understanding of the regulation mechanisms governing L-tryptophan synthesis in C. glutamicum and provides valuable insights for further optimization of industrial cell factories.

synthetic biology↗

bioRxiv2025

Sialylated CD43 is a glyco-immune checkpoint for macrophage phagocytosis

Macrophages in the tumor microenvironment exert potent anti-tumorigenic activity through phagocytosis. Yet therapeutics that enhance macrophage phagocytosis have not improved outcomes in clinical trials for patients with acute myeloid leukemia (AML) or myelodysplastic syndrome (MDS). To systematically identify regulators of phagocytosis, we performed genome-scale CRISPR knockout screens in human leukemia cells co-cultured with human monocyte-derived macrophages. Surprisingly, we found that whereas the classic "dont eat me" signal CD47 inhibited mouse macrophages, it did not inhibit phagocytosis by human macrophages. In contrast, the O-linked glycosylation and sialylation pathways were strong negative regulators of phagocytosis. In AML, the cell surface O-linked glycoprotein CD43 was the major effector of the O-linked glycosylation and sialylation pathways. Genetic deletion or antibody blockade of CD43 enhanced macrophage phagocytosis. This work highlights the importance of using human platforms to identify immune checkpoints, and nominates CD43 as a glyco-immune regulator of human macrophage phagocytosis.

cancer biology↗

bioRxiv2025

Hidden in plain sight: Bacterial genomes reveal thousands of lytic phages with therapeutic potential

Phages are typically classified as temperate, integrating into host genomes, or lytic, replicating and killing bacteria. Lytic phages are not expected in bacterial genome sequences, yet our analysis of 3.6 million bacterial assemblies from 1,226 species revealed >100,000 complete lytic phage genomes, which we term Bacterial Assembly-associated Phage Sequences (BAPS). This represents a [~]five-fold increase in the number of phages with known hosts and fundamentally reshapes our understanding of phage biology. Identifying BAPS has enabled the discovery of entirely novel phage clusters, including clusters distantly related to Salmonella Goslarviruses in E. coli, and Shigella, while significantly expanding known genera such as Seoulvirus (from 16 to >300 members). Notably, close relatives of therapeutic lytic phages were also detected, suggesting clinical isolate sequencing unknowingly archives viable phage candidates. The discovery of complete, lytic phage genomes within bacterial assemblies challenges long-standing assumptions and reveals a vast, untapped reservoir of phages.

microbiology↗

bioRxiv2025

Characterisation of putative retrotrapezoid nucleus (RTN) chemoreceptor neurons in the adult human brainstem

The retrotrapezoid nucleus (RTN) of rodents is located ventral to the facial motor nucleus (7N) and consists of acid-sensitive neurons that activate breathing and mediate the central component of the ventilatory response to hypercapnia. In rodents, RTN neurons can be histologically identified by the presence of paired-like homeobox 2B positive nuclei (Phox2b+) and the absence of cytoplasmic choline acetyltransferase (ChAT-) and tyrosine hydroxylase (TH-). Up to 50% of rodent RTN neurons synthesise galanin, and 88% express pituitary adenylate cyclase activating polypeptide (PACAP). The human RTN (hRTN) has not been mapped to date. This study aimed to map the location and cytoarchitecture of the adult hRTN and compare the findings to the homologies of rodents, macaques and human infants. Formalin-fixed, paraffin-embedded tissue blocks from two adult cases, spanning the medulla-pons, were serially sectioned (10{micro}m thick) and every four in thirty sections was assayed for immunohistochemistry for ChAT, or double-labelled Phox2b/TH, Phox2b/galanin and Phox2b/PACAP, followed by analysis using QuPath software. hRTN neurons, identified as Phox2b+/TH-/ChAT-, were located ventral to 7N and lateral to the superior olive, overlapped with the C1 or A5 catecholaminergic population and extended rostrocaudally from Obex +13 to +17 mm. In the parafacial area, 90% of Phox2b immunoreactive (-ir) neurons are hRTN neurons, totaling around 5000 bilaterally, and were surrounded by numerous TH-ir fibers. Galanin- and PACAP-ir was identified in 43% and 39% of Phox2b-ir parafacial neurons, respectively. This is the first study to characterise and quantitatively map the adult human RTN using a series of neurochemical markers.

neuroscience↗

bioRxiv2025

Strain-level metagenomic profiling using pangenome graphs with PanTax

Microbes are omnipresent, thriving in a range of habitats, from oceans to soils, and even within our gastrointestinal tracts. They play a vital role in maintaining ecological equilibrium and promoting the health of their hosts. Consequently, understanding the diversity in terms of strains in microbial communities is crucial, as variations between strains can lead to different phenotypic expressions or diverse biological functions. However, current methods for taxonomic classification from metagenomic sequencing data have several limitations, including their reliance solely on species resolution, support for either short or long reads, or their confinement to a given single species. Most notably, most existing strain-level taxonomic classifiers rely on the sequence representation of multiple linear reference genomes, which fails to capture the sequence correlations among these genomes, potentially introducing ambiguity and biases in metagenomic profiling. Here, we present PanTax, a pangenome graph-based taxonomic profiler that overcomes the short-comings of sequence-based approaches, because pangenome graphs possess the capability to depict the full range of genetic variability present across multiple evolutionarily or environmentally related genomes. PanTax provides a comprehensive solution to taxonomic classification for strain resolution, compatibility with both short and long reads, and compatibility with single or multiple species. Extensive benchmarking results demonstrate that PanTax drastically outperforms state-of-the-art approaches, primarily evidenced by its significantly higher precision or recall at strain level, while maintaining comparable or better performance in other aspects across various datasets. PanTax is an open-source user-friendly tool that is publicly available at https://github.com/LuoGroup2023/PanTax.

bioinformatics↗

bioRxiv2025

Expression and Characterization of SARS-CoV-2 Spike Protein in Thermothelomyces heterothallica C1

The COVID-19 pandemic demonstrated a pressing need for rapid, adaptive, and scalable manufacturing of vaccines and reagents. With the transition into an endemic disease and rising threats of other emerging pandemics, production of these biologicals requires a stable and sustainable supply chain and accessible distribution methods. In this study, we demonstrate the strength of an engineered filamentous fungal platform, Thermothelomyces heterothallica C1, for high volumetric productivity of the full-length spike glycoprotein. Spike protein produced in this system is highly thermostable and immunization of mice with spike made in C1 or mammalian platforms resulted in a similar humoral response. Additionally, it was shown that the native N-glycan profile can be redecorated with complex sialylated structures, if necessary, resulting in a more human-like glycan profile, without impacting binding characteristics as shown experimentally and in simulations. Through extensive physicochemical analysis, the C1 produced spike performs similarly to spike proteins produced in other commercially available systems. The data presented is evidence that C1 can be a strong platform for production of complex glycosylated recombinant proteins such as subunit antigen vaccines.

bioengineering↗

bioRxiv2025

EXPRESSION GRAPH NETWORK FRAMEWORK FOR BIOMARKERDISCOVERY

AO_SCPLOWBSTRACTC_SCPLOWBiomarker discovery for complex diseases like cancer hinges on uncovering molecular signatures that capture intricate, interconnected relationships within biological data, a challenge that traditional statistical and machine learning methods often fail to meet due to the complexity of high-dimensional gene expression profiles. To overcome this, we introduce the Expression Graph Network Framework (EGNF), a cutting-edge graph-based approach that integrates graph neural networks (GNNs) with network-based feature engineering to enhance predictive biomarker identification. EGNF constructs biologically informed networks by combining gene expression data and clinical attributes within a graph database, utilizing hierarchical clustering to generate dynamic, patient-specific representations of molecular interactions. Leveraging graph learning techniques, including Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs), our framework identifies statistically significant and biologically relevant gene modules for classification. Validated across three independent datasets consisting of contrasting tumor types and clinical scenarios, EGNF consistently outperforms traditional machine learning models, achieving superior classification accuracy and interpretability. Notably, it delivers perfect separation between normal and tumor samples while excelling in nuanced tasks such as classifying disease progression and treatment outcome. This scalable, interpretable, and robust framework provides a powerful tool for biomarker discovery, with wide-ranging applications in precision medicine and the elucidation of disease mechanisms across diverse clinical contexts.

bioinformatics↗

bioRxiv2025

SCRIPT: predicting single-cell long-range cis-regulation based on pretrained graph attention networks

Single-cell cis-regulatory relationships (CRRs) are essential for deciphering transcriptional regulation and understanding the pathogenic mechanisms of disease-associated non-coding variants. Existing computational methods struggle to accurately predict single-cell CRRs due to inadequately integrating causal biological principles and large-scale single-cell data. Here, we present SCRIPT (Single-cell Cis-regulatory Relationship Identifier based on Pre-Trained graph attention networks) that infers single-cell CRRs from transcriptomic and chromatin accessibility data. SCRIPT incorporates two key innovations: graph causal attention networks supported by empirical CRR evidence, and representation learning enhanced through pretraining on atlas-scale single-cell chromatin accessibility data. Validation using cell-type-specific chromatin contact data demonstrates that SCRIPT achieves a mean AUC of 0.9, significantly outperforming state-of-the-art methods (AUC: 0.68). Notably, SCRIPT obtains a threefold improvement in predicting long-range CRRs (>100 Kb) compared to existing methods. Applying SCRIPT to Alzheimers disease and schizophrenia, we establish a framework for prioritizing disease-causing variants and elucidating their functional effects in a cell-type-specific manner. By uncovering molecular genetic mechanisms undetected by existing computational methods, SCRIPT provides a roadmap for advancing genetic diagnosis and target discovery.

bioinformatics↗

bioRxiv2025

RECUR: Identifying recurrent amino acid substitutions from multiple sequence alignments

Identifying recurrent changes in biological sequences is important to multiple aspects of biological research -from understanding the molecular basis of convergent phenotypes, to pinpointing the causative sequence changes that give rise to antibiotic resistance and disease. Here, we present RECUR, a method for identifying recurrent amino acid substitutions from multiple sequence alignments that is fast, easy to use, and scalable to thousands of sequences. We demonstrate the utility and performance characteristics of RECUR on a data set of surface glycoprotein (S) protein sequences from SARS-CoV-2 - identifying widespread recurrent evolution throughout the protein. Structural analysis of the recurrently evolving sites revealed significant enrichment in the exposed receptor-binding S1 subunit and at the interface with the human angiotensin-converting enzyme 2 (hACE2), whereas recurrent substitutions were depleted at the trimeric interface of the S protein. Finally, in silico modelling showed that recurrent substitutions have primarily acted to stabilise the trimeric interface, but had no consistent effect at the hACE2 interface, suggesting that evolution at these sites has been shaped by opposing selection pressures - balancing the need to maintain or enhance hACE2 binding with pressures to diversify and evade host immune responses. A standalone implementation of the algorithm is available under the GPLv3 licence at https://github.com/OrthoFinder/RECUR.

bioinformatics↗

bioRxiv2025

Gut bacteria degrade purines via the 2,8-dioxopurine pathway

The loss of uricase during hominid evolution has predisposed humans to hyperuricemia and gout, conditions with high global prevalence. In healthy individuals, approximately one-third of urate is excreted into the intestinal tract1 where bacteria consume this purine aiding in its elimination2,3. However, the molecular details of purine metabolism in the gut microbiome remain unknown. Here we uncover the 2,8-dioxopurine pathway, a previously unrecognized anaerobic route for purine degradation in gut bacteria. This pathway uses a novel selenium-dependent enzyme, 2,8-dioxopurine dehydrogenase (DOPDH), and seven additional enzymes that together link purine metabolism to ATP generation and short chain fatty acid production. Competition experiments in gnotobiotic mice demonstrate that bacteria harboring this pathway exhibit a fitness advantage, with wild-type bacteria rapidly outcompeting a DOPDH-deficient strain. These findings highlight a host-microbe symbiosis, where host-secreted urate fosters a metabolic niche for bacteria that break it down. By elucidating this pathway, our study provides new avenues to modify and enhance intestinal elimination of urate, which has therapeutic implications for treating hyperuricemia and gout, conditions that affect millions of adults worldwide.

microbiology↗

bioRxiv2025

The landscape of structural variation in pediatric cancer

Structural variants (SVs) account for over 60% of the driver variants in pediatric cancer, and in many cases act as the cancer initiating event. To study SVs from a pan-cancer perspective, we analyzed 1,616 pediatric cancer genomes in 16 major cancer types of hematological malignancies (n = 908), brain tumors (n = 183), and solid tumors (n = 525) and compared their profiles to those of 2,203 adult cancers. The SV burden varied [~]100-fold across pediatric cancer types and demonstrated an 8-to 16-fold reduction compared to adult brain and solid tumors but was comparable in pediatric versus adult hematological malignancies. Recurrent SV hotspots occurred uniquely in pediatric acute lymphoblastic leukemias (ALLs) in proximity to RAG-mediated recombination signal sequences (RSS) and disrupted multiple immune-related loci as well as 69 genes, which often involved cryptic RSS sites. By contrast, such hotspots affected only immune-related loci but not driver genes in adult lymphoid cancers. Eight SV signatures extracted from the cohort had varying distributions across cancer types, with clustered translocations reflecting templated insertions in osteosarcoma, and medium-sized deletions (10 kb to 1 Mb) enriched in cancers with RAG-mediated deletions. Intra-patient evolutionary analysis in 13 patients with multiple spatiotemporally distinct samples revealed that RAG-mediated recombination in leukemia and complex rearrangements in solid tumors occurred both early in disease initiation and continuously during later diversification, contributing to clonal heterogeneity. Finally, we found that both driver genes and fragile sites were the two genomic regions most frequently disrupted by SVs. The unique and diverse SV landscapes that emerged from this comprehensive analysis expand the scope of RSS-mediated mutagenesis in pediatric ALL and will be a valuable resource for guiding future functional studies and the design of clinical genomic testing in pediatric cancer.

cancer biology↗

bioRxiv2025

Systematic evaluation of blood contamination in nanoparticle-based plasma proteomics

Circulating blood proteomics enables minimally invasive biomarker discovery. Nanoparticle (NP)-based circulating plasma proteomics studies have reported varying number of proteins, ranging from ca 2000 to 7000 but its not clear whether higher protein number is more informative. Here, we first develop OmniProt - a silica-NP workflow for plasma proteomics which is optimized through systematic evaluation of NP types and protein corona formation parameters. Next, we present an Astral spectral library for 10,109 protein groups expressed in human plasma. Using Astral with a throughput of 60 sample-per-day, OmniProt identifies ca 3000 to 6000 protein groups from human plasma. Notably, we found that platelet/erythrocyte/coagulation-related contamination artificially elevates protein identifications and compromises quantification accuracy for circulating plasma proteins in NP-enriched samples. Through controlled contamination experiments, we identified and validated protein biomarkers for contaminations of platelet, erythrocyte, and coagulation, respectively, in NP-based plasma proteomics experiments. We further developed an open-access software Baize for accessing plasma proteomics contamination. Finally, we validated our OmniProt workflow and Baize software in a clinical cohort of 193 patients with CT-indistinct benign nodules or early lung cancers. On average, we identified ca 4400 protein groups for each plasma sample without significant contamination. Five samples were flagged out as contaminated. In conclusion, this study reveals that contamination of plasma samples significantly alters the protein identification and quantification in NP-based plasma proteomics experiments, and presents an open-access software Baize to evaluate plasma proteome contamination.

biochemistry↗

bioRxiv2025

Structural insight into the substrate binding of the AMT complex via an inhibitor-trapped state

N6-adenine (6mA) DNA methylation plays an important role in gene regulation and genome stability. The 6mA methylation in Tetrahymena thermophila is mainly mediated by the AMT complex, comprised of the AMT1, AMT7, AMTP1 and AMTP2 subunits. To date, how this complex assembles on the DNA substrate remains elusive. Here we report the structure of the AMT complex bound to the OCR protein from bacteriophage T7, mimicking the AMT-DNA encounter complex. The AMT1-AMT7 heterodimer approaches OCR from one side, while AMTP1 N-terminal domain, assuming a homeodomain fold, binds to OCR from the other side, resulting in a saddle-shaped architecture reminiscent of what was observed for prokaryotic 6mA writers. Mutation of the AMT1, AMT7 and AMTP1 residues on the OCR-contact points led to impaired DNA methylation activity to various extents, supporting a role for these residues in DNA binding. Furthermore, structural comparison of the AMT1-AMT7 subunits with the evolutionarily related METTL3-METTL14 and AMT1-AMT6 complexes reveal sequence conservation and divergence on the region corresponding to the OCR-binding site, shedding light onto the substrate binding of the latter two complexes. Together, this study suggests a substrate binding-induced open-to-closed conformational transition of the AMT complex, with implications in its substrate binding and processive 6mA methylation.

biochemistry↗

bioRxiv2025

EasyMultiProfiler: An Efficient Multi-Omics Data Integration and Analysis Workflow for Microbiome Research

O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=141 SRC="FIGDIR/small/649266v1_ufig1.gif" ALT="Figure 1"> View larger version (60K): org.highwire.dtl.DTLVardef@14dd0b1org.highwire.dtl.DTLVardef@1c04a79org.highwire.dtl.DTLVardef@159894dorg.highwire.dtl.DTLVardef@1454a01_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical AbstractC_FLOATNO C_FIG HighlightsO_LIEasyMultiProfiler provides unified integration of microbial multi-omics data C_LIO_LIEasyMultiProfiler enables natural language-style analytical workflows C_LIO_LIEasyMultiProfiler solves key challenges in standardization and reproducibility C_LIO_LIEasyMultiProfiler extracts meaningful biological insights efficiently C_LI Host-microbiome interactions are crucial in maintaining physiological homeostasis and influencing disease progression. While traditional microbiome research, focused on microbial diversity and abundance, provided essential frameworks, the integration of multi-omics approaches established a far more comprehensive, systems-level understanding of microbiome functionality. However, significant methodological challenges in multi-omics data integration remain, including inconsistent sample coverage, heterogeneous data formats, and complex downstream analytical workflows. These challenges impact the reproducibility and reliability of results and underscore the critical requirement for developing standardized, systematic workflows. Thus, we developed the EasyMultiProfiler (EMP) workflow, a streamlined and efficient analytical framework for multi-omics data analysis. EMP provides a comprehensive infrastructure based on the SummarizedExperiment and MultiAssayExperiment classes, creating a unified framework for storing and analyzing omics data. The frameworks architecture comprises five interconnected functional modules: data extraction, preparation, support, analysis, and visualization. These modules are smoothly integrated into natural language-style analytical workflows, offering users an efficient and standardized solution for multi-omics analysis. EMP addresses critical challenges in multi-omics data analysis, including data integration, workflow standardization, and result reproducibility. The platforms modular architecture and intuitive interface provide researchers and clinicians with a robust, flexible workflow to systematically extract biologically relevant insights from complex multi-omics datasets.

bioinformatics↗

bioRxiv2025

Gut Microbiota Dysbiosis and Short-chain Fatty Acid alterations in Pediatric New-Onset Type 1 diabetes with Ketoacidosis

ObjectiveDiabetic ketoacidosis (DKA) stands as the most common acute hyperglycaemic complication with a relatively high mortality rate among children diagnosed with type 1 diabetes (T1D). Previous studies have shed light on gut dysbiosis in newly-onset T1D children, but research targeting the gut microbiota and microbial metabolites in patients with DKA remains scarce. MethodsShotgun metagenomic analysis was conducted on fecal samples from 96 newly diagnosed T1D children, including 32 patients who presented with DKA upon admission. Short-chain fatty acids (SCFAs) were quantified using gas chromatography/mass spectrometry (GC/MS). Comparative analyses were performed on the gut microbiome and SCFA levels between DKA and non-DKA subgroups. ResultsGut microbiota composition differed between DKA and non-DKA groups. While alpha-diversity remained consistent, beta-diversity showed marked differences between the two groups, and the phyla Firmicutes and Bacteroidetes exhibited predominance in the DKA group. DKA was associated with increased potential pathogens and a significant depletion of SCFA-producing genera (Anaerobutyricum, Dialister, Ruminococcus, Roseburia, Dorea, and Butyricicoccus), accompanied by reduced SCFA levels. Further correlation analysis revealed strong associations between SCFAs, SCFA-producing microbiota, and disease severity indicators. Propionic acid and butyric acid mediated the association between SCFA-producing bacteria and DKA. ConclusionThis study first reported reductions in SCFAs and SCFA-producing bacteria in the fecal samples of T1D children at DKA onset. Our findings highlight SCFAs and their producers as protective biomarkers of DKA and suggest that targeting gut microbiota and SCFA metabolism may offer a potential therapeutic strategy for DKA. ImportanceWhile gut microbiota imbalances have been linked to type 1 diabetes (T1D), little is known about the role of gut microbes and their metabolites during acute diabetic ketoacidosis (DKA), an acute and life-threatening complication. This study examined the gut microbiome and levels of short-chain fatty acids (SCFAs) in fecal samples from children with new-onset T1D with and without DKA. DKA was characterized by distinct microbial signatures, including increased potential pathogens, a loss of beneficial SCFA-producing bacteria, and consistently lower SCFA levels. These changes closely correlated with indicators of disease severity, and further mediation analysis revealed the role of SCFAs in linking gut microbiota to DKA. These findings suggest that SCFAs and their producers may serve as protective markers for DKA, highlighting the potential of microbiome-based interventions to improve outcomes in children facing this critical complication.

microbiology↗

bioRxiv2025

Parameter Estimation and Model Selection for the Quantitative Analysis of Oncolytic Virus Therapy in Zebrafish

Oncolytic virus therapy (OVT) is emerging as a potent alternative to conventional cancer treatments by employing engineered viruses that selectively infect and lyse tumor cells while sparing normal tissues. Although mathematical models have been developed to elucidate the dynamics of OVT and inform personalized therapies, they are often specific to certain organisms. Mathematical models tailored to more recently developed animal models of OVT, such as zebrafish, are not yet available. Here, we introduce the first mathematical model of OVT trained on zebrafish data from published studies to bridge the gap. We explore a variety of mathematical model structures and perform parameter estimation and model selection. The selected model effectively captures the observed tumor dynamics, i.e. delayed tumor shrinkage, and provides valuable insights into the underlying mechanisms of OVT in zebrafish. Our work establishes the groundwork for advancing experimental studies in zebrafish, contributing to the design of more effective cancer treatment strategies in the future.

systems biology↗