Biology subjects

Ye, Y.

Publications and source records attributed to Ye, Y..

At least 91 records · Page 5Linked to original sources

bioRxiv2024

KAT6A deficiency impairs cognitive functions through suppressing RSPO2/Wnt signaling in hippocampal CA3

Intellectual disability (ID) affects [~]2% of the general population and is often genetic in origin. ID-associated genes are enriched for epigenetic factors, including those encoding the largest family of histone lysine acetyltransferases (KAT5-KAT8). Among them is KAT6A, whose de novo heterozygous mutations cause KAT6A Syndrome (or Arboleda-Tham Syndrome), with ID as a common clinical feature. However, the underlying molecular mechanisms remain elusive. Here, we show that haploinsufficiency of Kat6a impairs learning and memory in mice, and specific deletion of Kat6a in excitatory neurons recapitulates the hippocampus-dependent memory deficits. Unexpectedly, KAT6A deficiency results in impaired synaptic structure and plasticity in hippocampal CA3, but not in CA1 region. Combining single-nucleus RNA-sequencing and chromatin analysis, we identify a CA3-enriched gene Rspo2, encoding a Wnt activator R-spondin 2, as a key transcriptional target of KAT6A. Moreover, deletion of Rspo2 in excitatory neurons phenocopies the loss of Kat6a, resulting in defective Wnt/{beta}-catenin signaling and synaptic plasticity in CA3, and abnormal cognitive behaviors in mice. Importantly, restoring RSPO2 expression in CA3 pyramidal neurons rescues the deficits in Wnt signaling and learning-associated behaviors in Kat6a mutant mice. Collectively, our results demonstrate that KAT6A plays a critical role in regulating synaptic plasticity and memory formation through RSPO2-mediated Wnt signaling in hippocampal CA3, shedding new light on the fundamental mechanisms of ID and providing potential therapeutic targets for the treatment of KAT6A Syndrome and related neurodevelopmental diseases.

neuroscience↗

bioRxiv2024

The Rhizobial effector NopT targets Nod factor receptors to regulate symbiosis in Lotus japonicus

It is well-documented that type-III effectors are required by Gram-negative pathogens to directly target different host cellular pathways to promote bacterial infection. However, in the context of legume-rhizobium symbiosis, the role of rhizobial effectors in regulating plant symbiotic pathways remains largely unexplored. Here, we show that NopT, a YopT-type cysteine protease of Sinorhizobium fredii NGR234 directly targets the plants symbiotic signaling pathway by associating with two Nod factor receptors (NFR1 and NFR5 of Lotus japonicus). NopT inhibits cell death triggered by co-expression of NFR1/NFR5 in Nicotiana benthamiana. Full-length NopT physically interacts with NFR1 and NFR5. NopT proteolytically cleaves NFR5 both in vitro and in vivo, but can be inactivated by NFR1 as a result of phosphorylation. NopT plays an essential role in mediating rhizobial infection in L. japonicus. Autocleaved NopT retains the ability to cleave NFR5 but no longer interacts with NFR1. Interestingly, genomes of certain Sinorhizobium species only harbor nopT genes encoding truncated proteins without the autocleavage site. These results reveal an intricate interplay between rhizobia and legumes, in which a rhizobial effector protease targets NFR5 to suppress symbiotic signaling. NFR1 appears to counteract this process by phosphorylating the effector. This discovery highlights the role of a bacterial effector in regulating a signaling pathway in plants and opens up the perspective of developing kinase-interacting proteases to fine-tune cellular signaling processes in general. One-sentence summaryThe rhizobial effector NopT and two Nod factor receptors of Lotus japonicus, NFR1 and NFR5, mutually interact to precisely regulate rhizobial symbiosis.

plant biology↗

bioRxiv2024

Telomere-to-telomere genome assembly of a male goat reveals novel variants associated with cashmere traits

A complete goat (Capra hircus) reference genome enhances analyses of genetic variation, thus providing novel insights into domestication and selection in goats and related species. Here, we assembled a telomere-to-telomere (T2T) gap-free genome (2.86 Gb) from a cashmere goat (T2T-goat1.0), which included a Y chromosome of 20.9 Mb. With a base accuracy of >99.999%, T2T-goat1.0 corrected numerous genome-wide structural and base errors in previous assemblies and added 288.5 Mb of previously unresolved regions (PURs) and 446 newly assembled genes to the reference genome. We sequenced the genomes of five representative goat breeds for PacBio reads, and used T2T-goat1.0 as a reference to identify a total of 63,417 structural variations (SVs) with up to 4711 SVs (7.41%) in the PURs and 507 overlapping genes significantly enriched (Padj. < 0.05) in the keratin filament pathway. T2T-goat1.0 was applied in population analyses of global wild and domestic goats, which revealed 32,419 SVs and 25,397,794 SNPs, including 870 SVs and 545,026 SNPs in the PURs. Also, our analyses revealed a set of novel selective variants and genes associated with domestication (e.g., NKG2D and ABCC4) and cashmere traits (e.g., ABCC4 and ASIP).

genomics↗

bioRxiv2024

Multi-gradient Permutation Survival Analysis Identifies Mitosis and Immune Signatures Steadily Associated with Cancer Patient Prognosis

The inconsistency of the association between genes and cancer prognosis is often attributed to many variables that contribute to patient survival. Whether there exist the Genes Steadily Associated with Prognosis (GEARs) and what their functions are remain largely elusive. Here we developed a novel method named "Multi-gradient Permutation Survival Analysis " (MEMORY) to screen the GEARs by using RNA-seq data from the TCGA database. We employed a network construction approach to identify hub genes from GEARs, and utilized them for cancer classification. In the case of lung adenocarcinoma (LUAD), the GEARs were found to be related to mitosis. Our analysis suggested that LUAD cell lines carrying PIK3CA mutations exhibit increased drug resistance. For breast invasive carcinoma (BRCA), the GEARs were related to immunity. Further analysis revealed that CDH1 mutation might regulate immune infiltration through the EMT process. Moreover, we explored the prognostic relevance of mitosis and immunity through their respective scores and demonstrated it as valuable biomarkers for predicting patient prognosis. In summary, our study offered significant biological insights into GEARs and highlights their potentials as robust prognostic indicators across diverse cancer types.

cancer biology↗

bioRxiv2024

Multitask Knowledge-primed Neural Network for Predicting Missing Metadata and Host Phenotype based on Human Microbiome

Microbial signatures in the human microbiome have been linked to various human diseases, and Machine Learning (ML) models have been developed for microbiome-based disease prediction, although improvements remain to be made in accuracy, reproducibility and interpretability. On the other hand, confounding factors, including hosts gender, age and BMI can have a significant impact on humans microbiome, complicating microbiome-based human phenotype predictions. We recently developed MicroKPNN, an interpretable ML model that achieved promising performance for human disease prediction based on microbiome data. MicroKPNN explicitly incorporates prior knowledge of microbial species into the neural network. Here we developed MicroKPNN-MT a unified model for predicting human phenotype based on microbiome data, as well as additional metadata including age, body mass index (BMI), gender and body site. In MicroKPNNMT, the metadata information, when available, will be used as additional input features for prediction, or otherwise will be predicted from microbiome data using additional decoders in the model. We applied MicroKPNN-MT to microbiome data collected in mBodyMap, covering healthy individuals and 25 different diseases, and demonstrated its potential as a predictive tool for multiple diseases, which at the same time provided predictions for much of the missing metadata (e.g., the BMI information was missing for 94% of the samples). Our results showed that incorporating real or predicted metadata helped improve the accuracy of disease predictions, and more importantly, helped improve the generalizability of the predictive models. Finally, our model enables the interpretation of predictive models and the identification of potential microbial markers affecting host phenotypes.

bioinformatics↗

bioRxiv2024

Multi-omic characterization of allele-specific regulatory variation in hybrid pigs

Genetic variation in the regulation of gene expression contributes substantially to phenotypic variation. Understanding how variation in DNA sequences and epigentic modifications leads to gene expression variation remains a challenging task. In hybrid animals where cellular environments are homogeneous, differences between the expression of paternal and maternal alleles must be due to cis sequence or epigenic differences. Therefore, hybrid mapping is a powerful approach to efficiently identify and characterize genes under regulation through mechanisms in cis. In this study, using reciprocal crosses of the phenotypically divergent Duroc and Lulai pig breeds, we performed a comprehensive multi-omic characterization of regulatory variation across brain, liver, muscle, and placenta in four developmental stages. We produced one of the largest multi-omic datasets to date in pigs, including 16 whole genome sequenced genomes, 48 whole genome bisulfite sequencing, 168 ATAC-Seq and 168 RNA-Seq samples. We developed a novel read count-based method to reliably assess allele-specific methylation, chromatin accessibility, and RNA expression. We showed that tissue specificity was much stronger than developmental stage specificity in all of DNA methylation, chromatin accessibility, and gene expression. We identified 573 genes showing allele specific expression, including those influenced by parent-of-origin as well as allele genotype effects. By integrating methylation, chromatin accessibility, and gene expression data, many of these allele specific expression can be explained by allele specific methylation and/or chromatin accessibility. This study provides one of the most comprehensive characterizations of regulatory variation across multiple tissues and developmental stages in pigs and offers new opportunity of genetic improvement for this important food animal species.

developmental biology↗

bioRxiv2024

Integrated analysis and systematic characterization of the regulatory network for human germline development

Primordial germ cells (PGCs) are the precursors of germline that are specified at embryonic stage. Recent studies reveal that humans employ different mechanisms for PGC specification compared with model organisms such as mice. Moreover, the specific regulatory machinery is still largely unexplored, mainly due to the inaccessible nature of this complex biological process. Here, we collect and integrate multi-omics data, including 581 RNA-seq, 54 ATAC-seq, 45 ChIP-seq, and 69 single-cell RNA-seq samples from different stages of human PGC development to recapitulate the precisely controlled and stepwise process, presenting an atlas in the human PGC database (hPGCdb). With these uniformly processed data and integrated analyses, we characterize the potential key transcription factors and regulatory networks governing human germ cell fate. We validate the important roles of some of the key factors in germ cell development by CRISPRi knockdown. We also identify the soma-germline interaction network and discover the involvement of SDC2 and LAMA4 for PGC development, as well as soma-derived NOTCH2 signaling for germ cell differentiation. Taken together, we have built a database for human PGCs and demonstrate that hPGCdb enables the identification of the missing pieces of mechanisms governing germline development, including both intrinsic and extrinsic regulatory programs.

developmental biology↗

bioRxiv2024

Hunting for peptide binders of specific targets with data-centric generative language models

Bioactive peptides have become strong candidates for a variety of clinical therapies due to their diverse advantages, which promotes the development of deep generative models for peptide design. Considering that existing methods cannot effectively deal with the conformational flexibility of peptides and are difficult to capture accurate residue-to-residue interaction dependencies, we propose a unified weakly order-dependent autoregressive language modeling architecture (PepGenWOA) for bioactive peptide generative design with tolerating out-of-order input as inductive bias. The superiority of PepGenWOA is demonstrated by generating three classes of therapeutic peptides, including antimicrobial peptides, anticancer peptides, and peptide binders. For antimicrobial and anticancer peptide generation, PepGenWOA not only comprehensively outperforms state-of-the-art baseline models, but also exhibits a significant propensity to incorporate specific types of residues that are beneficial for antimicrobial or anticancer bioactivity. For characteristic-guided peptide binder generation, the pretrained PepGenWOA is fine-tuned with Mixture-of-Experts-style plugins with lifelong learning paradigm, thereby achieving the best trade-off between memory stability for world knowledge and learning plasticity for new downstream tasks. The subsequent structure-based virtual screening anchors the 7 most promising candidates from the mega-scale synthetic sequence space, reducing the number of candidates for in vitro experimental validation by 5 orders of magnitude. A target binding rate of 28.6% with binding specificity further confirms the effectiveness of the fine-tuning strategy. Overall, PepGenWOA is a unified architecture for peptide generative design that can be flexibly customized for different task requirements, thereby harnessing generative language models to reach the "dark space" of biological language.

bioinformatics↗

bioRxiv2023

Unveiling Elevated Spontaneous Mutation Rates in Phyllostachys edulis (Moso Bamboo) through Whole Genome Sequencing (WGS) and Investigating the Impact of Atmospheric and Room Temperature Plasma (ARTP) Induced Mutagenesis

Moso bamboo, recognized for its wide distribution and economic importance, encounters challenges in varietal enhancement due to its protracted sexual reproduction cycle. This study employed whole-genome resequencing to uncover spontaneous mutations in Moso bamboo and investigated mutagenesis using atmospheric and room temperature plasma (ARTP). Through the sequencing results, we identified the population of flowering bamboo as an asexual breeding line. Notably, the flowering Moso bamboo population, exclusively derived from asexual reproduction, exhibited a high spontaneous mutation rate (4.54 x 10-4 to 1.15 x 10-3/bp) during sexual reproduction, considering parental and cross-pollination effects. Genetic disparities between offspring and parents exhibited a bimodal distribution, indicating a substantial cross-pollination rate. ARTP mutagenesis increased structural variations in offspring, while changes in SNPs and INDELs were less pronounced. Sanger sequencing validated a gene subset, providing a foundation for spontaneous mutation rate investigation via whole-genome sequencing. These insights, particularly from mutagenized offspring sequencing, contribute to Moso bamboo breeding strategies. HighlightMoso bamboo breeding revolutionized--high spontaneous mutations in asexually derived flowering population. ARTP mutagenesis boosts structural variations, shaping innovative breeding approaches.

genetics↗

bioRxiv2023

The E3 ubiquitin ligase RNF220 maintains hindbrain Hox expression patterns through regulation of WDR5 stability

The spatial and temporal linear expression of Hox genes establishes a regional Hox code, which is crucial for the antero-posterior (A-P) patterning, segmentation, and neuronal circuit development of the hindbrain. RNF220, an E3 ubiquitin ligase, is widely involved in neural development via targeting of multiple substrates. Here, we found that the expression of Hox genes in the pons was markedly up-regulated at the late developmental stage (post-embryonic day E15.5) in Rnf220-/- and Rnf220+/-mouse embryos. Single-nucleus RNA-seq analysis revealed different Hox de-repression profiles in different groups of neurons, including the pontine nuclei (PN). The Hox pattern was disrupted and the neural circuits were affected in the PN of Rnf220+/- mice. We showed that this phenomenon was mediated by WDR5, a key component of the TrxG complex, which can be polyubiquitinated and degraded by RNF220. Intrauterine injection of WDR5 inhibitor (WDR5-IN-4) and genetic ablation of Wdr5 in Rnf220+/- mice largely recovered the de-repressed Hox expression pattern in the hindbrain. In P19 embryonal carcinoma cells, the retinoic acid induced Hox expression was further stimulated by Rnf220 knockdown, which can also be rescued by Wdr5 knockdown. In short, our data suggest a new role of RNF220/WDR5 in Hox pattern maintenance and pons development in mice.

developmental biology↗

bioRxiv2023

ThermoLink: Bridging Disulfide Bond and EnzymeThermostability through Database Construction andMachine Learning Prediction

Disulfide bonds (SS bonds), covalently formed by sulfur atoms in cysteine residues, play a crucial role in protein folding and structure stability. Due to their significance, artificial disulfide bonds are often introduced to enhance the thermostability of proteins. Although an increasing number of tools can assist with this task, significant amounts of time and resources were often wasted due to inadequate consideration. To enhance the accuracy and efficiency of designing disulfide bonds for protein thermostability improvement, we first collected disulfide-bond data with protein thermostability data from extensive literature sources. Then, various sequence- and structure-based features were extracted, and machine learning models were constructed to predict whether a disulfide bond could improve protein thermostability. Among all models, the neighborhood context model using the Adaboost-DT algorithm performs the best, and the AUC-ROC score and accuracy are 0.773 and 0.714, respectively. Alongside this, we also found that AlphaFold2 exhibits a high superiority in predicting disulfide bonds, and the coevolutionary relationship between residue pairs, to some extent, could also guide artificial disulfide-bond design. The SS-bond data has been integrated into an online server, named ThermoLink, available at guolab.mpu.edu.mo/thermoLink. O_TEXTBOXKey MessagesO_LIWe manually curated a database of disulfide bonds and their impacts on protein thermostability from the literature. C_LIO_LIAlphaFold2 exhibits a high superiority in predicting disulfide bonds, and to some extent, the conservation and co-evolutionary information of the residue pairs involved in disulfide bonds could also guide artificial disulfide-bond design. C_LIO_LIMachine learning models were developed to predict potential disulfide bonds with the aim of improving protein thermostability, offering an extended design strategy beginning with existing SS-bond prediction tools to optimize variant selection. C_LIO_LIThis work provides valuable data and theoretical support for a more precise design of disulfide bonds for improving protein thermostability. C_LI C_TEXTBOX

bioinformatics↗

bioRxiv2023

Deciphering the Biosynthetic Potential of Microbial Genomes Using a BGC Language Processing Neural Network Model

Microbial secondary metabolites are usually synthesized by colocalized genes termed biosynthetic gene clusters (BGCs). A large portion of BGCs remain undiscovered in microbial genomes and metagenomes, representing a pressing challenge in unlocking the full potential of natural product diversity. In this work, we propose BGC-Prophet, a language model based on the transformer encoder that captures the distant location-dependent relationships among biosynthetic genes, allows accurately and efficiently identifies known BGCs and extrapolates novel BGCs among the microbial universe. BGC-Prophet is the first ultrahigh-throughput (UHT) method that is several orders of magnitude faster than existing tools such as DeepBGC, enabling pan-phylogenetic screening and whole-metagenome screening of BGCs. By analyzing 85,203 genomes and 9,428 metagenomes, new insights have been obtained about the diversity of BGCs on genomes from the majority of bacterial and archaeal lineages. The profound enrichment of BGCs in microbes after important geological events have been revealed: Both the Great Oxidation and Cambrian Explosion events led to a surge in BGC diversity and abundance, particularly in polyketides. These findings suggest that it is a general but constantly evolving approach for microbes to produce secondary metabolites for their adaptation in the changing environment. Taken together, BGC-Prophet enables accurate and fast detection of BGCs on a large scale, holds great promise for expanding BGC knowledge, and sheds light on the evolutionary patterns of BGCs for possible applications in synthetic biology. HighlightsO_LIBGC-Prophet shows superior performance to existing tools in terms of accuracy and speed. C_LIO_LIBGC-Prophet is the first ultrahigh-throughput (UHT) method that enables pan-phylogenetic screening and whole-metagenome screening of BGCs. C_LIO_LIBGC-Prophet builds the comprehensive profile of BGCs on 85,203 genomes and 9,428 metagenomes from the majority of bacterial and archaeal lineages. C_LIO_LIBGC-Prophet reveals the profound enrichment pattern of BGCs after important geological events. C_LI

bioinformatics↗

bioRxiv2023

Protein domain embeddings for fast and accurate similarity search

Recently developed protein language models have enabled a variety of applications with the protein contextual embeddings they produce. Per-protein representations (each protein is represented as a vector of fixed dimension) can be derived via averaging the embeddings of individual residues, or applying matrix transformation techniques such as the discrete cosine transformation to matrices of residue embeddings. Such protein-level embeddings have been applied to enable fast searches of similar proteins, however limitations have been found; for example, PROST is good at detecting global homologs but not local homologs, and knnProtT5 excels for proteins of single domains but not multi-domain proteins. Here we propose a novel approach that first segments proteins into domains (or subdomains) and then applies the discrete cosine transformation to the vectorized embeddings of residues in each domain to infer domain-level contextual vectors. Our approach, called DCTdomain, utilizes predicted contact maps from ESM-2 for domain segmentation, which is formulated as a domain segmentation problem and can be solved using a recursive cut algorithm (RecCut in short) in quadratic time to the protein length; for comparison, an existing approach for domain segmentation uses a cubic-time algorithm. We showed such domain-level contextual vectors (termed as DCT fingerprints) enable fast and accurate detection of similarity between proteins that share global similarities but with undefined extended regions between shared domains, and those that only share local similarities.

bioinformatics↗

bioRxiv2023

Altered hepatic metabolism mediates sepsis preventive effects of reduced glucose supply in infected preterm newborns

Preterm infants are susceptible to neonatal sepsis, a syndrome of pro-inflammatory activity, organ damage and altered metabolism following infection. Given the unique metabolic challenges and poor glucose regulatory capacity of preterm infants, their glucose intake during infection may have a high impact on the degree metabolism dysregulation and organ damage. Using a preterm pig model of neonatal sepsis, we previously showed that a drastic restriction in glucose supply during infection protects against sepsis via suppression of glycolysis-induced inflammation, but results in severe hypoglycemia. Now we explored clinically relevant options of reducing glucose intake to decrease sepsis risk, without causing hypoglycemia and further explore the involvement of the liver in these protective effects. We found that a reduced glucose regime during infection increased survival via reduced pro-inflammatory response, while maintaining normoglycemia. Mechanistically, this intervention enhanced hepatic oxidative phosphorylation and possibly gluconeogenesis, and dampened both circulating and hepatic inflammation. However, switching from a high to a reduced glucose supply after debut of clinical symptoms did not prevent sepsis, suggesting metabolic conditions at the start of infection are key in driving the outcome. Finally, an early therapy with purified human inter-alpha inhibitor protein, a liver derived anti-inflammatory protein, partially reversed the effects of low parenteral glucose provision, likely by inhibiting neutrophil functions that mediate pathogen clearance. Our findings suggest a clinically relevant regime of reduced glucose supply for infected preterm infants could prevent or delay the development of sepsis in vulnerable neonates.

immunology↗

bioRxiv2023

Mechanical Force Imprints Neutrophils to Orchestrate Pulmonary Homeostasis

Neutrophils are the most abundant immune cells that constantly patrol and marginate into the vascular beds of multiple tissues to support immune homeostasis. The extent to which neutrophils undergo adaptation in response to diverse tissue microenvironments, and the resultant biological implications of such adaptation, remains unclear. Here, we performed intravital imaging, transcriptional, and functional analyses of neutrophils in different tissues. Our findings showed that the lung harbors a transcriptionally distinct neutrophil population with unique migratory behaviors. These resident-like neutrophils guarantee a rapid response upon infection and play an important role in maintaining the homeostasis of pulmonary vasculature. Notably, pulmonary neutrophils are imprinted by mechanical cues via the mechanosensitive ion channel Piezo1. Mice with conditional Piezo1 ablation lost lung-specific neutrophil signatures, and showed impaired lung capillary angiogenesis. Furthermore, these mice displayed increased susceptibility to airway infection. Collectively, these data identify mechanical sensing via Piezo1 as an important driver for tissue-specialized neutrophils that support pulmonary homeostasis.

immunology↗

bioRxiv2023

Structural basis of EHEP-mediated offense against phlorotannin-induced defense from brown algae to protect akuBGL activity

The defensive-offensive associations between algae and herbivores determine marine ecology. Brown algae utilize phlorotannin as their chemical defense against the predator Aplysia kurodai, which uses {beta}-glucosidase (akuBGL) to digest the laminarin in algae to glucose. Moreover, A. kurodai employs Eisenia hydrolysis-enhancing protein (EHEP) as an offense to protect akuBGL activity from phlorotannin inhibition by precipitating phlorotannin. To underpin the molecular mechanism of this digestive-defensive-offensive system, we determined the structures of apo and tannic-acid (TNA, a phlorotannin-analog) bound form of EHEP, as well as apo akuBGL. EHEP consisted of three peritrophin-A domains formed in a triangle and bound TNA in the center without significant conformational changes. Structural comparison between EHEP and EHEP-TNA led us to find that EHEP can be resolubilized from phlorotannin-precipitation at an alkaline pH, which reflects a requirement in the digestive tract. akuBGL contained two GH1 domains, only one of which conserved the active site. Combining docking analysis, we propose the mechanisms by which phlorotannin inhibits akuBGL by occupying the substrate-binding pocket, and EHEP protects akuBGL against the inhibition by binding with phlorotannin to free the akuBGL pocket.

biochemistry↗

bioRxiv2023

Gut microbiota-derived gamma-aminobutyric acid from metformin treatment reduces hepatic ischemia/reperfusion injury through inhibiting ferroptosis.

Hepatic ischemia/reperfusion injury (HIRI) is a common and inevitable factor leading to poor prognosis in various liver diseases, making the outcomes of current treatments in clinic unsatisfactory. Metformin has been demonstrated to be beneficial to alleviate HIRI in recent studies, however, the underpinning mechanism remains unclear. In this study, we found metformin mitigates HIRI-induced ferroptosis through reshaped gut microbiota in mice, which was confirmed by the results of fecal microbiota transplantation (FMT) treatment but showed the elimination of the beneficial effects when gut bacteria were depleted using antibiotics. Detailedly, through 16S rRNA and metagenomic sequencing, we identified that the metformin-reshaped microbiota was characterized by the increase of gamma-aminobutyric acid (GABA) producing bacteria. This increase was further confirmed by the elevation of GABA synthesis key enzymes, glutamic acid decarboxylase (GAD) and putrescine aminotransferase (PAT), in gut microbes of metformin-treated mice and healthy volunteers. Furthermore, the benefit of GABA against HIRI-induced ferroptosis was demonstrated in GABA-treated mice. Collectively, our data indicate that metformin can mitigate HIRI-induced ferroptosis by reshaped gut microbiota, with GABA identified as a key metabolite.

microbiology↗

bioRxiv2023

Fingering instability accelerates population growth of an expanding cell collective

During the expansion of a cell collective, such as the development of microbial colonies and tumor progression, the local cell growth increases the local pressure, which in turn suppresses cell growth. How this pressure-growth coupling affects the expansion of a cell collective remains unclear. Here, we answer this question using a continuum model of cell collective. We find that a fast-growing leading front and a slow-growing interior of the cell collective emerge due to the pressure-dependent growth rate. The leading front can exhibit fingering instability and we confirm the predicted instability criteria numerically with the leading front explicitly simulated. Intriguingly, we find that fingering instability is not only a consequence of local cell growth but also enhances the entire populations growth rate as positive feedback. Our work unveils the fitness advantage of fingering formation quantitatively and suggests that the ability to form protrusion can be evolutionarily selected.

biophysics↗