Biology subjects

Zhang, C.

Publications and source records attributed to Zhang, C..

At least 181 records · Page 10Linked to original sources

bioRxiv2025

Sugar ABC transporter repertoires predict ecological dynamics in gut microbiome communities

The gut microbiome plays a central role in human health, but modern diets and lifestyles alter its composition. The microbial genomic traits that drive these ecological shifts, particularly in response to dietary sugars, remain poorly characterized. Here, we integrate a large dataset of longitudinal human diet-microbiome records and comparative genomics of human and murine gut isolates with in vitro and in vivo experiments to identify sugar ABC (ATP-binding cassette) transporters as key predictors of bacterial fitness and microbial community responses to dietary sugars. Strains encoding these transporters exhibit enhanced growth and consistently outcompete others in both monocultures and complex consortia across contexts. In gnotobiotic mice, dietary sugar supplementation selectively increases the expansion of sugar ABC transporter-positive bacteria, including the model gut pathobiont Escherichia coli. Systematic deletion of sugar transporter genes in E. coli revealed that a specific sugar ABC transporter gene was required to invade a model gut consortium, highlighting its importance in microbial competition. Together, these findings establish sugar ABC transporters as genomic predictors of microbial community dynamics in response to dietary sugars.

microbiology↗

bioRxiv2025

Cross-task, explainable, and real-time decoding of human emotion states by integrating grey and white matter intracranial neural activity

Decoding human emotion states from intracranial neural activity is key in developing brain-computer interfaces and new therapies for affective disorders. However, real-world application of decoding requires high performance that integrates neural activity from both grey and white matter, stable generalization across different contexts, sufficient neural encoding explainability, and robust real-time implementation, all of which remain elusive. Here, we simultaneously recorded intracranial electroencephalogram (iEEG) and abundant self-rated valence and arousal scores across two emotion-eliciting tasks in eighteen subjects, forming the largest intracranial neural dataset for emotion decoding. We then developed personalized decoding models within a hybrid deep learning framework involving both self-supervised and supervised components. The models achieved high-performance decoding of continuous valence/arousal states, doubling the R-squared performance of prior EEG/iEEG decoding. Critically, the models significantly improved performance by integrating grey and white matter signals and demonstrated cross-task generalization. The models further revealed shared and preferred mesolimbic-thalamo-cortical subnetworks encoding valence and arousal, showing neurophysiological explainability. Finally, the models realized robust real-time decoding in four new subjects. Our results have implications for advancing emotion decoding neurotechnology toward deployable affective brain-computer interfaces and closed-loop therapeutic systems for affective disorders.

neuroscience↗

bioRxiv2025

MicroRNA156 and its targeted SPL genes interact with the photoperiod, vernalization, and gibberellin pathways to regulate wheat heading time

Heading time has a large impact on adaptation to different environments and crop productivity. In this study, we characterized the effect of the endogenous age pathway on heading time and its interactions with the photoperiod and vernalization pathways in the leaves of tetraploid wheat (Triticum turgidum ssp. durum). Plants with reduced levels of microRNA156 or increased expression of its downstream targets, the SQUAMOSA PROMOTER BINDING PROTEIN-LIKE genes SPL3, SPL4, and SPL13 exhibited accelerated heading time, with stronger effects under suboptimal inductive conditions. Earlier heading was associated with the upregulation of miR172 and flowering-promoting genes VRN1, FUL2, and FT1 and the downregulation of flowering-repressing genes AP2L1 and VRN2. Additionally, we uncovered complex interactions among SPL, SQUAMOSA (VRN1 and FUL2) and DELLA proteins that modulate wheat heading time. We showed that DELLA proteins, which are negative regulators in the gibberellic acid pathway, can interact with SPL proteins reducing their ability to induce flowering. We also discovered previously unknown interactions between SQUAMOSA and DELLA proteins in wheat that compete with the DELLA-SPL interactions, likely reducing DELLAs ability to repress SPL3 and SPL4 activity. Since SPL3 and SPL4 directly promote VRN1 and FUL2 transcription, these interactions generate a positive regulatory feedback loop that accelerates wheat heading time. Finally, we developed dominant miR156-resistant alleles rSPL3, rSPL4, and rSPL13 that accelerate wheat heading time under both optimal and suboptimal inductive conditions. These publicly available genetic resources can be used to fine tune heading time and improve wheat adaptation to changing environments. Significance statementWheat heading time is critical for adaptation to diverse environments. We generated dominant mutations for the SPL3, SPL4 and SPL13 genes that accelerate heading time. Different combinations of these mutations can be used to modulate heading time and improve wheat adaptation to changing environments.

plant biology↗

bioRxiv2025

Deep Learning Enables Automated Segmentation and Quantification of Ultrastructure from Transmission Electron Microscopy Images

Transmission electron microscopy (TEM) has become an essential technique for observing subcellular ultrastructure, and is widely used in both clinical diagnosis and biomedical research. However, analysis of TEM data remains extremely labor-intensive and often inconsistent across operators due to the lack of dedicated computational methods. Here, we present TEAMKidney, a deep learning framework for accurate and scalable measurement of ultrastructures in TEM images across species, magnifications, and instrument platforms. We collected 12,991 TEM images from patients with multiple kidney diseases and from different animal models. By combining a self-training-based semantic segmentation stage with a TEM-tailored panoptic segmentation model, we address two major challenges in TEM data analysis: the lack of accurately labeled training data and the difficulty of achieving high segmentation accuracy for complex ultrastructure. Application of TEAMKidney to both human and animal images successfully reveals disease-associated changes in two critical glomerular ultrastructures: the glomerular basement membrane and podocyte foot processes. In addition to significantly outperforming existing tools, TEAMKidney shows close agreement with pathological expert measurements used in clinical assessment protocols. By reducing dependence on manual tracing while preserving expert-level accuracy, TEAMKidney demonstrates that deep learning can substantially reduce the burden of image analysis in both clinical pathology and biomedical research settings.

bioinformatics↗

bioRxiv2025

Differential Wnt/β-Catenin Signaling via TCF7L2/LEF1 Binding Specificity Shapes Cellular and Tumor Phenotypes

The mechanisms by which Wnt/{beta}-catenin signaling regulates gene expression in a tissue- and context-specific manner remain poorly understood, limiting our ability to target the aberrant cell growth typical of many Wnt-driven cancers. Here we focus on malignant liver tumors driven by activating CTNNB1 ({beta}-catenin) mutations that nevertheless display distinct phenotypic states and Wnt outputs. By profiling patient-derived organoids via single-cell transcriptomics and chromatin dynamics, we identify subtype-specific transcriptional and epigenetic profiles. Using CUT&RUN, we show that {beta}-catenin engages distinct genomic regions, dictated by differential association with TCF/LEF family transcription factors. Specifically, we define a novel sequence-specific regulatory element engaged by {beta}-catenin only upon interaction with TCF7L2, revealing that partner choice, independent of CTNNB1 mutational status, ultimately determines cell fate. Our findings, validated across multiple tumor models and patient tissues, offer a framework for understanding how differential {beta}-catenin-TCF/LEF interaction orchestrates context-specific Wnt signaling outcomes. SignificanceWnt/{beta}-catenin signaling is crucial for development and cancer, yet how it drives different gene programs across tissues is unclear. Using patient-derived liver tumor organoids, we show that {beta}-catenins transcriptional output depends on its binding partner: LEF1 or TCF7L2. These factors guide {beta}-catenin to distinct genomic regions, activating either stemness or differentiation genes. We identify a novel helper motif that directs {beta}-catenin-TCF7L2 binding and target selection. By linking partner choice and motif specificity to context-dependent gene regulation, our work provides a unifying mechanism explaining how Wnt/{beta}-catenin signaling produces diverse cellular outcomes.

cancer biology↗

bioRxiv2025

StrainOptimizer empowers rational cell factory design through multi-scale metabolic models with expression and proteome constraints

The rational design of microbial cell factories for high bioproduction remains a key challenge in metabolic engineering. While advanced modelling frameworks incorporating protein resource allocation, such as enzyme-constrained models (ecGEMs) and Expression and Thermodynamic Flux (ETFL), provide superior predictive power, their application is limited by a lack of user-friendly computational tools. Here, we present strainOptimizer, a comprehensive computational platform for rational strain design that systematically evaluates key resource allocation principles: the coupling of gene expression with metabolism, subcellular compartmentalization, and enzyme capacity limitations. Our benchmark analyses demonstrate that each principle offers distinct advantages: models coupling metabolism and expression (like ETFL) enable the identification of non-metabolic targets, organelle-level proteomic constraints improve precision for high-protein-cost products, and protein-usage-based objectives consistently outperformed traditional flux-based approaches. To demonstrate its practical utility, we applied strainOptimizer to an engineered sclareol-overproducing Saccharomyces cerevisiae strain. The platform identified novel targets, and experimental validation confirmed a 67% success rate, increasing the final sclareol titer by 14-26% and productivity by up to 45%. StrainOptimizer bridges the gap between resource allocation theory and applied engineering, providing a powerful, validated tool to accelerate the development of high-performance cell factories.

bioengineering↗

bioRxiv2025

SIMAIS: A swarm intelligence inspired biosensor for rapid protein detection

Swarm intelligence, the collective behaviour of animals such as birds, fish, and ants, arises from simple local interactions and enables survival, efficient foraging, and decision-making without central control. Inspired by this principle, we aimed to harness swarm intelligence for biosensing applications. Conventional assays often struggle with limited sensitivity, long processing times, and the need for specialised instruments, restricting their use in decentralised healthcare. Here we developed a swarm intelligence of microbead-inspired, artificial intelligence (AI)-assisted, and smartphone-based (SIMAIS) biosensing platform that can transform invisible molecular recognition into visible, macroscale patterns. Millions of antibody-coated magnetic microbeads (MBs) organise into magnetically induced structures upon binding protein biomarkers, with the resulting patterns correlating directly to biomarker concentration. Using interferon-gamma (IFN-{gamma}) as a model biomarker, we demonstrate that this approach delivers results within 10 minutes and achieves a tenfold increase in sensitivity compared to conventional lateral flow assays. Robust performance in human serum and urine confirms clinical applicability. By mimicking natural swarm behaviour, this platform introduces a new biosensing paradigm that bridges biology and engineering. Its versatility, speed, and ease of use highlight its potential for decentralised diagnostics, early disease detection, and broader applications in point-of-care healthcare.

biophysics↗

bioRxiv2025

DomDiff: protein family and domain annotation via diffusion model and ESM2 embedding

Accurate identification of conserved protein domain boundaries and their classification are fundamental to genome annotation, but are hindered by ambiguous boundaries, cross-domain interference, and limited samples for rare families. Here, we present DomDiff, a supervised conditional diffusion framework that reformulates the task as a generative process. Taking ESM2 embeddings, secondary structures, and biLSTM priors as inputs, it generates labels from Gaussian noise through iterative denoising, allowing coarse-to-fine optimization. We conducted a series of benchmark analyzes on publicly available protein sequence datasets, showing that DomDiff outperforms existing methods in domain boundary identification and classification, delivering performance gains of 12.6% in boundary detection and 4.2% in classification accuracy compared to other leading models. It excels particularly in annotating rare families, offering a powerful tool for specific applications such as large-scale genome annotation and functional characterization of novel proteins, thus providing a new paradigm for few-shot challenges in bioinformatics.

genomics↗

bioRxiv2025

A splice-switching antisense oligonucleotide approach for pediatric genetic epilepsies

Summary ParagraphVariants in ion channel genes are common causes of pediatric epilepsy, often leading to intractable seizures, developmental delay and other comorbidities, which increases risk of death. Pathogenic variants in the SCN8A gene, which encodes a voltage-gated sodium channel critical for action potential generation in the brain, account for [~]1% of genetic epilepsies. The voltage sensor in SCN8A domain 1 is encoded by one of two developmentally-regulated mutually exclusive alternative exons, 5N and 5A. We observe that variants in these exons are more likely to cause infantile spasms, a severe seizure type, than variants elsewhere in SCN8A, and that some pathogenic variants affect exon 5 splicing, impacting patient phenotype. Molecular and evolutionary analyses implicate the exon sequences of these and other voltage-gated ion channel alternative exons in splicing regulation. We identified antisense oligonucleotides (ASOs) that shift splicing of SCN8A exon 5N to 5A or vice versa. These ASOs normalize neuronal activity in patient-derived iPSC neurons, and reduce seizures and motor impairment and extend lifespan in a new exon 5N mutant mouse model. Our results demonstrate that splice-switching ASOs can effectively reduce the expression of pathogenic isoforms and rescue both seizure and non-seizure phenotypes. Similar approaches should be applicable to pediatric genetic epilepsies caused by mutations in other ion channel alternative exons.

neuroscience↗

bioRxiv2025

StoPred: Accurate Stoichiometry Prediction for ProteinComplexes Using Protein Language Models and GraphAttention

Proteins often function as part of complexes, and the specific stoichiometry of these assemblies is critical for their biological roles, but experimental determination of assembly composition remains challenging and existing computational methods for stoichiometry prediction are limited. Existing approaches rely on template-based searches or require predefined stoichiometry for structure prediction, hampering their applicability to proteins without close homologs or known assembly states. Recent advances using protein language models (pLM) have enabled sequence-based prediction of homo-oligomer stoichiometry, but these methods are not applicable to hetero-oligomeric complexes and do not fully leverage inter-subunit relationships. Here, we present StoPred, a method that predicts the stoichiometry of protein complexes by integrating pLM embeddings with a graph attention network to model subunit-level interactions. StoPred infers stoichiometry directly from sequence or structure features for both homo-and hetero-oligomers, without requiring template assemblies or predefined composition. We benchmark StoPred against deep learning-based and template-based methods, and show that it achieves improved accuracy and efficiency across curated and blind datasets, with up to 16% and 41% higher top-1 accuracy for homomeric and heteromeric complexes, respectively, compared to the strongest prior method on our held-out test dataset. More importantly, StoPred is the first deep learning-based method capable of accurately predicting the stoichiometry of hetero-oligomeric complexes.

bioinformatics↗

bioRxiv2025

RNA viruses in water shape the viromes of shrimp and fish in aquaculture systems

Aquatic environments host immense RNA viral diversity, yet the extent to which waterborne viruses influence the virome of aquatic animals remains unclear. Here, we conducted a comprehensive longitudinal metatranscriptomic analysis of water, shrimp, and fish across multiple culture stages in aquaculture systems. We identified 3,211 RNA viruses spanning 21 viral supergroups, with 97.4% representing novel species. Water exhibited the greatest viral diversity (3,093 species), followed by shrimp (1,280) and fish (398), with most viruses detected in shrimp and fish also detected in water. Diversity analyses revealed increasing richness and significant compositional shifts in 92 high-abundance viruses (>1,000 RPM) across culture stages. Viral abundance in aquatic animals was strongly correlated with that in water, and the temporal trajectories of the 16 most abundant viruses were closely synchronized between water and shrimp. Furthermore, 337 virus species were shared among shrimp and two fish species, of which 309 were present in water. These findings demonstrated that RNA viruses in water shape the composition and dynamics of aquatic animal viromes, underscoring the importance of water virome surveillance and management for disease prevention in shrimp aquaculture.

microbiology↗

bioRxiv2025

Cancer-specific sialylation of insulin-like growth factor 1 receptor impairs therapeutic antibody binding and efficacy

Despite extensive efforts to develop insulin-like growth factor (IGF1R)-targeted therapies for various malignancies, none has received clinical approval in the past two decades. Here, we reveal that N-glycan sialylation significantly decreases recognition by the humanized monoclonal anti-IGF1R antibody ganitumab across various cancer types, reducing its efficacy both in vitro and in vivo. Sialoforms of IGF1R are virtually absent in normal cells, indicating that the modification is tumor-specific. Pharmacological inhibition of sialyltransferases significantly sensitizes metastatic tumors to ganitumab in a ganitumab-resistant ovarian cancer model. Enzymatic removal of sialic acids from tissue sections resulted in marked enhancement in antibody binding to ovarian cancer patient tumors, but not normal tissues. Upregulation of 2-6 sialyltransferase ST6GAL1 in tumor tissues was found to be responsible for sialylation of IGF1R. Consequently, ST6GAL1-high tumors were more likely to benefit from desialylation-mediated enhancement of ganitumab binding. Furthermore, through comprehensive glycoproteomics analysis, structural prediction, and molecular dynamics simulation, we identify Asn-607 (N607) as a crucial site harboring sialylated glycans. Mechanistically, N607 glycosylation destabilizes the IGF1R-ganitumab complex. Overexpression of IGF1R Asn-607-Gln (N607Q) mutant in IGF1R-knockout cancer cells increases ganitumab efficacy compared to wild-type IGF1R in vivo. Taken together, these findings highlight sialylation as a common barrier in IGF1R-targeted therapies and provide crucial insights for therapy enhancement in cancer and patient stratification for future clinical trials. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=99 SRC="FIGDIR/small/682592v1_ufig1.gif" ALT="Figure 1"> View larger version (14K): org.highwire.dtl.DTLVardef@17c6d5corg.highwire.dtl.DTLVardef@1efe2f7org.highwire.dtl.DTLVardef@1dd05aorg.highwire.dtl.DTLVardef@159fb23_HPS_FORMAT_FIGEXP M_FIG C_FIG Synopsis2-6 sialylation of IGF1R Asn-607 by ST6GAL1 is prevalent in cancer. This modification disrupts the interaction of IGF1R with therapeutic mAb ganitumab. Removal of sialylation augments ganitumab efficacy.

cancer biology↗

bioRxiv2025

Polyketide Synthase-Like Functionality Acquired by Plant Fatty Acid Elongase

Fatty acid elongation (FAE) typically proceeds through a four-step cycle of condensation, reduction, dehydration, and reduction to yield very long-chain fatty acids. Here, we describe a variation of this pathway in Orychophragmus limprichtianus, whose seed oil was found to contains previously unknown C24-C28 keto-hydroxy fatty acids that account for [~]25% of total fatty acids. Our studies revealed that these compounds are produced through an endoplasmic reticulum-localized discontinuous elongation process in which a 3-keto hydroxy intermediate bypasses full reduction and is extended through a polyketide synthase-like mechanism. Transcriptomic and functional assays identified two divergent enzymes, OlFAE1-2 and the low-activity ketoreductase OlKCR1-1, as central to this process. Protein modeling and mutant analysis suggest that specific amino acid substitutions underlie the altered activity of OlKCR1-1, enabling accumulation of keto intermediates. A portion of these unusual fatty acids accumulated in estolides, complex triacylglycerols that expand the chemical repertoire of seed oils. Together, our findings reveal unexpected flexibility in plant fatty acid elongation and provide new tools for engineering plants and microbes to produce renewable oils with tailored industrial functions.

plant biology↗

bioRxiv2025

Interface-Sensitive Epi-Coherent Anti-Stokes Raman Scattering Microscopy for Imaging Cell Adhesion Dynamics

Studying cell adhesion dynamics is critical to understand how cells interact with the extracellular matrix and how they migrate. Conventional microscopy techniques for interfacial studies either lack chemical information or require fluorescence labeling. We found that homodyne epi-coherent anti-Stokes Raman scattering (epi-CARS) microscopy, when focused as interfaces, has intrinsic interfacial selectivity. The interference of the back-reflected nonresonant signals amplifies the contrast of cell adhesion areas, generating a distinctive negative contrast at attachment areas, which reflects the distance between the cell membrane and the substrate with tens of nanometers precision. The Epi-CARS configuration also provides high contrasts for cellular lipid droplets when applied in the inverted microscope configuration. Furthermore, by incorporating a pinhole into our confocal epi-CARS system, we can effectively reject out-of-focus reflections that degrade epi-CARS image quality, allowing us to amplify adhesion and lipid droplet contrasts for complex samples, such as cells. We applied this method to study cell-substrate adhesion dynamics of mitotic cells, revealing adhesion site splitting during mitosis and their outward development during postmitotic spreading. Furthermore, we found that after mitosis, the leading edges exhibited homogeneous adhesion regions with thicker water layers, whereas the retracting edges showed heterogeneous adhesion patterns with thinner layers at the contact sites.

biochemistry↗

bioRxiv2025

A correlative quantitative phase contrast and fluorescence super-resolution microscope for imaging molecules in their cellular context

Fluorescence microscopy has been widely used to reveal the spatial distribution of specifically labeled molecules, but it is blind to cellular context. Quantitative phase contrast microscopy (QPC) provides such complementary information. Here we have developed a platform that combines the QPC technique of correlative orientation-independent differential interference contrast (OI-DIC) microscopy with single-molecule super-resolution fluorescence microscopy. We demonstrate a detection sensitivity of 0.05 nm optical path difference, sufficient to detect single microtubules, and show its capability of 3D super-resolution fluorescence imaging in the cellular context. Additionally, we report deep-learning enabled digital staining, identifying nuclei, mitochondria and lipid droplets from OI-DIC data and demonstrate the potential of this approach for long-term live-cell imaging of organelles of interest without the need for fluorescence. OI-DIC can be easily integrated into most fluorescence microscopes and is readily adoptable by microscopy labs. ONE-SENTENCE TEASERA highly sensitive technique to visualize sub-cellular structures, dynamics, and molecules in their cellular context.

cell biology↗

bioRxiv2025

Diel rhythms shape viral community structure and activity across the host domains of life

Circadian rhythms, driven by endogenous clocks and synchronized with environmental cues, are fundamental to life on Earth. While extensively studied in diverse organisms, their influence on viral ecology remains largely unexplored. This study takes the temporal dynamics of viruses in Daya Bay as an example to uncover the rhythmic control of viral replication and activity. Using a high-resolution time-series dataset collected every 2 hours over a 3-day period, we identified a total of 22,151 viral operational taxonomic units (vOTUs) and 414 Nucleocytoplasmic Large DNA Virus (NCLDV) genomes. Our analysis revealed significant diel fluctuations, with 14.48% of vOTUs exhibiting diel patterns in metagenomic abundance and 1.97% showing diel transcriptional activity. We found that these abundant diel viruses infect hosts across the domains of life, including cyanobacteria, pelagibacteria, Marine Group II (MGII) archaea and protists, all known for their diel metabolism. The expanded spectrum of host diversity with diel viral interactions significantly broadens our understanding of virus-host rhythmic dynamics in natural environmental settings. A strong positive correlation was detected between the transcriptional activities of these diel viruses and their respective hosts. Contrary to bacteriophages, which mostly peaked during the day, we demonstrated that NCLDVs showed nocturnal diel abundance with a co-fluctuating diel transcriptomic activity pattern tightly hitched to their hosts, peaking at night and declining during the day. Furthermore, we identified a rich compendium of viral genes with significant diel expression patterns, including those related to structural protein production, DNA replication, and stress response. Notably, several essential viral genes involved in stress response and repair were found to be diel transcribed for the first time, including UV-endonuclease (UvdE), peroxidase, chaperones, and early light-induced protein (ELIP). Our findings suggest that viruses across host domains actively synchronize with environmental cues to optimize their replication and transmission, despite their dependence on host metabolism. This study provides novel insights into the rhythmic control of viral communities and their intricate interactions with hosts, with profound implications for microbial community succession and biogeochemical cycles.

ecology↗

bioRxiv2025

Macrophages display spatiotemporal specificity in intrahepatic cholangiocarcinoma and drive tumour progression via OSM and THBS1

Tumor-associated macrophages (TAMs) are abundant in intrahepatic cholangiocarcinoma (ICC) and correlate with poor prognosis, but their non-immunosuppressive roles remain unclear. This study aimed to define TAM transcriptional heterogeneity and map their interactions with ICC cells that promote malignancy beyond immune suppression. Single-cell and spatial transcriptome analysis with mouse and human ICC tissues revealed a TAM subpopulation, designated M{varphi}_VEGFA, that emerged as the major macrophage population from the intermediate stage of ICC development onward. Data from Gene Expression Omnibus (GEO) and spatial transcriptome indicate that M{varphi}_VEGFA was associated with poor prognosis and spatially localized to the ICC cells. Cell co-culture and cell-cell communication analyses revealed that M{varphi}_VEGFA promoted ICC cell proliferation via secreted OSM and downstream OSMR-STAT3-CCND1 axis, and enhanced ICC cell invasion through THBS1 and downstream CD47-mTOR/DBN1 signaling. Critically, multiplex immunofluorescence with human ICC tissue microarray indicated that OSM and THBS1 were co-elevated in human ICCs, and dual blockade of both targets inhibited ICC progression in mouse models. This study uncovers the direct and critical roles of M{varphi}_VEGFA in driving ICC progression with paracrine signaling and further highlights the potential of targeting macrophages in ICC treatment.

cancer biology↗

bioRxiv2025

Programming Microbe-Computer Interaction for Customized Life-Nonlife and Cross-Life Communication

This extension of human-computer interaction (HCI) paradigm to the non-intelligent microbe-computer interaction (MCI) is attractive for building generalized life-nonlife communication. Main challenges lie in how to construct biohybrid interface within individual MCI unit and thereafter program cross-life communication based on these MCI units. Here, we propose the design and construction of opto-interfaced MCI unit composed of engineered microbes and computer terminals, for enabling quantifiable transduction between microbial signals and digital signals. These units were able to serve as biohybrid sensors, actuators and transceivers, supporting biomonitoring, bio-actuation and microbe-centric bidirectional operations. Based on these functional units, we established programmable cross-life communication architectures capable of unicast, multicast, and logic-gate-driven communication with temporal dynamic control. The architectures were validated in biomedical scenarios including tumor hypoxia biomarking, epidermal heavy metal detoxification, and colitis intervention, demonstrating robust performance in closed-loop diagnostic-therapeutic applications. Additionally, we integrated MCI with an unmanned surface vessel for environmental monitoring, highlighting its cross-domain potential. Collectively, we provide a scalable strategy for building customized life-nonlife and cross-life communication, which is expected as an important complement to internet of things (IoT).

synthetic biology↗