Biology subjects

Kang, K.

Publications and source records attributed to Kang, K..

At least 19 recordsLinked to original sources

bioRxiv2026

KRASG12C Inhibition Remodels the Non-Small Cell Lung Cancer Surfaceome to Uncover Targetable Vulnerabilities

Mutant-selective and pan-RAS(ON) multi-selective inhibitors have improved clinical outcomes for patients with RAS-driven cancers; however, adaptive resistance limits the depth and durability of response. Using an integrated genetic, transcriptomic, and mass-spectrometry-based quantitative temporal proteomic platform, we profile the adaptive response to KRASG12C inhibitors (KRASi) in KRASG12C-mutant lung adenocarcinoma models. Our analyses reveal remodeling of signaling pathways and the cell surface proteome, including cell surface proteins selectively upregulated across cell lines, tumor models, and treatment durations. Folate receptor alpha (FOLR1) emerged as a therapeutically actionable target that is significantly upregulated with KRASi and is part of a broader acute cellular state transition characterized by acquisition of an epithelial identity. Combining KRASi with mirvetuximab soravtansine-gynx, a clinically approved FOLR1-targeting antibody drug conjugate, enhanced tumor cell cytotoxicity and deepened response and tumor control in cellular and in vivo models. Additionally, this work represents a comprehensive multi-omic resource characterizing non-genetic temporal adaptation to KRASi for future mechanistic and therapeutic investigations.

cancer biology↗

bioRxiv2026

KRAS inhibition drives adaptive surfaceome remodeling and reveals ALPP/ALPPL2 as a targetable vulnerability in pancreatic cancer

KRAS is mutated in over 90% of pancreatic ductal adenocarcinoma (PDAC) and is required for tumor initiation and maintenance. While clinical trials testing mutant-selective and pan-RAS inhibitors (KRASi) have demonstrated significant clinical benefit, genetic and non-genetic mechanisms of resistance limit the long-term efficacy of monotherapy strategies. Here, we used quantitative temporal analysis of the global, phosphorylation, and surface proteome to comprehensively identify non-genetic adaptive pathways and cell surface biomarkers in response to acute and long-term KRAS inhibition in PDAC. Through integrative machine learning analysis of the KRASi-proteome and publicly available datasets, we discovered top priority cell surface targets for combination therapy with KRASi. We identified Placental Alkaline Phosphatase (ALPP) as upregulated in response to KRASi through compensatory JAK2/STAT3 signaling. We developed a novel clinical grade ALPP/ALPPL2 (ALPG)-targeting antibody drug conjugate (ADC) with site-specific MMAE conjugation and demonstrated single agent efficacy in PDAC models, as well as enhanced internalization and anti-tumor efficacy in combination with KRASi. Together, these studies comprehensively define the KRASi-induced proteome and identify ALPP/ALPPL2 as a promising cell surface target for combination with KRASi to enhance response and forestall KRASi resistance.

cancer biology↗

bioRxiv2026

Individual-level expression deconvolution and assessment of cross-sample variation

Recovering cell-type-specific gene expression from bulk RNA sequencing would facilitate the study of transcriptional variation among individuals. However, accuracy can differ substantially among genes and cell types. We describe a reference-informed Bayesian deconvolution framework and a score that identifies gene--cell-type pairs likely to have more accurate estimates of cross-sample variation. The score uses bulk counts, reference expression profiles, and estimated RNA proportions. Known component expression is used to train and evaluate the score, but is not needed to calculate predictions from a trained model. We evaluated the approach in a ROSMAP-derived simulation with 40 target donors, 2,000 genes, and seven cell types. Median gene-wise correlation was 0.801 for raw allocated counts and 0.296 after normalization within each donor and cell type. To evaluate the score, we divided genes into five sets, kept linked genes together, and scored each set using a model trained on the other four. Retaining approximately 20\% of pairs within each cell type increased the median normalized correlation to 0.622. Ranking pairs only by the estimated share of a gene's bulk RNA contributed by the cell type yielded 0.570 at the same retained count. These results show that observable information can help prioritize pairs with more accurately recovered cross-sample variation.

bioinformatics↗

bioRxiv2026

Enhancing Mycobacterial Clearance via Heterologous BCG Prime Boost Strategy Using Recombinant Mycobacterium smegmatis

Bacille Calmette-Guerin (BCG) remains the only licensed tuberculosis (TB) vaccine, yet its protective efficacy against adult pulmonary TB is limited. To overcome this, we evaluated a novel recombinant Mycobacterium smegmatis (rMs) booster vaccine expressing Ag85B and ESAT-6 via the pMyong2 vector, combined with oat-derived {beta}-glucan adjuvant, to enhance BCG-primed immunity. BALB/c mice were primed with BCG and boosted twice with homologous BCG or varying doses of rMs with or without {beta}-glucan. Five weeks post-booster, mice were challenged intraperitoneally with M. tuberculosis H37Ra. Systemic bacterial clearance in liver tissue, cell-mediated immunity IFN-{gamma}, IL-17a, and antibody responses IgG, IgG2c, IgA were evaluated 3 weeks post-challenge. All rMs booster groups (rMs, high-dose rMs H, low-dose with {beta}-glucan, rMs {beta}) achieved complete systemic clearance of H37Ra with no detectable CFUs (colony forming unit), significantly outperforming mock-vaccinated and repeated BCG groups (**p < 0.01). Repeated BCG revaccination showed higher bacterial loads than single BCG (***p < 0.001), confirming no booster effect. Low-dose rMs with {beta}-glucan maintained 100% survival and total bacterial elimination despite a 50% dose reduction. Immune markers did not strictly correlate with clearance. Homologous BCG revaccination provides no additive protection, whereas pMyong2-based rMs confers robust systemic bacterial clearance. Adjuvanting with {beta}-glucan enables dose reduction while preserving maximum efficacy, establishing rMs as a promising heterologous TB booster candidate.

immunology↗

bioRxiv2026

Cell junction disruption drives translocation of gasdermin A and gasdermin B from cytoskeleton to plasma membrane during acantholysis

Gasdermins (GSDMs) are a family of pore forming protein that trigger pyroptosis by permeabilizing cell membranes. Pyroptotic cells often release the proinflammatory cytokines interleukin-1{beta} (IL-1{beta}), and IL-18, thereby promoting an inflammatory response. GSDMs are typically cleaved by caspases or granzymes, which enable their translocation to the membrane. Here, we showed GSDMA and GSMDB localize to the cytoskeletal fraction of keratinocytes. Disruption of cell junctions causes gasdermin A and B (GSDMA and GSDMB) to translocate to the membrane fraction in the absence of cleavage. Cell junction disrupted keratinocytes release post-translationally modified keratins, but not IL-1{beta} or IL-18. These events depend on endocytic mechanisms associated with recycling of cell junctional proteins. Our study suggests that cell junction disruption can drive translocation of GSDMA and GSDMB from cytoskeleton to plasma membrane in keratinocytes, however there may be a subsequent trigger that causes the conformational change allowing these gasdermins to form open pores.

immunology↗

bioRxiv2026

Multi-modal Graph Integration for Biologically Interpretable Domain Identification Using Spatial Transcriptomics

Functional domain identification in spatial transciptomics transforms spatial molecular measurements into mechanistic insights into tissue physiology and pathology. However, the inherent noise and sparsity of gene expression data, along with the locality-biased design of conventional graph-based approaches, fundamentally limit the accurate identification of complex tissue domains. In this study, we propose a novel Biologically Interpretable multi-modal Graph using Spatial Transcriptomics, called BIGraph-ST, that integrates pathway activity scores and histological image features for robust spatial domain identification. BIGraph-ST represents modality-specific similarity through affinity graphs and propagates spatial topology to capture higher-order connectivity within the tissue microenvironment. Experimental results demonstrated robust performance and notable improvements across multiple gold-standard benchmark datasets, particularly in cancer tissues. Moreover, BIGraph-ST provides biologically interpretable pathway-level representations of domains, which ultimately offers a valuable tool to gain biological in-sights into complex tissue architectures. The source code will be publicly available upon acceptance.

bioinformatics↗

bioRxiv2026

A multi-omics characterization reveals distinct molecular signatures in the human motor cortex and lumbar spinal cord in ALS

Amyotrophic lateral sclerosis (ALS) is a debilitating neurodegenerative disease characterized by the loss of upper motor neurons in the motor cortex (MTC) and lower motor neurons in the spinal cord, leading to muscle atrophy and ultimately respiratory failure. While motor neurons (MNs) are the selectively vulnerable cell type, their interactions with glia contribute to the progression of ALS pathology. However, it remains unclear whether the site of ALS symptom onset influences the molecular alterations underlying MN and glial dysfunction and whether these alterations are shared between the MTC and lumbar spinal cord (LSC). To address these questions, we constructed spatially-resolved gene expression maps of the MTC and LSC by combining spatial and single-nucleus transcriptomic profiles from a cohort of non-neurological controls and ALS donors clinically stratified by site of symptom onset. In the ventral horn of the LSC, we see a decrease in genes associated with MNs and synaptic signaling in ALS donors. We also identify region-specific alterations in endothelial- and glial-related functions. Notably, the severity of these MN deficits and endothelial-related functions is influenced by the site of symptom onset, whereas alterations in glial function largely are not. In contrast to the LSC, we observe layer-specific increases in synaptic signaling in the MTC of ALS donors. Comparing the molecular and cellular changes within the LSC and MTC in ALS indicates that they are predominantly non-overlapping, and have different molecular signatures.

genomics↗

bioRxiv2026

BRAID: RT-PCR-calibrated conformal intervals for splicing ΔPSI

Differential splicing workflows usually report a {Delta}PSI point estimate and a statistical score, but these outputs do not directly state whether the RNA-seq estimate is close enough to an orthogonal validation measurement. We developed BRAID as a post-processing calibration step for splicing analyses. BRAID estimates RNA-seq {Delta}PSI from rMATS inclusion and skipping counts, retains the upstream caller evidence, and adds a 95% interval whose width is calibrated from empirical RNA-seq-to-RT-PCR residuals using split conformal prediction. The packaged differential-splicing calibrator uses a residual half-width of q = 0.341, estimated from 162 RT-PCR-validated skipped-exon events. We evaluated BRAID on three RT-PCR validation datasets covering TRA2 knockdown, mouse cerebellum versus liver, and a prostate epithelial-to-mesenchymal comparison. On the pooled common set of 139 cassette-exon events, BRAID reached 0.971 RT-PCR coverage, whereas MAJIQ, betAS, and rMATS-derived intervals reached 0.518, 0.734, and 0.633, respectively. BRAID also had the lowest pooled interval score, 0.720, compared with 2.040 for MAJIQ, 1.414 for betAS, and 1.625 for rMATS. Applying the same residual calibration to other caller outputs brought MAJIQ, betAS, rMATS, and SUPPA2 {Delta}PSI estimates close to nominal RT-PCR coverage, indicating that the gain came from interval calibration rather than from a caller-specific point estimate. In a TRA2 positive-negative validation panel, using q as a hard rMATS effect-size cutoff reduced recall, whereas using q as an interval half-width improved RT-PCR coverage. Applied to a public DM1 skeletal-muscle rMATS table, BRAID reduced 967 large-effect significant events to 68 high-confidence interval-supported events and retained known DM1 and muscle-splicing signals. BRAID provides a practical calibrated reliability layer for RNA-seq splicing studies where downstream follow-up depends on the precision of reported {Delta}PSI estimates.

bioinformatics↗

bioRxiv2026

Aberrant 3'UTR splicing drives FUS-dependent mRNA condensates and prevents β-catenin from adherens junctions to promote cancer aggressiveness

The protein-coding sequence has long been considered the primary determinant of protein function. Alternative splicing within 3'UTRs (AS-3'UTRs) generates multiple transcript isoforms from a single gene, yet their roles in protein function and disease relevance remain largely unexplored. Through systematic transcriptome-wide identification of cancer-associated AS-3'UTRs, we uncover that AS-3'UTRs of {beta}-catenin mRNA direct distinct subcellular localization of {beta}-catenin mRNA isoforms. Specifically, an aberrantly spliced 3'UTR isoform promotes cytoplasmic mRNA condensate formation through FUS binding to a cancer-associated alternative exon (Exon 16A). Because {beta}-catenin function is exquisitely dependent on its subcellular distribution between adherens junctions and the nucleus, this aberrant 3'UTR isoform reprograms {beta}-catenin localization. By sequestering {beta}-catenin in the cytoplasm, the aberrant 3'UTR isoform prevents its incorporation into E-cadherin-based adherens junctions, thereby inducing epithelial-mesenchymal transition (EMT)-associated transcriptional programs. Notably, the expression signature of the aberrant 3'UTR isoform robustly correlates with poor clinical outcomes in colorectal cancer patients. Together, our findings reveal that AS-3'UTRs operate as a previously unrecognized post-transcriptional regulatory mechanism through which the untranslated region of mRNA, without altering a single amino acid, reprograms protein subcellular fate to drive oncogenic phenotypes.

cell biology↗

bioRxiv2026

A functional triangle relationship of topoisomerase IIβ, BARD1, and ERK2 in immediate early gene transcription

Immediate early genes (IEGs) encode master transcription factors, including EGR1, FOS, JUN, and MYC genes that drive robust transcription in the G1 phase of cell cycle. Our previous studies indicate that topoisomerase II{beta} (TOP2B) critically regulates IEG transcription and that the activities of TOP2B are dynamically controlled through post-translational modifications by BRCA1-BARD1 and ERKs. Here, we show that two E2 enzymes, UBCH5b and UBC13/MMS2, differentiate the effects and functions of TOP2B ubiquitination by BRCA1-BARD1. Comprehensive transcriptomics, proteomics, and biochemical and molecular cellular analyses revealed a close relationship between BARD1 and key mitogen-activated protein kinase pathway genes and identified activated ERK2 as a novel kinase that phosphorylates BARD1 at S391, a previously reported mitotic phosphorylation site, whose genetic mutation has been linked to tumorigenesis. Mechanistically, the catalytic activity of ERK2 stimulates TOP2B ubiquitination mediated by BRCA1-BARD1 in complex with UBCH5b and UBC13/MMS2, which controls the binding and function of TOP2B and BARD1 for transcriptional activation at representative IEGs. Taken together, our data propose that there is a functional regulatory circuit involving TOP2B, BARD1, and ERK2, three key transcriptional activators for IEG transcription, in which the gene association and catalytic activity of TOP2B are regulated through E2-differentiated ubiquitination by BRCA1-BARD1 and the phosphorylation of BARD1 by ERK2 for productive transcription.

molecular biology↗

bioRxiv2026

Synthetic Immunological Niche Reveals Early Immune Dysregulationand Stratifies Therapeutic Response in Type 1 Diabetes

In Type 1 Diabetes (T1D), disease onset and response to immunotherapy vary widely among individuals, reflecting heterogeneous stage-specific immune dysregulation that remains undefined. To investigate this heterogeneity, we engineered a microporous polycaprolactone scaffold that forms a synthetic immunological niche (IN) upon subcutaneous implantation, enabling in vivo capture of systemic immune dysregulation. In non-obese diabetic (NOD) mice, longitudinal transcriptomic profiling of IN-infiltrating cells identified early-stage genes relatively enriched for myeloid cells, followed by progressive increases in T cell-associated dysregulation at later stages that distinguished T1D progressors from non-progressors. We derived an early-stage IN-based T1D gene signature capturing immune alterations. The signature stratified NOD progressors from non-progressors as early as 6 weeks of age and was conserved across human T1D datasets, distinguishing T1D from non-diabetic individuals in spleen and pancreatic lymph node samples, but not peripheral blood. Signature-based stratification further revealed enrichment of macrophage-associated TNF- pathways in NOD progressors, validated in human T1D islets. Given heterogeneous response to anti-TNF- therapy, IN profiling identified resistance-associated mechanisms and enabled derivation of a pathway score that prospectively distinguished treatment-sensitive from resistant mice prior to therapy, establishing the IN as a minimally invasive platform for detecting stage-wise immune dysregulation and stratifying immunotherapy response in T1D.

bioengineering↗

bioRxiv2026

TACE reprograms RANKL-mediated differentiation of macrophages by activating the non-canonical pathway of IRF3

Inflammation is associated with an influx of inflammatory macrophages and increased activation and differentiation of osteoclasts. Receptor activator of NF-kB ligand (RANKL) is a key driver for osteoclast differentiation. However, the pathogenic mechanisms augmenting RANKL-induced osteoclast differentiation in inflammatory conditions are not fully elucidated. Here, we show that TNF- converting enzyme (TACE) plays a critical role in pathological bone erosion and enhances osteoclast differentiation in inflammatory conditions. Myeloid cell-specific TACE deletion in a murine arthritis model attenuates joint inflammation and bone destruction. TACE deficiency suppresses distal RANKL signaling in macrophages by increasing IRF3 activation, while enhancing proximal RANKL signaling, leading to the suppression of osteoclast differentiation. Mechanistically, IRF3 activation limits macrophage reprogramming by suppressing NFATc1 and HB-EGF in response to RANKL through a non-canonical pathway. HB-EGF, a TACE substrate, activates EGFR signaling and promotes osteoclastogenesis by inhibiting IRF3 activation. TACE regulates the reciprocal inhibition of the IRF3-HBEGF axis. Our study highlights a role for TACE as a rheostat, balancing both pro- and anti-osteoclastogenic signals.

cell biology↗

bioRxiv2026

PepMCP: A Graph-Based Membrane Contact Probability Predictor for Membrane-Lytic Antimicrobial Peptides

MotivationThe membrane-lytic mechanism of antimicrobial peptides (AMPs) is often overlooked during their in silico discovery process, largely due to the lack of a suitable metric for the membrane-binding propensity of peptides. Previously, we proposed a characteristic called membrane contact probability (MCP) and applied it to the identification of membrane proteins and membrane-lytic AMPs. However, previous MCP predictors were not trained on short peptides targeting bacterial membranes, which may result in unsatisfactory performance for peptide studies. ResultsIn this study, we present PepMCP, a peptide-tailored model for predicting MCP values of short peptides. We collected more than 500 membrane-lytic AMPs from the literature, conducted coarse-grained molecular dynamics (MD) simulations for these AMPs, and extracted their residue MCP labels from MD trajectories to train PepMCP. PepMCP employs the GraphSAGE framework to address this node regression task, encoding each peptide sequence as a graph with 4-hop edges. PepMCP achieved a Pearson correlation coefficient of 0. 883 and an RMSE of 0. 123 on th e node-level test set. It can recognize membrane-lytic AMPs with the predicted MCP values for each sequence, thereby facilitating mechanism-driven AMP discovery. Additionally, we provide a database, MemAMPdb, which includes the membrane-lytic AMPs, as well as the PepMCP web server for easy access. Availability and ImplementationThe code and data are available at https://github.com/ComputBiophys/PepMCP. Contactc.song@pku.edu.cn Supplementary InformationSupplementary data are available online.

bioinformatics↗

bioRxiv2026

Multi-modal and multi-region distance model for neuroimaging: Application to ABCD study

Large-scale neuroimaging studies often collect multiple modalities, such as task and resting-state functional MRI, diffusion MRI, and structural MRI. Joint inference across these modalities uses shared variation to improve statistical efficiency, increase replicability, and provide a more integrated view of brain-phenotype associations. In practice, however, such analyses are limited because complex cross-modality covariance cannot be flexibly modeled, which makes the resulting joint effects difficult to interpret. A recent distance-based ANOVA extension allows multimodal analysis and increases power for detecting group differences, but it cannot easily distinguish location from scale effects in distance space, offers only an omnibus pseudo-F test without interpretable parameters, and requires computationally intensive permutation inference. We propose a novel semiparametric, U-statistics-based Generalized Estimating Equation (UGEE) framework that unifies univariate and multivariate distance models. By regressing pairwise dissimilarities on covariates, this method yields interpretable regression coefficients that disentangle location and scale effects and quantify inter-modality differences, while flexibly accounting for correlations among modality distances. The estimator is based on efficient influence functions, ensuring asymptotic efficiency, robustness to misspecification, and computational scalability for large-scale data analysis. We evaluate the proposed method through extensive simulations and analyses of the Adolescent Brain Cognitive Development dataset. Results show that UGEE accurately estimates modality, group, and interaction effects and achieves a 100-fold speed-up compared with permutation-based approaches. This framework provides a general and computationally efficient tool for semiparametric inference on multimodal data, particularly suited for large neuroimaging applications.

neuroscience↗

bioRxiv2025

Improved Estimation of Correlation Accuracy for Machine Learning Brain-Phenotype Associations

Machine learning is used in neuroscience to examine brain-phenotype associations and facilitate individual prediction from high-dimensional brain imaging. For continuous phenotypes, Pearsons correlation between the observed and predicted phenotype is used to quantify model accuracy in testing data. However, recent research suggests millions of samples may be needed to reliably estimate the maximum achievable predictive accuracy (MAPA). We formally define the MAPA and show that Pearsons estimator is biased for this quantity and its confidence intervals fail to capture the target. We develop a semiparametric (double machine learning) one-step estimator that more accurately estimates the MAPA and yields valid confidence intervals across flexible machine learning settings. Analyzing data from the Reproducible Brain Charts dataset, we show that this estimator has smaller bias when estimating brain-phenotype associations of neuroimaging data with age and psychopathology phenotypes. We show that MAPA for psychopathology factor scores using machine learning models built on structural and functional imaging measures is not better than using demographic and nuisance covariates alone.

neuroscience↗

bioRxiv2025

Genetic and Epigenetic Reprogramming of Transposable Elements Drives ecDNA-Mediated Metastatic Prostate Cancer

Extrachromosomal DNAs (ecDNAs), which replicate and segregate in a non-Mendelian manner, serve as vectors for accelerated tumor evolution. By integrating chromatin accessibility, whole-genome sequencing, and Hi-C-based genome topology data from a cohort of metastatic Castration-Resistant Prostate Cancer (mCRPC) cases, we show that epigenetically activated repeat DNA, amplified in ecDNAs, drive oncogene overexpression. Specifically, we identify a subgroup of mCRPCs (20%) characterized by clusters of accessible LINE1 repeat DNA elements flanking the androgen receptor (AR) gene. These LINE1 elements are co-amplified with AR and provide binding sites for prostate-lineage transcription factors, including AR, FOXA1 and HOXB13. Accessible LINE1 elements establish novel 3D chromatin interactions with the AR gene, forging a new regulatory plexus driving AR overexpression and confers resistance to androgen signaling inhibitors. Our findings indicate how tumor evolution is driven by the convergence of genetic and epigenetic alterations on repeat DNA, activating and amplifying them to allow oncogene overexpression. Statement of significanceWe show how tumor evolution is driven by the convergence of genetic and epigenetic alterations on repeat DNA elements, resulting in their activation as regulatory elements and co-amplification in ecDNAs with oncogenes in mCRPC.

cancer biology↗

bioRxiv2025

Comparing Brain-Behavior Relationships Across Dimensional, Tail-Sampled, and Propensity-Matched Models

Large population cohorts are needed to perform brain-wide association studies (BWAS), with evidence that sampling from the tails of a distribution increases effect sizes and improves reproducibility. However, studies rarely compare how variability in sample sociodemographic characteristics relates to imaging or behavioral phenotypes within BWAS. To address this gap, we derived estimates for brain-behavior associations using multivariate regression models, comparing effect sizes for dimensional, tail-sampled, and propensity matched groups. Data were obtained from the Adolescent Brain Cognitive Development (ABCD) Study(R). The independent variables were brain structural imaging phenotypes, with a range of biological and psychological outcomes as dependent variables. We found expected increases in the magnitude of effect sizes moving from full-sample dimensional models to tail-sampled group-based models. However, findings for the propensity-matched group models suggested a non-uniform impact on BWAS (i.e., both increased and decreased effect sizes). Results suggest that sampling from the tails of the distribution of measures of brain structure generally increases effect sizes across biological, clinical, and cognitive outcomes.

neuroscience↗

bioRxiv2025

Sustainable production of plastic-degrading enzymes in Chlamydomonas pacifica

The discovery of a new extremophile alga, Chlamydomonas pacifica, provides an opportunity to expand on heterologous protein expression beyond the traditional Chlamydomonas reinhardtii. C. pacifica is a unicellular extremophile capable of surviving at high pH, high temperatures, and high salinity. These various growth conditions allow C. pacifica to outcompete any invading contaminants in open-air environments. Developing this novel species as a platform for recombinant protein production could significantly advance commercial microalgal recombinant protein production. We have previously shown that C. reinhardtii can secrete a plastic-degrading enzyme: a PETase known as PHL7. This PETase is capable of cleaving ester bonds and has been used commercially for the degradation of PET plastics. However, the expression of such an enzyme has yet to be done in open raceway ponds and on a large scale. Here, we describe the culturing of PHL7 transgenic C. pacifica strain in three 80L raceway ponds and the measurements of recombinant enzymatic expression and activity found in the culture media. Our work provides proof of concept that this new organism can produce functional PHL7 enzymes in addition to producing the valuable components that inherently exist in the C. pacifica algae biomass. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/654053v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@10065a3org.highwire.dtl.DTLVardef@110c456org.highwire.dtl.DTLVardef@1c9efc6org.highwire.dtl.DTLVardef@9ceea1_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗