Biology subjects

Yu, Y.

Publications and source records attributed to Yu, Y..

At least 145 records · Page 8Linked to original sources

bioRxiv2025

The antioxidant networks act as a firewall in maintenance of genomic integrity

Reactive oxygen species (ROS) threatens genomic integrity by both destruction of DNA metabolic enzymes and oxidative DNA damage. We find a negative correlation between oxidative DNA damage and activity of antioxidant enzymes. An antioxidant network formed by interactions between the antioxidant enzymes and the proteins involved in maintenance of genomic integrity was indentified in the nucleus by multi-proteomic investigations. The protein-protein interactions in the antioxidant network are up-regulated under oxidative stress, as proved by quantitative proximity ligation assays in situ within single cells. The antioxidant network catalyzes the ROS dismutation cascade before ROS attacks DNA-processing enzymes, thereby preventing DNA and DNA metabolic enzymes from oxidative damage and dysfunction. Therefore, the antioxidant network plays a firewall role in maintenance of genomic integrity.

biochemistry↗

bioRxiv2025

O-Acyltransferase Genes Involved in the Production of Volatile Sex Pheromones in Caenorhabditis elegans

Gene family expansions are critical for functional diversification, yet paralog contributions to metabolic pathways are often unclear. In Caenorhabditis, the expanded O-acyltransferase (OAC) family--enzymes that transfer acyl groups to hydroxylated substrates--remains poorly characterized despite having been implicated in lipid metabolism. Using CRISPR-Cas9 mutagenesis, behavioral assays, gas chromatographic-mass spectral (GC-MS) analyses, and metabolomics, we systematically analyzed 59 OAC-family protein-coding genes to define their roles in regulating signaling molecules. We found that four adjacent paralogs (oac-13, oac-16, oac-25, and oac-28) on chromosome I are required for synthesizing volatile sex pheromones (VSPs)--airborne signals critical for male mate-searching. Specifically, oac-13 and oac-16 are necessary for producing both major pheromone components, while the identical tandem paralogs oac-25 and oac-28 regulate the production of the later-eluting component in gas chromatography. Disruption of these genes reduced production of key pheromone components and impaired male attraction. Metabolomics revealed that oac-16 and other OACs also modulate synthesis and secretion of non-volatile ascaroside pheromones, indicating dual roles in chemical signaling. This work uncovers functional specialization within an expanded gene family, illustrating how redundancy and divergence enable adaptive evolution of communication systems.

biochemistry↗

bioRxiv2025

Robust CRISPR Screens Identify TPL1 as a Novel Long Noncoding RNA Driving Triple-Negative Breast Cancer Hallmarks

Despite the growing catalog of long noncoding RNAs (lncRNAs), the functional roles of their vast majority in cancer remain poorly defined. To systematically explore lncRNA dependencies in triple-negative breast cancer (TNBC), we compiled a comprehensive annotation by merging GENCODE, BIGTranscriptome, and MiTranscriptome databases and performed a CRISPR-Cas9 deletion screen targeting 1,029 TNBC-enriched lncRNAs. The screen revealed several essential lncRNAs and those modulating doxorubicin sensitivity, with TPL1 emerging among top hits. TPL1 silencing significantly impaired TNBC cell proliferation in both 2D and 3D cultures and reduced invasive capacity in an organ-on-chip model. Transcriptomic and proteomic profiling following TPL1 knockdown revealed downregulation of genes involved in ECM-receptor interaction, focal adhesion, cell migration, and PI3K-Akt signaling. Mechanistically, TPL1 directly interacted with key proteins including EIF4B, MDM2, TARBP2, TLE5, and GTPase RAN, suggesting TPL1 could regulate RNA processing, transcriptional repression, and translation, as well as modulate GTPase signaling pathways. Additionally, TPL1 functioned as a competing endogenous RNA (ceRNA), sequestering miR-10396b-5p, miR-486-3p, and miR-450a-2-3p, among others, thereby modulating expression of pro-tumorigenic targets. Clinically, TPL1 was significantly overexpressed in TNBC tissues, particularly in the BLIS subtype. Collectively, our findings highlight TPL1 as a key regulator of TNBC molecular networks and a promising therapeutic target.

cancer biology↗

bioRxiv2025

Distinguishing cortical indexes of arousal and awareness in sleeping patients with unresponsive wakefulness syndrome

Consciousness is assumed to be defined by two components: awareness and arousal. However, compared with awareness, the cortical index for arousal remains lacking in clues. Since patients with unresponsive wakefulness syndrome (UWS) are supposed to have no awareness but arousal, sleep-wake cycle in patients with UWS may reflect pure arousal fluctuations, and then could highlight the cortical indexes for arousal. This study recorded nighttime polysomnography in patients with UWS, patients with minimally conscious state and healthy controls. The EEG signal showed that spectral slope could index arousal, fluctuating among sleep stages in all three groups. Both spectral entropy and Lempel-Ziv complexity indexed awareness, showing a significant difference between conscious and unconscious states. These findings provide fundamental evidence for the two-component hypothesis of consciousness.

neuroscience↗

bioRxiv2025

The DDIT4L-TOM40-ATP5A pathway suppresses glioblastoma oncogenesis

The characteristics of glioblastoma (GBM), including resistance to cell death and aberrant energy metabolism, are associated with the function of mitochondria. To date, the inherent suppressors that couple mitochondrial function with cell death remain unclear. DNA damage-inducible transcript 4-like (DDIT4L) expression in human gliomas and its association with patient survival was determined by public databases, western-blot and immunostaining. The role of DDIT4L in regulating tumor growth was analyzed using lentivirus and PDX model. The mechanism of DDIT4L on GBM mitochondrial function was determined by different assays. Here, we show that DDIT4L is an endogenous inhibitor of GBM via suppressing mitochondrial function. DDIT4L was expressed at high levels in GBM and transported into mitochondria via single import channel TOM40 other than the import receptors TOM22, TOM20 or TOM70. Then, DDIT4L interacted with the subunit of ATP synthase, inhibited mitochondrial function and induced tumor cell apoptosis. Furthermore, the synthetic peptide DDIT4LV125-P132 suppressed GBM oncogenesis. Based on these results, DDIT4L suppresses GBM oncogenesis through both TOM40- and ATP5A-dependent mechanisms to curb mitochondrial dynamics and induce cell apoptosis, and the DDIT4LV125-P132 peptide is a potential therapy for GBM.

cancer biology↗

bioRxiv2025

Population-level super-pangenome reveals genome evolution and empowers precision breeding in watermelon

Pangenomes are increasingly critical for harnessing crop genetic diversity, yet their resolution and utility are often limited by insufficient sampling of high-quality genome assemblies. Here, we report a population-level watermelon super-pangenome constructed from 138 reference-grade assemblies, including 135 newly generated near-gapless genomes representing all seven watermelon species. The super-pangenome captures approximately one million structural variants (SVs), enabling accurate variant genotyping across ~900 watermelon accessions and substantially expanding variant discovery both across and within species. Broader sampling within the pangenome provides insights into genome evolution among watermelon species and sheds light on the origin of cultivated watermelon. SV-inclusive genome-wide association studies enhance trait mapping resolution and identify a copy number variation upstream of ClFCI1 that regulates flesh color intensity in a dosage-dependent manner. Leveraging this comprehensive variation map, we developed high-accuracy genomic prediction models for 18 agronomic traits. Together, our findings and genomic resources establish a foundational framework for dissecting complex traits and accelerating precision breeding in watermelon, while offering a valuable model for SV-resolved pangenomics in crop species.

plant biology↗

bioRxiv2025

Perceiving Material Qualities from Moving Contours

While research on the perception of line drawings has long demonstrated the importance of contours in object recognition, recent work shows that contours can also convey material properties. For example, even simple 2D shapes with varying contours have been shown to evoke vivid impressions of different materials (Pinna & Deiana, 2015). However, such static representations capture only a single moment in time. When a material moves, its contours shift, evolve, or deform over time, creating contour motion. Does this contour motion convey diagnostic information about material properties, independent of surface appearance? Existing studies on the role of dynamic cues in material perception either use fully rendered 3D stimuli, where contour motion is confounded with rich surface information, or motion-only displays (dynamic dot stimuli or noise patches), which eliminate surface cues but also lack clearly defined contours. As a result, the relative contribution of contour motion to material perception remains unclear. To address this gap, we measured how human observers perceive materials from dynamic line drawings ("line"), compared to animations of fully textured stimuli that carry optical and motion information ("full"), as well as dynamic dot stimuli ("dot"). Stimuli were rendered versions (full, dot, line) of material animations from five material categories (jelly, liquid, smoke, fabric, and rigid-breakable). In one experiment, participants rated five material attributes (dense, flexible, wobbly, fluid, airy motion), and in a second experiment, participants were asked to choose one of the two materials that is more similar to a third material across all possible combinations. Results from both experiments consistently reveal that 1) Dynamic line drawings vividly convey mechanical material properties, and 2) the similarity in material judgments between line and full conditions was larger than that between dot and full conditions. We conclude that contour motion carries rich information about mechanical material qualities.

neuroscience↗

bioRxiv2025

Large Impact of Genetic Data Processing Steps on Stability and Reproducibility of Set-Based Analyses in Genome-Wide Association Studies

Genome-wide association studies (GWAS) are crucial to human genetics research, yet their stability and reproducibility are often questioned. This work describes, analyzes, and provides tools for overcoming reproducibility challenges in two highly popular components of GWAS: set-based (a) hypothesis testing and (b) effect size estimation. Specifically, we focus on how the set-based natures of (a) and (b) often fuel non-reproducible results due to differences in data processing pipelines that are rarely discussed. First, we describe the processing challenges in a statistical model misspecification framework. Second, we analytically calculate the differences in power and amounts of bias that can arise in (a) and (b), respectively, due to small data processing choices. Third, we provide tools for quantifying and avoiding the data processing obstacles in GWAS. We validate our analytical calculations through a simulation study, and we demonstrate the aforementioned challenges empirically through analysis of a whole-exome sequencing study of pancreatic cancer. Author SummaryThe lack of reproducibility and stability in genome-wide association studies (GWAS) have been widely reported. Here, we demonstrate how such reproducibility challenges arise in a common component of GWAS, set-based hypothesis testing and estimation studies. Specifically, we show how minor, seemingly harmless decisions in how scientists prepare their data can lead to major differences in the final conclusions. These data processing steps are rarely reported in detail, further obscuring their importance. Our work precisely measures the impact of data processing steps on power and bias of common modeling approaches. As a partial solution for future GWAS, we also provide an R software package to interact with our results, which can be used to assess the impact of choices at the design stage of GWAS studies. We further analyze a pancreatic cancer dataset using two modern pipelines and show how the pipelines produce very different results for ATM, a gene that has been previously linked with pancreatic cancer. Using the tools provided by this work can help significantly improve the reproducibility and stability of set-based results, enhancing the translational potential of GWAS investigations.

genetics↗

bioRxiv2025

Receptor Tyrosine Kinase Profiling Identifies Chronic Constitutive Floodgate Oxidative Signaling in Glutathione-Independent Human Mammary Luminal Progenitor Cells

The human mammary epithelium contains a subset of luminal progenitor (LP) cells that are distinct from basal cells in both lineage potential and redox biology. LPs are uniquely equipped to tolerate oxidative stress through glutathione-independent mechanisms and have been implicated as candidate cells of origin in basal-like breast cancers. In this study, we identify the receptor tyrosine kinase (RTK) cKIT (CD117), as a defining feature of LPs and a key mediator of their expansion. cKIT is developmentally restricted to the LP compartment via Polycomb-mediated epigenetic repression in basal and luminal-committed cells. It is expressed in scattered epithelial cells within both ductal and alveolar regions of resting human mammary glands. Using RTK-engineered MCF10A models, we demonstrate that cKIT ligand/stem cell factor (SCF)-activated wildtype cKIT signaling is sufficient to drive proliferation in the absence of epidermal growth factor (EGF) and that cKIT is responsive not only to canonical ligands but also to hydrogen peroxide (H2O2). In primary human LPs, cKIT is rapidly phosphorylated upon exposure to SCF and H2O2, with concomitant AKT activation. These responses are enhanced when cKIT and EGFR signaling are co-engaged, suggesting a cooperative mitogenic program. In mammary gland, phosphorylation of the antioxidant enzyme PRDX1 is selectively detected in LPs, consistent with a floodgate model of redox signaling in which transient oxidative inactivation of peroxiredoxins (PRDXs) facilitates RTK signaling under elevated intracellular reactive oxygen species conditions. Clinically, elevated cKIT expression is associated with shorter progression-free survival in certain basal-like breast cancer, supporting a link between LP-like redox signaling states and aggressive tumor behavior. Together, these findings define a redox-integrated RTK signaling axis centered on cKIT that drives LP expansion and is associated with poor outcomes in a subset of basal breast cancers. This work establishes a mechanistic framework for targeting redox-responsive progenitor populations in both regenerative and oncologic context.

cancer biology↗

bioRxiv2025

QOBRA: A Quantum Operator-Based Autoencoder for De Novo Molecular Design

We introduce a variational quantum autoencoder tailored for de novo molecular design named QOBRA (Quantum Operator-Based Real-Amplitude autoencoder). QOBRA le circuits for real-amplitude encoding and the SWAP test to estimate reconstruction and latent-space regularization errors during back-propagation. Adjoint encoderand decoder unitary transformations and a generative process that ensures accurate reconstruction as well as novelty, uniqueness, and validity of the generated samples. We showcase QOBRA as applied to de novo design of Ca2+-, Mg2+-, and Zn2+-binding metalloproteins after training the generative model with a modest dataset. Significance StatementRecent advancements in classical generative machine learning have shown significant strides in molecular design for targeted applications. Nonetheless, these advancements are fundamentally limited by classical computation based on binary units. We introduce a quantum computation-based ML framework employing qubits, which exhibits the ability to synthesize de novo molecular instances with specified properties from limited datasets. Quantum networks require exponentially fewer parameters than classical ones, enhancing their trainability and efficiency. While our demonstration focuses on metalloprotein primary sequences, the paradigm is adaptable to diverse molecular designs. This integration of AI and quantum computing holds potential to expand the scientific and technological frontiers of both domains within a practical framework.

biochemistry↗

bioRxiv2025

Much higher covariation with foveation timing by superior colliculus than primary visual cortical neuronal activity

The superior colliculus (SC) is a potent driver for orienting movements, but it also possesses short-latency visual responses. Even though such responses largely derive from direct primary visual cortical (V1) input, their relationship to V1 activity has never been analyzed with the same experimental subjects and stimuli. Here, we revisited pioneering observations that trial-by-trial variability in either SC or V1 visual responses may predict eye movement timing variability. We found that V1 covariation with behavioral variability was virtually non-existent in comparison to the SC, whether with respect to visual response onset latency, visual response strength, or pre-stimulus state. By far, our largest predictor of behavioral variability was visual response strength in SC visual-motor neurons. These results suggest that the SC reformats its sensory inputs for exploitation of the SCs proximity to the motor periphery; V1 aids in jumpstarting the sensing process, but the SC much more directly supports visually-driven orienting.

neuroscience↗

bioRxiv2025

MuSHIN: A multi-way SMILES-based hypergraph inference network for metabolic model reconstruction

Genome-scale metabolic models (GEMs) are indispensable tools for probing cellular metabolism, enabling predictions of metabolic fluxes, guiding strain optimization, and advancing biomedical research. However, their predictive capacity is often compromised by incomplete reaction networks, stemming from gaps in biochemical knowledge, annotation inaccuracies, and insufficient experimental validations. Here we present MuSHIN (Multi-way SMILES-based Hypergraph Interface Network), a novel deep hypergraph learning method that integrates network topology with biochemical domain knowledge to predict missing reactions in GEMs. Evaluated on 926 high- and intermediate-quality GEMs with artificially removed reactions, MuSHIN significantly outperforms state-of-the-art methods, achieving up to a 17% improvement across multiple metrics and maintaining robust recovery even under severe network sparsity. Furthermore, MuSHIN substantially enhances phenotypic predictions in 24 draft GEMs associated with fermentation by resolving critical metabolic gaps, as validated against experimental measurements. Together, these findings highlight MuSHINs potential to advance GEM reconstruction and accelerate discoveries in systems biology, metabolic engineering, and precision medicine.

bioinformatics↗

bioRxiv2025

Stiffness sensing fuels matrix-driven metabolic reboot for kidney repair and regeneration

Kidney repair after acute kidney injury (AKI) relies on a finely tuned extracellular matrix (ECM) that provides structural integrity and mechanical cues. As primary ECM architects, fibroblasts and pericytes rapidly mobilize to the injury site post-AKI, yet the ECM-driven repair mechanisms remain incompletely defined. Here, leveraging tissue engineering, genetic and pharmacological models, and multi-omics, we profiled the proteome landscape of decellularized kidney matrix scaffold post-AKI and highlighted microfibrillar-associated protein 2 (Mfap2) as a key core matrisome component primarily sourced from fibroblasts and pericytes. Mfap2 loss disrupted kidney architecture and metabolism, aggravating AKI. Global proteomics revealed that Mfap2 deficiency suppressed tubular 3-hydroxy-3-methylglutaryl-CoA synthase 2 (Hmgcs2) expression via estrogen receptor 2 (Esr2)-mediated transcriptional repression and increased protein succinylation. Phosphoproteomics and spatial transcriptomics further demonstrated a shift in mechanical signaling, with Mfap2 loss hyperactivating mitogen-activated protein kinases and upregulating large tumor suppressor kinase 1 (Lats1) in tubular cells without altering integrin receptor activities. In turn, Lats1 suppressed Esr2 transcription independent of its canonical Yap/Taz effectors, without affecting ubiquitin-mediated Esr2 degradation. Therapeutically, Esr2 agonists restored kidney function in Mfap2-deficient models. These findings position Mfap2 as a key regulator of ECM dynamics and mechanosignaling, linking tissue stiffness to metabolic reprogramming required for kidney repair.

cell biology↗

bioRxiv2025

Non-inertial scan angle multiplier for expanded fields-of-view

In many laser scan engines, mechanical inertia imposes a tradeoff between mirror diameter (mass) and scan speed (cycle time) and/or scan angle amplitude. Ultimately, this limits the field-of-view and/or scan speed in multiphoton and confocal imaging, laser-based manufacturing, and other applications. To push parameters past this inertia limit, we present a non-inertial (stationary) add-on unit that doubles the scan angle amplitude while preserving both the beam size and cycle time, thus doubling the etendue (optical invariant) and overall system throughput. We demonstrate its use in two-photon calcium imaging of neural activity in living mice. We also adapt the approach to present a phase doubling unit that doubles the maximum range of phase modulators. Finally, we describe further variants including generalizing the approach to Nx scan angle multiplication (N = 2, 3, 4, ...) with diffraction limited performance across a wide scan angle range and over a broad wavelength bandwidth. These non-inertial scan angle multipliers (called Nisam2x, 3x, ...) can expand the capabilities of a range of technologies including imaging, adaptive optics, sensing, marking, and manufacturing.

neuroscience↗

bioRxiv2025

Species-specific oculomotor control tolerances predict saccadic suppression strength in macaques and zebrafish

Active sensing necessarily requires integrating information about both self-generated movements as well as past, present, and future afferent inputs. However, such information is inherently variable and relies, at least in part, on uncertain extrapolations. Here, using saccadic suppression of visual sensitivity as a classic example of sensory-motor integration, we show that suppression strength in two drastically different species, macaque monkeys and zebrafish larvae, may be a direct outcome of efficient state estimation in the presence of uncontrollable sensory and motor variability. Bayesian estimator models suggest that optimal saccadic suppression should rely not just on the sensory-motor information being processed, but also on the time-dependent magnitude of unexplained variability in the nervous system encoding it. In both macaques and zebrafish larvae, and using matching visual stimulation regimes optimized for each species receptive field properties, we experimentally measured saccadic suppression strength in the superior colliculus (SC) of the monkeys and the homologous optic tectum (OT) of the fish. We also experimentally quantified additive and multiplicative motor noise components in the oculomotor behavior of both species, and we furthermore estimated noise in the sensory systems. We found that inter-species differences in sensory and motor noise levels and their theoretically predicted impacts on visual sensitivity are consistent with our experimentally observed differences in saccadic suppression strength between the fish and the monkeys. Because sensory and motor noise levels can reflect the amounts of available neural resources committed to a given task, our results strongly underscore the value of incorporating computational resource limits into investigating performance differences that have evolved in homologous brain areas.

neuroscience↗

bioRxiv2025

Venus-MAXWELL: Efficient Learning of Protein-Mutation Stability Landscapes using Protein Language Models

In-silico prediction of protein mutant stability, measured by the difference in Gibbs free energy change ({Delta}{Delta}G), is fundamental for protein engineering. Current sequence-to-label methods typically employ the two-stage pipeline: (i) encoding mutant sequences using neural networks (e.g., transformers), followed by (ii) the {Delta}{Delta}G regression from the latent representations. Although these methods have demonstrated promising performance, their dependence on specialized neural network encoders significantly increases the complexity. Additionally, the requirement to individually compute latent representations for each mutant site negatively impacts computational efficiency and poses the risk of overfitting. This work proposes the Venus-MO_SCPLOWAXWELLC_SCPLOW framework, which reformulates mutation {Delta}{Delta}G prediction as a sequence-to-landscape task. In Venus-MO_SCPLOWAXWELLC_SCPLOW, mutations of a protein and their corresponding {Delta}{Delta}G values are organized into a landscape matrix, allowing our framework to learn the {Delta}{Delta}G landscape of a protein with a single forward and backward pass during training. Besides, to facilitate future works, we also curated a large-scale {Delta}{Delta}G dataset with strict controls on data leakage and redundancy to ensure robust evaluation. Venus-MO_SCPLOWAXWELLC_SCPLOW is compatible with multiple protein language models and enables these models for accurate and efficient {Delta}{Delta}G prediction. For example, when integrated with the ESM-IF, Venus-MO_SCPLOWAXWELLC_SCPLOW achieves higher accuracy than ThermoMPNN with 10x faster in inference speed (despite having 50x more parameters than ThermoMPNN). The training codes, model weights, and datasets are publicly available at https://github.com/ai4protein/Venus-MAXWELL.

bioinformatics↗

bioRxiv2025

Genetically Programmable Adhesive Protein Hydrogels for Sealing Perforating Corneal Trauma

The cornea, situated at the forefront of the eyeball, is often subjected to varying degrees of mechanical laceration injuries due to various traumatic incidents. Timely and precise suturing of corneal wounds to prevent the leakage of intraocular contents is a significant clinical challenge. Although some studies have attempted to use hydrogels to accurately cover corneal injuries, most of these hydrogels are only suitable for adhering to focal stromal defects that do not involve the corneal stroma. Here, a fully protein-based hydrogel, based on SpyTag-SpyCatcher chemistry, was investigated for repairing corneal perforating injury models in rats and rabbits. The hydrogel formation relies on covalent bonding between SpyTag (A) and SpyCatcher (B) under physiologically mild conditions, eliminating requirements for organic solvents, elevated temperatures, or photoactivation Notably, integration of recombinant mussel foot protein 3 (Mfp3) endowed the hydrogels with exceptional water-resistant adhesiveness, while maintaining their original mechanical properties and biocompatibility. This study demonstrates the great potential of Mfp hydrogels in the timely and precise repair of corneal perforating injuries in complex physicochemical environments. TeaserBioinspired protein hydrogel rapidly seals corneal perforations, minimizing scarring and inflammation without sutures in animal models.

bioengineering↗

bioRxiv2025

In-cell Proteomics Enables High-Resolution Spatial and Temporal Mapping of Early Xenopus tropicalis Embryos

Early embryonic development requires tightly regulated molecular programs to coordinate cell division, fate specification, and spatial patterning. While transcriptomic profiling is widely performed, proteomic analyses of early vertebrate embryos remain limited due to technical challenges in embryonic sample preparation. Here, we propose an "in-cell proteomics" approach, which bypasses cell lysis and yolk depletion, processes individual embryos directly in functionalized filter devices, and generates liquid chromatography-mass spectrometry (LC-MS)-friendly samples in an extremely robust and streamlined manner. Combined with a single-shot data-independent acquisition (DIA) MS workflow, this approach enabled us to consistently quantify [~]6,200 proteins from a single Xenopus tropicalis embryo, representing the deepest proteomic coverage of early X. tropicalis development reported to date. Investigation of the temporal proteomes across five cleavage stages (1- to 16-cell) revealed a drastic proteomic shift between 2- and 4-cell stages, followed by more gradual transitions thereafter. Spatial analysis of dissected 8-cell blastomeres uncovered pronounced molecular asymmetry along the animal-vegetal axis, while dorsal-ventral differences were minimal. This study establishes a novel in-cell proteomics technology in conjunction with DIA-MS as a robust platform for high-resolution, low-input developmental proteomics analysis, and provides a comprehensive spatiotemporal protein atlas for early X. tropicalis embryos.

biochemistry↗