Biology subjects

Pan, L.

Publications and source records attributed to Pan, L..

At least 19 recordsLinked to original sources

bioRxiv2026

Fluorescence-based sorting of extracellular vesicles enables detection and omics

Extracellular vesicles (EVs) are nanosized, lipid bilayer-enclosed particles that mediate intercellular communication and hold considerable promise as biomarkers and therapeutic agents. However, their functional heterogeneity remains incompletely understood, largely due to the absence of reliable methods for isolating specific EV subpopulations. Here, we established a Nanoscale Flow sorting (NanoFACS) workflow and evaluated its potential for EV detection, sorting, and molecular characterization. We assessed critical parameters, including fluorescent staining and labeling approaches, background signal discrimination, dilution and flow-rate effects, and scatter triggering. We achieved high-purity sorting of CFSE-stained and antibody-labeled EVs, preserving vesicle integrity and sufficient RNA yields for small RNA sequencing of sorted populations. We identified EV subtype-specific small RNAs despite partial cargo overlap in single-marker sorted populations. Our results establish a reproducible antibody-based NanoFACS workflow that defines key operational parameters for reliable EV detection and sorting, with potential adaptability across conventional and spectral flow cytometry platforms. By reducing background and non-EV-associated events, the workflow enables the isolation of phenotypically defined EV subpopulations suitable for downstream molecular analysis, providing a practical approach to investigate EV heterogeneity.

cell biology↗

bioRxiv2026

The Metabolic Organ Clock: A Computable Framework Linking Cell-Lineage Architecture to Emergent Organ Bioenergetics

Cell-type atlases now catalogue hundreds of distinct human cell identities across development, each annotated with mitochondrial abundance, dominant fuel pathway, and lineage of origin, yet no computable model connects the topology of the lineage tree to the metabolic rate the resulting organs display in the adult body. Using a curated 396-node human cell-lineage tree spanning the zygote to terminal somatic identities, we test whether organ-level standard metabolic rate (SMR) is better predicted by developmental time (lineage depth) or by terminal fate identity (anatomical compartment). We find that lineage depth explains essentially none of the variance in a cell's metabolic tier (r = 0.11, R2 approximately 1.2%), whereas compartment identity explains roughly three-quarters of it (approximately 75%), and that the mean mitochondrial volume fraction of an organ's constituent terminal cell types tracks the organ's classical literature-derived SMR with r = 0.90 across five canonical reference-man organ groups. These results motivate a five-layer computable framework: lineage as a formal tree grammar, information-theoretic structure of the tree, metabolic switches as runtime operators, compilation to organ-level energetic networks, and execution as organism-level bioenergetics. We also introduce the metabolic commitment-horizon model: the hypothesis that a cell's metabolic tier is fixed at a discrete lineage-commitment event and thereafter held constant, rather than accumulated continuously with differentiation time. In a worked held-out test (5-fold cross-validation, n = 396), a nearest-commitment-ancestor predictor beat a global-mean null (MAE 0.752 vs 0.809, p = 0.015) but was not significantly better than a lineage-depth regression (delta MAE = -0.042, 95% CI -0.098 to +0.012, p = 0.175), so we report the commitment-horizon model as supported descriptively but not yet discriminated predictively on these data. We connect this within-species result to the classical, cross-species rate-of-living hypothesis, treat insulin resistance as an acquired perturbation of the clock, survey machine-learning and deep-learning tools that could extend the framework on primary single-cell data, and close with eight testable predictions and falsification conditions.

systems biology↗

bioRxiv2026

An integrated platform for genome-wide functional analysis in Aspergillus niger through Ac/Ds insertion profiling and metabolic modeling

Genome-scale metabolic models guide rational strain engineering, while high-throughput insertional mutagenesis enables systematic investigation of gene function and condition-dependent growth requirements. However, a platform integrating high-density transposon insertion sequencing with matched stoichiometric, thermodynamic, enzyme-constrained and combined metabolic models has not, to our knowledge, been reported in Aspergillus. Here, we establish a unified experimental and computational platform in Aspergillus niger ATCC1015 by combining curated metabolic reconstruction, Ac/Ds transposon mutagenesis and machine-learning-based essentiality prediction. Our stoichiometric reconstruction, FBA1015, expands reaction coverage and compartmental resolution beyond four published reconstructions. From FBA1015, we developed three models sharing its biochemical network: GECKO light with enzyme-capacity constraints, TFA with thermodynamic constraints and ecTFA combining both. FBA1015 and its derivatives performed competitively in phenotype benchmarks, with added constraints improving agreement with measured growth and gas exchange. Insertion libraries grown in rich medium, glucose or xylose achieved genome-wide densities of one distinct site per 21--112bp across individual libraries. A machine-learning classifier integrated insertion patterns and gene structure to predict essentiality across 11,111 genes. Applying a stringent consensus criterion identified 709 genes predicted essential on both glucose and xylose as sole carbon sources, and 91 predicted essential on one but nonessential on the other. Integration with simulated gene deletions prioritizes candidate genes and testable pathway hypotheses, connecting high-throughput genetic screening with mechanistic analysis for fungal functional investigation and strain engineering.

microbiology↗

bioRxiv2026

A lifespan-scale single-cell atlas defines an early-childhood immunometabolic transition linked to age-referenced immune states

Childhood represents a critical period with rapid metabolic and immune remodeling, during which perturbations may have lasting consequences for health. We integrated single-cell RNA sequencing (scRNA-seq) profiles of peripheral blood mononuclear cells (PBMCs) from 2,008 healthy individuals aged 0-100 years. Age-resolved modeling highlighted an early-childhood transition centered at age 6, characterized by coordinated remodeling of metabolism-related transcriptional programs, particularly in monocyte and B-lineage cells. Matched serum metabolomics and lipidomics in a pediatric subcohort detected a temporally concordant wave of metabolite and lipid changes around ages 6-8 and extensive metabolite/lipid-transcript associations, prominently involving steroid and phospholipid species. Building on these findings, we developed an immunometabolic transition score and age-specific reference chart, which were evaluated in an independent healthy cohort and applied to quantify deviations in pediatric immune disorders. These findings highlight a coordinated immunometabolic transition in early childhood and provide an age-referenced framework for distinguishing atypical immune-state variation from healthy age-associated variation.

immunology↗

bioRxiv2026

IL27 exerts a powerful effect on systolic overload-induced cardiac inflammation, fibrosis, and heart failure development

BACKGROUNDInterleukin-27 (IL-27) is a heterodimeric cytokine that serves as a bifunctional rheostat rather than an inherently pro- or anti-inflammatory signaling protein. However, the specific role of IL-27 in regulating systolic overload-induced cardiac inflammation and heart failure (HF) pathogenesis remains unknown. METHODSWe investigated the effects of genetic IL-27 receptor deficiency (IL-27R knockout), pharmacological IL-27 blockade, and recombinant IL-27 administration on transverse aortic constriction (TAC)-induced HF in mice. RESULTSCardiac IL-27 expression was significantly elevated in both murine and human HF tissues. The global genetic ablation of the IL-27 receptor (IL-27R) significantly suppressed TAC-induced cardiac inflammation, fibrosis, hypertrophy, HF progression, and mortality. Corroborating these protective effects, transcriptomic analysis (RNA-seq) revealed that IL-27R deficiency drastically suppressed pathways driving immune responses and antigen presentation, alongside the significant downregulation of networks governing systemic inflammation, pathogen infection, and extracellular matrix remodeling. Furthermore, pharmacological neutralization of IL-27 effectively attenuated TAC-induced left ventricular dysfunction, chamber dilation, myocardial hypertrophy, fibrosis, and leukocyte infiltration. Conversely, the administration of recombinant mouse IL-27 exacerbated the TAC-induced cardiac accumulation of multiple immune cell subsets, resulting in worsened cardiac fibrosis, cardiomyocyte hypertrophy, and overall HF progression. CONCLUSIONSOur findings demonstrate that IL-27 acts as a critical pathogenic driver of cardiac inflammation and HF development by modulating both cardiac immune cells (predominantly T cells) and non-immune cells, highlighting the IL-27 signaling axis as a promising therapeutic target.

physiology↗

bioRxiv2026

Gram-scale one-pot enzymatic synthesis of CDP-ribitol by a designed bifunctional fusion enzyme

D-Ribitol-5-phosphate (Rbo5P) plays vital roles in bacterial and mammalian development. In mammals, Rbo5P is an essential component of the O-mannosyl glycan on -dystroglycan, and its defective biosynthesis causes a group of dystroglycanopathies. Supplementation with cytidine diphosphate ribitol (CDP-ribitol, CDP-Rbo) and its analogues has shown therapeutic potential for certain dystroglycanopathies. However, facile and scalable strategies to prepare CDP-Rbo remain unavailable. Here, we report a practical one-pot enzymatic strategy for gram-scale CDP-Rbo production. Starting from inexpensive ribitol, Rbo5P is first generated by L-ribulokinase (AraB) and then converted to CDP-Rbo by CDP-ribitol pyrophosphorylase (TarI) in 90% yield. To streamline the process, AraB and TarI were fused into a single bifunctional biocatalyst, AraB-TarI, which converts ribitol directly into CDP-Rbo on a gram-scale in a one-step reaction. Furthermore, a nucleotide recycling strategy was developed to lower the cost and increase the atom economy from 47% to 75%. This work provides a green and scalable route to CDP-Rbo and a reliable material supply for developing therapeutics against dystroglycanopathies.

biochemistry↗

bioRxiv2026

AINN-Express: A Leakage-Aware, Sequence-Only Predictor of VHH Antibody Expression Built on the AINN-P1 Protein Foundation Model

Expression -- whether an antibody can be produced at usable yield -- is one of the earliest and most expensive filters in therapeutic discovery. We present AINN-Express, a sequence-only predictor of VHH single-domain antibody (nanobody) expression built on AINN-P1, Ainnocences protein foundation model. AINN-Express encodes a VHH with a frozen AINN-P1 encoder and scores it with a lightweight gradient-boosted classifier: it takes only an amino-acid sequence, returns an expression probability, and needs no structure and no per-task model training. Under a leakage-safe, leave-program-out evaluation, AINN-Express reaches ROC-AUC 0.87 within known antibody programs and 0.81 on entirely new programs -- well above the majority baseline -- making it a practical tool for prioritizing candidates before wet-lab work. We further justify the encoder choice with a controlled, leakage-aware benchmark against general-purpose protein language models: on this task, AINN-P1 (167 M parameters) generalizes to unseen programs far better than a general-purpose ESM2 (650 M) -- 0.81 versus 0.68 new-program ROC-AUC -- and matches a domain-finetuned ESM2 (0.83) with no task-specific finetuning, at roughly one-quarter of the parameters. The gap is invisible under a random split, where all encoders score [~]0.88; only leave-program-out evaluation reveals that general-purpose embeddings largely encode program identity rather than transferable determinants of expression. Purpose-built representation quality, not parameter count, is what makes AINN-Express generalize.

bioinformatics↗

bioRxiv2026

{Sigma}0-EvoCell: An AI-Native Ontology that Unifies Evolutionary and Cell Biology in Latent Space

Foundation models for biology achieve impressive pattern recognition on molecular sequences and single-cell transcriptomics, yet they fail to outperform simple linear baselines for predicting genetic perturbation effects, exposing a gap between statistical correlation and mechanistic understanding. This gap is compounded by an interface problem: biological knowledge lives in human-readable formats (FASTA, SBML, ontology triples) that must be lossily re-encoded before a neural network can reason about them. Here we introduce the Evolutionary Cell Ontology (ECO), an AI-native formal language built on the {Sigma}0 substrate that represents biological knowledge directly as vector-encoded relational graphs. ECO uses 16 structural operators that serve simultaneously as knowledge glyphs, tensor operations, and--critically--carry a dual semantics spanning both evolutionary and cellular timescales, so the same operator that denotes speciation at the phylogenetic scale denotes irreversible APC/C commitment at the cell-cycle scale. We define a Latent Space Communication Protocol (LSCP) that maps ECO graphs into the residual stream of large language models, enabling systematic auditing of the biological knowledge a model actually contains. We illustrate ECO across three domains using controlled simulations: (i) globin protein-family evolution across roughly 1.5 billion years, where ECO epistatic attention recovers long-range coevolutionary couplings and ancestral-state reconstruction reaches 82-91% accuracy graded by conservation class; (ii) mammalian cell-cycle dynamics, where a CDK-cyclin attention graph with GATE checkpoints and FUSE commitment nodes reproduces two full oscillatory cycles with a four-attractor phase portrait emerging without explicit programming; and (iii) latent-space alignment, where ECO embedding distance tracks divergence across 60 protein families (Pearson r = 0.50) and an illustrative LSCP audit projects that relational, multi-scale concepts are encoded far more weakly than sequence-level facts. ECO replaces the human-readability constraint with an AI-processing constraint and, in doing so, turns the opacity of foundation models into a measurable, navigable coverage map.

bioinformatics↗

bioRxiv2026

Frozen Protein Foundation-Model Embeddings Improve Antibody-Antigen ΔΔG Ranking

We investigate whether representations from AINN-P1--a protein foundation model trained autoregressively on tens of millions of natural protein sequences--transfer to the task of ranking antibody-antigen pairs by binding affinity. Casting affinity maturation as a learning-to-rank problem over the change in binding free energy ({Delta}{Delta}G), we compare a task-specific sequence model trained end-to-end from scratch against lightweight downstream heads built on top of frozen AINN-P1 embeddings, all evaluated under an identical five-fold cross-validation protocol. A regularized linear probe on the frozen embeddings already surpasses the from-scratch baseline, and an optimized lightweight head raises the mean Spearman rank correlation from 0.42 to 0.53--a relative improvement of approximately 28%-- while training in seconds and without any fine-tuning of the foundation model. Because a linear probe alone exceeds the fully trained end-to-end baseline, the gain is attributable to representation quality rather than to added downstream-model capacity. These results position frozen foundation-model embeddings as a strong, data-efficient default for affinity ranking in antibody engineering and establish a conservative lower bound that task-adaptive fine-tuning is expected to exceed.

bioinformatics↗

bioRxiv2026

OGG1-Binding to Oxidized Guanine Base in Viral DNA Overcomes Epstein-Barr Virus Latency

Epstein-Barr virus (EBV) establishes lifelong latency in human cells and can periodically reactivate, contributing to several cancers. However, the molecular mechanisms that disrupt EBV latency, particularly those driven by oxidative stress, require further investigation. In this study, we provide evidence that oxidative DNA damage, particularly the formation of 8-oxoguanine and its repair enzyme OGG1, is involved in EBV reactivation. We demonstrated that OGG1 is recruited to EBV regulatory regions under oxidative stress conditions and is associated with the transcriptional activation of immediate-early and early lytic genes. Further experiments showed that pharmacological inhibition of OGG1 DNA binding significantly suppressed EBV lytic gene expression, supporting a functional role for OGG1 in viral reactivation. Mechanistically, our findings suggest that OGG1 may contribute to EBV lytic activation through a noncanonical mechanism independent of its glycosylase activity. These findings provide insight into how EBV may exploit host oxidative DNA damage responses to facilitate latency disruption and suggest that targeting OGG1 may offer a potential strategy to limit EBV reactivation and EBV-associated diseases. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=120 SRC="FIGDIR/small/728896v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@7866a7org.highwire.dtl.DTLVardef@9b5f35org.highwire.dtl.DTLVardef@87a87aorg.highwire.dtl.DTLVardef@461005_HPS_FORMAT_FIGEXP M_FIG C_FIG The model illustrates how oxidative DNA damage promotes Epstein-Barr virus (EBV) lytic reactivation through host base excision repair machineryReactive oxygen species (ROS) may induce oxidative modification of guanine to 8-oxoguanine (8-oxoGua) in latent EBV genomes, potentially contributing to OGG1 recruitment to viral regulatory regions. OGG1 binding facilitates the transcriptional activation of immediate-early and early lytic genes, likely through transcription factor recruitment, thereby contributing to the transition from latency to lytic replication. Pharmacological inhibition of OGG1-DNA interaction suppresses this process, highlighting OGG1 as a potential therapeutic target. 8-oxoGua, 7,8-dihydro-8-oxoguanine; OGG1, 8-oxoguanine DNA glycosylase-1. Significance StatementO_LIidentifies a potential mechanism by which ROS-induced oxidative DNA base damage and OGG1 recruitment within the EBV genome may contribute to viral reactivation from latency. C_LIO_LIsuggests that oxidative DNA repair intermediates may serve as regulatory platforms for viral gene expression. C_LIO_LIprovides pharmacological evidence supporting OGG1 as a potential therapeutic target for suppressing EBV reactivation. C_LI

microbiology↗

bioRxiv2026

Prediction of Transcription Factor DNA Binding Affinity with High-Throughput Kd Measurements and Deep Learning

Transcription factors (TFs) regulate gene expression through specific interactions with genomic DNA. While TF binding motifs from public databases describe sequence preferences, quantifying genome-wide affinity (Kd) is highly desirable for a more accurate thermodynamic description. Here, we report ivtFOODIE (in vitro FOOtprinting with DeamInasE), an assay that leverages deaminase-mediated cytosine-to-uracil conversion to measure Kd values for a given TF across accessible genomic regions from human cells. By pre-training on TF binding sites from JASPAR and fine-tuning with our ivtFOODIE data from 46 TFs representing 13 different DNA-binding domains (DBDs), we developed Seq2Kd, a deep learning model capable of predicting a TFs absolute binding affinity on DNA sequences. Seq2Kd enables de novo motif discovery of [~]500 previously uncharacterized human TFs and reveals the effects of genetic variation both in TF-coding regions and DNA-binding sites on gene expression and disease susceptibility. By correlating predicted affinity changes with the sign and magnitude of expression quantitative trait locus (eQTL) effects, we stratified TFs into activator-like and repressor-like groups. Compared to clinically benign variants, pathogenic single-nucleotide variants (SNVs) within regulatory and protein-coding regions show significantly larger predicted shifts in Kd. We provide an interactive web portal, the ENcyclopedia of Transcription-factor Interactions with Regulatory Elements (ENTIRE), which integrates the Seq2Kd model with the ivtFOODIE dataset. This resource offers thermodynamic prediction for TF-DNA interactions for functional genomics and human disease.

biophysics↗

bioRxiv2026

An orthogonal near-infrared optical switch for wireless neuromodulation in freely behaving mice

Modulating neuronal activity with light is a powerful tool for neuroscience research. However, currently available technologies often require invasive fibers for delivery of visible light, causing tissue damage and limiting behavioral studies. Although near-infrared (NIR) neuronal modulation improves tissue penetration depth, sustained NIR illumination during neuromodulation raises significant concerns about photothermal effects. Here, we introduce a dual-wavelength near-infrared switch (Dual-NIR Switch) that uses two transcranial NIR inputs to initiate and terminate neuronal activation, enabling tether-free neuromodulation in freely moving mice. Dual-NIR Switch employs orthogonal dichromatic upconversion nanoparticles that emit blue and green light under 980 nm and 808 nm excitation, respectively, to activate and inactivate the step-function opsin SOUL. Therefore, transient 980 nm NIR illumination initiates neuronal excitation, which will remain excited without further stimulation but will be rapidly terminated on demand by a subsequent 808 nm illumination. Upon transcranial 980 nm and 808 nm NIR illuminations in freely moving mice, we achieve on-demand control of behavioral paradigms across tunable timescales, ranging from seconds to minutes and even extending to sub-hour durations. By eliminating the need for sustained NIR irradiation, Dual-NIR Switch offers an on-demand, duration-tunable neuromodulation tool for both basic neuroscience and potential therapeutic applications in treating brain diseases.

neuroscience↗

bioRxiv2026

Correlation Between Information Entropy and Functions of Gene Sequences in the Evolutionary Context: A New Way to Construct Gene Regulatory Networks from Sequence

The information encoded in DNA sequences can be rigorously quantified using Shannon entropy and related measures. When placed in an evolutionary context, this quantification offers a principled yet underexplored route to constructing gene regulatory networks (GRNs) directly from sequence data. While most GRN inference methods rely exclusively on gene expression profiles, the regulatory code is ultimately written in the DNA sequence itself. Here we review the mathematical foundations of information theory as applied to gene sequences, survey existing computational methods for GRN inference--with emphasis on information-theoretic and sequence-based approaches--and examine how evolutionary conservation constrains sequence entropy to preserve biological function. We then propose a four-layer integrative framework that combines per-position Shannon entropy profiles, evolutionary conservation scoring via Jensen- Shannon divergence, expression-based mutual information and transfer entropy, and DNA foundation model embeddings to construct GRNs from sequence. Through worked examples on the Escherichia coli SOS regulatory sub-network, we demonstrate how conservation-weighted mutual information improves edge discrimination and how transfer entropy resolves regulatory directionality. The framework generates testable predictions: edges supported by low-entropy regulatory regions should show higher experimental validation rates, and cross-species entropy profile conservation should predict GRN topology conservation. This work bridges three scales of biological information--nucleotide-level entropy, evolutionary constraint patterns, and network-level regulatory logic--establishing information entropy as the natural mathematical language for sequence-to-network regulatory inference.

bioinformatics↗

bioRxiv2026

AINN-P1: A Compact Sequence-Only Protein Language Model Achieves Competitive Fitness Prediction on ProteinGym

Protein language models (PLMs) are increasingly central to protein engineering and drug discovery. Many high-performing systems, however, rely on large parameter counts, multiple sequence alignments (MSAs), explicit structural inputs, or computationally intensive attention mechanisms, limiting their accessibility and throughput. Here we present AINN-P1, a 167M-parameter protein language model trained exclusively on raw UniRef amino-acid sequences using an autoregressive next-token prediction objective. AINN-P1 employs a multiplicative LSTM (mLSTM) architecture--an attention-free, recurrent design that scales linearly with sequence length and avoids growing key-value caches during inference. We evaluate AINN-P1 on ProteinGym fitness prediction tasks spanning activity, binding, expression, and stability using a frozen-encoder protocol with lightweight few-shot regression heads. Under this protocol, AINN-P1 achieves an average Spearman{rho} of 0.441 across four task categories and a Spearman{rho} of 0.625 on stability--the highest among sequence-only models in our comparison set. Because our evaluation uses few-shot supervised regression rather than the zero-shot scoring employed by most ProteinGym leaderboard baselines, direct numerical comparison requires caution; we discuss this methodological distinction throughout. Beyond benchmark performance, AINN-P1 emphasizes practical deployability: its recurrent architecture avoids quadratic memory scaling, supports fixed-state inference on long sequences, and enables rapid adaptation through frozen embeddings rather than costly end-to-end fine-tuning. We discuss when sequence-only models are sufficient when structural information remains beneficial and how compact foundation models can serve as efficient front-end filters in drug discovery workflows.

bioinformatics↗

bioRxiv2026

A Systems Framework for Quantifying Programmability and Persistence Across Mammalian Cell Types

Cellular therapies, toxicity screening, and regenerative medicine depend on selecting mammalian cell types with optimal lifespan, persistence post-transplant, immunogenicity, and chemical resilience. This review synthesizes data from over 50 immune, parenchymal, stem, and emerging engineered cell populations--including gamma-delta T cells, iNKT cells, CAR-macrophages, and hypoimmune iPSC derivatives--drawing from in vivo lifespan studies (including 1{blacksquare}C birth-dating and deuterium labeling), engraftment dynamics, immune rejection risk, and stress sensitivity profiles. We introduce a Programmability & Persistence Score (PPS; 0-20) that integrates these features into a unified metric, complemented by Pareto frontier analysis to visualize multi-objective trade-offs. High-PPS cell types (e.g., HLA-matched HSCs, hypoimmune iPSCs, chondrocytes) are suited for long-term regenerative applications, while low-PPS sentinels (e.g., neutrophils, enterocytes) serve acute assays. We discuss mathematical extensions including multi-criteria decision analysis, fuzzy membership functions, and Bayesian frameworks that address limitations of linear additive scoring. Together, these integrated profiles support cell selection for gene editing, organ-on-chip systems, in vivo cell programming, and immunotherapy, bridging cell biology with translational engineering.

systems biology↗

bioRxiv2026

Catalytic Efficiency of Conserved Metabolic Enzymes Across Evolutionary Complexity

Metabolic enzymes are highly conserved across life, yet their catalytic efficiencies vary among organisms of differing complexity. We compiled turnover numbers (kcat) and catalytic efficiencies (kcat/Km) for key enzymes in glycolysis, the TCA cycle, and oxidative phosphorylation from bacteria to mammals, drawing on BRENDA, BioNumbers, and primary literature. Rather than a universal trend, we find enzyme-specific and pathway-dependent patterns: TCA cycle dehydrogenases (citrate synthase, malate dehydrogenase, isocitrate dehydrogenase) consistently show 3-4x higher kcat in E. coli than in mammals, whereas glycolytic kinases and transferases show the opposite pattern--human RBC pyruvate kinase ([~]1,375 s-1) is 2.8x faster than E. coli ([~]497 s-1), and yeast GAPDH ([~]1,000 s-1) is 10x faster than bacterial. OXPHOS complexes (ATP synthase, cytochrome c oxidase) show remarkably conserved rates across kingdoms. We interpret these patterns through the lens of metabolic demand, regulatory complexity, and the "rapid bursts and slow declines" framework of enzyme evolution, arguing that context-specific selection pressures--not organismal complexity per se--determine enzyme kinetic tuning.

biochemistry↗

bioRxiv2026

Multi-modal single-cell profiling reveals transcriptional states and commitment dynamics during sexual reproduction in the diatom Pseudo-nitzschia multistriata

Diatoms, single-celled photosynthetic eukaryotes, traditionally considered primarily influenced by resource availability, are now recognized to exhibit complex developmental processes reminiscent of those in multicellular organisms. Sexual reproduction is an obligate choice in many species, albeit with variable frequency and efficiency. In order to explore decision making mechanisms and cellular heterogeneity inherent to this specific phase of the diatom life cycle, we applied a multi-modal strategy to follow sexual reproduction events in the model diatom Pseudo-nitzschia multistriata: i) we employed image-enabled cell sorting to link sexual stages morphology with gene expression ii) we developed a single-nucleus RNA sequencing assay, and iii) conducted proteomic profiling. Combining phenotypes with single-cell profiling allowed us to identify sexual state-specific transcription programs and different states within morphologically identical parental cells, revealing a widespread commitment of the majority of the population to sexual reproduction. This implies that the bottleneck to successful sexual reproduction in this species can be placed downstream of the signalling phase between mating cells, with additional, yet unidentified, factors influencing mating success at sea.

microbiology↗

bioRxiv2026

Hierarchical binding of cognition to action in the human basal ganglia-thalamic circuit

Flexible behavior requires binding actions to their underlying cognitive variables, yet classical basal ganglia models emphasize a serial architecture where striatal action selection precedes a pallidothalamic motor gate. We tested this framework by recording single-neuron activity across the human pallidothalamic circuit while participants reported perceptual decisions with confidence-weighted reaching movements. Neurons in globus pallidus externus and internus, subthalamic nucleus, and motor thalamus exhibited sharp activity increases at movement onset that persisted through execution and feedback. Choice and confidence were continuously encoded before, during, and after movement. Population decoding revealed a transformation along the circuit, from dynamic upstream representations to stable, low-dimensional representations downstream, particularly in motor thalamus. After feedback, outcome signals emerged selectively in subthalamic nucleus and motor thalamus. In motor thalamus, confidence and outcome converged into a representation consistent with unsigned reward prediction error. These findings redefine the human pallidothalamic circuit as an interface that binds decision, action, and evaluation during behavior.

neuroscience↗