Biology subjects

Lin, S.

Publications and source records attributed to Lin, S..

At least 55 records · Page 3Linked to original sources

bioRxiv2026

Protein-peptide Interaction Representation Learning with Pretrained Language Models

Protein-peptide Interactions (PpIs) paly essential roles in diverse cellular processes, yet their systematic identification remains challenging due to the limited availability of experimentally annotated protein-peptide interaction data. To address this challenge, we present PepInter, a sequence-based Deep Learning (DL) framework that leverages large-scale pretraining on structurally derived pseudo protein-peptide pairs to learn interaction-relevant representations. Specifically, energy-dominant peptide fragments are extracted from protein complexes curated from non-redundant Protein Data Bank (PDB) structures, enabling the construction of pseudo protein-peptide interaction pairs that capture interface interaction patterns shared with canonical protein-protein interactions. This strategy allows the model to acquire interaction-aware priors in the absence of large-scale annotated protein-peptide complex datasets. Built upon the ESM-Cambrian (ESMC) architecture, PepInter adopts a two-stage pretraining strategy. In the first stage, masked language modeling is used to learn general protein sequence representations. In the second stage, the model is further trained to predict Rosetta-derived energetic scores, explicitly incorporating structural interaction signals into the learned embeddings. Following pretraining, PepInter is fine-tuned for both protein-peptide interaction classification and peptide bioactivity regression tasks. Across multiple benchmark datasets, including protein-peptide binding affinity prediction, PepInter consistently outperforms existing baseline methods and demonstrates strong generalization in identifying biologically meaningful PpIs. Case studies further highlight its ability to recover known interaction patterns and predict novel protein-peptide interactions. Together, these results establish PepInter as a scalable and effective framework for protein-peptide interaction prediction, with strong potential to accelerate peptide-based drug discovery.

bioinformatics↗

bioRxiv2025

Super-CUT&Tag: A Sensitive, Spatially Resolved Approach for Epigenomic Profiling in Tissue Sections

Spatial profiling of protein-DNA interactions is essential for understanding gene regulatory processes, but current methods suffer from low sensitivity, limited resolution, and complex workflows. Here we introduce Super-CUT&Tag (Solid-phase interface embedded with an Ultra-dense DNA barcode array to Profile Epigenomic landscapes for spatially Resolved CUT&Tag), a robust spatial epigenomic method that directly transfers in situ protein-DNA interactions from tissue sections onto a pre-fabricated spatially barcoded array. Incorporating 3D polyamidoamine (PAMAM) dendrimers markedly improves chromatin capture efficiency, achieving several-fold higher sensitivity compared to current spatial CUT&Tag approaches. Using Super-CUT&Tag, we profile H3K27ac spatial landscape and uncover previously hidden active-chromatin heterogeneity across mouse embryonic tissues and stages. Integration with spatial transcriptomics further reveals spatiotemporal enhancer regulation dynamics of Neurod2 during corticogenesis. By overcoming key bottlenecks in sensitivity, resolution and usability, Super-CUT&Tag establishes a powerful platform for spatial epigenomic profiling in complex tissues.

genetics↗

bioRxiv2025

Acute in vivo proximity labeling for membrane targeted proteomics in neuronal circuits

MOTIVATIONDespite the growing use of proximity labeling tools, there are limited techniques available to identify subcellularly localized proteins within targeted neuronal circuits on acute timescales in vivo. We engineered membrane targeted versions of the proximity labeling enzyme TurboID and validated their use for proteomic discovery in the mouse brain. We optimized an in vivo protocol to identify proteins in cell bodies and long range neuronal projections, and proteins differentially detected during acute drug labeling windows. SUMMARYA major goal within molecular systems neuroscience is to bridge the study of neuronal circuit function with changes in protein expression and localization in awake behaving animals. However, there are limited tools for capturing changes in subcellularly-defined proteomes within neuronal circuits during activity-gated timescales in vivo. Here, we engineered targeted versions of the proximity labeling enzyme TurboID, to tag proteins at the neuronal membrane during a user-delivered biotin injection. We optimized a labeling strategy that enables a one-to-two-hour labeling window and tagged proteins in medial prefrontal cortex (mPFC) cell bodies and corresponding axons in a downstream projection. We performed proteomics to identify proteins enriched in mPFC cell bodies and terminals, and upregulated in mPFC cell bodies following an acute cocaine injection. These advancements enable the detection of proteins at the subcellular level within short labeling windows, allowing identification of stimulus-specific proteomes in behaving mice.

neuroscience↗

bioRxiv2025

Tissue-to-Bytes: A Catalytic Digital Twin Platform for Consortium-Scale Integration of Single-Cell Omics Data in BRAIN Initiative Cell Atlas Network

Monolithic Electronic Health Record (EHR) architectures remain fundamentally misaligned with high-resolution, specialty-specific clinical workflows, contributing to significant physician burnout and challangies in secondary use of data. This study introduces the Enterprise Health Twin (EHT) framework, a paradigm grounded in the Digital Twin concept, with the goal to transform static clinical ledgers into dynamic decision-support systems. The EHT establishes continuous bidirectional feedback (feed-forward ingestion and feed-backward decision support) between the physical entities of clinical workflows and the digital counterparts of interfaces reflecting the evolving states of the workflow, for each individual patient in the EHT. We conceptualized, prototyped, and operationalized the EHT framework for epilepsy care for seven years, in production, at the point of care. Deployed at the Epilepsy Center of the University of Texas Health Science Center at Houston, a [~]400% increase productivity has been achieved in specialized surgical evaluation throughput without additional staffing. The EHT achieved de novo capture of ontology-anchored structured data at the point of care, mitigating cognitive load while growing the volume of high-resolution, annotated longitudinal datasets for clinical research, and driving an increase of care velocity in a learning healthcare system.

neuroscience↗

bioRxiv2025

Integrative spatial multi-omics reveals prognostic tumor niches in female genital tumors

Female genital tumors (FGTs), including ovarian, endometrial, and cervical cancers, pose a major global health challenge, yet their spatial and molecular determinants of progression and therapy resistance remain poorly understood. Here we present a large-scale integrative spatial and multi-omics atlas of FGTs, combining exome, transcriptome, methylome, and miRNA sequencing with high-resolution Visium HD spatial transcriptomics, profiling over 62 million cells from 100 tumors. We show that HPV-driven suppression of TGF{beta} signaling in cervical cancer disrupts myofibroblast (myCAF) formation and reshapes immune infiltration, while serous tumors exhibit extensive oncogene amplifications that activate MYC and PI3K pathways, linking chromosomal instability (CIN) to proliferative, immune-cold phenotypes. In high-grade serous ovarian cancer, we identify a previously unrecognized malignant domain, the Proliferating Core Desert (PCD) niche, defined by CIN-high epithelial enrichment, immune exclusion, stromal protection, and chemotherapy resistance, maintained through crosstalk with macrophages and fibroblasts. Finally, we develop POWER, a pathology foundation model that predicts PCD niche abundance from routine H&E slides, enabling robust stratification of prognosis and anti-angiogenic therapy response.

genomics↗

bioRxiv2025

The ID93 + GLA-3M-052-LS vaccine candidate elicits mucosal and systemic immunogenicity and protective efficacy against Mycobacterium tuberculosis challenge in BCG-primed Collaborative Cross inbred mice

New vaccine approaches are needed against tuberculosis (TB). We sought to optimize mucosal immunogenicity and protective efficacy by modulating the adjuvant component and route of immunization of a next-generation TB vaccine using the recombinant TB vaccine antigen (Ag) ID93. ID93-specific mucosal and systemic immunogenicity and protective efficacy were assessed in the Collaborative Cross 004 mouse strain, a mouse strain susceptible to Mycobacterium tuberculosis (Mtb) infection, as a suitable model of Mtb susceptible populations. Immunogenicity data from various vaccine candidates were used to select lead vaccine candidates with the most preferred immunostimulatory profiles using a pre-determined desirability index. A liposomal adjuvant system containing synthetic TLR4 and TLR7/8 ligands (GLA-3M-052-LS), administered by a heterologous intramuscular-intranasal regimen, induced an optimal comprehensive immune response profile including high levels of mucosal antibody and Th1 CD4+ T cells in the lungs. In BCG-primed mice, immunization with intramuscular followed by intranasal ID93 + GLA-3M-052-LS boosts significantly reduced Mtb burden in the lungs after challenge vs. BCG vaccinated mice alone. Thus, ID93 + GLA-3M-052-LS represents a promising next-generation TB vaccine candidate suitable for testing in additional preclinical models.

immunology↗

bioRxiv2025

Spatial learning in multi-scale environments: Roles of hippocampus, orbitofrontal cortex, and retrosplenial cortex

Navigation cognition involves the learning of multi-scale environments and the formation of cognitive maps. How do humans build cognitive maps in multi-scale environments? Cognitive maps are thought to be organized hierarchically, with local representations for subareas and global representations for the entire environment. However, it remains unclear how spatial learning influences the representations of multi-scale environments and their underlying neural mechanisms across different levels of representation. In the current study, we built a virtual environment (VE) with four orthogonally positioned rectangular rooms, each containing eight objects at the corners. Twenty-three healthy subjects completed a four-session spatial memory experiment conducted over two weeks. We measured their brain activity by using BOLD-fMRI at two stages: pre-learning stage and post-learning, when they were judging the relative direction between the objects within the VE. We found that with the progression of learning, the subjects shifted from relying on local, directional cues to using more global representations of the environment. At the neural level, the hippocampus (HIP), retrosplenial cortex (RSC), and orbitofrontal cortex (OFC) played distinct roles in encoding spatial information across the two learning stages. Specifically, after learning, the HIP shifted from local to global representations, while the RSC and OFC supported the integration of spatial information across these representational levels. In addition, the anterior cingulate cortex was involved in forming global representations, facilitating efficient spatial processing as learning advanced. These findings revealed how spatial learning leads to adaptive shifts in brain activity, contributing to the formation of cognitive maps in complex, multi-scale environments. HighlightsO_LIUsing fMRI to study neural mechanisms of cognitive map formation in a multi-scale environment. C_LIO_LIDetected that local and global spatial representations coexist and evolve during spatial learning. C_LIO_LIFound that learning progress is associated with activation changes in the entorhinal cortex, hippocampus, and retrosplenial cortex. C_LIO_LIRevealed the complementary roles of hippocampus and orbitofrontal cortex in representing the environment. C_LIO_LIShowed that the retrosplenial cortex improves both local and global representations, supporting the integration of multi-scale spatial information. C_LI

neuroscience↗

bioRxiv2025

Multiple Epigenetic Mechanisms Functionally Cooperate to Silence Expression of Somatostatin Receptor Type 2 in Pancreatic Neuroendocrine Tumors

Pancreatic neuroendocrine tumors (PNETs) are a rare and understudied set of cancers, with increasing incidence. Neuroendocrine tumors are unique in the fact that they express high levels of the somatostatin receptor type 2 (SSTR2), which represents a target for both tumor imaging and therapeutics. PNET grade inversely correlates with SSTR2 tumor staining and higher tumor grade is associated with poor patient prognosis. With no known mutations, SSTR2 expression is believed to be lost through aberrant epigenetic mechanisms. Enhanced knowledge of the epigenetic biology and players controlling SSTR2 expression may allow for identification of novel PNET imaging and treatment modalities. Through in-depth studies, we found that the specific de novo DNA methyltransferase (DNMT), DNMT3B, is responsible for SSTR2 gene CpG methylation and silencing. Using DNMT3B as a starting point, along with the concept of functional crosstalk between various epigenetic mechanisms, we further discovered that Polycomb Repressor Complexes 1 and 2 (PRC1 and PRC2) play important roles in silencing SSTR2. Moreover, we found several histone lysine demethylases, enzymes that remove activating histone H3K4 methylation marks, to be critical for silencing expression of SSTR2. We additionally identified several chromatin remodeling enzymes/complexes as cellular factors that negatively regulate SSTR2 expression. Finally, using the HiBiT luminescent reporter system, we exploited functional chemo-genomic screens to further expand our knowledge of SSTR2 epigenetic control. These screens both reinforced several of our initial findings and helped to identify additional silencing mechanism potentially regulating SSTR2 expression. A commonality in our findings point to the presence, or necessity, of Class I HDACs in nearly all the epigenetic silencing mechanisms characterized. Overall, our work demonstrates that SSTR2 gene expression is likely silenced through various dynamic and interconnected epigenetic events, resulting in a compacted, transcriptionally repressed chromatin environment. Our study offers novel potential therapeutic targets and combinations to best increase expression of SSTR2, which are currently being tested in pre-clinical studies from our group, with the goal of future clinical trials aimed at increasing SSTR2 expression in high-grade, SSTR2-low NET patients.

cancer biology↗

bioRxiv2025

Nutrient availability-driven changes in extracellular matrix biochemical and mechanical properties regulate pancreatic cancer cell biology

The extracellular matrix (ECM) provides key biochemical and biomechanical cues that govern fundamental cellular processes, including growth and migration. ECM dysregulation and altered cell-matrix interactions are a driver of cancer progression, exemplified by pancreatic ductal adenocarcinoma (PDAC), where an abnormally dense, collagen-rich, and stiff ECM correlates with poor patient outcomes. The PDAC microenvironment is poorly perfused, resulting in altered nutrient availability, yet how this metabolic stress shapes the ECM and its biological activity remains poorly understood. Herein, we demonstrate that glutamine, a key amino acid depleted in poorly perfused PDAC regions, regulates the biochemical composition, mechanical properties, and biological activity of fibroblast-derived ECM. As glutamine availability increases, fibroblasts shift from producing a basement membrane-like ECM toward an interstitial, mature ECM enriched in fibrillar collagens. The ECM generated under glutamine-rich conditions is stiffer, which limits PDAC cell growth, while simultaneously, the elevated collagen I content promotes migration in a 3D spheroid model. Mechanistically, glutamine-dependent collagen I engages integrin 2 (ITGA2) to activate focal adhesion kinase signaling, driving PDAC cell migration independent of growth. In PDAC patients, glutamine stress inversely correlates with collagen expression in CAFs, with collagen I-ITGA2 as the most enriched ECM receptor interaction pair compared to the normal pancreas. These findings establish nutrient availability as a key regulator of ECM biology and offer new avenues to therapeutically intervene with dysregulated cell-matrix interactions in PDAC.

cell biology↗

bioRxiv2025

Dual AAV gene therapy using laminin-linking proteins ameliorates muscle and nerve defects in LAMA2-related muscular dystrophy

Adeno-associated virus (AAV)-mediated gene replacement holds promise for treating genetic diseases but faces challenges due to AAVs limited packaging capacity and potential immune responses to transgene products, especially in patients lacking endogenous protein. LAMA2-related muscular dystrophy (LAMA2 MD), a severe congenital disorder caused by loss of laminin-2, presents both hurdles: the LAMA2 gene exceeds AAV capacity, and severely affected patients do not produce the native protein. Here, we developed an AAV-based therapy using two engineered linker proteins derived from endogenously expressed components. These linker proteins restore laminin receptor binding and polymerization, enabling reassembly of a functional basement membrane. Dual AAV delivery of the linkers in a severe LAMA2 MD mouse model resulted in robust expression and significant improvements in muscle histology and function. Employing myotropic capsids enabled therapeutic efficacy at lower vector doses. However, muscle-specific targeting unmasked a LAMA2-related peripheral neuropathy. To address this, we expressed one linker under a muscle-specific promoter and the other under a ubiquitous promoter, delivered via AAV9 or AAV8. This approach achieved near-complete phenotypic restoration when administered neonatally and provided significant benefit when given at progressed disease stages. Our strategy offers a mutation-independent, size-compatible, and potentially immune-tolerable treatment for LAMA2 MD with broad clinical potential.

neuroscience↗

bioRxiv2025

CASTLE: a training-free foundation-model pipeline for cross-species behavioral classification

Accurately and efficiently quantifying animal behavior at scale without intensive manual labeling is a long-standing challenge for neuroscience and ethology. Keypoint-based tracking emphasizes simplicity and efficiency but loses the richness of posture and context, while emerging foundation models capture pixel-level details, yet often require nontrivial efforts of retraining and can be more sensitive to backgrounds or lighting. Here, we present CASTLE, a training-free pipeline that addresses all these issues by synergistically combining foundation models for segmentation, tracking, and feature extraction. By isolating regions-of-interest (ROI), CASTLE first generates "focused (ROI-masked)" and orientation-invariant latent features, capturing rich postural details in zero-shot, fine-tuning-free manners. Following ROI isolation, CASTLE, through an interactive "Behavior Microscope" module, supports hierarchical clustering, for progressive, human-in-the-loop embedding and clustering. This enables raw-image-assisted discovery of behavioral classes without predefined categories. Across mice, Drosophila and C. elegans, CASTLE matches expert class annotations (>90%), reveals disease-relevant phenotypes in Parkinsonian mouse models. By eliminating purpose-specific model training and providing a raw-image-informed accessible workflow, CASTLE offers a scalable framework for interpretable, cross-species behavioral phenotyping.

neuroscience↗

bioRxiv2025

ProStab: Prediction of protein stability change upon mutations by protein language and inverse folding models

Predicting protein stability change upon mutation is critical for protein engineering, yet remains limited by the modeling assumptions of physics-based methods and the generalization bottlenecks of data-driven approaches. We present ProStab, a deep learning framework that integrates sequence- and structure-based information, including the mutation-aware sequence embeddings from protein language models and the geometric features extracted via an inverse folding model. Trained on the large-scale Megascale dataset, ProStab demonstrates strong performance across diverse test sets and robust generalization across distribution shifts between the training and test sets. In head-to-head comparisons, ProStab outperforms all state-of-the-art methods with consistently higher Spearman correlation and precision. To evaluate its practical utility, we experimentally validated ProStab-predicted mutations on the model enzyme transaminase. Among the 16 successfully expressed variants, 4 exhibited improved thermal stability. Remarkably, the 1st top-ranked predicted mutation yielded the highest observed enzymatic activity, retaining three-fold that of the wild type after 10 minutes at 40 {degrees}C. To facilitate broader application, a publicly accessible web server has been developed. We envisage that ProStab provides a scalable and accurate platform for intelligent protein stability design.

bioinformatics↗

bioRxiv2025

Novel Spatial-Structural-Zero-Aware Dissimilarity Measures for Subtype Discovery Using Single Cell Hi-C Data

High-throughput single-cell Hi-C (scHi-C) technologies have opened new avenues for investigating cell-to-cell variability in the three-dimensional organization of the genome within individual nuclei. Despite their potential, analyses of scHi-C data are hindered by data sparsity, which varies substantially across cells. To address this challenge, recent methods aim to denoise scHi-C data and differentiate between two types of zero entries: structural zeros (SZs), which reflect true absence of contacts due to biological structure, and dropouts (DOs), which arise from insufficient sequencing depth. However, current dissimilarity measures used in downstream analyses, such as Euclidean distance and Kendalls tau, treat all zeros as equivalent, thus do not distinguish between SZs and DOs nor recognize the special role of SZs in capturing cell-to-cell variability. Such oversight limits the ability to accurately capture biologically meaningful differences between cells. In this study, we introduce structural-zeros-aware Kendalls tau (szKendall), a novel dissimilarity metric that explicitly incorporates the spatial structure of 2D contact matrices and leverages the presence or absence of shared SZs across cells. Through comprehensive simulations and analysis of real scHi-C datasets, we demonstrate that szKendall more effectively captures key structural features and achieves superior performance in cell clustering tasks compared to existing approaches. Our results underscore the importance of SZ-aware dissimilarity measures in advancing single-cell 3D genomics.

genomics↗

bioRxiv2025

The ORB2 RNA-binding protein represses translation of its target transcripts during the Drosophila maternal-to-zygotic transition via its functionally conserved Zinc binding 'ZZ' domain

Post-transcriptional regulation is particularly prominent during the maternal-to-zygotic transition (MZT), a developmental phase during which a large proportion of maternally provided mRNAs is repressed and cleared from metazoan embryos. RNA-binding proteins (RBPs) are key components of the post-transcriptional regulatory machinery. We show that the ORB2 RBP, the Drosophila ortholog of human Cytoplasmic Polyadenylation Element Binding Protein (hCPEB) 2-4 protein subfamily, binds to hundreds of maternally provided, rare-codon-enriched mRNAs in early embryos; that ORB2 targets are translationally repressed and unstable during the MZT; identify a U-rich motif enriched in ORB2 targets 3'UTRs; and show that this motif confers ORB2 binding and repression to a luciferase reporter mRNA in S2 tissue culture cells. When tethered to a luciferase reporter, ORB2 and hCPEB2 (but not ORB and hCPEB1) repress translation; the C-terminal Zinc-binding ( ZZ) domain of ORB2 is necessary and sufficient for repression. ORB2 interacts with a suite of post-transcriptional regulators in early embryos; a subset of these interactions is lost upon deletion of the ZZ domain, notably with the Cup repressive complex. ORB2-targets significantly overlap with those previously identified for the repressive RBP, Smaug (SMG). Analysis of the early embryos translatome in the presence or absence of the endogenous ZZ domain shows that mRNAs bound by ORB2 but not by SMG move onto polysomes upon ZZ domain deletion whereas co-bound transcripts do not, consistent with co-regulation of the latter set of transcripts by both RBPs. Our results assign a function to the ZZ domain and position ORB2 in the post-transcriptional network that regulates maternal transcripts during the Drosophila MZT. ARTICLE SUMMARYWe show that Drosophila ORB2, the ortholog of the human CPEB2 RNA-binding protein, negatively regulates its target mRNAs during the maternal-to-zygotic transition via its C-terminal Zinc-binding ( ZZ) domain.

genetics↗

bioRxiv2025

Riboflavin drives nucleotide biosynthesis and iron-sulfur metabolism to promote acute myeloid leukemia

Riboflavin is a diet-derived vitamin in higher organisms that serves as a precursor for flavin mononucleotide and flavin adenine dinucleotide, key cofactors that participate in oxidoreductase reactions. Here, using proteomic, metabolomic and functional genomics approaches, we describe a specific riboflavin dependency in acute myeloid leukemia and demonstrate that, in addition to energy production via oxidative phosphorylation, a key biological role of riboflavin is to enable nucleotide biosynthesis and iron-sulfur cluster metabolism. Genetic perturbation of riboflavin metabolism pathways or exogenous depletion in physiological culture medium induce nucleotide imbalance and DNA damage responses, as well as impair the stability and activity of proteins which utilize [4Fe-4S] iron-sulfur clusters as cofactors. We identify a window of therapeutic opportunity upon riboflavin starvation or chemical riboflavin metabolism perturbation and demonstrate that this strongly synergizes with BCL-2 inhibition. Our work identifies riboflavin as a critical metabolic dependency in leukemia, with functions beyond energy production.

cancer biology↗

bioRxiv2025

Loss of cell-autonomously secreted laminin-α2 drives muscle stem cell dysfunction in LAMA2-related muscular dystrophy

The extracellular matrix protein laminin-2 is essential for preserving the integrity of skeletal muscle fibers during contraction. Its importance is reflected by the severe, congenital LAMA2-related muscular dystrophy (LAMA2 MD) caused by loss-of-function mutations in the LAMA2 gene. While laminin-2 has an established role in structurally supporting muscle fibers, it remains unclear whether it exerts additional functions that contribute to the maintenance of skeletal muscle integrity. Here, we report that in healthy muscle, activated muscle stem cells (MuSCs) express Lama2 and remodel their microenvironment with laminin-2. By characterizing LAMA2 MD-afflicted MuSCs and inducing MuSC-specific Lama2 knockouts, we show that MuSC-derived laminin-2 is essential for rapid MuSC expansion and regeneration. In humans, we identify LAMA2 expression in MuSCs and demonstrate that loss-of-function mutations impair the cell-cycle progression of myogenic precursors. In summary, we show that self-secreted laminin-2 supports MuSC proliferation post-injury, thus implicating MuSC dysfunction in LAMA2 MD pathology.

molecular biology↗

bioRxiv2025

Tryptophanol, a novel auxin analog found in marine diatoms, enhances nitrogen assimilation

Diatoms exhibit superior competitive capacity in nitrogen assimilation, largely contributing to their growth, although the mechanisms underpinning their success have not been completely understood. Here, a non-ribosomal peptide synthase-like (PtNRPS1, with an unusual domain structure A-T-R1-R2) gene was found to play a vital role in short-term nitrogen assimilation in the marine diatom Phaeodactylum tricornutum. In vitro biochemical assays and in vivo overexpression confirmed that PtNRPS1 catalyzed two sequential two-electron reductions of L-tryptophan to tryptophanol. Tryptophanol exhibits high structural and functional similarities to indole-3-acetic acid (IAA), the most typical phytohormone auxin. Surprisingly, the effective concentration of tryptophanol was lower than that of IAA by as much as 2-5 orders of magnitude for P. tricornutum. Compared with the action of IAA, a distinct molecular mode for tryptophanol was revealed by transcriptomic analysis, resulting mainly in enhanced short-term nitrogen assimilation, which was also confirmed by the elevated nitrogen uptake rates determined by stable-isotope tracking. Finally, global distribution of PtNRPS1 homologues from stramenopiles was found to be positively correlated with the abundance of genes involved in nitrogen assimilation pathways. Overall, our study provides evidence of an auxin-like derivative synthesized by an NRPS in a diatom. We speculate that tryptophanol may accelerate nitrogen assimilation, conferring advantages in the competition for nitrogen in the ocean.

plant biology↗

bioRxiv2025

Can Random Walking on a Hi-C Contact Matrix Lead to Data Quality Improvement? An Assessment

Hi-C and single cell Hi-C (scHi-C) data are now routinely generated for studying an array of biological questions of interest, including whole genome chromatin organization to gain a better understanding of the chromosome three-dimensional hierarchical structure: compartments, Topologically Associated Domains (TADs), and long-range interactions. Due to concerns about data quality, especially for scHi-C because of its sparsity, data quality improvement is seen as a necessary step before performing analyses to answer biological questions. As such, methods have been developed accordingly, among them is a set of methods that are "random walk"-based, including random walk with a limited number of steps (RWS) and random walk with restart (RWR). Nevertheless, there is little justification for the use of such methods, nor quantification of their performance success. Taking correct identification of TADs as the end point, in this paper, we describe the characteristics of random-walk-based approaches and carry out empirical investigation for identifying TADs before and after random walks. Due to the lack of practical guidelines for choosing tuning parameters necessary for performing random walks, it is difficult to know how many steps of random walk for RWS or how small a restart probability for RWR should one choose to achieve good performance. Even in the unrealistic scenario when one has the hindsight of using the optimal parameter values, little improvement in downstream studies by first performing random walk was observed. This conclusion was based on extensive analytical analyses, simulation study, and real data applications. Therefore, the current study provides a cautionary note to researchers who may consider using random-walk-based approaches prior to downstream analyses.

bioinformatics↗