Biology subjects

Chang, C.

Publications and source records attributed to Chang, C..

At least 19 recordsLinked to original sources

bioRxiv2026

Alpha-range neural oscillations are associated with multiple dynamic brain networks

We asked whether alpha range (8-12 Hz) EEG neural oscillations, a robust indicator of cortical excitability, are associated with dynamic BOLD brain networks. Two independent EEG-fMRI resting-state datasets (discovery: n = 94 and replication: n = 33) show that low- and high-alpha periods are associated with dynamic BOLD coactivation patterns (dCAPS) involving multiple dynamic networks rather than a single static network. Unique BOLD dCAPs consisting of visual cortex activation along with sensorimotor and attention networks in addition to the posterior thalamus, were mainly associated with periods of low-alpha power. However, overlapping BOLD dCAPs consisting of default, control, and attention networks were associated with periods of both low- and high-alpha power. The results suggest low-alpha dCAPS contain unique temporal-dependent configurations related to visual, sensorimotor, and attention networks while both low- and high-alpha dCAPs consist of overlapping temporal-dependent configurations involving CON, DMN, and attention networks involved in internal and external cognition. These findings reconcile discrepancies across static connectivity EEG-fMRI findings by showing that distinct transient BOLD configurations are associated with similar patterns of alpha EEG modulation in visual cortex, providing a dynamic framework for the gating function of alpha oscillations.

neuroscience↗

bioRxiv2026

Receptor-like kinase RLK15 binds a fungal RNase effector to enhance autophosphorylation and activate host defense

Fungal pathogens secrete diverse effectors to suppress host immunity, yet the plant receptors that perceive these virulence factors often remain elusive. Here, we identify Ss1624, a secreted RNase from the sugarcane smut pathogen Sporisorium scitamineum, and show that it is recognized by the plasma-membrane-localized CrRLK1L-family receptor kinase RLK15. This recognition triggers canonical immune outputs, including reactive oxygen species burst, callose deposition, defense gene activation, and induces hypersensitive cell death. Mechanistically, Ss1624 physically interacts with the receptor-like kinase RLK15, which is required for Ss1624-triggered cell death. Functional assays indicate that this interaction enhances RLK15 autophosphorylation in a kinase activity-dependent manner, suggesting that Ss1624 can act as an elicitor. Transcript profiling further reveals coordinated expression patterns of Ss1624 and RLK15 during infection, and perturbation of Ss1624 expression alters RLK15 transcript levels and pathogenic outcomes. Extending these findings to rice, the ortholog OsRLK15 contributes positively to resistance to bacterial and fungal diseases without yield penalty in rice. Our results support a model in which a secreted RNase effector is directly sensed by a receptor kinase to initiate broad-spectrum immune responses, providing insights into host-pathogen interactions and identifying RLK15 as a candidate for crop improvement.

microbiology↗

bioRxiv2026

CTR1-mediated copper uptake orchestrates metabolic-epigenetic regulation of pathogenic TH17 cells in autoimmune disease

Pathogenic T helper 17 (pTH17) cells are a subset of CD4+ T cells driving autoimmune diseases including multiple sclerosis (MS). Compared to homeostatic TH17 cells and other TH subsets, pTH17 have enhanced mitochondrial function and oxidative phosphorylation (OXPHOS) that supports their differentiation and pathogenic function. Here we identify Copper Transporter 1 (CTR1), encoded by Slc31a1, as essential for copper uptake in CD4+ T cells, OXPHOS and pTH17 cell differentiation and function. While copper levels are known to be higher in the cerebrospinal fluid of patients with MS compared to healthy individuals, and excess copper contributes to oligodendrocyte loss in murine models of MS, the effect of copper on T cell function and pathogenicity in MS are unclear. We demonstrate that deletion of Slc31a1 in CD4+ T cells decreased intracellular copper levels, disrupting mitochondrial respiration and rewiring metabolism. These changes altered the epigenetic landscape of pTH17 cells by impairing DNA demethylation capacity, leading to hypermethylated DNA and altered chromatin accessibility at key binding sites for AP-1 transcription factors essential for pTH17 differentiation. As a result, CTR1-deficient T cells showed defective differentiation into pTH17 cells, with decreased production of IL-17A and expression of TH17 signature genes, while the differentiation of other CD4+ T cell subsets remained largely unaffected. Moreover, T cell-specific deletion of Slc31a1 protected mice from central nervous system (CNS) inflammation in the experimental autoimmune encephalomyelitis (EAE) model of MS by suppressing clonal expansion of autoreactive CD4+ T cells. These findings establish copper as a critical regulator of pTH17 differentiation and function, revealing a previously unknown molecular link between copper homeostasis, metabolism and epigenetic regulation governing pTH17-mediated autoimmunity.

immunology↗

bioRxiv2026

π-MNovo improves de novo peptide sequencing through microbial-domain adaptation and evidence-guided candidate selection

The high taxonomic and strain-level diversity of microbial communities makes it difficult for reference databases to fully represent the protein sequences present in metaproteomic samples, limiting database-dependent peptide identification. De novo peptide sequencing can recover peptide sequences directly from tandem mass spectra without relying on reference databases, providing complementary peptide evidence for metaproteomics. However, most existing de novo sequencing models were developed largely from non-microbial proteomic data and lack specific adaptation to microbial spectra. Here, we established a dedicated microbial spectral resource comprising more than 10 million annotated high-quality tandem mass spectra from 72 cultured microbial isolates, with peptide digests from each isolate separated into five high-pH reversed-phase fractions to increase the opportunity for deeper and more diverse peptide sampling. Using this resource, we developed {pi}-MNovo, which combines adaptation to microbial spectra with evidence-guided candidate selection to improve full-length peptide sequencing precision. On a seven-species external benchmark, {pi}-MNovo achieved 66.30% complete-peptide recall under the same residue-mass-based complete-peptide matching criterion applied to all models, exceeding four published models by 6.9-21.7% in relative recall, with consistent gains across species and peptide-length groups. In two independent metaproteomic datasets, {pi}-MNovo increased reference-supported taxonomic coverage, while synthetic-community analysis recapitulated the designed abundance ranking. From pFind-unidentified spectra, {pi}-MNovo recovered 1,587 pFind-unreported, reference-matched peptides that passed spectrum-evidence filtering. These results show that {pi}-MNovo expands recoverable peptide evidence and enhances the utility of de novo sequencing for metaproteomic analysis.

bioinformatics↗

bioRxiv2026

Bioenergetic profiling of fresh human kidney tissue reveals compensatory metabolic adaptation and intrinsic mitochondrial dysfunction in diabetes

The kidney is a highly energetic organ, requiring substantial ATP production through mitochondrial oxidative phosphorylation to support tubular reabsorption. Metabolic reprogramming and impaired mitochondrial function are implicated in diabetic kidney disease, yet direct assessment of mitochondrial respiratory flux in the human kidney has been constrained by limited access to freshly obtained tissue. Consequently, much of the evidence supporting altered renal mitochondrial function in diabetes derives from animal models that do not fully recapitulate the human condition. We established a workflow for real-time bioenergetic profiling of fresh kidney cortex obtained during nephrectomy from living individuals with diabetes and preserved kidney function. Mitochondrial respiration, electron transport system activity and tubular mitochondrial morphology were compared with age- and sex-matched, histopathologically normal non-diabetic controls. High-resolution respirometry revealed increased mitochondrial respiratory flux in permeabilised diabetic kidney cortex. In contrast, mitochondria isolated from the same tissue exhibited reduced respiratory capacity and impaired complex I activity. Quantitative analysis of tubular cells demonstrated increased mitochondrial volume density together with greater mitochondrial fragmentation in diabetes. These findings reveal that the human kidney undergoes substantial metabolic adaptation early in diabetes, before measurable loss of kidney function. Increased tissue-level respiratory flux despite intrinsic mitochondrial impairment suggests that expansion and remodelling of the mitochondrial network may initially compensate for reduced organelle efficiency and sustain the kidneys high energetic demands. This compensatory state may, however, increase metabolic stress and vulnerability to subsequent kidney injury. To our knowledge, this study provides the first direct tissue-level functional evidence that mitochondrial metabolism is reprogrammed in the human kidney in diabetes before measurable kidney dysfunction develops. It defines an early bioenergetic signature characterised by tissue hypermetabolism despite impaired mitochondria-specific respiratory capacity, challenging the concept that diabetes produces a uniform decline in renal mitochondrial function. Failure to sustain this adaptive state may represent a critical transition towards diabetic kidney disease. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=134 SRC="FIGDIR/small/740003v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@101eb51org.highwire.dtl.DTLVardef@100187corg.highwire.dtl.DTLVardef@1478af2org.highwire.dtl.DTLVardef@1f31fc1_HPS_FORMAT_FIGEXP M_FIG C_FIG One Sentence SummaryDiabetes drives early metabolic reprogramming of the human kidney before measurable kidney dysfunction

pathology↗

bioRxiv2026

False discovery rate control for trustworthy AI-based de novo peptide sequencing

AI-based de novo peptide sequencing predicts peptide sequences from tandem mass spectra, enabling identification beyond predefined databases but leaving prediction reliability difficult to assess, particularly with respect to false discovery rate (FDR). In database search, FDR control is provided by target-decoy competition over a finite search space, whereas de novo predictions are generated in open sequence space and lack naturally matched sequence-level decoys. Here we introduce Counterpart Calibration Theory (CCT), a theory-guided framework that reframes de novo FDR control as a four-group score-ranking and threshold-selection problem over target-side predictions and matched counterpart-side comparators. Implemented in {pi}-NovoQC, CCT provides dual-level FDR control at the peptide-spectrum match and peptide levels. Across models, datasets, instruments and acquisition modes, {pi}-NovoQC achieves stable FDR control while preserving identification yield. In large-scale proteomic applications, {pi}-NovoQC recovers low-abundance in-database peptides missed by database search and provides de novo-supported protein-group and variant evidence.

bioinformatics↗

bioRxiv2026

Cells specify fate within an optimal window of positional information determined by morphogenesis

How cells sense position to adopt appropriate fates is a central problem in development. In a classic paradigm, cells read morphogen gradients encoding positional information (PI), with prolonged signal integration improving fate precision. However, how cells adapt this strategy in morphogenetic tissues remains unclear. Here, we reconstructed complete positional and signalling histories for individual cells during zebrafish neurulation, where the Sonic hedgehog (Shh) gradient patterns ventral progenitors as the neural plate folds into a tube. Despite steadily increasing Shh activity, Shh-encoded PI peaked early and then declined. Morphogenesis set this early readout window and imposed a [~]1.2-bit ceiling on Shh-encoded information about final position and fate. Fate mapping, transcriptomic analyses, and timed Shh inhibition showed that fate specification is temporally and functionally aligned with this early readout window. Thus, when morphogenesis decouples signal quality from signal strength, cells specify fate when signalling is most informative, not when signalling is strongest.

Developmental Biology↗

bioRxiv2026

Distinct Neural Systems Supporting Afferent and Efferent Autonomic Activity

Numerous studies have reported brain correlates of autonomic activity. However, ambiguity exists regarding whether those correlates reflect receiving or sending out autonomic signals. Additionally, little is known about how emotion and aging interact with brain-autonomic coordination. Based on time-varying heart rate variability (HRV) and blood oxygenation level dependent (BOLD) data (N = 104 younger; N = 51 older adults), we found insular and cingulate activity was negatively correlated with HRV during emotion regulation. We further examined the afferent and efferent nature of HRV-BOLD correlations during rest (N = 102 younger; N = 51 older adults). Functionally afferent regions where decreases in HRV were associated with subsequently increased BOLD activity included the posterior insula, postcentral gyrus and frontal pole. Functionally efferent regions where increased activity was associated with subsequently increased HRV included the anterior insula and cingulate. Information appeared to flow in one direction as the afferent regions activity Granger-predicted the efferent regions activity. Together, our findings suggest a feedback loop where decreased HRV increases the afferent regions activity, which activates the efferent regions, leading to increased HRV. Aging seems to affect this system; the efferent and afferent regions scarcely overlapped for younger adults, but did overlap for older adults.

neuroscience↗

bioRxiv2026

Wheat MYB transcription factor TaMYB83-7B regulates seed dormancy by influencing the balance between abscisic acid and gibberellin

In wheat, weak seed dormancy (SD) is related to an increased tendency for pre-harvest sprouting (PHS), which reduces yield and quality. However, the molecular mechanism underlying SD remains elusive. Here, we identified a wheat R2R3-MYB transcription factor (TaMYB83-7B) related to SD. Expression analysis showed that TaMYB83-7B was highly expressed in wheat seeds, and was more highly expressed in strong-dormancy varieties than in weak-dormancy varieties. Sequence and association analysis indicated that T/C mutations at -907 bp and -1133 bp in the TaMYB83-7B promoter were significantly associated with wheat SD, with C at both sites related to strong dormancy. Dual-luciferase reporter assays demonstrated that the transcriptional activity of the TaMYB83-7B promoter was significantly higher in strong-dormancy varieties than in weak-dormancy varieties. Further analyses indicated that TaMYB83-7B functions as a transcriptional inhibitor. Germination experiments revealed that overexpression of TaMYB83-7B significantly enhanced SD, while its loss-of-function reduced SD. Finally, TaMYB83-7B was found to regulate SD by influencing the balance between abscisic acid (ABA) and gibberellin (GA) in wheat seeds. Overall, the results of this study enhance our understanding of the complex regulatory mechanism underlying SD, and provide gene targets and molecular markers for the genetic improvement of PHS resistance in wheat.

molecular biology↗

bioRxiv2026

Parallel Activation and Interference CRISPR (PAIR) with Sequencing Uncovers DNA Repair Networks Guiding Precision Cell Engineering

The dynamic balance of cellular homeostasis is often maintained by opposing regulatory pathways, yet most genetic screens interrogate them in one direction and therefore miss the bidirectional gene-gene interactions that shape complex phenotypes such as DNA damage response (DDR). Here, we present PAIR (Parallel Activation and Interference CRISPR), a bidirectional perturbation platform that enables simultaneous activation and suppression of distinct genes within the same cell using CRISPR activation (CRISPRa) and Cas13d RNA knockdown. Applying PAIR to the CRISPR/Cas9 induced DSB repair screen, we mapped gene-gene interactions across competing repair branches and identify synergistic perturbations, including NBN activation combined with suppression of end-joining factors, that shift repair outcomes toward homology-directed repair (HDR) and improve the precision of CRISPR-based gene editing. Using coupled PAIR with single-cell transcriptomic, we further demonstrated that NBN activation induces inflammatory and interferon programs, whereas co-suppression of end-joining factors buffers this response, revealing transcriptional states missed by conventional unidirectional perturbations. To translate these findings into non-viral chimeric antigen receptor (CAR) T cell engineering, we developed an mRNA-based strategy for parallel overexpression and knockdown of NBN-anchored DDR effectors in primary T cells, priming the T cells into a transient HDR-favored state that enhances the efficiency of CAR knock-in on the TRAC locus. Together, the PAIR system provides a general framework for studying opposing regulatory networks, uncovering hidden cell states, and guiding cell-state engineering through bidirectional perturbation.

bioengineering↗

bioRxiv2026

AM-DMF-SCP-pro: An Ultrafast and Streamlined Nanolitre-Scale Lab-on-a-Chip Platform for Single-Cell Proteomics and Tumor Microenvironment Profiling

Single-cell proteomics (SCP) remains constrained by slow sample preparation and background interference in low-input, multistep workflows, limiting its broader practical use in complex biological samples. Here we report AM-DMF-SCP-pro, a streamlined active-matrix digital microfluidic workflow that enables rapid on-chip lysis and digestion in 6 nL droplets within a closed nanolitre-confined microenvironment. AM-DMF-SCP-pro reduces total sample-preparation time to as little as 30 min, including a 15 min digestion step, while maintaining quantitative reproducibility and digestion performance. Systematic optimization of mass spectrometry acquisition and nanolitre-scale digestion conditions enables deeper, more sensitive and lower-background proteome profiling from ultra-low peptide inputs. In murine tumor samples, AM-DMF-SCP-pro reliably resolves immune and tumor compartments, identifies treatment-associated tumor cell states and enables quantitative analysis of neutrophil-associated proteomic signatures. These results establish AM-DMF-SCP-pro as a practical workflow for rapid, low-background single-cell proteome profiling in complex biological samples.

systems biology↗

bioRxiv2026

π-MSNet: A billion-scale, AI-ready living proteomics data portal

Artificial intelligence (AI) is reshaping proteomics workflows, delivering remarkable gains in both peptide identification sensitivity and quantitative performance. However, the potential of deep learning models in proteomics has not been fully exploited due to the scarcity of large-scale, high-quality and consistently labeled datasets. Here, we present {pi}-MSNet, a billion-scale, AI-ready living mass spectrometry (MS) data portal. Using a uniform identification and quality control workflow, it comprises over 1.66 billion MS/MS spectra, 501 million peptide-spectrum matches (PSMs), and 9 million precursors from 36,356 LC-MS/MS runs across ten instrument types and 55 diverse species. Through community collaboration, the data are shared via international, interactive, and living web resources. Enabled by the built-in MSNetLoader Python API for seamless and scalable data access--with native support for PyTorch and TensorFlow--{pi}-MSNet provides an AI-ready data framework for efficient training and systematic benchmarking of multiple models across three representative tasks (e.g., MS/MS spectrum prediction, retention time prediction, and de novo peptide sequencing). In particular, by retraining multiple models on {pi}-MSNet, we achieved consistent performance improvements over their original versions. These improved models were subsequently integrated into the {pi}-MSNet agent to enable interactive, deployment-free use. Through SDRF (Sample and Data Relationship Format) metadata, an open-source cloud analysis workflow, and a community-driven interactive data portal that supports continuous data submission, {pi}-MSNet serves as a living, AI-ready resource for reproducible benchmarking, robust model training, and accelerated AI innovation in proteomics.

bioinformatics↗

bioRxiv2026

A comprehensive reference database to support untargeted metabolomics in Pseuudomonas putida

Pseudomonas putida strain KT2440 is a crucial model organism for synthetic biology and bioengineering applications, yet there currently exists no comprehensive metabolomics database comparable to those available for other model organisms. This gap hinders the use of untargeted metabolomics for exploratory analyses in this system. We developed the P. putida metabolome reference database (PPMDB v1) to address this limitation by consolidating metabolite information from multiple sources and expanding coverage through computational predictions. The database was constructed by curating metabolites from BioCyc, BiGG, and other literature sources, then computationally expanding this collection using BioTransformer environmental transformation predictions to generate additional predicted metabolites. We enhanced the databases utility for molecular annotation in metabolomics studies by incorporating analytical properties including collision cross-sections, tandem mass spectra, and gas-phase infrared spectra. These analytical properties were gathered from existing measurement data or predicted using computational tools. We further augmented the database through inclusion of reaction information and pathway annotations, facilitating biological interpretation of metabolomics data. This publicly available resource fills a critical gap in P. putida research infrastructure, supporting metabolite annotation and biological interpretation in untargeted metabolomics studies and enabling in-depth exploratory analyses of this important synthetic biology platform at the molecular level. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/713193v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@c8828forg.highwire.dtl.DTLVardef@1f3a5c5org.highwire.dtl.DTLVardef@1084535org.highwire.dtl.DTLVardef@1f7ca4a_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2026

Arousal state alters brain network switching and moderates cognitive task performance

Arousal relates to cognitive performance, but the neural underpinnings of this relationship remain unclear. One candidate marker is switching rate, a dynamic measure that has been linked to cognition and has been speculated to be sensitive to arousal. However, whether switching rate is altered across arousal states has not been directly tested. Here, using fMRI together with concurrent eye monitoring and EEG, we examined how the switching rates of the default mode, salience, and central executive networks are altered across arousal states. Default mode and anterior salience networks exhibited significant differences in switching rates across arousal states determined with eye tracking. Notably, thalamic subregions showed arousal-dependent changes in switching rate that were replicated across independent datasets and arousal measures. Additionally, arousal moderated the relationship between average network switching and performance on a relational processing task. Together, these findings suggest that switching rate may index neural underpinnings of arousal-dependent cognition.

neuroscience↗

bioRxiv2026

Elimination of intramuscular immunoglobulin accumulation alleviates Duchenne Muscular Dystrophy

Duchenne muscular dystrophy (DMD) is a devastating neuromuscular disorder due to loss of dystrophin, a cytoskeletal protein critical for muscle integrity and functionality. Despite recent therapeutic advances, there remains a significant unmet need for more effective and accessible therapeutics. Here, we discovered an early accumulation of immunoglobulin G (IgG) in the sarcolemma, which exacerbates tissue inflammation and disease progression. The IgG accumulation primarily resulted from ectopic localization of Fc{gamma}R1, a high-affinity IgG Fc receptor, on dystrophin-deficient myofibers. In two independent murine models, eliminating IgG accumulation via B cell depletion provided sustained benefit to alleviate DMD progression. Our findings uncover a novel disease accelerator in DMD and demonstrate the potential to target this mechanism as therapeutics for broader population of patients with DMD.

immunology↗

bioRxiv2026

MolSpecFlow: Mass-Constrained Hybrid Flow Matching for Joint Molecular-Spectral Analysis

Identifying the "dark matter" of the chemical universe requires bridging the fundamental gap between molecules and mass spectra. Existing approaches struggle to accurately map between these distinct data formats, often yielding results that are either chemically implausible or inconsistent with the physical evidence. We introduce MolSpecFlow, a unified foundation model pretrained on 100 million molecules and 42 million spectra, which leverages a hybrid flow matching framework to orchestrate optimal transport paths for spectral peaks and discrete probability flows for molecular tokens. Furthermore, to guarantee the physicochemical validity of the generated structures, we incorporate explicit rule-based constraints into the generative process, utilizing a token-level mass control mechanism that strictly enforces alignment with the precursor molecular weight. MolSpecFlow establishes a new state-of-the-art on the MassSpecGym benchmark, surpassing diffusion and retrieval baselines in de novo generation (Top-1 accuracy +35%), spectral simulation, and molecular retrieval, demonstrating the power of physics-grounded unified modeling.

bioinformatics↗

bioRxiv2025

Distributed fMRI patterns coupled to low-frequency cardiorespiratory dynamics provide markers of aging

How aging affects brain-body connections can be investigated through changes in the coupling between functional magnetic resonance imaging (fMRI) signals and bodily autonomic processes across the adult lifespan. Recent studies using univariate approaches have identified age-related changes in the association between fMRI signals from multiple individual brain regions and low-frequency respiratory and cardiac activity. Here, we investigate if whole-brain spatial fMRI patterns associated with low-frequency physiological processes (heart rate and respiratory volume fluctuations) present generalizable changes with age. Data from human participants of both sexes are included in the analysis. We find that chronological age can be predicted statistically beyond chance from patterns of low-frequency fMRI-physiology coupling, even after accounting for individual differences in physiological signal characteristics and brain anatomy. Notably, brain areas implicated in central autonomic regulation, including nodes within salience and ventral attention networks (e.g., insula and middle cingulate cortex), are amongst the strongest contributors to age prediction. Further, we observe that after removing physiological effects from fMRI data, the residual blood oxygen level-dependent (BOLD) signal variability is still a reliable indicator of age. Together, these findings underscore the close integration between brain and body physiology, and highlight this interaction as a potential biomarker of the aging process. Significance StatementThe association between brain activity and respiratory or cardiac activity is often dismissed as "noise" in functional magnetic resonance imaging (fMRI) studies. However, emerging evidence suggests that coupling between fMRI and peripheral physiological signals can provide valuable insight into the brain-body connection. In this study, we show that whole brain patterns of coupling between fMRI signals and low-frequency respiratory and cardiac processes can reliably predict age across the adult lifespan. Brain regions involved in autonomic regulation, such as insula and cingulate cortex, were among the most informative predictors of age. These findings suggest that fMRI-physiology coupling may capture aging-related changes in brain vascular health and autonomic function and may have broader relevance for tracking disease-related disruptions in brain-body interaction.

neuroscience↗

bioRxiv2025

Transposon Regulation in the Caenorhabditis elegans Germline and Soma

Transposons are parasitic nucleic acids present in most genomes. The ability of transposons to mobilize makes them a source of genetic diversity and a threat to genome integrity. Interestingly, mutations in the C. elegans gene mut-2/rde-3 increases the rate of transposition in the germline, but not in the soma, suggesting that the C. elegans germline and soma employ different strategies to regulate Tc1 transposition. Here, we develop fluorescence reporters for studying DNA transposon regulation in living C. elegans in a tissue-specific manner and we use candidate gene testing and genetic screening approaches to identify factors that regulate Tc1 mobility in the germline and/or the soma of the animal. We find that both cytoplasmic and nuclear components of the RNA interference (RNAi) pathway silence Tc1 in the germline, but not in the soma. We identify a novel pathway involving the C. elegans ortholog of HNRNPC, and a gene we term suppressor of transposon mobilization (stm)-1, which regulates Tc1 primarily in the soma, likely by binding Tc1 RNA and preventing its splicing. Our findings reveal tissue-specific strategies for regulating parasitic nucleic acids and pave the way for future studies exploring how and why different tissues adopt different transposon silencing systems.

genetics↗