Biology subjects

Li, S.

Publications and source records attributed to Li, S..

At least 307 records · Page 17Linked to original sources

bioRxiv2025

Augment rice ANNEXIN expression to counter planthopper NlAnnexin-like5 as an anti-virulence strategy against a major crop pest

The brown planthopper (BPH) is the most devastating insect pest in rice, posing a serious threat to global rice production. One attractive control strategy would be based on the understanding of the virulence mechanisms of BPH at the molecular level and then designing targeted methods to neutralize such mechanisms. Salivary proteins of BPH are important players in mediating rice-BPH interactions. Here, we describe a pivotal role of a watery saliva protein, NlAnnexin-like5 (NlANX5), in the rice-BPH interaction. RNA interference (RNAi) of NlANX5 greatly compromised BPH feeding performance and survival rate on rice plants. NlANX5-RNAi BPH triggered a rapid calcium ion influx in rice cells. The feeding and survival defects of NlANX5-RNAi BPH can be restored in NlANX5-expressing transgenic rice plants. NlANX5 targets rice annexin (OsANN) proteins, including OsANN2 and OsANN8. Further analysis with NlANX5 and OsANN2 as well as OsANN8 showed that NlANX5 displaces OsANN2 and OsANN8 from the rice cell membrane. The osann2 osann8 mutant rice plants are hypersusceptible to BPH infestation. In contrast, enhanced expression of OsANN2 and OsANN8 gene resulted in robust rice resistance against BPH. This study highlights a successful example of identifying and augmenting the expression of the host targets of a major BPH virulence effector as a promising anti-virulence strategy against an important crop pest. Significance StatementInsect pests pose a serious biotic threat to crop production worldwide. Understanding how insect pests attack plants could inspire innovative pest control measures to enhance global food security. The brown planthopper (BPH; Nilaparvata lugens St[a]l) is the most devastative insect pest in rice. In this study, we discovered that BPH secretes a salivary protein, NlAnnexin-like5 (NlANX5), to target rice host annexins that are associated with calcium fluxes and activation of multiple rice defense pathways. This knowledge led to employing enhanced expression of rice annexin-encoding genes to successfully defeat the virulence function of NlANX5. Results have significant implications in the development of anti-virulence breeding strategies against BPH.

plant biology↗

bioRxiv2025

SARS-CoV-2 S protein activates the HIV latent reservoir through the mTOR pathway

During the global COVID-19 pandemic, mRNA vaccines using the S protein as antigen were widely used.Vaccine-induced S proteins can persist in vivo for weeks, triggering low-level immune activation. HIV latent reservoir maintenance is a major challenge for ART therapy, especially when immune pressure is waning. This then raises critical questions for HIV-infected patients: does prolonged exposure to S proteins affect HIV latent reservoir stability? Recent studies have pointed out that S proteins may activate the mTOR signaling pathway, which in turn affects the immune response and metabolic processes of cells. And the mTOR pathway is closely related to the maintenance and activation of HIV latent reservoir. However, how S proteins affect the HIV latent reservoir and the mechanism of activation are unclear. To explore the mechanism of how SARS-CoV-2 S proteins regulate the HIV latent reservoir and to explore whether S proteins regulate the HIV latent reservoir through the mTOR pathway, we constructed an in vitro HIV latent reservoir model for our experiments.To evaluate the potential role of S protein in HIV latent reservoir activation, relevant markers of HIV latent reservoir activation were detected using ELISA, flow cytometry, and RT-qPCR; and the relationship between S protein and mTOR was also detected by WB, CO-IP, and IFC.It was found that S proteins activated the HIV latent reservoir while increasing mTOR expression. It was further observed that mTOR inhibitors significantly inhibited S protein-induced activation of the HIV latent reservoir, and mTOR activators reversed the inhibitory effect of mTOR inhibitors on HIV latent reservoir activation. In summary, we found that S proteins activated the HIV latent reservoir through the mTOR pathway. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=170 SRC="FIGDIR/small/670887v1_ufig1.gif" ALT="Figure 1"> View larger version (48K): org.highwire.dtl.DTLVardef@f6d87aorg.highwire.dtl.DTLVardef@1fe4cc0org.highwire.dtl.DTLVardef@60eefeorg.highwire.dtl.DTLVardef@1ea9a34_HPS_FORMAT_FIGEXP M_FIG S protein interacts with mTOR and activates the mTOR-p-p70S6K-pS6 pathway, which promotes HIV transcription C_FIG

immunology↗

bioRxiv2025

The competition between splicing and 3' processing shapes the human transcriptome

Eukaryotic pre-mRNA processing steps, including splicing and 3' processing, are tightly coordinated, yet the underlying mechanisms remain incompletely understood. U1 snRNP has been proposed to inhibit 3' processing at intronic polyadenylation (IPA) sites through a splicing-independent mechanism termed telescripting. In contrast, we discovered that disrupting splicing--by targeting various key components such as U1 snRNP, U2 snRNP, U2AF, and SF3b--activates 3' processing at thousands of IPA sites. Notably, splicing inhibition, especially of U1 snRNP, induced widespread premature transcription termination within gene bodies through both IPA-coupled and -independent mechanisms. Different splicing factors activated overlapping and distinct sets of IPA sites, reflecting their specific contributions to transcription and spliceosome function. Conversely, inhibition of 3' processing enhanced splicing globally. These findings support a model in which splicing and 3' processing are competing processes that intersect with transcription to shape the transcriptome landscape.

molecular biology↗

bioRxiv2025

Accurate and scalable multi-disease classification from adaptive immune repertoires

BackgroundMachine learning models trained on paratope-similarity networks have shown superior accuracy compared with clonotype-based models in binary disease classification. However, the computational demands of paratope networks hinder their use on large datasets and multi-disease classification. MethodsWe reanalyzed publicly available T cell receptor (TCR) repertoire data from 1,421 donors across 15 disease groups and a large control group, encompassing approximately 81 million TCR sequences. To address computational bottlenecks, we replaced the paratope-similarity network approach (Paratope Cluster Occupancy or PCO) with a new Fast Approximate Clustering Techniques (FACTS) pipeline, which is comprised of four main steps: (1) high-dimensional vector encoding of sequences; (2) efficient clustering of the resulting vectors; (3) donor-level feature construction from cluster distributions; and (4) gradient-boosted decision tree classification for multi-class disease prediction. FindingsFACTS processed 107 sequences in under 120 CPU hours. Using only TCR data, and evaluated with 5-fold cross-validation, it achieved a mean ROC AUC of 0.99 across 16 disease classes. Compared with the recently reported Mal-ID model, FACTS achieved higher donor-level classification accuracy for BCR (0.840 vs. 0.740), TCR (0.882 vs. 0.751), and combined BCR+TCR datasets (0.904 vs. 0.853) on the six-class Mal-ID benchmark. FACTS also preserved biologically meaningful signals, as shown by unsupervised t-SNE projections revealing distinct disease-associated and potentially age-associated clusters. InterpretationParatope-based encoding with FACTS-derived features provides a scalable and biologically grounded approach for adaptive immune receptor (AIR) repertoire classification. The resulting classifier achieves superior multi-disease diagnostic performance while maintaining interpretability, supporting its potential for clinical and population-scale health profiling. FundingThis study was supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI [JA23H034980], the Japan Agency for Medical Research and Development (AMED) [JP25am0101001], and the Kishimoto Foundation Fellowship. Research in contextO_ST_ABSEvidence before this studyC_ST_ABST and B cell receptor (TCR and BCR) repertoires encode lifelong immunological memory and antigen-specific responses, making them valuable biomarkers for disease diagnosis and prediction. Existing machine learning (ML) models for adaptive immune receptor (AIR) repertoires often rely on clonotype-based representations, which limit shared receptor detection between donors and thus reduce cross-individual disease signature detection. Most models also lack robust multi-disease, population-scale performance. Our previous work showed that representing repertoires as paratope-similarity networks increased the fraction of shared receptors between donors and improved disease classification. However, their computational complexity has limited their scalability for the large datasets required in multi-disease classification. Added value of this studyWe introduce FACTS, a unified ML framework integrating paratope similarity with scalable sequence encoding. Applied to TCR repertoires from 1,421 donors across 15 diseases and one control group, FACTS maintained high performance while efficiently processing 81 million sequences on standard CPU infrastructure. Compared to Mal-ID, our paratope-encoded method achieved significantly higher donor-level accuracy and revealed biologically meaningful disease- and potentially age-associated patterns. Implications of all the available evidenceFACTS offers high accuracy, and interpretability for multi-disease classification, bringing AIR repertoire-based diagnostics closer to clinical translation and potentially guiding precision immunotherapy and immune-based therapeutic discovery for a wide range of diseases.

bioinformatics↗

bioRxiv2025

Increased atherosclerosis and expression of inflammarafts in macrophage foam cells in AIBP-deficient mice

Atherosclerotic lesions comprise different populations of macrophages, including lipid-laden macrophage foam cells and non-foamy, inflammatory macrophages, which play distinct roles in disease progression. Non-foamy macrophages express higher levels of inflammarafts - enlarged, cholesterol-rich lipid rafts hosting assemblies of inflammatory receptors - compared to foam cells in atherosclerotic lesions of Ldlr-/-mice. Apolipoprotein A-I binding protein (AIBP) has been shown to control lipid raft dynamics. This study investigated the effect of systemic AIBP deficiency on inflammaraft expression in foam cells and non-foamy macrophages in atherosclerotic lesions of hypercholesterolemic mice. A larger number of foam cells, with increased neutral lipid accumulation, populated atherosclerotic lesions in Apoa1bp-/-Ldlr-/-mice compared to Ldlr-/- mice. Importantly, AIBP-deficient foam cells expressed higher levels of TLR4 dimers and lipid rafts (markers of inflammarafts) than control mice, accompanied by larger atherosclerotic lesions and larger necrotic cores compared to Ldlr-/-mice. In a model of foam cells, Apoa1bp-/- bone marrow-derived macrophages incubated with oxidized LDL had increased expression of inflammation- and atherosclerosis-related genes. These results indicate that AIBP deficiency is associated with a proinflammatory transition of foam cells in which increased lipid accumulation is paradoxically associated with an increased expression of inflammarafts and correlates with the development of advanced atherosclerotic plaques.

immunology↗

bioRxiv2025

Protein Function Prediction via Contig-Aware Multi-Level Feature Integration

Proteins play a central role in biological processes, and accurately predicting their functions is crucial for biomedical research. While computational methods have advanced significantly, most approaches rely solely on sequence or structure, neglecting critical inter-protein relationships, such as the topological arrangement of coding sequences (CDSs) within contigs. To address this gap, we propose CAML, a novel deep learning model that integrates intra-protein features including sequence and predicted structure with inter-protein features capturing functional linkages among CDSs in contigs. Specifically, CAML employs a Graph Isomorphism Network (GIN) to extract structural features from predicted protein contact graphs and ESM-2 for sequence embeddings. Additionally, it leverages kmer frequencies and a Bidirectional Long Short-Term Memory (BiLSTM) network to model functional relationships among colocalized CDSs within contigs, capturing operon-like associations. Extensive experiments demonstrate that CAML outperforms the state-of-the-art methods in accuracy, precision, recall and F1-score, achieving improvements of 11.24%, 12.43%, 13.59%, and 13.30%, respectively over the second-best model. Ablation studies further confirm the critical contribution of CAMLs multi-level biological feature integration in enhancing functional annotation accuracy. Our study demonstrates the importance of integrating structural, sequential, and CDSs topological features for accurate protein function prediction, providing a robust computational framework for genomics research.

bioinformatics↗

bioRxiv2025

Rationally Engineered Coronavirus-mimicking Protein Nanocage Platform for Glioblastoma Targeted Delivery and Immunity Regulation

Glioblastoma (GBM) is one of the most aggressive and treatment-resistant brain tumors, owing to the dual challenges of the blood-brain barrier (BBB) and a profoundly immunosuppressive tumor microenvironment. However, few existing strategies are capable of simultaneously overcoming these two barriers, underscoring a critical scientific gap and the need for innovative therapeutic platforms that enable both effective BBB traversal and tumor immune reprogramming. Here, we present a coronavirus-mimicking protein nanocage platform (EcomPC) derived from Thermotoga maritima encapsulin, rationally engineered for the targeted and sustained delivery of interferon- (IFN-) to GBM lesions. Through strategic insertion of flexible linkers, cysteine-to-serine mutations, and modular surface functionalization via SpyTag/SpyCatcher chemistry, EcomPC enables intracellular self-assembly of IFN- and glioma-specific targeting. In orthotopic GBM models, EcomPC-IFN demonstrates efficient BBB translocation, selective tumor accumulation, and potent anti-tumor efficacy. Mechanistically, localized IFN- release induces tumor cell apoptosis and reprograms the immune microenvironment--characterized by increased CD8 T cell infiltration, decreased Foxp3 regulatory T cells, and a favorable chemokine shift. These therapeutic effects are not recapitulated by free IFN- or untargeted nanocages, underscoring the essential role of both structural mimicry and ligand-guided delivery. Collectively, this work establishes EcomPC as a programmable, virus-inspired protein delivery platform capable of overcoming key physiological barriers in brain tumor immunotherapy, and lays the foundation for its broader application in CNS-targeted biologic delivery.

bioengineering↗

bioRxiv2025

Serotonin shapes the temporal window for associative fear learning

Fear learning is a critical adaptive mechanism that enables the association of an environmental cue (the conditioned stimulus, CS) with a potential threat (the unconditioned stimulus, US), even when these events are temporally discontiguous. When associations are instead formed between unrelated cues and threats, the maladaptive learning process can give rise to neurological disorders. Therefore, maintaining accurate temporal boundaries for forming such associations is crucial for distinguishing real threats from irrelevant stimuli. Despite its importance, the mechanisms defining this precise time window remain poorly understood. Here, we report that serotonin plays a crucial role in modulating the associable interval in trace fear conditioning. Specifically, we show that serotonergic projections from the dorsal raphe nuclei (DRN) to the CA1 region of the ventral hippocampus (vCA1) dynamically regulate the association of discontiguous CS and US. Mechanistically, we demonstrate that sustained serotonin release in the vCA1 maintains the ensemble activity of 5-HT2C receptor-expressing pyramidal neurons, facilitating the association between temporally separated events. In contrast, when the trace interval exceeds the associable window, transient serotonin release fails to drive vCA1 neuronal ensemble activity, thus preventing maladaptive learning. These findings highlight a key role for serotonin in regulating the temporal precision of fear learning and reveal the DRN-vCA1 serotonergic pathway as a potential therapeutic target for related disorders.

neuroscience↗

bioRxiv2025

Bioactive Enhanced Adjuvant Chemokine Oligonucleotide Nanoparticles (BEACONs) for Mucosal Vaccination Against Genital Herpes

Genital herpes, caused by herpes simplex virus-2 (HSV-2), remains a prevalent sexually transmitted infection with no available vaccine. Effective local immunity--including tissue-resident memory T cells (TRMs) and luminal antibodies--provides immediate viral control. Here, we developed Bioactive Enhanced Adjuvant Chemokine Oligonucleotide Nanoparticles (BEACON) formed via electrostatic interactions between CpG oligodeoxynucleotides (CpG ODN) and the chemokine CXCL9. This adjuvant enhances antigen-presenting cell engagement and innate immune signaling, promotes CD8+ T cell recruitment, and reduces local neutrophilic inflammation relative to CpG ODN. Co-administered vaginally with HSV-2 glycoproteins following intramuscular priming, BEACON improved protection against HSV-2 by increasing local CD8+ TRM populations and mucosal IgG and IgA responses. Vaccine-mediated protection required local delivery of both antigen and adjuvant, and was significantly reduced by CD8+ T cell or B cell depletion. These findings highlight the potential of engineered mucosal adjuvants for vaccines targeting genital herpes and other sexually transmitted infections. One-sentence summaryWe developed BEACON, a mucosal adjuvant that elicits protective immunity against genital herpes simplex virus 2 infection in mice.

immunology↗

bioRxiv2025

Interpretable spatial multi-omics data integration and dimension reduction with SpaMV

Spatial multi-omics technologies have revolutionized our understanding of biological systems by providing spatially resolved molecular profiles from multiple perspectives. Existing spatial multi-omics integration methods often assume that data from different modalities share a common underlying distribution, aiming to project them into a single unified latent space. This assumption, however, can obscure the unique insights offered by each modality, thereby limiting the full potential of multi-omics analyses. To address this limitation, we present the Spatial Multi-View (SpaMV) representation learning algorithm, which captures both the shared information across modalities and the distinct, modality-specific information, enabling a more comprehensive and interpretable representation of spatial multi-omics data. Through extensive evaluation on both simulated and real-world datasets, SpaMV demonstrates superior spatial domain clustering performance and provides users with more interpretable dimension reduction for downstream analysis. Moreover, SpaMV effectively annotates cell types within clusters of a mouse thymus dataset, highlighting its effectiveness in interpretable dimensionality reduction.

bioinformatics↗

bioRxiv2025

HPInet: Interpretable prediction of Host-Pathogen protein-protein Interactions using a transformer-based neural network

Gram-negative bacteria utilize a series of secretion systems (T1SS-T10SS) to deliver secreted effector proteins (T1SE-T10SE) into host cells, leading to infections and diseases. Understanding the interactions between these effector proteins and host proteins is crucial for unraveling the pathogenic mechanisms of bacterial pathogens. Despite advancements in sequencing technologies that have significantly enhanced our knowledge of effector protein diversity and structure, the molecular mechanisms underlying their interactions with human host proteins remain unclear. Experimental approaches to validate these interactions are labor-intensive, time-consuming, and insufficient to comprehensively map the extensive effector-host protein interaction (PPI) network. Additionally, specialized computational tools for predicting these interactions remain scarce. To address this gap, we developed HPInet, a transformer-based deep learning model for predicting interactions between bacterial effector proteins and human host proteins. HPInet introduces convolutional layers and global response normalization (GRN) into the feed-forward network (FFN) of the transformer architecture, enhancing the extraction of local sequence features. Compared to existing state-of-the-art methods, HPInet significantly improves prediction performance, increasing accuracy from 0.502 to 0.891 and boosting sensitivity by 35.3%. Furthermore, by leveraging the models attention mechanism, HPInet identifies critical residues at interaction sites, demonstrating its capability to capture local structural features of PPI sites solely from sequence information. To facilitate its application in research, HPInet has been implemented as a freely accessible web server available at https://bis.zju.edu.cn/hpinet. This platform provides a powerful and interpretable tool for studying effector-host protein interactions, offering valuable insights into bacterial pathogenesis and potential therapeutic targets.

bioinformatics↗

bioRxiv2025

gCoSRNA: Generalizable Coaxial-Stacking Prediction for RNA Junctions Using Secondary Structure

Coaxial stacking between adjacent stems is a key tertiary interaction that defines the spatial organization of RNA junctions, which are core structural motifs in folded RNAs. Accurate prediction of coaxial stacking is critical for RNA 3D structure modeling, yet existing computational tools remain limited, especially for junctions with variable numbers of branches or complex topologies. Here, we present gCoSRNA, a generalizable computational framework for predicting coaxial stacking configurations using RNA sequence and secondary structure as input. Instead of developing separate models for each junction type, gCoSRNA decomposes multi-way junctions into all possible adjacent stem pairs, termed pseudo two-way junctions, and uses a unified random forest classifier to evaluate stacking probabilities. Global stacking configurations are inferred by integrating these pairwise predictions, eliminating the need for explicit junction-type classification. Benchmarking on two independent test sets, including CASP15/16 and RNA-Puzzles targets, shows that gCoSRNA achieves consistently high accuracy (mean [~]0.87) across junctions with two to seven branches, outperforming existing junction-specific methods. These results highlight the models ability to capture higher-order structural features and its potential utility in RNA tertiary structure prediction pipelines. The source code is freely available at https://github.com/RNA-folding-lab/gCoSRNA.

bioinformatics↗

bioRxiv2025

An anterior-posterior gradient in hippocampal subfield volumes linking sleep health to cognition in young adults

Sleep plays a critical role in maintaining hippocampal integrity and supporting cognitive function. However, it remains unclear how variations in sleep are associated with the structurally heterogeneous subfields of the hippocampus and how this association relates to cognition in healthy young adults. This study aimed to elucidate the multivariate relationships among sleep duration, quality, and timing, the specific hippocampal subfield volumes, and cognitive functions in a large sample of healthy young adults. We applied multiset canonical correlation analysis to data from 942 young adults (ages 22-37) from the Human Connectome Project. We examined relationships among self-reported sleep parameters (via the Pittsburgh Sleep Quality Index), high-resolution hippocampal subfield volumes, and a broad set of cognitive performance measures. We identified a single significant mode of covariation, wherein better sleep health--characterized by a moderate sleep duration (reflecting a nonlinear, inverted U-shaped relationship), higher sleep quality (e.g., fewer awakenings, less discomfort), and optimal sleep timing--was robustly associated with larger hippocampal volumes, particularly in the anterior subfields. This sleep-hippocampus pattern was also associated with enhanced cognitive performance. Our findings reveal a specific link among sleep, an anterior-dominant pattern of hippocampal structure, and cognition in healthy young adulthood. The anterior hippocampus may serve as a key neural hub through which the cognitive benefits of healthy sleep are manifested early in life.

neuroscience↗

bioRxiv2025

FlyTomo: A streamlined software for on-the-fly cryo-ET data processing and diagnosis

Cryo-electron tomography (Cryo-ET) combined with subtomogram averaging (STA) enables the structural elucidation of macromolecular assemblies in their native environments. However, the widespread adoption of cryo-ET has been limited by its labor-intensive, expertise-dependent data processing workflow. Here we present FlyTomo, a software that streamlines data processing from frame alignment to STA with high-throughput for authentic cryo-ET scenarios. During data acquisition, FlyTomo performs real-time diagnosis, enabling prompt feedback on sample quality, microscope performance and structural features. After acquisition, it aggregates diagnostic metrics into an interactive overview, guiding users through data review and refinement. FlyTomo also curates raw and processed data into structured directories to simplify data management and archiving. We validated FlyTomo across a diverse set of authentic cryo-ET samples, including purified enveloped viruses and cryo-lamellae, on multiple microscopes and cameras, achieving structures at resolutions ranging from 3.4 to 5.7 [A]. Collectively, by integrating accuracy, scalability and usability, FlyTomo reduces the technical barrier for in situ structural biology using cryo-ET.

biophysics↗

bioRxiv2025

Xenopus IgX informs engineering strategies of IgM and IgG hexamers

Polymeric immunoglobulins are essential components of the immune system in jawed vertebrates. While mammalian immunoglobulin M (IgM) typically forms a pentamer linked by the joining chain (J-chain), Xenopus laevis IgX assembles into a J-chain-independent polymer. Here, we present the cryo-electron microscopy (cryo-EM) structure of IgX, revealing its hexameric configuration. By incorporating the IgX tailpiece into human IgM, we achieved efficient IgM hexamer formation. Truncating IgMs natural tailpiece to a range of 11-16 residues also substantially enhanced hexamerization efficiency. Furthermore, introducing a shortened IgM tailpiece to IgG resulted in effective IgG hexamer formation. We further show that the engineered IgM and IgG hexamers targeting CD20 demonstrated robust complement-dependent cytotoxicity (CDC) against several B lymphoma cells. Additionally, the IgG-Fc hexamer functioned as a decoy, attenuating CDC in cell cultures. These findings deepen our understanding of polymeric immunoglobulin evolution and introduce innovative strategies for the development of IgM- and IgG-based biologics. One Sentence SummaryThe cryo-EM structure of the African clawed frog immunoglobulin IgX reveals a uniform hexameric assembly, inspiring innovative strategies for the development of therapeutic human IgM and IgG hexamers.

immunology↗

bioRxiv2025

Engineering light robustness: Adaptive evolution uncovers new genetic determinants of HL tolerance in Synechocystis

Excess light absorption causes severe photo-oxidative damage and limits photosynthetic efficiency. Enhancing high-light (HL) tolerance and energy utilization in photosynthetic cyanobacteria holds great potential for sustainable biomanufacturing. Here, using Synechocystis sp. PCC 6803 (Syn6803) as a model, we generated eight independently evolved strains tolerant to hyper-HL (2000 mol photons/m2/s) through approximately two years of natural adaptive laboratory evolution (ALE). Remarkably, these strains circumvented common evolutionary trade-offs; all demonstrated markedly enhanced quantum yields and photosynthetic performance, with four strains exhibiting a 121.71%-168.36% increase in biomass accumulation under HL. When equipped with the heterologous sucrose transporter CscB, sucrose productivity under HL increased by up to 208.33%. Whole-genome resequencing unveiled a total of 77 mutations across the eight lineages, uncovering a diverse polygenic landscape for HL adaptation. Reverse genetics combined with structural modeling highlighted pivotal roles for regulatory and structural loci, including slr0758 (KaiC1) and slr1513 (SbtB), in modulating circadian rhythmicity and inorganic carbon uptake to prevent photo-oxidative overload. Overall, our findings uncover diverse evolutionary routes toward high-light acclimation and provide superior cyanobacterial chassis for photosynthetic biotechnology.

microbiology↗

bioRxiv2025

LeGO-Teknik reveals that the neurogenesis pathway is a clone-specific hallmark of brain metastasis in breast cancer

Breast cancer exhibits substantial inter- and intra-patient heterogeneity. Yet the molecular features underlying this diversity and their roles in tumour progression are not yet fully elucidated. Within a primary tumour, certain clones possess a unique capacity to metastasize to distant organs, such as the brain, and this propensity may be associated with specific gene expression profiles. To investigate this phenomenon, we developed a new optical barcoding library, LeGO-Teknik, compatible with high resolution imaging, flow cytometry, single-cell RNA sequencing and machine learning computational methods. Using this new tool, we systematically assessed the fitness and organ tropism of multiple human breast cancer clones in spontaneous metastasis assays. Linking transcriptomic profiles of individual cancer cells to their clonal identities and metastatic behaviours, we identified that clones predisposed to forming brain metastases exhibit distinct molecular signatures. Notably, individual cells from these clones showed enriched expression of neurogenesis-related genes. Using SCENIC transcription factor analysis, we found that some of these features are driven by the transcription factor SOX4. This hallmark, likely due to a process of cellular reprogramming, is a remarkable example of organ mimicry during clonal selection. Together, this work provides critical insights into brain metastasis and highlights the potential for identifying predictive biomarkers and therapeutic targets for metastatic breast cancer.

cancer biology↗

bioRxiv2025

Parabricks: GPU Accelerated Universal Pan-Instrument Genomics Analysis Software Suite

Next-generation sequencing (NGS) has transformed genomics, enabling breakthroughs in biotechnology, healthcare, and pharmaceuticals. However, exponential data growth-outpacing Moores Law-presents critical computational challenges for secondary analysis. We introduce Parabricks, a freely accessible, GPU-accelerated software suite supporting diverse workflows, including wholegenome, exome, transcriptome, and methylation analysis. Compatible across major short read and long read sequencing platforms, it reduces processing time from hours to minutes while maintaining concordance with established standards, this acceleration translates to significant cost savings. Deployable on-premises or through cloud platforms, Parabricks ensures scalable, genomic processing. Integrated AI-driven workflows and deep learning models further enhance precision, offering a universal solution for high-throughput genomic research and clinical diagnostics.

bioinformatics↗