Biology subjects

Singh, R.

Publications and source records attributed to Singh, R..

At least 91 records · Page 5Linked to original sources

bioRxiv2025

Extracellular vesicle-derived miR-425-5p (miR-425) activates astrocytes in the brain to promote breast cancer brain metastasis via the novel miR-425-ZNF24-CCL8 signaling axis

Mechanisms underlying breast cancer brain metastasis (BCBM) are still not well understood. Here, we identified that BCBM patient serum contained extracellular vesicles (EVs) with high levels of microRNAs (miRNAs)-107 and -425. Levels of miR-107 and miR-425 were elevated in brain metastases, and the elevation was associated with poor patient prognoses. Ectopic expression of miR-107 and miR-425 promoted mammospheres; inhibition of miR-425, but not miR-107, suppressed breast cancer mammosphere formation. EVs from miR-425-overexpressing breast cancer cells strongly activated astrocytes whereas their inhibitors abrogated the effect. Conditioned media from miR-425-activated astrocytes promoted mammospheres. Within astrocytes, miR-425 suppressed expression of transcription factor ZNF24, which downregulated CCL8 cytokine expression/secretion, leading to subsequent activation of astrocytes. We further determined the role of miR-425 in brain metastasis formation and observed that miR-425-overexpressing breast cancer cells exhibited significantly more aggressive growth in mouse brains compared to control cells. Immunohistochemistry and immunofluorescence analysis of mouse brain metastases revealed that miR-425 tumors exhibited significantly increased activation, intratumoral accumulation, and proliferation of astrocytes, and a decrease in ZNF24 expression compared to control tumors. Together, our findings demonstrate that breast cancer EV-derived miR-425 promotes BCBM via activating astrocytes in the brain microenvironment through the novel EV-miR-425-ZNF24-CCL8 signaling axis.

cancer biology↗

bioRxiv2025

SCOT+: A Comprehensive Software Suite for Single-Cell alignment Using Optimal Transport

SummaryNew advances in single-cell multi-omics experiments have allowed biologists to examine how various biological factors regulate processes in concert on the cellular level. However, measuring multiple cellular features for a single cell can be quite resource-intensive or impossible with the current technology. By using optimal transport (OT) to align cells and features across disparate datasets produced by separate assays, Single Cell alignment using Optimal Transport+ (SCOT+), our unsupervised single-cell alignment software suite, allows biologists to align their data without the need for any correspondence. SCOT+ has a generic optimal transport solution that can be reduced to multiple different OT optimization procedures, each of which provide state-of-the-art single-cell alignment performance. With our user-friendly website and tutorials, this new package will help improve biological analyses by allowing for more accurate downstream analyses on multi-omics single-cell measurements. Implementation and AvailabilityOur algorithm is implemented in Pytorch and available on PyPI and GitHub (https://github.com/scotplus/scotplus). Additionally, we have many tutorials available in a separate GitHub repository (https://github.com/scotplus/book_source) and on our website (https://scotplus.github.io/).

bioinformatics↗

bioRxiv2025

Blue light sensor-guided opto-modulation for enhanced production of valuable metabolites in microalgae

Light sensing proteins, photoreceptor, coordinate with photosynthetic machinery, influencing both photosynthetic efficiency and several metabolic outcomes. This work deals with bottleneck issue of astaxanthin production from Chlamydomonas reinhardtii. We report a non-genetic, illumination-based strategy for biomanufacturing of astaxanthin under optimised blue light illumination in C. reinhardtii. Notably, first time ever, we identified and report astaxanthin synthesis-associated BGC in C. reinhardtii using plantiSMASH analysis. We discover experimental evidences for phototropin-modulated biosynthetic gene clusters (BGC)-mediated metabolite production in C. reinhardtii. We established that the prolonged exposure to fine-tuned blue light illumination substantially enhances the biomanufacturing of astaxanthin (1.6 times compared to red light) and pigments via the phototropin. It suggests that fine-tuned illumination conditions modulate molecular components in specific metabolite production. We are providing biochemical, genetic, transcriptomic, quantitative proteomics and systems biology evidences for opto-biomanufacturing of bioactive from green lineage via modulation of phototropin network with artificial illumination (without any genetic modification). By integrating data-driven analytics and systems biology-based computational pipeline, we elucidate the functional crosstalk between biosynthetic gene clusters with the phototropin and carotenoid metabolism synthesis pathway in the green lineage (terrestrial alga and higher plants). Thus, highlighting a new opto-biotechnology approach in metabolite production from various phototrophic organisms. These results establish a new avenue for opto-biomanufacturing strategies to generate bioactive molecules from microorganisms and other green lineages simply by illumination. Highlights O_LIOpto-biomanufacturing of astaxanthin in C. reinhardtii is achieved simply by illumination. C_LIO_LIBiosynthetic gene clusters of metabolite production are also controlled by light. C_LIO_LIPhotoreceptor-based opto-biomanufacturing of valuable bioactives is established. C_LIO_LIOpto-biomanufacturing opens avenues for bioactive production in various organisms. C_LI

biochemistry↗

bioRxiv2025

p53-induced RNA-binding protein ZMAT3 inhibits transcription of a hexokinase to suppress mitochondrial respiration

The tumor suppressor p53 is a transcription factor that controls the expression of hundreds of genes. Emerging evidence indicates that the p53-induced RNA-binding protein ZMAT3 acts as a key splicing regulator that contributes to p53-dependent tumor suppression in vitro and in vivo. However, the mechanism by which ZMAT3 functions within the p53 pathway remains largely unclear. Here, we discovered a function of ZMAT3 in inhibiting transcription of HKDC1, a hexokinase that regulates glucose metabolism and mitochondrial respiration. Quantitative proteomics revealed HKDC1 as the most significantly upregulated protein in ZMAT3-depleted colorectal cancer cells. ZMAT3 depletion resulted in increased mitochondrial respiration, which was rescued by simultaneous depletion of HKDC1, suggesting that HKDC1 is a critical downstream effector of ZMAT3. Unexpectedly, ZMAT3 did not bind to HKDC1 RNA or DNA; however, proteomic analysis of the ZMAT3 interactome identified its interaction with the oncogenic transcription factor JUN. ZMAT3 depletion enhanced JUN binding to the HKDC1 locus, leading to increased HKDC1 transcription that was rescued upon JUN depletion, suggesting that JUN activates HKDC1 transcription in ZMAT3-depleted cells. Collectively, these findings uncover a mechanism by which ZMAT3 regulates transcription through JUN and demonstrate that HKDC1 is a key component of the ZMAT3-regulated transcriptome in the context of mitochondrial respiration regulation.

cancer biology↗

bioRxiv2025

A Cluster of Acidic Residues in the Cytoplasmic Domain of SARS-CoV-2 Spike is Required for Virion-Incorporation and Infectivity

Abstract/SummaryLike all coronaviruses, the infectivity of SARS-CoV-2 virus particles (virions) requires incorporation of the Spike glycoprotein. Yet, the mechanisms that support the virion-incorporation of Spike are incompletely defined. We noted an unusual feature of human sarbecovirus Spike proteins: their cytoplasmic domains (CDs) contain a stretch of acidic amino acids (DEDDSE). This sequence resembles a cluster of acidic residues, or acidic cluster (AC) motif, found in the cytoplasmic domain of the cellular endoprotease Furin. In Furin, the acidic cluster acts as a protein sorting signal, supporting its intracellular localization at the trans-Golgi network (TGN). We tested the contribution of the acidic cluster motif in the Spike CD to protein interactions and to the infectivity of SARS-CoV-2. We used virus-like particles (VLPs) as a model for viral "infection" (transduction). The SARS-CoV2 VLPs were produced by co-expressing Spike (S), Membrane (M), Envelope (E) and Nucleocapsid (N) proteins and deliver an RNA encoding luciferase to target cells expressing the ACE2 receptor. Remarkably, when all five acidic residues of the DEDDSE sequence were replaced with alanines, the VLPs were rendered non-infectious. The N-terminal DE residues provided most of the activity of the acidic cluster. These virologi-cally-impaired Spike mutants were able to reach the cell surface and induce the formation of syncytia, indicating that they are fusogenic and capable of anterograde traffic through the biosynthetic pathway to the plasma membrane. Despite this, they failed to efficiently incorporate into virions. We observed acidic cluster motif-dependent interactions of the Spike CD with several cellular proteins that could potentially support its role in virion-incorporation, including the ERM proteins Ezrin, Radixin, and Moesin; the retromer subunit Vps35, and the medium subunits of the clathrin adaptor complexes AP1 and AP2. While the key cofactor and mechanism of action remains to be defined, this region of acidic residues in the Spike CD appears to be a novel determinant of SARS-CoV-2 infectivity.

molecular biology↗

bioRxiv2025

Deep Learning-coupled Proximity Proteomics to Deconvolve Kinase Signaling In Vivo

Deconvolving the substrates of hundreds of kinases linked to phosphorylation networks driving cellular behavior is a fundamental, unresolved biological challenge, largely due to the poorly understood interplay of kinase selectivity and substrate proximity. We introduce KolossuS, a deep learning framework leveraging protein language models to decode kinase-substrate specificity. KolossuS achieves superior prediction accuracy and sensitivity across mammalian kinomes, enabling proteome-wide predictions and evolutionary insights. By integrating KolossuS with CRISPR-based proximity proteomics in vivo, we capture kinase-substrate recognition and spatial context, obviating prior limitations. We show this combined framework identifies kinase substrates associated with physiological states such as sleep, revealing both known and novel Sik3 substrates during sleep deprivation. This novel integrated computational-experimental approach promises to transform systematic investigations of kinase signaling in health and disease.

cell biology↗

bioRxiv2025

Protective effects of central leptin on whole-body energy homeostasis upon acute olanzapine exposure

Second-generation antipsychotic use is associated with severe metabolic side effects such as obesity and type 2 diabetes. Leptin is a hormone that is secreted by adipose tissue, and it acts on the brain to decrease body weight by reducing food intake and stimulating energy expenditure. Leptin also improves glucose and lipid metabolism. We examined the short-term impact of olanzapine, a commonly used second-generation antipsychotic, on the central leptin-mediated regulation of energy balance, lipid metabolism, and hypothalamic kinase activity. Male Sprague Dawley rats were given an acute intracerebroventricular (ICV, 3rd ventricle) injection of either leptin or vehicle, combined with subcutaneous olanzapine or vehicle. As expected, ICV leptin decreased food intake and importantly, olanzapine did not block this effect. Administration of leptin, olanzapine, or their combination reduced the average respiratory exchange ratio (RER) during the light cycle, which indicates that fat oxidation was increased. In the dark cycle, leptin decreased the average RER regardless of olanzapine administration, and in the presence of leptin, olanzapine did not affect the average RER. Leptin did not alter the olanzapine-induced increase in serum triglyceride concentrations. Olanzapine and central leptin treatment differentially activated hypothalamic kinases. In conclusion, regulation of food intake and fuel preference by central leptin is intact following acute olanzapine administration.

neuroscience↗

bioRxiv2025

ANOMALY: A Snakemake pipeline for identifying NuMTs from Long-Read Sequencing Data

MotivationNuclear mitochondrial DNA segments (NuMT) can significantly affect cellular processes, including cancer development and disease progression. Current methods to call NuMTs rely on short-read sequencing data but struggle to resolve complex NuMTs. These limitations can be overcome by employing long-read sequencing data. However, no such workflow exists to capture NuMTs from long-read sequencing data. ResultsHere, we introduce ANOMALY, a novel, easy-to-use workflow for detecting NuMTs from long-read sequencing data. The pipeline takes raw sequencing data or aligned data and calls and visualizes sample NuMTs. On 50 simulated datasets, the pipeline demonstrated high accuracy, with a precision of 1.000, a recall of 0.989, and an F1-score of 0.994. The pipeline underscores the limitations of short-read data in resolving and capturing complex NuMTs while demonstrating that long-read data enables their accurate identification. Availability and ImplementationThe Snakemake pipeline employs Python, Bash and R and is published under an open-source GNU GPL v3 license. Detailed information about setting up and running the pipeline and the source code can be accessed at https://github.com/Nirmal2310/ANOMALY.

bioinformatics↗

bioRxiv2025

scContrast: A contrastive learning based approach for encoding single-cell gene expression data

Single-cell RNA sequencing (scRNA-seq) captures gene expression at a individual cell resolution, which reveals critical insights into cellular diversity, disease processes, and developmental biology. However, a key challenge in scRNA-seq analysis is clustering similar cells across multiple batches, particularly when distinct sequencing protocols are used. In this work, we present scContrast, a semi-supervised contrastive learning method tailored for embedding scRNA-seq data from both plate- and droplet-based protocols into a universal representation space. By leveraging five simple augmentations, scContrast extracts biologically relevant signals from gene expression data while filtering out batch effects and technical artifacts. We trained scContrast on a subset of Tabula Muris tissues and evaluated its zero-shot performance on unseen tissues. Our results demonstrate that scContrast generalizes effectively to new tissues and outperforms the leading UCE approach in integrating scRNA-seq data from droplet- and plate-based sequencing protocols.

genomics↗

bioRxiv2025

Tracing the Shared Foundations of Gene Expression and Chromatin Structure

The three-dimensional organization of chromatin into topologically associating domains (TADs) may impact gene regulation by bringing distant genes into contact. However, studies of TADs function and their influence on transcription have been constrained by ambiguities in TAD boundary definitions and challenges in directly measuring their regulatory effects. We overcome these limitations by developing species-level consensus TAD maps for human and mouse using a bag-of-genes approach that exposes emergent regulatory structure. To quantify TAD-mediated gene relationships, we leverage a generative AI foundation model computed from 33 million transcriptomes to define a contextual similarity metric, revealing higher-order gene relationships elusive to co-expression analysis. We find that TADs are regions of elevated gene co-regulation, and our analytical framework directly leads to testable hypotheses about chromatin organization across cellular states. We discover that the TAD-linked enhancement of transcriptional context is strongest in early developmental stages and systematically declines with aging. Investigation of cancer cells show distinct patterns of TAD usage that shift with chemotherapy treatment. Enhancing our understanding of cellular plasticity in differentiation and disease, these findings suggest that chromatin organization may act through probabilistic mechanisms rather than deterministic rules. Software availabilityhttps://singhlab.net/tadmap

bioinformatics↗

bioRxiv2025

Vocal ontogeny in Mus musculus

Infants of many species produce distress calls when in need of parental care. As they mature and gain independence from caregivers, juveniles stop producing infant calls and begin producing adult-like vocalizations in a variety of species-typical contexts. Neonatal mice (Mus musculus) produce "isolation" ultrasonic vocalizations (USVs) when separated from the nest, and in contrast, adolescent and adult mice produce "social" USVs during interactions with conspecifics. Although this transition in vocal behavior is known to occur, its developmental timing remains poorly described. In the current study, we performed longitudinal measurements of mouse USVs to establish the age at which juvenile mice begin producing social USVs. Every 4 days (postnatal day (P)12-P28), USVs were first recorded from individual mice in solo sessions to establish rates of isolation/spontaneous USVs and then were recorded again during interactions between same-sex and opposite-sex pairs of age-matched, socially novel (non-littermate) C57BL/6J mice. We report that juvenile mice begin producing social USVs earlier than previously reported, at ages P20-P24. Moreover, although these early social USVs are produced at low rates compared to adult social USVs, they are nonetheless temporally coordinated with active social interaction. Our characterization of the timing of the transition from isolation USVs to social USVs forms a foundation to investigate brain mechanisms that enable the context-dependent regulation of vocal communication over development.

animal behavior and cognition↗

bioRxiv2025

Decoding the causal drivers of spatial cellular topology

Decoding how cells influence and communicate with each other in space is fundamental for understanding tissue organization. However, existing approaches either overlook spatial context entirely or rely solely on local cell-cell adjacency, failing to capture how global tissue topology shapes cellular communication. Here, we present GLACIER, which introduces spatial Granger causality to infer transcriptional and signaling relationships that emerge from tissue organization. By combining GASTONs global isodepth coordinate with Veloramas graph-based causal inference framework, we enable bidirectional inference of regulatory relationships along spatial axes, identifying transcription factortarget interactions and ligand-receptor pairs that operate across spatial domains. Applying GLACIER to single-cell spatial transcriptomics data from the mouse cerebellum, we identify both continuous within-cell-type regulatory gradients and discontinuous drivers at layer interfaces, while distinguishing between forward and backward cellular communication along the isodepth axis. Our approach reveals how tissue architecture directs patterns of cellular communication, providing a framework for understanding spatially-encoded regulatory programs. Software availabilityhttps://github.com/rohitsinghlab/glacier.

bioinformatics↗

bioRxiv2025

Development of a thermostable and broadly neutralizing pan-sarbecovirus vaccine candidate

Zoonotic spillover of sarbecoviruses to humans resulted in theSARS-CoV-1 outbreak in 2003 and the current COVID-19 pandemic caused by SARS-CoV-2. In both cases, the viral spike protein (S) is the principal target of neutralising antibodies that prevent infection. Within spike, the immunodominant receptor-binding domain (RBD) is the primary target of neutralising antibodies in COVID-19 convalescent sera and vaccine recipients. We have constructed stabilized RBD derivatives of different sarbecoviruses: SARS-CoV-1 (Clade 1a), WIV-1 (Clade 1a), RaTG13 (Clade 1b), RmYN02 (Clade 2) and BtKY72 (Clade 3). Stabilization enhanced yield by an 3-23-fold. The RBD derivatives were conformationally intact as assayed by binding to multiple broadly neutralizing antibodies. The stabilized RBDs show significant enhancement in apparent Tm, exhibit resistance to a 2-hour incubation at temperatures up to 60 in PBS in contrast to corresponding WT RBDs, and show prolonged stability of over 15 days at 37 after lyophilization. In mice immunizations, both stabilization and trimerization significantly enhanced elicited neutralization titers by [~]100 fold. The stabilized RBD cocktail elicited high neutralizing titers against both homologous and heterologous pseudoviruses. The immunogenicity of the vaccine formulation was assessed in both naive and SARS-CoV-2 pre-immunized mice, revealing an absence of immune imprinting, thus indicating its suitability for use in future sarbecovirus-origin epidemics or pandemics. Author summaryThe COVID-19 pandemic was caused by the sarbecovirus SARS-CoV-2. Phylogenetically, sarbecoviruses are divided into four clades: Clade 1a, Clade 1b, Clade 2 and Clade 3 and within these clades, are many other sarbecovirus strains with pandemic or epidemic potential. It is therefore important to develop a broadly protective, pan-sarbecovirus vaccine formulation that can be cheaply and rapidly produced. While mRNA vaccine formulations are efficacious, they have stringent low temperature storage requirements and there is limited manufacturing expertise in low and middle income countries for this modality. Neutralizing antibodies are important for protection and in the case of SARS-CoV-2 are primarily directed against the Receptor Binding Domain (RBD) of the surface spike protein. In this study, we have designed and developed an adjuvanted, protein subunit, pan sarbecovirus vaccine formulation using stabilized RBD derivatives from diverse sarbecoviruses as immunogens. We demonstrate that the stabilized RBD derivatives have considerably enhanced yield and thermal stability relative to corresponding WT proteins and that the formulation remains stable for up to two weeks at 37{degrees}C. The formulation was found to be highly immunogenic in both naive and pre-immunized mice, eliciting neutralizing titers well above the known protective threshold, indicating its suitability for use in future sarbecovirus-origin epidemics or pandemics.

immunology↗

bioRxiv2025

Improved Spatial Transcriptomics Clustering with Nested Graph Neural Networks

We introduce a novel approach, STING, for spatial transcriptomic clustering analysis. Unlike existing state-of-the-art techniques that use graph-based neural networks (GNNs) trained on graphs generated from the spatial proximity of tissue locations (or spots), STING incorporates spot-specific related genes. This feature allows STING to better distinguish between clusters and identify meaningful gene-gene relations for knowledge discovery. It is a nested GNN framework that simultaneously models gene-gene and spatial relations. Using the gene expression, we generate a spot-specific gene-gene co-expression graph. We implement an inner GNN for these graphs to generate embeddings for each location. Next, we utilize these embeddings as features in a sample-wide graph generated using spatial information. We implement an outer GNN for this graph to reconstruct the original gene expression data. Finally, STING is trained end-to-end to generate embeddings that capture gene-gene and spatial information, which we input to a clustering algorithm to produce the spatial clusters. Experiments for 26 samples across 7 datasets and 5 spatial sequencing technologies show that STING outperforms the existing state-of-the-art techniques with a 1.58% to 4.07% improvement in the clustering evaluation metric, thus confirming that integrating gene-gene relation information with the clustering task leads to more informative embeddings and better clusters. Furthermore, experiments on a human breast cancer dataset show that STING identifies relevant genes and gene-gene relations, enabling biological hypothesis generation.

bioinformatics↗

bioRxiv2025

Learning a CoNCISE language for small-molecule binding

Rapid advances in deep learning have improved in silico methods for drug-target interaction (DTI) prediction. However, current methods do not scale to the massive catalogs that list millions or billions of commercially-available small molecules. Here, we introduce CoNCISE, a method that accelerates drug-target interaction (DTI) prediction by 2-3 orders of magnitude while maintaining high accuracy. CoNCISE uses a novel vector-quantized codebook approach and a residual-learning based training of hierarchical codes. Strikingly, we find that much of binding-specificity information in the small molecule space can be compressed into just 15 bits of information per compound, characterizing all small molecules into 32,768 hierarchically-organized binding categories. Our DTI architecture, which combines these compact ligand representations with fixed-length protein embeddings in a cross-attention framework, achieves state-of-the-art prediction accuracy at unprecedented speed. We demonstrate CoNCISEs practical utility by indexing 6.4 billion ligands in the Enamine dataset, enabling researchers to query vast chemical libraries against a protein target in seconds. A "CoNCISE + docking" pipeline screened Enamine to propose strong binders (predicted KD {approx} 10-20 {micro}M) of three difficult-to-drug targets, each within two hours. CoNCISEs advance could democratize access to largescale computational drug discovery, potentially enabling rapid identification of promising molecules for therapeutic targets and cellular perturbations.

bioinformatics↗

bioRxiv2025

Nucleotide-resolution Mapping of RNA N6-Methyladenosine (m6A) modifications and comprehensive analysis of global polyadenylation events in mRNA 3' end processing in malaria pathogen Plasmodium falciparum

Plasmodium falciparum is an obligate human parasite of the phylum Apicomplexa and is the causative agent of the most lethal form of human malaria. Although N6-methyladenosine modification is thought to be one of the major post-transcriptional regulatory mechanisms for stage-specific gene expression in apicomplexan parasites, the precise base position of m6A in mRNAs or noncoding RNAs in these parasites remains unknown. Here, we report global nucleotide-resolution mapping of m6A residues in P. falciparum using DART-seq technology, which quantitatively displayed a stage-specific, dynamic distribution pattern with enrichment near mRNA 3 ends. In this process we identified 894, 788, and 1,762 m6A-modified genes in Ring, Trophozoite and Schizont stages respectively, with an average of 5-7 m6A sites per-transcript at the individual gene level. Notably, several genes involved in malaria pathophysiology, such as KAHRP, ETRAMPs, SERA and stress response genes, such as members of Heat Shock Protein (HSP) family are highly enriched in m6A and therefore could be regulated by this RNA modification. Since we observed preferential methylation at the 3 ends of P. falciparum transcripts and because malaria polyadenylation specificity factor PfCPSF30 harbors an m6A reader YTH domain, we reasoned that m6A might play an important role in 3-end processing of malaria mRNAs. To investigate this, we used two complementary high-throughput RNA 3-end mapping approaches, which provided an initial framework to explore potential roles of m6A in the regulation of alternative polyadenylation (APA) during malaria development in human hosts.

genomics↗

bioRxiv2024

Phototropin localization and interactions regulate photophysiological processes in Chlamydomonas reinhardtii

Phototropin, a blue-light sensing serine/threonine kinase, plays a pivotal role in regulating diverse photophysiological processes in both plants and algae. In Chlamydomonas reinhardtii, phototropin (CrPhot) localizes to the eyespot and flagella, coordinating key cellular functions such as phototaxis, photosynthesis, gametogenesis, and chlorophyll biosynthesis. While previous research has identified phototropin interactions with signaling proteins such as channelrhodopsins and light-harvesting complex proteins, many aspects of its interaction network and regulatory mechanisms remain unresolved. In this study, we explored novel interacting protein partners of phototropin and their roles in modulating its regulatory functions in Chlamydomonas reinhardtii. Employing a suite of intraflagellar transport (IFT) mutants of C. reinhardtii such as IFT172, IFT52, IFT88, IFT139, kinesin/dynein, CEP290 etc., we elucidate that phototropin localization within the flagella and eyespot is IFT-mediated. Our study highlights interaction of phototropin with other photoreceptors-channelrhodopsins (ChR1 and ChR2), chlamyopsin 6, LOV-histidine kinases (LOV-HK1, LOV-HK2) and signaling protein-14-3-3. CRISPR-Cas9 knockouts of phototropin showed reduced ChR1, 14- 3-3 levels and exhibited impaired photomotility. Moreover, two LOV-domain containing histidine kinases, LOV-HK1 and LOV-HK2, were identified in C. reinhardtii. Gene expression of LOV-HK1 and LOV-HK2 were found to be elevated in UV-light in C. reinhardtii and their genes expression was found to be altered in phototropin CRISPR-Cas9 knockouts. This study provides new insights into phototropin signalosome and highlights molecular mechanisms governing its function. The research outcomes advances our understanding of phototropin trafficking and signal modulation in Chlamydomonas reinhardtii, and sets the stage for further exploration into the broader physiological roles of phototropin in cellular responses. Graphical abstractPhototropin, a blue-light receptor in Chlamydomonas reinhardtii, localizes to the flagella and eyespot, mediates phototaxis and photosynthesis. Its trafficking is mediated by intraflagellar transport (IFT) machinery, with mutations in IFT components (kinesin, dynein, IFT172, IFT52, IFT88, IFT139, CEP290) disrupting phototropin localization. Phototropin interacts with other photoreceptors like channelrhodospins (ChR1/2), chlamyopsin 6, LOV-histidine kinases (LOV-HK1, LOV-HK2) and signaling proteins (14-3-3), coordinating light-driven responses. These findings underscore the details of phototropin trafficking and phototropin signaling impacting light-induced physiological processes in C. reinhardtii. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=159 SRC="FIGDIR/small/630506v1_ufig1.gif" ALT="Figure 1000"> View larger version (51K): org.highwire.dtl.DTLVardef@29f6b3org.highwire.dtl.DTLVardef@10382aeorg.highwire.dtl.DTLVardef@1786f0dorg.highwire.dtl.DTLVardef@1c2ed65_HPS_FORMAT_FIGEXP M_FIG C_FIG Highlights* Phototropin localizes in eyepot and flagella in Chlamydomonas reinhardtii. * Intraflagellar transport (IFT) mutants of C. reinhardtii suggest role of different IFT proteins in phototropin trafficking and localization. * Phototropin interacts with other photoreceptors (ChR1 & ChR2, COP6, LOV-HK1 & LOV-HK2) and signaling proteins (14-3-3), contributing to various physiological processes. * CRISPR-Cas9 knockouts of phototropin showed reduced 14-3-3 protein content and photomotility response in C. reinhardtii.

plant biology↗

bioRxiv2024

TimeFlies: an snRNA-seq aging clock for the fruit fly head sheds light on sex-biased aging

Although multiple high-performing epigenetic aging clocks exist, few are based directly on gene expression. Such transcriptomic aging clocks allow us to identify potential age-associated genes directly. However, most existing transcriptomic clocks model a subset of genes and are limited in their ability to predict novel biomarkers. With the growing application of single-cell sequencing, there is a need for robust single-cell transcriptomic aging clocks. Moreover, aging clocks have yet to be applied to investigate the elusive phenomenon of sex differences in aging. We introduce TimeFlies, a pan-cell-type snRNA-seq aging clock for the Drosophila melanogaster head. TimeFlies uses deep learning to classify the donor age of cells based on genome-wide gene expression profiles. Using explainability methods, we identified key marker genes contributing to the classification, with lncRNAs showing up as highly enriched among predicted biomarkers. lncRNA:roX1 and lncRNA:roX2 are top clock genes across cell types. Both are regulators of X chromosome dosage compensation, a pathway previously found to be significantly affected by aging in the mouse brain. We validated these findings experimentally in Drosophila, showing a decrease in survival when dosage compensation is inhibited in vivo. Furthermore, we trained sex-specific TimeFlies clocks and noted significant differences in model predictions and explanations between male and female clocks, suggesting that different pathways drive aging in males and females.

genomics↗