Biology subjects

Ghosh, A.

Publications and source records attributed to Ghosh, A..

At least 91 records · Page 5Linked to original sources

bioRxiv2025

Proximal relationships of moonlighting Proteins in Escherichia coli: a mathematical genomic perspective

Moonlighting proteins in Escherichia coli (E.coli) perform multiple independent functions without altering their primary amino acid sequence, challenging the "one gene-one enzyme" hypothesis. Bacterial proteins serve various functions, including host cell adhesion, extracellular matrix interaction, and immune modulation, while also supporting essential physiological processes within the bacteria. Identifying these proteins in pathogens and tracking their genetic changes is crucial for understanding bacterial survival and virulence. A quantitative understanding of these proteins is pivotal as it enables the identification of specific patterns and relationships between amino acid composition, protein stability, and functional versatility. This study quantitatively analyzes fifty E. coli moonlighting proteins, focusing on their structural and functional features. Key findings include variability in amino acid composition, with alanine predominating, and a preference for non-polar residues, which may enhance protein stability. Quantitative features analyses identified seven distinct proximal sets, reflecting the proteins spatial arrangements of amino acids, structural diversity, and functional roles in processes such as metabolism, stress response, and gene regulation. These results deepen our understanding of the multifunctionality of E. coli moonlighting proteins, indicating their adaptability and implications for bacterial survival and pathogenicity.

genomics↗

bioRxiv2025

A Machine Learning-based Approach for Quantification of Protein Secondary Structures from Discrete Frequency Infrared Images

Discrete frequency infrared (IR) imaging is an exciting experimental technique that has shown promise in various applications in biomedical science. This technique often involves acquiring IR absorptive images at specific frequencies of interest that enable pathologically relevant chemical contrast. However, certain applications, such as tracking the spatial variations in protein secondary structure of tissue specimens, necessary for the characterization of neurodegenerative diseases, require deeper analysis of spectral data. In such cases, the conventional analytical approach involves band fitting the hyperspectral data to extract the relative populations of different structures through their fitted areas under the curve (AUCs). While Gaussian spectral fitting for one spectrum is viable, expanding that to an image with millions of pixels, as often applicable for tissue specimens, becomes a computationally expensive process. Alternatives like Principal Component Analysis (PCA) are less structurally interpretable and incompatible with sparsely sampled data. Furthermore, this detracts from the key advantages of discrete frequency imaging by necessitating acquisition of a more finely sampled spectral data that is optimal for curve fitting, resulting in significantly longer data acquisition times, larger datasets and additional computational overhead. In this work we demonstrate that a simple two-step regressive neural network model can be utilized to mitigate these challenges and employ discrete frequency imaging for retrieving the results from band fitting without significant loss of fidelity. Our model reduces the data acquisition time nearly 6-fold by requiring only seven wavenumbers to accurately interpolate spectral information at a higher resolution, and subsequently using the upscaled spectra to accurate predict the component AUCs, which is more than 3000 times faster than spectral fitting. Our approach thus drastically cuts down the data acquisition and analysis time and predicts key differences in protein structure that can be vital towards broadening potential applications of discrete frequency imaging.

biophysics↗

bioRxiv2024

A novel isoform of Tensin1 promotes actin filament assembly for efficient erythroblast enucleation

Mammalian red blood cells are generated via a terminal erythroid differentiation pathway culminating in cell polarization and enucleation. Actin filament polymerization is critical for enucleation, but the molecular regulatory mechanisms remain poorly understood. We utilized publicly available RNA-seq and proteomics datasets to mine for actin-binding proteins and actin- nucleation factors differentially expressed during human erythroid differentiation and discovered that a focal adhesion protein--Tensin-1--dramatically increases in expression late in differentiation. Remarkably, we found that differentiating human CD34+ cells express a novel truncated form of Tensin-1 (eTNS1; Mr [~]125 kDa) missing the N-terminal half of the protein, due to an internal mRNA translation start site resulting in a unique exon 1. eTNS1 localized to the cytoplasm during terminal erythroid differentiation, with no apparent membrane association or focal adhesion formation. Knocking out eTNS1 had no effect on assembly of the spectrin membrane skeleton but led to impaired enucleation and absent or mis-localized actin filament foci in enucleating erythroblasts. We conclude that eTNS1 is a novel regulator of actin filament assembly during human erythroid terminal differentiation required for efficient enucleation.

cell biology↗

bioRxiv2024

Social isolation reduces metabolic rate and social preference in Wild-type Zebrafish (Danio rerio)

Group living is a common feature of many animals and serves a variety of functions, including predator avoidance, collective foraging, and reproduction. Individuals from group-living species may face isolation from their social group in their natural environment. Prior research has shown that social isolation in zebrafish (Danio rerio), a highly social fish, results in physiological and behavioural changes, including reduced locomotor activity and loss of interest in their environment. We investigated whether social isolation was associated with differences in dissolved oxygen consumption rate, movement speed, and social preference in adult zebrafish. We subjected fish to one month of visual and olfactory social isolation, and measured metabolic rate via dissolved oxygen consumption rate before and after social isolation. A control group was not subjected to any form of social isolation but held in comparable housing. We measured movement speed and social interaction before and after social isolation using a social preference assay. We found that socially isolated fish show a significant decrease in oxygen consumption rate compared to Day 0 isolation baseline levels, whereas control fish did not show this decrease. After a month of social isolation fish also showed lower social preference, and lower movement speed. These findings could suggest reduced energy expenditure in the context of social isolation in this species. This work enhances our understanding of the physiological and behavioural consequences of isolation in social animals.

ecology↗

bioRxiv2024

Design and synthesis of triazine-based hydrogel for combined targeted doxorubicin delivery and PI3Kinhibition

Melanoma, an aggressive skin cancer originating from melanocytes, presents substantial challenges due to its high metastatic potential and resistance to conventional therapies. Hydrogels, three-dimensional networks of hydrophilic polymers with high water-retention capacities, offer significant promise for controlled drug delivery applications. In this study, we report the synthesis and characterization of hydrogelators based on the triazine molecular scaffold, which self-assemble into fibrous networks conducive to hydrogel formation. Rheological analysis confirmed their hydrogelation properties, while microscopic techniques including FE-SEM and FEG-TEM provided insights into their morphological networks. The drug delivery capability of these hydrogelators was evaluated using doxorubicin, a widely employed anticancer agent, demonstrating enhanced biocompatibility and reduced side effects compared to free doxorubicin. Additionally, the hydrogelators exhibited inhibitory activity against phosphoinositide 3-kinase (PI3K), a key enzyme frequently mutated in cancer, and also involved in melanoma progression. The dual functionality of this delivery system - controlled drug release and PI3K inhibition - highlights the potential of triazine-based hydrogelators as innovative therapeutic platforms for melanoma treatment.

cancer biology↗

bioRxiv2024

Mechano-sensing role of oncogenic lncRNA NEAT1 on soft vs. stiff substrates and its implications for glioblastoma progression

Long non-coding RNAs (lncRNAs) have gained increasing recognition as key regulators of cancer biology, overturning their earlier classification as non-functional genomic elements. Among them, NEAT1 (Nuclear-Enriched Abundant Transcript 1) has emerged as a prominent oncogenic lncRNA involved in multiple solid tumors, including liver, prostate, and gastric cancers, as well as gliomas. Although the role of NEAT1 in cancer has been extensively studied, most of these investigations have focused on biochemical factors, leaving the mechano-sensitivity of NEAT1 to mechanical alterations in the tumor microenvironment (TME) largely unexplored. As tumors progress, the TME undergoes significant mechanical changes, particularly extracellular matrix (ECM) stiffening, which modulates cellular behavior through mechano-transduction pathways. While much research has focused on how these mechanical cues influence protein-coding gene expression, their impact on lncRNAs such as NEAT1 remains understudied. This study aims to address this gap by investigating the mechano-sensitivity of NEAT1 in glioblastoma (GBM) cells cultured on polyacrylamide (PAA) gels that mimic the brains physiological stiffness (0.5 kPa) versus conventional, stiff tissue culture plastic (TCP). NEAT1 expression, quantified using qRT-PCR, revealed a significant increase on softer, brain-mimicking substrates compared to stiffer TCP. Overexpression of NEAT1 in brain-mimicking stiffness also showed higher expression levels of cancer progression markers, quantified by qRT-PCR. To further explore NEAT1s role in oncogenesis due to its mechano-sensing capabilities, siRNA-mediated downregulation of NEAT1 in GBM cells cultured on soft PAA gels was performed to reduce NEAT1 levels to those comparable to TCP. The results showed reduced cancer aggressiveness, characterized by decreased expression of invasion, stemness, and epithelial-mesenchymal transition (EMT) markers, further supported by migration and invasion assays. These findings highlight NEAT1 as a mechanosensitive regulator in response to substrate stiffness, underscoring its role in tumor progression.

cancer biology↗

bioRxiv2024

Syntopic call diversity among morphologically similar calling morphs ofthe paleotropical katydid of genus Mecopoda (Tettigoniidae) from an Indo-Burma biodiversity hotspot.

The acoustically diverse family of Tettigoniidae (Ensifera, Orthoptera) (commonly called bushcrickets or katydids), constitutes an excellent system to study the evolution of calling songs. Mecopoda is a genus that is particularly interesting because of the many morphologically cryptic calling morphs that have been described across South, East, and Southeast Asia, some of which are considered endemic to India. We describe five new syntopic and sympatric calling morphs of the genus Mecopoda that are morphologically similar but acoustically diverse from a sub-tropical forest in Meghalaya, in the Indo-Burma biodiversity hotspot. We report the morphological and acoustic characterization of these five call types, finding morphological overlap but well-separated acoustic clusters based on a PCA analysis of call temporal and spectral features. We also sequenced the COI gene mitochondrial gene of these five call types as well as other call types previously reported from India and performed a phylogenetic analysis of this gene relative to previously reported COI gene sequences from genus Mecopoda. Phylogenetic results suggest our five call types are not mutually monophyletic and relate from different subspecies of Mecopoda, of which three call types are genetically well separated from other calling morphs. This broadens our understanding of the evolution of acoustic diversity among paleotropical katydids.

zoology↗

bioRxiv2024

Cellular moonlighting function of Hsp20 directs morphological and pathogenic development in Ustilago maydis

Ustilago maydis Hsp20 is involved in the pathogenicity of the fungus. In this study we have investigated the molecular basis of contribution of Hsp20 to U. maydis pathogenicity. Through biochemical studies we have demonstrated environment-dependent oligomeric plasticity associated with Hsp20. Hsp20 was also found to form higher order oligomers that undergo phase separation in vitro. Within cells Hsp20 was found to form distinct punctate structures that we believe play a pivotal role in its function. These punctate structures were demonstrated to sequester proteins such as actin and septin within it. Absence of Hsp20 was found to significantly affect key cellular processes like endocytosis, budding, cell polarity determination and mating in U. maydis cells. The deletion mutant failed to sporulate and complete pathogenic life cycle. This study presents a comprehensive understanding of the pathogenic development of U. maydis in reference to the moonlighting function of Hsp20 within the cell.

microbiology↗

bioRxiv2024

Methuselah Proteins in Longevity: Unraveling Their Impact Through Mathematical Genomics

This study provides a quantitative and comprehensive analysis of 18 Methuselah (mth) protein variants from fruit flies, focusing on their evolutionary relationships, structural features, and functional roles in aging and longevity. Phylogenetic analysis identified two major clades of mth proteins, with the first clade indicating conserved functions across Drosophila species and the second clade reflecting gene duplication and diversification. The study found five distinct functional subclasses of mth proteins through amino acid frequency and poly-string analyses, linked to their structural diversity and role in longevity. Structural topology and post-translational modifications reveal similarities with G-protein-coupled receptors (GPCRs), suggesting that mth proteins are crucial for signal transduction and cellular health. Variability in propeptide cleavage sites and intrinsic protein disorder further highlight adaptive roles in signaling. The findings underscore the importance of a quantitative and comprehensive approach to studying Methuselah genes, offering insights into their functional versatility and evolutionary dynamics. This enhanced quantitative understanding contributes to advancing research on aging and longevity.

genomics↗

bioRxiv2024

Brain-like learning with exponentiated gradients

Computational neuroscience relies on gradient descent (GD) for training artificial neural network (ANN) models of the brain. The advantage of GD is that it is effective at learning difficult tasks. However, it produces ANNs that are a poor phenomenological fit to biology, making them less relevant as models of the brain. Specifically, it violates Dales law, by allowing synapses to change from excitatory to inhibitory, and leads to synaptic weights that are not log-normally distributed, contradicting experimental data. Here, starting from first principles of optimisation theory, we present an alternative learning algorithm, exponentiated gradient (EG), that respects Dales Law and produces log-normal weights, without losing the power of learning with gradients. We also show that in biologically relevant settings EG outperforms GD, including learning from sparsely relevant signals and dealing with synaptic pruning. Altogether, our results show that EG is a superior learning algorithm for modelling the brain with ANNs.

neuroscience↗

bioRxiv2024

Differential expression of oncogenic lncRNAs NEAT1 and MALAT1 in 2D monolayer vs. 3D tumoroid culture and its implications in cancer progression

Long non-coding RNAs (lncRNAs) have emerged as crucial regulators of cellular processes, overturning their previous classification as "junk" DNA. Their involvement in various cellular functions makes them potential therapeutic targets in a range of diseases, particularly in tumorigenesis and cancer. Among these lncRNAs, MALAT1 (Metastasis-Associated Lung Adenocarcinoma Transcript 1) and NEAT1 (Nuclear-Enriched Abundant Transcript 1) are prominent oncogenic lncRNAs with complex regulatory roles across multiple cancer types. Although the roles of these lncRNAs in cancer have been extensively studied, the majority of these investigations have been conducted in 2D culture systems. While 2D cell cultures are well-established and widely accepted models in cell biology, they lack the physiological complexity of 3D tumor architecture. In contrast, 3D cultures, where cells grow as three-dimensional clusters, better mimic in vivo conditions, making them essential for understanding tumor microenvironment (TME) dynamics. Despite the importance of 3D models, there is a lack of literature exploring lncRNA expression in 2D monolayers versus 3D cultures across different cancer types. This study examines the expression and function of the lncRNAs NEAT1 and MALAT1 in 3D tumoroids compared to 2D monolayer cultures, aiming to bridge the gap between in vitro models and the complex in vivo tumor microenvironment. We addressed this gap by quantifying the expression differences of NEAT1 and MALAT1 using qRT-PCR in breast cancer, liver cancer, cervical cancer, and glioblastoma (GBM) cells grown in both 2D and 3D cultures. Significant variations in NEAT1 and MALAT1 expression were observed between 2D and 3D cultures across these cancer types, signifying the need to study lncRNAs in 3D micro-environments. Furthermore, we established correlations between NEAT1 expression and cancer traits such as stemness, invasion, glucose transporter expression, and epithelial-mesenchymal transition (EMT) in GBM 3D tumoroids compared to 2D monolayers. Using siRNA to downregulate NEAT1 in GBM tumoroids, we demonstrated that reducing NEAT1 expression to the levels comparable to 2D cultures led to a decrease in the expression of mRNA markers associated with stemness, invasion, glucose transporters, and EMT. Additionally, siRNA-mediated downregulation of NEAT1 in 3D tumoroids directly impacted cancer properties, as validated by phenotypic assays, where reduced proliferation, migration, and invasion were observed in GBM (comparing 3D to 2D models). Therefore, our work provides new insights into the expression of two key oncogenic lncRNAs in 3D microenvironments of various cancers and lays the foundation for integrating lncRNAs as important molecular players within 3D culture systems, offering a better understanding of the in vivo complexity of the TME.

cancer biology↗

bioRxiv2024

Sex-specific role of epigenetic modification of a leptin upstream enhancer in the adipose tissue

ObjectiveMaternal hormonal status can have long-term effects on offspring metabolic health, likely regulated via epigenetic mechanisms. We elucidated the effect of maternal thyroid hormones on epigenetic regulation of Leptin (Lep) transcription in adipose tissue (AT) and investigated the role of DNA methylation at a Lep upstream enhancer (UE) in adipocyte biology. MethodsPregnant mice were treated with Triiodothyronine (T3). Offspring body composition and adipose tissue were analysed at 6 months (treatment: N = 8, control: N = 12). DNA methylation at the Lep UE and Lep mRNA levels in gonadal white adipose tissue (gWAT) and leptin serum levels were measured. The Lep UE was hypomethylated in murine preadipocytes using a dCas9-SunTag-TET1 system, followed by differentiation. In human visceral AT samples (N = 52) from the Leipzig Obesity BioBank (LOBB) LEP UE methylation and LEP mRNA levels were assessed alongside leptin serum levels and correlated with body fat percentage. Causal relationships were explored using mediation analysis. ResultsHigh maternal T3 levels reduced offspring body weight, total fat, and gWAT mass. Female offspring of T3-treated mothers showed lower Lep mRNA levels and higher Lep UE methylation compared to controls. CRISPR/dCas9 editing reduced Lep UE methylation by [~]20% in preadipocytes without altering Lep expression or lipid accumulation after differentiation. In humans, a mediation of sex-specific difference in body fat percentage via Lep UE methylation was found. ConclusionMaternal thyroid hormones might affect offspring gWAT Lep expression in a sex-specific manner, potentially driven by DNA methylation at the Lep UE, influencing body fat percentage.

genetics↗

bioRxiv2024

Effect of different lining paper materials and infusions on oviposition preference of Aedes aegypti (Diptera: Culicidae) gravid mosquitoes

Aedes aegypti (Linnaeus) mosquito is a vector, responsible for increasing global public health concerns due to its rapid geographical spread and increasing vectorial capacity. Understanding the breeding behavior of Ae. aegypti is crucial to control this vector and thereby limiting the spread of diseases. To study this mosquito breeding in the laboratories and ovitraps, different types of lining-papers, surrounding inner surface of a container along with different types of added infusions, are used. Lining-papers serve as surfaces for oviposition and infusions provide the essential environment for nurturing. The types of oviposition surfaces and infusions used in laboratory rearing facilities or ovitraps influence oviposition preference, changing the breeding behavior of Ae. aegypti gravid mosquitoes. This study presents a comparative study on oviposition preferences for six different types of lining-papers and six different types of infusions in a bioassay cage. ANOVA analysis shows a significant effect on oviposition preference of different types of lining-papers that served as oviposition surfaces. The highest oviposition activity was observed for the agri seed germination paper-75 lining-paper when normal tap water infusion was used. Likewise, statistical analysis shows that when a plain printing offset paper 80 GSM lining-paper was used, a highly statistically significant effect on oviposition preference is observed for different types of infusions used in the oviposition cups.

animal behavior and cognition↗

bioRxiv2024

The binary protein interactome mapping of the Giardia lamblia proteasome lid reveals extra proteasomal functions of GlRpn11

Giardia lamblia does not encode Rpn12 and Sem1, two proteins crucial for assembling the proteasome lid. To understand how the interactions between the giardial proteasome lid subunits may have changed to compensate for their absence, we used the yeast two-hybrid assay to generate a binary protein interaction map of the Giardia lid subunits. Most interactions within the Giardia proteasome lid are stronger than those within the Saccharomyces cerevisiae lid. These may compensate for the absence of Rpn12 and Sem1. A notable exception was the weaker interaction between GlRpn11 and GlRpn8, compared to the strong interaction between Rpn11-Rpn8 of yeast. The Rpn11-Rpn8 dimer provides a platform for lid assembly and their interaction involves the insertion of a methionine residue of Rpn11 into a hydrophobic pocket of Rpn8. Molecular modeling indicates that GlRpn8s pocket is wider, reconciling the experimental observation of its weak interaction with GlRpn11. This weaker interaction may have evolved to support extra proteasomal functions of GlRpn11, which localizes to multiple subcellular regions where other proteasome subunits have not been detected. One such location is the mitosome. Functional complementation in yeast shows that GlRpn11 can influence mitochondrial function and distribution. This, together with its mitosomal localization, indicates that GlRpn11 functions at the mitosome. Thus, this parasites proteasome lid has a simpler subunit architecture and structural attributes that may support dual functionalities for GlRpn11. Such parasite-specific proteasome features could provide new avenues for controlling the transmission of Giardia. 1 Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=81 SRC="FIGDIR/small/613619v1_ufigs1.gif" ALT="Figure 0"> View larger version (33K): org.highwire.dtl.DTLVardef@989e69org.highwire.dtl.DTLVardef@1c8134aorg.highwire.dtl.DTLVardef@6a9794org.highwire.dtl.DTLVardef@5ac20_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIGiardia genome does not encode two proteasomal lid subunits: Rpn12 and Sem1 C_LIO_LIUnique interactions within the lid may compensate for the absence of these two C_LIO_LIGlRpn8:GlRpn11 weakly interacts to support GlRpn11s extra-proteasomal distribution C_LIO_LIGlRpn11 localizes at mitosomes, OZ of VD, and to the VFP C_LIO_LIThe 182-218 fragment of GlRpn11 may regulate mitosomal function C_LI

cell biology↗

bioRxiv2024

Network-based estimation of therapeutic efficacy and adverse reaction potential for prioritisation of anti-cancer drug combinations

Drug combinations, although a key therapeutic agent against cancer, are yet to reach their full applicability potential due to the challenges involved in the identification of effective and safe drug pairs. In vitro or in vivo screening would have been the optimal approach if combinatorial explosion was not an issue. In silico methods, on the other hand, can enable rapid screening of drug pairs to prioritise for experimental validation. Here we present a novel network medicine approach that systematically models the proximity of drug targets to disease-associated genes and adverse effect- associated genes, through the combination of network propagation algorithm and gene set enrichment analysis. The proposed approach is applied in the context of identifying effective drug combinations for cancer treatment starting from a training set of drug combinations curated from DrugComb and DrugBank databases. We observed that effective drug combinations usually enrich disease-related gene sets while adverse drug combinations enrich adverse-effect gene sets. We use this observation to systematically train classifiers distinguishing drug combinations with higher therapeutic effects and no known adverse reaction from combinations with lower therapeutic effects and potential adverse reactions in six cancer types. The approach is tested and validated using drug combinations curated from in vitro screening data and clinical reports. Trained classification models are also used to identify novel potential anti-cancer drug combinations for experimental validation. We believe our framework would be a key addition to the anti-cancer drug combination identification pipeline by enabling rapid yet robust estimation of therapeutic efficacy or adverse reaction potential.

bioinformatics↗

bioRxiv2024

Coralline companions: Exploring microbes in coral ecosystems

Coral reefs exhibit remarkable resilience to natural disturbances but face challenges due to global changes and emerging diseases. In the present study, we recorded a total of 51 ciliates and over 400 bacteria associated with healthy and diseased corals from the Ramsar site Gulf of Mannar Marine National Park, India, based on morphology, silver staining, and Next Generation Sequencing. Our study contributes to the growing understanding of ciliate diversity and provides insights into the dynamics of ciliate-associated coral diseases, towards developing effective biocontrol agents for coral bleaching and conservation strategies to safeguard coral reef ecosystems.

ecology↗

bioRxiv2024

Stochastic Wiring of Cell Types Enhances Fitness by Generating Phenotypic Variability

The development of neural connectivity is a crucial biological process that gives rise to diverse brain circuits and behaviors. Neural development is a stochastic process, but this stochasticity is often treated as a nuisance to overcome rather than as a functional advantage. Here we use a computational model, in which connection probabilities between discrete cell types are genetically specified, to investigate the benefits of stochasticity in the development of neural wiring. We show that this model can be viewed as a generalization of a powerful class of artificial neural networks--Bayesian neural networks--where each network parameter is a sample from a distribution. Our results reveal that stochasticity confers a greater benefit in large networks and variable environments, which may explain its role in organisms with larger brains. Surprisingly, we find that the average fitness over a population of agents is higher than a single agent defined by the average connection probability. Our model reveals how developmental stochasticity, by inducing a form of non-heritable phenotypic variability, can increase the probability that at least some individuals will survive in rapidly changing, unpredictable environments. Our results suggest how stochasticity may be an important feature rather than a bug in neural development.

neuroscience↗

bioRxiv2024

The conserved biochemical activity and function of an early metazoan phosphatidylinositol 5 phosphate 4-kinase regulates growth and development.

The ability to co-ordinate function between multiple cells is a critical requirement for multi-cellularity. This co-ordination is mediated by hormones or growth factors, molecules secreted by one cell type that can convey information to the other cells and influence their behaviour. Hormone-dependent signalling is mediated by second messenger systems;phosphoinositides (PIs) generated by lipid kinase activity are one such key second messenger system. Phosphatidylinositol 5 phosphate 4-kinase (PIP4K) is a lipid kinase that phosphorylates phosphatidylinositol 5-phosphate (PI5P) to generate phosphatidylinositol 4,5 bisphosphate [PI(4,5)P2]. Following a comprehensive bioinformatics analysis of ca. 23296 proteomes covering the tree of life, we find that PIP4K is a metazoan-specific enzyme, although its homologs are also found in choanoflagellate genomes. To understand their function in early metazoans, we experimentally analysed the biochemical activity and physiological function of PIP4K from several early metazoans. We find that the PIP4K enzyme from an early branching metazoan sponge Amphimedon queenslandica (AqPIP4K), regarded as the earliest evolved metazoan, shows a biochemical activity highly conserved with human PIP4K; AqPIP4K is able to selectively phosphorylate PI5P to generate PI(4,5)P2 just as effectively as the human enzyme. Further, AqPIP4K was able to rescue the reduced cell size, growth and development phenotype in larvae of a null mutant in Drosophila PIP4K. These phenotypes are regulated through activity of the hormone insulin, acting via the cell surface insulin receptor, a member of the receptor tyrosine kinase family, that is unique to metazoans. Together, our findings indicate that in early metazoans, AqPIP4K is likely to function in a signal transduction pathway that is required for receptor tyrosine kinase signalling. Overall, our work defines PIP4K as a signal transduction motif required to regulate receptor tyrosine kinase signalling for intercellular communication in the earliest forms of metazoa.

biochemistry↗