Biology subjects

Pramanik, S.

Publications and source records attributed to Pramanik, S..

14 recordsLinked to original sources

bioRxiv2026

Giant Vesicle Platform for Synthetic Endoplasmic Reticulum Networks

The endoplasmic reticulum (ER) is a membrane-bound organelle found in all eukaryotic cells. This organelle has a fascinating morphology, which involves a network of membrane nanotubes and three-way junctions connecting three nanotubes. Previous reconstitution studies were based on proteo-liposomes with a size of 100 to 200 nanometers, using electron microscopy as the main method to image the resulting membrane morphologies. These studies revealed the importance of membrane GTPases, that is, membrane proteins that hydrolyse GTP. Here, we introduce an alternative platform for the formation of synthetic ER networks starting from giant unilamellar vesicles with a typical size of 10 mirometers and containing a single membrane GTPase such as atlastin from fruit flies or Sey1 from yeast. This giant vesicle approach makes the networks accessible to optical microscopy, by which one can elucidate both their structure and their dynamics. We first demonstrate that the membranes' propensity to form synthetic ER networks depends strongly on the lipid composition. More precisely, we show that this propensity varies non-monotonically with the mole fraction of cholesterol and that the frequency of three-way junctions has a maximum for an optimal mole fraction between 10 and 15 mol%. This optimal value is comparable to the mole fraction of cholesterol as estimated for ER membranes in vivo. We also demonstrate the formation of synthetic ER networks when the membranes contain only the membrane GTPase Sey1 but no other membrane protein, which implies that Sey1 is able on its own to generate a high membrane curvature. Based on the reconstitution approach introduced here, the synthetic ER networks can now be further investigated using the versatile toolbox that has been developed for giant vesicles.

synthetic biology↗

bioRxiv2026

Lipid nanoparticle induced model endosomal membrane disruption

Endosomal escape remains the central bottleneck limiting lipid nanoparticle (LNP)-mediated mRNA delivery. In this study, we use giant unilamellar vesicles (GUVs) mimicking the endosomal membrane to directly visualize the interaction and membrane disruptions caused by two model LNPs constituted by two different ionizable lipids: LP01 and BiP-20 (a Branched ionizable Phospholipid) that are both benchmark ionizable lipids for in vivo gene editing in liver. Despite identical TNS-measured apparent pKa (6.6) for both lipids in our conditions, LP01 LNP showed maximal membrane engagement at pH 5.8, while BiP20 LNP required pH 5.1, demonstrating that bulk pKa alone cannot predict the pH at which functional membrane interaction occurs. Using confocal microscopy, we show that the LNP engagement with the endosomal membrane proceeds through first electrostatic attraction, clustering, membrane disruption by forming highly curved morphologies, and finally leading to the rupture of the whole endosomal mimicking membrane. Notably, the mRNA cargo is released only upon complete lysis of the membrane. This indicates that the membrane destabilization and cargo release are mechanistically distinct steps. These findings establish a cell-free platform for dissecting individual sub-steps of endosomal escape.

biophysics↗

bioRxiv2025

Protein-free membrane fusion: a refined view of the delicate fusogenic properties of calcium

In the late 20th century, calcium took on the identity of an independent fusogen, when it was found to induce fusion of anionic large unilamellar vesicles (LUVs), yet its ability to drive fusion in cell-sized membranes remains poorly understood. Here, we directly quantify calcium-mediated fusion of giant unilamellar vesicles (GUVs) using a microfluidic trapping platform combined with confocal microscopy, enabling simultaneous measurement of lipid mixing, content mixing, and fusion outcomes across hundreds of single vesicles. We systematically map fusion efficiency as a function of calcium concentration, membrane composition, and mechanically imposed tension. We find that calcium-induced fusion of GUVs in the absence of proteins is remarkably fickle and composition-sensitive, as the vesicles need to be sufficiently instable to allow the opening of the fusion pore, yet stable enough to prevent bursting and collapse. Negatively charged GUVs containing high fractions of DOPE exhibit the highest fusogenic responsiveness, whereas other compositions undergo extensive lipid mixing without pore formation. Increasing membrane tension can shift this balance and promote full fusion, revealing a narrow parameter space in which calcium acts as an effective protein-free fusogen for cell-sized membranes. These findings clarify longstanding discrepancies between LUV- and GUV-based calcium fusion assays and provide quantitative design rules for employing calcium as a fusogen in synthetic biology and membrane-reconstitution studies, where controlled membrane growth, vesicle-vesicle fusion, and module integration are central to building and sustaining artificial cells. SIGNIFICANCECalcium is widely regarded as a potent, protein-free fusogen in nanometer-scale lipid vesicles, yet its relevance for fusion between cell-sized membranes remains unresolved. This gap limits our ability to translate decades of LUV-based fusion studies to more physiologically realistic systems and restricts the use of calcium in synthetic cell engineering. By directly quantifying calcium-mediated fusion in giant unilamellar vesicles (GUVs), we identify the membrane compositions and mechanical conditions under which calcium can reliably drive full fusion rather than mere lipid mixing. Our findings provide the first systematic map of calciums fusogenic parameter space in cell-sized membranes, enabling more informed design of fusion-based assays, reconstitution experiments, and strategies for membrane growth in artificial cells.

biophysics↗

bioRxiv2025

Deep Learning for Sorghum Yield Forecasting using Uncrewed Aerial Systems and Lab-Derived Imagery

The AI revolution, advanced Graphics Processing Units (GPUs), and open-source platforms have enabled Machine Learning (ML) and Deep Learning (DL) algorithms to rapidly and accurately extract phenotypic features from Uncrewed Aerial System (UAS)-derived imagery. Such advancement leads to phenotypic digitization and sorghum yield forecasting. Yield analytics are critical for breeding programs to assess the genetics and breeding potential of genotypes to enhance cultivar development. This trial followed a three-replicated Randomized Complete Block Design (RCBD) with 36 diverse sorghum genotypes in 2023 at Ashland Bottoms, Kansas. The field images were captured 6 meters above using a DJI M300 drone equipped with the P1 sensor at 90{degrees}nadir and 45{degrees} oblique angles. This research trained YOLO and the Faster R-CNN (Detectron2) models to harness yield attributes from UAS field and lab images. The YOLO models outperformed the Faster R-CNN model in detecting sorghum panicles, achieving a mean average precision at 50% IoU (mAP@0.50) ranging from 0.92 to 0.98, compared to 0.61 to 0.89. Panicle detection from field imagery correlated at 0.86 with ground truth. Lab imagery analyses measured panicle size, seed counts, and seed area with correlation coefficients of 0.71, 0.95, and 0.25, respectively. Three machine learning models: Support Vector Regression (SVR), Decision Tree Regression (DTR), and Random Forest Regression (RFR) are used to predict yield with correlation coefficients of 0.58, 0.76, and 0.70, respectively. We observed that YOLO models are well-suited for extracting yield-attributing traits from images, which are then incorporated into ML regression models to improve yield prediction performance.

plant biology↗

bioRxiv2025

In vitro and in vivo synergy of Vancomycin and β-lactams against drug-resistant Mycobacterium tuberculosis and Non-tuberculous mycobacteria

The unabated increase in antimicrobial resistance has underlined the importance of identifying novel drug combinations which eliminate infections more potently and likely reduce the emergence of resistance. In this context, we have identified Vancomycin and many {beta}-lactams as being potently active against drug-resistant Mycobacterium tuberculosis and non-tuberculous mycobacteria including M. abscessus, emerging as pathogens of concern owing to their inherent drug resistance profile. In this study, we have identified combinations of Vancomycin, a glycopeptide and {beta}-lactams, especially Ceftriaxone, Ceftazidime and Meropenem, in the presence or absence of Sulbactam, a {beta}- lactamase inhibitor, as possessing potent antimicrobial activity against several drug-resistant mycobacterial strains. The combination of Vancomycin and {beta}-lactams exhibited potent bactericidal activity and reduced the bacterial load better than either drug alone. The molecular basis of synergy was mediated by increase in permeability of mycobacterial cell as demonstrated by ethidium bromide assay. In the murine model of mycobacterial infection, synergistic combination of Vancomycin and {beta}-lactams outperformed clinically utilized drugs including Isoniazid, Rifampicin and Ethambutol against M. tuberculosis and Amikacin, Clarithromycin against M. abscessus. The combinations caused a significant reduction in bacterial load in various organs in M. tuberculosis and M. abscessus infected mice. Thus, the synergistic combination of Vancomycin and {beta}-lactams could potentially be utilized for treatment of recalcitrant mycobacterial infection especially those caused due to drug-resistant pathogens.

microbiology↗

bioRxiv2025

Insights into CPSFL1 Induced Membrane Dynamics: A Multifaceted Regulator Linking Vesicle Formation to Thylakoid Biogenesis

Light drives plant life through photosynthesis, a process that takes place in the thylakoid membrane of the chloroplast, an organelle of cyanobacterial origin. The formation of thylakoid membranes within the chloroplast involves the eukaryote-specific factor CHLOROPLAST SEC14 LIKE PROTEIN 1 (CPSFL1), which shares strong sequence homology with the vesicle trafficking regulator SEC14. CSPFL1 is essential for vesicle formation, yet its specific molecular function in this process has remained unclear. In this study, we characterized CSPFL1 functions both in vitro and in vivo. Using a minimal membrane system of giant unilamellar vesicles (GUVs), we show that CPSFL1 alone can induce vesiculation. This process is mediated by lipid binding and membrane deformation, driven by curvature sensing and lipid-protein electrostatics. When expressed in the prokaryote E. coli, the eukaryote-specific CSPFL1 induces membrane curvature and vesicle formation. Plastid CPSFL1 co-purifies with vesicular structures. Lipid compositional analysis of CPSFL1-induced vesicles from bacteria reveals the presence of quinone precursors as cargo, linking CSPFL-mediated vesicle formation to prenylquinone transport. Together, our data suggest that during plant evolution, the eukaryotic vesicle formation system was co-opted for the transport of membrane integral metabolites from the inner envelope to the thylakoid membrane.

plant biology↗

bioRxiv2024

EEG data quality in large scale field studies in India and Tanzania

There is a growing imperative to understand the neurophysiological impact of our rapidly changing and diverse technological, social, chemical, and physical environments. To untangle the multidimensional and interacting effects requires data at scale across diverse populations, taking measurement out of a controlled lab environment and into the field. Electroencephalography (EEG), which has correlates with various environmental factors as well as cognitive and mental health outcomes, has the advantage of both portability and cost-effectiveness for this purpose. However, with numerous field researchers spread across diverse locations, data quality issues and researcher idle time due to insufficient participants can quickly become unmanageable and expensive problems. In programs we have established in India and Tanzania, we demonstrate that with appropriate training, structured teams, and daily automated analysis and feedback on data quality, non-specialists can reliably collect EEG data alongside various survey and assessments with consistently high throughput and quality. Over a 30-week period, research teams were able to maintain an average of 25.6 subjects per week, collecting data from a diverse sample of 7,933 participants ranging from Hadzabe hunter-gatherers to office workers. Furthermore, data quality, computed on the first 2,400 records using two common methods, PREP and FASTER, was comparable to benchmark datasets from controlled lab conditions. Altogether this resulted in a cost per subject of under $50, a fraction of the cost typical of such data collection, opening up the possibility for large-scale programs particularly in low- and middle-income countries. Significance StatementWith wide human diversity, a rapidly changing environment and growing rates of neurological and mental health disorders, there is an imperative for large scale neuroimaging studies across diverse populations that can deliver high quality data and be affordably sustained. Here we demonstrate, across two large-scale field data acquisition programs operating in India and Tanzania, that with appropriate systems it is possible to generate high throughput EEG data of quality comparable to controlled lab settings. With effective costs of under $50 per subject, this opens new possibilities for low- and middle-income countries to implement large-scale programs, and to do so at scales that previously could not be considered.

neuroscience↗

bioRxiv2024

UniGEN-DDI: Computing drug-drug interactions using a unified graph embedding network

Finding drug-drug interaction is crucial for patient safety and treatment efficacy. Two drugs may show a synergistic effect but may sometimes cause a severe health issue, including lethality. Wet lab studies are often performed to understand such interactions but are limited by cost and time. However, the biochemical data generated can be explored to compute unknown interactions. Here, we developed a computational model named UniGEN-DDI (Unified Graph Embedding Network for Drug-Drug Interaction) for the estimation of interactions between drugs. It is a simple unified network model containing the biochemical information of drug association developed using the compiled data from DrugBank 5.1.0. The feature learning of the drugs was carried out using a combination of GraphSAGE and Node2Vec algorithms, which were found efficient in extracting diverse features. The simple architecture of our model led to a significant reduction in computational time compared to the baselines, while maintaining a high prediction accuracy. The model performed well on the data, which was equally distributed between interacting and non-interacting drugs. As a more challenging evaluation, we performed non-overlapping splitting of the data based on the drug action on different parts of the body, and our model performed well in both interaction estimation and time efficiency. Our model successfully identified the 12 unknown drug interactions in DrugBank 5.1.0, which were updated in DrugBank 6.0. HighlightsO_LIUniGEN-DDI: A simple unified network model was developed for drug-drug interactions. C_LIO_LINode embeddings were generated using GraphSAGE and Node2Vec algorithms. C_LIO_LIA significantly higher computational efficiency was achieved compared to baselines. C_LIO_LIModel validated in non-overlapping splitting of data targetting multiple body parts. C_LIO_LIUniGEN-DDI estimated unknown interactions verified in the updated DrugBank 6.0. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=76 SRC="FIGDIR/small/615812v1_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@909caaorg.highwire.dtl.DTLVardef@1d7cfdorg.highwire.dtl.DTLVardef@1f8ca34org.highwire.dtl.DTLVardef@a340d6_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

Construction of ceRNA networks in endocrine therapy resistance model systems

BackgroundEndocrine therapy resistance (ETR) in breast cancer is achieved via multiple pathways including a decrease in ER, dysregulation of cell cycle genes, and/or mutations in ER/co-activators/co-repressors. We have reported earlier that high expression of Jumonji domaining containing protein 6 (JMJD6) induced ETR by depleting ER expression. In this study, 3 cellular models representing distinct ETR pathways; Tamoxifen resistant (TAMR), Long-term Estrogen deprived (LTEDI), JMJD6 overexpressing (JOE) cells, and parental MCF7 were subjected to RNA-sequencing, CNC, and ceRNA network analysis. We hypothesised that post-comparison RNA regulations that are common to all cell lines, will reveal actionable markers and targets. These will be shared by all patients with ET-resistant disease, independent of the initiating event. Results170 differentially expressed genes were found, of these, 73 maintained the same directionality in expression (ETR cassette genes). These genes segregated TCGA ER+ tumors into two groups, one intermixing with ER-tumors. Pathway-based curation of ETR genes identified 21 genes (7 up- and 14 down-regulated) that participated in multiple cancer hallmark pathways. Genes upregulated in ETR cells were less expressed in ER+ tumors at diagnosis when compared to normal breast samples but their higher expression indicated adverse survival outcomes. Next, these genes were used for CNC and ceRNA network construction and a triad FLT4:MIR503HG:miR-497/195/424 was discovered. The expression levels of miRNAs were predicted via network analysis and quantitative RT-PCR was used to validate the down regulation of miR-497/195/424 and upregulation of their targets, FLT4 and MIR503HG in ETR cells. ConclusionsWe show that total RNA-seq data can be successfully used to predict actionable miRNAs that achieve drug resistance. Re-expression of ETR genes such as FLT4 in tumor cells, that are less expressed at diagnosis, may be indicative of ETR onset. Finally, ETR may arise due to suppression of miR-424/497/195 leading to higher expression of FLT4 and MIR503HG. We posit that FLT4 may be a suitable target and RT-PCR analysis of this RNA triad could be developed as a detection strategy for ETR in ER+ breast cancer.

cancer biology↗

bioRxiv2024

Nanoscale interaction of endonuclease APE-1 with DNA characterized by atomic force microscopy

Apurinic/apyrimidinic endonuclease 1 (APE1) is involved in DNA replication, repair, and transcriptional regulation mechanisms. This multifunctional activity of APE1 should be supported by specific structural properties of APE1 that have not yet been elucidated. Here we applied atomic force microscopy (AFM) to characterize the interactions of APE1 with DNA. Complexes of APE1 with DNA containing G-rich segments were visualized, and analysis of the complexes revealed the affinity of APE1 to G-rich DNA sequences. Furthermore, loops in the DNA-APE1 complexes were visualized, and their yield was as high as 53 %. However, the loops were non-specific, with quantitative analysis revealing the yield of loops bridging two G-rich DNA segments to be 41%. Analysis of protein size in various complexes was performed, and these data showed that loops are formed by APE1 monomer, suggesting that APE1 has two DNA binding sites. The data lead us to a model for the interaction of APE1 with DNA that describes its molecular site search mechanism. The new properties of APE1 in organizing DNA, by bringing two distant sites together, may be important for facilitating the scanning for damage and coordinating repair and transcription.

biophysics↗

bioRxiv2022

Binding of His-tagged fluorophores to lipid bilayers and giant vesicles

His-tagged molecules can be attached to lipid bilayers via certain anchor lipids, a method that has been widely used for the bio-functionalization of membranes and vesicles. To observe the membrane-bound molecules, it is useful to consider His-tagged molecules that are fluorescent as well. Here, we study two such molecules, green fluorescence protein (GFP) and green-fluorescent fluorescein isothiocyanate (FITC), both of which are tagged with a chain of six histidines (6H) that bind to the anchor lipids within the bilayers. The His-tag 6H is much smaller than the GFP molecule but somewhat larger than the FITC dye. The lipid bilayers form giant unilamellar vesicles (GUVs), the behavior of which can be directly observed in the optical microscope. We apply and compare three well-established preparation methods for GUVs: electroformation on platinum wire, polyvinyl alcohol (PVA) hydrogel swelling, and electroformation on indium tin oxide (ITO) glass. Microfluidics is used to expose the GUVs to a constant fluorophore concentration in the exterior solution. The brightness of membrane-bound 6H-GFP exceeds the brightness of membrane-bound 6H-FITC, in contrast to the quantum yields of the two fluorophores in solution. In fact, 6H-FITC is observed to be strongly quenched by the anchor lipids which bind the fluorophores via Ni2+ ions. For both 6H-GFP and 6H-FITC, the membrane fluorescence is measured as a function of the fluorophores molar concentration. The theoretical analysis of these data leads to the equilibrium dissociation constants Kd = 37.5 nM for 6H-GFP and Kd = 18.5 nM for 6H-FITC. We also observe a strong pH-dependence of the membrane fluorescence.

biophysics↗

bioRxiv2021

A universal pocket in FAALs ensures redirection of fatty acid pool away from CoA-based activation

Fatty acyl-AMP ligases (FAALs) channelize fatty acids towards biosynthesis of virulent lipids in mycobacteria and other pharmaceutically or ecologically important polyketides and lipopeptides in other microbes. They do so by bypassing the ubiquitous coenzyme A-dependent activation and rely on the acyl carrier protein-tethered 4-phosphopantetheine (holo-ACP). The molecular basis of how FAALs strictly reject chemically identical and abundant acceptors like coenzyme A (CoA) and accept holo-ACP unlike other members of the ANL superfamily remains elusive. We show FAALs have plugged the promiscuous canonical CoA-binding pockets and utilize highly selective alternative binding sites. These alternative pockets can distinguish adenosine 3, 5-bisphosphate-containing CoA from holo-ACP and thus FAALs can distinguish between CoA and holo-ACP. These exclusive features helped identify the omnipresence of FAAL-like proteins and their emergence in plants, fungi, and animals with unconventional domain organisations. The universal distribution of FAALs suggests they are parallelly evolved with FACLs for ensuring a CoA-independent activation and redirection of fatty acids towards lipidic metabolites.

biochemistry↗

bioRxiv2020

Modulation of proteasome activity by curcumin and didemethylcurcumin

Modulation of proteasome function by pharmacological interventions and molecular biology tools is an active area of research in cancer biology and neurodegenerative diseases. Curcumin (diferuloylmethane) is a naturally occurring polyphenol that affects multiple signaling pathways and known to modulate PKC activities. The therapeutic significance of curcumin is often considered to reside in its anti-inflammatory, antioxidant, anti-angiogenic, or anti-apoptotic properties. However, recent research suggests that the therapeutic efficacy of curcumin may be due to its activity as a potent inhibitor of the proteasome. In this study, we show that both curcumin and its synthetic polyphenolic derivative (didemethylcurcumin), CUIII modulated proteasome activity in a biphasic manner. Curcumin and CUIII increased proteasome activity at nanomolar concentrations but inhibited proteasome activity at micromolar concentrations. Curcumin was more effective than CUIII in relative proteasome activity increase at nanomolar concentrations. Also, curcumin was more effective than CUIII in relative proteasome activity inhibition at micromolar concentration. The docking study was conducted on the 20S proteasome catalytic subunit. Estimated Kd values for curcumin and didemethylcurcumin are 0.0054M and 1.3167M, respectively. These values correlate well with the results of the effectiveness of modulation by curcumin compare to CUIII. The small size of CUIII makes its dock to the narrow cavity of the active site, but the binding interaction is not strong compare to curcumin. This study suggests that curcumin and its didemethyl derivative can be used to modulate proteasome activity. This communication implicates the reason why curcumin and its didemethyl derivative can be used to two different disease mechanisms, neurodegeneration, and cancer.

neuroscience↗

bioRxiv2020

Red Panda: A novel method for detecting variants in single-cell RNA sequencing

Single-cell sequencing enables us to better understand genetic diseases, such as cancer or autoimmune disorders, which are often affected by changes in rare cells. Currently, no existing software is aimed at identifying single nucleotide variations or micro (1-50bp) insertions and deletions in single-cell RNA sequencing (scRNA-seq) data. Generating high-quality variant data is vital to the study of the aforementioned diseases, among others. In this study, we report the design and implementation of Red Panda, a novel method to accurately identify variants in scRNA-seq data. Variants were called on scRNA-seq data from human articular chondrocytes, mouse embryonic fibroblasts (MEFs), and simulated data stemming from the MEF alignments. Red Panda had the highest Positive Predictive Value at 45.0%, while other tools--FreeBayes, GATK HaplotypeCaller, GATK UnifiedGenotyper, Monovar, and Platypus--ranged from 5.8%-41.53%. From the simulated data, Red Panda had the highest sensitivity at 72.44%. We show that our method provides a novel and improved mechanism to identify variants in scRNA-seq as compared to currently-existing software. AvailabilitySource code freely available under the MIT License at https://github.com/adambioi/red_panda, and is supported on Linux

bioinformatics↗