Biology subjects

Naskar, A.

Publications and source records attributed to Naskar, A..

8 recordsLinked to original sources

bioRxiv2025

Curvature-Encoded Photochemistry for Programmable Isolation and Fractionation of Extracellular and Plasma Membrane Vesicles

AbstractLipid-based nanoparticles, including extracellular vesicles (EVs), cell membrane vesicles (CMVs), and biomimetic nanovesicles, are emerging as powerful carriers for therapy and diagnosis due to their ability to transport diverse payloads across biological barriers. EVs, in particular, show promise for liquid biopsy and systemic therapy because of their endogenous origin, biocompatibility, and intrinsic targeting capacity. Cell-derived plasma membrane nanovesicles complement EVs by preserving the source cells membrane architecture and receptor landscape, creating customizable scaffolds for biomimetic drug delivery. However, the full potential of these lipid nanovesicles has been challenging because current isolation workflows are labor-intensive, low-yield, and often lack purity, forming a critical bottleneck for clinical translation and standardized manufacturing. To overcome these challenges, we developed a programmable electrostatic and light-activated platform for isolating lipid vesicles directly from complex biological fluids in a rapid, high-purity, and scalable manner. Our approach employs photocleavable lipid nanoprobes (PLNs) that insert into vesicle membranes in a size-dependent fashion and, together with a cationic polymer, enable efficient capture on a solid support. A brief light exposure cleaves the probes to trigger on-demand release and fractionation of intact vesicles with high speed and precision. Using this platform, we demonstrate high-purity EV isolation with preserved membrane protein functionality, as well as single-step enrichment of right-side-out (RSO) cell membrane nanovesicles that maintain native membrane orientation for effective drug delivery. The capability to rapidly isolate and tailor vesicle subpopulations by size and orientation establishes a unified materials platform for precision nanomedicine and advanced therapeutics, paving the way toward clinical-grade vesicle production and next-generation liquid biopsy applications.

bioengineering↗

bioRxiv2024

Cooperativity of weak actomyosin interaction

We report the discovery of a new regulatory mechanism of the actomyosin system in muscle. We show that the weak binding of the myosin-nucleotide complex with unregulated F-actin is a cooperative process. Hundreds of myosin heads must work together for efficient force production in muscle, but the precise mechanism by which they coordinate remains elusive. It is known that myosin initially binds actin weakly, then transitions into a strongly bound state to produce force. Using the contiguous cooperative binding model, we interpreted our experimental results in terms of a cooperativity parameter defined as an increased probability for a myosin head to bind to the actin filament next to the already bound head. Considering the geometric organization of a sarcomere, we propose the formation of cross-bridge clusters composed of up to six myosin heads bound consecutively to actin. The cooperativity of weak actomyosin interaction may explain several challenging questions in muscle physiology, such as the role of myosin isoforms in mixed-isoform hybrid muscles, or the yield of supramaximal rate of force production in decorated skinned muscle fibers. Significance StatementForce in striated muscle results from myosin interacting with actin. Initially, myosin attaches weakly to the thin filament, transitioning to a strongly bound state, generating force. Our experiments show high cooperativity in myosins weak interaction with unregulated actin filament. This cooperative behavior may facilitate the formation of cross-bridge clusters and the cooperative steps of myosin heads between clusters. Consequently, the thin- and thick-filament regulation could govern the spacing between cross-bridge clusters and influence the probability of a myosin head stepping along the thin filament during force development in muscle.

biophysics↗

bioRxiv2023

Fibrinogen and complement factor H: The two crucial markers of Parkinson's disease with cognitive impairment

Animal model-assisted validation is vital for biomarker studies. It provides better means for understanding disease pathogenesis and opens avenues for addressing therapeutics. Our earlier non-targeted label free proteomics-based biomarker study on CSF of Parkinsons disease (PD) patients with cognitive impairment (PDCI), revealed the presence of elevated levels of fibrinogen and complement factor H (CFAH) in PDCI-CSF. In the present study, we aimed to determine if these proteins harbor a pathogenic potential, when present above physiological levels. Native fibrinogen and recombinant CFAH were intraperitoneally injected in adult C57BL/6J mice and 48h later, motor and cognitive behavioral deficits as well as their neuroanatomical correlates were noted. The reduction in stride length in fibrinogen and CFAH injected mice indicate shuffling gait, often seen in PD patients. Motor deficit was complemented by the loss of dopaminergic (DA) neurons in SNpc, compensatory hypertrophy of the surviving neurons, and reduction in TH expression in striatum, thus reminiscing PD pathology. The low discrimination index in (novel object recognition) NOR test complemented by morphological alterations in Nissl-stained CA-1 and subiculum neurons in the fibrinogen-injected mice imply recognition memory deficits. Thus, our study provides objective evidence that fibrinogen and CFAH harbor the potential to induce motor deficits and cognitive impairments in mice, akin to the PDCI-associated neuro-pathological deficits, and thus are potential biomarkers.

neuroscience↗

bioRxiv2023

Chromatin states contribute to coordinated allelic transcriptional bursting to drive iPSC reprogramming

Molecular mechanisms behind the reprogramming of somatic cells to induced pluripotent stem cells (iPSC) remain poorly understood. While dynamic changes in gene expression are considered to drive reprogramming, the contribution of individual alleles of genes to reprogramming remains unexplored. It is thought that two alleles of a gene can transcribe independently or coordinatedly, which in turn can lead to temporal expression heterogeneity with potentially distinct impacts on cell fate. Here, we profiled genome-wide transcriptional burst kinetics with an allelic resolution during the reprogramming of mouse embryonic fibroblast (MEF) to iPSC. We show that many genes involved in iPSC reprogramming pathways exhibit bursty expression and contribute to dynamic autosomal random monoallelic expression (aRME). Moreover, we find that the degree of coordination of allelic bursting differs among genes and changes dynamically during iPSC reprogramming. Importantly, we show that alleles of many reprogramming-related genes burst in a highly coordinated fashion. ATAC-seq analysis revealed that coordination of allelic bursting is linked to allelic chromatin accessibility. Consistently, we show that highly coordinated genes are enriched with chromatin accessibility regulators such as H3K36me3, H3K27ac, histone variant H3.3 and BRD4. Collectively, our study demonstrates that chromatin states contribute to coordinated allelic bursting to fine-tune the expression of genes involved in iPSC reprogramming and provides insights into the implications of allelic bursting coordination in cell fate specification. O_FIG O_LINKSMALLFIG WIDTH=195 HEIGHT=200 SRC="FIGDIR/small/548864v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@162ec20org.highwire.dtl.DTLVardef@17c2db0org.highwire.dtl.DTLVardef@19b9d59org.highwire.dtl.DTLVardef@1775052_HPS_FORMAT_FIGEXP M_FIG C_FIG

genetics↗

bioRxiv2023

Neuromolecular interactions guiding homeostatic mechanisms underlying healthy ageing: A view from computational microscope

Ageing brain is associated with a slow drift in structural network properties over the lifespan accompanied by reorganization in neuromolecular interactions giving rise to changes in global functional markers. What are the guiding principles of the homeostatic mechanisms that maintain the desired performance of functional neural circuits and preserve brain health during healthy ageing? We hypothesize that an ageing brain alters two primary neurotransmitters, glutamate and{gamma} -aminobutyric acid (GABA), responsible for excitation-inhibition regulation, concomitant with anatomical demyelination to preserve critical neural dynamics that are necessary to uphold optimal network performance. Thus, often observed re-organized functional connectivity with age by several investigations is a byproduct of this adaptive process. We demonstrate that the adaptive mechanism is driven by the tuning of glutamate and GABA concentration over a very slow time scale (lifespan) that can be estimated by tracking criticality from co-ordinated neural dynamics at a resting state via a biophysically driven computational framework, introduced as a computational microscope. We validate several empirical observations and model predictions across three independent aging cohorts using this computational microscope. One of the key mechanisms we discover is the reduction in local glutamate levels employed by brain regions to maintain a homeostatic balance with aging. This is further supported by the invariance of measures of global functional integration during the healthy ageing process.

neuroscience↗

bioRxiv2021

How synchrony and metastable network dynamics are affected in fast and slow timescales with aging: Implication for Cognition

Both healthy and pathological aging exhibits gradual deterioration of structure but in-terestingly in healthy aging adults often maintains a high level of cognitive performance in a variety of cognitively demanding task till late age. What are the relevant network measures that could possibly track these dynamic changes which may be critically relevant for maintenance of cognitive functions through lifespan and how does these measures affected by the specific alterations in underlying anatomical connectivity till day remains an open question. In this work, we propose that whole-brain computational models are required to test the hypothesis that aging affects the brain network dynamics through two highly relevant network measures synchrony and metastability. Since aging entails complex processes involving multiple timescales we test the additional hypothesis that whether these two network measures remain invariant or exhibit different behavior in the fast and slow timescales respectively. The altered global synchrony and metastability with aging can be related to shifts in the dynamic working point of the system based on biophysical parameters e.g., time delay, and inter-areal coupling constrained by the underlying structural connectivity matrix.Using diffusion tensor imaging (DTI) data, we estimate structural connectivity (SC) of individual group of participants and obtain network level synchrony, metastability indexing network dynamics from resting state functional MRI data for both young and elderly participants in the age range of 18-89 years. Subsequently, we simulate a whole-brain Kuramoto model of coupled oscillators with appropriate conduction delay and interareal coupling strength to test the hypothesis of shifting of dynamic working point with age-associated alteration in network dynamics in both neural and ultraslow BOLD signal time scales. Specifically, we investigate the age-associated difference in metastable brain dynamics across large-scale neurocognitive brain networks e.g., salience network (SN), default mode network (DMN), and central executive network (CEN) to test spatio-temporal changes in default to executive coupling hypothesis with age. Interestingly, we find that the metastability of the SN increases substantially with age, whereas the metastability of the CEN and DMN networks do not substantially vary with age suggesting a clear role of conduction delay and global coupling in mediating altered dynamics in these networks. Moreover, our finding suggests that the metastability changes from slow to fast timescales confirming previous findings that variability of brain signals relates differently in slower and faster time scales with aging. However, synchrony remains invariant network measure across timescales and agnostic to the filtering of fast signals. Finally, we demonstrate both numerically and analytically longrange anatomical connections as oppose to shot-range or mid-range connection alterations is responsible for the overall neural difference in large-scale brain network dynamics captured by the network measure metastability. In summary, we propose a theoretical framework providing a systematic account of tracking age-associated variability and synchrony at multiple time scales across lifespan which may pave the way for developing dynamical theories of cognitive aging.

neuroscience↗

bioRxiv2021

Fibrinogen and Complement Factor H are promising CSF protein biomarker(s) for Parkinson's disease with cognitive impairment- A Proteomics and ELISA based study

Cognitive impairment is a debilitating non-motor symptom of Parkinsons disease (PD). The diagnosis of PD with cognitive impairment (PDCI) is essentially through clinical and neuropsychological examinations. There is an emerging need to identify biomarker(s) to foresee cognitive decline in PD patients, at an early stage. We performed label-free unbiased nontargeted proteomics (Q-TOF LC/MS-MS) in CSF of non-neurological control (NNC); PDCI; PD and normal pressure hydrocephalus (NPH), followed by targeted ELISA for validation. The diagnosis was confirmed by neuropsychological and MRI assessments prior to CSF collection. Of the 282 proteins identified by mass spectrometry, 42 were differentially altered in PD, PDCI and NPH. Further, 28 proteins were altered in PDCI and 25 in NPH. An interesting overlap of certain proteins was noted both in PDCI and NPH. Five significantly upregulated proteins in PDCI were fibrinogen, gelsolin, complement factor-H, apolipoprotein A-IV and apolipoprotein A-I. Whereas carnosine dipeptidase 1, carboxypeptidase E, dickkpof 3 and secretogranin 3 precursor proteins were down-regulated. NPH also had few uniquely altered proteins viz. insulin-like growth factor-binding protein, ceruloplasmin, -1 antitrypsin, VGF nerve growth factor, neural cell adhesion molecule L1 like protein. Interestingly, the ELISA-derived protein concentrations correlated well with the neuropsychological scores of certain cognitive domains. Executive function was affected both in PDCI and NPH. In PD, Wisconsin card sorting test (WCST) percentile correlated positively with ApoA-IV and negatively with the ratio of ApoAI: ApoA-IV. Thus assessment of a battery of proteins like fibrinogen--chain, CFAH and ApoAI: ApoA-IV ratio alongside neuropsychological could be reliable biomarkers to distinguish PDCI and NPH.

neuroscience↗

bioRxiv2018

Multiscale dynamic mean field (MDMF) model to relate resting state brain dynamics with local cortical excitatory-inhibitory neurotransmitter homeostasis in health and disease.

Previous neuro-computational studies have established the connection of spontaneous resting-state brain activity with "large-scale" neuronal ensembles using dynamic mean field approach and showed the impact of local excitatory-inhibitory (E-I) balance in sculpting dynamical patterns. Here, we argue that whole brain models that link multiple scales of physiological organization namely brain metabolism that governs synaptic concentrations of gamma-aminobutyric acid (GABA) and glutamate on one hand and neural field dynamics that operate on the macroscopic scale. The multiscale dynamic mean field (MDMF) model captures the synaptic gating dynamics over a cortical macrocolumn as a function of neurotransmitter kinetics. Multiple MDMF units were placed in brain locations guided by an anatomical parcellation and connected by tractography data from diffusion tensor imaging. The resulting whole-brain model generates the resting-state functional connectivity and also reveal that optimal configurations of glutamate and GABA captures the dynamic working point of the brain, that is the state of maximum metsatability as observed in BOLD signals. To demonstrate test-retest reliability we validate the observation that healthy resting brain dynamics is governed by optimal glutamate-GABA configurations using two different brain parcellations for model set-up. Furthermore, graph theoretical measures of segregation (modularity and clustering coefficient) and integration (global efficiency and characteristic path length) on the functional connectivity generated from healthy and pathological brain network studies could be explained by the MDMF model. In conclusion, the MDMF model could relate the various scales of observations from neurotransmitter concentrations to dynamics of synaptic gating to whole-brain resting-state network topology in health and disease.

neuroscience↗