Biology subjects

Purohit, P.

Publications and source records attributed to Purohit, P..

6 recordsLinked to original sources

bioRxiv2026

Reelin coordinates neuronal positioning and Müller glia scaffold maturation during retinal development

Reelin is a secreted extracellular matrix protein that regulates neuronal migration and layer formation in the developing brain, yet its role in retinal development remains incompletely defined. Here, we investigated Reelin function in retinal lamination using wild-type and Reeler (Reln-/-) mice, combining stage-resolved RNA in situ hybridization, immunohistochemistry, and single-nucleus RNA sequencing. We show that Reln is dynamically expressed in ganglion cell layer and inner nuclear layer neurons during retinal development and persists in discrete adult neuronal populations. Loss of Reelin leads to widespread defects in retinal organization affecting both neurons and Muller glia. In Reln-/- retinas, Muller glia exhibit reduced Glul positive extensions, indicating impaired glial scaffold maturation. Early-born neuronal populations are also disrupted, with altered spatial organization markers associated with retinal ganglion cell differentiation within the ganglion cell layer at postnatal day 9. Horizontal cells are significantly reduced with dorsal-predominant vulnerability, while cone photoreceptors are generated in normal numbers but show incomplete positioning within the outer nuclear layer. Together, these findings identify Reelin as a key regulator of retinal lamination that coordinates neuronal positioning with Muller glia morphogenesis, extending its canonical role in brain development to the vertebrate retina.

developmental biology↗

bioRxiv2026

BRIDGE: A Computational Workflow from Single Neurons to Network of Mean-Field Models

Mean-field models are extensively used in large-scale brain simulations because they provide a wieldy description of population dynamics while preserving key features of neural activity. Despite their widespread adoption, no common and reproducible methodology currently exists to systematically derive and validate mean-field models starting from biologically grounded single neuron dynamics. As a result, implementations are often ad hoc, difficult to reproduce and rarely reusable. Here we introduce BRIDGE, a modular, open-source Python pipeline that enables the bottom-up reconstruction, analysis, validation, and simulation of mean-field models from single neurons. The framework integrates single neurons modelling, network simulations, extraction of population statistics, parameters analysis, quantitative comparisons between spiking neural networks and corresponding mean-field representations, and simulation of network of mean-fields. Its flexible architecture allows users to incorporate different neuron models and to generate region-specific or state-dependent mean-field formulations. BRIDGE provides a reproducible foundation for developing biologically informed mean-field models suitable for large-scale and whole-brain simulations, supporting the transition from generic homogeneous population models toward region-specific ones. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=91 SRC="FIGDIR/small/742067v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@1124404org.highwire.dtl.DTLVardef@2f8b2aorg.highwire.dtl.DTLVardef@1598f37org.highwire.dtl.DTLVardef@c9814b_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2026

The Role of Fibrinogen-Mediated Platelet Aggregation in Subsequent Platelet-Driven Blood Clot Contraction

BackgroundBlood clot contraction/retraction depends on the force-generating actomyosin and on the platelet integrin IIb{beta}3, which transmits intracellular forces to fibrin. Before clotting, fibrinogen binds to activated integrin IIb{beta}3, mediating platelet aggregation. The relationship between platelet aggregation and subsequent platelet-driven clot contraction remains unclear. MethodsWe investigated the effects of platelet aggregation on clot contraction by selectively blocking the IIb{beta}3-fibrinogen binding using the RGDW peptide. The ability of RGDW to disrupt IIb{beta}3-fibrinogen binding was assessed by platelet aggregometry. The time-course of clot contraction was monitored optically in whole blood or platelet-rich plasma and modeled mathematically. Clot stiffness was assessed using Thromboelastography. The effect of the RGDW peptide on the structure of PRP-clots was examined using scanning electron microscopy. ResultsThe RGDW peptide dose-dependently inhibited TRAP-induced platelet aggregation. Both in whole blood and in plasma, the peptide dose-dependently prolonged the lag-period and slowed the rate without affecting the final extent of contraction. Thromboelastography showed that RGDW dose-dependently increased maximum clot stiffness in blood. Scanning electron microscopy revealed that RGDW treatment resulted in formation of smaller fibrin agglomerates surrounding non-aggregated platelets. A theoretical model allowed us to decipher mechanisms underlying the kinetic effects of RGDW. ConclusionBlocking the binding of integrin IIb{beta}3 to fibrinogen and preventing platelet aggregation delays and slows subsequent clot contraction without affecting the final degree of shrinkage. These findings indicate a modulatory role of fibrinogen-mediated platelet aggregation in clot contraction and highlight the unforeseen effects of selective inhibitors of platelet aggregation on the contraction of blood clots and thrombi.

Cell Biology↗

bioRxiv2025

Understudied and underprotected: biodiversity and conservation challenges in the Konkan region of Maharashtra

In biodiversity hotspots, such as the Western Ghats, periodic synthesis of existing ecological research can help identify knowledge gaps and address critical threats to biodiversity. The Maharashtra part of the Konkan region, situated between the Sahyadri foothills and the west coast, with diverse tropical vegetation, harbours unique open ecosystems, such as lateritic plateaus, and supports a multitude of threatened species, including hornbills and tigers. However, the region remains relatively understudied and has not received adequate protection through state or national conservation policies and laws. The area is undergoing rapid human-driven land-use change. These activities can impact the regions biodiversity, necessitating an effort to identify knowledge gaps and critical threats. Through a combination of a literature review and focus group discussions with 44 participants from various institutions and non-governmental organisations, we synthesised existing published information on the Konkan region of Maharashtra and identified key research gaps and conservation challenges. Our review of 138 studies found that while agroforestry and human-wildlife interactions have received some research attention, the effects of climate change on the regions biodiversity remain poorly understood. Focus group discussions highlighted major threats, including land-use changes due to expanding monoculture plantations, clear-felling of forests, forest fires, environmental pollution, and rapid infrastructure development, which are leading to habitat loss and fragmentation. Some of these concerns were validated by publicly available regional data, which revealed a 30% increase in roads, a 14% expansion of cashew plantations, and associated forest loss. This synthesis offers valuable insights for government and non-governmental organisations to inform future research and conservation efforts in the region.

ecology↗

bioRxiv2020

Kinetics of self-assembly of inclusions due to lipid membrane thickness interactions

Self-assembly of proteins on lipid membranes underlies many important processes in cell biology, such as, exo- and endo-cytosis, assembly of viruses, etc. An attractive force that can cause self-assembly is mediated by membrane thickness interactions between proteins. The free energy profile associated with this attractive force is a result of the overlap of thickness deformation fields around the proteins. The thickness deformation field around proteins of various shapes can be calculated from the solution of a boundary value problem and is relatively well understood. Yet, the time scales over which self-assembly occurs has not been explored. In this paper we compute this time scale as a function of the initial distance between two inclusions by viewing their coalescence as a first passage time problem. The first passage time is computed using both Langevin dynamics and a partial differential equation, and both methods are found to be in excellent agreement. Inclusions of three different shapes are studied and it is found that for two inclusions separated by about hundred nanometers the time to coalescence is hundreds of milliseconds irrespective of shape. Our Langevin dynamics simulation of self-assembly required an efficient computation of the interaction energy of inclusions which was accomplished using a finite difference technique. The interaction energy profiles obtained using this numerical technique were in excellent agreement with those from a previously proposed semi-analytical method based on Fourier-Bessel series. The computational strategies described in this paper could potentially lead to efficient methods to explore the kinetics of self-assembly of proteins on lipid membranes. Author summarySelf-assembly of proteins on lipid membranes occurs during exo- and endo-cytosis and also when viruses exit an infected cell. The forces mediating self-assembly of inclusions on membranes have therefore been of long standing interest. However, the kinetics of self-assembly has received much less attention. As a first step in discerning the kinetics, we examine the time to coalescence of two inclusions on a membrane as a function of the distance separating them. We use both Langevin dynamics simulations and a partial differential equation to compute this time scale. We predict that the time to coalescence is on the scale of hundreds of milliseconds for two inclusions separated by about hundred nanometers. The deformation moduli of the lipid membrane and the membrane tension can affect this time scale.

biophysics↗

bioRxiv2018

Rapamycin, an Immunosuppressant and mTORC1 Inhibitor, Triples the antidepressant Response Rate of Ketamine at 2 Weeks Following Treatment: A double-blind, placebo-controlled, cross-over, randomized clinical trial

BACKGROUNDKetamine exerts rapid and robust antidepressant effects thought to be mediated by activation of the mechanistic target of rapamycin complex 1 (mTORC1). To test this hypothesis, depressed patients were pretreated with rapamycin, an mTORC1 inhibitor, prior to receiving ketamine.\n\nMETHODSTwenty-three patients suffering a major depressive episode were randomized to oral rapamycin (6 mg) or placebo, each was followed 2 hours later by ketamine 0.5 mg/kg in a double-blind cross-over design with treatment days separated by at least 2 weeks. Depression severity was assessed using Montgomery-[A]sberg Depression Rating Scale (MADRS). Antidepressant response was defined as a MADRS improvement of 50% or more.\n\nRESULTSOver the two-week follow-up, we found a significant treatment by time interaction (F(8,245) = 2.02, p = 0.04), reflecting prolonged antidepressant effects post rapamycin+ketamine treatment. At 2 weeks, we found a significantly higher response rate following rapamycin+ketamine (41%) compared to placebo+ketamine (13%, p = 0.04). However, rapamycin pretreatment did not alter the acute effects of ketamine.\n\nCONCLUSIONUnexpectedly, pretreatment with rapamycin prolonged rather than blocked the acute antidepressant effects of ketamine. This observation raises questions about the role of mTORC1 in the antidepressant effects of keta-mine, raises the possibility that rapamycin may extend the benefits of ketamine, and thereby potentially sheds light on mechanisms that limit the duration of ketamine effects. The supplementing of ketamine with rapamycin may be a treat-ment strategy for reducing the frequency of ketamine infusions during maintenance treatment.

neuroscience↗