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Parashar, D.

Publications and source records attributed to Parashar, D..

7 recordsLinked to original sources

Biophysical Design Space for Cellular Self-assembly and Dynamics

In natural biological systems, cells organize into tissues through interactions of several processes, including cellular signaling, collective migration, contractile activity of cytoskeletal elements and interactions with their surroundings. In recent decades, advancements in microscopy, genetic engineering, biochemistry, and computational modeling have enabled a more quantitative understanding of these processes. In this article, we present an integrated computational framework that couples various physical mechanisms such as cell-cell adhesion, strength and persistence of cellular motility, and the background stiffness. We use this framework to study how they collectively interact to determine the self-organization of the cells from a pseudo-random structure as well as the migration behavior of the cells. Notably, our simulations predict that motility has a two-way effect on cellular self-assembly: it promotes aggregation at moderate levels but disrupts clusters when excessively strong, yielding an optimal motility for formation of multicellular clusters. On the other hand, adhesion shows a two-stage effect: At lower value it self-assembles the structure, at higher value it compacts it. Furthermore, we experimentally demonstrate the motility-assisted self-aggregation of cells using cancer cells in a granular mechanical milieu. Finally we show that cell-cell adhesion and background medium tune the strength and persistence of cellular migration. Altogether, this work presents a computational framework that allows us to design phase behavior of collective of cells tuning their interaction, motility, and the background mechanics.

biophysics↗

Orphan nuclear receptor NR2E3 and its small-molecule agonist induce cancer cell apoptosis through regulating p53, IFNα and MYC pathways

Orphan nuclear receptor NR2E3 activates p53 and induces cancer cell apoptosis. Further studies on p53-dependent and -independent functions of wild-type and mutated NR2E3 are needed. Herein, we showed that NR2E3 enhanced p53-DNA interactions in diverse cancer cells and up-regulated p53 and IFN pathways while down-regulating MYC pathway in cervical cancer cells. Studies of "All of Us" and TCGA databases showed NR2E3 nonsynonymous mutations associating with four cancers. We stratified NR2E3 SNVs for their cancer implications with the p53 reporter. A cancer-associated NR2E3R97Hmutation not only lost the wild-types tumor-suppressing functions but also prohibited the wild-type from enhancing p53 acetylation. These observations implicated the potential for pharmaceutically activating NR2E3 to suppress cancer. Indeed, NR2E3s small-molecule agonist 11a repressed 2-D and 3-D cultures of primary cells and cell lines of cervical cancer, in which screening FDA-approved anti-cancer drugs identified HDAC-1/2 inhibitor Romidepsin operating synergistically with 11a. The underlying molecular mechanisms included 11as down-regulating the transcription of Multidrug Resistance Protein ABCB1 that Romidepsin up-regulated. Transcriptomics studies revealed three synergy modes: (1) "sum-up" mode that the p53 pathway activated individually by 11a and Romidepsin got stronger by the combo; (2) "antagonism" mode that Romidepsin counteracted the activation of the Kras pathway by 11a; and (3) "de novo" mode that the combo instead of each individual drug repressed the MYC pathway. Conclusively, our experiments provide new data supporting tumor-suppressor like functions for wild-type NR2E3, reveal roles of mutated NR2E3 in cancer, and address values of NR2E3s agonist 11a in cancer therapy alone and combined.

cancer biology↗

Do Support Vector Machines play a role in stratifying patient population based on cancer biomarkers?

Biomarkers are known to be the key driver behind targeted cancer therapies by either stratifying the patients into risk categories or identifying patient subgroups most likely to benefit. However, the ability of a biomarker to stratify patients relies heavily on the type of clinical endpoint data being collected. Of particular interest is the scenario when the biomarker involved is a continuous one where the challenge is often to identify cut-offs or thresholds that would stratify the population according to the level of clinical outcome or treatment benefit. On the other hand, there are well-established Machine Learning (ML) methods such as the Support Vector Machines (SVM) that classify data, both linear as well as non-linear, into subgroups in an optimal way. SVMs have proven to be immensely useful in data-centric engineering and recently researchers have also sought its applications in healthcare. Despite their wide applicability, SVMs are not yet in the mainstream of toolkits to be utilised in observational clinical studies or in clinical trials. This research investigates the very role of SVMs in stratifying the patient population based on a continuous biomarker across a variety of datasets. Based on the mathematical framework underlying SVMs, we formulate and fit algorithms in the context of biomarker stratified cancer datasets to evaluate their merits. The analysis reveals their superior performance for certain data-types when compared to other ML methods suggesting that SVMs may have the potential to provide a robust yet simplistic solution to stratify real cancer patients based on continuous biomarkers, and hence accelerate the identification of subgroups for improved clinical outcomes or guide targeted cancer therapies.

bioinformatics↗

PRAK-03202: A triple antigen VLP vaccine candidate against SARS CoV-2

The rapid development of safe and effective vaccines against SARS CoV-2 is the need of the hour for the coronavirus outbreak. Here, we have developed PRAK-03202, the worlds first triple antigen VLP vaccine candidate in a highly characterized S. cerevisiae-based D-CryptTM platform, which induced SARS CoV-2 specific neutralizing antibodies in BALB/c mice. Immunizations using three different doses of PRAK-03202 induces antigen specific (Spike, envelope and membrane proteins) humoral response and neutralizing potential. PBMCs from convalescent patients, when exposed to PRAK-03202, showed lymphocyte proliferation and elevated IFN-{gamma} levels suggestive of conservation of epitopes and induction of T helper 1 (Th1)-biased cellular immune responses. These data support the clinical development and testing of PRAK-03202 for use in humans.

immunology↗

Efficiency of a Randomized Confirmatory Basket Trial Design Constrained to Control the False Positive Rate by Indication

PURPOSEMolecular oncology determines biomarker-defined niche indications. Basket trials pool histologic indications sharing molecular pathophysiology, potentially improving development efficiency. Currently basket trials have been confirmatory only for exceptional therapies. Our previous randomized basket design may be generally suitable in the resource-intensive confirmatory phase, maintains high power, and provides nearly k-fold increased efficiency for k indications, but controls false positives for the pooled result only. Since false positive control by indications (FWER) may sometimes be required, we now simulate a variant of this basket design controlling FWER at 0.025k, the total FWER of k separate randomized trials. METHODSThe previous design eliminated indications at an interim analysis, conducting a final pooled analysis of remaining indications. To control FWER, we rechecked individual indications at a prospectively defined level of statistical significance after any positive pooled result. We simulated this modified design under numerous scenarios varying design parameters. Only designs controlling FWER and minimizing estimation bias were allowable. RESULTSSequential analyses (interim, pooled, and post-individual tests)) result in cumulative power losses. Optimal performance results when k = 3,4. We report efficiency (expected # true positives/expected sample size) relative to k parallel studies, at 90% power ("uncorrected") or at the power achieved in the basket trial ("corrected", because conventional designs could also increase efficiency by sacrificing power). Efficiency and power (percentage active indications identified) improve with higher percentage of initial indications active. Up to 92% uncorrected and 38% corrected efficiency improvement is possible, with power {approx} 60%. CONCLUSIONSEven under FWER control, randomized confirmatory basket trials substantially improve development efficiency. Initial indication selection is critical. The design is particularly attractive when enrollment challenges preclude full powering of individual indications.

cancer biology↗

RNA-binding protein FXR1 drives cMYC translation by mRNA circularization through eIF4F recruitment in ovarian cancer

BackgroundThe RNA-binding protein FXR1 (fragile X-related protein 1) has been implicated as an important regulator of post-transcriptional changes of mRNAs. However, its role in mRNA circularization and recruitment of eukaryotic translation initiation factors for protein translation remains obscure. Here, we aimed to investigate the molecular mechanisms and potential clinical applications of FXR1 in ovarian cancer growth and progression. MethodsFXR1 copy number variation, mRNA expression, protein levels, and their association with prognosis were determined in clinical datasets. An orthotopic ovarian cancer model and bioluminescence imaging were used for preclinical evaluation of FXR1 in vivo. Reverse phase protein arrays (RPPA) and qPCR arrays were performed to identify FXR1s key targets and downstream effects. SUnSET and polysome profiling were used to determine the translational effects of FXR1. Immunoprecipitation and immunofluorescence were performed to identify the interaction between FXR1 and cMYC mRNA and eIF4F complex. RNA-immunoprecipitation (RIP), RNA electrophoretic mobility shift assays (REMSA), proximity ligation assays (PLA), and biochemical assays were used to identify the specific site on cMYC mRNA to which FXR1 binds to promote mRNA circularization and translation. ResultsWe found that amplification and copy-gain of FXR1 increased the expression of FXR1 mRNA and FXR1 protein in ovarian cancer patients, and these events associated with poor prognosis. We demonstrated that FXR1 binds to AU-rich elements (ARE) within the 3 untranslated region (3UTR) of cMYC. As a consequence, FXR1 binding to cMYC 3UTR leads to the circularization of mRNA and facilitated the recruitment of eukaryotic translation initiation factors (eIFs) to translation start site for improving protein synthesis. ConclusionWe found that FXR1 upregulates a known oncogene, cMYC, by binding to AU-rich elements within the 3UTR, leading to the recruitment of the eIF4F complex for cMYC translation. Our findings uncover a novel mechanism of action of FXR1 in tumorigenesis and provides opportunities to use FXR1 and its downstream effectors as biomarkers or therapeutic targets in ovarian and other cancers.

cancer biology↗

Multi-Antigenic Virus-like Particle of SARS CoV-2 produced in Saccharomyces cerevisiae as a vaccine candidate

Spike, Envelope and Membrane proteins from the SARS CoV-2 virus surface coat are important vaccine targets. We hereby report recombinant co-expression of the three proteins (Spike, Envelope and Membrane) in a engineered Saccharomyces cerevisiae platform (D-Crypt) and their self-assembly as Virus-like particle (VLP). This design as a multi-antigenic VLP for SARS CoV-2 has the potential to be a scalable vaccine candidate. The VLP is confirmed by transmission electron microscopy (TEM) images of the SARS CoV-2, along with supportive HPLC, Dynamic Light Scattering (DLS) and allied analytical data. The images clearly outline the presence of a "Corona" like morphology, and uniform size distribution.

biochemistry↗