Biology subjects

Jindal, A.

Publications and source records attributed to Jindal, A..

5 recordsLinked to original sources

bioRxiv2026

homeRNA Just Dropped! Streamlining Remote Blood Collection and RNA Stabilization for Smaller Sample Volumes with homeRNAdrop

At-home sampling can address obstacles to participation in transcriptomics research. We previously developed the homeRNA and homeRNAmax platforms; these custom-designed tubes containing RNA stabilizer interface with a blood tube compatible with upper-arm blood collection devices, allowing for blood collection and RNA stabilization to occur from a participant's home. In this work we introduce homeRNAdrop, an insert for a commercially available blood tube (BD Microtainer) that divides the tube into two compartments: the top compartment holds 200 L blood and the bottom compartment holds ~590 L liquid stabilizer (RNAlater). By combining blood collection and RNA stabilization into the same tube, homeRNAdrop streamlines sample preparation for participants and allows for more compact sample storage for the lab. In a remote pilot study, n=25 participants from across the United States tested the homeRNAdrop kit. The n=23 samples successfully received by the lab show that samples collected using homeRNAdrop are of sufficient RNA quality (all RINs > 6, a common cutoff for analysis) and yield (mean yield=1.44 g) for downstream gene expression analysis. Almost all participants who successfully collected blood using the Tasso Mini device found the homeRNAdrop device easy to use (n=23/24). Overall, this work adds a simplified, user-friendly platform to the growing suite of tools for remote blood RNA stabilization.

bioengineering↗

bioRxiv2024

Protrusion force and cell-cell adhesion play a critical role in collective tumor cluster migration

Collective migration refers to the coordinated movement of cells as a single unit during migration. While collective migration enhances invasive and metastatic potential in cancer, the mechanisms driving this behavior and regulating tumor migration plasticity remain poorly understood. This study provides a mechanistic model explaining the emergence of different modes of collective migration under hypoxia-induced secretome. We focus on the interplay between cellular protrusion force and cell-cell adhesion using collectively migrating three-dimensional microtumors as models with well-defined microenvironments. Large microtumors show directional migration due to intrinsic hypoxia, while small microtumors exhibit radial migration when exposed to hypoxic secretome. Here, we developed an in silico multi-scale microtumor model (MSMM) based on the cellular Potts model and implemented in CompuCell3D to elucidate underlying mechanisms. We identified distinct migration modes within specific regions of protrusion force and cell-cell adhesion parameter space and studied these modes using in vitro experimental microtumor models. We show that sufficient cellular protrusion force is crucial for radial and directional collective microtumor migration. Radial migration emerges when sufficient cellular protrusion force is generated, driving neighboring cells to move collectively in diverse directions. Within migrating tumors, strong cell-cell adhesion enhances the alignment of cell polarity, breaking the symmetric angular distribution of protrusion forces and leading to directional microtumor migration. The integrated results from the experimental and computational models provide fundamental insights into collective migration in response to different microenvironmental stimuli. Our computational and experimental models can adapt to various scenarios, providing valuable insights into cancer migration mechanisms. Statement of SignificanceWhile single-cell metastasis is well-studied, mechanisms of collective cluster migration are less understood. Significant challenges include the lack of a fundamental physics perspective on collective cluster migration mechanisms and suitable physiologically relevant three-dimensional (3D) in vitro models that can recapitulate collective cluster migration. In this article, we developed a computational model depicting microtumor migration behaviors. Collective cell migration, with varying correlation lengths, exhibits different migratory modes such as directional and radial migration. These modes are predicted by in silico models and confirmed using experimental microtumor models. Machine learning methods were exploited to identify migratory modes. Our computational and experimental models are flexible in various circumstances, offering insights into cancer migration mechanisms.

cancer biology↗

bioRxiv2023

Epigenetic mediated functional reprogramming of immune cells leads to HBsAg seroconversion in Hepatitis B Virus Reactivation patients

BackgroundHepatitis B virus (HBV) modulates epigenetic landscape by epigenetic regulators. HBsAg seroconversion is possible with immune activation, therefore we aimed to investigate epigenetic modulation in HBV reactivation (rHBV) for viral clearance and seroconversion. MethodsSixteen retrospectively collected rHBV patients [Seroconverters (SC, n=7, HBsAg loss and anti-HBs>10 IU/ml), non- seroconverters (NSC, n=9)], chronic hepatitis B treatment naive (nCHBV, n=7) patients and healthy controls (HC, n=7) were included in this study. Genome methylation, gene expression, plasma-cytokines, and immune cell profiling was analysed by Reduced Representation Bisulfite Sequencing (RRBS), QRT-PCR, multiplex-cytokine-bead array and flow-cytometry. ResultsrHBV patients having high HBV DNA and ALT showed epigenetic remodellers; KDM2B, NCOR2 and GATA6, immune and metabolic genes; TGF-{beta}, IL-6, IRF8, RPTOR, HK3 significantly (p<0.05) hypomethylated at specific CpG islands compared to nCHBV. TOX was hypomethylated in nCHBV suggesting immune-exhaustion. At-baseline, seroconverters showed hypomethylation of KDM2B, COX19, IRF8, TLR5 and hypermethylation of LAG3 compared to non-seroconverters. Further, in seroconverters at week-24, IL17RA, IFN-{gamma}, TGF-{beta}, and STAT5B (p<0.05) were additionally hypomethylated at specific CpG islands suggesting immune activation. Cytokine-bead analysis revealed increased IL-6 (p=0.009) and decreased LAG3 plasma levels (p=0.01) also imply on significantly differentiated HBV specific CD8, Tfh and Th1/17 cells in seroconverters at baseline and week-24. However, both nCHBV and non-seroconverters had consistent hypomethylation of LAG3 and TOX, which leads to immune exhaustion. ConclusionIn rHBV, seroconversion is driven by position specific CpG islands methylation in epigenetic remodellers, immune and metabolic genes. Immune metabolic reprograming is reflected by Th1/17 differentiation, extensive interleukin production for HBsAg seroconversion. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=116 SRC="FIGDIR/small/554133v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@48e206org.highwire.dtl.DTLVardef@ef0fa7org.highwire.dtl.DTLVardef@ecb710org.highwire.dtl.DTLVardef@e6b34e_HPS_FORMAT_FIGEXP M_FIG C_FIG Lay summaryEpigenetic landscape in nCHBV depicts exhaustion and immune dysfunction. Out of many hypermethylated CpG islands of nCHBV, few become hypomethylated in rHBV and drives immune and metabolic reprogramming. This study provides insights into the cellular and molecular basis of epigenomic programs that regulate the differentiation and activation of immune cells leading to viral clearance and seroconversion. Targeting epigenetic mechanism could be promising strategy for the treatment of nCHBV and non-seroconverters.

immunology↗

bioRxiv2021

Improved Docking of Protein Models by a Combination of Alphafold2 and ClusPro

It has been demonstrated earlier that the neural network based program AlphaFold2 can be used to dock proteins given the two sequences separated by a gap as the input. The protocol presented here combines AlphaFold2 with the physics based docking program ClusPro. The monomers of the model generated by AlphaFold2 are separated, re-docked using ClusPro, and the resulting 10 models are refined by AlphaFold2. Finally, the five original AlphaFold2 models are added to the 10 AlphaFold2 refined ClusPro models, and the 15 models are ranked by their predicted aligned error (PAE) values obtained by AlphaFold2. The protocol is applied to two benchmark sets of complexes, the first based on the established protein-protein docking benchmark, and the second consisting of only structures released after May 2018, the cut-off date for training AlphaFold2. It is shown that the quality of the initial AlphaFold2 models improves with each additional step of the protocol. In particular, adding the AlphaFold2 refined ClusPro models to the AlphaFold2 models increases the success rate by 23% in the top 5 predictions, whereas considering the 10 models obtained by the combined protocol increases the success rate to close to 40%. The improvement is similar for the second benchmark that includes only complexes distinct from the proteins used for training the neural network.

bioinformatics↗

bioRxiv2020

Deep learning discerns cancer mutation exclusivity

The exclusivity of a vast majority of cancer mutations remains poorly understood, despite the availability of large amounts of whole genome and exome sequencing data. In clinical settings, this markedly hinders the identification of the previously uncharacterized deleterious mutations due to the unavailability of matched normal samples. We employed state of the art deep learning algorithms for cross-exome learning of mutational embeddings and demonstrated their utility in sequence based detection of cancer-specific Single Nucleotide Variants (SNVs).

bioinformatics↗