Biology subjects

Soni, A.

Publications and source records attributed to Soni, A..

12 recordsLinked to original sources

bioRxiv2026

Nup153 regulates neuronal responsiveness through HDAC1-mediated epigenetic modulation

Neural activity-dependent gene regulation is central to the development of neural networks and neuronal plasticity. Induction of activity-dependent gene programs is equally important as repression of these programs, and both need to be balanced carefully. However, little is known about how repressive mechanisms modulate neuronal responsiveness across the genome before stimulation. Here, we identify nucleoporin-dependent regulation of neuronal responsiveness, in which Nup153 represses neuronal genes including activity-regulated genes (ARGs), in the basal state. By characterizing the genome-wide landscape of chromatin accessibility, histone modifications and Nup153 chromatin binding, we show that Nup153 bidirectionally regulates chromatin states through both basal activity-dependent and -independent mechanisms and influences associated genes. Mechanistically, Nup153 associates with HDAC1 to modulate histone acetylation and chromatin accessibility at target regulatory regions. Our data suggests Nup153 organizes chromatin states that regulate neuronal gene programs involved in maintaining neuronal responsiveness. TeaserNup153 primes neuronal responsiveness through multi-layered epigenetic regulation.

neuroscience↗

bioRxiv2025

Murine models for triple-negative breast cancer with differential responsiveness to immunotherapy

Breast cancer is the most common cancer diagnosis in women. Clinical studies with triple-negative breast cancer (TNBC) are encouraging for immunotherapy combined with chemotherapy (anti-PD-1 with paclitaxel and/or carboplatin). However, additional clinical advances may be pursued more rapidly with assistance from preclinical TNBC models including syngeneic mammary tumor cell lines. Here, we report two mammary tumor cell lines that exhibit differential responsiveness to immunotherapy in vivo. Spontaneous mammary tumors from C57BL/6J MMTV-Cre Trp53fl/+ animals were passaged serially in cell culture and in vivo in the mammary fat pad of fully wildtype animals. The resulting lines, MM001i and MM008i, lost Trp53 and formed 1000 mm3 tumors in the mammary fat pad within 21-28 days. Despite originating from the same genetic background, these lines exhibit differential responses to immunotherapy. For anti-PD-1 therapy, MM001i is poorly responsive and MM008i is strongly responsive with near-complete tumor regression. In comparison, both MM001i and MM008i respond rapidly to anti-CTLA-4 therapy. Both models express unique tumor antigens as evidenced by immunity to subsequent engraftments. Primary MM008i tumors exhibit greater T cell infiltration, and CD8-positive T lymphocytes are required for anti-PD-1 responses. These TNBC models are promising for further mechanistic studies and testing future single and combinatorial therapies.

cancer biology↗

bioRxiv2025

Synergistic RAS-MAPK and AKT Activation in MYC-Driven Tumors via Adjacent PVT1 Rearrangements

MYC-driven (MYC+) cancers are aggressive and often fatal. MYC dysregulation is a key event in these cancers, but overexpression of MYC alone is not always enough to cause cancer. Plasmocytoma Variant Translocation 1 (PVT1), a long non-coding RNA (lncRNA) adjacent to MYC on chromosome 8 is a rearrangement hotspot in many MYC+ cancers. In addition to being co-amplified with MYC, the genomic rearrangement at PVT1 involves translocation, which has had obscure functional consequences. We report that translocation at the PVT1 locus cause asymmetric enrichment of 5-PVT1 and loss of 3-PVT1. Despite being classified as a non-coding RNA, the retained 5 region of PVT1 generates a circular RNA (CircPVT1) that codes for the novel peptide we call Firefox (FFX). FFX augments AKT signaling and synergistically activates MYC and mTORC1 in these cells. Further, the 3 end of PVT1, which is lost during the translocation, codes for a tumor-suppressing micropeptide we named as Honeybadger (HNB). We demonstrate that HNB interacts with KRAS and disrupts the activation of KRAS effectors. Loss of HNB leads to activation of RAS/MAPK signaling pathway, and enhances MYC stability by promoting phosphorylation of MYC at Ser62. These findings identify PVT1 as a critical node that synchronizes MYC, AKT, and RAS-MAPK activities in cancer. Our study thus identifies a key mechanism by which rearrangements at the PVT1 locus activate additional oncogenic pathways that synergize with MYC to exacerbate the aggressiveness of MYC+ cancers. This newfound understanding explains the poor prognosis associated with MYC+ cancers and offers potential therapeutic targets that could be leveraged in treatment strategies for these cancers.

cancer biology↗

bioRxiv2024

Re-calibration of flow cytometry standards for plant genome size estimation

The evolution of long-read sequencing technologies has advanced the development of genome assemblies that are now frequently presented as gap-free or nearly gapless, reflecting substantial improvements in sequencing accuracy and completeness. However, discrepancies between the genome size estimates derived from genome assemblies and flow cytometry create ambiguity regarding the accuracy of these complementary approaches. Accurate genome size estimation via flow cytometry relies on use of an internal standard with a genome of known size. Historically, the genome size of these standards was often calibrated against incomplete genome assemblies or non-plant genomes, such as the human male genome, which was previously considered to be 7 pg but is now known to be around 6.15 pg after 20 years of advancements. Calibrating plant references against non-plant standards is not recommended due to differential staining properties. Therefore, we recalibrated the size of five plant genomes commonly used as reference standards in flow cytometry, by utilizing a recent gapless, telomere-to-telomere (T2T) genome assembly of Nipponbare rice. Our results indicate a significant overestimation of around 20% in previous flow cytometry-based estimates for Pisum sativum and Nicotiana benthamiana, around 10% for Arabidopsis thaliana, and less than 5% for Sorghum bicolor, and Gossypium hirsutum. The close alignment of the recalibrated GS estimates to the reference genome assemblies and recalculated estimates from different studies confirms their suitability as reference standards for more accurate measurement of plant genome size.

molecular biology↗

bioRxiv2024

Conclusions about Neural Network to Brain Alignment are Profoundly Impacted by the Similarity Measure

Deep neural networks are widely used to model biological perception and behavior, making the similarities and differences between artificial and biological systems consequential. If a principle (e.g. self-supervised learning) produces a model resembling biology, this is taken as evidence the same principle shapes the biological system. But what it means for an artificial system to be similar to a biological one is complex. A popular approach compares representations of identical stimuli using a similarity measure like Representational Similarity Analysis. Yet scientific questions rarely specify which measure is appropriate, raising a key question: do conclusions depend on this choice? Focusing on vision, we show that measure choice influences both hierarchical correspondence between systems and the ranking of which artificial systems are most biological. Reanalyzing prior studies, we find the choice hugely consequential: models best under one measure are often worst under another, and prior conclusions can flip or dissolve. Different metrics capture fundamentally different aspects of similarity, warranting healthy skepticism toward such comparisons.

neuroscience↗

bioRxiv2024

Adaptive chunking improves effective working memory capacity in a prefrontal cortex and basal ganglia circuit

How and why is working memory (WM) capacity limited? Traditional cognitive accounts focus either on limitations on the number or items that can be stored (slots models), or loss of precision with increasing load (resource models). Here we show that a neural network model of prefrontal cortex and basal ganglia can learn to reuse the same prefrontal populations to store multiple items, leading to resource-like constraints within a slot-like system, and inducing a trade-off between quantity and precision of information. Such "chunking" strategies are adapted as a function of reinforcement learning and WM task demands, mimicking human performance and normative models. Moreover, adaptive performance requires a dynamic range of dopaminergic signals to adjust striatal gating policies, providing a new interpretation of WM difficulties in patient populations such as Parkinsons disease, ADHD and schizophrenia. These simulations also suggest a computational rather than anatomical limit to WM capacity.

neuroscience↗

bioRxiv2024

A flow cytometry protocol for accurate and precise measurement of plant genome size using frozen material

Flow cytometry is a technique widely applied to infer the ploidy and genome size of plant nuclei. The conventional approach of sample preparation, reliant on fresh plant material to release intact nuclei, requires protocol optimisation for application to many species. The approach often results in poor yields of nuclei, impeding the accurate measurement of genome size and confines the optimal resource allocation and efficiency in genome sequencing which relies on genome size estimation. Here, we present a novel method using frozen plant material that facilitates the release of intact nuclei for genome size estimation. Genome estimates from frozen material are similar to those from fresh material. Accurate and precise estimates can be made by complementing the fluorescence of frozen nuclei with histogram modelling and debris compensation algorithms. This method of nuclei isolation from frozen plant material for flow cytometry-based genome size estimations has special value in estimating the genome size of samples collected and frozen for use in plant genome sequencing. Plant material can be conveniently stored, resampled, and used for DNA or RNA extractions. HighlightFrozen leaf material can be used to isolate nuclei for the accurate estimation of genome size The method proved suitable for difficult samples and did not require specific optimization. The method was especially useful where plant material could not be immediately processed through flow cytometry and allowed the same sample to be used for genomes size estimation and genome sequencing.

genomics↗

bioRxiv2024

Microtubule polymerization tuned by macromolecular crowdant size and density

Microtubule (MT) polymerization is regulated by biochemical as well as physical factors such as macromolecular crowding. Crowding agents or crowdants affect MT elongation rates differently depending on crowdant size due to opposing ef-fects on polymerization: microviscosity reduces polymer elongation, while volume exclusion increases reaction rates by local concentration. In order to address how crowdant size and concentration collectively affect MT populations, we combine in vitro MT polymerization experiments with kinetic Monte Carlo simulations. Our experiments in bulk with nucleators validate decreasing MT elongation rates with increasing concentrations of small molecular weight crowdants in bulk assays and a corresponding increase for large crowdants. Kinetic Monte Carlo simulations can explain the result with packing fractions dependence of small as compared to large crowdants increasing microviscosity more dramatically. In contrast MT bulk polymerization rates in absence of nucleators increased with crowdant con-centration, irrespective of their size, with a corresponding decrease in the critical concentration. Microscopy of filament growth dynamics demonstrates that small crowdants result in shorter filaments in a concentration dependent manner, consis-tent with their role in reducing elongation rates, but this decrease is compensated by increased number of filaments. Large crowdants increase the filament numbers while elongation is slightly decreased. Our results provide evidence for MT nucle-ation being rate-limited and elongation diffusion limited, resulting in differences in the effect of crowdant sizes on nucleation and elongation. These results are of gen-eral relevance to understand physical effects of crowding on collective cytoskeletal polymerization dynamics.

biophysics↗

bioRxiv2021

CDK1 and HSP90AA1 appears as novel regulatory gene in Non-Small Cell Lung Cancer: A Bioinformatics Approach

Lung cancer is one of the most invasive cancer affecting over a million of population. Non-small cell lung cancer constitutes up to 85% of all lung cancer cases. Therefore, it is important to identify prognostic biomarkers of NSCLC for therapeutic purpose. The complex behaviour of the NSCLC gene-regulatory network interaction is investigated using a network theoretical approach. We used eight NSCLC microarray datasets GSE19188, GSE118370, GSE10072, GSE101929, GSE7670, GSE33532, GSE31547, GSE31210 and meta analyse them to find differentially expressed genes (DEGs), construct protein-protein interaction (PPI) network, analysed its topological properties, significant modules using network analyser with MCODE, construct a PPI-MCODE network using the genes of the significant modules. We used topological properties such as Maximal Clique Centrality (MCC) and bottleneck from the PPI-MCODE network. We compare them with hub genes (those with highest degrees) to find key regulator (KR) gene. This result is also validated by finding of common genes among top twenty hub genes, genes with highest betweenness, closeness centrality and eigenvector values. It was found that two genes, CDK1 and HSP90AA1 were common in PPI-MCODE combined analysis, and it was also found that CDK1, HSP90AA1 and HSPA8 were common among hub and bottle neck properties and suggesting significant regulatory role of CDK1 in non-small cell lung cancer. After validation, the common genes among top twenty hubs and centrality values like Betweenness Centrality, Closeness Centrality and eigen vector properties, CDK1 again appeared as the common gene. Our study as a summary suggested CDK1 as key regulator gene in complex NSCLC network interaction using network theoretical approach and described the complex topological properties of the network.

systems biology↗

bioRxiv2020

Dugesia japonica is the best suited of three planarian species for high-throughput toxicology screening

High-throughput screening (HTS) using new approach methods is revolutionizing toxicology. Asexual freshwater planarians are a promising invertebrate model for neurotoxicity HTS because their diverse behaviors can be used as quantitative readouts of neuronal function. Currently, three planarian species are commonly used in toxicology research: Dugesia japonica, Schmidtea mediterranea, and Girardia tigrina. However, only D. japonica has been demonstrated to be suitable for HTS. Here, we assess the two other species for HTS suitability by direct comparison with D. japonica. Through quantitative assessments of morphology and multiple behaviors, we assayed the effects of 4 common solvents (DMSO, ethanol, methanol, ethyl acetate) and a negative control (sorbitol) on neurodevelopment. Each chemical was screened blind at 5 concentrations at two time points over a twelve-day period. We obtained two main results: First, G. tigrina and S. mediterranea planarians showed significantly reduced movement compared to D. japonica under HTS conditions, due to decreased health over time and lack of movement under red lighting, respectively. This made it difficult to obtain meaningful readouts from these species. Second, we observed species differences in sensitivity to the solvents, suggesting that care must be taken when extrapolating chemical effects across planarian species. Overall, our data show that D. japonica is best suited for behavioral HTS given the limitations of the other species. Standardizing which planarian species is used in neurotoxicity screening will facilitate data comparisons across research groups and accelerate the application of this promising invertebrate system for first-tier chemical HTS, helping streamline toxicology testing.

pharmacology and toxicology↗

bioRxiv2019

Learning supervised embeddings for large scale sequence comparisons

Similarity-based search of sequence collections is a core task in bioinformatics, one dominated for most of the genomic era by exact and heuristic alignment-based algorithms. However, even efficient heuristics such as BLAST may not scale to the data sets now emerging, motivating a range of alignment-free alternatives exploiting the underlying lexical structure of each sequence.\n\nIn this paper, we introduce SuperVec, a novel supervised approach to learning sequence embeddings. Our method extends earlier Representation Learning (RL) based methods to include jointly contextual and class-related information for each sequence during training. This ensures that related sequence fragments have proximal representations in the target space, better reflecting the structure of the domain.\n\nSuch representations may be used for downstream machine learning tasks or employed directly. Here, we apply SuperVec embeddings to a sequence retrieval task, where the goal is to retrieve sequences with the same family label as a given query. The SuperVec approach is extended further through H-SuperVec, a tree-based hierarchical method which learns embeddings across a range of feature spaces based on the class labels and their exclusive and exhaustive subsets.\n\nExperiments show that supervised learning of embeddings based on sequence labels using SuperVec and H-SuperVec provides a substantial improvement in retrieval performance over existing (unsupervised) RL-based approaches. Further, the new methods are an order of magnitude faster than BLAST for the database retrieval task, supporting hybrid approaches in which SuperVec rapidly filters the collection so that only potentially relevant records remain, allowing slower, more accurate methods to be executed quickly over a far smaller dataset. Thus, we may achieve faster query processing and higher precision than before.\n\nFinally, for some problems, direct use of embeddings is already sufficient to yield high levels of precision and recall. Extending this work to encompass weaker homology is the subject of ongoing research.

bioinformatics↗

bioRxiv2018

CAN NEUROPATHIC PAIN PREDICT RESPONSE TO ARTHROPLASTY IN KNEE OSTEOARTHRITIS? A PROSPECTIVE OBSERVATIONAL COHORT STUDY.

A significant proportion of patients with knee osteoarthritis (OA) continue to have severe ongoing pain following knee replacement surgery. Central sensitization and features suggestive of neuropathic pain before surgery may result in a poor outcome post-operatively. In this prospective observational study of patients undergoing primary knee arthroplasty (n=120), the modified PainDETECT score was used to divide patients, with primary knee OA, into nociceptive (<13), unclear (13-18) and neuropathic -like pain (>18) groups pre-operatively. Response to surgery was compared between groups using the Oxford Knee Score (OKS) and the presence of moderate to severe long-term pain 12 months after arthroplasty. The analyses were replicated in a larger independent cohort study (n=404). 120 patients were recruited to the main study cohort: 63 (52%) nociceptive pain; 32 (27%) unclear pain; 25 (21%) neuropathic-like pain. Patients with neuropathic-like pain had significantly worse OKS pre and post-operatively, compared to the nociceptive pain group, independent of age, sex and BMI. At 12-months post-operatively the mean OKS was 4 points lower in the neuropathic-like group compared with the nociceptive group in the study cohort (non-significant); with a difference of 5 points in the replication cohort (p<0.001). Moderate to severe long-term pain after arthroplasty at 12-months was present in 50% of the neuropathic-like pain group versus 24% in the nociceptive pain group, in the replication cohort (p<0.001). Neuropathic pain is common and targeted therapy pre, peri and post-operatively may improve treatment response.

epidemiology↗