Biology subjects

Rana, P.

Publications and source records attributed to Rana, P..

13 recordsLinked to original sources

bioRxiv2026

CRISPR-Mediated Targeting of BRAF Oncogenes in Pediatric Low-Grade Glioma

SummaryA catch-all intronic guide RNA pair excises the KIAA1549--BRAF oncofusion across its major variants, with productive junction excision confirmed by gain-of-function PCR in patient-derived glioma cells. An allele-specific guide selectively disrupts BRAF V600E, in patient-derived pediatric low-grade glioma cells. Pediatric low-grade glioma (pLGG) is the most common brain tumor of childhood, accounting for 30--50% of all pediatric central nervous system malignancies1. The disease is almost universally driven by activating mutations in the BRAF serine/threonine kinase: a chromosomal tandem duplication generating the KIAA1549--BRAF oncofusion in approximately 70% of cases, or the BRAF V600E gain-of-function point mutation in approximately 15%2. Current targeted pharmacotherapies, including the RAF inhibitor tovorafenib, require continuous dosing, are not allele-specific, and carry risks of long-term toxicity in children. A one-time genomic intervention that permanently disables the oncogenic BRAF alteration while preserving wild-type BRAF signaling represents a compelling therapeutic alternative. In this study, we describe the design and experimental validation of allele-specific CRISPR guide RNAs targeting both the KIAA1549--BRAF oncofusion and the BRAF V600E point mutation. For the oncofusion, we developed a double-cut intronic excision strategy in which a guide RNA targeting KIAA1549 intron 14 is paired with a guide RNA targeting BRAF intron 11. Because the genomic breakpoints of all four major fusion variants (KB 16:9, 15:9, 16:11, and 15:11) fall within these introns, a single guide pair can address the full landscape of fusion heterogeneity in a single intervention. For BRAF V600E, we exploited a unique PAM sequence created by the pathogenic TBA transversion at codon 600, enabling allele-specific SpCas9 and AsCas12a guide designs that distinguish the mutant from the wild-type allele at single-nucleotide resolution. We screened guide RNA candidates by ribonucleoprotein (RNP) nucleofection in A375 human melanoma cells (BRAF V600E homozygous) and in patient-derived 3635 PXA glioma cells (BRAF V600E heterozygous). The top KIAA1549 intron 14 guide, K9_i14_A_Cas9, achieved 66% indel frequency in A375 cells. The top BRAF intron 11 guides, B_i11_A_Cas9 and B_i11_D_Cas9, achieved 84% and 85% indel frequency, respectively. For BRAF V600E, the best allele-specific SpCas9 guide achieved l57% editing in A375 cells and l74% editing in 3635 PXA patient-derived glioma cells. Dual-cut excision of the KIAA1549--BRAF junction was confirmed by a gain-of-function PCR assay designed to detect the excision junction amplicon ([~]191 bp) produced by NHEJ-mediated rejoining of the KIAA1549 intron 14 and BRAF intron 11 cut ends.

cancer biology↗

bioRxiv2025

OS2CR-Diff: A Self-Refining Diffusion Framework for CD8 Imputation from One-Step Inference to Conditional Representation

Stain imputation in multiplex immunofluorescence (mIF) imaging addresses the challenge of missing or damaged biomarker channels by reconstructing target biomarker images from a limited set of available stains. This approach offers a faster and more efficient alternative to full-panel staining, enabling detailed analysis of the tumour microenvironment. Existing One-Step Inference Models (OSIMs), primarily based on generative adversarial networks (GAN) or autoencoders, often generate suboptimal images with significant artifacts or reduced signal intensity. These limitations impair visual interpretability and reliability of the downstream immunotherapy response assessment. The challenge is further amplified when imputing cytoplasmic biomarkers such as CD8 from commonly used stains such as DAPI, due to the limited spatial correlation and the inherently complex structure of cytoplasmic signals. To address these limitations, we propose a self-refining diffusion model, OS2CR-Diff, which utilises the results from OSIMs as additional conditional representations. Unlike prior studies that rely on a single or limited conditional inputs, OS2CR-Diff incorporates three conditional inputs: the OSIM-imputed target biomarker image, OSIM-imputed complementary biomarker images, and non-antibody-stained images. Furthermore, we propose a feature fusion module that employs a cross-gated attention mechanism to effectively integrate these inputs, enabling context-aware feature refinement and improving the quality and reliability of imputed biomarker images. We evaluated OS2CR-Diff for CD8 biomarker imputation on mIF images of melanoma tissues. Our method out-performed state-of-the-art methods, achieving a 73.4% increase in the Structural Similarity Index Measure (SSIM), a 28.9% gain in the Peak Signal-to-Noise Ratio (PSNR), a 61.2% improvement in Mean Absolute Error (MAE), and significantly lower false positive rates compared to OSIM.

bioinformatics↗

bioRxiv2025

Right-Lateralized Maladaptive Topological Reorganization in Hyperthyroidism

Hyperthyroidism (HT) has been associated with cognitive impairments, but the structural connectomic and molecular mechanisms underlying these deficits remain poorly understood. In this study, we investigated large-scale brain network reorganization using structural connectivity derived from diffusion MRI in patients with 30 hyperthyroidism and 28 matched control participants. Subject level connectomes were analyzed to characterize network topology, with a specific focus on hub architecture, nodal alterations, and hemispheric reorganization. To probe molecular underpinnings, we integrated normative neurotransmitter receptor and transporter density maps with network metrics, assessing their correspondence to observed connectomic disruptions. Individuals with hyperthyroidism exhibited increased structural connectivity, particularly in mid- and long-range connections, alongside disrupted network topology. These changes were characterized by reduced modularity, increased characteristic path length and hub reorganization, most prominently in subcortical and right-hemisphere regions. Importantly, network alterations were functionally relevant, as metrics showed significant associations with clinical and cognitive variables, including FT4 levels, BMI, and perceptuomotor performance in the hyperthyroid group. Spatial neuromolecular correspondence analyses further revealed that DAT, 5-HT2A, and NET were closely aligned with topological changes. Moreover, neurotransmitter-weighted within-module degree z-scores moderately predicted TSH levels in HT. Our findings demonstrate that hyperthyroidism is associated with an increase in redundant, spatially extended white matter connections and a marked right-lateralized asymmetry in network organization. Furthermore, serotonergic (5-HT1a) and dopaminergic (DAT) systems consistently contributed to the modulation of intra-modular hubness, suggesting their role in maladaptive structural reorganization that may underlie perceptuomotor dysfunction in hyperthyroidism.

neuroscience↗

bioRxiv2025

AdSI-MIMO: Adaptive stain imputation with multi-input and multi-output learning for multiplex immunofluorescence imaging

Multiplex immunofluorescence (mIF) imaging plays a crucial role in studying multiple biomarkers and their interactions within the tumour microenvironment. However, acquiring mIF images that include all desired biomarkers presents significant challenges due to the need for specialised equipment and costly reagents, which increase technical complexity, time and expense. Stain imputation offers a promising solution by synthesising target biomarker images using generative models, thereby eliminating the need for additional staining procedures. Existing deep learning-based stain imputation methods lack flexibility in generating multiple biomarker images from various input combinations. To overcome this limitation, we propose AdSI-MIMO, a novel stain imputation framework for mIF images. Our method features a multibranch deep learning architecture capable of generating multiple outputs and incorporates an adaptive progressive masking strategy to accommodate the varying combinations of input biomarkers. This approach not only improves the quality of the generated biomarker images, but also eliminates the need to train separate models for each target biomarker. We evaluated AdSI-MIMO using two datasets, including a local dataset comprising mIF images from 257 melanoma patients and a public dataset of 55 urothelial carcinoma samples. Our method achieved substantial improvements over state-of-the-art methods, particularly in the imputation of key T-cell and activation biomarkers, such as CD8 and PD-L1. Specifically, across both public and proprietary datasets, our model achieved a 18.4% improvement in Pearson Correlation Coefficient (Pearson-r) for CD8 imputation and a 48.1% improvement for PD-L1 imputation under various input biomarker configurations on the external test set.

bioinformatics↗

bioRxiv2023

Conservation of heat stress acclimation by the inositol polyphosphate multikinase, IPMK responsible for 4/6-InsP7 production in land plants

Inositol pyrophosphates (PP-InsPs) are soluble cellular messengers that integrate environmental cues to induce adaptive responses in eukaryotes. In plants, the biological functions of various PP-InsP species are poorly understood, largely due to the absence of canonical enzymes present in other eukaryotes. The recent identification of a new PP-InsP isomer with yet unknown enantiomeric identity, 4/6-InsP7 in the eudicot Arabidopsis thaliana, further highlights the intricate PP-InsP signalling network employed by plants. The abundance of 4/6-InsP7 in land plants, the enzyme(s) responsible for its synthesis, and the physiological functions of this species are all currently unknown. In this study, we show that 4/6-InsP7 is the major PP-InsP species present across land plants. Our findings demonstrate that the Arabidopsis inositol polyphosphate multikinase (IPMK) homolog, AtIPK2 generates 4/6-InsP7 in vitro. Furthermore, the cellular level of 4/6-InsP7 is controlled by the two Arabidopsis IPMK isoforms, AtIPK2 and AtIPK2{beta}. Notably, the activity of these IPMK proteins is critical for heat stress acclimation in Arabidopsis. During heat stress, the expression of genes encoding various heat shock proteins controlled by the heat shock factors (HSFs) is affected in the AtIPK2-deficient plants. Furthermore, we show that the transcription activity of HSF is regulated by the AtIPK2 proteins. Our parallel investigations using the liverwort Marchantia polymorpha suggest that the InsP6 kinase activity of IPMK and the role of IPMK in regulating the heat stress response are evolutionarily conserved. Collectively, our study indicates that IPMK has played a critical role in transducing environmental cues for biological processes during land plant evolution.

plant biology↗

bioRxiv2023

Assessment of population dynamics of T. putitora (Ham, 1822) in the Nayar river basin

The present study analyzed the population characteristics of Tor putitora in their breeding and nursery grounds located in Western Himalayan region. From a sample size of 400 individuals, growth parameters such as asymptotic length, growth rate and growth performance index were estimated as 80.01 cm, 0.810 per year and 3.71 per year while the potential longevity was estimated as 4.6 years. The mortality parameters such as total, natural and fishing mortality were estimated as 2.88 /year, 1.05/ year and 1.83 / year respectively. The length at 25 %, 50% and 75% susceptibility to capture was estimated as 40.87 cm, 38.11 cm and 35.34 cm respectively. The fishing mortality and exploitation beyond optimum level and maximum sustainable exploitation ratio (Emax) indicate the overly exploitation of Mahseer resources in their breeding and nursery grounds. This indicates that the examined breeding and nursery grounds in Western Himalaya are facing intense "growth and recruitment overfishing" which may result in stock collapse in the forthcoming years.

zoology↗

bioRxiv2023

Neuron Enriched Exosomal MicroRNA Expression Profiles as a Marker of Early Life Alcohol Consumption

BackgroundAlcohol consumption may impact and shape brain development through perturbed biological pathways and impaired molecular functions. We investigated the relationship between alcohol consumption rates and neuron-enriched exosomal microRNA (miRNA) expression to better understand the impact of alcohol use on early life brain biology. MethodsNeuron-enriched exosomal miRNA expression was measured from plasma samples collected from young people using a commercially available microarray platform while alcohol consumption was measured using the Alcohol Use Disorders Identification Test. Linear regression and network analyses were used to identify significantly differentially expressed miRNAs and to characterize the implicated biological pathways, respectively. ResultsCompared to alcohol naive controls, young people reporting high alcohol consumption exhibited significantly higher expression of four neuron-enriched exosomal miRNAs including miR-30a-5p, miR-194-5p, and miR-339-3p, although only miR-30a-5p and miR-194-5p survived multiple test correction. The miRNA-miRNA interaction network inferred by a network inference algorithm did not detect any differentially expressed miRNAs with a high cutoff on edge scores. However, when the cutoff of the algorithm was reduced, five miRNAs were identified as interacting with miR-194-5p and miR-30a-5p. These seven miRNAs were associated with 25 biological functions; miR-194-5p was the most highly connected node and was highly correlated with the other miRNAs in this cluster. ConclusionsOur observed association between neuron-enriched exosomal miRNAs and alcohol consumption concurs with results from experimental animal models of alcohol use and suggests that high rates of alcohol consumption during the adolescent/young adult years may impact brain functioning and development by modulating miRNA expression.

genetics↗

bioRxiv2022

Imbalanced classification for protein subcellularlocalisation with multilabel oversampling

MotivationSubcellular localisation of human proteins is essential to comprehend their functions and roles in physiological processes, which in turn helps in diagnostic and prognostic studies of pathological conditions and impacts clinical decision making. Since proteins reside at multiple locations at the same time and few subcellular locations host far more proteins than other locations, the computational task for their subcellular localisation is to train a multilabel classifier while handling data imbalance. In imbalanced data, minority classes are underrepresented, thus leading to a heavy bias towards the majority classes and the degradation of predictive capability for the minority classes. Furthermore, data imbalance in multilabel settings is an even more complex problem due to the coexistence of majority and minority classes. ResultsOur studies reveal that based on the extent of concurrence of majority and minority classes, oversampling of minority samples through appropriate data augmentation techniques holds promising scope for boosting the classification performance for the minority classes. We measured the magnitude of data imbalance per class and the concurrence of majority and minority classes in the dataset. Based on the obtained values, we identified minority and medium classes, and a new oversampling method is proposed that includes nonlinear mixup, geometric and colour transformations for data augmentation and a sampling approach to prepare minibatches. Performance evaluation on the Human Protein Atlas Kaggle challenge dataset shows that the proposed method is capable of achieving better predictions for minority classes than existing methods. AvailabilityData used in this study is available at https://www.kaggle.com/competitions/human-protein-atlas-image-classification/data. Contactyang.song1@unsw.edu.au Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2022

Data augmentation for imbalanced blood cell image classification

Due to progression in cell-cycle or duration of storage, classification of morphological changes in human blood cells is important for correct and effective clinical decisions. Automated classification systems help avoid subjective outcomes and are more efficient. Deep learning and more specifically Convolutional Neural Networks have achieved state-of-the-art performance on various biomedical image classification problems. However, real-world data often suffers from the data imbalance problem, owing to which the trained classifier is biased towards the majority classes and does not perform well on the minority classes. This study presents an imbalanced blood cells classification method that utilises Wasserstein divergence GAN, mixup and novel nonlinear mixup for data augmentation to achieve oversampling of the minority classes. We also present a minority class focussed sampling strategy, which allows effective representation of minority class samples produced by all three data augmentation techniques and contributes to the classification performance. The method was evaluated on two publicly available datasets of immortalised human T-lymphocyte cells and Red Blood Cells. Classification performance evaluated using F1-score shows that our proposed approach outperforms existing methods on the same datasets.

bioinformatics↗

bioRxiv2021

AAV-Txnip prolongs cone survival and vision in mouse models of retinitis pigmentosa

Retinitis pigmentosa (RP) is an inherited retinal disease, affecting >20 million people worldwide. Loss of daylight vision typically occurs due to the dysfunction/loss of cone photoreceptors, the cell type that initiates our color and high acuity vision. Currently, there is no effective treatment for RP, other than gene therapy for a limited number of specific disease genes. To develop a gene-agnostic therapy, we screened {approx}20 genes for their ability to prolong cone photoreceptor survival in vivo. Here, we report an adeno-associated virus (AAV) vector expressing Txnip, which prolongs the survival of cone photoreceptors and improves visual acuity in RP mouse models. A Txnip allele, C247S, which blocks the association of Txnip with thioredoxin, provides an even greater benefit. Additionally, the rescue effect of Txnip depends on lactate dehydrogenase b (Ldhb), and correlates with the presence of healthier mitochondria, suggesting that Txnip saves RP cones by enhancing their lactate catabolism.

physiology↗

bioRxiv2020

Estimation of Three-Dimensional Chromatin Morphology for Nuclear Classification and Characterisation

Classification and characterisation of cellular morphological states are vital for understanding cell differentiation, development, proliferation and diverse pathological conditions. As the onset of morphological changes transpires following genetic alterations in the chromatin configuration inside the nucleus, the nuclear texture as one of the low-level properties if detected and quantified accurately has the potential to provide insights on nuclear organisation and enable early diagnosis and prognosis. This study presents a three dimensional (3D) nuclear texture description method for cell nucleus classification and variation measurement in chromatin patterns on the transition to another phenotypic state. The proposed approach includes third plane information using hyperplanes into the design of the Sorted Random Projections (SRP) texture feature. The significance of including third plane information for low-resolution volumetric images is investigated by comparing the performance of 3D texture descriptor with its respective pseudo 3D form that ignores the interslice intensity correlations. Following classification, changes in chromatin pattern are estimated by computing the ratio of heterochromatin and euchromatin corresponding to their respective intensities and image gradient obtained by 3D SRP. The proposed method is evaluated on two publicly available 3D image datasets of human fibroblast and human prostate cancer cell lines in two phenotypic states obtained from the public Statistics Online Computational Resource. Experimental results show that 3D SRP and 3D Local Binary Pattern provide better results than other utilised handcrafted descriptors and deep learning features extracted using a pre-trained model. The results also show the advantage of utilising 3D feature descriptor for classification over its corresponding pseudo version. In addition, the proposed method validates that as the cell passes to another phenotypic state, there is a change in intensity and aggregation of heterochromatin. Author SummaryAutomated classification and measurement of cellular phenotypic traits can significantly impact clinical decision making. Early detection of diseases requires an accurate description of low-level cellular features to detect small-scale abnormalities in the few abnormal cells in the tissue microenvironment. The challenge is the development of a computational approach for 3D textural feature description that can capture the heterogeneous information in multiple dimensions and characterise the cells in their ultimate and intermediate phenotypic states effectively. Our work has proposed the method and metrics to measure chromatin condensation pattern and classify the phenotypic states. Experimental evaluation on the 3D image set of human fibroblast and human prostate cancer cell collections validates the proposed method for the classification of cell states. Results also signify the credibility of proposed metrics to characterise the cellular phenotypic states and contributes to studies related to early diagnosis, prognosis and drug resistance.

bioinformatics↗

bioRxiv2019

Glucose availability impacts proteotoxic stress in Caenorhabditis elegans

Alterations in protein folding may lead to aggregation of misfolded proteins, which is strongly correlated with neurotoxicity and cell death. Protein aggregation has been shown as a normal consequence of aging, but it is largely associated with age-related disease, particularly neurodegenerative diseases like Huntington disease (HD). Huntington disease is caused by a CAG repeat expansion in the huntingtin gene and serves as a useful model for neurodegeneration due to its strictly genetic origin. Research in the model organism Caenorhabditis elegans suggests that glucose protects against cell stress, including proteotoxicity related to aggregation, despite the well-known, lifespan-shortening effects of glucose. We hypothesized that glucose could be beneficial by alleviating energy deficiency, a well-characterized phenomenon in HD, or by upregulating stress resistance pathways. We used C. elegans expressing polyglutamine repeats to quantify lifespan, motility, reproduction, learning, and activity of succinate dehydrogenase (SDH), with and without glucose, to identify the role of glucose in proteotoxicity and neuroprotection. Our data show HD worms on glucose plates exhibited shorter lifespans, no change in motility, learning, or SDH product formation, but had altered reproductive phenotypes similar to dietary restriction. Additionally, worms expressing toxic polyglutamine repeats were unable to learn association of food with a neutral odorant. We also observed tissue-specific differences; polyglutamine appeared to be slightly more toxic to muscle cells than neurons. Rather than increasing energy production, glucose appeared to decrease mitochondrial metabolism, as SDH formation decreases with added glucose. Future work investigating glucose-mediated neuroprotection should focus on connecting metabolism, sirtuin activation, and DAF-16 activation.

neuroscience↗

bioRxiv2019

A game theoretic approach to deciphering the dynamics of amyloid-β aggregation along competing pathways

Aggregation of amyloid {beta} (A{beta}) peptides is a significant event that underpins Alzheimer disease (AD). A{beta} aggregates, especially the low-molecular weight oligomers, are the primary toxic agents in AD pathogenesis. Therefore, there is increasing interest in understanding their formation and behavior. In this paper, we use our previously established investigations on heterotypic interactions between A{beta} and fatty acids (FAs) that adopt off-fibril formation pathway under the control of FA concentrations, to develop a mathematical framework in defining this complex mechanism. We bring forth the use of novel game theoretic framework based on the principles of Nash equilibria to define and simulate the competing on- and off-pathways of A{beta} aggregation. Together with detailed simulations and biophysical experiments, our mathematical models define the dynamics involved in the mechanisms of A{beta} aggregation in the presence of FAs to adopt multiple pathways. Specifically, our game theoretic model indicates that the emergence of off- or on-pathway aggregates are tightly controlled by a narrow set of rate constant parameters, and one could alter such parameters to populate a particular oligomeric species. These models agree with the detailed simulations and experimental data on using FA as a heterotypic partner to modulate temporal parameters. Predicting spatiotemporal landscape along competing pathways for a given heterotypic partner such as biological lipids is a first step towards simulating physiological scenarios in which the generation of specific conformeric strains of A{beta} could be predicted. Such an approach could be profoundly significant in deciphering the biophysics of amyloid aggregation and oligomer generation, which is ubiquitously observed in many neurodegenerative diseases.

biophysics↗