Biology subjects

Arora, A.

Publications and source records attributed to Arora, A..

At least 19 recordsLinked to original sources

bioRxiv2026

KIF18A Inhibition as a Therapeutic Strategy in Cancers with Rb Pathway Inactivation

KIF18A inhibition has emerged as a therapeutic strategy for chromosomally unstable cancers, but clinical development is limited by the absence of a deployable predictive biomarker. Here we identify strong, diffuse p16INK4a expression, a well-established surrogate marker of Rb-pathway inactivation, as a predictive biomarker of response to KIF18A inhibition, and show that Rb-pathway inactivation marks a biologically distinct subset of cancers sensitive to this therapeutic approach. In sensitive models, low Rb activity is associated with robust spindle assembly checkpoint signaling and prolonged mitotic arrest following KIF18A inhibition. Weakening the spindle assembly checkpoint in this context is sufficient to confer resistance. Across three independent pan-cancer sensitivity datasets generated with distinct KIF18A inhibitors, Rb-pathway altered models were significantly more sensitive than histology-matched Rb-intact comparators, with the strongest association observed in cancers harboring direct RB1 loss or inactivating mutation. Guided by this mechanism, we retrospectively analyzed p16INK4a expression by immunohistochemistry (IHC) in pre-treatment tumor biopsies from 79 heavily pre-treated high-grade serous ovarian cancer patients across three dose-escalation or expansion cohorts and treated with two different KIF18A inhibitors (sovilnesib and VLS-1488) sharing a common mechanism of action. p16INK4a-high tumors showed substantially higher objective response rates than their p16INK4a-low counterparts (36.0% versus 2.2%; P = 0.0002) and markedly longer progression-free survival (median 24.3 versus 7.9 weeks; hazard ratio, 0.16; P < 0.0001). These findings establish p16INK4a as a mechanistically-based, clinically implementable biomarker of clinical response to KIF18A inhibition that is poised to support pan-cancer development of KIF18A inhibitors guided by Rb-pathway inactivation.

cancer biology↗

bioRxiv2026

Candida albicans Snf2 modulates the response to DNA damage by regulating gene expression and uptake of the genotoxic stressors

The SWI/SNF complex comprising of the catalytic subunit, Snf2, is a key regulator of gene expression and DNA damage repair in eukaryotic cell. Candida albicans Snf2 is known to regulate hyphal formation. In this paper, we have investigated the role of this protein in DNA damage response. We show that CaSnf2 is required for cell division as deletion of both copies of SNF2 leads to increased duplication time. The mutant cells form clumps with increased chitin and {beta}-glucan deposition on the cell wall. The altered cell wall phenotype leads to reduced uptake of genotoxic stressors leading to increased resistance to both methyl methane sulfonate (MMS) and hydroxyurea (HU). In addition, resistance of Casnf2{Delta} cells to MMS also appears to be mediated by upregulation of CaRAD9 expression by CaFun30, an ATP-dependent chromatin remodeling protein, and CaRtt109, a fungal-specific histone acetyltransferase. The response of Casnf2{Delta} to genotoxic stressors is at variance with the response of Scsnf2{Delta} mutant, highlighting the differences in DNA damage response/repair pathway between the two organisms. Finally, we show that Casnf2{Delta} mutants are extremely sensitive to azoles due to downregulation of multi-drug resistance pumps leading to reduced efflux of the drug.

microbiology↗

bioRxiv2025

TIP60, a key lysine acetyltransferase, acts as a wound-induced factor essential for efficient wound response in planaria

Chromatin modifiers are essential regulators of gene expression, DNA repair, replication, and cell division. Among them, histone acetyltransferases (HATs) such as TIP60 play a central role in modulating chromatin dynamics through acetylation of histone and non-histone proteins. TIP60, a member of the MYST family of HATs, is known to regulate key cellular processes, including transcriptional activation, DNA damage response, and cell cycle progression. Although TIP60s role in stem cell maintenance and differentiation is well established, its function in stem cell-driven regeneration has remained unexplored. In this study, we characterize the role of the TIP60 homolog, SMED-TIP60, in the planarian Schmidtea mediterranea, a model organism renowned for its regenerative capacity. Biochemical assays confirmed SMED-TIP60s acetyltransferase and auto-acetylation activity. RNAi-mediated knockdown of Smed-tip60 resulted in severe defects in tissue homeostasis, survival, and regeneration, including impaired blastema formation and failure to regenerate tissues. In situ hybridization and immunofluorescence analyses revealed a marked reduction in stem cell populations and mitotic activity. Western blotting showed a peak in SMED-TIP60 expression at 5 days post-amputation, suggesting a role during regeneration. RNA-seq analysis revealed widespread dysregulation of gene expression at both anterior and posterior wounds, correlating with increased TIP60 expression post-injury. Notably, wound-response gene expression was aberrant in Smed-tip60 RNAi animals, indicating TIP60s essential role in initiating wound responses and resetting positional cues. Together, these findings establish TIP60 as a critical regulator of stem cell-mediated regeneration and wound healing in planarians.

cell biology↗

bioRxiv2025

High-Fidelity Simulation of Cell Adhesion and Flow with Coarse Grain Modelling: A Numerical and Experimental Approach

A coupled CFD-DEM framework was developed to investigate red blood cell (RBC) transport and cellular adhesion in constricted microchannels, with a primary objective of reducing computational cost while preserving physiological accuracy. Initially, numerical softening factors were implemented, allowing greater overlap between particles to expedite computations, with an optimal value of 0.03 selected. Adhesion forces between RBCs and the channel wall were incorporated, with a minimum effective distance of 0.4{square}{micro}m identified as computationally feasible. However, RBC-RBC adhesion led to artificial clustering and was therefore omitted. Coarse-graining methods were applied up to a factor of 2 to further reduce simulation time, with a value of 1.8 chosen based on consistent cell-free layer (CFL) thickness and drag force characteristics. To correct for the increased drag in coarse-grained models, rolling resistance (Type C) with a coefficient of 0.8 was employed, bringing deviations within 10%. The validated model was then extended to simulate the detachment of adherent C2C12 myoblasts under physiological flow. Experimental validation showed increasing cell retention with seeding time, and simulations reproduced this trend with <5% deviation. The resulting Detachment Force Ratio (DFR) at 6 hours indicates near-equivalence between adhesion and hydrodynamic forces, establishing the models robustness for cell-substrate interaction studies in microfluidics.

cell biology↗

bioRxiv2025

Scaffolded hiPSC liver organoids recapitulating bile duct tubulogenesis and periportal architecture.

Recapitulating the liver periportal area in vitro remains a major challenge due to its complex cellular composition and the coordinated development of both ductal and endothelial networks. Most existing bile duct organoid models fail to reproduce tubular extension and multicellular organization. We present a 3D ECM micro-rods system biofunctionalized with grafted growth factors mimicking fetal liver paracrine signaling to initiate and to coordinate the development of bile ducts, parenchymal cells, and vascular structures. Our system recapitulates key developmental stages, from the formation of the ductal plate to the emergence of elongated tubular ducts. The ECM rod alone provides the biophysical cues initiating the elongation of the bile ducts and the spatial structuration of the cellular organization, while the biofunctionalization with growth factors enhance the organization and maturation of the biliary and vascular systems. We further demonstrate that this approach is scalable and amenable to quantitative analysis through label-free Optical Coherence Tomography combined with AI-driven 3D segmentation.

cell biology↗

bioRxiv2025

Single-cell transcriptomics reveal ddx43+ cells as regulators of slow-cycling neoblasts in planaria

Cell cycle dynamics are fundamental to stem cell maintenance and differentiation. While the G1 phase is known to influence stem cell fate decisions, the mechanisms regulating its duration remain poorly understood. Using Schmidtea mediterranea, a highly tractable model for studying regeneration, we uncovered how the interplay of systemic and local cues from specialized cell types regulates G1 phase progression, thereby priming the cells in a slow-cycling state poised to differentiate. Single-cell transcriptomics identified G1-enriched, non-committed neoblasts that give rise to a previously uncharacterized ddx43 cell type, localized near sub-epidermal muscle and closely associated with surrounding neoblasts. Functional analyses revealed that ddx43 cells act as gatekeepers, maintaining adjacent neoblasts in an extended G1 phase and a differentiation-primed state. ddx43 knockdown led to neoblast hyperproliferation, highlighting their niche-like regulatory role. Following injury, extracellular signal-regulated kinase (Erk) signalling that propagates to distal regions via subepidermal muscle cells is critical for suppression of ddx43 cells by downregulating Notch signalling. This establishes a critical cross-talk between sub-epidermal muscle and ddx43 cells, mediated by the Erk-Notch axis, essential for maintaining G1 phase extension. In summary, we reveal a ddx43-dependent, non-cell autonomous mechanism that regulates a transient, slow-cycling neoblast population, offering new insights into how extrinsic cues orchestrate stem cell cycling during tissue homeostasis and regeneration.

developmental biology↗

bioRxiv2025

Saliva-based Biomarkers for Predicting Gastric Cancer

Saliva-based biomarkers offer a non-invasive, convenient, and patient-friendly approach for gastric cancer (GC) diagnosis, eliminating the need for uncomfortable procedures. This study aimed to identify salivary extracellular RNA (exRNA) biomarkers capable of distinguishing GC from normal samples. We used GEO dataset containing exRNA expression profiles of 98 GC and 100 normal samples. We filtered the data and applied the Mann-Whitney U test to identify statistically significant salivary exRNAs (p < 0.05). Initially, a single gene-based classification method identified potential biomarkers including GSDMA, SMR3B, NPTXR, TBCD, KCNC4, and KRT26, achieving accuracies ranging from 61.61% to 64.14%. Then, we applied AI-based methods to identify GC biomarkers. We developed machine learning models on different sets of biomarkers extracted from the original set of statistically significant exRNA using feature selection methods. An optimal primary biomarker set of eight mRNAs (GSDMA, CCDC141, TBCD, STARD13, WRB, ARHGAP23, CDHR3, BX842679.1) was identified, yielding an AUC of 0.905 and MCC of 0.770 with an ensemble stacking classifier. We performed comparative analysis with existing GC biomarker studies which demonstrated that our identified primary biomarker set outperformed previously reported biomarker panels. Out of the eight identified genes, seven have been reported in the literature to be associated with Gastric Cancer. Our results demonstrate the potential of salivary exRNA biomarkers as a non-invasive tool for GC diagnosis. Key pointsO_LIGC is often diagnosed at advanced stages using invasive procedures C_LIO_LISalivary exRNA has potential for non-invasive GC detection C_LIO_LIA machine learning model was developed to predict GC C_LIO_LIA panel of 8 salivary exRNA biomarkers for GC were identified C_LIO_LIThe biomarker panel outperformed existing salivary biomarkers for GC C_LI

bioinformatics↗

bioRxiv2025

Exploring non-invasive sexing of early chick embryos in intact eggs using Laser Speckle Contrast Imaging (LSCI) and Deep Neural Network (DNN)

The ability to image blood flow in early-stage avian embryos has significant applications in developmental biology, drug and vaccine testing, as well as determining sex differentiation. In this project, we used our recently developed laser speckle contrast imaging (LSCI) system to non-invasively image extraembryonic blood vessels and used these images to attempt early sex identification of chick embryos. Specifically, we captured images of blood vessels from 1,251 living chicken embryos between day three and day four of incubation. We then applied deep neural network (DNN) models to evaluate whether it is possible to differentiate sex based on vascular patterns. Using ResNetBiT and YOLOv5 models, our results indicate that sex differentiation from extraembryonic blood vessel images was not achievable with sufficiently high accuracy or statistical significance for practical use. Specifically, ResNetBiT had a five-fold cross-validated average accuracy of 59%{+/-}5% (fold-wise p-value, p [≤] 0.3) at day 3 and 61%{+/-}3% (fold-wise, p [≤] 0.04) at day 4. YOLOv5 had a five-fold cross-validated average accuracy of 55%{+/-}3% (fold-wise, p [≤] 0.3) at day 3 and 53%{+/-}3% (fold-wise, p [≤] 0.5) at day 4. Our findings suggest that using vascular pattern imaging alone is inconclusive for reliable early sex identification in chicken embryos.

bioengineering↗

bioRxiv2025

Antagonistic Regulation of LINE-1/Alu Elements and Their Repressor APOBEC3B in Cellular Senescence

Long Interspersed Nuclear Elements-1 (LINE-1 or L1) make up approximately 21% of the human genome, with some L1 loci containing intact open reading frames (ORFs) that facilitate retrotransposition. Because retrotransposition can have deleterious effects leading to mutations and genomic instability, L1 activity is typically suppressed in somatic cells through transcriptional and post-transcriptional mechanisms. However, L1 elements are derepressed in senescent cells causing age-associated inflammation. Despite the recognition of L1 activity as a hallmark of aging, the underlying molecular mechanisms governing L1 derepression in these cells are not fully understood. In this study, we employed high throughput sequencing datasets and validated our findings through independent experiments to investigate the regulation of L1 elements in senescent cells. Our results reveal that both replicative and oncogene-induced senescence are associated with reduced expression of the cytidine deaminase APOBEC3B, a known suppressor of L1 retrotransposition. Consequently, senescent cells exhibited diminished levels of C-to-U editing of full-length L1 elements. Moreover, Ribo-seq profiling indicated that progression to senescence is not only associated with increased L1 transcription, but also translation of L1 ORFs. In summary, our results suggest that the depletion of APOBEC3B contributes to enhanced activity of L1 in senescent cells and promotion of L1-induced DNA damage and aging.

genomics↗

bioRxiv2025

Extracellular matrix deposition controls early differentiation patterns in human cardiac gastruloids

Amongst the various factors that affect differentiation and tissue organization, the autonomous deposition of extracellular matrix (ECM) has hardly been considered in the context of gastruloids. Using biofunctionalized colloidal particles as artificial organizing centers (aOCs), we patterned differentiation loci within 3D embryonic bodies. Our findings reveal that the spatial distribution of the aOCs induces various pattern organizations, mediated by endogenous deposition of ECM. We investigated how the interplay between meso-endoderm cell epithelial-to-mesenchymal transition, migration into germ layers, and the prolonged maintenance of local stem-cell niches orchestrates gastruloid structure and composition. These factors collectively regulate the complexity of emergent cardiac structures observed at later time points. This work uncovers a critical feedback loop between cell differentiation rates and endogenous ECM deposition patterns, which governs the differentiation paths in scaffolded cardiac gastruloids.

cell biology↗

bioRxiv2025

AdmirePred: A method for predicting abundant miRNAs in Exosomes

Non-invasive disease diagnosis is a key application of blood exosomes in liquid biopsy, as they carry diverse biological molecules, including microRNAs (miRNAs) derived from their parent cells. Developing miRNA-based disease biomarkers requires prediction of highly abundant miRNAs in exosomes under normal conditions for establishing a baseline for understanding their physiological roles and disease-specific variations. In this study, we present models for predicting highly abundant miRNAs in exosomes from their nucleotide sequences. The models were trained, tested, and evaluated on a dataset comprising 348 abundant and 349 non-abundant miRNAs. Initially, we applied alignment-based approaches, such as motif and similarity searches, but these methods yielded poor coverage. We then explored alignment-free approaches, particularly machine learning models leveraging a broad range of features. Our Extra Trees classifier, developed using binary profiles and TF-IDF features, achieved the highest performance with an AUC of 0.77. To further enhance predictive accuracy, we developed a hybrid method that combines machine learning models with alignment-based approaches, achieving an AUC of 0.854 on an independent dataset. To support research in non-invasive diagnostics and therapeutics, we have developed a web server, standalone tool, and Python package for AdmirePred, available at https://webs.iiitd.edu.in/raghava/admirepred/. Key pointsO_LImiRNA abundant in blood exosomes are promising biomarkers for liquid biopsy C_LIO_LIClassification of abundant and non-abundant miRNA in healthy individuals C_LIO_LIA hybrid method that combine alignment based and alignment free approach C_LIO_LIPrediction of miRNAs that are highly expressed in blood exosomes C_LIO_LIA web server, a python package, and a standalone tool have been created C_LI Authors BiographyO_LIAkanksha Arora is currently pursuing a Ph.D. in Computational Biology at Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIGajendra P. S. Raghava is currently working as a Professor and Head of Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LI

bioinformatics↗

bioRxiv2025

Identification of Multiple Prognostic Biomarker Sets for Risk Stratification in SKCM

Most of the existing studies have identified a single profile of prognostic biomarkers for predicting high-risk cancer patients using transcriptomics data. In this study, we propose multiple distinct sets of prognostic biomarkers for predicting high-risk Skin Cutaneous Melanoma (SKCM) patients. Our primary analysis reveals that the expression of certain genes, such as CREG1, PCGF5, and VPS13C, strongly correlates with overall survival (OS) in SKCM patients. We developed machine learning-based prognostic models to predict 1-, 3-, and 5-year overall survival using gene expression profiles. State-of-the-art feature selection techniques were employed to identify the first prognostic biomarkers set consisting of 20 genes. Machine learning models built to predict high-risk patients using this set of biomarkers and achieved an AUC of 0.90 and a Kappa of 0.58 in distinguishing high-risk SKCM patients. Similarly, a second independent set of 20 prognostic genes was identified, with no overlap with the first set. The best model trained on this second set achieved an AUC of 0.89 with a Kappa of 0.56, while the fifth biomarker set achieved the highest performance with an AUC of 0.91 and a Kappa of 0.64. This process was repeated to obtain a total of seven distinct prognostic biomarker sets, each containing 20 unique genes. The predictive performance of these models varied between 0.84 and 0.91 for AUC and 0.48 to 0.64 for Kappa on the validation dataset. These findings demonstrate that it is possible to identify multiple independent sets of prognostic biomarkers, each capable of accurately predicting high-risk SKCM patients. HighlightsO_LIPrediction of high-risk SKCM patients from transcriptomics data C_LIO_LIIdentification of multiple set of prognostic biomarkers C_LIO_LIIdentification of set of biomarkers using feature selection technique C_LIO_LIDevelopment of machine learning based models for prediction C_LIO_LIPathway analysis of selected set of biomarker genes C_LI

bioinformatics↗

bioRxiv2025

TDP-43 directly inhibits RNA accumulation in neurites through modulation of RNA stability

The subcellular localization of hundreds of RNAs to neuronal projections allows neurons to efficiently and rapidly react to spatially restricted external cues. However, for the vast majority of these RNAs, the mechanisms that govern their localization are unknown. Here we demonstrate that the ALS-associated RNA binding protein TDP-43 primarily acts to keep RNAs out of neuronal projections. Using subcellular fractionation and single molecule RNA FISH we find that TDP-43 loss results in the increased neurite accumulation of hundreds of RNAs. These RNAs are highly enriched for known TDP-43 binding sites, suggesting that TDP-43 directly binds them to regulate their localization. We then identified precise regions within RNAs that mediate their TDP-43-dependent localization and interaction with TDP-43 using high-throughput functional assays in cells and high-throughput binding assays in vitro. We found that these regions also mediated TDP-43-dependent RNA instability, identifying the mechanism by which TDP-43 regulates RNA localization. ALS-associated mutations in TDP-43 resulted in similar RNA mislocalization phenotypes as did TDP-43 loss in human iPS-derived motor neurons. These findings establish TDP-43 as a direct negative regulator of RNA abundance in neurites and suggest that mislocalization of specific transcripts may occur in ALS patients.

molecular biology↗

bioRxiv2025

Molecular interactome of HNRNPU reveals regulatory networks in neuronal differentiation and DNA methylation

HNRNPU is an RNA-binding protein with diverse roles in transcriptional and post-transcriptional regulation. Pathogenic genetic variants of HNRNPU cause a severe neurodevelopmental disorder (NDD), but the underlying molecular mechanisms are unclear. Here, we comprehensively investigate the HNRNPU molecular interactome by integrating protein-protein interaction (PPI) mapping, RNA target identification, and genome-wide DNA methylation profiling in human neuroepithelial stem cells and differentiating neural cells. We identified extensive HNRNPU-centered networks, including a strong association with the mammalian SWI/SNF chromatin-remodelling complex, and uncovered a previously unrecognized role in translation. We present evidence that HNRNPU associates with mRNAs encoding proteins important for neuronal development, including several linked to NDDs. Silencing HNRNPU reprogrammed methylation dynamics at regulatory regions, particularly at active and bivalent promoters of neurodevelopmental transcription factors. Integrative analysis across PPI, RNA, and methylome datasets identified 19 converging genes at all three molecular levels, including NDD genes within the SWI/SNF complex and RNA-processing machinery such as SYNCRIP. Together, these data showcase HNRNPU as a central coordinator of RNA metabolism and epigenetic remodelling during neural differentiation, linking RNA-binding, chromatin organization, and DNA methylation to the pathogenesis of HNRNPU-related NDDs.

molecular biology↗

bioRxiv2024

Implications of diabetes mellitus on pathophysiology of Tuberculosis: Role of AGE-RAGE Axis and Glycated Proteins

Chronic hyperglycemia in diabetes mellitus promotes the formation of advanced glycation end products (AGEs) via non-enzymatic glycation of plasma proteins. AGEs and their receptor RAGE are central to diabetic complications, as they activate inflammatory pathways and oxidative stress. We studied the AGE-RAGE axis in uncontrolled diabetic individuals having pulmonary TB (PTB+DM), uncontrolled diabetic individuals with no infection (DM), and pulmonary TB patients (PTB). Levels of AGEs and RAGE were measured across these groups and compared to controls. The results showed significantly higher AGEs and RAGE levels in the diabetic milieu, reflecting chronic hyperglycemia. These AGEs were shown to be associated with impaired macrophage function, evidenced by reduced phagocytic capacity. Increased AGEs correlated with disease severity in tuberculosis patients co-affected by diabetes, indicating compromised immune responses. RAGE expression also showed a higher trend in TB patients, although not statistically significant, potentially influencing infection outcomes. Glycated calmodulin was also elevated in diabetic and TB-diabetes comorbid patients and negatively correlated with nitric oxide (NO) levels, suggesting that calmodulin glycation hinders NO production in diabetic conditions. Molecular dynamic studies revealed that glycosylation disrupts calmodulins interaction with inducible nitric oxide synthase (iNOS), potentially explaining the decreased NO levels in TB-diabetes comorbid patients. These findings underscore the detrimental impact of AGEs and RAGE in diabetic immune dysregulation, highlighting the need for therapeutic strategies to mitigate their effects on macrophage function and restore immune responses. HighlightsO_LIWe conducted this study to examine how hyperglycemia-driven AGEs and RAGE contribute to immune dysfunction and disease severity in diabetic and TB-diabetes patients. C_LIO_LIHigh AGEs and RAGE impairs macrophage function, decreasing phagocytic capacity and worsening disease severity in TB-diabetes comorbidity. C_LIO_LIGlycosylation disrupts calmodulins interaction with iNOS, contributing to lower NO levels and weakened immune responses in TB-diabetes comorbidity. C_LIO_LIThe AGE-RAGE axis should be studied further for these comorbid conditions to assess and improve macrophage function and restore immune responses. C_LI

immunology↗

bioRxiv2024

Improving Accuracy of Somatic Mutation Profiling in Large Epidemiologic Studies: Addressing Cases without Matched Normal Samples

Ideally, detection of somatic mutations in a tumor is accomplished using a patient-matched sample of normal cells as the benchmark. In this way somatic mutations can be distinguished from rare germline mutations. In large retrospective studies, archival tissue collection can pose challenges in obtaining samples of normal DNA. In this article we propose a protocol that improves somatic mutation analysis in the absence of a matched normal sample. The method was motivated by the InterMEL study, a large-scale epidemiologic investigation involving multiomic, multi-institutional genomic profiling of 1000 primary melanoma samples. The key insight for accomplishing improved mutation calling is the fact that germline mutations should produce a variant allele frequency (VAF) of around 50%. While a similar VAF of 50% would also be expected for somatic mutations in pure tumor samples, typically the tumor purity is much less than 50%, resulting in a considerably lower VAF. Making use of a technique that can simultaneously estimate both tumor purity and VAF from tumor-only samples we have developed a method for better distinguishing somatic versus germline variants. Based on 137 melanomas from the InterMEL Study with matched normal tissue to provide a gold standard we show that the conventional pipeline using a panel of (unmatched) normal samples has a false positive rate of 15.6% and a false negative rate of 3.5%. Our new technique improves these error rates to 6.4% and 2.1%, respectively.

genomics↗

bioRxiv2024

Prediction of exosomal miRNA-based biomarkers for liquid biopsy

In this study, we investigated the properties of exosomal miRNAs to identify potential biomarkers for liquid biopsy. We collected 956 exosomal and 956 non-exosomal miRNA sequences from RNALocate and miRBase to develop predictive models. Our initial analysis reveals that specific nucleotides are preferred at certain positions in miRNAs associated with exosomes. We employed an alignment-based approach, artificial intelligence (AI) models, and ensemble methods for predicting exosomal miRNAs. For the alignment-based approach, we used a motif-based method with MERCI and a similarity-based method with BLAST, achieving high precision but low coverage of about 29%. The AI models, developed using machine learning, deep learning techniques, and large language models, achieved a maximum AUC of 0.707 and an MCC of 0.268 on an independent dataset. Finally, our ensemble method, combining alignment-based and AI-based models, reached a maximum AUC of 0.73 and an MCC of 0.352 on an independent dataset. We have developed a web server, EmiRPred, to assist the scientific community in predicting and designing exosomal miRNAs and identifying associated motifs (https://webs.iiitd.edu.in/raghava/emirpred/). Key pointsO_LIExosomal miRNAs have potential applications in liquid biopsy C_LIO_LIAn ensemble method has been developed to predict and design exosomal miRNA C_LIO_LIAn array of predictive models were built using alignment-based approaches and AI-based approaches (ML, DL, LLM) C_LIO_LIA variety of important features and motifs for exosomal miRNA have been identified C_LIO_LIA webserver, a python package, a github, and a standalone software have been created C_LI

bioinformatics↗

bioRxiv2024

Vascular regional analysis unveils differential responses to anti-angiogenic therapy in pancreatic xenografts through macroscopic photoacoustic imaging

Pancreatic cancer (PC) is a highly lethal malignancy and the third leading cause of cancer deaths in the U.S. Despite major innovations in imaging technologies, there are limited surrogate radiographic indicators to aid in therapy planning and monitoring. Amongst the various imaging techniques Ultrasound-guided photoacoustic imaging (US-PAI) is a promising modality based on endogenous blood (hemoglobin) and blood oxygen saturation (StO2) contrast to monitor response to anti-angiogenic therapies. Adaptation of US-PAI to the clinical realm requires macroscopic configurations for adequate depth visualization, illuminating the need for surrogate radiographic markers, including the tumoral microvessel density (MVD). In this work, subcutaneous xenografts with PC cell lines AsPC-1 and MIA-PaCa-2 were used to investigate the effects of receptor tyrosine kinase inhibitor (sunitinib) treatment on MVD and StO2. Through histological correlation, we have shown that regions of high and low vascular density (HVD and LVD) can be identified through frequency domain filtering of macroscopic PA images which could not be garnered from purely global analysis. We utilized vascular regional analysis (VRA) of treatment-induced StO2 and total hemoglobin (HbT) changes. VRA as a tool to monitor treatment response allowed us to identify potential timepoints of vascular remodeling, highlighting its ability to provide insights into the TME not only for sunitinib treatment but also other anti-angiogenic therapies.

bioengineering↗