Biology subjects

Koirala, N.

Publications and source records attributed to Koirala, N..

4 recordsLinked to original sources

bioRxiv2026

Targeting NAT10 Activates Tumor-Intrinsic Immunity and Suppresses Tumor Progression in Head and Neck Squamos Cell Carcinoma

Head and neck squamous cell carcinoma (HNSCC) remain a major clinical challenge due to its high heterogeneity and limited therapeutic response, resulting in a 5-year overall survival rate of only [~]50%. Identifying molecular pathways that drive tumor progression while suppressing anti-tumor immunity is therefore critical for developing more effective therapies. N-acetyltransferase 10 (NAT10) is the only known enzyme responsible for catalyzing the RNA modification N4-acetylcytidine (ac4C) on rRNA, tRNA, and mRNA, and has been implicated in tumor progression in several cancers. In this study, we identify NAT10 as a key suppressor of tumor-intrinsic immune signaling in HNSCC. NAT10 expression was significantly elevated in tumor tissues and HNSCC cell lines and was associated with poor overall survival. Moreover, high-risk HPV, a major etiological factor in HNSCC, upregulated NAT10 protein expression through the viral oncoproteins E6 and E7. Functional inhibition of NAT10, either by genetic depletion or the small-molecule inhibitor Remodelin, activated tumor-intrinsic innate immune responses, as evidenced by increased IRF3 phosphorylation and induction of type I/II interferons and interferon-stimulated genes. Depletion of NAT10 was able to suppress tumorigenic phenotypes, including cell proliferation, migration, and colony formation in HNSCC cells. Importantly, activation of the STING signaling pathway using agonist cyclic di-GMP further amplified immune activation in NAT10-inhibited cancer cells. Together, our findings establish NAT10 as a previously unrecognized negative regulator of tumor-intrinsic immunity in HNSCC and support NAT10 targeting, particularly in combination with STING agonists, as a promising immunotherapeutic strategy.

Cancer Biology↗

bioRxiv2025

Structural brain correlates of poor reading comprehension

Poor comprehenders have typical word reading skill and intelligence but poorer than expected reading comprehension. While the prevalence of poor comprehenders is similar to that of poor decoders (individuals who have difficulty fluently converting written text into spoken language), less is known about the neurobiological substrates of poor comprehension. Extant studies have found small differences in grey matter volume between poor comprehenders and poor or typically reading peers. However, a detailed quantification of cortical morphometric features and white matter integrity remains unexplored. Data from 2,100 children (1,200 with imaging data), aged 8-16 were analyzed to determine if there is a distinct neuroanatomy associated with poor reading comprehension. We computed grey matter volume, cortical thickness, and surface area, and white matter measures including mean diffusivity, fractional anisotropy, neurite orientation, and neurite density for poor comprehenders and compared to that of poor decoders and typical readers. Results revealed small but widespread white matter differences, but no grey matter differences between poor comprehenders and other readers. Poor comprehenders showed decreased white matter integrity (increased mean diffusivity, decreased neurite density) in tracts previously associated with reading, including the Superior Longitudinal Fasciculus and Inferior Longitudinal Fasciculus, and in tracts that have been associated with cognitive performance such as the Uncinate Fasciculus. These results suggest that diffuse structural connectivity differences may underlie reading comprehension weaknesses in the face of intact decoding skills. This is consistent with the behavioral profile of poor comprehenders who exhibit a broad pattern of subclinical impairments in language and integrative cognitive processes.

neuroscience↗

bioRxiv2019

Community driven dynamics of oscillatory network responses to threat

Physiological responses to threat stimuli involve neural synchronized oscillations in cerebral networks with distinct organization properties. Community architecture within these networks and its dynamic adaptation could play a critical role in achieving optimal physiological responses.\n\nHere we applied dynamic network analyses to address the early phases of threat processing at the millisecond level, describing multi-frequency (theta and alpha) integration and basic reorganization properties (flexibility and clustering) that drive physiological responses. We quantified cortical and subcortical network interactions and captured illustrative reconfigurations using community allegiance as essential fingerprints of large-scale adaptation.\n\nA theta band driven community reorganization of key anatomical regions forming the threat network (TN) along with transitions of nodes from the dorsal attention (DAN) and salience (SN) circuits predict the optimal physiological response to threat. We show that increase flexibility of the community network architecture drives the physiological responses during instructed threat processing. Nodal switches modulate the directionality of information flows in the involved circuits.\n\nThese results provide a captivating perspective of flexible network responses to threat and shed new light on basic physiological principles relevant for the development of stress- and threat-related mental disorders.

neuroscience↗

bioRxiv2018

Cortical network fingerprints predict deep brain stimulation outcome in dystonia

BackgroundDeep brain stimulation (DBS) is an effective evidence-based therapy for dystonia. However, no unequivocal predictors of therapy responses exist. We investigate whether patients optimally responding to DBS present distinct brain network organization and structural patterns.\n\nMethodsBased on a German multicentre cohort of eighty-two dystonia patients with segmental and generalized dystonia, who received DBS implantation in the globus pallidus internus patients were classified based on the clinical response 36 months after DBS, as superior-outcome group or moderate-outcome group, as above or below 70% motor improvement, respectively. Fifty-one patients met MRI-quality and treatment response requirements (mean age 51.3 {+/-} 13.2 years; 25 female) and were included into further analysis. From preoperative MRI we assessed cortical thickness and structural covariance, which were then fed into network analysis using graph theory. We designed a support vector machine to classify subjects for the clinical response based on group network properties and individual grey matter fingerprints.\n\nResultsThe moderate-outcome group showed cortical atrophy mainly in the sensorimotor and visuomotor areas and disturbed network topology in these regions. From all the structural integrity of the cortical mantle explained about 45% of the stimulation amplitude. Classification analyses achieved 88% of accuracy using individual grey matter atrophy patterns to predict DBS outcome.\n\nConclusionsThe analysis of cortical integrity and network properties could be developed into independent predictors to identify dystonia patients who benefit from DBS.

neuroscience↗