Biology subjects

Varma, P.

Publications and source records attributed to Varma, P..

8 recordsLinked to original sources

bioRxiv2026

Plasma Micro-RNA Signatures of Type 1 Ryanodine Receptor Related Myopathies

Pathogenic RYR1 variants are associated with a set of rare neuromuscular disorders termed RYR1-related disorders (RYR1-RD). Clinical manifestations of RYR1-RD include proximal/axial muscle weakness, delayed motor milestones, impaired mobility, muscle pain, and fatigue. Muscle-specific microRNAs (miRNAs) are mostly expressed in muscle tissue and can be detected peripherally in plasma. Using a digital detection system, here we identified and quantified differential amounts of miRNAs in six adult (four monoallelic and two biallelic) RYR1-RD patient plasma samples compared to controls. Overall, 51 differentially expressed miRNAs were identified and hsa-miR-4454+hsa-miR-7975, in particular, was significantly overexpressed relative to controls (+ 39-fold, P=0.00285). Exploration of these differentially expressed miRNAs warrant further investigation as potential biomarkers of RYR1-RD.

molecular biology↗

bioRxiv2024

APOE4 impacts cortical neurodevelopment and alters network formation in human brain organoids

Apolipoprotein E4 (APOE4) is the leading genetic risk factor for Alzheimers disease. While most studies examine the role of APOE4 in aging, imaging, and cognitive assessments reveal that APOE4 influences brain structure and function as early as infancy. Here, we examined human-relevant cellular phenotypes across neurodevelopment using induced pluripotent stem cell (iPSC) derived cortical and ganglionic eminence organoids (COs and GEOs). In COs, we showed that APOE4 decreased BRN2+ and SATB2+ cortical neurons, increased astrocytes and outer radial glia, and was associated with increased cell death and dysregulated GABA-related gene expression. In GEOs, APOE4 accelerated maturation of neural progenitors and neurons. Multi-electrode array recordings in assembloids revealed that APOE4 disrupted network formation and altered response to GABA, resulting in heightened excitability and synchronicity. Together, our data provides new insights into how APOE4 may influence cortical neurodevelopmental processes and network formation in the human brain.

neuroscience↗

bioRxiv2024

Dual effects of ARX poly-alanine mutations in human cortical and interneuron development

Infantile spasms, with an incidence of 1.6 to 4.5 per 10,000 live births, are a relentless and devastating childhood epilepsy marked by severe seizures but also leads to lifelong intellectual disability. Alarmingly, up to 5% of males with this condition carry a mutation in the Aristaless-related homeobox (ARX) gene. Our current lack of human-specific models for developmental epilepsy, coupled with discrepancies between animal studies and human data, underscores the gap in knowledge and urgent need for innovative human models, organoids being one of the best available. Here, we used human neural organoid models, cortical organoids (CO) and ganglionic eminences organoids (GEO) which mimic cortical and interneuron development respectively, to study the consequences of PAE mutations, one of the most prevalent mutation in ARX. ARXPAE produces a decrease expression of ARX in GEOs, and an enhancement in interneuron migration. That accelerated migration is cell autonomously driven, and it can be rescued by inhibiting CXCR4. We also found that PAE mutations result in an early increase in radial glia cells and intermediate progenitor cells, followed by a subsequent loss of cortical neurons at later timepoints. Moreover, ARX expression is upregulated in COs derived from patients at 30 DIV and is associated with alterations in the expression of CDKN1C. Furthermore, ARXPAE assembloids had hyperactivity which were evident at early stages of development. With effective treatments for infantile spasms and developmental epilepsies still elusive, delving into the role of ARXPAE mutations in human brain organoids represents a pivotal step toward uncovering groundbreaking therapeutic strategies.

neuroscience↗

bioRxiv2023

Multiomics-assisted characterization of rice-Yellow Stem Borer interaction provides genomic and mechanistic insights into stem borer tolerance in rice

Yellow stem borer (YSB), Scirpophaga incertulas (Walker) (Lepidoptera: Crambidae), is a major pest of rice in India, that can lead to 20-60% loss in rice production. Effective management of YSB infestation is challenged by the non-availability of adequate source of resistance and poor understanding of resistance mechanisms, thus necessitating studies for generating resources to breed YSB resistant rice and to understand rice-YSB interaction. In this study, by using bulk-segregant analysis in combination with next-generation sequencing, Quantitative Trait Loci (QTL) intervals in five rice chromosomes were mapped that could be associated with YSB tolerance at vegetative phase in a highly tolerant rice line named SM92. Further, multiple SNP markers that showed significant association with YSB tolerance in rice chromosomes 1, 5, 10, and 12 were developed. RNA-sequencing of the susceptible and tolerant lines revealed multiple genes present in the candidate QTL intervals to be differentially regulated upon YSB infestation. Comparative transcriptome analysis revealed a putative candidate gene that was predicted to encode an alpha-amylase inhibitor. Analysis of the transcriptome and metabolite profiles further revealed a possible link between phenylpropanoid metabolism and YSB tolerance. Taken together, our study provides insights on rice-YSB interaction at genomic, transcriptomic and metabolomic level, thereby facilitating the understanding of tolerance mechanism. Importantly, a promising breeding line and markers for YSB tolerance have been developed that can potentially aid in marker-assisted breeding of YSB resistance among elite rice cultivars. SIGNIFICANCE STATEMENTGlobal rice production is threatened by various pests, among which stem borers pose serious challenges. Hence, understanding the molecular intricacies of rice-stem borer interaction is necessary for effective pest management. Here, we used a multi-omics approach to unravel the mechanisms that might help rice combat yellow stem borer infestation, thus providing insights and scope for developing YSB tolerant rice varieties. To facilitate the latter, we developed markers that co-segregate with tolerance.

plant biology↗

bioRxiv2021

In situ Nuclear Matrix preparation in Drosophila melanogaster and its use in studying the components of nuclear architecture

The study of Nuclear Matrix (NuMat) over the last 40 years has been limited to either isolated nuclei from tissues or cells grown in culture. Here, we provide a protocol for NuMat preparation in intact Drosophila melanogaster embryos and its use in dissecting the components of nuclear architecture. The protocol does not require isolation of nuclei and therefore maintains the three-dimensional milieu of an intact embryo, which is biologically more relevant compared to cells in culture. One of the advantages of this protocol is that only a small number of embryos are required. The protocol can be extended to larval tissues like salivary glands and imaginal discs with little modification. Taken together, it becomes possible to carry out such studies in parallel to genetic experiments using mutant and transgenic flies. This protocol, therefore, opens the powerful field of fly genetics to cell biology in the study of nuclear architecture. SummaryNuclear Matrix is a biochemically defined entity and a basic component of the nuclear architecture. Here we present a protocol to isolate and visualize Nuclear Matrix in situ in the intact embryos and tissues of Drosophila melanogaster and its potential applications.

cell biology↗

bioRxiv2020

Buprenorphine exposure alters the development and migration of interneurons in the cortex

The misuse of opioids has reached epidemic proportions over the last decade, with over 2.1 million people in the U.S. suffering from substance use disorders related to prescription opioid pain relievers. This increase in opioid misuse affects all demographics of society, including women of child-bearing age, which has led to a rise in opioid use during pregnancy. Opioid use during pregnancy has been associated with increased risk of obstetric complications and adverse neonatal outcomes, including neonatal abstinence syndrome. Currently, opioid use disorder in pregnant women is treated with long-acting opioid agonists, including buprenorphine. Although buprenorphine reduces illicit opioid use during pregnancy and improves infant outcomes at birth, few long-term studies of the neurodevelopmental consequences have been conducted. The goal of the current experiments was to examine the effects of buprenorphine on the development of the cortex using fetal brain tissue, 3D brain cultures, and rodent models. First, we demonstrated that we can grow cortical and subpallial spheroids, which model the cellular diversity, connectivity, and activity of the developing human brain. Next, we show that cells in the developing human cortex express the nociceptin opioid (NOP) receptor and that buprenorphine can signal through this receptor in cortical spheroids. Using subpallial spheroids to grow inhibitory interneurons, we show that buprenorphine can alter interneuron development and migration into the cortex. Finally, using a rodent model of prenatal buprenorphine exposure, we demonstrate that alterations in interneuron distribution can persist into adulthood. Together, these results suggest that more research is needed into the long-lasting consequences of buprenorphine exposure on the developing human brain.

developmental biology↗

bioRxiv2018

Weakly supervised classification of rare aortic valve malformations using unlabeled cardiac MRI sequences

Biomedical repositories such as the UK Biobank provide increasing access to prospectively collected cardiac imaging, however these data are unlabeled which creates barriers to their use in supervised machine learning. We develop a weakly supervised deep learning model for classification of aortic valve malformations using up to 4,000 unlabeled cardiac MRI sequences. Instead of requiring highly curated training data, weak supervision relies on noisy heuristics defined by domain experts to programmatically generate large-scale, imperfect training labels. For aortic valve classification, models trained with imperfect labels substantially outperform a supervised model trained on hand-labeled MRIs. In an orthogonal validation experiment using health outcomes data, our model identifies individuals with a 1.8-fold increase in risk of a major adverse cardiac event. This work formalizes a learning baseline for aortic valve classification and outlines a general strategy for using weak supervision to train machine learning models using unlabeled medical images at scale.

bioinformatics↗

bioRxiv2018

Parameterizing neural power spectra

Electrophysiological signals across species and recording scales exhibit both periodic and aperiodic features. Periodic oscillations have been widely studied and linked to numerous physiological, cognitive, behavioral, and disease states, while the aperiodic \"background\" 1/f component of neural power spectra has received far less attention. Most analyses of oscillations are conducted on a priori, canonically-defined frequency bands without consideration of the underlying aperiodic structure, or verification that a periodic signal even exists in addition to the aperiodic signal. This is problematic, as recent evidence shows that the aperiodic signal is dynamic, changing with age, task demands, and cognitive state. It has also been linked to the relative excitation/inhibition of the underlying neuronal population. This means that standard analytic approaches easily conflate changes in the periodic and aperiodic signals with one another because the aperiodic parameters--along with oscillation center frequency, power, and bandwidth--are all dynamic in physiologically meaningful, but likely different, ways. In order to overcome the limitations of traditional narrowband analyses and to reduce the potentially deleterious effects of conflating these features, we introduce a novel algorithm for automatic parameterization of neural power spectral densities (PSDs) as a combination of the aperiodic signal and putative periodic oscillations. Notably, this algorithm requires no a priori specification of band limits and accounts for potentially-overlapping oscillations while minimizing the degree to which they are confounded with one another. This algorithm is amenable to large-scale data exploration and analysis, providing researchers with a tool to quickly and accurately parameterize neural power spectra.

neuroscience↗