Biology subjects

Ramaswamy, M.

Publications and source records attributed to Ramaswamy, M..

4 recordsLinked to original sources

bioRxiv2025

Naegleria amoebae seek confinement and crawl persistently through narrow spaces

The "brain-eating amoeba" Naegleria fowleri dwells in ponds where it normally feeds on bacteria, but if it enters the brain it can cause a deadly infection. To establish infection, N. fowleri must migrate through different environments--along olfactory axons, through openings in the cribriform plate, and within brain tissue--yet how it does so remains unknown. As a model for N. fowleri migration within these environments, we examine how its non-pathogenic relative, Naegleria gruberi, navigates environments of distinct geometries. We show that Naegleria uses both actin-rich protrusions and membrane blebs to crawl across or between flat surfaces. We also explore how Naegleria interact with narrow channels and find that, unlike Dictyostelium amoebae that we show frequently disengage from channel interfaces, Naegleria amoebae probe channels until they enter. Once inside, Naegleria crawls quickly (>50 m/min) and unidirectionally over long distances (>1 mm) using only bleb-based motility. We also introduced Naegleria to granular hydrogel matrices that mimic pond sediments and found that cells readily enter and migrate through these three-dimensional matrices using both blebs and lamellar protrusions. Although cells in matrices showed lower persistence at short timescales, longer time scales correlate with increased persistence, suggesting Naegleria cells may retain memory of past orientation. We propose that pond life may select for three behaviors that prime Naegleria for pathogenesis: memory-guided motility that would facilitate exploration of sinus cavities, confinement-seeking ("claustrophilia") that would promote entry into narrow passages along olfactory axons, and persistent bleb-based migration that would allow rapid transit along axons to the brain.

cell biology↗

bioRxiv2024

Machine learning model to predict risk assessment of a child inheriting a genetic disorder

Advancements in Machine Learning (ML) have revolutionised precision medicine, particularly in predicting and preventing genetic disorders. This study provides a comprehensive analysis of ML models designed for the risk assessment of genetic disorders in children, based on clinical history and pedigree analysis. Leveraging a diverse dataset of familial genetic profiles, clinical outcomes, and medical histories, we employed ML algorithms to identify inheritance patterns, assess genetic risks for Mendelian disorders, and predict disease recurrence. The analysis incorporates several ML algorithms, including Gradient Boosting, XGBoost, Random Forest, Logistic Regression, Naive Bayes, and Support Vector Machines (SVM), with the Gradient Boosting model achieving the highest mean cross-validation score of over 0.99. Designed for primary care physicians and healthcare professionals, this model aids in genetic counselling by predicting genetic disorder recurrence based on family history. The paper also addresses ethical and legal considerations, emphasising the importance of genetic counselling and informed decision-making. This tool is intended to support, not replace, medical professionals. This work advances personalised risk assessment for Mendelian single-gene disorders, including autosomal dominant, autosomal recessive, and X-linked recessive disorders, contributing to the field of genomic medicine and facilitating effective family planning strategies. Author summaryGenetic disorders are posing significant socio-economic, health, and psychological burdens due to the risk of inheritance, which can impact future generations with health complications. Genetic counselling has emerged as an important tool to help manage this issue, offering guidance to susceptible families. Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) have introduced new avenues for predicting the risk of genetic disorders with greater accuracy. The aim was to leverage machine learning models that would predict the risk assessment for single gene chromosomal Mendelian disorders. To train these models, patient data was collected from a clinical geneticist at a renowned hospital, all of whom had confirmed genetic testing results. This data was used to train the models that predicted the likelihood of the next child inheriting a genetic disorder. The initial results are promising, demonstrating a good fit with the data. However, it should be noted that larger sample sizes are needed to improve the accuracy of any model. With more extensive data, its predictive capabilities can be significantly enhanced. This tool has the potential to be a resource for genetic counsellors and primary healthcare physicians, aiding them in providing more accurate risk assessments and personalised guidance to families.

genetics↗

bioRxiv2019

Identification of GABAergic neurons innervating the zebrafish lateral habenula

Habenula neurons are constantly active. The level of activity affects mood and behaviour, with increased activity in the lateral habenula reflecting exposure to punishment and a switch to passive coping and depression. Here, we identify GABAergic neurons that could reduce activity in the lateral habenula of larval zebrafish. GAD65/67 immunohistochemistry and imaging of gad1b:DsRed transgenic fish suggest the presence of GABAergic terminals in the neuropil and between cell bodies in the lateral habenula. Retrograde tracing with the lipophilic dye DiD suggests that the former derives from the thalamus, while the latter originates from a group of cells in the posterior hypothalamus that are located between the posterior tuberal nucleus and hypothalamic lobes. Two-photon calcium imaging indicates that blue light causes excitation of thalamic GABAergic neurons and terminals in the neuropil, while a subpopulation of lateral habenula neurons show reduced intracellular calcium levels. Whole-cell electrophysiological recording indicates that blue-light reduces membrane potential of lateral habenula neurons. These observations suggest that GABAergic input from the thalamus may mediate inhibition in the zebrafish lateral habenula. Mechanisms governing release of GABA from the neurons in the posterior hypothalamus, which are likely to be in the tuberomammillary nucleus, remain to be defined.

neuroscience↗

bioRxiv2018

Features of criticality are asymmetric in the zebrafish habenula

The habenula is an evolutionarily conserved structure of the vertebrate brain that is essential for behavioural flexibility and mood control. It is spontaneously active and is able to access diverse states when the animal is exposed to sensory stimuli or reward. Here we analyze two-photon calcium imaging time-series of the habenula of larval zebrafish and find that percolation occurs, indicating the presence of long-range spatial correlations within each side of the habenula, with percolation occurring independently in each side. On the other hand, the analysis of neuronal avalanches suggests that the system is subcritical, implying that the flexibility in its dynamics may result from other dynamical processes.

neuroscience↗