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Mahalingam, S.

Publications and source records attributed to Mahalingam, S..

3 recordsLinked to original sources

Vaginal epithelial estrogen receptor α coordinates glycogen deposition, microbial stability, and pH regulation in mice

Estrogen plays a central role in regulating the vaginal environment, but the specific contribution of epithelial estrogen receptor (ESR1) to microbial and biochemical homeostasis has not been fully defined. In our previous work, we showed that epithelial ESR1 is indispensable for estrogen-induced epithelial proliferation, cornification, and MUC1 expression. Here, using mice with conditional deletion of Esr1 in vaginal epithelial cells, called epithelial Esr1d/d, we extend these findings to demonstrate that epithelial ESR1 also regulates glycogen deposition, luminal pH, and microbial stability. Compared to control littermates, epithelial Esr1d/d mice reduced glycogen abundance, elevated vaginal pH, and a compositional shift in the vaginal microbiome, marked by enrichment of Comamonadaceae and loss of Lactobacillus species, without significant differences in alpha diversity. These changes parallel features of postmenopausal dysbiosis in women. Together, our findings identify epithelial ESR1 as a master regulator of multiple pathways that sustain vaginal homeostasis, integrating epithelial metabolism, barrier function, and host-microbe interactions. This work provides a mechanistic framework to understand postmenopausal vaginal dysbiosis and suggests epithelial estrogen signaling as a potential therapeutic target for genitourinary syndrome of menopause. Significance StatementThe vaginal environment is essential for reproductive and gynecologic health, yet the mechanisms by which estrogen shapes this niche remain incompletely understood. We show that epithelial estrogen receptor (ESR1) regulates glycogen deposition, luminal pH, and microbial composition in the murine vagina. Loss of epithelial ESR1 reduced glycogen and increased luminal pH without altering overall microbial diversity, but shifted community structure toward enrichment of Comamonadaceae, a family associated with neutral to mildly alkaline environments. These findings identify epithelial ESR1 as a key regulator of the metabolic and physicochemical conditions that maintain vaginal microbial balance and provide a mechanistic framework for understanding postmenopausal dysbiosis.

physiology↗

Ocular tropism of SARS-CoV-2 with retinal inflammation through neuronal invasion in animal models

Although ocular manifestations are commonly reported in patients with coronavirus disease 2019 (COVID-19), there is currently no consensus on ocular tropism of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). To investigate this, we infected K18-hACE2 mice with SARS-CoV-2 using various routes. We observed ocular manifestation and retinal inflammation with cytokine production in the eyes of intranasally (IN) infected mice. An intratracheal (IT) injection resulted in virus spread from the lungs to the brain and eyes via trigeminal and optic nerves. Ocular and neuronal invasion were confirmed by an intracerebral (IC) infection. Notably, eye-dropped (ED) virus did not infect the lungs and was undetectable with time. Using infectious SARS-CoV-2-mCherry clones, we demonstrated the ocular and neurotropic distribution of the virus in vivo by a fluorescence-imaging system. Evidence for the ocular tropic and neuroinvasive characteristics of SARS-CoV-2 was confirmed in wild-type Syrian hamsters. Our data provides further understanding of the viral transmission; SARS-CoV-2 clinical characteristics; and COVID-19 control procedures. SummarySARS-CoV-2 can spread from the respiratory tract to the brain and eyes via trigeminal and optic nerves in animal models. This ocular tropism of SARS-CoV-2 through neuronal invasion likely causes ocular manifestation and retinal inflammation. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=148 SRC="FIGDIR/small/488607v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@151c2d5org.highwire.dtl.DTLVardef@ce3aeforg.highwire.dtl.DTLVardef@17f453aorg.highwire.dtl.DTLVardef@99e9c2_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

Construction of Feed Forward MultiLayer Perceptron Model For Genetic Dataset in Leishmaniasis Using Cognitive Computing

Leishmaniasis is an endemic parasitic disease, predominantly found in the poor locality of Africa, Asia and Latin America. It is associated with malnutrition, weak immune system of people and their housing locality. At present, it is diagnosed by microscopic identification, molecular and biochemical characterisation or serum analysis for parasitic compounds. In this study, we present a new approach for diagnosing Leishmaniasis using cognitive computing. The Genetic datasets of leishmaniasis are collected from Gene Expression Omnibus database and its then processed. The algorithm for training and developing a model, based on the data is prepared and coded using python. The algorithm and their corresponding datasets are integrated using TensorFlow dataframe. A feed forward Artificial Neural Network trained model with multi-layer perceptron is developed as a diagnosing model for Leishmaniasis, using genetic dataset. It is developed using recurrent neural network. The cognitive model of the trained network is interpreted using the maps and mathematical formula of the influencing parameters. The credit of the system is measured using the accuracy, loss and error of the system. This integrated system of the leishmaniasis genetic dataset and neural network proved to be the good choice for diagnosis with higher accuracy and lower error. Through this approach, all records of the data are effectively incorporated into the system. The experimental results of feed forward multilayer perceptron model after normalization; mean square error (219.84), loss function (1.94) and accuracy (85.71%) of the model, shows good fit of model with the process and it could possibly serve as a better solution for diagnosing Leishmaniasis in future, using genetic datasets.\n\nThe code is available in Github repository:\n\nhttps://github.com/shailzasingh/Machine-Learning-code-for-analyzing-genetic-dataset-in-Leishmaniasis

bioinformatics↗