Biology subjects

Singh, A. M.

Publications and source records attributed to Singh, A. M..

6 recordsLinked to original sources

bioRxiv2026

Mitochondrial Transplantation in the Eye: A Review and Evaluation of Surgical Approaches

PurposeMitochondrial dysfunction contributes to major blinding diseases, including age-related macular degeneration and glaucoma. Although mitochondrial transplantation has shown therapeutic potential in multiple organ systems, translation to the eye remains limited, partly due to uncertainty regarding optimal delivery. We summarize the biologic rationale and preclinical evidence supporting ocular mitochondrial transplantation and present feasibility data evaluating clinically relevant delivery routes. MethodsWe conducted a focused narrative review of ocular mitochondrial transplantation. For feasibility experiments, mitochondria with an endogenous fluorescent dye were isolated from liver donor mice. Postnatal day 7 pups received subretinal injections, and adult CD1 mice received intravitreal injections, including optic nerve head directed delivery. Eyes were analyzed using fluorescence microscopy and immunohistochemistry. Mitochondrial uptake was assessed in cultured retinal pigmental epithelial (RPE) cells using co-incubation assays. Suprachoroidal delivery feasibility was evaluated in cadaveric human near-real surgical specimens using a novel dedicated suprachoroidal injector. ResultsThe literature on ocular mitochondrial transplantation remains limited and consists primarily of small preclinical studies using intravitreal delivery and imaging-based detection. In our experiments, intravitreal delivery produced donor signals predominantly within inner retinal layers, with enrichment along retinal nerve fiber bundles when directed toward the optic nerve head. Cultured RPE cells demonstrated dose-dependent uptake of exogenous mitochondria. Subretinal delivery localized donors signal to the RPE and adjacent outer retina. Suprachoroidal injections demonstrated procedural feasibility with reliable access to the suprachoroidal space and visible injectate distribution. ConclusionsOcular mitochondrial transplantation is in an early stage of investigation. Our feasibility data indicate that established posterior-segment delivery routes expose distinct retinal compartments and that route selection strongly influences anatomic distribution. Further studies are needed to verify intracellular uptake, define dosing and durability, and evaluate safety in disease-relevant models.

cell biology↗

bioRxiv2026

miRNA-mRNA Interaction Network Analysis in Alzheimer's Disease for Biomarker Discovery

--Alzheimers disease (AD) is a complex neurodegenerative disorder characterized by widespread dysregulation of gene expression and regulatory pathways. MicroRNAs (miRNAs) act as key post-transcriptional regulators by modulating messenger RNAs (mRNAs), and their disruption can influence synaptic function, neuroinflammation, and neuronal survival. In this study, we present an integrative transcriptome-driven framework to identify AD-associated miRNA-mRNA regulatory signatures and potential biomarkers. Transcriptomic and clinical data were obtained from the Alzheimers Disease Neuroimaging Initiative (ADNI) and the GEO dataset GSE48552. Differential expression analysis (Welchs t-test with FDR correction) identified 148 significantly dysregulated genes (35 up-regulated, 113 down-regulated) between AD and cognitively normal controls. Experimentally validated and predicted miRNA-target interactions were integrated using miRTarBase and additional target resources, yielding 1,669,089 miRNA-gene interactions involving 3,055 unique miRNAs, with strong enrichment toward down-regulated gene targeting. Functional enrichment analysis revealed convergence of miRNA-regulated genes on synaptic signaling, neuronal communication, intracellular transport, apoptosis, oxidative stress, and PI3K-Akt/MAPK-related pathways. A bipartite miRNA-mRNA regulatory network (2,343 nodes; 14,603 edges) was constructed and analyzed using centrality metrics, highlighting key hub regulators including PBX1 and SLC7A5. Finally, supervised machine learning models trained on selected molecular features achieved strong performance, with ensemble approaches (XGBoost/LightGBM) demonstrating robust discrimination of AD from controls.

bioinformatics↗

bioRxiv2025

Disrupting Akt-Wnt/β-catenin signaling suppresses glioblastoma stem cell growth and tumor progression in immunocompetent mice

Glioblastoma (GBM) is an aggressive primary malignant brain tumor in adults with a median patient survival of 12-18 months post-diagnosis. The PI3K/Akt and Wnt/{beta}-catenin signaling pathways promote GBM cell growth, survival, invasiveness and therapeutic resistance. We hypothesize that inhibiting Akt and {beta}-catenin, which are central regulatory proteins of the PI3K/Akt and Wnt/{beta}-catenin pathway, will suppress GBM growth and progression. Our in vitro studies demonstrate that MK-2206, a pan-Akt inhibitor, effectively reduced cell viability, induced apoptosis, and inhibited {beta}-catenin activity; consistently outperforming iCRT3, a {beta}-catenin-TCF interaction inhibitor, in CT-2A mouse glioma cells, and N08-30 human glioma stem cells. Luciferase-expressing CT-2A cells were then intracranially implanted in C57BL/6J mice followed by MK-2206 treatment, and we observed a reduction in phosphorylated Akt and GSK-3{beta} levels, consistent with disruption of the Akt and Wnt/{beta}-catenin signaling axis causing tumor suppression. In summary, MK-2206 outperformed iCRT3 efficacy in vitro, and suppressed GBM progression, in vivo. These findings suggest that Akt inhibition via MK-2206 may offer a promising therapeutic strategy for treating GBM characterized by dysregulation of PI3K/Akt or Wnt/{beta}-catenin pathways.

cancer biology↗

bioRxiv2024

Farm exposure is associated with human breast milk immune profile and microbiome

Prenatal and early life farm exposure, and breastfeeding, are associated with protection from allergic diseases. We hypothesize that farm exposure influences the human breast milk microbiome and immune proteins. The immune protein profiles and microbial communities of 152 human breast milk samples were compared among three maternal farm exposure groups (traditional agrarian, farm, and non-farm) in rural Wisconsin to identify signatures associated with farm status and atopic disease. We found significant differences between farm groups for 23 immune proteins (p-adj<0.05), microbiome diversity (p=2.2E-05), and microbiome richness (p=8.0e-06). Traditional agrarian human breast milk had the highest immune protein levels and microbiome diversity and richness, followed by farm and non-farm human breast milk. Furthermore, Gram-positive bacterial species correlated with IL-23 mediated signaling events (p-adj<1.0E-05). These data suggest that increased farm exposures promotes human breast milk that is more microbially-diverse and rich in immune-associated proteins, ultimately influencing immune development in the infant.

microbiology↗

bioRxiv2022

lsaBGC provides a comprehensive framework for evolutionary analysis of biosynthetic gene clusters within focal taxa

We developed lsaBGC, a bioinformatics suite that introduces several new methods to expand on the available infrastructure for genomic and metagenomic-based comparative and evolutionary investigation of biosynthetic gene clusters (BGCs). Through application of the suite to four genera commonly found in skin microbiomes, we uncover multiple novel findings on the evolution and diversity of their BGCs. We show that the virulence associated carotenoid staphyloxanthin in Staphylococcus aureus is ubiquitous across the Staphylococcus genus but has largely been lost in the skin-commensal species Staphylococcus epidermidis. We further identify thousands of novel single nucleotide variants (SNVs) within BGCs from the Corynebacterium tuberculostearicum sp. complex, which we describe here to be a narrow, multi-species clade that features the most prevalent Corynebacterium in healthy skin microbiomes. Although novel SNVs were approximately ten times as likely to correspond to synonymous changes when located in the top five percentile of conserved sites, lsaBGC identified SNVs which defied this trend and are predicted to underlie amino acid changes within functionally key enzymatic domains. Ultimately, beyond supporting evolutionary investigations, lsaBGC provides important functionalities to aid efforts for the discovery or synthesis of natural products.

microbiology↗

bioRxiv2017

Feature Engineering Coupled Machine Learning Algorithms For Epileptic Seizure Forecasting From Intracranial EEGs

Epilepsy is one of the major neurological disorders affecting nearly 1 percentage of the global population. The major blunt is born by under developed and developing countries due to expensive treatment of epileptic conditions. Further, the lack of proper forecasting methods for an occurrence of epileptic seizures in epileptic-drug resistant patients or patients not amenable for surgery affects their psychological behaviour and restricts their daily activities. The forecasting is usually performed by human experts that leave a wide gap for human-bias and human error. Therefore, in the current work, we have evaluated the efficiency of several machine learning algorithms to automatically identify the preictal patterns corresponding to epileptic seizures from intracranial EEG signals. The robustness of the machine learning algorithms were tested after the data set was pre-processed using carefully chosen feature engineering strategies viz. denoised Fourier transforms as well as cross-correlation across electrodes in time and frequency domain. Extensive experimentations were carried out to determine the best combination of feature engineering techniques and machine learning algorithms. The best combination of feature engineering techniques and machine learning algorithm resulted in 0.7685 AUC (Area under the Receiver Operating Characteristic curve) on the random test samples. The suggested approach was fairly good at prediction of epilepsy in random samples and therefore, it can be used in epileptic seizure forecasting in patients where medication/surgery is ineffective. Eventually, our strategy reveals a robust method for brain disorders forecasting from EEGs.

bioengineering↗