Biology subjects

KAUR, N.

Publications and source records attributed to KAUR, N..

2 recordsLinked to original sources

bioRxiv2024

Generating human AMN and cALD iPSC-derived astrocytes with potential for modeling X-linked adrenoleukodystrophy phenotypes

X-adrenoleukodystrophy (X-ALD) is a peroxisomal metabolic disorder caused by mutations in the ABCD1 gene encoding the peroxisomal ABC transporter adrenoleukodystrophy protein (ALDP). Similar mutations in ABCD1 may result in a spectrum of phenotypes in males with slow progressing adrenomyeloneuropathy (AMN) and fatal cerebral adrenoleukodystrophy (cALD) dominating the majority of cases. Mouse model of X-ALD does not capture the phenotype differences and an appropriate model to investigate mechanism of disease onset and progress remains a critical need. Induced pluripotent stem cell (iPSC)-derived and cell models derived from them have provided useful tools for investigating cell-type specific disease mechanisms. Here, we generated induced pluripotent stem cell (iPSC) lines from skin fibroblasts of two each of apparently healthy control, AMN and cALD patients with non-integrating mRNA-based reprogramming. iPSC lines expanded normally and expressed pluripotency markers Oct4, SOX2, Nanog, SSEA and TRA-1-60. Expression of markers SOX17, brachyury, Desmin, Oxt2 and beta tubulin III demonstrated the ability of the iPSCs to differentiate into all three germ layers. iPSC-derived lines from CTL, AMN and cALD male patients were differentiated into astrocytes. Differentiated AMN and cALD astrocytes lacked ABCD1 expression and accumulated VLCFA, a hallmark of X-ALD. These patient astrocytes provide disease-relevant tools to investigate mechanism of differential neuroinflammatory response and metabolic reprogramming in X-ALD. Further these patient-derived human astrocyte cell models will be valuable for testing new therapeutics.

neuroscience↗

bioRxiv2021

Amino Acid Composition and Charge Based Prediction of Antisepsis Peptides by Random Forest Machine Learning Algorithm

Sepsis is a severe infectious disease with high mortality, and it occurs when chemicals released in the bloodstream to fight an infection trigger inflammation throughout the body and it can cause a cascade of changes that damage multiple organ systems, leading them to fail, even resulting in death. In order to reduce the possibility of sepsis or infection antiseptics are used and process is known as antisepsis. Antiseptic peptides (ASPs) show properties similar to antigram-negative peptides, antigram-positive peptides and many more. Machine learning algorithms are useful in screening and identification of therapeutic peptides and thus provide initial filters or built confidence before using time consuming and laborious experimental approaches. In this study, various machine learning algorithms like Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbour (KNN) and Logistic Regression (LR) were evaluated for prediction of ASPs. Moreover, the characteristics physicochemical features of ASPs were also explored to use them in machine learning. Both manual and automatic feature selection methodology was employed to achieve best performance of machine learning algorithms. A 5-fold cross validation and independent data set validation proved RF as the best model for prediction of ASPs. Our RF model showed an accuracy of 97%, Matthews Correlation Coefficient (MCC) of 0.93, which are indication of a robust and good model. To our knowledge this is the first attempt to build a machine learning classifier for prediction of ASPs.

bioinformatics↗