Biology subjects

Sevilimedu, A.

Publications and source records attributed to Sevilimedu, A..

3 recordsLinked to original sources

bioRxiv2024

Impaired ovarian development in a zebrafish fmr1 knockout model

Fragile X syndrome (FXS) is an inherited neurodevelopmental disorder and the leading genetic cause of autism spectrum disorders. FXS is caused by loss of function mutations in Fragile X mental retardation protein (FMRP), an RNA binding protein that is known to regulate translation of its target mRNAs, predominantly in the brain and gonads. The molecular mechanisms connecting FMRP function to neurodevelopmental phenotypes are well understood. However, neither the full extent of reproductive phenotypes, nor the underlying molecular mechanisms have been as yet determined. Here, we developed new fmr1 knockout zebrafish lines and show that they mimic key aspects of FXS neuronal phenotypes across both larval and adult stages. Results from the fmr1 knockout females also showed that altered gene expression in the brain, via the neuroendocrine pathway contribute to distinct abnormal phenotypes during ovarian development and oocyte maturation. We identified at least three mechanisms underpinning these defects, including altered neuroendocrine signaling in sexually mature females resulting in accelerated ovarian development, altered expression of germ cell and meiosis promoting genes at various stages during oocyte maturation, and finally a strong mitochondrial impairment in late stage oocytes from knockout females. Our findings have implications beyond FXS in the study of reproductive function and female infertility. Dissection of the translation control pathways during ovarian development using models like the knockout lines reported here may reveal novel approaches and targets for fertility treatments. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=133 SRC="FIGDIR/small/579749v1_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@15ea8b3org.highwire.dtl.DTLVardef@195838corg.highwire.dtl.DTLVardef@ef8940org.highwire.dtl.DTLVardef@1bd58a_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical AbstractC_FLOATNO C_FIG

molecular biology↗

bioRxiv2022

An efficient and cost-effective method for directed mutagenesis at multiple dispersed sites - a case study with Omicron Spike DNA

Site directed mutagenesis is an invaluable technique which enables the elucidation of the contribution of specific residues to protein structure and function. The simultaneous introduction of mutations at a large number of sites (>10), singly and in multiple combinations is often necessary to fully understand the functional contributions. We report a simple, efficient, time and cost effective method to achieve this using commonly available molecular biology reagents and protocols, as an alternative to gene synthesis. We demonstrate this method using the Omicron Spike DNA construct as an example, and create a construct bearing 37 mutations (as compared to wild-type Spike DNA), as well as four other constructs bearing subsets of the full spectrum of mutations. We believe that this method can be an excellent alternative to gene synthesis, especially when three or more variants are required. Graphical Abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

molecular biology↗

bioRxiv2020

Drug REpurposing using AI/ML tools - for Rare Diseases (DREAM-RD): A case study with Fragile X Syndrome (FXS)

Drug repositioning is emerging as an increasingly relevant option for rare disease therapy and management. Various methods for identifying suitable drug candidates have been tried and range from clinical symptomatic repurposing to data driven strategies which are based on the disease-specific gene or protein expression, modification, signalling and physiological perturbation profiles. The use of Artificial Intelligence (AI) and machine learning algorithms (ML) allows one to combine diverse data sets, and extract disease-specific data profiles which may not be intuitive or apparent from a subset of data. In this case study with Fragile X syndrome and autism, we have used multiple computational methodologies to extract profiles, which are then combined to arrive at a comprehensive signature (disease DEG). This DEG was then used to interrogate the large collection of drug-induced perturbation profiles present in public databases, to find appropriate small molecules to reverse or mimic the disease-profiles. We have labelled this pipeline Drug Repurposing using AI/ML tools - for Rare Diseases (DREAM-RD). We have shortlisted over 100 FDA approved drugs using the aforementioned pipeline, which may potentially be useful to ameliorate autistic phenotypes associated with FXS.

bioinformatics↗