The invention presents a proteomic landscape of lipid accumulation pathways in Chlorella sp. FC2 IITG, an oleaginous microalga, during nitrogen (N) starvation. Through sophisticated proteomic techniques, including iTRAQ and DIGE, validated by MRM and Western blotting, the study identifies key proteins associated with various metabolic processes, stress response, and photosynthesis. Notably, the temporal proteome analysis reveals critical time points correlating with significant changes in lipid content, shedding light on the intricate interplay of metabolic pathways under nutrient stress. The findings not only deepen our understanding of lipid biosynthesis in microalgae but also present potential targets for genetic manipulation to enhance lipid yields. Ultimately, this research contributes to the development of sustainable bioenergy resources, particularly algae-based biofuels, addressing the global imperative to transition from fossil fuels.
Figure (1) Schematic representation of the experimental strategy used for comparative; (2) Schematic representation of the experimental strategy used for comparative.
The current challenge lies in optimizing the efficiency of biofuel generation from renewable sources, particularly microalgae. Despite their high lipid content and adaptability to non-arable land, the full potential of microalgae remains untapped. Understanding the molecular mechanisms driving lipid accumulation in microalgae, especially under stress conditions like nitrogen starvation, is crucial for enhancing biofuel production efficiency.
- Biomarker Panel: This invention features a panel comprising specific protein biomarkers or their analogs that target multiple biological pathways involved in the development and progression of gliomas.
- Grade-Specific Detection: The panel is designed to include biomarkers that can detect and differentiate between various grades of gliomas, providing clinicians with valuable information about disease severity and aiding in prognosis.
- Biospecimen Versatility: The invention supports glioma detection across different biospecimens, including tissue and cerebrospinal fluid, making it adaptable to a range of clinical conditions and diagnostic requirements.
A pilot-scale cultivation of the Chlorella FC2 strain was demonstrated in a 5L automated bioreactor under controlled nitrogen starvation. The system enabled lipid induction up to ~50% w/w DCW. Proteomic and metabolomic profiling were integrated to identify key molecular targets. The validated workflow includes growth protocol, stress induction, and lipid quantification, forming a scalable prototype for biofuel and bioproduct development.
Validated at lab and pilot scale. Chlorella FC2 strain shows enhanced lipid accumulation under nitrogen starvation. Multi-omics profiling (proteomics and metabolomics) completed with identified molecular targets for biofuel enhancement. Technology is ready for scale-up and available for licensing.
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Early and accurate detection of gliomas through this biomarker panel can lead to improved patient outcomes by enabling timely medical intervention, which may enhance survival rates and overall well-being. Precise grading of gliomas also contributes to a reduced healthcare burden by allowing for better resource allocation and minimizing the costs associated with treating advanced-stage cases. Furthermore, the panel supports personalized medicine by facilitating treatment strategies tailored to each patient's specific condition and prognosis.
- Medical Diagnostics: This technology enables the development of diagnostic assays for detecting and grading gliomas, improving patient outcomes.
- Cancer Research: It aids cancer researchers in uncovering molecular mechanisms driving glioma development, fostering the discovery of new therapeutic targets and biomarkers.
- Neuroscience: This technology contributes to the understanding of brain tumor biology and pathology.
- Pharmaceutical Development: Pharmaceutical companies can utilize this technology for patient stratification in clinical trials.
Geography of IP
Type of IP
201821021744
481659