Biology subjects

Saadat, N.

Publications and source records attributed to Saadat, N..

3 recordsLinked to original sources

bioRxiv2024

The RNA-binding activity of the TRIM-NHL protein NHL-2 is essential for miRNA-mediated gene regulation

The conserved TRIM-NHL protein, NHL-2, plays a key role in small RNA pathways in Caenorhabditis elegans. NHL-2 has been shown to interact with U-rich RNA through its NHL domain, but the importance to its biological function is unknown. We defined the crystal structure of the NHL domain to 1.4 [A] resolution and identified residues that affect affinity for U-rich RNA. Functional analysis of an NHL-2 RNA-binding loss-of-function mutant demonstrated defects in the heterochronic pathway, suggesting that RNA binding is essential for its role in this miRNA pathway. Processing bodies were enlarged in the NHL-2 RNA-binding mutant, suggesting a defect in mRNA decay. We also identified the eIF4E binding protein IFET-1 as a strong synthetic interactor with NHL-2 and the DEAD box RNA helicase CGH-1 (DDX6), linking NHL-2 function to translation repression. We demonstrated that in the absence of NHL-2, there was an enrichment of miRNA transcripts associated with the miRNA pathway Argonaute proteins ALG-2 and ALG-2. We demonstrate that NHL-2 RNA-binding activity is essential for let-7 family miRNA-mediated translational repression. We conclude that the NHL-2, CGH-1, and IFET-1 regulatory axes work with the core miRISC components to form an effector complex that is required for some, but not all, miRNAs.

genetics↗

bioRxiv2023

What controls carbon sequestration in plants under which conditions?

Plants use photosynthesis to harvest sunlight and convert the solar energy into chemical energy, which is then used to reduce atmospheric carbon dioxide into organic molecules. This process forms the basis of all life on Earth, and stands at the beginning of the food chain which feeds the world population. Not surprisingly, many research efforts are currently ongoing aiming at improving plant growth and crop yield, and several of these activities directly target the photosynthetic pathways. Metabolic Control Analysis (MCA) shows that, in general, the control over a metabolic flux, such as carbon fixation, is distributed among several steps and highly dependent on the external conditions. Therefore, the concept of a single rate-limiting step is hardly ever applicable, and as a consequence, any strategy relying on improving a single molecular process in a complex metabolic system is bound to fail to yield the expected results. In photosynthesis, reports on which processes exert the highest control over carbon fixation are contradictory. This refers to both, the photosynthetic light reactions harvesting photons, and the dark reactions of the CalvinBenson-Bassham Cycle (CBB cycle). Here, we employ a recently developed mathematical model, which describes photosynthesis as an interacting supply-demand system, to systematically study how external conditions affect the control over carbon fixation fluxes.

systems biology↗

bioRxiv2023

Helixer--de novo Prediction of Primary Eukaryotic Gene Models Combining Deep Learning and a Hidden Markov Model.

AO_SCPLOWBSTRACTC_SCPLOWGene structural annotation is a critical step in obtaining biological knowledge from genome sequences yet remains a major challenge in genomics projects. Current de novo Hidden Markov Models are limited in their capacity to model biological complexity; while current pipelines are resource-intensive and their results vary in quality with the available extrinsic data. Here, we build on our previous work in applying Deep Learning to gene calling to make a fully applicable, fast and user friendly tool for predicting primary gene models from DNA sequence alone. The quality is state-of-the-art, with predictions scoring closer by most measures to the references than to predictions from other de novo tools. Helixers predictions can be used as is or could be integrated in pipelines to boost quality further. Moreover, there is substantial potential for further improvements and advancements in gene calling with Deep Learning. Helixer is open source and available at https://github.com/weberlab-hhu/Helixer A web interface is available at https://www.plabipd.de/helixer_main.html

bioinformatics↗