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Lami, K.

Publications and source records attributed to Lami, K..

2 recordsLinked to original sources
bioRxiv2024

NUDIX Hydrolases Target Specific Inositol Pyrophosphates and Regulate Phosphate and Iron Homeostasis, and the Expression of Defense Genes in Arabidopsis

Inositol pyrophosphates (PP-InsPs) are important signaling molecules that regulate diverse cellular processes in eukaryotes, including energy homeostasis, phosphate (Pi) signaling, and phytohormone perception. Yet, in plants, the enzymes responsible for their turnover remain largely unknown. Using a non-hydrolysable PP-InsP analog in a pull-down approach, we identified a family of Arabidopsis NUDIX hydrolases (NUDTs) that group into two closely related subclades. Through in vitro assays, heterologous expression systems, and higher-order gene-edited mutants, we explored the substrate specificities and physiological roles of these hydrolases. Using a combination of strong anion exchange (SAX)-HPLC, PAGE, and capillary electrophoresis electrospray ionization mass spectrometry (CE-ESI-MS), we found that their PP-InsP pyrophosphatase activity is enantiomer-selective and Mg2+-dependent. Specifically, subclade I NUDTs preferentially hydrolyze 4-InsP7, while subclade II NUDTs target 3-InsP7, with minor activity against other PP-InsPs, including 5-InsP7. In higher-order mutants of subclade II NUDTs, we observed defects in both Pi and iron homeostasis, accompanied by increased levels of 1/3-InsP7 and 5-InsP7, with a markedly larger increase in 1/3-InsP7. Ectopic expression of NUDTs from both subclades induced local Pi starvation responses (PSRs), while RNA-seq analysis comparing wildtype (WT) and subclade II nudt12/13/16 loss-of-function plants indicates additional PSR-independent roles, potentially involving 1/3-InsP7 in the regulation of plant defense. Consistently, nudt12/13/16 mutants displayed enhanced resistance to Pseudomonas syringae infection, indicating a role in bacterial pathogen susceptibility. Expanding beyond subclade II NUDTs, we demonstrated susceptibility of the 3PP-position of PP-InsPs to enzymatic activities unrelated to NUDTs, and found that such activities are conserved across plants and humans. Additionally, we found that NUDT effectors from pathogenic ascomycete fungi exhibit a substrate specificity similar to subclade I NUDTs. Collectively, our findings reveal new roles for NUDTs in PP-InsP signaling, plant nutrient and immune responses, and highlight a cross-kingdom conservation of PP-InsP-metabolizing enzymes.

plant biology↗
bioRxiv2022

Deep learning for histopathological subtyping and grading of lung adenocarcinoma

The histopathological distinction of lung adenocarcinoma (LADC) subtypes is subject to high inter-observer variability, which can compromise the optimal assessment of the patient prognosis. Therefore, this study developed convolutional neural networks (CNNs) capable of distinguishing LADC subtypes and predicting disease-specific survival, according to the LADC tumour grades established recently by the International Association for the Study of Lung Cancer pathology committee. Consensus LADC ground truth histopathological images were obtained from seventeen expert pulmonary pathologists and one pathologist in training. Two deep learning models (AI-1 and AI-2) were trained with EfficientNet b3 architecture to predict eight different LADC classes (lepidic, acinar, papillary, micropapillary, solid, invasive mucinous adenocarcinoma, other carcinoma types, and no carcinoma cells). Furthermore, the trained models were tested on an independent cohort of 133 patients. The models achieved high precision, recall, and F1-scores exceeding 0.90 for most of the LADC classes. Clear stratification of the three LADC grades was reached in predicting the disease-specific survival by the two models. Moreover, the grading prediction of one of the trained models was more accurate than those of 14 out of 15 pulmonary pathologists involved in the study (p=0.0003). Both trained models showed high stability in the segmentation of each pair of predicted grades with low variation in the hazard ratio across 200 bootstrapped samples. These findings indicate that the trained CNNs improve the diagnostic accuracy of the pathologist, standardise LADC subtype recognition, and refine LADC grade assessment. Thus, the trained models are promising tools that may assist in the routine evaluation of LADC subtypes and grades in clinical practice.

pathology↗