Systematic monitoring of 2-Cys peroxiredoxin-derived redox signals unveiled its role in attenuating carbon assimilation rate.

Transmission of reductive and oxidative cues from the photosynthetic electron transport chain to redox regulatory protein networks plays a crucial role in coordinating photosynthetic activities. The tight balance between these two signals dictates the cellular response to changing light conditions. While the role of reductive signals in activating chloroplast metabolism is well established, the role of their counterbalanced oxidative signals is still unclear, mainly due to monitoring difficulties. Here, we introduced chl-roGFP2-PrxΔCR, a 2-Cys peroxiredoxin-based biosensor, into Arabidopsis thaliana chloroplasts to monitor the dynamic changes in photosynthetically derived oxidative signaling. We showed that chl-roGFP2-PrxΔCR oxidation states reflected oxidation patterns similar to those of endogenous 2-Cys peroxiredoxin under varying light conditions. By employing a set of genetically encoded biosensors, we showed the induction of 2-Cys peroxiredoxin-dependent oxidative signals, throughout the day, under varying light intensities and their inverse relationship with NADPH levels, unraveling the combined activity of reducing and oxidizing signals. Furthermore, we demonstrated the induction of 2-Cys peroxiredoxin-derived oxidative signals during a dark­to­low-light transition and uncovered a faster increase in carbon assimilation rates during the photosynthesis induction phase in plants deficient in 2-Cys peroxiredoxins compared with wild type, suggesting the involvement of oxidative signals in attenuating photosynthesis. The presented data highlight the role of oxidative signals under nonstress conditions and suggest that oxidative signals measured by peroxiredoxin-based biosensors reflect the limitation to photosynthesis imposed by the redox regulatory system.

A data-driven approach for constructing mutation categories for mutational signature analysis.

Mutational processes shape the genomes of cancer patients and their understanding has important applications in diagnosis and treatment. Current modeling of mutational processes by identifying their characteristic signatures views each base substitution in a limited context of a single flanking base on each side. This context definition gives rise to 96 categories of mutations that have become the standard in the field, even though wider contexts have been shown to be informative in specific cases. Here we propose a data-driven approach for constructing a mutation categorization for mutational signature analysis. Our approach is based on the assumption that tumor cells that are exposed to similar mutational processes, show similar expression levels of DNA damage repair genes that are involved in these processes. We attempt to find a categorization that maximizes the agreement between mutation and gene expression data, and show that it outperforms the standard categorization over multiple quality measures. Moreover, we show that the categorization we identify generalizes to unseen data from different cancer types, suggesting that mutation context patterns extend beyond the immediate flanking bases.

From Leiden to Tel-Aviv University (TAU): exploring clustering solutions via a genetic algorithm.

Graph clustering is a fundamental problem in machine learning with numerous applications in data science. State-of-the-art approaches to the problem, Louvain and Leiden, aim at optimizing the modularity function. However, their greedy nature leads to fast convergence to sub-optimal solutions. Here, we design a new approach to graph clustering, Tel-Aviv University (TAU), that efficiently explores the solution space using a genetic algorithm. We benchmark TAU on synthetic and real data sets and show its superiority over previous methods both in terms of the modularity of the computed solution and its similarity to a ground-truth partition when such exists. TAU is available at https://github.com/GalGilad/TAU.

Functional dependency and COVID-19 in elderly patients with mild to moderate disease. Experience of tertiary geriatric hospital.

Clinical course of COVID-19 may be associated with functional dependency of geriatric patients. Data from the records of patients admitted to the COVID-19 Geriatric Unit were gathered during three months, including background, clinical aspects, time to resolution of infection and functional status. Functionally dependent patients had higher rates of diabetes (p = 0.03) and stroke (p = 0.004), as well as longer time to resolution of infection (p < 0.001), but less respiratory COVID-19 symptoms (p = 0.007), compared to independent patients. Time to resolution of infection was longer in women (p = 0.01) and positively associated with WBC level (p < 0.01) and age (p < 0.001). An adjusted analysis which controlled these variables confirmed the significant effect of functional status on the time to resolution of infection (p = 0.015). Functionally dependent geriatric patients with mild to moderate infection had less respiratory COVID-19 symptoms but showed longer time to resolution of infection compared to independent. Assessment of functional status in the elderly population may contribute to decision making for care of geriatric inpatients with COVID-19.