Biology subjects

Shahar, M.

Publications and source records attributed to Shahar, M..

5 recordsLinked to original sources

bioRxiv2026

Mechanistic Constraints on ClpM Expression Underlie Apicoplast Genome Retention in Malaria Parasites

The apicoplast of malaria parasites retains a reduced genome encoding a small set of genes with unknown functions. Among these genes is a putative ClpM chaperone, which unlike other apicoplast Clp-family members is not nuclear but plastid-encoded. In this study, we used ClpM as a model case to investigate evolutionary and molecular basis for plastid genome retention. Phylogenetic analyses across plastid-containing eukaryotes revealed that ClpM orthologues are broadly conserved and consistently plastid-encoded in all organisms with a red alga-derived plastid, irrespective of parasitism, photosynthesis or physiology. This broad phenomenon suggested gene-specific evolutionary constraints that were subsequently tested experimentally. To test whether clpM can be functionally expressed from the nucleus, we generated transgenic parasites carrying a nuclear ClpM copy fused to an apicoplast-targeting transit peptide. Unexpectedly, standard transgenesis resulted in transcriptional silencing, and we therefore forced transcription using integration into an endogenous essential locus. This led to robust clpM mRNA, however no detectable ClpM protein was observed. Multiple analyses ruled out apicoplast-dependent instability, ER-associated degradation, misfolding or membrane sequestration. Attempts to express clpM or other plastid-derived genes using endogenous sequences were found to be toxic, suggesting nucleotide-sequence incompatibility. In contrast, a transgene carrying a second copy of the nuclear ClpC ortholog was readily expressed. Comparative analysis of ClpM and ClpC domain architecture showed that their ATPase domains form distinct evolutionary clusters, suggesting conserved but functionally divergent roles. Subsequently, domain-swap experiments between ClpC and ClpM rescued partial expression and identified specific domains as contributors to the nuclear-expression barrier. Together, these findings demonstrate that clpM retention in the apicoplast genome is enforced by multilayered constraints involving evolutionary conservation, nucleotide-sequence incompatibility, transcriptional block and protein-intrinsic translational barriers. This work provides experimental evidence for mechanisms that restrict organelle-to-nucleus gene transfer and contribute to organelle genome retention.

microbiology↗

bioRxiv2025

From Big Data to Small Scales: Machine Learning Enhances Microclimate Model Predictions

1. Microclimates are critical for understanding how organisms interact with their environments, influencing behaviour, physiology, and species distributions. However, traditional physical heat-balance models for predicting ground temperatures in microhabitats often exhibit biases due to unaccounted environmental complexities and poorly constrained parameters. These limitations can hinder ecological research and conservation planning, particularly in the context of climate change. 2. In this study, we demonstrate how high-resolution drone-based mapping and machine learning can improve the accuracy of microclimate models. Using drone imagery, we generated detailed environmental maps, including solar radiation, vegetation indices, and skyview factors, to parameterize a physical heat-balance model. Validation with thermal maps derived from drone-mounted infrared cameras revealed systematic errors in the physical models predictions, including over- and underestimations under specific environmental conditions. To address these errors, we applied a random forest machine learning model to predict and correct biases in new prediction maps. 3. Our results show that machine learning reduced mean absolute errors by over 30% and mean square errors by 50%, while consistently narrowing the range of prediction inaccuracies. Key factors driving biases, such as vegetation cover, solar radiation, and height above ground, were identified, offering valuable insights for improving physical models. The machine learning corrections not only improved accuracy but also highlighted parameters and processes that were previously underrepresented or oversimplified in traditional models. 4. These findings illustrate the potential of combining machine learning with physical modelling to enhance microclimate predictions. This approach provides ecologists and conservation practitioners with a powerful tool to generate accurate, fine-scale microclimate maps, enabling better understanding of species responses to climate change and informing climate-resilient habitat management and conservation strategies.

ecology↗

bioRxiv2025

Organelle Development and Inheritance are Driven by Independent Nuclear and Organellar Mechanisms in Malaria Parasites

The apicoplast organelle of Plasmodium falciparum is essential for parasites replication, however the details of its biogenesis, inheritance and regulation throughout the cell cycle are unknown. Here, we report the development of a dynamic imaging platform coupled with an analytical pipeline that enables us to follow and measure subcellular structures throughout the 48-hour cell cycle of live parasites. We reveal a predetermined sequence of four discrete morphological steps in organelle development, which are tightly correlated with nuclear replication. We show that one of these steps, which we term the Crown morphology, is required for nucleus-apicoplast attachment. During Crown, apicoplast is stretched over multiple nuclei, fastened by centriolar tubulin. A complementary molecular approach was used to discover the basic ploidy of apicoplast and mitochondrial genomes, their replication rates and association with nuclear DNA replication. We inhibited nuclear DNA replication and found that it completely blocks apicoplast biogenesis in its most initial stages, demonstrating dependency on S-phase initiation. Conversely, specific inhibition of apicoplast genome replication resulted in an almost-undisturbed organelle development and division. However, it affected the Crown step, preventing association to tubulin-nuclear structures, leading to failure in accurate organelle sorting into daughter cells. Collectively, these experiments reveal a central cellular pathway linking apicoplast development to the parasites cell cycle, and a second independent organellar mechanism responsible for segregation into daughter cells.

microbiology↗

bioRxiv2024

A Chaperonin Complex Regulates Organelle Proteostasis in Malaria Parasites

The apicoplast of Plasmodium parasites serves as a metabolic hub that synthesize essential biomolecules. Like other endosymbiotic organelles, 90% of the apicoplast proteome is encoded by the cell nucleus and transported to the organelle. Evidence suggests that the apicoplast has minimal control over the synthesis of its proteome and therefore it is unclear how organelle proteostasis is regulated. Here, we identified and investigated a large and conserved chaperonin (CPN) complex with a previously unknown function. Using genetic tools, we demonstrated that ablation of the apicoplast CPN60 subunit leads to parasite death due to organellar damage, immediately within its first replication cycle, deviating from the delayed death phenotype commonly observed for apicoplast translation inhibitors. Unlike its close orthologues in other prokaryotic and eukaryotic cells, CPN60 is not upregulated during heat shock (HS) and does not affect HS response in the parasite. Instead, we found that it is directly involved in proteostasis through interaction with the Clp (caseinolytic protease) proteolytic complex. We showed that CPN60 physically binds both the active and inactive forms of the Clp complex, and manipulates its stability. A computational structural model of a possible interaction between these two large complexes suggests a stable interface. Finally, we screened a panel of inhibitors for the bacterial CPN60 orthologue GroEL, to test the potential of chaperonin inhibition as antimalarial. These inhibitors demonstrated an anti-Plasmodium activity that was not restricted to apicoplast function, with additional targets outsides of this organelle. Taken together, this work reveals how balanced activities of proteolysis and refolding safeguard the apicoplast proteome, and is essential for organelle biogenesis. Author SummaryThe cell of the human malaria parasite Plasmodium falciparum has a unique organelle called the apicoplast that produces essential metabolites, but it is unclear how it maintains a stable proteome. Here, we address the question of organelle proteostasis by investigating the function of a large chaperonin complex and its main subunit CPN60. We show that CPN60 mutants die due to organellar damage immediately within the first replication cycle, avoiding the typical apicoplast-delayed cell death. We demonstrate that it binds and stabilizes another large proteolytic complex and use computational predicting tools to demonstrate how a stable interface is attained. We use bacterial inhibitors to explore their potential as an antimalarial drug target. This study reveals how balanced refolding and proteolysis safeguard the apicoplast proteome and opens a new avenue for antimalarial drug discovery.

microbiology↗

bioRxiv2022

Predicting individual skill learning, a cautionary tale

People show vast variability in skill learning. What determines a persons individual learning ability? In this study we explored the possibility to predict participants future learning, based on their behavior during initial skill acquisition. We recruited a large online multi-session sample of participants performing a sequential tapping skill learning task. We trained machine learning models to predict future skill learning from raw data acquired during initial skill acquisition, and from engineered features calculated from the raw data. While the models did not explain learning, strong correlations were observed between initial and final performance. In addition, the results suggest that in correspondence with other empirical fields testing human behavior, canonical experimental tasks developed and selected to detect average effects may constrain insights regarding individual variability, relevant for real-life scenarios. Overall, implementing machine learning tools on large-scale data sets may provide a powerful approach towards revealing what differentiates between high and low innate learning abilities, paving the way for learning optimization techniques which may generalize beyond motor skill learning to broad learning abilities.

neuroscience↗