Biology subjects

Maleki, S.

Publications and source records attributed to Maleki, S..

5 recordsLinked to original sources

bioRxiv2026

The Pentose Phosphate Pathway Regulates Myelo-Lymphoid Lineage Specification

Following infection, hematopoietic stem and progenitor cells (HSPCs) support immunity by increasing the rate of innate immune cell production but the metabolic cues that guide this process are unknown. To address this question, we combined in situ RNA barcoding and metabolomics approaches to perform metabolic state-fate mapping in vivo. This approach revealed a subset of myeloid-biased HSPCs that express a distinct set of metabolic enzymes and transporters as well as the surface marker CD62L. Metabolically, CD62Lhigh HSPCs have differential activity of the pentose phosphate pathway (PPP), OXPHOS and translation, as well as differential levels of S-adenosylmethionine (SAM) cycle metabolites associated with epigenetic modifications. Inhibition of the PPP skews HSPC lineage fate decisions by disrupting myeloid associated enhancers, while simultaneously increasing enhancer activity for the master B-lymphoid regulator Ikaros. In vivo, overexpression of glucose-6-phosphate dehydrogenase, a rate limiting enzyme of the PPP, skewed HSPC output from B-lymphocytes. In summary, our data shows that HSPCs undergo significant metabolic changes to facilitate the bioenergetic and epigenetic demands of myeloid versus lymphoid lineage specification. We highlight a key role for the pentose phosphate pathway which modulates myeloid rather than lymphoid commitment by shaping the HSPC enhancer landscape, providing proof of principle that HSPC metabolism can be targeted to modulate immune system dynamics.

immunology↗

bioRxiv2026

Behavioural plasticity in Caenorhabditis elegans navigating dynamic granular environments

The mechanisms that steer behavioural plasticity in complex environments remain poorly understood. Here, we introduce Peb2les, a quasi-two-dimensional granular arena, for the behavioural assessment of Caenorhabditis elegans nematodes, as they interact with deformable, dynamic terrains. Using customized deep-learning-based methods, we track both nematodes and particles to characterize coupled animal-environment dynamics. We find that nematodes locomotor performance depends on particle density and surface properties, and is largely independent of mechanosensation, whereas functional touch sense is required for the initial decision to enter rough-textured arenas. In addition, we identify a previously undescribed touch-seeking behaviour that partially depends on mechanosensation, in which C. elegans actively return to the granular arena after exiting. In parallel, locomoting nematodes rearrange and stack surrounding particles, continuously modifying the granular terrain. Together, these findings establish Peb2les as a versatile platform for probing the mechanism and limits of behavioural plasticity and locomotory adaptability of nematodes in sensory-enriched, soil-like environments, while also providing insight into the potential evolutionary origins of adaptive touch-seeking behaviours in animals.

animal behavior and cognition↗

bioRxiv2025

Gene-embedding-based prediction and functional evaluation of perturbation expression responses with PRESAGE

Understanding the impact of genetic perturbations on cellular behavior is crucial for biological research, but comprehensive experimental mapping remains infeasible. We introduce PRESAGE (Perturbation Response EStimation with Aggregated Gene Embeddings), a simple, modular, and interpretable framework that predicts perturbation-induced expression changes by integrating diverse knowledge sources via gene embeddings. PRESAGE transforms gene embeddings through an attention-based model to predict perturbation expression outcomes. To assess model performance, we introduce a comprehensive evaluation suite with novel functional metrics that move beyond traditional regression tasks, including measures of accuracy in effect size prediction, in identifying perturbations with similar expression profiles (phenocopy), and in prediction of perturbations with the strongest impact on specific gene set scores. PRESAGE outperforms existing methods in both classical regression metrics and our novel functional evaluations. Through ablation studies, we demonstrate that knowledge source selection is more critical for predictive performance than architectural complexity, with cross-system Perturb-seq data providing particularly strong predictive power. We also find that performance saturates quickly with training set size, suggesting that experimental design strategies might benefit from collecting sparse perturbation data across multiple biological systems rather than exhaustive profiling of individual systems. Overall, PRESAGE establishes a robust framework for advancing perturbation response prediction and facilitating the design of targeted biological experiments, significantly improving our ability to predict cellular responses across diverse biological systems.

bioinformatics↗

bioRxiv2021

Identifying Protein Features and Pathways Responsible for Toxicity using Machine learning, CANDO, and Tox21 datasets: Implications for Predictive Toxicology

Humans are exposed to numerous compounds daily, some of which have adverse effects on health. Computational approaches for modeling toxicological data in conjunction with machine learning algorithms have gained popularity over the last few years. Machine learning approaches have been used to predict toxicity-related biological activities using chemical structure descriptors. However, toxicity-related proteomic features have not been fully investigated. In this study, we construct a computational pipeline using machine learning models for predicting the most important protein features responsible for the toxicity of compounds taken from the Tox21 dataset that is implemented within the multiscale Computational Analysis of Novel Drug Opportunities (CANDO) therapeutic discovery platform. Tox21 is a highly imbalanced dataset consisting of twelve in vitro assays, seven from the nuclear receptor (NR) signaling pathway and five from the stress response (SR) pathway, for more than 10,000 compounds. For the machine learning model, we employed a random forest with the combination of Synthetic Minority Oversampling Technique (SMOTE) and Edited Nearest Neighbor (ENN) method (SMOTE+ENN), which is a resampling method to balance the activity class distribution. Within the NR and SR pathways, the activity of the aryl hydrocarbon receptor (NR-AhR), and mitochondrial membrane potential (SR-MMP) were two of the top-performing twelve toxicity endpoints with AUCROCs of 0.90 and 0.92, respectively. The top extracted features for evaluating compound toxicity were analyzed for enrichment to highlight the implicated biological pathways and proteins. We validated our enrichment results for the activity of the AhR using a thorough literature search. Our case study showed that the selected enriched pathways and proteins from our computational pipeline are not only correlated with AhR toxicity but also form a cascading upstream/downstream arrangement. Our work elucidate significant relationships between protein and compound interactions computed using CANDO and the associated biological pathways to which the proteins belong for twelve toxicity endpoints. This novel study uses machine learning not only to predict and understand toxicity but also elucidates therapeutic mechanisms at a proteomic level for a variety of toxicity endpoints.

bioinformatics↗

bioRxiv2020

Structural Variants in SARS-CoV-2 Occur at Template-Switching Hotspots

The evolutionary dynamics of SARS-CoV-2 have been carefully monitored since the COVID-19 pandemic began in December 2019, however, analysis has focused primarily on single nucleotide polymorphisms and largely ignored the role of structural variants (SVs) as well as recombination in SARS-CoV-2 evolution. Using sequences from the GISAID database, we catalogue over 100 insertions and deletions in the SARS-CoV-2 consensus sequences. We hypothesize that these indels are artifacts of imperfect homologous recombination between SARS-CoV-2 replicates, and provide four independent pieces of evidence. (1) The SVs from the GISAID consensus sequences are clustered at specific regions of the genome. (2) These regions are also enriched for 5 and 3 breakpoints in the transcription regulatory site (TRS) independent transcriptome, presumably sites of RNA-dependent RNA polymerase (RdRp) template-switching. (3) Within raw reads, these structural variant hotspots have cases of both high intra-host heterogeneity and intra-host homogeneity, suggesting that these structural variants are both consequences of de novo recombination events within a host and artifacts of previous recombination. (4) Within the RNA secondary structure, the indels occur in "arms" of the predicted folded RNA, suggesting that secondary structure may be a mechanism for TRS-independent template-switching in SARS-CoV-2 or other coronaviruses. These insights into the relationship between structural variation and recombination in SARS-CoV-2 can improve our reconstructions of the SARS-CoV-2 evolutionary history as well as our understanding of the process of RdRp template-switching in RNA viruses.

genetics↗