Biology subjects

Slonim, D.

Publications and source records attributed to Slonim, D..

4 recordsLinked to original sources

bioRxiv2026

Anti-inflammatory and pro-proliferative effects of fasudil in human trisomy 21 neural progenitor cells

Down syndrome (DS) results from trisomy for human chromosome 21 and is the most frequent genetic cause of intellectual disability. No effective treatments currently exist that improve neurodevelopment and cognition. Atypical brain development in individuals with DS is apparent before birth, which suggests that the optimal time to begin administration of therapies is prenatally. Human neural progenitor cell (NPC) cultures provide a tractable in vitro model system to examine the effects of trisomy 21 (T21) on neurodevelopment and to measure the effects of pharmacological interventions. Here we report the results of preclinical studies evaluating 24 candidate therapies. RNA-Seq analyses found that euploid and T21 NPCs showed different transcriptomic responses to five candidate pharmacotherapies. The Rho-associated coiled-coil kinase (ROCK) inhibitor fasudil increased proliferation of T21 NPCs, reduced expression of inflammatory pathway genes in T21 NPCs, and reduced markers of inflammation in LPS-stimulated microglia model systems. These results demonstrate that fasudil can alter multiple T21-associated abnormalities in a beneficial manner, suggesting that fasudil warrants further study as a candidate prenatal pharmacotherapy for DS.

pharmacology and toxicology↗

bioRxiv2023

MELISSA: Semi-Supervised Embedding for Protein Function Prediction Across Multiple Networks

MotivationSeveral popular methods exist to predict function from multiple protein-protein association networks. For example, both the Mashup algorithm, introduced by Cho, Peng and Berger, and deepNF, introduced by Gligorijevi[c], Barotand, and Bonneau, analyze the diffusion in each network first, to characterize the topological context of each node. In Mashup, the high-dimensional topological patterns in individual networks are canonically represented using low-dimensional vectors, one per gene or protein, to yield the multi-network embedding. In deepNF, a multimodal autoencoder is trained to extract common network features across networks that yield a low-dimensional embedding. Neither embedding takes into account known functional labels; rather, these are then used by the machine learning methods applied after embedding. ResultsWe introduce MELISSA (MultiNetwork Embedding with Label Integrated Semi-Supervised Augmentation) which incorporates functional labels in the embedding stage. The function labels induce sets of "must link" and "cannot link" constraints which guide a further semi-supervised dimension reduction to yield an embedding that captures both the network topology and the information contained in the annotations. We find that the MELISSA embedding improves on both the Mashup and deepNF embeddings in creating more functionally enriched neighborhoods for predicting GO labels for multiplex association networks in both yeast and humans. AvailabilityMELISSA is available at https://github.com/XiaozheHu/melissa

bioinformatics↗

bioRxiv2023

GRN-VAE: A Simplified and Stabilized SEM Model for Gene Regulatory Network Inference

A major challenge in working with single-cell RNA sequencing data is the prevalence of "dropout," when some transcripts expression values are erroneously not captured. Addressing this issue, which produces zero-inflated count data, is crucial for many downstream data analyses including the inference of gene regulatory networks (GRNs). In this paper, we introduce two novel contributions. First, we propose Dropout Augmentation (DA), a simple but effective model regularization method to address the zero inflation problem in single-cell data by augmenting the data with synthetic dropout events. DA offers a new perspective to solve the "dropout" problem beyond imputation. Second, we present DAZZLE, a stabilized and robust version of the autoencoder-based structure equation model for GRN inference using the DA concept. Benchmark experiments illustrate the improved performance and increased stability of the proposed DAZZLE model over existing approaches. The practical application of the DAZZLE model on a longitudinal mouse microglia dataset containing over 15,000 genes illustrates its ability to handle real-world single cell data with minimal gene filtration. The improved robustness and stability of DAZZLE make it a practical and valuable addition to the toolkit for GRN inference from single-cell data. Finally, we propose that Dropout Augmentation may have wider applications beyond the GRN-inference problem. Project website: https://bcb.cs.tufts.edu/DAZZLE. Author summaryThe prevalence of false zeros in single-cell data, or "dropout," affects many downstream analyses. A common approach is to eliminate these zeros through data imputation. We propose an alternative solution that focuses on regularizing the model and increasing model robustness against dropout noise. Counter-intuitively, this is done by augmenting the input data with a small number of zeros to simulate additional dropout noise. Validation is performed on the task of gene regulatory network inference. Our proposed model, DAZZLE, which uses the dropout augmentation idea, shows improved performance and robustness.

bioinformatics↗

bioRxiv2020

Improving cell-specific drug connectivity mapping with collaborative filtering

MotivationDrug repositioning allows expedited discovery of new applications for existing compounds, but re-screening vast compound libraries is often prohibitively expensive. "Connectivity mapping" is a process that links drugs to diseases by identifying compounds whose impact on expression in a collection of cells reverses the diseases impact on expression in disease-relevant tissues. The high throughput LINCS project has expanded the universe of compounds and cell types for which data are available, but even with this effort, many potentially clinically useful combinations are missing. To evaluate the possibility of repurposing drugs this way despite missing data, we compared collaborative filtering with either neighborhood-based or SVD imputation methods to two naive approaches via cross-validation. ResultsMethods were evaluated for their ability to predict drug connectivity despite missing data. Predictions improved when cell type was taken into account. Neighborhood-based collaborative filtering was the most successful method, with the best improvements in non-immortalized primary cells. We also explored which classes of compounds are most and least reliant on cell type for accurate imputation, and we identified connections between related compounds even when many were not measured in the relevant cells. We conclude that even for cells in which drug responses have not been fully characterized, it is possible to identify unassayed drugs that reverse in those cells the expression signatures observed in disease. Contactdonna.slonim@tufts.edu

bioinformatics↗