Biology subjects

Maan, H.

Publications and source records attributed to Maan, H..

10 recordsLinked to original sources

bioRxiv2023

scGPT: Towards Building a Foundation Model for Single-Cell Multi-omics Using Generative AI

Generative pre-trained models have achieved remarkable success in various domains such as natural language processing and computer vision. Specifically, the combination of large-scale diverse datasets and pre-trained transformers has emerged as a promising approach for developing foundation models. Drawing parallels between linguistic constructs and cellular biology -- where texts comprise words, similarly, cells are defined by genes -- our study probes the applicability of foundation models to advance cellular biology and genetics research. Utilizing the burgeoning single-cell sequencing data, we have pioneered the construction of a foundation model for single-cell biology, scGPT, which is based on generative pre-trained transformer across a repository of over 33 million cells. Our findings illustrate that scGPT, a generative pre-trained transformer, effectively distills critical biological insights concerning genes and cells. Through the further adaptation of transfer learning, scGPT can be optimized to achieve superior performance across diverse downstream applications. This includes tasks such as cell-type annotation, multi-batch integration, multi-omic integration, genetic perturbation prediction, and gene network inference. The scGPT codebase is publicly available at https://github.com/bowang-lab/scGPT.

bioinformatics↗

bioRxiv2022

scFormer: A Universal Representation Learning Approach for Single-Cell Data Using Transformers

AO_SCPLOWBSTRACTC_SCPLOWSingle-cell sequencing has emerged as a promising technique to decode cellular heterogeneity and analyze gene functions. With the high throughput of modern techniques and resulting large-scale sequencing data, deep learning has been used extensively to learn representations of individual cells for downstream tasks. However, most existing methods rely on fully connected networks and are unable to model complex relationships between both cell and gene representations. We hereby propose scFormer, a novel transformer-based deep learning framework to jointly optimize cell and gene embeddings for single-cell biology in an unsupervised manner. By drawing parallels between natural language processing and genomics, scFormer applies self-attention to learn salient gene and cell embeddings through masked gene modelling. scFormer provides a unified framework to readily address a variety of downstream tasks such as data integration, analysis of gene function, and perturbation response prediction. Extensive experiments using scFormer show state-of-the-art performance on seven datasets across the relevant tasks. The scFormer model implementation is available at https://github.com/bowang-lab/scFormer.

bioinformatics↗

bioRxiv2022

The differential impacts of dataset imbalance in single-cell data integration

Single-cell transcriptomic data measured across distinct samples has led to a surge in computational methods for data integration. Few studies have explicitly examined the common case of cell-type imbalance between datasets to be integrated, and none have characterized its impact on downstream analyses. To address this gap, we developed the Iniquitate pipeline for assessing the stability of single-cell RNA sequencing (scRNA-seq) integration results after perturbing the degree of imbalance between datasets. Through benchmarking 5 state-of-the-art scRNA-seq integration techniques in 1600 perturbed integration scenarios for a multi-sample peripheral blood mononuclear cell (PBMC) dataset, our results indicate that sample imbalance has significant impacts on downstream analyses and the biological interpretation of integration results. We observed significant variation in clustering, cell-type classification, marker gene-based annotation, and query-to-reference mapping in imbalanced settings. Two key factors were found to lead to quantitation differences after scRNA-seq integration - the cell-type imbalance within and between samples (relative cell-type support) and the relatedness of cell-types across samples (minimum cell-type center distance). To account for evaluation gaps in imbalanced contexts, we developed novel clustering metrics robust to sample imbalance, including the balanced Adjusted Rand Index (bARI) and balanced Adjusted Mutual Information (bAMI). Our analysis quantifies biologically-relevant effects of dataset imbalance in integration scenarios and introduces guidelines and novel metrics for integration of disparate datasets. The Iniquitate pipeline and balanced clustering metrics are available at https://github.com/hsmaan/Iniquitate and https://github.com/hsmaan/balanced-clustering, respectively.

bioinformatics↗

bioRxiv2022

Context-dependent differences in the functional responses of Lactobacillaeca strains to fermentable sugars

Lactobacillaceae are Gram-positive rods, facultative anaerobes, and belong to the lactic acid bacteria (LAB) that frequently serve as probiotics. We systematically compared five LAB strains for the effects of different carbohydrates on their free-living and biofilm lifestyles. We found that fermentable sugars triggered a heterogeneous response in LAB strains, frequently manifested specifically in altered carrying capacity during planktonic growth and colony development. The fermentation capacities of the strains were compatible and could not account for heterogeneity in their differential carrying capacity in liquid and on a solid medium. Among tested LAB strains, L. paracasei, and L. rhamanosus GG survived self-imposed acid stress while L. acidophilus was extremely sensitive to its own glucose utilization acidic products. The addition of a buffering system during growth on a solid medium significantly improved the survival of most tested probiotic strains during fermentation. We suggest that the optimal performance of the beneficial microbiota members belonging to lactobacilli is heterogeneous and varies as a function of the growth model and the dependency on a buffering system.

microbiology↗

bioRxiv2022

DeepVelo: Deep Learning extends RNA velocity to multi-lineage systems with cell-specific kinetics

1The introduction of RNA velocity in single-cell studies has opened new ways of examining cell differentiation and tissue development. Existing RNA velocity estimation methods rely on strong assumptions of predefined dynamics and cell-agnostic constant transcriptional kinetic rates, which are often violated in complex and heterogeneous single-cell RNA sequencing (scRNA-seq) data. To overcome these limitations, we propose DeepVelo, a novel method that estimates the cell-specific dynamics of splicing kinetics using Graph Convolution Networks (GCNs). DeepVelo generalizes RNA velocity to cell populations containing time-dependent kinetics and multiple lineages, which are common in developmental and pathological systems. We applied DeepVelo to disentangle multifaceted kinetics in the processes of dentate gyrus neurogenesis, pancreatic endocrinogenesis, and hindbrain development. The method infers time-varying cellular rates of transcription, splicing and degradation, recovers each cells stage in the underlying differentiation process, and detects functionally relevant driver genes regulating these processes. DeepVelo relaxes the constraints of previous techniques, facilitates the study of more complex differentiation and lineage decision events in heterogeneous scRNA-seq data, and is more computationally efficient than previous techniques.

bioinformatics↗

bioRxiv2021

Imaging Flow Cytometry reveals a dual role for exopolysaccharides in biofilms: To promote self-adhesion while repelling non-self-community members

In nature, bacteria are establishing differentiated communities referred to as biofilms. These multicellular communities are held together by self-produced polymers that allow the community members to adhere to the surface as well as to neighbor bacteria. Here, we report that exopolysaccharides prevent Bacillus subtilis from co-aggregating with a distantly related bacterium Bacillus mycoides, while maintaining their role in promoting self-adhesion and co-adhesion with phylogenetically related bacterium, Bacillus atrophaeus. The defensive role of the exopolysaccharides is due to the specific regulation of bacillaene. Single cell analysis of biofilm and free-living bacterial cells using imaging flow cytometry confirmed a specific role for the exopolysaccharides in microbial competition repelling B. mycoides. Unlike exopolysaccharides, the matrix protein TasA induced bacillaene but inhibited the expression of the biosynthetic clusters for surfactin, and therefore its overall effect on microbial competition during floating biofilm formation was neutral. Thus, the exopolysaccharides provide a dual fitness advantage for biofilm-forming cells, as it acts to promote co-aggregation of related species, as well as, a secreted cue for chemical interference with non-compatible partners. These results experimentally demonstrate a general assembly principle of complex communities and provides an appealing explanation for how closely related species are favored during community assembly. Furthermore, the differential regulation of surfactin and bacillaene by the extracellular matrix may explain the spatio-temporal gradients of antibiotic production within biofilms.

microbiology↗

bioRxiv2021

Quantifying the effects of the plant root on antibiotic production in the beneficial bacterium B. subtilis

Beneficial and probiotic bacteria play an important role in conferring the immunity of their hosts against a wide range of bacterial, viral and fungal diseases. B. subtilis is a bacterium that protects the plant from various pathogens due to its capacity to produce an extensive repertoire of antibiotics. At the same time, the plant microbiome is a highly competitive niche, with multiple microbial species competing for space and resources, a competition that can be determined by the antagonistic potential of each microbiome member. Therefore, regulating antibiotic production in the rhizosphere is of great significance to eliminate pathogens and to establish beneficial host-associated communities. In this work, we used Bacillus subtilis as a model to investigate the role of plant colonization in antibiotic production. Flow cytometry and Image-stream cytometry analysis supported the notion that A. thaliana specifically induced the transcription of the biosynthetic clusters for the non-ribosomal peptides surfactin, bacilysin and plipastatin and the polyketide bacillaene. This induction could be beneficial for the root as all clusters were shown to antagonize plant pathogens. Consistently, the root failed to induce PenP, a {beta}-lactamase that increases only the fitness of the bacteria. Our results can be translated to improve the performance and competitiveness of beneficial members of the plant microbiome.

microbiology↗

bioRxiv2021

Multiscale interactome analysis coupled with off-target drug predictions reveals drug repurposing candidates for human coronavirus disease

The COVID-19 pandemic has led to an urgent need for the identification of new antiviral drug therapies that can be rapidly deployed to treat patients with this disease. COVID-19 is caused by infection with the human coronavirus SARS-CoV-2. We developed a computational approach to identify new antiviral drug targets and repurpose clinically-relevant drug compounds for the treatment of COVID-19. Our approach is based on graph convolutional networks (GCN) and involves multiscale host-virus interactome analysis coupled to off-target drug predictions. Cellbased experimental assessment reveals several clinically-relevant repurposing drug candidates predicted by the in silico analyses to have antiviral activity against human coronavirus infection. In particular, we identify the MET inhibitor capmatinib as having potent and broad antiviral activity against several coronaviruses in a MET-independent manner, as well as novel roles for host cell proteins such as IRAK1/4 in supporting human coronavirus infection, which can inform further drug discovery studies.

cell biology↗

bioRxiv2021

Resolving the conflict between antibiotic production and rapid growth by recognition of peptidoglycan of susceptible competitors

Microbial communities employ a variety of complex strategies to compete successfully against competitors sharing their niche, with antibiotic production being a common strategy of aggression. Here, by systematic evaluation of all non-ribosomal peptides (NRP) produced by B. subtilis clade, we revealed that they acted either synergistically or additively to effectively eliminate phylogenetically distinct competitors. All four major NRP biosynthetic clusters were also imperative for the survival of B. subtilis in a complex community extracted from the rhizosphere. The production of NRP came with a fitness cost manifested in growth inhibition, rendering NRP synthesis uneconomical when growing in proximity to a phylogenetically close species, carrying resistance against the same antibiotics. To resolve this conflict and ease the fitness cost, NRP production was only induced by the presence of peptidoglycan cue from a sensitive competitor. These results experimentally demonstrate a general ecological concept - closely related communities ("self") are favoured during competition, due to compatibility in attack and defence mechanisms.

microbiology↗

bioRxiv2020

Early temporal dynamics of cellular responses to SARS-CoV-2

Type I interferons (IFNs) are our first line of defence against a virus. Protein over-expression studies have suggested the ability of SARS-CoV-2 proteins to block IFN responses. Emerging data also suggest that timing and extent of IFN production is associated with manifestation of COVID-19 severity. In spite of progress in understanding how SARS-CoV-2 activates antiviral responses, mechanistic studies into wildtype SARS-CoV-2-mediated induction and inhibition of human type I IFN responses are lacking. Here we demonstrate that SARS-CoV-2 infection induces a mild type I IFN response in vitro and in moderate cases of COVID-19. In vitro stimulation of type I IFN expression and signaling in human airway epithelial cells is associated with activation of canonical transcriptions factors, and SARS-CoV-2 is unable to inhibit exogenous induction of these responses. Our data demonstrate that SARS-CoV-2 is not adept in blocking type I IFN responses and provide support for ongoing IFN clinical trials. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/158154v2_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@193c540org.highwire.dtl.DTLVardef@7b106forg.highwire.dtl.DTLVardef@1741cfforg.highwire.dtl.DTLVardef@1bde68_HPS_FORMAT_FIGEXP M_FIG GRAPHICAL SUMMARY C_FIG

immunology↗