Biology subjects

Romano, J. D.

Publications and source records attributed to Romano, J. D..

12 recordsLinked to original sources

bioRxiv2026

The live attenuated DGAT1-knockout whole-cell Toxoplasma vaccine confers protective immunity against acute and chronic toxoplasmosis

The intravacuolar parasite Toxoplasma gondii scavenges fatty acids from host mammalian cells and stores excess in lipid droplets. To investigate the physiological relevance of neutral lipid storage in Toxoplasma, we generated a mutant lacking DGAT1, an ER-localized enzyme that synthesizes triacylglycerols, from the virulent type I RH strain of T. gondii. Compared to WT, RH {Delta}DGAT1 parasites grow poorly in mammalian cells, form few LD, suffer from lipotoxicity, and do not cause disease or lethality in immunocompetent or immunodeficient mice. Importantly, mice immunized with RH {Delta}DGAT1 parasites mount strong, long-term immune responses involving both cellular and humoral components, with higher levels of T. gondii-specific IgG antibodies, effector memory T cells, and both pro-inflammatory and anti-inflammatory cytokines, indicating a mixed Th1/Th2 response with Th1 predominance. This immunity provides complete, long-lasting protection (up to 6 months) against rechallenge from homologous type I (acute infection) and heterologous cyst-forming type II (chronic infection) T. gondii strains. Additional analyses reveal that IFN-{gamma}, CD8+ T cells, as well as B cells are crucial for defending against type I T. gondii in immunized mice. Overall, our live-attenuated RH {Delta}DGAT1 strain is a promising vaccine candidate and a model for studying immune responses that control T. gondii infections.

microbiology↗

bioRxiv2024

CASTER-DTA: Equivariant Graph Neural Networks for Predicting Drug-Target Affinity

Accurately determining the binding affinity of a ligand with a protein is important for drug design, development, and screening. With the advent of accessible protein structure prediction methods such as AlphaFold, predicted protein 3D structures are readily available; however, methods for predicting binding affinity currently do not take full advantage of 3D protein information. Here, we present CASTER-DTA (Cross-Attention with Structural Target Equivariant Representations for Drug-Target Affinity), which uses an equivariant graph neural network to learn more robust protein representations alongside a standard graph neural network to learn molecular representations to predict drug-target affinity. We augment these representations by incorporating an attention-based mechanism between protein residues and drug atoms to improve interpretability. We show that CASTER-DTA represents a state-of-the-art improvement on multiple benchmarks for predicting drug-target affinity and that it generates novel insights for several related tasks. We then apply CASTER-DTA to create a large resource of the binding affinities of every FDA-approved drug against every protein in the human proteome and make these predictions freely available for download. We also make available a web server for researchers to apply a pretrained CASTER-DTA model for predicting binding affinities between arbitrary proteins and drugs. Key MessagesO_LICASTER-DTA is a novel architecture that makes use of equivariant graph neural networks to predict drug-target affinity, enabling rapid and interpretable predictions about how proteins and molecules bind to each other. C_LIO_LIEquivariant graph neural networks like those used in CASTER-DTA allow for more robust usage of protein 3D structural information and improve performance over non-equivariant approaches. C_LIO_LICASTER-DTA includes a cross-attention layer to update protein residue and molecule atom embeddings that allows for interpretability about which residues are involved in the prediction. C_LIO_LIWe can use this architecture for a variety of downstream tasks, and we have used CASTER-DTA to generate a resource of predicted binding affinities for every FDA-approved drug against every protein in the human proteome. C_LI

bioinformatics↗

bioRxiv2022

Host MOSPD2 enrichment at the parasitophorous vacuole membrane varies between Toxoplasma strains and involves complex interactions

Toxoplasma gondii is an obligate, intracellular parasite capable of causing severe disease in warm-blooded animals. Infection of a cell produces a unique niche for the parasite named the parasitophorous vacuole (PV) initially composed of host plasma membrane invaginated during invasion. The PV and its membrane (PVM) are subsequently decorated with a variety of parasite proteins allowing the parasite to optimally grow in addition to manipulate host processes. Recently, we reported a proximity-labeling screen at the PVM-host interface and identified host ER-resident MOSPD2 as being enriched at this location. Here we extend these findings in several important respects. First, we show that the extent and pattern of host MOSPD2 association with the PVM differs dramatically in cells infected with different strains of Toxoplasma. Second, in cells infected with Type I RH strain, the MOSPD2 staining is mutually exclusive with regions of the PVM that associate with mitochondria. Third, immunoprecipitation and LC-MS/MS with epitope-tagged MOSPD2-expressing host cells reveals strong enrichment of several PVM-localized parasite proteins, although none appear to play an essential role in MOSPD2 association. Lastly, most MOSPD2 associating with the PVM is newly translated after infection of the cell and requires the major functional domains of MOSPD2, identified as the CRAL/TRIO domain and tail anchor, although these domains were not sufficient for PVM association. Collectively, these studies provide new insight into the molecular interactions involving MOSPD2 at the dynamic interface between the PVM and the host cytosol. ImportanceToxoplasma gondii is an intracellular pathogen that lives within a membranous vacuole inside of its host cell. This vacuole is decorated by a variety of parasite proteins that allow it to defend against host attack, acquire nutrients, and interact with the host cell. Recent work identified and validated host proteins enriched at this host-pathogen interface. Here, we follow up on one candidate named MOSPD2 shown to be enriched at the vacuolar membrane and describe it as having a dynamic interaction at this location depending on a variety of factors. Some of these include the presence of host mitochondria, intrinsic domains of the host protein, and whether translation is active. Importantly, we show that MOSPD2 enrichment at the vacuole membrane differs between strains indicating active involvement of the parasite with this phenotype. Altogether, these results shed light on the mechanism and role of protein associations in the host-pathogen interaction.

microbiology↗

bioRxiv2022

KNOWLEDGE GRAPH AIDS COMPREHENSIVE EXPLANATION OF DRUG TOXICITY

In computational toxicology, prediction of complex endpoints has always been challenging, as they often involve multiple distinct mechanisms. State-of-the-art models are either limited by low accuracy, or lack of interpretability due to their black-box nature. Here we introduce AIDTox, an interpretable deep learning model which incorporates curated knowledge of chemical-gene connections, gene-pathway annotations, and pathway hierarchy. AIDTox accurately predicts cytotoxicity outcomes in HepG2 and HEK293 cells. It also provides comprehensive explanations of cytotoxicity covering multiple aspects of drug activity including target interaction, metabolism, and elimination. In summary, AIDTox provides a computational framework for unveiling cellular mechanisms for complex toxicity endpoints.

pharmacology and toxicology↗

bioRxiv2022

Toxoplasma scavenges mammalian host organelles through usurpation of host ESCRT-III and Vps4

Intracellular pathogens exploit cellular resources through host cell manipulation. Within its nonfusogenic parasitophorous vacuole (PV), Toxoplasma targets host nutrient-filled organelles and sequesters them into the PV through deep invaginations of the PV membrane (PVM) that ultimately detach from this membrane. Some of these invaginations are generated by an intravacuolar network (IVN) of parasite-derived tubules fusing with the PVM. Here, we examine the parasite usurpation of host ESCRT-III and Vps4 to create PVM buds and vesicles. CHMP4B associates with the PVM/IVN and dominant negative (DN) CHMP4B forms many long PVM invaginations containing CHMP4B filaments; the invaginations are shorter in IVN-deficient parasites, suggesting cooperation between IVN and ESCRT. In infected cells expressing Vps4-DN, enlarged intra-PV structures containing host endo-lysosomes accumulate, reflecting defects in PVM scission. Parasite mutants lacking TgGRA14 or TgGRA64 that interact with ESCRT have reduced CHMP4B-DN-induced PVM invaginations and intra-PV host organelles, with greater defects in a double-knockout, revealing the exploitation of ESCRT to scavenge host organelles by Toxoplasma. SummaryThe parasite Toxoplasma sequesters host nutrient-filled organelles into its parasitophorous vacuole through its exploitation of host ESCRT-III and Vps4 for vacuolar membrane-remodeling and fission processes utilizing the parasite proteins TgGRA14 and TgGRA64 that interact with ESCRT.

microbiology↗

bioRxiv2022

Knowledge-guided deep learning models of drug toxicity improve interpretation

In drug development, a major reason for attrition is the lack of understanding of cellular mechanisms governing drug toxicity. The black-box nature of conventional classification models has limited their utility in identifying toxicity pathways. Here we developed DTox (Deep learning for Toxicology), an interpretation framework for knowledge-guided neural networks, which can predict compound response to toxicity assays and infer toxicity pathways of individual compounds. We demonstrate that DTox can achieve the same level of predictive performance as conventional models with a significant improvement in interpretability. Using DTox, we were able to rediscover mechanisms of transcription activation by three nuclear receptors, recapitulate cellular activities induced by aromatase inhibitors and PXR agonists, and differentiate distinctive mechanisms leading to HepG2 cytotoxicity. Virtual screening by DTox revealed that compounds with predicted cytotoxicity are at higher risk for clinical hepatic phenotypes. In summary, DTox provides a framework for deciphering cellular mechanisms of toxicity in silico.

pharmacology and toxicology↗

bioRxiv2022

Toxoplasma gondii-s basal complex: the other apicomplexan business end is multifunctional

The Apicomplexa are famously named for their apical complex, a constellation of organelles at their apical end dedicated to invasion of their host cells. In contrast, at the other end of the cell, the basal complex (BC) has been in the limelight since it is much less prominent and specific functions were not immediately obvious. However, in the past decade a staggering array of functions have been associated with the BC, and strides have been made in understanding its structure. Here, these collective insights are supplemented with new data to provide an overview of the understanding of the BC in Toxoplasma gondii. The emerging picture is that the BC is a dynamic and multifunctional complex, with a series of (putative) functions. The BC has multiple roles in cell division: it is the site where building blocks are added to the cytoskeleton scaffold; it exerts a two-step stretch and constriction mechanism as contractile ring; and it is key in organelle division. Furthermore, the BC has numerous putative roles in import, such as the recycling of mother cell remnants, the acquisition of host-derived vesicles, possibly the uptake of lipids derived from the extracellular medium, and the endocytosis of micronemal proteins. The latter process ties the BC to motility, whereas an additional role in motility is conferred by Myosin C. Furthermore, the BC acts on the assembly and/or function of the intravacuolar network, which may directly or indirectly contribute to the establishment of chronic tissue cysts. Here we provide experimental support for molecules acting in several of these processes, and identify several new BC proteins critical to maintaining the cytoplasmic bridge between divided parasites. However, the dispensable nature of many BC components leaves many questions unanswered regarding its function. In conclusion, the BC in T. gondii is a dynamic and multifunctional structure at the posterior end of the parasite.

cell biology↗

bioRxiv2021

Dense granule protein, GRA64 interacts with host cell ESCRT proteins during Toxoplasma gondii infection

The intracellular parasite Toxoplasma gondii adapts to diverse host cell environments within a replicative compartment that is heavily decorated by secreted proteins. In attempts to identify novel parasite secreted proteins that influence host cell activity, we identified and characterized a trans-membrane dense granule protein dubbed GRA64 (TGME49_202620). We found that GRA64 is on the parasitophorous vacuolar membrane (PVM) and is partially exposed to the host cell cytoplasm in both tachyzoite and bradyzoite parasitophorous vacuoles. Using co-immunoprecipitation and proximity-based biotinylation approaches, we demonstrate that GRA64 appears to interact with certain components of the host Endosomal Sorting Complexes Required for Transport (ESCRT). Genetic disruption of GRA64 does not affect acute Toxoplasma virulence in mice nor encystation as observed via tissue cyst burdens in mice during chronic infection. However, ultrastructural analysis of {Delta}gra64 tissue cysts using electron tomography revealed enlarged vesicular structures underneath the cyst membrane, suggesting a role for GRA64 in organizing the recruitment of ESCRT proteins and subsequent intracystic vesicle formation. This study uncovers a novel host-parasite interaction that contributes to an emerging paradigm in which specific host ESCRT proteins are recruited to the limiting membranes (PVMs) of tachyzoite and bradyzoite vacuoles formed during acute and chronic Toxoplasma infection. IMPORTANCEToxoplasma gondii is a widespread foodborne parasite that causes congenital disease and life-threatening complications in immune compromised individuals. Part of this parasites success lies in its ability to infect diverse organisms and host cells, as well as to persist as a latent infection within parasite constructed structures called tissue cysts. In this study, we characterized a protein secreted by T. gondii into its parasitophorous vacuole during intracellular infection, which we dub GRA64. On the vacuole, this protein is exposed to the host cell and interacts with specific host ESCRT proteins. Parasites lacking the GRA64 protein exhibit ultrastructural changes in tissue cysts during chronic infection. This study lays the foundation for future studies on the mechanics and consequences of host ESCRT-parasite protein interactions.

microbiology↗

bioRxiv2021

Improving QSAR Modeling for Predictive Toxicology using Publicly Aggregated Semantic Graph Data and Graph Neural Networks

Quantitative Structure-Activity Relationship (QSAR) modeling is the most common computational technique for predicting chemical toxicity, but a lack of methodological innovations in QSAR have led to underwhelming performance. We show that contemporary QSAR modeling for predictive toxicology can be substantially improved by incorporating semantic graph data aggregated from open-access public databases, and analyzing those data in the context of graph neural networks (GNNs). Furthermore, we introspect the GNNs to demonstrate how they can lead to more interpretable applications of QSAR, and use ablation analysis to explore the contribution of different data elements to the final models performance.

pharmacology and toxicology↗

bioRxiv2021

The Rab11-Family Interacting Proteins reveal selective interaction of mammalian recycling endosomes with the Toxoplasma parasitophorous vacuole in a Rab11- and Arf6-dependent manner

After invasion of mammalian cells, the parasite Toxoplasma gondii multiplies in a self-made membrane-bound compartment, the parasitophorous vacuole (PV). We previously showed that intravacuolar Toxoplasma interacts with many host cell organelles, especially recycling endosomes, and further manipulates the host endocytic recycling through the sequestration of Rab11 vesicles into the PV. Mammalian Rab-PV interactions are likely mediated by Toxoplasma and host proteins that remain to be identified. In this context, we have examined the specificity of host Rab vesicle interaction with the PV by monitoring the recruitment of subtypes of Rab11 vesicles differing in their composition in Rab11-Family Interacting Proteins (FIPs). We found that vesicles with FIPs from Class I (FIP1C, FIP2, FIP5) or Class II (FIP3, FIP4) are distributed at the PV and detected to varying degrees inside the PV. The PV delivery of vesicles with FIPs from Class I, but not Class II, is Rab11-dependent. In addition to Rab11, FIP3 binds to Arf6, and vesicles associated with FIP3-Arf6 complexes are observed within the PV. Binding of FIP3 to either Rab11 or Arf6 significantly increases the internalization of vesicles into the PV. These data point to a selective process of host recycling endosome recognition and scavenging mediated by Toxoplasma.

cell biology↗

bioRxiv2020

Embedding covariate adjustments in tree-based automated machine learning for biomedical big data analyses

BackgroundA typical task in bioinformatics consists of identifying which features are associated with a target outcome of interest and building a predictive model. Automated machine learning (AutoML) systems such as the Tree-based Pipeline Optimization Tool (TPOT) constitute an appealing approach to this end. However, in biomedical data, there are often baseline characteristics of the subjects in a study or batch effects that need to be adjusted for in order to better isolate the effects of the features of interest on the target. Thus, the ability to perform covariate adjustments becomes particularly important for applications of AutoML to biomedical big data analysis. ResultsWe present an approach to adjust for covariates affecting features and/or target in TPOT. Our approach is based on regressing out the covariates in a manner that avoids leakage during the cross-validation training procedure. We then describe applications of this approach to toxicogenomics and schizophrenia gene expression data sets. The TPOT extensions discussed in this work are available at https://github.com/EpistasisLab/tpot/tree/v0.11.1-resAdj. ConclusionsIn this work, we address an important need in the context of AutoML, which is particularly crucial for applications to bioinformatics and medical informatics, namely covariate adjustments. To this end we present a substantial extension of TPOT, a genetic programming based AutoML approach. We show the utility of this extension by applications to large toxicogenomics and differential gene expression data. The method is generally applicable in many other scenarios from the biomedical field.

bioinformatics↗

bioRxiv2019

Discovering therapeutic activities from venoms using differential gene expression

Venoms are a diverse and complex group of natural toxins that have been adapted to treat many types of human disease, but rigorous computational approaches for discovering new therapeutic activities are scarce. We have designed and validated a new platform--named VenomSeq--to systematically identify putative associations between venoms and drugs/diseases via high-throughput transcriptomics and perturbational differential gene expression analysis. In this study, we describe the architecture of VenomSeq and its evaluation using the crude venoms from 25 diverse animal species and 9 purified teretoxin peptides. By integrating comparisons to public repositories of differential expression, associations between regulatory networks and disease, and existing knowledge of venom activity, we provide a number of new therapeutic hypotheses linking venoms to human diseases supported by multiple layers of preliminary evidence.

pharmacology and toxicology↗