Biology subjects

Branda, S. S.

Publications and source records attributed to Branda, S. S..

7 recordsLinked to original sources

bioRxiv2026

Intracellular screening of nanobodies reveals an intrabody that inhibits ITCH E3 ubiquitin ligase

Nanobodies are a class of small, monomeric camelid antibody fragments that can bind target antigens with high affinity and specificity. Their small size, structural simplicity, and limited reliance on disulfide bonding makes them attractive for intracellular expression for labeling and perturbing cellular processes in live cells. However, screening campaigns carried out exclusively in vitro often yield antigen binders that fail to perform well in live cells due to low expression, misfolding, or mistargeting. We demonstrate that traditional in vitro screening of a nanobody library combined with an intracellular bioluminescence resonant energy transfer (BRET) proximity sensor approach for sequence down-selection can yield strong in vitro binders that also perform well as intrabodies, in this case capable of binding to, and inhibiting the enzymatic activity of, ITCH E3 ubiquitin ligase in human cells. This strategy allows a more direct and scalable path toward intrabody discovery.

cell biology↗

bioRxiv2025

Challenges in predicting protein-protein interactions of understudied viruses: Arenavirus-Human interactions

Understanding protein-protein interactions (PPIs) between viruses and human proteins is crucial for uncovering infection mechanisms and identifying potential therapeutic targets. The ability to generalize PPI predictive models across understudied viruses presents a significant challenge. In this work, we use arenavirus-human PPIs to illustrate the difficulties associated with model generalization, which are compounded by a lack of both positive and negative data. We employ a Transfer Learning approach to investigate arenavirus-human PPI by utilizing models trained on better-studied virus-human and human-human interactions. Additionally, we curate and assess four types of negative sampling datasets to evaluate their impact on model performance. Despite the overall high accuracies (93-99%) and AUPRC scores (0.8-0.9) appearing promising, further analysis indicates that these performance metrics can be misleading due to data leakage, data bias, and overfitting, especially concerning under-represented viral proteins. We reveal these gaps and assess the impact of data imbalance through standard k-fold cross-validation and Independent Blind Testing with a Balanced Dataset, leading to a drop in accuracy below 50%. We propose a viral protein-specific evaluation framework that groups viral proteins into majority and minority classes based on their representation in the dataset, allowing for comparison of model performance across these groups using balanced accuracies. This framework offers a more robust evaluation of model generalizability, addressing biases inherent in standard evaluation techniques and paving the way for more reliable PPI prediction models for understudied viruses.

bioinformatics↗

bioRxiv2024

Niche exclusion of a lung pathogen in mice with designed probiotic communities

For years, the airway microbiota have been theorized to be gatekeepers of respiratory health, as pathogens entering the airway make contact with resident microbes prior to or coincident with their interaction with host cells. Thus, modification of the native airway community may serve as a means of altering the local environment in favor of health. In this work, we hypothesize that synthetic bacterial communities introduced into the airway can serve as prophylactic countermeasures against infection by Burkholderia thailandensis in mice. We demonstrate that understanding of antagonistic interactions between a pathogen and airway microbiota in vitro can guide identification of probiotics with protective capabilities in vivo. Specifically, we show that niche overlap between the probiotic and pathogen is indicative of probiotic performance in vivo. This work serves as a foundation for the rational design of probiotic communities for protection against and treatment of respiratory infections.

microbiology↗

bioRxiv2021

Shotgun Immunoproteomic Approach for the Discovery of Linear B Cell Epitopes in Biothreat Agents Francisella tularensis and Burkholderia pseudomallei

Peptide-based subunit vaccines are coming to the forefront of current vaccine approaches, with safety and cost-effective production among their top advantages. Peptide vaccine formulations consist of multiple synthetic linear epitopes that together trigger desired immune responses that can result in robust immune memory. The advantages of peptide epitopes are their simple structure, ease of synthesis, and ability to stimulate immune responses by means that do not require complex 3D conformation. Identification of linear epitopes is currently an inefficient process that requires thorough characterization of previously identified full-length protein antigens, or laborious techniques involving genetic manipulation of organisms. In this study, we apply a newly developed generalizable screening method that enables efficient identification of B cell epitopes in the proteomes of pathogenic bacteria. As a test case, we used this method to identify epitopes in the proteome of Francisella tularensis (Ft), a Select Agent with a well-characterized immunoproteome. Our screen identified many peptides that map to known antigens, including verified and predicted outer membrane proteins and extracellular proteins, validating the utility of this approach. We then used the method to identify seroreactive peptides in the less characterized immunoproteome of Select Agent Burkholderia pseudomallei (Bp). This screen revealed known Bp antigens as well as proteins that have not been previously identified as antigens. The present workflow is easily adaptable to detecting peptide targets relevant to the immune systems of other mammalian species, including humans (depending upon the availability of convalescent sera from patients), and could aid in accelerating the discovery of B cell epitopes and development of vaccines to counter emerging biological threats.

immunology↗

bioRxiv2020

Augmentation of Antibacterial Activity in Mesenchymal Stromal Cells Through Systems-Level Analysis and CRISPR-mediated Activation of CD14

Mesenchymal stromal cells (MSCs) have broad-ranging therapeutic capabilities, however MSC use is confounded by cell-to-cell heterogeneity, and source-to-source phenotypic inconsistencies. We utilized a systems-based approach to compare MSCs which displayed different capacity for antibacterial activity. Although MSCs from both sources satisfied traditional MSC-defining criteria, comparative transcriptomics and quantitative membrane proteomics demonstrated two unique molecular profiles. The antibacterial MSCs respond rapidly to bacterial lipopolysaccharide (LPS) and have elevated levels of the LPS co-receptor CD14. CRISPR-mediated overexpression of endogenous CD14 in non-antibacterial MSCs resulted in faster LPS response and enhanced antimicrobial activity. Single-cell transcriptome profiling of CD14-activated MSCs revealed uniform enhancement of LPS response kinetics, and a shift in the ground state of these MSCs. Our results demonstrate that systems-level analysis can reveal critical molecular targets to optimize desirable properties in MSCs, and that overexpression of CD14 in these cells can shift their state to be more responsive to future bacterial challenge.

molecular biology↗

bioRxiv2020

Computational front end to diverse therapeutic phage cocktails

New therapies are necessary to combat increasingly antibiotic-resistant bacterial pathogens. We have developed a technology platform of computational, molecular biology, and microbiology tools which together enable on-demand production of phages that target virtually any given bacterial isolate. Two complementary computational tools that identify and precisely map prophages and other integrative genetic elements (IGEs) in bacterial genomes are used to identify prophage-laden bacteria that are close relatives of the target strain. Phage genomes are engineered to disable lysogeny, through use of long amplicon PCR and Gibson assembly. Finally, the engineered phage genomes are introduced into host bacteria for phage production. As an initial demonstration, we used this approach to produce a phage cocktail against the opportunistic pathogen Pseudomonas aeruginosa PAO1. Two prophage-laden P. aeruginosa strains closely related to PAO1 were identified, ATCC 39324 and ATCC 27853. Deep sequencing revealed that mitomycin C treatment of these strains induced seven phages that grow on P. aeruginosa PAO1. The most diverse five of these were engineered for non-lysogeny by deleting the integrase gene (int), which is readily identifiable and typically conveniently located at one end of the prophage. The {Delta}int phages, individually and in cocktails, showed killing of P. aeruginosa PAO1 in vitro as well as in a waxworm (Galleria mellonella) model of infection. SIGNIFICANCE STATEMENTThe antibiotic-resistance crisis in medicine and agriculture has led to renewed interest in phage therapy as an alternative means of treating infection. However, conventional methods for isolating pathogen-specific phage are slow, labor-intensive, and frequently unsuccessful. We have demonstrated that prophages carried by near-neighbor bacteria can serve as starting material for production of engineered phages that kill the target pathogen. Our approach and technology platform offer new opportunity for rapid development of phage therapies against most, if not all, bacterial pathogens, a foundational advance for use of phage in treating infectious disease.

microbiology↗

bioRxiv2020

Genome Sequences of Burkholderia thailandensis strains E421, E426, and DW503

We present the draft genome sequences of three Burkholderia thailandensis strains: E421, E426, and DW503. E421 consists of 90 contigs of 6,639,935bp and 67.73% GC content. E426 consists of 106 contigs of 6,587,853bp and 67.73% GC content. DW503 consists of 102 contigs of 6,458,767bp and 67.64% GC content.

genomics↗