Biology subjects

Yim, K.

Publications and source records attributed to Yim, K..

7 recordsLinked to original sources

bioRxiv2026

Pan-neutralization of parainfluenza viruses by a hemagglutinin-neuraminidase antibody

Human parainfluenza viruses (HPIVs) can cause severe respiratory illnesses, such as croup, bronchiolitis, and pneumonia, particularly in children, the elderly and immunocompromised individuals. No vaccines or specific therapeutics are available for use in humans. Here, we report the discovery of a human monoclonal antibody designated PVA269 that broadly and potently neutralizes all four HPIV subtypes, PIV5, and Sendai virus by targeting the hemagglutinin-neuraminidase (HN) glycoprotein. We show that PVA269 inhibits neuraminidase activity and hemagglutination of erythrocytes through insertion of a long heavy chain complementary-determining region 3 in the enzyme active site. We reveal that the antibody markedly remodels its interactions to accommodate distinct viral features across HPIV subtypes, such as HPIV2 N-linked glycans. These results define the molecular basis for the unique PVA269 pan-neutralizing activity of human and animal viruses spanning two genera. PVA269 provides potent prophylactic activity against HPIV3 replication in the upper and lower airways of the clinically predictive cotton rat model, thus supporting translation of its protective efficacy to humans. These data establish PVA269 as a best-in-class monoclonal antibody and a promising clinical candidate to prevent HPIV infection, transmission, and disease in vulnerable populations.

immunology↗

bioRxiv2026

Genomic analysis of BCG unresponsive non-muscle-invasive bladder cancer identifies drivers of sensitivity to intravesical Gemcitabine/Docetaxel

Background and ObjectivesIntravesical gemcitabine/docetaxel (Gem/Doce) is an effective therapy for Bacillus Calmette- Guerin (BCG)-unresponsive non-muscle-invasive bladder cancer (NMIBC), achieving 50% complete responses at 2 years. However, the genomic determinants underlying response and resistance to Gem/Doce remain poorly defined. Our objective was to define the mutational landscape of BCG-unresponsive NMIBC and nominate genomic features associated with response or resistance Gem/Doce. MethodsPatients with BCG-unresponsive NMIBC treated with Gem/Doce were classified as responders (recurrence-free survival [RFS] >12 months) or non-responders (RFS <12 months). Whole-exome sequencing was performed on tumors prior to Gem/Doce treatment (n=23). Single nucleotide variants were identified and annotated using a Cancer Genome Analysis pipeline. Copy number alterations were inferred with ABSOLUTE, and clonal architecture was reconstructed using PhylogicNDT. Key Findings and LimitationsResponders demonstrated significantly prolonged time to high-grade recurrence (3.5 vs 42 months, p<0.001) and cystectomy compared with non-responders (9.5 months vs not reached; p<0.001). Non-responders exhibited higher tumor mutational burden (13.66 vs 8.71; p=0.02) and more frequent whole-genome doubling (2/2 non-responders vs 0/1 responders; p=0.33). Phylogenetic analyses revealed clonal BAP1 and subclonal BRCA2 mutations in responders, whereas non-responders harbored clonal FGFR3 mutations. Limitations include small sample size and retrospective design. Conclusions and Clinical ImplicationsDistinct genomic features underlie differential response to Gem/Doce in BCG-unresponsive NMIBC. In responders, alterations in DNA repair pathways (e.g., BRCA2) may sensitize tumors to chemotherapy, while non-responders with FGFR3 mutations may benefit from alternative targeted strategies. These findings warrant validation in larger cohorts and support the development of biomarker-driven clinical trials. Patient summaryIn this report we analyzed bladder tumors and found that some tumors respond well to treatment because they have defects in repairing DNA, making them more vulnerable to chemotherapy. In contrast, tumors that do not respond to chemotherapy harbor different genetic changes that help them survive and grow. These findings may help physicians choose more effective and personalized treatments in the future.

genomics↗

bioRxiv2024

Systematic computer-aided disulfide design as a general strategy to stabilize prefusion class I fusion proteins

Numerous enveloped viruses, such as coronaviruses, influenza, and respiratory syncytial virus (RSV), utilize class I fusion proteins for cell entry. During this process, the proteins transition from a prefusion to a postfusion state, undergoing substantial and irreversible conformational changes. The prefusion conformation has repeatedly shown significant potential in vaccine development. However, the instability of this state poses challenges for its practical application in vaccines. While non-native disulfides have been effective in maintaining the prefusion structure, identifying stabilizing disulfide bonds remains an intricated task. Here, we present a general computational approach to systematically identify prefusion-stabilizing disulfides. Our method assesses the geometric constraints of disulfide bonds and introduces a ranking system to estimate their potential in stabilizing the prefusion conformation. We found, for the RSV F protein, that disulfides restricting the initial stages of the conformational switch can offer higher stability to the prefusion state than those preventing unfolding at a later stage. The implementation of our algorithm on the RSV F protein led to the discovery of prefusion-stabilizing disulfides, providing evidence that supports our hypothesis. Furthermore, the evaluation of our top design as a vaccine candidate in a cotton rat model demonstrated robust protection against RSV infection, highlighting the potential of our approach for vaccine development. Significance statementClass I fusion proteins, pivotal for cellular entry, constitute primary targets for neutralizing antibodies across a wide range of highly pathogenic viruses. Notably, the prefusion conformation of these proteins has demonstrated to be a robust immunogen, presenting a promising avenue for vaccine development. Despite of this, the inherent structural instability of this conformation poses a substantial hurdle for vaccine formulations. To overcome this limitation and to optimize these proteins as vaccines candidates, we developed an automated computational approach to enhance the structural stability of the prefusion conformation without compromising its immunogenic properties. Given the clinical significance of class I fusion proteins, our computational tool offers a mechanism to accelerate the development of preventive measures against a multitude of viral infections.

bioengineering↗

bioRxiv2022

Assessing extracellular vesicles in human biofluids using flow-based analyzers

Extracellular vesicles (EVs) are increasingly being analyzed by flow cytometry. Yet, their miniscule size and low refractive index, causes the scatter intensity of most EVs to fall below the detection limit of most flow cytometers. A new class of devices, known as spectral flow analyzers, are becoming standards in cell phenotyping studies. Largely, due to their unique capacity of detecting a vast panel of markers with higher sensitivity for light scatter detection. Another class of devices, known as nano-analyzers, provides high resolution detection of sub-micron sized particles. Here, we aim to compare the EVs phenotyping performance between the Aurora (Cytek) spectral cell analyzer and the NanoFCM (nFCM) nanoflow analyzer. These two devices were specifically chosen given their lead in becoming gold standards in their respective fields. Immune cell-derived EVs remain poorly characterized despite their clinical potentials. We therefore, used B- and T- cell line-derived EVs and donor-matched human biofluid-derived EVs from serum, urine, and saliva in combination with a panel of established immune markers for this comparative study. A comparative evaluation of both cytometry platforms was performed, discussing their potential and suitability for different applications. We found that nFCM can accurately i) analyze small EVs (40 to 200 nm) matching the size accuracy of electron microscopy; ii) measure concentration of single EV particle per volume; iii) identify underrepresented EV marker subsets; and iv) provide co-localization of EV surface markers. We could also show that human sample biofluids have unique EV marker signatures that could have future clinical relevance.

immunology↗

bioRxiv2019

The Human Accelerated Region HACNS1 modifies developmental gene expression in humanized mice

The evolution of uniquely human traits likely entailed changes in developmental gene regulation. Human Accelerated Regions (HARs), which include transcriptional enhancers harboring a significant excess of human-specific sequence changes, are leading candidates for driving gene regulatory modifications in human development. However, insight into whether HARs alter the level, distribution and timing of endogenous gene expression remains limited. We examined the role of the HAR HACNS1 (HAR2) in human evolution by interrogating its molecular functions in a humanized mouse model. We find that HACNS1 maintains its human-specific enhancer activity in humanized mice and that it modifies expression of Gbx2, which encodes a homeobox transcription factor, during limb development. Using single-cell RNA-sequencing, we demonstrate that Gbx2 is upregulated in the chondrogenic mesenchyme of humanized limbs, supporting that HACNS1 alters gene expression in cell types involved in skeletal patterning. Our findings illustrate that humanized mouse models provide mechanistic insight into how HARs modified gene expression in human evolution.

genetics↗

bioRxiv2017

Exploring Single-Cell Data with Multitasking Deep Neural Networks

Biomedical researchers are generating high-throughput, high-dimensional single-cell data at a staggering rate. As costs of data generation decrease, experimental design is moving towards measurement of many different single-cell samples in the same dataset. These samples can correspond to different patients, conditions, or treatments. While scalability of methods to datasets of these sizes is a challenge on its own, dealing with large-scale experimental design presents a whole new set of problems, including batch effects and sample comparison issues. Currently, there are no computational tools that can both handle large amounts of data in a scalable manner (many cells) and at the same time deal with many samples (many patients or conditions). Moreover, data analysis currently involves the use of different tools that each operate on their own data representation, not guaranteeing a synchronized analysis pipeline. For instance, data visualization methods can be disjoint and mismatched with the clustering method. For this purpose, we present SAUCIE, a deep neural network that leverages the high degree of parallelization and scalability offered by neural networks, as well as the deep representation of data that can be learned by them to perform many single-cell data analysis tasks, all on a unified representation.\n\nA well-known limitation of neural networks is their interpretability. Our key contribution here are newly formulated regularizations (penalties) that render features learned in hidden layers of the neural network interpretable. When large multi-patient datasets are fed into SAUCIE, the various hidden layers contain denoised and batch-corrected data, a low dimensional visualization, unsupervised clustering, as well as other information that can be used to explore the data. We show this capability by analyzing a newly generated 180-sample dataset consisting of T cells from dengue patients in India, measured with mass cytometry. We show that SAUCIE, for the first time, can batch correct and process this 11-million cell data to identify cluster-based signatures of acute dengue infection and create a patient manifold, stratifying immune response to dengue on the basis of single-cell measurements.

bioinformatics↗

bioRxiv2017

HSP90 inhibitors disrupt a transient HSP90-HSF1 interaction and identify a noncanonical model of HSP90-mediated HSF1 regulation

Heat shock factor 1 (HSF1) initiates a broad transcriptional response to proteotoxic stress while also mediating a cancer-specific transcriptional program. HSF1 is thought to be regulated by molecular chaperones, including Heat Shock Protein 90 (HSP90). HSP90 is proposed to sequester HSF1 in unstressed cells, but visualization of this interaction in vivo requires protein crosslinking. In this report, we show that HSP90 binding to HSF1 depends on HSP90 conformation and is only readily visualized for the ATP-dependent, N-domain dimerized chaperone, a conformation only rarely sampled by mammalian HSP90. We have used this mutationally fixed conformation to map HSP90 binding sites on HSF1. Further, we show that ATP-competitive, N-domain targeted HSP90 inhibitors disrupt this interaction, resulting in the increased duration of HSF1 occupancy of the hsp70 promoter and significant prolongation of both the constitutive and heat-induced HSF1 transcriptional activity. While our data do not support a role for HSP90 in sequestering HSF1 monomers to suppress HSF1 transcriptional activity, our findings do identify a noncanonical role for HSP90 in providing dynamic modulation of HSF1 activity by participating in removal of HSF1 trimers from heat shock elements in DNA, thus terminating the heat shock response.

biochemistry↗