Biology subjects

Deo, R.

Publications and source records attributed to Deo, R..

7 recordsLinked to original sources

bioRxiv2026

Seipin and perilipin 3 promote distinct early steps of nascent lipid droplet biogenesis

Lipid droplets (LDs) are lipid storage organelles with critical roles in metabolism. Cells form LDs in response to various metabolic stresses (1). LD biogenesis occurs in the endoplasmic reticulum (ER) membrane and begins when triglyceride (TG) in the membrane phase separates (2-4). After nucleation, nascent LDs grow by absorbing TG from the membrane (5,6). How cells determine when TG in the ER membrane is added to existing LDs or is used to form new LDs or is not known. Here, we developed the first LD formation assay that allows the synthesis of TG in membranes, mimicking what occurs in cells. LDs arise stochastically after TG production begins. LD nucleation is affected by TG production rate and membrane phospholipid composition. Membrane-embedded LDs suppress nascent LD formation by sequestering TG and destabilizing nascent LDs by Ostwald ripening. We show that cells overcome this suppression in two ways. The ER membrane protein seipin accelerates LD nucleation where it localizes in a membrane, probably by increasing the kinetics of TG phase separation. The LD coat perilipin 3 inhibits nascent droplet dissipation by ripening. Together, our findings reveal a minimal LD formation machinery, and the mechanisms cells use to control whether TG in the ER drives new LD formation.

cell biology↗

bioRxiv2026

Short-term, Long-term, and Genetic Determinants of Human Plasma Proteome Variability

Plasma proteomics is increasingly used for biomarker discovery and predictive modeling, yet diurnal protein trajectories remain insufficiently characterized. In our review of recent proteomic biomarker studies, 43% of the identified biomarkers had previously been reported to display 24-h rhythmicity. We demonstrate that ignoring these short-term dynamic effects compromises the robustness of reported models predicting health outcomes. We integrated a population-scale multi-ethnic longitudinal cohort with repeated measures over 10 years, with two cohorts of healthy adults undergoing frequent plasma sampling across days under controlled circadian, sleep and food-intake conditions. This design enabled estimation of short-term intraindividual variability (ST), long-term intraindividual variability (LT), population-level variability (POP) and genetic effects (GEN) across 7,289 protein targets. ST, LT, POP, and GEN define diverse protein trajectories, including rapid dynamics, long-term change, and individual-specific signatures. Using and generalizing this framework will facilitate covariate selection, study design, biomarker prioritization, and variability-aware modeling by users of proteomic data.

genomics↗

bioRxiv2026

Extending structural surfaceomics to identify aberrant conformations of tumor surface proteins as potential immunotherapy targets

The complement of tumor cell surface proteins, or "surfaceome", is a rich source of potential immunotherapy targets. To move beyond expression-based target discovery, we previously described "structural surfaceomics," combining crosslinking mass spectrometry (XL-MS) with surface protein biotinylation to identify conformation-selective targets. In our prior work, we applied this method to a single model of acute myeloid leukemia (AML), identifying active integrin beta-2 as a promising target. Here, we expand structural surfaceomics to identify additional immunotherapy targets and surface protein biology across additional models of AML, multiple myeloma, and prostate cancer, as well as donor peripheral blood mononuclear cells. Utilizing these models and different chemical crosslinkers, we compile an extensive database of 5,209 crosslinks. We characterize both shared and unique crosslink-based features, identifying 1,612 disease model-specific crosslinks, including 212 potentially defining tumor-specific conformations based on distance constraint violations relative to AlphaFold predictions. We further implement a suite of emerging modeling tools to predict tumor-specific protein structures. We probe crosslinking patterns suggesting multiple myeloma-specific CD48 and AML-specific integrin 1/{beta}4 heterodimer conformations. This work establishes a resource for cancer structural biology by implementation of structural surfaceomics. Our findings also point toward more realistic protein design models, potentially enabling systematic detection of targetable cancer-specific epitopes for next-generation immunotherapies.

cancer biology↗

bioRxiv2026

Machine learning cross-platform proteomic imputation enables protein quality scoring and replication of epidemiological associations

High-throughput affinity-based proteomics has advanced biomedical research, yet fundamental, persistent discordance between mainstream platforms (SomaScan and Olink) routinely undermines the replication of findings. This platform-driven non-replication complicates downstream biological validation and biomarker prioritization. Here, we develop a machine learning-based framework for cross-platform protein value imputation to resolve this translational bottleneck. Using paired proteomic data measured by both SomaScan and Olink from 5,325 participants of the Multi-Ethnic Study of Atherosclerosis, we developed models to impute cross-platform measurements and applied them to two independent and demographically distinct cohorts (Cardiovascular Health Study [N=3,171] and UK Biobank [UKB; N=41,405]) for external validation. Our bi-directional model 1) established an imputation performance-based protein fidelity index, validated against gold-standard measurements from Atherosclerosis Risk in Communities study (N=101) and Nurses Health Study (N=54), 2) enabled imputation of platform-exclusive protein measurements, and 3) facilitated calibration of overlapping proteins. We demonstrate the utility of this framework through three applications: 1) fidelity-informed analyses enhanced the replication of biomarker discovery, 2) recovery of SomaScan signals that were previously inaccessible in UKBs original Olink measurements, and 3) improved replication performance for overlapping proteins. Our study offers a translational roadmap that allows researchers to achieve reliable epidemiological replication, target specific assays for future optimization, and prioritize biological signal over platform noise.

bioinformatics↗

bioRxiv2023

Modeling the emergence of viral resistance for SARS-CoV-2 during treatment with an anti-spike monoclonal antibody

The COVID-19 pandemic has led to over 760 million cases and 6.9 million deaths worldwide. To mitigate the loss of lives, emergency use authorization was given to several anti-SARS-CoV-2 monoclonal antibody (mAb) therapies for the treatment of mild-to-moderate COVID-19 in patients with a high risk of progressing to severe disease. Monoclonal antibodies used to treat SARS-CoV-2 target the spike protein of the virus and block its ability to enter and infect target cells. Monoclonal antibody therapy can thus accelerate the decline in viral load and lower hospitalization rates among high-risk patients with susceptible variants. However, viral resistance has been observed, in some cases leading to a transient viral rebound that can be as large as 3-4 orders of magnitude. As mAbs represent a proven treatment choice for SARS-CoV-2 and other viral infections, evaluation of treatment-emergent mAb resistance can help uncover underlying pathobiology of SARS-CoV-2 infection and may also help in the development of the next generation of mAb therapies. Although resistance can be expected, the large rebounds observed are much more difficult to explain. We hypothesize replenishment of target cells is necessary to generate the high transient viral rebound. Thus, we formulated two models with different mechanisms for target cell replenishment (homeostatic proliferation and return from an innate immune response anti-viral state) and fit them to data from persons with SARS-CoV-2 treated with a mAb. We showed that both models can explain the emergence of resistant virus associated with high transient viral rebounds. We found that variations in the target cell supply rate and adaptive immunity parameters have a strong impact on the magnitude or observability of the viral rebound associated with the emergence of resistant virus. Both variations in target cell supply rate and adaptive immunity parameters may explain why only some individuals develop observable transient resistant viral rebound. Our study highlights the conditions that can lead to resistance and subsequent viral rebound in mAb treatments during acute infection. Author summaryMonoclonal antibodies have been used as a treatment for SARS-CoV-2. However, viral evolution and development of variants has compromised the use of all currently authorized monoclonal antibodies for SARS-CoV-2. In some individuals treated with one such monoclonal antibody, bamlanivimab, transient nasal viral rebounds of 3-4 logs associated with resistant viral strains occur. To better understand the mechanisms underlying resistance emergence with high viral load rebounds, we developed two different models that incorporate drug sensitive and drug resistant virus as well as target cell replenishment and fit them to data. The models accurately capture the observed viral dynamics as well as the proportion of resistant virus for each studied individual with little variation in model parameters. In the models with best-fit parameters, bamlanivimab selects for resistance mutants that can expand to high levels due to target cell replenishment. The ultimate clearance of virus however depends on the development of adaptive immunity.

immunology↗

bioRxiv2023

Tempered IL-2 Signals Program PD-1 Checkpoint Blockade-Responsive Stem-like Exhausted T Cells During Priming

Stem-like progenitor exhausted CD8 T cells are critical for maintaining long-term resistance during chronic infections and cancer, and represent an important checkpoint blockade immunotherapy target for functional reinvigoration and disease control. Hence, there is vigorous interest in understanding the ontogenesis of TCF-1Hi stem-like exhausted CD8 T cells, and in defining the signals that promote their development. Here, we show that virus-specific TCF1HiGzmBLo CD8 T cells develop during early stages of chronic viral infection, and are progressively deprogrammed from the stem-like lineage towards terminal differentiation by strong IL-2 signals. In vivo fate-tracking studies show that strong T cell-intrinsic IL-2 signals through the high affinity heterotrimeric IL-2R/{beta}/{gamma} receptor drive skewed development of terminally differentiated TCF-1LoGzmBHi cells, which largely die in chronic antigenic environment. In contrast, tempered IL-2 signals through the intermediate affinity IL-2R{beta}/{gamma} heterodimer, or delayed priming in diminished IL-2 milieu support preferential development of TCF-1Hi stem-like exhausted CD8 T cells, capable of long-term persistence and potent responsiveness to PD-1 therapy in later stages of chronic viral infection. In human tumors as well, single cell RNA-seq analyses of tumor infiltrating lymphocytes from melanoma, human papillomavirus+ head and neck cancer and lung cancer patients revealed an inverse relationship between IL-2 signaling signature and T cell stemness. Moreover, melanoma patients with enriched IL-2 signaling signature showed poor responses to checkpoint blockade immunotherapy. Collectively, these findings bear relevance to clinical immunotherapies of chronic viral infections and cancers, and support exogenous IL-2 signal manipulation prior to therapy as a strategy for augmenting long-term clinical outcomes. ONE SENTENCE SUMMARYProgramming of exhausted T cell fates by differential IL-2

immunology↗

bioRxiv2019

The crystal structure of a cardiac calsequestrin filament reveals an atomic mechanism of calsequestrin-associated catecholaminergic polymorphic ventricular tachycardia

Mutations in the calcium-binding protein calsequestrin cause a highly lethal familial arrhythmia, catecholaminergic polymorphic ventricular tachycardia (CPVT). In vivo, calsequestrin multimerizes into filaments, but a compelling atomic-resolution structure of a calsequestrin filament is lacking. We report a crystal structure of a cardiac calsequestrin filament with supporting mutation analysis provided by an in vitro fomentation assay. We also report and characterize a novel disease-associated calsequestrin mutation, S173I, which localizes to the filament-forming interface. In addition, we show that a previously reported dominant disease mutation, K180R, maps to the same multimerization surface. Both mutations disrupt filamentation, suggesting that dominant disease arises from defects in multimer formation. A ytterbium-derivatized structure pinpoints multiple credible calcium sites at filament-forming interfaces, explaining the atomic basis of calsequestrin filamentation in the presence of calcium. This work advances our understanding of calsequestrin biochemistry and provides a unifying structure-function molecular mechanism by which dominant-acting calsequestrin mutations provoke lethal arrhythmias.

biochemistry↗